FN Clarivate Analytics Web of Science
VR 1.0
PT J
AU Croft, WL
   Sack, JR
AF Croft, William L.
   Sack, Joerg-Rudiger
TI Predicting the citation count and CiteScore of journals one year in
   advance
SO JOURNAL OF INFORMETRICS
LA English
DT Article
DE Impact metrics; Predictive modeling; Neural networks; Long short-term
   memory; CiteScore
ID IMPACT FACTOR; SCIENCE; SCORES
AB Prediction of the future performance of academic journals is a task that can benefit a variety of stakeholders including editorial staff, publishers, indexing services, researchers, university admin-istrators and granting agencies. Using historical data on journal performance, this can be framed as a machine learning regression problem. In this work, we study two such regression tasks: 1) prediction of the number of citations a journal will receive during the next calendar year, and 2) prediction of the Elsevier CiteScore a journal will be assigned for the next calendar year. To address these tasks, we first create a dataset of historical bibliometric data for journals indexed in Scopus. We propose the use of neural network models trained on our dataset to predict the future performance of journals. To this end, we perform feature selection and model configuration for a Multi-Layer Perceptron and a Long Short-Term Memory. Through experimental comparisons to heuristic prediction baselines and classical machine learning models, we demonstrate superior performance in our proposed models for the prediction of future citation and CiteScore values.
C1 [Croft, William L.; Sack, Joerg-Rudiger] Carleton Univ, Sch Comp Sci, Ottawa, ON, Canada.
C3 Carleton University
RP Croft, WL (corresponding author), Carleton Univ, Sch Comp Sci, Ottawa, ON, Canada.
EM leecroft@cmail.carleton.ca; sack@scs.carleton.ca
FU Natural Sciences and Engineering Research Council of Canada (NSERC); 
   [RGPIN-2016-06253]
FX Funding: This work was supported by the Natural Sciences and Engineering
   Research Council of Canada (NSERC) [grant number RGPIN-2016-06253] .
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NR 39
TC 5
Z9 5
U1 3
U2 31
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1751-1577
EI 1875-5879
J9 J INFORMETR
JI J. Informetr.
PD NOV
PY 2022
VL 16
IS 4
AR 101349
DI 10.1016/j.joi.2022.101349
EA NOV 2022
PG 19
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 6D9KY
UT WOS:000883004100006
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Muppidi, S
   Gorripati, SK
   Kishore, B
AF Muppidi, Satish
   Gorripati, Satya Keerthi
   Kishore, B.
TI An approach for bibliographic citation sentiment analysis using deep
   learning
SO INTERNATIONAL JOURNAL OF KNOWLEDGE-BASED AND INTELLIGENT ENGINEERING
   SYSTEMS
LA English
DT Article
DE Citation sentiment analysis; deep learning models; CNN; CNN-LSTM
AB Sentiment analysis of scientific citations is a novel and remarkable research area. Most of the work on opinion or sentiment analysis has been suggested on social platforms such as Blogs, Twitter, and Facebook. Nevertheless, when it comes to recognizing sentiments from scientific citation papers, investigators used to face difficulties due to the implied and unseen natures of sentiments or opinions. As the citation references are reflected implicitly positive in opinion, famous ranking and indexing prototypes frequently disregard the sentiment existence while citing. Hence, in the proposed framework the paper emphasizes the issue of classifying positive and negative polarity of reference sentiments in scientific research papers. First, the paper scraps the PDF articles from arxiv.org under the computer science group consisting of articles that are comprised of 'autism' in their title, then the paper extracted cited references and assigns polarity scores to each cited reference. The paper uses a supervised classifier with a combination of significant feature sets and compared the performance of the models. Experimental results show that a combined CNN-LSTM deep neural network model results in 85% of accuracy while traditional models result in less accuracy.
C1 [Muppidi, Satish] GMRIT, Dept CSE, Rajam, Andhra Pradesh, India.
   [Gorripati, Satya Keerthi] GVP Coll Engn Autonomous, Dept CSE, Visakhapatnam, Andhra Pradesh, India.
   [Kishore, B.] Oregon State Univ, Sch Elect Engn & Comp Sci, Corvallis, OR 97331 USA.
C3 GMR Institute of Technology; Gayatri Vidya Parishad College of
   Engineering; Oregon State University
RP Muppidi, S (corresponding author), GMRIT, Dept CSE, Rajam, Andhra Pradesh, India.
EM satish.m@gmrit.edu.in
RI Muppidi, Satish/E-4016-2016
OI Muppidi, Satish/0000-0003-1714-1769; B, Kishore/0000-0002-7577-4911
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NR 14
TC 4
Z9 4
U1 1
U2 10
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1327-2314
EI 1875-8827
J9 INT J KNOWL-BASED IN
JI Int. J. Knowl.-Based Intell. Eng. Syst.
PY 2020
VL 24
IS 4
BP 353
EP 362
DI 10.3233/KES-200087
PG 10
WC Computer Science, Artificial Intelligence
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA PW7HI
UT WOS:000610841000009
DA 2024-09-05
ER
PT J
AU Kumar, S
AF Kumar, Suresh
TI An evaluation of author productivity in artificial neural networks
   research in India during 1991-2014
SO ANNALS OF LIBRARY AND INFORMATION STUDIES
LA English
DT Article
DE Lotka Law; Bibliometrics; Artificial Neural Networks; India
ID LOTKAS LAW; COLLABORATION
AB The study examines the conformity of Lotka's law to authorship distribution in the field of Artificial Neural Networks research (ANNs) in India during 1991-2014 using Science Citation Index-Expanded. There were 3411 articles contributed by 5654 unique authors. Lotka's law was tested using methodology suggested by Pao and compared with maximum likelihood method advocated by Nicholls. The main elements involved in fitting in Lotka's law were identified. These includes criterion for taking a certain pair of observed data points for calculating Lotka's gradient, the constant for measurement of single author productivity and assessing goodness-of-fit. The results suggested that author productivity distribution, predicted by the modified Lotka's Law suggested by Pao, was confirmed to the ANNs discipline in India whereas methodology suggested by Nicholls was not able to explain the author productivity distribution for the same. Evaluation of the prolific authors indicated that most of them are among the top position in their respective institutions. However, they were not listed as first author in their publications supporting that all the authors should be considered while analysing author productivity.
C1 [Kumar, Suresh] CSIR Natl Inst Sci Technol & Dev Studies, Dr KS Krishnan Marg, New Delhi 110012, India.
C3 Council of Scientific & Industrial Research (CSIR) - India; CSIR -
   National Institute of Science Communication & Policy Research (NIScPR)
RP Kumar, S (corresponding author), CSIR Natl Inst Sci Technol & Dev Studies, Dr KS Krishnan Marg, New Delhi 110012, India.
EM sureshkr@nistads.res.in
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NR 17
TC 0
Z9 0
U1 0
U2 3
PU NATL INST SCIENCE COMMUNICATION-NISCAIR
PI NEW DELHI
PA DR K S KRISHNAN MARG, PUSA CAMPUS, NEW DELHI 110 012, INDIA
SN 0972-5423
EI 0975-2404
J9 ANN LIBR INF STUD
JI Ann. Libr. Inf. Stud.
PY 2016
VL 63
IS 2
BP 126
EP 131
DI 10.56042/alis.v63i2.11697
PG 6
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA FI2HU
UT WOS:000411760500006
DA 2024-09-05
ER
PT J
AU Ausloos, M
AF Ausloos, M.
TI Assessing the true role of coauthors in the <i>h</i>-index measure of an
   author scientific impact
SO PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
LA English
DT Article
DE Co-authorship; h-index; Principal component analysis; Scientific
   production; Fractional weight
ID JOURNAL LITERATURE; HIRSCH INDEX; GROWTH-MODEL; CO-AUTHORS; VARIANTS;
   COLLABORATION; MANIFESTATION; INDICATORS; REFERENCES
AB A method based on the classical principal component analysis leads to demonstrate that the role of co-authors should give a h-index measure to a group leader higher than usually accepted. The method rather easily gives what is usually searched for, i.e. an estimate of the role (or "weight") of co-authors, as the additional value to an author papers' popularity. The construction of the co-authorship popularity, H-matrix is exemplified and the role of eigenvalues and the main eigenvector component are discussed. Examples illustrate the points and serve as the basis for suggesting a generally practical application of the concept. (C) 2015 Elsevier B.V. All rights reserved.
C1 [Ausloos, M.] KNAW, E Humanities Grp, NL-1096 CJ Amsterdam, Netherlands.
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C3 Royal Netherlands Academy of Arts & Sciences
RP Ausloos, M (corresponding author), GRAPES, Rue Belle Jardiniere, B-4031 Liege, Federation Wall, Belgium.
EM marcel.ausloos@ulg.ac.be
RI Ausloos, Marcel/AAC-8812-2020
OI Ausloos, Marcel/0000-0001-9973-0019
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NR 39
TC 16
Z9 16
U1 0
U2 38
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0378-4371
EI 1873-2119
J9 PHYSICA A
JI Physica A
PD MAR 15
PY 2015
VL 422
BP 136
EP 142
DI 10.1016/j.physa.2014.12.004
PG 7
WC Physics, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Physics
GA CC1EX
UT WOS:000350085100013
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Momeni, F
   Mayr, P
   Dietze, S
AF Momeni, Fakhri
   Mayr, Philipp
   Dietze, Stefan
TI Investigating the contribution of author-and publication-specific
   features to scholars' h-index prediction
SO EPJ DATA SCIENCE
LA English
DT Article
DE h-index prediction; Feature importance; Academic mobility; Machine
   learning; Open access publishing
ID INTERNATIONAL COLLABORATION; CITATION IMPACT; PRODUCTIVITY
AB Evaluation of researchers' output is vital for hiring committees and funding bodies, and it is usually measured via their scientific productivity, citations, or a combined metric such as the h-index. Assessing young researchers is more critical because it takes a while to get citations and increment of h-index. Hence, predicting the h-index can help to discover the researchers' scientific impact. In addition, identifying the influential factors to predict the scientific impact is helpful for researchers and their organizations seeking solutions to improve it. This study investigates the effect of the author, paper/venue-specific features on the future h-index. For this purpose, we used a machine learning approach to predict the h-index and feature analysis techniques to advance the understanding of feature impact. Utilizing the bibliometric data in Scopus, we defined and extracted two main groups of features. The first relates to prior scientific impact, and we name it 'prior impact-based features' and includes the number of publications, received citations, and h-index. The second group is 'non-prior impact-based features' and contains the features related to author, co-authorship, paper, and venue characteristics. We explored their importance in predicting researchers' h-index in three career phases. Also, we examined the temporal dimension of predicting performance for different feature categories to find out which features are more reliable for long- and short-term prediction. We referred to the gender of the authors to examine the role of this author's characteristics in the prediction task. Our findings showed that gender has a very slight effect in predicting the h-index. Although the results demonstrate better performance for the models containing prior impact-based features for all researchers' groups in the near future, we found that non-prior impact-based features are more robust predictors for younger scholars in the long term. Also, prior impact-based features lose their power to predict more than other features in the long term.
C1 [Momeni, Fakhri; Mayr, Philipp; Dietze, Stefan] GESIS Leibniz Inst Social Sci, Unter Sachsenhausen 6-8, D-50667 Cologne, Germany.
   [Dietze, Stefan] Heinrich Heine Univ, Univ Str 1, D-40225 Dusseldorf, Germany.
C3 Leibniz Institut fur Sozialwissenschaften (GESIS); Heinrich Heine
   University Dusseldorf
RP Momeni, F (corresponding author), GESIS Leibniz Inst Social Sci, Unter Sachsenhausen 6-8, D-50667 Cologne, Germany.
EM fakhri.momeni@t-online.de
RI Momeni, Fakhri/I-8012-2018
OI Momeni, Fakhri/0000-0002-5572-575X
FU BMBF project OASE [01PU17005A];  [01PQ17001]
FX We acknowledge the support of the German Competence Center for
   Bibliometrics (grant: 01PQ17001) for maintaining the used dataset for
   the analyses.; Open Access funding enabled and organized by Projekt
   DEAL. This work is financially supported by BMBF project OASE, grant
   number 01PU17005A.
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NR 57
TC 1
Z9 1
U1 4
U2 4
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
EI 2193-1127
J9 EPJ DATA SCI
JI EPJ Data Sci.
PD OCT 6
PY 2023
VL 12
IS 1
AR 45
DI 10.1140/epjds/s13688-023-00421-6
PG 21
WC Mathematics, Interdisciplinary Applications; Social Sciences,
   Mathematical Methods
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Mathematics; Mathematical Methods In Social Sciences
GA LS8H5
UT WOS:001188878200001
OA gold, Green Submitted
DA 2024-09-05
ER
PT C
AU Adetiba, E
   Adeyemi-Kayode, T
   Moninuola, F
   Akinrinmade, A
   Abolarin, O
   Moyo, S
AF Adetiba, Emmanuel
   Adeyemi-Kayode, Temitope
   Moninuola, Funmilayo
   Akinrinmade, Adekunle
   Abolarin, Olusegun
   Moyo, Sibusiso
BE Soliman, KS
TI A Mini Bibliometric Review to Explore Decades of Research in Artificial
   Neural Networks
SO EDUCATION EXCELLENCE AND INNOVATION MANAGEMENT: A 2025 VISION TO SUSTAIN
   ECONOMIC DEVELOPMENT DURING GLOBAL CHALLENGES
LA English
DT Proceedings Paper
CT 35th International-Business-Information-Management-Association
   Conference (IBIMA)
CY APR 01-02, 2020
CL Seville, SPAIN
DE Artificial Neural Networks; ANN; Neurofuzzy; Kohonen; Hopfield;
   Bibliometrics
AB In this mini bibliometric review, we explore and report on the contributions made in the field of Artificial Neural Networks (ANN) over more than three decades of research endeavors. The review covers the period from 1986 to 2018 with a total of 380,086 documents extracted from Scopus database. The extracted bibliometric information was thoroughly pre-processed while the analysis of authorship was performed using the VOSviewer (www.vosviewer.com). The result of the bibliometric analysis unearths the types of publications, the languages used for the publications, the evolution of the scientific outputs, the distribution of publications by regions and institutions, the distribution of outputs in subject categories and the networks of authorship. The results presented in this paper could help researchers seeking for collaborations in the field of ANN to promptly locate reputable scholars. Funding agencies could also leverage on the results in this study to properly disburse grants for ground-breaking work and measure impact and accountability of investment to support further research in artificial intelligence.
C1 [Adetiba, Emmanuel; Adeyemi-Kayode, Temitope; Moninuola, Funmilayo; Akinrinmade, Adekunle] Covenant Univ, Coll Engn, Dept Elect & Informat Engn, Ota, Nigeria.
   [Adetiba, Emmanuel] Durban Univ Technol, Inst Syst Sci, HRA, POB 1334, Durban, South Africa.
   [Abolarin, Olusegun] Schlumberger Technol Corp, 14910 Airline Rd, Rosharon, TX 77583 USA.
   [Moyo, Sibusiso] Durban Univ Technol, Inst Syst Sci, DVC Res Innovat & Engagement, Durban, South Africa.
C3 Covenant University; Durban University of Technology; Schlumberger;
   Durban University of Technology
RP Adetiba, E (corresponding author), Covenant Univ, Coll Engn, Dept Elect & Informat Engn, Ota, Nigeria.; Adetiba, E (corresponding author), Durban Univ Technol, Inst Syst Sci, HRA, POB 1334, Durban, South Africa.
EM emmanuel.adetiba@covenantuniversity.edu.ng;
   mercy.john@covenantuniversity.edu.ng; funmiadefemi@gmail.com;
   adekunleakinrinmade@gmail.com; oablarin@slb.com; moyos@dut.ac.za
RI Adetiba, Emmanuel/AAC-4129-2022; Moyo, Sibusiso/GRE-7858-2022
OI Moyo, Sibusiso/0000-0001-5613-7290
FU Covenant University Center for Research Innovation and Discovery
   (CUCRID)
FX The authors wish to acknowledge the Covenant University Center for
   Research Innovation and Discovery (CUCRID) for sponsoring the
   publication of this study.
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NR 27
TC 0
Z9 0
U1 0
U2 1
PU INT BUSINESS INFORMATION MANAGEMENT ASSOC-IBIMA
PI NORRISTOWN
PA 34 E GERMANTOWN PIKE, NO. 327, NORRISTOWN, PA 19401 USA
BN 978-0-9998551-4-0
PY 2020
BP 12026
EP 12043
PG 18
WC Business; Green & Sustainable Science & Technology; Economics;
   Management
WE Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Business & Economics; Science & Technology - Other Topics
GA BR6JR
UT WOS:000661489802092
DA 2024-09-05
ER
PT J
AU Wu, JH
   Liu, TY
   Mu, KL
   Zhou, L
AF Wu, Jinhong
   Liu, Tianye
   Mu, Keliang
   Zhou, Lei
TI Identification and causal analysis of predatory open access journals
   based on interpretable machine learning
SO SCIENTOMETRICS
LA English
DT Article
DE Open access; Journal alerts; Causal analysis; Interpretable machine
   learning
ID RANK INDICATOR; IMPACT FACTOR; SCIENCE; INDEX
AB Predatory journals have been a recent phenomenon, drawing attention from the academic community in the last decade. However, as the open access (OA) movement has gained momentum, the indiscriminate growth of predatory journals has had significant negative impacts on academic communication, scholarly publishing, and effective utilization of scientific resources. This rampant growth poses a serious threat to the healthy development of the OA movement and also undermines the integrity of research and the research ecosystem. Identifying predatory journals from the massive number of OA journals would assist scholars in evading negative consequences in areas of monetary investment, reputation, academic influence, and occupational advancement. Traditional methods for identifying predatory journals have relied heavily on the knowledge of domain experts. However, a large number of predatory journals exhibit latent and covert characteristics, and the growth rate of OA journals is extremely rapid, making it difficult for experts to identify these predatory journals from the vast number of OA journals. This paper proposes an interpretable machine learning model for early warning of predatory OA journals, which identifies predatory journals through the ensemble of multiple machine learning algorithms. Specifically, the proposed methodology first constructs an OA journal early warning indicator system and integrates multiple machine learning algorithms to compute the early warning values of OA journals. Then, the SHAP interpretable framework is introduced to analyze the causal factors of the early warning risks in a novel way. To verify the accuracy of the model's causal factors, we conduct a comparative analysis of domestic and foreign medical OA journals using case studies. The empirical analysis conducted in this study demonstrates the efficacy of the ensemble algorithm in accurately identifying the risk of predatory OA journals.
C1 [Wu, Jinhong; Liu, Tianye; Mu, Keliang; Zhou, Lei] Wuhan Text Univ, Wuhan, Peoples R China.
C3 Wuhan Textile University
RP Mu, KL (corresponding author), Wuhan Text Univ, Wuhan, Peoples R China.
EM 704266922@qq.com
FU 2020 Hubei Provincial Social Science Foundation Pre-Funded Projects;
   China Scholarship Council
FX This work was supported by the China Scholarship Council.
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NR 63
TC 0
Z9 0
U1 16
U2 16
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD APR
PY 2024
VL 129
IS 4
BP 2131
EP 2158
DI 10.1007/s11192-024-04969-6
EA MAR 2024
PG 28
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA QV6H9
UT WOS:001180362500007
DA 2024-09-05
ER
PT J
AU Tian, S
   Mo, SS
   Wang, LW
   Peng, ZY
AF Tian, Shan
   Mo, Songsong
   Wang, Liwei
   Peng, Zhiyong
TI Deep Reinforcement Learning-Based Approach to Tackle Topic-Aware
   Influence Maximization
SO DATA SCIENCE AND ENGINEERING
LA English
DT Article
DE Social network; Influence maximization; Graph embedding; Reinforcement
   learning
AB Motivated by the application of viral marketing, the topic-aware influence maximization (TIM) problem has been proposed to identify the most influential users under given topics. In particular, it aims to find k seeds (users) in social network G, such that the seeds can maximize the influence on users under the specific query topics and diffusion model such as independent cascade (IC) or linear threshold (LT). This problem has been proved to be NP-hard, and most of the proposed techniques suffer from the efficiency issue due to the lack of generalization. Even worse, the design of these algorithms requires significant specialized knowledge which is hard to be understood and implemented. To overcome these issues, this paper aims to learn a generalized heuristic framework to solve TIM problems by meta-learning. To this end, we first propose two topic-aware social influence propagation models based on IC and LT model, respectively, which is conducive to better advertising injections. We then encode the feature of each node by a vector and introduce a model, called deep influence evaluation model , to evaluate the user influence under different circumstances. Based on this model, we can construct the solution according to the influence evaluations efficiently, rather than spending a high cost to compute the exact influence by considering the complex graph structure. We conducted experiments on generated graph instances and real-world social networks. The results show the superiority in performance and comparable quality of our framework.
C1 [Tian, Shan; Mo, Songsong; Wang, Liwei; Peng, Zhiyong] Wuhan Univ, Sch Comp Sci, Wuhan, Hubei, Peoples R China.
C3 Wuhan University
RP Peng, ZY (corresponding author), Wuhan Univ, Sch Comp Sci, Wuhan, Hubei, Peoples R China.
EM tianshan14@whu.edu.cn; songsong945@whu.edu.cn; liwei.wang@whu.edu.cn;
   peng@whu.edu.cn
FU National Key Research and Development Program of China [2018YFB1003400];
   Fundamental Research Funds for the Central Universities [2042017kf1017]
FX This work is supported by the National Key Research and Development
   Program of China (Project Number: 2018YFB1003400), and the Fundamental
   Research Funds for the Central Universities (Project Number:
   2042017kf1017).
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NR 33
TC 57
Z9 59
U1 1
U2 6
PU SPRINGERNATURE
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND
SN 2364-1185
EI 2364-1541
J9 DATA SCI ENG
JI Data Sci. Eng.
PD MAR
PY 2020
VL 5
IS 1
BP 1
EP 11
DI 10.1007/s41019-020-00117-1
PG 11
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA SL8HQ
UT WOS:000657155000001
OA gold
DA 2024-09-05
ER
PT J
AU Seo, YW
   Lee, KC
   Lee, S
AF Seo, Young Wook
   Lee, Kun Chang
   Lee, Sangjae
TI Decision quality of the research project evaluation mechanism by using
   particle swarm optimization
SO MANAGEMENT DECISION
LA English
DT Article
DE Research performance; Particle swarm optimization; Research impact;
   K-means clustering method; Research fund; Knowledge-based society
ID EFFICIENCY; SUPPORT; IMPACT
AB Purpose - For those who plan research funds and assess the research performance from the funds, it is necessary to overcome the limitations of the conventional classification of evaluated papers published by the research funds. Besides, they need to promote the objective, fair clustering of papers, and analysis of research performance. Therefore, the purpose of this paper is to find the optimum clustering algorithm using the MATLAB tools by comparing the performances of and the hybrid particle swarm optimization algorithms using the particle swarm optimization (PSO) algorithm and the conventional K-means clustering method.
   Design/methodology/approach - The clustering analysis experiment for each of the three fields of study - health and medicine, physics, and chemistry - used the following three algorithms: "K-means+Simulated annealing (SA)+ Adjustment of parameters+PSO" (KASA-PSO clustering), "K-means+SA+PSO" clustering, "K-means+PSO" clustering.
   Findings - The clustering analyses of all the three fields showed that KASA-PSO is the best method for the minimization of fitness value. Furthermore, this study administered the surveys intended for the "performance measurement of decision- making process" with 13 members of the research fund organization to compare the group clustering by the clustering analysis method of KASA-PSO algorithm and the group clustering by research funds. The results statistically demonstrated that the group clustering by the clustering analysis method of KASA-PSO algorithm was better than the group clustering by research funds.
   Originality/value - There are still too few studies that assess the research project evaluation mechanisms and its effectiveness perceived by the research fund managers. To fill the research void like this, this study aims to propose PSO and successfully proves validity of the proposed approach.
C1 [Seo, Young Wook] Daejeon Univ, Dept Business Consulting, Daejeon, South Korea.
   [Lee, Kun Chang] Sungkyunkwan Univ, SKK Business Sch, Seoul, South Korea.
   [Lee, Kun Chang] Sungkyunkwan Univ, Digital Hlth Dept, SAIHST, Seoul, South Korea.
   [Lee, Sangjae] Sejong Univ, Sch Management, Seoul, South Korea.
C3 Daejeon University; Sungkyunkwan University (SKKU); Sungkyunkwan
   University (SKKU); Sejong University
RP Lee, KC (corresponding author), Sungkyunkwan Univ, SKK Business Sch, Seoul, South Korea.; Lee, KC (corresponding author), Sungkyunkwan Univ, Digital Hlth Dept, SAIHST, Seoul, South Korea.
EM kunchanglee@gmail.com
RI Lee, Sangjae/F-4383-2014
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NR 41
TC 3
Z9 3
U1 0
U2 15
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 0025-1747
EI 1758-6070
J9 MANAGE DECIS
JI Manag. Decis.
PY 2017
VL 55
IS 4
BP 745
EP 765
DI 10.1108/MD-03-2016-0141
PG 21
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA EX0UW
UT WOS:000402937300007
DA 2024-09-05
ER
PT C
AU Nugraha, YR
   Wibawa, AP
   Zaeni, IAE
AF Nugraha, Youngga Rega
   Wibawa, Aji Prasetya
   Zaeni, Ilham Ari Elbaith
BE Hidayati, A
   Oktaviana, S
   Ismail, IE
   Zain, AR
   Nugrahani, F
   Kurniawati, D
   Permatasari, I
TI Particle Swarm Optimization - Support Vector Machine (PSO-SVM) Algorithm
   for Journal Rank Classification
SO 2019 2ND INTERNATIONAL CONFERENCE OF COMPUTER AND INFORMATICS
   ENGINEERING (IC2IE 2019): ARTIFICIAL INTELLIGENCE ROLES IN INDUSTRIAL
   REVOLUTION 4.0
LA English
DT Proceedings Paper
CT 2nd International Conference of Computer and Informatics Engineering
   (IC2IE) - Artificial Intelligence Roles in Industrial Revolution 4.0
CY SEP 10-11, 2019
CL Politeknik Negeri Jakarta, Banyuwangi, INDONESIA
HO Politeknik Negeri Jakarta
DE support vector machine; particle swarm optimization; SCImago journal
   rank; classification
AB Support Vector Machine (SVM) is a method with basic classification principles for data that can be separated linearly. As it developed, SVM is designed to work on non-linear problems by incorporating kernel concepts in high-dimensional space. The SVM method implemented in this study for classifying international journals using the SCImago Journal Rank (SJR) dataset. To overcome the disadvantages of SVM performance, the researchers used Particle Swarm Optimization (PSO) to optimize its performance. The purpose of using PSO is to get a better classification performance based on the parameters and functions of the kernel used and to approach the SJR classification system. The process includes normalizing and processing the data on the PSO, followed by implementation using the SVM method. The accuracy results obtained from PSO-SVM are 63.12% using Linear kernels. Based on these results, it assumed that PSO-SVM is still unable to optimize the approach in the SJR classification system if the system is 100% accurate.
C1 [Nugraha, Youngga Rega; Wibawa, Aji Prasetya; Zaeni, Ilham Ari Elbaith] Univ Negeri Malang, Elect Engn Dept, Malang, Indonesia.
C3 Universitas Negeri Malang
RP Nugraha, YR (corresponding author), Univ Negeri Malang, Elect Engn Dept, Malang, Indonesia.
EM younggarega@gmail.com; aji.prasetya.ft@um.ac.id; ilham.ari.ft@um.ac.id
RI wibawa, aji prasetya/AAI-7475-2021; Zaeni, Ilham Ari
   Elbaith/AAJ-9600-2020
OI Zaeni, Ilham Ari Elbaith/0000-0001-9665-8613
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NR 20
TC 3
Z9 3
U1 0
U2 1
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-7281-2384-4
PY 2019
BP 69
EP 73
PG 5
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BO7UI
UT WOS:000525626200014
DA 2024-09-05
ER
PT J
AU Mi, HC
   Gao, ZH
   Zhang, QR
   Zheng, YF
AF Mi, Huichao
   Gao, Zhanghao
   Zhang, Qiaorong
   Zheng, Yafeng
TI Research on Constructing Online Learning Performance Prediction Model
   Combining Feature Selection and Neural Network
SO INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING
LA English
DT Article
DE learning performance prediction; machine learning; deep neural networks;
   multiple linear regression; feature selection
ID STUDENTS PERFORMANCE; DROPOUT
AB Learning performance prediction can help teachers find students who tend to fail as early as possible so as to give them timely help, which is of great significance for online education. With the availability of online data and the continuous development of machine learning technology, learning performance prediction in large-scale online education is gaining new momentum. Traditional prediction methods include statistical methods, machine learning and neural networks. Among them, statistical methods and machine learning have low prediction efficiency. Although neural network can improve prediction efficiency, it ignores the impact of artificial feature filtering on model performance, and cannot find key factors for performance prediction, making predictions uninterpretable. Therefore, this paper proposes an online academic performance prediction model that integrates feature selection and neural network. Multiple linear regression analysis is used for feature extraction to obtain key influence features, and then deep neural networks is used for prediction. The results show that the F1 score of our model on large-scale data set is 99.25%, which is 1.25% higher than that of other related models.
C1 [Mi, Huichao; Gao, Zhanghao; Zhang, Qiaorong; Zheng, Yafeng] Henan Univ Econ & Law, Coll Comp & Informat Engn, Zhengzhou, Peoples R China.
C3 Henan University of Economics & Law
RP Zheng, YF (corresponding author), Henan Univ Econ & Law, Coll Comp & Informat Engn, Zhengzhou, Peoples R China.
EM mmhhcc@126.com
FU Industry-university cooperative education project [202101045002];
   Science and Technology Research Project of Henan Province ("Research on
   text classification model and application for massive open online
   courses")
FX This material is based upon work supported by Industry-university
   cooperative education project (202101045002) and the Science and
   Technology Research Project of Henan Province ("Research on text
   classification model and application for massive open online courses").
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NR 28
TC 2
Z9 2
U1 5
U2 29
PU INT ASSOC ONLINE ENGINEERING
PI WIEN
PA KIRCHENGASSE 10-200, WIEN, A-1070, AUSTRIA
SN 1863-0383
J9 INT J EMERG TECHNOL
JI Int. J. Emerg. Technol. Learn.
PY 2022
VL 17
IS 7
BP 94
EP 111
DI 10.3991/ijet.v17i07.25587
PG 18
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA 0W3EO
UT WOS:000788914700007
OA gold
DA 2024-09-05
ER
PT J
AU Du, WM
   Li, ZM
   Xie, Z
AF Du, Wumei
   Li, Zhemin
   Xie, Zheng
TI A modified LSTM network to predict the citation counts of papers
SO JOURNAL OF INFORMATION SCIENCE
LA English
DT Article
DE Citation analysis; citation count prediction; deep learning; long
   short-term memory
ID IMPACT; ARTICLES; INDEXES
AB Quantifiable predictability in the citation counts of articles is significant in scientometrics and informetrics. Many metrics based on the citation counts can evaluate the scientific impact of research articles and journals. Utilising time series models, an article's citation counts up to the yth year after publication can be predicted by those up to the previous years. However, the typically used models cannot predict the fat tail of the actual citation distributions. Thus, based on cumulative advantage of the citation behaviour, we propose a method to predict the accumulated citation counts, by using a random number sampled from a power-law distribution to modify the results given by a recurrent neural network (RNN), long short-term memory. Extensive experiments on the data set including 17 journals in information science verified the effectiveness of our method by the good fittings on distributions and evolutionary trends of the citation counts of articles. Our method has the potential to be extended to predict other popular assessment measures such as impact factor and h-index for journals.
C1 [Du, Wumei; Li, Zhemin; Xie, Zheng] Natl Univ Def Technol, Coll Liberal Arts & Sci, Changsha 410000, Peoples R China.
C3 National University of Defense Technology - China
RP Xie, Z (corresponding author), Natl Univ Def Technol, Coll Liberal Arts & Sci, Changsha 410000, Peoples R China.
EM xiezheng81@nudt.edu.cn
FU National Natural Science Foundation of China [61773020]
FX The author(s) disclosed receipt of the following financial support for
   the research, authorship, and/or publication of this article: This work
   was supported by the National Natural Science Foundation of China (grant
   no. 61773020).
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NR 44
TC 3
Z9 3
U1 16
U2 94
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 0165-5515
EI 1741-6485
J9 J INF SCI
JI J. Inf. Sci.
PD AUG
PY 2024
VL 50
IS 4
BP 894
EP 909
DI 10.1177/01655515221111000
EA AUG 2022
PG 16
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA C3E8I
UT WOS:000837348100001
DA 2024-09-05
ER
PT C
AU Bhardwaj, A
   Mercier, D
   Dengel, A
   Ahmed, S
AF Bhardwaj, Akansha
   Mercier, Dominik
   Dengel, Andreas
   Ahmed, Sheraz
BE Liu, D
   Xie, S
   Li, Y
   Zhao, D
   ElAlfy, ESM
TI DeepBIBX: Deep Learning for Image Based Bibliographic Data Extraction
SO NEURAL INFORMATION PROCESSING (ICONIP 2017), PT II
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT 24th International Conference on Neural Information Processing (ICONIP)
CY NOV 14-18, 2017
CL Guangzhou, PEOPLES R CHINA
DE Deep learning; Machine learning; Bibliographic data; Reference linking
AB Extraction of structured bibliographic data from document images of non-native-digital academic content is a challenging problem that finds its application in the automation of cataloging systems in libraries and reference linking domain. The existing approaches discard the visual cues and focus on converting the document image to text and further identifying citation strings using trained segmentation models. Apart from the large training data, which these existing methods require, they are also language dependent. This paper presents a novel approach (DeepBIBX) which targets this problem from a computer vision perspective and uses deep learning to semantically segment the individual citation strings in a document image. DeepBIBX is based on deep Fully Convolutional Networks and uses transfer learning to extract bibliographic references from document images. Unlike existing approaches which use textual content to semantically segment bibliographic references, DeepBIBX utilizes image based contextual information, which makes it applicable to documents of any language. To gauge the performance of the presented approach, a dataset consisting of 286 document images containing 5090 bibliographic references is collected. Evaluation results reveals that the DeepBIBX outperforms state-of-the-art method (ParsCit, 71.7%) for bibliographic references extraction and achieved an accuracy of 84.9% in comparison to 71.7%. Furthermore, in terms of pixel classification task, DeepBIBX achieved a precision and a recall rate of 96.2%, 94.4% respectively.
C1 [Bhardwaj, Akansha; Mercier, Dominik; Dengel, Andreas; Ahmed, Sheraz] DFKI Kaiserslautern, Smart Data & Serv, Kaiserslautern, Germany.
   [Bhardwaj, Akansha] Univ Fribourg, eXascale Infolab, Fribourg, Switzerland.
C3 German Research Center for Artificial Intelligence (DFKI); University of
   Fribourg
RP Bhardwaj, A (corresponding author), DFKI Kaiserslautern, Smart Data & Serv, Kaiserslautern, Germany.; Bhardwaj, A (corresponding author), Univ Fribourg, eXascale Infolab, Fribourg, Switzerland.
EM akansha.bhardwaj@dfki.de; dominik.mercier@dfki.de;
   andreas.dengel@dfki.de; sheraz.ahmed@dfki.de
OI Mercier, Dominique/0000-0001-8817-2744
FU DFG [DE 420/18-1]; Swiss National Science Foundation [407540 167320];
   Swiss National Science Foundation (SNF) [407540_167320] Funding Source:
   Swiss National Science Foundation (SNF)
FX This work was partially supported by the DFG under contract DE 420/18-1
   and by the Swiss National Science Foundation under grant number 407540
   167320.
CR [Anonymous], 2016, ARXIV161105725
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NR 10
TC 4
Z9 4
U1 0
U2 1
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 0302-9743
EI 1611-3349
BN 978-3-319-70096-0; 978-3-319-70095-3
J9 LECT NOTES COMPUT SC
PY 2017
VL 10635
BP 286
EP 293
DI 10.1007/978-3-319-70096-0_30
PN II
PG 8
WC Computer Science, Artificial Intelligence; Computer Science, Theory &
   Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BQ1LI
UT WOS:000576766300030
DA 2024-09-05
ER
PT J
AU Bhatt, SM
   Noortgate, WV
   Verbert, K
AF Bhatt, Sohum M.
   Noortgate, Wim Van Den
   Verbert, Katrien
TI Investigating the Use of Deep Learning and Implicit Feedback in K12
   Educational Recommender Systems
SO IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES
LA English
DT Article
DE Recommender systems; Deep learning; Education; Market research;
   Recurrent neural networks; Long short term memory; Context; E-learning
   tools; evaluation; K12 education; personalized E-learning; recommender
   systems
AB Recommender systems are increasingly being used in university or online education. However, recommender systems still have not found major usage in K12 education. This may be because of the unique challenges that recommender systems face when used by a young and diverse population. However, recommender systems for K12 education could provide many benefits for students and teachers, such as the simplification of personalized learning. Some of the issues with K12 educational recommender systems may be solved by the use of deep learning and implicit feedback. As such, we investigated the use of deep recommendation algorithms and implicit feedback for K12 educational recommender systems. To do this, we compared metrics for highly cited traditional and deep recommendation algorithms trained on explicit and implicit data. We found that recommendation algorithms using deep learning as a group do not differ in performance compared to traditional recommendation algorithms. We also found that the use of implicit feedback led to higher performance than using explicit feedback. The best performing algorithm used both deep learning and implicit feedback. We conclude that deep learning can be a benefit for K12 recommender systems, particularly when the ordered sequence of items is carefully accounted for. We also conclude that researchers and developers must carefully consider which feedback contributes the most information for learning.
C1 [Bhatt, Sohum M.; Noortgate, Wim Van Den] Katholieke Univ Leuven, IMEC Res Grp ITEC, B-8500 Kortrijk, Belgium.
   [Bhatt, Sohum M.; Noortgate, Wim Van Den] Fac Psychol & Educ Sci, KU Leuven Campus KulakKortrijk, B-8500 Kortrijk, Belgium.
   [Verbert, Katrien] Katholieke Univ Leuven, Dept Comp Sci, Human & Comp Interact Grp, B-3001 Heverlee, Belgium.
C3 KU Leuven; KU Leuven
RP Bhatt, SM (corresponding author), Katholieke Univ Leuven, IMEC Res Grp ITEC, B-8500 Kortrijk, Belgium.; Bhatt, SM (corresponding author), Fac Psychol & Educ Sci, KU Leuven Campus KulakKortrijk, B-8500 Kortrijk, Belgium.
EM sohummandar.bhatt@kuleuven.be; wim.vandennoortgate@kuleuven.be;
   katrien.verbert@kuleuven.be
OI Bhatt, Sohum/0000-0002-8917-292X; verbert, katrien/0000-0001-6699-7710
FU Flanders Agency of Innovation amp; Entrepreneurship
FX No Statement Available
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NR 59
TC 0
Z9 0
U1 14
U2 14
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1939-1382
J9 IEEE T LEARN TECHNOL
JI IEEE Trans. Learn. Technol.
PY 2024
VL 17
BP 112
EP 123
DI 10.1109/TLT.2023.3273422
PG 12
WC Computer Science, Interdisciplinary Applications; Education &
   Educational Research
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Education & Educational Research
GA EU8T8
UT WOS:001141545900043
DA 2024-09-05
ER
PT J
AU Meireles, MRG
   Cendón, BV
AF Meireles, Magali Rezende Gouvea
   Cendon, Beatriz Valadares
TI Citation-Based Document Categorization: An Approach Using Artificial
   Neural Networks
SO QUALITATIVE & QUANTITATIVE METHODS IN LIBRARIES
LA English
DT Article
DE Bibliometrics; Document Clustering; Information Science; Classification;
   Information Retrieval Systems; Artificial Neural Networks
AB The automatic organization of large collections of documents becomes more important with the growth of the amount of information available in digital form. This study contributes to this issue evaluating the use of Artificial Neural Networks (ANNs) to automatically categorize documents through the analysis of the references cited in these documents. The article describes the method developed to generate clusters of documents based on bibliometric concepts. The method is grounded on the premise that the presence of common citations is indicative of relationships among documents and thus publications are categorized using citations as the main input information. ANNs are typically used to solve problems related to approximation, prediction, classification, categorization and optimization. Many of the experiments reported in the literature describe the use of SOM networks, Self Organizing Maps, in the organization of documents for information retrieval. SOM networks are used in this work in order to categorize documents in a test database. In this categorization process, the semantic relationships among documents are defined not by the identification of terms in common, but by the presence of common cited references and their years of publication. After validation of the method, through the use of a prototype, a database was created, containing the references cited in 200 articles published in the IEEE Transactions on Neural Networks Journal, between years of 2001 and 2010. The publications were categorized by the ANN and presented in groups organized by their common citations. The results obtained show that the ANN successfully identified clusters of authors and texts, through their cited references. These clusters, formed through automatic classification of documents, evidence the existence of semantic relationships between the documents. They can be useful, for example, to automatically identify groups of researchers working in related fields or for identifying research trends in specific domains of knowledge. Another application would be in the process of information retrieval, where they could assist users in the development or reformulation of their queries.
C1 [Meireles, Magali Rezende Gouvea] Pontificia Univ Catolica Minas Gerais, Inst Math Sci & Informat, Belo Horizonte, MG, Brazil.
   [Cendon, Beatriz Valadares] Univ Fed Minas Gerais, Sch Informat Sci, Belo Horizonte, MG, Brazil.
C3 Pontificia Universidade Catolica de Minas Gerais; Universidade Federal
   de Minas Gerais
RP Meireles, MRG (corresponding author), Pontificia Univ Catolica Minas Gerais, Inst Math Sci & Informat, Belo Horizonte, MG, Brazil.
RI Cendon, Beatriz/G-6141-2011; Meireles, Magali RG/F-6563-2013
OI Cendon, Beatriz/0000-0002-3276-0114; 
CR ALVARENGA L, 1998, CIENCIA INFORM BRASI, V27
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NR 14
TC 1
Z9 1
U1 0
U2 4
PU INT SOC ART SCIENCE & TECHNOLOGY-ISAST
PI ATHINA
PA INT SOC ART SCIENCE & TECHNOLOGY-ISAST, ATHINA, 00000, GREECE
SN 2241-1925
J9 QUAL QUANT METHODS L
JI Qual. Quant. Methods Libr.
PD JAN
PY 2015
SI SI
BP 71
EP 79
PG 9
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA CV9ZK
UT WOS:000364647800008
DA 2024-09-05
ER
PT J
AU Egger, J
   Pepe, A
   Gsaxner, C
   Jin, Y
   Li, JN
   Kern, R
AF Egger, Jan
   Pepe, Antonio
   Gsaxner, Christina
   Jin, Yuan
   Li, Jianning
   Kern, Roman
TI Deep learning-a first meta-survey of selected reviews across scientific
   disciplines, their commonalities, challenges and research impact
SO PEERJ COMPUTER SCIENCE
LA English
DT Article
DE Deep learning; Artificial neural networks; Machine learning; Data
   analysis; Image analysis; Language processing; Speech recognition; Big
   data; Medical image analysis; Meta-review
ID IMAGE; RECOGNITION
AB Deep learning belongs to the field of artificial intelligence, where machines perform tasks that typically require some kind of human intelligence. Deep learning tries to achieve this by drawing inspiration from the learning of a human brain. Similar to the basic structure of a brain, which consists of (billions of) neurons and connections between them, a deep learning algorithm consists of an artificial neural network, which resembles the biological brain structure. Mimicking the learning process of humans with their senses, deep learning networks are fed with (sensory) data, like texts, images, videos or sounds. These networks outperform the state-of-the-art methods in different tasks and, because of this, the whole field saw an exponential growth during the last years. This growth resulted in way over 10,000 publications per year in the last years. For example, the search engine PubMed alone, which covers only a sub-set of all publications in the medical field, provides already over 11,000 results in Q3 2020 for the search term 'deep learning', and around 90% of these results are from the last three years. Consequently, a complete overview over the field of deep learning is already impossible to obtain and, in the near future, it will potentially become difficult to obtain an overview over a subfield. However, there are several review articles about deep learning, which are focused on specific scientific fields or applications, for example deep learning advances in computer vision or in specific tasks like object detection. With these surveys as a foundation, the aim of this contribution is to provide a first high-level, categorized meta-survey of selected reviews on deep learning across different scientific disciplines and outline the research impact that they already have during a short period of time. The categories (computer vision, language processing, medical informatics and additional works) have been chosen according to the underlying data sources In we review the common architectures, methods, pros, cons, evaluations, challenges and future directions for every sub-category.
C1 [Egger, Jan; Pepe, Antonio; Gsaxner, Christina; Jin, Yuan; Li, Jianning] Graz Univ Technol, Fac Comp Sci & Biomed Engn, Inst Comp Graph & Vis, Graz, Austria.
   [Egger, Jan; Pepe, Antonio; Gsaxner, Christina; Jin, Yuan; Li, Jianning] Comp Algorithms Med Lab, Graz, Austria.
   [Egger, Jan; Gsaxner, Christina] Med Univ Graz, Dept Oral & Maxillofacial Surg, Graz, Austria.
   [Egger, Jan; Li, Jianning] Univ Med Essen, Inst AI Med IKIM, Essen, Germany.
   [Jin, Yuan] Res Ctr Connected Healthcare Big Data, Zhejiang Lab, Hangzhou, Zhejiang, Peoples R China.
   [Li, Jianning] Med Univ Graz, Dept Neurosurg, Res Unit Expt Neurotraumatol, Graz, Austria.
   [Kern, Roman] Know Ctr, Knowledge Discovery, Graz, Austria.
   [Kern, Roman] Graz Univ Technol, Inst Interact Syst & Data Sci, Graz, Austria.
C3 Graz University of Technology; Medical University of Graz; Zhejiang
   Laboratory; Medical University of Graz; Graz University of Technology
RP Egger, J (corresponding author), Graz Univ Technol, Fac Comp Sci & Biomed Engn, Inst Comp Graph & Vis, Graz, Austria.; Egger, J (corresponding author), Comp Algorithms Med Lab, Graz, Austria.; Egger, J (corresponding author), Med Univ Graz, Dept Oral & Maxillofacial Surg, Graz, Austria.; Egger, J (corresponding author), Univ Med Essen, Inst AI Med IKIM, Essen, Germany.
EM egger@tugraz.at
RI Pepe, Antonio/AAI-9317-2020; Kern, Roman/ABG-3805-2020
OI Pepe, Antonio/0000-0002-5843-6275; Kern, Roman/0000-0003-0202-6100; Jin,
   Yuan/0000-0001-8695-1525
FU Austrian Science Fund (FWF) [KLI 678-B31]; TU Graz Lead Project
   (Mechanics, Modeling and Simulation of Aortic Dissection); Austrian
   Federal Ministry of Transport, Innovation and Technology (BMVIT)
   [871132]; Austrian Federal Ministry for Digital and Economic Affairs
   (BMDW) [871132]; Styrian Business Promotion Agency (SFG)
FX The authors received funding from the Austrian Science Fund (FWF) KLI
   678-B31: 'enFaced: Virtual and Augmented Reality Training and Navigation
   Module for 3D-Printed Facial Defect Reconstructions' and the TU Graz
   Lead Project (Mechanics, Modeling and Simulation of Aortic Dissection).
   Moreover, this work was supported by CAMed (COMET K-Project 871132),
   which is funded by the Austrian Federal Ministry of Transport,
   Innovation and Technology (BMVIT), and the Austrian Federal Ministry for
   Digital and Economic Affairs (BMDW), and the Styrian Business Promotion
   Agency (SFG). The funders had no role in study design, data collection
   and analysis, decision to publish, or preparation of the manuscript.
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NR 99
TC 15
Z9 15
U1 1
U2 10
PU PEERJ INC
PI LONDON
PA 341-345 OLD ST, THIRD FLR, LONDON, EC1V 9LL, ENGLAND
EI 2376-5992
J9 PEERJ COMPUT SCI
JI PeerJ Comput. Sci.
PD NOV 17
PY 2021
VL 7
AR e773
DI 10.7717/peerj-cs.773
PG 83
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA XA9AB
UT WOS:000720930400002
PM 34901429
OA gold, Green Published, Green Submitted
DA 2024-09-05
ER
PT J
AU Melnikova, EV
AF Melnikova, E. V.
TI Needs of Scientometry and Possibilities of Modern Machine Learning as a
   Field of Artificial Intelligence
SO SCIENTIFIC AND TECHNICAL INFORMATION PROCESSING
LA English
DT Article
DE scientometry; scientometric indicators; bibliometry; classification; Web
   of Science; Scopus; artificial intelligence; machine learning; deep
   learning; artificial neural networks; big data
ID CITATION INDEXES
AB A general description of modern scientometry, its main tasks, and its research methods is presented. The issues of the application of conventional machine learning and deep learning algorithms as tools of artificial intelligence in the thematic classification of scientific literature are considered. The problems and limitations of the classification of literature by sections of science in the systems of indexing and citing of scientific information are outlined. The author presents a specific example of a deep learning application for by-article thematic classification based on convolutional neural networks that was designed by scientists from the United Arab Emirates and Jordan. The article emphasizes the importance of the use of deep learning applications and models for creating correct classifications of the scientific literature that correspond to the realities of the development of science and that are capable of increasing the accuracy of calculating scientometric indicators.
C1 [Melnikova, E. V.] Russian Acad Sci, All Russian Res Inst Sci & Tech Informat, Moscow, Russia.
C3 Russian Academy of Sciences
RP Melnikova, EV (corresponding author), Russian Acad Sci, All Russian Res Inst Sci & Tech Informat, Moscow, Russia.
EM verden.mel@yandex.ru
FU All-Russian Research Institute for Scientific and Technical Information,
   Russian Academy of Sciences [FFFU-2022-0007]
FX The article was supported by the state assignment of the All-Russian
   Research Institute for Scientific and Technical Information, Russian
   Academy of Sciences (project no.FFFU-2022-0007).
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NR 27
TC 1
Z9 1
U1 5
U2 5
PU PLEIADES PUBLISHING INC
PI NEW YORK
PA PLEIADES HOUSE, 7 W 54 ST, NEW YORK,  NY, UNITED STATES
SN 0147-6882
EI 1934-8118
J9 SCI TECH INF PROCESS
JI Sci. Tech. Inf. Process.
PD JUN
PY 2023
VL 50
IS 2
BP 114
EP 120
DI 10.3103/S0147688223020089
PG 7
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA Q5HV3
UT WOS:001057838600004
DA 2024-09-05
ER
PT J
AU Ji, TR
   Self, N
   Fu, KQ
   Chen, ZQ
   Ramakrishnan, N
   Lu, CT
AF Ji, Taoran
   Self, Nathan
   Fu, Kaiqun
   Chen, Zhiqian
   Ramakrishnan, Naren
   Lu, Chang-Tien
TI Citation Forecasting with Multi-Context Attention-Aided Dependency
   Modeling
SO ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
LA English
DT Article
DE Citation analysis; recurrent neural networks; deep learning
ID COUNT PREDICTION; NETWORKS; IMPACT; INDEX
AB Forecasting citations of scientific patents and publications is a crucial task for understanding the evolution and development of technological domains and for foresight into emerging technologies. By construing citations as a time series, the task can be cast into the domain of temporal point processes. Most existing work on forecasting with temporal point processes, both conventional and neural network-based, only performs single-step forecasting. In citation forecasting, however, the more salient goal is n-step forecasting: predicting the arrival of the next n citations. In this article, we propose Dynamic Multi-Context Attention Networks (DMA-Nets), a novel deep learning sequence-to-sequence (Seq2Seq) model with a novel hierarchical dynamic attention mechanism for long-term citation forecasting. Extensive experiments on two real-world datasets demonstrate that the proposed model learns better representations of conditional dependencies over historical sequences compared to state-of-the-art counterparts and thus achieves significant performance for citation predictions.
C1 [Ji, Taoran] Texas A&M Univ, Dept Comp Sci, CI 317,6300 Ocean Dr, Corpus Christi, TX 78412 USA.
   [Self, Nathan; Ramakrishnan, Naren] Virginia Tech, Dept Comp Sci, Arlington, VA USA.
   [Self, Nathan; Ramakrishnan, Naren] VTRC Arlington, Dept Comp Sci, 900 North Glebe Rd, Arlington, VA 22203 USA.
   [Fu, Kaiqun] South Dakota State Univ, Dept Comp Sci, Daktron Eng Hall 123,Elect Engn Comp Sci Box 2222, Brookings, SD 57007 USA.
   [Chen, Zhiqian] Mississippi State Univ, Comp Sci & Engn Dept, Starkville, MS USA.
   [Chen, Zhiqian] Mississippi State Univ, Comp Sci & Engn Dept, 304 Butler Hall,75 BS Hood Rd, Mississippi State, MS 39762 USA.
   [Lu, Chang-Tien] Virginia Tech, Dept Comp Sci, Falls Church, VA USA.
   [Lu, Chang-Tien] Northern Virginia Ctr, Dept Comp Sci, 7054 Haycock Rd,Room 312, Falls Church, VA 22043 USA.
C3 Texas A&M University System; Virginia Polytechnic Institute & State
   University; South Dakota State University; Mississippi State University;
   Mississippi State University; Virginia Polytechnic Institute & State
   University
RP Ji, TR (corresponding author), Texas A&M Univ, Dept Comp Sci, CI 317,6300 Ocean Dr, Corpus Christi, TX 78412 USA.
EM taoran.ji@tamucc.edu; nwself@vt.edu; kaiqun.fu@sdstate.edu;
   zchen@cse.msstate.edu; ctlu@vt.edu
RI ; Fu, Kaiqun/L-7587-2015
OI Ji, Taoran/0000-0001-9438-3038; Fu, Kaiqun/0000-0003-4307-9938; Chen,
   Zhiqian/0000-0003-4112-9647
FU National Science Foundation [CCF-1918770, NRT DGE-1545362, OAC-1835660,
   IIS-2153369]
FX This work is supported in part by the National Science Foundation via
   grants Expeditions CCF-1918770, NRT DGE-1545362, OAC-1835660, and
   IIS-2153369. The US Government is authorized to reproduce and distribute
   reprints of this work for Governmental purposes notwithstanding any
   copyright annotation thereon. Disclaimer: The views and conclusions
   contained herein are those of the authors and should not be interpreted
   as necessarily representing the official policies or endorsements,
   either expressed or implied, of NSF, or the U.S. Government.
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NR 60
TC 0
Z9 0
U1 4
U2 4
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY USA
SN 1556-4681
EI 1556-472X
J9 ACM T KNOWL DISCOV D
JI ACM Trans. Knowl. Discov. Data
PD JUL
PY 2024
VL 18
IS 6
AR 144
DI 10.1145/3649140
PG 23
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA OU9C2
UT WOS:001209901400011
OA hybrid, Green Accepted
DA 2024-09-05
ER
PT J
AU Ibáñez, A
   Bielza, C
   Larrañaga, P
AF Ibanez, Alfonso
   Bielza, Concha
   Larranaga, Pedro
TI Cost-sensitive selective naive Bayes classifiers for predicting the
   increase of the <i>h</i>-index for scientific journals
SO NEUROCOMPUTING
LA English
DT Article
DE Cost-sensitive learning approach; Selective naive Bayes; h-index;
   Neurosciences journals
ID CLASSIFICATION; MODEL
AB Machine learning community is not only interested in maximizing classification accuracy, but also in minimizing the distances between the actual and the predicted class. Some ideas, like the cost-sensitive learning approach, are proposed to face this problem. In this paper, we propose two greedy wrapper forward cost-sensitive selective naive Bayes approaches. Both approaches readjust the probability thresholds of each class to select the class with the minimum-expected cost. The first algorithm (CS-SNB-Accuracy) considers adding each variable to the model and measures the performance of the resulting model on the training data. The variable that most improves the accuracy, that is, the percentage of well classified instances between the readjusted class and actual class, is permanently added to the model. In contrast, the second algorithm (CS-SNB-Cost) considers adding variables that reduce the misclassification cost, that is, the distance between the readjusted class and actual class. We have tested our algorithms on the bibliometric indices prediction area. Considering the popularity of the well-known h-index, we have researched and built several prediction models to forecast the annual increase of the h-index for Neurosciences journals in a four-year time horizon. Results show that our approaches, particularly CS-SNB-Accuracy, achieved higher accuracy values than the analyzed cost-sensitive classifiers and Bayesian classifiers. Furthermore, we also noted that the CS-SNB-Cost always achieved a lower average cost than all analyzed cost-sensitive and cost-insensitive classifiers. These cost-sensitive selective naive Bayes approaches outperform the selective naive Bayes in terms of accuracy and average cost, so the cost-sensitive learning approach could be also applied in different probabilistic classification approaches. (c) 2014 Elsevier B.V. All rights reserved.
C1 [Ibanez, Alfonso; Bielza, Concha; Larranaga, Pedro] Univ Politecn Madrid, Fac Informat, Dept Inteligencia Artificial, Computat Intelligence Grp, E-28660 Madrid, Spain.
C3 Universidad Politecnica de Madrid
RP Ibáñez, A (corresponding author), Univ Politecn Madrid, Fac Informat, Dept Inteligencia Artificial, Computat Intelligence Grp, E-28660 Madrid, Spain.
EM aibanez@fi.upm.es; mcbielza@fi.upm.es; pedro.larranaga@fi.upm.es
RI Ibáñez, Alfonso/B-3423-2010; Larranaga, Pedro/F-9293-2013; Bielza,
   Concha/F-9277-2013
OI Larranaga, Pedro/0000-0003-0652-9872; Bielza, Concha/0000-0001-7109-2668
FU Spanish Ministry of Economy and Competitiveness (MINECO) projects
   [TIN2010-20900-004-04]; Cajal Blue Brain
FX This work has been partially supported by the Spanish Ministry of
   Economy and Competitiveness (MINECO) projects TIN2010-20900-004-04 and
   Cajal Blue Brain.
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NR 52
TC 14
Z9 16
U1 2
U2 39
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD JUL 5
PY 2014
VL 135
SI SI
BP 42
EP 52
DI 10.1016/j.neucom.2013.08.042
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA AH1HM
UT WOS:000335871200006
DA 2024-09-05
ER
PT C
AU Velez-Estevez, A
   Garcia-Sanchez, P
   Moral-Munoz, JA
   Cobo, MJ
AF Velez-Estevez, A.
   Garcia-Sanchez, P.
   Moral-Munoz, J. A.
   Cobo, M. J.
BE Castellano, G
   Castiello, C
   Mencar, C
TI Thematical and Impact Differences Between National and International
   Collaboration on Artificial Intelligence Research
SO 2020 IEEE INTERNATIONAL CONFERENCE ON EVOLVING AND ADAPTIVE INTELLIGENT
   SYSTEMS (EAIS)
SE IEEE Conference on Evolving and Adaptive Intelligence Systems
LA English
DT Proceedings Paper
CT 12th IEEE International Conference on Evolving and Adaptive Intelligent
   Systems (IEEE EAIS)
CY MAY 27-29, 2020
CL ELECTR NETWORK
DE Research impact; international collaboration; national collaboration;
   local collaboration and bibliographic networks
AB In this paper, an analysis to discover the impact differences in terms of citations between three levels of research collaboration is carried out. Thus, the objective, further than discover what kind of collaboration is more rewarding in terms of impact, is to determine if researchers cover different themes when they collaborate with people from other countries or universities within the same country, and if those themes get more citations. To this end, 12610 documents belonging to the "Computer Science & Artificial Intelligence" Web of Science subject category, and published in 2015 were analyzed. The whole corpus was divided into three different datasets, according to the defined collaboration levels (local, national and international). Results indicate that papers by authors from different countries receive more citations in average, but also, there exist some differences with respect to the research themes between the datasets.
C1 [Velez-Estevez, A.; Cobo, M. J.] Univ Cadiz, Dept Comp Sci & Engn, Cadiz, Spain.
   [Garcia-Sanchez, P.] Univ Granada, Dept Software Engn, Granada, Spain.
   [Moral-Munoz, J. A.] Univ Cadiz, Dept Nursing & Physiotherapy, Cadiz, Spain.
   [Moral-Munoz, J. A.] Inst Res & Innovat Biomed Sci Prov Cadiz INiBICA, Cadiz, Spain.
C3 Universidad de Cadiz; University of Granada; Universidad de Cadiz
RP Velez-Estevez, A (corresponding author), Univ Cadiz, Dept Comp Sci & Engn, Cadiz, Spain.
EM antonio.velezestevez@alum.uca.es; pablogarcia@ugr.es;
   joseantonio.moral@uca.es; manueljesus.cobo@uca.es
RI FCADIZ, INIBICA/AFS-0591-2022; Cobo Martí­n, Manuel Jesús/C-5581-2011;
   García-Sánchez, Pablo/G-2166-2010; Velez-Estevez, Antonio/AAX-7661-2020;
   Moral-Munoz, Jose A./A-5893-2014
OI Cobo Martí­n, Manuel Jesús/0000-0001-6575-803X; García-Sánchez,
   Pablo/0000-0003-4644-2894; Velez-Estevez, Antonio/0000-0002-0109-0293;
   Moral-Munoz, Jose A./0000-0002-6465-982X
FU FEDER funds [TIN2016-75850-R, TIN2017-85727-C4-2-P]
FX The authors want to thank the support of FEDER funds (TIN2016-75850-R
   and TIN2017-85727-C4-2-P).
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NR 19
TC 2
Z9 2
U1 0
U2 0
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2330-4863
BN 978-1-7281-4384-2
J9 IEEE CONF EVOL ADAPT
PY 2020
DI 10.1109/eais48028.2020.9122769
PG 8
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BQ8DH
UT WOS:000619398100032
DA 2024-09-05
ER
PT J
AU Zhao, WF
   Liu, WJ
   Zhang, RY
AF Zhao, Weifan
   Liu, Wenjun
   Zhang, Ruiyu
TI New methods to quantify an individual's scientific research output based
   on h-index and various factors
SO QUALITATIVE & QUANTITATIVE METHODS IN LIBRARIES
LA English
DT Article
DE h-index; principal component analysis; h(f)-index
AB Hirsch proposed h-index in 2005. To overcome some limitations of h-index in practical application, many scholars have proposed a series of derivative indexes. So far, derivative indexes designed by the scholars are mostly based on the citations and total number of papers. Although they are simple to use, they have certain oneness on the data source. To overcome this situation, two methods are used in this paper to analyze various factors. Firstly, we use the principal component analysis method to analyze original data such as the total number of papers, citation and so on, and then draw a comprehensive evaluation result of Z to quantify scientific research output, which avoid the oneness on the factor. Then, based on the "Gold priority" rule and h(a)-index proposed by Xu, we introduce a new comprehensive scientific research evaluation index, named as h(f)-index, to enhance the degree of differentiation and sensitivity of h-index. For two methods above, examples are given respectively.
C1 [Zhao, Weifan; Liu, Wenjun; Zhang, Ruiyu] Nanjing Univ Informat Sci & Technol, Coll Math & Stat, Nanjing 210044, Jiangsu, Peoples R China.
C3 Nanjing University of Information Science & Technology
RP Zhao, WF (corresponding author), Nanjing Univ Informat Sci & Technol, Coll Math & Stat, Nanjing 210044, Jiangsu, Peoples R China.
RI Liu, Wenjun/A-3643-2009
OI Liu, Wenjun/0000-0002-4500-6559
CR Alonso S, 2010, SCIENTOMETRICS, V82, P391, DOI 10.1007/s11192-009-0047-5
   Anderson TR, 2008, SCIENTOMETRICS, V76, P577, DOI 10.1007/s11192-007-2071-2
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   Qiu J. P., 2006, KNOWLEDGE LIB INFORM, V4, P101
   Wei R. B., 2009, CHINESE J SCI TECHNI, V20, P220
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   Zhang X. M., 2007, LIB INFORM SERVICE, V51, P116
NR 15
TC 2
Z9 2
U1 0
U2 22
PU INT SOC ART SCIENCE & TECHNOLOGY-ISAST
PI ATHINA
PA INT SOC ART SCIENCE & TECHNOLOGY-ISAST, ATHINA, 00000, GREECE
SN 2241-1925
J9 QUAL QUANT METHODS L
JI Qual. Quant. Methods Libr.
PD MAR
PY 2016
BP 221
EP 234
PG 14
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA EM4FN
UT WOS:000395269000021
DA 2024-09-05
ER
PT J
AU Wu, ZM
   Lin, WW
   Liu, P
   Chen, JB
   Mao, L
AF Wu, Ziming
   Lin, Weiwei
   Liu, Pan
   Chen, Jingbang
   Mao, Li
TI Predicting Long-Term Scientific Impact Based on Multi-Field Feature
   Extraction
SO IEEE ACCESS
LA English
DT Article
DE H-index prediction; scientific impact; machine learning; heterogeneous
   network
AB Nowadays, there have been many studies on evaluating the scientific impact of scholars. However, we still lack effective methods to predict long-term impact, especially 10 years in the future. Therefore, we propose a long-term scientific impact prediction model based on multi-field feature extraction. The workflow of our proposed model consists of feature engineering and model ensemble. In feature engineering, we extract attribute feature, time-series feature, and heterogeneous network feature based on three different fields. Moreover, when extracting heterogeneous network feature, we propose a scientific impact evaluation method based on heterogeneous academic network, which considers both the time of publication and author order factors. In the model ensemble, we adjust the basic model and noise model to the different training set to make full use of the information from the original dataset. The experiment results demonstrate that the proposed model can stably improve the accuracy of scholars' scientific impact prediction, and it also offers a prediction pattern for long-term prediction problem.
C1 [Wu, Ziming; Lin, Weiwei] South China Univ Technol, Sch Comp Sci & Engn, Guangzhou 510006, Guangdong, Peoples R China.
   [Liu, Pan; Chen, Jingbang] Zhejiang Univ, Chu Kochen Coll, Hangzhou 310000, Zhejiang, Peoples R China.
   [Mao, Li] Guangdong Police Coll, Dept Comp Sci, Guangzhou 510440, Guangdong, Peoples R China.
C3 South China University of Technology; Zhejiang University
RP Lin, WW (corresponding author), South China Univ Technol, Sch Comp Sci & Engn, Guangzhou 510006, Guangdong, Peoples R China.
EM linww@scut.edu.cn
OI Liu, Pan/0009-0003-3764-7772; lin, weiwei/0000-0001-6876-1795
FU National Natural Science Foundation of China [61772205, 61872084];
   Science and Technology Planning Project of Guangdong Province
   [2017B010126002, 2016A010101018, 2016A010119171, 2018KJYZ009]; Guangzhou
   Science and Technology Projects [201610010092, 201807010052,
   201802010010]; Nansha Science and Technology Projects [2017GJ001];
   Special Funds for the Development of Industry and Information of
   Guangdong Province (Big Data Demonstrated Applications) in 2017; Young
   Teachers Training of Guangdong Police Officer College [2018QNGG06];
   Fundamental Research Funds for the Central Universities, SCUT
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61772205 and Grant 61872084, in part by
   the Science and Technology Planning Project of Guangdong Province under
   Grant 2017B010126002, Grant 2016A010101018, Grant 2016A010119171, and
   Grant 2018KJYZ009, in part by the Guangzhou Science and Technology
   Projects under Grant 201610010092, Grant 201807010052, and Grant
   201802010010, in part by the Nansha Science and Technology Projects
   under Grant 2017GJ001, in part by the Special Funds for the Development
   of Industry and Information of Guangdong Province (Big Data Demonstrated
   Applications) in 2017, in part by the Young Teachers Training of
   Guangdong Police Officer College under Grant 2018QNGG06, and in part by
   the Fundamental Research Funds for the Central Universities, SCUT.
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NR 37
TC 10
Z9 11
U1 1
U2 25
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2019
VL 7
BP 51759
EP 51770
DI 10.1109/ACCESS.2019.2910239
PG 12
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering; Telecommunications
GA HW5CR
UT WOS:000466707200001
OA gold
DA 2024-09-05
ER
PT C
AU Su, X
   Prasad, A
   Kan, MY
   Sugiyama, K
AF Su, Xuan
   Prasad, Animesh
   Kan, Min-Yen
   Sugiyama, Kazunari
BE Bonn, M
   Wu, D
   Downie, SJ
   Martaus, A
TI Neural Multi-Task Learning for Citation Function and Provenance
SO 2019 ACM/IEEE JOINT CONFERENCE ON DIGITAL LIBRARIES (JCDL 2019)
SE ACM-IEEE Joint Conference on Digital Libraries JCDL
LA English
DT Proceedings Paper
CT 19th ACM/IEEE Joint Conference on Digital Libraries (JCDL)
CY JUN 02-06, 2019
CL IL
DE Citation Analysis; Multi-Task Learning; Neural Networks
AB Citation function and provenance are two cornerstone tasks in citation analysis. Given a citation, the former task determines its rhetorical role, while the latter locates the text in the cited paper that contains the relevant cited information. We hypothesize that these two tasks are synergistically related, and build a model that validates this claim. For both tasks, we show that a single-layer convolutional neural network (CNN) outperforms existing state-of-the-art baselines. More importantly, we show that the two tasks are indeed synergistic: by jointly training both tasks using multi-task learning, we demonstrate additional performance gains.
C1 [Su, Xuan; Prasad, Animesh; Kan, Min-Yen; Sugiyama, Kazunari] Natl Univ Singapore, Sch Comp, Singapore, Singapore.
C3 National University of Singapore
RP Su, X (corresponding author), Natl Univ Singapore, Sch Comp, Singapore, Singapore.
EM suxuan@comp.nus.edu.sg; animesh@comp.nus.edu.sg; kanmy@comp.nus.edu.sg;
   sugiyama@comp.nus.edu.sg
RI Sugiyama, Kazunari/JRY-8592-2023
OI Sugiyama, Kazunari/0000-0003-3962-821X; Prasad,
   Animesh/0000-0002-9865-6993
CR Bird S, 2008, SIXTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, LREC 2008, P1755
   Caruana R, 1998, LEARNING TO LEARN, P95, DOI 10.1007/978-1-4615-5529-2_5
   Jaidka K, 2018, INT J DIGIT LIBRARIE, V19, P163, DOI 10.1007/s00799-017-0221-y
   Low HengWee, 2011, THESIS
   Prasad Animesh, 2017, BIRNDL SIGIR, P26
   Yulianto Eric, 2012, THESIS
NR 6
TC 12
Z9 14
U1 0
U2 6
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2575-7865
EI 2575-8152
BN 978-1-7281-1547-4
J9 ACM-IEEE J CONF DIG
PY 2019
BP 394
EP 395
DI 10.1109/JCDL.2019.00122
PG 2
WC Computer Science, Information Systems; Computer Science,
   Interdisciplinary Applications; Computer Science, Theory & Methods;
   Information Science & Library Science
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Information Science & Library Science
GA BP5KE
UT WOS:000555928200080
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Safdar, M
   Siddique, N
   Gulzar, A
   Adil, SA
   Yasin, H
   Khan, MA
AF Safdar, Muhammad
   Siddique, Nadeem
   Gulzar, Ayesha
   Adil, Syed Adnan
   Yasin, Haisim
   Khan, Muhammad Ajmal
TI A bibliometric analysis of literature published on ChatGPT and GPT
SO GLOBAL KNOWLEDGE MEMORY AND COMMUNICATION
LA English
DT Article; Early Access
DE AI tools; ChatGPT; GPT; Artificial intelligence; Bibliometric analysis
AB Purpose-This study aims to analyse the literature published on ChatGPT and generative pre-trained transformer (GPT) available through Scopus to identify the top countries, institutions, authors, journals and titles in terms of publishing and citations in the area. The research also intends to determine the collaborative trends among countries and authors as well as top-used keywords on the topic identified from the analysed literature. Design/methodology/approach-The researchers searched the Scopus database to collect and assess the literature on the topic. The paper used six applications such as Biblioshiny, VosViewer, Python, MS Access and Excel and Endnote to collect and analyse the literature. Findings-It is found that European countries such as the USA, the UK and Germany took the lead in terms of publishing and impact in the area but the USA stood first with 90 publications and 1,720 citations in this connection. Likewise, the organization "Rheinisch-Westf & auml;lische Technische Hochschule Aachen" scored the top position regarding publishing, but Open AI remained on top for getting the highest citations (1,384). Apropos, the author "Choi, Y" produced the highest number of publications. The research also shares the collaborative patterns, top journals and publications in the area, as well as the top-used keywords on the topic. Originality/value-To the best of the authors' knowledge, this is the first study that shares a bibliometric analysis of literature published on GPT and ChatGPT. The research not only fills the research gap on the topic but also shares implications for relevant stakeholders and future research directions for future researchers.
C1 [Safdar, Muhammad; Siddique, Nadeem] Lahore Univ Management Sci, Gad & Birgit Rausing Lib, Lahore, Pakistan.
   [Gulzar, Ayesha] Univ Sargodha, Sargodha, Pakistan.
   [Adil, Syed Adnan] UCL, London, England.
   [Yasin, Haisim] Systems Ltd, Lahore, Pakistan.
   [Khan, Muhammad Ajmal] Imam Abdulrahman Bin Faisal Univ, Deanship Lib Affairs, Dammam, Saudi Arabia.
C3 Lahore University of Management Sciences; University of Sargodha;
   University of London; University College London; Imam Abdulrahman Bin
   Faisal University
RP Safdar, M (corresponding author), Lahore Univ Management Sci, Gad & Birgit Rausing Lib, Lahore, Pakistan.
EM safdargr8@gmail.com
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NR 53
TC 0
Z9 0
U1 6
U2 6
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 2514-9342
EI 2514-9350
J9 GLOB KNOWL MEM COMMU
JI Glob. Knowl. Mem. Commun.
PD 2024 MAY 20
PY 2024
DI 10.1108/GKMC-08-2023-0304
EA MAY 2024
PG 18
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA QX5V7
UT WOS:001224189500001
DA 2024-09-05
ER
PT J
AU Fang, DB
   Yang, HX
   Gao, BJ
   Li, XJ
AF Fang, Debin
   Yang, Haixia
   Gao, Baojun
   Li, Xiaojun
TI Discovering research topics from library electronic references using
   latent Dirichlet allocation
SO LIBRARY HI TECH
LA English
DT Article
DE Academic libraries; Big data; Accounting research; Latent Dirichlet
   allocation (LDA); Topic model; Topic trends
ID INFORMATION-SCIENCE; WORD; DISSERTATIONS; TRENDS
AB Purpose Discovering the research topics and trends from a large quantity of library electronic references is essential for scientific research. Current research of this kind mainly depends on human justification. The purpose of this paper is to demonstrate how to identify research topics and evolution in trends from library electronic references efficiently and effectively by employing automatic text analysis algorithms.
   Design/methodology/approach The authors used the latent Dirichlet allocation (LDA), a probabilistic generative topic model to extract the latent topic from the large quantity of research abstracts. Then, the authors conducted a regression analysis on the document-topic distributions generated by LDA to identify hot and cold topics.
   Findings First, this paper discovers 32 significant research topics from the abstracts of 3,737 articles published in the six top accounting journals during the period of 1992-2014. Second, based on the document-topic distributions generated by LDA, the authors identified seven hot topics and six cold topics from the 32 topics.
   Originality/value The topics discovered by LDA are highly consistent with the topics identified by human experts, indicating the validity and effectiveness of the methodology. Therefore, this paper provides novel knowledge to the accounting literature and demonstrates a methodology and process for topic discovery with lower cost and higher efficiency than the current methods.
C1 [Fang, Debin; Yang, Haixia; Gao, Baojun] Wuhan Univ, Econ & Management Sch, Wuhan, Hubei, Peoples R China.
   [Li, Xiaojun] Yunnan Univ Finance & Econ, Coll Accounting, Kunming, Yunnan, Peoples R China.
C3 Wuhan University; Yunnan University of Finance & Economics
RP Gao, BJ (corresponding author), Wuhan Univ, Econ & Management Sch, Wuhan, Hubei, Peoples R China.
EM gaobj@whu.edu.cn
RI Lobo, Diele/I-9106-2012; lan, xueyao/JZD-4201-2024; gao,
   baojun/AAQ-1613-2020
OI Gao, Baojun/0000-0002-9190-8158; Fang, Debin/0000-0001-8410-8358
FU National Natural Science Foundation Programs of China (NSFC) [71771182,
   71673210, 71725007]
FX The authors would like to thank all the supports from the National
   Natural Science Foundation Programs of China (NSFC) (71771182, 71673210,
   71725007).
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NR 45
TC 18
Z9 22
U1 5
U2 97
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 0737-8831
J9 LIBR HI TECH
JI Libr. Hi Tech
PY 2018
VL 36
IS 3
SI SI
BP 400
EP 410
DI 10.1108/LHT-06-2017-0132
PG 11
WC Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science
GA GI3EB
UT WOS:000434253200003
DA 2024-09-05
ER
PT J
AU Wulff, P
   Westphal, A
   Mientus, L
   Nowak, A
   Borowski, A
AF Wulff, Peter
   Westphal, Andrea
   Mientus, Lukas
   Nowak, Anna
   Borowski, Andreas
TI Enhancing writing analytics in science education research with machine
   learning and natural language processing-Formative assessment of science
   and non-science preservice teachers' written reflections
SO FRONTIERS IN EDUCATION
LA English
DT Article
DE machine learning; natural language processing; teachers' reflections;
   assessment; deep learning
ID PROFESSIONAL VISION; KNOWLEDGE; SUPPORT; OPPORTUNITIES
AB IntroductionScience educators use writing assignments to assess competencies and facilitate learning processes such as conceptual understanding or reflective thinking. Writing assignments are typically scored with holistic, summative coding rubrics. This, however, is not very responsive to the more fine-grained features of text composition and represented knowledge in texts, which might be more relevant for adaptive guidance and writing-to-learn interventions. In this study we examine potentials of machine learning (ML) in combination with natural language processing (NLP) to provide means for analytic, formative assessment of written reflections in science teacher education. MethodsML and NLP are used to filter higher-level reasoning sentences in physics and non-physics teachers' written reflections on a standardized teaching vignette. We particularly probe to what extent a previously trained ML model can facilitate the filtering, and to what extent further fine-tuning of the previously trained ML model can enhance performance. The filtered sentences are then clustered with ML and NLP to identify themes and represented knowledge in the teachers' written reflections. ResultsResults indicate that ML and NLP can be used to filter higher-level reasoning elements in physics and non-physics preservice teachers' written reflections. Furthermore, the applied clustering approach yields specific topics in the written reflections that indicate quality differences in physics and non-physics preservice teachers' texts. DiscussionOverall, we argue that ML and NLP can enhance writing analytics in science education. For example, previously trained ML models can be utilized in further research to filter higher-level reasoning sentences, and thus provide science education researchers efficient mean to answer derived research questions.
C1 [Wulff, Peter] Heidelberg Univ Educ, Phys & Phys Educ Res, Heidelberg, Germany.
   [Westphal, Andrea] Univ Greifswald, Dept Educ Res, Greifswald, Mecklenburg Vor, Germany.
   [Mientus, Lukas; Nowak, Anna; Borowski, Andreas] Univ Potsdam, Phys Educ Res Grp, Brandenburg, Germany.
C3 Ruprecht Karls University Heidelberg; Universitat Greifswald; University
   of Potsdam
RP Wulff, P (corresponding author), Heidelberg Univ Educ, Phys & Phys Educ Res, Heidelberg, Germany.
EM peter.wulff@ph-heidelberg.de
RI Wulff, Peter/GSI-9069-2022
OI Wulff, Peter/0000-0002-5471-7977; Mientus, Lukas/0000-0001-5344-4770
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NR 107
TC 8
Z9 8
U1 7
U2 25
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 2504-284X
J9 FRONT EDUC
JI Front. Educ.
PD JAN 9
PY 2023
VL 7
AR 1061461
DI 10.3389/feduc.2022.1061461
PG 18
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA 8E7XN
UT WOS:000919182700001
OA gold
DA 2024-09-05
ER
PT C
AU Castanha, J
   Indrawati
   Pillai, SKB
   Ramantoko, G
   Widarmanti, T
AF Castanha, Jick
   Indrawati
   Pillai, Subhash K. B.
   Ramantoko, Gadang
   Widarmanti, Tri
GP IEEE
TI A Systematic Literature Review on Natural Language Processing (NLP)
SO 2022 INTERNATIONAL CONFERENCE ON ADVANCED CREATIVE NETWORKS AND
   INTELLIGENT SYSTEMS, ICACNIS
LA English
DT Proceedings Paper
CT International Conference on Advanced Creative Networks and Intelligent
   Systems (ICACNIS) - Blockchain Technology, Intelligent Systems, and the
   Applications for Human Life
CY NOV 23, 2022
CL Bandung, INDONESIA
DE Natural Language Processing; Artificial Intelligence; Systematic Review;
   Bibliometric analysis; NLP
ID TEXT; INFORMATION
AB Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) technology used by machines to understand, analyze and interpret human languages. In the past decade, NLP received more recognition due to innovation in information and communication technology which led to various research. Thus, it is essential to understand the development taken in the knowledge of literature. The present study aims to present a systematic literature review using bibliometric analysis in NLP research. The study identifies the publication trends, influential journals, cited articles, influential authors, institutions, countries, key research areas, and research clusters in the NLP field. 12541 NLP publications were extracted from the Web of Science (WoS) database and further analyzed using bibliometric analysis. The result indicated that the first NLP publication was in 1989, with the highest publication recorded in 2021. The IEEE access journal was the leading journal with the highest number of publications, and the highest number of citations received for NLP articles is 3174. The most productive author in the NLP field is Liu HF, whereas Harward university is the most influential institution. The US is the leading country in the total number of publications. Researchers extensively researched applied sciences area. The findings further revealed that most of the NLP research focused on five main clusters: modeling, neural networks, artificial intelligence, data mining using social media platforms, and data capturing and learning.
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C3 Goa University; Telkom University
RP Castanha, J (corresponding author), Goa Univ, Goa Business Sch, Taleigao, Goa, India.
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NR 33
TC 1
Z9 1
U1 10
U2 28
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 979-8-3503-3444-9
PY 2022
BP 130
EP 135
DI 10.1109/ICACNIS57039.2022.10055568
PG 6
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BV2XB
UT WOS:001012145500022
DA 2024-09-05
ER
PT J
AU Garg, M
   Rangra, P
AF Garg, Mohit
   Rangra, Priya
TI Bibliometric Analysis of Latent Dirichlet Allocation
SO DESIDOC JOURNAL OF LIBRARY & INFORMATION TECHNOLOGY
LA English
DT Article
DE Bibliometrics; Big data; Citation analysis; Latent dirichlet allocation
ID QUESTION; SCIENCE
AB Latent Dirichlet Allocation (LDA) has emerged as an important algorithm in big data analysis that finds the group of topics in the text data. It posits that each text document consists of a group of topics, and each topic is a mixture of words related to it. With the emergence of a plethora of text data, the LDA has become a popular algorithm for topic modeling among researchers from different domains. Therefore, it is essential to understand the trends of LDA researches. Bibliometric techniques are established methods to study the research progress of a topic. In this study, bibliographic data of 18715 publications that have cited the I,DA were extracted from the Scopus database. The software R and Vosviewer were used to carry out the analysis. The analysis revealed that research interest in LDA had grown exponentially. The results showed that most authors preferred "Book Series" followed by "Conference Proceedings" as the publication venue. The majority of the institutions and authors were from the USA, followed by China. The co-occurrence analysis of keywords indicated that text mining and machine learning were dominant topics in LDA research with significant interest in social media. This study attempts to provide a comprehensive analysis and intellectual structure of LDA compared to previous studies.
C1 [Garg, Mohit] Indian Inst Technol, Delhi 110016, India.
   [Rangra, Priya] Cent Univ Himachal Pradesh, Dept Lib & Informat Sci, Shahpur 176206, India.
C3 Indian Institute of Technology System (IIT System); Indian Institute of
   Technology (IIT) - Delhi; Central University of Himachal Pradesh
RP Rangra, P (corresponding author), Cent Univ Himachal Pradesh, Dept Lib & Informat Sci, Shahpur 176206, India.
EM priyarangra26494@gmail.com
OI GARG, MOHIT/0000-0001-5787-7143; , Priya/0000-0003-3015-9963
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NR 34
TC 5
Z9 5
U1 10
U2 41
PU DEFENCE SCIENTIFIC INFORMATION DOCUMENTATION CENTRE
PI DELHI
PA METCALFE HOUSE, DELHI 110054, INDIA
SN 0974-0643
EI 0976-4658
J9 DESIDOC J LIB INF TE
JI DESIDOC J. Lib. Inf. Technol.
PD MAR
PY 2022
VL 42
IS 2
BP 105
EP 113
DI 10.14429/djlit.42.2.17307
PG 9
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA ZQ8QW
UT WOS:000767363700005
OA gold
DA 2024-09-05
ER
PT J
AU Ma, AQ
   Liu, Y
   Xu, XJ
   Dong, T
AF Ma, Anqi
   Liu, Yu
   Xu, Xiujuan
   Dong, Tao
TI A deep-learning based citation count prediction model with paper
   metadata semantic features
SO SCIENTOMETRICS
LA English
DT Article
DE Citation count prediction; Metadata semantic features; Deep learning;
   Sentence embedding; Semantic information
ID CONVOLUTIONAL NEURAL-NETWORKS; IMPACT; ARTICLES; SCIENCE; TITLES; INDEX
AB Predicting the impact of academic papers can help scholars quickly identify the high-quality papers in the field. How to develop efficient predictive model for evaluating potential papers has attracted increasing attention in academia. Many studies have shown that early citations contribute to improving the performance of predicting the long-term impact of a paper. Besides early citations, some bibliometric features and altmetric features have also been explored for predicting the impact of academic papers. Furthermore, paper metadata text such as title, abstract and keyword contains valuable information which has effect on its citation count. However, present studies ignore the semantic information contained in the metadata text. In this paper, we propose a novel citation prediction model based on paper metadata text to predict the long-term citation count, and the core of our model is to obtain the semantic information from the metadata text. We use deep learning techniques to encode the metadata text, and then further extract high-level semantic features for learning the citation prediction task. We also integrate early citations for improving the prediction performance of the model. We show that our proposed model outperforms the state-of-the-art models in predicting the long-term citation count of the papers, and metadata semantic features are effective for improving the accuracy of the citation prediction models.
C1 [Ma, Anqi; Liu, Yu; Xu, Xiujuan; Dong, Tao] Dalian Univ Technol, Sch Software, Dalian 116621, Peoples R China.
C3 Dalian University of Technology
RP Liu, Y (corresponding author), Dalian Univ Technol, Sch Software, Dalian 116621, Peoples R China.
EM maanqi@mail.dlut.edu.cn; yuliu@dlut.edu.cn; xjxu@dlut.edu.cn;
   dongtao2019@mail.dlut.edu.cn
RI Liu, Yu/ABD-6335-2021
OI Ma, Anqi/0000-0002-2683-0657
FU Natural Science Foundation of China [61672128]
FX This work was supported by the Natural Science Foundation of China grant
   61672128.
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TC 20
Z9 21
U1 8
U2 87
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD AUG
PY 2021
VL 126
IS 8
BP 6803
EP 6823
DI 10.1007/s11192-021-04033-7
EA JUN 2021
PG 21
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA TM5GC
UT WOS:000658116700003
DA 2024-09-05
ER
PT J
AU Bornmann, L
   Ganser, C
   Tekles, A
AF Bornmann, Lutz
   Ganser, Christian
   Tekles, Alexander
TI Simulation of the h index use at university departments within the
   bibliometrics-based heuristics framework: Can the indicator be used to
   compare individual researchers?
SO JOURNAL OF INFORMETRICS
LA English
DT Article
DE Bibliometrics; Bibliometrics-based heuristics; Bibliometrics-based
   decision trees; h index
ID HIRSCH-INDEX; QUANTITATIVE-ANALYSIS; CITATION IMPACT; MODELS; FRUGAL;
   SCIENCES; CHOICE
AB Bornmann and Marewski (2019) have adapted the concept of fast-and-frugal heuristics to scientometrics in order to study and guide the application of bibliometrics in research evaluation. Bibliometrics-based heuristics (BBHs) are simple decision strategies for evaluative purposes based on bibliometric indicators. One aim of the heuristics research program is to develop methods for studying the use of BBHs in research evaluation. Many deans probably evaluate rough performance differences between researchers in their departments based on h index values. Bornmann, Ganser, Tekles, and Leydesdorff (2020) developed the Stata command h_index and R package hindex which can be deployed in a fast and frugal way to decide on the following question: can the h index be used to compare all researchers in a university department, or are the citation cultures so different between sub-groups in the department that not all researchers can be compared with one another? The command and package can be used for simulations that might answer the question before extensive processes of data collection start. If the citation cultures are very different in the sub-groups, the researchers should be compared with field-normalized indicators (instead of the h index). This paper shows how the h_index command and hindex package can be employed for the decision on the h index use in the BBH.
C1 [Bornmann, Lutz; Tekles, Alexander] Max Planck Gesell, Sci Policy & Strategy Dept Adm Headquarters, Hofgartenstr 8, D-80539 Munich, Germany.
   [Ganser, Christian; Tekles, Alexander] Ludwig Maximilians Univ Munchen, Dept Sociol, Konradstr 6, D-80801 Munich, Germany.
C3 Max Planck Society; University of Munich
RP Bornmann, L (corresponding author), Max Planck Gesell, Sci Policy & Strategy Dept Adm Headquarters, Hofgartenstr 8, D-80539 Munich, Germany.
EM bornmann@gv.mpg.de
RI Bornmann, Lutz/A-3926-2008
OI Ganser, Christian/0000-0002-2790-7353
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NR 71
TC 1
Z9 1
U1 2
U2 36
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1751-1577
EI 1875-5879
J9 J INFORMETR
JI J. Informetr.
PD FEB
PY 2022
VL 16
IS 1
AR 101237
DI 10.1016/j.joi.2021.101237
PG 17
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 1H0CB
UT WOS:000796212300002
DA 2024-09-05
ER
PT J
AU Cantu-Ortiz, FJ
AF Javier Cantu-Ortiz, Francisco
TI Advancing artificial intelligence research and dissemination through
   conference series: Benchmark, scientific impact and the MICAI experience
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Artificial intelligence; AI research and development; Scientific impact
   of AI
AB This article presents an overview, analysis and benchmark of the best-known artificial intelligence (AI) conferences, including the Mexican International Conference on Artificial Intelligence (MICAI) conference series, and describes how MICAI has contributed to both the growth of artificial intelligence (AI) research in Mexico and the advancement of AI research worldwide. Among the prestigious AI conferences examined are the IJCAI, AAAI, ECAI, IBERAMIA, AAJCAI and PRICAI. Features analyzed include number of papers, acceptance rate and the h index as a measure of the scientific impact. The MICAI has been held in Mexico since 2000, when the National Meeting on AI, held by the Mexican Society for Artificial Intelligence (SMIA) since 1983, and the International Symposium on Artificial Intelligence (ISAI), organized by Tecnologico de Monterrey (ITESM) since 1988, merged into a single conference. Conference trends and future developments are also explained. (C) 2013 Elsevier Ltd. All rights reserved.
C1 [Javier Cantu-Ortiz, Francisco] Tecnol Monterrey, Monterrey 64849, NL, Mexico.
C3 Tecnologico de Monterrey
RP Cantu-Ortiz, FJ (corresponding author), 2501 Eugenio Garza Sada Ave, Monterrey 64849, NL, Mexico.
EM fcantu@itesm.mx
RI Cantu-Ortiz, Francisco Javier/K-2942-2019
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NR 15
TC 9
Z9 9
U1 3
U2 46
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0957-4174
EI 1873-6793
J9 EXPERT SYST APPL
JI Expert Syst. Appl.
PD FEB 15
PY 2014
VL 41
IS 3
SI SI
BP 781
EP 785
DI 10.1016/j.eswa.2013.08.008
PG 5
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Operations Research & Management Science
GA 272EU
UT WOS:000328443900002
DA 2024-09-05
ER
PT J
AU Nannini, L
   Manerba, MM
   Beretta, I
AF Nannini, Luca
   Manerba, Marta Marchiori
   Beretta, Isacco
TI Mapping the landscape of ethical considerations in explainable AI
   research
SO ETHICS AND INFORMATION TECHNOLOGY
LA English
DT Article
DE Explainable AI (XAI); AI ethics; Ethical analysis; Bibliometric study
ID VIRTUE; FAIR
AB With its potential to contribute to the ethical governance of AI, eXplainable AI (XAI) research frequently asserts its relevance to ethical considerations. Yet, the substantiation of these claims with rigorous ethical analysis and reflection remains largely unexamined. This contribution endeavors to scrutinize the relationship between XAI and ethical considerations. By systematically reviewing research papers mentioning ethical terms in XAI frameworks and tools, we investigate the extent and depth of ethical discussions in scholarly research. We observe a limited and often superficial engagement with ethical theories, with a tendency to acknowledge the importance of ethics, yet treating it as a monolithic and not contextualized concept. Our findings suggest a pressing need for a more nuanced and comprehensive integration of ethics in XAI research and practice. To support this, we propose to critically reconsider transparency and explainability in regards to ethical considerations during XAI systems design while accounting for ethical complexity in practice. As future research directions, we point to the promotion of interdisciplinary collaborations and education, also for underrepresented ethical perspectives. Such ethical grounding can guide the design of ethically robust XAI systems, aligning technical advancements with ethical considerations.
C1 [Nannini, Luca] Univ Santiago De Compostela, Ctr Singular Invest Tecnoloxias Intelixentes, Santiago De Compostela, Spain.
   [Manerba, Marta Marchiori; Beretta, Isacco] Univ Pisa, Comp Sci Dept, Pisa, Italy.
   [Manerba, Marta Marchiori] CNR, KDD Lab, ISTI, Pisa, Italy.
   [Nannini, Luca] Indra Sistemas, Minsait, Madrid, Spain.
C3 Universidade de Santiago de Compostela; University of Pisa; Consiglio
   Nazionale delle Ricerche (CNR); Istituto di Scienza e Tecnologie
   dell'Informazione "Alessandro Faedo" (ISTI-CNR); Indra
RP Nannini, L (corresponding author), Univ Santiago De Compostela, Ctr Singular Invest Tecnoloxias Intelixentes, Santiago De Compostela, Spain.; Nannini, L (corresponding author), Indra Sistemas, Minsait, Madrid, Spain.
EM l.nannini@usc.es; marta.marchiori@phd.unipi.it;
   isacco.beretta@phd.unipi.it
OI Nannini, Luca/0000-0002-4733-9760
FU HORIZON EUROPE Framework Programme; ITN project NL4XAI Natural Language
   for Explainable AI [860621]; European Union; Marie Curie Actions (MSCA)
   [860621] Funding Source: Marie Curie Actions (MSCA)
FX Funding contribution from the ITN project NL4XAI Natural Language for
   Explainable AI. This project has received funding from the European
   Union's Horizon 2020 research and innovation programme under the Marie
   Sk & lstrok;odowska-Curie grant agreement No 860621. This document
   reflects the views of the author(s) and does not necessarily reflect the
   views or policy of the European Commission. The REA cannot be held
   responsible for any use that may be made of the information this
   document contains.
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NR 181
TC 0
Z9 0
U1 10
U2 10
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1388-1957
EI 1572-8439
J9 ETHICS INF TECHNOL
JI Ethics Inf. Technol.
PD SEP
PY 2024
VL 26
IS 3
AR 44
DI 10.1007/s10676-024-09773-7
PG 22
WC Ethics; Information Science & Library Science; Philosophy
WE Social Science Citation Index (SSCI); Arts &amp; Humanities Citation Index (A&amp;HCI)
SC Social Sciences - Other Topics; Information Science & Library Science;
   Philosophy
GA WI2G6
UT WOS:001254169000001
OA hybrid
DA 2024-09-05
ER
PT J
AU Curiel-Marín, E
   Passoni, I
   Olmedo-Moreno, EM
   Fernández-Cano, A
AF Curiel-Marin, Elvira
   Passoni, Isabel
   Olmedo-Moreno, Eva M.
   Fernandez-Cano, Antonio
TI Self-organizing maps for research evaluation of doctoral dissertations:
   the case of teaching Social Sciences in Spain
SO RELIEVE-REVISTA ELECTRONICA DE INVESTIGACION Y EVALUACION EDUCATIVA
LA English
DT Article
DE Scientometrics; Research Evaluation; Doctoral Theses; Self Organizing
   Maps; Neural Networks; Methodological Tools; Social Science Teaching
ID ORGANIZATION; THESES
AB This paper has as main objective to highlight the potential use of neural networks, self-organized maps type (SOM), as a clarifying tool in the treatment, analysis and visualization of scientometric data, specifically, in the case of the analysis of the Spanish doctoral theses in teaching Social Sciences, indexed in TESEO (Spanish national database of dissertations), and defended between 1976 and 2014. A census of 301 doctoral theses has been recovered, analyzed according to autonomous communities (Andalusia and Catalonia), five-year term groups, thematic categories and educational stages. In Andalusia, the production is highest in the five-year period 1986-1990 and 2001-2005. In Catalonia, the most productive five-year periods were 1991-1995, 1996-2000, 2001-2005 and 2006-2010. More agreement is needed in the nomenclature of the teaching Social Sciences area, as well as an update in the operation of the TESEO database. As a general conclusion, it can be inferred that the resulting SOM allow to update the understanding of the state of the art in the area based on the various variables considered. The potentiality of SOM as an exploratory approximation of multivariate data becomes evident.
C1 [Curiel-Marin, Elvira] Univ Granada, Dept Res Methods & Diag Educ, Fac Educ Econ & Technol, C Cortadura Valle S-N, Ceuta, Spain.
   [Olmedo-Moreno, Eva M.; Fernandez-Cano, Antonio] Univ Granada, Dept Res Methods & Diag Educ, Ceuta, Spain.
   [Passoni, Isabel] Natl Univ Mar del Plata, Dept Elect & Comp Engn, Fac Engn, Mar Del Plata, Buenos Aires, Argentina.
C3 University of Granada; University of Granada; National University of Mar
   del Plata
RP Curiel-Marín, E (corresponding author), Univ Granada, Fac Educ Econ & Technol, C Cortadura Valle S-N, Ceuta, Spain.
EM ecuriel@ugr.es
RI Fernández-Cano, Antonio/GLS-0532-2022; FERNANDEZ-CANO,
   ANTONIO/B-7376-2008; Curiel-Marín, Elvira/C-1064-2016; OLMEDO,
   EVA/K-6810-2014
OI FERNANDEZ-CANO, ANTONIO/0000-0003-3991-4443; OLMEDO,
   EVA/0000-0003-0558-1513
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NR 41
TC 3
Z9 3
U1 0
U2 6
PU ASOC INTERUNIVERSITARIA INVESTIGACION PEDAGOGICA
PI VALENCIA
PA AVE BLASCO IBANEZ NO 30, VALENCIA, 46010, SPAIN
SN 1134-4032
J9 RELIEVE
JI RELIEVE
PY 2018
VL 24
IS 1
AR 2
DI 10.7203/relieve.24.1.12345
PG 18
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA GM2BZ
UT WOS:000437884500002
OA Green Submitted, gold
DA 2024-09-05
ER
PT J
AU Prado-Romero, MA
   Prenkaj, B
   Stilo, G
   Giannotti, F
AF Prado-Romero, Mario Alfonso
   Prenkaj, Bardh
   Stilo, Giovanni
   Giannotti, Fosca
TI A Survey on Graph Counterfactual Explanations: Definitions, Methods,
   Evaluation, and Research Challenges
SO ACM COMPUTING SURVEYS
LA English
DT Article
DE Explainability; explainable AI; counterfactual explainability; post-hoc
   explanation; graphs; graph neural networks; graph learning; molecular
   recourse; black box problem; fairness in AI; machine learning
ID PREDICTION; DATABASE
AB Graph Neural Networks (GNNs) perform well in community detection and molecule classification. Counter-factual Explanations (CE) provide counter-examples to overcome the transparency limitations of black-box models. Due to the growing attention in graph learning, we focus on the concepts of CE for GNNs. We analysed the SoA to provide a taxonomy, a uniform notation, and the benchmarking datasets and evaluation metrics. We discuss fourteen methods, their evaluation protocols, twenty-two datasets, and nineteen metrics. We integrated the majority of methods into the GRETEL library to conduct an empirical evaluation to understand their strengths and pitfalls. We highlight open challenges and future work.
C1 [Prado-Romero, Mario Alfonso] Gran Sasso Sci Inst, I-67100 Laquila, Italy.
   [Prenkaj, Bardh] Sapienza Univ Rome, I-00198 Rome, Italy.
   [Stilo, Giovanni] Univ Aquila, I-67100 Laquila, Italy.
   [Giannotti, Fosca] Scuola Normale Super Pisa, I-56126 Pisa, Italy.
C3 Gran Sasso Science Institute (GSSI); Sapienza University Rome;
   University of L'Aquila; Scuola Normale Superiore di Pisa
RP Prado-Romero, MA (corresponding author), Gran Sasso Sci Inst, I-67100 Laquila, Italy.
EM marioalfonso.prado@gssi.it; prenkaj@di.uniroma1.it;
   giovanni.stilo@univaq.it; fosca.giannotti@sns.it
RI Prenkaj, Bardh/AAL-6461-2020
OI Prenkaj, Bardh/0000-0002-2991-2279; stilo, giovanni/0000-0002-2092-0213
FU European Union - NextGenerationEU - National Recovery and Resilience
   Plan [IR0000013, 3264, 834756]; HPC & Big Data Laboratory of DISIM,
   University of L'Aquila
FX This work is partially supported by the European Union -
   NextGenerationEU - National Recovery and Resilience Plan (Piano
   Nazionale di Ripresa e Resilienza, PNRR) - Project: SoBigData.it -
   Strengthening the Italian RI for Social Mining and Big Data Analytics -
   Prot. IR0000013 - Avviso n. 3264 del 28/12/2021, XAI: Science and
   technology for the eXplanation of AI decision - ERC Advanced Grant 2018
   G.A. 834756 and by the HPC & Big Data Laboratory of DISIM, University of
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NR 90
TC 1
Z9 1
U1 5
U2 5
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY USA
SN 0360-0300
EI 1557-7341
J9 ACM COMPUT SURV
JI ACM Comput. Surv.
PD JUL
PY 2024
VL 56
IS 7
AR 171
DI 10.1145/3618105
PG 37
WC Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA OQ7S3
UT WOS:001208811000010
OA hybrid, Green Submitted
DA 2024-09-05
ER
PT J
AU Marzouk, M
   Elhakeem, A
   Adel, K
AF Marzouk, Mohamed
   Elhakeem, Ahmed
   Adel, Kareem
TI Artificial neural networks applications in construction and building
   engineering (1991-2021): Science mapping and visualization
SO APPLIED SOFT COMPUTING
LA English
DT Article
DE ANN; Neural Networks; Bibliometrics; Science Mapping; VOSviewer;
   Biblioshiny
ID COMPRESSIVE STRENGTH PREDICTION; HIGH-PERFORMANCE CONCRETE; PARTICLE
   SWARM OPTIMIZATION; SELF-COMPACTING CONCRETE; NATURAL-GAS CONSUMPTION;
   SUPPORT VECTOR MACHINE; RECYCLED AGGREGATE; DAMAGE DETECTION; CRACK
   DETECTION; NANO-SILICA
AB Artificial neural network (ANN) has acquired noticeable interest from the research community to handle complex problems in Construction and Building engineering (CB). This interest has led to an enormous amount of scientific publications in diverse CB domains over the last three decades. This study introduces a scientometric review to quantitatively explore and visually map the development pathways and trends of ANN -CB literature. Via the Web of Science (WoS) database, 2406 peer -reviewed journal articles are identified and included for analysis as follows. First, the publication growth over time is inspected and evaluated. Second, the collaboration patterns between key contributors (researchers, countries, and organizations) are explored and mapped using the co -authorship analysis. Third, the key sources' productivity and influence are explored and mapped using the direct citation analysis. Fourth, the publications clusters and research themes are analyzed and visualized via the keyword co -occurrence analysis and document trend topics mapping. The study outcomes include but are not limited to i) recognizing pioneer ANN -CB researchers for future collaboration opportunities, ii) identifying reliable sources of information or suitable ones for publishing new ANN -CB works, and iii) fostering probable academic partnerships with the leading ANN -CB organizations. These outcomes help researchers to comprehend ANN -CB literature and direct research policy -makers and editorial boards to adopt the promising ANN -CB themes for further research and development.
C1 [Marzouk, Mohamed] Cairo Univ, Fac Engn, Struct Engn Dept, Construct Engn & Management, Giza, Egypt.
   [Elhakeem, Ahmed; Adel, Kareem] Arab Acad Sci Technol & Maritime Transport AASTMT, Coll Engn & Technol, Construct & Bldg Engn Dept, Cairo, Egypt.
C3 Egyptian Knowledge Bank (EKB); Cairo University; Egyptian Knowledge Bank
   (EKB); Arab Academy for Science, Technology & Maritime Transport
RP Marzouk, M (corresponding author), Cairo Univ, Fac Engn, Struct Engn Dept, Construct Engn & Management, Giza, Egypt.
EM mmarzouk@cu.edu.eg
RI adel, kareem/GVS-4750-2022; Elhakeem, Ali/U-9543-2019
OI Elhakeem, Ali/0000-0002-2752-1207; Marzouk, Mohamed/0000-0002-8594-8452;
   Adel, Kareem/0000-0002-8193-0204
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   Zou ZB, 2020, BUILD ENVIRON, V168, DOI 10.1016/j.buildenv.2019.106535
NR 340
TC 4
Z9 4
U1 9
U2 14
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1568-4946
EI 1872-9681
J9 APPL SOFT COMPUT
JI Appl. Soft. Comput.
PD FEB
PY 2024
VL 152
AR 111174
DI 10.1016/j.asoc.2023.111174
EA JAN 2024
PG 31
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA GN8K2
UT WOS:001153439800001
DA 2024-09-05
ER
PT J
AU Ran, N
AF Ran, Na
TI Association Between Immediacy of Citations and Altmetrics in COVID-19
   Research by Artificial Neural Networks
SO DISASTER MEDICINE AND PUBLIC HEALTH PREPAREDNESS
LA English
DT Article
DE journal impact factor; social media; COVID-19; scholarly communication;
   neural networks
ID CORE; SCORE
AB Objectives: Both citations and Altmetrics are indexes of influence of a publication, potentially useful, but to what extent that the professional-academic citation and media-dominated Altmetrics are consistent with each other is a topic worthy of being investigated. The objective is to show their correlation. Methods: DOI and citation information of coronavirus disease 2019 (COVID-19) researches were obtained from the Web of Science, its Altmetric indicators were collected from the Altmetrics. Correlation between the immediacy of citation and Altmetrics of COVID-19 research was studied by artificial neural networks. Results: Pearson coefficients are 0.962, 0.254, 0.222, 0.239, 0.363, 0.218, 0.136, 0.134, and 0.505 (P < 0.01) for dimensions citation, attention score, journal impact factor, news, blogs, Twitter, Facebook, video, and Mendeley correlated with the SCI citation, respectively. The citations from the Web of Science and that from the Altmetrics have deviance large enough in the current. Altmetric score is not precise to describe the immediacy of citations of academic publication in COVID-19 research. Conclusions: The effects of news, blogs, Twitter, Facebook, video, and Mendeley on SCI citations are similar to that of the journal impact factor. This paper performs a pioneer study for investigating the role of academic topics across Altmetric sources on the dissemination of scholarly publications.
C1 [Ran, Na] Univ Sci & Technol Beijing, Beijing, Peoples R China.
C3 University of Science & Technology Beijing
RP Ran, N (corresponding author), Univ Sci & Technol Beijing, Beijing, Peoples R China.
EM ranna@ustb.edu.cn
OI na, ran/0000-0003-2204-2183
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TC 5
Z9 5
U1 4
U2 34
PU CAMBRIDGE UNIV PRESS
PI NEW YORK
PA 32 AVENUE OF THE AMERICAS, NEW YORK, NY 10013-2473 USA
SN 1935-7893
EI 1938-744X
J9 DISASTER MED PUBLIC
JI Dis. Med. Public Health Prep.
PD AUG 31
PY 2021
VL 17
AR PII S1935789321002779
DI 10.1017/dmp.2021.277
EA AUG 2021
PG 6
WC Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Public, Environmental & Occupational Health
GA 8R0OF
UT WOS:000757087500001
PM 34462034
OA Green Published
DA 2024-09-05
ER
PT J
AU Makhashen, GMB
   Al-Jamimi, HA
AF Makhashen, Galal M. Bin
   Al-Jamimi, Hamdi A.
TI An Intelligent Prediction of the Next Highly Cited Paper Using Machine
   Learning
SO JOURNAL OF SCIENTOMETRIC RESEARCH
LA English
DT Article
DE Artificial Intelligence; Machine Learning; Highly Cited Paper
   Indicators; Digital; Libraries; Bibliometric Analysis
ID CITATION COUNTS; IMPACT; IMPROVE
AB Highly cited articles capture the attention of significant contributors in the research community as an opportunity to improve knowledge, source of ideas or solutions, and advance their research in general. Typically, these articles are authored by a large number of scientists with international collaboration. However, this could not be the only reason for an article to be highly cited, there might be several other characteristics for an article to be more attractive to researchers and readers. In other words, there are a few other characteristics that help articles/papers to be more than others to appear in search engines or to grab readers' attention. In this study, we modeled several machine-learning methods with a set of articles, and journal characteristics including authors-count, title characteristics, abstract length, international collaboration, number of keywords, funding information, journal characteristics, etc. We extracted 20 characteristics and developed multiple machine-learning models to automate highly-cited papers recognition from regular papers. In experiments conducted with an ensemble machine learning algorithm, 97% recognition accuracy was achieved. Other algorithms including a deep learning method using LSTMs also achieved high recognition accuracy. Such high performances can be utilized for a promising HCP auto-detection system in the future.
C1 [Makhashen, Galal M. Bin; Al-Jamimi, Hamdi A.] King Fahd Univ Petr & Minerals, Res Inst, Dhahran 31261, Saudi Arabia.
C3 King Fahd University of Petroleum & Minerals
RP Makhashen, GMB (corresponding author), King Fahd Univ Petr & Minerals, Res Inst, Dhahran 31261, Saudi Arabia.
EM binmakhashen@kfupm.edu.sa
RI Al-Jamimi, Hamdi A./G-5734-2016
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NR 34
TC 0
Z9 0
U1 5
U2 10
PU PHCOG NET
PI KARNATAKA
PA 17, 2ND FLR, BUDDHA VIHAR RD, NEAR SPORTS ZONE, COX TOWN, BENGALURU,
   KARNATAKA, 560005, INDIA
SN 2321-6654
EI 2320-0057
J9 J SCIENTOMETR RES
JI J. Scientometr. Res.
PD JAN-APR
PY 2023
VL 12
IS 1
BP 44
EP 53
DI 10.5530/jscires.12.1.008
PG 10
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA O6RV0
UT WOS:001045065000006
OA hybrid
DA 2024-09-05
ER
PT J
AU Melnikova, EV
AF Melnikova, E. V.
TI Deep Machine Learning in Optimization of Scientific Research Activities
SO SCIENTIFIC AND TECHNICAL INFORMATION PROCESSING
LA English
DT Article
DE artificial intelligence; deep learning; machine learning; artificial
   neural networks; big data; scientometrics; research productivity;
   predictive models; research data repositories
AB -This article provides a general overview of machine learning, a subdomain of artificial intelligence. The substance of the deep learning process is explained, and key features of deep learning as a high-level artificial intelligence technology are outlined. Differences between deep and conventional machine learning are analyzed. The architecture of deep learning models is considered. Issues with using deep learning in neural networks are outlined, and key processes of the functioning of neural networks are described. The importance of deep learning neural networks for processing big data is noted. Specific examples of application of deep learning algorithms in various research fields, specifically, scientometrics, bibliometrics, medicine, geoseismic research, and others, are provided. It is shown that deep learning plays an important role in optimizing research activities and improving research productivity.
C1 [Melnikova, E. V.] Russian Acad Sci, All Russian Inst Sci & Tech Informat VINITI, Moscow 125315, Russia.
C3 Russian Academy of Sciences; Institute for Scientific & Technical
   Information of the Russian Academy of Sciences
RP Melnikova, EV (corresponding author), Russian Acad Sci, All Russian Inst Sci & Tech Informat VINITI, Moscow 125315, Russia.
EM verden.mel@yandex.ru
FU All-Russian Institute for Scientific and Technical Information VINITI,
   Russian Academy of Sciences [FFFU-2021-0002]; Russian Foundation for
   Basic Research [20-07-00014]
FX The work is supported by the State assignment of the All-Russian
   Institute for Scientific and Technical Information VINITI, Russian
   Academy of Sciences (project no. FFFU-2021-0002) and by the Russian
   Foundation for Basic Research (project no. 20-07-00014).
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NR 22
TC 1
Z9 1
U1 15
U2 46
PU PLEIADES PUBLISHING INC
PI NEW YORK
PA PLEIADES HOUSE, 7 W 54 ST, NEW YORK,  NY, UNITED STATES
SN 0147-6882
EI 1934-8118
J9 SCI TECH INF PROCESS
JI Sci. Tech. Inf. Process.
PD MAR
PY 2023
VL 50
IS 1
BP 53
EP 58
DI 10.3103/S0147688223010082
PG 6
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA H0EW6
UT WOS:000992792300006
DA 2024-09-05
ER
PT C
AU Sadaf, F
   Shahid, MH
   Islam, MA
AF Sadaf, Fatima
   Shahid, Muzammil Hussain
   Islam, Muhammad Arshad
GP IEEE
TI Predicting Most Influential Paper Award Using Citation Count
SO 2021 INTERNATIONAL CONFERENCE ON DIGITAL FUTURES AND TRANSFORMATIVE
   TECHNOLOGIES (ICODT2)
LA English
DT Proceedings Paper
CT IEEE International Conference on Digital Futures and Transformative
   Technologies (ICoDT2)
CY MAY 20-21, 2021
CL Natl Univ Sci & Technol, Islamabad, PAKISTAN
HO Natl Univ Sci & Technol
DE Citation Count Prediction; Time series forecasting; Most influential
   paper award; Feature Engineering; Machine Learning
ID IMPACT; NETWORK
AB The early identification of the influential papers is of great significance for assessing the scientific achievements of researchers and institutions as it can help in addressing the processes in an academic and scientific field, such as promotions, recruitment decisions, and funding allocation. This work evaluates features for predicting the most influential paper award that is given by several renowned conferences, ten years subsequent to their publication. The data of five renowned conferences, i.e., ICSE, ICFP, POPL, PLDI, and OOPSLA is used to predict the long-term citations to identify the most influential paper of the respective conference. GD boost model is considered to be better performing among the five different machine learning algorithms. The results show that a three to five years of the time window is good enough to evaluate the most influential paper award. Additionally, the assessment of time window and the citation trajectory of awarded and non awarded papers shows that the citation trajectory of the awarded paper vary from the Citation gain patterns of non-awarded paper.
C1 [Sadaf, Fatima; Shahid, Muzammil Hussain; Islam, Muhammad Arshad] Natl Univ Comp & Emerging Sci, Islamabad, Pakistan.
RP Sadaf, F (corresponding author), Natl Univ Comp & Emerging Sci, Islamabad, Pakistan.
EM i181257@nu.edu.pk; i171090@nu.edu.pk; arshad.islam@nu.edu.pk
RI Islam, Muhammad Arshad/M-2385-2019
OI Islam, Muhammad Arshad/0000-0002-7503-5086; Shahid,
   Muzammil/0000-0001-7013-335X
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NR 16
TC 1
Z9 1
U1 1
U2 7
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-6654-1285-8
PY 2021
DI 10.1109/ICoDT252288.2021.9441487
PG 7
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BS7GM
UT WOS:000760235700015
DA 2024-09-05
ER
PT C
AU Montelongo, A
   Becker, JL
AF Montelongo, Alfredo
   Becker, Joao Luiz
BE Wu, XT
   Jermaine, C
   Xiong, L
   Hu, XH
   Kotevska, O
   Lu, SY
   Xu, WJ
   Aluru, S
   Zhai, CX
   Al-Masri, E
   Chen, ZY
   Saltz, J
TI A bibliometric network analysis of Deep Learning publications applied
   into legal documents
SO 2020 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA)
SE IEEE International Conference on Big Data
LA English
DT Proceedings Paper
CT 8th IEEE International Conference on Big Data (Big Data)
CY DEC 10-13, 2020
CL ELECTR NETWORK
DE Deep Learning; Neural Networks; bibliometric; legal; applications
ID NEURAL-NETWORKS; CLASSIFICATION
AB Deep Learning has been gradually adopted as the main methodology to perform Natural Language Processing tasks on legal documents. In this work we provide a bibliometric network analysis of Deep Learning publications (formerly Neural Networks) applied to the analysis of legal documents. Our study includes a total sample of 138 works published between 1987 and 2020 that used DL as primary methodology. We focused on three specific objectives: identification of the journals with more publications on the subject, a co-authorship network analysis, and an examination of the most cited works. Our results show that the publications are concentrated in a small number of specialized journals on the topic, and consequently, the number of works that use DL methodologies in the legal context is smaller compared to other areas. The co-authorship network analysis reveals four broad clusters of researchers that are time-dependent concentrated into Connectionism (1), Neural Networks (1) and Deep Learning (2). Our analysis of highly cited publications delivered 10 articles with two particular authors that centralize the network. Finally, we have found that collaboration between groups of researchers from different areas is minimal, showing a window of opportunity to increase interdisciplinary research, particularly between computer and legal research groups.
C1 [Montelongo, Alfredo] Univ Fed Rio Grande do Sul EA PPGA, Porto Alegre, RS, Brazil.
   [Becker, Joao Luiz] Fundacao Getulio Vargas FGV EAESP, Sao Paulo, Brazil.
C3 Getulio Vargas Foundation
RP Montelongo, A (corresponding author), Univ Fed Rio Grande do Sul EA PPGA, Porto Alegre, RS, Brazil.
EM alfredo.montelongo@ufrgs.br; joao.becker@fgv.br
RI Becker, João Luiz/R-2088-2016
OI Becker, João Luiz/0000-0003-4176-7374
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NR 67
TC 0
Z9 0
U1 1
U2 11
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2639-1589
BN 978-1-7281-6251-5
J9 IEEE INT CONF BIG DA
PY 2020
BP 2131
EP 2138
DI 10.1109/BigData50022.2020.9377970
PG 8
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BR6NZ
UT WOS:000662554702034
DA 2024-09-05
ER
PT J
AU Le, TQ
   Huynh, NV
AF Thanh Quynh Le
   Nam Van Huynh
TI Grading Sewing Operator Skill Using Principal Component Analysis and
   Ordinal Logistic Regression
SO INTERNATIONAL JOURNAL OF KNOWLEDGE AND SYSTEMS SCIENCE
LA English
DT Article
DE Delphi Method; Generalized Ordered Logit; Logistic Regression Method;
   Operation Research; Ordinal Logistic Regression; Performance Rating;
   Principal Component Analysis; Worker Skill Level
ID PARTIAL PROPORTIONAL ODDS; DELPHI METHOD; PERFORMANCE; MODELS; VARIABLES
AB In the apparel manufacturing process, productivity and quality are somewhat determined by operator skill level. Predicting worker skill level is very important for effective production operation management. However, the current methods for ranking skill level in the manufacturing industry have been based on the subjective evaluation of managers and have failed both in predicting the operator skill level needed for planning and in encouraging operators to develop new skills for quality and productivity. This article develops a new method for grading sewing worker skill levels that employs updated knowledge from experts involved in training, coaching and managing operations in factories. This approach uses the Delphi method combined with principal component analysis to define and classify six qualitative variables that effect on three aspects of operator skill, including coordination skill, sustaining skill, and tool operating skill. Based on these three variables, ordinal logistic regression is applied to grade skill levels, with a statistically significance result.
C1 [Thanh Quynh Le] Japan Adv Inst Sci & Technol, Sch Knowledge Sci, Nomi, Japan.
   [Nam Van Huynh] Japan Adv Inst Sci & Technol, Nomi, Japan.
C3 Japan Advanced Institute of Science & Technology (JAIST); Japan Advanced
   Institute of Science & Technology (JAIST)
RP Le, TQ (corresponding author), Japan Adv Inst Sci & Technol, Sch Knowledge Sci, Nomi, Japan.
OI Le, Thanh Quynh/0000-0002-1099-7807
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NR 29
TC 2
Z9 2
U1 0
U2 6
PU IGI GLOBAL
PI HERSHEY
PA 701 E CHOCOLATE AVE, STE 200, HERSHEY, PA 17033-1240 USA
SN 1947-8208
EI 1947-8216
J9 INT J KNOWL SYST SCI
JI Int. J. Knowl. Syst. Sci.
PD APR-JUN
PY 2018
VL 9
IS 2
BP 28
EP 44
AR 2
DI 10.4018/IJKSS.2018040102
PG 17
WC Operations Research & Management Science
WE Emerging Sources Citation Index (ESCI)
SC Operations Research & Management Science
GA HH1UK
UT WOS:000455505100002
DA 2024-09-05
ER
PT C
AU Montelongo, A
   Becker, JL
AF Montelongo, Alfredo
   Becker, Joao Luiz
BE DiFatta, G
   Sheng, V
   Cuzzocrea, A
   Zaniolo, C
   Wu, X
TI Tasks performed in the legal domain through Deep Learning: A
   bibliometric review (1987-2020)
SO 20TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW 2020)
SE International Conference on Data Mining Workshops
LA English
DT Proceedings Paper
CT 20th IEEE International Conference on Data Mining (ICDM)
CY NOV 17-20, 2020
CL ELECTR NETWORK
DE Legal Corpus; Deep Learning; Neural Networks
ID NEURAL-NETWORKS
AB Deep Learning (DL) has become the state-of-the-art method for Natural Language Processing (NLP). During the last 5 years DL became the primary Artificial Intelligence (AI) method in the legal domain. In this work we provide a systematic bibliometric review of the publications that have utilized DL as the primary methodology. In particular we analyzed the performed objectives (performed tasks), the corpus utilized to train the models and promising areas of research. The sample includes a total of 137 works published between 1987 and 2020. This analysis starts with the first DL models (formerly Neural Networks) in the legal domain until the latest articles in the ongoing year. Our results show an increment of 300% on the total number of publications during the last 5 years, mainly on information extraction and classification tasks. Moreover, classification is the category with most publications with 39% of the total sample. Finally, we have identified that summarization and text generation as promising areas of research. These findings show that DL in the legal domain is currently in a growing stage, and hence it will be a promising topic of research in the coming years.
C1 [Montelongo, Alfredo] Univ Fed Rio Grande do Sul, Porto Alegre, RS, Brazil.
   [Becker, Joao Luiz] Fundacao Getulio Vargas FGV EAESP, Sao Paulo, Brazil.
C3 Universidade Federal do Rio Grande do Sul; Getulio Vargas Foundation
RP Montelongo, A (corresponding author), Univ Fed Rio Grande do Sul, Porto Alegre, RS, Brazil.
EM alfredo.montelongo@ufrgs.br; joao.becker@fgv.br
RI Becker, João Luiz/R-2088-2016
OI Becker, João Luiz/0000-0003-4176-7374
CR Abood A., 2018, ARTIF INTELL LAW, P1
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   Tran V, 2020, ARTIFICIAL INTELLIGE, P1
   Undavia S, 2018, FED CONF COMPUT SCI, P515, DOI 10.15439/2018F227
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   Vogel F, 2018, LAW SOCIAL INQUIRY, V43, P1340, DOI 10.1111/lsi.12305
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   Zhang QS, 2018, FRONT INFORM TECH EL, V19, P27, DOI 10.1631/FITEE.1700808
NR 60
TC 0
Z9 0
U1 1
U2 11
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA
SN 2375-9232
BN 978-1-7281-9012-9
J9 INT CONF DAT MIN WOR
PY 2020
BP 775
EP 781
DI 10.1109/ICDMW51313.2020.00113
PG 7
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BR5SN
UT WOS:000657112800105
DA 2024-09-05
ER
PT J
AU Nazir, S
   Asif, M
   Ahmad, S
   Aljuaid, H
   Iftikhar, R
   Nawaz, Z
   Ghadi, YY
AF Nazir, Shahzad
   Asif, Muhammad
   Ahmad, Shahbaz
   Aljuaid, Hanan
   Iftikhar, Rimsha
   Nawaz, Zubair
   Ghadi, Yazeed Yasin
TI Important Citation Identification by Exploding the Sentiment Analysis
   and Section-Wise In-Text Citation Weights
SO IEEE ACCESS
LA English
DT Article
DE Sentiment analysis; Support vector machines; Random forests; Training
   data; Neural networks; Multilayer perceptrons; Linguistics; Machine
   learning; Citation analysis; Important citation identification;
   sentiment analysis; weight assignment; machine learning
ID QUALITY; INDEX
AB A massive research corpus is generated in this epoch based on some previously established concepts or findings. For the acknowledgment of the base knowledge, researchers perform citations. Citations are the key considerations used in finding the different research measures, such as ranking the institutions, researchers, countries, computing the impact factor of journals, allocating research funds, etc. But in calculating these critical measures, citations are treated equally. However, researchers have argued that all citations can never be equally influential. Therefore, researchers have proposed other techniques to identify the important content-based, meta-data-based, and bibliographic-based citations. However, the produced results by the state-of-the-art still need to be improved. In this research work, we proposed an approach based on two primary modules, 1) The section-wise citation count and 2) Sentiment based analysis of citation sentences. The first technique is based on extracting the different sections of the research articles and performing citation count. We applied Neural Network and Multiple Regression on section-wise citations for automatic weight assignment. The citation sentences were extracted in the second approach, and sentiment analysis was used for sentences. Citations were classified with Support Vector Machine, Multilayer Perceptron, and Random Forest. F-measure, Recall, and Precision were considered to evaluate the results, compared with the state-of-the-art results. The value of precision with the proposed approach was enhanced to 0.94.
C1 [Nazir, Shahzad; Asif, Muhammad; Ahmad, Shahbaz; Iftikhar, Rimsha] Natl Text Univ, Dept Comp Sci, Faisalabad 37610, Pakistan.
   [Aljuaid, Hanan] Princess Nourah Bint Abdulrahman Univ PNU, Coll Comp & Informat Sci, Dept Comp Sci, Riyadh 11671, Saudi Arabia.
   [Nawaz, Zubair] Univ Punjab, Dept Data Sci, Lahore 54590, Pakistan.
   [Ghadi, Yazeed Yasin] Al Ain Univ, Dept Comp Sci Software Engn, Abu Dhabi, U Arab Emirates.
C3 National Textile University - Pakistan; Princess Nourah bint Abdulrahman
   University; University of Punjab
RP Asif, M (corresponding author), Natl Text Univ, Dept Comp Sci, Faisalabad 37610, Pakistan.
EM asif@ntu.edu.pk
RI Asif, Muhammad/B-6072-2012; aljuaid, Hanan/AAJ-4910-2020; Ghadi, Yazeed
   Yasin/AAW-6774-2021; Ahmad, Shahbaz/AAA-1264-2021
OI Asif, Muhammad/0000-0003-1839-2527; aljuaid, Hanan/0000-0001-6042-0283;
   Ghadi, Yazeed Yasin/0000-0002-7121-495X; Ahmad,
   Shahbaz/0000-0003-0148-4521
FU Princess Nourah bint Abdulrahman University Researchers Supporting
   Project, Princess Nourah bint Abdulrahman University, Riyadh, Saudi
   Arabia [PNURSP2022R54]
FX This work was supported by the Princess Nourah bint Abdulrahman
   University Researchers Supporting Project, Princess Nourah bint
   Abdulrahman University, Riyadh, Saudi Arabia, under Grant PNURSP2022R54.
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NR 40
TC 2
Z9 2
U1 9
U2 32
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2022
VL 10
BP 87990
EP 88000
DI 10.1109/ACCESS.2022.3199420
PG 11
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA 4E9UC
UT WOS:000848162600001
OA gold
DA 2024-09-05
ER
PT J
AU Manogna, RL
   Anand, A
AF Manogna, R. L.
   Anand, Aayush
TI A bibliometric analysis on the application of deep learning in finance:
   status, development and future directions
SO KYBERNETES
LA English
DT Article; Early Access
DE Bibliometric analysis; Financial markets; Deep learning; Neural
   networks; Co-citation analysis; Keyword analysis
ID NEURAL-NETWORKS; BANKRUPTCY PREDICTION; STATISTICAL ARBITRAGE; MARKET;
   SUPPORT; PERCEPTRON; OUTRANKING; SELECTION; RATIOS; MODEL
AB PurposeDeep learning (DL) is a new and relatively unexplored field that finds immense applications in many industries, especially ones that must make detailed observations, inferences and predictions based on extensive and scattered datasets. The purpose of this paper is to answer the following questions: (1) To what extent has DL penetrated the research being done in finance? (2) What areas of financial research have applications of DL, and what quality of work has been done in the niches? (3) What areas still need to be explored and have scope for future research?Design/methodology/approachThis paper employs bibliometric analysis, a potent yet simple methodology with numerous applications in literature reviews. This paper focuses on citation analysis, author impacts, relevant and vital journals, co-citation analysis, bibliometric coupling and co-occurrence analysis. The authors collected 693 articles published in 2000-2022 from journals indexed in the Scopus database. Multiple software (VOSviewer, RStudio (biblioshiny) and Excel) were employed to analyze the data.FindingsThe findings reveal significant and renowned authors' impact in the field. The analysis indicated that the application of DL in finance has been on an upward track since 2017. The authors find four broad research areas (neural networks and stock market simulations; portfolio optimization and risk management; time series analysis and forecasting; high-frequency trading) with different degrees of intertwining and emerging research topics with the application of DL in finance. This article contributes to the literature by providing a systematic overview of the DL developments, trajectories, objectives and potential future research topics in finance.Research limitations/implicationsThe findings of this paper act as a guide for literature review for anyone interested in doing research in the intersection of finance and DL. The article also explores multiple areas of research that have yet to be studied to a great extent and have abundant scope.Originality/valueVery few studies have explored the applications of machine learning (ML), namely, DL in finance, which is a much more specialized subset of ML. The authors look at the problem from the aspect of different techniques in DL that have been used in finance. This is the first qualitative (content analysis) and quantitative (bibliometric analysis) assessment of current research on DL in finance.
C1 [Manogna, R. L.; Anand, Aayush] Birla Inst Technol & Sci Pilani, Dept Econ & Finance, KK Birla Goa Campus, Zuarinagar, India.
C3 Birla Institute of Technology & Science Pilani (BITS Pilani)
RP Manogna, RL (corresponding author), Birla Inst Technol & Sci Pilani, Dept Econ & Finance, KK Birla Goa Campus, Zuarinagar, India.
EM leshma2020@gmail.com
OI R L, Manogna/0000-0002-8882-6434
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NR 103
TC 7
Z9 7
U1 8
U2 17
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 0368-492X
EI 1758-7883
J9 KYBERNETES
JI Kybernetes
PD 2023 OCT 12
PY 2023
DI 10.1108/K-04-2023-0637
EA OCT 2023
PG 21
WC Computer Science, Cybernetics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA U0IS3
UT WOS:001081735000001
DA 2024-09-05
ER
PT J
AU Melnikova, EV
AF Melnikova, E. V.
TI Relevance of Application of Artificial Intelligence Toolkit in Modern
   Scientometric Research
SO SCIENTIFIC AND TECHNICAL INFORMATION PROCESSING
LA English
DT Article
DE scientometry; monitoring of scientific research; effectiveness of
   science; artificial intelligence; neural networks; deep learning;
   machine learning; representation learning; unsupervised learning
AB The main tasks of modern scientometrics are considered, including monitoring the effectiveness of science, and the possibility of solving them with the use of high-performance artificial intelligence tools is analyzed. The characteristics of artificial intelligence as a branch of computer science are presented, and the contribution of neuroinformatics to its development is noted. The common features and differences of the main types of machine learning developed to date are considered: classical, deep, hybrid, and automatic learning. The features of the functioning of artificial neural networks are presented, including their internal structure, order of operation, distinctive features, areas, and conditions of application. Examples of the practical use of artificial intelligence tools in modern scientometric research are given: central attention is paid to the advanced developments of the Indian scientific school. The urgently demanded method of article-by-article classification of scientific literature, as proposed by Arab scientists, is also outlined. A conclusion is drawn about the great importance of artificial intelligence and the relevance of its application for the implementation of new opportunities in optimizing scientometric research.
C1 [Melnikova, E. V.] Russian Acad Sci, All Russian Inst Sci & Tech Informat, Dept Theoret & Appl Problems Informat, Moscow, Russia.
C3 Russian Academy of Sciences; Institute for Scientific & Technical
   Information of the Russian Academy of Sciences
RP Melnikova, EV (corresponding author), Russian Acad Sci, All Russian Inst Sci & Tech Informat, Dept Theoret & Appl Problems Informat, Moscow, Russia.
EM verden.mel@yandex.ru
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NR 17
TC 0
Z9 0
U1 2
U2 2
PU PLEIADES PUBLISHING INC
PI NEW YORK
PA PLEIADES HOUSE, 7 W 54 ST, NEW YORK,  NY, UNITED STATES
SN 0147-6882
EI 1934-8118
J9 SCI TECH INF PROCESS
JI Sci. Tech. Inf. Process.
PD MAR
PY 2024
VL 51
IS 1
BP 57
EP 63
DI 10.3103/S014768822401009X
PG 7
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA RK4R2
UT WOS:001227549600008
DA 2024-09-05
ER
PT J
AU Xu, C
   Zong, QJ
AF Xu, Chuer
   Zong, Qianjin
TI The effects of international research collaboration on the policy impact
   of research: A causal inference drawing on the journal <i>Lancet</i>
SO JOURNAL OF INFORMATION SCIENCE
LA English
DT Article; Early Access
DE Causal inference; international research collaboration; policy citation
   counts; policy impact
ID PROPENSITY SCORE; SCIENTIFIC COLLABORATION; CITATION; SCIENCE; DOCUMENTS
AB Research findings have been widely used as evidence for policy-making. The internationalisation of research activities has been increasing in recent decades, particularly during the COVID-19 pandemic. Previous studies have revealed that international research collaboration can enhance the academic impact of research. However, the effects that international research collaboration exerts on the policy impact of research are still unknown. This study aims to examine the effects of international research collaboration on the policy impact of research (as measured by the number of citations in policy documents) using a causal inference approach. Research articles published by the journal Lancet between 2000 and 2019 were selected as the study sample (n = 6098). The number of policy citations of each article was obtained from Overton, the largest database of policy citations. Propensity score matching analysis, which takes a causal inference approach, was used to examine the dataset. Four other matching methods and alternative datasets of different sizes were used to test the robustness of the results. The results of this study reveal that international research collaboration has significant and positive effects on the policy impact of research (coefficient = 4.323, p < 0.001). This study can provide insight to researchers, research institutions and grant funders for improving the policy impact of research.
C1 [Xu, Chuer; Zong, Qianjin] South China Normal Univ, Sch Econ & Management, Guangzhou 510006, Peoples R China.
C3 South China Normal University
RP Zong, QJ (corresponding author), South China Normal Univ, Sch Econ & Management, Guangzhou 510006, Peoples R China.
EM zongqj@m.scnu.edu.cn
RI Zong, Qianjin/ABD-0454-2022
OI Zong, Qianjin/0000-0002-7517-8191
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NR 50
TC 2
Z9 2
U1 14
U2 43
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 0165-5515
EI 1741-6485
J9 J INF SCI
JI J. Inf. Sci.
PD 2023 MAY 11
PY 2023
DI 10.1177/01655515231174381
EA MAY 2023
PG 11
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA F8UE2
UT WOS:000985034900001
DA 2024-09-05
ER
PT J
AU Kilicoglu, H
   Peng, ZS
   Tafreshi, S
   Tran, T
   Rosemblat, G
   Schneider, J
AF Kilicoglu, Halil
   Peng, Zeshan
   Tafreshi, Shabnam
   Tung Tran
   Rosemblat, Graciela
   Schneider, Jodi
TI Confirm or refute?: A comparative study on citation sentiment
   classification in clinical research publications
SO JOURNAL OF BIOMEDICAL INFORMATICS
LA English
DT Article
DE Citation analysis; Sentiment analysis; Natural language processing;
   Supervised machine learning; Neural networks
ID SCIENCE; IMPACT
AB Quantifying scientific impact of researchers and journals relies largely on citation counts, despite the acknowledged limitations of this approach. The need for more suitable alternatives has prompted research into developing advanced metrics, such as h-index and Relative Citation Ratio (RCR), as well as better citation categorization schemes to capture the various functions that citations serve in a publication. One such scheme involves citation sentiment whether a reference paper is cited positively (agreement with the findings of the reference paper), negatively (disagreement), or neutrally. The ability to classify citation function in this manner can be viewed as a first step toward a more fine-grained bibliometrics. In this study, we compared several approaches, varying in complexity, for classification of citation sentiment in clinical trial publications. Using a corpus of 285 discussion sections from as many publications (a total of 4,182 citations), we developed a rule-based method as well as supervised machine learning models based on support vector machines (SVM) and two variants of deep neural networks; namely, convolutional neural network (CNN) and bidirectional long shortterm memory (BiLSTM). A CNN model augmented with hand-crafted features yielded the best performance (0.882 accuracy and 0.721 macro-F-1 on held-out set). Our results show that baseline performances of traditional supervised learning algorithms and deep neural network architectures are similar and that hand-crafted features based on sentiment dictionaries and rhetorical structure allow neural network approaches to outperform traditional machine learning approaches for this task. We make the rule-based method and the best-performing neural network model publicly available at: https://github.com/kilicogluh/clinical-citation-sentiment.
C1 [Kilicoglu, Halil; Peng, Zeshan; Rosemblat, Graciela] Natl Lib Med, Lister Hill Natl Ctr Biomed Commun, Bethesda, MD 20894 USA.
   [Tafreshi, Shabnam] George Washington Univ, Dept Comp Sci, Washington, DC 20052 USA.
   [Tung Tran] Univ Kentucky, Dept Comp Sci, Lexington, KY 40506 USA.
   [Schneider, Jodi] Univ Illinois, Sch Informat Sci, Champaign, IL 61820 USA.
C3 National Institutes of Health (NIH) - USA; NIH National Library of
   Medicine (NLM); George Washington University; University of Kentucky;
   University of Illinois System; University of Illinois Urbana-Champaign
RP Kilicoglu, H (corresponding author), Natl Lib Med, Lister Hill Natl Ctr Biomed Commun, Bethesda, MD 20894 USA.
EM kilicogluh@mail.nih.gov
RI Schneider, Jodi/AAK-2236-2020; Tafreshi, Shabnam/GXV-9636-2022
OI Schneider, Jodi/0000-0002-5098-5667; 
FU U.S. National Library of Medicine, National Institutes of Health
FX HK, ZP, and GR were supported by the intramural research program at the
   U.S. National Library of Medicine, National Institutes of Health. ST and
   TT took part in this study during their participation in the Lister Hill
   National Center for Biomedical Communications (LHNCBC) Research Program
   in Medical Informatics for Graduate students at the U.S. National
   Library of Medicine. JS was supported by an appointment to the NLM
   Research Participation Program, administered by the Oak Ridge Institute
   for Science and Education through an interagency agreement between the
   U.S. Department of Energy and the National Library of Medicine Research
   Participation Program.
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NR 47
TC 14
Z9 17
U1 3
U2 34
PU ACADEMIC PRESS INC ELSEVIER SCIENCE
PI SAN DIEGO
PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA
SN 1532-0464
EI 1532-0480
J9 J BIOMED INFORM
JI J. Biomed. Inform.
PD MAR
PY 2019
VL 91
AR 103123
DI 10.1016/j.jbi.2019.103123
PG 11
WC Computer Science, Interdisciplinary Applications; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Medical Informatics
GA LC9XS
UT WOS:000525688200006
PM 30753947
OA Green Accepted
DA 2024-09-05
ER
PT J
AU Deng, GF
   Lin, WT
AF Deng, Guang-Feng
   Lin, Woo-Tsong
TI Citation analysis and bibliometric approach for ant colony optimization
   from 1996 to 2010
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Citation analysis; Bibliometric analysis; Ant colony optimization (ACO);
   Bradford Law; Lotka's Law
ID QUADRATIC ASSIGNMENT PROBLEM; SYSTEM; ALGORITHMS
AB To build awareness of the development of ant colony optimization (ACO), this study clarifies the citation and bibliometric analysis of research publications of ACO during 1996-2010. This study analysed 12,960 citations from a total of 1372 articles dealing with ACO published in 517 journals based on the databases of SCIE, SSCI and AH&CI, retrieved via the Web of Science. Bradford Law and Lotka's Law, respectively, examined the distribution of journal articles and author productivity. Furthermore, this study determines the citation impact of ACO using parameters such as extent of citation received in terms of number of citations per study, distribution of citations over time, distribution of citations among domains, citation of authors, citation of institutions, highly cited papers and citing journals and impact factor of 12,960 citations. This study can help researchers to better understand the history, current status and trends of ACO in the advanced study of it. (C) 2011 Elsevier I.td. All rights reserved.
C1 [Deng, Guang-Feng; Lin, Woo-Tsong] Natl Chengchi Univ, Dept Management Informat Syst, Taipei 116, Taiwan.
C3 National Chengchi University
RP Deng, GF (corresponding author), Natl Chengchi Univ, Dept Management Informat Syst, 64,Sec 2,Chihnan Rd, Taipei 116, Taiwan.
EM deng@nccu.edu.tw; lin@mis.nccu.edu.tw
RI Deng, Guang-Feng/HNS-1201-2023
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NR 20
TC 21
Z9 23
U1 1
U2 33
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0957-4174
J9 EXPERT SYST APPL
JI Expert Syst. Appl.
PD MAY
PY 2012
VL 39
IS 6
BP 6229
EP 6237
DI 10.1016/j.eswa.2011.12.001
PG 9
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering; Operations Research & Management Science
GA 902BN
UT WOS:000301013700013
DA 2024-09-05
ER
PT J
AU Gupta, G
   Katarya, R
AF Gupta, Garima
   Katarya, Rahul
TI Research on Understanding the Effect of Deep Learning on User
   Preferences
SO ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
LA English
DT Article
DE Recommender systems; Machine learning; Deep learning
ID RECOMMENDER SYSTEMS; NETWORKS
AB Recommender systems are becoming more essential than ever as the data available online is increasing manifold. The increasing data presents us with an opportunity to build complex systems that can model the user interactions more accurately and extract sophisticated features to provide recommendations with better accuracy. To construct these complex models, deep learning is emerging as one of the most powerful tools. It can process large amounts of data to learn the structure and patterns that can be exploited. It has been used in recommender systems to solve cold-start problem, better estimate the interaction functions, and extract deep feature representations, among other facets that plague the traditional recommender systems. As big data is becoming more prevalent, there is a need to use tools that can take advantage of such explosive data. An extensive study on recommender systems using deep learning has been performed in the paper. The literature review spans in-depth analysis and comparative study of the research domain. The paper exhibits a vast range of scope for efficient recommender systems in future.
C1 [Gupta, Garima; Katarya, Rahul] Delhi Technol Univ, Dept Comp Sci & Engn, Delhi 110042, India.
C3 Delhi Technological University
RP Katarya, R (corresponding author), Delhi Technol Univ, Dept Comp Sci & Engn, Delhi 110042, India.
EM garimacsdtu@gmail.com; rahulkatarya@dtu.ac.in
OI Katarya, Prof. Rahul/0000-0001-7763-291X
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NR 135
TC 10
Z9 10
U1 2
U2 17
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 2193-567X
EI 2191-4281
J9 ARAB J SCI ENG
JI Arab. J. Sci. Eng.
PD APR
PY 2021
VL 46
IS 4
BP 3247
EP 3286
DI 10.1007/s13369-020-05112-2
EA NOV 2020
PG 40
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA QU1RC
UT WOS:000593093000001
DA 2024-09-05
ER
PT J
AU Nam, S
   Kim, D
   Jung, W
   Zhu, YJ
AF Nam, Seojin
   Kim, Donghun
   Jung, Woojin
   Zhu, Yongjun
TI Understanding the Research Landscape of Deep Learning in Biomedical
   Science: Scientometric Analysis
SO JOURNAL OF MEDICAL INTERNET RESEARCH
LA English
DT Article
DE deep learning; scientometric analysis; research publications; research
   landscape; research collaboration; knowledge diffusion
ID CONVOLUTIONAL NEURAL-NETWORKS; MODEL; SEGMENTATION; WEB; CLASSIFICATION;
   PREDICTION; CNN; MRI
AB Background: Advances in biomedical research using deep learning techniques have generated a large volume of related literature. However, there is a lack of scientometric studies that provide a bird's-eye view of them. This absence has led to a partial and fragmented understanding of the field and its progress.
   Objective: This study aimed to gain a quantitative and qualitative understanding of the scientific domain by analyzing diverse bibliographic entities that represent the research landscape from multiple perspectives and levels of granularity.
   Methods: We searched and retrieved 978 deep learning studies in biomedicine from the PubMed database. A scientometric analysis was performed by analyzing the metadata, content of influential works, and cited references.
   Results: In the process, we identified the current leading fields, major research topics and techniques, knowledge diffusion, and research collaboration. There was a predominant focus on applying deep learning, especially convolutional neural networks, to radiology and medical imaging, whereas a few studies focused on protein or genome analysis. Radiology and medical imaging also appeared to be the most significant knowledge sources and an important field in knowledge diffusion, followed by computer science and electrical engineering. A coauthorship analysis revealed various collaborations among engineering-oriented and biomedicine-oriented clusters of disciplines.
   Conclusions: This study investigated the landscape of deep learning research in biomedicine and confirmed its interdisciplinary nature. Although it has been successful, we believe that there is a need for diverse applications in certain areas to further boost the contributions of deep learning in addressing biomedical research problems. We expect the results of this study to help researchers and communities better align their present and future work.
C1 [Nam, Seojin; Kim, Donghun; Jung, Woojin] Sungkyunkwan Univ, Dept Lib & Informat Sci, Seoul, South Korea.
   [Zhu, Yongjun] Yonsei Univ, Dept Lib & Informat Sci, 50 Yonsei Ro, Seoul 03722, South Korea.
C3 Sungkyunkwan University (SKKU); Yonsei University
RP Zhu, YJ (corresponding author), Yonsei Univ, Dept Lib & Informat Sci, 50 Yonsei Ro, Seoul 03722, South Korea.
EM zhu@yonsei.ac.kr
RI jung, woojin/GRY-0374-2022; Zhu, Yongjun/K-2486-2015
OI Jung, Woojin/0000-0003-2229-169X; Zhu, Yongjun/0000-0003-4787-5122; Kim,
   Donghun/0000-0001-5441-1532
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NR 143
TC 2
Z9 2
U1 2
U2 18
PU JMIR PUBLICATIONS, INC
PI TORONTO
PA 130 QUEENS QUAY East, Unit 1100, TORONTO, ON M5A 0P6, CANADA
SN 1438-8871
J9 J MED INTERNET RES
JI J. Med. Internet Res.
PD APR 22
PY 2022
VL 24
IS 4
AR e28114
DI 10.2196/28114
PG 25
WC Health Care Sciences & Services; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Health Care Sciences & Services; Medical Informatics
GA 1N3WN
UT WOS:000800589600004
PM 35451980
OA Green Published, gold
DA 2024-09-05
ER
PT J
AU Hameed, A
   Omar, M
   Bilal, M
   Park, HW
AF Hameed, Abdul
   Omar, Muhammad
   Bilal, Muhammad
   Park, Han Woo
TI Toward the consolidation of a multi-metric-based journal ranking and
   categorization system for computer science subject areas
SO PROFESIONAL DE LA INFORMACION
LA English
DT Article
DE Journal rankings; Research evaluation; Indicators; Scientific journals;
   Metrics; Algorithms; Machine learning; Cluster analysis; Principal
   component analysis (PCA); t-distributed stochastic neighbor embedding
   (t-SNE); Cross tabulation; ESA index
ID IMPACT FACTOR
AB The evaluation of scientific journals poses challenges owing to the existence of various impact measures. This is because journal ranking is a multidimensional construct that may not be assessed effectively using a single metric such as an impact factor. A few studies have proposed an ensemble of metrics to prevent the bias induced by an individual metric. In this study, a multi-metric journal ranking method based on the standardized average index (SA index) was adopted to develop an extended standardized average index (ESA index). The ESA index utilizes six metrics: the CiteScore, Source Normalized Impact per Paper (SNIP), SCImago Journal Rank (SJR), Hirsh index (H-index), Eigenfactor Score, and Journal Impact Factor from three well-known databases (Scopus, SCImago Journal & Country Rank, and Web of Science). Experiments were conducted in two computer science subject areas: (1) artificial intelligence and (2) computer vision and pattern recognition. Comparing the results of the multi-metric-based journal ranking system with the SA index, it was demonstrated that the multi-metric ESA index exhibited high correlation with all other indicators and significantly outperformed the SA index. To further evaluate the performance of the model and determine the aggregate impact of bibliometric indices with the ESA index, we employed unsupervised machine learning techniques such as clustering coupled with principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE). These techniques were utilized to measure the clustering impact of various bibliometric indicators on both the complete set of bibliometric features and the reduced set of features. Furthermore, the results of the ESA index were compared with those of other ranking systems, including the internationally recognized Scopus, SJR, and HEC Journal Recognition System (HJRS) used in Pakistan. These comparisons demonstrated that the multi-metric-based ESA index can serve as a valuable reference for publishers, journal editors, researchers, policymakers, librarians, and practitioners in journal selection, decision making, and professional assessment.
C1 [Hameed, Abdul; Omar, Muhammad; Bilal, Muhammad] Islamia Univ, Dept Comp Sci, Bahawalpur, Pakistan.
   [Park, Han Woo] Yeungnam Univ, 257 Humanities Hall,280 Dae Dong, Gyongsan 38541, Gyeongsangbuk D, South Korea.
C3 Yeungnam University
RP Omar, M (corresponding author), Islamia Univ, Dept Comp Sci, Bahawalpur, Pakistan.; Park, HW (corresponding author), Yeungnam Univ, 257 Humanities Hall,280 Dae Dong, Gyongsan 38541, Gyeongsangbuk D, South Korea.
EM abdulhameedattari@gmail.com; m.omar.nazeer@gmail.com;
   m_bilalcsiub@yahoo.com; hanpark@ynu.ac.kr
OI Bilal, Muhammad/0009-0004-4757-3153
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TC 2
Z9 2
U1 6
U2 6
PU  EDICIONES PROFESIONALES INFORMACION SL-EPI
PI BARCELONA
PA MISTRAL, 36, BARCELONA, ALBOLOTE, SPAIN
SN 1386-6710
EI 1699-2407
J9 PROF INFORM
JI Prof. Inf.
PY 2023
VL 32
IS 7
AR e320703
DI 10.3145/epi.2023.dic.03
PG 17
WC Communication; Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Communication; Information Science & Library Science
GA GZ6U9
UT WOS:001156551800002
OA Green Accepted, hybrid
DA 2024-09-05
ER
PT J
AU Yu, FX
   Liu, XR
AF Yu, Fuxing
   Liu, Xinran
TI Research on Student Performance Prediction Based on Stacking Fusion
   Model
SO ELECTRONICS
LA English
DT Article
DE performance prediction; bagging; stacking; XGBoost; LightGBM; model
   fusion
ID CLASSIFICATION; NETWORK
AB Online learning is gradually becoming popular with the continuous development of Internet technology and the rapid development of educational informatization. It plays a key role in predicting students' course performance based on their online learning behavior. It can optimize the effects of teaching and improve teaching strategies. Student performance prediction models that are built with a single algorithm currently have limited prediction accuracy. Meanwhile, model fusion improvement technology can combine many algorithms into a single model, thereby enhancing the overall effect of the model and providing better performance. In this paper, a stacking fusion model based on RF-CART-XGBoost-LightGBM is proposed. The first layer of the model uses a decision tree (CART), random forest, XGBoost and LightGBM as the base models. The second layer uses the LightGBM model. We used the Kalboard360 student achievement dataset, and features related to online learning behavior were selected as the model's input for model training. Finally, we employed five-fold cross-validation to assess the model's performance. In comparison with the four single models, the two fusion models based on the four single models both show significantly better performance. The prediction accuracies of the bagging fusion model and stacking fusion model are 83% and 84%, respectively. This proves that the proposed stacking fusion model has better performance, which helps to improve the accuracy of the performance prediction model further. It also provides an effective basis for optimizing the effects of teaching.
C1 [Yu, Fuxing; Liu, Xinran] North China Univ Sci & Technol, Inst Artificial Intelligence, Tangshan 063210, Peoples R China.
   [Yu, Fuxing] Hebei Prov Key Lab Ind Intelligent Sensing, Tangshan 063210, Peoples R China.
C3 North China University of Science & Technology
RP Liu, XR (corresponding author), North China Univ Sci & Technol, Inst Artificial Intelligence, Tangshan 063210, Peoples R China.
EM liuxinran9652@163.com
OI Fuxing, Yu/0000-0002-5481-2413
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NR 35
TC 3
Z9 3
U1 12
U2 43
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2079-9292
J9 ELECTRONICS-SWITZ
JI Electronics
PD OCT
PY 2022
VL 11
IS 19
AR 3166
DI 10.3390/electronics11193166
PG 13
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Physics
GA 5F9SO
UT WOS:000866650200001
OA gold
DA 2024-09-05
ER
PT J
AU Reich, Y
   Barai, SV
AF Reich, Y
   Barai, SV
TI Evaluating machine learning models for engineering problems
SO ARTIFICIAL INTELLIGENCE IN ENGINEERING
LA English
DT Article
DE artificial neural networks; research methodology; performance
   evaluation; statistical tests; modeling
ID CROSS-VALIDATION; ERROR RATE; ALGORITHMS; SELECTION
AB The use of machine learning (ML), and in particular, artificial neural networks (ANN), in engineering applications has increased dramatically over the last years. However, by and large, the development of such applications or their report lack proper evaluation. Deficient evaluation practice was observed in the general neural networks community and again in engineering applications through a survey we conducted of articles published in Al in Engineering and elsewhere. This status hinders understanding and prevents progress. This article goal is to remedy this situation. First, several evaluation methods are discussed with their relative qualities. Second, these qualities are illustrated by using the methods to evaluate ANN performance in two engineering problems. Third, a systematic evaluation procedure for ML is discussed. This procedure will lead to better evaluation of studies, and consequently to improved research and practice in the area of ML in engineering applications. (C) 1999 Elsevier Science Ltd. All rights reserved.
C1 Tel Aviv Univ, Fac Engn, Dept Solid Mech Mat & Struct, IL-69978 Tel Aviv, Israel.
   Indian Inst Technol, Dept Civil Engn, Kharagpur 721302, W Bengal, India.
C3 Tel Aviv University; Indian Institute of Technology System (IIT System);
   Indian Institute of Technology (IIT) - Kharagpur
RP Reich, Y (corresponding author), Tel Aviv Univ, Fac Engn, Dept Solid Mech Mat & Struct, IL-69978 Tel Aviv, Israel.
RI Reich, Yoram/AAE-6262-2020; Barai, Sudhirkumar/AAI-5698-2020; Reich,
   Yoram/G-4894-2010
OI Reich, Yoram/0000-0002-0922-8381; Barai,
   Sudhirkumar/0000-0001-5100-0607; 
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NR 36
TC 114
Z9 126
U1 1
U2 17
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0954-1810
J9 ARTIF INTELL ENG
JI Artif. Intell. Eng.
PD JUL
PY 1999
VL 13
IS 3
BP 257
EP 272
DI 10.1016/S0954-1810(98)00021-1
PG 16
WC Computer Science, Artificial Intelligence; Engineering,
   Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA 221AT
UT WOS:000081702600006
DA 2024-09-05
ER
PT J
AU Thakuria, A
   Deka, D
AF Thakuria, Abhijit
   Deka, Dipen
TI A decadal study on identifying latent topics and research trends in open
   access LIS journals using topic modeling approach
SO SCIENTOMETRICS
LA English
DT Article
DE Topic modeling; LDA; Open access; LIS
ID INFORMATION-SCIENCE; AUTHOR COCITATION; RESEARCH FRONT; LIBRARY;
   EVOLUTION; PUBLICATIONS; ARTICLES
AB The study utilized Latent Dirichlet Allocation (LDA) Topic modeling to identify prevalent latent topics within Open Access (OA) Library and Information Science (LIS) journals from 2013 to 2022. Eight core OA Scopus indexed journals were selected based on their SJR scores and DOAJ listing. Titles, Abstracts and keywords of 2589 articles were extracted from the Scopus database. R software packages were used to perform data analysis and LDA topic modeling. The optimal value of k was determined to be 9. The analysis revealed that 53.89% of documents comprise over 50% of a certain topic (theta > 0.50). Notably, 'Scholarly Communication' and 'Information Systems, Models and Frameworks' emerged as dominant topics with the highest proportions of research literature in the corpus. The topic 'Scholarly Communication' experienced significant growth with an average annual growth rate (AAGR) of 4.37%, while 'Collection development and E-resources' exhibited the lowest research proportion and a negative AAGR of - 4.22%. Additionally, topics such as 'Information Seeking Behaviour and User Studies', 'User Education and Information Literacy', and 'Information Retrieval and Systematic Review' remained stable and persistent topics. Conversely, research on traditional topics like 'Librarianship and Profession', 'Bibliometrics' and 'Medical Library and Health Information' showed a gradual decline. The LDA topic modeling approach unveiled previously unknown or unexplored topics in open access LIS research literature, enhancing our understanding of emerging trends.
C1 [Thakuria, Abhijit; Deka, Dipen] Gauhati Univ, Dept Lib & Informat Sci, Gauhati, Assam, India.
C3 Gauhati University
RP Thakuria, A (corresponding author), Gauhati Univ, Dept Lib & Informat Sci, Gauhati, Assam, India.
EM abhijitthakuria97@gmail.com
OI Thakuria, Abhijit/0000-0002-3852-1982
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NR 76
TC 1
Z9 1
U1 18
U2 18
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD JUL
PY 2024
VL 129
IS 7
BP 3841
EP 3869
DI 10.1007/s11192-024-05058-4
EA JUN 2024
PG 29
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA ZU5Q7
UT WOS:001238196700002
DA 2024-09-05
ER
PT J
AU Zhou, YH
   Wang, RJ
   Zeng, A
AF Zhou, Yuhao
   Wang, Ruijie
   Zeng, An
TI Predicting the impact and publication date of individual scientists'
   future papers
SO SCIENTOMETRICS
LA English
DT Article
DE Citation counts; H-index; Deep learning; Scientific impact; Prediction
ID CITATION COUNTS; NEURAL-NETWORK
AB Predicting the future career of individual scientists is an important yet challenging problem with numerous applications such as recruitment of scientific research positions, promoting outstanding academic staff, and managing scientific grant proposals. Despite that much effort has been devoted to predict scientists' future performance and success, yet these works focus on the macro future performance of scholars from the perspective of their career ages. A related but different task is to predict the impact and publication date of each future paper. We regard this micro level prediction problem as a dynamic series auto-regression task, and a deep learning method is designed to solve it. The experiments show that our method outperforms the state-of-the-art method in this issue.
C1 [Zhou, Yuhao; Wang, Ruijie] Univ Fribourg, Dept Phys, CH-1700 Fribourg, Switzerland.
   [Zeng, An] Beijing Normal Univ, Sch Syst Sci, Beijing 100875, Peoples R China.
C3 University of Fribourg; Beijing Normal University
RP Zeng, A (corresponding author), Beijing Normal Univ, Sch Syst Sci, Beijing 100875, Peoples R China.
EM anzeng@bnu.edu.cn
FU National Natural Science Foundation of China [71731002]; China
   Scholarship Council (CSC)
FX This work is supported by the National Natural Science Foundation of
   China under Grant 71731002. Rui-Jie Wang acknowledges the support from
   the China Scholarship Council (CSC).
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NR 60
TC 3
Z9 3
U1 6
U2 63
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD APR
PY 2022
VL 127
IS 4
BP 1867
EP 1882
DI 10.1007/s11192-022-04286-w
EA FEB 2022
PG 16
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 0M5LI
UT WOS:000755102600003
DA 2024-09-05
ER
PT C
AU Lin, YY
   Lin, L
AF Lin, Yi-yong
   Lin, Lei
GP Destech Publicat Inc
TI Research on the Algorithm of LAN Performance Evaluation Based on Deep
   Learning
SO INTERNATIONAL CONFERENCE ON MECHANICAL, ELECTRONIC AND INFORMATION
   TECHNOLOGY (ICMEIT 2018)
SE DEStech Transactions on Engineering and Technology Research
LA English
DT Proceedings Paper
CT International Conference on Mechanical, Electronic and Information
   Technology (ICMEIT)
CY APR 15-16, 2018
CL Shanghai, PEOPLES R CHINA
DE Deep learning; Deep neural network; Recurrent neural network; Evaluation
   algorithm; K-line cross-validation
ID CLASSIFICATION; NETWORK
AB A network performance evaluation algorithm is presented based on deep learning framework RNN on this paper. The algorithm collects 8 index parameters in the private LAN and realizes the automatic evaluation of the three levels of the private LAN: excellent, good, and unqualified. The results of the example verification and analysis show that the algorithm proposed in this paper evaluates the performance of the private area network using K-fold cross-validation. The correctness of the algorithm is over 90%. The robustness and high efficiency of the algorithm, the computing time of milliseconds, can be effectively applied to the real-time performance evaluation of the local area network.
C1 [Lin, Yi-yong] Xichang Satellite Launch Ctr, Xichang, Sichuan, Peoples R China.
   [Lin, Lei] Chengdu Flight Ind Refco Grp Ltd, Chengdu, Sichuan, Peoples R China.
RP Lin, YY (corresponding author), Xichang Satellite Launch Ctr, Xichang, Sichuan, Peoples R China.
FU Equipment pre-research project
FX This research was financially supported by the Equipment pre-research
   project.
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NR 25
TC 0
Z9 0
U1 0
U2 0
PU DESTECH PUBLICATIONS, INC
PI LANCASTER
PA 439 DUKE STREET, LANCASTER, PA 17602-4967 USA
SN 2475-885X
BN 978-1-60595-548-3
J9 DESTECH TRANS ENG
PY 2018
BP 411
EP 418
PG 8
WC Engineering, Multidisciplinary
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Engineering
GA BM7ZE
UT WOS:000468596500069
DA 2024-09-05
ER
PT J
AU Daradkeh, M
   Abualigah, L
   Atalla, S
   Mansoor, W
AF Daradkeh, Mohammad
   Abualigah, Laith
   Atalla, Shadi
   Mansoor, Wathiq
TI Scientometric Analysis and Classification of Research Using
   Convolutional Neural Networks: A Case Study in Data Science and
   Analytics
SO ELECTRONICS
LA English
DT Article
DE scientific literature; thematic classification; scientometric; deep
   learning; convolutional neural network (CNN)
ID BIG DATA; DETERMINANTS; KNOWLEDGE; ADOPTION
AB With the increasing development of published literature, classification methods based on bibliometric information and traditional machine learning approaches encounter performance challenges related to overly coarse classifications and low accuracy. This study presents a deep learning approach for scientometric analysis and classification of scientific literature based on convolutional neural networks (CNN). Three dimensions, namely publication features, author features, and content features, were divided into explicit and implicit features to form a set of scientometric terms through explicit feature extraction and implicit feature mapping. The weighted scientometric term vectors are fitted into a CNN model to achieve dual-label classification of literature based on research content and methods. The effectiveness of the proposed model is demonstrated using an application example from the data science and analytics literature. The empirical results show that the scientometric classification model proposed in this study performs better than comparable machine learning classification methods in terms of precision, recognition, and F1-score. It also exhibits higher accuracy than deep learning classification based solely on explicit and dominant features. This study provides a methodological guide for fine-grained classification of scientific literature and a thorough investigation of its practice.
C1 [Daradkeh, Mohammad; Atalla, Shadi; Mansoor, Wathiq] Univ Dubai, Coll Engn & Informat Technol, Dubai 14143, U Arab Emirates.
   [Daradkeh, Mohammad] Yarmouk Univ, Fac Informat Technol & Comp Sci, Irbid 21163, Jordan.
   [Abualigah, Laith] Amman Arab Univ, Fac Comp Sci & Informat, Amman 11953, Jordan.
   [Abualigah, Laith] Middle East Univ, Fac Informat Technol, Amman 11831, Jordan.
C3 University of Dubai; Yarmouk University; Middle East University
RP Daradkeh, M (corresponding author), Univ Dubai, Coll Engn & Informat Technol, Dubai 14143, U Arab Emirates.; Daradkeh, M (corresponding author), Yarmouk Univ, Fac Informat Technol & Comp Sci, Irbid 21163, Jordan.
EM mdaradkehc@ud.ac.ae; aligah.2020@gmail.com; satalla@ud.ac.ae;
   wmansoor@ud.ac.ae
RI Abualigah, Laith/ABC-9695-2020; Atalla, Shadi/KAO-2626-2024; mansoor,
   wathiq/D-8297-2018
OI Abualigah, Laith/0000-0002-2203-4549; Atalla, Shadi/0000-0003-3017-9243;
   Daradkeh, Mohammad/0000-0003-2693-7363; mansoor,
   wathiq/0000-0003-2784-5188
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NR 65
TC 20
Z9 20
U1 2
U2 17
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2079-9292
J9 ELECTRONICS-SWITZ
JI Electronics
PD JUL
PY 2022
VL 11
IS 13
AR 2066
DI 10.3390/electronics11132066
PG 22
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Physics
GA 2X9VE
UT WOS:000825543500001
OA gold
DA 2024-09-05
ER
PT J
AU Li, Q
   Li, JX
   Sheng, JW
   Cui, SY
   Wu, J
   Hei, YM
   Peng, H
   Guo, S
   Wang, LH
   Beheshti, A
   Yu, PS
AF Li, Qian
   Li, Jianxin
   Sheng, Jiawei
   Cui, Shiyao
   Wu, Jia
   Hei, Yiming
   Peng, Hao
   Guo, Shu
   Wang, Lihong
   Beheshti, Amin
   Yu, Philip S.
TI A Survey on Deep Learning Event Extraction: Approaches and Applications
SO IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
LA English
DT Article
DE Task analysis; Deep learning; Electronic mail; Data mining; Measurement;
   Feature extraction; Technological innovation; evaluation metrics; event
   extraction (EE); research trends
ID JOINT ENTITY; TEXT; TRANSFORMER
AB Event extraction (EE) is a crucial research task for promptly apprehending event information from massive textual data. With the rapid development of deep learning, EE based on deep learning technology has become a research hotspot. Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey. This article fills the research gap by reviewing the state-of-the-art approaches, especially focusing on the general domain EE based on deep learning models. We introduce a new literature classification of current general domain EE research according to the task definition. Afterward, we summarize the paradigm and models of EE approaches, and then discuss each of them in detail. As an important aspect, we summarize the benchmarks that support tests of predictions and evaluation metrics. A comprehensive comparison among different approaches is also provided in this survey. Finally, we conclude by summarizing future research directions facing the research area.
C1 [Li, Qian; Li, Jianxin] Beihang Univ, Beijing Adv Innovat Ctr Big Data & Brain Comp, Sch Comp Sci & Engn, Beijing 100083, Peoples R China.
   [Sheng, Jiawei; Cui, Shiyao] Chinese Acad Sci, Inst Informat Engn, Beijing 100083, Peoples R China.
   [Sheng, Jiawei; Cui, Shiyao] Univ Chinese Acad Sci, Sch Cyber Secur, Beijing 100083, Peoples R China.
   [Wu, Jia; Beheshti, Amin] Macquarie Univ, Sch Comp, Sydney, NSW 2109, Australia.
   [Hei, Yiming] Beihang Univ, Sch Cyber Sci & Technol, Beijing 100083, Peoples R China.
   [Peng, Hao] Beihang Univ, Beijing Adv Innovat Ctr Big Data & Brain Comp, Sch Comp Sci & Engn, Beijing 100083, Peoples R China.
   [Peng, Hao] Beihang Univ, Beijing Adv Innovat Ctr Big Data & Brain Comp, Beijing 100083, Peoples R China.
   [Guo, Shu] Natl Comp Network Emergency Response Tech Team Coo, Beijing 100029, Peoples R China.
   [Wang, Lihong; Yu, Philip S.] Univ Illinois, Dept Comp Sci, Chicago, IL 60607 USA.
C3 Beihang University; Chinese Academy of Sciences; Chinese Academy of
   Sciences; University of Chinese Academy of Sciences, CAS; Macquarie
   University; Beihang University; Beihang University; Beihang University;
   University of Illinois System; University of Illinois Chicago;
   University of Illinois Chicago Hospital
RP Li, JX (corresponding author), Beihang Univ, Beijing Adv Innovat Ctr Big Data & Brain Comp, Sch Comp Sci & Engn, Beijing 100083, Peoples R China.
EM liqian@act.buaa.edu.cn; lijx@act.buaa.edu.cn; shengjiawei@iie.ac.cn;
   cuishiyao@iie.ac.cn; jia.wu@mq.edu.au; black@buaa.edu.cn;
   penghao@act.buaa.edu.cn; guoshu@cert.org.cn; wlh@cert.org.cn;
   amin.beheshti@mq.edu.au; psyu@uic.edu
FU NSFC
FX No Statement Available
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NR 252
TC 0
Z9 0
U1 2
U2 2
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2162-237X
EI 2162-2388
J9 IEEE T NEUR NET LEAR
JI IEEE Trans. Neural Netw. Learn. Syst.
PD MAY
PY 2024
VL 35
IS 5
BP 6301
EP 6321
DI 10.1109/TNNLS.2022.3213168
PG 21
WC Computer Science, Artificial Intelligence; Computer Science, Hardware &
   Architecture; Computer Science, Theory & Methods; Engineering,
   Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA PM9I4
UT WOS:001214608800104
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Zhang, X
   Wang, XH
   Zhao, HK
   de Pablos, PO
   Sun, YQ
   Xiong, H
AF Zhang, Xi
   Wang, Xianhai
   Zhao, Hongke
   Ordonez de Pablos, Patricia
   Sun, Yongqiang
   Xiong, Hui
TI An effectiveness analysis of altmetrics indices for different levels of
   artificial intelligence publications
SO SCIENTOMETRICS
LA English
DT Article
DE Altmetrics; Bibliometrics; Artificial intelligence; Highly cited
   publication; Increase of citation count; Citation analysis
ID CITATIONS; IMPACT; MODEL
AB Altmetrics indices are increasingly applied to measure scholarly influence in recent years because they can reflect the influence of research outputs more timely comparing with traditional measurements. Simultaneously, artificial intelligence (AI), as an emerging interdiscipline, has a rapid development in these years. Traditional indices can't reflect the influence of the AI research outputs quickly, thus more timely altmetrics indices are needed. In this paper, we conduct four studies about altmetrics indices and AI research outputs based on the datasets collected from Altmetric.com and Scopus database. First, we provide a review of the research status in the AI field. Second, we show the AI researches that attracted the most attention. Third, we demonstrate the general effectiveness of altmetrics indices in the AI field. Last, we examine the effectiveness of altmetrics indices for different levels of AI journal papers and AI conference papers. Our results indicate that there is a rapid increase of AI publications and the public has paid more attention to AI research outputs since 2011. It is found that altmetrics indices are effective to discriminate highly cited publications and publications whose citation counts increase quickly. Among all Altmetric sub-indicators, Number of Mendeley readers is the most effective. Moreover, the results indicate that altmetrics indices are more effective in high levels of AI journal papers and AI conference papers. The main contribution of this paper is investigating the effectiveness of altmetrics indices from the perspective of different levels of publications. This study lays the foundation for further investigations about effectiveness of altmetrics indices from new perspectives, and it has important implication for the studies about the impact of social media on the scientific community.
C1 [Zhang, Xi; Wang, Xianhai; Zhao, Hongke] Tianjin Univ, Coll Management & Econ, Tianjin 300072, Peoples R China.
   [Ordonez de Pablos, Patricia] Univ Oviedo, Dept Business Adm, Oviedo, Spain.
   [Sun, Yongqiang] Wuhan Univ, Sch Informat Management, Wuhan 430072, Hubei, Peoples R China.
   [Xiong, Hui] Rutgers State Univ, Rutgers Business Sch Newark & New Brunswick, Newark, NJ USA.
C3 Tianjin University; University of Oviedo; Wuhan University; Rutgers
   University System; Rutgers University Newark; Rutgers University New
   Brunswick
RP Zhang, X (corresponding author), Tianjin Univ, Coll Management & Econ, Tianjin 300072, Peoples R China.
EM jackyzhang@tju.edu.cn
RI Ordóñez de Pablos, Patricia/AAC-9329-2022; Zhang, Jacky/HHS-9302-2022;
   Sun, Yongqiang/K-4074-2019; Jiang, Cheng/JHU-0179-2023
OI Ordonez de Pablos, Patricia/0000-0002-8388-6382; Xiong,
   Hui/0000-0001-6016-6465; Sun, Yongqiang/0000-0001-8753-9268; Zhang,
   Xi/0000-0002-1105-9417
FU National Natural Science Foundation of China [71722005, 71571133,
   71790594, 71790590]; Natural Science Foundation of Tianjin
   [18JCJQJC45900]; Humanities and Social Sciences Foundation of the
   Ministry of Education, China [16YJC870011]
FX The study is supported by funds from National Natural Science Foundation
   of China (Nos: 71722005 and 71571133 and 71790594 and 71790590). And
   from Natural Science Foundation of Tianjin (No. 18JCJQJC45900), the
   Humanities and Social Sciences Foundation of the Ministry of Education,
   China (Project No. 16YJC870011). We are grateful to Altmetric.com for
   providing the data.
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NR 61
TC 26
Z9 28
U1 7
U2 146
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD JUN
PY 2019
VL 119
IS 3
BP 1311
EP 1344
DI 10.1007/s11192-019-03088-x
PG 34
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA HZ7UB
UT WOS:000469058000002
DA 2024-09-05
ER
PT J
AU Oblizanov, A
   Shevskaya, N
   Kazak, A
   Rudenko, M
   Dorofeeva, A
AF Oblizanov, Alexandr
   Shevskaya, Natalya
   Kazak, Anatoliy
   Rudenko, Marina
   Dorofeeva, Anna
TI Evaluation Metrics Research for Explainable Artificial Intelligence
   Global Methods Using Synthetic Data
SO APPLIED SYSTEM INNOVATION
LA English
DT Article
DE explainable artificial intelligence; XAI; explanation metrics; synthetic
   data
ID SMOTE
AB In recent years, artificial intelligence technologies have been developing more and more rapidly, and a lot of research is aimed at solving the problem of explainable artificial intelligence. Various XAI methods are being developed to allow the user to understand the logic of how machine learning models work, and in order to compare the methods, it is necessary to evaluate them. The paper analyzes various approaches to the evaluation of XAI methods, defines the requirements for the evaluation system and suggests metrics to determine the various technical characteristics of the methods. A study was conducted, using these metrics, which determined the degradation in the explanation quality of the SHAP and LIME methods with increasing correlation in the input data. Recommendations are also given for further research in the field of practical implementation of metrics, expanding the scope of their use.
C1 [Oblizanov, Alexandr; Shevskaya, Natalya] St Petersburg Electrotech Univ Leti, Fac Comp Sci & Technol, St Petersburg 197376, Russia.
   [Kazak, Anatoliy; Dorofeeva, Anna] VI Vernadsky Crimean Fed Univ, Humanitarian Pedag Acad, Simferopol 295007, Russia.
   [Rudenko, Marina] VI Vernadsky Crimean Fed Univ, Inst Phys & Technol, Simferopol 295007, Russia.
C3 Saint Petersburg State Electrotechnical University; VI Vernadsky Crimean
   Federal University; VI Vernadsky Crimean Federal University
RP Kazak, A (corresponding author), VI Vernadsky Crimean Fed Univ, Humanitarian Pedag Acad, Simferopol 295007, Russia.
EM kazak@cfuv.ru
RI Kazak, Anatoliy/R-8222-2019
OI Kazak, Anatoliy/0000-0001-7678-9210; Marina, Rudenko/0000-0002-8334-8453
FU International Alexander Popov's Innovation Institute for Artificial
   Intelligence, Cybersecurity and Communications of Saint Petersburg
   Electrotechnical University"LETI"
FX Authors thank International Alexander Popov's Innovation Institute for
   Artificial Intelligence, Cybersecurity and Communications of Saint
   Petersburg Electrotechnical University"LETI" for support in work and
   research
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NR 45
TC 2
Z9 2
U1 4
U2 14
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2571-5577
J9 APPL SYST INNOV
JI Appl. Syst. Innov.
PD FEB
PY 2023
VL 6
IS 1
AR 26
DI 10.3390/asi6010026
PG 13
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Emerging Sources Citation Index (ESCI)
SC Computer Science; Engineering; Telecommunications
GA 9H4MF
UT WOS:000938806500001
OA gold
DA 2024-09-05
ER
PT C
AU Mercier, D
   Rizvi, STR
   Rajashekar, V
   Ahmed, S
   Dengel, A
AF Mercier, Dominique
   Rizvi, Syed Tahseen Raza
   Rajashekar, Vikas
   Ahmed, Sheraz
   Dengel, Andreas
BE Rocha, AP
   Steels, L
   VanDenHerik, J
TI Utilizing Out-Domain Datasets to Enhance Multi-task Citation Analysis
SO AGENTS AND ARTIFICIAL INTELLIGENCE, ICAART 2021
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT 13th International Conference on Agents and Artificial Intelligence
   (ICAART)
CY FEB 04-06, 2021
CL ELECTR NETWORK
DE Artificial intelligence; Natural language processing; Scientific
   citation analysis; Multi-task; Transformers; Sentiment analysis; Intent
   analysis; Multi-domain
AB Citations are generally analyzed using only quantitative measures while excluding qualitative aspects such as sentiment and intent. However, qualitative aspects provide deeper insights into the impact of a scientific research artifact and make it possible to focus on relevant literature free from bias associated with quantitative aspects. Therefore, it is possible to rank and categorize papers based on their sentiment and intent. For this purpose, larger citation sentiment datasets are required. However, from a time and cost perspective, curating a large citation sentiment dataset is a challenging task. Particularly, citation sentiment analysis suffers from both data scarcity and tremendous costs for dataset annotation. To overcome the bottleneck of data scarcity in the citation analysis domain we explore the impact of out-domain data during training to enhance the model performance. Our results emphasize the use of different scheduling methods based on the use case. We empirically found that a model trained using sequential data scheduling is more suitable for domain-specific usecases. Conversely, shuffled data feeding achieves better performance on a cross-domain task. Based on our findings, we propose an end-to-end trainable multi-task model that covers the sentiment and intent analysis that utilizes out-domain datasets to overcome the data scarcity.
C1 [Mercier, Dominique; Rizvi, Syed Tahseen Raza; Rajashekar, Vikas; Ahmed, Sheraz; Dengel, Andreas] German Res Ctr Artificial Intelligence DFKI GmbH, Trippstadter Str 122, D-67663 Kaiserslautern, Germany.
   [Mercier, Dominique; Rizvi, Syed Tahseen Raza; Dengel, Andreas] TU Kaiserslautern, Erwin Schrodinger Str 52, D-67663 Kaiserslautern, Germany.
C3 German Research Center for Artificial Intelligence (DFKI); University of
   Kaiserslautern
RP Mercier, D (corresponding author), German Res Ctr Artificial Intelligence DFKI GmbH, Trippstadter Str 122, D-67663 Kaiserslautern, Germany.; Mercier, D (corresponding author), TU Kaiserslautern, Erwin Schrodinger Str 52, D-67663 Kaiserslautern, Germany.
EM dominique.mercier@dfki.de; syed.rizvi@dfki.de; vikas.rajashekar@dfki.de;
   sheraz.ahmed@dfki.de; andreas.dengel@dfki.de
OI Mercier, Dominique/0000-0001-8817-2744; Rajashekar,
   Vikas/0000-0002-3664-5156; Rizvi, Syed Tahseen Raza/0000-0002-4359-4772;
   Dengel, Andreas/0000-0002-6100-8255
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NR 37
TC 1
Z9 1
U1 0
U2 2
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 0302-9743
EI 1611-3349
BN 978-3-031-10161-8; 978-3-031-10160-1
J9 LECT NOTES COMPUT SC
PY 2022
VL 13251
BP 113
EP 134
DI 10.1007/978-3-031-10161-8_6
PG 22
WC Computer Science, Artificial Intelligence; Computer Science, Theory &
   Methods; Mathematics, Applied
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Mathematics
GA BU1DA
UT WOS:000876376200006
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Abrishami, A
   Aliakbary, S
AF Abrishami, Ali
   Aliakbary, Sadegh
TI Predicting citation counts based on deep neural network learning
   techniques
SO JOURNAL OF INFORMETRICS
LA English
DT Article
DE Informetrics; Citation count prediction; Neural networks; Deep learning;
   Scientific impact; Time series prediction
ID SLEEPING BEAUTIES; LINK PREDICTION; IMPACT; SCIENCE
AB With the growing number of published scientific papers world-wide, the need to evaluation and quality assessment methods for research papers is increasing. Scientific fields such as scientometrics, informetrics, and bibliometrics establish quantified analysis methods and measurements for evaluating scientific papers. In this area, an important problem is to predict the future influence of a published paper. Particularly, early discrimination between influential papers and insignificant papers may find important applications. In this regard, one of the most important metrics is the number of citations to the paper, since this metric is widely utilized in the evaluation of scientific publications and moreover, it serves as the basis for many other metrics such as h-index. In this paper, we propose a novel method for predicting long-term citations of a paper based on the number of its citations in the first few years after publication. In order to train a citation count prediction model, we employed artificial neural network which is a powerful machine learning tool with recently growing applications in many domains including image and text processing. The empirical experiments show that our proposed method outperforms state-of-the-art methods with respect to the prediction accuracy in both yearly and total prediction of the number of citations. (C) 2019 Elsevier Ltd. All rights reserved.
C1 [Abrishami, Ali; Aliakbary, Sadegh] Shahid Beheshti Univ, Fac Comp Sci & Engn, Tehran, Iran.
C3 Shahid Beheshti University
RP Aliakbary, S (corresponding author), Shahid Beheshti Univ, Fac Comp Sci & Engn, Tehran, Iran.
EM a.abrishami@mail.sbu.ac.ir; s_aliakbary@sbu.ac.ir
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NR 62
TC 88
Z9 94
U1 11
U2 166
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1751-1577
EI 1875-5879
J9 J INFORMETR
JI J. Informetr.
PD MAY
PY 2019
VL 13
IS 2
BP 485
EP 499
DI 10.1016/j.joi.2019.02.011
PG 15
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA IB0FF
UT WOS:000469932800002
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Sun, ZL
AF Sun, Zhuanlan
TI Textual features of peer review predict top-cited papers: An
   interpretable machine learning perspective
SO JOURNAL OF INFORMETRICS
LA English
DT Article
DE Peer review reports; Textual features; Machine learning; Explainable
   artificial intelligence; Research impact
AB Peer review is crucial in improving the quality and reliability of scientific research. However, the mechanisms through which peer review practices ensure papers become top-cited papers (TCPs) after publication are not well understood. In this study, by collecting a data set containing 13, 066 papers published between 2016 and 2020 from Nature communications with open peer review reports, we aim to examine how textual features embedded within the peer review reports of papers that reflect the reviewers' emotions may predict the papers to be TCPs. We compiled a list of 15 textual features and classified them into three categories: peer review features, linguistic features, and sentiment features. We then chose the XGBoost machine learning model with the best performance in predicting TCPs, and utilized the explainable artificial intelligence techniques SHAP to interpret the role of feature importance on the prediction results. The distribution of feature importance ranking results demonstrates that sentiment features play a crucial role in determining papers' potential to be highly cited. This conclusion still holds, even when the ranking of the feature importance changes in the subgroup analysis of dividing the samples into four disciplines (biological sciences, health sciences, physical sciences, and earth and environmental sciences), as well as two groups based on whether reviewers' identities were revealed. This research emphasizes the textual features retrieved from peer review reports that play role in improving manuscript quality can predict the post-publication research impact.
C1 [Sun, Zhuanlan] Nanjing Univ Posts & Telecommun, High Qual Dev Evaluat Res Inst, Nanjing 210003, Peoples R China.
C3 Nanjing University of Posts & Telecommunications
RP Sun, ZL (corresponding author), Nanjing Univ Posts & Telecommun, High Qual Dev Evaluat Res Inst, Nanjing 210003, Peoples R China.
EM zlsuen@njupt.edu.cn
OI Sun, Zhuanlan/0000-0003-4958-7351
FU Teachers Research Foundation Project of Nanjing University of Posts and
   Telecommunications [NYY222042]
FX Funding This work is funded by Teachers Research Foundation Project of
   Nanjing University of Posts and Telecommunications (NYY222042) .
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NR 69
TC 3
Z9 3
U1 29
U2 29
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1751-1577
EI 1875-5879
J9 J INFORMETR
JI J. Informetr.
PD MAY
PY 2024
VL 18
IS 2
AR 101501
DI 10.1016/j.joi.2024.101501
EA JAN 2024
PG 20
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA JY0I5
UT WOS:001176598700001
DA 2024-09-05
ER
PT J
AU Love, PED
AF Love, Peter E. D.
TI A Pragmatist Research Agenda for Employing Psychological Heuristics in
   Construction: Context, Design, Artificial Intelligence, and Performance
   Evaluation
SO IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
LA English
DT Article
DE Artificial intelligence (AI); construction; decision-making;
   pragmaticism; psychological heuristics; uncertainty
ID HOMO HEURISTICUS; DECISION-MAKING; MODELS; FRUGAL; SAFETY; RATIONALITY;
   UNCERTAINTY; DEFINITION; PROJECTS; JUDGMENT
AB Psychological heuristics are formal models of decision making relying on core psychological capacities, such as perception, attention, memory, language, emotion regulation, and social cognition. They function under conditions of limited information, which is processed using simple computations that are easy to understand, apply, and explain. Under conditions characterized by Knightian uncertainty, psychological heuristics embrace the "less-is-more" effect and have demonstrated to achieve equal and better performance in inference problems than optimality methods. Despite these salient performance outcomes, the construction and engineering management literature remains silent on their role in decision making. Filling this void, in this article, we use a narrative review to address the following research question: How can psychological heuristics be effectively used for decision making under uncertainty in construction? A research agenda comprising four interconnected themes integral to decision making under uncertainty is examined to deal with this question: understanding the context; developing and designing heuristics; the integration of heuristics with artificial intelligence; and performance evaluation. Underpinning these themes is the methodological lens of pragmatism and mixed method research design to foster the actualities of psychological heuristics in real-world situations. Thus, we aim to stimulate new lines of inquiry and the development of a repertoire of decision-making strategies of the mind that can be employed in construction where uncertainty reigns.
C1 [Love, Peter E. D.] Curtin Univ, Sch Civil & Mech Engn, Perth, WA, Australia.
C3 Curtin University
RP Love, PED (corresponding author), Curtin Univ, Sch Civil & Mech Engn, Perth, WA, Australia.
EM p.love@curtin.edu.au
RI Love, Peter/D-7418-2017
OI Love, Peter/0000-0002-3239-1304
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NR 107
TC 0
Z9 0
U1 1
U2 1
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0018-9391
EI 1558-0040
J9 IEEE T ENG MANAGE
JI IEEE Trans. Eng. Manage.
PY 2024
VL 71
BP 11183
EP 11197
DI 10.1109/TEM.2024.3411656
PG 15
WC Business; Engineering, Industrial; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Engineering
GA WQ7F8
UT WOS:001256396800001
DA 2024-09-05
ER
PT J
AU Talha, MM
   Khan, HU
   Iqbal, S
   Alghobiri, M
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AF Talha, Mian Muhammad
   Khan, Hikmat Ullah
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   Alghobiri, Mohammed
   Iqbal, Tassawar
   Fayyaz, Muhammad
TI Deep learning in news recommender systems: A comprehensive survey,
   challenges and future trends
SO NEUROCOMPUTING
LA English
DT Article
DE Recommender system; News recommender systems; Research challenges;
   Evaluation metrics; Datasets; Deep learning
AB Nowadays, people prefer to read news articles from online sources worldwide due to their easiness and availability. For the last few years, online searching for required information or content has been replaced by item recommendation, and news recommendation is also not an exception. For news recommendations, News Recommender System (NRS) helps the users to find the appropriate and pertinent content, alleviate the problem of information overload, and propose news that would be of interest to news readers. NRS also assists different users all around the world in this regard by recommending the most recent news articles based on their interests and past preferences. Many techniques such as traditional, Deep Learning (DL), and hybrid have been proposed to solve the NRS challenges and issues. DL techniques are considered one of the best techniques and have been successfully applied in various fields such as Natural Language Processing (NLP) and Computer Vision (CV). This survey article provides a detailed analysis of DL models-based techniques to build NRS. In this regard, firstly, a comprehensive comparison is provided between published survey articles on NRS and this research work. Secondly, it discusses the background of recommendation systems and their techniques. Furthermore, NRS is explored along with its current research challenges. Then background knowledge of DL and its methods have been discussed along with the analysis of year-wise published relevant articles having DL as the applied technique. The survey also presents widely used datasets and performance evaluation metrics used in the relevant literature. Finally, a detailed discussion provides several future directions and open research challenges for the researchers to consider DL applications in NRS.
C1 [Talha, Mian Muhammad; Khan, Hikmat Ullah; Iqbal, Tassawar] COMSATS Univ Islamabad, Dept Comp Sci, Wah Campus, Wah Cantt 47040, Pakistan.
   [Iqbal, Saqib] Al Ain Univ, Coll Engn, Al Ain, U Arab Emirates.
   [Alghobiri, Mohammed] King Khalid Univ, Dept MIS, Abha, Saudi Arabia.
   [Fayyaz, Muhammad] FAST Natl Univ Comp & Emerging Sci, Dept Comp Sci, Chiniot Faisalabad Campus, Chiniot, Pakistan.
   [Khan, Hikmat Ullah] Univ Sargodha, Dept Informat Management, Sargodha, Pakistan.
C3 COMSATS University Islamabad (CUI); King Khalid University; University
   of Sargodha
RP Khan, HU (corresponding author), COMSATS Univ Islamabad, Dept Comp Sci, Wah Campus, Wah Cantt 47040, Pakistan.; Khan, HU (corresponding author), Univ Sargodha, Dept Informat Management, Sargodha, Pakistan.
EM hikmat.ullah@ciitwah.edu.pk
RI Fayyaz, Muhammad/AAP-8145-2021; Khan, Hikmat Ullah/GZG-2251-2022
OI Fayyaz, Muhammad/0000-0002-0909-4539; 
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NR 129
TC 1
Z9 1
U1 7
U2 9
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD DEC 28
PY 2023
VL 562
AR 126881
DI 10.1016/j.neucom.2023.126881
EA OCT 2023
PG 23
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA Y2ZD5
UT WOS:001103991000001
DA 2024-09-05
ER
PT J
AU Cai, RA
   Tian, WC
   Luo, RD
   Hu, ZG
AF Cai, Ruonan
   Tian, Wencan
   Luo, Rundong
   Hu, Zhigang
TI The generation mechanism of research leadership in international
   collaboration based on GERGM: a case from the field of artificial
   intelligence
SO SCIENTOMETRICS
LA English
DT Article; Early Access
DE International collaboration; Research leaderships; GERGM; Artificial
   intelligence
ID SCIENTIFIC COLLABORATION; CO-AUTHORSHIP; PROXIMITY; DISTANCE;
   PERFORMANCE; IMPACT; ROLES; CHINA
AB Conducting an in-depth analysis of 235,746 research papers in the field of artificial intelligence spanning from 2001 to 2020, this study quantified the extent of research leadership in international collaborations by discerning the country of the corresponding author. To comprehensively investigate both endogenous and exogenous effects, we employed the Generalized Exponential Random Graph Model, an advanced methodology adept at characterizing network structures with real-valued edges. This research elucidates the pivotal role of intrinsic structural factors influenced by edge dependencies and evaluates their impact on research leadership in international collaborations. Specifically, our findings reveal a positive and significant effect of the mutual effect and the transitivity effect. Furthermore, language and geography no longer play a significant role in generating international research collaborations between two countries. Additionally, scientific productivity also holds an important position in generating research leadership. However, R&D expenditures no longer facilitate the establishment of leadership for international research collaboration.
C1 [Cai, Ruonan; Luo, Rundong] Shandong Univ, Sch Business, Weihai, Peoples R China.
   [Tian, Wencan] Dalian Univ Technol, Inst Sci Sci & S&T Management, WISE Lab, Dalian, Peoples R China.
   [Hu, Zhigang] South China Normal Univ, Inst Sci Technol & Soc, Guangzhou, Peoples R China.
C3 Shandong University; Dalian University of Technology; South China Normal
   University
RP Hu, ZG (corresponding author), South China Normal Univ, Inst Sci Technol & Soc, Guangzhou, Peoples R China.
EM huzhigang@scnu.edu.cn
RI zhigang, hu/C-6880-2009
OI zhigang, hu/0000-0003-1835-4264; , Ruonan Cai/0000-0002-2111-1362; Tian,
   Wencan/0000-0001-7420-9315
FU National Natural Science Foundation of China [2023 (ISSI 2023)]; Major
   Projects of National Social Science Foundation of China [22ZD194];
   National Natural Science Foundation of China [71974030]; LiaoNing
   Revitalization Talents Program [XLYC2007149]; China Scholarship Council
   [202106060134]
FX The present study is an extended version of a paper presented at the
   19th International Conference on Scientometrics and Informetrics 2023
   (ISSI 2023), Bloomington, Indiana (USA), 2-5 July 2023 (Cai et al.,
   2023). This study is partially supported by the Major Projects of
   National Social Science Foundation of China (22&ZD194), the National
   Natural Science Foundation of China (71974030), and the LiaoNing
   Revitalization Talents Program (XLYC2007149). Wencan Tian is financially
   supported by the China Scholarship Council (202106060134). The authors
   are grateful to the anonymous reviewers for their helpful comments and
   suggestions.
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NR 76
TC 1
Z9 1
U1 22
U2 22
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD 2024 MAR 11
PY 2024
DI 10.1007/s11192-024-04974-9
EA MAR 2024
PG 19
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA KM4E0
UT WOS:001180362500002
DA 2024-09-05
ER
PT J
AU Zhu, YJ
   Kim, D
   Jiang, T
   Zhao, Y
   He, JE
   Chen, XY
   Lou, W
AF Zhu, Yongjun
   Kim, Donghun
   Jiang, Ting
   Zhao, Yi
   He, Jiangen
   Chen, Xinyi
   Lou, Wen
TI Dependency, reciprocity, and informal mentorship in predicting long-term
   research collaboration: A co-authorship matrix-based multivariate time
   series analysis
SO JOURNAL OF INFORMETRICS
LA English
DT Article
DE Long-term research collaboration; Co-authorship prediction; dependency;
   Reciprocity; Informal mentorship; Interpretable machine learning
ID PATTERNS; IMPACT; PRODUCTIVITY; 20TH-CENTURY
AB In this study, we examine the roles of dependency, reciprocity, and informal mentorship in the prediction of long-term research collaboration in five disciplines. We use co-authorship matrixbased multivariate time series features and interpretable machine learning to train long-term collaboration prediction models and interpret the feature importance of trained models. Overall, long-term research collaboration that is defined using various standards was rare across the examined disciplines, and the prediction results were moderate to good. We found dependency, reciprocity, and informal mentorship to have different roles in different disciplines. Among the three, informal mentorship was important in predicting long-term research collaboration in Agriculture, Geology, and Library and Information Science. Reciprocity, which measures the interdependence between two researchers was important to prediction in the fields of Agriculture and Geology. Finally, dependency was important in all the disciplines with varying degrees of importance.
C1 [Zhu, Yongjun; Kim, Donghun; Jiang, Ting] Yonsei Univ, Dept Lib & Informat Sci, Seoul, South Korea.
   [Zhao, Yi] Nanjing Univ Sci & Technol, Sch Econ & Management, Nanjing, Peoples R China.
   [He, Jiangen] Univ Tennessee, Sch Informat Sci, Knoxville, TN USA.
   [Chen, Xinyi] Yonsei Univ, Dept Cultural Media, Seoul, South Korea.
   [Lou, Wen] East China Normal Univ, Sch Econ & Management, Shanghai, Peoples R China.
C3 Yonsei University; Nanjing University of Science & Technology;
   University of Tennessee System; University of Tennessee Knoxville;
   Yonsei University; East China Normal University
RP Lou, W (corresponding author), East China Normal Univ, Sch Econ & Management, Shanghai, Peoples R China.
EM wlou@infor.ecnu.edu.cn
RI lan, xueyao/JZD-4201-2024; Zhu, Yongjun/AAU-5726-2020; Lou,
   Wen/I-7966-2019; Zhu, Yongjun/K-2486-2015
OI Zhu, Yongjun/0000-0002-2099-2209; Lou, Wen/0000-0002-1770-6734; Zhu,
   Yongjun/0000-0003-4787-5122
FU Yonsei University [2022-22-0394]
FX This work was supported by the Yonsei University Research Grant of 2022
   (2022-22-0394).
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NR 39
TC 0
Z9 0
U1 16
U2 18
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1751-1577
EI 1875-5879
J9 J INFORMETR
JI J. Informetr.
PD FEB
PY 2024
VL 18
IS 1
AR 101486
DI 10.1016/j.joi.2023.101486
EA DEC 2023
PG 11
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA FS5S1
UT WOS:001147863300001
DA 2024-09-05
ER
PT C
AU Huang, SZ
AF Huang, Shengzhong
BE Wu, YW
TI Research and Application of wavelet neural networks of particle swarm
   optimization algorithm in the performance prediction of centrifugal
   compressor
SO SPORTS MATERIALS, MODELLING AND SIMULATION
SE Advanced Materials Research
LA English
DT Proceedings Paper
CT International Conference on Sport Material, Modelling and Simulation
CY JAN 27-28, 2011
CL Shenzhen, PEOPLES R CHINA
DE Particle swarm optimization; Wavelet neural network; Centrifugal
   compressor; Performance prediction
AB The traditional method of centrifugal compressor performance prediction is usually the BP neural network, however, the problems are that prediction accuracy is not high enough, convergence is slow and it is apt to fall into local optimal solution. In order to predict the performance of centrifugal compressors more accurately and identify the implicit problems in advance, now we combine the particle swarm optimization, wavelet theory and neural networks, to establish performance prediction model of centrifugal compressor based on wavelet neural network of PSO. First, set the various parameters of wavelet neural network as the particle position vector X and the energy function of mean square error as the optimized objective function. By particle swarm optimization algorithm to iterate the basic formula to obtain the corresponding WNN coefficient and then use back-propagation algorithm to train WNN to approach any nonlinear function. Simulation results show that application of the prediction model can achieve the accurate prediction of performance and monitoring of centrifugal compressor. The prediction model has the advantages of simple algorithm, stable structure, fast calculation of convergence speed and strong generalization ability with a prediction accuracy of 99%, 13% higher than prediction accuracy of traditional methods, which has a certain theoretical research value and practical value.
C1 Liuzhou Teachers Coll, Dept Math & Comp Sci, Liuzhou 545004, Guangxi, Peoples R China.
RP Huang, SZ (corresponding author), Liuzhou Teachers Coll, Dept Math & Comp Sci, Liuzhou 545004, Guangxi, Peoples R China.
EM gxhsz@126.com
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NR 13
TC 0
Z9 1
U1 2
U2 15
PU TRANS TECH PUBLICATIONS LTD
PI STAFA-ZURICH
PA LAUBLSRUTISTR 24, CH-8717 STAFA-ZURICH, SWITZERLAND
SN 1022-6680
BN 978-3-03785-041-1
J9 ADV MATER RES-SWITZ
PY 2011
VL 187
BP 271
EP 276
DI 10.4028/www.scientific.net/AMR.187.271
PG 6
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Materials Science, Multidisciplinary
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Materials Science
GA BVV36
UT WOS:000292888500051
DA 2024-09-05
ER
PT J
AU Srinivasa, G
AF Srinivasa, Gowri
TI Relevance of Innovations in Machine Learning to Scientometrics
SO JOURNAL OF SCIENTOMETRIC RESEARCH
LA English
DT Article
DE Feature engineering; Machine learning; Deep learning; Scientometrics
ID REGRESSION; PREDICTION; ADVANTAGES; NETWORKS; IMPACT; INDEX
AB Machine learning envisages building models that either classify, predict, cluster or determine the relative relevance of features to a problem and the associations between them. This paper briefy describes how these tasks are relevant to Scientometrics. Through this brief survey of selected tasks, it is observed that most solution approaches in Scientometric literature are built on the strong foundation of understanding and debating in uencing factors and the process of feature engineering, requiring the descriptors to be intuitive and methods used for classication, prediction, etc., to be amenable to interpretation. Recent trends in machine learning, particularly, deep learning methods, however, pose an interesting question: can we build models that automatically determine what features are important and thereby bypass the step of feature engineering? This paper discusses how such techniques could also be harnessed in Scientometrics.
C1 [Srinivasa, Gowri] PES Univ, Ctr Pattern Recognit, EC Campus, Bengaluru, Karnataka, India.
   [Srinivasa, Gowri] PES Univ, Dept Comp Sci & Engn, EC Campus,Hosur Rd, Bengaluru 560100, Karnataka, India.
C3 PES University; PES University
RP Srinivasa, G (corresponding author), PES Univ, Dept Comp Sci & Engn, EC Campus,Hosur Rd, Bengaluru 560100, Karnataka, India.
EM gsrinivasa@pes.edu
RI Srinivasa, Gowri/ABP-9131-2022
OI Srinivasa, Gowri/0000-0002-3568-6749
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NR 45
TC 5
Z9 5
U1 2
U2 17
PU PHCOG NET
PI KARNATAKA
PA 17, 2ND FLR, BUDDHA VIHAR RD, NEAR SPORTS ZONE, COX TOWN, BENGALURU,
   KARNATAKA, 560005, INDIA
SN 2321-6654
EI 2320-0057
J9 J SCIENTOMETR RES
JI J. Scientometr. Res.
PD MAY-AUG
PY 2019
VL 8
IS 2
SI SI
BP S39
EP S43
DI 10.5530/jscires.8.2.23
PG 5
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA KA8FQ
UT WOS:000506037200004
OA hybrid
DA 2024-09-05
ER
PT C
AU Golowko, N
   Tamla, P
   Stein, H
   Böhm, T
   Hemmje, M
   Onete, CB
AF Golowko, Nina
   Tamla, Philippe
   Stein, Holger
   Boehm, Thilo
   Hemmje, Matthias
   Onete, Christian Bogdan
BE Soliman, KS
TI On the Trail of Future Management Topics with Digital Technology - How
   Can Artificial Intelligence Influence the Didactic Content of Higher
   Education in Economics?
SO EDUCATION EXCELLENCE AND INNOVATION MANAGEMENT THROUGH VISION 2020
LA English
DT Proceedings Paper
CT 33rd International-Business-Information-Management-Association (IBIMA)
   Conference
CY APR 10-11, 2019
CL Granada, SPAIN
DE Higher education; management trends; Latent Dirichlet Allocation (LDA)
   topic modeling; artificial intelligence; transfer didactics; curricular
   contents
AB Universities can as little ignore the ever-faster pace of change in their environment as companies can. To anticipate the increase in the quantity of knowledge and the increasing competition in the higher education sector, universities can access their own knowledge base. This paper aims to show that universities can use machine learning to uncover potentials in their own knowledge base and use them for curriculum development. The final theses written at a German university are examined using software based on the Latent Dirichlet Allocation (LDA) topic model. Topic blocks are disclosed that reflect current developments and trends on the market. This paper describes the process and essential features in the preparation of the corpus and the derivation of the thematic areas. The added value of this method is pointed out and subsequently discussed using examples. This results in an innovative approach to future-oriented curriculum development. The study presented here gives only a small insight into the fundamental changes which artificial intelligence will imply for all of us in the future.
C1 [Golowko, Nina; Onete, Christian Bogdan] Bucharest Univ Econ Studies, Bucharest, Romania.
   [Tamla, Philippe; Boehm, Thilo; Hemmje, Matthias] Fernuniv, Fac Multimedia & Comp Sci, Hagen, Germany.
   [Stein, Holger] FOM Univ Appl Sci, Frankfurt, Germany.
C3 Bucharest University of Economic Studies; Fern University Hagen
RP Golowko, N (corresponding author), Bucharest Univ Econ Studies, Bucharest, Romania.
EM golowko@ninagolowko.de; philippe.tamla@studium.fernuni-hagen.de;
   holger.stein@fom.de; thilo.boehm@fernuni-hagen.de;
   mhemmje@fernuni-hagen.de; bogdan.onete@com.ase.ro
RI Onete, Cristian Bogdan/E-6579-2017; Prof. Dr.-Ing. Tamla,
   Philippe/KPB-3066-2024
OI Prof. Dr.-Ing. Tamla, Philippe/0000-0002-0786-4253
FU FOM University of Applied Sciences [576]
FX This research was supported by FOM University of Applied Sciences,
   implemented through the project "LeMaKoTA - Lebenszyklen von
   Managementmethoden - Korpusanalyse auf Basis der Titel von
   Abschlussarbeiten" (ID 576) under the expert guidance of Prof. Dr.
   Matthias Gehrke.
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NR 18
TC 0
Z9 0
U1 1
U2 8
PU INT BUSINESS INFORMATION MANAGEMENT ASSOC-IBIMA
PI NORRISTOWN
PA 34 E GERMANTOWN PIKE, NO. 327, NORRISTOWN, PA 19401 USA
BN 978-0-9998551-2-6
PY 2019
BP 8145
EP 8155
PG 11
WC Education & Educational Research; Management
WE Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Education & Educational Research; Business & Economics
GA BO3KC
UT WOS:000510675603070
DA 2024-09-05
ER
PT J
AU Shan, SQ
   Wang, L
   Wang, J
   Hao, Y
   Hua, F
AF Shan, Siqing
   Wang, Li
   Wang, Jing
   Hao, Yi
   Hua, Fan
TI Research on e-Government evaluation model based on the principal
   component analysis
SO INFORMATION TECHNOLOGY & MANAGEMENT
LA English
DT Article
DE e-Government evaluation; Local e-Government; Evaluation model; Principal
   component analysis; K-means clustering
ID INFORMATION-SYSTEMS SUCCESS; SERVICES; DELONE; ACCEPTANCE; QUALITY
AB Over the last few years, the area of electronic government (e-Government) has received increasing prominence and attention; people are interacting with e-Government systems to an ever greater extent. It is therefore important to measure the development of e-Government. Adopting principal component analysis (PCA), this study presents, validates and updates an evaluation model with 5 dimensions based on Socio-Technical model and Stakeholder Theory, which captures the multidimensional and interdependent nature of e-Government system. The validity of the model is empirically investigated using a sample of local e-Government of 18 cities in China, all of which have high Internet penetration and mature ICT use. The five dimensions of the evaluation model include project construction, information security management, special construction, transparency of government affairs and informationized ability. K-means clustering is applied in the subspace created by PCA to evaluate the local e-Government stages of growth of these 18 cities. The findings provide several important implications for e-Government research and practice.
C1 [Shan, Siqing; Wang, Li; Wang, Jing; Hao, Yi; Hua, Fan] Beihang Univ, Sch Econ & Management, Beijing 100191, Peoples R China.
C3 Beihang University
RP Shan, SQ (corresponding author), Beihang Univ, Sch Econ & Management, Beijing 100191, Peoples R China.
EM shansiqing@buaa.edu.cn; wl2000bh@yahoo.com.cn;
   wangjing.sem.buaa@gmail.com; haoyi@buaa.edu.cn; huafan@buaa.edu.cn
RI hao, yi/KHY-8135-2024
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NR 68
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Z9 41
U1 5
U2 91
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1385-951X
EI 1573-7667
J9 INFORM TECHNOL MANAG
JI Inf. Technol. Manag.
PD JUN
PY 2011
VL 12
IS 2
SI SI
BP 173
EP 185
DI 10.1007/s10799-011-0083-8
PG 13
WC Information Science & Library Science; Management
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science; Business & Economics
GA 769TE
UT WOS:000291040400012
DA 2024-09-05
ER
PT J
AU Wamba, SF
   Queiroz, MM
AF Wamba, Samuel Fosso
   Queiroz, Maciel M.
TI Responsible Artificial Intelligence as a Secret Ingredient for Digital
   Health: Bibliometric Analysis, Insights, and Research Directions
SO INFORMATION SYSTEMS FRONTIERS
LA English
DT Article
DE Artificial intelligence; Machine learning; Digital health; Responsible
   AI; Bibliometric analysis
ID OF-THE-ART; BIG-DATA; EXPERIENCE; ROBOT; CHALLENGES; RECOVERY; MEDICINE;
   THERAPY; FUTURE
AB With the unparallel advance of leading-edge technologies like artificial intelligence (AI), the healthcare systems are transforming and shifting for more digital health. In recent years, scientific productions have reached unprecedented levels. However, a holistic view of how AI is being used for digital health remains scarce. Besides, there is a considerable lack of studies on responsible AI and ethical issues that identify and suggest practitioners' essential insights towards the digital health domain. Therefore, we aim to rely on a bibliometric approach to explore the dynamics of the interplay between AI and digital health approaches, considering the responsible AI and ethical aspects of scientific production over the years. We found four distinct periods in the publication dynamics and the most popular approaches of AI in the healthcare field. Also, we highlighted the main trends and insightful directions for scholars and practitioners. In terms of contributions, this work provides a framework integrating AI technologies approaches and applications while discussing several barriers and benefits of AI-based health. In addition, five insightful propositions emerged as a result of the main findings. Thus, this study's originality is regarding the new framework and the propositions considering responsible AI and ethical issues on digital health.
C1 [Wamba, Samuel Fosso] TBS Business Sch, Informat Operat & Management Sci, 1 Pl Alphonse Jourdain, F-31068 Toulouse, France.
   [Queiroz, Maciel M.] Paulista Univ UNIP, Postgrad Program Business Adm, Dr Bacelar St 1212, BR-04026002 Sao Paulo, Brazil.
   [Queiroz, Maciel M.] Univ Prebiteriana Mackenzie, Sch Engn, Consolacao St 930, BR-01302000 Sao Paulo, Brazil.
C3 Universidade Paulista
RP Queiroz, MM (corresponding author), Paulista Univ UNIP, Postgrad Program Business Adm, Dr Bacelar St 1212, BR-04026002 Sao Paulo, Brazil.; Queiroz, MM (corresponding author), Univ Prebiteriana Mackenzie, Sch Engn, Consolacao St 930, BR-01302000 Sao Paulo, Brazil.
EM s.fosso-wamba@tbs-education.fr; maciel.queiroz@docente.unip.br
RI Queiroz, Maciel M./U-8499-2019; Queiroz, Maciel M./F-1274-2014; Fosso
   Wamba, Samuel/AAB-4953-2019
OI Queiroz, Maciel M./0000-0002-6025-9191; Fosso Wamba,
   Samuel/0000-0002-1073-058X
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NR 60
TC 48
Z9 48
U1 12
U2 81
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1387-3326
EI 1572-9419
J9 INFORM SYST FRONT
JI Inf. Syst. Front.
PD DEC
PY 2023
VL 25
IS 6
SI SI
BP 2123
EP 2138
DI 10.1007/s10796-021-10142-8
EA MAY 2021
PG 16
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA AU9X6
UT WOS:000650816700001
PM 34025210
OA Green Published, Bronze
DA 2024-09-05
ER
PT J
AU Prahani, BK
   Imah, EM
   Maureen, IY
   Rakhmawati, L
   Saphira, HV
AF Prahani, Binar Kurnia
   Imah, Elly Matul
   Maureen, Irena Yolanita
   Rakhmawati, Lusia
   Saphira, Hanandita Veda
TI Trend and Visualization of Artificial Intelligence Research in the Last
   10 Years
SO TEM JOURNAL-TECHNOLOGY EDUCATION MANAGEMENT INFORMATICS
LA English
DT Article
DE - artificial intelligence; bibliometric; deep learning; machine learning
ID BIBLIOMETRICS; TECHNOLOGIES; NETWORK
AB - Technology is permeating every aspect of our everyday lives, and businesses are increasingly turning to learning algorithms. These technologies are commonly associated with Artificial Intelligence (AI), Artificial Neural Network (ANN). Due to previous research exciting findings, implications, and limitations, the research publication on AI, ML, DL, and ANN will increase each year. Finally, the growth analysis of AI, ML, DL, and ANN publications in last 10 years brings up the results that the publications significantly increase each year. This research utilizes descriptive analyses and bibliometrics. Implications of the investigation of specific keyword mapping visualization results on AI, ML, DL, and ANN, can be created and enhanced for more study. A different area of future research might focus on using fewer terms, particularly to study learning methods like AI, ML, DL, and ANN in a convolutional neural network.
C1 [Prahani, Binar Kurnia; Imah, Elly Matul; Maureen, Irena Yolanita; Rakhmawati, Lusia; Saphira, Hanandita Veda] Univ Negeri Surabaya, Surabaya, Indonesia.
C3 Universitas Negeri Surabaya
RP Prahani, BK (corresponding author), Univ Negeri Surabaya, Surabaya, Indonesia.
EM binarprahani@unesa.ac.id
RI Saphira, Hanandita Veda/KIB-3061-2024; Imah, Elly Matul/D-1258-2015
OI Saphira, Hanandita Veda/0000-0002-3609-952X; Imah, Elly
   Matul/0000-0003-1008-4837
FU LPPM Competitive PNBP Research, Universitas Negeri Surabaya, Indonesia
   [B/35071/UN38.9/K.04.00/2022]
FX This paper is part of the LPPM Competitive PNBP Research, Universitas
   Negeri Surabaya, Indonesia which is fully funded with a contract number
   [B/35071/UN38.9/K.04.00/2022].
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NR 40
TC 1
Z9 1
U1 3
U2 8
PU UIKTEN - ASSOC INFORMATION COMMUNICATION TECHNOLOGY EDUCATION & SCIENCE
PI NOVI PAZAR
PA HILMA ROZAJCA 15, NOVI PAZAR, 36300, SERBIA
SN 2217-8309
EI 2217-8333
J9 TEM J
JI TEM J.
PD MAY
PY 2023
VL 12
IS 2
BP 918
EP 927
DI 10.18421/TEM122-38
PG 10
WC Computer Science, Information Systems
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA I6PD4
UT WOS:001003974800038
OA gold
DA 2024-09-05
ER
PT J
AU Yamazaki, T
   Sakata, I
AF Yamazaki, Tomomi
   Sakata, Ichiro
TI Exploration of Interdisciplinary Fusion and Interorganizational
   Collaboration With the Advancement of AI Research: A Case Study on
   Natural Language Processing
SO IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
LA English
DT Article; Early Access
DE Citation network analysis; emerging technology; interdisciplinarity
   fusion; interorganizational collaboration; natural language processing
   (NLP)
ID ARTIFICIAL-INTELLIGENCE; TECHNOLOGY
AB Network analysis is increasingly being used as a decision-making support tool for science and technology policy and technology management through the understanding of the trends and structure of innovation activities and the prediction of their changes. Since convergence and diversity are generally regarded as opportunities for groundbreaking innovation, analysis of interdisciplinary fusion and interorganizational collaboration using network analysis is expected to provide useful suggestions for policy formation and technology management. Nevertheless, little quantitative analysis has been conducted to date, especially for interorganizational collaboration. Meanwhile, regarding artificial intelligence research, its effectiveness has accelerated its application in various fields, and in the process, interdisciplinary fusion and interorganizational collaboration are developing. In this study, we focused on "natural language processing (NLP)," which is said to have recently surpassed human performance, and conducted a microanalysis by visualizing community formation, topic transition including technology integration with other fields and organizations, and their collaboration engaged in research front. Specifically, using the Scopus dataset, we extracted NLP papers and conducted a network analysis. As a result, we visualized these developments in the fast-changing field, and second, we showed that our method is applicable to many other fields. These results will help researchers interested in the application of NLP.
C1 [Yamazaki, Tomomi; Sakata, Ichiro] Univ Tokyo, Dept Technol Management Innovat, Bunkyo Ku, Tokyo 1138654, Japan.
C3 University of Tokyo
RP Yamazaki, T (corresponding author), Univ Tokyo, Dept Technol Management Innovat, Bunkyo Ku, Tokyo 1138654, Japan.
EM yamazaki-tomomi@g.ecc.u-tokyo.ac.jp; isakata@ipr-ctr.t.u-tokyo.ac.jp
OI SAKATA, ICHIRO/0000-0001-5881-3790
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NR 43
TC 1
Z9 1
U1 6
U2 19
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0018-9391
EI 1558-0040
J9 IEEE T ENG MANAGE
JI IEEE Trans. Eng. Manage.
PD 2023 NOV 1
PY 2023
DI 10.1109/TEM.2023.3327209
EA NOV 2023
PG 14
WC Business; Engineering, Industrial; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Engineering
GA Y2WY1
UT WOS:001103933100001
DA 2024-09-05
ER
PT J
AU Tolcheev, VO
AF Tolcheev, V. O.
TI Research and Analysis of the Subject Area of Deep Learning
SO AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS
LA English
DT Article
DE deep learning; deep neural networks; factual and multimedia data;
   scientometric analysis; bibliographic database Web of Science;
   publication activity; descriptor analysis
AB This paper analyzes the rapidly growing scientific direction of Deep Learning, as one of the most significant parts of artificial intelligence. Using scientometric methods, the growth rates of publications in leading countries and the level of their international cooperation are estimated. The terminological structure of the subject area is investigated and the most perspective directions of studies are revealed. We compare scientometric indicators of Deep Learning with another booming scientific area, Quantum Technology. The conclusion is made that publication activity on Deep Learning is growing faster. It is noted that in both these areas the United States and China are the leaders according to the number of papers. Scientometric analysis showed a fairly low level of publication activity of Russian scientists on Deep Learning and their weak involvement in international cooperation.
C1 [Tolcheev, V. O.] Moscow Power Engn Inst, Moscow 111250, Russia.
C3 Moscow Power Engineering Institute
RP Tolcheev, VO (corresponding author), Moscow Power Engn Inst, Moscow 111250, Russia.
EM tolcheevvo@mail.ru
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NR 23
TC 1
Z9 1
U1 0
U2 6
PU PLEIADES PUBLISHING INC
PI NEW YORK
PA PLEIADES HOUSE, 7 W 54 ST, NEW YORK,  NY, UNITED STATES
SN 0005-1055
EI 1934-8371
J9 AUTOMAT DOC MATH LIN
JI Autom. Doc. Math. Linguist.
PD MAY
PY 2019
VL 53
IS 3
BP 103
EP 113
DI 10.3103/S000510551903004X
PG 11
WC Computer Science, Information Systems
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA IT5QJ
UT WOS:000482919700001
DA 2024-09-05
ER
PT J
AU Djeffal, C
   Siewert, MB
   Wurster, S
AF Djeffal, Christian
   Siewert, Markus B.
   Wurster, Stefan
TI Role of the state and responsibility in governing artificial
   intelligence: a comparative analysis of AI strategies
SO JOURNAL OF EUROPEAN PUBLIC POLICY
LA English
DT Article
DE Artificial intelligence; AI governance; policy instruments; state types;
   responsible research innovation; technology assessment
ID TECHNOLOGY; GOVERNANCE; INNOVATION
AB Technologies based on artificial intelligence (AI) represent a crucial governance challenge for policymakers. This study contributes to the understanding of how states plan to govern AI with respect to the role they assume and to the way they develop AI in a responsible manner. In different policy instruments across 22 countries plus the European Union, there is considerable variation in how governments approach the governance of AI, both regarding the policy measures proposed and their focus on public responsibility. Analysing a set of policy instruments we find multiple modes of AI governance, with the major difference being between self-regulation-promoting and market-based approaches, and a combination of entrepreneurial and regulatory governance approaches. Our analysis also indicates that the approach to public responsibility is largely independent of the chosen policy mix of AI governance. Therefore, responsibility seems to be a cross-cutting issue that cannot be tied to a specific approach of states towards technology.
C1 [Djeffal, Christian; Wurster, Stefan] Tech Univ Munich, TUM Sch Social Sci & Technol, Munich, Germany.
   [Siewert, Markus B.; Wurster, Stefan] Munich Sch Polit & Publ Policy, Munich, Germany.
C3 Technical University of Munich
RP Siewert, MB (corresponding author), Munich Sch Polit & Publ Policy, Munich, Germany.
EM markus.siewert@hfp.tum.de
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NR 65
TC 20
Z9 20
U1 29
U2 120
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1350-1763
EI 1466-4429
J9 J EUR PUBLIC POLICY
JI J. Eur. Public Policy
PD NOV 2
PY 2022
VL 29
IS 11
SI SI
BP 1799
EP 1821
DI 10.1080/13501763.2022.2094987
EA JUL 2022
PG 23
WC Political Science; Public Administration
WE Social Science Citation Index (SSCI)
SC Government & Law; Public Administration
GA 6M4NX
UT WOS:000836050700001
OA Green Published
DA 2024-09-05
ER
PT J
AU Chen, H
   Deng, ZJ
AF Chen, Huie
   Deng, Zhenjie
TI Bibliometric Analysis of the Application of Convolutional Neural Network
   in Computer Vision
SO IEEE ACCESS
LA English
DT Article
DE Bibliometrics; Convolutional neural networks; Computer vision; Market
   research; Indexes; Technological innovation; Convolutional neural
   networks; computer vision; bibliometric analysis
ID FAULT-DIAGNOSIS; ARCHITECTURES; RECOGNITION
AB This article analyzes the research progress in field of Convolutional Neural Networks (CNNs) using the bibliometric method. Literature samples of CNNs are analyzed by a basic statistic and co-citation network. Experimental results show that CNNs are being utilized in many computer vision applications, such as fault and image recognition diagnosis, seismic detection, positioning, and automatic detection of cracks and signals, image classification and image segmentation. In addition, there is systematic research on unbalanced problems in CNNs. Quantitative experimental research, extensive application fields, and market research informatization will be the three vital research tendencies in the future. The ideas and conclusions of this article provide insights to the academic research of CNNs and their practical application in the corporate world.
C1 [Chen, Huie; Deng, Zhenjie] Guangdong Univ Finance, Sch Internet Finance & Informat Engn, Guangzhou 510521, Peoples R China.
C3 Guangdong University of Finance
RP Deng, ZJ (corresponding author), Guangdong Univ Finance, Sch Internet Finance & Informat Engn, Guangzhou 510521, Peoples R China.
EM 47-047@gduf.edu.cn
OI deng, zhenjie/0000-0002-3432-2169; Chen, Huie/0000-0002-4621-6718
FU First Batch of Reform of Innovation and Business Running Education
   [201801193131]; Characteristic Innovation Project of Guangdong Province
   Ordinary University [2019KTSCX113]
FX This work was supported in part by the First Batch of Reform of
   Innovation and Business Running Education with cooperation between
   production and learning of the Ministry of Education in 2018 under
   Project 201801193131, and in part by the Characteristic Innovation
   Project of Guangdong Province Ordinary University under Project
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NR 45
TC 4
Z9 4
U1 5
U2 32
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2020
VL 8
BP 155417
EP 155428
DI 10.1109/ACCESS.2020.3019336
PG 12
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA NK4GG
UT WOS:000566690000001
OA gold
DA 2024-09-05
ER
PT C
AU Kim, J
   Le, DX
   Thoma, GR
AF Kim, Jongwoo
   Le, Daniel X.
   Thoma, George R.
BE Likforman-Sulem, L
   Agam, G
TI Naive Bayes and SVM classifiers for classifying Databank Accession
   Number sentences from online biomedical articles
SO DOCUMENT RECOGNITION AND RETRIEVAL XVII
SE Proceedings of SPIE
LA English
DT Proceedings Paper
CT Conference on Document Recognition and Retrieval XVII
CY JAN 19-21, 2010
CL San Jose, CA
DE Naive Bayes; Support Vector Machine (SVM); databank; labeling; text
   classification; bibliographic information
AB This paper describes two classifiers, Nave Bayes and Support Vector Machine (SVM), to classify sentences containing Databank Accession Numbers, a key piece of bibliographic information, from online biomedical articles. The correct identification of these sentences is necessary for the subsequent extraction of these numbers. The classifiers use words that occur most frequently in sentences as features for the classification. Twelve sets of word features are collected to train and test the classifiers. Each set has a different number of word features ranging from 100 to 1,200. The performance of each classifier is evaluated using four measures: Precision, Recall, F-Measure, and Accuracy. The Naive Bayes classifier shows performance above 93.91% at 200 word features for all four measures. The SVM shows 98.80% Precision at 200 word features, 94.90% Recall at 500 and 700, 96.46% F-Measure at 200, and 99.14% Accuracy at 200 and 400. To improve classification performance, we propose two merging operators, Max and Harmonic Mean, to combine results of the two classifiers. The final results show a measureable improvement in Recall, F-Measure, and Accuracy rates.
C1 [Kim, Jongwoo; Le, Daniel X.; Thoma, George R.] Natl Lib Med, Bethesda, MD 20894 USA.
C3 National Institutes of Health (NIH) - USA; NIH National Library of
   Medicine (NLM)
RP Kim, J (corresponding author), Natl Lib Med, 8600 Rockville Pike, Bethesda, MD 20894 USA.
EM jongkim@mail.nih.gov
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U2 3
PU SPIE-INT SOC OPTICAL ENGINEERING
PI BELLINGHAM
PA 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA
SN 0277-786X
EI 1996-756X
BN 978-0-8194-7927-3
J9 PROC SPIE
PY 2010
VL 7534
AR 75340U
DI 10.1117/12.838961
PG 8
WC Computer Science, Information Systems; Optics; Imaging Science &
   Photographic Technology
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Optics; Imaging Science & Photographic Technology
GA BRR44
UT WOS:000283495700030
DA 2024-09-05
ER
PT J
AU Kaban, A
AF Kaban, Abdullatif
TI Artificial Intelligence in Education: A Science Mapping Approach
SO INTERNATIONAL JOURNAL OF EDUCATION IN MATHEMATICS SCIENCE AND TECHNOLOGY
LA English
DT Article
DE Education; Artificial intelligence; Machine learning; Deep learning;
   Bibliometric analysis
ID MANAGEMENT
AB While using artificial intelligence in education is a popular field of study for researchers, it has become a joint application for educational institutions. Educational institutions are trying to establish artificial intelligence-based systems to improve the existing education systems. On the other hand, education researchers want to determine which artificial intelligence models are the most effective. To provide an in-depth resource for both researchers and educators on the use of artificial intelligence in education, this study aims to make a bibliometric analysis of articles related to artificial intelligence in education. After the query was made in the Web of Science database, 1153 articles related to the subject were obtained. As a result of the bibliometric analysis of the articles obtained, the most influential journals are Education and Information Technologies and Computers & Education, and the most influential authors are Scouller, Biggs, and Hwang. After 2019, it has been observed that there has been a significant increase in the number of studies, the first examples of which were found in 1985. It is thought that this study, which provides results on the most cited publications, trending topics, thematic map of keywords, and co-occurrence network, will serve as a bedside resource for both educators and researchers. Implications of the findings for theory and practice are discussed.
C1 [Kaban, Abdullatif] Ataturk Univ, Erzurum, Turkiye.
C3 Ataturk University
RP Kaban, A (corresponding author), Ataturk Univ, Erzurum, Turkiye.
EM abdullatif.kaban@gmail.com
RI Kaban, Abdullatif/ABC-3989-2021
OI Kaban, Abdullatif/0000-0003-4465-3145
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NR 40
TC 1
Z9 1
U1 39
U2 95
PU NECMETTIN ERBAKAN UNIV
PI KONYA
PA AHMET KELESOGLU FAC EDUCATION, DEPT COMPUTER EDUCATION & INSTRUCTIONAL
   TECHNOLOGY, KONYA, 42090, Turkiye
SN 2147-611X
J9 INT J EDUC MATH SCI
JI Int. J. Educ. Math. Sci. Technol.
PY 2023
VL 11
IS 4
BP 844
EP 861
DI 10.46328/ijemst.3368
PG 19
WC Education, Scientific Disciplines
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA T7VD9
UT WOS:001080014900002
OA gold
DA 2024-09-05
ER
PT J
AU Zhou, MZ
   Liu, HJ
   Hu, YR
AF Zhou, Maoze
   Liu, Hongjiu
   Hu, Yanrong
TI Research on corporate financial performance prediction based on
   self-organizing and convolutional neural networks
SO EXPERT SYSTEMS
LA English
DT Article
DE CNN; financial performance; performance forecasting; SOM
ID BANKRUPTCY PREDICTION; DEFAULT PREDICTION; LISTED COMPANIES; RATIOS;
   DISTRESS; MODELS; ABILITY; TIME
AB Economic risks faced by manufacturing enterprises are gradually increasing and risk reduction whilst maintaining high financial performance has become key to their survival and development of enterprises. Enterprise performance affects not only enterprise development but also does the interests of investors and creditors. Therefore, a well-performing model for financial performance prediction is particularly important. In this paper, we combine unsupervised and supervised learning, fusing self-organizing mapping neural networks and convolutional neural networks, and apply deep learning to financial analysis to construct a new financial performance prediction model, called SNN-CNN. This paper uses crawler technology to obtain financial data of listed manufacturing enterprises and classifies their financial performance into five levels. It finds that enterprises with high financial performance tend to have balanced financial indicators, strong corporate vitality and stable development of various capabilities, while enterprises with low financial performance have poor repayment and profitability, significant risks in corporate operation and limited growth and development. Compared with traditional risk prediction models, the SOM-CNN model has a higher accuracy rate, up to 95.69%.
C1 [Zhou, Maoze; Liu, Hongjiu; Hu, Yanrong] Zhejiang A&F Univ, Sch Math & Comp Sci, Hangzhou 311300, Peoples R China.
   [Zhou, Maoze; Liu, Hongjiu; Hu, Yanrong] Zhejiang A&F Univ, Key Lab Intelligent Forestry Monitoring & Informa, Hangzhou, Peoples R China.
   [Zhou, Maoze; Liu, Hongjiu; Hu, Yanrong] Zhejiang A&F Univ, Key Lab Forestry Sensing Technol & Intelligent Eq, State Forestry & Grassland Bur, Hangzhou, Peoples R China.
C3 Zhejiang A&F University; Zhejiang A&F University; Zhejiang A&F
   University
RP Liu, HJ (corresponding author), Zhejiang A&F Univ, Sch Math & Comp Sci, Hangzhou 311300, Peoples R China.
EM joe_hunter@zafu.edu.cn
FU Humanity and Social Science Foundation of Ministry of Education of China
   [18YJA630037, 21YJA630054]; Zhejiang Philosophy and Social Science
   Program of China [19NDJC240YB, 17NDJC262YB]; Zhejiang Provincial Natural
   Science Foundation of China [LY18G010005, LY17G020025]
FX The Humanity and Social Science Foundation of Ministry of Education of
   China, Grant/Award Numbers: 18YJA630037, 21YJA630054; Zhejiang
   Philosophy and Social Science Program of China, Grant/Award Numbers:
   19NDJC240YB, 17NDJC262YB; Zhejiang Provincial Natural Science Foundation
   of China, Grant/Award Numbers: LY18G010005, LY17G020025
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NR 56
TC 5
Z9 5
U1 13
U2 69
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0266-4720
EI 1468-0394
J9 EXPERT SYST
JI Expert Syst.
PD NOV
PY 2022
VL 39
IS 9
SI SI
AR e13042
DI 10.1111/exsy.13042
EA JUN 2022
PG 17
WC Computer Science, Artificial Intelligence; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 5F3HH
UT WOS:000807113800001
DA 2024-09-05
ER
PT C
AU Iqbal, S
   Shaheen, M
   Fazl-e-Basit
AF Iqbal, Saeed
   Shaheen, Muhammad
   Fazl-e-Basit
GP IEEE
TI A Machine Learning Based Method for Optimal Journal Classification
SO 2013 8TH INTERNATIONAL CONFERENCE FOR INTERNET TECHNOLOGY AND SECURED
   TRANSACTIONS (ICITST)
SE International Conference for Internet Technology and Secured
   Transactions
LA English
DT Proceedings Paper
CT 8th International Conference for Internet Technology and Secured
   Transactions (ICITST)
CY DEC 09-12, 2013
CL London, UNITED KINGDOM
DE Journal ranking; Impact Factor; Eigenfactor; Article Influence; Prestige
   of Journal
ID IMPACT
AB We present a hypothetical and realistic examination and exploration of a number of bibliometric indicators of journal performance. In this paper, the indicators we have focused upon are Eigenfactor indicator, Impact factor, audience factor and Article influence weight indicator. Our focus is to find the missing parameters and some limitations that have not been conducted in previous algorithms. To find the influential parameters and to propose a new journal performance factor, that ranked a journal in best accepted manner. For calssification and verification purpose we use a machine learning classification technique (Bayesian classification). It is one of the most common learning algorithms in machine learning classification. Using bayesain classification, we classify several journals according to our proposed methods and compare results with the previous methods.
C1 [Iqbal, Saeed; Shaheen, Muhammad; Fazl-e-Basit] Natl Univ Comp & Emerging Scinece, Dept Compter Sci, FAST NUCES, Peshawar, Pakistan.
RP Iqbal, S (corresponding author), Natl Univ Comp & Emerging Scinece, Dept Compter Sci, FAST NUCES, Peshawar, Pakistan.
EM saeediqbalkhattak@gmail.com; muhammad.shaheen@nu.edu.pk;
   fazl.basit@nu.edu.pk
RI Shaheen, Prof. Dr. Muhammad/AGH-3143-2022
OI Shaheen, Prof. Dr. Muhammad/0000-0003-3647-1261; Iqbal,
   Saeed/0000-0002-3176-4658
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NR 40
TC 4
Z9 5
U1 0
U2 0
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2164-7046
BN 978-1-908320-20-9
J9 INT CONF INTERNET
PY 2013
BP 259
EP 264
PG 6
WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Engineering
GA BC1AN
UT WOS:000349902500038
DA 2024-09-05
ER
PT J
AU Wang, YB
   Ding, D
AF Wang, Yanbing
   Ding, Ding
TI Deep Learning Algorithm Research and Performance Optimization of
   Financial Treasury Big Data Monitoring Platform
SO INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS
LA English
DT Article
DE Deep learning; financial database big data monitoring; algorithm
   research; performance optimization
AB With the rapid development of information technology and the advent of the digital age, the management of fiscal treasury is facing unprecedented challenges and opportunities. In order to improve the efficiency and effectiveness of deep learning algorithms in the financial and treasury big data monitoring platform, this paper further studies the performance optimization methods of the model. This paper deeply studies deep learning algorithm research and performance optimization of financial Treasury big data monitoring platforms. This paper reviews the basic concepts, methods, and applications of deep learning and their application in the financial database big data monitoring platform. In the financial Treasury big data monitoring platform, deep learning algorithms are widely used in image recognition, natural language processing, recommendation systems and other fields. This article first conducts in-depth theoretical research on deep learning algorithms, including various neural network structures (such as convolutional neural network CNN, recurrent neural network RNN, etc.), optimization algorithms (such as gradient descent method and its variants), regularization techniques, etc. In addition, we also studied the practical applications of deep learning in fields such as image processing, natural language processing, and recommendation systems. In order to verify the effectiveness of deep learning algorithms in the financial and treasury big data monitoring platform, we designed corresponding experiments. These experiments include using deep learning algorithms for image recognition of financial documents, natural language processing, and building recommendation systems. We collected real fiscal treasury data as the experimental dataset and preprocessed and annotated the data.
C1 [Wang, Yanbing] Huishang Vocat Coll, Dept Elect Informat, Hefei 231201, Peoples R China.
   [Ding, Ding] AnHui Audit Coll, Hefei 230601, Peoples R China.
RP Ding, D (corresponding author), AnHui Audit Coll, Hefei 230601, Peoples R China.
FU Natural Science Foundation of Anhui Provincial [2023AH053112]
FX This work was sponsored by Natural Science Foundation of Anhui
   Provincial (2023AH053112) .
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NR 18
TC 0
Z9 0
U1 0
U2 0
PU SCIENCE & INFORMATION SAI ORGANIZATION LTD
PI WEST YORKSHIRE
PA 19 BOLLING RD, BRADFORD, WEST YORKSHIRE, 00000, ENGLAND
SN 2158-107X
EI 2156-5570
J9 INT J ADV COMPUT SC
JI Int. J. Adv. Comput. Sci. Appl.
PD JUN
PY 2024
VL 15
IS 6
BP 322
EP 331
PG 10
WC Computer Science, Theory & Methods
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA ZT9D4
UT WOS:001277649400001
DA 2024-09-05
ER
PT J
AU Serenko, A
   Dohan, M
AF Serenko, Alexander
   Dohan, Michael
TI Comparing the expert survey and citation impact journal ranking methods:
   Example from the field of Artificial Intelligence
SO JOURNAL OF INFORMETRICS
LA English
DT Article
DE Artificial Intelligence; Journal ranking; Academic journal; Google
   Scholar; Survey; Citation impact; H-index; G-index; Hc-index
ID MANAGEMENT JOURNALS; GLOBAL PERCEPTIONS; BUSINESS; QUALITY; PEER;
   SCHOLARSHIP; RESEARCHERS; DECISION; SCIENCE; FACULTY
AB The purpose of this study is to: (1) develop a ranking of peer-reviewed AI journals; (2) compare the consistency of journal rankings developed with two dominant ranking techniques, expert surveys and journal impact measures; and (3) investigate the consistency of journal ranking scores assigned by different categories of expert judges. The ranking was constructed based on the survey of 873 active AI researchers who ranked the overall quality of 182 peer-reviewed AI journals. It is concluded that expert surveys and citation impact journal ranking methods cannot be used as substitutes. Instead, they should be used as complementary approaches. The key problem of the expert survey ranking technique is that in their ranking decisions, respondents are strongly influenced by their current research interests. As a result, their scores merely reflect their present research preferences rather than an objective assessment of each journal's quality. In addition, the application of the expert survey method favors journals that publish more articles per year. (C) 2011 Elsevier Ltd. All rights reserved.
C1 [Serenko, Alexander] Lakehead Univ, Fac Business Adm, Thunder Bay, ON P7B 5E1, Canada.
   [Dohan, Michael] McMaster Univ, DeGroote Sch Business, Hamilton, ON L8S 4M4, Canada.
C3 Lakehead University; McMaster University
RP Serenko, A (corresponding author), Lakehead Univ, Fac Business Adm, 955 Oliver Rd, Thunder Bay, ON P7B 5E1, Canada.
EM aserenko@lakeheadu.ca; dohanms@mcmaster.ca
RI Serenko, Alexander/AAT-2082-2020
OI Serenko, Alexander/0000-0003-4881-2932
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NR 74
TC 61
Z9 65
U1 0
U2 70
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1751-1577
EI 1875-5879
J9 J INFORMETR
JI J. Informetr.
PD OCT
PY 2011
VL 5
IS 4
BP 629
EP 648
DI 10.1016/j.joi.2011.06.002
PG 20
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 841PI
UT WOS:000296524200014
DA 2024-09-05
ER
PT J
AU Goepp, V
   Matta, N
   Caillaud, E
   Feugeas, F
AF Goepp, Virginie
   Matta, Nada
   Caillaud, Emmanuel
   Feugeas, Francoise
TI Systematic community of Practice activities evaluation through Natural
   Language Processing: application to research projects
SO AI EDAM-ARTIFICIAL INTELLIGENCE FOR ENGINEERING DESIGN ANALYSIS AND
   MANUFACTURING
LA English
DT Article
DE Community of practice; Natural Language Processing; performance
   evaluation; pragmatics; research project
AB Community of Practice (CoP) efficiency evaluation is a great deal in research. Indeed, having the possibility to know if a given CoP is successful or not is essential to better manage it over time. The existing approaches for efficiency evaluation are difficult and time-consuming to put into action on real CoPs. They require either to evaluate subjective constructs making the analysis unreliable, either to work out a knowledge interaction matrix that is difficult to set up. However, these approaches build their evaluation on the fact that a CoP is successful if knowledge is exchanged between the members. It is the case if there are some interactions between the actors involved in the CoP. Therefore, we propose to analyze these interactions through the exchanges of emails thanks to Natural Language Processing. Our approach is systematic and semi-automated. It requires the e-mails exchanged and the definition of the speech-acts that will be retrieved. We apply it on a real project-based CoP: the SEPOLBE research project that involves different expertise fields. It allows us to identify the CoP core group and to emphasize learning processes between members with different backgrounds (Microbiology, Electrochemistry and Civil engineering).
C1 [Goepp, Virginie; Feugeas, Francoise] INSA Strasbourg, Icube 24, F-67084 Strasbourg, France.
   [Matta, Nada] Univ Technol Troyes, TechCICO 12 Rue Marie Curie CS 42060, F-10004 Troyes, France.
   [Caillaud, Emmanuel] Unistra, ICube 3,Rue Univ, F-67084 Strasbourg, France.
C3 Universites de Strasbourg Etablissements Associes; Universite de
   Strasbourg; Universite de Technologie de Troyes
RP Goepp, V (corresponding author), INSA Strasbourg, Icube 24, F-67084 Strasbourg, France.
EM virginie.goepp@insa-strasbourg.fr
RI Caillaud, Emmanuel/O-7735-2019
OI Caillaud, Emmanuel/0000-0002-9198-6041; GOEPP,
   Virginie/0000-0003-2294-7919
CR [Anonymous], 2011, 2011149536 WHO APW, V1, P22
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NR 33
TC 1
Z9 1
U1 1
U2 9
PU CAMBRIDGE UNIV PRESS
PI NEW YORK
PA 32 AVENUE OF THE AMERICAS, NEW YORK, NY 10013-2473 USA
SN 0890-0604
EI 1469-1760
J9 AI EDAM
JI AI EDAM-Artif. Intell. Eng. Des. Anal. Manuf.
PD MAY
PY 2019
VL 33
IS 2
SI SI
BP 160
EP 171
DI 10.1017/S0890060419000076
PG 12
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications; Engineering, Multidisciplinary;
   Engineering, Manufacturing
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering
GA ID8ZV
UT WOS:000471978100006
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Si, YT
AF Si, Yutong
TI Co-authorship in energy justice studies: Assessing research
   collaboration through social network analysis and topic modeling
SO ENERGY STRATEGY REVIEWS
LA English
DT Article; Proceedings Paper
CT Conference on Energy Modelling Platform for Europe (EMP-E) on Modelling
   the implementation of a Clean Planet for All Strategy
CY OCT 08-09, 2019
CL Brussels, BELGIUM
DE Energy justice; Co-authorship; Social network analysis; Topic modeling;
   Giant component
AB Diverse research collaboration is important to perform innovative science and drive transformational change. In the field of energy justice, the need for transformative team science is no longer bigger, given the urgency of a just energy transition. This study is the first research utilizing social network analysis (SNA) and computational methods to understand the structure of scientific collaboration networks around energy justice, while also exploring existing research topics in the field. The bibliographic data were obtained from Web of Science (1975-2021), including the information of 192 journal articles. Based on the linear regression quadratic assignment procedure (LR-QAP), this study shows that pairs of authors who share the same country (i.e., where an author's institution/affiliation is located) are significantly more likely to collaborate with each other. In addition, according to different actor-level centrality measures, this paper identifies the top four leading authors, who are all within the giant component of the network (about 17.07% of all nodes). Results of topic modeling also show that the articles authored or coauthored by the scholars involved in the giant component can capture all identified topics, suggesting the efficacy of the giant component and the significant impact of leading scientists on shaping hot research topics in the field. This study calls for more research collaboration across different countries in the energy justice research community, which can result in more comparative studies, especially the ones exploring energy justice issues between developing and developed countries. It also calls for a more decentralized network open to outside connections to diversify research collaboration, thus enriching research topics and advancing innovative work on energy justice.
C1 [Si, Yutong] Northeastern Univ, Sch Publ Policy & Urban Affairs, Boston, MA 02115 USA.
C3 Northeastern University
RP Si, YT (corresponding author), Northeastern Univ, Sch Publ Policy & Urban Affairs, Boston, MA 02115 USA.
EM si.yut@northeastern.edu
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NR 35
TC 6
Z9 7
U1 1
U2 13
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2211-467X
EI 2211-4688
J9 ENERGY STRATEG REV
JI Energy Strateg. Rev.
PD MAY
PY 2022
VL 41
AR 100859
DI 10.1016/j.esr.2022.100859
EA MAY 2022
PG 10
WC Energy & Fuels
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Energy & Fuels
GA 5U9HO
UT WOS:000876851000001
OA gold
DA 2024-09-05
ER
PT C
AU Rizvi, STR
   Lucieri, A
   Dengel, A
   Ahmed, S
AF Rizvi, Syed Tahseen Raza
   Lucieri, Adriano
   Dengel, Andreas
   Ahmed, Sheraz
GP IEEE
TI Benchmarking Object Detection Networks for Image based Reference
   Detection in Document Images
SO 2019 DIGITAL IMAGE COMPUTING: TECHNIQUES AND APPLICATIONS (DICTA)
LA English
DT Proceedings Paper
CT APRS International Conference on Digital Image Computing - Techniques
   and Applications (DICTA)
CY DEC 02-04, 2019
CL Perth, AUSTRALIA
DE bibliographic reference detection; Image based Reference Detection;
   Benchmarking; Convolutional Neural Networks
AB In this paper we study the performance evaluation of state-of-the-art object detection models for the task of bibliographic reference detection from document images. The motivation of evaluating object detection models for the task in hand is inspired from how human perceive a document containing bibliographic references. Humans can easily distinguish between different references just by exploiting the layout with a glimpse of an eye, without understanding the content. Existing state-of-the-art systems for bibliographic reference detection are purely based on textual content. By contrast, we employed four state-of-the art object detection models and compared their performance with state-of-the-art text based reference extraction models. Evaluations are performed on the publicly available dataset (ICONIP) for image based reference detection, containing 455 scanned bibliographic documents with 8766 references from Social Sciences books and journals. Evaluation results reveal the superiority of image based methods for the task of reference detection in document images.
C1 [Rizvi, Syed Tahseen Raza; Dengel, Andreas] Tech Univ Kaiserslautern, Kaiserslautern, Germany.
   [Rizvi, Syed Tahseen Raza; Lucieri, Adriano; Dengel, Andreas; Ahmed, Sheraz] German Res Ctr Artificial Intelligence DFKI, Kaiserslautern, Germany.
C3 University of Kaiserslautern; German Research Center for Artificial
   Intelligence (DFKI)
RP Rizvi, STR (corresponding author), Tech Univ Kaiserslautern, Kaiserslautern, Germany.; Rizvi, STR (corresponding author), German Res Ctr Artificial Intelligence DFKI, Kaiserslautern, Germany.
EM syed_tahseen_raza.rizvi@dfki.de; adriano.lucieri@dfki.de;
   andreas.dengel@dfki.de; sheraz.ahmed@dfki.de
RI Lucieri, Adriano/KFS-5924-2024
OI Lucieri, Adriano/0000-0003-1473-4745; Rizvi, Syed Tahseen
   Raza/0000-0002-4359-4772
CR Bhardwaj A., 2018, ICONIP DATASAET LABE
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NR 23
TC 6
Z9 6
U1 0
U2 8
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-7281-3857-2
PY 2019
BP 400
EP 407
DI 10.1109/dicta47822.2019.8945991
PG 8
WC Computer Science, Theory & Methods; Imaging Science & Photographic
   Technology
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Imaging Science & Photographic Technology
GA BO8QY
UT WOS:000529063300056
DA 2024-09-05
ER
PT J
AU WOELFEL, J
AF WOELFEL, J
TI ARTIFICIAL NEURAL NETWORKS IN POLICY RESEARCH - A CURRENT ASSESSMENT
SO JOURNAL OF COMMUNICATION
LA English
DT Article
ID BACK PROPAGATION
AB Recent advances in neuroscience, computer science, psychology, and other fields have led to the development of computer programs that are modeled, in principle, on idealizations Of organic neural structures.  These artificial neural networks (ANNs) exhibit important properties that promise great usefulness for policy researchers. Most important among these are ANNs' ability to learn to identify complex patterns of information and to associate them with other patterns. Furthermore, like their biological predecessors, ANNs can recognize and recall these patterns and associations in spite of noisy, incomplete, or otherwise defective information inputs. ANNs can also generalize information learned about one or more patterns to other related patterns. As a result, ANNs have already found extensive use in areas once reserved for multivariate statistical programs such as regression and multiple classification analysis, and are developing an extensive community of advocates for processing text and other qualitative information.
C1 SUNY BUFFALO,ROCKFELLER INST GOVT,BUFFALO,NY 14260.
C3 State University of New York (SUNY) System; State University of New York
   (SUNY) Buffalo
RP WOELFEL, J (corresponding author), SUNY BUFFALO,DEPT COMMUN,BUFFALO,NY 14260, USA.
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NR 25
TC 43
Z9 53
U1 0
U2 7
PU OXFORD UNIV PRESS INC
PI CARY
PA JOURNALS DEPT, 2001 EVANS RD, CARY, NC 27513
SN 0021-9916
J9 J COMMUN
JI J. Commun.
PD WIN
PY 1993
VL 43
IS 1
BP 63
EP 80
DI 10.1111/j.1460-2466.1993.tb01249.x
PG 18
WC Communication
WE Social Science Citation Index (SSCI)
SC Communication
GA KP598
UT WOS:A1993KP59800005
DA 2024-09-05
ER
PT J
AU Pillai, M
   Griffin, AC
   Kronk, CA
   McCall, T
AF Pillai, Malvika
   Griffin, Ashley C.
   Kronk, Clair A.
   McCall, Terika
TI Toward Community-Based Natural Language Processing (CBNLP): Cocreating
   With Communities
SO JOURNAL OF MEDICAL INTERNET RESEARCH
LA English
DT Article
DE ChatGPT; natural language processing; community-based participatory
   research; research design; artificial intelligence; participatory;
   co-design; machine learning; co-creation; community based; lived
   experience; lived experiences; collaboration; collaborative
ID PARTICIPATORY RESEARCH; ENGAGEMENT; BIAS
AB Rapid development and adoption of natural language processing (NLP) techniques has led to a multitude of exciting and innovative societal and health care applications. These advancements have also generated concerns around perpetuation of historical injustices and that these tools lack cultural considerations. While traditional health care NLP techniques typically include clinical subject matter experts to extract health information or aid in interpretation, few NLP tools involve community stakeholders with lived experiences. In this perspective paper, we draw upon the field of community-based participatory research, which gathers input from community members for development of public health interventions, to identify and examine ways to equitably involve communities in developing health care NLP tools. To realize the potential of community-based NLP (CBNLP), research and development teams must thoughtfully consider mechanisms and resources needed to effectively collaborate with community members for maximal societal and ethical impact of NLP-based tools.
C1 [Pillai, Malvika] Stanford Univ, Sch Med, Ctr Biomed Informat Res, Stanford, CA USA.
   [Pillai, Malvika; Griffin, Ashley C.] Vet Affairs Palo Alto Hlth Care Syst, Palo Alto, CA USA.
   [Griffin, Ashley C.] Stanford Univ, Sch Med, Dept Hlth Policy, Stanford, CA USA.
   [Kronk, Clair A.; McCall, Terika] Yale Sch Med, Ctr Med Informat, New Haven, CT USA.
   [McCall, Terika] Yale Sch Publ Hlth, Dept Biostat, Div Hlth Informat, New Haven, CT USA.
   [McCall, Terika] Yale Sch Med, Sect Biomed Informat & Data Sci, New Haven, CT USA.
   [Pillai, Malvika] Stanford Univ, Sch Med, Ctr Biomed Informat Res, 1265 Welch Rd, Stanford, CA 94305 USA.
C3 Stanford University; US Department of Veterans Affairs; Veterans Health
   Administration (VHA); VA Palo Alto Health Care System; Stanford
   University; Yale University; Yale University; Yale University; Stanford
   University
RP Pillai, M (corresponding author), Stanford Univ, Sch Med, Ctr Biomed Informat Res, 1265 Welch Rd, Stanford, CA 94305 USA.
EM mpillai@stanford.edu
OI Pillai, Malvika/0000-0001-8739-189X; McCall, Terika/0000-0002-8143-5393
FU Department of Veterans Affairs (VA) Big Data-Scientist Training
   Enhancement Program; VA advanced fellowship in medical informatics;
   National Library of Medicine [R01LM013477]
FX Acknowledgments MP is currently supported by the Department of Veterans
   Affairs (VA) Big Data-Scientist Training Enhancement Program. ACG is
   currently supported by a VA advanced fellowship in medical informatics.
   The opinions expressed are those of the authors and not necessarily
   those of the VA or those of the US government. TM is currently supported
   by funding from the National Library of Medicine (award R01LM013477) .
   The authors would like to thank Mary Peng for her support in creating
   the community-based natural language processing framework figure.
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NR 31
TC 1
Z9 1
U1 2
U2 7
PU JMIR PUBLICATIONS, INC
PI TORONTO
PA 130 QUEENS QUAY East, Unit 1100, TORONTO, ON M5A 0P6, CANADA
SN 1438-8871
J9 J MED INTERNET RES
JI J. Med. Internet Res.
PD AUG 4
PY 2023
VL 25
AR e48498
DI 10.2196/48498
PG 10
WC Health Care Sciences & Services; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Health Care Sciences & Services; Medical Informatics
GA P1EN0
UT WOS:001048139500001
PM 37540551
OA Green Published, gold
DA 2024-09-05
ER
PT J
AU Vallurupalli, V
   Bose, I
AF Vallurupalli, Vamsi
   Bose, Indranil
TI Exploring thematic composition of online reviews: A topic modeling
   approach
SO ELECTRONIC MARKETS
LA English
DT Article
DE Latent Dirichlet allocation; Online reviews; Review influence; Thematic
   content; Topic modeling; Yelp
AB Online reviews are a critical component of the retail business ecosystem today. They help consumers share feedback and readers make informed choices. As such, it is important to understand the mechanism driving the creation of reviews and identify factors which make them useful for readers. Extant work in this field has largely ignored the distribution of thematic content in reviews and its role in review diagnosticity. This article attempts to bridge the gap. A novel approach is proposed to explore the distribution of thematic content in reviews, in terms of underlying topics, and test its impact on influence of reviews. The approach is illustrated through a case study using data from Yelp. Implications of the study for theory and practice are discussed.
C1 [Vallurupalli, Vamsi; Bose, Indranil] Indian Inst Management Calcutta, Diamond Harbour Rd, Kolkata 700104, India.
C3 Indian Institute of Management (IIM System); Indian Institute of
   Management Calcutta
RP Bose, I (corresponding author), Indian Inst Management Calcutta, Diamond Harbour Rd, Kolkata 700104, India.
EM vallurupalliv13@email.iimcal.ac.in; bose@iimcal.ac.in
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PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1019-6781
EI 1422-8890
J9 ELECTRON MARK
JI Electron. Mark.
PD DEC
PY 2020
VL 30
IS 4
BP 791
EP 804
DI 10.1007/s12525-020-00397-5
PG 14
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA PD6VV
UT WOS:000597821100009
DA 2024-09-05
ER
PT J
AU Okagbue, HI
   Nzeadibe, CA
   Teixeira da Silva, JA
AF Okagbue, Hilary, I
   Nzeadibe, Chinyere A.
   Teixeira da Silva, Jaime A.
TI Predicting access mode of multidisciplinary and library and information
   sciences journals using machine learning
SO COLLNET JOURNAL OF SCIENTOMETRICS AND INFORMATION MANAGEMENT
LA English
DT Article
DE Adaptive boosting; Gradient boosting; CiteScore; Journal impact factor;
   Metrics; Web of Science
AB Academics and librarians might want to identify whether a journal is open access (OA) or subscription-based. While indexes and digital libraries might provide such information for known collections, it is possible that the access mode of a journal or body of journals might be unknown a priori. In this short analysis, a machine learning-based method is used to classify a journal's access mode, OA or subscription, using its CiteScore and Journal Impact Factor (JIF). Using an initial pool of 91 multidisciplinary journals with a CiteScore, 38 journals with both a JIF and a CiteScore were selected (24 = OA; 14 = subscription). Using a data mining tool (Orange), ten machine learning models were applied (k nearest neighbor (kNN), Tree, support vector machine (SVM), Random forest, Neural network, Naive Bayes, Logistic regression, Adaptive boosting (Adaboost)), Gradient Boosting (Scikit-learn) (GBS) and Gradient Boosting (catboost) (GBC). Adaboost, GBS and GBC showed the highest (100%) precision, sensitivity, and specificity. The 3 models correctly classify the access mode with zero error. The 3 optimum models were validated using then to predict the access mode of 54 (7 = OA; 47 = subscription) library and information science (LIS) journals and Adaboost and GBS gave perfect results with no misclassification. With these model, the access mode of multidisciplinary and LIS journals can be accurately and correctly predicted using only JIF-CiteScore data. Libraries in low-resource settings will benefit from the implementation of this research by designing a decision support system for the selection of journals.
C1 [Okagbue, Hilary, I] Covenant Univ, Dept Math, Coll Sci & Technol, KM 10 Idiroko Rd, Ota 112104, Nigeria.
   [Nzeadibe, Chinyere A.] Univ Nigeria, Dept Sci Educ, Nsukka, Nigeria.
   [Teixeira da Silva, Jaime A.] POB 7,Miki Cho PO,Ikenobe 3011-2, Miki, Kagawa 7610799, Japan.
C3 Covenant University; University of Nigeria
RP Okagbue, HI (corresponding author), Covenant Univ, Dept Math, Coll Sci & Technol, KM 10 Idiroko Rd, Ota 112104, Nigeria.
EM hilary.okagbue@covenantuniversity.edu.ng; chinyere.nzeadibe@unn.edu.ng;
   jaimetex@yahoo.com
RI Okagbue, Hilary Izuchukwu/AAD-1102-2020
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Z9 3
U1 1
U2 3
PU TARU PUBLICATIONS
PI NEW DELHI
PA G-159, PUSHKAR ENCLAVE, PASHCHIM VIHAR, NEW DELHI, 110 063, INDIA
SN 0973-7766
EI 2168-930X
J9 COLLNET J SCIENTOMET
JI Collnet J. Scientometr. Inf. Manag.
PD JAN 2
PY 2022
VL 16
IS 1
BP 117
EP 124
DI 10.1080/09737766.2021.2009745
PG 8
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA 3F7VX
UT WOS:000830873200008
DA 2024-09-05
ER
PT J
AU Hajibabaei, A
   Schiffauerova, A
   Ebadi, A
AF Hajibabaei, Anahita
   Schiffauerova, Andrea
   Ebadi, Ashkan
TI Gender-specific patterns in the artificial intelligence scientific
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SO JOURNAL OF INFORMETRICS
LA English
DT Article
DE Gender disparity; Interdisciplinary research; Artificial intelligence;
   Research performance; Collaboration
ID RESEARCH COLLABORATION; SEX-DIFFERENCES; RESEARCH PRODUCTIVITY; SCIENCE;
   CENTRALITY; DIVERSITY; PERFORMANCE; PIPELINE; CAREERS; TEAMS
AB Gender disparity in science is one of the most focused debating points among authorities and the scientific community. Over the last few decades, numerous initiatives have endeavored to accelerate gender equity in academia and research society. However, despite the ongoing efforts, gaps persist across the world, and more measures need to be taken. Using social network analysis, natural language processing, and machine learning, in this study, we comprehensively analyzed gender-specific patterns in the highly interdisciplinary and evolving field of artificial intelligence for the period of 2000-2019. Our findings suggest an overall increasing rate of mixed-gender collaborations. From the observed gender-specific collaborative patterns, the existence of disciplinary homophily at both dyadic and team levels is confirmed. However, a higher preference was observed for female researchers to form homophilous collaborative links. Our core-periphery analysis indicated a significant positive association between having diverse collaboration and scientific performance and experience. We found evidence in support of expecting the rise of new female superstar researchers in the artificial intelligence field.
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C3 Concordia University - Canada; National Research Council Canada
RP Ebadi, A (corresponding author), Concordia Univ, Concordia Inst Informat Syst Engn, Montreal, PQ H3G 2W1, Canada.
EM ashkan.ebadi@nrc-cnrc.gc.ca
RI Ebadi, Ashkan/AAI-5123-2020; Ebadi, Ashkan/GWZ-9018-2022
OI Ebadi, Ashkan/0000-0002-4542-9105; Schiffauerova,
   Andrea/0000-0003-3349-3991
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NR 96
TC 2
Z9 2
U1 7
U2 60
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1751-1577
EI 1875-5879
J9 J INFORMETR
JI J. Informetr.
PD MAY
PY 2022
VL 16
IS 2
AR 101275
DI 10.1016/j.joi.2022.101275
EA MAR 2022
PG 17
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 1A1UP
UT WOS:000791550800001
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Chen, XL
   Xie, HR
AF Chen, Xieling
   Xie, Haoran
TI A Structural Topic Modeling-Based Bibliometric Study of Sentiment
   Analysis Literature
SO COGNITIVE COMPUTATION
LA English
DT Article
DE Sentiment analysis; Bibliometric; Structural topic modeling; Social
   network analysis
ID SOCIAL EMOTION CLASSIFICATION; NEWS IMPACT; EXTRACTION; TEXT;
   PREDICTION; RESOURCES; EVOLUTION; NETWORKS; SYSTEM
AB Sentiment analysis is an increasingly evolving field of research in computer science. With the considerable number of studies on innovative sentiment analysis available, it is worth the effort to present a review to understand the research on sentiment analysis comprehensively. This study aimed to investigate issues involved in sentiment analysis; for instance, (1) What types of research topics had been covered in sentiment analysis research? (2) How did the research topics evolve with time? (3) What were the topic distributions for major contributors? (4) How did major contributors collaborate in sentiment analysis research? Based on articles retrieved from the Web of Science, this study presented a bibliometric review of sentiment analysis with the basis of a structural topic modeling method to obtain an extensive overview of the research field. We also utilized methods such as regression analysis, geographic visualization, social network analysis, and the Mann-Kendal trend test. Sentiment analysis research had, overall, received a growing interest in academia. In addition, institutions and authors within the same countries/regions were liable to collaborate closely. Highly discussed topics weresentiment lexicons and knowledge bases,aspect-based sentiment analysis, andsocial network analysis. Several current and potential future directions, such asdeep learning for natural language processing,web services,recommender systems and personalization, andeducation and social issues, were revealed. The findings provided a thorough understanding of the trends and topics regarding sentiment analysis, which could help in efficiently monitoring future research works and projects. Through this study, we proposed a framework for conducting a comprehensive bibliometric analysis.
C1 [Chen, Xieling] Educ Univ Hong Kong, Dept Math & Informat Technol, Hong Kong, Peoples R China.
   [Xie, Haoran] Lingnan Univ, Dept Comp & Decis Sci, Hong Kong, Peoples R China.
C3 Education University of Hong Kong (EdUHK); Lingnan University
RP Xie, HR (corresponding author), Lingnan Univ, Dept Comp & Decis Sci, Hong Kong, Peoples R China.
EM hrxie2@gmail.com
RI Xie, Haoran/AFS-3515-2022
OI Xie, Haoran/0000-0003-0965-3617; PV, THAYYIB/0000-0001-8929-0398
FU Interdisciplinary Research Scheme of Dean's Research Fund 2018-19
   [FLASS/DRF/IDS-3]; Departmental Collaborative Research Fund 2019
   [MIT/DCRF-R2/18-19]; Small Grant for Academic Staff of The Education
   University of Hong Kong [MIT/SGA04/19-20]; HKIBS Research Seed Fund
   2019/20 [190-009]; Lingnan University, Hong Kong [102367]
FX The research presented in this study has been supported by the
   Interdisciplinary Research Scheme of Dean's Research Fund 2018-19
   (FLASS/DRF/IDS-3), Departmental Collaborative Research Fund 2019
   (MIT/DCRF-R2/18-19), Small Grant for Academic Staff (MIT/SGA04/19-20) of
   The Education University of Hong Kong, HKIBS Research Seed Fund 2019/20
   (190-009), and Research Seed Fund (102367) of Lingnan University, Hong
   Kong.
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NR 144
TC 25
Z9 25
U1 8
U2 78
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1866-9956
EI 1866-9964
J9 COGN COMPUT
JI Cogn. Comput.
PD NOV
PY 2020
VL 12
IS 6
BP 1097
EP 1129
DI 10.1007/s12559-020-09745-1
EA JUL 2020
PG 33
WC Computer Science, Artificial Intelligence; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Neurosciences & Neurology
GA OU4WX
UT WOS:000554346600001
DA 2024-09-05
ER
PT J
AU Schepers, I
   Medvedeva, M
   Bruijn, M
   Wieling, M
   Vols, M
AF Schepers, Iris
   Medvedeva, Masha
   Bruijn, Michelle
   Wieling, Martijn
   Vols, Michel
TI Predicting citations in Dutch case law with natural language processing
SO ARTIFICIAL INTELLIGENCE AND LAW
LA English
DT Article
DE Machine learning; Case law; Natural language processing; Citation
   analysis; Judicial decisions
ID NETWORK ANALYSIS
AB With the ever-growing accessibility of case law online, it has become challenging to manually identify case law relevant to one's legal issue. In the Netherlands, the planned increase in the online publication of case law is expected to exacerbate this challenge. In this paper, we tried to predict whether court decisions are cited by other courts or not after being published, thus in a way distinguishing between more and less authoritative cases. This type of system may be used to process the large amounts of available data by filtering out large quantities of non-authoritative decisions, thus helping legal practitioners and scholars to find relevant decisions more easily, and drastically reducing the time spent on preparation and analysis. For the Dutch Supreme Court, the match between our prediction and the actual data was relatively strong (with a Matthews Correlation Coefficient of 0.60). Our results were less successful for the Council of State and the district courts (MCC scores of 0.26 and 0.17, relatively). We also attempted to identify the most informative characteristics of a decision. We found that a completely explainable model, consisting only of handcrafted metadata features, performs almost as well as a less well-explainable system based on all text of the decision.
C1 [Schepers, Iris; Bruijn, Michelle; Vols, Michel] Univ Groningen, Fac Law, Dept Legal Methods, Groningen, Netherlands.
   [Schepers, Iris; Wieling, Martijn] Univ Groningen, Fac Arts, Ctr Language & Cognit Groningen, Groningen, Netherlands.
   [Medvedeva, Masha] Leiden Univ, Fac Law, Ctr Law & Digital Technol, Leiden, Netherlands.
C3 University of Groningen; University of Groningen; Leiden University -
   Excl LUMC; Leiden University
RP Schepers, I (corresponding author), Univ Groningen, Fac Law, Dept Legal Methods, Groningen, Netherlands.; Schepers, I (corresponding author), Univ Groningen, Fac Arts, Ctr Language & Cognit Groningen, Groningen, Netherlands.
EM i.schepers@rug.nl
OI Medvedeva, Masha/0000-0002-2972-8447; Bruijn, Larissa
   Michelle/0000-0002-1904-8122; Schepers, Iris/0000-0003-4036-5904; Vols,
   Michel/0000-0002-5762-8697; Wieling, Martijn/0000-0003-0434-1526
FU European Union [949316]; Center for Information Technology of the
   University of Groningen; European Research Council (ERC) [949316]
   Funding Source: European Research Council (ERC)
FX The research presented in this paper has received funding from the
   European Union's ERC Research Grant under grant agreement No 949316. We
   would like to thank the Center for Information Technology of the
   University of Groningen for their support and for providing access to
   the Peregrine High Performance Computing Cluster. We would like to thank
   dr. Marc van Opijnen for making it possible to use the updated LIDO
   dataset and for his feedback in the early stages of this research.
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NR 45
TC 0
Z9 0
U1 5
U2 12
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0924-8463
EI 1572-8382
J9 ARTIF INTELL LAW
JI Artif. Intell. Law
PD SEP
PY 2024
VL 32
IS 3
BP 807
EP 837
DI 10.1007/s10506-023-09368-5
EA JUN 2023
PG 31
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications; Law
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Government & Law
GA A3L4V
UT WOS:001018103700001
PM 39099768
OA hybrid, Green Published
DA 2024-09-05
ER
PT C
AU Newlin, M
   Smathers, K
   DeYoung, ME
AF Newlin, Marvin
   Smathers, Kyle
   DeYoung, Mark E.
GP ASSOC COMP MACHINERY
TI ARC Containers for AI Workloads Singularity Performance Overhead
SO PEARC '19: PROCEEDINGS OF THE PRACTICE AND EXPERIENCE IN ADVANCED
   RESEARCH COMPUTING ON RISE OF THE MACHINES (LEARNING)
LA English
DT Proceedings Paper
CT Conference on Practice and Experience in Advanced Research Computing on
   Rise of the Machines (learning) (PEARC)
CY JUL 28-AUG 01, 2019
CL Chicago, IL
DE Advanced Research Computing; High Performance Computing; Artificial
   Intelligence; Machine Learning; Deep Learning
AB Containerization has taken the software world by storm. Deployment complications, like requiring elevated (i.e. "root") permissions to run, have slowed the adoption of containers in shared advanced research computing (ARC) environments. Singularity is a containerization approach that is designed for ARC in shared high performance computing (HPC) clusters. With the creation of the Singularity, there is finally a viable scientific container solution. However very few papers have looked at the performance tradeoffs of deploying applications using a container based model. The authors are not aware of any published studies evaluating the tradeoffs of the deployment models with complex Artificial Intelligence (AI) workloads. Without detailed evaluations of the performance trade-offs scientists and engineers are unable to make an informed decision on deployment model for time sensitive training or low power inference. Furthering previous research in this area and using emerging community developed benchmarks, we examine performance trade-offs of running AI workloads in a containerized Singularity environment.
C1 [Newlin, Marvin; Smathers, Kyle; DeYoung, Mark E.] Air Force Inst Technol Elect & Comp Engn, Wright Patterson AFB, OH 45433 USA.
C3 Air Force Institute of Technology (AFIT)
RP Newlin, M (corresponding author), Air Force Inst Technol Elect & Comp Engn, Wright Patterson AFB, OH 45433 USA.
EM marvin.newlin@afit.edu; kyle.smathers@afit.edu; mark.deyoung@afit.edu
NR 0
TC 0
Z9 0
U1 0
U2 0
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES
BN 978-1-4503-7227-5
PY 2019
DI 10.1145/3332186.3333048
PG 8
WC Computer Science, Artificial Intelligence; Computer Science, Theory &
   Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BT7QE
UT WOS:000850448600001
DA 2024-09-05
ER
PT J
AU de la Paz-Marín, M
   Campoy-Muñoz, P
   Hervás-Martínez, C
AF de la Paz-Marin, Monica
   Campoy-Munoz, Pilar
   Hervas-Martinez, Cesar
TI Non-linear multiclassifier model based on Artificial Intelligence to
   predict research and development performance in European countries
SO TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
LA English
DT Article
DE R&D performance; Neural networks; Evolutionary algorithms; k-means
   clustering; Multiclassification; European Union
ID NEURAL-NETWORKS; PRODUCT-UNIT; TECHNOLOGICAL CAPABILITIES;
   LOGISTIC-REGRESSION; ENDOGENOUS GROWTH; INNOVATION; SYSTEMS; DESIGN;
   EXTENSION
AB This paper deals with one of the most important keys for economic growth: scientific knowledge and innovation, following the linear Research and Development (R&D) model. Patents, scientific publications and expenditure in R&D as well as the personnel involved in these activities are taken into account as proxy indicators, together with variables related to education and economy in order to classify R&D performance in 25 European Union (EU) Member States. This study classifies these countries using a set of variables which characterize them from 2005 to 2008 and analyses the most relevant ones for this classification. The Multilayer Perceptron Model (MLP) and the Product-Unit Neural Network (EPUNN) models, both trained by evolutionary algorithms (EA), were used to classify yearly country observations in clusters previously defined by employing unsupervised algorithm k-means clustering, obtaining four different classes of national R&D performance: low, moderate, high and innovation driven economies. Finally, our methodology is compared to other classification methods normally used in machine learning. The results show that while various methods of classification exist, our methodology obtains models with a significantly lower number of coefficients without decreasing their accuracy in predicting the classification of other European countries or in these countries in the following years. (C) 2012 Elsevier Inc. All rights reserved.
C1 [de la Paz-Marin, Monica; Campoy-Munoz, Pilar] Fac Business Adm, ETEA, Dept Management & Quantitat Methods, Cordoba 14004, Spain.
   [Hervas-Martinez, Cesar] Univ Cordoba, Dept Comp & Numer Anal, E-14071 Cordoba, Spain.
C3 Universidad Loyola Andalucia; Universidad de Cordoba
RP de la Paz-Marín, M (corresponding author), Fac Business Adm, ETEA, Dept Management & Quantitat Methods, Escritor Castilla Aguayo 4, Cordoba 14004, Spain.
EM mpaz@uco.es; mpcampoy@etea.com; chervas@uco.com
RI Campoy-Muñoz, Pilar/J-8879-2012; Campoy-Muñoz, Pilar/GLS-8345-2022;
   Hervas-Martinez, Cesar/A-3979-2009
OI Campoy-Muñoz, Pilar/0000-0003-4163-3907; Hervas-Martinez,
   Cesar/0000-0003-4564-1816
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NR 66
TC 19
Z9 20
U1 0
U2 79
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0040-1625
EI 1873-5509
J9 TECHNOL FORECAST SOC
JI Technol. Forecast. Soc. Chang.
PD NOV
PY 2012
VL 79
IS 9
BP 1731
EP 1745
DI 10.1016/j.techfore.2012.06.001
PG 15
WC Business; Regional & Urban Planning
WE Social Science Citation Index (SSCI)
SC Business & Economics; Public Administration
GA 031UI
UT WOS:000310667500013
DA 2024-09-05
ER
PT S
AU Okada, T
   Takasu, A
   Adachi, J
AF Okada, T
   Takasu, A
   Adachi, J
BE Heery, R
   Lyon, L
TI Bibliographic component extraction using support vector machines and
   hidden Markov models
SO RESEARCH AND ADVANCED TECHNOLOGY FOR DIGITAL LIBRARIES
SE Lecture Notes in Computer Science
LA English
DT Article; Proceedings Paper
CT 8th European Conference on Research and Advanced Technology for Digital
   Libraries (ECDL 2004)
CY SEP 12-17, 2004
CL Univ Bath, Bath, ENGLAND
HO Univ Bath
AB Article citations are composed of subfields such as 'author', 'title', 'journal', and 'year'. It is useful to automatically identify attributes of these subfields, since they are used for linking a citation with the actual cited article. In this article, we employ a Support Vector Machine (SVM), a method of machine learning, to automatically identify subfields. We then employ a Hidden Markov Model (HMM) to improve the identification accuracy. Information from the subfields identified by the SVM, and syntactic information analyzed by the HMM, are integrated to make an accurate identification.
C1 Univ Tokyo, Tokyo, Japan.
   Natl Inst Informat, Chiyoda Ku, Tokyo, Japan.
C3 University of Tokyo; Research Organization of Information & Systems
   (ROIS); National Institute of Informatics (NII) - Japan
RP Univ Tokyo, 7-3-1 Bunkyo Ku, Tokyo, Japan.
EM takashi@nii.ac.jp; takasu@nii.ac.jp; adachi@nii.ac.jp
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NR 12
TC 4
Z9 5
U1 0
U2 2
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 0302-9743
EI 1611-3349
BN 3-540-23013-0
J9 LECT NOTES COMPUT SC
PY 2004
VL 3232
BP 501
EP 512
PG 12
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Conference Proceedings Citation Index - Science (CPCI-S); Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA BAX48
UT WOS:000224092600046
DA 2024-09-05
ER
PT J
AU Huang, H
   Zhu, DH
   Wang, XF
AF Huang, Heng
   Zhu, Donghua
   Wang, Xuefeng
TI Evaluating scientific impact of publications: combining citation
   polarity and purpose
SO SCIENTOMETRICS
LA English
DT Article
DE Scientific impact; Citation polarity; Citation purpose; CNN; Word2Vec
ID SCIENCE; COLLABORATION; RANKING; INDEX; TOOL
AB Citation counts are commonly used to evaluate the scientific impact of a publication on the general premise that more citations probably mean more endorsements. However, two questionable assumptions underpin this idea: a) that all authors contributed equally to the paper; and b) that the endorsement is positive. Obviously, neither of these assumptions hold true. Hence, with this study, we examine two components of citations-their purpose, i.e., the reason for the citation, and polarity, being the author's attitude toward the cited work. Our findings provide a new perspective on the scientific impact of highly-cited publications. Our methodology consists of three steps. Firstly, a pre-trained model composed of a Word2Vec-a well-known word embedding approach-and a convolutional neural network (CNN) is used to identify citation polarity and purpose. Secondly, in a set of highly-cited papers, we compare eight categories of purpose from foundational to critical and three categories of polarity: positive, negative, and neutral. We further explore how different types of papers-those discussing discoveries or those discussing utilitarian topics-influence the evaluation of scientific impact of papers. Finally, we mine and discover the knowledge (e.g. method, concept, tool or data) to explain the actual scientific impact of a highly-cited paper. To demonstrate how combining citation polarity with purpose can provide far greater details of a paper's scientific impact, we undertake a case study with 370 highly-cited journal articles spanning "Biochemistry & Molecular Biology" and "Genetics & Heredity". The results yield valuable insights into the assumption about citation counts as a metric for evaluating scientific impact.
C1 [Huang, Heng; Zhu, Donghua; Wang, Xuefeng] Beijing Inst Technol, Sch Management & Econ, Beijing 100081, Peoples R China.
C3 Beijing Institute of Technology
RP Wang, XF (corresponding author), Beijing Inst Technol, Sch Management & Econ, Beijing 100081, Peoples R China.
EM wxf5122@bit.edu.cn
OI Wang, xuefeng/0000-0002-4857-6944
FU General Program of the National Natural Science Foundation of China
   [72074020, 71774012]
FX This work was supported by the General Program of the National Natural
   Science Foundation of China under Grant Nos. 72074020 and 71774012. The
   findings and observations in this paper are those of the authors and do
   not necessarily reflect the views of the supporters.
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NR 53
TC 9
Z9 9
U1 4
U2 67
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD SEP
PY 2022
VL 127
IS 9
BP 5257
EP 5281
DI 10.1007/s11192-021-04183-8
EA OCT 2021
PG 25
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 4L1UW
UT WOS:000712768100001
DA 2024-09-05
ER
PT J
AU Sousa, AL
   Ribeiro, TP
   Relvas, S
   Barbosa-Póvoa, A
AF Sousa, Ana L.
   Ribeiro, Tiago P.
   Relvas, Susana
   Barbosa-Povoa, Ana
TI Using Machine Learning for Enhancing the Understanding of Bullwhip
   Effect in the Oil and Gas Industry
SO MACHINE LEARNING AND KNOWLEDGE EXTRACTION
LA English
DT Article
DE artificial neural networks; bullwhip effect; oil and gas industry;
   research proposal; supply networks; machine learning
ID ARTIFICIAL NEURAL-NETWORKS; SUPPLY CHAIN MANAGEMENT; MODEL-PREDICTIVE
   CONTROL; DECISION-SUPPORT-SYSTEM; INVENTORY MANAGEMENT; BIG DATA;
   FEEDFORWARD NETWORKS; LEAD TIME; DEMAND; IMPACT
AB Several suppliers of oil and gas (O & G) equipment and services have reported the necessity of making frequent resources planning adjustments due to the variability of demand, which originates in unbalanced production levels. The occurrence of these specific problems for the suppliers and operators is often related to the bullwhip effect. For studying such a problem, a research proposal is herein presented. Studying the bullwhip effect in the O & G industry requires collecting data from different levels of the supply chain, namely: services, upstream and midstream suppliers, and downstream clients. The first phase of the proposed research consists of gathering the available production and financial data. A second phase will be the statistical treatment of the data in order to evaluate the importance of the bullwhip effect in the oil and gas industry. The third phase of the program involves applying artificial neural networks (ANN) to forecast the demand. At this stage, ANN based on different training methods will be used. Further on, the attained mathematical model will be used to simulate the effects of demand fluctuations and assess the bullwhip effect in an oil and gas supply chain.
C1 [Sousa, Ana L.] Univ Lisbon, Inst Super Tecn, CERENA, P-1049001 Lisbon, Portugal.
   [Ribeiro, Tiago P.] Tal Projecto Lda, P-1350252 Lisbon, Portugal.
   [Relvas, Susana; Barbosa-Povoa, Ana] Univ Lisbon, Inst Super Tecn, CEG IST, P-1049001 Lisbon, Portugal.
C3 Universidade de Lisboa; Universidade de Lisboa
RP Sousa, AL (corresponding author), Univ Lisbon, Inst Super Tecn, CERENA, P-1049001 Lisbon, Portugal.
EM ana.margarida.sousa@tecnico.ulisboa.pt; tpribeiro@gmail.com;
   susana.relvas@tecnico.ulisboa.pt; apovoa@tecnico.ulisboa.pt
RI Ribeiro, Tiago Pinto/Q-6280-2018; Barbosa-Povoa, Ana/AFM-0470-2022;
   Barbosa-Povoa, Ana/AAH-2812-2022; Relvas, Susana/D-7556-2011; Sousa,
   Ana/AAB-3319-2019
OI Ribeiro, Tiago Pinto/0000-0002-5967-0864; Barbosa-Povoa,
   Ana/0000-0001-6594-9653; Barbosa-Povoa, Ana/0000-0001-6594-9653; Relvas,
   Susana/0000-0003-3043-6086; Sousa, Ana/0000-0003-2869-2195
FU Fundacao para a Ciencia e a Tecnologia, I. P [SFRH/BD/131005/2017];
   Fundação para a Ciência e a Tecnologia [SFRH/BD/131005/2017] Funding
   Source: FCT
FX This research was funded by Fundacao para a Ciencia e a Tecnologia, I.
   P, grant number SFRH/BD/131005/2017.
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NR 137
TC 8
Z9 8
U1 3
U2 12
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2504-4990
J9 MACH LEARN KNOW EXTR
JI Mach. Learn. Knowl. Extr.
PD SEP
PY 2019
VL 1
IS 3
AR 57
DI 10.3390/make1030057
PG 19
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications; Engineering, Electrical & Electronic
WE Emerging Sources Citation Index (ESCI)
SC Computer Science; Engineering
GA VJ9LO
UT WOS:000646949500001
OA gold
DA 2024-09-05
ER
PT J
AU Zheng, B
   Ma, X
   Zhang, XQ
   Gao, HY
AF Zheng, Bo
   Ma, Xin
   Zhang, Xiaoqiang
   Gao, Huiying
TI Research Article Team Collaboration Particle Swarm Optimization and Its
   Application on Reliability Optimization
SO INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS
LA English
DT Article
DE Particle swarm optimization; Premature convergence; Reliability
   optimization; Optimization&nbsp; performance improvement
ID ALGORITHM; PSO
AB Particle swarm optimization (PSO) tends to be premature convergence due to easily trapping into local suboptimal areas. In order to overcome the PSO's defects, the reasons causing the defects are analyzed and summarized as population diversity deficiency, insufficient information sharing, unbalance of exploitation and exploration, and single update strategy. On this basis, inspired by human team collaboration behavior, a team collaboration particle swarm optimization (TCPSO) is proposed. Diversified updates strategies, dynamic grouping strategy, selectivity vector, and decreasing and increasing inertia weight are designed in TCPSO to solve the defects' reasons and improve the optimization performance. Eight typical test functions have been used to evaluate and compare the performance of different PSO variants, and the results have been proven that the optimal results found by TCPSO are better compared with other PSO variants, which demonstrates the rationality and effectiveness of TCPSO. Finally, a real-world problem for reliability optimization are solved by five algorithms, and the results prove the convergence rate and stable optimization performance of TCPSO, TCPSO can provide better support for reliability optimization of complex system. (c) 2021 The Authors. Published by Atlantis Press B.V. This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
C1 [Zheng, Bo; Zhang, Xiaoqiang; Gao, Huiying] Civil Aviat Flight Univ China, Aviat Engn Inst, Guanghan 618307, Sichuan, Peoples R China.
   [Ma, Xin] Civil Aviat Flight Univ China, Coll Air Traff Management, Guanghan 618307, Sichuan, Peoples R China.
C3 Civil Aviation Flight University of China; Civil Aviation Flight
   University of China
RP Zheng, B (corresponding author), Civil Aviat Flight Univ China, Aviat Engn Inst, Guanghan 618307, Sichuan, Peoples R China.
EM b_zheng1@126.com
FU Project of Sichuan Province Sci-ence and Technology program
   [2021YJ0519]; China Civil Avia-tion Administration Development
   Foundation Educational Talents Program [14002600100018J034]; General
   Foundation of Civil Aviation Flight University of China [Q2019053];
   Youth Foun-dation of Civil Aviation Flight University of China
   [Q2018139]
FX This study was supported by the Project of Sichuan Province Sci-ence and
   Technology program (No. 2021YJ0519) ; China Civil Avia-tion
   Administration Development Foundation Educational Talents Program (No.
   14002600100018J034) ; General Foundation of Civil Aviation Flight
   University of China (No. Q2019053) ; Youth Foun-dation of Civil Aviation
   Flight University of China (No. Q2018139) .
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NR 59
TC 2
Z9 2
U1 1
U2 30
PU SPRINGERNATURE
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND
SN 1875-6891
EI 1875-6883
J9 INT J COMPUT INT SYS
JI Int. J. Comput. Intell. Syst.
PY 2021
VL 14
IS 1
BP 1842
EP 1855
DI 10.2991/ijcis.d.210625.001
PG 14
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA TP5YR
UT WOS:000677675400002
OA gold
DA 2024-09-05
ER
PT J
AU Correia, A
   Grover, A
   Jameel, S
   Schneider, D
   Antunes, P
   Fonseca, B
AF Correia, Antonio
   Grover, Andrea
   Jameel, Shoaib
   Schneider, Daniel
   Antunes, Pedro
   Fonseca, Benjamim
TI A hybrid human-AI tool for scientometric analysis
SO ARTIFICIAL INTELLIGENCE REVIEW
LA English
DT Article
DE Artificial intelligence; Bibliometric-enhanced information retrieval;
   Crowdsourcing; Human-AI interaction; Reinforcement learning from human
   feedback; Scientometrics
ID DESIGN SCIENCE; SYSTEM
AB Solid research depends on systematic, verifiable and repeatable scientometric analysis. However, scientometric analysis is difficult in the current research landscape characterized by the increasing number of publications per year, intersections between research domains, and the diversity of stakeholders involved in research projects. To address this problem, we propose SciCrowd, a hybrid human-AI mixed-initiative system, which supports the collaboration between Artificial Intelligence services and crowdsourcing services. This work discusses the design and evaluation of SciCrowd. The evaluation is focused on attitudes, concerns and intentions towards use. This study contributes a nuanced understanding of the interplay between algorithmic and human tasks in the process of conducting scientometric analysis.
C1 [Correia, Antonio; Fonseca, Benjamim] INESC TEC, Apartado 1013, Vila Real, Portugal.
   [Correia, Antonio; Fonseca, Benjamim] Univ Tras Os Montes & Alto Douro, UTAD, Apartado 1013, Vila Real, Portugal.
   [Correia, Antonio; Grover, Andrea] Univ Nebraska Omaha, Coll Informat Sci & Technol, Omaha, NE 68182 USA.
   [Jameel, Shoaib] Univ Southampton, Southampton SO17 1BJ, England.
   [Schneider, Daniel] Univ Fed Rio de Janeiro, Tercio Pacitti Inst Comp Applicat & Res NCE, Rio De Janeiro, Brazil.
   [Antunes, Pedro] LASIGE, P-1749016 Lisbon, Portugal.
   [Antunes, Pedro] Univ Lisbon, P-1749016 Lisbon, Portugal.
C3 INESC TEC; University of Tras-os-Montes & Alto Douro; University of
   Nebraska System; University of Nebraska Omaha; University of
   Southampton; Universidade Federal do Rio de Janeiro; Universidade de
   Lisboa; Universidade de Lisboa
RP Correia, A (corresponding author), INESC TEC, Apartado 1013, Vila Real, Portugal.; Correia, A (corresponding author), Univ Tras Os Montes & Alto Douro, UTAD, Apartado 1013, Vila Real, Portugal.; Correia, A (corresponding author), Univ Nebraska Omaha, Coll Informat Sci & Technol, Omaha, NE 68182 USA.
EM antonio.g.correia@inesctec.pt
RI Antunes, Pedro A/B-8664-2008; Correia, António/AAJ-3347-2021
OI Antunes, Pedro A/0000-0002-5411-8798; Correia,
   António/0000-0002-2736-3835
FU Portuguese Foundation for Science and Technology (FCT)
   [SFRH/BD/136211/2018]
FX This research was mainly performed during an internship of Antonio
   Correia at Microsoft Research, Cambridge, UK. The work was supported in
   part by the Portuguese Foundation for Science and Technology (FCT),
   national funding through the individual research Grant
   SFRH/BD/136211/2018. The authors would like to thank Sian Lindley from
   Microsoft Research for the important role in understanding and modifying
   the human-AI scientometric workflow that supports the SciCrowd system,
   as well as Jorge Santos for the help while building the necessary
   infrastructure. Our thanks extend to Hugo Paredes for the helpful
   discussions and valuable insights in the early stages of this work.
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NR 89
TC 2
Z9 2
U1 7
U2 24
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0269-2821
EI 1573-7462
J9 ARTIF INTELL REV
JI Artif. Intell. Rev.
PD OCT
PY 2023
VL 56
IS SUPPL 1
SU 1
BP 983
EP 1010
DI 10.1007/s10462-023-10548-7
EA JUL 2023
PG 28
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA U9KW4
UT WOS:001026679400001
DA 2024-09-05
ER
PT J
AU Gokcimen, T
   Das, B
AF Gokcimen, Tunahan
   Das, Bihter
TI Exploring climate change discourse on social media and blogs using a
   topic modeling analysis
SO HELIYON
LA English
DT Article
DE Bibliometric Analysis; Latent Dirichlet Allocation(LDA); BERTopic; Topic
   modeling; Climate change; Sentence similarity
AB Climate change is one of the most pressing global issues of our time, and understanding public perception and awareness of the topic is crucial for developing effective policies to mitigate its effects. While traditional survey methods have been used to gauge public opinion, advances in natural language processing (NLP) and data visualization techniques offer new opportunities to analyze user-generated content from social media and blog posts. In this study, a new dataset of climate change-related texts was collected from social media sources and various blogs. The dataset was analyzed using BERTopic and LDA to identify and visualize the most important topics related to climate change. The study also used sentence similarity to determine the similarities in the comments written and which topic categories they belonged to. The performance of different techniques for keyword extraction and text representation, including OpenAI, Maximal Marginal Relevance (MMR), and KeyBERT, was compared for topic modeling with BERTopic. It was seen that the best coherence score and topic diversity metric were obtained with OpenAI-based BERTopic. The results provide insights into the public's attitudes and perceptions towards climate change, which can inform policy development and contribute to efforts to reduce activities that cause climate change.
C1 [Gokcimen, Tunahan; Das, Bihter] Firat Univ, Technol Fac, Dept Software Engn, TR-23119 Elazig, Turkiye.
C3 Firat University
RP Das, B (corresponding author), Firat Univ, Technol Fac, Dept Software Engn, TR-23119 Elazig, Turkiye.
EM tunahangokcimen@gmail.com; bihterdas@firat.edu.tr
FU Republic of Turkey, Ministry of Science, Technology and Industry
   [AR-22-087-0001]; Arcelik Digital Transformation, Big Data and
   Artificial Intelligence RD Center [5746]
FX This work is supported by the Republic of Turkey, Ministry of Science,
   Technology and Industry project named "AI Based Smart Digital Assistant
   Customer Dialog Bot project" and project code AR-22-087-0001. It is
   funded by R&D project within the scope of law 5746 by the Arcelik
   Digital Transformation, Big Data and Artificial Intelligence R&D Center.
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PU CELL PRESS
PI CAMBRIDGE
PA 50 HAMPSHIRE ST, FLOOR 5, CAMBRIDGE, MA 02139 USA
EI 2405-8440
J9 HELIYON
JI Heliyon
PD JUN 15
PY 2024
VL 10
IS 11
AR e32464
DI 10.1016/j.heliyon.2024.e32464
PG 16
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA UX2K2
UT WOS:001251296800001
PM 38947458
OA gold
DA 2024-09-05
ER
PT J
AU Li, Y
   Xu, ZS
   Wang, XX
   Wang, XZ
AF Li, Yang
   Xu, Zeshui
   Wang, Xinxin
   Wang, Xizhao
TI A bibliometric analysis on deep learning during 2007-2019
SO INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
LA English
DT Article
DE Deep learning; Machine learning; Bibliometric analysis; Hot topic;
   Development trend
ID NEURAL-NETWORKS; CLASSIFICATION; ALGORITHM
AB As an emerging and applicable method, deep learning (DL) has attracted much attention in recent years. With the development of DL and the massive of publications and researches in this direction, a comprehensive analysis of DL is necessary. In this paper, from the perspective of bibliometrics, a comprehensive analysis of publications of DL is deployed from 2007 to 2019 (the first publication with keywords "deep learning" and "machine learning" was published in 2007). By preprocessing, 5722 publications are exported from Web of Science and they are imported into the professional science mapping tools: VOS viewer and Cite Space. Firstly, the publication structures are analyzed based on annual publications, and the publication of the most productive countries/regions, institutions and authors. Secondly, by the use of VOS viewer, the co-citation networks of countries/regions, institutions, authors and papers are depicted. The citation structure of them and the most influential of them are further analyzed. Thirdly, the cooperation networks of countries/regions, institutions and authors are illustrated by VOS viewer. Time-line review and citation burst detection of keywords are exported from Cite Space to detect the hotspots and research trend. Finally, some conclusions of this paper are given. This paper provides a preliminary knowledge of DL for researchers who are interested in this area, and also makes a conclusive and comprehensive analysis of DL for these who want to do further research on this area.
C1 [Li, Yang; Xu, Zeshui; Wang, Xinxin] Sichuan Univ, Business Sch, Chengdu 610064, Peoples R China.
   [Wang, Xizhao] Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Peoples R China.
C3 Sichuan University; Shenzhen University
RP Xu, ZS (corresponding author), Sichuan Univ, Business Sch, Chengdu 610064, Peoples R China.
EM liyang_ly18@163.com; xuzeshui@263.net; wangxinxin_cd@163.com;
   xizhaowang@ieee.org
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NR 56
TC 40
Z9 42
U1 10
U2 109
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1868-8071
EI 1868-808X
J9 INT J MACH LEARN CYB
JI Int. J. Mach. Learn. Cybern.
PD DEC
PY 2020
VL 11
IS 12
BP 2807
EP 2826
DI 10.1007/s13042-020-01152-0
EA JUN 2020
PG 20
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA OG5CX
UT WOS:000543945600001
DA 2024-09-05
ER
PT J
AU Keramatfar, A
   Rafiee, M
   Amirkhani, H
AF Keramatfar, Abdalsamad
   Rafiee, Mohadeseh
   Amirkhani, Hossein
TI Graph Neural Networks: A bibliometrics overview
SO MACHINE LEARNING WITH APPLICATIONS
LA English
DT Article
DE Bibliometrics; Graph Convolutional Network; Graph Neural Network; Graph
   representation learning
ID MODEL; PREDICTION; SCIENCE; INDEX
AB Recently, graph neural networks (GNNs) have become a hot topic in machine learning community. This paper presents a Scopus -based bibliometric overview of the GNNs' research since 2004 when GNN papers were first published. The study aims to evaluate GNN research trends, both quantitatively and qualitatively. We provide the trend of research, distribution of subjects, active and influential authors and institutions, sources of publications, most cited documents, and hot topics. Our investigations reveal that the most frequent subject categories in this field are computer science, engineering, and telecommunications. In addition, the most active source of GNN publications is Lecture Notes in Computer Science. The most prolific or impactful institutions are found in the United States, China, and Canada. We also provide must -read papers based on citation count and future directions. Our analysis reveals that node classification is the most popular task, followed by link prediction, and graph classification in the GNN literature. Moreover, the results suggest that the application of graph convolutional networks and attention mechanisms are now among hot topics of GNN research. Finally, scalability, generalization, over -smoothing, and explainability of graph neural networks are some research directions to pursue.
C1 [Keramatfar, Abdalsamad; Rafiee, Mohadeseh] Acad Ctr Educ Culture & Res ACECR, Tehran, Iran.
   [Amirkhani, Hossein] Univ Qom, Fac Engn, Dept Comp Engn & IT, Qom, Iran.
C3 Academic Center for Education, Culture & Research (ACECR); University of
   Qom
RP Keramatfar, A (corresponding author), Acad Ctr Educ Culture & Res ACECR, Tehran, Iran.
EM samad@sid.com; mohadeseh.rafie2012@gmail.com; amirkhani@qom.ac.ir
OI Keramatfar, Abdalsamad/0000-0001-6826-4692
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NR 164
TC 14
Z9 13
U1 3
U2 3
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
EI 2666-8270
J9 MACH LEARN APPL
JI Mach. Learn. Appl.
PD DEC 15
PY 2022
VL 10
AR 100401
DI 10.1016/j.mlwa.2022.100401
PG 18
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Interdisciplinary Applications
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA QT9C3
UT WOS:001223227900014
OA Green Submitted, gold
DA 2024-09-05
ER
PT J
AU Serenko, A
AF Serenko, Alexander
TI The development of an AI journal ranking based on the revealed
   preference approach
SO JOURNAL OF INFORMETRICS
LA English
DT Article
DE Artificial intelligence; Journal ranking; Academic journal; Google
   scholar; Citation impact; h-Index; g-Index; hc-Index
ID H-INDEX; CITATION ANALYSIS; SCIENTOMETRIC ANALYSIS; KNOWLEDGE
   MANAGEMENT; GLOBAL PERCEPTIONS; BUSINESS; QUALITY; IMPACT; SCIENCE;
   PRODUCTIVITY
AB This study presents a ranking of 182 academic journals in the field of artificial intelligence. For this, the revealed preference approach, also referred to as a citation impact method, was utilized to collect data from Google Scholar. This list was developed based on three relatively novel indices: h-index, g-index, and hc-index. These indices correlated almost perfectly with one another (ranging from 0.97 to 0.99), and they correlated strongly with Thomson's Journal Impact Factors (ranging from 0.64 to 0.69). It was concluded that journal longevity (years in print) is an important but not the only factor affecting an outlet's ranking position. Inclusion in Thomson's Journal Citation Reports is a must for a journal to be identified as a leading A+ or A level outlet. However, coverage by Thomson does not guarantee a high citation impact of an outlet. The presented list may be utilized by scholars who want to demonstrate their research output, various academic committees, librarians and administrators who are not familiar with the AI research domain. (C) 2010 Elsevier Ltd. All rights reserved.
C1 Lakehead Univ, Fac Business Adm, 955 Oliver Rd, Thunder Bay, ON P7B 5E1, Canada.
C3 Lakehead University
RP Serenko, A (corresponding author), Lakehead Univ, Fac Business Adm, 955 Oliver Rd, Thunder Bay, ON P7B 5E1, Canada.
EM aserenko@lakeheadu.ca
RI Serenko, Alexander/AAT-2082-2020
OI Serenko, Alexander/0000-0003-4881-2932
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NR 75
TC 39
Z9 39
U1 0
U2 32
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1751-1577
EI 1875-5879
J9 J INFORMETR
JI J. Informetr.
PD OCT
PY 2010
VL 4
IS 4
BP 447
EP 459
DI 10.1016/j.joi.2010.04.001
PG 13
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 647KA
UT WOS:000281616200001
DA 2024-09-05
ER
PT J
AU Hori, Y
   Hayashi, E
AF Hori, Yoshiki
   Hayashi, Eiji
TI A Research on a System Using Deep Learning for Inferring Piano
   Performance
SO JOURNAL OF ROBOTICS NETWORKING AND ARTIFICIAL LIFE
LA English
DT Article
DE Automatic piano; Computer music; Deep learning
AB Achieving expressive and human-like automated piano performances has proven challenging. This study proposes a deep learning system to infer expressive nuances from musical scores, addressing the limitations of traditional rule-based approaches. By leveraging neural networks to learn the mapping between scores and expert performances, the system automates the inference process, improving accuracy while enhancing efficiency. This novel application of deep learning shows promise for advancing automated music performance and enabling more artistically expressive renditions. The insights gained could have broader implications for computer-aided musical interpretation and synthesis.
C1 [Hori, Yoshiki; Hayashi, Eiji] Kyushu Inst Technol, 680-4 Kawazu, Iizuka Shi, Fukuoka 8208502, Japan.
C3 Kyushu Institute of Technology
RP Hori, Y (corresponding author), Kyushu Inst Technol, 680-4 Kawazu, Iizuka Shi, Fukuoka 8208502, Japan.
EM hori.yoshiki311@mail.kyutech.jp; haya@mse.kyutech.ac.jp
CR HAYASHI E, 1994, INT J JPN S PREC ENG, V28, P164
NR 1
TC 0
Z9 0
U1 0
U2 0
PU ALife Robotics Corp Ltd
PI Oita
PA Handadai Higashi 2-8-4, Oita, JAPAN
SN 2352-6386
J9 J ROBOT NETW ARTIF L
JI J. Robot Netw. Artif. Life
PD DEC
PY 2023
VL 10
IS 3
BP 282
EP 285
PG 4
WC Robotics
WE Emerging Sources Citation Index (ESCI)
SC Robotics
GA A9L6X
UT WOS:001285686500014
DA 2024-09-05
ER
PT J
AU Hanif, O
   Zhu, DH
   Wang, XF
   Nawaz, MS
AF Hanif, Omer
   Zhu Donghua
   Wang Xuefeng
   Nawaz, M. Saqib
TI Refining the Measurement of Topic Similarities Through Bibliographic
   Coupling and LDA
SO IEEE ACCESS
LA English
DT Article
DE Bibliographic coupling; brain cancer; technology mining; Latent
   Dirichlet allocation; topic similarity
ID GROWTH-HORMONE; THERAPY; CANCER; TECHNOLOGY; TRACKING; CITATION;
   DISEASE; BRAIN; MODEL; AXIS
AB Generally, two topics with vastly different terminology probably indicate different implied concepts. However, these topics themselves might share common references (bibliographic coupling), which suggest the underlying joint concept. Therefore, searching for these joint concepts in different topics would be of scientific interest. Previous studies have measured the similarity between topics based on comparison of the topics' word probability distributions. In contrast, this paper presents an approach for measuring the similarity between topics based on the bibliographic coupling. Besides, the similarity is independent of the topic's word probability distributions generated by a Latent Dirichlet Allocation (LDA) model. The proposed approach was evaluated using its counterpart (intra-topic similarity), baseline topic similarity matrices, and cosine measure. The method was exampled on brain cancer patents. A cross-topic similarity network of eight topics showcases 28 cross-topic pairs to profile which topics were associated with particular topics. Interestingly, some of the 28 combinations may be of scientific interest. For instance, the findings of the top five cross-topic pairs suggest that "growth of cancer cells" and "imbalances in the hormones" have common knowledge sources with the highest similarity value. These two entirely different concepts may suggest some common causative factors within the field. We believe that finding such an association between unrelated innovative inventions across various industries may help public and private research units in planning research direction and serve as a reference for future research.
C1 [Hanif, Omer; Zhu Donghua; Wang Xuefeng] Beijing Inst Technol, Sch Management & Econ, Lab Knowledge Management & Data Anal, Beijing 100081, Peoples R China.
   [Nawaz, M. Saqib] Harbin Inst Technol, Sch Comp Sci & Technol, Shenzhen 518000, Peoples R China.
C3 Beijing Institute of Technology; Harbin Institute of Technology
RP Hanif, O (corresponding author), Beijing Inst Technol, Sch Management & Econ, Lab Knowledge Management & Data Anal, Beijing 100081, Peoples R China.
EM omerhanif@bit.edu.cn
RI Nawaz, M. Saqib/AAV-3633-2020; Nawaz, M Saqib/JDM-5962-2023; Hanif,
   Omer/AAQ-3868-2020
OI Nawaz, M. Saqib/0000-0001-9856-2885; Wang, xuefeng/0000-0002-4857-6944;
   HANIF, OMER/0000-0002-3829-5620
FU National Natural Science Foundation of China [71774012, 71373019,
   71673024]; Chinese Scholarship Council [278401]
FX This work was supported in part by the General Program of National
   Natural Science Foundation of China under Grant 71774012, Grant
   71373019, and Grant 71673024, and in part by Chinese Scholarship Council
   through the Omer Hanif under Grant 2014-CSC#278401.
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NR 68
TC 4
Z9 4
U1 0
U2 15
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2019
VL 7
BP 179997
EP 180011
DI 10.1109/ACCESS.2019.2958489
PG 15
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering; Telecommunications
GA KF8JT
UT WOS:000509483800210
OA gold
DA 2024-09-05
ER
PT J
AU Cascella, M
   Perri, F
   Ottaiano, A
   Cuomo, A
   Wirz, S
   Coluccia, S
AF Cascella, Marco
   Perri, Francesco
   Ottaiano, Alessandro
   Cuomo, Arturo
   Wirz, Stefan
   Coluccia, Sergio
TI Trends in Research on Artificial Intelligence in Anesthesia: A
   VOSviewer-Based Bibliometric Analysis
SO INTELIGENCIA ARTIFICIAL-IBEROAMERICAL JOURNAL OF ARTIFICIAL INTELLIGENCE
LA English
DT Article
DE Artificial Intelligence; Anesthesia; Bibliometric analysis; Machine
   Learning; Deep Learning; Network analysis
AB Background: The scientific literature on Artificial Intelligence (AI) in anesthesia is rapidly growing. Considering that applications of AI strategies can offer paramount support in clinical decision processes, it is crucial to delineate the research features. Bibliometric analyses can provide an overview of research tendencies useful for supplementary investigations in a research field. Methods: The comprehensive literature about AI in anesthesia was checked in the Web of Science (WOS) core collection. Year of publication, journal metrics including impact factor and quartile, title, document type, topic, and article metric (citations) were extracted. The software tool VOSviewer (version 1.6.17) was implemented for the co-occurrence of keywords and the co-citation analyses, and for evaluating research networks (countries and institutions). Results: Altogether, 288 documents were retrieved from the WOS and 154 articles were included in the analysis. The number of articles increased from 4 articles in 2017 to 37 in 2021. Only 34 were observational investigations and 7 RCTs. The most relevant topic is "anesthesia management". The research network for countries and institutions shows severe gaps. Conclusion: Research on AI in anesthesia is rapidly developing. Further clinical studies are needed. Although different topics are addressed, scientific collaborations must be implemented.
C1 [Cascella, Marco; Cuomo, Arturo] Ist Nazl Tumori IRCCS Fdn Pascale, Div Anesthesia & Pain Med, Naples, Italy.
   [Perri, Francesco] IRCCS Fdn G Pascale, Ist Nazl Tumori, Med & Expt Head & Neck Oncol Unit, Naples, Italy.
   [Ottaiano, Alessandro] IRCCS G Pascale, Ist Nazl Tumori Napoli, SSD Innovat Therapies Abdominal Metastases, Via M Semmola, I-80131 Naples, Italy.
   [Wirz, Stefan] GFO Kliniken Bonn, Cura Krankenhaus, Zent Schmerzmed, Abt Anasthesie Interdisziplinare Intens Med Schmer, Schulgenstr 15, D-53604 Bad Honnef, Germany.
   [Coluccia, Sergio] IRCCS Fdn G Pascale, Ist Nazl Tumori, Epidemiol & Biostat Unit, I-80100 Naples, Italy.
C3 IRCCS Fondazione Pascale; IRCCS Fondazione Pascale; Fondazione IRCCS
   Istituto Nazionale Tumori Milan; IRCCS Fondazione Pascale; IRCCS
   Fondazione Pascale; Fondazione IRCCS Istituto Nazionale Tumori Milan
RP Cascella, M (corresponding author), Ist Nazl Tumori IRCCS Fdn Pascale, Div Anesthesia & Pain Med, Naples, Italy.
EM m.cascella@istitutotumori.na.it
RI cuomo, arturo/AAL-4416-2020; cascella, marco/N-1316-2018; Coluccia,
   Sergio/AAC-6043-2022; Wirz, Stefan/KGM-5077-2024
OI cascella, marco/0000-0002-5236-3132; Ottaiano,
   Alessandro/0000-0002-2901-3855
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   Zhao H, 2021, FRONT SURG, V8, DOI 10.3389/fsurg.2021.634629
NR 28
TC 3
Z9 3
U1 4
U2 24
PU ASOC ESPANOLA INTELIGENCIA ARTIFICIAL
PI VALENCIA
PA FAC INFORMATICA, UNIV POLITECNICA VALENCIA, VALENCIA, SPAIN
SN 1137-3601
EI 1988-3064
J9 INTELIGENCIA ARTIFIC
JI Inteligencia Artif.
PD DEC
PY 2022
VL 25
IS 70
BP 126
EP 137
DI 10.4114/intartif.vol25iss70pp126-137
PG 12
WC Computer Science, Artificial Intelligence
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA 7M8UM
UT WOS:000906926400001
OA Green Submitted, gold
DA 2024-09-05
ER
PT J
AU Ho, YS
   Wang, MH
AF Ho, Yuh-Shan
   Wang, Ming-Huang
TI A bibliometric analysis of artificial intelligence publications from
   1991 to 2018
SO COLLNET JOURNAL OF SCIENTOMETRICS AND INFORMATION MANAGEMENT
LA English
DT Article
DE AI; Neural networks; Hybrid; Machine learning; Support vector machine
ID CHEMICAL INFERENCE; COMPUTER-PROGRAM; ELUCIDATION; SIMULATION; ARTICLES;
   GENOA
AB This study aimed to analyze the characteristics of artificial intelligence-related publications in Science Citation Index Expanded (SCI-EXPANDED) from 1991 to 2018. The analyzed aspects covered distribution of annual publications, citations per publication, journals, Web of Science categories, countries, institutions, as well as research foci and their trends. A total of 13,251 artificial intelligence-related articles were found. Articles were published in a wide range of journals and Web of Science categories. The United States took the lead position in total, single country, international collaboration, and first, corresponding, and single author articles as well as citations per publication among 119 countries. Chinese Academy of Sciences in China, Islamic Azad University in Iran, and Massachusetts Institute of Technology (MIT) in USA were the three most productive institutions. MIT had higher citations per publication. An international collaborative article by authors from Canada, the United States, and Switzerland was the most frequently cited article with the most total citations from Web of Science Core Collection since publication through the end of 2018. Results from word cluster analysis showed that models, neural networks, learning, and prediction were the most popular topics and features, classification, and optimization might be focus in artificial intelligence research.
C1 [Ho, Yuh-Shan; Wang, Ming-Huang] Asia Univ, Trend Res Ctr, 500 Lioufeng Rd, Wufeng 41354, Taichung County, Taiwan.
C3 Asia University Taiwan
RP Ho, YS (corresponding author), Asia Univ, Trend Res Ctr, 500 Lioufeng Rd, Wufeng 41354, Taichung County, Taiwan.
EM ysho@asia.edu.tw
RI Wang, Ming-Huang/F-5445-2012
OI Wang, Ming-Huang/0000-0002-2193-9864
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NR 82
TC 11
Z9 11
U1 0
U2 17
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0973-7766
EI 2168-930X
J9 COLLNET J SCIENTOMET
JI Collnet J. Scientometr. Inf. Manag.
PD JUL 2
PY 2020
VL 14
IS 2
BP 369
EP 392
DI 10.1080/09737766.2021.1918032
PG 24
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA SM9LO
UT WOS:000657918700011
DA 2024-09-05
ER
PT J
AU Lokker, C
   Bagheri, E
   Abdelkader, W
   Parrish, R
   Afzal, M
   Navarro, T
   Cotoi, C
   Germini, F
   Linkins, L
   Haynes, RB
   Chu, LY
   Iorio, A
AF Lokker, Cynthia
   Bagheri, Elham
   Abdelkader, Wael
   Parrish, Rick
   Afzal, Muhammad
   Navarro, Tamara
   Cotoi, Chris
   Germini, Federico
   Linkins, Lori
   Haynes, R. Brian
   Chu, Lingyang
   Iorio, Alfonso
TI Deep learning to refine the identification of high-quality clinical
   research articles from the biomedical literature: Performance evaluation
SO JOURNAL OF BIOMEDICAL INFORMATICS
LA English
DT Article
DE Bioinformatics; Machine learning; Evidence-based medicine; Literature
   retrieval; Medical informatics; Natural Language Processing
ID RETRIEVAL; MEDLINE; CARE
AB Background: Identifying practice-ready evidence-based journal articles in medicine is a challenge due to the sheer volume of biomedical research publications. Newer approaches to support evidence discovery apply deep learning techniques to improve the efficiency and accuracy of classifying sound evidence.Objective: To determine how well deep learning models using variants of Bidirectional Encoder Representations from Transformers (BERT) identify high-quality evidence with high clinical relevance from the biomedical literature for consideration in clinical practice. Methods: We fine-tuned variations of BERT models (BERTBASE, BioBERT, BlueBERT, and PubMedBERT) and compared their performance in classifying articles based on methodological quality criteria. The dataset used for fine-tuning models included titles and abstracts of >160,000 PubMed records from 2012 to 2020 that were of interest to human health which had been manually labeled based on meeting established critical appraisal criteria for methodological rigor. The data was randomly divided into 80:10:10 sets for training, validating, and testing. In addition to using the full unbalanced set, the training data was randomly undersampled into four balanced datasets to assess performance and select the best performing model. For each of the four sets, one model that maintained sensitivity (recall) at & GE;99% was selected and were ensembled. The best performing model was evaluated in a prospective, blinded test and applied to an established reference standard, the Clinical Hedges dataset.Results: In training, three of the four selected best performing models were trained using BioBERTBASE. The ensembled model did not boost performance compared with the best individual model. Hence a solo BioBERT-based model (named DL-PLUS) was selected for further testing as it was computationally more efficient. The model had high recall (>99%) and 60% to 77% specificity in a prospective evaluation conducted with blinded research associates and saved >60% of the work required to identify high quality articles. Conclusions: Deep learning using pretrained language models and a large dataset of classified articles produced models with improved specificity while maintaining >99% recall. The resulting DL-PLUS model identifies high-quality, clinically relevant articles from PubMed at the time of publication. The model improves the efficiency of a literature surveillance program, which allows for faster dissemination of appraised research.
C1 [Lokker, Cynthia; Bagheri, Elham; Abdelkader, Wael; Parrish, Rick; Navarro, Tamara; Cotoi, Chris; Germini, Federico; Haynes, R. Brian; Iorio, Alfonso] McMaster Univ, Dept Hlth Res Methods Evidence & Impact, Hlth Informat Res Unit, Hamilton, ON, Canada.
   [Afzal, Muhammad] Birmingham City Univ, Dept Comp, Birmingham, England.
   [Germini, Federico; Linkins, Lori; Haynes, R. Brian; Iorio, Alfonso] McMaster Univ, Dept Med, Hamilton, ON, Canada.
   [Chu, Lingyang] McMaster Univ, Dept Comp & Software, Hamilton, ON, Canada.
C3 McMaster University; Birmingham City University; McMaster University;
   McMaster University
RP Lokker, C (corresponding author), McMaster Univ, Dept Hlth Res Methods Evidence & Impact, Hlth Informat Res Unit, Hamilton, ON, Canada.
EM lokkerc@mcmaster.ca
RI Afzal, Muhammad/D-3741-2019; Germini, Federico/K-6881-2016
OI Afzal, Muhammad/0000-0002-7851-2327; Germini,
   Federico/0000-0002-0802-3616; Abdelkader, Wael/0000-0002-9581-1521;
   Lokker, Cynthia/0000-0003-2436-4290
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NR 47
TC 4
Z9 4
U1 5
U2 9
PU ACADEMIC PRESS INC ELSEVIER SCIENCE
PI SAN DIEGO
PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA
SN 1532-0464
EI 1532-0480
J9 J BIOMED INFORM
JI J. Biomed. Inform.
PD JUN
PY 2023
VL 142
AR 104384
DI 10.1016/j.jbi.2023.104384
EA MAY 2023
PG 9
WC Computer Science, Interdisciplinary Applications; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Medical Informatics
GA J8TJ9
UT WOS:001012292000001
PM 37164244
OA hybrid, Green Accepted
DA 2024-09-05
ER
PT J
AU Pobiedina, N
   Ichise, R
AF Pobiedina, Nataliia
   Ichise, Ryutaro
TI Citation count prediction as a link prediction problem
SO APPLIED INTELLIGENCE
LA English
DT Article
DE Citation count; Graph pattern mining; Feature selection
ID IMPACT
AB The citation count is an important factor to estimate the relevance and significance of academic publications. However, it is not possible to use this measure for papers which are too new. A solution to this problem is to estimate the future citation counts. There are existing works, which point out that graph mining techniques lead to the best results. We aim at improving the prediction of future citation counts by introducing a new feature. This feature is based on frequent graph pattern mining in the so-called citation network constructed on the basis of a dataset of scientific publications. Our new feature improves the accuracy of citation count prediction, and outperforms the state-of-the-art features in many cases which we show with experiments on two real datasets.
C1 [Pobiedina, Nataliia] Vienna Univ Technol, Inst Software Technol & Interact Syst, A-1040 Vienna, Austria.
   [Ichise, Ryutaro] Natl Inst Informat, Principles Informat Res Div, Tokyo, Japan.
C3 Technische Universitat Wien; Research Organization of Information &
   Systems (ROIS); National Institute of Informatics (NII) - Japan
RP Pobiedina, N (corresponding author), Vienna Univ Technol, Inst Software Technol & Interact Syst, A-1040 Vienna, Austria.
EM pobiedina@ec.tuwien.ac.at; ichise@nii.ac.jp
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NR 24
TC 30
Z9 33
U1 3
U2 43
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0924-669X
EI 1573-7497
J9 APPL INTELL
JI Appl. Intell.
PD MAR
PY 2016
VL 44
IS 2
BP 252
EP 268
DI 10.1007/s10489-015-0657-y
PG 17
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA DF0SB
UT WOS:000371048100002
DA 2024-09-05
ER
PT C
AU Li, JX
   Hu, YL
AF Li Jiaxu
   Hu Yuling
GP IEEE
TI Research Framework of Risk Assessment in Evacuation Based on Deep
   Learning
SO 2020 CHINESE AUTOMATION CONGRESS (CAC 2020)
SE Chinese Automation Congress
LA English
DT Proceedings Paper
CT Chinese Automation Congress (CAC)
CY NOV 06-08, 2020
CL Shanghai, PEOPLES R CHINA
DE risk assessment; emergency evacuation; deep learning; convolutional
   neural network
ID AUTOENCODER; ACCIDENTS
AB Aim at the occurrence of security accidents, emergency evacuation has become an important means that cannot be ignored. At present, most of the risk assessment studies are focused on accident analysis, while only a few studies conduct risk assessments on evacuation issues. Without the basis for risk assessment, it is easy to ignore some important factors when formulating an evacuation emergency plan, resulting in lacking of science in the evacuation plan and even a negative impact in actual implementation. Therefore, the research on risk assessment of evacuation is a very significant research direction. In addition, with the advent of the era of big data and the development of artificial intelligence, the data required for risk assessment is also increasing, and traditional risk assessment methods are difficult to deal with these huge amounts of data. Therefore, the use of deep learning methods into risk assessment and deal with this problem is also one trend in future. The major objective of this study Is as to apply deep learning method to evacuation risk assessment, and give a framework of risk assessment in evacuation based on convolutional neural networks.
C1 [Li Jiaxu; Hu Yuling] Beijing Univ Civil Engn & Architecture, Beijing Key Lab Intelligent Proc Jbr Bldg Big Dat, Beijing, Peoples R China.
C3 Beijing University of Civil Engineering & Architecture
RP Li, JX (corresponding author), Beijing Univ Civil Engn & Architecture, Beijing Key Lab Intelligent Proc Jbr Bldg Big Dat, Beijing, Peoples R China.
EM 454453502@qq.com; huyuling@bucea.edu.cn
RI hu, yuling/KLE-4059-2024
OI hu, yuling/0000-0003-0884-0699
FU Fundamental Research Funds for Beijing University of Civil Engineering
   and Architecture [X18304]
FX This work was supported by Fundamental Research Funds for Beijing
   University of Civil Engineering and Architecture (X18304).
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NR 28
TC 0
Z9 0
U1 1
U2 12
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2688-092X
EI 2688-0938
BN 978-1-7281-7687-1
J9 CHIN AUTOM CONGR
PY 2020
BP 1860
EP 1864
DI 10.1109/CAC51589.2020.9326940
PG 5
WC Automation & Control Systems
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Automation & Control Systems
GA BR9MR
UT WOS:000678697001175
DA 2024-09-05
ER
PT J
AU Fu, LD
   Aphinyanaphongs, Y
   Wang, LL
   Aliferis, CF
AF Fu, Lawrence D.
   Aphinyanaphongs, Yindalon
   Wang, Lily
   Aliferis, Constantin F.
TI A comparison of evaluation metrics for biomedical journals, articles,
   and websites in terms of sensitivity to topic
SO JOURNAL OF BIOMEDICAL INFORMATICS
LA English
DT Article
DE Information retrieval; Machine learning; PageRank; Journal impact
   factor; Topic-sensitivity; Bibliometrics
ID IMPACT FACTORS; PAGERANK; SEARCH; LINK
AB Evaluating the biomedical literature and health-related websites for quality are challenging information retrieval tasks. Current commonly used methods include impact factor for journals, PubMed's clinical query filters and machine learning-based filter models for articles, and PageRank for websites. Previous work has focused on the average performance of these methods without considering the topic, and it is unknown how performance varies for specific topics or focused searches. Clinicians, researchers, and users should be aware when expected performance is not achieved for specific topics. The present work analyzes the behavior of these methods for a variety of topics. Impact factor, clinical query filters, and PageRank vary widely across different topics while a topic-specific impact factor and machine learning-based filter models are more stable. The results demonstrate that a method may perform excellently on average but struggle when used on a number of narrower topics. Topic-adjusted metrics and other topic robust methods have an advantage in such situations. Users of traditional topic-sensitive metrics should be aware of their limitations. (C) 2011 Elsevier Inc. All rights reserved.
C1 [Fu, Lawrence D.; Aphinyanaphongs, Yindalon; Aliferis, Constantin F.] NYU, Med Ctr, Ctr Hlth Informat & Bioinformat, New York, NY 10016 USA.
   [Wang, Lily] Vanderbilt Univ, Sch Med, Dept Biostat, Nashville, TN 37232 USA.
C3 New York University; Vanderbilt University
RP Fu, LD (corresponding author), NYU, Med Ctr, Ctr Hlth Informat & Bioinformat, 227 E 30th St,7th Floor, New York, NY 10016 USA.
EM lawrence.fu@nyumc.org; yin.a@nyumc.org; lily.wang@vanderbilt.edu;
   constantin.aliferis@nyumc.org
OI Aphinyanaphongs, Yin/0000-0001-8605-5392
FU  [R56 LM007948-04A1];  [1UL1RR029893]
FX The authors gratefully acknowledge support from Grants R56 LM007948-04A1
   and 1UL1RR029893.
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NR 22
TC 4
Z9 4
U1 0
U2 17
PU ACADEMIC PRESS INC ELSEVIER SCIENCE
PI SAN DIEGO
PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA
SN 1532-0464
J9 J BIOMED INFORM
JI J. Biomed. Inform.
PD AUG
PY 2011
VL 44
IS 4
BP 587
EP 594
DI 10.1016/j.jbi.2011.03.006
PG 8
WC Computer Science, Interdisciplinary Applications; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Medical Informatics
GA 800OO
UT WOS:000293371500009
PM 21419864
OA Green Accepted
DA 2024-09-05
ER
PT J
AU Zhou, R
   He, ZH
   Lu, XB
   Gao, Y
AF Zhou, Rui
   He, Zhihua
   Lu, Xiaobiao
   Gao, Ying
TI Applying Deep Learning in the Training of Communication Design Talents
   Under University-Industrial Research Collaboration
SO FRONTIERS IN PSYCHOLOGY
LA English
DT Article
DE deep learning; RNN; higher vocational computer science specialty; talent
   cultivation; fused attention model; communication design courses
ID TIME-SERIES MODEL; ALGORITHM
AB The purpose of the study was to solve the problem of the mismatching between the supply and demand of the talents that universities provide for society, whose major is communication design. The correlations between social post demand and university cultivation, as well as between social post demand and the demand indexes of enterprises for posts, are explored under the guidance of University-Industrial Research Collaboration. The backpropagation neural network (BPNN) is used, and the advantages of the Seasonal Autoregressive Integrated Moving Average model (SARIMA) model are combined to design the SARIMA-BPNN (SARIMA-BP) model after the relevant parameters are adjusted. Through the experimental analysis, it is found that the error of the root mean square of the designed SARIMA-BP model in post prediction is 7.523 and that of the BPNN model is 16.122. The effect of the prediction model that was designed based on deep learning is smaller than that of the previous model based on the neural network, and it can predict future posts more accurately for colleges and universities. Guided by the "University-Industrial Research Collaboration," students will have more practice in the teaching process in response to social needs. "University-Industrial Research Collaboration" guides the teaching direction for communication design majors and can help to cultivate communication design talents who are competent for the post provided.
C1 [Zhou, Rui; Lu, Xiaobiao] Anhui Agr Univ, Sch Text Engn & Art, Hefei, Peoples R China.
   [He, Zhihua; Gao, Ying] Zhejiang Gongshang Univ, Art & Design Coll, Hangzhou, Peoples R China.
C3 Anhui Agricultural University; Zhejiang Gongshang University
RP He, ZH (corresponding author), Zhejiang Gongshang Univ, Art & Design Coll, Hangzhou, Peoples R China.
EM hezhihua@mail.zjgsu.edu.cn
FU Key Research Projects of Humanities and Social Sciences in Colleges and
   Universities in Anhui Province [SK2020A0127]
FX Funding This work was supported by Key Research Projects of Humanities
   and Social Sciences in Colleges and Universities in Anhui Province (The
   Research on the Communication Design of Huizhou Gate Decoration Art from
   the Perspective of Omnimedi, Project Number: SK2020A0127).
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NR 26
TC 2
Z9 2
U1 3
U2 31
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 1664-1078
J9 FRONT PSYCHOL
JI Front. Psychol.
PD DEC 15
PY 2021
VL 12
AR 742172
DI 10.3389/fpsyg.2021.742172
PG 10
WC Psychology, Multidisciplinary
WE Social Science Citation Index (SSCI)
SC Psychology
GA YA0RS
UT WOS:000738052000001
PM 34975631
OA gold, Green Published
DA 2024-09-05
ER
PT C
AU Kang, Q
   Wang, L
   Xiao, H
   Wu, Q
AF Kang Qi
   Wang Lei
   Xiao Hui
   Wu Qidi
GP IEEE
TI Evaluation mode research on particle swarm optimization algorithm
SO 2007 IEEE INTERNATIONAL CONFERENCE ON NETWORKING, SENSING, AND CONTROL,
   VOLS 1 AND 2
SE IEEE International Conference on Networking Sensing and Control
LA English
DT Proceedings Paper
CT IEEE International Conference on Networking, Sensing and Control
CY APR 15-17, 2007
CL London, ENGLAND
DE particle swarm optimization; evaluation mode; general optimization
   performance; population dynamics
AB This paper presents a series of evaluation indexes for intelligent particle swarm optimization based on the basal evaluation index system in the field of intelligent optimization. A kind of evaluation model used to evaluate synthetically the general optimization performance and population dynamics of particle swarm optimization is proposed. This evaluation model is simulated and validated by function optimization problems.
C1 [Kang Qi; Wang Lei; Xiao Hui] Tongji Univ, Coll Elect & Informat Engn, Siping Rd 1239, Shanghai 200092, Peoples R China.
   [Wu Qidi] Minist Educ China, Beijing 100816, Peoples R China.
C3 Tongji University
RP Kang, Q (corresponding author), Tongji Univ, Coll Elect & Informat Engn, Siping Rd 1239, Shanghai 200092, Peoples R China.
EM kangqi_kz@hotmail.com
RI Kang, Qi/M-1037-2018
FU National Science Foundation of China [70531020]; National 973 project
   [2002CB312202]; National committee Development and Innovation of China
   [CNGI-04-15-5A-2]
FX The workof this paper is supported by National Science Foundation of
   China (70531020), sub-item of National 973 project (2002CB312202) and
   National committee Development and Innovation of China(CNGI-04-15-5A-2).
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PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 1810-7869
BN 978-1-4244-1075-0
J9 IEEE INT C NETW SENS
PY 2007
BP 846
EP +
PG 2
WC Automation & Control Systems; Computer Science, Theory & Methods;
   Engineering, Electrical & Electronic
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Automation & Control Systems; Computer Science; Engineering
GA BHB32
UT WOS:000252065800151
DA 2024-09-05
ER
PT J
AU Amon, J
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AF Amon, Julian
   Hornik, Kurt
TI Is it all bafflegab? - Linguistic and meta characteristics of research
   articles in prestigious economics journals
SO JOURNAL OF INFORMETRICS
LA English
DT Article
DE Research impact; SJR indicator; NLP; Readability; Gradient boosting;
   GLMLSS
ID R-PACKAGE; VARIABLE SELECTION; BETA REGRESSION; IMPACT; READABILITY;
   COLLABORATION; COMPLEXITY; MANAGEMENT; CITATIONS; RANKINGS
AB In competitive research environments, scholars have a natural interest to maximize the prestige associated with their scientific work. In order to identify factors that might help them address this goal more effectively, the scientometric literature has tried to link linguistic and meta characteristics of academic papers to the associated degree of scientific prestige, conceptualized as cumulative citation counts. In this paper, we take an alternative approach that instead understands scientific prestige in terms of the rankings of the journals that the articles appeared in, as such rankings are routinely used as surrogate research quality indicators. For the purpose of determining the most important drivers of suchlike prestige, we use state-of-the-art text mining tools to extract 344 interpretable features from a large corpus of over 200,000 journal articles in economics. We then estimate beta regression models to investigate the relationship between these predictors and a cross-sectionally standardized version of SCImago Journal Rank (SJR) in multiple topically homogeneous clusters. In so doing, we also reinvestigate the bafflegab theory, according to which more prestigious research papers tend to be less readable, in a methodologically novel way. Our results show the consistently most informative predictors to be associated with the length of the paper, the span of coreference chains in its full text, the deployment of a personal and moderately informal writing style, the "density" of the article in terms of sentences per page, international and institutional collaboration in research teams and the references cited in the paper. Moreover, we identify various linguistic intricacies that matter in the association between readability and scientific prestige, which suggest this relationship to be more complicated than previously assumed.
C1 [Amon, Julian; Hornik, Kurt] Vienna Univ Econ & Business Adm, Inst Stat & Math, Welthandelspl 1, A-1020 Vienna, Austria.
C3 Vienna University of Economics & Business
RP Amon, J (corresponding author), Vienna Univ Econ & Business Adm, Inst Stat & Math, Welthandelspl 1, A-1020 Vienna, Austria.
EM julian.amon@wu.ac.at
RI ; Hornik, Kurt/S-8548-2017
OI Amon, Julian/0000-0002-1677-8349; Hornik, Kurt/0000-0003-4198-9911
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NR 73
TC 1
Z9 1
U1 2
U2 23
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1751-1577
EI 1875-5879
J9 J INFORMETR
JI J. Informetr.
PD MAY
PY 2022
VL 16
IS 2
AR 101284
DI 10.1016/j.joi.2022.101284
EA APR 2022
PG 17
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 2S3GW
UT WOS:000821684900004
OA hybrid
DA 2024-09-05
ER
PT J
AU Muntean, M
   Militaru, FD
AF Muntean, Mihaela
   Militaru, Florin Daniel
TI Design Science Research Framework for Performance Analysis Using Machine
   Learning Techniques
SO ELECTRONICS
LA English
DT Article
DE design science research; performance analysis; machine learning;
   classification algorithms; clustering algorithms
AB We propose a methodological framework based on design science research for the design and development of data and information artifacts in data analysis projects, particularly managerial performance analysis. Design science research methodology is an artifact-centric creation and evaluation approach. Artifacts are used to solve real-life business problems. These are key elements of the proposed approach. Starting from the main current approaches of design science research, we propose a framework that contains artifact engineering aspects for a class of problems, namely data analysis using machine learning techniques. Several classification algorithms were applied to previously labelled datasets through clustering. The datasets contain values for eight competencies that define a manager's profile. These values were obtained through a 360 feedback evaluation. A set of metrics for evaluating the performance of the classifiers was introduced, and a general algorithm was described. Our initiative has a predominant practical relevance but also ensures a theoretical contribution to the domain of study. The proposed framework can be applied to any problem involving data analysis using machine learning techniques.
C1 [Muntean, Mihaela; Militaru, Florin Daniel] West Univ Timisoara, Fac Econ & Business Adm, Business Informat Syst Dept, Timisoara 300223, Romania.
C3 West University of Timisoara
RP Muntean, M (corresponding author), West Univ Timisoara, Fac Econ & Business Adm, Business Informat Syst Dept, Timisoara 300223, Romania.
EM mihaela.muntean@e-uvt.ro
RI Muntean, Mihaela/KII-9080-2024
OI Muntean, Mihaela/0000-0001-8428-4415
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NR 53
TC 3
Z9 3
U1 0
U2 13
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2079-9292
J9 ELECTRONICS-SWITZ
JI Electronics
PD AUG
PY 2022
VL 11
IS 16
AR 2504
DI 10.3390/electronics11162504
PG 18
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Physics
GA 4B6LF
UT WOS:000845885600001
OA gold
DA 2024-09-05
ER
PT C
AU Jaca-Madariaga, M
   Bilbao, EZ
   Rio-Belver, RM
   de la Torre, AR
AF Jaca-Madariaga, Maite
   Zarrabeitia Bilbao, Enara
   Rio-Belver, Rosa Maria
   Ruiz de la Torre, Aitor
BE Bautista-Valhondo, J
   Mateo-Doll, M
   Lusa, A
   Pastor-Moreno, R
TI Is the Impact of Management Research Predictable Through the Title? - A
   BERT Model to Find a Response
SO PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON INDUSTRIAL
   ENGINEERING AND INDUSTRIAL MANAGEMENT, ICIEIM-XXVII CONGRESO DE
   INGENIERIA DE ORGANIZACION, CIO 2023
SE Lecture Notes on Data Engineering and Communications Technologies
LA English
DT Proceedings Paper
CT 17th International Conference on Industrial Engineering and Industrial
   Management (ICIEIM) / 27th Organization Engineering Congress (CIO)
CY JUL 06-07, 2023
CL Barcelona, SPAIN
DE BERT; Management science; Text classification; Prediction; Research
   impact measurement
AB In academia, the impact a research paper can generate is a matter of concern to most researchers. Therefore, in this study a model is proposed to evaluate whether the impact is predictive by considering the title of the article. To measure this impact, the number of times an article is cited is taken into account. In addition, the aim is to create a tool that, when a new article title is introduced, will go through the designed model and output the impact it will have in five years' time. This paper focuses specifically on the management research field, so a dataset has been created with data downloaded belonging to this specific domain. This dataset has been labeled, preprocessed, tokenized, padded, masked and split into training and validation sets. The data were then trained and evaluated across a BERT model. The F1-score performance metric achieved is 0.56. Finally, some possible improvements are proposed.
C1 [Jaca-Madariaga, Maite; Zarrabeitia Bilbao, Enara] Univ Basque Country UPV EHU, Ind Org & Management Engn Dept, Fac Engn Bilbao, Bilbao 48013, Spain.
   [Rio-Belver, Rosa Maria; Ruiz de la Torre, Aitor] Univ Basque Country UPV EHU, Ind Org Management Engn Dept, Fac Engn Vitoria Gasteiz, Vitoria 01006, Spain.
C3 University of Basque Country; University of Basque Country
RP Jaca-Madariaga, M (corresponding author), Univ Basque Country UPV EHU, Ind Org & Management Engn Dept, Fac Engn Bilbao, Bilbao 48013, Spain.
EM maite.jacamadariaga@ehu.eus
CR Agarwal Ayush, 2021, 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS), P233, DOI 10.1109/ICACCS51430.2021.9441715
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   Web of Science, 2023, About us
NR 16
TC 0
Z9 0
U1 1
U2 1
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 2367-4512
BN 978-3-031-57995-0; 978-3-031-57996-7
J9 LECT NOTE DATA ENG
PY 2024
VL 206
BP 379
EP 384
DI 10.1007/978-3-031-57996-7_65
PG 6
WC Computer Science, Interdisciplinary Applications; Engineering,
   Industrial; Operations Research & Management Science
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Engineering; Operations Research & Management Science
GA BX2NG
UT WOS:001265092200065
DA 2024-09-05
ER
PT J
AU Karger, E
   Kureljusic, M
AF Karger, Erik
   Kureljusic, Marko
TI Using Artificial Intelligence for Drug Discovery: A Bibliometric Study
   and Future Research Agenda
SO PHARMACEUTICALS
LA English
DT Article
DE drug discovery; drug development; artificial intelligence; machine
   learning; deep learning; bibliometric study
ID SUPPORT VECTOR MACHINES; DESIGN SCIENCE RESEARCH; LEARNING APPLICATIONS;
   NEURAL-NETWORKS; PREDICTION; SYSTEMS; CITATIONS; EVOLUTION; BUSINESS;
   FIELD
AB Drug discovery is usually a rule-based process that is carefully carried out by pharmacists. However, a new trend is emerging in research and practice where artificial intelligence is being used for drug discovery to increase efficiency or to develop new drugs for previously untreatable diseases. Nevertheless, so far, no study takes a holistic view of AI-based drug discovery research. Given the importance and potential of AI for drug discovery, this lack of research is surprising. This study aimed to close this research gap by conducting a bibliometric analysis to identify all relevant studies and to analyze interrelationships among algorithms, institutions, countries, and funding sponsors. For this purpose, a sample of 3884 articles was examined bibliometrically, including studies from 1991 to 2022. We utilized various qualitative and quantitative methods, such as performance analysis, science mapping, and thematic analysis. Based on these findings, we furthermore developed a research agenda that aims to serve as a foundation for future researchers.
C1 [Karger, Erik] Univ Duisburg Essen, Informat Syst & Strateg IT Management, D-45141 Essen, Germany.
   [Kureljusic, Marko] Univ Duisburg Essen, Int Accounting, D-45141 Essen, Germany.
C3 University of Duisburg Essen; University of Duisburg Essen
RP Karger, E (corresponding author), Univ Duisburg Essen, Informat Syst & Strateg IT Management, D-45141 Essen, Germany.
EM erik.karger@uni-due.de
OI Kureljusic, Marko/0000-0003-4382-0669
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NR 127
TC 6
Z9 6
U1 12
U2 47
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1424-8247
J9 PHARMACEUTICALS-BASE
JI Pharmaceuticals
PD DEC
PY 2022
VL 15
IS 12
AR 1492
DI 10.3390/ph15121492
PG 22
WC Chemistry, Medicinal; Pharmacology & Pharmacy
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Pharmacology & Pharmacy
GA 7H9ZR
UT WOS:000903556000001
PM 36558943
OA Green Published, gold
DA 2024-09-05
ER
PT C
AU Xu, JZ
   Ren, AS
AF Xu, Jianzhong
   Ren, Jiasong
BE VandeWalle, B
   Song, Y
   Zlatanova, S
   Li, J
TI Research on Management Performance Evaluation of Intellectual Property
   for Research-Oriented Universities Based on Bayesian Networks
SO 3RD INTERNATIONAL CONFERENCE ON INFORMATION SYSTEMS FOR CRISIS RESPONSE
   AND MANAGEMENT & 4TH INTERNATIONAL SYMPOSIUM ON GEO-INFORMATION FOR
   DISASTER MANAGEMENT
LA English
DT Proceedings Paper
CT Joint Conference of the 3rd International Conference on Information
   Systems for Crisis Response and Management/4th International Symposium
   on Geo-Information for Disaster Management
CY AUG 04-06, 2008
CL Harbin Engn Univ, Harbin, PEOPLES R CHINA
HO Harbin Engn Univ
DE Bayesian networks; research-oriented university; intellectual property
   management; management performance evaluation
AB This paper designs a comprehensive evaluation index system of intellectual property management performance for research-oriented university based on intellectual property management features. Taking into account the qualitative characteristics and feasibility of the evaluation; we innovatively use Bayesian Networks to analyze the management performance evaluation of intellectual property for research-oriented universities. We end the study with the useful implications obtained from this study, including the problems needed to pay attention to in applying Bayesian Networks to conduct the management performance evaluation of intellectual property.
C1 [Xu, Jianzhong; Ren, Jiasong] Harbin Engn Univ, Sch Econ & Management, Harbin 150001, Peoples R China.
C3 Harbin Engineering University
EM XuJianZhongXJZ@163.com; Goodrjs@Yahoo.com
CR ALEXANDER C, 2004, OPERATIONAL RISK REG, P307
   BARNES T, 2003, EUROPEAN MANAGEMENT, P35
   CARACA J, 2002, HIGHER ED POLICY, P45
   DU XJ, 2004, R&D MANAGE, V2, P80
   HE JQ, 2002, SCI TECHNOLOGY REV, V3, P33
   HU XM, 2004, RES SOFTWARE RISK MA, P33
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   PENG YY, 2004, R&D MANAGE, V3, P110
   THOMAS H, 2004, IND INSIDER, P13
   ZHOU YM, 2006, J ZHENGZHOU U, V2, P16
NR 10
TC 0
Z9 0
U1 0
U2 3
PU HARBIN ENGINEERING UNIV
PI HARBIN
PA ADMINISTRATION BLDG, 145 NANTONG ST, HARBIN, 150001, PEOPLES R CHINA
BN 978-7-81133-251-3
PY 2008
BP 291
EP 296
PG 6
WC Computer Science, Information Systems; Computer Science,
   Interdisciplinary Applications; Management; Regional & Urban Planning;
   Public Administration; Urban Studies
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Business & Economics; Public Administration; Urban
   Studies
GA BIQ89
UT WOS:000262092900048
DA 2024-09-05
ER
PT J
AU Iqbal, S
   Saeed-Ul Hassan
   Aljohani, NR
   Alelyani, S
   Nawaz, R
   Bornmann, L
AF Iqbal, Sehrish
   Hassan, Saeed-Ul
   Aljohani, Naif Radi
   Alelyani, Salem
   Nawaz, Raheel
   Bornmann, Lutz
TI A decade of in-text citation analysis based on natural language
   processing and machine learning techniques: an overview of empirical
   studies
SO SCIENTOMETRICS
LA English
DT Article
DE In-text citation analysis; Citation context analysis; Citation content
   analysis; Citation classification; Citation sentiment analysis;
   Summarisation; Recommendation; Bibliometrics
ID SCIENTIFIC ARTICLES; CONTEXT ANALYSIS; LINGUISTIC PATTERNS; COUNTS
   MEASURE; SENTIMENT; CLASSIFICATION; PSYCHOLOGY; REFERENCES; RETRIEVAL;
   AUTHOR
AB In-text citation analysis is one of the most frequently used methods in research evaluation. We are seeing significant growth in citation analysis through bibliometric metadata, primarily due to the availability of citation databases such as the Web of Science, Scopus, Google Scholar, Microsoft Academic, and Dimensions. Due to better access to full-text publication corpora in recent years, information scientists have gone far beyond traditional bibliometrics by tapping into advancements in full-text data processing techniques to measure the impact of scientific publications in contextual terms. This has led to technical developments in citation classifications, citation sentiment analysis, citation summarisation, and citation-based recommendation. This article aims to narratively review the studies on these developments. Its primary focus is on publications that have used natural language processing and machine learning techniques to analyse citations.
C1 [Iqbal, Sehrish; Hassan, Saeed-Ul] Informat Technol Univ, Dept Comp Sci, 346-B,Ferozepur Rd, Lahore, Pakistan.
   [Aljohani, Naif Radi] King Abdulaziz Univ, Fac Comp & Informat Technol, Jeddah, Saudi Arabia.
   [Alelyani, Salem] King Khalid Univ, Ctr Artificial Intelligence CAI, POB 9004, Abha 61413, Saudi Arabia.
   [Alelyani, Salem] King Khalid Univ, Coll Comp Sci, POB 9004, Abha 61413, Saudi Arabia.
   [Nawaz, Raheel] Manchester Metropolitan Univ, Dept Operat Technol Events & Hospitality Manageme, Manchester, Lancs, England.
   [Bornmann, Lutz] Max Planck Gesell, Div Sci & Innovat Studies, Hofgartenstr 8, D-80539 Munich, Germany.
C3 King Abdulaziz University; King Khalid University; King Khalid
   University; Manchester Metropolitan University; Max Planck Society
RP Saeed-Ul Hassan (corresponding author), Informat Technol Univ, Dept Comp Sci, 346-B,Ferozepur Rd, Lahore, Pakistan.
EM sehrishiqbal@itu.edu.pk; saeed-ul-hassan@itu.edu.pk;
   nraljohani@kau.edu.sa; s.alelyani@kku.edu.sa; R.Nawaz@mmu.ac.uk;
   lutz.bornmann@gv.mpg.de
RI Nawaz, Raheel/AAX-5293-2021; Aljohani, Naif R/S-1109-2017; Bornmann,
   Lutz/A-3926-2008; Hassan, Saeed-Ul/G-1889-2016
OI Nawaz, Raheel/0000-0001-9588-0052; Hassan, Saeed-Ul/0000-0002-6509-9190
FU King Khalid University [R.G.P2/100/41]
FX The authors (Salem Alelyani and Saeed-Ul Hassan) are grateful for the
   financial support received from King Khalid University for this research
   Under Grant No. R.G.P2/100/41.
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NR 141
TC 21
Z9 24
U1 17
U2 132
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD AUG
PY 2021
VL 126
IS 8
BP 6551
EP 6599
DI 10.1007/s11192-021-04055-1
EA JUN 2021
PG 49
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA TM5GC
UT WOS:000664849500019
OA Green Submitted, Green Accepted
DA 2024-09-05
ER
PT J
AU Shen, Y
   Lei, C
AF Shen, Yue
   Lei, Cao
TI Research on evaluation of university education informatization level
   based on clustering technique
SO HELIYON
LA English
DT Article
DE Education informatization; Machine learning; Clustering; Reinforcement
   learning; Support vector machine
AB Today, the utilization of Information Technology tools is considered an inevitable path in the education system. In this regard, assessing the effective integration of Information Technology tools in the educational system holds significant importance. This process can be automated using artificial intelligence techniques, which are the subject of the current study. In this research, initially, a set of 14 indicators related to levels of Education Informatization (EI) in higher education is introduced. Subsequently, a clustering-based strategy is proposed to rank the indicators and determine an optimal subset of these features. Based on this framework, it is demonstrated that using 11 indicators related to educational behaviors can achieve the highest accuracy in evaluating EI levels. The proposed approach employs a group of Support Vector Machines (SVMs) for EI level assessment, where classifier hyperparameters are tuned using reinforcement learning strategy. The performance of the proposed method is evaluated on real-world data and compared with previous works. The results indicate that the proposed method can assess EI levels in universities with an average accuracy of 93.64 %, outperforming compared methods by at least 4.09 %.
C1 [Shen, Yue] Jiangsu Food & Pharmaceut Sci Coll, Huaian 223003, Jiangsu, Peoples R China.
   [Lei, Cao] China West Normal Univ, Educ Coll, Nanchong 637000, Sichuan, Peoples R China.
C3 Jiangsu Food & Pharmaceutical Science College; China West Normal
   University
RP Shen, Y (corresponding author), Jiangsu Food & Pharmaceut Sci Coll, Huaian 223003, Jiangsu, Peoples R China.; Lei, C (corresponding author), China West Normal Univ, Educ Coll, Nanchong 637000, Sichuan, Peoples R China.
EM 20163014@jsfpc.edu.cn; caolei115@cwnu.edu.cn
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NR 31
TC 0
Z9 0
U1 11
U2 11
PU CELL PRESS
PI CAMBRIDGE
PA 50 HAMPSHIRE ST, FLOOR 5, CAMBRIDGE, MA 02139 USA
EI 2405-8440
J9 HELIYON
JI Heliyon
PD FEB 29
PY 2024
VL 10
IS 4
AR e25215
DI 10.1016/j.heliyon.2024.e25215
EA FEB 2024
PG 14
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA KZ7S9
UT WOS:001183863100001
PM 38370245
OA gold, Green Published
DA 2024-09-05
ER
PT C
AU Zhu, XP
   Ban, ZJ
AF Zhu, XinPing
   Ban, ZhiJie
BE Barolli, L
   Takizawa, M
   Enokido, T
   Ogiela, MR
   Javaid, N
TI Citation Count Prediction Based on Academic Network Features
SO PROCEEDINGS 2018 IEEE 32ND INTERNATIONAL CONFERENCE ON ADVANCED
   INFORMATION NETWORKING AND APPLICATIONS (AINA)
SE International Conference on Advanced Information Networking and
   Applications
LA English
DT Proceedings Paper
CT 32nd IEEE International Conference on Advanced Information Networking
   and Applications (AINA)
CY MAY 16-18, 2018
CL Krakow, POLAND
DE citation count prediction; academic social network; feature selection
ID JOURNALS IMPACT FACTOR; PUBLICATION
AB Citation count is an important factor to measure the influence of academic publications. Identifying future citation count in advance can help scientists to find references and research area. There are many academic network features which are related to citation count. However, these features have not been completely explored in the existing studies. In this paper, we propose a citation count prediction model based on academic network features. Firstly, some important features are introduced and analyzed in detail. Then, we verify the importance of each feature and use a neural network model to select a set of optimal features. Finally, we present several machine learning methods and one multiple linear regression strategy to predict a paper's future citation. Experimental results on real datasets demonstrate that our model significantly outperforms the baseline method.
C1 [Zhu, XinPing; Ban, ZhiJie] Inner Mongolia Univ, Sch Comp Sci, Hohhot, Peoples R China.
C3 Inner Mongolia University
RP Zhu, XP (corresponding author), Inner Mongolia Univ, Sch Comp Sci, Hohhot, Peoples R China.
EM zhuxinping1993@163.com; banzhijie@imu.edu.cn
FU Natural Science Foundation of China [61662053]
FX We thank the anonymous reviewers for their constructive comments. This
   work was supported by the Natural Science Foundation of China (Grant No.
   61662053). We thank Jie Tang for providing the basic data set. The
   corresponding author is Zhijie Ban in this paper.
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NR 32
TC 12
Z9 13
U1 1
U2 14
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 1550-445X
BN 978-1-5386-2195-0
J9 INT CON ADV INFO NET
PY 2018
BP 534
EP 541
DI 10.1109/AINA.2018.00084
PG 8
WC Computer Science, Hardware & Architecture; Engineering, Electrical &
   Electronic
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Engineering
GA BL7AK
UT WOS:000454817500071
DA 2024-09-05
ER
PT J
AU Dong, XL
   Xu, JH
   Bu, Y
   Zhang, CW
   Ding, Y
   Hu, BB
   Ding, Y
AF Dong, Xianlei
   Xu, Jiahui
   Bu, Yi
   Zhang, Chenwei
   Ding, Ying
   Hu, Beibei
   Ding, Yang
TI Beyond correlation: Towards matching strategy for causal inference in
   Information Science
SO JOURNAL OF INFORMATION SCIENCE
LA English
DT Article
DE Causal inference; citation analysis; matching; scientometrics
ID INSTRUMENTAL VARIABLES ESTIMATION; RANDOMIZED CLINICAL-TRIALS; CITATION
   IMPACT; PROPENSITY; RISK; PERSPECTIVE
AB Correlation has become a fundamental method for information science. However, correlations are limited in making concrete decisions. In this article, we detail how causal inference could be utilised in the field of information science. There are six main steps of implementing matching for causal inference, namely, selecting candidate control variables, determining control variables, calculating similarities among all samples, forming control group, examining the performance of control group and estimating causal effects. As an example, this article applies causal inference to investigate whether Nobel Physics award increases the after-award citations. The method is presented in a step-by-step manner so that researchers can reproduce our analysis in the future.
C1 [Dong, Xianlei; Xu, Jiahui; Hu, Beibei; Ding, Yang] Shandong Normal Univ, Jinan, Peoples R China.
   [Bu, Yi] Peking Univ, 5 Yiheyuan Rd, Beijing 100871, Peoples R China.
   [Zhang, Chenwei] Univ Hong Kong, Hong Kong, Peoples R China.
   [Ding, Ying] Univ Texas Austin, Austin, TX 78712 USA.
C3 Shandong Normal University; Peking University; University of Hong Kong;
   University of Texas System; University of Texas Austin
RP Bu, Y (corresponding author), Peking Univ, 5 Yiheyuan Rd, Beijing 100871, Peoples R China.
EM buyi@pku.edu.cn
RI Bu, Yi/B-4964-2018; xu, jiawen/KGK-4238-2024; Xu, Jiawen/JEF-5028-2023;
   xu, jia/GSD-6347-2022
OI Bu, Yi/0000-0003-2549-4580; Zhang, Chenwei/0000-0002-0488-4603; Ding,
   Yang/0000-0002-0683-2655
FU programmes of National Natural Science Foundation of China [71904110];
   Humanities and Social Science Foundation of Ministry of Education of the
   People's Republic of China [19YJCGJW014]; National Natural Science
   Foundation of China [71701115]; China Postdoctoral Science Foundation
   [2017M610440]; National Nature Science Foundation of Shandong Province
   [ZR2017MF058]
FX The author(s) disclosed receipt of the following financial support for
   the research, authorship and/or publication of this article: The authors
   are grateful to two anonymous reviewers for their constructive comments.
   X.D. thanks the programmes of National Natural Science Foundation of
   China (Grant No. 71904110) and Humanities and Social Science Foundation
   of Ministry of Education of the People's Republic of China (Grant No.
   19YJCGJW014) for their support. B.H. thanks the programmes of National
   Natural Science Foundation of China (Grant No. 71701115), China
   Postdoctoral Science Foundation (Grant No. 2017M610440) and the National
   Nature Science Foundation of Shandong Province (Grant No. ZR2017MF058)
   for their support.
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NR 64
TC 6
Z9 6
U1 6
U2 86
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 0165-5515
EI 1741-6485
J9 J INF SCI
JI J. Inf. Sci.
PD DEC
PY 2022
VL 48
IS 6
BP 735
EP 748
AR 0165551520979868
DI 10.1177/0165551520979868
EA JUN 2021
PG 14
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 5M6KX
UT WOS:000667859600001
DA 2024-09-05
ER
PT C
AU ElAbdi, M
   Smine, B
   Ben Yahia, S
AF ElAbdi, Mariem
   Smine, Boutheina
   Ben Yahia, Sadok
GP IEEE
TI DFBICA: A New Distributed Approach For Sentiment Analysis of
   Bibliographic Citations
SO 2018 12TH INTERNATIONAL CONFERENCE ON RESEARCH CHALLENGES IN INFORMATION
   SCIENCE (RCIS)
SE International Conference on Research Challenges in Information Science
LA English
DT Proceedings Paper
CT 12th International Conference on Research Challenges in Information
   Science (RCIS)
CY MAY 29-31, 2018
CL Nantes, FRANCE
DE Sentiment analysis; Scientific paper; Word2vec; MapReduce
AB Sentiment analysis of citations in scientific papers is a new and interesting research area. In this paper, we focus on the problem of automatic identification of positive and negative sentiment polarity of citations in scientific papers. In this work, we conducted empirical research to investigate the classification of positive and negative citations. It is based on word vectors as a feature space, to which the examined citation context was mapped to. In order to handle with the huge amount of data, we have implemented our proposed approach in a distributed manner according to MapReduce paradigm through the Hadoop framework.
C1 [ElAbdi, Mariem; Ben Yahia, Sadok] Univ Tunis El Manar, Fac Sci Tunis, LIPAH LR 11ES14, Tunis 2092, Tunisia.
   [Smine, Boutheina] Univ Carthage, Fac Sci Econ & Gest Nabeul, Tunis 1054, Tunisia.
C3 Universite de Tunis-El-Manar; Faculte des Sciences de Tunis (FST);
   Universite de Carthage
RP ElAbdi, M (corresponding author), Univ Tunis El Manar, Fac Sci Tunis, LIPAH LR 11ES14, Tunis 2092, Tunisia.
EM elabdi.mariem@gmail.com; boutheina.smine@yahoo.fr;
   sadok.benyahia@fst.rnu.tn
RI BEN YAHIA, Sadok/C-8239-2019
OI BEN YAHIA, Sadok/0000-0001-8939-8948
CR Abu-Jbara A., 2013, NAACL, P596
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NR 13
TC 4
Z9 4
U1 0
U2 5
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2151-1357
BN 978-1-5386-6517-6
J9 INT CONF RES CHAL
PY 2018
PG 6
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Information Science & Library Science
GA BL3WY
UT WOS:000450067100033
DA 2024-09-05
ER
PT J
AU da Silva, RL
   de Sousa, BP
AF da Silva, Renata Lima
   de Sousa, Brisa Pozzi
TI Artificial Intelligence and ChatGPT: perspectives and challenges for
   Bibliographic Classification
SO REVISTA IBERO-AMERICANA DE CIENCIA DA INFORMACAO
LA English
DT Article
DE Library Science; Bibliographic Classification; ChatGPT; Artificial
   Intelligence
AB It presents perspectives and challenges of applying Artificial Intelligence in the field of Library Science. To this end, it focuses on the practice of Bibliographic Classification and conducts a comparative study between the results obtained through classification performed by a human and by ChatGPT, the chatbot tool used in this analysis. The study is based on the Dewey Decimal Classification and the Universal Decimal Classification. The results revealed significant divergences between both methods, exposing mistakes and hallucinations of the generative AI model GPT-3.5, on which the current free version of ChatGPT is based. Challenges and limitations to the effective applicability of ChatGPT in the context of Bibliographic Classification are highlighted, as well as the relevance of the librarian in the practice of classification and thematic analysis, given the importance of mental exercise and critical analysis in interpreting the subject to be classified. It should be noted that the study does not conduct a comprehensive comparative analysis. The need for future, more comprehensive investigations with different perspectives from human experts and AI models is emphasized.
C1 [da Silva, Renata Lima; de Sousa, Brisa Pozzi] Univ Fed Estado Rio De Janeiro, Curso Bibliotecon, Rio De Janeiro, RJ, Brazil.
C3 Universidade Federal do Estado do Rio de Janeiro
RP da Silva, RL (corresponding author), Univ Fed Estado Rio De Janeiro, Curso Bibliotecon, Rio De Janeiro, RJ, Brazil.
EM limarenataa@gmail.com; brisa.pozzi@unirio.br
CR Almeida Vergilio, 2023, IA Generativa: oportunidades, riscos e governanca
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NR 23
TC 0
Z9 0
U1 31
U2 31
PU UNIV BRASILIA, DEPT CIENCIA INFORMACAO
PI BRASILIA
PA CAIXA POSTAL 15-3011, BRASILIA, DF 00000, BRAZIL
SN 1983-5213
J9 REV IBERI-AM CIENC I
JI Rev. Ibero-Am. Cienc. Inf.
PD JAN-APR
PY 2024
VL 17
IS 1
BP 44
EP 65
DI 10.26512/rici.v17.n1.2024.50429
PG 22
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA MQ0I8
UT WOS:001194970100006
OA gold
DA 2024-09-05
ER
PT J
AU Hassan, SU
   Saleem, A
   Soroya, SH
   Safder, I
   Iqbal, S
   Jamil, S
   Bukhari, F
   Aljohani, NR
   Nawaz, R
AF Hassan, Saeed-Ul
   Saleem, Aneela
   Soroya, Saira Hanif
   Safder, Iqra
   Iqbal, Sehrish
   Jamil, Saqib
   Bukhari, Faisal
   Aljohani, Naif Radi
   Nawaz, Raheel
TI Sentiment analysis of tweets through Altmetrics: A machine learning
   approach
SO JOURNAL OF INFORMATION SCIENCE
LA English
DT Article
DE Altmetrics; comparative analysis; machine learning; sentiment analysis;
   Twitter
ID SOCIAL MEDIA; TWITTER; NETWORK
AB The purpose of the study is to (a) contribute to annotating an Altmetrics dataset across five disciplines, (b) undertake sentiment analysis using various machine learning and natural language processing-based algorithms, (c) identify the best-performing model and (d) provide a Python library for sentiment analysis of an Altmetrics dataset. First, the researchers gave a set of guidelines to two human annotators familiar with the task of related tweet annotation of scientific literature. They duly labelled the sentiments, achieving an inter-annotator agreement (IAA) of 0.80 (Cohen's Kappa). Then, the same experiments were run on two versions of the dataset: one with tweets in English and the other with tweets in 23 languages, including English. Using 6388 tweets about 300 papers indexed in Web of Science, the effectiveness of employed machine learning and natural language processing models was measured by comparing with well-known sentiment analysis models, that is, SentiStrength and Sentiment140, as the baseline. It was proved that Support Vector Machine with uni-gram outperformed all the other classifiers and baseline methods employed, with an accuracy of over 85%, followed by Logistic Regression at 83% accuracy and Naive Bayes at 80%. The precision, recall and F1 scores for Support Vector Machine, Logistic Regression and Naive Bayes were (0.89, 0.86, 0.86), (0.86, 0.83, 0.80) and (0.85, 0.81, 0.76), respectively.
C1 [Hassan, Saeed-Ul; Saleem, Aneela; Safder, Iqra; Iqbal, Sehrish] Informat Technol Univ, Lahore, Pakistan.
   [Soroya, Saira Hanif] Univ Punjab, Dept Informat Management, Lahore 54590, Pakistan.
   [Jamil, Saqib] Univ Okara, Dept Management Sci, Okara, Pakistan.
   [Bukhari, Faisal] Univ Punjab, Punjab Univ Coll Informat Technol PUCIT, Lahore, Pakistan.
   [Aljohani, Naif Radi] King Abdulaziz Univ, Fac Comp & Informat Technol, Jeddah, Saudi Arabia.
   [Nawaz, Raheel] Manchester Metropolitan Univ, Sch Comp Sci, Manchester, Lancs, England.
C3 University of Punjab; University of Punjab; King Abdulaziz University;
   Manchester Metropolitan University
RP Soroya, SH (corresponding author), Univ Punjab, Dept Informat Management, Lahore 54590, Pakistan.
EM sairasroya@gmail.com
RI Aljohani, Naif R/S-1109-2017; Nawaz, Raheel/AAX-5293-2021; Safder,
   Iqra/JXN-8069-2024; Hassan, Saeed-Ul/G-1889-2016
OI Nawaz, Raheel/0000-0001-9588-0052; Hassan, Saeed-Ul/0000-0002-6509-9190;
   soroya, Dr. Saira Hanif/0000-0002-8153-1529
FU Higher Education Commission; National Centre for Big Data and Cloud
   Computing (NCBC)
FX The author(s) disclosed receipt of the following financial support for
   the research, authorship, and/or publication of this article: The author
   Saeed-Ul Hassan is grateful for the financial support received from the
   Higher Education Commission for this research for Crime Investigation
   and Prevention Lab (CIPL) affiliated with National Centre for Big Data
   and Cloud Computing (NCBC).
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NR 61
TC 18
Z9 18
U1 7
U2 77
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 0165-5515
EI 1741-6485
J9 J INF SCI
JI J. Inf. Sci.
PD DEC
PY 2021
VL 47
IS 6
BP 712
EP 726
AR 0165551520930917
DI 10.1177/0165551520930917
EA JUN 2020
PG 15
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA WW0WF
UT WOS:000550209800001
OA Green Accepted
DA 2024-09-05
ER
PT C
AU Guo, MR
AF Guo Meirong
BE Su, Y
   Hakim, L
TI Research on Journal Evaluation Based on Principal Component Analysis
SO PROCEEDINGS OF THE 5TH INTERNATIONAL CONFERENCE ON COOPERATION AND
   PROMOTION OF INFORMATION RESOURCES IN SCIENCE AND TECHNOLOGY(COINFO 10)
LA English
DT Proceedings Paper
CT 5th International Conference on Cooperation and Promotion of Information
   Resources in Science and Technology
CY NOV 27-29, 2010
CL Beijing, PEOPLES R CHINA
DE principal component analysis; journal evaluation; weight; SPSS
AB Based on the Journal Citation measurement Index and data provided by the expand Chinese Journal Citation Reports 2009, using principal component analysis to reduce the number of related indicators into a few weak correlation indicators, solved the problem of distortion index weights. Put these composite indicators in accordance with the linear combination of weights, and ultimately evaluate the scientific and technical journals.
C1 Inst Sci & Tech Informat China, Beijing 100038, Peoples R China.
RP Guo, MR (corresponding author), Inst Sci & Tech Informat China, Beijing 100038, Peoples R China.
EM meirong1750@126.com
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   [贺颖 HE Ying], 2007, [中国软科学, China Soft Science], P107
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NR 14
TC 0
Z9 0
U1 0
U2 5
PU SCI RES PUBL, INC-SRP
PI IRVIN
PA 5005 PASEO SEGOVIA, IRVIN, CA 92603-3334 USA
BN 978-1-935068-43-3
PY 2010
BP 711
EP 716
PG 6
WC Computer Science, Interdisciplinary Applications; Engineering,
   Multidisciplinary; Multidisciplinary Sciences
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Engineering; Science & Technology - Other Topics
GA BST09
UT WOS:000285731100138
DA 2024-09-05
ER
PT J
AU Ferrell, B
   Raskin, SE
   Zimmerman, EB
AF Ferrell, Brian
   Raskin, Sarah E.
   Zimmerman, Emily B.
TI Calibrating a Transformer-Based Model's Confidence on Community-Engaged
   Research Studies: Decision Support Evaluation Study
SO JMIR FORMATIVE RESEARCH
LA English
DT Article
DE explainable artificial intelligence; XAI; Bidirectional Encoder
   Representations From Transformers; BERT; transformer-based models; text
   classification; community engagement; community-engaged research; deep
   learning; decision support; trust; confidence
ID DEEP LEARNING-PERFORMANCE; SCIENCE
AB Background: Deep learning offers great benefits in classification tasks such as medical imaging diagnostics or stock trading, especially when compared with human-level performances, and can be a viable option for classifying distinct levels within community-engaged research (CEnR). CEnR is a collaborative approach between academics and community partners with the aim of conducting research that is relevant to community needs while incorporating diverse forms of expertise. In the field of deep learning and artificial intelligence (AI), training multiple models to obtain the highest validation accuracy is common practice; however, it can overfit toward that specific data set and not generalize well to a real-world population, which creates issues of bias and potentially dangerous algorithmic decisions. Consequently, if we plan on automating human decision-making, there is a need for creating techniques and exhaustive evaluative processes for these powerful unexplainable models to ensure that we do not incorporate and blindly trust poor AI models to make real-world decisions.Objective: We aimed to conduct an evaluation study to see whether our most accurate transformer-based models derived from previous studies could emulate our own classification spectrum for tracking CEnR studies as well as whether the use of calibrated confidence scores was meaningful.Methods: We compared the results from 3 domain experts, who classified a sample of 45 studies derived from our university's institutional review board database, with those from 3 previously trained transformer-based models, as well as investigated whether calibrated confidence scores can be a viable technique for using AI in a support role for complex decision-making systems.Results: Our findings reveal that certain models exhibit an overestimation of their performance through high confidence scores, despite not achieving the highest validation accuracy.Conclusions: Future studies should be conducted with larger sample sizes to generalize the results more effectively. Although our study addresses the concerns of bias and overfitting in deep learning models, there is a need to further explore methods that allow domain experts to trust our models more. The use of a calibrated confidence score can be a misleading metric when determining our AI model's level of competency.(JMIR Form Res 2023;7:e41516) doi: 10.2196/41516
C1 [Ferrell, Brian] Virginia Commonwealth Univ, Richmond, VA USA.
   [Raskin, Sarah E.] Virginia Commonwealth Univ, L Douglas Wilder Sch Govt & Publ Affairs, Richmond, VA USA.
   [Zimmerman, Emily B.] Virginia Commonwealth Univ, Ctr Soc & Hlth, Richmond, VA USA.
   [Ferrell, Brian] Virginia Commonwealth Univ, 907 Floyd Ave, Richmond, VA 23284 USA.
C3 Virginia Commonwealth University; Virginia Commonwealth University;
   Virginia Commonwealth University; Virginia Commonwealth University
RP Ferrell, B (corresponding author), Virginia Commonwealth Univ, 907 Floyd Ave, Richmond, VA 23284 USA.
EM ferrellbj@vcu.edu
RI Raskin, Sarah/AAC-2435-2022
OI Raskin, Sarah/0000-0002-1652-6678; Zimmerman, Emily/0000-0003-2678-6657;
   Ferrell, Brian/0000-0003-2301-4926
FU National Institutes of Health (National Center for Advancing
   Translational Sciences Clinical and Translational Science Awards
   Program) [UL1TR002649]; Wright Center for Clinical and Translational
   Research at Virginia Commonwealth University
FX Acknowledgments The National Institutes of Health (National Center for
   Advancing Translational Sciences Clinical and Translational Science
   Awards Program UL1TR002649) and the Wright Center for Clinical and
   Translational Research at Virginia Commonwealth University have
   supported our work.
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NR 41
TC 1
Z9 1
U1 0
U2 1
PU JMIR PUBLICATIONS, INC
PI TORONTO
PA 130 QUEENS QUAY East, Unit 1100, TORONTO, ON M5A 0P6, CANADA
EI 2561-326X
J9 JMIR FORM RES
JI JMIR Form. Res.
PY 2023
VL 7
DI 10.2196/41516
PG 13
WC Health Care Sciences & Services; Medical Informatics
WE Emerging Sources Citation Index (ESCI)
SC Health Care Sciences & Services; Medical Informatics
GA H8MD9
UT WOS:000998428700031
PM 36939830
OA Green Published, gold
DA 2024-09-05
ER
PT J
AU Elgendi, M
AF Elgendi, Mohamed
TI Characteristics of a Highly Cited Article: A Machine Learning
   Perspective
SO IEEE ACCESS
LA English
DT Article
DE Natural language processing; text mining; artificial intelligence;
   scientific writing; citation analysis; bibliometrics
ID TITLES; NUMBER
AB Machine learning (ML) is a fast-growing topic that enables the extraction of patterns from varying types of datasets, ranging from medical data to financial data. However, the application of the ML methodology to understand the key characteristics of highly cited research articles has not been thoroughly investigated, despite the potential practical guidance that ML can provide for researchers during the publication process. To address this research gap, an ML algorithm known as principal component (PC) analysis is used to detect patterns in highly and lowly cited papers. In this paper, eight features (number of citations, number of views, number of characters with no spaces, number of figures, number of tables, number of equations, number of authors, and title length) are extracted from highly and lowly cited papers, leading to eight PCs (PC1-PC8). PC1 shows that the numbers of citations are positively correlated with the character count and negatively correlated with the title length. PC2 shows that the number of tables is positively correlated with the title length. PC3 shows that the number of figures is positively correlated with the number of tables. PC4-PC8 rank the importance of individual features in the descending order: number of equations, number of characters with no spaces, number of figures, number of views, and then the number of authors. The results of the ML analysis provide interesting and valuable tips for researchers, students, and all academic and non-academic writers who are seeking to improve their citation rates.
C1 [Elgendi, Mohamed] Univ British Columbia, Fac Med, Vancouver, BC V6T 1Z3, Canada.
   [Elgendi, Mohamed] BC Childrens & Womens Hosp, Vancouver, BC V6H 3N1, Canada.
   [Elgendi, Mohamed] Univ British Columbia, Sch Elect & Comp Engn, Vancouver, BC V6T 1Z4, Canada.
C3 University of British Columbia; University of British Columbia;
   University of British Columbia
RP Elgendi, M (corresponding author), Univ British Columbia, Fac Med, Vancouver, BC V6T 1Z3, Canada.; Elgendi, M (corresponding author), BC Childrens & Womens Hosp, Vancouver, BC V6H 3N1, Canada.; Elgendi, M (corresponding author), Univ British Columbia, Sch Elect & Comp Engn, Vancouver, BC V6T 1Z4, Canada.
EM moe.elgendi@gmail.com
RI Elgendi, Mohamed/I-8596-2016
OI Elgendi, Mohamed/0000-0003-1831-0202
FU Mining for Miracles, BC Children's Hospital Foundation, Vancouver,
   British Columbia, Canada
FX The author is grateful for the support from Mining for Miracles, BC
   Children's Hospital Foundation, Vancouver, British Columbia, Canada.
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NR 20
TC 17
Z9 18
U1 1
U2 35
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2019
VL 7
BP 87977
EP 87986
DI 10.1109/ACCESS.2019.2925965
PG 10
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering; Telecommunications
GA IK7YT
UT WOS:000476810500030
OA gold
DA 2024-09-05
ER
PT J
AU Baba, T
   Baba, K
   Ikeda, D
AF Baba, Takahiro
   Baba, Kensuke
   Ikeda, Daisuke
TI Citation Count Prediction using Abstracts
SO JOURNAL OF WEB ENGINEERING
LA English
DT Article
DE Citation count prediction; Document classification; Text analysis;
   Machine learning
AB Researchers are expected to find previous literature that is related to their research and potentially has a scientific impact from among a large number of publications. This paper addresses the problem of predicting the citation count of each research paper, that is, the number of citations from other papers to that paper. Previous literature related to the problem claims that the textual data of papers do not deeply affect the prediction compared with data about the authors and venues of publication. In contrast, the authors of this paper detected the citation counts of papers using only the paper abstracts. Additionally, they investigated the effect of technical terms used in the abstracts on the detection. They classified abstracts of papers with high and low citation counts and applied the classification to the abstracts modified by hiding the technical terms used in them. The results of their experiments indicate that the high and low of citation counts of research papers can be detected using their abstracts, and the effective features used in the prediction are related to the trend of research topics.
C1 [Baba, Takahiro] Kyushu Univ, Fukuoka, Fukuoka 8190395, Japan.
   [Ikeda, Daisuke] Kyushu Univ, Dept Informat, Fukuoka, Fukuoka 8190395, Japan.
   [Ikeda, Daisuke] Kyushu Univ, Comp Ctr, Fukuoka, Fukuoka 8190395, Japan.
   [Baba, Kensuke] Fujitsu Labs, Artificial Intelligence Lab, Kawasaki, Kanagawa 2118588, Japan.
C3 Kyushu University; Kyushu University; Kyushu University; Fujitsu Ltd;
   Fujitsu Laboratories Ltd
RP Baba, T (corresponding author), Kyushu Univ, Fukuoka, Fukuoka 8190395, Japan.
EM baba.takahiro.414@m.kyushu-u.ac.jp
RI Baba, Kensuke/J-8426-2017
OI Baba, Kensuke/0000-0002-8118-0175
FU JSPS KAKENHI [19K12133]; Grants-in-Aid for Scientific Research
   [19K12133] Funding Source: KAKEN
FX We thank Kimberly Moravec, PhD, from Edanz Group (www.edanzedi
   ting.com/ac) for editing a draft of this manuscript. This work was
   supported by JSPS KAKENHI Grant Number 19K12133.
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NR 12
TC 4
Z9 4
U1 2
U2 24
PU RIVER PUBLISHERS
PI GISTRUP
PA ALSBJERGVEJ 10, GISTRUP, 9260, DENMARK
SN 1540-9589
EI 1544-5976
J9 J WEB ENG
JI J. Web Eng.
PD JAN
PY 2019
VL 18
IS 1-3
BP 207
EP 228
DI 10.13052/jwe1540-9589.18136
PG 22
WC Computer Science, Software Engineering; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA IF0EP
UT WOS:000472749400006
OA Bronze
DA 2024-09-05
ER
PT J
AU Kim, H
   Jang, H
AF Kim, Huijae
   Jang, Hoon
TI Predicting research projects' output using machine learning for tailored
   projects management
SO ASIAN JOURNAL OF TECHNOLOGY INNOVATION
LA English
DT Article
DE Research and development; research project output; prediction;
   classification; artificial intelligence
ID RESEARCH PRODUCTIVITY; DELPHI METHOD; SELECTION; IMPACT; METHODOLOGY;
   PERFORMANCE
AB With the increasing interest and investment in research and development (R & D), the need for more efficient research project management has grown. Accordingly, we built prediction models to classify research projects that were expected to show excellent research output. Specifically, we applied five machine learning techniques to build prediction models. In an empirical analysis of data on research projects funded by South Korea over the last five years (2014-2018), we found that the automated machine learning model (autoML), in which the machine builds the most suitable learning model, shows relatively greater and more robust performance than models based on other techniques. We also established that research funding and project type played the most important roles in predicting excellent research projects. This study is significant because it shows the need for a paradigm shift in building an evidence-based project management system by verifying the utility and applicability of a data-driven approach in R & D project management.
C1 [Kim, Huijae] Korea Adv Inst Sci & Technol, Dept Ind & Syst Engn, Daejeon, South Korea.
   [Jang, Hoon] Korea Univ, Coll Global Business, Sejong Campus, Sejong, South Korea.
   [Jang, Hoon] Korea Univ, Coll Global Business, Sejong Campus,2511 Sejong Ro, Sejong 30019, South Korea.
C3 Korea Advanced Institute of Science & Technology (KAIST); Korea
   University; Korea University
RP Jang, H (corresponding author), Korea Univ, Coll Global Business, Sejong Campus,2511 Sejong Ro, Sejong 30019, South Korea.
EM hoonjang@korea.ac.kr
FU National Research Foundation of Korea (NRF) - Korean government
   [2019R1F1A1063365]
FX This work was supported by the National Research Foundation of Korea
   (NRF) grant funded by the Korean government [grant number
   2019R1F1A1063365].
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NR 45
TC 0
Z9 0
U1 2
U2 14
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1976-1597
EI 2158-6721
J9 ASIAN J TECHNOL INNO
JI Asian J. Technol. Innov.
PD MAY 3
PY 2024
VL 32
IS 2
BP 346
EP 363
DI 10.1080/19761597.2023.2243611
EA AUG 2023
PG 18
WC Business; Economics
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA YS4U6
UT WOS:001043184500001
DA 2024-09-05
ER
PT J
AU Zhang, BJ
   Fan, T
AF Zhang, Bijun
   Fan, Ting
TI Knowledge structure and emerging trends in the application of deep
   learning in genetics research: A bibliometric analysis [2000-2021]
SO FRONTIERS IN GENETICS
LA English
DT Article
DE deep learning; machine learning; genetics; bibliometric; knowledge graph
ID MODELS
AB Introduction: Deep learning technology has been widely used in genetic research because of its characteristics of computability, statistical analysis, and predictability. Herein, we aimed to summarize standardized knowledge and potentially innovative approaches for deep learning applications of genetics by evaluating publications to encourage more research.
   Methods: The Science Citation Index Expanded (TM) (SCIE) database was searched for deep learning applications for genomics-related publications. Original articles and reviews were considered. In this study, we derived a clustered network from 69,806 references that were cited by the 1,754 related manuscripts identified. We used CiteSpace and VOSviewer to identify countries, institutions, journals, co-cited references, keywords, subject evolution, path, current characteristics, and emerging topics.
   Results: We assessed the rapidly increasing publications concerned about deep learning applications of genomics approaches and identified 1,754 articles that published reports focusing on this subject. Among these, a total of 101 countries and 2,487 institutes contributed publications, The United States of America had the most publications (728/1754) and the highest h-index, and the US has been in close collaborations with China and Germany. The reference clusters of SCI articles were clustered into seven categories: deep learning, logic regression, variant prioritization, random forests, scRNA-seq (single-cell RNA-seq), genomic regulation, and recombination. The keywords representing the research frontiers by year were prediction (2016-2021), sequence (2017-2021), mutation (2017-2021), and cancer (2019-2021).
   Conclusion: Here, we summarized the current literature related to the status of deep learning for genetics applications and analyzed the current research characteristics and future trajectories in this field. This work aims to provide resources for possible further intensive exploration and encourages more researchers to overcome the research of deep learning applications in genetics.
C1 [Zhang, Bijun] China Med Univ, Dept Clin Genet, Shengjing Hosp, Shenyang, Peoples R China.
   [Fan, Ting] China Med Univ, Sch Intelligent Med, Dept Comp, Shenyang, Peoples R China.
C3 China Medical University; China Medical University
RP Fan, T (corresponding author), China Med Univ, Sch Intelligent Med, Dept Comp, Shenyang, Peoples R China.
EM tfan@cmu.edu.cn
FU Shengjing Hospital [M0347]; Research on the application of genetic big
   data analysis in the prevention and control of newborn birth defects
   [2900021013-CMU-013]
FX This study was supported by the 345 Talent Project of Shengjing Hospital
   (M0347) and " Research on the application of genetic big data analysis
   in the prevention and control of newborn birth defects"
   (2900021013-CMU-013).
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NR 49
TC 2
Z9 2
U1 3
U2 17
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 1664-8021
J9 FRONT GENET
JI Front. Genet.
PD AUG 23
PY 2022
VL 13
AR 951939
DI 10.3389/fgene.2022.951939
PG 13
WC Genetics & Heredity
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Genetics & Heredity
GA 4V4IP
UT WOS:000859442400001
PM 36081985
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU Rossi, D
   van Rensburg, H
   Harreveld, RE
   Beer, C
   Clark, D
   Danaher, PA
AF Rossi, Dolene
   van Rensburg, Henriette
   Harreveld, R. E. (Bobby)
   Beer, Colin
   Clark, Damien
   Danaher, P. A.
TI Exploring a cross-institutional research collaboration and innovation:
   Deploying social software and Web 2.0 technologies to investigate online
   learning designs and interactions in two Australian universities
SO JOURNAL OF LEARNING DESIGN
LA English
DT Article
DE Cross-institutional collaboration; interactions; online learning design;
   research; social software; Web 2.0 technologies
AB One significant manifestation of the proposition of a "classroom without walls" is the online learning environments evident in most contemporary Australian universities. A key element of the effectiveness of those environments is the quality of the interactions that they foster. Planning and implementing rigorous research into that quality is crucial if these particular "classrooms without walls" are to deliver enhanced and sustained learning outcomes. This article explores selected aspects of a cross-institutional collaboration linking two Australian universities researching the quality of learning interactions in their online courses. In particular, the authors analyse the utility of the social software and Web 2.0 technologies that have been deployed to facilitate their collaborative research. Despite the constraints and tensions attendant on within-and cross-organisational learning, teaching and research activities, the article records evidence of a developing innovation in investigating both the online learning designs and the research project developed to evaluate the effectiveness and impact of those designs.
C1 [Rossi, Dolene] CQUniversity, Sch Nursing & Midwifery, Melbourne, Vic, Australia.
   [van Rensburg, Henriette; Danaher, P. A.] Univ So Queensland, Fac Educ, Toowoomba, Qld 4350, Australia.
   [Harreveld, R. E. (Bobby)] CQUniversity, Sch Educ, Melbourne, Vic, Australia.
   [Beer, Colin; Clark, Damien] CQUniversity, Off Learning & Teaching, Melbourne, Vic, Australia.
C3 Central Queensland University; University of Southern Queensland;
   Central Queensland University; Central Queensland University
RP Rossi, D (corresponding author), CQUniversity, Sch Nursing & Midwifery, Melbourne, Vic, Australia.
CR Anderson T, 2008, THEORY AND PRACTICE OF ONLINE LEARNING, 2ND EDITION, P1
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NR 36
TC 1
Z9 1
U1 0
U2 4
PU QUEENSLAND UNIV TECHNOLOGY
PI BRISBANE
PA GPO  BOX 2434, BRISBANE, QLD 4001, AUSTRALIA
SN 1832-8342
J9 J LEARN DES
JI J. Learn. Des.
PY 2015
VL 8
IS 3
BP 81
EP 91
PG 11
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA CZ4IC
UT WOS:000367065700013
DA 2024-09-05
ER
PT J
AU Zheng, ET
   Fang, ZC
   Fu, HZ
AF Zheng, Er -Te
   Fang, Zhichao
   Fu, Hui-Zhen
TI Is gold open access helpful for academic purification? A causal
   inference analysis based on retracted articles in biochemistry
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
DE Open access; Academic misconduct; Scientometrics; Retraction time lag;
   Post-retraction citation; Causal inference
ID PROPENSITY SCORE; JOURNALS; SCIENCE; REPRODUCIBILITY; PUBLICATIONS;
   CREDIBILITY; CITATIONS
AB The relationship between transparency and credibility has long been a subject of theoretical and analytical exploration within the realm of social sciences, and it has recently attracted increasing attention in the context of scientific research. Retraction serves as a pivotal mechanism in addressing concerns about research integrity. This study aims to empirically examining the relationship between open access level and the effectiveness of current mechanism, specifically academic purification centered on retracted articles. In this study, we used matching and Difference-in-Difference (DiD) methods to examine whether gold open access is helpful for academic purification in biochemistry field. We collected gold open access (Gold OA) and non -open access (non-OA) biochemistry retracted articles as the treatment group, and matched them with corresponding unretracted articles as the control group from 2005 to 2021 based on Web of Science and Retraction Watch database. The results showed that compared to non-OA, Gold OA is advantageous in reducing the retraction time of flawed articles, but does not demonstrate a significant advantage in reducing citations after retraction. This indicates that Gold OA may help expedite the detection and retraction of flawed articles, ultimately promoting the practice of responsible research.
C1 [Zheng, Er -Te; Fang, Zhichao] Renmin Univ China, Sch Informat Resource Management, Beijing, Peoples R China.
   [Fang, Zhichao] Leiden Univ, Ctr Sci & Technol Studies CWTS, Leiden, Netherlands.
   [Fu, Hui-Zhen] Zhejiang Univ, Sch Publ Affairs, Dept Informat Resources Management, Hangzhou, Peoples R China.
C3 Renmin University of China; Leiden University; Leiden University - Excl
   LUMC; Zhejiang University
RP Fu, HZ (corresponding author), Zhejiang Univ, Sch Publ Affairs, Dept Informat Resources Management, Hangzhou, Peoples R China.
EM zhengerte@ruc.edu.cn; z.fang@cwts.leidenuniv.nl; fuhuizhen@zju.edu.cn
RI Zheng, Er-Te/JNE-7783-2023
OI Zheng, Er-Te/0000-0001-8759-3643
FU Soft Science Research Program of the Zhejiang Provincial Department of
   Science of Technology [2021C35040]
FX <B>Acknowledgments</B> This study is supported by the Soft Science
   Research Program of the Zhejiang Provincial Department of Science of
   Technology (No. 2021C35040) . We gratefully acknowledge the contribution
   of team members who collected the data, Jinyuan Huang, Hong Tao, Shichu
   Xu, Yawen Yin, Qin Zhang, Yangliu Cui.
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NR 98
TC 0
Z9 0
U1 37
U2 37
PU ELSEVIER SCI LTD
PI London
PA 125 London Wall, London, ENGLAND
SN 0306-4573
EI 1873-5371
J9 INFORM PROCESS MANAG
JI Inf. Process. Manage.
PD MAY
PY 2024
VL 61
IS 3
AR 103640
DI 10.1016/j.ipm.2023.103640
EA JAN 2024
PG 18
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA JZ1E8
UT WOS:001176881600001
DA 2024-09-05
ER
PT J
AU Rossi, D
   Harreveld, RE
   Clark, D
   van Rensburg, H
   Beer, C
   Danaher, PA
AF Rossi, Dolene
   Harreveld, R. E. (Bobby)
   Clark, Damien
   van Rensburg, Henriette
   Beer, Colin
   Danaher, P. A.
TI Exploring a cross-institutional research collaboration and innovation:
   Deploying social software and Web 2.0 technologies to investigate online
   learning designs and interactions in two Australian Universities
SO JOURNAL OF LEARNING DESIGN
LA English
DT Article
DE Cross-institutional collaboration; interactions; online learning design;
   research; social software; Web 2.0 technologies
AB One significant manifestation of the proposition of a 'classroom without walls' is the online learning environments evident in most contemporary Australian universities. A key element of the effectiveness of those environments is the quality of the interactions that they foster. Planning and implementing rigorous research into that quality is crucial if these particular 'classrooms without walls' are to deliver enhanced and sustained learning outcomes. This article explores selected aspects of a cross-institutional collaboration linking two Australian universities researching the quality of learning interactions in their online courses. In particular, the authors analyse the utility of the social software and Web 2.0 technologies that have been deployed to facilitate their collaborative research. Despite the constraints and tensions attendant on within-and cross-organisational learning, teaching and research activities, the article records evidence of a developing innovation in investigating both the online learning designs and the research project developed to evaluate the effectiveness and impact of those designs.
C1 [Rossi, Dolene] CQ Univ, Sch Nursing & Midwifery, Rockhampton, Qld, Australia.
   [Harreveld, R. E. (Bobby)] CQ Univ, Sch Educ, Rockhampton, Qld, Australia.
   [Clark, Damien; Beer, Colin] CQ Univ, Off Learning & Teaching, Rockhampton, Qld, Australia.
   [van Rensburg, Henriette; Danaher, P. A.] Univ Southern Queensland, Fac Educ, Toowoomba, Qld, Australia.
C3 Central Queensland University; Central Queensland University; Central
   Queensland University; University of Southern Queensland
RP Rossi, D (corresponding author), CQ Univ, Sch Nursing & Midwifery, Rockhampton, Qld, Australia.
EM d.rossi@cqu.edu.au; b.harreveld@cqu.edu.au; d.clark@cqu.edu.au;
   henriette.vanrensburg@usq.edu.au; c.beer@cqu.edu.au;
   patrick.danaher@usq.edu.au
RI Rossi, Dolene M/G-5036-2015; Danaher, Patrick A/N-7315-2014; Beer,
   Colin/GZA-5917-2022
OI Rossi, Dolene M/0000-0002-5093-6443; Danaher, Patrick
   A/0000-0002-2289-7774; Beer, Colin/0000-0002-1827-1365
FU DeHub Consortium
FX This article reports on a project funded in 2011-2012 by the DeHub
   Consortium, hosted by the University of New England. Administrative
   support was provided by Ms Tash Toon, Ms Chriss Lenz and Ms Mary
   Cranston at the Learning and Teaching Education Research Centre at
   CQUniversity, Australia. The project evaluator was Professor Michael
   Singh in the Centre for Educational Research at the University of
   Western Sydney, Australia. The project reference group was chaired by
   Professor Terry Evans (School of Education, Deakin University,
   Australia) and included Professor Nita Temmerman (Faculty of Education,
   University of Southern Queensland, Australia), Dr Abdurrahman Umar
   (Commonwealth of Learning, Canada) and Associate Professor Steve
   McKillup and Ms Beth Tennent (CQUniversity, Australia). The authors
   thank the guest editors of this special theme issue of the journal and
   acknowledge the feedback of two anonymous peer reviewers.
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NR 33
TC 6
Z9 7
U1 0
U2 0
PU QUEENSLAND UNIV TECHNOLOGY
PI BRISBANE
PA GPO  BOX 2434, BRISBANE, QLD 4001, AUSTRALIA
SN 1832-8342
J9 J LEARN DES
JI J. Learn. Des.
PY 2012
VL 5
IS 2
SI SI
BP 1
EP 11
DI 10.5204/jld.v5i2.108
PG 11
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA V01JA
UT WOS:000213597300002
OA Green Accepted, hybrid
DA 2024-09-05
ER
PT J
AU Ul Haq, MI
   Li, QM
   Hou, J
AF Ul Haq, Muhammad Inaam
   Li, Qianmu
   Hou, Jun
TI Analyzing the Research Trends of IoT Using Topic Modeling
SO COMPUTER JOURNAL
LA English
DT Article
DE topic modeling; text analysis; topic trends; research communities;
   topic; correlation
ID OF-THE-ART; INDUSTRY 4.0; COMMUNITY STRUCTURE; HEALTH-CARE; BIG DATA;
   INTERNET; THINGS; CHALLENGES; FUTURE; SECURITY
AB The internet of things (IoT) is one of the most rapidly growing technologies. Therefore, the interest in industry and academia has been increasing. The published research data have evolved in IoT because of scientific advances in this field. Since science plays a vital role in decision-making, this study examines the thematic landscape of research on IoT, which may contribute to understanding the research field's structure allows for critical reflections and the identification of blind spots for advancing this field. The current study applies a text mining approach on 25966 Scopus-indexed abstracts and titles published from 2008 to 2020 on a latent Dirichlet allocation-based topic model. In this study, various models in the range of 1-100 topics were created. Examination of coherence scores was combined with manual analysis; the 25-topic model was chosen as an optimal one. The statistical methods employed highlight the timely trends of the extracted topics, intellectual topic structure and resulting communities in the topic network. The study carpingly depicts the quantitative results from an IoT perspective. The statistical analysis depicts that IoT publications has exponential growth rate. The hotspot of the IoT research can be concluded as 'intrusion attack detection', 'cloud and edge computing', 'energy consumption', 'access channels', 'algorithm optimization' and 'healthcare and medical'. The topics that reflect the wireless sensor networks, security and privacy, high-range signal, devices and context aware computing and sensor control and monitoring have stable trends. This study identifies research focus on the development of low-energy consumption systems (Green IoT), application of high-range signals and their performance in tracking and identification, and data analytics (Big data IoT). Furthermore, the research focuses on industrial solutions towards diseases diagnosis and its treatment in health sector. Finally, in agriculture sector for intelligent manufacturing, research focuses on the application of image recognition for plant and food analysis.
C1 [Ul Haq, Muhammad Inaam; Li, Qianmu] Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China.
   [Hou, Jun] Nanjing Vocat Univ Ind Technol, Sch Social Sci, Nanjing 210046, Peoples R China.
   [Ul Haq, Muhammad Inaam] COMSATS Univ Islamabad, Dept Comp Sci, Sahiwal Campus, Sahiwal 57000, Pakistan.
C3 Nanjing University of Science & Technology; Nanjing Vocational
   University of Industry Technology; COMSATS University Islamabad (CUI)
RP Ul Haq, MI; Li, QM (corresponding author), Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China.; Ul Haq, MI (corresponding author), COMSATS Univ Islamabad, Dept Comp Sci, Sahiwal Campus, Sahiwal 57000, Pakistan.
EM minaamulhaq@hotmail.com; qianmu@njust.edu.cn
RI Inaam ul haq, Muhammad/HKO-1217-2023
OI Inaam ul haq, Muhammad/0000-0002-5759-073X
FU National Key R&D Program of China [2020YFB1805503]; Jiangsu Province
   Modern Education Technology Research Project [84365]; National
   Vocational Education Teacher Enterprise Practice Base "Integration of
   Industry and Education'' Special Project(study on Evaluation Standard of
   Artificial Intelligence Vocational Skilled Level)
FX This work is supported by the National Key R&D Program of China (Funding
   No.2020YFB1805503), Jiangsu Province Modern Education Technology
   Research Project (84365); National Vocational Education Teacher
   Enterprise Practice Base "Integration of Industry and Education''
   Special Project(study on Evaluation Standard of Artificial Intelligence
   Vocational Skilled Level).
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NR 124
TC 3
Z9 3
U1 3
U2 30
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 0010-4620
EI 1460-2067
J9 COMPUT J
JI Comput. J.
PD OCT 17
PY 2022
VL 65
IS 10
BP 2589
EP 2609
DI 10.1093/comjnl/bxab091
EA JUL 2021
PG 21
WC Computer Science, Hardware & Architecture; Computer Science, Information
   Systems; Computer Science, Software Engineering; Computer Science,
   Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 5L8FZ
UT WOS:000789359600001
DA 2024-09-05
ER
PT J
AU Zamit, I
   Musa, IH
   Jiang, LM
   Wei, YJ
   Tang, JJ
AF Zamit, Ibrahim
   Musa, Ibrahim Hussein
   Jiang, Limin
   Wei, Yanjie
   Tang, Jijun
TI Trends and features of autism spectrum disorder research using
   artificial intelligence techniques: a bibliometric approach
SO CURRENT PSYCHOLOGY
LA English
DT Article
DE Autism spectrum disorder; Artificial intelligence; Machine learning;
   Bibliometric analysis; Scientific collaboration; Research hotspots
ID MACHINE; SCIENCE; IDENTIFICATION; METAANALYSIS; DIAGNOSIS; DISEASE
AB The prevalence of autism spectrum disorder (ASD) has risen rapidly in recent decades. Owing to its success across disciplines, the use of artificial intelligence (AI) in the screening of ASD has emerged as a prominent solution. We conducted a bibliometric analysis on AI-powered ASD screening research with a unit of 2090 publications retrieved from Scopus database in the period 2010-2021. Our findings show, among other things, that the annual growth rate of publications was 33.05% and scientific production drastically increased 23-fold from 22 in 2010 to 509 in 2021 with nearly two thirds (1307; 62,54%) of the retrieved documents being published between 2019-2021. The USA was the global leader in terms of scientific output with 730 publications followed by China (255), and India (251). Stanford university, the scientific journal NeuroImage, and Dennis P. Wall were the most globally prolific institution, publication source, and author, respectively. Using VOSviewer's clustering algorithms, keyword and topic analysis identified neuroimaging techniques and genetic research as hot and emerging research trends. Interestingly, three of the top ten prolific authors were women, indicating a significant milestone for gender rebalancing efforts in the AI workforce. The findings will help both experienced and aspiring scientists better understand the structure and current state of knowledge, uncover patterns of collaboration, and identify emerging trends in ASD research using AI.
C1 [Zamit, Ibrahim; Jiang, Limin; Wei, Yanjie; Tang, Jijun] Shenzhen Inst Adv Technol, Inst Adv Comp & Digital Engn, Ctr High Performance Comp Technol, Shenzhen, Peoples R China.
   [Zamit, Ibrahim] Univ Chinese Acad Sci, Beijing, Peoples R China.
   [Musa, Ibrahim Hussein] Southeast Univ, Sch Comp Sci & Engn, Key Lab Comp Network & Informat Integrat, Nanjing, Peoples R China.
C3 Chinese Academy of Sciences; Shenzhen Institute of Advanced Technology,
   CAS; Chinese Academy of Sciences; University of Chinese Academy of
   Sciences, CAS; Southeast University - China
RP Wei, YJ; Tang, JJ (corresponding author), Shenzhen Inst Adv Technol, Inst Adv Comp & Digital Engn, Ctr High Performance Comp Technol, Shenzhen, Peoples R China.
EM yj.wei@siat.ac.cn; jj.tang@siat.ac.cn
OI Zamit, Ibrahim/0000-0002-5517-5102
FU ANSO; National Natural Science Foundation of China [NSFC 61772362,
   61972280]; Shenzhen KQTD Project [KQTD20200820113106007]; Research and
   Development Project of Guangdong Province [2021B0101310002]; Strategic
   Priority CAS Project [XDB38050100]; National Science Foundation of China
   [62272449]; Shenzhen Basic Research Fund [KQTD20200820113106007,
   RCYX202007141 1473419, JCYJ20200109114818703]; CAS Key Lab
   [2011DP173015]; Youth Innovation Promotion Association, CAS [Y2021101]
FX I. Z acknowledges support from the ANSO Scholarship for Young Talents.
   J.T. was supported by The National Natural Science Foundation of China
   (NSFC 61772362, 61972280) and Shenzhen KQTD Project
   [KQTD20200820113106007]. W.Y. was partly supported by the Research and
   Development Project of Guangdong Province under grant no.
   2021B0101310002, the Strategic Priority CAS Project XDB38050100,
   National Science Foundation of China under grant no. 62272449, the
   Shenzhen Basic Research Fund under grant no KQTD20200820113106007,
   RCYX202007141 1473419, JCYJ20200109114818703, CAS Key Lab under grant
   no. 2011DP173015, the Youth Innovation Promotion Association (Y2021101),
   CAS.
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NR 69
TC 0
Z9 0
U1 9
U2 25
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1046-1310
EI 1936-4733
J9 CURR PSYCHOL
JI Curr. Psychol.
PD DEC
PY 2023
VL 42
IS 35
BP 31317
EP 31332
DI 10.1007/s12144-022-03977-0
EA DEC 2022
PG 16
WC Psychology, Multidisciplinary
WE Social Science Citation Index (SSCI)
SC Psychology
GA EE1X1
UT WOS:000901937100002
DA 2024-09-05
ER
PT J
AU Kushkowski, JD
   Shrader, CB
   Anderson, MH
   White, RE
AF Kushkowski, Jeffrey D.
   Shrader, Charles B.
   Anderson, Marc H.
   White, Robert E.
TI Information flows and topic modeling in corporate governance
SO JOURNAL OF DOCUMENTATION
LA English
DT Article
DE Corporate governance; Bibliometrics; Citation analysis; Latent Dirichlet
   allocation; Agency theory; Director; Interdisciplinary
ID INTELLECTUAL STRUCTURE; BIBLIOMETRIC ANALYSIS; MANAGEMENT RESEARCH;
   COCITATION ANALYSIS; CITATION; SCIENCE; FIELD; UNIVERSITY; LOGISTICS;
   EVOLUTION
AB Purpose Multiple disciplines such as finance, management and economics have contributed to governance research over time. However, the full intellectual structure of the governance "field" including the exchange of knowledge across disciplines and the large variety of governance topics remains to be uncovered. To appreciate the breadth of corporate governance research, it is necessary to understand the disciplinary sources from which the research stems. This manuscript focuses on the interdisciplinary underpinnings of corporate governance research. Design/methodology/approach This paper employs bibliometric analysis to trace the evolution of corporate governance using articles included in the ISI Web of Science database between 1990 and 2015. Journals included in these categories encompass a full range of business disciplines and provide evidence of the multi-disciplinary nature of corporate governance. It also uncovers the topics treated by disciplines under the governance umbrella using a machine learning method called latent Dirichtlet allocation (LDA). Findings Corporate governance research deals with a number of strategy-related topics. Unlike strategy topics that reside in a single discipline, corporate governance crosses disciplinary boundaries and includes contributions from accounting, finance, economics, law and management. Our analysis shows that over 80% of corporate governance articles come from outside the field of management. Our LDA solution indicates that the major topics in governance research include corporate governance theory, control of family firms, executive compensation and audit committees. Originality/value The results illustrate that corporate governance is far more interdisciplinary than previously thought. This is an important insight for corporate governance academics and may lead to collaborative research. More importantly, this research illustrates the usefulness of LDA for investigating interdisciplinary fields. This method is easily transferable to other interdisciplinary fields and it provides a powerful alternative to existing bibliometric methods. We suggest a number of topic areas within library and information science where this method may be applied, including collection development, support for interdisciplinary faculty and basic research into emerging interdisciplinary areas.
C1 [Kushkowski, Jeffrey D.] Iowa State Univ, Univ Lib, Ames, IA 50011 USA.
   [Shrader, Charles B.; Anderson, Marc H.; White, Robert E.] Iowa State Univ, Dept Management, Ivy Coll Business, Ames, IA USA.
C3 Iowa State University; Iowa State University
RP Kushkowski, JD (corresponding author), Iowa State Univ, Univ Lib, Ames, IA 50011 USA.
EM kushkows@iastate.edu; cshrader@iastate.edu; mha@iastate.edu;
   rewhite@iastate.edu
RI Anderson, Marc/AAD-5445-2020; Kushkowski, Jeffrey/IYJ-9478-2023
OI Anderson, Marc/0000-0001-7379-0902; Kushkowski,
   Jeffrey/0000-0002-5331-8149
FU Iowa State University Ivy College of Business
FX The authors wish to thank Florence Honore of the University of Wisconsin
   for help with the early stages of this project and feedback on the
   manuscript; Kyle Hansen, Manaswi Podduturi and Hope Scheffert of
   Kingland Systems for their help with the latent Dirichlet allocation
   analysis; and, Kelly Moore for assistance with data collection. Thanks
   to the Iowa State University Ivy College of Business and the University
   Library for financial support of the open access fees.
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NR 54
TC 6
Z9 7
U1 3
U2 57
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 0022-0418
EI 1758-7379
J9 J DOC
JI J. Doc.
PD OCT 5
PY 2020
VL 76
IS 6
BP 1313
EP 1339
DI 10.1108/JD-10-2019-0207
EA JUN 2020
PG 27
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA PB2EL
UT WOS:000541942200001
OA Green Submitted, hybrid
DA 2024-09-05
ER
PT J
AU Macri, C
   Bacchi, S
   Teoh, SC
   Lim, WY
   Lam, L
   Patel, S
   Slee, M
   Casson, R
   Chan, WG
AF Macri, Carmelo
   Bacchi, Stephen
   Teoh, Sheng Chieh
   Lim, Wan Yin
   Lam, Lydia
   Patel, Sandy
   Slee, Mark
   Casson, Robert
   Chan, WengOnn
TI Evaluating the Ability of Open-Source Artificial Intelligence to Predict
   Accepting-Journal Impact Factor and Eigenfactor Score Using Academic
   Article Abstracts: Cross-sectional Machine Learning Analysis
SO JOURNAL OF MEDICAL INTERNET RESEARCH
LA English
DT Article
DE journal impact factor; artificial intelligence; ophthalmology;
   radiology; neurology; eye; neuroscience; impact factor; research
   quality; journal recommender; publish; open source; predict; machine
   learning; academic journal; scientometric; scholarly literature
ID AUTHORS
AB Background: Strategies to improve the selection of appropriate target journals may reduce delays in disseminating research results. Machine learning is increasingly used in content-based recommender algorithms to guide journal submissions for academic articles.
   Objective: We sought to evaluate the performance of open-source artificial intelligence to predict the impact factor or Eigenfactor score tertile using academic article abstracts.
   Methods: PubMed-indexed articles published between 2016 and 2021 were identified with the Medical Subject Headings (MeSH) terms "ophthalmology," "radiology," and "neurology." Journals, titles, abstracts, author lists, and MeSH terms were collected. Journal impact factor and Eigenfactor scores were sourced from the 2020 Clarivate Journal Citation Report. The journals included in the study were allocated percentile ranks based on impact factor and Eigenfactor scores, compared with other journals that released publications in the same year. All abstracts were preprocessed, which included the removal of the abstract structure, and combined with titles, authors, and MeSH terms as a single input. The input data underwent preprocessing with the inbuilt ktrain Bidirectional Encoder Representations from Transformers (BERT) preprocessing library before analysis with BERT. Before use for logistic regression and XGBoost models, the input data underwent punctuation removal, negation detection, stemming, and conversion into a term frequency-inverse document frequency array. Following this preprocessing, data were randomly split into training and testing data sets with a 3:1 train:test ratio. Models were developed to predict whether a given article would be published in a first, second, or third tertile journal (0-33rd centile, 34th-66th centile, or 67th-100th centile), as ranked either by impact factor or Eigenfactor score. BERT, XGBoost, and logistic regression models were developed on the training data set before evaluation on the hold-out test data set. The primary outcome was overall classification accuracy for the best-performing model in the prediction of accepting journal impact factor tertile.
   Results: There were 10,813 articles from 382 unique journals. The median impact factor and Eigenfactor score were 2.117 (IQR 1.102-2.622) and 0.00247 (IQR 0.00105-0.03), respectively. The BERT model achieved the highest impact factor tertile classification accuracy of 75.0%, followed by an accuracy of 71.6% for XGBoost and 65.4% for logistic regression. Similarly, BERT achieved the highest Eigenfactor score tertile classification accuracy of 73.6%, followed by an accuracy of 71.8% for XGBoost and 65.3% for logistic regression.
   Conclusions: Open-source artificial intelligence can predict the impact factor and Eigenfactor score of accepting peer-reviewed journals. Further studies are required to examine the effect on publication success and the time-to-publication of such recommender systems.
C1 [Macri, Carmelo] Univ Adelaide, Discipline Ophthalmol & Visual Sci, Adelaide, Australia.
   [Bacchi, Stephen; Teoh, Sheng Chieh; Lam, Lydia; Casson, Robert; Chan, WengOnn] Royal Adelaide Hosp, Dept Ophthalmol, Adelaide, Australia.
   [Lim, Wan Yin; Patel, Sandy] Royal Adelaide Hosp, Dept Radiol, Adelaide, Australia.
   [Slee, Mark] Flinders Univ S Australia, Coll Med & Publ Hlth, Adelaide, Australia.
   [Macri, Carmelo] Univ Adelaide, Discipline Ophthalmol & Visual Sci, Adelaide 5000, Australia.
C3 University of Adelaide; Royal Adelaide Hospital; Royal Adelaide
   Hospital; Flinders University South Australia; University of Adelaide
RP Macri, C (corresponding author), Univ Adelaide, Discipline Ophthalmol & Visual Sci, Adelaide 5000, Australia.
EM carmelo.macri@adelaide.edu.au
RI ; Slee, Mark/J-4731-2015
OI Teoh, Ian/0000-0002-8562-6277; Lam, Lydia/0000-0002-1484-5202; Slee,
   Mark/0000-0003-4323-2453; Macri, Carmelo/0000-0002-1110-3780; Bacchi,
   Stephen/0000-0001-5130-8628
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NR 33
TC 0
Z9 0
U1 5
U2 14
PU JMIR PUBLICATIONS, INC
PI TORONTO
PA 130 QUEENS QUAY East, Unit 1100, TORONTO, ON M5A 0P6, CANADA
SN 1438-8871
J9 J MED INTERNET RES
JI J. Med. Internet Res.
PD MAR 7
PY 2023
VL 25
AR e42789
DI 10.2196/42789
PG 7
WC Health Care Sciences & Services; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Health Care Sciences & Services; Medical Informatics
GA I9QU6
UT WOS:001006062500001
PM 36881455
OA Green Published, gold
DA 2024-09-05
ER
PT C
AU Di Rosa, E
   Durante, A
AF Di Rosa, Emanuele
   Durante, Alberto
BE Esposito, F
   Basili, R
   Ferilli, S
   Lisi, FA
TI Evaluating Industrial and Research Sentiment Analysis Engines on
   Multiple Sources
SO AI*IA 2017 ADVANCES IN ARTIFICIAL INTELLIGENCE
SE Lecture Notes in Artificial Intelligence
LA English
DT Proceedings Paper
CT 16th International Conference of the Italian Association for Artificial
   Intelligence (AI*IA)
CY NOV 14-17, 2017
CL Bari, ITALY
DE Sentiment analysis; Natural language processing; Machine learning;
   Experimental evaluation; Industrial and research tools comparison;
   Cross-domain sentiment classification
ID CLASSIFICATION
AB Sentiment Analysis has a fundamental role in analyzing users opinions in all kinds of textual sources. Computing accurately sentiment expressed in huge amount of textual data is a key task largely required by the market, and nowadays industrial engines make available ready-to-use APIs for sentiment analysis-related tasks. However, building sentiment engines showing high accuracy on structurally different textual sources (e.g. reviews, tweets, blogs, etc.) is not a trivial task. Papers about cross-source evaluation lack of a comparison with industrial engines, which are instead specifically designed for dealing with multiple sources.
   In this paper, we compare the results of research and industrial engines on an extensive experimental evaluation, considering the document-level polarity detection task performed on different textual sources: tweets, apps reviews and general products reviews, in both English and Italian. The experimental evaluation results help the reader to quantify the performance gap between industrial and research sentiment engines when both are tested on heterogeneous textual sources and on different languages (English/Italian). Finally, we present the results of our multi-source solution X2Check. Considering an overall cross-source average F-score on all of the results, X2Check shows a performance that is 9.1% and 5.1% higher than Google CNL, respectively on Italian and English benchmarks. Compared to the research engines, X2Check shows a F-score that is always higher than tools not specifically trained on the test set under evaluation; it is lower at most of 3.4% in Italian and 11.6% on English benchmarks, compared to the best research tools specifically trained on the target source.
C1 [Di Rosa, Emanuele] Finsa Spa, Artificial Intelligence, Genoa, Italy.
   [Durante, Alberto] Finsa Spa, Genoa, Italy.
RP Di Rosa, E (corresponding author), Finsa Spa, Artificial Intelligence, Genoa, Italy.
EM emanuele.dirosa@finsa.it; alberto.durante@finsa.it
RI Di Rosa, Emanuele/AAP-3475-2021
CR [Anonymous], 2016, P ACM S APPL COMP 20
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NR 22
TC 1
Z9 1
U1 0
U2 4
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 0302-9743
EI 1611-3349
BN 978-3-319-70169-1; 978-3-319-70168-4
J9 LECT NOTES ARTIF INT
PY 2017
VL 10640
BP 141
EP 155
DI 10.1007/978-3-319-70169-1_11
PG 15
WC Computer Science, Artificial Intelligence
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BL5GC
UT WOS:000451442200011
DA 2024-09-05
ER
PT C
AU Ochi, M
   Shiro, M
   Mori, J
   Sakata, I
AF Ochi, Masanao
   Shiro, Masanori
   Mori, Jun'ichiro
   Sakata, Ichiro
BE Mayo, FD
   Marchiori, M
   Filipe, J
TI Which Is More Helpful in Finding Scientific Papers to Be Top-cited in
   the Future: Content or Citations? Case Analysis in the Field of Solar
   Cells 2009
SO PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON WEB INFORMATION
   SYSTEMS AND TECHNOLOGIES (WEBIST)
LA English
DT Proceedings Paper
CT 17th International Conference on Web Information Systems and
   Technologies (WEBIST)
CY OCT 26-28, 2021
CL ELECTR NETWORK
DE Citation Analysis; Scientific Impact; Graph Neural Network; BERT
ID H-INDEX
AB With the increasing digital publication of scientific literature and the fragmentation of research, it is becoming more and more difficult to find promising papers. Of course, we can examine the contents of a large number of papers, but it is easier to look at the references cited. Therefore, we want to know whether a paper is promising or not based only on its content and citation information. This paper proposes a method of extracting and clustering the content and citations of papers as distributed representations and comparing them using the same criteria. This method clarifies whether the future promising papers will be biased toward content or citations. We evaluated the proposed method by comparing the distribution of the papers that would become the top-cited papers three years later among the papers published in 2009. As a result, we found that the citation information is 39.9% easier to identify the papers that will be the top-cited papers in the future than the content information. This analysis will provide a basis for developing more general models for early prediction of the impact of various scientific researches and trends in science and technology.
C1 [Ochi, Masanao; Mori, Jun'ichiro; Sakata, Ichiro] Univ Tokyo, Grad Sch Engn, Dept Technol Management Innovat, Bunkyo Ku, Hongo 7-3-1, Tokyo, Japan.
   [Shiro, Masanori] Natl Inst Adv Ind Sci & Technol, HIRI, Umezono 1-1-1, Tsukuba, Ibaraki, Japan.
C3 University of Tokyo; National Institute of Advanced Industrial Science &
   Technology (AIST)
RP Ochi, M (corresponding author), Univ Tokyo, Grad Sch Engn, Dept Technol Management Innovat, Bunkyo Ku, Hongo 7-3-1, Tokyo, Japan.
RI Ochi, Masanao/KND-7312-2024
OI Ochi, Masanao/0000-0002-6661-6735; Mori, Junichiro/0000-0002-9787-3857
FU New Energy and Industrial Technology Development Organization (NEDO)
   [JPNP20006]
FX This article is based on results obtained from a project, JPNP20006,
   commissioned by the New Energy and Industrial Technology Development
   Organization (NEDO).
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NR 16
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U1 0
U2 1
PU SCITEPRESS
PI SETUBAL
PA AV D MANUELL, 27A 2 ESQ, SETUBAL, 2910-595, PORTUGAL
BN 978-989-758-536-4
PY 2021
BP 360
EP 364
DI 10.5220/0010689100003058
PG 5
WC Computer Science, Information Systems; Telecommunications
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Telecommunications
GA BT0ZP
UT WOS:000795868100038
OA hybrid
DA 2024-09-05
ER
PT J
AU Thelwall, M
   Kousha, K
   Wilson, P
   Makita, M
   Abdoli, M
   Stuart, E
   Levitt, J
   Knoth, P
   Cancellieri, M
AF Thelwall, Mike
   Kousha, Kayvan
   Wilson, Paul
   Makita, Meiko
   Abdoli, Mahshid
   Stuart, Emma
   Levitt, Jonathan
   Knoth, Petr
   Cancellieri, Matteo
TI Predicting article quality scores with machine learning: The UK Research
   Excellence Framework
SO QUANTITATIVE SCIENCE STUDIES
LA English
DT Article
DE artificial intelligence; bibliometrics; citation analysis; machine
   learning; scientometrics
ID RESEARCH COLLABORATION; CITATION COUNTS; NEURAL-NETWORK; IMPACT;
   AUTHORS; BIAS; PERFORMANCE; INDICATOR; FEATURES
AB National research evaluation initiatives and incentive schemes choose between simplistic quantitative indicators and time-consuming peer/expert review, sometimes supported by bibliometrics. Here we assess whether machine learning could provide a third alternative, estimating article quality using more multiple bibliometric and metadata inputs. We investigated this using provisional three-level REF2021 peer review scores for 84,966 articles submitted to the U.K. Research Excellence Framework 2021, matching a Scopus record 2014-18 and with a substantial abstract. We found that accuracy is highest in the medical and physical sciences Units of Assessment (UoAs) and economics, reaching 42% above the baseline (72% overall) in the best case. This is based on 1,000 bibliometric inputs and half of the articles used for training in each UoA. Prediction accuracies above the baseline for the social science, mathematics, engineering, arts, and humanities UoAs were much lower or close to zero. The Random Forest Classifier (standard or ordinal) and Extreme Gradient Boosting Classifier algorithms performed best from the 32 tested. Accuracy was lower if UoAs were merged or replaced by Scopus broad categories. We increased accuracy with an active learning strategy and by selecting articles with higher prediction probabilities, but this substantially reduced the number of scores predicted.
C1 [Thelwall, Mike; Kousha, Kayvan; Wilson, Paul; Makita, Meiko; Abdoli, Mahshid; Stuart, Emma; Levitt, Jonathan] Univ Wolverhampton, Stat Cybermetr & Res Evaluat Grp, Wolverhampton, England.
   [Knoth, Petr; Cancellieri, Matteo] Open Univ, Knowledge Media Inst, Milton Keynes, England.
C3 University of Wolverhampton; Open University - UK
RP Thelwall, M (corresponding author), Univ Wolverhampton, Stat Cybermetr & Res Evaluat Grp, Wolverhampton, England.
EM m.thelwall@wlv.ac.uk
RI Thelwall, Mike/JDV-4700-2023
OI Thelwall, Mike/0000-0001-6065-205X; Wilson, Paul/0000-0002-1265-543X;
   Cancellieri, Matteo/0000-0002-9558-9772; Kousha,
   Kayvan/0000-0003-4827-971X; Abdoli, Mahshid/0000-0001-9251-5391
FU Scottish Funding Council; Research England; Higher Education Funding
   Council for Wales; Department for the Economy, Northern Ireland; Future
   Research Assessment Programme
FX This study was funded by Research England, Scottish Funding Council,
   Higher Education Funding Council for Wales, and Department for the
   Economy, Northern Ireland as part of the Future Research Assessment
   Programme (https://www.jisc.ac.uk/future-research-assessment-programme).
   The content is solely the responsibility of the authors and does not
   necessarily represent the official views of the funders.
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NR 72
TC 6
Z9 7
U1 7
U2 13
PU MIT PRESS
PI CAMBRIDGE
PA ONE ROGERS ST, CAMBRIDGE, MA 02142-1209 USA
EI 2641-3337
J9 QUANT SCI STUD
JI Quant. Sci. Stud.
PD MAY 1
PY 2023
VL 4
IS 2
BP 547
EP 573
DI 10.1162/qss_a_00258
PG 27
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA K5CJ2
UT WOS:001016613900013
OA Green Accepted, Green Submitted, gold
DA 2024-09-05
ER
PT C
AU Zeng, LQ
   Hu, H
   Han, QW
   Ye, L
   Lei, Y
AF Zeng, Lingqiu
   Hu, Han
   Han, Qingwen
   Ye, Lei
   Lei, Yu
GP IEEE
TI Research on Task Offloading and Typical Application Based on Deep
   Reinforcement Learning and Device-Edge-Cloud Collaboration
SO 2024 AUSTRALIAN & NEW ZEALAND CONTROL CONFERENCE, ANZCC
SE Australian and New Zealand Control Conference
LA English
DT Proceedings Paper
CT Australian and New Zealand Control Conference (ANZCC)
CY FEB 01-02, 2024
CL Gold Coast, AUSTRALIA
AB The ever evolving intelligent transportation systems may be able to provide low latency and high-quality service for intelligent connected vehicles (ICVs) on the basis of device-edge-cloud architecture. To match the requirement of vehicle-oriented task computing, the task offloading technology has received extensive attention, while making correct and fast offloading decisions to improve highly dynamic vehicular users' experience is still a considerable challenge. In this paper, we study a device-edge-cloud architecture, where tasks from vehicles can be partially offloaded with a dynamically offloading proportion. To deal with this problem, we firstly introduce SPSO (serial particle swarm optimization) algorithm to search optimal connected MEC (Multi-Access Edge Computing) node. Then we further design a novel offloading strategy based on the deep Q network (DQN), prioritized experience replay based double deep Q-learning network (PERDDQN), which considers priority weight of the sample and sampling probability in loss function definition. A typical complex task, bus remote takeover, is selected to verify the performance of proposed approach. Simulation results show that PERDDQN has lower system cost, faster convergence speed and higher task success rate than the other comparison algorithms.
C1 [Zeng, Lingqiu; Hu, Han] Chongqing Univ, Coll Comp Sci, Chongqing 400044, Peoples R China.
   [Han, Qingwen; Ye, Lei] Chongqing Univ, Sch Microelect & Commun Engn, Chongqing 400044, Peoples R China.
   [Lei, Yu] Chongqing Univ, Coll Mech & Vehicle Engn, Chongqing 400044, Peoples R China.
C3 Chongqing University; Chongqing University; Chongqing University
RP Zeng, LQ (corresponding author), Chongqing Univ, Coll Comp Sci, Chongqing 400044, Peoples R China.
EM zenglq@cqu.edu.cn; 20174328@cqu.edu.cn; hqw@cqu.edu.cn;
   Yelei@cqu.edu.cn; leiyyu@cqu.edu.cn
FU special key project of Chongqing Technology Innovation and Application
   Development [cstc2021jscx-gksbX0057]; special major project of Chongqing
   Technology Innovation and Application Development [CSTB2022TIAD-STX0003]
FX This research is supported by the special key project of Chongqing
   Technology Innovation and Application Development under Grant
   No.cstc2021jscx-gksbX0057, and the special major project of Chongqing
   Technology Innovation and Application Development under Grant
   No.CSTB2022TIAD-STX0003.
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NR 12
TC 0
Z9 0
U1 3
U2 3
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2767-7230
EI 2767-7257
BN 979-8-3503-1497-7
J9 AUST N Z C CONF
PY 2024
BP 13
EP 18
DI 10.1109/ANZCC59813.2024.10432815
PG 6
WC Automation & Control Systems
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Automation & Control Systems
GA BW6GM
UT WOS:001173666600003
DA 2024-09-05
ER
PT J
AU Lou, TF
   Hung, WH
AF Lou, Ta-Feng
   Hung, Wei-Hsi
TI Revival of Classical Algorithms: A Bibliometric Study on the Trends of
   Neural Networks and Genetic Algorithms
SO SYMMETRY-BASEL
LA English
DT Article
DE algorithm; neural network; artificial neural network; genetic algorithm;
   bibliometric; artificial intelligence; AI; Lotka's law
ID LOTKA LAW
AB The purpose of our bibliometric research was to capture and analyze the trends of two types of well-known classical artificial intelligence (AI) algorithms: neural networks (NNs) and genetic algorithms (GAs). Symmetry is a very popular international and interdisciplinary scientific journal that cover six major research subjects of mathematics, computer science, engineering science, physics, biology, and chemistry which are all related to our research on classical AI algorithms; therefore, we referred to the most innovative research articles of classical AI algorithms that have been published in Symmetry, which have also introduced new advanced applications for NNs and Gas. Furthermore, we used the keywords of "neural network algorithm" or "artificial neural network" to search the SSCI database from 2002 to 2021 and obtained 951 NN publications. For comparison purposes, we also analyzed GA trends by using the keywords "genetic algorithm" to search the SSCI database over the same period and we obtained 878 GA publications. All of the NN and GA publication results were categorized into eight groups for deep analyses so as to investigate their current trends and forecasts. Furthermore, we applied the Kolmogorov-Smirnov test (K-S test) to check whether our bibliometric research complied with Lotka's law. In summary, we found that the number of applications for both NNs and GAs are continuing to grow but the use of NNs is increasing more sharply than the use of GAs due to the boom in deep learning development. We hope that our research can serve as a roadmap for other NN and GA researchers to help them to save time and stay at the cutting edge of AI research trends.
C1 [Lou, Ta-Feng; Hung, Wei-Hsi] Natl Chengchi Univ, Dept Management Informat Syst, Taipei 116302, Taiwan.
C3 National Chengchi University
RP Lou, TF (corresponding author), Natl Chengchi Univ, Dept Management Informat Syst, Taipei 116302, Taiwan.
EM 103356505@nccu.edu.tw
OI Hung, Wei-Hsi/0000-0002-8480-8079
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NR 76
TC 2
Z9 2
U1 5
U2 14
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2073-8994
J9 SYMMETRY-BASEL
JI Symmetry-Basel
PD FEB
PY 2023
VL 15
IS 2
AR 325
DI 10.3390/sym15020325
PG 23
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA 9J2EX
UT WOS:000940008000001
OA Green Published, gold
DA 2024-09-05
ER
PT C
AU Liu, Y
   Li, L
   Wan, SH
   Gao, ZQ
AF Liu, Yu
   Li, Lei
   Wan, Shuhong
   Gao, Zhiqiao
BE Ma, H
   Wang, W
   Zhang, Y
TI RESEARCH ON CHINESE MULTI-DOCUMENT HIERARCHICAL TOPIC MODELING AUTOMATIC
   EVALUATION METHODS
SO 2014 IEEE 3RD INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND
   INTELLIGENCE SYSTEMS (CCIS)
SE International Conference on Cloud Computing and Intelligence Systems
LA English
DT Proceedings Paper
CT 3rd IEEE International Conference on Cloud Computing and Intelligence
   Systems (CCIS)
CY NOV 27-29, 2014
CL PEOPLES R CHINA
DE Hierarchical LDA; Hierarchical Topic Modeling; Automatic Evaluation
   Methods
ID CLUSTER VALIDITY MEASURE
AB Hierarchical Latent Dirichlet Allocation (hLDA) has achieved good results in the supervised and unsupervised multi-document hierarchical topic modeling. However, the result is diversified. The results maintain randomness even with the same parameters. Thus, this paper proposed automatic evaluation methods for unsupervised multi-document hLDA modeling results over previous studies. This paper used 10 topics of corpus of ACL2013 multilingual multi-document summarization and found 90 topics of news as experimental corpus, then compared the different modeling results. The results showed that automatic evaluation method can provide a good reference for the optimization of the modeling results.
C1 [Liu, Yu; Li, Lei; Wan, Shuhong; Gao, Zhiqiao] Beijing Univ Posts & Telecommun, Sch Comp Sci, Ctr Intelligence Sci & Technol, Beijing 100864, Peoples R China.
C3 Beijing University of Posts & Telecommunications
RP Liu, Y (corresponding author), Beijing Univ Posts & Telecommun, Sch Comp Sci, Ctr Intelligence Sci & Technol, Beijing 100864, Peoples R China.
EM 147121500@qq.com
RI Chen, Qi/GVU-3024-2022; Li, Lei/AAN-5959-2020
OI Chen, Qi/0000-0002-6568-7267; 
FU National Natural Science Foundation of China [61202247, 71231002,
   61202248, 61472046]; EU FP7 IRSES MobileCloud Project [612212]; 111
   Project of China [B08004]; Engineering Research Center of Information
   Networks, Ministry of Education; Fundamental Research Funds for the
   Central Universities [2013RC0304]; Beijing Institute of Science and
   Technology Information
FX This work was supported by the National Natural Science Foundation of
   China under Grant 61202247, 71231002, 61202248 and 61472046; EU FP7
   IRSES MobileCloud Project (Grant No. 612212); the 111 Project of China
   under Grant B08004; Engineering Research Center of Information Networks,
   Ministry of Education; the Fundamental Research Funds for the Central
   Universities under Grant2013RC0304; the project of Beijing Institute of
   Science and Technology Information.
CR Blei DM, 2004, ADV NEUR IN, V16, P17
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NR 15
TC 0
Z9 0
U1 0
U2 1
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2376-5933
BN 978-1-4799-4719-5
J9 INT CONF CLOUD COMPU
PY 2014
BP 444
EP 449
PG 6
WC Computer Science, Artificial Intelligence; Computer Science, Theory &
   Methods; Engineering, Electrical & Electronic
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Engineering
GA BG8UT
UT WOS:000392727800084
DA 2024-09-05
ER
PT C
AU Zhang, QW
   Zhang, HX
AF Zhang, Qingqing
   Zhang, Hongxin
GP IEEE
TI Research on evaluation metric of cross device bypass attack based on
   deep learning
SO 2022 CROSS STRAIT RADIO SCIENCE & WIRELESS TECHNOLOGY CONFERENCE,
   CSRSWTC
SE Cross Strait Quad Regional Radio Science and Wireless Technology
   Conference
LA English
DT Proceedings Paper
CT Cross Strait Radio Science and Wireless Technology Conference (CSRSWTC)
CY DEC 17-18, 2022
CL Haidian, PEOPLES R CHINA
DE side channel attack; SPECK; deep learning; cross device; evaluation
   metric
AB Based on the bypass analysis of SPECK encryption algorithm, this paper considers the feasibility of using commonly used in-depth learning evaluation index to evaluate the attack efficiency in real cross-device scenarios.We considered the impact of in-depth learning model evaluation indicators for side channel attacks in three different scenarios. We showed that using different devices and keys or changing the chip base during the analysis and attack phase would significantly affect the accuracy indicators compared with changing the probe measurement position.Our experimental results show that, although accuracy is the most commonly used indicator for monitoring and evaluating neural networks, it can result in a significant underestimation of attack efficiency under cross-device conditions.
C1 [Zhang, Qingqing; Zhang, Hongxin] Beijing Univ Posts & Telecommun, Sch Elect Engn, Beijing, Peoples R China.
C3 Beijing University of Posts & Telecommunications
RP Zhang, QW (corresponding author), Beijing Univ Posts & Telecommun, Sch Elect Engn, Beijing, Peoples R China.
EM zhangqingqing24630@bupt.edu.cn; hongxinzhang@bupt.edu.cn
RI Zhang, Hongxin/T-3714-2019
CR Beaulieu R, 2015, DES AUT CON, DOI 10.1145/2744769.2747946
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NR 8
TC 0
Z9 0
U1 0
U2 2
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2378-1297
BN 978-1-6654-6096-5
J9 Cross Strait Quad Re
PY 2022
DI 10.1109/CSRSWTC56224.2022.10098470
PG 3
WC Engineering, Electrical & Electronic; Telecommunications
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Engineering; Telecommunications
GA BV1QQ
UT WOS:000995192000176
DA 2024-09-05
ER
PT J
AU Heringa, PW
   Hessels, LK
   van der Zouwen, M
AF Heringa, Pieter W.
   Hessels, Laurens K.
   van der Zouwen, Marielle
TI The influence of proximity dimensions on international research
   collaboration: an analysis of European water projects
SO INDUSTRY AND INNOVATION
LA English
DT Article
DE Proximity; international research collaboration; Framework Programmes;
   logistic regression; O31; R12
ID EMPIRICAL-EVIDENCE; COOPERATION; NETWORKS; PATTERNS; DISTANCE; SCIENCE
AB In this paper we investigate the effect of geographical, organisational and social proximity on the propensity of organisations to collaborate internationally in knowledge production. We apply logistic regression models on data from water research projects in the European Union's Framework Programme 1-7. Although the main challenges in the water sector typically cut across borders, knowledge development is traditionally organised in national systems. These systems have a long tradition in collaborating across societal sectors. Despite the fact that about half of the collaborations in the Framework Programmes are not proximate at all, we show that all three proximity dimensions contribute to the propensity to collaborate. The three dimensions of proximity are weakly correlated, and there is a small substitution effect between organisational and geographical proximity.
C1 [Heringa, Pieter W.] Minist Econ Affairs, Directorate Gen Enterprise & Innovat, The Hague, Netherlands.
   [Hessels, Laurens K.; van der Zouwen, Marielle] KWR Watercycle Res Inst, Nieuwegein, Netherlands.
   [Heringa, Pieter W.; Hessels, Laurens K.] Rathenau Inst, The Hague, Netherlands.
C3 KWR Watercycle Research Institute; Royal Netherlands Academy of Arts &
   Sciences; Rathenau Institute (KNAW)
RP Heringa, PW (corresponding author), Minist Econ Affairs, Directorate Gen Enterprise & Innovat, The Hague, Netherlands.; Heringa, PW (corresponding author), Rathenau Inst, The Hague, Netherlands.
EM p.w.heringa@minez.nl
OI Hessels, Laurens/0000-0002-6399-7050
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NR 45
TC 16
Z9 16
U1 2
U2 37
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1366-2716
EI 1469-8390
J9 IND INNOV
JI Ind. Innov.
PD NOV
PY 2016
VL 23
IS 8
BP 753
EP 772
DI 10.1080/13662716.2016.1215240
PG 20
WC Economics; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA DV5JU
UT WOS:000382964100006
DA 2024-09-05
ER
PT J
AU Patino-Ramirez, F
   O'Sullivan, C
AF Patino-Ramirez, F.
   O'Sullivan, C.
TI Analysis of the growth, trends and prevalent topics in Geotechnical
   Engineering (1998-2022) using topic modelling
SO GEOTECHNIQUE LETTERS
LA English
DT Article
DE Research output; Latent Dirichlet Allocation (LDA); Topic modelling;
   Geotechnical Engineering; Text Mining; History; Data
AB Data gathered from 65,922 articles published in 30 Geotechnical Engineering journals between 1998-2022 were used to build a topic model and study the evolution of research output in the field. Over this period, the number of journal publications has grown exponentially with the number of articles published per year doubling every 6.3 years. The average citations per article (32.7) are skewed by highly-cited articles, which make up 7.8% of the articles but account for 34.5 of the total number of citations. The articles' country of origin has become increasingly centralised, with nearly half the articles published between 2018-2022 coming from China (39%) and the United States (10%). The topic distribution in the field has become highly specialised, with emerging topics being fueled by: i) new technologies and methods (i.e., data science and sensing techniques - the fastest-growing topics), and ii) rising applications seemingly related to mitigation (e.g. offshore and energy geotechnics) and adaptation (e.g., geohazards, mass movements) to climate change. As a consequence, traditional topics (e.g. soil and rock mechanics) have reduced their share in the field, but still remain among the most cited topics in geotechnical engineering.
C1 [Patino-Ramirez, F.; O'Sullivan, C.] Imperial Coll, Dept Civil & Environm Engn, London, England.
C3 Imperial College London
RP Patino-Ramirez, F (corresponding author), Imperial Coll, Dept Civil & Environm Engn, London, England.
EM lpatinor@ic.ac.uk
CR Burland J. B., 2012, ICE manual of geotechnical engineering, VI, P17, DOI [10.1680/moge.57074.0017, DOI 10.1680/MOGE.57074.0017]
   Chauhan U, 2021, ACM COMPUT SURV, V54, DOI 10.1145/3462478
   Das B.M., 1999, PRINCIPLES FDN ENG, Vfourth
   Elsevier B.V, 2023, Elsevier developer portal
   Melis G, 2017, Arxiv, DOI arXiv:1707.05589
   National Science Board. (NSB), 2021, PUBL OUTP US TRENDS
   Rose ME, 2019, SOFTWAREX, V10, DOI 10.1016/j.softx.2019.100263
   Scimago Lab, 2023, Journal rankings on geotechnical engineering and engineering geology
   The MathWorks Inc, 2022, Text analytics toolbox version: 9.4 (r2022b)
NR 9
TC 0
Z9 0
U1 7
U2 7
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 2049-825X
EI 2045-2543
J9 GEOTECH LETT
JI Geotech. Lett.
PD FEB 28
PY 2024
VL 14
IS 1
BP 1
EP 19
DI 10.1680/jgele.23.00097
PG 19
WC Engineering, Geological
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA KO3K0
UT WOS:001180865500001
DA 2024-09-05
ER
PT J
AU Di Caro, L
   Cataldi, M
   Schifanella, C
AF Di Caro, Luigi
   Cataldi, Mario
   Schifanella, Claudio
TI The <i>d</i>-index: Discovering dependences among scientific
   collaborators from their bibliographic data records
SO SCIENTOMETRICS
LA English
DT Article
DE Evaluation metrics; Social networks; Research evaluation; Collaboration
   analysis; Data visualization
ID SOCIAL NETWORK; H-INDEX
AB The evaluation of the work of a researcher and its impact on the research community has been deeply studied in literature through the definition of several measures, first among all the h-index and its variations. Although these measures represent valuable tools for analyzing researchers' outputs, they usually assume the co-authorship to be a proportional collaboration between the parts, missing out their relationships and the relative scientific influences. In this work, we propose the d-index, a novel measure that estimates the dependence degree between authors on their research environment along their entire scientific publication history. We also present a web application that implements these ideas and provides a number of visualization tools for analyzing and comparing scientific dependences among all the scientists in the DBLP bibliographic database. Finally, relying on this web environment, we present case and user studies that highlight both the validity and the reliability of the proposed evaluation measure.
C1 [Di Caro, Luigi; Cataldi, Mario; Schifanella, Claudio] Univ Turin, Dept Comp Sci, Turin, Italy.
C3 University of Turin
RP Di Caro, L (corresponding author), Univ Turin, Dept Comp Sci, Turin, Italy.
EM dicaro@di.unito.it
RI Schifanella, Claudio/JVP-2818-2024; Di, Luigi/C-8149-2011
OI Schifanella, Claudio/0000-0001-7449-6529; Di, Luigi/0000-0002-7570-637X
CR [Anonymous], 2010 2 INT C COMM SY
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PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD DEC
PY 2012
VL 93
IS 3
BP 583
EP 607
DI 10.1007/s11192-012-0762-1
PG 25
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 035RD
UT WOS:000310964500002
DA 2024-09-05
ER
PT J
AU Daud, A
   Li, JZ
   Zhou, LZ
   Muhammad, F
AF Daud, Ali
   Li, Juanzi
   Zhou, Lizhu
   Muhammad, Faqir
TI Temporal expert finding through generalized time topic modeling
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Temporal expert finding; Conferences influence; Generalized time topic
   modeling; Unsupervised learning
ID MIXTURE MODEL
AB This paper addresses the problem of semantics-based temporal expert finding, which means identifying a person with given expertise for different time periods. For example, many real world applications like reviewer matching for papers and finding hot topics in newswire articles need to consider time dynamics. Intuitively there will be different reviewers and reporters for different topics during different time periods. Traditional approaches used graph-based link structure by using keywords based matching and ignored semantic information, while topic modeling considered semantics-based information without conferences influence (richer text semantics and relationships between authors) and time information simultaneously. Consequently they result in not finding appropriate experts for different time periods. We propose a novel Temporal-Expert-Topic (TET) approach based on Semantics and Temporal Information based Expert Search (STMS) for temporal expert finding, which simultaneously models conferences influence and time information. Consequently, topics (semantically related probabilistic clusters of words) occurrence and correlations change over time, while the meaning of a particular topic almost remains unchanged. By using Bayes Theorem we can obtain topically related experts for different time periods and show how experts' interests and relationships change over time Experimental results on scientific literature dataset show that the proposed generalized time topic modeling approach significantly outperformed the non-generalized time topic modeling approaches, due to simultaneously capturing conferences influence with time information (C) 2010 Elsevier B.V. All rights reserved.
C1 [Daud, Ali; Li, Juanzi; Zhou, Lizhu] Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China.
   [Muhammad, Faqir] Allama Iqbal Open Univ, Dept Math & Stat, Sector H 8, Islamabad 44000, Pakistan.
C3 Tsinghua University
RP Daud, A (corresponding author), Tsinghua Univ, Dept Comp Sci & Technol, 1-308 FIT Bldg, Beijing 100084, Peoples R China.
RI Daud, Adil/T-3079-2019; Daud, Ali/ABD-4485-2020; Li,
   Zhiyuan/AAT-1121-2020; Daud, Ali/G-6568-2017
OI Daud, Adil/0000-0002-6617-8421; Daud, Ali/0000-0002-8284-6354
FU National Natural Science Foundation of China [60973102, 60703059];
   Chinese National Key Foundation Research and Development Plan
   [2007CB310803]; Higher Education Commission, Islamabad, Pakistan
FX The work is supported by the National Natural Science Foundation of
   China under Grant (60973102, 60703059). Chinese National Key Foundation
   Research and Development Plan under Grant (2007CB310803) and Higher
   Education Commission, Islamabad, Pakistan for providing scholarship to
   the first author the main contributor of this work. We are thankful to
   Jie Tang for sharing his topic modeling codes.
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Z9 44
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U2 23
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD AUG
PY 2010
VL 23
IS 6
BP 615
EP 625
DI 10.1016/j.knosys.2010.04.008
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 633UO
UT WOS:000280532400015
DA 2024-09-05
ER
PT J
AU Besimi, N
   Çiço, B
   Shehu, V
   Besimi, A
AF Besimi, Nuhi
   Cico, Betim
   Shehu, Visar
   Besimi, Adrian
TI EVALUATION OF MACHINE LEARNING TECHNIQUES FOR RESEARCH ARTICLES
   RECOMMANDATION
SO INTERNATIONAL JOURNAL ON INFORMATION TECHNOLOGIES AND SECURITY
LA English
DT Article
DE recommendation system; supervised learning; unsupervised learning; text
   mining
AB Recently, application of machine learning techniques on textual data has become a crucial factor in terms of extracting useful and unknown information from textual documents. This research adds to the machine learning community by evaluating some of the most significant text mining techniques for unsupervised and supervised learning that will supposedly ease the process of literature review for researchers. Furthermore, it evaluates the accuracy and execution time for all the phases of the model by comparing multiple techniques. Results showed that our proposed model can have a positive impact in terms of easing the processing of literature reviews and identify trend topics for a given field. On the other hand, this solution does not perform very well in execution time as the volume of data increases.
C1 [Besimi, Nuhi] South East European Univ, Tetovo, North Macedonia.
   [Shehu, Visar; Besimi, Adrian] South East European Univ, Fac Contemporary Sci & Technol, Tetovo, North Macedonia.
   [Cico, Betim] Epoka Univ, Skopje, North Macedonia.
RP Besimi, N (corresponding author), South East European Univ, Tetovo, North Macedonia.
EM nuhibesimi@gmail.com; bcico@epoka.edu.al; v.shehu@seeu.edu.mk;
   a.besimi@seeu.edu.mk
RI Cico, Betim/N-4797-2019; Cico, Betim/ABH-8348-2020; Shehu,
   Visar/HLX-2722-2023; Besimi, Adrian/U-5817-2019
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Z9 1
U1 1
U2 4
PU UNION SCIENTISTS BULGARIA
PI SOFIA
PA 1505 SOFIA 39, MADRID BLVD, FLR 2, SOFIA, 00000, BULGARIA
SN 1313-8251
J9 INT J INF TECHNOL SE
JI Int. J. Inf. Technol. Secur.
PY 2020
VL 12
IS 1
BP 75
EP 86
PG 12
WC Computer Science, Information Systems
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA KR9LD
UT WOS:000517933700006
DA 2024-09-05
ER
PT J
AU Manzoor, A
   Asghar, S
   Amjad, T
AF Manzoor, Ayesha
   Asghar, Sohail
   Amjad, Tehmina
TI Toward a New Paradigm for Author Name Disambiguation
SO IEEE ACCESS
LA English
DT Article
DE Semantics; Deep learning; Convolutional neural networks; Training;
   Support vector machines; Libraries; Collaboration; Author name
   disambiguation; digital library; deep learning; classification; word
   embedding; journal descriptor; semantic type
AB Author Name Disambiguation (AND) has emerged as a significant challenge in the bibliometric context with the growing volume of scientific literature. When citations written by different authors have the same names (polysemy or homonym names), and when an author has different names, there is ambiguity (synonyms or name variants). It is difficult to associate a citation with the correct author. Polysemy and synonyms cause merging and splitting anomalies in the citations. These anomalies affect the quantification of an author's productivity (bibliometric analysis) and the reliability and quality of the information retrieved. Many techniques for AND have been proposed in the literature; most of them do not go beyond string matching or text matching. Most of the existing work do not consider the context or semantics of the terms used in the citations. In this study, the AND problem is resolved semantically using the deep learning technique on the PubMed dataset. The experimental results show that the proposed method achieves overall (11.72 %, 12.5 %, and 12.1 %) higher precision, recall, and f-measure than the pairwise class classification.
C1 [Manzoor, Ayesha; Amjad, Tehmina] Int Islamic Univ Islamabad, Dept Comp Sci & Software Engn, Islamabad 44000, Pakistan.
   [Asghar, Sohail] COMSATS Univ Islamabad, Dept Comp Sci, Islamabad 44000, Pakistan.
C3 International Islamic University, Pakistan; COMSATS University Islamabad
   (CUI)
RP Manzoor, A (corresponding author), Int Islamic Univ Islamabad, Dept Comp Sci & Software Engn, Islamabad 44000, Pakistan.
EM ayeshamanzoor1@gmail.com
RI Amjad, Tehmina/GLS-0209-2022
OI asghar, sohail/0000-0001-6883-3584; Amjad, Tehmina/0000-0003-1201-498X;
   manzoor, ayesha/0000-0002-4754-2929
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NR 37
TC 1
Z9 1
U1 1
U2 13
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2022
VL 10
BP 76055
EP 76068
DI 10.1109/ACCESS.2022.3190088
PG 14
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA 3G0NR
UT WOS:000831054800001
OA gold
DA 2024-09-05
ER
PT C
AU Zhang, QP
   Shan, W
AF Zhang Qing-pu
   Shan Wei
BE Lan, H
TI Research on enterprise tacit knowledge management performance appraisal
   based on artificial neural networks
SO PROCEEDINGS OF THE 2006 INTERNATIONAL CONFERENCE ON MANAGEMENT SCIENCE &
   ENGINEERING (13TH), VOLS 1-3
LA English
DT Proceedings Paper
CT 13th International Conference on Management Science and Engineering
CY OCT 05-07, 2006
CL Lille, FRANCE
DE BP algorithm; knowledge management; performance appraisal; tacit
   knowledge
AB Based on knowledge management theory and performance appraisal methodology, the enterprise tacit knowledge management performance appraisal index system is established, and in view of the neural network structure characteristic, self-adapted and self-taught function, enterprise tacit knowledge management performance appraisal model based on BP neural algorithm is proposed. This model is feasible and suitable in terms of convergence rate, the network adaptability aspect. By using these research results, it can appraise the level of the enterprise tacit knowledge management scientifically, and provide the policy-making basis to correctly instruct enterprise knowledge management development.
C1 Harbin Inst Technol, Sch Management, Harbin 150001, Peoples R China.
C3 Harbin Institute of Technology
RP Zhang, QP (corresponding author), Harbin Inst Technol, Sch Management, Harbin 150001, Peoples R China.
RI Wang, Charles/B-5565-2011
OI Wang, Charles/0000-0001-9331-8437
CR AIM JH, 2002, P 35 HAW INT C SYST
   [Anonymous], P 35 HAW INT C SYST
   CHENG GH, 2004, T NW U, V2
   LI SC, 2001, SCI RES MANAGEMENT, V22, P73
   LI XF, 2000, T SICHUAN U, V32, P105
   Tiwana A., 2001, The Essential Guide to Knowledge Management: E-Business and CRM Applications
   [王军霞 Wang Junxia], 2002, [科学学研究, Studies in Science of Science], V20, P84
   WANG Y, 2002, T BEIJING BROADCASTI, V3, P33
   YAN GH, 2001, MANAGEMENT REV NANKA, V6, P26
   ZHANG LM, 1995, MODEL APPL ARTIFICIA
   Zhang Q., 2003, Chinese Wheat Improvement and Pedigree Analysis, P88
NR 11
TC 2
Z9 2
U1 0
U2 4
PU HARBIN INSTITUTE TECHNOLOGY PUBLISHERS
PI HARBIN
PA 16 FUXINGJIE NANGANGQU, HARBIN 150006, HEILONGJIANG, PEOPLES R CHINA
BN 7-5603-2355-3
PY 2006
BP 1333
EP 1337
PG 5
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Engineering, Industrial; Engineering, Electrical & Electronic;
   Operations Research & Management Science
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Engineering; Operations Research & Management Science
GA BFI54
UT WOS:000242037601110
DA 2024-09-05
ER
PT J
AU Usman, M
   Mustafa, G
   Afzal, MT
AF Usman, Muhammad
   Mustafa, Ghulam
   Afzal, Muhammad Tanvir
TI Ranking of author assessment parameters using Logistic Regression
SO SCIENTOMETRICS
LA English
DT Article
DE Parameters ranking; Citation count; Citation intensity; Variants of
   h-index; Civil engineering subject classification; ASCE; CSCE; ACI; ICE
ID H-INDEX; GOOGLE-SCHOLAR; VARIANTS; IMPACT
AB The renowned international scientific societies nominate researchers for awards based on qualitative judgments every year. Qualitative judgment uses subjective assessments based on information that is not quantifiable. The way of assessing the quality of the work has not been established or disclosed, nor do we have any qualitative evaluation criteria. We can assess the quality of the researcher's work by mapping the quantitative parameters to qualitative judgments. To date, the scientific community has presented more than 50 research assessment quantitative parameters, including publication count, citation count, h-index, and its variants. The contemporary state-of-the-art in authors ranking does not determine the best parameter that effectively maps on experts' qualitative evaluation. Moreover, these parameters have been evaluated by using same scenarios. In such scenarios, the value and effect of each parameter over the others are complicated to ascertain. Therefore, they must be assessed in inequitable scenarios. The purpose of this research is to identify the significant parameters that map on qualitative judgments of international scientific societies in Civil Engineering (CE) for award nominations. We will identify the rank of author assessment parameters, which includes published papers, citations, No of years since 1st publication, citations in h-core, authors/paper, citations/paper, citations/year, h-index, g-index, hg-index, A-index, R-index, e-index, and f-index. We have evaluated these parameters on the dataset from the discipline of Civil Engineering (CE). The data set contains 250 non-award winners and 250 award winners from prestigious scientific societies of CE. The h-index and its variants have been ranked based on their effectiveness for awardees using Logistic Regression. The award-winning researchers have less number of average authors/paper than the non-awardees. The authors/paper has achieved the highest effectiveness of 67% for awardees. Furthermore, we have also analyzed the ratio of awardees in the ranked list of 50, 100, and 150 researchers by author assessment parameters. The authors/papers have outperformed all other indices by elevating 62% and 66% of the award recipients in its ranked list of 100 and 150 researchers. In the ranked list of 50 researchers, publications elevate 54% awardees, and Authors/papers achieved the second-highest elevation score of awardees of 50%.
C1 [Usman, Muhammad] FAST Natl Univ Comp & Emerging Sci, Islamabad Campus, Islamabad, Pakistan.
   [Mustafa, Ghulam] Capital Univ Sci & Technol, Islamabad, Pakistan.
   [Afzal, Muhammad Tanvir] Namal Inst Mianwali, Mianwali, Pakistan.
C3 Capital University of Science & Technology
RP Afzal, MT (corresponding author), Namal Inst Mianwali, Mianwali, Pakistan.
EM m.usman@nu.edu.pk; ghulam.mustafa@cust.edu.pk; tanvir.afzal@namal.edu.pk
RI Mustafa, Ghulam/IQV-2174-2023; Mustafa, Ghulam/JPY-1274-2023; Afzal,
   Muhammad/D-3741-2019
OI Mustafa, Ghulam/0000-0003-4467-2987; Mustafa,
   Ghulam/0000-0002-0354-8229; Afzal, Muhammad/0000-0002-7851-2327; Afzal,
   Muhammad Tanvir/0000-0002-9765-8815; Usman, Muhammad/0000-0002-6154-6256
CR Abe N, 2005, LECT NOTES ARTIF INT, V3684, P689
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NR 33
TC 14
Z9 14
U1 3
U2 24
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD JAN
PY 2021
VL 126
IS 1
BP 335
EP 353
DI 10.1007/s11192-020-03769-y
EA NOV 2020
PG 19
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA PU7XL
UT WOS:000591124400001
DA 2024-09-05
ER
PT J
AU Chen, XL
   Zhang, XX
   Xie, HR
   Tao, XH
   Wang, F
   Xie, NF
   Hao, TY
AF Chen, Xieling
   Zhang, Xinxin
   Xie, Haoran
   Tao, Xiaohui
   Wang, Fu Lee
   Xie, Nengfu
   Hao, Tianyong
TI A bibliometric and visual analysis of artificial intelligence
   technologies-enhanced brain MRI research
SO MULTIMEDIA TOOLS AND APPLICATIONS
LA English
DT Article
DE Magnetic resonance imaging; Artificial intelligence; Latent Dirichlet
   allocation; Research topics
ID MILD COGNITIVE IMPAIRMENT; MAGNETIC-RESONANCE IMAGE; FUNCTIONAL NETWORK;
   PATTERN-RECOGNITION; FMRI DATA; CLASSIFICATION; SEGMENTATION; SINGLE;
   DIAGNOSIS; DISORDER
AB With the advances and development of imaging and computer technologies, the application of artificial intelligence (AI) in the processing of magnetic resonance imaging (MRI) data has become a significant research field. Based on 2572 research articles concerning AI-enhanced brain MRI processing, this study provides a latent Dirichlet allocation based bibliometric analysis for the exploration of the status, trends, major research issues, and potential future directions of the research field. The trend analyses of articles and citations demonstrate a flourishing and increasing impact of the research.Neuroimageis the most prolific and influential journal. The USA andUniversity College Londonhave contributed the most to the research. The collaboration between European countries is very close. Essential research issues such asImage segmentation,Mental disorder,Functional network connectivity, andAlzheimer's diseasehave been uncovered. Potential inter-topic research directions such asFunctional network connectivityandMental disorder,Image segmentationandImage classification,Cognitive impairmentandDiffusion imaging, as well asSense and memoryandEmotion and feedback, have been highlighted.
C1 [Chen, Xieling] Educ Univ Hong Kong, Dept Math & Informat Technol, Hong Kong, Peoples R China.
   [Zhang, Xinxin] South China Normal Univ, Inst Brain Res & Rehabil, Guangzhou, Peoples R China.
   [Xie, Haoran] Lingnan Univ, Dept Comp & Decis Sci, Hong Kong, Peoples R China.
   [Tao, Xiaohui] Univ Southern Queensland, Sch Sci, Toowoomba, Qld, Australia.
   [Wang, Fu Lee] Open Univ Hong Kong, Sch Sci & Technol, Hong Kong, Peoples R China.
   [Xie, Nengfu] Chinese Acad Agr Sci, Inst Agr Informat, Beijing, Peoples R China.
   [Hao, Tianyong] South China Normal Univ, Sch Comp Sci, Guangzhou, Peoples R China.
C3 Education University of Hong Kong (EdUHK); South China Normal
   University; Lingnan University; University of Southern Queensland; Hong
   Kong Metropolitan University; Chinese Academy of Agricultural Sciences;
   South China Normal University
RP Hao, TY (corresponding author), South China Normal Univ, Sch Comp Sci, Guangzhou, Peoples R China.
EM haoty@m.scnu.edu.cn
RI tao, xiaohui/KCK-2677-2024; Xie, Haoran/AFS-3515-2022; Tao,
   Xiaohui/JKI-2330-2023; Hao, Tianyong/HJH-2742-2023; Xie,
   Haoran/AAW-8845-2020; Wang, Fu Lee/AAD-9782-2021
OI Xie, Haoran/0000-0003-0965-3617; Hao, Tianyong/0000-0002-9792-3949;
   Wang, Fu Lee/0000-0002-3976-0053; PV, THAYYIB/0000-0001-8929-0398
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NR 146
TC 10
Z9 11
U1 8
U2 56
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1380-7501
EI 1573-7721
J9 MULTIMED TOOLS APPL
JI Multimed. Tools Appl.
PD MAY
PY 2021
VL 80
IS 11
BP 17335
EP 17363
DI 10.1007/s11042-020-09062-7
EA JUN 2020
PG 29
WC Computer Science, Information Systems; Computer Science, Software
   Engineering; Computer Science, Theory & Methods; Engineering, Electrical
   & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA SE7XP
UT WOS:000539519200002
OA Green Accepted
DA 2024-09-05
ER
PT C
AU Zhang, ZQ
   Zhang, AH
AF Zhang, Zhiqiang
   Zhang, Aihua
GP Northeastern Univ, China
TI Research on Performance of Audio Frequency Equalizer Evaluation Based on
   Support Vector Regression
SO 2010 CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-5
SE Chinese Control and Decision Conference
LA English
DT Proceedings Paper
CT 22nd Chinese Control and Decision Conference
CY MAY 26-AUG 28, 2010
CL Xuzhou, PEOPLES R CHINA
DE Support Vector Regression Machine; Performance of Audio Frequency
   Equalizer Evaluation; Amplifier Amplitude Frequency Characteristics
AB A evaluate method of audio frequency equalizer performance based on Support Vector Machine was presented. Taking BPF that applied to audio frequency equalizer as experiment object. First, obtaining dataset of the amplitude frequency of the BPF by amplitude frequency characteristics testing equipment to get samples, and then using support vector regression to get the approach function of the amplifier amplitude frequency characteristics of the BPF, using the function to test four performance parameters. The experimental results show that this method improves the precision of the parameters testing. It is suitable for the evaluate of the electronic production performance that tested by oscillograph.
C1 [Zhang, Zhiqiang; Zhang, Aihua] BoHai Univ, Coll Informat Sci & Engn, Jinzhou 121000, Peoples R China.
C3 Bohai University
RP Zhang, ZQ (corresponding author), BoHai Univ, Coll Informat Sci & Engn, Jinzhou 121000, Peoples R China.
EM Jsxinxi_zzq@163.com; Jsxinxi_zah@163.com
CR CRISTIANINI N, 2004, INTRO SUPPORT VECTOR, P125
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PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 1948-9439
BN 978-1-4244-5182-1
J9 CHIN CONT DECIS CONF
PY 2010
BP 810
EP 814
DI 10.1109/CCDC.2010.5498116
PG 5
WC Automation & Control Systems; Engineering, Electrical & Electronic;
   Operations Research & Management Science
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Automation & Control Systems; Engineering; Operations Research &
   Management Science
GA BUV61
UT WOS:000290460300163
DA 2024-09-05
ER
PT J
AU Zhou, H
   Sun, JQ
   Zhao, ZY
   Yang, YH
   Xie, AL
   Chiclana, F
AF Zhou, Hui
   Sun, Jinqing
   Zhao, Zhongying
   Yang, Yonghao
   Xie, Ailei
   Chiclana, Francisco
TI Attention-Based Deep Learning Model for Predicting Collaborations
   Between Different Research Affiliations
SO IEEE ACCESS
LA English
DT Article
DE Relationship prediction; collaboration analysis; coauthor networks; deep
   learning
ID LINK PREDICTION; INFLUENCE MAXIMIZATION; NETWORKS
AB It is challenging but important to predict the collaborations between different entities which in academia, for example, would enable finding evaluating trends of scientific research collaboration and the provision of decision support for policy formulation and incentive measures. In this paper, we propose an attention-based Long Short-Term Memory Convolutional Neural Network (LSTM-CNN) model to predict the collaborations between different research affiliations, which takes both the influence of research articles and time (year) relationships into consideration. The experimental results show that the proposed model outperforms the competitive Support Vector Machine (SVM), CNN and LSTM methods. It significantly improves the prediction precision by a minimum of 3.23 percent points and up to 10.80 percent points when compared with the mentioned competitive methods, while in terms of the F1-score, the performance is improved by 13.48, 4.85 and 4.24 percent points, respectively.
C1 [Zhou, Hui; Sun, Jinqing; Zhao, Zhongying; Yang, Yonghao] Shandong Univ Sci & Technol, Coll Comp Sci & Engn, Shandong Prov Key Lab Wisdom Mine Informat Techno, Qingdao 266590, Shandong, Peoples R China.
   [Xie, Ailei] Guangzhou Univ, Sch Educ, Guangzhou 510006, Guangdong, Peoples R China.
   [Chiclana, Francisco] De Montfort Univ, Sch Comp Sci & Informat, Inst Artificial Intelligence, Leicester LE1 9BH, Leics, England.
   [Chiclana, Francisco] Univ Granada, Andalusian Res Inst Data Sci & Computat Intellige, E-18071 Granada, Spain.
C3 Shandong University of Science & Technology; Guangzhou University; De
   Montfort University; University of Granada
RP Zhao, ZY (corresponding author), Shandong Univ Sci & Technol, Coll Comp Sci & Engn, Shandong Prov Key Lab Wisdom Mine Informat Techno, Qingdao 266590, Shandong, Peoples R China.
EM zzysuin@163.com
RI zhao, zhongying/V-6991-2019; hui, zhou/JOZ-7380-2023; Chiclana,
   Francisco/B-9031-2008; 谢, 爱磊/GWD-0986-2022
OI Chiclana, Francisco/0000-0002-3952-4210; 
FU Humanities and Social Science Research Project of the Ministry of
   Education in China [17YJCZH262, 18YJAZH136]; National Natural Science
   Foundation of China [61303167, 61702306, 61433012, U1435215, 71772107];
   Natural Science Foundation of Shandong Province [ZR2018BF013,
   ZR2017BF015]; Innovative Research Foundation of Qingdao [18-2-2-41-jch];
   Key Project of Industrial Transformation and Upgrading in China
   [TC170A5SW]; Scientific Research Foundation of SDUST for Innovative Team
   [2015TDJH102]
FX This work was supported in part by the Humanities and Social Science
   Research Project of the Ministry of Education in China under Grant
   17YJCZH262 and Grant 18YJAZH136, in part by the National Natural Science
   Foundation of China under Grant 61303167, Grant 61702306, Grant
   61433012, Grant U1435215, and Grant 71772107, in part by the Natural
   Science Foundation of Shandong Province under Grant ZR2018BF013 and
   Grant ZR2017BF015, in part by the Innovative Research Foundation of
   Qingdao under Grant 18-2-2-41-jch, in part by the Key Project of
   Industrial Transformation and Upgrading in China under Grant TC170A5SW,
   and in part by the Scientific Research Foundation of SDUST for
   Innovative Team under Grant 2015TDJH102.
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NR 39
TC 1
Z9 1
U1 1
U2 23
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2019
VL 7
BP 118068
EP 118076
DI 10.1109/ACCESS.2019.2936745
PG 9
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA IV6BV
UT WOS:000484354800001
OA Green Submitted, gold
DA 2024-09-05
ER
PT J
AU Vinayak
   Raghuvanshi, A
   Kshitij, A
AF Vinayak
   Raghuvanshi, Adarsh
   Kshitij, Avinash
TI Signatures of capacity development through research collaborations in
   artificial intelligence and machine learning
SO JOURNAL OF INFORMETRICS
LA English
DT Article
DE Artificial intelligence; Co-authorship network; Network statistics;
   Closeness centrality; Machine learning; Social network analysis
ID CO-AUTHORSHIP NETWORKS; SCIENTIFIC COLLABORATION; CENTRALITY; IMPACT;
   PRODUCTIVITY; PROXIMITY; EVOLUTION; PATTERNS
AB Extant studies suggest that the proximity between the researchers and their structural position-ing in the collaboration network may influence productivity and performance in collaboration research. In this paper, we analyze the co-authorship networks of the three countries, viz. the USA, China, and India, constructed in consecutive non-overlapping 5-year long time windows from bibliometric data of research papers published in the past decade in the rapidly evolving area of Artificial Intelligence and Machine Learning (AI&ML). Our analysis relies on the observa-tions ensued from a comparison of the statistical properties of the evolving networks. We consider macro-level network properties which describe the global characteristics, such as degree distri-bution, assortativity, and large-scale cohesion etc., as well as micro-level properties associated with the actors who have assumed central positions, defining a core in the network assembly with respect to closeness centrality measure. For the analysis of the core actors, who are well connected with a large number of other actors, we consider share of their affiliations with do-mestic institutes. We find dominant representation of domestic affiliations of the core actors for high productivity cases, such as China in the second time window and the USA in the first and second both. Our study, therefore, suggests that the domestic affiliation of the core actors, who could access network resources more efficiently than other actors, influences and catalyzes the collaborative research.
C1 [Vinayak; Raghuvanshi, Adarsh; Kshitij, Avinash] CSIR Natl Inst Sci Commun & Policy Res, Dr KS Krishnan Marg, New Delhi 110012, India.
   [Vinayak; Raghuvanshi, Adarsh; Kshitij, Avinash] Acad Sci & Innovat Res AcSIR, Ghaziabad 201002, India.
C3 Council of Scientific & Industrial Research (CSIR) - India; CSIR -
   National Institute of Science Communication & Policy Research (NIScPR);
   Academy of Scientific & Innovative Research (AcSIR)
RP Vinayak (corresponding author), CSIR Natl Inst Sci Commun & Policy Res, Dr KS Krishnan Marg, New Delhi 110012, India.; Vinayak (corresponding author), Acad Sci & Innovat Res AcSIR, Ghaziabad 201002, India.
EM vinayaksps2003@gmail.com
FU University Grant Commission, India [191620064987]
FX Adarsh Raghuvanshi acknowledges University Grant Commission, India (NTA
   Ref. No.: 191620064987) for financial support. The authors acknowledge
   Anirban Chakraborty and are indebted to anonymous reviewers for their
   insightful comments and suggestions.
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NR 48
TC 1
Z9 1
U1 9
U2 35
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1751-1577
EI 1875-5879
J9 J INFORMETR
JI J. Informetr.
PD FEB
PY 2023
VL 17
IS 1
AR 101358
DI 10.1016/j.joi.2022.101358
EA DEC 2022
PG 17
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 7E0LW
UT WOS:000900872200003
DA 2024-09-05
ER
PT C
AU Wang, GJ
AF Wang, GuoJun
GP IEEE
TI Research on the Construction of Classroom Teaching Evaluation System
   Based on Artificial Intelligence
SO 2021 INTERNATIONAL CONFERENCE ON BIG DATA ENGINEERING AND EDUCATION
   (BDEE 2021)
LA English
DT Proceedings Paper
CT International Conference on Big Data Engineering and Education (BDEE)
CY AUG 12-14, 2021
CL Guiyang, PEOPLES R CHINA
DE artificial intelligence; classroom teaching; evaluation; construction
   research
AB Human beings are accelerating into the era of artificial intelligence. In all areas of society, artificial intelligence is playing an irreplaceable and huge role. The development of artificial intelligence in the field of education is accelerating. The development of artificial intelligence in the field of education is accelerating. It promotes the development of teaching and makes classroom teaching more lively and interesting. Classroom teaching evaluation occupies a very important position in education and teaching, and it is a ruler to measure the quality of classroom teaching. Based on the study of traditional classroom teaching evaluation, this paper analyzes the problems in traditional teaching evaluation from three aspects: single teaching evaluation method, untimely feedback of evaluation results, and inability to achieve comprehensive evaluation. An artificial intelligence-based classroom teaching evaluation system is constructed from three aspects: system structure, procedural evaluation of students' listening to lectures, and procedural evaluation of teachers' teaching. On this basis, it analyzes the technical, ethical, and systematic problems of artificial intelligence in classroom teaching evaluation. In response to these problems, the development strategy of artificial intelligence in classroom teaching evaluation is proposed from several aspects, such as breaking through the technical bottleneck and establishing a teaching evaluation simulation system. Finally, the prospects for the development of artificial intelligence in teaching evaluation are prospected.
C1 [Wang, GuoJun] Shandong Technician Coll Water Conservancy, Dept Intelligent Mfg, Zibo, Peoples R China.
RP Wang, GJ (corresponding author), Shandong Technician Coll Water Conservancy, Dept Intelligent Mfg, Zibo, Peoples R China.
EM 155098193@qq.com
FU Shandong University [SSD-2020-002]
FX This article is part of the research results of the 2020 Shandong
   University Student Ideological and Political Education Theory and
   Practice Research Project "Curriculum Ideology and Politics Integration
   into the Course Evaluation System of Mechatronics Professional Course
   Teaching" (Project Number: SSD-2020-002), Project host: Guojun Wang
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   Hongcheng Jiang, 2021, International Journal of Information and Education Technology, V11, P262, DOI 10.18178/ijiet.2021.11.6.1521
   Hu Z. H., 2019, EDUC REV, P128
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NR 9
TC 1
Z9 1
U1 7
U2 37
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-6654-3957-2
PY 2021
BP 101
EP 105
DI 10.1109/BDEE52938.2021.00024
PG 5
WC Computer Science, Information Systems; Computer Science,
   Interdisciplinary Applications
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BT8LI
UT WOS:000853884200018
DA 2024-09-05
ER
PT J
AU Alahmadi, D
   Babour, A
   Saeedi, K
   Visvizi, A
AF Alahmadi, Dimah
   Babour, Amal
   Saeedi, Kawther
   Visvizi, Anna
TI Ensuring Inclusion and Diversity in Research and Research Output: A Case
   for a Language-Sensitive NLP Crowdsourcing Platform
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE human-centric technology; inclusion; decision-making; natural language
   processing (NLP); NLP crowdsourcing platforms
ID CORPUS
AB In the context of the debate on the need to place citizens at the center of the technological revolution, this paper makes a case for a natural language processing (NLP) crowdsourcing platform that ensures inclusion and diversity, thus making the research outcome relevant and applicable across issues and domains. This paper also makes the case that by enabling participation for a wide variety of stakeholders, this NLP crowdsourcing platform might ultimately prove useful in the decision- and policy-making processes at city, community, and country levels. Against the backdrop of the debates on artificial intelligence (AI) and NLP research, and considering substantial differentiation specific to the Arab language, this paper introduces and evaluates an Arab language-sensitive NLP crowdsourcing platform. The value of the platform and its accuracy are measured via the System Usability Scale (SUS), where it scores 72.5, i.e., above the accepted usability average. These findings are crucial for NLP research and the research community in general. They are equally promising in view of the practical application of the research findings.
C1 [Alahmadi, Dimah; Babour, Amal; Saeedi, Kawther] King Abdulaziz Univ, Dept Informat Syst, Fac Comp & Informat Technol, Jeddah 21589, Saudi Arabia.
   [Visvizi, Anna] Effat Univ, Effat Coll Business, Jeddah 34689, Saudi Arabia.
C3 King Abdulaziz University; Effat University
RP Alahmadi, D (corresponding author), King Abdulaziz Univ, Dept Informat Syst, Fac Comp & Informat Technol, Jeddah 21589, Saudi Arabia.
EM dalahmadi@kau.edu.sa; ababor@kau.edu.sa; ksaeedi@kau.edu.sa;
   avisvizi@gmail.com
RI Saeedi, Kawther/JDD-5107-2023; Visvizi, Anna/AFV-1944-2022
OI Alahmadi, Dimah/0000-0001-5576-8832; Babour, Amal/0000-0002-0365-3889;
   Saeedi, Kawther/0000-0002-5295-4485; Visvizi, Anna/0000-0003-3240-3771
FU Deanship of Scientific Research (DSR), King Abdulaziz University,
   Jeddah, Saudi Arabia [G: 1473-612-1440, 62467]
FX This project was funded by the Deanship of Scientific Research (DSR),
   King Abdulaziz University, Jeddah, Saudi Arabia, under grant G:
   1473-612-1440 and grant No. 62467. The authors D.A., A.B., and K.S.
   therefore gratefully acknowledge the DSR technical and financial
   support.
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NR 36
TC 3
Z9 3
U1 1
U2 21
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2076-3417
J9 APPL SCI-BASEL
JI Appl. Sci.-Basel
PD SEP
PY 2020
VL 10
IS 18
AR 6216
DI 10.3390/app10186216
PG 18
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Chemistry; Engineering; Materials Science; Physics
GA OE2KI
UT WOS:000580365600001
OA gold
DA 2024-09-05
ER
PT J
AU Tao, KM
   Osman, ZA
   Tzou, PL
   Rhee, SY
   Ahluwalia, V
   Shafer, RW
AF Tao, Kaiming
   Osman, Zachary A.
   Tzou, Philip L.
   Rhee, Soo-Yon
   Ahluwalia, Vineet
   Shafer, Robert W.
TI GPT-4 performance on querying scientific publications: reproducibility,
   accuracy, and impact of an instruction sheet
SO BMC MEDICAL RESEARCH METHODOLOGY
LA English
DT Article
DE Large language model; HIV drug resistance; Systematic review; GPT-4;
   Data extraction
AB BackgroundLarge language models (LLMs) that can efficiently screen and identify studies meeting specific criteria would streamline literature reviews. Additionally, those capable of extracting data from publications would enhance knowledge discovery by reducing the burden on human reviewers.MethodsWe created an automated pipeline utilizing OpenAI GPT-4 32 K API version "2023-05-15" to evaluate the accuracy of the LLM GPT-4 responses to queries about published papers on HIV drug resistance (HIVDR) with and without an instruction sheet. The instruction sheet contained specialized knowledge designed to assist a person trying to answer questions about an HIVDR paper. We designed 60 questions pertaining to HIVDR and created markdown versions of 60 published HIVDR papers in PubMed. We presented the 60 papers to GPT-4 in four configurations: (1) all 60 questions simultaneously; (2) all 60 questions simultaneously with the instruction sheet; (3) each of the 60 questions individually; and (4) each of the 60 questions individually with the instruction sheet.ResultsGPT-4 achieved a mean accuracy of 86.9% - 24.0% higher than when the answers to papers were permuted. The overall recall and precision were 72.5% and 87.4%, respectively. The standard deviation of three replicates for the 60 questions ranged from 0 to 5.3% with a median of 1.2%. The instruction sheet did not significantly increase GPT-4's accuracy, recall, or precision. GPT-4 was more likely to provide false positive answers when the 60 questions were submitted individually compared to when they were submitted together.ConclusionsGPT-4 reproducibly answered 3600 questions about 60 papers on HIVDR with moderately high accuracy, recall, and precision. The instruction sheet's failure to improve these metrics suggests that more sophisticated approaches are necessary. Either enhanced prompt engineering or finetuning an open-source model could further improve an LLM's ability to answer questions about highly specialized HIVDR papers.
C1 [Tao, Kaiming; Osman, Zachary A.; Tzou, Philip L.; Rhee, Soo-Yon; Shafer, Robert W.] Stanford Univ, Dept Med, Div Infect Dis, Stanford, CA 94305 USA.
   [Ahluwalia, Vineet] Aphorism Labs, Palo Alto, CA USA.
C3 Stanford University
RP Shafer, RW (corresponding author), Stanford Univ, Dept Med, Div Infect Dis, Stanford, CA 94305 USA.
EM rshafer@stanford.edu
FU National Institutes of Health
FX This work was funded by a grant from the National Institutes of Health:
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NR 18
TC 0
Z9 0
U1 0
U2 0
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
EI 1471-2288
J9 BMC MED RES METHODOL
JI BMC Med. Res. Methodol.
PD JUN 25
PY 2024
VL 24
IS 1
AR 139
DI 10.1186/s12874-024-02253-y
PG 10
WC Health Care Sciences & Services
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Health Care Sciences & Services
GA WI1Y8
UT WOS:001254161100001
PM 38918736
OA gold
DA 2024-09-05
ER
PT C
AU Urooj, A
   Khan, HU
   Iqbal, S
   Althebyan, Q
AF Urooj, Amber
   Khan, Hikmat Ullah
   Iqbal, Saqib
   Althebyan, Qutaibah
BE Guetl, C
   Ceravolo, P
   Jararweh, Y
   Benkhelifa, E
   Adedugbe, O
TI On Prediction of Research Excellence using Data Mining and Deep Learning
   Techniques
SO 2021 EIGHTH INTERNATIONAL CONFERENCE ON SOCIAL NETWORK ANALYSIS,
   MANAGEMENT AND SECURITY (SNAMS)
LA English
DT Proceedings Paper
CT 8th International Conference on Social Network Analysis, Management and
   Security (SNAMS)
CY DEC 06-09, 2021
CL ELECTR NETWORK
DE Scientometrics; Machine Learning Techniques; Deep Learning Techniques;
   Research Excellence and Data Mining Techniques
ID AUTHORS
AB Scientometrics analyses the science, technology and innovation. It measures and analyses the scientific literature. The goal of our research is to predict excellence of the researchers and examine the relationship between scientometric indicators. Data Mining Techniques are used to study research excellence in this paper. A dataset used in this research study consisted of 406 researcher's data which is extracted from MathSciNet (MSN) databases. Data mining classification algorithms like Naive Bayes, Decision Tree, Random Forest, Support Vector Machine, Logistic Regression and Deep Learning are applied on the dataset for the prediction of research excellence. The performance of these algorithms is also compared on the basis of some performance measures.
C1 [Urooj, Amber; Khan, Hikmat Ullah] COMSATS Univ Islamabad, Dept Comp Sci, Wah, Pakistan.
   [Iqbal, Saqib; Althebyan, Qutaibah] Al Ain Univ, Coll Engn, Al Ain, U Arab Emirates.
C3 COMSATS University Islamabad (CUI)
RP Urooj, A (corresponding author), COMSATS Univ Islamabad, Dept Comp Sci, Wah, Pakistan.
EM amberurooj1122@gmail.com; hikmat.ullah@ciitwah.edu.pk;
   saqib.iqbal@aau.ac.ae; qutaibah.althebyan@aau.ac
RI Khan, Hikmat Ullah/GZG-2251-2022
OI Khan, Hikmat/0000-0002-8178-6652
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NR 13
TC 0
Z9 0
U1 0
U2 0
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-6654-9495-3
PY 2021
BP 50
EP 55
DI 10.1109/SNAMS53716.2021.9732153
PG 6
WC Computer Science, Artificial Intelligence; Computer Science, Theory &
   Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BT2PA
UT WOS:000813133100007
DA 2024-09-05
ER
PT J
AU Iqbal, AM
   Iqbal, S
   Khan, AS
   Senin, AA
AF Iqbal, Abeda Muhammad
   Iqbal, Saima
   Khan, Adnan Shahid
   Senin, Aslan Amat
TI A Novel Cost Efficient Evaluation Model for Assessing Research-Based
   Technology Transfer between University and Industry
SO JURNAL TEKNOLOGI
LA English
DT Article
DE University-industry research collaboration; evaluation metrics;
   evaluation model; technology transfer
AB Innovations and inventions are not outcomes of single activity of any organization. This is a result of collaboration of different partners. The evaluation of collaborated research between university and industry has created the greatest interest amongst the collaboration researchers as it can determine the feasibility and value of the collaboration. Despite the enormous importance of this collaboration, there have been certain problems in successful collaboration, for instance issues related to time, trainings, differences in their perceptions, orientations and goals, intellectual property right issues, some other technological competency and fund and financial matters are the key constraints that generates some how proportional to this collaboration. Thus to tackle the basis of these problems and to analyse the strength and weaknesses of these technological linkage, evaluation of such collaboration is highly demanded. This paper intends to illustrate an evaluation model to evaluate the university-industry collaboration and to enhance their technological linkage. For bridging the model, four important variables, constraints, evaluation parameter, success criteria and tangible outcome has been identified. The novelty of this model is, it is cost and time efficient and can be applied for any university-industry research collaboration.
C1 [Iqbal, Abeda Muhammad; Iqbal, Saima; Senin, Aslan Amat] Univ Teknol Malaysia, Fac Management & Human Resource Dev, Utm Johor Bahru 81310, Johor, Malaysia.
   [Khan, Adnan Shahid] Univ Teknol Malaysia, Fac Elect Engn, UTM MIMOS Ctr Excellence, Skudai 81310, Malaysia.
C3 Universiti Teknologi Malaysia; Universiti Teknologi Malaysia
RP Iqbal, AM (corresponding author), Univ Teknol Malaysia, Fac Management & Human Resource Dev, Utm Johor Bahru 81310, Johor, Malaysia.
EM abidaiqbal49@yahoo.com
RI Khan, Adnan Shahid S/N-7113-2018; Iqbal, Abeda Muhammad/AAM-2154-2020;
   Senin, Aslan Amat/B-1757-2017
OI Senin, Aslan Amat/0000-0002-8004-8746; Khan, Adnan
   Shahid/0000-0002-3924-3646
FU Faculty of Management and Human Resource Development (FPPSM); Research
   Management Centre (RMC); Universiti Teknologi Malaysia (UTM)
FX The authors would like to thanks Faculty of Management and Human
   Resource Development (FPPSM) and Research Management Centre (RMC),
   Universiti Teknologi Malaysia (UTM) for their partial funding
   contributions.
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NR 16
TC 0
Z9 0
U1 0
U2 0
PU PENERBIT UTM PRESS
PI JOHOR
PA PENERBIT UTM PRESS, SKUDAI, JOHOR, 81310, MALAYSIA
SN 0127-9696
EI 2180-3722
J9 J TEKNOL
JI J. Teknol.
PY 2013
VL 64
IS 2
PG 5
WC Engineering, Multidisciplinary
WE Emerging Sources Citation Index (ESCI)
SC Engineering
GA V3P1I
UT WOS:000218458900031
DA 2024-09-05
ER
PT C
AU Barakhnin, VB
   Duisenbayeva, AN
   Kozhemyakina, OY
   Yergaliyev, YN
   Muhamedyev, RI
AF Barakhnin, V. B.
   Duisenbayeva, A. N.
   Kozhemyakina, O. Yu
   Yergaliyev, Y. N.
   Muhamedyev, R., I
GP IOP
TI The automatic processing of the texts in natural language. Some
   bibliometric indicators of the current state of this research area
SO BIGDATA CONFERENCE (FORMERLY INTERNATIONAL CONFERENCE ON BIG DATA AND
   ITS APPLICATIONS)
SE Journal of Physics Conference Series
LA English
DT Proceedings Paper
CT 5th Big Data Conference (BDC)
CY SEP 13, 2018
CL Moscow, RUSSIA
DE Natural language processing; Machine Learning; Bibliometric Indicators;
   Scientometrics; Deep Learning; Neural Networks; Information Extraction;
   Text Categorization; Dialog Systems; Speech Recognition; Machine
   Translation; Information Retrieval; Question Answering; Opinion Mining;
   Smart advisors; D1; D2; semantic network
AB This work reviews the bibliometric indicators of a rapidly developing field of research as automatic text processing (Natural language processing). The differential indicators of speed and acceleration were used to evaluate the development dynamics of NLP domains. The evaluation was based on the data from the Science direct bibliometric database. The evaluation of the Russian research segment was conducted according to e-library data. The calculations for the following subdomains of NLP were performed: Grammar Checking, Information Extraction, Text Categorization, Dialog Systems, Speech Recognition, Machine Translation, Information Retrieval, Question Answering, Opinion Mining, Smart advisors and others. The areas with high growth rates (Grammar Checking, Information Extraction, Machine Translation and Question Answering) and the areas that have lost the previously existing dynamics of growth of the publication activity (Information Retrieval, Opinion Mining, Text Categorization) have been identified.
C1 [Barakhnin, V. B.; Kozhemyakina, O. Yu] Inst Computat Technol SB RAS, Moscow, Russia.
   [Duisenbayeva, A. N.; Yergaliyev, Y. N.; Muhamedyev, R., I] Inst Informat & Computat Technol MES RK, Alma Ata, Kazakhstan.
   [Duisenbayeva, A. N.; Yergaliyev, Y. N.; Muhamedyev, R., I] Satbayev Univ, Alma Ata, Kazakhstan.
   [Muhamedyev, R., I] ISMA Univ, Riga, Latvia.
C3 Russian Academy of Sciences; Federal Research Center for Information &
   Computational Technologies; Institute of Information & Computational
   Technologies; Satbayev University
RP Barakhnin, VB (corresponding author), Inst Computat Technol SB RAS, Moscow, Russia.
EM bar@ict.nsc.ru; a.duisenbayeva@gmail.com; olgakozhemyakina@mail.ru;
   erlan21@mail.ru; ravil.muhamedyev@gmail.com
RI Kozhemyakina, Olga Yu./AAG-8715-2019; Mukhamediev, Ravil I./X-1461-2019;
   Yergaliyev, Yerlan/AAB-2507-2020; Barakhnin, Vladimir/A-5856-2014
OI Kozhemyakina, Olga Yu./0000-0003-3619-1120; Mukhamediev, Ravil
   I./0000-0002-3727-043X; Barakhnin, Vladimir/0000-0003-3299-0507;
   Yergaliyev, Yerlan/0000-0001-9632-3784
FU Ministry of Education and Science of the Republic of Kazakhstan
   [BR05236839]
FX The work was funded by grant No. BR05236839 of the Ministry of Education
   and Science of the Republic of Kazakhstan.
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NR 20
TC 9
Z9 9
U1 0
U2 11
PU IOP PUBLISHING LTD
PI BRISTOL
PA DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND
SN 1742-6588
EI 1742-6596
J9 J PHYS CONF SER
PY 2018
VL 1117
AR 012001
DI 10.1088/1742-6596/1117/1/012001
PG 9
WC Computer Science, Artificial Intelligence; Computer Science, Theory &
   Methods; Physics, Multidisciplinary
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Physics
GA BO1FH
UT WOS:000495570900001
OA gold
DA 2024-09-05
ER
PT J
AU Gupta, BM
   Dhawan, SM
AF Gupta, B. M.
   Dhawan, S. M.
TI Machine Translation Research: A Scientometric Assessment of Global
   Publications Output during 2007-16
SO DESIDOC JOURNAL OF LIBRARY & INFORMATION TECHNOLOGY
LA English
DT Article
DE Machine translation; Global publications; Scientometrics; Bibliometrics
AB The present study provides a quantitative and qualitative description of global machine translation research published during 2007-16 and as indexed in Scopus database. The study profiles research in the field on a series of measures, such as publications growth rate, global share, citation impact, share of international collaborative papers and distribution of publications by sub-areas. The study also profiles top contributing countries, organisations and authors in machine translation research on a series of bibliometric indicators. The study further reports characteristics of highly cited papers in the field.
C1 [Gupta, B. M.] 1173 Sect 15, Panchkula 134113, India.
   [Dhawan, S. M.] 114 Dayanand Vihar, Delhi 110092, India.
RP Dhawan, SM (corresponding author), 114 Dayanand Vihar, Delhi 110092, India.
EM smdhawan@yahoo.com
RI Technology, DESIDOC Journal of Library and Information/AHA-3068-2022
CR Brownlee Jason, 2017, NATURAL LANGUAGE PRO
   Dong DH, 2015, SCIENTOMETRICS, V105, P1111, DOI 10.1007/s11192-015-1769-1
   Gile D, 2015, PERSPECT STUD TRANSL, V23, P240, DOI 10.1080/0907676X.2014.972418
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   Zanettin F, 2015, PERSPECT STUD TRANSL, V23, P161, DOI 10.1080/0907676X.2015.1010551
NR 8
TC 3
Z9 3
U1 1
U2 13
PU DEFENCE SCIENTIFIC INFORMATION DOCUMENTATION CENTRE
PI DELHI
PA METCALFE HOUSE, DELHI 110054, INDIA
SN 0974-0643
EI 0976-4658
J9 DESIDOC J LIB INF TE
JI DESIDOC J. Lib. Inf. Technol.
PD JAN
PY 2019
VL 39
IS 1
BP 31
EP 38
DI 10.14429/djlit.39.1.13558
PG 8
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA HO5AO
UT WOS:000460935300005
OA Green Submitted, gold
DA 2024-09-05
ER
PT J
AU Ahmed, B
   Wang, L
   Al-Shamayleh, AS
   Afzal, MT
   Mustafa, G
   Alrawagfeh, W
   Akhunzada, A
AF Ahmed, Bilal
   Wang, Li
   Al-Shamayleh, Ahmad Sami
   Afzal, Muhammad Tanvir
   Mustafa, Ghulam
   Alrawagfeh, Wagdi
   Akhunzada, Adnan
TI Machine Learning Approach for Effective Ranking of Researcher Assessment
   Parameters
SO IEEE ACCESS
LA English
DT Article
DE Measurement; Indexes; Mathematics; Metadata; Reliability; Random
   forests; Current measurement; Research evaluation; H index and variants;
   research assessment parameters; ranking of researchers; math subject
   classification
ID H-INDEX; COMPREHENSIVE EVALUATION; GOOGLE SCHOLAR; PUBLICATION;
   EXTENSIONS; VARIANTS; JOURNALS; METRICS; IMPACT
AB The measurement and assessment of academic performance is now a fact of scientific life. This assessment guides the scientific community in making significant judgments such as selecting appropriate candidates for various positions, nominating individuals for scientific awards, and awarding scholarships or grants. Several research assessment parameters have been proposed by researchers to identify the most influential scholars. In the literature, researchers have employed a combination of hypothetical and fictional scenarios, as well as manual approaches, to identify the best assessment parameters. Moreover, there is no established benchmark available for assessing these parameters. The current study employs an innovative machine learning approach, the Dynamic Random Forest with Brouta Optimizer called "BorutaRanked Forest", to prioritize the assessment metrics for researchers by calculating the importance score for each metric. Thirty different assessment metrics have been evaluated on a comprehensive dataset of researchers that contains awardees researchers and non-awardees researchers of three decades from (1990 to 2023). The main purpose of this evaluation is to determine the potential value and significance of each parameter relative to others. In addition, the position of awardees researchers is examined at different percentile ranges form Top 10% to Top 100% in the ranked lists of each parameter. During the individual evaluation of each parameter, we uncovered several intriguing patterns in the data. Our findings indicate that the normalized h-index is a particularly effective assessment parameter for the impact evaluation of researchers in the domain of mathematics. An analysis has been conducted to explore the correlation between parameters and awarding societies, examining the associations between different metrics and specific awarding societies.
C1 [Ahmed, Bilal; Wang, Li] Taiyuan Univ Technol, Coll Informat & Comp, Taiyuan, Peoples R China.
   [Al-Shamayleh, Ahmad Sami] Al Ahliyya Amman Univ, Fac Informat Technol, Dept Data Sci & Artificial Intelligence, Amman 19328, Jordan.
   [Afzal, Muhammad Tanvir; Mustafa, Ghulam] Shifa Tameer E Millat Univ, Dept Comp, Islamabad 44000, Pakistan.
   [Alrawagfeh, Wagdi; Akhunzada, Adnan] Univ Doha Sci & Technol, Coll Comp & IT, Doha, Qatar.
C3 Taiyuan University of Technology; Al-Ahliyya Amman University
RP Alrawagfeh, W (corresponding author), Univ Doha Sci & Technol, Coll Comp & IT, Doha, Qatar.
EM wagdi.alrawagfeh@udst.edu.qa
RI Al-Shamayleh, Ahmad Sami/IVU-8846-2023; Akhunzada, Adnan/N-7917-2017;
   Afzal, Muhammad/D-3741-2019
OI Akhunzada, Adnan/0000-0001-8370-9290; Afzal,
   Muhammad/0000-0002-7851-2327; Alrawagfeh, Wagdi/0000-0003-4227-9276;
   Ahmed, Bilal/0000-0002-9458-9412; Wang, Li/0000-0002-7385-1426;
   Al-Shamayleh, Dr. Ahmad Sami/0000-0002-7222-2433
FU Qatar National Library
FX No Statement Available
CR Ahmed B., 2020, P 6 INT C DAT SCI IC, P331
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NR 49
TC 3
Z9 3
U1 4
U2 8
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2023
VL 11
BP 133294
EP 133312
DI 10.1109/ACCESS.2023.3336950
PG 19
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA AB6N6
UT WOS:001116036800001
OA Green Submitted, gold
DA 2024-09-05
ER
PT J
AU Huang, L
   Cai, YJ
   Zhao, ED
   Zhang, ST
   Shu, Y
   Fan, J
AF Huang, Lu
   Cai, Yijie
   Zhao, Erdong
   Zhang, Shengting
   Shu, Yue
   Fan, Jiao
TI Measuring the interdisciplinarity of Information and Library Science
   interactions using citation analysis and semantic analysis
SO SCIENTOMETRICS
LA English
DT Article
DE Interdisciplinary interactions; Citation analysis; Semantic analysis;
   LDA; Word2Vec
ID TOPIC EXTRACTION; DIVERSITY; NETWORK; MODEL; NORMALIZATION; EXPLORATION;
   INDICATORS; FRAMEWORK; INDEXES
AB Interdisciplinary interaction and integration have become major features of current science and technology development. Hence, ways to measure the strength of the interdisciplinary interactions between two given disciplines has become a crucial issue. In this study, we propose a novel framework for measuring interdisciplinary interaction that is based on both citation analysis and semantic analysis. Within the framework, direct citations combined with bibliographic coupling reflect citation relationship of interdisciplinary knowledge, while an LDA model combined with a word embedding model are used to explore the integration and diffusion of knowledge via semantic similarity. The strength of the interdisciplinary interactions is then assessed with an entropy weighting method. A case study on the interactions between Information & Library Science and six other disciplines demonstrates the efficacy and reliability of the framework.
C1 [Huang, Lu; Cai, Yijie; Zhao, Erdong; Zhang, Shengting; Shu, Yue; Fan, Jiao] Beijing Inst Technol, Sch Management & Econ, 5 South Zhongguancun St, Beijing 100081, Peoples R China.
C3 Beijing Institute of Technology
RP Zhao, ED (corresponding author), Beijing Inst Technol, Sch Management & Econ, 5 South Zhongguancun St, Beijing 100081, Peoples R China.
EM huanglu628@163.com; m15001185835@163.com; teacherzed@163.com;
   bhgszst@163.com; shuyue.1997@163.com; 18844116182@163.com
RI lan, xueyao/JZD-4201-2024
FU National Science Foundation of China [71673086, 71774013]
FX This work was supported by the National Science Foundation of China
   [Grant No. 71673086; 71774013]. Our heartfelt appreciation goes to
   Changtian Wang for his contributions to this paper.
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NR 82
TC 10
Z9 11
U1 24
U2 134
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD NOV
PY 2022
VL 127
IS 11
BP 6733
EP 6761
DI 10.1007/s11192-022-04401-x
EA JUN 2022
PG 29
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 5U5FU
UT WOS:000807322400004
DA 2024-09-05
ER
PT C
AU Fan, LP
   Wang, YF
   Ding, SC
AF Fan, Lipeng
   Wang, Yuefen
   Ding, Shengchun
BE Catalano, G
   Daraio, C
   Gregori, M
   Moed, HF
   Ruocco, G
TI Scientific research collaboration in Artificial Intelligence: global
   trends and citations at the institution level
SO 17TH INTERNATIONAL CONFERENCE ON SCIENTOMETRICS & INFORMETRICS
   (ISSI2019), VOL I
SE Proceedings of the International Conference on Scientometrics and
   Informetrics
LA English
DT Proceedings Paper
CT 17th International Conference of the
   International-Society-for-Scientometrics-and-Informetrics (ISSI) on
   Scientometrics and Informetrics
CY SEP 02-05, 2019
CL Sapienza Univ Rome, Rome, ITALY
HO Sapienza Univ Rome
DE Collaboration pattern; Global trends; Citation; Institution level;
   Artificial Intelligence
ID INTERNATIONAL COLLABORATION; COMPUTER-SCIENCE; PATTERNS
AB In order to gain a deeper understanding of collaboration and the relationship between collaboration patterns and citations in global Artificial Intelligence (AI) research, this present paper defines institution types and collaboration patterns from a new perspective. According to a variation of H-index, it classifies institutions into two types: Main institutions and Normal institutions. Based on institution types of the first and remaining institutions in a paper, it divides collaboration publications into six parts: M, M&M, M&N, N, N&M and N&N. In this study, all publications were collected from papers listed in Web of science from 1997 to 2017, published in the field of AI. According the number of units in a paper, results show that five or more authors have a great chance to be the primary pattern in Al field in the future; single-institution papers are the primary pattern but decreasing sharply during a long time; single-country papers keep playing a dominant role in past almost 20 years. According to different collaboration types, results show that five or more author publications are the primary form in M&M, M&N and N&M types, while three-author papers in N & N; Domestic two-institution papers in M&N and N&N are obviously more than that in M&M and N&M types; Single-country papers account for a large share in M&N, N&N and N&M, while two-country papers are more than single-country papers and become the most important part since 2010 in M&M. According to the relationship between collaboration types and citations, results show that the number of Main institutions has a positive relationship with the citation values, while the number of Normal institutions has a little negative influence on N&N type.
C1 [Fan, Lipeng; Wang, Yuefen; Ding, Shengchun] Nanjing Univ Sci & Technol, Sch Econ & Management, Nanjing, Peoples R China.
   [Wang, Yuefen] Jiangsu Collaborat Innovat Ctr Social Safety Sci, Nanjing, Peoples R China.
C3 Nanjing University of Science & Technology
RP Fan, LP (corresponding author), Nanjing Univ Sci & Technol, Sch Econ & Management, Nanjing, Peoples R China.
EM funnypower@126.com; yuefen163@163.com; todingding@163.com
RI fan, lipeng/IWM-3409-2023
FU National Social Science of China [16ZDA224]
FX The authors are grateful to anonymous referees and editors for their
   invaluable and insightful comments, and thank for the support by the
   National Social Science of China (16ZDA224).
CR Abramo G, 2011, SCIENTOMETRICS, V86, P347, DOI 10.1007/s11192-010-0297-2
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NR 19
TC 0
Z9 0
U1 1
U2 15
PU INT SOC SCIENTOMETRICS & INFORMETRICS-ISSI
PI LEUVEN
PA KATHOLIEKE UNIV LEUVEN, FACULTEIT E T E W, DEKENSTRAAT 2, LEUVEN,
   B-3000, BELGIUM
SN 2175-1935
BN 978-88-3381-118-5
J9 PRO INT CONF SCI INF
PY 2019
BP 596
EP 607
PG 12
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Information Science & Library Science
GA BO2SN
UT WOS:000508217900057
DA 2024-09-05
ER
PT J
AU Shao, Z
   Yuan, S
   Wang, YL
   Xu, J
AF Shao, Zhou
   Yuan, Sha
   Wang, Yongli
   Xu, Jing
TI Evolutions and trends of artificial intelligence (AI): research, output,
   influence and competition
SO LIBRARY HI TECH
LA English
DT Article
DE Science of science; Artificial intelligence; KG4AI; Knowledge graph
ID COMPUTER-SCIENCE; IMPACT; PUBLICATIONS; SYSTEMS
AB Purpose This paper throws light on some of the nature of artificial intelligence (AI) development, which will serve as a starter for helping to advance its development. Design/methodology/approach This work reveals the evolutions and trends of AI from four dimensions: research, output, influence and competition through leveraging academic knowledge graph with 130,750 AI scholars and 43,746 scholarly articles. Findings The authors unearth that the "research convergence" phenomenon becomes more evident in current AI research for scholars' highly similar research interests in different regions. The authors notice that Pareto's principle applies to AI scholars' outputs, and the outputs have been increasing at an explosive rate in the past two decades. The authors discover that top works dominate the AI academia, for they attracted considerable attention. Finally, the authors delve into AI competition, which accelerates technology development, talent flow, and collaboration. Originality/value The work aims to throw light on the nature of AI development, which will serve as a starter for helping to advance its development. The work will help us to have a more comprehensive and profound understanding of the evolutions and trends, which bridge the gap between literature research and AI development as well as enlighten the way the authors promote AI development and its strategy formulation.
C1 [Shao, Zhou; Wang, Yongli] Nanjing Univ Sci & Technol, Nanjing, Peoples R China.
   [Yuan, Sha] Beijing Acad Artificial Intelligence, Beijing, Peoples R China.
   [Xu, Jing] Tsinghua Univ, Beijing, Peoples R China.
C3 Nanjing University of Science & Technology; Tsinghua University
RP Wang, YL (corresponding author), Nanjing Univ Sci & Technol, Nanjing, Peoples R China.
EM shaozhou0001@gmail.com; yuansha@baal.ac.cn; yongllwang@njust.edu.cn;
   xjqh@mail.tsinghua.edu.cn
OI Shao, Zhou/0000-0002-6265-7310
FU National Natural Science Foundation of China [61941113, 82074580,
   61806111]; Fundamental Research Fund for the Central Universities
   [30918015103, 30918012204]; Nanjing Science and Technology Development
   Plan Project [201805036]; China Academy of Engineering Consulting
   Research Project [2019-ZD-1-02-02]; National Social Science Foundation
   [18BTQ073]; NSFC for Distinguished Young Scholar [61825602]; National
   Key R&D Program of China [2020AAA010520002]
FX This article has been awarded by the National Natural Science Foundation
   of China (61941113, 82074580, 61806111), the Fundamental Research Fund
   for the Central Universities (30918015103, 30918012204), Nanjing Science
   and Technology Development Plan Project (201805036), China Academy of
   Engineering Consulting Research Project (2019-ZD-1-02-02), National
   Social Science Foundation (18BTQ073), NSFC for Distinguished Young
   Scholar under Grant No. 61825602 and National Key R&D Program of China
   under Grant No. 2020AAA010520002.
CR [Anonymous], 2010, Commun ACM, DOI [10.1145/1839676.1839701, DOI 10.1145/1839676.1839701]
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NR 37
TC 20
Z9 20
U1 17
U2 88
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 0737-8831
J9 LIBR HI TECH
JI Libr. Hi Tech
PD MAY 27
PY 2022
VL 40
IS 3
BP 704
EP 724
DI 10.1108/LHT-01-2021-0018
EA JUL 2021
PG 21
WC Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science
GA 1M5FJ
UT WOS:000675934600001
DA 2024-09-05
ER
PT J
AU Martin, P
   Surian, D
   Bashir, R
   Bourgeois, FT
   Dunn, AG
AF Martin, Paige
   Surian, Didi
   Bashir, Rabia
   Bourgeois, Florence T.
   Dunn, Adam G.
TI Trial2rev: Combining machine learning and crowd-sourcing to create a
   shared space for updating systematic reviews
SO JAMIA OPEN
LA English
DT Article
DE review literature as topic; semi-supervised learning; databases as
   topic; bibliographic databases
ID PUBLICATION; SIMILARITY; WORKLOAD
AB Objectives: Systematic reviews of clinical trials could be updated faster by automatically monitoring relevant trials as they are registered, completed, and reported. Our aim was to provide a public interface to a database of curated links between systematic reviews and trial registrations.
   Materials and Methods: We developed the server-side system components in Python, connected them to a PostgreSQL database, and implemented the web-based user interface using Javascript, HTML, and CSS. All code is available on GitHub under an open source MIT license and registered users can access and download all available data.
   Results: The trial2rev system is a web-based interface to a database that collates and augments information from multiple sources including bibliographic databases, the ClinicalTrials.gov registry, and the actions of registered users. Users interact with the system by browsing, searching, or adding systematic reviews, verifying links to trials included in the review, and adding or voting on trials that they would expect to include in an update of the systematic review. The system can trigger the actions of software agents that add or vote on included and relevant trials, in response to user interactions or by scheduling updates from external resources.
   Discussion and Conclusion: We designed a publicly-accessible resource to help systematic reviewers make decisions about systematic review updates. Where previous approaches have sought to reactively filter published reports of trials for inclusion in systematic reviews, our approach is to proactively monitor for relevant trials as they are registered and completed.
C1 [Martin, Paige; Surian, Didi; Bashir, Rabia; Dunn, Adam G.] Macquarie Univ, Ctr Hlth Informat, Australian Inst Hlth Innovat, Level 6,75 Talavera Rd, Sydney, NSW 2109, Australia.
   [Bourgeois, Florence T.] Childrens Hosp Boston, Computat Hlth Informat Program, Boston, MA USA.
   [Bourgeois, Florence T.] Harvard Med Sch, Dept Pediat, Boston, MA 02115 USA.
C3 Macquarie University; Harvard University; Boston Children's Hospital;
   Harvard University; Harvard Medical School
RP Martin, P (corresponding author), Macquarie Univ, Ctr Hlth Informat, Australian Inst Hlth Innovat, Level 6,75 Talavera Rd, Sydney, NSW 2109, Australia.
EM paige.newman@mq.edu.au
RI Dunn, Adam/H-4425-2019; Bashir, Rabia/Q-3225-2019
OI Dunn, Adam/0000-0002-1720-8209; Bashir, Rabia/0000-0002-9613-8957;
   Surian, Didi/0000-0003-2299-2971; Martin, Paige/0000-0002-6157-4740
FU Agency for Healthcare Research and Quality [R03HS024798]
FX This work was supported by the Agency for Healthcare Research and
   Quality (R03HS024798 to FTB).
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NR 42
TC 16
Z9 16
U1 1
U2 4
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
EI 2574-2531
J9 JAMIA OPEN
JI JAMIA Open
PD APR
PY 2019
VL 2
IS 1
BP 15
EP 22
DI 10.1093/jamiaopen/ooy062
PG 8
WC Health Care Sciences & Services; Medical Informatics
WE Emerging Sources Citation Index (ESCI)
SC Health Care Sciences & Services; Medical Informatics
GA VJ9DU
UT WOS:000645417700004
PM 31984340
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU Yang, Q
AF Yang, Qin
TI RESEARCH ON E-COMMERCE CUSTOMER SATISFACTION EVALUATION METHOD BASED ON
   PSO-LSTM AND TEXT MINING
SO 3C EMPRESA
LA English
DT Article
DE text mining; PSO-LSTM; particle swarm algorithm; long short-term memory
   network
ID SENTIMENT ANALYSIS
AB With the increase of social technology, e-commerce platforms have entered a period of rapid development. Improving customer satisfaction , purchase rate is the key to the survival of e-commerce platforms. Text mining and analysis of customer evaluation data will help to grasp the focus of customers and optimize the e -commerce platform. To this end, through text mining technology, the text comment data of five e-commerce platforms such as Amazon, eBay, Alibaba, Jingdong , Taobao are collected, and the cleaned text is analyzed by particle swarm algorithm (PSO)-long short-term memory (LSTM) model. The data is subject to time scale extraction, and the extraction results are visualized and interpreted. The research shows that the logistics, price, freshness, quality and packaging of e-commerce platform merchants are important factors that affect the evaluation of e-commerce customer satisfaction.
C1 [Yang, Qin] Jinling Inst Sci & Technol, Sch Marxism, Nanjing 211169, Jiangsu, Peoples R China.
RP Yang, Q (corresponding author), Jinling Inst Sci & Technol, Sch Marxism, Nanjing 211169, Jiangsu, Peoples R China.
EM yang2161980@sina.com
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NR 27
TC 0
Z9 0
U1 3
U2 16
PU AREA INNOVACION & DESARROLLO
PI ALICANTE
PA C/ELS ALZAMORA NO 17, ALCOY, ALICANTE, 03802, SPAIN
SN 2254-3376
J9 3C EMPRESA
JI 3C EMPRESA
PD JAN-MAR
PY 2023
VL 12
IS 1
BP 51
EP 66
DI 10.17993/3cemp.2023.120151.51-66
PG 16
WC Business
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA 9A2TK
UT WOS:000933915300003
OA gold, Green Submitted
DA 2024-09-05
ER
PT J
AU Jang, H
AF Jang, Hoon
TI Predicting funded research project performance based on machine learning
SO RESEARCH EVALUATION
LA English
DT Article
DE research and development; project output prediction; commercialization;
   artificial intelligence; autoML
ID RESEARCH-AND-DEVELOPMENT; RESEARCH PRODUCTIVITY; RESEARCH COLLABORATION;
   DELPHI METHOD; SELECTION; IMPACT; INNOVATION; MODEL; MANAGEMENT; SUCCESS
AB Increasing investment and interest in research and development (R&D) requires an efficient management system for achieving better research project outputs. In tandem with this trend, there is a growing need to develop a method for predicting research project outputs. Motivated by this, using information gathered in the early stage of projects, this study addresses the problem of predicting research projects' output, which is binary coded as either successful or not. To build the prediction model, we apply six machine learning algorithms: five are well-known supervised learning algorithms and the other is autoML, characterized by its ability to produce a learning model appropriate to the data characteristics on its own, with minimal user intervention. Our empirical analysis with real R&D data provided by the South Korean government over 5 years (2014-8) confirms that the autoML-based model performs better than models based on other machine learning algorithms for this task. We also find that project duration and research funding are important factors in predicting R&D project outputs. Based on the results, our study provides insightful implications leading to a paradigm shift for data-based R&D project management.
C1 [Jang, Hoon] Korea Univ, Coll Global Business, Sejong Campus,2511 Sejong Ro, Sejong 30019, South Korea.
C3 Korea University
RP Jang, H (corresponding author), Korea Univ, Coll Global Business, Sejong Campus,2511 Sejong Ro, Sejong 30019, South Korea.
EM hoonjang@korea.ac.kr
FU National Research Foundation of Korea (NRF) - Korean government
   [2019R1F1A1063365]
FX This work was supported by the National Research Foundation of Korea
   (NRF) grant funded by the Korean government (2019R1F1A1063365).
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NR 72
TC 1
Z9 1
U1 3
U2 29
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 0958-2029
EI 1471-5449
J9 RES EVALUAT
JI Res. Evaluat.
PD APR 28
PY 2022
VL 31
IS 2
BP 257
EP 270
DI 10.1093/reseval/rvac005
EA MAR 2022
PG 14
WC Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science
GA 0V3QC
UT WOS:000769089500001
DA 2024-09-05
ER
PT J
AU González-Tejero, CB
   Ancillo, AD
   Gavrila, SG
   Blanco, AG
AF Gonzalez-Tejero, Cristina Blanco
   Ancillo, Antonio de Lucas
   Gavrila, Sorin Gavrila
   Blanco, Antonio Garcia
TI Uncovering the Complexities of Intellectual Property Management in the
   era of AI: Insights from a Bibliometric Analysis
SO JOURNAL OF COMPETITIVENESS
LA English
DT Article
DE Intellectual Property; Artificial Intelligence; Natural Language
   Processing; Machine Learning
ID OF-THE-ART; ARTIFICIAL-INTELLIGENCE; BIG DATA; RIGHTS; INNOVATION;
   COMPETITIVENESS; WATERMARKING; PERFORMANCE; MODELS
AB Intellectual property (IP) management has posed continuous problems in the digital world, so understanding its associated concepts and the particularities they present is crucial. Within artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) have enabled the intelligent processing and analysis of large volumes of data, making them widely used tools. In order to help fill the research gap that exists due to the novelty of the concepts, a bibliometric analysis is proposed of 404 scientific documents linked to AI, ML, NLP and IP, extracted from the Web of Science (WoS) core collection repository. The results demonstrate a current trend in research on the management of IP, related to digital tools and highlight the issues that arise from the management of IP stemming from their use. This research also identifies how these tools have been used to facilitate the management and identification of IP. In this sense, this study brings originality to the field of intellectual property management by examining previous studies and proposing new avenues for future research, thus broadening the current understanding of the subject. Entrepreneurs and business leaders can benefit from this study as it uncovers the complexities of IP management and thus enhances understanding of the opportunities and challenges in the AI era.
C1 [Gonzalez-Tejero, Cristina Blanco; Ancillo, Antonio de Lucas; Gavrila, Sorin Gavrila; Blanco, Antonio Garcia] Univ Alcala, Fac Econ Business & Tourism, Dept Econ & Business Adm, Alcala De Henares, Spain.
C3 Universidad de Alcala
RP González-Tejero, CB (corresponding author), Univ Alcala, Fac Econ Business & Tourism, Dept Econ & Business Adm, Alcala De Henares, Spain.
EM cristina.blancog@uah.es; antonio.lucas@uah.es; sorin.gavrila@uah.es;
   antonio.garciab@uah.es
RI De Lucas, Antonio/AAB-2463-2019; Gavrila Gavrila, Sorin/AAG-9867-2021
OI De Lucas, Antonio/0000-0002-8876-7753; Gavrila Gavrila,
   Sorin/0000-0002-7574-5504
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NR 76
TC 0
Z9 0
U1 20
U2 20
PU UNIV TOMASE BATI & ZLINE, FAK MANAGEMENTU EKONOMIKY
PI ZLIN
PA NAM T G MASARYKA 5555, ZLIN, 760 01, CZECH REPUBLIC
SN 1804-171X
EI 1804-1728
J9 J COMPETITIVENESS
JI J. Competitiveness
PD DEC
PY 2023
VL 15
IS 4
BP 69
EP 86
DI 10.7441/joc.2023.04.05
PG 18
WC Business; Economics; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA FD8Q1
UT WOS:001143918000002
DA 2024-09-05
ER
PT J
AU Sotudeh, H
   Saber, Z
   Aloni, FG
   Mirzabeigi, M
   Khunjush, F
AF Sotudeh, Hajar
   Saber, Zeinab
   Aloni, Farzin Ghanbari
   Mirzabeigi, Mahdieh
   Khunjush, Farshad
TI A longitudinal study of the evolution of opinions about open access and
   its main features: a twitter sentiment analysis
SO SCIENTOMETRICS
LA English
DT Article
DE Open access; Opinion mining; Sentiment analysis; Twitter; Tweets
ID ACADEMICS BEHAVIORS; ATTITUDES; AWARENESS; JOURNALS; IMPACT; USAGE;
   PERCEPTIONS; CHALLENGES; ALTMETRICS; CITATIONS
AB The present study aimed to explore how tweeters' opinions about open access publishing and its main features evolved over time. Using a quantitative content analysis method through an opinion mining approach, it explored a sample of English tweets on open access posted from 2007 to December 2019. The main terms related to open access were first identified through reviewing the related literature and were then categorized into five features including "costs & funding", "impact", "models", "publishing & publications", and "quality & quality control". The terms were composed in the form of search formulae. The searches on Twitter led to retrieving 629,123 tweets. A cleansing process was carried out to remove duplicates, non-English, and low-relevant tweets. The final sample reached 80,629 tweets. The tweets were then tagged with the five features. The KNIME data mining tool and SentiStrength were used respectively for processing the tweets' contents and calculating their opinion scores. According to the results, the open-access-related tweets have been growing based on a sigmoidal model. They were mostly neutral and opinion tweets were far lower in number. The tweets in different polarities have been increasing based on a power-law model, with the negative tweets experiencing a disproportionately higher increase. The positive and negative opinions have remained almost stable in strength, with the former being stronger. The results were almost in line with the previous surveys confirming the co-existence of the positive and negative attitudes about open access. However, the social sphere has been gradually becoming more negative. As attitudes are likely to go viral on social networks, and thereby affect users' perceptions and behaviors, the results call for devising appropriate measures to empower the movement and to find solutions for the problems and concerns leading to the negative opinions.
C1 [Sotudeh, Hajar; Saber, Zeinab; Mirzabeigi, Mahdieh] Shiraz Univ, Sch Educ & Psychol, Dept Knowledge & Informat Sci, Eram Campus, Shiraz, Iran.
   [Aloni, Farzin Ghanbari; Khunjush, Farshad] Shiraz Univ, Sch Comp Engn, Dept Comp Sci & Engn & IT, Shiraz, Iran.
C3 Shiraz University; Shiraz University
RP Sotudeh, H (corresponding author), Shiraz Univ, Sch Educ & Psychol, Dept Knowledge & Informat Sci, Eram Campus, Shiraz, Iran.
EM sotudeh@shirazu.ac.ir; zeinabsaber92@gmail.com;
   f.ghanbari@shirazu.ac.ir; mmirzabeigi@gmail.com; khunjush@shirazu.ac.ir
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NR 141
TC 5
Z9 5
U1 4
U2 37
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD OCT
PY 2022
VL 127
IS 10
BP 5587
EP 5611
DI 10.1007/s11192-022-04502-7
EA SEP 2022
PG 25
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 4P1FY
UT WOS:000849303100001
DA 2024-09-05
ER
PT J
AU Yang, C
   Wang, CH
   Zheng, RZ
   Geng, S
AF Yang, Chen
   Wang, Chuhan
   Zheng, Ruozhen
   Geng, Shuang
TI Link prediction in research collaboration: a multi-network
   representation learning framework with joint training
SO MULTIMEDIA TOOLS AND APPLICATIONS
LA English
DT Article
DE Research collaboration; Link prediction; Network representation
   learning; Machine learning
ID PERFORMANCE
AB With the rapid advancement of scientific research, collaboration in this area is becoming increasingly important. One of the major challenges is the link prediction problem for research collaboration. Recently, learning-based link prediction methods have received much attention. However, most of these studies have solely concentrated on exploiting a single network and its topology features for prediction, and ignore other factors that may influence link formation. To address this issue, in this paper we propose a link prediction model based on multi-network representation learning. Specifically, we develop new features based on the author's institutions and published papers, and three networks incorporating these features are modeled. Then, the network representation method based on joint training is proposed to embed the networks in a low-dimensional space. Finally, the authors' feature vectors are combined in finer granularity, and collaboration prediction is performed in a supervised manner. The performance of our model is evaluated by comparing it with other link prediction methods on a real-world dataset, and the experimental results show the effectiveness of our model.
C1 [Yang, Chen; Wang, Chuhan; Zheng, Ruozhen; Geng, Shuang] Shenzhen Univ, Coll Management, Shenzhen, Peoples R China.
C3 Shenzhen University
RP Geng, S (corresponding author), Shenzhen Univ, Coll Management, Shenzhen, Peoples R China.
EM gs@szu.edu.cn
FU National Natural Science Foundation of China [71901150, 71701134];
   Guangdong Basic and Applied Basic Research Foundation [2023A1515012515,
   2 022A1515012077]; Guangdong Province Innovation Team "Intelligent
   Management and Interdisciplinary Innovation" [2021WCXTD002]; Shenzhen
   Higher Education Support Plan [20200826144104001]
FX This work was supported by grants from National Natural Science
   Foundation of China [71901150, 71701134]; Guangdong Basic and Applied
   Basic Research Foundation [2023A1515012515, 2 022A1515012077]; Guangdong
   Province Innovation Team "Intelligent Management and Interdisciplinary
   Innovation" [2021WCXTD002]; Shenzhen Higher Education Support Plan
   [20200826144104001].
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TC 2
Z9 2
U1 1
U2 15
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1380-7501
EI 1573-7721
J9 MULTIMED TOOLS APPL
JI Multimed. Tools Appl.
PD DEC
PY 2023
VL 82
IS 30
BP 47215
EP 47233
DI 10.1007/s11042-023-15720-3
EA MAY 2023
PG 19
WC Computer Science, Information Systems; Computer Science, Software
   Engineering; Computer Science, Theory & Methods; Engineering, Electrical
   & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA Z9FI2
UT WOS:000983956900004
DA 2024-09-05
ER
PT C
AU Baba, T
   Baba, K
AF Baba, Takahiro
   Baba, Kensuke
BE Gervasi, O
   Murgante, B
   Misra, S
   Stankova, E
   Torre, CM
   Rocha, AMAC
   Taniar, D
   Apduhan, BO
   Tarantino, E
   Ryu, Y
TI Citation Count Prediction Using Non-technical Terms in Abstracts
SO COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2018, PT I
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT 18th International Conference on Computational Science and Its
   Applications (ICCSA)
CY JUL 02-05, 2018
CL Monash Univ, Caulfield Campus, Melbourne, AUSTRALIA
HO Monash Univ, Caulfield Campus
DE Citation count prediction; Document classification; Text analysis;
   Machine learning
AB Researchers are required to find previous literature which is related to their research and has a scientific impact efficiently from a large number of publications. The target problem of this paper is predicting the citation count of each scholarly paper, that is, the number of citations from other scholarly papers, as the scientific impact. The authors tried to detect the high and low of the citation count of scholarly papers using only their abstracts, especially, non-technical terms used in them. They conducted a classification of abstracts of scholarly papers with high and low citation counts, and applied the classification also to the abstracts modified by deleting technical terms from them. The results of their experiments indicate that the scientific impact of a scholarly paper can be detected from information which is written in its abstract and is not related to the trend of research topics. The classification accuracy for detecting scholarly papers with the top or bottom 1% citation counts was 0.93, and that using the abstracts without technical terms was 0.90.
C1 [Baba, Takahiro] Kyushu Univ, Fukuoka, Fukuoka 8190395, Japan.
   [Baba, Kensuke] Fujitsu Labs, Kawasaki, Kanagawa 2118588, Japan.
C3 Kyushu University; Fujitsu Ltd; Fujitsu Laboratories Ltd
RP Baba, K (corresponding author), Fujitsu Labs, Kawasaki, Kanagawa 2118588, Japan.
EM baba.kensuke@jp.fujitsu.com
RI Baba, Kensuke/J-8426-2017
OI Baba, Kensuke/0000-0002-8118-0175; ma chang, long
   kuan/0000-0001-7910-2728
FU JSPS KAKENHI [15K00310]; Grants-in-Aid for Scientific Research
   [15K00310] Funding Source: KAKEN
FX This work was supported by JSPS KAKENHI Grant Number 15K00310.
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PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 0302-9743
EI 1611-3349
BN 978-3-319-95162-1; 978-3-319-95161-4
J9 LECT NOTES COMPUT SC
PY 2018
VL 10960
BP 366
EP 375
DI 10.1007/978-3-319-95162-1_25
PN I
PG 10
WC Computer Science, Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BM1UH
UT WOS:000460577300025
DA 2024-09-05
ER
PT J
AU Wang, KT
   Tan, FB
   Zhu, ZM
   Kong, LY
AF Wang, Kangtao
   Tan, Fengbo
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   Kong, Lingyu
TI Exploring changes in depression and radiology-related publications
   research focus: A bibliometrics and content analysis based on natural
   language processing
SO FRONTIERS IN PSYCHIATRY
LA English
DT Article
DE major depressive disorder; radiology; bibliometric analysis; Latent
   Dirichlet allocation; machine learning
ID CORTICAL SPREADING DEPRESSION; FUNCTIONAL CONNECTIVITY; MRI; MIGRAINE;
   DEPOLARIZATION; METAANALYSIS; RELEVANCE; NETWORKS; DISORDER; BIPOLAR
AB ObjectiveThis study aims to construct and use natural language processing and other methods to analyze major depressive disorder (MDD) and radiology studies' publications in the PubMed database to understand the historical growth, current state, and potential expansion trend. MethodsAll MDD radiology studies publications from January 2002 to January 2022 were downloaded from PubMed using R, a statistical computing language. R and the interpretive general-purpose programming language Python were used to extract publication dates, geographic information, and abstracts from each publication's metadata for bibliometric analysis. The generative statistical algorithm "Latent Dirichlet allocation" (LDA) was applied to identify specific research focus and trends. The unsupervised Leuven algorithm was used to build a network to identify relationships between research focus. ResultsA total of 5,566 publications on MDD and radiology research were identified, and there is a rapid upward trend. The top-cited publications were 11,042, and the highly-cited publications focused on improving diagnostic performance and establishing imaging standards. Publications came from 76 countries, with the most from research institutions in the United States and China. Hospitals and radiology departments take the lead in research and have an advantage. The extensive field of study contains 12,058 Medical Subject Heading (MeSH) terms. Based on the LDA algorithm, three areas were identified that have become the focus of research in recent years, "Symptoms and treatment," "Brain structure and imaging," and "Comorbidities research." ConclusionLatent Dirichlet allocation analysis methods can be well used to analyze many texts and discover recent research trends and focus. In the past 20 years, the research on MDD and radiology has focused on exploring MDD mechanisms, establishing standards, and constructing imaging methods. Recent research focuses are "Symptoms and sleep," "Brain structure study," and "functional connectivity." New progress may be made in studies on MDD complications and the combination of brain structure and metabolism.
C1 [Wang, Kangtao; Tan, Fengbo] Cent South Univ, Xiangya Hosp, Dept Gen Surg, Changsha, Hunan, Peoples R China.
   [Wang, Kangtao; Tan, Fengbo] Cent South Univ, Xiangya Hosp, Natl Clin Res Ctr Geriatr Disorders, Changsha, Hunan, Peoples R China.
   [Zhu, Zhiming; Kong, Lingyu] Cent South Univ, Xiangya Hosp, Dept Radiol, Changsha, Hunan, Peoples R China.
C3 Central South University; Central South University; Central South
   University
RP Kong, LY (corresponding author), Cent South Univ, Xiangya Hosp, Dept Radiol, Changsha, Hunan, Peoples R China.
EM kong_lingyu@csu.edu.cn
RI 令煜, 孔/HME-1741-2023; Zhu, Zhiming/ABH-9561-2020
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NR 50
TC 2
Z9 2
U1 6
U2 26
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 1664-0640
J9 FRONT PSYCHIATRY
JI Front. Psychiatry
PD NOV 30
PY 2022
VL 13
AR 978763
DI 10.3389/fpsyt.2022.978763
PG 15
WC Psychiatry
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Psychiatry
GA 6Y3PV
UT WOS:000897010800001
PM 36532194
OA Green Published, gold
DA 2024-09-05
ER
PT J
AU Xu, MY
   Du, JP
   Xue, Z
   Guan, ZL
   Kou, FF
   Shi, L
AF Xu, Mingying
   Du, Junping
   Xue, Zhe
   Guan, Zeli
   Kou, Feifei
   Shi, Lei
TI A scientific research topic trend prediction model based on multi-LSTM
   and graph convolutional network
SO INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
LA English
DT Article
DE graph convolutional networks; long short-term memory; scientific
   Influence modeling; time series prediction; topic trend prediction
ID EVOLUTION; DEMAND; SYSTEM
AB Predicting the development trend of future scientific research not only provides a reference for researchers to understand the development of the discipline, but also provides support for decision-making and fund allocation for decision-makers. The continuous growth of scientific publications has brought challenges to track the development trends of scientific research topics. The existing topic trend prediction methods have proved that the research topic trend of a publication is influenced by other peer publications. However, they ignore the fact that the research topics of different publications belong to different research topic space. Moreover, the existing topic prediction methods do not fully consider the interactive influence among publications that the research topic of one publication affects the topics of other publications, it is also influenced by the research topics of other publications. In line with this, this paper proposes a scientific research topic trend prediction model based on multi-long short-term memory (multi-LSTM) and Graph Convolutional Network. Specifically, multiple LSTMs are employed to map research topics of different publications into their respective topic space. Then, the graph convolutional neural network is applied to learn the scientific influence context of each publication, so that the research topic of each publication not only integrates the influence of neighbor nodes, but also considers the influence of the neighbors of the neighbor node on the research topic of the publication, so as to more accurately fuse scientific influence context of research topic of peer publications. Experiments results on the data set of scientific research papers in the field of artificial intelligence and data mining demonstrate that the model improves the prediction precision and achieves the state-of-the-art research topic trend prediction effect compared with the other baseline models.
C1 [Xu, Mingying; Du, Junping; Xue, Zhe; Guan, Zeli; Kou, Feifei] Beijing Univ Posts & Telecommun, Sch Comp Sci, Beijing Key Lab Intelligent Telecommun Software &, Beijing 100876, Peoples R China.
   [Shi, Lei] Commun Univ China, State Key Lab Media Convergence & Commun, Beijing, Peoples R China.
C3 Beijing University of Posts & Telecommunications; Communication
   University of China
RP Du, JP (corresponding author), Beijing Univ Posts & Telecommun, Sch Comp Sci, Beijing Key Lab Intelligent Telecommun Software &, Beijing 100876, Peoples R China.
EM junpingdu@126.com
RI Shi, Lei/JAX-8444-2023; shi, lei/ADU-8999-2022
OI Shi, Lei/0000-0002-5570-7818; Xu, Mingying/0000-0002-4175-199X
FU National Key R&D Program of China [2018YFB1402600]; Major Program of
   National Natural Science Foundation of China [62192784]; National
   Natural Science Foundation of China (NSFC) [61772083, 61802028,
   61877006]
FX National Key R&D Program of China, Grant/Award Number: 2018YFB1402600;
   Major Program of National Natural Science Foundation of China,
   Grant/Award Number: 62192784; National Natural Science Foundation of
   China (NSFC), Grant/Award Numbers: 61772083, 61802028, 61877006
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NR 57
TC 15
Z9 16
U1 6
U2 104
PU WILEY-HINDAWI
PI LONDON
PA ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON, WIT 5HE, ENGLAND
SN 0884-8173
EI 1098-111X
J9 INT J INTELL SYST
JI Int. J. Intell. Syst.
PD SEP
PY 2022
VL 37
IS 9
BP 6331
EP 6353
DI 10.1002/int.22846
EA FEB 2022
PG 23
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 3K1XR
UT WOS:000752787800001
OA gold
DA 2024-09-05
ER
PT J
AU Alshdadi, AA
   Usman, M
   Alassafi, MO
   Afzal, MT
   AlGhamdi, R
AF Alshdadi, Abdulrahman A.
   Usman, Muhammad
   Alassafi, Madini O.
   Afzal, Muhammad Tanvir
   AlGhamdi, Rayed
TI Formulation of rules for the scientific community using deep learning
SO SCIENTOMETRICS
LA English
DT Article
DE Scientific quantitative rules; Rule mining; Citations; Publications;
   h-index; g-index; Variants of h-index; ASCE; CSCE; ACI; ICE; AMS; LMS;
   IMU; ANS; CNS; FENS; IBRO; SFN
ID H-INDEX; GOOGLE-SCHOLAR; VARIANTS
AB In a deluge of scientific literature, it is important to build scientific quantitative rules (SQR) that can be applied to researchers' quantitative data in order to produce a uniform format for making decisions regarding the nomination of outstanding researchers. Google Scholar and other search engines track scholars' papers, citations, etc. However, the scientific community hasn't agreed on standards a researcher must meet to be regarded as important. In this paper, we suggest rules for the scientific community based on the top five quantitative scientific parameters. The significance of the parameters is measured based on two factors: (i) parameters' impact on the model's performance while classifying awardees and non-awardees, and (ii) the number of award-winning researchers elevated in the ranking of researchers through each respective parameter. The experimental dataset includes information from researchers in the civil engineering, mathematics, and neuroscience domains. There are 250 awardees and 250 non-awardees from each field. The SQR for each discipline has attained an accuracy of 70% or more for their respective award-winning researchers. In addition to this, the top ranked parameters from each discipline have elevated more than 50% of the award-winning researchers into their respective ranked lists of the top 100 researchers. These findings can guide individual researchers to be on the list of prestigious scientists, and scientific societies can use the SQR to filter the list of researchers for subjective evaluation in order to reward prolific researchers in the domain.
C1 [Alshdadi, Abdulrahman A.] Univ Jeddah, Jeddah, Saudi Arabia.
   [Alassafi, Madini O.; AlGhamdi, Rayed] King Abdulaziz Univ, Fac Comp & Informat Technol, Jeddah, Saudi Arabia.
   [Usman, Muhammad; Afzal, Muhammad Tanvir] Shifa Tameer e Millat Univ, Islamabad, Pakistan.
C3 University of Jeddah; King Abdulaziz University
RP Usman, M (corresponding author), Shifa Tameer e Millat Univ, Islamabad, Pakistan.
EM alshdadi@uj.edu.sa; usman.alot761@gmail.com; malasafi@kau.edu.sa;
   director.ssc@stmu.edu.pk; raalghamdi8@kau.edu.sa
RI Alshdadi, Abdulrahman A./KXQ-6244-2024; Alassafi, Madini
   O./AGY-4104-2022; Afzal, Muhammad/D-3741-2019; AlGhamdi,
   Rayed/H-8753-2012
OI Alshdadi, Abdulrahman A./0000-0002-9815-0319; Alassafi, Madini
   O./0000-0001-9919-8368; Afzal, Muhammad/0000-0002-7851-2327; Usman,
   Muhammad/0000-0002-6154-6256; Afzal, Muhammad Tanvir/0000-0002-9765-8815
FU Institutional Fund Project [812-611-1442]; Ministry of Education; King
   Abdulaziz University, DSR, Jeddah, Saudi Arabia
FX This research work was funded by the Institutional Fund Project under
   grant no. (IFPIP; 812-611-1442). Therefore, the authors gratefully
   acknowledge technical and financial support from the Ministry of
   Education and King Abdulaziz University, DSR, Jeddah, Saudi Arabia.
   Moreover, there is no conflict of interest between the authors and the
   funding department.
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NR 33
TC 9
Z9 9
U1 6
U2 25
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD MAR
PY 2023
VL 128
IS 3
BP 1825
EP 1852
DI 10.1007/s11192-023-04633-5
EA JAN 2023
PG 28
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 9M3IZ
UT WOS:000922480200001
DA 2024-09-05
ER
PT C
AU Cheng, X
   Zhao, YF
AF Cheng, Xin
   Zhao, YiFan
GP IEEE
TI Analysis of Scholars' influence evaluation Based on Data collection and
   integration in the Context of Big Data - Taking the Hospital Scientific
   Research Output Database as an Example
SO 2021 3RD INTERNATIONAL CONFERENCE ON MACHINE LEARNING, BIG DATA AND
   BUSINESS INTELLIGENCE (MLBDBI 2021)
LA English
DT Proceedings Paper
CT 3rd International Conference on Machine Learning, Big Data and Business
   Intelligence (MLBDBI)
CY DEC 03-05, 2021
CL ELECTR NETWORK
DE Academic influence of scholars; Medical field; Principal component
   analysis; First author; Big Data
AB [Purpose/Significance] Evaluating the academic influence of scholars is an important basis for identifying excellent scholars and selecting talents. At present, there is no strict and unified standard for the evaluation index of scholars' academic influence. Using a single index or traditional indexes such as the number of documents has certain limitations and is not comprehensive enough. [Methods / Process] This paper constructs the evaluation index system of academic influence of scholars in the medical field from the perspective of multiple indicators. By dividing the number of scholars' papers into two data sources: the total number of published papers and the number of papers only containing scholars as the first author, the principal component analysis method is used to empirically study the data, obtain the evaluation index model and calculate the corresponding scholar ranking. [Results / Conclusion] It is found that the total number of scholars' papers is quite different from the ranking of only considering the first author, and whether it is the first author has a great impact on the influence evaluation. Therefore, whether it is the first author and the weight of the first author and non first author should be considered when evaluating the influence of scholars.
C1 [Cheng, Xin; Zhao, YiFan] Shanxi Univ Finance & Econ, Sch Informat, Taiyuan, Peoples R China.
C3 Shanxi University Finance & Economics
RP Zhao, YF (corresponding author), Shanxi Univ Finance & Econ, Sch Informat, Taiyuan, Peoples R China.
EM 347063513@qq.com
RI Zhao, Yifan/AAN-7735-2021
OI Zhao, Yifan/0000-0001-6911-3183
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TC 0
Z9 0
U1 0
U2 18
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-6654-1790-7
PY 2021
BP 402
EP 407
DI 10.1109/MLBDBI54094.2021.00082
PG 6
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BT1XR
UT WOS:000804043100075
DA 2024-09-05
ER
PT C
AU Li, L
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AF Li, Li
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   Lin, Kuo-Yi
GP IEEE
TI A Survey on federated learning
SO 2020 IEEE 16TH INTERNATIONAL CONFERENCE ON CONTROL & AUTOMATION (ICCA)
SE IEEE International Conference on Control and Automation ICCA
LA English
DT Proceedings Paper
CT 16th IEEE International Conference on Control and Automation (ICCA)
CY OCT 09-11, 2020
CL ELECTR NETWORK
DE Federated learning; Literature survey; Citation analysis; Research front
AB Federated learning (FL) is an emerging setting which implement machine learning in a distributed environment while protecting privacy. Research activities relating to FLhave grown at a fast rate recently in control. Exactly what activities have been carrying the research momentum forward is a question of interest to the research community. This study finds these research activities and optimization path of FL based on survey. Thus, this study aims to review related studies of FL to base on the baseline a universal definition gives a guiding for the future work. Besides, this study presents the prevailing FL applications and the evolution of federated learning. In the end, this study also identifies four research fronts to enrich the FL literature and help advance our understanding of the field. A comprehensive taxonomy of FL can also be developed through analyzing the results of this review.
C1 [Li, Li; Fan, Yuxi; Lin, Kuo-Yi] Tongji Univ, Shanghai 201804, Peoples R China.
C3 Tongji University
RP Li, L (corresponding author), Tongji Univ, Shanghai 201804, Peoples R China.
EM lili@tongji.edu.cn; 1830749@tongji.edu.cn; 19603@tongji.edu.cn
RI Fan, Yuxi/ABG-6950-2021; yuan, lin/JDW-7387-2023; Lin,
   Kuo-Yi/HLH-8727-2023
FU National Key R&D Program of China [2018YFE0105000]; National Natural
   Science Foundation of China [51475334]; Shanghai Municipal Commission of
   science and technology [19511132100]; Fundamental Research Funds for the
   Central Universities of China [22120170077]
FX Research supported by National Key R&D Program of China, No.
   2018YFE0105000, the National Natural Science Foundation of China under
   Grant No. 51475334, the Shanghai Municipal Commission of science and
   technology No. 19511132100 and the Fundamental Research Funds for the
   Central Universities of China under Grant No. 22120170077.
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NR 61
TC 20
Z9 20
U1 5
U2 40
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 1948-3449
BN 978-1-7281-9093-8
J9 IEEE INT CONF CON AU
PY 2020
BP 791
EP 796
DI 10.1109/icca51439.2020.9264412
PG 6
WC Automation & Control Systems; Engineering, Electrical & Electronic;
   Operations Research & Management Science
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Automation & Control Systems; Engineering; Operations Research &
   Management Science
GA BR3HM
UT WOS:000646357300135
DA 2024-09-05
ER
PT J
AU Porwal, P
   Devare, MH
AF Porwal, Priya
   Devare, Manoj H.
TI Citation count prediction using weighted latent semantic analysis (wlsa)
   and three-layer-deep-learning paradigm: a meta-heuristic approach
SO MULTIMEDIA TOOLS AND APPLICATIONS
LA English
DT Article
DE Citation count prediction; Class distribution factor-based Term
   Frequency-Inverse Document Frequency (CDF-TFIDF); Weighted Latent
   Semantic Analysis (WLSA); Red fox optimization (RFO); Convolutional
   Neural Network (CNN)
ID IMPACT; PUBLICATION
AB Citation count prediction is a subfield of bibliometrics, which involves using mathematical and statistical models to predict the quantity of citations that a scholarly research paper will accept in the future. This study suggests a novel approach for forecasting the number of citations academic papers will receive in future. The proposed model includes four major phases: "(a) pre-processing, (b) feature extraction, (c) feature selection, (d) Three-Layer-Deep-Learning-based-citation-count-prediction". In this paper, the Raw data collected is pre-processed via stop word removal, stemming, lemmatization, tokenization. As a resultant, the tokens are acquired. The features are extracted from the tokens. The features like bag-of-words, Part-of-Speech (POS) Tagger, Pointwise Mutual Information (PMI), Class distribution factor-based Term Frequency-Inverse Document Frequency (CDF-TFIDF), Doc2vec (document to vector) and Weighted Latent Semantic Analysis (WLSA). Subsequently, from the extracted features, the optimal features are selected using the new hybrid optimization model. The proposed hybrid optimization model- Polar Red Fox Optimization (PRFO), is the agglomeration of concepts of the standard "Polar Bear Optimization (PBO)" and "Red the fox optimization (RFO)," respectively. The citation counts are predicted using the new three-layer-deep-learning paradigm that includes the "Long Short-Term Memory (LSTM), Recurrent Neural Net Language Model (RNNL), and Convolutional Neural Network (CNN)," respectively. The input to LSTM and RNNL is the selected optimal features. The outcome from LSTM and RNNL is fed as input to the CNN. The outcome is acquired from CNN. Finally, the proposed prototypical is validated over current models, to manifest its efficiency in accurate citation count prediction.
C1 [Porwal, Priya] Amity Univ, Comp Engn Dept, Mumbai, India.
   [Devare, Manoj H.] Amity Univ, Prof & Head Inst, Amity Inst Informat Technol, Mumbai, India.
RP Porwal, P (corresponding author), Amity Univ, Comp Engn Dept, Mumbai, India.
EM priya.porwal20@gmail.com; mhdevare@mum.amity.edu
RI Devare, Manoj/H-2442-2016
OI Devare, Manoj/0000-0002-9530-3914
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NR 32
TC 0
Z9 0
U1 5
U2 11
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1380-7501
EI 1573-7721
J9 MULTIMED TOOLS APPL
JI Multimed. Tools Appl.
PD MAR
PY 2024
VL 83
IS 11
BP 32245
EP 32276
AR s11042-023-16957-8
DI 10.1007/s11042-023-16957-8
EA SEP 2023
PG 32
WC Computer Science, Information Systems; Computer Science, Software
   Engineering; Computer Science, Theory & Methods; Engineering, Electrical
   & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA KF5H9
UT WOS:001070386400005
DA 2024-09-05
ER
PT J
AU Wang, ZW
   Jia, YH
   Fu, PH
   Li, HY
   Song, L
   Yang, BQ
   Zhang, LJ
   Yuan, L
   Shi, K
AF Wang, Zhiwei
   Jia, Yanhao
   Fu, Penghao
   Li, Haiyin
   Song, Li
   Yang, Bingqing
   Zhang, Lijun
   Yuan, Liang
   Shi, Kan
TI Research on the online evaluation of the straightness error of
   hydrostatic guideways based on deep learning
SO INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
LA English
DT Article
DE Recess pressure; Hydrostatic guideway; Online evaluation; Straightness
   error; Deep learning
ID MOTION ERRORS; RAILS
AB Currently, the measurement of guide rail straightness error is basically a direct measurement, which cannot meet the requirements of online straightness error measurement in the machining process of the machine tool. Therefore, this paper proposes a straightness error evaluation model based on the recess pressure, which can realize the online measurement of the straightness error of hydrostatic guideways. To address the nonlinear relationship between the recess pressure and the linear deviation data of the guideway in an experiment, based on deep learning, the hydrostatic guideway straightness error was evaluated. First, the experimental data are analyzed and processed by feature, and a sliding window is processed using the data time sequence. Second, a long short-term memory network model is constructed based on an attention mechanism, the model parameters are obtained through orthogonal experiments, and the theoretical straightness error of the hydrostatic guideway is obtained via training. Finally, the theoretical values and experimental values of straightness error are compared and evaluated with the multilayer perceptron and recurrent neural network models. The results show that the model can effectively evaluate the straightness error of hydrostatic guideways.
C1 [Wang, Zhiwei; Jia, Yanhao; Fu, Penghao; Li, Haiyin; Song, Li; Yang, Bingqing; Yuan, Liang; Shi, Kan] Shandong Univ Sci & Technol, Coll Mech & Elect Engn, 579 Qianwangang Rd, Qingdao 266590, Peoples R China.
   [Wang, Zhiwei] Xi An Jiao Tong Univ, State Key Lab Mfg Syst Engn, 28 Xianning West Rd, Xian 710049, Peoples R China.
   [Zhang, Lijun] China Univ Petr, Coll Mech & Elect Engn, 66 Yangtze West Rd, Qingdao 266580, Peoples R China.
C3 Shandong University of Science & Technology; Xi'an Jiaotong University;
   China University of Petroleum
RP Shi, K (corresponding author), Shandong Univ Sci & Technol, Coll Mech & Elect Engn, 579 Qianwangang Rd, Qingdao 266590, Peoples R China.
EM kan.shi@hotmail.com
FU National Natural Science Foundation of China [52275497]; Natural
   Foundation of Shandong Province [ZR2020ME141]
FX This work was supported by the National Natural Science Foundation of
   China (52275497) and Natural Foundation of Shandong Province
   (ZR2020ME141).
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NR 30
TC 0
Z9 0
U1 2
U2 2
PU SPRINGER LONDON LTD
PI LONDON
PA 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND
SN 0268-3768
EI 1433-3015
J9 INT J ADV MANUF TECH
JI Int. J. Adv. Manuf. Technol.
PD SEP
PY 2024
VL 134
IS 3-4
BP 2023
EP 2034
DI 10.1007/s00170-024-14052-2
EA AUG 2024
PG 12
WC Automation & Control Systems; Engineering, Manufacturing
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Automation & Control Systems; Engineering
GA D7X5A
UT WOS:001290981800004
DA 2024-09-05
ER
PT J
AU Hilmi, MF
   Mustapha, Y
AF Hilmi, Mohd Faiz
   Mustapha, Yanti
TI A BIBLIOMETRIC AND TOPIC MODELING OVERVIEW OF<i> KAJIAN</i><i>
   MALAYSIA</i> BETWEEN 2011 AND 2020: A RESEARCH NOTE
SO KAJIAN MALAYSIA
LA English
DT Article
DE Kajian Malaysia; bibliometric analysis; topic modeling; latent dirichlet
   allocation; social science; humanities; Malaysia
ID CORPORATE SOCIAL-RESPONSIBILITY; COMMUNITY; PROSPECTS; EDUCATION;
   SOCIETY
AB Kajian Malaysia, published by Penerbit Universiti Sains Malaysia, is an interdisciplinary journal which provides a forum for a broad range of social sciences and humanities research. This research note presents a bibliometric review of the articles published in the journal Kajian Malaysia between 2011 and 2020. The purpose of this research note is to evaluate publication patterns and the topic model of articles published in Kajian Malaysia. The bibliographical material applied in this study was retrieved from the Scopus database. This study bibliometrically examines 192 documents published in Kajian Malaysia from 2011 to 2020 to rank the most productive countries, institutions, authors, keywords, influential articles and the topic model. This research note assists researchers with an understanding of the development of Kajian Malaysia, provides an important reference for Kajian Malaysia's future trajectory as well as provides an effective method of analysis for the future evaluation of journals.
C1 [Hilmi, Mohd Faiz] Univ Sains Malaysia, Sch Distance Educ, George Town, Malaysia.
   [Mustapha, Yanti] Univ Teknol MARA, Fac Business & Management, Kedah, Malaysia.
C3 Universiti Sains Malaysia
RP Hilmi, MF (corresponding author), Univ Sains Malaysia, Sch Distance Educ, George Town, Malaysia.
EM faiz@usm.my
RI Hilmi, Mohd Faiz/GVL-9141-2022; Mustapha, Yanti Aspha
   Ameira/HJH-8582-2023; Hilmi, Mohd Faiz/B-9197-2009
OI Hilmi, Mohd Faiz/0000-0003-4548-0565; Mustapha, Yanti Aspha
   Ameira/0000-0002-1871-9457
CR Abd Rahim RA, 2011, KAJI MALAYS, V29, P91
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   Raman M, 2012, KAJI MALAYS, V30, P71
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   Scopus, 2021, SOURCE DETAILS
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   Tuck-Po L, 2011, KAJI MALAYS, V29, P23
   Yahya Z, 2017, KAJI MALAYS, V35, P39, DOI 10.21315/km2017.35.2.3
   Yean TF, 2015, KAJI MALAYS, V33, P141
NR 31
TC 0
Z9 0
U1 0
U2 2
PU PENERBIT UNIV SAINS MALAYSIA
PI PULAU PINANG
PA PENERBIT UNIVERSITI SAINS MALAYSIA, PULAU PINANG, PINANG 11800, MALAYSIA
SN 2180-4273
J9 KAJI MALAYS
JI Kaji. Malays.
PY 2022
VL 40
IS 2
BP 255
EP 268
DI 10.21315/km2022.40.2.11
PG 14
WC Area Studies
WE Emerging Sources Citation Index (ESCI)
SC Area Studies
GA 5Z6US
UT WOS:000880106600011
OA gold
DA 2024-09-05
ER
PT J
AU Liu, R
   Liu, Q
   Shi, JW
   Yu, WY
   Gong, X
   Chen, N
   Yang, Y
   Huang, JL
   Wang, ZX
AF Liu, Rui
   Liu, Qian
   Shi, Jianwei
   Yu, Wenya
   Gong, Xin
   Chen, Ning
   Yang, Yan
   Huang, Jiaoling
   Wang, Zhaoxin
TI Application of a feature extraction and normalization method to improve
   research evaluation across clinical disciplines
SO ANNALS OF TRANSLATIONAL MEDICINE
LA English
DT Article
DE Research evaluation; clinical discipline; feature extraction;
   normalization
AB Background: To deal with the large disparity across disciplines using impact factor, which is widely used in hospitals and has recently come under attack for distorting good scientific practices, we propose a set of systematic methods to improve the equality of research evaluations of various clinical disciplines. Methods: We used bibliometric information on 18 clinical disciplines from 2016 to 2018. We first sought to clarify disciplinary characteristics with the aim of identifying the characteristic fields for each clinical discipline, and we constructed a keyword database. To minimize the disparity across various clinical disciplines, we used normalized evaluation, referring to the calculation of the normalized coefficient of a specific discipline, to enable a relatively clear evaluation across different disciplines. Results: Feature extraction was performed, and over 700,000 journals were retrieved each year. Using this information, the journal correlation coefficient was calculated. From 2016 to 2018, oncology had the largest normalized coefficient (0.133, 0.136, 0.146 respectively), which reflects the highest correlation between the characteristic journals of the discipline. The findings showed a clear distinction in journal coverage and journal correlations for different disciplines. Conclusions: The new evaluation indicator and normalized process measure different features of disciplines, providing a basis for the further balancing of evaluations, and considering differences across disciplines.
C1 [Liu, Rui] Tongji Univ, Shanghai Peoples Hosp 10, Shanghai, Peoples R China.
   [Liu, Qian; Yang, Yan] Tongji Univ, Sch Econ & Management, Shanghai, Peoples R China.
   [Shi, Jianwei; Yu, Wenya; Huang, Jiaoling; Wang, Zhaoxin] Shanghai Jiao Tong Univ, Sch Publ Hlth, Sch Med, South Chongqing Rd 227, Shanghai 200025, Peoples R China.
   [Gong, Xin] Tongji Univ, Sch Med, Shanghai East Hosp, Shanghai, Peoples R China.
   [Chen, Ning] Tongji Univ, Sch Med, Shanghai, Peoples R China.
   [Wang, Zhaoxin] Southern Med Univ, Nanhai Hosp, Gen Practice Ctr, Foshan, Peoples R China.
C3 Tongji University; Tongji University; Shanghai Jiao Tong University;
   Tongji University; Tongji University; Southern Medical University -
   China
RP Huang, JL; Wang, ZX (corresponding author), Shanghai Jiao Tong Univ, Sch Publ Hlth, Sch Med, South Chongqing Rd 227, Shanghai 200025, Peoples R China.
EM jiaoling_huang@sina.com; supercell002@sina.com
RI zhang, min/IYI-9869-2023; Wang, Ling/AGR-4917-2022; Liu,
   qianhong/HDO-6033-2022; Huang, Joy/HJP-2358-2023
OI Wang, Ling/0000-0003-0272-2974; Huang, Jiaoling/0000-0003-1975-3937
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NR 24
TC 1
Z9 1
U1 1
U2 8
PU AME PUBLISHING COMPANY
PI SHATIN
PA FLAT-RM C 16F, KINGS WING PLAZA 1, NO 3 KWAN ST, SHATIN, HONG KONG
   00000, PEOPLES R CHINA
SN 2305-5839
EI 2305-5847
J9 ANN TRANSL MED
JI ANN. TRANSL. MED.
PD OCT
PY 2021
VL 9
IS 20
AR 1580
DI 10.21037/atm-21-5046
PG 15
WC Oncology; Medicine, Research & Experimental
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Oncology; Research & Experimental Medicine
GA WR8GG
UT WOS:000714733000006
PM 34790786
OA Green Published, gold
DA 2024-09-05
ER
PT J
AU Lu, W
   Huang, SZ
   Yang, JQ
   Bu, Y
   Cheng, QK
   Huang, Y
AF Lu, Wei
   Huang, Shengzhi
   Yang, Jinqing
   Bu, Yi
   Cheng, Qikai
   Huang, Yong
TI Detecting research topic trends by author-defined keyword frequency
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
DE Scientometrics; Bibliometrics; Deep learning; Word frequency prediction
ID WORD ANALYSIS; SCIENCE; TECHNOLOGY; EVOLUTION; NETWORKS; COCITATION;
   CITATIONS; KNOWLEDGE; SYSTEM; IMPACT
AB Detecting research trends helps researchers and decision makers to promptly identify and analyze research topics. However, due to citation and publication delay, previous studies on trend analysis are more likely to identify ex-post trends. In this study, we employ author-defined keywords to represent topics and propose a simple, effective, and ex-ante approach, called authordefined keyword frequency prediction (AKFP), to detect research trends. More specifically, the proposed AKFP relies on the long short-term memory (LSTM) neural network. Four categories of features are proposed as input variables: Temporal feature, Persistence, Community size, and Community development potential. To verify the effectiveness and feasibility of the AKFP, we also proposed a simple but effective method to build a balanced and sufficient data set and conducted extensive comparative experiments, based on data extracted from the ACM Digital Library. Our empirical result confirms the feasibility of word frequency prediction by forecasting precision. Specifically, the short- and medium-term word frequency prediction achieved excellent performance, and the long-term word frequency prediction obtained acceptable prediction accuracy. In addition, we found that these proposed features have a significant but inconsistent impact on the AKFP. Specifically, the temporal feature is always an unignorable factor. The persistence has a strong correlation with the community size, and both are more important in the short- and medium-term prediction. In contrast, the community development potential is particularly significant in the long-term prediction.
C1 [Lu, Wei; Huang, Shengzhi; Yang, Jinqing; Cheng, Qikai; Huang, Yong] Wuhan Univ, Sch Informat Management, Wuhan, Hubei, Peoples R China.
   [Lu, Wei; Huang, Shengzhi; Yang, Jinqing; Cheng, Qikai; Huang, Yong] Wuhan Univ, Informat Retrieval & Knowledge Min Lab, Wuhan, Hubei, Peoples R China.
   [Bu, Yi] Peking Univ, Dept Informat Management, Beijing, Peoples R China.
C3 Wuhan University; Wuhan University; Peking University
RP Huang, Y (corresponding author), Wuhan Univ, Sch Informat Management, Wuhan, Hubei, Peoples R China.; Huang, Y (corresponding author), Wuhan Univ, Informat Retrieval & Knowledge Min Lab, Wuhan, Hubei, Peoples R China.
EM weilu@whu.edu.cn; ShengzhiHuang@whu.edu.cn; Jinq_yang@163.com;
   buyi@pku.edu.cn; chengqikai0806@163.com; yonghuang1991@whu.edu.cn
OI Huang, Shengzhi/0000-0002-7035-4627
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NR 56
TC 29
Z9 31
U1 9
U2 108
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0306-4573
EI 1873-5371
J9 INFORM PROCESS MANAG
JI Inf. Process. Manage.
PD JUL
PY 2021
VL 58
IS 4
AR 102594
DI 10.1016/j.ipm.2021.102594
EA MAR 2021
PG 18
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA SN6BG
UT WOS:000658372100033
DA 2024-09-05
ER
PT J
AU Qayyum, F
   Jamil, H
   Iqbal, N
   Kim, D
   Afzal, MT
AF Qayyum, Faiza
   Jamil, Harun
   Iqbal, Naeem
   Kim, DoHyeun
   Afzal, Muhammad Tanvir
TI Toward potential hybrid features evaluation using MLP-ANN binary
   classification model to tackle meaningful citations
SO SCIENTOMETRICS
LA English
DT Article
DE Binary Classification; Citation Count; Information Retrieval; Logistic
   Regression; Multi-layer Perceptron; Naive Bayes; Support vector machine
ID QUALITY; INDEX
AB Citation analysis-based systems are premised on assuming that all citations are equally important. The scientific community argues that a citation may hold divergent reasons and thus, should not be treated at par. In this regard, a plethora of existing studies classifies citations for varying reasons. Presently, the community has a propensity toward binary citation classification with the notion of contemplating only important reasons while employing quantitative analysis-based measures. We argue that outcomes yielded by the contemporary state-of-the-art models cannot be deemed ideal as the plethora of them has been evaluated on a data set with minimal number of instances due to which the outcomes cannot be generalized. The scope of results from such approaches is restricted to a single domain only which may exhibit entirely different behavior for the different data sets. Most of the studies are ruled by the content based features evaluated by harnessing traditional classification models like Support Vector Machine (SVM), and random forest (RF), while an inconsiderable number of studies employ metadata which holds the potential to serve as a quintessential indicator to tackle meaningful citations. In this study, we introduce Multilayer perceptron artificial neural network (MLP-ANN) binary citation classifier, which exploits the best combinations of features formed using both sources. We also introduce a new benchmark data set from the electrical engineering domain which is consolidated with two existing benchmark data sets for model evaluation. The outcomes reveal that the results produced by the proposed MLP model outperform the contemporary models achieving a precision of 0.92.
C1 [Qayyum, Faiza; Iqbal, Naeem; Kim, DoHyeun] Jeju Natl Univ, Comp Engn Dept, Jeju 63243, South Korea.
   [Jamil, Harun] Jeju Natl Univ, Dept Elect Engn, Jeju 63243, South Korea.
   [Afzal, Muhammad Tanvir] Shifa Tameer E Milat Univ, Dept Comp Sci, Islamabad 46000, Pakistan.
C3 Jeju National University; Jeju National University
RP Qayyum, F; Kim, D (corresponding author), Jeju Natl Univ, Comp Engn Dept, Jeju 63243, South Korea.
EM faizaqayyum@jejunu.ac.kr; harunjamil@hotmail.com;
   naeemiqbal@jejunu.ac.kr; kimdh@jejunu.ac.kr; tanvirqau@hotmail.com
RI Iqbal, Naeem/ABG-1525-2021; Afzal, Muhammad/D-3741-2019
OI Iqbal, Naeem/0000-0003-2749-6344; Afzal, Muhammad/0000-0002-7851-2327;
   Afzal, Muhammad Tanvir/0000-0002-9765-8815
FU Energy Cloud R&D Program through the National Research Foundation of
   Korea(NRF) - Ministry of Science, ICT [2019M3F2A1073387]; Institute for
   Information & communications Technology Promotion (IITP) [2022-0-00980]
FX This research was supported by Energy Cloud R&D Program through the
   National Research Foundation of Korea(NRF) funded by the Ministry of
   Science, ICT (2019M3F2A1073387), and this work was supported by the
   Institute for Information & communications Technology Promotion (IITP)
   (NO. 2022-0-00980, Cooperative Intelligence Framework of Scene
   Perception for Autonomous IoT Device).
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NR 46
TC 8
Z9 8
U1 4
U2 39
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD NOV
PY 2022
VL 127
IS 11
BP 6471
EP 6499
DI 10.1007/s11192-022-04530-3
EA OCT 2022
PG 29
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 5U5FU
UT WOS:000870916600001
DA 2024-09-05
ER
PT J
AU Yan, YY
   Fan, GX
   Liao, X
   Zhao, XD
AF Yan, Yangye
   Fan, Guoxin
   Liao, Xiang
   Zhao, Xudong
TI Research trends and hotspots on connectomes from 2005 to 2021: A
   bibliometric and latent Dirichlet allocation application study
SO FRONTIERS IN NEUROSCIENCE
LA English
DT Article
DE connectome; bibliometric; latent Dirichlet allocation; Web of Science;
   neuroscience
ID FUNCTIONAL CONNECTIVITY; GRAPH-THEORY; RICH-CLUB; BRAIN; NETWORKS;
   SCIENCE; PROMISE; STROKE
AB BackgroundThis study aimed to conduct a bibliometric analysis of publications on connectomes and illustrate its trends and hotspots using a machine-learning-based text mining algorithm. MethodsDocuments were retrieved from the Web of Science Core Collection (WoSCC) and Scopus databases and analyzed in Rstudio 1.3.1. Through quantitative and qualitative methods, the most productive and impactful academic journals in the field of connectomes were compared in terms of the total number of publications and h-index over time. Meanwhile, the countries/regions and institutions involved in connectome research were compared, as well as their scientific collaboration. The study analyzed topics and research trends by R package "bibliometrix." The major topics of connectomes were classified by Latent Dirichlet allocation (LDA). ResultsA total of 14,140 publications were included in the study. NEUROIMAGE ranked first in terms of publication volume (1,427 articles) and impact factor (h-index:122) among all the relevant journals. The majority of articles were published by developed countries, with the United States having the most. Harvard Medical School and the University of Pennsylvania were the two most productive institutions. Neuroimaging analysis technology and brain functions and diseases were the two major topics of connectome research. The application of machine learning, deep learning, and graph theory analysis in connectome research has become the current trend, while an increasing number of studies were concentrating on dynamic functional connectivity. Meanwhile, researchers have begun investigating alcohol use disorders and migraine in terms of brain connectivity in the past 2 years. ConclusionThis study illustrates a comprehensive overview of connectome research and provides researchers with critical information for understanding the recent trends and hotspots of connectomes.
C1 [Yan, Yangye] Tongji Univ, Sch Med, Shanghai Eastern Hosp, Shanghai, Peoples R China.
   [Fan, Guoxin; Liao, Xiang] Huazhong Univ Sci & Technol, Union Shenzhen Hosp, Dept Pain Med, Shenzhen, Peoples R China.
   [Fan, Guoxin; Liao, Xiang] Shenzhen Univ, Sch Biomed Engn, Sch Med, Guangdong Key Lab Biomed Measurements & Ultrasound, Shenzhen, Peoples R China.
   [Fan, Guoxin] Sun Yat Sen Univ, Affiliated Hosp 3, Dept Spine Surg, Guangzhou, Peoples R China.
   [Zhao, Xudong] Tongji Univ, Chinese German Inst Mental Hlth, Clin Res Ctr Mental Disorders, Shanghai Pudong New Area Mental Hlth Ctr,Sch Med, Shanghai, Peoples R China.
C3 Tongji University; Huazhong University of Science & Technology; Shenzhen
   University; Sun Yat Sen University; Tongji University
RP Liao, X (corresponding author), Huazhong Univ Sci & Technol, Union Shenzhen Hosp, Dept Pain Med, Shenzhen, Peoples R China.; Liao, X (corresponding author), Shenzhen Univ, Sch Biomed Engn, Sch Med, Guangdong Key Lab Biomed Measurements & Ultrasound, Shenzhen, Peoples R China.; Zhao, XD (corresponding author), Tongji Univ, Chinese German Inst Mental Hlth, Clin Res Ctr Mental Disorders, Shanghai Pudong New Area Mental Hlth Ctr,Sch Med, Shanghai, Peoples R China.
EM digitalxiang@163.com; zhaoxd@tongji.edu.cn
FU Outstanding Clinical Discipline Project of Shanghai Pudong; Guangdong
   Basic and Applied Basic Research Foundation; National Natural Science
   Foundation of China;  [PWYgy2021-02];  [2019A1515111171];  [82102640]
FX Funding This work was supported by the Outstanding Clinical Discipline
   Project of Shanghai Pudong (Grant No.: PWYgy2021-02), Guangdong Basic
   and Applied Basic Research Foundation (2019A1515111171), and the
   National Natural Science Foundation of China (82102640). The funders
   played no part in the study design, data collection, analysis,
   interpretation, writing, or the decision to submit the manuscript for
   publication.
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NR 63
TC 1
Z9 1
U1 4
U2 12
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 1662-453X
J9 FRONT NEUROSCI-SWITZ
JI Front. Neurosci.
PD DEC 22
PY 2022
VL 16
AR 1046562
DI 10.3389/fnins.2022.1046562
PG 12
WC Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Neurosciences & Neurology
GA 7N7PG
UT WOS:000907527100001
PM 36620450
OA Green Published, gold, Green Submitted
DA 2024-09-05
ER
PT C
AU Viloria, A
   Lis-Gutiérrez, JP
   Gaitán-Angulo, M
   Stanescu, CLV
   Crissien, T
AF Viloria, Amelec
   Paola Lis-Gutierrez, Jenny
   Gaitan-Angulo, Mercedes
   Vasquez Stanescu, Carmen Luisa
   Crissien, Tito
BE Rocha, A
   Reis, JL
   Peter, MK
   Bogdanovic, Z
TI Machine Learning Applied to the H Index of Colombian Authors with
   Publications in Scopus
SO MARKETING AND SMART TECHNOLOGIES, ICMARKTECH 2019
SE Smart Innovation Systems and Technologies
LA English
DT Proceedings Paper
CT International Conference on Marketing and Technologies (ICMarkTech)
CY NOV 27-29, 2019
CL Porto, PORTUGAL
AB Our research aims to establish how to predict the H index of Colombian authors with publications in Scopus until 2016. The selection of the date was because, as mentioned earlier, the number of documents indexed per year exceeded 10,000 and they obtained the highest number of documents cited. To accomplish this purpose, a quantitative, nonexperimental, cross-sectional, descriptive, explanatory, and predictive research was designed using supervised learning algorithms. These were applied to information from 8,840 Colombian authors. Among the findings we can highlight that: (i) Colombia is in the fifth position in the scope of countries of South America and the Caribbean, in terms of the number of products and citations; (ii) the largest number of Colombian authors with products in Scopus until 2016, belonged mainly to the area of natural sciences, followed by medical sciences and health; (iii) most of the Colombian authors were men (64.2%, or 5,442) and they have higher H index rates than women; (iv) using random cross validation for 10 iterations, the methods with the best predictive value using R2 and the minimization of mean absolute error (MAE) correspond to: AdaBoost (96.6% and 0.397, respectively); Random Forest (96.8% and 0.431, respectively); KNN (94.4% and 0.525, respectively); Tree (94.9% and 0.53, respectively); and Neural Network (93.3% and 0.7, respectively); and (v) the variables that help predict the H index in the case of the Colombian authors, in addition to the citations, correspond to: the quantity of products, number of products in Q1, and international collaboration.
C1 [Viloria, Amelec; Crissien, Tito] Univ Costa, Barranquilla, Colombia.
   [Paola Lis-Gutierrez, Jenny] Corp Univ Meta, Villavicencio, Colombia.
   [Paola Lis-Gutierrez, Jenny] Univ Nacl Colombia, Bogota, Colombia.
   [Gaitan-Angulo, Mercedes] Corp Univ Salamanca, Barranquilla, Colombia.
   [Vasquez Stanescu, Carmen Luisa] Univ Nacl Expt Politecn Antonio Jose de Sucre, Barquisimeto, Venezuela.
C3 Universidad de la Costa; Universidad Nacional de Colombia
RP Viloria, A (corresponding author), Univ Costa, Barranquilla, Colombia.
EM aviloria@cuc.edu.co; jenny.lis@unimeta.edu.co; m_gaitan689@cues.edu.co;
   cvasquez@unexpo.edu.ve; rectoria@cuc.edu.co
RI Gaitán-Angulo, Mercedes/U-3365-2019
OI Gaitán-Angulo, Mercedes/0000-0002-8248-8788; Lis-Gutierrez,
   Jenny-Paola/0000-0002-1438-7619; Vasquez Stanescu, Carmen
   Luisa/0000-0002-0657-3470
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NR 19
TC 0
Z9 0
U1 0
U2 0
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 2190-3018
EI 2190-3026
BN 978-981-15-1564-4; 978-981-15-1563-7
J9 SMART INNOV SYST TEC
PY 2020
VL 167
BP 388
EP 397
DI 10.1007/978-981-15-1564-4_36
PG 10
WC Business; Computer Science, Artificial Intelligence
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Business & Economics; Computer Science
GA BT4QS
UT WOS:000833547500036
DA 2024-09-05
ER
PT J
AU Venkatesan, VK
   Ramakrishna, MT
   Batyuk, A
   Barna, A
   Havrysh, B
AF Venkatesan, Vinoth Kumar
   Ramakrishna, Mahesh Thyluru
   Batyuk, Anatoliy
   Barna, Andrii
   Havrysh, Bohdana
TI High-Performance Artificial Intelligence Recommendation of Quality
   Research Papers Using Effective Collaborative Approach
SO SYSTEMS
LA English
DT Article
DE recommender system; quality; artificial intelligence; publications;
   research paper; collaborative approach; accuracy; precision; recall
AB The Artificial Intelligence Recommender System has emerged as a significant research interest. It aims at helping users find things online by offering recommendations that closely fit their interests. Recommenders for research papers have appeared over the last decade to make it easier to find publications associated with the field of researchers' interests. However, due to several issues, such as copyright constraints, these methodologies assume that the recommended articles' contents are entirely openly accessible, which is not necessarily the case. This work demonstrates an efficient model, known as RPRSCA: Research Paper Recommendation System Using Effective Collaborative Approach, to address these uncertain systems for the recommendation of quality research papers. We make use of contextual metadata that are publicly available to gather hidden relationships between research papers in order to personalize recommendations by exploiting the advantages of collaborative filtering. The proposed system, RPRSCA, is unique and gives personalized recommendations irrespective of the research subject. Thus, a novel collaborative approach is proposed that provides better performance. Using a publicly available dataset, we found that our proposed method outperformed previous uncertain methods in terms of overall performance and the capacity to return relevant, valuable, and quality publications at the top of the recommendation list. Furthermore, our proposed strategy includes personalized suggestions and customer expertise, in addition to addressing multi-disciplinary concerns.
C1 [Venkatesan, Vinoth Kumar] VIT Univ, Sch Informat Technol & Engn, Vellore 632014, Tamil Nadu, India.
   [Ramakrishna, Mahesh Thyluru] JAIN, Fac Engn & Technol, Dept Comp Sci & Engn, Bangalore 562112, Karnataka, India.
   [Batyuk, Anatoliy] Lviv Polytech Natl Univ, Dept Automated Control Syst, UA-79013 Lvov, Ukraine.
   [Barna, Andrii] Lviv Polytech Natl Univ, Dept Artificial Intelligence, UA-79013 Lvov, Ukraine.
   [Havrysh, Bohdana] Lviv Polytech Natl Univ, Dept Publishing Informat Technol, UA-79013 Lvov, Ukraine.
C3 Vellore Institute of Technology (VIT); VIT Vellore; Jain University;
   Ministry of Education & Science of Ukraine; Lviv Polytechnic National
   University; Ministry of Education & Science of Ukraine; Lviv Polytechnic
   National University; Ministry of Education & Science of Ukraine; Lviv
   Polytechnic National University
RP Ramakrishna, MT (corresponding author), JAIN, Fac Engn & Technol, Dept Comp Sci & Engn, Bangalore 562112, Karnataka, India.
EM t.mahesh@jainuniversity.ac.in
RI Batyuk, Anatoliy Ye./S-4988-2017; Havrysh, Bohdana/W-5413-2018
OI Batyuk, Anatoliy Ye./0000-0001-7650-7383; TR,
   MAHESH/0000-0002-5589-8992; Havrysh, Bohdana/0000-0003-3213-9747
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NR 28
TC 4
Z9 4
U1 2
U2 16
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2079-8954
J9 SYSTEMS-BASEL
JI Systems-Basel
PD FEB
PY 2023
VL 11
IS 2
AR 81
DI 10.3390/systems11020081
PG 14
WC Social Sciences, Interdisciplinary
WE Social Science Citation Index (SSCI)
SC Social Sciences - Other Topics
GA 9L2OA
UT WOS:000941392700001
OA gold
DA 2024-09-05
ER
PT J
AU Ma, HZ
   Zhang, QJ
AF Ma, Huizhu
   Zhang, Qiuju
TI Research on cultural-based multi-objective particle swarm optimization
   in image compression quality assessment
SO OPTIK
LA English
DT Article
DE Image compression; Multi-objective optimization; Cultural algorithms;
   Particle swarm optimization
AB Quantization is a main factor which affects the performance of the JPEG compression. Compression ratio and the quality of the decoded images are both determined by quantization tables. Appropriate choice of the quantization tables is the key to the Compression performance. To select different optimal quantization tables for different classes of images is multi-objective optimal problem. A cultural-based multi-objective particle swarm optimization model is proposed in this paper. And, different trade-offs between image compression and quality is presented. The simulation result shows that the proposed model is effective to the choice of image compression quality. (C) 2012 Elsevier GmbH. All rights reserved.
C1 [Ma, Huizhu; Zhang, Qiuju] Harbin Engn Univ, Informat & Commun Engn Coll, Harbin 150001, Peoples R China.
C3 Harbin Engineering University
RP Zhang, QJ (corresponding author), Harbin Engn Univ, Informat & Commun Engn Coll, Harbin 150001, Peoples R China.
EM mahuizhu@hrbeu.edu.cn; zhangqiuju_hrbeu@yahoo.com.cn
RI zhang, qiu/GXG-5600-2022
FU Fundamental Research Funds for the Central Universities
FX This work was supported by the Fundamental Research Funds for the
   Central Universities.
CR [Anonymous], 2010 IEEE C EV COMP
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NR 6
TC 9
Z9 12
U1 0
U2 5
PU ELSEVIER GMBH
PI MUNICH
PA HACKERBRUCKE 6, 80335 MUNICH, GERMANY
SN 0030-4026
EI 1618-1336
J9 OPTIK
JI Optik
PY 2013
VL 124
IS 10
BP 957
EP 961
DI 10.1016/j.ijleo.2012.02.041
PG 5
WC Optics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Optics
GA 154XV
UT WOS:000319710900020
DA 2024-09-05
ER
PT J
AU Antons, D
   Joshi, AM
   Salge, TO
AF Antons, David
   Joshi, Amol M.
   Salge, Torsten Oliver
TI Content, Contribution, and Knowledge Consumption: Uncovering Hidden
   Topic Structure and Rhetorical Signals in Scientific Texts
SO JOURNAL OF MANAGEMENT
LA English
DT Article
DE scientific impact; management research; citation analysis; text mining;
   latent Dirichlet allocation
ID STRATEGIC MANAGEMENT RESEARCH; INTELLECTUAL STRUCTURE; CO-AUTHORSHIP;
   LANGUAGE USE; WORDS; CITATIONS; JOURNALS; ARTICLE; SCIENCE; IMPACT
AB Knowledge production and scientific discourse are observable in published scholarly texts. Citations capture knowledge consumption and impact. Drawing from the sociology of science, our theoretical framework posits scientific communities as thought collectives with distinctive thought styles that embed a hidden topic structure and rhetorical signals into a journal's published articles. We hypothesize and uncover how an article's topic attributes (structure, focus, and newness) and rhetorical attributes (inclusiveness, exclusiveness, tentativeness, and certainty) are related to future knowledge consumption. We empirically test our ideas by applying text mining algorithms to model topics and extract rhetorical signals from 1,646 strategy articles composed of nearly 18 million words generating 172,237 citations over 35 years. We find that strategy articles' hidden topic structure explains 14% of variance in scientific impact. We also show that topic focus and topic newness each independently, directly, and significantly increase impact. As for newness, the first two articles published on a new topic each generate a citation premium >100%, which is higher within the focal thought collective than outside. Importantly, we find that the citation premium of newness increases with greater topic focus (which attracts attention) and greater inflow of prior intracollective knowledge (which enhances absorption). Impact also increases when authors present new topics using a rhetorical style that is more tentative than certain. Overall, our findings demonstrate that topic and rhetorical attributes, as constitutive elements of scientific content, are independently and interdependently related to the consumption of strategy articles across thought collectives in management research.
C1 [Antons, David; Salge, Torsten Oliver] Rhein Westfal TH Aachen, Aachen, Germany.
   [Joshi, Amol M.] Oregon State Univ, Corvallis, OR 97331 USA.
C3 RWTH Aachen University; Oregon State University
RP Joshi, AM (corresponding author), Oregon State Univ, Coll Business, 443 Austin Hall, Corvallis, OR 97331 USA.
EM amol.joshi@oregonstate.edu
RI Joshi, Amol M/C-8433-2013; Joshi, Amol/JBQ-4510-2023
OI Joshi, Amol M/0000-0002-3023-6749; 
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NR 96
TC 39
Z9 40
U1 6
U2 89
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 0149-2063
EI 1557-1211
J9 J MANAGE
JI J. Manag.
PD SEP
PY 2019
VL 45
IS 7
BP 3035
EP 3076
DI 10.1177/0149206318774619
PG 42
WC Business; Psychology, Applied; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics; Psychology
GA IR5QB
UT WOS:000481488500015
DA 2024-09-05
ER
PT C
AU Purwitasari, D
   Ilmi, AB
   Fatichah, C
   Fauzi, WA
   Sumpeno, S
   Purnomo, MH
AF Purwitasari, Diana
   Ilmi, Akhmad Bakhrul
   Fatichah, Chastine
   Fauzi, Willy Achmat
   Sumpeno, Surya
   Purnomo, Mauridhi Hery
GP IEEE
TI Conflict of Interest based Features for Expert Classification in
   Bibliographic Network
SO 2018 INTERNATIONAL CONFERENCE ON COMPUTER ENGINEERING, NETWORK AND
   INTELLIGENT MULTIMEDIA (CENIM)
LA English
DT Proceedings Paper
CT Joint International Conference on Computer Engineering, Network and
   Intelligent Multimedia (CENIM) / 11th AUN/SEED-Net Regional Conference
   on Computer and Information Engineering (RCCIE)
CY NOV 26-27, 2018
CL Surabaya, INDONESIA
DE expert classification; bibliographic data; citation analysis; conflict
   of interest feature; word embedding; deep learning
AB Countless approaches of feature extraction in the expert classification problem employ text contents and network structures from bibliographic metadata of published articles. The content part often use title and abstract while the structure part utilize co-authorship and citation. On citation data, the classifier method works on a feature of citation quantity since a frequently cited author is presumed to have more expertise. Citation misconduct occurs if there is no subject relation between citing and cited articles. Therefore, the misconduct becomes a challenge for evaluation of citation quality. Here, the problem is to classify experts with features that can indicate citation misconduct. To address this problem, our contribution exploited the quality and the quantity of citations in feature extraction designed for classifying experts. Co-authorship that influence the misconducts is called as Conflict of Interest (CoI) situation. Accordingly, the class labels are experts with or without CoI indication. We proposed three ratio features of (1) self-citation to represent the citation quantity, then (2) subject similarity of author interests and article contents, as well as (3) subject similarity of citing and cited articles to determine the citation quality. There are various word phrases used in subjects with similar contexts. Therefore the proposed CoI-based features for the citation quality took on deep learning approaches for understanding natural language. Our experiments exercised a selection of data from one of the common datasets in bibliographic related problems called as AMiner. We selected +/- 15K articles from the original data of +/- 2M articles in the experiments. The results showed that our proposed features classified experts with CoI indication by accuracy value of +/- 60%. Although the first feature of citation quantity was not significant for categorizing experts, other features of citation quality confirmed more profound evidence.
C1 [Purwitasari, Diana] Inst Teknol Sepuluh Nopember, Fac Informat Technol, Dept Informat, Dept Elect Engn,Fac Elect Technol, Surabaya, Indonesia.
   [Ilmi, Akhmad Bakhrul; Fatichah, Chastine] Kampus Inst Teknol Sepuluh Nopember ITS Sukolilo, Inst Teknol Sepuluh Nopember ITS, Fac Informat Technol, Dept Informat, Surabaya 60111, East Java, Indonesia.
   [Fauzi, Willy Achmat] Inst Teknol Sepuluh Nopember, Dept Elect Engn, Fac Elect Technol, Surabaya, Indonesia.
   [Sumpeno, Surya; Purnomo, Mauridhi Hery] Inst Teknol Sepuluh Nopember, Dept Elect Engn, Fac Elect Technol, Dept Comp Engn, Surabaya, Indonesia.
C3 Institut Teknologi Sepuluh Nopember; Institut Teknologi Sepuluh
   Nopember; Institut Teknologi Sepuluh Nopember; Institut Teknologi
   Sepuluh Nopember
RP Purwitasari, D (corresponding author), Inst Teknol Sepuluh Nopember, Fac Informat Technol, Dept Informat, Dept Elect Engn,Fac Elect Technol, Surabaya, Indonesia.
EM diana@if.its.ac.id; chastine@cs.its.ac.id; surya@ee.its.ac.id;
   hery@ee.its.ac.id
RI Fatichah, Chastine/W-2402-2019; Setijadi, Eko/A-2128-2012; s,
   surya/AER-2052-2022; Fauzi, Willy Achmat/GLN-6678-2022; Purwitasari,
   Diana/X-3953-2019
OI Fatichah, Chastine/0000-0002-7348-9762; Fauzi, Willy
   Achmat/0000-0002-2919-6575; Purwitasari, Diana/0000-0001-7000-7628;
   Sumpeno, Surya/0000-0002-1744-1342
FU Indonesia Endowment Fund for Education, Indonesian called as Lembaga
   Pengelola Dana Pendidikan, LPDP, under Indonesian Education Scholarship
   for Master and Doctoral Programs of the LPDP Doctoral Scholarship
   Programme fiscal year 2017-2020 [PRJ-4228/LPDP.3/2016]
FX This work was supported by the Indonesia Endowment Fund for Education,
   or in Indonesian called as Lembaga Pengelola Dana Pendidikan, LPDP,
   under Indonesian Education Scholarship for Master and Doctoral Programs
   with the grant number PRJ-4228/LPDP.3/2016 of the LPDP Doctoral
   Scholarship Programme fiscal year 2017-2020.
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NR 19
TC 1
Z9 1
U1 0
U2 0
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-5386-7509-0
PY 2018
BP 54
EP 59
PG 6
WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Engineering
GA BM8IQ
UT WOS:000469263900010
DA 2024-09-05
ER
PT J
AU Zerva, C
   Nghiem, MQ
   Nguyen, NTH
   Ananiadou, S
AF Zerva, Chrysoula
   Minh-Quoc Nghiem
   Nguyen, Nhung T. H.
   Ananiadou, Sophia
TI Cited text span identification for scientific summarisation using
   pre-trained encoders
SO SCIENTOMETRICS
LA English
DT Article
DE Cited text span identification; Citation analysis; Scientific
   summarisation; Neural networks; BERT; Fine-tuning
ID MODELS
AB We present our approach for the identification of cited text spans in scientific literature, using pre-trained encoders (BERT) in combination with different neural networks. We further experiment to assess the impact of using these cited text spans as input in BERT-based extractive summarisation methods. Inspired and motivated by the CL-SciSumm shared tasks, we explore different methods to adapt pre-trained models which are tuned for generic domain to scientific literature. For the identification of cited text spans, we assess the impact of different configurations in terms of learning from augmented data and using different features and network architectures (BERT, XLNET, CNN, and BiMPM) for training. We show that identifying and fine-tuning the language models on unlabelled or augmented domain specific data can improve the performance of cited text span identification models. For the scientific summarisation we implement an extractive summarisation model adapted from BERT. With respect to the input sentences taken from the cited paper, we explore two different scenarios: (1) consider all the sentences (full-text) of the referenced article as input and (2) consider only the text spans that have been identified to be cited by other publications. We observe that in certain experiments, by using only the cited text-spans we can achieve better performance, while minimising the input size needed.
C1 [Zerva, Chrysoula; Minh-Quoc Nghiem; Nguyen, Nhung T. H.; Ananiadou, Sophia] Univ Manchester, Natl Ctr Text Min, Sch Comp Sci, Manchester, Lancs, England.
   [Ananiadou, Sophia] Alan Turing Inst, London, England.
C3 University of Manchester
RP Zerva, C (corresponding author), Univ Manchester, Natl Ctr Text Min, Sch Comp Sci, Manchester, Lancs, England.
EM chrysoula.zerva@manchester.ac.uk; minh-quoc.nghiem@manchester.ac.uk;
   nhung.nguyen@manchester.ac.uk; sophia.ananiadou@manchester.ac.uk
RI Zerva, Chrysoula/JOK-8942-2023
OI Zerva, Chrysoula/0000-0002-4031-9492
FU EPSRC [EP/N509565/1]; HSE Discovering Safety, Lloyd's Register
   Foundation; Thomas Ashton Institute
FX This work was partly supported by the EPSRC Doctoral Prize award
   [EP/N509565/1]; the HSE Discovering Safety, Lloyd's Register Foundation;
   and the Thomas Ashton Institute.
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NR 86
TC 16
Z9 16
U1 1
U2 16
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD DEC
PY 2020
VL 125
IS 3
BP 3109
EP 3137
DI 10.1007/s11192-020-03455-z
EA MAY 2020
PG 29
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA PE1YL
UT WOS:000531002500001
OA hybrid
DA 2024-09-05
ER
PT J
AU Hernández, IMD
   Urdaneta, AS
   Carayannis, E
AF De la Vega Hernandez, Ivan Manuel
   Serrano Urdaneta, Angel
   Carayannis, Elias
TI Global bibliometric mapping of the frontier of knowledge in the field of
   artificial intelligence for the period 1990-2019
SO ARTIFICIAL INTELLIGENCE REVIEW
LA English
DT Article
DE Artificial intelligence; Machine learning; Deep learning; Big Data;
   Bibliometric mapping; Knowledge networks; WoS; Radical changes
AB Artificial Intelligence (AI) has emerged as a field of knowledge that is displacing and disrupting technologies, leading to changes in human life. Therefore, the purpose of this study is to scientifically map this topic and its ramifications, in order to analyze its growth. The study was developed under the bibliometric approach and considered the period 1990-2019. The steps followed were (i) Identification and selection of keyword terms in three methodological layers by a panel of experts. (ii) Design and application of an algorithm to identify these selected keywords in titles, abstracts, and keywords using terms in Web of Science to contrast them. (iii) Performing data processing based on the Journals of the Journal Citation Report during 2020. Knowing the evolution of a field of knowledge such as AI from a bibliometric study and subsequently establishing the ramifications of new research streams is in itself a relevant finding. Addressing a broad field of knowledge as AI from a multidisciplinary approach given the convergence it generates with other disciplines and specialties is of high strategic value for decision makers such as governments, academics, scientists, and entrepreneurs.
C1 [De la Vega Hernandez, Ivan Manuel; Serrano Urdaneta, Angel] CTR Catolica Grad Business Sch, Lima, Peru.
   [De la Vega Hernandez, Ivan Manuel; Serrano Urdaneta, Angel] Pontificia Univ Catolica Peru, Lima, Peru.
   [Carayannis, Elias] George Washington Univ, GWU Sch Business, Washington, DC 20052 USA.
C3 Pontificia Universidad Catolica del Peru; George Washington University
RP Hernández, IMD (corresponding author), CTR Catolica Grad Business Sch, Lima, Peru.; Hernández, IMD (corresponding author), Pontificia Univ Catolica Peru, Lima, Peru.
EM idelavega@pucp.edu.pe; angel.serrano@pucp.pe; caraye@gwu.edu
RI CARAYANNIS, ELIAS/H-3075-2014; de la Vega Hernandez, Ivan
   Manuel/AAD-5460-2019
OI CARAYANNIS, ELIAS/0000-0003-2348-4311; de la Vega Hernandez, Ivan
   Manuel/0000-0002-8554-0510
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NR 51
TC 17
Z9 17
U1 9
U2 34
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0269-2821
EI 1573-7462
J9 ARTIF INTELL REV
JI Artif. Intell. Rev.
PD FEB
PY 2023
VL 56
IS 2
BP 1699
EP 1729
DI 10.1007/s10462-022-10206-4
EA JUN 2022
PG 31
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 8L6FZ
UT WOS:000807969300001
PM 35693001
OA Bronze, Green Published
DA 2024-09-05
ER
PT J
AU La Quatra, M
   Cagliero, L
   Baralis, E
AF La Quatra, Moreno
   Cagliero, Luca
   Baralis, Elena
TI Leveraging full-text article exploration for citation analysis
SO SCIENTOMETRICS
LA English
DT Article
DE Citation analysis; Deep natural language processing; Citation
   classification
ID SENTIMENT
AB Scientific articles often include in-text citations quoting from external sources. When the cited source is an article, the citation context can be analyzed by exploring the article full-text. To quickly access the key information, researchers are often interested in identifying the sections of the cited article that are most pertinent to the text surrounding the citation in the citing article. This paper first performs a data-driven analysis of the correlation between the textual content of the sections of the cited article and the text snippet where the citation is placed. The results of the correlation analysis show that the title and abstract of the cited article are likely to include content highly similar to the citing snippet. However, the subsequent sections of the paper often include cited text snippets as well. Hence, there is a need to understand the extent to which an exploration of the full-text of the cited article would be beneficial to gain insights into the citing snippet, considering also the fact that the full-text access could be restricted. To this end, we then propose a classification approach to automatically predicting whether the cited snippets in the full-text of the paper contain a significant amount of new content beyond abstract and title. The proposed approach could support researchers in leveraging full-text article exploration for citation analysis. The experiments conducted on real scientific articles show promising results: the classifier has a 90% chance to correctly distinguish between the full-text exploration and only title and abstract cases.
C1 [La Quatra, Moreno; Cagliero, Luca; Baralis, Elena] Politecn Torino, Corso Duca degli Abruzzi 24, I-10129 Turin, Italy.
C3 Polytechnic University of Turin
RP La Quatra, M (corresponding author), Politecn Torino, Corso Duca degli Abruzzi 24, I-10129 Turin, Italy.
EM moreno.laquatra@polito.it; luca.cagliero@polito.it;
   elena.baralis@polito.it
RI La Quatra, Moreno/AET-2693-2022
OI La Quatra, Moreno/0000-0001-8838-064X; Cagliero,
   Luca/0000-0002-7185-5247
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NR 42
TC 1
Z9 1
U1 2
U2 44
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD OCT
PY 2021
VL 126
IS 10
BP 8275
EP 8293
DI 10.1007/s11192-021-04117-4
EA AUG 2021
PG 19
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA UW2MR
UT WOS:000686089100011
OA hybrid
DA 2024-09-05
ER
PT J
AU Thelwell, M
AF Thelwell, Mike
TI Can the quality of published academic journal articles be assessed with
   machine learning?
SO QUANTITATIVE SCIENCE STUDIES
LA English
DT Article
DE citation analysis; machine learning; research evaluation; text mining
ID CITATION COUNTS; IMPACT; SCIENCE; READABILITY; JUDGMENT; FEATURES;
   MODELS; FIELD
AB Formal assessments of the quality of the research produced by departments and universities are now conducted by many countries to monitor achievements and allocate performance-related funding. These evaluations are hugely time consuming if conducted by postpublication peer review and are simplistic if based on citations or journal impact factors. I investigate whether machine learning could help reduce the burden of peer review by using citations and metadata to learn how to score articles from a sample assessed by peer review. An experiment is used to underpin the discussion, attempting to predict journal citation thirds, as a proxy for article quality scores, for all Scopus narrow fields from 2014 to 2020. The results show that these proxy quality thirds can be predicted with above baseline accuracy in all 326 narrow fields, with Gradient Boosting Classifier, Random Forest Classifier, or Multinomial Naive Bayes being the most accurate in nearly all cases. Nevertheless, the results partly leverage journal writing styles and topics, which are unwanted for some practical applications and cause substantial shifts in average scores between countries and between institutions within a country. There may be scope for predicting articles' scores when the predictions have the highest probability.
C1 [Thelwell, Mike] Univ Wolverhampton, Wolverhampton, England.
C3 University of Wolverhampton
RP Thelwell, M (corresponding author), Univ Wolverhampton, Wolverhampton, England.
EM m.thetwall@wlv.ac.uk
RI Thelwall, Mike/JDV-4700-2023
OI Thelwall, Mike/0000-0001-6065-205X
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NR 47
TC 8
Z9 9
U1 5
U2 25
PU MIT PRESS
PI CAMBRIDGE
PA ONE ROGERS ST, CAMBRIDGE, MA 02142-1209 USA
EI 2641-3337
J9 QUANT SCI STUD
JI Quant. Sci. Stud.
PD APR 12
PY 2022
VL 3
IS 1
BP 208
EP 226
DI 10.1162/qss_a_00185
PG 19
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA 4J6XG
UT WOS:000851408900010
OA Green Published, gold
DA 2024-09-05
ER
PT J
AU Collado-Villaverde, A
   Muñoz, P
   Cid, C
AF Collado-Villaverde, Armando
   Munoz, Pablo
   Cid, Consuelo
TI Comment on "Prediction of the SYM-H Index Using a Bayesian Deep Learning
   Method With Uncertainty Quantification" by Abduallah et al. (2024)
SO SPACE WEATHER-THE INTERNATIONAL JOURNAL OF RESEARCH AND APPLICATIONS
LA English
DT Article
DE machine learning; uncertainty; geomagnetic indices forecasting
AB Abduallah et al. (2024b, ) proposed a novel approach using a deep neural network model, which includes a graph neural network and a bidirectional LSTM layer, named SYMHnet, to forecast the SYM-H index one and 2 hr in advance. Additionally, the network also provides an uncertainty quantification of the predictions. While the approach is innovative, there are some areas where the model's design and implementation may not align with best practices in uncertainty quantification and predictive modeling. We focus on discrepancies in the input and output of the model, which can limit the applicability in real-world forecasting scenarios. This comment aims to clarify these issues, offering detailed insights into how such discrepancies could compromise the model's interpretability and reliability, thereby contributing to the advancement of predictive modeling in space weather research.
   The use of Machine learning to predict geomagnetic storms is becoming a trend. A recent study by Abduallah et al. (2024b, ) introduces a novel approach to forecast the SYM-H index, which measures geomagnetic activity on a global scale, while also quantifying the uncertainty of these predictions. However, this commentary highlights methodological concerns with their approach, such as data selection issues and the reliability of uncertainty calculations. These factors could significantly affect the model's accuracy and applicability in real-time forecasting scenarios.
   Examination of the model input and output reveals oversights that could undermine the model's predictive accuracy and applicability The process to estimate uncertainty has limited applicability for use in real time There are no coverage metrics regarding the uncertainty intervals
C1 [Collado-Villaverde, Armando; Munoz, Pablo] Univ Alcala, Dept Comp Engn, Madrid, Spain.
   [Cid, Consuelo] Univ Alcala, Dept Phys & Math, Madrid, Spain.
C3 Universidad de Alcala; Universidad de Alcala
RP Collado-Villaverde, A (corresponding author), Univ Alcala, Dept Comp Engn, Madrid, Spain.
EM armando.collado@uah.es
OI Munoz, Pablo/0000-0003-0581-5383
FU ESA project Deep Neural Networks for Geomagnetic Forecasting
   [4000137421/22/NL/GLC/my]; MICINN [PID2020-119407GB-I00]; University of
   Alcala [2022/00464/001]
FX The authors are also thankful to the reviewer for their suggestions
   which helped us to improve the paper and the ESA's technical officer
   Alexi Glover for her support. Armando Collado-Villaverde is supported by
   the ESA project Deep Neural Networks for Geomagnetic Forecasting
   4000137421/22/NL/GLC/my. Consuelo Cid acknowledges the support by the
   MICINN (Grant PID2020-119407GB-I00). Pablo Munoz acknowledges the
   support by the University of Alcala (Grant 2022/00464/001).
CR Abduallah Y., 2024, Zenodo, DOI [10.5281/zenodo.10602474, DOI 10.5281/ZENODO.10602474]
   Abduallah Y, 2024, SPACE WEATHER, V22, DOI 10.1029/2023SW003824
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NR 15
TC 0
Z9 0
U1 0
U2 0
PU AMER GEOPHYSICAL UNION
PI WASHINGTON
PA 2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA
EI 1542-7390
J9 SPACE WEATHER
JI Space Weather
PD AUG
PY 2024
VL 22
IS 8
AR e2024SW003909
DI 10.1029/2024SW003909
PG 6
WC Astronomy & Astrophysics; Geochemistry & Geophysics; Meteorology &
   Atmospheric Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Astronomy & Astrophysics; Geochemistry & Geophysics; Meteorology &
   Atmospheric Sciences
GA D5A3T
UT WOS:001296304100001
OA hybrid
DA 2024-09-05
ER
PT J
AU Kumar, R
   Althaqafi, E
   Patro, SGK
   Simic, V
   Babbar, A
   Pamucar, D
   Singh, SK
   Verma, A
AF Kumar, Raman
   Althaqafi, Essam
   Patro, S. Gopal Krishna
   Simic, Vladimir
   Babbar, Atul
   Pamucar, Dragan
   Singh, Sanjeev Kumar
   Verma, Amit
TI Machine and deep learning methods for concrete strength Prediction: A
   bibliometric and content analysis review of research trends and future
   directions
SO APPLIED SOFT COMPUTING
LA English
DT Article
DE Review; Machine Learning; Deep Learning; Concrete Strength Prediction;
   Construction Engineering
ID HIGH-PERFORMANCE CONCRETE; COMPRESSIVE STRENGTH; SHEAR-STRENGTH;
   BRADFORD LAW; ACCURACY; SCIENCE; MODELS
AB This review paper provides a detailed evaluation of the existing landscape and future trends in applying machine learning and deep learning approaches for predicting concrete strength in construction engineering. The study contextualizes the investigation of machine learning and deep learning in concrete strength prediction, emphasizing the need for precise strength forecasting in construction. This hybrid review uses quantitative analysis of an extensive collection of 1005 research publications from the Scopus database (2010-2023) to identify clusters, hotspots, and gaps in this area, giving a systematic way to analyze the field's dynamics. This review reveals major research clusters such as concrete characteristics, sustainability, error analysis, and optimization. It identifies research hotspots like compressive strength prediction, reinforced concrete, and neural networks. The review illuminates future research paths, ethical concerns, and environmental implications. It emphasizes the relevance of fairness, bias reduction, and sustainability in developing and deploying machine and deep learning models in the construction sector and the necessity for specialized models in forecasting concrete durability, sustainable concrete strength, and shear strength.
C1 [Kumar, Raman] Guru Nanak Dev Engn Coll, Dept Mech & Prod Engn, Ludhiana 141006, Punjab, India.
   [Althaqafi, Essam] King Khalid Univ, Coll Engn, Civil Engn Dept, Abha 61421, Saudi Arabia.
   [Patro, S. Gopal Krishna] Woxsen Univ, Sch Technol, Hyderabad, Telangana, India.
   [Simic, Vladimir] Univ Belgrade, Fac Transport & Traff Engn, Vojvode Stepe 305, Belgrade 11010, Serbia.
   [Simic, Vladimir] Yuan Ze Univ, Coll Engn, Dept Ind Engn & Management, Taoyuan 320315, Taiwan.
   [Simic, Vladimir] Korea Univ, Coll Informat, Dept Comp Sci & Engn, Seoul 02841, South Korea.
   [Babbar, Atul] SGT Univ, Dept Mech Engn, Gurugram 122505, Haryana, India.
   [Pamucar, Dragan] Univ Belgrade, Fac Org Sci, Dept Operat Res & Stat, Belgrade, Serbia.
   [Pamucar, Dragan] Western Caspian Univ, Dept Mech & Math, Baku, Azerbaijan.
   [Pamucar, Dragan] Sunway Univ, Sch Engn & Technol, Selangor, Malaysia.
   [Singh, Sanjeev Kumar] Galgotia Coll Engn, Galgotias Coll Engn & Technol, Knowledge Pk 1, Greater Noida 201310, Uttar Pradesh, India.
   [Verma, Amit] Chandigarh Univ, Univ Ctr Res & Dev, Gharuan Mohali 140413, Punjab, India.
C3 Guru Nanak Dev Engineering College Ludhiana; King Khalid University;
   University of Belgrade; Yuan Ze University; Korea University; University
   of Belgrade; Ministry of Education of Azerbaijan Republic; Western
   Caspian University; Sunway University; Galgotias College of Engineering
   & Technology (GCET); Chandigarh University
RP Simic, V (corresponding author), Univ Belgrade, Fac Transport & Traff Engn, Vojvode Stepe 305, Belgrade 11010, Serbia.
EM sehgal91@yahoo.co.in; ealthaqafi@kku.edu.sa; sgkpatro2008@gmail.com;
   vsima@sf.bg.ac.rs; atulbabbar123@gmail.com; dragan.pamucar@fon.bg.ac.rs;
   sksingh72@gmail.com; amit.e9679@cumail.in
RI Patro, Dr. S Gopal Krishna/CAF-9606-2022; Simic, Vladimir/B-8837-2011
OI Patro, Dr. S Gopal Krishna/0000-0003-3130-339X; Simic,
   Vladimir/0000-0001-5709-3744
FU Deanship of Scientific Research at King Khalid University, Abha, Saudi
   Arabia [RGP2/563/44]
FX The authors extend their appreciation to the Deanship of Scientific
   Research at King Khalid University, Abha, Saudi Arabia for providing
   financial support to this research work through Large Groups Research
   Project under grant number RGP2/563/44.
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NR 228
TC 0
Z9 0
U1 13
U2 13
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1568-4946
EI 1872-9681
J9 APPL SOFT COMPUT
JI Appl. Soft. Comput.
PD OCT
PY 2024
VL 164
AR 111956
DI 10.1016/j.asoc.2024.111956
EA JUL 2024
PG 28
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA YG7E5
UT WOS:001267393300001
DA 2024-09-05
ER
PT J
AU Daud, A
   Ghaffar, S
   Amjad, T
AF Daud, Ali
   Ghaffar, Sehrish
   Amjad, Tehmina
TI Citation Count Is Not Enough: Citation's Context-Based Scientific Impact
   Evaluation
SO IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
LA English
DT Article; Early Access
DE Sentiment analysis; Task analysis; Blogs; Software; Social networking
   (online); Protocols; Prediction algorithms; Article ranking; citation
   context; conflict of interest; context-based article impact factor
   (CBAIF); impact evaluation; sentiment analysis
ID SENTIMENT ANALYSIS; RANKING AUTHORS
AB Qualitative analysis of citations received by a scientific manuscript is a challenging task in the field of citation analysis. In most cases, the existing approaches that involve citations for the scientific impact evaluation normally employ a quantitative parameter, such as the number of received citations, while ignoring the qualitative feature, such as the context of citations, while, in reality, a received citation might hold positive feedback and negative or neutral feedback. In this study, a measure is purposed for the scientific evaluation of the articles based on the context of the citations named the context-based article impact factor (CBAIF). CBAIF not only considers the positive, negative, or neutral context of the citations but also involves the citing and cited author's conflict-of-interest relationship for the evaluation of their scientific impact. With the help of experimentation, it is observed that CBAIF performs a fair ranking of articles based on citation's context, whether it is cited positively or being criticized by some authors. Experimental results show that the CBAIF value with the context of citations revealed accurate results rather than the article impact factor (AIF) value without the context of citations.
C1 [Daud, Ali] Abu Dhabi Sch Management, Abu Dhabi, U Arab Emirates.
   [Ghaffar, Sehrish] Int Islamic Univ, Islamabad 44000, Pakistan.
   [Amjad, Tehmina] Int Islamic Univ, Dept Comp Sci & Software Engn, Islamabad 44000, Pakistan.
C3 International Islamic University, Pakistan; International Islamic
   University, Pakistan
RP Amjad, T (corresponding author), Int Islamic Univ, Dept Comp Sci & Software Engn, Islamabad 44000, Pakistan.
EM tehmina.amj@gmail.com
RI Amjad, Tehmina/GLS-0209-2022; Daud, Ali/ABA-8422-2020
OI Daud, Ali/0000-0002-8284-6354
CR Amjad T, 2022, LIBR HI TECH, V40, P685, DOI 10.1108/LHT-05-2021-0154
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NR 27
TC 0
Z9 0
U1 3
U2 27
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2329-924X
J9 IEEE T COMPUT SOC SY
JI IEEE Trans. Comput. Soc. Syst.
PD 2022 AUG 5
PY 2022
DI 10.1109/TCSS.2022.3193508
EA AUG 2022
PG 7
WC Computer Science, Cybernetics; Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 3T7ZW
UT WOS:000840490800001
DA 2024-09-05
ER
PT C
AU Zhao, ZB
   Gao, Q
   Huang, LY
AF Zhao, Zhenbing
   Gao, Qiang
   Huang, Liyan
GP IEEE
TI Research on the Order and Specific Component of the Output Signals of
   Independent Component Analysis
SO 2008 7TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-23
LA English
DT Proceedings Paper
CT 7th World Congress on Intelligent Control and Automation
CY JUN 25-27, 2008
CL Chongqing, PEOPLES R CHINA
DE Independent component analysis; Independent component ordering; Apriori
   knowledge
ID CONSTANT-MODULUS ALGORITHM; NON-GAUSSIAN SIGNALS; BLIND
AB Independent Component Analysis (ICA) is a new signal processing technique that can recover source signals from the observed mixtures. In some applications of ICA, it is desired that the order of the recovered source signals can be predicted and the component which we are interested in can be extracted separately. But the order of recovered source signals is unpredictable. Facing this problem, a new algorithm based on ICA is presented for determining the order of recovered source signals in this paper. First it gives a brief introduction about the problem of ICA, and then by using the observed vectors and apriori knowledge of components from the first run, it also constructs the linear system of equations statistically and achieves the separating matrix. The results of simulation show that the proposed method can separate the source signals in specified order effectively.
C1 [Zhao, Zhenbing; Gao, Qiang] N China Elect Power Univ, Sch Elect & Elect Engn, Baoding 071003, Hebei Province, Peoples R China.
   [Huang, Liyan] Tianjin Elect Corp, Elect Power Commun Co, Tianjin 300010, Peoples R China.
C3 North China Electric Power University
RP Zhao, ZB (corresponding author), N China Elect Power Univ, Sch Elect & Elect Engn, Baoding 071003, Hebei Province, Peoples R China.
EM diligencyzhao@yahoo.com.cn; gaoqiang0001@sohu.com; huangliyan08@163.com
CR [Anonymous], P INT C ART NEUR NET
   [Anonymous], INTRO RANDOM PROCESS
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NR 15
TC 0
Z9 0
U1 1
U2 2
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-4244-2113-8
PY 2008
BP 3569
EP +
DI 10.1109/WCICA.2008.4593491
PG 2
WC Automation & Control Systems; Computer Science, Artificial Intelligence;
   Computer Science, Cybernetics; Engineering, Electrical & Electronic;
   Robotics
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Automation & Control Systems; Computer Science; Engineering; Robotics
GA BIJ02
UT WOS:000259965702210
DA 2024-09-05
ER
PT J
AU Umer, M
   Sadiq, S
   Missen, MMS
   Hameed, Z
   Aslam, Z
   Siddique, MA
   Nappi, M
AF Umer, Muhammad
   Sadiq, Saima
   Missen, Malik Muhammad Saad
   Hameed, Zahid
   Aslam, Zahid
   Siddique, Muhammad Abubakar
   Nappi, Michele
TI Scientific papers citation analysis using textual features and SMOTE
   resampling techniques
SO PATTERN RECOGNITION LETTERS
LA English
DT Article
DE Citation sentiment analysis; Machine learning; Feature engineering;
   TF-IDF; SMOTE
ID SENTIMENT ANALYSIS; IMPACT; CLASSIFICATION; EXTRACTION; INDEX
AB Ascertaining the impact of research is significant for the research community and academia of all disciplines. The only prevalent measure associated with the quantification of research quality is the citationcount. Although a number of citations play a significant role in academic research, sometimes citations can be biased or made to discuss only the weaknesses and shortcomings of the research. By considering the sentiment of citations and recognizing patterns in text can aid in understanding the opinion of the peer research community and will also help in quantifying the quality of research articles. Efficient feature representation combined with machine learning classifiers has yielded significant improvement in text classification. However, the effectiveness of such combinations has not been analyzed for citation sentiment analysis. This study aims to investigate pattern recognition using machine learning models in combination with frequency-based and prediction-based feature representation techniques with and without using Synthetic Minority Oversampling Technique (SMOTE) on publicly available citation sentiment dataset. Sentiment of citation instances are classified into positive, negative or neutral. Results indicate that the Extra tree classifier in combination with Term Frequency-Inverse Document Frequency achieved 98.26% accuracy on the SMOTE-balanced dataset. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )
C1 [Umer, Muhammad; Sadiq, Saima; Siddique, Muhammad Abubakar] Khwaja Fareed Univ Engn & Informat Technol, Dept Comp Sci, Rahim Yar Khan, Pakistan.
   [Umer, Muhammad; Missen, Malik Muhammad Saad; Aslam, Zahid] Islamia Univ Bahawalpur, Dept Comp Sci & Informat Technol, Bahawalpur 63100, Pakistan.
   [Nappi, Michele] Univ Salerno, Dept Comp Sci, Via Giovanni Paolo II 132, I-84084 Salerno, Italy.
   [Hameed, Zahid] Khwaja Fareed Univ Engn & Informat Technol, Dept Management Sci, Rahim Yar Khan, Pakistan.
C3 Khwaja Fareed University of Engineering & Information Technology,
   Pakistan; Islamia University of Bahawalpur; University of Salerno;
   Khwaja Fareed University of Engineering & Information Technology,
   Pakistan
RP Umer, M (corresponding author), Khwaja Fareed Univ Engn & Informat Technol, Dept Comp Sci, Rahim Yar Khan, Pakistan.; Umer, M; Missen, MMS (corresponding author), Islamia Univ Bahawalpur, Dept Comp Sci & Informat Technol, Bahawalpur 63100, Pakistan.
EM umersabir1996@gmail.com; s.kamrran@gmail.com; Saad.missen@iub.edu.pk;
   zahid.hameed@kfueit.edu.pk; zahid.aslam@iub.edu.pk;
   abubakar.ahmadani@gmail.com; mnappi@unisa.it
RI Missen, Malik Muhammad Saad/LEM-0611-2024; Umer, Muhammad/AAX-4594-2020;
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NR 55
TC 23
Z9 23
U1 2
U2 23
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0167-8655
EI 1872-7344
J9 PATTERN RECOGN LETT
JI Pattern Recognit. Lett.
PD OCT
PY 2021
VL 150
BP 250
EP 257
DI 10.1016/j.patrec.2021.07.009
EA AUG 2021
PG 8
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA UO5DU
UT WOS:000694715500011
OA hybrid, Green Published
DA 2024-09-05
ER
PT J
AU He, CC
   Wu, J
   Zhang, QP
AF He, Chaocheng
   Wu, Jiang
   Zhang, Qingpeng
TI Proximity-aware research leadership recommendation in research
   collaboration via deep neural networks
SO JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY
LA English
DT Article
ID SCIENTIFIC COLLABORATION; CHINA; INSTITUTIONS; FUSION
AB Collaborator recommendation is of great significance for facilitating research collaboration. Proximities have been demonstrated to be significant factors and determinants of research collaboration. Research leadership is associated with not only the capability to integrate resources to launch and sustain the research project but also the production and academic impact of the collaboration team. However, existing studies mainly focus on social or cognitive proximity, failing to integrate critical proximities comprehensively. Besides, existing studies focus on recommending relationships among all the coauthors, ignoring leadership in research collaboration. In this article, we propose a proximity-aware research leadership recommendation (PRLR) model to systematically integrate critical node attribute information (critical proximities) and network features to conduct research leadership recommendation by predicting the directed links in the research leadership network. PRLR integrates cognitive, geographical, and institutional proximity as node attribute information and constructs a leadership-aware coauthorship network to preserve the research leadership information. PRLR learns the node attribute information, the local network features, and the global network features with an autoencoder model, a joint probability constraint, and an attribute-aware skip-gram model, respectively. Extensive experiments and ablation studies have been conducted, demonstrating that PRLR significantly outperforms the state-of-the-art collaborator recommendation models in research leadership recommendation.
C1 [He, Chaocheng; Wu, Jiang] Wuhan Univ, Sch Informat Management, Wuhan, Hubei, Peoples R China.
   [He, Chaocheng; Zhang, Qingpeng] City Univ Hong Kong, Sch Data Sci, Kowloon, Hong Kong, Peoples R China.
C3 Wuhan University; City University of Hong Kong
RP Wu, J (corresponding author), Wuhan Univ, Sch Informat Management, Wuhan, Hubei, Peoples R China.; Zhang, QP (corresponding author), City Univ Hong Kong, Sch Data Sci, Kowloon, Hong Kong, Peoples R China.
EM jiangw@whu.edu.cn; qingpeng.zhang@cityu.edu.hk
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OI Zhang, Qingpeng/0000-0002-6819-0686; He, Chaocheng/0000-0002-9885-0331
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NR 48
TC 16
Z9 16
U1 8
U2 94
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 2330-1635
EI 2330-1643
J9 J ASSOC INF SCI TECH
PD JAN
PY 2022
VL 73
IS 1
BP 70
EP 89
DI 10.1002/asi.24546
EA JUN 2021
PG 20
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA XJ2PG
UT WOS:000667522800001
DA 2024-09-05
ER
PT J
AU Du, JC
   Soysal, E
   Wang, D
   He, L
   Lin, B
   Wang, JQ
   Manion, FJ
   Li, YR
   Wu, E
   Yao, LX
AF Du, Jingcheng
   Soysal, Ekin
   Wang, Dong
   He, Long
   Lin, Bin
   Wang, Jingqi
   Manion, Frank J.
   Li, Yeran
   Wu, Elise
   Yao, Lixia
TI Machine learning models for abstract screening task - A systematic
   literature review application for health economics and outcome research
SO BMC MEDICAL RESEARCH METHODOLOGY
LA English
DT Article
DE Machine learning; Deep learning; Text classification; Article screening;
   Systematic literature review
AB Objective Systematic literature reviews (SLRs) are critical for life-science research. However, the manual selection and retrieval of relevant publications can be a time-consuming process. This study aims to (1) develop two disease-specific annotated corpora, one for human papillomavirus (HPV) associated diseases and the other for pneumococcal-associated pediatric diseases (PAPD), and (2) optimize machine- and deep-learning models to facilitate automation of the SLR abstract screening.Methods This study constructed two disease-specific SLR screening corpora for HPV and PAPD, which contained citation metadata and corresponding abstracts. Performance was evaluated using precision, recall, accuracy, and F1-score of multiple combinations of machine- and deep-learning algorithms and features such as keywords and MeSH terms.Results and conclusions The HPV corpus contained 1697 entries, with 538 relevant and 1159 irrelevant articles. The PAPD corpus included 2865 entries, with 711 relevant and 2154 irrelevant articles. Adding additional features beyond title and abstract improved the performance (measured in Accuracy) of machine learning models by 3% for HPV corpus and 2% for PAPD corpus. Transformer-based deep learning models that consistently outperformed conventional machine learning algorithms, highlighting the strength of domain-specific pre-trained language models for SLR abstract screening. This study provides a foundation for the development of more intelligent SLR systems.
C1 [Du, Jingcheng; Soysal, Ekin; He, Long; Lin, Bin; Wang, Jingqi; Manion, Frank J.] Intelligent Med Objects, Houston, TX USA.
   [Wang, Dong; Li, Yeran; Wu, Elise; Yao, Lixia] Merck & Co Inc, Rahway, NJ 07065 USA.
   [Soysal, Ekin] Univ Texas Hlth Sci Ctr Houston, McWilliams Sch Biomed Informat, Houston, TX USA.
C3 Merck & Company; University of Texas System; University of Texas Health
   Science Center Houston
RP Yao, LX (corresponding author), Merck & Co Inc, Rahway, NJ 07065 USA.
EM Lixia.cn.yao@gmail.com
OI Wang, Dong/0009-0003-3322-6284
FU Merck Sharp Dohme LLC; Merck & Co., Inc., Rahway, NJ, USA
FX This research was supported by Merck Sharp & Dohme LLC, a subsidiary of
   Merck & Co., Inc., Rahway, NJ, USA.
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NR 21
TC 0
Z9 0
U1 5
U2 5
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
EI 1471-2288
J9 BMC MED RES METHODOL
JI BMC Med. Res. Methodol.
PD MAY 9
PY 2024
VL 24
IS 1
AR 108
DI 10.1186/s12874-024-02224-3
PG 7
WC Health Care Sciences & Services
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Health Care Sciences & Services
GA PX4H9
UT WOS:001217362100002
PM 38724903
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU Loan, FA
   Bashir, B
   Nasreen, N
AF Loan, Fayaz Ahmad
   Bashir, Bisma
   Nasreen, Nahida
TI Applied artificial intelligence : A bibliometric study of an
   International Journal
SO COLLNET JOURNAL OF SCIENTOMETRICS AND INFORMATION MANAGEMENT
LA English
DT Article
DE Artificial intelligence; Applied artificial intelligence; Bibliometrics;
   Scientometrics; Citation analysis; Keyword analysis
ID SCIENTOMETRIC ANALYSIS; RESEARCH OUTPUT; SCIENCE; PRODUCTIVITY;
   MANAGEMENT; AMERICAN; PATTERNS; IMPACT
AB Purpose: The study aims to conduct a bibliometric analysis of an international journal "Applied Artificial Intelligence (AAI)" to analyze publication trends, authorship patterns, collaborative networks, citation behaviors, and research hotspots of authors, organizations, and countries.
   Research Design/Methodology: "Applied Artificial Intelligence" is a peer-reviewed international journal, published by Taylor & Francis. The journal has published more than 1100 articles in 34 volumes so far. The idea was conceived to conduct the bibliometric study of the AAI journal. The data were collected from the Web of Science (WOS) database, owned by the Clarivate Analytics. A total of 1109 articles were retrieved and their metadata was collected for further analysis and interpretation. Additionally, the VOS viewer software was used for mapping and visualization of the bibliographic information.
   Findings: The journal has experienced positive growth in research productivity and negative growth in citations. Authors of 74 prominent countries have contributed to the journal and the USA has occupied the first position in publication count followed by Italy, India and England respectively. All the countries work in close collaboration and created a collaborative network and sub-networks. The USA is the pivot of the collaborative network, mostly collaborating with England, Japan, Italy, China and Germany. The keywords like the classification, optimization, algorithms and neural networks are the most common and hence the hot topics of research in the journal.
   Originality/Value: The main advantage of this study is that it provides profound knowledge of the content structure and developmental process of the journal to date. It is also valuable for researchers in the field of artificial intelligence to identify the research hotspots in this field.
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C3 University of Kashmir
RP Loan, FA (corresponding author), Univ Kashmir, Ctr Cent Asian Studies, Srinagar, Jammu & Kashmir, India.
EM fayazlib@yahoo.co.in; bismabashir53@gmail.com;
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TC 4
Z9 4
U1 3
U2 29
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0973-7766
EI 2168-930X
J9 COLLNET J SCIENTOMET
JI Collnet J. Scientometr. Inf. Manag.
PD JAN 2
PY 2021
VL 15
IS 1
BP 27
EP 45
DI 10.1080/09737766.2021.1938742
PG 19
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA TI9DQ
UT WOS:000673096200003
DA 2024-09-05
ER
PT J
AU Tao, FF
   Pi, YL
   Deng, MH
   Tang, YJ
   Yuan, C
AF Tao, Feifei
   Pi, Yanling
   Deng, Menghua
   Tang, Yongjun
   Yuan, Chi
TI Research on Intelligent Grading Evaluation of Water Conservancy Project
   Safety Risks Based on Deep Learning
SO WATER
LA English
DT Article
DE deep learning; hazard sources; risk evaluation; transformer model; task
   scenarios; a priori knowledge
ID MANAGEMENT
AB With the rise of artificial intelligence and big data technologies, it is increasingly significant to apply these emerging technologies to scientific decision-making in water conservancy project construction management in the face of many problems in the process of water conservancy project construction. Different from using traditional assessment methods for risk classification of water conservancy construction hazards, this paper integrates a priori attention and constructs a transformer risk prediction model based on a sliding window, which deeply explores the data value of water conservancy construction hazards information, further predicts the risk level of water conservancy construction hazards and realizes efficient and intelligent management of water conservancy project construction hazard identification management.
C1 [Tao, Feifei; Pi, Yanling; Yuan, Chi] Hohai Univ, Coll Comp & Informat, Nanjing 210098, Peoples R China.
   [Deng, Menghua; Tang, Yongjun] Hohai Univ, Business Sch, Nanjing 210098, Peoples R China.
C3 Hohai University; Hohai University
RP Pi, YL (corresponding author), Hohai Univ, Coll Comp & Informat, Nanjing 210098, Peoples R China.
EM 211607010123@hhu.edu.cn
RI Tang, Yongjun/AAX-9631-2021
OI Tang, Yongjun/0000-0002-7061-728X; Tao, Feifei/0000-0002-4217-635X
FU National Natural Science Foundation of China [42001250]; Jiangsu Water
   Conservancy Science and Technology Foundation [2020014]
FX This research was funded by the National Natural Science Foundation of
   China, grant number. 42001250; Jiangsu Water Conservancy Science and
   Technology Foundation, grant number. 2020014.
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TC 0
Z9 0
U1 65
U2 138
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2073-4441
J9 WATER-SUI
JI Water
PD APR
PY 2023
VL 15
IS 8
AR 1607
DI 10.3390/w15081607
PG 15
WC Environmental Sciences; Water Resources
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology; Water Resources
GA E8IT6
UT WOS:000977922000001
OA gold
DA 2024-09-05
ER
PT J
AU Ihsan, I
   Qadir, MA
AF Ihsan, Imran
   Qadir, M. Abdul
TI An NLP-based citation reason analysis using CCRO
SO SCIENTOMETRICS
LA English
DT Article
DE NLP-based citation analysis; Qualitative research evaluation; Text
   classification; Ontology
ID REPORTING VERBS; SCIENCE
AB In recent scientific advances, Artificial Intelligence and Natural Language Processing are the major contributors to classifying documents and extracting information. Classifying citations in different classes have gathered a lot of attention due to the large volume of citations available in different digital libraries. Typical citation classification uses sentiment analysis, where various techniques are applied to citations texts to mainly classify them in "Positive", "Negative" and "Neutral" sentiments. However, there can be innumerable reasons why an author selects another research for citation. Citations' Context and Reasons Ontology-CCRO uses a clear scientific method to articulate eight basic reasons for citing by using an iterative process of sentiment analysis, collaborative meanings, and experts' opinions. Using CCRO, this research paper adopts an ontology-based approach to extract citation's reasons and instantiate ontology classes and properties on two different corpora of citation sentences. One corpus of citation sentences is a publicly available dataset, while the other is our own manually curated. The process uses a two-step approach. The first part is an interface to manually annotate each citation text in the selected corpora on CCRO properties. A team of carefully selected annotators has annotated each citation to achieve a high inter-annotator agreement. The second part focuses on the automatic extraction of these reasons. Using Natural Language Processing, Mapping Graph, and Reporting Verb in a citation sentence, citation's reason is extracted and mapped onto a CCRO property. After comparing both manual and automatic mapping, accuracy is calculated. Based on experiments and results, accuracy is calculated for both publicly available and own corpora of citation sentences.
C1 [Ihsan, Imran; Qadir, M. Abdul] Capital Univ Sci & Technol, Dept Comp Sci, Islamabad, Pakistan.
   [Ihsan, Imran] Air Univ, Fac Comp & AI, Dept Creat Technol, Islamabad, Pakistan.
C3 Capital University of Science & Technology; Air University Islamabad
RP Ihsan, I (corresponding author), Capital Univ Sci & Technol, Dept Comp Sci, Islamabad, Pakistan.; Ihsan, I (corresponding author), Air Univ, Fac Comp & AI, Dept Creat Technol, Islamabad, Pakistan.
EM iimranihsan@gmail.com; aqadir@cust.edu.pk
RI ihsan, imran/ABA-7494-2021; ihsan, imran/AAZ-6236-2021
OI ihsan, imran/0000-0002-3447-4576; Qadir, Muhammad
   Abdul/0000-0003-4634-9016
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NR 55
TC 2
Z9 2
U1 4
U2 64
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD JUN
PY 2021
VL 126
IS 6
BP 4769
EP 4791
DI 10.1007/s11192-021-03955-6
EA MAR 2021
PG 23
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA SI9BA
UT WOS:000633283300003
DA 2024-09-05
ER
PT J
AU Chen, XL
   Xie, HR
   Cheng, G
   Poon, LKM
   Leng, MM
   Wang, FL
AF Chen, Xieling
   Xie, Haoran
   Cheng, Gary
   Poon, Leonard K. M.
   Leng, Mingming
   Wang, Fu Lee
TI Trends and Features of the Applications of Natural Language Processing
   Techniques for Clinical Trials Text Analysis
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE natural language processing; clinical trials text; bibliometrics;
   collaboration; structural topic modeling
ID BIBLIOMETRIC ANALYSIS; INFORMATION EXTRACTION; RADIOLOGY REPORTS;
   SCIENCE; WEB; ENGLISH; INTERVENTION; QUALITY; SYSTEMS; SCOPUS
AB Natural language processing (NLP) is an effective tool for generating structured information from unstructured data, the one that is commonly found in clinical trial texts. Such interdisciplinary research has gradually grown into a flourishing research field with accumulated scientific outputs available. In this study, bibliographical data collected from Web of Science, PubMed, and Scopus databases from 2001 to 2018 had been investigated with the use of three prominent methods, including performance analysis, science mapping, and, particularly, an automatic text analysis approach named structural topic modeling. Topical trend visualization and test analysis were further employed to quantify the effects of the year of publication on topic proportions. Topical diverse distributions across prolific countries/regions and institutions were also visualized and compared. In addition, scientific collaborations between countries/regions, institutions, and authors were also explored using social network analysis. The findings obtained were essential for facilitating the development of the NLP-enhanced clinical trial texts processing, boosting scientific and technological NLP-enhanced clinical trial research, and facilitating inter-country/region and inter-institution collaborations.
C1 [Chen, Xieling; Cheng, Gary; Poon, Leonard K. M.] Educ Univ Hong Kong, Dept Math & Informat Technol, Tai Po, Hong Kong 999077, Peoples R China.
   [Xie, Haoran; Leng, Mingming] Lingnan Univ, Dept Comp & Decis Sci, Tuen Mun, Hong Kong 999077, Peoples R China.
   [Wang, Fu Lee] Open Univ Hong Kong, Sch Sci & Technol, Ho Man Tin, Kowloon, Hong Kong 999077, Peoples R China.
C3 Education University of Hong Kong (EdUHK); Lingnan University; Hong Kong
   Metropolitan University
RP Wang, FL (corresponding author), Open Univ Hong Kong, Sch Sci & Technol, Ho Man Tin, Kowloon, Hong Kong 999077, Peoples R China.
EM xielingchen0708@gmail.com; hrxie@ln.edu.hk; chengks@eduhk.hk;
   kmpoon@eduhk.hk; mmleng@ln.edu.hk; pwang@ouhk.edu.hk
RI Wang, Fu Lee/AAD-9782-2021; Xie, Haoran/AFS-3515-2022; LENG,
   Mingming/O-7640-2017
OI Wang, Fu Lee/0000-0002-3976-0053; Xie, Haoran/0000-0003-0965-3617; POON,
   Leonard K. M./0000-0002-8394-1492; PV, THAYYIB/0000-0001-8929-0398;
   LENG, Mingming/0000-0002-3377-2387; Cheng, Gary/0000-0002-5614-3348
FU Interdisciplinary Research Scheme of the Dean's Research Fund 2018-19 of
   The Education University of Hong Kong, Research Seed Fund
   [FLASS/DRF/IDS-3]; Departmental Collaborative Research Fund 2019 of The
   Education University of Hong Kong, Research Seed Fund
   [MIT/DCRF-R2/18-19]; One-o ff Special Fund from the Central and Faculty
   Fund in Support of Research entitled "Facilitating Artificial
   Intelligence and Big Data Analytics Research in Education" of The
   Education University of Hong Kong, Research Seed Fund [MIT02/19-20];
   Hong Kong Institute of Business Studies Research Seed Fund [HKIBS
   RSF-190-009]; LEO Dr. David P. Chan Institute of Data Science, Lingnan
   University, Hong Kong
FX The work has been supported by the Interdisciplinary Research Scheme of
   the Dean's Research Fund 2018-19 (FLASS/DRF/IDS-3), Departmental
   Collaborative Research Fund 2019 (MIT/DCRF-R2/18-19), and One-o ff
   Special Fund from the Central and Faculty Fund in Support of Research
   (MIT02/19-20) entitled "Facilitating Artificial Intelligence and Big
   Data Analytics Research in Education" of The Education University of
   Hong Kong, Research Seed Fund, Hong Kong Institute of Business Studies
   Research Seed Fund (HKIBS RSF-190-009) and LEO Dr. David P. Chan
   Institute of Data Science, Lingnan University, Hong Kong.
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NR 131
TC 21
Z9 23
U1 1
U2 31
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2076-3417
J9 APPL SCI-BASEL
JI Appl. Sci.-Basel
PD MAR
PY 2020
VL 10
IS 6
AR 2157
DI 10.3390/app10062157
PG 36
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Chemistry; Engineering; Materials Science; Physics
GA LI1NZ
UT WOS:000529252800258
OA gold
DA 2024-09-05
ER
PT J
AU Salehpour, A
   Samadzamini, K
AF Salehpour, Arash
   Samadzamini, Karim
TI A bibliometric analysis on the application of deep learning in
   economics, econometrics, and finance
SO INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING
LA English
DT Article
DE deep learning; bibliometric; economics; econometrics; finance
ID NETWORK; EVOLUTION; TOOL
AB This research looked at the deep learning applications in economics, econometrics, and finance. Two hundred fifty articles from the Scopus database's index of journals published between 2013 and 2022 were gathered using a bibliometric technique. The data was analysed using many programs (R studio, Excel, and Biblioshiny), and in terms of countries, organisations, publications, papers, and authors, the most prominent scientific players were highlighted. Our research found that as of 2019, the quantity of publications has increased. The literature analysis received the most contributions from China and the USA. The most significant findings and discussions came from the following analyses: estimation of share prices, asset management price fluctuations and liquidity, forecast of bankruptcies, evaluation of credit risk, risk assessment, commodity prices top trend analysis, citation analysis, thematic evolution, and thematic map. Our findings offer practical recommendations on how deep learning may be implemented into decision-making processes for market participants, particularly those working in fintech and finance.
C1 [Salehpour, Arash] Islamic Azad Univ, Dept Comp Engn, Rasht Branch, Rasht, Gilan, Iran.
   [Samadzamini, Karim] Univ Coll Nabi Akram, Dept Comp Engn, Tabriz, Iran.
C3 Islamic Azad University
RP Salehpour, A (corresponding author), Islamic Azad Univ, Dept Comp Engn, Rasht Branch, Rasht, Gilan, Iran.
EM arash.salehpour4@gmail.com; samadzamini@ucna.ac.ir
RI Salehpour, Arash/KIJ-9771-2024
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NR 58
TC 1
Z9 1
U1 6
U2 6
PU INDERSCIENCE ENTERPRISES LTD
PI GENEVA
PA WORLD TRADE CENTER BLDG, 29 ROUTE DE PRE-BOIS, CASE POSTALE 856, CH-1215
   GENEVA, SWITZERLAND
SN 1742-7185
EI 1742-7193
J9 INT J COMPUT SCI ENG
JI Int. J. Comput. Sci. Eng.
PY 2024
VL 27
IS 2
DI 10.1504/IJCSE.2024.137286
PG 16
WC Computer Science, Interdisciplinary Applications
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA LB9L4
UT WOS:001184435200004
DA 2024-09-05
ER
PT C
AU Lipitakis, AD
   Lipitakis, EAEC
AF Lipitakis, Anastasia-Dimitra
   Lipitakis, Evangelia A. E. C.
BE Akhgar, B
   Arabnia, HR
TI On Machine Learning with Imbalanced Data and Research Quality Evaluation
   Methodologies
SO 2014 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND COMPUTATIONAL
   INTELLIGENCE (CSCI), VOL 1
LA English
DT Proceedings Paper
CT International Conference on Computational Science and Computational
   Intelligence (CSCI)
CY MAR 10-13, 2014
CL Las Vegas, NV
DE Bibliometric Indicators; Business Intelligence; Citation Analysis;
   Computational Intelligence; Data Mining; Learning Algorithms; Imbalanced
   Data; Machine Learning; Quantitative Methods; Research Quality
   Evaluation
ID FIRM PERFORMANCE; ROTATION FOREST; E-BUSINESS; CLASSIFICATION;
   INTELLIGENCE; PREDICTION; STRATEGY
AB In this article a synoptic review of machine learning techniques with imbalanced data and a class of corresponding learning algorithms is presented. This class of algorithms includes the meta-algorithms: Cost sensitive, Metacost, Rotation forest-cost sensitive, rotation forest-smote. Four learning algorithms (with base classifiers J48 and part processing with F-measure and a predetermined imbalanced data set) are compared in the computational environment WEKA leading to comparative numerical results. The basic concepts of research quality evaluation methodologies are presented, an adaptive citation qualitative-quantitative approach and advanced bibliometric indicators are given. Basic components of research quality performance such as research journal cited publications, citing publications and research quality evaluations at various academic levels are considered and corresponding numerical results are given. An alternative approach using certain machine learning algorithms with imbalanced data in the case of research quality evaluation methodologies is proposed.
C1 [Lipitakis, Anastasia-Dimitra] Univ Patras, Dept Math, Patras 26504, Hellas, Greece.
   [Lipitakis, Evangelia A. E. C.] Univ Kent, Kent Business Sch, Canterbury CT2 7PE, Kent, England.
C3 University of Patras; University of Kent
RP Lipitakis, AD (corresponding author), Univ Patras, Dept Math, Patras 26504, Hellas, Greece.
EM adlipitaki@gmail.com; eael2@kentforlife.net
RI Lipitakis, Evangelia/N-2952-2013; Lipitakis, Anastasia
   Dimitra/AAO-8831-2020
OI Lipitakis, Evangelia/0000-0002-9506-4319; Lipitakis,
   Anastasia-Dimitra/0000-0001-5058-5463
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NR 87
TC 0
Z9 0
U1 1
U2 18
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-4799-3009-8
PY 2014
BP 451
EP 457
DI 10.1109/CSCI.2014.81
PG 7
WC Computer Science, Artificial Intelligence; Computer Science, Theory &
   Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BC8NS
UT WOS:000355911900077
DA 2024-09-05
ER
PT J
AU Jiang, HC
   Qiang, MS
   Lin, P
AF Jiang HanChen
   Qiang MaoShan
   Lin Peng
TI Finding academic concerns of the Three Gorges Project based on a topic
   modeling approach
SO ECOLOGICAL INDICATORS
LA English
DT Article
DE Hydropower project; Three Gorges Project; Topic modeling; Scientific
   documents; Latent Dirichlet Allocation; Bibliometric indicators
ID YANGTZE-RIVER; SCIENCE; POLICY; CHINA; TEXT
AB The Three Gorges Project (TGP) has gone into the overall completion acceptance stage in 2014. As the world's largest hydropower project, the TGP has attracted worldwide attention over the past few decades. Previous studies mainly focused on a single aspect, such as engineering technologies, social impacts and environmental impacts, of the TGP. However, a large-scale review gathering systematic data to find academic concerns about the TGP is missing. Topic model is a text mining approach for discovering latent topics in a collection of documents. In this article, an emerging topic modeling approach, Latent Dirichlet Allocation (LDA), was introduced to uncover the intellectual structure of the academic literature focusing on the TGP. A collection of 8280 Chinese research articles highly related to the TGP was established with a time frame ranging from 2001 to 2013, and an 18-topic model was used to describe the intellectual structure. Two novel bibliometric indicators, including topic proportion and topic trend, were constructed to describe the academic concerns of the TGP. Topic proportion analysis shows that post-construction issues, including the social and environmental impacts brought by the TGP, have attracted more attention than the construction issues. "Ecology", "Reservoir Operation", "Land Administration", and "Water Pollution", have become the dominant research topics regarding the TGP during these years. Meanwhile, "Construction Technology" and "Design", have gradually lost scholars' interest. The results show that the approach reported in this study can provide sound and credible conclusions of the major academic concerns for a hydropower project. The topic modeling approach is expected to be widely applied as a methodological strategy in future hydropower and other infrastructure project assessment. (C) 2015 Elsevier Ltd. All rights reserved.
C1 [Jiang HanChen; Qiang MaoShan; Lin Peng] Tsinghua Univ, State Key Lab Hydrosci & Engn, Beijing 100084, Peoples R China.
C3 Tsinghua University
RP Qiang, MS (corresponding author), Tsinghua Univ, State Key Lab Hydrosci & Engn, Beijing 100084, Peoples R China.
EM jhc13@mails.tsinghua.edu.cn; qiangms@mail.tsinghua.edu.cn;
   celinpe@tsinghua.edu.cn
RI Lin, Peng/J-4015-2013
FU National Natural Science Foundation of China [51479100, 51179086,
   11272178, 51379104]; State Key Laboratory of Hydroscience and
   Engineering of China [2015-KY-5, 2013-KY-5]
FX This work was supported by the National Natural Science Foundation of
   China under Grant Nos. 51479100, 51179086, 11272178, and 51379104; and
   State Key Laboratory of Hydroscience and Engineering of China under
   Grant 2015-KY-5 and 2013-KY-5.
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NR 49
TC 29
Z9 34
U1 5
U2 83
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1470-160X
EI 1872-7034
J9 ECOL INDIC
JI Ecol. Indic.
PD JAN
PY 2016
VL 60
BP 693
EP 701
DI 10.1016/j.ecolind.2015.08.007
PG 9
WC Biodiversity Conservation; Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Biodiversity & Conservation; Environmental Sciences & Ecology
GA CZ9GW
UT WOS:000367407000070
DA 2024-09-05
ER
PT J
AU Amos, AJ
   Lee, KYM
   Gupta, TS
   Malau-Aduli, BS
AF Amos, Andrew James
   Lee, Kyungmi
   Gupta, Tarun Sen
   Malau-Aduli, Bunmi S.
TI Validating the knowledge represented by a self-organizing map with an
   expert-derived knowledge structure
SO BMC MEDICAL EDUCATION
LA English
DT Article
DE Artificial intelligence; Machine learning; Curriculum development;
   Scientometrics; Medical education; Explainable AI
AB Background Professionals are reluctant to make use of machine learning results for tasks like curriculum development if they do not understand how the results were generated and what they mean. Visualizations of peer reviewed medical literature can summarize enormous amounts of information but are difficult to interpret. This article reports the validation of the meaning of a self-organizing map derived from the Medline/PubMed index of peer reviewed medical literature by its capacity to coherently summarize the references of a core psychiatric textbook.Methods Reference lists from ten editions of Kaplan and Sadock's Comprehensive Textbook of Psychiatry were projected onto a self-organizing map trained on Medical Subject Headings annotating the complete set of peer reviewed medical research articles indexed in the Medline/PubMed database (MedSOM). K-means clustering was applied to references from every edition to examine the ability of the self-organizing map to coherently summarize the knowledge contained within the textbook.Results MedSOM coherently clustered references into six psychiatric knowledge domains across ten editions (1967-2017). Clustering occurred at the abstract level of broad psychiatric practice including General/adult psychiatry, Child psychiatry, and Administrative psychiatry.Conclusions The uptake of visualizations of published medical literature by medical experts for purposes like curriculum development depends upon validation of the meaning of the visualizations. The current research demonstrates that a self-organizing map (MedSOM) can validate the stability and coherence of the references used to support the knowledge claims of a standard psychiatric textbook, linking the products of machine learning to a widely accepted standard of knowledge.
C1 [Amos, Andrew James; Gupta, Tarun Sen] James Cook Univ, Coll Med & Dent, Townsville, Australia.
   [Lee, Kyungmi] James Cook Univ, Coll Sci & Engn, Cairns, Australia.
   [Malau-Aduli, Bunmi S.] Univ Newcastle, Sch Med & Publ Hlth, Newcastle, Australia.
C3 James Cook University; James Cook University; University of Newcastle
RP Amos, AJ (corresponding author), James Cook Univ, Coll Med & Dent, Townsville, Australia.
EM Andrew.Amos@jcu.edu.au
RI Malau-Aduli, Bunmi/J-9388-2014
OI Malau-Aduli, Bunmi/0000-0001-6054-8498
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NR 31
TC 0
Z9 0
U1 1
U2 1
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
EI 1472-6920
J9 BMC MED EDUC
JI BMC Med. Educ.
PD APR 16
PY 2024
VL 24
IS 1
AR 416
DI 10.1186/s12909-024-05352-y
PG 16
WC Education & Educational Research; Education, Scientific Disciplines
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Education & Educational Research
GA OA4L6
UT WOS:001204524700002
PM 38627742
OA gold
DA 2024-09-05
ER
PT C
AU Hamadicharef, B
AF Hamadicharef, Brahim
BE Deng, H
   Miao, DQ
   Lei, JS
   Wang, FL
TI Bibliometric Analysis of Particle Swarm Optimization (PSO) Research
   2000-2010
SO ARTIFICIAL INTELLIGENCE AND COMPUTATIONAL INTELLIGENCE, PT III
SE Lecture Notes in Artificial Intelligence
LA English
DT Proceedings Paper
CT 3rd International Conference on Artificial Intelligence and
   Computational Intelligence (AICI 2011)
CY SEP 23-25, 2011
CL Taiyuan, PEOPLES R CHINA
DE Particle Swarm Optimization; bibliometric study; citations
ID PUBLICATION
AB In the last decade, Particle Swarm Optimization (PSO) has grown in popularity as one important method for optimization, compared to recent Differential Evolution (DE) and Harmony Search (HS). In this paper a bibliometric study is presented, carried out on the PSO research literature from 2000 to 2010. The Thomson Reuters Web of Science (WoS) was used to collect publication records and analyzed to identify authorship, co-authorship, top journals, profile the distribution of citations and references. The study also includes the use keyword co-occurrence frequency from the articles' title, to help getting insights into PSO research trends and fields of applications.
C1 Tiara, Singapore 239403, Singapore.
RP Hamadicharef, B (corresponding author), Tiara, 1 Kim Seng Walk, Singapore 239403, Singapore.
EM bhamadicharef@hotmail.com
RI HAMADICHAREF, Brahim/B-8686-2009
OI HAMADICHAREF, Brahim/0000-0003-4192-3517
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NR 15
TC 3
Z9 3
U1 0
U2 1
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 0302-9743
EI 1611-3349
BN 978-3-642-23895-6; 978-3-642-23896-3
J9 LECT NOTES ARTIF INT
PY 2011
VL 7004
BP 404
EP 411
PN III
PG 8
WC Computer Science, Artificial Intelligence
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BBZ34
UT WOS:000309149800050
DA 2024-09-05
ER
PT J
AU Kartal, G
   Yesilyurt, YE
AF Kartal, Galip
   Yesilyurt, Yusuf Emre
TI A bibliometric analysis of artificial intelligence in L2 teaching and
   applied linguistics between 1995 and 2022
SO RECALL
LA English
DT Article; Early Access
DE artificial intelligence (AI); L2; applied linguistics; bibliometric
   analysis; co-citation analysis
ID LEARNER LANGUAGE; COMPREHENSION; EXPERIENCES; CALL; AI
AB This study offers a comprehensive bibliometric analysis of artificial intelligence (AI) applications in the field of second language (L2) teaching and applied linguistics, spanning from the early developments in 1995 to 2022. It aims to uncover current trends, prominent themes, and influential authors, documents, and sources. A total of 185 relevant articles published in Social Sciences Citation Index (SSCI) indexed journals were analyzed using the VOSviewer bibliometric software tool. Our investigation reveals a highly multidisciplinary and interconnected field, with four main clusters identified: AI, natural language processing (NLP), robot-assisted language learning, and chatbots. Notable themes include the increasing use of intelligent tutoring systems, the importance of syntactic complexity and vocabulary in L2 learning, and the exploration of robots and gamification in language education. The study also highlights the potential of NLP and AI technologies to enhance personalized feedback and instruction for language learners. The findings emphasize the growing interest in AI applications in L2 teaching and applied linguistics, as well as the need for continued research to advance the field and improve language instruction and assessment. By providing a quantitative and rigorous overview of the literature, this study contributes valuable insights into the current state of research in AI-assisted L2 teaching and applied linguistics and identifies key areas for future exploration and development.
C1 [Kartal, Galip] Necmettin Erbakan Univ, Konya, Turkiye.
   [Yesilyurt, Yusuf Emre] Burdur Mehmet Akif Ersoy Univ, Burdur, Turkiye.
C3 Necmettin Erbakan University; Mehmet Akif Ersoy University
RP Kartal, G (corresponding author), Necmettin Erbakan Univ, Konya, Turkiye.
EM kartalgalip@gmail.com; yeyesilyurtnew@gmail.com
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NR 59
TC 0
Z9 0
U1 70
U2 70
PU CAMBRIDGE UNIV PRESS
PI CAMBRIDGE
PA EDINBURGH BLDG, SHAFTESBURY RD, CB2 8RU CAMBRIDGE, ENGLAND
SN 0958-3440
EI 1474-0109
J9 RECALL
JI ReCALL
PD 2024 FEB 26
PY 2024
DI 10.1017/S0958344024000077
EA FEB 2024
PG 17
WC Education & Educational Research; Linguistics; Language & Linguistics
WE Social Science Citation Index (SSCI); Arts &amp; Humanities Citation Index (A&amp;HCI)
SC Education & Educational Research; Linguistics
GA JF5K4
UT WOS:001171763000001
OA hybrid
DA 2024-09-05
ER
PT J
AU Gupta, BM
   Dhawan, SM
AF Gupta, B. M.
   Dhawan, S. M.
TI Artificial Intelligence Research in India: A Scientometric Assessment of
   Publications Output during 2007-16
SO DESIDOC JOURNAL OF LIBRARY & INFORMATION TECHNOLOGY
LA English
DT Article
DE Artificial intelligence; India; Publications; Highly cited papers;
   Scientometrics; Bibliometrics
ID COMPUTER-SCIENCE RESEARCH
AB The paper examines the world output in artificial intelligence research, a total of 1,52,655 publications, as seen from Scopus database, covering the period during 2007-16. The top 10 countries of the world in artificial intelligence research accounted for 74.32 per cent global publication share. Individually their global share varied from 3.68 per cent to 19.46 per cent, with China accounting for 19.46 per cent global share, followed by the USA (17.96 %), India (6.37 %), and the U.K. (6.33 %), etc. The paper also examines publications output by India in artificial intelligence research. India cumulated a total of 9730 publications in 10 years during 2007-16, registered an annual average growth rate of 27.45 per cent, averaged citation impact to 2.76 citations per paper, and contributed 10.34 per cent share of its total country output as international collaborative publications during 2007-16. Computer science accounted for the largest publication share (86.99 %), followed by engineering (30.69 %), mathematics (15.95 %), biochemistry, genetics & molecular biology (4.66 %), and several other disciplines. The top 10 organizations and 10 authors together accounted for 19.31 per cent and 2.71 per cent national publications share respectively and 29.78 per cent share and 6.85 per cent national citation share respectively during 2007-16. Top 10 journals accounted for 15.45 per cent share of the country output appearing in journal medium (1650 papers). India accounted for 24 highly cited papers, averaging to 162.46 citations per paper. These 24 highly cited papers involved the participation of 109 authors from 70 organizations, published in 15 journals.
C1 [Gupta, B. M.] 1173,Sect 15, Panchkula 134113, Haryana, India.
   [Dhawan, S. M.] 114 Dayanand Vihar, Delhi 110092, India.
RP Gupta, BM (corresponding author), 1173,Sect 15, Panchkula 134113, Haryana, India.
EM bmgupta1@gmail.com
CR Cheng Saiyan, 2012, IEEE P 5 INT JOINT C
   Gupta BM, 2017, J SCIENTOMETR RES, V6, P74, DOI 10.5530/jscires.6.2.13
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NR 9
TC 8
Z9 8
U1 0
U2 25
PU DEFENCE SCIENTIFIC INFORMATION DOCUMENTATION CENTRE
PI DELHI
PA METCALFE HOUSE, DELHI 110054, INDIA
SN 0974-0643
EI 0976-4658
J9 DESIDOC J LIB INF TE
JI DESIDOC J. Lib. Inf. Technol.
PD NOV
PY 2018
VL 38
IS 6
BP 416
EP 422
DI 10.14429/djlit.38.6.12309
PG 7
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA GZ0NB
UT WOS:000449059400006
OA Green Submitted, hybrid
DA 2024-09-05
ER
PT J
AU Chen, XL
   Xie, HR
   Li, ZX
   Zhang, D
   Cheng, GRY
   Wang, FL
   Dai, HN
   Li, Q
AF Chen, Xieling
   Xie, Haoran
   Li, Zongxi
   Zhang, Dian
   Cheng, Gary
   Wang, Fu Lee
   Dai, Hong-Ning
   Li, Qing
TI Leveraging deep learning for automatic literature screening in
   intelligent bibliometrics
SO INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
LA English
DT Article
DE Automatic literature screening; Deep neural networks; Intelligent
   bibliometrics; Big data analytics
ID SYSTEMATIC REVIEWS; CLASSIFICATION; PATTERNS
AB Intelligent bibliometrics, by providing sufficient statistical information based on large-scale literature data analytics, is promising for understanding innovative pathways, addressing meaningful insights with the assistance of expert knowledge, and indicating key areas of scientific inquiry. However, the exponential growth of global scientific publication output in most areas of modern science makes it extremely difficult and labor-intensive to analyze literature in large volumes. This study aims to accelerate intelligent bibliometrics-driven literature analysis by leveraging deep learning for automatic literature screening. The comparison of different machine learning algorithms for the automatic classification of literature regarding relevance to a given research topic reveals the outstanding performance of deep learning. This study also compares different features as model input and provides suggestions about training dataset size. By leveraging deep learning's abilities in predictive and big data analytics, this study makes contributions to intelligent bibliometrics by promoting literature screening and is promising to track technological changes and scientific evolutionary pathways.
C1 [Chen, Xieling] Guangzhou Univ, Sch Educ, Guangzhou, Peoples R China.
   [Xie, Haoran] Lingnan Univ, Dept Comp & Decis Sci, Hong Kong, Peoples R China.
   [Li, Zongxi; Wang, Fu Lee] Hong Kong Metropolitan Univ, Sch Sci & Technol, Hong Kong, Peoples R China.
   [Zhang, Dian] Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen, Peoples R China.
   [Cheng, Gary] Educ Univ Hong Kong, Dept Math & Informat Technol, Hong Kong, Peoples R China.
   [Dai, Hong-Ning] Hong Kong Baptist Univ, Dept Comp Sci, Hong Kong, Peoples R China.
   [Li, Qing] Hong Kong Polytech Univ, Dept Comp, Hong Kong, Peoples R China.
C3 Guangzhou University; Lingnan University; Hong Kong Metropolitan
   University; Shenzhen University; Education University of Hong Kong
   (EdUHK); Hong Kong Baptist University; Hong Kong Polytechnic University
RP Xie, HR (corresponding author), Lingnan Univ, Dept Comp & Decis Sci, Hong Kong, Peoples R China.
EM xielingchen0708@gmail.com; hrxie@ieee.org; zoli@hkmu.edu.hk;
   zhangd@szu.edu.cn; chengks@eduhk.hk; pwang@hkmu.edu.hk;
   henrydai@comp.hkbu.edu.hk; qing-prof.li@polyu.edu.hk
RI Li, Qing/JMH-1365-2023; zheng, yi/JOZ-7204-2023; Dai,
   Hong-Ning/B-1931-2012; Xie, Haoran/AFS-3515-2022; Wang, Fu
   Lee/AAD-9782-2021
OI Li, Qing/0000-0003-3370-471X; Dai, Hong-Ning/0000-0001-6165-4196; Xie,
   Haoran/0000-0003-0965-3617; Wang, Fu Lee/0000-0002-3976-0053; PV,
   THAYYIB/0000-0001-8929-0398
FU Research Grants Council of the Hong Kong Special Administrative Region,
   China [UGC/FDS16/E01/19]; Lingnan University, Hong Kong [DR22A2, DB22B4,
   DB22B7]; One-off Special Fund from Central and Faculty Fund in Support
   of Research [MIT02/19-20]; Interdisciplinary Research Scheme of Dean's
   Research Fund 2021/22 of The Education University of Hong Kong
   [FLASS/DRF/IDS-3]
FX The research described in this article has been supported by a grant
   from the Research Grants Council of the Hong Kong Special Administrative
   Region, China (UGC/FDS16/E01/19), the Direct Grant (DR22A2) and the
   Faculty Research Grants (DB22B4 and DB22B7) of Lingnan University, Hong
   Kong, the One-off Special Fund from Central and Faculty Fund in Support
   of Research from 2019/20 to 2021/22 (MIT02/19-20), and Interdisciplinary
   Research Scheme of Dean's Research Fund 2021/22 (FLASS/DRF/IDS-3) of The
   Education University of Hong Kong.
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NR 109
TC 1
Z9 2
U1 10
U2 37
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1868-8071
EI 1868-808X
J9 INT J MACH LEARN CYB
JI Int. J. Mach. Learn. Cybern.
PD APR
PY 2023
VL 14
IS 4
BP 1483
EP 1525
DI 10.1007/s13042-022-01710-8
EA DEC 2022
PG 43
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA A1ZL5
UT WOS:000899155600001
DA 2024-09-05
ER
PT J
AU Gong, YL
   Jia, LG
AF Gong, Yunlu
   Jia, Lianguo
TI Research on SVM environment performance of parallel computing based on
   large data set of machine learning
SO JOURNAL OF SUPERCOMPUTING
LA English
DT Article
DE SVM environment; Parallel computing; Large data set; Machine learning
ID BIG DATA; ALGORITHM; SCHEME
AB The support vector machine (SVM) algorithm is widely used in various fields because of its good classification effect, simplicity and practicability. However, the support vector machine calculates the support vector by quadratic programming, and the solution of quadratic programming will calculate the n-order matrix. When the amount of data is large, the calculation and storage of the n-order matrix will make the optimization speed very slow, even lead to memory overflow and interrupt operation. Using the big data computing platform Spark to improve the support vector machine algorithm can solve the above problems, but it's not competent for multi-classification problems. Therefore, this paper starts with constructing multiple classifiers, combines the Spark framework of big data programming model and the classification characteristics of support vector machine to realize a parallel one-to-many SVM optimization algorithm based on large data sets and compares them through UCI data sets. In the experiments, the one-to-many support vector machine improved by Spark is obviously better than the one-to-many support vector machine in the single-machine environment. The simulation results show that the proposed algorithm has better performance.
C1 [Gong, Yunlu] Shanghai Univ, Dept Math, Shanghai, Peoples R China.
   [Jia, Lianguo] Wuxi Huoqiupuhui Co Ltd, Wuxi, Jiangsu, Peoples R China.
C3 Shanghai University
RP Gong, YL (corresponding author), Shanghai Univ, Dept Math, Shanghai, Peoples R China.
EM ylgong@shu.edu.cn
FU Natural Science Foundation of Jiangsu Province [BK20150204]
FX The authors acknowledge the Natural Science Foundation of Jiangsu
   Province (Grant: BK20150204).
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TC 8
Z9 8
U1 0
U2 22
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0920-8542
EI 1573-0484
J9 J SUPERCOMPUT
JI J. Supercomput.
PD SEP
PY 2019
VL 75
IS 9
BP 5966
EP 5983
DI 10.1007/s11227-019-02894-7
PG 18
WC Computer Science, Hardware & Architecture; Computer Science, Theory &
   Methods; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA JA2JV
UT WOS:000487643900019
DA 2024-09-05
ER
PT C
AU Sleeman, J
   Halem, M
   Finin, T
   Cane, M
AF Sleeman, Jennifer
   Halem, Milton
   Finin, Tim
   Cane, Mark
BE Nie, JY
   Obradovic, Z
   Suzumura, T
   Ghosh, R
   Nambiar, R
   Wang, C
   Zang, H
   BaezaYates, R
   Hu, X
   Kepner, J
   Cuzzocrea, A
   Tang, J
   Toyoda, M
TI Discovering Scientific Influence using Cross-Domain Dynamic Topic
   Modeling
SO 2017 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA)
SE IEEE International Conference on Big Data
LA English
DT Proceedings Paper
CT IEEE International Conference on Big Data (IEEE Big Data)
CY DEC 11-14, 2017
CL Boston, MA
DE big data; topic model; cross-domain correlation; data integration;
   domain influence
AB We describe an approach using dynamic topic modeling to model influence and predict future trends in a scientific discipline. Our study focuses on climate change and uses assessment reports of the Intergovernmental Panel on Climate Change (IPCC) and the papers they cite. Since 1990, an IPCC report has been published every five years that includes four separate volumes, each of which has many chapters. Each report cites tens of thousands of research papers, which comprise a correlated dataset of temporally grounded documents. We use a custom dynamic topic modeling algorithm to generate topics for both datasets and apply cross-domain analytics to identify the correlations between the IPCC chapters and their cited documents. The approach reveals both the influence of the cited research on the reports and how previous research citations have evolved over time. For the IPCC use case, the report topic model used 410 documents and a vocabulary of 5911 terms while the citations topic model was based on 200K research papers and a vocabulary more than 25K terms. We show that our approach can predict the importance of its extracted topics on future IPCC assessments through the use of cross domain correlations, Jensen-Shannon divergences and cluster analytics.
C1 [Sleeman, Jennifer; Halem, Milton; Finin, Tim] Univ Maryland, Comp Sci & Elect Engn, Baltimore, MD 21250 USA.
   [Cane, Mark] Columbia Univ, Lamont Doherty Earth Observ, New York, NY 10027 USA.
C3 University System of Maryland; University of Maryland Baltimore;
   Columbia University
RP Sleeman, J (corresponding author), Univ Maryland, Comp Sci & Elect Engn, Baltimore, MD 21250 USA.
EM jsleem1@umbc.edu; halem@umbc.edu; finin@umbc.edu; mac6@columbia.edu
RI Cane, Mark A/I-8086-2012; Finin, Tim/IUO-4834-2023
OI Finin, Tim/0000-0002-6593-1792; Cane, Mark/0000-0001-5408-2388
FU NSF [1439663]; Division Of Computer and Network Systems; Direct For
   Computer & Info Scie & Enginr [1439663] Funding Source: National Science
   Foundation
FX This work was partially supported by NSF award #1439663 and a gift from
   IBM.
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TC 4
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U2 3
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2639-1589
BN 978-1-5386-2715-0
J9 IEEE INT CONF BIG DA
PY 2017
BP 1325
EP 1332
PG 8
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BJ8DN
UT WOS:000428073701043
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Mazrou, H
AF Mazrou, Hakim
TI Performance improvement of artificial neural networks designed for
   safety key parameters prediction in nuclear research reactors
SO NUCLEAR ENGINEERING AND DESIGN
LA English
DT Article
AB The present work explores, through a comprehensive sensitivity study, a new methodology to find a suitable artificial neural network architecture which improves its performances capabilities in predicting two significant parameters in safety assessment i.e. the multiplication factor k(eff) and the fuel powers peaks P-max of the benchmark 10 MW IAEA LEU core research reactor. The performances under consideration were the improvement of network predictions during the validation process and the speed up of computational time during the training phase.
   To reach this objective, we took benefit from Neural Network MATLAB Toolbox to carry out a widespread sensitivity study. Consequently, the speed up of several popular algorithms has been assessed during the training process. The comprehensive neural system was subsequently trained on different transfer functions, number of hidden neurons, levels of error and size of generalization corpus.
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C1 CRNA, Div Phys Radiol, Fanon 16000, Alger, Algeria.
RP Mazrou, H (corresponding author), CRNA, Div Phys Radiol, 02 Blvd Frantz,BP 399, Fanon 16000, Alger, Algeria.
EM mazrou_h@crna.dz
RI MAZROU, Hakim/AAM-5146-2021
OI MAZROU, Hakim/0000-0001-5394-1021
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NR 15
TC 13
Z9 13
U1 0
U2 2
PU ELSEVIER SCIENCE SA
PI LAUSANNE
PA PO BOX 564, 1001 LAUSANNE, SWITZERLAND
SN 0029-5493
EI 1872-759X
J9 NUCL ENG DES
JI Nucl. Eng. Des.
PD OCT
PY 2009
VL 239
IS 10
BP 1901
EP 1910
DI 10.1016/j.nucengdes.2009.06.004
PG 10
WC Nuclear Science & Technology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Nuclear Science & Technology
GA 501VO
UT WOS:000270411500020
DA 2024-09-05
ER
PT J
AU Wang, QY
   Liao, J
   Lapata, M
   Macleod, M
AF Wang, Qianying
   Liao, Jing
   Lapata, Mirella
   Macleod, Malcolm
TI Risk of bias assessment in preclinical literature using natural language
   processing
SO RESEARCH SYNTHESIS METHODS
LA English
DT Article
DE automatic assessment; natural language processing; preclinical research
   synthesis; risk of bias
AB We sought to apply natural language processing to the task of automatic risk of bias assessment in preclinical literature, which could speed the process of systematic review, provide information to guide research improvement activity, and support translation from preclinical to clinical research. We use 7840 full-text publications describing animal experiments with yes/no annotations for five risk of bias items. We implement a series of models including baselines (support vector machine, logistic regression, random forest), neural models (convolutional neural network, recurrent neural network with attention, hierarchical neural network) and models using BERT with two strategies (document chunk pooling and sentence extraction). We tune hyperparameters to obtain the highest F1 scores for each risk of bias item on the validation set and compare evaluation results on the test set to our previous regular expression approach. The F1 scores of best models on test set are 82.0% for random allocation, 81.6% for blinded assessment of outcome, 82.6% for conflict of interests, 91.4% for compliance with animal welfare regulations and 46.6% for reporting animals excluded from analysis. Our models significantly outperform regular expressions for four risk of bias items. For random allocation, blinded assessment of outcome, conflict of interests and animal exclusions, neural models achieve good performance; for animal welfare regulations, BERT model with a sentence extraction strategy works better. Convolutional neural networks are the overall best models. The tool is publicly available which may contribute to the future monitoring of risk of bias reporting for research improvement activities.
C1 [Wang, Qianying; Liao, Jing; Macleod, Malcolm] Univ Edinburgh, Ctr Clin Brain Sci, 49 Little France Crescent, Edinburgh EH16 4SB, Midlothian, Scotland.
   [Lapata, Mirella] Univ Edinburgh, Sch Informat, Edinburgh, Midlothian, Scotland.
C3 University of Edinburgh; University of Edinburgh
RP Macleod, M (corresponding author), Univ Edinburgh, Ctr Clin Brain Sci, 49 Little France Crescent, Edinburgh EH16 4SB, Midlothian, Scotland.
EM malcolm.macleod@ed.ac.uk
RI Macleod, Malcolm Robert/B-2052-2010; Wang, Junzhe/KCK-4991-2024; Wang,
   Yitong/KBA-1959-2024
OI Macleod, Malcolm Robert/0000-0001-9187-9839; L, Jing/0000-0002-9591-8070
FU China Scholarship Council; UK Reproducibility Network - John Climax PhD
   studentship; University of Edinburgh - Edinburgh Global Research
   Scholarship; MRC [MR/N015665/1] Funding Source: UKRI
FX China Scholarship Council; UK Reproducibility Network -John Climax PhD
   studentship; University of Edinburgh -Edinburgh Global Research
   Scholarship
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NR 48
TC 7
Z9 7
U1 1
U2 8
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1759-2879
EI 1759-2887
J9 RES SYNTH METHODS
JI Res. Synth. Methods
PD MAY
PY 2022
VL 13
IS 3
BP 368
EP 380
DI 10.1002/jrsm.1533
EA NOV 2021
PG 13
WC Mathematical & Computational Biology; Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology; Science & Technology - Other
   Topics
GA 1B5RJ
UT WOS:000714691800001
PM 34709718
OA Green Published, Green Submitted
DA 2024-09-05
ER
PT J
AU Wu, RL
   Kang, D
   Chen, Y
   Chen, CF
AF Wu, Renli
   Kang, Donghyun
   Chen, Yi
   Chen, Chuanfu
TI Assessing academic impacts of machine learning applications on a social
   science: Bibliometric evidence from economics
SO JOURNAL OF INFORMETRICS
LA English
DT Article
DE Academic impact; Machine learning application; Citation analysis;
   Interdisciplinary impact; Economics research
ID ARTIFICIAL-INTELLIGENCE; WEIGHTED CITATION; TRENDS
AB Machine learning (ML) methods have recently been applied in diverse fields of study. ML methods provide new toolkits and opportunities for social sciences, but they have also raised concerns with their black-box nature, irreproducibility, and emphasis on prediction rather than explanation. Against this backdrop, we study the bibliometric impact of leveraging ML methods in economics using publications indexed in Microsoft Academic Graph. We use our four-dimensional bibliometric framework by which we gage citation intensity, speed, breadth, and disruption to compare two groups of publications in economics (2001-2020)-those using ML methods and others not. We find that economics papers applying ML methods started to have advantages in citation counts and speed after 2010. Our analysis also shows that they received attention from more diverse research communities and had more disruptive citations over the past two decades. Then, we demonstrate that economics papers using ML methods obtained more disruptive citations within economics than outside. These findings suggest bibliometric advantages for applying ML methods in economics, especially in the recent decade, but we also discuss cautions and potential opportunities missed.
C1 [Wu, Renli; Chen, Yi; Chen, Chuanfu] Wuhan Univ, Sch Informat Management, Wuhan 430072, Hubei, Peoples R China.
   [Wu, Renli; Chen, Yi; Chen, Chuanfu] Wuhan Univ, Ctr Studies Informat Resources, Wuhan 430072, Hubei, Peoples R China.
   [Kang, Donghyun] Univ Chicago, Dept Sociol, Chicago, IL 60637 USA.
   [Wu, Renli; Kang, Donghyun] Univ Chicago, Knowledge Lab, Chicago, IL 60637 USA.
C3 Wuhan University; Wuhan University; University of Chicago; University of
   Chicago
RP Chen, CF (corresponding author), Wuhan Univ, Sch Informat Management, Wuhan 430072, Hubei, Peoples R China.; Chen, CF (corresponding author), Wuhan Univ, Ctr Studies Informat Resources, Wuhan 430072, Hubei, Peoples R China.
EM cfchen@whu.edu.cn
RI Kang, Donghyun/GQP-7643-2022
OI Kang, Donghyun/0000-0001-6241-8910; Wu, Renli/0000-0003-2370-6236
FU National Natural Science Foundation of China (NSFC) [71921002]
FX We are deeply grateful for the insightful comments from two anonymous
   reviewers and the editors. This work was supported by the National
   Natural Science Foundation of China (NSFC) under Grant Number 71921002.
   This work was completed in part with resources provided by the
   University of Chicago Research Computing Center. We thank Professor
   James Evans for his support towards this collaboration. We also
   appreciate Wenxuan Shi for her assistance in the submission process.
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NR 82
TC 1
Z9 1
U1 8
U2 42
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1751-1577
EI 1875-5879
J9 J INFORMETR
JI J. Informetr.
PD AUG
PY 2023
VL 17
IS 3
AR 101436
DI 10.1016/j.joi.2023.101436
EA JUL 2023
PG 19
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA P4DI0
UT WOS:001050163300001
DA 2024-09-05
ER
PT J
AU Ma, YC
   Teng, Y
   Deng, ZZ
   Liu, L
   Zhang, Y
AF Ma, Yongchao
   Teng, Ying
   Deng, Zhongzhun
   Liu, Li
   Zhang, Yi
TI Does writing style affect gender differences in the research performance
   of articles?: An empirical study of BERT-based textual sentiment
   analysis
SO SCIENTOMETRICS
LA English
DT Article
DE Gender; Gender inequalities; Female; Research performance; Marketing;
   SDGs; Writing style
ID CITATION ADVANTAGES; SELF-PROMOTION; 1ST AUTHORS; PRODUCTIVITY;
   JOURNALS; IMPACT; SCIENTISTS; SCIENCE; COLLABORATION; PUBLICATIONS
AB "Achieve gender equality and empower all women and girls" is essential to reduce gender disparity and improve the status of women. But it remains a challenge to narrow gender differences and improve gender equality in academic research. In this paper, we propose that the impact of articles is lower and writing style of articles is less positive when the article's first author is female relative to male first authors, and writing style mediates this relationship. Focusing on the positive writing style, we attempt to contribute and explain the research on gender differences in research performance. We use BERT-based textual sentiment analysis to analyse 87 years of 9820 articles published in the top four marketing journals and prove our hypotheses. We also consider a set of control variables and conduct a set of robustness checks to ensure the robustness of our findings. We discuss the theoretical and managerial implications of our findings for researchers.
C1 [Ma, Yongchao] Huazhong Univ Sci & Technol, Sch Management, Wuhan, Hubei, Peoples R China.
   [Ma, Yongchao; Teng, Ying] City Univ Hong Kong, Dept Mkt, Hong Kong, Peoples R China.
   [Deng, Zhongzhun] Sichuan Univ, Business Sch, Chengdu, Sichuan, Peoples R China.
   [Liu, Li; Zhang, Yi] Huazhong Univ Sci & Technol, Coll Publ Adm, Wuhan, Hubei, Peoples R China.
C3 Huazhong University of Science & Technology; City University of Hong
   Kong; Sichuan University; Huazhong University of Science & Technology
RP Deng, ZZ (corresponding author), Sichuan Univ, Business Sch, Chengdu, Sichuan, Peoples R China.
EM martin@hust.edu.cn; danieldeng@scu.edu.cn
RI Ma, Yongchao Martin/ABG-4449-2021; Liu, Enlong/AFM-5097-2022
OI Ma, Yongchao Martin/0000-0002-7272-9779; Liu,
   Enlong/0000-0002-3507-1963; TENG, Ying/0000-0002-7198-4823;
   /0000-0001-7822-5064
FU National Natural Science Foundation of China [72202149, 71672063,
   72072065]; Major Program of the National Social Science Fund Projects
   [19ZDA104]; Fundamental Research Funds for the Central Universities
   [2022ZY-SX004]
FX The authors thank the editor, the editorial assistant, and anonymous
   reviewers for their insightful comments and suggestions. The authors
   thank Maikun Li, Nibing Zhu, Kexin Wu, and Shuai Jin for their
   assistance in this paper. The authors gratefully acknowledge the grants
   from the National Natural Science Foundation of China (projects
   72202149, 71672063 and 72072065), the grant from the Major Program of
   the National Social Science Fund Projects (project 19ZDA104), and the
   Fundamental Research Funds for the Central Universities (project
   2022ZY-SX004) for financial support. The computation is completed in the
   HPC Platform of Huazhong University of Science and Technology.
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EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD APR
PY 2023
VL 128
IS 4
BP 2105
EP 2143
DI 10.1007/s11192-023-04666-w
EA MAR 2023
PG 39
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA M8WZ2
UT WOS:000945307700001
PM 37095862
OA Bronze
DA 2024-09-05
ER
PT J
AU Upshall, M
AF Upshall, Michael
TI An AI toolkit for libraries
SO INSIGHTS-THE UKSG JOURNAL
LA English
DT Article
DE AI; NLP; evaluation; metrics; research support
AB Now that artificial intelligence (AI) tools are being widely used across academic publishing, how can we make informed assessments of these utilities? There is a need for a set of skills for evaluating new tools and measuring existing ones, which should enable anyone commissioning or managing AI utilities to understand what questions to ask, what parameters to measure and possible pitfalls to avoid when introducing a new utility. The skills required are not technical. Potential problems include bias in the corpus, a poor training set or poor use of metrics for evaluation. This article gives a quick overview of some of areas where AI tools are being used and how they work. It then provides a checklist for assessment. The goal is not to discredit AI, but to make effective use of it.
EM michael@consultmu.co.uk
OI Upshall, Michael/0000-0003-1115-6847
CR [Anonymous], 2020, IBM CLOUD LEARN HUB
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NR 42
TC 1
Z9 1
U1 7
U2 21
PU UBIQUITY PRESS LTD
PI LONDON
PA Unit 3.22, East London Works, 65-75 Whitechapel Road, LONDON, E1 1DU,
   ENGLAND
SN 2048-7754
J9 INSIGHTS
JI Insights
PD NOV 1
PY 2022
VL 35
DI 10.1629/uksg.592
PG 16
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA 6H2RG
UT WOS:000885293100001
OA gold
DA 2024-09-05
ER
PT J
AU Gursoy, F
   Kakadiaris, IA
AF Gursoy, Furkan
   Kakadiaris, Ioannis A.
TI Artificial intelligence research strategy of the United States: critical
   assessment and policy recommendations
SO FRONTIERS IN BIG DATA
LA English
DT Article
DE artificial intelligence; research; development; policy; strategy;
   accountable AI
ID DECISION-MAKING; PROJECT
AB The foundations of Artificial Intelligence (AI), a field whose applications are of great use and concern for society, can be traced back to the early years of the second half of the 20th century. Since then, the field has seen increased research output and funding cycles followed by setbacks. The new millennium has seen unprecedented interest in AI progress and expectations with significant financial investments from the public and private sectors. However, the continual acceleration of AI capabilities and real-world applications is not guaranteed. Mainly, accountability of AI systems in the context of the interplay between AI and the broader society is essential for adopting AI systems via the trust placed in them. Continual progress in AI research and development (R & D) can help tackle humanity's most significant challenges to improve social good. The authors of this paper suggest that the careful design of forward-looking research policies serves a crucial function in avoiding potential future setbacks in AI research, development, and use. The United States (US) has kept its leading role in R & D, mainly shaping the global trends in the field. Accordingly, this paper presents a critical assessment of the US National AI R & D Strategic Plan and prescribes six recommendations to improve future research strategies in the US and around the globe.
C1 [Gursoy, Furkan; Kakadiaris, Ioannis A.] Univ Houston, Computat Biomed Lab, Houston, TX 77204 USA.
C3 University of Houston System; University of Houston
RP Kakadiaris, IA (corresponding author), Univ Houston, Computat Biomed Lab, Houston, TX 77204 USA.
EM ioannisk@uh.edu
RI Gürsoy, Furkan/HSE-4101-2023
FU National Science Foundation [CCF-2131504]
FX Funding This material is based upon work supported by the National
   Science Foundation under Grant CCF-2131504.
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NR 39
TC 0
Z9 0
U1 8
U2 14
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 2624-909X
J9 FRONT BIG DATA
JI Front. Big Data
PD AUG 7
PY 2023
VL 6
AR 1206139
DI 10.3389/fdata.2023.1206139
PG 5
WC Computer Science, Information Systems; Computer Science,
   Interdisciplinary Applications; Multidisciplinary Sciences
WE Emerging Sources Citation Index (ESCI)
SC Computer Science; Science & Technology - Other Topics
GA P4WV6
UT WOS:001050691000001
PM 37609602
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU van de Venter, R
   Skelton, E
   Matthew, J
   Woznitza, N
   Tarroni, G
   Hirani, SP
   Kumar, A
   Malik, R
   Malamateniou, C
AF van de Venter, Riaan
   Skelton, Emily
   Matthew, Jacqueline
   Woznitza, Nick
   Tarroni, Giacomo
   Hirani, Shashivadan P.
   Kumar, Amrita
   Malik, Rizwan
   Malamateniou, Christina
TI Artificial intelligence education for radiographers, an evaluation of a
   UK postgraduate educational intervention using participatory action
   research: a pilot study
SO INSIGHTS INTO IMAGING
LA English
DT Article
DE Artificial intelligence; Radiography; Education; Evaluation; Action
   research
ID HEALTH-CARE; TELEHEALTH; INNOVATION; ONLINE
AB BackgroundArtificial intelligence (AI)-enabled applications are increasingly being used in providing healthcare services, such as medical imaging support. Sufficient and appropriate education for medical imaging professionals is required for successful AI adoption. Although, currently, there are AI training programmes for radiologists, formal AI education for radiographers is lacking. Therefore, this study aimed to evaluate and discuss a postgraduate-level module on AI developed in the UK for radiographers.MethodologyA participatory action research methodology was applied, with participants recruited from the first cohort of students enrolled in this module and faculty members. Data were collected using online, semi-structured, individual interviews and focus group discussions. Textual data were processed using data-driven thematic analysis.ResultsSeven students and six faculty members participated in this evaluation. Results can be summarised in the following four themes: a. participants' professional and educational backgrounds influenced their experiences, b. participants found the learning experience meaningful concerning module design, organisation, and pedagogical approaches, c. some module design and delivery aspects were identified as barriers to learning, and d. participants suggested how the ideal AI course could look like based on their experiences.ConclusionsThe findings of our work show that an AI module can assist educators/academics in developing similar AI education provisions for radiographers and other medical imaging and radiation sciences professionals. A blended learning delivery format, combined with customisable and contextualised content, using an interprofessional faculty approach is recommended for future similar courses.
C1 [van de Venter, Riaan] Nelson Mandela Univ, Fac Hlth Sci, Sch Clin Care Sci, Dept Radiog, Port Elizabeth, South Africa.
   [van de Venter, Riaan; Skelton, Emily; Malik, Rizwan; Malamateniou, Christina] City Univ London, Sch Hlth & Psychol Sci, Div Midwifery & Radiog, London, England.
   [Skelton, Emily; Matthew, Jacqueline; Malamateniou, Christina] Kings Coll London, Dept Perinatal Imaging & Hlth, London, England.
   [Matthew, Jacqueline] Guys & St Thomas NHS Fdn Trust, London, England.
   [Woznitza, Nick] Univ Coll London Hosp, Radiol Dept, London, England.
   [Woznitza, Nick] Canterbury Christ Church Univ, Sch Allied & Publ Hlth Profess, Canterbury, England.
   [Tarroni, Giacomo] City Univ London, Dept Comp Sci, Cit AI, London, England.
   [Tarroni, Giacomo] Imperial Coll London, Dept Comp, BioMedIA, London, England.
   [Hirani, Shashivadan P.] City Univ London, Ctr Healthcare Innovat Res, London, England.
   [Kumar, Amrita] Frimley Hlth NHS Fdn Trust, London, England.
   [Malik, Rizwan] Royal Bolton Hosp, Farnworth, England.
   [Malamateniou, Christina] HESAV Univ, Dept Radiog, Lausanne, Switzerland.
C3 Nelson Mandela University; City St Georges, University of London;
   University of London; King's College London; Guy's & St Thomas' NHS
   Foundation Trust; University College London Hospitals NHS Foundation
   Trust; University of London; University College London; Canterbury
   Christ Church University; City St Georges, University of London;
   Imperial College London; City St Georges, University of London; Royal
   Bolton Hospital
RP van de Venter, R (corresponding author), Nelson Mandela Univ, Fac Hlth Sci, Sch Clin Care Sci, Dept Radiog, Port Elizabeth, South Africa.; van de Venter, R; Malamateniou, C (corresponding author), City Univ London, Sch Hlth & Psychol Sci, Div Midwifery & Radiog, London, England.; Malamateniou, C (corresponding author), Kings Coll London, Dept Perinatal Imaging & Hlth, London, England.; Malamateniou, C (corresponding author), HESAV Univ, Dept Radiog, Lausanne, Switzerland.
EM riaan.vandeventer@mandela.ac.za; christina.malamateniou@city.ac.uk
RI Matthew, Jackie/HQZ-6583-2023; van de Venter, Riaan/JFK-9683-2023
OI Matthew, Jackie/0000-0003-4754-0322; van de Venter,
   Riaan/0000-0003-4384-9234; Skelton, Emily/0000-0003-0132-7948;
   Malamateniou, Christina/0000-0002-2352-8575
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NR 62
TC 9
Z9 9
U1 4
U2 15
PU SPRINGER WIEN
PI Vienna
PA Prinz-Eugen-Strasse 8-10, A-1040 Vienna, AUSTRIA
SN 1869-4101
J9 INSIGHTS IMAGING
JI Insights Imaging
PD FEB 3
PY 2023
VL 14
IS 1
AR 25
DI 10.1186/s13244-023-01372-2
PG 13
WC Radiology, Nuclear Medicine & Medical Imaging
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Radiology, Nuclear Medicine & Medical Imaging
GA 8O2MV
UT WOS:000925674900003
PM 36735172
OA Green Accepted, Green Published, gold
DA 2024-09-05
ER
PT J
AU Fan, P
AF Fan, Peng
TI Application of deep learning and cloud data platform in college teaching
   quality evaluation (Publication with Expression of Concern)
SO JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
LA English
DT Article; Publication with Expression of Concern
DE Deep learning; teaching quality; cloud computing; data fusions
ID REVIEWS; MODEL
AB In this paper, the author introduces the theory of fuzzy mathematics into the evaluation of higher education. By determining the set of evaluation factors and comments, the author constructs the relevant mathematical model and processes the data, thus turning the evaluation problem into the multiplication problem of the fuzzy matrix. Deep learning is a very active branch of machine learning research in recent years. By increasing the depth and breadth of the model, i.e. increasing the number of operations from the input end to the output end and the number of channels of the model, the scale of parameters of the model is increased, so that the model has the ability to express complex functions. It is appropriate to use deep learning in teaching quality evaluation. The simulation results show that the deep learning model is very effective in dealing with data diversity and extracting complex implicit rules. It can effectively model experts' professional knowledge and experience. Deep neural network has powerful expressive ability, and can effectively extract the deep-seated laws affecting the teaching quality. It can be used as an assistant technology for the evaluation of teaching quality in Colleges.
C1 [Fan, Peng] Xidian Univ, Xian, Peoples R China.
C3 Xidian University
RP Fan, P (corresponding author), Xidian Univ, Xian, Peoples R China.
EM fanpengxidian@163.com
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NR 30
TC 16
Z9 16
U1 0
U2 34
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1064-1246
EI 1875-8967
J9 J INTELL FUZZY SYST
JI J. Intell. Fuzzy Syst.
PY 2020
VL 39
IS 4
BP 5547
EP 5558
DI 10.3233/JIFS-189036
PG 12
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA OH1HF
UT WOS:000582322000069
DA 2024-09-05
ER
PT J
AU Saputra, NA
   Hamidah, I
   Setiawan, A
AF Saputra, Nisa Aulia
   Hamidah, Ida
   Setiawan, Agus
TI A BIBLIOMETRIC ANALYSIS OF DEEP LEARNING FOR EDUCATION RESEARCH
SO JOURNAL OF ENGINEERING SCIENCE AND TECHNOLOGY
LA English
DT Article
DE Bibliometric analysis; Deep learning in education; Learning media;
   Technology 4; 0; VOSviewer
ID COVID-19
AB The purpose of this study was to determine the role, trend, and development of deep learning (DL) in education. The research method used is a bibliometric analysis method using the VOSviewer tool. VOSviewer is used to analyse the distribution of documents each year in various countries, institutions, journals, authors, and the relationship between keywords that appear. The results of this study show that the growth of publications on DL articles in the world of education increased by 31.69%, while the growth of DL articles as learning media increased by 11%. The most productive country in publishing articles related to DL in education is the United States with a total of 460 related documents and 13,162 citations. The most productive institution that researches DL in education is Stanford University with a total of 21 articles published. Furthermore, the most productive journal in IEEE Access with a total publication of 58,219 articles and a citation score of 4.8. The relationship between authors shows that the co-authoring network with Zhang Y. is the largest network with a total of 24 co-authored articles. The keyword that appears the most is the keyword "deep learning" which is directly related to "Data Analytics" and "AI". It is also seen that the topics that may arise for future research are topics related to the keyword "deep learning" which is related to "Virtual Reality" or "Educational Psychology". This research can be useful to find research gaps regarding the development or implementation of deep learning in the field of education to improve the quality of education and solving problems related to the world of education.
C1 [Saputra, Nisa Aulia; Hamidah, Ida; Setiawan, Agus] Univ Pendidikan Indonesia, Bandung, Indonesia.
C3 Universitas Pendidikan Indonesia
RP Hamidah, I (corresponding author), Univ Pendidikan Indonesia, Bandung, Indonesia.
EM idahamidah@upi.edu
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NR 83
TC 0
Z9 0
U1 2
U2 7
PU TAYLORS UNIV SDN BHD
PI SELANGOR
PA 1 JALAN SS15-8, SUBANG JAYA, SELANGOR, 47500, MALAYSIA
EI 1823-4690
J9 J ENG SCI TECHNOL
JI J. Eng. Sci. Technol.
PD APR
PY 2023
VL 18
IS 2
BP 1258
EP 1276
PG 19
WC Engineering, Multidisciplinary
WE Emerging Sources Citation Index (ESCI)
SC Engineering
GA F9SA3
UT WOS:000985660800026
DA 2024-09-05
ER
PT C
AU Wang, MY
AF Wang Mingyan
BE Zhu, KL
   Zhang, H
TI Research on E-procurement Supplier Performance Assessment Model Based on
   SPSS Principal Component Analysis and Gray Relational Theory
SO COMPREHENSIVE EVALUATION OF ECONOMY AND SOCIETY WITH STATISTICAL SCIENCE
LA English
DT Proceedings Paper
CT 2nd Conference of the
   International-Institute-of-Applied-Statistics-Studies
CY JUL 24-29, 2009
CL Qingdao, PEOPLES R CHINA
DE Principal Component Analysis; Grey Theory; Supplier's Performance
   Evaluation Model; SPSS
AB The supplier of e-procurement under at the whole supply chain environment are the main operators of the generalized one. Implementation of ongoing supplier performance evaluation is to control the stable operation of the supply chain the key aspect. In this paper, with theoretical analysis and empirical analysis, put forward the gray relational theory and the principal component analysis SPSS combined to build he supplier performance evaluation index system model under e-procurement, and carry out the relevant empirical research. Examples of analysis showed that the evaluation model for supplier performance evaluation under e-procurement provides a new effective way.
C1 [Wang Mingyan] Shanghai Univ Engn Sci, Informat Management Dept, Inst Management, Shanghai 201620, Peoples R China.
C3 Shanghai University of Engineering Science
RI wang, ming/HPC-6329-2023; wang, ming/ITV-5378-2023
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NR 5
TC 1
Z9 1
U1 0
U2 1
PU AUSSINO ACAD PUBL HOUSE
PI MARRICKVILLE
PA PO BOX 893, MARRICKVILLE, NSW 2204 00000, AUSTRALIA
BN 978-0-9806057-7-8
PY 2009
BP 745
EP 751
PG 7
WC Agricultural Economics & Policy; Economics; Education & Educational
   Research; Environmental Sciences; Environmental Studies; Social
   Sciences, Mathematical Methods; Statistics & Probability
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Agriculture; Business & Economics; Education & Educational Research;
   Environmental Sciences & Ecology; Mathematical Methods In Social
   Sciences; Mathematics
GA BOE48
UT WOS:000276383800129
DA 2024-09-05
ER
PT J
AU Poole, G
   Egan, JP
   Iqbal, I
AF Poole, Gary
   Egan, John P.
   Iqbal, Isabeau
TI Innovation in collaborative health research training: The role of active
   learning
SO JOURNAL OF INTERPROFESSIONAL CARE
LA English
DT Article
DE Active learning; collaboration; community health; research training
ID TEAMS
AB This paper describes and discusses the essential pedagogical elements of the Partnering in Community Health Research (PCHR) program, which was designed to address the training needs of researchers who participate in collaborative, interdisciplinary health research. These elements were intended to foster specific skills that helped learners develop research partnerships featuring knowledge, capabilities, values and attitudes needed for successful research projects. By establishing research teams called "clusters'', PCHR provided research training and experience for graduate students and post-doctoral fellows, as well as for community health workers and professionals. Pedagogical elements relied on active learning approaches such as inquiry-based and experience-based learning. Links between these elements and learning approaches are explained. Through their work in cluster-based applied research projects, the development of learning plans, and cross-cluster learning events, trainees acquired collaborative research competencies that were valuable, relevant and theoretically informed.
C1 [Poole, Gary] Univ British Columbia, Fac Med, Sch Populat & Publ Hlth, Vancouver, BC V6T 1Z3, Canada.
   [Egan, John P.] Univ British Columbia, Fac Med, Ctr Clin Epidemiol & Evaluat, Vancouver, BC V6T 1Z3, Canada.
   [Iqbal, Isabeau] Univ British Columbia, Dept Educ Studies & Educ Developer, Ctr Teaching & Acad Growth, Vancouver, BC V6T 1Z3, Canada.
C3 University of British Columbia; University of British Columbia;
   University of British Columbia
RP Poole, G (corresponding author), Univ British Columbia, Fac Med, Sch Populat & Publ Hlth, 5804 Fairview Ave, Vancouver, BC V6T 1Z3, Canada.
EM gary.poole@ubc.ca
RI Egan, John P/JHS-5622-2023
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TC 4
Z9 6
U1 0
U2 10
PU TAYLOR & FRANCIS INC
PI PHILADELPHIA
PA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA
SN 1356-1820
EI 1469-9567
J9 J INTERPROF CARE
JI J. Interprofessional Care
PY 2009
VL 23
IS 2
BP 148
EP 155
DI 10.1080/13561820802634894
PG 8
WC Health Care Sciences & Services; Health Policy & Services
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Health Care Sciences & Services
GA 483PB
UT WOS:000268978600005
PM 19283545
DA 2024-09-05
ER
PT J
AU Zhang, ZP
   Yin, H
AF Zhang, Zipeng
   Yin, Hu
TI Research on design forms based on artificial intelligence collaboration
   model
SO COGENT ENGINEERING
LA English
DT Article
DE AI; industrial design; design morphology; AIGC; co-design
AB With the advent of the era of great intersection and integration, the development of generative artificial intelligence has caused the renewal of design methods, promoting a new paradigm of research in design fundamentals. The study seeks to investigate the research method of design form in the collaborative mode of artificial intelligence, to provide new ideas for design to conduct interdisciplinary research, and to promote design innovation under AI collaboration. This research begins with the design morphology theory, integrates interdisciplinary theories such as bionic design, and topology research, and collaborates with AIGC tools such as Midjourney, Stable Diffusion, and Chilloutmix to conduct case-specific research. To improve the accuracy of the morphological study, parametric design, bi-directional progressive topology optimization, genetic algorithm and simulation analysis, and other methods were also used in the research process to carry out a comprehensive design experiment exploration. This study also summarizes the AIGC prompt formula for the industrial design field and proposes an innovative seven-step design form research method with shape finding and shape making. This study also summarizes the AIGC prompt formula for the industrial design field and proposes an innovative seven-step design form research method with shape finding and shape making. Simultaneously, the pearl shell design morphology research is conducted in collaboration with AI technology, the full case design of the autonomous underwater vehicle is completed, and the efficacy of the seven-step design morphology research method is validated through fluid simulation. AI synergy provides new ideas for complex morphology research, extends and complements design, and plays a crucial role in the phases of morphology exploration, concept generation, and solution implementation, thereby assisting in the exploration of the central content of design morphology.
C1 [Zhang, Zipeng; Yin, Hu] Beihang Univ, Sch Mech Engn & Automat, Dept Ind Design, Beijing, Peoples R China.
   [Zhang, Zipeng] Tsinghua Univ, Acad Art & Design, Beijing, Peoples R China.
C3 Beihang University; Tsinghua University
RP Yin, H (corresponding author), Beihang Univ, Sch Mech Engn & Automat, Dept Ind Design, Beijing, Peoples R China.
EM 19375309@buaa.edu.cn
FX Thanks to my supervisor Hu Yin, for his valuable advice on the selection
   of the research subjects, the setting of the design evaluation criteria,
   and the verification of the morphological laws, which gave me great
   encouragement and help in the process of writing the paper.
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NR 43
TC 0
Z9 0
U1 11
U2 11
PU TAYLOR & FRANCIS AS
PI OSLO
PA KARL JOHANS GATE 5, NO-0154 OSLO, NORWAY
SN 2331-1916
J9 COGENT ENG
JI Cogent Eng.
PD DEC 31
PY 2024
VL 11
IS 1
AR 2364051
DI 10.1080/23311916.2024.2364051
PG 18
WC Engineering, Multidisciplinary
WE Emerging Sources Citation Index (ESCI)
SC Engineering
GA XH5J4
UT WOS:001260801700001
OA gold
DA 2024-09-05
ER
PT J
AU Ibáñez, A
   Larrañaga, P
   Bielza, C
AF Ibanez, Alfonso
   Larranaga, Pedro
   Bielza, Concha
TI Using Bayesian networks to discover relationships between bibliometric
   indices. A case study of computer science and artificial intelligence
   journals
SO SCIENTOMETRICS
LA English
DT Article
DE Bibliometric indices; Bayesian networks; Conditional dependencies and
   conditional independencies; Computer science and artificial intelligence
ID H-INDEX; PROBABILISTIC NETWORKS; CITATION ANALYSIS; R-INDEX; IMPACT
AB As they are used to evaluate the importance of research at different levels by funding agencies and promotion committees, bibliometric indices have received a lot of attention from the scientific community over the last few years. Many bibliometric indices have been developed in order to take into account aspects not previously covered. The result is that, nowadays, the scientific community faces the challenge of selecting which of this pool of indices meets the required quality standards. In view of the vast number of bibliometric indices, it is necessary to analyze how they relate to each other (irrelevant, dependent and so on). Our main purpose is to learn a Bayesian network model from data to analyze the relationships among bibliometric indices. The induced Bayesian network is then used to discover probabilistic conditional (in) dependencies among the indices and, also for probabilistic reasoning. We also run a case study of 14 well-known bibliometric indices on computer science and artificial intelligence journals.
C1 [Ibanez, Alfonso; Larranaga, Pedro; Bielza, Concha] Univ Politecn Madrid, Computat Intelligence Grp, Dept Inteligencia Artificial, E-28660 Madrid, Spain.
C3 Universidad Politecnica de Madrid
RP Ibáñez, A (corresponding author), Univ Politecn Madrid, Computat Intelligence Grp, Dept Inteligencia Artificial, E-28660 Madrid, Spain.
EM aibanez@fi.upm.es; plarranaga@fi.upm.es; mcbielza@fi.upm.es
RI Bielza, Concha/F-9277-2013; Larranaga, Pedro/F-9293-2013; Ibáñez,
   Alfonso/B-3423-2010
OI Bielza, Concha/0000-0001-7109-2668; Larranaga,
   Pedro/0000-0003-0652-9872; 
FU Spanish Ministry of Science and Innovation [TIN2008-04528-E,
   TIN2010-20900-C04-04]; Cajal Blue Brain and Consolider Ingenio
   [2010-CSD2007-00018]
FX Research supported by Spanish Ministry of Science and Innovation,
   Project TIN2008-04528-E. The study has also been partially supported by
   Spanish Ministry of Science and Innovation, grants TIN2010-20900-C04-04,
   Cajal Blue Brain and Consolider Ingenio 2010-CSD2007-00018.
CR Alonso S, 2010, SCIENTOMETRICS, V82, P391, DOI 10.1007/s11192-009-0047-5
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NR 33
TC 10
Z9 10
U1 1
U2 79
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
J9 SCIENTOMETRICS
JI Scientometrics
PD NOV
PY 2011
VL 89
IS 2
BP 523
EP 551
DI 10.1007/s11192-011-0486-7
PG 29
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 840WU
UT WOS:000296473400004
DA 2024-09-05
ER
PT J
AU Huang, W
   Song, FB
   Zhang, SY
   Xia, T
AF Huang, Wei
   Song, Fangbin
   Zhang, Shenyu
   Xia, Tian
TI Influence of deep learning-based journal reading guidance system on
   students' national cognition and cultural acceptance
SO FRONTIERS IN PSYCHOLOGY
LA English
DT Article
DE educational psychology; art journal; national identity; guidance system;
   deep learning
ID MENTAL-HEALTH; COLLEGE-STUDENTS
AB The purpose is to explore new cultivation modes of college students' national cognition and cultural acceptance. Deep learning (DL) technology and Educational Psychology theory are introduced, and the influence of art journal reading on college students' national cognition and cultural acceptance is analyzed under Educational Psychology. Firstly, the background of Educational Psychology, national cognition and cultural acceptance, and learning system are discussed following a literature review. The DL technology is introduced to construct the journal reading guidance system. The system can provide users with art journals and record the user habits like reading duration and preferences. Secondly, hypotheses are proposed, and a questionnaire survey is designed, with 12 specific indicators to investigate and collect research data. Finally, the collected data are analyzed. The results show that women's cognition of Chinese traditional culture, Chinese excellent revolutionary culture, and Chinese national identity is higher than that of men. By comparison, men's cognition of Chinese advanced socialist culture is higher than women's. After using the journal reading guidance system, the cognition of female college students on traditional Chinese culture is improved by 16.3%. Before and after reading art journals, the overall national cognition and cultural acceptance of Minority students are higher than that of Han students. The overall cognition of Literature and History students is higher than that of Science and Engineering students in traditional Chinese culture and China's excellent revolutionary culture and lower in advanced Chinese socialist culture and Chinese national identity. The overall cognition of college students' party members to the advanced socialist culture is higher than league members. As students read more art journals through the guidance system, their overall national cognition and cultural acceptance have increased. Therefore, reading art journals can promote college students' national cognition and cultural acceptance. A national cognition and cultural acceptance promotion system that conforms to the current situation of college students is constructed. The finding provides a reference for developing complex emotion recognition technology in human-computer interaction.
C1 [Huang, Wei] Southeast Univ, Sch Arts, Nanjing, Peoples R China.
   [Song, Fangbin] Nanjing Univ Sci & Technol, Sch Design Art & Media, Nanjing, Peoples R China.
   [Zhang, Shenyu] Nanjing Normal Univ, Sch Liberal Arts, Nanjing, Peoples R China.
   [Xia, Tian] Southwestern Univ Finance & Econ, Sch Econ, Chengdu, Peoples R China.
C3 Southeast University - China; Nanjing University of Science &
   Technology; Nanjing Normal University; Southwestern University of
   Finance & Economics - China
RP Huang, W (corresponding author), Southeast Univ, Sch Arts, Nanjing, Peoples R China.
EM 230198768@seu.edu.cn
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NR 40
TC 1
Z9 1
U1 8
U2 45
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 1664-1078
J9 FRONT PSYCHOL
JI Front. Psychol.
PD AUG 25
PY 2022
VL 13
AR 950412
DI 10.3389/fpsyg.2022.950412
PG 17
WC Psychology, Multidisciplinary
WE Social Science Citation Index (SSCI)
SC Psychology
GA 6Q6PA
UT WOS:000891733200001
PM 36092117
OA gold, Green Published
DA 2024-09-05
ER
PT C
AU Ma, J
AF Ma, Jian
BE Li, D
   Li, Z
TI Principal Component Analysis Method-Based Research on Agricultural
   Science and Technology Website Evaluation
SO COMPUTER AND COMPUTING TECHNOLOGIES IN AGRICULTURE IX, CCTA 2015, PT II
SE IFIP Advances in Information and Communication Technology
LA English
DT Proceedings Paper
CT 9th IFIP WG 5.14 International Conference on Computer and Computing
   Technologies in Agriculture (CCTA)
CY SEP 27-30, 2015
CL China Agr Univ, Beijing, PEOPLES R CHINA
HO China Agr Univ
DE Agricultural science and technology website; Principal component
   analysis method; Website evaluation
AB Agricultural science and technology website is a very important supporter of driving agricultural information and servicing agriculture. An evaluation method is proposed on agricultural science and technology website based on objective data and artificial ratings, using principal component analysis method. Finally the author used the model to evaluate 18 agricultural science and technology websites, and proposed some suggestions on development of agricultural science and technology websites based on the evaluation result which would act as reference to agricultural science and technology website construction.
C1 [Ma, Jian] Chinese Acad Agr Sci, Agr Informat Inst, Beijing 100081, Peoples R China.
   [Ma, Jian] Minist Agr, Key Lab Agr Informat Serv Technol 2006 2010, Beijing 100081, Peoples R China.
C3 Chinese Academy of Agricultural Sciences; Agriculture Information
   Institute, CAAS; Ministry of Agriculture & Rural Affairs
RP Ma, J (corresponding author), Chinese Acad Agr Sci, Agr Informat Inst, Beijing 100081, Peoples R China.; Ma, J (corresponding author), Minist Agr, Key Lab Agr Informat Serv Technol 2006 2010, Beijing 100081, Peoples R China.
EM majian@caas.cn
RI JIAN, MA/KQU-7977-2024
CR Du J., 2010, EVALUATION SYSTEM AG
   Li C., 2005, J NANJING U FINANCES
   Liu Y., 2010, EVALUATION METHOD EV
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NR 4
TC 0
Z9 0
U1 0
U2 11
PU SPRINGER INT PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 1868-4238
BN 978-3-319-48354-2; 978-3-319-48353-5
J9 IFIP ADV INF COMM TE
PY 2016
VL 479
BP 369
EP 381
DI 10.1007/978-3-319-48354-2_37
PG 13
WC Agricultural Engineering; Computer Science, Information Systems;
   Computer Science, Interdisciplinary Applications
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Agriculture; Computer Science
GA BH5KJ
UT WOS:000401099100037
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Rychalski, A
   Aubry, M
AF Rychalski, Aude
   Aubry, Mathilde
TI Diversify Approaches to Better Understand the Compatibility of
   Artificial Intelligence and Sustainability: "I Love You... Me Neither"
SO JOURNAL OF INNOVATION ECONOMICS & MANAGEMENT
LA English
DT Article
DE Artificial Intelligence; Sustainability; Bibliometrics; Co-Citation
   Analysis (CCA); Bibliographic Coupling Analysis (BCA); Ethics
ID BIBLIOMETRIC ANALYSIS; DECISION-MAKING; BIG DATA; IMPACT; MODELS; LAND
AB The aim of this article is twofold: 1. Suggest an overview of current knowledge and understanding of both concepts and 2. Present the six contributions and their positioning in relation to current the literature linked to artificial intelligence and sustainability. For that, we use different but complementary sources. First, we ask artificial intelligence to reveal the mainstream view. Then we call on human intelligence to provide a critical perspective. Finally, we carry out a bibliometric analysis using the SCOPUS database and two different statistical analyses (the CCA - co -citation analysis, the BCA - bibliographic coupling analysis). The diversity of the sources used, and their complementarity allow us to propose a holistic vision of the subject, highlighting the concerns that surround it and identifying future avenues of research for academics. The articles selected in this special issue fill some of the gaps raised and call for further research.
C1 [Rychalski, Aude] ESSCA Sch Management, Angers, France.
   [Aubry, Mathilde] EM Normandie Business Sch, Metis Lab, Paris, France.
C3 ESSCA School of Management
RP Rychalski, A (corresponding author), ESSCA Sch Management, Angers, France.
EM aude.rychalski@essca.fr; maubry@em-normandie.fr
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NR 84
TC 0
Z9 0
U1 0
U2 0
PU DE BOECK UNIV
PI LOUVAIN-LA-NEUVE
PA FOND JEAN-PAQUES 4,, B-1348 LOUVAIN-LA-NEUVE, BELGIUM
EI 2032-5355
J9 J INNOV ECON MANAG
JI J. INNOV. ECON. MANAG.
PY 2024
IS 44
DI 10.3917/jie.044.0001
PG 23
WC Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA XD8L0
UT WOS:001259836400001
DA 2024-09-05
ER
PT J
AU Chen, HH
   Nguyen, H
   Alghamdi, A
AF Chen, Haihua
   Huyen Nguyen
   Alghamdi, Asmaa
TI Constructing a high-quality dataset for automated creation of summaries
   of fundamental contributions of research articles
SO SCIENTOMETRICS
LA English
DT Article
DE Academic literature; Research contribution; Dataset; Text
   classification; Machine learning; BERT
AB Research contributions, which indicate how a research paper contributes new knowledge or new understanding in contrast to prior research on the topic, are the most valuable type of information for researchers to understand the main content of a paper. However, there is little research using research contributions to identify and recommend valuable knowledge in academic literature for users. Instead, most existing studies mainly focus on the analysis of other elements in academic literature, such as keywords, citations, rhetorical structure, discourse, and others. This paper first introduces a fine-grained annotation scheme with six categories for research contributions in academic literature. To evaluate the reliability of our annotation scheme, we conduct annotation on 5024 sentences collected from Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL Anthology) and an academic journal Information Processing & Management (IP &M). We reach an inter-annotator agreement of Cohen's kappa = 0.91 and Fleiss' kappa = 0.91, demonstrating the high quality of the dataset. We then built two types of classifiers for automated research contribution identification based on the dataset: classic feature-based machine learning (ML) and transformer-based deep learning (DL). Our experimental results show that SCI-BERT, a pretrained language model for scientific text, achieves the best performance with an F1 score of 0.58, improving the best classic ML model (nouns + verbs + tf-idf + random forest) by 2%. This also indicates a comparable power of classic feature-based ML models to DL-based model like SCI-BERT on this dataset. The fine-grained annotation scheme can be applied for large-scale analysis for research contributions in academic literature. The automated research contribution classifiers built in this paper provide the basis for the automatic research contributions extraction and knowledge fragment recommendation. The high-quality research contribution dataset developed in this research is publicly available on Zenodo https://zenodo.org/record/6284137#.YhkZ7-iZO4Q. The code for the data analysis and experiments will be released at: https://github.com/HuyenNguyenHelen/Contribution-Sentence-Classification.
C1 [Chen, Haihua; Huyen Nguyen] Univ North Texas, Dept Informat Sci, Denton, TX 76203 USA.
   [Alghamdi, Asmaa] Univ North Texas, Dept Comp Sci & Engn, Denton, TX 76203 USA.
C3 University of North Texas System; University of North Texas Denton;
   University of North Texas System; University of North Texas Denton
RP Chen, HH (corresponding author), Univ North Texas, Dept Informat Sci, Denton, TX 76203 USA.
EM haihua.chen@unt.edu
OI Chen, Haihua/0000-0002-7088-9752
CR Allard Oelen, 2019, CEUR WORKSHOP PROC, V2526, P21, DOI DOI 10.15488/9388
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NR 40
TC 3
Z9 4
U1 6
U2 40
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD DEC
PY 2022
VL 127
IS 12
BP 7061
EP 7075
DI 10.1007/s11192-022-04380-z
EA APR 2022
PG 15
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 6U3BO
UT WOS:000788455400002
DA 2024-09-05
ER
PT J
AU Aytaç, E
AF Aytac, Ersin
TI EXPLORING ELECTROCOAGULATION THROUGH DATA ANALYSIS AND TEXT MINING
   PERSPECTIVES
SO ENVIRONMENTAL ENGINEERING AND MANAGEMENT JOURNAL
LA English
DT Article
DE bibliometric analysis; decision tree; exploratory data analysis; k-means
   clustering; sentiment analysis; t-SNE
ID ALGORITHM; CLASSIFICATION; ORANGE; CURVE; AREA
AB This study is a bibliometric analysis of electrocoagulation with data analysis and text mining aspects. Related research was conducted to take a picture of the current state of electrocoagulation in the literature to find out the less-used wastewater, electrode, and pollutants types, to discover the common words used in article titles, to understand how many pages an average article has, to figure out if electrocoagulation has passed its prime time and to provide helpful information to the researchers in developing of their research strategies. The first part of the study was the statistical analysis of the raw data. Some valuable information such as cited reference count, publication year, number of pages, and times cited - all databases have been revealed with density plots. Then a word cloud approach was used to inspect the abstracts, the titles, and the keywords. Afterward, the abstracts were classed into two, using word embedding and a k-means algorithm. Descriptive statistics, word cloud, and sentiment analysis were performed for each cluster. Finally, a classification process was conducted depending on research areas with the decision tree algorithm. The decision tree method could not classify the data set sufficiently depending on whether the abstracts of the papers were not compatible with the research area classes or because there were too many research area categories.
C1 [Aytac, Ersin] Zonguldak Bulent Ecevit Univ, Dept Environm Engn, TR-67100 Zonguldak, Turkey.
C3 Zonguldak Bulent Ecevit University
RP Aytaç, E (corresponding author), Zonguldak Bulent Ecevit Univ, Dept Environm Engn, TR-67100 Zonguldak, Turkey.
EM ersin.aytac@beun.edu.tr
RI Aytaç, Ersin/ACU-4789-2022
OI Aytac, Ersin/0000-0002-7124-4438
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NR 62
TC 7
Z9 7
U1 4
U2 15
PU GH ASACHI TECHNICAL UNIV IASI
PI IASI
PA 71 MANGERON BLVD, IASI, 700050, ROMANIA
SN 1582-9596
EI 1843-3707
J9 ENVIRON ENG MANAG J
JI Environ. Eng. Manag. J.
PD APR
PY 2022
VL 21
IS 4
BP 671
EP 685
PG 15
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology
GA 1V6UR
UT WOS:000806222800013
DA 2024-09-05
ER
PT J
AU Batista, AD
   Gouveia, FC
   Mena-Chalco, JP
AF Batista-Jr, Antonio de Abreu
   Gouveia, Fabio Castro
   Mena-Chalco, Jesus P.
TI Predicting the Q of junior researchers using data from the first years
   of publication
SO JOURNAL OF INFORMETRICS
LA English
DT Article
DE Junior researcher; Research performance; Deep learning; Linear
   regression
ID JOURNAL IMPACT FACTOR; EARLY CAREER; INFLATION
AB A researcher's Q denotes their ability in scientific research as a real number. Due to their short presence in the academic environment, junior researchers have unstable Q values. This article aims to present a model that uses data from junior researchers' first years of publication to predict their stable Q values. We tested the deep model and the linear regression model and compared their accuracies. We have obtained reliable results showing that the predicted values estimated with both models are better than the estimated Q values computed with the Q model itself when using only data from the first five years of publication. Lastly, we note that both approaches are robust approaches to deal with the inflation of citation bias.
   (c) 2021 Elsevier Ltd. All rights reserved.
C1 [Batista-Jr, Antonio de Abreu] Univ Fed Maranhao, Dept Informat, Sao Luis, Maranhao, Brazil.
   [Gouveia, Fabio Castro] Fundacao Oswaldo Cruz, Museum Life, House Oswaldo Cruz, Rio De Janeiro, RJ, Brazil.
   [Batista-Jr, Antonio de Abreu; Mena-Chalco, Jesus P.] Fed Univ ABC, Ctr Math Computat & Cognit, Santo Andre, SP, Brazil.
C3 Universidade Federal do Maranhao; Fundacao Oswaldo Cruz; Universidade
   Federal do ABC (UFABC)
RP Batista, AD (corresponding author), Univ Fed Maranhao, Dept Informat, Sao Luis, Maranhao, Brazil.; Batista, AD (corresponding author), Fed Univ ABC, Ctr Math Computat & Cognit, Santo Andre, SP, Brazil.
EM antonio.batista@ufma.br; fgouveia@gmail.com; jesus.mena@ufabc.edu.br
RI Mena-Chalco, Jesus P./C-7550-2014
OI Mena-Chalco, Jesus P./0000-0001-7509-5532
FU Maranhao Research Foundation (FAPEMA) [BD-08792/17]
FX Research partially supported by grant #BD-08792/17, Maranhao Research
   Foundation (FAPEMA).
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NR 22
TC 4
Z9 4
U1 3
U2 33
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1751-1577
EI 1875-5879
J9 J INFORMETR
JI J. Informetr.
PD MAY
PY 2021
VL 15
IS 2
AR 101130
DI 10.1016/j.joi.2021.101130
EA JAN 2021
PG 8
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA SL1FJ
UT WOS:000656661900020
DA 2024-09-05
ER
PT J
AU Gao, Y
   Wang, XC
   Liu, X
AF Gao, Yuan
   Wang, Xuechun
   Liu, Xu
TI Mapping Higher Education Internationalisation as a Research Space via
   Natural Language Processing (NLP) Techniques
SO JOURNAL OF STUDIES IN INTERNATIONAL EDUCATION
LA English
DT Article; Early Access
DE higher education internationalisation; natural language processing
   techniques; bibliometrics; spatial theory; sociology of science
ID CURRICULA
AB The productivity of a specific research field hinges on the periodic examination of both the knowledge produced and the knowledge production activities. By harnessing the strength of traditional bibliometric analyses and a variety of Natural language processing (NLP) techniques, this study portrayed a holistic landscape of higher education internationalisation (HEI) research that incorporated time and region through a spatial lens. The findings reveal the field's evolution into establishment, significant regional variations in research focus, and the expansion of networks for disseminating knowledge. These factors collectively contribute to a diverse 'lived' space of HEI research. However, the dominance of Western-centric key concepts, theories, and discourses highlights a homogenous 'conceived' space, pointing to an underlying tension between these spaces. Despite these challenges, opportunities for breakthroughs exist. Additionally, the study underscores the immense potential of NLP techniques in facilitating the exploration of how research fields evolve, further enriching our understanding of HEI.
C1 [Gao, Yuan] Victoria Univ, Ctr Int Res Educ Syst, Melbourne, Australia.
   [Gao, Yuan; Liu, Xu] Southern Univ Sci & Technol, Ctr Higher Educ Res, Shenzhen, Peoples R China.
   [Wang, Xuechun] Chinese Univ Hongkong, Fac Educ, Hong Kong, Peoples R China.
C3 Victoria University; Southern University of Science & Technology;
   Chinese University of Hong Kong
RP Gao, Y (corresponding author), Victoria Univ, Ctr Int Res Educ Syst, Melbourne, Australia.
EM catherineyuangao@gmail.com
RI Wang, Xuechun/JEO-9948-2023; Liu, May/GSE-0899-2022
OI Liu, May/0000-0002-1567-7925; Gao, Yuan/0000-0001-9290-4397
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NR 62
TC 0
Z9 0
U1 7
U2 7
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 1028-3153
EI 1552-7808
J9 J STUD INT EDUC
JI J. Stud. Int. Educ.
PD 2024 MAY 2
PY 2024
DI 10.1177/10283153241251924
EA MAY 2024
PG 24
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA PM7S5
UT WOS:001214566800001
DA 2024-09-05
ER
PT J
AU Zhuang, H
   Huang, TY
   Acuna, DE
AF Zhuang, Han
   Huang, Tzu-Yang
   Acuna, Daniel E.
TI A computational analysis of accessibility, readability, and
   explainability of figures in open access publications
SO EPJ DATA SCIENCE
LA English
DT Article
DE Accessibility; Open Access; Computer Vision
ID VISION; MODEL
AB Figures are an essential part of scientific communication. Yet little is understood about how accessible (e.g., color-blind safe), readable (e.g., good contrast), and explainable (e.g., contain captions and legends) they are. We develop computational techniques to measure these features and analyze a large sample of them from open access publications. Our method combines computer and human vision research principles, achieving high accuracy in detecting problems. In our sample, we estimated that around 20.6% of publications contain either accessibility, readability, or explainability issues (around 2% of all figures contain accessibility issues, 3% of diagnostic figures contain readability issues, and 23% of line charts contain explainability issues). We release our analysis as a dataset and methods for further examination by the scientific community.
C1 [Zhuang, Han] Syracuse Univ, Sch Informat Studies, Syracuse, NY 13244 USA.
   [Huang, Tzu-Yang] Amazon Inc, New York, NY USA.
   [Acuna, Daniel E.] Univ Colorado Boulder, Dept Comp Sci, Boulder, CO USA.
C3 Syracuse University; Amazon.com; University of Colorado System;
   University of Colorado Boulder
RP Zhuang, H (corresponding author), Syracuse Univ, Sch Informat Studies, Syracuse, NY 13244 USA.
EM hzhuang@syr.edu
RI Zhuang, Han/KPY-6284-2024; Acuna, Daniel/JJF-1452-2023
OI Huang, Tzu-Yang/0000-0002-5357-1267
FU NSF [1800956]; US Office of Research Integrity (ORI) [ORIIR180041,
   ORIIIR200052, ORIIIR190049, ORIIIR210062]; Syracuse University
   Dissertation Fellowship; 2022 Summer Fellowship of the School of
   Information Studies at Syracuse University; DHHS-ORI grants
   [ORIIR180041, ORIIIR190049]; Alfred P. Sloan Foundation grant
   [G-2020-12618]; SBE Off Of Multidisciplinary Activities; Direct For
   Social, Behav & Economic Scie [1800956] Funding Source: National Science
   Foundation
FX HZ and DEA were funded by NSF award #1800956, and the US Office of
   Research Integrity (ORI) awards ORIIR180041, ORIIIR190049, ORIIIR200052,
   and ORIIIR210062. HZ was funded by the Syracuse University Dissertation
   Fellowship and the 2022 Summer Fellowship of the School of Information
   Studies at Syracuse University. TYH was partially funded by the DHHS-ORI
   grants ORIIR180041 and ORIIIR190049. DEA was funded by the Alfred P.
   Sloan Foundation grant #G-2020-12618.
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NR 26
TC 0
Z9 0
U1 4
U2 8
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
EI 2193-1127
J9 EPJ DATA SCI
JI EPJ Data Sci.
PD MAR 2
PY 2023
VL 12
IS 1
AR 5
DI 10.1140/epjds/s13688-023-00380-y
PG 16
WC Mathematics, Interdisciplinary Applications; Social Sciences,
   Mathematical Methods
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Mathematics; Mathematical Methods In Social Sciences
GA 9N3SX
UT WOS:000942836600002
OA gold
DA 2024-09-05
ER
PT C
AU Pang, NS
   Shi, YL
   Ji, CM
AF Pang, Nansheng
   Shi, Yingling
   Ji, Changming
GP IEEE
TI Research on Comprehensive Bid Evaluation of Construction Project Based
   on the Principal Component Analysis
SO 2008 4TH INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, NETWORKING
   AND MOBILE COMPUTING, VOLS 1-31
SE International Conference on Wireless Communications, Networking and
   Mobile Computing
LA English
DT Proceedings Paper
CT 4th International Conference on Wireless Communications, Networking and
   Mobile Computing
CY OCT 12-17, 2008
CL Dalian, PEOPLES R CHINA
DE principal component analysis; contribution ratio; factor load; linear
   weighted model
ID NEURAL NETWORKS
AB The bid evaluation of construction project is a multivariate evaluation. Based on the principal component analysis which is included in multivariate statistical analysis, this paper establishes the multi-index comprehensive bid evaluation model of construction project, and proposes that how to calculate the evaluation index weight by introducing the variance contribution ratio, and that how to determine the number of principal components by making comprehensive consideration with an introduction of accumulated contribution ratio and factor load. Finally, an example is given to prove the convenience and feasibility of this bid evaluation method. This method has some characteristics: It overcomes deficiencies like large number of evaluation indexes, the overlap of the information that contained in evaluation indexes, and the determination of weight is artificial. The results of bid evaluation are objective and reasonable, which are suit for the evaluation of multi-index systems of large construction projects.
C1 [Pang, Nansheng; Shi, Yingling; Ji, Changming] NCEPU, Inst Business Management, Beijing, Peoples R China.
C3 North China Electric Power University
RP Pang, NS (corresponding author), NCEPU, Inst Business Management, Beijing, Peoples R China.
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NR 6
TC 0
Z9 0
U1 0
U2 0
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-4244-2107-7
J9 I C WIREL COMM NETW
PY 2008
BP 5412
EP 5416
PG 5
WC Telecommunications
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Telecommunications
GA BIW81
UT WOS:000263466102287
DA 2024-09-05
ER
PT C
AU Bin-Obaidellah, O
   Al-Fagih, AE
AF Bin-Obaidellah, Omar
   Al-Fagih, Ashraf E.
GP IEEE
TI Scientometric Indicators and Machine Learning-Based Models for
   Predicting Rising Stars in Academia
SO 2019 7TH INTERNATIONAL CONFERENCE ON SMART COMPUTING & COMMUNICATIONS
   (ICSCC)
LA English
DT Proceedings Paper
CT 7th International Conference on Smart Computing & Communications (ICSCC)
CY JUN 28-30, 2019
CL Miri, MALAYSIA
DE kNN; machine learning; rising stars; scientometrics; SVM
ID NETWORKS
AB Newly recruited researchers who are expected to outstandingly surpass their peers in the quality of their work, are often considered as substantial assets in universities and research & development entities. Foreseeably identifying such Rising Stars is vital for highly competitive and profitable institutes and organizations. In this paper, we propose models based on a set of Scientometric Indicators to predict rising stars in academia. In addition, we define the rising stars problem in a comprehensive and methodological manner. Machine learning techniques are applied on actual data subsets collected from the Web of Science (WoS) data source. Our experimental results show that the proposed models and indicators can be used effectively in predicting future rising stars.
C1 [Bin-Obaidellah, Omar; Al-Fagih, Ashraf E.] King Fahd Univ Petr & Minerals, Dept Informat & Comp Sci, Dhahran 31261, Saudi Arabia.
C3 King Fahd University of Petroleum & Minerals
RP Bin-Obaidellah, O (corresponding author), King Fahd Univ Petr & Minerals, Dept Informat & Comp Sci, Dhahran 31261, Saudi Arabia.
EM omar.obaidellah@kfupm.edu.sa; alfagih@kfupm.edu.sa
FU King Fahd University of Petroleum & Minerals (KFUPM)
FX The authors would like to acknowledge the support provided by the
   Deanship of Scientific Research at King Fahd University of Petroleum &
   Minerals (KFUPM) for conducting this research. We also like to
   acknowledge Dr. Moataz Ahmed from the Information & Computer Science
   dept., KFUPM and Dr. Hosam Rowaihy from the Computer Engineering dept.,
   KFUPM.
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NR 26
TC 3
Z9 3
U1 1
U2 2
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-7281-1557-3
PY 2019
BP 1
EP 7
PG 7
WC Computer Science, Theory & Methods; Telecommunications
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Telecommunications
GA BS3DU
UT WOS:000709842500001
DA 2024-09-05
ER
PT C
AU Liu, RC
   Mei, WJ
   Liu, J
AF Liu Ruochen
   Mei Wenjuan
   Liu Jun
BE Wang, X
TI Research on Predicting Students' Performance Based on Machine Learning
SO 2018 INTERNATIONAL CONFERENCE ON BIG DATA AND ARTIFICIAL INTELLIGENCE
   (ICBDAI 2018)
LA English
DT Proceedings Paper
CT International Conference on Big Data and Artificial Intelligence
   (ICBDAI)
CY DEC 21-23, 2018
CL Ningbo, PEOPLES R CHINA
DE Teaching quality; support vector regression; decision tree
AB Machine learning is one of the most core and hot technology of artificial intelligence at present. It can automatically identify patterns and discover rules based on a large amount of data, predict students' learning performance, and provide possibilities for more reasonable teaching evaluation and personalized learning. Taking the final mathematics scores of students in two Portuguese schools in the medium education as an example, this paper analyzes the characteristics of students' stage scores, personal personality, social relations and daily performance. After dimensionality reduction and other preprocessing of data sets by PCA and other methods, the final mathematics scores of students in one academic year are classified and predicted by SVM and decision tree algorithm respectively, and relevant factors affecting students' scores were analyzed. Finally, it concludes that schools can focus on students' family status, bad habit and ordinary grades to enable students to perform better.
C1 [Liu Ruochen; Mei Wenjuan; Liu Jun] Nanjing Univ Finance & Econ, Sch Management Sci & Engn, 3 Wenyuan Rd, Nanjing, Jiangsu, Peoples R China.
C3 Nanjing University of Finance & Economics
RP Liu, J (corresponding author), Nanjing Univ Finance & Econ, Sch Management Sci & Engn, 3 Wenyuan Rd, Nanjing, Jiangsu, Peoples R China.
EM 965703636@qq.com; 1563477026@qq.com; 9120031038@nufe.edu.cn
FU Jiangsu province higher education education reform research project
   [2017JSJG218]; Postgraduate Research & Practice Innovation Program of
   Jiangsu Province [KYCX18_1325]
FX This paper was supported by the Jiangsu province higher education
   education reform research project, 2017, project number: 2017JSJG218 and
   Postgraduate Research & Practice Innovation Program of Jiangsu Province,
   2018, project number: KYCX18_1325.
CR [Anonymous], CYBERNETICS INFORM T
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NR 11
TC 0
Z9 0
U1 1
U2 12
PU FRANCIS ACAD PRESS
PI LONDON
PA 35 IVOR PL, LOWER GROUND, LONDON, NW1 6EA, ENGLAND
BN 978-1-912407-14-9
PY 2019
BP 40
EP 48
DI 10.25236/icbdai.2018.007
PG 9
WC Computer Science, Artificial Intelligence
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BM9TB
UT WOS:000471631800007
DA 2024-09-05
ER
PT J
AU Meireles, MRG
   Cendon, BV
   de Almeida, PEM
AF Gouvea Meireles, Magali Rezende
   Cendon, Beatriz Valadares
   Maciel de Almeida, Paulo Eduardo
TI Bibliometric Knowledge Organization: A Domain Analytic Method Using
   Artificial Neural Networks
SO KNOWLEDGE ORGANIZATION
LA English
DT Article
DE articles; documents; categorization; references; network
ID RESEARCH QUESTIONS; CATEGORIZATION; SOM; CLASSIFICATION; SCIENCE
AB The organization of large collections of documents has become more important with the increase in the amount of digital information available. In certain constricted domains of knowledge, keywords and subject descriptors tend to be similar and therefore insufficient to differentiate documents. In this context, instead of relying only on the presence of common terms, the identification of common cited references can be useful to define semantic relationship among documents. The purpose of this work is to add another instance on the research linking information retrieval and bibliometric techniques aided by information technology. A domain analytic method was developed to generate clusters of documents, which uses self-organizing maps, in the scope of artificial neural networks, to categorize documents. The results obtained show that this approach successfully identified clusters of authors and documents through their cited references. In addition, further qualitative analysis of these clusters demonstrates the existence of semantic relationships between the documents. This study can contribute to the development of the field of knowledge organization by evaluating the use of artificial neural networks in the automatic categorization of documents in a constricted knowledge domain based on the analysis of the references cited by these documents.
C1 [Gouvea Meireles, Magali Rezende] Pontificia Univ Catolica Minas Gerais, Inst Math Sci & Informat, BR-30535901 Belo Horizonte, MG, Brazil.
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   [Maciel de Almeida, Paulo Eduardo] Fed Ctr Technol Educ Minas Gerais, Dept Comp, BR-30510000 Belo Horizonte, MG, Brazil.
C3 Universidade Federal de Minas Gerais
RP Meireles, MRG (corresponding author), Pontificia Univ Catolica Minas Gerais, Inst Math Sci & Informat, Av Dom Jose Gaspar 500, BR-30535901 Belo Horizonte, MG, Brazil.
EM magali@pucminas.br; cendon@eci.ufmg.br; pema@lsi.cefetmg.br
RI Cendon, Beatriz/G-6141-2011; Almeida, Paulo/KHY-6445-2024; Meireles,
   Magali/F-6563-2013
OI Cendon, Beatriz/0000-0002-3276-0114; Almeida, Paulo/0009-0005-7350-6875;
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NR 35
TC 9
Z9 9
U1 0
U2 31
PU NOMOS VERLAGSGESELLSCHAFT MBH & CO KG
PI BADEN-BADEN
PA WALDSEESTR 3 5, BADEN-BADEN, 76530, GERMANY
SN 0943-7444
J9 KNOWL ORGAN
JI Knowl. Organ.
PY 2014
VL 41
IS 2
BP 145
EP 159
DI 10.5771/0943-7444-2014-2-145
PG 15
WC Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science
GA AF7OJ
UT WOS:000334904100005
DA 2024-09-05
ER
PT J
AU Do Cho, S
   Hyun, BH
   Kim, JK
AF Do Cho, Sung
   Hyun, Byung Hwan
   Kim, Jae Kyeom
TI Assessment of technological level of stem cell research using principal
   component analysis
SO SPRINGERPLUS
LA English
DT Article
DE Technology level assessment; Principal component analysis; Analysis of
   scientific literatures and patents; Stem cell
AB Background: In general, technological levels have been assessed based on specialist's opinion through the methods such as Delphi. But in such cases, results could be significantly biased per study design and individual expert.
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C1 [Do Cho, Sung] Univ Sci & Technol, Sci & Technol Management Policy, Daejeon 305350, South Korea.
   [Hyun, Byung Hwan] Daejeon Univ, Grad Sch, Dept Business Consulting, Daejeon 300716, South Korea.
   [Kim, Jae Kyeom] Univ Arkansas, Sch Human Environm Sci, Fayetteville, AR 72701 USA.
C3 University of Science & Technology (UST); Daejeon University; University
   of Arkansas System; University of Arkansas Fayetteville
RP Kim, JK (corresponding author), Univ Arkansas, Sch Human Environm Sci, Fayetteville, AR 72701 USA.
EM jkk003@uark.edu
OI Kim, Jae Kyeom/0000-0002-2837-9302
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NR 23
TC 1
Z9 1
U1 2
U2 12
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 2193-1801
J9 SPRINGERPLUS
JI SpringerPlus
PD JUN 24
PY 2016
VL 5
AR 857
DI 10.1186/s40064-016-2494-9
PG 17
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA DQ0XK
UT WOS:000378924900001
PM 27386306
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU Kapusuz, M
   Ozcan, H
   Yamin, JA
AF Kapusuz, Murat
   Ozcan, Hakan
   Yamin, Jehad Ahmad
TI Research of performance on a spark ignition engine fueled by
   alcohol-gasoline blends using artificial neural networks
SO APPLIED THERMAL ENGINEERING
LA English
DT Article
DE Artificial neural network; Spark ignition engine; Modeling; Ethanol;
   Methanol
ID EXHAUST EMISSIONS; METHANOL ADDITION; DIESEL-ENGINE; ETHANOL;
   PREDICTION; COMBUSTION
AB In this paper, we investigate various alcohol unleaded gasoline mixtures that can be used with no modifications in a spark-ignition engine. The mixtures consisted of 5%, 10% and 15% ethanol, methanol together and separately. Based on the recommendations of the Jordanian Petroleum Company (JoPetrol), total alcohol content should not exceed 15-20% owing to safety and ignition hazards. Optimizations for the use of alcohol were made for the maximum torque, maximum power and minimum specific fuel consumption values. For torque 0.9906, for brake power 0.997, and for brake specific fuel consumption 0.9312 regression values for tests have been obtained from models generated by the neural network. According to the modeling and optimizations, use of fuel mixture containing 11% methanol-1% ethanol for performance, and fuel mixture containing 2% methanol for BSFC were found to have better results.
   Moreover, the paper demonstrates that ANN (Artificial Neural Network) can be used successfully as an alternative type of modeling technique for internal combustion engines. (C) 2015 Elsevier Ltd. All rights reserved.
C1 [Kapusuz, Murat; Ozcan, Hakan] Ondokuz Mayis Univ, Dept Mech Engn, TR-55139 Samsun, Turkey.
   [Yamin, Jehad Ahmad] Univ Jordan, Dept Mech Engn, Amman 11942, Jordan.
C3 Ondokuz Mayis University; University of Jordan
RP Ozcan, H (corresponding author), Ondokuz Mayis Univ, Dept Mech Engn, TR-55139 Samsun, Turkey.
EM ozcanh@omu.edu.tr
RI Kapusuz, Murat/AAP-2014-2020; ÖZCAN, Hakan/AAG-6973-2019; Yamin,
   Jehad/D-9235-2016
OI Kapusuz, Murat/0000-0002-2243-8551; ÖZCAN, Hakan/0000-0002-7848-3650;
   Yamin, Jehad/0000-0002-7874-358X
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NR 29
TC 65
Z9 65
U1 0
U2 23
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 1359-4311
J9 APPL THERM ENG
JI Appl. Therm. Eng.
PD DEC 5
PY 2015
VL 91
BP 525
EP 534
DI 10.1016/j.applthermaleng.2015.08.058
PG 10
WC Thermodynamics; Energy & Fuels; Engineering, Mechanical; Mechanics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Thermodynamics; Energy & Fuels; Engineering; Mechanics
GA CW5RB
UT WOS:000365053200053
DA 2024-09-05
ER
PT J
AU Wang, RY
   Huang, S
   Wang, P
   Shi, XM
   Li, SQ
   Ye, YS
   Zhang, W
   Shi, L
   Zhou, X
   Tang, XW
AF Wang, Ruiyu
   Huang, Shu
   Wang, Ping
   Shi, Xiaomin
   Li, Shiqi
   Ye, Yusong
   Zhang, Wei
   Shi, Lei
   Zhou, Xian
   Tang, Xiaowei
TI Bibliometric analysis of the application of deep learning in cancer from
   2015 to 2023
SO CANCER IMAGING
LA English
DT Article
DE Deep learning; Cancer; Imaging; Bibliometric analysis; VOSviewer;
   CiteSpace
ID ARTIFICIAL-INTELLIGENCE; MITOSIS DETECTION; LUNG-CANCER; SKIN-CANCER;
   CLASSIFICATION
AB BackgroundRecently, the application of deep learning (DL) has made great progress in various fields, especially in cancer research. However, to date, the bibliometric analysis of the application of DL in cancer is scarce. Therefore, this study aimed to explore the research status and hotspots of the application of DL in cancer.MethodsWe retrieved all articles on the application of DL in cancer from the Web of Science database Core Collection database. Biblioshiny, VOSviewer and CiteSpace were used to perform the bibliometric analysis through analyzing the numbers, citations, countries, institutions, authors, journals, references, and keywords.ResultsWe found 6,016 original articles on the application of DL in cancer. The number of annual publications and total citations were uptrend in general. China published the greatest number of articles, USA had the highest total citations, and Saudi Arabia had the highest centrality. Chinese Academy of Sciences was the most productive institution. Tian, Jie published the greatest number of articles, while He Kaiming was the most co-cited author. IEEE Access was the most popular journal. The analysis of references and keywords showed that DL was mainly used for the prediction, detection, classification and diagnosis of breast cancer, lung cancer, and skin cancer.ConclusionsOverall, the number of articles on the application of DL in cancer is gradually increasing. In the future, further expanding and improving the application scope and accuracy of DL applications, and integrating DL with protein prediction, genomics and cancer research may be the research trends.
C1 [Wang, Ruiyu; Wang, Ping; Shi, Xiaomin; Li, Shiqi; Ye, Yusong; Zhang, Wei; Shi, Lei; Zhou, Xian; Tang, Xiaowei] Southwest Med Univ, Affiliated Hosp, Dept Gastroenterol, St Taiping 25, Luzhou 646099, Sichuan, Peoples R China.
   [Wang, Ruiyu; Wang, Ping; Shi, Xiaomin; Li, Shiqi; Ye, Yusong; Zhang, Wei; Shi, Lei; Zhou, Xian; Tang, Xiaowei] Nucl Med & Mol Imaging Key Lab Sichuan Prov, Luzhou, Peoples R China.
   [Huang, Shu] Lianshui Cty People Hosp, Dept Gastroenterol, Huaian, Peoples R China.
   [Huang, Shu] Nanjing Med Univ, Lianshui People Hosp, Kangda Coll, Dept Gastroenterol, Huaian, Peoples R China.
C3 Southwest Medical University; Nanjing Medical University
RP Zhou, X; Tang, XW (corresponding author), Southwest Med Univ, Affiliated Hosp, Dept Gastroenterol, St Taiping 25, Luzhou 646099, Sichuan, Peoples R China.; Zhou, X; Tang, XW (corresponding author), Nucl Med & Mol Imaging Key Lab Sichuan Prov, Luzhou, Peoples R China.
EM 853023378@qq.com; solitude5834@hotmail.com
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   Zou J, 2019, NAT GENET, V51, P12, DOI 10.1038/s41588-018-0295-5
NR 59
TC 0
Z9 0
U1 1
U2 1
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 1740-5025
EI 1470-7330
J9 CANCER IMAGING
JI Cancer Imaging
PD JUL 4
PY 2024
VL 24
IS 1
AR 85
DI 10.1186/s40644-024-00737-0
PG 17
WC Oncology; Radiology, Nuclear Medicine & Medical Imaging
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Oncology; Radiology, Nuclear Medicine & Medical Imaging
GA XQ4B7
UT WOS:001263122000001
PM 38965599
OA gold, Green Published
DA 2024-09-05
ER
PT C
AU Hou, XH
   Huang, L
   Li, XF
AF Hou, Xiaohui
   Huang, Lei
   Li, Xuefei
BE Zhang, R
   Zhang, Z
   Liu, K
   Zhang, J
TI Evaluation Method of Scientific Research Projects Based on the Neural
   Networks
SO LISS 2013
LA English
DT Proceedings Paper
CT 3rd International Conference on Logistics, Informatics and Service
   Science (LISS)
CY AUG 21-24, 2013
CL Beijing Jiaotong Univ, Sch Econ & Management, Reading, ENGLAND
HO Beijing Jiaotong Univ, Sch Econ & Management
DE Back propagation neural network; Linear neural network; Scientific
   research projects; Evaluation; Index system
AB The scientific research projects are evaluated by using the neural networks in this paper. The evaluation index system of scientific research projects is set up, and based on it, BP neural network model and linear neural network model are established to evaluate the scientific research projects. The Matlab software is used to set the parameters, to solve the two models which are built and by calculating an example, we get the objective evaluation results and then compare the results of these two different neural networks. The result shows that the two neural networks models which we set up have high accuracy and will promote the objective and efficient development of evaluation research of scientific research projects in China.
C1 [Hou, Xiaohui] Beijing Jiaotong Univ, Div Sci & Technol, Beijing 100044, Peoples R China.
   [Huang, Lei] Beijing Jiaotong Univ, Sch Econ & Management, Beijing 100044, Peoples R China.
   [Li, Xuefei] Beijing Jiaotong Univ, Sch Traff & Transportat, Beijing 100044, Peoples R China.
C3 Beijing Jiaotong University; Beijing Jiaotong University; Beijing
   Jiaotong University
RP Hou, XH (corresponding author), Beijing Jiaotong Univ, Div Sci & Technol, Beijing 100044, Peoples R China.
EM xhhou@bjtu.edu.cn; lhuang@bjtu.edu.cn; hopeandfuture2010@gmail.com
RI huang, lei/GQP-8739-2022; HUANG, LING/HTR-1819-2023; Huang,
   Li/IUQ-0909-2023
CR CAO ZG, 2007, AERONAUTICAL COMPUTI, V37, P51
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NR 5
TC 0
Z9 0
U1 0
U2 7
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
BN 978-3-642-40660-7; 978-3-642-40659-1
PY 2015
BP 489
EP 496
DI 10.1007/978-3-642-40660-7_72
PG 8
WC Engineering, Industrial; Operations Research & Management Science
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Engineering; Operations Research & Management Science
GA BD1WS
UT WOS:000358441900072
DA 2024-09-05
ER
PT J
AU Chen, K
   Zhai, X
   Wang, S
   Li, XY
   Lu, ZK
   Xia, DM
   Li, M
AF Chen, Kai
   Zhai, Xiao
   Wang, Sheng
   Li, Xiaoyu
   Lu, Zhikai
   Xia, Demeng
   Li, Ming
TI Emerging trends and research foci of deep learning in spine:
   bibliometric and visualization study
SO NEUROSURGICAL REVIEW
LA English
DT Article
DE Deep learning; Spine; Bibiometrics; Neural network; Image
ID CONVOLUTIONAL NEURAL-NETWORKS; SEGMENTATION; DISORDERS; ALGORITHM
AB As the cognition of spine develops, deep learning (DL) emerges as a powerful tool with tremendous potential for advancing research in this field. To provide a comprehensive overview of DL-spine research, our study utilized bibliometric and visual methods to retrieve relevant articles from the Web of Science database. VOSviewer and CiteSpace were primarily used for literature measurement and knowledge graph analysis. A total of 273 studies focusing on deep learning in the spine, with a combined total of 2302 citations, were retrieved. Additionally, the overall number of articles published on this topic demonstrated a continuous upward trend. China was the country with the highest number of publications, whereas the USA had the most citations. The two most prominent journals were "European Spine Journal" and "Medical Image Analysis," and the most involved research area was Radiology Nuclear Medicine Medical Imaging. VOSviewer identified three visually distinct clusters: "segmentation," "area," and "neural network." Meanwhile, CiteSpace highlighted "magnetic resonance image" and "lumbar" as the keywords with the longest usage, and "agreement" and "automated detection" as the most commonly used keywords. Although the application of DL in spine is still in its infancy, its future is promising. Intercontinental cooperation, extensive application, and more interpretable algorithms will invigorate DL in the field of spine.
C1 [Chen, Kai; Zhai, Xiao; Li, Xiaoyu; Li, Ming] Shanghai Changhai Hosp, Dept Orthoped, Shanghai 200433, Peoples R China.
   [Wang, Sheng] Shanghai Changhai Hosp, Dept Emergency, Shanghai, Peoples R China.
   [Lu, Zhikai] No 906 Hosp Joint Logist Support Force PLA, Dept Orthoped, Ningbo, Zhejiang, Peoples R China.
   [Xia, Demeng] Shanghai Univ, Shanghai Baoshan Luodian Hosp, Luodian Clin Drug Res Ctr, Shanghai, Peoples R China.
   [Xia, Demeng] Naval Hosp Eastern Theater, Emergency Dept, Zhoushan, Zhejiang, Peoples R China.
C3 Naval Medical University; Naval Medical University; Shanghai University
RP Li, M (corresponding author), Shanghai Changhai Hosp, Dept Orthoped, Shanghai 200433, Peoples R China.; Lu, ZK (corresponding author), No 906 Hosp Joint Logist Support Force PLA, Dept Orthoped, Ningbo, Zhejiang, Peoples R China.; Xia, DM (corresponding author), Shanghai Univ, Shanghai Baoshan Luodian Hosp, Luodian Clin Drug Res Ctr, Shanghai, Peoples R China.; Xia, DM (corresponding author), Naval Hosp Eastern Theater, Emergency Dept, Zhoushan, Zhejiang, Peoples R China.
EM spine_kai@smmu.edu.cn; zhaixiao@smmu.edu.cn; wschyy421304714@163.com;
   y15721434081@163.com; Lzkjfj113@163.com; demengxia@163.com;
   limingspine0103@126.com
RI Xia, Demeng/ADR-6215-2022
OI ZHAI, XIAO/0000-0003-4236-2264
FU National Natural Science Foundation of China [81701199]
FX This research was funded by the National Natural Science Foundation of
   China (81701199) of Ming Li
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PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 0344-5607
EI 1437-2320
J9 NEUROSURG REV
JI Neurosurg. Rev.
PD MAR 31
PY 2023
VL 46
IS 1
AR 81
DI 10.1007/s10143-023-01987-5
PG 14
WC Clinical Neurology; Surgery
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Neurosciences & Neurology; Surgery
GA C3WI9
UT WOS:000961253900001
PM 37000304
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Chaki, J
   Ghosh, D
AF Chaki, Jyotismita
   Ghosh, Dibyajyoti
TI Deep Learning in Leaf Disease Detection (2014-2024): A
   Visualization-Based Bibliometric Analysis
SO IEEE ACCESS
LA English
DT Article
DE Bibliometric study; biblioshiny; deep learning; leaf disease detection;
   VOSViewer
AB The agriculture industry is critical to delivering high-quality food and contributes significantly to the growth of economies and people which can be affected by the plant disease. This article demonstrates a visualization based bibliometric analysis to depict research trends in deep learning-based leaf disease detection from 2014 to January 2024. The publications used in this study are collected from the Scopus database. The research distributions with respect to sources and country, research trends, and research limits for deep learning in leaf disease detection studies are presented using Biblioshiny and VOSViewer software and visualization technologies. From 2014 to January 2024, the literature on this field has grown at an average rate of 53.41%. 1307 peer-reviewed publications from 54 countries are identified that are published in 594 distinct sources. India is the most productive country, accounting for 36.6% of total publications and 23% of total citations. Chitkara University Institute of Engineering and Technology was the most productive research institute, with 66 publications and 291 citations, while Computers and Electronics in Agriculture journal has the most citations in deep learning-based leaf disease detection research. The findings, in particular, show that "Convolution Neural Network", "Transfer Learning", "Ensemble Learning", etc., are the most widely used research topics in this field from 2014 to January 2024, and the research interest engrossed on applications of deep learning standard architectures. This study gives an insight into deep learning in leaf disease detection's general research patterns, which may assist researchers better understand and forecast the field's dynamic paths.
C1 [Chaki, Jyotismita] Vellore Inst Technol, Sch Comp Sci & Engn, Vellore 632014, India.
   [Ghosh, Dibyajyoti] Vellore Inst Technol, VIT Business Sch, Vellore 632014, India.
C3 Vellore Institute of Technology (VIT); VIT Vellore; Vellore Institute of
   Technology (VIT); VIT Vellore
RP Ghosh, D (corresponding author), Vellore Inst Technol, VIT Business Sch, Vellore 632014, India.
EM dibyajyoti.ghosh@vit.ac.in
RI Chaki, Jyotismita/T-4882-2019
OI Chaki, Jyotismita/0000-0003-1804-8590
FU Vellore Institute of Technology (VIT), Vellore
FX The authors are thankful to the Vellore Institute of Technology (VIT),
   Vellore for providing all the facilities and support.
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NR 57
TC 0
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U2 4
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2024
VL 12
BP 95291
EP 95308
DI 10.1109/ACCESS.2024.3425897
PG 18
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA YY9Q4
UT WOS:001272169500001
OA gold
DA 2024-09-05
ER
PT J
AU Sarin, G
   Kumar, P
   Mukund, M
AF Sarin, Gaurav
   Kumar, Pradeep
   Mukund, M.
TI Text classification using deep learning techniques: a bibliometric
   analysis and future research directions
SO BENCHMARKING-AN INTERNATIONAL JOURNAL
LA English
DT Article
DE Data mining; Text analytics; Classification; Deep learning; Bibliometric
   analysis
ID CONVOLUTIONAL NEURAL-NETWORK; ATTENTION; MODEL; SEMANTICS; ENSEMBLE;
   SYSTEMS; SUPPORT; LSTM; WEB
AB PurposeText classification is a widely accepted and adopted technique in organizations to mine and analyze unstructured and semi-structured data. With advancement of technological computing, deep learning has become more popular among academicians and professionals to perform mining and analytical operations. In this work, the authors study the research carried out in field of text classification using deep learning techniques to identify gaps and opportunities for doing research.Design/methodology/approachThe authors adopted bibliometric-based approach in conjunction with visualization techniques to uncover new insights and findings. The authors collected data of two decades from Scopus global database to perform this study. The authors discuss business applications of deep learning techniques for text classification.FindingsThe study provides overview of various publication sources in field of text classification and deep learning together. The study also presents list of prominent authors and their countries working in this field. The authors also presented list of most cited articles based on citations and country of research. Various visualization techniques such as word cloud, network diagram and thematic map were used to identify collaboration network.Originality/valueThe study performed in this paper helped to understand research gaps that is original contribution to body of literature. To best of the authors' knowledge, in-depth study in the field of text classification and deep learning has not been performed in detail. The study provides high value to scholars and professionals by providing them opportunities of research in this area.
C1 [Sarin, Gaurav] Delhi Sch Business, New Delhi, Delhi, India.
   [Kumar, Pradeep] Indian Inst Management Lucknow, Lucknow, India.
   [Mukund, M.] VDXtv, New Delhi, India.
C3 Indian Institute of Management (IIM System); Indian Institute of
   Management Lucknow
RP Sarin, G (corresponding author), Delhi Sch Business, New Delhi, Delhi, India.
EM gaurav.sarin@iiml.org; pradeep.kumar@iiml.ac.in;
   reachoutmukund@gmail.com
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NR 126
TC 0
Z9 0
U1 1
U2 10
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 1463-5771
EI 1758-4094
J9 BENCHMARKING
JI Benchmarking
PD AUG 30
PY 2024
VL 31
IS 8
BP 2743
EP 2766
DI 10.1108/BIJ-07-2022-0454
EA AUG 2023
PG 24
WC Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA E0R0X
UT WOS:001049091700001
DA 2024-09-05
ER
PT J
AU Polyakov, M
   Gibson, FL
   Pannell, DJ
AF Polyakov, Maksym
   Gibson, Fiona L.
   Pannell, David J.
TI Antipodean agricultural and resource economics at 60: Trends in topics,
   authorship and collaboration
SO AUSTRALIAN JOURNAL OF AGRICULTURAL AND RESOURCE ECONOMICS
LA English
DT Article
DE citation analysis; co-authorship; collaboration; Latent Dirichlet
   allocation
AB This study presents results of an analysis of 1060 academic articles published in the Australian Journal of Agricultural Economics and the Australian Journal of Agricultural and Resource Economics from 1957 to 2015. Trends in research topics over time identified by the study include a decline in research on agricultural topics offset by growth in publications related to natural resources, the environment, trade, food and international development. Other trends include an increase in the average number of co-authors on each paper, a gradual increase in authorship by females, changes in the shares of top contributing institutions, increases in collaboration between institutions and a steady increase in the number of authors from outside Australia or New Zealand.
C1 [Polyakov, Maksym; Gibson, Fiona L.; Pannell, David J.] Univ Western Australia, Ctr Environm Econ & Policy, Sch Agr & Resource Econ, Nedlands, WA, Australia.
C3 University of Western Australia
RP Polyakov, M (corresponding author), Univ Western Australia, Ctr Environm Econ & Policy, Sch Agr & Resource Econ, Nedlands, WA, Australia.
EM maksym.polyakov@uwa.edu.au
RI Pannell, David/B-4476-2008; Polyakov, Maksym/G-1523-2010
OI Pannell, David/0000-0001-5420-9908; Polyakov,
   Maksym/0000-0002-0193-6658; Dempster, Fiona/0000-0002-7989-3483
FU ARC Centre of Excellence for Environmental Decisions
FX Funding support from the ARC Centre of Excellence for Environmental
   Decisions is gratefully acknowledged.
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NR 19
TC 4
Z9 4
U1 3
U2 16
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1364-985X
EI 1467-8489
J9 AUST J AGR RESOUR EC
JI Aust. J. Agr. Resour. Econ.
PD OCT
PY 2016
VL 60
IS 4
BP 506
EP 515
DI 10.1111/1467-8489.12152
PG 10
WC Agricultural Economics & Policy; Economics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Agriculture; Business & Economics
GA EC7IV
UT WOS:000388311500004
OA Green Submitted, Green Published
DA 2024-09-05
ER
PT J
AU Liu, YM
   Chen, M
AF Liu, Yunmei
   Chen, Min
TI The Knowledge Structure and Development Trend in Artificial Intelligence
   Based on Latent Feature Topic Model
SO IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
LA English
DT Article
DE Artificial intelligence; Market research; Analytical models; Patents;
   Semantics; Collaboration; Computational modeling; development trend;
   knowl-edge structures; LDA models
AB Currently, with the rapid development of science and technology, the field of artificial intelligence presents characteristics such as a wide crossover of disciplines and fast update, and the field of artificial intelligence has become a new focus of international competition. As an interdisciplinary field, the field of artificial intelligence has rich knowledge and strategic management significance. This article conducts an in-depth study on the knowledge structure and evolution trends in the field of AI, and the main work is as follows. First, a new potential feature topic model New-LDA is proposed for the study of topic recognition, which enhances the feature learning ability of the traditional LDA model, and makes up for the deficiency of the traditional LDA model in the ability of recognizing topics in complex environments. Second, the knowledge structure in the field of AI is analyzed from two aspects: topic recognition and coword analysis. The time series model is introduced to establish the topic evolution network, and the high-frequency words in three periods are compared and analyzed to find the evolution regular of knowledge structure in the AI domain. Finally, taking the cross-discipline of AI as an example, the thematic evolution of the field and its cross-discipline is analyzed to determine the future development direction and evolutionary trend of the field of AI.
C1 [Liu, Yunmei] Shanghai Univ, Sch Cultural Heritage & Informat Management, Shanghai 200444, Peoples R China.
   [Chen, Min] Wenzhou Univ, Sch Business, Wenzhou 325035, Peoples R China.
C3 Shanghai University; Wenzhou University
RP Chen, M (corresponding author), Wenzhou Univ, Sch Business, Wenzhou 325035, Peoples R China.
EM emily0904@shu.edu.cn; minchen@wzu.edu.cn
RI Chen, Min/AAD-4064-2019
FU "Research on Nonstandard Citation Behavior of Scientific Data for
   Full-Text Citation Content" (Joint Laboratory Project Between Institute
   of Scientific and Technical Information of China and Elsevier in 2022);
   MultidimensionalEvaluation Paradigm of ScientificLiterature and
   Evaluation System of Scientific and Technological Talents under the
   Background of `Breaking Four Principles and Establishing New Standards'
   [21wsk169]
FX This work was supported in part by the Project of "Research on
   Nonstandard Citation Behavior of Scientific Data for Full-Text Citation
   Content" (Joint Laboratory Project Between Institute of Scientific and
   Technical Information of China and Elsevier in 2022) and in part by the
   Project of "MultidimensionalEvaluation Paradigm of ScientificLiterature
   and Evaluation System of Scientific and Technological Talents under the
   Background of `Breaking Four Principles and Establishing New Standards"'
   under Grant 21wsk169 (Zhejiang Soft Science Research Plan in 2022).
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PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0018-9391
EI 1558-0040
J9 IEEE T ENG MANAGE
JI IEEE Trans. Eng. Manage.
PY 2024
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BP 12593
EP 12604
DI 10.1109/TEM.2022.3232178
EA JAN 2023
PG 12
WC Business; Engineering, Industrial; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Engineering
GA C6R9A
UT WOS:000920705300001
HC Y
HP N
DA 2024-09-05
ER
PT C
AU Davidescu, AA
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   Strat, VA
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AF Davidescu, Adriana AnaMaria
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TI Mapping the Landscape: A Bibliometric Analysis of Rating Agencies in the
   Era of Artificial Intelligence and Machine Learning
SO PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON BUSINESS EXCELLENCE
LA English
DT Proceedings Paper
CT 18th International Conference on Business Excellence (ICBE) - Smart
   Solutions for a Sustainable Future
CY MAR 21-23, 2024
CL Bucharest, ROMANIA
DE Rating agencies; Artificial intelligence; Machine learning; Risk
   assessment; Accountable governance
ID BOND RATINGS; CREDIT; CLASSIFICATION; MODELS; INDEX; RISK
AB In the ever-evolving financial landscape, the integration of artificial intelligence (AI) and machine learning (ML) is revolutionising creditworthiness assessment. The vast body of literature on credit rating indicates a growing prevalence of these techniques in the rating processes. Although these methods boast high predictive accuracy, concerns about their robustness, equity, and explainability affect the confidence of various parties in rating agencies. This comprehensive study explores the dynamic intersection of these cutting-edge technologies with rating agencies, presenting an in-depth literature review employing a bibliometric analysis that uses the Bibliometrix and Biblioshiny packages from R. The paper makes a significant contribution by analysing the literature across three prominent databases: Web of Science, Scopus, and arXiv. The empirical findings indicate that despite a recent growing interest, the relatively limited number of documents implies that, while there is a wide literature about credit rating in general, when it comes to rating agencies, the literature is much more limited. This limitation may stem from a certain lack of transparency in the methods and processes used by rating agencies and the complex nature of these entities. The literature witnessed growth after the 2008 global financial crisis, where rating agencies faced significant criticism, and post-pandemic, indicating a need for more adaptable and precise ratings. The examination of the topic reveals a recent shift in focus within AI-driven rating agencies towards accountable governance. While traditional attention persists on artificial intelligence techniques and finance, the emerging emphasis on ethical considerations, societal impacts, and performance evaluation underscores a changing landscape. This transition underscores the growing importance of integrating ethical considerations and societal impacts into the operational frameworks of AI-powered rating agencies, emphasising the necessity for responsible and transparent decision-making practices.
C1 [Davidescu, Adriana AnaMaria; Agafitei, Marina-Diana; Strat, Vasile Alecsandru; Dima, Alina Mihaela] Bucharest Univ Econ Studies, Bucharest, Romania.
C3 Bucharest University of Economic Studies
RP Davidescu, AA (corresponding author), Bucharest Univ Econ Studies, Bucharest, Romania.
EM adriana.alexandru@csie.ase.ro; diana.agafitei@csie.ase.ro;
   vasile.strat@csie.ase.ro; alina.dima@ase.ro
FU EU's NextGenerationEU instrument through the National Recovery and
   Resilience Plan of Romania [Pillar III-C9-I8, 760049/23.05.2023,
   760047/23.05.2023]
FX This work was funded by the EU's NextGenerationEU instrument through the
   National Recovery and Resilience Plan of Romania - Pillar III-C9-I8,
   managed by the Ministry of Research, Innovation and Digitalization,
   within the project entitled "CauseFinder: Causality in the Era of Big
   Data and AI and its applications to innovation management", contract no.
   760049/23.05.2023, code CF 268/29.11.2023, and within the project
   entitled "Accountable Governance and Responsible Innovation in
   Artificial Intelligence", contract no. 760047/23.05.2023, code CF
   158/15.11.2022.
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NR 41
TC 0
Z9 0
U1 1
U2 1
PU SCIENDO
PI WARSAW
PA BOGUMILA ZUGA 32A, WARSAW, MAZOVIA, POLAND
SN 2502-0226
EI 2558-9652
J9 P INT CONF BUS EXCEL
JI Proc. Int. Conf. Bus. Excell.
PD JUN 1
PY 2024
VL 18
IS 1
BP 67
EP 85
DI 10.2478/picbe-2024-0007
PG 19
WC Business
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Business & Economics
GA XM4K5
UT WOS:001262084900007
DA 2024-09-05
ER
PT J
AU Lathabai, HH
   Nandy, A
   Singh, VK
AF Lathabai, Hiran H.
   Nandy, Abhirup
   Singh, Vivek Kumar
TI Institutional collaboration recommendation: An expertise-based framework
   using NLP and network analysis
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Institutional collaboration; Recommendation system; NLP; Network
   analysis; Research expertise; Expertise indices
ID PRODUCTIVITY
AB The shift from 'trust-based funding' to 'performance-based funding' is one of the factors that has forced institutions to strive for continuous improvement of performance. Several studies have established the importance of collaboration in enhancing the performance of paired institutions. However, identification of suitable institutions for collaboration is sometimes difficult and therefore institutional collaboration recommendation systems can be vital. Currently, there are no well-developed institutional collaboration recommendation systems. In order to bridge this gap, we design a framework that recognizes the thematic strengths and core competencies of institutions, which can in turn be used for collaboration recommendations. The framework, based on NLP and network analysis techniques, is capable of determining the strengths of an institution in different thematic areas within a field and thereby determining the core competency and potential core competency areas of that institution. It makes use of recently proposed expertise indices such as x and x(g) indices for determination of core and potential core competency areas and can toss two kinds of recommendations: (i) for enhancement of strength of strong areas or core competency areas of an institution and (ii) for complementing the potentially strong areas or potential core competency areas of an institution. A major advantage of the system is that it can help to determine and improve the research portfolio of an institution within a field through suitable collaboration, which may lead to the overall improvement of the performance of the institution in that field. The framework is demonstrated by analyzing the performance of 195 Indian institutions in the field of 'Computer Science'. Upon validation using standard metrics for novelty, coverage and diversity of recommendation systems, the framework is found to be of sufficient coverage and capable of tossing novel and diverse recommendations. The article thus presents an institutional collaboration recommendation system which can be used by institutions to identify potential collaborators.
C1 [Lathabai, Hiran H.] Indian Inst Sci, DST Ctr Policy Res, Bengaluru 560012, India.
   [Nandy, Abhirup; Singh, Vivek Kumar] Banaras Hindu Univ, Dept Comp Sci, Varanasi 221005, Uttar Pradesh, India.
C3 Indian Institute of Science (IISC) - Bangalore; Banaras Hindu University
   (BHU)
RP Singh, VK (corresponding author), Banaras Hindu Univ, Dept Comp Sci, Varanasi 221005, Uttar Pradesh, India.
EM hiranh@iisc.ac.in; vivek@bhu.ac.in
RI Nandy, Abhirup/AFR-0690-2022; Singh, Vivek Kumar/O-5699-2019
OI Nandy, Abhirup/0000-0001-8618-0847; Singh, Vivek
   Kumar/0000-0002-7348-6545; lathabai, hiran/0000-0002-5633-9842
FU DST-NSTMIS [DST/NSTMIS/05/04/2019-20]
FX The authors would like to acknowledge the support provided by the
   DST-NSTMIS funded project-'Design of a Computational Framework for
   Discipline-wise and Thematic Mapping of Research Performance of Indian
   Higher Education Institutions (HEIs)', bearing Grant No.
   DST/NSTMIS/05/04/2019-20, for this work.
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NR 73
TC 5
Z9 5
U1 5
U2 20
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0957-4174
EI 1873-6793
J9 EXPERT SYST APPL
JI Expert Syst. Appl.
PD DEC 15
PY 2022
VL 209
AR 118317
DI 10.1016/j.eswa.2022.118317
EA AUG 2022
PG 17
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Operations Research & Management Science
GA 4R4YQ
UT WOS:000856772000012
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Xu, S
   Hao, LY
   An, X
   Yang, GC
   Wang, FF
AF Xu, Shuo
   Hao, Liyuan
   An, Xin
   Yang, Guancan
   Wang, Feifei
TI Emerging research topics detection with multiple machine learning models
SO JOURNAL OF INFORMETRICS
LA English
DT Article
DE Emerging research topics; Topic modeling; Dynamic Influence Model;
   Citation Influence Model; Machine learning
ID CO-WORD ANALYSIS; INFORMATION-SCIENCE; RESEARCH FIELDS; RESEARCH FRONTS;
   IDENTIFY; EVOLUTION; TRENDS; PUBLICATIONS; TECHNOLOGIES; REFERENCES
AB Emerging research topic detection can benefit the research foundations and policy-makers. With the long-term and recent interest in detecting emerging research topics, various approaches are proposed in the literature. Though, there is still a lack of well-established linkages between the clear conceptual definition of emerging research topics and the proposed indicators for operationalization. This work follows the definition by Wang (2018), and several machine learning models are together used to detect and foresight the emerging research topics. Finally, experimental results on gene editing dataset discover three emerging research topics, which make clear that it is feasible to identify emerging research topics with our framework. (C) 2019 Elsevier Ltd. All rights reserved.
C1 [Xu, Shuo; Hao, Liyuan; Wang, Feifei] Beijing Univ Technol, Coll Econ & Management, Res Base Beijing Modern Mfg Dev, 100 PingLeYuan, Beijing 100124, Peoples R China.
   [An, Xin] Beijing Forestry Univ, Sch Econ & Management, 35 Qinghua East Rd, Beijing 100083, Peoples R China.
   [Yang, Guancan] Renmin Univ China, Sch Informat Resource Management, 59 Zhongguancun St, Beijing 100872, Peoples R China.
C3 Beijing University of Technology; Beijing Forestry University; Renmin
   University of China
RP An, X (corresponding author), Beijing Forestry Univ, Sch Econ & Management, 35 Qinghua East Rd, Beijing 100083, Peoples R China.
EM xushuo@bjut.edu.cn; Leanne.H@qq.com; anxin@bjfu.edu.cn;
   yanggc@ruc.edu.cn; feifeiwang@bjut.edu.cn
RI yang, guancan/HLP-8842-2023; Xu, Shuo/KVY-0402-2024; 王, 菲菲/R-1936-2019
OI yang, guancan/0000-0002-1706-1884; Xu, Shuo/0000-0002-8602-1819; 
FU Social Science Foundation of Beijing Municipality [17GLB074]; Natural
   Science Foundation of Guangdong Province [2018A030313695]
FX This research received the financial support from Social Science
   Foundation of Beijing Municipality under grant number 17GLB074, and
   Natural Science Foundation of Guangdong Province under grant number
   2018A030313695. Our gratitude also goes to the anonymous reviewers for
   their valuable comments.
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NR 75
TC 39
Z9 46
U1 17
U2 222
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1751-1577
EI 1875-5879
J9 J INFORMETR
JI J. Informetr.
PD NOV
PY 2019
VL 13
IS 4
AR 100983
DI 10.1016/j.joi.2019.100983
PG 19
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA JU7SB
UT WOS:000501871000011
DA 2024-09-05
ER
PT J
AU Zhang, Q
   Mao, R
   Li, R
AF Zhang, Qi
   Mao, Rui
   Li, Rui
TI Spatial-temporal restricted supervised learning for collaboration
   recommendation
SO SCIENTOMETRICS
LA English
DT Article
DE Spatial-temporal description; Academic influence; Supervised learning;
   Collaboration recommendation
ID DISCRIMINANT-ANALYSIS; RECOGNITION
AB Collaboration recommendation from scholarly big data is an important but challenging problem as it might suffer the difficulty of accurate recommendation from three aspects: how to efficiently integrate the available author-related information, how to precisely describe the characteristics of the scholarly data samples, and how to extract the intrinsic features that are more suitable for collaboration recommendation. Facing these challenges, we incorporate the temporal and academic-influence information of the publications with the spatial information of the researchers to present a spatial-temporal restricted supervised learning (STSL) model for collaboration recommendation. We first present a topic clustering model to determine the topic distribution vector of each researcher, where a temporal parameter is introduced to exponentially weight each topic distribution vector and an academic-influence parameter is further introduced to linearly combine all the topic distribution vectors of the publications. Then, inspired by the geographical-advantage phenomena in collaboration, spatial labels are generated by using the personal information of the researchers. Furthermore, considering that the publication data enhanced by spatial-temporal and academic-influence descriptions usually exhibit multimodal or mixmodal properties, we propose a data-driven supervised learning model to extract the intrinsic features inhered in data, which determines a low-dimensional recommendation subspace. A number of experiments are conducted to test the impact of the topic-clustering number, the temporal parameter, the academic-influence parameter, and the number of extracted features. Besides, several widely-used models are adopted to compare with the proposed STSL model for collaboration recommendation, with results verifying its feasibility and effectiveness.
C1 [Zhang, Qi] Univ Int Business & Econ, Sch Informat Technol & Management, Beijing 100029, Peoples R China.
   [Mao, Rui] PLA, Troop 93617, Beijing 101407, Peoples R China.
   [Li, Rui] Dalian Univ Technol, Sch Math Sci, Dalian 116024, Peoples R China.
C3 University of International Business & Economics; Dalian University of
   Technology
RP Zhang, Q (corresponding author), Univ Int Business & Econ, Sch Informat Technol & Management, Beijing 100029, Peoples R China.
EM zhangqi@uibe.edu.cn; mr_32@pku.edu.cn; rui_li@dlut.edu.cn
RI ARSLAN, Okan/AAA-3232-2020; Li, Rui/C-2155-2009
OI Zhang, Qi/0000-0003-1912-0523
FU NSFC [61503375]; Fundamental Research Funds for the Central Universities
   in UIBE [CXTD10-05,18QD18]
FX This work was supported by NSFC (No. 61503375), and the Fundamental
   Research Funds for the Central Universities in UIBE (CXTD10-05,18QD18).
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NR 28
TC 4
Z9 5
U1 0
U2 39
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD JUN
PY 2019
VL 119
IS 3
BP 1497
EP 1517
DI 10.1007/s11192-019-03100-4
PG 21
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA HZ7UB
UT WOS:000469058000009
DA 2024-09-05
ER
PT J
AU Wang, N
   Lv, XL
   Sun, SW
   Wang, QJ
AF Wang, Nan
   Lv, Xinlong
   Sun, Shanwu
   Wang, Qingjun
TI Research on the effect of government media and users' emotional
   experience based on LSTM deep neural network
SO NEURAL COMPUTING & APPLICATIONS
LA English
DT Article
DE Government media; Emotion analysis; LSTM; Deep learning; Social media
   platforms
AB Different government media have different communication effects and users' emotional experience. It carries on a comparative research on government media selecting three different types of government media which include China's Police Online, Central Committee of the Communist Youth League, and China's Fire Control in the context of public health emergencies. Based on the deep learning technique, the emotion classification model of long-term memory network is constructed to analyze the emotion of the users' comments of different government media; taking the number of contents, the number of retweets, the number of praises, and the number of comments as evaluating indicators to do comparative analysis to cross platform government medias. Through the comparative results, it is found that different types and platforms of government media have great differences in users' emotional experience; the emotion performance of users' comments is strongly related to the information communication power and effectiveness of government media.
C1 [Wang, Nan; Lv, Xinlong; Sun, Shanwu] Jilin Univ Finance & Econ, Changchun 130117, Peoples R China.
   [Wang, Qingjun] Shenyang Aerosp Univ, Shenyang 110136, Peoples R China.
   [Wang, Qingjun] Nanjing Univ Aeronaut & Astronaut, Nanjing 210016, Peoples R China.
C3 Jilin University of Finance & Economics; Shenyang Aerospace University;
   Nanjing University of Aeronautics & Astronautics
RP Wang, QJ (corresponding author), Shenyang Aerosp Univ, Shenyang 110136, Peoples R China.; Wang, QJ (corresponding author), Nanjing Univ Aeronaut & Astronaut, Nanjing 210016, Peoples R China.
EM ctuwangnan@126.com; xslvxinlong@163.com; ctusunshanwu@126.com;
   wangqingjun@sau.edu.cn
FU Key Project of Jilin Province Education Science During the 13th Five
   Year Plan in 2020: Research on new teaching mode in big data cloud
   education environment [ZD20024]
FX This work was supported by Key Project of Jilin Province Education
   Science During the 13th Five Year Plan in 2020: Research on new teaching
   mode in big data cloud education environment (ZD20024).
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NR 24
TC 3
Z9 3
U1 2
U2 45
PU SPRINGER LONDON LTD
PI LONDON
PA 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND
SN 0941-0643
EI 1433-3058
J9 NEURAL COMPUT APPL
JI Neural Comput. Appl.
PD AUG
PY 2022
VL 34
IS 15
SI SI
BP 12505
EP 12516
DI 10.1007/s00521-021-06567-6
EA OCT 2021
PG 12
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 3E6IS
UT WOS:000705779300003
PM 34642547
OA Bronze, Green Published
DA 2024-09-05
ER
PT C
AU Bae, S
   Hwang, C
   Lee, T
AF Bae, Sungho
   Hwang, Chanwoong
   Lee, Taejin
BE Kim, H
TI Research on Improvement of Anomaly Detection Performance in Industrial
   Control Systems
SO INFORMATION SECURITY APPLICATIONS
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT 22nd World Conference on Information Security Application (WISA)
CY AUG 11-13, 2021
CL SOUTH KOREA
DE Industrial Control System; Anomaly detection; Unsupervised stacked
   bidirectional LSTM; HAI dataset; TaPR
AB In the automated Industrial Control System (ICS) where advanced technology is being integrated with core infrastructure, technology development is ahead of the application of security solutions. Our city, power, and transportation control systems are getting smarter and more efficient, but new connectivity and interoperability are making them more vulnerable than ever before. Accordingly, various studies have been conducted for anomaly detection in ICS. In this paper, we propose an unsupervised stacked bidirectional Long Short-Term Memory (LSTM) model for automated anomaly detection in large-scale ICS and introduce a method for performance improvement. In addition, it was written based on participation in HAICon2020, an ICS security threat detection contest hosted by the National Security Research Institute. We use the HAI 2.0 dataset published at HAICon2020 and use Time-series Aware Precision and Recall (TaPR), which is suitable for anomaly detection evaluation in ICS. As a result of submission of test data, we were awarded 2nd place at HAICon2020. We have detected anomalies in ICS. As a follow-up work, we will do further research to identify the sensor and actuator that caused the anomaly and to quickly respond and recover.
C1 [Bae, Sungho; Hwang, Chanwoong; Lee, Taejin] Hoseo Univ, Dept Informat Secur, Asan 31499, South Korea.
C3 Hoseo University
RP Lee, T (corresponding author), Hoseo Univ, Dept Informat Secur, Asan 31499, South Korea.
EM baesungho21@naver.com
FU Institute for Information & communication Technology Planning &
   Evaluation (IITP) - Korea government (MSIT) [2019-0-00026]
FX This work was supported by Institute for Information & communication
   Technology Planning & Evaluation (IITP) grant funded by the Korea
   government (MSIT) (No. 2019-0-00026, ICT infra-structure protection
   against intelligent malware threats).
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NR 17
TC 0
Z9 0
U1 0
U2 1
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 0302-9743
EI 1611-3349
BN 978-3-030-89432-0; 978-3-030-89431-3
J9 LECT NOTES COMPUT SC
PY 2021
VL 13009
BP 76
EP 87
DI 10.1007/978-3-030-89432-0_7
PG 12
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods; Mathematics, Applied
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Mathematics
GA BS5DU
UT WOS:000728361600007
DA 2024-09-05
ER
PT C
AU Alonso, JM
   Castiello, C
   Mencar, C
AF Alonso, Jose M.
   Castiello, Ciro
   Mencar, Corrado
BE Medina, J
   OjedaAciego, M
   Verdegay, JL
   Pelta, DA
   Cabrera, IP
   BouchonMeunier, B
   Yager, RR
TI A Bibliometric Analysis of the Explainable Artificial Intelligence
   Research Field
SO INFORMATION PROCESSING AND MANAGEMENT OF UNCERTAINTY IN KNOWLEDGE-BASED
   SYSTEMS: THEORY AND FOUNDATIONS, IPMU 2018, PT I
SE Communications in Computer and Information Science
LA English
DT Proceedings Paper
CT 17th International Conference on Information Processing and Management
   of Uncertainty in Knowledge-Based Systems (IPMU)
CY JUN 11-15, 2018
CL Cadiz, SPAIN
DE Interpretability; Understandability; Comprehensibility; Explainable AI;
   Interpretable Fuzzy Systems
ID SYSTEMS; ACCURACY
AB This paper presents the results of a bibliometric study of the recent research on eXplainable Artificial Intelligence (XAI) systems. We took a global look at the contributions of scholars in XAI as well as in the subfields of AI that are mostly involved in the development of XAI systems. It is worthy to remark that we found out that about one third of contributions in XAI come from the fuzzy logic community. Accordingly, we went in depth with the actual connections of fuzzy logic contributions with AI to promote and improve XAI systems in the broad sense. Finally, we outlined new research directions aimed at strengthening the integration of different fields of AI, including fuzzy logic, toward the common objective of making AI accessible to people.
C1 [Alonso, Jose M.] Univ Santiago de Compostela, Ctr Singular Invest Tecnol Informac CiTIUS, Santiago De Compostela, Spain.
   [Castiello, Ciro; Mencar, Corrado] Univ Bari Aldo Moro, Dept Informat, Bari, Italy.
C3 Universidade de Santiago de Compostela; Universita degli Studi di Bari
   Aldo Moro
RP Alonso, JM (corresponding author), Univ Santiago de Compostela, Ctr Singular Invest Tecnol Informac CiTIUS, Santiago De Compostela, Spain.
EM josemaria.alonso.moral@usc.es; ciro.castiello@uniba.it;
   corrado.mencar@uniba.it
RI Alonso Moral, Jose Maria/A-4374-2017
OI Alonso Moral, Jose Maria/0000-0003-3673-421X; Mencar,
   Corrado/0000-0001-8712-023X
FU Ramon y Cajal contract - Spanish "Ministerio de Economia y
   Competitividad" [RYC-2016-19802]; MINECO - Spanish "Ministerio de
   Economia y Competitividad" [TIN2017-84796-C2-1-R, TIN2014-56633-C3-3-R];
   Xunta de Galicia (Centro singular de investigacion de Galicia); European
   Union (European Regional Development Fund - ERDF)
FX This work was supported by RYC-2016-19802 (Ramon y Cajal contract), and
   two MINECO projects TIN2017-84796-C2-1-R (BIGBISC) and
   TIN2014-56633-C3-3-R (ABS4SOW). All of them funded by the Spanish
   "Ministerio de Economia y Competitividad". Financial support from the
   Xunta de Galicia (Centro singular de investigacion de Galicia
   accreditation 2016-2019) and the European Union (European Regional
   Development Fund - ERDF), is gratefully acknowledged.
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NR 26
TC 41
Z9 45
U1 2
U2 34
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 1865-0929
EI 1865-0937
BN 978-3-319-91473-2; 978-3-319-91472-5
J9 COMM COM INF SC
PY 2018
VL 853
BP 3
EP 15
DI 10.1007/978-3-319-91473-2_1
PN I
PG 13
WC Computer Science, Artificial Intelligence; Computer Science, Theory &
   Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BN4AQ
UT WOS:000481659500001
DA 2024-09-05
ER
PT J
AU Guo, SC
   Zheng, YY
   Zhai, XM
AF Guo, Shuchen
   Zheng, Yuanyuan
   Zhai, Xiaoming
TI Artificial intelligence in education research during 2013-2023: A review
   based on bibliometric analysis
SO EDUCATION AND INFORMATION TECHNOLOGIES
LA English
DT Article; Early Access
DE Artificial intelligence (AI); AI in education (AIED); Machine learning
   (ML); Bibliometric analysis; Citespace software
ID TUTORING SYSTEMS; LEARNING ANALYTICS; SCIENCE; TRENDS; SPEECH; TEXT
AB Research on Artificial Intelligence in Education (AIED) has rapidly progressed in recent years, and understanding the research trends and development is essential for technological innovations and implementations in education. Using a bibliometric analysis of 6843 publications from Web of Science and Scopus, we found that China, US, India, Spain, and Germany led the research profuctivity. AIED research is concerned more with higher education compared to K-12 education. Fifteen research trends emerged from the analysis, such as Educational Robots and Large Data Mining. Research has primarily leveraged technologies of machine learning, decision trees, deep learning, speech recognition, and computer vision in AIED. The major implementations of AI include educational robots, automated grading, recommender systems, learning analytics, and intelligent tutoring systems. Among the implementations, a majority of AIED research was conducted in seven major subject domains, chief among them being science, technology, engineering and mathematics (STEM) and language disciplines, with a focus on computer science and English education.
C1 [Guo, Shuchen; Zheng, Yuanyuan] Nanjing Normal Univ, Sch Educ, Nanjing, Jiangsu, Peoples R China.
   [Zhai, Xiaoming] Univ Georgia, AI4STEM Educ Ctr, 125M Aderhold Hall,110 Carlton St, Athens, GA 30602 USA.
   [Zhai, Xiaoming] Univ Georgia, Dept Math Sci & Social Studies Educ, 125M Aderhold Hall,110 Carlton St, Athens, GA 30602, Georgia.
C3 Nanjing Normal University; University System of Georgia; University of
   Georgia
RP Zhai, XM (corresponding author), Univ Georgia, AI4STEM Educ Ctr, 125M Aderhold Hall,110 Carlton St, Athens, GA 30602 USA.; Zhai, XM (corresponding author), Univ Georgia, Dept Math Sci & Social Studies Educ, 125M Aderhold Hall,110 Carlton St, Athens, GA 30602, Georgia.
EM gsc44@njnu.edu.cn; 3048835050@qq.com; Xiaoming.Zhai@uga.edu
RI Zheng, Yuanyuan/HJA-4183-2022; Zhai, Xiaoming/AAB-7129-2021
OI Zhai, Xiaoming/0000-0003-4519-1931
FU National Science Foundation [2101104]; China Scholarship Council (CSC) -
   National Science Foundation (NSF); NSF
FX The authors thank China Scholarship Council (CSC) for supporting the
   study. The study was also partially funded by the National Science
   Foundation (NSF) (Award # 2101104, PI: Zhai). Any opinions, findings,
   conclusions, or recommendations expressed in this material are those of
   the author(s) and do not necessarily reflect the views of the CSC and
   NSF.
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NR 91
TC 0
Z9 0
U1 161
U2 174
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1360-2357
EI 1573-7608
J9 EDUC INF TECHNOL
JI Educ. Inf. Technol.
PD 2024 FEB 9
PY 2024
DI 10.1007/s10639-024-12491-8
EA FEB 2024
PG 23
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA HK6S0
UT WOS:001159441700001
DA 2024-09-05
ER
PT C
AU Hong, MH
   Marsh, LA
   Feuston, JL
   Ruppert, J
   Brubaker, JR
   Szafr, DA
AF Hong, Matt-Heun
   Marsh, Lauren A.
   Feuston, Jessica L.
   Ruppert, Janet
   Brubaker, Jed R.
   Szafr, Danielle Albers
GP ACM
TI Scholastic: Graphical Human-AI Collaboration for Inductive and
   Interpretive Text Analysis
SO PROCEEDINGS OF THE 35TH ANNUAL ACM SYMPOSIUM ON USER INTERFACE SOFTWARE
   AND TECHNOLOGY, UIST 2022
LA English
DT Proceedings Paper
CT 35th Annual ACM Symposium on User Interface Software and Technology
   (UIST)
CY OCT 29-NOV 02, 2022
CL Bend, OR
DE qualitative research; interpretive research methods; interactive topic
   modeling; interactive document clustering; human-AI collaboration;
   visual analytics; text data
ID GROUNDED THEORY; PITFALLS
AB Interpretive scholars generate knowledge from text corpora by manually sampling documents, applying codes, and refining and collating codes into categories until meaningful themes emerge. Given a large corpus, machine learning could help scale this data sampling and analysis, but prior research shows that experts are generally concerned about algorithms potentially disrupting or driving interpretive scholarship. We take a human-centered design approach to addressing concerns around machine-assisted interpretive research to build Scholastic, which incorporates a machine-in-the-loop clustering algorithm to scafold interpretive text analysis. As a scholar applies codes to documents and refines them, the resulting coding schema serves as structured metadata which constrains hierarchical document and word clusters inferred from the corpus. Interactive visualizations of these clusters can help scholars strategically sample documents further toward insights. Scholastic demonstrates how human-centered algorithm design and visualizations employing familiar metaphors can support inductive and interpretive research methodologies through interactive topic modeling and document clustering.
C1 [Hong, Matt-Heun] Univ Colorado Boulder, ATLAS Inst, Boulder, CO USA.
   [Marsh, Lauren A.] Univ Colorado Boulder, Dept Appl Math, Boulder, CO USA.
   [Feuston, Jessica L.; Ruppert, Janet; Brubaker, Jed R.] Univ Colorado Boulder, Dept Informat Sci, Boulder, CO USA.
   [Szafr, Danielle Albers] Univ North Carolina Chapel Hill, Dept Comp Sci, Chapel Hill, NC USA.
C3 University of Colorado System; University of Colorado Boulder;
   University of Colorado System; University of Colorado Boulder;
   University of Colorado System; University of Colorado Boulder;
   University of North Carolina School of Medicine; University of North
   Carolina; University of North Carolina Chapel Hill
RP Hong, MH (corresponding author), Univ Colorado Boulder, ATLAS Inst, Boulder, CO USA.
OI BRUBAKER, JED/0000-0003-4826-8324
FU NSF [1764092, 2046725]
FX The authors would like to thank Kandrea Wade and Casey Fiesler for their
   input at the conceptualization stages, students and faculty at the ATLAS
   Institute, CU Boulder during the design, development, and writing
   stages, and Matteo Abrate (https://bl.ocks.org/nitaku) for his valuable
   examples of geographical treemaps. This work was supported by NSF awards
   #1764092 & #2046725.
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TC 3
Z9 3
U1 2
U2 7
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES
BN 978-1-4503-9320-1
PY 2022
DI 10.1145/3526113.3545681
PG 12
WC Computer Science, Cybernetics; Computer Science, Hardware &
   Architecture; Computer Science, Software Engineering; Computer Science,
   Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BV5DH
UT WOS:001046841800070
OA Green Submitted, Bronze
DA 2024-09-05
ER
PT J
AU Ritchie, F
   Tilbrook, A
   Cole, C
   Jefferson, E
   Krueger, S
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   Smith, J
AF Ritchie, Felix
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   Jefferson, Emily
   Krueger, Susan
   Mansouri-Benssassi, Esma
   Rogers, Simon
   Smith, Jim
TI Machine learning models in trusted research environments - understanding
   operational risks
SO INTERNATIONAL JOURNAL OF POPULATION DATA SCIENCE (IJPDS)
LA English
DT Article
DE confidentiality; output checking; machine learning; artificial
   intelligence; data enclave; trusted research environment
AB Introduction
   Trusted research environments (TREs) provide secure access to very sensitive data for research. All TREs operate manual checks on outputs to ensure there is no residual disclosure risk. Machine learning (ML) models require very large amount of data; if this data is personal, the TRE is a well-established data management solution. However, ML models present novel disclosure risks, in both type and scale.
   Objectives
   As part of a series on ML disclosure risk in TREs, this article is intended to introduce TRE managers to the conceptual problems and work being done to address them.
   Methods
   We demonstrate how ML models present a qualitatively different type of disclosure risk, compared to traditional statistical outputs. These arise from both the nature and the scale of ML modelling.
   Results
   We show that there are a large number of unresolved issues, although there is progress in many areas. We show where areas of uncertainty remain, as well as remedial responses available to TREs.
   Conclusions
   At this stage, disclosure checking of ML models is very much a specialist activity. However, TRE managers need a basic awareness of the potential risk in ML models to enable them to make sensible decisions on using TREs for ML model development.
C1 [Ritchie, Felix] Univ West England, Bristol Business Sch, Coldharbour Lane, Bristol BS16 1QY, Avon, England.
   [Tilbrook, Amy] Univ Edinburgh, South Bridge, Edinburgh EH8 9YL, Midlothian, Scotland.
   [Cole, Christian; Jefferson, Emily] Ninewells Hosp & Med Sch, Div Populat Hlth & Gen, Dundee DD1 9SY, Scotland.
   [Krueger, Susan] Ninewells Hosp & Med Sch, Hlth Informat Ctr, Dundee DD1 9SY, Scotland.
   [Mansouri-Benssassi, Esma] AffectiveHalo Ltd, Tom Morris Dr, St Andrews KY16 8HS, Fife, Scotland.
   [Rogers, Simon] NHS Natl Serv Scotland, Gyle Sq,1 South Gyle Crescent, Edinburgh EH12 9EB, Midlothian, Scotland.
   [Smith, Jim] Univ West England, Sch Comp Sci & Creat Technol, Coldharbour Lane, Bristol BS16 1QY, Avon, England.
C3 University of West England; University of Edinburgh; University of
   Dundee; University of Dundee; NHS National Services Scotland; University
   of West England
RP Ritchie, F (corresponding author), Univ West England, Bristol Business Sch, Coldharbour Lane, Bristol BS16 1QY, Avon, England.
EM felix.ritchie@uwe.ac.uk
RI ; Smith, Jim/M-7533-2015
OI Jefferson, Emily/0000-0003-2992-7582; Rogers, Simon/0000-0003-3578-4477;
   Krueger, Susan/0000-0002-5219-1959; Ritchie, Felix/0000-0003-4097-4021;
   Cole, Christian/0000-0002-2560-2484; Smith, Jim/0000-0001-7908-1859
FU UK Medical Research Council as a DARE Phase 1 Sprint Project
FX The GRAIMATTER project was funded by the UK Medical Research Council as
   a DARE Phase 1 Sprint Project
   https://dareuk.org.uk/sprint-exemplar-project-graimatter/.The DARE
   project report [11] was the main project output. This post-project
   article was produced by members of the GRAIMATTER team most closely
   connected to this workstream. In addition, Amy Tilbrook contributed
   significantly to the writing of this document but did not receive any
   funding through DARE or MRC for her contribution. Susan Krueger also
   contributed to the background analysis and writing of this document but
   did not receive any GRAIMATTER funding. She does however received
   funding for the MRC PICTURES project developing AI applications of image
   data, which continues to help the research team tests recommendations in
   a genuine environment.
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NR 20
TC 0
Z9 0
U1 0
U2 0
PU SWANSEA UNIV
PI SWANSEA
PA SINGLETON PARK, SWANSEA, SA2 8PP, WALES
EI 2399-4908
J9 INT J POPUL DATA SCI
JI Int. J. Population Data Sci.
PY 2023
VL 8
IS 1
AR 30
DI 10.23889/ijpds.v8i1.2165
PG 9
WC Health Care Sciences & Services; Public, Environmental & Occupational
   Health; Social Sciences, Interdisciplinary
WE Emerging Sources Citation Index (ESCI)
SC Health Care Sciences & Services; Public, Environmental & Occupational
   Health; Social Sciences - Other Topics
GA GG6C4
UT WOS:001151542700002
PM 38414545
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU Lee, JW
   Lee, WK
   Sohn, SY
AF Lee, Jong Wook
   Lee, Won Kyung
   Sohn, So Young
TI Patenting trends in biometric technology of the Big Five patent offices
SO WORLD PATENT INFORMATION
LA English
DT Article
DE Biometrics; Citation analysis; Latent dirichlet allocation; Patent
   analysis; Technology trend analysis; Topic modeling
ID RECOGNITION; CENTRALITY; TEXT
AB We examined the overall trends in biometric technology based on patent documents. Using PATSTAT database, we extracted 37,462 patent documents applied at the Big Five patent offices between 1990 and 2016. Latent Dirichlet allocation was applied to their abstracts to observe annual trends by topic. Our results are as follows: Fingerprint-enabled car anti-theft systems have been undergoing rapid technological development since 2014. In response, biometric signal transmitting models are becoming popular owing to concerns about theft of biometric templates. While fingerprint, face, and iris authentication technologies continue to advance, finger vein, voice, and signature authentication technologies are lagging. Use of biometric technologies in financial transactions, server networks, and digital media content security are decreasing as well. A citation analysis discovered key topics and patent applicants: Surprisingly, the quantitative growth rate of topics and the effect on the knowledge network showed an inverse relationship. US firms had the most citations, but fewer backward citations of own work, unlike Japanese companies. We provide practical insights to stakeholders of biometric technology.
C1 [Lee, Jong Wook; Lee, Won Kyung; Sohn, So Young] Yonsei Univ, Dept Ind Engn, 134 Shinchon Dong, Seoul 120749, South Korea.
C3 Yonsei University
RP Sohn, SY (corresponding author), Yonsei Univ, Dept Ind Engn, 134 Shinchon Dong, Seoul 120749, South Korea.
EM sohns@yonsei.ac.kr
OI Sohn, So Young/0000-0002-3958-2269
FU National Research Foundation of Korea (NRF) - Korea government (MSIT)
   [2020R1A2C2005026]
FX This work was supported by the National Research Foundation of Korea
   (NRF) grant funded by the Korea government (MSIT) (2020R1A2C2005026) .
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NR 44
TC 7
Z9 7
U1 1
U2 14
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0172-2190
EI 1874-690X
J9 WORLD PAT INF
JI World Pat. Inf.
PD JUN
PY 2021
VL 65
AR 102040
DI 10.1016/j.wpi.2021.102040
EA APR 2021
PG 10
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA SU4ZJ
UT WOS:000663147300001
DA 2024-09-05
ER
PT C
AU Yang, C
   Liu, WX
AF Yang Chun
   Liu Weixin
BE Hung, J
   Zhao, RM
TI Research of the Staff Performance in the University Library Based on the
   Principal Component Analysis
SO PROCEEDINGS OF 2008 INTERNATIONAL SEMINAR ON EDUCATION MANAGEMENT AND
   ENGINEERING
LA English
DT Proceedings Paper
CT International Seminar on Education Management and Engineering
CY SEP 22, 2008
CL Chengdu, PEOPLES R CHINA
DE principal component analysis; staff performance; university library; the
   appraisal model
AB The competition among the university library is fundamentally intense gradually. The methoud to raise the competition ability of the university library lies in introducing the staff and training the staff. Only the stuff's performance are raised, can the university library be developed prosperously. The performance management of the staff is the basis to make the university library policy and carry on the important decision of the university library, which is also an important mechanism for the university library to set up the strategic targets. This ariticle introduces the principal component to judge the performance of the the staff of the university library, which can give the university library a useful result to introducing the highly efficient staff and training the low outstanding staff. Through enstablishing the appraisal model, the university library can get the stituation of the staff performance eassily, then they can take on some effective measures to raise the level of the staff performance so that the university library can be developed effectively and efficiently.
EM yangchun513383000@163.com
CR BING AI, 2006, RES PREFORMANCE APPR
   KE L, 2007, RES PERFORMANCE MANA
   LI GF, J ANHUI U TECHNOLOGY
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NR 7
TC 0
Z9 0
U1 0
U2 2
PU M D FORUM
PI ALLAWAH NSW
PA 17 AUGUSTA ST, ALLAWAH NSW, 2218, AUSTRALIA
BN 978-0-646-49806-5
PY 2008
BP 857
EP 864
PG 8
WC Education & Educational Research
WE Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Education & Educational Research
GA BIO55
UT WOS:000261358200168
DA 2024-09-05
ER
PT J
AU Heibi, I
   Peroni, S
AF Heibi, Ivan
   Peroni, Silvio
TI A quantitative and qualitative open citation analysis of retracted
   articles in the humanities
SO QUANTITATIVE SCIENCE STUDIES
LA English
DT Article
DE citation analysis; humanities; retraction; topic modeling
ID PUBLICATIONS
AB In this article, we show and discuss the results of a quantitative and qualitative analysis of open citations of retracted publications in the humanities domain. Our study was conducted by selecting retracted papers in the humanities domain and marking their main characteristics (e.g., retraction reason). Then, we gathered the citing entities and annotated their basic metadata (e.g., title, venue, subject) and the characteristics of their in-text citations (e.g., intent, sentiment). Using these data, we performed a quantitative and qualitative study of retractions in the humanities, presenting descriptive statistics and a topic modeling analysis of the citing entities' abstracts and the in-text citation contexts. As part of our main findings, we noticed that there was no drop in the overall number of citations after the year of retraction, with few entities that have either mentioned the retraction or expressed a negative sentiment toward the cited publication. In addition, on several occasions, we noticed a higher concern/awareness by citing entities belonging to the health sciences domain about citing a retracted publication, compared with the humanities and social science domains. Philosophy, arts, and history are the humanities areas that showed higher concern toward the retraction.
C1 [Heibi, Ivan; Peroni, Silvio] Univ Bologna, Res Ctr Open Scholarly Metadata, Dept Class Philol & Italian Studies, Bologna, Italy.
   [Heibi, Ivan; Peroni, Silvio] Univ Bologna, Digital Humanities Adv Res Ctr DHarc, Dept Class Philol & Italian Studies, Bologna, Italy.
C3 University of Bologna; University of Bologna
RP Heibi, I (corresponding author), Univ Bologna, Res Ctr Open Scholarly Metadata, Dept Class Philol & Italian Studies, Bologna, Italy.; Heibi, I (corresponding author), Univ Bologna, Digital Humanities Adv Res Ctr DHarc, Dept Class Philol & Italian Studies, Bologna, Italy.
EM ivan.heibi2@unibo.it
RI Heibi, Ivan/AAZ-9145-2021
OI Heibi, Ivan/0000-0001-5366-5194; Peroni, Silvio/0000-0003-0530-4305
FU European Union [101017452]
FX This work has been partially funded by the European Union's Horizon 2020
   research andinnovation program under grant agreement No 101017452
   (OpenAIRE-Nexus).
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NR 58
TC 6
Z9 6
U1 12
U2 31
PU MIT PRESS
PI CAMBRIDGE
PA ONE ROGERS ST, CAMBRIDGE, MA 02142-1209 USA
EI 2641-3337
J9 QUANT SCI STUD
JI Quant. Sci. Stud.
PD DEC 20
PY 2022
VL 3
IS 4
BP 953
EP 975
DI 10.1162/qss_a_00222
PG 23
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA 9B9MZ
UT WOS:000935055300004
OA Green Published, gold, Green Submitted
DA 2024-09-05
ER
PT J
AU Hua, V
   Huynh, B
AF Hua, Van
   Huynh, Bao
TI Forecasting a Journal Impact Factor Under Missing Values Based on
   Machine Learning
SO IEEE ACCESS
LA English
DT Article
DE Multivariate; time series; missing values; imputation; journal impact
   factor; machine learning; kNNI
ID MULTIVARIATE TIME-SERIES; IMPUTATION
AB Scientists not only engage in research, but also write articles based on their work, and naturally aim to submit their articles to prestigious, well-received, and highly regarded journals or conferences. One way to measure the prestige of a journal is to use the Journal Impact Factor (JIF), but in order to make best use of this information one needs to better understand the relationships among various JIF-related attributes. However, in recent years JIF values have been missing or unavailable for some journals, due to several objective and subjective factors, which significantly impacts the ability to forecast such values in the following years. In this article we study the factors that directly affect the ranking of journals and can be used to forecast the appropriate values in the next few years, in order to help researchers find the right place to submit their articles in certain journals.
C1 [Hua, Van; Huynh, Bao] HUTECH Univ, Fac Informat Technol, Ho Chi Minh City 700000, Vietnam.
RP Huynh, B (corresponding author), HUTECH Univ, Fac Informat Technol, Ho Chi Minh City 700000, Vietnam.
EM hq.bao@hutech.edu.vn
RI Huynh, Bao/O-9317-2018
OI Huynh, Bao/0000-0002-1882-6877
CR Andreadis A., 2022, Missing values imputation on multivariate time series in the field of agriculture
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NR 38
TC 0
Z9 0
U1 1
U2 1
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2024
VL 12
BP 85745
EP 85760
DI 10.1109/ACCESS.2024.3416345
PG 16
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA WC3K3
UT WOS:001252628000001
OA gold
DA 2024-09-05
ER
PT J
AU van der Zwaard, S
   de Leeuw, AW
   Meerhoff, LA
   Bodine, SC
   Knobbe, A
AF van der Zwaard, Stephan
   de Leeuw, Arie-Willem
   Meerhoff, L. (Rens) A.
   Bodine, Sue C.
   Knobbe, Arno
TI Articles with impact: insights into 10 years of research with machine
   learning
SO JOURNAL OF APPLIED PHYSIOLOGY
LA English
DT Article
DE altmetrics; bibliometrics; machine learning; natural language
   processing; scientometrics
ID MUSCLE PROTEIN-SYNTHESIS; HIGH-INTENSITY EXERCISE; MAXIMAL
   OXYGEN-UPTAKE; CARDIORESPIRATORY FITNESS; RESISTANCE EXERCISE; O-2 COST;
   IMPROVES PERFORMANCE; ACCEPTABLE ESTIMATE; ENDURANCE EXERCISE; VIEWPOINT
   V
AB Worldwide scientific output is growing faster and faster. Academics should not only publish much and fast, but also publish research with impact. The aim of this study is to use machine learning to investigate characteristics of articles that were published in the Journal of Applied Physiology between 2009 and 2018, and characterize high-impact articles. Article impact was assessed for 4,531 publications by three common impact metrics: the Altmetric Attention Scores, downloads, and citations. Additionally, a broad collection of (more than 200) characteristics was collected from the article's title, abstract, authors, keywords, publication, and article engagement. We constructed random forest (RF) regression models to predict article impact and articles with the highest impact (top-25% and top-10% for each impact metric), which were compared with a naive baseline method. RF models outperformed the baseline models when predicting the impact of unseen articles (P < 0.001 for each impact metric). Also, RF models predicted top-25% and top-10% high-impact articles with a high accuracy. Moreover, RF models revealed important article characteristics. Higher impact was observed for articles about exercise, training, performance and (V)over dot(O2max), reviews, human studies, articles from large collaborations, longer articles with many references and high engagement by scientists, practitioners and public or via news outlets and videos. Lower impact was shown for articles about respiratory physiology or sleep apnea, editorials, animal studies, and titles with a question mark or a reference to places or individuals. In summary, research impact can be predicted and better understood using a combination of article characteristics and machine learning.
   NEW & NOTEWORTHY Common measures of article impact are the Altmetric Attention Scores, number of downloads, and number of citations. To our knowledge, this is the first study that applies machine learning on a comprehensive collection of article characteristics to predict article attention scores, downloads, and citations. Using 10 years of research articles, we obtained accurate predictions of high-impact articles and discovered important article characteristics related to article impact.
C1 [van der Zwaard, Stephan; de Leeuw, Arie-Willem; Meerhoff, L. (Rens) A.; Knobbe, Arno] Leiden Univ, Leiden Inst Adv Comp Sci, Leiden, Netherlands.
   [van der Zwaard, Stephan] Vrije Univ Amsterdam, Dept Human Movement Sci, Amsterdam, Netherlands.
   [Bodine, Sue C.] Univ Iowa, Dept Internal Med Endocrinol & Metab, Iowa City, IA USA.
C3 Leiden University - Excl LUMC; Leiden University; Vrije Universiteit
   Amsterdam; University of Iowa
RP van der Zwaard, S (corresponding author), Leiden Univ, Leiden Inst Adv Comp Sci, Leiden, Netherlands.; van der Zwaard, S (corresponding author), Vrije Univ Amsterdam, Dept Human Movement Sci, Amsterdam, Netherlands.
EM s.van.der.zwaard@liacs.leidenuniv.nl
RI van der Zwaard, Stephan/J-9622-2019; Knobbe, Arno/AAE-5659-2020; de
   Leeuw, Arie-Willem/AAL-5634-2021
OI van der Zwaard, Stephan/0000-0002-8296-828X; de Leeuw,
   Arie-Willem/0000-0002-9857-0970; Bodine, Sue/0000-0002-5742-9145
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NR 114
TC 9
Z9 9
U1 2
U2 20
PU AMER PHYSIOLOGICAL SOC
PI Rockville
PA 6120 Executive Blvd, Suite 600, Rockville, MD, UNITED STATES
SN 8750-7587
EI 1522-1601
J9 J APPL PHYSIOL
JI J. Appl. Physiol.
PD OCT
PY 2020
VL 129
IS 4
BP 967
EP 979
DI 10.1152/japplphysiol.00489.2020
PG 13
WC Physiology; Sport Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Physiology; Sport Sciences
GA QX2XY
UT WOS:000629212200013
PM 32790596
OA Green Published, Green Submitted, Bronze
DA 2024-09-05
ER
PT J
AU Reong, S
   Wee, HM
   Hsiao, YL
AF Reong, Samuel
   Wee, Hui-Ming
   Hsiao, Yu-Lin
TI 20 Years of Particle Swarm Optimization Strategies for the Vehicle
   Routing Problem: A Bibliometric Analysis
SO MATHEMATICS
LA English
DT Article
DE particle swarm optimization; vehicle routing problem; bibliometric
   analysis; supply chain management; metaheuristics; combinatorial
   optimization; data mining
ID TIME WINDOWS; DELIVERY; IMPACT; PICKUP; FIELD; PSO
AB This study uses bibliometric analysis to examine the scientific evolution of particle swarm optimization (PSO) for the vehicle routing problem (VRP) over the past 20 years. Analyses were conducted to discover and characterize emerging trends in the research related to these topics and to examine the relationships between key publications. Through queries of the Web of Science and Scopus databases, the metadata for these particle swarm optimization (PSO) and vehicle routing problem (VRP) solution strategies were compared using bibliographic coupling and co-citation analysis using the Bibliometrix R software package, and secondly with VOSViewer. The bibliometric study's purpose was to identify the most relevant thematic clusters and publications where PSO and VRP research intersect. The findings of this study can guide future VRP research and underscore the importance of developing effective PSO metaheuristics.
C1 [Reong, Samuel; Wee, Hui-Ming; Hsiao, Yu-Lin] Chung Yuan Christian Univ, Dept Ind & Syst Engn, Taoyuan 320, Taiwan.
C3 Chung Yuan Christian University
RP Wee, HM (corresponding author), Chung Yuan Christian Univ, Dept Ind & Syst Engn, Taoyuan 320, Taiwan.
EM weehm@cycu.edu.tw
RI Wee, H/JXY-0919-2024
OI wee, hui ming/0000-0002-2935-9506
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NR 60
TC 5
Z9 5
U1 8
U2 32
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2227-7390
J9 MATHEMATICS-BASEL
JI Mathematics
PD OCT
PY 2022
VL 10
IS 19
AR 3669
DI 10.3390/math10193669
PG 19
WC Mathematics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematics
GA 5G6HI
UT WOS:000867097500001
OA gold
DA 2024-09-05
ER
PT J
AU Wang, J
AF Wang, Jun
TI RESEARCH ON THE REALIZATION MECHANISM AND EVALUATION SYSTEM OF
   HIGH-QUALITY UNDERGRADUATE EDUCATION IN PRIVATE UNIVERSITIES BASED ON
   DEEP LEARNING
SO 3C TIC
LA English
DT Article
DE Deep learning; private universities; high-quality undergraduate
   education; realization mechanism; assessment system
AB Due to the new development stage, it is especially important to improve the education quality of private undergraduate universities. As a result, it is a new hot issue for the construction of a mechanism and assessment system for the quality improvement of private undergraduate education. In this paper, after analyzing and researching the quality of undergraduate education in present-day universities, the mechanism of deep learning is applied to the establishment of the assessment system. Finally, 1082 samples collected from the data center platform of a private university are analyzed as the research object. From the results, the final size of the combined weights of the seven evaluation items constituting the assessment system differed basically little. They were 12.81%, 15.78%, 15.28%, 14.38%, 12.83%, 12.81%, 15.01%, and 13.27%, respectively. In the comparison of this paper's method with FAHP+TOPSIS combined evaluation, euclidean map method, and genetic algorithm assignment, the difference between the seven weight values of the euclidean map method is larger, 5.56%. The evaluation times of the four methods were 41 s, 38 s, 47 s, and 118 s. Compared with the other three methods, the genetic algorithm assignment took the most time.
C1 [Wang, Jun] Guangdong Inst Sci & Technol, Dongguan 523808, Guangdong, Peoples R China.
RP Wang, J (corresponding author), Guangdong Inst Sci & Technol, Dongguan 523808, Guangdong, Peoples R China.
EM wjshgz2022@126.com
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NR 30
TC 0
Z9 0
U1 2
U2 3
PU AREA INNOVACION & DESARROLLO
PI ALICANTE
PA C/ELS ALZAMORA NO 17, ALCOY, ALICANTE, 03802, SPAIN
SN 2254-6529
J9 3C TIC
JI 3C Tic
PD APR-JUN
PY 2023
VL 12
IS 2
BP 97
EP 115
DI 10.17993/3ctic.2023.122.97-115
PG 19
WC Computer Science, Theory & Methods
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA U8YQ8
UT WOS:001087605300004
OA gold, Green Submitted
DA 2024-09-05
ER
PT J
AU Zheng, QG
   Chen, HY
   Wang, Y
   Zhang, HB
   Hu, ZZ
AF Zheng, Qiangang
   Chen, Haoying
   Wang, Yong
   Zhang, Haibo
   Hu, Zhongzhi
TI Research on hybrid optimization and deep learning modeling method in the
   performance seeking control
SO PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF
   AEROSPACE ENGINEERING
LA English
DT Article
DE Performance seeking control; on-board turbo engine model; deep neural
   network; genetic algorithm particle swarm optimization; feasible
   sequential quadratic programming
AB A novel performance seeking control method based on hybrid optimization algorithm and deep learning modeling method is proposed to get a better engine performance. The deep learning modeling method, deep neural network, which has strong representation capability and can deal with big training data, is adopted to establish an on-board engine model. A hybrid optimization algorithm-genetic algorithm particle swarm optimization-feasible sequential quadratic programming-is proposed and applied to performance seeking control. The genetic algorithm particle swarm optimization-feasible sequential quadratic programming not only has the global search ability of genetic algorithm particle swarm optimization, but also has the high local search accuracy of feasible sequential quadratic programming. The final simulation experiments show that, compared with feasible sequential quadratic programming, genetic algorithm particle swarm optimization, and genetic algorithm, the proposed optimization algorithm can get more installed thrust, decrease fuel consumption between 2% to 3%, and decrease turbine blade temperature larger than 15k, while meeting all of the constraints. Moreover, it also shows that the proposed modeling method has high accuracy and real-time performance.
C1 [Zheng, Qiangang; Chen, Haoying; Wang, Yong; Zhang, Haibo; Hu, Zhongzhi] Nanjing Univ Aeronaut & Astronaut, Jiangsu Prov Key Lab Aerosp Power Syst, Yudao St 29, Nanjing 210016, Peoples R China.
C3 Nanjing University of Aeronautics & Astronautics
RP Zheng, QG (corresponding author), Nanjing Univ Aeronaut & Astronaut, Jiangsu Prov Key Lab Aerosp Power Syst, Yudao St 29, Nanjing 210016, Peoples R China.
EM zhqg@nuaa.edu.cn
RI Zhang, Haibo/HLP-9266-2023
FU National Natural Science Foundation of China [51906102]; National
   Science and Technology Major Project [2017-V-0004-0054]; Research on the
   Basic Problem of Intelligent Aero-engine [2017-JCJQZD-047-21]; China
   Postdoctoral Science Foundation [2019M661835]; Aeronautics Power
   Foundation [6141B09050385]; Fundamental Research Funds for the Central
   Universities [NT2019004]
FX The author(s) disclosed receipt of the following financial support for
   the research, authorship, and/or publication of this article: This study
   was supported in part by National Natural Science Foundation of China
   [Grant no. 51906102], National Science and Technology Major Project
   [Grant no. 2017-V-0004-0054], Research on the Basic Problem of
   Intelligent Aero-engine [Grant no. 2017-JCJQZD-047-21, China
   Postdoctoral Science Foundation Funded Project [Grant no. 2019M661835],
   Aeronautics Power Foundation [Grant no. 6141B09050385], and the
   Fundamental Research Funds for the Central Universities [Grant no.
   NT2019004].
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NR 33
TC 6
Z9 7
U1 0
U2 18
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 0954-4100
EI 2041-3025
J9 P I MECH ENG G-J AER
JI Proc. Inst. Mech. Eng. Part G-J. Aerosp. Eng.
PD JUN
PY 2020
VL 234
IS 7
BP 1340
EP 1355
AR 0954410020903151
DI 10.1177/0954410020903151
EA FEB 2020
PG 16
WC Engineering, Aerospace; Engineering, Mechanical
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA LM6CN
UT WOS:000513327500001
DA 2024-09-05
ER
PT J
AU Polyakov, M
   Chalak, M
   Iftekhar, MS
   Pandit, R
   Tapsuwan, S
   Zhang, F
   Ma, CB
AF Polyakov, Maksym
   Chalak, Morteza
   Iftekhar, Md. Sayed
   Pandit, Ram
   Tapsuwan, Sorada
   Zhang, Fan
   Ma, Chunbo
TI Authorship, Collaboration, Topics, and Research Gaps in Environmental
   and Resource Economics 1991-2015
SO ENVIRONMENTAL & RESOURCE ECONOMICS
LA English
DT Article
DE Topic analysis; Latent Dirichlet allocation; Co-authorship;
   Environmental and resource economics
ID ECOLOGICAL ECONOMICS; INFLUENTIAL PUBLICATIONS; TEXT
AB Environmental and Resource Economics is one of the premier journals in the field of environmental economics. It was established with an aspiration to focus more on applied and policy relevant research compared to other established journals, and to establish better channels of communication and collaboration between researchers from Europe and other parts of the world. We present a text based exploratory analysis of 1630 articles published in the Journal from 1991 to 2015 that suggests the Journal has been somewhat successful in meeting both these aims. Perhaps more importantly, it shows the Journal continues to progress toward these goals. The European authors are the largest contributors to the Journal, which is in contrast to other prominent journals (such as Journal of Environmental Economics and Management and Ecological Economics). And while most of the collaboration has occurred within this geographic region (e.g., European authors collaborated with other European authors more frequently), this trend appears to be changing as the proportion of articles written by international collaborators is gradually increasing. Topic analysis reveals that almost all of the articles could be grouped under applied and/or policy relevant topics, and almost two-thirds of the articles are empirical in nature, which suggest that the journal has been able to fulfil both of its commitments. We also investigate trends in research foci over the last 25 years and what kind of research gaps can be discerned.
C1 [Polyakov, Maksym; Chalak, Morteza; Iftekhar, Md. Sayed; Pandit, Ram; Zhang, Fan; Ma, Chunbo] Univ Western Australia, M089,35 Stirling Hwy, Crawley, WA 6009, Australia.
   [Tapsuwan, Sorada] Private Bag 5, Wembley, WA 6913, Australia.
C3 University of Western Australia
RP Ma, CB (corresponding author), Univ Western Australia, M089,35 Stirling Hwy, Crawley, WA 6009, Australia.
EM maksym.polyakov@uwa.edu.au; morteza.chalak@uwa.edu.au;
   mdsayed.iftekhar@uwa.edu.au; ram.pandit@uwa.edu.au;
   Sorada.Tapsuwan@csiro.au; fan.zhang@uwa.edu.au; chunbo.ma@uwa.edu.au
RI Iftekhar, Sayed/J-5298-2013; Ma, Chunbo/D-3478-2011; Polyakov,
   Maksym/G-1523-2010; Tapsuwan, Sorada/G-5869-2010; Lobo,
   Diele/I-9106-2012
OI Ma, Chunbo/0000-0002-9973-2943; Polyakov, Maksym/0000-0002-0193-6658;
   Pandit, Ram/0000-0003-4053-5694; Iftekhar, Md Sayed/0000-0002-2827-2943;
   Tapsuwan, Sorada/0000-0002-8160-3828
FU Australian Research Council Centre of Excellence for Environmental
   Decisions
FX This research was conducted with the support of funding from the
   Australian Research Council Centre of Excellence for Environmental
   Decisions. We acknowledge helpful comments and suggestions of Ms Tammie
   Harold and Mr Tas Thamo of UWA.
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NR 33
TC 6
Z9 6
U1 4
U2 33
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0924-6460
EI 1573-1502
J9 ENVIRON RESOUR ECON
JI Environ. Resour. Econ.
PD SEP
PY 2018
VL 71
IS 1
BP 217
EP 239
DI 10.1007/s10640-017-0147-2
PG 23
WC Economics; Environmental Studies
WE Social Science Citation Index (SSCI)
SC Business & Economics; Environmental Sciences & Ecology
GA GS2SD
UT WOS:000443405200010
DA 2024-09-05
ER
PT J
AU Bornmann, L
   Marewski, JN
AF Bornmann, Lutz
   Marewski, Julian N.
TI Heuristics as conceptual lens for understanding and studying the usage
   of bibliometrics in research evaluation
SO SCIENTOMETRICS
LA English
DT Article
DE Bibliometrics; Fast-and-frugal heuristics; Research evaluation
ID RECOGNITION-BASED JUDGMENTS; PARALLEL CONSTRAINT SATISFACTION; ADAPTIVE
   STRATEGY SELECTION; DECISION-MAKING; CREATIVE ACCOMPLISHMENTS; SCIENCE
   HISTORY; PROCESS MODELS; LINEAR-MODELS; ROBUST BEAUTY; FRUGAL
AB While bibliometrics are widely used for research evaluation purposes, a common theoretical framework for conceptually understanding, empirically studying, and effectively teaching its usage is lacking. In this paper, we outline such a framework: the fast-and-frugal heuristics research program, proposed originally in the context of the cognitive and decision sciences, lends itself particularly well for understanding and investigating the usage of bibliometrics in research evaluations. Such evaluations represent judgments under uncertainty in which typically not all possible options, their consequences, and those consequences' probabilities of occurring may be known. In these situations of incomplete information, candidate descriptive and prescriptive models of human behavior are heuristics. Heuristics are simple strategies that, by exploiting the structure of environments, can aid people to make smart decisions. Relying on heuristics does not mean trading off accuracy against effort: while reducing complexity, heuristics can yield better decisions than more information-greedy procedures in many decision environments. The prescriptive power of heuristics is documented in a cross-disciplinary literature, cutting across medicine, crime, business, sports, and other domains. We outline the fast-and-frugal heuristics research program, provide examples of past empirical work on heuristics outside the field of bibliometrics, explain why heuristics may be especially suitable for studying the usage of bibliometrics, and propose a corresponding conceptual framework.
C1 [Bornmann, Lutz] Adm Headquarters Max Planck Soc, Div Sci & Innovat Studies, Hofgartenstr 8, D-80539 Munich, Germany.
   [Marewski, Julian N.] Univ Lausanne, Fac Business & Econ, Quartier UNIL Dorigny, Batiment Internef, CH-1015 Lausanne, Switzerland.
C3 Max Planck Society; University of Lausanne
RP Bornmann, L (corresponding author), Adm Headquarters Max Planck Soc, Div Sci & Innovat Studies, Hofgartenstr 8, D-80539 Munich, Germany.
EM bornmann@gv.mpg.de; julian.marewski@unil.ch
RI Marewski, Julian N/A-6211-2012; Bornmann, Lutz/A-3926-2008
OI Marewski, Julian/0000-0002-8974-0667; Bornmann, Lutz/0000-0003-0810-7091
FU Max Planck Society
FX Open access funding provided by Max Planck Society. We thank Katrin
   Auspurg, Robin Haunschild, Sven Hug, and Alexander Tekles for very
   helpful comments on an earlier version of this paper. We also thank Marc
   Jekel and another anonymous reviewer for their very thoughtful comments
   and feedback.
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NR 177
TC 26
Z9 26
U1 1
U2 61
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD AUG
PY 2019
VL 120
IS 2
BP 419
EP 459
DI 10.1007/s11192-019-03018-x
PG 41
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA IL1FQ
UT WOS:000477044700004
OA hybrid
DA 2024-09-05
ER
PT J
AU Shu, H
   Zou, CL
   Chen, JY
   Wang, SH
AF Shu, Han
   Zou, Chunlong
   Chen, Jianyu
   Wang, Shenghuai
TI Research on Micro/Nano Surface Flatness Evaluation Method Based on
   Improved Particle Swarm Optimization Algorithm
SO FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
LA English
DT Article
DE flatness error; micro; nano surface; improved particle swarm
   optimization algorithm; minimum zone method; uncertainty; AFM
ID SEGMENTATION; ERROR
AB Flatness error is an important factor for effective evaluation of surface quality. The existing flatness error evaluation methods mainly evaluate the flatness error of a small number of data points on the micro scale surface measured by CMM, which cannot complete the flatness error evaluation of three-dimensional point cloud data on the micro/nano surface. To meet the needs of nano scale micro/nano surface flatness error evaluation, a minimum zone method on the basis of improved particle swarm optimization (PSO) algorithm is proposed. This method combines the principle of minimum zone method and hierarchical clustering method, improves the standard PSO algorithm, and can evaluate the flatness error of nano scale micro/nano surface image data point cloud scanned by atomic force microscope. The influence of the area size of micro/nano surface topography data on the flatness error evaluation results is analyzed. The flatness evaluation results and measurement uncertainty of minimum region method, standard least squares method, and standard PSO algorithm on the basis of the improved PSO algorithm are compared. Experiments show that the algorithm can stably evaluate the flatness error of micro/nano surface topography point cloud data, and the evaluation result of flatness error is more reliable and accurate than standard least squares method and standard PSO algorithm.
C1 [Shu, Han; Zou, Chunlong; Chen, Jianyu; Wang, Shenghuai] Hubei Univ Automot Technol, Sch Mech Engn, Shiyan, Hubei, Peoples R China.
C3 Hubei University of Automotive Technology
RP Wang, SH (corresponding author), Hubei Univ Automot Technol, Sch Mech Engn, Shiyan, Hubei, Peoples R China.
EM shwangkb@163.com
FU National Natural Science Foundation of China [51675167, 51475150];
   National Science and Technology Major Project of China [2018ZX04027001];
   Natural Science Foundation of Hubei Province of China [2020CFB755];
   Research Project of Education Department of Hubei Province of China
   [T2020018, Q20191801]
FX Funding This project is funded by the National Natural Science
   Foundation of China (nos. 51675167 and 51475150), National Science and
   Technology Major Project of China (no. 2018ZX04027001), Natural Science
   Foundation of Hubei Province of China (no. 2020CFB755), and Research
   Project of Education Department of Hubei Province of China (nos.
   T2020018 and Q20191801).
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NR 31
TC 6
Z9 6
U1 7
U2 35
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 2296-4185
J9 FRONT BIOENG BIOTECH
JI Front. Bioeng. Biotechnol.
PD DEC 15
PY 2021
VL 9
AR 775455
DI 10.3389/fbioe.2021.775455
PG 13
WC Biotechnology & Applied Microbiology; Engineering, Biomedical
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biotechnology & Applied Microbiology; Engineering
GA YA4CD
UT WOS:000738282300001
PM 34976973
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU Feng, JY
   Jia, DY
   Cui, L
   Cao, J
   Lin, Z
   Zhang, M
AF Feng, Jiayin
   Jia, Dongyan
   Cui, Li
   Cao, Jing
   Lin, Zhuo
   Zhang, Min
TI COMPARISON OF SVM ALGORITHM AND BP ALGORITHM: STUDY ON THE EVALUATION
   INDEX SYSTEM OF SCIENTIFIC RESEARCH PERFORMANCE OF VOCATIONAL COLLEGES
SO ITALIAN JOURNAL OF PURE AND APPLIED MATHEMATICS
LA English
DT Article
DE SVM algorithm; BP algorithm; vocational colleges; scientific research;
   performance evaluation
AB As the education in China develops rapidly, the scientific research of higher vocational education has gradually drawn extensive attentions. In order to construct a reasonable evaluation model of scientific research performance to further enhance the research enthusiasm of teachers, this study constructed a model based on relevant theories of the support vector machine (SVM) algorithm and the back propagation (BP) algorithm. In addition, the simulation of the model was performed and the accuracy rate and errors of these two algorithms were compared and analyzed. Then the most appropriate algorithm was applied to the evaluation index system. The simulation results showed that, simplified data of scientific research evaluation could be applied as the input data of the SVM algorithm to accurately and effectively construct an evaluation index system of scientific research performances of vocational colleges. Thus a more reasonable and accurate evaluation system was constructed.
C1 [Feng, Jiayin; Jia, Dongyan; Cui, Li; Cao, Jing; Lin, Zhuo; Zhang, Min] Hebei Normal Univ Sci & Technol, Qinhuangdao Haigang Dist 066000, Heibei, Peoples R China.
C3 Hebei Normal University of Science & Technology
RP Feng, JY (corresponding author), Hebei Normal Univ Sci & Technol, Qinhuangdao Haigang Dist 066000, Heibei, Peoples R China.
EM feng_ada2001@163.com
FU Research project on the development of social science in Hebei province
   2018 The people's livelihood project [201803040106]
FX Research project on the development of social science in Hebei province
   2018 The people's livelihood project No.201803040106.
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NR 15
TC 0
Z9 0
U1 0
U2 7
PU FORUM EDITRICE UNIV UDINESE
PI UDINE
PA VIA LARGA 38, UDINE, UD 33100, ITALY
SN 1126-8042
EI 2239-0227
J9 ITAL J PURE APPL MAT
JI Ital. J. Pure Appl. Math.
PD JUL
PY 2018
IS 40
BP 244
EP 255
PG 12
WC Mathematics
WE Emerging Sources Citation Index (ESCI)
SC Mathematics
GA HJ4HP
UT WOS:000457135400024
DA 2024-09-05
ER
PT J
AU Lan, GJ
   Wu, Y
   Hu, F
   Hao, Q
AF Lan, Gongjin
   Wu, Yu
   Hu, Fei
   Hao, Qi
TI Vision-Based Human Pose Estimation via Deep Learning: A Survey
SO IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS
LA English
DT Article
DE Action recognition; bibliometric; deep learning; human performance
   assessment; human pose estimation (HPE)
ID NETWORK
AB Human pose estimation (HPE) has attracted a significant amount of attention from the computer vision community in the past decades. Moreover, HPE has been applied to various domains, such as human-computer interaction, sports analysis, and human tracking via images and videos. Recently, deep learning-based approaches have shown state-of-the-art performance in HPE-based applications. Although deep learning-based approaches have achieved remarkable performance in HPE, a comprehensive review of deep learning-based HPE methods remains lacking in literature. In this article, we provide an up-to-date and in-depth overview of the deep learning approaches in vision-based HPE. We summarize these methods of 2-D and 3-D HPE, and their applications, discuss the challenges and the research trends through bibliometrics, and provide insightful recommendations for future research. This article provides a meaningful overview as introductory material for beginners to deep learning-based HPE, as well as supplementary material for advanced researchers.
C1 [Lan, Gongjin; Wu, Yu; Hao, Qi] Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen 518055, Peoples R China.
   [Hu, Fei] Univ Alabama, Dept Elect & Comp Engn, Tuscaloosa, AL 35487 USA.
C3 Southern University of Science & Technology; University of Alabama
   System; University of Alabama Tuscaloosa
RP Lan, GJ; Hao, Q (corresponding author), Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen 518055, Peoples R China.
EM langj@sustech.edu.cn; wuy@mail.sustech.edu.cn; fei@eng.ua.edu;
   hao.q@sustech.edu.cn
RI HAO, QI/IST-5581-2023
OI HAO, QI/0000-0002-2792-5965; Lan, Gongjin/0000-0003-2020-8186; Wu,
   Yu/0009-0009-4754-0892
FU National Natural Science Foundation of China [61773197]; Shenzhen
   Fundamental Research Program [JCYJ20200109141622964]; Intel ICRI-IACV
   Research Fund [CG 52514373]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61773197, in part by the Shenzhen
   Fundamental Research Program under Grant JCYJ20200109141622964, and in
   part by the Intel ICRI-IACV Research Fund under Grant CG#52514373. This
   article was recommended by Associate Editor Max Mulder. (Corresponding
   authors: Gongjin Lan; Qi Hao.)
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NR 153
TC 7
Z9 7
U1 6
U2 30
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2168-2291
EI 2168-2305
J9 IEEE T HUM-MACH SYST
JI IEEE T. Hum.-Mach. Syst.
PD FEB
PY 2023
VL 53
IS 1
BP 253
EP 268
DI 10.1109/THMS.2022.3219242
EA NOV 2022
PG 16
WC Computer Science, Artificial Intelligence; Computer Science, Cybernetics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 8B3CU
UT WOS:000890810300001
OA Green Submitted
DA 2024-09-05
ER
PT C
AU Lee, YS
   Poh, SW
AF Lee, Yik Sheng
   Poh, Siaw Way
BE Markauskaite, L
   Goodyear, P
   Reimann, P
TI The collaborative work between learning technologists and academics in
   implementing online learning
SO WHO'S LEARNING? WHOSE TECHNOLOGY?, PROCEEDINGS, VOLS 1 AND 2
LA English
DT Proceedings Paper
CT 23rd Annual Conference of the
   Australasian-Society-for-Computers-in-Learning-in-Tertiary-Education
CY DEC 03-06, 2006
CL Univ Sydney, Ctr Res Comp Supported Learning & Cognit, Sydney, AUSTRALIA
HO Univ Sydney, Ctr Res Comp Supported Learning & Cognit
DE online learning; higher education; collaboration; organisational change;
   action research
C1 [Lee, Yik Sheng; Poh, Siaw Way] Tunku Abdul Rahman Coll, Jalan Genting Kelang,Setapak, Kuala Lumpur 53300, Malaysia.
RP Lee, YS (corresponding author), Tunku Abdul Rahman Coll, Jalan Genting Kelang,Setapak, Kuala Lumpur 53300, Malaysia.
EM leeys@mail.tarc.edu.my; pohsw@mail.tarc.edu.my
NR 0
TC 0
Z9 0
U1 0
U2 0
PU SYDNEY UNIV PRESS
PI SYDNEY
PA UNIV SYDNEY LIBRARY F03, SYDNEY, NSW 2006, AUSTRALIA
BN 978-1-920898-48-9
PY 2006
BP 980
EP 980
PG 1
WC Computer Science, Information Systems; Computer Science,
   Interdisciplinary Applications; Education & Educational Research
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Education & Educational Research
GA BFX00
UT WOS:000245234700138
DA 2024-09-05
ER
PT C
AU Abuein, QQ
   Almahmoud, MH
   Elayan, ON
AF Abuein, Qusai Q.
   Almahmoud, Mothanna H.
   Elayan, Omar N.
BE Alsmirat, M
   Almaaitah, A
   Jararweh, Y
   Lloret, J
TI Improving QS Rank Based on The Classification of Authors Research
   Collaboration Using Machine Learning Techniques
SO 2021 12TH INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION
   SYSTEMS (ICICS)
SE International Conference on Information and Communication Systems
LA English
DT Proceedings Paper
CT 12th International Conference on Information and Communication Systems
   (ICICS)
CY MAY 24-26, 2021
CL ELECTR NETWORK
DE QS Ranking; Citation Per Faculty; Academic Collaboration; Co-authorship;
   Machine Learning
ID GLOBAL UNIVERSITY RANKINGS; CO-AUTHORSHIP; PERFORMANCE
AB The importance of universities' global ranking lies in providing a trusty resource, which helps students in choosing the right place to complete their academic future. The global ranking systems are based on several metrics that focus on the study environment, the quality of the provided services, the scientific publications, and the extent of the authors' strength. Quacquarelli Symonds (QS) is the most popular global ranking system, it has Citations Per Faculty (CPF) evaluation metric, which constitutes 20% of the total ranking score. In this research, we aim to find the effect of the research collaboration on increasing the CPF score, in which we apply descriptive analytics on a dataset for Jordan University of Science and Technology (JUST) authors, that is scrapped from the official websites of Google Scholar and Researchgate. Then, we find the authors who have a moderate collaboration through building a classification model using machine learning techniques. The results proved that the research collaboration has a significant impact in increasing authors publications that positively correlated with their total citations, which in turn gives a great opportunity to increase the CPF score. Also, the Support Vector Machine classifier has obtained a 95.27% level of accuracy, which considers as an efficient method in classifying the authors research collaboration into strong and moderate collaboration. Finally, the proposed method can be used to improve the QS ranking and obtain a high scientific standing level for academic institutes.
C1 [Abuein, Qusai Q.; Almahmoud, Mothanna H.; Elayan, Omar N.] Jordan Univ Sci & Technol, Comp Informat Syst, Irbid, Jordan.
C3 Jordan University of Science & Technology
RP Abuein, QQ (corresponding author), Jordan Univ Sci & Technol, Comp Informat Syst, Irbid, Jordan.
EM qabuein@just.edu.jo; mhalmahmood18@cit.just.edu.jo;
   onelayan18@cit.just.edu.jo
OI Abuein, Qusai/0009-0002-4323-4301
CR Anguita D., 2012, ESANN, P441
   [Anonymous], 2006, REGRESSION ANAL
   Batt S, 2020, J ECON EDUC, V51, P317, DOI 10.1080/00220485.2020.1804503
   Ding Shi-fei, 2011, Journal of University of Electronic Science and Technology of China, V40, P2, DOI 10.3969/j.issn.1001-0548.2011.01.001
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   Patro SGK., 2015, Normalization: a preprocessing stage, DOI [10.17148/IARJSET.2015.2305, DOI 10.17148/IARJSET.2015.2305]
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NR 23
TC 0
Z9 0
U1 2
U2 9
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2471-125X
BN 978-1-6654-3351-8
J9 INT CONF INFORM COMM
PY 2021
BP 63
EP 68
DI 10.1109/ICICS52457.2021.9464603
PG 6
WC Computer Science, Theory & Methods; Engineering, Electrical &
   Electronic; Telecommunications
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Engineering; Telecommunications
GA BS1QZ
UT WOS:000694853800010
DA 2024-09-05
ER
PT C
AU Han, H
   Zha, HY
   Giles, CL
AF Han, H
   Zha, HY
   Giles, CL
GP ACM
TI Name disambiguation spectral in author citations using a K-way
   clustering method
SO PROCEEDINGS OF THE 5TH ACM/IEEE JOINT CONFERENCE ON DIGITAL LIBRARIES,
   PROCEEDINGS
SE ACM-IEEE Joint Conference on Digital Libraries JCDL
LA English
DT Proceedings Paper
CT 5th ACM/IEEE Joint Conference on Digital Libraries
CY JUN 07-11, 2005
CL Denver, CO
DE name disambiguation; feature selection; unsupervised learning; spectral
   clustering
AB An author may have multiple names and multiple authors may share the same name simply due to name abbreviations, identical names, or name misspellings in publications or bibliographies (citations)(1). This can produce name ambiguity which can affect the performance of document retrieval, web search, and database integration, and may cause improper attribution of credit. Proposed here is an unsupervised learning approach using K-way spectral clustering that disambiguates authors in citations. The approach utilizes three types of citation attributes: co-author names, paper titles, and publication venue titles(2). The approach is illustrated with 16 name datasets with citations collected from the DBLP database bibliography and author home pages and shows that name disambiguation can be achieved using these citation attributes.
C1 Yahoo Inc, Sunnyvale, CA 95129 USA.
C3 Yahoo! Inc; Yahoo! Inc United States
EM huihan@yahoo-inc.com; zha@cse.psu.edu; giles@ist.psu.edu
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   [No title captured]
NR 50
TC 132
Z9 162
U1 1
U2 21
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA
SN 2575-7865
EI 2575-8152
BN 1-58113-876-8
J9 ACM-IEEE J CONF DIG
PY 2005
BP 334
EP 343
DI 10.1145/1065385.1065462
PG 10
WC Computer Science, Information Systems; Computer Science,
   Interdisciplinary Applications; Information Science & Library Science
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Information Science & Library Science
GA BCO70
UT WOS:000230429800058
DA 2024-09-05
ER
PT C
AU Frankowska, A
   Pawlik, B
AF Frankowska, Agata
   Pawlik, Bartosz
BE Biele, C
   Kacprzyk, J
   Kopec, W
   Owsinski, JW
   Romanowski, A
   Sikorski, M
TI A Decade of Artificial Intelligence Research in the European Union: A
   Bibliometric Analysis
SO DIGITAL INTERACTION AND MACHINE INTELLIGENCE, MIDI 2021
SE Lecture Notes in Networks and Systems
LA English
DT Proceedings Paper
CT 9th Machine Intelligence and Digital Interaction Conference (MIDI)
CY DEC 09-10, 2021
CL Warsaw, POLAND
DE Artificial intelligence; Bibliometric analysis; Bibliometric indicators;
   Clustering; European Union; Principal component analysis
AB In recent years, the body of research on artificial intelligence (AI) has grown rapidly. As the European Union strives for excellence in AI development, this study aims to establish the publication achievements in the field among its member states between 2010 and 2019. We applied clustering and principal component analysis (PCA) on a set of bibliometric data concerning research publications on AI obtained from Scopus. The results reveal that while the union's most populous countries-the United Kingdom, Germany, France, Spain, and Italy- were the most prolific producers of AI publications between 2010 and 2019, the highest impact was noted for publications that originated in the Nordic and Benelux countries, as well as in Austria and Ireland. Analysis confirms that the division between 'old' and 'new' member states has endured: the nations that joined the EU after 2004 recorded the lowest results in scientific output and impact in the AI field. This study can assist research agencies and researchers in developing a broad grasp of the current state of AI research.
C1 [Frankowska, Agata; Pawlik, Bartosz] Natl Informat Proc Inst, Warsaw, Poland.
C3 Information Processing Center - National Research Institute
RP Frankowska, A (corresponding author), Natl Informat Proc Inst, Warsaw, Poland.
EM agata.frankowska@opi.org.pl; bartosz.pawlik@opi.org.pl
RI Pawlik, Bogdan/W-2346-2018
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NR 34
TC 1
Z9 1
U1 2
U2 9
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 2367-3370
EI 2367-3389
BN 978-3-031-11432-8; 978-3-031-11431-1
J9 LECT NOTE NETW SYST
PY 2022
VL 440
BP 52
EP 62
DI 10.1007/978-3-031-11432-8_5
PG 11
WC Computer Science, Artificial Intelligence
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BU3OQ
UT WOS:000892355400005
OA hybrid
DA 2024-09-05
ER
PT C
AU Boukhers, Z
   Asundi, NB
AF Boukhers, Zeyd
   Asundi, Nagaraj Bahubali
BE Silvello, G
   Corcho, O
   Manghi, P
   DiNunzio, GM
   Golub, K
   Ferro, N
   Poggi, A
TI <i>Whois</i>? Deep Author Name Disambiguation Using Bibliographic Data
SO LINKING THEORY AND PRACTICE OF DIGITAL LIBRARIES (TPDL 2022)
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT 26th International Conference on Theory and Practice of Digital
   Libraries (TPDL)
CY SEP 20-23, 2022
CL Padua, ITALY
DE Author name disambiguation; Entity linkage; Bibliographic data; Neural
   networks; Classification
AB As the number of authors is increasing exponentially over years, the number of authors sharing the same names is increasing proportionally. This makes it challenging to assign newly published papers to their adequate authors. Therefore, Author Name Ambiguity (ANA) is considered a critical open problem in digital libraries. This paper proposes an Author Name Disambiguation (AND) approach that links author names to their real-world entities by leveraging their co-authors and domain of research. To this end, we use a collection from the DBLP repository that contains more than 5 million bibliographic records authored by around 2.6 million co-authors. Our approach first groups authors who share the same last names and same first name initials. The author within each group is identified by capturing the relation with his/her co-authors and area of research, which is represented by the titles of the validated publications of the corresponding author. To this end, we train a neural network model that learns from the representations of the co-authors and titles. We validated the effectiveness of our approach by conducting extensive experiments on a large dataset.
C1 [Boukhers, Zeyd; Asundi, Nagaraj Bahubali] Univ Koblenz Landau, Inst Web Sci & Technol WeST, Koblenz, Germany.
   [Boukhers, Zeyd] Fraunhofer Inst Appl Informat Technol, St Augustin, Germany.
C3 University of Koblenz & Landau; Fraunhofer Gesellschaft
RP Boukhers, Z (corresponding author), Univ Koblenz Landau, Inst Web Sci & Technol WeST, Koblenz, Germany.; Boukhers, Z (corresponding author), Fraunhofer Inst Appl Informat Technol, St Augustin, Germany.
EM boukhers@uni-koblenz.de; nagarajbahubali@uni-koblenz.de
RI Boukhers, Zeyd/HZL-0733-2023
OI Boukhers, Zeyd/0000-0001-9778-9164; Asundi, Nagaraj
   Bahubali/0000-0002-1044-7047
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NR 38
TC 6
Z9 6
U1 1
U2 11
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 0302-9743
EI 1611-3349
BN 978-3-031-16802-4; 978-3-031-16801-7
J9 LECT NOTES COMPUT SC
PY 2022
VL 13541
BP 201
EP 215
DI 10.1007/978-3-031-16802-4_16
PG 15
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods; Information Science & Library Science
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Information Science & Library Science
GA BU0DS
UT WOS:000867565900016
DA 2024-09-05
ER
PT J
AU Lodge, JM
   Thompson, K
   Corrin, L
AF Lodge, Jason M.
   Thompson, Kate
   Corrin, Linda
TI Mapping out a research agenda for generative artificial intelligence in
   tertiary education
SO AUSTRALASIAN JOURNAL OF EDUCATIONAL TECHNOLOGY
LA English
DT Article
DE generative artificial intelligence; research; assessment
AB Generative artificial intelligence (AI) has taken the world by storm. In this editorial, we outline some of the key areas of tertiary education impacted by large language models and associated applications that will require re-thinking and research to address in the short to medium term. Given how rapidly generative AI developments are currently occurring, this editorial is speculative. Although there is a long history of research on AI in education, the current situation is both unprecedented and seemingly not something that the AI in education community fully predicted. We also outline the editorial position of AJET in regards to generative AI to assist authors using tools such as ChatGPT as any part of the research or writing process. This is a rapidly evolving space. We have attempted to provide some clarity in this editorial while acknowledging that we may need to revisit some or all of what we offer here in the weeks and months ahead.
C1 [Lodge, Jason M.] Univ Queensland, St Lucia, Australia.
   [Thompson, Kate] Queensland Univ Technol, Brisbane, Australia.
   [Corrin, Linda] Deakin Univ, Burwood, Australia.
C3 University of Queensland; Queensland University of Technology (QUT);
   Deakin University
RP Lodge, JM (corresponding author), Univ Queensland, St Lucia, Australia.
EM jason.lodge@uq.edu.au
RI BUCCINI, FRANCESCA/HTM-4917-2023; Lodge, Jason M/F-8079-2018; Corrin,
   Linda/AAD-8545-2019
OI Thompson, Kate/0000-0003-0738-0205; Lodge, Jason/0000-0001-6330-6160;
   Corrin, Linda/0000-0002-1593-3271
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NR 18
TC 42
Z9 43
U1 128
U2 311
PU AUSTRALASIAN SOC COMPUTERS LEARNING TERTIARY EDUCATION-ASCILITE
PI TUGUN
PA UNIT 5, 202 COODE ST, PO BOX 350, TUGUN, 4224, AUSTRALIA
SN 1449-3098
EI 1449-5554
J9 AUSTRALAS J EDUC TEC
JI Australas. J. Educ. Technol.
PY 2023
VL 39
IS 1
BP 18
EP 18
DI 10.14742/ajet.8695
PG 1
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA G4FT6
UT WOS:000988740600002
OA gold
HC Y
HP N
DA 2024-09-05
ER
PT J
AU Liu, JY
   Kong, XJ
   Xia, F
   Bai, XM
   Wang, L
   Qing, Q
   Lee, I
AF Liu, Jiaying
   Kong, Xiangjie
   Xia, Feng
   Bai, Xiaomei
   Wang, Lei
   Qing, Qing
   Lee, Ivan
TI Artificial Intelligence in the 21st Century
SO IEEE ACCESS
LA English
DT Article
DE Artificial intelligence; data analytics; scientific impact; science of
   science; data science
ID SCIENTOMETRIC ANALYSIS; SCIENCE; TRENDS; DECADE
AB The field of artificial intelligence (AI) has shown an upward trend of growth in the 21st century (from 2000 to 2015). The evolution in AI has advanced the development of human society in our own time, with dramatic revolutions shaped by both theories and techniques. However, the multidisciplinary and fastgrowing features make AI a field in which it is difficult to be well understood. In this paper, we study the evolution of AI at the beginning of the 21st century using publication metadata extracted from 9 top-tier journals and 12 top-tier conferences of this discipline. We find that the area is in the sustainable development and its impact continues to grow. From the perspective of reference behavior, the decrease in self-references indicates that the AI is becoming more and more open-minded. The influential papers/researchers/institutions we identified outline landmarks in the development of this field. Last but not least, we explore the inner structure in terms of topics' evolution over time. We have quantified the temporal trends at the topic level and discovered the inner connection among these topics. These findings provide deep insights into the current scientific innovations, as well as shedding light on funding policies.
C1 [Liu, Jiaying; Kong, Xiangjie; Xia, Feng; Wang, Lei; Qing, Qing] Dalian Univ Technol, Sch Software, Key Lab Ubiquitous Network & Serv Software Liaoni, Dalian 116620, Peoples R China.
   [Bai, Xiaomei] Anshan Normal Univ, Comp Ctr, Anshan 114007, Peoples R China.
   [Lee, Ivan] Univ South Australia, Sch Informat Technol & Math Sci, Adelaide, SA 5095, Australia.
C3 Dalian University of Technology; Anshan Normal University; University of
   South Australia
RP Xia, F (corresponding author), Dalian Univ Technol, Sch Software, Key Lab Ubiquitous Network & Serv Software Liaoni, Dalian 116620, Peoples R China.
EM f.xia@ieee.org
RI Xia, Feng/Y-2859-2019; Liu, JY/GYJ-0138-2022; wang, lei/U-2378-2019;
   Kong, Xiangjie/B-8809-2016; Lee, Ivan/F-4131-2013
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NR 35
TC 92
Z9 98
U1 3
U2 54
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2018
VL 6
BP 34403
EP 34421
DI 10.1109/ACCESS.2018.2819688
PG 19
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering; Telecommunications
GA GN6RS
UT WOS:000439222700017
OA gold
DA 2024-09-05
ER
PT C
AU Zhang, WD
   Pang, L
   Yang, QL
   Zhao, CH
AF Zhang, Wendong
   Pang, Liang
   Yang, Qingliang
   Zhao, Chaohui
GP IEEE
TI Research on Electromagnetic Performance Optimization of Tangential
   Magnetizing Parallel Structure Hybrid Excitation Synchronous Motor Based
   on Particle Swarm Optimization Algorithm
SO 2022 6TH INTERNATIONAL CONFERENCE ON POWER AND ENERGY ENGINEERING, ICPEE
SE International Conference on Power and Energy Engineering
LA English
DT Proceedings Paper
CT 6th International Conference on Power and Energy Engineering (ICPEE)
CY NOV 25-27, 2022
CL ELECTR NETWORK
DE tangential focus type; hybrid excitation; torque ripple; particle swarm
   optimization
AB In order to reduce the torque ripple of the tangential magnetizing parallel structure hybrid excitation synchronous motor (TMPS-HESM) and improve the efficiency of the motor. The TMPS-HESM torque ripple model was established by analyzing the stator and rotor magnetic force. Then, using Maxwell & Workbench & optiSLong co-simulation tool, particle swarm optimization (PSO) algorithm was used to optimize the length of the air gap and the width and length of permanent magnet. The optimal solution is screened by sensitivity analysis. The results show that the optimized motor torque ripple and cogging torque are reduced, and the average torque is improved. Meanwhile, the optimization saves the use of permanent magnets, reduces the loss of the motor, and improves the maximum efficiency of the motor to 93%.
C1 [Zhang, Wendong; Pang, Liang; Zhao, Chaohui] Shanghai Dianji Univ, Sch Elect, Shanghai, Peoples R China.
   [Yang, Qingliang] Shanghai Maritime Univ, Logist Engn Coll, Shanghai, Peoples R China.
C3 Shanghai Dianji University; Shanghai Maritime University
RP Zhang, WD (corresponding author), Shanghai Dianji Univ, Sch Elect, Shanghai, Peoples R China.
EM 1427912006@qq.com; 1048789838@qq.com; yangql@sdju.edu.cn;
   zhaoch@sdju.edu.cn
RI hu, xin/KHT-2406-2024; Yang, Qingliang/GRY-1981-2022
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NR 17
TC 0
Z9 0
U1 0
U2 2
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-6654-6475-8
J9 Power and Energy Eng
PY 2022
BP 306
EP 311
DI 10.1109/ICPEE56418.2022.10050269
PG 6
WC Energy & Fuels; Engineering, Electrical & Electronic
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Energy & Fuels; Engineering
GA BV3SB
UT WOS:001022922300052
DA 2024-09-05
ER
PT J
AU Chen, XL
   Zou, D
   Xie, HR
   Cheng, G
   Liu, CX
AF Chen, Xieling
   Zou, Di
   Xie, Haoran
   Cheng, Gary
   Liu, Caixia
TI Two Decades of Artificial Intelligence in Education: Contributors,
   Collaborations, Research Topics, Challenges, and Future Directions
SO EDUCATIONAL TECHNOLOGY & SOCIETY
LA English
DT Article
DE Artificial intelligence in education; Structural topic modeling;
   Bibliometric analysis; Research topics; Research evolution
ID NEURAL-NETWORK
AB With the increasing use of Artificial Intelligence (AI) technologies in education, the number of published studies in the field has increased. However, no large-scale reviews have been conducted to comprehensively investigate the various aspects of this field. Based on 4,519 publications from 2000 to 2019, we attempt to fill this gap and identify trends and topics related to AI applications in education (AIEd) using topic-based bibliometrics. Results of the review reveal an increasing interest in using AI for educational purposes from the academic community The main research topics include intelligent tutoring systems for special education; natural language processing for language education; educational robots for AI education; educational data mining for performance prediction; discourse analysis in computer-supported collaborative learning; neural networks for teaching evaluation; affective computing for learner emotion detection; and recommender systems for personalized learning. We also discuss the challenges and future directions of AIEd.
C1 [Chen, Xieling; Cheng, Gary; Liu, Caixia] Educ Univ Hong Kong, Dept Math & Informat Technol, Hong Kong, Peoples R China.
   [Zou, Di] Educ Univ Hong Kong, Dept English Language Educ, Hong Kong, Peoples R China.
   [Xie, Haoran] Lingnan Univ, Dept Comp & Decis Sci, Hong Kong, Peoples R China.
   [Liu, Caixia] Nanjing Normal Univ, Inst EduInfo Sci & Engn, Nanjing, Peoples R China.
C3 Education University of Hong Kong (EdUHK); Education University of Hong
   Kong (EdUHK); Lingnan University; Nanjing Normal University
RP Zou, D (corresponding author), Educ Univ Hong Kong, Dept English Language Educ, Hong Kong, Peoples R China.
EM xielingchen0708@gmail.com; dizoudaisy@gmail.com; hrxie2@gmail.com;
   chengks@eduhk.hk; cxsqz@126.com
RI Xie, Haoran/AFS-3515-2022; BUCCINI, FRANCESCA/HTM-4917-2023
OI Xie, Haoran/0000-0003-0965-3617; PV, THAYYIB/0000-0001-8929-0398; ZOU,
   Di/0000-0001-8435-9739; Cheng, Gary/0000-0002-5614-3348
FU Lingnan University, Hong Kong [DR21A5, DB21A4]; Research Grants Council
   of Hong Kong SAR, China [18601118]; Education University of Hong Kong
   [MIT02/19-20]
FX An abstract entitled "A Bibliometric Study on Artificial Intelligence in
   Education for Two Decades" from this paper was presented at the
   International Conference on Education and Artificial Intelligence 2020,
   The Education University of Hong Kong, 9-11 November 2020, Hong Kong.
   Haoran Xie's work in this research is supported by the Direct Grant
   (DR21A5) and the Faculty Research Fund (DB21A4) at Lingnan University,
   Hong Kong. Gary Cheng's work is supported by General Research Fund (No.
   18601118) of Research Grants Council of Hong Kong SAR, China, and
   One-off Special Fund from Central and Faculty Fund in Support of
   Research from 2019/20 to 2021/22 (MIT02/19-20) of The Education
   University of Hong Kong.
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PU INT FORUM EDUCATIONAL TECHNOLOGY & SOC, NATL TAIWAN NORMAL UNIV
PI Taipei City
PA No.162, Sec. 1, Heping E. Rd., Da-an Dist, Taipei City, TAIWAN
SN 1176-3647
EI 1436-4522
J9 EDUC TECHNOL SOC
JI Educ. Technol. Soc.
PD JAN
PY 2022
VL 25
IS 1
BP 28
EP 47
PG 20
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA ZS7DL
UT WOS:000768622600003
HC Y
HP N
DA 2024-09-05
ER
PT J
AU Khosravi, H
   Shafie, MR
   Hajiabadi, M
   Raihan, AS
   Ahmed, I
AF Khosravi, Hamed
   Shafie, Mohammad Reza
   Hajiabadi, Morteza
   Raihan, Ahmed Shoyeb
   Ahmed, Imtiaz
TI Chatbots and ChatGPT: a bibliometric analysis and systematic review of
   publications in Web of Science and Scopus databases
SO INTERNATIONAL JOURNAL OF DATA MINING MODELLING AND MANAGEMENT
LA English
DT Article
DE chatbot; ChatGPT; bibliometrics; artificial intelligence; natural
   language processing; NLP; generative artificial intelligence
ID ARTIFICIAL-INTELLIGENCE; BRADFORD LAW; SCATTERING; FIELD
AB This paper presents a bibliometric analysis of the scientific literature related to chatbots, focusing specifically on ChatGPT. Chatbots have gained increasing attention recently, with an annual growth rate of 19.16% and 27.19% on the Web of Sciences (WoS) and Scopus, respectively. The research consists of two study phases: 1) an analysis of chatbot literature; 2) a comprehensive review of scientific documents on ChatGPT. In the first phase, a bibliometric analysis is conducted on all the published literature from both Scopus (5,839) and WoS (2,531) databases covering the period from 1998 to 2023. Consequently, bibliometric analysis has been carried out on ChatGPT publications, and 45 published studies have been analysed thoroughly based on their methods, novelty, and conclusions. Overall, the study aims to provide guidelines for researchers to conduct their research more effectively in the field of chatbots and specifically highlight significant areas for future investigation into ChatGPT.
C1 [Khosravi, Hamed; Raihan, Ahmed Shoyeb; Ahmed, Imtiaz] West Virginia Univ, Dept Ind & Management Syst Engn, Morgantown, WV 26505 USA.
   [Shafie, Mohammad Reza] Iran Univ Sci & Technol, Dept Elect Engn, Tehran, Iran.
   [Hajiabadi, Morteza] Iran Univ Sci & Technol, Sch Comp Engn, Tehran, Iran.
C3 West Virginia University; Iran University Science & Technology; Iran
   University Science & Technology
RP Khosravi, H (corresponding author), West Virginia Univ, Dept Ind & Management Syst Engn, Morgantown, WV 26505 USA.
EM hk00024@mix.wvu.edu; Mr.shafie7731@gmail.com; hajiabadi1377@gmail.com;
   ar00065@mix.wvu.edu; imtiaz.ahmed@mail.wvu.edu
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NR 94
TC 0
Z9 0
U1 8
U2 8
PU INDERSCIENCE ENTERPRISES LTD
PI GENEVA
PA WORLD TRADE CENTER BLDG, 29 ROUTE DE PRE-BOIS, CASE POSTALE 856, CH-1215
   GENEVA, SWITZERLAND
SN 1759-1163
EI 1759-1171
J9 INT J DATA MIN MODEL
JI Int. J. Data Min. Model. Manag.
PY 2024
VL 16
IS 2
DI 10.1504/IJDMMM.2024.138824
PG 36
WC Computer Science, Artificial Intelligence
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA SW5D2
UT WOS:001237489300005
OA Green Submitted
DA 2024-09-05
ER
PT C
AU Ping, L
AF Ping, Li
BE Wang, HS
TI The effect research of multimedia-assisted music teaching based on
   principal component analysis- For example HeNan xuchang college
SO PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON SCIENCE AND SOCIAL
   RESEARCH (ICSSR 2013)
SE Advances in Intelligent Systems Research
LA English
DT Proceedings Paper
CT 2nd International Conference on Science and Social Research (ICSSR)
CY 2013
CL Beijing, PEOPLES R CHINA
DE Multimedia-assisted instruction; Teaching effect; Principal component
   analysis
AB Currently, multimedia-assisted music instruction has been widely used in colleges and universities. The diversification trend of music teaching and greatly promoted students' absorbing ability have increased the extensive use of multimedia-assisted instruction in music teaching. To solve the problems like teachers rely too much on multimedia-assisted instruction in music teaching process, lack of effective interaction between teachers and students and lack of effectiveness, 126 pieces of music multimedia teaching effect questionnaires are analyzed and the music professional classes comprehensive scores of art major students of two sessions are compared. Principal component analysis method is applied to analyze the main factors influencing the music multimedia-assisted instruction effect. Conclusions have been drawn from the research that multimedia-assisted instruction can improve the absorbing ability of students' music learning, that teaching courseware of informative and interesting content may enhance students' interest in learning, that effective interaction between teachers and students can improve the effectiveness of multimedia music instruction, and that students accepting more multimedia-assisted music instruction turn to have better absorbing ability.
C1 Xuchang Univ, Xuchang 461000, Henan, Peoples R China.
C3 Xuchang University
RP Ping, L (corresponding author), Xuchang Univ, Xuchang 461000, Henan, Peoples R China.
EM 360008910@qq.com
CR Dou Wenyu, 2009, MULTIMEDIA TECHNOLOG
   Li Fei, 2006, PRELIMINARY STUDY AD
   Lu Xiaoxu, 2005, COMPUTER MUSIC TECHN
   Luo Hongmin, 2010, BIG STAGE
   Wang Chenglai, 2010, EFFECTIVE APPL MULTI
   zhang Jing, 2009, COMPREHENSIVE DIGEST
NR 6
TC 0
Z9 0
U1 0
U2 0
PU ATLANTIS PRESS
PI PARIS
PA 29 AVENUE LAVMIERE, PARIS, 75019, FRANCE
SN 1951-6851
BN 978-90-78677-75-8
J9 ADV INTEL SYS RES
PY 2013
VL 64
BP 861
EP 866
PG 6
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BIE82
UT WOS:000327937000196
DA 2024-09-05
ER
PT J
AU Wu, MF
   Yu, KF
   Zhao, ZG
   Zhu, B
AF Wu, Mingfen
   Yu, Kefu
   Zhao, Zhigang
   Zhu, Bin
TI Knowledge structure and global trends of machine learning in stroke over
   the past decade: A scientometric analysis
SO HELIYON
LA English
DT Article
DE Machine learning; Stroke; Deep learning; Global trends; Algorithm
ID ARTIFICIAL-INTELLIGENCE; RISK-FACTORS; CHINA; CARE; EPIDEMIOLOGY
AB Objective: Machine learning (ML) models have been widely applied in stroke prediction, diagnosis, treatment, and prognosis assessment. We aimed to conduct a comprehensive scientometrics analysis of studies related to ML in stroke and reveal its current status, knowledge structure, and global trends. Methods: All documents related to ML in stroke were retrieved from the Web of Science database on March 15, 2023. We refined the documents by including only original articles and reviews in the English language. The literature published over the past decade was imported into scientometrics software for influence detection and collaborative network analysis. Results: 2389 related publications were included. The annual publication outputs demonstrated explosive growth, with an average growth rate of 63.99 %. Among the 90 countries/regions involved, the United States (729 articles) and China (636 articles) were the most productive countries. Frontiers in Neurology was the most prolific journal with 94 articles. 234 highly cited articles, each with more than 31 citations, were detected. Keyword analysis revealed a total of 5333 keywords, with a predominant focus on the application of ML models in the early diagnosis, classification, and prediction of "acute ischemic stroke" and "atrial fibrillation-related stroke". The keyword "classification" had the first and longest burst, spanning from 2013 to 2018. 'Upport vector machine' got the strongest burst strength with 6.2. Keywords such as 'mechanical thrombectomy', 'expression', and 'prognosis' experienced bursts in 2022 and have continued to be prominent. Conclusion: The applications of ML in stroke are increasingly diverse and extensive, with researchers showing growing interest over the past decade. However, the clinical application of ML in stroke is still in its early stages, and several limitations and challenges need to be addressed for its widespread adoption in clinical practice.
C1 [Wu, Mingfen; Yu, Kefu; Zhao, Zhigang; Zhu, Bin] Capital Med Univ, Beijing Tiantan Hosp, Dept Pharm, Beijing 100070, Peoples R China.
   [Zhao, Zhigang; Zhu, Bin] Capital Med Univ, Beijing Tiantan Hosp, Dept Pharm, 119 South Fourth Ring West Rd, Beijing 100070, Peoples R China.
C3 Capital Medical University; Capital Medical University
RP Zhao, ZG; Zhu, B (corresponding author), Capital Med Univ, Beijing Tiantan Hosp, Dept Pharm, 119 South Fourth Ring West Rd, Beijing 100070, Peoples R China.
EM 1022zzg@sina.com; zbtcm@163.com
OI Zhu, Bin/0000-0003-1771-9874; Wu, Mingfen/0000-0002-2761-2299
FU Nature Foundation of Capital Medical University [PYZ23122]
FX <B>Acknowledgements</B> The authors would like to express their
   appreciation to Professor CM Chen who invented Citespace, and Professor
   Van Eck and Waltman who invented VOSviewer, which are free to use. This
   study was supported by the Nature Foundation of Capital Medical
   University (Number: PYZ23122) .
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NR 67
TC 1
Z9 1
U1 9
U2 10
PU CELL PRESS
PI CAMBRIDGE
PA 50 HAMPSHIRE ST, FLOOR 5, CAMBRIDGE, MA 02139 USA
EI 2405-8440
J9 HELIYON
JI Heliyon
PD JAN 30
PY 2024
VL 10
IS 2
AR e24230
DI 10.1016/j.heliyon.2024.e24230
EA JAN 2024
PG 15
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA HT4X3
UT WOS:001161756800001
PM 38288018
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU Yao, RF
   Shen, ZH
   Xu, XY
   Ling, GX
   Xiang, RW
   Song, TY
   Zhai, F
   Zhai, YX
AF Yao, Rufan
   Shen, Zhenhua
   Xu, Xinyi
   Ling, Guixia
   Xiang, Rongwu
   Song, Tingyan
   Zhai, Fei
   Zhai, Yuxuan
TI Knowledge mapping of graph neural networks for drug discovery: a
   bibliometric and visualized analysis
SO FRONTIERS IN PHARMACOLOGY
LA English
DT Article
DE bibliometric analysis; graph neural network; drug discovery; VOSviewer;
   Citespace
ID CONVOLUTIONAL NETWORK; EMERGING TRENDS; PREDICTION
AB Introduction In recent years, graph neural network has been extensively applied to drug discovery research. Although researchers have made significant progress in this field, there is less research on bibliometrics. The purpose of this study is to conduct a comprehensive bibliometric analysis of graph neural network applications in drug discovery in order to identify current research hotspots and trends, as well as serve as a reference for future research. Methods Publications from 2017 to 2023 about the application of graph neural network in drug discovery were collected from the Web of Science Core Collection. Bibliometrix, VOSviewer, and Citespace were mainly used for bibliometric studies. Results and Discussion In this paper, a total of 652 papers from 48 countries/regions were included. Research interest in this field is continuously increasing. China and the United States have a significant advantage in terms of funding, the number of publications, and collaborations with other institutions and countries. Although some cooperation networks have been formed in this field, extensive worldwide cooperation still needs to be strengthened. The results of the keyword analysis clarified that graph neural network has primarily been applied to drug-target interaction, drug repurposing, and drug-drug interaction, while graph convolutional neural network and its related optimization methods are currently the core algorithms in this field. Data availability and ethical supervision, balancing computing resources, and developing novel graph neural network models with better interpretability are the key technical issues currently faced. This paper analyzes the current state, hot spots, and trends of graph neural network applications in drug discovery through bibliometric approaches, as well as the current issues and challenges in this field. These findings provide researchers with valuable insights on the current status and future directions of this field.
C1 [Yao, Rufan; Shen, Zhenhua; Xu, Xinyi; Ling, Guixia; Xiang, Rongwu; Song, Tingyan; Zhai, Fei; Zhai, Yuxuan] Shenyang Pharmaceut Univ, Fac Med Device, Shenyang, Peoples R China.
C3 Shenyang Pharmaceutical University
RP Zhai, F; Zhai, YX (corresponding author), Shenyang Pharmaceut Univ, Fac Med Device, Shenyang, Peoples R China.
EM 106030309@syphu.edu.cn; 278763282@qq.com
FU Scientific Research Foundation of the Education Bureau of Liaoning
   Province [LJKR0302]; National Natural Science Foundation of China
   [U1908215]; Basic Scientific Research Youth Project of Liaoning
   Provincial Department of Education [JYTQN2023337]
FX The author(s) declare that financial support was received for the
   research, authorship, and/or publication of this article. The General
   Project supported by the Scientific Research Foundation of the Education
   Bureau of Liaoning Province (LJKR0302) Special Fund of the National
   Natural Science Foundation of China (U1908215) Basic Scientific Research
   Youth Project of Liaoning Provincial Department of Education
   (JYTQN2023337).
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NR 71
TC 0
Z9 0
U1 13
U2 13
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 1663-9812
J9 FRONT PHARMACOL
JI Front. Pharmacol.
PD MAY 10
PY 2024
VL 15
AR 1393415
DI 10.3389/fphar.2024.1393415
PG 19
WC Pharmacology & Pharmacy
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Pharmacology & Pharmacy
GA RU4S3
UT WOS:001230166300001
PM 38799167
OA Green Published, gold
DA 2024-09-05
ER
PT C
AU Papachristopoulos, L
   Kleidis, N
   Sfakakis, M
   Tsakonas, G
   Papatheodorou, C
AF Papachristopoulos, Leonidas
   Kleidis, Nikos
   Sfakakis, Michalis
   Tsakonas, Giannis
   Papatheodorou, Christos
BE Garoufallou, E
   Hartley, RJ
   Gaitanou, P
TI Discovering the Topical Evolution of the Digital Library Evaluation
   Community
SO METADATA AND SEMANTICS RESEARCH, MTSR 2015
SE Communications in Computer and Information Science
LA English
DT Proceedings Paper
CT 9th Conference on Metadata and Semantics Research (MTSR)
CY SEP 09-11, 2015
CL Manchester, ENGLAND
DE Research trends discovery; Digital library evaluation; Topic modeling;
   Metadata extraction; Latent Dirichlet Allocation
AB The successful management of textual information is a rising challenge for all the researchers' communities, in order firstly to assess its current and previous statuses and secondly to enrich the level of their metadata description. The huge amount of unstructured data that is produced has consequently populated text mining techniques for its interpretation, selection and metadata enrichment opportunities that provides. Scientific production regarding Digital Libraries (DLs) evaluation has been grown in size and has broaden the scope of coverage as it consists a complex and multidimensional field. The current study proposes a probabilistic topic modeling implemented on a domain corpus from the JCDL, ECDL/TDPL and ICADL conferences proceedings in the period 2001-2013, aiming at the unveiling of its topics and subject temporal analysis, for exploiting and extracting semantic metadata from large corpora in an automatic way.
C1 [Papachristopoulos, Leonidas; Sfakakis, Michalis; Papatheodorou, Christos] Ionian Univ, Dept Arch Lib Sci & Museol, Corfu, Greece.
   [Kleidis, Nikos] Athens Univ Econ & Business, Dept Informat, Athens, Greece.
   [Tsakonas, Giannis] Univ Patras, Lib & Informat Ctr, Patras, Greece.
   [Papachristopoulos, Leonidas; Papatheodorou, Christos] Athena Res Ctr, IMIS, Digital Curat Unit, Athens, Greece.
C3 Ionian University; Athens University of Economics & Business; University
   of Patras
RP Papachristopoulos, L (corresponding author), Ionian Univ, Dept Arch Lib Sci & Museol, Corfu, Greece.
EM l11papa@ionio.gr; klidisnik@aueb.gr; sfakakis@ionio.gr;
   john@lis.upatras.gr; papatheodor@ionio.gr
RI Papachristopoulos, Leonidas/AAP-8859-2020; Papatheodorou,
   Christos/AAD-2749-2020; Tsakonas, Giannis/M-3219-2019; Sfakakis,
   Michalis/AAR-6503-2021
OI Papachristopoulos, Leonidas/0000-0002-4148-2689; Papatheodorou,
   Christos/0000-0002-9025-6469; Tsakonas, Giannis/0000-0002-8786-9440;
   Sfakakis, Michalis/0000-0003-2973-7455
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NR 21
TC 4
Z9 4
U1 2
U2 14
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 1865-0929
EI 1865-0937
BN 978-3-319-24129-6; 978-3-319-24128-9
J9 COMM COM INF SC
PY 2015
VL 544
BP 101
EP 112
DI 10.1007/978-3-319-24129-6_9
PG 12
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Interdisciplinary Applications; Computer
   Science, Theory & Methods; Robotics
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Robotics
GA BE1NZ
UT WOS:000368260500009
DA 2024-09-05
ER
PT J
AU Ye, YE
   Na, JC
AF Ye, Yingxin Estella
   Na, Jin-Cheon
TI Who is mentioning COVID-19 articles on twitter? Classifying twitter
   users in the context of scholarly communication
SO SOCIAL NETWORK ANALYSIS AND MINING
LA English
DT Article
DE Altmetrics; Twitter; Scholarly communication; User classification; Graph
   neural networks; Social networks; Machine learning
ID TWEETS
AB This study aims to examine the demographics of participants engaged in scholarly communication on Twitter, which has been rebranded as X. Firstly, based on a dataset of tweets citing COVID-19 publications, it proposed a more precise classification system consisting of eleven user categories for individuals who tweeted academic publication. Secondly, it explores the effectiveness of graph neural network models (GNNs) in combination with a transformer-based text classification model (specifically, BERT) to classify these newly defined user categories. The findings of this research highlight that GNNs can effectively interpret the social networks within scholarly communication, and complement text classification models in characterizing user types. The best-performing model achieved an accuracy rate of 84.05 percent in classifying user categories for a dataset of 10,048 labeled users. Subsequently, this model was employed to analyze 393,030 tweeters in our dataset. The analysis revealed that relevant scholarly discussion on Twitter was dominated by members from the general public (over 71 percent). Academic researchers and institutions constituted 12.48 percent, while health science professionals and institutions made up 7.35 percent of the contributors to relevant scholarly discussions on Twitter. Notably, academic publishers and research feed accounts exhibited aggressive tweeting behaviors and were responsible for the highest volume of tweets on average. This study also demonstrates the active involvement of various non-academic members, including commercial businesses, mass media outlets, public authorities, politicians, and civil society organizations, in Twitter scholarly communication.
C1 [Ye, Yingxin Estella; Na, Jin-Cheon] Nanyang Technol Univ, Wee Kim Wee Sch Commun & Informat, 31 Nanyang Link, Singapore 637718, Singapore.
C3 Nanyang Technological University
RP Ye, YE (corresponding author), Nanyang Technol Univ, Wee Kim Wee Sch Commun & Informat, 31 Nanyang Link, Singapore 637718, Singapore.
EM yingxin001@e.ntu.edu.sg; tjcna@ntu.edu.sg
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TC 0
Z9 0
U1 4
U2 4
PU SPRINGER WIEN
PI Vienna
PA Prinz-Eugen-Strasse 8-10, A-1040 Vienna, AUSTRIA
SN 1869-5450
EI 1869-5469
J9 SOC NETW ANAL MIN
JI Soc. Netw. Anal. Min.
PD MAR 28
PY 2024
VL 14
IS 1
AR 72
DI 10.1007/s13278-024-01236-7
PG 17
WC Computer Science, Information Systems
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA MQ8G0
UT WOS:001195179000001
DA 2024-09-05
ER
PT J
AU Kaparthi, S
AF Kaparthi, Shashidhar
TI Business-Related Research in Neural Networks-Based Intelligent Decision
   Support Systems: A Bibliometric Analysis
SO JOURNAL OF DECISION SYSTEMS
LA English
DT Article
DE neural networks; business applications; bibliometric analysis; co-word
   method; intelligent decision support systems
AB Neural networks are used in business organizations for providing intelligent decision support. The ABI/Inform bibliographic database has over 1 000 publications on business-related research in neural networks. Historical trends in this area are examined by a bibliometric analysis of this research. Distributions by management function, by industries & markets, by business environment and by keywords are presented and insights into future research possibilities are drawn. Further, this corpus of literature is analyzed by using the co-word bibliometric methodology to assess the state-of-the-art in this research area. Several research themes are identified. Based on the analysis, suggestions for future research are outlined.
C1 [Kaparthi, Shashidhar] Univ Northern Iowa, Coll Business Adm, Dept Management, Cedar Falls, IA 50614 USA.
C3 University of Northern Iowa
RP Kaparthi, S (corresponding author), Univ Northern Iowa, Coll Business Adm, Dept Management, Cedar Falls, IA 50614 USA.
EM Shashi.Kaparthi@uni.edu
FU University of Northern Iowa
FX This research was supported in part by a professional development
   assignment from the University of Northern Iowa. An earlier version of
   this paper was presented at the 34th Annual Meeting of the Decision
   Sciences Institute, Washington DC, November 2003.
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NR 42
TC 1
Z9 1
U1 0
U2 3
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 1166-8636
EI 2116-7052
J9 J DECIS SYST
JI J. Decis. Syst.
PY 2005
VL 14
IS 1-2
BP 157
EP 177
DI 10.3166/jds.14.157-177
PG 21
WC Operations Research & Management Science
WE Emerging Sources Citation Index (ESCI)
SC Operations Research & Management Science
GA V88LS
UT WOS:000212725500008
DA 2024-09-05
ER
PT J
AU Sharma, H
   Kumar, H
   Gupta, A
   Shah, MA
AF Sharma, Himanshu
   Kumar, Harish
   Gupta, Ashulekha
   Shah, Mohd Asif
TI Computer vision in manufacturing: a bibliometric analysis and future
   research propositions
SO INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
LA English
DT Article
DE Bibliometric analysis; Bibliographic coupling; Computer vision;
   Manufacturing; Review
ID COORDINATE MEASURING MACHINE; SURFACE DEFECT DETECTION; QUALITY
   INSPECTION; MICROASSEMBLY SYSTEM; INDUSTRY 4.0; ROUGHNESS;
   CLASSIFICATION; ACCURATE; WEB; ALGORITHMS
AB Computer vision for the past two decades has been used to simulate human capabilities and automate tasks, and in the process, has benefited all of us. Specifically, its application within the manufacturing context has garnered ample attention and interest from both academics and practitioners. Due to its large-scale applicability and adoption potential, extensive research has been conducted to understand and appreciate it is working. However, extant research in this domain is rather disjointed, thereby delimiting the otherwise vast scope and knowledge boundaries. Thus, this study utilizes bibliometric analysis to synthesize extant literature within this field to address this lacuna. We analyzed 897 articles from Scopus, entailing contributions from 309 journals, 108 countries, 2138 authors, and 1334 organizations from 1981 to 2022. Additionally, we analyzed citation and co-authorship networks to acknowledge prominent authors, organizations, and countries within this domain. The thematic classification of extant literature through bibliographic coupling identified five major thematic areas: automated visual inspection, object tracking and process controlling, real-time monitoring, roughness inspection, and profile projection. Importantly, we used both knowledge and insights from our findings, and propose scope for future research.
C1 [Sharma, Himanshu; Kumar, Harish] Indian Inst Management IIM, Dept Informat Technol & Syst, Kashipur, India.
   [Gupta, Ashulekha] Graph Era Deemed Be Univ, Dept Management Studies, Dehra Dun, India.
   [Shah, Mohd Asif] Kebri Dehar Univ, Dept Econ, Kebri Dehar 250, Somali, Ethiopia.
   [Shah, Mohd Asif] Woxsen Univ, Sch Business, Sadasivpet 502345, Hyderabad, India.
   [Shah, Mohd Asif] Lovely Profess Univ, Div Res & Dev, Phagwara 144001, Punjab, India.
   [Shah, Mohd Asif] Sharda Univ, Sch Engn & Technol, Greater Noida 201310, India.
C3 Indian Institute of Management (IIM System); Indian Institute of
   Management Kashipur; Graphic Era University; Lovely Professional
   University; Sharda University
RP Shah, MA (corresponding author), Kebri Dehar Univ, Dept Econ, Kebri Dehar 250, Somali, Ethiopia.; Shah, MA (corresponding author), Woxsen Univ, Sch Business, Sadasivpet 502345, Hyderabad, India.; Shah, MA (corresponding author), Lovely Profess Univ, Div Res & Dev, Phagwara 144001, Punjab, India.; Shah, MA (corresponding author), Sharda Univ, Sch Engn & Technol, Greater Noida 201310, India.
EM himanshu.fpm1806@iimkashipur.ac.in; harishkr08@gmail.com;
   ashulekha26@gmail.com; drmohdasifshah@kdu.edu.et
RI Gupta, Ashulekha/AAY-1812-2020; Kumar, Harish/AAU-7634-2020; SHAH, MOHD
   ASIF/AAZ-4565-2021; Shah, Mohd Asif/GOJ-7931-2022
OI Gupta, Ashulekha/0000-0001-5155-6090; Kumar, Harish/0000-0002-7204-7321;
   SHAH, MOHD ASIF/0000-0001-6164-0915; Shah, Mohd Asif/0000-0002-0351-9559
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NR 114
TC 1
Z9 1
U1 3
U2 11
PU SPRINGER LONDON LTD
PI LONDON
PA 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND
SN 0268-3768
EI 1433-3015
J9 INT J ADV MANUF TECH
JI Int. J. Adv. Manuf. Technol.
PD AUG
PY 2023
VL 127
IS 11-12
BP 5691
EP 5710
DI 10.1007/s00170-023-11907-y
EA JUL 2023
PG 20
WC Automation & Control Systems; Engineering, Manufacturing
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Automation & Control Systems; Engineering
GA M9VB5
UT WOS:001033455200002
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Ebadi, A
   Tremblay, S
   Goutte, C
   Schiffauerova, A
AF Ebadi, Ashkan
   Tremblay, Stephane
   Goutte, Cyril
   Schiffauerova, Andrea
TI Application of machine learning techniques to assess the trends and
   alignment of the funded research output
SO JOURNAL OF INFORMETRICS
LA English
DT Article
DE Text mining; Topic modeling; Machine learning; Funded research;
   Publications; Government research priorities; Canada
ID SCIENTIFIC COLLABORATION; TOPIC MODEL; SCIENCE; SYSTEM
AB Research and development activities are regarded as one of the most influencing factors of the future of a country. Large investments in research can yield a tremendous outcome in terms of a country's overall wealth and strength. However, public financial resources of countries are often limited which calls for a wise and targeted investment. Scientific publications are considered as one of the main outputs of research investment. Although the general trend of scientific publications is increasing, a detailed analysis is required to monitor the research trends and assess whether they are in line with the top research priorities of the country. Such focused monitoring can shed light on scientific activities evolution as well as the formation of new research areas, thus helping governments to adjust priorities, if required. But monitoring the output of the funded research manually is not only very expensive and difficult, it is also subjective. Using structural topic models, in this paper we evaluated the trends in academic research performed by federally funded Canadian researchers during the time-frame of 2000-2018, covering more than 140,000 research publications. The proposed approach makes it possible to objectively and systematically monitor research projects, or any other set of documents related to research activities such as funding proposals, at large-scale. Our results confirm the accordance between the performed federally funded research projects and the top research priorities of Canada. Crown Copyright (C) 2020 Published by Elsevier Ltd. All rights reserved.
C1 [Ebadi, Ashkan; Tremblay, Stephane; Goutte, Cyril] Natl Res Council Canada, Ottawa, ON K1K 2E1, Canada.
   [Ebadi, Ashkan; Schiffauerova, Andrea] Concordia Univ, Concordia Inst Informat Syst Engn CIISE, Montreal, PQ H3G 2W1, Canada.
C3 National Research Council Canada; Concordia University - Canada
RP Ebadi, A (corresponding author), Natl Res Council Canada, Ottawa, ON K1K 2E1, Canada.; Ebadi, A (corresponding author), Concordia Univ, Concordia Inst Informat Syst Engn CIISE, Montreal, PQ H3G 2W1, Canada.
EM ashkan.ebadi@nrc-cnrc.gc.ca
RI Ebadi, Ashkan/AAI-5123-2020; Goutte, Cyril/A-5824-2009; Ebadi,
   Ashkan/GWZ-9018-2022
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NR 99
TC 11
Z9 12
U1 3
U2 42
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1751-1577
EI 1875-5879
J9 J INFORMETR
JI J. Informetr.
PD MAY
PY 2020
VL 14
IS 2
AR 101018
DI 10.1016/j.joi.2020.101018
PG 15
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA NG7GI
UT WOS:000564148300018
DA 2024-09-05
ER
PT J
AU Robert, C
   Arreto, CD
   Azerad, J
   Gaudy, JF
AF Robert, C
   Arreto, CD
   Azerad, J
   Gaudy, JF
TI Bibliometric overview of the utilization of artificial neural networks
   in medicine and biology
SO SCIENTOMETRICS
LA English
DT Article
ID EUROPEAN-UNION
AB The distribution of articles involving artificial neural networks (ANN) in the fields of medicine and biology and appearing in the ISI (Institute for Scientific Information) databases during the period 2000-2001 was analysed. The following parameters were considered: the number of articles, the total impact factor, the ISI journal category, the source country population, and the gross domestic product. Among the 803 articles and the 49 countries considered, the 5 most prolific (in term of the number of publications) were the USA, The United Kingdom, Germany, Italy, and Canada; other active countries included Sweden, Netherlands, Spain, France, Japan, and China. Comparison between the USA and the European Union, and the distribution of ANN publications among the subdisciplines of the life sciences and clinical medicine are also presented.
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C3 Universite Paris Cite; Universite Paris Cite
RP Robert, C (corresponding author), Univ Paris 05, Lab Anat Fonct, 1 Rue Maurice Arnoux, F-92120 Montrouge, France.
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NR 14
TC 16
Z9 16
U1 0
U2 19
PU KLUWER ACADEMIC PUBL
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
J9 SCIENTOMETRICS
JI Scientometrics
PY 2004
VL 59
IS 1
BP 117
EP 130
PG 14
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 767DD
UT WOS:000188424200008
DA 2024-09-05
ER
PT J
AU Farhat, F
   Athar, MT
   Ahmad, S
   Madsen, DO
   Sohail, SS
AF Farhat, Faiza
   Athar, Md Tanwir
   Ahmad, Sultan
   Madsen, Dag Oivind
   Sohail, Shahab Saquib
TI Antimicrobial resistance and machine learning: past, present, and future
SO FRONTIERS IN MICROBIOLOGY
LA English
DT Article
DE antimicrobial resistance; antibiotic resistance; machine learning; deep
   learning; bibliometric analysis; healthcare
ID IDENTIFICATION
AB Machine learning has become ubiquitous across all industries, including the relatively new application of predicting antimicrobial resistance. As the first bibliometric review in this field, we expect it to inspire further research in this area. The review employs standard bibliometric indicators such as article count, citation count, and the Hirsch index (H-index) to evaluate the relevance and impact of the leading countries, organizations, journals, and authors in this field. VOSviewer and Biblioshiny programs are utilized to analyze citation and co-citation networks, collaboration networks, keyword co-occurrence, and trend analysis. The United States has the highest contribution with 254 articles, accounting for over 37.57% of the total corpus, followed by China (103) and the United Kingdom (78). Among 58 publishers, the top four publishers account for 45% of the publications, with Elsevier leading with 15% of the publications, followed by Springer Nature (12%), MDPI, and Frontiers Media SA with 9% each. Frontiers in Microbiology is the most frequent publication source (33 articles), followed by Scientific Reports (29 articles), PLoS One (17 articles), and Antibiotics (16 articles). The study reveals a substantial increase in research and publications on the use of machine learning to predict antibiotic resistance. Recent research has focused on developing advanced machine learning algorithms that can accurately forecast antibiotic resistance, and a range of algorithms are now being used to address this issue.
C1 [Farhat, Faiza] Aligarh Muslim Univ, Dept Zool, Aligarh, India.
   [Athar, Md Tanwir] Buraydah Coll, Coll Dent & Pharm, Dept Pharmacognosy & Pharmaceut Chem, Buraydah, Al Qassim, Saudi Arabia.
   [Ahmad, Sultan] Prince Sattam bin Abdulaziz Univ, Coll Comp Engn & Sci, Dept Comp Sci, Al Kharj, Saudi Arabia.
   [Ahmad, Sultan] Chandigarh Univ, Univ Ctr Res & Dev UCRD, Dept Comp Sci & Engn, Mohali, Punjab, India.
   [Madsen, Dag Oivind] Univ South Eastern Norway, Sch Business, Honefoss, Norway.
   [Sohail, Shahab Saquib] Jamia Hamdard, Dept Comp Sci & Engn, New Delhi, India.
C3 Aligarh Muslim University; Buraydah Colleges; Prince Sattam Bin
   Abdulaziz University; Chandigarh University; University College of
   Southeast Norway; Jamia Hamdard University
RP Madsen, DO (corresponding author), Univ South Eastern Norway, Sch Business, Honefoss, Norway.; Sohail, SS (corresponding author), Jamia Hamdard, Dept Comp Sci & Engn, New Delhi, India.
EM dag.oivind.madsen@usn.no; shahabssohail@jamiahamdard.ac.in
RI FARHAT, FAIZA/KIK-8175-2024; Ahmad, Sultan/F-9146-2010; sohail,
   shahab/O-3263-2019; Madsen, Dag Øivind/I-1587-2016
OI Ahmad, Sultan/0000-0002-3198-7974; sohail, shahab/0000-0002-5944-7371;
   Madsen, Dag Øivind/0000-0001-8735-3332
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NR 40
TC 5
Z9 5
U1 11
U2 23
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 1664-302X
J9 FRONT MICROBIOL
JI Front. Microbiol.
PD MAY 26
PY 2023
VL 14
AR 1179312
DI 10.3389/fmicb.2023.1179312
PG 14
WC Microbiology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Microbiology
GA I5SH4
UT WOS:001003372600001
PM 37303800
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU He, F
AF He, Fei
TI RESEARCH ON THE EVALUATION MODEL OF STUDENTS' FOREIGN LANGUAGE LEARNING
   SITUATION BASED ON ORIENTED ONLINE TEACHING COLLABORATION PLATFORM
SO SCALABLE COMPUTING-PRACTICE AND EXPERIENCE
LA English
DT Article
DE Online Education; Foreign Language Learning; Evaluation Model;
   N-Adaboost; DBSCAN
ID COLLEGE ENGLISH; SYSTEM
AB "Internet + Education" makes online teaching gradually penetrate the education industry, and makes the industry enter a great revolution based on information technology. The traditional student learning evaluation system cannot satisfy the actual demand of current learning evaluation. This paper constructs an evaluation model for the foreign language learning of online students. Firstly, the DBSCAN algorithm with distance optimization is used to conduct cluster analysis on the description indicators of student behavior, and the student groups with different behavior characteristics are obtained. Then the ANOVA F-test was used to extract the features of different student groups. Finally, a novel N-Adaboost algorithm based on multiple classifiers is proposed and a model is constructed to evaluate students' foreign language learning. The experimental results show that the accuracy of the evaluation model is 74.02% in the pass and fail groups and 73.74% in the excellent and non-excellent groups. Students' listening, speaking, and reading abilities are in a state of upward development overall through the online teaching collaboration platform, but their writing ability is obviously declining. There is a great improvement in foreign language vocabulary. This study provides a new perspective of thinking for the improvement of the quality of school teaching management, the analysis of students' behavior, and the evaluation of learning situations, and provides a new solution for the problem of students' learning situations in modern information teaching.
C1 [He, Fei] Henan Polytech Univ, Sch Foreign Studies, Jiaozuo 454003, Peoples R China.
C3 Henan Polytechnic University
RP He, F (corresponding author), Henan Polytech Univ, Sch Foreign Studies, Jiaozuo 454003, Peoples R China.
EM feihefhfh@outlook.com
FU Henan Provincial Teaching Reform Research and Practice Project "Research
   and Practice on the Teaching Ecosystem of General Academic English in
   Science and Engineering Universities under the Double First Class
   Background" [2019SJGLX060]
FX The research is supported by the Henan Provincial Teaching Reform
   Research and Practice Project "Research and Practice on the Teaching
   Ecosystem of General Academic English in Science and Engineering
   Universities under the Double First Class Background" (No.
   2019SJGLX060).
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NR 21
TC 0
Z9 0
U1 2
U2 2
PU UNIV VEST TIMISOARA, WEST UNIV TIMISOARA
PI TIMISOARA
PA BLVD VASILE PARVAN 4, TIMISOARA, TIMIS 300223, ROMANIA
SN 1895-1767
J9 SCALABLE COMPUT-PRAC
JI Scalable Comput.-Pract. Exp.
PD JAN
PY 2024
VL 25
IS 1
SI SI
DI 10.12694/scpe.v25i1.2306
PG 16
WC Computer Science, Software Engineering
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA FE1M3
UT WOS:001143994100025
OA gold
DA 2024-09-05
ER
PT J
AU Zhu, YP
   Park, HW
AF Zhu, Yu Peng
   Park, Han Woo
TI Publication, Collaboration, Citation Performance, and Triple Helix
   Innovation Gene of Artificial Intelligence Research in the Communication
   Field: Comparing Asia to the Rest of the World
SO JOURNAL OF THE KNOWLEDGE ECONOMY
LA English
DT Article; Early Access
DE Artificial intelligence; Communication; Collaboration; Citation; Network
   analysis; Asia; Triple helix
ID QUADRUPLE HELIX; N-TUPLE
AB Artificial intelligence (AI) in the communication field has become increasingly popular in recent years. This study collected data from 482 documents and cited references in the Web of Science database. It explores the knowledge structure related to AI in communication, combined with the triple helix innovation gene model. The analysis employed collaborative network analysis, two-mode network analysis, citation analysis, and quadratic assignment procedure-based correlation analysis. The results show that the most popular hotspots are human-machine communication, automatically generated publications, social media-mediated fake news, and some other AI-based applied research. Academic collaborations can be facilitated by transnational disciplinary leaders. China emerged as the core academic country with the greatest growth potential in Asia, while the core non-Asian country is the United States. In addition, the trend in collaboration among scholars in Asia is better than in non-Asian countries. However, concerning the characteristics of collaborating institutions, the triple-helix collaboration among universities, government bodies, and industries remains insufficient. Particularly, the collaboration between industry and government necessitates further development.
C1 [Zhu, Yu Peng] Chongqing Univ, Sch Journalism & Commun, Chongqing Key Lab Intelligent Commun & Citys Int P, Chongqing, Peoples R China.
   [Park, Han Woo] Yeungnam Univ, Dept Media & Commun, Interdisciplinary Grad Programs Digital Convergenc, Gyongsan, South Korea.
C3 Chongqing University; Yeungnam University
RP Zhu, YP (corresponding author), Chongqing Univ, Sch Journalism & Commun, Chongqing Key Lab Intelligent Commun & Citys Int P, Chongqing, Peoples R China.; Park, HW (corresponding author), Yeungnam Univ, Dept Media & Commun, Interdisciplinary Grad Programs Digital Convergenc, Gyongsan, South Korea.
EM yu.peng.zhu@cqu.edu.cn; hanpark@ynu.ac.kr
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NR 42
TC 0
Z9 0
U1 0
U2 0
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1868-7865
EI 1868-7873
J9 J KNOWL ECON
JI J. Knowl. Econ.
PD 2024 AUG 28
PY 2024
DI 10.1007/s13132-024-02280-6
EA AUG 2024
PG 21
WC Economics
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA E0A0J
UT WOS:001299704800001
DA 2024-09-05
ER
PT J
AU Rybinski, K
AF Rybinski, Krzysztof
TI The forecasting power of the multi-language narrative of sell-side
   research: A machine learning evaluation
SO FINANCE RESEARCH LETTERS
LA English
DT Article
DE Economic research; Forecasting; Text mining; NLP; Sentiment analysis;
   Wordscores
ID TEXT; FINANCE
AB This is probably the first ever analysis of sell-side daily economic research to use Natural Language Processing, and it shows that the narrative of such reports can be used to predict economic time series. The NLP indexes are based on Polish and English language reports released at the same time and exhibit predictive power for different sets of economic variables. VAR models with the NLP indexes generate smaller forecast errors than ARIMA. The wordscores scaling model uses Monetary Policy Council statements to generate scores and allows NLP indexes to be created with better forecasting power than the sentiment-based ones.
C1 [Rybinski, Krzysztof] Vistula Univ, Warsaw, Poland.
   [Rybinski, Krzysztof] Synerise Jsc, Warsaw, Poland.
C3 Vistula University
RP Rybinski, K (corresponding author), Stoklosy 3, PL-02787 Warsaw, Poland.
EM rybinski@rybinski.eu
RI Rybinski, Krzysztof/AAM-1608-2020
OI Rybinski, Krzysztof/0000-0002-4604-7993
FU National Science Centre in Poland [2018/29/B/HS4/01462]
FX Financial support of the Opus 15 project 2018/29/B/HS4/01462
   `Construction and testing of country-specific measures of economic
   uncertainty' of the National Science Centre in Poland is gratefully
   acknowledged. The author would like to thank the anonymous referee and
   Wojciech Charemza for many insightful comments on the earlier version of
   this paper, and Robin Hazlehurst for his excellent language editing.
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U2 30
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PI SAN DIEGO
PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA
SN 1544-6123
EI 1544-6131
J9 FINANC RES LETT
JI Financ. Res. Lett.
PD MAY
PY 2020
VL 34
AR 101261
DI 10.1016/j.frl.2019.08.009
PG 7
WC Business, Finance
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA MO2EQ
UT WOS:000551346400034
DA 2024-09-05
ER
PT J
AU Long, YC
   Cao, ZW
   Mao, Y
   Liu, XR
   Gao, Y
   Zhou, CZ
   Zheng, X
AF Long, Yuchong
   Cao, Zhengwei
   Mao, Yan
   Liu, Xinran
   Gao, Yan
   Zhou, Chuanzhi
   Zheng, Xin
TI Research on Evaluation Elements of Urban Agricultural Green Bases: A
   Causal Inference-Based Approach
SO LAND
LA English
DT Article
DE urban agriculture; green base evaluation; Bayesian network; causal
   inference
ID FUTURE
AB The construction of agricultural green bases is an important part of sustainable agricultural development. This paper takes urban agriculture green bases in Shanghai as an example, choosing base construction elements, production, and ecological construction elements, as well as status assessment elements as evaluation indicators, in order to construct an evaluation system for urban agriculture green bases. Using a Bayesian network, typical urban agricultural green bases in six agricultural districts of Shanghai were evaluated. The construction of the evaluation system was analyzed by using intervention, counterfactual inference, and other methods to analyze the correlation and importance of the indicators. The results show that there are differences among the bases in various indicators, but they all reach a high level overall; base construction elements as well as production and ecological construction elements are the main factors affecting the level of urban agricultural green bases; improving the base management system (BMS), innovativeness (IN), and economic benefits (EBs) are key ways to improve the production capacity of agriculture green bases. Green base construction should pay attention to top-level design, coordinate the planning of industrial layout, technical mode, scientific and technological support, and supporting policies. Based on the conclusion, this paper provides some useful recommendations for creating urban agriculture green bases, which help promote urban agriculture transformation, upgrading, and coordinating development between urban and rural areas.
C1 [Long, Yuchong; Cao, Zhengwei; Liu, Xinran; Gao, Yan; Zhou, Chuanzhi; Zheng, Xin] Shanghai Jiao Tong Univ, Coll Agr & Biol, Dongchuan Rd, Shanghai 200240, Peoples R China.
   [Mao, Yan] Zhejiang Univ, Inst Publ Policy, Hangzhou 310058, Peoples R China.
C3 Shanghai Jiao Tong University; Zhejiang University
RP Cao, ZW (corresponding author), Shanghai Jiao Tong Univ, Coll Agr & Biol, Dongchuan Rd, Shanghai 200240, Peoples R China.
EM ayinmostima@sjtu.edu.cn; zhengweiskylark@sjtu.edu.cn;
   myy0716@outlook.com; roxas5@sjtu.edu.cn; gao.yan@sjtu.edu.cn;
   zhouchuanzhi@sjtu.edu.cn; zhwngxin@sjtu.edu.cn
OI cao, zhengwei/0000-0002-8101-9568; Mao, Yan/0009-0004-4772-1988; Liu,
   Xinran/0000-0003-2284-7547; Yuchong, Long/0000-0002-3271-9760
FU Shanghai Philosophy and Social Science Program [17Z2017030008]; Shanghai
   Agriculture Applied Technology Development Program, China [T20200201];
   Shanghai Pujiang Talent Program [16Z2022010010]
FX This work was co-supported by the Shanghai Philosophy and Social Science
   Program(17Z2017030008), Shanghai Agriculture Applied Technology
   Development Program, China (T20200201)and Shanghai Pujiang Talent
   Program (16Z2022010010).
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NR 54
TC 0
Z9 0
U1 2
U2 17
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2073-445X
J9 LAND-BASEL
JI Land
PD AUG
PY 2023
VL 12
IS 8
AR 1636
DI 10.3390/land12081636
PG 27
WC Environmental Studies
WE Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology
GA Q2FE9
UT WOS:001055719300001
OA Green Published, gold
DA 2024-09-05
ER
PT C
AU Cao, Y
   Tong, HF
   Yu, J
AF Cao Yan
   Tong Hefeng
   Yu Jie
BE Zhu, KL
   Zhang, H
TI Overall and by Fields Research Output Evaluation of Chinese Mainland
   Universities Based on ESI Database
SO COMPREHENSIVE EVALUATION OF ECONOMY AND SOCIETY WITH STATISTICAL SCIENCE
LA English
DT Proceedings Paper
CT 3rd International Institute of Statistics and Management Engineering
   Symposium
CY 2010
CL Wei Hai Lu, PEOPLES R CHINA
DE Bibliometrics; Research output; AI; CPP/FCSm
ID RESEARCH PERFORMANCE; BASIC RESEARCH; BIBLIOMETRIC ANALYSIS;
   MOLECULAR-BIOLOGY; DEPARTMENTS; PUBLICATIONS; EFFICIENCY; DIMENSION
AB The research performance of mainland China universities is evaluated in this study from the global perspective. Based on the Essential Science Indicators (ESI) database, the main research concern is the academic output in overall fields and the strength in certain fields of top 20 universities ranking by citation. Indicators about the numbers of paper and citation are obtained from ESI to interpret the general academic performance of universities. The other indicators are considered as the population of faculty, the activity index, as well as the relative impact index. The statistical results show that the universities in mainland China in terms of quality and quantity of research output have considerable gap. Meanwhile, a large degree of influence to research concentrated in some specific fields is existed.
C1 [Cao Yan; Tong Hefeng; Yu Jie] Inst Sci & Tech Informat China, Beijing 100038, Peoples R China.
EM caoyan@istic.ac.cn
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NR 22
TC 1
Z9 1
U1 0
U2 8
PU AUSSINO ACAD PUBL HOUSE
PI MARRICKVILLE
PA PO BOX 893, MARRICKVILLE, NSW 2204 00000, AUSTRALIA
BN 978-1-921712-09-8
PY 2010
BP 485
EP 494
PG 10
WC Economics; Social Sciences, Mathematical Methods
WE Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Business & Economics; Mathematical Methods In Social Sciences
GA BYE74
UT WOS:000298265300084
DA 2024-09-05
ER
PT J
AU Musa, IH
   Zamit, I
   Xu, K
   Boutouhami, K
   Qi, GL
AF Musa, Ibrahim Hussein
   Zamit, Ibrahim
   Xu, Kang
   Boutouhami, Khaoula
   Qi, Guilin
TI A comprehensive bibliometric analysis on opinion mining and sentiment
   analysis global research output
SO JOURNAL OF INFORMATION SCIENCE
LA English
DT Article
DE Bibliometric; opinion mining; Scopus; sentiment analysis
AB The rise of the Internet and social media (i.e. reviews, forum discussions, blogs and social networks) constituted an interesting source to detect user opinion trends. This study examines the global publication output on opinion mining and sentiment analysis from documents published in 2000 to 2020. Bibliometric indicators on the trends, most cited papers, authors, institutions, countries, funding agencies and research subject areas were independently screened and analysed using bibliometrix package in R. A total of 7603 eligible documents were identified from 2000 to 2020. The total number of citations for all publications was 129,251, with an average of 17.0 citations per publication. About 14,629 authors wrote those documents with 1.93 authors per document and a collaboration index of 1.98. The most prolific author was Cambria Erik, with 47 publications and h-index of 42. The leading countries for research were China with n = 824, India with n = 576 and the United States with n = 244 publications. Lecture Notes in Computer Science proceedings was the top-ranked venue for publications with n = 434, h-index of 32 and 4598 total citation scores. National Natural Science Foundation of China was the top-ranked funding agency for research, and most of the publications were computer science (n = 6320) documents. The study provides an in-depth assessment of the landmark of the hot research topic and acknowledges the contribution of the most productive and active authors across different countries in the world. In addition, the findings could support the younger scholars in their future research direction and improve the efficiency in potential future research collaborations and projects.
C1 [Musa, Ibrahim Hussein; Boutouhami, Khaoula; Qi, Guilin] Southeast Univ, Sch Comp Sci & Engn, Nanjing 211189, Jiangsu, Peoples R China.
   [Musa, Ibrahim Hussein; Boutouhami, Khaoula; Qi, Guilin] Southeast Univ, Key Lab Comp Network & Informat Integrat, Minist Educ, Nanjing, Jiangsu, Peoples R China.
   [Zamit, Ibrahim] Shenzhen Inst Adv Technol, Ctr High Performance Comp, Shenzhen, Guangdong, Peoples R China.
   [Xu, Kang] Nanjing Univ Posts & Telecommun, Sch Comp Sci, Nanjing, Jiangsu, Peoples R China.
C3 Southeast University - China; Southeast University - China; Chinese
   Academy of Sciences; Shenzhen Institute of Advanced Technology, CAS;
   Nanjing University of Posts & Telecommunications
RP Qi, GL (corresponding author), Southeast Univ, Sch Comp Sci & Engn, Nanjing 211189, Jiangsu, Peoples R China.
EM gqi@seu.edu.cn
RI Zhang, Yihao/JGM-3514-2023; Wang, Jiacheng/ABE-5948-2020; xu,
   lingzhi/JVZ-8748-2024; Liu, Song/KCX-6842-2024; Cheng,
   Yuan/JKJ-0794-2023; Musa, Ibrahim/GYJ-0462-2022; Wang,
   Peilin/JWP-6008-2024
OI Wang, Jiacheng/0000-0003-4327-1508; BOUTOUHAMI,
   khaoula/0000-0001-9631-9507
CR Akintunde TY, 2021, ASIAN J PSYCHIATR, V63, DOI 10.1016/j.ajp.2021.102753
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NR 27
TC 3
Z9 3
U1 3
U2 45
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 0165-5515
EI 1741-6485
J9 J INF SCI
JI J. Inf. Sci.
PD DEC
PY 2023
VL 49
IS 6
BP 1506
EP 1516
DI 10.1177/01655515211061866
EA SEP 2022
PG 11
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA Y8SE2
UT WOS:000852202900001
DA 2024-09-05
ER
PT J
AU Lee, JW
   Han, DH
AF Lee, Jea Woog
   Han, Doug Hyun
TI Data Analysis of Psychological Approaches to Soccer Research: Using LDA
   Topic Modeling
SO BEHAVIORAL SCIENCES
LA English
DT Article
DE soccer; psychology; research trends; data science; topic modeling;
   bibliometric
ID YOUTH SOCCER; ELITE SOCCER; TALENT DEVELOPMENT; INJURY PREVENTION; TEAM
   PERFORMANCE; DECISION-MAKING; PENALTY KICKS; FOOTBALL; PLAYERS;
   PERCEPTIONS
AB This study identifies the topical areas of research that have attempted a psychological approach to soccer research over the last 33 years (1990-2022) and explored the growth and stagnation of the topic as well as research contributions to soccer development. Data were obtained from 1863 papers from the Web of Science database. The data were collected through keyword text mining and data preprocessing to determine the keywords needed for analysis. Based on the keywords, latent Dirichlet allocation-based topic modeling analysis was performed to analyze the topic distribution of papers and explore research trends by topic area. The topic modeling process included four topic area and fifty topics. The "Coaching Essentials in Football" topic area had the highest frequency, but it was not statistically identified as a trend. However, coaching, including training, is expected to continue to be an important research topic, as it is a key requirement for success in the highly competitive elite football world. Interest in the research field of "Psychological Skills for Performance Development" has waned in recent years. This may be due to the predominance of other subject areas rather than a lack of interest. Various high-tech interventions and problem-solving attempts are being made in this field, providing opportunities for qualitative and quantitative expansion. "Motivation, cognition, and emotion" is a largely underrated subject area in soccer psychology. This could be because survey-based psychological evaluation attempts have decreased as the importance of rapid field application has been emphasized in recent soccer-related studies. However, measuring psychological factors contributes to the study of football psychology through a new methodology and theoretical background. Recognizing the important role of psychological factors in player performance and mental management, as well as presenting new research directions and approaches that can be directly applied to the field, will advance soccer psychology research.
C1 [Lee, Jea Woog] Chung Ang Univ, Intelligent Informat Proc Lab, Seoul 06974, South Korea.
   [Han, Doug Hyun] Chung Ang Univ Hosp, Dept Psychiat, Seoul 06974, South Korea.
C3 Chung Ang University; Chung Ang University; Chung Ang University
   Hospital
RP Han, DH (corresponding author), Chung Ang Univ Hosp, Dept Psychiat, Seoul 06974, South Korea.
EM yyizeuks@cau.ac.kr; hduk70@gmail.com
OI Han, Doug Hyun/0000-0002-8314-0767
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NR 128
TC 0
Z9 0
U1 5
U2 18
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2076-328X
J9 BEHAV SCI-BASEL
JI Behav. Sci.
PD OCT
PY 2023
VL 13
IS 10
AR 787
DI 10.3390/bs13100787
PG 19
WC Psychology, Multidisciplinary
WE Social Science Citation Index (SSCI)
SC Psychology
GA X0RR1
UT WOS:001095614600001
PM 37887437
OA Green Published, gold, Green Submitted
DA 2024-09-05
ER
PT J
AU Shao, Z
   Yuan, S
   Wang, YL
AF Shao, Zhou
   Yuan, Sha
   Wang, Yongli
TI Institutional Collaboration and Competition in Artificial Intelligence
SO IEEE ACCESS
LA English
DT Article
DE Collaboration; Artificial intelligence; Industries; Technological
   innovation; Market research; Computer science; Licenses; Artificial
   intelligence; Science of Science; cooperation and competition; data
   analytics; data science
ID INTERNATIONAL COLLABORATION; EVOLUTION; PATTERNS; IMPACT
AB The institutional collaboration and competition in academia have benefited the development of science, with inter-institutional scientific work promoting the exchange of ideas and competing fields developing rapidly. However, understanding of how the institutions collaborate and compete in science is sorely lacking, especially in emerging fields. Artificial intelligence is such a booming field currently, changing the way we live and work daily. To illustrate the problem, we try to reveal the evolution of institutional collaboration and competition in artificial intelligence by applying AI 2000 from the perspective of Science of Science. In this paper, we make multiple multidimensional statistical analyses by scrutinizing the collaboration network, research interests, talent flow, etc. We demonstrate the collaboration evolution in this field and find the advantage of inter-institutional collaboration is growing over time for papers that have been published more than 5 years. We discover the common cooperation modes of top institutions and visualize their closer cooperation. We highlight the critical resources competition among institutions in three dimensions and learn the recent trends in the field. In particular, we are concerned about the competition among institutions for cross-industry cooperation and notice the consistency of competitiveness and cross-industry collaboration. The research of this paper may support further research studies on institutional collaboration and competition as well as policy proposals for promoting scientific innovation, research management, and funding.
C1 [Shao, Zhou; Wang, Yongli] Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China.
   [Yuan, Sha] Beijing Acad Artificial Intelligence, Beijing 100190, Peoples R China.
   [Shao, Zhou; Yuan, Sha] Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China.
   [Shao, Zhou] Southwest Univ Sci & Technol, Sch Comp Sci & Technol, Mianyang 621010, Sichuan, Peoples R China.
   [Shao, Zhou] China Acad Engn Phys, Grad Sch, Mianyang 621010, Sichuan, Peoples R China.
C3 Nanjing University of Science & Technology; Tsinghua University;
   Southwest University of Science & Technology - China; Chinese Academy of
   Engineering Physics
RP Wang, YL (corresponding author), Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China.
EM yongliwang@mail.njust.edu.cn
OI Shao, Zhou/0000-0002-6265-7310
FU National Natural Science Foundation of China [61941113, 61806111];
   Fundamental Research Fund for the Central Universities [30918015103,
   30918012204]; Nanjing Science and Technology Development Plan Project
   [201805036]; China Academy of Engineering Consulting Research Project
   [2019-ZD-1-02-02]; National Social Science Foundation [18BTQ073]; State
   Grid Technology Project [5211XT190033]; 13th Five-Year Equipment Field
   Fund [61403120501]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61941113 and Grant 61806111, in part by
   the Fundamental Research Fund for the Central Universities under Grant
   30918015103 and Grant 30918012204, in part by the Nanjing Science and
   Technology Development Plan Project under Grant 201805036, in part by
   the 13th Five-Year Equipment Field Fund under Grant 61403120501, in part
   by the China Academy of Engineering Consulting Research Project under
   Grant 2019-ZD-1-02-02, in part by the National Social Science Foundation
   under Grant 18BTQ073, and in part by the State Grid Technology Project
   under Grant 5211XT190033.
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NR 49
TC 13
Z9 14
U1 7
U2 55
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2020
VL 8
BP 69734
EP 69741
DI 10.1109/ACCESS.2020.2986383
PG 8
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering; Telecommunications
GA MM0CJ
UT WOS:000549827900004
OA gold
DA 2024-09-05
ER
PT J
AU Chen, XL
   Cheng, GRY
   Zou, D
   Zhong, BC
   Xie, HR
AF Chen, Xieling
   Cheng, Gary
   Zou, Di
   Zhong, Baichang
   Xie, Haoran
TI Artificial Intelligent Robots for Precision Education: A Topic Modeling-
   Based Bibliometric Analysis
SO EDUCATIONAL TECHNOLOGY & SOCIETY
LA English
DT Article
DE Artificial intelligence robots; Topic modeling; Bibliometric analysis;
   Precision education; Research topics; Future of human-centered
   artificial intelligence
ID AI; COMMUNICATION; KNOWLEDGE; THINKING
AB As a human-friendly system, the artificial intelligence (AI) robot is one of the critical applications in promoting precision education. Alongside the call for humanity-oriented applications in education, AI robot supported precision education has developed into an active field, with increasing literature available. This study aimed to comprehensively analyze directions taken in the past in this research field to interpret a roadmap for future work. By adopting structural topic modeling, the Mann-Kendall trend test, and keyword analysis, we investigated the research topics and their dynamics in the field based on literature collected from Web of Science and Scopus databases up to 2021. Results showed that AI robots and chatbots had been widely used in different subject areas (e.g., early education, STEM education, medical, nursing, and healthcare education, and language education) for promoting collaborative learning, mobile/game-based learning, distance learning, and affective learning. However, a limited practice in developing true human-centered AI (HCAI)-supported educational robots is available. To advance HCAI in education and its application in educational robots for precision education, we suggested involving humans in AI robot design, thinking of individual learners, testing, and understanding the learner-AI robot interaction, taking an HCAI multidisciplinary approach in robot system development, and providing sufficient technical support for instructors during robot implementation.
C1 [Chen, Xieling; Zhong, Baichang] South China Normal Univ, Sch Informat Technol Educ, Guangzhou, Peoples R China.
   [Cheng, Gary] Educ Univ Hong Kong, Dept Math & Informat Technol, Hong Kong, Peoples R China.
   [Zou, Di] Educ Univ Hong Kong, Dept English Language Educ, Hong Kong, Peoples R China.
   [Xie, Haoran] Lingnan Univ, Dept Comp & Decis Sci, Hong Kong, Peoples R China.
C3 South China Normal University; Education University of Hong Kong
   (EdUHK); Education University of Hong Kong (EdUHK); Lingnan University
RP Zou, D (corresponding author), Educ Univ Hong Kong, Dept English Language Educ, Hong Kong, Peoples R China.
EM xielingchen0708@gmail.com; chengks@eduhk.hk; dizoudaisy@gmail.com;
   zhongbc@163.com; hrxie2@gmail.com
RI Xie, Haoran/AFS-3515-2022; Xie, Haoran/AAW-8845-2020
OI Xie, Haoran/0000-0003-0965-3617; ZOU, Di/0000-0001-8435-9739
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NR 55
TC 17
Z9 17
U1 37
U2 158
PU INT FORUM EDUCATIONAL TECHNOLOGY & SOC, NATL TAIWAN NORMAL UNIV
PI Taipei City
PA No.162, Sec. 1, Heping E. Rd., Da-an Dist, Taipei City, TAIWAN
SN 1176-3647
EI 1436-4522
J9 EDUC TECHNOL SOC
JI Educ. Technol. Soc.
PD JAN
PY 2023
VL 26
IS 1
BP 171
EP 186
DI 10.30191/ETS.202301_26(1).0013
PG 16
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA 7Q6QP
UT WOS:000909514000013
DA 2024-09-05
ER
PT J
AU Ermolayeva, A
   Birukou, A
   Matyushenko, S
   Kochetkov, D
AF Ermolayeva, Anna
   Birukou, Aliaksandr
   Matyushenko, Sergey
   Kochetkov, Dmitry
TI Statistical model and method for analyzing AI conference rankings: China
   vs USA
SO HELIYON
LA English
DT Article
DE Conference proceedings; Scientometrics; Research evaluation; Research
   assessment; Artificial intelligence
ID UNITED-STATES; SCIENCE; WEB; INDEX; QUALITY; SCOPUS
AB Artificial Intelligence (AI) is a rapidly developing field of research that attracts significant funding from both the state and industry players. Such interest is driven by a wide range of AI technology applications in many fields. Since many AI research topics relate to computer science, where a significant share of research results are published in conference proceedings, the same applies to AI. The world leaders in artificial intelligence research are China and the United States. The authors conducted a comparative analysis of the bibliometric indicators of AI conference papers from these two countries based on Scopus data. The analysis aimed to identify conferences that receive above-average citation rates and suggest publication strategies for authors from these countries to participate in conferences that are likely to provide better dissemination of their research results. The results showed that, although Chinese researchers publish more AI papers than those from the United States, US conference papers are cited more frequently. The authors also conducted a correlation analysis of the MNCS index, which revealed no high correlation between MNCS USA vs. MNCS China, MNCS China/MNCS USA vs. MSAR, and MNCS China/MNCS USA vs. CORE ranking indicators.
C1 [Ermolayeva, Anna; Matyushenko, Sergey; Kochetkov, Dmitry] Peoples Friendship Univ Russia, RUDN Univ, Inst Comp Sci & Telecommun, Dept Probabil Theory & Cyber Secur, 6 Miklukho Maklaya St, Moscow 117198, Russia.
   [Birukou, Aliaksandr] Springer Nat, Tiergartenstr 17, D-69121 Heidelberg, Germany.
   [Kochetkov, Dmitry] Leiden Univ, Ctr Sci & Technol Studies, Kolffpad 1, NL-2333 BN Leiden, Netherlands.
   [Kochetkov, Dmitry] Ural Fed Univ, 19 Mira St, Ekaterinburg 620002, Russia.
C3 Peoples Friendship University of Russia; Leiden University - Excl LUMC;
   Leiden University; Ural Federal University
RP Ermolayeva, A; Kochetkov, D (corresponding author), Peoples Friendship Univ Russia, RUDN Univ, Inst Comp Sci & Telecommun, Dept Probabil Theory & Cyber Secur, 6 Miklukho Maklaya St, Moscow 117198, Russia.; Kochetkov, D (corresponding author), Leiden Univ, Ctr Sci & Technol Studies, Kolffpad 1, NL-2333 BN Leiden, Netherlands.; Kochetkov, D (corresponding author), Ural Fed Univ, 19 Mira St, Ekaterinburg 620002, Russia.
EM ermolaevaanna@bk.ru; d.kochetkov@cwts.leidenuniv.nl
RI Matyushenko, Sergey/JQW-8543-2023; Kochetkov, Dmitry/I-4979-2015
OI Matyushenko, Sergey/0000-0001-8247-8988; Kochetkov,
   Dmitry/0000-0001-7890-7532; Birukou, Aliaksandr/0000-0002-4925-9131
FU RUDN University Strategic Academic Leadership Program
FX This paper has been supported by the RUDN University Strategic Academic
   Leadership Program (Anna Ermolayeva, Sergey Matyushenko, Dmitry
   Kochetkov) .
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NR 53
TC 0
Z9 0
U1 3
U2 5
PU CELL PRESS
PI CAMBRIDGE
PA 50 HAMPSHIRE ST, FLOOR 5, CAMBRIDGE, MA 02139 USA
EI 2405-8440
J9 HELIYON
JI Heliyon
PD NOV
PY 2023
VL 9
IS 11
AR e21592
DI 10.1016/j.heliyon.2023.e21592
EA NOV 2023
PG 12
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA Z3TJ8
UT WOS:001111330100001
PM 38027555
OA Green Published, gold
DA 2024-09-05
ER
PT J
AU Kumar, V
   Kumar, S
   Chatterjee, S
   Mariani, M
AF Kumar, Vinod
   Kumar, Sachin
   Chatterjee, Sheshadri
   Mariani, Marcello
TI Artificial Intelligence (AI) Capabilities and the R&D Performance of
   Organizations: The Moderating Role of Environmental Dynamism
SO IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
LA English
DT Article
DE Technological innovation; Organizations; Artificial intelligence;
   Business; Research and development; Dynamic scheduling; Industries;
   Artificial intelligence (AI) capability (AIC); environmental dynamism
   (ED); exploitative innovation (EXI); exploration innovation (EXO);
   research and development (R&D) performance
ID PLS-SEM; ANALYTICS CAPABILITY; FIRM PERFORMANCE; OPEN INNOVATION;
   INDUSTRY 4.0; EXPLOITATION; EXPLORATION; KNOWLEDGE; COMMITMENT;
   MANAGEMENT
AB The potential of artificial intelligence capabilities (AICs) extends beyond fostering both explorative and exploitative innovations (EXO and EXI); it may also enhance the overall performance of organizations. Despite this, the interplay between AIC and research and development performance (RDP) remains unexplored. In this article, we aim to fill this gap by investigating the influence of AIC on RDP, considering both EXO and EXI. Additionally, the study examines the potential moderating role of environmental dynamism in shaping the relationship between AIC and the two types of innovations, ultimately impacting the enhancement of RDP in organizations. To achieve this, a conceptual model was developed based on the existing literature and subsequently validated using the partial least square structural equation modeling. The research gathered 289 responses from a diverse group of industry professionals. The findings of this study contribute both theoretically and practically by shedding light on the pivotal role played by artificial intelligence (AI) capabilities, exploration, and EXI in improving the research and development (R&D) performance of organizations. Understanding these dynamics will provide valuable insights for organizations seeking to leverage AI for strategic advancement in their R&D endeavors.
C1 [Kumar, Vinod] FLAME Univ, FLAME Sch Commun, Pune 412115, Maharashtra, India.
   [Kumar, Sachin] Natl Inst Technol, Dept Management Studies, Hamirpur 177005, Himachal Prades, India.
   [Chatterjee, Sheshadri] Indian Inst Technol Kharagpur, Kharagpur, W Bengal, India.
   [Mariani, Marcello] Univ Reading Greenlands, Henley Business Sch, Henley On Thames RG9 3AU, Oxon, England.
   [Mariani, Marcello] Univ Bologna, I-40126 Bologna, Italy.
C3 National Institute of Technology (NIT System); National Institute of
   Technology Hamirpur; Indian Institute of Technology System (IIT System);
   Indian Institute of Technology (IIT) - Kharagpur; University of Reading;
   University of Bologna
RP Mariani, M (corresponding author), Univ Reading Greenlands, Henley Business Sch, Henley On Thames RG9 3AU, Oxon, England.
EM vinod.kumar@flame.edu.in; sachin@nith.ac.in;
   sheshadri.academic@gmail.com; m.mariani@henley.ac.uk
RI Kumar, Sachin/AEP-4946-2022; KUMAR, VINOD/AAL-2759-2020
OI Kumar, Sachin/0000-0003-2125-044X; KUMAR, VINOD/0000-0002-5014-0672;
   Chatterjee, Sheshadri/0000-0003-1075-5549
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NR 85
TC 0
Z9 0
U1 11
U2 11
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0018-9391
EI 1558-0040
J9 IEEE T ENG MANAGE
JI IEEE Trans. Eng. Manage.
PY 2024
VL 71
BP 11522
EP 11532
DI 10.1109/TEM.2024.3423669
PG 11
WC Business; Engineering, Industrial; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Engineering
GA ZP0P7
UT WOS:001276385800002
DA 2024-09-05
ER
PT J
AU Villaseñor, EA
   Arencibia-Jorge, R
   Carrillo-Calvet, H
AF Atenogenes Villasenor, Elio
   Arencibia-Jorge, Ricardo
   Carrillo-Calvet, Humberto
TI Multiparametric characterization of scientometric performance profiles
   assisted by neural networks: a study of Mexican higher education
   institutions
SO SCIENTOMETRICS
LA English
DT Article
DE Bibliometric rankings; Higher education; Institutional academic
   assessment; Scientometric indicators; Self-organized neural networks;
   Scientometric data mining; Mexico
ID BIBLIOMETRIC ANALYSIS; UNIVERSITY RANKINGS; TRIPLE-HELIX; SCIENCE;
   INDEX; COLLABORATION; CONSEQUENCES; PRODUCTIVITY; PUBLICATION;
   VISIBILITY
AB Development of accurate systems to assess academic research performance is an essential topic in national science agendas around the world. Providing quantitative elements such as scientometric rankings and indicators have contributed to measure prestige and excellence of universities, but more sophisticated computational tools are seldom exploited. We compare the evolution of Mexican scientific production in Scopus and the Web of Science as well as Mexico's scientific productivity in relation to the growth of the National Researchers System of Mexico is analyzed. As a main analysis tool we introduce an artificial intelligence procedure based on self-organizing neural networks. The neural network technique proves to be a worthy scientometric data mining and visualization tool which automatically carries out multiparametric scientometric characterizations of the production profiles of the 50 most productive Mexican Higher Education Institutions (in Scopus database). With this procedure we automatically identify and visually depict clusters of institutions that share similar bibliometric profiles in bidimensional maps. Four perspectives were represented in scientometric maps: productivity, impact, expected visibility and excellence. Since each cluster of institutions represents a bibliometric pattern of institutional performance, the neural network helps locate various bibliometric profiles of academic production, and the identification of groups of institutions which have similar patterns of performance. Also, scientometric maps allow for the identification of atypical behaviors (outliers) which are difficult to identify with classical tools, since they outstand not because of a disparate value in just one variable, but due to an uncommon combination of a set of indicators values.
C1 [Atenogenes Villasenor, Elio] Ctr Res & Innovat Informat & Commun Technol INFOT, Circuito Tecnopolo Sur 112, Mexico City, Aguascalientes, Mexico.
   [Arencibia-Jorge, Ricardo] Empresa Tecnol Informac, Havana, Cuba.
   [Carrillo-Calvet, Humberto] Univ Nacl Autonoma Mexico, Fac Sci, Lab Nonlinear Dynam, Mexico City, DF, Mexico.
   [Carrillo-Calvet, Humberto] Univ Nacl Autonoma Mexico, Ctr Complex Sci, Mexico City, DF, Mexico.
C3 Universidad Nacional Autonoma de Mexico; Universidad Nacional Autonoma
   de Mexico
RP Carrillo-Calvet, H (corresponding author), Univ Nacl Autonoma Mexico, Fac Sci, Lab Nonlinear Dynam, Mexico City, DF, Mexico.; Carrillo-Calvet, H (corresponding author), Univ Nacl Autonoma Mexico, Ctr Complex Sci, Mexico City, DF, Mexico.
EM elio.villasenor@infotec.mx; ricardo.arencibia@eti.biocubafarma.cu;
   carr@unam.mx
RI Atenogenes, Elio/GSE-4644-2022; Carrillo Calvet, Humberto/E-2265-2012;
   GARCÍA, ELIO ATENÓGENES VILLASEÑOR/W-7501-2019; Arencibia-Jorge,
   Ricardo/AAK-3567-2020; Arencibia-Jorge, Ricardo/B-1330-2016; CARRILLO
   CALVET, HUMBERTO/ITW-2657-2023
OI Atenogenes, Elio/0000-0002-8611-8661; Carrillo Calvet,
   Humberto/0000-0003-3659-6769; GARCÍA, ELIO ATENÓGENES
   VILLASEÑOR/0000-0002-8611-8661; Arencibia-Jorge,
   Ricardo/0000-0001-8907-2454; 
FU Proyecto CITMA-CONACyT [B330.166]; Empresa de Tecnologias Inteligentes y
   Modelacion de Sistemas S.A. de C.V.
FX This research was partially supported by the Proyecto CITMA-CONACyT
   (B330.166) and the Empresa de Tecnologias Inteligentes y Modelacion de
   Sistemas S.A. de C.V. The authors acknowledge the collaboration of Jose
   Luis Jimenez Andrade (UNAM, Mexico), and of Dr. Felix de Moya Anegon
   (CSIC, Spain) for the data support given from SCImago Institutions
   Rankings.
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NR 59
TC 25
Z9 26
U1 5
U2 91
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD JAN
PY 2017
VL 110
IS 1
BP 77
EP 104
DI 10.1007/s11192-016-2166-0
PG 28
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA EK2HL
UT WOS:000393748300005
DA 2024-09-05
ER
PT C
AU BinMakhashen, GM
   Al-Jamimi, HA
AF BinMakhashen, Galal M.
   Al-Jamimi, Hamdi A.
GP IEEE
TI Evaluation of Machine Learning to Early Detection of Highly Cited Papers
SO 2022 7TH INTERNATIONAL CONFERENCE ON DATA SCIENCE AND MACHINE LEARNING
   APPLICATIONS (CDMA 2022)
LA English
DT Proceedings Paper
CT 7th International Conference on Data Science and Machine Learning
   Applications (CDMA)
CY MAR 01-03, 2022
CL Prince Sultan Univ, Riyadh, SAUDI ARABIA
HO Prince Sultan Univ
DE Highly-cited Research; Bibliometric Analysis; Machine Learning; Digital
   Libraries
ID CITATION IMPACT; COLLABORATION; IMPROVE; PREDICT; COUNTS
AB As one of the fastest-growing topics, machine learning has many applications that span through different domains including image and signal recognition, text mining, information retrieval, robotics, etc. It enables information extraction and analysis for better insights and decision-based systems. The Web of Science(WoS) citation database is a leading organization that provides citation data of high-quality published research. WoS has its metrics to label published articles as Highly Cited Paper(HCP). Machine learning (ML) can help researchers in identifying the key characteristics of HCP. Moreover, it can allow research evaluation units forecasting significant scientific articles. In other words, it may allow researchers and/or research evaluators to detect potential scientific breakthrough ideas and stay current. In this study, more than 26 thousand records of published articles indexed by WoS were analyzed. All the records are drawn from the Technology research area as defined by WoS. Four ML algorithms are evaluated to verify the HCP common factors influence in raising citations and interest in scientific articles. The ensemble algorithms show promising results to identify HCP articles using only four factors.
C1 [BinMakhashen, Galal M.; Al-Jamimi, Hamdi A.] King Fahd Univ Petr & Minerals, Res Inst, Dhahran, Saudi Arabia.
C3 King Fahd University of Petroleum & Minerals
RP BinMakhashen, GM (corresponding author), King Fahd Univ Petr & Minerals, Res Inst, Dhahran, Saudi Arabia.
EM binmakhashen@kfupm.edu.sa; aljamimi@kfupm.edu.sa
RI Al-Jamimi, Hamdi A./G-5734-2016
FU King Fahd University of Petroleum and Minerals (KFUPM) [DF191012]
FX The authors would like to acknowledge the help and support provided by
   King Fahd University of Petroleum and Minerals (KFUPM) through funding
   the project number DF191012.
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NR 38
TC 3
Z9 3
U1 6
U2 17
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-6654-1014-4
PY 2022
BP 1
EP 6
DI 10.1109/CDMA54072.2022.00006
PG 6
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Engineering, Biomedical
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Engineering
GA BT2WE
UT WOS:000814738100002
DA 2024-09-05
ER
PT J
AU Liu, Y
   Chen, HX
   Thoff, A
AF Liu, Yang
   Chen, Huaxi
   Thoff, Anjahol
TI Research on evaluation method of students' classroom performance based
   on artificial intelligence
SO INTERNATIONAL JOURNAL OF CONTINUING ENGINEERING EDUCATION AND LIFE-LONG
   LEARNING
LA English
DT Article
DE artificial intelligence; classroom performance; evaluation method;
   students' performance; monitoring image
ID BEHAVIOR
AB In order to solve the problem of time-consuming and low reliability of the traditional evaluation method for students' classroom performance, an artificial intelligence-based evaluation method for students' classroom performance is proposed. This paper establishes the evaluation standard of students' classroom performance and quantifies the evaluation of students' classroom performance. Classroom monitoring device is driven by program code. The initial acquired image is processed by image enhancement and recognition, and the result is transmitted to the upper computer. This paper analyses the evaluation indexes of students' classroom performance, such as attendance, participation, attention and abnormal behaviour, and finally outputs the results of teachers' classroom performance. The experimental results show that compared with the traditional evaluation method, the artificial intelligence-based evaluation method saves 69 seconds on average and improves its reliability by 6.25%.
C1 [Liu, Yang] Chuzhou Univ, Sch Math & Finance, Chuzhou 329000, Peoples R China.
   [Chen, Huaxi] Bengbu Univ, Sch Sci, Bengbu 233000, Anhui, Peoples R China.
   [Thoff, Anjahol] Univ Copenhagen, Dept Comp Sci, DK-1017 Copenhagen, Denmark.
C3 Chuzhou University; Bengbu University; University of Copenhagen
RP Chen, HX (corresponding author), Bengbu Univ, Sch Sci, Bengbu 233000, Anhui, Peoples R China.
EM 946261665@qq.com; aiiiang@sina.com; anjahol.thoff@outlook.com
RI Valdiviezo, Lorgio/KRO-5493-2024
FU Anhui Provincial Education Department Foundation [2016jyxm0724]; Key
   project of teaching research in Anhui province [2017jyxm0541]
FX This work was supported by Anhui Provincial Education Department
   Foundation under grant no. 2016jyxm0724, and Key project of teaching
   research in Anhui province under grant no. 2017jyxm0541.
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NR 19
TC 1
Z9 1
U1 4
U2 29
PU INDERSCIENCE ENTERPRISES LTD
PI GENEVA
PA WORLD TRADE CENTER BLDG, 29 ROUTE DE PRE-BOIS, CASE POSTALE 856, CH-1215
   GENEVA, SWITZERLAND
SN 1560-4624
EI 1741-5055
J9 INT J CONTIN ENG EDU
JI Int. J. Contin. Eng. Educ. Life-Long Learn.
PY 2020
VL 30
IS 4
SI SI
BP 476
EP 491
PG 16
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA OP8WG
UT WOS:000588370800008
DA 2024-09-05
ER
PT J
AU Zhang, Y
   Lu, J
   Liu, F
   Liu, Q
   Porter, A
   Chen, HS
   Zhang, GQ
AF Zhang, Yi
   Lu, Jie
   Liu, Feng
   Liu, Qian
   Porter, Alan
   Chen, Hongshu
   Zhang, Guangquan
TI Does deep learning help topic extraction? A kernel k-means clustering
   method with word embedding
SO JOURNAL OF INFORMETRICS
LA English
DT Article
DE Bibliometrics; Topic analysis; Cluster analysis; Text mining
ID AUTHOR COCITATION; SCIENCE
AB Topic extraction presents challenges for the bibliometric community, and its performance still depends on human intervention and its practical areas. This paper proposes a novel kernel k-means clustering method incorporated with a word embedding model to create a solution that effectively extracts topics from bibliometric data. The experimental results of a comparison of this method with four clustering baselines (i.e., k-means, fuzzy c-means, principal component analysis, and topic models) on two bibliometric datasets demonstrate its effectiveness across either a relatively broad range of disciplines or a given domain. An empirical study on bibliometric topic extraction from articles published by three top tier bibliometric journals between 2000 and 2017, supported by expert knowledge-based evaluations, provides supplemental evidence of the method's ability on topic extraction. Additionally, this empirical analysis reveals insights into both overlapping and diverse research interests among the three journals that would benefit journal publishers, editorial boards, and research communities. (C) 2018 Elsevier Ltd. All rights reserved.
C1 [Zhang, Yi; Lu, Jie; Liu, Feng; Liu, Qian; Chen, Hongshu; Zhang, Guangquan] Univ Technol Sydney, Fac Engn & Informat Technol, Ctr Artificial Intelligence, Sydney, NSW, Australia.
   [Liu, Qian] Beijing Inst Technol, Sch Comp Sci, Beijing, Peoples R China.
   [Porter, Alan] Georgia Inst Technol, Technol Policy & Assessment Ctr, Atlanta, GA 30332 USA.
   [Porter, Alan] Search Technol Inc, Herndon, VA USA.
C3 University of Technology Sydney; Beijing Institute of Technology;
   University System of Georgia; Georgia Institute of Technology
RP Chen, HS (corresponding author), Univ Technol Sydney, Fac Engn & Informat Technol, Ctr Artificial Intelligence, Sydney, NSW, Australia.
EM yi.zhang@uts.edu.au; jie.lu@uts.edu.au; feng.liu-2@student.uts.edu.au;
   qian.liu-9@student.uts.edu.au; alan.porter@isye.gatech.edu;
   hongsue1114@hotmail.com; guangquan.zhang@uts.edu.au
RI Liu, Feng/I-1816-2016; Chen, Hongshu/O-2926-2017; Zhang,
   Yi/AAT-6945-2021; Zhang, Guangquan/G-2553-2017; Liu, Qian/AGD-5748-2022;
   porter, alan/A-7013-2009
OI Liu, Feng/0000-0002-5005-9129; Chen, Hongshu/0000-0002-0893-1817; Zhang,
   Yi/0000-0002-7731-0301; Liu, Qian/0000-0002-3162-935X; porter,
   alan/0000-0002-4520-6518
FU Australian Research Council [DP150101645]; United States National
   Science Foundation [1759960]; Direct For Social, Behav & Economic Scie;
   SBE Off Of Multidisciplinary Activities [1759960] Funding Source:
   National Science Foundation
FX This work is partially supported by the Australian Research Council
   under Discovery Grant DP150101645 and the United States National Science
   Foundation Award #1759960.
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NR 45
TC 80
Z9 83
U1 3
U2 117
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1751-1577
EI 1875-5879
J9 J INFORMETR
JI J. Informetr.
PD NOV
PY 2018
VL 12
IS 4
BP 1099
EP 1117
DI 10.1016/j.joi.2018.09.004
PG 19
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA HB5CH
UT WOS:000451074800008
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Riehl, K
AF Riehl, Kevin
TI On the scientometric value of full-text, beyond abstracts and titles:
   evidence from the business and economic literature
SO MANAGEMENT REVIEW QUARTERLY
LA English
DT Article; Early Access
DE Scientometrics; Topic modelling; Latent Dirichlet allocation; LDA;
   Textual analysis; Natural language processing; I23; M21; O31; O35; Z19
ID CO-WORD ANALYSIS; INTERNATIONAL-BUSINESS; BIBLIOMETRIC ANALYSIS;
   MANAGEMENT JOURNALS; CITATION ANALYSIS; ARTICLE; SCIENCE; INFORMATION;
   READABILITY; EVOLUTION
AB Are abstracts or titles a good proxy for what an article contains? The majority of scientometric studies have used easily accessible representations of publications such as reference and author lists, citations, keywords, titles, and abstracts, rather than full-texts. However, better accessibility to full-text databases is on the rise. First studies employing full-texts are promising, yet the extent to which scientometric exploration of papers beyond title and abstract is beneficial to gain further insights is still under discussion. In this paper, we analyse the similarity between a paper's title, abstract and full-text and examine whether scientometric analyses should better rely on full-texts. Our dataset includes 66,392 articles published in 27 leading journals in the business administration and economics literature. We examine the use of these representations in textual analysis, topic modelling and research evaluation. The results suggest that, unlike titles, abstracts and full-texts exhibit significant similarities and can be used interchangeably. While, abstracts contain less extraneous information and approximately 30% less noise compared to full-texts in topic modelling, full-text-based models to explain future number of citations yield a 5% higher explanatory power. Additionally, we recommend considering the influence of diverse writing styles as a textual and rhetorical property, as our analysis demonstrates its significant explanatory power for future publication success.
C1 [Riehl, Kevin] Tech Univ Darmstadt, Dept Law & Econ, Darmstadt, Germany.
C3 Technical University of Darmstadt
RP Riehl, K (corresponding author), Tech Univ Darmstadt, Dept Law & Econ, Darmstadt, Germany.
EM kevin.riehl.de@googlemail.com
OI Riehl, Kevin/0000-0003-4620-8379
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NR 141
TC 0
Z9 0
U1 1
U2 1
PU SPRINGERNATURE
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND
SN 2198-1620
EI 2198-1639
J9 MANAG REV Q
JI Manag. Rev. Q.
PD 2024 MAY 24
PY 2024
DI 10.1007/s11301-024-00439-8
EA MAY 2024
PG 55
WC Business; Business, Finance; Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA SJ3O2
UT WOS:001234049200001
DA 2024-09-05
ER
PT J
AU Crockett, C
   Finelli, CJ
   Demonbrun, M
   Nguyen, KA
   Tharayil, S
   Shekhar, P
   Rosenberg, RS
AF Crockett, Caroline
   Finelli, Cynthia J.
   Demonbrun, Matt
   Nguyen, Kevin A.
   Tharayil, Sneha
   Shekhar, Prateek
   Rosenberg, Robyn S.
TI Common Characteristics of High-quality Papers Studying Student Response
   to Active Learning
SO INTERNATIONAL JOURNAL OF ENGINEERING EDUCATION
LA English
DT Article
DE active learning; student response; systematic literature review
ID EDUCATION; SCIENCE
AB Active learning is increasingly used in engineering classrooms to improve student learning and engagement. Although students tend to respond positively to the introduction of active learning, some instructors experience negative student responses. Determining why and how to alleviate such negative responses is an open research question. Because there are many contextual variables to consider, we believe this question will best be addressed by increasing the number of faculty who are able to study their own implementation of active learning. This paper examines the underlying characteristics of 27 high-quality papers on student response to active learning. Using a six step research framework, this paper: (1) discusses common categories of research questions, (2) offers rules of thumb for literature reviews, (3) provides example theories, (4) discusses the data collected by qualitative, quantitative, and mixed methods studies and how the data is analyzed, (5) points to different approaches for data presentation, and (6) lists elements which authors typically include in their description of context and discussion sections. We offer literature-driven recommendations for faculty to help them quickly adopt good practices for how to share evidence based on their experiences.
C1 [Crockett, Caroline; Finelli, Cynthia J.] Univ Michigan, Elect Engn & Comp Sci, Ann Arbor, MI 48109 USA.
   [Demonbrun, Matt] Southern Methodist Univ, Enrollment Management Res Grp, Dallas, TX USA.
   [Nguyen, Kevin A.] Sonoma State Univ, Hutchins Sch Liberal Studies, Rohnert Pk, CA 94928 USA.
   [Tharayil, Sneha] Univ Texas Austin, STEM Educ, Austin, TX 78712 USA.
   [Shekhar, Prateek] New Jersey Inst Technol, Sch Appl Engn & Technol, Newark, NJ 07102 USA.
   [Rosenberg, Robyn S.] Harvard Univ, Cabot Sci Lib, Cambridge, MA 02138 USA.
C3 University of Michigan System; University of Michigan; Southern
   Methodist University; California State University System; Sonoma State
   University; University of Texas System; University of Texas Austin; New
   Jersey Institute of Technology; Harvard University
RP Crockett, C (corresponding author), Univ Michigan, Elect Engn & Comp Sci, Ann Arbor, MI 48109 USA.
EM cecroc@umich.edu; cfinelli@umich.edu; rdemonbrun@smu.edu;
   nguyenkevi@sonoma.edu; sneha.tharayil@utexas.edu;
   prateek.shekhar@njit.edu; robyn_rosenberg@harvard.edu
FU NSF [DUE-1744407]
FX The authors would like to thank Dr. Maura Borrego and Dr. Cindy Waters
   for their contributions to the overall systematic literature, without
   which this project would not have been possible. We also thank the
   reviewers for their thoughtful comments. We would like to gratefully
   acknowledge the NSF for their financial support (through the DUE-1744407
   grant). Any opinions, findings, and conclusions or recommendations
   expressed in this Report are those of the authors and do not necessarily
   reflect the views of the National Science Foundation; NSF has not
   approved or endorsed its content.
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NR 36
TC 2
Z9 2
U1 0
U2 2
PU TEMPUS PUBLICATIONS
PI DURRUS, BANTRY
PA IJEE , ROSSMORE,, DURRUS, BANTRY, COUNTY CORK 00000, IRELAND
SN 0949-149X
J9 INT J ENG EDUC
JI Int. J. Eng. Educ
PY 2021
VL 37
IS 2
BP 420
EP 432
PG 13
WC Education, Scientific Disciplines; Engineering, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Education & Educational Research; Engineering
GA RO4PJ
UT WOS:000641026600012
DA 2024-09-05
ER
PT J
AU Bai, XM
   Liu, H
   Zhang, FL
   Ning, ZL
   Kong, XJ
   Lee, I
   Xia, F
AF Bai, Xiaomei
   Liu, Hui
   Zhang, Fuli
   Ning, Zhaolong
   Kong, Xiangjie
   Lee, Ivan
   Xia, Feng
TI An Overview on Evaluating and Predicting Scholarly Article Impact
SO INFORMATION
LA English
DT Article
DE scholarly big data; article impact; machine learning; data mining
ID CITATION IMPACT; UNIVERSALITY; NETWORKS
AB Scholarly article impact reflects the significance of academic output recognised by academic peers, and it often plays a crucial role in assessing the scientific achievements of researchers, teams, institutions and countries. It is also used for addressing various needs in the academic and scientific arena, such as recruitment decisions, promotions, and funding allocations. This article provides a comprehensive review of recent progresses related to article impact assessment and prediction. The review starts by sharing some insight into the article impact research and outlines current research status. Some core methods and recent progress are presented to outline how article impact metrics and prediction have evolved to consider integrating multiple networks. Key techniques, including statistical analysis, machine learning, data mining and network science, are discussed. In particular, we highlight important applications of each technique in article impact research. Subsequently, we discuss the open issues and challenges of article impact research. At the same time, this review points out some important research directions, including article impact evaluation by considering Conflict of Interest, time and location information, various distributions of scholarly entities, and rising stars.
C1 [Bai, Xiaomei; Liu, Hui; Ning, Zhaolong; Kong, Xiangjie; Xia, Feng] Dalian Univ Technol, Sch Software, Dalian 116620, Peoples R China.
   [Bai, Xiaomei] Anshan Normal Univ, Ctr Comp, Anshan 114007, Peoples R China.
   [Zhang, Fuli] Anshan Normal Univ, Anshan 114007, Peoples R China.
   [Lee, Ivan] Univ South Australia, Sch Informat Technol & Math Sci, Mawson Lakes, SA 5095, Australia.
C3 Dalian University of Technology; Anshan Normal University; Anshan Normal
   University; University of South Australia
RP Liu, H (corresponding author), Dalian Univ Technol, Sch Software, Dalian 116620, Peoples R China.
EM xiaomeibai@outlook.com; liuhui1126@dlut.edu.cn; zfuli@outlook.com;
   Zhaolongning@dlut.edu.cn; xjkong@ieee.org; ivan.lee@unisa.edu.au;
   f.xia@ieee.org
RI Xia, Feng/Y-2859-2019; Kong, Xiangjie/B-8809-2016; Ning,
   Zhaolong/ABI-3626-2022; Lee, Ivan/F-4131-2013
OI Xia, Feng/0000-0002-8324-1859; Kong, Xiangjie/0000-0003-2698-3319; Ning,
   Zhaolong/0000-0002-7870-5524; Lee, Ivan/0000-0002-2826-6367
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PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2078-2489
J9 INFORMATION
JI Information
PD SEP
PY 2017
VL 8
IS 3
AR 73
DI 10.3390/info8030073
PG 14
WC Computer Science, Information Systems
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA FQ6YB
UT WOS:000418508900003
OA Green Submitted, gold
DA 2024-09-05
ER
PT J
AU Bickley, SJ
   Chan, HF
   Torgler, B
AF Bickley, Steve J.
   Chan, Ho Fai
   Torgler, Benno
TI Artificial intelligence in the field of economics
SO SCIENTOMETRICS
LA English
DT Article
DE Artificial intelligence; Machine learning; Economics; Scientometrics;
   Science of science; Bibliometrics
ID WEB-OF-SCIENCE; GOOGLE-SCHOLAR; BIG DATA; SCOPUS; AI; CREATIVITY;
   BUSINESS; CYBERNETICS; MANAGEMENT; LANDSCAPE
AB The history of AI in economics is long and winding, much the same as the evolving field of AI itself. Economists have engaged with AI since its beginnings, albeit in varying degrees and with changing focus across time and places. In this study, we have explored the diffusion of AI and different AI methods (e.g., machine learning, deep learning, neural networks, expert systems, knowledge-based systems) through and within economic subfields, taking a scientometrics approach. In particular, we centre our accompanying discussion of AI in economics around the problems of economic calculation and social planning as proposed by Hayek. To map the history of AI within and between economic sub-fields, we construct two datasets containing bibliometrics information of economics papers based on search query results from the Scopus database and the EconPapers (and IDEAs/RePEc) repository. We present descriptive results that map the use and discussion of AI in economics over time, place, and subfield. In doing so, we also characterise the authors and affiliations of those engaging with AI in economics. Additionally, we find positive correlations between quality of institutional affiliation and engagement with or focus on AI in economics and negative correlations between the Human Development Index and share of learning-based AI papers.
C1 [Bickley, Steve J.; Chan, Ho Fai; Torgler, Benno] Queensland Univ Technol, Sch Econ & Finance, 2 George St, Brisbane, Qld 4000, Australia.
   [Bickley, Steve J.; Chan, Ho Fai; Torgler, Benno] Ctr Behav Econ Soc & Technol BEST, 2 George St, Brisbane, Qld 4000, Australia.
   [Torgler, Benno] CREMA Ctr Res Econ Management & Arts, Sudstr 11, CH-8008 Zurich, Switzerland.
C3 Queensland University of Technology (QUT); Queensland University of
   Technology (QUT)
RP Chan, HF (corresponding author), Queensland Univ Technol, Sch Econ & Finance, 2 George St, Brisbane, Qld 4000, Australia.; Chan, HF (corresponding author), Ctr Behav Econ Soc & Technol BEST, 2 George St, Brisbane, Qld 4000, Australia.
EM hofai.chan@qut.edu.au
RI Torgler, Benno/W-4556-2019; Bickley, Steve/HZH-8985-2023; Chan, Ho
   Fai/G-9541-2015
OI Torgler, Benno/0000-0002-9809-963X; Chan, Ho Fai/0000-0002-7281-5212;
   Bickley, Steven/0000-0002-9579-4231
FU Australian Research Council [DP180101169]
FX Open Access funding enabled and organized by CAUL and its Member
   Institutions. The funding was provided by Australian Research Council,
   Grant No (DP180101169).
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NR 111
TC 12
Z9 13
U1 19
U2 105
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD APR
PY 2022
VL 127
IS 4
BP 2055
EP 2084
DI 10.1007/s11192-022-04294-w
EA MAR 2022
PG 30
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 0M5LI
UT WOS:000764911500001
OA Green Published, hybrid
DA 2024-09-05
ER
PT J
AU An, L
   Han, YX
   Yi, XY
   Li, G
   Yu, CM
AF An, Lu
   Han, Yuxin
   Yi, Xingyue
   Li, Gang
   Yu, Chuanming
TI Prediction and Evolution of the Influence of Microblog Entries in the
   Context of Terrorist Events
SO SOCIAL SCIENCE COMPUTER REVIEW
LA English
DT Article
DE influence prediction; microblogging; evolution; topic identification;
   sentiment analysis; h-index; terrorist event
ID COLLECTIVE SENSE-MAKING; SOCIAL MEDIA; TWITTER; CRISIS
AB The outbreak of terrorist events often causes tremendous damage to the country and society and arouses high attention from the public and an overwhelming response on the microblogging platform. Predicting the influence of microblogging in the context of terrorist events and revealing its evolutionary mode can help counterterrorism departments foresee potential risks, take effective countermeasures in time, and provide a reference for reducing public panic caused by terrorist events. In this study, Word2Vec is combined with the K-means clustering technique to discover the topics of microblogging, and an emotion analysis of microblogging is performed. The user features, time features, and content features of microblogging in the context of terrorist events are extracted. The prediction model of microblogging influence based on the logistic regression model was constructed and evaluated. The experimental results showed that the prediction accuracy of the model was 85.8%, which had superior performance over other six classification models. In addition, the high-influence characteristics of microblogging in the context of terrorist events were analyzed and summarized. Finally, a quantitative method of the influence of a microblogging topic based on the h-index was proposed. The evolution pattern of the influence of a microblogging topic was analyzed. The results can help predict microblog entries of high influence, understand the intensity and variation of public concern over terrorist events, and assist counterterrorism departments in taking scientific decisions.
C1 [An, Lu; Li, Gang] Wuhan Univ, Ctr Studies Informat Resources, 299 Bayi Rd, Wuhan 430072, Hubei, Peoples R China.
   [An, Lu; Han, Yuxin; Yi, Xingyue] Wuhan Univ, Sch Informat Management, Wuhan, Peoples R China.
   [Yu, Chuanming] Zhongnan Univ Econ & Law, Sch Informat & Safety Engn, Wuhan, Peoples R China.
C3 Wuhan University; Wuhan University; Zhongnan University of Economics &
   Law
RP Li, G (corresponding author), Wuhan Univ, Ctr Studies Informat Resources, 299 Bayi Rd, Wuhan 430072, Hubei, Peoples R China.
EM anlu97@163.com; yxhanccnu@163.com; yixingyue@126.com;
   imiswhu@aliyun.com; yuchuanming2003@126.com
RI Han, yuxin/LCD-8775-2024
FU National Natural Science Foundation of China [71790612, 71974202,
   71603189, 71921002]; Major Project of the Ministry of Education of China
   [17JZD034]; world class discipline of the Ministry of Education of China
   "Library, Information, and Data Science"
FX The authors disclosed receipt of the following financial support for the
   research, authorship, and/or publication of this article: This work was
   supported by the National Natural Science Foundation of China (Grant no.
   71921002), the Major Project of the Ministry of Education of China
   (Grant no. 17JZD034), the National Natural Science Foundation of China
   (Grant nos. 71790612, 71974202, and 71603189), and the world class
   discipline of the Ministry of Education of China "Library, Information,
   and Data Science."
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NR 31
TC 3
Z9 3
U1 11
U2 123
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 0894-4393
EI 1552-8286
J9 SOC SCI COMPUT REV
JI Soc. Sci. Comput. Rev.
PD FEB
PY 2023
VL 41
IS 1
BP 64
EP 82
AR 08944393211029193
DI 10.1177/08944393211029193
EA JUL 2021
PG 19
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science; Social Sciences, Interdisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science; Social Sciences
   - Other Topics
GA 7S7ZM
UT WOS:000676872600001
DA 2024-09-05
ER
PT C
AU Li, N
   Su, WT
   Li, Y
   Yu, H
   Xu, WZ
   Gao, BZ
AF Li, Na
   Su, Wentao
   Li, Yang
   Yu, Hui
   Xu, Weizhi
   Gao, Baozhong
GP IEEE
TI Research on Machine Translation Automatic Evaluation Based on Extended
   Reference
SO 2019 IEEE 2ND INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATION
   ENGINEERING TECHNOLOGY (CCET)
LA English
DT Proceedings Paper
CT 2nd IEEE International Conference on Computer and Communication
   Engineering Technology (CCET)
CY AUG 16-18, 2019
CL Beijing, PEOPLES R CHINA
DE machine translation; automatic evaluation; reference extension
AB Language is the main carrier of communication between cultures, but the translation between languages has become the biggest problem of people's communication. Machine translation is a process that uses computer to transform a natural language into another natural language. The automatic evaluation of machine translation is an important research content in machine translation technology. It can discover defects in translation system and promote its development. It has achieved rich fruits, and various evaluation methods emerge endlessly after several decades of development of the automatic evaluation method. In this paper, three kinds of representative evaluation methods are introduced and their respective advantages and disadvantages are analyzed. In addition, we describe the evaluation technique based on reference. It plays an important role in improving the performance of automatic evaluation methods although the coverage expansion of reference is not the main method. Finally, we summarize the development trends of automatic evaluation metric based on extended reference and related issues that need to be further addressed.
C1 [Li, Na; Yu, Hui] Shandong Normal Univ, Sch Business, Jinan, Peoples R China.
   [Su, Wentao] CNIPA, Ctr Patent Off, Patent Examinat Cooperat Beijing, Beijing, Peoples R China.
   [Li, Yang] China Natl Elect Import & Export Corp, Beijing, Peoples R China.
   [Xu, Weizhi; Gao, Baozhong] Shandong Normal Univ, Sch Informat Sci & Engn, Jinan, Peoples R China.
C3 Shandong Normal University; Shandong Normal University
RP Gao, BZ (corresponding author), Shandong Normal Univ, Sch Informat Sci & Engn, Jinan, Peoples R China.
EM gaobaozhong@sdnu.edu.cn
RI Yu, Hui/G-1115-2018; Zeng, Yun/JFK-6190-2023; su, wentao/AAK-3862-2021
OI Yu, Hui/0000-0002-7655-9228; su, wentao/0000-0002-3476-8259
FU NNSF of China [61602285, 61602284, 61602282]; Shandong Natural Science
   Foundation [ZR2015FQ 009, ZR2016FP07]; Project of Shandong Province
   Higher Educational Science and Technology Program [J16LN05]
FX This work was supported in part by NNSF of China (No. 61602285 and No.
   61602284 and No. 61602282), Shandong Natural Science Foundation
   (No.ZR2015FQ 009 and No. ZR2016FP07), and Project of Shandong Province
   Higher Educational Science and Technology Program (No.J16LN05)
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NR 35
TC 0
Z9 0
U1 0
U2 2
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-7281-2871-9
PY 2019
BP 41
EP 45
DI 10.1109/ccet48361.2019.8989400
PG 5
WC Computer Science, Theory & Methods; Telecommunications
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Telecommunications
GA BP2XS
UT WOS:000545638300009
DA 2024-09-05
ER
PT J
AU de la Paz, LD
   Collado, FNR
   Mendoza, JLG
   González, LMG
   Mederos, AAL
   Crispi, AT
AF Diaz de la Paz, Lisandra
   Riestra Collado, Francisco N.
   Garcia Mendoza, Juan L.
   Gonzalez Gonzalez, Luisa M.
   Leiva Mederos, Amed A.
   Taboada Crispi, Alberto
TI Weights Estimation in the Completeness Measurement of Bibliographic
   Metadata
SO COMPUTACION Y SISTEMAS
LA English
DT Article
DE Bibliographic metadata; completeness metric; particle swarm optimization
   (PSO); weights estimation
ID DATA QUALITY; DIGITAL REPOSITORIES; PARTICLE SWARM; LIBRARY; MANAGEMENT;
   FRAMEWORK
AB Y The weighted completeness metrics for metadata use a weighting factor to indicate the importance of each field. In the case of bibliographic metadata, a common way of representing the importance of a field is its frequency of appearance in a given repository. The inaccuracy of this method is why we need to recalculate the weights as the volume of the repository grows. In this paper, we used the Particle Swarm Optimization (PSO) method in the estimation of the weights for the completeness metrics of bibliographic metadata. This method is independent of the metadata format, of the collection and the volume of the repository used. As part of this work, we defined the fitness function of the PSO method to reflect the importance levels of the fields. Finally, we presented a case study with the estimated weights and the calculated completeness of the bibliographic records described at the full cataloging level in MARC 21 format.
C1 [Diaz de la Paz, Lisandra; Gonzalez Gonzalez, Luisa M.; Leiva Mederos, Amed A.; Taboada Crispi, Alberto] Univ Cent Marta Abreu Las Villas, Santa Clara, Cuba.
   [Diaz de la Paz, Lisandra; Taboada Crispi, Alberto] Ctr Invest Informat, Santa Clara, Cuba.
   [Riestra Collado, Francisco N.] Melia Dunas Cayo Santa Maria, Cayo De Santa Maria, Cuba.
   [Garcia Mendoza, Juan L.] Inst Nacl Astrofis Opt & Electr, Cholula, Mexico.
C3 Universidad Central "Marta Abreu" de Las Villas; Instituto Nacional de
   Astrofisica, Optica y Electronica
RP de la Paz, LD (corresponding author), Univ Cent Marta Abreu Las Villas, Santa Clara, Cuba.; de la Paz, LD (corresponding author), Ctr Invest Informat, Santa Clara, Cuba.
EM ldp@uclv.edu.cu; informatico.mld@mld.solmelia.cu; juanluis@inaoep.mx;
   luisagon@uclv.edu.cu; amed@uclv.edu.cu; ataboada@uclv.edu.cu
RI Majumder, Abhishek/AAV-3041-2020; Taboada-Crispi, Alberto/ABC-5178-2020;
   Majumder, Abhishek/GQO-9495-2022; Majumder, Abhishek/ABC-3221-2021;
   Balasubramanian, Gomathy/JUF-7312-2023; Taboada-Crispi,
   Alberto/K-4732-2019
OI Majumder, Abhishek/0000-0001-8451-0451; Taboada-Crispi,
   Alberto/0000-0002-7797-1441; Majumder, Abhishek/0000-0001-8451-0451;
   Balasubramanian, Gomathy/0000-0002-0418-2150; Taboada-Crispi,
   Alberto/0000-0002-7797-1441; Garcia Mendoza, Juan
   Luis/0000-0002-8165-9661
FU Project 3 ICT supporting the educational processes and the knowledge
   management in higher education (ELINF)of the NETWORK University
   Cooperation Strengthening of the role of ICT in Cuban Universities for
   the development of the society
FX This work is partially supported by Project 3 ICT supporting the
   educational processes and the knowledge management in higher education
   (ELINF)of the NETWORK University Cooperation Strengthening of the role
   of ICT in Cuban Universities for the development of the society.
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NR 81
TC 3
Z9 3
U1 0
U2 5
PU IPN, CENTRO INVESTIGAVION COMPUTACION
PI MEXICO CITY
PA AV JUAN DIOS BATIZ, S N ESQ M OTHON MENDIZABAL, UP ADOLFO LOPEZ MATEOS
   ZACATENCO, MEXICO CITY, 07738, MEXICO
SN 1405-5546
EI 2007-9737
J9 COMPUT SIST
JI Comput. Sist.
PY 2021
VL 25
IS 1
BP 47
EP 65
DI 10.13053/CyS-25-1-3355
PG 19
WC Computer Science, Information Systems
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA RY1WZ
UT WOS:000647710700005
DA 2024-09-05
ER
PT J
AU Rahman, A
   Raj, A
   Tomy, P
   Hameed, MS
AF Rahman, Abdur
   Raj, Antony
   Tomy, Prajeesh
   Hameed, Mohamed Sahul
TI A comprehensive bibliometric and content analysis of artificial
   intelligence in language learning: tracing between the years 2017 and
   2023
SO ARTIFICIAL INTELLIGENCE REVIEW
LA English
DT Article
DE Artificial intelligence; Language learning; Bibliometric analysis;
   Natural language processing; Review; Content analysis
ID RESEARCH TRENDS; EDUCATION; CHATBOT; FUTURE; FIELD
AB The rising pervasiveness of Artificial Intelligence (AI) has led applied linguists to combine it with language teaching and learning processes. In many cases, such implementation has significantly contributed to the field. The retrospective amount of literature dedicated on the use of AI in language learning (LL) is overwhelming. Thus, the objective of this paper is to map the existing literature on Artificial Intelligence in language learning through bibliometric and content analysis. From the Scopus database, we systematically explored, after keyword refinement, the prevailing literature of AI in LL. After excluding irrelevant articles, we conducted our study with 606 documents published between 2017 and 2023 for further investigation. This review reinforces our understanding by identifying and distilling the relationships between the content, the contributions, and the contributors. The findings of the study show a rising pattern of AI in LL. Along with the metrics of performance analysis, through VOSviewer and R studio (Biblioshiny), our findings uncovered the influential authors, institutions, countries, and the most influential documents in the field. Moreover, we identified 7 clusters and potential areas of related research through keyword analysis. In addition to the bibliographic details, this review aims to elucidate the content of the field. NVivo 14 and Atlas AI were used to perform content analysis to categorize and present the type of AI used in language learning, Language learning factors, and its participants.
C1 [Rahman, Abdur; Raj, Antony; Tomy, Prajeesh; Hameed, Mohamed Sahul] Vellore Inst Technol, Sch Social Sci & Languages, Dept English, Vellore 632104, Tamil Nadu, India.
C3 Vellore Institute of Technology (VIT); VIT Vellore
RP Rahman, A (corresponding author), Vellore Inst Technol, Sch Social Sci & Languages, Dept English, Vellore 632104, Tamil Nadu, India.
EM mrabdurrahmanofficial@gmail.com; antonyr311@gmail.com
RI raj, antony/HGF-3077-2022
OI raj, antony/0000-0003-0351-4245
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NR 87
TC 0
Z9 0
U1 27
U2 27
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0269-2821
EI 1573-7462
J9 ARTIF INTELL REV
JI Artif. Intell. Rev.
PD APR 1
PY 2024
VL 57
IS 4
AR 107
DI 10.1007/s10462-023-10643-9
PG 27
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA MR5M5
UT WOS:001195372800002
OA hybrid
DA 2024-09-05
ER
PT C
AU Bhattacharjee, KK
AF Bhattacharjee, Kalyan Kumar
GP IEEE
TI Research Output on the Usage of Artificial Intelligence in Indian Higher
   Education - A Scientometric Study
SO 2019 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND
   ENGINEERING MANAGEMENT (IEEM)
SE International Conference on Industrial Engineering and Engineering
   Management IEEM
LA English
DT Proceedings Paper
CT IEEE International Conference on Industrial Engineering and Engineering
   Management (IEEM)
CY DEC 15-18, 2019
CL Macao, MACAO
DE Scientometric analysis; Artificial Intelligence; Indian higher education
AB Scientrometrics is a branch of science which performs reproducible measurements of scientific activity. Scientometric analysis of research papers/ articles indexed in Scopus database (www.scopus.com) for last ten years (2009 to 2018) have been done. The study focuses on the research publications for the applications of Artificial Intelligence (AI) in higher education. A scientometric assessment of the trend of the research papers on AI usage in education sector have been presented in the study by way of analyzing; annual growth of research publications of AI (both globally and country wise) and growth trend of the "AI usage in education" publications (both country-wise as well as individual share). The study reveals the growth of AI in research publications both in international and in Indian context, but its applicability in the field of higher education is substantially low. The usage of AI in Indian education sector has tremendous scope for growth and in most likelihood research publications in the said field will expand considerably in years to come. This study will help subject specialists, researchers, policy makers for drafting effective policies, and those who wish to map the scientrometric patterns of research publications in the capacity of academic administrator or as an individual.
C1 [Bhattacharjee, Kalyan Kumar] Indian Inst Technol Delhi, New Delhi 16, India.
C3 Indian Institute of Technology System (IIT System); Indian Institute of
   Technology (IIT) - Delhi
RP Bhattacharjee, KK (corresponding author), Indian Inst Technol Delhi, New Delhi 16, India.
EM kalyan@admin.iitd.ac.in
RI Bhattacharjee, Kalyan/AAM-8341-2021
OI Bhattacharjee, Kalyan/0000-0001-9844-6091
CR ANDREWS S., 2016, Contingent Academic Employment in Australian Universities
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NR 17
TC 3
Z9 3
U1 3
U2 12
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2157-3611
BN 978-1-7281-3804-6
J9 IN C IND ENG ENG MAN
PY 2019
BP 916
EP 919
DI 10.1109/ieem44572.2019.8978798
PG 4
WC Engineering, Industrial; Operations Research & Management Science
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Engineering; Operations Research & Management Science
GA BP2CR
UT WOS:000541902500182
DA 2024-09-05
ER
PT J
AU Yeom, S
   Choi, C
   Kim, K
AF Yeom, Sungwoong
   Choi, Chulwoong
   Kim, Kyungbaek
TI LSTM-Based Collaborative Source-Side DDoS Attack Detection
SO IEEE ACCESS
LA English
DT Article
DE Collaboration; Adaptive systems; Denial-of-service attack; Neural
   networks; Market research; Logic gates; Time series analysis; Network
   security; DDoS attack; SDN; LSTM; collaborative detection; traffic
   seasonality embedding
ID NETWORK; DEFENSE
AB As denial of service attacks become more sophisticated, the source-side detection techniques are being studied to solve the limitations of target-side detection techniques such as delayed detection and difficulty in tracking attackers. Recently, some source-side detection techniques are being studied to use an adaptive attack detection threshold considering seasonal behavior of network traffic. However, because patterns of network traffic usage have become irregular with increased randomness and explosive traffic, the performance of the adaptive threshold technique has deteriorated. In addition, by limitations of the local view of a single site, distributed attacks from multiple sites may not be detected. In this paper, we propose a LSTM (Long Short Term Memory) based collaborative source-side DDoS (Distributed Denial of Service) attack detection framework which provides the attack detection result of a collaboration network in a global view. The proposed framework applies LSTM-based adaptive thresholds to each source-side network to mitigate performance degradation caused by irregular network traffic behavior. Also, in order to overcome the limitation of performance caused by the local view of single source-side network, the proposed framework constructs a collaborative network through multiple detection sites and aggregates feedback from each site, such as detection rates, local traffic patterns, and timestamp. The collaborative attack detection technique uses the aggregated feedback to determine whether the attack is finally detected and shares the finial detection results with multiple sites. Depending on this final detection result, the adaptive thresholds of each site are reset. Through extensive evaluation of actual network traffic data, the proposed collaborative source-side attack detection technique shows around 15% lower false positive rate than the single source-side attack detection technique while maintaining a high detection rate.
C1 [Yeom, Sungwoong; Choi, Chulwoong; Kim, Kyungbaek] Chonnam Natl Univ, Dept Artificial Intelligence Convergence, Gwangju 61186, South Korea.
C3 Chonnam National University
RP Kim, K (corresponding author), Chonnam Natl Univ, Dept Artificial Intelligence Convergence, Gwangju 61186, South Korea.
EM kyungbaekkim@jnu.ac.kr
OI Kim, Kyungbaek/0000-0001-9985-3051
FU Institute for Information & communications Technology Planning &
   Evaluation (IITP); Korea Government Ministry of Science and ICT (MSIT)
   [2019-0-01343]; Regional strategic Industry convergence Security Core
   Talent Training Business; Bio and Medical Technology Development Program
   of the National Research Foundation (NRF) through the Korean Government
   Ministry of Science and ICT (MSIT) [NRF-2019M3E5D1A02067961]
FX This work was supported by the Institute for Information &
   communications Technology Planning & Evaluation (IITP) and the Korea
   Government Ministry of Science and ICT (MSIT) under Grant 2019-0-01343,
   in part by the Regional strategic Industry convergence Security Core
   Talent Training Business, and in part by the Bio and Medical Technology
   Development Program of the National Research Foundation (NRF) through
   the Korean Government Ministry of Science and ICT (MSIT) under Grant
   NRF-2019M3E5D1A02067961.
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NR 31
TC 3
Z9 3
U1 2
U2 7
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2022
VL 10
BP 44033
EP 44045
DI 10.1109/ACCESS.2022.3169616
PG 13
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA 0Y9RE
UT WOS:000790719100001
OA gold
DA 2024-09-05
ER
PT J
AU Armenia, S
   Franco, E
   Iandolo, F
   Maielli, G
   Vito, P
AF Armenia, Stefano
   Franco, Eduardo
   Iandolo, Francesca
   Maielli, Giuliano
   Vito, Pietro
TI Zooming in and out the landscape: Artificial intelligence and system
   dynamics in business and management
SO TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
LA English
DT Article
DE System dynamics; Artificial intelligence; Bibliometrics; Topic modeling;
   Technology; Forecasting
ID SUPPLY CHAIN DYNAMICS; DECISION-MAKING; KNOWLEDGE MANAGEMENT;
   HEALTH-CARE; BANKRUPTCY PREDICTION; INNOVATION SYSTEM; NEURAL-NETWORKS;
   POLICY-ANALYSIS; BIG DATA; MODEL
AB Organizations are increasingly leveraging the ability of artificial intelligence to analyze and resolve complex problems. This can potentially reshape the interdependencies and interactions of complex systems, leading to our research question: To what extent and in which direction is the literature on Artificial Intelligence (AI) and System Dynamics (SD) converging within the business and management landscape? We conducted an extensive literature review using bibliometric and topic modeling methods to address this question. Through a bibliometric analysis, we identified the areas in which academic papers referred to both SD and AI literature. However, bibliometrics do not show a clear path towards convergence. The top modeling analysis highlights more details on how convergence is structured, providing insights into how SD and AI may be integrated. Two trajectories are identified. In the "soft convergence," AI supports system dynamics analysis and modeling more deeply characterized by social interaction. In the "hard convergence," AI shapes innovative ways of rethinking system design, dynamics, and interdependencies. Our analysis suggests that while soft convergence is more visible in the business and management landscape, hard convergence may well represent a new frontier in studying system dynamics with the potential to reshape the landscape.
C1 [Armenia, Stefano] IUL Univ, Rome, Italy.
   [Franco, Eduardo] Univ Sao Paulo, Sao Paulo, Brazil.
   [Iandolo, Francesca; Vito, Pietro] Sapienza Univ Rome, Rome, Italy.
   [Maielli, Giuliano] Queen Mary Univ London, Sch Business & Management, London, England.
C3 Universidade de Sao Paulo; Sapienza University Rome; University of
   London; Queen Mary University London
RP Iandolo, F (corresponding author), Sapienza Univ Rome, Rome, Italy.
EM francesca.iandolo@uniroma1.it
RI Armenia, Stefano/K-2167-2017; Maielli, Giuliano/KDM-8272-2024; Iandolo,
   Francesca/AFK-0311-2022
OI Armenia, Stefano/0000-0002-0777-4004; Iandolo,
   Francesca/0000-0002-2366-4892
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NR 253
TC 4
Z9 4
U1 33
U2 41
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0040-1625
EI 1873-5509
J9 TECHNOL FORECAST SOC
JI Technol. Forecast. Soc. Chang.
PD MAR
PY 2024
VL 200
AR 123131
DI 10.1016/j.techfore.2023.123131
EA JAN 2024
PG 20
WC Business; Regional & Urban Planning
WE Social Science Citation Index (SSCI)
SC Business & Economics; Public Administration
GA GR7Q5
UT WOS:001154469500001
OA hybrid, Green Submitted
DA 2024-09-05
ER
PT J
AU Galán, JJ
   Carrasco, RA
   LaTorre, A
AF Galan, Jose Javier
   Carrasco, Ramon Alberto
   LaTorre, Antonio
TI Military Applications of Machine Learning: A Bibliometric Perspective
SO MATHEMATICS
LA English
DT Article
DE machine learning; military; artificial intelligence; bibliometric
   analysis
ID STRESS; INTELLIGENCE; IDENTIFICATION; TECHNOLOGY; NETWORKS; VETERANS;
   TRENDS; MODEL
AB The military environment generates a large amount of data of great importance, which makes necessary the use of machine learning for its processing. Its ability to learn and predict possible scenarios by analyzing the huge volume of information generated provides automatic learning and decision support. This paper aims to present a model of a machine learning architecture applied to a military organization, carried out and supported by a bibliometric study applied to an architecture model of a nonmilitary organization. For this purpose, a bibliometric analysis up to the year 2021 was carried out, making a strategic diagram and interpreting the results. The information used has been extracted from one of the main databases widely accepted by the scientific community, ISI WoS. No direct military sources were used. This work is divided into five parts: the study of previous research related to machine learning in the military world; the explanation of our research methodology using the SciMat, Excel and VosViewer tools; the use of this methodology based on data mining, preprocessing, cluster normalization, a strategic diagram and the analysis of its results to investigate machine learning in the military context; based on these results, a conceptual architecture of the practical use of ML in the military context is drawn up; and, finally, we present the conclusions, where we will see the most important areas and the latest advances in machine learning applied, in this case, to a military environment, to analyze a large set of data, providing utility, machine learning and decision support.
C1 [Galan, Jose Javier] Univ Complutense Madrid, Fac Stat, Madrid 3728040, Spain.
   [Carrasco, Ramon Alberto] Univ Madrid, Fac Commerce & Tourism Complutense, Dept Management & Mkt, Madrid 28223, Spain.
   [LaTorre, Antonio] Univ Politecn Madrid, Ctr Computat Simulat CCS, Madrid 28660, Spain.
C3 Complutense University of Madrid; Universidad Politecnica de Madrid
RP Galán, JJ (corresponding author), Univ Complutense Madrid, Fac Stat, Madrid 3728040, Spain.
EM josejgal@ucm.es; ramoncar@ucm.es; a.latorre@upm.es
RI Galan Hernandez, Jose Javier/AHA-8253-2022; CARRASCO, RAMON
   ALBERTO/D-9973-2012; LaTorre, Antonio/A-5361-2011
OI Galan Hernandez, Jose Javier/0000-0002-1668-1731; CARRASCO, RAMON
   ALBERTO/0000-0001-7365-349X; LaTorre, Antonio/0000-0002-8718-5735
FU FEDER funds [PGC2018-096509-B-I00]
FX This research has been partially supported support from the FEDER funds
   provided by the National Spanish project PGC2018-096509-B-I00.
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NR 88
TC 10
Z9 10
U1 8
U2 36
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2227-7390
J9 MATHEMATICS-BASEL
JI Mathematics
PD MAY
PY 2022
VL 10
IS 9
AR 1397
DI 10.3390/math10091397
PG 27
WC Mathematics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Mathematics
GA 1G9BQ
UT WOS:000796143500001
OA gold
DA 2024-09-05
ER
PT C
AU Qi, J
   Liu, CY
   Guo, Y
   Gao, Y
   Hu, XX
AF Qi, Jin
   Liu, Chenya
   Guo, Yang
   Gao, Yu
   Hu, Xiaoxuan
GP IEEE
TI Research and Practice on the Talent Evaluation Model of the First-class
   Undergraduate Major in Network Engineering for Emerging engineering
SO 2023 35TH CHINESE CONTROL AND DECISION CONFERENCE, CCDC
SE Chinese Control and Decision Conference
LA English
DT Proceedings Paper
CT 35th Chinese Control and Decision Conference (CCDC)
CY MAY 20-22, 2023
CL Yichang, PEOPLES R CHINA
DE emerging engineering; outcome-based education; network engineering;
   talent evaluation; reinforcement learning
AB In view of the continuous quality improvement of network engineering education in the context of emerging engineering, we propose a talent evaluation method in the field of network engineering based on deep reinforcement learning with time windows. Firstly, a mathematical model of talent evaluation is established, and then a comprehensive index system is built from four aspects: engineering awareness, engineering foundation, engineering ability and engineering innovation. Secondly, the time window mode is used to divide the time slots of the data source, and the evaluation results are solved through the dynamic interaction between the agent and the environment. Further, the evaluation results are used as feedback factors to continuously improve the quality of education. Finally, we take "Nanjing University of Posts and Telecommunications-Network Engineering" as an example to conduct experimental simulation. The results show that the proposed method can obtain more accurate and reliable evaluation conclusions.
C1 [Qi, Jin; Liu, Chenya; Hu, Xiaoxuan] Nanjing Univ Posts & Telecommun, Sch Internet Things, Nanjing, Peoples R China.
   [Guo, Yang] Nanjing Vovat Univ Ind Technol, Sch Elect Engn, Nanjing, Peoples R China.
   [Gao, Yu] Nanjing Univ Posts & Telecommun, Coll Automat, Nanjing, Peoples R China.
   [Gao, Yu] Nanjing Univ Posts & Telecommun, Coll Artif Intelligence, Nanjing, Peoples R China.
C3 Nanjing University of Posts & Telecommunications; Nanjing University of
   Posts & Telecommunications; Nanjing University of Posts &
   Telecommunications
RP Qi, J (corresponding author), Nanjing Univ Posts & Telecommun, Sch Internet Things, Nanjing, Peoples R China.
EM qijin@njupt.edu.cn
FU Education Reform of Nanjing University of Posts and Telecommunications
   [JG01620JX04]; Ministry of Education 2020 School Cooperative Education
   Project [QT-HKEDU2021030800069]
FX This paper is a key project funded by Education Reform of Nanjing
   University of Posts and Telecommunications. Project
   number:JG01620JX04;Ministry of Education 2020 School Cooperative
   Education Project:QT- HKEDU2021030800069.
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NR 20
TC 0
Z9 0
U1 1
U2 1
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 1948-9439
BN 979-8-3503-3472-2
J9 CHIN CONT DECIS CONF
PY 2023
BP 1130
EP 1135
DI 10.1109/CCDC58219.2023.10327192
PG 6
WC Automation & Control Systems; Operations Research & Management Science
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Automation & Control Systems; Operations Research & Management Science
GA BW2HE
UT WOS:001116704301059
DA 2024-09-05
ER
PT C
AU Chandrasekaran, MK
   Jaidka, K
   Mayr, P
AF Chandrasekaran, Muthu Kumar
   Jaidka, Kokil
   Mayr, Philipp
GP ACM/SIGIR
TI Joint Workshop on Bibliometric-enhanced Information Retrieval and
   Natural Language Processing for Digital Libraries (BIRNDL 2017)
SO SIGIR'17: PROCEEDINGS OF THE 40TH INTERNATIONAL ACM SIGIR CONFERENCE ON
   RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL
LA English
DT Proceedings Paper
CT 40th International ACM SIGIR conference on research and development in
   Information Retrieval
CY AUG 07-11, 2017
CL Shinjuku, JAPAN
DE Scientometrics; Information Retrieval; Digital Libraries; NLP;
   Summarization; Information Extraction; Citation analysis
AB The large scale of scholarly publications poses a challenge for scholars in information seeking and sensemaking. Bibliometrics, information retrieval (IR), text mining and NLP techniques could help in these search and look-up activities, but are not yet widely used. This workshop is intended to stimulate IR researchers and digital library professionals to elaborate on new approaches in natural language processing, information retrieval, scientometrics, text mining and recommendation techniques that can advance the state-of-the-art in scholarly document understanding, analysis, and retrieval at scale. The BIRNDL workshop at SIGIR 2017 will incorporate an invited talk, paper sessions and the third edition of the Computational Linguistics (CL) Scientific Summarization Shared Task.
C1 [Chandrasekaran, Muthu Kumar] Natl Univ Singapore, Sch Comp, Singapore, Singapore.
   [Jaidka, Kokil] Univ Penn, Sch Arts & Sci, Philadelphia, PA 19104 USA.
   [Mayr, Philipp] GESIS Leibniz Inst Social Sci, Mannheim, Germany.
C3 National University of Singapore; University of Pennsylvania; Leibniz
   Institut fur Sozialwissenschaften (GESIS)
RP Chandrasekaran, MK (corresponding author), Natl Univ Singapore, Sch Comp, Singapore, Singapore.
EM muthu.chandra@comp.nus.edu.sg; jaidka@sas.upenn.edu;
   philipp.mayr@gesis.org
RI Jaidka, Kokil/AAK-2618-2020
OI Jaidka, Kokil/0000-0002-8127-1157
FU Microsoft Research Asia
FX We thank Microsoft Research Asia for their generous support in funding
   the development, dissemination and organization of the CL-SciSumm
   dataset and the Shared Task. We are also grateful to the co-organizers
   of the 1st BIRNDL workshop - Guillaume Cabanac, Ingo Frommholz, Min-Yen
   Kan and Dietmar Wolfram, for their continued support and involvement.
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NR 6
TC 0
Z9 0
U1 1
U2 4
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA
BN 978-1-4503-5022-8
PY 2017
BP 1421
EP 1422
DI 10.1145/3077136.3084370
PG 2
WC Computer Science, Information Systems
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BL6UH
UT WOS:000454711900249
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Fahd, K
   Miah, SJ
AF Fahd, Kiran
   Miah, Shah J.
TI Designing and evaluating a big data analytics approach for predicting
   students' success factors
SO JOURNAL OF BIG DATA
LA English
DT Article
DE Design Science Research (DSR); Big Data; Big Data Analytical Solution
   (BDAS); Machine Learning (ML); Deep Learning (DL); DSR evaluation;
   Artificial Intelligence (AI)
ID SCIENCE RESEARCH; HIGHER-EDUCATION; ARTIFACT DESIGN; SYSTEMS;
   MANAGEMENT; METHODOLOGY; CONTEXT
AB Reducing student attrition in tertiary education plays a significant role in the core mission and financial well-being of an educational institution. The availability of big data source from the Learning Management System (LMS) can be analysed to help with the attrition issues. This study aims to use an integrated Design Science Research (DSR) methodology to develop and evaluate a novel Big Data Analytical Solution (BDAS) as an educational decision support artefact. The BDAS as DSR artefact utilises Artificial Intelligence (AI) approaches to predict potential students at risk. Identifying students at risk helps to take timely intervention in the learning process to improve student academic progress for increasing their retention rate. To evaluate the performance of the predictive model, we compare the accuracy of the collection of representational AI algorithms in the literature. The study utilized an integrated DSR methodology founded on the similarities of DSR and design based research (DBR) to design and develop the proposed BDAS employing an specific evaluation framework that works on real data scenarios. The BDAS does not only aimto replace any existing practice but also support educators to implement a variety of pedagogical practices for improving students' academic performance.
C1 [Fahd, Kiran; Miah, Shah J.] Univ Newcastle, Newcastle Business Sch, Business Analyt, Hunter St, Newcastle, NSW 2300, Australia.
C3 University of Newcastle
RP Miah, SJ (corresponding author), Univ Newcastle, Newcastle Business Sch, Business Analyt, Hunter St, Newcastle, NSW 2300, Australia.
EM shah.miah@newcastle.edu.au
FU Not applicable.
FX Not applicable.
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NR 68
TC 0
Z9 0
U1 12
U2 30
PU SPRINGERNATURE
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND
EI 2196-1115
J9 J BIG DATA-GER
JI J. Big Data
PD OCT 13
PY 2023
VL 10
IS 1
AR 159
DI 10.1186/s40537-023-00835-z
PG 19
WC Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA U0LZ2
UT WOS:001081820100001
OA gold, Green Accepted
DA 2024-09-05
ER
PT C
AU Ye, QS
AF Ye, Qiusun
BE Wang, H
   Zhang, BJ
   Liu, XZ
   Luo, DZ
   Zhong, SB
TI AI-VCR Computational Research on Visibility of 3D Materials Solids
   Reality Pictures
SO SMART MATERIALS AND INTELLIGENT SYSTEMS, PTS 1 AND 2
SE Advanced Materials Research
LA English
DT Proceedings Paper
CT International Conference on Smart Materials and Intelligent Systems
CY DEC 17-20, 2010
CL Chongqing, PEOPLES R CHINA
DE Showing plane; sighting plane; visibility; 3D Materials solids;
   VCR(Variable Carrying Rules); AI(Artificial Intelligence)
AB An optional spatial 3D material solid reality picture may be showed on a 2D showing plane, its surface is commonly consisted of too many sub-surfaces of the 3D solid reality picture in both dispersal and continuation. It was a difficult problem to express all exact visibilities of solids subsurfaces; because of some numerals computational error properties of visibilities are always unknown. So far, we cannot choose but select a proper dispersal degree of materials sub-surfaces, an approaching function of an optional material sub-surfaces B-Rep (Bound Representation), and numerals computation without any perturbation motion, etc. A novel numeral computational method of expressing exact visibilities of 3D spatial materials solids reality pictures with intelligent properties of VCR is given in this paper.
C1 Wuyi Univ, Dept Math & Comp Sci Engn, Wuyishan 354300, Fujian, Peoples R China.
C3 Wuyi University, Fujian
RP Ye, QS (corresponding author), Wuyi Univ, Dept Math & Comp Sci Engn, Wuyishan 354300, Fujian, Peoples R China.
EM qsye2005@yahoo.com.cn
CR CAI W, 1997, EXTENSION METHOD ENG
   FU KS, 1988, ARTIFICIAL INTELLIGE
   GUAN QX, 1990, J COMPUTER AIDED DES
   LIANG YD, 1989, ALGORITHM BASIS COMP
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   2004, MASTER TOPICS NUMBER
NR 13
TC 0
Z9 0
U1 0
U2 0
PU TRANS TECH PUBLICATIONS LTD
PI STAFA-ZURICH
PA LAUBLSRUTISTR 24, CH-8717 STAFA-ZURICH, SWITZERLAND
SN 1022-6680
BN 978-0-87849-223-7
J9 ADV MATER RES-SWITZ
PY 2011
VL 143-144
BP 592
EP 597
DI 10.4028/www.scientific.net/AMR.143-144.592
PG 6
WC Computer Science, Artificial Intelligence; Materials Science,
   Multidisciplinary
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Materials Science
GA BUC64
UT WOS:000288884700116
DA 2024-09-05
ER
PT J
AU Doan, LP
   Nguyen, LH
   Auquier, P
   Boyer, L
   Fond, G
   Nguyen, HT
   Latkin, CA
   Vu, GT
   Hall, BJ
   Ho, CSH
   Ho, RCM
AF Doan, Linh Phuong
   Nguyen, Long Hoang
   Auquier, Pascal
   Boyer, Laurent
   Fond, Guillaume
   Nguyen, Hien Thu
   Latkin, Carl A.
   Vu, Giang Thu
   Hall, Brian J.
   Ho, Cyrus S. H.
   Ho, Roger C. M.
TI Social network and HIV/AIDS: A bibliometric analysis of global
   literature
SO FRONTIERS IN PUBLIC HEALTH
LA English
DT Article
DE social network; HIV; bibliometric; topic modeling; Latent Dirichlet
   Allocation
ID INJECTION-DRUG USERS; HIV-RELATED RISK; RANDOMIZED CONTROLLED-TRIAL;
   PREVENTION; BEHAVIORS; SUPPORT; WOMEN; SEX; MEN; INTERVENTIONS
AB Social networks (SN) shape HIV risk behaviors and transmission. This study was performed to quantify research development, patterns, and trends in the use of SN in the field of HIV/AIDS, and used Global publications extracted from the Web of Science Core Collection database. Networks of countries, research disciplines, and most frequently used terms were visualized. The Latent Dirichlet Allocation method was used for topic modeling. A linear regression model was utilized to identify the trend of research development. During the period 1991-2019, in a total of 5,698 publications, topics with the highest volume of publications consisted of (1) mental disorders (16.1%); (2) HIV/sexually transmitted infections prevalence in key populations (9.9%); and (3) HIV-related stigma (9.3%). Discrepancies in the geographical distribution of publications were also observed. This study highlighted (1) the rapid growth of publications on a wide range of topics regarding SN in the field of HIV/AIDS, and (2) the importance of SN in HIV prevention, treatment, and care. The findings of this study suggest the need for interventions using SN and the improvement of research capacity via regional collaborations to reduce the HIV burden in low- and middle-income countries.
C1 [Doan, Linh Phuong; Nguyen, Hien Thu] Duy Tan Univ, Inst Global Hlth Innovat, Da Nang, Vietnam.
   [Doan, Linh Phuong; Nguyen, Hien Thu] Duy Tan Univ, Fac Med, Da Nang, Vietnam.
   [Nguyen, Long Hoang] Karolinska Inst, Dept Global Publ Hlth, Stockholm, Sweden.
   [Auquier, Pascal; Boyer, Laurent; Fond, Guillaume] Aix Marseille Univ, Res Ctr Hlth Serv & Qual Life, Marseille, France.
   [Latkin, Carl A.] Johns Hopkins Univ, Bloomberg Sch Publ Hlth, Baltimore, MD USA.
   [Vu, Giang Thu] Nguyen Tat Thanh Univ, Ctr Excellence Hlth Serv & Syst Res, Ho Chi Minh, Vietnam.
   [Hall, Brian J.] NYU, Sch Global Publ Hlth, New York, NY USA.
   [Ho, Cyrus S. H.; Ho, Roger C. M.] Natl Univ Singapore, Yong Loo Lin Sch Med, Dept Psychol Med, Singapore, Singapore.
   [Ho, Roger C. M.] Natl Univ Singapore, Inst Hlth Innovat & Technol iHealthtech, Singapore, Singapore.
C3 Duy Tan University; Duy Tan University; Karolinska Institutet;
   Aix-Marseille Universite; Johns Hopkins University; Johns Hopkins
   Bloomberg School of Public Health; Nguyen Tat Thanh University (NTTU);
   New York University; National University of Singapore; National
   University of Singapore
RP Doan, LP (corresponding author), Duy Tan Univ, Inst Global Hlth Innovat, Da Nang, Vietnam.; Doan, LP (corresponding author), Duy Tan Univ, Fac Med, Da Nang, Vietnam.
EM doanphuonglinh@duytan.edu.vn
RI Hall, Brian J./B-7694-2016; Ho, Roger C./ABD-9061-2021; Nguyen, Long
   H/E-6145-2015; Nguyen, Hien Thi/JKH-4812-2023; nguyen,
   long/KHV-1588-2024; Boyer, Laurent/E-5728-2016
OI Hall, Brian J./0000-0001-9358-2377; Ho, Roger C./0000-0001-9629-4493;
   Nguyen, Hien Thi/0000-0003-1444-3120; Boyer,
   Laurent/0000-0003-1229-6622; Vu, Giang Thu/0000-0002-3470-4458; Ho,
   Cyrus SH/0000-0002-7092-9566
FU NUS Department of Psychological Medicine; 
   [R-177-000-100-001/R-177-000-003-001];  [R-722-000-004-731]
FX Funding The article process charge of this paper is supported by NUS
   Department of Psychological Medicine
   (R-177-000-100-001/R-177-000-003-001) and NUS iHeathtech Other Operating
   Expenses (R-722-000-004-731).
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NR 62
TC 0
Z9 1
U1 3
U2 14
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 2296-2565
J9 FRONT PUBLIC HEALTH
JI Front. Public Health
PD NOV 2
PY 2022
VL 10
AR 1015023
DI 10.3389/fpubh.2022.1015023
PG 10
WC Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Public, Environmental & Occupational Health
GA 6H7GP
UT WOS:000885603400001
PM 36408016
OA gold, Green Published
DA 2024-09-05
ER
PT C
AU Chandrasekaran, MK
   Mayr, P
   Yasunaga, M
   Freitag, D
   Radev, D
   Kan, MY
AF Chandrasekaran, Muthu Kumar
   Mayr, Philipp
   Yasunaga, Michihiro
   Freitag, Dayne
   Radev, Dragomir
   Kan, Min-Yen
GP ACM
TI Joint Workshop on Bibliometric-enhanced Information Retrieval and
   Natural Language Processing for Digital Libraries (BIRNDL 2019)
SO PROCEEDINGS OF THE 42ND INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH
   AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '19)
LA English
DT Proceedings Paper
CT 42nd Annual International ACM SIGIR Conference on Research and
   Development in Information Retrieval (SIGIR)
CY JUL 21-25, 2019
CL Paris, FRANCE
DE Scientometrics; Information Retrieval; Digital Libraries; NLP;
   Summarization; Information Extraction; Citation analysis
AB The deluge of scholarly publication poses a challenge for scholars find relevant research and policy makers to seek in-depth information and understand research impact. Information retrieval (IR), natural language processing (NLP) and bibliometrics could enhance scholarly search, retrieval and user experience, but their use in digital libraries is not widespread. To address this gap, we propose the 4th Joint Workshop on BIRNDL and the 5th CL-SciSumm Shared Task. We seek to foster collaboration among researchers in NLP, IR and Digital Libraries (DL), and to stimulate the development of new methods in NLP, IR, recommendation systems and scientometrics toward improved scholarly document understanding, analysis, and retrieval at scale.
C1 [Chandrasekaran, Muthu Kumar] SRI Int, 333 Ravenswood Ave, Menlo Pk, CA 94025 USA.
   [Mayr, Philipp] GESIS Leibniz Inst Social Sci, Mannheim, Germany.
   [Yasunaga, Michihiro; Radev, Dragomir] Yale Univ, New Haven, CT 06520 USA.
   [Freitag, Dayne] SRI Int, San Diego, CA USA.
   [Kan, Min-Yen] Natl Univ Singapore, Sch Comp, Singapore, Singapore.
C3 SRI International; Leibniz Institut fur Sozialwissenschaften (GESIS);
   Yale University; SRI International; National University of Singapore
RP Chandrasekaran, MK (corresponding author), SRI Int, 333 Ravenswood Ave, Menlo Pk, CA 94025 USA.
EM cmkumar087@gmail.com; philipp.mayr@gesis.org; michi@yale.edu;
   freitag@ai.sri.com; dragomir.radev@yale.edu; kanmy@comp.nus.edu.sg
RI Radev, Dragomir/E-9641-2012; Yasunaga, Michihiro/GPW-9499-2022
FU SRI International; Chan-Zuckerberg Initiative (CZI); CZI; Deutsche
   Forschungsgemeinschaft (DFG), the "Establishing Contextual Dataset
   Retrieval - transferring concepts from document to dataset retrieval
   (ConDATA)" project [MA 3964/10-1]
FX We thank SRI International and Chan-Zuckerberg Initiative (CZI) for
   their generous support in funding the organization of the CL-SciSumm
   Shared Task 2019. CZI sponsored Alex Wade's keynote. We immensely than
   Prof. Dragomir Radev and Michihiro Yasunaga from Yale University for
   sharing the SciSummNet dataset for CL-SciSumm 2019 and co-organising
   this time. This work by Philipp Mayr was partly funded by Deutsche
   Forschungsgemeinschaft (DFG) under grant number MA 3964/10-1, the
   "Establishing Contextual Dataset Retrieval - transferring concepts from
   document to dataset retrieval (ConDATA)" project.
CR Atanassova Iana, 2019, Front Res Metr Anal, V4, P2, DOI 10.3389/frma.2019.00002
   Cabanac G., 2017, SIGIR FORUM, V50, P36, DOI [10.1145/3053408.3053417, DOI 10.1145/3053408.3053417]
   Cabanac G, 2018, SCIENTOMETRICS, V116, P1225, DOI 10.1007/s11192-018-2861-0
   Jaidka Kokil, 2014, The computational linguistics summarization pilot task
   Mayr Philipp, 2014, Advances in Information Retrieval. 36th European Conference on IR Research, ECIR 2014. Proceedings: LNCS 8416, P798, DOI 10.1007/978-3-319-06028-6_99
   Mayr P, 2018, INT J DIGIT LIBRARIE, V19, P107, DOI 10.1007/s00799-017-0230-x
   Mayr Philipp, 2018, SIGIR FORUM, V52, P105
   Philipp Mayr, 2017, SIGIR FORUM, V51, P107
   Wolfram D, 2016, P JOINT WORKSH BIBL, P6
   Yasunaga Michihiro, 2019, SCISUMMNET LARGE ANN
NR 10
TC 0
Z9 1
U1 1
U2 8
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA
BN 978-1-4503-6172-9
PY 2019
BP 1441
EP 1443
DI 10.1145/3331184.3331650
PG 3
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Information Science & Library Science
GA BO1LU
UT WOS:000501488900248
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Romanov, D
   Molokanov, V
   Kazantsev, N
   Jha, AK
AF Romanov, Dmitry
   Molokanov, Valentin
   Kazantsev, Nikolai
   Jha, Ashish Kumar
TI Removing order effects from human-classified datasets: A machine
   learning method to improve decision making systems
SO DECISION SUPPORT SYSTEMS
LA English
DT Article
DE Order effect; Machine learning; Artificial intelligence;
   Decision-making; Information systems (IS) research
ID DESIGN SCIENCE RESEARCH; QUESTION ORDER; INFORMATION; BIAS; JUDGMENTS;
   MEMORY; MODEL
AB Although recent developments in Artificial Intelligence (AI) and machine learning (ML) aim to enhance the fairness and transparency of decision-making systems, research has found that neural networks (or other similar AI techniques) are still effected by human cognitive biases due to the training datasets. In this study, we focus on order effects, i.e., when the input of information impacts human perception and the decisions resulting from this information. We propose the Order Effect Removal Method (OERM) for handling the order effect which leads to bias and for helping organizations remove these biases from their training datasets and, therefore, from auto-mated decision-making systems. Using design science principles to theoretically create, test, and validate the method, we can eliminate the order bias even in basic classification systems. Furthermore, the method can be applied in a multidisciplinary context, where an AI-based algorithm substitutes for manual work.
C1 [Romanov, Dmitry] HSE Univ, Grad Sch Business, Business Informat, Moscow, Russia.
   [Molokanov, Valentin] IQMen Business Intelligence, Moscow, Russia.
   [Kazantsev, Nikolai] Univ Cambridge, Inst Mfg, Cambridge, England.
   [Jha, Ashish Kumar] Trinity Coll Dublin, Trinity Business Sch, Dublin, Ireland.
C3 HSE University (National Research University Higher School of
   Economics); University of Cambridge; Trinity College Dublin
RP Jha, AK (corresponding author), Trinity Coll Dublin, Trinity Business Sch, Dublin, Ireland.
EM dromanov@hse.ru; nk622@cam.ac.uk; akjha@tcd.ie
RI Jha, Ashish Kumar/AAG-9098-2021
OI Jha, Ashish Kumar/0000-0002-5450-9983
FU EPSRC [EP/T022566/1]; Science Foundation Ireland research Centre ADAPT
   [13/RC/2106_P2]
FX The authors wish to thank HSE University students for their assis- tance
   in this research: Ekaterina Semenova, Ekaterina Shilova, Daria Davydova,
   Elina Zaytseva (Edgeeva) , and Nikita Pronin. The third author
   acknowledges the EPSRC funding via ?Next Stage Digital Econ- omy Centre?
   and UKRI funding, grant reference EP/T022566/1. The last author
   acknowledges the support of Science Foundation Ireland research Centre
   ADAPT through Grant 13/RC/2106_P2.
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NR 77
TC 5
Z9 5
U1 18
U2 44
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0167-9236
EI 1873-5797
J9 DECIS SUPPORT SYST
JI Decis. Support Syst.
PD FEB
PY 2023
VL 165
AR 113891
DI 10.1016/j.dss.2022.113891
EA DEC 2022
PG 12
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Operations Research & Management Science
GA 8H7MO
UT WOS:000921214800001
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Savithri, S
   Prathap, G
AF Savithri, S.
   Prathap, Gangan
TI Indian and Chinese higher education institutions compared using an
   end-to-end evaluation
SO CURRENT SCIENCE
LA English
DT Article
DE Bibliometrics indicators; higher education institutions; principal
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ID BIBLIOMETRIC INDICATORS
AB The latest (2014) release of the SCImago Institutions Rankings (SIR) allows to compare the research performance of leading higher education institutions in India and China using an end-to-end bibliometric performance analysis procedure. Six carefully chosen primary and secondary bibliometric indicators summarize the chain of activity: input output excellence outcome productivity. From principal component analysis it is established that the primary indicators are orthogonal and represent size-dependent quantity and size-independent quality/productivity dimensions respectively. Using this insight two-dimensional maps can be used to visualize the results.
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C3 Council of Scientific & Industrial Research (CSIR) - India; CSIR -
   National Institute Interdisciplinary Science & Technology (NIIST)
RP Savithri, S (corresponding author), CSIR, Natl Inst Interdisciplinary Sci & Technol, Thiruvananthapuram 695019, Kerala, India.
EM sivakumarsavi@gmail.com
RI S, Savithri/H-1145-2013; Prathap, Gangan/T-4054-2019; s,
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TC 8
Z9 10
U1 1
U2 21
PU INDIAN ACAD SCIENCES
PI BANGALORE
PA C V RAMAN AVENUE, SADASHIVANAGAR, P B #8005, BANGALORE 560 080, INDIA
SN 0011-3891
J9 CURR SCI INDIA
JI Curr. Sci.
PD MAY 25
PY 2015
VL 108
IS 10
BP 1922
EP 1926
PG 5
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA CK0JF
UT WOS:000355890800033
DA 2024-09-05
ER
PT J
AU Xu, ZY
AF Xu, Ziyun
TI Doctoral Dissertations in Chinese Interpreting Studies: A Scientometric
   Survey Using Topic Modeling
SO FORUM-REVUE INTERNATIONALE D INTERPRETATION ET DE
   TRADUCTION-INTERNATIONAL JOURNAL OF INTERPRETATION AND TRANSLATION
LA English
DT Article
DE Machine learning; scientometrics; Chinese interpreting studies; doctoral
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C1 [Xu, Ziyun] Univ Rovira & Virgili, Intercultural Studies Grp, Tarragona, Spain.
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RP Xu, ZY (corresponding author), Univ Rovira & Virgili, Intercultural Studies Grp, Tarragona, Spain.
EM xuziyun@gmail.com
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NR 68
TC 3
Z9 3
U1 0
U2 3
PU JOHN BENJAMINS PUBLISHING CO
PI AMSTERDAM
PA PO BOX 36224, 1020 ME AMSTERDAM, NETHERLANDS
SN 1598-7647
EI 2451-909X
J9 FORUM-REV INT INTERP
JI Forum-Rev. Int. Interpret. Trad.
PY 2015
VL 13
IS 1
BP 131
EP 165
DI 10.1075/forum.13.1.07xuz
PG 35
WC Language & Linguistics
WE Emerging Sources Citation Index (ESCI)
SC Linguistics
GA V5R8G
UT WOS:000219880900007
DA 2024-09-05
ER
PT J
AU Siche, R
   Siche, N
AF Siche, Raul
   Siche, Nikol
TI The language model based on sensitive artificial intelligence-ChatGPT:
   Bibliometric analysis and possible uses in agriculture and livestock
SO SCIENTIA AGROPECUARIA
LA English
DT Article
DE autoregressive language model; deep learning; text production; text
   mining; data mining; artificial intelligence; chatbot
AB ChatGPT adds to the list of artificial intelligence-based systems designed to perform specific tasks and answer questions by interacting with ChatGPT works using OpenAI's GPT (Generative Pretrained Transformer) language model and is capable of learning from users' preferences scientific writing, communication, cell biology, and biotechnology, where there is already evidence. The aim of this work was to analyze the question: What are the main applications in which ChatGTP will revolutionize agriculture (or livestock) in the world? ChatGPT responded: (a) in the agricultural field: improvement of agricultural decision-making, optimization of agricultural production, detection and prevention of plant diseases, climate management, and supply chain management; and (b) in the livestock field: improvement of animal health and welfare, optimization of animal production, supply chain management, detection and prevention of zoonotic diseases, and climate is enough scientific evidence to conclude, in this case, that its answers were correct. While ChatGPT does not necessarily scientifically substantiate its answers, users should. There is a lack of studies on the use of Artificial Intelligence and its relationship with ethics.
C1 [Siche, Raul; Siche, Nikol] Univ Nacl Trujillo, Escuela Ingn Agroind, Fac Ciencias Agr, Trujillo, Peru.
   Univ Nacl Trujillo, Escuela Ingn Zootecnia, Fac Ciencias Agr, Trujillo, Peru.
C3 Universidad Nacional de Trujillo; Universidad Nacional de Trujillo
RP Siche, R (corresponding author), Univ Nacl Trujillo, Escuela Ingn Agroind, Fac Ciencias Agr, Trujillo, Peru.
EM rsiche@unitru.edu.pe
OI Siche, Nikol/0000-0002-7174-8337
CR Agtecher, 2022, OPENAI CHATGPT CAN U
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NR 30
TC 7
Z9 9
U1 21
U2 93
PU UNIV NACL TRUJILLO, FAC CIENCIAS AGROPECUARIAS
PI TRUJILLO
PA AV JUAN PABLO II S-N, TRUJILLO, 00000, PERU
SN 2077-9917
EI 2306-6741
J9 SCI AGROPEC
JI Sci. Agropecu.
PD JAN-MAR
PY 2023
VL 14
IS 1
BP 111
EP 116
DI 10.17268/sci.agropecu.2023.010
PG 6
WC Agriculture, Dairy & Animal Science
WE Emerging Sources Citation Index (ESCI)
SC Agriculture
GA E5HW5
UT WOS:000975859700010
OA gold
DA 2024-09-05
ER
PT J
AU Idnay, B
   Fang, YL
   Dreisbach, C
   Marder, K
   Weng, CH
   Schnall, R
AF Idnay, Betina
   Fang, Yilu
   Dreisbach, Caitlin
   Marder, Karen
   Weng, Chunhua
   Schnall, Rebecca
TI Clinical research staff perceptions on a natural language
   processing-driven tool for eligibility prescreening: An iterative
   usability assessment
SO INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
LA English
DT Article
DE Eligibility prescreening; Cohort identification; Clinical research;
   Natural language processing; Usability; Cognitive walkthrough
ID TRUSTWORTHINESS; CRITERIA
AB Background: Participant recruitment is a barrier to successful clinical research. One strategy to improve recruitment is to conduct eligibility prescreening, a resource-intensive process where clinical research staff manually reviews electronic health records data to identify potentially eligible patients. Criteria2Query (C2Q) was developed to address this problem by capitalizing on natural language processing to generate queries to identify eligible participants from clinical databases semi-autonomously.Objective: We examined the clinical research staff's perceived usability of C2Q for clinical research eligibility prescreening.Methods: Twenty clinical research staff evaluated the usability of C2Q using a cognitive walkthrough with a think-aloud protocol and a Post-Study System Usability Questionnaire. On-screen activity and audio were recorded and transcribed. After every-five evaluators completed an evaluation, usability problems were rated by informatics experts and prioritized for system refinement. There were four iterations of system refinement based on the evaluation feedback. Guided by the Organizational Framework for Intuitive Human-computer Interaction, we performed a directed deductive content analysis of the verbatim transcriptions. Results: Evaluators aged from 24 to 46 years old (33.8; SD: 7.32) demonstrated high computer literacy (6.36; SD:0.17); female (75 %), White (35 %), and clinical research coordinators (45 %). C2Q demonstrated high usability during the final cycle (2.26 out of 7 [lower scores are better], SD: 0.74). The number of unique usability issues decreased after each refinement. Fourteen subthemes emerged from three themes: seeking user goals, performing well-learned tasks, and determining what to do next.Conclusions: The cognitive walkthrough with a think-aloud protocol informed iterative system refinement and demonstrated the usability of C2Q by clinical research staff. Key recommendations for system development and implementation include improving system intuitiveness and overall user experience through comprehensive consideration of user needs and requirements for task completion.
C1 [Idnay, Betina; Schnall, Rebecca] Columbia Univ, Sch Nursing, New York, NY USA.
   [Idnay, Betina; Marder, Karen] Columbia Univ, Dept Neurol, New York, NY USA.
   [Idnay, Betina; Fang, Yilu; Weng, Chunhua] Columbia Univ, Dept Biomed Informat, New York, NY USA.
   [Dreisbach, Caitlin] Columbia Univ, Data Sci Inst, New York, NY USA.
   [Schnall, Rebecca] Columbia Univ, Mailman Sch Publ Hlth, Dept Populat & Family Hlth, New York, NY USA.
   [Idnay, Betina] Columbia Univ, Dept Biomed Informat, 622 West 168th St, New York, NY 10032 USA.
C3 Columbia University; Columbia University; Columbia University; Columbia
   University; Columbia University; Columbia University
RP Idnay, B (corresponding author), Columbia Univ, Dept Biomed Informat, 622 West 168th St, New York, NY 10032 USA.
EM bsi2102@cumc.columbia.edu
OI Fang, Yilu/0000-0002-2681-1931; Idnay, Betina/0000-0002-4318-5987;
   Schnall, Rebecca/0000-0003-2184-4045; Dreisbach,
   Caitlin/0000-0003-3964-3161
FU Agency for Healthcare Research and Quality [R36HS028752]; National
   Institute of Nursing Research [T32NR007969, P30NR016587, K24NR018621];
   National Library of Medicine [R01LM009886]; National Center for
   Advancing Clinical and Translational Science [UL1TR001873, OT2TR003434]
FX This work was supported by the Agency for Healthcare Research and
   Quality grant R36HS028752, the National Institute of Nursing Research
   grants T32NR007969, P30NR016587, and K24NR018621, the National Library
   of Medicine grant R01LM009886, and the National Center for Advancing
   Clinical and Translational Science grants UL1TR001873 and OT2TR003434.
   The content is solely the responsibility of the authors and does not
   necessarily represent the official views of the National Institutes of
   Health.
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NR 58
TC 3
Z9 3
U1 0
U2 5
PU ELSEVIER IRELAND LTD
PI CLARE
PA ELSEVIER HOUSE, BROOKVALE PLAZA, EAST PARK SHANNON, CO, CLARE, 00000,
   IRELAND
SN 1386-5056
EI 1872-8243
J9 INT J MED INFORM
JI Int. J. Med. Inform.
PD MAR
PY 2023
VL 171
AR 104985
DI 10.1016/j.ijmedinf.2023.104985
EA JAN 2023
PG 12
WC Computer Science, Information Systems; Health Care Sciences & Services;
   Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Health Care Sciences & Services; Medical Informatics
GA G7UE0
UT WOS:000991155600001
PM 36638583
OA Green Accepted
DA 2024-09-05
ER
PT C
AU Li, MJ
   Xu, JG
   Ge, BF
   Liu, J
   Jiang, J
   Zhao, QS
AF Li, Mengjun
   Xu, Jianguo
   Ge, Bingfeng
   Liu, Jia
   Jiang, Jiang
   Zhao, Qingsong
GP IEEE
TI A Deep Learning Methodology for Citation Count Prediction with
   Large-scale Biblio-Features
SO 2019 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC)
SE IEEE International Conference on Systems Man and Cybernetics Conference
   Proceedings
LA English
DT Proceedings Paper
CT IEEE International Conference on Systems, Man and Cybernetics (SMC)
CY OCT 06-09, 2019
CL Bari, ITALY
ID NETWORK
AB Recently, many efforts have been devoted to the impact of scientific papers within different citation windows. However, it is still an elusive task to predict scientific impact shortly after the publication date. In this paper, a deep learning methodology is proposed to predict long-term citation count. First, large-scale biblio-features are extracted from heterogeneous information network based on five types of links. Next, a deep learning model is designed by taking it as a regression and prediction task. Then Pearson coefficient is used to confirm the correlations between input features and future citation count. Finally, as a case study, papers of Markov Chain published in 1980 are analyzed. The result shows the proposed methodology outperforms the state-of-the-art models and gives accurate prediction of future citations.
C1 [Li, Mengjun; Xu, Jianguo; Ge, Bingfeng; Liu, Jia; Jiang, Jiang; Zhao, Qingsong] Natl Univ Def Technol, Coll Syst Engn, Changsha 410073, Peoples R China.
C3 National University of Defense Technology - China
RP Xu, JG (corresponding author), Natl Univ Def Technol, Coll Syst Engn, Changsha 410073, Peoples R China.
EM mjli11260774@sina.com; jgxu1992@hotmail.com; bingfengge@nudt.edu.cn;
   JLiu1223@163.com; jiangjiangnudt@nudt.com; qingsongzhao99@163.com
RI jiang, jiang/GRX-1861-2022; jiang, jun/GWC-9329-2022
OI zhao, qingsong/0000-0003-1392-845X
FU National Natural Science Foundation of China [71690233, 71671186,
   71571185]
FX Research supported in part by the National Natural Science Foundation of
   China under Grants 71690233, 71671186, and 71571185..
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NR 15
TC 10
Z9 10
U1 0
U2 7
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 1062-922X
BN 978-1-7281-4569-3
J9 IEEE SYS MAN CYBERN
PY 2019
BP 1172
EP 1176
PG 5
WC Computer Science, Cybernetics; Computer Science, Information Systems
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BO6NM
UT WOS:000521353901032
DA 2024-09-05
ER
PT J
AU ul Haq, MI
   Li, QM
   Hou, J
   Iftekhar, A
AF ul Haq, Muhammad Inaam
   Li, Qianmu
   Hou, Jun
   Iftekhar, Adnan
TI Detecting the research structure and topic trends of social media using
   static and dynamic probabilistic topic models
SO ASLIB JOURNAL OF INFORMATION MANAGEMENT
LA English
DT Article
DE Social media; Topic models; Latent dirichlet allocation; DTM; Topic
   trends; Temporal evolution
ID HEALTH-CARE; IMPACT; FUTURE; INFORMATION; CHALLENGES; ADOLESCENT;
   EVOLUTION; FRAMEWORK; TOURISM; TWITTER
AB PurposeA huge volume of published research articles is available on social media which evolves because of the rapid scientific advances and this paper aims to investigate the research structure of social media.Design/methodology/approachThis study employs an integrated topic modeling and text mining-based approach on 30381 Scopus index titles, abstracts, and keywords published between 2006 and 2021. It combines analytical analysis of top-cited reviews with topic modeling as means of semantic validation. The output sequences of the dynamic model are further analyzed using the statistical techniques that facilitate the extraction of topic clusters, communities, and potential inter-topic research directions.FindingsThis paper brings into vision the research structure of social media in terms of topics, temporal topic evolutions, topic trends, emerging, fading, and consistent topics of this domain. It also traces various shifts in topic themes. The hot research topics are the application of the machine or deep learning towards social media in general, alcohol consumption in different regions and its impact, Social engagement and media platforms. Moreover, the consistent topics in both models include food management in disaster, health study of diverse age groups, and emerging topics include drug violence, analysis of social media news for misinformation, and problems of Internet addiction.Originality/valueThis study extends the existing topic modeling-based studies that analyze the social media literature from a specific disciplinary viewpoint. It focuses on semantic validations of topic-modeling output and correlations among the topics and also provides a two-stage cluster analysis of the topics.
C1 [ul Haq, Muhammad Inaam; Li, Qianmu] Nanjing Univ Sci & Technol, Nanjing, Peoples R China.
   [Hou, Jun] Nanjing Vocat Univ Ind Technol, Nanjing, Peoples R China.
   [Iftekhar, Adnan] Wuhan Univ, Wuhan, Peoples R China.
C3 Nanjing University of Science & Technology; Nanjing Vocational
   University of Industry Technology; Wuhan University
RP ul Haq, MI (corresponding author), Nanjing Univ Sci & Technol, Nanjing, Peoples R China.
EM Minaamulhaq@hotmail.com
RI Iftekhar, Adnan/ABB-1590-2020; Inaam ul haq, Muhammad/HKO-1217-2023
OI Iftekhar, Adnan/0000-0002-8249-6876; Inaam ul haq,
   Muhammad/0000-0002-5759-073X
FU Research Project of Nanjing Polytechnic Institute [2020SKYJo3]; major
   project of philosophy and social science research in colleges and
   universities of Jiangsu Province "Research on the Construction of
   Selective Compulsory Courses of Ideological and Political Science in
   Higher vocational Colleges" [2022SJZDSZ011]
FX This work was supported by the major project of philosophy and social
   science research in colleges and universities of Jiangsu Province
   "Research on the Construction of Selective Compulsory Courses of
   Ideological and Political Science in Higher vocational Colleges"
   (2022SJZDSZ011) and the Research Project of Nanjing Polytechnic
   Institute (2020SKYJo3).
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NR 121
TC 1
Z9 1
U1 10
U2 56
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 2050-3806
EI 1758-3748
J9 ASLIB J INFORM MANAG
JI Aslib J. Inf. Manag.
PD MAR 23
PY 2023
VL 75
IS 2
BP 215
EP 245
DI 10.1108/AJIM-02-2022-0091
EA SEP 2022
PG 31
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA A4BU1
UT WOS:000853140500001
DA 2024-09-05
ER
PT J
AU Alp, ZZ
   Ögüdücü, SG
AF Alp, Zeynep Zengin
   Oguducu, Sule Gunduz
TI Identifying topical influencers on twitter based on user behavior and
   network topology
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Social influence; Topical influence; Twitter; User modeling; Page Rank;
   Topic modeling; LDA; Evaluation metrics; Viral marketing
ID MODELS
AB Social media web sites have become major media platforms to share personal information, news, photos, videos and more. Users can even share live streams whenever they want to reach out to many other. This prevalent usage of social media attracted companies, data scientists, and researchers who are trying to infer meaningful information from this vast amount of data. Information diffusion and maximizing the spread of words is one of the most important focus for researchers working on social media. This information can serve many purposes such as; user or content recommendation, viral marketing, and user modeling. In this research, finding topical influential/authority users on Twitter is addressed. Since Twitter is a good platform to spread knowledge as a word of mouth approach and it has many more public profiles than protected ones, it is a target media for marketers. In this paper, we introduce a novel methodology, called Personalized PageRank, that integrates both the information obtained from network topology and the information obtained from user actions and activities in Twitter. The proposed approach aims to determine the topical influencers who are experts on a specific topic. Experimental results on a large dataset consisting of Turkish tweets show that using user specific features like topical focus rate, activeness, authenticity and speed of getting reaction on specific topics positively affects identifying influencers and lead to higher information diffusion. Algorithms are implemented on a distributed computing environment which makes high-cost graph processing more efficient. (C) 2017 Elsevier B.V. All rights reserved.
C1 [Alp, Zeynep Zengin] Istanbul Tech Univ Maslak, Inst Sci & Technol, TR-34469 Istanbul, Turkey.
   [Oguducu, Sule Gunduz] Istanbul Tech Univ Maslak, Dept Comp Engn, TR-34469 Istanbul, Turkey.
C3 Istanbul Technical University; Istanbul Technical University
RP Alp, ZZ (corresponding author), Istanbul Tech Univ Maslak, Inst Sci & Technol, TR-34469 Istanbul, Turkey.
EM zzalp@itu.edu.tr; sgunduz@itu.edu.tr
RI Alp, Zeynep/KHX-4733-2024; Oguducu, Sule Gunduz/C-7710-2009
OI Oguducu, Sule Gunduz/0000-0002-0288-4757
FU 2211 - TUBITAK BIDEB Ph.D. Scholarship Fund
FX This research is partially funded by 2211 - TUBITAK BIDEB Ph.D.
   Scholarship Fund.
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NR 47
TC 72
Z9 75
U1 2
U2 133
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD FEB 1
PY 2018
VL 141
BP 211
EP 221
DI 10.1016/j.knosys.2017.11.021
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA FU1ZP
UT WOS:000423648300015
DA 2024-09-05
ER
PT J
AU Konkiel, S
AF Konkiel, Stacy
TI Approaches to creating 'humane' research evaluation metrics for the
   humanities
SO INSIGHTS-THE UKSG JOURNAL
LA English
DT Article
DE Humanities; research metrics; bibliometrics; evaluation; indicators
AB There are many complexities and challenges associated with developing 'humane' research evaluation metrics in the humanities. This monumental task can only be addressed by reverse engineering evaluation metrics based upon the practices and values that funders, institutions, professional societies and individuals want to encourage in their disciplines. The work of the HuMetricsHSS initiative is described in this article as a framework for doing so.
EM s.konkiel@digital-science.com
OI Konkiel, Stacy/0000-0002-0546-8257
FU Andrew W Mellon Foundation
FX The author gratefully acknowledges the support of the Andrew W Mellon
   Foundation, which has made the HuMetricsHSS initiative's work possible,
   and Nicky Agate, for her valuable input on this article.
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NR 19
TC 5
Z9 5
U1 0
U2 4
PU UBIQUITY PRESS LTD
PI LONDON
PA 2N, 6 OSBORNE ST, LONDON, E1 6TD, ENGLAND
SN 2048-7754
J9 INSIGHTS
JI Insights
PD NOV 15
PY 2018
VL 31
AR 44
DI 10.1629/uksg.445
PG 7
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA HY4EV
UT WOS:000468081700001
OA gold, Green Submitted
DA 2024-09-05
ER
PT J
AU Bai, XM
   Zhang, FL
   Hou, J
   Xia, F
   Tolba, A
   Elashkar, E
AF Bai, Xiaomei
   Zhang, Fuli
   Hou, Jie
   Xia, Feng
   Tolba, Amr
   Elashkar, Elsayed
TI Implicit Multi-Feature Learning for Dynamic Time Series Prediction of
   the Impact of Institutions
SO IEEE ACCESS
LA English
DT Article
DE Scientific impact; prediction; feature selection; machine learning;
   scientometrics
AB Predicting the impact of research institutions is an important tool for decision makers, such as resource allocation for funding bodies. Despite significant effort of adopting quantitative indicators to measure the impact of research institutions, little is known that how the impact of institutions evolves in time. Previous studies have focused on using the historical relevance scores of different institutions to predict potential future impact for these institutions. In this paper, we explore the factors that can drive the changes of the impact of institutions, finding that the impact of an institution, as measured by the number of the accepted papers of the institution, more is determined by the authors' influence of the institution. Geographic location of institution feature and state GDP can drive the changes of the impact of institutions. Identifying these features allows us to formulate a predictive model that integrates the effects of individual ability, location of institution, and state GDP. The model unveils the underlying factors driving the future impact of institutions, which can be used to accurately predict the future impact of institutions.
C1 [Bai, Xiaomei; Hou, Jie; Xia, Feng] Dalian Univ Technol, Key Lab Ubiquitous Network & Serv Software Liaoni, Sch Software, Dalian 116620, Peoples R China.
   [Bai, Xiaomei] Anshan Normal Univ, Ctr Comp, Anshan 114007, Peoples R China.
   [Zhang, Fuli] Anshan Normal Univ, Anshan 114007, Peoples R China.
   [Tolba, Amr] King Saud Univ, Dept Comp Sci, Community Coll, Riyadh 11437, Saudi Arabia.
   [Tolba, Amr] Menoufia Univ, Fac Sci, Math Dept, Shibin Al Kawm 32511, Egypt.
   [Elashkar, Elsayed] King Saud Univ, Adm Sci Dept, Community Coll, Riyadh 11437, Saudi Arabia.
   [Elashkar, Elsayed] Mansoura Univ, Fac Commerce, Dept Appl Stat, Mansoura 35516, Egypt.
C3 Dalian University of Technology; Anshan Normal University; Anshan Normal
   University; King Saud University; Egyptian Knowledge Bank (EKB); Menofia
   University; King Saud University; Egyptian Knowledge Bank (EKB);
   Mansoura University
RP Xia, F (corresponding author), Dalian Univ Technol, Key Lab Ubiquitous Network & Serv Software Liaoni, Sch Software, Dalian 116620, Peoples R China.
EM f.xia@ieee.org
RI Tolba, Amr/O-8464-2016; Xia, Feng/Y-2859-2019; Elashkar, Elsayed
   Elsherbini/AAV-9503-2021; Elashkar, Elsayed/O-6416-2018
OI Tolba, Amr/0000-0003-3439-6413; Xia, Feng/0000-0002-8324-1859; Elashkar,
   Elsayed/0000-0003-2326-6779
FU King Saud University [RGP-1438-27]
FX The authors extend their appreciation to the Deanship of Scientific
   Research at King Saud University for funding this work through research
   group NO (RGP-1438-27).
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NR 26
TC 28
Z9 30
U1 0
U2 16
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2017
VL 5
BP 16372
EP 16382
DI 10.1109/ACCESS.2017.2739179
PG 11
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering; Telecommunications
GA FF9PS
UT WOS:000409349400017
OA gold, Green Submitted
DA 2024-09-05
ER
PT J
AU Albanese, F
   Pinto, S
   Semeshenko, V
   Balenzuela, P
AF Albanese, Federico
   Pinto, Sebastian
   Semeshenko, Viktoriya
   Balenzuela, Pablo
TI Analyzing mass media influence using natural language processing and
   time series analysis
SO JOURNAL OF PHYSICS-COMPLEXITY
LA English
DT Article
DE mass media influence; sentiment analysis; topic detection; time series
   analysis
ID PUBLIC-OPINION; AGENDA; NEWS; ECONOMY
AB A key question of collective social behavior is related to the influence of mass media on public opinion. Different approaches have been developed to address quantitatively this issue, ranging from field experiments to mathematical models. In this work we propose a combination of tools involving natural language processing and time series analysis. We compare selected features of mass media news articles with measurable manifestation of public opinion. We apply our analysis to news articles belonging to the 2016 US presidential campaign. We compare variations in polls (as a proxy of public opinion) with changes in the connotation of the news (sentiment) or in the agenda (topics) of a selected group of media outlets. Our results suggest that the sentiment content by itself is not enough to understand the differences in polls, but the combination of topics coverage and sentiment content provides an useful insight of the context in which public opinion varies. The methodology employed in this work is far general and can be easily extended to other topics of interest.
C1 [Albanese, Federico] Univ Buenos Aires, Inst Ciencias Computac, CONICET, Buenos Aires, DF, Argentina.
   [Pinto, Sebastian; Balenzuela, Pablo] Consejo Nacl Invest Cient & Tecn, Inst Fis Buenos Aires IFIBA, Ave Cantilo S-N,Pabellon 1,Ciudad Univ, RA-1428 Buenos Aires, DF, Argentina.
   [Pinto, Sebastian; Balenzuela, Pablo] Univ Buenos Aires, Fac Ciencias Exactas & Nat, Dept Fis, Ave Cantilo S-N,Pabellon 1,Ciudad Univ, RA-1428 Buenos Aires, DF, Argentina.
   [Semeshenko, Viktoriya] Univ Buenos Aires, Fac Ciencias Econ, Buenos Aires, DF, Argentina.
   [Semeshenko, Viktoriya] Univ Buenos Aires, Inst Interdisciplinario Econ Polit Buenos Aires, CONICET, Ave Cordoba 2122,C1120 AAQ, Buenos Aires, DF, Argentina.
C3 Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET);
   University of Buenos Aires; Consejo Nacional de Investigaciones
   Cientificas y Tecnicas (CONICET); University of Buenos Aires; University
   of Buenos Aires; University of Buenos Aires; University of Buenos Aires;
   Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET)
RP Albanese, F (corresponding author), Univ Buenos Aires, Inst Ciencias Computac, CONICET, Buenos Aires, DF, Argentina.
EM falbanese@dc.uba.ar
RI Semeshenko, Viktoriya/HTS-8922-2023
OI Semeshenko, Viktoriya/0000-0003-0295-5946; Albanese,
   Federico/0000-0001-7140-2910; Balenzuela, Pablo/0000-0002-8581-4892
FU Agencia Nacional de Promocion Cientifica of Argentina [PICT 201-0215]
FX We thank Marcos Trevisan for careful reading of the manuscript and
   helpful comments. This work was partially funded by the Agencia Nacional
   de Promocion Cientifica of Argentina via grant PICT 201-0215.
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NR 39
TC 10
Z9 11
U1 3
U2 16
PU IOP Publishing Ltd
PI BRISTOL
PA TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
EI 2632-072X
J9 J PHYS-COMPLEXITY
JI J. Phys.-Complex.
PD SEP
PY 2020
VL 1
IS 2
AR 025005
DI 10.1088/2632-072X/ab8784
PG 12
WC Mathematics, Interdisciplinary Applications; Multidisciplinary Sciences;
   Physics, Mathematical
WE Emerging Sources Citation Index (ESCI)
SC Mathematics; Science & Technology - Other Topics; Physics
GA SQ6MF
UT WOS:000660466800001
OA gold, Green Submitted, Green Published
DA 2024-09-05
ER
PT C
AU Hong, XY
   Li, ZX
AF Yun Hong Xu
   Xian Li Zuo
BE Yang, BJ
   Chen, J
   Cai, XQ
   Qin, KD
   Zhou, C
TI A LDA Model Based Text-Mining Method to Recommend Reviewer for Proposal
   of Research Project Selection
SO 2016 13TH INTERNATIONAL CONFERENCE ON SERVICE SYSTEMS AND SERVICE
   MANAGEMENT
SE International Conference on Service Systems and Service Management
LA English
DT Proceedings Paper
CT 13th International Conference on Service Systems and Service Management
   (ICSSSM)
CY JUN 24-26, 2016
CL Kunming Univ Sci & Technol, Sch Management & Econ, Kumming, PEOPLES R
   CHINA
HO Kunming Univ Sci & Technol, Sch Management & Econ
DE Reviewer recommendation; Research project proposal; Latent Dirichlet
   Allocation Model; Text mining; Expert evaluation model
AB Reviewer recommendation for research projects proposals plays an indispensable role in funding agencies, because the opinions or feedback of reviewers will exert a direct impact on the result of the projects selection. Current methods mainly focus on grouping the proposals by declared disciplines or evaluating the reviewers with their individual profile, however, the two methods ignore the rich information with different types and formats of proposals and experts, such as subjective information (e.g., evaluation of colleague), objective information (e.g., publications' number). Besides, prior studies mostly applied to English documents, which has limitations when dealing with projects proposals in Chinese. In order to effectively solve the research gap that ignored the different information forms and Chinese contexts, this paper proposes firstly extract the topics words in proposal by LDA and expert's profile with text-mining; secondly automatically classify the information of proposal and profile, and integrate the information into several categories according to its different types. Each category represents the different dimensions of information of proposal and expert. Thirdly, we calculate the similarity of information in each category, and sort the similarity to select top 8 experts as candidate reviewers. Finally, we establish the evaluation model for the candidate reviewers to decide several reviewers to review proposal. A recommendation approach is proposed by integrating these categories of information. In future research, we will try to evaluate the proposed approach using real data.
C1 [Yun Hong Xu; Xian Li Zuo] Kunming Univ Sci & Technol, Fac Econ & Management, Kunming, Peoples R China.
C3 Kunming University of Science & Technology
RP Hong, XY (corresponding author), Kunming Univ Sci & Technol, Fac Econ & Management, Kunming, Peoples R China.
EM xuyunhong@gmail.com; 1029035822@qq.com
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NR 12
TC 0
Z9 0
U1 0
U2 5
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2161-1890
BN 978-1-5090-2842-9
J9 I C SERV SYST SERV M
PY 2016
PG 5
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Engineering
GA BG6DX
UT WOS:000390104400130
DA 2024-09-05
ER
PT J
AU Lopez-Martinez, RE
   Sierra, G
AF Lopez-Martinez, Roberto E.
   Sierra, Gerardo
TI Research Trends in the International Literature on Natural Language
   Processing, 2000-2019 - A Bibliometric Study
SO JOURNAL OF SCIENTOMETRIC RESEARCH
LA English
DT Article
DE Bibliometrics; Natural language processing; Computational linguistics
ID EVOLUTION; TECHNOLOGIES; INFORMATICS; NETWORKS; BUSINESS
AB This work presents a review, using bibliometric methods, of the state of research on the whole field of natural language processing (NLP), understanding this as the methods to process human language, including semantic techniques, statistical techniques or a combination of both. Particularly we focus on the trends of research in NLP, since there are not in the literature studies that embrace in an integral way bibliometric studies about natural language processing, its applications and related topics. Our work includes an identification of the main sources where research is published, the most productive and influential countries and research institutions, the main actors involved in research, as well as the main topics that are investigated. We found that research in the field and subfields has increased continuously during the period under study: conference proceedings are the preferred media to communicate results and that biomedical informatics is one relevant field of application of NLP. We conclude with both, a synchronic and a diachronic characterization of research topics carried out internationally on natural language processing and related topics, which showed that several subfields of artificial intelligence are closely related to natural language processing in recent years.
C1 [Lopez-Martinez, Roberto E.] Univ Nacl Autonoma Mexico, Inst Ingn, Mexico City, DF, Mexico.
C3 Universidad Nacional Autonoma de Mexico
RP Lopez-Martinez, RE (corresponding author), Edificio 12 Inst Ingn, Ciudad De Mexico 04510, Mexico.
EM robertol@unam.mx
RI Lopez-Martinez, Roberto Ernesto/A-4547-2009
OI Sierra, Gerardo/0000-0002-6724-1090
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NR 45
TC 7
Z9 7
U1 4
U2 31
PU PHCOG NET
PI KARNATAKA
PA 17, 2ND FLR, BUDDHA VIHAR RD, NEAR SPORTS ZONE, COX TOWN, BENGALURU,
   KARNATAKA, 560005, INDIA
SN 2321-6654
EI 2320-0057
J9 J SCIENTOMETR RES
JI J. Scientometr. Res.
PD SEP-DEC
PY 2020
VL 9
IS 3
BP 310
EP 318
DI 10.5530/jscires.9.3.38
PG 9
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA PP1WG
UT WOS:000605658100008
OA hybrid
DA 2024-09-05
ER
PT C
AU Wen, J
AF Wen, Ji
BE MizeraPietraszko, J
   Pichappan, P
TI Research on Effect Evaluation of Physical Education Teaching Based on
   Artificial Intelligence Expert Decision Making System
SO LECTURE NOTES IN REAL-TIME INTELLIGENT SYSTEMS (RTIS 2016)
SE Advances in Intelligent Systems and Computing
LA English
DT Proceedings Paper
CT 1st Beijing International Conference on Real-Time Intelligent Systems
   (RTIS)
CY SEP 01-03, 2016
CL Beijing, PEOPLES R CHINA
DE Artificial intelligence; Expert system; Physical education
AB Teaching Result Evaluation in physical education plays an extremely important role in the link of the teaching of Physical Education. The development is accompanied with the development of evaluation and evaluation of education. The principle, data, mathematical model and human computer interaction model were used in the evaluation of Physical Education teaching according to artificial intelligence expert decision system, and the index system of evaluation of physical education teaching work was constructed, based on this, the sports evaluation and monitoring system with functions of diagnostic evaluation, data statistics and assistant decision making was studied in this paper, then the math model was built by calling a variety of sports teaching information resources and a large number of analytical tools, and the simulation process of decision making and the environment of analysis and execution were provided. The results show that the evaluation results of physical education teaching effect based on artificial intelligence expert decision-making system can provide theoretical basis for decision-making and evaluation of relevant competent departments, which plays a positive role in promoting the reform of physical education and improving the quality of physical education.
C1 [Wen, Ji] Hohai Univ, Dept Phys Educ, Nanjing, Jiangsu, Peoples R China.
C3 Hohai University
RP Wen, J (corresponding author), Hohai Univ, Dept Phys Educ, Nanjing, Jiangsu, Peoples R China.
EM JIW2541JW@163.com
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PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 2194-5357
EI 2194-5365
BN 978-3-319-60744-3; 978-3-319-60743-6
J9 ADV INTELL SYST
PY 2018
VL 613
BP 289
EP 298
DI 10.1007/978-3-319-60744-3_31
PG 10
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications; Neurosciences
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Neurosciences & Neurology
GA BL5XT
UT WOS:000452919900031
DA 2024-09-05
ER
PT C
AU Chen, XL
   Zou, D
   Cheng, G
   Xie, HR
AF Chen, Xieling
   Zou, Di
   Cheng, Gary
   Xie, Haoran
BE Chang, M
   Chen, NS
   Sampson, DG
   Tlili, A
TI Artificial intelligence-assisted personalized language learning:
   systematic review and co-citation analysis
SO IEEE 21ST INTERNATIONAL CONFERENCE ON ADVANCED LEARNING TECHNOLOGIES
   (ICALT 2021)
SE IEEE International Conference on Advanced Learning Technologies
LA English
DT Proceedings Paper
CT 21st IEEE International Conference on Advanced Learning Technologies
   (ICALT)
CY JUL 12-15, 2021
CL ELECTR NETWORK
DE artificial intelligence; personalized language learning; systematic
   review; co-citation network analysis
ID TECHNOLOGY
AB Artificial intelligence (AI) for personalized learning has attracted increasing attention in various educational contexts and domains, including language learning. This study systematically reviewed academic studies on AI-assisted personalized language learning (PLL) from the perspectives of article trends, top journals, countries/regions and institutions, AI technology types, learning outcomes and supports, participants, scientific collaborations, and co-citation relations. Results indicated Taiwanese institutions' predominance in the field and the prevalent use of intelligent tutoring systems, natural language processing, and artificial neural network in facilitating personalized diagnosis and learning path and material recommendations in language learning. Furthermore, students' improved language outcomes and positive perception, satisfaction, or motivation towards language learning and AI technologies were commonly reported. The co-authorship analysis results indicated the close inter-regional collaborations, while the cross-regional collaborations are expected to be enhanced. The co-citation network analysis results highlighted the significance of fuzzy systems and item response theory. Additionally, learner profiling mining and learning resource adaptation were important directions to realize mobile- and web-based PLL.
C1 [Chen, Xieling; Cheng, Gary] Educ Univ Hong Kong, Dept Math & Informat Technol, Hong Kong, Peoples R China.
   [Zou, Di] Educ Univ Hong Kong, Dept English Language Educ, Hong Kong, Peoples R China.
   [Xie, Haoran] Lingnan Univ, Dept Comp & Decis Sci, Hong Kong, Peoples R China.
C3 Education University of Hong Kong (EdUHK); Education University of Hong
   Kong (EdUHK); Lingnan University
RP Chen, XL (corresponding author), Educ Univ Hong Kong, Dept Math & Informat Technol, Hong Kong, Peoples R China.
EM xielingchen0708@gmail.com; dizoudaisy@gmail.com; chengks@eduhk.hk;
   hrxie2@gmail.com
RI Xie, Haoran/AFS-3515-2022
OI Xie, Haoran/0000-0003-0965-3617; ZOU, Di/0000-0001-8435-9739; PV,
   THAYYIB/0000-0001-8929-0398
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NR 26
TC 9
Z9 9
U1 6
U2 69
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA
SN 2161-3761
BN 978-1-6654-4106-3
J9 IEEE INT CONF ADV LE
PY 2021
BP 241
EP 245
DI 10.1109/ICALT52272.2021.00079
PG 5
WC Computer Science, Information Systems; Computer Science,
   Interdisciplinary Applications; Computer Science, Theory & Methods;
   Education & Educational Research
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Education & Educational Research
GA BS4HI
UT WOS:000719352000072
DA 2024-09-05
ER
PT J
AU Corrales-Garay, D
   Rodríguez-Sánchez, JL
   Montero-Navarro, A
AF Corrales-Garay, Diego
   Rodriguez-Sanchez, Jose-Luis
   Montero-Navarro, Antonio
TI Co-Creating Value With Artificial Intelligence: A Bibliometric Approach
   to the Use of AI in Open Innovation Ecosystems
SO IEEE ACCESS
LA English
DT Article
DE AI; artificial intelligence; bibliometric analysis; innovation
   ecosystems; OI; open innovation; value co-creation
ID INDUSTRY 4.0; ABSORPTIVE-CAPACITY
AB Open innovation, which blurs the boundaries of organizations using inflows and outflows of knowledge to boost their innovation processes, has transformed the innovation paradigm, evolving from higher degrees of protectionism to cooperative relationships. Nevertheless, frequently the management of the huge amount of information and data generated in an open innovation ecosystem requires the use of information and communication technologies. In this context, artificial intelligence can be a major help to profit from all the opportunities derived from open innovation. Considering the growing body of academic literature dealing with the use of artificial intelligence tools in the context of open innovation environments, the objective of this article is revealing the main references, the academic trends and the hottest topics dealing with this subject, disentangling the knowledge structure of the research through a bibliometric analysis carried out over 63 papers selected from Web of Science database, using both co-word analysis and bibliographic coupling. The recent burst in the academic production anticipates a potentially massive interest in this topic, which is studied by the literature at three different levels (operational, managerial, and social). This study reveals the existence of relevant research opportunities, specially related with the management of the potential conflicts that may stem from the fuzzy ownership of the data generated by an artificial intelligence, and the roles of the different agents in such context.
C1 [Corrales-Garay, Diego; Rodriguez-Sanchez, Jose-Luis; Montero-Navarro, Antonio] Univ Rey Juan Carlos, Fac Ciencias Econ & Empresa, Dept Business Econ Adm Dir & Org, Appl Econ & Fundamentals Econ Anal 2, Madrid 28032, Spain.
C3 Universidad Rey Juan Carlos
RP Corrales-Garay, D (corresponding author), Univ Rey Juan Carlos, Fac Ciencias Econ & Empresa, Dept Business Econ Adm Dir & Org, Appl Econ & Fundamentals Econ Anal 2, Madrid 28032, Spain.
EM diego.corrales@urjc.es
RI castro wanderley, lorenna/HGA-7990-2022; Rodriguez-Sanchez,
   Jose-Luis/N-6040-2018; Corrales-Garay, Diego/N-6024-2018
OI Rodriguez-Sanchez, Jose-Luis/0000-0001-7913-8747; Corrales-Garay,
   Diego/0000-0003-2362-6367; Montero-Navarro, Antonio/0000-0001-8096-5352
FU OPENINNOVA High Performance Research Group (Grupo de investigacin de
   alto rendimiento en Innovacin abierta de la Universidad Rey Juan Carlos)
FX No Statement Available
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NR 102
TC 0
Z9 0
U1 15
U2 15
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2024
VL 12
BP 56860
EP 56871
DI 10.1109/ACCESS.2024.3391054
PG 12
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA OQ8Z6
UT WOS:001208844400001
OA gold
DA 2024-09-05
ER
PT J
AU Rover, A
AF Jose Rover, Aires
TI <i>A bibliometric overview of data protection and privacy in the context
   of the advance of artificial intelligence</i>
SO SCIRE-REPRESENTACION Y ORGANIZACION DEL CONOCIMIENTO
LA English
DT Article
DE Bibliographic reviews; Data protection; Artificial intelligence; Privacy
AB A new societal landscape is taking form, marked by a myriad of emerging themes warranting thorough examination. Chief among these discussions is the pivotal role of artificial intelligence (AI), particularly machine learning and its predictive applications. Despite extensive exploration of issues pertaining to personal data protection and privacy across scientific literature, there remains a conspicuous dearth of studies integrating this analysis with the latest AI methodologies. Hence, this study endeavors to undertake a predominantly quantitative mapping of the most influential scientific works addressing these interconnected themes. Our approach, conducted over the span of March and April 2024, employs a bibliometric methodology, prioritizing an inductive analysis framework and adopting the case study methodology to fulfill this objective.
C1 [Jose Rover, Aires] Univ Fed Santa Catarina, Fac Derecho, Florianopolis, SC, Brazil.
C3 Universidade Federal de Santa Catarina (UFSC)
RP Rover, A (corresponding author), Univ Fed Santa Catarina, Fac Derecho, Florianopolis, SC, Brazil.
EM aires.j.r@ufsc.br
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NR 12
TC 0
Z9 0
U1 2
U2 2
PU UNIV ZARAGOZA
PI ZARAGOZA
PA C/PEDRO CERBUNA 12, ZARAGOZA, 50009, SPAIN
SN 1135-3716
EI 2340-7042
J9 SCIRE
JI Scire
PD JAN-JUN
PY 2024
VL 30
IS 1
BP 49
EP 58
PG 10
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA WC9J0
UT WOS:001252784900003
DA 2024-09-05
ER
PT J
AU Shevchuk, R
   Martsenyuk, V
AF Shevchuk, Ruslan
   Martsenyuk, Vasyl
TI Neural Networks Toward Cybersecurity: Domaine Map Analysis of
   State-of-the-Art Challenges
SO IEEE ACCESS
LA English
DT Article
DE Neural networks; Computer security; Market research; Bibliometrics;
   Visualization; Knowledge engineering; Databases; Neural network;
   cybersecurity; scientometric database; domaine map analysis; CiteSpace
ID DEEP LEARNING APPROACH; CYBER SECURITY; ARTIFICIAL-INTELLIGENCE; ATTACK
   DETECTION; IOT; INTERNET; SCIENCE; DATASET; WEB
AB The growing interest in applying neural networks for cybersecurity has prompted a substantial increase in related research. This paper presents a comprehensive bibliometric analysis of research on cybersecurity towards neural networks published in the Web of Science over the past two decades (2003-2023) using bibliometric methods and CiteSpace software. The analysis encompasses yearly publication trends, types of publications, and trends across various dimensions such as publishing sources, organizations, researchers, countries, and keywords. Additionally, timeline and burst detection analyses were conducted to identify significant topic trends and citations in the last two decades. It also outlines the latest trends, under-explored topics, and open challenges.
C1 [Shevchuk, Ruslan; Martsenyuk, Vasyl] Univ Bielsko Biala, Dept Comp Sci & Automat, PL-43309 Biala, Poland.
   [Shevchuk, Ruslan] West Ukrainian Natl Univ, Dept Comp Sci, UA-46009 Ternopol, Ukraine.
C3 University of Bielsko-Biala; Ministry of Education & Science of Ukraine;
   West Ukrainian National University
RP Shevchuk, R (corresponding author), Univ Bielsko Biala, Dept Comp Sci & Automat, PL-43309 Biala, Poland.; Shevchuk, R (corresponding author), West Ukrainian Natl Univ, Dept Comp Sci, UA-46009 Ternopol, Ukraine.
EM rshevchuk@ubb.edu.pl
RI Shevchuk, Ruslan/G-5668-2017
OI Shevchuk, Ruslan/0000-0001-5381-9528
FU European Union through the ERASMUS+ Project: The Future is in Applied
   Artificial Intelligence
FX No Statement Available
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NR 104
TC 1
Z9 1
U1 2
U2 2
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2024
VL 12
BP 81265
EP 81280
DI 10.1109/ACCESS.2024.3411632
PG 16
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA UL2E1
UT WOS:001248140800001
OA gold
DA 2024-09-05
ER
PT J
AU Dai, R
   Donohue, L
   Drechsler, Q
   Jiang, W
AF Dai, Rui
   Donohue, Lawrence
   Drechsler, Qingyi (Freda)
   Jiang, Wei
TI Dissemination, Publication, and Impact of Finance Research: When Novelty
   Meets Conventionality*
SO REVIEW OF FINANCE
LA English
DT Article
DE Finance research; Impact; Publication; Innovation; Machine learning;
   Textual analysis
ID CAREER CONCERNS; PRODUCTIVITY; CITATIONS; NETWORKS; JOURNALS; ANALYSTS
AB Using numeric and textual data extracted from over 50,000 finance articles in Social Science Research Network (SSRN) during 2001-19, we examine the relationship between measured qualities and a paper's readership, eventual outlet, and impact. Conventionality (semantic similarity with existent research) helps boost readership and publication prospects. However, novelty in the forms of emerging topics and databases are associated with better publishing outcomes. Studies that do not easily map into established finance subfields or that introduce nonfinance elements face a higher hurdle. Finally, papers whose research questions span multiple fields are a hard sell, but those building on prior knowledge from multiple fields are valued.
C1 [Dai, Rui; Donohue, Lawrence; Drechsler, Qingyi (Freda)] Univ Penn, Wharton Res Data Serv WRDS, Philadelphia, PA 19104 USA.
   [Jiang, Wei] Columbia Business Sch, New York, NY USA.
C3 University of Pennsylvania; Columbia University
RP Dai, R (corresponding author), Univ Penn, Wharton Res Data Serv WRDS, Philadelphia, PA 19104 USA.
RI Jiang, Wei/KCX-8643-2024
OI Jiang, Wei/0000-0003-3256-4938; Dai, Rui/0000-0002-0690-1980
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NR 58
TC 3
Z9 3
U1 23
U2 65
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1572-3097
EI 1573-692X
J9 REV FINANC
JI Rev. Financ.
PD JAN 6
PY 2023
VL 27
IS 1
BP 79
EP 141
DI 10.1093/rof/rfac018
EA MAR 2022
PG 63
WC Business, Finance; Economics
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 7O9OO
UT WOS:000786168700001
OA hybrid
DA 2024-09-05
ER
PT C
AU Wan, YC
AF Wan Yucheng
BE Zhu, KL
   Zhang, H
TI The Evaluation Method for Science Research Project Based on Logistic
   Regression Analysis Model
SO COMPREHENSIVE EVALUATION OF ECONOMY AND SOCIETY WITH STATISTICAL SCIENCE
LA English
DT Proceedings Paper
CT 2nd Conference of the
   International-Institute-of-Applied-Statistics-Studies
CY JUL 24-29, 2009
CL Qingdao, PEOPLES R CHINA
DE Forecast; Logistic Regression; Research Project Supported by Science
   Foundation; Science Research Management
AB In order to improve the accurateness of science research project, a comprehensive evaluation model for evaluating the applying of science research project is presented by applying the logistic regression analysis model. To solve the parameter estimation problem by taking maximum likelihood method, the logistic module in SPSS package is used. Compared with general methods, it is very easy to calculate and realize on computer using logistic regression models. By using this method, science foundation can get more theoretical guidance in evaluating the applying of science research project. An example of practical application is given to show the high correct-rate of forecast backward, the effectiveness of this model and the rationality of the evaluating results.
C1 [Wan Yucheng] Xuzhou AF Coll, Dept Air Mat Management, Xuzhou 221002, Peoples R China.
EM wanyucheng@126.com
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NR 23
TC 0
Z9 0
U1 0
U2 2
PU AUSSINO ACAD PUBL HOUSE
PI MARRICKVILLE
PA PO BOX 893, MARRICKVILLE, NSW 2204 00000, AUSTRALIA
BN 978-0-9806057-7-8
PY 2009
BP 471
EP 476
PG 6
WC Agricultural Economics & Policy; Economics; Education & Educational
   Research; Environmental Sciences; Environmental Studies; Social
   Sciences, Mathematical Methods; Statistics & Probability
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Agriculture; Business & Economics; Education & Educational Research;
   Environmental Sciences & Ecology; Mathematical Methods In Social
   Sciences; Mathematics
GA BOE48
UT WOS:000276383800082
DA 2024-09-05
ER
PT J
AU Li, KC
   Wong, BTM
AF Li, Kam Cheong
   Wong, Billy Tak-Ming
TI Artificial intelligence in personalised learning: a bibliometric
   analysis
SO INTERACTIVE TECHNOLOGY AND SMART EDUCATION
LA English
DT Article; Early Access
DE Personalised learning; Personalised education; Personalisation;
   Artificial intelligence; AI; Bibliometric analysis
ID EMERGING TRENDS; REGENERATIVE MEDICINE; TUTORING SYSTEMS; EDUCATION;
   STYLE
AB PurposeThis paper aims to present a comprehensive overview of the patterns and trends of publications on artificial intelligence (AI) in personalised learning. It addresses the need to investigate the intellectual structure and development of this area in view of the growing amount of related research and practices. Design/methodology/approachA bibliometric analysis was conducted to cover publications on AI in personalised learning published from 2000 to 2022, including a total of 1,005 publications collected from the Web of Science and Scopus. The patterns and trends in terms of sources of publications, intellectual structure and major topics were analysed. FindingsResearch on AI in personalised learning has been widely published in various sources. The intellectual bases of related work were mostly on studies on the application of AI technologies in education and personalised learning. The relevant research covered mainly AI technologies and techniques, as well as the design and development of AI systems to support personalised learning. The emerging topics have addressed areas such as big data, learning analytics and deep learning. Originality/valueThis study depicted the research hotspots of personalisation in learning with the support of AI and illustrated the evolution and emerging trends in the field. The results highlight its latest developments and the need for future work on diverse means to support personalised learning with AI, the pedagogical issues, as well as teachers' roles and teaching strategies.
C1 [Li, Kam Cheong] Hong Kong Metropolitan Univ, Sch Open Learning, Kowloon, Hong Kong, Peoples R China.
   [Wong, Billy Tak-Ming] Hong Kong Metropolitan Univ, Inst Res Open & Innovat Educ, Kowloon, Hong Kong, Peoples R China.
C3 Hong Kong Metropolitan University; Hong Kong Metropolitan University
RP Wong, BTM (corresponding author), Hong Kong Metropolitan Univ, Inst Res Open & Innovat Educ, Kowloon, Hong Kong, Peoples R China.
EM kcli@hkmu.edu.hk; tamiwong@hkmu.edu.hk
FU Hong Kong Metropolitan University [2021/011]
FX The work described in this paper was partially supported by a grant from
   Hong Kong Metropolitan University (2021/011).
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NR 80
TC 6
Z9 6
U1 25
U2 31
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 1741-5659
EI 1758-8510
J9 INTERACT TECHNOL SMA
JI Interact. Technol. Smart Educ.
PD 2023 MAY 26
PY 2023
DI 10.1108/ITSE-01-2023-0007
EA MAY 2023
PG 24
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA H0EQ1
UT WOS:000992785800001
DA 2024-09-05
ER
PT J
AU Gelman, A
AF Gelman, Andrew
TI Criticism as asynchronous collaboration: An example from social science
   research
SO STAT
LA English
DT Article
DE causal inference; regression; social sciences; subject areas
ID FAILURE
AB I discuss a published paper in political science that made a claim that aroused skepticism. The reanalysis is an example of how we, as consumers as well as producers of science, can engage with published work. This can be viewed as a sort of collaboration performed implicitly between the authors of a published paper and later researchers who want to understand or use the published work.
C1 [Gelman, Andrew] Columbia Univ, New York, NY 10027 USA.
C3 Columbia University
RP Gelman, A (corresponding author), Columbia Univ, New York, NY 10027 USA.
EM gelman@stat.columbia.edu
OI Gelman, Andrew/0000-0002-6975-2601
FU U.S. Office of Naval Research, Institute for Education Sciences; Sloan
   Foundation
FX To appear in Stat., I thank Erik Gahner Larsen, Bill Harris, and two
   anonymous reviewers for helpful comments and the U.S. Office of Naval
   Research, Institute for Education Sciences, and Sloan Foundation for
   partial support of this work.
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NR 19
TC 2
Z9 2
U1 0
U2 1
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 2049-1573
J9 STAT-US
JI Stat
PD DEC
PY 2022
VL 11
IS 1
AR e464
DI 10.1002/sta4.464
PG 12
WC Statistics & Probability
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematics
GA 2J5IM
UT WOS:000815690400001
DA 2024-09-05
ER
PT J
AU Abbas, K
   Hasan, MK
   Abbasi, A
   Mokhtar, UA
   Khan, A
   Abdullah, SNHS
   Dong, S
   Islam, S
   Alboaneen, D
   Ahmed, FRA
AF Abbas, Khushnood
   Hasan, Mohammad Kamrul
   Abbasi, Alireza
   Mokhtar, Umi Asma
   Khan, Asif
   Abdullah, Siti Norul Huda Sheikh
   Dong, Shi
   Islam, Shayla
   Alboaneen, Dabiah
   Ahmed, Fatima Rayan Awad
TI Predicting the Future Popularity of Academic Publications Using Deep
   Learning by Considering It as Temporal Citation Networks
SO IEEE ACCESS
LA English
DT Article
DE Citation prediction; citation networks; node ranking; deep learning;
   temporal networks; and popularity prediction
ID RANKING; IMPACT; CENTRALITY; PAGERANK; INDEX
AB One of the key goals of Informetrics is to identify citation-based popular articles among so many other aspects, such as determining popular research topics, identifying influential scholars, and predicting hot trends in science. These can be achieved by applying network science approaches to scientific networks and formulating the problem as a popular (most-cited) node ranking task. To rank the papers based on their future citation gain. In this work a deep learning based framework is proposed. Which helps in automatic node level feature extraction and can make node level prediction in dynamic graphs such as citation networks. To achieve this we have learned global ranking preserve d dimensional node embedding. We have only considered temporal features, which makes it suitable for generalisation to other networks. Although our model can consider node level explicit features also. Further we have given novel cost function which can be easily solve ranking problem for dynamic graphs using probabilistic regression method. Which can be easily optimised. Another novelty of our work is that our model can be trained using different snapshots of the graph and different time. Further trained model can be used to make future prediction. The proposed model has been tested on an arXiv paper citation network using six standard information retrieval-based metrics. The results show that our proposed model outperforms, on average, other state-of-the-art static models as well as dynamic node ranking models. The outcome of this research study leads to informed data-driven decision-making in science, such as the allocation and distribution of research funds and investment in strategic research centers. When considering past time window size as 10 months and making prediction after 10 months our proposed model's performance on various ranking based evaluation metrics are as follows: AUC-0.974, Kendal's rank correlation tau-0.455, Precision- 0.643, Novelty-0.0456, Temporal novelty-0.375 and on NDCG-0.949. Our model is able to make long term trend prediction with just training on short time window.
C1 [Abbas, Khushnood; Dong, Shi] Zhoukou Normal Univ, Sch Comp Sci & Technol, Zhoukou 466000, Henan, Peoples R China.
   [Abbas, Khushnood; Abbasi, Alireza] Univ New South Wales UNSW, Sch Engn & IT, Sydney, NSW 2052, Australia.
   [Hasan, Mohammad Kamrul; Mokhtar, Umi Asma] Univ Kebangsaan Malaysia, Fac Informat Sci & Technol, Bangi 43600, Selangor, Malaysia.
   [Khan, Asif] Integral Univ, Sch Comp Applicat, Lucknow 226026, India.
   [Islam, Shayla] UCSI Univ, Inst Comp Sci & Digital Innovat, Kuala Lumpur 56000, Malaysia.
   [Alboaneen, Dabiah] Imam Abdulrahman Bin Faisal Univ, Coll Sci & Humanities, Comp Sci Dept, Al Jubail 34212, Saudi Arabia.
   [Ahmed, Fatima Rayan Awad] Prince Sattam Bin Abdulaziz Univ, Coll Comp Engn & Sci, Comp Sci Dept, Al Kharj 16273, Saudi Arabia.
C3 Zhoukou Normal University; University of New South Wales Sydney;
   Universiti Kebangsaan Malaysia; Integral University; UCSI University;
   Imam Abdulrahman Bin Faisal University; Prince Sattam Bin Abdulaziz
   University
RP Hasan, MK (corresponding author), Univ Kebangsaan Malaysia, Fac Informat Sci & Technol, Bangi 43600, Selangor, Malaysia.; Khan, A (corresponding author), Integral Univ, Sch Comp Applicat, Lucknow 226026, India.
EM hasankamrul@ieee.org; asif05amu@gmail.com
RI Abbasi, Alireza/HLW-8556-2023; dong, shi/AAC-6333-2022; Abbas,
   Khushnood/I-6735-2017; Sheikh Abdullah, Siti Norul Huda/J-7949-2015;
   mokhtar, umi asma/G-7121-2017
OI Abbasi, Alireza/0000-0001-9136-1837; dong, shi/0000-0003-4616-6519;
   Abbas, Khushnood/0000-0002-0096-3179; Hasan, Dr. Mohammad
   Kamrul/0000-0001-5511-0205; Islam, Dr. Shayla/0000-0002-0490-7799;
   Sheikh Abdullah, Siti Norul Huda/0000-0002-2602-7805; mokhtar, umi
   asma/0000-0002-9097-3441; Alboaneen, Dabiah/0000-0003-2215-9963
FU Ministry of Higher Education Malaysia [FRGS/1/2020/ICT03/UKM/02/6]
FX This work was supported in part by the Ministry of Higher Education
   Malaysia through the Research Grant Scheme under Grant
   FRGS/1/2020/ICT03/UKM/02/6.
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NR 86
TC 9
Z9 9
U1 5
U2 16
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2023
VL 11
BP 83052
EP 83068
DI 10.1109/ACCESS.2023.3290906
PG 17
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA O9UA9
UT WOS:001047184200001
OA gold
DA 2024-09-05
ER
PT C
AU Liao, Q
   Zheng, JB
   Shen, Y
   Me, J
AF Liao, Qin
   Zheng, Jiabi
   Shen, Ying
   Me, Jing
GP Univ Academic Press Toronto
TI Research on evaluation and optimized structure of managerial competence
SO INTERNATIONAL CONFERENCE ON MANAGEMENT INNOVATION, VOLS 1 AND 2
LA English
DT Proceedings Paper
CT 1st International Conference on Management Innovation
CY JUN 04-06, 2007
CL Shanghai, PEOPLES R CHINA
DE managerial competence; factor analysis; logistic regression; genetic
   algorithms
AB Managerial competence is a hot issue in the field of human resource (HR). Based on the questionnaires from Want Want group, Guangzhou Pepsi Cola group and other enterprises, this paper extracts the main influencing factors of managerial competence in specified industry with Factor Analysis and establishes evaluation & prediction & optimization model with combination of Logistic Regression and Genetic Algorithms. This paper designs fitness with the influence of factor score to competence, designs chromosome & crossover & mutation with different position and different competence, searches the optimal collocation of persons and structure of position in the condition of limited competence resource, so that enterprise & person & position can match each other authentically. Demonstration indicates that the way in this paper is valid and practicable for evaluation, prediction and optimization in HR management (HRM).
C1 S China Univ Technol, Sch Math Sci, Guangzhou 510640, Peoples R China.
C3 South China University of Technology
RP Liao, Q (corresponding author), S China Univ Technol, Sch Math Sci, Guangzhou 510640, Peoples R China.
EM maqliao@scut.edu.cn; zhengjiabi123@163.com
CR BUTLER FC, 1978, ED TECHNOLOGY
   CHEN YC, 2004, SCI RES MANAGEMENT
   GUANGMAN X, 2000, PUBLISHING HOUSE SCI
   JING X, 2006, THESIS S CHINA U TEC
   LONG Y, 2003, CHINA SOFT SCI
   MCCLELLAND, 1973, AM PSYCHOL
   Shippmann J. S., 2000, PERSONNEL PSYCHOL
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   ZHONG LF, 2003, NANKAI BUSINESS
NR 10
TC 0
Z9 0
U1 0
U2 1
PU UNIV ACADEMIC PRESS TORONTO
PI TORONTO
PA UNIV TORONTO, 409 HURON STREET, TORONTO, ON M5S 2G5, CANADA
BN 978-0-9783350-0-7
PY 2007
BP 761
EP 764
PG 4
WC Business; Business, Finance; Management
WE Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Business & Economics
GA BGN47
UT WOS:000248620600142
DA 2024-09-05
ER
PT J
AU Kanakaris, N
   Giarelis, N
   Siachos, I
   Karacapilidis, N
AF Kanakaris, Nikos
   Giarelis, Nikolaos
   Siachos, Ilias
   Karacapilidis, Nikos
TI Shall I Work with Them? A Knowledge Graph-Based Approach for Predicting
   Future Research Collaborations
SO ENTROPY
LA English
DT Article
DE knowledge graph; link prediction; natural language processing; document
   representation; future research collaborations; graph kernels; word
   embeddings
ID LINK PREDICTION; NEIGHBORS
AB We consider the prediction of future research collaborations as a link prediction problem applied on a scientific knowledge graph. To the best of our knowledge, this is the first work on the prediction of future research collaborations that combines structural and textual information of a scientific knowledge graph through a purposeful integration of graph algorithms and natural language processing techniques. Our work: (i) investigates whether the integration of unstructured textual data into a single knowledge graph affects the performance of a link prediction model, (ii) studies the effect of previously proposed graph kernels based approaches on the performance of an ML model, as far as the link prediction problem is concerned, and (iii) proposes a three-phase pipeline that enables the exploitation of structural and textual information, as well as of pre-trained word embeddings. We benchmark the proposed approach against classical link prediction algorithms using accuracy, recall, and precision as our performance metrics. Finally, we empirically test our approach through various feature combinations with respect to the link prediction problem. Our experimentations with the new COVID-19 Open Research Dataset demonstrate a significant improvement of the abovementioned performance metrics in the prediction of future research collaborations.
C1 [Kanakaris, Nikos; Giarelis, Nikolaos; Siachos, Ilias; Karacapilidis, Nikos] Univ Patras, Ind Management & Informat Syst Lab, MEAD, Rion 26504, Greece.
C3 University of Patras
RP Kanakaris, N (corresponding author), Univ Patras, Ind Management & Informat Syst Lab, MEAD, Rion 26504, Greece.
EM nkanakaris@upnet.gr; giarelis@ceid.upatras.gr; ilias.siachos@upnet.gr;
   karacap@upatras.gr
OI Kanakaris, Nikos/0000-0001-9352-5807; Karacapilidis,
   Nikos/0000-0002-6581-6831; Giarelis, Nikolaos/0000-0003-2611-3129;
   Siachos, Ilias/0000-0001-5489-3500
FU European Union; Greek national funds through the Operational Program
   Competitiveness, Entrepreneurship and Innovation, under the call
   RESEARCHCREATE-INNOVATE [T2EDK-04389]
FX The work presented in this paper is supported by the inPOINT project
   (https://inpointproject.eu/), which is co-financed by the European Union
   and Greek national funds through the Operational Program
   Competitiveness, Entrepreneurship and Innovation, under the call
   RESEARCHCREATE-INNOVATE (Project id: T2EDK-04389).
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NR 56
TC 6
Z9 6
U1 0
U2 20
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1099-4300
J9 ENTROPY-SWITZ
JI Entropy
PD JUN
PY 2021
VL 23
IS 6
AR 664
DI 10.3390/e23060664
PG 18
WC Physics, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Physics
GA SX7BG
UT WOS:000665355000001
PM 34070422
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU Safder, I
   Saeed-Ul Hassan
AF Safder, Iqra
   Saeed-Ul Hassan
TI Bibliometric-enhanced information retrieval: a novel deep feature
   engineering approach for algorithm searching from full-text publications
SO SCIENTOMETRICS
LA English
DT Article
DE Algorithm search; Bibliometric-enhanced information retrieval;
   Full-text; Deep learning; Bi-directional LSTM
ID SCHOLARLY DATA
AB Recently, tremendous advances have been observed in information retrieval systems designed to search for relevant knowledge in scientific publications. Although these techniques are quite powerful, there is still room for improvement in the area of searching for metadata relating to algorithms in full-text publication datasetsfor instance, efficiency-related metrics such as precision, recall, f-measure and accuracy, and other useful metadata such as the datasets deployed and the algorithmic run-time complexity. In this study, we proposed a novel deep learning-based feature engineering approach that improves search capabilities by mining algorithmic-specific metadata from full-text scientific publications. Typically, traditional term frequency-inverse document frequency (TF-IDF)-based approaches function like a bag of words' model and thus fail to capture either the text's semantics or the word sequence. In this work, we designed a semantically enriched synopsis of each full-text document by adding algorithmic-specific deep metadata text lines to enhance the search mechanism of algorithm search systems. These text lines are classified by our deployed deep learning-based bi-directional long short term memory (LSTM) model. The designed bi-directional LSTM model outperformed the support vector machine by 9.46%, with a 0.81f1-score on a dataset of 37,000 algorithm-specific deep metadata text lines that had been tagged by four human experts. Lastly, we present a case study on 21,940 full-text publications downloaded from ACL (https://aclweb.org/) to show the effectiveness of deep learning-based advanced feature engineering search compared to the conventional TF-IDF-based (Lucene) search.
C1 [Safder, Iqra; Saeed-Ul Hassan] Informat Technol Univ, 346-B Ferozepur Rd, Lahore, Pakistan.
RP Safder, I (corresponding author), Informat Technol Univ, 346-B Ferozepur Rd, Lahore, Pakistan.
EM iqra.safder@itu.edu.pk
RI Safder, Iqra/JXN-8069-2024; Hassan, Saeed-Ul/G-1889-2016
OI Hassan, Saeed-Ul/0000-0002-6509-9190
FU NRPU - Higher Education Commission of Pakistan [6857]
FX This research work is supported by the NRPU Grant #6857, funded by the
   Higher Education Commission of Pakistan.
CR Al-Zaidy RA, 2017, AAAI CONF ARTIF INTE, P4644
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NR 43
TC 34
Z9 34
U1 1
U2 39
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD APR
PY 2019
VL 119
IS 1
BP 257
EP 277
DI 10.1007/s11192-019-03025-y
PG 21
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA HS1QS
UT WOS:000463637600013
DA 2024-09-05
ER
PT J
AU Memisevic, H
   Pasalic, A
   Mujkanovic, E
   Memisevic, M
AF Memisevic, Haris
   Pasalic, Arnela
   Mujkanovic, Edin
   Memisevic, Mahira
TI In Search of a Silver Bullet: Evaluating Researchers' Performance in
   Bosnia and Herzegovina
SO JOURNAL OF SCIENTOMETRIC RESEARCH
LA English
DT Article
DE Academic performance; Evaluation; h-index; Principal component analysis;
   Bosnia and Herzegovina
ID GOOGLE-SCHOLAR; H-INDEX; SCOPUS; JOURNALS; SCIENCE; WEB; INDICATOR
AB Background: Evaluating academic production and researchers' impact has become a common practice in many areas of academic life. Researchers are being evaluated for many purposes such as getting employment, promotion, tenure and winning grants. Achieving full objectivity of the evaluation is a rather difficult, if not the impossible task. The goal of the present paper was to evaluate research performance of scholars from Bosnia and Herzegovina (BiH and to propose a single number that captures several scientometric indices. Methods: We took data from 303 scholars from 4 public universities in BiH on their number of citations and h-indexes derived from four databases/services: Web of Science, Scopus, Google Scholar and Research Gate. In addition to this, we performed a Principal Component Analysis of number of citations and h-indexes from these indices to come up with a single number that best captures the scientific impact of the researchers. Results: The results of this study indicate a strong relationship between all indices of scholarly achievement as measured through citations and h-indexes. Principal component analysis has shown that it is possible to obtain a single number that captures researchers' scientific impact. Conclusion: Many metrics can be useful in evaluating researchers' scientific impact. As the researchers in BiH have a low scientific production, universities in BiH need to adapt a strategy to stimulate the increase in their scientific productivity.
C1 [Memisevic, Haris; Pasalic, Arnela] Univ Sarajevo, Fac Educ Sci, Sarajevo 71000, Bosnia & Herceg.
   [Mujkanovic, Edin] Herzegovina Univ, Dept Special Educ, Mostar, Bosnia & Herceg.
   [Memisevic, Mahira] Univ Sarajevo, Fac Sci, Sarajevo, Bosnia & Herceg.
C3 University of Sarajevo; University of Sarajevo
RP Memisevic, H (corresponding author), Univ Sarajevo, Fac Educ Sci, Sarajevo 71000, Bosnia & Herceg.
EM hmemisevic@gmail.com
RI Memisevic, Haris/N-3458-2016
OI Memisevic, Haris/0000-0001-7340-3618
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NR 36
TC 4
Z9 4
U1 0
U2 2
PU PHCOG NET
PI KARNATAKA
PA 17, 2ND FLR, BUDDHA VIHAR RD, NEAR SPORTS ZONE, COX TOWN, BENGALURU,
   KARNATAKA, 560005, INDIA
SN 2321-6654
EI 2320-0057
J9 J SCIENTOMETR RES
JI J. Scientometr. Res.
PD SEP-DEC
PY 2019
VL 8
IS 3
BP 125
EP 130
DI 10.5530/jscires.8.3.27
PG 6
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA KA8GC
UT WOS:000506038800001
OA hybrid
DA 2024-09-05
ER
PT J
AU Geraci, M
   Esposti, M
AF Geraci, Marco
   Degli Esposti, M.
TI Where do Italian universities stand? An in-depth statistical analysis of
   national and international rankings
SO SCIENTOMETRICS
LA English
DT Article
DE Ranking; Higher education; Principal component analysis; Correlation;
   Reform law; h-index
AB In a previous article (Degli Esposti and Geraci. Bulletin of Italian Politics, 2011), we presented an historical survey of the university reform laws that took place in Italy in the last 30 years. On that occasion, we stressed how important is merit evaluation for academics and their institutions, especially in view of the much debated but not yet implemented 'Gelmini' reform with its long awaited new regulation for accessing academic positions (concorsi) and for determining individual weight in financial resource allocation among universities. Here, we present and compare several rankings used to evaluate the prestige and merit of Italian universities. We also consider alternative approaches to academic rankings that highlight peculiar aspects of the universities in Italy which cannot be reasonably accounted for by other international rankings. Finally, we propose a new approach that combines both national and international standing of Italian universities. It is hoped that this study will provide practical guidance to policy makers for establishing the criteria upon which merit should be assessed.
C1 [Geraci, Marco] UCL Inst Child Hlth, MRC Ctr Epidemiol Child Hlth, London WC1N 1EH, England.
   [Degli Esposti, M.] Univ Manchester, Fac Life Sci, Manchester M13 9PT, Lancs, England.
C3 University of London; University College London; University of
   Manchester
RP Geraci, M (corresponding author), UCL Inst Child Hlth, MRC Ctr Epidemiol Child Hlth, 30 Guilford St, London WC1N 1EH, England.
EM m.geraci@ucl.ac.uk
RI Geraci, Marco/B-9588-2009; Degli Esposti, Mauro/C-3270-2012
OI Geraci, Marco/0000-0002-6311-8685; 
FU MRC [G0400546] Funding Source: UKRI
CR *CAMP, 2009, GUIDA ALL U
   DEGLIESPOSTI M, 2011, B ITALIAN P IN PRESS
   Everitt B., 1998, The Cambridge Dictionary of Statistics
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   Tocci W, 2009, QUALE RIFORMA U
   *UDU, 2009, MIST MIN
   *VIS, 2009, U NUOV CLASS VIS
   *VIS, 2009, CLASS U IT
NR 11
TC 10
Z9 12
U1 0
U2 14
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
J9 SCIENTOMETRICS
JI Scientometrics
PD JUN
PY 2011
VL 87
IS 3
BP 667
EP 681
DI 10.1007/s11192-011-0350-9
PG 15
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 753PS
UT WOS:000289792600016
DA 2024-09-05
ER
PT C
AU Kongthon, A
   Haruechaiyasak, C
   Thaiprayoon, S
AF Kongthon, Alisa
   Haruechaiyasak, Choochart
   Thaiprayoon, Santipong
BE Buchanan, G
   Masoodian, M
   Cunningham, SJ
TI Enhancing the Literature Review Using Author-Topic Profiling
SO DIGITAL LIBRARIES: UNIVERSAL AND UBIQUITOUS ACCESS TO INFORMATION,
   PROCEEDINGS
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT 11th International Conference on Asian Digital Libraries
CY DEC 02-05, 2008
CL Bali, INDONESIA
DE Bibliographic data; text mining; Latent Dirichlet Allocation (LDA);
   author-topic profiling; literature review
AB In this paper, we utilize bibliographic data for identifying author-topic relations which call be used to enhance the traditional literature review. When writing a research paper, researchers often cite oil the order of tens of references which do not provide the complete coverage of the research context especially when the targeted research is multidisciplinary. Author-topic profiling can help researchers discover a broader picture of their topic of interest including topical relationships and research community. We apply the Latent Dirichlet Allocation (LDA) to generate multinomial distributions over words and topics to discover author-topic relations from text collections. As ail illustration, we apply the methodology to bibliographic abstracts related to Emerging Infectious Diseases (ElDs) research topic.
C1 [Kongthon, Alisa; Haruechaiyasak, Choochart; Thaiprayoon, Santipong] Natl Elect & Comp Technol Ctr NECTEC, Human Language Technol Lab HLT, Klongluang 12120, Pathumthani, Thailand.
C3 National Science & Technology Development Agency - Thailand; National
   Electronics & Computer Technology Center (NECTEC)
EM alisa.kon@nectec.or.th; choochart.har@nectec.or.th;
   santipong.tha@nectec.or.th
RI Kongthon, Alisa/E-4470-2011
OI Kongthon, Alisa/0009-0009-8682-5006
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NR 5
TC 0
Z9 0
U1 0
U2 3
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 0302-9743
BN 978-3-540-89532-9
J9 LECT NOTES COMPUT SC
PY 2008
VL 5362
BP 335
EP 338
PG 4
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods; Information Science & Library Science
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Information Science & Library Science
GA BIT31
UT WOS:000262503100038
OA Bronze
DA 2024-09-05
ER
PT C
AU Guo, J
   Yan, YM
   Zhang, B
   Ma, QM
AF Guo Jun
   Yan Yongming
   Zhang Bin
   Ma Qingmin
GP IEEE
TI SLA-Oriented Research on Prediction and Evaluation of Service Components
   Performance
SO 2015 12TH WEB INFORMATION SYSTEM AND APPLICATION CONFERENCE (WISA)
LA English
DT Proceedings Paper
CT 12th Web Information System and Application Conference (WISA)
CY SEP 11-13, 2015
CL Shandong Univ, Jinan, PEOPLES R CHINA
HO Shandong Univ
DE cloud service component; performance prediction; non-negative matrix
   factorization; response time sequence
AB The application services which deployed in cloud environment have some characteristics, such as the variability of concurrent requests and the differences of resource demands among components, which bring certain influence on service performance even cause some potential problems. Therefore, making an effective service performance prediction mechanism has become a research hotspot for application services based cloud service components. This paper proposes SLA-oriented research on prediction of service component and introduces the fundamental process of performance prediction. It adopts the non-negative matrix factorization method for performance prediction and makes an evaluation on component performance. The experiment result indicates the method in this paper is feasible and valid, and the accuracy is higher than the traditional approaches.
C1 [Guo Jun; Yan Yongming; Zhang Bin; Ma Qingmin] Northeastern Univ, Coll Informat Sci & Engn, Shenyang, Peoples R China.
C3 Northeastern University - China
RP Guo, J (corresponding author), Northeastern Univ, Coll Informat Sci & Engn, Shenyang, Peoples R China.
EM guojun@ise.neu.edu.cn; yym_sy@163.com; zhangbing@ise.neu.edu.cn;
   maqingmin10@163.com
RI Zhang, Bin/U-9174-2019
OI Zhang, Bin/0000-0002-2127-9560
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   Pacheco-Sanchez S., 2011, Proceedings of the 2011 IEEE 4th International Conference on Cloud Computing (CLOUD 2011), P147, DOI 10.1109/CLOUD.2011.100
   Patel SG, 2012, INT J ENG, V1
   Shao L, 2007, IEEE ICCE, P439, DOI 10.1109/ICWS.2007.140
   Song Huanhuan, 2011, DESIGN IMPLEMENT CLO
   Truong HL, 2010, PROCEDIA COMPUT SCI, V1, P2169, DOI 10.1016/j.procs.2010.04.243
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NR 20
TC 0
Z9 0
U1 0
U2 0
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-4673-9372-0
PY 2015
BP 136
EP 141
DI 10.1109/WISA.2015.33
PG 6
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods; Engineering, Electrical & Electronic
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Engineering
GA BF1EO
UT WOS:000380387900026
DA 2024-09-05
ER
PT J
AU Ye, ZY
   Li, Z
   Zhong, SY
   Xing, QC
   Li, KH
   Sheng, WC
   Shi, X
   Bao, YJ
AF Ye, Ziyin
   Li, Zhi
   Zhong, Shiyu
   Xing, Qichen
   Li, Kunhang
   Sheng, Weichen
   Shi, Xin
   Bao, Yijun
TI The recent two decades of traumatic brain injury: a bibliometric
   analysis and systematic review
SO INTERNATIONAL JOURNAL OF SURGERY
LA English
DT Article
DE bibliometric analysis; Latent Dirichlet Allocation; topic modeling;
   traumatic brain injury
ID RAISED INTRACRANIAL-PRESSURE; FIBRILLARY ACIDIC PROTEIN; C-TERMINAL
   HYDROLASE-L1; HYPERTONIC SALINE; DECOMPRESSIVE CRANIECTOMY;
   UNITED-STATES; CLINICAL-TRIAL; HEAD TRAUMA; CARE; ENCEPHALOPATHY
AB Background:Traumatic brain injury (TBI) is a serious public health burden worldwide, with a mortality rate of 20-30%; however, reducing the incidence and mortality rates of TBI remains a major challenge. This study provides a multidimensional analysis to explore the potential breakthroughs in TBI over the past two decades.Materials and methods:The authors used bibliometric and Latent Dirichlet Allocation (LDA) analyses to analyze publications focusing on TBI published between 2003 and 2022 from the Web of Science Core Collection (WOSCC) database to identify core journals and collaborations among countries/regions, institutions, authors, and research trends.Results:Over the past 20 years, 41 545 articles on TBI from 3043 journals were included, with 12 916 authors from 20 449 institutions across 145 countries/regions. The annual number of publications has increased 10-fold compared to previous publications. This study revealed that high-income countries, especially the United States, have a significant influence. Collaboration was limited to several countries/regions. The LDA results indicated that the hotspots included four main areas: 'Clinical finding', 'Molecular mechanism', 'Epidemiology', and 'Prognosis'. Epidemiological research has consistently increased in recent years. Through epidemiological topic analysis, the main etiology of TBI has shifted from traffic accidents to falls in a demographically aging society.Conclusion:Over the past two decades, TBI research has developed rapidly, and its epidemiology has received increasing attention. Reducing the incidence of TBI from a preventive perspective is emerging as a trend to alleviate the future social burden; therefore, epidemiological research might bring breakthroughs in TBI.
C1 [Ye, Ziyin; Zhong, Shiyu; Xing, Qichen; Li, Kunhang; Sheng, Weichen; Bao, Yijun] China Med Univ, Hosp 4, Dept Neurosurg, 4 Chongshandong, Shenyang 110084, Peoples R China.
   [Li, Zhi] China Med Univ, Hosp 1, Dept Oncol, Shenyang, Heping, Peoples R China.
   [Shi, Xin] China Med Univ, Sch Hlth Management, 77 Puhe Rd, Shenyang, Liaoning, Peoples R China.
C3 China Medical University; China Medical University; China Medical
   University
RP Bao, YJ (corresponding author), China Med Univ, Hosp 4, Dept Neurosurg, 4 Chongshandong, Shenyang 110084, Peoples R China.; Shi, X (corresponding author), China Med Univ, Sch Hlth Management, 77 Puhe Rd, Shenyang, Liaoning, Peoples R China.
EM y18924280072@163.com; zli@cmu.edu.cn; zsy757301823@163.com;
   tonyxxv@Hotmail.com; khli@cmu.edu.cn; sWeiccc1201@163.com;
   20221010@cmu.edu.cn; yjbao@cmu.edu.cn
FU Liaoning Provincial Natural Science Foundation [2020-MS-155]; China
   Medical University novel coronavirus pneumonia prevention and control
   research project [2020-12-11]; Shenyang Planning Foundation for Science
   and Technology [21-173-9-38]; China Medical University [YDJK2021011];
   National Science Foundation of China [72074104]; Immersive Smart Devices
   for Healthcare System R&D and Industrial Application Innovation Platform
   (2022) of Immersion Technology and Evaluation Shandong Engineering
   Research Center (2022); Research Project on Undergraduate Teaching
   Reform of Liaoning General Higher Education - Educational Department of
   Liaoning Province [2022-10159-479]; Research Project on Postgraduate
   Teaching Reform - Educational Department of Liaoning Province
   [2022-10159-311]; China Stroke Association Whole Course Management of
   Cerebrovascular Disease Sailing Fund [202001]; The 2023 Undergraduate
   Teaching Reform Research Project of China Medical University - 2022
   Provincial First-class Curriculum Construction Specialization; Natural
   Science Foundation of Liaoning Province of China [2021-MS-179]
FX This study was funded by Liaoning Provincial Natural Science Foundation
   (2020-MS-155), China Medical University novel coronavirus pneumonia
   prevention and control research project (2020-12-11), Shenyang Planning
   Foundation for Science and Technology (21-173-9-38), the first batch of
   medical education scientific research project of China Medical
   University for the 14th Five-Year Plan (YDJK2021011), National Science
   Foundation of China (72074104), Immersive Smart Devices for Healthcare
   System R&D and Industrial Application Innovation Platform (2022) of
   Immersion Technology and Evaluation Shandong Engineering Research Center
   (2022) , Research Project on Undergraduate Teaching Reform of Liaoning
   General Higher Education sponsored by Educational Department of Liaoning
   Province (2022-10159-479), Research Project on Postgraduate Teaching
   Reform sponsored by Educational Department of Liaoning Province
   (2022-10159-311), China Stroke Association Whole Course Management of
   Cerebrovascular Disease Sailing Fund (202001), 2023 Undergraduate
   Teaching Reform Research Project of China Medical University - 2022
   Provincial First-class Curriculum Construction Specialization, and The
   Natural Science Foundation of Liaoning Province of China (2021-MS-179).
   The researchers are grateful for the support of several organizations.
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NR 127
TC 1
Z9 1
U1 19
U2 19
PU LIPPINCOTT WILLIAMS & WILKINS
PI PHILADELPHIA
PA TWO COMMERCE SQ, 2001 MARKET ST, PHILADELPHIA, PA 19103 USA
SN 1743-9191
EI 1743-9159
J9 INT J SURG
JI Int. J. Surg.
PD JUN
PY 2024
VL 110
IS 6
BP 3745
EP 3759
DI 10.1097/JS9.0000000000001367
PG 15
WC Surgery
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Surgery
GA UL4X2
UT WOS:001248212000058
PM 38608040
OA hybrid
DA 2024-09-05
ER
PT J
AU Wang, Q
   Huang, R
   Li, RR
AF Wang, Qiang
   Huang, Rui
   Li, Rongrong
TI Renewable energy and sustainable development goals: Insights from latent
   dirichlet allocation thematic and bibliometric analysis
SO SUSTAINABLE DEVELOPMENT
LA English
DT Article; Early Access
DE comprehensive evolution; renewable energy; sustainable development
   goals; topic modeling
ID ECONOMIC-POLICY UNCERTAINTY; NONRENEWABLE ENERGY; CONSUMPTION;
   CHALLENGES; EMISSIONS; WIND; GROWTH; STATES; NEXUS
AB This research conducts a comprehensive analysis of the intricate relationship between renewable energy and the sustainable development goals (SDGs). Employing diverse methodologies including latent dirichlet allocation (LDA) topic modeling, bibliometrics, citation analysis, and regression modeling, the study explores the evolving landscape of renewable energy research and its implications for SDGs. The analysis identifies a pronounced scholarly interest in renewable energy, reflected in the escalating publication volumes and citations across environmental sciences, green technology, and energy studies. Through meticulous examination, it uncovers interconnected research themes; encompassing policy uncertainty, Environmental Kuznets Curve, and ecological footprint, elucidating the multifaceted impact of renewable energy on the SDGs. Geographical distributions underscore diverse regional focuses, emphasizing the need for nuanced, context-specific approaches. Regression analysis highlights influential factors like carbon dioxide emissions and gross domestic product (GDP) growth, delineating their pivotal roles in shaping scholarly attention toward renewable energy research. Furthermore, the study delves into the economic, environmental, and social dimensions of renewable energy's influence. It reveals its contributions to employment generation, sustainable production, energy access, and infrastructure development while navigating challenges related to climate change mitigation and biodiversity conservation. This comprehensive investigation offers crucial insights into the complex interplay between renewable energy and the SDGs. It predicts future research directions and highlights the urgency for interdisciplinary collaboration, international cooperation, and policy innovations to harness renewable energy's transformative potential for global sustainable development.
C1 [Wang, Qiang; Li, Rongrong] Xinjiang Univ, Sch Econ & Management, Urumqi, Peoples R China.
   [Wang, Qiang; Huang, Rui; Li, Rongrong] China Univ Petr East China, Sch Econ & Management, Qingdao 266580, Peoples R China.
C3 Xinjiang University; China University of Petroleum
RP Wang, Q; Li, RR (corresponding author), China Univ Petr East China, Sch Econ & Management, Qingdao 266580, Peoples R China.
EM wangqiang7@upc.edu.cn; lirr@upc.edu.cn
RI Wang, Qiang/F-4618-2011
OI Wang, Qiang/0000-0002-8751-8093
FU National Natural Science Foundation of China;  [72104246]
FX The authors would like to thank the editor and the anonymous reviewer
   for their helpful and constructive comments that greatly contributed to
   improving the final version of the manuscript. This work is supported by
   the National Natural Science Foundation of China (Grant No. 72104246).
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NR 75
TC 1
Z9 1
U1 24
U2 24
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0968-0802
EI 1099-1719
J9 SUSTAIN DEV
JI Sustain. Dev.
PD 2024 MAY 8
PY 2024
DI 10.1002/sd.3027
EA MAY 2024
PG 21
WC Development Studies; Green & Sustainable Science & Technology; Regional
   & Urban Planning
WE Social Science Citation Index (SSCI)
SC Development Studies; Science & Technology - Other Topics; Public
   Administration
GA PR9L1
UT WOS:001215926500001
DA 2024-09-05
ER
PT J
AU Seeber, M
   Alon, I
   Pina, DG
   Piro, FN
   Seeber, M
AF Seeber, Marco
   Alon, Ilan
   Pina, David G.
   Piro, Fredrik Niclas
   Seeber, Michele
TI Predictors of applying for and winning an ERC Proof-of-Concept grant: An
   automated machine learning model
SO TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
LA English
DT Article
DE Research proposals evaluation; Automated machine learning; ERC; PoC;
   Research valorization; Research funding; Likelihood to apply; Artificial
   intelligence
ID GENDER-DIFFERENCES; KNOWLEDGE TRANSFER; LIFE SCIENCES; UNIVERSITY;
   COMMERCIALIZATION; DETERMINANTS; ENGAGEMENT; BIAS; GAP; TECHNOLOGIES
AB Research often fails to be translated into applications because of lack of financial support. The Proof of Concept (PoC) funding scheme from the European Research Council (ERC) supports the early stages of the valorization process of the research conducted by its grantees. This article explores the factors that predict who will apply for ERC grants and which grant proposals will prove successful. By combining information from two datasets of 10,074 ERC grants (representing 8361 individual grantees) and 2186 PoC proposals, and using automated machine learning, we can identify the main predictors of the propensity to apply and to win. Doing so fills a void in the literature on likelihood to apply. The results reveal major differences between potential and actual ben-eficiaries, due to decisions about applying for a grant and evaluations of the proposals. The decision to apply is affected by the interaction between the characteristics of the PoC funding scheme, the ERC grantee, and his/her environment. Grantees in countries that invest little in innovation, with low cost of personnel, and strong collaboration in innovation are more likely to apply. Male grantees are more likely to apply but have similar chances of winning as women.
C1 [Seeber, Marco] Univ Agder, Dept Polit Sci & Management, Kristiansand, Norway.
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   [Alon, Ilan] Univ Agder, Sch Business & Law, POB 422, Kristiansand, Norway.
   [Pina, David G.] European Res Execut Agcy, European Commiss, Brussels, Belgium.
   [Piro, Fredrik Niclas] Nord Inst Studies Innovat, Educ & Res NIFU, Oslo, Norway.
   [Seeber, Michele] Univ Modena & Reggio Emilia, Dept Life Sci, Modena, Italy.
C3 University of Agder; Ariel University; University of Agder; Universita
   di Modena e Reggio Emilia
RP Seeber, M (corresponding author), Univ Agder, Dept Polit Sci & Management, Kristiansand, Norway.
EM marco.seeber@uia.no; ilanal@ariel.ac.il; david.pina@ec.europa.eu;
   fredrik.piro@nifu.no; michele.seeber@unimore.it
RI seeber, marco/AFO-9598-2022
OI seeber, marco/0000-0002-0162-6289; Pina, David/0000-0002-4930-748X
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NR 84
TC 5
Z9 5
U1 1
U2 14
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0040-1625
EI 1873-5509
J9 TECHNOL FORECAST SOC
JI Technol. Forecast. Soc. Chang.
PD NOV
PY 2022
VL 184
AR 122009
DI 10.1016/j.techfore.2022.122009
EA SEP 2022
PG 16
WC Business; Regional & Urban Planning
WE Social Science Citation Index (SSCI)
SC Business & Economics; Public Administration
GA 5B0AE
UT WOS:000863238800013
OA hybrid
DA 2024-09-05
ER
PT J
AU Tseng, CY
   Ting, PH
AF Tseng, Chun-Yao
   Ting, Ping-Ho
TI Patent analysis for technology development of artificial intelligence: A
   country-level comparative study
SO INNOVATION-ORGANIZATION & MANAGEMENT
LA English
DT Article
DE artificial intelligence; technology development; patent and citation
   analysis; technology flow
ID INDICATORS
AB Artificial intelligence (AI) plays a key role in knowledge economies, because it can be used to develop systems that think like humans, act like humans, think rationally, and act rationally (Russell & Norvig, 2010). In this study, we divide AI into four sub-technological fields: Problem reasoning and solving, machine learning, network structures, and knowledge processing systems. This study investigates three main issues related to the technology development of AI. First, the aggregate technology development of AI is examined, and the four sub-technological fields of AI are compared. Second, we employ measures of patent quantity and patent quality to demonstrate the technology development of AI in different countries. Finally, we investigate the technology positions of different countries in the four sub-technological fields of AI. By analyzing a patent and citation dataset comprised of all patents granted by the United States patent and trademark office from 1976 to 2010, we obtain empirical findings that help us understand the technology development of AI in different countries. The major contributions of this study are four measures of patent quantity (PCA, PCI, SHAI, and SHIA) and three measures of patent quality (citation ratios, CII, and TCT). These measures are helpful in understanding technological development of AI in different counties. Moreover, we use patent citation data and investigate the technology flow in AI, in order to determine the technology position of different countries in the four sub-technological fields of AI.
C1 [Tseng, Chun-Yao] Tunghai Univ, Dept Business Adm, Taichung 40704, Taiwan.
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   [Ting, Ping-Ho] Chi Nan Univ, Dept Leisure Studies & Tourism Management, Nantou, Taiwan.
C3 Tunghai University
RP Tseng, CY (corresponding author), Tunghai Univ, Dept Business Adm, Taichung 40704, Taiwan.
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NR 20
TC 37
Z9 40
U1 10
U2 120
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1447-9338
EI 2204-0226
J9 INNOV-ORGAN MANAG
JI Innov.-Organ. Manag.
PD DEC
PY 2013
VL 15
IS 4
BP 463
EP 475
DI 10.5172/impp.2013.15.4.463
PG 13
WC Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA AA4KT
UT WOS:000331065200006
DA 2024-09-05
ER
PT J
AU Gil, R
   Virgili-Goma, J
   López-Gil, JM
   García, R
AF Gil, Rosa
   Virgili-Goma, Jordi
   Lopez-Gil, Juan-Miguel
   Garcia, Roberto
TI Deepfakes: evolution and trends
SO SOFT COMPUTING
LA English
DT Article
DE Deepfakes; Artificial intelligence; Deep learning; Bibliometrics
ID ARTIFICIAL-INTELLIGENCE; FACE MANIPULATION; NEURAL-NETWORKS; DEEP FAKES;
   VIDEOS; CHALLENGES; IMAGES; TOOL; RECOGNITION; TECHNOLOGY
AB This study conducts research on deepfakes technology evolution and trends based on a bibliometric analysis of the articles published on this topic along with six research questions: What are the main research areas of the articles in deepfakes? What are the main current topics in deepfakes research and how are they related? Which are the trends in deepfakes research? How do topics in deepfakes research change over time? Who is researching deepfakes? Who is funding deepfakes research? We have found a total of 331 research articles about deepfakes in an analysis carried out on the Web of Science and Scopus databases. This data serves to provide a complete overview of deepfakes. Main insights include: different areas in which deepfakes research is being performed; which areas are the emerging ones, those that are considered basic, and those that currently have the most potential for development; most studied topics on deepfakes research, including the different artificial intelligence methods applied; emerging and niche topics; relationships among the most prominent researchers; the countries where deepfakes research is performed; main funding institutions. This paper identifies the current trends and opportunities in deepfakes research for practitioners and researchers who want to get into this topic.
C1 [Gil, Rosa; Virgili-Goma, Jordi; Garcia, Roberto] Univ Lleida, Lleida 25001, Spain.
   [Lopez-Gil, Juan-Miguel] Univ Basque Country, Donostia San Sebastian 20018, Spain.
C3 Universitat de Lleida; University of Basque Country
RP García, R (corresponding author), Univ Lleida, Lleida 25001, Spain.
EM rosamaria.gil@udl.cat; jordi.virgili@udl.cat; juanmiguel.lopez@ehu.eus;
   roberto.garcia@udl.cat
RI Garcia, Roberto/B-3388-2008; Lopez-Gil, Juan-Miguel/GVT-9867-2022
OI Garcia, Roberto/0000-0003-2207-9605; Lopez-Gil,
   Juan-Miguel/0000-0001-7730-0472; Virgili Goma, Jordi/0000-0002-7144-7489
FU CRUE-CSIC agreement; Springer Nature; MCIN/AEI [PID2020-117912RB-C22];
   Research Group program of the University of the Basque Country; 
   [GIU21/037]
FX Open Access funding provided thanks to the CRUE-CSIC agreement with
   Springer Nature. This work has been partially supported by the project
   "ANGRU: Applying kNowledge Graphs to research data ReUsability" with
   reference PID2020-117912RB-C22 and funded by
   MCIN/AEI/10.13039/501100011033. Additionally, this research benefits
   from funding from the Research Group program of the University of the
   Basque Country under contract GIU21/037.
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NR 311
TC 3
Z9 3
U1 26
U2 63
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1432-7643
EI 1433-7479
J9 SOFT COMPUT
JI Soft Comput.
PD AUG
PY 2023
VL 27
IS 16
BP 11295
EP 11318
DI 10.1007/s00500-023-08605-y
EA JUN 2023
PG 24
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA J9AG9
UT WOS:001008460000001
OA hybrid
DA 2024-09-05
ER
PT J
AU Thelwall, M
   Kousha, K
AF Thelwall, Mike
   Kousha, Kayvan
TI Technology assisted research assessment: algorithmic bias and
   transparency issues
SO ASLIB JOURNAL OF INFORMATION MANAGEMENT
LA English
DT Article; Early Access
DE Technology assisted research assessment; Bibliometrics; Research
   evaluation; Machine learning; Algorithmic bias; Transparency
ID CITATION BIAS; SCIENCE; COVERAGE; ARTICLES; DISTANCE; BEHAVIOR
AB PurposeTechnology is sometimes used to support assessments of academic research in the form of automatically generated bibliometrics for reviewers to consult during their evaluations or by replacing some or all human judgements. With artificial intelligence (AI), there is increasing scope to use technology to assist research assessment processes in new ways. Since transparency and fairness are widely considered important for research assessment and AI introduces new issues, this review investigates their implications.Design/methodology/approachThis article reviews and briefly summarises transparency and fairness concerns in general terms and through the issues that they raise for various types of Technology Assisted Research Assessment (TARA).FindingsWhilst TARA can have varying levels of problems with both transparency and bias, in most contexts it is unclear whether it worsens the transparency and bias problems that are inherent in peer review.Originality/valueThis is the first analysis that focuses on algorithmic bias and transparency issues for technology assisted research assessment.
C1 [Thelwall, Mike; Kousha, Kayvan] Univ Wolverhampton, Stat Cybermetr Res Grp, Wolverhampton, England.
C3 University of Wolverhampton
RP Thelwall, M (corresponding author), Univ Wolverhampton, Stat Cybermetr Res Grp, Wolverhampton, England.
EM m.a.thelwall@sheffield.ac.uk
RI Thelwall, Mike/JDV-4700-2023
OI Thelwall, Mike/0000-0001-6065-205X
FU Research England; Scottish Funding Council; Higher Education Funding
   Council for Wales; Department for the Economy, Northern Ireland, Future
   Research Assessment Programme
FX This study was funded by Research England, Scottish Funding Council,
   Higher Education Funding Council for Wales, and Department for the
   Economy, Northern Ireland as part of the Future Research Assessment
   Programme
   (https://www.jisc.ac.uk/future-research-assessment-programme).The
   content is solely the responsibility of the authors and does not
   necessarily represent the officialviews of the funders.
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NR 72
TC 0
Z9 0
U1 16
U2 36
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 2050-3806
EI 1758-3748
J9 ASLIB J INFORM MANAG
JI Aslib J. Inf. Manag.
PD 2023 OCT 2
PY 2023
DI 10.1108/AJIM-04-2023-0119
EA OCT 2023
PG 16
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA T0QX6
UT WOS:001075132200001
OA Green Accepted
DA 2024-09-05
ER
PT J
AU Kong, XJ
   Zhang, J
   Zhang, D
   Bu, Y
   Ding, Y
   Xia, F
AF Kong, Xiangjie
   Zhang, Jun
   Zhang, Da
   Bu, Yi
   Ding, Ying
   Xia, Feng
TI The Gene of Scientific Success
SO ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
LA English
DT Article
DE Scientific impact; academic networks; machine learning; feature
   selection
ID IMPACT
AB This article elaborates how to identify and evaluate causal factors to improve scientific impact. Currently, analyzing scientific impact can be beneficial to various academic activities including funding application, mentor recommendation, discovering potential cooperators, and the like. It is universally acknowledged that high-impact scholars often have more opportunities to receive awards as an encouragement for their hard work. Therefore, scholars spend great efforts in making scientific achievements and improving scientific impact during their academic life. However, what are the determinate factors that control scholars' academic success? The answer to this question can help scholars conduct their research more efficiently. Under this consideration, our article presents and analyzes the causal factors that are crucial for scholars' academic success. We first propose five major factors including article-centered factors, author-centered factors, venue-centered factors, institution-centered factors, and temporal factors. Then, we apply recent advanced machine learning algorithms and jackknife method to assess the importance of each causal factor. Our empirical results show that author-centered and article-centered factors have the highest relevancy to scholars' future success in the computer science area. Additionally, we discover an interesting phenomenon that the h-index of scholars within the same institution or university are actually very close to each other.
C1 [Kong, Xiangjie] Zhejiang Univ Technol, Coll Comp Sci & Technol, 288 Liuhe Rd, Hangzhou 310023, Zhejiang, Peoples R China.
   [Zhang, Jun] Dalian Univ Technol, Grad Sch Educ, 2 Linggong Rd, Dalian 116024, Liaoning, Peoples R China.
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   [Bu, Yi] Peking Univ, Dept Informat Management, 5 Yiheyuan Rd, Beijing 100871, Peoples R China.
   [Ding, Ying] Univ Texas Austin, Sch Informat, 1616 Guadalupe St, Austin, TX 78701 USA.
   [Xia, Feng] Federat Univ Australia, Sch Sci Engn & Informat Technol, POB 663, Ballarat, Vic 3353, Australia.
C3 Zhejiang University of Technology; Dalian University of Technology;
   University of Miami; Peking University; University of Texas System;
   University of Texas Austin; Federation University Australia
RP Xia, F (corresponding author), Federat Univ Australia, Sch Sci Engn & Informat Technol, POB 663, Ballarat, Vic 3353, Australia.
EM xjkong@acm.org; junzhang@dlut.edu.cn; zhang.1855@miami.edu;
   buyipku@gmail.com; ying.ding@austin.utexas.edu; f.xia@acm.org
RI Ding, Ying/X-3657-2019; Kong, Xiangjie/B-8809-2016; Xia,
   Feng/Y-2859-2019
OI Kong, Xiangjie/0000-0003-2698-3319; Xia, Feng/0000-0002-8324-1859;
   Zhang, Da/0000-0003-3321-1835
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NR 44
TC 19
Z9 19
U1 2
U2 37
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA
SN 1556-4681
EI 1556-472X
J9 ACM T KNOWL DISCOV D
JI ACM Trans. Knowl. Discov. Data
PD JUL
PY 2020
VL 14
IS 4
AR 41
DI 10.1145/3385530
PG 19
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA OI9ZB
UT WOS:000583626600004
OA Green Published, Green Submitted
DA 2024-09-05
ER
PT J
AU Thiem, A
AF Thiem, Alrik
TI Beyond the Facts: Limited Empirical Diversity and Causal Inference in
   Qualitative Comparative Analysis
SO SOCIOLOGICAL METHODS & RESEARCH
LA English
DT Article
DE Boolean algebra; causal inference; configurational comparative methods;
   empirical research methods; method evaluation; qualitative comparative
   analysis
ID MINIMIZATION
AB Qualitative Comparative Analysis (QCA) is a relatively young method of causal inference that continues to diffuse across the social sciences. However, recent methodological research has found the conservative (QCA-CS) and the intermediate solution type (QCA-IS) of QCA to fail fundamental tests of correctness. Even under conditions otherwise ideal for causal discovery, both solution types frequently committed causal fallacies by presenting inferences that were in direct disagreement with the underlying data-generating structure to be discovered by QCA. None of these problems affected the parsimonious solution type (QCA-PS). These findings conflict with conventional wisdom in the QCA literature, which has it that QCA-CS uses empirical information only and that QCA-IS is preferable to both QCA-CS and QCA-PS. The present article resolves these contradictions. It shows that QCA-CS and QCA-IS systematically supplement empirical data with matching artificial data. These artificial data, however, regularly induce causal fallacies of severe magnitude. Researchers who employ QCA-CS or QCA-IS in empirical analyses thus always risk moving further away from the truth rather than closer to it.
C1 [Thiem, Alrik] Univ Lucerne, Dept Polit Sci, Luzern, Switzerland.
C3 University of Lucerne
RP Thiem, A (corresponding author), Univ Lucerne, CH-6002 Luzern, Switzerland.
EM alrik.thiem@unilu.ch
OI Thiem, Alrik/0000-0002-4769-2185
FU Swiss National Science Foundation [PP00P1_170442]; Swiss National
   Science Foundation (SNF) [PP00P1_170442] Funding Source: Swiss National
   Science Foundation (SNF)
FX The author(s) disclosed receipt of the following financial support for
   the research, authorship, and/or publication of this article: Swiss
   National Science Foundation (PP00P1_170442).
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NR 40
TC 30
Z9 32
U1 24
U2 33
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 0049-1241
EI 1552-8294
J9 SOCIOL METHOD RES
JI Sociol. Methods. Res.
PD MAY
PY 2022
VL 51
IS 2
BP 527
EP 540
AR 0049124119882463
DI 10.1177/0049124119882463
EA NOV 2019
PG 14
WC Social Sciences, Mathematical Methods; Sociology
WE Social Science Citation Index (SSCI)
SC Mathematical Methods In Social Sciences; Sociology
GA 0N5YZ
UT WOS:000496361300001
OA hybrid
DA 2024-09-05
ER
PT J
AU Mohsen, MA
   Althebi, S
   Albahooth, M
AF Mohsen, Mohammed Ali
   Althebi, Sultan
   Albahooth, Mohammed
TI A scientometric study of three decades of machine translation research:
   Trending issues, hotspot research, and co-citation analysis
SO COGENT ARTS & HUMANITIES
LA English
DT Article
DE machine translation; research; scientometric; translation studies;
   co-citation
ID EMERGING TRENDS; COMMUNITY
AB This study aims to examine machine translation research in journals indexed in the Web of Science to find out the research trending issue, hotspot areas of research, and document co-citation analysis. To this end, 541 documents published between 1992 and 2022 were retrieved and analyzed using CiteSpace, and Bibexcel. Many metrics were analyzed such as document co-citation analysis, sources co-citation analyses, authors' keywords analysis, and Hirsch index. Data were coded and filtered to include research related to machine translation from the perspectives of language and translation studies. We identified 11 clusters that represented the hotspot research during the period of almost three decades of research. We also discovered that a significant focus of research in machine translation centered around enhancing the translation process through the implementation of neural networks integrated with artificial intelligence. Additionally, we observed the incorporation of human post-editing as a means to refine and improve machine-translated outputs. We found that translation studies journals were the most highly co-cited journals and Google translate was the most highly used machine translation. This study highlights the trending issues and hotspots in machine translation research within language and translation studies. The integration of neural networks with artificial intelligence and human post-editing emerged as prominent areas of focus for enhancing translation quality. The findings of the current study inform future research and technological advancements in machine translation, guiding efforts to improve translation processes and outcomes.
C1 [Mohsen, Mohammed Ali; Althebi, Sultan; Albahooth, Mohammed] Najran Univ, Coll Languages & Translat, Najran, Saudi Arabia.
C3 Najran University
RP Mohsen, MA (corresponding author), Najran Univ, Coll Languages & Translat, Najran, Saudi Arabia.
EM mamohsen@nu.edu.sa
RI AlThebi, Sultan/GQQ-3465-2022; MOHSEN, MOHAMMED/K-1861-2015
OI AlThebi, Sultan/0000-0002-8459-6969; MOHSEN,
   MOHAMMED/0000-0003-3169-102X
FU Literature, Publishing and Translation Commission, Ministry of Culture,
   Kingdom of Saudi Arabia [102/022]
FX This study was funded by the Literature, Publishing and Translation
   Commission, Ministry of Culture, Kingdom of Saudi Arabia under [102/022]
   as part of the Arabic Observatorof Translation.
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NR 46
TC 2
Z9 2
U1 6
U2 28
PU TAYLOR & FRANCIS AS
PI OSLO
PA KARL JOHANS GATE 5, NO-0154 OSLO, NORWAY
SN 2331-1983
J9 COGENT ARTS HUMANITE
JI Cogent Art Humanities
PD DEC 31
PY 2023
VL 10
IS 1
AR 2242620
DI 10.1080/23311983.2023.2242620
PG 16
WC Humanities, Multidisciplinary
WE Emerging Sources Citation Index (ESCI)
SC Arts & Humanities - Other Topics
GA O0ZX1
UT WOS:001041200900001
OA gold
DA 2024-09-05
ER
PT J
AU Zhang, J
   Yu, MX
   He, K
AF Zhang, Jia
   Yu, Mengxia
   He, Ke
TI Research on high-efficiency optimization algorithm applied to near-field
   effect error correction
SO INTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING
LA English
DT Article
DE BP-GA; hybrid algorithm; neural networks; PSO; PSO-SVM
ID PARTICLE SWARM OPTIMIZATION; HYBRID
AB In order to achieve a more efficient and accurate correction of near-field error in the semiphysical radio frequency simulation system, the precise control parameters of the three antenna elements need to be obtained. This article is based on the method of moments electromagnetic simulation, and propose corresponding improvement ideas for the problems of limited optimization accuracy and low calculation efficiency in the near-field error correction process. From the aspects of high-precision intelligent inversion algorithm and high-efficiency electromagnetic forward modeling, systematic optimization design and verification were carried out. The results prove that the control parameter filtering scheme based on PSO-GA hybrid method has better optimization efficiency and accuracy than single genetic algorithm or differential evolution algorithm, which can provide more ideal initial amplitude and phase parameters for the subsequent selection of electromagnetic simulation and forward verification. In order to solve the problem of time-consuming in the electromagnetic simulation, the multivariate vector forward model based on GA-BP network and PSO-SVM network are established, which can achieve high-precision positioning of synthetic vector target points. The neural network method has been proved to be feasible on the basis of the current sample size. The paper selects hybrid algorithms to improve the shortcomings of single algorithm and uses algorithms to optimize neural networks, thereby obtaining better optimization results and reducing the time-consuming of electromagnetic simulations, which can realize efficient correction of near-field error.
C1 [Zhang, Jia; Yu, Mengxia; He, Ke] Univ Elect Sci & Technol China, Sch Phys, Chengdu, Sichuan, Peoples R China.
C3 University of Electronic Science & Technology of China
RP Yu, MX (corresponding author), Univ Elect Sci & Technol China, Sch Phys, Chengdu, Sichuan, Peoples R China.
EM yumengxia@uestc.edu.cn
RI ripert, marion/HHN-5115-2022; Yu, Mengxia/AAD-9435-2021
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NR 21
TC 0
Z9 0
U1 2
U2 8
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1096-4290
EI 1099-047X
J9 INT J RF MICROW C E
JI Int. J. RF Microw. Comput-Aid. Eng.
PD DEC
PY 2022
VL 32
IS 12
DI 10.1002/mmce.23530
EA NOV 2022
PG 10
WC Computer Science, Interdisciplinary Applications; Engineering,
   Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA 7H4QP
UT WOS:000877594200001
DA 2024-09-05
ER
PT J
AU Zhen-Wu
AF Zhen-Wu
TI Application research of bel canto performance based on artificial
   intelligence technology
SO APPLIED MATHEMATICS AND NONLINEAR SCIENCES
LA English
DT Article; Early Access
DE artificial intelligence; bel canto; performance; science and technology
AB In the 21st century, with the rapid development of information processing technology, neurophysiology, non-linear dynamics, fuzzy mathematics and other disciplines, artificial intelligence (AI) has come to be widely used in many aspects. Considering AI music, music, art, AI and music media fuse together. For the perfect combination of modern technology and traditional art, a variety of technologies, including machine learning, machine perception, neural network, genetic algorithm, knowledge representation, knowledge system and so on, form a new category in AI. The advent of this technology has dramatically changed traditional music. Therefore, this paper applies AI technology to bel canto singing and combines it with AI technology, summarises the corresponding algorithm principle and analyses its development trend and characteristics with specific application cases, in order to better serve music.
C1 [Zhen-Wu] Xiamen Univ, Tan Kah Kee Coll, Zhangzhou 363105, Fujian, Peoples R China.
C3 Xiamen University
RP Zhen-Wu (corresponding author), Xiamen Univ, Tan Kah Kee Coll, Zhangzhou 363105, Fujian, Peoples R China.
EM Zhenwu1225@outlook.com
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NR 28
TC 0
Z9 0
U1 1
U2 8
PU WALTER DE GRUYTER GMBH
PI BERLIN
PA GENTHINER STRASSE 13, D-10785 BERLIN, GERMANY
EI 2444-8656
J9 APPL MATH NONLIN SCI
JI Appl. Math. Nonlinear Sci.
PD 2022 NOV 30
PY 2022
DI 10.2478/amns.2021.2.00255
EA NOV 2022
PG 12
WC Mathematics, Applied
WE Emerging Sources Citation Index (ESCI)
SC Mathematics
GA 7E2SQ
UT WOS:000901025000001
OA gold
DA 2024-09-05
ER
PT J
AU Anderson, BS
   Schueler, J
   Baum, M
   Wales, WJ
   Gupta, VK
AF Anderson, Brian S.
   Schueler, Jens
   Baum, Matthias
   Wales, William J.
   Gupta, Vishal K.
TI The Chicken or the Egg? Causal Inference in Entrepreneurial
   Orientation-Performance Research
SO ENTREPRENEURSHIP THEORY AND PRACTICE
LA English
DT Article
DE causality; endogeneity; research design; entrepreneurial orientation;
   firm performance
ID FIRM PERFORMANCE; STRATEGIC MANAGEMENT; MEDIATING ROLE; RESOURCE
   ORCHESTRATION; BUSINESS PERFORMANCE; MODERATING ROLE; ENDOGENEITY;
   CONSTRUCT; MARKET; RECOMMENDATIONS
AB While entrepreneurial orientation (EO) correlates with many organizational phenomena, we lack convincing evidence of causal relationships within EO's nomological network. We explore the challenges to establishing causal relationships with a systematic review of EO-performance research. We then use a simulation to illustrate how popular research designs in EO research limit our ability to make causal claims. We conclude by outlining the research design considerations to move from associational to causal EO-performance research. Our message is that while experiments may not be practical or feasible in many areas of organizational research, including EO, scholars can nevertheless move towards causal understanding.
C1 [Anderson, Brian S.] Univ Missouri Kansas City, Henry W Bloch Sch Management, 5108 Cherry St, Kansas City, MO 64110 USA.
   [Anderson, Brian S.] Univ Ghent, Ctr Entrepreneurship Res, Ghent, Belgium.
   [Schueler, Jens; Baum, Matthias] Unvers Kaiserslautern, Dept Law Business & Econ, Kaiserslautern, Germany.
   [Wales, William J.] SUNY Albany, Sch Business, Albany, NY 12222 USA.
   [Gupta, Vishal K.] Univ Alabama, Culverhouse Coll Business, Tuscaloosa, AL USA.
C3 University of Missouri System; University of Missouri Kansas City; Ghent
   University; State University of New York (SUNY) System; State University
   of New York (SUNY) Albany; University of Alabama System; University of
   Alabama Tuscaloosa
RP Anderson, BS (corresponding author), Univ Missouri Kansas City, Henry W Bloch Sch Management, 5108 Cherry St, Kansas City, MO 64110 USA.
EM andersonbri@umkc.edu
RI Wales, William/C-3837-2016; Anderson, Brian/E-6102-2017
OI Wales, William/0000-0001-6565-6439; Anderson, Brian/0000-0001-9749-4104;
   Schuler, Jens/0000-0002-8899-3718
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NR 107
TC 16
Z9 16
U1 12
U2 60
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 1042-2587
EI 1540-6520
J9 ENTREP THEORY PRACT
JI Entrep. Theory Pract.
PD NOV
PY 2022
VL 46
IS 6
BP 1569
EP 1596
AR 1042258720976368
DI 10.1177/1042258720976368
EA DEC 2020
PG 28
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 5D7FX
UT WOS:000631599500001
DA 2024-09-05
ER
PT J
AU Luechtefeld, T
   Bozada, T Jr
   Goel, R
   Wang, L
   Paller, CJ
AF Luechtefeld, Thomas
   Bozada Jr, Thomas
   Goel, Rahul
   Wang, Lin
   Paller, Channing J.
TI Applications for open access normalized synthesis in metastatic prostate
   cancer trials
SO FRONTIERS IN ARTIFICIAL INTELLIGENCE
LA English
DT Article
DE data curation; prostate cancer; biomarker; text mining; natural language
   processing; machine learning
ID ABIRATERONE ACETATE; SURVIVAL ANALYSIS; PLUS PREDNISONE; DOUBLE-BLIND;
   ENZALUTAMIDE; INHIBITORS
AB Recent metastatic castration-resistant prostate cancer (mCRPC) clinical trials have integrated homologous recombination and DNA repair deficiency (HRD/DRD) biomarkers into eligibility criteria and secondary objectives. These trials led to the approval of some PARP inhibitors for mCRPC with HRD/DRD indications. Unfortunately, biomarker-trial outcome data is only discovered by reviewing publications, a process that is error-prone, time-consuming, and laborious. While prostate cancer researchers have written systematic evidence reviews (SERs) on this topic, given the time involved from the last search to publication, an SER is often outdated even before publication. The difficulty in reusing previous review data has resulted in multiple reviews of the same trials. Thus, it will be useful to create a normalized evidence base from recently published/presented biomarker-trial outcome data that one can quickly update. We present a new approach to semi-automating normalized, open-access data tables from published clinical trials of metastatic prostate cancer using a data curation and SER platform. and were used to collect mCRPC clinical trial publications with HRD/DRD biomarkers. We extracted data from 13 publications covering ten trials that started before 22nd Apr 2021. We extracted 585 hazard ratios, response rates, duration metrics, and 543 adverse events. Across 334 patients, we also extracted 8,180 patient-level survival and biomarker values. Data tables were populated with survival metrics, raw patient data, eligibility criteria, adverse events, and timelines. A repeated strong association between HRD and improved PARP inhibitor response was observed. Several use cases for the extracted data are demonstrated via analyses of trial methods, comparison of treatment hazard ratios, and association of treatments with adverse events. Machine learning models are also built on combined and normalized patient data to demonstrate automated discovery of therapy/biomarker relationships. Overall, we demonstrate the value of systematically extracted and normalized data. We have also made our code open-source with simple instructions on updating the analyses as new data becomes available, which anyone can use even with limited programming knowledge. Finally, while we present a novel method of SER for mCRPC trials, one can also implement such semi-automated methods in other clinical trial domains to advance precision medicine.
C1 [Luechtefeld, Thomas; Bozada Jr, Thomas] Insilica LLC, Bethesda, MD USA.
   [Wang, Lin] Johns Hopkins Univ, Dept Epidemiol, Bloomberg Sch Publ Hlth, Baltimore, MD USA.
   [Paller, Channing J.] Johns Hopkins Univ, Sidney Kimmel Comprehens Canc Ctr, Sch Med, Baltimore, MD 21205 USA.
C3 Johns Hopkins University; Johns Hopkins Bloomberg School of Public
   Health; Johns Hopkins University; Johns Hopkins Medicine
RP Paller, CJ (corresponding author), Johns Hopkins Univ, Sidney Kimmel Comprehens Canc Ctr, Sch Med, Baltimore, MD 21205 USA.
EM cpaller1@jhmi.edu
RI goel, rahul/KMY-8257-2024
OI Bozada, Thomas/0000-0002-6100-0384; Wang, Lin/0000-0003-2046-4366
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NR 47
TC 0
Z9 0
U1 2
U2 2
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 2624-8212
J9 FRONT ARTIF INTELL
JI Front. Artif. Intell.
PD SEP 12
PY 2022
VL 5
AR 984836
DI 10.3389/frai.2022.984836
PG 15
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA 8D1JO
UT WOS:000918055500001
PM 36171797
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU Zong, QJ
   Huang, ZH
   Huang, JR
AF Zong, Qianjin
   Huang, Zhihong
   Huang, Jiaru
TI Can open access increase LIS research's policy impact? Using regression
   analysis and causal inference
SO SCIENTOMETRICS
LA English
DT Article
DE Policy impact; Open access; Evidence-based policy-making; Policy-makers;
   Policy citation counts; Unpaywall; Dimensions
ID HEALTH-POLICY; INFORMATION; GOVERNMENT; PUBLICATIONS; CITATIONS;
   JOURNALS; EFFICACY; DATABASE; LIBRARY; STATE
AB The relationship between open access and academic impact (usually measured as citations received from academic publications) has been extensively studied but remains a very controversial topic. However, the effect of open access on policy impact (measured as citations received from policy documents) is still unknown. The purpose of this study was to examine the effect of open access on the policy impact, which might initiate a new controversial topic. Research articles in the field of library and information science (LIS) were selected as the data sample (n = 48,884). Negative binomial regression models were used to examine the dataset. Furthermore, propensity score matching (PSM) analysis, a causal inference approach, was used to estimate the effect of open access on the policy impact based on a selected LIS journal (Scientometrics, n = 4019) that received the most policy citations among the LIS journals. Linear regression models, logit regression models, four other matching methods, open access status provided by different databases, and different sizes of data samples were used to check the robustness of the main results. This study revealed that open access had significant and positive effects on the policy impact.
C1 [Zong, Qianjin; Huang, Zhihong; Huang, Jiaru] South China Normal Univ, Sch Econ & Management, Guangzhou, Peoples R China.
C3 South China Normal University
RP Zong, QJ (corresponding author), South China Normal Univ, Sch Econ & Management, Guangzhou, Peoples R China.
EM zongqj@m.scnu.edu.cn
RI lan, xueyao/JZD-4201-2024; Zong, Qianjin/ABD-0454-2022
OI Zong, Qianjin/0000-0002-7517-8191; Huang, Zhihong/0000-0001-6679-2133
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NR 100
TC 4
Z9 4
U1 19
U2 80
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD AUG
PY 2023
VL 128
IS 8
BP 4825
EP 4854
DI 10.1007/s11192-023-04750-1
EA MAY 2023
PG 30
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA L4VQ8
UT WOS:000999292600007
DA 2024-09-05
ER
PT J
AU Hu, ZW
   Cui, JJ
   Lin, AEL
AF Hu, Zewen
   Cui, Jingjing
   Lin, Angela
TI Identifying potentially excellent publications using a citation-based
   machine learning approach
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
DE Machine learning; Artificial intelligence; Excellent papers; Highly
   cited papers; Sleeping beauty; Citation -based measures; Citation peak;
   Neural network; LightGBM; TabNet
ID HIGHLY CITED PAPERS; SLEEPING BEAUTIES; IMPACT; COUNTS; PREDICTION;
   PATTERNS; FEATURES; PROBE
AB Excellent research papers are vital to science and technology advances. Thus, the early identification of potentially excellent research papers and recognizing their value in science and technology is high on the research agenda. This study used a set of 5 static and 8 time-dependent citation features to explore six machine learning methods and identify the method with the best performance to identify potentially excellent papers. The study modelled Random Forest, LightGBM, Naive Bayes, Support Vector Machine, Neural Network, and TabNet to identify PEPs in the artificial intelligence field. The study defined highly cited papers using the threshold of the top 1% and top 5% and collected the data from the Web of Science (R). Bibliometric and citation data from 485,041 research articles, proceeding papers, and reviews published in AI between 1990 and 2010 were collected initially. The data was screened and processed, and the final dataset consists of 96,169 papers for the training and test sets. The findings suggest that the timedependent citation features are more important than the static features, and citation peak features are more significant than the citation features in identifying potentially excellent papers. The findings demonstrate the effect of threshold on machine learning outcomes (e.g., the top 1% and 5%); therefore, the study argues that the decision about threshold selection should be carefully made. LightGBM and Random Forest both performed with the given conditions and achieved the same score in accuracy and recall. Nevertheless, when comparing their performance in other indicators, such as F1 and cross-entropy loss, LightGBM performed better. The study concluded that LightGBM was the best-performing model for identifying potentially excellent papers. The papers identified the contributions and recommended future research.
C1 [Hu, Zewen; Cui, Jingjing] Nanjing Univ Informat Sci & Technol, Sch Management Sci & Engn, Nanjing 210044, Peoples R China.
   [Lin, Angela] Univ Sheffield, Informat Sch, Sheffield S10 2TN, England.
C3 Nanjing University of Information Science & Technology; University of
   Sheffield
RP Lin, AEL (corresponding author), Univ Sheffield, Informat Sch, Sheffield S10 2TN, England.
EM a.lin@sheffield.ac.uk
OI hu, ze wen/0000-0002-9518-7204
FU National Social Science Fund of China ? [20CTQ031]
FX This study was supported by the National Social Science Fund of China
   ?The Identification Method and Its Application in Iden-tifying the
   ?Hidden Treasures? from Massive Scientifical and Technical Literature?
   (Grant No. 20CTQ031) .
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PU ELSEVIER SCI LTD
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EI 1873-5371
J9 INFORM PROCESS MANAG
JI Inf. Process. Manage.
PD MAY
PY 2023
VL 60
IS 3
AR 103323
DI 10.1016/j.ipm.2023.103323
EA FEB 2023
PG 22
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 9V4SR
UT WOS:000948384700001
OA Green Accepted, hybrid
DA 2024-09-05
ER
PT J
AU Zhu, XD
   Turney, P
   Lemire, D
   Vellino, A
AF Zhu, Xiaodan
   Turney, Peter
   Lemire, Daniel
   Vellino, Andre
TI Measuring Academic Influence: Not All Citations Are Equal
SO JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY
LA English
DT Article
DE natural language processing
ID H-INDEX; FREQUENCY; ARTICLES; OUTCOMES
AB The importance of a research article is routinely measured by counting how many times it has been cited. However, treating all citations with equal weight ignores the wide variety of functions that citations perform. We want to automatically identify the subset of references in a bibliography that have a central academic influence on the citing paper. For this purpose, we examine the effectiveness of a variety of features for determining the academic influence of a citation. By asking authors to identify the key references in their own work, we created a data set in which citations were labeled according to their academic influence. Using automatic feature selection with supervised machine learning, we found a model for predicting academic influence that achieves good performance on this data set using only four features. The best features, among those we evaluated, were those based on the number of times a reference is mentioned in the body of a citing paper. The performance of these features inspired us to design an influence-primed h-index (the hip-index). Unlike the conventional h-index, it weights citations by how many times a reference is mentioned. According to our experiments, the hip-index is a better indicator of researcher performance than the conventional h-index.
C1 [Zhu, Xiaodan; Turney, Peter] Natl Res Council Canada, Ottawa, ON K1A 0R6, Canada.
   [Lemire, Daniel] Univ Quebec, TELUQ, Montreal, PQ H2S 3L5, Canada.
   [Vellino, Andre] Univ Ottawa, Sch Informat Studies, Ottawa, ON K1N 6N5, Canada.
C3 National Research Council Canada; University of Quebec; Universite
   TELUQ; University of Quebec Montreal; University of Ottawa
RP Zhu, XD (corresponding author), Natl Res Council Canada, 1200 Montreal Rd,Bldg M50, Ottawa, ON K1A 0R6, Canada.
EM Xiaodan.Zhu@nrc-cnrc.gc.ca; peter.turney@nrc-cnrc.gc.ca; lemire@acm.org;
   avellino@uottawa.ca
RI Turney, Peter/AAI-8278-2021; Lemire, Daniel/N-7632-2017; Vellino,
   Andre/E-8105-2017
OI Turney, Peter/0000-0003-0909-4085; Lemire, Daniel/0000-0003-3306-6922;
   Vellino, Andre/0000-0003-4304-2801
FU Natural Sciences and Engineering Research Council of Canada (NSERC)
   [26143]
FX We are grateful to the volunteers who identified key citations in their
   own work. Daniel Lemire acknowledges support from the Natural Sciences
   and Engineering Research Council of Canada (NSERC) with grant number
   26143. We thank M. Couture, V. Lariviere, and the anonymous reviewers
   for their helpful comments.
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NR 79
TC 155
Z9 170
U1 10
U2 118
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 2330-1635
EI 2330-1643
J9 J ASSOC INF SCI TECH
PD FEB
PY 2015
VL 66
IS 2
BP 408
EP 427
DI 10.1002/asi.23179
PG 20
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA CA2HR
UT WOS:000348730000013
OA Green Accepted, Green Submitted
DA 2024-09-05
ER
PT J
AU Ibáñez, A
   Armañanzas, R
   Bielza, C
   Larrañaga, P
AF Ibanez, Alfonso
   Armananzas, Ruben
   Bielza, Concha
   Larranaga, Pedro
TI Genetic algorithms and Gaussian Bayesian networks to uncover the
   predictive core set of bibliometric indices
SO JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY
LA English
DT Article
DE machine learning; predictive models; bibliometrics
ID H-INDEX; INDICATORS; MODEL; OPTIMIZATION
AB The diversity of bibliometric indices today poses the challenge of exploiting the relationships among them. Our research uncovers the best core set of relevant indices for predicting other bibliometric indices. An added difficulty is to select the role of each variable, that is, which bibliometric indices are predictive variables and which are response variables. This results in a novel multioutput regression problem where the role of each variable (predictor or response) is unknown beforehand. We use Gaussian Bayesian networks to solve the this problem and discover multivariate relationships among bibliometric indices. These networks are learnt by a genetic algorithm that looks for the optimal models that best predict bibliometric data. Results show that the optimal induced Gaussian Bayesian networks corroborate previous relationships between several indices, but also suggest new, previously unreported interactions. An extended analysis of the best model illustrates that a set of 12 bibliometric indices can be accurately predicted using only a smaller predictive core subset composed of citations, g-index, q(2)-index, and h(r)-index. This research is performed using bibliometric data on Spanish full professors associated with the computer science area.
C1 [Ibanez, Alfonso; Bielza, Concha; Larranaga, Pedro] Univ Politecn Madrid, Computat Intelligence Grp, Dept Inteligencia Artificial, Campus Montegancedo S-N, Boadilla Del Monte 28660, Spain.
   [Armananzas, Ruben] George Mason Univ, Krasnow Inst Adv Study, 4400 Univ Dr, Fairfax, VA 22030 USA.
C3 Universidad Politecnica de Madrid; George Mason University
RP Ibáñez, A (corresponding author), Univ Politecn Madrid, Computat Intelligence Grp, Dept Inteligencia Artificial, Campus Montegancedo S-N, Boadilla Del Monte 28660, Spain.
EM fonsoim@gmail.com; rarmanan@gmu.edu; mcbielza@fi.upm.es;
   plarranaga@fi.upm.es
RI Armañanzas, Rubén/O-7403-2019; Bielza, Concha/F-9277-2013; Ibáñez,
   Alfonso/B-3423-2010; Larranaga, Pedro/F-9293-2013
OI Armañanzas, Rubén/0000-0003-4049-0000; Bielza,
   Concha/0000-0001-7109-2668; Larranaga, Pedro/0000-0003-0652-9872
FU Spanish Ministry of Economy and Competitiveness [TIN2013-41592-P]; Cajal
   Blue Brain Project (Spanish partner of the Blue Brain Project initiative
   from EPFL); National Institutes of Health (NINDS) [R01 NS39600]
FX Research partially supported by the Spanish Ministry of Economy and
   Competitiveness (grant no. TIN2013-41592-P) and the Cajal Blue Brain
   Project (Spanish partner of the Blue Brain Project initiative from
   EPFL). R.A. is currently supported by grant R01 NS39600 from the
   National Institutes of Health (NINDS).
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NR 89
TC 7
Z9 7
U1 3
U2 47
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 2330-1635
EI 2330-1643
J9 J ASSOC INF SCI TECH
PD JUL
PY 2016
VL 67
IS 7
BP 1703
EP 1721
DI 10.1002/asi.23467
PG 19
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA DP6YB
UT WOS:000378644700011
OA Green Accepted
DA 2024-09-05
ER
PT C
AU Tkaczyk, D
   Collins, A
   Sheridan, P
   Beel, J
AF Tkaczyk, Dominika
   Collins, Andrew
   Sheridan, Paraic
   Beel, Joeran
GP Assoc Comp Machinery
TI Machine Learning vs. Rules and Out-of-the-Box vs. Retrained: An
   Evaluation of Open-Source Bibliographic Reference and Citation Parsers
SO JCDL'18: PROCEEDINGS OF THE 18TH ACM/IEEE JOINT CONFERENCE ON DIGITAL
   LIBRARIES
SE ACM-IEEE Joint Conference on Digital Libraries JCDL
LA English
DT Proceedings Paper
CT 18th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL)
CY JUN 03-07, 2018
CL Univ N Texas Coll Informat, Fort Worth, TX
HO Univ N Texas Coll Informat
DE bibliographic reference parsing; citation parsing; machine learning;
   sequence tagging
ID METADATA; METHODOLOGY; EXTRACTION
AB Bibliographic reference parsing refers to extracting machine-readable metadata, such as the names of the authors, the title, or journal name, from bibliographic reference strings. Many approaches to this problem have been proposed so far, including regular expressions, knowledge bases and supervised machine learning. Many open source reference parsers based on various algorithms are also available. In this paper, we apply, evaluate and compare ten reference parsing tools in a specific business use case. The tools are Anystyle-Parser, Biblio, CERMINE, Citation, Citation-Parser, GROBID, ParsCit, PDFSSA4MET, Reference Tagger and Science Parse, and we compare them in both their out-of-the-box versions and versions tuned to the project-specific data. According to our evaluation, the best performing out-of-the-box tool is GROBID (F1 0.89), followed by CERMINE (F1 0.83) and ParsCit (F1 0.75). We also found that even though machine learning-based tools and tools based on rules or regular expressions achieve on average similar precision (0.77 for ML-based tools vs. 0.76 for non-ML-based tools), applying machine learning-based tools results in a recall three times higher than in the case of non-ML-based tools (0.66 vs. 0.22). Our study also confirms that tuning the models to the task-specific data results in the increase in the quality. The retrained versions of reference parsers are in all cases better than their out-of-the-box counterparts; for GROBID F1 increased by 3% 0.92 vs. 0.89), for CERMINE by 11% (0.92 vs. 0.83), and for ParsCit by 16% (0.87 vs. 0.75).
C1 [Tkaczyk, Dominika; Collins, Andrew; Sheridan, Paraic; Beel, Joeran] Trinity Coll Dublin, ADAPT Ctr, Sch Comp Sci & Stat, Dublin, Ireland.
C3 Trinity College Dublin
RP Tkaczyk, D (corresponding author), Trinity Coll Dublin, ADAPT Ctr, Sch Comp Sci & Stat, Dublin, Ireland.
EM Dominika.Tkaczyk@adaptcentre.ie; Andrew.Collins@adaptcentre.ie;
   Paraic.Sheridan@adaptcentre.ie; Joeran.Beel@adaptcentre.ie
OI Beel, Joeran/0000-0002-4537-5573
FU Science Foundation Ireland (SFI) [13/RC/2016]; European Union [713567]
FX This publication has emanated from research conducted with the financial
   support of Science Foundation Ireland (SFI) under Grant Number
   13/RC/2016. The project has also received funding from the European
   Union's Horizon 2020 research and innovation programme under the Marie
   Sklodowska-Curie grant agreement No 713567.
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NR 33
TC 27
Z9 28
U1 0
U2 6
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA
SN 2575-7865
EI 2575-8152
BN 978-1-4503-5178-2
J9 ACM-IEEE J CONF DIG
PY 2018
BP 99
EP 108
DI 10.1145/3197026.3197048
PG 10
WC Computer Science, Information Systems; Computer Science,
   Interdisciplinary Applications; Information Science & Library Science
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Information Science & Library Science
GA BL9RX
UT WOS:000458178700017
DA 2024-09-05
ER
PT J
AU Blank, S
   Mason, C
   Steinicke, F
   Herzog, C
AF Blank, Sabrina
   Mason, Celeste
   Steinicke, Frank
   Herzog, Christian
TI Tailoring responsible research and innovation to the translational
   context: the case of AI-supported exergaming
SO ETHICS AND INFORMATION TECHNOLOGY
LA English
DT Article
DE Responsible research and innovation; Industry; Trustworthiness; Medical
   artificial intelligence; Interdisciplinary collaboration
ID DILEMMA
AB We discuss the implementation of Responsible Research and Innovation (RRI) within a project for the development of an AI-supported exergame for assisted movement training, outline outcomes and reflect on methodological opportunities and limitations. We adopted the responsibility-by-design (RbD) standard (CEN CWA 17796:2021) supplemented by methods for collaborative, ethical reflection to foster and support a shift towards a culture of trustworthiness inherent to the entire development process. An embedded ethicist organised the procedure to instantiate a collaborative learning effort and implement RRI in a translational context. Within the interdisciplinary setting of the collaboration and with the support of a technoethicist, we successfully identified relevant, project-specific challenges and developed a roadmap with derived actions, thus meaningfully integrating RRI into the development process. We discuss the methodological procedure in terms of its effectiveness and efficiency, the allocation of responsibilities and roles, particularly regarding potential frictions in the interdisciplinary context with embedded ethics, and the challenges of the translational context. We conclude that the responsibility-by-design standard effectively established a productive workflow for collaborative investigation and work on ethical challenges. We reflect on methodological difficulties and propose possible avenues to our approach.
C1 [Blank, Sabrina; Herzog, Christian] Univ Lubeck, Eth Innovat Hub, Ratzeburger Allee 160, D-23562 Lubeck, Germany.
   [Mason, Celeste; Steinicke, Frank] Univ Hamburg, Dept Informat, Vogt Kolln Str 30, D-22527 Hamburg, Germany.
C3 University of Lubeck; University of Hamburg
RP Blank, S (corresponding author), Univ Lubeck, Eth Innovat Hub, Ratzeburger Allee 160, D-23562 Lubeck, Germany.
EM sabrina.blank@uni-luebeck.de; celeste.mason@uni-hamburg.de;
   frank.steinicke@uni-hamburg.de; christian.herzog@uni-luebeck.de
RI Steinicke, Frank/AAC-2976-2020
OI Steinicke, Frank/0000-0001-9879-7414; Herzog,
   Christian/0000-0003-2513-2563
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NR 44
TC 0
Z9 0
U1 4
U2 4
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1388-1957
EI 1572-8439
J9 ETHICS INF TECHNOL
JI Ethics Inf. Technol.
PD JUN
PY 2024
VL 26
IS 2
AR 22
DI 10.1007/s10676-024-09753-x
PG 16
WC Ethics; Information Science & Library Science; Philosophy
WE Social Science Citation Index (SSCI); Arts &amp; Humanities Citation Index (A&amp;HCI)
SC Social Sciences - Other Topics; Information Science & Library Science;
   Philosophy
GA MB9W2
UT WOS:001191294700001
OA hybrid
DA 2024-09-05
ER
PT J
AU Arora, M
   Gupta, J
   Mittal, A
   Prakash, A
AF Arora, Meenal
   Gupta, Jaya
   Mittal, Amit
   Prakash, Anshika
TI Achieving sustainable development goals (SDGs) through corporate
   sustainability: a topic modeling-based bibliometric analysis approach
SO KYBERNETES
LA English
DT Article; Early Access
DE Bibliometric analysis; Corporate sustainability; VOSviewer; Latent
   Dirichlet allocation; Sustainable development goals
ID SOCIAL-RESPONSIBILITY; PERSPECTIVE; CHALLENGES; STRATEGIES; SYSTEMS;
   TRENDS; WASTE
AB PurposeConsidering the swift adoption of innovative sustainability practices in businesses to accomplish sustainable development goals (SDGs), research on corporate sustainability has increased significantly over the years. This research intends to analyze the published literature, emphasizing the existing, emerging and future research directions on achieving the SDGs through corporate sustainability.Design/methodology/approachThis research analyzed the growing trends in corporate sustainability by incorporating 2,038 Scopus articles published between 1999 and 2022 using latent Dirichlet allocation (LDA) topic modeling, bibliometrics and qualitative content analysis techniques. The bibliometric data were analyzed using performance and science mapping. Thereafter, topic modeling and content analysis uncovered the topics included under the corporate sustainability umbrella.FindingsThe findings indicate that investigation into corporate sustainability has considerably increased from 2015 to date. Additionally, the majority of studies on corporate sustainability are from the United States of America, the United Kingdom and Germany. Besides, the USA has the most collaboration in terms of co-authorship. S. Schaltegger was considered the most productive author. However, P. Bansal was ranked as the top author based on a co-citation analysis of authors. Further, bibliometric data were evaluated to analyze leading publications, journals and institutions. Besides, keyword co-occurrence analysis, topic modeling and content analysis highlighted the theoretical underpinnings and new patterns and provided directions for further research.Originality/valueThis study demonstrates various existing and emerging themes in corporate sustainability, which have various repercussions for academicians and organizations. This research also examines the lagging themes in the current domain.
C1 [Arora, Meenal; Mittal, Amit] Chitkara Univ, Chitkara Business Sch, Rajpura, India.
   [Gupta, Jaya] New Delhi Inst Management, New Delhi, India.
   [Prakash, Anshika] KR Mangalam Univ, Sch Management & Commerce, Gurugram, India.
C3 Chitkara University, Punjab
RP Arora, M (corresponding author), Chitkara Univ, Chitkara Business Sch, Rajpura, India.
EM meenal.bajaj20@gmail.com
RI MITTAL, AMIT/AAD-2112-2019; Arora, Meenal/ADH-7267-2022
OI MITTAL, AMIT/0000-0002-1191-4620; Arora, Meenal/0000-0001-7670-6948
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NR 104
TC 1
Z9 1
U1 11
U2 11
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 0368-492X
EI 1758-7883
J9 KYBERNETES
JI Kybernetes
PD 2024 MAR 7
PY 2024
DI 10.1108/K-09-2023-1802
EA MAR 2024
PG 27
WC Computer Science, Cybernetics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA KG0P2
UT WOS:001178689200001
DA 2024-09-05
ER
PT J
AU Bara, A
   Oprea, SV
AF Bara, Adela
   Oprea, Simona-Vasilica
TI The Impact of Academic Publications over the Last Decade on Historical
   Bitcoin Prices Using Generative Models
SO JOURNAL OF THEORETICAL AND APPLIED ELECTRONIC COMMERCE RESEARCH
LA English
DT Article
DE cryptocurrency; research publication; topic modelling; latent Dirichlet
   allocation; sentiment analysis; Bitcoin prices
ID BLOCKCHAIN; SENTIMENT; MARKET
AB Since 2012, researchers have explored various factors influencing Bitcoin prices. Up until the end of July 2023, more than 9100 research papers on cryptocurrencies were published and indexed in the Web of Science Clarivate platform. The objective of this paper is to analyze the impact of publications on Bitcoin prices. This study aims to uncover significant themes within these research articles, focusing on cryptocurrencies in general and Bitcoin specifically. The research employs latent Dirichlet allocation to identify key topics from the unstructured abstracts. To determine the optimal number of topics, perplexity and topic coherence metrics are calculated. Additionally, the abstracts are processed using BERT-transformers and Word2Vec and their potential to predict Bitcoin prices is assessed. Based on the results, while the research helps in understanding cryptocurrencies, the potential of academic publications to influence Bitcoin prices is not significant, demonstrating a weak connection. In other words, the movements of Bitcoin prices are not influenced by the scientific writing in this specific field. The primary topics emerging from the analysis are the blockchain, market dynamics, transactions, pricing trends, network security, and the mining process. These findings suggest that future research should pay closer attention to issues like the energy demands and environmental impacts of mining, anti-money laundering measures, and behavioral aspects related to cryptocurrencies.
C1 [Bara, Adela] Acad Romanian Scientists, Ilfov 3, Bucharest 050044, Romania.
   [Oprea, Simona-Vasilica] Bucharest Univ Econ Studies, Dept Econ Informat & Cybernet, 6 Piata Romana, Bucharest 010374, Romania.
C3 Romanian Academy of Sciences; Bucharest University of Economic Studies
RP Oprea, SV (corresponding author), Bucharest Univ Econ Studies, Dept Econ Informat & Cybernet, 6 Piata Romana, Bucharest 010374, Romania.
EM bara.adela@ie.ase.ro; simona.oprea@csie.ase.ro
OI Oprea, Simona Vasilica/0000-0002-9005-5181; Bara,
   Adela/0000-0002-0961-352X
FU Academy of Romanian Scientists
FX This paper was supported by Academy of Romanian Scientists, Ilfov 3,
   050044 Bucharest, Romania, project title: "Solutii informatice pentru
   analiza impactului retelelor de social media asupra instrumentelor
   investitionale cu grad ridicat de risc: cryptomonede si bursa".
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NR 77
TC 1
Z9 1
U1 8
U2 8
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
SN 0718-1876
J9 J THEOR APPL EL COMM
JI J. Theor. Appl. Electron. Commer. Res.
PD MAR
PY 2024
VL 19
IS 1
BP 538
EP 560
DI 10.3390/jtaer19010029
PG 23
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA MG3Y9
UT WOS:001192446400001
OA gold
DA 2024-09-05
ER
PT C
AU Woo, SH
   Choi, MS
   Duffy, VG
AF Woo, Seung Ho
   Choi, Min Soo
   Duffy, Vincent G.
BE Duffy, VG
   Kromker, H
   Streitz, NA
   Konomi, S
TI Artificial Intelligence and Transportations on Road Safety: A
   Bibliometric Review
SO HCI INTERNATIONAL 2023 LATE BREAKING PAPERS, HCII 2023,PT IV
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT 25th International Conference on Human-Computer Interaction (HCI
   International)
CY JUL 23-28, 2023
CL Copenhagen, DENMARK
DE Artificial Intelligence; machine learning; road safety; transportation;
   human-computer interaction
ID DRIVING SAFETY; SYSTEMS
AB The topic of road safety modeling by applying artificial intelligence has been aroused in the research field. The purpose of this study was to explore artificial intelligence enhancing road safety using bibliometric analyses. The data sources were collected from three databases: Scopus, ProQuest, and Web of Science. Numerous analysis tools were applied to visualize the trends and get meaningful outcomes, such as MaxQDA, Vicinitas, Scopus, etc. The measures of analysis were shown in five individual analysis results which include content, co-citation, keyword, trend, and statistical analysis. Statistical analysis was performed by ANOVA to distinguish the significant predictors in publication yields with interpretation. The recent trend in artificial intelligence and road safety has increased in the field of research. All analysis and findings are shown in the analysis section. We briefly mention the future work area ideas in various aspects of the study.
C1 [Woo, Seung Ho; Choi, Min Soo; Duffy, Vincent G.] Purdue Univ, Sch Ind Engn, W Lafayette, IN 47906 USA.
C3 Purdue University System; Purdue University
RP Choi, MS (corresponding author), Purdue Univ, Sch Ind Engn, W Lafayette, IN 47906 USA.
EM woo44@purdue.edu; choi502@purdue.edu; duffy@purdue.edu
OI Woo, Seung Ho/0000-0001-9839-8616
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NR 41
TC 0
Z9 0
U1 5
U2 5
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 0302-9743
EI 1611-3349
BN 978-3-031-48046-1; 978-3-031-48047-8
J9 LECT NOTES COMPUT SC
PY 2023
VL 14057
BP 450
EP 464
DI 10.1007/978-3-031-48047-8_30
PG 15
WC Computer Science, Artificial Intelligence; Computer Science,
   Cybernetics; Computer Science, Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BW5EV
UT WOS:001159620100030
DA 2024-09-05
ER
PT J
AU Jia, K
   Wang, PH
   Li, Y
   Chen, ZZ
   Jiang, XY
   Lin, CL
   Chin, T
AF Jia, Kan
   Wang, Penghui
   Li, Yang
   Chen, Zezhou
   Jiang, Xinyue
   Lin, Chien-Liang
   Chin, Tachia
TI Research Landscape of Artificial Intelligence and e-Learning: A
   Bibliometric Research
SO FRONTIERS IN PSYCHOLOGY
LA English
DT Article
DE artificial intelligence; online learning; technological education;
   bibliometrics research; Web of Science Publications
ID ONLINE TEACHER; SYSTEM; STYLE; IMPLEMENTATION; ENVIRONMENTS; PRECISION;
   ANALYTICS; PATTERNS; MODEL; TOOL
AB While an increasing number of organizations have introduced artificial intelligence as an important facilitating tool for learning online, the application of artificial intelligence in e-learning has become a hot topic for research in recent years. Over the past few decades, the importance of online learning has also been a concern in many fields, such as technological education, STEAM, AR/VR apps, online learning, amongst others. To effectively explore research trends in this area, the current state of online learning should be understood. Systematic bibliometric analysis can address this problem by providing information on publishing trends and their relevance in various topics. In this study, the literary application of artificial intelligence combined with online learning from 2010 to 2021 was analyzed. In total, 64 articles were collected to analyze the most productive countries, universities, authors, journals and publications in the field of artificial intelligence combined with online learning using VOSviewer through WOS data collection. In addition, the mapping of co-citation and co-occurrence was explored by analyzing a knowledge map. The main objective of this study is to provide an overview of the trends and pathways in artificial intelligence and online learning to help researchers understand global trends and future research directions.
C1 [Jia, Kan; Wang, Penghui; Chin, Tachia] Zhejiang Univ Technol, Sch Management, Hangzhou, Peoples R China.
   [Li, Yang] Commun Univ Zhejiang, Sch Cultural Creat & Management, Hangzhou, Peoples R China.
   [Chen, Zezhou; Jiang, Xinyue] Zhejiang Univ Technol, Sch Econ, Hangzhou, Peoples R China.
   [Lin, Chien-Liang] Ningbo Univ, Coll Sci & Technol, Ningbo, Peoples R China.
C3 Zhejiang University of Technology; Communication University of Zhejiang;
   Zhejiang University of Technology; Ningbo University
RP Li, Y (corresponding author), Commun Univ Zhejiang, Sch Cultural Creat & Management, Hangzhou, Peoples R China.; Lin, CL (corresponding author), Ningbo Univ, Coll Sci & Technol, Ningbo, Peoples R China.
EM 20040096@cuz.edu.com; linjianliang@nbu.edu.cn
FU First Batch of Industry University Collaborative Education Project of
   the Ministry of Education-"Social Practice Training Camp Plan Based on
   Science and Technology Innovation and Entrepreneurship Projects"
   [202002143051]; National Social Science Late Funding Project of China
   [20FXWB020]; KC Wong Magna Fund in Ningbo University [RC190015]; China
   Postdoctoral Science Foundation [2016M60283]
FX This research was supported by the First Batch of Industry University
   Collaborative Education Project of the Ministry of Education--"Social
   Practice Training Camp Plan Based on Science and Technology Innovation
   and Entrepreneurship Projects" (Grant No. 202002143051), the National
   Social Science Late Funding Project of China (Grant No. 20FXWB020), KC
   Wong Magna Fund in Ningbo University (Grant No. RC190015), and the China
   Postdoctoral Science Foundation (Grant No. 2016M60283).
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NR 99
TC 14
Z9 14
U1 11
U2 111
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 1664-1078
J9 FRONT PSYCHOL
JI Front. Psychol.
PD FEB 16
PY 2022
VL 13
AR 795039
DI 10.3389/fpsyg.2022.795039
PG 14
WC Psychology, Multidisciplinary
WE Social Science Citation Index (SSCI)
SC Psychology
GA ZW0OS
UT WOS:000770921500001
PM 35250730
OA Green Published, gold
DA 2024-09-05
ER
PT J
AU Huang, SZ
   Huang, Y
   Bu, Y
   Luo, ZR
   Lu, W
AF Huang, Shengzhi
   Huang, Yong
   Bu, Yi
   Luo, Zhuoran
   Lu, Wei
TI Disclosing the interactive mechanism behind scientists' topic selection
   behavior from the perspective of the productivity and the impact
SO JOURNAL OF INFORMETRICS
LA English
DT Article
DE Scientometrics; Topic selection behavior; Random walk; Reinforcement
   learning
ID RESEARCH PERFORMANCE
AB The productivity and the impact are two most recognized aspects to evaluate the research per-formance of scientists. Figuring out whether and how these two factors shape the evolution of scientists' research interests may facilitate researchers to go deep into scientists' topic selection behavior. In this paper, we employ Microsoft Academic Graph as our data source, and propose two correlation metrics, by which over 20,000 scientists' publication sequence from the com-puter science field are analyzed. We confirm that the productivity and the impact are related to the evolution of scientists' research interests, and scientists tend to select topics which help them produce the productivity and the impact. To further explore how these two factors affects topic selection behavior, we propose a novel Q seashore walk model based on the interactive mech-anism hypothesis. Our analysis results based on the simulation data are consistent with those based on the empirical data, which confirms the validity of our model and reports the evidence for the interactive mechanism. Based on the simulation data, we also analyze the role of reward for scientists' research performance, and find that "too much is as bad as too little ". This research may help researchers deeply understand the process of topic selection, and provide a theoretical basis for research and development policy formulation.
C1 [Huang, Shengzhi; Huang, Yong; Luo, Zhuoran; Lu, Wei] Wuhan Univ, Sch Informat Management, Wuhan, Hubei, Peoples R China.
   [Huang, Shengzhi; Huang, Yong; Luo, Zhuoran; Lu, Wei] Wuhan Univ, Informat Retrieval & Knowledge Min Lab, Wuhan, Hubei, Peoples R China.
   [Bu, Yi] Peking Univ, Dept Informat Management, Beijing, Peoples R China.
C3 Wuhan University; Wuhan University; Peking University
RP Lu, W (corresponding author), Wuhan Univ, Sch Informat Management, Wuhan, Hubei, Peoples R China.
EM weilu@whu.edu.cn
RI Bu, Yi/B-4964-2018
OI Bu, Yi/0000-0003-2549-4580; Luo, Zhuoran/0000-0003-0677-8350; lu,
   wei/0000-0002-0929-7416; Huang, Shengzhi/0000-0002-7035-4627
FU Youth Science Foundation of the National Natural Science Foundation of
   China [72004168]
FX Acknowledgments This work was supported by the Youth Science Foundation
   of the National Natural Science Foundation of China (grant no. 72004168)
   .
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NR 41
TC 3
Z9 3
U1 21
U2 65
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1751-1577
EI 1875-5879
J9 J INFORMETR
JI J. Informetr.
PD MAY
PY 2023
VL 17
IS 2
AR 101409
DI 10.1016/j.joi.2023.101409
EA APR 2023
PG 15
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA G5SA0
UT WOS:000989740500001
DA 2024-09-05
ER
PT J
AU Smith, JG
   Tissing, R
AF Smith, Justin G.
   Tissing, Reid
TI Using Computational Text Classification for Qualitative Research and
   Evaluation in Extension
SO JOURNAL OF EXTENSION
LA English
DT Article
DE qualitative research; natural language processing; machine learning;
   text classification
AB This article introduces a process for computational text classification that can be used in a variety of qualitative research and evaluation settings. The process leverages supervised machine learning based on an implementation of a multinomial Bayesian classifier. Applied to a community of inquiry framework, the algorithm was used to identify evidence of cognitive presence, social presence, and teaching presence in the text contributions (44,000 unique posts) of more than 4,000 participants in an online environmental education course. Results indicate that computational text classification can significantly reduce labor costs and can help Extension research faculty scale, accelerate, and ensure reproducibility of their research.
C1 [Smith, Justin G.; Tissing, Reid] Washington State Univ, Shelton, WA 98584 USA.
C3 Washington State University
RP Smith, JG (corresponding author), Washington State Univ, Shelton, WA 98584 USA.
EM justingriffis@wsu.edu; reid.tissing@wsu.edu
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PU UNIV OF WISCONSIN EXTENSION JOURNAL INC
PI MADISON
PA 605 EXTENSION BLDG 432 NORTH LAKE ST, MADISON, WI 53706 USA
SN 0022-0140
EI 1077-5315
J9 J EXT
JI J. Ext.
PD APR
PY 2018
VL 56
IS 2
AR 2TOT2
PG 7
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA GQ0LA
UT WOS:000441309200024
DA 2024-09-05
ER
PT J
AU Chen, YL
   Chen, XH
AF Chen, Yen-Liang
   Chen, Xiang-Han
TI An evolutionary PageRank approach for journal ranking with expert
   judgements
SO JOURNAL OF INFORMATION SCIENCE
LA English
DT Article
DE citation-based approach; experts' survey method; journal ranking;
   PageRank; Particle Swarm Optimization
ID PARTICLE SWARM OPTIMIZATION; IMPACT; MANAGEMENT; ALGORITHM; QUALITY;
   MIS; CITATIONS; SINGLE; FORUMS; MODEL
AB The journal ranking problem has drawn a great deal of attention from researchers in various fields due to its importance in the evaluation of academic performance. Most previous studies solved the problem with either a subjective approach, based on expert survey metrics, or an objective approach, based on citation-based metrics. Since both have their own advantages and disadvantages, and since they are usually complementary, this work proposes a brand new approach that integrates the two. In this work, we propose the Evolutionary PageRank algorithm, which first uses the PageRank algorithm to evaluate journal prestige and then uses the Multi-Objective Particle Swarm Optimization to balance citation analysis and expert opinion. Experiments evaluating ranking quality were carried out with citation records and experts' surveys to show the effectiveness of the proposed method. The results indicate that the proposed method can improve PageRank journal ranking results.
C1 [Chen, Yen-Liang; Chen, Xiang-Han] Natl Cent Univ, Dept Informat Management, Jung Li City 32001, Taoyuan County, Taiwan.
C3 National Central University
RP Chen, YL (corresponding author), Natl Cent Univ, Dept Informat Management, 300 Jung Da Rd, Jung Li City 32001, Taoyuan County, Taiwan.
EM ylchen@mgt.ncu.edu.tw
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NR 55
TC 11
Z9 12
U1 1
U2 30
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 0165-5515
EI 1741-6485
J9 J INF SCI
JI J. Inf. Sci.
PD JUN
PY 2011
VL 37
IS 3
BP 254
EP 272
DI 10.1177/0165551511402421
PG 19
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 775QC
UT WOS:000291476000004
DA 2024-09-05
ER
PT J
AU Warin, T
   Stojkov, A
AF Warin, Thierry
   Stojkov, Aleksandar
TI Discursive dynamics and local contexts on Twitter: The refugee crisis in
   Europe
SO DISCOURSE & COMMUNICATION
LA English
DT Article
DE Agenda-setting theory; authority; discourse analysis; influence; natural
   language processing; refugee crisis; social media; structural topic
   modeling
ID MEDIA BIAS; COVERAGE; NEWS
AB In today's hybrid media environment, traditional news organizations extend their presence on Online Social Networks (OSNs) and compete with political and civil society organizations, public figures, and other influential digital storytelling individuals. This article examines conversations on Twitter, one of the most widely used OSNs, about Europe's refugee crisis in 2014 and 2015. We use, in particular, topic modeling techniques to deduce the existence of a complex network of Twitter topics formed in response to coverage of and opinion formation surrounding the European refugee crisis. We collected more than 11 million tweets in six different languages. One of our most significant findings is that while most conversations happen in English, the refugee crisis has had different rhythms in other languages. Our assumption is that this could be evidence that the power of mainstream local media on Twitter to set the agenda is considerable, at least regarding refugee-related conversations in Europe.
C1 [Warin, Thierry] HEC Montreal, Montreal, PQ, Canada.
   [Stojkov, Aleksandar] Ss Cyril & Methodius Univ, Skopje, North Macedonia.
   [Warin, Thierry] Dept Int Business, 3000,chemin Cote Sainte Catherine, Montreal, PQ H3T2A7, Canada.
C3 Universite de Montreal; HEC Montreal; Saints Cyril & Methodius
   University of Skopje
RP Warin, T (corresponding author), Dept Int Business, 3000,chemin Cote Sainte Catherine, Montreal, PQ H3T2A7, Canada.
EM thierry.warin@hec.ca
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NR 43
TC 0
Z9 0
U1 3
U2 16
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 1750-4813
EI 1750-4821
J9 DISCOURSE COMMUN
JI Discourse Commun.
PD JUN
PY 2023
VL 17
IS 3
BP 354
EP 380
DI 10.1177/17504813231155739
EA MAR 2023
PG 27
WC Communication
WE Social Science Citation Index (SSCI)
SC Communication
GA AR8J0
UT WOS:000941813000001
OA hybrid
DA 2024-09-05
ER
PT J
AU Hou, JH
   Zheng, BL
   Wang, DY
   Zhang, Y
   Chen, CM
AF Hou, Jianhua
   Zheng, Bili
   Wang, Dongyi
   Zhang, Yang
   Chen, Chaomei
TI How Boundary-spanning Paper Sparkles Citation: From Citation Count to
   Citation Network
SO JOURNAL OF INFORMETRICS
LA English
DT Article
DE Boundary-spanning; Paper's role definition; Sentence-BERT; Knowledge
   diffusion; Citation network
ID PROPENSITY SCORE; IMPACT; CLASSIFICATION; INNOVATION; BEHAVIORS;
   KNOWLEDGE; DYNAMICS; ARTICLES; SEARCH; ROLES
AB Previous studies diverged on whether boundary-spanning papers are cited more heavily, how-ever, there is as yet no research that explains how boundary-spanning papers affect citation counts from a causal view and how they influence knowledge diffusion in citation network. To this end, we utilized Propensity Score Matching to clarify the relationship between the citation counts and the degree of boundary-spanning of 281,707 papers in the field of Astronomy (AS), Natural Language Processing (NLP), Library and Information Science (LIS) based on causal inference. We also adopted Sentence-BERT to compute the content similarity among seed papers, cited papers, citing papers, to imply how seed paper affects citation network when knowledge diffuses. According to the similarity between seed paper and citing paper, cited paper and citing paper, we defined paper's role in citation network as four types: Disseminator, Broker, Trigger, and Outlier. The major findings are as follows: (1) Papers with a higher degree of boundary-spanning are more likely to be cited heavily; (2) Disseminator and Outlier account for a larger proportion in three disciplines, while Broker and Trigger account for a smaller proportion; (3) The degree of boundary-spanning and citation counts of four types vary in three disciplines. This work, which reveals paper's value and role in citation network from the perspective of content, has implica-tions to provide some enlightenment for the paper's evaluation.
C1 [Hou, Jianhua; Zheng, Bili; Wang, Dongyi; Zhang, Yang] Sun Yat Sen Univ, Guangzhou Higher Educ Mega Ctr, Sch Informat Management, 132 Waihuan East Rd, Guangzhou 510006, Peoples R China.
   [Chen, Chaomei] Drexel Univ, Coll Comp & Informat, 3675 Market St, Philadelphia, PA 19104 USA.
C3 Sun Yat Sen University; Drexel University
RP Zhang, Y (corresponding author), Sun Yat Sen Univ, Guangzhou Higher Educ Mega Ctr, Sch Informat Management, 132 Waihuan East Rd, Guangzhou 510006, Peoples R China.
EM houjh5@mail.sysu.edu.cn; zhengbli@mail2.sysu.edu.cn;
   wangdy37@mail2.sysu.edu.cn; zhyang2@mail.sysu.edu.cn; cc345@drexel.edu
RI Chen, Chaomei/A-1252-2007; Hou, Jianhua/JQI-1081-2023
OI Chen, Chaomei/0000-0001-8584-1041; 
FU Natural Science Foundation of Guangdong Province [2021A1515012291]
FX This research was support by the Natural Science Foundation of Guangdong
   Province under Grant 2021A1515012291.
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NR 77
TC 4
Z9 4
U1 26
U2 69
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1751-1577
EI 1875-5879
J9 J INFORMETR
JI J. Informetr.
PD AUG
PY 2023
VL 17
IS 3
AR 101434
DI 10.1016/j.joi.2023.101434
EA JUL 2023
PG 19
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA Q6WJ8
UT WOS:001058905500001
DA 2024-09-05
ER
PT J
AU Gupta, BM
   Dhawan, SM
   Mamdapur, GMN
AF Gupta, B. M.
   Dhawan, S. M.
   Mamdapur, Ghouse Modin N.
TI Research trends in the field of natural language processing : A
   scientometric study based on global publications during 2001-2020
SO COLLNET JOURNAL OF SCIENTOMETRICS AND INFORMATION MANAGEMENT
LA English
DT Article
DE Natural language processing; Global publications; Scientometrics;
   Bibliometrics
AB The study provides a quantitative and qualitative description of global research in "Natural Language Processing" ( NLP) using bibliometric methods. The analysis is based on publications data sourced from Scopus database for the period 2001-2020. The purpose of the study is to understand the status of NLP research at the global, national, institutional, and author level. The study highlights the productivity and performance of NLP research on a series of metrics as well as provides a visual view of collaborative network relationship between authors, research institutions, and leading countries using standard software tools. In addition, the study identified the leading players in NLP research such as key countries, institutions, authors, and areas of research. According to the study, the USA leads in global publications output as well as it leads in terms of relative citation index.
C1 [Gupta, B. M.] CSIR NISTADS, Delhi 110012, India.
   [Dhawan, S. M.] CSIR NPL, Delhi 110012, India.
   [Mamdapur, Ghouse Modin N.] Synthite Ind P Ltd, New Prod Dev & Res, Kolenchery 682311, Kerala, India.
C3 Council of Scientific & Industrial Research (CSIR) - India; CSIR -
   National Institute of Science Communication & Policy Research (NIScPR);
   Council of Scientific & Industrial Research (CSIR) - India; CSIR -
   National Physical Laboratory (NPL)
RP Mamdapur, GMN (corresponding author), Synthite Ind P Ltd, New Prod Dev & Res, Kolenchery 682311, Kerala, India.
EM bmgupta1@gmail.com; smdhawan@yahoo.com; ghouse@synthite.com
OI Mamdapur, Ghouse Modin/0000-0003-4155-1987
CR Banerjee Dibyendu, 2020, NATURAL LANGUAGE PRO
   Chen XL, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10062157
   Chen XL, 2018, WIREL COMMUN MOB COM, DOI 10.1155/2018/1827074
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   Dhawan S. M., 2021, International Journal of Knowledge Content Development & Technology, V11, P29
   IBM Cloud Education, 2020, WHAT IS NAT LANG PRO
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   Reshamwala A., 2013, REV NATURAL LANGUAGE
   Rybkin Yuriy, 2020, NLP APPL BUSINESS
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NR 11
TC 0
Z9 0
U1 2
U2 4
PU TARU PUBLICATIONS
PI NEW DELHI
PA G-159, PUSHKAR ENCLAVE, PASHCHIM VIHAR, NEW DELHI, 110 063, INDIA
SN 0973-7766
EI 2168-930X
J9 COLLNET J SCIENTOMET
JI Collnet J. Scientometr. Inf. Manag.
PD JUN
PY 2023
VL 17
IS 1
BP 61
EP 79
DI 10.47974/CJSIM-2022-0023
PG 19
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA M8KC9
UT WOS:001032640000005
DA 2024-09-05
ER
PT J
AU Alnowaiser, K
AF Alnowaiser, Khaled
TI Scientific text citation analysis using CNN features and ensemble
   learning model
SO PLOS ONE
LA English
DT Article
ID BIBLIOMETRICS; CONTEXT; COUNTS; INDEX
AB Citation illustrates the link between citing and cited documents. Different aspects of achievements like the journal's impact factor, author's ranking, and peers' judgment are analyzed using citations. However, citations are given the same weight for determining these important metrics. However academics contend that not all citations can ever have equal weight. Predominantly, such rankings are based on quantitative measures and the qualitative aspect is completely ignored. For a fair evaluation, qualitative evaluation of citations is needed in addition to quantitative ones. Many existing works that use qualitative evaluation consider binary class and categorize citations as important or unimportant. This study considers multi-class tasks for citation sentiments on imbalanced data and presents a novel framework for sentiment analysis in in-text citations of research articles. In the proposed technique, features are retrieved using a convolutional neural network (CNN), and classification is performed using a voting classifier that combines Logistic Regression (LR) and Stochastic Gradient Descent (SGD). The class imbalance problem is handled by the synthetic minority oversampling technique (SMOTE). Extensive experiments are performed in comparison with the proposed approach using SMOTE-generated data and machine learning models by term frequency (TF), and term frequency-inverse document frequency (TF-IDF) to evaluate the efficacy of the proposed approach for citation analysis. It is found that the proposed voting classifier using CNN features achieves an accuracy, precision, recall, and F1 score of 0.99 for all. This work not only advances the field of sentiment analysis in academic citations but also underscores the importance of incorporating qualitative aspects in evaluating the impact and sentiments conveyed through citations.
C1 [Alnowaiser, Khaled] Prince Sattam Bin Abdulaziz Univ, Coll Comp Engn & Sci, Dept Comp Engn, Al Kharj, Saudi Arabia.
C3 Prince Sattam Bin Abdulaziz University
RP Alnowaiser, K (corresponding author), Prince Sattam Bin Abdulaziz Univ, Coll Comp Engn & Sci, Dept Comp Engn, Al Kharj, Saudi Arabia.
EM k.alnowaiser@psau.edu.sa
RI Alnowaiser, Khaled/GQY-5062-2022
OI Alnowaiser, Khaled/0009-0007-2717-6902
FU Prince Sattam bin Abdulaziz University [PSAU/2024/R/1445]
FX This study was supported by Prince Sattam bin Abdulaziz University in
   the form of a grant to KA [PSAU/2024/R/1445].
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NR 45
TC 0
Z9 0
U1 7
U2 7
PU PUBLIC LIBRARY SCIENCE
PI SAN FRANCISCO
PA 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA
SN 1932-6203
J9 PLOS ONE
JI PLoS One
PD MAY 28
PY 2024
VL 19
IS 5
AR e0302304
DI 10.1371/journal.pone.0302304
PG 19
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA SI9G2
UT WOS:001233936700036
PM 38805427
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU Lang, J
   Repp, H
AF Lang, Johannes
   Repp, Holger
TI Artificial intelligence in medical education and the meaning of
   interaction with natural intelligence - an interdisciplinary approach
SO GMS JOURNAL FOR MEDICAL EDUCATION
LA English
DT Article
DE artificial intelligence; interdisciplinary research; interdisciplinary
   learning; evaluation; teaching
AB Introduction: The practice of medicine is characterized by decision making in which digital techniques can provide good support. In this context, artificial intelligence (AI) is becoming increasingly important. The challenge for physicians, however, is to maintain an overview of the potential applications and usefulness of AI in order to be able to apply it efficiently and safely in their work. Therefore, appropriate skills must be imparted during the course of medical studies so that future practitioners can meet this requirement.
   Project description: The interdisciplinary research-related teaching and learning project "(Natural) Science and Technology in Medicine NWTmed" brings together students at the Justus-Liebig-University Giessen (JLU) from the fields of medicine and other (natural) scientific disciplines in structured courses with the aim of thinking, learning, and working in an interdisciplinary and research-oriented manner already during their medical education. With the involvement of local researchers, a "multi-disciplinary" seminar on the basic premises, methods, and applications of AI was established.
   Results: The participants of the course came from a wide variety of fields of study, which promoted an interdisciplinary exchange and animated discussions. A gain in knowledge and an increase in interest in the topic of AI was noted in the evaluations, and a willingness on the part of the students to pursue further independent study was also expressed.
   Discussion and conclusion: The topic of AI and its relevance to the field of medicine is not yet sufficiently represented in medical education. It will require integration in the curriculum and performance evaluations as well as interdisciplinary and research-related teaching formats.
C1 [Lang, Johannes; Repp, Holger] Justus Liebig Univ Giessen, Med Fac, Div Study & Teaching, Deans Off, Klin Str 29, D-35392 Giessen, Germany.
C3 Justus Liebig University Giessen
RP Lang, J (corresponding author), Justus Liebig Univ Giessen, Med Fac, Div Study & Teaching, Deans Off, Klin Str 29, D-35392 Giessen, Germany.
EM johannes.lang@dekanat.med.uni-giessen.de
FU central and decentralized QSL funds of the JLU Giessen; study structure
   program of the State of Hesse
FX The work is supported by central and decentralized QSL funds of the JLU
   Giessen as well as by funds from the study structure program of the
   State of Hesse.
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NR 8
TC 7
Z9 8
U1 1
U2 10
PU GERMAN MEDICAL SCIENCE-GMS
PI DUESSELDORF
PA UBIERSTRASSE 20, DUESSELDORF, 40223, GERMANY
SN 2366-5017
J9 GMS J MED EDU
JI GMS J. Med. Educ.
PY 2020
VL 37
IS 6
AR Doc59
DI 10.3205/zma001352
PG 3
WC Education, Scientific Disciplines
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA OU6XD
UT WOS:000591668800005
PM 33225051
DA 2024-09-05
ER
PT J
AU Ruiz-Sánchez, R
   Arencibia-Jorge, R
   Tagüeña, J
   Jiménez-Andrade, JL
   Carrillo-Calvet, H
AF Ruiz-Sanchez, Ricardo
   Arencibia-Jorge, Ricardo
   Taguena, Julia
   Jimenez-Andrade, Jose Luis
   Carrillo-Calvet, Humberto
TI Exploring research on ecotechnology through artificial intelligence and
   bibliometric maps
SO ENVIRONMENTAL SCIENCE AND ECOTECHNOLOGY
LA English
DT Article
DE Ecotechnologies; Bibliometrics; Sustainable development goals; Network
   analysis; SOM neural networks
ID WASTE-WATER TREATMENT; DEA ENVIRONMENTAL ASSESSMENT; CONSTRUCTED
   WETLANDS; TECHNOLOGY DEVELOPMENT; TREATMENT PERFORMANCES; CARBON;
   SUSTAINABILITY; EFFICIENCY; NIGERIA; RECOVERY
AB Ecotechnology, quintessential for crafting sustainable socio-environmental strategies, remains tantalizingly uncharted. Our analysis, steered by the nuances of machine learning and augmented by bibliometric insights, delineates the expansive terrain of this domain, elucidates pivotal research themes and conundrums, and discerns the vanguard nations in this field. Furthermore, we deftly connect our discoveries to the United Nations' 2030 Sustainable Development Goals, thereby accentuating the profound societal ramifications of ecotechnology. (c) 2024 The Authors. Published by Elsevier B.V. on behalf of Chinese Society for Environmental Sciences, Harbin Institute of Technology, Chinese Research Academy of Environmental Sciences. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
C1 [Ruiz-Sanchez, Ricardo] Inst Politecn Nacl, Unidad Profes Interdisciplinaria Ingn Palenque UPI, Palenque 29960, Chiapas, Mexico.
   [Ruiz-Sanchez, Ricardo; Arencibia-Jorge, Ricardo; Taguena, Julia; Jimenez-Andrade, Jose Luis; Carrillo-Calvet, Humberto] Natl Autonomous Univ Mex, Complex Sci Ctr, Circuito Ctr Cultural S-N, Coyoacan 04510, Mexico City, Mexico.
   [Taguena, Julia] Univ Nacl Autonoma Mexico, Inst Renewable Energies IER, Priv Xochicalco S-N, Temixco 62580, Morelos, Mexico.
   [Jimenez-Andrade, Jose Luis; Carrillo-Calvet, Humberto] Univ Nacl Autonoma Mexico, Fac Sci, Circuito Ctr Cultural S-N, Coyoacan 04510, Mexico City, Mexico.
C3 Universidad Nacional Autonoma de Mexico; Universidad Nacional Autonoma
   de Mexico
RP Arencibia-Jorge, R (corresponding author), Natl Autonomous Univ Mex, Complex Sci Ctr, Circuito Ctr Cultural S-N, Coyoacan 04510, Mexico City, Mexico.
EM ricardo.arencibia@c3.unam.mx
RI Carrillo Calvet, Humberto/E-2265-2012; Jiménez-Andrade, José-Luis Luis
   Jiménez/T-1666-2018; Arencibia-Jorge, Ricardo/B-1330-2016
OI Carrillo Calvet, Humberto/0000-0003-3659-6769; Jiménez-Andrade,
   José-Luis Luis Jiménez/0000-0003-3453-7159; Arencibia-Jorge,
   Ricardo/0000-0001-8907-2454
FU UNAM- DGAPA postdoctoral fellowship program;  [OPP-00534251STEAMINC]
FX This research is a result of the program "Scientometrics, Complexity,
   and Science of Science ", at the Complexity Science Center of the
   National Autonomous University of Mexico (UNAM) . Ricardo Ruiz-Sanchez
   was partially supported by the UNAM- DGAPA postdoctoral fellowship
   program. We are very grateful to Clarivate Analytics for granting us a
   temporary license to use In-Cites. Contract ID: OPP-00534251STEAMINC,
   Subscription Name: UNIVERSIDAD NACIONAL AUTONOMA DE MEXICO_OPP-
   00534251STEAMINC_1.
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NR 98
TC 0
Z9 0
U1 19
U2 20
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2666-4984
J9 ENVIRON SCI ECOTECH
JI Env. Sci. Ecotechnol.
PD SEP
PY 2024
VL 21
AR 100386
DI 10.1016/j.ese.2023.100386
EA FEB 2024
PG 8
WC Green & Sustainable Science & Technology; Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA KE7H7
UT WOS:001178342000001
PM 38328508
OA Green Published, gold
DA 2024-09-05
ER
PT J
AU Heo, GE
   Ko, YS
   Xie, Q
   Song, M
AF Heo, Go Eun
   Ko, Young Soo
   Xie, Qing
   Song, Min
TI High acknowledgement index: Characterizing research supporters with
   factors of acknowledgement affecting paper citation counts
SO JOURNAL OF INFORMETRICS
LA English
DT Article
DE Acknowledgement type; Named entity recognition; Academic performance;
   Citation count; Sensitivity analysis; Neural network
ID PATTERNS; TRENDS
AB The acknowledgments section of scientific papers is paramount in academic production, providing valuable insights into the individuals and organizations involved in the research process. Developing a standard index of acknowledgments would provide a significant metric for measuring institutional and researcher influence. However, the absence of standardized formats has impeded effective analysis of information in acknowledgement sections. This study develop an acknowledgement index utilizing automated text-mining techniques to address these limitations. In addition, we propose two methods to disambiguate research supporters in the acknowledgement section: entity scores based on specific keywords and research similarity scores calculated through co-references. Based on our investigation, we explore the characteristics of research supporters with high acknowledgement index scores and examine the correlation between each acknowledgement index and their performance. Notably, paper citation strongly correlates with research supporters' performance. Next, our analysis delves into the impact of acknowledgement and entity types on paper citations. Our findings reveal that acknowledgments with only person names exert the most significant impact on paper citation.
C1 [Heo, Go Eun; Ko, Young Soo; Song, Min] Yonsei Univ, Dept Lib & Informat Sci, 50 Yonsei Ro, Seoul 03722, South Korea.
   [Xie, Qing] Shenzhen Polytech, Sch Management, Shenzhen 518055, Guangdong, Peoples R China.
C3 Yonsei University; Shenzhen Polytechnic University
RP Song, M (corresponding author), Yonsei Univ, Dept Lib & Informat Sci, 50 Yonsei Ro, Seoul 03722, South Korea.
EM min.song@yonsei.ac.kr
RI song, min/KPA-7030-2024
OI Song, Min/0000-0003-3255-1600
FU Ministry of Education of the Republic of Korea; National Research
   Foundation of Korea [72204167]; Yonsei University; National Natural
   Science Foundation of China (NSFC);  [NRF-2020S1A5B1104865]
FX This work was supported by the Ministry of Education of the Republic of
   Korea and the National Research Foundation of Korea
   (NRF-2020S1A5B1104865) . In addition, this work was supported by the
   Yonsei University Research Grant of 2023. This work was also supported
   by the National Natural Science Foundation of China (NSFC) , Grant No.
   72204167.
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NR 24
TC 1
Z9 1
U1 4
U2 14
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1751-1577
EI 1875-5879
J9 J INFORMETR
JI J. Informetr.
PD NOV
PY 2023
VL 17
IS 4
AR 101447
DI 10.1016/j.joi.2023.101447
EA AUG 2023
PG 11
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA Q7FL9
UT WOS:001059143000001
DA 2024-09-05
ER
PT J
AU Bornmann, L
AF Bornmann, Lutz
TI Bibliometrics-based decision trees (BBDTs) based on bibliometrics-based
   heuristics (BBHs): Visualized guidelines for the use of bibliometrics in
   research evaluation
SO QUANTITATIVE SCIENCE STUDIES
LA English
DT Article
DE bibliometrics; heuristics; bibliometrics-based heuristic (BBH);
   bibliometrics-based decision tree (BBDT)
ID CITATION IMPACT; FRUGAL; NORMALIZATION; MODELS
AB Fast-and-frugal heuristics are simple strategies that base decisions on only a few predictor variables. In so doing, heuristics may not only reduce complexity but also boost the accuracy of decisions, their speed, and transparency. In this paper, bibliometrics-based decision trees (BBDTs) are introduced for research evaluation purposes. BBDTs visualize bibliometrics-based heuristics (BBHs), which are judgment strategies solely using publication and citation data. The BBDT exemplar presented in this paper can be used as guidance to find an answer on the question in which situations simple indicators such as mean citation rates are reasonable and in which situations more elaborated indicators (i.e., [sub-]field-normalized indicators) should be applied.
C1 [Bornmann, Lutz] Adm Headquarters Max Planck Soc, Div Sci & Innovat Studies, Hofgartenstr 8, D-80539 Munich, Germany.
C3 Max Planck Society
RP Bornmann, L (corresponding author), Adm Headquarters Max Planck Soc, Div Sci & Innovat Studies, Hofgartenstr 8, D-80539 Munich, Germany.
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NR 53
TC 3
Z9 3
U1 18
U2 19
PU MIT PRESS
PI CAMBRIDGE
PA ONE ROGERS ST, CAMBRIDGE, MA 02142-1209 USA
EI 2641-3337
J9 QUANT SCI STUD
JI Quant. Sci. Stud.
PD WIN
PY 2020
VL 1
IS 1
BP 171
EP 182
DI 10.1162/qss_a_00012
PG 12
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA UK2XH
UT WOS:000691837400009
OA gold
DA 2024-09-05
ER
PT J
AU Mazzei, D
   Chiarello, F
   Fantoni, G
AF Mazzei, Daniele
   Chiarello, Filippo
   Fantoni, Gualtiero
TI Analyzing Social Robotics Research with Natural Language Processing
   Techniques
SO COGNITIVE COMPUTATION
LA English
DT Article
DE Social robotics; Human-robot interaction; Bibliometric analysis; Topic
   modelling; Natural language processing
ID SOFT SKILLS; ANTHROPOMORPHISM; CHILDREN; EMOTION
AB The fast growth of social robotics (SR) has not been unidirectional, but rather towards a multidisciplinary scenario, creating a need for collaboration between different fields. This divergent expansion calls for a clear analysis of the field aimed at better orienting the research, thus paving the future of social robotics. This paper aims at understanding how the SR research field evolved in the last two decades by analyzing academic publications in SR and human-robot interaction using natural language processing (NLP) techniques. The analysis spotted an overlap between SR and human-robot interaction research fields that have been disambiguated using a data-driven approach that leads to the identification of a new group of papers we clustered under the concept of "soft HRI." This research topic has been analyzed by extracting trends and insights. Finally, another topic modelling step has been applied to identify seven sub-topics that have been discussed and analyzed picturing the current state of the art of SR. The paper reports a complete overview of the SR research field identifying various topics and sub-topics helping researchers in understanding the evolution of this field, thus supporting the strategic placing and evolution of their research activities.
C1 [Mazzei, Daniele] Univ Pisa, Comp Sci Dept, Largo Bruno Pontecorvo 3, I-56127 Pisa, Italy.
   [Chiarello, Filippo] Univ Pisa, Dept Energy Proc & Syst Engn, Pisa, Italy.
   [Fantoni, Gualtiero] Univ Pisa, Dept Civil & Ind Engn, Pisa, Italy.
C3 University of Pisa; University of Pisa; University of Pisa
RP Mazzei, D (corresponding author), Univ Pisa, Comp Sci Dept, Largo Bruno Pontecorvo 3, I-56127 Pisa, Italy.
EM daniele.mazzei@unipi.it; filippo.chiarello@unipi.it;
   gualtiero.fantoni@unipi.it
RI Fantoni, Gualtiero/GWZ-8445-2022; Mazzei, Daniele/AAB-9819-2019
OI Fantoni, Gualtiero/0000-0003-0772-600X; Mazzei,
   Daniele/0000-0001-8383-3355
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NR 60
TC 8
Z9 8
U1 3
U2 18
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1866-9956
EI 1866-9964
J9 COGN COMPUT
JI Cogn. Comput.
PD MAR
PY 2021
VL 13
IS 2
BP 308
EP 321
DI 10.1007/s12559-020-09799-1
EA JAN 2021
PG 14
WC Computer Science, Artificial Intelligence; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Neurosciences & Neurology
GA QU0DI
UT WOS:000608081500001
DA 2024-09-05
ER
PT J
AU Gao, H
   Ding, XH
AF Gao, Hui
   Ding, Xiuhao
TI The research landscape on the artificial intelligence: a bibliometric
   analysis of recent 20 years
SO MULTIMEDIA TOOLS AND APPLICATIONS
LA English
DT Article
DE Artificial intelligence (AI); Bibliometric methods; Patent analysis;
   Visualization
ID STRATEGIC-MANAGEMENT; PATTERNS; PERSPECTIVE; SCIENCE; TRENDS; DOMAIN;
   MODEL
AB Artificial intelligence (AI), a general term that implies the imitation of information process of intelligent behavior and sense with minimal intervention, is one of the most promising research areas and has received a considerable attention with coexisting pros and cons. In order to understand the research status quo and future trends on AI technology, this work uses bibliometric analysis method to obtain this objective. By analyzing the datasets including journal article data collected from Web of Science (WOS), conference paper data retrieved from Scopus and the patent data extracted from Derwent Innovations Index (DII) in the period of 2000-2019, we primarily provide a comprehensive overview to better understand the research status of AI. Bibliometric analysis results can also shed light on the evolution and trends in AI.
C1 [Gao, Hui] Hubei Univ, Sch Business, Youyi Ave 368, Wuhan, Peoples R China.
   [Ding, Xiuhao] Huazhong Univ Sci & Technol, Sch Management, Luoyu Rd 1037, Wuhan, Peoples R China.
C3 Hubei University; Huazhong University of Science & Technology
RP Gao, H (corresponding author), Hubei Univ, Sch Business, Youyi Ave 368, Wuhan, Peoples R China.
EM gaohui-hust@qq.com
OI Gao, Hui/0000-0001-8629-6532
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NR 38
TC 6
Z9 6
U1 15
U2 78
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1380-7501
EI 1573-7721
J9 MULTIMED TOOLS APPL
JI Multimed. Tools Appl.
PD APR
PY 2022
VL 81
IS 9
BP 12973
EP 13001
DI 10.1007/s11042-022-12208-4
EA FEB 2022
PG 29
WC Computer Science, Information Systems; Computer Science, Software
   Engineering; Computer Science, Theory & Methods; Engineering, Electrical
   & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering
GA 0P3ES
UT WOS:000760059700008
DA 2024-09-05
ER
PT J
AU Shi, JC
   Wang, YC
AF Shi, Jincheng
   Wang, Yingchun
TI Prerequisites for the Innovation Performance of Artificial Intelligence
   Laboratory: A Fuzzy-Set Qualitative Comparative Analysis
SO IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
LA English
DT Article
DE Artificial intelligence; Technological innovation; Research and
   development; Organizations; Ecosystems; Patents; Particle measurements;
   Artificial intelligence (AI) laboratory; basic research; engineering
   breakthrough; innovation performance; qualitative comparative analysis
   (QCA)
ID KNOWLEDGE; CONFIGURATIONS; MANAGEMENT; STRATEGY; SYSTEM
AB Artificial intelligence (AI) is widely adopted as a general-purpose technology, bringing about disruptive innovative changes. R&D laboratories (labs) from universities, enterprises, and public institutions drive AI innovation. However, research on the factors affecting AI innovation in R&D labs is rarely discussed. To address this gap, we constructed an adjusted technology-organization-environment framework to analyze different configurations that influence AI basic research and engineering breakthroughs. This article uses fuzzy set qualitative comparative analysis for analysis aimed at 43 international typical AI labs. The results indicate that technological, organizational, and environmental conditions jointly impact AI labs' innovation. Specifically, AI basic research depends on strong computing resources and a high-quality innovation ecology, and it is moving from academia to industry. AI Engineering breakthroughs rely on public R&D institutions and leading firms, and high-quality data has a significant impact on applications. The findings highlight the equivalent effect of different configurations in AI innovation. In addition, this study provides implications for the government's AI innovation policies and the technological management of AI labs.
C1 [Shi, Jincheng; Wang, Yingchun] Shanghai Artificial Intelligence Lab, Governance Res Ctr, Shanghai 200232, Peoples R China.
RP Shi, JC (corresponding author), Shanghai Artificial Intelligence Lab, Governance Res Ctr, Shanghai 200232, Peoples R China.
EM shijincheng0819@163.com; wangyingchun@pjlab.org.cn
RI Wang, Yingchun/N-1864-2018
OI Shi, Jincheng/0000-0002-9877-0710
FU National Key Ramp;D Program of China
FX No Statement Available
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NR 63
TC 0
Z9 0
U1 114
U2 114
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0018-9391
EI 1558-0040
J9 IEEE T ENG MANAGE
JI IEEE Trans. Eng. Manage.
PY 2024
VL 71
BP 5341
EP 5356
DI 10.1109/TEM.2024.3355235
PG 16
WC Business; Engineering, Industrial; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Engineering
GA IT6G4
UT WOS:001168619400001
OA hybrid
DA 2024-09-05
ER
PT J
AU O'Donovan, J
   Kahn, K
   MacRae, M
   Namanda, AS
   Hamala, R
   Kabali, K
   Geniets, A
   Lakati, A
   Mbae, SM
   Winters, N
AF O'Donovan, James
   Kahn, Ken
   MacRae, MacKenzie
   Namanda, Allan Saul
   Hamala, Rebecca
   Kabali, Ken
   Geniets, Anne
   Lakati, Alice
   Mbae, Simon M.
   Winters, Niall
TI Analysing 3429 digital supervisory interactions between Community Health
   Workers in Uganda and Kenya: the development, testing and validation of
   an open access predictive machine learning web app
SO HUMAN RESOURCES FOR HEALTH
LA English
DT Article
DE Machine learning; Artificial intelligence; Supervision; Community Health
   Worker; Digital Health; Training
ID COUNTRIES
AB Background Despite the growth in mobile technologies (mHealth) to support Community Health Worker (CHW) supervision, the nature of mHealth-facilitated supervision remains underexplored. One strategy to support supervision at scale could be artificial intelligence (AI) modalities, including machine learning. We developed an open access, machine learning web application (CHWsupervisor) to predictively code instant messages exchanged between CHWs based on supervisory interaction codes. We document the development and validation of the web app and report its predictive accuracy. Methods CHWsupervisor was developed using 2187 instant messages exchanged between CHWs and their supervisors in Uganda. The app was then validated on 1242 instant messages from a separate digital CHW supervisory network in Kenya. All messages from the training and validation data sets were manually coded by two independent human coders. The predictive performance of CHWsupervisor was determined by comparing the primary supervisory codes assigned by the web app, against those assigned by the human coders and calculating observed percentage agreement and Cohen's kappa coefficients. Results Human inter-coder reliability for the primary supervisory category of messages across the training and validation datasets was 'substantial' to 'almost perfect', as suggested by observed percentage agreements of 88-95% and Cohen's kappa values of 0.7-0.91. In comparison to the human coders, the predictive accuracy of the CHWsupervisor web app was 'moderate', suggested by observed percentage agreements of 73-78% and Cohen's kappa values of 0.51-0.56. Conclusions Augmenting human coding is challenging because of the complexity of supervisory exchanges, which often require nuanced interpretation. A realistic understanding of the potential of machine learning approaches should be kept in mind by practitioners, as although they hold promise, supportive supervision still requires a level of human expertise. Scaling-up digital CHW supervision may therefore prove challenging. Trial registration: This was not a clinical trial and was therefore not registered as such.
C1 [O'Donovan, James; MacRae, MacKenzie; Namanda, Allan Saul; Hamala, Rebecca; Kabali, Ken] Omni Med Uganda, Div Res & Hlth Equ, Makata, Uganda.
   [O'Donovan, James; Kahn, Ken; Geniets, Anne; Winters, Niall] Univ Oxford, Dept Educ, Learning & New Technol Res Grp, Oxford, England.
   [MacRae, MacKenzie] Tufts Univ, Sch Med, Dept Med, Boston, MA 02111 USA.
   [Lakati, Alice] Amref Int Univ, Div Hlth & Social Sci, Nairobi, Kenya.
   [Mbae, Simon M.] Med Mobile, Div Res, Nairobi, Kenya.
C3 University of Oxford; Tufts University
RP O'Donovan, J (corresponding author), Omni Med Uganda, Div Res & Hlth Equ, Makata, Uganda.
EM jamesodonovan@post.harvard.edu
OI Lakati, Alice Sipyian/0000-0003-3246-6924; O'Donovan,
   James/0000-0002-7248-5436
FU Economic and Social Research Council (ESRC) [ES/P000649/1]; ESRC-DFID
   Joint Scheme for Research on International Development [ES/J018619/2]
FX Funding for this study was provided jointly by grants from the Economic
   and Social Research Council (ESRC) (ES/P000649/1) and the ESRC-DFID
   Joint Scheme for Research on International Development (ES/J018619/2).
CR Abadi Martin, 2016, arXiv
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   GLOBAL EXPERIENCE CO
NR 30
TC 1
Z9 1
U1 2
U2 13
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
EI 1478-4491
J9 HUM RESOUR HEALTH
JI Hum. Resour. Health
PD MAR 16
PY 2022
VL 20
IS 1
AR 6
DI 10.1186/s12960-021-00699-5
PG 8
WC Health Policy & Services; Industrial Relations & Labor
WE Social Science Citation Index (SSCI)
SC Health Care Sciences & Services; Business & Economics
GA ZT9HJ
UT WOS:000769459300001
PM 35292073
OA Green Published, gold
DA 2024-09-05
ER
PT J
AU Wang, SM
   Yang, WP
   Chou, HP
   Chen, FM
   Hou, JL
   Sheu, JJ
AF Wang, Sheng-Ming
   Yang, Wei-Pang
   Chou, Hsin-Ping
   Chen, Fu-Mei
   Hou, Jia-Li
   Sheu, Jyh-Jian
TI Automatic Bibliographic Component Extraction Using Conditional Random
   Fields
SO JOURNAL OF INTERNET TECHNOLOGY
LA English
DT Article
DE Conditional random field; Citation analysis; Machine learning
AB Bibliographic data and publication data are composed of subficlds such as "author," "title," "journal," and "year." Citation analysis of articles in scholarly journals is a very effective method for their evaluation. This paper proposes a system for analyzing bibliographic component strings, which is based on the technique of Conditional Random Fields (CRF). The system is composed of two major modules: the Bibliographic Extraction Module (BEM) and the Statistical Evaluation Module (SEM). The objective of the Bibliographic Extraction Module is to extract the bibliographic components based on the machine learning technique, and the objective of the Statistical Evaluation Module is to turn the extracted bibliographic information into a statistical report.
   In this paper, we apply the CRF technique to build a probability model for dividing sequential data and giving proper tags to the components according to their characteristics. This is the framework for building the BEM to segment and label bibliographic information, identifying the author's name, journal's name, date of publication and so on. Then we employ the SEM to filter and match the intermediate representations produced by the BEM. In the end, the SEM will output the final evaluation report. Experimental results show that our system is reliable, with excellent overall efficiency.
C1 [Wang, Sheng-Ming] Natl Taipei Univ Technol, Grad Inst Interact Media Design, Taipei, Taiwan.
   [Yang, Wei-Pang; Chou, Hsin-Ping; Hou, Jia-Li] Natl Dong Hwa Univ, Dept Informat Management, Taipei, Taiwan.
   [Chen, Fu-Mei] Natl Taipei Univ Technol, Ctr Res & Dev, Taipei, Taiwan.
   [Sheu, Jyh-Jian] Natl Chengchi Univ, College Commun, Taipei, Taiwan.
C3 National Taipei University of Technology; National Dong Hwa University;
   National Taipei University of Technology; National Chengchi University
RP Wang, SM (corresponding author), Natl Taipei Univ Technol, Grad Inst Interact Media Design, Taipei, Taiwan.
EM ryan5885@mail.ntut.edu.tw; wpyang@mail.ndhu.edu.tw;
   shean@ms49.url.com.tw; chenfmei@mail.ntut.edu.tw;
   alexhou@mail.ndhu.edu.tw; jjshezi@nccu.edu.tw
RI Wang, Ryan/JFJ-0465-2023
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NR 26
TC 0
Z9 0
U1 0
U2 6
PU LIBRARY & INFORMATION CENTER, NAT DONG HWA UNIV
PI HUALIEN
PA LIBRARY & INFORMATION CENTER, NAT DONG HWA UNIV, HUALIEN, 00000, TAIWAN
SN 1607-9264
EI 2079-4029
J9 J INTERNET TECHNOL
JI J. Internet Technol.
PD SEP
PY 2012
VL 13
IS 5
BP 737
EP 747
PG 11
WC Computer Science, Information Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA 018UL
UT WOS:000309691900005
DA 2024-09-05
ER
PT J
AU Mariani, J
   Francopoulo, G
   Paroubek, P
AF Mariani, Joseph
   Francopoulo, Gil
   Paroubek, Patrick
TI Reuse and plagiarism in Speech and Natural Language Processing
   publications
SO INTERNATIONAL JOURNAL ON DIGITAL LIBRARIES
LA English
DT Article
DE Plagiarism; Detection; Text reuse; Natural Language Processing; Speech
   Processing; Scientometrics; Informetrics
AB The aim of this experiment is to present an easy way to compare fragments of texts in order to detect (supposed) results of copy and paste operations between articles in the domain of Natural Language Processing (NLP), including Speech Processing. The search space of the comparisons is a corpus labeled as NLP4NLP, which includes 34 different conferences and journals and gathers a large part of the NLP activity over the past 50 years. This study considers the similarity between the papers of each individual event and the complete set of papers in the whole corpus, according to four different types of relationship (self-reuse, self-plagiarism, reuse and plagiarism) and in both directions: a paper borrowing a fragment of text from another paper of the corpus (that we will call the source paper), or in the reverse direction, fragments of text from the source paper being borrowed and inserted in another paper of the corpus. The results show that self-reuse is rather a common practice, but that plagiarism seems to be very unusual, and that both stay within legal and ethical limits.
C1 [Mariani, Joseph; Francopoulo, Gil; Paroubek, Patrick] Univ Paris Saclay, CNRS, LIMSI, Orsay, France.
   [Francopoulo, Gil] Tagmatica, Paris, France.
C3 Centre National de la Recherche Scientifique (CNRS); Universite Paris
   Cite; Universite Paris Saclay
RP Mariani, J (corresponding author), Univ Paris Saclay, CNRS, LIMSI, Orsay, France.
EM joseph.mariani@limsi.fr; gil.francopoulo@wanadoo.fr; pap@limsi.fr
CR [Anonymous], 2014, Transactions of the Association for Computational Linguistics, DOI [DOI 10.1162/TACL_A_00179, DOI 10.1162/TACLA00179]
   [Anonymous], P 23 INT C COMP LING
   [Anonymous], ACL 2012 SPEC WORKSH
   [Anonymous], 2011, IJCNLP
   Barron-Cedeno A., 2010, P 7 INT C LANG RES E, P771
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NR 35
TC 5
Z9 5
U1 0
U2 11
PU SPRINGER
PI NEW YORK
PA 233 SPRING ST, NEW YORK, NY 10013 USA
SN 1432-5012
EI 1432-1300
J9 INT J DIGIT LIBRARIE
JI Int. J. Digit. Llibraries
PD SEP
PY 2018
VL 19
IS 2-3
SI SI
BP 113
EP 126
DI 10.1007/s00799-017-0211-0
PG 14
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA GQ5QB
UT WOS:000441741300002
DA 2024-09-05
ER
PT J
AU Momeni, F
   Dietze, S
   Mayr, P
   Biesenbender, K
   Peters, I
AF Momeni, Fakhri
   Dietze, Stefan
   Mayr, Philipp
   Biesenbender, Kristin
   Peters, Isabella
TI Which factors are associated with Open Access publishing? A Springer
   Nature case study
SO QUANTITATIVE SCIENCE STUDIES
LA English
DT Article
DE APC policies; bibliometrics; citation impact; machine learning; open
   access
ID AUTHOR-PAYS MODEL; CITATION ADVANTAGE; IMPACT; OA
AB Open Access (OA) facilitates access to research articles. However, authors or funders often must pay the publishing costs, preventing authors who do not receive financial support from participating in OA publishing and gaining citation advantage for OA articles. OA may exacerbate existing inequalities in the publication system rather than overcome them. To investigate this, we studied 522,411 articles published by Springer Nature. Employing correlation and regression analyses, we describe the relationship between authors affiliated with countries from different income levels, their choice of publishing model, and the citation impact of their papers. A machine learning classification method helped us to explore the importance of different features in predicting the publishing model. The results show that authors eligible for article processing charge (APC) waivers publish more in gold OA journals than others. In contrast, authors eligible for an APC discount have the lowest ratio of OA publications, leading to the assumption that this discount insufficiently motivates authors to publish in gold OA journals. We found a strong correlation between the journal rank and the publishing model in gold OA journals, whereas the OA option is mostly avoided in hybrid journals. Also, results show that the countries' income level, seniority, and experience with OA publications are the most predictive factors for OA publishing in hybrid journals.
C1 [Momeni, Fakhri; Dietze, Stefan; Mayr, Philipp] GESIS Leibniz Inst Social Sci, Cologne, Germany.
   [Dietze, Stefan] Heinrich Heine Univ Dusseldorf, Dept Comp Sci, Dusseldorf, Germany.
   [Biesenbender, Kristin; Peters, Isabella] ZBW Leibniz Informat Ctr Econ, Kiel, Germany.
C3 Leibniz Institut fur Sozialwissenschaften (GESIS); Heinrich Heine
   University Dusseldorf; Deutsche Zentralbibliothek fur
   Wirtschaftswissenschaften (ZBW)
RP Momeni, F (corresponding author), GESIS Leibniz Inst Social Sci, Cologne, Germany.
EM fakhri.momeni@t-online.de
RI Momeni, Fakhri/I-8012-2018; Mayr, Philipp/C-4359-2013; Peters,
   Isabella/C-9891-2012
OI Momeni, Fakhri/0000-0002-5572-575X; Mayr, Philipp/0000-0002-6656-1658;
   Biesenbender, Kristin/0000-0003-2497-5411; Peters,
   Isabella/0000-0001-5840-0806
FU BMBF project OASE [01PU17005A]; German Competence Center for
   Bibliometrics [01PQ17001]
FX This work is financially supported by BMBF project OASE, grant number
   01PU17005A. We acknowledge the support of the German Competence Center
   for Bibliometrics (grant:01PQ17001) for maintaining the used data set
   for the analyses.
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NR 48
TC 2
Z9 2
U1 10
U2 24
PU MIT PRESS
PI CAMBRIDGE
PA ONE ROGERS ST, CAMBRIDGE, MA 02142-1209 USA
EI 2641-3337
J9 QUANT SCI STUD
JI Quant. Sci. Stud.
PD MAY 1
PY 2023
VL 4
IS 2
BP 353
EP 371
DI 10.1162/qss_a_00253
PG 19
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA K5CJ2
UT WOS:001016613900003
OA Green Published, Green Submitted, gold, Green Accepted
DA 2024-09-05
ER
PT C
AU Schröder, S
   Thiele, T
   Jooss, C
   Vossen, R
   Richert, A
   Isenhardt, I
   Jeschke, S
AF Schroeder, Stefan
   Thiele, Thomas
   Jooss, Claudia
   Vossen, Rene
   Richert, Anja
   Isenhardt, Ingrid
   Jeschke, Sabina
BE Ribiere, V
   Worasinchai, L
TI Text Mining Analytics as a Method of Benchmarking Interdisciplinary
   Research Collaboration
SO PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON INTELLECTUAL CAPITAL
   KNOWLEDGE MANAGEMENT & ORGANISATIONAL LEARNING (ICICKM 2015)
SE Proceedings of the International Conference on Intellectual Capital
   Knowledge Management & Organizational Learning
LA English
DT Proceedings Paper
CT 12th International Conference on Intellectual Capital Knowledge
   Management and Organisational Learning (ICICKM)
CY NOV 05-06, 2015
CL Bangkok Univ, Inst Knowledge Innovat, Bangkok, THAILAND
HO Bangkok Univ, Inst Knowledge Innovat
DE benchmarking; interdisciplinarity; text mining; clustering; k-means;
   principal component analysis
AB This paper introduces the process of adopting and implementing modern text mining approaches of analysis within the Cluster of Excellence (CoE) Tailor-Made Fuels from Biomass (TMFB) at RWTH Aachen University and presents initial results of the analysis of research output by use of common clustering algorithms, namely Principal Component Analysis and k-means. As one main part of this paper the data driven approach is classified into benchmarking efforts, which are part of the research work of the so called Supplementary Cluster Activities. The SCA, supporting the cluster management, are initiated in order to promote interdisciplinary collaboration of CoE researchers with different disciplinary backgrounds. This cross-linking is aided by means of knowledge engineering and knowledge transfer strategies, such as the exploration of synergies and benchmarking of research results as well as progress. In this course an adoption of current benchmarking efforts to the specific cluster research framework conditions is described. At this, in case of differing data sources according to those used in widespread business organisational benchmarking, possible TMFB data sources are outlined and a selection for analysis is reasoned. While benchmarking is usually differentiated in internal and external benchmarking, in this case focus lies on internal analysis of publications in order to reflect research work. Benchmarking of publications is used and implemented to identify (best) methods, practices and processes of CoE to improve the research organization. Second major part and central question within the scope of this paper is in which way text mining respectively clustering algorithms are sensitive applicable to TMFB publications and are able to be used as benchmark for research clusters. Thus thematically priorities of TMFB researchers will be investigated in order to create an overview according to research topics, keywords and methods. In case of an outlook further steps, e.g. dealing with generated results, data visualisation or further acquisition of data corpora, are formulated.
C1 [Schroeder, Stefan] Rhein Westfal TH Aachen, Inst Informat Management Mech Engn IMA, Aachen, Germany.
   Rhein Westfal TH Aachen, Ctr Learning & Knowledge Management ZLW, Aachen, Germany.
   Rhein Westfal TH Aachen, Assoc Inst Management Cybernet IfU, Aachen, Germany.
C3 RWTH Aachen University; RWTH Aachen University; RWTH Aachen University
RP Schröder, S (corresponding author), Rhein Westfal TH Aachen, Inst Informat Management Mech Engn IMA, Aachen, Germany.
EM stefan.schroeder@ima-zlw-ifu.rwth-aachen.de;
   thomas.thiele@ima-zlw-ifu.rwth-aachen.de;
   claudia.jooss@ima-zlw-ifu.rwth-aachen.de;
   rene.vossen@ima-zlw-ifu.rwth-aachen.de;
   anja.richert@ima-zlw-ifu.rwth-aachen.de;
   ingrid.isenhardt@ima-zlw-ifu.rwth-aachen.de;
   sabina.jeschke@ima-zlw-ifu.rwth-aachen.de
RI Thiele, Thomas/J-7334-2016; Jeschke, Sabina/M-9453-2013
OI Isenhardt, Ingrid/0000-0002-5645-5341
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NR 36
TC 1
Z9 1
U1 0
U2 5
PU ACAD  CONFERENCES LTD
PI NR READING
PA CURTIS FARM, KIDMORE END, NR READING, RG4 9AY, ENGLAND
SN 2048-9803
BN 978-1-910810-74-3
J9 PROC INT CONF INTELL
PY 2015
BP 408
EP 417
PG 10
WC Business; Psychology, Applied; Management
WE Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Business & Economics; Psychology
GA BE4JO
UT WOS:000371799600049
DA 2024-09-05
ER
PT J
AU Chen, XL
   Xie, HR
   Wang, FL
   Liu, ZQ
   Xu, J
   Hao, TY
AF Chen, Xieling
   Xie, Haoran
   Wang, Fu Lee
   Liu, Ziqing
   Xu, Juan
   Hao, Tianyong
TI A bibliometric analysis of natural language processing in medical
   research
SO BMC MEDICAL INFORMATICS AND DECISION MAKING
LA English
DT Article; Proceedings Paper
CT 3rd China Health Information Processing Conference (CHIP)
CY NOV 24-25, 2017
CL Shenzhen, PEOPLES R CHINA
DE Natural language processing; Medical; Bibliometrics; Statistical
   characteristics; Scientific collaboration; Thematic discovery and
   evolution
ID NETWORK ANALYSIS; SCIENCE; ALGORITHM; EVOLUTION; EDUCATION; TRENDS;
   DOMAIN; FIELD
AB Background: Natural language processing (NLP) has become an increasingly significant role in advancing medicine. Rich research achievements of NLP methods and applications for medical information processing are available. It is of great significance to conduct a deep analysis to understand the recent development of NLP-empowered medical research field. However, limited study examining the research status of this field could be found. Therefore, this study aims to quantitatively assess the academic output of NLP in medical research field.
   Methods: We conducted a bibliometric analysis on NLP-empowered medical research publications retrieved from PubMed in the period 2007-2016. The analysis focused on three aspects. Firstly, the literature distribution characteristics were obtained with a statistics analysis method. Secondly, a network analysis method was used to reveal scientific collaboration relations. Finally, thematic discovery and evolution was reflected using an affinity propagation clustering method.
   Results: There were 1405 NLP-empowered medical research publications published during the 10 years with an average annual growth rate of 18.39%. 10 most productive publication sources together contributed more than 50% of the total publications. The USA had the highest number of publications. A moderately significant correlation between country's publications and GDP per capita was revealed. Denny, Joshua C was the most productive author. Mayo Clinic was the most productive affiliation. The annual co-affiliation and co-country rates reached 64.04% and 15.79% in 2016, respectively. 10 main great thematic areas were identified including Computational biology, Terminology mining, Information extraction, Text classification, Social medium as data source, Information retrieval, etc.
   Conclusions: A bibliometric analysis of NLP-empowered medical research publications for uncovering the recent research status is presented. The results can assist relevant researchers, especially newcomers in understanding the research development systematically, seeking scientific cooperation partners, optimizing research topic choices and monitoring new scientific or technological activities.
C1 [Chen, Xieling] Jinan Univ, Coll Econ, Guangzhou, Guangdong, Peoples R China.
   [Xie, Haoran] Educ Univ Hong Kong, Dept Math & Informat Technol, Hong Kong, Hong Kong, Peoples R China.
   [Wang, Fu Lee] Open Univ Hong Kong, Sch Sci & Technol, Hong Kong, Hong Kong, Peoples R China.
   [Liu, Ziqing] Guangzhou Univ Chinese Med, Clin Med Coll 2, Guangzhou, Guangdong, Peoples R China.
   [Xu, Juan] Nanjing Audit Univ, Res Inst Natl Supervis & Audit Law, Nanjing, Jiangsu, Peoples R China.
   [Hao, Tianyong] Guangdong Univ Foreign Studies, Sch Informat Sci & Technol, Guangzhou, Guangdong, Peoples R China.
   [Hao, Tianyong] South China Normal Univ, Sch Comp, Guangzhou, Guangdong, Peoples R China.
C3 Jinan University; Education University of Hong Kong (EdUHK); Hong Kong
   Metropolitan University; Guangzhou University of Chinese Medicine;
   Nanjing Audit University; Guangdong University of Foreign Studies; South
   China Normal University
RP Hao, TY (corresponding author), Guangdong Univ Foreign Studies, Sch Informat Sci & Technol, Guangzhou, Guangdong, Peoples R China.; Hao, TY (corresponding author), South China Normal Univ, Sch Comp, Guangzhou, Guangdong, Peoples R China.
EM haoty@gdufs.edu.cn
RI Hao, Tianyong/HJH-2742-2023; Wang, Fu Lee/AAD-9782-2021; Xie,
   Haoran/AFS-3515-2022; Xie, Haoran/AAW-8845-2020
OI Hao, Tianyong/0000-0002-9792-3949; Wang, Fu Lee/0000-0002-3976-0053;
   Xie, Haoran/0000-0003-0965-3617; Zhang, Dong/0000-0001-8186-4692; PV,
   THAYYIB/0000-0001-8929-0398; Chen, Xieling/0000-0003-3417-7421; xing,
   libo/0000-0002-8918-7128
FU National Natural Science Foundation of China [61772146]; Research Grants
   Council of Hong Kong Special Administrative Region, China
   [UGC/FDS11/E04/16]; Innovative School Project in Higher Education of
   Guangdong Province [YQ2015062]
FX Publication of the article is supported by grants from National Natural
   Science Foundation of China (No. 61772146), Research Grants Council of
   Hong Kong Special Administrative Region, China (UGC/FDS11/E04/16), and
   Innovative School Project in Higher Education of Guangdong Province (No.
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NR 65
TC 98
Z9 103
U1 3
U2 67
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
EI 1472-6947
J9 BMC MED INFORM DECIS
JI BMC Med. Inform. Decis. Mak.
PD MAR 22
PY 2018
VL 18
SU 1
AR 14
DI 10.1186/s12911-018-0594-x
PG 14
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI); Conference Proceedings Citation Index - Science (CPCI-S)
SC Medical Informatics
GA GA9CN
UT WOS:000428638800001
PM 29589569
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU Chuang, CW
   Chang, A
   Chen, MC
   Selvamani, MJP
   Shia, BC
AF Chuang, Chien-Wei
   Chang, Ariana
   Chen, Mingchih
   Selvamani, Maria John P.
   Shia, Ben-Chang
TI A Worldwide Bibliometric Analysis of Publications on Artificial
   Intelligence and Ethics in the Past Seven Decades
SO SUSTAINABILITY
LA English
DT Article; Data Paper
DE AI; ethics; bibliometric analysis; citation analysis; worldwide trend
ID BIG DATA ANALYTICS; AI
AB Issues related to artificial intelligence (AI) and ethics have gained much traction worldwide. The impact of AI on society has been extensively discussed. This study presents a bibliometric analysis of research results, citation relationships among researchers, and highly referenced journals on AI and ethics on a global scale. Papers published on AI and ethics were recovered from the Microsoft Academic Graph Collection data set, and the subject terms included "artificial intelligence" and "ethics." With 66 nations' researchers contributing to AI and ethics research, 1585 papers on AI and ethics were recovered, up to 5 July 2021. North America, Western Europe, and East Asia were the regions with the highest productivity. The top ten nations produced about 94.37% of the wide variety of papers. The United States accounted for 47.59% (286 articles) of all papers. Switzerland had the highest research production with a million-person ratio (1.39) when adjusted for populace size. It was followed by the Netherlands (1.26) and the United Kingdom (1.19). The most productive authors were found to be Khatib, O. (n = 10), Verner, I. (n = 9), Bekey, G. A. (n = 7), Gennert, M. A. (n = 7), and Chatila, R., (n = 7). Current research shows that research on artificial intelligence and ethics has evolved dramatically over the past 70 years. Moreover, the United States is more involved with AI and ethics research than developing or emerging countries.
C1 [Chuang, Chien-Wei; Chen, Mingchih; Shia, Ben-Chang] Fu Jen Catholic Univ, Grad Inst Business Adm, New Taipei 242062, Taiwan.
   [Chuang, Chien-Wei; Chen, Mingchih; Shia, Ben-Chang] Fu Jen Catholic Univ, Artificial Intelligence Dev Ctr, New Taipei 242062, Taiwan.
   [Chang, Ariana] Fu Jen Catholic Univ, Interdisciplinary Studies Program, New Taipei 242062, Taiwan.
   [Selvamani, Maria John P.] Fu Jen Catholic Univ, Sch Med, New Taipei 242062, Taiwan.
   [Selvamani, Maria John P.] Fu Jen Catholic Univ, Fu Jen Acad Catholica, New Taipei 242062, Taiwan.
C3 Fu Jen Catholic University; Fu Jen Catholic University; Fu Jen Catholic
   University; Fu Jen Catholic University; Fu Jen Catholic University
RP Shia, BC (corresponding author), Fu Jen Catholic Univ, Grad Inst Business Adm, New Taipei 242062, Taiwan.; Shia, BC (corresponding author), Fu Jen Catholic Univ, Artificial Intelligence Dev Ctr, New Taipei 242062, Taiwan.
EM 025674@mail.fju.edu.tw
OI Chuang, Chien-wei/0000-0002-3592-4794; Shia,
   Ben-Chang/0000-0003-2854-8361
FU  [7100397];  [A0110152]
FX This manuscript was partially funded byGrant number: 7100397 and Grant
   number: A0110152.
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TC 2
Z9 2
U1 9
U2 39
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD SEP
PY 2022
VL 14
IS 18
AR 11125
DI 10.3390/su141811125
PG 13
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA 4R9PZ
UT WOS:000857087700001
OA gold
DA 2024-09-05
ER
PT J
AU Nastasa, A
   Dumitra, TC
   Grigorescu, A
AF Nastasa, Anamaria
   Dumitra, Teodora-Catalina
   Grigorescu, Adriana
TI Artificial intelligence and sustainable development during the pandemic:
   An overview of the scientific debates
SO HELIYON
LA English
DT Article
DE Artificial intelligence; Sustainable development; Bibliometric analysis;
   Text mining; LDA
ID DEVELOPMENT GOALS; INFORMATION-TECHNOLOGY; USER ACCEPTANCE; R PACKAGE;
   PERSPECTIVE; EVOLUTION; ACHIEVE
AB The current work aims to analyze the main themes related to artificial intelligence (AI) and sustainable development during the pandemic period. This study provides an overview of the specialized literature related to AI and sustainability from the beginning of the pandemic through 2023. The present paper analyses scientific literature emphasizing both artificial intelligence's positive and negative impacts on sustainable development objectives (SDGs). To conduct the research, we employed bibliometric analysis and text-mining techniques to identify the major themes in the literature indexed in the Web of Science and Scopus databases. Firstly, we used descriptive measures to identify the authors' impact, the article production by country, the main keywords used, and other descriptive data. We further used data reduction methods based on coword analysis (such as multiple correspondence analysis) on authors' keywords to show patterns in the themes explored in the literature. Bibliometric analysis was supplemented by text mining using Latent Dirichlet allocation (LDA) and structural topic modeling on abstracts to provide a comprehensive view of scientific debates on AI and sustainable development. Our research has identified various themes in the literature related to AI and sustainable development. These themes include social sustainability, health-related issues, AI technologies for energy efficiency, sustainability in industry and innovation, IoT technologies for smart and sustainable cities, urban planning, technologies for education and knowledge production, and the impact of technologies on SDGs. We also found that there is a significant positivity bias in the literature when discussing the impact of AI on sustainable development. Despite acknowledging certain risks, the literature tends to focus on the potential benefits of AI across various sectors. In addition, the analysis shows a growing emphasis on energy efficiency, which is facilitated by the use of AI technologies. Our study contributes to a better understanding of current scholarly discussion trends and emerging scientific avenues regarding AI and sustainable development. It also highlights the areas where research is needed and the implications for practitioners and policymakers.
C1 [Nastasa, Anamaria; Dumitra, Teodora-Catalina] Natl Sci Res Inst Lab & Social Protect, 6-8 Povernei St, Bucharest 010643, Romania.
   [Nastasa, Anamaria] Univ Bucharest, Doctoral Sch Sociol, 36-46 Mihail Kogalniceanu Blvd, Bucharest 050107, Romania.
   [Dumitra, Teodora-Catalina] Bucharest Univ Econ Studies, Bucharest 010552, Romania.
   [Grigorescu, Adriana] Natl Univ Polit Studies & Publ Adm, 30A Expozitiei Bd, Bucharest 012104, Romania.
C3 University of Bucharest; Bucharest University of Economic Studies;
   National University of Political Studies & Public Administration (SNSPA)
   - Romania
RP Nastasa, A (corresponding author), Natl Sci Res Inst Lab & Social Protect, 6-8 Povernei St, Bucharest 010643, Romania.
EM anamaria.nastasa@incsmps.ro; teodora.dumitra@incsmps.ro;
   adriana.grigorescu@snspa.ro
RI Nastasa, Anamaria/AED-5337-2022
OI Nastasa, Anamaria/0000-0002-4641-1273
FU Romanian Ministry of Research, Innovation and Digitalization [PN
   22_10_0103]
FX Part of this work was supported by the NUCLEU Program funded by the
   Romanian Ministry of Research, Innovation and Digitalization (Project PN
   22_10_0103-"Development of digital skills for the new socio-economic
   normality"/"Dezvoltarea competentelor digitale pentru noua normalitate
   socio-economica")
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NR 120
TC 0
Z9 0
U1 8
U2 8
PU CELL PRESS
PI CAMBRIDGE
PA 50 HAMPSHIRE ST, FLOOR 5, CAMBRIDGE, MA 02139 USA
EI 2405-8440
J9 HELIYON
JI Heliyon
PD MAY 15
PY 2024
VL 10
IS 9
AR e30412
DI 10.1016/j.heliyon.2024.e30412
PG 27
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA TC2J1
UT WOS:001238990800001
PM 38711639
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU Fu, LD
   Aliferis, CF
AF Fu, Lawrence D.
   Aliferis, Constantin F.
TI Using content-based and bibliometric features for machine learning
   models to predict citation counts in the biomedical literature
SO SCIENTOMETRICS
LA English
DT Article
DE Bibliometrics; Citation analysis; Machine learning; Information
   retrieval
ID TEXT CATEGORIZATION
AB The most popular method for judging the impact of biomedical articles is citation count which is the number of citations received. The most significant limitation of citation count is that it cannot evaluate articles at the time of publication since citations accumulate over time. This work presents computer models that accurately predict citation counts of biomedical publications within a deep horizon of 10 years using only predictive information available at publication time. Our experiments show that it is indeed feasible to accurately predict future citation counts with a mixture of content-based and bibliometric features using machine learning methods. The models pave the way for practical prediction of the long-term impact of publication, and their statistical analysis provides greater insight into citation behavior.
C1 [Fu, Lawrence D.; Aliferis, Constantin F.] NYU Med Ctr, Ctr Hlth Informat & Bioinformat, New York, NY 10016 USA.
C3 New York University
RP Fu, LD (corresponding author), NYU Med Ctr, Ctr Hlth Informat & Bioinformat, 333 E 38th St,6th Floor, New York, NY 10016 USA.
EM lawrence.fu@nyumc.org
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NR 16
TC 75
Z9 84
U1 8
U2 108
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD OCT
PY 2010
VL 85
IS 1
BP 257
EP 270
DI 10.1007/s11192-010-0237-1
PG 14
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 639CC
UT WOS:000280947400019
OA Bronze
DA 2024-09-05
ER
PT J
AU Tam, J
   Lagisz, M
   Cornwell, W
   Nakagawa, S
AF Tam, Jessica
   Lagisz, Malgorzata
   Cornwell, Will
   Nakagawa, Shinichi
TI Quantifying research interests in 7,521 mammalian species with h-index:
   a case study
SO GIGASCIENCE
LA English
DT Article
DE bibliometrics; research bias; meta-research; scientific mapping;
   research on research; topic modeling
ID TAXONOMIC BIAS; MODELS
AB Background Taxonomic bias is a known issue within the field of biology, causing scientific knowledge to be unevenly distributed across species. However, a systematic quantification of the research interest that the scientific community has allocated to individual species remains a big data problem. Scalable approaches are needed to integrate biodiversity data sets and bibliometric methods across large numbers of species. The outputs of these analyses are important for identifying understudied species and directing future research to fill these gaps.
   Findings In this study, we used the species h-index to quantity the research interest in 7,521 species of mammals. We tested factors potentially driving species h-index, by using a Bayesian phylogenetic generalized linear mixed model (GLMM). We found that a third of the mammals had a species h-index of zero, while a select few had inflated research interest. Further, mammals with higher species h-index had larger body masses; were found in temperate latitudes; had their humans uses documented, including domestication; and were in lower-risk International Union for Conservation of Nature Red List categories. These results surprisingly suggested that critically endangered mammals are understudied. A higher interest in domesticated species suggested that human use is a major driver and focus in mammalian scientific literature.
   Conclusions Our study has demonstrated a scalable workflow and systematically identified understudied species of mammals, as well as identified the likely drivers of this taxonomic bias in the literature. This case study can become a benchmark for future research that asks similar biological and meta-research questions for other taxa.
C1 Univ New South Wales, Evolut & Ecol Res Ctr, Sydney, NSW 2052, Australia.
   Univ New South Wales, Sch Biol Earth & Environm Sci, Sydney, NSW 2052, Australia.
C3 University of New South Wales Sydney; University of New South Wales
   Sydney
RP Tam, J (corresponding author), UNSW, Level 5 West,Biol Sci South E26, Kensington, NSW 2052, Australia.
EM jessicatin-ying.tam@unsw.edu.au
RI Lagisz, Malgorzata/A-3100-2010; Nakagawa, Shinichi/B-5571-2011
OI Lagisz, Malgorzata/0000-0002-3993-6127; Nakagawa,
   Shinichi/0000-0002-7765-5182; Cornwell, Will/0000-0003-4080-4073; Tam,
   Jesse / Jess/0000-0003-3655-1974
FU Research Technology Services at UNSW Sydney
FX We are grateful for the comments from Prof. Ian Suthers and A/Prof.
   Tracy Ainsworth from UNSW Sydney. This research includes computations
   using the computational cluster Katana supported by Research Technology
   Services at UNSW Sydney.
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TC 4
Z9 4
U1 1
U2 5
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 2047-217X
J9 GIGASCIENCE
JI GigaScience
PY 2022
VL 11
AR giac074
DI 10.1093/gigascience/giac074
PG 11
WC Biology; Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Life Sciences & Biomedicine - Other Topics; Science & Technology - Other
   Topics
GA 4C9BL
UT WOS:000846739000088
PM 35962776
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU Isgüzar, S
   Fendoglu, E
   Simsek, AI
AF Isguzar, Seda
   Fendoglu, Eda
   Simsek, Ahmed Ihsan
TI INNOVATIVE APPLICATIONS IN BUSINESSES: AN EVALUATION ON GENERATIVE
   ARTIFICIAL INTELLIGENCE
SO AMFITEATRU ECONOMIC
LA English
DT Article
DE ChatGPT; artificial intelligence (AI); generative artificial
   intelligence (GenAI); OpenAI; business; business management; technology
   adoption; bibliometric analysis
AB The utilisation of Chat Generative Pre -Trained Transformer (ChatGPT) and generative artificial intelligence (GenAI) technologies has started to demonstrate its impact across several domains. The swift shift and widespread implementation of efficient artificial intelligence (AI) present distinct prospects such as optimisation, advancement, enhanced efficiency, boosted sales and marketing, expansion, reduced costs, and heightened profitability. GenAI has the potential to create a competition crisis between technologically advanced enterprises and less developed ones. Additionally, it may give rise to legal, moral, and ethical issues such as copyright infringement and the production of fake and false information. Hence, it is crucial for organisations to ensure that the productivity of AI is maximized in order to maximise its benefits and minimise any potential harm. The aim of this study is to provide suggestions regarding the use and potential of GenAI technologies in the corporate sector and to emphasise the potential research areas of future GenAI. This study contributes to research and practice in business and management and also identifies future research avenues. This study examines the benefits and disadvantages of using GenAI tools in businesses and individual departments, and it highlights the potential risks and dangers. A bibliometric analysis of 198 studies in the discipline of Business & Management from the Scopus database was conducted using the R program's bibliometrix package. The study focuses on descriptive data, annual scientific production, most productive journals, most productive authors and authors dominance factor, most cited publications, and most relevant keywords. The findings show that GenAI is likely to continue with a strong and rapidly rising trend in 2024 and beyond.
C1 [Isguzar, Seda; Fendoglu, Eda] Malatya Turgut Ozal Univ, Malatya, Turkiye.
   [Simsek, Ahmed Ihsan] Firat Univ, Elazig, Turkiye.
C3 Malatya Turgut Ozal University; Firat University
RP Fendoglu, E (corresponding author), Malatya Turgut Ozal Univ, Malatya, Turkiye.
EM eda.fendoglu@ozal.edu.tr
RI Simsek, Ahmed Ihsan/W-3881-2018
OI Simsek, Ahmed Ihsan/0000-0002-2900-3032
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TC 0
Z9 0
U1 34
U2 34
PU EDITURA ASE
PI BUCURESTI
PA PIATA ROMANA, NR 6, SECTOR 1, BUCURESTI, 701731, ROMANIA
SN 1582-9146
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J9 AMFITEATRU ECON
JI Amfiteatru Econ.
PD MAY
PY 2024
VL 26
IS 66
DI 10.24818/EA/2024/66/511
PG 283
WC Business; Economics; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA UL5C7
UT WOS:001248217500007
OA gold
DA 2024-09-05
ER
PT C
AU Dunn, AG
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AF Dunn, Adam G.
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BE Sarkar, IN
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   Marques, PMD
TI Identifying Clinical Study Types from PubMed Metadata: The Active
   (Machine) Learning Approach
SO MEDINFO 2015: EHEALTH-ENABLED HEALTH
SE Studies in Health Technology and Informatics
LA English
DT Proceedings Paper
CT 15th World Congress on Health and Biomedical Informatics (MEDINFO)
CY AUG 19-23, 2015
CL Int Med Informat Assoc, Brazilian Hlth Informat Soc, Sao Paulo, BRAZIL
HO Int Med Informat Assoc, Brazilian Hlth Informat Soc
DE Machine Learning; Databases; Bibliographic; Antidepressants
ID OPTIMAL SEARCH STRATEGIES; RETRIEVING SCIENTIFICALLY STRONG; MEDLINE;
   CLASSIFICATION; CATEGORIZATION; MESH
AB We examined a process for automating the classification of articles in MEDLINE aimed at minimising manual effort without sacrificing accuracy. From 22,808 articles pertaining to 19 antidepressants, 1000 were randomly selected and manually labelled according to article type (including, randomised controlled trials, editorials, etc.). We applied a machine learning approach termed 'active learning', where the learner (machine) selects the order in which the user (human) labels examples. Via simulation, we determined the number of articles a user needed to label to produce a classifier with at least 95% recall and 90% precision in three scenarios related to evidence synthesis. We found that the active learning process reduced the number of training instances required by 70%, 19%, and 14% in the three scenarios. The results show that the active learning method may be used in some scenarios to produce accurate classifiers that meet the needs of evidence synthesis tasks and reduce manual effort.
C1 [Dunn, Adam G.; Arachi, Diana] Macquarie Univ, Ctr Hlth Informat, Australian Inst Hlth Innovat, N Ryde, NSW 2109, Australia.
   [Bourgeois, Florence T.] Boston Childrens Hosp, Childrens Hosp Informat Program, Boston, MA USA.
   [Bourgeois, Florence T.] Harvard Med Sch, Dept Pediat, Boston, MA USA.
C3 Macquarie University; Harvard University; Boston Children's Hospital;
   Harvard University; Harvard Medical School
RP Dunn, AG (corresponding author), Macquarie Univ, Ctr Hlth Informat, Australian Inst Hlth Innovat, N Ryde, NSW 2109, Australia.
EM adam.dunn@mq.edu.au
RI Dunn, Adam/H-4425-2019; Arachi, Diana/AAY-8129-2020; Bourgeois,
   Florence/H-6710-2016
OI Dunn, Adam/0000-0002-1720-8209; Arachi, Diana/0000-0003-2446-7011;
   Bourgeois, Florence/0000-0001-7798-4560
CR [Anonymous], 2004, BMC MED
   [Anonymous], ECP
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NR 26
TC 2
Z9 2
U1 0
U2 0
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 0926-9630
EI 1879-8365
BN 978-1-61499-564-7; 978-1-61499-563-0
J9 STUD HEALTH TECHNOL
PY 2015
VL 216
BP 867
EP 871
DI 10.3233/978-1-61499-564-7-867
PG 5
WC Computer Science, Interdisciplinary Applications; Health Care Sciences &
   Services; Medical Informatics
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Health Care Sciences & Services; Medical Informatics
GA BL7ZK
UT WOS:000455836700178
PM 26262175
DA 2024-09-05
ER
PT J
AU Gendron, Y
   Andrew, J
   Cooper, C
AF Gendron, Yves
   Andrew, Jane
   Cooper, Christine
TI The perils of artificial intelligence in academic publishing*
SO CRITICAL PERSPECTIVES ON ACCOUNTING
LA English
DT Article
DE Academia; artificial intelligence; Deskilling; Editorial systems;
   Evaluation of research; Selection of reviewers; Algorithms
AB This essay aims to reflect on the potentially perilous implications of artificial intelligence in academic publishing. Our main point is that the colonization of academia by artificial intelligence technologies may erode, deskill and degrade core academic activities, where the role of key actors historically involved in the evaluation of research could become less and less tangible and significant. We are concerned particularly with the gradual removal of human involvement in journal editor and reviewer roles, as artificial intelligence and automated expert systems become increasingly influential across a range of tasks and judgments historically carried out and performed by people. Although these thoughts are exploratory, we believe it is imperative that researchers from all paradigmatic allegiances and geographies engage in initiatives that document, reflect, and debate the implications of artificial intelligence on the ways we evaluate research. The future of academic publishing as a meaningful human activity is at stake.
C1 [Gendron, Yves] Univ Laval, Fac Sci Adm, Pavillon Palasis Prince,2325 rue Terrasse,Local 26, Quebec City, PQ G1V 0A6, Canada.
   [Andrew, Jane] Univ Sydney, H69 Codrington, Sydney, NSW 2006, Australia.
   [Cooper, Christine] Univ Edinburgh, 29 Buccleuch Pl, Edinburgh EH8 9JS, Scotland.
C3 Laval University; University of Sydney; University of Edinburgh
RP Gendron, Y (corresponding author), Univ Laval, Fac Sci Adm, Pavillon Palasis Prince,2325 rue Terrasse,Local 26, Quebec City, PQ G1V 0A6, Canada.
EM yves.gendron@fsa.ulaval.ca; jane.andrew@sydney.edu.au;
   christine.cooper@ed.ac.uk
RI Cooper, Christine/KMY-8758-2024
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NR 47
TC 20
Z9 20
U1 8
U2 47
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1045-2354
EI 1095-9955
J9 CRIT PERSPECT ACCOUN
JI Crit. Perspect. Account.
PD SEP
PY 2022
VL 87
AR 102411
DI 10.1016/j.cpa.2021.102411
EA SEP 2022
PG 12
WC Business, Finance
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 4U8SF
UT WOS:000859056500004
OA Green Accepted, Green Submitted
DA 2024-09-05
ER
PT J
AU Hückstädt, M
AF Hueckstaedt, Malte
TI Ten reasons why research collaborations succeed-a random forest approach
SO SCIENTOMETRICS
LA English
DT Article
DE Research collaboration success; Team science; Collaboration
   effectiveness; Random forest; Machine Learning
ID INTERDISCIPLINARY RESEARCH; TEAM CLIMATE; PERFORMANCE; INNOVATION;
   STRATEGIES; CONFLICT; MODEL; TASK
AB The state of research in the Science of Team Science is characterised by a wide range of findings on how successful research collaboration should be structured. However, it remains unclear how the multitude of findings can be put into a hierarchical order with regard to their significance for the success of cooperation. This is where the article comes in: based on the state of research, the question of which intra- and interpersonal factors are most significant for the success of a research team is investigated. In order to explore the ten most important reasons for the success of a research collaboration, a Random Forest classifier is specified that predicts the success of research collaborations on the basis of 51 input variables. The analyses presented in the paper are based on representative survey data on n = 1.417 principal investigators and spokespersons of ongoing and completed research clusters funded by the German Research Foundation. The success of a research cluster is operationalised as the extent to which it has achieved the goals that it communicated to the funding agency before it began. Highly realistic and clear research objectives are central to the success of research clusters, as are comprehensive agreement on objectives, close interconnection of the subprojects' research work and a fair and trusting cooperation climate.
C1 [Hueckstaedt, Malte] German Ctr Higher Educ Res & Sci Studies, Lange Laube 12, D-30159 Hannover, Germany.
RP Hückstädt, M (corresponding author), German Ctr Higher Educ Res & Sci Studies, Lange Laube 12, D-30159 Hannover, Germany.
EM hueckstaedt@dzhw.eu
OI Huckstadt, Malte/0000-0002-0185-4230
FU Projekt DEAL; German Federal Ministry of Education and Research; Projekt
   DEAL [M527800]; German Federal Ministry of Education and Research
FX Open Access funding enabled and organized by Projekt DEAL. This research
   was supported by the German Federal Ministry of Education and Research
   [grant number M527800].
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NR 106
TC 5
Z9 6
U1 6
U2 27
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD MAR
PY 2023
VL 128
IS 3
BP 1923
EP 1950
DI 10.1007/s11192-022-04629-7
EA JAN 2023
PG 28
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 9M3IZ
UT WOS:000911272900003
OA hybrid
DA 2024-09-05
ER
PT C
AU Han, H
   Giles, L
   Zha, H
   Li, C
   Tsioutsiouliklis, K
AF Han, H
   Giles, L
   Zha, H
   Li, C
   Tsioutsiouliklis, K
BE Chen, H
   Christel, M
   Lim, EP
TI Two supervised learning approaches for name disambiguation in author
   citations
SO JCDL 2004: PROCEEDINGS OF THE FOURTH ACM/IEEE JOINT CONFERENCE ON
   DIGITAL LIBRARIES: GLOBAL REACH AND DIVERSE IMPACT
SE ACM-IEEE Joint Conference on Digital Libraries JCDL
LA English
DT Proceedings Paper
CT 4th Joint Conference on Digital Libraries
CY JUN 07-11, 2004
CL Tucson, AZ
DE naive Bayes; name disambiguation; Support Vector Machine
AB Due to name abbreviations, identical names, name misspellings, and pseudonyms in publications or bibliographies (citations), an author may have multiple names and multiple authors may share the same name. Such name ambiguity affects the performance of document retrieval, web search, database integration, and may cause improper attribution to authors. This paper investigates two supervised teaming approaches to disambiguate authors in the citations'. One approach uses the naive Bayes probability model, a generative model; the other uses Support Vector Machines(SVMs) [39] and the vector space representation of citations, a discriminative model. Both approaches utilize three types of citation attributes: co-author names, the title of the paper, and the title of the journal or proceeding. We illustrate these two approaches on two types of data, one collected from the web, mainly publication lists from homepages, the other collected from the DBLP citation databases.
C1 Penn State Univ, Dept Comp Sci & Engn, University Pk, PA 16802 USA.
C3 Pennsylvania Commonwealth System of Higher Education (PCSHE);
   Pennsylvania State University; Pennsylvania State University -
   University Park
EM hhan@cse.psu.edu; giles@ist.psu.edu; zha@cse.psu.edu;
   cli@hsph.harvard.edu; kt@nec-labs.com
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NR 41
TC 166
Z9 209
U1 1
U2 16
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA
SN 2575-7865
EI 2575-8152
BN 1-58113-832-6
J9 ACM-IEEE J CONF DIG
PY 2004
BP 296
EP 305
DI 10.1145/996350.996419
PG 10
WC Computer Science, Information Systems; Computer Science,
   Interdisciplinary Applications; Information Science & Library Science
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Information Science & Library Science
GA BAM92
UT WOS:000222881400052
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Sánchez-Bello, NF
   Quiroga, JEM
   Pérez-Martelo, CB
AF Sanchez-Bello, Nubia Fernanda
   Quiroga, Jorge Enrique Mejia
   Perez-Martelo, Constanza Beatriz
TI Factors Associated with Citation of Colombian Biomedical Articles:
   Analysis with Machine Learning
SO INVESTIGACION BIBLIOTECOLOGICA
LA English
DT Article
DE Citation Analysis; Machine Learning; Biomedical Research; Colombia
ID COLLABORATION; COUNTS
AB Citation indicators can be used to measure the impact or usefulness of research results in a scientific article; however, this usage can be controversial. Intrinsic and extrinsic factors influence the citation of an article, not to mention that citation behavior can differ between thematic areas, which hinders the comparison between articles and disciplines. Understanding that context can affect citation analysis is essential to interpret indicators properly; for this reason, we want to recognize the fac- tors that influence the citation of Colombian biomedical journals indexed in Scopus using Machine Learning al- gorithms. With 'Gradient Boosting Classifier' and 'Light Gradient Boosting Machine' algorithms, we find char- acteristics of importance such as the h -index of the first and last author, open access, number of authors and key- words of the article, in addition to identifying the num- ber of pages. These characteristics are relevant to the ar- ea of interest and can provide context for future analyses, always considering that what should be relevant about an article is not how many citations it attracts but whether it helps to fill gaps in knowledge.
C1 [Sanchez-Bello, Nubia Fernanda] Univ Cent, Fac Ingn & Ciencias Basicas, Bogota, Colombia.
   [Quiroga, Jorge Enrique Mejia; Perez-Martelo, Constanza Beatriz] Univ Cent, Fac Ingn & Ciencias Basicas, Grp Invest Prod Innovac Desarrollo & Org, Bogota, Colombia.
RP Sánchez-Bello, NF (corresponding author), Univ Cent, Fac Ingn & Ciencias Basicas, Bogota, Colombia.
EM nsanchezb1@ucentral.edu.co; jmejiaq@ucentral.edu.co;
   cperezm@ucentral.edu.co
CR Aksnes DW, 2019, SAGE OPEN, V9, DOI 10.1177/2158244019829575
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NR 35
TC 0
Z9 0
U1 0
U2 0
PU UNIV NACIONAL AUTONOMA MEXICO
PI MEXICO CITY
PA CIUDAD UNIV, CENTRO UNIV BIBLIOTECOLOGICAS, TORRE II HUMANIDADES, PISO
   11, 12 & 13, MEXICO CITY, CP 04510, MEXICO
SN 0187-358X
EI 2448-8321
J9 INVESTIG BIBLIOTECOL
JI Investig. Bibliotecol.
PD APR-JUN
PY 2024
VL 38
IS 99
BP 89
EP 107
DI 10.22201/iibi.24488321xe.2024.99.58857
PG 19
WC Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science
GA LY6J1
UT WOS:001190408500001
OA gold
DA 2024-09-05
ER
PT J
AU Huang, LT
   Shi, FF
   Hu, D
   Kang, DY
AF Huang, Litao
   Shi, Fanfan
   Hu, Dan
   Kang, Deying
TI Analysis of research topics and trends in investigator-initiated
   research/trials (IIRs/IITs): A topic modeling study
SO MEDICINE
LA English
DT Article
DE bibliometric; investigator-initiated trials; latent Dirichlet
   allocation; Web of Science
ID ATRIAL-FIBRILLATION; CLINICAL-TRIALS
AB Background:With the exponential growth of publications in the field of investigator-initiated research/trials (IIRs/IITs), it has become necessary to employ text mining and bibliometric analysis as tools for gaining deeper insights into this area of study. By using these methods, researchers can effectively identify and analyze research topics within the field.Methods:This study retrieved relevant publications from the Web of Science Core Collection and conducted bioinformatics analysis. The latent Dirichlet allocation model, which is based on machine learning, was utilized to identify subfield research topics.Results:A total of 4315 articles related to IIRs/IITs were obtained from the Web of Science Core Collection. After excluding duplicates and articles with missing abstracts, a final dataset of 3333 articles was included for bibliometric analysis. The number of publications showed a steady increase over time, particularly since 2000. The United States, Germany, the United Kingdom, the Netherlands, Canada, Denmark, Japan, Switzerland, and France emerged as the most productive countries in terms of IIRs/IITs. The citation analysis revealed intriguing trends, with certain highly cited articles showing a significant increase in citation frequency in recent years. A model with 45 topics was deemed the best fit for characterizing the extensively researched fields within IIRs/IITs. Our analysis revealed 10 top topics that have garnered significant attention, spanning domains such as community health, cancer treatment, brain development and disease mechanisms, nursing research, and stem cell therapy. These top topics offer researchers valuable directions for further investigation and innovation. Additionally, we identified 12 hot topics, which represent the most cutting-edge and highly regarded research areas within the field.Conclusion:This study contributes to a comprehensive understanding of the current research landscape and provides valuable insights for researchers working in this domain.
C1 [Huang, Litao] Sichuan Univ, West China Hosp, Chinese Evidence Based Med Ctr, Natl Clin Res Ctr Geriatr, Chengdu, Sichuan, Peoples R China.
   [Huang, Litao; Shi, Fanfan; Hu, Dan; Kang, Deying] Sichuan Univ, Dept Clin Res Management, West China Hosp, Chengdu, Peoples R China.
   [Kang, Deying] Sichuan Univ, West China Hosp, Dept Evidence Based Med & Clin Epidemiol, Chengdu, Peoples R China.
C3 Sichuan University; Sichuan University; Sichuan University
RP Kang, DY (corresponding author), Sichuan Univ, Dept Clin Res Management, West China Hosp, Chengdu, Peoples R China.; Kang, DY (corresponding author), Sichuan Univ, West China Hosp, Dept Evidence Based Med & Clin Epidemiol, Chengdu, Peoples R China.
EM huanglitao@wchscu.cn; shifanfan0706@163.com; deyingkang@126.com
FU National Clinical Research Center for Geriatrics, West China Hospital,
   Sichuan University [Z20192005]; The 1.3.5 project for disciplines of
   excellence, West China Hospital, Sichuan University [ZYGD23002]
FX This work was supported by the National Clinical Research Center for
   Geriatrics, West China Hospital, Sichuan University (Z20192005); 1.3.5
   project for disciplines of excellence, West China Hospital, Sichuan
   University (ZYGD23002).
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NR 33
TC 0
Z9 0
U1 0
U2 0
PU LIPPINCOTT WILLIAMS & WILKINS
PI PHILADELPHIA
PA TWO COMMERCE SQ, 2001 MARKET ST, PHILADELPHIA, PA 19103 USA
SN 0025-7974
EI 1536-5964
J9 MEDICINE
JI Medicine (Baltimore)
PD MAR 8
PY 2024
VL 103
IS 10
AR e37375
DI 10.1097/MD.0000000000037375
PG 8
WC Medicine, General & Internal
WE Science Citation Index Expanded (SCI-EXPANDED)
SC General & Internal Medicine
GA QS0C1
UT WOS:001222731500056
PM 38457583
OA gold
DA 2024-09-05
ER
PT J
AU Taskin, Z
   Al, U
AF Taskin, Zehra
   Al, Umut
TI Natural language processing applications in library and information
   science
SO ONLINE INFORMATION REVIEW
LA English
DT Article
DE Social network analysis; Bibliometrics; Library and information science;
   Citespace; VOSviewer; Natural language processing
ID COMBINING BIBLIOMETRICS; RETRIEVAL; RELEVANCE; CITATIONS; NLP
AB Purpose With the recent developments in information technologies, natural language processing (NLP) practices have made tasks in many areas easier and more practical. Nowadays, especially when big data are used in most research, NLP provides fast and easy methods for processing these data. The purpose of this paper is to identify subfields of library and information science (LIS) where NLP can be used and to provide a guide based on bibliometrics and social network analyses for researchers who intend to study this subject. Design/methodology/approach Within the scope of this study, 6,607 publications, including NLP methods published in the field of LIS, are examined and visualized by social network analysis methods. Findings After evaluating the obtained results, the subject categories of publications, frequently used keywords in these publications and the relationships between these words are revealed. Finally, the core journals and articles are classified thematically for researchers working in the field of LIS and planning to apply NLP in their research. Originality/value The results of this paper draw a general framework for LIS field and guides researchers on new techniques that may be useful in the field.
C1 [Taskin, Zehra; Al, Umut] Hacettepe Univ, Dept Informat Management, Ankara, Turkey.
C3 Hacettepe University
RP Taskin, Z (corresponding author), Hacettepe Univ, Dept Informat Management, Ankara, Turkey.
EM ztaskin@hacettepe.edu.tr; umutal@hacettepe.edu.tr
RI Al, Umut/E-9584-2013; Taskin, Zehra/H-3025-2011
OI Taskin, Zehra/0000-0001-7102-493X
FU Turkish Scientific and Technological Research Center [115K440]
FX This paper was supported in part by a research grant from the Turkish
   Scientific and Technological Research Center (115K440).
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NR 60
TC 19
Z9 22
U1 6
U2 107
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 1468-4527
EI 1468-4535
J9 ONLINE INFORM REV
JI Online Inf. Rev.
PD AUG 12
PY 2019
VL 43
IS 4
BP 676
EP 690
DI 10.1108/OIR-07-2018-0217
PG 15
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA IX7UX
UT WOS:000485891600012
DA 2024-09-05
ER
PT J
AU Cao, EW
AF Cao, Enwei
TI Research on students' classroom performance evaluation algorithm based
   on machine learning
SO INTERNATIONAL JOURNAL OF CONTINUING ENGINEERING EDUCATION AND LIFE-LONG
   LEARNING
LA English
DT Article
DE machine learning; students' classroom performance; evaluation; test
   statistics; intelligent teaching
AB In order to overcome the poor accuracy of traditional classroom performance evaluation algorithm, a machine learning-based classroom performance evaluation algorithm was designed. This paper makes an empirical analysis of the statistical data and constructs a statistical information analysis model for students' classroom performance evaluation. According to the mining results of students' classroom performance evaluation information, the adaptive mining and feature clustering of students' classroom performance evaluation data are carried out. This paper uses quantitative game method to evaluate students' classroom performance, constructs the explanatory variable and control variable model of students' classroom performance evaluation, and then uses machine learning method to optimise the evaluation of students' classroom performance. The simulation results show that the evaluation accuracy of the proposed method is always above 0.77, which has high reliability and adaptability, and improves the quantitative evaluation ability of students' classroom performance.
C1 [Cao, Enwei] Jingdezhen Ceram Inst, Sch Management & Econ, Jingdezhen 333000, Jiangxi, Peoples R China.
C3 Jingdezhen Ceramic Institute
RP Cao, EW (corresponding author), Jingdezhen Ceram Inst, Sch Management & Econ, Jingdezhen 333000, Jiangxi, Peoples R China.
EM enweicao@36haojie.com
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NR 21
TC 0
Z9 0
U1 2
U2 43
PU INDERSCIENCE ENTERPRISES LTD
PI GENEVA
PA WORLD TRADE CENTER BLDG, 29 ROUTE DE PRE-BOIS, CASE POSTALE 856, CH-1215
   GENEVA, SWITZERLAND
SN 1560-4624
EI 1741-5055
J9 INT J CONTIN ENG EDU
JI Int. J. Contin. Eng. Educ. Life-Long Learn.
PY 2022
VL 32
IS 2
BP 227
EP 239
PG 13
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA 0I4VV
UT WOS:000779420000008
DA 2024-09-05
ER
PT J
AU Nivash, JP
   Babu, LDD
AF Nivash, J. P.
   Babu, L. D. Dhinesh
TI Analyzing the impact of news trends on research publications and
   scientific collaboration networks
SO CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
LA English
DT Article
DE bibliometric analysis; citation networks; LDA; scientific collaboration
   networks
ID CITATION; SCIENCE
AB Scientific collaboration plays a vital role in generating novel ideas and innovative research progress among the researchers. Similarly, news diffusion also has an important role among the research communities. Though the collaboration networks have made an impact in scientific activities and attracted the attention of scientific communities, no work so far analyzed the cause which can determine the future research publications. The objective of this paper is to study influence of news trends on scientific collaboration and research publications. For this purpose, we have collected the top technological news trends and applied the LDA model to identify top research keywords from the articles. The results show that the news trends play a significant role on scientific collaborations and innovative research progress. It is found that the researchers identify their research gap, make future collaborations and does interdisciplinary research. Our results highlight the important role of diffusion of news, which influence the young researchers to generate novel ideas and tend to collaborate more with different scientific communities.
C1 [Nivash, J. P.; Babu, L. D. Dhinesh] VIT Univ, Sch Informat Technol & Engn, Vellore 632014, Tamil Nadu, India.
C3 Vellore Institute of Technology (VIT); VIT Vellore
RP Nivash, JP (corresponding author), VIT Univ, Sch Informat Technol & Engn, Vellore 632014, Tamil Nadu, India.
EM nivash.jeevan@vit.ac.in
RI d, d/GZL-3202-2022; D, D/HNJ-1774-2023; L D, Dhinesh Babu/K-6683-2017
OI L D, Dhinesh Babu/0000-0002-3354-8713; JP, NIVASH/0000-0002-2977-5593
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NR 29
TC 3
Z9 3
U1 3
U2 30
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1532-0626
EI 1532-0634
J9 CONCURR COMP-PRACT E
JI Concurr. Comput.-Pract. Exp.
PD JUL 25
PY 2019
VL 31
IS 14
AR e5058
DI 10.1002/cpe.5058
PG 10
WC Computer Science, Software Engineering; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA IF0NW
UT WOS:000472775300015
DA 2024-09-05
ER
PT J
AU Lyu, DQ
   Gong, KL
   Ruan, XM
   Cheng, Y
   Li, J
AF Lyu, Dongqing
   Gong, Kaile
   Ruan, Xuanmin
   Cheng, Ying
   Li, Jiang
TI Does research collaboration influence the "disruption" of articles?
   Evidence from neurosciences
SO SCIENTOMETRICS
LA English
DT Article
DE Disruption index; Collaboration; Neurosciences; Logistic regression
ID INTERNATIONAL COLLABORATION; SCIENTIFIC COLLABORATION; TECHNOLOGICAL
   NOVELTY; TEAM SCIENCE; IMPACT; INNOVATION; KNOWLEDGE; BEHAVIOR; AUTHORS;
   SEARCH
AB A new indicator (the disruption index) quantifying the extent to which a paper disrupts or consolidates established knowledge was recently introduced from the perspective of subsequent use of the current knowledge. This study explored whether different types of collaboration (i.e., at the author, institution, and country levels) equally affect the disruption of papers. We selected 505,168 papers from Neurosciences indexed in the Web of Science from 1954-2011 and employed logistic regression analysis. Our principal findings are that team size and international collaboration are negatively associated with the disruption of articles, while an additional increase in the number of domestic institutions of a team statistically favors disruption.
C1 [Lyu, Dongqing; Ruan, Xuanmin; Cheng, Ying; Li, Jiang] Nanjing Univ, Sch Informat Management, Nanjing 210023, Peoples R China.
   [Gong, Kaile] Nanjing Normal Univ, Sch Journalism & Commun, Nanjing 210097, Peoples R China.
C3 Nanjing University; Nanjing Normal University
RP Li, J (corresponding author), Nanjing Univ, Sch Informat Management, Nanjing 210023, Peoples R China.
EM lijiang@nju.edu.cn
RI Cheng, Yongzhi/AAY-9109-2020; Li, Jiang/JHV-1585-2023; Li,
   Jiang/Z-1709-2019; Gong, Kaile/T-2945-2019
OI Li, Jiang/0000-0001-5769-8647; Gong, Kaile/0000-0001-9269-8669
FU National Natural Science Foundation of China [71874077]
FX This work uses Web of Science data by Clarivate Analytics provided by
   the Indiana University Network Science Institute and the
   Cyberinfrastructure for Network Science Center at Indiana University. We
   also acknowledge the National Natural Science Foundation of China Grant
   71874077 for financial support.
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NR 90
TC 11
Z9 15
U1 18
U2 185
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD JAN
PY 2021
VL 126
IS 1
BP 287
EP 303
DI 10.1007/s11192-020-03757-2
EA OCT 2020
PG 17
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA PU7XL
UT WOS:000582810200011
DA 2024-09-05
ER
PT C
AU Li, HJ
   Li, L
   Yu, YQ
AF Li Haijun
   Li Lin
   Yu Yongqing
BE Fang, J
   Wang, Z
TI Research on simulation methods of evaluation for diagnostic Bayesian
   networks
SO SIGNAL ANALYSIS, MEASUREMENT THEORY, PHOTO-ELECTRONIC TECHNOLOGY, AND
   ARTIFICIAL INTELLIGENCE, PTS 1 AND 2
SE Proceedings of SPIE
LA English
DT Proceedings Paper
CT 6th International Symposium on Instrumentation and Control Technology
CY OCT 13-15, 2006
CL Beijing, PEOPLES R CHINA
DE Bayesian Networks; diagnostic models; evaluation; algorithm; faults
   injection; faults propagation
AB Bayesian Networks that based on probability inference is proposed to solve problems of uncertainty and imperfection. It has more advantages to solve faults caused by uncertainty and relevancy of complex devices. Diagnostic models of Bayesian Networks must be evaluated roundly before used for diagnosis. The usual way to evaluate diagnostic models is using standard cases to test the model, but the cases are limited and the quality of these cases depend on their source, and these cases could not include all instances. Based on sample means of Monte Carlo, an algorithm of evaluation for diagnostic models is proposed this article; this algorithm does not need special diagnostic cases. Faults injecting algorithm with equal probability of every components are adopted, test cases are produced by this algorithm of system itself, and course of faults propagation is simulated by this algorithm of system too. This algorithm could test diagnostic models roundly, and make overall evaluation of diagnostic models.
C1 [Li Haijun; Li Lin] Naval Aero Engn Acad, Yantai 264001, Peoples R China.
   [Yu Yongqing] Mil Deputy Off NAD 8357 Inst, Tianjin 300141, Peoples R China.
RP Li, HJ (corresponding author), Naval Aero Engn Acad, Yantai 264001, Peoples R China.
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NR 6
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Z9 0
U1 0
U2 1
PU SPIE-INT SOC OPTICAL ENGINEERING
PI BELLINGHAM
PA 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA
SN 0277-786X
EI 1996-756X
BN 0-8194-6452-X
J9 PROC SPIE
PY 2006
VL 6357
AR 63574Q
DI 10.1117/12.717469
PN 1-2
PG 5
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic; Instruments & Instrumentation; Optics; Imaging Science &
   Photographic Technology
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Engineering; Instruments & Instrumentation; Optics;
   Imaging Science & Photographic Technology
GA BFM84
UT WOS:000243123200170
DA 2024-09-05
ER
PT J
AU Krishna, D
   Mohan, SR
   Murthy, BSN
   Rao, AR
AF Krishna, D
   Mohan, SR
   Murthy, BSN
   Rao, AR
TI Performance evaluation of public research institutes using principal
   component analysis
SO JOURNAL OF SCIENTIFIC & INDUSTRIAL RESEARCH
LA English
DT Article
AB Public research institutes are the organisations, which carry out scientific research and tender technological services. Owing to the phenomenon of globalisation, resource constraints and increased accountability, public research institutes, especially in the developing countries, are under tremendous pressure to improve their performance. The need to improve performance of these organizations necessitates evaluating the performance of the institution in relation to other institutes. In this study, public research institutes are considered as systems and some models based on statistical concepts are utilized to compare performance of different research institutes by measuring productivity index of those institutes. Performance ranking is done, based on productivity index values. Ranks obtained with the study are also compared with those of the experts judgment for studying the accuracy of the methodologies.
C1 Indian Inst Chem Technol, Hyderabad 500007, Andhra Pradesh, India.
   Sri Venkateswara Univ, Coll Engn, Tirupati 517502, Andhra Pradesh, India.
C3 Council of Scientific & Industrial Research (CSIR) - India; CSIR -
   Indian Institute of Chemical Technology (IICT); Sri Venkateswara
   University
RP Mohan, SR (corresponding author), Indian Inst Chem Technol, Hyderabad 500007, Andhra Pradesh, India.
CR Anderson T.W, 1998, An Introduction to Multivariate Statistical Analysis
   BROWN MG, 1988, RES TECHNOL MANAGE, V31, P11, DOI 10.1080/08956308.1988.11670531
   Coccia M, 2001, R&D MANAGE, V31, P453, DOI 10.1111/1467-9310.00231
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   Kendall M., 1980, Multivariate Analysis
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   MOHAN SR, R D MANAGE
   MORRISON, 1978, MULIVARIATE STAT MET
   MUNDEL, 1976, IND ENG J, V10, P50
NR 10
TC 7
Z9 7
U1 0
U2 7
PU NATL INST SCIENCE COMMUNICATION
PI NEW DELHI
PA DR K S KRISHNAN MARG, NEW DELHI 110 012, INDIA
SN 0022-4456
J9 J SCI IND RES INDIA
JI J. Sci. Ind. Res.
PD NOV
PY 2002
VL 61
IS 11
BP 940
EP 947
PG 8
WC Engineering, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA 614RK
UT WOS:000179204100004
DA 2024-09-05
ER
PT J
AU Wao, H
   Wang, Y
   Wao, MA
   Were, JA
AF Wao, Hesborn
   Wang, Yan
   Wao, Melvin A.
   Were, Juliana A.
TI Factors associated with North-South research collaboration focusing on
   HIV/AIDS: lessons from ClinicalTrials.gov
SO AIDS RESEARCH AND THERAPY
LA English
DT Article
DE Clinical trials; HIV; AIDS; Logistic regression; North-South
   collaborations; Research capacity strengthening
ID HEALTH; CAPACITY
AB Background A North-South (N-S) research collaboration is one way through which research capacity of developing countries can be strengthened. Whereas N-S collaboration in HIV/AIDS area may result in research capacity strengthening of Southern partners, it is not clear what factors are associated with this type of collaboration. The study aims to characterize N-S research collaboration focusing on HIV/AIDS and to determine factors associated with such N-S research collaborations. Methods Clinical trial data on HIV/AIDS-related studies conducted between 2000 and 2019 were obtained from ClinicalTrials.gov. Using these data, we characterized N-S collaborative studies focusing on HIV/AIDS and summarized them using frequencies and percentages. To determine factors associated with these studies, we used logistic regression and reported results as adjusted odds ratios with Wald 95% confidence intervals. Results and discussion Of the 4,832 HIV/AIDS-related studies retrieved from the registry, less than one-quarter (n = 1133, 23%) involved a Southern institution, with 77% of these studies classified as N-S collaborations. Majority of these studies have single PI (50%), are conducted at single location (39%); have large sample sizes (41%); are federally-funded (32%) or receive funding from other sources (32%); are intervention studies (64%); and involve a mixture of male and female participants (58%) and adult participants (54%). Single PIs (as opposed to multiple PIs) were more likely to be from the North than South institution (odds ratio = 5.59, 95%CI: 4.16 - 11.57). Trend analyses showed that N-S research collaborations produced HIV/AIDS-related studies at a faster rate than S-S research collaborations. N-S collaborations involving female or children produced HIV/AIDS-related studies between 2000 and 2019 at a significantly faster rate than S-S collaborations involving females and children during the same period. Holding other factors constant, N-S collaborative research focusing on HIV/AIDS are associated with: multiple PIs as opposed to single PI, multiple institutions as opposed to a single institution, multiple locations as opposed to a single location, large number of participants as opposed to small sample sizes, and public funding as opposed to industry funding. Almost half of these studies had a Northern PI only, about one-third had a Southern PI only, and much fewer had PIs from both North and South. However, these studies were less likely to receive funding from other sources than industry funding. Conclusions HIV/AIDS-related research is increasingly becoming a more collaborative global research involving more N-S collaborations than S-S collaborations. Factors associated with N-S collaborative studies focusing on HIV/AIDS include multiple PIs, institutions, and locations; large sample sizes; publicly funded; and involve vulnerable populations such as women and children. Whereas almost half of these studies have a Northern PI only, about one-third have a Southern PI only, and much fewer have PIs from both North and South. Our results inform future design and implementation of N-S research collaborations in this area. Suggestions for improvement of ClinicalTrials.gov registry are provided.
C1 [Wao, Hesborn; Wang, Yan] African Populat & Hlth Res Ctr, APHRC Campus,Manga Close,Kirawa Rd,POB 10-8-00100, Nairobi, Kenya.
   [Wang, Yan] Drexel Univ, Dornsife Sch Publ Hlth, Urban Hlth Collaborat, 3600 Market St,7th Floor, Philadelphia, PA 19104 USA.
   [Wang, Yan] Univ Calif Los Angeles, Div Infect Dis, 10833 Conte Ave, Los Angeles, CA 90095 USA.
   [Wao, Melvin A.] US Int Univ Africa USIU Africa, USIU Rd,Thika Rd Exit 7,POB 14634-00800, Nairobi, Kenya.
   [Were, Juliana A.] Management Univ Africa MUA, Popo Rd,Mombasa Rd,Belleview,South C,POB 29677-00, Nairobi, Kenya.
C3 African Population & Health Research Centre; Drexel University;
   University of California System; University of California Los Angeles
RP Wang, Y (corresponding author), African Populat & Hlth Res Ctr, APHRC Campus,Manga Close,Kirawa Rd,POB 10-8-00100, Nairobi, Kenya.; Wang, Y (corresponding author), Drexel Univ, Dornsife Sch Publ Hlth, Urban Hlth Collaborat, 3600 Market St,7th Floor, Philadelphia, PA 19104 USA.; Wang, Y (corresponding author), Univ Calif Los Angeles, Div Infect Dis, 10833 Conte Ave, Los Angeles, CA 90095 USA.
EM wangyan@ucla.edu
RI Wao, Melvin/AAB-8890-2022
FU NIH [T32MH080634]; National Institute on Minority Health and Health
   Disparities [1T37MD014251]
FX This work was supported by NIH [Grant Number T32MH080634] and National
   Institute on Minority Health and Health Disparities [Grant Number NIMHD
   #1T37MD014251].
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NR 30
TC 2
Z9 2
U1 0
U2 2
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 1742-6405
J9 AIDS RES THER
JI Aids Res. Ther.
PD AUG 25
PY 2021
VL 18
IS 1
AR 54
DI 10.1186/s12981-021-00376-6
PG 10
WC Infectious Diseases
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Infectious Diseases
GA UF4IH
UT WOS:000688538400001
PM 34433475
OA Green Published, gold
DA 2024-09-05
ER
PT C
AU Tsuji, K
   Yoshikane, F
   Sato, S
   Itsumura, H
AF Tsuji, Keita
   Yoshikane, Fuyuki
   Sato, Sho
   Itsumura, Hiroshi
GP IEEE
TI Book Recommendation Using Machine Learning Methods Based on Library Loan
   Records and Bibliographic Information
SO 2014 IIAI 3RD INTERNATIONAL CONFERENCE ON ADVANCED APPLIED INFORMATICS
   (IIAI-AAI 2014)
LA English
DT Proceedings Paper
CT 3rd IIAI International Conference on Advanced Applied Informatics
   (IIAI-AAI)
CY AUG 31-SEP 04, 2014
CL Kitakyushu, JAPAN
DE Book Recommendation; Recommender System; Library Loan Records; Support
   Vector Machine (SVM); Random Forest; Adaboost
AB We propose a method to recommend books through machine learning modules based on several features, including library loan records. We evaluated the most effective method among ones using (a) a Support Vector Machine (SVM), (b) Random Forest and (c) Adaboost, as well as the most effective combination of relevant features among (1) library loan records, (2) book titles, (3) Nippon Decimal Classification categories, (4) publication year and (5) frequencies at which books were borrowed. We performed an experiment involving 40 subjects who are students at T University. The books that our methods recommended and the loan records that we used were obtained from the T University Library. The results show that books recommended by the SVM based on features (1), (2), (3) and (5) were rated most favorably by the subjects. Our method outperforms preceding ones, such as the method proposed by Tsuji et al. (2013), and is comparable in performance to the recommendation by the website Amazon.co.jp.
C1 [Tsuji, Keita] Univ Tsukuba, Fac Lib Informat & Media Sci, Tsuchiura, Ibaraki 3058550, Japan.
   Doshisha Univ, Fac Social Studies, Kamigyo Ku, Kyoto 6028580, Japan.
C3 University of Tsukuba; Doshisha University
RP Tsuji, K (corresponding author), Univ Tsukuba, Fac Lib Informat & Media Sci, 1-2 Kasuga, Tsuchiura, Ibaraki 3058550, Japan.
EM keita@slis.tsukuba.ac.jp; fuyuki@slis.tsukuba.ac.jp; min2fly@gmail.com;
   hits@slis.tsukuba.ac.jp
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NR 9
TC 7
Z9 8
U1 1
U2 18
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-4799-4173-5
PY 2014
BP 76
EP 79
DI 10.1109/IIAI-AAI.2014.26
PG 4
WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Engineering
GA BD1PY
UT WOS:000358256400014
OA Green Submitted
DA 2024-09-05
ER
PT C
AU Guo, PF
   Wang, PY
   Zhou, JY
   Jiang, SS
   Patel, VM
AF Guo, Pengfei
   Wang, Puyang
   Zhou, Jinyuan
   Jiang, Shanshan
   Patel, Vishal M.
GP IEEE COMP SOC
TI Multi-institutional Collaborations for Improving Deep Learning-based
   Magnetic Resonance Image Reconstruction Using Federated Learning
SO 2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION,
   CVPR 2021
SE IEEE Conference on Computer Vision and Pattern Recognition
LA English
DT Proceedings Paper
CT IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
CY JUN 19-25, 2021
CL ELECTR NETWORK
AB Fast and accurate reconstruction of magnetic resonance (MR) images from under-sampled data is important in many clinical applications. In recent years, deep learning-based methods have been shown to produce superior performance on MR image reconstruction. However, these methods require large amounts of data which is difficult to collect and share due to the high cost of acquisition and medical data privacy regulations. In order to overcome this challenge, we propose a federated learning (FL) based solution in which we take advantage of the MR data available at different institutions while preserving patients' privacy. However, the generalizability of models trained with the FL setting can still be suboptimal due to domain shift, which results from the data collected at multiple institutions with different sensors, disease types, and acquisition protocols, etc. With the motivation of circumventing this challenge, we propose a cross-site modeling for MR image reconstruction in which the learned intermediate latent features among different source sites are aligned with the distribution of the latent features at the target site. Extensive experiments are conducted to provide various insights about FL for MR image reconstruction. Experimental results demonstrate that the proposed framework is a promising direction to utilize multi-institutional data without compromising patients' privacy for achieving improved MR image reconstruction.
C1 [Guo, Pengfei; Wang, Puyang; Zhou, Jinyuan; Jiang, Shanshan; Patel, Vishal M.] Johns Hopkins Univ, Baltimore, MD 21218 USA.
C3 Johns Hopkins University
RP Guo, PF (corresponding author), Johns Hopkins Univ, Baltimore, MD 21218 USA.
EM pguo4@jhu.edu; pwang47@jhu.edu; jzhou2@jhmi.edu; sjiang21@jhmi.edu;
   vpatel36@jhu.edu
RI guo, peng/AAG-4052-2019; Jiang, Shanshan/O-8265-2019; Wang,
   Puyang/ITU-4535-2023; Guo, Peng/GWC-0572-2022; Guo, Peng/IZQ-0331-2023
OI Jiang, Shanshan/0000-0003-2853-9991; 
FU National Science Foundation [1910141]; National Institutes of Health
   [R01CA248077]; Direct For Computer & Info Scie & Enginr; Div Of
   Information & Intelligent Systems [1910141] Funding Source: National
   Science Foundation
FX Vishal M. Patel was supported by the National Science Foundation grant
   1910141. This work was supported in part by grants from the National
   Institutes of Health (R01CA248077).
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NR 41
TC 92
Z9 99
U1 3
U2 10
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA
SN 1063-6919
BN 978-1-6654-4509-2
J9 PROC CVPR IEEE
PY 2021
BP 2423
EP 2432
DI 10.1109/CVPR46437.2021.00245
PG 10
WC Computer Science, Artificial Intelligence; Imaging Science &
   Photographic Technology
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Imaging Science & Photographic Technology
GA BS5QS
UT WOS:000739917302060
PM 35444379
OA Green Accepted, Green Submitted
DA 2024-09-05
ER
PT C
AU Chakraborty, J
   Maity, B
   Pradhan, DK
   Nandi, S
AF Chakraborty, Joyita
   Maity, Biswajit
   Pradhan, Dinesh K.
   Nandi, Subrata
GP ACM
TI CiteDEK: A hybrid EMD-kNN-DTW model for classification of paper citation
   trajectories
SO PROCEEDINGS OF 7TH JOINT INTERNATIONAL CONFERENCE ON DATA SCIENCE AND
   MANAGEMENT OF DATA, CODS-COMAD 2024
LA English
DT Proceedings Paper
CT 7th ACM India Joint International Conference on Data Science and
   Management of Data (CODS-COMAD) / 11th ACM IKDD CODS Conference / 29th
   COMAD Conference
CY JAN 04-07, 2024
CL IIIT Bangalore, Bangalore, INDIA
HO IIIT Bangalore
DE Time-series classification; Citation trajectory classification;
   Supervised machine learning; EMD; kNN; DTW; Bibliographic databases
AB Classifying citation trajectories of scientific publications is crucial. However, they diffuse anomalously due to non-linear, non-stationary, and long-ranged correlations. Previous studies define hard thresholds, arbitrary parameters, and subjective rules to classify based on their rise and fall patterns. It leads to substantial variance and, thus, ambiguous classification. This paper proposes CiteDEK, a hybrid EMD-kNN-DTW classification model framework. It predicts the nature of 5,039 trajectories, each 30 years in length, using only raw time series. We get a classification accuracy of similar to 76%, and Cohen's kappa-statistic is 0.63, which is significant.
C1 [Chakraborty, Joyita; Nandi, Subrata] Natl Inst Technol, Durgapur, India.
   [Maity, Biswajit] Inst Engn & Management, Kolkata, India.
   [Pradhan, Dinesh K.] Dr BC Roy Engn Coll, Durgapur, India.
C3 National Institute of Technology (NIT System); National Institute of
   Technology Durgapur; Institute of Engineering & Management (IEM),
   Kolkata; Dr. B. C. Roy Engineering College
RP Chakraborty, J (corresponding author), Natl Inst Technol, Durgapur, India.
EM joyita.ckra@gmail.com; biswajit.maity1@gmail.com; dineshkrp@gmail.com;
   subrata.nandi@gmail.com
RI Pradhan, Dinesh K./AAE-4386-2019
OI Pradhan, Dinesh K./0000-0001-9132-9255; MAITY,
   BISWAJIT/0000-0002-3891-7469; Nandi, Subrata/0000-0002-8743-4770
CR Chakraborty J, 2023, Arxiv, DOI arXiv:2309.04949
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   Colavizza G, 2016, J INFORMETR, V10, P1037, DOI 10.1016/j.joi.2016.07.009
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NR 8
TC 0
Z9 0
U1 1
U2 1
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES
BN 979-8-4007-1634-8
PY 2024
BP 576
EP 577
DI 10.1145/3632410.3632481
PG 2
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BW5HU
UT WOS:001160848200085
DA 2024-09-05
ER
PT J
AU Parteka, A
   Kordalska, A
AF Parteka, Aleksandra
   Kordalska, Aleksandra
TI Artificial intelligence and productivity: global evidence from AI patent
   and bibliometric data
SO TECHNOVATION
LA English
DT Article
DE Technological innovation; Productivity paradox; Productivity growth;
   Artificial intelligence; Patents
ID UNITED-STATES; GROWTH; TECHNOLOGY; ROBOTS; SLOWDOWN; PARADOX; ICT;
   INFORMATION; INDICATORS; EUROPE
AB In this paper we analyse the relationship between technological innovation in the artificial intelligence (AI) domain and macroeconomic productivity. We embed recently released data on patents and publications related to AI in an augmented model of productivity growth, which we estimate for the OECD countries and compare to an extended sample including non-OECD countries. Our estimates provide evidence in favour of the modern productivity paradox. We show that the development of AI technologies remains a niche innovation phenomenon with a negligible role in the officially recorded productivity growth process. This general result, i.e. a lack of a strong relationship between AI and registered macroeconomic productivity growth, is robust to changes in the country sample, in the way we quantify labour productivity and technology (including AI stock), in the specification of the empirical model (control variables) and in estimation methods.
C1 [Parteka, Aleksandra; Kordalska, Aleksandra] Gdansk Univ Technol, Fac Management & Econ, Narutowicza 11-12, PL-80233 Gdansk, Poland.
C3 Fahrenheit Universities; Gdansk University of Technology
RP Parteka, A (corresponding author), Gdansk Univ Technol, Fac Management & Econ, Narutowicza 11-12, PL-80233 Gdansk, Poland.
EM aparteka@zie.pg.edu.pl; Aleksandra.Kordalska@zie.pg.edu.pl
RI Kordalska, Aleksandra/R-8011-2016; Parteka, Aleksandra/AAN-5881-2020
OI Kordalska, Aleksandra/0000-0002-8842-4777; Parteka,
   Aleksandra/0000-0003-1149-6614
FU National Science Centre, Poland [2020/37/B/HS4/01302]
FX Acknowledgment The research has been conducted within the project
   financed by the National Science Centre, Poland (2020/37/B/HS4/01302) .
   All remain-ing errors are the authors' responsibility.
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NR 100
TC 11
Z9 11
U1 93
U2 194
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0166-4972
EI 1879-2383
J9 TECHNOVATION
JI Technovation
PD JUL
PY 2023
VL 125
AR 102764
DI 10.1016/j.technovation.2023.102764
EA MAY 2023
PG 15
WC Engineering, Industrial; Management; Operations Research & Management
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Engineering; Business & Economics; Operations Research & Management
   Science
GA H8AN2
UT WOS:000998126000001
OA Green Submitted, hybrid
DA 2024-09-05
ER
PT J
AU Singh, A
   Kanaujia, A
   Singh, VK
   Vinuesa, R
AF Singh, Aakash
   Kanaujia, Anurag
   Singh, Vivek Kumar
   Vinuesa, Ricardo
TI Artificial intelligence for Sustainable Development Goals: Bibliometric
   patterns and concept evolution trajectories
SO SUSTAINABLE DEVELOPMENT
LA English
DT Article
DE artificial intelligence (AI); bibliometrics; path analysis; Sustainable
   Development Goals (SDGs)
ID CITATION NETWORK ANALYSIS; INTEGRATED APPROACH; PARADIGM SHIFTS;
   PATH-ANALYSIS; TECHNOLOGY; PREDICTION; MANAGEMENT; RADIOMICS
AB The development of artificial intelligence (AI) as a field has impacted almost all aspects of human life. More recently it has found a role in addressing developmental challenges, specifically the Sustainable Development Goals (SDGs). However, there are not enough systematic studies on analysis of the role of AI research towards the SDGs. Therefore, this article attempts to bridge this gap by identifying the major bibliometric trends and concept-evolution trajectories in the area of AI applications for sustainable-development goals. The research publication data for the last 20 years in the areas of artificial intelligence, machine learning, deep learning, and so forth, is obtained and computationally analysed using a framework comprising bibliometrics, path analysis and content analysis. The findings show an incremental trend in overall publications on the application of AI for SDGs across the different regions of the world. SDGs 3 (good health & well-being) and 7 (affordable and clean energy) are found as the areas with the most applications of AI. In SDG3, the literature reflects application of AI techniques such as deep learning for precision and personalised medicine while in SDG7, a number of studies have employed AI techniques for the integration of systems for efficient generation of solar power and improving the energy efficiency of a building. Furthermore, SDG 4 (quality education), SDG 13 (climate action), SDG 11 (sustainable cities and communities) and SDG 16 (peace, justice and strong institutions) are the other SDGs where AI approaches and techniques are applied. The analytical results present a detailed insight of application of AI for achieving the SDGs.
C1 [Singh, Aakash; Kanaujia, Anurag; Singh, Vivek Kumar] Banaras Hindu Univ, Dept Comp Sci, Varanasi 221005, India.
   [Vinuesa, Ricardo] KTH Royal Inst Technol, FLOW, Engn Mech, SE-10044 Stockholm, Sweden.
C3 Banaras Hindu University (BHU); Royal Institute of Technology
RP Singh, VK (corresponding author), Banaras Hindu Univ, Dept Comp Sci, Varanasi 221005, India.; Vinuesa, R (corresponding author), KTH Royal Inst Technol, FLOW, Engn Mech, SE-10044 Stockholm, Sweden.
EM vivek@bhu.ac.in; rvinuesa@mech.ktc.se
RI Vinuesa, Ricardo/ABG-6234-2020; Singh, Vivek Kumar/O-5699-2019;
   Kanaujia, Anurag/GOK-0097-2022
OI Vinuesa, Ricardo/0000-0001-6570-5499; Singh, Vivek
   Kumar/0000-0002-7348-6545; Singh, Aakash/0000-0002-6213-718X
FU Science and Engineering Research Board (SERB), India [MTR/2020/000625]; 
   [M-22-69]
FX ACKNOWLEDGEMENTSThe authors would like to acknowledge the support in
   form of the extramural research grant no. MTR/2020/000625 from Science
   and Engineering Research Board (SERB), India, and by HPE Aruba Centre
   for Research in Information Systems at BHU (No. M-22-69 of BHU).
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NR 63
TC 21
Z9 21
U1 25
U2 59
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0968-0802
EI 1099-1719
J9 SUSTAIN DEV
JI Sustain. Dev.
PD FEB
PY 2024
VL 32
IS 1
BP 724
EP 754
DI 10.1002/sd.2706
EA JUL 2023
PG 31
WC Development Studies; Green & Sustainable Science & Technology; Regional
   & Urban Planning
WE Social Science Citation Index (SSCI)
SC Development Studies; Science & Technology - Other Topics; Public
   Administration
GA HI0K6
UT WOS:001038163500001
OA hybrid
DA 2024-09-05
ER
PT J
AU Mukhamediev, RI
   Yakunin, K
   Mussabayev, R
   Buldybayev, T
   Yan, KC
   Murzakhmetov, S
   Yelis, M
AF Mukhamediev, Ravil, I
   Yakunin, Kirill
   Mussabayev, Rustam
   Buldybayev, Timur
   Yan Kuchin
   Murzakhmetov, Sanzhar
   Yelis, Marina
TI Classification of Negative Information on Socially Significant Topics in
   Mass Media
SO SYMMETRY-BASEL
LA English
DT Article
DE Bayesian rules; journalism; mass media; multimodal mass media
   assessment; natural language processing; social influence; social
   significance; topic modeling
ID RENEWABLE ENERGY; DECISION-MAKING; SYSTEM
AB Mass media not only reflect the activities of state bodies but also shape the informational context, sentiment, depth, and significance level attributed to certain state initiatives and social events. Multilateral and quantitative (to the practicable extent) assessment of media activity is important for understanding their objectivity, role, focus, and, ultimately, the quality of the society's "fourth power". The paper proposes a method for evaluating the media in several modalities (topics, evaluation criteria/properties, classes), combining topic modeling of the text corpora and multiple-criteria decision making. The evaluation is based on an analysis of the corpora as follows: the conditional probability distribution of media by topics, properties, and classes is calculated after the formation of the topic model of the corpora. Several approaches are used to obtain weights that describe how each topic relates to each evaluation criterion/property and to each class described in the paper, including manual high-level labeling, a multi-corpora approach, and an automatic approach. The proposed multi-corpora approach suggests assessment of corpora topical asymmetry to obtain the weights describing each topic's relationship to a certain criterion/property. These weights, combined with the topic model, can be applied to evaluate each document in the corpora according to each of the considered criteria and classes. The proposed method was applied to a corpus of 804,829 news publications from 40 Kazakhstani sources published from 01 January 2018 to 31 December 2019, to classify negative information on socially significant topics. A BigARTM model was derived (200 topics) and the proposed model was applied, including to fill a table of the analytical hierarchical process (AHP) and all of the necessary high-level labeling procedures. Experiments confirm the general possibility of evaluating the media using the topic model of the text corpora, because an area under receiver operating characteristics curve (ROC AUC) score of 0.81 was achieved in the classification task, which is comparable with results obtained for the same task by applying the BERT (Bidirectional Encoder Representations from Transformers) model.
C1 [Mukhamediev, Ravil, I; Yakunin, Kirill; Yelis, Marina] Satbayev Univ KazNRTU, Inst Cybernet & Informat Technol, Satpayev Str 22A, Alma Ata 050013, Kazakhstan.
   [Mukhamediev, Ravil, I] ISMA Univ, Dept Nat Sci & Comp Technol, Lomonosov Str 1, LV-1011 Riga, Latvia.
   [Mukhamediev, Ravil, I; Yakunin, Kirill; Mussabayev, Rustam; Yan Kuchin; Murzakhmetov, Sanzhar] Inst Informat & Computat Technol, Pushkin Str 125, Alma Ata 050010, Kazakhstan.
   [Buldybayev, Timur] Informat Analyt Ctr, Dostyk Str 18, Nur Sultan 010000, Kazakhstan.
C3 Institute of Information & Computational Technologies
RP Mukhamediev, RI; Yakunin, K; Yelis, M (corresponding author), Satbayev Univ KazNRTU, Inst Cybernet & Informat Technol, Satpayev Str 22A, Alma Ata 050013, Kazakhstan.; Mukhamediev, RI (corresponding author), ISMA Univ, Dept Nat Sci & Comp Technol, Lomonosov Str 1, LV-1011 Riga, Latvia.; Mukhamediev, RI; Yakunin, K; Mussabayev, R (corresponding author), Inst Informat & Computat Technol, Pushkin Str 125, Alma Ata 050010, Kazakhstan.
EM ravil.muhamedyev@gmail.com; Yakunin.k@mail.ru; rustam@iict.kz;
   Timur.Buldybayev@iac.kz; ykuchin@mail.ru; sanzharmrz@gmail.com;
   k.marina92@gmail.com
RI Kuchin, Yan/IUO-8562-2023; Mussabayev, Rustam/AAQ-9781-2020; Yelis,
   Marina/AAD-8506-2021; Murzakhmetov, Sanzhar/AAD-8637-2021; Mukhamediev,
   Ravil I./X-1461-2019
OI Kuchin, Yan/0000-0002-5271-9071; Mussabayev, Rustam/0000-0001-7283-5144;
   Mukhamediev, Ravil I./0000-0002-3727-043X; Yelis,
   Marina/0000-0003-4203-800X; Yakunin, Kirill/0000-0002-7378-9212
FU Committee of Science under the Ministry of Education and Science of the
   Republic of Kazakhstan [BR05236839]
FX This research was funded by the Committee of Science under the Ministry
   of Education and Science of the Republic of Kazakhstan, grant
   BR05236839.
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   Yakunin K., MEDIA MONITORING SYS
NR 55
TC 9
Z9 9
U1 1
U2 15
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2073-8994
J9 SYMMETRY-BASEL
JI Symmetry-Basel
PD DEC
PY 2020
VL 12
IS 12
AR 1945
DI 10.3390/sym12121945
PG 23
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics
GA PK3WF
UT WOS:000602378900001
OA gold
DA 2024-09-05
ER
PT J
AU Umer, M
   Aljrees, T
   Ullah, S
   Bashir, AK
AF Umer, Muhammad
   Aljrees, Turki
   Ullah, Saleem
   Bashir, Ali Kashif
TI Novel approach for quantitative and qualitative authors research
   profiling using feature fusion and tree-based learning approach
SO PEERJ COMPUTER SCIENCE
LA English
DT Article
DE Citation sentiment analysis; Ensemble learning; Feature engineering;
   Feature fusion; Intelligent recommendation and text analysis; Authors
   research profiling; Self citation analysis
ID SELF-CITATION RATES; H-INDEX; IMPACT; CLASSIFICATION; PATTERNS; MACRO;
   SMOTE
AB Article citation creates a link between the cited and citing articles and is used as a basis for several parameters like author and journal impact factor, H-index, i10 index, etc., for scientific achievements. Citations also include self-citation which refers to article citation by the author himself. Self-citation is important to evaluate an author's research profile and has gained popularity recently. Although different criteria are found in the literature regarding appropriate self-citation, self-citation does have a huge impact on a researcher's scientific profile. This study carries out two cases in this regard. In case 1, the qualitative aspect of the author's profile is analyzed using hand-crafted feature engineering techniques. The sentiments conveyed through citations are integral in assessing research quality, as they can signify appreciation, critique, or serve as a foundation for further research. Analyzing sentiments within in-text citations remains a formidable challenge, even with the utilization of automated sentiment annotations. For this purpose, this study employs machine learning models using term frequency (TF) and term frequency-inverse document frequency (TF-IDF). Random forest using TF with Synthetic Minority Oversampling Technique (SMOTE) achieved a 0.9727 score of accuracy. Case 2 deals with quantitative analysis and investigates direct and indirect self-citation. In this study, the top 2% of researchers in 2020 is considered as a baseline. For this purpose, the data of the top 25 Pakistani researchers are manually retrieved from this dataset, in addition to the citation information from the Web of Science (WoS). The self citation is estimated using the proposed model and results are compared with those obtained from WoS. Experimental results show a substantial difference between the two, as the ratio of self-citation from the proposed approach is higher than WoS. It is observed that the citations from the WoS for authors are overstated. For a comprehensive evaluation of the researcher's profile, both direct and indirect self citation must be included.
C1 [Umer, Muhammad; Ullah, Saleem] Khwaja Fareed Univ Engn & IT, Dept Comp Sci, Rahim Yar Khan, Punjab, Pakistan.
   [Aljrees, Turki] Univ Hafr Al Batin, Dept Comp Sci & Engn, Hafar Al Batin, Saudi Arabia.
   [Bashir, Ali Kashif] Manchester Metropolitan Univ, Dept Comp & Math, Manchester, England.
C3 Hafr Albatin University; Manchester Metropolitan University
RP Ullah, S (corresponding author), Khwaja Fareed Univ Engn & IT, Dept Comp Sci, Rahim Yar Khan, Punjab, Pakistan.
EM saleem.ullah@kfueit.edu.pk
RI Aljrees, Turki/HLQ-3139-2023; Umer, Muhammad/AAX-4594-2020; Umer,
   Muhammad/KHU-2339-2024; Ullah, Dr. Saleem/D-2644-2014
OI Aljrees, Turki/0000-0002-7473-7115; Umer, Muhammad/0000-0002-6015-9326;
   Umer, Muhammad/0009-0001-8751-6100; Bashir, Ali
   Kashif/0000-0003-2601-9327; Ullah, Dr. Saleem/0000-0003-3747-1263
FU Turki Aljrees
FX The funding is supported by Turki Aljrees with the support of the
   University of Hafr-Al Batin. The funders had no role in study design,
   data collection and analysis, decision to publish, or preparation of the
   manuscript.
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NR 64
TC 0
Z9 0
U1 3
U2 3
PU PEERJ INC
PI LONDON
PA 341-345 OLD ST, THIRD FLR, LONDON, EC1V 9LL, ENGLAND
EI 2376-5992
J9 PEERJ COMPUT SCI
JI PeerJ Comput. Sci.
PD DEC 19
PY 2023
VL 9
AR e1752
DI 10.7717/peerj-cs.1752
PG 26
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA DK4Q8
UT WOS:001131925400003
PM 38192451
OA gold
DA 2024-09-05
ER
PT J
AU Nadathur, SG
   Warren, JR
AF Nadathur, Shyamala G.
   Warren, James R.
TI Formal-Transfer In and Out of Stroke Care Units: An Analysis Using
   Bayesian Networks
SO INTERNATIONAL JOURNAL OF HEALTHCARE INFORMATION SYSTEMS AND INFORMATICS
LA English
DT Article
DE Administrative Data; Bayesian Networks; Health Services Research;
   Outcome and Process Assessment; Patient Care; Stroke Units
AB The positive impact of stroke care units (SCUs) on patient outcome has been previously reported. In this study, long-term stroke patients that are formally admitted to teaching-hospitals are compared with and without SCUs. The authors focus on the patients' experience with ongoing care or formal transfers following current care as this cohort is often high users of the system with associated high costs. Bayesian Networks were employed to analyze routinely collected public-hospital administrative data. The results illustrate that the teaching-hospitals with SCUs, while achieving shorter length of stay, in fact deal with younger patients with lower overall patient complexity than non-SCU teaching-hospitals. Other differences include SCUs predominantly treating subarachnoid hemorrhages whereas the non-SCUs treat more cerebral infarctions. This study illustrates the power of Bayesian Networks to expose the nature of caseload and outcomes recorded in hospital-administrative data as a means to gain insight on current practice and create opportunities for benchmarking and improving care.
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   [Warren, James R.] Univ Auckland, Sch Populat Hlth, Auckland, New Zealand.
   [Warren, James R.] Univ Natl Inst Hlth Innovat, Auckland, New Zealand.
C3 Monash University; University of Auckland; University of Auckland
RP Nadathur, SG (corresponding author), Monash Univ, Clayton, Vic, Australia.
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   Valderas JM, 2009, ANN FAM MED, V7, P357, DOI 10.1370/afm.983
   van der Gaag L.C., 2008, Aligning Bayesian Network Classifiers with Medical Contexts
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   Zhu HF, 2009, STROKE, V40, P18, DOI 10.1161/STROKEAHA.108.527606
NR 66
TC 0
Z9 0
U1 0
U2 0
PU IGI GLOBAL
PI HERSHEY
PA 701 E CHOCOLATE AVE, STE 200, HERSHEY, PA 17033-1240 USA
SN 1555-3396
EI 1555-340X
J9 INT J HEALTHC INF SY
JI Int. J. Healthc. Inf. Syst. Inf.
PD JUL-SEP
PY 2011
VL 6
IS 3
BP 32
EP 45
DI 10.4018/jhisi.2011070103
PG 14
WC Medical Informatics
WE Emerging Sources Citation Index (ESCI)
SC Medical Informatics
GA V14ZL
UT WOS:000214518800003
DA 2024-09-05
ER
PT J
AU Lee, C
AF Lee, Changsoo
TI A topic modeling analysis of Korea's T&I research trends in the 2010s
SO BABEL-REVUE INTERNATIONALE DE LA TRADUCTION-INTERNATIONAL JOURNAL OF
   TRANSLATION
LA English
DT Article
DE research trends; bibliographic study; text mining; topic modeling;
   Korean T&I research
ID TRANSLATION
AB The present study aims to demonstrate the relevance of topic modeling as a new research tool for analyzing research trends in the T&I field. Until now, most efforts to this end have relied on manual classification based on pre-established typologies. This method is time- and labor-consuming, prone to subjective biases, and limited in describing a vast amount of research output. As a key component of text mining, topic modeling offers an efficient way of summarizing topic structure and trends over time in a collection of documents while being able to describe the entire system without having to rely on sampling. As a case study, the present paper applies the technique to analyzing a collection of abstracts from four Korean Language T&I journals for the 2010s decade (from 2010 to 2019). The analysis proves the technique to be highly successful in uncovering hidden topical structure and trends in the abstract corpus. The results are discussed along with implications of the technique for the T&I field.
C1 [Lee, Changsoo] Hankuk Univ Foreign Studies, Seoul, South Korea.
C3 Hankuk University Foreign Studies
RP Lee, C (corresponding author), Hankuk Univ Foreign Studies, Grad Sch Interpretat & Translat, Imunro 107, Seoul 02450, South Korea.
EM dewywag@gmail.com
FU Hankuk University of Foreign Studies Research Fund
FX This work was supported by the Hankuk University of Foreign Studies
   Research Fund of 2021.
CR [Anonymous], 2012, DESCRIPTIVE TRANSLAT
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U2 4
PU JOHN BENJAMINS PUBLISHING CO
PI AMSTERDAM
PA PO BOX 36224, 1020 ME AMSTERDAM, NETHERLANDS
SN 0521-9744
EI 1569-9668
J9 BABEL-AMSTERDAM
JI Babel
PY 2021
VL 67
IS 4
BP 482
EP 499
DI 10.1075/babel.00228.lee
PG 18
WC Linguistics; Language & Linguistics
WE Social Science Citation Index (SSCI); Arts &amp; Humanities Citation Index (A&amp;HCI)
SC Linguistics
GA WT6EP
UT WOS:000715956200005
DA 2024-09-05
ER
PT C
AU Correia, A
   Jameel, S
   Schneider, D
   Paredes, H
   Fonseca, B
AF Correia, Antonio
   Jameel, Shoaib
   Schneider, Daniel
   Paredes, Hugo
   Fonseca, Benjamim
BE Wu, XT
   Jermaine, C
   Xiong, L
   Hu, XH
   Kotevska, O
   Lu, SY
   Xu, WJ
   Aluru, S
   Zhai, CX
   Al-Masri, E
   Chen, ZY
   Saltz, J
TI A Workflow-Based Methodological Framework for Hybrid Human-AI Enabled
   Scientometrics
SO 2020 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA)
SE IEEE International Conference on Big Data
LA English
DT Proceedings Paper
CT 8th IEEE International Conference on Big Data (Big Data)
CY DEC 10-13, 2020
CL ELECTR NETWORK
DE artificial intelligence; crowdsourcing; human-AI hybrid interaction;
   human-machine symbiosis; science mapping; research evaluation;
   scientometrics; workflows
ID INFORMATION-SCIENCE; SOFTWARE; KNOWLEDGE; TOOL; NETWORKS; MODEL
AB With cutting edge scientific breakthroughs, human-centred algorithmic approaches have proliferated in recent years and information technology (IT) has begun to redesign socio-technical systems in the context of human-AI collaboration. As a result, distinct forms of interaction have emerged in tandem with the proliferation of infrastructures aiding interdisciplinary work practices and research teams. Concomitantly, large volumes of heterogeneous datasets are produced and consumed at a rapid pace across many scientific domains. This results in difficulties in the reliable analysis of scientific production since current tools and algorithms are not necessarily able to provide acceptable levels of accuracy when analyzing the content and impact of publication records from large continuous scientific data streams. On the other hand, humans cannot consider all the information available and may be adversely influenced by extraneous factors. Using this rationale, we propose an initial design of a human-AI enabled pipeline for performing scientometric analyses that exploits the intersection between human behavior and machine intelligence. The contribution is a model for incorporating central principles of human-machine symbiosis (HMS) into scientometric workflows, demonstrating how hybrid intelligence systems can drive and encapsulate the future of research evaluation.
C1 [Correia, Antonio; Paredes, Hugo; Fonseca, Benjamim] INESC TEC, Apartado 1013, Vila Real, Portugal.
   [Correia, Antonio; Paredes, Hugo; Fonseca, Benjamim] Univ Tras Os Montes & Alto Douro, UTAD, Apartado 1013, Vila Real, Portugal.
   [Jameel, Shoaib] Univ Essex, Sch Comp Sci & Elect Engn, Colchester Campus, Colchester, Essex, England.
   [Schneider, Daniel] Univ Fed Rio de Janeiro, Tercio Pacitti Inst Comp Applicat & Res NCE, Rio De Janeiro, RJ, Brazil.
C3 INESC TEC; University of Tras-os-Montes & Alto Douro; University of
   Essex; Universidade Federal do Rio de Janeiro
RP Correia, A (corresponding author), INESC TEC, Apartado 1013, Vila Real, Portugal.; Correia, A (corresponding author), Univ Tras Os Montes & Alto Douro, UTAD, Apartado 1013, Vila Real, Portugal.
EM antonio.g.correia@inesctec.pt
RI Correia, António/AAJ-3347-2021; Paredes, Hugo/D-8347-2010
OI Correia, António/0000-0002-2736-3835; Paredes, Hugo/0000-0002-4274-4783;
   Fonseca, Benjamim/0000-0002-0850-9755
FU National Funds through the Portuguese funding agency, FCT -Fundacao para
   a Ciencia e a Tecnologia [UIDB/50014/2020]
FX This work is financed by National Funds through the Portuguese funding
   agency, FCT -Fundacao para a Ciencia e a Tecnologia within project
   UIDB/50014/2020.
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NR 87
TC 4
Z9 4
U1 5
U2 37
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2639-1589
BN 978-1-7281-6251-5
J9 IEEE INT CONF BIG DA
PY 2020
BP 2876
EP 2883
DI 10.1109/BigData50022.2020.9378096
PG 8
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BR6NZ
UT WOS:000662554703001
DA 2024-09-05
ER
PT J
AU Ivanovic, I
AF Ivanovic, Igor
TI CAN AI-ASSISTED ESSAY ASSESSMENT SUPPORT TEACHERS? A CROSS-SECTIONAL
   MIXED-METHODS RESEARCH CONDUCTED AT THE UNIVERSITY OF MONTENEGRO
SO ANNALES-ANALI ZA ISTRSKE IN MEDITERANSKE STUDIJE-SERIES HISTORIA ET
   SOCIOLOGIA
LA English
DT Article
DE ChatGPT; automated grading; AI language models; essay assessment; essay
   feedback; assessment metrics; natural language processing
ID MAGICAL NUMBER
AB In this study, we will try to answer the question if an AI language model can provide teachers with essay assessment solutions that are on a par with the solutions provided by experienced professors. We designed a study with the aim of comparing the essay assessment outputs of the AI language model and three of our colleagues working at the University of Montenegro. The main aim of this paper is to investigate if this AI language model can be a viable teachers' assistance tool that provides immediate and meaningful feedback to teachers and students. Our hypothesis is, with some caveats, that the AI language model is more than a viable and useful tool, capable of providing meaningful and immediate feedback, greatly reducing the assessment time, and thus helping the teachers become more efficient and consistent. We will compare the results of 78 essays assessed by three teachers with the results provided by ChatGPT and see where the two sets of results converge or diverge in terms of their individual and overall scores.
C1 [Ivanovic, Igor] Univ Montenegro, Fac Philol, Danila Bojov Bb, Niksic 81400, Montenegro.
C3 University of Montenegro
RP Ivanovic, I (corresponding author), Univ Montenegro, Fac Philol, Danila Bojov Bb, Niksic 81400, Montenegro.
EM iggybosnia@ucg.ac.me
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NR 31
TC 0
Z9 0
U1 11
U2 11
PU HISTORICAL SOC SOUTHERN PRIMORSKA KOPER-HSSP
PI KOPER
PA GARIBALDIJEVA 18, KOPER, 6000, SLOVENIA
SN 1408-5348
EI 2591-1775
J9 ANN-ANAL ISTRSKE MED
JI Ann.-Anal. Istrske Mediteranske
PY 2023
VL 33
IS 3
BP 571
EP 590
DI 10.19233/ASHS.2023.30
PG 20
WC Humanities, Multidisciplinary
WE Arts &amp; Humanities Citation Index (A&amp;HCI)
SC Arts & Humanities - Other Topics
GA HX9Y5
UT WOS:001162939500010
DA 2024-09-05
ER
PT J
AU Liu, JHC
AF Liu, Juhong Christie
TI Evaluating Online Learning Orientation Design With a Readiness Scale
SO ONLINE LEARNING
LA English
DT Article
DE student online learning readiness; orientation for online learning;
   design research; structure and interaction; evaluation
ID HIGHER-EDUCATION; SOCIAL PRESENCE; MOTIVATION; STUDENTS; SATISFACTION;
   SUCCESS
AB Student online learning readiness (SOLR) has been identified as being closely associated with the success of learning in online environments. Online learning orientations have also been used as a key intervention to support students. However, the evaluation practice and research of online learning orientation design are limited. This research studied the effects of an orientation course on SOLR, using a multiyear design-based research with a one-group pretest and posttest method as the evaluation measurement. The design and implementation of a self-paced orientation course in Canvas learning management system was detailed as the intervention. A 20-item SOLR questionnaire was selected as the pretest and posttest instrument. After the initial cycles, a sample of 2,590 college students were invited to participate in the 2017 orientation and respond to the pretest and posttest. Because separate consent forms were distributed and collected at the pretest and posttest stages, the researcher was able to use 445 pretest and 624 posttest datasets. The independent samples t-test results indicated statistically significant improvement of SOLR competencies. The exploratory factor analysis results also indicated changes of items associated with the SOLR constructs. The reliability coefficients of all subscales were > .90, with an increase in the reliability of the SOLR instrument as a whole from pretest (alpha = .92) to posttest (alpha = .95). Implications for the design and evaluation of online learning orientations and preparing student online learning readiness are discussed toward future design and implementation.
C1 [Liu, Juhong Christie] James Madison Univ, Harrisonburg, VA 22807 USA.
C3 James Madison University
RP Liu, JHC (corresponding author), James Madison Univ, Harrisonburg, VA 22807 USA.
OI Liu, Juhong Christie/0000-0002-3384-4379
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NR 59
TC 20
Z9 36
U1 0
U2 13
PU ONLINE LEARNING CONSORTIUM
PI NEWBURYPORT
PA PO BOX 1238, NEWBURYPORT, MA 01950 USA
SN 2472-5749
EI 2472-5730
J9 ONLINE LEARN
JI Online Learn.
PD DEC
PY 2019
VL 23
IS 4
BP 42
EP 61
DI 10.24059/olj.v23i4.2078
PG 20
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA JR9TU
UT WOS:000499959500004
OA gold
DA 2024-09-05
ER
PT J
AU Sekli, GM
AF Sekli, Giulio Marchena
TI The research landscape on generative artificial intelligence: a
   bibliometric analysis of transformer-based models
SO KYBERNETES
LA English
DT Article; Early Access
DE Generative artificial intelligence; Transformer-based models;
   Bibliometrics; Co-citation analysis; Co-word analysis; Strategic diagram
ID ABSORPTIVE-CAPACITY; INFORMATION; COCITATION; INNOVATION; EVOLUTION;
   INTERVIEW; NETWORK; TOOL
AB Purpose - The aim of this study is to offer valuable insights to businesses and facilitate better understanding on transformer-based models (TBMs), which are among the widely employed generative artificial intelligence (GAI) models, garnering substantial attention due to their ability to process and generate complex data. Design/methodology/approach - Existing studies on TBMs tend to be limited in scope, either focusing on specific fields or being highly technical. To bridge this gap, this study conducts robust bibliometric analysis to explore the trends across journals, authors, affiliations, countries and research trajectories using science mapping techniques - co-citation, co-words and strategic diagram analysis. Findings - Identified research gaps encompass the evolution of new closed and open-source TBMs; limited exploration across industries like education and disciplines like marketing; a lack of in-depth exploration on TBMs' adoption in the health sector; scarcity of research on TBMs' ethical considerations and potential TBMs' performance research in diverse applications, like image processing. Originality/value - The study offers an updated TBMs landscape and proposes a theoretical framework for TBMs' adoption in organizations. Implications for managers and researchers along with suggested research questions to guide future investigations are provided.
C1 [Sekli, Giulio Marchena] Ctr Catolica Grad Business Sch, Lima, Peru.
   [Sekli, Giulio Marchena] Pontificia Univ Catolica Peru, Lima, Peru.
C3 Pontificia Universidad Catolica del Peru
RP Sekli, GM (corresponding author), Ctr Catolica Grad Business Sch, Lima, Peru.; Sekli, GM (corresponding author), Pontificia Univ Catolica Peru, Lima, Peru.
EM gmarchena@pucp.pe
RI MARCHENA SEKLI, GIULIO FRANZ/IST-0279-2023
OI Marchena Sekli, Giulio/0000-0003-3854-2879
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NR 181
TC 0
Z9 0
U1 3
U2 3
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 0368-492X
EI 1758-7883
J9 KYBERNETES
JI Kybernetes
PD 2024 JUL 19
PY 2024
DI 10.1108/K-03-2024-0554
EA JUL 2024
PG 37
WC Computer Science, Cybernetics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZB4L0
UT WOS:001272816000001
DA 2024-09-05
ER
PT J
AU Habib, R
   Afzal, MT
AF Habib, Raja
   Afzal, Muhammad Tanvir
TI Paper recommendation using citation proximity in bibliographic coupling
SO TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES
LA English
DT Article
DE Paper recommendation; bibliographic coupling; citation proximity
   analysis; DBSCAN
AB Research paper recommendation has been a hot research area for the last few decades. Thus far, numerous different paper recommendation approaches have been proposed. Some of these include methods based on metadata, content similarity, collaborative filtering, and citation analysis, among others. Citation analysis methods include bibliographic coupling and co-citation analysis. Much research has been done in the area of co-citation analysis. Researchers have also performed experiments using the proximity of in-text citations in co-citation analysis and have found that it improves the accuracy of paper recommendation. In co-citation analysis, the similarity is discovered based on the frequency of co-cited papers in different research papers and those citing papers may belong to different areas. However, when proximity is used to calculate co-citation, the accuracy of recommendations improves significantly. Bibliographic coupling finds bibliographic coupling strength based on the common references between two papers. In bibliographic coupling, a large number of common references of two papers means that they belong to the same area, unlike co-citation analysis, in which there is a possibility that the citing papers may belong to different areas. Based on the observation that with the use of proximity analysis the accuracy in cases of co-citation analysis has improved, this paper investigates if the accuracy of paper recommendation can be further improved by using proximity analysis in bibliographic coupling. This paper proposes an approach that extends the traditional bibliographic coupling by exploiting the proximity of in-text citations of bibliographically coupled articles. The proposed approach takes into account the proximity of in-text citations by clustering the in-text citations using a density-based algorithm called DBSCAN. Experiments on a data set of research papers are presented to show that there is a substantial increase in accuracy of the recommendations produced by DBSCAN based on proximity analysis of in-text citations compared to traditional bibliographic coupling and content-based approaches.
C1 [Habib, Raja; Afzal, Muhammad Tanvir] Capital Univ Sci & Technol, Dept Comp Sci, Islamabad, Pakistan.
C3 Capital University of Science & Technology
RP Habib, R (corresponding author), Capital Univ Sci & Technol, Dept Comp Sci, Islamabad, Pakistan.
EM r_habib_pk@yahoo.com
RI Afzal, Muhammad/D-3741-2019
OI Afzal, Muhammad/0000-0002-7851-2327; Afzal, Muhammad
   Tanvir/0000-0002-9765-8815
FU Higher Education Commission (HEC) of Pakistan
FX This research was supported by Higher Education Commission (HEC) of
   Pakistan. We thank the Capital University of Science and Technology
   Islamabad for assistance. We thank Ansar Mehmood for assistance with
   compilation of the gold standard dataset and for important
   recommendations and comments during this research and previous versions
   of the manuscript.
CR Afzal MT, 2007, J UNIVERS COMPUT SCI, V13, P1234
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NR 21
TC 18
Z9 20
U1 3
U2 40
PU Tubitak Scientific & Technological Research Council Turkey
PI ANKARA
PA ATATURK BULVARI NO 221, KAVAKLIDERE, TR-06100 ANKARA, TURKIYE
SN 1300-0632
EI 1303-6203
J9 TURK J ELECTR ENG CO
JI Turk. J. Electr. Eng. Comput. Sci.
PY 2017
VL 25
IS 4
BP 2708
EP 2718
DI 10.3906/elk-1608-180
PG 11
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA FC7AR
UT WOS:000406993300013
OA Bronze
DA 2024-09-05
ER
PT J
AU Miranda, O
   Kiehl, SM
   Qi, XG
   Brannock, MD
   Kosten, T
   Ryan, ND
   Kirisci, L
   Wang, YS
   Wang, LR
AF Miranda, Oshin
   Kiehl, Sophie Marie
   Qi, Xiguang
   Brannock, M. Daniel
   Kosten, Thomas
   Ryan, Neal David
   Kirisci, Levent
   Wang, Yanshan
   Wang, Lirong
TI Enhancing post-traumatic stress disorder patient assessment: leveraging
   natural language processing for research of domain criteria
   identification using electronic medical records
SO BMC MEDICAL INFORMATICS AND DECISION MAKING
LA English
DT Article
DE Post-traumatic stress disorder; Research of domain criteria; Real-world
   evidence; Clinical notes; Natural language processing
ID SYMPTOM SEVERITY; PTSD; ALCOHOL
AB Background Extracting research of domain criteria (RDoC) from high-risk populations like those with post-traumatic stress disorder (PTSD) is crucial for positive mental health improvements and policy enhancements. The intricacies of collecting, integrating, and effectively leveraging clinical notes for this purpose introduce complexities.Methods In our study, we created a natural language processing (NLP) workflow to analyze electronic medical record (EMR) data and identify and extract research of domain criteria using a pre-trained transformer-based natural language model, all-mpnet-base-v2. We subsequently built dictionaries from 100,000 clinical notes and analyzed 5.67 million clinical notes from 38,807 PTSD patients from the University of Pittsburgh Medical Center. Subsequently, we showcased the significance of our approach by extracting and visualizing RDoC information in two use cases: (i) across multiple patient populations and (ii) throughout various disease trajectories.Results The sentence transformer model demonstrated high F1 macro scores across all RDoC domains, achieving the highest performance with a cosine similarity threshold value of 0.3. This ensured an F1 score of at least 80% across all RDoC domains. The study revealed consistent reductions in all six RDoC domains among PTSD patients after psychotherapy. We found that 60.6% of PTSD women have at least one abnormal instance of the six RDoC domains as compared to PTSD men (51.3%), with 45.1% of PTSD women with higher levels of sensorimotor disturbances compared to men (41.3%). We also found that 57.3% of PTSD patients have at least one abnormal instance of the six RDoC domains based on our records. Also, veterans had the higher abnormalities of negative and positive valence systems (60% and 51.9% of veterans respectively) compared to non-veterans (59.1% and 49.2% respectively). The domains following first diagnoses of PTSD were associated with heightened cue reactivity to trauma, suicide, alcohol, and substance consumption.Conclusions The findings provide initial insights into RDoC functioning in different populations and disease trajectories. Natural language processing proves valuable for capturing real-time, context dependent RDoC instances from extensive clinical notes.
C1 [Miranda, Oshin; Qi, Xiguang; Wang, Lirong] Univ Pittsburgh, Computat Chem Genom Screening Ctr, Sch Pharm, Dept Pharmaceut Sci, Pittsburgh, PA 15213 USA.
   [Kiehl, Sophie Marie] Colorado State Univ, Ft Collins, CO 80521 USA.
   [Brannock, M. Daniel] RTI Int, Durham, NC 27709 USA.
   [Kosten, Thomas] Baylor Coll Med, Menninger Dept Psychiat, Houston, TX 77030 USA.
   [Ryan, Neal David] Univ Pittsburgh, Sch Med, Dept Psychiat, Pittsburgh, PA 15213 USA.
   [Kirisci, Levent] Univ Pittsburgh, Sch Pharm, Pittsburgh, PA 15213 USA.
   [Wang, Yanshan] Univ Pittsburgh, Sch Hlth & Rehabil Sci, Pittsburgh, PA 15213 USA.
C3 Pennsylvania Commonwealth System of Higher Education (PCSHE); University
   of Pittsburgh; Colorado State University; Research Triangle Institute;
   Baylor College of Medicine; Pennsylvania Commonwealth System of Higher
   Education (PCSHE); University of Pittsburgh; Pennsylvania Commonwealth
   System of Higher Education (PCSHE); University of Pittsburgh;
   Pennsylvania Commonwealth System of Higher Education (PCSHE); University
   of Pittsburgh
RP Wang, LR (corresponding author), Univ Pittsburgh, Computat Chem Genom Screening Ctr, Sch Pharm, Dept Pharmaceut Sci, Pittsburgh, PA 15213 USA.
EM liw30@pitt.edu
OI Brannock, Daniel/0000-0001-8095-547X
FU Office of the Assistant Secretary of Defense for Health Affairs through
   the Alcohol and Substance Abuse Research Program; University of
   Pittsburgh Center for Research Computing through the NIH
   [S10OD028483-01A1]; NIH [UL1 TR001857];  [W81XWH-22-2-0081 (PASA3)]
FX The U.S. Army Medical Research Acquisition Activity, 820 Chandler
   Street, Fort Detrick MD 21702-5014 is the awarding and administering
   acquisition office. This work was supported by the Office of the
   Assistant Secretary of Defense for Health Affairs through the Alcohol
   and Substance Abuse Research Program under Award No. W81XWH-22-2-0081
   (PASA3). Opinions, interpretations, conclusions, and recommendations are
   those of the author and are not necessarily endorsed by the Department
   of Defense. This research was supported in part by the University of
   Pittsburgh Center for Research Computing through the NIH
   S10OD028483-01A1 grant and NIH UL1 TR001857 grant.
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NR 59
TC 0
Z9 0
U1 1
U2 1
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
EI 1472-6947
J9 BMC MED INFORM DECIS
JI BMC Med. Inform. Decis. Mak.
PD JUN 4
PY 2024
VL 24
IS 1
AR 154
DI 10.1186/s12911-024-02554-8
PG 14
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Medical Informatics
GA TB0M8
UT WOS:001238679900001
PM 38835009
OA gold, Green Published
DA 2024-09-05
ER
PT C
AU Saravanan, TM
   Ajmal, MM
   Manoranjith, M
   Sanjaay, BG
   Mishra, JP
AF Saravanan, T. M.
   Ajmal, M. Mohammed
   Manoranjith, M.
   Sanjaay, B. G.
   Mishra, Jay Prakash
TI Rumour influence minimization and topic modelling for twitter dataset
   using machine learning schemes
SO MATERIALS TODAY-PROCEEDINGS
LA English
DT Proceedings Paper
CT International Conference on Artificial Intelligence and Energy Systems
   (AIES)
CY DEC 08-09, 2021
CL St Josephs Coll Engn & Technol, Palai, INDIA
HO St Josephs Coll Engn & Technol
DE Sentiment analysis; Support Vector Machine (SVM); Greedy and Dynamic
   Blocking Algorithm; Tweet
AB We advocate a joint combined trend sentiment categorization method to guide sentiment classifiers for a couple of tweets at the same time. Distinctively, putrefy the sentiment classifier of all trends linked to two additives, namely the specific trend and the global trend. Our method gives a green way to as it should sort out trendy subjects devoid of the lack of outside facts making news channels to find out the infringement news in a particular time or to rush out viral memes to enhance marketing decisions with competitors. The analysis of social functions also reveals styles associated with all kinds of trends, such as tweets with approximately ongoing activities of trendsetters. The unique version of the Trends Greedy and Dynamic Blocking Algorithms can capture the precise expressions of sentiment in each Trend. In addition, we extract trends unique sentimental knowledge from each of the classified and unmarked samples in each Trend and use it to embellish the mastery of Trends precise sentiment clas-sifiers. In addition, we are introducing green algorithms to resolve the version of our technique. These schemes can effectively boost the performance of the combined trends group and outperform traditional methods with experimental effects on benchmark datasets. Copyright (C) 2022 Elsevier Ltd. All rights reserved.
C1 [Saravanan, T. M.; Ajmal, M. Mohammed; Manoranjith, M.; Sanjaay, B. G.; Mishra, Jay Prakash] Kongu Engn Coll, Dept Comp Applicat, Perundurai 638060, Tamil Nadu, India.
C3 Kongu Engineering College
RP Mishra, JP (corresponding author), Kongu Engn Coll, Dept Comp Applicat, Perundurai 638060, Tamil Nadu, India.
EM tmskec@gmail.com
NR 0
TC 0
Z9 0
U1 0
U2 1
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2214-7853
J9 MATER TODAY-PROC
JI Mater. Today-Proc.
PY 2022
VL 58
SI SI
BP 535
EP 539
DI 10.1016/j.matpr.2022.03.059
EA MAY 2022
PN 1
PG 5
WC Materials Science, Multidisciplinary
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Materials Science
GA 1S5DF
UT WOS:000804070100028
DA 2024-09-05
ER
PT J
AU Sandu, A
   Cotfas, LA
   Stanescu, A
   Delcea, C
AF Sandu, Andra
   Cotfas, Liviu-Adrian
   Stanescu, Aurelia
   Delcea, Camelia
TI A Bibliometric Analysis of Text Mining: Exploring the Use of Natural
   Language Processing in Social Media Research
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE text mining; natural language processing; social media; bibliometric;
   Biblioshiny
ID SENTIMENT ANALYSIS
AB Natural language processing (NLP) plays a pivotal role in modern life by enabling computers to comprehend, analyze, and respond to human language meaningfully, thereby offering exciting new opportunities. As social media platforms experience a surge in global usage, the imperative to capture and better understand the messages disseminated within these networks becomes increasingly crucial. Moreover, the occurrence of adverse events, such as the emergence of a pandemic or conflicts in various parts of the world, heightens social media users' inclinations towards these platforms. In this context, this paper aims to explore the scientific literature dedicated to the utilization of NLP in social media research, with the goal of highlighting trends, keywords, and collaborative networks within the authorship that contribute to the proliferation of papers in this field. To achieve this objective, we extracted and analyzed 1852 papers from the ISI Web of Science database. An initial observation reveals a remarkable annual growth rate of 62.18%, underscoring the heightened interest of the academic community in this domain. This paper includes an n-gram analysis and a review of the most cited papers in the extracted database, offering a comprehensive bibliometric analysis. The insights gained from these efforts provide essential perspectives and contribute to identifying pertinent issues in social media analysis addressed through the application of NLP.
C1 [Sandu, Andra; Cotfas, Liviu-Adrian; Delcea, Camelia] Bucharest Univ Econ Studies, Dept Econ Informat & Cybernet, Bucharest 010552, Romania.
   [Stanescu, Aurelia] Bucharest Univ Econ Studies, Dept Management, Bucharest 010552, Romania.
C3 Bucharest University of Economic Studies; Bucharest University of
   Economic Studies
RP Cotfas, LA (corresponding author), Bucharest Univ Econ Studies, Dept Econ Informat & Cybernet, Bucharest 010552, Romania.
EM liviu.cotfas@ase.ro
RI Delcea, Camelia/C-4343-2011
OI Delcea, Camelia/0000-0003-3589-1969
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NR 55
TC 3
Z9 3
U1 5
U2 5
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2076-3417
J9 APPL SCI-BASEL
JI Appl. Sci.-Basel
PD APR
PY 2024
VL 14
IS 8
AR 3144
DI 10.3390/app14083144
PG 34
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA OW1B1
UT WOS:001210212300001
OA gold
DA 2024-09-05
ER
PT J
AU Curiac, CD
   Micea, MV
AF Curiac, Christian-Daniel
   Micea, Mihai V.
TI Identifying Hot Information Security Topics Using LDA and Multivariate
   Mann-Kendall Test
SO IEEE ACCESS
LA English
DT Article
DE Market research; Bibliometrics; Natural language processing; Databases;
   Data mining; Time series analysis; Information security; Metadata; LDA
   topic modeling; multivariate Mann-Kendall test; natural language
   processing; paper metadata; research theme; research trend
ID NONPARAMETRIC-TESTS; TREND
AB Discovering promising research themes in a scientific domain by evaluating semantic information extracted from bibliometric databases represents a challenging task for Natural Language Processing (NLP). While existing NLP methods generally characterize the research topics using unique key terms, we take a step further by more accurately modeling the research themes as finite sets of key terms. The proposed approach involves two stages: identifying the research themes from paper metadata using LDA topic modeling; and, evaluation of research theme trends by employing a version of the Mann-Kendall test that is able to cope with multivariate time series of term occurrences. The results obtained by applying this general methodology to Information Security domain confirm its viability.
C1 [Curiac, Christian-Daniel; Micea, Mihai V.] Politehn Univ Timisoara, Comp & Informat Technol Dept, Timisoara 300223, Romania.
C3 Universitatea Politehnica Timisoara
RP Curiac, CD (corresponding author), Politehn Univ Timisoara, Comp & Informat Technol Dept, Timisoara 300223, Romania.
EM christian.curiac@cs.upt.ro
OI Curiac, Christian-Daniel/0000-0002-2253-7226
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NR 35
TC 0
Z9 0
U1 4
U2 26
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2023
VL 11
BP 18374
EP 18384
DI 10.1109/ACCESS.2023.3247588
PG 11
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA 9M5IO
UT WOS:000942263400001
OA gold
DA 2024-09-05
ER
PT J
AU McCardle, JR
AF McCardle, JR
TI The challenge of integrating AI & Smart Technology in design education
SO INTERNATIONAL JOURNAL OF TECHNOLOGY AND DESIGN EDUCATION
LA English
DT Article
DE artificial intelligence; industrial design; research dissemination;
   technology integration; tertiary education
AB This paper examines some of the many problems and issues associated with integrating new and developing technologies into the education of future designers. As technology in general races ahead challenges arise for both commercial designers and educators on how best to keep track and utilise the advances. The challenge is particularly acute within tertiary education where the introduction of new cutting edge technology is often encouraged. Although this is generally achieved through the feedback of research activity, integrating new concepts at an appropriate level is a major task. Of particular concern is how focussed areas of applied technology can be made part of the multidisciplinary scope of design education.
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C1 Loughborough Univ Technol, Bridgeman Ctr, Dept Design & Technol, Loughborough LE11 3TU, Leics, England.
C3 Loughborough University
RP McCardle, JR (corresponding author), Loughborough Univ Technol, Bridgeman Ctr, Dept Design & Technol, Loughborough LE11 3TU, Leics, England.
RI McCardle, John/AAP-4557-2020
OI McCardle, John/0000-0002-5358-5765
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NR 18
TC 12
Z9 12
U1 3
U2 54
PU KLUWER ACADEMIC PUBL
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0957-7572
J9 INT J TECHNOL DES ED
JI Int. J. Technol. Des. Educ.
PY 2002
VL 12
IS 1
BP 59
EP 76
DI 10.1023/A:1013089404168
PG 18
WC Education & Educational Research; Education, Scientific Disciplines;
   Engineering, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Education & Educational Research; Engineering
GA 497HG
UT WOS:000172448900004
OA Green Accepted
DA 2024-09-05
ER
PT J
AU Zheng, B
   Chen, WF
   Zhao, H
AF Zheng, Bin
   Chen, Wenfeng
   Zhao, Hui
TI The Spatial and Temporal Characteristics of Industry-University Research
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SO SUSTAINABILITY
LA English
DT Article
DE IUR collaboration; potential; spatio-temporal distribution; principal
   component analysis
ID INNOVATION; KNOWLEDGE; COOPERATION; PERFORMANCE; EDUCATION
AB The aim of this study was to investigate the spatio-temporal characteristics of the industry-university research (IUR) collaboration efficiency of Chinese mainland colleges and universities, from 2008 to 2018. A comparative analysis method was used to analyze the data from the Statistical Yearbook of China's Education Funds, the Compilation of Science and Technology Statistics of Colleges and Universities, and the China Statistical Yearbook. The principal components were extracted from relevant indicators of IUR capability in colleges and universities, with a principal component analysis (PCA) method. The principal component scores and comprehensive scores of 31 provinces in mainland China were calculated. The results showed that the efficiency of IUR collaboration in Chinese colleges and universities has increased rapidly within the 11 years studied. The efficiency in the eastern region has grown faster than that in the western region, and the gap between the southern region and the northern region has also continued to widen. The results also showed that the development of IUR collaboration efficiency of colleges and universities in mainland China is unbalanced. Scientific and technological funds, and scientific and technological manpower, were excessively concentrated in the southeast. Therefore, there is large room for improvement in the overall development of IUR collaboration in Chinese colleges and universities.
C1 [Zheng, Bin] China Univ Min & Technol, Sch Management, Xuzhou 221116, Jiangsu, Peoples R China.
   [Zheng, Bin] Wuxi Taihu Univ, Scotland Acad, Wuxi 214063, Jiangsu, Peoples R China.
   [Chen, Wenfeng] Wuxi Univ, Grad Sch, Wuxi 214105, Jiangsu, Peoples R China.
   [Zhao, Hui] Fudan Univ, Dept Environm Sci & Engn, Shanghai 200438, Peoples R China.
C3 China University of Mining & Technology; Wuxi University; Fudan
   University
RP Zhao, H (corresponding author), Fudan Univ, Dept Environm Sci & Engn, Shanghai 200438, Peoples R China.
EM zhengbin5202@163.com; wenf_chen@163.com; zhaohui_nuist@163.com
RI Zheng, Bin/ACR-9720-2022; Zheng, Bin/CAF-2106-2022
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NR 48
TC 4
Z9 4
U1 2
U2 40
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD DEC
PY 2021
VL 13
IS 23
AR 13180
DI 10.3390/su132313180
PG 13
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA XU9YJ
UT WOS:000734610600001
OA gold
DA 2024-09-05
ER
PT J
AU Verstegen, DML
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AF Verstegen, D. M. L.
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TI How do Virtual Teams Collaborate in Online Learning Tasks in a MOOC?
SO INTERNATIONAL REVIEW OF RESEARCH IN OPEN AND DISTRIBUTED LEARNING
LA English
DT Article
DE MOOC; problem-based learning; PBL; open educational resources; online
   learning; virtual teams; team collaboration; design-based research
AB Modern learning theories stress the importance of student-centered and self-directed learning. Problem-Based Learning (PBL) supports this by focusing on small group learning centered around authentic problems. PBL, however, usually relies heavily on face-to-face team collaboration and tutor guidance. Yet, when applied in online/blended environments, such elements may not be feasible or even desirable. This study explores how virtual teams collaborate in online learning tasks in the context of a nine-week Massive Open Online Course (MOOC) where international, virtual teams worked on PBL-like tasks. Twenty-one self-formed teams were observed. An inductive thematic analysis resulted in five themes: 1) team formation and team composition, 2) team process (organization and leadership), 3) approach to task work (task division and interaction), 4) use of tools, and 5) external factors (MOOC design and interaction with others). Overall findings revealed that online, virtual teams can collaborate on learning tasks without extensive guidance, but this requires additional communication and technological skills and support. Explicit discussion about group organization and task work, a positive atmosphere, and acceptance of unequal contributions seem to be positive factors. Additional support is required to prepare participants for virtual team work, develop digital literacy, and stimulate more elaborate brainstorming and discussion.
C1 [Verstegen, D. M. L.] Maastricht Univ, Sch Hlth Profess Educ SHE, Maastricht, Netherlands.
   [Dailey-Hebert, A.] Pk Univ, Parkville, MO USA.
   [Dailey-Hebert, A.] Maastricht Univ, SBE, Maastricht, Netherlands.
   [Fonteijn, H. T. H.] Maastricht Univ, Dept Work & Social Psychol, Maastricht, Netherlands.
   [Clarebout, G.] Univ Leuven, Ctr Instruct Psychol & Technol, Leuven, Belgium.
   [Clarebout, G.; Spruijt, A.] Maastricht Univ, SHE, Maastricht, Netherlands.
   [Spruijt, A.] Univ Utrecht, Fac Vet Med, Utrecht, Netherlands.
C3 Maastricht University; Maastricht University; Maastricht University; KU
   Leuven; Maastricht University; Utrecht University
RP Verstegen, DML (corresponding author), Maastricht Univ, Sch Hlth Profess Educ SHE, Maastricht, Netherlands.
RI Spruijt, Annemarie/ABB-2416-2021
OI Spruijt, Annemarie/0000-0002-0995-5503
CR Ahn J., 2013, J ONLINE LEARNING TE, V9, P2
   [Anonymous], 1980, Problem-based learning: An approach to medical education. Volume
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NR 37
TC 10
Z9 13
U1 0
U2 32
PU ATHABASCA UNIV PRESS
PI ATHABASCA
PA 1 UNIVERSITY DR, ATHABASCA, AB T9S 3A3, CANADA
SN 1492-3831
J9 INT REV RES OPEN DIS
JI Int. Rev. Res. Open Distrib. Learn.
PD SEP
PY 2018
VL 19
IS 4
BP 39
EP 55
PG 17
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA GV1BS
UT WOS:000445802700003
DA 2024-09-05
ER
PT J
AU Koltcov, S
   Ignatenko, V
   Boukhers, Z
   Staab, S
AF Koltcov, Sergei
   Ignatenko, Vera
   Boukhers, Zeyd
   Staab, Steffen
TI Analyzing the Influence of Hyper-parameters and Regularizers of Topic
   Modeling in Terms of Renyi Entropy
SO ENTROPY
LA English
DT Article
DE topic modeling; Renyi entropy; regularization
ID ADDITIVE REGULARIZATION
AB Topic modeling is a popular technique for clustering large collections of text documents. A variety of different types of regularization is implemented in topic modeling. In this paper, we propose a novel approach for analyzing the influence of different regularization types on results of topic modeling. Based on Renyi entropy, this approach is inspired by the concepts from statistical physics, where an inferred topical structure of a collection can be considered an information statistical system residing in a non-equilibrium state. By testing our approach on four models-Probabilistic Latent Semantic Analysis (pLSA), Additive Regularization of Topic Models (BigARTM), Latent Dirichlet Allocation (LDA) with Gibbs sampling, LDA with variational inference (VLDA)-we, first of all, show that the minimum of Renyi entropy coincides with the "true" number of topics, as determined in two labelled collections. Simultaneously, we find that Hierarchical Dirichlet Process (HDP) model as a well-known approach for topic number optimization fails to detect such optimum. Next, we demonstrate that large values of the regularization coefficient in BigARTM significantly shift the minimum of entropy from the topic number optimum, which effect is not observed for hyper-parameters in LDA with Gibbs sampling. We conclude that regularization may introduce unpredictable distortions into topic models that need further research.
C1 [Koltcov, Sergei; Ignatenko, Vera] Natl Res Univ Higher Sch Econ, Soyuza Pechatnikov St 16, St Petersburg 190121, Russia.
   [Boukhers, Zeyd] Univ Koblenz Landau, Inst Web Sci & Technol, Univ Str 1, D-56070 Koblenz, Germany.
   [Staab, Steffen] Univ Stuttgart, Inst Parallel & Distributed Syst IPVS, Univ Str 32, D-50569 Stuttgart, Germany.
   [Staab, Steffen] Univ Southampton, Web & Internet Sci Res Grp, Univ Rd, Southampton SO17 1BJ, Hants, England.
C3 HSE University (National Research University Higher School of
   Economics); University of Koblenz & Landau; University of Stuttgart;
   University of Southampton
RP Koltcov, S (corresponding author), Natl Res Univ Higher Sch Econ, Soyuza Pechatnikov St 16, St Petersburg 190121, Russia.
EM skoltsov@hse.ru; vignatenko@hse.ru; boukhers@uni-koblenz.de;
   Steffen.Staab@ipvs.uni-stuttgart.de
RI Boukhers, Zeyd/HZL-0733-2023
OI Boukhers, Zeyd/0000-0001-9778-9164; Ignatenko, Vera/0000-0003-1407-0168;
   Staab, Steffen/0000-0002-0780-4154; Koltcov, Sergei/0000-0002-2932-2746
FU Basic Research Program at the National Research University Higher School
   of Economics in 2019; German Research Foundation (DFG) through the
   project grant 'Extraction of Citations from PDF Documents (EXCITE)' [STA
   572/14-1]; German Research Foundation (DFG) [STA 572/18-1]
FX Sergei Koltcov and Vera Ignatenko were supported by the Basic Research
   Program at the National Research University Higher School of Economics
   in 2019. Zeyd Boukhers and Steffen Staab were previously supported by
   the German Research Foundation (DFG) through the project grant
   'Extraction of Citations from PDF Documents (EXCITE)' under grant number
   STA 572/14-1. Steffen Staab is now supported by the German Research
   Foundation (DFG) through the project grant "Open Argument Mining" (grant
   number STA 572/18-1).
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NR 41
TC 8
Z9 9
U1 0
U2 4
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1099-4300
J9 ENTROPY-SWITZ
JI Entropy
PD APR
PY 2020
VL 22
IS 4
AR 394
DI 10.3390/e22040394
PG 13
WC Physics, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Physics
GA LT7BS
UT WOS:000537222600057
PM 33286169
OA Green Published, Green Submitted, gold
DA 2024-09-05
ER
PT J
AU Bornmann, L
   Hug, S
AF Bornmann, Lutz
   Hug, Sven
TI Bibliometrics-based heuristics: What is their definition and how can
   they be studied? - Research note
SO PROFESIONAL DE LA INFORMACION
LA English
DT Article
DE Bibliometrics; Heuristics; Bibliometrics-based heuristics; Research
   evaluation; Decision strategies
ID SCIENCE; FRUGAL; MODELS; INDEX
AB When scientists study the phenomena they are interested in, they apply sound methods and base their work on theoretical considerations. In contrast, when the fruits of their research are being evaluated, basic scientific standards do not seem to matter. Instead, simplistic bibliometric indicators (i.e., publication and citation counts) are, paradoxically, both widely used and criticized without any methodological and theoretical framework that would serve to ground both use and critique. Recently, however Bornmann and Marewski (2019) proposed such a framework. They developed bibliometrics-based heuristics (BBHs) based on the fast-and-frugal heuristics approach (Gigerenzer; Todd; ABC Research Group, 1999) to decision making, in order to conceptually understand and empirically investigate the quantitative evaluation of research as well as to effectively train end-users of bibliometrics (e.g., science managers, scientists). Heuristics are decision strategies that use part of the available information and ignore the rest. By exploiting the statistical structure of task environments, they can aid to make accurate, fast, effortless, and cost-efficient decisions without that trade-offs are incurred. Because of their simplicity, heuristics are easy to understand and communicate, enhancing the transparency of decision processes. In this commentary, we explain several BBHs and discuss how such heuristics can be employed in practice (using the evaluation of applicants for funding programs as one example). Furthermore, we outline why heuristics can perform well, and how they and their fit to task environments can be studied. In pointing to the potential of research on BBHs and to the risks that come with an under-researched, mindless usage of bibliometrics, this commentary contributes to make research evaluation more scientific.
C1 [Bornmann, Lutz] Max Planck Gesell, Div Sci & Innovat Studies, Hofgartenstr 8, D-80539 Munich, Germany.
   [Hug, Sven] Univ Zurich, Dept Psychol, Binzmuhlestr 14, CH-8050 Zurich, Switzerland.
C3 Max Planck Society; University of Zurich
RP Bornmann, L (corresponding author), Max Planck Gesell, Div Sci & Innovat Studies, Hofgartenstr 8, D-80539 Munich, Germany.
EM bornmann@gv.mpg.de; sven.hug@uzh.ch
RI Hug, Sven E./G-7810-2015; Bornmann, Lutz/A-3926-2008
OI Hug, Sven E./0000-0002-7624-9529; Bornmann, Lutz/0000-0003-0810-7091
CR [Anonymous], 2014, Research Excellence Framework
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NR 43
TC 2
Z9 3
U1 2
U2 38
PU  EDICIONES PROFESIONALES INFORMACION SL-EPI
PI BARCELONA
PA MISTRAL, 36, BARCELONA, ALBOLOTE, SPAIN
SN 1386-6710
EI 1699-2407
J9 PROF INFORM
JI Prof. Inf.
PD JUL-AUG
PY 2020
VL 29
IS 4
AR e290420
DI 10.3145/epi.2020.jul.20
PG 9
WC Communication; Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Communication; Information Science & Library Science
GA NK5HY
UT WOS:000566763500016
OA Bronze
DA 2024-09-05
ER
PT C
AU Alexandru, D
   Iftene, A
   Gîfu, D
AF Alexandru, Dan
   Iftene, Adrian
   Gifu, Daniela
BE Mititelu, VB
   Irimia, E
   Tufis, D
   Cristea, D
TI WHAT INDICATORS TELL US ABOUT MAKING ACCURATE RANK OF THE BEST PAPER
   PREDICTIONS
SO PROCEEDINGS OF THE 15TH INTERNATIONAL CONFERENCE LINGUISTIC RESOURCES
   AND TOOLS FOR NATURAL LANGUAGE PROCESSING
SE Linguistic Resources and Tools for Romanian Language Processing
LA English
DT Proceedings Paper
CT 15th International Conference on Linguistic Resources and Tools for
   Natural Language Processing
CY OCT 14-15, 2020
CL ELECTR NETWORK
DE arXiv collection; h-index; machine learning; paper ranking
ID QUALITY
AB We propose a pilot research methodology intended to predict the best paper ranking, including machine learning algorithms based on arXiv collection. Our approach plans to link the author's h-index, identified in the Semantic Scholar, with the quality of a paper. It is well known that the h-index is a significant indicator of research impact realised by one author or a team, based on citation measurement. Out of these considerations of paper ranking use, we will concentrate in this survey only on the one or more of the authors on the search results page, by checking the h-index for each of them.
C1 [Alexandru, Dan; Iftene, Adrian; Gifu, Daniela] Alexandru Ioan Cuza Univ, Fac Comp Sci, 16 Berthelot St, Iasi, Romania.
   [Gifu, Daniela] Romanian Acad, Inst Comp Sci, 2 Codrescu St, Iasi, Romania.
C3 Alexandru Ioan Cuza University; Romanian Academy of Sciences
RP Alexandru, D (corresponding author), Alexandru Ioan Cuza Univ, Fac Comp Sci, 16 Berthelot St, Iasi, Romania.
EM dan.alexandru@info.uaic.ro; adiftene@info.uaic.ro;
   daniela.gifu@info.uaic.ro
RI Gifu, Daniela/D-1805-2015
FU  [848098];  [H2020-SC1-BHC2018-2020/H2020-SC1-2019-Two-Stage-RTD]
FX This work was supported by project REVERT (taRgeted thErapy for adVanced
   colorEctal canceR paTients), Grant Agreement number: 848098,
   H2020-SC1-BHC2018-2020/H2020-SC1-2019-Two-Stage-RTD.
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NR 24
TC 0
Z9 0
U1 0
U2 2
PU EDITURA UNIV ALEXANDRU IOAN CUZA IASI
PI IASI
PA STR PINULUI NR 1A, IASI, 700109, ROMANIA
SN 1843-911X
J9 LING RES T ROM L PRO
PY 2020
BP 173
EP 182
PG 10
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications; Linguistics; Language & Linguistics
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Linguistics
GA BR6BE
UT WOS:000659362800016
DA 2024-09-05
ER
PT J
AU Kuppler, M
AF Kuppler, Matthias
TI Predicting the future impact of Computer Science researchers: Is there a
   gender bias?
SO SCIENTOMETRICS
LA English
DT Article
DE Impact prediction; h-index; Gender; Discrimination; Machine learning
ID RISING STARS; EMPLOYMENT; STEREOTYPES; SCIENTISTS; WOMEN; INDEX
AB The advent of large-scale bibliographic databases and powerful prediction algorithms led to calls for data-driven approaches for targeting scarce funds at researchers with high predicted future scientific impact. The potential side-effects and fairness implications of such approaches are unknown, however. Using a large-scale bibliographic data set of N = 111,156 Computer Science researchers active from 1993 to 2016, I build and evaluate a realistic scientific impact prediction model. Given the persistent under-representation of women in Computer Science, the model is audited for disparate impact based on gender. Random forests and Gradient Boosting Machines are used to predict researchers' h-index in 2010 from their bibliographic profiles in 2005. Based on model predictions, it is determined whether the researcher will become a high-performer with an h-index in the top-25% of the discipline-specific h-index distribution. The models predict the future h-index with an accuracy of R-2 = 0.875 and correctly classify 91.0% of researchers as high-performers and low-performers. Overall accuracy does not vary strongly across researcher gender. Nevertheless, there is indication of disparate impact against women. The models underestimate the true h-index of female researchers more strongly than the h-index of male researchers. Further, women are 8.6% less likely to be predicted to become high-performers than men. In practice, hiring, tenure, and funding decisions that are based on model predictions risk to perpetuate the under-representation of women in Computer Science.
C1 [Kuppler, Matthias] Univ Siegen, Dept Social Sci, Adolf Reichwein Str 2, D-57076 Siegen, Germany.
C3 Universitat Siegen
RP Kuppler, M (corresponding author), Univ Siegen, Dept Social Sci, Adolf Reichwein Str 2, D-57076 Siegen, Germany.
EM matthias.kuppler@uni-siegen.de
OI Kuppler, Matthias/0000-0002-7324-4722
FU Projekt DEAL
FX Open Access funding enabled and organized by Projekt DEAL.
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NR 85
TC 4
Z9 4
U1 7
U2 29
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD NOV
PY 2022
VL 127
IS 11
BP 6695
EP 6732
DI 10.1007/s11192-022-04337-2
EA APR 2022
PG 38
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 5U5FU
UT WOS:000779267100001
OA hybrid
DA 2024-09-05
ER
PT J
AU Chen, H
   Hu, WZ
AF Chen, Hong
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TI Research on exchange rate pass-through effect based on artificial
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SO CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
LA English
DT Article
DE artificial intelligence; exchange rate pass-through; Hermite polynomial;
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ID IMPORT PRICES; COMPETITION; QUALITY; TRADE
AB Artificial intelligence is widely believed to reshape many industries in the future. This paper analyzes the chaotic characteristics of Chinese export price index, exchange rate data, and studies the predictive ability of the neural network model in exchange rate passthrough (ERPT) prediction. The Hermite neural network model, one of artificial intelligence models, is selected to forecast the rate of pass-through effect. Hermite neural network model demonstrates a superior forecasting accuracy, and its forecasting error is less than that of the general neural network model.
C1 [Chen, Hong; Hu, WenZhe] Wuhan Univ, Econ & Management Sch, Wuhan 430072, Peoples R China.
C3 Wuhan University
RP Hu, WZ (corresponding author), Wuhan Univ, Econ & Management Sch, Wuhan 430072, Peoples R China.
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NR 29
TC 2
Z9 2
U1 0
U2 28
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1532-0626
EI 1532-0634
J9 CONCURR COMP-PRACT E
JI Concurr. Comput.-Pract. Exp.
PD MAY 10
PY 2019
VL 31
IS 9
SI SI
AR e4986
DI 10.1002/cpe.4986
PG 9
WC Computer Science, Software Engineering; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA HU1NF
UT WOS:000465038400010
DA 2024-09-05
ER
PT C
AU Jangid, N
   Saha, S
   Narasimhamurthy, A
   Mathur, A
AF Jangid, Neelam
   Saha, Snehanshu
   Narasimhamurthy, Anand
   Mathur, Archana
BE Mitra, S
   McIntosh, S
   Nair, I
   Bedi, P
   Rajasree, MS
TI Computing the Prestige of a journal: A Revised Multiple Linear
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SO PROCEEDING OF THE THIRD INTERNATIONAL SYMPOSIUM ON WOMEN IN COMPUTING
   AND INFORMATICS (WCI-2015)
LA English
DT Proceedings Paper
CT 3rd International Symposium on Women in Computing and Informatics (WCI)
CY AUG 10-13, 2015
CL SCMS Sch Engn & Technol, Aluva, INDIA
HO SCMS Sch Engn & Technol
DE Revised Multiple Linear Regression Model (RMLRM); Journal ranking; PCA
ID SCIENCE; IMPACT
AB The evaluation of journals based on their influence is of interest for numerous reasons. Various methods of computing a score have been proposed for measuring the scientific influence of scholarly journals. Typically the computation of any of these scores involves compiling the citation information pertaining to the journal under consideration. This involves significant overhead since the article citation information of not only the journal under consideration but also that of other journals for the recent few years need to be stored. Our work is motivated by the idea of developing a computationally lightweight scheme that does not require any data storage, yet yields a score which is useful for measuring the importance of journals. In this paper, a Journal Influence Score is mooted and a regression analysis based method is proposed to calculate the score. We validated our model using historical data from the SCImago portal. The results are promising, the rankings obtained using the proposed method compare favourably with the SCImago Journal Rank, thus indicating that the proposed approach is a feasible and effective method of calculating scientific impact of journals.
C1 [Jangid, Neelam; Saha, Snehanshu; Mathur, Archana] PESIT South, Dept Comp Sci & Engn, Bangalore, Karnataka, India.
   [Narasimhamurthy, Anand] BITS, Dept Comp Sci & Engn, Hyderabad, Andhra Pradesh, India.
C3 PES University; Birla Institute of Technology & Science Pilani (BITS
   Pilani)
RP Jangid, N (corresponding author), PESIT South, Dept Comp Sci & Engn, Bangalore, Karnataka, India.
EM neelu.jangid88@gmail.com; snehanshusaha@pes.edu; anandmnl@gmail.com;
   archanamathur@pes.edu
RI ; Saha, Snehanshu/R-1028-2018
OI Mathur, Archana/0000-0003-4522-6890; Saha, Snehanshu/0000-0002-8458-604X
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NR 8
TC 2
Z9 2
U1 0
U2 1
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA
BN 978-1-4503-3361-0
PY 2015
BP 1
EP 4
DI 10.1145/2791405.2791407
PG 4
WC Computer Science, Information Systems; Computer Science,
   Interdisciplinary Applications; Computer Science, Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BH0PF
UT WOS:000395824100001
DA 2024-09-05
ER
PT C
AU Cabanac, G
   Chandrasekaran, MK
   Frommholz, I
   Jaidka, K
   Kan, MY
   Mayr, P
   Wolfram, D
AF Cabanac, Guillaume
   Chandrasekaran, Muthu Kumar
   Frommholz, Ingo
   Jaidka, Kokil
   Kan, Min-Yen
   Mayr, Philipp
   Wolfram, Dietmar
GP IEEE
TI Joint Workshop on Bibliometric-enhanced Information Retrieval and
   Natural Language Processing for Digital Libraries (BIRNDL 2016)
SO 2016 IEEE/ACM JOINT CONFERENCE ON DIGITAL LIBRARIES (JCDL)
SE ACM-IEEE Joint Conference on Digital Libraries JCDL
LA English
DT Proceedings Paper
CT 16th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL)
CY JUN 19-23, 2016
CL Newark, NJ
DE Bibliometrics; Information Retrieval; Digital Libraries; Natural
   Language Processing; Text Mining
AB The large scale of scholarly publications poses a challenge for scholars in information-seeking and sensemaking. Bibliometric, information retrieval (IR), text mining and NLP techniques could help in these activities, but are not yet widely used in digital libraries. This workshop is intended to stimulate IR researchers and digital library professionals to elaborate on new approaches in natural language processing, information retrieval, scientometric and recommendation techniques which can advance the state-of-the-art in scholarly document understanding, analysis and retrieval at scale.
C1 [Cabanac, Guillaume] Univ Toulouse, Toulouse, France.
   [Chandrasekaran, Muthu Kumar; Kan, Min-Yen] NUS Sch Comp, Singapore, Singapore.
   [Frommholz, Ingo] Univ Bedfordshire, Luton, Beds, England.
   [Jaidka, Kokil] Adobe Syst Inc, Bangalore, Karnataka, India.
   [Mayr, Philipp] GESIS Leibniz Inst Social Sci, Leibniz, Germany.
   [Wolfram, Dietmar] Univ Wisconsin, Milwaukee, WI 53201 USA.
C3 Universite de Toulouse; University of Bedfordshire; Adobe Systems Inc.;
   Leibniz Institut fur Sozialwissenschaften (GESIS); University of
   Wisconsin System; University of Wisconsin Milwaukee
RP Cabanac, G (corresponding author), Univ Toulouse, Toulouse, France.
EM guillaume.cabanac@univ-tlse3.fr; muthu.chandra@comp.nus.edu.sg;
   ingo.frommholz@beds.ac.uk; jaidka@adobe.com; kanmy@comp.nus.edu.sg;
   philipp.mayr@gesis.org; dwolfram@uwn.edu
RI Jaidka, Kokil/AAK-2618-2020; Cabanac, Guillaume/C-5913-2011; Frommholz,
   Ingo/IUM-8186-2023
OI Jaidka, Kokil/0000-0002-8127-1157; Cabanac,
   Guillaume/0000-0003-3060-6241; Frommholz, Ingo/0000-0002-5622-5132
CR Atanassova Iana, 2015, P 1 WORKSH MIN SCI C
   Jaidka Kokil, 2014, P TEXT AN C GAITH
   Mayr Philipp, 2016, P 3 WORKSH BIBL ENH
   Nakov P. I., 2004, P SIGIR 04 WORKSH SE, P81
NR 4
TC 2
Z9 2
U1 2
U2 10
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2575-7865
EI 2575-8152
BN 978-1-4503-4229-2
J9 ACM-IEEE J CONF DIG
PY 2016
BP 299
EP 300
DI 10.1145/2910896.2926734
PG 2
WC Computer Science, Interdisciplinary Applications
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BG5HY
UT WOS:000389502300078
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Bollen, J
   Van de Sompel, H
   Hagberg, A
   Chute, R
AF Bollen, Johan
   Van de Sompel, Herbert
   Hagberg, Aric
   Chute, Ryan
TI A Principal Component Analysis of 39 Scientific Impact Measures
SO PLOS ONE
LA English
DT Article
ID JOURNAL IMPACT
AB Background: The impact of scientific publications has traditionally been expressed in terms of citation counts. However, scientific activity has moved online over the past decade. To better capture scientific impact in the digital era, a variety of new impact measures has been proposed on the basis of social network analysis and usage log data. Here we investigate how these new measures relate to each other, and how accurately and completely they express scientific impact.
   Methodology: We performed a principal component analysis of the rankings produced by 39 existing and proposed measures of scholarly impact that were calculated on the basis of both citation and usage log data.
   Conclusions: Our results indicate that the notion of scientific impact is a multi-dimensional construct that can not be adequately measured by any single indicator, although some measures are more suitable than others. The commonly used citation Impact Factor is not positioned at the core of this construct, but at its periphery, and should thus be used with caution.
RP Bollen, J (corresponding author), Los Alamos Natl Lab, Res Lib, Digital Lib Res & Prototyping Team, Los Alamos, NM 87545 USA.
EM jbollen@lanl.gov
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NR 29
TC 222
Z9 226
U1 2
U2 160
PU PUBLIC LIBRARY SCIENCE
PI SAN FRANCISCO
PA 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA
SN 1932-6203
J9 PLOS ONE
JI PLoS One
PD JUN 29
PY 2009
VL 4
IS 6
AR e6022
DI 10.1371/journal.pone.0006022
PG 11
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics
GA 463XB
UT WOS:000267465900001
OA Green Published, Green Submitted, gold
DA 2024-09-05
ER
PT J
AU Thijs, B
   Zhang, L
   Glänzel, W
AF Thijs, Bart
   Zhang, Lin
   Glanzel, Wolfgang
TI Bibliographic coupling and hierarchical clustering for the validation
   and improvement of subject-classification schemes
SO SCIENTOMETRICS
LA English
DT Article
DE Bibliographic coupling; Journal clustering; Second order similarities;
   Subject classification
AB An attempt is made to cluster journals from the complete Web of Science database by using bibliographic coupling similarities. Since the sparseness of the underlying similarity matrix proved inappropriate for this exercise, second-order similarities have been used. Only 0.12 % out of 8282 journals had to be removed from the classification as being singletons. The quality at three hierarchical levels with 6, 14 and 24 clusters substantiated the applicability of this method. Cluster labelling was made on the basis of the about 70 subfields of the Leuven-Budapest subject-classification scheme that also allowed the comparison with the existing two-level journal classification system developed in Leuven. The further comparison with the 22 field classification system of the Essential Science Indicators does, however, reveal larger deviations.
C1 [Thijs, Bart; Zhang, Lin; Glanzel, Wolfgang] Katholieke Univ Leuven, Dept MSI, ECOOM, Louvain, Belgium.
   [Zhang, Lin] North China Univ Water Conservancy & Elect Power, Dept Management & Econ, Zhengzhou, Peoples R China.
   [Glanzel, Wolfgang] Lib Hungarian Acad Sci, Dept Sci Policy & Scientometr, Budapest, Hungary.
C3 KU Leuven; North China University of Water Resources & Electric Power;
   Hungarian Academy of Sciences
RP Thijs, B (corresponding author), Katholieke Univ Leuven, Dept MSI, ECOOM, Louvain, Belgium.
EM Bart.Thijs@econ.kuleuven.be
RI Glanzel, Wolfgang/AAE-4395-2021; zhang, lin/M-3007-2017
OI zhang, lin/0000-0003-0526-9677
FU National Natural Science Foundation of China [71103064]
FX This is a revised and extended version of a paper presented at the 14th
   International Conference on Scientometrics and Informetrics, Vienna,
   Austria, 15-19 July 2013 (Thijs et al. 2013b). The authors wish to thank
   the reviewers for their comments which helped us to improve and extend
   the paper. Lin Zhang acknowledges the support from the National Natural
   Science Foundation of China under Grant 71103064.
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NR 19
TC 23
Z9 25
U1 1
U2 97
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD DEC
PY 2015
VL 105
IS 3
BP 1453
EP 1467
DI 10.1007/s11192-015-1641-3
PG 15
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA CW6TG
UT WOS:000365130100006
DA 2024-09-05
ER
PT J
AU Sharma, D
   Mittal, R
   Sekhar, R
   Shah, PT
   Renz, M
AF Sharma, Deepak
   Mittal, Ruchi
   Sekhar, Ravi
   Shah, Pritesh
   Renz, Matthias
TI A bibliometric analysis of cyber security and cyber forensics research
SO RESULTS IN CONTROL AND OPTIMIZATION
LA English
DT Article
DE Anomaly detection; Cyber security; Cyber forensics; Cyber attack;
   Malware detection; Machine learning; Deep learning; Bibliometry
ID SOCIAL NETWORK ANALYSIS; TOURISM; JOURNALS; SCIENCE
AB Cybersecurity is one of the most important concerns associated with ever expanding internet based technologies, products, services and networks. If cybersecurity is prevention then cyber forensics is the cure. Both are equally important pillars of digital security. This paper presents an extensive bibliometric analysis of cybersecurity and cyberforensic research published in Web of Science during the past decade (2011-2021). The analysis included yearly publications, publication types and trends across different verticals such as publishing sources, organizations, researchers, countries and keywords. Full counting method was used for citation analysis, whereas fractional counting method was implemented to analyze co-citation, co-author collaborations as well as keyword co-occurrences across all these verticals. Furthermore, timeline and burst detection analyses were carried out to unravel significant topic trends and citations in the last decade. The study presents bibliometric results in terms of the authors, organizations, countries, keywords, sources and documents with the highest collaborative link strengths worldwide in the field of cybersecurity and forensics. Latest trends, under-investigated topics and future directions are also presented.
C1 [Sharma, Deepak; Renz, Matthias] Christian Albrechts Univ Kiel, Dept Comp Sci, D-24118 Kiel, Germany.
   [Mittal, Ruchi] Ganga Inst Technol & Management, Dept Comp Sci, Bahadurgarh Jhajjar Rd Kablana, Jhajjar 124104, Haryana, India.
   [Sekhar, Ravi; Shah, Pritesh] Symbiosis Int Deemed Univ, Symbiosis Inst Technol, Pune 412115, India.
C3 University of Kiel; Symbiosis International University; Symbiosis
   Institute of Technology (SIT)
RP Shah, PT (corresponding author), Symbiosis Int Deemed Univ, Symbiosis Inst Technol, Pune 412115, India.
EM deepak.btg@gmail.com; ruchi.mittal138@gmail.com;
   ravi.sekhar@sitpune.edu.in; pritesh.ic@gmail.com;
   mr@informatik.uni-kiel.de
RI dbs, hunzla/HZL-0324-2023; Shah, Pritesh/Q-7269-2016
OI Shah, Pritesh/0000-0002-7504-2323
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NR 79
TC 4
Z9 4
U1 2
U2 2
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
EI 2666-7207
J9 RESULTS CONTROL OPTI
JI Results Control Optim.
PD MAR
PY 2023
VL 10
AR 100204
DI 10.1016/j.rico.2023.100204
PG 32
WC Mathematics, Applied
WE Emerging Sources Citation Index (ESCI)
SC Mathematics
GA OR3Z4
UT WOS:001208974200005
OA gold
DA 2024-09-05
ER
PT J
AU de Winter, J
AF de Winter, Joost
TI Can ChatGPT be used to predict citation counts, readership, and social
   media interaction? An exploration among 2222 scientific abstracts
SO SCIENTOMETRICS
LA English
DT Article
DE Citation prediction; Scientometrics; Altmetrics; ChatGPT; GPT-4;
   Scientific abstracts; Artificial intelligence
AB This study explores the potential of ChatGPT, a large language model, in scientometrics by assessing its ability to predict citation counts, Mendeley readers, and social media engagement. In this study, 2222 abstracts from PLOS ONE articles published during the initial months of 2022 were analyzed using ChatGPT-4, which used a set of 60 criteria to assess each abstract. Using a principal component analysis, three components were identified: Quality and Reliability, Accessibility and Understandability, and Novelty and Engagement. The Accessibility and Understandability of the abstracts correlated with higher Mendeley readership, while Novelty and Engagement and Accessibility and Understandability were linked to citation counts (Dimensions, Scopus, Google Scholar) and social media attention. Quality and Reliability showed minimal correlation with citation and altmetrics outcomes. Finally, it was found that the predictive correlations of ChatGPT-based assessments surpassed traditional readability metrics. The findings highlight the potential of large language models in scientometrics and possibly pave the way for AI-assisted peer review.
C1 [de Winter, Joost] Delft Univ Technol, Fac Mech Engn, Dept Cognit Robot, Delft, Netherlands.
C3 Delft University of Technology
RP de Winter, J (corresponding author), Delft Univ Technol, Fac Mech Engn, Dept Cognit Robot, Delft, Netherlands.
EM j.c.f.dewinter@tudelft.nl
OI de Winter, Joost/0000-0002-1281-8200
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NR 70
TC 3
Z9 3
U1 55
U2 61
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD APR
PY 2024
VL 129
IS 4
BP 2469
EP 2487
DI 10.1007/s11192-024-04939-y
EA FEB 2024
PG 19
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA QV6H9
UT WOS:001162583700002
OA hybrid
DA 2024-09-05
ER
PT J
AU Hove, D
   Olugbara, O
   Singh, A
AF Hove, Dickson
   Olugbara, Oludayo
   Singh, Alveen
TI Bibliometric Analysis of Recent Trends in Machine Learning for Online
   Credit Card Fraud Detection
SO JOURNAL OF SCIENTOMETRIC RESEARCH
LA English
DT Article
DE Bibliometric analysis; Credit card; Deep learning; Ensemble learning;
   Machine; learning; Online fraud.; transaction. Online Credit Card Fraud
   (OCCF) occurs exclusively
AB Online credit card fraud (OCCF) is the malicious act of using credit card details belonging to another person to complete fraudulent transactions over the Internet. Naturally, masses of researchers have engaged in the imperative search for effective solutions across a wide range of disciplines. The result is a rich tapestry of methodologies, models, frameworks, and inventions exhibiting dramatic spread and growth. However, this also results in an unorganized research domain. In this state, a bibliometric analysis is a useful technique for establishing a reconciled snapshot of the OCCF research domain. This paper has particular interest in determining the intellectual structure of the knowledge of machine learning, deep learning, and ensemble learning models for early detection of OCCF. This bibliometric analysis is conducted using 524 publications between 2013 and 2022 extracted from the SCOPUS core collection database. Microsoft Excel, VOSViewer, and Biblioshiny software tools were used for data analysis. The findings indicate that ensemble learning models are trending and the three most authoritative authors have been exposed in this study. There is a sharp rise in global publications annually and India has the most publications with the most impactful authors. Five broad clusters of knowledge are imbalanced data, anomaly detection, machine learning, decision trees, and ensemble learning. Intellectual collaboration across regions is strong amongst Asia, Europe, and North America with weak associations between Africa and South America. This is the first bibliometric analysis in the domain of OCCF detection to the best of the author's ability. The findings significantly contribute to the application of OCCF detection through the creation of intellectual patterns in existing literature. The results bring about synthesis within a domain of research that is currently disorganized. This in turn helps researchers to identify research gaps, and areas for further research and formulate a curriculum.
C1 [Hove, Dickson; Olugbara, Oludayo; Singh, Alveen] Durban Univ Technol, MICT SETA Ctr Excellence 4IR, Dept Informat Technol, Durban, South Africa.
C3 Durban University of Technology
RP Hove, D (corresponding author), Durban Univ Technol, MICT SETA Ctr Excellence 4IR, Dept Informat Technol, Durban, South Africa.
EM hovedickson@gmail.com
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NR 45
TC 0
Z9 0
U1 2
U2 2
PU PHCOG NET
PI KARNATAKA
PA 17, 2ND FLR, BUDDHA VIHAR RD, NEAR SPORTS ZONE, COX TOWN, BENGALURU,
   KARNATAKA, 560005, INDIA
SN 2321-6654
EI 2320-0057
J9 J SCIENTOMETR RES
JI J. Scientometr. Res.
PD JAN-APR
PY 2024
VL 13
IS 1
BP 43
EP 57
DI 10.5530/jscires.13.1.4
PG 15
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA TZ7J6
UT WOS:001245143700004
OA hybrid
DA 2024-09-05
ER
PT C
AU Terziyan, V
   Kaikova, O
   Golovianko, M
   Vitko, O
AF Terziyan, Vagan
   Kaikova, Olena
   Golovianko, Mariia
   Vitko, Oleksandra
BE Longo, F
   Shen, W
   Padovano, A
TI Can ChatGPT Challenge the Scientific Impact of Published Research,
   Particularly in the Context of Industry 4.0 and Smart Manufacturing?
SO 5TH INTERNATIONAL CONFERENCE ON INDUSTRY 4.0 AND SMART MANUFACTURING,
   ISM 2023
SE Procedia Computer Science
LA English
DT Proceedings Paper
CT 5th International Conference on Industry 4.0 and Smart Manufacturing
   (ISM)
CY NOV 22-24, 2023
CL Lisbon, PORTUGAL
DE Artificial Intelligence; ChatGPT; Industry 4.0; Smart Manufacturing;
   academic impact
AB The released ChatGPT as a powerful language model is capable of assisting with a wide range of tasks, including answering questions, summarizing, paraphrasing, proofreading, classifying, and integrating texts. In this study, we tested ChatGPT capability to assist researchers in evaluating the academic articles' contribution. We suggest a dialogue schema in which ChatGPT is asked to answer research questions from the target article and then to compare its own answers with the answers from the article. Finally, ChatGPT is asked to integrate both solutions coherently. We experimented with Proceedings of ISM-2022 Conference on Industry 4.0 and Smart Manufacturing, utilizing explicit research questions. The chat context enabled assessing studied articles' contributions to Industry 4.0, uncovering advancements beyond the state-of-the-art. However, ChatGPT demonstrates limitations in content understanding and contribution evaluation. We conclude that while it collaborates with humans on academic tasks, human guidance remains essential, while ChatGPT's assistance efficiently complements traditional academic processes.
C1 [Terziyan, Vagan; Kaikova, Olena] Univ Jyvaskyla, Fac Informat Technol, Jyvaskyla 40014, Finland.
   [Golovianko, Mariia; Vitko, Oleksandra] Kharkiv Natl Univ Radio Elect, Dept Artificial Intelligence, UA-61166 Kharkiv, Ukraine.
C3 University of Jyvaskyla; Ministry of Education & Science of Ukraine;
   Kharkiv National University of Radio Electronics
RP Terziyan, V (corresponding author), Univ Jyvaskyla, Fac Informat Technol, Jyvaskyla 40014, Finland.
EM vagan.terziyan@jyu.fi
RI Terziyan, Vagan/C-4899-2018
OI Terziyan, Vagan/0000-0001-7732-2962; Kaikova, Olena/0000-0002-8427-6236
CR Ali Omar, 2023, Procedia Comput Sci, V217, P205, DOI 10.1016/j.procs.2022.12.216
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NR 18
TC 0
Z9 0
U1 1
U2 1
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA SARA BURGERHARTSTRAAT 25, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 1877-0509
J9 PROCEDIA COMPUT SCI
PY 2024
VL 232
BP 2540
EP 2550
DI 10.1016/j.procs.2024.02.072
PG 11
WC Computer Science, Theory & Methods; Engineering, Industrial;
   Engineering, Manufacturing
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Engineering
GA BW7UP
UT WOS:001196800602057
OA Green Published, gold
DA 2024-09-05
ER
PT J
AU Loi, CK
   Lim, JMH
   Suki, NM
   Lee, HA
AF Loi, Chek-Kim
   Lim, Jason Miin-Hwa
   Suki, Norazah Mohd
   Lee, Hock -Ann
TI Exploring University Students' Online Learning Readiness: A Mixed
   Methods Study of Forced Online Learning
SO JOURNAL OF LANGUAGE AND EDUCATION
LA English
DT Article
DE institutional support; online learning; social influence; students'
   readiness; technology accessibility
ID DENTAL EDUCATION; USER ACCEPTANCE; SAUDI-ARABIA; TECHNOLOGY;
   SATISFACTION; ENVIRONMENTS; PERCEPTIONS; ASSESSMENTS; SUPPORT
AB Background: Despite the advancement achieved in previous research into online learning, few studies have used both quantitative and qualitative data to examine how students' readiness to learn online is affected by three different external factors, comprising (i) the degrees to which technology is available to students, (ii) the support provided by the institutions of learning, and (iii) the social influence affecting the students engaged in forced online learning in a pandemic situation. Purpose: To fill this research gap, this study explored university students' forced online learning readiness in relation to technological accessibility, institutional support and social influence during a pandemic, in an attempt to furnish insights into how educators can maximize the benefits of adopting online learning methods. Method: A mixed methods research design was employed in this study. Quantitative data, elicited via self-administered questionnaires completed by 211 participants, was analyzed using the frequencies, means, standard deviations and Pearson correlation analysis involving the Statistical Package for the Social Sciences (SPSS) software version 27. Qualitative data, elicited via 11 open-ended questions posed to 41 students through in-depth interviews, was then studied using a thematic analysis of the participants' feedback concerning the three constructs in online learning. Results: Our quantitative analysis showed that institutional support had the strongest positive correlation with online learning readiness, and this was followed by technology accessibility and social influence in relation to students' readiness to learn online. Qualitative findings further indicated that students were largely concerned about Internet accessibility and the setting where their roles were restricted to being mere listeners in online sessions. Apart from being apprehensive about excessive online assignments, students also acknowledged that their online interactions were influenced by their friends and family members, and they would prefer practical work that could inspire them to reflect and engage actively with the course material given during the pandemic. Conclusion: While lecturers can make online classes more interactive and discussion -generative, university administrators need to aptly facilitate their institution's transition to the forced online learning mode, moderate social influence, improve the learning management system, and provide training to teachers and students on the use of emerging technology.
C1 [Loi, Chek-Kim; Lee, Hock -Ann] Univ Malaysia Sabah, Kota Kinabalu, Malaysia.
   [Lim, Jason Miin-Hwa] Jiangsu Univ Technol, Zhenjiang, Peoples R China.
   [Suki, Norazah Mohd] Univ Utara Malaysia, Sintok, Malaysia.
C3 Universiti Malaysia Sabah; Jiangsu University of Technology; Universiti
   Utara Malaysia
RP Loi, CK (corresponding author), Univ Malaysia Sabah, Kota Kinabalu, Malaysia.; Lim, JMH (corresponding author), Jiangsu Univ Technol, Zhenjiang, Peoples R China.
EM lck734@yahoo.com; drjasonlim@gmail.com
RI MOHD SUKI, NORAZAH/C-9312-2016
OI MOHD SUKI, NORAZAH/0000-0002-8422-2449
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NR 68
TC 0
Z9 0
U1 1
U2 1
PU NATL RESEARCH UNIV HIGHER SCH ECONOMICS
PI MOSCOW
PA SHABOLOVKA, 26, MOSCOW, 119049, RUSSIA
EI 2411-7390
J9 J LANG EDUC
JI J. Lang. Educ.
PY 2024
VL 10
IS 1
BP 49
EP 67
DI 10.17323/jle.2024.16016
PG 19
WC Education & Educational Research; Linguistics
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research; Linguistics
GA RN1Q4
UT WOS:001228253300009
OA gold
DA 2024-09-05
ER
PT J
AU Baminiwatta, A
AF Baminiwatta, Anuradha
TI Global trends of machine learning applications in psychiatric research
   over 30 years: A bibliometric analysis
SO ASIAN JOURNAL OF PSYCHIATRY
LA English
DT Article
DE Machine learning; Artificial intelligence; Psychiatry; Mental health;
   Trends; Bibliometrics
ID BIOMARKERS; DISEASE
AB This bibliometric analysis aimed to identify active research areas and trends in machine learning applications within the psychiatric literature. An exponential growth in the number of related publications indexed in Web of Science during the last decade was noted. Document co-citation analysis revealed 10 clusters of knowledge, which included several mental health conditions, albeit with visible structural overlap. Several influential publications in the co-citation network were identified. Keyword trends illustrated a recent shift of focus from "psychotic" to "neurotic" conditions. Despite a relative lack of literature from the developing world, a recent rise in publications from Asian countries was observed.
   Data Availability: Bibliographic data for this study were downloaded from the Web of Science. The search strategy is included in the Supplementary file.
C1 [Baminiwatta, Anuradha] Univ Kelaniya, Fac Med, Dept Psychiat, Colombo, Sri Lanka.
C3 University Kelaniya; University of Colombo
RP Baminiwatta, A (corresponding author), Univ Kelaniya, Fac Med, Dept Psychiat, Colombo, Sri Lanka.
EM baminiwatta@kln.ac.lk
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NR 10
TC 6
Z9 6
U1 5
U2 38
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1876-2018
EI 1876-2026
J9 ASIAN J PSYCHIATR
JI Asian J. Psychiatr.
PD MAR
PY 2022
VL 69
AR 102986
DI 10.1016/j.ajp.2021.102986
EA JAN 2022
PG 4
WC Psychiatry
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Psychiatry
GA YF1CN
UT WOS:000741553300003
PM 34990914
DA 2024-09-05
ER
PT C
AU Zhang, W
   Zeng, XY
   Ming, DY
   Wang, JH
AF Zhang, Wei
   Zeng, Xinyao
   Ming, Daoyang
   Wang, Jihan
GP IEEE
TI Research on the Construction of Evaluation Indicators of Students'
   Computational Thinking Based on Spectral Clustering
SO 2022 10TH INTERNATIONAL CONFERENCE ON INFORMATION AND EDUCATION
   TECHNOLOGY (ICIET 2022)
LA English
DT Proceedings Paper
CT 10th International Conference on Information and Education Technology
   (ICIET)
CY APR 09-11, 2022
CL Matsue, JAPAN
DE CT concepts; spectral clustering; evaluation indicators; CT practice; CT
   perspective
AB The scientific and reasonable evaluation indicators that can fully reflect the various dimensions of students' computational thinking (CT) skills is the basis and premise of accurately evaluating students' CT skills, which is of great significance to the cultivation of students' CT skills. However, the current research on how to construct the evaluation indicators is still inadequate, and most of the research is put forward by the subjective experience of researchers, lacking objectivity and the universality of ability. In the paper, we comprehensively reviewed the concepts of CT in the theoretical literature of CT, aiming to construct the comprehensive and effective evaluation indicators of CT for students by clustering the keywords of CT concepts and extracting indicators. The validity of indicators is verified by qualitative analysis, quantitative analysis and expert evaluation. The results show that the evaluation indicators of CT constructed by spectral clustering technology are a more scientific, more comprehensive reflection of the ability dimensions of CT. It has unique advantages in constructing objective and comprehensive evaluation indicators and provides an evaluation basis for the evaluation practice of CT skills.
C1 [Zhang, Wei; Zeng, Xinyao; Ming, Daoyang; Wang, Jihan] Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan, Peoples R China.
C3 Central China Normal University
RP Zhang, W (corresponding author), Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan, Peoples R China.
EM zwccnu@mail.ccnu.edu.cn; zengxy_98@mails.ccuu.edu.cn;
   mingdaoyang@qq.com; 2110401398@qq.com
FU National Natural Science Foundation of China [61977031]
FX This study was funded by the National Natural Science Foundation of
   China [grant number 61977031].
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NR 23
TC 0
Z9 0
U1 3
U2 8
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-6654-8048-2
PY 2022
BP 104
EP 112
DI 10.1109/ICIET55102.2022.9779003
PG 9
WC Computer Science, Cybernetics; Computer Science, Information Systems;
   Computer Science, Interdisciplinary Applications
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BT5WU
UT WOS:000839122100021
DA 2024-09-05
ER
PT J
AU Li, JY
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   Bao, Qingfen
TI Research on the coordinated development of agglomeration economy and
   environmental performance based on artificial intelligence
SO PHYSICS AND CHEMISTRY OF THE EARTH
LA English
DT Article
DE Artificial intelligence; Agglomeration economy; Environmental
   performance; Coordinated development
ID TAX
AB In order to promote the coordinated development of agglomeration economy and environmental performance, this paper combines artificial intelligence technology to conduct research on the coordinated development of agglomeration economy and environmental performance, and presents an easy-to-implement controller design. For discrete linear multi-agent systems under asynchronous packet loss, all communication edges are indepen-dent and random. In this paper, from the perspective of the overall change of communication topology, a switching model based on Markov packet loss channel is constructed and the sufficient conditions for the system to achieve almost certain consistency are given. In addition, this paper constructs an analysis model for the coordinated development of agglomeration economy and environmental performance based on artificial intel-ligence. The simulation study verifies that the model proposed in this paper has certain effects.
C1 [Li, Junying; Bao, Qingfen] Inner Mongolia Agr Univ, Coll Econ Management, Hohhot 010018, Peoples R China.
   [Li, Junying] Inner Mongolia Univ Finance & Econ, Coll Business Adm, Hohhot 010070, Peoples R China.
C3 Inner Mongolia Agricultural University; Inner Mongolia University of
   Finance & Economics
RP Li, JY (corresponding author), Inner Mongolia Agr Univ, Coll Econ Management, Hohhot 010018, Peoples R China.
EM ljyljgf@163.com
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NR 20
TC 0
Z9 0
U1 5
U2 13
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 1474-7065
EI 1873-5193
J9 PHYS CHEM EARTH
JI Phys. Chem. Earth
PD JUN
PY 2023
VL 130
AR 103371
DI 10.1016/j.pce.2023.103371
EA FEB 2023
PG 11
WC Geosciences, Multidisciplinary; Meteorology & Atmospheric Sciences;
   Water Resources
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Geology; Meteorology & Atmospheric Sciences; Water Resources
GA 9C2YZ
UT WOS:000935290300001
DA 2024-09-05
ER
PT C
AU Pride, D
   Cancellieri, M
   Knoth, P
AF Pride, David
   Cancellieri, Matteo
   Knoth, Petr
BE Alonso, O
   Cousijn, H
   Silvello, G
   Marrero, M
   Lopes, CT
   Marchesin, S
TI CORE-GPT: Combining Open Access Research and Large Language Models for
   Credible, Trustworthy Question Answering
SO LINKING THEORY AND PRACTICE OF DIGITAL LIBRARIES, TPDL 2023
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT 27th International Conference on Theory and Practice of Digital
   Libraries (TPDL)
CY SEP 26-29, 2023
CL Zadar, CROATIA
AB In this paper, we present CORE-GPT, a novel question-answering platform that combines GPT-based language models and more than 32 million full-text open access scientific articles from CORE (https://core.ac.uk). We first demonstrate that GPT3.5 and GPT4 cannot be relied upon to provide references or citations for generated text. We then introduce CORE-GPT which delivers evidence-based answers to questions, along with citations and links to the cited papers, greatly increasing the trustworthiness of the answers and reducing the risk of hallucinations. CORE-GPT's performance was evaluated on a dataset of 100 questions covering the top 20 scientific domains in CORE, resulting in 100 answers and links to 500 relevant articles. The quality of the provided answers and relevance of the links were assessed by two annotators. Our results demonstrate that CORE-GPT can produce comprehensive and trustworthy answers across the majority of scientific domains, complete with links to genuine, relevant scientific articles.
C1 [Pride, David; Cancellieri, Matteo; Knoth, Petr] Open Univ, Knowledge Media Inst, Milton Keynes, Bucks, England.
C3 Open University - UK
RP Pride, D (corresponding author), Open Univ, Knowledge Media Inst, Milton Keynes, Bucks, England.
EM david.pride@open.ac.uk; matteo.cancellieri@open.ac.uk;
   petr.knoth@open.ac.uk
OI Cancellieri, Matteo/0000-0002-9558-9772
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NR 16
TC 5
Z9 5
U1 7
U2 8
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 0302-9743
EI 1611-3349
BN 978-3-031-43848-6; 978-3-031-43849-3
J9 LECT NOTES COMPUT SC
PY 2023
VL 14241
BP 146
EP 159
DI 10.1007/978-3-031-43849-3_13
PG 14
WC Computer Science, Information Systems; Computer Science,
   Interdisciplinary Applications; Information Science & Library Science
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Information Science & Library Science
GA BW4WR
UT WOS:001156311600014
DA 2024-09-05
ER
PT J
AU Kochhar, SK
   Ojha, U
AF Kochhar, Sarabjeet Kaur
   Ojha, Uma
TI Index for objective measurement of a research paper based on sentiment
   analysis
SO ICT EXPRESS
LA English
DT Article
DE Impact factor; Index; Metric; Sentiment analysis; SentiWordNet; Citation
   sentiment analysis; Sentiment scores; Citation extraction; Publication
   impact factor; ACL anthology
ID IMPACT FACTOR; CITATION; TRACKING
AB Establishing impact of a research paper is essential for academia, industry and research community alike. The attempts made in this direction are hitherto limited to some objective metrics, largely based on the citation count. The number of citations has always been used as a measure for ascertaining quality and popularity of research papers. Though, citations play an essential role in academic research, sometimes researchers may cite a paper to just point out its weaknesses and infirmities. A subjective look into the sentiments behind citations of a research paper aids in understanding the opinion of the peer research community for a paper. Objective measures such as citing author's impact factor and the publication's impact factor, may help to quantify the weightage of citations themselves and should also be included in the assessment of impact of a research paper. In this paper, we formulate a model that combines both the objective and subjective metrics and forms basis for an index to objectively convey the impact of a research paper. (C) 2020 The Korean Institute of Communications and Information Sciences (KICS). Publishing services by Elsevier B.V.
C1 [Kochhar, Sarabjeet Kaur] Univ Delhi, Dept Comp Sci, Indraprastha Coll Women, New Delhi, India.
   [Ojha, Uma] Univ Delhi, Dept Comp Sci, Atma Ram Sanatan Dharma Coll, New Delhi, India.
C3 University of Delhi; Atma Ram Sanatan Dharma College; University of
   Delhi
RP Ojha, U (corresponding author), Univ Delhi, Dept Comp Sci, Atma Ram Sanatan Dharma Coll, New Delhi, India.
EM sarabjeet.kochhar@gmail.com; uojha@arsd.du.ac.in
RI Kochhar, Sarabjeet Kaur/HMV-8788-2023
OI Kochhar, Sarabjeet Kaur/0000-0001-9406-7414
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NR 28
TC 4
Z9 4
U1 3
U2 14
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2405-9595
J9 ICT EXPRESS
JI ICT Express
PD SEP
PY 2020
VL 6
IS 3
BP 253
EP 257
DI 10.1016/j.icte.2020.02.001
PG 5
WC Computer Science, Information Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA NF8SG
UT WOS:000563562300019
OA gold
DA 2024-09-05
ER
PT J
AU Zhu, WD
   Liu, F
   Chen, YW
   Yang, JB
   Xu, DL
   Wang, DP
AF Zhu, Wei-dong
   Liu, Fang
   Chen, Yu-wang
   Yang, Jian-bo
   Xu, Dong-ling
   Wang, Dong-peng
TI Research project evaluation and selection: an evidential reasoning
   rule-based method for aggregating peer review information with
   reliabilities
SO SCIENTOMETRICS
LA English
DT Article
DE Research project evaluation and selection; Evidential reasoning;
   Reliability; Confusion matrix
ID DECISION-ANALYSIS; NETWORK; MODEL
AB Research project evaluation and selection is mainly concerned with evaluating a number of research projects and then choosing some of them for implementation. It involves a complex multiple-experts multiple-criteria decision making process. Thus this paper presents an effective method for evaluating and selecting research projects by using the recently-developed evidential reasoning (ER) rule. The proposed ER rule based evaluation and selection method mainly includes (1) using belief structures to represent peer review information provided by multiple experts, (2) employing a confusion matrix for generating experts' reliabilities, (3) implementing utility based information transformation to handle qualitative evaluation criteria with different evaluation grades, and (4) aggregating multiple experts' evaluation information on multiple criteria using the ER rule. An experimental study on the evaluation and selection of research proposals submitted to the National Science Foundation of China demonstrates the applicability and effectiveness of the proposed method. The results show that (1) the ER rule based method can provide consistent and informative support to make informed decisions, and (2) the reliabilities of the review information provided by different experts should be taken into account in a rational research project evaluation and selection process, as they have a significant influence to the selection of eligible projects for panel review.
C1 [Zhu, Wei-dong] Hefei Univ Technol, Sch Econ, Hefei 230009, Anhui, Peoples R China.
   [Liu, Fang; Yang, Jian-bo; Xu, Dong-ling; Wang, Dong-peng] Hefei Univ Technol, Sch Management, Hefei 230009, Anhui, Peoples R China.
   [Liu, Fang; Chen, Yu-wang; Yang, Jian-bo; Xu, Dong-ling] Univ Manchester, Manchester Business Sch, Manchester M15 6PB, Lancs, England.
C3 Hefei University of Technology; Hefei University of Technology;
   University of Manchester
RP Liu, F (corresponding author), Hefei Univ Technol, Sch Management, 193 Tunxi Rd, Hefei 230009, Anhui, Peoples R China.
EM liu_fang2014@163.com
RI Xu, Dong-Ling/AFO-9481-2022; Chen, Yu-wang/A-1129-2019; Yang,
   Jian-Bo/D-8047-2016
OI Xu, Dong-Ling/0000-0003-4480-1611; Chen, Yu-wang/0000-0002-2007-1821;
   Yang, Jian-Bo/0000-0001-8953-1550; Yang, Jian-Bo/0000-0002-1368-5294
FU National Natural Science Foundation of China [71071048]; China
   Scholarship Council [201306230047]
FX This research is partially supported by the National Natural Science
   Foundation of China under Grant No. 71071048 and the Scholarship from
   China Scholarship Council under Grant No. 201306230047.
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NR 34
TC 25
Z9 27
U1 2
U2 73
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD DEC
PY 2015
VL 105
IS 3
BP 1469
EP 1490
DI 10.1007/s11192-015-1770-8
PG 22
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA CW6TG
UT WOS:000365130100007
DA 2024-09-05
ER
PT C
AU Cao, L
   Zhang, A
   Wang, QA
AF Cao, Lu
   Zhang, An
   Wang, Qiang
BE Li, K
   Fei, MR
   Jia, L
   Irwin, GW
TI Research on Situation Assessment of UCAV Based on Dynamic Bayesian
   Networks in Complex Environment
SO LIFE SYSTEM MODELING AND INTELLIGENT COMPUTING, PT II
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT International Conference on Life System Modeling and Simulation /
   International Conference on Intelligent Computing for Sustainable Energy
   and Environment (LSMS-ICSEE)
CY SEP 17-20, 2010
CL Wuxi, PEOPLES R CHINA
DE situation assessment; UCAV; dynamic Bayesian networks; complex
   environment
AB UCAV is an inevitable trend of the future intelligent and uninhabited flight platform. Situation assessment (SA) is an effective method to solve the problem of the autonomous decision-making in UCAV investigation. The concepts, contents and process of SA are put forward and the methods about the implementation of SA are analyzed. Then the concept and inference of dynamic Bayesian networks (DBN) are introduced, and SA configuration of UCAV autonomous decision system is given. Finally, the SA is applied to the UCAV autonomous decision system, especially SA based on DBN is used and the model is propounded. The simulation result indicates that the inference results are consistent with the theoretical analysis. The subjectivity of the assessment is reduced and the accuracy is greatly improved.
C1 [Cao, Lu; Zhang, An; Wang, Qiang] NW Ploytech Univ, Dept Elect & Informat, Xian 710129, Peoples R China.
RP Cao, L (corresponding author), NW Ploytech Univ, Dept Elect & Informat, Xian 710129, Peoples R China.
EM cao1u2563@126.com; zhangan@nwpu.edu.cn; 15076686@qq.com
RI zhang, an/JMR-3763-2023; Wang, Qiang/AAV-7131-2021
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NR 12
TC 1
Z9 3
U1 0
U2 1
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 0302-9743
EI 1611-3349
BN 978-3-642-15596-3
J9 LECT NOTES COMPUT SC
PY 2010
VL 6329
BP 58
EP 68
DI 10.1007/978-3-642-15597-0_7
PG 11
WC Automation & Control Systems; Computer Science, Artificial Intelligence;
   Computer Science, Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Automation & Control Systems; Computer Science
GA BTD67
UT WOS:000286579100007
DA 2024-09-05
ER
PT J
AU Burns, GA
   Li, XC
   Peng, NY
AF Burns, Gully A.
   Li, Xiangci
   Peng, Nanyun
TI Building deep learning models for evidence classification from the open
   access biomedical literature
SO DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION
LA English
DT Article
AB We investigate the application of deep learning to biocuration tasks that involve classification of text associated with biomedical evidence in primary research articles. We developed a large-scale corpus of molecular papers derived from PubMed and PubMed Central open access records and used it to train deep learning word embeddings under the GloVe, FastText and ELMo algorithms. We applied those models to a distant supervised method classification task based on text from figure captions or fragments surrounding references to figures in the main text using a variety or models and parameterizations. We then developed document classification (triage) methods for molecular interaction papers by using deep learning mechanisms of attention to aggregate classification-based decisions over selected paragraphs in the document. We were able to obtain triage performance with an accuracy of 0.82 using a combined convolutional neural network, bi-directional long short-term memory architecture augmented by attention to produce a single decision for triage. In this work, we hope to encourage biocuration systems developers to apply deep learning methods to their specialized tasks by repurposing large-scale word embedding to apply to their data.
C1 [Burns, Gully A.] Chan Zuckerberg Initiat, Redwood City, CA 94063 USA.
   [Li, Xiangci; Peng, Nanyun] Univ Southern Calif, Viterbi Sch Engn, Informat Sci Inst, Marina Del Rey, CA 90292 USA.
C3 Chan Zuckerberg Initiative (CZI); University of Southern California
RP Burns, GA (corresponding author), Chan Zuckerberg Initiat, Redwood City, CA 94063 USA.
EM gullyburns@gmail.com
OI Li, Xiangci/0000-0002-7493-9534
FU National Institute of Health's National Library of Medicine [LM012592]
FX National Institute of Health's National Library of Medicine (LM012592).
CR [Anonymous], CORR
   [Anonymous], DATABASE OXFORD
   [Anonymous], CORR
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   Björne J, 2018, SIGBIOMED WORKSHOP ON BIOMEDICAL NATURAL LANGUAGE PROCESSING (BIONLP 2018), P98
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NR 26
TC 16
Z9 24
U1 0
U2 8
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1758-0463
J9 DATABASE-OXFORD
JI Database
PD APR 2
PY 2019
AR baz034
DI 10.1093/database/baz034
PG 9
WC Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology
GA IA9NG
UT WOS:000469883300001
PM 30938776
OA gold, Green Published, Green Submitted
DA 2024-09-05
ER
PT C
AU Zeng, XF
AF Zeng Xianfeng
GP IEEE
TI Research on Security Assessment and Maintenance Decision of Trains based
   on Bayesian Networks
SO 2014 SIXTH INTERNATIONAL CONFERENCE ON MEASURING TECHNOLOGY AND
   MECHATRONICS AUTOMATION (ICMTMA)
SE International Conference on Measuring Technology and Mechatronics
   Automation
LA English
DT Proceedings Paper
CT 6th International Conference on Measuring Technology and Mechatronics
   Automation (ICMTMA)
CY JAN 10-11, 2014
CL Zhangjiajie, PEOPLES R CHINA
DE Magler Train; Security Assessment; Fault Tree; Bayesian Networks;
   Maintenance Decision
AB With the medium and low speed maglev train plays the role of commercial operation gradually, people put forward higher requirements for train safety reliability, which makes train security assessment even more prominent. Aiming at the characteristics of the maglev train equipments as well as the limitations of traditional security assessment, the establishment of a multi-state security assessment based on Bayesian network model has better diagnostic reasoning and causal reasoning ability. Finally, using the model to analysis the train traction system quantitatively, fmding the weaknesses of the system and the relationship between the equipments to make rational maintenance decision. This will provide a basis to improve the reliability of train equipment and repair and maintenance work.
C1 Guangzhou Inst Railway Technol, Guangzhou, Guangdong, Peoples R China.
RP Zeng, XF (corresponding author), Guangzhou Inst Railway Technol, Guangzhou, Guangdong, Peoples R China.
EM wujing2013c@163.com
CR Bobbio A., 2001, RELIABILITY ENG SYST, V71
   Li Haijun, 2003, APPL EQUIPMENT FAILU
   Yasuda Y., 2004, MAGLEV 2004 PROC 18
   Zhou Zhongbao, 2009, SYSTEM ENG ACAD J, V24
NR 4
TC 1
Z9 1
U1 0
U2 5
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2157-1473
BN 978-1-4799-3434-8
J9 INT CONF MEAS
PY 2014
BP 534
EP 537
DI 10.1109/ICMTMA.2014.129
PG 4
WC Automation & Control Systems; Engineering, Electrical & Electronic;
   Engineering, Mechanical
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Automation & Control Systems; Engineering
GA BC7WU
UT WOS:000355260500126
DA 2024-09-05
ER
PT C
AU Yuan, XE
   Shi, MW
   Song, CM
AF Yuan, Xiu-e
   Shi, Meng-wei
   Song, Chun-mei
BE Muhin, VE
   Ye, Z
TI Research on Supply Chain Performance Evaluation Based on Rough set and
   SVM
SO IEEC 2009: FIRST INTERNATIONAL SYMPOSIUM ON INFORMATION ENGINEERING AND
   ELECTRONIC COMMERCE, PROCEEDINGS
LA English
DT Proceedings Paper
CT 1st International Symposium on Information Engineering and Electronic
   Commerce
CY MAY 16-17, 2009
CL Ternopil, UKRAINE
DE Supply Chain; Rough set; SVM
AB For implementing supply chain management effectively, a manufacturing supply chain performance evaluation nethod was very important. Based on the comprehensive evaluation index system of supply Chain Performance Evaluation, a new evaluation model with Support vector machine (SVM) and Rough set Is founded. Rough Set is introduced to reduce numbers of evaluation indicators, thus reducing the dimensions of the input space of SVM. Finally, a example is provided to validate the proposed method. The findings indicate that the method proposed in this paper is a useful tool for solving the supply chain performance evaluation.
C1 [Yuan, Xiu-e; Shi, Meng-wei] North China Elect Power Univ, Business & Management Dept, Beijing, Peoples R China.
   [Song, Chun-mei] ShiJiaZhuang Foreign Affair Coll, Foreign Affair Management Dept, Shijiazhuang, Peoples R China.
C3 North China Electric Power University
RP Yuan, XE (corresponding author), North China Elect Power Univ, Business & Management Dept, Beijing, Peoples R China.
EM hdyxe@126.com; scm780929@sina.com
CR [Anonymous], INTELLIGENT CONTROL
   [Anonymous], 1991, ROUGH SETS THEORETIC, DOI [DOI 10.1007/978-94-011-3534-4, 10.1007/978-94-011-3534-4]
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   LIU H, 2005, STAT DECISION, P142
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NR 10
TC 0
Z9 0
U1 0
U2 1
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA
BN 978-0-7695-3686-6
PY 2009
BP 276
EP +
DI 10.1109/IEEC.2009.182
PG 2
WC Computer Science, Information Systems; Computer Science, Software
   Engineering; Engineering, Electrical & Electronic
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Engineering
GA BNT86
UT WOS:000275528800057
DA 2024-09-05
ER
PT J
AU Saarela, M
   Kärkkäinen, T
AF Saarela, Mirka
   Kaerkkaeinen, Tommi
TI Can we automate expert-based journal rankings? Analysis of the Finnish
   publication indicator
SO JOURNAL OF INFORMETRICS
LA English
DT Article
DE Performance-based research funding system; Machine learning; Automation;
   Feature importance
ID NORWEGIAN MODEL; SOCIAL-SCIENCES; PERFORMANCE; INTERNATIONALIZATION;
   CITATIONS; QUALITY; IMPACT; SNIP
AB The publication indicator of the Finnish research funding system is based on a manual ranking of scholarly publication channels. These ranks, which represent the evaluated quality of the channels, are continuously kept up to date and thoroughly reevaluated every four years by groups of nominated scholars belonging to different disciplinary panels. This expert-based decision-making process is informed by available citation-based metrics and other relevant metadata characterizing the publication channels. The purpose of this paper is to introduce various approaches that can explain the basis and evolution of the quality of publication channels, i.e., ranks. This is important for the academic community, whose research work is being governed using the system. Data-based models that, with sufficient accuracy, explain the level of or changes in ranks provide assistance to the panels in their multi-objective decision making, thus suggesting and supporting the need to use more cost-effective, automated ranking mechanisms. The analysis relies on novel advances in machine learning systems for classification and predictive analysis, with special emphasis on local and global feature importance techniques (C) 2020 Published by Elsevier Ltd.
C1 [Saarela, Mirka; Kaerkkaeinen, Tommi] Univ Jyvaskyla, Fac Informat Technol, POB 35, FI-40014 Jyvaskyla, Finland.
C3 University of Jyvaskyla
RP Saarela, M (corresponding author), Univ Jyvaskyla, Fac Informat Technol, POB 35, FI-40014 Jyvaskyla, Finland.
EM mirka.saarela@jyu.fi
RI Saarela, Mirka/AAC-4087-2019
OI Saarela, Mirka/0000-0002-1559-154X; Karkkainen,
   Tommi/0000-0003-0327-1167
FU Academy of Finland [315550, 311877]; Academy of Finland (AKA) [315550]
   Funding Source: Academy of Finland (AKA)
FX This research was supported by the Academy of Finland (grants no. 315550
   and 311877) and is related to the the matic research area DEMO (Decision
   Analytics Utilizing Causal Models and Multiobjective Optimization,
   jyu.fi/demo) of the University of Jyvaskyla.
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NR 73
TC 17
Z9 17
U1 4
U2 48
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1751-1577
EI 1875-5879
J9 J INFORMETR
JI J. Informetr.
PD MAY
PY 2020
VL 14
IS 2
AR 101008
DI 10.1016/j.joi.2020.101008
PG 17
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA NG7GI
UT WOS:000564148300006
OA Green Accepted
DA 2024-09-05
ER
PT J
AU Timko, C
   Niederstadt, M
   Goel, N
   Faltings, B
AF Timko, Christina
   Niederstadt, Malte
   Goel, Naman
   Faltings, Boi
TI Incentive Mechanism Design for Responsible Data Governance: A
   Large-scale Field Experiment
SO ACM JOURNAL OF DATA AND INFORMATION QUALITY
LA English
DT Article
DE Data quality assessment; experimental; quantitative research; incentive
   mechanism design; responsible AI
ID PREDICTION
AB A crucial building block of responsible artificial intelligence is responsible data governance, including data collection. Its importance is also underlined in the latest EU regulations. The data should be of high quality, foremost correct and representative, and individuals providing the data should have autonomy over what data is collected. In this article, we consider the setting of collecting personally measured fitness data (physical activity measurements), in which some individuals may not have an incentive to measure and report accurate data. This can significantly degrade the quality of the collected data. On the other hand, high-quality collective data of this nature could be used for reliable scientific insights or to build trustworthy artificial intelligence applications. We conduct a framed field experiment (N = 691) to examine the effect of offering fixed and quality-dependent monetary incentives on the quality of the collected data. We use a peer-based incentive-compatible mechanism for the quality-dependent incentives without spot-checking or surveilling individuals. We find that the incentive-compatible mechanism can elicit good-quality data while providing a good user experience and compensating fairly, although, in the specific study context, the data quality does not necessarily differ under the two incentive schemes. We contribute new design insights from the experiment and discuss directions that future field experiments and applications on explainable and transparent data collection may focus on.
C1 [Timko, Christina; Niederstadt, Malte] Ruhr Univ Bochum, Univ Str 150, D-44801 Bochum, Germany.
   [Goel, Naman] Univ Oxford, Wolf Son Bldg,Parks Rd, Oxford OX1 3QD, England.
   [Faltings, Boi] EPFL IC IINFCOM LIA, Stn 14, CH-1015 Lausanne, Switzerland.
C3 Ruhr University Bochum; University of Oxford
RP Timko, C (corresponding author), Ruhr Univ Bochum, Univ Str 150, D-44801 Bochum, Germany.
EM christina.timko@rub.de; malte.niederstadt@rub.de;
   naman.goel@cs.ox.ac.uk; boi.faltings@epfl.ch
FU Research Department Closed Carbon Cycle Economy at the Ruhr-Universitat
   Bochum
FX Christina Timko was supported by a personal research grant of the
   Research Department Closed Carbon Cycle Economy at the Ruhr-Universitat
   Bochum.
CR Abiteboul S, 2019, ACM J DATA INF QUAL, V11, DOI 10.1145/3310231
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NR 38
TC 0
Z9 0
U1 4
U2 7
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY USA
SN 1936-1955
J9 ACM J DATA INF QUAL
JI ACM J. Data Inf. Qual.
PD JUN
PY 2023
VL 15
IS 2
AR 16
DI 10.1145/3592617
PG 18
WC Computer Science, Information Systems
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA L0RD5
UT WOS:001020404600007
OA Green Submitted, Bronze
DA 2024-09-05
ER
PT J
AU Radu, C
   Ciocoiu, CN
   Veith, C
   Dobrea, RC
AF Radu, Catalina
   Ciocoiu, Carmen Nadia
   Veith, Cristina
   Dobrea, Razvan Catalin
TI ARTIFICIAL INTELLIGENCE AND COMPETENCY-BASED EDUCATION: A BIBLIOMETRIC
   ANALYSIS
SO AMFITEATRU ECONOMIC
LA English
DT Article
DE artificial intelligence (AI); competency-based education (CBE);
   bibliometric analysis; thematic map; Web of Science (WoS)
AB In the context of the educational transition toward a competency -based approach, this study aimed to identify trends, challenges, and emerging opportunities generated by the intersection of Artificial Intelligence (AI) and Competency -Based Education (CBE). The research was carried out using a bibliometric analysis of 1,028 articles included in the Web of Science database and based on reports provided by the biblioshiny application, the graphical interface of the bibliometrix R package. The results included a quantitative analysis of scientific production, collaborations, and cocitations, as well as the evolution and thematic map of the field. These revealed an annual increase of 8.43% in publications with acceleration after 2017 and global involvement, with the United States and China in leading positions. Thematic analyses have shown the field's evolution from technological foundations to an interdisciplinary approach, highlighting the influences of global events, such as COVID-19. The research confirmed the profound interaction between AI and CBE, demonstrating its potential, complexity, and the need for collaborative and interdisciplinary approaches. The bibliometric analysis performed can serve as a guide for future research directions and for identifying strategic directions in the implementation of AI in education.
C1 [Radu, Catalina; Ciocoiu, Carmen Nadia; Dobrea, Razvan Catalin] Bucharest Univ Econ Studies, Bucharest, Romania.
   [Veith, Cristina] Univ Bucharest, Bucharest, Romania.
C3 Bucharest University of Economic Studies; University of Bucharest
RP Radu, C (corresponding author), Bucharest Univ Econ Studies, Bucharest, Romania.
EM catalina.radu@man.ase.ro
RI Veith, Cristina/KIH-8901-2024
OI Veith, Cristina/0000-0002-3592-8779
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NR 38
TC 0
Z9 0
U1 6
U2 6
PU EDITURA ASE
PI BUCURESTI
PA PIATA ROMANA, NR 6, SECTOR 1, BUCURESTI, 701731, ROMANIA
SN 1582-9146
EI 2247-9104
J9 AMFITEATRU ECON
JI Amfiteatru Econ.
PD FEB
PY 2024
VL 26
IS 65
BP 220
EP 240
DI 10.24818/EA/2024/65/220
PG 21
WC Business; Economics; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA UX2M3
UT WOS:001251298900013
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU Pitkäaho, T
   Ryynänen, OP
   Partanen, P
   Vehviläinen-Julkunen, K
AF Pitkaaho, Taina
   Ryynanen, Olli-Pekka
   Partanen, Pirjo
   Vehvilainen-Julkunen, Katri
TI Data-based nurse staffing indicators with Bayesian networks explain
   nurse job satisfaction: a pilot study
SO JOURNAL OF ADVANCED NURSING
LA English
DT Article
DE Bayesian networks; nurse job satisfaction; outcome research; register
   data; time series
ID LARGE DATA SETS; PATIENT MORTALITY; WORK ENVIRONMENTS; OUTCOMES; CARE;
   QUALITY; MODELS; DISSATISFACTION; REGRESSION; DATABASES
AB P>Aim.
   This paper is a report of a pilot study to examine the relationship of nursing intensity, work environment intensity and nursing resources to nurse job satisfaction.
   Background.
   There is an ever increasing amount of information in hospital information systems; however, still very little of it is actually used in nursing management and leadership.
   Methods.
   The combination of a retrospective time series and cross-sectional survey data was used. The time series patient data of 9704 in/outpatients and nurse data of 110 nurses were collected from six inpatient units in a medical clinic of a university hospital in Finland in 2006. A unit-level measure of nurse job satisfaction was collected with a survey (n = 98 nurses) in the autumn of 2006. Bayesian networks were applied to examine a model that explains nurse job satisfaction.
   Results.
   In a hospital data system, 18 usable nurse staffing indicators were identified. There were four nurse staffing indicators: patient acuity from nursing intensity subgroup, diagnosis-related group volume from work environment subgroup, and skill mix and nurse turnover from nursing resources subgroup that explained the likelihood of nurse job satisfaction in the final model. The Bayesian networks also revealed the elusive non-linear relationship between nurse job satisfaction and patient acuity.
   Conclusion.
   Survey-based information on nurse job satisfaction can be modelled with data-based nurse staffing indicators. Nurse researchers could use the Bayesian approach to obtain information about the effects of nurse staffing on nursing outcomes.
C1 [Pitkaaho, Taina; Partanen, Pirjo; Vehvilainen-Julkunen, Katri] Univ Eastern Finland, Dept Nursing Sci, Kuopio, Finland.
   [Pitkaaho, Taina; Ryynanen, Olli-Pekka; Vehvilainen-Julkunen, Katri] Kuopio Univ Hosp, SF-70210 Kuopio, Finland.
   [Ryynanen, Olli-Pekka] Univ Eastern Finland, Fac Med, Kuopio, Finland.
C3 University of Eastern Finland; University of Eastern Finland; Kuopio
   University Hospital; University of Eastern Finland
RP Pitkäaho, T (corresponding author), Univ Eastern Finland, Dept Nursing Sci, Kuopio Campus, Kuopio, Finland.
EM taina.pitkaaho@kuh.fi
FU Northern-Savo Cultural Foundation; Kuopio University Hospital
FX We are grateful to Northern-Savo Cultural Foundation and Kuopio
   University Hospital for funding this study.
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   2002, B COURS VERS 2 0 0 W
NR 60
TC 18
Z9 26
U1 3
U2 26
PU WILEY-BLACKWELL
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0309-2402
J9 J ADV NURS
JI J. Adv. Nurs.
PD MAY
PY 2011
VL 67
IS 5
BP 1053
EP 1066
DI 10.1111/j.1365-2648.2010.05538.x
PG 14
WC Nursing
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Nursing
GA 749LW
UT WOS:000289469600014
PM 21198804
DA 2024-09-05
ER
PT C
AU Sridhar, P
   Karanji, D
   Sampatrao, GS
   Danda, S
   Saha, S
AF Sridhar, Pragnya
   Karanji, Deepika
   Sampatrao, Gambhire Swati
   Danda, Sravan
   Saha, Snehanshu
BE Kotsis, G
   Tjoa, AM
   Khalil, I
   Moser, B
   Mashkoor, A
   Sametinger, J
   Fensel, A
   Martinez-Gil, J
   Fischer, L
   Czech, G
   Sobieczky, F
   Khan, S
TI Semantic Influence Score: Tracing Beautiful Minds Through Knowledge
   Diffusion and Derivative Works
SO DATABASE AND EXPERT SYSTEMS APPLICATIONS - DEXA 2021 WORKSHOPS
SE Communications in Computer and Information Science
LA English
DT Proceedings Paper
CT 32nd International Conference on Database and Expert Systems
   Applications (DEXA)
CY SEP 27-30, 2021
CL ELECTR NETWORK
DE Big scholarly data; Semantic influence; Reference mining; Citation
   analysis; Machine learning; Research impact networks; AI
ID TECHNOLOGY DOMAIN
AB Articles judged on the basis of raw citations or citation counts (or similar) are biased with "Rich gets Richer" conjecture, and continue to propagate a perceived notion of paper quality and influence among scientific communities. This perception of preferential attachment, overlooking important factors such as context and the age of the paper has been criticized recently. In this paper, we propose 'Semantic Influence Score (SIS)', an unbiased alternative to metrics which rely on raw citation counts. We compute the semantic influence of a paper on its derivative works by developing a multilevel influence network, which takes into account references, domain intersection and influence scores of the articles in the network. SIS provides a robust alternative to the widely used mechanism of raw citation counts i.e., the number of citations it receives.
C1 [Sridhar, Pragnya; Karanji, Deepika; Sampatrao, Gambhire Swati] PES Univ, Dept CSE, Bengaluru, India.
   [Danda, Sravan; Saha, Snehanshu] BITS Pilani, CSIS, KK Birla Goa Campus, Sancoale, Goa, India.
   [Danda, Sravan; Saha, Snehanshu] BITS Pilani, APPCAIR, KK Birla Goa Campus, Sancoale, Goa, India.
C3 PES University; Birla Institute of Technology & Science Pilani (BITS
   Pilani); Birla Institute of Technology & Science Pilani (BITS Pilani)
RP Sridhar, P (corresponding author), PES Univ, Dept CSE, Bengaluru, India.
RI Danda, Sravan/E-7881-2019; Saha, Snehanshu/R-1028-2018
OI Saha, Snehanshu/0000-0002-8458-604X; Karanji,
   Deepika/0000-0002-1290-9233
FU BITS Pilani K K Birla Goa Campus
FX Supported by BITS Pilani K K Birla Goa Campus.
CR [Anonymous], GOOGL SCHOL TOP AI J
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NR 13
TC 0
Z9 0
U1 0
U2 1
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 1865-0929
EI 1865-0937
BN 978-3-030-87101-7; 978-3-030-87100-0
J9 COMM COM INF SC
PY 2021
VL 1479
BP 106
EP 115
DI 10.1007/978-3-030-87101-7_11
PG 10
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Interdisciplinary Applications; Computer
   Science, Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BS3IK
UT WOS:000711896000011
DA 2024-09-05
ER
PT C
AU Khiat, H
AF Khiat, Henry
BE Chova, LG
   Martinez, AL
   Torres, IC
TI WHAT CONSTITUTES AN EFFECTIVE ONLINE LEARNING EXPERIENCE FOR MATHEMATICS
   STUDENTS?
SO INTED2012: INTERNATIONAL TECHNOLOGY, EDUCATION AND DEVELOPMENT
   CONFERENCE
SE INTED Proceedings
LA English
DT Proceedings Paper
CT 6th International Conference of Technology, Education and Development
   (INTED)
CY MAR 05-07, 2012
CL Valencia, SPAIN
DE Mathematics learning; online learning; content delivery; self
   evaluation; qualitative research
AB This study reported on the students' perceptions of an effective online mathematics learning programme. 194 sets of qualitative data were collected from an open ended question that was part of a survey used to capture student perceptions of an online preparatory mathematics programme in a polytechnic. There were 376 students who responded to this online survey, from a total of 1095 students who had completed this programme. From the analysis, through coding and categorisation, the students identified important features of an effective online mathematics programme in two areas. In the area of content delivery, an online mathematics programme has to be diagnostic, recapitulative, connected, incremental, dynamic, meaningful, challenging, sufficient and monitoring. On the other hand, in the domain of self evaluation, an online mathematics programme has to be consistent, convenient, friendly, scaffolding, detailed, intelligent, challenging, sufficient and monitoring. In summary, this study has implications in the future design of online courses, specifically in mathematics, as it illuminated some of its desirable unique features as compared to other content areas.
C1 [Khiat, Henry] SIM Univ, Singapore, Singapore.
C3 Singapore University of Social Sciences (SUSS)
EM henrykhiat@unisim.edu.sg
CR Chang CC, 2008, COMPUT HUM BEHAV, V24, P1753, DOI 10.1016/j.chb.2007.07.005
   Dinov ND, 2008, COMPUT EDUC, V50, P284, DOI 10.1016/j.compedu.2006.06.003
   Golanics JD, 2008, J COMPUT ASSIST LEAR, V24, P167, DOI 10.1111/j.1365-2729.2007.00251.x
   Grant LK, 2007, PSYCHOL REC, V57, P265, DOI 10.1007/BF03395576
   Janicki T., 2001, J ASYNCHRONOUS LEARN, V5, P58
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   Tselios N.K., 2001, International Journal of Educational Telecommunications, V7, P355
   Zhang DS, 2005, AM J DISTANCE EDUC, V19, P149, DOI 10.1207/s15389286ajde1903_3
NR 8
TC 0
Z9 0
U1 0
U2 2
PU IATED-INT ASSOC TECHNOLOGY EDUCATION & DEVELOPMENT
PI VALENICA
PA LAURI VOLPI 6, VALENICA, BURJASSOT 46100, SPAIN
SN 2340-1079
BN 978-84-615-5563-5
J9 INTED PROC
PY 2012
BP 6754
EP 6760
PG 7
WC Education & Educational Research
WE Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Education & Educational Research
GA BHQ76
UT WOS:000326396406100
DA 2024-09-05
ER
PT C
AU Wu, D
   Zhang, QP
AF Wu, David
   Zhang, Qiping
GP IEEE
TI An Altmetric Study of Artificial Intelligence in Medicine
SO 2021 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND COMPUTATIONAL
   INTELLIGENCE (CSCI 2021)
LA English
DT Proceedings Paper
CT International Conference on Computational Science and Computational
   Intelligence (CSCI)
CY DEC 15-17, 2021
CL Las Vegas, NV
DE Altmetric; artificial intelligence; medicine
AB Artificial Intelligence (AI) and its powered technologies have been crucial for medical research and clinical practice In While AI in medicine is a rapidly evolving field, this study provides a new perspective to review its related bodies of literature through a bibliometric analysis of Altmetric data. Altimetric is a system that tracks the attention that research outputs such as scholarly articles receive from different web sources (e.g. social media, mainstream news, blogs). Altmetric Explorer is an online platform that enables users to browse and report on citation-based attention metric data for a given scholarly output (including journal articles and dataset). The purpose of this study is to perform an Altmetric analysis to systematically study research trends on AI in medicine. The study identifies various aspects of research outputs on AI in medicine (mentions, attention scores, a timeline of mentions, a timeline of yearly publication, top journals, top research affiliations, and twitter demographics). These findings would be of interest to both researchers and practitioners in the field. In conclusion, research of AI in medicine has been attracting significant attention in the past 4-5 years and top journals and top research affiliations follow the trend of Bradford's law and Lotka's law.
C1 [Wu, David] Great Neck North High Sch, 35 Polo Rd, Great Neck, NY 11023 USA.
   [Zhang, Qiping] Long Isl Univ, Palmer Sch Lib & Informat Sci, Brookville, NY USA.
C3 Long Island University Post
RP Wu, D (corresponding author), Great Neck North High Sch, 35 Polo Rd, Great Neck, NY 11023 USA.
EM david.zn.wu@gmail.com; Qiping.Zhang@liu.edu
CR Amisha, 2019, J FAM MED PRIM CARE, V8, P2328, DOI 10.4103/jfmpc.jfmpc_440_19
   Briganti G, 2020, FRONT MED-LAUSANNE, V7, DOI 10.3389/fmed.2020.00027
   Buch VH, 2018, BRIT J GEN PRACT, V68, P143, DOI 10.3399/bjgp18X695213
   Donepudi PK., 2018, ABC J Adv Res, V7, P109, DOI DOI 10.18034/ABCJAR.V7I2.514
   Gameiro J, 2020, J CLIN MED, V9, DOI 10.3390/jcm9030678
   Laguarta J, 2020, IEEE OPEN J ENG MED, V1, P275, DOI 10.1109/OJEMB.2020.3026928
   Porumb M, 2020, SCI REP-UK, V10, DOI 10.1038/s41598-019-56927-5
NR 7
TC 0
Z9 0
U1 0
U2 10
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-6654-5841-2
PY 2021
BP 196
EP 198
DI 10.1109/CSCI54926.2021.00105
PG 3
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications; Computer Science, Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BT4NL
UT WOS:000832229300034
DA 2024-09-05
ER
PT C
AU Leidner, JL
AF Leidner, Jochen L.
GP ACM
TI Nobody Said it Would be Easy: A Decade of R&D Projects in Information
   Access from Thomson over Reuters to Refinitiv
SO PROCEEDINGS OF THE 42ND INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH
   AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '19)
LA English
DT Proceedings Paper
CT 42nd Annual International ACM SIGIR Conference on Research and
   Development in Information Retrieval (SIGIR)
CY JUL 21-25, 2019
CL Paris, FRANCE
DE Information retrieval; information extraction; natural language
   processing; machine learning; innovation; academic collaboration;
   professional information services; corporate research & development
AB In this talk, I survey a small, non-random sample of research projects in information access carried out as part of the Thomson Reuters family of companies over the course of a 10+-year period.
   I analyse into how these projects are similar and different when compared to academic research efforts and attempt a critical (and personal, so certainly subjective) assessment of what academia can do for industry, and what industry can do for research in terms of R&D efforts. I will conclude with some advice for academic-industry collaboration initiatives in several areas of vertical information services (legal, finance, pharma and regulatory/compliance) as well as news.
C1 [Leidner, Jochen L.] Refinitiv Labs, London, England.
   [Leidner, Jochen L.] Univ Sheffield, Sheffield, S Yorkshire, England.
C3 Refinitiv; University of Sheffield
RP Leidner, JL (corresponding author), Refinitiv Labs, London, England.; Leidner, JL (corresponding author), Univ Sheffield, Sheffield, S Yorkshire, England.
EM leidner@acm.org
RI Leidner, Jochen L./AAP-6871-2021
OI Leidner, Jochen L./0000-0002-1219-4696
CR Leidner J. L., 2017, P 1 ACL WORKSHOP ETH, P30, DOI [10.18653/v1/W17-1604, DOI 10.18653/V1/W17-1604]
   Leidner JL., 2010, Proceedings of the association for computational linguistics (ACL), association for computational linguistics, Stroudsburg, PA, P54
   Nugent T, 2016, INT CONF DAT MIN WOR, P1308, DOI [10.1109/ICDMW.2016.176, 10.1109/ICDMW.2016.0191]
   Petroni F, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P626, DOI 10.1145/3219819.3219827
   Pfeifer Daniel, 2019, Advances in Information Retrieval. 41st European Conference on IR Research, ECIR 2019. Proceedings: Lecture Notes in Computer Science (LNCS 11437), P590, DOI 10.1007/978-3-030-15712-8_38
   Plachouras V., 2016, International Conference on Social Media Society, P1, DOI DOI 10.1145/2930971.2930977
   Plachouras V, 2016, SIGIR'16: PROCEEDINGS OF THE 39TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P1121, DOI 10.1145/2911451.2911457
   Smiley C., 2016, P 9 INT NAT LANG GEN, P36, DOI DOI 10.18653/V1/W16-6606
NR 8
TC 0
Z9 0
U1 1
U2 11
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA
BN 978-1-4503-6172-9
PY 2019
BP 1387
EP 1388
DI 10.1145/3331184.3331444
PG 2
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Information Science & Library Science
GA BO1LU
UT WOS:000501488900224
DA 2024-09-05
ER
PT J
AU Whipple, EE
   Hughes, A
   Bowden, S
AF Whipple, Ellen
   Hughes, Anne
   Bowden, Susan
TI Evaluation of a BSW Research Experience: Improving Student Research
   Competency
SO JOURNAL OF TEACHING IN SOCIAL WORK
LA English
DT Article
DE active learning; evaluation; mentoring; research methods; social work
   education
ID SOCIAL-WORK EDUCATION
AB This article examines the experience of 24 BSW students in a faculty- mentored undergraduate research experience (URE) over the course of 1 academic year. In particular, we sought to better understand students' self-perceived sense of competency across 15 specific research skills. In addition, we examined the URE's impact on students' knowledge about and attitudes toward research, as well as anxiety levels about research. A cross-sectional pre- and posttest design utilized both quantitative (survey) and qualitative (focus group) methodologies. All of the students' ratings of their 15 research skills improved over time; 3 were statistically significant. Students demonstrated the most gain in evidence-based practice, ability to use statistical software, and data entry and analysis. Both knowledge about and attitude toward research improved significantly. Anxiety levels were surprisingly low. The importance of faculty mentoring is discussed, and suggestions for future research are provided.
C1 [Whipple, Ellen; Hughes, Anne; Bowden, Susan] Michigan State Univ, Sch Social Work, 655 Auditorium Rd,244 Baker Hall, E Lansing, MI 48824 USA.
C3 Michigan State University
RP Whipple, EE (corresponding author), Michigan State Univ, Sch Social Work, 655 Auditorium Rd,244 Baker Hall, E Lansing, MI 48824 USA.
EM whipple@msu.edu
CR Adedokun OA., 2011, J STEM ED INNOVATION, V12
   [Anonymous], J BACCALAUREATE SOCI
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NR 19
TC 11
Z9 14
U1 0
U2 7
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0884-1233
EI 1540-7349
J9 J TEACH SOC WORK
JI J. Teach. Soc. Work
PY 2015
VL 35
IS 4
BP 397
EP 409
DI 10.1080/08841233.2015.1063568
PG 13
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA V56RN
UT WOS:000210577400004
DA 2024-09-05
ER
PT J
AU Ghosal, T
   Tiwary, P
   Patton, R
   Stahl, C
AF Ghosal, Tirthankar
   Tiwary, Piyush
   Patton, Robert
   Stahl, Christopher
TI Towards establishing a research lineage via identification of
   significant citations
SO QUANTITATIVE SCIENCE STUDIES
LA English
DT Article
DE academic influence; citation classification; citation graph; citation
   significance detection; machine learning; research lineage
ID MEASURING ACADEMIC INFLUENCE
AB Finding the lineage of a research topic is crucial for understanding the prior state of the art and advancing scientific displacement. The deluge of scholarly articles makes it difficult to locate the most relevant previous work. It causes researchers to spend a considerable amount of time building up their literature list. Citations play a crucial role in discovering relevant literature. However, not all citations are created equal. The majority of the citations that a paper receives provide contextual and background information to the citing papers. In those cases, the cited paper is not central to the theme of citing papers. However, some papers build upon a given paper and further the research frontier. In those cases, the concerned cited paper plays a pivotal role in the citing paper. Hence, the nature of the citation that the former receives from the latter is significant. In this work, we discuss our investigations towards discovering significant citations of a given paper. We further show how we can leverage significant citations to build a research lineage via a significant citation graph. We demonstrate the efficacy of our idea with two real-life case studies. Our experiments yield promising results with respect to the current state of the art in classifying significant citations, outperforming the earlier ones by a relative margin of 20 points in terms of precision. We hypothesize that such an automated system can facilitate relevant literature discovery and help identify knowledge flow for a particular category of papers.
C1 [Ghosal, Tirthankar] Charles Univ Prague, Fac Math & Phys, Inst Formal & Appl Linguist, Prague, Czech Republic.
   [Tiwary, Piyush] Indian Inst Sci, Bengaluru, India.
   [Patton, Robert; Stahl, Christopher] Oak Ridge Natl Lab, Oak Ridge, TN USA.
C3 Charles University Prague; Indian Institute of Science (IISC) -
   Bangalore; United States Department of Energy (DOE); Oak Ridge National
   Laboratory
RP Ghosal, T (corresponding author), Charles Univ Prague, Fac Math & Phys, Inst Formal & Appl Linguist, Prague, Czech Republic.
EM ghosal@ufal.mff.cuni.cz
FU U.S. Department of Energy (DOE) [DE-AC0500OR22725]; Oak Ridge Institute
   for Science and Education (ORISE); Digital India Corporation under
   Ministry of Electronics and Information Technology, Government of India
   [VISPHD-MEITY-2518]
FX This manuscript has been authored by UT-Battelle, LLC under Contract No.
   DE-AC0500OR22725 with the U.S. Department of Energy (DOE). The views
   expressed in the article do not necessarily represent the views of the
   DOE or the U.S. government. The U.S. government retains and the
   publisher, by accepting the article for publication, acknowledges that
   the U.S. government retains a nonexclusive, paid-up, irrevocable,
   world-wide license to publish or reproduce the published form of this
   manuscript, or allow others to do so, for U.S. government purposes. The
   Department of Energy will provide public access to these results of
   federally sponsored research in accordance with the DOE Public Access
   Plan (https://energy.gov/downloads/doe-public-access-plan).TG also
   thanks the Oak Ridge Institute for Science and Education (ORISE) for
   sponsorship for the Advanced Short-Term Research Opportunity (ASTRO)
   program at the Oak Ridge National Laboratory (ORNL). The ASTRO program
   is administered by the Oak Ridge Institute for Science and Education
   (ORISE) for the U.S. Department of Energy. TG also acknowledges the
   Visvesvaraya PhD fellowship award VISPHD-MEITY-2518 from Digital India
   Corporation under Ministry of Electronics and Information Technology,
   Government of India.
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NR 78
TC 5
Z9 6
U1 1
U2 12
PU MIT PRESS
PI CAMBRIDGE
PA ONE ROGERS ST, CAMBRIDGE, MA 02142-1209 USA
EI 2641-3337
J9 QUANT SCI STUD
JI Quant. Sci. Stud.
PD FEB 4
PY 2022
VL 2
IS 4
BP 1511
EP 1528
DI 10.1162/qss_a_00170
PG 18
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA YU4AT
UT WOS:000751988600016
OA gold
DA 2024-09-05
ER
PT J
AU Zhang, MJ
   Li, JT
   Huang, QX
   Kan, HB
AF Zhang, Mengjie
   Li, Jingtao
   Huang, Qixuan
   Kan, Haibin
TI Learning counterfactual outcomes of MOOC multiple learning behaviors
SO COMPUTER APPLICATIONS IN ENGINEERING EDUCATION
LA English
DT Article
DE action research; assessment tools; causal inference; counterfactual
   outcome; research methods
ID PROPENSITY SCORE
AB The absence of counterfactual outcomes presents a fundamental challenge in causal inference. However, existing work typically does not apply to multiple learning behaviors of Massive Open Online Courses. This paper proposes a counterfactual representation learning model based on multitask learning, applicable to any dimension, and any type of treatment. The model consists of a potential outcome network and a propensity score encoder, which shares feature information from the base layer. The propensity scores calculated by the encoder are then utilized in the potential outcome network to mitigate selection bias. Experiments based on real-world data sets demonstrate the superior performance of our model compared with baselines.
C1 [Zhang, Mengjie; Li, Jingtao; Huang, Qixuan] Fudan Univ, Software Sch, Shanghai, Peoples R China.
   [Li, Jingtao; Kan, Haibin] Shanghai Engn Res Ctr Blockchain, Shanghai, Peoples R China.
   [Kan, Haibin] Fudan Univ, Sch Comp Sci, Shanghai Key Lab Intelligent Informat Proc, Shanghai, Peoples R China.
   [Li, Jingtao] Fudan Univ, Cross 2 Bldg,Jiangwan Campus, Shanghai, Peoples R China.
C3 Fudan University; Fudan University; Fudan University
RP Li, JT (corresponding author), Fudan Univ, Cross 2 Bldg,Jiangwan Campus, Shanghai, Peoples R China.
EM lijt@fudan.edu.cn
RI zhang, mengjie/GRN-9277-2022; HUANG, QIXUAN/JXN-6677-2024; Li,
   Jingtao/IST-9889-2023
OI Li, Jingtao/0000-0002-0263-9831
FU Computer Course Teaching Reform Project of Shanghai Municipal Education
   Commission "Information Security Course" [202111]; National Natural
   Science Foundation of China [62272107, U19A2066]; National Key Ramp;D
   Program of China [2019YFB2101703]; Key Ramp;D Program of Guangdong
   Province [2020B0101090001]; Undergraduate Key Course construction
   Project of Shanghai Municipal Education Commission "Computer Network"
   [2019021]; Innovation Action Plan of Shanghai Science and Technology
   [21511102200]
FX Computer Course Teaching Reform Project of Shanghai Municipal Education
   Commission "Information Security Course", Grant/Award Number: 202111;
   National Natural Science Foundation of China, Grant/Award Number:
   62272107;U19A2066; National Key R&D Program of China, Grant/Award
   Number: 2019YFB2101703; The Key R&D Program of Guangdong Province,
   Grant/Award Number: 2020B0101090001; Undergraduate Key Course
   construction Project of Shanghai Municipal Education Commission
   "Computer Network", Grant/Award Number: 2019021; The Innovation Action
   Plan of Shanghai Science and Technology, Grant/Award Number: 21511102200
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NR 36
TC 0
Z9 0
U1 3
U2 7
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1061-3773
EI 1099-0542
J9 COMPUT APPL ENG EDUC
JI Comput. Appl. Eng. Educ.
PD NOV
PY 2023
VL 31
IS 6
BP 1678
EP 1689
DI 10.1002/cae.22666
EA JUL 2023
PG 12
WC Computer Science, Interdisciplinary Applications; Education, Scientific
   Disciplines; Engineering, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Education & Educational Research; Engineering
GA Y7FP1
UT WOS:001036516100001
OA Bronze
DA 2024-09-05
ER
PT J
AU Xie, Y
   Seth, I
   Rozen, WM
   Hunter-Smith, DJ
AF Xie, Yi
   Seth, Ishith
   Rozen, Warren M. M.
   Hunter-Smith, David J. J.
TI Evaluation of the Artificial Intelligence Chatbot on Breast
   Reconstruction and Its Efficacy in Surgical Research: A Case Study
SO AESTHETIC PLASTIC SURGERY
LA English
DT Article
DE ChatGPT; Artificial intelligence; Chatbot; Breast reconstruction
AB Background ChatGPT is an open-source artificial intelligence (AI) chatbot that uses deep learning to produce human-like text dialog. Its potential applications in the scientific community are vast; however, its efficacy on performing comprehensive literature searches, data analysis and report writing in aesthetic plastic surgery topics remains unknown. This study aims to evaluate both the accuracy and comprehensiveness of ChatGPT's responses to assess its suitability for use in aesthetic plastic surgery research.Methods Six questions were prompted to ChatGPT on post-mastectomy breast reconstruction. First two questions focused on the current evidence and options for breast reconstruction post-mastectomy, and remaining four questions focused specifically on autologous breast reconstruction. Using the Likert framework, the responses provided by ChatGPT were qualitatively assessed for accuracy and information content by two specialist plastic surgeons with extensive experience in the field.Results ChatGPT provided relevant, accurate information; however, it lacked depth. It could provide no more than a superficial overview in response to more esoteric questions and generated incorrect references. It created non-existent references, cited wrong journal and date, which poses a significant challenge in maintaining academic integrity and caution of its use in academia.Conclusion While ChatGPT demonstrated proficiency in summarizing existing knowledge, it created fictitious references which poses a significant concern of its use in academia and healthcare. Caution should be exercised in interpreting its responses in the aesthetic plastic surgical field and should only be used for such with sufficient oversight.
C1 [Xie, Yi; Seth, Ishith; Rozen, Warren M. M.; Hunter-Smith, David J. J.] Peninsula Hlth, Dept Plast Surg, Melbourne, Vic 3199, Australia.
   [Seth, Ishith; Rozen, Warren M. M.; Hunter-Smith, David J. J.] Monash Univ, Fac Med, Melbourne, Vic 3004, Australia.
C3 Peninsula Health; Monash University
RP Seth, I (corresponding author), Peninsula Hlth, Dept Plast Surg, Melbourne, Vic 3199, Australia.; Seth, I (corresponding author), Monash Univ, Fac Med, Melbourne, Vic 3004, Australia.
EM ishithseth1@gmail.com
RI Seth, Ishith/IXX-0725-2023
OI Seth, Ishith/0000-0001-5444-8925
FU CAUL
FX Open Access funding enabled and organized by CAUL and its Member
   Institutions. No authors have received any funding orsupport.
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TC 16
Z9 16
U1 3
U2 26
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 0364-216X
EI 1432-5241
J9 AESTHET PLAST SURG
JI Aesthet. Plast. Surg.
PD DEC
PY 2023
VL 47
IS 6
BP 2360
EP 2369
DI 10.1007/s00266-023-03443-7
EA JUN 2023
PG 10
WC Surgery
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Surgery
GA MM6Z2
UT WOS:001010426300003
PM 37314466
OA hybrid
DA 2024-09-05
ER
PT J
AU Deike, M
AF Deike, Michael
TI Evaluating the performance of ChatGPT and Perplexity AI in Business
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SO JOURNAL OF BUSINESS & FINANCE LIBRARIANSHIP
LA English
DT Article
DE Artificial intelligence; assessment; business reference services;
   business libraries; business librarians; ChatGPT; Perplexity AI;
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AB The Thomas Mahaffey Jr. Business Library conducted a study to assess the performance of two competing generative AI products, ChatGPT and Perplexity AI, in answering business reference questions. The study used a data set consisting of a sample of anonymized reference questions submitted through the library's ServiceNow ticketing system between January 2018 and May 2022. The questions were input as prompts to each competing AI. Responses were collected and evaluated by their performance in four separate dimensions relevant to business reference: accessibility, library referral, quality, and serendipity. Each dimension was scored on a 0-5 Likert scale resulting in a final composite performance score for each AI. Results showed similar and underwhelming performance between each AI at the composite level. Analysis of scores in each individual scoring dimension showed greater variance in the score distributions between the competing AI. Through the evaluation process, key strengths, weaknesses, and trends emerged between each AI. The study provides a quantitative measure of where generative AI stands in its capabilities in a business library reference context, and it recommends, based on the results of the evaluation, making use of generative AI in its current iteration as a supplementary tool for business reference as opposed to considering it as a replacement.
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RP Deike, M (corresponding author), Univ Notre Dame, Notre Dame, IN 46556 USA.
EM mdeike@nd.edu
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EI 1547-0644
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JI J. Bus. Financ. Libr.
PD APR 2
PY 2024
VL 29
IS 2
BP 125
EP 154
DI 10.1080/08963568.2024.2317534
EA FEB 2024
PG 30
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA LI0S1
UT WOS:001170173900001
DA 2024-09-05
ER
PT J
AU Bao, T
   Gao, JS
   Wang, JY
   Chen, Y
   Xu, F
   Qiao, GZ
   Li, F
AF Bao, Tong
   Gao, Jiasi
   Wang, Jinyi
   Chen, Yang
   Xu, Feng
   Qiao, Guanzhong
   Li, Fei
TI A global bibliometric and visualized analysis of gait analysis and
   artificial intelligence research from 1992 to 2022
SO FRONTIERS IN ROBOTICS AND AI
LA English
DT Article
DE gait analysis; artificial intelligence (AI); wearable device; sensor;
   bibliometric analysis
ID ANTERIOR CRUCIATE LIGAMENT; CEREBRAL-PALSY; PARKINSONS-DISEASE;
   PATHOLOGICAL GAIT; CLASSIFICATION; CHILDREN; MOVEMENT; WALKING;
   RECOGNITION; PARAMETERS
AB Gait is an important basic function of human beings and an integral part of life. Many mental and physical abnormalities can cause noticeable differences in a person's gait. Abnormal gait can lead to serious consequences such as falls, limited mobility and reduced life satisfaction. Gait analysis, which includes joint kinematics, kinetics, and dynamic Electromyography (EMG) data, is now recognized as a clinically useful tool that can provide both quantifiable and qualitative information on performance to aid in treatment planning and evaluate its outcome. With the assistance of new artificial intelligence (AI) technology, the traditional medical environment has undergone great changes. AI has the potential to reshape medicine, making gait analysis more accurate, efficient and accessible. In this study, we analyzed basic information about gait analysis and AI articles that met inclusion criteria in the WoS Core Collection database from 1992-2022, and the VosViewer software was used for web visualization and keyword analysis. Through bibliometric and visual analysis, this article systematically introduces the research status of gait analysis and AI. We introduce the application of artificial intelligence in clinical gait analysis, which affects the identification and management of gait abnormalities found in various diseases. Machine learning (ML) and artificial neural networks (ANNs) are the most often utilized AI methods in gait analysis. By comparing the predictive capability of different AI algorithms in published studies, we evaluate their potential for gait analysis in different situations. Furthermore, the current challenges and future directions of gait analysis and AI research are discussed, which will also provide valuable reference information for investors in this field.
C1 [Bao, Tong; Wang, Jinyi] Tsinghua Univ, Sch Med, Beijing, Peoples R China.
   [Bao, Tong; Li, Fei] Tsinghua Univ, Inst Precis Med, Beijing, Peoples R China.
   [Bao, Tong; Wang, Jinyi; Chen, Yang; Xu, Feng; Qiao, Guanzhong; Li, Fei] Tsinghua Univ, Affiliated Hosp 1, Orthoped Dept, Beijing, Peoples R China.
   [Gao, Jiasi] Tsinghua Univ, Inst AI Ind Res, Beijing, Peoples R China.
C3 Tsinghua University; Tsinghua University; Tsinghua University; Tsinghua
   University
RP Li, F (corresponding author), Tsinghua Univ, Inst Precis Med, Beijing, Peoples R China.; Li, F (corresponding author), Tsinghua Univ, Affiliated Hosp 1, Orthoped Dept, Beijing, Peoples R China.
EM lifeisulker@126.com
FU Tsinghua University Initiative Scientific Research Program of Precision
   Medicine [2022TS008]
FX The authors declare financial support was received for the research,
   authorship, and/or publication of this article. Supported by Tsinghua
   University Initiative Scientific Research Program of Precision Medicine
   (2022TS008).
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NR 157
TC 2
Z9 2
U1 6
U2 12
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 2296-9144
J9 FRONT ROBOT AI
JI Front. Robot. AI
PD NOV 17
PY 2023
VL 10
AR 1265543
DI 10.3389/frobt.2023.1265543
PG 23
WC Robotics
WE Emerging Sources Citation Index (ESCI)
SC Robotics
GA Z4IG7
UT WOS:001111722400001
PM 38047061
OA gold, Green Published
DA 2024-09-05
ER
PT C
AU Butkute, V
   Lapin, K
AF Butkute, Viktorija
   Lapin, Kristina
BE Targamadze, A
   Butleris, R
   Butkiene, R
TI USABILITY HEURISTICS FOR ONLINE VIRTUAL WORLDS
SO INFORMATION TECHNOLOGIES' 2010
SE Information Technologies Conference Proceedings
LA English
DT Proceedings Paper
CT 16th International conference on Information and Software Technologies
   (IT 2010)
CY APR 21-23, 2010
CL Kaunas, LITHUANIA
DE virtual world; usability evaluation; heuristic evaluations; online
   communication and collaboration; user research; user needs
AB This paper explores a set of usability heuristics developed for the EU FP7 ICT VirtualLife project. The purpose of the project is to create a three-dimensional online virtual world - in the form of a safe, democratic and legally ruled collaboration platform. 3D virtual worlds are a relatively new expression of online communication and collaboration tools. The most essential aspect - usability - still is in tentative phase. Consequently, conventional usability heuristics are not suitable for 3D virtual worlds. The paper identifies major differences between 3D virtual worlds and other online collaboration and communication tools. Specific user needs are discussed considering the users' real experience, mistakes and problems.
C1 [Butkute, Viktorija; Lapin, Kristina] Vilnius State Univ, Fac Math & Informat, LT-03225 Vilnius, Lithuania.
C3 Vilnius University
RP Butkute, V (corresponding author), Vilnius State Univ, Fac Math & Informat, Naugarduko 24, LT-03225 Vilnius, Lithuania.
EM Viktorija.Butkute@mif.stud.vu.lt; Kristina.Lapin@mif.vu.lt
RI Lapin, Kristina/ISS-9336-2023
CR [Anonymous], 2003, HUM FAC ER
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NR 19
TC 0
Z9 0
U1 0
U2 2
PU KAUNAS UNIV TECHNOLOGY PRESS
PI KAUNAS
PA K DONELAICIO 73, KAUNAS LT 3006, LITHUANIA
SN 2029-0020
J9 INFORM TECHNOL C PR
PY 2010
BP 285
EP 291
PG 7
WC Computer Science, Information Systems
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BDN77
UT WOS:000314082000036
DA 2024-09-05
ER
PT J
AU Liu, XY
   Zhu, HR
AF Liu, Xueying
   Zhu, Haoran
TI Linguistic positivity in soft and hard disciplines: temporal dynamics,
   disciplinary variation, and the relationship with research impact
SO SCIENTOMETRICS
LA English
DT Article
DE Linguistic positivity; Sentiment analysis; Disciplinary variation;
   Research impact
ID ARTICLES; SCIENCE; SENTIMENT; LANGUAGE; CITATION; QUALITY; PUBLISH; HYPE
AB Previous studies have investigated the use of positive/negative language in academic discourse, and have found a tendency toward using more positive language in academic writing. However, little is known about whether the features and dynamics of linguistic positivity vary across disciplines. In addition, the relationship between linguistic positivity and research impact deserves further evaluation. To address these issues, the present study investigated linguistic positivity in academic writing from a cross-disciplinary perspective. Based on a 111-million-word corpus of research article abstracts collected from the Web of Science, the study examined the diachronic trends of positive/negative language in eight academic disciplines, and explored the relationship between linguistic positivity and citation counts. The results demonstrated that the increase in linguistic positivity is a common phenomenon across the examined academic disciplines. In addition, hard disciplines showed a higher and faster-growing degree of linguistic positivity compared with soft disciplines. Last, a significant positive correlation was identified between citation counts and the degree of linguistic positivity. Reasons for the temporal dynamics and disciplinary variation of linguistic positivity were explored, and implications for the scientific community were discussed.
C1 [Liu, Xueying; Zhu, Haoran] Huazhong Univ Sci & Technol, 1037 Luoyu Rd, Wuhan, Peoples R China.
C3 Huazhong University of Science & Technology
RP Zhu, HR (corresponding author), Huazhong Univ Sci & Technol, 1037 Luoyu Rd, Wuhan, Peoples R China.
EM hrzhu@hust.edu.cn
RI Haoran, Zhu/GPW-7084-2022
FU Humanities and Social Sciences Youth Foundation, Ministry of Education
   of the People's Republic of China [21YJC740085]
FX This study was supported by Humanities and Social Sciences Youth
   Foundation, Ministry of Education of the People's Republic of China
   (Grant No. 21YJC740085).
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NR 67
TC 6
Z9 6
U1 13
U2 43
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD MAY
PY 2023
VL 128
IS 5
BP 3107
EP 3127
DI 10.1007/s11192-023-04679-5
EA MAR 2023
PG 21
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA U5FW4
UT WOS:000954476500005
PM 37101976
OA Bronze, Green Published
DA 2024-09-05
ER
PT J
AU Bevern, R
   Komusiewicz, C
   Niedermeier, R
   Sorge, M
   Walsh, T
AF van Bevern, Rene
   Komusiewicz, Christian
   Niedermeier, Rolf
   Sorge, Manuel
   Walsh, Toby
TI H-index manipulation by merging articles: Models, theory, and
   experiments
SO ARTIFICIAL INTELLIGENCE
LA English
DT Article
DE Citation index; Hirsch index; Parameterized complexity; Exact
   algorithms; AI's 10 to watch
ID MULTIVARIATE ALGORITHMICS; GOOGLE SCHOLAR; COMPLEXITY
AB An author's profile on Google Scholar consists of indexed articles and associated data, such as the number of citations and the H-index. The author is allowed to merge articles; this may affect the H-index. We analyze the (parameterized) computational complexity of maximizing the H-index using article merges. Herein, to model realistic manipulation scenarios, we define a compatibility graph whose edges correspond to plausible merges. Moreover, we consider several different measures for computing the citation count of a merged article. For the measure used by Google Scholar, we give an algorithm that maximizes the H-index in linear time if the compatibility graph has constant-size connected components. In contrast, if we allow to merge arbitrary articles (that is, for compatibility graphs that are cliques), then already increasing the H-index by one is NP-hard. Experiments on Google Scholar profiles of AI researchers show that the H-index can be manipulated substantially only if one merges articles with highly dissimilar titles. (C) 2016 Elsevier B.V. All rights reserved.
C1 [van Bevern, Rene] Novosibirsk State Univ, Ul Pirogova 2, Novosibirsk 630090, Russia.
   [van Bevern, Rene] Russian Acad Sci, Siberian Branch, Sobolev Inst Math, Novosibirsk, Russia.
   [Komusiewicz, Christian] Univ Jena, Inst Informat, D-07745 Jena, Germany.
   [van Bevern, Rene; Komusiewicz, Christian; Niedermeier, Rolf; Sorge, Manuel; Walsh, Toby] TU Berlin, Inst Softwaretech & Theoret Informat, Berlin, Germany.
   [Walsh, Toby] Univ New South Wales, Sydney, NSW, Australia.
   [Walsh, Toby] Data61, Sydney, NSW, Australia.
C3 Novosibirsk State University; Russian Academy of Sciences; Sobolev
   Institute of Mathematics; Friedrich Schiller University of Jena;
   Technical University of Berlin; University of New South Wales Sydney;
   Commonwealth Scientific & Industrial Research Organisation (CSIRO)
RP Bevern, R (corresponding author), Novosibirsk State Univ, Ul Pirogova 2, Novosibirsk 630090, Russia.
EM rvb@nsu.ru; christian.komusiewicz@uni-jena.de;
   rolf.niedermeier@tu-berlin.de; manuel.sorge@tu-berlin.de;
   toby.walsh@nicta.com.au
RI van Bevern, René/L-1374-2016; Komusiewicz, Christian/X-2452-2019; Walsh,
   Toby/Q-9043-2016
OI van Bevern, René/0000-0002-4805-218X; Komusiewicz,
   Christian/0000-0003-0829-7032; Walsh, Toby/0000-0003-2998-8668
FU German Research Foundation (DFG), project DAPA [NI 369/12]; Russian
   Foundation for Basic Research (RFBR) [16-31-60007 mol_a_dk]; DFG project
   MAGZ [KO 3669/4-1]; Alexander von Humboldt Foundation, Bonn, Germany
FX Rene van Bevern was supported by the German Research Foundation (DFG),
   project DAPA (NI 369/12), at TU Berlin, and by the Russian Foundation
   for Basic Research (RFBR), project 16-31-60007 mol_a_dk, at Novosibirsk
   State University. Christian Komusiewicz was supported by the DFG project
   MAGZ (KO 3669/4-1) and Manuel Sorge was supported by the DFG project
   DAPA (NI 369/12). Toby Walsh was supported by the Alexander von Humboldt
   Foundation, Bonn, Germany, while at TU Berlin. The main work was done
   while Toby Walsh was affiliated with University of New South Wales and
   Data61, Sydney, Australia.
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NR 33
TC 13
Z9 13
U1 1
U2 15
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0004-3702
EI 1872-7921
J9 ARTIF INTELL
JI Artif. Intell.
PD NOV
PY 2016
VL 240
BP 19
EP 35
DI 10.1016/j.artint.2016.08.001
PG 17
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA DY1JM
UT WOS:000384851300002
OA Green Submitted, Green Published, hybrid
DA 2024-09-05
ER
PT J
AU Lu, QQ
   Chai, YX
   Ren, LH
   Ren, PY
   Zhou, JH
   Lin, CL
AF Lu, Qianqian
   Chai, Yongxiang
   Ren, Lihui
   Ren, Pengyu
   Zhou, Junhui
   Lin, Chunlei
TI Research on quality evaluation of innovation and entrepreneurship
   education for college students based on random forest algorithm and
   logistic regression model
SO PEERJ COMPUTER SCIENCE
LA English
DT Article
DE Innovation; Entrepreneurship; Random forest; Logistic regression;
   Entrepreneurial education; Curriculum development
AB The quality evaluation of innovation and entrepreneurship (I&E) in the education sector is achieving worldwide attention as empowering nations with high quality talents is quintessential for economic progress. China, a pioneer in the world market in almost all sectors have transformed its educational policies and incorporated entrepreneurial skills as a part of their education models to further catalyst the country's economic progress. This research focuses on building a novel hybrid Machine Learning (ML) model by integrating two powerful algorithms namely Random Forest (RF) and Logistic Regression (LR) to assess the intensity of the I&E in education from the data acquired from 25 leading Higher Educational Institution's (HEI) in different provinces. The major contributions to the work are, (1) construction of quality index for each topic of interest using individual RF, (2) ranking the indicators based on the quality index to assess the strength and weaknesses, (3) and finally use the LR algorithm study the quality of each indicator. The efficacy of the proposed hybrid model is validated using the benchmark classification metrics to assess its learning and prediction performance in evaluating the quality of I&E education. The result of the research portrays that the universities have now started to integrate entrepreneurship skills as a part of the curriculum, which is evident from the better ranking of the topic curriculum development which is followed by the enrichment of skills. This comprehensive research will help the institutions to identify the potential areas of growth to boost the economic development and improve the skill set necessary for I&E education among college students.
C1 [Lu, Qianqian] Zhejiang Guangsha Vocat & Tech Univ Construct, Adm Off, Dongyang, Peoples R China.
   [Chai, Yongxiang] Zhejiang Guangsha Vocat & Tech Univ Construct, Sch Informat, Dongyang, Peoples R China.
   [Ren, Lihui; Ren, Pengyu; Zhou, Junhui] Zhejiang Guangsha Vocat & Tech Univ Construct, Dept Publ Phys Educ, Dongyang, Peoples R China.
   [Lin, Chunlei] Zhejiang Guangsha Vocat & Tech Univ Construct, Dept Educ & Engn, Dongyang, Peoples R China.
RP Ren, LH (corresponding author), Zhejiang Guangsha Vocat & Tech Univ Construct, Dept Publ Phys Educ, Dongyang, Peoples R China.
EM renlihui578@163.com
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NR 46
TC 1
Z9 1
U1 8
U2 18
PU PEERJ INC
PI LONDON
PA 341-345 OLD ST, THIRD FLR, LONDON, EC1V 9LL, ENGLAND
EI 2376-5992
J9 PEERJ COMPUT SCI
JI PeerJ Comput. Sci.
PD APR 17
PY 2023
VL 9
AR e1329
DI 10.7717/peerj-cs.1329
PG 22
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA P8TE7
UT WOS:001053334400005
PM 37346726
OA gold, Green Published
DA 2024-09-05
ER
PT C
AU Zhang, K
AF Zhang, Ke
BE Li, K
   Fei, M
   Irwin, GW
   Ma, SW
TI Research on coaxiality errors evaluation based on ant colony
   optimization algorithm
SO BIO-INSPIRED COMPUTATIONAL INTELLIGENCE AND APPLICATIONS
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT International Conference on Life System Modeling and Simulation (LSMS)
CY SEP 14-17, 2007
CL Shanghai, PEOPLES R CHINA
ID FLATNESS
AB Based on the analysis of existent evaluation methods for coaxiality errors, an intelligent evaluation method is provided in this paper. The evolutional optimum model and the calculation process are introduced in detail. According to characteristics of coaxiality error evaluation, ant colony optimization (ACO) algorithm is proposed to evaluate the minimum zone error. Compared with conventional optimum evaluation methods such as simplex search and Powell method, it can find the global optimal solution, and the precision of calculating result is very good. Then, the objective function calculation approaches for using the ACO algorithm to evaluate minimum zone error are formulated. Finally, the control experiment results evaluated by different method such as the least square, simplex search, Powell optimum methods and GA, indicate that the proposed method does provide better accuracy on coaxiality error evaluation, and it has fast convergent speed as well as using computer expediently and popularizing application easily.
C1 [Zhang, Ke] Shanghai Inst Technol, Sch Mech & Automat Engn, Shanghai 200235, Peoples R China.
C3 Shanghai Institute of Technology
RP Zhang, K (corresponding author), Shanghai Inst Technol, Sch Mech & Automat Engn, Shanghai 200235, Peoples R China.
EM zkwy@hotmail.com
CR [Anonymous], 2004, ANT COLONY OPTIMIZAT
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NR 11
TC 0
Z9 0
U1 0
U2 1
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 0302-9743
EI 1611-3349
BN 978-3-540-74768-0
J9 LECT NOTES COMPUT SC
PY 2007
VL 4688
BP 267
EP 276
PG 10
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Software Engineering; Computer Science,
   Theory & Methods; Mathematical & Computational Biology
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Mathematical & Computational Biology
GA BGV86
UT WOS:000250830800030
DA 2024-09-05
ER
PT J
AU Romanello, M
   Najem-Meyer, S
AF Romanello, Matteo
   Najem-Meyer, Sven
TI A Named Entity-Annotated Corpus of 19<SUP>th</SUP> Century Classical
   Commentaries
SO JOURNAL OF OPEN HUMANITIES DATA
LA English
DT Article; Data Paper
DE historical commentaries; classics; named entity recognition; entity
   linking; bibliographic reference extraction
AB We release a multilingual named entity (NE) corpus of 19th century commentaries to Sophocles' Ajax. Selected commentaries are written in English, German and French, but are also replete with Latin and Greek quotes. Bibliographic entities were annotated along traditional named entities following our guidelines (Romanello & Najem-Meyer, 2022). The corpus contains about 300 annotated pages, 111,216 tokens and 7,334 entity mentions and was featured in the HIPE-2022 shared task. Although named entity recognition (NER) showed reassuring results, optical character recognition (OCR) mistakes and extensive use of abbreviation kept entity linking (EL) a challenging task. With such characteristics, this corpus offers an excellent way to assess the adaptability of information extraction systems to noisy, domain-specific multilingual and multiscript environments.
C1 [Romanello, Matteo] Univ Lausanne, Inst Archeol & Class Studies, Lausanne, Switzerland.
   [Najem-Meyer, Sven] Swiss Fed Inst Technol Lausanne, Digital Humanities Lab, Lausanne, Switzerland.
C3 University of Lausanne; Swiss Federal Institutes of Technology Domain;
   Ecole Polytechnique Federale de Lausanne
RP Romanello, M (corresponding author), Univ Lausanne, Inst Archeol & Class Studies, Lausanne, Switzerland.
EM matteo.romanello@unil.ch
OI Romanello, Matteo/0000-0002-7406-6286
FU Swiss National Science Foundation under the Ambizione scheme
   [PZ00P1_186033]; Swiss National Science Foundation (SNF) [PZ00P1_186033]
   Funding Source: Swiss National Science Foundation (SNF)
FX This research was funded by the Swiss National Science Foundation under
   the Ambizione scheme (Grant number: PZ00P1_186033) .
CR Bugert M., 2018, P 27 INT C COMP LING, P5
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   SCHwETER Stefan, 2022, P CLEF 2022 C LABS E, P1109
NR 9
TC 0
Z9 0
U1 0
U2 0
PU UBIQUITY PRESS LTD
PI LONDON
PA Unit 3N, 6 Osborn Street, LONDON, E1 6TD, ENGLAND
EI 2059-481X
J9 J OPEN HUMANIT DATA
JI J. Open Humanit. Data
PY 2024
VL 10
AR 1
DI 10.5334/johd.150
PG 7
WC Humanities, Multidisciplinary; Social Sciences, Interdisciplinary
WE Emerging Sources Citation Index (ESCI)
SC Arts & Humanities - Other Topics; Social Sciences - Other Topics
GA OR0S7
UT WOS:001208889500008
OA gold
DA 2024-09-05
ER
PT J
AU Yu, SS
   Carroll, F
   Bentley, BL
AF Yu, Shasha
   Carroll, Fiona
   Bentley, Barry L.
TI Insights Into Privacy Protection Research in AI
SO IEEE ACCESS
LA English
DT Article
DE AI; artificial intelligence; bibliometric analysis; privacy protection
ID ARTIFICIAL-INTELLIGENCE; BIBLIOMETRIC ANALYSIS; SCIENCE; SECURITY; INDEX
AB This paper presents a systematic bibliometric analysis of the artificial intelligence (AI) domain to explore privacy protection research as AI technologies integrate and data privacy concerns rise. Understanding evolutionary patterns and current trends in this research is crucial. Leveraging bibliometric techniques, the authors analyze 8,322 papers from the Web of Science (WoS) database, spanning 1990 to 2023. The analysis highlights IEEE Transactions on Knowledge and Data Engineering and IEEE Access journals as highly influential, the former being an early contributor and the latter emerging as a pivotal source. The study demonstrates substantial disparities in scientific productivity across countries. Specifically, the top 10 countries collectively accounted for 74.8% of the articles, with China and the USA making up nearly half of the total contribution (46.1%). In contrast, regions in Africa and South America exhibited lower scientific production. The evolution of privacy preservation research is reflected, shifting from an algorithm-oriented approach to a focus on data orientation, and subsequently, to privacy solutions centered around Cloud Computing. In recent years, there has been a shift towards embracing Federated Learning and Differential Privacy. The analysis brings to light emerging themes and identifies research gaps, notably a global disparity in research output and a lag in ethical and legal inquiry. It asserts that enhanced interdisciplinary collaboration is imperative to formulate comprehensive privacy solutions for AI. Specifically, the paper imparts invaluable insights that are pivotal for effectively addressing the evolving privacy concerns in the era of AI and big data.
C1 [Yu, Shasha] Clark Univ, Sch Profess Studies, Worcester, MA 01610 USA.
   [Carroll, Fiona; Bentley, Barry L.] Cardiff Metropolitan Univ, Cardiff Sch Technol, Cardiff CF5 2YB, Wales.
C3 Clark University; Cardiff Metropolitan University
RP Yu, SS (corresponding author), Clark Univ, Sch Profess Studies, Worcester, MA 01610 USA.
EM ShaYu@clarku.edu
RI Bentley, Barry L./K-2802-2017
OI Bentley, Barry L./0000-0002-4360-5902; Carroll,
   Fiona/0000-0002-9967-2207
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NR 94
TC 0
Z9 0
U1 14
U2 14
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2024
VL 12
BP 41704
EP 41726
DI 10.1109/ACCESS.2024.3378126
PG 23
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA MF5R3
UT WOS:001192229700001
OA gold
DA 2024-09-05
ER
PT C
AU Ho, TKT
   Bui, QV
   Bui, M
AF Thi Kim Thoa Ho
   Quang Vu Bui
   Bui, Marc
GP Assoc Comp Machinery
TI Co-author Relationship Prediction in Bibliographic Network: A New
   Approach Using Geographic Factor and Latent Topic Information
SO SOICT 2019: PROCEEDINGS OF THE TENTH INTERNATIONAL SYMPOSIUM ON
   INFORMATION AND COMMUNICATION TECHNOLOGY
LA English
DT Proceedings Paper
CT 10th International Symposium on Information and Communication Technology
   (SoICT)
CY DEC 04-06, 2019
CL VIETNAM
DE Link prediction; bibliographic network; multi-relation network; topic
   modeling
AB In this research, we propose a novel approach for co-author relationship prediction in a bibliographic network utilizing geographic factor and latent topic information. We utilize a supervised method to predict the co-author relationship formation where combining dissimilar features with the dissimilar measuring coefficient. Firstly, besides existing relations have been studied in previous researches, we exploit new relation related to the geographic factor which contributes as a topological feature. Moreover, we discover content feature based on textual information from author's papers using topic modeling. Finally, we amalgamate topological features and content feature in co-author relationship prediction. We conducted experiments on dissimilar datasets of the bibliographic network and have attained satisfactory results.
C1 [Thi Kim Thoa Ho; Bui, Marc] PSL Res Univ, CHArt Lab EA 4004, EPHE, Paris, France.
   [Thi Kim Thoa Ho] Hue Univ, Univ Educ, Hue, Vietnam.
   [Quang Vu Bui] Hue Univ Vietnam, Univ Sci, Hue, Vietnam.
C3 Universite PSL; Ecole Pratique des Hautes Etudes (EPHE); Universite
   Paris-Est-Creteil-Val-de-Marne (UPEC); Hue University; Hue University
RP Ho, TKT (corresponding author), PSL Res Univ, CHArt Lab EA 4004, EPHE, Paris, France.; Ho, TKT (corresponding author), Hue Univ, Univ Educ, Hue, Vietnam.
EM thi-kim-thoa.ho@etu.ephe.psl.eu; buiquangvu@hueuni.edu.vn;
   marc.bui@ephe.psl.eu
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NR 18
TC 1
Z9 1
U1 0
U2 1
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA
BN 978-1-4503-7245-9
PY 2019
BP 69
EP 77
DI 10.1145/3368926.3369668
PG 9
WC Computer Science, Theory & Methods; Engineering, Electrical &
   Electronic; Telecommunications
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Engineering; Telecommunications
GA BP3MI
UT WOS:000548118100011
DA 2024-09-05
ER
PT J
AU Hu, ZW
   Luo, JM
   Chi, CGQ
   Gursoy, D
AF Hu, Zongwei
   Luo, Jian Ming
   Chi, Christina Geng-Qing
   Gursoy, Dogan
TI Examination of experience attributes of parks in urban tourist
   destinations and their influence on visitor satisfaction: a topic
   modelling approach
SO LEISURE STUDIES
LA English
DT Article; Early Access
DE Urban destinations; park experience; user-generated content (UGC);
   latent Dirichlet allocation (LDA); seasonal
ID CITY; REVIEWS; HEALTH; MANAGEMENT; BENEFITS; SEASON; SPACES
AB Understanding visitors' park experience in urban tourist destinations to enhance their satisfaction is crucial for effective park management and urban planning. Through a topic modelling analysis using user-generated data from TripAdvisor and employing the Latent Dirichlet Allocation (LDA) algorithm, 20 attributes of urban park experience are identified and grouped into three categories and five dimensions. Besides confirming some of the attributes of park experience identified in previous research, this study also uncovers new attributes that provide insights into visitors' urban park experience related to the historical and cultural aspects (e.g. filming location, carriage rides and rickshaws). Furthermore, positive and negative effects of each attribute on visitor satisfaction are identified. Findings also suggest significant seasonal difference in attributes of visitors' urban park experience.
C1 [Hu, Zongwei] Macau Univ Sci & Technol, Fac Hospitality & Tourism Management, Macau, Peoples R China.
   [Luo, Jian Ming] Macau Univ Sci & Technol, Sch Liberal Arts, Macau, Peoples R China.
   [Chi, Christina Geng-Qing; Gursoy, Dogan] Washington State Univ, Carson Coll Business, Sch Hospitality Business Management, Pullman, WA USA.
   [Chi, Christina Geng-Qing; Gursoy, Dogan] Univ Johannesburg, Sch Tourism & Hospitality, Johannesburg, South Africa.
C3 Macau University of Science & Technology; Macau University of Science &
   Technology; Washington State University; University of Johannesburg
RP Luo, JM (corresponding author), Macau Univ Sci & Technol, Sch Liberal Arts, Macau, Peoples R China.
EM kenny.luo@connect.polyu.hk
RI ; Chi, Christina G/AGE-9108-2022; Gursoy, Dogan/A-3493-2008
OI Hu, Zongwei/0000-0002-7456-0006; Chi, Christina G/0000-0002-8265-1768;
   Gursoy, Dogan/0000-0002-3602-9433
FU Macau University of Science and Technology Faculty Research Grants
   [FRG-24-004-SLA]
FX This work was supported by the Macau University of Science and
   Technology Faculty Research Grants [FRG-24-004-SLA].
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NR 80
TC 0
Z9 0
U1 0
U2 0
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0261-4367
EI 1466-4496
J9 LEISURE STUD
JI Leis. Stud.
PD 2024 AUG 23
PY 2024
DI 10.1080/02614367.2024.2392583
EA AUG 2024
PG 16
WC Hospitality, Leisure, Sport & Tourism
WE Social Science Citation Index (SSCI)
SC Social Sciences - Other Topics
GA D4Z7V
UT WOS:001296288700001
DA 2024-09-05
ER
PT J
AU Murphy, J
AF Murphy, Jackie
TI Exploring the Impact of an Open Access Mindfulness Course with Online
   Graduate Students: A Mixed Methods Explanatory Sequential Study
SO ONLINE LEARNING
LA English
DT Article
DE mindfulness; stress; online learning; graduate students; open access;
   mind wandering
ID RANDOMIZED CONTROLLED-TRIAL; STRESS REDUCTION; NURSING-STUDENTS; MIND;
   INTERVENTION; PERFORMANCE; ATTENTION; ANXIETY; STATES
AB As enrollment in online graduate education increases, retention continues to be problematic for many colleges and universities across the United States. Non-traditional students, who represent the majority of online graduate student enrollment, have unique issues related to persistence considering they often must juggle the demands of graduate school with work and families. The competing demands can lead to increased levels of perceived stress, which can impact academic performance due to increased mind wandering and decreased attention. Mindfulness is a practice that has been shown in the literature to decrease levels of perceived stress and mind wandering, therefore, the integration of mindfulness practice could have a positive effect on student persistence in online graduate education. Therefore, an online open access mindfulness course was created at one large urban university. The purpose of this explanatory sequential study was to explore the impact of teaching mindfulness to online graduate students. Self-report levels of perceived stress and mind wandering were significantly lower after students completed Module One of an open access mindfulness course. Self-reported perceived persistence levels were found to be significantly higher after Module One with students in the first or second quarter of their program, students with little or no mindfulness experience, and students who meditated four or more times a week. Furthermore, students interviewed felt that the course provided excellent foundational information about mindfulness that could be immediately applied, and therefore should be a requirement for all incoming students.
C1 [Murphy, Jackie] Drexel Univ, Philadelphia, PA 19104 USA.
C3 Drexel University
RP Murphy, J (corresponding author), Drexel Univ, Philadelphia, PA 19104 USA.
OI Murphy, Jackie/0000-0003-3736-2032
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NR 65
TC 3
Z9 6
U1 1
U2 11
PU ONLINE LEARNING CONSORTIUM
PI NEWBURYPORT
PA PO BOX 1238, NEWBURYPORT, MA 01950 USA
SN 2472-5749
EI 2472-5730
J9 ONLINE LEARN
JI Online Learn.
PD JUN
PY 2021
VL 25
IS 2
BP 299
EP 323
DI 10.24059/olj.v25i2.2292
PG 25
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA SO1PR
UT WOS:000658752000014
OA gold
DA 2024-09-05
ER
PT C
AU Wang, YH
   He, XC
AF Wang, Yong-Hong
   He, Xiang-Chun
BE Rodrigo, MMT
   Iyer, S
   Mitrovic, A
TI Research on the Application of College Students' Online Learning
   Cognitive Engagement Evaluation
SO 29TH INTERNATIONAL CONFERENCE ON COMPUTERS IN EDUCATION (ICCE 2021), VOL
   I
LA English
DT Proceedings Paper
CT 29th International Conference on Computers in Education (ICCE)
CY NOV 22-26, 2021
CL ELECTR NETWORK
DE Online learning; cognitive engagement; evaluation index system
AB College Students' online learning is gradually becoming more and more normalized. There is a correlation between learning engagement and learning quality. Cognitive engagement is important components of online learning engagement. Through literature research, expert consultation and analytic hierarchy process, this paper constructs the "online cognitive engagement evaluation index system of college students", which includes two first-class indicators, four second-class indicators, and determines the weight of each level of indicators. Through the design and development of the evaluation index system, based on the structural equation model of the observed variables on the corresponding latent variables of the factor load, the experimental class of college students online cognitive engagement was evaluated and analyzed, which provides reference for the development of online learning engagement evaluation of college students.
C1 [Wang, Yong-Hong; He, Xiang-Chun] Northwest Normal Univ, Coll Educ Technol, Lanzhou, Peoples R China.
C3 Northwest Normal University - China
RP Wang, YH (corresponding author), Northwest Normal Univ, Coll Educ Technol, Lanzhou, Peoples R China.
EM wangyh4437@nwnu.edu.cn
RI Wang, Yonghong/AAN-4845-2020
FU Northwest Normal University Young Teachers' scientific research ability
   improvement plan project [SKQN2021-29]
FX This research is supported by the 2021 Northwest Normal University Young
   Teachers' scientific research ability improvement plan project "Research
   on data literacy model construction and cultivation strategy of primary
   and secondary school teachers under the background of big data". Project
   No: NWNU-SKQN2021-29.
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   Lee E, 2015, TECHTRENDS, V59, P54, DOI 10.1007/s11528-015-0871-9
NR 2
TC 0
Z9 0
U1 0
U2 8
PU ASIA PACIFIC SOC COMPUTERS IN EDUCATION
PI TAOYUAN CITY
PA NO 300, JUNGDA RD, JHONGLI DISTRICT, TAOYUAN CITY, 320, TAIWAN
BN 978-986-97214-7-9
PY 2021
BP 182
EP 184
PG 3
WC Computer Science, Interdisciplinary Applications; Education &
   Educational Research
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Education & Educational Research
GA BS8GL
UT WOS:000772144100027
DA 2024-09-05
ER
PT J
AU Altememy, HA
   Mohammed, BA
   Hsony, MK
   Hassan, AY
   Mazhair, R
   Dawood, II
   Al Jouani, ISH
   Zearah, SA
   Sharif, HR
AF Altememy, Haady Abdilnibi
   Mohammed, Bahira Abdulrazzaq
   Hsony, Mizher Khlif
   Hassan, Aalaa Yaseen
   Mazhair, Rabaa
   Dawood, Imad Ibrahim
   Al Jouani, Isra Shakir Hassan
   Zearah, Sajad Ali
   Sharif, Hedab Rasoul
TI The influence of the artificial intelligence capabilities of higher
   education institutions in Iraq on students' academic performance: The
   role of AI-based technology application as a mediator
SO EURASIAN JOURNAL OF EDUCATIONAL RESEARCH
LA English
DT Article
DE Artificial intelligence; Capability; higher education; Iraq
AB Objective: Artificial intelligence ( AI) plays a crucial role in promoting unbiased and effective advancements in the field of education. The benefits of these state-of-the-art technologies in the areas of teaching methods and the acquisition of knowledge have attracted considerable attention in current discussions. The present study aims to investigate the influence of artificial intelligence capabilities (AIC) within Iraqi higher education institutions on students' academic performance. Additionally, the study has also investigated the mediating role of AI-based technology applications (AIBTA). Methodology: The research employed a quantitative survey-based approach to gather data, utilizing a questionnaire as the data collection instrument. For the analysis, Smart-PLS 3, a software tool known for its implementation of the PLS-SEM (partial least squares structural equation modeling) analysis method, was utilized (Sarstedt et al., 2022). The study achieved a response rate of 64.3 percent from the participants. Results: The findings of the study reveal a significant influence of AIBTA (Artificial Intelligence-Based Teaching and Assessment) on student performance within the context of Iraqi higher education. Moreover, there exists a positive and notable correlation between the AI capabilities integrated into Iraqi higher education and the academic achievements of university students in Iraq. The study's results also suggest that AI capabilities substantially impact the AIBTA framework within Iraqi higher education. Furthermore, the mediating role of AIBTA in Iraqi higher education is identified in relation to the relationship between the AI capabilities of the educational system and the academic performance of university students in Iraq. Implication: The outcomes of this study hold considerable implications for the landscape of higher education in Iraq. The results underscore that the incorporation of AI-driven technology bears a substantial influence on students' academic achievements. This relationship serves as a pivotal connection, linking the AI capabilities present within higher education institutions to the attainment of academic excellence by students. Novelty: This study stands as one of the pioneering efforts in exploring the realm of AIBTA within the context of Iraqi higher education.
   (c) 2023 Ani Publishing Ltd. All rights reserved.
C1 [Altememy, Haady Abdilnibi] Islamic Univ Najaf, Coll Islamic Sci, Najaf, Iraq.
   [Mohammed, Bahira Abdulrazzaq] Al Hadi Univ Coll, Dept Med Engn, Baghdad 10011, Iraq.
   [Hsony, Mizher Khlif] Al Manara Coll Med Sci, Maysan, Iraq.
   [Hassan, Aalaa Yaseen] Al Nisour Univ Coll, Dept Educ, Baghdad, Iraq.
   [Mazhair, Rabaa] Al Nisour Univ Coll, Coll Educ, English Dept, Baghdad, Iraq.
   [Dawood, Imad Ibrahim] Mazaya Univ Coll, Dept Media, Nasiriyah, Iraq.
   [Al Jouani, Isra Shakir Hassan] Al Esraa Univ, Dept Media, Coll Arts, Baghdad, Iraq.
   [Zearah, Sajad Ali] Al ayen Univ, Sci Res Ctr, Thi Qar, Iraq.
   [Sharif, Hedab Rasoul] Natl Univ Sci & Technol, Coll Nursing, Dhi Qar, Iraq.
C3 Islamic University College; Al-Nisour University College; Al-Nisour
   University College; Al-Esraa University College; Al-Ayen University
RP Altememy, HA (corresponding author), Islamic Univ Najaf, Coll Islamic Sci, Najaf, Iraq.
EM haady.altememy@gmail.com; dr.bahera@huc.edu.iq;
   mizherkhlifhsony@uomanara.edu.iq; alaa.y.english@nuc.edu.iq;
   mizherrabaa4@gmail.com; prof.dr.imad.i.dawood@mpu.edu.iq;
   isramasar@gmail.com; sajad@alayen.edu.iq; hadab.r.sh@nust.edu.iq
OI Ali Zearah, Sajad/0009-0001-4827-0128
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NR 37
TC 1
Z9 1
U1 16
U2 36
PU ANI YAYINCILIK
PI BAKANLIKLAR
PA KIZILIRMAK SOK NO 10-A, BAKANLIKLAR, ANKARA 00000, Turkiye
SN 1302-597X
EI 2528-8911
J9 EURASIAN J EDUC RES
JI Egit. Arast.
PY 2023
IS 104
BP 267
EP 282
DI 10.14689/ejer.2023.104.015
PG 16
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA R4HA0
UT WOS:001063960700015
DA 2024-09-05
ER
PT C
AU Bredikhin, S
   Scherbakova, N
AF Bredikhin, Sergey
   Scherbakova, Natalya
GP IEEE
TI Normalized Spectral Clustering of the Journal Citation Network
SO 2019 15TH INTERNATIONAL ASIAN SCHOOL-SEMINAR OPTIMIZATION PROBLEMS OF
   COMPLEX SYSTEMS (OPCS 2019)
LA English
DT Proceedings Paper
CT 15th International Asian School-Seminar on Optimization Problems of
   Complex Systems (OPCS)
CY AUG 26-30, 2019
CL Novosibirsk, RUSSIA
DE spectral clustering; normalized cut; generalized weighted cut; journal
   citation network
AB The task of clustering of a set of objects is treated as the graph partitioning problem. The normalized cut criterion is used for a weighted graph partitioning and the approach is applied in two implemented algorithms. The results of testing the method for the journal citation network clustering are presented.
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C3 Russian Academy of Sciences
RP Bredikhin, S (corresponding author), SB RAS, Inst Computat Math & Math Geophys, Novosibirsk, Russia.
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NR 14
TC 0
Z9 0
U1 0
U2 3
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-7281-2986-0
PY 2019
BP 17
EP 20
DI 10.1109/opcs.2019.8880205
PG 4
WC Engineering, Electrical & Electronic; Operations Research & Management
   Science
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Engineering; Operations Research & Management Science
GA BO7WE
UT WOS:000525749600004
DA 2024-09-05
ER
PT J
AU Chikhaoui, B
   Chiazzaro, M
   Wang, S
   Sotir, M
AF Chikhaoui, Belkacem
   Chiazzaro, Mauricio
   Wang, Shengrui
   Sotir, Martin
TI Detecting Communities of Authority and Analyzing Their Influence in
   Dynamic Social Networks
SO ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
LA English
DT Article
DE Community of authority; meta-community; topic modeling; betweenness
   centrality; community influence; granger causality
AB Users in real-world social networks are organized into communities that differ from each other in terms of influence, authority, interest, size, etc. This article addresses the problems of detecting communities of authority and of estimating the influence of such communities in dynamic social networks. These are new issues that have not yet been addressed in the literature, and they are important in applications such as marketing and recommender systems. To facilitate the identification of communities of authority, our approach first detects communities sharing common interests, which we call "meta-communities,"by incorporating topic modeling based on users' community memberships. Then, communities of authority are extracted with respect to each meta-community, using a new measure based on the betweenness centrality. To assess the influence between communities over time, we propose a new model based on the Granger causality method. Through extensive experiments on a variety of social network datasets, we empirically demonstrate the suitability of our approach for community-of-authority detection and assessment of the influence between communities over time.
C1 [Chikhaoui, Belkacem; Sotir, Martin] Comp Res Inst Montreal, 405 Ave Ogilvy,Bur 101, Montreal, PQ, Canada.
   [Chiazzaro, Mauricio; Wang, Shengrui] Univ Sherbrooke, Dept Comp Sci, Prospectus Lab, 2500 Blvd Univ, Sherbrooke, PQ, Canada.
C3 University of Sherbrooke
RP Chikhaoui, B (corresponding author), Comp Res Inst Montreal, 405 Ave Ogilvy,Bur 101, Montreal, PQ, Canada.
EM belkacem.chikhaoui@crim.ca; mauricio.chiazzaro@usherbrooke.ca;
   shengrui.wang@usherbrooke.ca; martin.sotir@crim.ca
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NR 55
TC 1
Z9 1
U1 1
U2 19
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA
SN 2157-6904
EI 2157-6912
J9 ACM T INTEL SYST TEC
JI ACM Trans. Intell. Syst. Technol.
PD SEP
PY 2017
VL 8
IS 6
AR 82
DI 10.1145/3070658
PG 28
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA FL5XE
UT WOS:000414319000011
DA 2024-09-05
ER
PT J
AU Aljohani, NR
   Fayoumi, A
   Hassan, SU
AF Aljohani, Naif Radi
   Fayoumi, Ayman
   Hassan, Saeed-Ul
TI A Novel Deep Neural Network-Based Approach to Measure Scholarly Research
   Dissemination Using Citations Network
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE citations context classification; citation network analysis; deep
   learning
ID ARCHITECTURE; KNOWLEDGE; SYSTEMS; IMPACT; GRAPH
AB We investigated the scientific research dissemination by analyzing the publications and citation data, implying that not all citations are significantly important. Therefore, as alluded to existing state-of-the-art models that employ feature-based techniques to measure the scholarly research dissemination between multiple entities, our model implements the convolutional neural network (CNN) with fastText-based pre-trained embedding vectors, utilizes only the citation context as its input to distinguish between important and non-important citations. Moreover, we speculate using focal-loss and class weight methods to address the inherited class imbalance problems in citation classification datasets. Using a dataset of 10 K annotated citation contexts, we achieved an accuracy of 90.7% along with a 90.6% f1-score, in the case of binary classification. Finally, we present a case study to measure the comprehensiveness of our deployed model on a dataset of 3100 K citations taken from the ACL Anthology Reference Corpus. We employed state-of-the-art graph visualization open-source tool Gephi to analyze the various aspects of citation network graphs, for each respective citation behavior.
C1 [Aljohani, Naif Radi; Fayoumi, Ayman] King Abdulaziz Univ, Fac Comp & Informat Technol, Jeddah 21589, Saudi Arabia.
   [Hassan, Saeed-Ul] Manchester Metropolitan Univ, Dept Comp & Math, Manchester M15 6BH, Lancs, England.
C3 King Abdulaziz University; Manchester Metropolitan University
RP Aljohani, NR (corresponding author), King Abdulaziz Univ, Fac Comp & Informat Technol, Jeddah 21589, Saudi Arabia.
EM nraljohani@kau.edu.sa; afayoumi@kau.edu.sa; s.ul-hassan@mmu.ac.uk
RI Aljohani, Naif R/S-1109-2017; Fayoumi, Ayman/E-7236-2014; Hassan,
   Saeed-Ul/G-1889-2016
OI Fayoumi, Ayman/0000-0002-4160-3305; Hassan, Saeed-Ul/0000-0002-6509-9190
FU Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah
   [RG-14-611-40]
FX This project was funded by the Deanship of Scientific Research (DSR),
   King Abdulaziz University, Jeddah, under Grant No. RG-14-611-40. The
   authors, therefore, gratefully acknowledge DSR's technical and financial
   support.
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NR 62
TC 7
Z9 7
U1 3
U2 18
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2076-3417
J9 APPL SCI-BASEL
JI Appl. Sci.-Basel
PD NOV
PY 2021
VL 11
IS 22
AR 10970
DI 10.3390/app112210970
PG 14
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Chemistry; Engineering; Materials Science; Physics
GA XJ3JO
UT WOS:000726689000001
OA gold
DA 2024-09-05
ER
PT J
AU Senter, MS
AF Senter, Mary Scheuer
TI Integrating Program Assessment and a Career Focus into a Research
   Methods Course
SO TEACHING SOCIOLOGY
LA English
DT Article
DE research methods; program assessment; active learning; careers
ID TEACHING-RESEARCH METHODS; STUDENTS
AB Sociology research methods students in 2013 and 2016 implemented a series of real world data gathering activities that enhanced their learning while assisting the department with ongoing program assessment and program review. In addition to the explicit collection of program assessment data on both students' development of sociological concepts and skills while undergraduates and alumni's use of such knowledge after graduation, an effort was made throughout the semesters to highlight key research methods knowledge using examples that focused on job searching and careers appropriate for baccalaureate-trained sociologists. Students reported that these real-world activities both increased their interest in and their learning about research methods. These explicit and implicit experiences with an employment focus also led them to increase their own thinking about their eventual careers and preparing job search materials, such as resumes, that include skills developed in their undergraduate sociology courses.
C1 [Senter, Mary Scheuer] Cent Michigan Univ, Sociol, Mt Pleasant, MI 48859 USA.
C3 Central Michigan University
RP Senter, MS (corresponding author), Cent Michigan Univ, Dept Sociol, 312B Anspach Hall, Mt Pleasant, MI 48859 USA.
EM Mary.Senter@cmich.edu
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   Crull SR, 2004, TEACH SOCIOL, V32, P206, DOI 10.1177/0092055X0403200206
   Earl Babbie., 2014, The basics of social research
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   Jennings PK, 2006, TEACH SOCIOL, V34, P286, DOI 10.1177/0092055X0603400307
   Kathleen McKinney, 2004, LIBERAL LEARNING SOC
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NR 20
TC 7
Z9 7
U1 0
U2 5
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 0092-055X
EI 1939-862X
J9 TEACH SOCIOL
JI Teach. Sociol.
PD APR
PY 2017
VL 45
IS 2
BP 131
EP 141
DI 10.1177/0092055X16686151
PG 11
WC Education & Educational Research; Sociology
WE Social Science Citation Index (SSCI)
SC Education & Educational Research; Sociology
GA ET2XH
UT WOS:000400138500003
DA 2024-09-05
ER
PT J
AU Fan, LP
   Wang, YF
   Ding, SC
   Qi, BB
AF Fan, Lipeng
   Wang, Yuefen
   Ding, Shengchun
   Qi, Binbin
TI Productivity trends and citation impact of different institutional
   collaboration patterns at the research units' level
SO SCIENTOMETRICS
LA English
DT Article
DE Institutional collaboration pattern; Productivity trends; Citation
   impact; Negative binomial; Artificial intelligence
ID INTERNATIONAL COLLABORATION; SCIENTIFIC COLLABORATION; COMPUTER-SCIENCE;
   UNIVERSITIES; PERFORMANCE
AB In order to gain a deeper understanding of how research performance and collaboration patterns of institutions affect productivity trends and citations, this paper classifies institutions into two types: main and normal institutions, and then divides the dataset into six types: M and N as intra-institution collaboration types, and M&M, M&N, N&M, N&N as inter-institution types (M: main institutions, N: normal institutions). After analysing the productivity trends and citation impact at the research units' level, the main results are shown as following: through a large-scale and long-span data, M papers account for the highest percentage, and play an important leading role in the beginning, and the average citation value of M&M papers is significantly higher than other types; although the number of papers with multi-authors is increasing over time, the impact of the number of authors on citations may vary from discipline to discipline, and there is a slightly negative relationship between them in artificial intelligence field in our data; despite the number of institutions and countries has a positive impact on citations in whole dataset, it differs when considering different institutional collaboration patterns and the first author's country; no matter what institutional collaboration pattern is, the papers with USA as first author's country always have a significant greater impact than China as first author's country. After analysing two negative binomial regression models, some results support the above conclusions. Moreover, we find that the number of M institutions has a significant greatest impact on citations, while M institution as first author's affiliation only has a slightly influence; China as first author's country has a negative impact, while USA as first author's country has a moderately positive impact, and slightly lower than that of the number of countries, moderately higher than that of the number of institutions.
C1 [Fan, Lipeng; Wang, Yuefen; Ding, Shengchun] Nanjing Univ Sci & Technol, Sch Econ & Management, Nanjing, Peoples R China.
   [Wang, Yuefen] Jiangsu Collaborat Innovat Ctr Social Safety Sci, Nanjing, Peoples R China.
   [Qi, Binbin] Nanjing Univ, Sch Informat Management, Nanjing, Peoples R China.
C3 Nanjing University of Science & Technology; Nanjing University
RP Wang, YF (corresponding author), Nanjing Univ Sci & Technol, Sch Econ & Management, Nanjing, Peoples R China.; Wang, YF (corresponding author), Jiangsu Collaborat Innovat Ctr Social Safety Sci, Nanjing, Peoples R China.
EM yuefen163@163.com
RI fan, lipeng/IWM-3409-2023
FU National Social Science of China [16ZDA224]
FX The authors are grateful to anonymous referees and editors for their
   invaluable and insightful comments, and thank for the support by the
   National Social Science of China (16ZDA224).
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   Wang WC, 2014, SCIENTOMETRICS, V98, P1535, DOI 10.1007/s11192-013-1072-y
   Yuan LL, 2018, SCIENTOMETRICS, V116, P401, DOI 10.1007/s11192-018-2753-3
NR 27
TC 8
Z9 9
U1 8
U2 73
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD NOV
PY 2020
VL 125
IS 2
BP 1179
EP 1196
DI 10.1007/s11192-020-03609-z
EA JUL 2020
PG 18
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA OT3PI
UT WOS:000547804200009
DA 2024-09-05
ER
PT C
AU Koni, I
AF Koni, I
BE Chova, LG
   Martinez, AL
   Torres, IC
TI HOW TO SUCCESSFULLY COLLABORATE IN AN ONLINE COURSE?
SO 12TH INTERNATIONAL CONFERENCE OF EDUCATION, RESEARCH AND INNOVATION
   (ICERI2019)
SE ICERI Proceedings
LA English
DT Proceedings Paper
CT 12th Annual International Conference of Education, Research and
   Innovation (ICERI)
CY NOV 11-13, 2019
CL Seville, SPAIN
DE Collaboration; online learning; study skills; action research
AB The contemporary educational system evaluates life-long learning and knowledgeable application of skills [1], [2]. One of these skills that help the learner to learn and relearn in new contexts are study skills. Although the field of study skills is rich in variety, one of the essential study skills, emphasized both in learning and working context, is a skill to collaborate [3], [4]. The collaboration skill is, also, considered as one of the 21st-century learning skills [5]. Research has shown that collaborative learning leads to higher achievement, less stress and greater student satisfaction [6]. Moreover, the online environment, as compared to the classroom, allows students to gain deeper experience in team-work and communication [7]. In recent years, the number of online courses has increased along with the development of technology. These courses might challenge collaboration between participants who are strangers to each other but working together towards the same aim in the learning process. This action research is about restructuring the online course with the team-work assignment to enhance collaboration among participants taking the online course.
   The restructuring process involved adding a team-work assignment to previous assignments (in 2018). In the process of this assignment, the student looked back to their learning experience acquired from the online course and created a virtual poster in team-work following assessment rubric. Therefore, to study the effectiveness of the assignment, the aim was to find out student opinions about what developed in their collaboration as a study skill within the assignment and their suggestions on how to enhance the collaboration within the assignment.
   This action research involves 43 high school students participating in an online course on study skills in 2018. The data was gathered via the feedback questionnaire and analyzed using qualitative inductive content analysis. The results indicate that within the assignment, participants had an opportunity to develop their skills related to leadership, communication, and compromising. To enhance the collaboration within the assignment, the participants suggested that the lecturer should make random groups to facilitate collaboration between the students who are strangers to each other, and the personal data for contacting each other should be provided. Participants also highlighted that the technical tool for forming the groups should prevent the possibility to erase other members from the team. Further research will address the question of why some of the participants 'disappear' from the team-work assignment or prefer to do the assignment alone, from which the latter annuls the aim of the team-work.
C1 [Koni, I] Tartu Univ, Tartu, Estonia.
C3 University of Tartu
RP Koni, I (corresponding author), Tartu Univ, Tartu, Estonia.
CR [Anonymous], 2006, J ASYNCHRONOUS LEARN, DOI DOI 10.24059/OLJ.V10I1.1770
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   Fadel C., 2008, 21 CENTURY SKILLS CA
   Garrison D. R., 1999, Internet and Higher Education, V2, P87, DOI 10.1016/S1096-7516(00)00016-6
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NR 10
TC 0
Z9 0
U1 0
U2 0
PU IATED-INT ASSOC TECHNOLOGY EDUCATION & DEVELOPMENT
PI VALENICA
PA LAURI VOLPI 6, VALENICA, BURJASSOT 46100, SPAIN
SN 2340-1095
BN 978-84-09-14755-7
J9 ICERI PROC
PY 2019
BP 6841
EP 6845
PG 5
WC Education & Educational Research
WE Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Education & Educational Research
GA BO8YP
UT WOS:000530212402115
DA 2024-09-05
ER
PT C
AU Liu, HM
AF Liu Huimin
GP IEEE COMP SOC
TI Research on Practice Evaluation System of Higher Vocational Preschool
   Education Based on Artificial Intelligence
SO 2021 13TH INTERNATIONAL CONFERENCE ON MEASURING TECHNOLOGY AND
   MECHATRONICS AUTOMATION (ICMTMA 2021)
SE International Conference on Measuring Technology and Mechatronics
   Automation
LA English
DT Proceedings Paper
CT 13th International Conference on Measuring Technology and Mechatronics
   Automation (ICMTMA)
CY JAN 16-17, 2021
CL Beihai, PEOPLES R CHINA
DE BP neural network; GA; artificial intelligence; practice evaluation;
   higher vocational college teaching
AB To improve the quality of practical teaching and promote the synchronization of practical teaching and practice, the theory of artificial intelligence network is introduced into the evaluation of practical teaching quality, and the relevant mathematical model is established. Firstly, a perfect practical teaching evaluation index system is provided, and the related index system and quality standard are established. Then, when the convergence speed of BP algorithm training network is slow, genetic algorithm is used to optimize the operation parameters of the network, the optimization results are taken as the initial value of BP algorithm, and BP algorithm is used to train the network until the required accuracy is reached. Finally, by using data cleaning, network training and rationalization test, a more reasonable result is obtained, which provides a new idea for teaching quality evaluation.
C1 [Liu Huimin] Shanxi Vocat Acad Art, Xian 710054, Shaanxi, Peoples R China.
RP Liu, HM (corresponding author), Shanxi Vocat Acad Art, Xian 710054, Shaanxi, Peoples R China.
EM a0Zhangyanzz@outlook.com
FU Infant School Education Research Project of Shaanxi Province [ZdKT1914]
FX This work was supported by Infant School Education Research Project of
   Shaanxi Province in 2019 (Key Project) under grant no. ZdKT1914. Title
   of the Project: Research on the Talent Cultivation of Infant School
   Education in Higher Vocational Colleges Based on Market Demand.
CR Cox JL, 2010, J EVAL CLIN PRACT, V16, P315, DOI 10.1111/j.1365-2753.2010.01391.x
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NR 9
TC 0
Z9 0
U1 2
U2 11
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA
SN 2157-1473
BN 978-1-6654-3892-6
J9 INT CONF MEAS
PY 2021
BP 615
EP 618
DI 10.1109/ICMTMA52658.2021.00142
PG 4
WC Automation & Control Systems; Engineering, Electrical & Electronic;
   Engineering, Mechanical; Instruments & Instrumentation
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Automation & Control Systems; Engineering; Instruments & Instrumentation
GA BR8SH
UT WOS:000672818900134
DA 2024-09-05
ER
PT C
AU Ramírez-de-la-Rosa, G
   Villatoro-Tello, E
   Jiménez-Salazar, H
   Sánchez-Sánchez, C
AF Ramirez-de-la-Rosa, Gabriela
   Villatoro-Tello, Esau
   Jimenez-Salazar, Hector
   Sanchez-Sanchez, Christian
BE Gelbukh, A
   Espinoza, FC
   GaliciaHaro, SN
TI Towards Automatic Detection of User Influence in Twitter by Means of
   Stylistic and Behavioral Features
SO HUMAN-INSPIRED COMPUTING AND ITS APPLICATIONS, PT I
SE Lecture Notes in Artificial Intelligence
LA English
DT Proceedings Paper
CT 13th Mexican International Conference on Artificial Intelligence (MICAI)
CY NOV 16-22, 2014
CL Tuxtla Gutierrez, MEXICO
DE Opinion Leaders; User Influence; Author Profiling; Machine Learning;
   Natural Language Processing
AB Online communities are filled with comments of loyal readers or first-time viewers, that are constantly creating and sharing information at an unprecedented level, resulting in millions of messages containing opinions, ideas, needs and beliefs of Internet users. Therefore, businesses companies are very interested in finding influential users and encouraging them to create positive influence. Influential users represent users with the ability to influence individual's attitudes in a desired way with relative frequency. We present an empirical analysis on influential users identification problem in Twitter. Our proposed approach considers that the influential level of users can be detected by considering its communication patterns, by means of particular writing style features as well as behavioral features. Performed experiments on more that 7000 users profiles, indicate that it is possible to automatically identify influential users among the members of a social networking community, and also it obtains competitive results against several state-of-the-art methods
C1 [Ramirez-de-la-Rosa, Gabriela; Villatoro-Tello, Esau; Jimenez-Salazar, Hector; Sanchez-Sanchez, Christian] Univ Autonoma Metropolitana, Dept Tecnol Informac, Unidad Cuajimalpa, Mexico City, DF, Mexico.
C3 Universidad Autonoma Metropolitana - Mexico
RP Ramírez-de-la-Rosa, G (corresponding author), Univ Autonoma Metropolitana, Dept Tecnol Informac, Unidad Cuajimalpa, Mexico City, DF, Mexico.
EM gramirez@correo.cua.uam.mx; evillatoro@correo.cua.uam.mx;
   hjimenez@correo.cua.uam.mx; csanchez@correo.cua.uam.mx
OI VILLATORO-TELLO, ESAU/0000-0002-1322-0358
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NR 13
TC 13
Z9 13
U1 0
U2 12
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 0302-9743
EI 1611-3349
BN 978-3-319-13647-9; 978-3-319-13646-2
J9 LECT NOTES ARTIF INT
PY 2014
VL 8856
BP 245
EP 256
PG 12
WC Computer Science, Artificial Intelligence; Computer Science, Theory &
   Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BC7JE
UT WOS:000354931600023
DA 2024-09-05
ER
PT J
AU Ebadi, A
   Schiffauerova, A
AF Ebadi, Ashkan
   Schiffauerova, Andrea
TI iSEER: an intelligent automatic computer system for scientific
   evaluation of researchers
SO SCIENTOMETRICS
LA English
DT Article; Proceedings Paper
CT 15th International Conference of the
   International-Society-for-Scientometrics-and-Informetrics (ISSI) on
   Scientometrics and Informetrics
CY JUN 29-JUL 04, 2015
CL Bogazici Univ, Istanbul, TURKEY
HO Bogazici Univ
DE Machine learning; Scientific output; Funding; Research performance;
   Scientific evaluation
ID RESEARCH PRODUCTIVITY; CITATION; IMPACT; PERFORMANCE; COLLABORATION;
   UNIVERSITIES; DYNAMICS; MODELS
AB Funding is one of the crucial drivers of scientific activities. The increasing number of researchers and the limited financial resources have caused a tight competition among scientists to secure research funding. On the other side, it is now even harder for funding allocation organizations to select the most proper researchers. Number of publications and citation counts based indicators are the most common methods in the literature for analyzing the performance of researchers. However, the mentioned indicators are highly correlated with the career age and reputation of the researchers, since they accumulate over time. This makes it almost impossible to evaluate the performance of a researcher based on quantity and impact of his/her articles at the time of the publication. This article proposes an intelligent machine learning framework for scientific evaluation of researchers (iSEER). iSEER may help decision makers to better allocate the available funding to the distinguished scientists through providing fair comparative results, regardless of the career age of the researchers. Our results show that iSEER performs well in predicting the performance of the researchers with high accuracy, as well as classifying them based on collaboration patterns, research performance, and efficiency.
C1 [Ebadi, Ashkan; Schiffauerova, Andrea] Concordia Univ, CIISE, Montreal, PQ, Canada.
   [Schiffauerova, Andrea] Masdar Inst Sci & Technol, Dept Engn Syst & Management, Abu Dhabi, U Arab Emirates.
C3 Concordia University - Canada; Khalifa University of Science &
   Technology
RP Ebadi, A (corresponding author), Concordia Univ, CIISE, Montreal, PQ, Canada.
EM a_ebad@encs.concordia.ca
RI Ebadi, Ashkan/AAI-5123-2020; Ebadi, Ashkan/GWZ-9018-2022
OI Ebadi, Ashkan/0000-0002-4542-9105; 
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NR 66
TC 5
Z9 5
U1 0
U2 54
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD MAY
PY 2016
VL 107
IS 2
BP 477
EP 498
DI 10.1007/s11192-016-1852-2
PG 22
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH); Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Information Science & Library Science
GA DM3VA
UT WOS:000376273600009
DA 2024-09-05
ER
PT C
AU Freire, N
   Borbinha, J
   Martins, B
AF Freire, Nuno
   Borbinha, Jose
   Martins, Bruno
BE Buchanan, G
   Masoodian, M
   Cunningham, SJ
TI Consolidation of References to Persons in Bibliographic Databases
SO DIGITAL LIBRARIES: UNIVERSAL AND UBIQUITOUS ACCESS TO INFORMATION,
   PROCEEDINGS
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT 11th International Conference on Asian Digital Libraries
CY DEC 02-05, 2008
CL Bali, INDONESIA
DE Entity resolution; bibliographic metadata; similarity metrics; machine
   learning
AB Entity resolution is the process of determining if, in a specific context, two or more references correspond to the same entity. In this work, we address this problem in the context of references to persons as they are found in bibliographic data, specifically in the case of consolidating multiple datasets. Or solution follows the extraction, transformation and loading (ETL) process, typical in data warehouses. It computes the similarities of the attribute values for the references, and employs a decision tree to decide when the references match. We describe the characteristics of these references within bibliographic datasets, and how we explored those characteristics by developing new similarity metrics to improve the quality of the consolidation process. We evaluated our work by designing an experiment with data from four national libraries. The results show that the proposed similarity metrics contribute significantly to the consolidation process.
C1 [Freire, Nuno; Borbinha, Jose; Martins, Bruno] Univ Tecn Lisboa, Inst Super Tecn, P-1049001 Lisbon, Portugal.
C3 Universidade de Lisboa
EM nuno.freire@ist.utl.pt; jlb@ist.utl.pt; bruno.g.martins@ist.utl.pt
RI da Graça Martins, Bruno Emanuel/J-9735-2015; Freire, Nuno/AAD-9410-2022;
   Borbinha, Jose/A-7355-2010
OI da Graça Martins, Bruno Emanuel/0000-0002-3856-2936; Freire,
   Nuno/0000-0002-3632-8046; Borbinha, Jose/0000-0001-5463-8438
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   Pasula H., 2002, Advances in Neural Information Processing
NR 13
TC 1
Z9 1
U1 0
U2 4
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 0302-9743
EI 1611-3349
BN 978-3-540-89532-9
J9 LECT NOTES COMPUT SC
PY 2008
VL 5362
BP 256
EP 265
PG 10
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods; Information Science & Library Science
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Information Science & Library Science
GA BIT31
UT WOS:000262503100026
DA 2024-09-05
ER
PT J
AU Wang, J
   Feng, QM
   Tam, A
   Sun, T
   Zhou, PJ
   So, S
AF Wang, Jing
   Feng, Qiming
   Tam, Andrew
   Sun, Tong
   Zhou, Peijing
   So, Samuel
TI Evaluation of the first open-access hepatitis B and safe injection
   online training course for health professionals in China
SO BMC MEDICAL EDUCATION
LA English
DT Article
DE Hepatitis B; Internet based education; Open-access education; Online
   learning; Continuing medical education; Online learning evaluation
ID BURDEN; CARE; DISEASES
AB Background: Despite the high prevalence of chronic hepatitis B virus (HBV) infection in China, HBV infection prevention and long-term care knowledge of health professionals is inadequate. To address this knowledge gap, we developed an open-access evidence-based online training course, "KnowHBV", to train health professionals on prevention of HBV transmission and safe injections. We conducted an evaluation of the course with health professionals in China to examine its effectiveness in improving knowledge and learner's satisfaction of the course.
   Methods: Between July and December 2011, 1015 health professionals from selected hospitals and disease control institutions of Shandong province registered for the course and 932 (92 %) completed the three-module course. Participants' demographic information, pre- and post-course knowledge test results and learner's feedback were collected through the course website.
   Results: Pre-course knowledge assessment confirmed gaps in HBV transmission routes, prevention and long-term care knowledge. Only 50.4 % of participants correctly identified all of the transmission routes of HBV, and only 40.7 % recognized all of the recommended tests to monitor chronically infected persons. The number of participants that answered all six multi-part multiple-choice knowledge questions correctly increased from 183 (19.7 %) before taking the course to 395 (42.4 %) on their first attempt upon completion of the course. Over 90 % of the 898 participants who completed the learner-feedback questionnaire rated the course as 'good' or 'very good'; over 94 % found the course instructional design helpful; 57.5 %, 65.7 % and 68.5 % reported that half or more than half of the course content in modules 1, 2 and 3 respectively provided new information; and 93.2 % of the participants indicated they preferred the online learning over traditional face-to-face classroom learning.
   Conclusions: The "KnowHBV" online training course appears to be an effective online training tool to improve HBV prevention and care knowledge of the health professionals in China.
C1 [Wang, Jing; Tam, Andrew; So, Samuel] Stanford Univ, Asian Liver Ctr, 780 Welch Rd,CJ130, Palo Alto, CA 94304 USA.
   [Feng, Qiming] Guangxi Med Univ, Sch Publ Hlth, 22 Shuangyong Rd, Nanning, Guangxi Provinc, Peoples R China.
   [Sun, Tong; Zhou, Peijing] Shandong Prov Ctr Dis Control & Prevent, 16992 Jingshi Rd, Jinan, Shandong, Peoples R China.
C3 Stanford University; Guangxi Medical University
RP Wang, J; So, S (corresponding author), Stanford Univ, Asian Liver Ctr, 780 Welch Rd,CJ130, Palo Alto, CA 94304 USA.
EM alcjwang@gmail.com; samso@stanford.edu
FU Zeshan Foundation (Hongkong); Zeshan Foundation (Hongkong)
FX We would like to thank Zeshan Foundation (Hongkong) and Mr. Paul Davis
   for providing funding support for this project. Dr. Yvan Hutin from the
   China office of the World Health Organization provided suggestions for
   the course content development. We are also most grateful to Michael
   Mohrman and Paul Davis at Videx Inc for their technical support. Dr.
   Mehlika Toy contributed to the editing of the manuscript. Portions of
   this study have been presented at the poster session at the 14th
   International Symposium on Viral Hepatitis and Liver Disease (ISVHLD) in
   Shanghai, China, 2012. The study group and the study have no conflict of
   interests with any other institutions.
CR [Anonymous], 2011, Xinhua News Agency
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NR 25
TC 5
Z9 5
U1 0
U2 25
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
EI 1472-6920
J9 BMC MED EDUC
JI BMC Med. Educ.
PD MAR 8
PY 2016
VL 16
AR 81
DI 10.1186/s12909-016-0608-2
PG 8
WC Education & Educational Research; Education, Scientific Disciplines
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Education & Educational Research
GA DG0ON
UT WOS:000371764700001
PM 26952079
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU Garay-Jiménez, LI
   Romero-Lujambio, JF
   Santiago-Horta, A
   Tovar-Corona, B
   Gómez-Miranda, P
   Mata-Rivera, MF
AF Garay-Jimenez, Laura I.
   Romero-Lujambio, Jose Fausto
   Santiago-Horta, Amaury
   Tovar-Corona, Blanca
   Gomez-Miranda, Pilar
   Mata-Rivera, Miguel Felix
TI Collaboration System for Multidisciplinary Research with Essential Data
   Analysis Toolkit Built-In
SO INFORMATION
LA English
DT Article
DE data visualization; multidimensional dataset; fuzzy searching;
   collaborative research; t-SNE; microservices; approximate
   string-matching; knowledge-sharing behaviors
ID INFORMATION
AB Environmental research calls for a multidisciplinary approach, where highly specialized research teams collaborate in data analysis. Nevertheless, managing the data lifecycle and research artifacts becomes challenging because the project teams require techniques and tools tailored to their study fields. Another pain point is the unavailability of essential analysis and data representation formats for querying and interpreting the shared results. In addition, managing progress reports across the teams is demanding because they manage different platforms and systems. These concerns discourage the knowledge-sharing process and lead to researchers' low adherence to the system. A hybrid methodology based on Design Thinking and an Agile approach enables us to understand and attend to the research process needs. As a result, a microservices-based architecture of the system, which can be deployed in cloud, hybrid, or standalone environments and adapt the computing resources according to the actual requirements with an access control system based on users and roles, enables the security and confidentiality, allowing the team's lead to share or revoke access. Additionally, intelligent assistance is available for document searches and dataset analyses. A multidisciplinary researchers' team that uses this system as a knowledge-sharing workspace reported an 83% acceptance.
C1 [Garay-Jimenez, Laura I.; Tovar-Corona, Blanca] Inst Politecn Nacl, UPIITA, SEPI, LIPS, Mexico City 07340, Mexico.
   [Romero-Lujambio, Jose Fausto; Santiago-Horta, Amaury] Inst Politecn Nacl, UPIITA, Mexico City 07340, Mexico.
   [Gomez-Miranda, Pilar] Inst Politecn Nacl, UPIICSA, SEPI, Mexico City 08400, Mexico.
   [Mata-Rivera, Miguel Felix] Inst Politecn Nacl, UPIITA, SEPI, Lab Geospatial Intelligence & Mobile Comp, Mexico City 07340, Mexico.
C3 Instituto Politecnico Nacional - Mexico; Instituto Politecnico Nacional
   - Mexico; Instituto Politecnico Nacional - Mexico; Instituto Politecnico
   Nacional - Mexico
RP Garay-Jiménez, LI (corresponding author), Inst Politecn Nacl, UPIITA, SEPI, LIPS, Mexico City 07340, Mexico.
EM lgaray@ipn.mx; jromerol0902@alumno.ipn.mx; asantiagoh2300@alumno.ipn.mx;
   bltovar@ipn.mx; pgomezm@ipn.mx; mmatar@ipn.mx
RI Gomez Miranda, Pilar/KFB-8604-2024; Garay Jimenez, Laura/S-5637-2018
OI Garay Jimenez, Laura/0000-0001-9478-4835; Santiago Horta,
   Amaury/0000-0003-3497-5546; Romero-Lujambio, Jose
   Fausto/0009-0001-5416-9771
FU Instituto Politcnico National
FX No Statement Available
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NR 44
TC 0
Z9 0
U1 0
U2 0
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2078-2489
J9 INFORMATION
JI Information
PD DEC
PY 2023
VL 14
IS 12
AR 626
DI 10.3390/info14120626
PG 21
WC Computer Science, Information Systems
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA DI4N5
UT WOS:001131389900001
OA gold
DA 2024-09-05
ER
PT J
AU Kulakli, A
   Osmanaj, V
AF Kulakli, Atik
   Osmanaj, Valmira
TI Global Research on Big Data in Relation with Artificial Intelligence (A
   Bibliometric Study: 2008-2019)
SO INTERNATIONAL JOURNAL OF ONLINE AND BIOMEDICAL ENGINEERING
LA English
DT Article
DE Bibliometric analysis; big data; artificial intelligence; business
   intelligence; citations; research evaluation and Impact; SCI-Expanded;
   SSCI
ID CHALLENGES; PREDICTION
AB The purpose of this paper is to analyze and explore the research studies on Big Data in relation with Artificial Intelligence domain, which published in Peer Review Journals and indexed in Web of Science Core Collection for the period of 2008-2019 years.
   The publication data for our research analysis "Big Data in relation with Artificial Intelligence" has been derived from the Web of Science (WoS) Core Collection database (Indexes included SCI Expanded and SSCI). The Bibliometric Analysis Methods is applied for the study in order to find out the relations between two domains and to investigate the status of scientific development level in the research era. Therefore, our research concentrates and highlights the current issues discussed and studied by the scholars around the globe. This paper would useful for researchers to show the publication trends on big data in relation with artificial intelligence research outcomes in highly reputable SCI-Exp and SSCI journal (ranked by WoS).
C1 [Kulakli, Atik; Osmanaj, Valmira] Amer Univ Middle East, Coll Business Adm, Egaila, Kuwait.
C3 American University of the Middle East
RP Kulakli, A (corresponding author), Amer Univ Middle East, Coll Business Adm, Egaila, Kuwait.
EM atik.kulakli@aum.edu.kw
RI Kulakli, Atik/D-6033-2016; Osmanaj, Valmira Hysen/B-7175-2019
OI Kulakli, Atik/0000-0002-2368-3225; Osmanaj, Valmira
   Hysen/0000-0002-9864-8627
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NR 27
TC 12
Z9 13
U1 0
U2 18
PU INT ASSOC ONLINE ENGINEERING
PI WIEN
PA KIRCHENGASSE 10-200, WIEN, A-1070, AUSTRIA
EI 2626-8493
J9 INT J ONLINE BIOMED
JI Int. J. Online Biomed. Eng.
PY 2020
VL 16
IS 2
BP 31
EP 46
DI 10.3991/ijoe.v16i02.12617
PG 16
WC Computer Science, Interdisciplinary Applications
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA KM5WB
UT WOS:000514209100003
OA gold
DA 2024-09-05
ER
PT J
AU Chen, KH
   Tang, MC
   Wang, CM
   Hsiang, J
AF Chen, Kuang-hua
   Tang, Muh-chyun
   Wang, Chun-mei
   Hsiang, Jieh
TI Exploring alternative metrics of scholarly performance in the social
   sciences and humanities in Taiwan
SO SCIENTOMETRICS
LA English
DT Article
DE Research evaluation; Bibliometrics; Evaluation metrics; Altmetrics
ID BIBLIOMETRIC INDICATORS; IMPACT; WEB; COMMUNICATION; WEBOMETRICS
AB Research output and impact metrics derived from commercial citation databases such as Web of Science and Scopus have become the de facto indicators of scholarly performance across different disciplines and regions. However, it has been pointed out that the existing metrics are largely inadequate to reflect scholars' overall peer-mediated performance, especially in the social sciences and humanities (SSH) where publication channels are more diverse. In this paper alternative metrics exploring a variety of formal and informal communication channels were proposed, with the aim of better reflecting SSH scholarship. Data for a group of SSH scholars in Taiwan on these metrics were collected. Principal component analysis revealed four underlying dimensions represented by the 18 metrics. Multiple-regression analyses were then performed to examine how well each of these dimensions predicted the academic standing of the scholars, measured by the number of public grants awarded and prestigious research awards received. Differences in the significance of the predictors were found between the social sciences and humanities. The results suggest the need to consider disciplinary differences when evaluating scholarly performance.
C1 [Chen, Kuang-hua; Tang, Muh-chyun; Wang, Chun-mei] Natl Taiwan Univ, Dept Lib & Informat Sci, Taipei 10617, Taiwan.
   [Hsiang, Jieh] Natl Taiwan Univ, Dept Comp Sci & Informat Engn, Taipei 10617, Taiwan.
C3 National Taiwan University; National Taiwan University
RP Tang, MC (corresponding author), Natl Taiwan Univ, Dept Lib & Informat Sci, 1,Sec 4,Roosevelt Rd, Taipei 10617, Taiwan.
EM mctang@ntu.edu.tw
RI muh-chyun, Tang/O-9385-2019
OI muh-chyun, Tang/0000-0001-7321-6927; HSIANG, JIEH/0000-0002-2649-4331
FU "The Aim for the Top University Project, Integrated Platform of Digital
   Humanities" at National Taiwan University in Taiwan
FX The study was sponsored by "The Aim for the Top University Project,
   Integrated Platform of Digital Humanities" at National Taiwan University
   in Taiwan.
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NR 35
TC 32
Z9 34
U1 2
U2 120
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD JAN
PY 2015
VL 102
IS 1
BP 97
EP 112
DI 10.1007/s11192-014-1420-6
PG 16
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA AY0OZ
UT WOS:000347297400006
DA 2024-09-05
ER
PT J
AU Scaccia, JP
   Scott, VC
AF Scaccia, Jonathan P.
   Scott, Victoria C.
TI 5335 days of <i>Implementation Science</i>: using natural language
   processing to examine publication trends and topics
SO IMPLEMENTATION SCIENCE
LA English
DT Article
DE Implementation science; Natural language processing; Synthesis and
   translation; Bibliometric study; Systematic review
ID HEALTH; FRAMEWORK; SUPPORT; SCOPE
AB Introduction Moving evidence-based practices into the hands of practitioners requires the synthesis and translation of research literature. However, the growing pace of scientific publications across disciplines makes it increasingly difficult to stay abreast of research literature. Natural language processing (NLP) methods are emerging as a valuable strategy for conducting content analyses of academic literature. We sought to apply NLP to identify publication trends in the journal Implementation Science, including key topic clusters and the distribution of topics over time. A parallel study objective was to demonstrate how NLP can be used in research synthesis. Methods We examined 1711 Implementation Science abstracts published from February 22, 2006, to October 1, 2020. We retrieved the study data using PubMed's Application Programming Interface (API) to assemble a database. Following standard preprocessing steps, we use topic modeling with Latent Dirichlet allocation (LDA) to cluster the abstracts following a minimization algorithm. Results We examined 30 topics and computed topic model statistics of quality. Analyses revealed that published articles largely reflect (i) characteristics of research, or (ii) domains of practice. Emergent topic clusters encompassed key terms both salient and common to implementation science. HIV and stroke represent the most commonly published clinical areas. Systematic reviews have grown in topic prominence and coherence, whereas articles pertaining to knowledge translation (KT) have dropped in prominence since 2013. Articles on HIV and implementation effectiveness have increased in topic exclusivity over time. Discussion We demonstrated how NLP can be used as a synthesis and translation method to identify trends and topics across a large number of (over 1700) articles. With applicability to a variety of research domains, NLP is a promising approach to accelerate the dissemination and uptake of research literature. For future research in implementation science, we encourage the inclusion of more equity-focused studies to expand the impact of implementation science on disadvantaged communities.
C1 [Scaccia, Jonathan P.] Dawn Chorus Grp, 1014 Hartman Rd, Reading, PA 19606 USA.
   [Scott, Victoria C.] Univ North Carolina Charlotte, Dept Psychol Sci, 9201 Univ City Blvd, Charlotte, NC 28223 USA.
C3 University of North Carolina; University of North Carolina Charlotte
RP Scaccia, JP (corresponding author), Dawn Chorus Grp, 1014 Hartman Rd, Reading, PA 19606 USA.
EM jon@dawnchrousgroup.com
OI Scaccia, Jonathan/0000-0001-6800-1286
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NR 41
TC 14
Z9 17
U1 2
U2 19
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 1748-5908
J9 IMPLEMENT SCI
JI Implement. Sci.
PD APR 26
PY 2021
VL 16
IS 1
AR 47
DI 10.1186/s13012-021-01120-4
PG 12
WC Health Care Sciences & Services; Health Policy & Services
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Health Care Sciences & Services
GA RT7QQ
UT WOS:000644652300001
PM 33902657
OA Green Submitted, gold, Green Published
DA 2024-09-05
ER
PT J
AU Wu, J
   Ou, GY
   Liu, XH
   Dong, K
AF Wu, Jiang
   Ou, Guiyan
   Liu, Xiaohui
   Dong, Ke
TI How does academic education background affect top researchers'
   performance? Evidence from the field of artificial intelligence
SO JOURNAL OF INFORMETRICS
LA English
DT Article
DE Educational background; Research performance; AI; Academic career
ID FACULTY RESEARCH PRODUCTIVITY; PUBLICATION PRODUCTIVITY;
   GENDER-DIFFERENCES; SCIENTIFIC PERFORMANCE; UNIVERSITY RANKINGS; IMPACT;
   DETERMINANTS; OUTPUT; COLLABORATION; MANAGEMENT
AB The early academic beginning is critical in the development of a researcher's academic career because it helps determine one's further success. We aim to shed light on the path that drives the success of talents in the field of artificial intelligence (AI) by investigating the academic education background of distinguished AI researchers and analyzing the contribution of different educational factors to their research performance. In this study, we collected and coded the curriculum vitae of 1832 AI researchers. Results show that most AI researchers were educated in the United States and obtained their highest degrees from top universities. As for their educational background, approximately 18.27% of AI researchers chose non-AI majors, such as mathematics, physics, and chemistry, instead of AI-related majors, such as computer science. Furthermore, negative binomial regression analysis demonstrates that individuals who publish more during study period will have better research output, whether they are currently in academia or industry. Researchers in academia with overseas degrees published more articles than those without overseas degrees. In terms of interdisciplinary education, a mathematics background leads to increased research visibility of AI researchers in the industry but depresses the scholarly productivity of AI researchers in academia. Academic qualification is the main factor determining the scientific performance of AI researchers in industry, which is not the case in academia. The analysis also showed that individuals who graduated from more prestigious universities tended to receive more citations than those graduating from less famous universities. Moreover, AI researchers in academia who have graduated from prestigious universities seem to pay more attention to the quality of the papers rather than the quantity.
C1 [Wu, Jiang; Ou, Guiyan; Dong, Ke] Wuhan Univ, Ctr Studies Informat Resources, Wuhan 430072, Peoples R China.
   [Wu, Jiang; Ou, Guiyan; Liu, Xiaohui; Dong, Ke] Wuhan Univ, Sch Informat Management, Wuhan 430072, Peoples R China.
C3 Wuhan University; Wuhan University
RP Dong, K (corresponding author), Wuhan Univ, Ctr Studies Informat Resources, Wuhan 430072, Peoples R China.
EM dongke@whu.edu.cn
FU Ministry of Education Key Projects of Philosophy and Social Sciences
   Research, People's Republic of China [20JZD024]; National Social Science
   Foundation of China [21CTQ017]
FX This research was supported by Ministry of Education Key Projects of
   Philosophy and Social Sciences Research, People's Republic of China
   (Grant No. 20JZD024) and National Social Science Foundation of China
   (Grant No. 21CTQ017) . The authors would like to thank the anonymous
   reviewers for their kind help.
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NR 84
TC 5
Z9 6
U1 30
U2 136
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1751-1577
EI 1875-5879
J9 J INFORMETR
JI J. Informetr.
PD MAY
PY 2022
VL 16
IS 2
AR 101292
DI 10.1016/j.joi.2022.101292
EA MAY 2022
PG 17
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 1T2KG
UT WOS:000804563600002
DA 2024-09-05
ER
PT C
AU Lin, LC
   Wang, F
AF Lin, Longcheng
   Wang, Fang
BE ACM
TI Research on the relationship between learning engagement and learning
   performance in online learning
SO PROCEEDINGS OF THE 15TH INTERNATIONAL CONFERENCE ON EDUCATION TECHNOLOGY
   AND COMPUTERS, ICETC 2023
LA English
DT Proceedings Paper
CT 15th International Conference on Education Technology and Computers
   (ICETC)
CY SEP 26-28, 2023
CL Univ Barcelona, Barcelona, SPAIN
HO Univ Barcelona
DE online learning; learning engagement; learning performance; relationship
AB With the continuous development and popularisation of internet technology, online learning has become an indispensable learning method in the field of education. However, the relationship between learning engagement and learning performance in online learning has always been of great concern. This paper investigates the relationship between learning engagement and learning performance in an online learning environment. This study used scale surveys and experimental research methods to analyse learning engagement from behavioural, cognitive and emotional perspectives and to investigate the relationship between learning engagement and learning performance. The research findings indicate that in online learning, emotional engagement is relatively high and students have a strong willingness to learn, but cognitive and behavioural engagement are relatively low. There is a positive correlation between learning engagement and learning performance, and students with high learning engagement tend to have higher learning performance. Classifying students into three categories based on learning engagement and learning performance is useful for understanding the characteristics of learners' learning engagement and for providing data support and reference for improving the quality of learning.
C1 [Lin, Longcheng; Wang, Fang] Nantong Hlth Coll Jiangsu Prov, Nantong, Peoples R China.
RP Lin, LC (corresponding author), Nantong Hlth Coll Jiangsu Prov, Nantong, Peoples R China.
EM 754289407@qq.com
FU Jiangsu Vocational Education Research Project [XHYBLX2023136]
FX This study is supported by the Jiangsu Vocational Education Research
   Project (Project Number: XHYBLX2023136): Research on the Impact
   Mechanism and Improvement Strategies of Vocational School Students'
   Learning Engagement under Blended Learning.
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NR 14
TC 0
Z9 0
U1 33
U2 33
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES
BN 979-8-4007-0911-1
PY 2023
BP 201
EP 206
DI 10.1145/3629296.3629327
PG 6
WC Computer Science, Interdisciplinary Applications; Education &
   Educational Research; Education, Scientific Disciplines
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Education & Educational Research
GA BW5RO
UT WOS:001166851900031
DA 2024-09-05
ER
PT J
AU Liu, G
   Pan, L
   Jiang, WQ
   Fan, S
   Buhari, A
AF Liu, Gang
   Pan, Lei
   Jiang, Weiqiang
   Fan, Shuai
   Buhari, Abudhahir
TI Dynamic performance and optimization research for six-link mechanism
   considering the coupling effect of flexible structure and wear
   clearances
SO NONLINEAR DYNAMICS
LA English
DT Article
DE Wear clearance; Flexible structure; Six-link mechanism; Dynamic
   performance; Optimization research; Simulated annealing algorithm
ID SYSTEM
AB The adverse effects of flexible structure and wear clearances on dynamic performance of mechanical systems cannot be ignored. At present, scholars have carried out extensive research on mechanism with clearance, but few consider the coupling effect of wear clearance and flexible structure on dynamic performance of mechanism. Therefore, this paper develops a dynamic model of six-link mechanism considering multiple wear clearances and flexible structure through Lagrange method. The influence of clearance size and frictional coefficient on dynamic performance and nonlinear characteristics of mechanism is investigated. In view of the adverse effects of wear clearances and flexible structure on the performance of mechanism, a new optimization method of mechanism based on simulated annealing algorithm (SAA) is proposed. This method takes the mass parameters of each component for mechanism as the design variables and minimizes the maximal wear depth of clearance as the objective function. The results indicate that the optimization method can reduce the vibration and error, and improve the overall dynamic performance of mechanism.
C1 [Liu, Gang; Buhari, Abudhahir] Infrastruct Univ Kuala Lumpur, Fac Engn Sci & Technol, Jln Ikram Uniten, Kajang 43000, Selangor, Malaysia.
   [Liu, Gang; Pan, Lei; Jiang, Weiqiang] Weifang Vocat Coll, Weifang 261041, Shandong, Peoples R China.
   [Fan, Shuai] Chengdu Univ Technol, Sch Mech & Elect Engn, Chengdu 610059, Peoples R China.
C3 Infrastructure University Kuala Lumpur (IUKL); Chengdu University of
   Technology
RP Jiang, WQ (corresponding author), Weifang Vocat Coll, Weifang 261041, Shandong, Peoples R China.
EM jiangweiqiang_wfvc@163.com
FU Development Path and Industrialization Research Project of Kinetic
   Energy Coupling Technology for Large Composite Intelligent Agricultural
   Machinery Equipment [2022RKX027]
FX This work was supported by the [Development Path and Industrialization
   Research Project of Kinetic Energy Coupling Technology for Large
   Composite Intelligent Agricultural Machinery Equipment: 2022RKX027].
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NR 28
TC 0
Z9 0
U1 27
U2 29
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0924-090X
EI 1573-269X
J9 NONLINEAR DYNAM
JI Nonlinear Dyn.
PD MAR
PY 2024
VL 112
IS 6
BP 4299
EP 4320
DI 10.1007/s11071-023-09247-3.
PG 22
WC Engineering, Mechanical; Mechanics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Mechanics
GA ZO5V5
UT WOS:001276261600013
DA 2024-09-05
ER
PT J
AU Buehling, K
AF Buehling, Kilian
TI Changing research topic trends as an effect of publication rankings-The
   case of German economists and the Handelsblatt Ranking
SO JOURNAL OF INFORMETRICS
LA English
DT Article
DE Research rankings; Topic modeling; Text classification; Topic change;
   Research field mapping
ID PERFORMANCE; INNOVATION; BUSINESS; SCIENCE; IMPACT; DYNAMICS; JOURNALS;
   VALIDITY; SYSTEMS
AB In order to arrive at informed judgments about the quality of research institutions and individual scholars, funding agencies, academic employers and researchers have turned to publication rankings. While such rankings, often based on journal citations, promise a more efficient and transparent funding allocation, individual researchers are at risk of showing adaptive behavior. This paper investigates whether the use of journal rankings in assessing the quality of scholarly research results in the unintended consequence of researchers adapting their research topics to the publishing interests of high-ranked journals. The introduction of the Handelsblatt Ranking (HBR) for economists in German language institutions serves as a quasi-natural experiment, allowing for an examination of research topic dynamics in economics via topic modeling and text classification . It is found that the Handelsblatt Ranking did not cause a significant shift of topics researched by German-affiliated authors in comparison to their international counterparts, even though topic convergence is apparent.
C1 [Buehling, Kilian] Tech Univ Dresden, Res Grp Knowledge & Technol Transfer, Muenchner Pl 2-3, D-01062 Dresden, Germany.
C3 Technische Universitat Dresden
RP Buehling, K (corresponding author), Tech Univ Dresden, Res Grp Knowledge & Technol Transfer, Muenchner Pl 2-3, D-01062 Dresden, Germany.
EM kilian.buehling@tu-dresden.de
OI Buehling, Kilian/0000-0002-5244-7547
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NR 92
TC 8
Z9 8
U1 0
U2 18
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1751-1577
EI 1875-5879
J9 J INFORMETR
JI J. Informetr.
PD AUG
PY 2021
VL 15
IS 3
AR 101199
DI 10.1016/j.joi.2021.101199
EA AUG 2021
PG 13
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA WB5KY
UT WOS:000703611800002
OA Green Accepted
DA 2024-09-05
ER
PT C
AU Liu, ZD
   Wang, BH
AF Liu, Zhendong
   Wang, Beihai
BE Long, S
   Dhillon, BS
TI Research on Text Visual Effect of Multimedia Courseware for Mobile
   Online Learning
SO MAN-MACHINE-ENVIRONMENT SYSTEM ENGINEERING, MMESE
SE Lecture Notes in Electrical Engineering
LA English
DT Proceedings Paper
CT 21st International Conference on Man-Machine-Environment System
   Engineering (MMESE)
CY OCT 23-25, 2021
CL Beijing, PEOPLES R CHINA
DE Online learning; Mobile learning; Visual design; Courseware
AB The purpose of this study is to make the multimedia courseware, which designed on the computer, can terminal obtain satisfactory visual effect on the mobile screen. Methods interviews and questionnaires were conducted to online learning students to determine the factors that affect visual effect. Then an experiment on visual effect with font, font size, color and spacing as variables was conducted with 20 subjects. Results the influence of each variable on visual effect was obtained. Conclusion the study gives the clear suggestions on the parameterization of font designed on computer to ensure good visual effect on the mobile screen.
C1 [Liu, Zhendong; Wang, Beihai] Wuhan Polytech Univ, Wuhan, Peoples R China.
C3 Wuhan Polytechnic University
RP Wang, BH (corresponding author), Wuhan Polytech Univ, Wuhan, Peoples R China.
RI ZHAO, S/IWV-4219-2023
CR Cheng H, 2015, EFFECTS FONT SIZE SP
   Ge L, 2016, ENG PSHCHOLOGY
   [宫殿坤 Gong Diankun], 2009, [心理科学, Psychological Science], V32, P1142
   Li H., 2011, APPL PSYCHOL, V17, P62
   Li Xixia, 2016, Renmin chubanshe, P48
   Li Y, 2012, EFFECTS FONT SIZE SP
   Liu L, 2015, EFFECTS FONT SIZE FO
   Peng Y., 2010, SCI TECHNOL ASS FORU, V6, P92
   Zhang L., 2014, ERGONOMICS, V20, P32
NR 9
TC 0
Z9 0
U1 0
U2 11
PU SPRINGER-VERLAG SINGAPORE PTE LTD
PI SINGAPORE
PA 152 BEACH ROAD, #21-01/04 GATEWAY EAST, SINGAPORE, 189721, SINGAPORE
SN 1876-1100
EI 1876-1119
BN 978-981-16-5963-8; 978-981-16-5962-1
J9 LECT NOTES ELECTR EN
PY 2022
VL 800
BP 841
EP 847
DI 10.1007/978-981-16-5963-8_115
PG 7
WC Automation & Control Systems; Engineering, Electrical & Electronic
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Automation & Control Systems; Engineering
GA BS4UW
UT WOS:000722635300115
DA 2024-09-05
ER
PT C
AU Djen, RAM
   Nurmandi, A
   Muallidin, I
   Kurniawan, D
   Loilatu, MJ
AF Djen, Risman A. M.
   Nurmandi, Achmad
   Muallidin, Isnaini
   Kurniawan, Danang
   Loilatu, Mohammad Jafar
BE Yang, XS
   Sherratt, S
   Dey, N
   Joshi, A
TI Artificial Intelligence: Bibliometric Analysis in Government Studies
SO PROCEEDINGS OF SEVENTH INTERNATIONAL CONGRESS ON INFORMATION AND
   COMMUNICATION TECHNOLOGY, VOL 4
SE Lecture Notes in Networks and Systems
LA English
DT Proceedings Paper
CT 7th International Congress of Information and Communication Technology
   (ICICT)
CY FEB 21-24, 2022
CL London, ENGLAND
DE Artificial intelligence; Government; Public policy
AB Artificial intelligence systems have become a massive thing in all sectors, both in the business world and government institutions and in the development of technology and information. Therefore, this research focuses on mapping the trend of the study of artificial Intelligence in a government study. This research uses a qualitative method with a literature study. In this bibliometric research, the data collected are journals and articles taken from Scopus using the keyword "artificial intelligence" and processed through NVivo 12 Plus and VOSviewer. Through this research, the researcher finds a tendency in the artificial intelligence discourse more directed to alternative system improvements in government, both in policymaking and public sector management. This research is expected to be a trigger for improving the government system, especially in a more transparent and democratic policymaking plan, seeing that several countries have started to develop research on artificial intelligence, of course, this research will help analyze the direction of the development of synthetic intelligence studies which are more directed toward the improvement of the policymaking system in government institutions.
C1 [Djen, Risman A. M.; Nurmandi, Achmad; Muallidin, Isnaini; Kurniawan, Danang; Loilatu, Mohammad Jafar] Univ Muhammadiyah Yogyakarta, Jusuf Kalla Sch Govt, Dept Govt Affairs & Adm, Yogyakarta, Indonesia.
C3 Universitas Muhammadiyah Yogyakarta
RP Djen, RAM (corresponding author), Univ Muhammadiyah Yogyakarta, Jusuf Kalla Sch Govt, Dept Govt Affairs & Adm, Yogyakarta, Indonesia.
EM djenrisman03@gmail.com; nurmandi_achmad@umy.ac.id
RI Nurmandi, Achmad/J-4428-2019
OI Nurmandi, Achmad/0000-0002-6730-0273; Kurniawan,
   Danang/0000-0003-2013-6821; Jafar Loilatu, Mohammad/0000-0001-6921-6879
CR Janssen M, 2022, SOC SCI COMPUT REV, V40, P478, DOI 10.1177/0894439320980118
   Kim J, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10010115
   Kindylidi I, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su132112064
   MotomuraY, 2021, SYNTHESIOLOGY, V13, P1, DOI [10.5571/synth.13.1_1, DOI 10.5571/SYNTH.13.1_1]
   Richardson L, 2019, GEOFORUM, V99, P278, DOI 10.1016/j.geoforum.2017.09.014
   Sætra HS, 2020, TECHNOL SOC, V62, DOI 10.1016/j.techsoc.2020.101283
   Scholz RW, 2018, SUSTAINABILITY SWITZ, V10
   Sima V, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12104035
   Sipior JC, 2020, INT J INFORM MANAGE, V55, DOI 10.1016/j.ijinfomgt.2020.102170
NR 9
TC 1
Z9 1
U1 10
U2 40
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 2367-3370
EI 2367-3389
BN 978-981-19-2397-5; 978-981-19-2396-8
J9 LECT NOTE NETW SYST
PY 2023
VL 465
BP 411
EP 418
DI 10.1007/978-981-19-2397-5_39
PG 8
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods; Telecommunications
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Telecommunications
GA BU3ZT
UT WOS:000894285700038
DA 2024-09-05
ER
PT J
AU Ghalambaz, S
   Abbaszadeh, M
   Sadrehaghighi, I
   Younis, O
   Ghalambaz, M
   Ghalambaz, M
AF Ghalambaz, Sepideh
   Abbaszadeh, Mohammad
   Sadrehaghighi, Ideen
   Younis, Obai
   Ghalambaz, Mehdi
   Ghalambaz, Mohammad
TI A forty years scientometric investigation of artificial intelligence for
   fluid-flow and heat-transfer (AIFH) during 1982 and 2022
SO ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
LA English
DT Article
DE Scientometric; Bibliometric analysis; Artificial intelligence (AI);
   Fluid -flow and heat -transfer (FH)
ID SOLAR-RADIATION; NEURAL-NETWORKS; PREDICTION
AB A scientometric approach is utilized to investigate the dynamic maps of relationships among researchers, institutes, and countries in the field of Artificial Intelligence for Fluid-flow and Heat-transfer (AIFH). The Web of Science database was searched for related publications during the last 40 years (1982 and 2022). A total of 6151 articles were discovered, which were analyzed in detail. Using a bibliometric analysis, the most relevant and most cited sources of publications were identified. The most active researchers, institutions, and countries leading AIFH were reported. Then, the worldwide dynamic collaboration maps and coupling maps of relation-ships were reported. The Islamic Azad University (1893 T.C.), the Chinese Academy of Sciences (1374 T.C.), and Beihang University (1266 T.C.) were the most influential institutes in AIFH. The most influential countries were China, the USA, and Iran. The dynamic map of collaborations shows a good worldwide collaboration distribution. The USA and China established the most connection with the rest of the world. ANNs are the most studied topic (19.5% of publications), followed by Machine Learning (17.9%) and Neural Networks (15.4%). Support Vector Machines lag behind at 1.4%. ANNs boast the highest total citations (17,064) and H-index (63). Most ANIF papers were published by Medical Physics (119 T.P.). Half of the articles in AIFH were published by five journals of Medical Physics, Neurocomputing, International Journal of Heat and Mass Transfer, International Journal of Radiation Oncology Biology Physics, and IEEE Access. The International Journal of Heat and Mass Transfer received the most citations in AIFH.
C1 [Ghalambaz, Sepideh] Payame Noor Univ, Dept Knowledge & Informat Sci, Tehran, Iran.
   [Abbaszadeh, Mohammad] Shiraz Univ, Hydroaeronaut Res Ctr, Shiraz, Iran.
   [Sadrehaghighi, Ideen] Old Dominion Univ, Mech Engn, Norfolk, VA USA.
   [Younis, Obai] Prince Sattam Bin Abdulaziz Univ, Coll Engn Wadi Alddwasir, Dept Mech Engn, Al Kharj, Saudi Arabia.
   [Younis, Obai] Univ Khartoum, Fac Engn, Dept Mech Engn, Khartoum, Sudan.
   [Ghalambaz, Mehdi] Almaaqal Univ, Coll Engn, Basra 61003, Iraq.
   [Ghalambaz, Mohammad] Tomsk State Univ, Lab Convect Heat & Mass Transfer, Tomsk 634050, Russia.
C3 Payame Noor University; Shiraz University; Old Dominion University;
   Prince Sattam Bin Abdulaziz University; University of Khartoum; Tomsk
   State University
RP Ghalambaz, S (corresponding author), Payame Noor Univ, Dept Knowledge & Informat Sci, Tehran, Iran.; Ghalambaz, M (corresponding author), Tomsk State Univ, Lab Convect Heat & Mass Transfer, Tomsk 634050, Russia.
EM sepideh_ghalambaz@pnu.ac.ir; mabbaszadeh@shirazu.ac.ir;
   ghalambaz.mehdi@gmail.com; m.ghalambaz@gmail.com
RI Ghalambaz, Mehdi/AGI-3994-2022; younis, obai/HHZ-8864-2022
OI Ghalambaz, Mehdi/0000-0001-8762-5510; younis, obai/0000-0002-1701-9387;
   Ghalambaz, Sepideh/0000-0001-8916-8245; Abbaszadeh,
   Mohammad/0000-0002-7754-4191
FU Prince sattam bin Abdulaziz University [PSAU/2023/R/1445]
FX This study is supported via funding from Prince sattam bin Abdulaziz
   University project number (PSAU/2023/R/1445) .
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NR 42
TC 2
Z9 2
U1 0
U2 8
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0952-1976
EI 1873-6769
J9 ENG APPL ARTIF INTEL
JI Eng. Appl. Artif. Intell.
PD JAN
PY 2024
VL 127
AR 107334
DI 10.1016/j.engappai.2023.107334
EA OCT 2023
PN B
PG 19
WC Automation & Control Systems; Computer Science, Artificial Intelligence;
   Engineering, Multidisciplinary; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Automation & Control Systems; Computer Science; Engineering
GA Y3IB9
UT WOS:001104228600001
DA 2024-09-05
ER
PT C
AU Plancher, B
   Reddi, VJ
AF Plancher, Brian
   Reddi, Vijay Janapa
GP ACM
TI TinyMLedu: The Tiny Machine Learning Open Education Initiative
SO PROCEEDINGS OF THE 53RD ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE
   EDUCATION (SIGCSE 2022), VOL 2
LA English
DT Proceedings Paper
CT 53rd Annual ACM SIGCSE Technical Symposium on Computer Science Education
   (SIGCSE)
CY MAR 02-05, 2022
CL Providence, RI
DE Computing Education; TinyML; Applied Machine Learning; Embedded Systems;
   Open-Access Materials; Global Network
AB TinyML is a cutting-edge field that brings the transformative power of machine learning (ML) to the performance and power-constrained domain of embedded systems. This opens new avenues of opportunity for a smarter and cheaper internet of things (IoT). TinyML is also a great educational tool as it touches on topics from across the computer science curriculum, ranging from machine learning to embedded systems. TinyMLedu is working to build an international coalition of researchers and practitioners advancing TinyML in the developing world, and to develop and share highquality, open-access educational materials globally. To date, we have helped launch two courses derived from our materials, taught in Portuguese in Brazil, held an outreach workshop for middle and high school teachers and students of the Navajo nation, and launched an Academic Network of over 20 universities from around the globe. Moving forward we want to grow our impact by helping develop more workshops and courses, in more languages, targeting an even broader audience, to introduce the world to TinyML.
C1 [Plancher, Brian; Reddi, Vijay Janapa] Harvard John A Paulson Sch Engn & Appl Sci, Boston, MA 02134 USA.
RP Plancher, B (corresponding author), Harvard John A Paulson Sch Engn & Appl Sci, Boston, MA 02134 USA.
RI Plancher, Brian/HKW-7966-2023
OI Plancher, Brian/0000-0002-0078-3653
CR Reddi Vijay Janapa, 2021, arXiv
   Warden Pete, 2019, TinyML: Machine Learning with TensorflowLite on Arduino and Ultra Low Power Microcontrollers
NR 2
TC 0
Z9 0
U1 0
U2 0
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES
BN 978-1-4503-9070-5
PY 2022
BP 1159
EP 1159
DI 10.1145/3478432.3499093
PG 1
WC Computer Science, Theory & Methods; Education, Scientific Disciplines
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Education & Educational Research
GA BU8GR
UT WOS:000947124200113
DA 2024-09-05
ER
PT J
AU Yan, RH
   Liu, TY
   Peng, YG
   Peng, XX
AF Yan, Ruohua
   Liu, Tianyi
   Peng, Yaguang
   Peng, Xiaoxia
TI Can statistical adjustment guided by causal inference improve the
   accuracy of effect estimation? A simulation and empirical research based
   on meta-analyses of case-control studies
SO BMC MEDICAL INFORMATICS AND DECISION MAKING
LA English
DT Article
DE Simulation; Confounder; Causal inference; Case&#8211; control study;
   Meta-analysis
ID BREAST-CANCER; PASSIVE SMOKING; SYSTEMATIC REVIEWS; RISK
AB Background Statistical adjustment is often considered to control confounding bias in observational studies, especially case-control studies. However, different adjustment strategies may affect the estimation of odds ratios (ORs), and in turn affect the results of their pooled analyses. Our study is aimed to investigate how to deal with the statistical adjustment in case-control studies to improve the validity of meta-analyses. Methods Three types of adjustment strategies were evaluated including insufficient adjustment (not all preset confounders were adjusted), full adjustment (all confounders were adjusted under the guidance of causal inference), and improper adjustment (covariates other than confounders were adjusted). We carried out a series of Monte Carlo simulation experiments based on predesigned scenarios, and assessed the accuracy of effect estimations from meta-analyses of case-control studies by combining ORs calculated according to different adjustment strategies. Then we used the data from an empirical review to illustrate the replicability of the simulation results. Results For all scenarios with different strength of causal relations, combining ORs that were comprehensively adjusted for confounders would get the most precise effect estimation. By contrast, combining ORs that were not sufficiently adjusted for confounders or improperly adjusted for mediators or colliders would easily introduce bias in causal interpretation, especially when the true effect of exposure on outcome was weak or none. The findings of the simulation experiments were further verified by the empirical research. Conclusions Statistical adjustment guided by causal inference are recommended for effect estimation. Therefore, when conducting meta-analyses of case-control studies, the causal relationship formulated by exposure, outcome, and covariates should be firstly understood through a directed acyclic graph, and then reasonable original ORs could be extracted and combined by suitable methods.
C1 [Yan, Ruohua; Liu, Tianyi; Peng, Yaguang; Peng, Xiaoxia] Capital Med Univ, Beijing Childrens Hosp, Ctr Clin Epidemiol & Evidence Based Med, Natl Ctr Childrens Hlth, Nanlishilu 56, Beijing 100045, Peoples R China.
   [Liu, Tianyi] AstraZenaca, Int Fortune Ctr, Evidence Generat, Med Affairs, Level 22,Jianguomenwai Ave 8, Beijing 100010, Peoples R China.
C3 Capital Medical University
RP Peng, XX (corresponding author), Capital Med Univ, Beijing Childrens Hosp, Ctr Clin Epidemiol & Evidence Based Med, Natl Ctr Childrens Hlth, Nanlishilu 56, Beijing 100045, Peoples R China.
EM pengxiaoxia@bch.com.cn
RI Peng, Xiao/ITR-9448-2023; Yan, Ruohua/HGE-2321-2022; Guo,
   yongqing/KDS-5864-2024; Li, Huizhen/JPX-2563-2023
OI Peng, Yaguang/0000-0002-7987-6464; Yan, Ruohua/0000-0003-1423-5962
FU Beijing Municipal Administration of Hospitals Clinical Medicine
   Development of Special Funding Support [ZYLX201840]; Special Fund of the
   Pediatric Medical Coordinated Development Center of Beijing Hospitals
   Authority [XTCX201812]
FX This study is funded by the Beijing Municipal Administration of
   Hospitals Clinical Medicine Development of Special Funding Support (No.
   ZYLX201840) and the Special Fund of the Pediatric Medical Coordinated
   Development Center of Beijing Hospitals Authority (No. XTCX201812). The
   funding bodies had no role in the design of the study and collection,
   analysis, and interpretation of data and in writing the manuscript.
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NR 38
TC 3
Z9 4
U1 0
U2 7
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
EI 1472-6947
J9 BMC MED INFORM DECIS
JI BMC Med. Inform. Decis. Mak.
PD DEC 11
PY 2020
VL 20
IS 1
AR 333
DI 10.1186/s12911-020-01343-3
PG 11
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Medical Informatics
GA PD6RU
UT WOS:000597810600001
PM 33308213
OA Green Submitted, gold, Green Published
DA 2024-09-05
ER
PT J
AU Khalid, S
   Wu, SL
   Zhang, F
AF Khalid, Shah
   Wu, Shengli
   Zhang, Fang
TI A multi-objective approach to determining the usefulness of papers in
   academic search
SO DATA TECHNOLOGIES AND APPLICATIONS
LA English
DT Article
DE Academic search; Citation analysis; Usefulness; Relevance; Impact;
   Publication age; Evaluation metrics
AB Purpose How to provide the most useful papers for searchers is a key issue for academic search engines. A lot of research has been carried out to address this problem. However, when evaluating the effectiveness of an academic search engine, most of the previous investigations assume that the only concern of the user is the relevancy of the paper to the query. The authors believe that the usefulness of a paper is determined not only by its relevance to the query but also by other aspects including its publication age and impact in the research community. This is vital, especially when a large number of papers are relevant to the query. Design/methodology/approach This paper proposes a group of metrics to measure the usefulness of a ranked list of papers. When defining these metrics, three factors, including relevance, publication age and impact, are considered at the same time. To accommodate this, the authors propose a framework to rank papers by a combination of their relevance, publication age and impact scores. Findings The framework is evaluated with the ACL (Association for Computational Linguistics Anthology Network) dataset. It demonstrates that the proposed ranking algorithm is effective for improving usefulness when two or three aspects of academic papers are considered at the same time, while the relevance of the retrieved papers is slightly down compared with the relevance-only retrieval. Originality/value To the best of the authors' knowledge, the proposed multi-objective academic search framework is the first of its kind that is proposed and evaluated with a group of new evaluation metrics.
C1 [Khalid, Shah; Wu, Shengli; Zhang, Fang] Jiangsu Univ, Sch Comp Sci & Commun Engn, Zhenjiang, Jiangsu, Peoples R China.
   [Khalid, Shah] Natl Univ Sci & Technol, Sch Comp Sci, Islamabad, Pakistan.
C3 Jiangsu University; National University of Sciences & Technology -
   Pakistan
RP Wu, SL (corresponding author), Jiangsu Univ, Sch Comp Sci & Commun Engn, Zhenjiang, Jiangsu, Peoples R China.
EM shahkhalid@ujs.edu.cn; swu@ujs.edu.cn; fangzh_1014@126.com
RI Khalid, Shah/AAC-8325-2021
OI Khalid, Shah/0000-0001-5735-5863; wu, shengli/0000-0003-2008-1736
CR Agrawal Rakesh, 2009, P 2 INT C WEB SEARCH, P5, DOI DOI 10.1145/1498759.1498766
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NR 32
TC 12
Z9 12
U1 2
U2 31
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 2514-9288
EI 2514-9318
J9 DATA TECHNOL APPL
JI Data Technol. Appl.
PD OCT 11
PY 2021
VL 55
IS 5
BP 734
EP 748
DI 10.1108/DTA-05-2020-0104
EA MAY 2021
PG 15
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA WF5PG
UT WOS:000653156700001
DA 2024-09-05
ER
PT J
AU Liu, G
   Pan, L
   Jiang, WQ
   Fan, S
   Buhari, A
AF Liu, Gang
   Pan, Lei
   Jiang, Weiqiang
   Fan, Shuai
   Buhari, Abudhahir
TI Dynamic performance and optimization research for six-link mechanism
   considering the coupling effect of flexible structure and wear
   clearances
SO NONLINEAR DYNAMICS
LA English
DT Article
DE Wear clearance; Flexible structure; Six-link mechanism; Dynamic
   performance; Optimization research; Simulated annealing algorithm
ID SYSTEM
AB The adverse effects of flexible structure and wear clearances on dynamic performance of mechanical systems cannot be ignored. At present, scholars have carried out extensive research on mechanism with clearance, but few consider the coupling effect of wear clearance and flexible structure on dynamic performance of mechanism. Therefore, this paper develops a dynamic model of six-link mechanism considering multiple wear clearances and flexible structure through Lagrange method. The influence of clearance size and frictional coefficient on dynamic performance and nonlinear characteristics of mechanism is investigated. In view of the adverse effects of wear clearances and flexible structure on the performance of mechanism, a new optimization method of mechanism based on simulated annealing algorithm (SAA) is proposed. This method takes the mass parameters of each component for mechanism as the design variables and minimizes the maximal wear depth of clearance as the objective function. The results indicate that the optimization method can reduce the vibration and error, and improve the overall dynamic performance of mechanism.
C1 [Liu, Gang; Buhari, Abudhahir] Infrast Univ Kuala Lumpur, Fac Engn Sci & Technol, Jln Ikram Uniten, Kajang 43000, Selangor, Malaysia.
   [Liu, Gang; Pan, Lei; Jiang, Weiqiang] Weifang Vocat Coll, Weifang 261041, Shandong, Peoples R China.
   [Fan, Shuai] Chengdu Univ Technol, Sch Mech & Elect Engn, Chengdu 610059, Sichuan, Peoples R China.
C3 Chengdu University of Technology
RP Jiang, WQ (corresponding author), Weifang Vocat Coll, Weifang 261041, Shandong, Peoples R China.
EM jiangweiqiang_wfvc@163.com
FU Development Path and Industrialization Research Project of Kinetic
   Energy Coupling Technology for Large Composite Intelligent Agricultural
   Machinery Equipment;  [2022RKX027]
FX This work was supported by the [Development Path and Industrialization
   Research Project of Kinetic Energy Coupling Technology for Large
   Composite Intelligent Agricultural Machinery Equipment: 2022RKX027].
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NR 28
TC 0
Z9 0
U1 27
U2 29
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0924-090X
EI 1573-269X
J9 NONLINEAR DYNAM
JI Nonlinear Dyn.
PD MAR
PY 2024
VL 112
IS 6
BP 4929
EP 4950
DI 10.1007/s11071-023-09247-3
EA JAN 2024
PG 22
WC Engineering, Mechanical; Mechanics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Mechanics
GA ZO5V5
UT WOS:001147898600007
DA 2024-09-05
ER
PT J
AU Gurcan, F
   Ayaz, A
   Dalveren, GGM
   Derawi, M
AF Gurcan, Fatih
   Ayaz, Ahmet
   Dalveren, Gonca Gokce Menekse
   Derawi, Mohammad
TI Business Intelligence Strategies, Best Practices, and Latest Trends:
   Analysis of Scientometric Data from 2003 to 2023 Using Machine Learning
SO SUSTAINABILITY
LA English
DT Article
DE business intelligence; topic modeling; text mining; trend analysis;
   machine learning
ID SEMANTIC CONTENT-ANALYSIS; BIG DATA ANALYTICS; EXPLORATORY ANALYSIS;
   SYSTEMS; MANAGEMENT; INTERESTS; EVOLUTION; INTENTION; IMPACT
AB The widespread use of business intelligence products, services, and applications piques the interest of researchers in this field. The interest of researchers in business intelligence increases the number of studies significantly. Identifying domain-specific research patterns and trends is thus a significant research problem. This study employs a topic modeling approach to analyze domain-specific articles in order to identify research patterns and trends in the business intelligence field over the last 20 years. As a result, 36 topics were discovered that reflect the field's research landscape and trends. Topics such as "Organizational Capability", "AI Applications", "Data Mining", "Big Data Analytics", and "Visualization" have recently gained popularity. A systematic taxonomic map was also created, revealing the research background and BI perspectives based on the topics. This study may be useful to researchers and practitioners interested in learning about the most recent developments in the field. Topics generated by topic modeling can also be used to identify gaps in current research or potential future research directions.
C1 [Gurcan, Fatih] Karadeniz Tech Univ, Fac Econ & Adm Sci, Dept Management Informat Syst, TR-61080 Trabzon, Turkiye.
   [Ayaz, Ahmet] Karadeniz Tech Univ, Digital Transformat Off, TR-61080 Trabzon, Turkiye.
   [Dalveren, Gonca Gokce Menekse] Atilim Univ, Fac Engn, Dept Software Engn, TR-06830 Ankara, Turkiye.
   [Derawi, Mohammad] Norwegian Univ Sci & Technol, Fac Informat Technol & Elect Engn, Dept Elect Syst, N-7034 Gjovik, Norway.
C3 Karadeniz Technical University; Karadeniz Technical University; Atilim
   University; Norwegian University of Science & Technology (NTNU)
RP Derawi, M (corresponding author), Norwegian Univ Sci & Technol, Fac Informat Technol & Elect Engn, Dept Elect Syst, N-7034 Gjovik, Norway.
EM mohammad.derawi@ntnu.no
RI Ayaz, Ahmet/JBJ-2146-2023; Menekse Dalveren, Gonca Gokce/HHS-4591-2022;
   GURCAN, Fatih/AAJ-7503-2021
OI Menekse Dalveren, Gonca Gokce/0000-0002-8649-1909; GURCAN,
   Fatih/0000-0001-9915-6686; Ayaz, Ahmet/0000-0003-1405-0546
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NR 84
TC 3
Z9 3
U1 11
U2 27
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD JUL
PY 2023
VL 15
IS 13
AR 9854
DI 10.3390/su15139854
PG 23
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA M1VP2
UT WOS:001028127500001
OA gold
DA 2024-09-05
ER
PT J
AU Enduri, MK
   Sankar, VU
   Hajarathaiah, K
AF Enduri, Murali Krishna
   Sankar, V. Udaya
   Hajarathaiah, Koduru
TI Empirical Study on Citation Count Prediction of Research Articles
SO JOURNAL OF SCIENTOMETRIC RESEARCH
LA English
DT Article
DE PACS codes; Citation; APS journals; Machine learning
ID IMPACT; FEATURES; TITLE
AB Citation is a measure that quantifies the impact of the researcher, research article and journal's quality. Investigating the citation of articles and/or researchers is one of the important tasks in the research community. So, understanding and predicting citation patterns of research articles has become popular in scientific research fields. In this work, we give a machine learning approach to predict the citations of research articles using the keywords. We study the citation impact based on keywords motioned in the articles using the data set of publications which are published in the various physical review journals from 1985-2012. In this dataset, for each publication is allocated some PACS codes (keywords) by their authors which represent a sub-field of Physics. In this work, we are investigating the impact of PACS codes of article on article's citation. We are performing our analysis on the first (sub-field of physics), second (sub area of sub-field of physics) and third level of PACS codes. We observed that compared to the first level, every pair of citation patterns of the second level is highly correlated. We also obtained a universal approximation curve for the third level that matches with the average value of the first level. This curve looks like a shifted and scaled version of the Gaussian function and is right skewed. We can also predict the citations based on the keywords by using this universal curve.
C1 [Enduri, Murali Krishna; Hajarathaiah, Koduru] SRM Univ, Dept Comp Sci & Engn, Amaravati, Andhra Pradesh, India.
   [Sankar, V. Udaya] SRM Univ, Dept Elect & Commun Engn, Amaravati, Andhra Pradesh, India.
   [Enduri, Murali Krishna] SRM Univ, Dept Comp Sci & Engn, Amaravati 522502, Andhra Pradesh, India.
C3 SRM University-AP; SRM University-AP; SRM University-AP
RP Enduri, MK (corresponding author), SRM Univ, Dept Comp Sci & Engn, Amaravati 522502, Andhra Pradesh, India.
EM muralikrishna.e@srmap.edu.in
RI Enduri, Murali Krishna/AEK-0968-2022
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NR 30
TC 3
Z9 3
U1 1
U2 10
PU PHCOG NET
PI KARNATAKA
PA 17, 2ND FLR, BUDDHA VIHAR RD, NEAR SPORTS ZONE, COX TOWN, BENGALURU,
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SN 2321-6654
EI 2320-0057
J9 J SCIENTOMETR RES
JI J. Scientometr. Res.
PD MAY-AUG
PY 2022
VL 11
IS 2
BP 155
EP 163
DI 10.5530/jscires.11.2.17
PG 9
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA 5B1PC
UT WOS:000863346300002
DA 2024-09-05
ER
PT J
AU Yin, YM
AF Yin, Yamei
TI Research on ideological and political evaluation model of university
   students based on data mining artificial intelligence technology
SO JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
LA English
DT Article
DE Data mining; artificial intelligence; ideology and politics; teaching
   evaluation
AB The teaching evaluation index system based on artificial intelligence not only evaluates and reflects the teaching situation of ideological and political theory courses in universities as a whole, but also provides specific feasible goals and direction guidance for the construction of ideological and political theory courses in universities. Based on data mining technology, this paper combines machine learning algorithms and dimensional analysis to study the ideological and political evaluation model of colleges and universities and builds an artificial intelligence teaching evaluation model based on actual needs. Moreover, this study transforms the model selection problem into a hybrid optimization algorithm optimization problem, and the algorithm attempts to find the optimal model from the model set. In addition, this study designs a control experiment to perform model performance analysis. The results of the study show that the performance of the model meets the expected goals and can be applied to practice.
C1 [Yin, Yamei] Hebei Inst Int Business & Econ, Qinhuangdao 066311, Hebei, Peoples R China.
RP Yin, YM (corresponding author), Hebei Inst Int Business & Econ, Qinhuangdao 066311, Hebei, Peoples R China.
EM yameiyin@tutanota.com
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PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1064-1246
EI 1875-8967
J9 J INTELL FUZZY SYST
JI J. Intell. Fuzzy Syst.
PY 2021
VL 40
IS 2
BP 3689
EP 3698
DI 10.3233/JIFS-189403
PG 10
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA QH1ZP
UT WOS:000618076700173
DA 2024-09-05
ER
PT J
AU Nakatoh, T
   Hirokawa, S
   Minami, T
   Nanri, T
   Funamori, M
AF Nakatoh, Tetsuya
   Hirokawa, Sachio
   Minami, Toshiro
   Nanri, Takeshi
   Funamori, Miho
TI Attribute-based quality classification of academic papers
SO ARTIFICIAL LIFE AND ROBOTICS
LA English
DT Article
DE Bibliometrics; Academic paper; Feature selection; Machine learning; SVM
AB Investigating the relevant literature is very important for research activities. However, it is difficult to select the most appropriate and important academic papers from the enormous number of papers published annually. Researchers search paper databases by combining keywords, and then select papers to read using some evaluation measure-often, citation count. However, the citation count of recently published papers tends to be very small because citation count measures accumulated importance. This paper focuses on the possibility of classifying high-quality papers superficially using attributes such as publication year, publisher, and words in the abstract. To examine this idea, we construct classifiers by applying machine-learning algorithms and evaluate these classifiers using cross-validation. The results show that our approach effectively finds high-quality papers.
C1 [Nakatoh, Tetsuya; Hirokawa, Sachio; Nanri, Takeshi] Kyushu Univ, Res Inst Informat Technol, Nishi Ku, 744 Motooka, Fukuoka, Fukuoka 8190395, Japan.
   [Minami, Toshiro] Kyushu Inst Informat Sci, Fukuoka, Fukuoka, Japan.
   [Funamori, Miho] Natl Inst Informat, Tokyo, Japan.
C3 Kyushu University; Research Organization of Information & Systems
   (ROIS); National Institute of Informatics (NII) - Japan
RP Nakatoh, T (corresponding author), Kyushu Univ, Res Inst Informat Technol, Nishi Ku, 744 Motooka, Fukuoka, Fukuoka 8190395, Japan.
EM nakatoh@cc.kyushu-u.ac.jp; hirokawa@cc.kyushu-u.ac.jp;
   minamitoshiro@gmail.com; nanri.takeshi.995@m.kyushu-u.ac.jp;
   funamori@nii.ac.jp
FU JSPS KAKENHI Grant [JP15K00426]
FX This work was supported by JSPS KAKENHI Grant Number JP15K00426. The
   computation was mainly carried out using the computer facilities at
   Research Institute for Information Technology, Kyushu University.
CR Ashok V.G., 2013, P 2013 C EMPIRICAL M, P1753
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NR 9
TC 0
Z9 0
U1 0
U2 7
PU SPRINGER
PI NEW YORK
PA 233 SPRING ST, NEW YORK, NY 10013 USA
SN 1433-5298
EI 1614-7456
J9 ARTIF LIFE ROBOT
JI Artif. Life Robot.
PD JUN
PY 2018
VL 23
IS 2
BP 235
EP 240
DI 10.1007/s10015-017-0412-z
PG 6
WC Robotics
WE Emerging Sources Citation Index (ESCI)
SC Robotics
GA GT0UR
UT WOS:000444164800010
DA 2024-09-05
ER
PT J
AU Zhang, BJ
AF Zhang, Bangjin
TI Research on performance evaluation of intelligent manufacturing
   enterprises supported by machine learning and big data technology
SO INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
LA English
DT Article
DE Machine learning; Big data technology; Intelligent manufacturing;
   Performance evaluation
AB Intelligent manufacturing, as a pillar of the manufacturing industry, provides crucial support for achieving China's strategic goal of becoming a manufacturing powerhouse. The operation and management process of intelligent manufacturing enterprises requires a large amount of capital investment, and the efficiency of capital utilization directly affects the economic benefits of intelligent manufacturing enterprises. Relevant enterprises continue to improve the performance evaluation mechanism and enhance their performance evaluation ability, so as to effectively promote the realization of corporate strategic objectives and continue to become a solid support for the steady operation of China's economy. On this basis, the performance of intelligent manufacturing enterprises was deeply explored, and a set of performance evaluation indicator framework for intelligent manufacturing enterprises was established by using machine learning and big data technology. Through empirical analysis, machine learning methods are introduced into performance evaluation to establish an assessment mechanism for intelligent manufacturing enterprises that is suitable for China's national conditions. For ridge regression, lasso regression, and elastic network regression in machine algorithm, we have conducted in-depth research and built a set of performance evaluation system using the model of lightGBM. Construct a framework of performance evaluation system for intelligent manufacturing enterprises based on machine learning and big data technology support, and use TOPSIS method to conduct empirical analysis, thus further highlighting the importance of evaluation and feedback mechanism for intelligent manufacturing enterprises.
C1 [Zhang, Bangjin] Guangzhou Coll Commerce, Sch Accounting, Guangzhou 511363, Peoples R China.
C3 Guangzhou College of Commerce
RP Zhang, BJ (corresponding author), Guangzhou Coll Commerce, Sch Accounting, Guangzhou 511363, Peoples R China.
EM zhangbangjin001@163.com
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NR 16
TC 0
Z9 0
U1 15
U2 21
PU SPRINGER LONDON LTD
PI LONDON
PA 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND
SN 0268-3768
EI 1433-3015
J9 INT J ADV MANUF TECH
JI Int. J. Adv. Manuf. Technol.
PD JAN
PY 2024
VL 130
IS 5-6
BP 2811
EP 2832
DI 10.1007/s00170-023-12864-2
EA DEC 2023
PG 22
WC Automation & Control Systems; Engineering, Manufacturing
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Automation & Control Systems; Engineering
GA HH7Q7
UT WOS:001127127300002
DA 2024-09-05
ER
PT J
AU Zhang, XY
   Shi, WB
AF Zhang, Xiaoyue
   Shi, Wanbing
TI Research about the university teaching performance evaluation under the
   data envelopment method
SO COGNITIVE SYSTEMS RESEARCH
LA English
DT Article
DE Principal component analysis; Data envelopment analysis; Performance
   evaluation
AB How to take active and effective measures to evaluate the university scientifically and rationally has been an eternal topic that the educational circles are constantly exploring. Based on the principle of index construction, the current educational performance evaluation index system is improved and a more reasonable evaluation index system is formed. On this basis, taking the sample data in 2017 as an example, the principal component analysis method is used to reduce the dimension of input and output indicators and eliminate the correlation between indicators, and three principal components of input and three principal components of output are obtained. Secondly, data envelopment analysis model is established, and the data processed are analyzed with the help of MATLAB and DEAP2.1 operation software. The efficiency of these 24 colleges and universities is compared to understand the efficiency and differences of each college. Moreover, projection analysis of non-DEA effective DMU is completed and the direction of improvement and the specific adjustment value are pointed out. (C) 2018 Elsevier B.V. All rights reserved.
C1 [Zhang, Xiaoyue; Shi, Wanbing] Northeastern Univ, Sch Humanities & Law, Shenyang, Liaoning, Peoples R China.
C3 Northeastern University - China
RP Zhang, XY (corresponding author), Northeastern Univ, Sch Humanities & Law, Shenyang, Liaoning, Peoples R China.
EM sherylyue@live.com
FU National Fund Pedagogy Project of Social Sciences "The Evaluation System
   Research of Universities' Scientific Research Performance of Humanistic
   and Social Science" [BFA150043]
FX The project was supported by the National Fund Pedagogy Project of
   Social Sciences "The Evaluation System Research of Universities'
   Scientific Research Performance of Humanistic and Social Science"
   (Project No.: BFA150043).
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NR 14
TC 32
Z9 32
U1 5
U2 54
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2214-4366
EI 1389-0417
J9 COGN SYST RES
JI Cogn. Syst. Res.
PD AUG
PY 2019
VL 56
BP 108
EP 115
DI 10.1016/j.cogsys.2018.11.004
PG 8
WC Computer Science, Artificial Intelligence; Neurosciences; Psychology,
   Experimental
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Neurosciences & Neurology; Psychology
GA HZ6PP
UT WOS:000468975100015
DA 2024-09-05
ER
PT J
AU Griggs, DM
   Crain-Dorough, M
AF Griggs, Dana M.
   Crain-Dorough, Mindy
TI Appreciative inquiry's potential in program evaluation and research
SO QUALITATIVE RESEARCH JOURNAL
LA English
DT Article
DE Appreciative inquiry; Qualitative research; AI as a research tool; AI
   and program evaluation
ID TOOL
AB Purpose The purposes of this paper are to provide a description of AI and to document and compare two applications of AI, one in program evaluation and another in an applied research study. Design/methodology/approach Focus groups, interviews and observations were used to gather rich qualitative data which was used to detail Appreciative Inquiry's value in evaluation and research. Findings AI aided the researcher in connecting with the participants and valuing what they shared. In both studies, the use of AI amassed information that answered the research questions, provided a rich description of the context and findings, and led to data saturation. The authors describe and compare experiences with two applications of AI: program evaluation and a research study. This paper contributes further understanding of the use of AI in public education institutions. The researchers also explore the efficacy of using AI in qualitative research and recommend its use for multiple purposes. Research limitations/implications Limitations occurred in the AI-Design Stage by using a positive viewpoint and because both program and partnership studied were new with limited data to use for designing a better future. So, the authors recommend a revisit of both studies through the same 4D Model. Practical implications This manuscript shows that AI is useful for evaluation and research. It amplifies the participants' voices through favorite stories and successes. AI has many undiscovered uses. Social implications Through the use of AI the authors can: improve theoretical perspectives; conduct research that yields more authentic data; enable participants to deeply reflect on their practice and feel empowered; and ultimately impact and improve the world. Originality/value AI is presented as an evaluation tool for a high-school program and as a research approach identifying strengths and perceptions of an educational partnership. In both studies, AI crumbled the walls that are often erected by interviewees when expecting to justify or defend decisions and actions. This paper contributes further understanding of the use of AI in public education institutions.
C1 [Griggs, Dana M.] Columbus State Univ, Coll Educ & Hlth Profess, Educ Leadership, Columbus, GA 31907 USA.
   [Crain-Dorough, Mindy] Southeastern Louisiana Univ, Educ Leadership & Technol, Hammond, LA 70402 USA.
C3 University System of Georgia; Columbus State University; University of
   Louisiana System; Southeastern Louisiana University
RP Griggs, DM (corresponding author), Columbus State Univ, Coll Educ & Hlth Profess, Educ Leadership, Columbus, GA 31907 USA.
EM dgriggs0011@gmail.com; Mindy.Dorough@southeastern.edu
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NR 43
TC 1
Z9 6
U1 2
U2 9
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 1443-9883
EI 1448-0980
J9 QUAL RES J
JI Qual. Res. J.
PD OCT 12
PY 2021
VL 21
IS 4
BP 375
EP 393
DI 10.1108/QRJ-06-2020-0059
EA JAN 2021
PG 19
WC Social Sciences, Interdisciplinary
WE Emerging Sources Citation Index (ESCI)
SC Social Sciences - Other Topics
GA WF4QN
UT WOS:000614469100001
DA 2024-09-05
ER
PT J
AU Hajkowicz, S
   Sanderson, C
   Karimi, S
   Bratanova, A
   Naughtin, C
AF Hajkowicz, Stefan
   Sanderson, Conrad
   Karimi, Sarvnaz
   Bratanova, Alexandra
   Naughtin, Claire
TI Artificial intelligence adoption in the physical sciences, natural
   sciences, life sciences, social sciences and the arts and humanities: A
   bibliometric analysis of research publications from 1960-2021
SO TECHNOLOGY IN SOCIETY
LA English
DT Article
DE Artificial intelligence; Machine learning; Bibliometric analysis;
   Technology adoption; Technology diffusion
ID DEFINITION
AB Analysing historical patterns of artificial intelligence (AI) adoption can inform decisions about AI capability uplift, but research to date has provided a limited view of AI adoption across different fields of research. In this study we examine worldwide adoption of AI technology within 333 fields of research during 1960-2021. We do this by using bibliometric analysis with 137 million peer-reviewed publications captured in The Lens database. We define AI using a list of 214 phrases developed by expert working groups at the Organisation for Economic Cooperation and Development (OECD). We found that 3.1 million of the 137 million peer-reviewed research publications during the entire period were AI-related, with a surge in AI adoption across practically all research fields (physical science, natural science, life science, social science and the arts and humanities) in recent years. The diffusion of AI beyond computer science was early, rapid and widespread. In 1960 14% of 333 research fields were related to AI (many in computer science), but this increased to cover over half of all research fields by 1972, over 80% by 1986 and over 98% in current times. We note AI has experienced boom-bust cycles historically; the AI "springs" and "winters". We conclude that the context of the current surge appears different, and that interdisciplinary AI application is likely to be sustained.
C1 [Hajkowicz, Stefan; Sanderson, Conrad; Karimi, Sarvnaz; Bratanova, Alexandra; Naughtin, Claire] CSIRO, Eveleigh, Australia.
C3 Commonwealth Scientific & Industrial Research Organisation (CSIRO)
RP Hajkowicz, S (corresponding author), CSIRO, Eveleigh, Australia.
EM stefan.hajkowicz@csiro.au
RI Naughtin, Claire/LCD-8181-2024
OI Karimi, Sarvnaz/0000-0002-4927-3937
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NR 75
TC 12
Z9 12
U1 18
U2 56
PU ELSEVIER SCI LTD
PI London
PA 125 London Wall, London, ENGLAND
SN 0160-791X
EI 1879-3274
J9 TECHNOL SOC
JI Technol. Soc.
PD AUG
PY 2023
VL 74
AR 102260
DI 10.1016/j.techsoc.2023.102260
EA JUN 2023
PG 8
WC Social Issues; Social Sciences, Interdisciplinary
WE Social Science Citation Index (SSCI)
SC Social Issues; Social Sciences - Other Topics
GA K6JX9
UT WOS:001017495300001
OA Green Published, Green Submitted, hybrid
DA 2024-09-05
ER
PT C
AU Tang, L
   Li, XM
   He, L
   Zhang, SH
AF Tang Li
   Li Xiaomei
   He Li
   Zhang Shuhua
BE Zhang, Y
TI A Research on the Evaluation of Scientific Researchers in Chinese
   Universities from the Perspective of Sustainable Development
SO PROCEEDINGS OF THE 12TH EURO-ASIA CONFERENCE ON ENVIRONMENT AND CSR:
   TOURISM, SOCIETY AND EDUCATION SESSION, PT II
LA English
DT Proceedings Paper
CT 12th Euro-Asian Conference on Corporate Social Responsibility and
   Environmental Management - Tourism, Society and Education
CY AUG 29-30, 2016
CL Hanover, GERMANY
DE Support Vector Machine (SVM); Evaluation of Scientific Researchers;
   Scientific Research Management; Machine Learning Theory
AB The evaluation of scientific researchers is an important part of scientific research management in Chinese colleges and universities from the perspective of sustainable development. Focusing on the evaluation of scientific researchers in universities, this paper designs an evaluation model, and constructs an evaluation index system by combining the basic personal information of scientific researchers with their achievements. Furthermore, a new evaluation method for scientific researchers based on Support Vector Machine (EMSR-SVM) is proposed. Finally, the comparative experiments have been done with a large number of data sets from the scientific research platform of the university. The experimental results show that this method is simple, rapid and accurate. It can efficiently improve the evaluation of scientific researchers.
C1 [Tang Li; He Li] Tianjin Univ Finance & Econ, Inst Technol, Tianjin, Peoples R China.
   [Li Xiaomei] Tianjin Univ Finance & Econ, Sci Res Off, Tianjin, Peoples R China.
   [Zhang Shuhua] Tianjin Univ Finance & Econ, Coordinated Innovat Ctr Computable Modeling Manag, Tianjin, Peoples R China.
C3 Tianjin University of Finance & Economics; Tianjin University of Finance
   & Economics; Tianjin University of Finance & Economics
RP Tang, L (corresponding author), Tianjin Univ Finance & Econ, Inst Technol, Tianjin, Peoples R China.
EM tangli0831@tjufe.edu.cn; lixiaomei@tjufe.edu.cn; renkeheli@163.com;
   shuhua55@126.com
FU General project of Humanities and social science research of the
   Ministry of Education [14YJA630025]; Tianjin Natural Science Foundation
   [15JCYBJC16000]; Tianjin Social Science Foundation [TJYY15-017]
FX This work is partly supported by the General project of Humanities and
   social science research of the Ministry of Education (14YJA630025),
   Tianjin Natural Science Foundation (15JCYBJC16000) and Tianjin Social
   Science Foundation (TJYY15-017).
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NR 7
TC 0
Z9 0
U1 0
U2 3
PU WISSENSCHAFTLICHER VERLAG BERLIN
PI BERLIN
PA OLAF GAUDIG & KLAUS-PETER VEIT GBR, VERKEHRSNUMMER 96258, KORTESTR 10,
   BERLIN, 10967, GERMANY
BN 978-3-86573-963-6
PY 2016
BP 31
EP 37
PG 7
WC Hospitality, Leisure, Sport & Tourism
WE Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Social Sciences - Other Topics
GA BH4RP
UT WOS:000400597800005
DA 2024-09-05
ER
PT C
AU Hassan, SU
   Iqbal, S
   Imran, M
   Aljohani, NR
   Nawaz, R
AF Hassan, Saeed-Ul
   Iqbal, Sehrish
   Imran, Mubashir
   Aljohani, Naif Radi
   Nawaz, Raheel
BE Dobreva, M
   Hinze, A
   Zumer, M
TI Mining the Context of Citations in Scientific Publications
SO MATURITY AND INNOVATION IN DIGITAL LIBRARIES, ICADL 2018
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT 20th International Conference on Asia-Pacific Digital Libraries (ICADL)
CY NOV 19-22, 2018
CL Univ Waikato, Hamilton, NEW ZEALAND
HO Univ Waikato
DE Citation context analysis; Influential citations; Machine learning;
   Self-organizing maps
AB Recent advancements in information retrieval systems significantly rely on the context-based features and semantic matching techniques to provide relevant information to users from ever-growing digital libraries. Scientific communities seek to understand the implications of research, its importance and its applicability for future research directions. To mine this information, absolute citations merely fail to measure the importance of scientific literature, as a citation may have a specific context in full text. Thus, a comprehensive contextual understanding of cited references is necessary. For this purpose, numerous techniques have been proposed that tap the power of artificial intelligence models to detect important or incidental (non-important) citations in full text scholarly publications. In this paper, we compare and build upon on four state-of-the-art models that detect important citations using 450 manually annotated citations by experts - randomly selected from 20,527 papers from the Association for Computational Linguistics corpus. Of the total 64 unique features proposed by the four selected state-of-the-art models, the top 29 were chosen using the Extra-Trees classifier. These were then fed it to our supervised machine learning based models: Random Forest (RF) and Support Vector Machine. The RF model outperforms existing selected systems by more than 10%, with 89% precision-recall curve. Finally, we qualitatively assessed important and non-important citations by employing and self-organizing maps. Overall, our research work supports information retrieval algorithms that detect and fetch scientific articles on the basis of both qualitative and quantitative indices in scholarly big data.
C1 [Hassan, Saeed-Ul; Iqbal, Sehrish] Informat Technol Univ, Ferozepur Rd, Lahore, Pakistan.
   [Imran, Mubashir] Univ Queensland, St Lucia, Qld 4072, Australia.
   [Aljohani, Naif Radi] King Abdulaziz Univ, Al Malaeb St, Jeddah, Saudi Arabia.
   [Nawaz, Raheel] Manchester Metropolitan Univ, Manchester M15 6BH, Lancs, England.
C3 University of Queensland; King Abdulaziz University; Manchester
   Metropolitan University
RP Hassan, SU (corresponding author), Informat Technol Univ, Ferozepur Rd, Lahore, Pakistan.
EM saeed-ul-hassan@itu.edu.pk
RI Aljohani, Naif R/S-1109-2017; Nawaz, Raheel/AAX-5293-2021; Hassan,
   Saeed-Ul/G-1889-2016
OI Nawaz, Raheel/0000-0001-9588-0052; Imran, Mubashir/0000-0003-4721-499X;
   Hassan, Saeed-Ul/0000-0002-6509-9190; Iqbal, Sehrish/0000-0003-1956-1572
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NR 14
TC 4
Z9 5
U1 2
U2 5
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 0302-9743
EI 1611-3349
BN 978-3-030-04257-8; 978-3-030-04256-1
J9 LECT NOTES COMPUT SC
PY 2018
VL 11279
BP 316
EP 322
DI 10.1007/978-3-030-04257-8_32
PG 7
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Interdisciplinary Applications; Information
   Science & Library Science
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Information Science & Library Science
GA BS8PU
UT WOS:000775537800032
DA 2024-09-05
ER
PT J
AU Eke, G
   Holttum, S
   Hayward, M
AF Eke, Gemma
   Holttum, Sue
   Hayward, Mark
TI Testing a Model of Research Intention Among UK Clinical Psychologists: A
   Logistic Regression Analysis
SO JOURNAL OF CLINICAL PSYCHOLOGY
LA English
DT Article
DE clinical psychologist; research output; research intention; research
   training environment (RTE); subjective norm; research self-efficacy;
   control belief; outcome expectancy; value
ID RESEARCH TRAINING ENVIRONMENT; SCIENTIST-PRACTITIONER MODEL; HOLLAND
   PERSONALITY TYPE; SOCIAL COGNITIVE THEORY; RESEARCH SELF-EFFICACY;
   RESEARCH PRODUCTIVITY; SCHOLARLY PRODUCTIVITY; STUDENTS; CAREER; IMPACT
AB Objectives: Previous research highlights barriers to clinical psychologists conducting research, but has rarely examined U. K. clinical psychologists. The study investigated U. K. clinical psychologists' self-reported research output and tested part of a theoretical model of factors influencing their intention to conduct research. Methods: Questionnaires were mailed to 1,300 U.K. clinical psychologists. Results: Three hundred and seventy-four questionnaires were returned (29% response-rate). This study replicated in a U.K. sample the finding that the modal number of publications was zero, highlighted in a number of U.K. and U.S. studies. Research intention was bimodally distributed, and logistic regression classified 78% of cases successfully. Outcome expectations, perceived behavioral control and normative beliefs mediated between research training environment and intention. Conclusions: Further research should explore how research is negotiated in clinical roles, and this issue should be incorporated into prequalification training. (C) 2012 Wiley Periodicals, Inc. J. Clin. Psychol 68:263-278, 2012.
C1 [Eke, Gemma] Sussex Partnership NHS Fdn Trust, Linwood Community Mental Hlth Ctr, Haywards Heath RH16 4BE, W Sussex, England.
   [Holttum, Sue] Canterbury Christ Church Univ, Sussex Partnership NHS Fdn Trust, Canterbury, New Zealand.
C3 University of Canterbury
RP Eke, G (corresponding author), Sussex Partnership NHS Fdn Trust, Linwood Community Mental Hlth Ctr, Butlers Green Rd, Haywards Heath RH16 4BE, W Sussex, England.
EM gemma.eke@sussexpartnership.nhs.uk
RI Hayward, Mark/AAJ-9858-2020
OI Hayward, Mark/0000-0001-6567-7723; Holttum, Sue/0000-0003-2618-8518
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NR 62
TC 9
Z9 13
U1 0
U2 12
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0021-9762
EI 1097-4679
J9 J CLIN PSYCHOL
JI J. Clin. Psychol.
PD MAR
PY 2012
VL 68
IS 3
BP 263
EP 278
DI 10.1002/jclp.20860
PG 16
WC Psychology, Clinical
WE Social Science Citation Index (SSCI)
SC Psychology
GA 908XJ
UT WOS:000301526100004
PM 22422562
DA 2024-09-05
ER
PT J
AU Lee, JL
   Kim, Y
AF Lee, Jung Lim
   Kim, Youngji
TI Research Topic Trends on Turnover Intention among Korean Registered
   Nurses: An Analysis Using Topic Modeling
SO HEALTHCARE
LA English
DT Article
DE nurses; employee turnover; data mining; social network analysis
AB This study aimed to explore research topic trends on turnover intention among Korean hospital nurses by analyzing the keywords and topics of related articles. Methods: This text-mining study collected, processed, and analyzed text data from 390 nursing articles published between 1 January 2010 and 30 June 2021 that were collected via search engines. The collected unstructured text data were preprocessed, and the NetMiner program was used to perform keyword analysis and topic modeling. Results: The word with the highest degree centrality was "job satisfaction", the word with the highest betweenness centrality was "job satisfaction", and the word with the highest closeness centrality and frequency was "job stress". The top 10 keywords in both the frequency analysis and the 3 centrality analyses included "job stress", "burnout", "organizational commitment", "emotional labor", "job", and "job embeddedness". The 676 preprocessed key words were categorized into five topics: "job", "burnout", "workplace bullying", "job stress", and "emotional labor". Since many individual-level factors have already been thoroughly investigated, future research should concentrate on enabling successful organizational interventions that extend beyond the microsystem.
C1 [Lee, Jung Lim] Daejeon Univ, Dept Nursing, Daejeon Si 34520, South Korea.
   [Kim, Youngji] Kongju Natl Univ, Coll Nursing & Hlth, Dept Nursing, Kongju Si 32588, South Korea.
C3 Daejeon University; Kongju National University
RP Kim, Y (corresponding author), Kongju Natl Univ, Coll Nursing & Hlth, Dept Nursing, Kongju Si 32588, South Korea.
EM leejl@dju.kr; superdr1@hanmail.net
OI Lee, Jung Lim/0000-0001-9464-383X
FU Daejeon University fund
FX This research was supported by the Daejeon University fund (2021).
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NR 29
TC 0
Z9 0
U1 2
U2 5
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2227-9032
J9 HEALTHCARE-BASEL
JI Healthcare
PD APR
PY 2023
VL 11
IS 8
AR 1139
DI 10.3390/healthcare11081139
PG 10
WC Health Care Sciences & Services; Health Policy & Services
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Health Care Sciences & Services
GA F7IM8
UT WOS:000984042800001
PM 37107972
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU Campbell, DG
   Mayhew, A
AF Campbell, D. Grant
   Mayhew, Alex
TI Repositioning the Base Level of Bibliographic Relationships: Or, a
   Cataloguer, a Post-Modernist and a Chatbot Walk Into a Bar
SO KNOWLEDGE ORGANIZATION
LA English
DT Article
DE bibliographic relationships; superwork; cataloguing; literary theory;
   artificial intelligence
AB Designers and maintainers of library catalogues are facing fresh challenges representing bibliographic relationships, due both to changes in cataloguing standards and to a broader information environment that has grown increasingly diverse, sophisticated and complex. This paper presents three different paradigms, drawn from three different fields of study, for representing relationships between bibliographic entities beyond the FRBR/LRM models: superworks, as developed in information studies; adaptation, as developed in literary studies; and artificial intelligence, as developed in computer science. Theories of literary adaptation remain focused on "the work," as traditionally conceived. The concept of the superwork reminds us that there are some works which serve as ancestors for entire families of works, and that those familial relationships are still useful. Crowd-sourcing projects often make more granular connections, a trend which has escalated significantly with current and emerging artificial intelligence systems. While the artificial intelligence paradigm is proving more pervasive outside conventional library systems, it could lead to a seismic shift in knowledge organization, a shift in which the power both to arrange information and to use it are moving beyond the control of users and intermediaries alike.
C1 [Campbell, D. Grant; Mayhew, Alex] Univ Western Ontario, Fac Informat & Media Studies, London, ON, Canada.
C3 Western University (University of Western Ontario)
RP Campbell, DG (corresponding author), Univ Western Ontario, Fac Informat & Media Studies, London, ON, Canada.
EM gcampbel@uwo.ca; amayhew@uwo.ca
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NR 18
TC 0
Z9 0
U1 5
U2 5
PU NOMOS VERLAGSGESELLSCHAFT MBH & CO KG
PI BADEN-BADEN
PA WALDSEESTR 3 5, BADEN-BADEN, 76530, GERMANY
SN 0943-7444
J9 KNOWL ORGAN
JI Knowl. Organ.
PY 2023
VL 50
IS 8
BP 519
EP 525
DI 10.5771/0943-7444-2023-8-519
PG 7
WC Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science
GA IF8G5
UT WOS:001165000100002
DA 2024-09-05
ER
PT C
AU Triebus, C
   Geiger, C
AF Triebus, Charlotte
   Geiger, Christian
BE Olivero, LF
   DaVeiga, PA
   Araujo, AB
   Dourado, P
   DaSilva, BM
TI Precious Camouflage - A Dance Performance Interweaving Human Movement
   and Artificial Intelligence Artistic Research for Exploring the
   Communication Between Dancers and AI
SO PROCEEDINGS OF THE 11TH INTERNATIONAL CONFERENCE ON DIGITAL AND
   INTERACTIVE ARTS, ARTECH 2023
LA English
DT Proceedings Paper
CT 11th International Conference on Digital and Interactive Arts (ARTECH) -
   Digital Creation Processes
CY NOV 28-30, 2023
CL Faro, PORTUGAL
DE Performance Art; Dance; AI; Digital Art; Agency; Body; Live Interaction;
   Ethics
AB This paper presents the layout created for Precious Camouflage, a dance performance that entangles human movement and artificial intelligence (AI) in a museal space. The performance, which features four dancers and five AI systems, challenges traditional notions of performance and authorship, and serves as a reflection on the rapid development of digital technologies and their societal implications. This is done by staging different kinds of agency in a communicative setup and a mesh of interactions accompanied by extended research on ethical questions with a focus point on bodies in datasets. In this paper we present the research to develop the piece. Background information and the recorded performance can be found at http://www.precious.dance.
C1 [Triebus, Charlotte; Geiger, Christian] Univ Appl Sci, Dept Media, Dusseldorf, Nrw, Germany.
RP Triebus, C (corresponding author), Univ Appl Sci, Dept Media, Dusseldorf, Nrw, Germany.
EM charlotte.triebus@hs-duesseldorf.de; geiger@hs-duesseldorf.de
FU Nationales Performance Netz, Neustart Kultur and Dachverband Tanz;
   Federal Government Commissioner for Culture and Media; German Federal
   Ministry of Education and Research (BMBF) [16SV8878]
FX This work has been supported by Nationales Performance Netz, Neustart
   Kultur and Dachverband Tanz. Funded by the Federal Government
   Commissioner for Culture and Media within the framework of the
   initiative NEUSTART KULTUR. Assistance Program for Dance tanz:digital by
   German Dance Association. The research was partially funded by the
   German Federal Ministry of Education and Research (BMBF) within the
   scope of the HIVAM project, Grant Number: 16SV8878.
CR Gatys LA, 2015, Arxiv, DOI [arXiv:1508.06576, DOI 10.48550/ARXIV.1508.06576, 10.1167/16.12.326]
   Chicau Joana, 2022, Anatomies of Intelligence
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   Juliane Laura, State of the deep
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NR 15
TC 0
Z9 0
U1 0
U2 0
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES
BN 979-8-4007-0872-5
PY 2023
AR 49
DI 10.1145/3632776.3632810
PG 4
WC Art; Computer Science, Interdisciplinary Applications
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Art; Computer Science
GA BW9DF
UT WOS:001211709700049
DA 2024-09-05
ER
PT C
AU Pradhan, DK
   Chakraborty, J
   Nandi, S
AF Pradhan, Dinesh K.
   Chakraborty, Joyita
   Nandi, Subrata
GP Assoc Comp Machinery
TI Applications of Machine Learning in Analysis of Citation Network
SO PROCEEDINGS OF THE 6TH ACM IKDD CODS AND 24TH COMAD
LA English
DT Proceedings Paper
CT ACM India Joint International Conference on Data Science and Management
   of Data (CoDS-COMAD)
CY JAN 03-05, 2019
CL Kolkata, INDIA
DE Machine learning models; bibliographic dataset analysis; clustering;
   temporal citation profiles
ID PREDICTION
AB Research standard and quality should be continuously monitored to direct progress of science in right direction. With exponential growth and continuous expansion in citation network, manual and static analysis is becoming insignificant. To fill in the gap, application of machine learning models might prove to be useful. In this paper, we propose some of the problems that we intend to solve using machine learning. Among various applications outlier analysis for early detection of anomalies in citation network, long term prediction of high impact and seminal authors, papers and field of study, deriving inherent features on diverse temporal and demographic scale governing citation structure etc. Starting with empirical analysis of open academic graph dataset, we try to understand the complex relational citation structure of entities. As a preliminary step, we do time series clustering of citation data and study characteristics of diverse profiles of citation curves. When compared to static classification in past literature, we overcome drawbacks of past study and get better insights.
C1 [Pradhan, Dinesh K.; Chakraborty, Joyita; Nandi, Subrata] NIT Durgapur, Dept Comp Sci & Engn, Durgapur, W Bengal, India.
C3 National Institute of Technology (NIT System); National Institute of
   Technology Durgapur
RP Pradhan, DK (corresponding author), NIT Durgapur, Dept Comp Sci & Engn, Durgapur, W Bengal, India.
RI Pradhan, Dinesh K/AAN-3592-2021; Pradhan, Dinesh K./AAE-4386-2019
OI Pradhan, Dinesh K./0000-0001-9132-9255
FU West Bengal | Department of Higher Education, Science Technology
FX We would like to thank `West Bengal | Department of Higher Education,
   Science & Technology' for funding the research work.
CR Chakraborty Joyita, 2018, J COMPUTER SCI
   Chakraborty T, 2015, COMMUN ACM, V58, P82, DOI 10.1145/2701412
   Daud A, 2015, SCIENTOMETRICS, V102, P1687, DOI 10.1007/s11192-014-1455-8
   Dey R, 2017, SCIENTOMETRICS, V113, P1645, DOI 10.1007/s11192-017-2543-3
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NR 11
TC 12
Z9 12
U1 0
U2 8
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA
BN 978-1-4503-6207-8
PY 2019
BP 330
EP 333
DI 10.1145/3297001.3297053
PG 4
WC Computer Science, Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BP5OG
UT WOS:000557252800052
DA 2024-09-05
ER
PT J
AU Zhang, WY
   Mukerjee, S
   Qin, HZ
AF Zhang, Weiyu
   Mukerjee, Subhayan
   Qin, Huazhi
TI Topics and Sentiments Influence Likes: A Study of Facebook Public Pages'
   Posts About COVID-19 Vaccination
SO CYBERPSYCHOLOGY BEHAVIOR AND SOCIAL NETWORKING
LA English
DT Article
DE COVID-19; Facebook; likes; sentiment analysis; topic modeling
ID EMOTION; NEWS; MEDIA
AB In this study, we analyzed >200,000 posts collected from Facebook public pages that were published in December 2020 during the rollout of the first dose of the Pfizer-BioNTech vaccine to the American public. We ran both topic modeling and sentiment analysis of the posts and found that first Facebook posts talked about not only treatment effectiveness such as trial results and testing approaches but also other issues that surround vaccines such as approval and distribution. Second, although the general sentiment during this period was positive and anticipation was the highest emotion, Facebook posts expressed a salient amount of fear and sadness, especially when discussing emergency approval and trial results. Finally, we found that both topics and sentiments have a significant influence on user likes. Using the topic of vaccine distribution as a baseline, posts that discuss related aspects of vaccines (e.g., effectiveness, shipment, and testing), call for actions (e.g., use of masks) and indicate care to vulnerable groups (e.g., health care workers and seniors) received more likes. Posts with bad news (e.g., new cases and deaths) and doubts over the usefulness of vaccines received fewer likes. Regardless of their valences, approach emotions lead to more likes whereas withdrawal emotions lead to fewer likes. Our study suggests that to facilitate actions, using certain topics and approach emotions in the posts could be helpful.
C1 [Zhang, Weiyu; Mukerjee, Subhayan] Natl Univ Singapore, Dept Commun & New Media, Singapore, Singapore.
   [Qin, Huazhi] Univ Chicago, Div Social Sci, Chicago, IL USA.
C3 National University of Singapore; University of Chicago
RP Zhang, WY (corresponding author), Natl Univ Singapore, Dept Commun & New Media, Singapore, Singapore.
EM weiyu.zhang@nus.edu.sg
RI Zhang, Weiyu/KIE-1785-2024; Mukerjee, Subhayan/AAK-6864-2020; Qin,
   Huazhi/JAX-6031-2023
OI Mukerjee, Subhayan/0000-0002-1885-5440; Qin, Huazhi/0009-0006-5365-2282
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NR 24
TC 3
Z9 3
U1 15
U2 28
PU MARY ANN LIEBERT, INC
PI NEW ROCHELLE
PA 140 HUGUENOT STREET, 3RD FL, NEW ROCHELLE, NY 10801 USA
SN 2152-2715
EI 2152-2723
J9 CYBERPSYCH BEH SOC N
JI Cyberpsychology Behav. Soc. Netw.
PD SEP 1
PY 2022
VL 25
IS 9
BP 552
EP 560
DI 10.1089/cyber.2022.0063
EA AUG 2022
PG 9
WC Psychology, Social
WE Social Science Citation Index (SSCI)
SC Psychology
GA 6J0MB
UT WOS:000840772800001
PM 35969378
DA 2024-09-05
ER
PT J
AU Zhong, ZL
   Guo, H
   Qian, K
AF Zhong, Zilong
   Guo, Hui
   Qian, Kun
TI Deciphering the impact of machine learning on education: Insights from a
   bibliometric analysis using bibliometrix R-package
SO EDUCATION AND INFORMATION TECHNOLOGIES
LA English
DT Article; Early Access
DE Artificial intelligence; Machine learning; Educational research;
   Bibliometric analysis; Bibliometrix R-package
ID PREDICTING STUDENTS PERFORMANCE
AB This study leverages bibliometric analysis through the bibliometrix R-package to dissect the expansive influence of machine learning on education, a field where machine learning's adaptability and data-processing capabilities promise to revolutionize teaching and learning methods. Despite its potential, the integration of machine learning in education requires a nuanced understanding to navigate the associated challenges and ethical considerations effectively. Our investigation spans articles from 2000 to 2023, focusing on identifying growth patterns, key contributors, and emerging trends within this interdisciplinary domain. By analyzing 970 selected articles, this study uncovers the developmental trajectory of machine learning in education, revealing significant insights into publication trends, prolific authors, influential institutions, and the geographical distribution of research. Furthermore, it highlights the journals pivotal in disseminating machine learning education research, the most cited works that shape the field, and the dynamic evolution of research themes. This bibliometric exploration not only charts the current landscape but also anticipates future directions, suggesting areas for further inquiry and potential breakthroughs. Through a detailed examination of empirical evidence and a critical analysis of machine learning applications in educational settings, this study aims to provide a foundational understanding of the field's complexities and potentials. The anticipated outcome is a comprehensive roadmap that guides researchers, educators, and policymakers towards a thoughtful integration of machine learning in education, balancing innovation with ethical stewardship.
C1 [Zhong, Zilong] Beijing Foreign Studies Univ, Res Inst Foreign Languages, 2 Xisanhuan North Rd, Beijing 100089, Peoples R China.
   [Guo, Hui] Harbin Normal Univ, Coll Comp Sci & Informat Engn, Harbin, Peoples R China.
   [Qian, Kun] Chongqing Univ, Sch Elect Engn, Chongqing, Peoples R China.
C3 Beijing Foreign Studies University; Harbin Normal University; Chongqing
   University
RP Zhong, ZL (corresponding author), Beijing Foreign Studies Univ, Res Inst Foreign Languages, 2 Xisanhuan North Rd, Beijing 100089, Peoples R China.
EM zhongzilong1106@bfsu.edu.cn
RI Zhong, Zilong/HSC-9312-2023
OI Zhong, Zilong/0000-0002-8512-4701
FU Zhejiang Provincial Philosophy and Social Science Planning Project
FX We thank editors and reviewers for their valuable feedback, which has
   greatly improved this manuscript. Their insights and suggestions have
   been instrumental in refining our work.
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NR 56
TC 0
Z9 0
U1 27
U2 27
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1360-2357
EI 1573-7608
J9 EDUC INF TECHNOL
JI Educ. Inf. Technol.
PD 2024 MAY 6
PY 2024
DI 10.1007/s10639-024-12734-8
EA MAY 2024
PG 28
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA PN5T9
UT WOS:001214778100006
DA 2024-09-05
ER
PT J
AU Ofer, D
   Kaufman, H
   Linial, M
AF Ofer, Dan
   Kaufman, Hadasah
   Linial, Michal
TI What's next? Forecasting scientific research trends
SO HELIYON
LA English
DT Article
DE PubMed; NLP; MeSH; Bibliometrics; Time series; Machine learning;
   Citation analysis
ID CITATION; PUBLICATION; PATENTS; SCIENCE
AB Scientific research trends and interests evolve over time. The ability to identify and forecast these trends is vital for educational institutions, practitioners, investors, and funding organizations. In this study, we predict future trends in scientific publications using heterogeneous sources, including historical publication time series from PubMed, research and review articles, pre -trained language models, and patents. We demonstrate that scientific topic popularity levels and changes (trends) can be predicted five years in advance across 40 years and 125 diverse topics, including life-science concepts, biomedical, anatomy, and other science, technology, and engineering topics. Preceding publications and future patents are leading indicators for emerging scientific topics. We find the ratio of reviews to original research articles informative for identifying increasing or declining topics, with declining topics having an excess of reviews. We find that language models provide improved insights and predictions into temporal dynamics. In temporal validation, our models substantially outperform the historical baseline. Our findings suggest that similar dynamics apply across other scientific and engineering research topics. We present SciTrends, a user-friendly webtool for predicting future publication trends for any topic covered in PubMed.
C1 [Ofer, Dan; Kaufman, Hadasah; Linial, Michal] Hebrew Univ Jerusalem, Inst Life Sci, Dept Biol Chem, Jerusalem, Israel.
   [Linial, Michal] Hebrew Univ Jerusalem, Inst Life Sci, Dept Biol Chem, IL-91904 Jerusalem, Israel.
C3 Hebrew University of Jerusalem; Hebrew University of Jerusalem
RP Linial, M (corresponding author), Hebrew Univ Jerusalem, Inst Life Sci, Dept Biol Chem, IL-91904 Jerusalem, Israel.
EM dan.ofer@mail.huji.ac.il; hadasah.kaufman@mail.huji.ac.il;
   michall@cc.huji.ac.il
RI Linial, Michal/AAQ-9259-2020; Ofer, Dan/GPK-5264-2022
OI Linial, Michal/0000-0002-9357-4526; Ofer, Dan/0000-0001-5136-8014
FU Center for Interdisciplinary Data Science Research (CIDR) at the Hebrew
   University, Jerusalem [3035000440]
FX We thank the members from D. Shahaf and M. Linial laboratories for
   sharing their ideas and valuable discussions. We thank N. Rappoport and
   R. Zucker for supporting the web application. This work was partially
   supported by the Center for Interdisciplinary Data Science Research
   (CIDR, #3035000440) at the Hebrew University, Jerusalem.
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NR 50
TC 0
Z9 0
U1 4
U2 8
PU CELL PRESS
PI CAMBRIDGE
PA 50 HAMPSHIRE ST, FLOOR 5, CAMBRIDGE, MA 02139 USA
EI 2405-8440
J9 HELIYON
JI Heliyon
PD JAN 15
PY 2024
VL 10
IS 1
AR e23781
DI 10.1016/j.heliyon.2023.e23781
EA DEC 2023
PG 12
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA FE3C3
UT WOS:001144036600001
PM 38223716
OA Green Submitted, gold
DA 2024-09-05
ER
PT S
AU Ho, DE
   Rubin, DB
AF Ho, Daniel E.
   Rubin, Donald B.
BE Hagan, J
   Scheppele, KL
   Tyler, TR
TI Credible Causal Inference for Empirical Legal Studies
SO ANNUAL REVIEW OF LAW AND SOCIAL SCIENCE, VOL 7
SE Annual Review of Law and Social Science
LA English
DT Article; Book Chapter
DE research design; policy evaluation; matching; regression discontinuity
ID REGRESSION DISCONTINUITY DESIGNS; PROPENSITY SCORE; MATCHING METHODS;
   PRINCIPAL STRATIFICATION; RANDOMIZED EXPERIMENTS; CLASS-SIZE; BIAS;
   CLASSIFICATION; IDENTIFICATION; ENVIRONMENT
AB We review advances toward credible causal inference that have wide application for empirical legal studies. Our chief point is simple: Research design trumps methods of analysis. We explain matching and regression discontinuity approaches in intuitive (nontechnical) terms. To illustrate, we apply these to existing data on the impact of prison facilities on inmate misconduct, which we compare to experimental evidence. What unifies modern approaches to causal inference is the prioritization of research design to create-without reference to any outcome data-subsets of comparable units. Within those subsets, outcome differences may then be plausibly attributed to exposure to the treatment rather than control condition. Traditional methods of analysis play a small role in this venture. Credible causal inference in law turns on substantive legal, not mathematical, knowledge.
C1 [Ho, Daniel E.] Stanford Univ, Stanford Law Sch, Stanford, CA 94305 USA.
   [Rubin, Donald B.] Harvard Univ, Dept Stat, Cambridge, MA 02138 USA.
C3 Stanford University; Harvard University
RP Ho, DE (corresponding author), Stanford Univ, Stanford Law Sch, Stanford, CA 94305 USA.
EM dho@law.stanford.edu; rubin@stat.harvard.edu
OI Ho, Daniel/0000-0002-2195-5469
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NR 141
TC 37
Z9 41
U1 1
U2 29
PU ANNUAL REVIEWS
PI PALO ALTO
PA 4139 EL CAMINO WAY, PO BOX 10139, PALO ALTO, CA 94303-0897 USA
SN 1550-3585
BN 978-0-8243-4107-7
J9 ANNU REV LAW SOC SCI
JI Annu. Rev. Law. Soc. Sci.
PY 2011
VL 7
BP 17
EP 40
DI 10.1146/annurev-lawsocsci-102510-105423
PG 24
WC Law; Sociology
WE Book Citation Index – Social Sciences & Humanities (BKCI-SSH); Social Science Citation Index (SSCI)
SC Government & Law; Sociology
GA BYL92
UT WOS:000299297100002
DA 2024-09-05
ER
PT J
AU Zheng, C
   Zhang, Q
   Long, GD
   Zhang, CQ
   Young, SD
   Wang, W
AF Zheng, Cheng
   Zhang, Qin
   Long, Guodong
   Zhang, Chengqi
   Young, Sean D.
   Wang, Wei
TI Measuring Time-Sensitive and Topic-Specific Influence in Social Networks
   With LSTM and Self-Attention
SO IEEE ACCESS
LA English
DT Article
DE Social influence; time-sensitive; topic-specific; LSTM; self-attention
AB Influence measurement in social networks is vital to various real-world applications, such as online marketing and political campaigns. In this paper, we investigate the problem of measuring time-sensitive and topic-specific influence based on streaming texts and dynamic social networks. A user & x2019;s influence can change rapidly in response to a new event and vary on different topics. For example, the political influence of Douglas Jones increased dramatically after winning the Alabama special election, and then rapidly decreased after the election week. During the same period, however, Douglas Jones & x2019; influence on sports remained low. Most existing approaches can only model the influence based on static social network structures and topic distributions. Furthermore, as popular social networking services embody many features to connect their users, multi-typed interactions make it hard to learn the roles that different interactions play when propagating information. To address these challenges, we propose a Time-sensitive and Topic-specific Influence Measurement (TTIM) method, to jointly model the streaming texts and dynamic social networks. We simulate the influence propagation process with a self-attention mechanism to learn the contributions of different interactions and track the influence dynamics with a matrix-adaptive long short-term memory. To the best of our knowledge, this is the first attempt to measure time-sensitive and topic-specific influence. Furthermore, the TTIM model can be easily adapted to supporting online learning which consumes constant training time on newly arrived data for each timestamp. We comprehensively evaluate the proposed TTIM model on five datasets from Twitter and Reddit. The experimental results demonstrate promising performance compared to the state-of-the-art social influence analysis models and the potential of TTIM in visualizing influence dynamics and topic distribution.
C1 [Zheng, Cheng; Wang, Wei] Univ Calif Los Angeles, Dept Comp Sci, Los Angeles, CA 90095 USA.
   [Zhang, Qin; Long, Guodong; Zhang, Chengqi] Univ Technol Sydney, Ctr Artificial Intelligence, Fac Engn & IT FEIT, Ultimo, NSW 2007, Australia.
   [Young, Sean D.] Univ Calif Irvine, Irvine, CA 92697 USA.
C3 University of California System; University of California Los Angeles;
   University of Technology Sydney; University of California System;
   University of California Irvine
RP Wang, W (corresponding author), Univ Calif Los Angeles, Dept Comp Sci, Los Angeles, CA 90095 USA.
EM weiwang@cs.ucla.edu
RI zhang, chao/IXD-9965-2023; zhang, chao/HTO-2468-2023; Zhang,
   Cheng/JAD-2236-2023
OI Wang, Wei/0000-0002-8180-2886; Zhang, Chengqi/0000-0001-5715-7154
FU National Institutes of Health [U01HG008488]; National Institute of
   Allergy and Infectious Diseases [R56AI125105, 7R01AI132030]; National
   Institute of Mental Health [5R01MH106415]
FX The work was supported in part by the National Institutes of Health
   under Grant U01HG008488, in part by the National Institute of Allergy
   and Infectious Diseases under Grant R56AI125105 and Grant 7R01AI132030,
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NR 43
TC 15
Z9 16
U1 1
U2 8
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2020
VL 8
BP 82481
EP 82492
DI 10.1109/ACCESS.2020.2991683
PG 12
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA ML5JL
UT WOS:000549502200065
PM 32577335
OA Green Submitted, Green Accepted, gold
DA 2024-09-05
ER
PT C
AU Amato, F
   Di Cicco, F
   Fonisto, M
   Giacalone, M
AF Amato, Flora
   Di Cicco, Francesco
   Fonisto, Mattia
   Giacalone, Marco
BE Barolli, L
   Hussain, F
   Enokido, T
TI A Survey on Neural Recommender Systems: Insights from a Bibliographic
   Analysis
SO ADVANCED INFORMATION NETWORKING AND APPLICATIONS, AINA-2022, VOL 3
SE Lecture Notes in Networks and Systems
LA English
DT Proceedings Paper
CT 36th International Conference on Advanced Information Networking and
   Applications (AINA)
CY APR 13-15, 2022
CL Sydney, AUSTRALIA
DE Recommender systems; Deep learning; Research trend analysis
AB In recent years, deep learning has gotten a lot of attention, notably in fields like Computer Vision and Natural Language Processing. With the growing amount of online information, recommender systems have shown to be an effective technique for coping with information overload. The purpose of this article is to provide a comprehensive overview of recent deep learning-based recommender systems. Furthermore, it provides an experimental assessment of prominent topics within the latest published papers in the field. Results showed that explainable AI and Graph Neural Networks are two of the most attractive topics in the field to this day, and that the adoption of deep learning methods is increasing over.
C1 [Amato, Flora; Fonisto, Mattia] Univ Naples Federico II, I-80125 Naples, Italy.
   [Di Cicco, Francesco] Univ Turin, I-10124 Turin, Italy.
   [Giacalone, Marco] Vrije Univ Brussel, B-1050 Brussels, Belgium.
C3 University of Naples Federico II; University of Turin; Vrije
   Universiteit Brussel
RP Fonisto, M (corresponding author), Univ Naples Federico II, I-80125 Naples, Italy.
EM flora.amato@unina.it; francesco.dicicco@unito.it;
   mattia.fonisto@unina.it; marco.giacalone@vub.be
RI Giacalone, Marco/IAN-6488-2023; Amato, Flora/N-1408-2016
OI Giacalone, Marco/0000-0001-7097-4394; Amato, Flora/0000-0002-5128-5558
FU European Union [101046629]
FX This paper has been produced with the financial support of the Justice
   Programme of the European Union, 101046629 CREA2, JUST-2021-EJUSTICE,
   JUST2027 Programme. The contents of this report are the sole
   responsibility of the authors and can in no way be taken to reflect the
   views of the European Commission.
NR 0
TC 0
Z9 0
U1 3
U2 4
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 2367-3370
EI 2367-3389
BN 978-3-030-99619-2; 978-3-030-99618-5
J9 LECT NOTE NETW SYST
PY 2022
VL 451
BP 104
EP 114
DI 10.1007/978-3-030-99619-2_10
PG 11
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods; Telecommunications
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Telecommunications
GA BU6OK
UT WOS:000926702400010
DA 2024-09-05
ER
PT C
AU Boukhers, Z
   Mayr, P
   Peroni, S
AF Boukhers, Zeyd
   Mayr, Philipp
   Peroni, Silvio
GP ASSOC COMP MACHINERY
TI BiblioDAP'21: The 1st Workshop on Bibliographic Data Analysis and
   Processing
SO KDD '21: PROCEEDINGS OF THE 27TH ACM SIGKDD CONFERENCE ON KNOWLEDGE
   DISCOVERY & DATA MINING
LA English
DT Proceedings Paper
CT 27th ACM SIGKDD International Conference on Knowledge Discovery and Data
   Mining (KDD)
CY AUG 14-18, 2021
CL ELECTR NETWORK
DE Bibliographic data; Digital libraries; Machine Learning; Data Science
AB Automatic processing of bibliographic data becomes very important in digital libraries, data science and machine learning due to its importance in keeping pace with the significant increase of published papers every year from one side and to the inherent challenges from the other side. This processing has several aspects including but not limited to I) Automatic extraction of references from PDF documents, II) Building an accurate citation graph, III) Author name disambiguation, etc. Bibliographic data is heterogeneous by nature and occurs in both structured (e.g. citation graph) and unstructured (e.g. publications) formats. Therefore, it requires data science and machine learning techniques to be processed and analysed. Here we introduce BiblioDAP'21: The 1st Workshop on Bibliographic Data Analysis and Processing.
C1 [Boukhers, Zeyd] Univ Koblenz Landau, Inst Web Sci & Technol, Koblenz, Germany.
   [Mayr, Philipp] GESIS Leibniz Inst Social Sci, Cologne, Germany.
   [Peroni, Silvio] Univ Bologna, Dept Class Philol & Italian Studies, Res Ctr Open Scholarly Metadata, Bologna, Italy.
C3 University of Koblenz & Landau; Leibniz Institut fur
   Sozialwissenschaften (GESIS); University of Bologna
RP Boukhers, Z (corresponding author), Univ Koblenz Landau, Inst Web Sci & Technol, Koblenz, Germany.
EM boukhers@uni-koblenz.de; philipp.mayr@gesis.org; silvio.peroni@unibo.it
RI Boukhers, Zeyd/HZL-0733-2023
OI Boukhers, Zeyd/0000-0001-9778-9164
CR Boukhers Zeyd, 2021, 2021 ACM IEEE JOINT, P1
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NR 8
TC 0
Z9 0
U1 0
U2 1
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES
BN 978-1-4503-8332-5
PY 2021
BP 4110
EP 4111
DI 10.1145/3447548.3469482
PG 2
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BS6LU
UT WOS:000749556804051
OA Green Submitted, Green Published
DA 2024-09-05
ER
PT J
AU Wu, H
   Gu, XM
   Zhao, YJ
   Liu, W
AF Wu, Hui
   Gu, Xiaomin
   Zhao, Yuanjun
   Liu, Wei
TI Research on the Relationship between Structural Hole Location, Knowledge
   Management and Cooperative Innovation Performance in Artificial
   Intelligence
SO KNOWLEDGE MANAGEMENT RESEARCH & PRACTICE
LA English
DT Article; Early Access
DE Structure hole location; collaborative innovation performance; knowledge
   management; artificial intelligence
ID SOCIAL NETWORK; DECISION
AB This paper builds a conceptual model of the relationship between structural hole location, knowledge management, and collaborative innovation performance in the artificial intelligence environment. A questionnaire survey was conducted on 267 companies, and sample data were analysed using SEM statistics. In the artificial intelligence environment, under the influence of the embedded relationship of the collaborative innovation network structure, the influence of network structure hole position on the performance of collaborative innovation is discussed. The research results show that the location of network structure holes has a direct impact on the performance of collaborative innovation; At the same time, the location of network structure holes has a significant positive impact on knowledge management, and knowledge management plays a part of the mediating role in the process of network structure hole locations affecting collaborative innovation performance.
C1 [Wu, Hui; Gu, Xiaomin] Shanghai Lixin Univ Accounting & Finance, Sch Financial Technol, Shanghai, Peoples R China.
   [Zhao, Yuanjun] Shanghai Lixin Univ Accounting & Finance, Sch Business Adm, Shanghai, Peoples R China.
   [Liu, Wei] Qingdao Univ, Business Sch, Qingdao, Peoples R China.
C3 Shanghai Lixin University of Accounting & Finance; Shanghai Lixin
   University of Accounting & Finance; Qingdao University
RP Gu, XM (corresponding author), 2800 Wenxiang Rd, Shanghai 201620, Peoples R China.
EM guxiaomin126@163.com
RI Liu, Wei/ABB-7317-2021
FU National Social Science Fund of China [18CGL015]
FX This work was supported by the National Social Science Fund of China
   [(Grant No. 18CGL015)].
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NR 18
TC 11
Z9 11
U1 17
U2 170
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 1477-8238
EI 1477-8246
J9 KNOWL MAN RES PRACT
JI Knowl. Manag. Res. Pract.
PD 2020 SEP 3
PY 2020
DI 10.1080/14778238.2020.1813642
EA SEP 2020
PG 10
WC Information Science & Library Science; Management
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science; Business & Economics
GA NH9YD
UT WOS:000565015600001
DA 2024-09-05
ER
PT C
AU Wen, Z
   Rui, W
AF Wen, Zhang
   Rui, Wang
BE Deng, MG
   Ye, JM
   Kaminishi, K
   Duysters, G
   DeHoyos, A
TI Research on the Multilevel Indicator Evaluation Model of Online Learning
   Based on Fuzzy Set
SO PROCEEDINGS OF THE 7TH INTERNATIONAL CONFERENCE ON INNOVATION AND
   MANAGEMENT, VOLS I AND II
LA English
DT Proceedings Paper
CT 7th International Conference on Innovation and Management
CY DEC 04-05, 2010
CL Wuhan Univ Technol, Wuhan, PEOPLES R CHINA
HO Wuhan Univ Technol
DE Entropy; Online learning; Weight; Fuzzy Comprehensive evaluation
AB Learning evaluation is an effective way to ensure the quality of online education. However, most of online learning evaluation models are not fully consider the characteristics of online learning. This paper, from the view of the characteristics of online learning, establishes an index system of evaluation of the online learning's effect. Besides, weight of every factor affecting online learning is calculated by using entropy method. At the same time, a model based on entropy weight of the fuzzy comprehensive evaluation is established and its calculation is given out. At last, the learning effects of a learner are evaluated and its result is analyzed by this model.
C1 [Wen, Zhang] Gannan Normal Univ, Off Recruitment & Employment, Ganzhou 341000, Peoples R China.
   [Rui, Wang] Gannan Normal Univ, Sch Business, Ganzhou 341000, Peoples R China.
C3 Gannan Normal University; Gannan Normal University
RP Wen, Z (corresponding author), Gannan Normal Univ, Off Recruitment & Employment, Ganzhou 341000, Peoples R China.
EM djwhsdlk1982@126.com; 80772557@qq.com
RI ruirui, WANG/KCY-0880-2024
CR BUCKLEY JJ, 1995, FUZZY SET SYSTEMS, P233
   Hrieko Mary, 2005, ONLINE ASSESSMENT ME, V10, P299
   Qi Huan, 2004, SYSTEM MODELING SIMU, P28
NR 3
TC 0
Z9 0
U1 0
U2 2
PU WUHAN UNIV TECHNOLOGY PRESS
PI WUHAN
PA 122 LUOSHI RD, WUHAN 430070, PEOPLES R CHINA
BN 978-7-5629-3370-0
PY 2010
BP 1456
EP +
PG 2
WC Business; Computer Science, Information Systems; Management; Operations
   Research & Management Science
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Business & Economics; Computer Science; Operations Research & Management
   Science
GA BUX78
UT WOS:000290638901026
DA 2024-09-05
ER
PT J
AU Montague-Hellen, B
AF Montague-Hellen, Beth
TI Empowering knowledge through AI: open scholarship proactively supporting
   well trained generative AI
SO INSIGHTS-THE UKSG JOURNAL
LA English
DT Article
DE AI; licensing; Creative Commons; open access; ChatGPT; copyright; BETH
   MONTAGUE
AB Generative AI has taken the world by storm over the last few years, and the world of scholarly communications has not been immune to this. Most discussions in this area address how we can integrate these tools into our workflows, concerns about how researchers and students might misuse the technology or the unauthorised use of copyrighted work. This article argues for a novel viewpoint that librarians and publishers should be encouraging the use of their scholarly content in the training of AI algorithms. Inclusion of scholarly works would advance the reliability and accuracy of the information in training datasets and ensure that this content is included in new knowledge discovery platforms. The article also argues that inclusion can be achieved by improving linkage to content, and, by making sure that licences explicitly allow inclusion in AI training datasets, it advocates for a more collaborative approach to shaping the future of the information landscape in academia.
C1 [Montague-Hellen, Beth] Francis Crick Inst, Lib & Informat Serv, London, England.
C3 Francis Crick Institute
RP Montague-Hellen, B (corresponding author), Francis Crick Inst, Lib & Informat Serv, London, England.
EM beth.montague-hellen@crick.ac.uk
OI Montague-Hellen, Beth/0000-0003-0946-1842
FU Francis Crick Institute - Cancer Research UK [CC0103]; UK Medical
   Research Council [CC0103]; Wellcome Trust [CC0103]
FX This work was supported by the Francis Crick Institute which receives
   its core funding from Cancer Research UK (CC0103) , the UK Medical
   Research Council (CC0103) and the Wellcome Trust (CC0103) .
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NR 62
TC 0
Z9 0
U1 2
U2 2
PU UBIQUITY PRESS LTD
PI LONDON
PA Unit 3N, 6 Osborn Street, LONDON, E1 6TD, ENGLAND
SN 2048-7754
J9 INSIGHTS
JI Insights
PD JUN 18
PY 2024
VL 37
BP 1
EP 9
DI 10.1629/uksg.649
PG 9
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA XA1C5
UT WOS:001258858200001
OA gold
DA 2024-09-05
ER
PT J
AU Javed, S
   Adewumi, TP
   Liwicki, FS
   Liwicki, M
AF Javed, Saleha
   Adewumi, Tosin P.
   Liwicki, Foteini Simistira
   Liwicki, Marcus
TI Understanding the Role of Objectivity in Machine Learning and Research
   Evaluation
SO PHILOSOPHIES
LA English
DT Article
DE objectivity; machine learning; ethics; naturalism; philosophy of science
AB This article makes the case for more objectivity in Machine Learning (ML) research. Any research work that claims to hold benefits has to be scrutinized based on many parameters, such as the methodology employed, ethical considerations and its theoretical or technical contribution. We approach this discussion from a Naturalist philosophical outlook. Although every analysis may be subjective, it is important for the research community to keep vetting the research for continuous growth and to produce even better work. We suggest standardizing some of the steps in ML research in an objective way and being aware of various biases threatening objectivity. The ideal of objectivity keeps research rational since objectivity requires beliefs to be based on facts. We discuss some of the current challenges, the role of objectivity in the two elements (product and process) that are up for consideration in ML and make recommendations to support the research community.
C1 [Javed, Saleha; Adewumi, Tosin P.; Liwicki, Foteini Simistira; Liwicki, Marcus] Lulea Univ Technol, EISLAB, Dept Comp Sci Elect & Space Engn, Machine Learning Grp, S-97187 Lulea, Sweden.
C3 Lulea University of Technology
RP Javed, S (corresponding author), Lulea Univ Technol, EISLAB, Dept Comp Sci Elect & Space Engn, Machine Learning Grp, S-97187 Lulea, Sweden.
EM saleha.javed@ltu.se; oluwatosin.adewumi@ltu.se; foteini.liwicki@ltu.se;
   marcus.liwicki@ltu.se
RI Javed, saleha/GWZ-8529-2022; Liwicki, Marcus/D-5572-2014
OI Javed, saleha/0000-0002-2123-8187; Adewumi, Tosin/0000-0002-5582-2031;
   Liwicki, Marcus/0000-0003-4029-6574
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NR 46
TC 6
Z9 6
U1 0
U2 7
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2409-9287
J9 PHILOSOPHIES
JI Philosophies
PD MAR
PY 2021
VL 6
IS 1
AR 22
DI 10.3390/philosophies6010022
PG 8
WC History & Philosophy Of Science; Philosophy
WE Emerging Sources Citation Index (ESCI)
SC History & Philosophy of Science; Philosophy
GA RG6WP
UT WOS:000635676500001
OA gold
DA 2024-09-05
ER
PT J
AU van de Laar, M
   West, RE
   Cosma, P
   Katwal, D
   Mancigotti, C
AF van de Laar, Mindel
   West, Richard E.
   Cosma, Paris
   Katwal, Dennis
   Mancigotti, Cristina
TI The value of educational microcredentials in open access online
   education: a doctoral education case
SO OPEN LEARNING
LA English
DT Article; Early Access
DE Educational open microcredentials; online learning; Higher Education;
   open access; Sub-Saharan Africa; doctoral education
ID PERFORMANCE
AB This research explores the effect of implementing educational open microcredentials on student motivation, engagement, and completion in open access online courses. Our case study reviews the impact on the Community of Learning for African PhD Fellows, a capacity-building project supporting PhD fellows in Sub-Saharan Africa. It builds on an analysis of data from learning analytics, surveys, and semi-structured interviews. Our case study findings indicate that course completion was low, in course offering rounds with and without online certification. Main hurdles to completion are lack of time and lack of direct career benefits or academic value attached to the course completion. We found that, while open access online courses are appreciated by PhD fellows, the implementation of open microcredentials did not provide an incentive towards completion of online courses for this population. Hard and soft copy certificates at this point are more appreciated.
C1 [van de Laar, Mindel; Cosma, Paris; Katwal, Dennis; Mancigotti, Cristina] Maastricht Univ, UNU MERIT, Boschstr 24, NL-6211 AX Maastricht, Netherlands.
   [West, Richard E.] Brigham Young Univ, McKay Sch Educ, Provo, UT 84602 USA.
C3 Maastricht University; Brigham Young University
RP van de Laar, M (corresponding author), Maastricht Univ, UNU MERIT, Boschstr 24, NL-6211 AX Maastricht, Netherlands.
EM mindel.vandelaar@maastrichtuniversity.nl
OI West, Richard/0000-0002-1417-0823; van de Laar,
   Mindel/0000-0003-4028-9630; Mancigotti, Cristina/0000-0002-6222-6027
FU Sustainablity Theme Group of the School of Business and Economics,
   Maastricht University
FX This work was supported by The Sustainablity Theme Group of the School
   of Business and Economics, Maastricht University, which supported the
   educational round offered to the African Doctoral students in Fall 2019
   financially.
CR African Union, 2020, POL GUID DIG TEACH L
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NR 21
TC 0
Z9 1
U1 3
U2 13
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0268-0513
EI 1469-9958
J9 OPEN LEARN
JI Open Learn.
PD 2022 MAY 11
PY 2022
DI 10.1080/02680513.2022.2072721
EA MAY 2022
PG 14
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA 1C3VR
UT WOS:000793051400001
OA hybrid
DA 2024-09-05
ER
PT J
AU Liu, H
AF Liu, Huan
TI Research on Performance Prediction of Technological Innovation
   Enterprises Based on Deep Learning
SO WIRELESS COMMUNICATIONS & MOBILE COMPUTING
LA English
DT Article
ID SYSTEM; NETWORK
AB High-tech enterprises are the leaders in promoting economic development. The study of the relationship between their scientific and technological innovation capabilities and corporate performance is of far-reaching practical significance for guiding companies to formulate independent innovation strategies scientifically, improving their independent innovation capabilities, and promoting further transformation into an innovative country. In view of the large-scale technological innovation enterprise network, the traditional technological innovation enterprise performance prediction method cannot fully reflect the real-time technological innovation enterprise status. Aiming at the deficiencies of the existing short-term technology innovation enterprise forecasting methods, this paper proposes a technology innovation enterprise performance forecasting method based on deep learning. I analyze the temporal and spatial characteristics of the data of technological innovation enterprises and divide the data according to the temporal characteristics of technological innovation enterprises. According to the spatial relevance of technological innovation enterprises, grouping is carried out by setting different correlation coefficient thresholds. The method of spectral decomposition is used to divide the data of scientific and technological innovation enterprises into trend items and random fluctuation items, to decompose the matrix of scientific and technological innovation enterprises, and to construct a compressed matrix using correlation. Using the deep belief network model in deep learning combined with support vector regression to establish a prediction model for technological innovation enterprises, this paper proposes a convolutional neural network model for performance prediction of scientific and technological innovation enterprises. Through the convolution operation and subsampling operation based on the concept of local window, the feature learning from the local to the whole is completed. This article uses the Naive Bayes model, logistic regression model, support vector regression model, and other mainstream methods to predict and compare the performance of technological innovation enterprises. I use the dropout method to reduce the impact of overfitting during training. The experimental results show that the deep neural network model method used in this article can achieve better prediction results than mainstream methods under the same characteristics. The experimental results on the data set confirm that the method of performance prediction of technology innovation enterprises based on deep learning used in this paper can effectively improve the results of performance prediction of technology innovation enterprises.
C1 [Liu, Huan] Northwest Univ, Sch Econ & Management, Xian 710000, Shaanxi, Peoples R China.
C3 Northwest University Xi'an
RP Liu, H (corresponding author), Northwest Univ, Sch Econ & Management, Xian 710000, Shaanxi, Peoples R China.
EM jiuyue010318@163.com
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NR 30
TC 1
Z9 1
U1 2
U2 30
PU WILEY-HINDAWI
PI LONDON
PA ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON, WIT 5HE, ENGLAND
SN 1530-8669
EI 1530-8677
J9 WIREL COMMUN MOB COM
JI Wirel. Commun. Mob. Comput.
PD SEP 25
PY 2021
VL 2021
AR 1682163
DI 10.1155/2021/1682163
PG 12
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA WB1IF
UT WOS:000703332200004
OA gold
DA 2024-09-05
ER
PT J
AU Yang, YQ
   Chen, LBY
   He, WM
   Sun, DN
   Salas-Pilco, SZ
AF Yang, Yuqin
   Chen, Linbaiyu
   He, Wenmeng
   Sun, Daner
   Salas-Pilco, Sdenka Zobeida
TI Artificial Intelligence for Enhancing Special Education for K-12: A
   Decade of Trends, Themes, and Global Insights (2013-2023)
SO INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION
LA English
DT Article; Early Access
DE Artificial intelligence (AI); Special education; Special needs students;
   Bibliometric analysis
ID ASSISTIVE TECHNOLOGIES; LEARNING-DISABILITIES; BIBLIOMETRIC ANALYSIS;
   AMBIENT INTELLIGENCE; MOBILE TECHNOLOGY; AUGMENTED REALITY;
   SCHOOL-STUDENTS; CHILDREN; DESIGN; NEEDS
AB This paper provided a review of 210 studies on AI-enhanced special education from 2013 to 2023. Through bibliometric analysis, this review aimed to explore trends, focus areas, developments, and evolving themes of the field of AI for enhancing special education. Several noteworthy findings emerged from our analysis. The trend analysis of publications and citations revealed distinct phases, including an initial exploratory phase (2013-2016) followed by a period of rapid development (2017-2023). keyword co-occurrence networks and emergent word mapping highlight AI's transformative potential, especially in autism spectrum disorder interventions and advancements in learning environments. Emerging trends focus on mathematics learning outcomes and educational equity, evolving through phases of understanding AI's support and integrating advanced tools like virtual reality and educational robots. Topic clustering analysis revealed categories including cognitive rehabilitation and ethical AI integration, emphasizing personalized instructional environments. Implications for research stress the need to bolster foundational skills and explore innovative teaching methods, including addressing challenges in gamified learning and integrating AI seamlessly. The review reveals a need for larger sample sizes and longitudinal studies to enhance statistical robustness and real-world relevance. In educational practices, using AI tools like apps, robots, and simulations can boost engagement and support social and academic progress. Tailored interventions for specific learning difficulties, such as dyslexia and dyscalculia, through intelligent tutoring systems, offer promise for positive learning outcomes. Policymakers are crucial in facilitating technology integration by ensuring comprehensive teacher training, increased funding for tech infrastructure, and strong leadership. Initiatives targeting underserved communities aim to promote equity and access to transformative resources. This study highlights AI's transformative potential in special education, advocating for inclusive and personalized learning environments with ethical Al solutions to address unique challenges faced by special needs students.
C1 [Chen, Linbaiyu; Salas-Pilco, Sdenka Zobeida] Cent China Normal Univ, Fac Artificial Intelligence Educ, 152 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.
   [He, Wenmeng] Wuhan Univ Technol, Wuhan, Hubei, Peoples R China.
   [Sun, Daner] Educ Univ Hong Kong, Dept Math & Informat Technol, Hong Kong, Peoples R China.
   [Yang, Yuqin] Cent China Normal Univ, Fac Artificial Intelligence Educ, Hubei Key Lab Digital Educ, Wuhan, Peoples R China.
C3 Central China Normal University; Wuhan University of Technology;
   Education University of Hong Kong (EdUHK); Central China Normal
   University
RP Salas-Pilco, SZ (corresponding author), Cent China Normal Univ, Fac Artificial Intelligence Educ, 152 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.; Yang, YQ (corresponding author), Cent China Normal Univ, Fac Artificial Intelligence Educ, Hubei Key Lab Digital Educ, Wuhan, Peoples R China.
EM yangyuqin@ccnu.edu.cn; chenlinbaiyu@mails.ccnu.edu.cn;
   hwm20041016@whut.edu.cn; dsun@eduhk.hk; sdenkasp@ccnu.edu.cn
OI Sun, Daner/0000-0002-9813-6306
FU National Natural Science Foundation of China
FX No Statement AvailableDAS:The data of this study are available upon
   reasonable request from the corresponding author.
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NR 143
TC 0
Z9 0
U1 3
U2 3
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1560-4292
EI 1560-4306
J9 INT J ARTIF INTELL E
JI Int. J. Artif. Intell. Educ.
PD 2024 AUG 19
PY 2024
DI 10.1007/s40593-024-00422-0
EA AUG 2024
PG 49
WC Computer Science, Interdisciplinary Applications
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA D0V9T
UT WOS:001293460100001
DA 2024-09-05
ER
PT J
AU Mubin, O
   Alnajjar, F
   Trabelsi, Z
   Ali, L
   Parambil, MMA
   Zou, Z
AF Mubin, Omar
   Alnajjar, Fady
   Trabelsi, Zouheir
   Ali, Luqman
   Parambil, Medha Mohan Ambali
   Zou, Zhao
TI Tracking ChatGPT Research: Insights From the Literature and the Web
SO IEEE ACCESS
LA English
DT Article
DE Chatbots; Bibliometrics; Education; Artificial intelligence; Task
   analysis; Market research; Search engines; Natural language processing;
   Open Access; ChatGPT; artificial intelligence; natural language
   processing; NLM
ID ARTIFICIAL-INTELLIGENCE; SCIENCE
AB This article presents a scientometric and literature analysis of current research on ChatGPT, a conversational AI technology developed by OpenAI. Using various databases, 103 relevant articles were retrieved and analyzed through scientometric, quantitative, and application-based approaches. A Google trend analysis and comparison with other generative AI and chatbot technologies were also carried out. The study provides insights into the distribution of ChatGPT publications across different countries and regions, the network of co-occurring keywords, authorship analysis, article typology, and publishing entities. The findings offer a comprehensive overview of the current state of ChatGPT research, highlighting key directions for future research. The study finds that ChatGPT has gained significant attention and interest in online platforms, particularly in technology, education, and healthcare, and highlights potential ethical and legal concerns related to its use. Its applications extend to several literary and text generation areas. We do note that the sample of extracted publications is lower than anticipated due to the niche area of investigation. The article is relevant to researchers, practitioners, and policymakers interested in the field of AI-powered language models, especially ChatGPT.
C1 [Mubin, Omar; Zou, Zhao] Western Sydney Univ, Sch Comp Data & Math Sci, Sydney, NSW 2751, Australia.
   [Alnajjar, Fady; Trabelsi, Zouheir; Ali, Luqman; Parambil, Medha Mohan Ambali] United Arab Emirates Univ UAEU, Coll IT, Al Ain, U Arab Emirates.
C3 Western Sydney University
RP Alnajjar, F (corresponding author), United Arab Emirates Univ UAEU, Coll IT, Al Ain, U Arab Emirates.
EM fady.alnajjar@uaeu.ac.ae
RI ALI, LUQMAN/LDF-2311-2024; Alnajjar, Fady/GRX-4246-2022
OI Alnajjar, Fady/0000-0001-6102-3765; Zou, Zhao/0000-0002-2867-7246; ,
   Medha Mohan Ambali Parambil/0000-0002-9336-2902
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NR 75
TC 0
Z9 0
U1 19
U2 19
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2024
VL 12
BP 30518
EP 30532
DI 10.1109/ACCESS.2024.3356584
PG 15
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA JW4V1
UT WOS:001176194200001
OA gold
DA 2024-09-05
ER
PT J
AU Tomic, BB
   Kijevcanin, AD
   Sevarac, ZV
   Jovanovic, JM
AF Tomic, Bojan B.
   Kijevcanin, Anisja D.
   Sevarac, Zoran, V
   Jovanovic, Jelena M.
TI An AI-based Approach for Grading Students' Collaboration
SO IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES
LA English
DT Article
DE Collaboration; Codes; Software engineering; Measurement; Teamwork;
   Market research; Electronic mail; Automatic assessment tools;
   collaboration; computer science education; fuzzy systems; machine
   learning (ML); soft skills
ID SOFT SKILLS
AB Soft skills (such as communication and collaboration) are rarely addressed in programming courses, mostly because they are difficult to teach, assess, and grade. A quantitative, modular, AI-based approach for assessing and grading students' collaboration has been examined in this article. The pedagogical underpinning of the approach includes a pedagogical framework and a quantitative soft skill assessment rubric, which have been adapted and used in an extracurricular Java programming course. The objective was to identify pros and cons of using different AI methods within this approach when it comes to assessing and grading collaboration in group programming projects. More specifically, fuzzy rules and several machine learning methods (ML onward) have been examined to see which one would yield the best results regarding performance, interpretability/explainability of recommendations, and feasibility/practicality. The data used for training and testing span four academic years, and the results suggest that almost all of the examined AI methods, when used within the proposed AI-based approach, can provide adequate grading recommendations as long as teachers cover other aspects of the assessment not covered by the rubrics: code quality, plagiarism, and project completion. The fuzzy-rule-based method requires time and effort to be spent on (manual) creation and tuning of fuzzy rules and sets, whereas the examined ML methods require lesser initial investments but do need historical data for training. On the other hand, the fuzzy-rule-based method can provide the best explanations on how the assessment/grading was made-something that proved to be very important to teachers.
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C3 University of Belgrade
RP Tomic, BB (corresponding author), Univ Belgrade, Fac Org Sci, Dept Software Engn, Belgrade 11000, Serbia.
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SN 1939-1382
J9 IEEE T LEARN TECHNOL
JI IEEE Trans. Learn. Technol.
PD JUN 1
PY 2023
VL 16
IS 3
BP 292
EP 305
DI 10.1109/TLT.2022.3225432
PG 14
WC Computer Science, Interdisciplinary Applications; Education &
   Educational Research
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Education & Educational Research
GA J9IH3
UT WOS:001012684000001
DA 2024-09-05
ER
PT J
AU Wang, KS
AF Wang, Kuansan
TI Opportunities in Open Science With AI
SO FRONTIERS IN BIG DATA
LA English
DT Article
DE open science; big data; microsoft academic graph; artificial
   intelligence; research assessment
AB Bolstered by ever affordable computational power and open big datasets, artificial intelligence (AI) technologies are bringing revolutionary changes to our lives. This article examines the current trends and elaborates the future potentials of AI in its role for making science more open and accessible. Based on the experience derived from a research project called Microsoft Academic, the advocates have reasons to be optimistic about the future of open science as the advanced discovery, ranking, and distribution technologies enabled by AI are offering strong incentives for scientists, funders and research managers to make research articles, data and software freely available and accessible.
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C3 Microsoft
RP Wang, KS (corresponding author), Microsoft Res, Redmond, WA 98052 USA.
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U2 21
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 2624-909X
J9 FRONT BIG DATA
JI Front. Big Data
PD SEP 27
PY 2019
VL 2
AR 26
DI 10.3389/fdata.2019.00026
PG 4
WC Computer Science, Information Systems; Computer Science,
   Interdisciplinary Applications; Multidisciplinary Sciences
WE Emerging Sources Citation Index (ESCI)
SC Computer Science; Science & Technology - Other Topics
GA TW9KI
UT WOS:000682708800001
PM 33693349
OA Green Published, gold
DA 2024-09-05
ER
PT J
AU Oosthuizen, R
   Pretorius, L
AF Oosthuizen, Rudolph
   Pretorius, Leon
TI Analysis of INCOSE Systems Engineering journal and international
   symposium research topics
SO SYSTEMS ENGINEERING
LA English
DT Article
DE bibliometrics; natural language processing; research; systems
   engineering; topic modeling
ID TECHNOLOGY; SCIENCE
AB The pressure on systems engineering is ever-increasing to support the development and implementation of systems that meet a complex environment's demands. As a growing discipline, systems engineering requires insight into past research to identify opportunities for future growth. Analyzing the bibliometric data on published research provides valuable information on a scientific discipline's past progress and future prospects. Therefore, this paper extracts the research topics published in INCOSE's journal Systems Engineering and the annual international symposium proceedings to analyze their composition and allocation to papers. The implemented process applies natural language processing and topic modeling to extract the main topics from these papers' titles and abstracts. Analyzing these research topics' composition and mapping them to processed articles helps to understand their relative importance. The analysis's output confirms the importance of modeling in systems engineering, as it is the most popular topic. The additional focus of research papers on the systems engineering process, practice, and methodologies also indicates that the field is still growing and evolving. Some important topics to systems engineering, which were not found as prominent topics, are humans' roles in systems, verification and validation, and other specialty fields. This new knowledge about the structure of research into systems engineering can identify future research project opportunities to continue growing the field.
C1 [Oosthuizen, Rudolph] CSIR, Def & Safety, Pretoria, South Africa.
   [Oosthuizen, Rudolph; Pretorius, Leon] Univ Pretoria, Grad Sch Technol Management, Pretoria, South Africa.
C3 Council for Scientific & Industrial Research (CSIR) - South Africa;
   University of Pretoria
RP Oosthuizen, R (corresponding author), Univ Pretoria, Grad Sch Technol Management, Pretoria, South Africa.
EM rudolph.oosthuizen@up.ac.za
RI Pretorius, Leon/M-7573-2017; Oosthuizen, Rudolph/AAH-9253-2021
OI Pretorius, Leon/0000-0002-2842-3596; Oosthuizen,
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NR 41
TC 1
Z9 3
U1 3
U2 15
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1098-1241
EI 1520-6858
J9 SYSTEMS ENG
JI Syst. Eng.
PD JUL
PY 2021
VL 24
IS 4
BP 203
EP 220
DI 10.1002/sys.21575
EA MAR 2021
PG 18
WC Engineering, Industrial; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Engineering; Operations Research & Management Science
GA TH9HD
UT WOS:000635351900001
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Zhou, YZ
   Lin, ZP
   Tu, L
   Shi, JH
   Yang, YL
AF Zhou, Yuzhong
   Lin, Zhengping
   Tu, Liang
   Shi, Jiahao
   Yang, Yuliang
TI Research on the Performance of Text Mining and Processing in Power Grid
   Networks
SO EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS
LA English
DT Article
DE Text mining; performance analysis; deep learning
ID TRANSMISSION
AB This paper employs deep learning technique to perform the research of text mining for power grid networks, focusing on fundamental elements such as loss and activation functions. Through some analysis and formulas, we explain how these functions contribute to deep learning. We also introduce major deep learning training models, including CNN and RNN, and provide visual aids to aid understanding. To demonstrate the impact of various factors on deep learning training, we employ control variable experiments to analyze the influence of factors such as learning rate, batch size, and data noise on model training trends. While the influence of hyperparameters and data noise are covered in this paper, other factors such as CPU and memory frequency, as well as GPU performance, also play a crucial role in deep learning training. Therefore, continuous adjustments to various factors are necessary to achieve optimal training results for deep learning models in power grid networks.
C1 [Zhou, Yuzhong; Lin, Zhengping; Tu, Liang; Shi, Jiahao; Yang, Yuliang] Res Inst China Southern Power Grid, Guangzhou, Peoples R China.
RP Zhou, YZ (corresponding author), Res Inst China Southern Power Grid, Guangzhou, Peoples R China.
EM yuzhong_zhou@hotmail.com
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NR 19
TC 0
Z9 0
U1 2
U2 2
PU INST COMPUTER SCIENCES, SOCIAL INFORMATICS & TELECOMMUNICATIONS ENG-ICST
PI GHENT
PA BEGIJNHOFLAAN 93, GHENT, 90000, BELGIUM
SN 2032-9407
J9 EAI ENDORSED TRANS S
JI EAI Endorsed Trans. Scalable Inform. Syst.
PY 2023
VL 10
IS 5
AR 3094
DI 10.4108/eetsis.v10i4.3094
PG 7
WC Computer Science, Information Systems
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA IE4R4
UT WOS:001164642900002
OA gold
DA 2024-09-05
ER
PT J
AU Das, K
   Patel, JD
   Sharma, A
   Shukla, Y
AF Das, Kallol
   Patel, Jayesh D.
   Sharma, Anuj
   Shukla, Yupal
TI Creativity in marketing: Examining the intellectual structure using
   scientometric analysis and topic modeling
SO JOURNAL OF BUSINESS RESEARCH
LA English
DT Article
DE Creativity; Marketing; Scientometric Analysis; Topic Modeling
ID CONSUMER-BRAND RELATIONSHIPS; SERVICE EMPLOYEE CREATIVITY; ADVERTISING
   CREATIVITY; SOCIAL MEDIA; PRODUCT CREATIVITY; INNOVATION; CONSEQUENCES;
   TECHNOLOGY; IMPACT; FUTURE
AB Creativity helps marketers better address customer needs, competitive actions, and challenges of an unpredictable environment. However, marketing academics have been debating the value added by creativity. This confusion can be best addressed by a comprehensive analysis of the creativity in marketing (CiM) literature, which we attempt to achieve. In this endeavor, we conducted citation, keyword, and authorship analyses from a corpus comprising 375 scholarly papers from 1973 to 2021. The most frequent keywords (e.g., advertising, co-creation, consumer creativity) may aid interested researchers in effectively exploring/understanding this domain. The domain's most productive journals (e.g., JBR, JA, P&M) are recommended target journals. We used structural topic modeling to extract ten key topics and content-analyzed them to develop an organizing framework. Furthermore, we used the six trending topics (e.g., creativity and branding, consumer creativity, new product creativity) to suggest implications for theory, practice, and future research.
C1 [Das, Kallol] MICA, Ahmadabad, Gujarat, India.
   [Patel, Jayesh D.] Ganpat Univ, VM Patel Inst Management Ganpat, Ganpat Vidyanagar, Gujarat, India.
   [Sharma, Anuj] OP Jindal Global Univ, Jindal Global Business Sch, Sonipat, Haryana, India.
   [Shukla, Yupal] Univ Bologna, Dept Management, Bologna, Italy.
C3 MICA; Ganpat University; O.P. Jindal Global University; University of
   Bologna
RP Das, K (corresponding author), MICA, Ahmadabad, Gujarat, India.
EM kallol.das@micamail.in; jayesh.patel@ganpatuniversity.ac.in;
   f09anujs@iimidr.ac.in; yupal.shukla3@unibo.it
RI Patel, Jayesh D./AAM-4121-2020; Sharma, Anuj/JTS-4887-2023
OI Sharma, Anuj/0000-0002-6281-6115; Das, Kallol/0000-0002-3601-707X;
   Sharma, Anuj/0000-0001-6602-9285
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NR 133
TC 9
Z9 9
U1 14
U2 53
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0148-2963
EI 1873-7978
J9 J BUS RES
JI J. Bus. Res.
PD JAN
PY 2023
VL 154
AR 113384
DI 10.1016/j.jbusres.2022.113384
EA OCT 2022
PG 21
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 6Y1TQ
UT WOS:000896885200011
DA 2024-09-05
ER
PT J
AU Yang, ZB
   Zhang, SH
   Shen, WJ
   Xing, XF
   Gao, Y
AF Yang, Zongbao
   Zhang, Shaohong
   Shen, Wenjun
   Xing, Xiaofei
   Gao, Ying
TI Artificial Intelligence Related Publication Analysis Based on Citation
   Counting
SO IEEE ACCESS
LA English
DT Article
DE Artificial intelligence; citation counting; recommendation lists
ID POWER-LAW DISTRIBUTIONS; CREDIT ALLOCATION; ARTICLES
AB Artificial intelligence is one of the most popular technologies in recently years. Journals and conferences are widely viewed as major tools to track the development of technologies. Citation counting analysis is one of the most acknowledged metrics in spite of its controversial drawbacks. To the best of our knowledge, most methods based on citation counting do not taken into account the citation weight in different years. In this paper, we focused on citation counting and designed a scheme to calculate both the citation weight and weighting of the cited credits of different publications, which are used to verify the efficiency of the proposed scheme. We also evaluated the popularity of publications by calculating their popularity scores. Unlike other ranking regulations, our proposed measure was able to compare journals and conferences simultaneously. In addition, we extracted ranking results to calculate the pairwise similarity via a generalized measure, which provided a more objective insight into the differences between publications. Several interesting observations were found from the experimental results with real data.
C1 [Yang, Zongbao; Zhang, Shaohong; Xing, Xiaofei; Gao, Ying] Guangzhou Univ, Sch Comp Sci & Educ Software, Guangzhou 510006, Guangdong, Peoples R China.
   [Shen, Wenjun] Shantou Univ, Med Coll, Shantou 515041, Peoples R China.
C3 Guangzhou University; Shantou University
RP Zhang, SH (corresponding author), Guangzhou Univ, Sch Comp Sci & Educ Software, Guangzhou 510006, Guangdong, Peoples R China.
EM zimzsh@qq.com
RI shen, Wen-jun/ACG-0761-2022
OI shen, Wen-jun/0000-0001-5150-1698
FU Scientific and Technological Project of Guangzhou [201607010053,
   201607010191, 201604016045, 201707010284]; Natural Science Foundation of
   China [61772007, 61502292]; Guangdong Natural Science Foundation of
   China [2014A030313524, 2016A030313540]; Science and Technology Projects
   of Guangdong Province, China [2016B010127001]; Guangzhou Education
   Scientific Research Project [1201730714]; Education Reform Project of
   Guangdong Province under Research on Construction and Mining methods in
   Knowledge Graphs of Computer Sciences Courses Project [426, (2016)236];
   Guangzhou Education Bureau Science Foundation [1201430560]; Postgraduate
   Educational Reform Project of Guangdong Province [2017JGXM-MS45];
   Graduate Innovative Research Grant Program of Guangzhou University
   [2017GDJC-M15]
FX This work was supported by the Scientific and Technological Project of
   Guangzhou under Project 201607010053, Project 201607010191, Project
   201604016045, and Project 201707010284, in part by the Natural Science
   Foundation of China under Grant 61772007 and Grant 61502292, in part by
   the Guangdong Natural Science Foundation of China under Grant
   2014A030313524 and Grant 2016A030313540, in part by the Science and
   Technology Projects of Guangdong Province, China, under Grant
   2016B010127001, in part by the Funding of the Guangzhou Education
   Scientific Research Project under Grant 1201730714, in part by the 2016
   Education Reform Project of Guangdong Province under Research on
   Construction and Mining methods in Knowledge Graphs of Computer Sciences
   Courses Project under Grant 426, File [(2016)236], in part bqy the
   Guangzhou Education Bureau Science Foundation under Grant 1201430560, in
   part by the Postgraduate Educational Reform Project of Guangdong
   Province under Grant 2017JGXM-MS45, and in part by the Graduate
   Innovative Research Grant Program of Guangzhou University under Grant
   2017GDJC-M15.
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NR 30
TC 2
Z9 2
U1 0
U2 22
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2018
VL 6
BP 52205
EP 52217
DI 10.1109/ACCESS.2018.2869140
PG 13
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering; Telecommunications
GA GX5OC
UT WOS:000447797200001
OA gold
DA 2024-09-05
ER
PT J
AU Jha, R
   Jbara, AA
   Qazvinian, V
   Radev, DR
AF Jha, Rahul
   Jbara, Amjad-Abu
   Qazvinian, Vahed
   Radev, Dragomir R.
TI NLP-driven citation analysis for scientometrics
SO NATURAL LANGUAGE ENGINEERING
LA English
DT Article
ID SCIENCE; INDEX; PREDICTORS; REFERENCES; AGREEMENT; ARTICLES; IMPACT;
   USAGE
AB This paper summarizes ongoing research in Natural-Language-Processing-driven citation analysis and describes experiments and motivating examples of how this work can be used to enhance traditional scientometrics analysis that is based on simply treating citations as a ` vote' from the citing paper to cited paper. In particular, we describe our dataset for citation polarity and citation purpose, present experimental results on the automatic detection of these indicators, and demonstrate the use of such annotations for studying research dynamics and scientific summarization. We also look at two complementary problems that show up in Natural-Language-Processing-driven citation analysis for a specific target paper. The first problem is extracting citation context, the implicit citation sentences that do not contain explicit anchors to the target paper. The second problem is extracting reference scope, the target relevant segment of a complicated citing sentence that cites multiple papers. We show how these tasks can be helpful in improving sentiment analysis and citation-based summarization.
C1 [Jha, Rahul; Jbara, Amjad-Abu] Microsoft Corp, Redmond, WA 98052 USA.
   [Jha, Rahul; Jbara, Amjad-Abu; Qazvinian, Vahed] Univ Michigan, Ann Arbor, MI 48109 USA.
   [Radev, Dragomir R.] Univ Michigan, EECS & SI, Ann Arbor, MI 48109 USA.
C3 Microsoft; University of Michigan System; University of Michigan;
   University of Michigan System; University of Michigan
RP Jha, R (corresponding author), Microsoft Corp, Redmond, WA 98052 USA.
EM rajh@microsoft.com; amjada@microsoft.com; vahed@umich.edu;
   radev@umich.edu
RI Radev, Dragomir/E-9641-2012
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NR 97
TC 55
Z9 62
U1 5
U2 71
PU CAMBRIDGE UNIV PRESS
PI CAMBRIDGE
PA EDINBURGH BLDG, SHAFTESBURY RD, CB2 8RU CAMBRIDGE, ENGLAND
SN 1351-3249
EI 1469-8110
J9 NAT LANG ENG
JI Nat. Lang. Eng.
PD JAN
PY 2017
VL 23
IS 1
BP 93
EP 130
DI 10.1017/S1351324915000443
PG 38
WC Computer Science, Artificial Intelligence; Linguistics; Language &
   Linguistics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI); Arts &amp; Humanities Citation Index (A&amp;HCI)
SC Computer Science; Linguistics
GA EI1VN
UT WOS:000392274300005
DA 2024-09-05
ER
PT J
AU Ali, N
   Halim, Z
   Hussain, SF
AF Ali, Nisar
   Halim, Zahid
   Hussain, Syed Fawad
TI An artificial intelligence-based framework for data-driven
   categorization of computer scientists: a case study of world's Top 10
   computing departments
SO SCIENTOMETRICS
LA English
DT Article
DE Scientists ranking; Data driven decision-making; Artificial
   intelligence; Clustering; Classification; Research output measurement
ID RANKING AUTHORS; CITATION ANALYSIS; INDEX
AB The total number of published articles and the resulting citations are generally acknowledged as suitable criteria of the scientist's evaluation. However, it is challenging to determine the ranking of scientists as the value of their scientific work (at times) is not directly reflective of the abovementioned aspects. In this regard, multiple other elements needs to be examined in combination for better evaluating the scientific worth of an individual. This work presents a learning-based technique, i.e., an Artificial Intelligence (AI)-based solution towards categorizing scientists utilizing a multifaceted criteria. In this context, a novel ranking metric is proposed which is grounded on authorship, experience, publications count, total citations, i10-index, and h-index. To assess the proposed framework's performance, a dataset is collected considering the world's top ten computing departments and ten domestic ones. This results in a data of 1000 computer scientists. The dataset is preprocessed and afterwards three techniques for feature selection are employed, i.e., Mutual Information (MI), Chi-Square (X-2), and Fisher-Test (F-Test) to rank the features in the data. To validate the collected data, the framework has three clustering techniques as well, namely, k-medoids, k-means, and spectral clustering to identify the optimum number of heterogeneous groups. Three cluster validity indices are used to evaluate the clustering outcomes, namely, Calinski-Harabasz Index (CHI), Davies Bouldin Index (DBI), and Silhouette Coefficient (SC). Once the optimum clusters are obtained, five classification procedures are used, including, Artificial Neural Network (ANN), k-Nearest Neighbor (k-NN), Decision Tree (DT), Gaussian Naive Bayes (GNB), and Linear Regression Classifier (LRC) to predict the category of a previously unknown scientist. Among all classifiers, an average accuracy of 94.44% is shown by the ANN to predict an unknown/new scientist category. The current proposal is also compared with closely related past works. The proposed framework offers the possibility to independently classify scientists based on AI techniques.
C1 [Ali, Nisar; Halim, Zahid] Ghulam Ishaq Khan Inst Engn Sci & Technol, Fac Comp Sci & Engn, Machine Intelligence Res Grp MInG, Topi, Pakistan.
   [Ali, Nisar] Univ Regina, Fac Engn & Appl Sci, Regina, SK, Canada.
   [Hussain, Syed Fawad] Univ Birmingham, Sch Comp Sci, Birmingham, England.
C3 GIK Institute Engineering Science & Technology; University of Regina;
   University of Birmingham
RP Halim, Z (corresponding author), Ghulam Ishaq Khan Inst Engn Sci & Technol, Fac Comp Sci & Engn, Machine Intelligence Res Grp MInG, Topi, Pakistan.
EM zahid.halim@giki.edu.pk
RI Ali, Nisar/HMD-4203-2023
OI Ali, Nisar/0000-0001-5926-0569
FU GIK Institute graduate research fund under PSS; GIK Institute graduate
   research fund under PSS [CS1917]
FX This work was sponsored by the GIK Institute graduate research fund
   under PSS scheme. Grant number CS1917.
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NR 53
TC 6
Z9 6
U1 4
U2 13
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD MAR
PY 2023
VL 128
IS 3
BP 1513
EP 1545
DI 10.1007/s11192-022-04627-9
EA DEC 2022
PG 33
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 9M3IZ
UT WOS:000906311800003
DA 2024-09-05
ER
PT J
AU Sued, GE
AF Sued, Gabriela Elisa
TI Mexican Scientific Production on Artificial Intelligence: A Bibliometric
   Analysis
SO INVESTIGACION BIBLIOTECOLOGICA
LA English
DT Article
DE Artificial Intelligence; Bibliometrics; Scientific Production; Mexico
ID SCIENCE
AB This article surveys the current state of AI scientific pro - duction in Mexico using bibliometric techniques. It examines AI specialization across six subfields. As a meth - odology, it utilizes metadata from 13 265 publications -collected from the OpenAlex catalogue- and conducts a quantitative productivity analysis based on metrics of publications, authors, citations, and international collaborations, identifying key research themes and their development. Findings reveal a broad local scientific structure with significant international collaborations. Both mature subfields, developed over three decades, including robotics and neural networks, and emerging subfields, developed in the last five years, encompassing machine learning, natural language processing, and computer vision, were identified. The article highlights recent applications in the health, environment, finance, natural language processing, and acoustics fields.
C1 [Sued, Gabriela Elisa] Univ Nacl Autonoma Mexico, Inst Invest Matemat Aplicadas & Sistemas, Coyoacan, Mexico.
C3 Universidad Nacional Autonoma de Mexico
RP Sued, GE (corresponding author), Univ Nacl Autonoma Mexico, Inst Invest Matemat Aplicadas & Sistemas, Coyoacan, Mexico.
EM gabriela.sued@iimas.unam.mx
OI Sued, Gabriela Elisa/0000-0002-4516-678X
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NR 28
TC 0
Z9 0
U1 2
U2 2
PU UNIV NACIONAL AUTONOMA MEXICO
PI MEXICO CITY
PA CIUDAD UNIV, CENTRO UNIV BIBLIOTECOLOGICAS, TORRE II HUMANIDADES, PISO
   11, 12 & 13, MEXICO CITY, CP 04510, MEXICO
SN 0187-358X
EI 2448-8321
J9 INVESTIG BIBLIOTECOL
JI Investig. Bibliotecol.
PD JUL-SEP
PY 2024
VL 38
IS 100
BP 87
EP 105
DI 10.22201/iibi.24488321xe.2024.100.58893
PG 19
WC Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science
GA UK4Z2
UT WOS:001247952400002
OA gold
DA 2024-09-05
ER
PT C
AU Han, XD
   Baldwin, T
   Cohn, T
AF Han, Xudong
   Baldwin, Timothy
   Cohn, Trevor
BE Vlachos, A
   Augenstein, I
TI Fair Enough: Standardizing Evaluation and Model Selection for Fairness
   Research in NLP
SO 17TH CONFERENCE OF THE EUROPEAN CHAPTER OF THE ASSOCIATION FOR
   COMPUTATIONAL LINGUISTICS, EACL 2023
LA English
DT Proceedings Paper
CT 17th Conference of the European-Chapter of the
   Association-for-Computational-Linguistics (EACL)
CY MAY 02-06, 2023
CL Dubrovnik, CROATIA
AB Modern NLP systems exhibit a range of biases, which a growing literature on model debiasing attempts to correct. However current progress is hampered by a plurality of definitions of bias, means of quantification, and oftentimes vague relation between debiasing algorithms and theoretical measures of bias. This paper seeks to clarify the current situation and plot a course for meaningful progress in fair learning, with two key contributions: (1) making clear inter-relations among the current gamut of methods, and their relation to fairness theory; and (2) addressing the practical problem of model selection, which involves a trade-off between fairness and accuracy and has led to systemic issues in fairness research. Putting them together, we make several recommendations to help shape future work.1
C1 [Han, Xudong; Baldwin, Timothy; Cohn, Trevor] Univ Melbourne, Melbourne, Australia.
   [Han, Xudong; Baldwin, Timothy] MBZUAI, Abu Dhabi, U Arab Emirates.
C3 University of Melbourne; Mohamed Bin Zayed University of Artificial
   Intelligence
RP Han, XD (corresponding author), Univ Melbourne, Melbourne, Australia.; Han, XD (corresponding author), MBZUAI, Abu Dhabi, U Arab Emirates.
EM xudongh1@student.unimelb.edu.au; tbaldwin@unimelb.edu.au;
   t.cohn@unimelb.edu.au
FU Australian Research Council [DP200102519]; Australian Research Council
   [DP200102519] Funding Source: Australian Research Council
FX We thank Lea Frermann, Aili Shen, and Shivashankar Subramanian for their
   discussions and inputs. We thank the anonymous reviewers for their
   helpful feedback and suggestions. This work was funded by the Australian
   Research Council, Discovery grant DP200102519.
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NR 41
TC 0
Z9 0
U1 0
U2 0
PU ASSOC COMPUTATIONAL LINGUISTICS-ACL
PI STROUDSBURG
PA 209 N EIGHTH STREET, STROUDSBURG, PA 18360 USA
BN 978-1-959429-44-9
PY 2023
BP 297
EP 312
PG 16
WC Computer Science, Artificial Intelligence; Computer Science, Theory &
   Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BW6RX
UT WOS:001181056902006
DA 2024-09-05
ER
PT J
AU Bittermann, A
   Fischer, A
AF Bittermann, Andre
   Fischer, Andreas
TI How to Identify Hot Topics in Psychology Using Topic Modeling
SO ZEITSCHRIFT FUR PSYCHOLOGIE-JOURNAL OF PSYCHOLOGY
LA English
DT Article
DE topic modeling; hotspots; scientometrics; trends; controlled terms
ID BIG DATA; SCIENCE
AB Latent topics and trends in psychological publications were examined to identify hotspots in psychology. Topic modeling was contrasted with a classification-based scientometric approach in order to demonstrate the benefits of the former. Specifically, the psychological publication output in the German-speaking countries containing German-and English-language publications from 1980 to 2016 documented in the PSYNDEX database was analyzed. Topic modeling based on latent Dirichlet allocation (LDA) was applied to a corpus of 314,573 publications. Input for topic modeling was the controlled terms of the publications, that is, a standardized vocabulary of keywords in psychology. Based on these controlled terms, 500 topics were determined and trending topics were identified. Hot topics, indicated by the highest increasing trends in this data, were facets of neuropsychology, online therapy, cross-cultural aspects, traumatization, and visual attention. In conclusion, the findings indicate that topics can reveal more detailed insights into research trends than standardized classifications. Possible applications of this method, limitations, and implications for research synthesis are discussed.
C1 [Bittermann, Andre] Leibniz Inst Psychol Informat ZPID, Univ Ring 15, D-54296 Trier, Germany.
   [Fischer, Andreas] F Bb, Nurnberg, Germany.
C3 Leibniz Institute for Psychology Information & Documentation
RP Bittermann, A (corresponding author), Leibniz Inst Psychol Informat ZPID, Univ Ring 15, D-54296 Trier, Germany.
EM abi@leibniz-psychology.org
RI Bittermann, André/AAA-5600-2019; Fischer, Andreas/E-9202-2012
OI Bittermann, André/0000-0003-2942-9831; Fischer,
   Andreas/0009-0006-0748-6076
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NR 46
TC 34
Z9 36
U1 4
U2 44
PU HOGREFE & HUBER PUBLISHERS
PI GOTTINGEN
PA MERKELSTR 3, D-37085 GOTTINGEN, GERMANY
SN 2190-8370
EI 2151-2604
J9 Z PSYCHOL
JI Z. Psychol.-J. Psychol.
PD JAN
PY 2018
VL 226
IS 1
BP 3
EP 13
DI 10.1027/2151-2604/a000318
PG 11
WC Psychology, Multidisciplinary
WE Social Science Citation Index (SSCI)
SC Psychology
GA FZ0XO
UT WOS:000427298600002
DA 2024-09-05
ER
PT J
AU Tomas, C
   Jessop, T
AF Tomas, Carmen
   Jessop, Tansy
TI Struggling and juggling: a comparison of student assessment loads across
   research and teaching-intensive universities
SO ASSESSMENT & EVALUATION IN HIGHER EDUCATION
LA English
DT Article
DE Assessment load; programme assessment; deep learning
ID FORMATIVE ASSESSMENT; ASSESSMENT PATTERNS; FEEDBACK; EXPERIENCE; DESIGN;
   WORK
AB In spite of the rising tide of metrics in UK higher education, there has been scant attention paid to assessment loads, when evidence demonstrates that heavy demands lead to surface learning. Our study seeks to redress the situation by defining assessment loads and comparing them across research and teaching intensive universities. We clarify the concept of 'assessment load' in response to findings about high volumes of summative assessment on modular degrees. We define assessment load across whole undergraduate degrees, according to four measures: the volume of summative assessment; volume of formative assessment; proportion of examinations to coursework; number of different varieties of assessment. All four factors contribute to the weight of an assessment load, and influence students' approaches to learning. Our research compares programme assessment data from 73 programmes in 14 UK universities, across two institutional categories. Research-intensives have higher summative assessment loads and a greater proportion of examinations; teaching-intensives have higher varieties of assessment. Formative assessment does not differ significantly across both university groups. These findings pose particular challenges for students in different parts of the sector. Our study questions the wisdom that 'more' is always better, proposing that lighter assessment loads may make room for 'slow' and deep learning.
C1 [Tomas, Carmen] Univ Nottingham, Teaching Transformat Programme, Nottingham, England.
   [Jessop, Tansy] Solent Univ, Solent Learning & Teaching Inst, Southampton, Hants, England.
C3 University of Nottingham
RP Jessop, T (corresponding author), Solent Univ, Solent Learning & Teaching Inst, Southampton, Hants, England.
EM tansy.jessop@solent.ac.uk
OI Tomas, Carmen/0000-0001-6163-2907
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NR 51
TC 21
Z9 24
U1 2
U2 20
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0260-2938
EI 1469-297X
J9 ASSESS EVAL HIGH EDU
JI Assess. Eval. High. Educ.
PD JAN 2
PY 2019
VL 44
IS 1
BP 1
EP 10
DI 10.1080/02602938.2018.1463355
PG 10
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA HD7LT
UT WOS:000452734400001
DA 2024-09-05
ER
PT J
AU Xu, JG
   Li, MY
   Gao, Y
   Liu, M
   Shi, SZ
   Shi, JY
   Yang, KL
   Zhou, Z
   Tian, JH
AF Xu, Jianguo
   Li, Muyang
   Gao, Ya
   Liu, Ming
   Shi, Shuzhen
   Shi, Jiyuan
   Yang, Kelu
   Zhou, Zheng
   Tian, Jinhui
TI Using Mendelian randomization as the cornerstone for causal inference in
   epidemiology
SO ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
LA English
DT Article
DE Mendelian randomization analysis; Bibliometric analysis; Citation
   analysis; Visualization
ID GENETIC EPIDEMIOLOGY; FUTURE-PROSPECTS; RISK; DETERMINANTS;
   METAANALYSIS; DISEASE; TRENDS
AB Mendelian randomization (MR) is attracting considerable critical attention. This paper aimed to explore the characteristics of the publications of MR, to reach an insight in this field and prospect the future trend. A bibliometric analysis was performed to identify published MR-related research. The articles were selected from the Web of Science Core Collection database. Excel 2019, VOSviewer 1.6.9, and CiteSpace 5.7.R3 were used to analyze the information. A total of 1783 papers of MR were identified, and the first included literature appeared in 2003. A total of 2829 institutions from 72 countries participated in the relevant research, while the UK contributed to 852 articles and were in a leading position. The most productive institution was the University of Bristol, and Smith GD who has posted the most articles (n=202) was also from there. The Int J Epidemiol (100 publications, 6861 citations) was the most prolific and high citation journal. Related topics of frontiers will still focus on coronary heart disease, diabetes, cancer, psychiatric disorder, body mass index, and lifestyle factors. We summarized the publication information of MR-related literature from 2003 to 2020, including country and institution of origin, authors, and publication journal. We analyzed former research hotspots in the field of MR and predicted future areas of interest. Exposures and outcomes detected in this paper will be the hotspots and frontiers of research in the next few years.
C1 [Xu, Jianguo; Gao, Ya; Liu, Ming; Shi, Shuzhen; Shi, Jiyuan; Yang, Kelu; Zhou, Zheng; Tian, Jinhui] Lanzhou Univ, Evidence Based Med Ctr, Lanzhou, Peoples R China.
   [Xu, Jianguo; Gao, Ya; Liu, Ming; Shi, Shuzhen; Zhou, Zheng; Tian, Jinhui] Lanzhou Univ, Sch Basic Med Sci, Lanzhou, Peoples R China.
   [Li, Muyang] Lanzhou Univ, Clin Med Coll 2, Lanzhou, Peoples R China.
   [Tian, Jinhui] Lanzhou Univ, Evidence Based Med Ctr, Sch Basic Med Sci, 199 Donggang West Rd, Lanzhou 730000, Gansu, Peoples R China.
C3 Lanzhou University; Lanzhou University; Lanzhou University; Lanzhou
   University
RP Tian, JH (corresponding author), Lanzhou Univ, Evidence Based Med Ctr, Lanzhou, Peoples R China.; Tian, JH (corresponding author), Lanzhou Univ, Sch Basic Med Sci, Lanzhou, Peoples R China.; Tian, JH (corresponding author), Lanzhou Univ, Evidence Based Med Ctr, Sch Basic Med Sci, 199 Donggang West Rd, Lanzhou 730000, Gansu, Peoples R China.
EM tjh996@163.com
RI Gao, Ya/HGA-2705-2022; shi, jiyuan/ACB-6953-2022; xu,
   jianguo/JZD-9552-2024
OI Gao, Ya/0000-0002-9280-4457
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NR 56
TC 15
Z9 17
U1 7
U2 38
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 0944-1344
EI 1614-7499
J9 ENVIRON SCI POLLUT R
JI Environ. Sci. Pollut. Res.
PD JAN
PY 2022
VL 29
IS 4
BP 5827
EP 5839
DI 10.1007/s11356-021-15939-3
EA AUG 2021
PG 13
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology
GA YH3ZR
UT WOS:000687964300009
PM 34431050
DA 2024-09-05
ER
PT C
AU Chatzopoulos, S
   Deligiannis, P
   Vergoulis, T
   Kanellos, I
   Tryfonopoulos, C
   Dalamagas, T
AF Chatzopoulos, Serafeim
   Deligiannis, Panagiotis
   Vergoulis, Thanasis
   Kanellos, Ilias
   Tryfonopoulos, Christos
   Dalamagas, Theodore
BE Doucet, A
   Isaac, A
   Golub, K
   Aalberg, T
   Jatowt, A
TI SciTo Trends: Visualising Scientific Topic Trends
SO DIGITAL LIBRARIES FOR OPEN KNOWLEDGE, TPDL 2019
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT 23rd International Conference on Theory and Practice of Digital
   Libraries (TPDL)
CY SEP 09-12, 2019
CL Oslo Metropolitan Univ, Oslo, NORWAY
HO Oslo Metropolitan Univ
DE Information retrieval; Scientific impact; Topic modeling
AB Monitoring trends in scientific disciplines is a common task for researchers and other professionals in the broad research and academic community, like research and innovation policy makers and research fund managers. We demonstrate SciTo, a powerful tool that assists in the monitoring of trends in scientific disciplines. SciTo supports keyword-based search for the identification of scientific topics of interest and comparison of interesting topics to each other in terms of their popularity inside the academic community.
C1 [Chatzopoulos, Serafeim; Deligiannis, Panagiotis; Tryfonopoulos, Christos] Univ Peloponnese, Dept Informat & Tel Tions, Tripoli 22100, Greece.
   [Chatzopoulos, Serafeim; Vergoulis, Thanasis; Kanellos, Ilias; Dalamagas, Theodore] IMSI Athena Res & Innovat Ctr, Athens 15125, Greece.
   [Kanellos, Ilias] NTUA, Sch Elect & Comp Engn, Athens 15780, Greece.
C3 University of Peloponnese; National Technical University of Athens
RP Vergoulis, T (corresponding author), IMSI Athena Res & Innovat Ctr, Athens 15125, Greece.
EM schatz@athenarc.gr; cst11017@uop.gr; vergoulis@athenarc.gr;
   ilias.kanellos@athenarc.gr; trifon@uop.gr; dalamag@athenarc.gr
RI Dalamagas, Theodore/ABE-9542-2020; Tryfonopoulos,
   Christos/AAL-8960-2021; Tryfonopoulos, Christos/HKW-5651-2023;
   Vergoulis, Thanasis/GSO-2837-2022
OI Dalamagas, Theodore/0000-0002-5002-7901; Vergoulis,
   Thanasis/0000-0003-0555-4128; Tryfonopoulos,
   Christos/0000-0003-0640-9088; Chatzopoulos,
   Serafeim/0000-0003-1714-5225; Kanellos, Ilias/0000-0003-2146-3795
FU Operational Programme "Competitiveness, Entrepreneurship and Innovation"
   (NSRF) [MIS 5002437/3]; European Union (European Regional Development
   Fund)
FX We acknowledge support of this work by the project "Moving from Big Data
   Management to Data Science" (MIS 5002437/3) which is implemented under
   the Action "Re-inforcement of the Research and Innovation
   Infrastructure", funded by the Operational Programme "Competitiveness,
   Entrepreneurship and Innovation" (NSRF 2014-2020) and co-financed by
   Greece and the European Union (European Regional Development Fund).
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NR 5
TC 1
Z9 1
U1 0
U2 3
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 0302-9743
EI 1611-3349
BN 978-3-030-30760-8; 978-3-030-30759-2
J9 LECT NOTES COMPUT SC
PY 2019
VL 11799
BP 393
EP 396
DI 10.1007/978-3-030-30760-8_41
PG 4
WC Information Science & Library Science
WE Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Information Science & Library Science
GA BP4BL
UT WOS:000550576600041
DA 2024-09-05
ER
PT J
AU Keramatfar, A
   Amirkhani, H
AF Keramatfar, Abdalsamad
   Amirkhani, Hossein
TI Bibliometrics of sentiment analysis literature
SO JOURNAL OF INFORMATION SCIENCE
LA English
DT Article
DE Bibliometrics; keyword analysis; opinion mining; sentiment analysis;
   Twitter
ID SOCIAL MEDIA; MARKET PREDICTION; FEATURES; WEB; COLLABORATION; OPINIONS;
   SCIENCE; TWITTER; SYSTEM; MOOD
AB This article provides a bibliometric study of the sentiment analysis literature based on Web of Science (WoS) until the end of 2016 to evaluate current research trends, quantitatively and qualitatively. We concentrate on the analysis of scientific documents, distribution of subject categories, languages of documents and languages that have been more investigated in sentiment analysis, most prolific and impactful authors and institutions, venues of publications and their geographic distribution, most cited and hot documents, trends of keywords and future works. Our investigations demonstrate that the most frequent subject categories in this field are computer science, engineering, telecommunications, linguistics, operations research and management science, information science and library science, business and economics, automation and control systems, robotics and social sciences. In addition, the most active venue of publication in this field is Lecture Notes in Computer Science (LNCS). The United States, China and Singapore have the most prolific or impactful institutions. A keyword analysis demonstrates that sentiment analysis is a more accepted term than opinion mining. Twitter is the most used social network for sentiment analysis and Support Vector Machine (SVM) is the most used classification method. We also present the most cited and hot documents in this field and authors' suggestions for future works.
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RP Keramatfar, A (corresponding author), Univ Qom, Tehran, Iran.
EM keramatfar.a.s@gmail.com
RI Keramatfar, Abdalsamad/AAH-7414-2019; Keramatfar, Abdalsamad/D-7117-2015
OI Keramatfar, Abdalsamad/0000-0001-6826-4692; Amirkhani,
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NR 83
TC 43
Z9 44
U1 6
U2 167
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 0165-5515
EI 1741-6485
J9 J INF SCI
JI J. Inf. Sci.
PD FEB
PY 2019
VL 45
IS 1
BP 3
EP 15
DI 10.1177/0165551518761013
PG 13
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA HG4KY
UT WOS:000454945400001
DA 2024-09-05
ER
PT C
AU Zhang, Q
   Liu, TN
AF Zhang, Qian
   Liu, Tongna
BE Mahadevan, V
   Jianhong, Z
TI Research on Performance Evaluation of Project Management Based on SVM
   and Wavelet Neural Network
SO 2010 2ND INTERNATIONAL CONFERENCE ON COMPUTER AND AUTOMATION ENGINEERING
   (ICCAE 2010), VOL 2
SE International Conference on Computer and Automation Engineering
LA English
DT Proceedings Paper
CT 2nd International Conference on Computer and Automation Engineering
   (ICCAE)
CY FEB 26-28, 2010
CL Singapore, SINGAPORE
DE SVM; Project Management; Performance Evaluation; Neural Network
AB The principle and step of performance evaluation of project management based on SVM and wavelet neural network are studied. The index system of performance evaluation of project management is set up. Then we built up the evaluation model on SVM and wavelet neural network. Finally, take some samples of project for an example, we carry on this model to instance. It can take a preferably evaluation, so that it is a viable method.
C1 [Zhang, Qian] North China Elect Power Univ, Dept Econ Management, Baoding 071000, Hebei, Peoples R China.
   [Liu, Tongna] North China Elect Power Univ, Dept Elect & Commun Engn, Baoding, Hebei, Peoples R China.
C3 North China Electric Power University; North China Electric Power
   University
RP Zhang, Q (corresponding author), North China Elect Power Univ, Dept Econ Management, Baoding 071000, Hebei, Peoples R China.
EM hdzhq@yeah.net; hdltn@yeah.net
FU Hebei Natural Science Fund [G2009001410]
FX This research was supported by Hebei Natural Science Fund. (G2009001410)
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NR 7
TC 17
Z9 17
U1 0
U2 5
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2154-4352
BN 978-1-4244-5569-0
J9 INT CONF COMPUT AUTO
PY 2010
BP 91
EP 94
DI 10.1109/ICCAE.2010.5451393
PG 4
WC Automation & Control Systems; Computer Science, Theory & Methods;
   Engineering, Electrical & Electronic
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Automation & Control Systems; Computer Science; Engineering
GA BH0ZA
UT WOS:000397218200021
DA 2024-09-05
ER
PT J
AU Saeed-Ul Hassan
   Safder, I
   Akram, A
   Kamiran, F
AF Saeed-Ul Hassan
   Safder, Iqra
   Akram, Anam
   Kamiran, Faisal
TI A novel machine-learning approach to measuring scientific knowledge
   flows using citation context analysis
SO SCIENTOMETRICS
LA English
DT Article
DE Knowledge flows; Machine learning; Citation context classification;
   Influential citations; Citation analysis
ID INFORMATION-SCIENCE; PATENT CITATIONS; INSTITUTIONS; SPECIALTY;
   DIFFUSION; SPACE; US
AB We measure the knowledge flows between countries by analysing publication and citation data, arguing that not all citations are equally important. Therefore, in contrast to existing techniques that utilize absolute citation counts to quantify knowledge flows between different entities, our model employs a citation context analysis technique, using a machine-learning approach to distinguish between important and non-important citations. We use 14 novel features (including context-based, cue words-based and text-based) to train a Support Vector Machine (SVM) and Random Forest classifier on an annotated dataset of 20,527 publications downloaded from the Association for Computational Linguistics anthology (http://allenai.org/data.html). Our machine-learning models outperform existing state-of-the-art citation context approaches, with the SVM model reaching up to 61% and the Random Forest model up to a very encouraging 90% Precision-Recall Area Under the Curve, with 10-fold cross-validation. Finally, we present a case study to explain our deployed method for datasets of PLoS ONE full-text publications in the field of Computer and Information Sciences. Our results show that a significant volume of knowledge flows from the United States, based on important citations, are consumed by the international scientific community. Of the total knowledge flow from China, we find a relatively smaller proportion (only 4.11%) falling into the category of knowledge flow based on important citations, while The Netherlands and Germany show the highest proportions of knowledge flows based on important citations, at 9.06 and 7.35% respectively. Among the institutions, interestingly, the findings show that at the University of Malaya more than 10% of the knowledge produced falls into the category of important. We believe that such analyses are helpful to understand the dynamics of the relevant knowledge flows across nations and institutions.
C1 [Saeed-Ul Hassan; Safder, Iqra; Akram, Anam; Kamiran, Faisal] Informat Technol Univ, 346-B,Ferozepur Rd, Lahore 54700, Pakistan.
RP Saeed-Ul Hassan (corresponding author), Informat Technol Univ, 346-B,Ferozepur Rd, Lahore 54700, Pakistan.
EM saeedulhassan@gmail.com
RI Safder, Iqra/JXN-8069-2024; Hassan, Saeed-Ul/G-1889-2016
OI Hassan, Saeed-Ul/0000-0002-6509-9190
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NR 50
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Z9 51
U1 10
U2 120
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD AUG
PY 2018
VL 116
IS 2
BP 973
EP 996
DI 10.1007/s11192-018-2767-x
PG 24
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA GP1SS
UT WOS:000440597800015
DA 2024-09-05
ER
PT J
AU Xu, HL
   Ge, SL
   Yuan, F
AF Xu, Hailing
   Ge, Shilun
   Yuan, Feng
TI Research on the Mechanism of Influence of Game Competition Mode on
   Online Learning Performance
SO BEHAVIORAL SCIENCES
LA English
DT Article
DE online learning; learning performance; competition; gamification;
   curriculum platform
ID TO-STUDENT CONNECTEDNESS; GAMIFICATION; MOTIVATION; CLASSROOM
AB With the rapid development of information technology and the influence of the COVID-19 pandemic, online learning has become an important supplement to the teaching organization form of basic education and higher education. In order to increase user stickiness and improve learning performance, gamification elements are widely introduced into online learning situations. However, scholars have drawn different conclusions on the impact of game-based competition on online learning performance. This study is based on field theory and constructivist learning theory. Taking the online interaction of the curriculum platform as the situation, psychological capital as the intermediary variable and connected classroom atmosphere as the adjustment variable, this paper constructs an interaction model between game competition and online learning performance and discusses in depth the intermediary effect of psychological capital and the adjustment effect of a connected classroom atmosphere. The results show that game-based competition has a significant positive effect on learning performance, and the effect of direct competition is better than that of indirect competition; the self-efficacy dimension of psychological capital plays an intermediary role between direct competition and learning performance, and the resilience dimension plays an intermediary role between competition and learning performance; and a connected classroom atmosphere plays a regulating role in the dimensions of game competition, knowledge mastery and knowledge innovation.
C1 [Xu, Hailing] Jiangsu Univ Sci & Technol, Acad Affairs Off, Zhenjiang 212003, Jiangsu, Peoples R China.
   [Xu, Hailing; Ge, Shilun] Jiangsu Univ Sci & Technol, Sch Econ & Management, Zhenjiang 212003, Jiangsu, Peoples R China.
   [Yuan, Feng] Jiangsu Univ Sci & Technol, Grad Sch, Zhenjiang 212003, Jiangsu, Peoples R China.
C3 Jiangsu University of Science & Technology; Jiangsu University of
   Science & Technology; Jiangsu University of Science & Technology
RP Xu, HL (corresponding author), Jiangsu Univ Sci & Technol, Acad Affairs Off, Zhenjiang 212003, Jiangsu, Peoples R China.; Xu, HL (corresponding author), Jiangsu Univ Sci & Technol, Sch Econ & Management, Zhenjiang 212003, Jiangsu, Peoples R China.
EM xhl@just.edu.cn; jzgsl@jzerp.com; yfjuster1979@163.com
FU Humanities and Social Sciences Project of Ministry of Education
   [21JDSZ3091]; Jiangsu Higher Education Reform Research Project
   [2021JSJG365]; Philosophy and Social Sciences Project of Universities in
   Jiangsu Province [2021SJZDA169, 2021SJA2102]
FX Humanities and Social Sciences Project of Ministry of Education
   (21JDSZ3091); Jiangsu Higher Education Reform Research Project
   (2021JSJG365); Philosophy and Social Sciences Project of Universities in
   Jiangsu Province (2021SJZDA169 and 2021SJA2102).
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NR 30
TC 5
Z9 5
U1 7
U2 81
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2076-328X
J9 BEHAV SCI-BASEL
JI Behav. Sci.
PD JUL
PY 2022
VL 12
IS 7
AR 225
DI 10.3390/bs12070225
PG 16
WC Psychology, Multidisciplinary
WE Social Science Citation Index (SSCI)
SC Psychology
GA 3G8KI
UT WOS:000831596400001
PM 35877295
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU Özköse, H
   Ozyurt, O
   Ayaz, A
AF Ozkose, Hakan
   Ozyurt, Ozcan
   Ayaz, Ahmet
TI Management Information Systems Research: A Topic Modeling Based
   Bibliometric Analysis
SO JOURNAL OF COMPUTER INFORMATION SYSTEMS
LA English
DT Article
DE Management information systems; topic modeling; bibliometric analysis;
   research trends
ID COVID-19; POINTS; WORK; MIS
AB Management information systems (MIS) have an interdisciplinary structure. Naturally, it develops and changes with the influence of other fields. This study tries to analyze MIS through academic studies on this topic. In this context, the analysis included 25304 articles published in the Scopus database from 2016 to 2021. Then, the performance analysis, part of bibliometric analysis, was used to measure the research productivity and impact of authors, institutions, countries, and journals. This way, the field's most influential authors, institutions, countries, and journals were determined. Afterward, the articles were gathered under 15 categories with topic modeling. These categories were examined on an annual basis, and more detailed results were tried to be revealed. This study both revealed the primary lines of the MIS field and ended up being a guide for researchers about the topics they can focus on in the future.
C1 [Ozkose, Hakan] Bartin Univ, Dept Management Informat Syst, Bartin, Turkey.
   [Ozyurt, Ozcan] Karadeniz Tech Univ, Dept Software Engn, Trabzon, Turkey.
   [Ayaz, Ahmet] Karadeniz Tech Univ, Digital Transformat Off, Trabzon, Turkey.
C3 Bartin University; Karadeniz Technical University; Karadeniz Technical
   University
RP Ayaz, A (corresponding author), Karadeniz Tech Univ, Digital Transformat Off, Trabzon, Turkey.
EM ahmetayaz@ktu.edu.tr
RI Ayaz, Ahmet/JBJ-2146-2023; ÖZYURT, Özcan/AAG-4556-2019; Özköse,
   Hakan/HLX-2774-2023; Ayaz, Ahmet/ABF-5870-2021
OI ÖZYURT, Özcan/0000-0002-0047-6813; Özköse, Hakan/0000-0002-4628-3056;
   Ayaz, Ahmet/0000-0003-1405-0546
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NR 47
TC 7
Z9 7
U1 8
U2 50
PU TAYLOR & FRANCIS INC
PI PHILADELPHIA
PA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA
SN 0887-4417
EI 2380-2057
J9 J COMPUT INFORM SYST
JI J. Comput. Inf. Syst.
PD SEP 3
PY 2023
VL 63
IS 5
BP 1166
EP 1182
DI 10.1080/08874417.2022.2132429
EA OCT 2022
PG 17
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA U2VV9
UT WOS:000869520900001
DA 2024-09-05
ER
PT J
AU Williams, K
   Berman, G
   Michalska, S
AF Williams, Kate
   Berman, Glen
   Michalska, Sandra
TI Investigating hybridity in artificial intelligence research
SO BIG DATA & SOCIETY
LA English
DT Article
DE Artificial intelligence; hybridity; knowledge production; research
   value; bibliometrics
ID INSTITUTIONAL LOGICS; RESEARCH IMPACT; ALTMETRICS; INDUSTRY; PATENT
AB Research in the global field of artificial intelligence is increasingly hybrid in orientation. Researchers are beholden to the requirements of multiple intersecting spheres, such as scholarly, public, and commercial, each with their own language and logic. Relatedly, collaboration across disciplinary, sector and national borders is increasingly expected, or required. Using a dataset of 93,482 artificial intelligence publications, this article operationalises scholarly, public, and commercial spheres through citations, news mentions, and patent mentions, respectively. High performing publications (99th percentile) for each metric were separated into eight categories of influence. These comprised four blended categories of influence (news, patents and citations; news and patents; news and citations; patents and citations) and three single categories of influence (citations; news; patents), in addition to the 'Other' category of non-high performing publications. The article develops and applies two components of a new hybridity lens: evaluative hybridity and generative hybridity. Using multinomial logistic regression, selected aspects of knowledge production - research context, focus, artefacts, and collaborative configurations - were examined. The results elucidate key characteristics of knowledge production in the artificial intelligence field and demonstrate the utility of the proposed lens.
C1 [Williams, Kate] Univ Melbourne, Sch Social & Polit Sci, Melbourne, Vic, Australia.
   [Berman, Glen] Australian Natl Univ, Sch Engn, Canberra, ACT, Australia.
   [Michalska, Sandra] Kings Coll London, Policy Inst, London, England.
   [Williams, Kate] Univ Melbourne, Sch Social & Polit Sci, 420 John Medley Bldg, Melbourne, Vic 3010, Australia.
C3 University of Melbourne; Australian National University; University of
   London; King's College London; University of Melbourne
RP Williams, K (corresponding author), Univ Melbourne, Sch Social & Polit Sci, 420 John Medley Bldg, Melbourne, Vic 3010, Australia.
EM kate.williams@unimelb.edu.au
OI Berman, Glen/0000-0003-3249-0190
FU Economic and Social Research Council [ES/V004123/1]; ESRC [ES/V004123/1]
   Funding Source: UKRI
FX The author(s) disclosed receipt of the following financial support
   forthe research, authorship, and/or publication of this article: This
   workwas supported by the Economic and Social Research Council grant
   ES/V004123/1, awarded to Kate Williams and Jonathan Grant.
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NR 55
TC 3
Z9 3
U1 8
U2 28
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 2053-9517
J9 BIG DATA SOC
JI Big Data Soc.
PD JUL
PY 2023
VL 10
IS 2
AR 20539517231180577
DI 10.1177/20539517231180577
PG 17
WC Social Sciences, Interdisciplinary
WE Social Science Citation Index (SSCI)
SC Social Sciences - Other Topics
GA L5MG1
UT WOS:001023699400001
OA gold
DA 2024-09-05
ER
PT C
AU Zhang, ZY
   Xie, X
AF Zhang, Zhenyu
   Xie, Xiaoyao
BE Salem, MA
   ElHadidi, MT
TI Intelligent cognitive radio: Research on learning and evaluation of CR
   based on neural network
SO MEDIA CONVERGENCE: MOVING TO THE NEXT GENERATION
LA English
DT Proceedings Paper
CT ITI 5th International Conference on Information and Communications
   Technology
CY DEC 16-18, 2007
CL Cairo, EGYPT
DE cognitive radio; artificial intelligence; neural network; cognitive
   radio engine; genetic algorithms
AB This paper introduces the research of cognitive engine and application of artificial intelligence techniques in cognitive radio. The limitation of CR engine based on GA is analyzed, propose for improvement is proposed. The decision maker of CR engine should consider both the changeable factors and the unchangeable factors such as cost, bandwidth, signal rate and ARQ. Based on Neural Network, the method of evaluating and learning best decision is proposed. Several key architectural issues for cognitive radio engine based on Neural Network are discussed, including knowledge base information model and learning model Neural Network design.
C1 [Zhang, Zhenyu] Guizhoi Univ, Inst Comp Sci, Guizhoi, Peoples R China.
   [Xie, Xiaoyao] Guizhou Normal Univ, Comp Sci Guizhou, Key Lab Informat, Guiyang 550002, Peoples R China.
C3 Guizhou Normal University
RP Zhang, ZY (corresponding author), Guizhoi Univ, Inst Comp Sci, Guizhoi, Peoples R China.
EM zhangzy3@vip.sina.com; xyx@gznu.edu.cn
FU International Cooperate Science Foundation of GuiZhou Province (China)
FX This work was supported by the International Cooperate Science
   Foundation of GuiZhou Province (China).
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BN 978-1-4244-1430-7
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EP 37
PG 5
WC Computer Science, Hardware & Architecture; Computer Science, Information
   Systems; Computer Science, Theory & Methods; Information Science &
   Library Science; Social Issues
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Information Science & Library Science; Social Issues
GA BHM92
UT WOS:000254392000006
DA 2024-09-05
ER
PT J
AU Vinkenburg, CJ
   Ossenkop, C
   Schiffbaenker, H
AF Vinkenburg, Claartje J.
   Ossenkop, Carolin
   Schiffbaenker, Helene
TI Selling science: optimizing the research funding evaluation and decision
   process
SO EQUALITY DIVERSITY AND INCLUSION
LA English
DT Article
DE Research funding; Panel evaluation; Decision making; Bias mitigation;
   Discretion elimination; Process optimization; Inclusion; Selling science
ID LINGUISTIC ANALYSIS; GENDER
AB Purpose In this contribution to EDI's professional insights, the authors develop practical and evidence-based recommendations that are developed for bias mitigation, discretion elimination and process optimization in panel evaluations and decisions in research funding. An analysis is made of how the expectation of "selling science" adds layers of complexity to the evaluation and decision process. The insights are relevant for optimization of similar processes, including publication, recruitment and selection, tenure and promotion. Design/methodology/approach The recommendations are informed by experiences and evidence from commissioned projects with European research funding organizations. The authors distinguish between three aspects of the evaluation process: written applications, enacted performance and group dynamics. Vignettes are provided to set the stage for the analysis of how bias and (lack of) fit to an ideal image makes it easier for some than for others to be funded. Findings In research funding decisions, (over)selling science is expected but creates shifting standards for evaluation, resulting in a narrow band of acceptable behavior for applicants. In the authors' recommendations, research funding organizations, evaluators and panel chairs will find practical ideas and levers for process optimization, standardization and customization, in terms of awareness, accountability, biased language, criteria, structure and time. Originality/value Showing how "selling science" in research funding adds to the cumulative disadvantage of bias, the authors offer design specifications for interventions to mitigate the negative effects of bias on evaluations and decisions, improve selection habits, eliminate discretion and create a more inclusive process.
C1 [Ossenkop, Carolin] Connectify, Wijchen, Netherlands.
   [Ossenkop, Carolin] Radboud Univ Nijmegen, Inst Management Res, Nijmegen, Netherlands.
   [Schiffbaenker, Helene] Joanneum Res Forsch Gesell MbH, Graz, Austria.
C3 Radboud University Nijmegen
EM c.j.vinkenburg@gmail.com
RI ; Vinkenburg, Claartje/F-1664-2013
OI Ossenkop, Carolin/0000-0002-2139-9984; Vinkenburg,
   Claartje/0000-0002-4607-7287
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NR 22
TC 2
Z9 2
U1 0
U2 8
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 2040-7149
EI 2040-7157
J9 EQUAL DIVERS INCL
JI Equal. Divers. Incl.
PD OCT 27
PY 2021
VL 41
IS 9
BP 1
EP 14
DI 10.1108/EDI-01-2021-0028
EA OCT 2021
PG 14
WC Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA 0D9YO
UT WOS:000711609600001
OA hybrid, Green Submitted
DA 2024-09-05
ER
PT J
AU Engström, A
   Pittino, D
   Mohlin, A
   Johansson, A
   Mirzaei, NE
AF Engstrom, Annika
   Pittino, Daniel
   Mohlin, Alice
   Johansson, Anette
   Mirzaei, Nina Edh
TI Artificial intelligence and work transformations: integrating
   sensemaking and workplace learning perspectives
SO INFORMATION TECHNOLOGY & PEOPLE
LA English
DT Article; Early Access
DE Action research; Sensemaking; Socio-technical theory; Organizational
   learning; Collaboration; Organizational change; Management practices
ID INFORMATION-TECHNOLOGY; USER ACCEPTANCE; FOCUS GROUP; FUTURE; FRAMEWORK;
   INSIGHTS; LEVEL
AB PurposeThe purpose of this study is to explore the process of initial sensemaking that organizational members activate when they reflect on AI adoption in their work settings, and how the perceived features of AI technologies trigger sensemaking processes which in turn have the potential to influence workplace learning modes and trajectories.Design/methodology/approachWe adopted an explorative qualitative and interactive approach to capture free fantasies and imaginative ideas of AI among people within the industry. We adopt a conceptual perspective that combines theories on initial sensemaking and workplace learning as a theoretical lens to analyze data collected during 23 focus groups held at four large Swedish manufacturing companies. The data were analyzed using the Gioia method.FindingsTwo aggregated dimensions were defined and led to the development of an integrated conceptualization of the initial sensemaking of AI technology adoption. Specifically, sensemaking triggered by abstract features of AI technology mainly pointed to an exploitative learning path. Sensemaking triggered by concrete features of the technology mainly pointed to explorative paths, where socio-technical processes appear to be crucial in the process of AI adoption.Originality/valueThis is one of the first studies that attempts to explore and conceptualize how organizations make sense of prospective workplace learning in the context of AI adoption.
C1 [Engstrom, Annika; Mohlin, Alice; Johansson, Anette; Mirzaei, Nina Edh] Jonkoping Univ, Sch Engn, Jonkoping, Sweden.
   [Pittino, Daniel] Jonkoping Univ, Jonkoping Int Business Sch, Jonkoping, Sweden.
   [Pittino, Daniel] Univ Udine, DMIF, Udine, Italy.
C3 Jonkoping University; Jonkoping University; University of Udine
RP Engström, A (corresponding author), Jonkoping Univ, Sch Engn, Jonkoping, Sweden.
EM annika.engstrom@ju.se
FU Knowledge Intensive Product Realization SPARK at Joenkoeping University,
   Sweden [20200223]
FX The authors acknowledge the Knowledge Foundation, Joenkoeping
   University, and the industrial partners for funding the research and
   education environment on Knowledge Intensive Product Realization SPARK
   at Joenkoeping University, Sweden. Project: AFAIR (No: 20200223).
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NR 62
TC 0
Z9 0
U1 4
U2 4
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 0959-3845
EI 1758-5813
J9 INFORM TECHNOL PEOPL
JI Inf. Technol. People
PD 2024 JUL 15
PY 2024
DI 10.1108/ITP-01-2023-0048
EA JUL 2024
PG 21
WC Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science
GA YF1E0
UT WOS:001266970800001
DA 2024-09-05
ER
PT J
AU Sahran, F
   Altarturi, HHM
   Anuar, NB
   Bouras, CJ
   Liu, YM
   Zhang, Z
   Meng, Y
AF Sahran, Firdaus
   Altarturi, Hamza H. M.
   Anuar, Nor Badrul
   Bouras, Christos J.
   Liu, Yiming
   Zhang, Zhi
   Meng, Yue
TI Exploring the Landscape of AI-SDN: A Comprehensive Bibliometric Analysis
   and Future Perspectives
SO ELECTRONICS
LA English
DT Article
DE artificial intelligence; Software-Defined Networking; bibliometrics;
   machine learning; data visualization
ID SOFTWARE-DEFINED NETWORKING; INTRUSION DETECTION; FRAMEWORK; SCIENCE
AB The rising influence of artificial intelligence (AI) enables widespread adoption of the technology in every aspect of computing, including Software-Defined Networking (SDN). Technological adoption leads to the convergence of AI and SDN, producing solutions that overcome limitations present in traditional networking architecture. Although numerous review articles discuss the convergence of these technologies, there is a lack of bibliometric trace in this field, which is important for identifying trends, new niches, and future directions. Therefore, this study aims to fill the gap by presenting a thorough bibliometric analysis of AI-related SDN studies, referred to as AI-SDN. The study begins by identifying 474 unique documents in the Web of Science (WoS) database published from 2009 until recently. The study uses bibliometric analysis to identify the general information, countries, authorship, and content of the selected articles, thereby providing insights into the geographical and institutional landscape shaping AI-SDN research. The findings provide a robust roadmap for further investigation in this field, including the background and taxonomy of the AI-SDN field. Finally, the article discusses several challenges and the future of AI-SDN in academic research.
C1 [Sahran, Firdaus; Altarturi, Hamza H. M.; Anuar, Nor Badrul] Univ Malaya, Fac Comp Sci & Informat Technol, Dept Comp Syst & Technol, Kuala Lumpur 50603, Malaysia.
C3 Universiti Malaya
RP Anuar, NB (corresponding author), Univ Malaya, Fac Comp Sci & Informat Technol, Dept Comp Syst & Technol, Kuala Lumpur 50603, Malaysia.
EM firdaussahran@um.edu.my; hamza.altarturi@um.edu.my; badrul@um.edu.my
RI Yiming, Liu/GYJ-8249-2022; wang, yi/HOF-6668-2023
OI Sahran, Firdaus/0000-0002-3470-0086
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NR 62
TC 0
Z9 0
U1 13
U2 16
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2079-9292
J9 ELECTRONICS-SWITZ
JI Electronics
PD JAN
PY 2024
VL 13
IS 1
AR 26
DI 10.3390/electronics13010026
PG 33
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Physics
GA EM3J5
UT WOS:001139301200001
OA gold
DA 2024-09-05
ER
PT J
AU Della Corte, D
   Morris, CJ
   Billings, WM
   Stern, J
   Jarrett, AJ
   Hedelius, B
   Bennion, A
AF Della Corte, Dennis
   Morris, Connor J.
   Billings, Wendy M.
   Stern, Jacob
   Jarrett, Austin J.
   Hedelius, Bryce
   Bennion, Adam
TI Training undergraduate research assistants with an outcome-oriented and
   skill-based mentoring strategy
SO ACTA CRYSTALLOGRAPHICA SECTION D-STRUCTURAL BIOLOGY
LA English
DT Article
DE mentoring; undergraduates; CASP; protein structure prediction; deep
   learning
AB Effective mentoring of undergraduate students is a growing requirement for the promotion of faculty at many universities. It is often challenging for young investigators to define a successful mentoring strategy, partially due to the absence of a broadly accepted definition of what mentoring should entail. To overcome this, an outcome-oriented mentoring framework was developed and used with more than 25 students over three years. It was found that a systematic mentoring approach can help students quickly realize their scientific potential and result in meaningful contributions to science. This report especially shows how the Critical Assessment of Protein Structure Prediction (CASP14) challenge was used to amplify student research efforts. As a result of this challenge, multiple publications, presentations and scholarships were awarded to the participating students. The mentoring framework continues to see much success in allowing undergraduate students, including students from under-represented groups, to foster scientific talent and make meaningful contributions to the scientific community.
C1 [Della Corte, Dennis; Morris, Connor J.; Billings, Wendy M.; Stern, Jacob; Jarrett, Austin J.; Hedelius, Bryce; Bennion, Adam] Brigham Young Univ, Dept Phys & Astron, Provo, UT 84602 USA.
C3 Brigham Young University
RP Della Corte, D; Bennion, A (corresponding author), Brigham Young Univ, Dept Phys & Astron, Provo, UT 84602 USA.
EM dennis.dellacorte@byu.edu; adam_bennion@byu.edu
OI Della Corte, Dennis/0000-0002-8884-9724; Bennion,
   Adam/0000-0003-2524-7360; Jarrett, Austin/0000-0001-8522-0309
FU College of Physical and Mathematical Sciences at BYU
FX DDC thanks the College of Physical and Mathematical Sciences at BYU for
   start-up and undergraduate funds. All authors thank the Office of
   Research Computing at BYU for access to compute resources. Author
   contributions are as follows. DDC developed the strategy and mentored
   the students. CM, WMB and BH prepared and executed the CASP experiment.
   JS and AJ contributed to the CASP conference and related publications.
   AB performed the postexperiment interviews and analysis. All authors
   contributed to the writing of this article.
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NR 27
TC 0
Z9 0
U1 1
U2 3
PU INT UNION CRYSTALLOGRAPHY
PI CHESTER
PA 2 ABBEY SQ, CHESTER, CH1 2HU, ENGLAND
SN 2059-7983
J9 ACTA CRYSTALLOGR D
JI Acta Crystallogr. Sect. D-Struct. Biol.
PD AUG 1
PY 2022
VL 78
BP 936
EP 944
DI 10.1107/S2059798322005861
PN 8
PG 9
WC Biochemical Research Methods; Biochemistry & Molecular Biology;
   Biophysics; Crystallography
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Biophysics; Crystallography
GA 3O7WU
UT WOS:000837045300002
PM 35916219
OA hybrid, Green Published
DA 2024-09-05
ER
PT C
AU Mohamed, B
AF Mohamed, Bahaaeldin
BE Chova, LG
   Martinez, AL
   Torres, IC
TI RESEARCH FOR SILLY BILLY: TECHNICAL COLLEGES' STUDENTS CONDUCT A
   SCIENTIFIC RESEARCH
SO 9TH INTERNATIONAL CONFERENCE ON EDUCATION AND NEW LEARNING TECHNOLOGIES
   (EDULEARN17)
SE EDULEARN Proceedings
LA English
DT Proceedings Paper
CT 9th International Conference on Education and New Learning Technologies
   (EDULEARN)
CY JUL 03-05, 2017
CL Barcelona, SPAIN
DE Conducting research; Digital research; e-Science; communication;
   collaboration; learning evaluation; Social online learning;
   Research-based learning; Academic writing; Research methodology;
   teaching to conduct research
ID ATTITUDES
AB The purpose of this study is to create a new framework to help the Saudi Technical College students to conduct a first piece of research mainly, the bachelor's thesis. The research proposed a framework in helping students to understand not only resource referencing, data collection, data analysis and problem solving but the academic writing and project management as well. The data were collected from Saudi Bachelor students at Technical Trainer College in Riyadh, Kingdom of Saudi Arabia. Students were asked to conduct a bachelor's thesis within 3 months in order for them to obtain their Bachelor's Degree in Engineering. The researcher conducted an experiment to test the impact of the proposed framework on students' learning and attitudes towards research. The proposed framework consists of 7 weekly lectures and individuals' consultation face to face and online. The students were asked to fill out a survey expressing their attitudes towards the program. In addition, the researcher conducted a brainstorming within the focused group to investigate qualitative data. The results indicate not only a positive attitude among students for learning academic writing and research methodology, but a reduced number of falsifications and acquisition of plagiarism.
C1 [Mohamed, Bahaaeldin] Lincoln Coll Int Riyadh, Res Methodol & Vocat Pedag, Bachelor Thesis, Riyadh, Saudi Arabia.
RP Mohamed, B (corresponding author), Lincoln Coll Int Riyadh, Res Methodol & Vocat Pedag, Bachelor Thesis, Riyadh, Saudi Arabia.
FU Lincoln College International in Saudi Arabia
FX This research was supported by Lincoln College International in Saudi
   Arabia. The researcher would like to give his deepest appreciation to
   Mr. Riccy Longden, the Vice Dean and the Bachelor Thesis Committee Head
   for his trust and support. The researcher would also like to show his
   gratitude to Dr. Garry Sharkey, the Head of English and Education
   Enterprise (EEE) department and the Bachelor's Thesis Committee support
   team for his trust and support. The researcher also appreciates Mr.
   Gavin Jenkins, the General Coordinator and Bachelor's Thesis Committee
   support team for the assistance and support.
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NR 22
TC 0
Z9 0
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U2 1
PU IATED-INT ASSOC TECHNOLOGY EDUCATION & DEVELOPMENT
PI VALENICA
PA LAURI VOLPI 6, VALENICA, BURJASSOT 46100, SPAIN
SN 2340-1117
BN 978-84-697-3777-4
J9 EDULEARN PROC
PY 2017
BP 8985
EP 8995
PG 11
WC Education & Educational Research; Education, Scientific Disciplines
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Education & Educational Research
GA BO0RJ
UT WOS:000493048104014
DA 2024-09-05
ER
PT C
AU Xiao, T
   Liu, GH
   Xu, GP
   Li, Y
   Cheng, XZ
   Xu, LX
   Cheng, C
   Zhou, SY
AF Xiao, Tian
   Liu, Guanghai
   Xu, Guoping
   Li, Yi
   Cheng, Xinzhou
   Xu, Lexi
   Cheng, Chen
   Zhou, Shiyu
GP IEEE
TI Research on Coverage Ability Assessment of High and Low Frequency based
   on Machine Learning
SO 2021 INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION
   TECHNOLOGIES FOR DISASTER MANAGEMENT (ICT-DM)
LA English
DT Proceedings Paper
CT International Conference on Information and Communication Technologies
   for Disaster Management (ICT-DM)
CY DEC 03-05, 2021
CL Hangzhou, PEOPLES R CHINA
DE Coverage Ability; Propagation Model; Machine Learning
AB With the rapid construction of 5G network in China, how to guide reasonable network planning and construction through accurate network coverage ability assessment, and build a 5G high-low-frequency hybrid network with low cost and high efficiency, has become an important topic urgently needed to be studied by telecommunication suppliers. Firstly, the propagation models applicable to 2.1G and 3.5G are studied and theoretically calculated. Next, reasonable suggestions are put forward for the problems existed in the calibration for traditional Propagation Model, and the accuracy of the propagation model is improved by adopting the machine learning algorithm and model. Finally, based on outfield test results, the propagation model calibrations for 3.5G and 2.1G bands are conducted, and reasonable suggestions are put forward for 5G high and low frequency hybrid networking scheme.
C1 [Xiao, Tian; Liu, Guanghai; Li, Yi; Cheng, Xinzhou; Xu, Lexi; Cheng, Chen; Zhou, Shiyu] China United Network Commun Corp, Res Inst, Beijing, Peoples R China.
   [Xu, Guoping] China United Network Commun Grp Co Ltd, Beijing, Peoples R China.
C3 China United Network Communications Limited; China United Network
   Communications Limited
RP Xiao, T (corresponding author), China United Network Commun Corp, Res Inst, Beijing, Peoples R China.
EM xiaot6@chinaunicom.cn; liugh124@chinaunicom.cn; xugp5@chinaunicom.cn;
   liy360@chinaunicom.cn; chengxz11@chinaunicom.cn; xulx29@chinaunicom.cn;
   chengc40@chinaunicom.cn; zhousy60@chinaunicom.cn
RI zhang, luyu/JJC-4227-2023; Xu, Lexi/ABT-2601-2022; LI,
   Xiang-Yang/JZE-0275-2024; cheng, cheng/JBR-8359-2023
OI Xu, Lexi/0000-0003-4338-7252; 
FU Ministry of Industry and Information Technology of P.R.China
FX This work was supported in partially by Ministry of Industry and
   Information Technology of P.R.China -Big data industry development pilot
   demonstration project: Integrated heterogeneous data and deep learning
   based civil big data innovative application and pilot demonstration, 5G
   big data cross industry heterogeneous integration innovation application
   pilot demonstration.
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NR 33
TC 0
Z9 0
U1 0
U2 4
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-6654-3285-6
PY 2021
BP 86
EP 92
DI 10.1109/ICT-DM52643.2021.9664166
PG 7
WC Computer Science, Information Systems; Telecommunications
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Telecommunications
GA BT7HR
UT WOS:000848659700014
DA 2024-09-05
ER
PT J
AU Ali, Z
   Qi, GL
   Muhammad, K
   Bhattacharyya, S
   Ullah, I
   Abro, W
AF Ali, Zafar
   Qi, Guilin
   Muhammad, Khan
   Bhattacharyya, Siddhartha
   Ullah, Irfan
   Abro, Waheed
TI Citation recommendation employing heterogeneous bibliographic network
   embedding
SO NEURAL COMPUTING & APPLICATIONS
LA English
DT Article
DE Recommender systems; Citation recommendations; Network embedding; Deep
   learning; Network sparsity
ID GRAPH
AB The massive number of research articles on the Web makes it troublesome for researchers to identify related works that could meet their preferences and interests. Consequently, various network representation learning-based models have been proposed to produce citation recommendations. Nevertheless, these models do not exploit semantic relations and contextual information between the objects of bibliographic papers' networks, which can result in inadequate citation recommendations. Moreover, existing citation recommendation methods face problems such as lack of personalization, cold-start, and network sparsity. To mitigate such problems and produce individualized citation recommendations, we propose a heterogeneous network embedding model that jointly learns node representations by exploiting semantics corresponding to the author, time, context, field of study, citations, and topics. Compared to baseline models, the results produced by the proposed model over the DBLP datasets prove 10% and 12% improvement on mean average precision (MAP) and normalized discounted cumulative gain (nDCG@10) metrics, respectively. Also, the effectiveness of our model is analyzed on the cold-start papers and network sparsity problems, where it gains 12% and 9% better MAP and recall@10 scores, respectively.
C1 [Ali, Zafar; Qi, Guilin; Abro, Waheed] Southeast Univ, Sch Comp Sci & Engn, Nanjing, Peoples R China.
   [Muhammad, Khan] Sejong Univ, Dept Software, Visual Analyt Knowledge Lab VIS2KNOW Lab, Seoul 143747, South Korea.
   [Bhattacharyya, Siddhartha] Rajnagar Mahavidyalaya, Birbhum, India.
   [Ullah, Irfan] Shaheed Benazir Bhutto Univ, Dept Comp Sci, Sheringal, Pakistan.
C3 Southeast University - China; Sejong University
RP Ali, Z (corresponding author), Southeast Univ, Sch Comp Sci & Engn, Nanjing, Peoples R China.; Muhammad, K (corresponding author), Sejong Univ, Dept Software, Visual Analyt Knowledge Lab VIS2KNOW Lab, Seoul 143747, South Korea.
EM zafarali@seu.edu.cn; khan.muhammad@ieee.org
RI Ullah, Irfan/C-9213-2014; Muhammad, Khan/L-9059-2016; Zhang,
   Yihao/JGM-3514-2023; Khan, Muhammad/IXN-8470-2023; Ullah,
   Irfan/CAA-4310-2022; Cheng, Yuan/JKJ-0794-2023; Wang,
   Peilin/JWP-6008-2024; Ali, Zafar/D-7320-2017
OI Ullah, Irfan/0000-0003-0693-5467; Muhammad, Khan/0000-0003-4055-7412;
   Ullah, Irfan/0000-0003-0693-5467; Ullah, Irfan/0000-0003-3961-888X;
   Muhammad, Khan/0000-0002-5302-1150; Ali, Zafar/0000-0002-6404-645X
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NR 58
TC 16
Z9 17
U1 2
U2 38
PU SPRINGER LONDON LTD
PI LONDON
PA 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND
SN 0941-0643
EI 1433-3058
J9 NEURAL COMPUT APPL
JI Neural Comput. Appl.
PD JUL
PY 2022
VL 34
IS 13
SI SI
BP 10229
EP 10242
DI 10.1007/s00521-021-06135-y
EA AUG 2021
PG 14
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2J4QQ
UT WOS:000690716700002
DA 2024-09-05
ER
PT C
AU Liu, YL
   Molenaar, M
   Jiao, LM
   Liu, YF
AF Liu, YL
   Molenaar, M
   Jiao, LM
   Liu, YF
BE Owe, M
   DUrso, G
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TI Research on land evaluation based on fuzzy neural network
SO REMOTE SENSING FOR AGRICULTURE, ECOSYSTEMS, AND HYDROLOGY V
SE Proceedings of SPIE
LA English
DT Proceedings Paper
CT Conference on Remote Sensing for Agriculture, Ecosystems and Hydrology V
CY SEP 08-10, 2003
CL Barcelona, SPAIN
DE land suitability evaluation; fuzzy neural networks (FNN); fuzzy system
AB This paper focuses on application of artificial neural networks (ANN) in land suitability evaluation. There are some problems in applying fuzzy system to land suitability evaluation such as self-adjusting ability of the membership functions and rules of fuzzy evaluation system. In this paper, the model of fuzzy neural network is designed for land suitability evaluation. This model is the result of integrated fuzzy system and artificial neural network. This fuzzy neural network model has five layers. The learning algorithm of the model has been designed based on the principle of error back propagation of neural networks. The learning strategy, algorithm and efficiency of the model have been tested and the results of test are satisfied.
C1 Wuhan Univ, Sch Resource & Environm, Wuhan 430079, Peoples R China.
C3 Wuhan University
RP Wuhan Univ, Sch Resource & Environm, Wuhan 430079, Peoples R China.
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Z9 3
U1 0
U2 4
PU SPIE-INT SOC OPTICAL ENGINEERING
PI BELLINGHAM
PA 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA
SN 0277-786X
EI 1996-756X
BN 0-8194-5115-0
J9 PROC SPIE
PY 2004
VL 5232
BP 565
EP 574
DI 10.1117/12.510895
PG 10
WC Agriculture, Multidisciplinary; Environmental Sciences; Remote Sensing;
   Water Resources
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Agriculture; Environmental Sciences & Ecology; Remote Sensing; Water
   Resources
GA BY77H
UT WOS:000189459600056
DA 2024-09-05
ER
PT J
AU Williams, K
   Michalska, S
   Cohen, E
   Szomszor, M
   Grant, J
AF Williams, Kate
   Michalska, Sandra
   Cohen, Eliel
   Szomszor, Martin
   Grant, Jonathan
TI Exploring the application of machine learning to expert evaluation of
   research impact
SO PLOS ONE
LA English
DT Article
ID REF 2014; READABILITY
AB The objective of this study is to investigate the application of machine learning techniques to the large-scale human expert evaluation of the impact of academic research. Using publicly available impact case study data from the UK's Research Excellence Framework (2014), we trained five machine learning models on a range of qualitative and quantitative features, including institution, discipline, narrative style (explicit and implicit), and bibliometric and policy indicators. Our work makes two key contributions. Based on the accuracy metric in predicting high- and low-scoring impact case studies, it shows that machine learning models are able to process information to make decisions that resemble those of expert evaluators. It also provides insights into the characteristics of impact case studies that would be favoured if a machine learning approach was applied for their automated assessment. The results of the experiments showed strong influence of institutional context, selected metrics of narrative style, as well as the uptake of research by policy and academic audiences. Overall, the study demonstrates promise for a shift from descriptive to predictive analysis, but suggests caution around the use of machine learning for the assessment of impact case studies.
C1 [Williams, Kate] Univ Melbourne, Sch Social & Polit Sci, Melbourne, Vic, Australia.
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   [Grant, Jonathan] Different Angles, Cambridge, England.
C3 University of Melbourne; University of London; King's College London
RP Williams, K (corresponding author), Univ Melbourne, Sch Social & Polit Sci, Melbourne, Vic, Australia.
EM kate.williams@unimelb.edu.au
RI Szomszor, Martin/N-9188-2018
OI Cohen, Eliel/0000-0002-7064-4552
FU UK Economic and Social Research Council (ESRC) [ES/V004123/1]; ESRC
   [ES/V004123/1] Funding Source: UKRI
FX KW & JG. ES/V004123/1 UK Economic and Social Research Council (ESRC)
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   design, data collection and analysis, decision to publish, or
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NR 66
TC 1
Z9 1
U1 1
U2 5
PU PUBLIC LIBRARY SCIENCE
PI SAN FRANCISCO
PA 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA
SN 1932-6203
J9 PLOS ONE
JI PLoS One
PD AUG 3
PY 2023
VL 18
IS 8
AR e0288469
DI 10.1371/journal.pone.0288469
PG 18
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA O4DI4
UT WOS:001043333000039
PM 37535633
OA Green Published, gold
DA 2024-09-05
ER
PT J
AU Gao, XY
   Wu, Q
   Liu, YY
   Yang, RL
AF Gao, Xingyu
   Wu, Qiang
   Liu, Yuanyuan
   Yang, Ruilu
TI Pasteur's quadrant in AI: do patent-cited papers have higher scientific
   impact?
SO SCIENTOMETRICS
LA English
DT Article
DE Artificial intelligence; Pasteur's quadrant; Patent citation; Scientific
   citation; Usage count
ID CITATION COUNTS; COMPUTER-SCIENCE; H-INDEX; PUBLICATIONS; TECHNOLOGY;
   INNOVATION; KNOWLEDGE; TIME; DETERMINANTS; RESEARCHERS
AB In scientific research, basic research that is both curiosity-driven and use-inspired is known as Pasteur's Quadrant. The research on the impact and attention of Pasteur's Quadrant is an essential research topic in academia. In view of the many milestone breakthroughs that Artificial Intelligence (AI) has brought to humanity, this paper delves into Pasteur's Quadrant in AI through the citation of papers by patents. We empirically analyse the scientific impact of 3322 patent-cited papers and 6587 non-patent-cited papers published from 1999 to 2013, where scientific impact is measured by scientific citations and usage counts. Our main results show that patent-cited papers have a stronger scientific impact than non-patent-cited papers, and this impact is further enhanced in conference publications than in journal publications. Further, the relationship between the multidimensional characteristics of patent citations and scientific impact is investigated in terms of patent-cited papers. We find an inverted U-shaped relationship between the intensity of a paper's patent citations and its scientific citations, as well as between the breadth of a paper's patent citations and its scientific citations. In addition, the patent citation lag of a paper negatively relates to its scientific impact.
C1 [Gao, Xingyu; Wu, Qiang; Liu, Yuanyuan; Yang, Ruilu] Univ Sci & Technol China, Sch Management, 96 Jinzhai Rd, Hefei 230026, Peoples R China.
C3 Chinese Academy of Sciences; University of Science & Technology of
   China, CAS
RP Wu, Q (corresponding author), Univ Sci & Technol China, Sch Management, 96 Jinzhai Rd, Hefei 230026, Peoples R China.
EM qiangwu@ustc.edu.cn
OI Gao, Xingyu/0000-0002-0905-3227; Wu, Qiang/0000-0002-1308-1669
FU National Natural Science Foundation of China [71874173, 72374188];
   National Natural Science Foundation of China [FSSF-A-230204]; Featured
   Social Science Fund of the University of Science and Technology of China
FX This research was supported by the National Natural Science Foundation
   of China (Grant No. 71874173 and 72374188) and the Featured Social
   Science Fund of the University of Science and Technology of China
   (FSSF-A-230204). We would like to thank the editor and anonymous
   reviewers for their constructive comments and suggestions, which helped
   us to improve the paper.
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TC 1
Z9 1
U1 36
U2 49
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD FEB
PY 2024
VL 129
IS 2
BP 909
EP 932
DI 10.1007/s11192-023-04925-w
EA JAN 2024
PG 24
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA HK8Z0
UT WOS:001137293300001
DA 2024-09-05
ER
PT J
AU Nicholson, JM
   Mordaunt, M
   Lopez, P
   Uppala, A
   Rosati, D
   Rodrigues, NP
   Grabitz, P
   Rife, SC
AF Nicholson, Josh M.
   Mordaunt, Milo
   Lopez, Patrice
   Uppala, Ashish
   Rosati, Domenic
   Rodrigues, Neves P.
   Grabitz, Peter
   Rife, Sean C.
TI scite: A smart citation index that displays the context of citations and
   classifies their intent using deep learning
SO QUANTITATIVE SCIENCE STUDIES
LA English
DT Article
DE bibliometrics; citations; evaluation; machine learning; publishing;
   scientometrics
ID INCREASED AMYGDALA; ASSOCIATION; ACTIVATION; ANXIETY; SCIENCE; FACES
AB Citation indices are tools used by the academic community for research and research evaluation that aggregate scientific literature output and measure impact by collating citation counts. Citation indices help measure the interconnections between scientific papers but fall short because they fail to communicate contextual information about a citation. The use of citations in research evaluation without consideration of context can be problematic because a citation that presents contrasting evidence to a paper is treated the same as a citation that presents supporting evidence. To solve this problem, we have used machine learning, traditional document ingestion methods, and a network of researchers to develop a "smart citation index" called scite, which categorizes citations based on context. Scite shows how a citation was used by displaying the surrounding textual context from the citing paper and a classification from our deep learning model that indicates whether the statement provides supporting or contrasting evidence for a referenced work, or simply mentions it. Scite has been developed by analyzing over 25 million full-text scientific articles and currently has a database of more than 880 million classified citation statements. Here we describe how scite works and how it can be used to further research and research evaluation.
C1 [Nicholson, Josh M.; Mordaunt, Milo; Uppala, Ashish; Rosati, Domenic; Rodrigues, Neves P.; Grabitz, Peter; Rife, Sean C.] Scite, Brooklyn, NY 11211 USA.
   [Lopez, Patrice] Sci Miner, Naves, France.
   [Grabitz, Peter] Charite Univ Med Berlin, Berlin, Germany.
   [Rife, Sean C.] Murray State Univ, Murray, KY 42071 USA.
C3 Berlin Institute of Health; Free University of Berlin; Humboldt
   University of Berlin; Charite Universitatsmedizin Berlin; Murray State
   University
RP Nicholson, JM (corresponding author), Scite, Brooklyn, NY 11211 USA.
EM josh@scite.al
OI Rosati, Domenic/0000-0003-2666-7615; Rife, Sean/0000-0002-6748-0841;
   Grabitz, Peter/0000-0001-5658-2482; Mordaunt, Milo/0000-0001-5395-4252;
   Nicholson, Joshua/0000-0002-1111-1828
FU NIDA grant [4R44DA050155-02]
FX This work was supported by NIDA grant 4R44DA050155-02.
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NR 56
TC 40
Z9 43
U1 4
U2 37
PU MIT PRESS
PI CAMBRIDGE
PA ONE ROGERS ST, CAMBRIDGE, MA 02142-1209 USA
EI 2641-3337
J9 QUANT SCI STUD
JI Quant. Sci. Stud.
PD NOV 5
PY 2021
VL 2
IS 3
BP 882
EP 898
DI 10.1162/qss_a_00146
PG 17
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA XQ8PQ
UT WOS:000731804900003
OA gold, Green Submitted
DA 2024-09-05
ER
PT J
AU Yee, JY
   Tsai, CJ
   Hsu, TY
   Lin, JY
   Cheng, PC
AF Yee, Jen-Yuan
   Tsai, Cheng-Jung
   Hsu, Tien-Yu
   Lin, Jung-Yi
   Cheng, Pei-Cheng
TI FEATURE SELECTION AND CLASSIFICATION INTEGRATED METHOD FOR IDENTIFYING
   CITED TEXT SPANS FOR CITANCES ON IMBALANCED DATA
SO MALAYSIAN JOURNAL OF COMPUTER SCIENCE
LA English
DT Article
DE Citation analysis; cited text spans identification; feature selection;
   classification; class imbalance; performance evaluation; scientific
   paper summarization
AB Recent studies in scientific paper summarization have explored a new form of structured summary for a reference paper by grouping all cited and citing sentences together by facet. This involves three main tasks: (1) identifying cited text spans for citances (i.e., citing sentences), (2) classifying their discourse facets, and (3) generating a structured summary from the cited text spans and citances. This paper focuses on the first task, and approaches the task as binary classification to distinguish relevant pairs of citances and reference sentences from irrelevant pairs. We propose a new method that integrates feature selection and classification techniques to enhance classification performance. The proposed method investigates combinations of six feature selection methods (chi(2)-Statistics, Information Gain, Gain Ratio, Relief-F, Significance Attribute Evaluation, and Symmetrical Uncertainty), and five classification algorithms (k-Nearest Neighbors, Decision Tree, Support Vector Machine, Naive Bayes, and Random Forest). Additionally, to address imbalanced data during training, we apply SMOTE (Synthetic Minority Over sampling Technique) to introduce synthetic biases towards the minority. Experiments are conducted using the CLSciSumm corpora to compare the effect of feature selection applied to classification. The results reveal the benefits of feature selection in significantly boosting performance of F-1 score metric, and show that our method is competitive to the state-of-the-art methods in the CL-SciSumm evaluations.
C1 [Yee, Jen-Yuan] Natl Museum Nat Sci, Visitor Serv, Dept Operat, Collect & Informat Management, Taichung 40453, Taiwan.
   [Tsai, Cheng-Jung] Natl Changhua Univ Educ, Grad Inst Stat & Informat Sci, Changhua 50007, Taiwan.
   [Hsu, Tien-Yu] Natl Museum Nat Sci, Dept Sci Educ, Taichung 40453, Taiwan.
   [Lin, Jung-Yi] Hon Hai Precis IndCo Ltd Foxconn, IP Affairs Div, Taipei 11492, Taiwan.
   [Cheng, Pei-Cheng] Chien Hsin Univ Sci & Technol, Dept Informat Management, Taoyuan 32097, Taiwan.
C3 National Changhua University of Education; Chien Hsin University of
   Science & Technology
RP Yee, JY (corresponding author), Natl Museum Nat Sci, Visitor Serv, Dept Operat, Collect & Informat Management, Taichung 40453, Taiwan.
EM jenyuan@nmns.edu.tw; cjtsai@cc.ncue.edu.tw; dan@nmns.edu.tw;
   jungyilin@gmail.com; pccheng@uch.edu.tw
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PU UNIV MALAYA, FAC COMPUTER SCIENCE & INFORMATION TECH
PI KUALA LUMPUR
PA UNIV MALAYA, FAC COMPUTER SCIENCE & INFORMATION TECH, KUALA LUMPUR,
   50603, MALAYSIA
SN 0127-9084
J9 MALAYS J COMPUT SCI
JI Malayas. J. Comput. Sci.
PY 2021
VL 34
IS 4
BP 355
EP 373
DI 10.22452/mjcs.vol34no4.3
PG 19
WC Computer Science, Artificial Intelligence; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WS2WM
UT WOS:000715047500003
OA Bronze
DA 2024-09-05
ER
PT J
AU Ashraf, QM
   Tahir, M
   Habaebi, MH
   Isoaho, J
AF Ashraf, Qazi Mamoon
   Tahir, Mohammad
   Habaebi, Mohamed Hadi
   Isoaho, Jouni
TI Toward Autonomic Internet of Things: Recent Advances, Evaluation
   Criteria, and Future Research Directions
SO IEEE INTERNET OF THINGS JOURNAL
LA English
DT Article
DE Artificial intelligence (AI); autonomic comput-ing; blockchain; edge
   computing; Internet of Things (IoT); machine learning (ML); self-*
   paradigm
ID MOBILE EDGE; RESOURCE-MANAGEMENT; IOT; CHALLENGES; SECURITY;
   INTELLIGENCE; MECHANISMS; PARADIGM; SYSTEMS; CLOUD
AB With the rise of the Internet of Things (IoT), tiny devices capable of computation and data transmission are being deployed across various technological domains. Due to the wide deployment of these devices, manual setup and management are infeasible and inefficient. To address this inefficiency, intelligent procedures must be established to enable autonomy that allows devices and networks to operate efficiently with minimal human intervention. In the traditional client-server paradigm, autonomic computing has been proven effective in minimizing user intervention in computer systems management and will benefit IoT networks. However, IoT networks tend to be heterogeneous, distributed, and resource constrained, mandating the need for new approaches to implement autonomic principles compared to traditional approaches. We begin by introducing the basic principles of autonomic computing and its significance in IoT. We then discuss the self-* paradigm and monitor, analyze, plan, and execute (MAPE) loop from an IoT perspective, followed by recent works in IoT and key enabling technologies for enabling autonomic properties in IoT. Based on the self-* paradigm and MAPE loop analysis from the existing literature, we propose a set of qualitative characteristics for evaluating the autonomy of the IoT network. Finally, we provide a comprehensive list of challenges associated with achieving autonomic IoT and directions for future research.
C1 [Ashraf, Qazi Mamoon] Telekom Malaysia Res & Dev, Dept Res & Innovat, Cyberjaya 63000, Malaysia.
   [Tahir, Mohammad; Isoaho, Jouni] Univ Turku, Dept Comp, Turku 20014, Finland.
   [Habaebi, Mohamed Hadi] Int Islamic Univ Malaysia, Dept Elect & Comp Engn, Kuala Lumpur 53100, Malaysia.
C3 University of Turku; International Islamic University Malaysia
RP Tahir, M (corresponding author), Univ Turku, Dept Comp, Turku 20014, Finland.
EM mamoon@tmrnd.com.my; tahir.mohammad@utu.fi; habaebi@iium.edu.my;
   jisoaho@utu.fi
RI Tahir, Mohammad/ABA-8684-2020; Habaebi, Mohamed Hadi/P-2128-2017
OI Tahir, Mohammad/0000-0002-6273-4603; Habaebi, Mohamed
   Hadi/0000-0002-2263-0850; Isoaho, Jouni/0000-0002-5789-3992
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NR 106
TC 3
Z9 3
U1 3
U2 5
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2327-4662
J9 IEEE INTERNET THINGS
JI IEEE Internet Things J.
PD AUG 15
PY 2023
VL 10
IS 16
BP 14725
EP 14748
DI 10.1109/JIOT.2023.3285359
PG 24
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA O7WQ3
UT WOS:001045875700042
OA hybrid
DA 2024-09-05
ER
PT J
AU Borah, A
   Skiera, B
AF Borah, Abhishek
   Skiera, Bernd
TI Marketing and investor behavior: Insights, introspections, and
   indications
SO INTERNATIONAL JOURNAL OF RESEARCH IN MARKETING
LA English
DT Article
DE Marketing-finance; Firm value; Historical analysis; Citation analysis;
   Topic modelling; Latent dirichlet allocation
AB This article introduces three exemplary articles that speak to novel, current, and important issues in marketing and investor behavior. One proposes a novel measure of quality using warranties, another examines the context of common ownership, and another provides a sweeping review of the marketing-finance interface. In addition to introducing these articles in the special section, we examine the major topics and their impact in a quartercentury of research on the marketing-finance interface encompassing 373 articles. We identify ten major topics from which the topic stock performance had the highest coverage (19%). The remaining nine topics are covered roughly equally. The topic with the highest average number of citations is marketing spending followed by new products & innovations. We also identify an increasing number of articles over the last quarter-century that underlines the importance of the research on the marketing-finance interface. Finally, we put forward opportunities for the future of marketing-finance interface fusing novel data sources with decisive firm value outcomes. (C) 2021 Elsevier B.V. All rights reserved.
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   [Skiera, Bernd] Goethe Univ Frankfurt Main, Fac Econ & Business, Dept Mkt, Theodor W Adorno Pl 4, D-60629 Frankfurt, Germany.
C3 INSEAD Business School; Goethe University Frankfurt
RP Borah, A (corresponding author), INSEAD, Mkt Area, Europe Campus Blvd Constance, F-77305 Fontainebleau, France.
EM abhishek.borah@insead.edu; skiera@wiwi.uni-frankfurt.de
RI Skiera, Bernd/B-6978-2013
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NR 10
TC 4
Z9 5
U1 6
U2 17
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0167-8116
EI 1873-8001
J9 INT J RES MARK
JI Int. J. Res. Mark.
PD DEC
PY 2021
VL 38
IS 4
BP 811
EP 816
DI 10.1016/j.ijresmar.2021.09.011
EA DEC 2021
PG 6
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA YY9HM
UT WOS:000755095900003
OA hybrid
DA 2024-09-05
ER
PT J
AU Yun, GW
   Lee, KM
   Choi, HH
AF Yun, Gawon
   Lee, Kewman M.
   Choi, Hailey Hyunjin
TI Empowering Student Learning Through Artificial Intelligence: A
   Bibliometric Analysis
SO JOURNAL OF EDUCATIONAL COMPUTING RESEARCH
LA English
DT Article; Early Access
DE artificial intelligence; student learning; education; bibliometric
   analysis
ID EDUCATION; CHATGPT; MODELS
AB Scholarly interest in artificial intelligence (AI) has surged as researchers delve into its transformative impact on various aspects of our lives. AI poses both benefits and challenges, particularly in the context of educators' endeavors to comprehend the intricacies of students' learning processes. Although the use of AI to enhance and assist student learning is relatively new, the exponential growth of scholarly attention and publications in AI and student learning in recent years underscores the compelling necessity for further inquiry. Investigating this area is crucial for understanding the emerging trends in this research domain. This study aims to provide insights into the burgeoning research trajectories on AI from a student learning perspective. Using a bibliometric approach, this study examined 663 scholarly articles pertaining to the interface between AI and student learning published between 1961 and 2024. Our findings reveal four major thematic areas including AI in education and educational technology, AI-driven learning environments, essential AI enablers, and human learning and highlight promising avenues at this intersection.
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C3 Missouri State University; Missouri State University
RP Choi, HH (corresponding author), Missouri State Univ, Coll Educ, 901 S Natl Ave Hill Hall 307, Springfield, MO 65897 USA.
EM hchoi@missouristate.edu
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U1 0
U2 0
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 0735-6331
EI 1541-4140
J9 J EDUC COMPUT RES
JI J. Educ. Comput. Res.
PD 2024 AUG 28
PY 2024
DI 10.1177/07356331241278636
EA AUG 2024
PG 34
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA E0S4D
UT WOS:001300188900001
DA 2024-09-05
ER
PT J
AU Zhang, Y
   Wu, MJ
   Tian, GY
   Zhang, GQ
   Lu, J
AF Zhang, Yi
   Wu, Mengjia
   Tian, George Yijun
   Zhang, Guangquan
   Lu, Jie
TI Ethics and privacy of artificial intelligence: Understandings from
   bibliometrics
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Artificial intelligence; Ethics; Privacy; Bibliometrics; Topic analysis
ID TOPIC EXTRACTION; TECHNOLOGY; AI; METHODOLOGY; SCIENCE
AB Artificial intelligence (AI) and its broad applications are disruptively transforming the daily lives of human beings and a discussion of the ethical and privacy issues surrounding AI is a topic of growing interest, not only among academics but also the general public This review identifies the key entities (i.e., leading research institutions and their affiliated countries/regions, core research journals, and communities) that contribute to the research on the ethical and privacy issues in relation to AI and their intersections using co-occurrence analysis. Topic analyses profile the topical landscape of AI ethics using a topical hierarchical tree and the changing interest of society in AI ethics over time through scientific evolutionary pathways. We also paired 15 selected AI techniques with 17 major ethical issues and identify emerging ethical issues from a core set of the most recent articles published in Nature, Science, and Proceedings of the National Science Academy of the United States. These insights bridging the knowledge base of AI techniques and ethical issues in the literature, are of interest to the AI community and audiences in science policy, technology management, and public administration. (C) 2021 Elsevier B.V. All rights reserved.
C1 [Zhang, Yi; Wu, Mengjia; Zhang, Guangquan; Lu, Jie] Univ Technol Sydney, Australian Artificial Intelligence Inst, Fac Engn & Informat Technol, Sydney, NSW, Australia.
   [Tian, George Yijun] Univ Technol Sydney, Fac Law, Sydney, NSW, Australia.
C3 University of Technology Sydney; University of Technology Sydney
RP Zhang, Y (corresponding author), Univ Technol Sydney, Australian Artificial Intelligence Inst, Fac Engn & Informat Technol, Sydney, NSW, Australia.
EM yi.zhang@uts.edu.au; mengjia.wu@student.uts.edu.au;
   yijun.tian@uts.edu.au; guangquan.zhang@uts.edu.au; jie.lu@uts.edu.au
RI Wu, Mengjia/AFU-9852-2022; Zhang, Yi/AAT-6945-2021; Lu, Jie/S-3581-2016;
   Zhang, Guangquan/G-2553-2017
OI Wu, Mengjia/0000-0003-3956-7808; Zhang, Yi/0000-0002-7731-0301; Tian,
   George/0000-0003-4472-5428; Lu, Jie/0000-0003-0690-4732; Zhang,
   Guangquan/0000-0003-3960-0583
FU Australian Research Council [DE190100994]; Australian Research Council
   [DE190100994] Funding Source: Australian Research Council
FX This work is supported by the Australian Research Council under
   Discovery Early Career Researcher Award DE190100994.
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NR 52
TC 29
Z9 29
U1 30
U2 209
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD JUN 21
PY 2021
VL 222
AR 106994
DI 10.1016/j.knosys.2021.106994
EA APR 2021
PG 14
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA RS5ZQ
UT WOS:000643857400001
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Shueb, S
   Gul, S
   Hussain, A
AF Shueb, Sheikh
   Gul, Sumeer
   Hussain, Aabid
TI Are Pay-Walled Doors of Access Open During the Pandemic? Analysing the
   Open-Access Landscape of COVID-19 Research
SO JOURNAL OF SCHOLARLY PUBLISHING
LA English
DT Article
DE altmetrics; bibliometrics; big data analytics; indexing databases;
   information retrieval; open access; predatory publishers; scholarly
   communication; scientometrics; sentiment analysis; social media
AB Open access (OA) to research results is indispensable for knowing more about COVID-19 and ways to contain it. The study investigates the OA status of the research output on COVID-19 using the Web of Science. The results show that about 85 per cent of the publications are available as OA, which shows a decline over time. Almost an equal proportion of articles are funded and non-funded, with the Department of Health and Human Services and the National Institutes of Health, both in the United States, as the leading sponsors. Although the United States and China were the top contributors, Sweden and the Netherlands share the highest percentage of OA articles. Among publishers, Elsevier, Springer Nature, the Multidisciplinary Publishing Institute, and Wiley were the leading OA publishers, and universities mainly dominated OA research on COVID-19. This study will be helpful for researchers and policymakers to identify the leading contributors to OA research during public health emergencies of international concern.
C1 [Shueb, Sheikh] Islamic Univ Sci & Technol, Rumi Lib, Awantipora, Jammu & Kashmir, India.
   [Gul, Sumeer] Univ Kashmir, Dept Lib & Informat Sci, Jammu, Jammu & Kashmir, India.
   [Hussain, Aabid] Film Div, Directorate Informat & Publ Relat, Jammu, Jammu & Kashmir, India.
C3 University of Kashmir
RP Shueb, S (corresponding author), Islamic Univ Sci & Technol, Rumi Lib, Awantipora, Jammu & Kashmir, India.
EM shkhshb@gmail.com
RI Gul, Sumeer/H-8253-2012; Shueb, Sheikh/AAT-9257-2021
OI Shueb, Sheikh/0000-0002-4323-4363; Gul, Sumeer/0000-0002-0258-1182
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NR 27
TC 0
Z9 0
U1 5
U2 5
PU UNIV TORONTO PRESS INC
PI TORONTO
PA JOURNALS DIVISION, 5201 DUFFERIN ST, DOWNSVIEW, TORONTO, ON M3H 5T8,
   CANADA
SN 1198-9742
EI 1710-1166
J9 J SCHOLARLY PUBL
JI J. Sch. Publ.
PD JAN 1
PY 2024
VL 55
IS 1
BP 54
EP 83
DI 10.3138/jsp-2023-0005
PG 30
WC Humanities, Multidisciplinary; Information Science & Library Science
WE Social Science Citation Index (SSCI); Arts &amp; Humanities Citation Index (A&amp;HCI)
SC Arts & Humanities - Other Topics; Information Science & Library Science
GA RC6D1
UT WOS:001225502100001
DA 2024-09-05
ER
PT J
AU Rivest, M
   Vignola-Gagné, E
   Archambault, É
AF Rivest, Maxime
   Vignola-Gagne, Etienne
   Archambault, Eric
TI Article-level classification of scientific publications: A comparison of
   deep learning, direct citation and bibliographic coupling
SO PLOS ONE
LA English
DT Article
AB Classification schemes for scientific activity and publications underpin a large swath of research evaluation practices at the organizational, governmental, and national levels. Several research classifications are currently in use, and they require continuous work as new classification techniques becomes available and as new research topics emerge. Convolutional neural networks, a subset of "deep learning" approaches, have recently offered novel and highly performant methods for classifying voluminous corpora of text. This article benchmarks a deep learning classification technique on more than 40 million scientific articles and on tens of thousands of scholarly journals. The comparison is performed against bibliographic coupling-, direct citation-, and manual-based classifications-the established and most widely used approaches in the field of bibliometrics, and by extension, in many science and innovation policy activities such as grant competition management. The results reveal that the performance of this first iteration of a deep learning approach is equivalent to the graph-based bibliometric approaches. All methods presented are also on par with manual classification. Somewhat surprisingly, no machine learning approaches were found to clearly outperform the simple label propagation approach that is direct citation. In conclusion, deep learning is promising because it performed just as well as the other approaches but has more flexibility to be further improved. For example, a deep neural network incorporating information from the citation network is likely to hold the key to an even better classification algorithm.
C1 [Rivest, Maxime; Vignola-Gagne, Etienne; Archambault, Eric] Sci Metrix Inc, Montreal, PQ, Canada.
   [Rivest, Maxime; Vignola-Gagne, Etienne; Archambault, Eric] Elsevier BV, Amsterdam, Netherlands.
   [Archambault, Eric] 1Science, Montreal, PQ, Canada.
C3 Reed Elsevier; Elsevier
RP Rivest, M (corresponding author), Sci Metrix Inc, Montreal, PQ, Canada.; Rivest, M (corresponding author), Elsevier BV, Amsterdam, Netherlands.
EM maxime.rivest@science-metrix.com
RI Archambault, Eric JA/G-5808-2019
OI Archambault, Eric JA/0000-0002-4422-1054; Vignola-Gagne,
   Etienne/0000-0002-4948-4363; Rivest, Maxime/0000-0002-1196-4679
FU Elsevier BV
FX The funder, Elsevier BV and its daughter company Science-Metrix Inc.,
   1science, provided support in the form of salaries for authors MR, EVG,
   EA, but did not have any additional role in the study design, data
   collection and analysis, decision to publish, or preparation of the
   manuscript. The specific roles of these authors are articulated in the
   `author contributions' section.
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NR 29
TC 18
Z9 20
U1 0
U2 22
PU PUBLIC LIBRARY SCIENCE
PI SAN FRANCISCO
PA 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA
SN 1932-6203
J9 PLOS ONE
JI PLoS One
PD MAY 11
PY 2021
VL 16
IS 5
AR e0251493
DI 10.1371/journal.pone.0251493
PG 18
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics
GA SW6LY
UT WOS:000664626600046
PM 33974653
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU Wang, N
   Guo, JL
   Zhang, J
   Fan, Y
AF Wang, Ning
   Guo, Jinling
   Zhang, Jian
   Fan, Yu
TI Comparing eco-civilization theory and practice: Big-data evidence from
   China
SO JOURNAL OF CLEANER PRODUCTION
LA English
DT Article
DE Eco-civilization; Bibliometric analysis; Latent Dirichlet Allocation
   (LDA); Big data; Difference analysis
ID ECOLOGICAL CIVILIZATION; BEAUTIFUL CHINA; YELLOW-RIVER; CITY; TRENDS
AB Constructing an eco-civilization is crucial in achieving green, low-carbon development, and thus bridging the gap between theory and practice is imperative to better promote urban ecological transformation. At present, there are inconsistencies and imbalances between theoretical research and practical efforts to achieve an eco-civilization. This paper uses bibliometric analysis and Latent Dirichlet Allocation to analyze China's progress towards an eco-civilization at the theoretical and practical levels. The 'theory' is analyzed using 632 articles, while the 'practice' is analyzed using 100 eco-civilization pilot zone reports. The two are compared, with results showing: First: from 2015 onwards, the theory is moving in an interdisciplinary direction, with seven themes focusing on macro topics. Second: eco-civilization construction projects have multiple overlapping themes, and the growing connection between energy consumption, economic growth and environmental impacts driven by significant projects is the key to improving the level of regional ecological development. Third: the theory and practice are similar in that both are concerned with environmental protection, ecological education, and ecotourism. However, eco-civilization construction projects lack forward-looking and dynamic development tracking of low-carbon research. Finally: the paper proposes that dynamic evaluation and project tracking methods should be applied to monitor critical indicators to achieve a solid link between the theory and practice during eco-construction projects. This paper also proposes academia should do more research on townships and other micro level phenomena to promote climate change and energy revolution in the construction of a Chinese eco-civilization.
C1 [Wang, Ning; Zhang, Jian; Fan, Yu] Beijing Informat Sci & Technol Univ, Beijing 100192, Peoples R China.
   [Wang, Ning; Zhang, Jian] Beijing Int Sci & Technol Cooperat Base Intelligen, Beijing 100192, Peoples R China.
   [Wang, Ning; Zhang, Jian] Beijing Key Lab Big Data Decis Making Green Dev, Beijing 100192, Peoples R China.
   [Guo, Jinling] China Univ Min & Technol Beijing, Beijing 100083, Peoples R China.
C3 Beijing Information Science & Technology University; China University of
   Mining & Technology
RP Zhang, J (corresponding author), Beijing Informat Sci & Technol Univ, Beijing 100192, Peoples R China.
EM zhangjian@bistu.edu.cn
OI Wang, Ning/0000-0002-6155-212X
FU National Natural Science Foundationof China;  [72204027]
FX Acknowledgments This work was supported by the National Natural Science
   Foundationof China (72204027) . We would like to thank the anonymous
   reviewers for their valuable suggestions. The contents of this paper
   reflect the views of the authors and do not necessarily indicate
   acceptance by the sponsors.
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NR 46
TC 13
Z9 13
U1 9
U2 57
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0959-6526
EI 1879-1786
J9 J CLEAN PROD
JI J. Clean Prod.
PD DEC 20
PY 2022
VL 380
AR 134754
DI 10.1016/j.jclepro.2022.134754
EA NOV 2022
PN 1
PG 10
WC Green & Sustainable Science & Technology; Engineering, Environmental;
   Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics; Engineering; Environmental Sciences
   & Ecology
GA 6F5KC
UT WOS:000884098500002
DA 2024-09-05
ER
PT J
AU Mertala, P
   López-Pernas, S
   Vartiainen, H
   Saqr, M
   Tedre, M
AF Mertala, Pekka
   Lopez-Pernas, Sonsoles
   Vartiainen, Henriikka
   Saqr, Mohammed
   Tedre, Matti
TI Digital natives in the scientific literature: A topic modeling approach
SO COMPUTERS IN HUMAN BEHAVIOR
LA English
DT Article
DE Digital natives; Bibliometrics; Structured topic modeling; Digital
   immigrants
ID HEALTH INFORMATION-SEEKING; AUGMENTED REALITY; NET GENERATION; SOCIAL
   MEDIA; STUDENTS; ONLINE; IMMIGRANTS; EDUCATION; MYTH; CAPACITY
AB The term "digital natives" was introduced in 2001 to describe a generation that has grown up surrounded by technology and the internet. The accompanying claims of a new way of thinking among digital natives were influential in shaping educational policy. Still, they were challenged by research that found no evidence of generation-wide cognitive changes in learners. Yet, the digital natives narrative persists in popular media and the education discourse. This study set out to investigate the reasons for the persistence of the digital native myth. It analyzed the metadata from 1886 articles related to the term between 2001 and 2022 using bibliometric methods and structural topic modeling. The results show that the concept of "digital native" is still both warmly embraced and fiercely criticized by scholars mostly from western and high income countries, and the volume of research on the topic is growing. However, the results suggest that what appears as the persistence of the idea is actually evolution and complete reinvention: The way the "digital native" concept is operationalized has shifted over time through a series of (metaphorical) mutations. The concept of digital native is one (albeit a highly successful) mutation of the generational gap discourse dating back to the early 1900s. While the initial digital native literature relied on Prensky's unvalidated claims and waned upon facing empirical challenges, subsequent versions have sought more nuanced interpretations. Notably, a burgeoning third mutation now co-opts the "digital native" terminology for diverse purposes, often completely decoupled from the foundational literature and its critiques. This study explains the concept's persistence as dynamic evolution of the digital native discourse in contemporary academic and public spheres.
C1 [Mertala, Pekka] Univ Jyvaskyla, Fac Educ & Psychol, PL 35, Jyvaskyla 40014, Finland.
   [Lopez-Pernas, Sonsoles; Saqr, Mohammed; Tedre, Matti] Univ Eastern Finland, Sch Comp, Joensuu 80100, Finland.
   [Vartiainen, Henriikka] Univ Eastern Finland, Sch Appl Educ Sci & Teacher Educ, Joensuu 80100, Finland.
C3 University of Jyvaskyla; University of Eastern Finland; University of
   Eastern Finland
RP Mertala, P (corresponding author), Univ Jyvaskyla, Fac Educ & Psychol, PL 35, Jyvaskyla 40014, Finland.
EM pekka.o.mertala@jyu.fi; sonsoles.lopez@uef.fi;
   henriikka.vartiainen@uef.fi; mohammed.saqr@uef.fi; matti.tedre@uef.fi
RI Saqr, Mohammed/AAH-2520-2020; López-Pernas, Sonsoles/M-7375-2019
OI Saqr, Mohammed/0000-0001-5881-3109; López-Pernas,
   Sonsoles/0000-0002-9621-1392
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NR 129
TC 1
Z9 1
U1 12
U2 19
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0747-5632
EI 1873-7692
J9 COMPUT HUM BEHAV
JI Comput. Hum. Behav.
PD MAR
PY 2024
VL 152
AR 108076
DI 10.1016/j.chb.2023.108076
EA DEC 2023
PG 12
WC Psychology, Multidisciplinary; Psychology, Experimental
WE Social Science Citation Index (SSCI)
SC Psychology
GA EE8O4
UT WOS:001137337500001
OA Green Published, hybrid
DA 2024-09-05
ER
PT J
AU Pérez, CP
   Perdomo, B
AF Perez, Carlos Perez
   Perdomo, Bexi
TI Artificial Intelligence in Communication: A Biblio- metric Review in Web
   of Science
SO INVESTIGACION BIBLIOTECOLOGICA
LA English
DT Article
DE Bibliometrics; Artificial Intelligence; Communication; Scientometrics
ID BIG DATA; RISE
AB The accelerated increase of scientific production on Ar - tificial Intelligence requires investigating trends and orienting researchers on new research areas. The study aimed to analyze the scientific production of AI in com - munications. A bibliometric review was performed in the Web of Science database using a five -phase methodology and different bibliometric techniques. We analyzed 994 papers published between 2013 and 2023 and recurred to RS tudio, Bibliometrix, Microsoft Excel and IBM SPSS for the analysis and visualization processes. The United States of America stands out as the country with higher publication rates and it must be said that there is no Latin American representation among the top ten countries with higher publishing production. The thematic analysis shows gaps and emerging topics that contribute to build scientific evidence on AI in communications. In conclu - sion, the article shows an increasing trend in the produc - tion of this topic and that AI from the human perspec - tive seems to be the focus of study in communications. New studies are needed to fill the observed gaps and to strengthen both the driving and basic topics.
C1 [Perez, Carlos Perez] Ctr Invest Innovac Desarrollo & Gest CIIDEG SAC, Lima, Peru.
   [Perdomo, Bexi] Univ Ciencias & Artes Amer Latina UCAL, Ctr Invest Creat, Lima, Peru.
RP Pérez, CP (corresponding author), Ctr Invest Innovac Desarrollo & Gest CIIDEG SAC, Lima, Peru.
EM Carlosperez100@gmail.com; bjperdomod@crear.ucal.edu.pe
RI Pérez Pérez, Carlos/JPA-2212-2023
OI Pérez Pérez, Carlos/0000-0001-8425-1875
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NR 49
TC 0
Z9 0
U1 5
U2 5
PU UNIV NACIONAL AUTONOMA MEXICO
PI MEXICO CITY
PA CIUDAD UNIV, CENTRO UNIV BIBLIOTECOLOGICAS, TORRE II HUMANIDADES, PISO
   11, 12 & 13, MEXICO CITY, CP 04510, MEXICO
SN 0187-358X
EI 2448-8321
J9 INVESTIG BIBLIOTECOL
JI Investig. Bibliotecol.
PD APR-JUN
PY 2024
VL 38
IS 99
BP 165
EP 185
DI 10.22201/iibi.24488321xe.2024.99.58882
PG 21
WC Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science
GA PZ1U9
UT WOS:001217817300001
OA gold
DA 2024-09-05
ER
PT J
AU Khalid, N
AF Khalid, Nadeem
TI Artificial intelligence learning and entrepreneurial performance among
   university students: evidence from malaysian higher educational
   institutions
SO JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
LA English
DT Article
DE Artificial intelligence learning; higher educational institutions;
   strategic entrepreneurship; government funding; entrepreneurial
   orientation; performance; entrepreneurial attitude
ID STRATEGIC ENTREPRENEURSHIP; FIRM PERFORMANCE; EXPLOITATION; ORIENTATION;
   EXPLORATION; AMBIDEXTERITY; OPPORTUNITIES; INFORMATION; TECHNOLOGY;
   NETWORKS
AB Artificial intelligence learning at higher educational institutions is one of the emerging concepts having vital importance to promote entrepreneurship activities among the university students. However, Malaysian Universities are lacking with the artificial intelligence learning activities. The objective of the study is to examine the role of artificial intelligence learning to promote entrepreneurship performance with the help of entrepreneurial orientation and strategic entrepreneurship. Moreover, the moderating role of government funding and attitude towards entrepreneurship is also examined. To achieve the objective of this study, a survey was carried out among the Malaysian universities. 500 questionnaires were distributed among the universities and data were collected from the teaching staff. After collection of data, it was analysed with the help of Partial Least Square (PLS)-Structural Equation Modeling (SEM). It is concluded that artificial intelligence learning is most significant to promote entrepreneurial performance among university students. Entrepreneurial orientation and strategic entrepreneurship play a key role to transfer the positive effect of artificial intelligence learning on entrepreneurial performance. Additionally, government funding and attitude towards entrepreneurship also has significant role.
C1 [Khalid, Nadeem] KIMEP Univ, Alma Ata, Kazakhstan.
C3 KIMEP University
RP Khalid, N (corresponding author), KIMEP Univ, Alma Ata, Kazakhstan.
EM nadeem.k@kimep.kz
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NR 66
TC 16
Z9 16
U1 14
U2 64
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1064-1246
EI 1875-8967
J9 J INTELL FUZZY SYST
JI J. Intell. Fuzzy Syst.
PY 2020
VL 39
IS 4
BP 5417
EP 5435
DI 10.3233/JIFS-189026
PG 19
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA OH1HF
UT WOS:000582322000059
DA 2024-09-05
ER
PT J
AU Rokach, L
AF Rokach, Lior
TI Applying the Publication Power Approach to Artificial Intelligence
   Journals
SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY
LA English
DT Article
DE bibliographic records
ID AUTHOR-AFFILIATION-INDEX; IMPACT FACTOR; SCIENTIFIC JOURNALS; H-INDEX;
   QUALITY; MANAGEMENT; BUSINESS; SYSTEMS; RANKINGS
AB This study evaluates the utility of a publication power approach (PPA) for assessing the quality of journals in the field of artificial intelligence. PPA is compared with the Thomson-Reuters Institute for Scientific Information (TR) 5-year and 2-year impact factors and with expert opinion. The ranking produced by the method under study is only partially correlated with citation-based measures (TR), but exhibits close agreement with expert survey rankings. A simple average of TR and power rankings results in a new ranking that is highly correlated with the expert survey rankings. This evidence suggests that power ranking can contribute to evaluating artificial intelligence journals.
C1 Ben Gurion Univ Negev, Dept Informat Syst Engn, IL-84105 Beer Sheva, Israel.
C3 Ben Gurion University
RP Rokach, L (corresponding author), Ben Gurion Univ Negev, Dept Informat Syst Engn, POB 653, IL-84105 Beer Sheva, Israel.
EM liorrk@bgu.ac.il
RI Rokach, Lior/F-8247-2010
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NR 38
TC 6
Z9 7
U1 0
U2 16
PU WILEY-BLACKWELL
PI MALDEN
PA COMMERCE PLACE, 350 MAIN ST, MALDEN 02148, MA USA
SN 1532-2882
J9 J AM SOC INF SCI TEC
JI J. Am. Soc. Inf. Sci. Technol.
PD JUN
PY 2012
VL 63
IS 6
BP 1270
EP 1277
DI 10.1002/asi.22616
PG 8
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 943OO
UT WOS:000304133900014
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Chakraborti, N
AF Chakraborti, Nirupam
TI Critical Assessment 3: The unique contributions of multi-objective
   evolutionary and genetic algorithms in materials research
SO MATERIALS SCIENCE AND TECHNOLOGY
LA English
DT Article
DE Genetic algorithms; Multi-objective optimisation; Materials design and
   processing; Reviews; Critical assessments
ID ZN-COATED FE; NEURAL-NETWORK; MATERIALS SCIENCE; OPTIMAL-DESIGN;
   OPTIMIZATION; MODEL; PART; SIMULATION
AB The current state of the art of materials research using multi-objective genetic and evolutionary algorithms is briefly presented with critical analyses. The basic concepts of multi-objective optimisation and Pareto optimality are explained in simple terms and the advantages of an evolutionary approach are emphasised. Current materials related research in this area is summarised, focusing on the achievements to date and the specific needs for further improvement.
C1 Indian Inst Technol, Kharagpur 721302, W Bengal, India.
C3 Indian Institute of Technology System (IIT System); Indian Institute of
   Technology (IIT) - Kharagpur
RP Chakraborti, N (corresponding author), Indian Inst Technol, Kharagpur 721302, W Bengal, India.
EM nchakrab@iitkgp.ac.in
RI Chakraborti, Nirupam/C-7082-2011; Chakraborti, Nirupam/HDN-8793-2022
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NR 56
TC 33
Z9 34
U1 0
U2 22
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 0267-0836
EI 1743-2847
J9 MATER SCI TECH-LOND
JI Mater. Sci. Technol.
PD SEP
PY 2014
VL 30
IS 11
BP 1259
EP 1262
DI 10.1179/1743284714Y.0000000578
PG 4
WC Materials Science, Multidisciplinary; Metallurgy & Metallurgical
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Materials Science; Metallurgy & Metallurgical Engineering
GA AQ9WU
UT WOS:000343209500001
DA 2024-09-05
ER
PT J
AU SPROW, FB
AF SPROW, FB
TI EVALUATION OF RESEARCH EXPENDITURES USING TRIANGULAR DISTRIBUTION
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SO INDUSTRIAL AND ENGINEERING CHEMISTRY
LA English
DT Article
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   1955, MILLION RANDOM DIGIT
NR 8
TC 16
Z9 17
U1 0
U2 0
PU AMER CHEMICAL SOC
PI WASHINGTON
PA 1155 16TH ST, NW, WASHINGTON, DC 20036
SN 0019-7866
J9 IND ENG CHEM
PY 1967
VL 59
IS 7
BP 35
EP &
DI 10.1021/ie50691a009
PG 0
WC Chemistry, Applied
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Chemistry
GA 94973
UT WOS:A19679497300007
DA 2024-09-05
ER
PT J
AU Stepanov, VK
   Madzhumder, MS
   Begunova, DD
AF Stepanov, V. K.
   Madzhumder, M. Sh.
   Begunova, D. D.
TI Exploring the Potential of Applying the Artificial Intelligence Language
   Model ChatGPT-3.5 in Library and Bibliographic Activities
SO SCIENTIFIC AND TECHNICAL INFORMATION PROCESSING
LA English
DT Article
DE library processes; artificial intelligence; benchmarking; enhancement of
   library work efficiency; ChatGPT-3.5
AB An experiment on the use of the artificial intelligence model ChatGPT-3.5 to perform typical tasks in the field of library and bibliographic activities as well as similar tasks of virtual reference services in several federal libraries in the Russian Federation is described. The strong and weak aspects of the language model were identified. The results of the experiment demonstrate that ChatGPT even in its current version 3.5 is well suited to perform a number of information processes with the necessary supervision and control by a qualified library specialist.
C1 [Stepanov, V. K.] Russian Acad Sci INION RAN, Inst Sci Informat Social Sci, Moscow, Russia.
   [Stepanov, V. K.; Madzhumder, M. Sh.; Begunova, D. D.] Moscow State Linguist Univ, Informat & Analyt Act Dept, Moscow, Russia.
C3 Russian Academy of Sciences; Institute of Scientific Information on
   Social Sciences of the Russian Academy of Sciences; Moscow State
   Linguistic University
RP Stepanov, VK (corresponding author), Russian Acad Sci INION RAN, Inst Sci Informat Social Sci, Moscow, Russia.; Stepanov, VK (corresponding author), Moscow State Linguist Univ, Informat & Analyt Act Dept, Moscow, Russia.
EM stepanov@vadimstepanov.ru; mmadzhumder@gmail.com; dbegunova01@gmail.com
RI Stepanov, Vadim K./HNR-6340-2023
OI Stepanov, Vadim K./0000-0002-3439-9537
CR Achiam OJ, 2023, Arxiv, DOI [arXiv:2303.08774, DOI 10.48550/ARXIV.2303.08774]
   [Anonymous], AI in focus: Artificial intelligence and libraries
   [Anonymous], And there are clouds in the sky
   Introducing ChatGPT, OpenAI
   Mizintseva M.F., 2020, The COVID-19 Pandemic. Biology and Economy
NR 5
TC 1
Z9 1
U1 35
U2 72
PU PLEIADES PUBLISHING INC
PI NEW YORK
PA PLEIADES HOUSE, 7 W 54 ST, NEW YORK,  NY, UNITED STATES
SN 0147-6882
EI 1934-8118
J9 SCI TECH INF PROCESS
JI Sci. Tech. Inf. Process.
PD SEP
PY 2023
VL 50
IS 3
BP 166
EP 175
DI 10.3103/S0147688223030036
PG 10
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA W7HT8
UT WOS:001093305800002
DA 2024-09-05
ER
PT J
AU Prabowo, H
   Ikhsan, RB
   Yuniarty, Y
AF Prabowo, Hartiwi
   Ikhsan, Ridho Bramulya
   Yuniarty, Yuniarty
TI Student performance in online learning higher education: A preliminary
   research
SO FRONTIERS IN EDUCATION
LA English
DT Article
DE overall quality; course quality; e-learning technology; online learning;
   student engagement; student and institutional factors; instructor
   characteristics; student performance
ID HIGH-SCHOOL-STUDENTS; CONTINUANCE INTENTION; INSTRUCTIONAL-DESIGN;
   ACCEPTANCE; PERCEPTIONS; ENGAGEMENT; EXTENSION; MODEL; TTF
AB The impact of student performance is the focus of online learning because it can determine the success of students and higher education institutions to get good ratings and public trust. This study explores comprehensively the factors that can affect the impact of student performance in online learning. An empirical model of the impact of student performance has been developed from the literature review and previous research. The test of reliability and validity of the empirical model was evaluated through linguist reviews and statistically tested with construct reliability coefficients and confirmatory factor analysis (CFA). Overall, the results of this study prove that the structural model with second-order measurements produces a good fit, while the structural model with first-order measurements shows a poor fit.
C1 [Prabowo, Hartiwi; Ikhsan, Ridho Bramulya; Yuniarty, Yuniarty] Bina Nusantara Univ, Dept Management, Binus Online Learning, Jakarta, Indonesia.
C3 Universitas Bina Nusantara
RP Ikhsan, RB (corresponding author), Bina Nusantara Univ, Dept Management, Binus Online Learning, Jakarta, Indonesia.
EM ridho.bramulya.i@binus.ac.id
RI Ikhsan, Ridho Bramulya/AAA-3716-2019
OI Ikhsan, Ridho Bramulya/0000-0002-1499-3264
FU  [3481/LL3/KR/2021]
FX Funding This work was supported by "Model Evaluasi Pembelajaran Daring
   Dalam Menilai Kualitas Sistem Pendidikan Tinggi Di Indonesia" (contract
   number: 3481/LL3/KR/2021 and contract date: 12 July 2021).
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NR 82
TC 0
Z9 0
U1 3
U2 23
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 2504-284X
J9 FRONT EDUC
JI Front. Educ.
PD NOV 3
PY 2022
VL 7
AR 916721
DI 10.3389/feduc.2022.916721
PG 16
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA 6H8WE
UT WOS:000885711500001
OA gold
DA 2024-09-05
ER
PT J
AU Hoppe, TA
   Arabi, S
   Hutchins, BI
AF Hoppe, Travis A.
   Arabi, Salsabil
   Hutchins, B. Ian
TI Predicting substantive biomedical citations without full text
SO PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF
   AMERICA
LA English
DT Article
DE science policy; machine learning; citation analysis; artificial
   intelligence; bench to bedside translation
ID PREPRINTS
AB Insights from biomedical citation networks can be used to identify promising avenues for accelerating research and its downstream bench-to-bedside translation. Citation analy-sis generally assumes that each citation documents substantive knowledge transfer that informed the conception, design, or execution of the main experiments. Citations may exist for other reasons. In this paper, we take advantage of late-stage citations added during peer review because these are less likely to represent substantive knowledge flow. Using a large, comprehensive feature set of open access data, we train a predictive model to identify late-stage citations. The model relies only on the title, abstract, and citations to previous articles but not the full-text or future citations patterns, making it suitable for publications as soon as they are released, or those behind a paywall (the vast majority). We find that high prediction scores identify late-stage citations that were likely added during the peer review process as well as those more likely to be rhetorical, such as journal self-citations added during review. Our model conversely gives low prediction scores to early-stage citations and citation classes that are known to represent substantive knowledge transfer. Using this model, we find that US federally funded biomedical research publications represent 30% of the predicted early-stage (and more likely to be substantive) knowledge transfer from basic studies to clinical research, even though these comprise only 10% of the literature. This is a threefold overrepresentation in this important type of knowledge flow.
C1 [Hoppe, Travis A.] CDCP, Off Director, Natl Ctr Hlth Stat, Hyattsville, MD 20782 USA.
   [Arabi, Salsabil; Hutchins, B. Ian] Univ Wisconsin Madison, Informat Sch, Sch Comp Data & Informat Sci, Coll Letters & Sci, Madison, WI 53706 USA.
C3 Centers for Disease Control & Prevention - USA; CDC National Center for
   Health Statistics (NCHS); University of Wisconsin System; University of
   Wisconsin Madison
RP Hutchins, BI (corresponding author), Univ Wisconsin Madison, Informat Sch, Sch Comp Data & Informat Sci, Coll Letters & Sci, Madison, WI 53706 USA.
EM bihutchins@wisc.edu
OI Hutchins, B. Ian/0000-0001-7657-552X; Hoppe, Travis/0000-0002-4694-3050
FU Office of the Vice Chancellor for Research and Graduate Education at the
   University of Wisconsin-Madison; Wisconsin Alumni Research Foundation
FX B.I.H. is funded through the Office of the Vice Chancellor for Research
   and Graduate Education at the University of Wisconsin-Madison and
   through funding from the Wisconsin Alumni Research Foundation.
   Disclaimer: This work was performed prior to T.A.H. joining the Centers
   for Disease Control and Prevention and should not be considered a
   research product of that agency.
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NR 63
TC 1
Z9 1
U1 6
U2 11
PU NATL ACAD SCIENCES
PI WASHINGTON
PA 2101 CONSTITUTION AVE NW, WASHINGTON, DC 20418 USA
SN 0027-8424
EI 1091-6490
J9 P NATL ACAD SCI USA
JI Proc. Natl. Acad. Sci. U. S. A.
PD JUL 25
PY 2023
VL 120
IS 30
AR e2213697120
DI 10.1073/pnas.2213697120
PG 11
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA U2WO5
UT WOS:001083458500002
PM 37463199
OA hybrid, Green Published
DA 2024-09-05
ER
PT J
AU Halepoto, H
   Gong, T
   Noor, S
   Memon, H
AF Halepoto, Habiba
   Gong, Tao
   Noor, Saleha
   Memon, Hafeezullah
TI Bibliometric Analysis of Artificial Intelligence in Textiles
SO MATERIALS
LA English
DT Article
DE bibliometric analysis; textiles; research trend; artificial
   intelligence; Web of Science
ID FABRIC DEFECT DETECTION
AB Generally, comprehensive documents are needed to provide the research community with relevant details of any research direction. This study conducted the first descriptive bibliometric analysis to examine the most influential journals, institutions, and countries in the field of artificial intelligence in textiles. Furthermore, bibliometric mapping analysis was also used to examine diverse research topics of artificial intelligence in textiles. VOSviewer was used to process 996 articles retrieved from Web of Science-Core Collection from 2007 to 2020. The results show that China and the United States have the largest number of publications, while Donghua University and Jiangnan University have the highest output. These three themes have also appeared in textile artificial intelligence publications and played a significant role in the textile structure, textile inspection, and textile clothing production. The authors believe that this research will unfold new research domains for researchers in computer science, electronics, material science, imaging science, and optics and will benefit academic and industrial circles.
C1 [Halepoto, Habiba; Gong, Tao] Donghua Univ, Engn Res Ctr Digitized Text & Fash Technol, Shanghai 201620, Peoples R China.
   [Gong, Tao] Donghua Univ, Coll Informat Sci & Technol, Shanghai 201620, Peoples R China.
   [Noor, Saleha] East China Sci & Technol Univ, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China.
   [Memon, Hafeezullah] Zhejiang Sci Tech Univ, Coll Text Sci & Engn, Hangzhou 310018, Peoples R China.
C3 Donghua University; Donghua University; Zhejiang Sci-Tech University
RP Gong, T (corresponding author), Donghua Univ, Engn Res Ctr Digitized Text & Fash Technol, Shanghai 201620, Peoples R China.; Gong, T (corresponding author), Donghua Univ, Coll Informat Sci & Technol, Shanghai 201620, Peoples R China.
EM 317111@mail.dhu.edu.cn; taogong@dhu.edu.cn; saleha.noor@yahoo.com;
   hm@zstu.edu.cn
RI Memon, Hafeezullah/K-7126-2015
OI Memon, Hafeezullah/0000-0001-5985-5394; Gong, Tao/0000-0003-0248-9404;
   Halepoto, Habiba/0000-0003-1045-6530
FU National Natural Science Foundation of China [61673007]; Research Fund
   for International Scientists [RFIS-52150410416]; National Natural
   Science Foundation of China; Research Startup grant of ZSTU [20202294-Y]
FX This work was supported by the National Natural Science Foundation of
   China (No. 61673007), Research Fund for International Scientists
   (RFIS-52150410416), National Natural Science Foundation of China, and
   the Research Startup grant of ZSTU (20202294-Y).
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NR 58
TC 15
Z9 15
U1 16
U2 70
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1996-1944
J9 MATERIALS
JI Materials
PD APR
PY 2022
VL 15
IS 8
AR 2910
DI 10.3390/ma15082910
PG 14
WC Chemistry, Physical; Materials Science, Multidisciplinary; Metallurgy &
   Metallurgical Engineering; Physics, Applied; Physics, Condensed Matter
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Chemistry; Materials Science; Metallurgy & Metallurgical Engineering;
   Physics
GA 0Q7GA
UT WOS:000785081100001
PM 35454603
OA Green Published, gold
DA 2024-09-05
ER
PT J
AU Yigitcanlar, T
   Senadheera, S
   Marasinghe, R
   Bibri, SE
   Sanchez, T
   Cugurullo, F
   Sieber, R
AF Yigitcanlar, Tan
   Senadheera, Sajani
   Marasinghe, Raveena
   Bibri, Simon Elias
   Sanchez, Thomas
   Cugurullo, Federico
   Sieber, Renee
TI Artificial intelligence and the local government: A five-decade
   scientometric analysis on the evolution, state-of-the-art, and emerging
   trends
SO CITIES
LA English
DT Article
DE Artificial intelligence (AI); GeoAI; Local government; Municipality;
   Technology adoption; Smart city
ID EXPERT SYSTEMS; SMART; TECHNOLOGY; CHALLENGES; PREDICTION; MANAGEMENT
AB In recent years, the rapid advancement of artificial intelligence (AI) technologies has significantly impacted various sectors, including public governance at the local level. However, there exists a limited understanding of the overarching narrative surrounding the adoption of AI in local governments and its future. Therefore, this study aims to provide a comprehensive overview of the evolution, current state-of-the-art, and emerging trends in the adoption of AI in local government. A comprehensive scientometric analysis was conducted on a dataset comprising 7112 relevant literature records retrieved from the Scopus database in October 2023, spanning over the last five decades. The study findings revealed the following key insights: (a) exponential technological advancements over the last decades ushered in an era of AI adoption by local governments; (b) the primary purposes of AI adoption in local governments include decision support, automation, prediction, and service delivery; (c) the main areas of AI adoption in local governments encompass planning, analytics, security, surveillance, energy, and modelling; and (d) under-researched but critical research areas include ethics of and public participation in AI adoption in local governments. This study informs research, policy, and practice by offering a comprehensive understanding of the literature on AI applications in local governments, providing valuable insights for stakeholders and decision-makers.
C1 [Yigitcanlar, Tan] Queensland Univ Technol, Sch Architecture & Built Environm, City 4 0 Lab, 2 George St, Brisbane, Qld 4000, Australia.
   [Senadheera, Sajani; Marasinghe, Raveena] Queensland Univ Technol, Sch Architecture & Built Environm, City 4 0 Lab, Brisbane, Australia.
   [Bibri, Simon Elias] Ecole Polytech Fed Lausanne, Sch Architecture Civil & Environm Engn, Lausanne, Switzerland.
   [Sanchez, Thomas] Texas A&M Univ, Dept Landscape Architecture & Urban Planning, College Stn, TX USA.
   [Cugurullo, Federico] Trinity Coll Dublin, Sch Nat Sci, Dublin, Ireland.
   [Sieber, Renee] McGill Univ, Dept Geog, Montreal, PQ, Canada.
C3 Queensland University of Technology (QUT); Queensland University of
   Technology (QUT); Swiss Federal Institutes of Technology Domain; Ecole
   Polytechnique Federale de Lausanne; Texas A&M University System; Texas
   A&M University College Station; Trinity College Dublin; McGill
   University
RP Yigitcanlar, T (corresponding author), Queensland Univ Technol, Sch Architecture & Built Environm, City 4 0 Lab, 2 George St, Brisbane, Qld 4000, Australia.
EM tan.yigitcanlar@qut.edu.au; sajanisuwanka.senadheera@hdr.qut.edu.au;
   raveena.pelige@hdr.qut.edu.au; simon.bibri@epfl.ch; twsanchez@tamu.edu;
   cugurulf@tcd.ie; renee.sieber@mcgill.ca
RI Yigitcanlar, Tan/J-1142-2012
OI Yigitcanlar, Tan/0000-0001-7262-7118; Cugurullo,
   Federico/0000-0002-0625-8868
FU Australian Research Council Discovery Grant Scheme [DP220101255]
FX This research was funded by the Australian Research Council Discovery
   Grant Scheme, grant number DP220101255.
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NR 134
TC 1
Z9 1
U1 9
U2 9
PU ELSEVIER SCI LTD
PI London
PA 125 London Wall, London, ENGLAND
SN 0264-2751
EI 1873-6084
J9 CITIES
JI Cities
PD SEP
PY 2024
VL 152
AR 105151
DI 10.1016/j.cities.2024.105151
EA JUN 2024
PG 21
WC Urban Studies
WE Social Science Citation Index (SSCI)
SC Urban Studies
GA WV6X2
UT WOS:001257697800001
OA hybrid
DA 2024-09-05
ER
PT J
AU Chen, HS
   Jin, QQ
   Wang, XM
   Xiong, F
AF Chen, Hongshu
   Jin, Qianqian
   Wang, Ximeng
   Xiong, Fei
TI Profiling academic-industrial collaborations in bibliometric-enhanced
   topic networks: A case study on digitalization research
SO TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
LA English
DT Article
DE Topic networks; Topic vectorization; Topic modeling; Word2Vec;
   Academic-industrial collaboration
ID KNOWLEDGE NETWORKS; TECHNOLOGY; FIELD; TRANSFORMATION; EVOLUTION;
   SCIENCE
AB Collaborations between industry and academia provide a key pathway for innovation and serve as a stimulus for basic and applied research. The collaborative innovations of the two communities are embedded in both the collaborative networks of these organizations and the knowledge networks established by coupling among knowledge elements in the collaborative content. However, existing studies on academic-industrial collabora-tions have mainly been concerned with analyzing these interactions at the institutional level. To fill the gap of profiling collaborative content and to inspire related studies, this paper provides a bibliometric-enhanced method of mapping topic networks and measuring the semantic structures of academic-industrial collabora-tion. Via this method, topics can be extracted, vectorized, and correlated to construct a bibliometric-enhanced topic network as a representation of the collaborative content generated by these partnerships. Examining the structural properties of the topic network can provide comprehensive insights for future academic-industrial research collaborations. To showcase these insights, we conducted a case study involving both articles and patents in the field of digitalization. As the case study shows, the method provided in this paper can serve as a tool for cooperative research planning, innovation management, and problem-solving in a given target area of research.
C1 [Chen, Hongshu; Jin, Qianqian] Beijing Inst Technol, Sch Management & Econ, Beijing, Peoples R China.
   [Wang, Ximeng] Postal Savings Bank China, Cyber Finance Dept, Beijing, Peoples R China.
   [Xiong, Fei] Beijing Jiaotong Univ, Sch Elect & Informat Engn, Beijing, Peoples R China.
C3 Beijing Institute of Technology; Beijing Jiaotong University
RP Chen, HS (corresponding author), Beijing Inst Technol, Sch Management & Econ, Beijing, Peoples R China.
EM hongshu.chen@bit.edu.cn
RI Chen, Hongshu/O-2926-2017
OI Chen, Hongshu/0000-0002-0893-1817; Wang, Ximeng/0000-0002-2445-6737
FU National Natural Science Foundation of China [72004009, 61872033];
   Beijing Institute of Technology Research Fund Program for Young Scholars
   and Beijing Nova Program under the Beijing Municipal Science &
   Technology Commission [Z201100006820015]
FX This work was supported by: the National Natural Science Foundation of
   China under Grant Nos. 72004009 and 61872033; the Beijing Institute of
   Technology Research Fund Program for Young Scholars and Beijing Nova
   Program (Z201100006820015) under the Beijing Municipal Science &
   Technology Commission.
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NR 53
TC 18
Z9 18
U1 14
U2 99
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0040-1625
EI 1873-5509
J9 TECHNOL FORECAST SOC
JI Technol. Forecast. Soc. Chang.
PD FEB
PY 2022
VL 175
AR 121402
DI 10.1016/j.techfore.2021.121402
EA DEC 2021
PG 12
WC Business; Regional & Urban Planning
WE Social Science Citation Index (SSCI)
SC Business & Economics; Public Administration
GA XN3NS
UT WOS:000729415700003
DA 2024-09-05
ER
PT C
AU Wang, W
   Liu, XT
AF Wang Wei
   Liu Xi-tao
BE Zhu, XN
TI Research on the Performance Evaluation of Government Portal Based on
   Public Satisfaction Degree
SO PROCEEDINGS OF 2009 INTERNATIONAL CONFERENCE ON PUBLIC ADMINISTRATION
   (5TH), VOL II
LA English
DT Proceedings Paper
CT 5th International Conference on Public Administration
CY OCT 23-25, 2009
CL UESTC, Sch Polit  Sci & Public Adm, Chengdu, PEOPLES R CHINA
HO UESTC, Sch Polit  Sci & Public Adm
DE Government portal; Performance evaluation; Public satisfaction degree;
   Principal component analysis
AB In order to promote the openness of government affairs, improve public services and the effectiveness of administration, we must pay attention to the development of government portal. According to the analysis of government portal characteristics, this paper forms an appraisal index system of government portal website, based on public satisfaction, and then designs a questionnaire used for data collection. Besides this paper uses principal component analysis and regression equation studies public satisfaction to government portal. Finally this paper suggests the mainly solution to improve the public satisfaction degree of government portal, that is increase the awareness of service and government websites establishment, improve the efficiency of the government online website and enhance the interactive capabilities of the public.
C1 [Wang Wei; Liu Xi-tao] Harbin Univ Commerce, Sch Publ Finance & Management, Harbin 150028, Peoples R China.
C3 Harbin University of Commerce
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NR 20
TC 0
Z9 0
U1 0
U2 2
PU UNIV ELECTRONIC SCIENCE & TECHNOLOGY CHINA PRESS
PI CHENGDU
PA UESTC PRESS, CHENGDU, 610054, PEOPLES R CHINA
BN 978-7-5647-0139-0
PY 2009
BP 633
EP 640
PG 8
WC Public Administration
WE Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Public Administration
GA BMV79
UT WOS:000273673900092
DA 2024-09-05
ER
PT J
AU Edelmann, A
   Moody, J
   Light, R
AF Edelmann, Achim
   Moody, James
   Light, Ryan
TI Disparate foundations of scientists' policy positions on contentious
   biomedical research
SO PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF
   AMERICA
LA English
DT Article
DE science-public relations; opinion formation; biomedical research; topic
   modeling; collaboration networks
ID AIRBORNE TRANSMISSION; SOCIAL-INFLUENCE; DECISION-MAKING;
   INFLUENZA-VIRUS; ADVICE; SCIENCE
AB What drives scientists' position taking on matters where empirical answers are unavailable or contradictory? We examined the contentious debate on whether to limit experiments involving the creation of potentially pandemic pathogens. Hundreds of scientists, including Nobel laureates, have signed petitions on the debate, providing unique insights into how scientists take a public stand on important scientific policies. Using 19,257 papers published by participants, we reconstructed their collaboration networks and research specializations. Although we found significant peer associations overall, those opposing "gain-of-function" research are more sensitive to peers than are proponents. Conversely, specializing in fields directly related to gain-of-function research (immunology, virology) predicts public support better than specializing in fields related to potential pathogenic risks (such as public health) predicts opposition. These findings suggest that different social processes might drive support compared with opposition. Supporters are embedded in a tight-knit scholarly community that is likely both more familiar with and trusting of the relevant risk mitigation practices. Opponents, on the other hand, are embedded in a looser federation of widely varying academic specializations with cognate knowledge of disease and epidemics that seems to draw more heavily on peers. Understanding how scientists' social embeddedness shapes the policy actions they take is important for helping sides interpret each other's position accurately, avoiding echo-chamber effects, and protecting the role of scientific expertise in social policy.
C1 [Edelmann, Achim] Univ Bern, Inst Sociol, CH-3012 Bern, Switzerland.
   [Edelmann, Achim; Moody, James] Duke Univ, Duke Network Anal Ctr, Durham, NC 27708 USA.
   [Moody, James] Duke Univ, Dept Sociol, Durham, NC 27708 USA.
   [Moody, James] King Abdulaziz Univ, Jeddah 21589, Saudi Arabia.
   [Light, Ryan] Univ Oregon, Dept Sociol, Eugene, OR 97403 USA.
C3 University of Bern; Duke University; Duke University; King Abdulaziz
   University; University of Oregon
RP Edelmann, A (corresponding author), Univ Bern, Inst Sociol, CH-3012 Bern, Switzerland.; Edelmann, A (corresponding author), Duke Univ, Duke Network Anal Ctr, Durham, NC 27708 USA.
EM achim.edelmann@gmail.com
RI Light, Ryan/AAA-1684-2019
OI Edelmann, Achim/0000-0001-8293-674X; Light, Ryan/0000-0002-1508-154X
FU James S. McDonnell Foundation Complexity Scholars award; NIH [HD075712]
FX We thank participants in the Duke Network Analysis Center Seminar Series
   and Katharina V. Koelle for valuable input on earlier drafts, the editor
   and two anonymous reviewers for their careful reading of our manuscript
   and constructive comments, as well as Faculty of 1000 for providing us
   with a list of their members. We acknowledge partial support from the
   James S. McDonnell Foundation Complexity Scholars award and from an NIH
   Grant (HD075712).
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NR 40
TC 15
Z9 15
U1 1
U2 13
PU NATL ACAD SCIENCES
PI WASHINGTON
PA 2101 CONSTITUTION AVE NW, WASHINGTON, DC 20418 USA
SN 0027-8424
EI 1091-6490
J9 P NATL ACAD SCI USA
JI Proc. Natl. Acad. Sci. U. S. A.
PD JUN 13
PY 2017
VL 114
IS 24
BP 6262
EP 6267
DI 10.1073/pnas.1613580114
PG 6
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics
GA EX4BV
UT WOS:000403179300045
PM 28559310
OA Green Published, Bronze
DA 2024-09-05
ER
PT J
AU Li, XL
   Long, YJ
   Fan, MX
   Chen, Y
AF Li, Xueling
   Long, Yujie
   Fan, Meixi
   Chen, Yong
TI Drilling down artificial intelligence in entrepreneurial management: A
   bibliometric perspective
SO SYSTEMS RESEARCH AND BEHAVIORAL SCIENCE
LA English
DT Article
DE artificial intelligence; bibliometrics; entrepreneurial management;
   knowledge mapping
ID PERFORMANCE; SCIENCE; IMPACT; AI; TECHNOLOGY; KNOWLEDGE; FRAMEWORK;
   INTERNET; SYSTEMS; SERVICE
AB Artificial intelligence (AI) has been adopted in entrepreneurial practices and generates huge economic benefits. It creates a number of digital start-ups and changes the way in which entrepreneurial research and practice interact. Existing studies on the cross-field of AI and entrepreneurial management are scattered. Accordingly, this paper conducts a comprehensive and systematic review of existing studies on AI in entrepreneurial management by applying VOSviewer, a knowledge graph tool, based on the data obtained from the Web of Science. The study standardizes and analyses prominent research topics, interprets research hotspots and points out directions for further research.
C1 [Li, Xueling; Long, Yujie; Fan, Meixi] Jilin Univ, Sch Business & Management, 2699 Qianjin St, Changchun 130015, Peoples R China.
   [Chen, Yong] Texas A&M Int Univ, Sch Business, Laredo, TX USA.
C3 Jilin University; Texas A&M University System; Texas A&M International
   University
RP Long, YJ (corresponding author), Jilin Univ, Sch Business & Management, 2699 Qianjin St, Changchun 130015, Peoples R China.
EM jlulyj@163.com
OI Long, Yujie/0000-0001-7307-5773
FU National Natural Science Foundation of China [71872068,
   72091310-72091315]; Social Science Foundation of Jilin Province
   [2020A06]; 2021 Jilin University "New Liberal Arts" Innovation Team
   Project; Postgraduate Education and Teaching Reform Project of Jilin
   University [2021JGZ11]; School-based Application Project of Jilin
   University [2020XGZX07]; Postgraduate Innovation Research Program of
   Jilin University [101832020CX053]
FX National Natural Science Foundation of China, Grant/Award Numbers:
   71872068, 72091310-72091315; Social Science Foundation of Jilin
   Province, Grant/Award Number: 2020A06; 2021 Jilin University "New
   Liberal Arts" Innovation Team Project; Postgraduate Education and
   Teaching Reform Project of Jilin University, Grant/Award Number:
   2021JGZ11; School-based Application Project of Jilin University,
   Grant/Award Number: 2020XGZX07; Postgraduate Innovation Research Program
   of Jilin University, Grant/Award Number: 101832020CX053
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NR 103
TC 2
Z9 2
U1 16
U2 106
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1092-7026
EI 1099-1743
J9 SYST RES BEHAV SCI
JI Syst. Res. Behav. Sci.
PD MAY
PY 2022
VL 39
IS 3
SI SI
BP 379
EP 396
DI 10.1002/sres.2855
EA JUN 2022
PG 18
WC Management; Social Sciences, Interdisciplinary
WE Social Science Citation Index (SSCI)
SC Business & Economics; Social Sciences - Other Topics
GA 2P0YQ
UT WOS:000804474900001
DA 2024-09-05
ER
PT C
AU Parra-Domínguez, J
   Manzano, S
   De la Prieta, F
   Prieto, J
AF Parra-Dominguez, Javier
   Manzano, Sergio
   De la Prieta, Fernando
   Prieto, Javier
BE Omatu, S
   Mehmood, R
   Sitek, P
   Cicerone, S
   Rodriguez, S
TI The Importance of Classifying Artificial Intelligence as a Digital
   Asset. A Bibliometric Study.
SO 19TH INTERNATIONAL SYMPOSIUM ON DISTRIBUTED COMPUTING AND ARTIFICIAL
   INTELLIGENCE
SE Lecture Notes in Networks and Systems
LA English
DT Proceedings Paper
CT 19th International Symposium on Distributed Computing and Artificial
   Intelligence
CY JUL 13-15, 2022
CL L'Aquila, ITALY
DE Digital assets; Artificial intelligence; Bibliometrics
ID BLOCKCHAIN; PRODUCTIVITY
AB The importance of Artificial Intelligence technology is manifested in the advancement of society. However, there is a growing need for the economic and business support that Artificial Intelligence can offer to be considered in its involvement as a digital asset. The motivation of this paper is precise to reflect the importance for academia of the concept of Artificial Intelligence as a digital asset since its capacity to generate intangible value for companies will make them more competitive. The main result is that Artificial Intelligence represents a significant percentage of the studies on digital assets, but not the other way around; this shows that most writings published so far have addressed the issue by focusing more on the technical aspect of the study (understood as the development of IT systems or solutions, for example). All shows the concern of the scientific community for the technological progress of the link between Artificial Intelligence and digital assets, but not in the sense of progress towards greater valuation of entities and organisations in economic and financial terms, which will make companies more competitive in their access to financing, for example. All of the above is refuted by the conceptualisation of current work, which works from the point of view of the advancement of artificial intelligence in specific areas of the company, such as marketing or finance, or sectors such as manufacturing, but not from an aggregate point of view as presented here.
C1 [Parra-Dominguez, Javier; Manzano, Sergio; De la Prieta, Fernando; Prieto, Javier] Univ Salamanca, BISITE Res Grp, Salamanca, Spain.
   [Parra-Dominguez, Javier; Manzano, Sergio; De la Prieta, Fernando; Prieto, Javier] IoT Digital Innovat Hub, Valladolid, Spain.
   [Prieto, Javier] AIR Inst, Deep Tech Lab, Valladolid, Spain.
C3 University of Salamanca
RP Manzano, S (corresponding author), Univ Salamanca, BISITE Res Grp, Salamanca, Spain.; Manzano, S (corresponding author), IoT Digital Innovat Hub, Valladolid, Spain.
EM javierparra@usal.es; smanzano@usal.es; fer@usal.es; javierp@usal.es
RI Parra, Javier/ABE-5866-2021; Prieto, Javier/H-3704-2015; De la Prieta,
   Fernando/H-4738-2015
OI Parra, Javier/0000-0002-1088-9152; Prieto, Javier/0000-0001-8175-2201;
   De la Prieta, Fernando/0000-0002-8239-5020
FU European Regional Development Fund (ERDF) through the Interreg
   Spain-Portugal V -A Program (POCTEP) [0677_DISRUPTIVE_2_E]
FX This work has been partially supported by the European Regional
   Development Fund (ERDF) through the Interreg Spain-Portugal V -A Program
   (POCTEP) under gran 0677_DISRUPTIVE_2_E (Intensifying the activity of
   Digital Innovation Hubs within the PocTep region to boost the
   development of disruptive and last generation ICTs through cross-border
   cooperation).
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NR 43
TC 0
Z9 0
U1 2
U2 10
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 2367-3370
EI 2367-3389
BN 978-3-031-20858-4; 978-3-031-20859-1
J9 LECT NOTE NETW SYST
PY 2023
VL 583
BP 154
EP 164
DI 10.1007/978-3-031-20859-1_16
PG 11
WC Computer Science, Artificial Intelligence; Computer Science, Theory &
   Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BU6TO
UT WOS:000929025600016
DA 2024-09-05
ER
PT J
AU de Stefano, E
   Santos, MPD
   Balassiano, R
AF de Stefano, Ercilia
   de Sequeira Santos, Marcio Peixoto
   Balassiano, Ronaldo
TI Development of a software for metric studies of transportation
   engineering journals
SO SCIENTOMETRICS
LA English
DT Article
DE Scientometrics; Informetrics; Bibliometrics; Artificial intelligence;
   Natural language processing; Transportation engineering
AB This study intends to describe the development and results of a software designed to analyze millions of articles in the area of Transportation Engineering. This tool intends to support Transportation Planning activities by providing additional information about trends, references and technologies. In order to develop this software, techniques from scientometrics, bibliometrics and informetrics were employed with the support of tools from Computer Science, such as Artificial Intelligence, Data Mining and Natural Language Processing. The result of this study is a structured database that allows browsing the change of interest in different topics along the years in areas related to Transportation Engineering. When analyzing a given area, the database is capable of identifying which authors published works in that area, allowing the identification of specialists and related papers. In addition, the software responsible for creating this database is capable of performing the same analysis in academic corpora of other areas of study.
C1 [de Stefano, Ercilia; de Sequeira Santos, Marcio Peixoto; Balassiano, Ronaldo] COPPE UFRJ, Transportat Engn Program PET, Rio De Janeiro, Brazil.
C3 Universidade Federal do Rio de Janeiro
RP de Stefano, E (corresponding author), COPPE UFRJ, Transportat Engn Program PET, Rio De Janeiro, Brazil.
EM ercilia@pet.coppe.ufrj.br; marcio@pet.coppe.ufrj.br;
   ronaldo@pet.coppe.ufrj.br
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NR 21
TC 3
Z9 3
U1 0
U2 53
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD DEC
PY 2016
VL 109
IS 3
BP 1579
EP 1591
DI 10.1007/s11192-016-2152-6
PG 13
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA EE1JA
UT WOS:000389336100010
DA 2024-09-05
ER
PT C
AU Scanlon, E
   O'Shea, T
   McAndrew, P
AF Scanlon, Eileen
   O'Shea, Tim
   McAndrew, Patrick
GP Assoc Comp Machinery
TI Learning in the Open: A Research Agenda for MOOCs
SO CSCW'17: COMPANION OF THE 2017 ACM CONFERENCE ON COMPUTER SUPPORTED
   COOPERATIVE WORK AND SOCIAL COMPUTING
LA English
DT Proceedings Paper
CT ACM Conference on Computer Supported Cooperative Work and Social
   Computing (CSCW)
CY FEB 25-MAR 01, 2017
CL Portland, OR
DE collaboration; distance learning; online learning; research agenda;
   evidence hub
AB The development of online distance learning and the early years of the recent MOOC phenomenon leads to a mix of lessons from experience and emergent findings from studies in new contexts that require further reflection and research. Seven issues are identified from this combination each of which require attention to allow evidence-based practice in MOOC design and development. Some preliminary hypotheses are presented and an approach to interrogating evidence to develop and evaluate these hypotheses by the construction of an Evidence Hub is proposed as a next step to this work in progress.
C1 [Scanlon, Eileen; McAndrew, Patrick] IET Open Univ, Milton Keynes MK7 6AA, Bucks, England.
   [O'Shea, Tim] Univ Edinburgh, Edinburgh EH8 9YL, Midlothian, Scotland.
C3 University of Edinburgh
RP Scanlon, E (corresponding author), IET Open Univ, Milton Keynes MK7 6AA, Bucks, England.
EM eileen.scanlon@open.ac.uk; principal@ed.ac.uk; IET-Director@open.ac.uk
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NR 11
TC 0
Z9 0
U1 0
U2 1
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA
BN 978-1-4503-4688-7
PY 2017
BP 303
EP 306
DI 10.1145/3022198.3026323
PG 4
WC Computer Science, Information Systems; Computer Science, Software
   Engineering; Social Sciences, Interdisciplinary
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Social Sciences - Other Topics
GA BL7JF
UT WOS:000455085000075
DA 2024-09-05
ER
PT J
AU Saeed, MA
   Al Qunayeer, HS
AF Saeed, Murad Abdu
   Al Qunayeer, Huda Suleiman
TI Can we engage postgraduates in active research methodology learning?
   Challenges, strategies and evaluation of learning
SO INTERNATIONAL JOURNAL OF RESEARCH & METHOD IN EDUCATION
LA English
DT Article
DE Research methodology; active learning; postgraduates; challenges;
   evaluation of learning
ID TEACHING-RESEARCH METHODS; STUDENTS; PEDAGOGY; SCIENCE
AB Due to the complex nature of research methodology courses, the current study focused on implementing the active teaching and learning approach to a postgraduate research method course in a Malaysian university over an academic semester. A qualitative analysis of observations, instructor-learner interactional exchanges, students' drafts of tasks and pre-course and focus group discussion was performed. The findings revealed three types of challenges: student-oriented challenges, subject-matter-related challenges and instructor-oriented challenges. Three main pedagogical strategies: instructional scaffolding, peer scaffolding and engaging the postgraduates in drafting their tasks were employed as a response to these challenges. Although the active teaching and learning practices resulted into students' enhancement of the assigned research methodology tasks and positive research learning experience, such practices were time and effort-consuming. Therefore, future research will need to examine the applicability of our active teaching and learning approach to research methodology courses in different contexts.
C1 [Saeed, Murad Abdu] Qassim Univ, Unaizah Coll Sci & Arts, Qasim, Saudi Arabia.
   [Al Qunayeer, Huda Suleiman] Qassim Univ, Unaizah Coll Sci & Arts, Dept English, Qasim, Saudi Arabia.
C3 Qassim University; Qassim University
RP Saeed, MA (corresponding author), Qassim Univ, Unaizah Coll Sci & Arts, Qasim, Saudi Arabia.
EM muradsaeed16@yahoo.com
RI Saeed, Murad Abdu/R-7669-2017
OI Saeed, Murad Abdu/0000-0003-2933-7929
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NR 32
TC 4
Z9 4
U1 1
U2 9
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1743-727X
EI 1743-7288
J9 INT J RES METHOD EDU
JI Int. J. Res. Method Educ.
PD JAN 1
PY 2021
VL 44
IS 1
BP 3
EP 19
DI 10.1080/1743727X.2020.1728526
EA FEB 2020
PG 17
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA PW9WL
UT WOS:000514976200001
DA 2024-09-05
ER
PT C
AU Sayce, S
AF Sayce, Susan
BE Remenyi, D
TI Managing the Fear Factor (or how a Mini-Viva Assessment can Improve the
   Process of Learning for International Students)
SO ECRM 2007: 6TH EUROPEAN CONFERENCE ON RESEARCH METHODOLOGY FOR BUSINESS
   AND MANAGEMENT STUDIES
LA English
DT Proceedings Paper
CT 6th European Conference on Research Methodology for Business and
   Management Studies (ECRM)
CY JUL 09-10, 2007
CL Univ Nova Lisboa, Lisbon, PORTUGAL
HO Univ Nova Lisboa
DE International students; research methods; mini-viva; deep learning;
   assessment
ID EDUCATION
AB This paper is about an exploration of international business student's learning through the use of mini-vivas as a form of assessment. It also includes an investigation of the meaning of a mini-viva for students who have a wide range of nationalities. Pedagogical research has indicated that using this form of summative assessment for large cohorts of international students maybe problematic (Carless 2002). However, experimentation with this model of assessment with MA business students in research methods has indicated that mini-vivas can enhance and consolidate the learning potential of international students. So in effect this paper is also about explaining why this has happened in relation to student's learning.
C1 [Sayce, Susan] Bournemouth Univ, Bournemouth, Dorset, England.
C3 Bournemouth University
EM ssayce@bournemouth.ac.uk
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NR 19
TC 0
Z9 0
U1 0
U2 1
PU ACAD  CONFERENCES LTD
PI NR READING
PA CURTIS FARM, KIDMORE END, NR READING, RG4 9AY, ENGLAND
BN 978-1-905305-50-6
PY 2007
BP 275
EP 282
PG 8
WC Business; Management
WE Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Business & Economics
GA BYT73
UT WOS:000300195700030
DA 2024-09-05
ER
PT J
AU Shaikh, AR
   Alhoori, H
   Sun, MY
AF Shaikh, Abdul Rahman
   Alhoori, Hamed
   Sun, Maoyuan
TI YouTube and science: models for research impact
SO SCIENTOMETRICS
LA English
DT Article
DE Social media; YouTube; Societal impact; Research impact; Science of
   science; MetaScience; Machine learning; Altmetrics; Scientometrics;
   Scholarly communication
ID ALTMETRICS
AB Video communication has been rapidly increasing over the past decade, with YouTube providing a medium where users can post, discover, share, and react to videos. There has also been an increase in the number of videos citing research articles, especially since it has become relatively commonplace for academic conferences to require video submissions. However, the relationship between research articles and YouTube videos is not clear, and the purpose of the present paper is to address this issue. We created new datasets using YouTube videos and mentions of research articles on various online platforms. We found that most of the articles cited in the videos are related to medicine and biochemistry. We analyzed these datasets through statistical techniques and visualization, and built machine learning models to predict (1) whether a research article is cited in videos, (2) whether a research article cited in a video achieves a level of popularity, and (3) whether a video citing a research article becomes popular. The best models achieved F1 scores between 80% and 94%. According to our results, research articles mentioned in more tweets and news coverage have a higher chance of receiving video citations. We also found that video views are important for predicting citations and increasing research articles' popularity and public engagement with science.
C1 [Shaikh, Abdul Rahman; Alhoori, Hamed; Sun, Maoyuan] Northern Illinois Univ, De Kalb, IL 60115 USA.
C3 Northern Illinois University
RP Shaikh, AR (corresponding author), Northern Illinois Univ, De Kalb, IL 60115 USA.
EM ashaikh2@niu.edu; alhoori@niu.edu; smaoyuan@niu.edu
RI Rahman, Abdul/KBD-0934-2024
OI Alhoori, Hamed/0000-0002-4733-6586; Shaikh, Abdul
   Rahman/0000-0002-6046-4638; Sun, Maoyuan/0000-0002-0990-2620
FU NSF; Research and Artistry Opportunity Grant from Northern Illinois
   University;  [SMA-2022443];  [IIS-2002082]
FX AcknowledgementsThis research is supported in part by NSF Grants
   SMA-2022443, IIS-2002082, and the Research and Artistry Opportunity
   Grant from Northern Illinois University.
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NR 48
TC 6
Z9 7
U1 6
U2 38
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD FEB
PY 2023
VL 128
IS 2
BP 933
EP 955
DI 10.1007/s11192-022-04574-5
EA DEC 2022
PG 23
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 8W6QB
UT WOS:000895039100004
PM 36530773
OA Green Published, Bronze
DA 2024-09-05
ER
PT J
AU Singh, N
AF Singh, Nitin
TI Big data technology: developments in current research and emerging
   landscape
SO ENTERPRISE INFORMATION SYSTEMS
LA English
DT Article
DE Big data; analytics; research themes; principal component analysis;
   citation analysis; co-citation analysis
ID DATA ANALYTICS; INFORMATION-SYSTEMS; BUSINESS ANALYTICS; CITATION
   ANALYSIS; OPPORTUNITIES; CHALLENGES; COCITATION; IMPACT
AB In this study, big data studies (01/2015-6/2018) are reviewed and several highly cited papers are identified, which indicates a growing interest in the area of big data. The papers and proceedings from international peer-reviewed journals and ranked conferences were reviewed. We employed Principal component analysis and citation and co-citation analysis to identify themes of research emanating from these studies. Citation and co-citation analysis reveals that there is cross-functional nature of big data research, which permeates different business sectors and is influenced by themes in engineering and information management.
C1 [Singh, Nitin] IIM, Kashipur, India.
C3 Indian Institute of Management (IIM System); Indian Institute of
   Management Kashipur
RP Singh, N (corresponding author), IIM, Kashipur, India.
EM nitin.singh@iimkashipur.ac.in
RI SINGH, NITIN/JEO-5380-2023
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NR 88
TC 18
Z9 18
U1 4
U2 34
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 1751-7575
EI 1751-7583
J9 ENTERP INF SYST-UK
JI Enterp. Inf. Syst.
PD JUL 3
PY 2019
VL 13
IS 6
BP 801
EP 831
DI 10.1080/17517575.2019.1612098
PG 31
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA IL0BG
UT WOS:000476960400002
DA 2024-09-05
ER
PT J
AU Zhang, J
AF Zhang, Jing
TI Research on Sentiment Analysis and Satisfaction Evaluation of Online
   Teaching in Universities During Epidemic Prevention
SO FRONTIERS IN PSYCHOLOGY
LA English
DT Article
DE sentiment analysis; online teaching; satisfaction evaluation; fuzzy
   Bayesian theory; evaluation index system of online teaching
AB Sentiment analysis of online and offline integrated teaching in universities is being paid more and more attention. Many universities have carried out online teaching activities. However, due to the lack of face-to-face teaching, the lack of emotional communication is the key problem affecting the quality of online teaching. We analyze the relations from the perspectives of the change of teaching mode, the reconstruction of teacher-student relationship, and the transmission of emotional attitude of teachers and students in this paper. Then based on the Bayesian network (BN) theory, the satisfaction of online teaching can be evaluated from the aspects of emotion analysis, learning investment, and teaching interaction. Further, some suggestions are put forward to improve the satisfaction of online teaching.
C1 [Zhang, Jing] Wuxi Inst Technol, Sch Mech Technol, Wuxi, Jiangsu, Peoples R China.
C3 Wuxi Institute of Technology
RP Zhang, J (corresponding author), Wuxi Inst Technol, Sch Mech Technol, Wuxi, Jiangsu, Peoples R China.
EM zhangjing@wxit.edu.cn
FU Jiangsu Province education system party building research key project
   [2019JSJYDJ01018]; Jiangsu University philosophy and social science
   research project [2019SJB281, 2021SJB1394]; Jiangsu Social Science
   Application Research Project [21SZB-013]
FX This work was supported in part by the Jiangsu Province education system
   party building research key project (Grant No. 2019JSJYDJ01018); the
   Jiangsu University philosophy and social science research project (Grant
   Nos. 2019SJB281 and 2021SJB1394); the Jiangsu Social Science Application
   Research Project (Special Topic of Ideological and Political Education
   in Colleges): (Grant No. 21SZB-013).
CR Guo R.R., 2020, J COMMUN U CHINA SCI, V27, P48
   Li Li, 2021, J MICROCOMPUT APPL, V37, P45
   Liu L., 2019, APPL RES COMPUT, V36, P1619
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   Zhou T.H., 2020, WIREL INTERNET TECHN, V9, P115
NR 7
TC 2
Z9 2
U1 0
U2 83
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 1664-1078
J9 FRONT PSYCHOL
JI Front. Psychol.
PD OCT 18
PY 2021
VL 12
AR 738776
DI 10.3389/fpsyg.2021.738776
PG 7
WC Psychology, Multidisciplinary
WE Social Science Citation Index (SSCI)
SC Psychology
GA WS6EB
UT WOS:000715270900001
PM 34733212
OA Green Published, gold
DA 2024-09-05
ER
PT J
AU Pandey, AK
   Chakraborty, A
   Khandal, V
AF Pandey, Ajay Kumar
   Chakraborty, Arnab
   Khandal, Vijay
TI Scientometric Study of Research on AI & ML Application in Defence
   Technology and Military Operations
SO DESIDOC JOURNAL OF LIBRARY & INFORMATION TECHNOLOGY
LA English
DT Article
DE Scientometrics; Artificial intelligence; Machine learning; Defence
   technology; Military operations; Research trend
AB Application of AI and machine learning in different domains of defence system in increasing rapidly to bring automation and to facilitate all the benefits of modern technologies in military. This article conducts a scientometric analysis on articles that are on application of Ai and Ml in military equipment, military intelligence, cyber security, decision making, military operations, defence medical systems etc. This study has executed a search query on Web of Science for identifying peer reviewed current resources that are contributing to the application of modern technologies in military systems. With extensive query and filtering this study has identified 417 articles with in the period of 1991 to 2023. With analysing all the data, it determines that a lot of varied research is there on the defence system that promotes use of modern technologies in development of weapon, conducting strategic military operation, prioritising military society etc. Prioritising legal and ethical parameters. This study has also highlighted legal, and security concerns surrounding using autonomous systems in military applications. The authorship pattern, document types, country production over time, and most cited countries have also been studied. Bradford's scattering law was applied to identify the core journals, and Lotka's law to check authors' productivity patterns.
C1 [Pandey, Ajay Kumar] DRDO Armament Res & Dev Estab ARDE, Pune 411021, India.
   [Chakraborty, Arnab] Indian Inst Trop Meteorol, Pune 411021, India.
   [Khandal, Vijay] Rashtrasant Tukadoji Maharaj Nagpur Univ, Nagpur 440033, India.
C3 Ministry of Earth Sciences (MoES) - India; Indian Institute of Tropical
   Meteorology (IITM); Rashtrasant Tukadoji Maharaj Nagpur University
RP Pandey, AK (corresponding author), DRDO Armament Res & Dev Estab ARDE, Pune 411021, India.
EM akpandey.arde@gov.in
OI , Arnab Chakraborty/0009-0000-1423-6610
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NR 18
TC 0
Z9 0
U1 5
U2 5
PU DEFENCE SCIENTIFIC INFORMATION DOCUMENTATION CENTRE
PI DELHI
PA METCALFE HOUSE, DELHI 110054, INDIA
SN 0974-0643
EI 0976-4658
J9 DESIDOC J LIB INF TE
JI DESIDOC J. Lib. Inf. Technol.
PD MAR
PY 2024
VL 44
IS 2
BP 61
EP 68
DI 10.14429/djlit.44.2.19496
PG 8
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA NW7G9
UT WOS:001203551800008
OA gold
DA 2024-09-05
ER
PT J
AU Zhang, GT
   Du, YH
   Zhu, XB
   Liu, XL
AF Zhang, Guoting
   Du, Yonghao
   Zhu, Xiaobin
   Liu, Xiaolu
TI Hybrid Operator and Strengthened Diversity Improving for Multimodal
   Multi-Objective Optimization: Electronic Supplementary Material
SO TSINGHUA SCIENCE AND TECHNOLOGY
LA English
DT Article
DE Sensitivity; Topology; Particle swarm optimization; Optimization; Open
   Access; Benchmark testing
RI Liu, Xiaolu/P-1404-2019
CR Yue CT, 2018, IEEE T EVOLUT COMPUT, V22, P805, DOI 10.1109/TEVC.2017.2754271
NR 1
TC 0
Z9 0
U1 0
U2 0
PU TSINGHUA UNIV PRESS
PI BEIJING
PA B605D, XUE YAN BUILDING, BEIJING, 100084, PEOPLES R CHINA
SN 1007-0214
EI 1878-7606
J9 TSINGHUA SCI TECHNOL
JI Tsinghua Sci. Technol.
PD OCT
PY 2024
VL 29
IS 5
BP 1
EP 4
PG 4
WC Computer Science, Information Systems; Computer Science, Software
   Engineering; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA PP6N7
UT WOS:001215322000005
DA 2024-09-05
ER
PT J
AU Luo, ZL
   Shao, XF
   Ma, XC
AF Luo, Zhilin
   Shao, Xuefeng
   Ma, Xiaochun
TI Enhancing Learners' Performance in Contest Through Knowledge Mapping
   Algorithm: The Roles of Artificial Intelligence and Blockchain in
   Scoring and Data Integrity
SO JOURNAL OF ORGANIZATIONAL AND END USER COMPUTING
LA English
DT Article
DE Artificial Intelligence; Blockchain; Contest Research; End User; Learner
   Performance; Learning Motivation; Vocational Education
ID ACADEMIC-PERFORMANCE; EXTRINSIC MOTIVATIONS; LANGUAGE; ENGLISH;
   ACHIEVEMENT; EXPECTANCY; ANXIETY
AB The fairness of vocational contest scoring is key to generating reliable competency assessments. This study examined the performance impact of the motivation of English-as-a-foreign-language learners in contests with vocabulary knowledge antecedents in the contexts of artificial intelligence (AI) and blockchain (BC). The sample comprised 185 participants of an oral English contest at higher vocational institution in China. AI-powered scoring of learners' contest performance and a survey were used to collect data. The findings revealed that learners' intrinsic drive was the main positive factor, outweighing their extrinsic motivation, and that AI and BC increased the trustworthiness and integrity of contest records, thus providing new opportunities to build learner trust and form psychological incentives. This study enriches foreign language motivation theory in the context of contest research and highlights the importance of using AI and BC to enhance the scoring accuracy and credibility of contests as authoritative evaluation instruments in vocational education.
C1 [Luo, Zhilin] Chongqing Ind Polytech Coll, Gen Educ Sch, Chongqing, Peoples R China.
   [Shao, Xuefeng] Univ Newcastle, Management, Callaghan, Australia.
   [Ma, Xiaochun] Chongqing Coll Elect Engn, Gen Educ Sch, Chongqing, Peoples R China.
   [Ma, Xiaochun] Chongqing Coll Elect Engn, Gen Educ Sch, Chongqing 400030, Peoples R China.
   [Shao, Xuefeng] Univ Newcastle, Newcastle, NSW 2300, Australia.
C3 Chongqing Industry Polytechnic College; University of Newcastle;
   Chongqing College of Electronic Engineering; Chongqing College of
   Electronic Engineering; University of Newcastle
RP Ma, XC (corresponding author), Chongqing Coll Elect Engn, Gen Educ Sch, Chongqing 400030, Peoples R China.; Shao, XF (corresponding author), Univ Newcastle, Newcastle, NSW 2300, Australia.
EM david.shao@newcastle.edu.au; maxiaochun@cqcet.edu.cn
RI Luo, Zhilin/IXD-8511-2023
OI Luo, Zhilin/0000-0003-4352-1984; Shao, Xuefeng/0000-0002-4267-9600
FU Hubei Education Science Planning 2022 Annual general project: Research
   on the deep integration mode and practice Path of "Five-dimensional
   Integration", Production and Education in applicationoriented
   undergraduate colleges [2022GB088]
FX Hubei Education Science Planning 2022 Annual general project: Research
   on the deep integration mode and practice Path of "Five-dimensional
   Integration", Production and Education in applicationoriented
   undergraduate colleges (2022GB088). Funding for this research was
   covered by the author(s) of the article.
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NR 84
TC 0
Z9 0
U1 20
U2 20
PU IGI GLOBAL
PI HERSHEY
PA 701 E CHOCOLATE AVE, STE 200, HERSHEY, PA 17033-1240 USA
SN 1546-2234
EI 1546-5012
J9 J ORGAN END USER COM
JI J. Organ. End User Comput.
PY 2024
VL 36
IS 1
AR 336277
DI 10.4018/JOEUC.336277
PG 21
WC Computer Science, Information Systems; Information Science & Library
   Science; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science; Business &
   Economics
GA II1V4
UT WOS:001165615200004
OA gold
DA 2024-09-05
ER
PT J
AU Yaniasih, Y
   Budi, I
AF Yaniasih, Yaniasih
   Budi, Indra
TI Systematic Design and Evaluation of a Citation Function Classification
   Scheme in Indonesian Journals
SO PUBLICATIONS
LA English
DT Article
DE citation function; classification scheme; annotator agreement; machine
   learning; deep learning
ID REFERENCES; CONTEXT
AB Classifying citations according to function has many benefits when it comes to information retrieval tasks, scholarly communication studies, and ranking metric developments. Many citation function classification schemes have been proposed, but most of them have not been systematically designed for an extensive literature-based compilation process. Many schemes were also not evaluated properly before being used for classification experiments utilizing large datasets. This paper aimed to build and evaluate new citation function categories based upon sufficient scientific evidence. A total of 2153 citation sentences were collected from Indonesian journal articles for our dataset. To identify the new categories, a literature survey was conducted, analyses and groupings of category meanings were carried out, and then categories were selected based on the dataset's characteristics and the purpose of the classification. The evaluation used five criteria: coherence, ease, utility, balance, and coverage. Fleiss' kappa and automatic classification metrics using machine learning and deep learning algorithms were used to assess the criteria. These methods resulted in five citation function categories. The scheme's coherence and ease of use were quite good, as indicated by an inter-annotator agreement value of 0.659 and a Long Short-Term Memory (LSTM) F1-score of 0.93. According to the balance and coverage criteria, the scheme still needs to be improved. This research data was limited to journals in food science published in Indonesia. Future research will involve classifying the citation function using a massive dataset collected from various scientific fields and published from some representative countries, as well as applying improved annotation schemes and deep learning methods.
C1 [Yaniasih, Yaniasih; Budi, Indra] Univ Indonesia, Fac Comp Sci, Depok 16424, Indonesia.
   [Yaniasih, Yaniasih] Indonesian Inst Sci LIPI, Res Ctr Informat, Bandung 40135, Indonesia.
C3 University of Indonesia; National Research & Innovation Agency of
   Indonesia (BRIN); Indonesian Institute of Sciences (LIPI)
RP Budi, I (corresponding author), Univ Indonesia, Fac Comp Sci, Depok 16424, Indonesia.
EM yaniasih@ui.ac.id; indra@cs.ui.ac.id
OI Yaniasih, Yaniasih/0000-0002-3389-6742; Budi, Indra/0000-0002-2107-6552
FU Universitas Indonesia [BA-733/UN2.RST/PPM.00.03.01/2020]
FX This study was supported by research grants from Universitas Indonesia
   (Hibah Publikasi Doktoral Tahun 2021 and Publikasi Terindeks
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NR 37
TC 1
Z9 1
U1 1
U2 4
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2304-6775
J9 PUBLICATIONS
JI Publications
PD SEP
PY 2021
VL 9
IS 3
AR 27
DI 10.3390/publications9030027
PG 14
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA UZ2MO
UT WOS:000702044600001
OA Green Published, gold
DA 2024-09-05
ER
PT J
AU Jadhav, MS
   Dhas, J
   Joshi, D
   Jadhavrao, N
   Kadam, S
AF Jadhav, Madhumita Satish
   Dhas, Jyoti
   Joshi, Deepali
   Jadhavrao, Namrata
   Kadam, Sayali
TI Citation Clustering for Identifying Research Contribution
SO JOURNAL OF COMPUTERS
LA English
DT Article
DE Leacock-Chodorow similarity vector; WordNet; hierarchical clustering
ID QUALITY; SCIENCE
AB The h-index is an index that measures productivity and citation impact of the published work but it has been criticized because it does not consider context of citation and reason behind citation. This indicates that there is a need for an improved h-index by a new approach which includes important citations received by a paper instead of the whole list of citations. Citation classification is an emerging area of research that categorizes citations based on the purpose behind the citation.
   To perform citation classification there is need of a standard set of classes called as classification scheme. Such standard scheme is not available so we aim to generate a citation classification scheme automatically i.e. by using hierarchical clustering. The clustering is performed by using similarity vectors. The main contribution of this research is to generate similarity distance matrix of keywords and verbs extracted from the citation sentences with the help of WordNet.
C1 [Jadhav, Madhumita Satish; Dhas, Jyoti; Joshi, Deepali; Jadhavrao, Namrata; Kadam, Sayali] Vishwakarma Inst Technol, Dept Comp, Pune, Maharashtra, India.
RP Jadhav, MS (corresponding author), Vishwakarma Inst Technol, Dept Comp, Pune, Maharashtra, India.
EM mitujadhav@gmail.com
RI Joshi, Deepali Jayant/ACG-9456-2022
OI Joshi, Deepali Jayant/0000-0002-8832-9294
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NR 9
TC 0
Z9 0
U1 0
U2 4
PU ACAD PUBL
PI OULU
PA PO BOX 40, OULU, 90571, FINLAND
SN 1796-203X
J9 J COMPUT
JI J. Comput.
PD NOV
PY 2015
VL 10
IS 6
BP 406
EP 411
DI 10.17706/jcp.10.6.406-411
PG 6
WC Computer Science, Interdisciplinary Applications
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA CY4UP
UT WOS:000366404400006
OA gold
DA 2024-09-05
ER
PT J
AU Galgani, F
   Compton, P
   Hoffmann, A
AF Galgani, Filippo
   Compton, Paul
   Hoffmann, Achim
TI LEXA: Building knowledge bases for automatic legal citation
   classification
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Knowledge acquisition; Natural language processing; Citation analysis;
   Legal documents
ID DOCUMENT MANAGEMENT; ACQUISITION; INFORMATION; SYSTEMS
AB This paper presents a new approach to building legal citation classification systems. Our approach is based on Ripple-down Rules (RDA), an efficient knowledge acquisition methodology. The main contributions of the paper (over existing expert-systems approaches) are extensions to the traditional RDR approach introducing new automatic methods to assist in the creation of rules: using the available dataset to provide performance estimates and relevant examples, automatically suggesting and validating synonyms, re-using exceptions in different portions of the knowledge base. We compare our system LEXA with baseline machine learning techniques. LEXA obtains better results both in clean and noisy subsets of our corpus. Compared to machine learning approaches, LEXA also has other advantages such as supporting continuous extension of the rule base, and the opportunity to proceed without an annotated data set and to validate class labels while building rules. Crown Copyright (C) 2015 Published by Elsevier Ltd. All rights reserved.
C1 [Galgani, Filippo; Compton, Paul; Hoffmann, Achim] Univ New S Wales, Sch Engn & Comp Sci, Sydney, NSW 2052, Australia.
C3 University of New South Wales Sydney
RP Galgani, F (corresponding author), Univ New S Wales, Sch Engn & Comp Sci, Sydney, NSW 2052, Australia.
EM galganif@cse.unsw.edu.au; compton@cse.unsw.edu.au; achim@cse.unsw.edu.au
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   [No title captured]
   [No title captured]
   [No title captured]
   [No title captured]
NR 72
TC 15
Z9 19
U1 0
U2 36
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0957-4174
EI 1873-6793
J9 EXPERT SYST APPL
JI Expert Syst. Appl.
PD OCT
PY 2015
VL 42
IS 17-18
BP 6391
EP 6407
DI 10.1016/j.eswa.2015.04.022
PG 17
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering; Operations Research & Management Science
GA CK3NE
UT WOS:000356122000011
DA 2024-09-05
ER
PT C
AU Han, Y
   Guo, C
   Fang, ZY
   Chen, FX
AF Han, Yu
   Guo, Cheng
   Fang, Zhengyun
   Chen, Fengxian
BE Liu, J
   Liu, Y
TI Voltage Overrun Evaluation and Prediction Research Based on
   Combinatorial Weighting and CNN-GRU
SO 2023 2ND ASIAN CONFERENCE ON FRONTIERS OF POWER AND ENERGY, ACFPE
LA English
DT Proceedings Paper
CT 2nd Asian Conference on Frontiers of Power and Energy (ACFPE)
CY OCT 20-22, 2023
CL Chengdu, PEOPLES R CHINA
DE Voltage overrun; Combinatorial assignment; Convolutional neural network;
   Gated recurrent unit; Comprehensive evaluation; Predictive warning
AB Aiming at the problem of assessing the severity of voltage overrun in distribution networks, a voltage overrun assessment and prediction and warning method based on combinatorial assignment and the CNN-GRU model is proposed. Firstly, a new type of voltage overrun assessment index considering the influence of user density is selected, the subjective and objective weights are optimized and coordinated based on the theory of combinatorial assignment, and the regional division of monitoring points is used to give appropriate weight correction from the perspective of voltage propagation to establish the voltage deviation severity assessment level and determine the voltage deviation severity degree of assessment samples; the data features are extracted based on the assessment results after the overrun assessment of the data; then the data features are extracted through the gating network (CNN), and the data features are extracted through the gating network (CNN). (CNN) for the extraction of data features, and then through the gated recirculation unit (GRU) for the prediction and judgment of the voltage overrun problem in the next time period and timely warning; finally, some of the measured voltage data in a certain area are analyzed as an example to verify the effectiveness of the assessment level system and prediction and warning model
C1 [Han, Yu] Kunming Univ Sci & Technol, Sch Mech & Elect Engn, Kunming, Yunnan, Peoples R China.
   [Guo, Cheng] Kunming Univ Sci & Technol, Sch Elect Power Engn, Kunming, Yunnan, Peoples R China.
   [Fang, Zhengyun] Kunming Univ Sci & Technol, Sch Land & Resources Engn, Kunming, Yunnan, Peoples R China.
   [Chen, Fengxian] Yunnan Power Grid Co Ltd, Qujing Power Supply Bur, Qujing, Peoples R China.
C3 Kunming University of Science & Technology; Kunming University of
   Science & Technology; Kunming University of Science & Technology; China
   Southern Power Grid
RP Han, Y (corresponding author), Kunming Univ Sci & Technol, Sch Mech & Elect Engn, Kunming, Yunnan, Peoples R China.
EM 1512497432@qq.com; gc325@126.com; 57592715@qq.com; 690704018@qq.com
FU Yunnan Major Scientific and Technological Projects [202202AG050002];
   Special project of Yunnan Provincial Joint Fund [202201BE070001-15]
FX The authors would like to acknowledge the support of Yunnan Major
   Scientific and Technological Projects (grant NO. 202202AG050002);
   Special project of Yunnan Provincial Joint Fund(202201BE070001-15).
CR CHEN Haihua, 2023, Journal of Power Supply., P1
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   He Chunguang, 2022, Journal of Electric Power Science and Technology, V37, P161
   Jiao Zhan, 2021, Science, Technology and Engineering., V21, P14769
   Li Jinyou, 2022, Journal of Solar Energy., V43, P340
   Liu Wei-Lin, 2017, Thermal limit and voltage overrun solution based on online network topology optimization
   Zou Wenjun, 2022, Urban Rail Transportation Research., V25, P70
NR 8
TC 0
Z9 0
U1 1
U2 1
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 979-8-3503-0389-6
PY 2023
BP 94
EP 98
DI 10.1109/ACFPE59335.2023.10455120
PG 5
WC Computer Science, Artificial Intelligence; Energy & Fuels; Engineering,
   Electrical & Electronic
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Energy & Fuels; Engineering
GA BW6SH
UT WOS:001181159800017
DA 2024-09-05
ER
PT S
AU Atibuni, DZ
AF Atibuni, Dennis Zami
BE Cross, M
   Long, C
   Ndlovu, S
   Nyoni, P
TI 'Assessment for Learning' over 'Assessment of Learning' <i>A Quest for
   Mastery Rather Than Performance Orientation in Postgraduate Research
   Degrees</i>
SO TRANSFORMATIVE CURRICULA, PEDAGOGIES AND EPISTEMOLOGIES: Teaching and
   Learning in Diverse Higher Education Contexts
SE African Higher Education-Developments and Perspectives
LA English
DT Article; Book Chapter
DE assessment of learning; assessment for learning; deep learning; surface
   learning; mastery orientation; performance orientation
ID MOTIVATION; GOALS; STUDENTS; SELF
AB At higher education, students are terminally assessed through a research output that demonstrates their originality, creativity, innovativeness, and contribution to knowledge and problem solving in society. However, the research assessment process, unlike the traditional pencil-and-paper and other performance assessments which are thoroughly proctored by the examiner, is one that is loosely structured. Depending on whether the student engrosses in undertaking research as an assessment by mastery orientation or performance orientation or both will determine whether the research process serves as an assessment for learning rather than assessment of learning. In this chapter it is argued using a critical review of literature that postgraduate students who use mastery orientation in carrying out their research will pursue a deep learning of both the theoretical and practical demands of the research process, in which case what is learned is enduring. Hence research as a terminal assessment will serve as an assessment for learning. On the other hand, students engaged in research through performance orientation are likely to engage in surface learning; taking ethical shortcuts in the pursuit and just wanting the work done, presented, and passed. In this case, what is learned from the research process is not enduring, and hence the process serves as assessment of learning for a short while. It is recommended among others that institutional policies and faculty practices regarding research conduct should engender deep learning through mastery orientation as opposed to surface learning through performance orientation so as to foster research as an assessment for learning rather than assessment of learning.
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PU BRILL
PI LEIDEN
PA PO BOX 9000, 2300 PA LEIDEN, NETHERLANDS
SN 2666-2663
BN 978-90-04-46842-9; 978-90-04-46844-3; 978-90-04-46843-6
J9 AFRICA HIGH ED-DEVEL
PY 2021
VL 11
BP 194
EP 213
DI 10.1163/9789004468443_011
PG 20
WC Education & Educational Research; Social Issues; Social Sciences,
   Interdisciplinary
WE Book Citation Index – Social Sciences & Humanities (BKCI-SSH)
SC Education & Educational Research; Social Issues; Social Sciences - Other
   Topics
GA BV8GF
UT WOS:001077070100012
DA 2024-09-05
ER
PT J
AU Rahman, QM
   Corke, P
   Dayoub, F
AF Rahman, Quazi Marufur
   Corke, Peter
   Dayoub, Feras
TI Run-Time Monitoring of Machine Learning for Robotic Perception: A Survey
   of Emerging Trends
SO IEEE ACCESS
LA English
DT Article
DE Monitoring; Robots; Predictive models; Market research; Task analysis;
   Training; Safety; Machine learning; performance evaluation; reliability;
   robot learning
AB As deep learning continues to dominate all state-of-the-art computer vision tasks, it is increasingly becoming an essential building block for robotic perception. This raises important questions concerning the safety and reliability of learning-based perception systems. There is an established field that studies safety certification and convergence guarantees of complex software systems at design-time. However, the unknown future deployment environments of an autonomous system and the complexity of learning-based perception make the generalization of design-time verification to run-time problematic. In the face of this challenge, more attention is starting to focus on run-time monitoring of performance and reliability of perception systems with several trends emerging in the literature in the face of this challenge. This paper attempts to identify these trends and summarize the various approaches to the topic.
C1 [Rahman, Quazi Marufur; Corke, Peter; Dayoub, Feras] Queensland Univ Technol, ARC Ctr Excellence Robot Vis, Brisbane, Qld 4000, Australia.
C3 Queensland University of Technology (QUT); Australian Centre for Robotic
   Vision
RP Rahman, QM (corresponding author), Queensland Univ Technol, ARC Ctr Excellence Robot Vis, Brisbane, Qld 4000, Australia.
EM quazi.rahman@qut.edu.au
RI Corke, Peter/C-6770-2009
OI Corke, Peter/0000-0001-6650-367X; Rahman, Quazi
   Marufur/0000-0001-6538-0225; Dayoub, Feras/0000-0002-4234-7374
FU Australian Research Council (ARC) Centre of Excellence for Robotic
   Vision [CE140100016]; QUT Centre for Robotics
FX This work was supported in part by the Australian Research Council (ARC)
   Centre of Excellence for Robotic Vision under Grant CE140100016, and in
   part by the QUT Centre for Robotics.
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NR 78
TC 26
Z9 26
U1 0
U2 8
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2021
VL 9
BP 20067
EP 20075
DI 10.1109/ACCESS.2021.3055015
PG 9
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA QC7VN
UT WOS:000615041000001
OA Green Submitted, gold
DA 2024-09-05
ER
PT J
AU Yu, ZG
AF Yu, Zhonggen
TI Visualizing Artificial Intelligence Used in Education Over Two Decades
SO JOURNAL OF INFORMATION TECHNOLOGY RESEARCH
LA English
DT Article
DE Artificial Intelligence; Bibliometric Evaluation; Cluster; Education;
   Taxonomy
ID ENTREPRENEURSHIP EDUCATION; COCITATION ANALYSIS; LEARNING COMPANION;
   PERSPECTIVES; PERFORMANCE; JOURNALS; SYSTEM; IMPACT; FIELD
AB With the rapid development of computer science, use of artificial intelligence (AI) in education has caught much attention across the world although it is still a young field with many under-explored research elements. Through visualizing study with bibliometric evaluation and taxonomy of the literature using both VOSviewer and CiteSpace, this study provided references for readers in terms of cluster mapping on the basis of keywords, bibliographic coupling of countries, cluster mapping on the basis of co-citations, citation counts, bursts, betweenness centrality, and sigma. Researchers could also take the findings of this study into serious consideration when they set about researching effectiveness, efficiency, or usefulness of AI in education. Future research into use of AI in education will most likely need interdisciplinary cooperation between computer science, statistics, education, cognition, and robotics.
C1 [Yu, Zhonggen] Beijing Language & Culture Univ, Dept English Studies, Fac Foreign Studies, Beijing, Peoples R China.
C3 Beijing Language & Culture University
RP Yu, ZG (corresponding author), Beijing Language & Culture Univ, Dept English Studies, Fac Foreign Studies, Beijing, Peoples R China.
RI Yu, Zhonggen/AAE-5514-2020; Yu, Zhonggen/AAJ-3063-2020
OI Yu, Zhonggen/0000-0002-3873-980X; Yu, Zhonggen/0000-0002-3873-980X
FU Chinese national fund for the humanities and social sciences (Chinese
   Academic translation) [17WSS005]; MOOCs of Beijing Language and Culture
   University (Important) "An introduction to Linguistics" in 2019
   [MOOC201902]; Beijing Language and Culture University; research and
   reform fund of the "Undergraduate Teaching Reform and Innovation
   Project" of Beijing higher education in 2020-innovative "multilingual +"
   excellent talent training system
FX We would like to extend our gratitude to those who contributed to this
   work and the funds that supported this research: Chinese national fund
   for the humanities and social sciences (Chinese Academic translation)
   (17WSS005); MOOCs of Beijing Language and Culture University (Important)
   "An introduction to Linguistics" in 2019 (MOOC201902); An online and
   offline hybrid course "Introduction to Linguistics" of Beijing Language
   and Culture University in 2020; The research and reform fund of the
   "Undergraduate Teaching Reform and Innovation Project" of Beijing higher
   education in 2020-innovative "multilingual +" excellent talent training
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NR 43
TC 20
Z9 20
U1 14
U2 124
PU IGI GLOBAL
PI HERSHEY
PA 701 E CHOCOLATE AVE, STE 200, HERSHEY, PA 17033-1240 USA
SN 1938-7857
EI 1938-7865
J9 J INF TECHNOL RES
JI J. Inf. Technol. Res.
PD OCT-DEC
PY 2020
VL 13
IS 4
BP 32
EP 46
DI 10.4018/JITR.2020100103
PG 15
WC Computer Science, Interdisciplinary Applications
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA OH4QC
UT WOS:000582557600003
DA 2024-09-05
ER
PT C
AU Song, XM
   Xiong, T
AF Song, Xinmeng
   Xiong, Ting
GP IEEE
TI A Survey of Published Literature on Conversational Artificial
   Intelligence
SO 2021 7TH INTERNATIONAL CONFERENCE ON INFORMATION MANAGEMENT (ICIM 2021)
LA English
DT Proceedings Paper
CT 7th International Conference on Information Management (ICIM)
CY MAR 27-29, 2021
CL Imperial Coll London, S Kensington Campus, London, ENGLAND
HO Imperial Coll London, S Kensington Campus
DE conversational artificial intelligence; literature review;
   scientometrics; CiteSpace
ID EMERGING TRENDS
AB Conversational artificial intelligence (AI), as a rapidly emerging technology, has made a huge impact in the fields of e-commerce, education, entertainment, health, journalism and productivity, and thus arousing the interest of governments, businesses and research institutions. Therefore, it is necessary to generate an overview of the recent developments in the field in order to plan further study effectively. To achieve the goal, we analyze the conversational AI literature between 2015 and 2020 with CiteSpace, which is used for the systematic evaluation of the knowledge structures. Based on the results of the analysis, we find the following insights: (1) The evolution of conversational AI research involves many categories while two major disciplines-computer science and ergonomics-lead the way. (2) The current research can be divided into two research areas: underlying technology architecture and smart scene applications. By using multiple complementary scientometric methods, our study visually presents the research history, current research hotspots and emerging trends in the field of conversational AI, to further promote its technology and application research.
C1 [Song, Xinmeng; Xiong, Ting] Sichuan Univ, Sch Publ Adm, Chengdu, Peoples R China.
C3 Sichuan University
RP Song, XM (corresponding author), Sichuan Univ, Sch Publ Adm, Chengdu, Peoples R China.
EM songxinmeng@stu.scu.edu.cn; xiongting@stu.scu.edu.cn
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NR 13
TC 3
Z9 3
U1 2
U2 33
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-6654-4380-7
PY 2021
BP 113
EP 117
DI 10.1109/ICIM52229.2021.9417135
PG 5
WC Computer Science, Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BR8WH
UT WOS:000674460200021
DA 2024-09-05
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PT J
AU Gyory, JT
   Kotovsky, K
   Cagan, J
AF Gyory, Joshua T.
   Kotovsky, Kenneth
   Cagan, Jonathan
TI The Influence of Process Management: Uncovering the Impact of Real-Time
   Managerial Interventions via a Topic Modeling Approach
SO JOURNAL OF MECHANICAL DESIGN
LA English
DT Article
DE cognitive-based design; design representation; design teams; topic
   modeling; process management
ID DESIGN
AB Computationally studying team discourse can provide valuable, real-time insights into the state of design teams and design cognition during problem-solving. The particular experimental design, adopted from previous work by the authors, places one of the design team conditions under the guidance of a human process manager. In that work, teams under this process management outperformed the unmanaged teams in terms of their design performance. This opens the opportunity to not only model design discourse during problem-solving, but more critically, to explore process manager interventions and their impact on design cognition. Utilizing this experimental framework, a topic model is trained on the discourse of human designers of both managed and unmanaged teams collaboratively solving a conceptual engineering design task. Results show that the two team conditions significantly differ in a number of the extracted topics and, in particular, those topics that most pertain to the manager interventions. A dynamic look during the design process reveals that the largest differences between the managed and unmanaged teams occur during the latter half of problem-solving. Furthermore, a before and after analysis of the topic-motivated interventions reveals that the process manager interventions significantly shift the topic mixture of the team members' discourse immediately after intervening. Taken together, these results from this work not only corroborate the effect of the process manager interventions on design team discourse and cognition but provide promise for the computational detection and facilitation of design interventions based on real-time, discourse data.
C1 [Gyory, Joshua T.; Cagan, Jonathan] Carnegie Mellon Univ, Dept Mech Engn, Pittsburgh, PA 15213 USA.
   [Kotovsky, Kenneth] Carnegie Mellon Univ, Dept Psychol, Pittsburgh, PA 15213 USA.
C3 Carnegie Mellon University; Carnegie Mellon University
RP Cagan, J (corresponding author), Carnegie Mellon Univ, Dept Mech Engn, Pittsburgh, PA 15213 USA.
EM jgyory@andrew.cmu.edu; kotovsky@cmu.edu; cagan@cmu.edu
OI Gyory, Joshua/0000-0001-9946-4179; Cagan, Jonathan/0000-0002-3935-9219
FU Air Force Office of Scientific Research (AFOSR) [FA9550-18-0088]
FX This work was supported by the Air Force Office of Scientific Research
   (AFOSR) under Grant No. FA9550-18-0088. Any opinions, findings, and
   conclusions or recommendations expressed in this paper are those of the
   authors and do not necessarily reflect the views of the sponsors. A
   previous version of this paper was published in the proceedings of the
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NR 64
TC 6
Z9 6
U1 1
U2 3
PU ASME
PI NEW YORK
PA TWO PARK AVE, NEW YORK, NY 10016-5990 USA
SN 1050-0472
EI 1528-9001
J9 J MECH DESIGN
JI J. Mech. Des.
PD NOV 1
PY 2021
VL 143
IS 11
AR 111401
DI 10.1115/1.4050748
PG 12
WC Engineering, Mechanical
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA UZ8RJ
UT WOS:000702466200011
OA Bronze
DA 2024-09-05
ER
PT J
AU Haga, WJ
   Zviran, M
AF Haga, W. J.
   Zviran, M.
TI Information systems effectiveness: research designs for causal inference
SO INFORMATION SYSTEMS JOURNAL
LA English
DT Article
DE effectiveness; effectiveness measurement; productivity; productivity
   assessment; research design; validity
AB This paper examines the capacity of the research designs of 37 empirical studies of information systems (IS) effectiveness to provide a basis for the development of theories about behaviour related to IS effectiveness. The power of each study to support causal inference was evaluated in terms of (a) its handling of the time dimension, (b) its ability to weigh differences and (c) its resistance to internal validity threats that pose alternative explanations for its reported findings. Of the reviewed studies, 29.7% could account for the time dimension, 32.4% employed a comparison group and 16.2% were not susceptible to any internal validity threats. Only 13.5% of the studies combined an accounting for the time dimension with the use of a comparison group. Of these, however, only 5.4% were also invulnerable to internal validity threats. The research designs of nearly 95% of these published studies were deficient in supporting causal inference. In those studies, suggestions that one variable was causally related to another variable could not be substantiated. Encouragement for the future capacity of IS effectiveness research to support causal inference was found in a trend towards the use of quasiexperimental designs. Recommendations are made regarding ways to increase the inferential capacity of research designs employed in the study of IS effectiveness.
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C3 United States Department of Defense; United States Navy; Naval
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RP Haga, WJ (corresponding author), Naval Postgrad Sch, Dept Syst Management, Monterey, CA 93943 USA.
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NR 121
TC 4
Z9 6
U1 2
U2 11
PU WILEY-BLACKWELL
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1350-1917
EI 1365-2575
J9 INFORM SYST J
JI Inf. Syst. J.
PD APR
PY 1994
VL 4
IS 2
BP 141
EP 166
DI 10.1111/j.1365-2575.1994.tb00048.x
PG 26
WC Information Science & Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Information Science & Library Science
GA V34KM
UT WOS:000209085300005
DA 2024-09-05
ER
PT J
AU Ballestar, MT
   Doncel, LM
   Sainz, J
   Ortigosa-Blanch, A
AF Teresa Ballestar, Maria
   Miguel Doncel, Luis
   Sainz, Jorge
   Ortigosa-Blanch, Arturo
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SO TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
LA English
DT Article
DE Research evaluation; Machine learning; Longitudinal clustering;
   Incentive-based policies
ID FINANCIAL INCENTIVES; PREDICTION; PRODUCTIVITY; VALIDATION; SCIENCE;
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C1 [Teresa Ballestar, Maria; Ortigosa-Blanch, Arturo] ESIC Business & Mkt Sch, Barcelona, Spain.
   [Miguel Doncel, Luis; Sainz, Jorge] Univ Rey Juan Carlos, Mostoles, Spain.
   [Sainz, Jorge] Univ Bath, Bath, Avon, England.
C3 ESIC; ESIC Business & Marketing School; Universidad Rey Juan Carlos;
   University of Bath
RP Ballestar, MT (corresponding author), ESIC Business & Mkt Sch, Barcelona, Spain.
EM mariateresa.ballestar@esic.edu; luismiguel.doncel@urjc.es;
   js3189@bath.ac.uk; arturo.ortigosa@esic.edu
RI SAINZ, JORGE/AAG-5379-2021; Sainz, jorge/AGW-3813-2022; Doncel-Pedrera,
   Luis/H-4711-2015
OI SAINZ, JORGE/0000-0001-8491-3154; Ortigosa-Blanch,
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NR 59
TC 20
Z9 20
U1 3
U2 47
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0040-1625
EI 1873-5509
J9 TECHNOL FORECAST SOC
JI Technol. Forecast. Soc. Chang.
PD DEC
PY 2019
VL 149
AR 119756
DI 10.1016/j.techfore.2019.119756
PG 9
WC Business; Regional & Urban Planning
WE Social Science Citation Index (SSCI)
SC Business & Economics; Public Administration
GA JU8TU
UT WOS:000501943200021
DA 2024-09-05
ER
PT J
AU LoPresto, MC
   Slater, TF
AF LoPresto, Michael C.
   Slater, Timothy F.
TI A New Comparison Of Active Learning Strategies To Traditional Lectures
   For Teaching College Astronomy
SO JOURNAL OF ASTRONOMY AND EARTH SCIENCES EDUCATION
LA English
DT Article
DE Astronomy Education Research; Active Learning; Lecture-Tutorials;
   Visual-Assessments; Formative Assessment
AB Although traditional lectures are still the dominant form of undergraduate instruction, there have been relatively few studies comparing various learner-centered and active learning teaching strategies to one another in order to guide professors in making informed instructional decisions. To study the impact of different active learning approaches, pre-test to posttest learning gains for students receiving instruction on introductory astronomy solar system topics through a combination of collaborative learning activities and formative assessment-driven activities were compared to the gains of students being taught the same topics by traditional lectures only. After traditional lectures, students improved from a pre-test score of 42% (n=144) to 49% (n=49). After lecture tutorials and classroom voting response systems improvement was to 73% (n=72) Using a multiple-group comparison approach, similar earning gains were also observed when using visual-assessment and tutorial activities. Moreover, data from a Likert-style attitude survey of 264 undergraduates showed that, although they did not report a clear preference for one instructional mode over the other, the majority of students believed that the active and collaborative nature of the activities helped them learn. The results of this study add weight to the notion that most modern pedagogies are superior to traditional lecture, and that although the relative impacts of particular pedagogies are mostly indistinguishable from one another, they are all are better than traditional lecture alone.
C1 [LoPresto, Michael C.] Henry Ford Coll, Dearborn, MI 48128 USA.
   [LoPresto, Michael C.] Henry Ford Coll, Introductory Phys & Astron, Dearborn, MI USA.
   [LoPresto, Michael C.] Univ Michigan, Dept Astron, Ann Arbor, MI 48109 USA.
   [Slater, Timothy F.] Univ Wyoming, Sci Educ, Laramie, WY 82071 USA.
C3 University of Michigan System; University of Michigan; University of
   Wyoming
RP LoPresto, MC (corresponding author), Henry Ford Coll, Dearborn, MI 48128 USA.
EM lopresto@hfcc.edu; tslater@uwyo.edu
FU NASA/JPL Center for Astronomy Education (CAE) at the University of
   Arizona; University of Michigan Department of Astronomy - University's
   Third Century Initiative; NSF [AST-1514835]
FX This work was supported in part by the NASA/JPL Center for Astronomy
   Education (CAE) at the University of Arizona as part of the
   Collaboration of Astronomy Teaching Scholars (CATS) project, Solar
   System Concept Inventory (SSCI) & Solar System Lecture-Tutorials Project
   and a through a Post-Doctoral Fellowship in the University of Michigan
   Department of Astronomy funded by University's Third Century Initiative
   and NSF grant AST-1514835.
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NR 40
TC 13
Z9 29
U1 0
U2 3
PU CLUTE INT
PI LITTLETON
PA 6901 S PIERCE STR, STE 301, LITTLETON, CO 80128 USA
SN 2374-6246
EI 2374-6254
J9 J ASTRON EARTH SCI E
JI J. Astron. Earth Sci. Educ.
PD JUN
PY 2016
VL 3
IS 1
BP 59
EP 75
DI 10.19030/jaese.v3i1.9685
PG 17
WC Education, Scientific Disciplines
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA FF2ZF
UT WOS:000408764600004
OA gold
DA 2024-09-05
ER
PT C
AU Wang, Y
   Kanagavelu, R
   Wei, QS
   Yang, YC
   Liu, Y
AF Wang, Yuan
   Kanagavelu, Renuga
   Wei, Qingsong
   Yang, Yechao
   Liu, Yong
BE Bakas, S
   Crimi, A
   Baid, U
   Malec, S
   Pytlarz, M
   Baheti, B
   Zenk, M
   Dorent, R
TI Model Aggregation for Federated Learning Considering Non-IID and
   Imbalanced Data Distribution
SO BRAINLESION: GLIOMA, MULTIPLE SCLEROSIS, STROKE AND TRAUMATIC BRAIN
   INJURIES, BRAINLES 2022, PT II
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT 8th International Workshop on Brain Lesion - Glioma, Multiple Sclerosis,
   Stroke and Traumatic Brain Injuries (BrainLes)
CY SEP 18-22, 2022
CL Singapore, SINGAPORE
DE Federated Learning; Multi-institutional Collaboration; Medical Imaging;
   Brain Tumor Segmentation
AB With the ever increasing importance and requirements to ensure data privacy, federated learning emerges as an promising technology for training deep learning models without having hospitals to share the raw data. MICCAI Federated Tumor Segmentation Challenge 2021 is the first international challenge on federated learning to strengthen the understanding of real-world challenges and create practical solutions in the related area. In the challenge of this year, we proposed a series of new aggregation strategies towards improving the learning performance in the context of non-IID and imbalanced data distribution. We also designed a simple collaborator selection scheme to shorten the training time while achieving a good level of model performance for brain tumor segmentation.
C1 [Wang, Yuan; Kanagavelu, Renuga; Wei, Qingsong; Yang, Yechao; Liu, Yong] Inst High Performance Comp, Singapore, Singapore.
C3 Agency for Science Technology & Research (A*STAR); A*STAR - Institute of
   High Performance Computing (IHPC)
RP Wang, Y (corresponding author), Inst High Performance Comp, Singapore, Singapore.
EM wang_yuan@ihpc.a-star.edu.sg; renuga_k@ihpc.a-star.edu.sg;
   wei_qingsong@ihpc.a-star.edu.sg; yang_yechao@ihpc.a-star.edu.sg;
   liuyong@ihpc.a-star.edu.sg
CR Baid U, 2021, Arxiv, DOI [arXiv:2107.02314, 10.48550/arXiv.2107.02314, 10.48550/ARXIV.2107.02314]
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   cnbc, Modern Medicine
   FeTS, 2022, Challenge
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NR 17
TC 0
Z9 0
U1 0
U2 0
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 0302-9743
EI 1611-3349
BN 978-3-031-44152-3; 978-3-031-44153-0
J9 LECT NOTES COMPUT SC
PY 2023
VL 14092
BP 196
EP 208
DI 10.1007/978-3-031-44153-0_19
PG 13
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications; Radiology, Nuclear Medicine & Medical
   Imaging
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Radiology, Nuclear Medicine & Medical Imaging
GA BW8RG
UT WOS:001206018200019
DA 2024-09-05
ER
PT J
AU Han, N
   Wen, YY
   Wang, BW
   Huang, F
   Liu, XQ
   Li, LY
   Zhu, TS
AF Han, Nuo
   Wen, Yeye
   Wang, Bowen
   Huang, Feng
   Liu, Xiaoqian
   Li, Linyan
   Zhu, Tingshao
TI Developing a machine learning-based instrument for subjective well-being
   assessment on Weibo and its psychological significance: An evaluative
   and interpretive research
SO APPLIED PSYCHOLOGY-HEALTH AND WELL BEING
LA English
DT Article; Early Access
DE domain knowledge; life satisfaction; machine learning; social media;
   subjective well-being; Weibo
ID AFFECT SCHEDULE PANAS; SOCIAL MEDIA; PERSONALITY; FACEBOOK; TWITTER;
   HAPPINESS; VALIDITY; REFLECT; CULTURE; EVENTS
AB Demystifying machine learning (ML) approaches through the synergy of psychology and artificial intelligence can achieve a balance between predictive and explanatory power in model development while enhancing rigor in validation and reporting standards. Accordingly, this study aimed to bridge this research gap by developing a subjective well-being (SWB) prediction model on Weibo, serving as a psychological assessment instrument and explaining the model construction based on psychological knowledge. The model establishment involved the collection of SWB scores and posts from 1,427 valid Weibo users. Multiple machine learning algorithms were employed to train the model and fine-tune its parameters. The optimal model was selected by comparing its criterion validity and split-half reliability performance. Furthermore, SHAP values were calculated to rank the importance of features, which were then used for model interpretation. The criterion validity for the three dimensions of SWB ranged from 0.50 to 0.52 (P < 0.001), and the split-half reliability ranged from 0.94 to 0.96 (P < 0.001). The identified relevant features were related to four main aspects: cultural values, emotions, morality, and time and space. This study expands the application scope of SWB-related psychological theories from a data-driven perspective and provides a theoretical reference for further well-being prediction.
C1 [Han, Nuo] Beijing Normal Univ, Fac Arts & Sci, Dept Psychol, Zhuhai, Peoples R China.
   [Han, Nuo; Li, Linyan] City Univ Hong Kong, Sch Data Sci, Hong Kong, Peoples R China.
   [Wen, Yeye] Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing, Peoples R China.
   [Wang, Bowen] Helmholtz Ctr Potsdam, GFZ German Res Ctr Geosci, Potsdam, Germany.
   [Huang, Feng; Liu, Xiaoqian; Zhu, Tingshao] Chinese Acad Sci, Inst Psychol, CAS Key Lab Behav Sci, Beijing, Peoples R China.
   [Huang, Feng; Zhu, Tingshao] Univ Chinese Acad Sci, Dept Psychol, Beijing, Peoples R China.
   [Li, Linyan] City Univ Hong Kong, Jockey Club Coll Vet Med & Life Sci, Dept Infect Dis & Publ Hlth, Hong Kong, Peoples R China.
C3 Beijing Normal University; City University of Hong Kong; Chinese Academy
   of Sciences; University of Chinese Academy of Sciences, CAS; Helmholtz
   Association; Helmholtz-Center Potsdam GFZ German Research Center for
   Geosciences; Chinese Academy of Sciences; Institute of Psychology, CAS;
   Chinese Academy of Sciences; University of Chinese Academy of Sciences,
   CAS; City University of Hong Kong
RP Zhu, TS (corresponding author), Chinese Acad Sci, Inst Psychol, 16 Lincui Rd, Beijing, Peoples R China.; Li, LY (corresponding author), City Univ Hong Kong, Kowloon, Tat Chee Ave, Hong Kong, Peoples R China.
EM linyanli@cityu.edu.hk; tszhu@psych.ac.cn
RI Wang, Bowen/GZN-1670-2022; Han, Nuo/GYU-5016-2022
OI Wang, Bowen/0000-0002-0975-1412; Han, Nuo/0000-0002-8090-4581; Wen,
   Yeye/0000-0001-6009-2101; HUANG, Feng/0000-0003-2156-0915; Li,
   Linyan/0000-0001-5736-2115; Zhu, Tingshao/0000-0003-0020-3812
FX We sincerely thank all participants and staff who participated in this
   study.
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NR 65
TC 0
Z9 0
U1 1
U2 1
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1758-0846
EI 1758-0854
J9 APPL PSYCHOL-HLTH WE
JI Appl. Psychol.-Health Well Being
PD 2024 AUG 21
PY 2024
DI 10.1111/aphw.12590
EA AUG 2024
PG 20
WC Psychology, Applied
WE Social Science Citation Index (SSCI)
SC Psychology
GA D3R9F
UT WOS:001295402200001
PM 39168488
DA 2024-09-05
ER
PT J
AU Kurtz, JB
   Lourie, MA
   Holman, EE
   Grob, KL
   Monrad, SU
AF Kurtz, Josh B.
   Lourie, Michael A.
   Holman, Elizabeth E.
   Grob, Karri L.
   Monrad, Seetha U.
TI Creating assessments as an active learning strategy: what are students'
   perceptions? A mixed methods study
SO MEDICAL EDUCATION ONLINE
LA English
DT Article
DE Qualitative research methods; quantitative research methods; curriculum
   development; evaluation; active learning; multiple-choice questions
ID MULTIPLE-CHOICE QUESTIONS; MEDICAL-EDUCATION; GENERATION
AB Background: Teaching students how to create assessments, such as those involving multiple-choice questions (MCQs), has the potential to be a useful active learning strategy. In order to optimize students' learning, it is essential to understand how they engage with such activities. Objective: To explore medical students' perceptions of how completing rigorous MCQ training and subsequently writing MCQs affects their learning. Design: In this mixed methods exploratory qualitative study, eighteen second-year medical students, trained in MCQ-writing best practices, collaboratively generated a question bank. Subsequently, the authors conducted focus groups with eight students to probe impressions of the process and the effect on learning. Responses partially informed a survey consisting of open-ended and Likert rating scale questions that the remaining ten students completed. Focus group and survey data from the eighteen participants were iteratively coded and categorized into themes related to perceptions of training and of collaborative MCQ writing. Results: Medical students felt that training in MCQ construction affected their appreciation for MCQ examinations and their test-taking strategy. They perceived that writing MCQs required more problem-solving and content-integration compared to their preferred study strategies. Specifically, generating plausible distractors required the most critical reasoning to make subtle distinctions between diagnoses and treatments. Additionally, collaborating with other students was beneficial in providing exposure to different learning and question-writing approaches. Conclusions: Completing MCQ-writing training increases appreciation for MCQ assessments. Writing MCQs requires medical students to make conceptual connections, distinguish between diagnostic and therapeutic options, and learn from colleagues, but requires extensive time and knowledge base.
C1 [Kurtz, Josh B.; Lourie, Michael A.; Holman, Elizabeth E.; Grob, Karri L.; Monrad, Seetha U.] Univ Michigan, Med Sch, Div Rheumatol, Dept Internal Med, Ann Arbor, MI 48109 USA.
C3 University of Michigan System; University of Michigan
RP Monrad, SU (corresponding author), Univ Michigan, Med Sch, Taubman Hlth Sci Lib 6125, 1135 Catherine St, Ann Arbor, MI 48109 USA.
EM seetha@med.umich.edu
OI Monrad, Seetha/0000-0002-3374-2989; Kurtz, Joshua/0000-0001-7528-1722
FU Whitaker fund grant at the University of Michigan; Summer Biological
   Research Program at the University of Michigan Medical School; Whitaker
   Fund - Institutional Grant
FX This study was funded by the Whitaker fund grant at the University of
   Michigan and the Summer Biological Research Program at the University of
   Michigan Medical School; Whitaker Fund - Institutional Grant [(none)];
CR [Anonymous], 2015, RES MED ED
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NR 37
TC 18
Z9 20
U1 1
U2 7
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 1087-2981
J9 MED EDUC ONLINE
JI Med. Educ. Online
PD JAN 1
PY 2019
VL 24
IS 1
AR 1630239
DI 10.1080/10872981.2019.1630239
PG 10
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA IF5VE
UT WOS:000473149700001
PM 31248355
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU Liang, Y
   Tan, JW
   Xie, ZS
   Chen, ZT
   Lin, DQ
   Yang, ZH
AF Liang, Yong
   Tan, Junwen
   Xie, Zhisong
   Chen, Zetao
   Lin, Daoqian
   Yang, Zhenhao
TI Research on Convolutional Neural Network Inference Acceleration and
   Performance Optimization for Edge Intelligence
SO SENSORS
LA English
DT Article
DE FPGA; HLS; edge intelligence; deep learning; heterogeneous computing
ID DNN INFERENCE; ARCHITECTURE
AB In recent years, edge intelligence (EI) has emerged, combining edge computing with AI, and specifically deep learning, to run AI algorithms directly on edge devices. In practical applications, EI faces challenges related to computational power, power consumption, size, and cost, with the primary challenge being the trade-off between computational power and power consumption. This has rendered traditional computing platforms unsustainable, making heterogeneous parallel computing platforms a crucial pathway for implementing EI. In our research, we leveraged the Xilinx Zynq 7000 heterogeneous computing platform, employed high-level synthesis (HLS) for design, and implemented two different accelerators for LeNet-5 using loop unrolling and pipelining optimization techniques. The experimental results show that when running at a clock speed of 100 MHz, the PIPELINE accelerator, compared to the UNROLL accelerator, experiences an 8.09% increase in power consumption but speeds up by 14.972 times, making the PIPELINE accelerator superior in performance. Compared to the CPU, the PIPELINE accelerator reduces power consumption by 91.37% and speeds up by 70.387 times, while compared to the GPU, it reduces power consumption by 93.35%. This study provides two different optimization schemes for edge intelligence applications through design and experimentation and demonstrates the impact of different quantization methods on FPGA resource consumption. These experimental results can provide a reference for practical applications, thereby providing a reference hardware acceleration scheme for edge intelligence applications.
C1 [Liang, Yong; Tan, Junwen] Guilin Univ Technol, Educ Dept Guangxi Zhuang, Key Lab Adv Mfg & Automat Technol, Guilin 541006, Peoples R China.
   [Liang, Yong; Tan, Junwen; Xie, Zhisong; Chen, Zetao; Lin, Daoqian; Yang, Zhenhao] Guilin Univ Technol, Coll Mech & Control Engn, Guilin 541006, Peoples R China.
C3 Guilin University of Technology; Guilin University of Technology
RP Tan, JW (corresponding author), Guilin Univ Technol, Educ Dept Guangxi Zhuang, Key Lab Adv Mfg & Automat Technol, Guilin 541006, Peoples R China.; Tan, JW (corresponding author), Guilin Univ Technol, Coll Mech & Control Engn, Guilin 541006, Peoples R China.
EM void414@163.com; 2120211120@glut.edu.cn; xiezs1972@163.com;
   chenzetao2021@163.com; lindq1997@163.com; 19950909667@163.com
RI Tan, Junwen/ISA-4718-2023
OI Liang, Yong/0000-0001-6021-2791
FU Science and Technology Program of Guangxi, China
FX No Statement Available
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NR 45
TC 0
Z9 0
U1 5
U2 11
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1424-8220
J9 SENSORS-BASEL
JI Sensors
PD JAN
PY 2024
VL 24
IS 1
AR 240
DI 10.3390/s24010240
PG 16
WC Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments
   & Instrumentation
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Instruments & Instrumentation
GA ER0L4
UT WOS:001140531900001
PM 38203102
OA Green Published, gold
DA 2024-09-05
ER
PT J
AU Rüdiger, MS
   Antons, D
   Salge, TO
AF Ruediger, Matthias Sebastian
   Antons, David
   Salge, Torsten-Oliver
TI The explanatory power of citations: a new approach to unpacking impact
   in science
SO SCIENTOMETRICS
LA English
DT Article
DE Citation analysis; Content-based citation analysis; Topic modeling;
   Information systems
ID INFORMATION-TECHNOLOGY; SCIENTIFIC ARTICLES; USER ACCEPTANCE; COUNTS
   MEASURE; REFERENCES; CLASSICS; SECURITY; CONTEXT
AB Citation analysis has been applied to map the landscape of scientific disciplines and to assess the impact of publications. However, it is limited in that it assumes all citations to be of equal weight. Doing away with this assumption could make such studies even more insightful. Current developments in this regard focus on the evaluation of the syntactic and semantic qualities of the text that surrounds citations. Still lacking, however, are computational techniques to unpack the thematic context in which citations appear. It is against this backdrop that we propose a text clustering approach to derive contextual aspects of individual citations and the relationship between cited and citing work in an automated and scalable fashion. The method reveals a focal publication's absorption and use within the scientific community. It can also facilitate impact assessments at all levels. In addition to analyzing individual publications, the method can also be extended to creating impact profiles for authors, institutions, disciplines, and regions. We illustrate our results based on a large corpus of full-text articles from the field of Information systems (IS) with the help of exemplary visualizations. In addition, we provide a case study, the scientific impact of the Technology acceptance model. This way, we not only show the usefulness of our method in comparison to existing techniques but also enhance the understanding of the field by providing an in-depth analysis of the absorption of a key IS theoretical base.
C1 [Ruediger, Matthias Sebastian; Antons, David; Salge, Torsten-Oliver] Rhein Westfal TH Aachen, Inst Technol & Innovat Management, Aachen, Germany.
C3 RWTH Aachen University
RP Rüdiger, MS (corresponding author), Rhein Westfal TH Aachen, Inst Technol & Innovat Management, Aachen, Germany.
EM ruediger@time.rwth-aachen.de; antons@time.rwth-aachen.de;
   salge@time.rwth-aachen.de
OI Rudiger, Matthias/0000-0002-7086-1370
FU Federal Ministry of Education and Research (BMBF) Germany [01PU17020]
FX Open Access funding enabled and organized by Projekt DEAL. This work was
   supported by Federal Ministry of Education and Research (BMBF) Germany,
   Grant No. 01PU17020.
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NR 71
TC 6
Z9 7
U1 2
U2 30
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD DEC
PY 2021
VL 126
IS 12
BP 9779
EP 9809
DI 10.1007/s11192-021-04103-w
EA AUG 2021
PG 31
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA XF4WA
UT WOS:000681571100001
OA hybrid
DA 2024-09-05
ER
PT J
AU Maphosa, V
   Maphosa, M
AF Maphosa, Vusumuzi
   Maphosa, Mfowabo
TI Artificial intelligence in higher education: a bibliometric analysis and
   topic modeling approach
SO APPLIED ARTIFICIAL INTELLIGENCE
LA English
DT Article
ID BIG DATA; ANALYTICS; STUDENTS; INDEX
AB Artificial intelligence (AI) has brought unprecedented growth and productivity in every socioeconomic sector. AI adoption in education is transformational through reduced teacher workload, individualized learning, intelligent tutors, profiling and prediction, high-precision education, collaboration, and learner tracking. This paper highlights the trajectory of AI research in higher education (HE) through bibliometric analysis and topic modeling approaches. We used the PRISMA guidelines to select 304 articles published in the Scopus database between 2012 and 2021. VOSviewer was used for visualization and text-mining to identify hotspots in the field. Latent Dirichlet Allocation analysis reveals distinct topics in the dynamic relationship between AI and HE. Only 9.6% of AI research in HE was achieved in the first seven years, with the last three years contributing 90.4%. China, the United States, Russia and the United Kingdom dominated publications. Four themes emerged - data as the catalyst, the development of AI, the adoption of AI in HE and emerging trends and the future of AI in HE. Topic modeling on the abstracts revealed the 10 most frequent topics and the top 30 most salient terms. This research contributes to the literature by synthesizing AI adoption opportunities in HE, topic modeling and future research areas.
C1 [Maphosa, Vusumuzi] Natl Univ Sci & Technol, Dept Informat & Commun Technol Serv, Bulawayo, Zimbabwe.
   [Maphosa, Mfowabo] Univ Pretoria, Fac Engn Built Environm & Informat Technol, Pretoria, South Africa.
C3 National University of Science & Technology - Zimbabwe; University of
   Pretoria
RP Maphosa, V (corresponding author), Natl Univ Sci & Technol, Dept Informat & Commun Technol Serv, Bulawayo, Zimbabwe.
EM vusumuzi.maphosa@nust.ac.zw
RI ; Maphosa, Vusumuzi/HGU-1754-2022
OI Maphosa, Mfowabo/0000-0003-3702-6821; Maphosa,
   Vusumuzi/0000-0002-2595-3890
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NR 68
TC 7
Z9 7
U1 29
U2 79
PU TAYLOR & FRANCIS INC
PI PHILADELPHIA
PA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA
SN 0883-9514
EI 1087-6545
J9 APPL ARTIF INTELL
JI Appl. Artif. Intell.
PD DEC 31
PY 2023
VL 37
IS 1
AR 2261730
DI 10.1080/08839514.2023.2261730
PG 23
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA U6IU4
UT WOS:001085827700001
OA gold
DA 2024-09-05
ER
PT J
AU Sonni, AF
   Putri, VCC
   Irwanto, I
AF Sonni, Alem Febri
   Putri, Vinanda Cinta Cendekia
   Irwanto, Irwanto
TI Bibliometric and Content Analysis of the Scientific Work on Artificial
   Intelligence in Journalism
SO JOURNALISM AND MEDIA
LA English
DT Article
DE artificial intelligence; journalism; bibliometric; news; Scopus
AB This paper presents a comprehensive bibliometric review of the development of artificial intelligence (AI) in journalism based on the analysis of 331 articles indexed in the Scopus database between 2019 and 2023. This research combines bibliometric approaches and quantitative content analysis to provide an in-depth conceptual and structural overview of the field. In addition to descriptive measures, co-citation and co-word analyses are also presented to reveal patterns and trends in AI- and journalism-related research. The results show a significant increase in the number of articles published each year, with the largest contributions coming from the United States, Spain, and the United Kingdom, serving as the most productive countries. Terms such as "fake news", "algorithms", and "automated journalism" frequently appear in the reviewed articles, reflecting the main topics of concern in this field. Furthermore, ethical aspects of journalism were highlighted in every discussion, indicating a new paradigm that needs to be considered for the future development of journalism studies and professionalism.
C1 [Sonni, Alem Febri; Putri, Vinanda Cinta Cendekia] Hasanuddin Univ, Fac Social & Polit Sci, Commun Studies, Makassar 90245, Indonesia.
   [Irwanto, Irwanto] Bina Nusantara Univ, Sch Design, Film Dept, Jakarta 15143, Indonesia.
C3 Universitas Hasanuddin; Universitas Bina Nusantara
RP Sonni, AF (corresponding author), Hasanuddin Univ, Fac Social & Polit Sci, Commun Studies, Makassar 90245, Indonesia.
EM alemfebris@unhas.ac.id; vinanda.cinta@gmail.com; irwanto001@binus.ac.id
RI Sonni, Alem Febri/GOV-6689-2022; Irwanto, Irwanto/KFQ-9741-2024
OI Sonni, Alem Febri/0000-0002-6785-5033; Irwanto,
   Irwanto/0000-0003-0474-5141
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NR 45
TC 0
Z9 0
U1 1
U2 1
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2673-5172
J9 JOURNAL MEDIA
JI Journal. Media
PD JUN
PY 2024
VL 5
IS 2
BP 787
EP 798
DI 10.3390/journalmedia5020051
PG 12
WC Communication
WE Emerging Sources Citation Index (ESCI)
SC Communication
GA WO9C2
UT WOS:001255924600001
OA gold
DA 2024-09-05
ER
PT J
AU Al-Jaishi, AA
   Taljaard, M
   Al-Jaishi, MD
   Abdullah, SS
   Thabane, L
   Devereaux, PJ
   Dixon, SN
   Garg, AX
AF Al-Jaishi, Ahmed A.
   Taljaard, Monica
   Al-Jaishi, Melissa D.
   Abdullah, Sheikh S.
   Thabane, Lehana
   Devereaux, P. J.
   Dixon, Stephanie N.
   Garg, Amit X.
TI Machine learning algorithms to identify cluster randomized trials from
   MEDLINE and EMBASE
SO SYSTEMATIC REVIEWS
LA English
DT Article
DE Cluster randomized controlled trial; Machine learning; Bibliographic
   databases; Sensitivity and specificity; Prediction
ID PRAGMATIC TRIALS
AB Background: Cluster randomized trials (CRTs) are becoming an increasingly important design. However, authors of CRTs do not always adhere to requirements to explicitly identify the design as cluster randomized in titles and abstracts, making retrieval from bibliographic databases difficult. Machine learning algorithms may improve their identification and retrieval. Therefore, we aimed to develop machine learning algorithms that accurately determine whether a bibliographic citation is a CRT report.
   Methods: We trained, internally validated, and externally validated two convolutional neural networks and one support vector machine (SVM) algorithm to predict whether a citation is a CRT report or not. We exclusively used the information in an article citation, including the title, abstract, keywords, and subject headings. The algorithms' output was a probability from 0 to 1. We assessed algorithm performance using the area under the receiver operating characteristic (AUC) curves. Each algorithm's performance was evaluated individually and together as an ensemble. We randomly selected 5000 from 87,633 citations to train and internally validate our algorithms. Of the 5000 selected citations, 589 (12%) were confirmed CRT reports. We then externally validated our algorithms on an independent set of 1916 randomized trial citations, with 665 (35%) confirmed CRT reports.
   Results: In internal validation, the ensemble algorithm discriminated best for identifying CRT reports with an AUC of 98.6% (95% confidence interval: 97.8%, 99.4%), sensitivity of 97.7% (94.3%, 100%), and specificity of 85.0% (81.8%, 88.1%). In external validation, the ensemble algorithm had an AUC of 97.8% (97.0%, 98.5%), sensitivity of 97.6% (96.4%, 98.6%), and specificity of 78.2% (75.9%, 80.4%)). All three individual algorithms performed well, but less so than the ensemble.
   Conclusions: We successfully developed high-performance algorithms that identified whether a citation was a CRT report with high sensitivity and moderately high specificity. We provide open-source software to facilitate the use of our algorithms in practice.
C1 [Al-Jaishi, Ahmed A.; Dixon, Stephanie N.; Garg, Amit X.] Lawson Hlth Res Inst, 800 Commissioners Rd E, London, ON, Canada.
   [Taljaard, Monica] Univ Ottawa, Ottawa Hosp Res Inst, Sch Epidemiol & Publ Hlth, Clin Epidemiol Program, 501 Smyth Rd, Ottawa, ON, Canada.
   [Al-Jaishi, Melissa D.] London Hlth Sci Ctr, 800 Commissioners Rd E, London, ON, Canada.
   [Abdullah, Sheikh S.] Western Univ, Dept Comp Sci, 1151 Richmond St, Richmond, ON, Canada.
   [Thabane, Lehana; Devereaux, P. J.] McMaster Univ, Dept Hlth Res Methods Evidence & Impact, 1280 Main St W, Hamilton, ON, Canada.
C3 Western University (University of Western Ontario); University of
   Ottawa; Ottawa Hospital Research Institute; London Health Sciences
   Centre; McMaster University
RP Al-Jaishi, AA (corresponding author), Lawson Hlth Res Inst, 800 Commissioners Rd E, London, ON, Canada.
EM Ahmed.AlJaishi@lhsc.on.ca
RI Taljaard, Monica/AFJ-8820-2022
OI Taljaard, Monica/0000-0002-3978-8961; Al-Jaishi,
   Ahmed/0000-0003-0376-2214
FU Allied Health Doctoral Fellowship from the Kidney Foundation of Canada;
   CIHR Doctoral Award; McMaster University Michael DeGroote Scholarship;
   Canadian Institutes of Health Research (CIHR) [MYG-151209]; Dr. Adam
   Linton Chair in Kidney Health Analytics; CIHR
FX Ahmed Al-Jaishi was supported by the Allied Health Doctoral Fellowship
   from the Kidney Foundation of Canada, CIHR Doctoral Award, and McMaster
   University Michael DeGroote Scholarship. Stephanie Dixon's research is
   supported through a SPOR Innovative Clinical Trial Multi-Year Grant
   (Grant number: MYG-151209) from the Canadian Institutes of Health
   Research (CIHR). Amit Garg was supported by the Dr. Adam Linton Chair in
   Kidney Health Analytics and a Clinician Investigator Award from the
   CIHR. The funders had no role in study design, data collection and
   analysis, decision to publish, or manuscript preparation.
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NR 49
TC 0
Z9 0
U1 0
U2 0
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
EI 2046-4053
J9 SYST REV-LONDON
JI Syst. Rev.
PD OCT 25
PY 2022
VL 11
IS 1
AR 229
DI 10.1186/s13643-022-02082-4
PG 10
WC Medicine, General & Internal
WE Science Citation Index Expanded (SCI-EXPANDED)
SC General & Internal Medicine
GA 5N7NZ
UT WOS:000871976500001
PM 36284336
OA gold, Green Published, Green Submitted
DA 2024-09-05
ER
PT J
AU Burstein, J
   Elliot, N
   Molloy, H
AF Burstein, Jill
   Elliot, Norbert
   Molloy, Hillary
TI Informing Automated Writing Evaluation Using the Lens of Genre: Two
   Studies
SO CALICO JOURNAL
LA English
DT Article
DE AUTOMATED WRITING EVALUATION; NATURAL LANGUAGE PROCESSING; WRITING
   RESEARCH
ID HIGH-SCHOOL; UNIVERSITY; STUDENTS
AB Genre serves as a useful lens to investigate the range of evidence derived from automated writing evaluation ( AWE). To support construct-relevant systems used for writing instruction and assessment, two investigations were conducted that focused on postsecondary writing requirements and faculty perceptions of student writing proficiency. Survey research is described from a national study and a second site study at American University, a 4-year private university in Washington, DC, to illustrate writing requirements and perceptions of writing proficiency in school and workplace settings. A mixed-methods analysis of faculty focus groups in the site study afforded more detailed discussions that were used to understand student writing support needs. Through the lens of genre, study results illustrated how the role of AWE might be expanded and aligned with instruction in four-year postsecondary institutions.
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   [Elliot, Norbert] New Jersey Inst Technol, Newark, NJ 07102 USA.
C3 Educational Testing Service (ETS); New Jersey Institute of Technology
RP Burstein, J (corresponding author), Educ Testing Serv, Princeton, NJ 08541 USA.
EM jburstein@ets.org
CR [Anonymous], 25 INT C COMP LING C
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NR 57
TC 19
Z9 20
U1 1
U2 14
PU EQUINOX PUBLISHING LTD
PI SHEFFIELD
PA 415, THE WORKSTATION, 15 PATERNOSTER ROW, SHEFFIELD, S1 2BX, ENGLAND
SN 2056-9017
J9 CALICO J
JI CALICO J.
PY 2016
VL 33
IS 1
BP 117
EP 141
DI 10.1558/cj.v33i1.26374
PG 25
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA ER8CR
UT WOS:000399044800007
DA 2024-09-05
ER
PT J
AU Arnesen, K
   Walters, S
   Borup, J
   Barbour, MK
AF Arnesen, Karen
   Walters, Shea
   Borup, Jered
   Barbour, Michael K.
TI Irrelevant, Overlooked, or Lost? Trends in 20 Years of Uncited and
   Low-cited K-12 Online Learning Articles
SO ONLINE LEARNING
LA English
DT Article
DE distance education; telelearning; e-learning; K-12 online learning;
   virtual school; cyber school; citation analysis; journal analysis
ID DISTANCE EDUCATION; SCIENCE; SCHOOLS
AB In this study, we analyzed a subset of uncited or low-cited articles from the data reported in Arnesen, Hveem, Short, West, and Barbour (2019), who examined the trends in K-12 online learning articles from 1994 to 2016. We identified 62 articles that had 5 or fewer citations, and analyzed them for trends in authorship, publication outlets, dates of publication, and topics that could help explain their low citation numbers. We also analyzed topics to see what contribution they might have made and can still make to the field of K-12 online learning. We found that the majority of these articles had been published in many different, less well-known journals. We also found that these articles may have attracted fewer readers because they addressed topics that seemed to have a narrow focus, often outside of the U.S. The articles were also authored by both well-known researchers in the field, as well as a number of one-time authors. What we did not find were articles that were uninteresting, poorly researched, or irrelevant. Many of the articles described and discussed programs that grappled with and overcame some of the same challenges online learning still faces today: issues of interaction, community, technology, management, etc. Some of the early articles gave interesting insights into the history of K-12 online learning, especially as it involved rural learners and programs. Others addressed less mainstream but still interesting topics such as librarians in online learning, cross-border AP history classes, policies that helped or hindered the growth of online learning, and practical considerations of cost and access.
C1 [Arnesen, Karen] Brigham Young Univ, Provo, UT 84602 USA.
   [Walters, Shea; Borup, Jered] George Mason Univ, Fairfax, VA 22030 USA.
   [Barbour, Michael K.] Touro Univ Calif, Vallejo, CA USA.
C3 Brigham Young University; George Mason University; Touro University
   California
RP Arnesen, K (corresponding author), Brigham Young Univ, Provo, UT 84602 USA.
RI Barbour, Michael/F-9514-2011
OI Barbour, Michael/0000-0001-9037-3350
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NR 69
TC 4
Z9 8
U1 2
U2 11
PU ONLINE LEARNING CONSORTIUM
PI NEWBURYPORT
PA PO BOX 1238, NEWBURYPORT, MA 01950 USA
SN 2472-5749
EI 2472-5730
J9 ONLINE LEARN
JI Online Learn.
PD JUN
PY 2020
VL 24
IS 2
BP 187
EP 206
DI 10.24059/olj.v24i2.2080
PG 20
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA LU8QU
UT WOS:000538014200012
OA gold
DA 2024-09-05
ER
PT J
AU Ribeiro, GAS
   Barbosa, RM
   Reis, MD
   Costa, NL
AF Ribeiro, Guilherme Alberto Sousa
   Barbosa, Rommel Melgaco
   da Cunha Reis, Marcio
   Costa, Nattane Luiza
TI From bibliometrics to text mining: exploring feature selection methods
   in microarray research
SO COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
LA English
DT Article; Early Access
DE Bibliometric; Feature selection; Microarray; Text mining
ID MACHINE; CLASSIFICATION; BUSINESS; DATASETS; MODEL; TOOL
AB Text mining (TM) is a technique that aims to extract knowledge from unstructured data sources by transforming them into structured data. TM algorithms can be used to detect hidden patterns in large amounts of data, including bibliometric data. Feature selection has been used to reduce the high dimensionality and complexity of computational problems, including microarray data that have a large number of features. In this context, this study aims to use text mining to discover trends in the use of feature selection techniques on microarray data based on bibliometric data such as titles, abstracts, and keywords. A total of 1448 studies published in journals indexed in the Web of Science database were collected to perform a bibliometric and TM analysis. One of the main goals of this study was to determine the patterns related to the roles of medical and machine learning methods. The results demonstrated the trends between microarray and other medical/biological topics, and machine learning techniques such as feature selection and classification, including the identification of commonly used databases and algorithms. Colon, lung, and breast were the most commonly studied cancers identified using microarray data and feature selection techniques. In addition, SVM was frequently used for dimensionality reduction and classification tasks. Despite the insightful results based on text mining, more studies are needed to investigate the performance, strength, and weakness of different types of feature selectors to microarray data.
C1 [Ribeiro, Guilherme Alberto Sousa; Barbosa, Rommel Melgaco] Univ Fed Goias, Inst Informat, Samambaia Campus, BR-74690900 Goiania, Go, Brazil.
   [Ribeiro, Guilherme Alberto Sousa; da Cunha Reis, Marcio] Fed Inst Goias IFG, Studies & Res Sci & Technol Grp GCITE, Goiania, Go, Brazil.
   [Barbosa, Rommel Melgaco; da Cunha Reis, Marcio] Hosp Israelita Albert Einstein, Image Ctr, Sao Paulo, SP, Brazil.
   [Costa, Nattane Luiza] Fed Inst Goiano, Urutai, Go, Brazil.
C3 Universidade Federal de Goias; Instituto Federal de Goias (IFG);
   Hospital Israelita Albert Einstein; Instituto Federal Goiano
RP Ribeiro, GAS (corresponding author), Univ Fed Goias, Inst Informat, Samambaia Campus, BR-74690900 Goiania, Go, Brazil.
EM guilherme.ufma@gmail.com
OI Sousa Ribeiro, Guilherme Alberto/0000-0002-2230-9573
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NR 85
TC 0
Z9 0
U1 8
U2 8
PU TAYLOR & FRANCIS INC
PI PHILADELPHIA
PA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA
SN 0361-0918
EI 1532-4141
J9 COMMUN STAT-SIMUL C
JI Commun. Stat.-Simul. Comput.
PD 2024 MAR 25
PY 2024
DI 10.1080/03610918.2024.2331083
EA MAR 2024
PG 17
WC Statistics & Probability
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematics
GA MQ1D9
UT WOS:001194992900001
DA 2024-09-05
ER
PT C
AU Zhang, CT
   Zhang, HX
   Yuan, DF
   Zhang, MG
AF Zhang, Chuanting
   Zhang, Haixia
   Yuan, Dongfeng
   Zhang, Minggao
BE Liu, X
   Qiu, T
   Guo, B
   Lu, K
   Ning, Z
   Dong, M
   Li, Y
TI Deep Learning Based Link Prediction with Social Pattern and External
   Attribute Knowledge in Bibliographic Networks
SO 2016 IEEE INTERNATIONAL CONFERENCE ON INTERNET OF THINGS (ITHINGS) AND
   IEEE GREEN COMPUTING AND COMMUNICATIONS (GREENCOM) AND IEEE CYBER,
   PHYSICAL AND SOCIAL COMPUTING (CPSCOM) AND IEEE SMART DATA (SMARTDATA)
LA English
DT Proceedings Paper
CT 9th IEEE Int Conf on Internet of Things / 12th IEEE Int Conf on Green
   Comp and Communicat / 9th IEEE Int Conf on Cyber, Phys, and Social Comp
   / IEEE Int Conf on Smart Data
CY DEC 16-19, 2016
CL Chengdu, PEOPLES R CHINA
AB The problem of predicting links for information entities is an important task in network analysis. In this regard, link prediction between authors in bibliographic networks has attracted much attention. However, most of these works only center around exploiting network topology features to do prediction, and other factors affecting link formation are rarely considered. In this paper, we introduce two kinds of novel features based on social pattern and external attribute knowledge (SPEAK), then integrate the SPEAK features and topological features into a deep learning framework using deep neural networks (DNNs). We present the performance based on a real world academic social network from AMiner. Experimental results demonstrate that the SPEAK features can significantly boost the link prediction performance especially when potential links span large geodesic distance. In addition, these features are helpful in understanding the mechanisms behind the link formation.
C1 [Zhang, Chuanting; Zhang, Haixia; Yuan, Dongfeng; Zhang, Minggao] Shandong Univ, Shandong Prov Key Lab Wireless Commun Technol, Jinan 250100, Peoples R China.
C3 Shandong University
RP Zhang, CT (corresponding author), Shandong Univ, Shandong Prov Key Lab Wireless Commun Technol, Jinan 250100, Peoples R China.
EM chuanting.zhang@gmail.com; haixia.zhang@sdu.edu.cn; dfyuan@sdu.edu.cn
RI Zhang, Chuanting/AAD-4183-2020
FU Special Project for Independent Innovation and Achievement
   Transformation of Shandong Province [2013ZHZX2C0102, 2014ZZCX03401]
FX The work presented in this paper was supported in part by the Special
   Project for Independent Innovation and Achievement Transformation of
   Shandong Province (2013ZHZX2C0102, 2014ZZCX03401).
CR AlHasan M., 2006, P SDM 06 WORKSH LINK
   [Anonymous], 2005, ACM SIGKDD EXPLOR NE
   [Anonymous], 2010, KDD
   Backstrom L., 2011, WSDM, P635
   Dong YX, 2012, IEEE DATA MINING, P181, DOI 10.1109/ICDM.2012.140
   Grover A, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P855, DOI 10.1145/2939672.2939754
   Han X, 2015, IEEE ICC, P1250, DOI 10.1109/ICC.2015.7248494
   Hinton G, 2012, IEEE SIGNAL PROC MAG, V29, P82, DOI 10.1109/MSP.2012.2205597
   LeCun Y, 2015, NATURE, V521, P436, DOI 10.1038/nature14539
   Li X., 2014, Proceedings of the 2014 SIAM international conference on data mining, P289, DOI DOI 10.1137/1.9781611973440.33
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NR 15
TC 7
Z9 7
U1 1
U2 2
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-5090-5880-8
PY 2016
BP 815
EP 821
DI 10.1109/iThings-GreenCom-CPSCom-SmartData.2016.170
PG 7
WC Computer Science, Theory & Methods; Green & Sustainable Science &
   Technology; Telecommunications
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Science & Technology - Other Topics;
   Telecommunications
GA BS5HS
UT WOS:000730823200143
DA 2024-09-05
ER
PT C
AU Omotayo, AH
   Gamal, M
   Ehab, E
   Dovonon, G
   Akinjobi, Z
   Lukman, I
   Turki, H
   Abdien, M
   Tondji, I
   Oppong, A
   Pimi, Y
   Gamal, K
   Roya
   Siam, M
AF Omotayo, Abdul-Hakeem
   Gamal, Mai
   Ehab, Eman
   Dovonon, Gbetondji
   Akinjobi, Zainab
   Lukman, Ismaila
   Turki, Houcemeddine
   Abdien, Mahmod
   Tondji, Idriss
   Oppong, Abigail
   Pimi, Yvan
   Gamal, Karim
   Roya
   Siam, Mennatullah
GP ACM
TI Towards a Better Understanding of the Computer Vision Research Community
   in Africa
SO PROCEEDINGS OF 2023 ACM CONFERENCE ON EQUITY AND ACCESS IN ALGORITHMS,
   MECHANISMS, AND OPTIMIZATION, EAAMO 2023
LA English
DT Proceedings Paper
CT ACM Conference on Equity and Access in Algorithms, Mechanisms, and
   Optimization (EAAMO)
CY OCT 30-NOV 01, 2023
CL Boston Univ, Boston, MA
HO Boston Univ
DE computer vision; participatory approach; bibliometric study
ID BIBLIOMETRIC ANALYSIS; SCIENCE; HEALTH; BIAS
AB Computer vision is a broad field of study that encompasses different tasks (e.g., object detection, semantic segmentation, 3D reconstruction). Although computer vision is relevant to the African communities in various applications, yet computer vision research is under-explored in the continent and constructs only 0.06% of top-tier publications in the last ten years. In this paper, our goal is to have a better understanding of the computer vision research conducted in Africa and provide pointers on whether there is equity in research or not. We do this through an empirical analysis of the African computer vision publications that are Scopus indexed, where we collect around 63,000 publications over the period 2012-2022. We first study the opportunities available for African institutions to publish in top-tier computer vision venues. We show that African publishing trends in top-tier venues over the years do not exhibit consistent growth, unlike other continents such as North America or Asia. Moreover, we study all computer vision publications beyond top-tier venues in different African regions to find that mainly Northern and Southern Africa are publishing in computer vision with 68.5% and 15.9% of publications, resp. Nonetheless, we highlight that both Eastern and Western Africa are exhibiting a promising increase with the last two years closing the gap with Southern Africa. Additionally, we study the collaboration patterns in these publications to find that most of these exhibit international collaborations rather than African ones. We also show that most of these publications include an African author that is a key contributor as the first or last author. Finally, we present the most recurring keywords in computer vision publications per African region. In summary, our analysis reveals that African researchers are key contributors to African research, yet there exists multiple barriers to publish in top-tier venues and establish African collaborations. Additionally, the question on whether there is an alignment between the current computer vision topics published in Africa and the most urgent needs in African communities remains unanswered. In this work we took the first step of documenting per-region published topics and we leave it for future work to investigate the latter question. This work is part of a community based effort that is focused on improving the computer vision research in Africa, where we question whether researchers across the different regions have access to equal opportunities to lead their research or not.
C1 [Omotayo, Abdul-Hakeem] Univ Calif Davis, Davis, CA 95616 USA.
   [Gamal, Mai] German Univ Cairo, Cairo, Egypt.
   [Ehab, Eman] Nile Univ, Giza, Egypt.
   [Dovonon, Gbetondji] UCL, London, England.
   [Akinjobi, Zainab] New Mexicos State Univ, Las Cruces, NM USA.
   [Lukman, Ismaila] Univ Angers, Angers, France.
   [Turki, Houcemeddine] Univ Sfax, Sfax, Tunisia.
   [Abdien, Mahmod; Gamal, Karim] Queens Univ, Kingston, ON, Canada.
   [Tondji, Idriss; Pimi, Yvan] African Masters Machine Intelligence AMMI AIMS, Mbour Thies, Senegal.
   [Oppong, Abigail] Ashesi Univ, Berekuso, Ghana.
   [Roya] Grassroots, Comp Vis Africa, Kampala, Uganda.
   [Siam, Mennatullah] Ontario Tech Univ, Oshawa, ON, Canada.
C3 University of California System; University of California Davis;
   Egyptian Knowledge Bank (EKB); German University in Cairo; Egyptian
   Knowledge Bank (EKB); Nile University; University of London; University
   College London; Universite d'Angers; Universite de Sfax; Queens
   University - Canada
RP Roya (corresponding author), Grassroots, Comp Vis Africa, Kampala, Uganda.
EM ormorteey@gmail.com; mai.tharwat@guc.edu.eg; e.ehab@nu.edu.eg;
   gbetondji.dovonon.22@ucl.ac.uk; akinzayn@gmail.com;
   ismailukman@gmail.com; turkiabdelwaheb@hotmail.fr; 21mmah@queensu.ca;
   itondji@aimsammi.org; abigoppong@gmail.com; ypimi@aimsammi.org;
   21kgmm@queensu.ca; roya.cv4africa@gmail.com;
   mennatullah.siam@ontariotechu.ca
RI Turki, Houcemeddine/J-8929-2013; Omotayo, Abdul-Hakeem/KDP-2885-2024
OI Turki, Houcemeddine/0000-0003-3492-2014; Omotayo,
   Abdul-Hakeem/0009-0009-4558-0356
CR African Development Bank Group, African Countries
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NR 47
TC 0
Z9 0
U1 0
U2 0
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES
BN 979-8-4007-0381-2
PY 2023
AR 1
DI 10.1145/3617694.3623221
PG 12
WC Computer Science, Interdisciplinary Applications; Social Sciences,
   Mathematical Methods; Social Sciences, Interdisciplinary
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Mathematical Methods In Social Sciences; Social
   Sciences - Other Topics
GA BW2SD
UT WOS:001124266900001
OA Green Submitted
DA 2024-09-05
ER
PT C
AU Dogan, RI
   Wilbur, WJ
   Comeau, DC
AF Dogan, Rezarta Islamaj
   Wilbur, W. John
   Comeau, Donald C.
BE Calzolari, N
   Choukri, K
   Declerck, T
   Loftsson, H
   Maegaard, B
   Mariani, J
   Moreno, A
   Odijk, J
   Piperidis, S
TI BioC and Simplified Use of the PMC Open Access Dataset for Biomedical
   Text Mining
SO LREC 2014 - NINTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND
   EVALUATION
LA English
DT Proceedings Paper
CT 9th International Conference on Language Resources and Evaluation (LREC)
CY MAY 26-31, 2014
CL Reykjavik, ICELAND
DE interoperability; PubMed Central; biomedical natural language
   processing; BioC; annotations
ID INFORMATION EXTRACTION; FULL-TEXT
AB High quality easily-accessible resources are crucial for developing reliable applications in the health and biomedical domain. At the same time, interoperability, broad use, and reuse are vital considerations when developing useful systems. As a response, BioC has recently been put forward as a convenient XML format to share text documents and annotations, and as an accompanying input/output library to promote interoperability of data and tools. The BioC approach allows a large number of different textual annotations to be represented, and permits developers to more easily and efficiently share training data, supportive software modules and produced results. Here we give a brief overview of BioC resources. We also present the BioC-PMC dataset as a new resource, which contains all the articles available from the PubMed Central Open Access collection conveniently packaged in the BioC format. We show how this valuable resource can be easily used for text-mining tasks. Code and data are available for download at the BioC site: http://bioc.sourceforge.net.
C1 [Dogan, Rezarta Islamaj; Wilbur, W. John; Comeau, Donald C.] Natl Lib Med, Natl Ctr Biotechnol Informat, Bethesda, MD 20894 USA.
C3 National Institutes of Health (NIH) - USA; NIH National Library of
   Medicine (NLM)
RP Dogan, RI (corresponding author), Natl Lib Med, Natl Ctr Biotechnol Informat, Bethesda, MD 20894 USA.
EM Rezarta.Islamaj@nih.gov; wilbur@ncbi.nlm.nih.gov;
   comeau@ncbi.nlm.nih.gov
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NR 29
TC 0
Z9 0
U1 0
U2 1
PU EUROPEAN LANGUAGE RESOURCES ASSOC-ELRA
PI PARIS
PA 55-57, RUE BRILLAT-SAVARIN, PARIS, 75013, FRANCE
BN 978-2-9517408-8-4
PY 2014
PG 8
WC Linguistics; Language & Linguistics
WE Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Linguistics
GA BC8FH
UT WOS:000355611000008
DA 2024-09-05
ER
PT C
AU Marshall, GC
   Jay, C
   Freitas, A
AF Marshall, Guy Clarke
   Jay, Caroline
   Freitas, Andre
BE Basu, A
   Stapleton, G
   Linker, S
   Legg, C
   Manalo, E
   Viana, P
TI Number and Quality of Diagrams in Scholarly Publications is Associated
   with Number of Citations
SO DIAGRAMMATIC REPRESENTATION AND INFERENCE, DIAGRAMS 2021
SE Lecture Notes in Artificial Intelligence
LA English
DT Proceedings Paper
CT 12th International Conference on the Theory and Application of Diagrams
   (Diagrams)
CY SEP 28-30, 2021
CL ELECTR NETWORK
DE Neural networks; Scholarly diagrams; Corpus analysis; Bibliometrics;
   Graphicacy
AB Diagrams are often used in scholarly communication. We analyse a corpus of diagrams found in scholarly computational linguistics conference proceedings (ACL 2017), and find inclusion of a system diagram to be correlated with higher numbers of citations after three years. Inclusion of more than three diagrams in this 8-page limit conference was found to correlate with a lower citation count. Focusing on neural network system diagrams, we find a correlation between highly cited papers and "good diagramming practice" quantified by level of compliance with a set of diagramming guidelines. This study suggests that diagrams may be a useful source of quality data for predicting citations, and that "graphicacy" is a key skill for scholars with insufficient support at present.
C1 [Marshall, Guy Clarke; Jay, Caroline; Freitas, Andre] Univ Manchester, Dept Comp Sci, Manchester, Lancs, England.
   [Freitas, Andre] Idiap Res Inst, Martigny, Switzerland.
C3 University of Manchester
RP Marshall, GC (corresponding author), Univ Manchester, Dept Comp Sci, Manchester, Lancs, England.
EM guy.marshall@postgrad.manchester.ac.uk; caroline.jay@manchester.ac.uk;
   andre.freitas@manchester.ac.uk
RI Freitas, Andre/AAT-9885-2020
OI Freitas, Andre/0000-0002-4430-4837; Jay, Caroline/0000-0002-6080-1382
CR Abrishami A, 2019, J INFORMETR, V13, P485, DOI 10.1016/j.joi.2019.02.011
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NR 20
TC 2
Z9 2
U1 0
U2 3
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 2945-9133
EI 1611-3349
BN 978-3-030-86062-2; 978-3-030-86061-5
J9 LECT NOTES ARTIF INT
PY 2021
VL 12909
BP 512
EP 519
DI 10.1007/978-3-030-86062-2_52
PG 8
WC Computer Science, Artificial Intelligence; Computer Science, Software
   Engineering; Mathematics, Applied; Logic
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Mathematics; Science & Technology - Other Topics
GA BS3IM
UT WOS:000711901600052
DA 2024-09-05
ER
PT C
AU Olensky, M
AF Olensky, Marlies
BE Gorraiz, J
   Schiebel, E
   Gumpenberger, C
   Horlesberger, M
   Moed, H
TI ACCURACY ASSESSMENT FOR BIBLIOGRAPHIC DATA
SO 14TH INTERNATIONAL SOCIETY OF SCIENTOMETRICS AND INFORMETRICS CONFERENCE
   (ISSI)
SE Proceedings of the International Conference on Scientometrics and
   Informetrics
LA English
DT Proceedings Paper
CT 14th International-Society-of-Scientometrics-and-Informetrics Conference
   (ISSI)
CY JUL 15-20, 2013
CL Vienna, AUSTRIA
C1 Humboldt Univ, Berlin Sch Lib & Informat Sci, D-10099 Berlin, Germany.
C3 Humboldt University of Berlin
RP Olensky, M (corresponding author), Humboldt Univ, Berlin Sch Lib & Informat Sci, Unter Linden 6, D-10099 Berlin, Germany.
EM marlies.olensky@ibi.hu-berlin.de
OI Kirchner (Olensky), Marlies/0000-0002-4727-8531
CR [Anonymous], 1997, Data Quality for the Information Age
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NR 7
TC 2
Z9 2
U1 0
U2 3
PU INT SOC SCIENTOMETRICS & INFORMETRICS-ISSI
PI LEUVEN
PA KATHOLIEKE UNIV LEUVEN, FACULTEIT E T E W, DEKENSTRAAT 2, LEUVEN,
   B-3000, BELGIUM
SN 2175-1935
BN 978-3-200-03135-7
J9 PRO INT CONF SCI INF
PY 2013
BP 1850
EP 1853
PG 4
WC Information Science & Library Science
WE Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Information Science & Library Science
GA BC6IG
UT WOS:000353961700146
DA 2024-09-05
ER
PT J
AU Malkin, A
   Rehfeldt, RA
   Shayter, AM
AF Malkin, Albert
   Rehfeldt, Ruth Anne
   Shayter, Ashley M.
TI An Investigation of the Efficacy of Asynchronous Discussion on Students'
   Performance in an Online Research Method Course
SO BEHAVIOR ANALYSIS IN PRACTICE
LA English
DT Article
DE Teaching behavior analysis; Online learning; Asynchronous discussion;
   Active learning
ID SATISFACTION
AB Online instruction has become increasingly a commonplace in higher education, broadly and within the field of behavior analysis. Given the increased availability of online instruction, it is important to establish how learning outcomes are influenced by various teaching methods, in order to effectively train the next generation of behavior analysts. This study used a between-group design to evaluate the use of asynchronous online class discussion. Results indicate greater group mean performance on quizzes for students who were required to participate in asynchronous discussion as a component of instruction.Demonstration of the effectiveness of a typical component of online instructionProcedures can be used to evaluate instructional methods in behavior analytic courseworkAsynchronous online discussion is a promising component of online courseworkActive learning pedagogy is more effective when compared with passive learning pedagogy
C1 [Malkin, Albert; Rehfeldt, Ruth Anne; Shayter, Ashley M.] Southern Illinois Univ, Behav Anal & Therapy Program, Carbondale, IL 62901 USA.
C3 Southern Illinois University System; Southern Illinois University
RP Malkin, A (corresponding author), Southern Illinois Univ, Behav Anal & Therapy Program, Carbondale, IL 62901 USA.
EM amalkin@siu.edu
RI Malkin, Albert/AAB-1492-2021
OI Malkin, Albert/0000-0002-8652-6390
CR Austin J.L., 2000, HDB APPL BEHAV ANAL, P449
   Behavior Analyst Certification Board, 2016, APPR U TRAIN
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NR 9
TC 19
Z9 33
U1 0
U2 17
PU SPRINGER
PI NEW YORK
PA 233 SPRING ST, NEW YORK, NY 10013 USA
SN 1998-1929
EI 2196-8934
J9 BEHAV ANAL PRACT
JI Behav. Anal. Pract.
PD SEP
PY 2018
VL 11
IS 3
SI SI
BP 274
EP 278
DI 10.1007/s40617-016-0157-5
PG 5
WC Psychology, Clinical
WE Emerging Sources Citation Index (ESCI)
SC Psychology
GA GW7HQ
UT WOS:000447138100016
PM 30363852
OA Green Published
DA 2024-09-05
ER
PT J
AU Carrasco, RC
   Serrano, A
   Castillo-Buergo, R
AF Carrasco, Rafael C.
   Serrano, Aureo
   Castillo-Buergo, Reydi
TI A parser for authority control of author names in bibliographic records
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
DE Digital libraries; Cataloguing standards; Natural language processing
ID DISAMBIGUATION
AB Bibliographic collections in traditional libraries often compile records from distributed sources where variable criteria have been applied to the normalization of the data. Furthermore, the source records often follow classical standards, such as MARC21, where a strict normalization of author names is not enforced. The identification of equivalent records in large catalogues is therefore required, for example, when migrating the data to new repositories which apply modern specifications for cataloguing, such as the FRBR and RDA standards. An open-source tool has been implemented to assist authority control in bibliographic catalogues when external features (such as the citations found in scientific articles) are not available for the disambiguation of creator names. This tool is based on similarity measures between the variants of author names combined with a parser which interprets the dates and periods associated with the creator. An efficient data structure (the unigram frequency vector trie) has been used to accelerate the identification of variants. The algorithms employed and the attribute grammar are described in detail and their implementation is distributed as an open-source resource to allow for an easier uptake. (C) 2016 Elsevier Ltd. All rights reserved.
C1 [Carrasco, Rafael C.; Serrano, Aureo] Univ Alicante, Dept Lenguajes & Sistemas Informat, Alicante, Spain.
   [Castillo-Buergo, Reydi] Univ Agr La Habana, Dept Computac, Havana, Cuba.
C3 Universitat d'Alacant
RP Carrasco, RC (corresponding author), Univ Alicante, Dept Lenguajes & Sistemas Informat, Alicante, Spain.
EM carrasco@ua.es
RI Carrasco, Rafael C/JWP-5402-2024
OI Carrasco, Rafael C./0000-0002-4726-9694
FU Spanish Government [TIN2012-32615]
FX This work has been partially supported by the Spanish Government through
   Project TIN2012-32615.
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NR 26
TC 2
Z9 3
U1 2
U2 29
PU ELSEVIER SCI LTD
PI London
PA 125 London Wall, London, ENGLAND
SN 0306-4573
EI 1873-5371
J9 INFORM PROCESS MANAG
JI Inf. Process. Manage.
PD SEP
PY 2016
VL 52
IS 5
BP 753
EP 764
DI 10.1016/j.ipm.2016.02.002
PG 12
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA DT5RP
UT WOS:000381540600003
DA 2024-09-05
ER
PT J
AU Harnal, S
   Sharma, G
   Malik, S
   Kaur, G
   Khurana, S
   Kaur, P
   Simaiya, S
   Bagga, D
AF Harnal, Shilpi
   Sharma, Gaurav
   Malik, Swati
   Kaur, Gagandeep
   Khurana, Savita
   Kaur, Prabhjot
   Simaiya, Sarita
   Bagga, Deepak
TI Bibliometric Mapping of Trends, Applications and Challenges of
   Artificial Intelligence in Smart Cities
SO EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS
LA English
DT Article
DE Artificial Intelligence; Education; Smart cities; Science mapping; Text
   mining; Data analysis; Artificial Intelligence Trends; Artificial
   Intelligence Survey; Artificial Intelligence Applications; Artificial
   Intelligence challenges; Health care; Traffic management; E-governance;
   Surveillance; Environment; Water management; Energy management; Garbage
   management; Mobility
ID ENERGY-SYSTEMS; BIG DATA; IOT; EDGE; ENVIRONMENTS; CONSUMPTION; INTERNET
AB INTRODUCTION: The continued growth of urbanization presents new challenges. This, in turn, will lead to pressure for sustainable environment initiatives, with demands for more and better infrastructure in the diminishing space available and improved quality of life for city dwellers at a more affordable cost. Smart Cities are part of the solution to the growing challenges of urbanization. The adoption of new technologies like artificial intelligence (AI) is transforming cities, making them smarter, faster, and predicting opportunities for improvement.
   OBJECTIVES: This study is conducting a detailed bibliometric survey to investigate the applications and trends of Artificial Intelligence research for different areas of smart cities and emphasizing the potential effects and challenges of AI adaptation in smart cities over the past 30.5 years.
   METHODS: For this study, the Scopus database was used to collect a total of 1925 documents published between 1991-2021 (July). The bibliometric analysis includes document types, subject categorization, document growth, as well as top contributing sources, countries, authors, and funding sponsors. It also analyses keywords, abstracts, titles, and characteristics of most cited documents.
   RESULTS: The analyzed findings of this research study reflect not only the significance of AI technology for various applications within numerous sectors in the smart city but also major obstacles in AI research for various sectors of smart cities.
   CONCLUSION: The research demonstrates that AI has the ability to construct today's and tomorrow's smart cities, but that each region's potentials, conditions, and circumstances must be addressed in order to achieve a smooth internet city development.
C1 [Harnal, Shilpi; Malik, Swati; Kaur, Gagandeep; Kaur, Prabhjot; Simaiya, Sarita] Chitkara Univ, Inst Engn & Technol, Rajpura, Punjab, India.
   [Sharma, Gaurav; Khurana, Savita] Seth Jai Parkash Mukand Lal Inst Engn & Technol, Radaur, India.
   [Bagga, Deepak] SafeXplore, Yamunanagar, Haryana, India.
C3 Chitkara University, Punjab
RP Harnal, S (corresponding author), Chitkara Univ, Inst Engn & Technol, Rajpura, Punjab, India.
EM shilpi13n@gmail.com
RI Harnal, Shilpi/JVM-8172-2024; Kaur, Prabhjot/JXN-0073-2024
OI Harnal, Shilpi/0000-0002-7692-2349; khurana, savita/0000-0002-8657-0017;
   Malik, Swati/0000-0002-3125-4332; Kaur, Prabhjot/0000-0002-3539-0622;
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   SARITA/0000-0001-7686-8496; sharma, gaurav/0000-0002-9306-4227
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NR 57
TC 1
Z9 1
U1 5
U2 22
PU INST COMPUTER SCIENCES, SOCIAL INFORMATICS & TELECOMMUNICATIONS ENG-ICST
PI GHENT
PA BEGIJNHOFLAAN 93, GHENT, 90000, BELGIUM
SN 2032-9407
J9 EAI ENDORSED TRANS S
JI EAI Endorsed Trans. Scalable Inform. Syst.
PY 2022
VL 9
IS 4
AR e8
DI 10.4108/eetsis.vi.489
EA JUN 2022
PG 21
WC Computer Science, Information Systems
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA DL9V8
UT WOS:000846907900001
OA gold
DA 2024-09-05
ER
PT J
AU Cruz-Jesus, F
   Castelli, M
   Oliveira, T
   Mendes, R
   Nunes, C
   Sa-Velho, M
   Rosa-Louro, A
AF Cruz-Jesus, Frederico
   Castelli, Mauro
   Oliveira, Tiago
   Mendes, Ricardo
   Nunes, Catarina
   Sa-Velho, Mafalda
   Rosa-Louro, Ana
TI Using artificial intelligence methods to assess academic achievement in
   public high schools of a European Union country
SO HELIYON
LA English
DT Article
DE Education; Applied computing; Information systems; Data analysis;
   Evaluation in education; Teaching research; Achievement; Education
   reform; Quantitative research; Artificial intelligence; Data science
ID PARENTAL INVOLVEMENT; STUDENT-ACHIEVEMENT; CLASS-SIZE;
   GENDER-DIFFERENCES; INTERNET USE; DATA-DRIVEN; PERFORMANCE; ANALYTICS;
   TEACHERS; GROWTH
AB Understanding academic achievement (AA) is one of the most global challenges, as there is evidence that it is deeply intertwined with economic development, employment, and countries' wellbeing. However, the research conducted on this topic grounds in traditional (statistical) methods employed in survey (sample) data. This paper presents a novel approach, using state-of-the-art artificial intelligence (AI) techniques to predict the academic achievement of virtually every public high school student in Portugal, i.e., 110,627 students in the academic year of 2014/2015. Different AI and non-Al methods are developed and compared in terms of performance Moreover, important insights to policymakers are addressed.
C1 [Cruz-Jesus, Frederico; Castelli, Mauro; Oliveira, Tiago; Mendes, Ricardo; Nunes, Catarina; Sa-Velho, Mafalda; Rosa-Louro, Ana] Univ Nova Lisboa, NOVA Informat Management Sch NOVA IMS, Campus Campolide, P-1070312 Lisbon, Portugal.
C3 Universidade Nova de Lisboa
RP Cruz-Jesus, F (corresponding author), Univ Nova Lisboa, NOVA Informat Management Sch NOVA IMS, Campus Campolide, P-1070312 Lisbon, Portugal.
EM fjesus@novaims.unl.pt
RI Castelli, Mauro/O-8809-2019; Oliveira, Tiago/B-4090-2011; Oliveira,
   Tiago/GSD-3675-2022; Castelli, Mauro/U-5599-2017
OI Oliveira, Tiago/0000-0001-6523-0809; Costa-Mendes,
   Ricardo/0000-0002-9259-4576; Cruz-Jesus, Frederico/0000-0002-4446-5980;
   Castelli, Mauro/0000-0002-8793-1451
FU national funds through FCT (Fundacao para a Ciencia e a Tecnologia)
   [DSAIPA/DS/0032/2018 (DS4AA)]; Fundação para a Ciência e a Tecnologia
   [DSAIPA/DS/0032/2018] Funding Source: FCT
FX This work was partially supported by national funds through FCT
   (Fundacao para a Ciencia e a Tecnologia) under project
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NR 86
TC 32
Z9 34
U1 12
U2 49
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
EI 2405-8440
J9 HELIYON
JI Heliyon
PD JUN
PY 2020
VL 6
IS 6
AR e04081
DI 10.1016/j.heliyon.2020.e04081
PG 11
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA NW9XK
UT WOS:000575372400009
PM 32551378
OA Green Published, gold
DA 2024-09-05
ER
PT J
AU Li, X
   Chen, HC
   Dang, Y
   Lin, YL
   Larson, CA
   Roco, MC
AF Li, Xin
   Chen, Hsinchun
   Dang, Yan
   Lin, Yiling
   Larson, Catherine A.
   Roco, Mihail C.
TI A longitudinal analysis of nanotechnology literature: 1976-2004
SO JOURNAL OF NANOPARTICLE RESEARCH
LA English
DT Article
DE Bibliographic analysis; Citation analysis; Information visualization;
   Self-organizing maps; Nanoscale science and engineering; Nanotechnology
   papers; Research and development (R&D); Technological innovation
ID TECHNOLOGY; INTERDISCIPLINARITY; COLLABORATION; INSTITUTION;
   NANOSCIENCE; PATTERNS; COUNTRY; SCIENCE; FIELD; USPTO
AB Nanotechnology research and applications have experienced rapid growth in recent years. We assessed the status of nanotechnology research worldwide by applying bibliographic, content map, and citation network analysis to a data set of about 200,000 nanotechnology papers published in the Thomson Science Citation Index Expanded database (SCI) from 1976 to 2004. This longitudinal study shows a quasi-exponential growth of nanotechnology articles with an average annual growth rate of 20.7% after 1991. The United States had the largest contribution of nanotechnology research and China and Korea had the fastest growth rates. The largest institutional contributions were from the Chinese Academy of Sciences and the Russian Academy of Sciences. The high-impact papers generally described tools, theories, technologies, perspectives, and over-views of nanotechnology. From the top 20 institutions, based on the average number of paper citations in 1976-2004, 17 were in the Unites States, 2 in France and I in Germany. Content map analysis identified the evolution of the major topics researched from 1976 to 2004, including investigative tools, physical phenomena, and experiment environments. Both the country citation network and the institution citation network had relatively high clustering, indicating the existence of citation communities in the two networks, and specific patterns in forming citation communities. The United States, Germany, Japan, and China were major citation centers in nanotechnology research with close inter-citation relationships.
C1 [Li, Xin; Chen, Hsinchun; Dang, Yan; Lin, Yiling; Larson, Catherine A.] Univ Arizona, Artificial Intelligence Lab, Dept Management Informat Syst, Eller Coll Management, Tucson, AZ 85721 USA.
   [Roco, Mihail C.] Natl Sci Fdn, Arlington, VA 22230 USA.
C3 University of Arizona; National Science Foundation (NSF)
RP Li, X (corresponding author), Univ Arizona, Artificial Intelligence Lab, Dept Management Informat Syst, Eller Coll Management, Tucson, AZ 85721 USA.
EM xinli@email.arizona.edu; mroco@nsf.gov
RI ; Li, Xin/K-8045-2015
OI Lin, Yi-Ling/0000-0003-0004-1278; Li, Xin/0000-0002-0041-3134
FU National Science Foundation (NSF); Directorate for Engineering, NSF; 
   [CMMI-0549663];  [CMMI-0533749]
FX This research was supported by the following awards: National Science
   Foundation (NSF), "Mapping Nanotechnology Development Based on the ISI
   Literature-Citation Database,"CMMI-0549663 and "Mapping Nanotechnology
   Development,"CMMI-0533749. The last coauthor was supported by the
   Directorate for Engineering, NSF. The literature data was purchased from
   Thomson ISI and we thank them for their support.
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NR 21
TC 29
Z9 35
U1 0
U2 24
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1388-0764
EI 1572-896X
J9 J NANOPART RES
JI J. Nanopart. Res.
PY 2008
VL 10
SU 1
BP 3
EP 22
DI 10.1007/s11051-008-9473-1
PG 20
WC Chemistry, Multidisciplinary; Nanoscience & Nanotechnology; Materials
   Science, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Chemistry; Science & Technology - Other Topics; Materials Science
GA 404QA
UT WOS:000263166900002
DA 2024-09-05
ER
PT J
AU Dao, SD
   Abhary, K
   Marian, R
AF Dao, Son Duy
   Abhary, Kazem
   Marian, Romeo
TI A bibliometric analysis of Genetic Algorithms throughout the history
SO COMPUTERS & INDUSTRIAL ENGINEERING
LA English
DT Article
DE Genetic algorithms; Bibliometric analysis; Publication statistics;
   Survey
ID SELECTION; DESIGN
AB In this article, a bibliometric analysis of Genetic Algorithms (GA) throughout the history is conducted. A big picture of publications associated with GA is created. A number of dominant statistics of GA publications by years, research fields, document types, source titles, countries, institutions and authors are provided herein. In addition, some insights as well as future perspectives of publications associated with GA are discussed. (C) 2017 Elsevier Ltd. All rights reserved.
C1 [Dao, Son Duy; Abhary, Kazem; Marian, Romeo] Univ South Australia, Sch Engn, Room M2-14,Bldg M,Mawson Lakes Campus, Toki, Gifu 5095, Japan.
RP Dao, SD (corresponding author), Univ South Australia, Sch Engn, Room M2-14,Bldg M,Mawson Lakes Campus, Toki, Gifu 5095, Japan.
EM son.dao@mymail.unisa.edu.au
RI Dao, Son Duy/D-8696-2012; Abhary, Kazem/F-3976-2013
OI Dao, Son Duy/0000-0002-5253-6398; 
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NR 20
TC 51
Z9 52
U1 3
U2 29
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0360-8352
EI 1879-0550
J9 COMPUT IND ENG
JI Comput. Ind. Eng.
PD AUG
PY 2017
VL 110
BP 395
EP 403
DI 10.1016/j.cie.2017.06.009
PG 9
WC Computer Science, Interdisciplinary Applications; Engineering,
   Industrial
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA FD6SE
UT WOS:000407657400035
DA 2024-09-05
ER
PT J
AU Marques, PC
   Reis, J
   Santos, R
AF Marques, P. Carmona
   Reis, Joao
   Santos, Ricardo
TI Artificial Intelligence and Disruptive Technologies in Service Systems:
   A Bibliometric Analysis
SO INTERNATIONAL JOURNAL OF INNOVATION AND TECHNOLOGY MANAGEMENT
LA English
DT Article
DE Artificial intelligence; service systems; bibliometric analysis; smart
   services
ID USER ACCEPTANCE; INTERNET; THINGS; SMART; FUTURE; IOT; MANAGEMENT;
   KNOWLEDGE; PARADIGM; CITIES
AB Artificial intelligence (AI) is being used in our daily lives, in all situations and in particular those concerning service systems. However, there is an absence of the ability of the conceptual structure, thematic structure, intellectual structure, and research trends of AI and disruptive technologies in service systems. The main purpose of this study was to carry out a bibliometric analysis of the scientific production of AI and disruptive technologies in service systems based on Elsevier's Scopus database. To do so, keywords were chosen and then data outputs such as the number of published documents, top authors and citations, top journals, countries, and affiliations with the highest number of productions, and network analysis using R-based "biblioshiny" software. The main results showed the growing interest in the subject in the last five years, pointed out current themes and research trends, and revealed the intellectual structure of the field, namely the importance of smart services, cloud computing, and smart sustainable cities. The number of articles for this study reached 1,323, the growth rate has increased in the last five years and the main sources have been reported. China, South Korea and the USA were the leading countries on the subject, and the top 10 authors of influence showed. The word cloud and word growth were presented, as well as the co-citation clusters and co-occurrence network revealed important aspects, and finally the thematic map and the thematic evolution of the subject showed the important concepts. It is hoped that this research will supply future directions for researchers in the area while highlighting the potential of quantitative methods.
C1 [Marques, P. Carmona; Reis, Joao] Lusofona Univ, Ind Engn & Management, Fac Engn, P-1749024 Lisbon, Portugal.
   [Marques, P. Carmona] Inst Politecn Lisboa, Inst Super Engn Lisboa ISEL, P-1959007 Lisbon, Portugal.
   [Santos, Ricardo] Univ Aveiro, Competitiveness Governance & Publ Policy GOVCOPP U, P-3810193 Aveiro, Portugal.
C3 Lusofona University; Polytechnic Institute of Lisbon; Universidade de
   Aveiro
RP Marques, PC (corresponding author), Lusofona Univ, Ind Engn & Management, Fac Engn, P-1749024 Lisbon, Portugal.; Marques, PC (corresponding author), Inst Politecn Lisboa, Inst Super Engn Lisboa ISEL, P-1959007 Lisbon, Portugal.
EM p4803@ulusofona.pt
RI Marques, Pedro/H-3387-2013; Santos, Ricardo/KUD-2638-2024; dos Reis,
   João Carlos Gonçalves/L-6686-2017
OI Marques, Pedro/0000-0003-4891-1754; dos Reis, João Carlos
   Gonçalves/0000-0002-8504-0065
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NR 50
TC 0
Z9 0
U1 6
U2 15
PU WORLD SCIENTIFIC PUBL CO PTE LTD
PI SINGAPORE
PA 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE
SN 0219-8770
EI 1793-6950
J9 INT J INNOV TECHNOL
JI Int. J. Innov. Technol. Manag.
PD NOV
PY 2023
VL 20
IS 07
DI 10.1142/S0219877023300033
EA JUN 2023
PG 33
WC Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA W1KX4
UT WOS:001013913000002
DA 2024-09-05
ER
PT J
AU Huang, CQ
   Zhang, Q
AF Huang, Cuiqing
   Zhang, Qiang
TI Research on Music Emotion Recognition Model of Deep Learning Based on
   Musical Stage Effect
SO SCIENTIFIC PROGRAMMING
LA English
DT Article
ID FRIGHT
AB The change of life style of the times has also prompted the reform of many art forms (including musicals). Nowadays, the audience can not only enjoy the wonderful performances of offline musicals but also feel the charm of musicals online. However, how to bring the emotional integrity of musicals to the audience is a technical problem. In this paper, the deep learning music emotion recognition model based on musical stage effect is studied. Firstly, there is little difference between the emotional results identified by the CRNN model test and the actual feelings of people, and the coincidence degree of emotional responses is as high as 95.68%. Secondly, the final recognition rate of the model is 98.33%, and the final average accuracy rate is as high as 93.22%. Finally, compared with other methods on CASIA emotion set, the CRNN-AttGRU has only 71.77% and 71.60% of WAR and UAR, and only this model has the highest recognition degree. This model also needs to update iteration and use other learning methods to learn at different levels so as to make this model widely used and bring more perfect enjoyment to the audience.
C1 [Huang, Cuiqing; Zhang, Qiang] Chengdu Univ, China ASEAN Coll Arts, Sch Mus & Dance, Chengdu 610106, Sichuan, Peoples R China.
C3 Chengdu University
RP Zhang, Q (corresponding author), Chengdu Univ, China ASEAN Coll Arts, Sch Mus & Dance, Chengdu 610106, Sichuan, Peoples R China.
EM huangcuiqing1234@163.com; zhangqiang01@cdu.edu.cn
FU key project of Curriculum Ideological and Political Research of Chengdu
   University [2021KCSZ01]
FX This research was supported by the phased achievement of the Hidden
   Infiltration of Red Culture in Instrumental Music Teaching (2021KCSZ01)
   and the key project of Curriculum Ideological and Political Research of
   Chengdu University in 2021.
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NR 25
TC 1
Z9 1
U1 0
U2 8
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1058-9244
EI 1875-919X
J9 SCI PROGRAMMING-NETH
JI Sci. Program.
PD OCT 26
PY 2021
VL 2021
AR 3807666
DI 10.1155/2021/3807666
PG 10
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA WW1LF
UT WOS:000717686300006
OA gold
DA 2024-09-05
ER
PT C
AU Tan, L
   Chen, YL
   Yang, RH
   Lai, L
AF Tan, Lin
   Chen, Yali
   Yang, Runhan
   Lai, Li
GP Assoc Comp Machinery
TI Empirical Research on the Effect of Collaborative Learning in Blended
   Learning Mode Based on KNN Algorithm
SO ICIET 2020: 2020 8TH INTERNATIONAL CONFERENCE ON INFORMATION AND
   EDUCATION TECHNOLOGY
LA English
DT Proceedings Paper
CT 8th International Conference on Information and Education Technology
   (ICIET)
CY MAR 28-30, 2020
CL ELECTR NETWORK
DE Blended Learning; Collaborative Learning; KNN algorithm
ID CRITICAL THINKING
AB The quality of collaborative learning is one of the essential factors that determine the quality of teaching. Therefore, it is a significant work for educators to explore scientific and reasonable grouping methods. In this paper, first we design a Blended Learning mode in which there are a variety of online and offline learning activities. The quantified learning behavior information becomes the original data and basis for grouping. Then we combined KNN (k-Nearest Neighbor) algorithm and grouping principle to implement grouping for the pilot class. Finally, the effect of this grouping method is demonstrated by comparing the final examination results and analyzing the number of students who have finished the preview. The results show that the class with the new grouping method has achieved good performance in the final examination.
C1 [Tan, Lin; Chen, Yali; Yang, Runhan; Lai, Li] Southwest Petr Univ, Sch Sci, Chengdu, Sichuan, Peoples R China.
C3 Southwest Petroleum University
RP Tan, L (corresponding author), Southwest Petr Univ, Sch Sci, Chengdu, Sichuan, Peoples R China.
EM 673233182@qq.com; 21620285@qq.com; 1930858707@qq.com; laili_swpu@126.com
RI Liu, Shao/JFK-0166-2023
CR Barker R, 2002, BUS WEEK, P85
   Bodemer D, 2011, COMPUT HUM BEHAV, V27, P1043, DOI 10.1016/j.chb.2010.07.014
   COVER TM, 1967, IEEE T INFORM THEORY, V13, P21, DOI 10.1109/TIT.1967.1053964
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NR 16
TC 0
Z9 0
U1 0
U2 4
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA
BN 978-1-4503-7705-8
PY 2020
BP 48
EP 52
DI 10.1145/3395245.3395251
PG 5
WC Computer Science, Information Systems
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BR0KK
UT WOS:000629453900009
DA 2024-09-05
ER
PT J
AU Gordon, S
   Li, Z
   Marthinsen, J
AF Gordon, Steven
   Li, Zhi
   Marthinsen, John
TI A deep analysis of the economics and finance research on
   cryptocurrencies
SO ECONOMICS LETTERS
LA English
DT Article
DE Cryptocurrency; Topic modeling; Bibliographic analysis
AB Growth in academic research on cryptocurrencies has paralleled crypto-markets' development. Still unknown are the research areas receiving the greatest attention, remaining relatively unexplored, and changing significantly. This article uses semantic topic analysis of top-journal publications to address these questions.(c) 2023 Elsevier B.V. All rights reserved.
C1 [Gordon, Steven; Li, Zhi; Marthinsen, John] Babson Coll, Babson Pk, MA 02457 USA.
C3 Babson College
RP Gordon, S (corresponding author), Babson Coll, Babson Pk, MA 02457 USA.
EM gordon@babson.edu; zli@babson.edu; marthinsen@babson.edu
OI Marthinsen, John/0000-0001-7288-7569; Gordon, Steven/0000-0002-9015-2547
CR Akyildirim E, 2023, FINANC RES LETT, V53, DOI 10.1016/j.frl.2023.103643
   Briola A, 2023, FINANC RES LETT, V51, DOI 10.1016/j.frl.2022.103358
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   Schmiedel T, 2019, ORGAN RES METHODS, V22, P941, DOI 10.1177/1094428118773858
NR 6
TC 0
Z9 0
U1 0
U2 3
PU ELSEVIER SCIENCE SA
PI LAUSANNE
PA PO BOX 564, 1001 LAUSANNE, SWITZERLAND
SN 0165-1765
EI 1873-7374
J9 ECON LETT
JI Econ. Lett.
PD JUL
PY 2023
VL 228
AR 111136
DI 10.1016/j.econlet.2023.111136
EA MAY 2023
PG 4
WC Economics
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA J1SF6
UT WOS:001007472200001
DA 2024-09-05
ER
PT J
AU Schultz, SK
   Slater, TF
AF Schultz, Sara K.
   Slater, Timothy F.
TI Use Of Formative Assessment-Based Active Learning By Astronomy Educators
   Teaching In Live Planetarium Learning Environments
SO JOURNAL OF ASTRONOMY AND EARTH SCIENCES EDUCATION
LA English
DT Article
DE Discipline-Based Astronomy Education Research; Planetarium Education;
   Active Learning; Formative Assessment
AB Planetariums were created to teach astronomy by simulating motions of the star-filled night sky; however, simply having a virtual reality facility to immerse learners beneath a projected night sky in and of itself is insufficient to automatically ensure student learning occurs. Modern teaching strategies, like active learning, have consistently shown to move students toward deeper understanding in classrooms; yet, active learning approaches seem to be only rarely observed in planetariums. Use of Ruiz-Primo and Furtak's (2006) coding scheme to define and analyze formative assessment conversations between classroom teachers and students reveals that unless teachers are formally taught how to use formative assessment-based active learning, such approaches are largely absent in classrooms studied. The goal of this 2-phase study was to evaluate the nature of active learning-based formative assessment conversation cycles in the planetarium. The first phase systematically analyzes 26 recordings of live planetarium programs to describe and document presence of active learning teaching strategies. The second phase conducts interviews to determine rewards and barriers to using formative assessment-based active learning in the planetarium. Analysis suggests scant evidence of complete formative assessment conversation cycles, despite that varying degrees of interactivity between the planetarium lecturer and the audience do exist. It is not that planetarians don't ask questions, but responses rarely serve to systematically guide instructional decisions aligned with modern pedagogy. Moreover, these planetarians hold a wide range of definitions of what constitutes active learning and often view their primary responsibility as inspiration rather than education, lending explanatory power to why active learning is largely absent.
C1 [Schultz, Sara K.] Minnesota State Univ, Moorhead, MN 56563 USA.
   [Slater, Timothy F.] Univ Wyoming, Excellence Higher Educ Endowed Chair Sci Educ, Laramie, WY 82071 USA.
C3 Minnesota State Colleges & Universities; Minnesota State University
   Moorhead; University of Wyoming
RP Schultz, SK (corresponding author), Minnesota State Univ, Moorhead, MN 56563 USA.
EM sarakschultz@gmail.com
CR Abbatantuono B.P., 1995, PLANETARIAN, V24, P14
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   Schultz SK, 2020, J ASTRON EARTH SCI E, V7, P25
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NR 41
TC 0
Z9 0
U1 0
U2 3
PU CLUTE INT
PI LITTLETON
PA 6901 S PIERCE STR, STE 301, LITTLETON, CO 80128 USA
SN 2374-6246
EI 2374-6254
J9 J ASTRON EARTH SCI E
JI J. Astron. Earth Sci. Educ.
PD JUN
PY 2021
VL 8
IS 1
BP 27
EP 38
PG 12
WC Education, Scientific Disciplines
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA 1X5SN
UT WOS:000807513500003
DA 2024-09-05
ER
PT C
AU Wang, JY
   Guo, YK
   Fang, Y
   Zhang, XY
AF Wang, Jin-Ying
   Guo, Yu-Kun
   Fang, Yin
   Zhang, Xin-Ye
BE Hu, Q
TI Evaluation Research on Micro-blog Marketing Effect of 5A Scenic Spot in
   China
SO PROCEEDINGS OF THE 2014 INTERNATIONAL CONFERENCE ON MANAGEMENT SCIENCE
   AND MANAGEMENT INNOVATION
LA English
DT Proceedings Paper
CT International Conference on Management Science and Management Innovation
   (MSMI)
CY JUN 14-15, 2014
CL Changsha, PEOPLES R CHINA
DE Micro-blog; Marketing effect; 5A Scenic Spot; Principal component
   analysis (PCA)
AB In order to evaluate the micro-blog marketing effect of the 5A Scenic Spots in China scientifically and provide some constructive suggestions, the thesis establishes a kind of evaluation index system on the basis of the user coverage, micro-blog active degree, and the propagation force. With Sina micro-blog as a platform, we select the officially certificated micro-blogs of 74 5A Scenic Spots as data sources, and collect the related data. Using the principal component analysis (PCA), we find out the four principal factors which have great influences on the micro-blog marketing of scenic spots, i.e., the customers' attention to the resort, the scenic spots' emphasis on the micro-blog, the interaction between scenic spots and their fans, and the innovation degrees of scenic spots. Through analysis, the thesis classifies the official micro-blogs of these scenic spots: type I (wonderful), type II (fine), type III (ordinary), type IV (poor). The thesis also evaluates the basic situation of the 5A Scenic Spots in China and the shortcomings in micro-blogs' operation.
C1 [Wang, Jin-Ying; Guo, Yu-Kun; Fang, Yin; Zhang, Xin-Ye] Southwest Univ Nationalities, Inst Management, Chengdu, Sichuan, Peoples R China.
C3 Southwest Minzu University
RP Wang, JY (corresponding author), Southwest Univ Nationalities, Inst Management, Chengdu, Sichuan, Peoples R China.
EM 1039497813@qq.com; 77528934@qq.com; 362467183@qq.com; 248638743@qq.com
CR Du Zijian, 2011, ENTERPRISE MICROBLOG
   Jianhua Xu, 2002, MATH METHOD MODERN G, P85
   Jin Yongsheng, 2011, MANAGE SCI, V2, P71
   Murdough Chris, 2009, J INTERACTIVE ADVERT, P94
   Wang Lepeng, 2011, J INNER MONGOLIA SCI, V231, P31
   Wiser Neal Mr., 2009, MARKET WATCH TECHNOL, P16
   Zhang Wenyong, 2012, MODERN DECORATION, P187
NR 7
TC 1
Z9 1
U1 0
U2 5
PU ATLANTIS PRESS
PI PARIS
PA 29 AVENUE LAVMIERE, PARIS, 75019, FRANCE
BN 978-94-6252-015-8
PY 2014
BP 479
EP 484
PG 6
WC Operations Research & Management Science
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Operations Research & Management Science
GA BB8BS
UT WOS:000346261200085
DA 2024-09-05
ER
PT C
AU Oliveira, LR
   Fontes, R
   Collus, J
   Cerisier, JF
AF Oliveira, L. R.
   Fontes, R.
   Collus, J.
   Cerisier, J. F.
BE Chova, LG
   Martinez, AL
   Torres, IC
TI VIDEO AND ONLINE LEARNING IN HIGHER EDUCATION: A BIBLIOMETRIC ANALYSIS
   OF THE OPEN ACCESS SCIENTIFIC PRODUCTION, THROUGH WEB OF SCIENCE
SO 13TH INTERNATIONAL TECHNOLOGY, EDUCATION AND DEVELOPMENT CONFERENCE
   (INTED2019)
SE INTED Proceedings
LA English
DT Proceedings Paper
CT 13th International Technology, Education and Development Conference
   (INTED)
CY MAR 11-13, 2019
CL Valencia, SPAIN
DE Educational video; podcasting; MOOC; OCW; OER; university pedagogy;
   online learning; online distance education; e-learning; bibliometric
   review; web of science
AB Video is used, today and generally, as a preferential technology for the online broadcast of educational contents, in higher education. This exploratory bibliometric analysis surveys scientific publications written in English, in Open Access, in the field of Education, in the period between 2007 and 2017, considering the following topics: video, higher education, online learning, distance learning and MOOC. Three strings of searches have been used, in total, 96 publications have been identified, mainly articles in journals and respective affiliated authors, also primarily, to institutions of European countries.
C1 [Oliveira, L. R.; Collus, J.] Univ Minho, Braga, Portugal.
   [Fontes, R.] Univ Vigo, Vigo, Spain.
   [Cerisier, J. F.] Univ Poitiers, Poitiers, France.
C3 Universidade do Minho; Universidade de Vigo; Universite de Poitiers
RP Oliveira, LR (corresponding author), Univ Minho, Braga, Portugal.
RI Fontes, Rosa/GRS-0711-2022
FU CIEd-Research Centre on Education, Institute of Education, University of
   Minho, Portugal, through national funds of FCT/MCTES-PT
   [UID/CED/1661/2013, UID/CED/1661/2016]; FCT/MCTES-PT
   [SFRH/BSAB/135571/2018]; Fundação para a Ciência e a Tecnologia
   [SFRH/BSAB/135571/2018, UID/CED/1661/2016] Funding Source: FCT
FX This work is funded by CIEd-Research Centre on Education, projects
   UID/CED/1661/2013 and UID/CED/1661/2016, Institute of Education,
   University of Minho, Portugal, through national funds of FCT/MCTES-PT.
   The first author is also funded by FCT/MCTES-PT, sabbatical grant
   SFRH/BSAB/135571/2018 and hosted by Laboratoire TECHNE, University of
   Poitiers, France.
CR Bufrem L., 2005, Ciencia da Informacao, V34, P9, DOI 10.1590/S0100-19652005000200002
   Daniel J, 2012, J INTERACT MEDIA EDU, DOI 10.5334/2012-18
   Downes S., 2012, RISE MOOCS
   Hansch A., 2015, HIIG Discussion Paper Series
   JOCE, 2002, EAC4602 JOCE DG
   Lankshear C., 2008, New Literacies: Everyday Practices Classroom Learning, VSecond
   Lattanzio L., 2003, MEDIA ED JAPON
   Martins ACR, 2015, ICERI PROC, P1909
   Oliveira L. R., 2009, AT 10 C INT GAL PORT, p[5570, 5570]
   Oliveira L. R., 2002, P E LEARN 2002 WORLD, P2001
   Oliveira L. R., 2013, 12 C INT GAL PORT PS, P6482
   Peraya D., 2017, DISTANCES MEDIATIONS, DOI [10.4000/dms.1738, DOI 10.4000/DMS.1738]
   Peraya D, 2016, INT J TECHNOL HIGH E, V13, P6
   PRITCHARD A, 1969, J DOC, V25, P348
NR 14
TC 2
Z9 2
U1 3
U2 19
PU IATED-INT ASSOC TECHNOLOGY EDUCATION & DEVELOPMENT
PI VALENICA
PA LAURI VOLPI 6, VALENICA, BURJASSOT 46100, SPAIN
SN 2340-1079
BN 978-84-09-08619-1
J9 INTED PROC
PY 2019
BP 8562
EP 8567
DI 10.21125/inted.2019.2137
PG 6
WC Education & Educational Research; Psychology, Educational
WE Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Education & Educational Research; Psychology
GA BP1XC
UT WOS:000541042203099
DA 2024-09-05
ER
PT J
AU Chen, XL
   Zou, D
   Xie, HR
   Cheng, GR
AF Chen, Xieling
   Zou, Di
   Xie, Haoran
   Cheng, Gary
TI Twenty Years of Personalized Language Learning: Topic Modeling and
   Knowledge Mapping
SO EDUCATIONAL TECHNOLOGY & SOCIETY
LA English
DT Article
DE Personalized language learning; Topic modeling; Knowledge mapping;
   Bibliometrics; Precision education
ID VOCABULARY; ANALYTICS; SYSTEM
AB Personalized language learning (PLL), a popular approach to precision language education, plays an increasingly essential role in effective language education to meet diverse learner needs and expectations. Research on PLL has become an active sub-field of research on technology-enhanced language learning and artificial intelligence applications in education. Based on the PLL literature from the Web of Science and Scopus databases, this study identified trends and prominent research issues within the field from 2000 to 2019 using structural topic modeling and bibliometrics. Trend analysis of articles demonstrated increasing interest in PLL research. Journals such as Educational Technology & Society and Computers & Education had contributed much to PLL research. PLL associated closely with mobile learning, game-based learning, and online/web-based learning. Moreover, personalized feedback and recommendations were important issues in PLL. Additionally, there was an increasing interest in adopting learning analytics and artificial intelligence in PLL research. Results obtained could help practitioners and scholars better understand the trends and status of PLL research and become aware of the hot topics and future directions.
C1 [Chen, Xieling; Cheng, Gary] Educ Univ Hong Kong, Dept Math & Informat Technol, Hong Kong, Peoples R China.
   [Zou, Di] Educ Univ Hong Kong, Dept English Language Educ, Hong Kong, Peoples R China.
   [Xie, Haoran] Lingnan Univ, Dept Comp & Decis Sci, Hong Kong, Peoples R China.
C3 Education University of Hong Kong (EdUHK); Education University of Hong
   Kong (EdUHK); Lingnan University
RP Zou, D (corresponding author), Educ Univ Hong Kong, Dept English Language Educ, Hong Kong, Peoples R China.
EM xielingchen0708@gmail.com; dizoudaisy@gmail.com; hrxie2@gmail.com;
   chengks@eduhk.hk
RI Xie, Haoran/AFS-3515-2022
OI Xie, Haoran/0000-0003-0965-3617; Cheng, Gary/0000-0002-5614-3348; PV,
   THAYYIB/0000-0001-8929-0398; ZOU, Di/0000-0001-8435-9739
FU Faculty Research Fund of Lingnan University, Hong Kong [102041]; Lam Woo
   Research Fund of Lingnan University, Hong Kong [LWI20011]; One-off
   Special Fund from Central and Faculty Fund in Support of Research from
   2019/20 to 2021/22 [MIT02/19-20]; Research Cluster Fund of The Education
   University of Hong Kong, Hong Kong [RG 78/2019-2020R]; Interdisciplinary
   Research Scheme of the Dean's Research Fund 2019-20 of The Education
   University of Hong Kong, Hong Kong [FLASS/DRF/IDS-2]
FX This research was supported by the Faculty Research Fund (102041) and
   the Lam Woo Research Fund (LWI20011) of Lingnan University, Hong Kong,
   the One-off Special Fund from Central and Faculty Fund in Support of
   Research from 2019/20 to 2021/22 (MIT02/19-20), the Research Cluster
   Fund (RG 78/2019-2020R), and the Interdisciplinary Research Scheme of
   the Dean's Research Fund 2019-20 (FLASS/DRF/IDS-2) of The Education
   University of Hong Kong, Hong Kong.
CR Chen CM, 2016, INTERACT LEARN ENVIR, V24, P681, DOI 10.1080/10494820.2014.917107
   Chen CM, 2010, INTERACT LEARN ENVIR, V18, P341, DOI 10.1080/10494820802602329
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NR 38
TC 53
Z9 54
U1 12
U2 66
PU INT FORUM EDUCATIONAL TECHNOLOGY & SOC-IFETS
PI DOULIU CITY
PA NATL YUNLIN UNIV SCIENCE & TECHNOLOGY, NO 123, SECTION 3, DAXUE RD,
   DOULIU CITY, YUNLIN COUNTY, TAIWAN
SN 1176-3647
EI 1436-4522
J9 EDUC TECHNOL SOC
JI Educ. Technol. Soc.
PD JAN
PY 2021
VL 24
IS 1
BP 205
EP 222
PG 18
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA QQ5RX
UT WOS:000624582400016
DA 2024-09-05
ER
PT J
AU An, X
   Sun, X
   Xu, S
AF An, Xin
   Sun, Xin
   Xu, Shuo
TI Important citations identification with semi-supervised classification
   model
SO SCIENTOMETRICS
LA English
DT Article
DE Important citation; Semi-supervised learning; Self-training;
   Expert-labeled dataset; Author-labeled dataset
AB Given that citations are not equally important, various techniques have been presented to identify important citations on the basis of supervised machine learning models. However, only a small volume of instances have been annotated manually with the labels. To make full use of unlabeled instances and promote the identification performance, the semi-supervised self-training technique is utilized here to identify important citations in this work. After six groups of features are engineered, the SVM and RF models are chosen as the base classifiers for self-training strategy. Then two experiments based on two different types of datasets are conducted. The experiment on the expert-labeled dataset from one single discipline shows that the semi-supervised versions of SVM and RF models significantly improve the performance of the conventional supervised versions when unannotated samples under 75% and 95% confidence level are rejoined to the training set, respectively. The AUC-PR and AUC-ROC of SVM model are 0.8102 and 0.9622, and those of RF model reach 0.9248 and 0.9841, which outperform their counterparts and the benchmark methods in the literature. This demonstrates the effectiveness of our semi-supervised self-training strategy for important citation identification. Another experiment on the author-labeled dataset from multiple disciplines, semi-supervised learning models can perform better than their supervised learning counterparts in term of AUC-PR when the ratio of labeled instances is less than 20%. Compared to our first experiment, insufficient amount of instances from each discipline in our second experiment enables the performance of the models to be unsatisfactory.
C1 [An, Xin] Beijing Forestry Univ, Sch Econ & Management, Beijing 100083, Peoples R China.
   [Sun, Xin] Inst Sci & Tech Informat China, Beijing 100038, Peoples R China.
   [Xu, Shuo] Beijing Univ Technol, Coll Econ & Management, Beijing 100124, Peoples R China.
C3 Beijing Forestry University; Beijing University of Technology
RP An, X (corresponding author), Beijing Forestry Univ, Sch Econ & Management, Beijing 100083, Peoples R China.
EM anxin@bjfu.edu.cn; sunx@istic.ac.cn; xushuo@bjut.edu.cn
RI Xu, Shuo/KVY-0402-2024
OI Xu, Shuo/0000-0002-8602-1819
FU National Natural Science Foundation of China [72004012, 72074014]
FX The present study is an extended version of an article (An et al.,
   2021b) presented at the first Workshop on AI + Informetrics at the
   iConference 2021, 17 March, 2021. This research received the financial
   support from the National Natural Science Foundation of China under
   grant number 72004012 and 72074014.
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NR 44
TC 6
Z9 6
U1 5
U2 35
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD NOV
PY 2022
VL 127
IS 11
BP 6533
EP 6555
DI 10.1007/s11192-021-04212-6
EA JAN 2022
PG 23
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 5U5FU
UT WOS:000744421100002
DA 2024-09-05
ER
PT C
AU Zhang, YY
   Jiang, YR
   Wu, Y
   Su, J
AF Zhang, Yuyao
   Jiang, Yuru
   Wu, Yu
   Su, Jing
BE Liu, M
   Kit, C
   Su, Q
TI A Research on the Generation Model and Evaluation Model of Chinese
   Wu-Qing Couplets
SO CHINESE LEXICAL SEMANTICS (CLSW 2020)
SE Lecture Notes in Artificial Intelligence
LA English
DT Proceedings Paper
CT 21st Chinese Lexical Semantics Workshop (CLSW)
CY MAY 28-30, 2020
CL City Univ Hong Kong, Dept Linguist & Translat, ELECTR NETWORK
HO City Univ Hong Kong, Dept Linguist & Translat
DE Wu-Qing couplet; Transfer learning; Sequence to sequence
AB The Wu-Qing couplet is a unique form of expressions in couplets and an important part of Chinese traditional culture. It shows the profoundness and interesting features of Chinese. However, there is still no research on the automatic generation of Wu-Qing couplets because the corpus of Wu-Qing couplets is scarce and it cannot support the training process of deep learning. This paper proposes a sequence-to-sequence Wu-Qing couplet generation model based on the idea of transfer learning. At the same time, in order to further improve the effectiveness of the model, based on the characteristics of Wu-Qing couplets, such as coherence, rhythm change, and semantic separation, this paper proposes an evaluation model, which can reorder the output of the generation model for better results. Finally, a complete Chinese Wu-Qing couplet automatic generation system is constructed based on the generation model and the evaluation model.
C1 [Zhang, Yuyao; Jiang, Yuru; Wu, Yu; Su, Jing] Beijing Informat Sci & Technol Univ, Beijing, Peoples R China.
C3 Beijing Information Science & Technology University
RP Jiang, YR (corresponding author), Beijing Informat Sci & Technol Univ, Beijing, Peoples R China.
EM jiangyuru@bistu.edu.cn
RI Zhang, Yuyao/KEH-7175-2024
FU National Natural Science Foundation of China [61602044]; Beijing
   Information Science and Technology University [5102010805]
FX Thiswork is supported by the National Natural Science Foundation of
   China (Grant No. 61602044) and the funds for improving the quality of
   personnel training in 2020 of Beijing Information Science and Technology
   University (Grant No. 5102010805).
CR Bahdanau D, 2016, Arxiv, DOI arXiv:1409.0473
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   Fei Y, 1999, THESIS I AUTOMATION
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   Zou Z, 2008, CHONGQING SOC SCI, P105
NR 11
TC 0
Z9 0
U1 0
U2 3
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 2945-9133
EI 1611-3349
BN 978-3-030-81197-6; 978-3-030-81196-9
J9 LECT NOTES ARTIF INT
PY 2021
VL 12278
BP 536
EP 548
DI 10.1007/978-3-030-81197-6_46
PG 13
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications; Linguistics; Language & Linguistics
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Linguistics
GA BS4ZP
UT WOS:000724573500046
DA 2024-09-05
ER
PT J
AU Ozyurt, O
   Ayaz, A
AF Ozyurt, Ozcan
   Ayaz, Ahmet
TI Twenty-five years of education and information technologies: Insights
   from a topic modeling based bibliometric analysis
SO EDUCATION AND INFORMATION TECHNOLOGIES
LA English
DT Article
DE Topic modeling; Bibliometric analysis; Research themes and trends
ID ACCEPTANCE; DECADES; MANAGEMENT; KNOWLEDGE
AB Education and Information Technologies (EAIT) has been a leading journal in education & educational research since 1996. To celebrate its 25th anniversary and provide a comprehensive overview of the field, a topic modeling-based bibliometric analysis was conducted on the articles published in this journal. The study is constructed upon two methods, bibliometric analysis, and topic modeling. The study aims to find out the trends in publications and citations, prominent countries, affiliations and the status of authors, the prominent topics, and the thematic characteristics of these topics, as well as research interests and trends. The results show that the articles are grouped under the 21 topics. The top five most studied of them have been determined as "Technology acceptance", "Social network-based learning", "Teacher education", "Satisfaction of e-learning" and "E-learning". Finally, the acceleration results of each topic within itself and compared to other topics show that the most accelerated topic is "Gamification", while the most accelerated topic compared to other topics has been determined as "Technology acceptance". The general results of the study shed light on future studies in terms of determining the research interests and trends of publications in the field of educational technologies, EAIT.
C1 [Ozyurt, Ozcan] Karadeniz Tech Univ, OF Technol Fac, Dept Software Engn, Trabzon, Turkey.
   [Ayaz, Ahmet] Karadeniz Tech Univ, Digital Transformat Off, Trabzon, Turkey.
C3 Karadeniz Technical University; Karadeniz Technical University
RP Ozyurt, O (corresponding author), Karadeniz Tech Univ, OF Technol Fac, Dept Software Engn, Trabzon, Turkey.
EM oozyurt@ktu.edu.tr; ahmetayaz@ktu.edu.tr
RI Ayaz, Ahmet/JBJ-2146-2023; ÖZYURT, Özcan/AAG-4556-2019; Ayaz,
   Ahmet/ABF-5870-2021
OI ÖZYURT, Özcan/0000-0002-0047-6813; Ayaz, Ahmet/0000-0003-1405-0546
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NR 62
TC 22
Z9 22
U1 6
U2 60
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1360-2357
EI 1573-7608
J9 EDUC INF TECHNOL
JI Educ. Inf. Technol.
PD SEP
PY 2022
VL 27
IS 8
BP 11025
EP 11054
DI 10.1007/s10639-022-11071-y
EA APR 2022
PG 30
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA 5N0UV
UT WOS:000794835400002
PM 35502161
OA Bronze, Green Published
DA 2024-09-05
ER
PT J
AU Qin, Y
   Irshad, A
AF Qin, Ying
   Irshad, Azeem
TI Research on the evaluation method of English textbook readability based
   on the TextCNN model and its application in teaching design
SO PEERJ COMPUTER SCIENCE
LA English
DT Article
DE English reading; Deep learning; TextCNN; Readability assessment
ID DIFFICULTY
AB English is a world language, and the ability to use English plays an important role in the improvement of college students' comprehensive quality and career development. However, quite a lot of Chinese college students feel that English learning is difficult; it is difficult to understand the learning materials, and they cannot effectively improve their English ability. This study uses a convolutional neural network to evaluate the readability of English reading materials. It provides students with English reading materials of suitable difficulty based on their English reading ability so as to improve the effect of English learning. Aiming at the high dispersion of students' English reading level, a text readability evaluation model for English reading textbooks based on deep learning is designed. First, the legibility dataset is constructed based on college English textbooks; second, the TextCNN text legibility evaluation model is constructed; finally, the model training is completed through parameter adjustment and optimization, and the evaluation accuracy rate on the self-built dataset reaches 90%. We use the text readability method based on TextCNN model to conduct experimental teaching, and divided the two groups into comparative experiments. The experimental results showed that the reading level and reading interest of students in the experimental group were significantly improved, which proved that the text readability evaluation method based on deep learning was scientific and effective. In addition, we will further expand the capacity of the English legibility dataset and invite more university classes and students to participate in comparative experiments to improve the generality of the model.
C1 [Qin, Ying] Wuzhou Univ, Sch Foreign Languages, Wuzhou, Peoples R China.
   [Irshad, Azeem] Int Islamic Univ, Dept Comp Sci & Software Engn, Islamabad, Pakistan.
C3 Wuzhou University; International Islamic University, Pakistan
RP Qin, Y (corresponding author), Wuzhou Univ, Sch Foreign Languages, Wuzhou, Peoples R China.
EM qinying@gxuwz.edu.cn
FU Education and Teaching Reform Project of Guangxi Autonomous Region level
   A "The Research and Practice of Smart Education Leading the New
   Development of Universities' Smart Classroom Ecosystem of Business
   English in the Post-Epidemic Era" [2022JGA341]; Key project of the "14th
   Five-Year Plan" of Guangxi Education Science in 2022, "A Practical Study
   on the Intelligent Classroom Teaching Model of English Subject Core
   Literacy from Activity Theory" [2022ZJY1227]; Education and Teaching
   Reform Project of Wuzhou University "New Development of Universities '
   Smart Education Ecosystem Based on Smart Classrooms in the Post-Epidemic
   Era" [Wyjg2021C001]
FX This work was supported by the Education and Teaching Reform Project of
   Guangxi Autonomous Region level A "The Research and Practice of Smart
   Education Leading the New Development of Universities' Smart Classroom
   Ecosystem of Business English in the Post-Epidemic Era" (Project No.
   2022JGA341) ; The key project of the "14th Five-Year Plan" of Guangxi
   Education Science in 2022, "A Practical Study on the Intelligent
   Classroom Teaching Model of English Subject Core Literacy from Activity
   Theory" (Project No. 2022ZJY1227) ; and the Education and Teaching
   Reform Project of Wuzhou University "New Development of Universities '
   Smart Education Ecosystem Based on Smart Classrooms in the Post-Epidemic
   Era" (Project No. Wyjg2021C001) . The funders had no role in study
   design, data collection and analysis, decision to publish, or
   preparation of the manuscript.
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PU PEERJ INC
PI LONDON
PA 341-345 OLD ST, THIRD FLR, LONDON, EC1V 9LL, ENGLAND
EI 2376-5992
J9 PEERJ COMPUT SCI
JI PeerJ Comput. Sci.
PD FEB 29
PY 2024
VL 10
AR e1895
DI 10.7717/peerj-cs.1895
PG 21
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA JR6W8
UT WOS:001174942400006
PM 38435600
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU Pluhacek, M
   Kazikova, A
   Viktorin, A
   Kadavy, T
   Senkerik, R
AF Pluhacek, Michal
   Kazikova, Anezka
   Viktorin, Adam
   Kadavy, Tomas
   Senkerik, Roman
TI Chaos in popular metaheuristic optimizers - a bibliographic analysis
SO JOURNAL OF DIFFERENCE EQUATIONS AND APPLICATIONS
LA English
DT Article
DE Evolutionary computing; chaos; metaheuristic; genetic algorithm;
   differential evolution; particle swarm optimization
ID PARTICLE SWARM OPTIMIZATION; NON-WANDERING POINTS; CONTINUOUS-MAPS;
   GRAPH MAPS; DENDRITES; ALGORITHM; DEPTH; DYNAMICS; DRIVEN; SETS
AB This paper presents an overview of the history and recent efforts in combining chaos theory and evolutionary computation techniques. Various algorithms from the evolutionary computation domain, also known as metaheuristic algorithms, have been successfully enhanced with chaotic components in the past. Numerous ways to incorporate chaos have been examined, and many impressive results have been reported. Implementations of discrete chaotic maps such as Lozi, Henon, and logistic map as generators of chaotic pseudo-random sequences for controlling evolution operators in metaheuristics have achieved significant popularity. In this survey, we focus on the research field itself and perform a bibliographical analysis to show how broad and active is nowadays the research field of chaos-enhanced metaheuristics and what are some of the most recent works published.
C1 [Pluhacek, Michal; Kazikova, Anezka; Viktorin, Adam; Kadavy, Tomas; Senkerik, Roman] Tomas Bata Univ Zlin, Fac Appl Informat, Nam TG Masaryka 5555, Zlin 76001, Czech Republic.
C3 Tomas Bata University Zlin
RP Pluhacek, M (corresponding author), Tomas Bata Univ Zlin, Fac Appl Informat, Nam TG Masaryka 5555, Zlin 76001, Czech Republic.
EM pluhacek@utb.cz
RI Šenkeřík, Roman/H-6353-2012; Pluhacek, Michal/AAF-3155-2019; Kadavy,
   Tomas/A-6914-2018
OI Pluhacek, Michal/0000-0002-3692-2838; Kadavy, Tomas/0000-0002-3341-4336
FU Internal Grant Agency of Tomas Bata University [IGA/CebiaTech/2023/004]
FX This work was supported by the Internal Grant Agency of Tomas Bata
   University under the Projects no. IGA/CebiaTech/2023/004, and further by
   the resources of A.I.Lab at the Faculty of Applied Informatics, Tomas
   Bata University in Zlin (ailab.fai.utb.cz).
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NR 35
TC 3
Z9 3
U1 7
U2 15
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 1023-6198
EI 1563-5120
J9 J DIFFER EQU APPL
JI J. Differ. Equ. Appl.
PD DEC 2
PY 2023
VL 29
IS 9-12
SI SI
DI 10.1080/10236198.2023.2203779
EA APR 2023
PG 16
WC Mathematics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematics
GA EY6P8
UT WOS:000974831300001
DA 2024-09-05
ER
PT J
AU Barbieri, N
   Bonchi, F
   Manco, G
AF Barbieri, Nicola
   Bonchi, Francesco
   Manco, Giuseppe
TI Topic-aware social influence propagation models
SO KNOWLEDGE AND INFORMATION SYSTEMS
LA English
DT Article
DE Social influence; Topic modeling; Topic-aware propagation model; Viral
   marketing
AB The study of influence-driven propagations in social networks and its exploitation for viral marketing purposes has recently received a large deal of attention. However, regardless of the fact that users authoritativeness, expertise, trust and influence are evidently topic-dependent, the research on social influence has surprisingly largely overlooked this aspect. In this article, we study social influence from a topic modeling perspective. We introduce novel topic-aware influence-driven propagation models that, as we show in our experiments, are more accurate in describing real-world cascades than the standard (i.e., topic-blind) propagation models studied in the literature. In particular, we first propose simple topic-aware extensions of the well-known Independent Cascade and Linear Threshold models. However, these propagation models have a very large number of parameters which could lead to overfitting. Therefore, we propose a different approach explicitly modeling authoritativeness, influence and relevance under a topic-aware perspective. Instead of considering user-to-user influence, the proposed model focuses on user authoritativeness and interests in a topic, leading to a drastic reduction in the number of parameters of the model. We devise methods to learn the parameters of the models from a data set of past propagations. Our experimentation confirms the high accuracy of the proposed models and learning schemes.
C1 [Barbieri, Nicola; Bonchi, Francesco] Yahoo Res, Web Min Res Grp, Barcelona, Spain.
   [Manco, Giuseppe] ICAR CNR, Cosenza, Italy.
C3 Yahoo! Inc; Yahoo! Inc Spain; Consiglio Nazionale delle Ricerche (CNR);
   Istituto di Calcolo e Reti ad Alte Prestazioni (ICAR-CNR)
RP Bonchi, F (corresponding author), Yahoo Res, Web Min Res Grp, Barcelona, Spain.
EM barbieri@yahoo-inc.com; bonchi@yahoo-inc.com; manco@icar.cnr.it
RI Manco, Giuseppe/O-2428-2015; Manco, Giuseppe/KDN-6515-2024
OI Manco, Giuseppe/0000-0001-9672-3833; Manco, Giuseppe/0000-0001-9672-3833
FU Torres Quevedo Program of the Spanish Ministry of Science and
   Innovation; European Union [270239]
FX This research was partially supported by the Torres Quevedo Program of
   the Spanish Ministry of Science and Innovation and partially funded by
   the European Union 7th Framework Programme (FP7/2007-2013) under Grant
   No. 270239 (ARCOMEM).
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NR 30
TC 145
Z9 164
U1 1
U2 33
PU SPRINGER LONDON LTD
PI LONDON
PA 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND
SN 0219-1377
EI 0219-3116
J9 KNOWL INF SYST
JI Knowl. Inf. Syst.
PD DEC
PY 2013
VL 37
IS 3
BP 555
EP 584
DI 10.1007/s10115-013-0646-6
PG 30
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 251NL
UT WOS:000326936100003
DA 2024-09-05
ER
PT J
AU Lang, W
AF Lang, Wang
TI Research on College English Teaching Quality Assessment Method Based on
   K-Means Clustering Algorithm
SO MATHEMATICAL PROBLEMS IN ENGINEERING
LA English
DT Article
AB The evaluation of college teachers' teaching ability is very important. Currently, the indicators for evaluating the quality of college English teaching are unclear and insufficient. This paper evaluates the quality of university classroom teaching from two aspects: students' learning effect and teachers' teaching work. This paper employs the K-means algorithm to analyze the relationship between the indicators in the evaluation model and teachers' teaching ability, Snds out the speciSc factors that affect teaching activities, and guides the implementation of teachers' teaching work. At the same time, the K-means model is used to evaluate students' learning effect, identify the relationship between the indicators in the model and teachers' teaching ability, and Snd out the speciSc factors that affect teachers to guide the implementation of teachers' teaching work. Experiments show that the method proposed in this paper can solve the problem that the evaluation indicators of traditional evaluation methods are not clear and insufficient and can be better applied to teaching evaluation.
C1 [Lang, Wang] Jingdezhen Ceram Univ, Sch Int Studies, Jingdezhen 333403, Jiangxi, Peoples R China.
C3 Jingdezhen Ceramic Institute
RP Lang, W (corresponding author), Jingdezhen Ceram Univ, Sch Int Studies, Jingdezhen 333403, Jiangxi, Peoples R China.
EM 005301@jcu.edu.cn
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NR 16
TC 2
Z9 2
U1 3
U2 19
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1024-123X
EI 1563-5147
J9 MATH PROBL ENG
JI Math. Probl. Eng.
PD AUG 8
PY 2022
VL 2022
AR 4134827
DI 10.1155/2022/4134827
PG 8
WC Engineering, Multidisciplinary; Mathematics, Interdisciplinary
   Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Mathematics
GA 6A4EJ
UT WOS:000880609500009
OA gold
DA 2024-09-05
ER
PT J
AU Lawelai, H
   Iswanto, I
   Raharja, NM
AF Lawelai, Herman
   Iswanto, Iswanto
   Raharja, Nia Maharani
TI Use of Artificial Intelligence in Public Services: A Bibliometric
   Analysis and Visualization
SO TEM JOURNAL-TECHNOLOGY EDUCATION MANAGEMENT INFORMATICS
LA English
DT Article
DE artificial intelligence; public services; bibliometric; vosviewer;
   Scopus publications
ID SMART; AI
AB The aim of this research is to survey the current state of research related to the utilization of AI in public services as indexed by Scopus. VOSviewer is used in this research to assess keywords from 183 articles. Furthermore, the Scopus database search results analysis visualizes the characteristics and trends of journals, authors, and subjects connected to the application of artificial intelligence in public service research. Based on the results of this study, it is possible that AI could assist in making cities more intelligent by improving the quality of life for citizens and enhancing the efficiency of their access to public goods and services. It surpasses benchmark models in predicted accuracy because of its more transparent and modifiable model structure. By strategically designing and deploying AI-based technology, traditional public services may be turned into intelligent services. This study contributes to the formation of more relevant research by serving as a reference for future researchers in identifying the position of contribution to the creation of more relevant research.
C1 [Lawelai, Herman] Univ Muhammadiyah Buton, Dept Govt Studies, Betoambari St, Baubau City 93724, Indonesia.
   [Iswanto, Iswanto] Univ Muhammadiyah Yogyakarta, Dept Engineer Profess Program, Brawijaya St, Yogyakarta City 55183, Indonesia.
   [Raharja, Nia Maharani] UIN Sunan Kalijaga Yogyakarta, Dept Informat Engn, Laksda Adisucipto St, Yogyakarta City 55281, Indonesia.
   [Lawelai, Herman] Univ Muhammadiyah Buton, Dept Govt Studies, Betoambari St, Baubau City 93724, Indonesia.
C3 Universitas Muhammadiyah Buton; Universitas Muhammadiyah Yogyakarta;
   Sunan Kalijaga State Islamic University; Universitas Muhammadiyah Buton
RP Lawelai, H (corresponding author), Univ Muhammadiyah Buton, Dept Govt Studies, Betoambari St, Baubau City 93724, Indonesia.
EM herman.lawelai@umbuton.ac.id
RI Lawelai, Herman/AAQ-8062-2021
OI Lawelai, Herman/0000-0002-7266-4557
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NR 41
TC 1
Z9 1
U1 4
U2 14
PU UIKTEN - ASSOC INFORMATION COMMUNICATION TECHNOLOGY EDUCATION & SCIENCE
PI NOVI PAZAR
PA HILMA ROZAJCA 15, NOVI PAZAR, 36300, SERBIA
SN 2217-8309
EI 2217-8333
J9 TEM J
JI TEM J.
PD MAY
PY 2023
VL 12
IS 2
BP 798
EP 807
DI 10.18421/TEM122-24
PG 10
WC Computer Science, Information Systems
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA I6PD4
UT WOS:001003974800024
OA gold
DA 2024-09-05
ER
PT J
AU Kajikawa, Y
   Abe, K
   Noda, S
AF Kajikawa, Yuya
   Abe, Koji
   Noda, Suguru
TI Filling the gap between researchers studying different materials and
   different methods: a proposal for structured keywords
SO JOURNAL OF INFORMATION SCIENCE
LA English
DT Article
DE structured keywords; bibliographic approach; natural language
   processing; ontology
ID LITERATURE-BASED DISCOVERY; RESEARCH-AND-DEVELOPMENT; KNOWLEDGE
   MANAGEMENT; FISH OIL; ONTOLOGIES; INFORMATION; PRINCIPLES; ABSTRACTS;
   RAYNAUDS; SUPPORT
AB Scientific publications written in natural language still play a central role as our knowledge source. However, due to the flood of publications, obtaining a comprehensive view even on a topic of limited scope, from a stack of publications is becoming an arduous task. Examples are presented from our recent experiences in the materials science field, where information is not shared among researchers studying different materials and different methods. To overcome the limitation, we propose a structured keywords method to reinforce the functionality of a future e-library.
C1 Univ Tokyo, Sch Engn, Inst Engn Innovat, Bunkyo Ku, Tokyo 1138656, Japan.
   Univ Tokyo, Intelligent Modelling Lab, Tokyo, Japan.
   Univ Tokyo, Grad Sch Engn, Dept Chem Syst Engn, Tokyo, Japan.
C3 University of Tokyo; University of Tokyo; University of Tokyo
RP Kajikawa, Y (corresponding author), Univ Tokyo, Sch Engn, Inst Engn Innovat, Bunkyo Ku, 2-11-16 Yayoi, Tokyo 1138656, Japan.
EM kaji@biz-model.t.u-tokyo.ac.jp
RI Noda, Suguru/C-1365-2008; Kajikawa, Yuya/C-1996-2015
OI Noda, Suguru/0000-0002-7305-5307; Kajikawa, Yuya/0000-0003-3577-5167
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   2003, NASDA
NR 63
TC 26
Z9 27
U1 0
U2 26
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 0165-5515
EI 1741-6485
J9 J INF SCI
JI J. Inf. Sci.
PY 2006
VL 32
IS 6
BP 511
EP 524
DI 10.1177/0165551506067125
PG 14
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 126GE
UT WOS:000243499800002
DA 2024-09-05
ER
PT C
AU Chi, DAW
   Huang, YY
AF Chi, Dianwei
   Huang, Yinyin
GP IEEE
TI Research on Application of Online Teaching Performance Prediction Based
   on Data Mining Algorithm
SO 2021 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS AND COMPUTER
   ENGINEERING (ICCECE)
LA English
DT Proceedings Paper
CT IEEE International Conference on Consumer Electronics and Computer
   Engineering (ICCECE)
CY JAN 15-17, 2021
CL Guangzhou, PEOPLES R CHINA
DE performance warning; naive bayes; software programming; classification
AB The application of data mining in teaching has entered the stage of development, During the epidemic, colleges and universities have accumulated a lane amount of online teaching statistical data. These data can he used to establish a classification model for predicting student performance. In this paper, the Naive Bayes algorithm is improved to wise the problem of underflosw when the data feature values are too large. The student performance prediction classification model is constructed, and the classification efficiency and accuracy are improved to a certain extent. The improved model is used to predict and warn the performance in the mid-term stage to prevent The phenomenon of a large proportion of missing subjects, thereby ensuring the quality of students' learning throughout the semester.
C1 [Chi, Dianwei] Shandong Vocat Univ Foreign Affairs, Coll Informat & Control Engn, Weihai, Peoples R China.
   [Huang, Yinyin] WKW Automot Parts Co Ltd, South Taishan Rd, Beijing, Peoples R China.
RP Chi, DAW (corresponding author), Shandong Vocat Univ Foreign Affairs, Coll Informat & Control Engn, Weihai, Peoples R China.
EM dianwei.chi@163.com; miaomiao6983@163.com
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   Lulu Dong, 2014, RES IMPLEMENTATION C
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   Wei Huijian, 2014, RES NAIVE BAYES CLAS
   Wu Bei, 2019, RES APPL PERFORMANCE
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NR 16
TC 1
Z9 1
U1 1
U2 10
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-7281-8319-0
PY 2021
BP 394
EP 397
DI 10.1109/ICCECE51280.2021.9342597
PG 4
WC Computer Science, Interdisciplinary Applications; Engineering,
   Electrical & Electronic; Telecommunications
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Engineering; Telecommunications
GA BS0AM
UT WOS:000680655600075
DA 2024-09-05
ER
PT J
AU Rodrigues, M
   Silva, R
   Borges, AP
   Franco, M
   Oliveira, C
AF Rodrigues, Margarida
   Silva, Rui
   Borges, Ana Pinto
   Franco, Mario
   Oliveira, Cidalia
TI Artificial intelligence: threat or asset to academic integrity? A
   bibliometric analysis
SO KYBERNETES
LA English
DT Article; Early Access
DE Artificial intelligence; Academic integrity; Plagiarism; Dishonesty;
   Students
ID ENGINEERING STUDENTS; CONTEXTUAL INFLUENCES; COCITATION ANALYSIS;
   DISHONESTY; PLAGIARISM; PERCEPTIONS; ENVIRONMENTS; MISCONDUCT;
   EDUCATION; BEHAVIOR
AB Purpose- This study aims to address a systematic literature review (SLR) using bibliometrics on the relationship between academic integrity and artificial intelligence (AI), to bridge the scattering of literature on this topic, given the challenge and opportunity for the educational and academic community. Design/methodology/approach- This review highlights the enormous social influence of COVID-19 by mapping the extensive yet distinct and fragmented literature in AI and academic integrity fields. Based on 163 publications from the Web of Science, this paper offers a framework summarising the balance between AI and academic integrity. Findings- With the rapid advancement of technology, AI tools have exponentially developed that threaten to destroy students' academic integrity in higher education. Despite this significant interest, there is a dearth of academic literature on how AI can help in academic integrity. Therefore, this paper distinguishes two significant thematical patterns: academic integrity and negative predictors of academic integrity. Practical implications-This study also presents several contributions by showing that tools associated with AI can act as detectors of students who plagiarise. That is, they can be useful in identifying students with fraudulent behaviour. Therefore, it will require a combined effort of public, private academic and educational institutions and the society with affordable policies. Originality/value- This study proposes a new, innovative framework summarising the balance between AI and academic integrity.
C1 [Rodrigues, Margarida] Inst Europeu Estudos Super Fafe, CEFAGE UBI Res Ctr, Covilha, Portugal.
   [Silva, Rui] Univ Tras Os Montes & Alto Douro, CETRAD Res Ctr, Vila Real, Portugal.
   [Borges, Ana Pinto] ISAG, Res Ctr Business Sci & Tourism CICET, Porto, Portugal.
   [Franco, Mario] Univ Beira Interior, CEFAGE UBI Res Ctr, Dept Management & Econ, Covilha, Portugal.
   [Oliveira, Cidalia] Univ Portucalense, REMIT, Braga, Portugal.
   [Oliveira, Cidalia] Univ Minho, Braga, Portugal.
C3 University of Tras-os-Montes & Alto Douro; Universidade da Beira
   Interior; Universidade Portucalense Infante D. Henrique; Universidade do
   Minho
RP Franco, M (corresponding author), Univ Beira Interior, CEFAGE UBI Res Ctr, Dept Management & Econ, Covilha, Portugal.
EM mfranco@ubi.pt
RI Franco, Mário/ABG-1980-2021; Oliveira, Cidália/AAV-3204-2020; Pinto
   Borges, Ana/AAO-9585-2020
OI Franco, Mário/0000-0001-7818-0206; Oliveira,
   Cidália/0000-0002-3512-6151; Pinto Borges, Ana/0000-0002-4942-079X
FU National Funds of the FCT - Portuguese Foundation for Science and
   Technology [UIDB/04007/2020, UIDB/05105/2020, UIDB/04630/2020,
   UI/BD/151029/2021, UIDB/04011/2020, UIDB/04630/2022,
   CEECINST/00127/2018/CP1501/CT0010]
FX The authors are grateful to the journal's anonymous referees for their
   extremely useful suggestions to improve the quality of the paper. The
   authors gratefully acknowledge financial support from National Funds of
   the FCT - Portuguese Foundation for Science and Technology within the
   project UIDB/04007/2020, UIDB/05105/2020, UIDB/04630/2020,
   UI/BD/151029/2021, UIDB/04011/2020
   (https://doi.org/10.54499/UIDB/04011/2020), UIDB/04630/2022 and by
   CEECINST/00127/2018/CP1501/CT0010.
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NR 179
TC 1
Z9 1
U1 35
U2 44
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 0368-492X
EI 1758-7883
J9 KYBERNETES
JI Kybernetes
PD 2024 JAN 29
PY 2024
DI 10.1108/K-09-2023-1666
EA JAN 2024
PG 32
WC Computer Science, Cybernetics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA GG3H1
UT WOS:001151468600001
DA 2024-09-05
ER
PT C
AU Qing, Z
   Edara, P
AF Qing, Zhu
   Edara, Praveen
GP IEEE Comp Soc
TI Human Vision vs. Computer Vision: A Readability Study in a Virtual
   Reality Environment
SO 2022 IEEE CONFERENCE ON VIRTUAL REALITY AND 3D USER INTERFACES ABSTRACTS
   AND WORKSHOPS (VRW 2022)
LA English
DT Proceedings Paper
CT IEEE Conference on Virtual Reality and 3D User Interfaces (IEEE VR)
CY MAR 12-16, 2022
CL ELECTR NETWORK
DE Text readability; Evaluation methods; Computer vision; Transportation;
   Road sign readability; User study; Human factors; Human-centered
   computing; Visualization; Visualization design and evaluation methods;
   Applied computing; Operations research; Transportation
AB Text plays an important role in conveying information to users in a virtual reality (VR) environment. Both VR software and hardware are evolving rapidly to improve text display quality. However, evaluation of text readability still relies on human participants. In this study, cloud computer vision was used to evaluate text readability in VR. Human subjects were recruited to test the same text scenarios. The cloud computer vision-based approach produced results that were consistent with human vision-based recognition. The use of computer vision to automate text readability evaluation could significantly reduce the overall effort and time in developing readable text in VR.
C1 [Qing, Zhu; Edara, Praveen] Univ Missouri, Columbia, MO 65211 USA.
C3 University of Missouri System; University of Missouri Columbia
RP Qing, Z (corresponding author), Univ Missouri, Columbia, MO 65211 USA.
EM zqing@missouri.edu; edarap@missouri.edu
OI Qing, Zhu/0000-0002-3219-6971
CR [Anonymous], 2021, VISION AI DERIVE HNA
   [Anonymous], PRICING CLOUD VISION
   [Anonymous], 2021, VR VISIBILITY EXPT
   [Anonymous], BEST PRACTICES RIFT
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NR 30
TC 0
Z9 1
U1 1
U2 9
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA
BN 978-1-6654-8402-2
PY 2022
BP 61
EP 64
DI 10.1109/VRW55335.2022.00023
PG 4
WC Computer Science, Artificial Intelligence; Computer Science,
   Cybernetics; Computer Science, Software Engineering
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BT2FN
UT WOS:000808111800016
DA 2024-09-05
ER
PT J
AU Mistrano, A
AF Mistrano, Al
TI Practitioner research regarding independent learning in sixth-form
   education within eight Bedfordshire schools
SO TEACHER DEVELOPMENT
LA English
DT Article
DE practitioner research; independent learning; deep learning; performance
   management; monitoring learning
AB The aim of this study was to conduct research about independent learning in post-16 education in order to assist the author, a teacher and senior leader, with developing college-wide improvement strategies. The research consisted of observations and surveys of teachers and students at eight schools in Bedfordshire in order to investigate the extent to which students and teachers work towards the creation of deep learning, in the sense defined, discussed and developed by Adey, Entwistle, Dibdin and DeakinCrick. The results point to students juggling between deep and surface learning strategies in conjunction with mixed messages from teachers and institutional practices. Conclusions drawn include the recognition that teachers, with the support of senior managers, need to take greater control of the discourse of performance management in order that the rush for examination success does not prove detrimental to the progress of young people.
C1 [Mistrano, Al] Samuel Whitbread Community Coll, Shefford, England.
RP Mistrano, A (corresponding author), Samuel Whitbread Community Coll, Shefford, England.
EM amistrano@swcc.beds.sch.uk
CR Adey P., 1999, Learning styles strategies: a review of research
   Adey P., 1994, REALLY RAISING STAND
   [Anonymous], 1988, LEARNING STRATEGIES, DOI DOI 10.1007/978-1-4899-2118-5_2
   [Anonymous], TIMES HIGHER ED SUPP
   Coleman M., 1999, PRACTITIONER RES ED, P83
   Deakin-Crick R., 2006, LEARNING POWER PRACT
   Department for Education and Skills, 2007, PART 16 18 YEAR OLDS
   Department for Education and Skills, 2004, PED IN PRACT
   Dibdin John, 2004, SAIL PROJECT STRATEG
   Dweck C.S., 1999, SELF THEORIES
   Harlen W., 2002, Research Evidence in Education Library
   Joyce B., 2002, MODELS LEARNING TOOL, V2nd
   Leat David, 2004, THINKING GEOGRAPHY
   Office for Standards in Education, 2006, HDB INSP SCH
   Office for Standards in Education, 2007, COND INSP GUID INSP
   Wilson A., 2007, KNOWLEDGE POWER PART
NR 16
TC 0
Z9 2
U1 0
U2 0
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1366-4530
EI 1747-5120
J9 TEACH DEV
JI Teach. Dev.
PY 2008
VL 12
IS 3
BP 165
EP 177
DI 10.1080/13664530802259198
PG 13
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA V94HK
UT WOS:000213119900001
DA 2024-09-05
ER
PT J
AU Lamers, WS
   Boyack, K
   Larivière, V
   Sugimoto, CR
   van Eck, NJ
   Waltman, L
   Murray, D
AF Lamers, Wout S.
   Boyack, Kevin
   Lariviere, Vincent
   Sugimoto, Cassidy R.
   van Eck, Nees Jan
   Waltman, Ludo
   Murray, Dakota
TI Investigating disagreement in the scientific literature
SO ELIFE
LA English
DT Article
DE meta-research; disagreement; citation analysis; natural language
   processing; metascience; None
ID CONSENSUS; CITATIONS; SCIENCE
AB Disagreement is essential to scientific progress but the extent of disagreement in science, its evolution over time, and the fields in which it happens remain poorly understood. Here we report the development of an approach based on cue phrases that can identify instances of disagreement in scientific articles. These instances are sentences in an article that cite other articles. Applying this approach to a collection of more than four million English-language articles published between 2000 and 2015 period, we determine the level of disagreement in five broad fields within the scientific literature (biomedical and health sciences; life and earth sciences; mathematics and computer science; physical sciences and engineering; and social sciences and humanities) and 817 meso-level fields. Overall, the level of disagreement is highest in the social sciences and humanities, and lowest in mathematics and computer science. However, there is considerable heterogeneity across the meso-level fields, revealing the importance of local disciplinary cultures and the epistemic characteristics of disagreement. Analysis at the level of individual articles reveals notable episodes of disagreement in science, and illustrates how methodological artifacts can confound analyses of scientific texts.
C1 [Lamers, Wout S.; van Eck, Nees Jan; Waltman, Ludo] Leiden Univ, Ctr Sci & Technol Studies, Leiden, Netherlands.
   [Boyack, Kevin] SciTech Strategies Inc, Albuquerque, NM USA.
   [Lariviere, Vincent] Univ Montreal, Ecole Bibliothecon & Sci Informat, Montreal, PQ, Canada.
   [Sugimoto, Cassidy R.] Georgia Inst Technol, Sch Publ Policy, Atlanta, GA 30332 USA.
   [Murray, Dakota] Indiana Univ, Sch Informat Comp & Engn, Bloomington, IN 47405 USA.
C3 Leiden University - Excl LUMC; Leiden University; Universite de
   Montreal; University System of Georgia; Georgia Institute of Technology;
   Indiana University System; Indiana University Bloomington
RP Lamers, WS (corresponding author), Leiden Univ, Ctr Sci & Technol Studies, Leiden, Netherlands.; Murray, D (corresponding author), Indiana Univ, Sch Informat Comp & Engn, Bloomington, IN 47405 USA.
EM w.s.lamers@cwts.leidenuniv.nl; dakmurra@iu.edu
RI Sugimoto, Cassidy R/AAV-2705-2021; Waltman, Ludo/B-5561-2008; van Eck,
   Nees Jan/B-6042-2008
OI Sugimoto, Cassidy R/0000-0001-8608-3203; Waltman,
   Ludo/0000-0001-8249-1752; van Eck, Nees Jan/0000-0001-8448-4521
FU Air Force Office of Scientific Research [FA9550-19-1-039]; Canada
   Research Chairs
FX Air Force Office of Scientific Research FA9550-19-1-039 Dakota Murray;
   Canada Research Chairs Vincent Lariviere; The funders had no role in
   study design, data collection and interpretation, or the decision to
   submit the work for publication.
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NR 63
TC 22
Z9 22
U1 3
U2 32
PU eLIFE SCIENCES PUBL LTD
PI CAMBRIDGE
PA SHERATON HOUSE, CASTLE PARK, CAMBRIDGE, CB3 0AX, ENGLAND
SN 2050-084X
J9 ELIFE
JI eLife
PD DEC 24
PY 2021
VL 10
AR e72737
DI 10.7554/eLife.72737; 10.7554/eLife.72737.sa1; 10.7554/eLife.72737.sa2
PG 20
WC Biology
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Life Sciences & Biomedicine - Other Topics
GA XV0WA
UT WOS:000734672100001
PM 34951588
OA Green Published, Green Submitted, gold
DA 2024-09-05
ER
PT J
AU Schochet, PZ
AF Schochet, Peter Z.
TI Estimators for Clustered Education RCTs Using the Neyman Model for
   Causal Inference
SO JOURNAL OF EDUCATIONAL AND BEHAVIORAL STATISTICS
LA English
DT Article
DE Statistics; experimental design; program evaluation; research
   methodology
AB This article examines the estimation of two-stage clustered designs for education randomized control trials (RCTs) using the nonparametric Neyman causal inference framework that underlies experiments. The key distinction between the considered causal models is whether potential treatment and control group outcomes are considered to be fixed for the study population (the finite-population model) or randomly selected from a vaguely defined universe (the super-population model). Both approaches allow for heterogeneity of treatment effects. Appropriate estimation methods and asymptotic moments are discussed for each model using simple differences-in-means estimators and those that include baseline covariates. An empirical application using a large-scale education RCT shows that the choice of the finite- or super-population approach can matter. Thus, the choice of framework and sensitivity analyses should be specified and justified in the analysis protocols.
C1 Math Policy Res Inc, Princeton, NJ 08543 USA.
C3 Mathematica
RP Schochet, PZ (corresponding author), Math Policy Res Inc, POB 2393, Princeton, NJ 08543 USA.
EM pschochet@mathematica-mpr.com
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NR 32
TC 21
Z9 22
U1 0
U2 4
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 1076-9986
EI 1935-1054
J9 J EDUC BEHAV STAT
JI J. Educ. Behav. Stat.
PD JUN
PY 2013
VL 38
IS 3
BP 219
EP 238
DI 10.3102/1076998611432176
PG 20
WC Education & Educational Research; Social Sciences, Mathematical Methods;
   Psychology, Mathematical
WE Social Science Citation Index (SSCI)
SC Education & Educational Research; Mathematical Methods In Social
   Sciences; Psychology
GA 298CX
UT WOS:000330302700001
DA 2024-09-05
ER
PT C
AU Hao, YH
   Li, WB
AF Hao, Yunhong
   Li, Wenbo
BE Chen, J
   Xu, QR
   Wu, XB
TI Research on the comprehensive evaluation of firms' technology innovation
   ability based on neural network
SO ISMOT'07: Proceedings of the Fifth International Symposium on Management
   of Technology, Vols 1 and 2: MANAGING TOTAL INNOVATION AND OPEN
   INNOVATION IN THE 21ST CENTURY
LA English
DT Proceedings Paper
CT 5th International Symposium on Management of Technology
CY JUN 01-03, 2007
CL Hangzhou, PEOPLES R CHINA
DE neural networks; back propagation algorithm; technology innovation
   ability; comprehensive evaluation
AB Nowadays, technology innovation is a fundamental determinant of value creation in enterprises and economic growth. Therefore, the comprehensive evaluation of technology innovation ability has become a significant concern both for enterprises and governments. The artificial neural network (ANN) is a technique that is studied heavily and used in applications for engineering and scientific fields for various purposes ranging from control systems to urban planning. Its generalization powers have not only received admiration from the engineering and scientific fields, but in recent years, many innovation researchers are taking an interest in the application of artificial neural networks. In this paper, we propose a comprehensive evaluation method based on the neural networks, applying the artificial neural network to the comprehensive evaluation of technology innovation ability is a breakthrough in concept and technology. The paper is organized as follows. The first section is introduction. The second section constructs the evaluation index system of firms' technology innovation ability. The third section is the basic model, and first, it introduces some basis elements and concepts to the readers that are central to understand the approach. The fourth section, using survey data on some firms, the neural networks are trained to provide some intelligent decisions, and the simulation results demonstrate that the method performs well in the evaluation of innovation. ability. Last, it contains some concluding comments.
C1 Zhejiang Gongshang Univ, Sch Business Adm, Hangzhou 310018, Zhejiang, Peoples R China.
C3 Zhejiang Gongshang University
RP Hao, YH (corresponding author), Zhejiang Gongshang Univ, Sch Business Adm, Hangzhou 310018, Zhejiang, Peoples R China.
RI li, wenbo/GZM-8930-2022; li, wenbo/JAC-7955-2023
CR [Anonymous], 1987, IEEE ASP MAGAZINE, DOI DOI 10.1109/MASSP.1987.1165576
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   Tidd J., 2020, MANAGING INNOVATION
NR 6
TC 0
Z9 0
U1 0
U2 0
PU ZHEJIANG UNIV PRESS
PI HANGZHOU
PA YUGU ROAD 20,, HANGZHOU, ZHEJIANG 310027, PEOPLES R CHINA
BN 978-7-89490-375-4
PY 2007
BP 413
EP 416
PG 4
WC Management; Operations Research & Management Science
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Business & Economics; Operations Research & Management Science
GA BGL01
UT WOS:000248091800085
DA 2024-09-05
ER
PT J
AU Calderon, K
   Serrano, N
   Blanco, C
   Gutierrez, I
AF Calderon, Kevin
   Serrano, Nicolas
   Blanco, Carmen
   Gutierrez, Inigo
TI Automated and continuous assessment implementation in a programming
   course
SO COMPUTER APPLICATIONS IN ENGINEERING EDUCATION
LA English
DT Article
DE automated assessment tool; continuous assessment; deep learning;
   design-based research; programming subject
ID DESIGN SCIENCE RESEARCH; EDUCATION; IMPACT; TOOL
AB Continuous assessment is an assessment methodology whose objective is to assess students on an ongoing basis. However, designing, organizing, correcting, and evaluating continuous assessment increases the workload of teachers. Moreover, this methodology may not promote deep learning if it is not implemented properly. In this study, we implemented continuous assessment in an undergraduate programming subject using an automated assessment tool to reduce the workload of professors. We used design-based research (DBR) to implement a prototype of assessment methodology which includes an automated assessment tool developed by our research group. DBR provides us with a scientific background for this implementation through an iterative process in which we progressively come to assess all the activities that students perform in the course. In the different iterations of this process, we have collected students' final and project grades, and their opinions through surveys about the assessments we have implemented. These results allow us to demonstrate that the performance of at least two types of students improves after the implementation of continuous assessment, while at the same time, the depth of learning in the class is not affected. We have also found that students are more motivated and committed to the course when continuous assessment is used as they prefer automated assessment over the traditional exercises. In addition, the implementation of the continuous assessment has shown us some unexpected outcomes about flexibility in methodology design, collection of large amounts of data from the learning process, and students acquiring useful skills for programming. In reality, this can result in students gaining deeper knowledge if they are confronted with a greater number of situations during this time in which they test their knowledge.
C1 [Calderon, Kevin; Serrano, Nicolas; Blanco, Carmen; Gutierrez, Inigo] Univ Navarra, TECNUN Sch Engn, Pamplona, Spain.
   [Calderon, Kevin] Paseo de Manuel Lardizabal,N 13, Donostia San Sebastian 20018, Spain.
C3 University of Navarra
RP Calderon, K (corresponding author), Paseo de Manuel Lardizabal,N 13, Donostia San Sebastian 20018, Spain.
EM kcalderon@unav.es
RI Blanco, Carmen/AAA-5446-2019; Serrano, Nicolas/V-7304-2017
OI Blanco, Carmen/0000-0001-6907-5367; Calderon Maceda,
   Kevin/0000-0001-8743-5063
CR Bloxham S., 2007, Developing Effective Assessment in Higher Education: a practical guide
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NR 32
TC 0
Z9 0
U1 4
U2 9
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1061-3773
EI 1099-0542
J9 COMPUT APPL ENG EDUC
JI Comput. Appl. Eng. Educ.
PD JAN
PY 2024
VL 32
IS 1
DI 10.1002/cae.22681
EA SEP 2023
PG 14
WC Computer Science, Interdisciplinary Applications; Education, Scientific
   Disciplines; Engineering, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Education & Educational Research; Engineering
GA FA5I4
UT WOS:001074661100001
OA Green Published, hybrid
DA 2024-09-05
ER
PT J
AU Castro, C
   Leiva, V
   Garrido, D
   Huerta, M
   Minatogawa, V
AF Castro, Cecilia
   Leiva, Victor
   Garrido, Diego
   Huerta, Mauricio
   Minatogawa, Vinicius
TI Blockchain in clinical trials: Bibliometric and network studies of
   applications, challenges, and future prospects based on data analytics
SO COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
LA English
DT Article
DE Artificial intelligence; Bibliometry; Blockchain technology; Data
   management; Interdisciplinary research; Latent Dirichlet allocation;
   Network analysis; PRISMA methodology
ID TOOL
AB This study conducts a comprehensive analysis on the usage of the blockchain technology in clinical trials, based on a curated corpus of 107 scientific articles from the year 2016 through the first quarter of 2024. Utilizing a methodological framework that integrates bibliometric analysis, network analysis, thematic mapping, and latent Dirichlet allocation, the study explores the terrain and prospective developments within this usage based on data analytics. Through a meticulous examination of the analyzed articles, the present study identifies seven key thematic areas, highlighting the diverse applications and interdisciplinary nature of blockchain in clinical trials. Our findings reveal blockchain capability to enhance data management, participant consent processes, as well as overall trial transparency, efficiency, and security. Additionally, the investigation discloses the emerging synergy between blockchain and advanced technologies, such as artificial intelligence and federated learning, proposing innovative directions for improving clinical research methodologies. Our study underscores the collaborative efforts in dealing with the complexities of integrating blockchain into the areas of clinical trials and healthcare, delineating the transformative potential of blockchain technology in revolutionizing these areas by addressing challenges and promoting practices of efficient, secure, and transparent research. The delineated themes and networks of collaboration provide a blueprint for future inquiry, showing the importance of empirical research to narrow the gap between theoretical promise and practical implementation.
C1 [Castro, Cecilia] Univ Minho, Ctr Math, Braga, Portugal.
   [Leiva, Victor; Garrido, Diego; Huerta, Mauricio] Pontificia Univ Catolica Valparaiso, Escuela Ingn Ind, Valparaiso, Chile.
   [Minatogawa, Vinicius] Pontificia Univ Catolica Valparaiso, Escuela Ingn Construcc & Transporte, Valparaiso, Chile.
C3 Universidade do Minho; Pontificia Universidad Catolica de Valparaiso;
   Pontificia Universidad Catolica de Valparaiso
RP Leiva, V (corresponding author), Pontificia Univ Catolica Valparaiso, Escuela Ingn Ind, Valparaiso, Chile.
EM victorleivasanchez@gmail.com
RI Leiva, Victor/AAM-7834-2021; Costa e Castro, Cecilia Maria
   Vasconcelos/ACU-7420-2022
OI Leiva, Victor/0000-0003-4755-3270; Costa e Castro, Cecilia Maria
   Vasconcelos/0000-0001-9897-8186
FU Chilean funds from the Vice-rectorate for Research; Creation, and
   Innovation (VINCI) of the Pontificia Universidad Catolica de Valparaiso
   [039.470/2024]; Portuguese funds through the CMAT-Research Centre of
   Mathematics of University of Minho, Portugal [UIDB/00013/2020,
   UIDP/00013/2020]
FX This research was partially supported by Chilean funds from the
   Vice-rectorate for Research, Creation, and Innovation (VINCI) of the
   Pontificia Universidad Catolica de Valparaiso, within project grant
   number 039.470/2024 (Victor Leiva) ; and by Portuguese funds through the
   CMAT-Research Centre of Mathematics of University of Minho, Portugal,
   within projects UIDB/00013/2020
   (https://doi.org/10.54499/UIDB/00013/2020) and UIDP/00013/2020
   (https://doi.org/10.54499/UIDP/00013/2020) (Cecilia Castro) . The
   authors would also like to thank the Editors and reviewers for their
   constructive comments, which led to improvements in the presentation of
   the article.
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NR 92
TC 0
Z9 0
U1 2
U2 2
PU ELSEVIER IRELAND LTD
PI CLARE
PA ELSEVIER HOUSE, BROOKVALE PLAZA, EAST PARK SHANNON, CO, CLARE, 00000,
   IRELAND
SN 0169-2607
EI 1872-7565
J9 COMPUT METH PROG BIO
JI Comput. Meth. Programs Biomed.
PD OCT
PY 2024
VL 255
AR 108321
DI 10.1016/j.cmpb.2024.108321
PG 20
WC Computer Science, Interdisciplinary Applications; Computer Science,
   Theory & Methods; Engineering, Biomedical; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Medical Informatics
GA A2F6P
UT WOS:001280744300001
PM 39053350
DA 2024-09-05
ER
PT C
AU Guo, FQ
   Xiao, H
AF Guo, Fengqun
   Xiao, Hui
BE Fei, M
   Peng, C
   Su, Z
   Song, Y
   Han, Q
TI Research on Visual Environment Evaluation System of Subway Station Space
SO COMPUTATIONAL INTELLIGENCE, NETWORKED SYSTEMS AND THEIR APPLICATIONS
SE Communications in Computer and Information Science
LA English
DT Proceedings Paper
CT International Conference of Life System Modeling and Simulation (LSMS) /
   International Conference on Intelligent Computing for Sustainable Energy
   and Environment (ICSEE)
CY SEP 20-23, 2014
CL Shanghai, PEOPLES R CHINA
DE LED lighting; visual environment; evaluation system; particle swarm
   optimization
AB Based on the energy crisis, LED with its energy-saving and environmental friendly is gradually used to the subway station space lighting. But now, there are little materials about the visual environment evaluation for semiconductor lighting, so that the use of LED lighting lacks theoretical basis and data support. So, in order to promote the LED lighting in subway station space, it's very important to evaluate the visual environment. Therefore, the core of this paper was to build a theoretical model to evaluate the visual environment of subway station space using Particle Swarm Optimization. Firstly, chose 16 evaluation indexes which were fit for the subway station visual environment evaluation and got the initial judgment matrix through pair wise comparison, after that, established the non-linear consistency correction model. Finally, used Particle Swarm Optimization to calculate the judgment matrix with better consistency and the corresponding index weight, and constructed the theoretical model.
C1 [Guo, Fengqun; Xiao, Hui] Tongji Univ, Coll Elect & Informat Engn, Shanghai 201804, Peoples R China.
C3 Tongji University
RP Guo, FQ (corresponding author), Tongji Univ, Coll Elect & Informat Engn, Shanghai 201804, Peoples R China.
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NR 7
TC 1
Z9 1
U1 1
U2 5
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 1865-0929
BN 978-3-662-45260-8
J9 COMM COM INF SC
PY 2014
VL 462
BP 169
EP 179
PG 11
WC Computer Science, Artificial Intelligence; Robotics
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Robotics
GA BD3QP
UT WOS:000360077500018
DA 2024-09-05
ER
PT J
AU Cohan, A
   Goharian, N
AF Cohan, Arman
   Goharian, Nazli
TI Scientific document summarization via citation contextualization and
   scientific discourse
SO INTERNATIONAL JOURNAL ON DIGITAL LIBRARIES
LA English
DT Article
DE Scientific document summarization; Text summarization; Citation
   analysis; Natural language processing
ID MODELS
AB The rapid growth of scientific literature has made it difficult for the researchers to quickly learn about the developments in their respective fields. Scientific summarization addresses this challenge by providing summaries of the important contributions of scientific papers. We present a framework for scientific summarization which takes advantage of the citations and the scientific discourse structure. Citation texts often lack the evidence and context to support the content of the cited paper and are even sometimes inaccurate. We first address the problem of inaccuracy of the citation texts by finding the relevant context from the cited paper. We propose three approaches for contextualizing citations which are based on query reformulation, word embeddings, and supervised learning. We then train a model to identify the discourse facets for each citation. We finally propose a method for summarizing scientific papers by leveraging the faceted citations and their corresponding contexts. We evaluate our proposed method on two scientific summarization datasets in the biomedical and computational linguistics domains. Extensive evaluation results show that our methods can improve over the state of the art by large margins.
C1 [Cohan, Arman; Goharian, Nazli] Georgetown Univ, Informat Retrieval Lab, Dept Comp Sci, Washington, DC 20057 USA.
C3 Georgetown University
RP Cohan, A (corresponding author), Georgetown Univ, Informat Retrieval Lab, Dept Comp Sci, Washington, DC 20057 USA.
EM arman@ir.cs.georgetown.edu; nazli@ir.cs.georgetown.edu
OI Cohan, Arman/0000-0002-8954-2724
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NR 78
TC 45
Z9 48
U1 0
U2 11
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1432-5012
EI 1432-1300
J9 INT J DIGIT LIBRARIE
JI Int. J. Digit. Llibraries
PD SEP
PY 2018
VL 19
IS 2-3
SI SI
BP 287
EP 303
DI 10.1007/s00799-017-0216-8
PG 17
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA GQ5QB
UT WOS:000441741300014
DA 2024-09-05
ER
PT J
AU Yang, LQ
   Wang, P
   Wang, J
AF Yang, Liqiang
   Wang, Pan
   Wang, Jie
TI Research on evaluation model for vehicle interior sound quality based on
   an optimized BiLSTM using genetic algorithm
SO MECHANICAL SYSTEMS AND SIGNAL PROCESSING
LA English
DT Article
DE Sound quality; Vehicle interior noise; Evaluation model; Bidirectional
   long short-term memory; Genetic algorithm
ID PREDICTION; NOISE; TRANSFORM
AB Interior sound quality strongly affects passengers' physiological and psychological perceptions. Therefore, it is important to evaluate vehicle interior sound quality. Compared with assessing by humans, the artificial intelligent-based evaluation model can acquire an evaluation efficiently. However, this type of models determines initial learnable parameters at random before training, which is easy to cause final trained model to trap in local optima. This paper proposes an evaluation model based on an optimized bidirectional long short-term memory using genetic algorithm. Firstly, interior noise measurement and subjective evaluation are completed. Secondly, to obtain the time-frequency information in line with human auditory perception, log-mel spectrum is used to preprocess the noise. Thirdly, the evaluation model is constructed, which consists of two bidirectional long short-term memory layers, two fully connected layers and one Softmax output unit. Next, to avoid model trapping in local optima, initial learnable parameters are optimized using genetic algorithm. After optimization, average fitness and best fitness decreased by 6.5136% and 1.4415%, respectively. The training accuracy is 95.79%. The validation accuracy is 93.15%. The testing accuracy is 93.33%. Only two samples are misclassified in the confusion matrix of testing stage. These suggest that genetic algorithm can greatly enhance the model's performance by optimizing initial learnable parameters. The evaluation obtained by the optimized model is very close to human subjective evaluation.
C1 [Yang, Liqiang; Wang, Pan] Chongqing Univ, Coll Mech & Vehicle Engn, Chongqing 400044, Peoples R China.
   [Wang, Jie] Zuoyebang Educ Technol Beijing Co Ltd, Beijing 100085, Peoples R China.
C3 Chongqing University
RP Wang, P (corresponding author), Chongqing Univ, Coll Mech & Vehicle Engn, Chongqing 400044, Peoples R China.
EM yangliqiang@cqu.edu.cn; wangpan@cqu.edu.cn; wangj996@foxmail.com
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NR 35
TC 2
Z9 2
U1 7
U2 18
PU ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
PI LONDON
PA 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND
SN 0888-3270
EI 1096-1216
J9 MECH SYST SIGNAL PR
JI Mech. Syst. Signal Proc.
PD DEC 1
PY 2023
VL 204
AR 110827
DI 10.1016/j.ymssp.2023.110827
EA OCT 2023
PG 15
WC Engineering, Mechanical
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA W4BO1
UT WOS:001091096900001
DA 2024-09-05
ER
PT J
AU Han, YJ
   Park, SY
   Park, SB
AF Han, Yong-Jin
   Park, Se-Young
   Park, Seong-Bae
TI A single-directional influence topic model using call and proximity logs
   simultaneously
SO SOFT COMPUTING
LA English
DT Article; Proceedings Paper
CT 12th International Conference on Advances in Mobile Computing and
   Multimedia (MoMM)
CY DEC 08-10, 2014
CL Kaohsiung, TAIWAN
DE Social interaction pattern; Call; Proximity; Topic model; Latent
   Dirichlet allocation
AB Understanding social interactions is one of the key factors in the development of context-aware ubiquitous applications. Identifying interaction patterns sensed by a mobile device is one possible way for understanding social interactions. Most previous studies on this problem have employed call and proximity logs to represent social interactions. Because these interactions can be characterized by topics, the studies have applied topic models based on latent Dirichlet allocation (LDA) to identifying interaction patterns from social interactions. However, these previous studies regarded calls and proximities as independent interaction types. As a result, they lost the information obtainable when calls and proximities were analyzed simultaneously. This paper proposes a topic-based method that simultaneously considers calls and proximities, allowing interaction patterns to be identified from a mobile log. For this purpose, the proposed method regards calls and proximities as a homogeneous information type that are drawn from the same temporal space expressed by the same distribution, but with different parameters. From the observation that the number of proximities in a mobile log usually overwhelms that of calls and the proximities are observed regularly, the proposed method models a single-directional influence from proximities to calls, where both call and proximity are modeled by LDA. The experiments with three different data sets from the Massachusetts Institute of Technology's Reality Mining project show that the proposed method outperforms the method that considers calls and proximities independently; this proves the plausibility of the proposed method.
C1 [Han, Yong-Jin; Park, Se-Young; Park, Seong-Bae] Kyungpook Natl Univ, Sch Comp Sci & Engn, Daegu, South Korea.
C3 Kyungpook National University (KNU)
RP Park, SB (corresponding author), Kyungpook Natl Univ, Sch Comp Sci & Engn, Daegu, South Korea.
EM yjhan@sejong.knu.ac.kr; sbpark@sejong.knu.ac.kr; sypark@sejong.knu.ac.kr
RI Park, Young/D-6811-2013
FU BK21 Plus project (SW Human Resource Development Program for Supporting
   Smart Life) - Ministry of Education, School of Computer Science and
   Engineering, Kyungpook National University, Korea [21A20131600005]
FX This study was supported by the BK21 Plus project (SW Human Resource
   Development Program for Supporting Smart Life) funded by the Ministry of
   Education, School of Computer Science and Engineering, Kyungpook
   National University, Korea (21A20131600005).
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NR 25
TC 1
Z9 1
U1 0
U2 10
PU SPRINGER
PI NEW YORK
PA 233 SPRING ST, NEW YORK, NY 10013 USA
SN 1432-7643
EI 1433-7479
J9 SOFT COMPUT
JI Soft Comput.
PD AUG
PY 2017
VL 21
IS 15
BP 4179
EP 4195
DI 10.1007/s00500-015-1898-8
PG 17
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA FA5RL
UT WOS:000405501100003
DA 2024-09-05
ER
PT C
AU Koksalmis, E
   Koksalmis, GH
AF Koksalmis, Emrah
   Koksalmis, Gulsah Hancerliogullari
GP IEEE
TI Artificial Intelligence in Air and Space Technologies: A Scientometric
   Analysis
SO 2023 10TH INTERNATIONAL CONFERENCE ON RECENT ADVANCES IN AIR AND SPACE
   TECHNOLOGIES, RAST
LA English
DT Proceedings Paper
CT 10th International Conference on Recent Advances in Air and Space
   Technologies (RAST)
CY JUN 07-09, 2023
CL Istanbul, TURKEY
DE artificial intelligence; air and space technologies; aerospace;
   bibliometric analysis; scientometric analysis; publication trends
AB The aim of this paper is to conduct a bibliometric analysis and evaluate publication trends of research papers related to artificial intelligence in air and space technologies for the last 50 years. The data was obtained from Scopus' database. The keyword for the search was "artificial intelligence" "aerospace". This paper seeks to identify top publishing authors, universities, countries, and reference articles to highlight collaboration between authors, institutions, and countries in the region, and to gain insight into research topics that scholars have been working on recently. Moreover, distribution of publications according to their languages, document types, and research areas are evaluated. Articles from 1975 to 2023 were considered, and a total of 1,111 artificial intelligence in air and space technologies-related publications were discovered. There were five main document types in artificial intelligence in air and space technologies related publications and conference paper and article were mostly used among all document types. The United States was found to be the most productive country and English was the most frequently used language among all publications. Furthermore, engineering is the main distribution channel, and it is followed by computer science and physics and astronomy as the top popular research areas.
C1 [Koksalmis, Emrah] Natl Def Univ, Dept Ind Engn, Istanbul, Turkiye.
   [Koksalmis, Gulsah Hancerliogullari] Istanbul Tech Univ, Dept Ind Engn, Istanbul, Turkiye.
C3 Istanbul Technical University
RP Koksalmis, E (corresponding author), Natl Def Univ, Dept Ind Engn, Istanbul, Turkiye.
EM ekoksalmis@hho.msu.edu.tr; ghancerliogullari@itu.edu.tr
CR Blasch E., 2022, 2022 IEEE AEROSPACE, P1
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NR 6
TC 0
Z9 0
U1 2
U2 4
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 979-8-3503-2302-3
PY 2023
DI 10.1109/RAST57548.2023.10197660
PG 4
WC Engineering, Aerospace; Remote Sensing
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Engineering; Remote Sensing
GA BV6BY
UT WOS:001055074600008
DA 2024-09-05
ER
PT J
AU Boukhers, Z
   Asundi, NB
AF Boukhers, Zeyd
   Asundi, Nagaraj Bahubali
TI Deep author name disambiguation using DBLP data
SO INTERNATIONAL JOURNAL ON DIGITAL LIBRARIES
LA English
DT Article; Early Access
DE Author name disambiguation; Entity linkage; Bibliographic data; Neural
   networks; Classification; DBLP
AB In the academic world, the number of scientists grows every year and so does the number of authors sharing the same names. Consequently, it is challenging to assign newly published papers to their respective authors. Therefore, author name ambiguity is considered a critical open problem in digital libraries. This paper proposes an author name disambiguation approach that links author names to their real-world entities by leveraging their co-authors and domain of research. To this end, we use data collected from the DBLP repository that contains more than 5 million bibliographic records authored by around 2.6 million co-authors. Our approach first groups authors who share the same last names and same first name initials. The author within each group is identified by capturing the relation with his/her co-authors and area of research, represented by the titles of the validated publications of the corresponding author. To this end, we train a neural network model that learns from the representations of the co-authors and titles. We validated the effectiveness of our approach by conducting extensive experiments on a large dataset.
C1 [Boukhers, Zeyd] Univ Koblenz Landau, Inst Web Sci & Technol WeST, Univ str 1, D-56070 Koblenz, Germany.
   [Boukhers, Zeyd; Asundi, Nagaraj Bahubali] Fraunhofer Inst Appl Informat Technol, Dept Data Sci & Artificial Intelligence FIT, Schloss Birlinghoven 1, D-53757 St Augustin, Germany.
C3 University of Koblenz & Landau; Fraunhofer Gesellschaft
RP Boukhers, Z (corresponding author), Univ Koblenz Landau, Inst Web Sci & Technol WeST, Univ str 1, D-56070 Koblenz, Germany.; Boukhers, Z (corresponding author), Fraunhofer Inst Appl Informat Technol, Dept Data Sci & Artificial Intelligence FIT, Schloss Birlinghoven 1, D-53757 St Augustin, Germany.
EM zeyd.boukhers@fit.fraunhofer.de;
   nagaraj.bahubali.asundi@fit.fraunhofer.de
RI Boukhers, Zeyd/HZL-0733-2023
OI Boukhers, Zeyd/0000-0001-9778-9164; Asundi, Nagaraj
   Bahubali/0000-0002-1044-7047
FU Projekt DEAL
FX Open Access funding enabled and organized by Projekt DEAL.
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NR 39
TC 0
Z9 0
U1 1
U2 13
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1432-5012
EI 1432-1300
J9 INT J DIGIT LIBRARIE
JI Int. J. Digit. Llibraries
PD 2023 MAY 4
PY 2023
DI 10.1007/s00799-023-00361-6
EA MAY 2023
PG 11
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA F3JJ7
UT WOS:000981337900001
OA hybrid
DA 2024-09-05
ER
PT J
AU Aliya
   Liu, S
   Zhang, DN
   Cao, YF
   Sun, JY
   Jiang, S
   Liu, Y
AF Aliya, Shi
   Liu, Shi
   Zhang, Danni
   Cao, Yufa
   Sun, Jinyuan
   Jiang, Shui
   Liu, Yuan
TI Research on the Evaluation of Baijiu Flavor Quality Based on Intelligent
   Sensory Technology Combined with Machine Learning
SO CHEMOSENSORS
LA English
DT Article
DE Baijiu; flavor evaluation; intelligent sensory; machine learning
AB Baijiu, one of the world's six major distilled spirits, has an extremely rich flavor profile, which increases the complexity of its flavor quality evaluation. This study employed an electronic nose (E-nose) and electronic tongue (E-tongue) to detect 42 types of strong-aroma Baijiu. Linear discriminant analysis (LDA) was performed based on the different production origins, alcohol content, and grades. Twelve trained Baijiu evaluators participated in the quantitative descriptive analysis (QDA) of the Baijiu samples. By integrating characteristic values from the intelligent sensory detection data and combining them with the human sensory evaluation results, machine learning was used to establish a multi-submodel-based flavor quality prediction model and classification model for Baijiu. The results showed that different Baijiu samples could be well distinguished, with a prediction model R2 of 0.9994 and classification model accuracy of 100%. This study provides support for the establishment of a flavor quality evaluation system for Baijiu.
C1 [Jiang, Shui; Liu, Yuan] Shanghai Jiao Tong Univ, Sch Agr & Biol, Dept Food Sci & Technol, Shanghai 200240, Peoples R China.
   [Aliya, Shi; Liu, Shi; Cao, Yufa] Suqian Prod Qual Supervis & Testing Inst, Suqian 223800, Peoples R China.
   [Zhang, Danni] Shanghai Jiao Tong Univ, Instrumental Anal Ctr, Shanghai 200240, Peoples R China.
   [Sun, Jinyuan] Beijing Technol & Business Univ, China Food Flavor & Nutr Hlth Innovat Ctr, Beijing 102401, Peoples R China.
   [Liu, Yuan] Ningxia Univ, Sch Food Sci & Engn, Yinchuan 750021, Peoples R China.
C3 Shanghai Jiao Tong University; Shanghai Jiao Tong University; Beijing
   Technology & Business University; Ningxia University
RP Liu, Y (corresponding author), Shanghai Jiao Tong Univ, Sch Agr & Biol, Dept Food Sci & Technol, Shanghai 200240, Peoples R China.; Liu, S (corresponding author), Suqian Prod Qual Supervis & Testing Inst, Suqian 223800, Peoples R China.; Sun, JY (corresponding author), Beijing Technol & Business Univ, China Food Flavor & Nutr Hlth Innovat Ctr, Beijing 102401, Peoples R China.; Liu, Y (corresponding author), Ningxia Univ, Sch Food Sci & Engn, Yinchuan 750021, Peoples R China.
EM aly122150910101@sjtu.edu.cn; liushisqzj@163.com;
   dannizhang2019@sjtu.edu.cn; qcyf@163.com; sunjinyuan@btbu.edu.cn;
   jiangshui@sjtu.edu.cn; y_liu@sjtu.edu.cn
RI ; Liu, Yuan/J-4453-2012
OI Sun, Jinyuan/0000-0001-6717-9787; Liu, Yuan/0000-0003-1420-0276
FU Suqian Sci Tech Program [L202205, L202305]; Open Project of the China
   Food Flavor and Nutrition Health Innovation Center [CFC2023B-007]
FX This work was supported by the Suqian Sci & Tech Program (Grant No.
   L202205 and No. L202305) and the Open Project of the China Food Flavor
   and Nutrition Health Innovation Center (CFC2023B-007).
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NR 36
TC 0
Z9 0
U1 4
U2 4
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2227-9040
J9 CHEMOSENSORS
JI Chemosensors
PD JUL
PY 2024
VL 12
IS 7
AR 125
DI 10.3390/chemosensors12070125
PG 15
WC Chemistry, Analytical; Electrochemistry; Instruments & Instrumentation
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Electrochemistry; Instruments & Instrumentation
GA ZQ3D4
UT WOS:001276712500001
OA gold
DA 2024-09-05
ER
PT J
AU Mustafa, G
   Usman, M
   Afzal, MT
   Shahid, A
   Koubaa, A
AF Mustafa, Ghulam
   Usman, Muhammad
   Afzal, Muhammad Tanvir
   Shahid, Abdul
   Koubaa, Anis
TI A Comprehensive Evaluation of Metadata-Based Features to Classify
   Research Paper's Topics
SO IEEE ACCESS
LA English
DT Article
DE Metadata; Computer science; Deep learning; Tools; Support vector
   machines; Licenses; Libraries; Research paper classification;
   Word2Vector (W2V); metadata; association of computing machinery (ACM);
   k-nearest neighbor's (KNN); decision tree (DT); random forest (RF); term
   frequency (TF); term frequency and inverse document frequency (TFIDF);
   bag of word (BOW)
ID CLASSIFICATION; NOISE
AB The existing plethora of document classification techniques exploits different data sources either from the content or metadata of research articles. Various journal publishers like Springer, Elsevier, IEEE, etc., do not provide open access to the content of research articles, whereas metadata is freely available there. Metadata like title, keyword, and abstract can serve as a better alternative to the content in various scenarios. In the current literature, researchers have assessed the role of some of the metadata individually. We believe that the collective contribution of metadata parameters can play a significant role in classifying research papers. This paper presents a comprehensive evaluation of the role of metadata, individually as well as in combinations to achieve the objective of research paper classification. Moreover, we have classified the research articles into ACM hierarchy root categories (e.g. general literature, hardware, software, etc.). In this comprehensive evaluation, we have assessed all the possible combinations of metadata features against different classifiers such as Random Forest, K Nearest Neighbor, and Decision Tree. The results of this research reveal that the title & keywords combination outperforms other combinations with an F-measure score of 0.88.
C1 [Mustafa, Ghulam] Capital Univ Sci & Technol, Dept Comp Sci, Islamabad 46000, Pakistan.
   [Usman, Muhammad] Natl Univ Comp & Emerging Sci, Dept Comp Sci, Islamabad 44000, Pakistan.
   [Afzal, Muhammad Tanvir] Namal Inst Mianwali, Dept Comp Sci, Mianwali 42250, Pakistan.
   [Shahid, Abdul] Kohat Univ Sci & Technol, Inst Comp, Kohat 26000, Pakistan.
   [Koubaa, Anis] Prince Sultan Univ, Robot & Internet Things Lab, Riyadh 12435, Saudi Arabia.
   [Koubaa, Anis] Polytech Inst Porto, CISTER INESC TEC, P-4200 Porto, Portugal.
C3 Capital University of Science & Technology; Kohat University of Science
   & Technology; Prince Sultan University; Instituto Politecnico do Porto;
   INESC TEC
RP Shahid, A (corresponding author), Kohat Univ Sci & Technol, Inst Comp, Kohat 26000, Pakistan.
EM ashahid@kust.edu.pk
RI Afzal, Muhammad/D-3741-2019; Mustafa, Ghulam/JPY-1274-2023; Koubaa,
   Anis/T-7414-2018
OI Afzal, Muhammad/0000-0002-7851-2327; Mustafa,
   Ghulam/0000-0002-0354-8229; Koubaa, Anis/0000-0003-3787-7423; Usman,
   Muhammad/0000-0002-6154-6256; Afzal, Muhammad Tanvir/0000-0002-9765-8815
FU Prince Sultan University
FX The authors would like to acknowledge the support of Prince Sultan
   University for paying the Article Processing Charges (APC) of this
   publication.
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NR 45
TC 3
Z9 3
U1 1
U2 27
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2021
VL 9
BP 133500
EP 133509
DI 10.1109/ACCESS.2021.3115148
PG 10
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA WC2MQ
UT WOS:000704096400001
OA gold
DA 2024-09-05
ER
PT J
AU Park, S
   Ceulemans, E
   Van Deun, K
AF Park, S.
   Ceulemans, E.
   Van Deun, K.
TI A critical assessment of sparse PCA (research): why (one should
   acknowledge that) weights are not loadings
SO BEHAVIOR RESEARCH METHODS
LA English
DT Article
DE Sparse principal component analysis; Principal component analysis;
   Exploratory data analysis; Dimension reduction; Sparse weights; Sparse
   loadings
ID PRINCIPAL COMPONENT ANALYSIS; VARIABLE SELECTION; POWER METHOD;
   CONSISTENCY; MATRIX; MODEL; APPROXIMATION; EIGENVALUE; ROTATION
AB Principal component analysis (PCA) is an important tool for analyzing large collections of variables. It functions both as a pre-processing tool to summarize many variables into components and as a method to reveal structure in data. Different coefficients play a central role in these two uses. One focuses on the weights when the goal is summarization, while one inspects the loadings if the goal is to reveal structure. It is well known that the solutions to the two approaches can be found by singular value decomposition; weights, loadings, and right singular vectors are mathematically equivalent. What is often overlooked, is that they are no longer equivalent in the setting of sparse PCA methods which induce zeros either in the weights or the loadings. The lack of awareness for this difference has led to questionable research practices in sparse PCA. First, in simulation studies data is generated mostly based only on structures with sparse singular vectors or sparse loadings, neglecting the structure with sparse weights. Second, reported results represent local optima as the iterative routines are often initiated with the right singular vectors. In this paper we critically re-assess sparse PCA methods by also including data generating schemes characterized by sparse weights and different initialization strategies. The results show that relying on commonly used data generating models can lead to over-optimistic conclusions. They also highlight the impact of choice between sparse weights versus sparse loadings methods and the initialization strategies. The practical consequences of this choice are illustrated with empirical datasets.
C1 [Park, S.; Van Deun, K.] Tilburg Univ, Methods & Stat, Tilburg, Netherlands.
   [Ceulemans, E.] Katholieke Univ Leuven, Psychol & Educ Sci, Leuven, Belgium.
C3 Tilburg University; KU Leuven
RP Park, S (corresponding author), Tilburg Univ, Methods & Stat, Tilburg, Netherlands.
EM s.park_1@tilburguniversity.edu
RI Ceulemans, Eva/AAQ-6617-2020
OI Ceulemans, Eva/0000-0002-7611-4683
FU Netherlands Organisation for Scientific Research [NWO-VIDI 452.16.012]
FX AcknowledgementsThis research was funded by a personal grant from the
   Netherlands Organisation for Scientific Research [NWO-VIDI 452.16.012]
   awarded to Katrijn Van Deun. The funder did not have any additional role
   in the study design, data collection and analysis, decision to publish,
   or preparation of the manuscript. We thank the anonymous reviewers for
   providing their valuable comments and suggestions on improving the
   manuscript.
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NR 78
TC 0
Z9 0
U1 0
U2 1
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1554-351X
EI 1554-3528
J9 BEHAV RES METHODS
JI Behav. Res. Methods
PD MAR
PY 2024
VL 56
IS 3
BP 1413
EP 1432
DI 10.3758/s13428-023-02099-0
EA AUG 2023
PG 20
WC Psychology, Mathematical; Psychology, Experimental
WE Social Science Citation Index (SSCI)
SC Psychology
GA WN8I1
UT WOS:001042482800001
PM 37540466
OA Green Published, hybrid
DA 2024-09-05
ER
PT J
AU Sienkiewicz, J
   Altmann, EG
AF Sienkiewicz, Julian
   Altmann, Eduardo G.
TI Impact of lexical and sentiment factors on the popularity of scientific
   papers
SO ROYAL SOCIETY OPEN SCIENCE
LA English
DT Article
DE citation analysis; sentiment analysis; quantile regression
ID CITATION IMPACT
AB We investigate how textual properties of scientific papers relate to the number of citations they receive. Our main finding is that correlations are nonlinear and affect differently the most cited and typical papers. For instance, we find that, in most journals, short titles correlate positively with citations only for the most cited papers, whereas for typical papers, the correlation is usually negative. Our analysis of six different factors, calculated both at the title and abstract level of 4.3 million papers in over 1500 journals, reveals the number of authors, and the length and complexity of the abstract, as having the strongest (positive) influence on the number of citations.
C1 [Sienkiewicz, Julian; Altmann, Eduardo G.] Max Planck Inst Phys Komplexer Syst, D-01187 Dresden, Germany.
C3 Max Planck Society
RP Sienkiewicz, J (corresponding author), Max Planck Inst Phys Komplexer Syst, D-01187 Dresden, Germany.
EM julas@if.pw.edu.pl
RI Sienkiewicz, Julian/AAB-4900-2020; Altmann, Eduardo/A-3320-2009;
   Altmann, Eduardo G./ABD-2012-2020
OI Sienkiewicz, Julian/0000-0003-2097-1499; Altmann,
   Eduardo/0000-0002-1932-3710; Altmann, Eduardo G./0000-0002-1932-3710
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NR 35
TC 17
Z9 18
U1 0
U2 50
PU ROYAL SOC
PI LONDON
PA 6-9 CARLTON HOUSE TERRACE, LONDON SW1Y 5AG, ENGLAND
SN 2054-5703
J9 ROY SOC OPEN SCI
JI R. Soc. Open Sci.
PD JUN
PY 2016
VL 3
IS 6
AR 160140
DI 10.1098/rsos.160140
PG 10
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics
GA DR9RF
UT WOS:000380233100013
PM 27429773
OA Green Published, Green Submitted, gold
DA 2024-09-05
ER
PT J
AU Yu, Y
   Ren, JS
   Zhang, Q
   Yang, WM
   Jiao, ZW
AF Yu, Yuan
   Ren, Jinsheng
   Zhang, Qi
   Yang, Weimin
   Jiao, Zhiwei
TI Research on Tire Marking Point Completeness Evaluation Based on K-Means
   Clustering Image Segmentation
SO SENSORS
LA English
DT Article
DE machine vision; tire marking point; completeness; image segmentation
ID VISION
AB The tire marking points of dynamic balance and uniformity play a crucial guiding role in tire installation. Incomplete marking points block the recognition of tire marking points, and then affect the installation of tires. It is usually necessary to evaluate the marking point completeness during the quality inspection of finished tires. In order to meet the high-precision requirements of the evaluation of tire marking point completeness in the smart factories, the K-means clustering algorithm is introduced to segment the image of marking points in this paper. The pixels within the contour of the marking point are weighted to calculate the marking point completeness on the basis of the image segmentation. The completeness is rated and evaluated by completeness calculation. The experimental results show that the accuracy of the marking point completeness ratings is 95%, and the accuracy of the marking point evaluations is 99%. The proposed method has an important guiding significance of practice to evaluate the tire marking point completeness during the tire quality inspection based on machine vision.
C1 [Yu, Yuan; Ren, Jinsheng; Zhang, Qi; Yang, Weimin; Jiao, Zhiwei] Beijing Univ Chem Technol, Coll Mech & Elect Engn, Beijing 100029, Peoples R China.
C3 Beijing University of Chemical Technology
RP Jiao, ZW (corresponding author), Beijing Univ Chem Technol, Coll Mech & Elect Engn, Beijing 100029, Peoples R China.
EM yuyuanjd@263.net; renjs630@163.com; zhangq0618@163.com;
   yangwm@mail.buct.edu.cn; jiaozw@mail.buct.edu.cn
RI Yang, Wei/GWV-4107-2022; Yang, Wei/JBJ-1928-2023; Yang,
   Wei/HIA-0360-2022
OI Yu, Yuan/0000-0001-7087-9223
FU Shandong Major Scientific and Technological Innovation Project
   (Intelligent manufacturing technology and equipment for electromagnetic
   induction heating and direct pressure vulcanization of tire with
   ultra-high performance) [12874]
FX This research paper was funded by Shandong Major Scientific and
   Technological Innovation Project (Intelligent manufacturing technology
   and equipment for electromagnetic induction heating and direct pressure
   vulcanization of tire with ultra-high performance. No. 12874).
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NR 19
TC 4
Z9 4
U1 0
U2 16
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1424-8220
J9 SENSORS-BASEL
JI Sensors
PD SEP
PY 2020
VL 20
IS 17
AR 4687
DI 10.3390/s20174687
PG 16
WC Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments
   & Instrumentation
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Instruments & Instrumentation
GA NP1OZ
UT WOS:000569951900001
PM 32825149
OA gold, Green Published
DA 2024-09-05
ER
PT C
AU Chen, CY
   Lin, ML
AF Chen, CY
   Lin, ML
BE Qi, JM
   Cui, JP
TI Research on the performance of SVM with Fourier-Kernel function and
   application on regression
SO ICEMI 2005: CONFERENCE PROCEEDINGS OF THE SEVENTH INTERNATIONAL
   CONFERENCE ON ELECTRONIC MEASUREMENT & INSTRUMENTS, VOL 3
LA English
DT Proceedings Paper
CT 7th International Conference on Electronic Measurement and Instruments
CY AUG 16-18, 2005
CL Beijing, PEOPLES R CHINA
DE SVM; Fourier kernel; regression
AB Contrast to traditional methods, Support Vector Machine (SVM) has better performance on generalization. It has widely applications on pattern recognition.. but less on regression now. And the common choice of kernel function is Radial Basis Function, so few Studies on other special kernels. In this paper, the performance of SVM based on Fourier kernel is studied which aims at the regression in signal processing problems, and the influence of parameter q on performance of SVM is analyzed. A conclusion is drawn that the integral of Fourier kernel in one period is a constant and the concept of equivalent kernel function width is proposed. At last, Simulation verifies that SVM based on Fourier kernel has better performance than the one,based on RBF kernel in the field of signal processing.
C1 Harbin Inst Technol, Harbin 150001, Peoples R China.
C3 Harbin Institute of Technology
RP Harbin Inst Technol, Harbin 150001, Peoples R China.
CR BOSER B, 1922, P 5 ANN ACM WORKSH C, P144
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NR 7
TC 0
Z9 0
U1 0
U2 0
PU INTERNATIONAL ACADEMIC PUBLISHERS LTD
PI HONG KONG
PA UNIT 1205, 12 FLOOR, SINO PLAZA, 255 GLOUCESTER ROAD, HONG KONG 00000,
   CAUSEWAY BAY, PEOPLES R CHINA
BN 7-5062-7443-4
PY 2005
BP 671
EP 675
PG 5
WC Engineering, Electrical & Electronic; Instruments & Instrumentation
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Engineering; Instruments & Instrumentation
GA BDR67
UT WOS:000235120400143
DA 2024-09-05
ER
PT J
AU Sengöz, A
   Orhun, BN
   Konyalilar, N
AF Sengoz, Ayse
   Orhun, Beste Nisa
   Konyalilar, Nil
TI A holistic approach to artificial intelligence-related research in the
   transportation system: bibliometric analysis
SO WORLDWIDE HOSPITALITY AND TOURISM THEMES
LA English
DT Article
DE Artificial intelligence; Transportation; Bibliometric analysis;
   VOSviewer; Tourism
AB Purpose Developments regarding the use of artificial intelligence (AI) in transportation systems, one of the important stakeholders of tourism, are remarkable. However, no review thus far has provided a comprehensive overview of research on AI in transportation systems.Design/methodology/approach To fill this gap, this study uses the VOSviewer software to present a bibliometric review of the current scientific literature in the field of AI-related tourism research. The theme of AI in transportation systems was explored in the Web of Science database.Findings The original search yielded 642 documents, which were then filtered by parameters. For publications related to AI in transportation systems, the most cited documents, leading authors, productive countries, co-occurrence analysis of keywords and bibliographic matching of documents were examined. This report shows that there has been a recent increase in research on AI in transport systems. However, there is only one study on tourism. The country that contributed the most is China with 298 studies. The most used keyword in the documents was intelligent transportation system.Originality/value The bibliometric analysis of the existing work provided a valuable and seminal reference for researchers and practitioners in AI-related in transportation system.
C1 [Sengoz, Ayse] Akdeniz Univ, Antalya, Turkiye.
   [Orhun, Beste Nisa] Van Yuzuncu Yil Univ, Van, Turkiye.
   [Konyalilar, Nil] Istanbul Topkapi Univ, Istanbul, Turkiye.
C3 Akdeniz University; Yuzuncu Yil University; Istanbul Topkapi University
RP Sengöz, A (corresponding author), Akdeniz Univ, Antalya, Turkiye.
EM aysesengoz@akdeniz.edu.tr
RI SENGOZ, Ayse/IAQ-7228-2023
OI SENGOZ, Ayse/0000-0002-0311-9141; ORHUN, Beste Nisa/0000-0001-5578-2546
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NR 32
TC 0
Z9 0
U1 6
U2 6
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 1755-4217
EI 1755-4225
J9 WORLDW HOSP TOUR THE
JI Worldw. Hosp. Tour. Themes
PD MAY 30
PY 2024
VL 16
IS 2
SI SI
BP 138
EP 149
DI 10.1108/WHATT-03-2024-0059
EA APR 2024
PG 12
WC Hospitality, Leisure, Sport & Tourism
WE Emerging Sources Citation Index (ESCI)
SC Social Sciences - Other Topics
GA SC3L8
UT WOS:001199471200001
DA 2024-09-05
ER
PT J
AU Djogbenou, R
   Adjiwanou, V
   Lardoux, S
AF Djogbenou, Robert
   Adjiwanou, Visseho
   Lardoux, Solene
TI Exploring Sixty-Two Years of Research on Immigrants' Integration Using
   Structural Topic Modeling-Based Bibliometric Analysis
SO JOURNAL OF INTERNATIONAL MIGRATION AND INTEGRATION
LA English
DT Article; Early Access
DE Immigration; Immigrants' integration; Ethnic diversity; Topic modeling;
   Bibliometric analysis
ID ENGAGEMENT; EARNINGS; SCIENCE; HEALTH; YOUTH
AB The research on immigrant integration is a dynamic interdisciplinary domain with rich and diverse literature. Considering the numerous studies in this domain, it is valuable to provide a comprehensive mapping to understand the research landscape resource and facilitate collaborations. This article combines Structural Topic modeling with bibliometric analysis to identify key research topics on immigrant integration. Applying these methods to 70890 abstracts published between 1960 and 2022, we identified 30 key research topics. We also tracked their prevalence over time, correlations and distributions within institutions and countries, funding effects, and countries' collaborations. The results indicate that the most discussed topics are integration theory, economic integration, education, residential segregation, integration policy, language, religious diversity, cultural participation, identity & belonging, racism & discrimination, political participation, health & welfare, research methods, demographic issues, gender & violence. Moreover, some of these topics were highly prominent in earlier periods and nearly non-existent in later years, while others emerged only recently. Specific topics maintained consistent significance over time. Analyses of correlations and trends reveal clusters of topics and diverse distributions across countries and research institutions. The implications of these results can benefit researchers, helping them better understand the current state of research and design future research projects. The finding could also help stakeholders in migration and integration governance and funding agencies to guide policies regarding the integration of immigrants.
C1 [Djogbenou, Robert; Lardoux, Solene] Univ Montreal, Dept Demog, Montreal, PQ H3C 3J7, Canada.
   [Adjiwanou, Visseho] Univ Quebec & Montreal, Dept Sociol, Montreal, PQ H2L 2C4, Canada.
C3 Universite de Montreal; University of Quebec; University of Quebec
   Montreal
RP Djogbenou, R (corresponding author), Univ Montreal, Dept Demog, Montreal, PQ H3C 3J7, Canada.
EM yao.robert.djogbenou@umontreal.ca
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NR 70
TC 0
Z9 0
U1 2
U2 2
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1488-3473
EI 1874-6365
J9 J INT MIGR INTEGR
JI J. Int. Migr. Integr.
PD 2024 APR 3
PY 2024
DI 10.1007/s12134-024-01139-8
EA APR 2024
PG 28
WC Demography
WE Emerging Sources Citation Index (ESCI)
SC Demography
GA MV2L6
UT WOS:001196345000002
DA 2024-09-05
ER
PT C
AU Luo, FH
   Zheng, H
   Erdt, M
   Raamkumar, AS
   Theng, YL
AF Luo, Feiheng
   Zheng, Han
   Erdt, Mojisola
   Raamkumar, Aravind Sesagiri
   Theng, Yin-Leng
BE Erdt, M
   Raamkumar, AS
   Rasmussen, E
   Theng, YL
TI A Comparative Investigation on Citation Counts and Altmetrics Between
   Papers Authored by Top Universities and Companies in the Research Field
   of Artificial Intelligence
SO ALTMETRICS FOR RESEARCH OUTPUTS MEASUREMENT AND SCHOLARLY INFORMATION
   MANAGEMENT
SE Communications in Computer and Information Science
LA English
DT Proceedings Paper
CT International Workshop on Altmetrics for Research Outputs Measurement
   and Scholarly Information Management (AROSIM)
CY JAN 26, 2018
CL Nanyang Technol Univ Singapore, Wee Kim Wee Sch Commun & Informat,
   Singapore, SINGAPORE
HO Nanyang Technol Univ Singapore, Wee Kim Wee Sch Commun & Informat
DE Citation analysis; Altmetrics; Industrial research; Academic research
ID NEURAL-NETWORKS
AB Artificial Intelligence is currently a popular research field. With the development of deep learning techniques, researchers in this area have achieved impressive results in a variety of tasks. In this initial study, we explored scientific papers in Artificial Intelligence, making comparisons between papers authored by the top universities and companies from the dual perspectives of bibliometrics and altmetrics. We selected publication venues according to the venue rankings provided by Google Scholar and Scopus, and retrieved related papers along with their citation counts from Scopus. Altmetrics such as Altmetric Attention Scores and Mendeley reader counts were collected from Altmetric.com and PlumX. Top universities and companies were identified, and the retrieved papers were classified into three groups accordingly: university-authored papers, company-authored papers, and co-authored papers. Comparative results showed that university-authored papers received slightly higher citation counts than company-authored papers, while company-authored papers gained considerably more attention online. In addition, when we focused on the most impactful papers, i.e., the papers with the highest numbers of citation counts, and the papers with the largest amount of online attention, companies seemed to make a larger contribution by publishing more impactful papers than universities.
C1 [Luo, Feiheng; Zheng, Han; Erdt, Mojisola; Raamkumar, Aravind Sesagiri; Theng, Yin-Leng] Nanyang Technol Univ, Wee Kim Wee Sch Commun & Informat, Singapore 637718, Singapore.
C3 Nanyang Technological University
RP Raamkumar, AS (corresponding author), Nanyang Technol Univ, Wee Kim Wee Sch Commun & Informat, Singapore 637718, Singapore.
EM fhluo@ntu.edu.sg; zhenghan@ntu.edu.sg; mojisola.erdt@ntu.edu.sg;
   aravind0002@ntu.edu.sg; tyltheng@ntu.edu.sg
RI Zheng, Han/AAD-6949-2020; Sesagiri Raamkumar, Aravind/G-9502-2017
OI Zheng, Han/0000-0003-4032-4299; Sesagiri Raamkumar,
   Aravind/0000-0001-7200-7787
FU National Research Foundation, Prime Minister's Office, Singapore under
   its Science of Research, Innovation and Enterprise programme (SRIE)
   [NRF2014-NRF-SRIE001-019]
FX This research is supported by the National Research Foundation, Prime
   Minister's Office, Singapore under its Science of Research, Innovation
   and Enterprise programme (SRIE Award No. NRF2014-NRF-SRIE001-019). We
   also thank Altmetric.com for providing access to Fetch API.
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NR 10
TC 1
Z9 1
U1 0
U2 9
PU SPRINGER-VERLAG SINGAPORE PTE LTD
PI SINGAPORE
PA 152 BEACH ROAD, #21-01/04 GATEWAY EAST, SINGAPORE, 189721, SINGAPORE
SN 1865-0929
EI 1865-0937
BN 978-981-13-1053-9; 978-981-13-1052-2
J9 COMM COM INF SC
PY 2018
VL 856
BP 105
EP 114
DI 10.1007/978-981-13-1053-9_9
PG 10
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods; Information Science & Library Science
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Information Science & Library Science
GA BP2HH
UT WOS:000542570100009
OA Green Accepted
DA 2024-09-05
ER
PT J
AU Yahia, Y
   Lopes, JC
   Lopes, RP
AF Yahia, Youssef
   Lopes, Julio Castro
   Lopes, Rui Pedro
TI Computer Vision Algorithms for 3D Object Recognition and Orientation: A
   Bibliometric Study
SO ELECTRONICS
LA English
DT Article
DE 3D object; object detection; object orientation; bibliometric analysis
ID NETWORK
AB This paper consists of a bibliometric study that covers the topic of 3D object detection from 2022 until the present day. It employs various analysis approaches that shed light on the leading authors, affiliations, and countries within this research domain alongside the main themes of interest related to it. The findings revealed that China is the leading country in this domain given the fact that it is responsible for most of the scientific literature as well as being a host for the most productive universities and authors in terms of the number of publications. China is also responsible for initiating a significant number of collaborations with various nations around the world. The most basic theme related to this field is deep learning, along with autonomous driving, point cloud, robotics, and LiDAR. The work also includes an in-depth review that underlines some of the latest frameworks that took on various challenges regarding this topic, the improvement of object detection from point clouds, and training end-to-end fusion methods using both camera and LiDAR sensors, to name a few.
C1 [Yahia, Youssef; Lopes, Julio Castro; Lopes, Rui Pedro] Inst Politecn Braganca, Res Ctr Digitalizat & Intelligent Robot CeDRI, P-5300252 Braganca, Portugal.
C3 Instituto Politecnico de Braganca
RP Yahia, Y (corresponding author), Inst Politecn Braganca, Res Ctr Digitalizat & Intelligent Robot CeDRI, P-5300252 Braganca, Portugal.
EM youssefyahia@ipb.pt; juliolopes@ipb.pt; rlopes@ipb.pt
RI Al-obaidi, Abdullah Thair/P-8487-2017; Lopes, Rui Pedro/A-1947-2010
OI Al-obaidi, Abdullah Thair/0000-0002-9971-5895; Lopes, Rui
   Pedro/0000-0002-9170-5078; Castro Lopes, Julio/0000-0003-3354-8956; bel
   haj yahia, youssef/0009-0009-4880-6218
FU Foundation for Science and Technology (FCT, Portugal) [UIDB/05757/2020,
   UIDP/05757/2020, LA/P/0007/2021]
FX This research was funded by the Foundation for Science and Technology
   (FCT, Portugal) for financial support through national funds FCT/MCTES
   (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC
   (LA/P/0007/2021).
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NR 29
TC 1
Z9 1
U1 2
U2 4
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2079-9292
J9 ELECTRONICS-SWITZ
JI Electronics
PD OCT
PY 2023
VL 12
IS 20
AR 4218
DI 10.3390/electronics12204218
PG 16
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Physics
GA W2JL4
UT WOS:001089943900001
OA Green Published, gold
DA 2024-09-05
ER
PT J
AU Corvi, JO
   McKitrick, A
   Fernández, JM
   Fuenteslópez, CV
   Gelpí, JL
   Ginebra, MP
   Capella-Gutierrez, S
   Hakimi, O
AF Corvi, Javier O.
   McKitrick, Austin
   Fernandez, Jose M.
   Fuenteslopez, Carla V.
   Gelpi, Josep L.
   Ginebra, Maria-Pau
   Capella-Gutierrez, Salvador
   Hakimi, Osnat
TI DEBBIE: The Open Access Database of Experimental Scaffolds and
   Biomaterials Built Using an Automated Text Mining Pipeline
SO ADVANCED HEALTHCARE MATERIALS
LA English
DT Article
DE biomaterials; databases; natural language processing; text mining;
   tissue scaffolds
AB Biomaterials research output has experienced an exponential increase over the last three decades. The majority of research is published in the form of scientific articles and is therefore available as unstructured text, making it a challenging input for computational processing. Computational tools are becoming essential to overcome this information overload. Among them, text mining systems present an attractive option for the automated extraction of information from text documents into structured datasets. This work presents the first automated system for biomaterial related information extraction from the National Library of Medicine's premier bibliographic database (MEDLINE) research abstracts into a searchable database. The system is a text mining pipeline that periodically retrieves abstracts from PubMed and identifies research and clinical studies of biomaterials. Thereafter, the pipeline identifies sixteen concept types of interest in the abstract using the Biomaterials Annotator, a tool for biomaterials Named Entity Recognition (NER). These concepts of interest, along with the abstract and relevant metadata are then deposited in DEBBIE, the Database of Experimental Biomaterials and their Biological Effect. DEBBIE is accessible through a web application that provides keyword searches and displays results in an intuitive and meaningful manner, aiming to facilitate an efficient mapping and organization of biomaterials information.
C1 [Corvi, Javier O.; McKitrick, Austin; Fernandez, Jose M.; Capella-Gutierrez, Salvador] Barcelona Supercomp Ctr BSC, Barcelona 08034, Spain.
   [Fuenteslopez, Carla V.] Univ Oxford, Inst Biomed Engn, Botnar Res Ctr, Oxford OX3 7LD, England.
   [Gelpi, Josep L.] Univ Barcelona, Dept Biochem & Mol Biol, Barcelona 08014, Spain.
   [Ginebra, Maria-Pau] Tech Univ Catalonia, Dept Mat Sci & Engn, Barcelona 08222, Spain.
   [Hakimi, Osnat] Univ Int Catalunya, Fac Med & Hlth Sci, Barcelona 08017, Spain.
C3 Universitat Politecnica de Catalunya; Barcelona Supercomputer Center
   (BSC-CNS); University of Oxford; University of Barcelona; Universitat
   Politecnica de Catalunya; Universitat Internacional de Catalunya (UIC)
RP Capella-Gutierrez, S (corresponding author), Barcelona Supercomp Ctr BSC, Barcelona 08034, Spain.; Hakimi, O (corresponding author), Univ Int Catalunya, Fac Med & Hlth Sci, Barcelona 08017, Spain.
EM salvador.capella@bsc.es; Osnat@aMoon.fund
RI Ginebra, Maria-Pau/J-8149-2017; Capella-Gutierrez, Salvador/H-5053-2015;
   Fernandez Gonzalez, Jose Maria/N-5920-2014
OI Ginebra, Maria-Pau/0000-0002-4700-5621; Corvi,
   Javier/0000-0003-3241-3400; Capella-Gutierrez,
   Salvador/0000-0002-0309-604X; Hakimi, Osnat/0000-0002-8839-4846;
   Fuenteslopez, Carla Veronica/0000-0003-1469-2629; Fernandez Gonzalez,
   Jose Maria/0000-0002-4806-5140
FU Innovative Medicines Initiative two Joint Undertaking [777365];
   eTRANSAFE (Innovative Medicines Initiative) [777365]; eTRANSAFE
   (European Union's Horizon 2020 research and innovation programme);
   eTRANSAFE (EFPIA); European Union Horizon 2020 programme under the Marie
   Sklodowska-Curie [751277]; MSCA; Bosch-Aymerich fellowship; INB
   [PT17/0009/0001 - ISCIII-SGEFI / ERDF]; Generalitat de~Catalunya; Marie
   Curie Actions (MSCA) [751277] Funding Source: Marie Curie Actions (MSCA)
FX J.C. was partly supported by eTRANSAFE (received funding from the
   Innovative Medicines Initiative two Joint Undertaking under grant
   agreement no 777365 and support from the European Union's Horizon 2020
   research and innovation programme and EFPIA). This project had received
   funding from the European Union Horizon 2020 programme under the Marie
   Sklodowska-Curie grant agreement DEBBIE (project number: 751277). O.H.
   was funded through an MSCA and a Bosch-Aymerich fellowship. J-M.F,
   S.C-G, and J-L.P. were partly supported by INB Grant (PT17/0009/0001 -
   ISCIII-SGEFI / ERDF). M-P.G. acknowledges the ICREA Academia Award from
   Generalitat de & nbsp;Catalunya.
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NR 39
TC 4
Z9 4
U1 0
U2 10
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 2192-2640
EI 2192-2659
J9 ADV HEALTHC MATER
JI Adv. Healthc. Mater.
PD OCT 6
PY 2023
VL 12
IS 25
DI 10.1002/adhm.202300150
EA AUG 2023
PG 13
WC Engineering, Biomedical; Nanoscience & Nanotechnology; Materials
   Science, Biomaterials
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Science & Technology - Other Topics; Materials Science
GA IT7I6
UT WOS:001045313100001
PM 37563883
DA 2024-09-05
ER
PT J
AU Abbas, NN
   Ahmed, T
   Shah, SHU
   Omar, M
   Park, HW
AF Abbas, Naveed Naeem
   Ahmed, Tanveer
   Shah, Syed Habib Ullah
   Omar, Muhammad
   Park, Han Woo
TI Investigating the applications of artificial intelligence in cyber
   security
SO SCIENTOMETRICS
LA English
DT Article
DE Artificial intelligence; Cyber security; Scientometric; Visualization;
   Emerging trend; Research hotspot
AB Artificial Intelligence (AI) provides instant insights to pierce through the noise of thousands of daily security alerts. The recent literature focuses on AI's application to cyber security but lacks visual analysis of AI applications. Structural changes have been observed in cyber security since the emergence of AI. This study promotes the development of theory about AI in cyber security, helps researchers establish research directions, and provides a reference that enterprises and governments can use to plan AI applications in the cyber security industry. Many countries, institutions and authors are densely connected through collaboration and citation networks. Artificial neural networks, an AI technique, gave birth to today's research on cloud cyber security. Many research hotspots such as those on face recognition and deep neural networks for speech recognition may create future hotspots on emerging technology, such as on artificial intelligence systems for security. This study visualizes the structural changes, hotspots and emerging trends in AI studies. Five evaluation factors are used to judge the hotspots and trends of this domain and a heat map is used to identify the areas of the world that are generating research on AI applications in cyber security. This study is the first to provide an overall perspective of hotspots and trends in the research on AI in the cyber security domain.
C1 [Abbas, Naveed Naeem; Shah, Syed Habib Ullah; Omar, Muhammad] Islamia Univ Bahawalpur, Dept Comp Sci & IT, Bahawalpur, Pakistan.
   [Abbas, Naveed Naeem] H 39-A Jamal E Sarwar Colony, Chowk Churratah, Dera Ghazi Khan, Pakistan.
   [Ahmed, Tanveer] COMSATS Univ, Dept Comp Sci, Islamabad, Pakistan.
   [Shah, Syed Habib Ullah] H 2147,Block 18, Coll Chowk, Dera Ghazi Khan, Pakistan.
   [Park, Han Woo] Yeungnam Univ, Interdisciplinary Program Digital Convergence Bus, Dept Media & Commun, 214-1 Dae Dong, Gyongsan 712749, Gyeongsangbuk D, South Korea.
C3 Islamia University of Bahawalpur; COMSATS University Islamabad (CUI);
   Yeungnam University
RP Omar, M (corresponding author), Islamia Univ Bahawalpur, Dept Comp Sci & IT, Bahawalpur, Pakistan.; Park, HW (corresponding author), Yeungnam Univ, Interdisciplinary Program Digital Convergence Bus, Dept Media & Commun, 214-1 Dae Dong, Gyongsan 712749, Gyeongsangbuk D, South Korea.
EM naveednaeemabbas@gmail.com; tanveerahmed@comsats.edu.pk;
   syedhabib7779@gmail.com; m.omar.nazeer@gmail.com; hanpark@ynu.ac.kr
RI Park, Han Woo/F-4051-2011; Abbas, Naveed/JAV-9478-2023
OI Park, Han Woo/0000-0002-1378-2473; Abbas, Naveed/0000-0003-1204-250X;
   omar, muhammad/0000-0002-7071-5760
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NR 38
TC 19
Z9 21
U1 18
U2 175
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD NOV
PY 2019
VL 121
IS 2
BP 1189
EP 1211
DI 10.1007/s11192-019-03222-9
PG 23
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA JF2FJ
UT WOS:000491201000025
DA 2024-09-05
ER
PT J
AU Heradio, R
   Fernandez-Amoros, D
   Cerrada, C
   Cobo, MJ
AF Heradio, Ruben
   Fernandez-Amoros, David
   Cerrada, Cristina
   Cobo, Manuel J.
TI Group Decision-Making Based on Artificial Intelligence: A Bibliometric
   Analysis
SO MATHEMATICS
LA English
DT Article
DE group decision-making; consensus decision-making; artificial
   intelligence; bibliometrics; science mapping
ID CO-WORD ANALYSIS; CONSENSUS MODEL; ALLOCATION; NETWORK; INFORMATION;
   SELECTION; MAKERS
AB Decisions concerning crucial and complicated problems are seldom made by a single person. Instead, they require the cooperation of a group of experts in which each participant has their own individual opinions, motivations, background, and interests regarding the existing alternatives. In the last 30 years, much research has been undertaken to provide automated assistance to reach a consensual solution supported by most of the group members. Artificial intelligence techniques are commonly applied to tackle critical group decision-making difficulties. For instance, experts' preferences are often vague and imprecise; hence, their opinions are combined using fuzzy linguistic approaches. This paper reports a bibliometric analysis of the ample literature published in this regard. In particular, our analysis: (i) shows the impact and upswing publication trend on this topic; (ii) identifies the most productive authors, institutions, and countries; (iii) discusses authors' and journals' productivity patterns; and (iv) recognizes the most relevant research topics and how the interest on them has evolved over the years.
C1 [Heradio, Ruben; Fernandez-Amoros, David; Cerrada, Cristina] Univ Nacl Educ Distancia UNED, Dept Comp Syst & Software Engn, Madrid 28040, Spain.
   [Cobo, Manuel J.] Univ Cadiz, Dept Comp Sci & Engn, Cadiz 11519, Spain.
C3 Universidad Nacional de Educacion a Distancia (UNED); Universidad de
   Cadiz
RP Heradio, R; Fernandez-Amoros, D; Cerrada, C (corresponding author), Univ Nacl Educ Distancia UNED, Dept Comp Syst & Software Engn, Madrid 28040, Spain.; Cobo, MJ (corresponding author), Univ Cadiz, Dept Comp Sci & Engn, Cadiz 11519, Spain.
EM rheradio@issi.uned.es; david@issi.uned.es; ccerrada01@gmail.com;
   manueljesus.cobo@uca.es
RI Fernandez-Amoros, David/ABG-7972-2020; Cobo Martí­n, Manuel
   Jesús/C-5581-2011; Heradio, Ruben/D-3675-2013
OI Fernandez-Amoros, David/0000-0003-3758-0195; Cobo Martí­n, Manuel
   Jesús/0000-0001-6575-803X; Heradio, Ruben/0000-0002-7131-0482
FU Spanish Ministry of Science, Innovation and Universities
   [DPI2016-77677-P, PID2019-105381GA-I00 (iScience)]; Community of Madrid,
   under the research network CAM [RoboCity2030 S2013/MIT-2748]
FX This work has been supported by (i) the Spanish Ministry of Science,
   Innovation and Universities, under grants with reference DPI2016-77677-P
   and PID2019-105381GA-I00 (iScience), and (ii) the Community of Madrid,
   under the research network CAM RoboCity2030 S2013/MIT-2748.
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NR 61
TC 7
Z9 7
U1 3
U2 42
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2227-7390
J9 MATHEMATICS-BASEL
JI Mathematics
PD SEP
PY 2020
VL 8
IS 9
AR 1566
DI 10.3390/math8091566
PG 20
WC Mathematics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Mathematics
GA OF7XA
UT WOS:000581414200001
OA Green Submitted, gold
DA 2024-09-05
ER
PT J
AU Meyers, SD
   Azevedo, L
   Luther, ME
AF Meyers, Steven D.
   Azevedo, Laura
   Luther, Mark E.
TI A Scopus-based bibliometric study of maritime research involving the
   Automatic Identification System
SO TRANSPORTATION RESEARCH INTERDISCIPLINARY PERSPECTIVES
LA English
DT Article
DE Automatic Identification System; Maritime data; Artificial intelligence;
   Maritime policy; Scientific collaboration; Research trends
ID ARTIFICIAL-INTELLIGENCE; AIS DATA; TIME; COLLABORATION; TRAJECTORIES;
   PREDICTION; LOGISTICS; NETWORKS; PATTERNS; SCIENCE
AB Vessel traffic records from the Automatic Identification System (AIS) are a useful source of information for maritime data analytics, and of training data for maritime artificial intelligence systems. Researchers utilizing these data are developing the foundations for operational maritime tools essential to economic expansion and security. The global growth and distribution of this research effort from 1997 to 2019 was examined through a bibliometric study of 817 Scopus-listed publications. Indications of both collaboration and accelerating competition were found by examining the number of publications and authors, national and institutional affiliations of the authors, and number of citations received. Prior to 2003 about 1-5 publications per year appeared in the literature. The annual number of publications has doubled roughly every 5 years since the mid-2000s, reaching 140 in 2019. About 82% of publications were by authors based in a single country. Overall, authors affiliated with China contributed to 27% of all publications, followed by the US (9%) and Italy (8%). Authors from EU countries, taken collectively, were most common (37%). From 2016 to 2019 the number of authors from China quadrupled, and the number of publications with at least one China-affiliated author quintupled, producing about 39% of all publications in that time period. Some policy questions arising from this study are presented, and the need for continuing international collaboration and cooperative development are discussed.
C1 [Meyers, Steven D.; Azevedo, Laura; Luther, Mark E.] Univ S Florida, Coll Marine Sci, Ctr Maritime & Port Studies, St Petersburg, FL 33701 USA.
C3 State University System of Florida; University of South Florida
RP Meyers, SD (corresponding author), Univ S Florida, Coll Marine Sci, Ctr Maritime & Port Studies, St Petersburg, FL 33701 USA.
EM smeyers@usf.edu
RI Meyers, Steven/I-9170-2014
OI Meyers, Steven/0000-0003-1592-9050; C Azevedo, Laura/0000-0002-6732-8612
FU Southeast Coastal Ocean Observing Regional Association [IOOS.16 (028)
   USF.ML.OBS.1]; Gulf of Mexico Coastal Ocean Observing System
   [02-S160275]; Greater Tampa Bay Marine Advisory Council-PORTS, Inc.
   [2500-1066-00]; Tampa Bay Estuary Program [6911]; USF College of Marine
   Science Von Rosenstiel Fellowship
FX This effort received partial support from the Southeast Coastal Ocean
   Observing Regional Association (Sub Award #IOOS.16 (028) USF.ML.OBS.1) ,
   the Gulf of Mexico Coastal Ocean Observing System (Award #02-S160275) ,
   the Greater Tampa Bay Marine Advisory Council-PORTS, Inc. (Award
   #2500-1066-00) and the Tampa Bay Estuary Program (PO#6911) .
   https://marinecadastre.gov/ais/Author L.A. was supported by the USF
   College of Marine Science Von Rosenstiel Fellowship. USF librarian
   Theresa Burress helped with the initial Scopus search. Undergraduate
   researchers Ayden Muellier and Ste-fanie Barbulescu assisted with the
   Scopus search, and undergraduate assistant Gerald Meyers created the
   network figures.
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NR 62
TC 10
Z9 10
U1 3
U2 4
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2590-1982
J9 TRANSP RES INTERDISC
JI Transp. Res. Interdiscip. Perspect.
PD JUN
PY 2021
VL 10
AR 100387
DI 10.1016/j.trip.2021.100387
PG 10
WC Transportation
WE Emerging Sources Citation Index (ESCI)
SC Transportation
GA W1GY9
UT WOS:001089193200010
OA Green Published, gold
DA 2024-09-05
ER
PT J
AU Arencibia-Jorge, R
   Vega-Almeida, RL
   Jiménez-Andrade, JL
   Carrillo-Calvet, H
AF Arencibia-Jorge, Ricardo
   Lidia Vega-Almeida, Rosa
   Luis Jimenez-Andrade, Jose
   Carrillo-Calvet, Humberto
TI Evolutionary stages and multidisciplinary nature of artificial
   intelligence research
SO SCIENTOMETRICS
LA English
DT Article
DE Artificial intelligence; Scientific production; Multidisciplinarity;
   Bibliometric indicators; Thematic dispersion index
ID SCIENTIFIC DISCIPLINE; SCIENCE FIELDS; INTERDISCIPLINARITY; INDICATORS;
   DIVERSITY; DOMAIN; MAPS; TECHNOLOGY; KNOWLEDGE; INDEXES
AB This paper analyzed the growth and multidisciplinary nature of Artificial Intelligence research during the last 60 years. Web of Science coverage since 1960 was considered, and a descriptive research was performed. A top-down approach using Web of Science subject categories as a proxy to measure multidisciplinarity was developed. Bibliometric indicators based on the core of subject categories involving articles and citing articles related to this area were applied. The data analysis within a historical and epistemological perspective allowed to identify three main evolutionary stages: an emergence period (1960-1979), based on foundational literature from 1950s; a re-emergence and consolidation period (1980-2009), involving a "paradigmatic" phase of development and first industrial approach; and a period of re-configuration of the discipline as a technoscience (2010-2019), where an explosion of solutions for productive systems, wide collaboration networks and multidisciplinary research projects were observed. The multidisciplinary dynamics of the field was analyzed using a Thematic Dispersion Index. This indicator clearly described the transition from the consolidation stage to the re-configuration of the field, finding application in a wide diversity of scientific and technological domains. The results demonstrated that epistemic changes and qualitative leaps in Artificial Intelligence research have been associated to variations in multidisciplinarity patterns.
C1 [Arencibia-Jorge, Ricardo] Univ Nacl Autonoma Mexico, Complex Sci Ctr C3, Mexico City 04510, DF, Mexico.
   [Lidia Vega-Almeida, Rosa] BioCubaFarma, Empresa Tecnol Informac ETI, Havana, Cuba.
   [Luis Jimenez-Andrade, Jose] Univ Nacl Autonoma Mexico, Fac Sci, Mexico City 04510, DF, Mexico.
   [Carrillo-Calvet, Humberto] Univ Nacl Autonoma Mexico, Fac Sci, Complex Sci Ctr C3, Mexico City 04510, DF, Mexico.
C3 Universidad Nacional Autonoma de Mexico; Universidad Nacional Autonoma
   de Mexico; Universidad Nacional Autonoma de Mexico
RP Arencibia-Jorge, R (corresponding author), Univ Nacl Autonoma Mexico, Complex Sci Ctr C3, Mexico City 04510, DF, Mexico.
EM ricardo.arencibia@c3.unam.mx; vegaalmeida.rosa@gmail.com;
   jlja@ciencias.unam.mx; humberto.carrillo@c3.unam.mx
RI Vega-Almeida, Rosa Lidia/KDP-2481-2024; CARRILLO CALVET,
   HUMBERTO/ITW-2657-2023; Carrillo Calvet, Humberto/E-2265-2012;
   Jiménez-Andrade, José-Luis Luis Jiménez/T-1666-2018; Arencibia-Jorge,
   Ricardo/AAK-3567-2020
OI Vega-Almeida, Rosa Lidia/0000-0003-4203-6207; Carrillo Calvet,
   Humberto/0000-0003-3659-6769; Jiménez-Andrade, José-Luis Luis
   Jiménez/0000-0003-3453-7159; Arencibia-Jorge,
   Ricardo/0000-0001-8907-2454
FU program "Scientometrics, Complexity, and Science of Science", at the
   Complexity Science Center of the National Autonomous University of
   Mexico (UNAM)
FX This research was supported by the program "Scientometrics, Complexity,
   and Science of Science", at the Complexity Science Center of the
   National Autonomous University of Mexico (UNAM). We would like to thank
   Dr. Javier Garcia-Garcia for reviewing an earlier version of this
   article.
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NR 98
TC 3
Z9 3
U1 13
U2 50
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD SEP
PY 2022
VL 127
IS 9
BP 5139
EP 5158
DI 10.1007/s11192-022-04477-5
EA AUG 2022
PG 20
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 4L1UW
UT WOS:000841043900001
DA 2024-09-05
ER
PT J
AU Chen, XL
   Tao, XH
   Wang, FL
   Xie, HR
AF Chen, Xieling
   Tao, Xiaohui
   Wang, Fu Lee
   Xie, Haoran
TI Global research on artificial intelligence-enhanced human
   electroencephalogram analysis
SO NEURAL COMPUTING & APPLICATIONS
LA English
DT Article
DE Artificial intelligence technologies; Human brain; Electroencephalogram;
   Bibliometrics; Research topics; Visualization
ID EPILEPTIC SEIZURE DETECTION; EMPIRICAL MODE DECOMPOSITION; DISCRETE
   WAVELET TRANSFORM; EEG SIGNAL CLASSIFICATION; FEATURE-EXTRACTION;
   EMOTION RECOGNITION; NEURAL-NETWORKS; FEATURE-SELECTION; AUTOMATED
   IDENTIFICATION; FRONTOTEMPORAL DEMENTIA
AB The application of artificial intelligence (AI) technologies in assisting human electroencephalogram (EEG) analysis has become an active scientific field. This study aims to present a comprehensive review of the research field of AI-enhanced human EEG analysis. Using bibliometrics and topic modeling, research articles concerning AI-enhanced human EEG analysis collected from the Web of Science database during the period 2009-2018 were analyzed. After examining 2053 research articles published around the world, it was found that the annual number of articles had significantly grown from 78 to 468, with the USA and China being the most influential and prolific. The results of the keyword analysis showed that "electroencephalogram," "brain-computer interface," "classification," "support vector machine," "electroencephalography," and "signal" were the most frequently used. The results of topic modeling and evolution analyses highlighted several important issues, including epileptic seizure detection, brain-machine interface, EEG classification, mental disorders, emotion, and alcoholism and anesthesia. The findings suggest that such visualization and analysis of the research articles could provide a comprehensive overview of the field for communities of practice and inquiry worldwide.
C1 [Chen, Xieling] Educ Univ Hong Kong, Dept Math & Informat Technol, Tai Po, Hong Kong, Peoples R China.
   [Tao, Xiaohui] Univ Southern Queensland, Sch Sci, Toowoomba, Qld, Australia.
   [Wang, Fu Lee] Open Univ Hong Kong, Sch Sci & Technol, Ho Man Tin, Hong Kong, Peoples R China.
   [Xie, Haoran] Lingnan Univ, Dept Comp & Decis Sci, Tuen Mun, Hong Kong, Peoples R China.
C3 Education University of Hong Kong (EdUHK); University of Southern
   Queensland; Hong Kong Metropolitan University; Lingnan University
RP Wang, FL (corresponding author), Open Univ Hong Kong, Sch Sci & Technol, Ho Man Tin, Hong Kong, Peoples R China.
EM xielingchen0708@gmail.com; Xiaohui.Tao@usq.edu.au; pwang@ouhk.edu.hk;
   hrxie2@gmail.com
RI Wang, Fu Lee/AAD-9782-2021; Xie, Haoran/AFS-3515-2022; tao,
   xiaohui/KCK-2677-2024; Tao, Xiaohui/JKI-2330-2023
OI Wang, Fu Lee/0000-0002-3976-0053; Xie, Haoran/0000-0003-0965-3617; PV,
   THAYYIB/0000-0001-8929-0398
FU HKIBS Research Seed Fund 2019/20 [190-009]; Research Seed Fund [102367];
   LEO Dr David P. Chan Institute of Data Science of Lingnan University,
   Hong Kong
FX This work was supported by the HKIBS Research Seed Fund 2019/20
   (190-009), the Research Seed Fund (102367), and LEO Dr David P. Chan
   Institute of Data Science of Lingnan University, Hong Kong.
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NR 282
TC 26
Z9 31
U1 7
U2 86
PU SPRINGER LONDON LTD
PI LONDON
PA 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND
SN 0941-0643
EI 1433-3058
J9 NEURAL COMPUT APPL
JI Neural Comput. Appl.
PD JUL
PY 2022
VL 34
IS 14
SI SI
BP 11295
EP 11333
DI 10.1007/s00521-020-05588-x
EA JAN 2021
PG 39
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 2Y7TS
UT WOS:000605918300007
DA 2024-09-05
ER
PT J
AU Chen, XL
   Zou, D
   Xie, HR
AF Chen, Xieling
   Zou, Di
   Xie, Haoran
TI A decade of learning analytics: Structural topic modeling based
   bibliometric analysis
SO EDUCATION AND INFORMATION TECHNOLOGIES
LA English
DT Article
DE Learning analytics; Research topics; Topic evolution; Structural topic
   modeling; Social network analysis
ID HIGHER-EDUCATION; PERFORMANCE; CLASSIFICATION; TECHNOLOGIES;
   SATISFACTION; CHALLENGES; PACKAGE; SYSTEMS
AB Learning analytics (LA) has become an increasingly active field focusing on leveraging learning process data to understand and improve teaching and learning. With the explosive growth in the number of studies concerning LA, it is significant to investigate its research status and trends, particularly the thematic structure. Based on 3900 LA articles published during the past decade, this study explores answers to questions such as "what research topics were the LA community interested in?" and "how did such research topics evolve?" by adopting structural topic modeling and bibliometrics. Major publication sources, countries/regions, institutions, and scientific collaborations were examined and visualized. Based on the analyses, we present suggestions for future LA research and discussions about important topics in the field. It is worth highlighting LA combining various innovative technologies (e.g., visual dashboards, neural networks, multimodal technologies, and open learner models) to support classroom orchestration, personalized recommendation/feedback, self-regulated learning in flipped classrooms, interaction in game-based and social learning. This work is useful in providing an overview of LA research, revealing the trends in LA practices, and suggesting future research directions.
C1 [Chen, Xieling] Educ Univ Hong Kong, Dept Math & Informat Technol, 10 Ping Rd, Hong Kong, Peoples R China.
   [Zou, Di] Educ Univ Hong Kong, Dept English Language Educ, 10 Ping Rd, Hong Kong, Peoples R China.
   [Xie, Haoran] Lingnan Univ, Dept Comp & Decis Sci, Hong Kong, Peoples R China.
C3 Education University of Hong Kong (EdUHK); Education University of Hong
   Kong (EdUHK); Lingnan University
RP Zou, D (corresponding author), Educ Univ Hong Kong, Dept English Language Educ, 10 Ping Rd, Hong Kong, Peoples R China.
EM xielingchen0708@gmail.com; dizoudaisy@gmail.com; hrxie2@gmail.com
RI Xie, Haoran/AFS-3515-2022; Xie, Haoran/AAW-8845-2020
OI Xie, Haoran/0000-0003-0965-3617; PV, THAYYIB/0000-0001-8929-0398; ZOU,
   Di/0000-0001-8435-9739
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NR 103
TC 13
Z9 13
U1 12
U2 88
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1360-2357
EI 1573-7608
J9 EDUC INF TECHNOL
JI Educ. Inf. Technol.
PD SEP
PY 2022
VL 27
IS 8
BP 10517
EP 10561
DI 10.1007/s10639-022-11046-z
EA APR 2022
PG 45
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA 5N0UV
UT WOS:000784393100001
DA 2024-09-05
ER
PT J
AU Ogrean, C
AF Ogrean, Claudia
TI Interplays Between Artificial Intelligence and Sustainability in
   Business/Management. A Bibliometric Analysis
SO STUDIES IN BUSINESS AND ECONOMICS
LA English
DT Article
DE artificial intelligence; bibliometric analysis; business/management;
   sustainability; twin transition.
ID BIG DATA; DIGITAL TRANSFORMATION; CIRCULAR ECONOMY; SUPPLY CHAIN; SMART;
   MANAGEMENT; BLOCKCHAIN; BUSINESS; FUTURE; CHALLENGES
AB The paper aims to identify the main research (threads and) trends and evaluate the relationships between (and the impact of) the publications/articles investigating the interplays between artificial intelligence (AI) and sustainability against a business or management related context. To reach this objective, 863 articles from Web of Science Core Collection were analyzed, using VOSviewer as a bibliometric tool. Performance analysis was employed to mainly explore the interest and popularity of the topic, assess the main interest areas and fields of both the sources and the publications, determine the most relevant SDGs for the topic, and identify the most popular journals hosting articles in the analyzed field. Science mapping was carried out to identify the most influential articles in the field, understand the antecedent topics/ideas (in the fields of AI and sustainability, respectively) contributing to the emergence of a new interest area at the intersection between AI and sustainability, appraise the current developments in the analyzed interest area, and discover new trends / areas for future research.
C1 [Ogrean, Claudia] Lucian Blaga Univ Sibiu, Sibiu, Romania.
C3 Lucian Blaga University of Sibiu
RP Ogrean, C (corresponding author), Lucian Blaga Univ Sibiu, Sibiu, Romania.
RI Zenn, Sebastian/KQU-0681-2024
FU Ministry of Research, Innovation and Digitization;  [28PFE / 30.12.2021]
FX This work is supported by the Ministry of Research, Innovation and
   Digitization through Program 1 -Development of the national
   research-development system, Subprogram 1.2 -Institutional
   performance-Projects for financing excellence in RDI, contract no. 28PFE
   / 30.12.2021.
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NR 91
TC 2
Z9 2
U1 18
U2 28
PU SCIENDO
PI WARSAW
PA BOGUMILA ZUGA 32A, WARSAW, MAZOVIA, POLAND
SN 1842-4120
EI 2344-5416
J9 STUD BUS ECON-ROM
JI Stud. Bus. Econ.
PD AUG 1
PY 2023
VL 18
IS 2
BP 336
EP 357
DI 10.2478/sbe-2023-0041
PG 22
WC Economics
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA S2YD9
UT WOS:001069869700021
OA gold
DA 2024-09-05
ER
PT J
AU Rojek, M
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   Palkij, Kacper
   Mielcarska, Sylwia
   Bartnikowska, Wiktoria
TI Exploring the performance of ChatGPT3.5 in addressing dermatological
   queries: a research investigation into AI capabilities
SO PRZEGLAD DERMATOLOGICZNY
LA English
DT Article
DE medical education; artificial intelligence; dermatology; venereology;
   ChatGPT-3.5
ID ARTIFICIAL-INTELLIGENCE
AB Introduction: In the 21 st century's era of rapid technological advancement, ChatGPT-3.5, an artificial intelligence (AI) language model, is scrutinized for its application in dermatology. Using 119 questions from the National Specialist Examination (PES), we assess ChatGPT-3.5's performance by comparing it to human skills and addressing ethical implications. Objective: Our primary aim is to evaluate ChatGPT-3.5's proficiency in responding to 119 dermatology questions from the PES. The study emphasizes ethical considerations and compares the model's knowledge and skills to those of human dermatologists. Material and methods: Utilizing the 2023 PES question database, questions were categorized by Bloom's taxonomy and thematic content. ChatGPT-3.5, version of 3 August 2023, answered 119 questions in five sessions, allowing for a probabilistic evaluation. Statistical analyses, conducted using R Studio, assessed correctness, confidence, and dif - ficulty. Results: ChatGPT-3.5 achieved a 49.58% correct response rate, below the 60% passing threshold. No significant differences in difficulty or correlations between difficulty and certainty were observed. Varied per - formance across question types highlighted strengths and weaknesses. Despite suboptimal results, ChatGPT-3.5's differential performance offers insights, suggesting future improvements. The study advocates for ongoing research into AI integration in dermatology, envisioning a promising role for AI in assisting dermatologists. Conclusions: Ethical considerations are crucial for effective AI intro - duction, minimizing errors, and enhancing dermatological healthcare quality, fostering optimism for AI's evolving role in dermatology.
C1 [Rojek, Marcin; Bielowka, Michal; Mitrega, Adam; Kaczynska, Dominika; Czogalik, Lukasz] Med Univ Siles, Dept Radiol & Nucl Med, Students Sci Assoc Comp Anal & Artificial Intellig, Katowice, Poland.
   [Kufel, Jakub] Med Univ Silesia, Dept Radiodiagnost Intervent Radiol & Nucl Med, Katowice, Poland.
   [Kufel, Jakub] Med Univ Siles, Dept Radiol & Nucl Med, Katowice, Poland.
   [Kondol, Dominika; Palkij, Kacper] Multispecialty Dist Hosp SA Dr B Hager Pyskowicka, Tarnowskie Gory, Poland.
   [Mielcarska, Sylwia] Med Univ Siles, Fac Med Sci Zabrze, Dept Med & Mol Biol, Katowice, Poland.
   [Bartnikowska, Wiktoria] Med Univ Siles, Fac Med Sci Katowice, Katowice, Poland.
C3 Medical University Silesia; Medical University Silesia; Medical
   University Silesia; Medical University Silesia; Medical University
   Silesia
RP Bielówka, M (corresponding author), Med Univ Siles, Dept Radiol & Nucl Med, Students Sci Assoc Comp Anal & Artificial Intellig, Katowice, Poland.
CR [Anonymous], Centrum Egzaminow Medycznych Internet
   [Anonymous], 2023, S4 wyniki jesiennego naboru na specjalizacje. Hitem m.in. radiologia, dermatologia i psychiatria
   [Anonymous], Introducing GPTs Internet
   [Anonymous], 2023, Centrum Egzaminow Medycznych
   [Anonymous], Introducing ChatGPT Internet
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   Foreland M., 2010, Emerging Perspectives on Learning, Teaching, and Technology Internet
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   Kufel J, 2023, POL J RADIOL, V88, pE430, DOI 10.5114/pjr.2023.131215
   Lewandowski M, 2023, CLIN EXP DERMATOL, V49, P686, DOI 10.1093/ced/llad255
   Passby L, 2023, CLIN EXP DERMATOL, V49, P722, DOI 10.1093/ced/llad197
   Patel S, 2021, CLIN DERMATOL, V39, P667, DOI 10.1016/j.clindermatol.2021.03.012
   Porter E, 2023, J EUR ACAD DERMATOL, V37, pE943, DOI 10.1111/jdv.19174
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NR 15
TC 0
Z9 0
U1 1
U2 1
PU TERMEDIA PUBLISHING HOUSE LTD
PI POZNAN
PA KLEEBERGA 2, POZNAN, 61-615, POLAND
SN 0033-2526
EI 2084-9893
J9 PRZ DERMATOL
JI Prz. Dermatol.
PY 2024
VL 111
IS 1
BP 26
EP 30
DI 10.5114/dr.2024.140796
PG 5
WC Dermatology
WE Emerging Sources Citation Index (ESCI)
SC Dermatology
GA XT4O5
UT WOS:001263918000003
OA gold
DA 2024-09-05
ER
PT J
AU Durmusoglu, A
AF Durmusoglu, Alptekin
TI A pre-assessment of past research on the topic of environmental-friendly
   electronics
SO JOURNAL OF CLEANER PRODUCTION
LA English
DT Article
DE Text mining; Environmental-friendly electronic; Technology forecasting;
   Bibliometrics; Singular value decomposition; K-means clustering
ID TEXT; TECHNOLOGY; EVOLUTION; TRENDS
AB Environmental-friendly products, processes, tools, methods and etc ... have been under the interest of both industry and academy. However, this interest has not been analyzed systematically for the academic studies. On the other hand, bibliometric analyses have been a widely used approach to measure and to analyze the interest of academic world on a certain topic. In this regard, this study intends to provide insight about the research on environmental-friendly electronic using the related literatures from the Thomson Reuters Web of Knowledge database during the period of 1980-2016. This study consists of two parts. In the first part, 7288 academic papers having the "environmental" and "electronic" phrases on "Title", "Abstract" or "Keywords" were retrieved and analyzed using bibliometric analysis methodology. These two adjectives has been selected on purpose since current studies on textual analysis indicate that phrase building most frequently starts after adjectives. In the second part of this work, Singular Value Decomposition (SVD) method, concept extraction and k-means clustering method was performed to gain more insight about the textual structure of the retrieved articles. Findings indicate that, approximately one third of publications were written by the authors addressing USA. It is also clarified that the topic was not in the agenda of researchers between 1980 and 1990. The number of publications on the area had significantly increased and had reached its peak in 2014. Text mining results showed that, the most important research focus was on "life-cycle" that was followed by "e-waste, sensor, recycling and solder" respectively. (C) 2016 Elsevier Ltd. All rights reserved.
C1 [Durmusoglu, Alptekin] Gaziantep Univ, Dept Ind Engn, TR-27310 Gaziantep, Turkey.
C3 Gaziantep University
RP Durmusoglu, A (corresponding author), Gaziantep Univ, Dept Ind Engn, TR-27310 Gaziantep, Turkey.
EM durmusoglu@gantep.edu.tr
RI , alptekind/J-8067-2019
OI , alptekind/0000-0001-9800-5747
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NR 49
TC 18
Z9 18
U1 1
U2 64
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0959-6526
EI 1879-1786
J9 J CLEAN PROD
JI J. Clean Prod.
PD AUG 15
PY 2016
VL 129
BP 305
EP 314
DI 10.1016/j.jclepro.2016.04.068
PG 10
WC Green & Sustainable Science & Technology; Engineering, Environmental;
   Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Engineering; Environmental Sciences
   & Ecology
GA DP0MM
UT WOS:000378183900029
DA 2024-09-05
ER
PT C
AU Cheung, SKS
AF Cheung, Simon K. S.
BE Li, R
   Cheung, SKS
   Iwasaki, C
   Kwok, LF
   Kageto, M
TI Implication on Perceived Usefulness of Open Educational Resources After
   a Rapid Switch to Online Learning Mode
SO BLENDED LEARNING: RE-THINKING AND RE-DEFINING THE LEARNING PROCESS, ICBL
   2021
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT 14th International Conference on Blended Learning (ICBL)
CY AUG 10-13, 2021
CL Nihon Fukushi Univ, ELECTR NETWORK
HO Nihon Fukushi Univ
DE Open educational resources; Open courseware; Online courses; Open access
   e-books; Learning tools; Online learning; Learning effectiveness
AB Evolved as open courseware, open online courses and tutorials, open access e-books and open source learning tools, open educational resources or OER are generally perceived as useful resources by university students. Over the last year, many higher education institutions have rapidly switched their usual classroom-based learning to online learning in order to accommodate the social distancing requirements arising from the outbreak of COVID-19. This paper investigates the implication on the students' perceived usefulness of OER after the switch to online learning. Based on two identical surveys conducted in a university in Hong Kong before and after the change, it is revealed that the students' perceived usefulness was generally increased, where the increase in perceived usefulness was more significant on open online complete courses, online tutorials, and open access e-books than other types of OER. It is also revealed that the students have become more aware of the shortcomings of OER, especially on accuracy and comprehensiveness. OER, especially open online courses, tutorials, and open access textbooks, are perceived to be useful for students to accommodate a rapid shift to online learning mode.
C1 [Cheung, Simon K. S.] Open Univ Hong Kong, Homantin, Kowloon, Good Shepherd St, Hong Kong, Peoples R China.
C3 Hong Kong Metropolitan University
RP Cheung, SKS (corresponding author), Open Univ Hong Kong, Homantin, Kowloon, Good Shepherd St, Hong Kong, Peoples R China.
EM kscheung@ouhk.edu.hk
RI Cheung, Simon K.S./AAC-4241-2022
OI Cheung, Simon K.S./0000-0002-7323-0961
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NR 18
TC 5
Z9 5
U1 1
U2 7
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 0302-9743
EI 1611-3349
BN 978-3-030-80504-3; 978-3-030-80503-6
J9 LECT NOTES COMPUT SC
PY 2021
VL 12830
BP 298
EP 308
DI 10.1007/978-3-030-80504-3_25
PG 11
WC Computer Science, Software Engineering; Computer Science, Theory &
   Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BS8LB
UT WOS:000773457100025
DA 2024-09-05
ER
PT C
AU Gogoglou, A
   Manolopoulos, Y
AF Gogoglou, Antonia
   Manolopoulos, Yannis
BE Nguyen, NT
   Papadopoulos, GA
   Jedrzejowicz, P
   Trawinski, B
   Vossen, G
TI Predicting the Evolution of Scientific Output
SO COMPUTATIONAL COLLECTIVE INTELLIGENCE, ICCCI 2017, PT I
SE Lecture Notes in Artificial Intelligence
LA English
DT Proceedings Paper
CT 9th International Conference on Computational Collective Intelligence
   (ICCCI)
CY SEP 27-29, 2017
CL Nicosia, CYPRUS
DE Scientometrics; Bibliographic data; Predictive modeling
ID SLEEPING BEAUTIES; LINK PREDICTION; NETWORKS; IMPACT; INDEX
AB Various efforts have been made to quantify scientific impact and identify the mechanisms that influence its future evolution. The first step is the identification of what constitutes scholarly impact and how it is measured. In this direction, various approaches focus on future citation count or h-index prediction at author or publication level, on fitting the distribution of citation accumulation or accurately identifying award winners, upcoming hot research topics or academic rising stars. A plethora of features have been contemplated as possible influential factors and assorted machine-learning methodologies have been adopted to ensure timely and accurate estimations. Here, we provide an overview of the field challenges, as well as a taxonomy of the existing approaches to identify the open issues that are yet to be addressed.
C1 [Gogoglou, Antonia; Manolopoulos, Yannis] Aristotle Univ Thessaloniki, Dept Informat, Thessaloniki 54124, Greece.
C3 Aristotle University of Thessaloniki
RP Gogoglou, A (corresponding author), Aristotle Univ Thessaloniki, Dept Informat, Thessaloniki 54124, Greece.
EM agogoglou@csd.auth.gr; manolopo@csd.auth.gr
RI Manolopoulos, Yannis/AAI-7767-2020
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NR 41
TC 2
Z9 2
U1 0
U2 6
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 0302-9743
EI 1611-3349
BN 978-3-319-67074-4; 978-3-319-67073-7
J9 LECT NOTES ARTIF INT
PY 2017
VL 10448
BP 244
EP 254
DI 10.1007/978-3-319-67074-4_24
PG 11
WC Computer Science, Artificial Intelligence; Computer Science, Theory &
   Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BL9II
UT WOS:000457470500024
DA 2024-09-05
ER
PT J
AU Liu, XH
   Glänzel, W
   De Moor, B
AF Liu, Xinhai
   Glanzel, Wolfgang
   De Moor, Bart
TI Optimal and hierarchical clustering of large-scale hybrid networks for
   scientific mapping
SO SCIENTOMETRICS
LA English
DT Article; Proceedings Paper
CT 13th International Conference on Scientometrics and Informetrics
CY JUL 04-07, 2011
CL Univ Zululand, Durban, SOUTH AFRICA
HO Univ Zululand
DE Optimal and hierarchical clustering; Text mining; Bibliometric analysis;
   Modularity optimization; Network analysis
ID COMMUNITY STRUCTURE; COMBINED COCITATION; WORD ANALYSIS; INFORMATION;
   SCIENCE
AB Previous studies have shown that hybrid clustering methods based on textual and citation information outperforms clustering methods that use only one of these components. However, former methods focus on the vector space model. In this paper we apply a hybrid clustering method which is based on the graph model to map the Web of Science database in the mirror of the journals covered by the database. Compared with former hybrid clustering strategies, our method is very fast and even achieves better clustering accuracy. In addition, it detects the number of clusters automatically and provides a top-down hierarchical analysis, which fits in with the practical application. We quantitatively and qualitatively asses the added value of such an integrated analysis and we investigate whether the clustering outcome provides an appropriate representation of the field structure by comparing with a text-only or citation-only clustering and with another hybrid method based on linear combination of distance matrices. Our dataset consists of about 8,000 journals published in the period 2002-2006. The cognitive analysis, including the ranked journals, term annotation and the visualization of cluster structure demonstrates the efficiency of our strategy.
C1 [Liu, Xinhai] Peoples Bank China, Credit Reference Ctr, Dept Postdoctoral Res, Beijing 100800, Peoples R China.
   [Liu, Xinhai] Peoples Bank China, Financial Res Inst, Dept Postdoctoral Res, Beijing 100800, Peoples R China.
   [Glanzel, Wolfgang] Katholieke Univ Leuven, Dept MSI, Ctr R&D Monitoring ECOOM, B-3000 Louvain, Belgium.
   [Glanzel, Wolfgang] Hungarian Acad Sci, IRPS, Budapest, Hungary.
   [De Moor, Bart] Katholieke Univ Leuven, ESAT SCD, B-3001 Louvain, Belgium.
   [De Moor, Bart] Katholieke Univ Leuven, KU Leuven IBBT Future Hlth Dept, B-3001 Louvain, Belgium.
C3 People's Bank of China; People's Bank of China; KU Leuven; Hungarian
   Academy of Sciences; KU Leuven; KU Leuven
RP Liu, XH (corresponding author), Peoples Bank China, Credit Reference Ctr, Dept Postdoctoral Res, Chengfangjie 32, Beijing 100800, Peoples R China.
EM xinhai.liu@yahoo.com
RI Glanzel, Wolfgang/AAE-4395-2021; Glanzel, Wolfgang/A-6280-2008
OI Glanzel, Wolfgang/0000-0001-7529-5198
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NR 37
TC 16
Z9 16
U1 0
U2 53
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD MAY
PY 2012
VL 91
IS 2
BP 473
EP 493
DI 10.1007/s11192-011-0600-x
PG 21
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH); Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Information Science & Library Science
GA 921LD
UT WOS:000302478200013
DA 2024-09-05
ER
PT J
AU Hren, D
   Pina, DG
   Norman, CR
   Marusic, A
AF Hren, Darko
   Pina, David G.
   Norman, Christopher R.
   Marusic, Ana
TI What makes or breaks competitive research proposals? A mixed-methods
   analysis of research grant evaluation reports
SO JOURNAL OF INFORMETRICS
LA English
DT Article
DE European Commission; Machine learning; Marie Curie Actions; Peer review
   outcome; Qualitive analysis; Research grants
ID LINGUISTIC ANALYSIS; DECISION-MAKING; NEGATIVITY BIAS; PEER; REVIEWERS
AB The evaluation of grant proposals is an essential aspect of competitive research funding. Funding bodies and agencies rely in many instances on external peer reviewers for grant assessment. Most of the research available is about quantitative aspects of this assessment, and there is little evidence from qualitative studies. We used a combination of machine learning and qualitative analysis methods to analyse the reviewers' comments in evaluation reports from 3667 grant applications to the Initial Training Networks (ITN) of the Marie Curie Actions under the Seventh Framework Programme (FP7). Our results show that the reviewers' comments for each evaluation criterion were aligned with the Action's prespecified criteria and that the evaluation outcome was more influenced by the proposals' weaknesses than by their strengths.
C1 [Hren, Darko] Univ Split, Fac Humanities & Social Sci, Dept Psychol, Split, Croatia.
   [Pina, David G.] European Commiss, European Res Execut Agcy, Brussels, Belgium.
   [Norman, Christopher R.] Sciome LLC, Res Triangle Pk, NC USA.
   [Marusic, Ana] Univ Split, Sch Med, Dept Res Biomed & Hlth, Split, Croatia.
   [Marusic, Ana] Univ Split, Sch Med, Ctr Evidence Based Med, Split, Croatia.
C3 University of Split; University of Split; University of Split
RP Marusic, A (corresponding author), Univ Split, Sch Med, Dept Res Biomed & Hlth, Split, Croatia.; Marusic, A (corresponding author), Univ Split, Sch Med, Ctr Evidence Based Med, Split, Croatia.
EM ana.marusic@mefst.hr
RI Marusic, Ana/E-7683-2013; Hren, Darko/H-1819-2017
OI Marusic, Ana/0000-0001-6272-0917; Hren, Darko/0000-0001-6465-6568; Pina,
   David/0000-0002-4930-748X
FU Croatian Science Foundation "Professionalism in Health -Decision making
   in practice and research" (ProDeM) [IP-2019-04-4882]
FX This study was funded by the Croatian Science Foundation
   "Professionalism in Health -Decision making in practice and research"
   (ProDeM) under Grant agreement No. IP-2019-04-4882. The funder had no
   role in the design of this study, its execution, analyses,
   interpretation of the data, or decision to submit results.
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NR 44
TC 4
Z9 4
U1 9
U2 29
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1751-1577
EI 1875-5879
J9 J INFORMETR
JI J. Informetr.
PD MAY
PY 2022
VL 16
IS 2
AR 101289
DI 10.1016/j.joi.2022.101289
EA APR 2022
PG 17
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 2S3GW
UT WOS:000821684900009
OA hybrid
DA 2024-09-05
ER
PT J
AU Grecov, P
   Prasanna, AN
   Ackermann, K
   Campbell, S
   Scott, D
   Lubman, DI
   Bergmeir, C
AF Grecov, Priscila
   Prasanna, Ankitha Nandipura
   Ackermann, Klaus
   Campbell, Sam
   Scott, Debbie
   Lubman, Dan I.
   Bergmeir, Christoph
TI Probabilistic Causal Effect Estimation With Global Neural Network
   Forecasting Models
SO IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
LA English
DT Article
DE Time series analysis; Predictive models; Forecasting; Biological system
   modeling; Probabilistic logic; Estimation; Market research; Causal
   effect; counterfactual analysis; global time series forecasting; neural
   networks (NNs); probabilistic forecasting
ID SERIES; DIFFERENCE; INFERENCE
AB We introduce a novel method to estimate the causal effects of an intervention over multiple treated units by combining the techniques of probabilistic forecasting with global forecasting methods using deep learning (DL) models. Considering the counterfactual and synthetic approach for policy evaluation, we recast the causal effect estimation problem as a counterfactual prediction outcome of the treated units in the absence of the treatment. Nevertheless, in contrast to estimating only the counterfactual time series outcome, our work differs from conventional methods by proposing to estimate the counterfactual time series probability distribution based on the past preintervention set of treated and untreated time series. We rely on time series properties and forecasting methods, with shared parameters, applied to stacked univariate time series for causal identification. This article presents DeepProbCP, a framework for producing accurate quantile probabilistic forecasts for the counterfactual outcome, based on training a global autoregressive recurrent neural network model with conditional quantile functions on a large set of related time series. The output of the proposed method is the counterfactual outcome as the spline-based representation of the counterfactual distribution. We demonstrate how this probabilistic methodology added to the global DL technique to forecast the counterfactual trend and distribution outcomes overcomes many challenges faced by the baseline approaches to the policy evaluation problem. Oftentimes, some target interventions affect only the tails or the variance of the treated units' distribution rather than the mean or median, which is usual for skewed or heavy-tailed distributions. Under this scenario, the classical causal effect models based on counterfactual predictions are not capable of accurately capturing or even seeing policy effects. By means of empirical evaluations of synthetic and real-world datasets, we show that our framework delivers more accurate forecasts than the state-of-the-art models, depicting, in which quantiles, the intervention most affected the treated units, unlike the conventional counterfactual inference methods based on nonprobabilistic approaches.
C1 [Grecov, Priscila; Prasanna, Ankitha Nandipura; Bergmeir, Christoph] Monash Univ, Dept Data Sci & Artificial Intelligence, Melbourne, Vic 3800, Australia.
   [Ackermann, Klaus] Monash Univ, Dept Econometr & Business Stat, Melbourne, Vic 3800, Australia.
   [Campbell, Sam; Scott, Debbie; Lubman, Dan I.] Monash Univ, Eastern Hlth & Eastern Hlth Clin Sch, Turning Point, Melbourne, Vic 3800, Australia.
C3 Monash University; Monash University; Monash University; Turning Point
   Alcohol & Drug Centre - Australia
RP Grecov, P (corresponding author), Monash Univ, Dept Data Sci & Artificial Intelligence, Melbourne, Vic 3800, Australia.
EM priscila.grecov@monash.edu
RI Scott, Debbie/KHU-0258-2024; Bergmeir, Christoph/Q-9911-2019
OI Bergmeir, Christoph/0000-0002-3665-9021; Grecov,
   Priscila/0000-0001-6131-0238; Ackermann, Klaus/0000-0001-7693-8538
FU Australian Research Council [DE190100045]; Monash University Graduate
   Research Funding; MASSIVE High Performance Computing Facility,
   Australia; Australian Research Council [DE190100045] Funding Source:
   Australian Research Council
FX This work was supported in part by the Australian Research Council under
   Grant DE190100045, in part by the Monash University Graduate Research
   Funding, and in part by the MASSIVE High Performance Computing Facility,
   Australia.
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NR 48
TC 2
Z9 2
U1 4
U2 18
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2162-237X
EI 2162-2388
J9 IEEE T NEUR NET LEAR
JI IEEE Trans. Neural Netw. Learn. Syst.
PD APR
PY 2024
VL 35
IS 4
BP 4999
EP 5013
DI 10.1109/TNNLS.2022.3190984
EA JUL 2022
PG 15
WC Computer Science, Artificial Intelligence; Computer Science, Hardware &
   Architecture; Computer Science, Theory & Methods; Engineering,
   Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA NB2Q3
UT WOS:000829191700001
PM 35853064
DA 2024-09-05
ER
PT J
AU Ruiz-Real, JL
   Uribe-Toril, J
   Torres, JA
   De Pablo, J
AF Luis Ruiz-Real, Jose
   Uribe-Toril, Juan
   Antonio Torres, Jose
   De Pablo, Jaime
TI ARTIFICIAL INTELLIGENCE IN BUSINESS AND ECONOMICS RESEARCH: TRENDS AND
   FUTURE
SO JOURNAL OF BUSINESS ECONOMICS AND MANAGEMENT
LA English
DT Article
DE artificial intelligence; business; economics; bibliometrics; research
   trends; decision-making
ID EXTREME LEARNING-MACHINE; NEURAL-NETWORKS; KNOWLEDGE; INNOVATION;
   RESPONSES; SCIENCE; INDEXES; SYSTEMS; DESIGN; EXPERT
AB Artificial Intelligence is a disruptive technology developed during the 20th century, which has undergone an accelerated evolution, underpinning solutions to complex problems in the business world. Neural Networks, Machine Learning, or Deep Learning are concepts currently associated with terms such as digital marketing, decision making, industry 4.0 and business digital transformation. Interest in this technology will increase as the competitive advantages of the use of Artificial Intelligence by economic entities is realised. The aim of this research is to analyse the state-of-the-art research of Artificial Intelligence in business. To this end, a bibliometric analysis has been implement using the Web of Science and Scopus online databases. By using a fractional counting method, this paper identifies 11 clusters and the most frequent terms used in Artificial Intelligence research. The present study identifies the main trends in research on Artificial Intelligence in business and proposes future lines of inquiry.
C1 [Luis Ruiz-Real, Jose; Uribe-Toril, Juan; De Pablo, Jaime] Univ Almeria, Fac Econ & Business, Almeria, Spain.
   [Antonio Torres, Jose] Univ Almeria, Dept Comp Sci, Almeria, Spain.
C3 Universidad de Almeria; Universidad de Almeria
RP Uribe-Toril, J (corresponding author), Univ Almeria, Fac Econ & Business, Almeria, Spain.
EM juribe@ual.es
RI de Pablo Valenciano, Jaime/T-1455-2019; Silva, Flavio/JTT-2763-2023;
   Uribe-Toril, Juan/W-8568-2018; DE+PABLO+VALENCIANO, JAIME/ABA-6206-2021
OI de Pablo Valenciano, Jaime/0000-0002-9451-8956; Uribe-Toril,
   Juan/0000-0002-0227-801X; 
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NR 75
TC 38
Z9 40
U1 62
U2 376
PU VILNIUS GEDIMINAS TECH UNIV
PI VILNIUS
PA SAULETEKIO AL 11, VILNIUS, LT-10223, LITHUANIA
SN 1611-1699
EI 2029-4433
J9 J BUS ECON MANAG
JI J. Bus. Econ. Manag.
PY 2021
VL 22
IS 1
BP 98
EP 117
DI 10.3846/jbem.2020.13641
PG 20
WC Business; Economics
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA QB2AQ
UT WOS:000613944700006
OA gold
DA 2024-09-05
ER
PT C
AU Khan, A
   Koh, RGL
   Hassan, S
   Liu, T
   Tucci, V
   Kumbhare, D
   Doyle, TE
AF Khan, Asif
   Koh, Ryan G. L.
   Hassan, Samah
   Liu, Theodore
   Tucci, Victoria
   Kumbhare, Dinesh
   Doyle, Thomas E.
GP IEEE
TI STAR-ML: A Rapid Screening Tool for Assessing Reporting of Machine
   Learning in Research
SO 2022 IEEE CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING
   (CCECE)
SE Canadian Conference on Electrical and Computer Engineering
LA English
DT Proceedings Paper
CT Canadian Conference on Electrical and Computer Engineering (CCECE)
CY SEP 18-20, 2022
CL Halifax, CANADA
DE machine learning; screening tool; reporting assessment; quality scoring;
   checklist; research methodology; literature review
AB Literature review provides researchers with an overview of the field and when presented as a systematic assessment, it summarizes state-of-the-art information and identifies knowledge gaps. While there are many tools for assessing quality and risk-of-bias within studies, there is currently no generalized tool for evaluating the transparency, reproducibility, and correctness of machine learning (ML) reporting in the literature. This study proposes a new tool (Screening Tool for Assessing Reporting of Machine Learning; STAR-ML) that can be used to screen articles for a systematic or scoping review focusing on the reporting of the ML algorithm. This paper describes the development of the tool to assess the quality of ML research reporting and how it can be applied to improve the literature review methodology. The tool was tested and updated using three independent raters on 15 studies. The inter-rater reliability and the time used to review an article were evaluated. The current version of STAR-ML has a very high inter-rater reliability of 0.923, and the average time to screen an article was 4.73 minutes. This new tool will allow for filtering ML-related papers that can be included in a systematic or scoping review by ensuring transparent, reproducible, and correct screening of research for inclusion in the review article.
C1 [Khan, Asif; Doyle, Thomas E.] McMaster Univ, Dept Elect & Comp Engn, Hamilton, ON, Canada.
   [Koh, Ryan G. L.; Hassan, Samah; Kumbhare, Dinesh] Toronto Rehabil Inst UHN, KITE, Toronto, ON, Canada.
   [Liu, Theodore] McMaster Univ, Fac Engn, Hamilton, ON, Canada.
   [Tucci, Victoria] McMaster Univ, Fac Hlth Sci, Hamilton, ON, Canada.
   [Doyle, Thomas E.] McMaster Univ, Sch Biomed Engn, Hamilton, ON, Canada.
   [Doyle, Thomas E.] Vector Inst Artificial Intelligence, Toronto, ON, Canada.
C3 McMaster University; University of Toronto; University Health Network
   Toronto; Toronto Rehabilitation Institute; McMaster University; McMaster
   University; McMaster University; Vector Institute for Artificial
   Intelligence
RP Khan, A (corresponding author), McMaster Univ, Dept Elect & Comp Engn, Hamilton, ON, Canada.
EM khanm382@mcmaster.ca; ryan.koh@uhn.ca; samah.hassam@uhn.ca;
   liu102@mcmaster.ca; tucciv1@mcmaster.ca; dinesh.kumbhare@uhn.ca;
   doylet@mcmaster.ca
RI Hassan, Samah/ABA-6467-2021; Hassan, Samah/CAA-2067-2022; Izquierdo,
   Mikel/A-4894-2010
OI Izquierdo, Mikel/0000-0002-1506-4272; Doyle, Thomas/0000-0003-1059-110X;
   Liu, Theodore/0000-0001-7334-8129; Khan, Md Asif/0000-0001-8395-347X
FU Canadian Department of National Defence IDEaS [CFPMN2-17]; Department of
   Electrical and Computer Engineering at McMaster University, Canada
FX This work was funded and supported by the Canadian Department of
   National Defence IDEaS under award number CFPMN2-17 and the Department
   of Electrical and Computer Engineering at McMaster University, Canada.
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NR 32
TC 1
Z9 1
U1 0
U2 1
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 0840-7789
BN 978-1-6654-8432-9
J9 CAN CON EL COMP EN
PY 2022
BP 336
EP 341
DI 10.1109/CCECE49351.2022.9918312
PG 6
WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Engineering
GA BU7ZN
UT WOS:000945894600046
DA 2024-09-05
ER
PT C
AU Xiao, TT
   Han, P
   Hong, B
   Dong, Z
AF Xiao, Tiantian
   Han, Pu
   Hong, Bo
   Dong, Ze
GP IEEE
TI Research on The Effect of Selection of Objective Function on
   Optimization Results
SO 2014 11TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA)
LA English
DT Proceedings Paper
CT 11th World Congress on Intelligent Control and Automation
CY JUN 29-JUL 04, 2014
CL Shenyang, PEOPLES R CHINA
DE Particle Swarm Optimization algorithm; Objective function; Parameter
   optimization
AB In this paper, based on Particle Swarm Optimization (PSO) algorithm to observe the different optimization results by changing the objective function. By comparing indicators of various types of objective function, clearly showing its intuitive respective advantages and disadvantages. Herein we can derived from the comprehensive objective function is a relatively good target function, stability, accuracy and rapidity performance can better meet the requirements of people.
C1 [Xiao, Tiantian; Han, Pu; Hong, Bo; Dong, Ze] North China Elect Power Univ, Hebei Engn Res Ctr Simulat & Optimized Control Po, Dept Automat, Baoding 071003, Peoples R China.
C3 North China Electric Power University
RP Xiao, TT (corresponding author), North China Elect Power Univ, Hebei Engn Res Ctr Simulat & Optimized Control Po, Dept Automat, Baoding 071003, Peoples R China.
EM smilesweetsweet@163.com
CR Han P., 2007, CONTROL SYSTEM DIGIT
   Han P., 1993, N CHINA U ELECT POWE, V20, P50
   Han P., 2013, INTELLIGENT CONTROL
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   Zhang J., 2001, COMPUTER SIMULATION, V27, P191
NR 6
TC 0
Z9 0
U1 0
U2 0
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-4799-5825-2
PY 2014
BP 4988
EP 4990
PG 3
WC Automation & Control Systems
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Automation & Control Systems
GA BG9CY
UT WOS:000393066205003
DA 2024-09-05
ER
PT J
AU Matrix, S
AF Matrix, Sidneyeve
TI Teaching with Infographics: Practicing New Digital Competencies and
   Visual Literacies
SO JOURNAL OF PEDAGOGIC DEVELOPMENT
LA English
DT Article
DE online learning; communications; graphic design; Internet research;
   peerto-peer collaboration; teaching
AB This position paper examines the use of infographics as a teaching assignment in the online college classroom. It argues for the benefits of adopting this type of creative assignment for teaching and learning, and considers the pedagogic and technical challenges that may arise in doing so. Data and insights are drawn from two case studies, both from the communications field, one online class and a blended one, taught at two different institutions. The paper demonstrates how incorporating a research-based graphic design assignment into coursework challenges and encourages students' visual digital literacies. The paper includes practical insights and identifies best practices emerging from the authors' classroom experience with the infographic assignment, and from student feedback. The paper suggests that this kind of creative assignment requires students to practice exactly those digital competencies required to participate in an increasingly visual digital culture.
C1 [Matrix, Sidneyeve] Queens Univ, Kingston, ON, Canada.
   [Matrix, Sidneyeve] Jaigris Hodson Ryerson Univ, Toronto, ON, Canada.
C3 Queens University - Canada
RP Matrix, S (corresponding author), Queens Univ, Kingston, ON, Canada.
EM sidney.eve@queensu.ca
OI Hodson, Jaigris/0000-0002-2235-3718
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NR 28
TC 30
Z9 44
U1 1
U2 9
PU UNIV BEDFORDSHIRE, CENTRE LEARNING EXCELLENCE
PI BEDS
PA UNIVERSITY SQ, LUTON, BEDS, LU1 3JU, ENGLAND
SN 2047-3257
EI 2047-3265
J9 J PEDAGOG DEV
JI J. Pedagog. Dev.
PD JUL
PY 2014
VL 4
IS 2
BP 17
EP 27
PG 11
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA V5B4R
UT WOS:000219455600002
DA 2024-09-05
ER
PT J
AU Koopmann, T
   Stubbemann, M
   Kapa, M
   Paris, M
   Buenstorf, G
   Hanika, T
   Hotho, A
   Jäschke, R
   Stumme, G
AF Koopmann, Tobias
   Stubbemann, Maximilian
   Kapa, Matthias
   Paris, Michael
   Buenstorf, Guido
   Hanika, Tom
   Hotho, Andreas
   Jaeschke, Robert
   Stumme, Gerd
TI Proximity dimensions and the emergence of collaboration: a HypTrails
   study on German AI research
SO SCIENTOMETRICS
LA English
DT Article
DE Dimensions of proximity; Co-authorships; Co-inventorships; Embedding
   techniques; Collaboration
ID KNOWLEDGE; INNOVATION; NETWORKS; SPILLOVERS; GEOGRAPHY; INDUSTRY
AB Creation and exchange of knowledge depends on collaboration. Recent work has suggested that the emergence of collaboration frequently relies on geographic proximity. However, being co-located tends to be associated with other dimensions of proximity, such as social ties or a shared organizational environment. To account for such factors, multiple dimensions of proximity have been proposed, including cognitive, institutional, organizational, social and geographical proximity. Since they strongly interrelate, disentangling these dimensions and their respective impact on collaboration is challenging. To address this issue, we propose various methods for measuring different dimensions of proximity. We then present an approach to compare and rank them with respect to the extent to which they indicate co-publications and co-inventions. We adapt the HypTrails approach, which was originally developed to explain human navigation, to co-author and co-inventor graphs. We evaluate this approach on a subset of the German research community, specifically academic authors and inventors active in research on artificial intelligence (AI). We find that social proximity and cognitive proximity are more important for the emergence of collaboration than geographic proximity.
C1 [Koopmann, Tobias; Hotho, Andreas] Univ Wurzburg, Data Sci Chair, Wurzburg, Germany.
   [Stubbemann, Maximilian; Jaeschke, Robert; Stumme, Gerd] L3S Res Ctr, Hannover, Germany.
   [Kapa, Matthias; Buenstorf, Guido] Univ Kassel, INCHER, Kassel, Germany.
   [Kapa, Matthias; Buenstorf, Guido] Univ Kassel, Inst Econ, Kassel, Germany.
   [Paris, Michael; Jaeschke, Robert] Humboldt Univ, Berlin, Germany.
   [Buenstorf, Guido] Univ Gothenburg, Innovat & Entrepreneurship Unit, Gothenburg, Sweden.
   [Hanika, Tom] Univ Kassel, Knowledge & Data Engn Grp, Kassel, Germany.
C3 University of Wurzburg; Leibniz University Hannover; Universitat Kassel;
   Universitat Kassel; Humboldt University of Berlin; University of
   Gothenburg; Universitat Kassel
RP Koopmann, T (corresponding author), Univ Wurzburg, Data Sci Chair, Wurzburg, Germany.
EM koopmann@informatik.uni-wuerzburg.de; stubbemann@13s.de;
   kapa@incher.uni-kassel.de; michael.paris@hu-berlin.de;
   buenstorf@uni-kassel.de; hanika@cs.uni-kassel.de;
   hotho@informatik.uni-wuerzburg.de; robert.jaeschke@hu-berlin.de;
   stumme@13s.de
RI Koopmann, Tobias/JKJ-5457-2023
OI Paris, Michael/0000-0003-2077-6984; Hanika, Tom/0000-0002-4918-6374;
   Koopmann, Tobias/0000-0002-7736-9864
FU German Federal Ministry of Education and Research(BMBF) [01PU17012A-D]
FX Open Access funding enabled and organized by Projekt DEAL. This work is
   funded by the German Federal Ministry of Education and Research(BMBF)
   under grant numbers 01PU17012A-D.
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NR 68
TC 4
Z9 4
U1 3
U2 35
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD DEC
PY 2021
VL 126
IS 12
BP 9847
EP 9868
DI 10.1007/s11192-021-03922-1
EA MAR 2021
PG 22
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA XF4WA
UT WOS:000630848600008
OA hybrid, Green Published
DA 2024-09-05
ER
PT J
AU Genkina, D
AF Genkina, Dina
TI Don't Start a Career as an AI Prompt Engineer AI will Take Your Job
SO IEEE SPECTRUM
LA English
DT Article
DE Engineering profession; Chatbots; Internet; Artificial intelligence;
   Employment; Career development; Market research; Mathematical models;
   Kirk field collapse effect
AB Since ChatGPT dropped in the fall of 2022, everyone and their donkey has tried their hand at prompt engineering-finding a clever way to phrase their query to a large language model (LLM) or AI art or video generator to get the best results (or sidestep protections). The Internet is replete with prompt-engineering guides, cheat sheets, and advice threads to help you get the most out of an LLM.
NR 0
TC 0
Z9 0
U1 7
U2 7
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0018-9235
EI 1939-9340
J9 IEEE SPECTRUM
JI IEEE Spectr.
PD MAY
PY 2024
VL 61
IS 5
BP 30
EP 34
DI 10.1109/MSPEC.2024.10523015
PG 5
WC Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA WE2L1
UT WOS:001253126000009
DA 2024-09-05
ER
PT C
AU Yu, LY
AF Yu, Lingyun
GP IEEE
TI Application Research of SVM-based Mining Algorithm in Evaluation of
   College English Teaching
SO 2016 INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION, BIG DATA &
   SMART CITY (ICITBS)
LA English
DT Proceedings Paper
CT International Conference on Intelligent Transportation, Big Data & Smart
   City (ICITBS)
CY DEC 17-18, 2016
CL Changsha, PEOPLES R CHINA
DE evaluation; English teaching; SVM; data mining
AB It is a problem of obtaining fair, accurate and fast evaluation of teacher in education and teaching, which is also an important premise of modern management in colleges. There exist some disadvantages as being subjective, poor accuracy and complex operation in traditional schemes. We proposed an improved method by combining data mining algorithm and the evaluation indicators of English teachers. SVM is used to classify the sample data. Then we attain the training model through training the sample data in the evaluation system and take the intelligent evaluation and analysis on the prediction data with the training model. Our method is testified to have advantage in comprehensive performance and application value by experiments.
C1 [Yu, Lingyun] Sias Int Univ, Sch Foreign Languages, Xinzheng 451150, Peoples R China.
RP Yu, LY (corresponding author), Sias Int Univ, Sch Foreign Languages, Xinzheng 451150, Peoples R China.
EM 709740726@qq.com
CR Jing Yang, 2011, Information Technology Journal, V10, P2140, DOI 10.3923/itj.2011.2140.2146
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NR 5
TC 1
Z9 1
U1 0
U2 1
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-5090-6061-0
PY 2017
BP 73
EP 76
DI 10.1109/ICITBS.2016.124
PG 4
WC Computer Science, Information Systems; Computer Science,
   Interdisciplinary Applications; Telecommunications
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Telecommunications
GA BJ7EQ
UT WOS:000427175000018
DA 2024-09-05
ER
PT C
AU Magalhaes, GV
   Vieira, JPA
   Santos, RLD
   Barbosa, JLN
   Neto, PDD
   Moura, RS
AF Magalhaes Junior, Gilvan Veras
   Albuquerque Vieira, Joao Paulo
   de Sales Santos, Roney Lira
   Nascimento Barbosa, Jardeson Leandro
   dos Santos Neto, Pedro de Alcantara
   Moura, Raimundo Santos
GP IEEE
TI A Study of the Influence of Textual Features in Learning Medical Prior
   Authorization
SO 2019 IEEE 32ND INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS
   (CBMS)
SE IEEE International Symposium on Computer-Based Medical Systems
LA English
DT Proceedings Paper
CT 32nd IEEE International Symposium on Computer-Based Medical Systems
   (IEEE CBMS)
CY JUN 05-07, 2019
CL Inst Maimonides Investigac Biomedica Cordoba, Cordoba, SPAIN
HO Inst Maimonides Investigac Biomedica Cordoba
DE Text Mining; Natural Language Processing; Machine Learning; Medical
   Prior Authorization; Health Insurance; Health Plan
AB In Brazil, a current health problem is the low capacity of meeting an increasing demand for medical services. As a result, some people have resorted to supplementary health care, which involves the operation of private health plans and health insurance. However, many health maintenance organizations (HMO) face financial difficulties due to unnecessary procedures, fraud or abuses in the use of health services. In order to avoid unnecessary expenses, the HMO began to use a mechanism called prior authorization, where a prior analysis of each user's need is made to authorize or deny the required requests. This work aims to study the influence of the use of textual features in automatic prior authorization evaluation, by using Text Mining, Natural Language Processing and Machine Learning techniques. Experiments were performed using several machine learning algorithms combined with textual features, increasing the performance of the automatic prior authorization. Results indicate not only the textual features influence to the evaluation of the automatic prior authorization process but also improved the prediction of the classifiers.
C1 [Magalhaes Junior, Gilvan Veras; Nascimento Barbosa, Jardeson Leandro; dos Santos Neto, Pedro de Alcantara] Infoway eHlth Co, Teresina, Brazil.
   [Magalhaes Junior, Gilvan Veras; Albuquerque Vieira, Joao Paulo; Nascimento Barbosa, Jardeson Leandro; dos Santos Neto, Pedro de Alcantara] Univ Fed Piaui, Teresina, Brazil.
   [de Sales Santos, Roney Lira] Univ Sao Paulo, Sao Carlos, SP, Brazil.
   [Moura, Raimundo Santos] Univ Fed Piaui, Dept Comp, Teresina, Brazil.
C3 Universidade Federal do Piaui; Universidade de Sao Paulo; Universidade
   Federal do Piaui
RP Magalhaes, GV (corresponding author), Infoway eHlth Co, Teresina, Brazil.; Magalhaes, GV (corresponding author), Univ Fed Piaui, Teresina, Brazil.
EM gilvanvmj@gmail.com; joaopauloalbu@gmail.com; roneysantos@usp.br;
   jardeson@infoway-pi.com.br; pasn@ufpi.edu.br; rsm@ufpi.edu.br
RI Neto, Pedro Santos/JXY-0805-2024; Veras, Gilvan/HLW-3131-2023
FU CNPq
FX [5th] Researcher scholarship - DT Level 2, sponsored by CNPq
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TC 1
Z9 2
U1 1
U2 2
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2372-9198
BN 978-1-7281-2286-1
J9 COMP MED SY
PY 2019
BP 56
EP 61
DI 10.1109/CBMS.2019.00021
PG 6
WC Computer Science, Information Systems; Computer Science,
   Interdisciplinary Applications; Engineering, Biomedical
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Engineering
GA BO1SY
UT WOS:000502356600012
DA 2024-09-05
ER
PT J
AU Nica, I
   Delcea, C
   Chirita, N
AF Nica, Ionut
   Delcea, Camelia
   Chirita, Nora
TI Mathematical Patterns in Fuzzy Logic and Artificial Intelligence for
   Financial Analysis: A Bibliometric Study
SO MATHEMATICS
LA English
DT Article
DE fuzzy logic; financial management; artificial intelligence; bibliometric
   analysis
ID SYSTEMS; SETS
AB In this study, we explored the dynamic field of fuzzy logic and artificial intelligence (AI) in financial analysis from 1990 to 2023. Utilizing the bibliometrix package in RStudio and data from the Web of Science, we focused on identifying mathematical models and the evolving role of fuzzy information granulation in this domain. The research addresses the urgent need to understand the development and impact of fuzzy logic and AI within the broader scope of evolving technological and analytical methodologies, particularly concentrating on their application in financial and banking contexts. The bibliometric analysis involved an extensive review of the literature published during this period. We examined key metrics such as the annual growth rate, international collaboration, and average citations per document, which highlighted the field's expansion and collaborative nature. The results revealed a significant annual growth rate of 19.54%, international collaboration of 21.16%, and an average citation per document of 25.52. Major journals such as IEEE Transactions on Fuzzy Systems, Fuzzy Sets and Systems, the Journal of Intelligent & Fuzzy Systems, and Information Sciences emerged as significant contributors, aligning with Bradford's Law's Zone 1. Notably, post-2020, IEEE Transactions on Fuzzy Systems showed a substantial increase in publications. A significant finding was the high citation rate of seminal research on fuzzy information granulation, emphasizing its mathematical importance and practical relevance in financial analysis. Keywords like "design", "model", "algorithm", "optimization", "stabilization", and terms such as "fuzzy logic controller", "adaptive fuzzy controller", and "fuzzy logic approach" were prevalent. The Countries' Collaboration World Map indicated a strong pattern of global interconnections, suggesting a robust framework of international collaboration. Our study highlights the escalating influence of fuzzy logic and AI in financial analysis, marked by a growth in research outputs and global collaborations. It underscores the crucial role of fuzzy information granulation as a mathematical model and sets the stage for further investigation into how fuzzy logic and AI-driven models are transforming financial and banking analysis practices worldwide.
C1 [Nica, Ionut; Delcea, Camelia; Chirita, Nora] Bucharest Univ Econ Studies, Dept Econ Informat & Cybernet, Bucharest 0105552, Romania.
C3 Bucharest University of Economic Studies
RP Delcea, C (corresponding author), Bucharest Univ Econ Studies, Dept Econ Informat & Cybernet, Bucharest 0105552, Romania.
EM ionut.nica@csie.ase.ro; camelia.delcea@csie.ase.ro;
   nora.chirita@csie.ase.ro
RI Nica, Ionut/ABA-4243-2021; Delcea, Camelia/C-4343-2011
OI Nica, Ionut/0000-0003-2118-3654; Delcea, Camelia/0000-0003-3589-1969;
   Chirita, Nora/0009-0005-6633-9466
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NR 63
TC 1
Z9 1
U1 8
U2 8
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2227-7390
J9 MATHEMATICS-BASEL
JI Mathematics
PD MAR
PY 2024
VL 12
IS 5
AR 782
DI 10.3390/math12050782
PG 35
WC Mathematics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematics
GA KO0E1
UT WOS:001180780800001
OA gold
DA 2024-09-05
ER
PT J
AU Dai, T
   Yan, WJ
   Zhang, KQ
   Qiu, C
   Zhao, XM
   Pan, SR
AF Dai, Tao
   Yan, Wenjun
   Zhang, Kaiqi
   Qiu, Chen
   Zhao, Xiangmo
   Pan, Shirui
TI Gated relational stacked denoising autoencoder with localized author
   embedding for global citation recommendation
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Global citation recommendation; Stacked denoising autoencoder; Topic
   model; Machine learning; Deep learning
AB Citation recommendation is an effective and efficient way to facilitate authors finding desired references. This paper presents a novel neural network based model, called gated relational probabilistic stacked denoising autoencoder with localized author (GRSLA) embedding, for global citation recommendation task. Our model is comprised of two modules with different neural network architecture. For each citing and cited papers, we use a gated paper embedding module, which is extended from probabilistic stacked denoising autoencoder (PSDAE) by adding gated units, to obtain their paper vectors. The added gated units are able to utilize text information of cited paper to refine the vector representation of citing paper in multiple semantic levels. For an author in papers, we first apply topic model to obtain his/her semantic neighbors, and then use a localized author embedding (LAE) module to excavate author vector representation from semantic and explicit neighbors. Unlike most graph convolutional network (GCN) based methods, the LAE module is able to avoid computing global Laplacian in whole graph by taking limited neighbors. Moreover, the LAE module can also be stacked to absorb more neighbors, which makes our model have high extendibility. Based on the generation process of GRSLA, we also derive a learning algorithm of our model by maximum a posteriori (MAP) estimation. We conduct experiments on the AAN, DBLP and CORD-19 datasets, and the results show that GRSLA model works well than previous global citation recommendation methods.
C1 [Dai, Tao; Yan, Wenjun; Zhang, Kaiqi] Changan Univ, Sch Econ & Management, Xian 710064, Shaanxi, Peoples R China.
   [Zhao, Xiangmo] Changan Univ, Sch Informat Engn, Xian 710064, Shaanxi, Peoples R China.
   [Qiu, Chen] Walmart Inc, Sams Club Innovat Ctr, Dallas, TX 75202 USA.
   [Pan, Shirui] Monash Univ, Fac Informat Technol, Melbourne, Vic, Australia.
C3 Chang'an University; Chang'an University; Wal-Mart Stores Inc; Monash
   University
RP Yan, WJ (corresponding author), Changan Univ, Sch Econ & Management, Xian 710064, Shaanxi, Peoples R China.
EM daitao@chd.edu.cn; ywj@chd.edu.cn; kaiqizhang@chd.edu.cn;
   chen.qiu@ymail.com; xmzhao@chd.edu.cn; Shirui.Pan@monash.edu
RI Qiu, Chen/P-9429-2017; Pan, Shirui/K-6763-2018; Zhang, Kai/HWQ-4396-2023
OI Pan, Shirui/0000-0003-0794-527X; 
FU Natural Science Foundation in Shaanxi Province of China [2019JQ-531,
   2021JQ-289]; Social Science in Shaanxi Province of China [2020R007];
   Major Theoretical and Practical Problems Research Project of Social
   Science in Shaanxi Province of China [2020Z357]; Fundamental Research
   Funds for the Central Universities, CHD [300102231302]
FX This work was partially support by the Natural Science Foundation in
   Shaanxi Province of China (Project No. 2019JQ-531; No. 2021JQ-289) ,
   Social Science in Shaanxi Province of China (Project No. 2020R007) , the
   Major Theoretical and Practical Problems Research Project of Social
   Science in Shaanxi Province of China (Project No. 2020Z357) and the
   Fundamental Research Funds for the Central Universities, CHD (Project
   No. 300102231302).
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NR 56
TC 9
Z9 9
U1 2
U2 26
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0957-4174
EI 1873-6793
J9 EXPERT SYST APPL
JI Expert Syst. Appl.
PD DEC 1
PY 2021
VL 184
AR 115359
DI 10.1016/j.eswa.2021.115359
EA JUN 2021
PG 18
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering; Operations Research & Management Science
GA WI0YM
UT WOS:000708093400002
DA 2024-09-05
ER
PT J
AU Bhanage, DA
   Pawar, AV
AF Bhanage, Deepali Arun
   Pawar, Ambika Vishal
TI Bibliometric survey of IT Infrastructure Management to Avoid Failure
   Conditions
SO INFORMATION DISCOVERY AND DELIVERY
LA English
DT Article
DE Bibliometric analysis; Failure prediction; Deep learning; Machine
   learning; Log analysis; System log
AB Purpose The purpose of this paper is to present the bibliometric study of articles IT Infrastructure Management to Avoid Failure Conditions. As in today's era of IT Industries, IT infrastructure management plays a crucial role. As a result, substantial research is going on to improve the reliability and availability of assets in IT infrastructure.
   Design/methodology/approach The paper analyzes and focuses the results acquired from articles accessed from Scopus for the past 15 years by examining in terms of frequently used keywords, the amount of work done in different countries and year-wise progression of the research, prolific authors, article citation frequencies, etc. Tools such as Gephi, Word Cloud, BiblioShiny, GPS visualizer, etc. are used for bibliometric analysis.
   Findings The study comes out with maximum publications of IT infrastructure management from conferences and journals. Anomaly detection, log analysis and learning system are the most frequently used keywords in the publications. Significant research has been done in the USA, followed by China under the area of Computer Science with an increase in publication since 2018.
   Originality/value This paper provides an accurate idea about the amount of work done in different countries and year-wise progression of the research. This bibliometric analysis will be useful for beginners to conduct a literature survey using appropriate literature available on the Scopus database.
C1 [Bhanage, Deepali Arun; Pawar, Ambika Vishal] Symbiosis Int Deemed Univ, Symbiosis Inst Technol, Pune, Maharashtra, India.
C3 Symbiosis International University; Symbiosis Institute of Technology
   (SIT)
RP Pawar, AV (corresponding author), Symbiosis Int Deemed Univ, Symbiosis Inst Technol, Pune, Maharashtra, India.
EM deepali.bhanage.phd2019@sitpune.edu.in; ambikap@sitpune.edu.in
RI B, Deepali/AEU-3127-2022; Institute of Technology, Symbiosis Institute
   of Technology Symbiosis/AAC-5693-2019
OI B, Deepali/0000-0001-6022-4565; 
CR [Anonymous], 2018, P WORKSH BIG DAT AN
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NR 20
TC 8
Z9 8
U1 0
U2 21
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 2398-6247
J9 INF DISCOV DELIV
JI Inf. Discov. Deliv.
PY 2021
VL 49
IS 1
BP 45
EP 56
DI 10.1108/IDD-06-2020-0060]
PG 12
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA RF9OI
UT WOS:000635166800005
DA 2024-09-05
ER
PT C
AU Zelenkov, Y
AF Zelenkov, Yuri
BE Uden, L
   Ting, IH
   Corchado, JM
TI The Topics Dynamics in Knowledge Management Research
SO KNOWLEDGE MANAGEMENT IN ORGANIZATIONS, KMO 2019
SE Communications in Computer and Information Science
LA English
DT Proceedings Paper
CT 14th International Conference on Knowledge Management in Organizations
   (KMO) - Synergistic Role of Knowledge Management in Organization
CY JUL 15-18, 2019
CL Univ Salamanca, Zamora, SPAIN
HO Univ Salamanca
DE Knowledge management; Bibliometrics; Topic modeling; LDA
ID TRENDS; PERFORMANCE; THEMES
AB The intellectual structure of an academic discipline can be viewed as a set of interacting topics evolving over time. Dynamics of those topics i.e. changes in their popularity and impact is the subject of special attention because it reflects a shift in actual researchers' interest. This paper analyzes topics of knowledge management (KM) on the base of the topic modeling technique (namely Latent Dirichlet Allocation). Studying the flow of academic publications in 7 leading journals in 2010-2018, we identified 8 topics that concern different aspects of knowledge management science. Three topics, what focus on the social aspects of knowledge management (namely the context supporting knowledge transfer, the employees' incentives to share knowledge, and innovation), grow in terms of popularity and impact. Opposite, popularity and impact of topics, which focus on the practice of the knowledge management and organizational learning also as on the impact of intellectual capital on performance, decline. It is consistent with the opinion of other researchers that in the contemporary flow of scientific publication role of KM is identified more as a social process than a management engineering method.
C1 [Zelenkov, Yuri] Natl Res Univ Higher Sch Econ, Moscow, Russia.
C3 HSE University (National Research University Higher School of Economics)
RP Zelenkov, Y (corresponding author), Natl Res Univ Higher Sch Econ, Moscow, Russia.
EM yuri.zelenkov@gmail.com
RI Zelenkov, Yuri/S-1331-2016
OI Zelenkov, Yuri/0000-0002-2248-1023
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NR 30
TC 2
Z9 2
U1 2
U2 6
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 1865-0929
EI 1865-0937
BN 978-3-030-21451-7; 978-3-030-21450-0
J9 COMM COM INF SC
PY 2019
VL 1027
BP 324
EP 335
DI 10.1007/978-3-030-21451-7_28
PG 12
WC Computer Science, Theory & Methods; Operations Research & Management
   Science
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Operations Research & Management Science
GA BP5RT
UT WOS:000558101000028
DA 2024-09-05
ER
PT J
AU Jung, S
   Yoon, WC
AF Jung, Sukhwan
   Yoon, Wan Chul
TI An alternative topic model based on Common Interest Authors for topic
   evolution analysis
SO JOURNAL OF INFORMETRICS
LA English
DT Article
DE Topic modeling; Bibliographic network; Topic evolution; Scientometric
ID MULTILAYER GRAPHS; COMMUNITY DETECTION; SCIENCE; TRENDS
AB y Topic modeling methods aim to extract semantic topics from unstructured documents, and topic evolution is one of its branches seeking to analyze how temporal topics in a set of documents evolve and has shown successful identification of content transitions within static topics over time; yet, the inherent limitations of topic modeling methods inhibit traditional topic evolution methods from highlighting topical correlations between different, dynamic topics. The authors propose an alternative topic modeling method conscious of the topical correlation in the academic domain by introducing the notion of the common interest authors (CIA(1)), defining a topic as a set of shared common research interests of a researcher group. Publication records related to the Human Computer Interaction field were extracted from the Microsoft Academic Graph dataset, with virtual reality as the target field of research. The result showed that the proposed alternative topic modeling is capable of successfully model coherent topics regardless of the topic size with only the meta-data of the document set, indicating that the alternative approach is not only capable of allowing topic correlation analysis during the topic evolution but also able to generate coherent topics at the same time. (C) 2020 Published by Elsevier Ltd.
C1 [Jung, Sukhwan] Univ S Alabama, Dept Comp Sci, 307 N Univ Blvd, Mobile, AL 36688 USA.
   [Yoon, Wan Chul] Korea Adv Inst Sci & Technol, Dept Knowledge Serv Engn, 291 Daehak Ro, Daejeon 34141, South Korea.
C3 University of South Alabama; Korea Advanced Institute of Science &
   Technology (KAIST)
RP Yoon, WC (corresponding author), Korea Adv Inst Sci & Technol, Dept Knowledge Serv Engn, 291 Daehak Ro, Daejeon 34141, South Korea.
EM shjung@southalabama.edu
RI Jung, Suk hwan/HIK-1039-2022; Yoon, Wan Chul/C-1982-2011
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NR 66
TC 27
Z9 31
U1 2
U2 98
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1751-1577
EI 1875-5879
J9 J INFORMETR
JI J. Informetr.
PD AUG
PY 2020
VL 14
IS 3
AR 101040
DI 10.1016/j.joi.2020.101040
PG 19
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA NN5JF
UT WOS:000568824100005
DA 2024-09-05
ER
PT C
AU Knop, S
   Merchel, R
   Poeppelbuss, J
AF Knop, Sebastian
   Merchel, Robin
   Poeppelbuss, Jens
BE Huang, GQ
   Qu, T
   Thurer, M
   Xu, S
   Khalgui, M
TI Author Collaboration in Ten Years of IPS<SUP>2</SUP>: A Bibliometric
   Analysis
SO 11TH CIRP CONFERENCE ON INDUSTRIAL PRODUCT-SERVICE SYSTEMS
SE Procedia CIRP
LA English
DT Proceedings Paper
CT 11th CIRP Conference on Industrial Product-Service Systems
CY MAY 29-31, 2019
CL PEOPLES R CHINA
DE Author Collaboration; Industrial Product-Service Systems; Bibliometric
   Analysis; Machine Learning; Random Forests
ID WEIGHTED KAPPA; SERVITIZATION
AB This paper investigates author collaboration at the Conference on Industrial Product-Service Systems (IPS2). Previous work showed that there is only a loose collaboration between authors from different countries in the field of Product-Service System research. This study aims to extend and refine these findings by also taking the authors' disciplines and affiliations into account. We analyze 694 articles written by a total of 1,131 authors using both bibliometric analysis and a machine learning technique. We identify collaboration patterns that illustrate how researcher communities collaborate within their country, either on countrywide or regional level. Furthermore, the authors' disciplines also influence their tendency to collaborate with authors from other disciplines. We conclude that a shared cultural background, language, and discipline promote the collaboration of authors from the IPS2 community. (C) 2019 The Authors. Published by Elsevier B.V.
C1 [Knop, Sebastian; Merchel, Robin; Poeppelbuss, Jens] Ruhr Univ Bochum, Ind Sales & Serv Engn, Univ Str 150, D-44801 Bochum, Germany.
C3 Ruhr University Bochum
RP Knop, S (corresponding author), Ruhr Univ Bochum, Ind Sales & Serv Engn, Univ Str 150, D-44801 Bochum, Germany.
EM sebastian.knop@isse.rub.de
OI Poeppelbuss, Jens/0000-0003-4960-7818; Knop,
   Sebastian/0000-0002-9187-9254
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NR 33
TC 0
Z9 0
U1 1
U2 4
PU ELSEVIER
PI AMSTERDAM
PA Radarweg 29, PO Box 211, AMSTERDAM, NETHERLANDS
SN 2212-8271
J9 PROC CIRP
PY 2019
VL 83
BP 22
EP 27
DI 10.1016/j.procir.2019.03.092
PG 6
WC Engineering, Industrial
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Engineering
GA BP8WE
UT WOS:000568146700004
OA gold
DA 2024-09-05
ER
PT J
AU Li, JZ
   Liu, ZX
   Zhou, JH
AF Li, Jizhen
   Liu, Zixu
   Zhou, Jianghua
TI How to become the chosen one in the artificial intelligence market: the
   evidence from China
SO INTERNATIONAL JOURNAL OF TECHNOLOGY MANAGEMENT
LA English
DT Article
DE artificial intelligence market; small and medium-sized enterprises;
   SMEs; innovation performance; social investment; institutional
   intermediaries; public funding; Innofund; signalling effects; China
ID RESEARCH-AND-DEVELOPMENT; DEVELOPMENT SUBSIDIES; ENTREPRENEURIAL FIRMS;
   PUBLIC SUPPORT; POLITICAL TIES; INNOVATION; PERFORMANCE; INVESTMENT;
   STRATEGIES; LEGITIMACY
AB This study aims to explore how firms' innovation performance is related to their possibility of receiving public support, and the boundary conditions of this relationship. Specifically, we focus on the firms in the Chinese artificial intelligence (AI) market, and study a specific public support, namely, Innofund. The results suggest that a firm's innovation performance has an inverted U-shaped effect on its probability of receiving Innofund. The effect, moreover, is moderated by whether a firm has received social investment, that is, the relationship between innovation performance and the probability of receiving funding is flattened by the receipt of social investment. Besides, a firm's ties to institutional intermediaries further strengthen the moderating effect of social investment. The findings carry implications for future research and technology policy.
C1 [Li, Jizhen; Liu, Zixu] Tsinghua Univ, Res Ctr Competit Dynam & Innovat Strategy, Sch Econ & Management, Beijing 100084, Peoples R China.
   [Zhou, Jianghua] Beijing Normal Univ, Business Sch, Beijing 100875, Peoples R China.
C3 Tsinghua University; Beijing Normal University
RP Zhou, JH (corresponding author), Beijing Normal Univ, Business Sch, Beijing 100875, Peoples R China.
EM lijzh@sem.tsinghua.edu.cn; liuzx.18@sem.tsinghua.edu.cn;
   zhoujh@bnu.edu.cn
RI zhou, jiang/KZQ-3297-2024; LI, Jizhen/A-7113-2018
OI LI, Jizhen/0000-0003-1940-5633
FU National Natural Science Foundation of China [71772103, 71772014]; MOE
   Project of Key Research Institute of Humanities and Social Sciences at
   Universities [16JJD630005]
FX This paper is supported by National Natural Science Foundation of China
   (Project Nos. 71772103; 71772014) and MOE Project of Key Research
   Institute of Humanities and Social Sciences at Universities
   (16JJD630005).
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NR 37
TC 3
Z9 3
U1 3
U2 42
PU INDERSCIENCE ENTERPRISES LTD
PI GENEVA
PA WORLD TRADE CENTER BLDG, 29 ROUTE DE PRE-BOIS, CASE POSTALE 856, CH-1215
   GENEVA, SWITZERLAND
SN 0267-5730
EI 1741-5276
J9 INT J TECHNOL MANAGE
JI Int. J. Technol. Manage.
PY 2020
VL 84
IS 1-2
SI SI
BP 8
EP 24
PG 17
WC Engineering, Multidisciplinary; Management; Operations Research &
   Management Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Engineering; Business & Economics; Operations Research & Management
   Science
GA PS3BF
UT WOS:000607800400001
DA 2024-09-05
ER
PT J
AU Wong, LH
   Park, H
   Looi, CK
AF Wong, Lung-Hsiang
   Park, Hyejin
   Looi, Chee-Kit
TI From hype to insight: Exploring ChatGPT's early footprint in education
   via altmetrics and bibliometrics
SO JOURNAL OF COMPUTER ASSISTED LEARNING
LA English
DT Article
DE AI in education; altmetrics; bibliometrics; ChatGPT in education;
   probing publication deluge; research impact
ID IMPACT
AB Background: The emergence of ChatGPT in the education literature represents a transformative phase in educational technology research, marked by a surge in publications driven by initial research interest in new topics and media hype. While these publications highlight ChatGPT's potential in education, concerns arise regarding their quality, methodology, and uniqueness. Objective: Our study employs unconventional methods by combining altmetrics and bibliometrics to explore ChatGPT in education comprehensively. Methods: Two scholarly databases, Web of Science and Altmetric, were adopted to retrieve publications with citations and those mentioned on social media, respectively. We used a search query, "ChatGPT," and set the publication date between November 30th, 2022, and August 31st, 2023. Both datasets were within the education-related domains. Through a filtering process, we identified three publication categories: 49 papers with both altmetrics and citations, 60 with altmetrics only, and 66 with citations only. Descriptive statistical analysis was conducted on all three lists of papers, further dividing the entire collection into three distinct periods. All the selected papers underwent detailed coding regarding open access, paper types, subject domains, and learner levels. Furthermore, we analysed the keywords occurring and visualized clusters of the co-occurring keywords. Results and Conclusions: An intriguing finding is the significant correlation between media/social media mentions and academic citations in ChatGPT in education papers, underscoring the transformative potential of ChatGPT and the urgency of its incorporation into practice. Our keyword analysis also reveals distinctions between the themes of the papers that received both mentions and citations and those that received only citations but no mentions. Additionally, we noticed a limitation that authors' choice of keywords might be influenced by individual subjective judgements, potentially skewing results in thematic analysis based solely on author-assigned keywords such as keyword co-occurrence analysis. Henceforth, we advocate for developing a standardized keyword taxonomy in the educational technology field and integrating Large Language Models to enhance keyword analysis in altmetric and bibliometric tools. This study reveals that ChatGPT in education literature is evolving from rapid publication to rigorous research.
C1 [Wong, Lung-Hsiang] Nanyang Technol Univ, Natl Inst Educ, Singapore, Singapore.
   [Park, Hyejin] Korea Inst Sci & Technol Informat, Daejeon, South Korea.
   [Looi, Chee-Kit] Educ Univ Hong Kong, Dept Curriculum & Instruct, Hong Kong, Peoples R China.
   [Park, Hyejin] Korea Inst Sci & Technol Informat, 245 Daehak Ro, Daejeon 34141, South Korea.
C3 Nanyang Technological University; National Institute of Education (NIE)
   Singapore; Korea Institute of Science & Technology Information (KISTI);
   Education University of Hong Kong (EdUHK); Korea Institute of Science &
   Technology Information (KISTI)
RP Park, H (corresponding author), Korea Inst Sci & Technol Informat, 245 Daehak Ro, Daejeon 34141, South Korea.
EM hpark7@kisti.re.kr
OI Park, Hyejin/0000-0001-9695-8456; Looi, Chee-Kit/0000-0001-9905-2713;
   Wong, Lung-Hsiang/0000-0002-0402-9199
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NR 75
TC 0
Z9 0
U1 43
U2 43
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0266-4909
EI 1365-2729
J9 J COMPUT ASSIST LEAR
JI J. Comput. Assist. Learn.
PD AUG
PY 2024
VL 40
IS 4
BP 1428
EP 1446
DI 10.1111/jcal.12962
EA FEB 2024
PG 19
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA ZD1B9
UT WOS:001174528300001
DA 2024-09-05
ER
PT J
AU Fu, CB
   Luo, HG
   Liang, XJ
   Yu, SQ
AF Fu, Chenbo
   Luo, Haogeng
   Liang, Xuejiao
   Yu, Shanqing
TI The profit and risk in the interdisciplinary behavior
SO FRONTIERS IN PHYSICS
LA English
DT Article
DE interdisciplinary behavior; scientific influence; large and small
   disciplines; interdisciplinary distance; causal inference
ID PROPENSITY SCORE; SCIENCE; IMPACT; DIVERSITY
AB Evaluating the influence of interdisciplinary research is important to the development of science. This work considers the large and small disciplines, calculates the interdisciplinary distance, and analyzes the influence of interdisciplinary behavior and interdisciplinary distance in the academic network. The results show that the risk of interdisciplinary behavior in the large discipline is more significant than the benefits. The peer in the small disciplines will tend to agree with the results of the small discipline across the large discipline. We further confirmed this conclusion by utilizing PSM-DID. The analysis between interdisciplinary distance and scientists' influence shows that certain risks will accompany any distance between disciplines. However, there still exists a "Sweet Spot " which could bring significant rewards. Overall, this work provides a feasible approach to studying and understanding interdisciplinary behaviors in science.
C1 [Fu, Chenbo; Luo, Haogeng; Liang, Xuejiao; Yu, Shanqing] Zhejiang Univ Technol, Inst Cyberspace Secur, Hangzhou, Peoples R China.
   [Fu, Chenbo; Luo, Haogeng; Liang, Xuejiao; Yu, Shanqing] Zhejiang Univ Technol, Coll Informat Engn, Hangzhou, Peoples R China.
C3 Zhejiang University of Technology; Zhejiang University of Technology
RP Fu, CB; Yu, SQ (corresponding author), Zhejiang Univ Technol, Inst Cyberspace Secur, Hangzhou, Peoples R China.; Fu, CB; Yu, SQ (corresponding author), Zhejiang Univ Technol, Coll Informat Engn, Hangzhou, Peoples R China.
EM cbfu@zjut.edu.cn; yushanqing@zjut.edu.cn
FU National Natural Science Foundation of China [62103374]; Basic Public
   Welfare Research Project of Zhejiang Province [LGF20F020016]
FX Funding This work was supported by the National Natural Science
   Foundation of China under Grant (62103374) and the Basic Public Welfare
   Research Project of Zhejiang Province under Grant (LGF20F020016).
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NR 71
TC 1
Z9 1
U1 10
U2 28
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 2296-424X
J9 FRONT PHYS-LAUSANNE
JI Front. Physics
PD JAN 19
PY 2023
VL 11
AR 1107446
DI 10.3389/fphy.2023.1107446
PG 10
WC Physics, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Physics
GA 8L8OA
UT WOS:000924038100001
OA gold
DA 2024-09-05
ER
PT J
AU Yazi, FS
   Vong, WT
   Raman, V
   Then, PHH
   Lunia, MJ
AF Yazi, Fatin Syafiqah
   Vong, Wan-Tze
   Raman, Valliappan
   Then, Patrick Hang Hui
   Lunia, Mukulraj J.
TI AN EXPERIMENTAL EVALUATION OF DEEP NEURAL NETWORK MODEL PERFORMANCE FOR
   THE RECOGNITION OF CONTRADICTORY MEDICAL RESEARCH CLAIMS USING SMALL AND
   MEDIUM-SIZED CORPORA
SO MALAYSIAN JOURNAL OF COMPUTER SCIENCE
LA English
DT Article
DE Evidence-based medicine; contradiction detection; medical literature;
   deep neural network; deep learning
AB Corpora come in various shapes and sizes and play an essential role in facilitating Natural Language Processing (NLP) tasks. However, the availability of corpora specialized for Evidence-Based Medicine (EBM) related tasks is limited. The study is aimed to discover how the size of a corpus influence the performance of our Deep Neural Network (DNN) model developed for contradiction detection in medical literature. We explored the potential of the EBM Summarizer corpus by Molla and Santiago-Martinez, a medium-sized corpus to be used with our contradiction detection model. The dataset preparation involves the filtering of open-ended questions, duplicates of claims, and vague claims. As a result, two datasets were created with the claim input represented by sniptext in one dataset and longtext in the other. Experiments were conducted with varying numbers of hidden layers and units of the model using different datasets. The performance of the DNN model was recorded and compared with the result of using a small-sized corpus. It was found that the DNN model performance did not improve even after it was trained with a larger dataset derived from the medium-sized corpus. The factors may include the limitation of the DNN model itself and the quality of the datasets.
C1 [Yazi, Fatin Syafiqah; Vong, Wan-Tze; Raman, Valliappan; Then, Patrick Hang Hui] Swinburne Univ Technol, Fac Engn Comp & Sci, Kuching 93350, Malaysia.
   [Lunia, Mukulraj J.] Sri Krishna Coll Informat Technol, Dept Informat Technol, Coimbatore, Tamil Nadu, India.
C3 Swinburne University of Technology Sarawak; Swinburne University of
   Technology
RP Yazi, FS (corresponding author), Swinburne Univ Technol, Fac Engn Comp & Sci, Kuching 93350, Malaysia.
EM fyazi@swinburne.edu.my; wvong@swinburne.edu.my; vraman@swinburne.edu.my;
   pthen@swinburne.edu.my; mukul_lunia99@outlook.com
RI mo, mo/JRW-0535-2023; raman, Valliappan/E-6393-2018
OI raman, Valliappan/0000-0002-9363-2319
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NR 27
TC 1
Z9 1
U1 4
U2 8
PU UNIV MALAYA, FAC COMPUTER SCIENCE & INFORMATION TECH
PI KUALA LUMPUR
PA UNIV MALAYA, FAC COMPUTER SCIENCE & INFORMATION TECH, KUALA LUMPUR,
   50603, MALAYSIA
SN 0127-9084
J9 MALAYS J COMPUT SCI
JI Malayas. J. Comput. Sci.
PY 2021
SI SI
BP 68
EP 77
DI 10.22452/mjcs.sp2021no2.5
PG 10
WC Computer Science, Artificial Intelligence; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA YQ8SY
UT WOS:000749575900005
OA Bronze
DA 2024-09-05
ER
PT J
AU Cen, H
   Huang, DL
   Liu, Q
   Zong, ZL
   Tang, AP
AF Cen, Hang
   Huang, Delong
   Liu, Qiang
   Zong, Zhongling
   Tang, Aiping
TI Application Research on Risk Assessment of Municipal Pipeline Network
   Based on Random Forest Machine Learning Algorithm
SO WATER
LA English
DT Article
DE machine learning; random forest; municipal pipeline network; monitoring
   data; risk assessment
ID SOLID PARTICLE EROSION; NEURAL-NETWORK; WATER; PREDICTION; FAILURE;
   CLASSIFICATION; PERFORMANCE; REGRESSION; LOCATION; SYSTEM
AB Urban municipal water supply is an important part of underground pipelines, and their scale continues to expand. Due to the continuous improvement in the quality and quantity of data available for pipeline systems in recent years, traditional pipeline network risk assessment cannot cope with the improvement of various monitoring methods. Therefore, this paper proposes a machine learning-based risk assessment method for municipal pipe network operation and maintenance and builds a model example based on the data of a pipeline network base in a park in Suzhou. We optimized the random forest learning model, compared it with other centralized learning methods, and finally evaluated the model's learning effect. Finally, the risk probability associated with each pipe segment sample was obtained, the risk factors affecting the pipe segment's failure were determined, and their relevance and importance ranking was established. The results showed that the most influential factors are pipe material, soil properties, service life, and the number of past failures. The random forest algorithm demonstrated better prediction accuracy and robustness on the dataset.
C1 [Cen, Hang; Huang, Delong; Zong, Zhongling] Jiangsu Ocean Univ, Sch Civil & Ocean Engn, Lianyungang 222005, Peoples R China.
   [Liu, Qiang; Tang, Aiping] Harbin Inst Technol, Sch Civil Engn, Harbin 150090, Peoples R China.
C3 Jiangsu Ocean University; Harbin Institute of Technology
RP Huang, DL (corresponding author), Jiangsu Ocean Univ, Sch Civil & Ocean Engn, Lianyungang 222005, Peoples R China.
EM cen1364570502@gmail.com; huang06080601@163.com; qiangliu_hit@163.com;
   jouzongzhl@jou.edu.cn; tangap@hit.edu.cn
FU National Natural Science Foundation of China [41672287, 51778197];
   Hainan Province Key R&D Program (Social Development) Project of China
   [ZDYF2022SHFZ089]; Jiangsu Province Key R&D Program (Social Development)
   Project of China [BE2021681]
FX This research was supported by the National Natural Science Foundation
   of China (No. 41672287, 51778197), the Hainan Province Key R&D Program
   (Social Development) Project of China (No. ZDYF2022SHFZ089). and the
   Jiangsu Province Key R&D Program (Social Development) Project of China
   (No. BE2021681). The supports are gratefully acknowledged.
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NR 46
TC 2
Z9 2
U1 21
U2 50
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2073-4441
J9 WATER-SUI
JI Water
PD MAY 22
PY 2023
VL 15
IS 10
AR 1964
DI 10.3390/w15101964
PG 17
WC Environmental Sciences; Water Resources
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology; Water Resources
GA H6WY3
UT WOS:000997354000001
OA gold
DA 2024-09-05
ER
PT J
AU Pandey, S
   Verma, MK
   Shukla, R
AF Pandey, Shriram
   Verma, Manoj Kumar
   Shukla, Ravi
TI A Scientometric Analysis of Scientific Productivity of Artificial
   Intelligence Research in India
SO JOURNAL OF SCIENTOMETRIC RESEARCH
LA English
DT Article
DE Scientometrics; Artificial Intelligence; Collaboration Coefficient;
   Collaborative Index; Relative Growth Rate
ID OUTPUT
AB The study presents a scientometric analsyis of publications related to 'Artificial Intelligence' research in India during 2009-2018. In today's ICT driven world, artificial intelligence has taken up some tasks of our daily life to make it easier. As a consequence, extensive research is going on "Artificial Intelligence" to find out it's potential in knowledge development. The paper analyses the bibliographic data retrived from Scopus database extracted with a suitable search query. The study was conducted taking the chronological growth of research publications, relative growth rate, doubling time, scientometric profile of authors, document type of publications, source profile, keyword analysis, institution wise distribution of publications, funding agency wise distribution. The analysis was conducted using the MS-Excel. The study reveals that a maximum number of publications are in the form of conference procedings and articles. Artificial Intelligence, Learning system, algorithms, data mining are the keywords with maximum number of occurences. The findings of the study implies India need become more competitive with the world leaders in artificial intelligence research. To get more return from AI applications, the stakeholders are required to play a catalytic role to build and strengthen research capacity in the nation by paving quality research environment, adequate funding, research incentives, and development of IT infrastructure.
C1 [Pandey, Shriram] Banaras Hindu Univ, Dept Lib & Informat Sci, Varanasi, Uttar Pradesh, India.
   [Verma, Manoj Kumar; Shukla, Ravi] Mizoram Univ, Dept Lib & Informat Sci, Aizawl 796001, Mizoram, India.
C3 Banaras Hindu University (BHU); Mizoram University
RP Verma, MK (corresponding author), Mizoram Univ, Dept Lib & Informat Sci, Aizawl 796001, Mizoram, India.
EM manojdlis@mzu.edu.in
RI Verma, Manoj Kumar/ABE-4906-2020; Pandey, Shri Ram/HNS-6652-2023
OI Pandey, Shri Ram/0000-0002-1690-6603; Shukla, Dr.
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NR 13
TC 2
Z9 2
U1 2
U2 13
PU PHCOG NET
PI KARNATAKA
PA 17, 2ND FLR, BUDDHA VIHAR RD, NEAR SPORTS ZONE, COX TOWN, BENGALURU,
   KARNATAKA, 560005, INDIA
SN 2321-6654
EI 2320-0057
J9 J SCIENTOMETR RES
JI J. Scientometr. Res.
PD MAY-AUG
PY 2021
VL 10
IS 2
BP 245
EP 250
DI 10.5530/jscires.10.2.38
PG 6
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA WK3EL
UT WOS:000709612000011
OA hybrid
DA 2024-09-05
ER
PT J
AU Varsha, PS
   Akter, S
   Kumar, A
   Gochhait, S
   Patagundi, B
AF Varsha, P. S.
   Akter, Shahriar
   Kumar, Amit
   Gochhait, Saikat
   Patagundi, Basanna
TI The Impact of Artificial Intelligence on Branding: A Bibliometric
   Analysis (1982-2019)
SO JOURNAL OF GLOBAL INFORMATION MANAGEMENT
LA English
DT Article
DE Artificial Intelligence; Bibliometric Analysis; Branding; Chatbot;
   Neural Network; VOS Viewer
ID SOCIAL-MEDIA; NEURAL-NETWORKS; BIG DATA; BUSINESS; CHAIN; INDUSTRY;
   USER; AI; ENVIRONMENTS; MOTIVATIONS
AB Understanding the growth paths of artificial intelligence (AI) and its impact on branding is extremely pertinent of technology-driven marketing. This explorative research covers a complete bibliometric analysis of the impact of AI on branding. The sample for this research included all 117 articles from the period of 1982-2019 in the Scopus database. A bibliometric study was conducted using co-occurrence, citation analysis and co-citation analysis. The empirical analysis investigates the value propositions of AI on branding. The study revealed the nine clusters of co-occurrence: Social Media Analytics and Brand Equity; Neural Networks and Brand Choice; Chat Bots-Brand Intimacy; Twitter, Facebook, Instagram-Luxury Brands; Interactive Agent-Brand Love and User Choice; Algorithm Recommendations and E-Brand Experience; User-Generated Content-Brand Sustainability; Brand Intelligence Analytics; and Digital Innovations and Brand Excellence. The findings also identify four clusters of citation analysis-Social Media Analysis and Brand Photos, Network Analysis and E-Commerce, Hybrid Simulating Modelling, and Real-time Knowledge-Based Systems-and four clusters of co-citation analysis: B2B Technology Brands, AI Fostered E-Brands, Information Cascades and Online Brand Ratings, and Voice Assistants-Brand Eureka Moments. Overall, the study presents the patterns of convergence and divergence of themes, narrowing to the specific topic, and multidisciplinary engagement in research, thus offering the recent insights in the field of AI on branding.
C1 [Varsha, P. S.; Patagundi, Basanna] Cambridge Inst Technol, Bangalore, Karnataka, India.
   [Akter, Shahriar] Univ Wollongong, Digital Mkt Analyt & Innovat, Sch Management & Mkt, Wollongong, NSW, Australia.
   [Kumar, Amit] Univ Newcastle, Newcastle Business Sch, Callaghan, NSW, Australia.
   [Gochhait, Saikat] Symbiosis Int Univ Deemed, Symbiosis Inst Digital & Telecom Management, Pune, Maharashtra, India.
C3 University of Wollongong; University of Newcastle; Symbiosis
   International University; Symbiosis Institute of Digital & Telecom
   Management (SIDTM)
RP Varsha, PS (corresponding author), Cambridge Inst Technol, Bangalore, Karnataka, India.
RI Kumar, Amit/GQA-4556-2022; Akter, Shahriar/S-2888-2019; Gochhait,
   Saikat/G-2182-2014; Gochhait (Honoris Causa), Saikat/AAP-4107-2021
OI Kumar, Amit/0000-0001-5883-6693; Akter, Shahriar/0000-0002-2050-9985;
   Gochhait (Honoris Causa), Saikat/0000-0003-4583-9208; Kumar,
   Amit/0000-0002-8367-9981; PS, Varsha/0000-0002-2194-0363
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NR 146
TC 30
Z9 32
U1 41
U2 288
PU IGI GLOBAL
PI HERSHEY
PA 701 E CHOCOLATE AVE, STE 200, HERSHEY, PA 17033-1240 USA
SN 1062-7375
EI 1533-7995
J9 J GLOB INF MANAG
JI J. Glob. Inf. Manag.
PD JUL-AUG
PY 2021
VL 29
IS 4
BP 221
EP 246
DI 10.4018/JGIM.20210701.oa10
PG 26
WC Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science
GA SA7KG
UT WOS:000649479400010
OA gold
DA 2024-09-05
ER
PT C
AU Makady, H
   Liu, FJ
AF Makady, Heidi
   Liu, Fanjue
BE Kurosu, M
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SE Lecture Notes in Computer Science
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DT Proceedings Paper
CT Human Computer Interaction Thematic Area Conference Held as Part of the
   24th International Conference on Human-Computer Interaction (HCII)
CY JUN 26-JUL 01, 2022
CL ELECTR NETWORK
DE Systematic review; Human-machine communication; LDA topic modeling;
   Affiliation; Funding; Technology; Method; Theory; Research trends
ID RESPONSES
AB This study explores the trends in Human-Machine Communication (HMC) scholarship in the past decade. We examined 444 peer-reviewed empirical studies published between 2010 and 2021 across journals with highest impact factor according to Social Sciences Citation Index (SSCI). Through a systematic review, we looked at theoretical frameworks, methodological approaches, studied technologies, funding sources, and contributing countries in HMC studies. Using an LDA topic modeling on article abstracts, we further explore the top topic composition in the field and topic distribution across the journals in the past decade. Our analysis revealed diversity among contributing countries. The United States-led studies saw the highest share in HMC research, followed by Asia and Europe. Funding saw a dominant contribution from government and university. A diversity in thematic focus was observed with some topics' dominance among domain-specific journals. Significant differences among journals in terms of theory, method, investigated technology and contributing disciplinary affiliation were also found.
C1 [Makady, Heidi; Liu, Fanjue] Univ Florida, Gainesville, FL 32611 USA.
C3 State University System of Florida; University of Florida
RP Makady, H (corresponding author), Univ Florida, Gainesville, FL 32611 USA.
EM Makady.h@ufl.edu; fanjueliu@ufl.edu
RI Liu, Fanjue/JBS-1440-2023
OI Liu, Fanjue/0000-0001-5007-900X; Makady, Heidi/0000-0003-3083-6522
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NR 47
TC 0
Z9 0
U1 3
U2 13
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 0302-9743
EI 1611-3349
BN 978-3-031-05311-5; 978-3-031-05310-8
J9 LECT NOTES COMPUT SC
PY 2022
VL 13302
BP 83
EP 103
DI 10.1007/978-3-031-05311-5_6
PG 21
WC Computer Science, Cybernetics; Computer Science, Theory & Methods;
   Ergonomics
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Engineering
GA BU0OC
UT WOS:000870723000006
DA 2024-09-05
ER
PT J
AU Fernández, JMM
   Moreno, JJG
   Vergara-González, EP
   Iglesias, GA
AF Mesa Fernandez, Jose Manuel
   Gonzalez Moreno, Juan Jose
   Vergara-Gonzalez, Eliseo P.
   Alonso Iglesias, Guillermo
TI Bibliometric Analysis of the Application of Artificial Intelligence
   Techniques to the Management of Innovation Projects
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE research; innovation; artificial intelligence; project management
AB Due to their specific characteristics, innovation projects are developed in contexts with great volatility, uncertainty, complexity, and even ambiguity. Project management has needed to adopt changes to ensure success in this type of project. Artificial intelligence (AI) techniques are being used in these changing environments to increase productivity. This work collected and analyzed those areas of technological innovation project management, such as risk management, costs, and deadlines, in which the application of artificial-intelligence techniques is having the greatest impact. With this objective, a search was carried out in the Scopus database including the three areas involved, that is, artificial intelligence, project management, and research and innovation. The resulting document set was analyzed using the co-word bibliographic method. Then, the results obtained were analyzed first from a global point of view and then specifically for each of the domains that the Project Management Institute (PMI) defines in project management. Some of the findings obtained indicate that sectors such as construction, software and product development, and systems such as knowledge management or decision-support systems have studied and applied the possibilities of artificial intelligence more intensively.
C1 [Mesa Fernandez, Jose Manuel; Gonzalez Moreno, Juan Jose; Vergara-Gonzalez, Eliseo P.; Alonso Iglesias, Guillermo] Univ Oviedo, Dept Min Exploitat & Prospecting, Project Engn Area, Independencia St 13, Oviedo 33004, Spain.
C3 University of Oviedo
RP Fernández, JMM (corresponding author), Univ Oviedo, Dept Min Exploitat & Prospecting, Project Engn Area, Independencia St 13, Oviedo 33004, Spain.
EM jmmesa@uniovi.es
RI Fernández, José Manuel Mesa/A-4994-2010
OI Fernández, José Manuel Mesa/0000-0002-0754-7426; Alonso Iglesias,
   Guillermo/0000-0003-4282-0376
FU Regional Ministry of Science and Innovation; University of the
   Principality of Asturias [AYUD/2021/50953]
FX This study was funded by the Regional Ministry of Science and Innovation
   and the University of the Principality of Asturias (grant number
   AYUD/2021/50953).
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NR 71
TC 3
Z9 3
U1 24
U2 81
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2076-3417
J9 APPL SCI-BASEL
JI Appl. Sci.-Basel
PD NOV
PY 2022
VL 12
IS 22
AR 11743
DI 10.3390/app122211743
PG 15
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA 6J8OV
UT WOS:000887078700001
OA gold
DA 2024-09-05
ER
PT J
AU Chen, XL
   Zou, D
   Xie, HR
   Su, F
AF Chen, Xieling
   Zou, Di
   Xie, Haoran
   Su, Fan
TI Twenty-five years of computer-assisted language learning: A topic
   modeling analysis
SO LANGUAGE LEARNING & TECHNOLOGY
LA English
DT Article
DE Computer Assisted Language Learning; Structural Topic Modeling;
   Bibliometrics; Mobile Assisted Language Learning
ID MEDIATED COMMUNICATION; TECHNOLOGIES; SLA; METAANALYSIS; PERFORMANCE;
   CULTURE; IMPACT; SENSE; EFL; CMC
AB The advance of educational technologies and digital devices have made computer-assisted language learning (CALL) an active interdisciplinary field with increasing research potential and topic diversity. Questions like "what topics and technologies attract the interest of the CALL community?," "how have these topics and technologies evolved?," and "what is the future of CALL?" are key to understanding where the CALL field has been and where it is going. To help answer these questions, the present review combined structural topic modeling, the Mann-Kendall trend test, and hierarchical clustering with bibliometrics to investigate the research status, trends, and prominent issues in CALL from 1,295 articles over the past 25 years ending in 2020. Major findings revealed that Social Sciences Citation Indexed journals such as Computer Assisted Language Learning, Language Learning & Technology, and ReCALL contributed most to the field. Topics that drew the most interest included mobile-assisted language learning, project-based learning, and blended learning. Topics drawing increasing research interest include mobile-assisted language learning, seamless learning, wiki-based learning, and virtual world and virtual reality. Additionally, the development of mobile devices, games, and virtual worlds continuously promote research attention. Finally, the review showed that scholars and educators are integrating different technologies, such as the mixed use of mobile technology and glosses/annotations for vocabulary learning, and their application into various contexts; one such context being the integration of digital multimodal composing into blended project-based learning.
C1 [Chen, Xieling; Zou, Di; Su, Fan] Educ Univ Hong Kong, Hong Kong, Peoples R China.
   [Xie, Haoran] Lingnan Univ, Dept Comp & Decis Sci, Hong Kong, Peoples R China.
C3 Education University of Hong Kong (EdUHK); Lingnan University
RP Zou, D (corresponding author), Educ Univ Hong Kong, Hong Kong, Peoples R China.
EM xielingchen0708@gmail.com; dizoudaisy@gmail.com; hrxie2@gmail.com;
   s1134959@s.eduhk.hk
RI Xie, Haoran/AFS-3515-2022
OI Xie, Haoran/0000-0003-0965-3617; ZOU, Di/0000-0001-8435-9739
FU Lingnan University [102489]; Education University of Hong Kong
   [RG15/20-21R, KT16/20-21]
FX We are grateful for the anonymous reviewers' helpful comments and
   suggestions on earlier drafts of this paper. This research was funded by
   the Teaching Development Grant (102489) from Lingnan University, and the
   Internal Research Grants (RG15/20-21R, KT16/20-21) from the Education
   University of Hong Kong.
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NR 73
TC 36
Z9 38
U1 13
U2 88
PU UNIV HAWAII, NATL FOREIGN LANGUAGE RESOURCE CENTER
PI HONOLULU
PA 1859 EAST WEST RD, 106, HONOLULU, HI 96822 USA
SN 1094-3501
J9 LANG LEARN TECHNOL
JI Lang. Learn. Technol.
PD OCT
PY 2021
VL 25
IS 3
BP 151
EP 185
PG 35
WC Education & Educational Research; Linguistics
WE Social Science Citation Index (SSCI)
SC Education & Educational Research; Linguistics
GA WG0MM
UT WOS:000706693200012
DA 2024-09-05
ER
PT J
AU Aryadoust, V
AF Aryadoust, Vahid
TI The vexing problem of validity and the future of second language
   assessment
SO LANGUAGE TESTING
LA English
DT Article
DE Artificial intelligence (AI); authenticity; interdisciplinary research;
   language assessment; neuroscience; validity; validity arguments
AB Construct validity and building validity arguments are some of the main challenges facing the language assessment community. The notion of construct validity and validity arguments arose from research in psychological assessment and developed into the gold standard of validation/validity research in language assessment. At a theoretical level, construct validity and validity arguments conflate the scientific reasoning in assessment and policy matters of ethics. Thus, a test validator is expected to simultaneously serve the role of conducting scientific research and examining the consequential basis of assessments. I contend that validity investigations should be decoupled from the ethical and social aspects of assessment. In addition, the near-exclusive focus of empirical construct validity research on cognitive processing has not resulted in sufficient accuracy and replicability in predicting test takers' performance in real language use domains. Accordingly, I underscore the significance of prediction in validation, in contrast to explanation, and propose that the question to ask might not so much be about what a test measures as what type of methods and tools can better generate language use profiles. Finally, I suggest that interdisciplinary alliances with cognitive and computational neuroscience and artificial intelligence (AI) fields should be forged to meet the demands of language assessment in the 21st century.
C1 [Aryadoust, Vahid] Nanyang Technol Univ, Natl Inst Educ, Singapore, Singapore.
   [Aryadoust, Vahid] Nanyang Technol Univ, Natl Inst Educ, Singapore 637616, Singapore.
C3 Nanyang Technological University; National Institute of Education (NIE)
   Singapore; Nanyang Technological University; National Institute of
   Education (NIE) Singapore
RP Aryadoust, V (corresponding author), Nanyang Technol Univ, Natl Inst Educ, Singapore 637616, Singapore.
EM Vahid.aryadoust@nie.edu.sg
RI Aryadoust, Vahid/AAJ-1764-2020
OI Aryadoust, Vahid/0000-0001-6960-2489
CR Aryadoust V, 2020, FRONT PSYCHOL, V11, DOI 10.3389/fpsyg.2020.01941
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NR 24
TC 7
Z9 7
U1 10
U2 25
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 0265-5322
EI 1477-0946
J9 LANG TEST
JI Lang. Test.
PD JAN
PY 2023
VL 40
IS 1
BP 8
EP 14
DI 10.1177/02655322221125204
EA JAN 2023
PG 7
WC Linguistics; Language & Linguistics
WE Social Science Citation Index (SSCI); Arts &amp; Humanities Citation Index (A&amp;HCI)
SC Linguistics
GA 7W5MS
UT WOS:000911148000001
DA 2024-09-05
ER
PT J
AU Wang, PL
   Su, J
AF Wang, Peiling
   Su, Jing
TI Post-publication expert recommendations in faculty opinions
   (F1000Prime): Recommended articles and citations
SO JOURNAL OF INFORMETRICS
LA English
DT Article
DE Post-publication expert recommendations; Cognitive authorities; Citation
   analysis; Sentiment analysis; F1000Prime; Faculty Opinions
AB A B S T R A C T This exploratory study of the post-publication expert recommendations (PPER) of biomedical articles in Faculty Opinions observed whether the recommended articles were cited differently from other articles in the same journal. The collected data include 830 research articles published in Cell, Nature Genetics, Nature Medicine , and PLoS Biology in 2010 and their 205,976 citations in Web of Science (WoS) from 2010 to 2019. Of the 830 articles, 417 were recommended in Faculty Opinions. A recommendation made by a Faculty Member (FM) includes a star rating and optional classification and commentary. For Nature Genetics, Nature Medicine , and PLoS Biology , the recommended articles (dataset.FM) were cited significantly more than other articles (dataset.other). Certain correlations were found between recommendation level and citedness, but a scaled mapping showed no linear relationship between the two measurements. The majority of the articles reached a citation peak two years after publication. The most assigned classification tags are New Finding, Interesting Hypothesis, Technical Advance, and Novel Drug Target. Sentiment analysis of the 118 recommendations of the 30 top articles found that FM ratings were correlated with sentiment intensity level. The repeated measures ANOVA did not show the Matthew effect of citations. Suggestions include refining Faculty Opinions' rating schema.
C1 [Wang, Peiling] Univ Tennessee, Sch Informat Sci, 1345 Circle Pk Dr, Knoxville, TN 37932 USA.
   [Su, Jing] Vanderbilt Univ, Ctr Knowledge Management, Med Ctr, 3401 West End Ave, Nashville, TN 37203 USA.
C3 University of Tennessee System; University of Tennessee Knoxville;
   Vanderbilt University
RP Wang, PL (corresponding author), Univ Tennessee, Sch Informat Sci, 1345 Circle Pk Dr, Knoxville, TN 37932 USA.
EM peilingw@utk.edu; jing.su@vumc.org
OI Wang, Peiling/0000-0003-4202-7570; Su, Jing/0000-0001-6699-6806
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NR 25
TC 2
Z9 2
U1 10
U2 38
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1751-1577
EI 1875-5879
J9 J INFORMETR
JI J. Informetr.
PD AUG
PY 2021
VL 15
IS 3
AR 101174
DI 10.1016/j.joi.2021.101174
EA JUN 2021
PG 20
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA WB5JH
UT WOS:000703607500017
DA 2024-09-05
ER
PT J
AU Sripathi, KN
   Moscarella, RA
   Steele, M
   Yoho, R
   You, HYS
   Prevost, LB
   Urban-Lurain, M
   Merrill, J
   Haudek, KC
AF Sripathi, Kamali N. N.
   Moscarella, Rosa A. A.
   Steele, Matthew
   Yoho, Rachel
   You, Hyesun
   Prevost, Luanna B. B.
   Urban-Lurain, Mark
   Merrill, John
   Haudek, Kevin C. C.
TI Machine Learning Mixed Methods Text Analysis: An Illustration From
   Automated Scoring Models of Student Writing in Biology Education
SO JOURNAL OF MIXED METHODS RESEARCH
LA English
DT Article
DE machine learning; predictive models; constructed response assessments;
   biology assessment; biology education research
ID EXPLANATIONS
AB Assessing student knowledge based on their writing using traditional qualitative methods is time-consuming. To improve speed and consistency of text analysis, we present our mixed methods development of a machine learning predictive model to analyze student writing. Our approach involves two stages: first an exploratory sequential design, and second an iterative complex design. We first trained our predictive model using qualitative coding of categories (ideas) in student writing. We next revised our model based on feedback from instructor-users. The model itself highlighted categories in need of revision. The contribution to mixed methods research lies in our innovative use of the machine learning tool as a rapid, consistent additional coder, and a resource that can predict codes for new student writing.
C1 [Sripathi, Kamali N. N.] Univ Calif Davis, UC Davis Genome Ctr, Davis, CA USA.
   [Moscarella, Rosa A. A.] Univ Massachusetts Amherst, Biol Dept, Amherst, MA USA.
   [Steele, Matthew; Urban-Lurain, Mark] Michigan State Univ, CREATE STEM Inst, E Lansing, MI USA.
   [Yoho, Rachel] George Mason Univ, Stearns Ctr Teaching & Learning, Fairfax, VA USA.
   [You, Hyesun] Univ Iowa, Coll Educ, Iowa, LA USA.
   [Prevost, Luanna B. B.] Univ S Florida, Dept Integrat Biol, Tampa, FL USA.
   [Merrill, John] Michigan State Univ, Dept Microbiol & Mol Genet, E Lansing, MI USA.
   [Haudek, Kevin C. C.] Michigan State Univ, Dept Biochem & Mol Biol, E Lansing, MI USA.
   [Sripathi, Kamali N. N.] Univ Calif Davis, UC Davis Genome Ctr, Davis, CA 95616 USA.
C3 University of California System; University of California Davis;
   University of Massachusetts System; University of Massachusetts Amherst;
   Michigan State University; George Mason University; State University
   System of Florida; University of South Florida; Michigan State
   University; Michigan State University; University of California System;
   University of California Davis
RP Sripathi, KN (corresponding author), Univ Calif Davis, UC Davis Genome Ctr, Davis, CA 95616 USA.
EM ksripathi@ucdavis.edu
RI Haudek, Kevin/AAU-9879-2021
OI Haudek, Kevin/0000-0003-1422-6038; Urban-Lurain,
   Mark/0000-0002-2243-8252
FU National Science Foundation [DUE 1323162, 1347740]; Direct For Education
   and Human Resources; Division Of Undergraduate Education [1347740]
   Funding Source: National Science Foundation
FX The author(s) disclosed receipt of the following financial support for
   the research, authorship, and/or publication of this article: We
   gratefully acknowledge members of the Automated Analysis of Constructed
   Response research group for helpful conversations. This material is
   based upon work supported by the National Science Foundation (DUE
   1323162 and 1347740).
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NR 66
TC 5
Z9 5
U1 6
U2 17
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 1558-6898
EI 1558-6901
J9 J MIX METHOD RES
JI J. Mix Methods Res.
PD JAN
PY 2024
VL 18
IS 1
BP 48
EP 70
DI 10.1177/15586898231153946
EA FEB 2023
PG 23
WC Social Sciences, Interdisciplinary
WE Social Science Citation Index (SSCI)
SC Social Sciences - Other Topics
GA AO0N2
UT WOS:000926059800001
DA 2024-09-05
ER
PT J
AU Hinojo-Lucena, FJ
   Aznar-Díaz, I
   Cáceres-Reche, MP
   Romero-Rodríguez, JM
AF Hinojo-Lucena, Francisco-Javier
   Aznar-Diaz, Inmaculada
   Caceres-Reche, Maria-Pilar
   Romero-Rodriguez, Jose-Maria
TI Artificial Intelligence in Higher Education: A Bibliometric Study on its
   Impact in the Scientific Literature
SO EDUCATION SCIENCES
LA English
DT Article
DE artificial intelligence; emerging technologies; higher education;
   bibliometric study
ID PHYSICAL-EDUCATION; INDICATORS; QUALITY; WEB; JCR
AB Artificial intelligence has experienced major developments in recent years and represents an emerging technology that will revolutionize the ways in which human beings live. This technology is already being introduced in the field of higher education, although many teachers are unaware of its scope and, above all, of what it consists of. Therefore, the purpose of this paper was to analyse the scientific production on artificial intelligence in higher education indexed in Web of Science and Scopus databases during 2007-2017. A bespoke methodology of bibliometric studies was used in the most relevant databases in social science. The sample was composed of 132 papers in total. From the results obtained, it was observed that there is a worldwide interest in the topic and that the literature on this subject is just at an incipient stage. We conclude that, although artificial intelligence is a reality, the scientific production about its application in higher education has not been consolidated.
C1 [Hinojo-Lucena, Francisco-Javier; Aznar-Diaz, Inmaculada; Caceres-Reche, Maria-Pilar; Romero-Rodriguez, Jose-Maria] Univ Granada, Dept Didact & Sch Org, E-18071 Granada, Spain.
C3 University of Granada
RP Romero-Rodríguez, JM (corresponding author), Univ Granada, Dept Didact & Sch Org, E-18071 Granada, Spain.
EM fhinojo@ugr.es; iaznar@ugr.es; caceres@ugr.es; romejo@ugr.es
RI HINOJO-LUCENA, FRANCISCO JAVIER/K-2517-2014; Romero-Rodríguez,
   José-María/O-2233-2019; BUCCINI, FRANCESCA/HTM-4917-2023; RECHE, MARÍA
   PILAR CÁCERES/AAE-4164-2020; AZNAR DIAZ, INMACULADA/K-2486-2014
OI Romero-Rodríguez, José-María/0000-0002-9284-8919; RECHE, MARÍA PILAR
   CÁCERES/0000-0002-6323-8054; AZNAR DIAZ, INMACULADA/0000-0002-0018-1150
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Z9 97
U1 24
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PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2227-7102
J9 EDUC SCI
JI Educ. Sci.
PD MAR 8
PY 2019
VL 9
IS 1
AR 51
DI 10.3390/educsci9010051
PG 9
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA HS9DZ
UT WOS:000464169300002
OA Green Submitted, Green Published, gold
DA 2024-09-05
ER
PT J
AU Su, K
   Peng, ZS
   Zhu, D
   Liu, RQ
   Wang, Q
   Cao, R
   He, J
AF Su, Kai
   Peng, Zhongshan
   Zhu, Dan
   Liu, Ruiqian
   Wang, Qin
   Cao, Rong
   He, Jun
TI Water quality evaluation based on water quality index and multiple
   linear regression: A research on Hanyuan Lake in southern Sichuan
   Province, China
SO WATER ENVIRONMENT RESEARCH
LA English
DT Article
DE Hanyuan Lake; multiple linear regression; water quality evaluation;
   water quality index (WQI)
AB This study aims to understand the changes in the water quality of Hanyuan Lake and to show these changes over time. In this study, monthly sampling was conducted at three sampling sites in Hanyuan Lake, and water samples were measured for water quality indicators in the laboratory according to the methods specified in the Environmental Quality Standards for Surface Water (GB3838-2002). Based on the monitoring data from January to December 2019, the WQI comprehensive evaluation method was used to conduct multiple linear stepwise regression analysis, extract key indicators, and establish the WQI(min) model. The results show that according to the WQI comprehensive evaluation method, the WQI values of Hanyuan Lake are all above 90, and the grade is excellent. The overall water quality of Hanyuan Lake is excellent, and most of the water quality indexes reach the Class I standard in the Environmental Quality Standards for Surface Water (GB3838-2002). WQI(min1) (R-2 = 0.86, p < 0.001, PE = 4.28) as the best WQI(min) model. In this study, a model with fewer parameters was established by multiple linear regression method, which is conducive to better monitoring of water quality at monitoring stations while saving costs. Practitioner Points According to the WQI comprehensive evaluation method, the WQI values of Hanyuan Lake are all above 90, the rating is excellent. From January 2019 to September 2020, the monthly change trend of each section is roughly the same, showing a trend of first decreasing, then rising, then decreasing, and finally rising and flattening. The WQI(min) model was developed to completely describe the change in the water body.
C1 [Su, Kai; Peng, Zhongshan; Zhu, Dan; Liu, Ruiqian; Wang, Qin; Cao, Rong] Southwest Jiaotong Univ, Sch Environm Sci & Engn, Chengdu 611756, Peoples R China.
   [He, Jun] Hanyuan Ecol Environm Monitoring Stn Yaan, Yaan, Peoples R China.
C3 Southwest Jiaotong University
RP Su, K (corresponding author), Southwest Jiaotong Univ, Sch Environm Sci & Engn, Chengdu 611756, Peoples R China.
EM ksu@swjtu.edu.cn
RI He, jun/LCW-1246-2024
OI Su, Kai/0000-0002-6291-3628
FU Sichuan Science and Technology Program;  [2021YFS0284]
FX This work was supported by Sichuan Science and Technology Program
   (2021YFS0284).
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NR 27
TC 0
Z9 0
U1 12
U2 12
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1061-4303
EI 1554-7531
J9 WATER ENVIRON RES
JI Water Environ. Res.
PD JUN
PY 2024
VL 96
IS 6
AR e11055
DI 10.1002/wer.11055
PG 12
WC Engineering, Environmental; Environmental Sciences; Limnology; Water
   Resources
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Environmental Sciences & Ecology; Marine & Freshwater
   Biology; Water Resources
GA SC9Q2
UT WOS:001232380400001
PM 38804065
DA 2024-09-05
ER
PT C
AU Zhang, L
   Bai, XZ
   Chen, YJ
   Wang, T
   Hu, FH
   Li, MZ
   Peng, J
AF Zhang Lin
   Bai Xingzhong
   Chen Yajun
   Wang Ting
   Hu Fei-hu
   Li Mingzhu
   Peng Jian
GP IEEE
TI Research on Power Market User Credit Evaluation Based on K-Means
   Clustering and Contour Coefficient
SO 2020 3RD INTERNATIONAL CONFERENCE ON ROBOTICS, CONTROL AND AUTOMATION
   ENGINEERING (RCAE 2020)
LA English
DT Proceedings Paper
CT 3rd International Conference on Robotics, Control and Automation
   Engineering (RCAE)
CY NOV 05-08, 2020
CL Chongqing, PEOPLES R CHINA
DE component; power market; power user credit evaluation; k-means;
   silhouette coefficient; power market
AB Power market trading centers establish and improve the market member's credit system can regulate the market order, reduce transaction costs and prevent economic risk. As the credit evaluation system of most of the domestic power market trading center is still being improved, the design of the evaluation system at this stage must consider the practicability. Based on the actual business data of S Province Power market trading Center, this paper designs the credit evaluation index system of power users which including 11 indexes from the aspects of basic information, market behavior, payment ability and credit record, collects the relevant index data of power users. Evaluate the credit of power users based on K-means Clustering and Silhouette Coefficient method. The credit rating of power users is divided into three grades and four types. The credit evaluation method proposed in this paper is simple and easy to use, easy to understand, and the division results are consistent with the actual situation of most users. It shows that the proposed evaluation index system, clustering algorithm and clustering effect evaluation method are practical.
C1 [Zhang Lin; Bai Xingzhong; Chen Yajun] State Grid Shaanxi Elect Power Co Econ Res Inst, Xian, Peoples R China.
   [Wang Ting] State Grid Beijing Elect Power Co, Maintenance Branch, Beijing, Peoples R China.
   [Hu Fei-hu; Li Mingzhu] Xi An Jiao Tong Univ, Sch Elect Engn, Xian, Peoples R China.
   [Peng Jian] State Grid Xintong Yili Technol Co Ltd, Xian, Peoples R China.
C3 State Grid Corporation of China; Xi'an Jiaotong University
RP Hu, FH (corresponding author), Xi An Jiao Tong Univ, Sch Elect Engn, Xian, Peoples R China.
EM zhanglin1116@139.com; baixingzhong@sn.sgcc.com.cn; junya_chen@163.com;
   xjtuwangting@163.com; hufeihu@mail.xjtu.edu.cn;
   limingzhu@stu.xjtu.edu.cn; 13799375384@139.com
RI WANG, TING/ACE-5023-2022
OI WANG, TING/0000-0003-2481-2890
FU National Natural Science Foundation of China [61174154]; Fundamental
   Research Fund for the Central Universities [xjj2016004]
FX This work is supported by the National Natural Science Foundation of
   China (No. 61174154) and Fundamental Research Fund for the Central
   Universities (No. xjj2016004).
CR Chen Xiaodong, 2016, AUTOMATION POWER SYS, V42, P98
   Credit system construction of the national energy administration, EN IND IMPL OP 2016
   DENTON M, 2018, IEEE TRANSACTION POW, P494
   Liu Jun, 2018, PRICE THEORY PRACTIC, P71
   Ren Baorui., 2011, MODERN ELECT POWER, V28, P90
   ROUSSEEUW PJ, 1987, J COMPUT APPL MATH, V20, P53, DOI 10.1016/0377-0427(87)90125-7
   Saroj Kavita, 2016, INT J COMPUTER SCI E, V6, P279
   Tian Lin, 2019, CHINA SO POWER GRID, V13, P50
   Wang Yuping, 2018, POWER DEMAND SIDE MA, V20, P52
   [徐宏 Xu Hong], 2020, [电网技术, Power System Technology], V44, P2582
   Zhang Yunlei, 2018, CHINA ELECT POWER, V51, P128
   Zou Yunfeng, 2019, POWER DEMAND SIDE MA, V21, P37
NR 12
TC 0
Z9 0
U1 2
U2 6
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-7281-8638-2
PY 2020
BP 64
EP 68
DI 10.1109/rcae51546.2020.9294725
PG 5
WC Automation & Control Systems; Engineering, Mechanical; Robotics
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Automation & Control Systems; Engineering; Robotics
GA BT8HO
UT WOS:000853363700012
DA 2024-09-05
ER
PT C
AU Müller, MC
   Bannister, A
   Reitz, F
AF Mueller, Mark-Christoph
   Bannister, Adam
   Reitz, Florian
BE Doucet, A
   Isaac, A
   Golub, K
   Aalberg, T
   Jatowt, A
TI Off-the-shelf Semantic Author Name Disambiguation for Bibliographic Data
   Bases
SO DIGITAL LIBRARIES FOR OPEN KNOWLEDGE, TPDL 2019
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT 23rd International Conference on Theory and Practice of Digital
   Libraries (TPDL)
CY SEP 09-12, 2019
CL Oslo Metropolitan Univ, Oslo, NORWAY
HO Oslo Metropolitan Univ
DE Author name disambiguation; Semantic similarity; Word embeddings; API;
   Open source software
AB The demo presents a minimalist, off-the-shelf AND tool which provides a fundamental AND operation, the comparison of two publications with ambiguous authors, as an easily accessible HTTP interface. The tool implements this operation using standard AND functionality, but puts particular emphasis on advanced methods from natural language processing (NLP) for comparing publication title semantics.
C1 [Mueller, Mark-Christoph] Heidelberg Inst Theoret Studies gGmbH, Heidelberg, Germany.
   [Bannister, Adam] FIZ Karlsruhe, Math Dept, Berlin, Germany.
   [Reitz, Florian] Schloss Dagstuhl LZI, Wadern, Germany.
C3 Heidelberg Institute for Theoretical Studies; FIZ Karlsruhe - Leibniz
   Institut fur Informationsinfrastruktur
RP Müller, MC (corresponding author), Heidelberg Inst Theoret Studies gGmbH, Heidelberg, Germany.
EM mark-christoph.mueller@h-its.org; adam.bannister@fiz-karlsruhe.de;
   florian.reitz@dagstuhl.de
RI Reitz, Florian/N-8934-2019
OI Reitz, Florian/0000-0001-6114-3388; Muller,
   Mark-Christoph/0000-0001-5639-7682; Bannister, Adam/0000-0002-8849-1152
FU Leibniz Association [SAW-2015-LZI-2]; Klaus Tschira Foundation
FX The work described in this paper was conducted in the project SCAD
   -Scalable Author Disambiguation, funded in part by the Leibniz
   Association (grant SAW-2015-LZI-2), and in part by the Klaus Tschira
   Foundation.
CR Ferreira AA, 2012, SIGMOD REC, V41, P15, DOI 10.1145/2350036.2350040
   Hussain Ijaz, 2017, KNOWL ENG REV, V32, P1
   Kang IS, 2011, INFORM PROCESS MANAG, V47, P452, DOI 10.1016/j.ipm.2010.10.001
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   Strube M., 2018, COLING SYSTEM DEMONS, P53
NR 10
TC 0
Z9 0
U1 0
U2 2
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 0302-9743
EI 1611-3349
BN 978-3-030-30760-8; 978-3-030-30759-2
J9 LECT NOTES COMPUT SC
PY 2019
VL 11799
BP 397
EP 400
DI 10.1007/978-3-030-30760-8_42
PG 4
WC Information Science & Library Science
WE Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Information Science & Library Science
GA BP4BL
UT WOS:000550576600042
DA 2024-09-05
ER
PT J
AU Saeed-Ul Hassan
   Aljohani, NR
   Idrees, N
   Sarwar, R
   Nawaz, R
   Martínez-Cámara, E
   Ventura, S
   Herrera, F
AF Saeed-Ul Hassan
   Aljohani, Naif R.
   Idrees, Nimra
   Sarwar, Raheem
   Nawaz, Raheel
   Martinez-Camara, Eugenio
   Ventura, Sebastian
   Herrera, Francisco
TI Predicting literature's early impact with sentiment analysis in Twitter
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Altmetrics; Twitter; Sentiment analysis; User category; Predicting
   citations
ID RESEARCH EXCELLENCE; TWEETS LINKING; ALTMETRICS; AGREEMENT
AB Traditional bibliometric techniques gauge the impact of research through quantitative indices based on the citations data. However, due to the lag time involved in the citation-based indices, it may take years to comprehend the full impact of an article. This paper seeks to measure the early impact of research articles through the sentiments expressed in tweets about them. We claim that cited articles in either positive or neutral tweets have a more significant impact than those not cited at all or cited in negative tweets. We used the SentiStrength tool and improved it by incorporating new opinion-bearing words into its sentiment lexicon pertaining to scientific domains. Then, we classified the sentiment of 6,482,260 tweets linked to 1,083,535 publications covered by Altmetric.com. Using positive and negative tweets as an independent variable, and the citation count as the dependent variable, linear regression analysis showed a weak positive prediction of high citation counts across 16 broad disciplines in Scopus. Introducing an additional indicator to the regression model, i.e. 'number of unique Twitter users', improved the adjusted R-squared value of regression analysis in several disciplines. Overall, an encouraging positive correlation between tweet sentiments and citation counts showed that Twitter-based opinion may be exploited as a complementary predictor of literatures early impact. (C) 2019 Elsevier B.V. All rights reserved.
C1 [Saeed-Ul Hassan; Idrees, Nimra; Sarwar, Raheem] Informat Technol Univ, 346-B,Ferozepur Rd, Lahore, Pakistan.
   [Aljohani, Naif R.; Ventura, Sebastian; Herrera, Francisco] King Abdulaziz Univ, Fac Comp & Informat Technol, Jeddah, Saudi Arabia.
   [Nawaz, Raheel] Manchester Metropolitan Univ, Dept Operat Technol Events & Hospitality Manageme, Manchester, Lancs, England.
   [Martinez-Camara, Eugenio; Herrera, Francisco] Univ Granada, Andalusian Res Inst Data Sci & Computat Intellige, E-18071 Granada, Spain.
   [Ventura, Sebastian] Univ Granada, Andalusian Res Inst Data Sci & Computat Intellige, Cordoba 14071, Spain.
C3 King Abdulaziz University; Manchester Metropolitan University;
   University of Granada; University of Granada
RP Martínez-Cámara, E (corresponding author), Univ Granada, Andalusian Res Inst Data Sci & Computat Intellige, E-18071 Granada, Spain.
EM saeed-ul-hassan@itu.edu.pk; nraljohani@kau.edu.sa;
   nimraidrees@yahoo.com; raheem.bwl@gmail.com; r.nawaz@mmu.ac.uk;
   emcamara@decsai.ugr.es; sventura@uco.es; herrera@decsai.ugr.es
RI Martínez Cámara, Eugenio/C-5539-2014; Aljohani, Naif R/S-1109-2017;
   Ventura, Sebastian/A-7753-2008; Nawaz, Raheel/AAX-5293-2021; Hassan,
   Saeed-Ul/G-1889-2016
OI Martínez Cámara, Eugenio/0000-0002-5279-8355; Ventura,
   Sebastian/0000-0003-4216-6378; Nawaz, Raheel/0000-0001-9588-0052;
   /0000-0002-0640-807X; Hassan, Saeed-Ul/0000-0002-6509-9190
FU Spanish Ministry of Science and Technology [TIN2017-89517-P,
   T1N2017-83445-P]; Fondo Europeo de Desarrollo Regional (FEDER); NRPU
   from Higher Education Commission of Pakistan [6857]; Juan de la Cierva
   Formacion Programme of the Spanish government [FJCI-2016-28353]
FX We should like to thank Altmetric.com for granting us access to their
   dataset for research purposes. This work was partially supported by the
   Spanish Ministry of Science and Technology under the projects
   TIN2017-89517-P and T1N2017-83445-P and a grant from the Fondo Europeo
   de Desarrollo Regional (FEDER). Saeed Ul Hassan was supported by NRPU
   Grant #6857, received from Higher Education Commission of Pakistan.
   Eugenio Martinez Camara was supported by the Juan de la Cierva Formacion
   Programme (FJCI-2016-28353) of the Spanish government.
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NR 40
TC 41
Z9 40
U1 3
U2 47
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29a, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD MAR 15
PY 2020
VL 192
AR 105383
DI 10.1016/j.knosys.2019.105383
PG 10
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA KT9MF
UT WOS:000519335400040
OA Green Accepted
DA 2024-09-05
ER
PT J
AU Gao, DK
   Haverly, A
   Mittal, S
   Wu, JM
   Chen, JD
AF Gao, Di Kevin
   Haverly, Andrew
   Mittal, Sudip
   Wu, Jiming
   Chen, Jingdao
TI AI Ethics: A Bibliometric Analysis, Critical Issues, and Key Gaps
SO INTERNATIONAL JOURNAL OF BUSINESS ANALYTICS
LA English
DT Article
DE AI Ethics; AI Identification; Artificial Intelligence Ethics;
   Bibliometric Analysis; Human-Like Machine; Large Ethics Model (LEM);
   Literature Review; Machine-Like Human
AB Artificial intelligence (AI) ethics has emerged as a burgeoning yet pivotal area of scholarly research. This study conducts a comprehensive bibliometric analysis of the AI ethics literature over the past two decades. The analysis reveals a discernible tripartite progression, characterized by an incubation phase, followed by a subsequent phase focused on imbuing AI with human-like attributes, culminating in a third phase emphasizing the development of human-centric AI systems. After that, they present seven key AI ethics issues, encompassing the Collingridge dilemma, the AI status debate, challenges associated with AI transparency and explainability, privacy protection complications, considerations of justice and fairness, concerns about algocracy and human enfeeblement, and the issue of superintelligence. Finally, they identify two notable research gaps in AI ethics regarding the large ethics model (LEM) and AI identification and extend an invitation for further scholarly research.
C1 [Gao, Di Kevin; Wu, Jiming] Calif State Univ East Bay, Hayward, CA 94542 USA.
   [Haverly, Andrew; Mittal, Sudip; Chen, Jingdao] Mississippi State Univ, Mississippi State, MS USA.
C3 California State University System; California State University East
   Bay; Mississippi State University
RP Gao, DK (corresponding author), Calif State Univ East Bay, Hayward, CA 94542 USA.
OI Gao, Di Kevin/0009-0008-7391-5208
FU National Science Foundation (NSF) [2246920]
FX The work reported herein was supported by the National Science
   Foundation (NSF) (Award #2246920) . Any opinions, findings, conclusions,
   or recommendations expressed in this material are those of the authors
   and do not necessarily reflect the views of the NSF.
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NR 81
TC 0
Z9 0
U1 33
U2 33
PU IGI GLOBAL
PI HERSHEY
PA 701 E CHOCOLATE AVE, STE 200, HERSHEY, PA 17033-1240 USA
SN 2334-4547
EI 2334-4555
J9 INT J BUS ANAL
JI Int. J. Bus. Anal.
PY 2024
VL 11
IS 1
AR 338367
DI 10.4018/IJBAN.338367
PG 19
WC Business
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA IR6A2
UT WOS:001168083600001
OA gold
DA 2024-09-05
ER
PT J
AU Heibi, I
   Peroni, S
AF Heibi, Ivan
   Peroni, Silvio
TI A qualitative and quantitative analysis of open citations to retracted
   articles: the Wakefield 1998 et al.'s case
SO SCIENTOMETRICS
LA English
DT Article
DE Citation analysis; Retraction; Topic modeling; Science of Science
AB In this article, we show the results of a quantitative and qualitative analysis of open citations on a popular and highly cited retracted paper: "Ileal-lymphoid-nodular hyperplasia, non-specific colitis and pervasive developmental disorder in children" by Wakefield et al., published in 1998. The main purpose of our study is to understand the behavior of the publications citing one retracted article and the characteristics of the citations the retracted article accumulated over time. Our analysis is based on a methodology which illustrates how we gathered the data, extracted the topics of the citing articles and visualized the results. The data and services used are all open and free to foster the reproducibility of the analysis. The outcomes concerned the analysis of the entities citing Wakefield et al.'s article and their related in-text citations. We observed a constant increasing number of citations in the last 20 years, accompanied with a constant increment in the percentage of those acknowledging its retraction. Citing articles have started either discussing or dealing with the retraction of Wakefield et al.'s article even before its full retraction happened in 2010. Articles in the social sciences domain citing the Wakefield et al.'s one were among those that have mostly discussed its retraction. In addition, when observing the in-text citations, we noticed that a large number of the citations received by Wakefield et al.'s article has focused on general discussions without recalling strictly medical details, especially after the full retraction. Medical studies did not hesitate in acknowledging the retraction of the Wakefield et al.'s article and often provided strong negative statements on it.
C1 [Heibi, Ivan; Peroni, Silvio] Univ Bologna, Res Ctr Open Scholarly Metadata, Dept Class Philol & Italian Studies, Bologna, Italy.
   [Heibi, Ivan; Peroni, Silvio] Univ Bologna, Digital Humanities Adv Res Ctr DHArc, Dept Class Philol & Italian Studies, Bologna, Italy.
C3 University of Bologna; University of Bologna
RP Heibi, I (corresponding author), Univ Bologna, Res Ctr Open Scholarly Metadata, Dept Class Philol & Italian Studies, Bologna, Italy.; Heibi, I (corresponding author), Univ Bologna, Digital Humanities Adv Res Ctr DHArc, Dept Class Philol & Italian Studies, Bologna, Italy.
EM ivan.heibi2@unibo.it; silvio.peroni@unibo.it
RI Heibi, Ivan/AAZ-9145-2021; Heibi, Ivan/GQZ-8619-2022
OI Heibi, Ivan/0000-0001-5366-5194; Peroni, Silvio/0000-0003-0530-4305
FU Alma Mater Studiorum -Universita di Bologna within the CRUI-CARE
   Agreement
FX Open access funding provided by Alma Mater Studiorum -Universita di
   Bologna within the CRUI-CARE Agreement.
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NR 53
TC 14
Z9 14
U1 7
U2 48
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD OCT
PY 2021
VL 126
IS 10
BP 8433
EP 8470
DI 10.1007/s11192-021-04097-5
EA AUG 2021
PG 38
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA UW2MR
UT WOS:000681571100002
PM 34376878
OA Green Published, hybrid
DA 2024-09-05
ER
PT J
AU Yu, LH
   Yu, ZG
AF Yu, Liheng
   Yu, Zhonggen
TI Qualitative and quantitative analyses of artificial intelligence ethics
   in education using VOSviewer and CitNetExplorer
SO FRONTIERS IN PSYCHOLOGY
LA English
DT Article
DE artificial intelligence; ethics; bibliometric analysis; VOSviewer;
   CitNetExplorer
AB The new decade has been witnessing the wide acceptance of artificial intelligence (AI) in education, followed by serious concerns about its ethics. This study examined the essence and principles of AI ethics used in education, as well as the bibliometric analysis of AI ethics for educational purposes. The clustering techniques of VOSviewer (n = 880) led the author to reveal the top 10 authors, sources, organizations, and countries in the research of AI ethics in education. The analysis of clustering solution through CitNetExplorer (n = 841) concluded that the essence of AI ethics for educational purposes included deontology, utilitarianism, and virtue, while the principles of AI ethics in education included transparency, justice, fairness, equity, non-maleficence, responsibility, and privacy. Future research could consider the influence of AI interpretability on AI ethics in education because the ability to interpret the AI decisions could help judge whether the decision is consistent with ethical criteria.
C1 [Yu, Liheng] Univ Birmingham, Sch Engn, Birmingham, England.
   [Yu, Zhonggen] Beijing Language & Culture Univ, Fac Foreign Studies, Beijing, Peoples R China.
C3 University of Birmingham; Beijing Language & Culture University
RP Yu, ZG (corresponding author), Beijing Language & Culture Univ, Fac Foreign Studies, Beijing, Peoples R China.
EM 401373742@qq.com
RI liu, xinyu/IWD-6630-2023; Liu, Yuan/JFB-4766-2023; yan,
   yan/JVN-1800-2024; zhang, xiang/JJD-7003-2023; liu, bing/JJD-5566-2023;
   li, wl/JJC-0768-2023; Zhou, Yue/JHS-8791-2023; li, wei/IUQ-2973-2023;
   Zhang, Han/JMR-0670-2023; Yang, Fei/JLM-3367-2023; sun,
   chen/JCP-0396-2023; yang, rui/JHI-3328-2023; li, jiaxin/JNT-5073-2023;
   Liu, Yixuan/JFJ-2820-2023; Zhu, Li/JTT-9093-2023; liu, xy/JEP-3175-2023;
   wang, zhiwen/JDV-9990-2023; Yu, Zhonggen/AAE-5514-2020; li,
   yansong/JXL-5023-2024; li, qing/JEF-9044-2023; li, jing/JEF-8436-2023;
   Yang, Jie/JDM-6213-2023; WANG, YANG/JFA-8821-2023; zhang,
   yan/JGL-8022-2023; Zhao, Yi/JFA-7988-2023; yang, li/JGM-1009-2023; Yu,
   Zhonggen/AAJ-3063-2020; wang, KiKi/JFZ-3334-2023; Yuan,
   Yu/KBQ-0606-2024; ZHOU, YUE/IZE-6277-2023; Wang, Yanlin/JGC-6782-2023;
   zhang, xiao/JCN-8822-2023; Chen, Yu/JLL-0171-2023; li,
   zhang/JHV-1750-2023; Chen, Xin/JDN-2017-2023; Wang, Guang/JFS-8374-2023;
   WANG, HUI/JFA-9683-2023; liu, lin/JFK-3401-2023; liu,
   jianyang/JXL-6273-2024; Zhang, Yun/JCN-7026-2023; yang,
   yun/IZE-1092-2023; .., What/IXW-6776-2023; liu, lingling/IUQ-7478-2023;
   李, 嘉馨/IWM-4023-2023; zhang, ly/JMB-7214-2023; Yan,
   Miaochen/JLL-5061-2023
OI Yu, Zhonggen/0000-0002-3873-980X; Yang, Jie/0000-0002-3941-0053; Yu,
   Zhonggen/0000-0002-3873-980X; 
FU 2019 MOOC of Beijing Language and Culture University (Important)
   "Introduction to Linguistics" [MOOC201902]; "Introduction to
   Linguistics" of online and offline mixed courses in Beijing Language and
   Culture University in 2020; Special fund of Beijing Co-construction
   Project-Research and reform of the "Undergraduate Teaching Reform and
   Innovation Project" of Beijing higher education in 2020-innovative
   "multilingual +" excellent talent training system [202010032003];
   research project of Graduate Students of Beijing Language and Culture
   University "Xi Jinping: The Governance of China" [SJTS202108]
FX This work was supported by 2019 MOOC of Beijing Language and Culture
   University (MOOC201902) (Important) "Introduction to Linguistics";
   "Introduction to Linguistics" of online and offline mixed courses in
   Beijing Language and Culture University in 2020; Special fund of Beijing
   Co-construction Project-Research and reform of the "Undergraduate
   Teaching Reform and Innovation Project" of Beijing higher education in
   2020-innovative "multilingual +" excellent talent training system
   (202010032003); The research project of Graduate Students of Beijing
   Language and Culture University "Xi Jinping: The Governance of China"
   (SJTS202108).
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NR 54
TC 9
Z9 11
U1 55
U2 195
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 1664-1078
J9 FRONT PSYCHOL
JI Front. Psychol.
PD MAR 9
PY 2023
VL 14
AR 1061778
DI 10.3389/fpsyg.2023.1061778
PG 14
WC Psychology, Multidisciplinary
WE Social Science Citation Index (SSCI)
SC Psychology
GA A4WU4
UT WOS:000955152700001
PM 36968737
OA Green Published, gold
DA 2024-09-05
ER
PT C
AU Wamba, SF
   Bawack, RE
   Carillo, KDA
AF Wamba, Samuel Fosso
   Bawack, Ransome Epie
   Carillo, Kevin Daniel Andre
BE Pappas, IO
   Mikalef, P
   Dwivedi, YK
   Jaccheri, L
   Krogstie, J
   Mantymaki, M
TI The State of Artificial Intelligence Research in the Context of National
   Security: Bibliometric Analysis and Research Agenda
SO DIGITAL TRANSFORMATION FOR A SUSTAINABLE SOCIETY IN THE 21ST CENTURY
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT 18th International-Federation-of-Information-Processing (IFIP) WG 6.11
   International Conference on E-Business, E-Services, and E-Society (I3E)
CY SEP 18-20, 2019
CL Norwegian Univ Sci & Technol, Fac Informat Technol & Elect Engn, Dept
   Comp, Trondheim, NORWAY
HO Norwegian Univ Sci & Technol, Fac Informat Technol & Elect Engn, Dept Comp
DE Artificial intelligence; National security; Military; Defense;
   Bibliometrics
ID SYSTEMS; SUPPORT; SCIENCE
AB Artificial intelligence (AI) is a growing research topic in national security due to the growing need for peaceful and inclusive societies, as well as for the maintenance of strong institutions of justice. As e-societies continue to evolve due to the advancements made in information and communication technologies (ICT), AI has proven crucial to guarantee the development of security measures, especially against growing cyberthreats and cyberattacks. This relevance has been translated into an explosive growth of AI applications for the improvement of decision support systems, expert systems, robotics, surveillance, and military operations that aim at ensuring national security. However, there is no bibliometric research on AI in national security, especially one that highlights current debates on the topic. This paper presents an overview of research on AI and national security, with emphasis on the research focus areas and debates central to research on the topic. We analyzed 94 references collected from the Web of Science (WoS) Core Collection and used VOS viewer software to analyze them. Based on these analyses, we identified 7 focus areas and 8 debates on AI in national security. We also identified the state and evolution of research on the topic in terms of main journals, authors, institutions, and countries. Our findings help researchers and practitioners better understand the state of the art of AI research on national security, and guides future research and development projects on the topic.
C1 [Wamba, Samuel Fosso; Bawack, Ransome Epie; Carillo, Kevin Daniel Andre] Toulouse Business Sch, 20 Blvd Lascrosses, F-31068 Toulouse, France.
   [Bawack, Ransome Epie] Toulouse 1 Univ Capitole, 2 Rue Doyen Gabriel Marty, F-31042 Toulouse, France.
C3 Universite Federale Toulouse Midi-Pyrenees (ComUE); Universite de
   Toulouse; TBS Education
RP Bawack, RE (corresponding author), Toulouse Business Sch, 20 Blvd Lascrosses, F-31068 Toulouse, France.; Bawack, RE (corresponding author), Toulouse 1 Univ Capitole, 2 Rue Doyen Gabriel Marty, F-31042 Toulouse, France.
EM ransome.bawack@tsm-education.fr
RI Bawack, Ransome/H-6050-2018; Fosso Wamba, Samuel/AAB-4953-2019; Bawack,
   Ransome/GLT-8064-2022
OI Fosso Wamba, Samuel/0000-0002-1073-058X; Bawack,
   Ransome/0000-0002-5441-604X
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NR 24
TC 1
Z9 1
U1 8
U2 27
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 0302-9743
EI 1611-3349
BN 978-3-030-29374-1; 978-3-030-29373-4
J9 LECT NOTES COMPUT SC
PY 2019
VL 11701
BP 255
EP 266
DI 10.1007/978-3-030-29374-1_21
PG 12
WC Computer Science, Information Systems; Computer Science,
   Interdisciplinary Applications
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BQ6GP
UT WOS:000611600800021
DA 2024-09-05
ER
PT J
AU Su, YY
   Wang, SX
   Li, Y
AF Su, Yingying
   Wang, Shengxu
   Li, Yi
TI Research on the improvement effect of machine learning and neural
   network algorithms on the prediction of learning achievement
SO NEURAL COMPUTING & APPLICATIONS
LA English
DT Article
DE Machine learning; Neural network; Student performance; Prediction
ID PARAMETER-IDENTIFICATION; SEARCH; PATTERN; MODEL
AB In order to improve the effect of college student performance prediction, based on machine learning and neural network algorithms, this paper improves the traditional data processing algorithms and proposes a similarity calculation method for courses. Moreover, this paper uses cosine similarity to calculate the similarity of courses. Simultaneously, this paper proposes an improved hybrid multi-weight improvement algorithm to improve the cold start problem that cannot be solved by traditional algorithms. In addition, this paper combines the neural network structure to construct a model framework structure, sets the functional modules according to actual needs, and analyzes and predicts students' personal performance through student portraits. Finally, this paper designs experiments to analyze the effectiveness of the model proposed in this paper. From the experimental data, it can be seen that the model proposed in this paper basically meets the expected requirements.
C1 [Su, Yingying; Wang, Shengxu; Li, Yi] Shenyang Univ, Sch Mech Engn, Shenyang 110044, Liaoning, Peoples R China.
C3 Shenyang University
RP Su, YY (corresponding author), Shenyang Univ, Sch Mech Engn, Shenyang 110044, Liaoning, Peoples R China.
EM suyingying@syu.edu.cn
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NR 22
TC 6
Z9 6
U1 2
U2 37
PU SPRINGER LONDON LTD
PI LONDON
PA 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND
SN 0941-0643
EI 1433-3058
J9 NEURAL COMPUT APPL
JI Neural Comput. Appl.
PD JUN
PY 2022
VL 34
IS 12
SI SI
BP 9369
EP 9383
DI 10.1007/s00521-021-06333-8
EA JUL 2021
PG 15
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA 1H6NM
UT WOS:000675747400006
DA 2024-09-05
ER
PT C
AU Bose, A
   Behzadan, V
   Aguirre, C
   Hsu, WH
AF Bose, Avishek
   Behzadan, Vahid
   Aguirre, Carlos
   Hsu, William H.
BE Spezzano, F
   Chen, W
   Xiao, X
TI A Novel Approach for Detection and Ranking of Trendy and Emerging Cyber
   Threat Events in Twitter Streams
SO PROCEEDINGS OF THE 2019 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN
   SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM 2019)
LA English
DT Proceedings Paper
CT IEEE/ACM International Conference on Advances in Social Networks
   Analysis and Mining (ASONAM)
CY AUG 27-30, 2019
CL Vancouver, CANADA
DE novelty detection; emerging topics; event detection; named entity
   recognition; threat intelligence; user influence; tweet analysis
AB We present a new machine learning and text information extraction approach to detection of cyber threat events in Twitter that are novel (previously non-extant) and developing (marked by significance with respect to similarity with a previously detected event). While some existing approaches to event detection measure novelty and trendiness, typically as independent criteria and occasionally as a holistic measure, this work focuses on detecting both novel and developing events using an unsupervised machine learning approach. Furthermore, our proposed approach enables the ranking of cyber threat events based on an importance score by extracting the tweet terms that are characterized as named entities, keywords, or both. We also impute influence to users in order to assign a weighted score to noun phrases in proportion to user influence and the corresponding event scores for named entities and keywords. To evaluate the performance of our proposed approach, we measure the efficiency and detection error rate for events over a specified time interval, relative to human annotator ground truth.
C1 [Bose, Avishek; Behzadan, Vahid; Aguirre, Carlos; Hsu, William H.] Kansas State Univ, Dept Comp Sci, Manhattan, KS 66506 USA.
C3 Kansas State University
RP Bose, A (corresponding author), Kansas State Univ, Dept Comp Sci, Manhattan, KS 66506 USA.
EM abose@ksu.edu; behzadan@ksu.edu; caguirre97@ksu.edu; bhsu@ksu.edu
RI Bose, Avishek/KEH-4123-2024; Behzadan, Vahid/AAZ-5344-2020; Aguirre,
   Carlos/B-7560-2014
OI Bose, Avishek/0000-0001-5936-3170; Behzadan, Vahid/0000-0002-6229-9365; 
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NR 22
TC 10
Z9 14
U1 0
U2 1
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES
BN 978-1-4503-6868-1
PY 2019
BP 871
EP 878
DI 10.1145/3341161.3344379
PG 8
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Telecommunications
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Telecommunications
GA BP5IK
UT WOS:000555683800149
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Moore, CL
   Wang, NN
   Johnson, J
   Manyibe, EO
   Washington, AL
   Muhammad, A
AF Moore, Corey L.
   Wang, Ningning
   Johnson, Jean
   Manyibe, Edward O.
   Washington, Andre L.
   Muhammad, Atashia
TI Return-to-Work Outcome Rates of African American Versus White Veterans
   Served by State Vocational Rehabilitation Agencies: A Randomized
   Split-Half Cross-Model Validation Research Design
SO REHABILITATION COUNSELING BULLETIN
LA English
DT Article
DE African American veterans; state vocational rehabilitation agencies;
   RSA-911 data assessment and cross-validation research methods; minority
   access and outcome rates
ID MULTIPLE-REGRESSION; DISABILITIES; PREDICTION; SERVICES
AB The purpose of this study was to identify disparities in successful return-to-work outcome rates based on race, gender, and level of educational attainment at closure among veterans with a signed Individualized Plan for Employment (IPE). A randomized split-half cross-model validation research design was used to develop and test a series of logistic regression models for goodness of fit across two samples (i.e., screening and calibration) of case records (N = 11,337) obtained from the national Fiscal Year (FY) 2013 Rehabilitation Services Administration (RSA)-911 database. The final predictive multinomial logistic regression model indicated that (a) the odds of White veterans successfully returning to work were nearly 11/2 times the odds of African American veterans returning to work and (b) African American female veterans had the lowest probability for successfully returning to work. Moreover, findings indicated that African American veterans' successful return-to-work rates in 5 of the 10 RSA regions were below the national benchmark. Recommendations for policy development and future research directions are presented.
C1 [Moore, Corey L.; Wang, Ningning; Johnson, Jean; Manyibe, Edward O.; Washington, Andre L.; Muhammad, Atashia] Langston Univ, LU RRTC Res & Capac Bldg,4205 N Lincoln Blvd, Oklahoma City, OK 73105 USA.
C3 Langston University
RP Moore, CL (corresponding author), Langston Univ, LU RRTC Res & Capac Bldg,4205 N Lincoln Blvd, Oklahoma City, OK 73105 USA.; Moore, CL (corresponding author), Delta Sigma Theta Sorority Inc, Oklahoma City, OK 73105 USA.
EM clmoore@langston.edu
RI Manyibe, Edward/KDN-0723-2024
OI Manyibe, Edward/0000-0002-4616-9798
FU Department of Education, NIDRR [H133B130023]
FX The author(s) disclosed receipt of the following financial support for
   the research, authorship, and/or publication of this article: This
   research was supported under a grant from the Department of Education,
   NIDRR Grant H133B130023.
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NR 54
TC 4
Z9 12
U1 0
U2 14
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 0034-3552
EI 1538-4853
J9 REHABIL COUNS BULL
JI Rehabil. Couns. Bull.
PD APR
PY 2016
VL 59
IS 3
BP 158
EP 171
DI 10.1177/0034355215579917
PG 14
WC Rehabilitation
WE Social Science Citation Index (SSCI)
SC Rehabilitation
GA DG7FG
UT WOS:000372249600004
DA 2024-09-05
ER
PT C
AU Tenório, K
   Olari, V
   Chikobava, M
   Romeike, R
AF Tenorio, Kamilla
   Olari, Viktoriya
   Chikobava, Margarita
   Romeike, Ralf
GP ACM
TI Artificial Intelligence Literacy Research Field: A Bibliometric Analysis
   from 1989 to 2021
SO PROCEEDINGS OF THE 54TH ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE
   EDUCATION, VOL 1, SIGCSE 2023
LA English
DT Proceedings Paper
CT 54th Annual ACM SIGCSE Technical Symposium on Computer Science Education
   (SIGCSE TS)
CY MAR 15-18, 2023
CL Toronto, CANADA
DE AI literacy; AI education; bibliometric analysis; secondary study
ID BIG DATA; CHALLENGES
AB Artificial Intelligence (AI) literacy is a rapidly evolving research field. Due to the broad scope of AI literacy-related publications, a comprehensive analysis of the field is needed in order to examine the main characteristics of the current scientific output. Based on it, we conducted a bibliometric analysis of the field where we investigated the publications' evolution over time, research constituents (authors, countries, institutions, publication venues), collaboration patterns, and emerging trends. The findings point out that the United States of America (USA), China, Spain, and Germany are the most contributing countries in the AI literacy field. Moreover, the organizations that most contribute to the AI literacy field are the Massachusetts Institute of Technology, the University of Eastern Finland, and the Georgia Institute of Technology. Furthermore, KI - Kunstliche Intelligenz, ACM Transactions on Computing Education and IEEE Access are the most disseminating journals, and FIE, AAAI, SIGCSE and CHI are the most disseminating conferences of AI literacy research. According to keywords co-occurrence analysis, machine learning, data, big data, deep learning, and ethics are the most addressed AI topics. Finally, based on the achieved results, this bibliometric analysis draws some conclusions regarding the AI literacy field and points out potential directions for future works.
C1 [Tenorio, Kamilla; Olari, Viktoriya; Romeike, Ralf] Free Univ Berlin, Berlin, Germany.
   [Chikobava, Margarita] German Res Ctr Artificial Intelligence, Berlin, Germany.
C3 Free University of Berlin
RP Tenório, K (corresponding author), Free Univ Berlin, Berlin, Germany.
EM kamilla.tenorio@fu-berlin.de; viktoriya.olari@fu-berlin.de;
   margarita.chikobava@dfki.de; ralf.romeike@fu-berlin.de
RI Chikobava, Margarita/JAD-2254-2023
OI Chikobava, Margarita/0000-0001-7692-6289; Olari,
   Viktoriya/0000-0002-5113-6624; Romeike, Ralf/0000-0002-2941-4288;
   Tenorio, Kamilla/0000-0002-8088-7507
FU German Federal Ministry of Education and Research (BMBF) [16DHBKI025]
FX The authors would like to thank the German Federal Ministry of Education
   and Research (BMBF) for financial support as part of the project "ENKIS
   -Establishment of sustainable AI-related courses for responsible
   artificial intelligence at Freie Universitat Berlin" (funding code:
   16DHBKI025) and the reviewers for the suggestions for improvement.
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NR 38
TC 9
Z9 9
U1 64
U2 76
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES
BN 978-1-4503-9431-4
PY 2023
BP 1083
EP 1089
DI 10.1145/3545945.3569874
PG 7
WC Computer Science, Theory & Methods; Education, Scientific Disciplines
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Education & Educational Research
GA BW2KI
UT WOS:001117817800156
DA 2024-09-05
ER
PT J
AU Vasishta, P
   Dhingra, N
   Vasishta, S
AF Vasishta, Prihana
   Dhingra, Navjyoti
   Vasishta, Seema
TI Application of artificial intelligence in libraries: a bibliometric
   analysis and visualisation of research activities
SO LIBRARY HI TECH
LA English
DT Article; Early Access
DE Artificial intelligence; Bibliometrics; Libraries; Scopus; Network
   analysis; Cluster analysis
ID SMART; SYSTEM
AB PurposeThis research aims to analyse the current state of research on the application of Artificial Intelligence (AI) in libraries by examining document type, publication year, keywords, country and research methods. The overarching aim is to enrich the existing knowledge of AI-powered libraries by identifying the prevailing research gaps, providing direction for future research and deepening the understanding needed for effective policy development.Design/methodology/approachThis study used advanced tools such as bibliometric and network analysis, taking the existing literature from the SCOPUS database extending to the year 2022. This study analysed the application of AI in libraries by identifying and selecting relevant keywords, extracting the data from the database, processing the data using advanced bibliometric visualisation tools and presenting and discussing the results. For this comprehensive research, the search strategy was approved by a panel of computer scientists and librarians.FindingsThe majority of research concerning the application of AI in libraries has been conducted in the last three years, likely driven by the fourth industrial revolution. Results show that highly cited articles were published by Emerald Group Holdings Ltd. However, the application of AI in libraries is a developing field, and the study highlights the need for more research in areas such as Digital Humanities, Machine Learning, Robotics, Data Mining and Big Data in Academic Libraries.Research limitations/implicationsThis study has excluded papers written in languages other than English that address domains beyond libraries, such as medicine, health, education, science and technology.Practical implicationsThis article offers insight for managers and policymakers looking to implement AI in libraries. By identifying clusters and themes, the article would empower managers to plan ahead, mitigate potential drawbacks and seize opportunities for sustainable growth.Originality/valuePrevious studies on the application of AI in libraries have taken a broad approach, but this study narrows its focus to research published explicitly in Library and Information Science (LIS) journals. This makes it unique compared to previous research in the field.
C1 [Vasishta, Prihana] Punjab Engn Coll, Ctr Management & Humanities, Chandigarh, India.
   [Dhingra, Navjyoti] Panjab Univ, Chandigarh, India.
   [Vasishta, Seema] Punjab Engn Coll, Cent Lib, Chandigarh, India.
C3 Punjab Engineering College (Deemed University); Panjab University;
   Punjab Engineering College (Deemed University)
RP Vasishta, P (corresponding author), Punjab Engn Coll, Ctr Management & Humanities, Chandigarh, India.
EM prihana.ubs@gmail.com
OI Vasishta, Prihana/0000-0002-3833-4992; Vasishta,
   Seema/0000-0003-2764-1724
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NR 43
TC 1
Z9 1
U1 82
U2 104
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 0737-8831
J9 LIBR HI TECH
JI Libr. Hi Tech
PD 2024 JAN 19
PY 2024
DI 10.1108/LHT-12-2023-0589
EA JAN 2024
PG 18
WC Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science
GA FA1F8
UT WOS:001142924300001
DA 2024-09-05
ER
PT J
AU Onan, A
AF Onan, Aytug
TI Two-Stage Topic Extraction Model for Bibliometric Data Analysis Based on
   Word Embeddings and Clustering
SO IEEE ACCESS
LA English
DT Article
DE Topic extraction; machine learning; cluster analysis; text mining
ID SCIENCE; ENSEMBLE
AB Topic extraction is an essential task in bibliometric data analysis, data mining and knowledge discovery, which seeks to identify significant topics from text collections. The conventional topic extraction schemes require human intervention and involve also comprehensive pre-processing tasks to represent text collections in an appropriate way. In this paper, we present a two-stage framework for topic extraction from scientific literature. The presented scheme employs a two-staged procedure, where word embedding schemes have been utilized in conjunction with cluster analysis. To extract significant topics from text collections, we propose an improved word embedding scheme, which incorporates word vectors obtained by word2vec, POS2vec, word-position2vec and LDA2vec schemes. In the clustering phase, an improved clustering ensemble framework, which incorporates conventional clustering methods (i.e., k-means, k-modes, k-means CC, self-organizing maps and DIANA algorithm) by means of the iterative voting consensus, has been presented. In the empirical analysis, we analyze a corpus containing 160,424 abstracts of articles from various disciplines, including agricultural engineering, economics, engineering and computer science. In the experimental analysis, performance of the proposed scheme has been compared to conventional baseline clustering methods (such as, k-means, k-modes, and k-means CC), LDA-based topic modelling and conventional word embedding schemes. The empirical analysis reveals that ensemble word embedding scheme yields better predictive performance compared to the baseline word vectors for topic extraction. Ensemble clustering framework outperforms the baseline clustering methods. The results obtained by the proposed framework show an improvement in Jaccard coefficient, Folkes & Mallows measure and F1 score.
C1 [Onan, Aytug] Izmir Katip Celebi Univ, Comp Engn Dept, TR-35620 Izmir, Turkey.
C3 Izmir Katip Celebi University
RP Onan, A (corresponding author), Izmir Katip Celebi Univ, Comp Engn Dept, TR-35620 Izmir, Turkey.
EM aytug.onan@ikc.edu.tr
RI ONAN, Aytuğ/L-4613-2018
OI ONAN, Aytuğ/0000-0002-9434-5880
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NR 59
TC 193
Z9 193
U1 17
U2 86
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2019
VL 7
BP 145614
EP 145633
DI 10.1109/ACCESS.2019.2945911
PG 20
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering; Telecommunications
GA JQ2YJ
UT WOS:000498816000002
OA gold
HC Y
HP N
DA 2024-09-05
ER
PT J
AU Wang, KL
   Dong, K
   Wu, JC
   Wu, J
AF Wang, Kaili
   Dong, Ke
   Wu, Jiachun
   Wu, Jiang
TI Patterns of artificial intelligence policies in China: a nationwide
   perspective
SO LIBRARY HI TECH
LA English
DT Article; Early Access
DE Artificial intelligence; Policy analysis; Bibliometrics; Policy
   instruments; Policy diffusion; Policy patterns
ID DIFFUSION
AB PurposeThe purpose of this paper is to identify the historical trends and status of the national development of artificial intelligence (AI) from a nationwide perspective and to enable governments at different administrative levels to promote AI development through policymaking.Design/methodology/approachThis paper analyzed 248 Chinese AI policies (36 issued by the state agencies and 212 by the regional agencies). Policy bibliometrics, policy instruments and network analysis were used to reveal the AI policy patterns. Three aspects were analyzed: the spatiotemporal distribution of issued policies, the policy foci and instruments of policy contents and the cooperation and citation among policy-issuing agencies.FindingsResults indicate that Chinese AI development is still in the initial phase. During the policymaking processes, the state and regional policy foci have strong consistency; however, the coordination among state and regional agencies is supposed to be strengthened. According to the issuing time of AI policies, Chinese AI development is in accordance with the global situation and has witnessed unprecedented growth in the last five years. And the coastal provinces have issued more targeted policies than the middle and western provinces. Governments at the state and regional levels have emphasized familiar policy foci and played the role of policymakers, along with regional governments that also functioned as policy executors as well. According to the three-dimension instruments coding, the authors found an uneven structure of policy instruments at both levels. Furthermore, weak cooperation appears at the state level, while little cooperation is found among regional agencies. Regional governments cite state policies, thus leading to the formation of top-down diffusion, lacking bottom-up diffusion.Originality/valueThe paper contributes to the literature by characterizing policy patterns from both external attributes and semantic contents, thus revealing features of policy distribution, contents and agencies. What is more, this research analyzes Chinese AI policies from a nationwide perspective, which contributes to clarifying the overall status and multi-level relationships of policies. The findings also benefit the coordinated development of governments during further policymaking processes.
C1 [Wang, Kaili; Dong, Ke; Wu, Jiachun; Wu, Jiang] Wuhan Univ, Sch Informat Management, Wuhan, Peoples R China.
C3 Wuhan University
RP Wu, J (corresponding author), Wuhan Univ, Sch Informat Management, Wuhan, Peoples R China.
EM jiangw@whu.edu.cn
OI Wu, Jiang/0000-0002-3342-9757
FU This research was supported by the Key Projects of Philosophy and Social
   Sciences Research, Ministry of Education (No. 20JZD024). [20JZD024]; Key
   Projects of Philosophy and Social Sciences Research, Ministry of
   Education
FX This research was supported by the Key Projects of Philosophy and Social
   Sciences Research, Ministry of Education (No. 20JZD024).
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NR 59
TC 2
Z9 2
U1 30
U2 65
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 0737-8831
J9 LIBR HI TECH
JI Libr. Hi Tech
PD 2023 SEP 15
PY 2023
DI 10.1108/LHT-04-2022-0168
EA SEP 2023
PG 31
WC Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science
GA T2MO7
UT WOS:001076377200001
DA 2024-09-05
ER
PT J
AU Wang, X
   Bendle, NT
   Mai, F
   Cotte, J
AF Wang, Xin (Shane)
   Bendle, Neil T.
   Mai, Feng
   Cotte, June
TI The <i>Journal of Consumer Research</i> at 40: A Historical Analysis
SO JOURNAL OF CONSUMER RESEARCH
LA English
DT Article
DE topic modeling; Journal of Consumer Research; historical analysis;
   citation analysis
AB This article reviews 40 years of the Journal of Consumer Research (JCR). Using text mining, we uncover the key phrases associated with consumer research. We use a topic modeling procedure to uncover 16 topics that have been featured in the journal since its inception and to show the trends in topics over time. For example, we highlight the decline in family decision-making research and the flourishing of social identity and influence research since the journal's inception. A citation analysis shows which JCR articles have had the most impact and compares the topics in top-cited articles with all JCR journal articles. We show that methodological and consumer culture articles tend to be heavily cited. We conclude by investigating the scholars who have been the top contributors to the journal across the four decades of its existence. And to better understand which schools have contributed most to the knowledge of consumer research over this history, we provide an analysis of where these top-performing scholars were trained. Our approach shows that the JCR archives can be an excellent source of data for scholars trying to understand the complicated, challenging, and dynamic field of consumer research.
C1 [Wang, Xin (Shane); Bendle, Neil T.; Cotte, June] Univ Western Ontario, Ivey Business Sch, Mkt, London, ON N6G 0N1, Canada.
   [Mai, Feng] Univ Cincinnati, Lindner Coll Business, Cincinnati, OH 45221 USA.
C3 Western University (University of Western Ontario); University System of
   Ohio; University of Cincinnati
RP Wang, X (corresponding author), Univ Western Ontario, Ivey Business Sch, Mkt, 1255 Western Rd, London, ON N6G 0N1, Canada.
EM xwang@ivey.ca; nbendle@ivey.ca; maifg@mail.uc.edu; jcot-te@ivey.ca
RI Bendle, Neil/AAZ-6717-2021; , June Cotte/HKW-4643-2023
OI Bendle, Neil/0000-0002-8557-5983; Cotte, June/0000-0003-0770-8338; Mai,
   Feng/0000-0001-6897-8935
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U1 17
U2 179
PU OXFORD UNIV PRESS INC
PI CARY
PA JOURNALS DEPT, 2001 EVANS RD, CARY, NC 27513 USA
SN 0093-5301
EI 1537-5277
J9 J CONSUM RES
JI J. Consum. Res.
PD JUN
PY 2015
VL 42
IS 1
BP 5
EP 18
DI 10.1093/jcr/ucv009
PG 14
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA CK9ZA
UT WOS:000356596900002
DA 2024-09-05
ER
PT J
AU Sarikoç, GÖ
AF Sarikoc, Gulhan Ozdogan
TI Artificial intelligence applications in the field of streamflow: a
   bibliometric analysis of recent trends
SO HYDROLOGICAL SCIENCES JOURNAL
LA English
DT Article
DE artificial intelligence; streamflow; bibliometric; research trends;
   Scopus; RStudio Bibliometrix
ID NEURAL-NETWORK; WATER-LEVEL; MODELS; PREDICTION; WAVELET; ALGORITHM;
   MACHINE; FLOW; TOOL
AB In this study, a bibliometric analysis technique is used for performance analysis and science mapping of artificial intelligence (AI) applications in streamflow research. This paper examines the current trends in the literature using the Scopus database over the last 37 years. RStudio Bibliometrix software was used to analyse the titles, keywords, abstracts, and full texts of 3000 publications to identify trends in AI models, publication types, journals, citations, authors, countries, and regions. The highest frequency AI-related keyword is "artificial neural networks," which was used in a total of 25587 times. The most common publication type, at 82.1%, is journal articles, and the highest rate of country production is 25% for China. In recent years, streamflow research studies have significantly increased their use of AI applications.
   [GRAPHICS]
   .
C1 [Sarikoc, Gulhan Ozdogan] Amasya Univ, Suluova Vocat Sch, Dept Vegetable & Anim Prod, Amasya, Turkiye.
C3 Ministry of National Education - Turkey; Amasya University
RP Sarikoç, GÖ (corresponding author), Amasya Univ, Suluova Vocat Sch, Dept Vegetable & Anim Prod, Amasya, Turkiye.
EM ozdogan.gulhan@gmail.com
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U1 2
U2 2
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 0262-6667
EI 2150-3435
J9 HYDROLOG SCI J
JI Hydrol. Sci. J.
PD JUL 3
PY 2024
VL 69
IS 9
BP 1141
EP 1157
DI 10.1080/02626667.2024.2356006
EA JUN 2024
PG 17
WC Water Resources
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Water Resources
GA ZJ7M4
UT WOS:001253039200001
DA 2024-09-05
ER
PT J
AU Bircan, T
   Salah, AAA
AF Bircan, Tuba
   Salah, Almila Alkim Akdag
TI A Bibliometric Analysis of the Use of Artificial Intelligence
   Technologies for Social Sciences
SO MATHEMATICS
LA English
DT Article
DE big data; artificial intelligence; computational social science; social
   sciences; bibliometrics
ID BIG DATA RESEARCH; WEB; COLLABORATION; COVERAGE; NETWORK
AB The use of Artificial Intelligence (AI) and Big Data analysis algorithms is complementary to theory-driven analysis approaches and becoming more popular also in social sciences. This paper describes the use of Big Data and computational approaches in social sciences by bibliometric analyses of articles indexed between 2015 and 2020 in Social Sciences Citation Index (SSCI) of the Web of Science repository. We have analysed especially the recent research direction called Computational Social Sciences (CSS) that bridges computer analytical approaches with social science challenges, generating new methodologies of Big Data and AI analytics for social sciences. The results indicate that AI and Big Data practices are not confined to CSS only and are diffused in a wide variety of disciplines under Social Sciences and are made use of in many main research lines as well. Thus, the anticipated overlap between the Social Sciences & AI specialization and CSS has yet to be crystallised. Moreover, the impact of computational social science studies is not permeated to social science citation networks yet. Lastly, we demonstrate that the AI and Big Data publications that appear under the SSCI index are more oriented towards computational studies than addressing social science concepts, concerns, and challenges.
C1 [Bircan, Tuba] Vrije Univ Brussel, Dept Sociol, Interface Demog, Pl Laan 5, B-1050 Brussels, Belgium.
   [Salah, Almila Alkim Akdag] Univ Utrecht, Dept Informat & Comp Sci, Human Ctr Comp Grp, Buys Ballotgebouw BBG 422,Princetonpl 5, NL-3584 CM Utrecht, Netherlands.
C3 Vrije Universiteit Brussel; Utrecht University
RP Bircan, T (corresponding author), Vrije Univ Brussel, Dept Sociol, Interface Demog, Pl Laan 5, B-1050 Brussels, Belgium.
EM tuba.bircan@vub.be
OI Bircan, Tuba/0000-0003-1956-0545
FU European Commission;  [870661]
FX This research is funded by the European Commission through the
   Horizon2020 European project: "HumMingBird-Enhanced migration measures
   from a multidimensional perspective" (GA: 870661).
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NR 64
TC 3
Z9 3
U1 19
U2 53
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2227-7390
J9 MATHEMATICS-BASEL
JI Mathematics
PD DEC
PY 2022
VL 10
IS 23
AR 4398
DI 10.3390/math10234398
PG 17
WC Mathematics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematics
GA 6Z7CO
UT WOS:000897930400001
OA Green Published, gold
DA 2024-09-05
ER
PT C
AU Anderson, DM
   Lantz, PA
AF Anderson, D. Michael
   Lantz, Penelope A.
BE Looi, CK
   Jonassen, D
   Ikeda, M
TI From Research and Development to Field Application: Collaboration
   Between Laboratory and Service Organization For Online Professional
   Education
SO TOWARDS SUSTAINABLE AND SCALABLE EDUCATIONAL INNOVATIONS INFORMED BY
   LEARNING SCIENCES
SE Frontiers in Artificial Intelligence and Applications
LA English
DT Proceedings Paper
CT 13th International Conference on Computers in Education (ICCCE 2005)
CY DEC 08-30, 2005
CL Singapore, SINGAPORE
DE Online learning; nurse training; dementia care; development and
   dissemination
ID DISEASE
AB Health Media Lab, an R & D firm, and The Alzheimer's Association, a large voluntary organization, are collaborating to create an online course to train supervisory nurses in dementia care staff supervision. This paper discusses this collaboration as an example of how researchers and service organizations can work toward mutually beneficial goals: the creation and evaluation of innovative educational materials, as well as their dissemination from laboratory to field use.
C1 [Anderson, D. Michael; Lantz, Penelope A.] Hlth Media Lab, Washington, DC USA.
EM dmichaela@healthmedialab.com
CR *ALZH ASS, 2004, LONG TERM CAR WORKF
   [Anonymous], SUCCESSFUL COMMUNICA
   [Anonymous], 2000, HLTH PEOPL 2010, V2nd
   Brookmeyer R, 1998, AM J PUBLIC HEALTH, V88, P1337, DOI 10.2105/AJPH.88.9.1337
   Hebert LE, 2003, ARCH NEUROL-CHICAGO, V60, P1119, DOI 10.1001/archneur.60.8.1119
   Innes A., 2000, TRAINING DEV DEMENTI
NR 6
TC 0
Z9 0
U1 0
U2 3
PU I O S PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 0922-6389
BN 978-1-58603-573-0
J9 FR ART INT
PY 2005
VL 133
BP 605
EP 608
PG 4
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications; Education & Educational Research
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Education & Educational Research
GA BPN21
UT WOS:000279364400077
DA 2024-09-05
ER
PT J
AU Haber, NA
   Wieten, SE
   Rohrer, JM
   Arah, OA
   Tennant, PWG
   Stuart, EA
   Murray, EJ
   Pilleron, S
   Lam, ST
   Riederer, E
   Howcutt, SJ
   Simmons, AE
   Leyrat, C
   Schoenegger, P
   Booman, A
   Dufour, MSK
   O'Donoghue, AL
   Baglini, R
   Do, S
   Takashima, MD
   Evans, TR
   Rodriguez-Molina, D
   Alsalti, TM
   Dunleavy, DJ
   Meyerowitz-Katz, G
   Antonietti, A
   Calvache, JA
   Kelson, MJ
   Salvia, MG
   Parra, CO
   Khalatbari-Soltani, S
   McLinden, T
   Chatton, A
   Seiler, J
   Steriu, A
   Alshihayb, TS
   Twardowski, SE
   Dabravolskaj, J
   Au, E
   Hoopsick, RA
   Suresh, S
   Judd, N
   Peña, S
   Axfors, C
   Khan, P
   Aguirre, AER
   Odo, NU
   Schmid, I
   Fox, MP
AF Haber, Noah A.
   Wieten, Sarah E.
   Rohrer, Julia M.
   Arah, Onyebuchi A.
   Tennant, Peter W. G.
   Stuart, Elizabeth A.
   Murray, Eleanor J.
   Pilleron, Sophie
   Lam, Sze Tung
   Riederer, Emily
   Howcutt, Sarah Jane
   Simmons, Alison E.
   Leyrat, Clemence
   Schoenegger, Philipp
   Booman, Anna
   Dufour, Mi-Suk Kang
   O'Donoghue, Ashley L.
   Baglini, Rebekah
   Do, Stefanie
   Takashima, Mari De la Rosa
   Evans, Thomas Rhys
   Rodriguez-Molina, Daloha
   Alsalti, Taym M.
   Dunleavy, Daniel J.
   Meyerowitz-Katz, Gideon
   Antonietti, Alberto
   Calvache, Jose A.
   Kelson, Mark J.
   Salvia, Meg G.
   Parra, Camila Olarte
   Khalatbari-Soltani, Saman
   McLinden, Taylor
   Chatton, Arthur
   Seiler, Jessie
   Steriu, Andreea
   Alshihayb, Talal S.
   Twardowski, Sarah E.
   Dabravolskaj, Julia
   Au, Eric
   Hoopsick, Rachel A.
   Suresh, Shashank
   Judd, Nicholas
   Pena, Sebastian
   Axfors, Cathrine
   Khan, Palwasha
   Aguirre, Ariadne E. Rivera
   Odo, Nnaemeka U.
   Schmid, Ian
   Fox, Matthew P.
TI Causal and Associational Language in Observational Health Research: A
   Systematic Evaluation
SO AMERICAN JOURNAL OF EPIDEMIOLOGY
LA English
DT Article
DE association; causal inference; causal language; observational study
AB We estimated the degree to which language used in the high-profile medical/public health/epidemiology literature implied causality using language linking exposures to outcomes and action recommendations; examined disconnects between language and recommendations; identified the most common linking phrases; and estimated how strongly linking phrases imply causality. We searched for and screened 1,170 articles from 18 high-profile journals (65 per journal) published from 2010-2019. Based on written framing and systematic guidance, 3 reviewers rated the degree of causality implied in abstracts and full text for exposure/outcome linking language and action recommendations. Reviewers rated the causal implication of exposure/outcome linking language as none (no causal implication) in 13.8%, weak in 34.2%, moderate in 33.2%, and strong in 18.7% of abstracts. The implied causality of action recommendations was higher than the implied causality of linking sentences for 44.5% or commensurate for 40.3% of articles. The most common linking word in abstracts was "associate" (45.7%). Reviewers' ratings of linking word roots were highly heterogeneous; over half of reviewers rated "association" as having at least some causal implication. This research undercuts the assumption that avoiding "causal" words leads to clarity of interpretation in medical research.
C1 [Haber, Noah A.; Wieten, Sarah E.; Axfors, Cathrine] Stanford Univ, Meta Res Innovat Ctr Stanford METRICS, Stanford, CA 94305 USA.
   [Rohrer, Julia M.] Univ Leipzig, Dept Psychol, Leipzig, Germany.
   [Arah, Onyebuchi A.] Univ Calif Los Angeles, Fielding Sch Publ Hlth, Dept Epidemiol, Los Angeles, CA USA.
   [Tennant, Peter W. G.] Univ Leeds, Leeds Inst Data Analyt, Leeds, W Yorkshire, England.
   [Stuart, Elizabeth A.; Schmid, Ian] Johns Hopkins Bloomberg Sch Publ Hlth, Dept Mental Hlth, Baltimore, MD USA.
   [Murray, Eleanor J.; Fox, Matthew P.] Boston Univ, Sch Publ Hlth, Dept Epidemiol, Boston, MA USA.
   [Pilleron, Sophie] Univ Oxford, Big Data Inst, Nuffield Dept Populat Hlth, Oxford, England.
   [Lam, Sze Tung] Natl Univ Singapore, Yong Loo Lin Sch Med, Singapore, Singapore.
   [Riederer, Emily] Capital One, Chicago, IL USA.
   [Howcutt, Sarah Jane] Oxford Brookes Univ, Fac Hlth & Life Sci, Psychol Hlth & Profess Dev, Oxford, England.
   [Simmons, Alison E.] Univ Toronto, Dalla Lana Sch Publ Hlth, Div Epidemiol, Toronto, ON, Canada.
   [Leyrat, Clemence] London Sch Hyg & Trop Med, Dept Med Stat, London, England.
   [Schoenegger, Philipp] Univ St Andrews, Sch Econ & Finance, St Andrews, Fife, Scotland.
   [Schoenegger, Philipp] Univ St Andrews, Sch Philosoph Anthropol & Film Studies, St Andrews, Fife, Scotland.
   [Booman, Anna] Oregon Hlth & Sci Univ Portland State Univ Sch Pu, Epidemiol Dept, Portland, OR USA.
   [Dufour, Mi-Suk Kang] Univ Calif Berkeley, Berkeley Publ Hlth, Berkeley, CA 94720 USA.
   [O'Donoghue, Ashley L.] Beth Israel Deaconess Med Ctr, Ctr Healthcare Delivery Sci, Boston, MA 02215 USA.
   [Baglini, Rebekah] Aarhus Univ, Interacting Minds Ctr Linguist Cognit Sci & Semio, Aarhus, Denmark.
   [Do, Stefanie] Leibniz Inst Prevent Res & Epidemiol BIPS, Dept Epidemiol Methods & Etiol Res, Bremen, Germany.
   [Takashima, Mari De la Rosa] Griffith Univ, Sch Med, Nathan, Qld, Australia.
   [Evans, Thomas Rhys] Univ Greenwich, Sch Human Sci, London, England.
   [Rodriguez-Molina, Daloha] Ludwig Maximilian Univ Munich, Inst & Clin Occupat Social & Environm Med Univ Ho, Occupat & Environm Epidemiol & NetTeaching Unit, Munich, Germany.
   [Alsalti, Taym M.] Free Univ Berlin, Dept Educ & Psychol, Berlin, Germany.
   [Dunleavy, Daniel J.] Florida State Univ, Ctr Translat Behav Sci, Tallahassee, FL 32306 USA.
   [Meyerowitz-Katz, Gideon] Univ Wollongong, Sch Hlth & Soc, Wollongong, NSW, Australia.
   [Antonietti, Alberto] Politecn Milan, Dept Elect Informat & Bioengn, Milan, Italy.
   [Calvache, Jose A.] Univ Cauca, Dept Anesthesiol, Cauca, Colombia.
   [Kelson, Mark J.] Univ Exeter, Dept Math, Exeter, Devon, England.
   [Salvia, Meg G.] Harvard Univ, Harvard TH Chan Sch Publ Hlth, Boston, MA 02115 USA.
   [Parra, Camila Olarte] Univ Bath, Dept Math Sci, Bath, Avon, England.
   [Khalatbari-Soltani, Saman; Au, Eric] Univ Sydney, Fac Med & Hlth, Sch Publ Hlth, Sydney, NSW, Australia.
   [McLinden, Taylor] British Columbia Ctr Excellence HIV AIDS, Epidemiol & Populat Hlth Program, Vancouver, BC, Canada.
   [Chatton, Arthur] Univ Nantes, UMR Inst Natl Sante & Rech Med 1246 SPHERE, Nantes, France.
   [Chatton, Arthur] Univ Tours, Nantes, France.
   [Seiler, Jessie] Washington Sch Publ Hlth, Seattle, WA USA.
   [Steriu, Andreea] Carol Davila Univ Med & Pharm, Fac Med, Bucharest, Romania.
   [Alshihayb, Talal S.] King Saud bin Abdulaziz Univ Hlth Sci, Coll Dent, Riyadh, Saudi Arabia.
   [Twardowski, Sarah E.] McGill Univ, Epidemiol Biostat & Occupat Hlth, Montreal, PQ, Canada.
   [Dabravolskaj, Julia] Univ Alberta, Sch Publ Hlth, Edmonton, AB, Canada.
   [Hoopsick, Rachel A.] Univ Illinois, Dept Kinesiol & Community Hlth, Champaign, IL USA.
   [Suresh, Shashank] Univ Pittsburgh, Med Ctr, Community Med, Pittsburgh, PA USA.
   [Judd, Nicholas] Karolinska Inst, Dept Neurosci, Stockholm, Sweden.
   [Pena, Sebastian] Finnish Inst Hlth & Welf, Helsinki, Finland.
   [Khan, Palwasha] London Sch Hyg & Trop Med, Clin Res Dept, London, England.
   [Aguirre, Ariadne E. Rivera] NYU, Grossman Sch Med, Dept Populat Hlth, Div Epidemiol, New York, NY USA.
   [Odo, Nnaemeka U.] Exponent Inc, Ctr Hlth Sci, Oakland, CA USA.
   [Fox, Matthew P.] Sch Publ Hlth, Dept Epidemiol, Boston, MA USA.
C3 Stanford University; Leipzig University; University of California
   System; University of California Los Angeles; University of Leeds; Johns
   Hopkins University; Johns Hopkins Bloomberg School of Public Health;
   Boston University; University of Oxford; National University of
   Singapore; Oxford Brookes University; University of Toronto; University
   of London; London School of Hygiene & Tropical Medicine; University of
   St Andrews; University of St Andrews; Oregon Health & Science
   University; Portland State University; University of California System;
   University of California Berkeley; Harvard University; Beth Israel
   Deaconess Medical Center; Aarhus University; Leibniz Institute for
   Prevention Research & Epidemiology (BIPS); Griffith University;
   University of Greenwich; University of Munich; Free University of
   Berlin; State University System of Florida; Florida State University;
   University of Wollongong; Polytechnic University of Milan; Universidad
   del Cauca; University of Exeter; Harvard University; Harvard T.H. Chan
   School of Public Health; University of Bath; University of Sydney; B.C.
   Centre for Excellence in HIV/AIDS; Nantes Universite; Universite de
   Tours; Carol Davila University of Medicine & Pharmacy; King Saud Bin
   Abdulaziz University for Health Sciences; McGill University; University
   of Alberta; University of Illinois System; University of Illinois
   Urbana-Champaign; Pennsylvania Commonwealth System of Higher Education
   (PCSHE); University of Pittsburgh; Karolinska Institutet; University of
   London; London School of Hygiene & Tropical Medicine; New York
   University; Exponent
RP Haber, NA (corresponding author), 1265 Welch Rd, Palo Alto, CA 94305 USA.
EM noahhaber@gmail.com
RI Antonietti, Alberto/M-8981-2014; khan, Palwasha/GRS-4835-2022; Steriu,
   Andreea/AAQ-5127-2021; Leyrat, Clémence/I-9413-2019; Evans, Thomas
   Rhys/H-5874-2019; Chatton, Arthur/AAY-4349-2021; Baglini,
   Rebekah/JQI-8521-2023; Hoopsick, Rachel/ABH-7046-2020; Pilleron,
   Sophie/O-8136-2018; Arah, Onyebuchi/KZT-8718-2024; Booman,
   Anna/IZD-6025-2023; Rohrer, Julia Marie/AEU-2491-2022;
   Khalatbari-Soltani, Saman/M-7161-2014
OI Antonietti, Alberto/0000-0003-0388-6321; Steriu,
   Andreea/0000-0002-2998-8644; Leyrat, Clémence/0000-0002-4097-4577;
   Evans, Thomas Rhys/0000-0002-6670-0718; Chatton,
   Arthur/0000-0002-0018-5899; Baglini, Rebekah/0000-0002-2836-5867;
   Hoopsick, Rachel/0000-0001-5992-9007; Pilleron,
   Sophie/0000-0001-7146-4740; Arah, Onyebuchi/0000-0002-9067-1697; Rohrer,
   Julia Marie/0000-0001-8564-4523; Khan, Palwasha
   Yousafzai/0000-0002-0873-8355; Haber, Noah/0000-0002-5672-1769;
   Alshihayb, Talal/0000-0001-5750-4144; Khalatbari-Soltani,
   Saman/0000-0001-8437-1906; Booman, Anna/0000-0002-8112-6929; Dunleavy,
   Daniel/0000-0002-3597-7714; Simmons, Alison E./0000-0001-8780-9467;
   Axfors, Cathrine/0000-0002-2706-1730; Schoenegger,
   Philipp/0000-0001-9930-487X; Tennant, Peter/0000-0003-1555-069X;
   Takashima, Mari/0000-0002-1167-9106; Lam, Sze Tung
   Walter/0000-0001-6033-262X; Au, Eric/0000-0002-6089-5913
FU Arnold Ventures LLC (Houston, Texas); European Union's Horizon 2020
   research and innovation program under the Marie Sklodowska-Curie grant
   [842817]; Australian Research Council Centre of Excellence in Population
   Aging Research [CE170100005]; National Institute of Mental Health
   [T32MH122357, R01MH115487]; Bloomberg American Health Initiative;
   National Institute of Biomedical Imaging and Bioengineering
   [R01EB027650]; National Center for Advancing Translational Sciences UCLA
   Clinical Translational Science Institute [UL1TR001881]; Marie Curie
   Actions (MSCA) [842817] Funding Source: Marie Curie Actions (MSCA); MRC
   [MR/T032448/1] Funding Source: UKRI
FX No funding was granted specifically for the support of this study. The
   Meta-Research Innovation Center at Stanford University is supported by
   Arnold Ventures LLC (Houston, Texas), formerly the Laura and John Arnold
   Foundation. S.P. was funded by the European Union's Horizon 2020
   research and innovation program under the Marie Sklodowska-Curie grant
   (agreement no. 842817). S.K.-S. is supported by the Australian Research
   Council Centre of Excellence in Population Aging Research (project
   number CE170100005). I.S. is supported by the National Institute of
   Mental Health (grant T32MH122357). E.A.S.'s time was supported by the
   National Institute of Mental Health (grant R01MH115487) and the
   Bloomberg American Health Initiative. A.L.O. is funded by a
   philanthropic gift from Google.org outside of the submitted work. O.A.A.
   is supported by the National Institute of Biomedical Imaging and
   Bioengineering (grant R01EB027650), National Center for Advancing
   Translational Sciences UCLA Clinical Translational Science Institute
   (grant UL1TR001881), and a philanthropic gift from the Karen Toffler
   Charity Trust. Data, data analysis code, and materials are available on
   the Open Science Framework project https://osf.io/jtdaz/.; This work was
   supported by many people who made contributions to this work. Turki
   Althunian contributed to the screening process. Jess Rohmann contributed
   to the piloting process. This work was additionally supported by
   comments and contributions from Alyssa Bilinksi, Pascal Goldsetzer,
   Caroline Blaine, Otto Kalliokoski, Eero Raittio, Tanya Colyer, Tim
   Watkins, Alexander Breskin, Arindam Basu, Jessica L. Rohmann, Luke A
   McGuinness, Todd Johnson, Mario Mali.cki, Sebastian Skejo, Scott Graham,
   Michael Chaiton-Murray, John Edlund, Katelyn Smalley, Danielle Newby,
   Anita Williams, Cord Phelps, Colleen Derkatch, Alexander Wolthon,
   Pallavi Rohella, Damien Croteau-Chonka, Steven Goodman, and John
   Ioannidis. Presented at the Annual Meeting of the Society for
   Epidemiologic Research, June 14-17, 2022, Chicago, Illinois. A preprint
   of this article has been published online. (Haber, NA, Wieten SE, Rohrer
   JM, et al. Causal and Associational Language in Observational Health
   Research: A Systematic Evaluation. medRxiv. 2021.
   https://doi.org/10.1101/2021.08.25.21262631).All errors are the sole
   responsibility of the authors, and no funders had any role in the
   collection, analysis, and interpretation of data; in the writing of the
   report; and in the decision to submit the article for publication. The
   researchers were independent from funders, and all authors, external and
   internal, had full access to all of the data (including statistical
   reports and tables) in the study and can take responsibility for the
   integrity of the data and the accuracy of the data analysis. Conflict of
   interest: none declared.
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NR 38
TC 19
Z9 19
U1 1
U2 7
PU OXFORD UNIV PRESS INC
PI CARY
PA JOURNALS DEPT, 2001 EVANS RD, CARY, NC 27513 USA
SN 0002-9262
EI 1476-6256
J9 AM J EPIDEMIOL
JI Am. J. Epidemiol.
PD NOV 19
PY 2022
VL 191
IS 12
BP 2084
EP 2097
DI 10.1093/aje/kwac137
EA AUG 2022
PG 14
WC Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Public, Environmental & Occupational Health
GA 8B3EP
UT WOS:000864758500001
PM 35925053
OA Green Submitted, Green Accepted, Green Published
DA 2024-09-05
ER
PT C
AU Jangid, N
   Saha, S
   Gupta, S
   Rao, JM
AF Jangid, Neelam
   Saha, Snehanshu
   Gupta, Siddhant
   Rao, Mukunda J.
BE Lee, G
TI Ranking of Journals in Science and Technology Domain: a Novel and
   Computationally Lightweight Approach
SO INTERNATIONAL CONFERENCE ON FUTURE INFORMATION ENGINEERING (FIE 2014)
SE IERI Procedia
LA English
DT Proceedings Paper
CT International Conference on Future Information Engineering (FIE)
CY JUL 07-08, 2014
CL Beijing, PEOPLES R CHINA
DE Multiple linear regression; scientific journal; Journal influence score;
   SCImago journal score; Quartile matching
AB In this paper, a regression analysis based method is proposed to calculate the Journal Influence Score. This Influence Score is used to measure the scientific influence of scholarly journals. Journal Influence Score is calculated by using various factors in a weighted manner. The Score is then compared with the SCImago Journal Score. The results show that the error is small between the existing and proposed methods, proving that the model is a feasible and effective way of calculating scientific impact of journals. (C) 2014 Published by Elsevier B.V.
C1 [Jangid, Neelam] Dept CSE, PESIT Bangalore South Campus, Bangalore 560100, Karnataka, India.
   CBIMMC, Bangalore 560100, Karnataka, India.
   Lib Informat Sci, Bangalore, Karnataka, India.
C3 PES University
RP Jangid, N (corresponding author), Dept CSE, PESIT Bangalore South Campus, Bangalore 560100, Karnataka, India.
EM neelu.jangid88@gmail.com; snehanshusaha@pes.edu; sidpro.pesit@gmail.com;
   pesselibrarian@pes.edu
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NR 8
TC 9
Z9 9
U1 0
U2 4
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA SARA BURGERHARTSTRAAT 25, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 2212-6678
J9 IERI PROC
PY 2014
VL 10
BP 57
EP 62
DI 10.1016/j.ieri.2014.09.091
PG 6
WC Computer Science, Information Systems; Computer Science,
   Interdisciplinary Applications
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BH1GM
UT WOS:000398020800009
OA hybrid
DA 2024-09-05
ER
PT J
AU Ludl, H
   Schope, K
   Mangelsdorf, I
AF Ludl, H
   Schope, K
   Mangelsdorf, I
TI Searching for information on toxicological data of chemical substances
   in selected bibliographic databases - Selection of essential databases
   for toxicological researches
SO CHEMOSPHERE
LA English
DT Article
DE bibliographic databases; literature research; toxicology; recall rates;
   duplicates; quantitative analysis
AB By using information from printed and online database guides, 18 online bibliographic databases (ED), which cover literature on toxicology were selected from 5 hosts. A search for literature containing information on three selected chemicals was carried out with each of the databases, and the number of documents relevant to toxicology found in them was compared by computer-assisted analysis. Some databases yielded very little information pertinent to toxicology, while others provided a considerable amount, In addition, the databases contained numerous duplicates (references common to more than one database). Most of the relevant documents could be obtained using only 8 of the 18 BDs selected. These databases are: Biosis Previews (BIOSIS), Chemical Abstracts (CA), Chemical Safety Newsbase (CSNB), Excerpta Medica (EMBASE), National Institute for Occupational Safety and Health (NIOSH), Scisearch, Toxicology Information Online (TOXLINE) and the former Toxicology Literature (TOXLIT).
C1 FRAUNHOFER INST TOXICOL & AEROSOL RES,D-30625 HANNOVER,GERMANY.
C3 Fraunhofer Gesellschaft
RP Ludl, H (corresponding author), GSF FORSCHUNGSZENTRUM UNWELT & GESUNDHEIT GMBH,INST TOXIKOL,INGOLSTADTER LANDSTR 1,D-85758 OBERSCHLEISSHEIM,GERMANY.
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NR 18
TC 15
Z9 15
U1 0
U2 2
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB
SN 0045-6535
J9 CHEMOSPHERE
JI Chemosphere
PD MAR
PY 1996
VL 32
IS 5
BP 867
EP 880
DI 10.1016/0045-6535(96)00012-4
PG 14
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology
GA UA156
UT WOS:A1996UA15600007
PM 8867141
DA 2024-09-05
ER
PT C
AU Cicco, G
AF Cicco, Gina
BE Chova, LG
   Martinez, AL
   Torres, IC
TI ONLINE LEARNING EXPERIENCES OF COUNSELORS-IN-TRAINING: IMPLICATIONS FOR
   ONLINE COURSE DESIGN, ASSESSMENT, AND RESEARCH
SO 7TH INTERNATIONAL TECHNOLOGY, EDUCATION AND DEVELOPMENT CONFERENCE
   (INTED2013)
SE INTED Proceedings
LA English
DT Proceedings Paper
CT 7th International Technology, Education and Development Conference
   (INTED)
CY MAR 04-06, 2013
CL Valencia, SPAIN
DE Counselor education; counselor preparation; learning styles; online
   counseling skills instruction; online courses; online learning
   experiences; online skills assessment; virtual classroom
AB The global expansion of online programs and course offerings in graduate education points to the need for researchers to examine the effectiveness of diverse pedagogical methods in the virtual learning environment as well as the corresponding assessment practices employed to confirm students' mastery of learning objectives (Glassmeyer, Dibbs, & Jensen, 2011; Meyers, 2008). Various studies have been conducted to measure the levels of student engagement and relationship-building within the virtual classroom and to identify the student learning styles that are most frequently accommodated in this setting (Cicco, 2009; Trepal, Haberstroh, Duffey, & Evans, 2007). This paper summarizes the findings based on a recent research investigation of the online learning experiences and perceptions of graduate students enrolled in a counselor preparation program in New York. The study design and methodology will be described, with an emphasis on the specific recommendations provided by the 53 student participants for improvement of online counseling courses. The instruction and assessment of counseling skills and techniques within the context of online courses continue to raise questions and concerns for counselor educators as they recognize the inherent differences between the in-class and online instructional environments. The emphasis on the practice, development, and evaluation of basic and complex interpersonal skills within counselor education programs has traditionally been experienced through live interaction among faculty, students, site supervisors, and clinical associates. Converting this rigorous communication experience to the virtual classroom may pose special challenges for counselor educators, particularly in appropriately planning experiences that allow students to participate in live practice exercises and to receive immediate feedback and opportunities for self-and peer-evaluation. Examples of such activities include role-playing, mock counseling sessions, and reflective journal writing (Ivey, Ivey, & Zalaquett, 2010). Instructional designers have provided several recommendations for the enhancement of online instruction, such as integration of synchronous methods and media, yet the ethical concerns of accurately assessing the preparedness of counseling professionals who will serve at times the most vulnerable client populations remain an issue of concern and controversy (Cicco, 2011; Scheuermann, 2010). This paper will elaborate on specific student reflections of their online learning experiences and the corresponding indications for online course design, planning, and instruction that allow students to receive rich and diverse counselor preparation exercises. Recommendations for the provision of formative and summative assessment throughout online courses will be discussed (Reiner & Arnold, 2010). In conclusion, the implications of this exploratory study for future empirical investigation on this important area of research will be addressed.
C1 [Cicco, Gina] St Johns Univ, Jamaica, NY 11439 USA.
C3 Saint John's University
EM ciccog@stjohns.edu
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NR 18
TC 0
Z9 0
U1 0
U2 7
PU IATED-INT ASSOC TECHNOLOGY EDUCATION & DEVELOPMENT
PI VALENICA
PA LAURI VOLPI 6, VALENICA, BURJASSOT 46100, SPAIN
SN 2340-1079
BN 978-84-616-2661-8
J9 INTED PROC
PY 2013
BP 293
EP 300
PG 8
WC Education & Educational Research
WE Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Education & Educational Research
GA BB8IX
UT WOS:000346699800043
DA 2024-09-05
ER
PT J
AU Trabelsi, Z
   Parambil, MMA
AF Trabelsi, Zouheir
   Parambil, Medha Mohan Ambali
TI A bibliometric study on recent trends in artificial intelligence-based
   suspicious activity recognition
SO SECURITY JOURNAL
LA English
DT Article
DE Artificial intelligence; Suspicious activity; Behavioral research;
   Security systems; Bibliometric analysis
AB Recent years have seen a dramatic increase in the use of artificial intelligence (AI) in suspicious activity recognition (SAR). To better understand the research work and recent trends in AI-based SAR, the paper carries out a bibliometric study to analyze the publications based on the recent developments and contributions of authors, publication source, country, and institutions, identifying the most productive items, and the partnership among each. The search on the Scopus database retrieved 1713 documents related to AI-based SAR. In this study, all document types from Scopus were included in the analysis. VOSviewer was used to perform coupling, cluster, and co-citation network analysis to identify research hotspots, while bibliometrix was used to generate keyword analysis, including word clouds, word dynamics, theme trends, and Sankey diagrams, to understand the evolution and future direction of the research field. This paper contributes valuable insights for researchers and audiences worldwide regarding emerging research areas.
C1 [Trabelsi, Zouheir; Parambil, Medha Mohan Ambali] United Arab Emirates Univ UAEU, Dept Informat Syst & Secur, Coll IT, Al Ain, U Arab Emirates.
RP Trabelsi, Z (corresponding author), United Arab Emirates Univ UAEU, Dept Informat Syst & Secur, Coll IT, Al Ain, U Arab Emirates.
EM trabelsi@uaeu.ac.ae; medhamohanap@uaeu.ac.ae
OI , Medha Mohan Ambali Parambil/0000-0002-9336-2902
FU UAE University UPAR Research Grant Program [31T122, 12T002]
FX The work was supported by UAE University UPAR Research Grant Program
   under Grants 31T122 and 12T002.
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NR 22
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U2 9
PU PALGRAVE MACMILLAN LTD
PI BASINGSTOKE
PA BRUNEL RD BLDG, HOUNDMILLS, BASINGSTOKE RG21 6XS, HANTS, ENGLAND
SN 0955-1662
EI 1743-4645
J9 SECUR J
JI Secur. J.
PD JUN
PY 2024
VL 37
IS 2
BP 399
EP 424
DI 10.1057/s41284-023-00382-5
EA MAY 2023
PG 26
WC Criminology & Penology
WE Social Science Citation Index (SSCI)
SC Criminology & Penology
GA UT7N8
UT WOS:000995746200001
DA 2024-09-05
ER
PT J
AU Yuan, XM
   Pan, PP
AF Yuan, Xiuming
   Pan, Peipei
TI Research on the Evaluation Model of Dance Movement Recognition and
   Automatic Generation Based on Long Short-Term Memory
SO MATHEMATICAL PROBLEMS IN ENGINEERING
LA English
DT Article
ID HUMAN MOTION CAPTURE
AB With the development of random image processing technology and in-depth learning, it is possible to recognize human movements, but it is difficult to recognize and evaluate dance movements automatically in artistic expression and emotional classification. Aiming at the problems of low efficiency, low accuracy, and unsatisfactory evaluation in dance motion recognition, this paper proposes a long short-term memory (LSTM) model based on deep learning to recognize dance motion and automatically generate corresponding features. This paper first introduces the related deep learning model recognition methods and describes the related research background. Secondly, the method of identifying dance movements is identified concretely, and the process of identifying concretely is given. Finally, through the comparison of different dance movements through experiments, it shows that there are obvious advantages in the accuracy of action recognition, error rate, similarity, and model evaluation method.
C1 [Yuan, Xiuming] St Paul Univ Manila, Coll Mus, Manila 1004, Philippines.
   [Pan, Peipei] Liaocheng Univ, Acad Mus & Dance, Liaocheng 252000, Peoples R China.
C3 Liaocheng University
RP Pan, PP (corresponding author), Liaocheng Univ, Acad Mus & Dance, Liaocheng 252000, Peoples R China.
EM 13047458974@163.com; panpeipei@lcu.edu.cn
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NR 25
TC 0
Z9 0
U1 2
U2 14
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1024-123X
EI 1563-5147
J9 MATH PROBL ENG
JI Math. Probl. Eng.
PD APR 28
PY 2022
VL 2022
AR 6405903
DI 10.1155/2022/6405903
PG 10
WC Engineering, Multidisciplinary; Mathematics, Interdisciplinary
   Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Mathematics
GA 1G2PZ
UT WOS:000795696400023
OA gold
DA 2024-09-05
ER
PT J
AU Ivanova, M
   Grosseck, G
   Holotescu, C
AF Ivanova, Malinka
   Grosseck, Gabriela
   Holotescu, Carmen
TI Unveiling Insights: A Bibliometric Analysis of Artificial Intelligence
   in Teaching
SO INFORMATICS-BASEL
LA English
DT Article
DE artificial intelligence; teaching; intelligent environment; learning
   analytics; large language models; ChatGPT
ID EDUCATION
AB The penetration of intelligent applications in education is rapidly increasing, posing a number of questions of a different nature to the educational community. This paper is coming to analyze and outline the influence of artificial intelligence (AI) on teaching practice which is an essential problem considering its growing utilization and pervasion on a global scale. A bibliometric approach is applied to outdraw the "big picture" considering gathered bibliographic data from scientific databases Scopus and Web of Science. Data on relevant publications matching the query "artificial intelligence and teaching" over the past 5 years have been researched and processed through Biblioshiny in R environment in order to establish a descriptive structure of the scientific production, to determine the impact of scientific publications, to trace collaboration patterns and to identify key research areas and emerging trends. The results point out the growth in scientific production lately that is an indicator of increased interest in the investigated topic by researchers who mainly work in collaborative teams as some of them are from different countries and institutions. The identified key research areas include techniques used in educational applications, such as artificial intelligence, machine learning, and deep learning. Additionally, there is a focus on applicable technologies like ChatGPT, learning analytics, and virtual reality. The research also explores the context of application for these techniques and technologies in various educational settings, including teaching, higher education, active learning, e-learning, and online learning. Based on our findings, the trending research topics can be encapsulated by terms such as ChatGPT, chatbots, AI, generative AI, machine learning, emotion recognition, large language models, convolutional neural networks, and decision theory. These findings offer valuable insights into the current landscape of research interests in the field.
C1 [Ivanova, Malinka] Tech Univ Sofia, Fac Appl Math & Informat, Dept Informat, Blvd Kl Ohridski 8, Sofia 1797, Bulgaria.
   [Grosseck, Gabriela] West Univ Timisoara, Fac Sociol & Psychol, Dept Psychol, 4 Bd Vasile Parvan, Timisoara 300223, Romania.
   [Holotescu, Carmen] Ioan Slavici Univ Timisoara, Fac Engn, Dept Informat Technol, 144 Str Paunescu Podeanu, Timisoara 300569, Romania.
C3 Technical University Sofia; West University of Timisoara; Ioan Slavici
   University
RP Ivanova, M (corresponding author), Tech Univ Sofia, Fac Appl Math & Informat, Dept Informat, Blvd Kl Ohridski 8, Sofia 1797, Bulgaria.
EM m_ivanova@tu-sofia.bg; gabriela.grosseck@e-uvt.ro;
   carmen.holotescu@islavici.ro
RI Ivanova, Malinka/AAE-1774-2019
OI Ivanova, Malinka/0000-0002-8474-6226
FU Bulgarian FNI fund
FX No Statement Available
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NR 56
TC 2
Z9 2
U1 35
U2 35
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2227-9709
J9 INFORMATICS-BASEL
JI Informatics-Basel
PD MAR
PY 2024
VL 11
IS 1
AR 10
DI 10.3390/informatics11010010
PG 21
WC Computer Science, Interdisciplinary Applications
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA MG5J0
UT WOS:001192482500001
OA gold
DA 2024-09-05
ER
PT J
AU Eckmann, M
   Rocha, A
   Wainer, J
AF Eckmann, Michael
   Rocha, Anderson
   Wainer, Jacques
TI Relationship between high-quality journals and conferences in computer
   vision
SO SCIENTOMETRICS
LA English
DT Article
DE Computer science; Computer vision; Publishing; Journal papers;
   Conference proceedings; Author survey; Bibliometrics
AB In computer science, as opposed to many other disciplines, papers published in conference and workshop proceedings count as formal publications when evaluating the scholarship of an academic. We consider the relationship between high quality journals and conferences in the computer vision (CV) subfield of computer science. We determined that 30% of papers in the top-3 CV journals base their work on top-3 conference papers by the same authors (which we call priors (See "Methods" section for the definition of a prior)). Journal papers with priors are significantly more cited than journal papers without priors. Also the priors themselves are cited more than other papers from the conferences. For a period of 3-5 years after the journal paper publication, the priors receive more citations than the follow-up journal paper. After that period, the journal paper starts receiving most of the citations. Furthermore, we found that having the prior conference paper did not make it any easier (faster) to publish in a journal. We also surveyed journal authors and based on their answers and the priors analysis, we discovered that authors seem to be divided into different groups depending on their preferred method of publication.
C1 [Eckmann, Michael] Skidmore Coll, Saratoga Springs, NY 12866 USA.
   [Rocha, Anderson; Wainer, Jacques] Univ Estadual Campinas, Campinas, SP, Brazil.
C3 Skidmore College; Universidade Estadual de Campinas
RP Eckmann, M (corresponding author), Skidmore Coll, 815 N Broadway, Saratoga Springs, NY 12866 USA.
EM meckmann@skidmore.edu; rocha@ic.unicamp.br; wainer@ic.unicamp.br
RI Wainer, Jacques/B-4241-2012; Wainer, Jacques/AAQ-6029-2021;
   Rocha-Buelvas, Anderson Iván/KHU-9621-2024
OI Wainer, Jacques/0000-0001-5201-1244; Wainer,
   Jacques/0000-0001-5201-1244; 
CR [Anonymous], 2010, Commun ACM, DOI [10.1145/1839676.1839701, DOI 10.1145/1839676.1839701]
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NR 11
TC 29
Z9 31
U1 2
U2 53
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD FEB
PY 2012
VL 90
IS 2
BP 617
EP 630
DI 10.1007/s11192-011-0527-2
PG 14
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 876DZ
UT WOS:000299088900017
DA 2024-09-05
ER
PT J
AU Davidovitch, N
   Eckhaus, E
AF Davidovitch, Nitza
   Eckhaus, Eyal
TI EFFECT OF FACULTY ON RESEARCH COOPERATION AND PUBLICATION: EMPLOYING
   NATURAL LANGUAGE PROCESSING
SO ECONOMICS & SOCIOLOGY
LA English
DT Article
DE academic conference; gender; faculty; academia
AB This study continues a series of studies on the effectiveness of scientific conferences. This topic has not been sufficiently investigated although it receives large funds, assuming that these conferences have added value for staff members' academic-professional development. Predicated on questionnaires filled by 96 academic staff members from 17 different departments, we found that when choosing conferences to attend, the type of faculty affect the search for cooperation. Moreover, staff members from the Faculty of Natural Sciences attribute more significance to conferences that result in publications than staff members from the Faculty of Health. The Faculty of Engineering creates negative mediation in the correlation between gender and cooperation. Namely, the Faculty of Engineering does not urge cooperation and even has a negative effect, but its effect is evident mainly among men. This finding complements prior research findings showing that women are more inclined to cooperation (Eckhaus & Davidovitch, 2018a). The current findings show that the inclination to cooperation is not related only to gender issues rather the faculty has an effect as well. The current findings might have a contribution to the significance of the faculty as an influential factor of conferences on cooperation - and in fact on the professional development of staff members.
C1 [Davidovitch, Nitza; Eckhaus, Eyal] Ariel Univ, Ariel, Israel.
C3 Ariel University
RP Davidovitch, N (corresponding author), Ariel Univ, Ariel, Israel.
EM d.nitza@ariel.ac.il; eyde@ariel.ac.il
RI Eckhaus, Eyal/AAX-2557-2020
OI Eckhaus, Eyal/0000-0002-1815-0045; Davidovitch,
   Nitza/0000-0001-7273-903X
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NR 17
TC 2
Z9 3
U1 1
U2 2
PU CENTER SOCIOLOGICAL RESEARCH
PI SZCZECIN
PA MICKIEWICZA STR, 64, SZCZECIN, 71-101, POLAND
SN 2071-789X
EI 2306-3459
J9 ECON SOCIOL
JI Econ. Sociol.
PY 2018
VL 11
IS 4
BP 173
EP 180
DI 10.14254/2071-789X.2018/11-4/11
PG 8
WC Economics
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA HJ2WF
UT WOS:000457030400011
OA gold
DA 2024-09-05
ER
PT J
AU Cobelli, N
   Blasi, S
AF Cobelli, Nicola
   Blasi, Silvia
TI Combining topic modeling and bibliometric analysis to understand the
   evolution of technological innovation adoption in the healthcare
   industry
SO EUROPEAN JOURNAL OF INNOVATION MANAGEMENT
LA English
DT Article
DE Digital transformation; Healthcare management; Bibliometric analysis;
   Topic modeling; UTAUT; UTAUT2
ID DEVELOPING-COUNTRY; INFORMATION-TECHNOLOGY; CONSUMER ACCEPTANCE; MOBILE
   BANKING; SUPPLY CHAIN; SERVICES; EXTENSION; UTAUT; INTENTION; COHERENCE
AB PurposeThis paper explores the Adoption of Technological Innovation (ATI) in the healthcare industry. It investigates how the literature has evolved, and what are the emerging innovation dimensions in the healthcare industry adoption studies.Design/methodology/approachWe followed a mixed-method approach combining bibliometric methods and topic modeling, with 57 papers being deeply analyzed.FindingsOur results identify three latent topics. The first one is related to the digitalization in healthcare with a specific focus on the COVID-19 pandemic. The second one groups up the word combinations dealing with the research models and their constructs. The third one refers to the healthcare systems/professionals and their resistance to ATI.Research limitations/implicationsThe study's sample selection focused on scientific journals included in the Academic Journal Guide and in the FT Research Rank. However, the paper identifies trends that offer managerial insights for stakeholders in the healthcare industry.Practical implicationsATI has the potential to revolutionize the health service delivery system and to decentralize services traditionally provided in hospitals or medical centers. All this would contribute to a reduction in waiting lists and the provision of proximity services.Originality/valueThe originality of the paper lies in the combination of two methods: bibliometric analysis and topic modeling. This approach allowed us to understand the ATI evolutions in the healthcare industry.
C1 [Cobelli, Nicola] Univ Verona, Dept Management, Verona, Italy.
C3 University of Verona
RP Cobelli, N (corresponding author), Univ Verona, Dept Management, Verona, Italy.
EM nicola.cobelli@univr.it
RI Cobelli, Nicola/AAI-7592-2021
OI Cobelli, Nicola/0000-0001-6710-6510
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NR 110
TC 2
Z9 2
U1 10
U2 10
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 1460-1060
EI 1758-7115
J9 EUR J INNOV MANAG
JI Eur. J. Innov. Manag.
PD FEB 13
PY 2024
VL 27
IS 9
BP 127
EP 149
DI 10.1108/EJIM-06-2023-0497
PG 23
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA KG9O1
UT WOS:001178924800001
OA hybrid
DA 2024-09-05
ER
PT J
AU Färber, M
   Tampakis, L
AF Faerber, Michael
   Tampakis, Lazaros
TI Analyzing the impact of companies on AI research based on publications
SO SCIENTOMETRICS
LA English
DT Article
DE Artificial intelligence; Impact quantification; Company influence;
   Industry-academia collaboration
ID CITATION ANALYSIS; ALTMETRICS; KNOWLEDGE
AB Artificial Intelligence (AI) is one of the most momentous technologies of our time. Thus, it is of major importance to know which stakeholders influence AI research. Besides researchers at universities and colleges, researchers in companies have hardly been considered in this context. In this article, we consider how the influence of companies on AI research can be made measurable on the basis of scientific publishing activities. We compare academic- and company-authored AI publications published in the last decade and use scientometric data from multiple scholarly databases to look for differences across these groups and to disclose the top contributing organizations. While the vast majority of publications is still produced by academia, we find that the citation count an individual publication receives is significantly higher when it is (co-)authored by a company. Furthermore, using a variety of altmetric indicators, we notice that publications with company participation receive considerably more attention online. Finally, we place our analysis results in a broader context and present targeted recommendations to safeguard a harmonious balance between academia and industry in the realm of AI research.
C1 [Faerber, Michael; Tampakis, Lazaros] Karlsruhe Inst Technol KIT, Inst AIFB, Karlsruhe, Germany.
C3 Helmholtz Association; Karlsruhe Institute of Technology
RP Färber, M (corresponding author), Karlsruhe Inst Technol KIT, Inst AIFB, Karlsruhe, Germany.
EM michael.faerber@kit.edu; lazaros.tampakis@student.kit.edu
RI Färber, Michael/AAA-4789-2021
OI Färber, Michael/0000-0001-5458-8645
FU Karlsruher Institut fr Technologie (KIT) (4220)
FX No Statement Available
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NR 49
TC 2
Z9 2
U1 8
U2 15
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD JAN
PY 2024
VL 129
IS 1
BP 31
EP 63
DI 10.1007/s11192-023-04867-3
EA NOV 2023
PG 33
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA ES5M9
UT WOS:001122585100001
OA hybrid, Green Submitted
DA 2024-09-05
ER
PT C
AU Shinde, SV
   Gawali, SZ
   Thakore, DM
AF Shinde, Sachin V.
   Gawali, Sangram Z.
   Thakore, Devendrasingh M.
GP IEEE
TI MAS a scalable framework for research effort evaluation by unsupervised
   machine learning-Hybrid plagiarism model
SO 2015 INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING (ICPC)
LA English
DT Proceedings Paper
CT International Conference on Pervasive Computing (ICPC)
CY JAN 08-10, 2015
CL Pune, INDIA
DE Document in Question; MAS; Inverted Index; Unsupervised Learning;
   Sentence vector; Term Vector; Cosine similarity; Mapper; fork; join;
   EMA; PMA; SMA; WEMA; WPMA; WSMA
AB In the era of web new information is upcoming day by day. Researches add their work for their research domains. Detecting of originality of research work is in hype. In Academic sector students researchers bring in innovative ideas, algorithms stating that their work outperforms prior research. They may implement NULL Hypothesis or alternative Hypothesis, detecting their effort is a challenge. By means of plagiarism detectors such academic efforts can be evaluated or graded. This reflects the essence of research in the field of Plagiarized content detection and grading. Some of our research issue highlights to technical scenario to design an algorithm which is adaptable to changing nature of dataset. The dataset grows, as new research work is added in due course of time. Data extraction from unstructured information is challenging, as no standard pattern is yet defined. Such patterns vary from research to research and are domain specific. A document in question i.e plagiarized or not? Is a join of one or more sentences that originate by the authors research or referenced from previous publications. Authors to prove originality use paraphrasing which may have semantic similarity, also some of the contents act as metaphor for upcoming research work. It is complex task point out such an activity.
   Methodology states that a document in question is a join of sentences, whereas each sentence is a join of terms. Thus we conclude by fork and join operations; plagiarism detection is possible in effective way. Document in question is split to produce a sentence vector. A term vector is generated by forking sentence to terms for each sentence in sentence vector. Mapper is implemented that maps term to sentence and sentence to source document. To enhance the accuracy of the model a Multi Agent Based System MAS frame is recommended to adapt varying similarity functions. Achieve parallelism in system and adaptability of new similarity measures as well remove one which are not suitable any more to the task.
C1 [Shinde, Sachin V.] BVDUs Coll Engineeering, Informat Technol, Pune, Maharashtra, India.
   [Gawali, Sangram Z.] BVDUs Coll Engineeering, Dept Informat Technol, Pune, Maharashtra, India.
   [Thakore, Devendrasingh M.] BVDUs Coll Engineeering, Dept Comp Engn, Pune, Maharashtra, India.
C3 Bharati Vidyapeeth Deemed University; Bharati Vidyapeeth Deemed
   University; Bharati Vidyapeeth Deemed University
RP Shinde, SV (corresponding author), BVDUs Coll Engineeering, Informat Technol, Pune, Maharashtra, India.
EM shinde_s_v@rediffmail.com; szgawali@bvucoep.edu.in;
   dmthakore@bvucoep.edu.in
RI Shinde, Sachin Vidhyasagar/KFA-7996-2024; Thakore, Devendrasingh
   M/K-7496-2017
OI Gawali, Sangram/0000-0002-8875-942X; Shinde, Sachin/0009-0007-9442-0186
CR Alzahrani Salha M., 2011, UNDERSTANDING PLAGIA
   [Anonymous], 2001, ADAP COMP MACH LEARN
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   [Anonymous], 2013, INT J COMPUTER APPL
   Bakhtiyari Kaveh, 2014, INT ED STUDIES, V7
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   Bhattacharjee Debotosh, 2013, PLAGIARISM DETECTION
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   Juziuk J., 2012, DESIGN PATTERNS MULT
   Luo Yuan, 2002, MULTIAGENT DECISION
   PDFlib GmbH, 2002, TEXT EXTR TOOLK TET
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   Tian Z., 2013, DKISB DYNAMIC KEY IN
NR 21
TC 0
Z9 0
U1 0
U2 1
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-4799-6272-3
PY 2015
PG 5
WC Engineering, Electrical & Electronic
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Engineering
GA BF1LK
UT WOS:000380407300069
DA 2024-09-05
ER
PT J
AU Shackelford, GE
   Kemp, L
   Rhodes, C
   Sundaram, L
   OhÉigeartaigh, SS
   Beard, S
   Belfield, H
   Weitzdörfer, J
   Avin, S
   Sorebo, D
   Jones, EM
   Hume, JB
   Price, D
   Pyle, D
   Hurt, D
   Stone, T
   Watkins, H
   Collas, L
   Cade, BC
   Johnson, TF
   Freitas-Groff, Z
   Denkenberger, D
   Levot, M
   Sutherland, WJ
AF Shackelford, Gorm E.
   Kemp, Luke
   Rhodes, Catherine
   Sundaram, Lalitha
   OhEigeartaigh, Sean S.
   Beard, Simon
   Belfield, Haydn
   Weitzdorfer, Julius
   Avin, Shahar
   Sorebo, Dag
   Jones, Elliot M.
   Hume, John B.
   Price, David
   Pyle, David
   Hurt, Daniel
   Stone, Theodore
   Watkins, Harry
   Collas, Lydia
   Cade, Bryony C.
   Johnson, Thomas Frederick
   Freitas-Groff, Zachary
   Denkenberger, David
   Levot, Michael
   Sutherland, William J.
TI Accumulating evidence using crowdsourcing and machine learning: A living
   bibliography about existential risk and global catastrophic risk
SO FUTURES
LA English
DT Article
DE Bibliographic databases; Crowdsourcing; Machine learning; Subject-wide
   evidence synthesis; Systematic maps; Systematic reviews
AB The study of existential risk - the risk of human extinction or the collapse of human civilization - has only recently emerged as an integrated field of research, and yet an overwhelming volume of relevant research has already been published. To provide an evidence base for policy and risk analysis, this research should be systematically reviewed. In a systematic review, one of many time-consuming tasks is to read the titles and abstracts of research publications, to see if they meet the inclusion criteria. We show how this task can be shared between multiple people (using crowdsourcing) and partially automated (using machine learning), as methods of handling an overwhelming volume of research. We used these methods to create The Existential Risk Research Assessment (TERRA), which is a living bibliography of relevant publications that gets updated each month (www.x-risk.net ). We present the results from the first ten months of TERRA, in which 10,001 abstracts were screened by 51 participants. Several challenges need to be met before these methods can be used in systematic reviews. However, we suggest that collaborative and cumulative methods such as these will need to be used in systematic reviews as the volume of research increases.
C1 [Shackelford, Gorm E.; Kemp, Luke; Rhodes, Catherine; Sundaram, Lalitha; OhEigeartaigh, Sean S.; Beard, Simon; Belfield, Haydn; Weitzdorfer, Julius; Avin, Shahar; Sutherland, William J.] Univ Cambridge, CSER, Cambridge, England.
   [Shackelford, Gorm E.; Kemp, Luke; Rhodes, Catherine; Sundaram, Lalitha; OhEigeartaigh, Sean S.; Sutherland, William J.] Univ Cambridge, Biosecur Res Initiat St Catharines BioRISC, St Catharines Coll, Cambridge, England.
   [Shackelford, Gorm E.; Collas, Lydia; Sutherland, William J.] Univ Cambridge, Dept Zool, David Attenborough Bldg, Cambridge, England.
   [Weitzdorfer, Julius] Harvard Univ, Belfer Ctr Sci & Int Affairs, Cambridge, MA 02138 USA.
   [Sorebo, Dag] Grindstone Secur, Oslo, Norway.
   [Jones, Elliot M.] Demos, London, England.
   [Hume, John B.] Michigan State Univ, E Lansing, MI 48824 USA.
   [Price, David] DebateGraph, London, England.
   [Pyle, David] Univ Oxford, Dept Earth Sci, Oxford, England.
   [Hurt, Daniel] Univ Cambridge, Cambridge, England.
   [Stone, Theodore] Univ Amsterdam, Amsterdam, Netherlands.
   [Watkins, Harry] Univ Sheffield, Dept Landscape, Sheffield, S Yorkshire, England.
   [Watkins, Harry] St Andrews Bot Garden, St Andrews, Fife, Scotland.
   [Johnson, Thomas Frederick] Univ Reading, Reading, Berks, England.
   [Freitas-Groff, Zachary] Stanford Univ, Stanford, CA 94305 USA.
   [Denkenberger, David] Univ Alaska, Fairbanks, AK 99701 USA.
   [Denkenberger, David] GCRI, Fairbanks, AK USA.
   [Denkenberger, David] Alliance Feed Earth Disasters ALLFED, Fairbanks, AK USA.
   [Levot, Michael] NYU, New York, NY 10003 USA.
C3 University of Cambridge; University of Cambridge; University of
   Cambridge; Harvard University; Michigan State University; University of
   Oxford; University of Cambridge; University of Amsterdam; University of
   Sheffield; University of Reading; Stanford University; University of
   Alaska System; University of Alaska Fairbanks; New York University
RP Shackelford, GE (corresponding author), Univ Cambridge, CSER, Cambridge, England.; Shackelford, GE (corresponding author), Univ Cambridge, Biosecur Res Initiat St Catharines BioRISC, St Catharines Coll, Cambridge, England.; Shackelford, GE (corresponding author), Univ Cambridge, Dept Zool, David Attenborough Bldg, Cambridge, England.
EM gorm.shackelford@gmail.com
RI Avin, Shahar/H-9639-2019; Sutherland, William/B-1291-2013
OI Avin, Shahar/0000-0001-7859-1507; Sundaram, Lalitha/0000-0002-9595-9753;
   Stone, Theodore/0000-0001-6385-161X; Groff, Zachary/0000-0003-4791-4494;
   Watkins, Harry/0000-0002-4038-7145
FU Templeton World Charity Foundation; David and Claudia Harding Foundation
FX This project was made possible through the support of a grant from
   Templeton World Charity Foundation. The opinions expressed in this
   publication are those of the authors and do not necessarily reflect the
   views of Templeton World Charity Foundation. Several of the authors were
   also supported by the David and Claudia Harding Foundation. We thank our
   funders, and we also thank Stuart Armstrong, Seth Baum, Sebastian
   Farquhar, Nancy Ockendon, Martin Rees, Jens Steffensen, Phil Torres, and
   all of the participants in TERRA.
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   [No title captured]
   [No title captured]
   [No title captured]
NR 27
TC 7
Z9 7
U1 1
U2 8
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0016-3287
EI 1873-6378
J9 FUTURES
JI Futures
PD FEB
PY 2020
VL 116
AR 102508
DI 10.1016/j.futures.2019.102508
PG 10
WC Economics; Regional & Urban Planning
WE Social Science Citation Index (SSCI)
SC Business & Economics; Public Administration
GA KM3AV
UT WOS:000513994800003
OA Green Submitted, hybrid
DA 2024-09-05
ER
PT J
AU Shukla, AK
   Janmaijaya, M
   Abraham, A
   Muhuri, PK
AF Shukla, Amit K.
   Janmaijaya, Manvendra
   Abraham, Ajith
   Muhuri, Pranab K.
TI Engineering applications of artificial intelligence: A bibliometric
   analysis of 30 years (1988-2018)
SO ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
LA English
DT Article
DE Bibliometric study; Engineering applications of artificial intelligence;
   Scientometric mapping; Web of science; VOSviewer
ID PARTICLE SWARM OPTIMIZATION; HYBRID NEURAL-NETWORK; OF-THE-ART; GENETIC
   ALGORITHM; MULTIOBJECTIVE OPTIMIZATION; MODEL; DESIGN; SYSTEMS;
   RECOGNITION; SIMULATION
AB The Engineering Applications of Artificial Intelligence (EAAI) is a journal of very high repute in the domain of Engineering and Computer Science. This paper gives a broad view of the publications in EAAI from 1988-2018, which are indexed in Web of Science (WoS) and Scopus. The main purpose of this research is to bring forward the prime impelling factors that bring about the EAAI publications and its citation structure. The publication and citation structure of EAAI is analyzed, which includes the distribution of publication over the years, citations per year and a bird's eye view of the citation structure. Then the co-citation analysis and over the year's trend of top keywords is given. The co-authorship networks and a geographic analysis of the sources is also provided. Further, a country-wise temporal and quantitative analysis of the publications is given along with the highly cited documents among the EAAI publications.
C1 [Shukla, Amit K.; Janmaijaya, Manvendra; Muhuri, Pranab K.] South Asian Univ, Dept Comp Sci, New Delhi 110021, India.
   [Abraham, Ajith] Univ Pretoria, Dept Comp Sci, ZA-0002 Pretoria, South Africa.
   [Abraham, Ajith] Machine Intelligence Res Labs MIR Labs, 3rd St,POB 2259, Auburn, WA 98071 USA.
C3 South Asian University (SAU); University of Pretoria
RP Muhuri, PK (corresponding author), South Asian Univ, Dept Comp Sci, New Delhi 110021, India.
EM pranabmuhuri@cs.sau.ac.in
RI Shukla, Amit k./AAX-5624-2021; MUHURI, PRANAB K./F-4301-2015; Abraham,
   Ajith/A-1416-2008; Manvendra, Janmaijaya/JOK-0347-2023
OI MUHURI, PRANAB K./0000-0001-7122-7622; Abraham,
   Ajith/0000-0002-0169-6738; 
FU Department of Science and Technology, Government of India; South Asian
   University, New Delhi, India
FX Authors are thankful to the anonymous reviewers for their valuable
   comments which have helped them a lot in improving the paper
   significantly. First author thankfully acknowledges the INSPIRE
   fellowship received from the Department of Science and Technology,
   Government of India. Second author is grateful to the South Asian
   University, New Delhi, India for the financial support in the form of a
   Ph.D. fellowship.
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NR 71
TC 86
Z9 88
U1 14
U2 110
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0952-1976
EI 1873-6769
J9 ENG APPL ARTIF INTEL
JI Eng. Appl. Artif. Intell.
PD OCT
PY 2019
VL 85
BP 517
EP 532
DI 10.1016/j.engappai.2019.06.010
PG 16
WC Automation & Control Systems; Computer Science, Artificial Intelligence;
   Engineering, Multidisciplinary; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Automation & Control Systems; Computer Science; Engineering
GA JC0UC
UT WOS:000488994300042
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Gordon, SM
   Edwards, JL
AF Gordon, Sue Marquis
   Edwards, Jennifer Lynne
TI Enhancing student research through a virtual participatory action
   research project: Student benefits and administrative challenges
SO ACTION RESEARCH
LA English
DT Article
DE doctoral studies; mentoring; online learning; participatory action
   research; project administration; virtual collaboration
ID TEACHERS
AB Can graduate students in a distance learning environment gain meaningful research experience through a virtual participatory action research project? The answer is an emphatic 'yes.' The purpose of this article is twofold: to demonstrate how and to what extent graduate students can gain research experience through participation with alumni and faculty in an action research project, and to examine administrative issues arising from adhering to the democratic participatory action research process under virtual constraints. Project success is determined through documents produced by participants in the Faculty-Student Mentoring Project and from two focus group evaluations held via conference calls. The data demonstrated that the students and alumni increased their research skills, used their skills and knowledge in other courses and their dissertations, presented, and published. The university gained valuable information. The democratic participatory aspect of action research and the virtual environment created administrative challenges such as scheduling and workload issues. Recommendations include: screening volunteers to determine their levels of expertise and commitment; providing time for participants to get to know each other and the technology at the outset of the project; setting expectations for participation; sharing the project management; and anticipating more time for virtual than face-to-face research.
C1 [Gordon, Sue Marquis; Edwards, Jennifer Lynne] Fielding Grad Univ, Sch Educ Leadership, Santa Barbara, CA USA.
   [Gordon, Sue Marquis; Edwards, Jennifer Lynne] Fielding Grad Univ, Change Doctoral Program, Santa Barbara, CA USA.
RP Edwards, JL (corresponding author), 3774 Mountainside Trail, Evergreen, CO 80439 USA.
EM jedwards@fielding.edu
RI Edwards, Jennifer/IAN-1163-2023
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NR 38
TC 4
Z9 8
U1 1
U2 23
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 1476-7503
EI 1741-2617
J9 ACTION RES-LONDON
JI Action Res.
PD JUN
PY 2012
VL 10
IS 2
BP 205
EP 220
DI 10.1177/1476750312439900
PG 16
WC Management; Social Sciences, Interdisciplinary
WE Social Science Citation Index (SSCI)
SC Business & Economics; Social Sciences - Other Topics
GA 951CJ
UT WOS:000304698000007
DA 2024-09-05
ER
PT J
AU Hancock, GRA
   Troscianko, J
AF Hancock, George R. A.
   Troscianko, Jolyon
TI CamoEvo: An open access toolbox for artificial camouflage evolution
   experiments
SO EVOLUTION
LA English
DT Article
DE CamoEvo; camouflage; evolution; genetic algorithms; optimization;
   selection
ID GENETIC ALGORITHMS; SELECTION; ECOLOGY
AB Camouflage research has long shaped our understanding of evolution by natural selection, and elucidating the mechanisms by which camouflage operates remains a key question in visual ecology. However, the vast diversity of color patterns found in animals and their backgrounds, combined with the scope for complex interactions with receiver vision, presents a fundamental challenge for investigating optimal camouflage strategies. Genetic algorithms (GAs) have provided a potential method for accounting for these interactions, but with limited accessibility. Here, we present CamoEvo, an open-access toolbox for investigating camouflage pattern optimization by using tailored GAs, animal and egg maculation theory, and artificial predation experiments. This system allows for camouflage evolution within the span of just 10-30 generations (similar to 1-2 min per generation), producing patterns that are both significantly harder to detect and that are optimized to their background. CamoEvo was built in ImageJ to allow for integration with an array of existing open access camouflage analysis tools. We provide guides for editing and adjusting the predation experiment and GA as well as an example experiment. The speed and flexibility of this toolbox makes it adaptable for a wide range of computer-based phenotype optimization experiments.
C1 [Hancock, George R. A.; Troscianko, Jolyon] Univ Exeter, Ctr Ecol & Conservat, Penryn TR10 9FE, England.
C3 University of Exeter
RP Hancock, GRA (corresponding author), Univ Exeter, Ctr Ecol & Conservat, Penryn TR10 9FE, England.
EM ghancockzoology@gmail.com
RI Hancock, George/HJH-0619-2022
OI Troscianko, Jolyon/0000-0001-9071-2594; Hancock,
   George/0000-0002-8771-545X
FU NERC GW4+ studentship [NE/S007504/1]; NERC Independent Research
   Fellowship [NE/P018084/1]; NERC [NE/P018084/1] Funding Source: UKRI
FX We are thankful to our two anonymous reviewers for their thorough
   feedback and recommendations on the manuscript. GRAH was funded by an
   NERC GW4+ studentship NE/S007504/1. JT was funded by an NERC Independent
   Research Fellowship NE/P018084/1.
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NR 67
TC 3
Z9 3
U1 0
U2 14
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 0014-3820
EI 1558-5646
J9 EVOLUTION
JI Evolution
PD MAY
PY 2022
VL 76
IS 5
BP 870
EP 882
DI 10.1111/evo.14476
EA MAR 2022
PG 13
WC Ecology; Evolutionary Biology; Genetics & Heredity
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology; Evolutionary Biology; Genetics &
   Heredity
GA 1I8GU
UT WOS:000775653100001
PM 35313008
OA Green Published
DA 2024-09-05
ER
PT J
AU da Silva, MAD
   Pereira, AC
   Walmsley, AD
AF Dias da Silva, Marco Antonio
   Pereira, Andresa Costa
   Walmsley, Anthony Damien
TI The availability of open-access videos offered by dental schools
SO EUROPEAN JOURNAL OF DENTAL EDUCATION
LA English
DT Article
DE complimentary content; online learning; teaching
ID STUDENTS PERCEPTIONS; TECHNOLOGY; INFORMATION; ONLINE; CLIPS
AB Background The Internet has become an established learning tool in dental education where students can access online videos on a range of dental subjects. However, finding reliable peer-reviewed content is not straightforward. Aim To evaluate the video content offered by UK and Republic of Ireland (RoI) Dental Schools on their YouTube channels and public websites. Methods Free videos offered on UK and RoI Dental schools websites and YouTube channels were watched and set according to its purpose, as educational or non-educational. The number of views, length, category and date of publication were analysed. Results A total of 627 videos offered by dental courses were evaluated. Videos were available on 83% of the websites, but only 9% was educational content. Dental courses YouTube channels received more than 2.3 million views, but less than 5% of the material offered is educational. Instructional videos found on the websites (3.2 min) were shorter than those found on YouTube (8.5 min) (P = .03). The majority of the videos, provided by Universities, were not educational and focused on promoting the dental courses. Most websites have demonstrated a password-protected area where quality content may be offered. Conclusion Students wishing to watch instructional videos will find limited educational content provided by UK and RoI dental courses. Therefore, they are likely to access course-related material elsewhere on the Internet that may not be necessarily peer-reviewed.
C1 [Dias da Silva, Marco Antonio; Pereira, Andresa Costa; Walmsley, Anthony Damien] Univ Birmingham, Birmingham, W Midlands, England.
   [Dias da Silva, Marco Antonio; Pereira, Andresa Costa] Univ Fed Campina Grande, Patos de Minas, Brazil.
C3 University of Birmingham; Universidade Federal de Campina Grande
RP da Silva, MAD (corresponding author), Univ Birmingham, Birmingham, W Midlands, England.
EM M.a.DiasdaSilva@bham.ac.uk
RI da Silva, Marco Antonio Dias/K-4730-2012
OI da Silva, Marco Antonio Dias/0000-0002-2774-4769; Walmsley, Anthony
   Damien/0000-0003-4970-0764; Pereira, Andresa Costa/0000-0002-3654-6123
FU H2020 Marie Sklodowska-Curie Actions [748609]; Marie Curie Actions
   (MSCA) [748609] Funding Source: Marie Curie Actions (MSCA)
FX H2020 Marie Sklodowska-Curie Actions, Grant/Award Number: 748609
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NR 26
TC 6
Z9 6
U1 1
U2 5
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1396-5883
EI 1600-0579
J9 EUR J DENT EDUC
JI Eur. J. Dent. Educ.
PD NOV
PY 2019
VL 23
IS 4
BP 522
EP 526
DI 10.1111/eje.12461
EA SEP 2019
PG 5
WC Dentistry, Oral Surgery & Medicine; Education, Scientific Disciplines
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Dentistry, Oral Surgery & Medicine; Education & Educational Research
GA JH4DN
UT WOS:000484611100001
PM 31429507
OA Green Accepted
DA 2024-09-05
ER
PT J
AU Yang, C
   Huang, C
AF Yang, Chao
   Huang, Cui
TI Quantitative mapping of the evolution of AI policy distribution, targets
   and focuses over three decades in China
SO TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
LA English
DT Article
DE Artificial intelligence (AI); China's policy; Bibliometrics; Policy
   evolution; Policy documents
ID ARTIFICIAL-INTELLIGENCE; SCIENCE; CHALLENGES; FRAMEWORK
AB Artificial intelligence (AI) technology policy plays a critical role to steer its applications to broadly relevant endpoints, and contributes to critical governance of innovations by governments, industry and society at large. In this paper, we adopt a bibliometrics-based research framework to characterize the development and evolution of China's AI policy. The framework integrates bibliometric methods, semantic analysis, and network analysis for identifying core policy elements and their evolution in the AI policy process. Specifically, we first collect China's central-level AI-related policies and identify four stages of its evolution based on policy-issuing frequency, policy trends, and core policy issuing time nodes. We then identify the core policies, core institutions, and core policy targets in each stage. Then we explore the policy issuing trends, policy distribution changes, and evolution of policy targets. Finally, patterns and characteristics of the policy process are identified, and trends are predicted. We used the PKULaw database to collect the policy-relevant data on AI in China, and the time frame is from 1990 to 2019. Our findings and the reported quantitative map might usefully inform AI policy in China and elsewhere around the world. It could also help broader stakeholder engagement in policy discussions on AI technology, industry and society.
C1 [Yang, Chao; Huang, Cui] Zhejiang Univ, Sch Publ Affairs, Dept Informat Resource Management, Hangzhou 310058, Peoples R China.
C3 Zhejiang University
RP Huang, C (corresponding author), Zhejiang Univ, Sch Publ Affairs, Dept Informat Resource Management, Hangzhou 310058, Peoples R China.
EM huangcui@zju.edu.cn
OI Yang, Chao/0000-0002-0607-9552
FU Innovative Research Group Project of the National Natural Science
   Foundation of China [71721002]; Excellent Youth Project of the National
   Natural Science Foundation of China [71722002]; key Project of Humanties
   and Social Sciences in Ministry of Education of China [18JZD056]; Youth
   Foundation Project of Humanities and Social Sciences in Ministry of
   Education of China [18YJC870022]; General Program of National Natural
   Science Foundation of China [71673164]
FX We acknowledge support from the Innovative Research Group Project of the
   National Natural Science Foundation of China (Grant No. 71721002) ,
   Excellent Youth Project of the National Natural Science Foundation of
   China (Grant No. 71722002) , The key Project of Humanties and Social
   Sciences in Ministry of Education of China (Grant No. 18JZD056) , Youth
   Foundation Project of Humanities and Social Sciences in Ministry of
   Education of China (Grant No. 18YJC870022) , and the General Program of
   National Natural Science Foundation of China (Grant No. 71673164) . The
   findings and observations contained in this paper are those of the
   authors and do not necessarily reflect the views of the supporters.
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NR 41
TC 33
Z9 35
U1 48
U2 287
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0040-1625
EI 1873-5509
J9 TECHNOL FORECAST SOC
JI Technol. Forecast. Soc. Chang.
PD JAN
PY 2022
VL 174
AR 121188
DI 10.1016/j.techfore.2021.121188
EA SEP 2021
PG 17
WC Business; Regional & Urban Planning
WE Social Science Citation Index (SSCI)
SC Business & Economics; Public Administration
GA UY7AO
UT WOS:000701672300009
OA hybrid
DA 2024-09-05
ER
PT C
AU Teodorescu, HN
AF Teodorescu, Horia-Nicolai
BE Burileanu, C
   Teodorescu, HN
TI How Central European countries fare in speech and language technology
   research?
SO 2019 10TH INTERNATIONAL CONFERENCE ON SPEECH TECHNOLOGY AND
   HUMAN-COMPUTER DIALOGUE (SPED)
LA English
DT Proceedings Paper
CT 10th International Conference on Speech Technology and Human-Computer
   Dialogue (SpeD)
CY OCT 10-12, 2019
CL Timisoara, ROMANIA
DE NLP; speech technology; speech analysis; text analysis; artificial
   intelligence; country performance; education; research; scientometry
ID FUZZY-LOGIC; SCIENCE
AB Speech and language technologies are parts of AI family of tools and methods that already have an economic impact; they are assumed to extend that impact in the near future. We analyze the contributions of several average and small central European countries to these fields in terms of papers published. We compare these contributions with those of France and Germany and establish relationships between the efficiency measured by scientific publications between subfields of speech and language technologies. We find that Czech Republic is more efficient in terms of papers per inhabitant even than Germany. Czech Republic excels too when the number of papers is divided by the population of tertiary education. Also, we find interesting strong and low correlations between subdomains.
C1 [Teodorescu, Horia-Nicolai] Romanian Acad, Iasi Branch, Inst Comp Sci, Bd Carol I 8, Iasi, Romania.
   [Teodorescu, Horia-Nicolai] Gheorghe Asachi Tech Univ Iasi, 67 Bd D Mangeron, Iasi 70050, Romania.
C3 Romanian Academy of Sciences; GH Asachi Technical University
RP Teodorescu, HN (corresponding author), Romanian Acad, Iasi Branch, Inst Comp Sci, Bd Carol I 8, Iasi, Romania.; Teodorescu, HN (corresponding author), Gheorghe Asachi Tech Univ Iasi, 67 Bd D Mangeron, Iasi 70050, Romania.
RI Teodorescu, Horia-Nicolai L/C-3287-2008
CR [Anonymous], Population, Total
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NR 15
TC 0
Z9 0
U1 1
U2 3
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-7281-0984-8
PY 2019
DI 10.1109/sped.2019.8906637
PG 6
WC Computer Science, Artificial Intelligence; Computer Science,
   Cybernetics; Telecommunications
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Telecommunications
GA BQ0ED
UT WOS:000571718700034
DA 2024-09-05
ER
PT C
AU Huang, R
   Liu, XF
   Zheng, X
AF Huang, Rui
   Liu, Xiaofang
   Zheng, Xiang
BE LuevanosRojas, A
   Ilewicz, G
   Jakobczak, DJ
   Weller, K
TI Research on Performance Quality Prediction Method of Missile Based on
   Grey Theory and SVM
SO PROCEEDINGS OF THE 2018 3RD INTERNATIONAL CONFERENCE ON MODELLING,
   SIMULATION AND APPLIED MATHEMATICS (MSAM 2018)
SE Advances in Intelligent Systems Research
LA English
DT Proceedings Paper
CT 3rd International Conference on Modelling, Simulation and Applied
   Mathematics (MSAM)
CY JUL 22-23, 2018
CL Shanghai, PEOPLES R CHINA
DE grey theory; linear weighted; SVM; quality prediction
AB Accurately grasping the performance quality status of the missile is a prerequisite for ensuring the completion of the operational task. At present, in a real missile launch exercise, in order to ensure the success of the launch, the method of fist passing the test and then launching is usually adopted. This method can hardly meet the large-scale, high-volume and high-efficiency operational requirements in the future battlefield. In order to meet the future operational requirements, based on the past test data of the performance parameters of the missile and the information of daily management and stored, combined the information of actual missile launch results, this paper uses grey theory, linear weighted comprehensive evaluation and SVM to accurately predict the performance quality status of the missile, and provide technical support for operational decision-making.
C1 [Huang, Rui; Liu, Xiaofang; Zheng, Xiang] High Tech Inst Xian, Dept Management, Xian 710025, Shaanxi, Peoples R China.
C3 Rocket Force University of Engineering
RP Huang, R (corresponding author), High Tech Inst Xian, Dept Management, Xian 710025, Shaanxi, Peoples R China.
RI huang, rui/JYP-3898-2024
CR [Anonymous], 2012, APPL SCI TECHNOL
   [Anonymous], GREY SYSTEMS THEORY
   Deng Ju-long, 2002, FDN GREYTHEORY
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NR 6
TC 1
Z9 1
U1 0
U2 4
PU ATLANTIS PRESS
PI PARIS
PA 29 AVENUE LAVMIERE, PARIS, 75019, FRANCE
SN 1951-6851
BN 978-94-6252-566-5
J9 ADV INTEL SYS RES
PY 2018
VL 160
BP 140
EP 145
PG 6
WC Mathematics, Applied
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Mathematics
GA BL7JB
UT WOS:000455077200031
DA 2024-09-05
ER
PT J
AU Piryani, R
   Madhavi, D
   Singh, VK
AF Piryani, R.
   Madhavi, D.
   Singh, V. K.
TI Analytical mapping of opinion mining and sentiment analysis research
   during 2000-2015
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
DE Affective computing; Opinion mining; Scientometrics; Sentiment analysis
ID ONLINE PRODUCT REVIEWS; SOCIAL MEDIA; SEMANTIC ORIENTATION;
   FEATURE-EXTRACTION; POLARITY CLASSIFICATION; EMOTION RECOGNITION;
   CHINESE REVIEWS; EXPRESSION RECOGNITION; CUSTOMER SATISFACTION;
   UNSUPERVISED APPROACH
AB The new transformed read-write Web has resulted in a rapid growth of user generated content on the Web resulting into a huge volume of unstructured data. A substantial part of this data is unstructured text such as reviews and blogs. Opinion mining and sentiment analysis (OMSA) as a research discipline has emerged during last 15 years and provides a methodology to computationally process the unstructured data mainly to extract opinions and identify their sentiments. The relatively new but fast growing research discipline has changed a lot during these years. This paper presents a scientometric analysis of research work done on OMSA during 2000-2016. For the scientometric mapping, research publications indexed in Web of Science (WoS) database are used as input data. The publication data is analyzed computationally to identify year-wise publication pattern, rate of growth of publications, types of authorship of papers on OMSA, collaboration patterns in publications on OMSA, most productive countries, institutions, journals and authors, citation patterns and an year-wise citation reference network, and theme density plots and keyword bursts in OMSA publications during the period. A somewhat detailed manual analysis of the data is also performed to identify popular approaches (machine learning and lexicon-based) used in these publications, levels (document, sentence or aspect-level) of sentiment analysis work done and major application areas of OMSA. The paper presents a detailed analytical mapping of OMSA research work and charts the progress of discipline on various useful parameters. (C) 2016 Elsevier Ltd. All rights reserved.
C1 [Piryani, R.] South Asian Univ, Dept Comp Sci, New Delhi, India.
   [Madhavi, D.] APJAKTU, Dept Comp Sci & Engn, Lucknow, Uttar Pradesh, India.
   [Singh, V. K.] Banaras Hindu Univ, Dept Comp Sci, Varanasi, Uttar Pradesh, India.
C3 South Asian University (SAU); Dr. A.P.J. Abdul Kalam Technical
   University (AKTU); Banaras Hindu University (BHU)
RP Singh, VK (corresponding author), Banaras Hindu Univ, Dept Comp Sci, Varanasi, Uttar Pradesh, India.
EM rajesh.piryani@gmail.com; madhavidevaraj@gmail.com; vivekks12@gmail.com
RI Piryani, Rajesh/AAF-8148-2020; Singh, Vivek Kumar/O-5699-2019;
   Freienberg, Selina/AAV-8829-2021
OI Piryani, Rajesh/0000-0003-3374-0657; Singh, Vivek
   Kumar/0000-0002-7348-6545; 
FU Department of Science and Technology, Government of India
   [INT/MEXICO/P-13/2012]; University Grants Commission India [41 -624/
   2012(SR)]
FX This work was supported by research grants from Department of Science
   and Technology, Government of India (Grant: INT/MEXICO/P-13/2012) and
   University Grants Commission India (Grant: F. No. 41 -624/ 2012(SR)).
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NR 488
TC 104
Z9 106
U1 3
U2 270
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0306-4573
EI 1873-5371
J9 INFORM PROCESS MANAG
JI Inf. Process. Manage.
PD JAN
PY 2017
VL 53
IS 1
BP 122
EP 150
DI 10.1016/j.ipm.2016.07.001
PG 29
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA EF7IW
UT WOS:000390504300008
DA 2024-09-05
ER
PT J
AU Bradley, C
   Romane, L
AF Bradley, Cara
   Romane, Leeanne
TI Changing the Tire Instead of Reinventing the Wheel: Customizing an
   Existing Online Information Literacy Tutorial
SO COLLEGE & UNDERGRADUATE LIBRARIES
LA English
DT Article
DE Academic libraries; information literacy; online tutorial; active
   learning; institutional collaboration; research skills; undergraduate
   student
AB Information literacy instruction has become a core responsibility of many academic librarian positions in recent years. Online information literacy tutorials have gained increasing popularity among librarians struggling to keep up with the growing demand for this type of instruction. The availability of high-quality, open source tutorials has prompted some librarians to customize existing tutorials rather than build their own resource from scratch. This article provides an overview and checklist for librarians who are considering customization of an existing online tutorial as a means of meeting student information literacy needs.
C1 [Bradley, Cara] Univ Regina, Dr John Archer Lib, Regina, SK S4S 0A2, Canada.
   [Romane, Leeanne] Saskatchewan Inst Appl Sci & Technol, Regina, SK S4P 3A3, Canada.
C3 University of Regina
RP Bradley, C (corresponding author), Univ Regina, Dr John Archer Lib, Regina, SK S4S 0A2, Canada.
EM bradleca@uregina.ca; lromane@library.uwaterloo.ca
OI Bradley, Cara/0000-0003-0934-9842
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NR 7
TC 5
Z9 5
U1 0
U2 2
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1069-1316
EI 1545-2530
J9 COLL UNDERGRAD LIBR
JI Coll. Undergrad. Libr.
PY 2007
VL 14
IS 4
BP 73
EP 86
DI 10.1080/10691310802128344
PG 14
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA VC3EC
UT WOS:000433667100007
DA 2024-09-05
ER
PT J
AU Rundle, AG
   Bader, MDM
   Branas, CC
   Lovasi, GS
   Mooney, SJ
   Morrison, CN
   Neckerman, KM
AF Rundle, Andrew G.
   Bader, Michael D. M.
   Branas, Charles C.
   Lovasi, Gina S.
   Mooney, Stephen J.
   Morrison, Christopher N.
   Neckerman, Kathryn M.
TI Causal Inference with Case-Only Studies in Injury Epidemiology Research
SO CURRENT EPIDEMIOLOGY REPORTS
LA English
DT Article
DE Study design; Case-only design; Injury research; Pedestrian injury;
   Etiologic heterogeneity; Effect modification
ID GENE-ENVIRONMENT INTERACTION; PEDESTRIAN FATALITIES;
   ALCOHOL-CONSUMPTION; UNITED-STATES; CASE-SERIES; ASSOCIATION; STRUCK;
   RISK
AB Purpose of Review We review the application and limitations of two implementations of the "case-only design" in injury epidemiology with example analyses of Fatality Analysis Reporting System data. Recent Findings The term "case-only design" covers a variety of epidemiologic designs; here, two implementations of the design are reviewed: (1) studies to uncover etiological heterogeneity and (2) studies to measure exposure effect modification. These two designs produce results that require different interpretations and rely upon different assumptions. The key assumption of case-only designs for exposure effect modification, the more commonly used of the two designs, does not commonly hold for injuries and so results from studies using this design cannot be interpreted. Case-only designs to identify etiological heterogeneity in injury risk are interpretable but only when the case-series is conceptualized as arising from an underlying cohort. The results of studies using case-only designs are commonly misinterpreted in the injury literature.
C1 [Rundle, Andrew G.; Branas, Charles C.; Morrison, Christopher N.; Neckerman, Kathryn M.] Columbia Univ, Mailman Sch Publ Hlth, Dept Epidemiol, 722 West 168th St,Room 727, New York, NY 10032 USA.
   [Bader, Michael D. M.] Johns Hopkins Univ, Dept Sociol, Baltimore, MD 21218 USA.
   [Lovasi, Gina S.] Drexel Univ, Dept Epidemiol & Biostat, Philadelphia, PA 19104 USA.
   [Mooney, Stephen J.] Univ Washington, Dept Epidemiol, Seattle, WA 98195 USA.
C3 Columbia University; Johns Hopkins University; Drexel University;
   University of Washington; University of Washington Seattle
RP Rundle, AG (corresponding author), Columbia Univ, Mailman Sch Publ Hlth, Dept Epidemiol, 722 West 168th St,Room 727, New York, NY 10032 USA.
EM Agr3@cumc.columbia.edu
RI Bader, Michael D. M./F-3422-2010; Lovasi, Gina/C-2781-2009; Rundle,
   Andrew/A-5282-2009
OI Lovasi, Gina/0000-0003-2613-9599; Morrison,
   Christopher/0000-0001-6522-6420; Rundle, Andrew/0000-0003-0211-7707
FU National Institute on Alcohol Abuse and Alcoholism [K01AA026327];
   National Library of Medicine [R00LM012868]
FX Drs. Rundle, Lovasi, Neckerman, Mooney, Branas, and Bader were supported
   by a grant from the National Institute on Alcohol Abuse and Alcoholism
   (R01AA028552); Dr. Morrison was supported by a grant from the National
   Institute on Alcohol Abuse and Alcoholism (K01AA026327); Dr. Mooney was
   also supported by a grant from the National Library of Medicine
   (R00LM012868).
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NR 37
TC 0
Z9 0
U1 0
U2 2
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
EI 2196-2995
J9 CURR EPIDEMIOL REP
JI Curr. Epidemiol. Rep.
PD DEC
PY 2022
VL 9
IS 4
BP 223
EP 232
DI 10.1007/s40471-022-00306-8
EA SEP 2022
PG 10
WC Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Public, Environmental & Occupational Health
GA 6S5BQ
UT WOS:000863122000001
PM 37152190
OA Green Accepted, hybrid
DA 2024-09-05
ER
PT J
AU Khan, ZA
   Zubair, S
   Imran, K
   Ahmad, R
   Butt, SA
   Chaudhary, NI
AF Khan, Zeshan Aslam
   Zubair, Syed
   Imran, Kashif
   Ahmad, Rehan
   Butt, Sharjeel Abid
   Chaudhary, Naveed Ishtiaq
TI A New Users Rating-Trend Based Collaborative Denoising Auto-Encoder for
   Top-N Recommender Systems
SO IEEE ACCESS
LA English
DT Article
DE Noise reduction; Collaboration; Recommender systems; Encoding; Market
   research; Deep learning; Auto-encoders; collaborative filtering;
   denoising; e-commerce; recommender systems; top-N recommendations
ID MATRIX FACTORIZATION; AUTOENCODER
AB To promote online businesses and sales, e-commerceindustry focuses to fulfill users demands by giving them top set ofrecommendations which are ranked through different ranking measures.Deep learning based auto-encoder models have further improved theperformance of recommender systems. Astate-of-the-art collaborative denoisingauto-encoder (CDAE) models user-item interactions as a corruptedversion of users rating inputs. However, this architecture stilllacks users ratings-trend information which is an important parameterto recommend top-N items to users. In this paper, buildingupon CDAE characteristics, we propose a novel users rating-trendbased collaborative denoising auto-encoder (UT-CDAE) whichdetermines user-item correlations by evaluating rating-trend(High or Low) of a user towards a set of items. This inclusion of ausers rating-trend provides additional regularization flexibilitywhich helps to predict improved top-N recommendations. Thecorrectness of the suggested method is verified through different rankingevaluation metrics i.e., (mean reciprocal rank, meanaverage precision and normalized discounted gain), for various inputcorruption values, learning rates and regularization parameters.Experiments on standard ML-100K and ML-1M datasets showthat suggested model has improved performance overstate-of-the-art denoising auto-encodermodels.
C1 [Khan, Zeshan Aslam; Zubair, Syed; Ahmad, Rehan; Butt, Sharjeel Abid; Chaudhary, Naveed Ishtiaq] Int Islamic Univ, Elect Engn Dept, Islamabad 44000, Pakistan.
   [Imran, Kashif] NUST, Dept Elect Power Engn, US Pakistan Ctr Adv Studies Energy, Islamabad 44000, Pakistan.
C3 International Islamic University, Pakistan; National University of
   Sciences & Technology - Pakistan
RP Khan, ZA (corresponding author), Int Islamic Univ, Elect Engn Dept, Islamabad 44000, Pakistan.
EM zeeshan.aslam@iiu.edu.pk
RI Chaudhary, Naveed/I-7754-2019; Butt, Sharjeel Abid/C-7011-2017
OI Chaudhary, Naveed Ishtiaq/0000-0002-9568-3216; Ahmad,
   Rehan/0000-0002-0194-6653; Butt, Sharjeel Abid/0000-0002-3396-8224
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NR 39
TC 9
Z9 11
U1 1
U2 4
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2019
VL 7
BP 141287
EP 141310
DI 10.1109/ACCESS.2019.2940603
PG 24
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA JN8QJ
UT WOS:000497156000093
OA gold
DA 2024-09-05
ER
PT J
AU Koh, RGL
   Khan, MA
   Rashidiani, S
   Hassan, S
   Tucci, V
   Liu, T
   Nesovic, K
   Kumbhare, D
   Doyle, TE
AF Koh, Ryan G. L.
   Khan, Md Asif
   Rashidiani, Sajjad
   Hassan, Samah
   Tucci, Victoria
   Liu, Theodore
   Nesovic, Karlo
   Kumbhare, Dinesh
   Doyle, Thomas E.
TI Check It Before You Wreck It: A Guide to STAR-ML for Screening Machine
   Learning Reporting in Research
SO IEEE ACCESS
LA English
DT Article
DE Checklist; literature review; machine learning; quality scoring;
   reporting assessment; research methodology; screening tool
ID CONVOLUTIONAL NEURAL-NETWORKS; CLASSIFICATION; TRENDS
AB Machine learning (ML) is a technique that learns to detect patterns and trends in data. However, the quality of reporting ML in research is often suboptimal, leading to inaccurate conclusions and hindering progress in the field, especially if disseminated in literature reviews that provide researchers with an overview of a field, current knowledge gaps, and future directions. While various tools are available to assess the quality and risk-of-bias of studies, there is currently no generalized tool for assessing the reporting quality of ML in the literature. To address this, this study presents a new screening tool called STAR-ML (Screening Tool for Assessing Reporting of Machine Learning), accompanied by a guide to using it. A pilot scoping review looking at ML in chronic pain was used to investigate the tool. The time it took to screen papers and how the selection of the threshold affected the papers included were explored. The tool provides researchers with a reliable and systematic way to evaluate the quality of reporting of ML studies and to make informed decisions about the inclusion of studies in scoping or systematic reviews. In addition, this study provides recommendations for authors on how to choose the threshold for inclusion and use the tool proficiently. Lastly, the STAR-ML tool can serve as a checklist for researchers seeking to develop or implement ML techniques effectively.
C1 [Koh, Ryan G. L.; Nesovic, Karlo; Kumbhare, Dinesh] Univ Hlth Network UHN, KITE Res Inst, Toronto Rehabil Inst, Toronto, ON M5G 2A2, Canada.
   [Khan, Md Asif; Rashidiani, Sajjad; Liu, Theodore; Doyle, Thomas E.] McMaster Univ, Dept Elect & Comp Engn, Hamilton, ON L8S 4L8, Canada.
   [Hassan, Samah] UHN, Inst Educ Res TIER, Toronto, ON M5T 1V4, Canada.
   [Tucci, Victoria] McMaster Univ, Fac Hlth Sci, Hamilton, ON L8S 4L8, Canada.
   [Doyle, Thomas E.] McMaster Univ, Sch Biomed Engn, Hamilton, ON L8S 4L8, Canada.
   [Doyle, Thomas E.] Vector Inst Artificial Intelligence, Toronto, ON M5G 1M1, Canada.
C3 University of Toronto; University Health Network Toronto; Toronto
   Rehabilitation Institute; McMaster University; University of Toronto;
   University Health Network Toronto; McMaster University; McMaster
   University; Vector Institute for Artificial Intelligence
RP Koh, RGL (corresponding author), Univ Hlth Network UHN, KITE Res Inst, Toronto Rehabil Inst, Toronto, ON M5G 2A2, Canada.
EM ryan.koh@mail.utoronto.ca
RI Izquierdo, Mikel/A-4894-2010
OI Izquierdo, Mikel/0000-0002-1506-4272; Koh, Ryan/0000-0001-8662-1008;
   Khan, Md Asif/0000-0001-8395-347X; Tucci, Victoria/0000-0002-2344-2560;
   Hassan, Samah/0000-0003-2526-4515; Liu, Theodore/0000-0001-7334-8129;
   Kumbhare, Dinesh/0000-0003-3889-7557; Doyle, Thomas/0000-0003-1059-110X;
   Nesovic, Karlo/0000-0002-1520-954X
FU Canadian Department of National Defence IDEaS [CFPMN2-17]; Department of
   Electrical and Computer Engineering, McMaster University, Hamilton, ON,
   Canada
FX This work was supported in part by the Canadian Department of National
   Defence IDEaS under Award CFPMN2-17; and in part by the Department of
   Electrical and Computer Engineering, McMaster University, Hamilton, ON,
   Canada.
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NR 95
TC 1
Z9 1
U1 0
U2 2
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2023
VL 11
BP 101567
EP 101579
DI 10.1109/ACCESS.2023.3316019
PG 13
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA S5RA7
UT WOS:001071724700001
OA gold
DA 2024-09-05
ER
PT J
AU Hood, A
AF Hood, Alison
TI Whose responsibility is it? Encouraging student engagement in the
   learning process
SO MUSIC EDUCATION RESEARCH
LA English
DT Article
DE action research; self-assessment; group work; student learning; student
   confidence; deep learning
AB This article presents the results of an action research project that focused on giving students more sense of control and responsibility over their own learning by engaging them more fully in assessment and helping them to understand the principles underpinning assessment criteria. The course is a second-year music module with approximately 85 students. I formed the class into groups to grade model answers and compiled a list of what they believed the assessment criteria should be based on this experience. I then used this list to compile a self-assessment criteria sheet, which the students filled out themselves and attached with each subsequent assessment. When I completed my analysis of data from the first cycle of action research, I implemented the learning from that cycle into a second and third cycle of action. This involved re-evaluating my initial plans in light of my findings; building on what was successful and changing what was not, and refocusing my research. The findings were significant, as their assessment results improved dramatically. Involving students throughout the assessment process, from initially setting the criteria right through to self-assessing their work, improved their grades, reduced student passivity and increased their self-confidence.
C1 Natl Univ Ireland Maynooth, Dept Mus, Maynooth, Kildare, Ireland.
C3 Maynooth University
RP Hood, A (corresponding author), Natl Univ Ireland Maynooth, Dept Mus, Maynooth, Kildare, Ireland.
EM Alison.Hood@nuim.ie
OI Hood, Alison/0000-0001-5272-8325
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U2 13
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1461-3808
EI 1469-9893
J9 MUSIC EDUC RES
JI Music Educ. Res.
PY 2012
VL 14
IS 4
BP 457
EP 478
DI 10.1080/14613808.2012.703174
PG 22
WC Education & Educational Research; Music
WE Social Science Citation Index (SSCI); Arts &amp; Humanities Citation Index (A&amp;HCI)
SC Education & Educational Research; Music
GA 041AL
UT WOS:000311368500005
OA Green Accepted
DA 2024-09-05
ER
PT J
AU Sharma, D
   Kumar, R
   Jung, KH
AF Sharma, Deepak
   Kumar, Rajeev
   Jung, Ki-Hyun
TI A Bibliometric Analysis of Convergence of Artificial Intelligence and
   Blockchain for Edge of Things
SO JOURNAL OF GRID COMPUTING
LA English
DT Article
DE Bibliometric analysis; Artificial intelligence; Convergence; Blockchain;
   Edge of things; EOT
ID SOCIAL NETWORK ANALYSIS; BIG DATA; INTERNET; TOURISM; CHALLENGES; IOT;
   JOURNALS; SCIENCE
AB The convergence of Artificial Intelligence (AI) and Blockchain technologies has emerged as a powerful paradigm to address the challenges of data management, security, and privacy in the Edge of Things (EoTs) environment. This bibliometric analysis aims to explore the research landscape and trends surrounding the topic of convergence of AI and Blockchain for EoTs to gain insights into its development and potential implications. For this, research published during the past six years (2018-2023) in the Web of Science indexed sources has been considered as it has been a new field. VoSViewer-based full counting methodology has been used to analyze citation, co-citation, and co-authorship based collaborations among authors, organizations, countries, sources, and documents. The full counting method in VoSViewer involves considering all authors or sources with equal weight when calculating various bibliometric indicators. Co-occurrence, timeline, and burst detection analysis of keywords and published articles were also carried out to unravel significant research trends on the convergence of AI and Blockchain for EoTs. Our findings reveal a steady growth in research output, indicating the increasing importance and interest in AI-enabled Blockchain solutions for EoTs. Further, the analysis uncovered key influential researchers and institutions driving advancements in this domain, shedding light on potential collaborative networks and knowledge hubs. Additionally, the study examines the evolution of research themes over time, offering insights into emerging areas and future research directions. This bibliometric analysis contributes to the understanding of the state-of-the-art in convergence of AI and Blockchain for EoTs, highlighting the most influential works and identifying knowledge gaps. Researchers, industry practitioners, and policymakers can leverage these findings to inform their research strategies and decision-making processes, fostering innovation and advancements in this cutting-edge interdisciplinary field.
C1 [Sharma, Deepak] Christian Albrechts Univ Kiel, Dept Comp Sci, D-24118 Kiel, Schleswig Holst, Germany.
   [Kumar, Rajeev] Delhi Technol Univ, Dept Comp Sci & Engn, Blockchain Technol Res Lab, New Delhi 110042, Delhi, India.
   [Jung, Ki-Hyun] Andong Natl Univ, Dept Software Convergence, Andong 36729, Gyeongbuk, South Korea.
C3 University of Kiel; Delhi Technological University; Andong National
   University
RP Kumar, R (corresponding author), Delhi Technol Univ, Dept Comp Sci & Engn, Blockchain Technol Res Lab, New Delhi 110042, Delhi, India.
EM rajeevkumar@dtu.ac.in
FU National Research Foundation of Korea
FX No Statement Available
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NR 52
TC 2
Z9 2
U1 6
U2 12
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1570-7873
EI 1572-9184
J9 J GRID COMPUT
JI J. Comput.
PD DEC
PY 2023
VL 21
IS 4
AR 79
DI 10.1007/s10723-023-09716-4
PG 35
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA AW8M3
UT WOS:001121578200003
DA 2024-09-05
ER
PT J
AU Hu, HT
   Wang, DB
   Deng, SH
AF Hu, Haotian
   Wang, Dongbo
   Deng, Sanhong
TI Global Collaboration in Artificial Intelligence: Bibliometrics and
   Network Analysis from 1985 to 2019
SO JOURNAL OF DATA AND INFORMATION SCIENCE
LA English
DT Article
DE Artificial intelligence; International collaboration; Collaboration
   pattern; Bibliometric analysis; Social network analysis
ID INFORMATION-SCIENCE; TRENDS
AB Purpose: This study aims to explore the trend and status of international collaboration in the field of artificial intelligence (AI) and to understand the hot topics, core groups, and major collaboration patterns in global AI research.
   Design/methodology/approach: We selected 38,224 papers in the field of AI from 1985 to 2019 in the core collection database of Web of Science (WoS) and studied international collaboration from the perspectives of authors, institutions, and countries through bibliometric analysis and social network analysis.
   Findings: The bibliometric results show that in the field of AI, the number of published papers is increasing every year, and 84.8% of them are cooperative papers. Collaboration with more than three authors, collaboration between two countries and collaboration within institutions are the three main levels of collaboration patterns. Through social network analysis, this study found that the US, the UK, France, and Spain led global collaboration research in the field of AI at the country level, while Vietnam, Saudi Arabia, and United Arab Emirates had a high degree of international participation. Collaboration at the institution level reflects obvious regional and economic characteristics. There are the Developing Countries Institution Collaboration Group led by Iran, China, and Vietnam, as well as the Developed Countries Institution Collaboration Group led by the US, Canada, the UK. Also, the Chinese Academy of Sciences (China) plays an important, pivotal role in connecting the these institutional collaboration groups.
   Research limitations: First, participant contributions in international collaboration may have varied, but in our research they are viewed equally when building collaboration networks. Second, although the edge weight in the collaboration network is considered, it is only used to help reduce the network and does not reflect the strength of collaboration.
   Originality/value: This work is the longest to date regarding international collaboration in the field of AI. This research explores the evolution, future trends, and major collaboration patterns of international collaboration in the field of AI over the past 35 years. It also reveals the leading countries, core groups, and characteristics of collaboration in the field of AI.
C1 [Hu, Haotian; Deng, Sanhong] Nanjing Univ, Sch Informat Management, Nanjing 210023, Peoples R China.
   [Wang, Dongbo] Nanjing Agr Univ, Coll Informat & Technol, Nanjing 210095, Peoples R China.
   [Hu, Haotian; Deng, Sanhong] Jiangsu Key Lab Data Engn & Knowledge Serv, Nanjing 210023, Peoples R China.
C3 Nanjing University; Nanjing Agricultural University
RP Wang, DB (corresponding author), Nanjing Agr Univ, Coll Informat & Technol, Nanjing 210095, Peoples R China.
EM hhtdlam@126.com; db.wang@njau.edu.cn; sanhong@inu.edu.cn
FU National Natural Science Foundation of China [71673143]; National Social
   Science Foundation of China [19BTQ062]
FX We acknowledge the National Natural Science Foundation of China (Grant
   No. 71673143) and the National Social Science Foundation of China (Grant
   No. 19BTQ062) for thier financial support.
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NR 21
TC 10
Z9 11
U1 3
U2 47
PU SCIENDO
PI WARSAW
PA BOGUMILA ZUGA 32A, WARSAW, MAZOVIA, POLAND
SN 2096-157X
EI 2543-683X
J9 J DATA INFO SCI
JI J. Data Info. Sci.
PD NOV
PY 2020
VL 5
IS 4
BP 86
EP 115
DI 10.2478/jdis-2020-0027
PG 30
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA OZ6NJ
UT WOS:000595040100008
OA gold
DA 2024-09-05
ER
PT C
AU Yan, Z
   Guo, WS
   Zhong, YL
   Sun, MS
AF Yan, Zhong
   Guo Wensi
   Zhong Yanling
   Sun Maosheng
GP IEEE
TI Research on Big Data Intelligent Application Effect Evaluation Overall
   Technology
SO 2020 INTERNATIONAL CONFERENCE ON BIG DATA & ARTIFICIAL INTELLIGENCE &
   SOFTWARE ENGINEERING (ICBASE 2020)
LA English
DT Proceedings Paper
CT International Conference on Big Data and Artificial Intelligence and
   Software Engineering (ICBASE)
CY OCT 23-25, 2020
CL Chengdu, PEOPLES R CHINA
DE component; big data; artificial intelligence; digital simulation;
   process customization
AB The big data intelligent application effect evaluation is aimed at panoramic, realistic, dynamic and quantitative displaying and analyzing the whole process and effect of big data intelligent application, and the main scene is the application of important points such as simulation deduction, simulation evaluation and situation analysis. By using the mature experience in the field of digital simulation evaluation, integrating the latest research achievements in the field of big data analysis and artificial intelligence, the overall technology route is creatively proposed with data integration scale, application modeling intelligent, process deduction customization, effect evaluation refined, situation display comprehensive. Research results will guide the prototype system construction.
C1 [Yan, Zhong; Guo Wensi; Zhong Yanling; Sun Maosheng] AMA, PLA, Inst Chem Def, Beijing, Peoples R China.
RP Guo, WS (corresponding author), AMA, PLA, Inst Chem Def, Beijing, Peoples R China.
EM yggong@pku.edu.cn
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NR 8
TC 0
Z9 0
U1 1
U2 6
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-7281-9619-0
PY 2020
BP 78
EP 81
DI 10.1109/ICBASE51474.2020.00024
PG 4
WC Computer Science, Artificial Intelligence; Computer Science, Software
   Engineering; Computer Science, Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BR8GI
UT WOS:000671885700018
DA 2024-09-05
ER
PT J
AU Khanfar, AA
   Mavi, RK
   Iranmanesh, M
   Gengatharen, D
AF Khanfar, Ahmad A.
   Mavi, Reza Kiani
   Iranmanesh, Mohammad
   Gengatharen, Denise
TI Determinants of artificial intelligence adoption: research themes and
   future directions
SO INFORMATION TECHNOLOGY & MANAGEMENT
LA English
DT Article; Early Access
DE Artificial intelligence; Technology adoption; Adoption models; Keyword
   analysis; Thematic analysis; Bibliometric analysis
ID ACCEPTANCE; CHALLENGES; EVOLUTION; NETWORK; SYSTEMS; TOOL
AB The adoption of artificial intelligence (AI) systems is on the rise owing to their many benefits. This study conducted a bibliometric analysis to identify (1) how the literature on AI adoption has evolved over the past few years, (2) key themes associated with AI adoption in the literature, and (3) the gaps in the literature. To achieve these objectives, we utilised the Biblioshiny of R-package bibliometric analysis tool to analyse the AI adoption literature. A total of 91 articles were reviewed and analysed in this study. Four major themes were identified: AI, machine learning, the unified theory of acceptance and use of technology (UTAUT) model and the technology acceptance model (TAM). Using a content analysis of the identified themes, the study gained additional insight into the studies on AI adoption. Previous studies have been limited to specific industries and systems, and adoption theories like the UTAUT and TAM have also been utilised to a limited extent. Directions for future studies were provided.
C1 [Khanfar, Ahmad A.; Mavi, Reza Kiani; Gengatharen, Denise] Edith Cowan Univ, Sch Business & Law, Joondalup, WA 6027, Australia.
   [Iranmanesh, Mohammad] La Trobe Univ, La Trobe Business Sch, Melbourne, Vic 3086, Australia.
C3 Edith Cowan University; La Trobe University
RP Khanfar, AA (corresponding author), Edith Cowan Univ, Sch Business & Law, Joondalup, WA 6027, Australia.
EM a.khanfar@ecu.edu.au
FU CAUL and its Member Institutions
FX Open Access funding enabled and organized by CAUL and its Member
   Institutions. This research received no specific grant from any funding
   agency in the public, commercial, or not-for-profit sectors.
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NR 92
TC 0
Z9 0
U1 1
U2 1
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1385-951X
EI 1573-7667
J9 INFORM TECHNOL MANAG
JI Inf. Technol. Manag.
PD 2024 AUG 23
PY 2024
DI 10.1007/s10799-024-00435-0
EA AUG 2024
PG 21
WC Information Science & Library Science; Management
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science; Business & Economics
GA D5L9N
UT WOS:001296606600002
OA hybrid
DA 2024-09-05
ER
PT J
AU Bonner, C
   McKinn, S
   Lau, A
   Jansen, J
   Doust, J
   Trevena, L
   McCaffery, K
AF Bonner, Carissa
   McKinn, Shannon
   Lau, Annie
   Jansen, Jesse
   Doust, Jenny
   Trevena, Lyndal
   McCaffery, Kirsten
TI Heuristics and biases in cardiovascular disease prevention: How can we
   improve communication about risk, benefits and harms?
SO PATIENT EDUCATION AND COUNSELING
LA English
DT Article
DE Cardiovascular disease; Risk communication; Risk assessment; Risk
   formats; Heuristics; Qualitative research
ID TOOL; INFORMATION
AB Objective: Cardiovascular disease (CVD) prevention guidelines recommend medication based on the probability of a heart attack/stroke in the next 5-10 years. However, heuristics and biases make risk communication challenging for doctors. This study explored how patients interpret personalised CVD risk results presented in varying formats and timeframes.
   Methods: GPs recruited 25 patients with CVD risk factors and varying medication history. Participants were asked to 'think aloud' while using two CVD risk calculators that present probabilistic risk in different ways, within a semi-structured interview. Transcribed audio-recordings were coded using Framework Analysis.
   Results: Key themes were: 1) numbers lack meaning without a reference point; 2) risk results need to be both credible and novel; 3) selective attention to intervention effects. Risk categories (low/moderate/high) provided meaningful context, but short-term risk results were not credible if they didn't match expectations. Colour-coded icon arrays showing the effect of age and interventions were seen as novel and motivating. Those on medication focused on benefits, while others focused on harms.
   Conclusion: CVD risk formats need to be tailored to patient expectations and experiences in order to counteract heuristics and biases.
   Practice implications: Doctors need access to multiple CVD risk formats to communicate effectively about CVD prevention. (C) 2017 Elsevier B.V. All rights reserved.
C1 [Bonner, Carissa; McKinn, Shannon; Jansen, Jesse; Trevena, Lyndal; McCaffery, Kirsten] Univ Sydney, Sydney Sch Publ Hlth, Sydney, NSW, Australia.
   [Lau, Annie] Macquarie Univ, Australian Inst Hlth Innovat, Ctr Hlth Informat, Sydney, NSW, Australia.
   [Doust, Jenny] Bond Univ, Fac Hlth Sci & Med, Robina, Australia.
C3 University of Sydney; Macquarie University; Bond University
RP Bonner, C (corresponding author), Univ Sydney, Edward Ford Bldg A27, Sydney, NSW 2006, Australia.
EM carissa.bonner@sydney.edu.au; shannon.mckinn@sydney.edu.au;
   annie.lau@mq.edu.au; jesse.jansen@sydney.edu.au; jdoust@bond.edu.au;
   lyndal.trevena@sydney.edu.au; kirsten.mccaffery@sydney.edu.au
RI McCaffery, Kirsten/K-7945-2019; Doust, Jenny/AGI-8773-2022; McKinn,
   Shannon/AAW-7934-2020; Bonner, Carissa/AFL-3726-2022
OI McCaffery, Kirsten/0000-0003-2696-5006; Doust,
   Jenny/0000-0002-4024-9308; McKinn, Shannon/0000-0001-6384-1745; Bonner,
   Carissa/0000-0002-4797-6460; Lau, Annie Y.S./0000-0002-3028-4222
FU National Health and Medical Research Council (NHMRC) through the
   Screening and Test Evaluation Program [633003]
FX The study was supported by the National Health and Medical Research
   Council (NHMRC) through the Screening and Test Evaluation Program (No.
   633003). The funders had no role in the design or conduct of the study;
   in the collection, analysis, and interpretation of the data; or in the
   preparation or approval of the manuscript.
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PU ELSEVIER IRELAND LTD
PI CLARE
PA ELSEVIER HOUSE, BROOKVALE PLAZA, EAST PARK SHANNON, CO, CLARE, 00000,
   IRELAND
SN 0738-3991
J9 PATIENT EDUC COUNS
JI Patient Educ. Couns.
PD MAY
PY 2018
VL 101
IS 5
BP 843
EP 853
DI 10.1016/j.pec.2017.12.003
PG 11
WC Public, Environmental & Occupational Health; Social Sciences,
   Interdisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Public, Environmental & Occupational Health; Social Sciences - Other
   Topics
GA GD3ZH
UT WOS:000430441800010
PM 29269097
OA Green Submitted
DA 2024-09-05
ER
PT J
AU LUDL, H
   SCHOPE, K
   MANGELSDORF, I
AF LUDL, H
   SCHOPE, K
   MANGELSDORF, I
TI SEARCHING FOR INFORMATION ON CHEMICAL-SUBSTANCES IN SELECTED BIOMEDICAL
   BIBLIOGRAPHIC DATABASES
SO CHEMOSPHERE
LA English
DT Article
DE BIBLIOGRAPHIC DATABASES; ONLINE RETRIEVAL; EVALUATION OF CHEMICALS;
   CHEMICAL SUBSTANCES; CAS NUMBER; SUBSTANCE NAMES; RECALL; PRECISION
AB A method was developed which allows effective searching for information on chemical substances in databases. Several searches for chemicals in bibliographic databases were carried out to analyse the method of indexing chemical names. The recall rates of documents were compared to evaluate information resources as well as searching strategies. Recall rates of documents searched with the CAS Nos. were compared to those searched with substance name. It turned out that searching for substances is most specific and fastest with CAS Nos. They should always be used whenever possible. However, this is not sufficient in many BDs, making an additional search using chemical names necessary. Specific search options that have to be considered for each database are reported.
RP LUDL, H (corresponding author), GSF FORSCHUNGSZENTRUM UNWELT & GESUNDHEIT,INST TOXIKOL,INGOLSTADTER LANDSTR 1,D-85758 OBERSCHLEISSHEIM,GERMANY.
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NR 24
TC 4
Z9 4
U1 0
U2 0
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB
SN 0045-6535
J9 CHEMOSPHERE
JI Chemosphere
PD JUL
PY 1995
VL 31
IS 2
BP 2611
EP 2628
DI 10.1016/0045-6535(95)00134-T
PG 18
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology
GA RL927
UT WOS:A1995RL92700004
PM 7663948
DA 2024-09-05
ER
PT C
AU Bioglio, L
   Rho, V
   Pensa, RG
AF Bioglio, Livio
   Rho, Valentina
   Pensa, Ruggero G.
BE Yamamoto, A
   Kida, T
   Uno, T
   Kuboyama, T
TI Measuring the Inspiration Rate of Topics in Bibliographic Networks
SO DISCOVERY SCIENCE, DS 2017
SE Lecture Notes in Artificial Intelligence
LA English
DT Proceedings Paper
CT 20th International Conference on Discovery Science (DS)
CY OCT 15-17, 2017
CL Kyoto, JAPAN
DE Information diffusion; Topic modeling; Citation networks
AB Information diffusion is a widely-studied topic thanks to its applications to social media/network analysis, viral marketing campaigns, influence maximization and prediction. In bibliographic networks, for instance, an information diffusion process takes place when some authors, that publish papers in a given topic, influence some of their neighbors (coauthors, citing authors, collaborators) to publish papers in the same topic, and the latter influence their neighbors in their turn. This well-accepted definition, however, does not consider that influence in bibliographic networks is a complex phenomenon involving several scientific and cultural aspects. In fact, in scientific citation networks, influential topics are usually considered those ones that spread most rapidly in the network. Although this is generally a fact, this semantics does not consider that topics in bibliographic networks evolve continuously. In fact, knowledge, information and ideas are dynamic entities that acquire different meanings when passing from one person to another. Thus, in this paper, we propose a new definition of influence that captures the diffusion of inspiration within the network. We propose a measure of the inspiration rate called inspiration rank. Finally, we show the effectiveness of our measure in detecting the most inspiring topics in a citation network built upon a large bibliographic dataset.
C1 [Bioglio, Livio; Rho, Valentina; Pensa, Ruggero G.] Univ Turin, Dept Comp Sci, Turin, Italy.
C3 University of Turin
RP Pensa, RG (corresponding author), Univ Turin, Dept Comp Sci, Turin, Italy.
EM livio.bioglio@unito.it; valentina.rho@unito.it; ruggero.pensa@unito.it
RI Pensa, Ruggero G./B-5994-2011
OI Pensa, Ruggero G./0000-0001-5145-3438
FU project MIMOSA (MultIModal Ontology-driven query system for the
   heterogeneous data of a SmArtcity, "Progetto di Ateneo Torino_call")
   [2014_L2_157]
FX This work is partially funded by project MIMOSA (MultIModal
   Ontology-driven query system for the heterogeneous data of a SmArtcity,
   "Progetto di Ateneo Torino_call2014_L2_157", 2015-17).
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NR 24
TC 3
Z9 3
U1 0
U2 2
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 2945-9133
EI 1611-3349
BN 978-3-319-67786-6; 978-3-319-67785-9
J9 LECT NOTES ARTIF INT
PY 2017
VL 10558
BP 309
EP 323
DI 10.1007/978-3-319-67786-6_22
PG 15
WC Computer Science, Artificial Intelligence; Computer Science, Theory &
   Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BK5YA
UT WOS:000439773400022
DA 2024-09-05
ER
PT J
AU Gálvez, RH
AF Galvez, Ramiro H.
TI Assessing author self-citation as a mechanism of relevant knowledge
   diffusion
SO SCIENTOMETRICS
LA English
DT Article
DE Author self-citation; Latent Dirichlet allocation; Semantic
   dissimilarity; Knowledge diffusion
ID INDEX; MACRO
AB Author self-citation is a practice that has been historically surrounded by controversy. Although the prevalence of self-citations in different scientific fields has been thoroughly analysed, there is a lack of large scale quantitative research focusing on its usefulness at guiding readers in finding new relevant scientific knowledge. In this work we empirically address this issue. Using as our main corpus the entire set of PLOS journals research articles, we train a topic discovery model able to capture semantic dissimilarity between pairs of articles. By dividing pairs of articles involved in intra-PLOS citations into self-citations (articles linked by a cite which share at least one author) and non-self-citations (articles linked by a cite which share no author), we observe the distribution of semantic dissimilarity between citing and cited papers in both groups. We find that the typical semantic distance between articles involved in self-citations is significantly smaller than the observed one for articles involved in non-self-citations. Additionally, we find that our results are not driven by the fact that authors tend to specialize in particular areas of research, make use of specific research methodologies or simply have particular styles of writing. Overall, assuming shared content as an indicator of relevance and pertinence of citations, our results indicate that self-citations are, in general, useful as a mechanism of knowledge diffusion.
C1 [Galvez, Ramiro H.] Univ Buenos Aires, Dept Comp, FCEyN, Buenos Aires, DF, Argentina.
C3 University of Buenos Aires
RP Gálvez, RH (corresponding author), Univ Buenos Aires, Dept Comp, FCEyN, Buenos Aires, DF, Argentina.
EM rgalvez@dc.uba.ar
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NR 37
TC 14
Z9 14
U1 5
U2 64
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD JUN
PY 2017
VL 111
IS 3
BP 1801
EP 1812
DI 10.1007/s11192-017-2330-1
PG 12
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA EV4RE
UT WOS:000401747900028
DA 2024-09-05
ER
PT J
AU Chavez, MD
   Ceballos, HG
   Cantu-Ortiz, FJ
AF Chavez, Mario D.
   Ceballos, Hector G.
   Cantu-Ortiz, Francisco J.
TI A data analytics approach to contrast the performance of teaching (only)
   vs. research professors
SO INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM
LA English
DT Article
DE Student Evaluation of Teaching (SET); Teaching Professor; Research
   Professor; Data Science; ANOVA and Logistic Regression; Innovation in
   Higher Education
ID STUDENT EVALUATION SCORES; HIGHER-EDUCATION; RATINGS
AB This research article presents a study to compare the teaching performance of teaching-only versus teaching-and-research professors at higher education institutions. It is a common belief that, generally, teaching professors outperform research professors in teaching-and-research universities according to student perceptions reflected in student surveys. This case study presents experimental evidence that shows this is not always the case and that, under certain circumstances, it can be the contrary. The case study is from Tecnologico de Monterrey (Tec), a teaching-and-research, private university in Mexico that has developed a research profile during the last two decades using a mix of teaching-only and teaching-and-research faculty members; during this time period, the university has had a growing ascendancy in world university rankings. Data from an institutional student survey called the ECOA was used. The data set contains more than 118,000 graduate and undergraduate courses for 5 semesters (January 2017 to May 2019). The results presented were derived from statistical to data mining methods, including Analysis of Variance and Logistic Regression, that were applied to this data set of more than nine thousand professors who taught those courses. The results show that teaching-and-research professors perform better or at least the same as teaching-only professors. The differences found in teaching with respect to attributes like professors' gender, age, and research level are also presented.
C1 [Chavez, Mario D.; Ceballos, Hector G.; Cantu-Ortiz, Francisco J.] Tecnol Monterrey, Sch Sci & Engn, Ave Eugenio Garza Sada 2501, Monterrey 64989, NL, Mexico.
C3 Tecnologico de Monterrey
RP Cantu-Ortiz, FJ (corresponding author), Tecnol Monterrey, Sch Sci & Engn, Ave Eugenio Garza Sada 2501, Monterrey 64989, NL, Mexico.
EM A00826797@itesm.mx; ceballos@tec.mx; fcantu@tec.mx
RI Ceballos, Hector G./AAC-3747-2022; Cantu-Ortiz, Francisco J./B-8457-2009
OI Ceballos, Hector G./0000-0002-2460-3442; Cantu-Ortiz, Francisco
   J./0000-0002-2015-0562
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NR 39
TC 1
Z9 1
U1 0
U2 11
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1955-2513
EI 1955-2505
J9 INT J INTERACT DES M
JI Int. J. Interact. Des. Manuf.-IJIDeM
PD DEC
PY 2020
VL 14
IS 4
BP 1577
EP 1592
DI 10.1007/s12008-020-00713-5
EA SEP 2020
PG 16
WC Engineering, Manufacturing
WE Emerging Sources Citation Index (ESCI)
SC Engineering
GA OQ9JH
UT WOS:000573412000001
DA 2024-09-05
ER
PT J
AU Ravindran, AC
   Kokjohn, SL
AF Ravindran, Arun C.
   Kokjohn, Sage L.
TI Evaluation of the sample size requirements of machine learning models
   used in engine design and research
SO INTERNATIONAL JOURNAL OF ENGINE RESEARCH
LA English
DT Article
DE Engine research; machine learning; Gaussian process regression; sample
   size requirements; CDC; RCCI; DISI
ID GAUSSIAN PROCESS REGRESSION; OPTIMIZATION
AB Machine Learning (ML) techniques have been effectively used to learn the intricate relationships between variables that play a significant role in the field of engine design. However, there are two challenges to this approach - (1) Identifying ML regression models that could capture the trends of a response variable, given a non-parametric training set of relatively small size, with an acceptable accuracy and response time, and (2) identifying the size of the dataset, with respect to the input parameters, to be used for training and validation of the ML models. There is not enough information in the literature to reach a consensus on the sampling size to be used for an engine design problem that would yield an acceptable measure of goodness-of-fit. This is evident from the varied size of the training/validation data size used within the engine research community, as will be elaborated on in the following sections. The objective of this paper is to provide an insight into the sampling size required by the different ML models to achieve an acceptable fit between the model and the data, to be used in three types of engine design/optimization problems - (1) conventional diesel combustion (CDC) engine performance over a wide range of speed and load, (2) cold-start operation of a direct-injected spark-ignition (DISI) engine, and (3) high-load performance of a dual-fuel reactivity-controlled compression ignition (RCCI) engine.
C1 [Ravindran, Arun C.; Kokjohn, Sage L.] Univ Wisconsin, Dept Mech Engn, 1513 Univ Ave, Madison, WI 53706 USA.
C3 University of Wisconsin System; University of Wisconsin Madison
RP Ravindran, AC (corresponding author), Univ Wisconsin, Dept Mech Engn, 1513 Univ Ave, Madison, WI 53706 USA.
EM aravindran3@wisc.edu
OI C RAVINDRAN, ARUN/0000-0002-8841-9150
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NR 26
TC 0
Z9 0
U1 1
U2 1
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 1468-0874
EI 2041-3149
J9 INT J ENGINE RES
JI Int. J. Engine Res.
PD JUL
PY 2023
VL 24
IS 7
BP 2973
EP 2990
DI 10.1177/14680874221137185
EA NOV 2022
PG 18
WC Thermodynamics; Engineering, Mechanical; Transportation Science &
   Technology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Thermodynamics; Engineering; Transportation
GA O6BN2
UT WOS:000890518800001
DA 2024-09-05
ER
PT C
AU Minglana, J
   Tobias, RR
   Roxas, RE
AF Minglana, Johanna
   Tobias, Rogelio Ruzcko
   Roxas, Rachel Edita
GP IEEE
TI Artificial Intelligence Applications in Quality Management System: A
   Bibliometric Study
SO 2021 IEEE REGION 10 CONFERENCE (TENCON 2021)
LA English
DT Proceedings Paper
CT IEEE Region 10 Conference (TENCON)
CY DEC 07-10, 2021
CL Auckland, NEW ZEALAND
DE artificial intelligence; bibliometrics; ISO 9001:2015; Industry 4.0;
   Quality 4.0; quality management system
AB This paper presents a systematic literature review and bibliometric analyses of publications in the field of Quality Management System (QMS) by authors who applied artificial intelligence (AI) in ISO 9001:2015 audits. Scopus-indexed papers that were published from 1998 to 2021 were evaluated based on the research publication metrics made available by Scopus. From the 142 extracted Scopus-indexed publications in September 2021, 109 or 76 percent of the publications remained after Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) procedure. Analyses and visualizations using VOSviewer reveal research productivity, affiliation and collaboration networks in various countries, and the corresponding relationship between research networks in the field of AI-enabled QMS. Findings reveal that QMS is leaning towards sustainability, big data, and applied technological innovations.
C1 [Minglana, Johanna; Tobias, Rogelio Ruzcko] Qual Management Off, Manila, Philippines.
   [Minglana, Johanna] Natl Univ, Coll Educ Arts & Sci, Manila, Philippines.
   [Tobias, Rogelio Ruzcko] Natl Univ, Coll Engn, Manila, Philippines.
   [Roxas, Rachel Edita] Natl Univ, Coll Comp & Informat Technol, Manila, Philippines.
C3 National University Philippines; National University Philippines;
   National University Philippines
RP Minglana, J (corresponding author), Qual Management Off, Manila, Philippines.; Minglana, J (corresponding author), Natl Univ, Coll Educ Arts & Sci, Manila, Philippines.
EM jgminglana@national-u.edu.ph; ruzcko@gmail.com;
   reoroxas@national-u.edu.ph
CR Alejandrino Jonnel, 2020, 2020 IEEE Region 10 Conference (TENCON), P1340, DOI 10.1109/TENCON50793.2020.9293829
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NR 35
TC 3
Z9 3
U1 6
U2 28
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-6654-9532-5
PY 2021
BP 947
EP 952
DI 10.1109/TENCON54134.2021.9707340
PG 6
WC Computer Science, Artificial Intelligence; Computer Science, Software
   Engineering; Engineering, Multidisciplinary; Telecommunications
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Engineering; Telecommunications
GA BT1OC
UT WOS:000799485900167
DA 2024-09-05
ER
PT J
AU Maroteau, G
   An, JS
   Murgier, J
   Hulet, C
   Ollivier, M
   Ferreira, A
AF Maroteau, Gaelle
   An, Jae-Sung
   Murgier, Jerome
   Hulet, Christophe
   Ollivier, Matthieu
   Ferreira, Alexandre
TI Evaluation of the impact of large language learning models on articles
   submitted to <i>Orthopaedics & Traumatology: Surgery & Research
   (OTSR</i>): A significant increase in the use of artificial intelligence
   in 2023
SO ORTHOPAEDICS & TRAUMATOLOGY-SURGERY & RESEARCH
LA English
DT Article
DE Artificial intelligence; ChatGPT; Large language learning models;
   Chatbot; Scientific article
AB Introduction: There has been an unprecedented rise is the use of artificial intelligence (AI) amongst med-ical fields. Recently, a dialogue agent called ChatGPT (Generative Pre-trained Transformer) has grown in popularity through its use of large language models (LLM) to clearly and precisely generate text on demand. However, the impact of AI on the creation of scientific articles is remains unknown. A retrospec-tive study was carried out with the aim of answering the following questions: identify the presence of text generated by LLM before and after the increased usage of ChatGPT in articles submitted in OTSR; deter-mine if the type of article, the year of submission, and the country of origin, influenced the proportion of text generated, at least in part by AI.Material and methods: A total of 390 English articles were submitted to OTSR in January, February and March 2022 (n = 204) and over the same months of 2023 (n = 186) were analyzed. All articles were ana-lyzed using the ZeroGPT tool, which provides an assumed rate of AI use expressed as a percentage. A comparison of the average rate of AI use was carried out between the articles submitted in 2022 and 2023. This comparison was repeated keeping only the articles with the highest percentage of suspected AI use (greater than 10 and 20%). A secondary analysis was carried out to identify risk factors for AI use.Results: The average percentage of suspected LLM use in the entire cohort was 11% +/- 6, with 160 articles (41.0%) having a suspected AI rate greater than 10% and 61 (15.6%) with an assumed AI rate greater than 20%. A comparison between articles submitted in 2022 and 2023 revealed a significant increase in the use of these tools after the launch of ChatGPT 3.5 (9.4% in 2022 and 12.6% in 2023 [p = 0.004]). The number of articles with suspected AI rates of greater than 10 and 20% were significantly higher in 2023: >10%: 71 articles (34.8%) versus 89 articles (47.8%) (p = 0.008) and >20%: 21 articles (10.3%) versus 40 articles (21.5%) (p = 0.002). A risk factor analysis for LLLM use, demonstrated that authors of Asian geographic origin, and the submission year 2023 were associated with a higher rate of suspected AI use. An AI rate >20% was associated to Asian geographical origin with an odds ratio of 1.79 (95% CI: 1.03-3.11) (p = 0.029), while the year of submission being 2023 had an odds ratio of 1.7 (95% CI: 1.1-2.5) (p = 0.02).Conclusion: This study highlights a significant increase in the use of LLM in the writing of articles sub-mitted to the OTSR journal after the launch of ChatGPT 3.5. The increasing use of these models raises questions about originality and plagiarism in scientific research. AI offers creative opportunities but also raises ethical and methodological challenges. Level of evidence: III; case control study.(c) 2023 Elsevier Masson SAS. All rights reserved.
C1 [Maroteau, Gaelle; Hulet, Christophe; Ferreira, Alexandre] Caen Univ Hosp, Dept Orthopaed & Traumatol, Unite Inserm Comete 1075, Ave Cote De Nacre, F-14000 Caen, France.
   [An, Jae-Sung] Tokyo Med & Dent Univ, 1 Chome 5-45 Yushima, Bunkyo, Tokyo 1138510, Japan.
   [Murgier, Jerome] Clin Aguilera, Serv Chirurg Orthoped, 21 Rue Estagnas, F-64200 Biarritz, France.
   [Ollivier, Matthieu] St Marguer Hosp, Inst Movement & Locomot, Dept Orthopaed & Traumatol, BP 29,270 Blvd St Marguer, F-13274 Marseille, France.
   [Ollivier, Matthieu] St Marguerite Hosp, AP HM, Dept Orthopaed & Traumatol, Aix Marseille Unit,Inst Locomot, Marseille, Brazil.
C3 CHU de Caen NORMANDIE; Universite de Caen Normandie; Tokyo Medical &
   Dental University (TMDU); Aix-Marseille Universite; Assistance
   Publique-Hopitaux de Marseille
RP Ferreira, A (corresponding author), Caen Univ Hosp, Dept Orthopaed & Traumatol, Unite Inserm Comete 1075, Ave Cote De Nacre, F-14000 Caen, France.
EM alexandreferreira0891@gmail.com
OI Maroteau, Gaelle/0000-0002-0923-7042; HULET,
   Christophe/0000-0002-1011-6141; An, Jae-Sung/0009-0006-4975-2600;
   Alexandre, FERREIRA/0000-0002-4624-3898
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NR 23
TC 4
Z9 4
U1 12
U2 14
PU ELSEVIER MASSON, CORP OFF
PI PARIS
PA 65 CAMILLE DESMOULINS CS50083 ISSY-LES-MOULINEAUX, 92442 PARIS, FRANCE
SN 1877-0568
J9 ORTHOP TRAUMATOL-SUR
JI Orthop. Traumatol.-Surg. Res.
PD DEC
PY 2023
VL 109
IS 8
AR 103720
DI 10.1016/j.otsr.2023.103720
EA NOV 2023
PG 6
WC Orthopedics; Surgery
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Orthopedics; Surgery
GA CV3W6
UT WOS:001127982300001
PM 37866509
DA 2024-09-05
ER
PT J
AU Song, XZR
   Zhang, Y
   Pan, R
   Wang, HS
AF Song, Xizhuoran
   Zhang, Yan
   Pan, Rui
   Wang, Hansheng
TI Link Prediction for Statistical Collaboration Networks Incorporating
   Institutes and Research Interests
SO IEEE ACCESS
LA English
DT Article
DE Collaboration; Measurement; Machine learning; Finance; Predictive
   models; Stochastic processes; Collaboration network; link prediction;
   nodal attribute; similarity-based approach
ID SOCIAL NETWORK; RANDOM-WALK; EVOLUTION; COAUTHORSHIP; PATTERNS
AB An interesting application of the link prediction technique is detecting the potential new links in collaboration networks. In this study, we construct collaboration networks based on the co-authorship information of the papers published in 43 statistical journals from 2001 to 2018. We construct training and testing networks according to the timestamps of the papers and construct a classification dataset for link prediction. We calculate 20 similarity indices based on the training network to perform link prediction. Additionally, we consider nodal attributes (institutes and research interests) to develop two novel predictors called the same institute (SIN) and keywords match count (KMC). Several machine-learning classifiers including support vector machine, XGBoost and random forest are implemented to combine all predictors. After incorporating SIN and KMC, we observe that the area under the receiver operating characteristic curve values of all classifiers improved, indicating that SIN and KMC can significantly improve classification accuracy. Finally, we provide collaborative recommendations for researchers based on the proposed model.
C1 [Song, Xizhuoran; Pan, Rui] Cent Univ Finance & Econ, Sch Stat & Math, Beijing 102206, Peoples R China.
   [Zhang, Yan] Xiamen Univ, Sch Econ, Xiamen 361005, Peoples R China.
   [Wang, Hansheng] Peking Univ, Guanghua Sch Management, Beijing 100871, Peoples R China.
C3 Central University of Finance & Economics; Xiamen University; Peking
   University
RP Pan, R (corresponding author), Cent Univ Finance & Econ, Sch Stat & Math, Beijing 102206, Peoples R China.; Zhang, Y (corresponding author), Xiamen Univ, Sch Econ, Xiamen 361005, Peoples R China.
EM zhangyan_elyssa@163.com; panrui_cufe@126.com
OI Zhang, Yan/0000-0002-4081-5189
FU National Natural Science Foundation of China (NSFC) [11971504];
   Disciplinary Funding of Central University of Finance and Economics;
   Emerging Interdisciplinary Project of the Central University of Finance
   and Economics; National Natural Science Foundation of China [11831008];
   Open Research Fund of Key Laboratory of Advanced Theory and Application
   in Statistics and Data Science [KLATASDS-MOE-ECNU-KLATASDS2101]
FX The work of Rui Pan was supported in part by the National Natural
   Science Foundation of China (NSFC) under Grant 11971504, and in part by
   the Disciplinary Funding of Central University of Finance and Economics,
   and in part by the Emerging Interdisciplinary Project of the Central
   University of Finance and Economics. The work of Hansheng Wang was
   supported in part by the National Natural Science Foundation of China
   under Grant 11831008, and in part by the Open Research Fund of Key
   Laboratory of Advanced Theory and Application in Statistics and Data
   Science under Grant KLATASDS-MOE-ECNU-KLATASDS2101.
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NR 50
TC 1
Z9 1
U1 5
U2 20
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2022
VL 10
BP 104954
EP 104965
DI 10.1109/ACCESS.2022.3210129
PG 12
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA 5F6QU
UT WOS:000866438900001
OA gold
DA 2024-09-05
ER
PT J
AU Catalini, C
   Lacetera, N
   Oettl, A
AF Catalini, Christian
   Lacetera, Nicola
   Oettl, Alexander
TI The incidence and role of negative citations in science
SO PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF
   AMERICA
LA English
DT Article
DE social studies of science; citation analysis; bibliometric techniques;
   natural-language processing; negative citations
ID KNOWLEDGE
AB Citations to previous literature are extensively used to measure the quality and diffusion of knowledge. However, we know little about the different ways in which a study can be cited; in particular, are papers cited to point out their merits or their flaws? We elaborated a methodology to characterize "negative" citations using bibliometric data and natural language processing. We found that negative citations concerned higher-quality papers, were focused on a study's findings rather than theories or methods, and originated from scholars who were closer to the authors of the focal paper in terms of discipline and social distance, but not geographically. Receiving a negative citation was also associated with a slightly faster decline in citations to the paper in the long run.
C1 [Catalini, Christian] MIT, MIT Sloan Sch Management, Cambridge, MA 02142 USA.
   [Lacetera, Nicola] Univ Toronto, Inst Management & Innovat, Mississauga, ON L5L 1C6, Canada.
   [Oettl, Alexander] Georgia Inst Technol, Scheller Coll Business, Atlanta, GA 30308 USA.
C3 Massachusetts Institute of Technology (MIT); University of Toronto;
   University Toronto Mississauga; University System of Georgia; Georgia
   Institute of Technology
RP Oettl, A (corresponding author), Georgia Inst Technol, Scheller Coll Business, Atlanta, GA 30308 USA.
EM Alexander.Oettl@scheller.gatech.edu
RI Oettl, Alex/AFN-9082-2022
OI Oettl, Alex/0000-0001-8908-4674; Lacetera, Nicola/0000-0002-3191-8792
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NR 28
TC 108
Z9 118
U1 1
U2 47
PU NATL ACAD SCIENCES
PI WASHINGTON
PA 2101 CONSTITUTION AVE NW, WASHINGTON, DC 20418 USA
SN 0027-8424
J9 P NATL ACAD SCI USA
JI Proc. Natl. Acad. Sci. U. S. A.
PD NOV 10
PY 2015
VL 112
IS 45
BP 13823
EP 13826
DI 10.1073/pnas.1502280112
PG 4
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics
GA CV7QY
UT WOS:000364470300045
PM 26504239
OA Green Published, Bronze
DA 2024-09-05
ER
PT J
AU Acosta-Angulo, B
   Diaz-Angulo, J
   Lara-Ramos, J
   Torres-Palma, R
   Martínez-Pachón, D
   Moncayo-Lasso, A
   Machuca-Martínez, F
AF Acosta-Angulo, Bryan
   Diaz-Angulo, Jennyfer
   Lara-Ramos, Jose
   Torres-Palma, Ricardo
   Martinez-Pachon, Diana
   Moncayo-Lasso, Alejandro
   Machuca-Martinez, Fiderman
TI Analysis of the Applications of Particle Swarm Optimization and Genetic
   Algorithms on Reaction Kinetics: A Prospective Study for Advanced
   Oxidation Processes
SO CHEMELECTROCHEM
LA English
DT Article
DE Bibliometrics; Electrochemistry; Forecasting; Kinetics; Photocatalysis
ID ELECTROCHEMICAL ADVANCED OXIDATION; MONTE-CARLO-SIMULATION;
   ELECTRO-FENTON PROCESS; WASTE-WATER; ORGANIC POLLUTANTS; PHOTOCATALYTIC
   DEGRADATION; NUMERICAL-SIMULATION; PARAMETER-ESTIMATION;
   REACTION-MECHANISM; UV/H2O2 OXIDATION
AB A bibliometric analysis of the Scopus database was implemented to assess the spread of two types of evolutionary algorithms (EAs), genetic algorithms (GA) and particle swarm optimization (PSO), on the study of reaction kinetics. Particular attention was given to applications for advanced oxidation processes (AOPs). The collaborations between countries and authors, as well as the keywords co-occurrences, were investigated. Finally, the Gompertz and Logistic Substitution models (LSM) were employed to forecast future scenarios. It was observed that GA methods were the preferred algorithms for reaction kinetic studies, and the USA was the most influential country in terms of collaboration, followed by China. On the other hand, there was still poor collaboration for most countries; this was also observed for authors' collaboration. In addition, literature concerning AOPs was still scarce. The forecasting suggested growth for implementing evolutionary algorithms, especially for the PSO, increasing its popularity regarding GA methods.
C1 [Acosta-Angulo, Bryan; Lara-Ramos, Jose; Machuca-Martinez, Fiderman] Univ Valle, Grp Invest Proc Avanzados Tratamientos Quim & Bio, Escuela Ingn Quim, Valle Del Cauca, Colombia.
   [Diaz-Angulo, Jennyfer] GITAM, Res & Technol Dev Water Treatment Proc Modelling, Cauca, Colombia.
   [Torres-Palma, Ricardo] Univ Antioquia, Grp Invest Remediac Ambiental & Biocatalisis GIRA, Inst Chem, Fac Exact & Nat Sci, Antioquia, Colombia.
   [Martinez-Pachon, Diana; Moncayo-Lasso, Alejandro] Univ Antonio Narino, Grp Invest Ciencias Biol & Quim, Fac Sci, Bogota, Colombia.
C3 Universidad del Valle; Universidad de Antioquia; Universidad Antonio
   Narino
RP Acosta-Angulo, B; Machuca-Martínez, F (corresponding author), Univ Valle, Grp Invest Proc Avanzados Tratamientos Quim & Bio, Escuela Ingn Quim, Valle Del Cauca, Colombia.
EM bryan.acosta@correounivalle.edu.co;
   fiderman.machuca@correounivalle.edu.co
RI Martínez-Pachón, Diana/ADC-9239-2022; Machuca-Martinez,
   Fiderman/G-2373-2017
OI Martínez-Pachón, Diana/0000-0001-5496-273X; Machuca-Martinez,
   Fiderman/0000-0002-4553-3957; Acosta Angulo, Bryan
   Enrique/0000-0002-3048-6684; Lara Ramos, Jose
   Antonio/0000-0001-6324-5150
FU MINCIENCIAS COLOMBIA (COLCIENCIAS) [80740-173-2021, 111585269594]
FX The authors gratefully acknowledge MINCIENCIAS COLOMBIA (before known as
   COLCIENCIAS) for funding through the program PRO-CEC-AGUA contract
   80740-173-2021 with code 111585269594.
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NR 205
TC 3
Z9 3
U1 3
U2 19
PU WILEY-V C H VERLAG GMBH
PI WEINHEIM
PA POSTFACH 101161, 69451 WEINHEIM, GERMANY
SN 2196-0216
J9 CHEMELECTROCHEM
JI ChemElectroChem
PD JUL 14
PY 2022
VL 9
IS 13
AR e202200229
DI 10.1002/celc.202200229
EA JUN 2022
PG 15
WC Electrochemistry
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Electrochemistry
GA 2P8OJ
UT WOS:000808198800001
DA 2024-09-05
ER
PT J
AU De Paoli, S
AF De Paoli, Stefano
TI Performing an Inductive Thematic Analysis of Semi-Structured Interviews
   With a Large Language Model: An Exploration and Provocation on the
   Limits of the Approach
SO SOCIAL SCIENCE COMPUTER REVIEW
LA English
DT Article
DE large language models; thematic analysis; qualitative research; human-
   AI collaboration
AB Large Language Models (LLMs) have emerged as powerful generative Artificial Intelligence solutions. This paper presents results and reflections of an experiment done with the LLM GPT 3.5-Turbo to perform an inductive Thematic Analysis (TA). Previous research has worked on conducting deductive analysis. Thematic Analysis is a qualitative method for analysis commonly used in social sciences and it is based on interpretations by the human analyst(s) and the identification of explicit and latent meanings in qualitative data. The paper presents the motivations for attempting this analysis; it reflects on how the six phases to a TA proposed by Braun and Clarke can partially be reproduced with the LLM and it reflects on what are the model's outputs. The paper uses two datasets of open access semi-structured interviews, previously analysed by other researchers. The first dataset contains interviews with videogame players, and the second is a dataset of interviews with lecturers teaching data science in a University. This paper used the analyses previously conducted on these datasets to compare with the results produced by the LLM. The results show that the model can infer most of the main themes from previous research. This shows that using LLMs to perform an inductive TA is viable and offers a good degree of validity. The discussion offers some recommendations for working with LLMs in qualitative analysis.
C1 [De Paoli, Stefano] Abertay Univ, Digital Soc, Dundee, Scotland.
   [De Paoli, Stefano] Abertay Univ, Sociol Div, Bell St, Dundee DD1 1HG, Scotland.
C3 University of Abertay Dundee; University of Abertay Dundee
RP De Paoli, S (corresponding author), Abertay Univ, Sociol Div, Bell St, Dundee DD1 1HG, Scotland.
EM s.depaoli@abertay.ac.uk
OI De Paoli, Stefano/0000-0003-1120-4773
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NR 32
TC 4
Z9 4
U1 33
U2 37
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 0894-4393
EI 1552-8286
J9 SOC SCI COMPUT REV
JI Soc. Sci. Comput. Rev.
PD AUG
PY 2024
VL 42
IS 4
BP 997
EP 1019
DI 10.1177/08944393231220483
EA DEC 2023
PG 23
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science; Social Sciences, Interdisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science; Social Sciences
   - Other Topics
GA A2P3C
UT WOS:001116391300001
OA Green Published, hybrid
DA 2024-09-05
ER
PT J
AU Mahalakshmi, GS
   Siva, R
   Sendhilkumar, S
AF MAHALAKSHMI, G. S.
   SIVA, R.
   SENDHILKUMAR, S.
TI On the Expressive Power of Scientific Manuscripts
SO IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING
LA English
DT Article
DE Machine learning; Bibliometrics; Semantics; Text mining; Measurement;
   Indexes; Analytical models; Citation analysis; semantic analysis;
   citation quality; machine learning; text mining; availability index;
   article metrics; deep learning; expressive power
AB Every research manuscript is appreciated in the form of citations. Citations are expected to carry the essence of the underlying base paper by some rhetorical means. However, this is not true in reality. Citation manipulations are equally possible which shall be identified using research semantics. This paper discusses machine learning based approaches for analyzing research citations with the aim of finding quality research citations. On analyzing the semantics of the research manuscript and the respective citations, this paper proposes various metrics for citation quality analysis including deep cite, raw expressive power, expressive power and normalized expressive power.
C1 [MAHALAKSHMI, G. S.; SENDHILKUMAR, S.] Anna Univ, Dept Comp Sci & Engn, Coll Engn Guindy Campus, Chennai 600025, TN, India.
   [SIVA, R.] KCG Coll Technol, Dept Comp Sci & Engn, Chennai 600097, TN, India.
C3 Anna University; Anna University Chennai; College of Engineering Guindy
RP Mahalakshmi, GS (corresponding author), Anna Univ, Dept Comp Sci & Engn, Coll Engn Guindy Campus, Chennai 600025, TN, India.
EM gsmaha@annauniv.edu; sivavb6@gmail.com; thamaraikumar@annauniv.edu
RI Selvaradjou, Sendhilkumar/AIF-2078-2022; R, SIVA/ABE-6532-2021
OI Selvaradjou, Sendhilkumar/0000-0001-6006-1866; R,
   SIVA/0000-0002-2006-8753; Antony, Betina/0000-0002-0491-6214
FU Ministry of Electronics & Information Technology, Government of India
   [VISPHD-MEITY-2959]
FX This Publication is an outcome of the R&D work undertaken in the project
   under the Visvesvaraya Ph.D. Scheme (awarded to Dr. S. Sendhilkumar,
   Unique Awardee Number: VISPHD-MEITY-2959) of Ministry of Electronics &
   Information Technology, Government of India, being implemented by
   Digital India Corporation (formerly Media Lab Asia).
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NR 35
TC 4
Z9 4
U1 0
U2 9
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2168-6750
J9 IEEE T EMERG TOP COM
JI IEEE Trans. Emerg. Top. Comput.
PD JAN 1
PY 2021
VL 9
IS 1
BP 269
EP 279
DI 10.1109/TETC.2018.2870179
PG 11
WC Computer Science, Information Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA QW8QH
UT WOS:000628912400025
OA Bronze
DA 2024-09-05
ER
PT J
AU Lee, CH
   Liu, CL
   Trappey, AJC
   Mo, JPT
   Desouza, KC
AF Lee, Ching-Hung
   Liu, Chien-Liang
   Trappey, Amy J. C.
   Mo, John P. T.
   Desouza, Kevin C.
TI Understanding digital transformation in advanced manufacturing and
   engineering: A bibliometric analysis, topic modeling and research trend
   discovery
SO ADVANCED ENGINEERING INFORMATICS
LA English
DT Article
DE Digital transformation; Advanced manufacturing and engineering;
   Bibliometric analysis; Topic modeling; Systematic review
ID INDUSTRY 4.0; BIG DATA; SUPPLY CHAIN; INNOVATION; SERVICE; CONSTRUCTION;
   TECHNOLOGIES; ARCHITECTURE; LOGISTICS; KNOWLEDGE
AB Digital transformation (DT) is the process of combining digital technologies with sound business models to generate great value for enterprises. DT intertwines with customer requirements, domain knowledge, and theoretical and empirical insights for value propagations. Studies of DT are growing rapidly and heterogeneously, covering the aspects of product design, engineering, production, and life-cycle management due to the fast and market-driven industrial development under Industry 4.0. Our work addresses the challenge of understanding DT trends by presenting a machine learning (ML) approach for topic modeling to review and analyze advanced DT technology research and development. A systematic review process is developed based on the comprehensive DT in manufacturing systems and engineering literature (i.e., 99 articles). Six dominant topics are identified, namely smart factory, sustainability and product-service systems, construction digital transformation, public infrastructure-centric digital transformation, techno-centric digital transformation, and business modelcentric digital transformation. The study also contributes to adopting and demonstrating the ML-based topic modeling for intelligent and systematic bibliometric analysis, particularly for unveiling advanced engineering research trends through domain literature.
C1 [Lee, Ching-Hung] Xi An Jiao Tong Univ, Sch Publ Policy & Adm, Xian 710049, Peoples R China.
   [Liu, Chien-Liang] Natl Yang Ming Chiao Tung Univ, Dept Ind Engn & Management, Hsinchu 300093, Taiwan.
   [Trappey, Amy J. C.] Natl Tsing Hua Univ, Dept Ind Engn & Engn Management, Hsinchu 300044, Taiwan.
   [Mo, John P. T.] RMIT Univ, Sch Engn, Melbourne, Vic 3000, Australia.
   [Desouza, Kevin C.] Queensland Univ Technol, QUT Business Sch, Brisbane, Qld 4001, Australia.
C3 Xi'an Jiaotong University; National Yang Ming Chiao Tung University;
   National Tsing Hua University; Royal Melbourne Institute of Technology
   (RMIT); Queensland University of Technology (QUT)
RP Liu, CL (corresponding author), Natl Yang Ming Chiao Tung Univ, Dept Ind Engn & Management, Hsinchu 300093, Taiwan.
EM leechinghung@xjtu.edu.cn; clliu@nycu.edu.tw; trappey@ie.nthu.edu.tw;
   john.mo@rmit.edu.au; kevin.desouza@qut.edu.au
RI Silva, Flavio/JTT-2763-2023; Trappey, Amy/KWV-0368-2024; Lee,
   Ching-Hung/IQV-9761-2023
OI Trappey, Amy/0000-0001-7651-7012; Lee, Ching-Hung/0000-0002-4093-556X;
   Desouza, Kevin/0000-0002-4734-3081; Liu, Chien-Liang/0000-0002-2724-7199
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NR 122
TC 54
Z9 57
U1 32
U2 285
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 1474-0346
EI 1873-5320
J9 ADV ENG INFORM
JI Adv. Eng. Inform.
PD OCT
PY 2021
VL 50
AR 101428
DI 10.1016/j.aei.2021.101428
EA OCT 2021
PG 17
WC Computer Science, Artificial Intelligence; Engineering,
   Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering
GA WN2YT
UT WOS:000711640000003
DA 2024-09-05
ER
PT J
AU Ngaile, G
   Wang, J
   Gau, JT
AF Ngaile, Gracious
   Wang, Jyhwen
   Gau, Jenn-Terng
TI Challenges in teaching modern manufacturing technologies
SO EUROPEAN JOURNAL OF ENGINEERING EDUCATION
LA English
DT Article
DE manufacturing education; collaboration; active learning; technical
   skills; advanced manufacturing; engineering education research
ID FUSED DEPOSITION; DESIGN
AB Teaching of manufacturing courses for undergraduate engineering students has become a challenge due to industrial globalisation coupled with influx of new innovations, technologies, customer-driven products. This paper discusses development of a modern manufacturing course taught concurrently in three institutions where students collaborate in executing various projects. Lectures are developed to contain materials featuring advanced manufacturing technologies, R&D trends in manufacturing. Pre- and post-surveys were conducted by an external evaluator to assess the impact of the course on increase in student's knowledge of manufacturing; increase students' preparedness and confidence in effective communication and; increase students' interest in pursuing additional academic studies and/or a career path in manufacturing and high technology. The surveyed data indicate that the students perceived significant gains in manufacturing knowledge and preparedness in effective communication. The study also shows that implementation of a collaborative course within multiple institutions requires a robust and collective communication platform.
C1 [Ngaile, Gracious] N Carolina State Univ, Dept Mech & Aerosp Engn, Raleigh, NC 27695 USA.
   [Wang, Jyhwen] Texas A&M Univ, Dept Engn Technol & Ind Distribut, College Stn, TX USA.
   [Gau, Jenn-Terng] No Illinois Univ, Dept Mech Engn, De Kalb, IL 60115 USA.
C3 North Carolina State University; Texas A&M University System; Texas A&M
   University College Station; Northern Illinois University
RP Gau, JT (corresponding author), No Illinois Univ, Dept Mech Engn, De Kalb, IL 60115 USA.
EM jgau@niu.edu
OI Wang, Jyhwen/0000-0001-9016-0566
FU National Science Foundation [0941042, 0941045, 0941079]; Direct For
   Education and Human Resources; Division Of Undergraduate Education
   [0941079, 0941042] Funding Source: National Science Foundation; Direct
   For Education and Human Resources; Division Of Undergraduate Education
   [0941045] Funding Source: National Science Foundation
FX The authors would like to acknowledge the National Science Foundation
   through which this work was funded [award number 0941042, 0941045, and
   0941079].
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NR 36
TC 3
Z9 4
U1 0
U2 9
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 0304-3797
EI 1469-5898
J9 EUR J ENG EDUC
JI Eur. J. Eng. Educ.
PD JUL 4
PY 2015
VL 40
IS 4
BP 432
EP 449
DI 10.1080/03043797.2014.1001814
PG 18
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA CK6OU
UT WOS:000356348200006
DA 2024-09-05
ER
PT J
AU Fanton, M
   Mota, HR
   Araújo, CDB
   da Silva, MS
   Canuto, R
AF Fanton, Marcos
   Mota, Hugo Ribeiro
   Araujo, Carolina de Melo Bomfim
   da Silva, Mitieli Seixas
   Canuto, Raquel
TI Philosophical research in Brazil: A structural topic modeling approach
   with a focus on temporal and gender trends
SO METAPHILOSOPHY
LA English
DT Article
DE gender gap; Latin American philosophy; metaphilosophy; scientometrics;
   structural topic modeling
AB This paper employs structural topic modeling (STM) to describe the academic philosophy landscape in Brazil. Based on a public national database, a corpus consisting of 12,515 abstracts of monographs defended in philosophy graduate programs between 1991 and 2021 was compiled. The final STM model identified 74 meaningful research topics, clustered into 7 thematic categories. This study discusses the prevalence of the most significant topics and categories, their trends across three decades, and their (positive or negative) association with the supervisor's gender. Results show the first empirical evidence that Brazilian philosophical research exhibits a greater focus on philosophers than on specific themes or problems. Moreover, by visualizing the variations in topic prevalence over time, it was possible to track the rise or decline of the major interest categories and topics. Finally, results also show which topics are more influenced or less influenced by gender.
C1 [Fanton, Marcos; da Silva, Mitieli Seixas] Fed Univ St Maria, Dept Philosophy, 1000 Roraima Ave, BR-97105900 Santa Maria, Rio Grande do S, Brazil.
   [Mota, Hugo Ribeiro] Univ Oslo, Dept Philosophy Class Hist Art & Ideas, Oslo, Norway.
   [Araujo, Carolina de Melo Bomfim] Univ Fed Rio de Janeiro, Dept Philosophy, Rio De Janeiro, Brazil.
   [Canuto, Raquel] Univ Fed Rio Grande do Norte, Dept Nutr, Natal, Brazil.
C3 University of Oslo; Universidade Federal do Rio de Janeiro; Universidade
   Federal do Rio Grande do Norte
RP Fanton, M (corresponding author), Fed Univ St Maria, Dept Philosophy, 1000 Roraima Ave, BR-97105900 Santa Maria, Rio Grande do S, Brazil.
EM marcos.fanton@ufsm.br
OI Ribeiro Mota, Hugo/0000-0001-9010-1064
FU Fundacao Carlos Chagas de Apoio a Pesquisa do Estado do Rio de Janeiro
   (FAPERJ) [E-26-201.027/2021]
FX Many thanks to Camila Palhares Barbosa for her thoughtful comments, as
   well as to Michael Baumtrog for proofreading our manuscript. We are also
   grateful to Otto Bohlmann for his meticulous editorial feedback and
   textual review. This research received funds from Fundacao Carlos Chagas
   de Apoio a Pesquisa do Estado do Rio de Janeiro (FAPERJ), process
   E-26-201.027/2021.
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NR 59
TC 0
Z9 0
U1 0
U2 0
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0026-1068
EI 1467-9973
J9 METAPHILOSOPHY
JI Metaphilosophy
PD JUL
PY 2024
VL 55
IS 3
BP 457
EP 501
DI 10.1111/meta.12700
EA JUL 2024
PG 45
WC Philosophy
WE Arts &amp; Humanities Citation Index (A&amp;HCI)
SC Philosophy
GA A1F9Q
UT WOS:001274087100001
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Wilson, J
   Pollard, B
   Aiken, JM
   Caballero, MD
   Lewandowski, HJ
AF Wilson, Joseph
   Pollard, Benjamin
   Aiken, John M.
   Caballero, Marcos D.
   Lewandowski, H. J.
TI Classification of open-ended responses to a research-based assessment
   using natural language processing
SO PHYSICAL REVIEW PHYSICS EDUCATION RESEARCH
LA English
DT Article
AB Surveys have long been used in physics education research to understand student reasoning and inform course improvements. However, to make analysis of large sets of responses practical, most surveys use a closed-response format with a small set of potential responses. Open-ended formats, such as written free response, can provide deeper insights into student thinking, but take much longer to analyze, especially with a large number of responses. Here, we explore natural language processing as a computational solution to this problem. We create a machine learning model that can take student responses from the Physics Measurement Questionnaire as input, and output a categorization of student reasoning based on different reasoning paradigms. Our model yields classifications with the same level of agreement as that between two humans categorizing the data, but can be done by a computer, and thus can be scaled for large datasets. In this work, we describe the algorithms and methodologies used to create, train, and test our natural language processing system. We also present the results of the analysis and discuss the utility of these approaches for analyzing open-response data in education research.
C1 [Wilson, Joseph; Pollard, Benjamin; Lewandowski, H. J.] Univ Colorado Boulder, Dept Phys, Boulder, CO 80309 USA.
   [Wilson, Joseph; Lewandowski, H. J.] NIST, JILA, Boulder, CO 80309 USA.
   [Pollard, Benjamin] Worcester Polytech Inst, Dept Phys, Worcester, MA 01609 USA.
   [Aiken, John M.] Univ Oslo, Njord Ctr, Dept Geosci, N-0316 Oslo, Norway.
   [Aiken, John M.] Univ Oslo, Njord Ctr, Dept Phys, N-0316 Oslo, Norway.
   [Aiken, John M.; Caballero, Marcos D.] Univ Oslo, Ctr Comp Sci Educ, N-0316 Oslo, Norway.
   [Aiken, John M.; Caballero, Marcos D.] Univ Oslo, Dept Phys, N-0316 Oslo, Norway.
   [Caballero, Marcos D.] Michigan State Univ, Dept Phys & Astron, Dept Computat Math Sci & Engn, E Lansing, MI 48824 USA.
   [Caballero, Marcos D.] Michigan State Univ, CREATE, STEM Inst, E Lansing, MI 48824 USA.
C3 University of Colorado System; University of Colorado Boulder; National
   Institute of Standards & Technology (NIST) - USA; Worcester Polytechnic
   Institute; University of Oslo; University of Oslo; University of Oslo;
   University of Oslo; Michigan State University; Michigan State University
RP Lewandowski, HJ (corresponding author), Univ Colorado Boulder, Dept Phys, Boulder, CO 80309 USA.; Lewandowski, HJ (corresponding author), NIST, JILA, Boulder, CO 80309 USA.
EM lewandoh@colorado.edu
OI Caballero, Marcos/0000-0003-0717-4583; Pollard,
   Benjamin/0000-0002-5109-6415
FU National Science Foundation [PHY-1734006]; Michigan State's
   LappanPhilips Foundation; INTPART project of the Research Council of
   Norway [288125]; Olav Thon Foundation; Norwegian Agency for
   International Cooperation and Quality Enhancement in Higher Education
   (DIKU)
FX We acknowledge Rajarshi Basak for valuable input and mentorship
   regarding NLP and machine learning. We acknowledge Robert Hobbs for
   producing the human-coded dataset that was central to this work. This
   work was supported by the National Science Foundation (Grant No.
   PHY-1734006) and by Michigan State's LappanPhilips Foundation. This
   project has received support from the INTPART project of the Research
   Council of Norway (Grant No. 288125), the Olav Thon Foundation, and the
   Norwegian Agency for International Cooperation and Quality Enhancement
   in Higher Education (DIKU), which supports the Center for Computing in
   Science Education. The initial idea for this project was suggested by B.
   P.; further discussions around the idea occurred with H. J. L. and with
   J. M. A., J. W. wrote all of the code and performed all of the analysis,
   with the joint mentorship of B. P., J. M. A., M. D. C., and H. J. L., B.
   P. acted as the "second human" coder for interrater reliability
   comparison. J. W. wrote the initial drafts of this manuscript, which was
   finished by B. P. and H. J. L. All authors contributed to editing of the
   manuscript.
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NR 47
TC 13
Z9 13
U1 7
U2 8
PU AMER PHYSICAL SOC
PI COLLEGE PK
PA ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA
SN 2469-9896
J9 PHYS REV PHYS EDUC R
JI Phys. Rev. Phys. Educ. Res.
PD JUN 2
PY 2022
VL 18
IS 1
AR 010141
DI 10.1103/PhysRevPhysEducRes.18.010141
PG 16
WC Education & Educational Research; Education, Scientific Disciplines
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Education & Educational Research
GA VN2GZ
UT WOS:001135789700002
OA Green Published, gold
DA 2024-09-05
ER
PT J
AU Shen, XH
   Wang, GS
   Wang, Y
AF Shen, Xiaohong
   Wang, Gaoshan
   Wang, Yue
TI The Influence of Research Reports on Stock Returns: The Mediating Effect
   of Machine-Learning-Based Investor Sentiment
SO DISCRETE DYNAMICS IN NATURE AND SOCIETY
LA English
DT Article
ID PATTERN-RECOGNITION; MARKET-REACTIONS; SOCIAL MEDIA; NEWS;
   PREDICTABILITY
AB This paper investigates whether and how the research reports issued by securities companies affect stock returns from the perspective of investor sentiment in China. By collecting research reports and investor comments from a popular Chinese investor community, i.e., East Money, we derive two indices that represent the information contained in research reports: one is the attention of research reports and the other is the average stock rating given by research reports; then we develop an investor sentiment indicator using the machine learning method. Based on behavioral finance theory, we hypothesize that research reports have a significant effect on stock returns and investor sentiment plays a mediating role in it. The empirical analysis results confirm the above hypotheses. Specifically, the average stock rating given by research reports can better predict future stock returns, and investor sentiment plays a partial mediating role in the relationship between stock rating and stock returns.
C1 [Shen, Xiaohong] Shandong Univ Finance & Econ, Sch Comp Sci & Technol, 7366 Erhuan Dong Rd, Jinan 250014, Peoples R China.
   [Wang, Gaoshan; Wang, Yue] Shandong Univ Finance & Econ, Sch Management Sci & Engn, 7366 Erhuan Dong Rd, Jinan 250014, Peoples R China.
   [Wang, Gaoshan] Shandong Univ Finance & Econ, Inst Digital Econ, 7366 Erhuan Dong Rd, Jinan 250014, Peoples R China.
C3 Shandong University of Finance & Economics; Shandong University of
   Finance & Economics; Shandong University of Finance & Economics
RP Wang, GS (corresponding author), Shandong Univ Finance & Econ, Sch Management Sci & Engn, 7366 Erhuan Dong Rd, Jinan 250014, Peoples R China.; Wang, GS (corresponding author), Shandong Univ Finance & Econ, Inst Digital Econ, 7366 Erhuan Dong Rd, Jinan 250014, Peoples R China.
EM gaoshanwang@126.com
RI Cai, Lin/C-3286-2016
OI Cai, Lin/0000-0002-1093-4865; Wang, Gaoshan/0000-0002-5140-8378
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NR 99
TC 1
Z9 1
U1 4
U2 58
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1026-0226
EI 1607-887X
J9 DISCRETE DYN NAT SOC
JI Discrete Dyn. Nat. Soc.
PD DEC 31
PY 2021
VL 2021
AR 5049179
DI 10.1155/2021/5049179
PG 14
WC Mathematics, Interdisciplinary Applications; Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Mathematics; Science & Technology - Other Topics
GA 0I1SN
UT WOS:000779206100004
OA gold
DA 2024-09-05
ER
PT C
AU Kobayashi, Y
   Shimbo, M
   Matsumoto, Y
AF Kobayashi, Yuta
   Shimbo, Masashi
   Matsumoto, Yuji
GP Assoc Comp Machinery
TI Citation Recommendation Using Distributed Representation of Discourse
   Facets in Scientific Articles
SO JCDL'18: PROCEEDINGS OF THE 18TH ACM/IEEE JOINT CONFERENCE ON DIGITAL
   LIBRARIES
SE ACM-IEEE Joint Conference on Digital Libraries JCDL
LA English
DT Proceedings Paper
CT 18th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL)
CY JUN 03-07, 2018
CL Univ N Texas Coll Informat, Fort Worth, TX
HO Univ N Texas Coll Informat
DE Scientific article; representation learning; natural language
   processing; discourse facet; co-citation analysis
ID INFORMATIVE ABSTRACTS
AB Scientific articles usually follow a common pattern of discourse, and their contents can be divided into several facets, such as objective, method, and result. We examine the efficacy of using these discourse facets for citation recommendation. A method for learning multi-vector representations of scientific articles is proposed, in which each vector encodes a discourse facet present in an article. With each facet represented as a separate vector, the similarity of articles can be measured not in their entirety, but facet by facet. The proposed representation method is tested on a new citation recommendation task called context-based co-citation recommendation. This task calls for the evaluation of article similarity in terms of citation contexts, wherein facets help to abstract and generalize the diversity of contexts. The experimental results show that the facet-based representation outperforms the standard monolithic representation of articles.
C1 [Kobayashi, Yuta; Shimbo, Masashi; Matsumoto, Yuji] Nara Inst Sci & Technol, Nara, Japan.
C3 Nara Institute of Science & Technology
RP Kobayashi, Y (corresponding author), Nara Inst Sci & Technol, Nara, Japan.
EM kobayashi.yuta.kp1@is.naist.jp; shimbo@is.naist.jp; matsu@is.naist.jp
FU JST CREST, Japan [JPMJCR1513]
FX This work was partly supported by JST CREST Grant Number JPMJCR1513,
   Japan.
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NR 35
TC 27
Z9 29
U1 0
U2 7
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA
SN 2575-7865
EI 2575-8152
BN 978-1-4503-5178-2
J9 ACM-IEEE J CONF DIG
PY 2018
BP 243
EP 251
DI 10.1145/3197026.3197059
PG 9
WC Computer Science, Information Systems; Computer Science,
   Interdisciplinary Applications; Information Science & Library Science
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Information Science & Library Science
GA BL9RX
UT WOS:000458178700035
DA 2024-09-05
ER
PT J
AU Chi, ZY
   Zhang, S
   Wang, Y
   Yang, L
   Yang, YM
   Li, XW
AF Chi, Zhenyu
   Zhang, Song
   Wang, Yang
   Yang, Lin
   Yang, Yimin
   Li, Xuwen
TI Research of gestational diabetes mellitus risk evaluation method
SO TECHNOLOGY AND HEALTH CARE
LA English
DT Article; Proceedings Paper
CT 4th International Conference on Biomedical Engineering and Biotechnology
   (iCBEB)
CY AUG 18-21, 2015
CL Shanghai, PEOPLES R CHINA
DE Gestational diabetes mellitus; logistic regression; risk factors
AB BACKGROUND: Gestational diabetes mellitus (GDM) is not easily detected. The difficulty in detecting GDM is largely due to the late onset of clinical symptoms as well as the various complications that result from GDM [1].
   OBJECTIVE: GDM greatly influences both mother and child. Therefore, the purpose of this study was to reduce the morbidity of GDM.
   METHODS: In this study, risk factors that influence GDM were selected through statistical analysis. Multivariable logistic regression analysis was used to obtain the regression equation and Odds Ratio (OR) value. The risk score of each factor was obtained according to the OR value.
   RESULTS: The score of every pregnant woman could be very intuitively used to show the risk of getting GDM.
   CONCLUSION: Through the above methods, a comprehensive risk evaluation method of detecting GDM was developed.
C1 [Chi, Zhenyu; Zhang, Song; Yang, Lin; Yang, Yimin; Li, Xuwen] Beijing Univ Technol, Coll Life Sci & Bioengn, Beijing, Peoples R China.
   [Wang, Yang] Shenzhen Huada Gene Res Inst, Shenzhen, Peoples R China.
C3 Beijing University of Technology
RP Yang, L (corresponding author), 100 Pingleyuan, Beijing, Peoples R China.
EM yanglin@bjut.edu.cn
RI Li, Zilong/JEZ-8642-2023
CR Albert Reece E., 2009, LANCET
   Anne Vambergue, 2013, GESTATIONAL DIABETES
   Bennett W.L., 2012, J WOMENS HLTH
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NR 19
TC 2
Z9 2
U1 0
U2 9
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 0928-7329
EI 1878-7401
J9 TECHNOL HEALTH CARE
JI Technol. Health Care
PY 2016
VL 24
SU 2
BP S499
EP S503
DI 10.3233/THC-161174
PG 5
WC Health Care Sciences & Services; Engineering, Biomedical
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Health Care Sciences & Services; Engineering
GA DQ6ZD
UT WOS:000379355100010
PM 27163310
OA Bronze
DA 2024-09-05
ER
PT J
AU Ma, HL
   Ismail, L
   Han, WJ
AF Ma, Huiling
   Ismail, Lilliati
   Han, Weijing
TI A bibliometric analysis of artificial intelligence in language teaching
   and learning (1990-2023): evolution, trends and future directions
SO EDUCATION AND INFORMATION TECHNOLOGIES
LA English
DT Article; Early Access
DE Artificial intelligence; Language learning; Language teaching;
   Bibliometric analysis
ID CHATBOT; TECHNOLOGY; IMPACT; BOTS
AB The advancement and application of Artificial Intelligence (AI) has introduced innovative changes in language learning and teaching. In particular, the widespread utilization of various chatbots as foreign language learning partners showcases their remarkable potential contribution to the field. Nevertheless, there are currently few studies that encompass extensive and holistic reviews and analyses of the relevant literature during this period. The study employs bibliometric analysis and a systematic review of representative research to present trends, the current status and future directions of AI research in language teaching and learning, providing language educators, policymakers, and research scholars with visually accessible and comprehensive insights. Results indicate that the field is in its early stages of development, growing rapidly with significant research potential. The study identified the most productive and influential sources, institutions, authors and countries and provided a summary for the most representative papers in the research field. Through keyword analysis, the study delineates the evolutionary progression of AI in the domain of language teaching and learning across different time periods, identifies prevailing research trends and proposes future research directions. Results indicate that influential research in this realm predominantly focuses on refining technological solutions and conducting empirical studies on AI applications in language teaching and learning. This highlights significant interest in the effectiveness of AI in language education and its implementation methods. However, research on the application of AI in language education is still in its infancy. Therefore, the study advocates for increased empirical research on AI's specific applications in language listening, speaking, reading, and writing, as well as the development of more effective pedagogical approaches. Furthermore, the findings reveal a lack of attention given to various concerns and challenges associated with AI utilization in language teaching and learning, such as concerns regarding academic integrity, content authenticity, potential bias, privacy and security issues, and environmental concerns. At present, there is a lack of suitable solutions or regulatory frameworks proposed to address these concerns adequately.
C1 [Ma, Huiling] Coll Arts & Sci Kunming, Kunming 650000, Yunnan, Peoples R China.
   [Ma, Huiling; Ismail, Lilliati; Han, Weijing] UPM Univ Putra Malaysia, Fak Pengajian Pendidikan, Seri Kembangan 43400, Selangor, Malaysia.
   [Han, Weijing] Yunnan Technol & Business Univ, Kunming 650000, Yunnan, Peoples R China.
RP Ma, HL (corresponding author), Coll Arts & Sci Kunming, Kunming 650000, Yunnan, Peoples R China.; Ma, HL (corresponding author), UPM Univ Putra Malaysia, Fak Pengajian Pendidikan, Seri Kembangan 43400, Selangor, Malaysia.
EM mahuiling00@gmail.com; lilliati@upm.edu.my; 303679843@qq.com
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NR 51
TC 0
Z9 0
U1 34
U2 34
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1360-2357
EI 1573-7608
J9 EDUC INF TECHNOL
JI Educ. Inf. Technol.
PD 2024 JUN 22
PY 2024
DI 10.1007/s10639-024-12848-z
EA JUN 2024
PG 25
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA UZ4U1
UT WOS:001251881400002
DA 2024-09-05
ER
PT J
AU Zhang, GJ
   Liang, YK
   Wei, FF
AF Zhang, Guijie
   Liang, Yikai
   Wei, Fangfang
TI Combining Bibliometric and Social Network Analysis to Understand the
   Scholarly Publications on Artificial Intelligence
SO JOURNAL OF SCHOLARLY PUBLISHING
LA English
DT Article
DE scholarly publications; artificial intelligence; bibliometric analysis;
   social network analysis; correlation analysis
ID CO-AUTHORSHIP NETWORKS
AB This article aims to conduct a comprehensive study employing bibliometric and social network analysis to explore scholarly publications in artificial intelligence (AI). A co-authorship network analysis of countries/regions and institutions, a thematic analysis based on the co-occurrence of keywords, and a Spearman rank correlation test of social network analysis are conducted using VOSviewer and SPSS, respectively. According to the research power analysis, the United States and China are the most significant contributors to the relevant publications and hold dominant positions in the co-authorship network. Universities play a crucial role in promoting and carrying out relevant research. AI has been increasingly applied to address new problems and challenges in various fields in recent years. The Spearman rank correlation analysis indicates that research performance in AI is significantly and positively correlated with social network indicators. This article reveals a systematic picture of the research landscape of AI, which can provide a potential guide for future research.
C1 [Zhang, Guijie; Liang, Yikai] Shandong Univ Finance & Econ, Sch Management Sci & Engn, Jinan, Peoples R China.
   [Wei, Fangfang] Univ Jinan, Business Sch, Jinan, Peoples R China.
C3 Shandong University of Finance & Economics; University of Jinan
RP Wei, FF (corresponding author), Univ Jinan, Business Sch, Jinan, Peoples R China.
EM zgjzxmtx@163.com; yikailiang@qq.com; weifftju@163.com
OI Zhang, Guijie/0000-0002-7847-5431; Liang, Yikai/0000-0002-8696-4446
FU National Social Science Foundation of China [22BGL271]; Humanities and
   Social Science Foundation of the Ministry of Education of China
   [22YJC630198]; Natural Science Foundation of Shandong Province
   [ZR2022MG086, ZR2023MG025]
FX This study was supported by the National Social Science Foundation of
   China (No. 22BGL271), the Humanities and Social Science Foundation of
   the Ministry of Education of China (No. 22YJC630198), and Natural
   Science Foundation of Shandong Province (No. ZR2022MG086 and
   ZR2023MG025).
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NR 32
TC 1
Z9 1
U1 10
U2 39
PU UNIV TORONTO PRESS INC
PI TORONTO
PA JOURNALS DIVISION, 5201 DUFFERIN ST, DOWNSVIEW, TORONTO, ON M3H 5T8,
   CANADA
SN 1198-9742
EI 1710-1166
J9 J SCHOLARLY PUBL
JI J. Sch. Publ.
PD OCT 1
PY 2023
VL 54
IS 4
BP 552
EP 568
DI 10.3138/jsp-2022-0070
EA JUL 2023
PG 17
WC Humanities, Multidisciplinary; Information Science & Library Science
WE Social Science Citation Index (SSCI); Arts &amp; Humanities Citation Index (A&amp;HCI)
SC Arts & Humanities - Other Topics; Information Science & Library Science
GA W6BV3
UT WOS:001036212700001
DA 2024-09-05
ER
PT C
AU Bharathwaj, SK
   Na, JC
   Sangeetha, B
   Sarathkumar, E
AF Bharathwaj, Sampathkumar Kuppan
   Na, Jin-Cheon
   Sangeetha, Babu
   Sarathkumar, Eswaran
BE Jatowt, A
   Maeda, A
   Syn, SY
TI Sentiment Analysis of Tweets Mentioning Research Articles in Medicine
   and Psychiatry Disciplines
SO DIGITAL LIBRARIES AT THE CROSSROADS OF DIGITAL INFORMATION FOR THE
   FUTURE, ICADL 2019
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT 21st International Conference on Asia-Pacific Digital Libraries (ICADL)
CY NOV 04-07, 2019
CL Kuala Lumpur, MALAYSIA
DE Altmetrics; Twitter; Medicine; Psychiatry; Sentiment analysis; Text
   classification
AB Recently altmetrics (short for alternative metrics) are gaining popularity among researchers to identify the impact of scholarly publications among the general public. Although altmetrics have been widely used nowadays, there has been a limited number of studies analyzing users' sentiments towards these scholarly publications on social media platforms. In this paper, we analyzed and compared user sentiments (positive, negative and neutral) towards scholarly publications in Medicine and Psychiatry domains by analyzing user-generated content (tweets) on Twitter. We explored various machine learning algorithms, and constructed the best model with Support Vector Machine (SVM) which gave an accuracy of 91.6%.
C1 [Bharathwaj, Sampathkumar Kuppan; Na, Jin-Cheon; Sangeetha, Babu; Sarathkumar, Eswaran] Nanyang Technol Univ, Wee Kim Wee Sch Commun & Informat, 31 Nanyang Link, Singapore 637718, Singapore.
C3 Nanyang Technological University
RP Na, JC (corresponding author), Nanyang Technol Univ, Wee Kim Wee Sch Commun & Informat, 31 Nanyang Link, Singapore 637718, Singapore.
EM bharathw001@e.ntu.edu.sg; tjcna@ntu.edu.sg; sangeeth006@e.ntu.edu.sg;
   sarathku002@e.ntu.edu.sg
OI Na, Jin-Cheon/0000-0002-2211-9382
CR [Anonymous], ALT METR IN PHAS 1 W
   Arredondo L., 2018, STUDY ALTMETRICS USI
   Na JC, 2015, LECT NOTES COMPUT SC, V9469, P197, DOI 10.1007/978-3-319-27974-9_20
   Raamkumar AS, 2018, LECT NOTES COMPUT SC, V11279, P71, DOI 10.1007/978-3-030-04257-8_7
NR 4
TC 1
Z9 1
U1 0
U2 2
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 0302-9743
EI 1611-3349
BN 978-3-030-34058-2; 978-3-030-34057-5
J9 LECT NOTES COMPUT SC
PY 2019
VL 11853
BP 303
EP 307
DI 10.1007/978-3-030-34058-2_29
PG 5
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods; Information Science &
   Library Science; Imaging Science & Photographic Technology
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Information Science & Library Science; Imaging Science
   & Photographic Technology
GA BQ3BB
UT WOS:000583740100029
DA 2024-09-05
ER
PT C
AU Gonzalez-Aragon, T
   Castro-Godinez, J
AF Gonzalez-Aragon, Tomas
   Castro-Godinez, Jorge
GP IEEE
TI Improving Performance of Error-Tolerant Applications: A Case Study of
   Approximations on an Off-the-Shelf Neural Accelerator
SO V JORNADAS COSTARRICENSES DE INVESTIGACION EN COMPUTACION E INFORMATICA
   (JOCICI 2021)
LA English
DT Proceedings Paper
CT 5th Costa Rican Conference on Research in Computing and Informatics
   (JoCICI)
CY OCT 25-29, 2021
CL San Jose, COSTA RICA
DE Approximate computing; deep learning; neural accelerator; edge computing
AB Trending workloads and applications are leading many of the new advances in computer architecture and design paradigms. For instance, deep learning applications are transforming the way we do computing. On one hand, specific architectures are currently commercialized as neural processing units, specialized hardware accelerators for these types of applications, achieving significant performance improvements. On the other hand, design paradigms, such as approximate computing, exploit existing inherent tolerance to imprecise computations in these applications to reduce their computation complexity and produce energy-efficient implementations. Relevant available approximations are limited to the software layer to improve the performance of deep learning applications when using an off-the-shelf specialized accelerator alongside edge computing platforms. In this work, we present a case study of performance improvement by introducing approximate computing techniques to three deep learning classification applications. Our test platform is a Raspberry Pi 4, as edge computing device, and a Movidius Myriad X, as neural accelerator. Our experimental results show that using a mixture of approximate techniques can achieve a performance improvement from 20x to 48x with no accuracy degradation for a compute-intensive classification application.
C1 [Gonzalez-Aragon, Tomas] Intel Costa Rica, Heredia, Costa Rica.
   [Castro-Godinez, Jorge] Inst Tecnol Costa Rica TEC, Cartago, Costa Rica.
C3 Instituto Tecnologico de Costa Rica
RP Gonzalez-Aragon, T (corresponding author), Intel Costa Rica, Heredia, Costa Rica.
EM tomas.gonzalez.aragon@intel.com; jocastro@tec.ac.cr
FU Instituto Tecnologico de Costa Rica
FX This work was supported by the Instituto Tecnologico de Costa Rica.
CR [Anonymous], OpenVINO toolkit
   [Anonymous], Intel Neural Compute Stick 2
   [Anonymous], MYRIAD 10 PRODUCT BR
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NR 15
TC 0
Z9 0
U1 0
U2 0
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-6654-9832-6
PY 2021
DI 10.1109/JoCICI54528.2021.9794353
PG 6
WC Computer Science, Interdisciplinary Applications; Computer Science,
   Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BT9IY
UT WOS:000861163300011
DA 2024-09-05
ER
PT C
AU Batanovic, V
AF Batanovic, Vuk
GP IEEE
TI Semantic Similarity and Sentiment Analysis of Short Texts in Serbian
SO 2021 29TH TELECOMMUNICATIONS FORUM (TELFOR)
LA English
DT Proceedings Paper
CT 29th Telecommunications Forum (TELFOR)
CY NOV 23-24, 2021
CL ELECTR NETWORK
DE open access datasets; semantic textual similarity; sentiment
   classification; Serbian language
AB This paper presents an overview of the open access datasets in Serbian that have been manually annotated for the tasks of semantic textual similarity and short-text sentiment classification. In addition, it describes several kinds of statistical models that have been trained and evaluated on these datasets and discusses their results.
C1 [Batanovic, Vuk] Univ Belgrade, Sch Elect Engn, Innovat Ctr, Bul Kralja Aleksandra 73, Belgrade 11120, Serbia.
C3 University of Belgrade
RP Batanovic, V (corresponding author), Univ Belgrade, Sch Elect Engn, Innovat Ctr, Bul Kralja Aleksandra 73, Belgrade 11120, Serbia.
EM vuk.batanovic@ic.etf.bg.ac.rs
RI Batanović, Vuk/AAD-8365-2021
OI Batanović, Vuk/0000-0003-2639-9091
FU Science Fund of the Republic of Serbia [6526093, AI - AVANTES]
FX This work was partially supported by the Science Fund of the Republic of
   Serbia, grant no. 6526093, AI - AVANTES.
CR Agirre E., 2013, SEMEVAL
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NR 37
TC 2
Z9 2
U1 0
U2 0
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-6654-2585-8
PY 2021
DI 10.1109/TELFOR52709.2021.9653390
PG 7
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Engineering; Telecommunications
GA BT5TR
UT WOS:000838086600149
DA 2024-09-05
ER
PT J
AU Yang, QL
   Zhang, WD
   Zhao, CH
AF Yang, Qingliang
   Zhang, Wendong
   Zhao, Chaohui
TI Research on Torque Performance of Marine Hybrid Excitation Synchronous
   Motors Based on PSO Optimization of Magnetic Permeability Structure
SO JOURNAL OF MARINE SCIENCE AND ENGINEERING
LA English
DT Article
DE hybrid excitation machine; particle swarm optimization; multi-objective
   optimization
ID DESIGN; OPERATION; DRIVE
AB The rotor magnetic shunt structure hybrid excitation synchronous motor (RMS-HESM) has been widely used in marine propulsion due to its advantages of low loss and high efficiency. The objective of this paper is to improve the output torque capability of the hybrid excitation motor with a rotor magnetic shunt structure by conducting a multi-objective optimization design for the magnetic permeability structure. The first step involved establishing a mathematical analytical model of average torque and torque ripple based on the fundamental principle of motor magnetization. Next, the parameters of the magnetic permeability structure were designed and analyzed using the finite element simulation method. The impact of the variations in the parameters of the magnetic permeability structure on motor torque and no-load back electromotive force was examined. Additionally, a sensitivity analysis was performed on the design variables of the magnetic permeability structure, leading to the determination of optimization parameters based on the obtained results. The adaptive inertia weight-based particle swarm algorithm (PSO) was employed to conduct a multi-objective optimization design analysis. A comparative analysis on the average torque, torque ripple, and no-load back electromotive force of the motor before and after optimization was performed using the Maxwell and Workbench and Optislong joint simulation tools. This enhancement significantly improves the torque performance of the marine motor while simultaneously optimizing the no-load back electromotive force.
C1 [Yang, Qingliang] Shanghai Dianji Univ, Coll Mech Engn, Shanghai 201306, Peoples R China.
   [Yang, Qingliang] Shanghai Maritime Univ, Logist Engn Coll, Shanghai 201306, Peoples R China.
   [Zhang, Wendong; Zhao, Chaohui] Shanghai Dianji Univ, Coll Elect Engn, Shanghai 201306, Peoples R China.
C3 Shanghai Dianji University; Shanghai Maritime University; Shanghai
   Dianji University
RP Zhao, CH (corresponding author), Shanghai Dianji Univ, Coll Elect Engn, Shanghai 201306, Peoples R China.
EM yangql@sdju.edu.cn; zhaoch@sdju.edu.cn
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NR 31
TC 0
Z9 0
U1 2
U2 2
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2077-1312
J9 J MAR SCI ENG
JI J. Mar. Sci. Eng.
PD JUL
PY 2024
VL 12
IS 7
AR 1064
DI 10.3390/jmse12071064
PG 18
WC Engineering, Marine; Engineering, Ocean; Oceanography
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Oceanography
GA ZS4V1
UT WOS:001277277100001
OA gold
DA 2024-09-05
ER
PT J
AU Kaur, M
   Saini, M
AF Kaur, Manpreet
   Saini, Munish
TI Role of Artificial Intelligence in the crime prediction and pattern
   analysis studies published over the last decade: a scientometric
   analysis
SO ARTIFICIAL INTELLIGENCE REVIEW
LA English
DT Article
DE Artificial Intelligence; Scientometrics; Crime prediction; Crime pattern
   analysis; Bibliometrics
ID PREVENTION
AB Crime is the intentional commission of an act usually suspected as socially detrimental and specifically defined, forbidden, and punishable under criminal law. Developing a society that is less susceptible to criminal acts makes crime prediction and pattern analysis (CPPA) a paramount topic for academic research interest. With the innovation in technology and rapid expansion of Artificial Intelligence (AI), the research in the field of CPPA has evolved radically to predict crime efficiently. While the number of publications is expanding substantially, we believe there is a dearth of thorough scientometric analysis for this topic. This work intends to analyze research conducted in the last decade using Scopus data and a scientometric technique, emphasizing citation trends and intriguing journals, nations, institutions, their collaborations, authors, and co-authorship networks in CPPA research. Furthermore, three field plots have been staged to visualize numerous associations between country, journal, keyword, and author. Besides, a comprehensive keyword analysis is carried out to visualize the CPPA research carried out with AI amalgamation. A total of five clusters have been identified depicting several AI methods used by the researchers in CPPA and the evolution of research trends over time from various perspectives.
C1 [Kaur, Manpreet; Saini, Munish] Guru Nanak Dev Univ, Dept Comp Engn & Technol, Amritsar, Punjab, India.
C3 Guru Nanak Dev University
RP Kaur, M (corresponding author), Guru Nanak Dev Univ, Dept Comp Engn & Technol, Amritsar, Punjab, India.
EM manpreet.csersh@gndu.ac.in; munish.cet@gndu.ac.in
RI Kaur, Manpreet/CAH-3123-2022; Saini, Munish/J-4196-2016
OI Kaur, Manpreet/0000-0002-7680-3075; Saini, Munish/0000-0003-4129-2591
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NR 63
TC 0
Z9 0
U1 2
U2 2
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0269-2821
EI 1573-7462
J9 ARTIF INTELL REV
JI Artif. Intell. Rev.
PD JUL 11
PY 2024
VL 57
IS 8
AR 202
DI 10.1007/s10462-024-10823-1
PG 35
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA YN6G5
UT WOS:001269200500003
OA hybrid
DA 2024-09-05
ER
PT J
AU Foran, DJ
   Chen, WJ
   Kurc, T
   Gupta, R
   Kaczmarzyk, JR
   Torre-Healy, LA
   Bremer, E
   Ajjarapu, S
   Do, N
   Harris, G
   Stroup, A
   Durbin, E
   Saltz, JH
AF Foran, David J.
   Chen, Wenjin
   Kurc, Tahsin
   Gupta, Rajarshi
   Kaczmarzyk, Jakub Roman
   Torre-Healy, Luke Austin
   Bremer, Erich
   Ajjarapu, Samuel
   Do, Nhan
   Harris, Gerald
   Stroup, Antoinette
   Durbin, Eric
   Saltz, Joel H.
TI An Intelligent Search & Retrieval System (IRIS) and Clinical and
   Research Repository for Decision Support Based on Machine Learning and
   Joint Kernel-based Supervised Hashing
SO CANCER INFORMATICS
LA English
DT Article
DE Multi-modal clinical research data warehouse; content based retrieval;
   decision support; machine learning; adaptable extraction; transform and
   load interface; large-scale multi-site collaboration
AB Large-scale, multi-site collaboration is becoming indispensable for a wide range of research and clinical activities in oncology. To facilitate the next generation of advances in cancer biology, precision oncology and the population sciences it will be necessary to develop and implement data management and analytic tools that empower investigators to reliably and objectively detect, characterize and chronicle the phenotypic and genomic changes that occur during the transformation from the benign to cancerous state and throughout the course of disease progression. To facilitate these efforts it is incumbent upon the informatics community to establish the workflows and architectures that automate the aggregation and organization of a growing range and number of clinical data types and modalities ranging from new molecular and laboratory tests to sophisticated diagnostic imaging studies. In an attempt to meet those challenges, leading health care centers across the country are making steep investments to establish enterprise-wide, data warehouses. A significant limitation of many data warehouses, however, is that they are designed to support only alphanumeric information. In contrast to those traditional designs, the system that we have developed supports automated collection and mining of multimodal data including genomics, digital pathology and radiology images. In this paper, our team describes the design, development and implementation of a multi-modal, Clinical & Research Data Warehouse (CRDW) that is tightly integrated with a suite of computational and machine-learning tools to provide actionable insight into the underlying characteristics of the tumor environment that would not be revealed using standard methods and tools. The System features a flexible Extract, Transform and Load (ETL) interface that enables it to adapt to aggregate data originating from different clinical and research sources depending on the specific EHR and other data sources utilized at a given deployment site.
C1 [Foran, David J.; Chen, Wenjin] Rutgers Canc Inst New Jersey, Ctr Biomed Informat, New Brunswick, NJ USA.
   [Kurc, Tahsin; Gupta, Rajarshi; Bremer, Erich; Saltz, Joel H.] SUNY Stony Brook, Dept Biomed Informat, Stony Brook, NY USA.
   [Kaczmarzyk, Jakub Roman; Torre-Healy, Luke Austin] SUNY Stony Brook, Renaissance Sch Med, Stony Brook, NY USA.
   [Ajjarapu, Samuel; Do, Nhan] VA Healthcare Syst, Jama Plain Campus, Boston, MA USA.
   [Harris, Gerald; Stroup, Antoinette] Rutgers Canc Inst New Jersey, New Jersey State Canc Registry, New Brunswick, NJ USA.
   [Durbin, Eric] Markey Canc Ctr, Kentucky Canc Registry, Lexington, KY USA.
   [Foran, David J.] Rutgers Canc Inst New Jersey, Biomed Informat, 195 Little Albany St, New Brunswick, NJ 08903 USA.
C3 Rutgers University System; Rutgers University New Brunswick; Rutgers
   University Biomedical & Health Sciences; Rutgers Cancer Institute of New
   Jersey; State University of New York (SUNY) System; State University of
   New York (SUNY) Stony Brook; State University of New York (SUNY) System;
   State University of New York (SUNY) Stony Brook; Stony Brook University
   Hospital; Rutgers University System; Rutgers University New Brunswick;
   Rutgers University Biomedical & Health Sciences; Rutgers Cancer
   Institute of New Jersey; Rutgers University System; Rutgers University
   New Brunswick; Rutgers University Biomedical & Health Sciences; Rutgers
   Cancer Institute of New Jersey
RP Foran, DJ (corresponding author), Rutgers Canc Inst New Jersey, Biomed Informat, 195 Little Albany St, New Brunswick, NJ 08903 USA.
EM foran@cinj.rutgers.edu
OI Torre-Healy, Luke/0000-0002-9513-4620; ajjarapu,
   samuel/0000-0002-0066-6086
CR Coudray N, 2018, NAT MED, V24, P1559, DOI 10.1038/s41591-018-0177-5
   Foran David J, 2022, J Pathol Inform, V13, P5, DOI 10.4103/jpi.jpi_31_21
   Foran DJ., 2017, Cancer Inform, V16, P1
   Hirshfield KM, 2016, ONCOLOGIST, V21, P1315, DOI 10.1634/theoncologist.2016-0049
   Le H, 2020, AM J PATHOL, V190, P1491, DOI 10.1016/j.ajpath.2020.03.012
   Payne PRO., 2021, Biomedical Informatics: Computer Applications in Health Care and Biomedicine, P913
   Platt JE, 2020, J MED INTERNET RES, V22, DOI 10.2196/17026
   Ren J, 2018, J MED IMAGING, V5, DOI 10.1117/1.JMI.5.4.047501
   Vanguri RS, 2022, NAT CANCER, V3, P1151, DOI 10.1038/s43018-022-00416-8
   Yoshida H, 2021, WORLD J GASTROENTERO, V27, P2818, DOI 10.3748/wjg.v27.i21.2818
   Zaldana F., 2018, Presented at the Newport, Rhode Island
NR 11
TC 0
Z9 0
U1 1
U2 1
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
EI 1176-9351
J9 CANCER INFORM
JI Cancer Inform.
PY 2024
VL 23
AR 11769351231223806
DI 10.1177/11769351231223806
PG 6
WC Oncology; Mathematical & Computational Biology
WE Emerging Sources Citation Index (ESCI)
SC Oncology; Mathematical & Computational Biology
GA GZ4U9
UT WOS:001156498400001
PM 38322427
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU Greenwood, CR
   Thiemann-Bourque, K
   Walker, D
   Buzhardt, J
   Gilkerson, J
AF Greenwood, Charles R.
   Thiemann-Bourque, Kathy
   Walker, Dale
   Buzhardt, Jay
   Gilkerson, Jill
TI Assessing Children's Home Language Environments Using Automatic Speech
   Recognition Technology
SO COMMUNICATION DISORDERS QUARTERLY
LA English
DT Article
DE language acquisition/development; language assessment; research
   methodology; applied research; technology
AB The purpose of this research was to replicate and extend some of the findings of Hart and Risley using automatic speech processing instead of human transcription of language samples. The long-term goal of this work is to make the current approach to speech processing possible by researchers and clinicians working on a daily basis with families and young children. Twelve hour-long, digital audio recordings were obtained repeatedly in the homes of middle to upper SES families for a sample of typically developing infants and toddlers (N = 30). These recordings were processed automatically using a measurement framework based on the work of Hart and Risley. Like Hart and Risley, the current findings indicated vast differences in individual children's home language environments (i.e., adult word count), children's vocalizations, and conversational turns. Automated processing compared favorably to the original Hart and Risley estimates that were based on transcription. Adding to Hart and Risley's findings were new descriptions of patterns of daily talk and relationships to widely used outcome measures, among others. Implications for research and practice are discussed.
C1 [Greenwood, Charles R.] Univ Kansas, Kansas City, KS USA.
   [Thiemann-Bourque, Kathy; Walker, Dale; Buzhardt, Jay] Univ Kansas, Life Span Inst, Kansas City, KS USA.
   [Gilkerson, Jill] LENA Fdn, Boulder, CO USA.
C3 University of Kansas; University of Kansas
RP Greenwood, CR (corresponding author), Juniper Gardens Childrens Project, 650 Minnesota Ave,2nd Floor, Kansas City, KS 66101 USA.
EM greenwood@ku.edu
RI Buzhardt, Jay/ACD-4708-2022
OI Bourque, Kathy/0000-0002-0672-5761; Greenwood,
   Charles/0000-0002-6274-3075; Walker, Dale/0000-0001-9692-1151
CR [Anonymous], 2009, LTR052 LENA FDN
   Bayley N, 2006, Bayley scales of infant and toddler development technical manual, V3rd
   Christakis DA, 2009, ARCH PEDIAT ADOL MED, V163, P554, DOI 10.1001/archpediatrics.2009.61
   Gilkerson J., 2009, LENA NATURAL LANGUAG
   Gilkerson J., 2008, Technical Report LTR-06-2
   Greenwood C. R., 2008, DIV EARL CHILDH C MI
   Hansen J. L., 2008, LANGUAGE ASSESSMENT
   HART B, 1978, EDUC URBAN SOC, V10, P407, DOI 10.1177/001312457801000402
   HART B, 1992, DEV PSYCHOL, V28, P1096, DOI 10.1037/0012-1649.28.6.1096
   Hart B., 1999, SOCIAL WORLD CHILDRE
   Hart B., 1989, Education and Treatment of Children, V12, P347
   Hart B, 1995, Meaningful Differences in the Everyday Experience of Young American Children
   Hart Betty, 2003, American Education, P4
   Infoture Inc., 2008, CHILD LANG DEV S DEP
   Lena Foundation, 2009, LENA PROS
   Miller P., 2009, LENA FDN CASE STUDY
   Montgomery J., 2008, LANGUAGE ASSESSMENT
   Oetting JB., 2009, ASHA LEADER, V14, P20, DOI DOI 10.1044/LEADER.FTR3.14062009.20
   Oller D. K., 2008, LANGUAGE ASSESSMENT
   STAR Reading, 2007, UND REL VAL
   Warren S., 2009, Journal of Autism Developmental Disorders, P1
   Warren S.F., 2005, HDB RES METHODS DEV, P249, DOI DOI 10.1002/9780470756676.CH13
   Xu D, 2008, LTR042 LENA FDN
   Zimmerman I.L., 2002, PRESCHOOL LANGUAGE S, VFourth
NR 24
TC 106
Z9 129
U1 0
U2 18
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 1525-7401
J9 COMM DISORD Q
JI Comm. Disord. Q.
PD FEB
PY 2011
VL 32
IS 2
BP 83
EP 92
DI 10.1177/1525740110367826
PG 10
WC Linguistics; Rehabilitation
WE Social Science Citation Index (SSCI)
SC Linguistics; Rehabilitation
GA V28NU
UT WOS:000208688300004
DA 2024-09-05
ER
PT J
AU Huang, XY
   Zou, D
   Cheng, G
   Chen, XL
   Xie, HR
AF Huang, Xinyi
   Zou, Di
   Cheng, Gary
   Chen, Xieling
   Xie, Haoran
TI Trends, Research Issues and Applications of Artificial Intelligence in
   Language Education
SO EDUCATIONAL TECHNOLOGY & SOCIETY
LA English
DT Article
DE Artificial Intelligence; Language Education; Bibliometric Analysis;
   Automated Writing Evaluation; Intelligent Tutoring System
ID FOREIGN-LANGUAGE; LEARNING-SYSTEM; ENGLISH; FUTURE; AI
AB Artificial Intelligence (AI) plays an increasingly important role in language education; however, the trends, research issues, and applications of AI in language learning remain largely under-investigated. Accordingly, the present paper, using bibliometric analysis, investigates these issues via a review of 516 papers published between 2000 and 2019, focusing on how AI was integrated into language education. Findings revealed that the frequency of studies on AI-enhanced language education increased over the period. The USA and Arizona State University were the most active country and institution, respectively. The 10 most popular topics were: (1) automated writing evaluation; (2) intelligent tutoring systems (ITS) for reading and writing; (3) automated error detection; (4) computer-mediated communication; (5) personalized systems for language learning; (6) natural language and vocabulary learning; (7) web resources and web-based systems for language learning; (8) ITS for writing in English for specific purposes; (9) intelligent tutoring and assessment systems for pronunciation and speech training; and (10) affective states and emotions. The results also indicated that AI was frequently used to assist students in learning writing, reading, vocabulary, grammar, speaking, and listening. Natural language processing, automated speech recognition, and learner profiling were commonly applied to develop automated writing evaluation, personalized learning, and intelligent tutoring systems.
C1 [Huang, Xinyi; Cheng, Gary; Chen, Xieling] Educ Univ Hong Kong, Dept Math & Informat Technol, Hong Kong, Peoples R China.
   [Zou, Di] Educ Univ Hong Kong, Dept English Language Educ, Hong Kong, Peoples R China.
   [Xie, Haoran] Lingnan Univ, Dept Comp & Decis Sci, Hong Kong, Peoples R China.
C3 Education University of Hong Kong (EdUHK); Education University of Hong
   Kong (EdUHK); Lingnan University
RP Zou, D (corresponding author), Educ Univ Hong Kong, Dept English Language Educ, Hong Kong, Peoples R China.
EM hxinyi@eduhk.hk; dizoudaisy@gmail.com; chengks@eduhk.hk;
   xielingchen0708@gmail.com; hrxie2@gmail.com
RI Xie, Haoran/AFS-3515-2022; Huang, Xinyi/AFI-7092-2022; Xie,
   Haoran/AAW-8845-2020
OI Xie, Haoran/0000-0003-0965-3617; Huang, Xinyi/0000-0001-9777-7905; ZOU,
   Di/0000-0001-8435-9739
FU Education University of Hong Kong and the Dean's Research Fund of The
   Education University of Hong Kong [RG 78/2019-2020R, IDS-22020]; Lingnan
   University, Hong Kong [DB21A9, LWI20011]
FX An abstract entitled ?Artificial Intelligence in Language Education?
   based on this paper was presented at the International Conference on
   Education and Artificial Intelligence 2020, The Education University of
   Hong Kong, 9-11 November 2020, Hong Kong. Gary Cheng?s work in this
   research is supported by the Research Cluster Fund (RG 78/2019-2020R) of
   The Education University of Hong Kong and the Dean's Research Fund
   2019/20 (IDS-22020) of The Education University of Hong Kong. Haoran
   Xie?s work in this research is supported by the Faculty Research Fund
   (DB21A9) and the Lam Woo Research Fund (LWI20011) of Lingnan University,
   Hong Kong.
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   Zhang RF, 2022, COMPUT ASSIST LANG L, V35, P696, DOI 10.1080/09588221.2020.1744666
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NR 64
TC 50
Z9 51
U1 241
U2 602
PU INT FORUM EDUCATIONAL TECHNOLOGY & SOC, NATL TAIWAN NORMAL UNIV
PI Taipei City
PA No.162, Sec. 1, Heping E. Rd., Da-an Dist, Taipei City, TAIWAN
SN 1176-3647
EI 1436-4522
J9 EDUC TECHNOL SOC
JI Educ. Technol. Soc.
PD JAN
PY 2023
VL 26
IS 1
BP 112
EP 131
DI 10.30191/ETS.202301_26(1).0009
PG 20
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA 7Q6QP
UT WOS:000909514000009
HC Y
HP N
DA 2024-09-05
ER
PT S
AU Earnshaw, R
AF Earnshaw, Rae
BA Earnshaw, R
   Dill, J
   Kasik, D
BF Earnshaw, R
   Dill, J
   Kasik, D
TI Data Science Institutes and Data Centers
SO DATA SCIENCE AND VISUAL COMPUTING
SE Advanced Information and Knowledge Processing
LA English
DT Article; Book Chapter
DE Infrastructure for data science; Interdisciplinary research
   collaboration; Artificial intelligence; Machine learning; Data mining;
   Statistical inference; Data management; Data visualization; Cultural
   change; Reward models
AB Optimum ways of addressing large data volumes across a variety of disciplines have led to the formation of national and institutional Data Science Institutes and Centers. The objectives and functions of such institutes and centers are summarized. In reflecting the driver of national priority, they are able to attract academic support within their institutions to bring together interdisciplinary expertise to address a wide variety of datasets from disciplines such as astronomy, bioinformatics, engineering, science, medicine, social science, and the humanities. All are generating increasing volumes of data, often in real time, and require efficient and effective solutions. The opportunities and challenges of data science are presented. The processes of knowledge discovery in data science often require new methods and software, new organizational arrangements, and new skills in order to be effective. Data science centers and institutes provide a focus for the development and implementation of such new structures and arrangements for the development of appropriate facilities, with academic leadership and professional support. These are summarized and reviewed.
C1 [Earnshaw, Rae] Univ Bradford, Ctr Visual Comp, Fac Engn & Informat, Bradford, W Yorkshire, England.
   [Earnshaw, Rae] Univ Durham, St Johns Coll, Durham, England.
   [Earnshaw, Rae] Wrexham Glyndwr Univ, Fac Arts Sci & Technol, Wrexham, Wales.
C3 University of Bradford; Durham University
RP Earnshaw, R (corresponding author), Univ Bradford, Ctr Visual Comp, Fac Engn & Informat, Bradford, W Yorkshire, England.; Earnshaw, R (corresponding author), Univ Durham, St Johns Coll, Durham, England.; Earnshaw, R (corresponding author), Wrexham Glyndwr Univ, Fac Arts Sci & Technol, Wrexham, Wales.
RI Earnshaw, Rae A/G-8917-2013
CR Berman F., 2017, REALIZING POTENTIAL
   Diamond I, 2015, MAKING MOST DATA DAT
   Earnshaw R, 2017, SPRINGERBRIEF COMPUT, P21, DOI 10.1007/978-3-319-61409-0_3
   Harvey C., 2017, BIG DATA CHALLENGES
   Kitzes J., 2018, The Practice of Reproducible Research: Case Studies and Lessons from the DataIntensive Sciences
   Nesta, 2015, TAL DAT REV
   The Moore-Sloan Data Science Environments: New York University and the U. of W. UC Berkeley, 2018, CREAT I CHANG DAT SC
NR 7
TC 0
Z9 0
U1 1
U2 4
PU SPRINGER-VERLAG LONDON LTD
PI GODALMING
PA SWEETAPPLE HOUSE CATTESHALL RD FARNCOMBE, GODALMING GU7 1NH, SURREY,
   ENGLAND
SN 1610-3947
BN 978-3-030-24367-8; 978-3-030-24366-1
J9 ADV INFORM KNOWL PRO
PY 2019
BP 93
EP 108
DI 10.1007/978-3-030-24367-8_7
D2 10.1007/978-3-030-24367-8
PG 16
WC Computer Science, Information Systems; Computer Science,
   Interdisciplinary Applications
WE Book Citation Index – Science (BKCI-S)
SC Computer Science
GA BN8MA
UT WOS:000488075500009
DA 2024-09-05
ER
PT J
AU Segura-Robles, A
   Parra-González, ME
   Gallardo-Vigil, MA
AF Segura-Robles, Adrian
   Elena Parra-Gonzalez, Maria
   Angel Gallardo-Vigil, Miguel
TI Bibliometric and Collaborative Network Analysis on Active Methodologies
   in Education
SO JOURNAL OF NEW APPROACHES IN EDUCATIONAL RESEARCH
LA English
DT Article
DE CITATION ANALYSIS; ACTIVE LEARNING; BIBLIOMETRICS; EDUCATION; ACTIVE
   METHODOLOGIES
ID IMPACT; PUBLICATION; INFORMATION
AB Teachers have gradually been making more use of active methodologies at all educational levels, and some even carry out research in this area. The objective of this research was to develop a bibliometric study in order to gain an in-depth view of the scientific literature on active methodologies in education. An analysis of the classic descriptions of bibliometrics, co-authorship indexes and collaboration networks was carried out, using documents indexed by the Web of Science on active methodologies in education between 2009 and 2019. The final data corpus is composed of 513 documents. The results show that publications on this type of research are booming, demonstrating a growing interest in these kinds of studies in the short and medium term. English is the predominant language in these studies, as occurs in the general scientific literature. The results indicate a limited range of topics being studied currently and likely growth in coming years. Therefore, this category of research can be considered as a relevant field of study for the scientific community in the short and medium term.
C1 [Segura-Robles, Adrian; Elena Parra-Gonzalez, Maria; Angel Gallardo-Vigil, Miguel] Univ Granada, Dept Metodos Invest & Diagnost Educ, Granada, Spain.
C3 University of Granada
RP Segura-Robles, A (corresponding author), Fac Educ Econ & Tecnol Ceuta, C Cortadura Valle S-N, Ceuta 51001, Ceuta, Spain.
EM adrianseg@ugr.es
RI Gallardo-Vigil, MA/K-5954-2012; Segura, Adrián/B-4963-2019
OI Gallardo-Vigil, MA/0000-0002-5462-077X; Segura,
   Adrián/0000-0003-0753-7129; Parra Gonzalez/0000-0002-6918-9126
CR Abramo G, 2019, J INFORMETR, V13, P32, DOI 10.1016/j.joi.2018.11.003
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NR 54
TC 18
Z9 19
U1 3
U2 31
PU UNIV ALICANTE, GRUPO INVESTIGACION EDUTIC-ADEI
PI ALICANTE
PA CARRETERA SAN VICENTE RASPEIG, SAN VICENTE DEL RASPEIG, ALICANTE, 03690,
   SPAIN
SN 2254-7339
J9 J NEW APPROACHES EDU
JI J. New Approaches Educ. Res.
PD JUL
PY 2020
VL 9
IS 2
BP 259
EP 274
DI 10.7821/naer.2020.7.575
PG 16
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA MO4AQ
UT WOS:000551471200008
OA gold
DA 2024-09-05
ER
PT J
AU Huang, XY
   Zou, D
   Cheng, GR
   Chen, XL
   Xie, HR
AF Huang, Xinyi
   Zou, Di
   Cheng, Gary
   Chen, Xieling
   Xie, Haoran
TI Trends, Research Issues and Applications of Artificial Intelligence in
   Language Education
SO EDUCATIONAL TECHNOLOGY & SOCIETY
LA English
DT Article
DE Artificial Intelligence; Language education; Bibliometric analysis;
   Automated writing evaluation; Intelligent Tutoring Systems
ID FOREIGN-LANGUAGE; LEARNING-SYSTEM; ENGLISH
AB Artificial Intelligence (AI) is playing an increasingly important role in language education; however, the trends, research issues and applications of AI in language learning remain largely underinvestigated. Accordingly, the present paper, using bibliometric analysis, investigates these issues via a review of 516 papers that were published between 2000 and 2019 focusing on how AI was integrated into language education. Findings revealed that the frequency of studies on AI-enhanced language education is increasing. We found the most active country and institution were the USA and Arizona State University respectively. The 10 most popular topics were: (1) automated writing evaluation; (2) intelligent tutoring systems (ITS) for reading and writing; (3) automated error detection; (4) computer-mediated communication; (5) personalized systems for language learning; (6) natural language and vocabulary learning; (7) web resources and web-based systems for language learning; (8) ITS for writing in English for specific purposes; (9) intelligent tutoring and assessment system for pronunciation and speech training; and (10) affective states and emotions. The results also indicated that AI has been frequently used to assist students in learning writing, reading, vocabulary and grammar learning, and speaking and listening. Natural language processing, automated speech recognition, and learner profiling were commonly applied to develop automated writing evaluation, personalized learning and intelligent tutoring systems.
C1 [Huang, Xinyi; Cheng, Gary; Chen, Xieling] Educ Univ Hong Kong, Dept Math & Informat Technol, Hong Kong, Peoples R China.
   [Zou, Di] Educ Univ Hong Kong, Dept English Language Educ, Hong Kong, Peoples R China.
   [Xie, Haoran] Lingnan Univ, Dept Comp & Decis Sci, Hong Kong, Peoples R China.
C3 Education University of Hong Kong (EdUHK); Education University of Hong
   Kong (EdUHK); Lingnan University
RP Zou, D (corresponding author), Educ Univ Hong Kong, Dept English Language Educ, Hong Kong, Peoples R China.
EM hxinyi@eduhk.hk; dizoudaisy@gmail.com; chengks@eduhk.hk;
   xielingchen0708@gmail.com; hrxie2@gmail.com
RI Huang, Xinyi/AFI-7092-2022; Xie, Haoran/AFS-3515-2022
OI Huang, Xinyi/0000-0001-9777-7905; Xie, Haoran/0000-0003-0965-3617
FU Research Cluster Fund of The Education University of Hong Kong [RG
   78/2019-2020R]; Education University of Hong Kong [IDS-2 2020]; Lingnan
   University, Hong Kong [DB21A9, LWI20011]
FX An abstract entitled "Artificial Intelligence in Language Education"
   based on this paper was presented at International Conference on
   Education and Artificial Intelligence 2020, The Education University of
   Hong Kong, 9-11 November 2020, Hong Kong. Gary Cheng's work in this
   research is supported by the Research Cluster Fund (RG 78/2019-2020R) of
   The Education University of Hong Kong and the Dean's Research Fund
   2019/20 (IDS-2 2020) of The Education University of Hong Kong. Haoran
   Xie's work in this research is supported by the Faculty Research Fund
   (DB21A9) and the Lam Woo Research Fund (LWI20011) of Lingnan University,
   Hong Kong.
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NR 54
TC 4
Z9 4
U1 40
U2 277
PU INT FORUM EDUCATIONAL TECHNOLOGY & SOC-IFETS
PI DOULIU CITY
PA NATL YUNLIN UNIV SCIENCE & TECHNOLOGY, NO 123, SECTION 3, DAXUE RD,
   DOULIU CITY, YUNLIN COUNTY, TAIWAN
SN 1176-3647
EI 1436-4522
J9 EDUC TECHNOL SOC
JI Educ. Technol. Soc.
PD JUL
PY 2021
VL 24
IS 3
BP 238
EP 255
PG 18
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA TD7SR
UT WOS:000669522300017
DA 2024-09-05
ER
PT J
AU Kirkwood, S
   Cree, V
   Winterstein, D
   Nuttgens, A
   Sneddon, J
AF Kirkwood, Steve
   Cree, Viviene
   Winterstein, Daniel
   Nuttgens, Alex
   Sneddon, Jenni
TI Encountering #Feminism on Twitter: Reflections on a Research
   Collaboration between Social Scientists and Computer Scientists
SO SOCIOLOGICAL RESEARCH ONLINE
LA English
DT Article
DE 'big data'; collaboration; feminism; machine learning; qualitative
   analysis; social media; Twitter
ID WOMENS MOVEMENT; MEDIA
AB The growth of social media presents an unparalleled opportunity for the study of social change. However, the speed and scale of this growth presents challenges for social scientists, particularly those whose methodologies tend to rely on the qualitative analysis of data that are gathered firsthand. Alongside the growth of social media, companies have emerged which have developed tools for interrogating 'big data', although often unconnected from social scientists. It is self-evident that collaboration between social scientists and social media analysis companies offers the potential for developing methods for analysing social change on large scales, bringing together their respective expertise in technological innovations and knowledge of social science. What is less well known is how such a partnership might work in practice. This article presents an example of such a collaboration, highlighting the opportunities and challenges that arose in the context of an exploration of feminism on Twitter. As will be shown, machine-learning technologies allow the analysis of data on a scale that would be impossible for human analysts, yet such approaches also heighten challenges regarding the study of social change and communication.
C1 [Kirkwood, Steve] Univ Edinburgh, Social Work, Chrystal Macmillan Bldg,15a George Sq, Edinburgh EH8 9LD, Midlothian, Scotland.
   [Cree, Viviene] Univ Edinburgh, Social Work Studies, Edinburgh, Midlothian, Scotland.
   [Winterstein, Daniel; Nuttgens, Alex; Sneddon, Jenni] SoDash, Edinburgh, Midlothian, Scotland.
C3 University of Edinburgh; University of Edinburgh
RP Kirkwood, S (corresponding author), Univ Edinburgh, Social Work, Chrystal Macmillan Bldg,15a George Sq, Edinburgh EH8 9LD, Midlothian, Scotland.
EM s.kirkwood@ed.ac.uk
RI Kirkwood, Steve/I-1966-2019
OI Kirkwood, Steve/0000-0003-1508-0835; Cree, Viviene/0000-0002-9995-1820
FU University of Edinburgh School of Social and Political Science
FX We would like to thank the University of Edinburgh School of Social and
   Political Science for the research grant that supported this project. We
   would also like to thank the anonymous reviewers for their helpful
   comments on an earlier version of this article.
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NR 33
TC 2
Z9 3
U1 1
U2 22
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 1360-7804
J9 SOCIOL RES ONLINE
JI Sociol. Res. Online
PD DEC
PY 2018
VL 23
IS 4
BP 763
EP 779
DI 10.1177/1360780418781615
PG 17
WC Sociology
WE Social Science Citation Index (SSCI)
SC Sociology
GA HE2FJ
UT WOS:000453091200004
OA Green Accepted
DA 2024-09-05
ER
PT J
AU Wulff, P
   Buschhüter, D
   Westphal, A
   Mientus, L
   Nowak, A
   Borowski, A
AF Wulff, Peter
   Buschhueter, David
   Westphal, Andrea
   Mientus, Lukas
   Nowak, Anna
   Borowski, Andreas
TI Bridging the Gap Between Qualitative and Quantitative Assessment in
   Science Education Research with Machine Learning - A Case for Pretrained
   Language Models-Based Clustering
SO JOURNAL OF SCIENCE EDUCATION AND TECHNOLOGY
LA English
DT Article
DE Attention to classroom events; Noticing; NLP; ML
ID AUTOMATED-ANALYSIS; UNREASONABLE EFFECTIVENESS; VIDEO; MATHEMATICS;
   KNOWLEDGE
AB Science education researchers typically face a trade-off between more quantitatively oriented confirmatory testing of hypotheses, or more qualitatively oriented exploration of novel hypotheses. More recently, open-ended, constructed response items were used to combine both approaches and advance assessment of complex science-related skills and competencies. For example, research in assessing science teachers' noticing and attention to classroom events benefitted from more open-ended response formats because teachers can present their own accounts. Then, open-ended responses are typically analyzed with some form of content analysis. However, language is noisy, ambiguous, and unsegmented and thus open-ended, constructed responses are complex to analyze. Uncovering patterns in these responses would benefit from more principled and systematic analysis tools. Consequently, computer-based methods with the help of machine learning and natural language processing were argued to be promising means to enhance assessment of noticing skills with constructed response formats. In particular, pretrained language models recently advanced the study of linguistic phenomena and thus could well advance assessment of complex constructs through constructed response items. This study examines potentials and challenges of a pretrained language model-based clustering approach to assess preservice physics teachers' attention to classroom events as elicited through open-ended written descriptions. It was examined to what extent the clustering approach could identify meaningful patterns in the constructed responses, and in what ways textual organization of the responses could be analyzed with the clusters. Preservice physics teachers (N = 75) were instructed to describe a standardized, video-recorded teaching situation in physics. The clustering approach was used to group related sentences. Results indicate that the pretrained language model-based clustering approach yields well-interpretable, specific, and robust clusters, which could be mapped to physics-specific and more general contents. Furthermore, the clusters facilitate advanced analysis of the textual organization of the constructed responses. Hence, we argue that machine learning and natural language processing provide science education researchers means to combine exploratory capabilities of qualitative research methods with the systematicity of quantitative methods.
C1 [Wulff, Peter] Heidelberg Univ Educ, Phys Educ Res Grp, Heidelberg, Germany.
   [Buschhueter, David; Mientus, Lukas; Nowak, Anna; Borowski, Andreas] Univ Potsdam, Phys Educ Res Grp, Potsdam, Germany.
   [Westphal, Andrea] Univ Greifswald, Dept Educ Res, Greifswald, Germany.
C3 Ruprecht Karls University Heidelberg; University of Potsdam; Universitat
   Greifswald
RP Wulff, P (corresponding author), Heidelberg Univ Educ, Phys Educ Res Grp, Heidelberg, Germany.
EM peter.wulff@ph-heidelberg.de
RI Wulff, Peter/GSI-9069-2022
OI Wulff, Peter/0000-0002-5471-7977; Mientus, Lukas/0000-0001-5344-4770
FU Projekt DEAL; Federal Ministry of Education and Research
FX Open Access funding enabled and organized by Projekt DEAL. This project
   is part of the "Qualitatsoffensive Lehrerbildung", a joint initiative of
   the Federal Government and the Lander which aims to improve the quality
   of teacher training. The program is funded by the Federal Ministry of
   Education and Research. The authors are responsible for the content of
   this publication.
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NR 77
TC 17
Z9 17
U1 7
U2 37
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1059-0145
EI 1573-1839
J9 J SCI EDUC TECHNOL
JI J. Sci. Educ. Technol.
PD AUG
PY 2022
VL 31
IS 4
BP 490
EP 513
DI 10.1007/s10956-022-09969-w
EA JUN 2022
PG 24
WC Education & Educational Research; Education, Scientific Disciplines
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Education & Educational Research
GA 2O1JP
UT WOS:000804522900001
OA hybrid
DA 2024-09-05
ER
PT J
AU Sadeghian, A
   Sundaram, L
   Wang, DZ
   Hamilton, WF
   Branting, K
   Pfeifer, C
AF Sadeghian, Ali
   Sundaram, Laksshman
   Wang, Daisy Zhe
   Hamilton, William F.
   Branting, Karl
   Pfeifer, Craig
TI Automatic semantic edge labeling over legal citation graphs
SO ARTIFICIAL INTELLIGENCE AND LAW
LA English
DT Article
DE Legal citation graph; Semantics; Automatic citation analysis;
   Conditional random fields; Word embeddings; Clustering
ID AGREEMENT
AB A large number of cross-references to various bodies of text are used in legal texts, each serving a different purpose. It is often necessary for authorities and companies to look into certain types of these citations. Yet, there is a lack of automatic tools to aid in this process. Recently, citation graphs have been used to improve the intelligibility of complex rule frameworks. We propose an algorithm that builds the citation graph from a document and automatically labels each edge according to its purpose. Our method uses the citing text only and thus works only on citations who's purpose can be uniquely identified by their surrounding text. This framework is then applied to the US code. This paper includes defining and evaluating a standard gold set of labels that cover a vast majority of citation types which appear in the "US Code'' but are still short enough for practical use. We also proposed a novel linear-chain conditional random field model that extracts the features required for labeling the citations from the surrounding text. We then analyzed the effectiveness of different clustering methods such as K-means and support vector machine to automatically label each citation with the corresponding label. Besides this, we talk about the practical difficulties of this task and give a comparison of human accuracy compared to our end-to-end algorithm.
C1 [Sadeghian, Ali; Sundaram, Laksshman; Wang, Daisy Zhe; Hamilton, William F.] Univ Florida, Gainesville, FL 32611 USA.
   [Sundaram, Laksshman] Stanford Univ, Stanford, CA 94305 USA.
   [Branting, Karl; Pfeifer, Craig] Mitre Corp, Mclean, VA USA.
C3 State University System of Florida; University of Florida; Stanford
   University; MITRE Corporation
RP Sadeghian, A (corresponding author), Univ Florida, Gainesville, FL 32611 USA.
EM asadeghian@ufl.edu; lakss@stanford.edu; daisyw@ufl.edu;
   hamiltonw@law.ufl.edu; lbranting@mitre.org; cpfeifer@mitre.org
RI Sadeghian, Ali/AAV-6773-2020
CR Adedjouma M, 2014, INT REQUIR ENG CONF, P63, DOI 10.1109/RE.2014.6912248
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NR 37
TC 10
Z9 13
U1 0
U2 6
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0924-8463
EI 1572-8382
J9 ARTIF INTELL LAW
JI Artif. Intell. Law
PD JUN
PY 2018
VL 26
IS 2
SI SI
BP 127
EP 144
DI 10.1007/s10506-018-9217-1
PG 18
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications; Law
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Government & Law
GA GR5LE
UT WOS:000442672600003
DA 2024-09-05
ER
PT J
AU Liu, N
   Shapira, P
   Yue, XX
AF Liu, Na
   Shapira, Philip
   Yue, Xiaoxu
TI Tracking developments in artificial intelligence research: constructing
   and applying a new search strategy
SO SCIENTOMETRICS
LA English
DT Article
DE Emerging technology; Artificial intelligence; Bibliometric analysis;
   Search strategy; Research trends
ID SCIENTIFIC-RESEARCH; TECHNOLOGY; SCIENCE; EMERGENCE; AI
AB Artificial intelligence, as an emerging and multidisciplinary domain of research and innovation, has attracted growing attention in recent years. Delineating the domain composition of artificial intelligence is central to profiling and tracking its development and trajectories. This paper puts forward a bibliometric definition for artificial intelligence which can be readily applied, including by researchers, managers, and policy analysts. Our approach starts with benchmark records of artificial intelligence captured by using a core keyword and specialized journal search. We then extract candidate terms from high frequency keywords of benchmark records, refine keywords and complement with the subject category "artificial intelligence". We assess our search approach by comparing it with other three recent search strategies of artificial intelligence, using a common source of articles from the Web of Science. Using this source, we then profile patterns of growth and international diffusion of scientific research in artificial intelligence in recent years, identify top research sponsors in funding artificial intelligence and demonstrate how diverse disciplines contribute to the multidisciplinary development of artificial intelligence. We conclude with implications for search strategy development and suggestions of lines for further research.
C1 [Liu, Na] Shandong Technol & Business Univ, Sch Management, Yantai 264005, Peoples R China.
   [Shapira, Philip] Univ Manchester, Alliance Manchester Business Sch, Manchester Inst Innovat Res, Manchester M13 9PL, Lancs, England.
   [Shapira, Philip] Georgia Inst Technol, Sch Publ Policy, Atlanta, GA 30332 USA.
   [Yue, Xiaoxu] Tsinghua Univ, Sch Publ Policy & Management, Beijing 100084, Peoples R China.
C3 Shandong Technology & Business University; University of Manchester;
   Alliance Manchester Business School; University System of Georgia;
   Georgia Institute of Technology; Tsinghua University
RP Shapira, P (corresponding author), Univ Manchester, Alliance Manchester Business Sch, Manchester Inst Innovat Res, Manchester M13 9PL, Lancs, England.; Shapira, P (corresponding author), Georgia Inst Technol, Sch Publ Policy, Atlanta, GA 30332 USA.
EM pshapira@manchester.ac.uk
RI Shapira, Philip/E-4638-2011
OI Shapira, Philip/0000-0003-2488-5985
FU National Natural Science Foundation of China [71702090]; Taishan
   Scholars Program of Shandong Province [Tsqn201909149]; Biotechnology and
   Biological Sciences Research Council [BB/M017702/1)]; Project on
   Anticipating Transformative Innovations and their Implications: AI
   innovation strategies in Canada and the UK (Partnership for the
   Organization of Innovation and New Technologies, Social Sciences and
   Humanities Research Council of Canada) [895-2018-1006]; BBSRC
   [BB/M017702/1] Funding Source: UKRI
FX Na Liu acknowledges support for this research from the National Natural
   Science Foundation of China (Grant No. 71702090) and the Taishan
   Scholars Program of Shandong Province (Grant No. Tsqn201909149). Philip
   Shapira acknowledges support for this research from the Biotechnology
   and Biological Sciences Research Council (Grant No. BB/M017702/1)
   (Manchester Synthetic Biology Research Centre for Fine and Speciality
   Chemicals) and the Project on Anticipating Transformative Innovations
   and their Implications: AI innovation strategies in Canada and the UK
   (Partnership for the Organization of Innovation and New Technologies,
   Social Sciences and Humanities Research Council of Canada, Grant No.
   895-2018-1006). The authors appreciate comments on an earlier draft
   received from Alan L. Porter.
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NR 62
TC 25
Z9 28
U1 33
U2 229
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD APR
PY 2021
VL 126
IS 4
BP 3153
EP 3192
DI 10.1007/s11192-021-03868-4
EA FEB 2021
PG 40
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA RK9JQ
UT WOS:000621730600002
PM 34720254
OA Green Published, hybrid
DA 2024-09-05
ER
PT J
AU Jia, FL
   Sun, DE
   Looi, CK
AF Jia, Fenglin
   Sun, Daner
   Looi, Chee-kit
TI Artificial Intelligence in Science Education (2013-2023): Research
   Trends in Ten Years
SO JOURNAL OF SCIENCE EDUCATION AND TECHNOLOGY
LA English
DT Article
DE Bibliometric literature review; Artificial Intelligence; Science
   Education; Primary and Secondary Schools
ID SCHOOL STUDENTS; COMPUTATIONAL THINKING; DESIGN; SYSTEM; GAMIFICATION;
   TECHNOLOGIES; GENERATION; FEEDBACK
AB The use of artificial intelligence has played an important role in science teaching and learning. The purpose of this study was to fill a gap in the current review of research on AI in science education (AISE) in the early stage of education by systematically reviewing existing research in this area. This systematic review examined the trends and research foci of AI in the science of early stages of education. This review study employed a bibliometric analysis and content analysis to examine the characteristics of 76 studies on Artificial Intelligence in Science Education (AISE) indexed in Web of Science and Scopus from 2013 to 2023. The analytical tool CiteSpace was utilized for the analysis. The study aimed to provide an overview of the development level of AISE and identify major research trends, keywords, research themes, high-impact journals, institutions, countries/regions, and the impact of AISE studies. The results, based on econometric analyses, indicate that AISE has experienced increasing influence over the past decade. Cluster and timeline analyses of the retrieved keywords revealed that AI in primary and secondary science education can be categorized into 11 main themes, and the chronology of their emergence was identified. Among the most prolific journals in this field are the International Journal of Social Robotics, Educational Technology Research and Development, and others. Furthermore, the analysis identified that institutions and countries/regions located primarily in the United States have made the most significant contributions to AISE research. To explore the learning outcomes and overall impact of AI technologies on learners in primary and secondary schools, content analysis was conducted, identifying five main categories of technology applications. This study provides valuable insights into the advancements and implications of AI in science education at the primary and secondary levels.
C1 [Jia, Fenglin; Looi, Chee-kit] Educ Univ Hong Kong, Dept Curriculum & Instruct, Hong Kong, Peoples R China.
   [Sun, Daner] Educ Univ Hong Kong, Dept Math & Informat Technol, Hong Kong, Peoples R China.
C3 Education University of Hong Kong (EdUHK); Education University of Hong
   Kong (EdUHK)
RP Sun, DE (corresponding author), Educ Univ Hong Kong, Dept Math & Informat Technol, Hong Kong, Peoples R China.
EM dsun@eduhk.hk
RI SUN, Daner/AAL-2567-2020; jia, fenglin/HNP-9112-2023; Looi,
   Chee-Kit/ABG-9043-2021; Jia, fenglin/HZI-8900-2023
OI SUN, Daner/0000-0002-9813-6306; Jia, fenglin/0000-0002-6233-9873; Looi,
   Chee-Kit/0000-0001-9905-2713
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NR 122
TC 8
Z9 8
U1 148
U2 268
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1059-0145
EI 1573-1839
J9 J SCI EDUC TECHNOL
JI J. Sci. Educ. Technol.
PD FEB
PY 2024
VL 33
IS 1
BP 94
EP 117
DI 10.1007/s10956-023-10077-6
EA OCT 2023
PG 24
WC Education & Educational Research; Education, Scientific Disciplines
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Education & Educational Research
GA FV0J7
UT WOS:001079844000001
DA 2024-09-05
ER
PT J
AU Mutz, R
   Daniel, HD
AF Mutz, Ruediger
   Daniel, Hans-Dieter
TI The generalized propensity score methodology for estimating unbiased
   journal impact factors
SO SCIENTOMETRICS
LA English
DT Article
DE Journal impact factor; Causal inference; Generalized propensity score;
   Rubin Causal Model
ID CAUSAL INFERENCE; SUBCLASSIFICATION; BIAS; PUBLICATION; SCIENCE; DESIGN
AB The journal impact factor (JIF) proposed by Garfield in the year 1955 is one of the most commonly used and prominent citation-based indicators of the performance and significance of a scientific journal. The JIF is simple, reasonable, clearly defined, and comparable over time and, what is more, can be easily calculated from data provided by Thomson Reuters, but at the expense of serious technical and methodological flaws. The paper discusses one of the core problems: The JIF is affected by bias factors (e.g., document type) that have nothing to do with the prestige or quality of a journal. For solving this problem, we suggest using the generalized propensity score methodology based on the Rubin Causal Model. Citation data for papers of all journals in the ISI subject category "Microscopy" (Journal Citation Report) are used to illustrate the proposal.
C1 [Mutz, Ruediger; Daniel, Hans-Dieter] Swiss Fed Inst Technol, CH-8001 Zurich, Switzerland.
   [Daniel, Hans-Dieter] Univ Zurich, Evaluat Off, CH-8001 Zurich, Switzerland.
C3 Swiss Federal Institutes of Technology Domain; ETH Zurich; University of
   Zurich
RP Mutz, R (corresponding author), Swiss Fed Inst Technol, Muehlegasse 21, CH-8001 Zurich, Switzerland.
EM mutz@gess.ethz.ch
RI Mutz, Ruediger/A-2226-2009; Mutz, Rüdiger/AAA-9629-2021; Daniel,
   Hans-Dieter/A-2419-2013
OI Mutz, Rüdiger/0000-0003-3345-6090; 
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TC 13
Z9 14
U1 1
U2 30
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD AUG
PY 2012
VL 92
IS 2
SI SI
BP 377
EP 390
DI 10.1007/s11192-012-0670-4
PG 14
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 975YF
UT WOS:000306547000015
OA Green Published
DA 2024-09-05
ER
PT J
AU Hoseini, FF
   Mansouri, A
AF Hoseini, Fatemeh Fadavi
   Mansouri, Ali
TI The Role of Articles in Science-Technology Relationship: A Topic
   Analysis of Non-patent Literature (NPL) References
SO SERIALS REVIEW
LA English
DT Article
DE Citation analysis; nanotechnology; non-patent literature; patents; topic
   modeling
ID PATENT CITATION ANALYSIS; INDUSTRY; INNOVATION; PUBLICATIONS;
   NANOTECHNOLOGY; DOCUMENTS; LINKAGES; SEARCH; REAL
AB Patents with non-patent literature (NPL) references indicate how the link between science and technology interact. Using topic modeling, this paper investigated the thematic relationship between patents and their cited articles in the field of Nanotechnology. For this purpose, patents in the field of nanotechnology (IPC Class: B82) were obtained from the United States Patent and Trademark Office from 1985 to 2019. Then, NPL references listed in "Other References" section of the patents was extracted and abstract of the NPL references was retrieved from Scopus database. R software, topic modeling, and Latent Dirichlet Allocation algorithm were used to analyze the data. Results showed that most of the subclasses in Nanotechnology use few NPL references and are more dependent on patents. In total, NPL references account for only 36% of patent citations. The topics of the NPL references in this field (nanotechnology) belonged to six categories: Physics, Electricity, Chemistry, Cellular and Molecular Biology, Medicine, and Nanotechnology. Consequently, it seems that nanotechnology patents are more technology-driven, and a medium to low relationship exists between science and nanotechnology. The topic modeling of NPL references uncovered that nanotechnology patents have been more influenced by non-nano scientific.
C1 [Hoseini, Fatemeh Fadavi; Mansouri, Ali] Univ Isfahan, Dept Knowledge & Informat Sci, Esfahan, Iran.
C3 University of Isfahan
RP Mansouri, A (corresponding author), Univ Isfahan, Dept Knowledge & Informat Sci, Esfahan, Iran.
EM a.mansouri@edu.ui.ac.ir
RI Mansouri, Ali/HKN-4172-2023; Mansouri, Ali/ABH-9666-2020
OI Mansouri, Ali/0000-0003-4047-2697; Mansouri, Ali/0000-0003-4047-2697;
   Fadavi Hoseini, Fateme/0000-0001-8371-8939
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NR 62
TC 3
Z9 3
U1 9
U2 36
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0098-7913
EI 1879-095X
J9 SERIALS REV
JI Ser. Rev.
PY 2022
VL 48
IS 1-2
SI SI
BP 137
EP 150
DI 10.1080/00987913.2022.2127403
EA SEP 2022
PG 14
WC Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science
GA 8M2CW
UT WOS:000863366700001
DA 2024-09-05
ER
PT C
AU Ahmad, A
   Bukhari, F
   Malik, K
AF Ahmad, Asma
   Bukhari, Faisal
   Malik, Kamran
GP IEEE
TI Predicting Article Sentiment Analysis Impact in Twitter: A Case Study in
   the Field of Information Sciences
SO 4TH INTERNATIONAL CONFERENCE ON INNOVATIVE COMPUTING (IC)2
LA English
DT Proceedings Paper
CT 4th International Conference on Innovative Computing (ICIC)
CY NOV 09-10, 2021
CL Univ Management & Technol, Lahore, PAKISTAN
HO Univ Management & Technol
DE Altmetrics; Twitter; Sentiment Analysis; Predicting Citations
ID RESEARCH EXCELLENCE; SOCIAL MEDIA; ALTMETRICS
AB T For a long time, the scholarly impact of a researcher has been evaluated by its citation count. But this method usually takes years to improve its impact rate. Twitter is one of the famous and rapidly growing microblogging platforms used in the context of "Altmetrics" as a solid alternative scale to identify quality research work. This research test whether Twitter sentiment is helpful to identify quality research work and there is a correlation between citation count and Altmetrics. We classified a small dataset of 6613 tweets into neutral, positive, and negative in information sciences. Our findings show that most of the sentiments of the tweets are neutral because of irrelevant discussion about the articles. Twitter comments do not provide important information about quality research papers except in exceptional cases. We cannot use tweets as a solid indicator to identify quality research work in the field of Information Sciences.
C1 [Ahmad, Asma; Bukhari, Faisal; Malik, Kamran] Univ Punjab, Fac Comp & Informat Technol, Dept Comp Sci, Lahore, Pakistan.
C3 University of Punjab
RP Ahmad, A (corresponding author), Univ Punjab, Fac Comp & Informat Technol, Dept Comp Sci, Lahore, Pakistan.
EM mscsf17m518@pucit.edu.pk; faisal.bukhari@pucit.edu.pk;
   kamran.amlik@pucit.edu.pk
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NR 36
TC 0
Z9 0
U1 2
U2 3
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-6654-0091-6
PY 2021
BP 488
EP 493
DI 10.1109/ICIC53490.2021.9692959
PG 6
WC Computer Science, Artificial Intelligence; Computer Science, Software
   Engineering; Computer Science, Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BT3XY
UT WOS:000824668100067
DA 2024-09-05
ER
PT J
AU Zhou, ZG
   Shi, C
   Hu, MX
   Liu, YH
AF Zhou, Zhiguang
   Shi, Chen
   Hu, Miaoxin
   Liu, Yuhua
TI Visual ranking of academic influence via paper citation
SO JOURNAL OF VISUAL LANGUAGES AND COMPUTING
LA English
DT Article
DE Visual analysis; Citation network; Word2vec model; PageRank model
ID SCHOLARLY DATA; VISUALIZATION; EXPLORATION; PAGERANK
AB With rapid growth of digital publishing, a great deal of document datum has been published online for a widely spread of knowledge innovations, which is an important resource for human survival and social development. However, it is a time-consuming and difficult task to conduct a high-efficiency access of valuable papers from an extremely large document database. A set of ranking techniques have been proposed to evaluate the influence of articles by counting the number and quality of citations, such as PageRank. In fact, the influence of an article does not merely depend on the account of citations, which is also highly related to the citation network. In this paper, we propose a visual analytics system for visual ranking of academic influence of articles, based on an insightful analysis of citation network. Firstly, a characterization of articles is established through word2vec model, based on an analogy between the articles in citation network and natural language processing (NPL) terms. Then, the difference between articles in the vectorized space is employed to optimize the PageRank model and achieve desired influence ranking results. A set of meaningful visual encodings are also designed to present the relationships among articles, such as the visualization of high-dimensional vectors and time-varying citation networks. At last, a visualization framework is implemented for visual ranking of academic influence of articles, with the ranking models and visual designs integrated. Case studies based on real-world datasets and interviews with domain experts have demonstrated the effectiveness of our system in the evaluation of academic influence of articles.
C1 [Zhou, Zhiguang; Shi, Chen; Hu, Miaoxin; Liu, Yuhua] Zhejiang Univ Finance & Econ, Sch Informat, Hangzhou, Zhejiang, Peoples R China.
C3 Zhejiang University of Finance & Economics
RP Liu, YH (corresponding author), Zhejiang Univ Finance & Econ, Sch Informat, Hangzhou, Zhejiang, Peoples R China.
EM zhgzhou1983@163.com; ChenShi4066@163.com; humiaoxin@zufe.edu.cn;
   liuyuhua@zufe.edu.cn
OI Shi, Chen/0000-0002-4615-4123
FU National Natural Science Foundation of China [61872314, 61802339];
   Humanities and Social Sciences Foundation of Ministry of Education in
   China [18YJC910017]; Natural Science Foundation of Zhejiang Province
   [LY18F020024]; Open Project Program of the State Key Lab of CADAMP;CG of
   Zhejiang University [A1806]; First Class Discipline of Zhejiang -A
   (Zhejiang University of Finance and Economics Statistics)
FX The authors would like to thank the anonymous reviewers for their
   valuable comments. This work was supported by the National Natural
   Science Foundation of China (61872314, 61802339), the Humanities and
   Social Sciences Foundation of Ministry of Education in China
   (18YJC910017), the Natural Science Foundation of Zhejiang Province
   (LY18F020024), the Open Project Program of the State Key Lab of CAD&CG
   of Zhejiang University (A1806), and the First Class Discipline of
   Zhejiang -A (Zhejiang University of Finance and Economics Statistics).
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NR 51
TC 14
Z9 16
U1 0
U2 41
PU ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
PI LONDON
PA 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND
SN 1045-926X
EI 1095-8533
J9 J VISUAL LANG COMPUT
JI J. Vis. Lang. Comput.
PD OCT
PY 2018
VL 48
BP 134
EP 143
DI 10.1016/j.jvlc.2018.08.007
PG 10
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA GY5NQ
UT WOS:000448625700016
DA 2024-09-05
ER
PT J
AU Romano, R
   Davino, C
AF Romano, Rosaria
   Davino, Cristina
TI Assessing scientific research activity evaluation models using
   multivariate analysis
SO STATISTICS AND ITS INTERFACE
LA English
DT Article
DE Composite indicators; Stability; Analysis of variance; Principal
   component analysis; Scientific research activity
ID INDICATORS
AB The authors of this paper propose a method, based both on confirmatory and exploratory data analysis, aiming to assess the variability arising from the Composite Indicators (CIs) construction process. This research refers to an evaluation exercise very important for universities: the assessment of scientific research. The aim of every evaluation system is to synthesize all the information collected at universities into a unique CI, which will allow comparison of performances or ranks of the objects under evaluation. Since the methodology adopted to construct the CI is just one possible solution among several acceptable alternatives, it is reasonable to wonder about the results from the other options. The proposed approach investigates the impact of the different sources of variability occurring in CIs construction, also taking into account the external information available for each statistical unit. The term CI variability is used in the meaning of CI stability and it refers to differences emerging among CIs obtained using different subjective choices to construct the CI. Furthermore, the stability of the results is assessed through a combination of graphical tools and resampling methods. An empirical analysis is provided to discuss the methodological proposal. The research refers to the 'University Planning and Evaluation 2007-2009' system, implemented by the Italian government to finance public universities.
C1 [Romano, Rosaria] Univ Calabria, Dept Econ Stat & Finance, I-87030 Commenda Di Rende, Italy.
   [Davino, Cristina] Univ Macerata, Dept Polit Sci Commun & Int Relat, Macerata, Italy.
C3 University of Calabria; University of Macerata
RP Davino, C (corresponding author), Univ Macerata, Dept Polit Sci Commun & Int Relat, Macerata, Italy.
EM rosaria.romano@unical.it; cristina.davino@unimc.it
OI Romano, Rosaria/0000-0002-9708-1753
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NR 17
TC 1
Z9 1
U1 0
U2 9
PU INT PRESS BOSTON, INC
PI SOMERVILLE
PA PO BOX 43502, SOMERVILLE, MA 02143 USA
SN 1938-7989
EI 1938-7997
J9 STAT INTERFACE
JI Stat. Interface
PY 2016
VL 9
IS 3
BP 303
EP 313
DI 10.4310/SII.2016.v9.n3.a5
PG 11
WC Mathematical & Computational Biology; Mathematics, Interdisciplinary
   Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology; Mathematics
GA DF4AV
UT WOS:000371290600005
DA 2024-09-05
ER
PT J
AU Nolasco, D
   Oliveira, J
AF Nolasco, Diogo
   Oliveira, Jonice
TI Mining social influence in science and vice-versa: A topic correlation
   approach
SO INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT
LA English
DT Article
DE Topic modeling; Social networks; Science networks; Topic labeling;
   Influence mining; Topic similarity
ID NETWORKS; MENDELEY; ADOPTION; TWITTER; TWEETS; IMPACT
AB There is no doubt that scientific discoveries have always brought changes to society. New technologies help solve social problems such as transportation and education, while research brings benefits such as curing diseases and improving food production. Despite the impacts caused by science and society on each other, this relationship is rarely studied and they are often seen as different universes. Previous literature focuses only on a single domain, detecting social demands or research fronts for example, without ever crossing the results for new insights. In this work, we create a system that is able to assess the relationship between social and scholar data using the topics discussed in social networks and research topics. We use the articles as science sensors and humans as social sensors via social networks. Topic modeling algorithms are used to extract and label social subjects and research themes and then topic correlation metrics are used to create links between them if they have a significant relationship. The proposed system is based on topic modeling, labeling and correlation from heterogeneous sources, so it can be used in a variety of scenarios. We make an evaluation of the approach using a large-scale Twitter corpus combined with a PubMed article corpus. In both of them, we work with data of the Zika epidemic in the world, as this scenario provides topics and discussions on both domains. Our work was capable of discovering links between various topics of different domains, which suggests that some of the relationships can be automatically inferred by the sensors. Results can open new opportunities for forecasting social behavior, assess community interest in a scientific subject or directing research to the population welfare.
C1 [Nolasco, Diogo] Univ Fed Rio de Janeiro, Programa Posgrad Informat, Av Athos,Silveira Ramos,274 Bl CCMN NCE, Rio De Janeiro, RJ, Brazil.
   [Oliveira, Jonice] Univ Fed Rio de Janeiro, Dept Ciencia Comp, Av Athos,Silveira Ramos,274 Bl Room 1038 NCE, Rio De Janeiro, RJ, Brazil.
C3 Universidade Federal do Rio de Janeiro; Universidade Federal do Rio de
   Janeiro
RP Nolasco, D (corresponding author), Univ Fed Rio de Janeiro, Programa Posgrad Informat, Av Athos,Silveira Ramos,274 Bl CCMN NCE, Rio De Janeiro, RJ, Brazil.
EM diogo.sousa@ppgi.ufrj.br; jonice@dcc.ufrj.br
RI Oliveira, Jonice/AAR-8798-2021
OI Oliveira, Jonice/0000-0002-2495-1463; Nolasco, Diogo/0000-0001-6125-0140
FU CAPES; CNPq; FAPERJ
FX The authors would like to thank CAPES, CNPq, and FAPERJ for supporting
   this work.
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NR 73
TC 9
Z9 9
U1 1
U2 33
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0268-4012
EI 1873-4707
J9 INT J INFORM MANAGE
JI Int. J. Inf. Manage.
PD APR
PY 2020
VL 51
AR 102017
DI 10.1016/j.ijinfomgt.2019.10.002
PG 14
WC Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science
GA KQ3OC
UT WOS:000516835200016
DA 2024-09-05
ER
PT J
AU Saheb, T
   Saheb, T
   Carpenter, DO
AF Saheb, Tahereh
   Saheb, Tayebeh
   Carpenter, David O.
TI Mapping research strands of ethics of artificial intelligence in
   healthcare: A bibliometric and content analysis
SO COMPUTERS IN BIOLOGY AND MEDICINE
LA English
DT Article
DE Artificial intelligence; Healthcare; Robotics; Bibliometric analysis;
   Content analysis; Network visualization; Ethics
ID CLINICAL IMAGING DATA; FINANCIAL BURDEN; CHALLENGES; DISEASE;
   TECHNOLOGY; MANAGEMENT; ISSUES; FIELD; HOPE; BIAS
AB The growth of artificial intelligence in promoting healthcare is rapidly progressing. Notwithstanding its promising nature, however, AI in healthcare embodies certain ethical challenges as well. This research aims to delineate the most influential elements of scientific research on AI ethics in healthcare by conducting bibliometric, social network analysis, and cluster-based content analysis of scientific articles. Not only did the bibliometric analysis identify the most influential authors, countries, institutions, sources, and documents, but it also recognized four ethical concerns associated with 12 medical issues. These ethical categories are composed of normative, meta-ethics, epistemological and medical practice. The content analysis complemented this list of ethical categories and distinguished seven more ethical categories: ethics of relationships, medico-legal concerns, ethics of robots, ethics of ambient intelligence, patients' rights, physicians' rights, and ethics of predictive analytics. This analysis likewise identified 40 general research gaps in the literature and plausible future research strands. This analysis furthers conversations on the ethics of AI and associated emerging technologies such as nanotech and biotech in healthcare, hence, advances convergence research on the ethics of AI in healthcare. Practically, this research will provide a map for policymakers and AI engineers and scientists on what dimensions of AI-based medical interventions require stricter policies and guidelines and robust ethical design and development.
C1 [Saheb, Tahereh] Tarbiat Modares Univ, Management Studies Ctr, Tehran, Iran.
   [Saheb, Tayebeh] Tarbiat Modares Univ, Fac Law, Tehran, Iran.
   [Carpenter, David O.] SUNY Albany, Sch Publ Hlth, Inst Hlth & Environm, Albany, NY 12222 USA.
C3 Tarbiat Modares University; Tarbiat Modares University; State University
   of New York (SUNY) System; State University of New York (SUNY) Albany
RP Saheb, T (corresponding author), Tarbiat Modares Univ, Management Studies Ctr, Tehran, Iran.
EM t.saheb@modares.ac.ir; t-saheb@modares.ac.ir; dcarpenter@albany.edu
RI Saheb, Tayebeh/ABY-9272-2022
OI Saheb, Tayebeh/0000-0002-2672-1776; Saheb, Tahereh/0000-0002-6426-609X;
   Carpenter, David/0000-0003-4841-394X
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NR 133
TC 39
Z9 39
U1 21
U2 164
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0010-4825
EI 1879-0534
J9 COMPUT BIOL MED
JI Comput. Biol. Med.
PD AUG
PY 2021
VL 135
AR 104660
DI 10.1016/j.compbiomed.2021.104660
EA AUG 2021
PG 19
WC Biology; Computer Science, Interdisciplinary Applications; Engineering,
   Biomedical; Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Life Sciences & Biomedicine - Other Topics; Computer Science;
   Engineering; Mathematical & Computational Biology
GA UE4NL
UT WOS:000687867000001
PM 34346319
DA 2024-09-05
ER
PT J
AU Mardle, S
   Pascoe, S
AF Mardle, S
   Pascoe, S
TI Use of evolutionary methods for bioeconomic optimization models: an
   application to fisheries
SO AGRICULTURAL SYSTEMS
LA English
DT Article
DE genetic algorithms; optimization; bioeconomic modeling; fisheries; open
   access
AB Bioeconomic optimization models are regularly used in fisheries policy analysis. However, the use of these models has been restricted in fisheries,as in other similar fields, where there are a large number of non-linear interactions. In this paper, the basic features, advantages and disadvantages of the use of the evolutionary methods, specifically genetic algorithms, are discussed. A large non-linear model of the UK component of the English Channel fisheries is developed using genetic algorithms. The results are compared with those from a linearized version of the model solved using traditional optimization techniques. The results suggest that genetic algorithms may provide better solutions for large non-linear bioeconomic models thar cannot be solved using traditional techniques without the use of simplifying assumptions. (C) 2000 Elsevier Science Ltd. All rights reserved.
C1 Univ Portsmouth, Ctr Econ & Management Aquat Resources, Portsmouth PO4 8JF, Hants, England.
C3 University of Portsmouth
RP Mardle, S (corresponding author), Univ Portsmouth, Ctr Econ & Management Aquat Resources, Locksway Rd, Portsmouth PO4 8JF, Hants, England.
RI Pascoe, Sean/D-9710-2011
OI Pascoe, Sean/0000-0001-6581-2649
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NR 39
TC 7
Z9 7
U1 0
U2 6
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0308-521X
J9 AGR SYST
JI Agric. Syst.
PD OCT
PY 2000
VL 66
IS 1
BP 33
EP 49
DI 10.1016/S0308-521X(00)00035-4
PG 17
WC Agriculture, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Agriculture
GA 371BR
UT WOS:000165158300003
DA 2024-09-05
ER
PT J
AU Heckman, JJ
   Flyer, F
   Loughlin, C
AF Heckman, James J.
   Flyer, Fredrick
   Loughlin, Colleen
TI An assessment of causal inference in smoking initiation research and a
   framework for future research
SO ECONOMIC INQUIRY
LA English
DT Article; Proceedings Paper
CT 81st Annual Meeting of the Western-Economic-Association-International
CY JUN 29-JUL 02, 2006
CL San Diego, CA
ID CIGARETTE; SCHOOLCHILDREN; ONSET
AB Reliably identifying the causal factors underlying youth smoking initiation is an important part of developing effective smoking prevention programs and shaping other types of smoking-related policies. The establishment of reliable scientific evidence in support of a causal link between cigarette advertising and youth smoking initiation depends on both rich longitudinal data as well as careful empirical applications. We examine basic principles of empirical scientific investigation of potential causal relationships, discuss findings of recent research on causal factors of youth smoking, and evaluate evidence from the public health literature regarding the effects of cigarette advertising on youth smoking.
C1 [Heckman, James J.] Univ Chicago, Chicago, IL 60637 USA.
C3 University of Chicago
RP Heckman, JJ (corresponding author), Univ Chicago, 1126 E 59th St, Chicago, IL 60637 USA.
EM jjh@uchicago.edu; fflyer@lexecon.com; cloughlin@lexecon.com
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NR 37
TC 16
Z9 17
U1 1
U2 12
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0095-2583
EI 1465-7295
J9 ECON INQ
JI Econ. Inq.
PD JAN
PY 2008
VL 46
IS 1
BP 37
EP 44
DI 10.1111/j.1465-7295.2007.00078.x
PG 8
WC Economics
WE Social Science Citation Index (SSCI); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Business & Economics
GA 279AJ
UT WOS:000254327100007
DA 2024-09-05
ER
PT J
AU Ionescu, S
   Delcea, C
   Chirita, N
   Nica, I
AF Ionescu, Stefan
   Delcea, Camelia
   Chirita, Nora
   Nica, Ionut
TI Exploring the Use of Artificial Intelligence in Agent-Based Modeling
   Applications: A Bibliometric Study
SO ALGORITHMS
LA English
DT Article
DE bibliometric analysis; agent-based modelling; artificial intelligence;
   complex systems; RStudio; VOSviewer; Bibliometrix
ID COMPLEX
AB This research provides a comprehensive analysis of the dynamic interplay between agent-based modeling (ABM) and artificial intelligence (AI) through a meticulous bibliometric study. This study reveals a substantial increase in scholarly interest, particularly post-2006, peaking in 2021 and 2022, indicating a contemporary surge in research on the synergy between AI and ABM. Temporal trends and fluctuations prompt questions about influencing factors, potentially linked to technological advancements or shifts in research focus. The sustained increase in citations per document per year underscores the field's impact, with the 2021 peak suggesting cumulative influence. Reference Publication Year Spectroscopy (RPYS) reveals historical patterns, and the recent decline prompts exploration into shifts in research focus. Lotka's law is reflected in the author's contributions, supported by Pareto analysis. Journal diversity signals extensive exploration of AI applications in ABM. Identifying impactful journals and clustering them per Bradford's Law provides insights for researchers. Global scientific production dominance and regional collaboration maps emphasize the worldwide landscape. Despite acknowledging limitations, such as citation lag and interdisciplinary challenges, our study offers a global perspective with implications for future research and as a resource in the evolving AI and ABM landscape.
C1 [Ionescu, Stefan; Delcea, Camelia; Chirita, Nora; Nica, Ionut] Bucharest Univ Econ Studies, Dept Econ Informat & Cybernet, Bucharest 0105552, Romania.
C3 Bucharest University of Economic Studies
RP Delcea, C (corresponding author), Bucharest Univ Econ Studies, Dept Econ Informat & Cybernet, Bucharest 0105552, Romania.
EM stefion09@gmail.com; camelia.delcea@csie.ase.ro;
   nora.chirita@csie.ase.ro; ionut.nica@csie.ase.ro
RI Nica, Ionut/ABA-4243-2021; Delcea, Camelia/C-4343-2011
OI Nica, Ionut/0000-0003-2118-3654; Delcea, Camelia/0000-0003-3589-1969;
   Ionescu, Stefan-Andrei/0000-0002-0469-3022; Chirita,
   Nora/0009-0005-6633-9466
FU Bucharest University of Economic Studies
FX This paper was co-financed by the Bucharest University of Economic
   Studies during the Ph.D. program.
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NR 79
TC 5
Z9 5
U1 5
U2 8
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1999-4893
J9 ALGORITHMS
JI Algorithms
PD JAN
PY 2024
VL 17
IS 1
AR 21
DI 10.3390/a17010021
PG 38
WC Computer Science, Artificial Intelligence; Computer Science, Theory &
   Methods
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA FW8K1
UT WOS:001148979300001
OA gold
DA 2024-09-05
ER
PT J
AU Harder, VS
   Stuart, EA
   Anthony, JC
AF Harder, Valerie S.
   Stuart, Elizabeth A.
   Anthony, James C.
TI Propensity Score Techniques and the Assessment of Measured Covariate
   Balance to Test Causal Associations in Psychological Research
SO PSYCHOLOGICAL METHODS
LA English
DT Article
DE observational studies; causal inference; standardized bias; cannabis
ID INFERENCE; BIAS; SUBCLASSIFICATION; SENSITIVITY; ADJUSTMENT; REGRESSION;
   MODELS
AB There is considerable interest in using propensity score (PS) statistical techniques to address questions of causal inference in psychological research. Many PS techniques exist, yet few guidelines are available to aid applied researchers in their understanding, use, and evaluation. In this study, the authors give an overview of available techniques for PS estimation and PS application. They also provide a way to help compare PS techniques, using the resulting measured covariate balance as the criterion for selecting between techniques. The empirical example for this study involves the potential causal relationship linking early-onset cannabis problems and subsequent negative mental health outcomes and uses data from a prospective cohort study. PS techniques are described and evaluated on the basis of their ability to balance the distributions of measured potentially confounding covariates for individuals with and without early-onset cannabis problems. This article identifies the PS techniques that yield good statistical balance of the chosen measured covariates within the context of this particular research question and cohort.
C1 [Harder, Valerie S.] Univ Vermont, Coll Med, Dept Psychiat, Burlington, VT 05401 USA.
   [Stuart, Elizabeth A.] Johns Hopkins Bloomberg Sch Publ Hlth, Dept Mental Hlth, Baltimore, MD USA.
   [Stuart, Elizabeth A.] Johns Hopkins Bloomberg Sch Publ Hlth, Dept Biostat, Baltimore, MD USA.
   [Anthony, James C.] Michigan State Univ, Coll Human Med, Dept Epidemiol, E Lansing, MI 48824 USA.
C3 University of Vermont; Johns Hopkins University; Johns Hopkins Bloomberg
   School of Public Health; Johns Hopkins University; Johns Hopkins
   Bloomberg School of Public Health; Michigan State University; Michigan
   State University College of Human Medicine
RP Harder, VS (corresponding author), Univ Vermont, Coll Med, Dept Psychiat, 1 S Prospect St, Burlington, VT 05401 USA.
EM vharder@uvm.edu
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NR 74
TC 562
Z9 664
U1 1
U2 53
PU AMER PSYCHOLOGICAL ASSOC
PI WASHINGTON
PA 750 FIRST ST NE, WASHINGTON, DC 20002-4242 USA
SN 1082-989X
EI 1939-1463
J9 PSYCHOL METHODS
JI Psychol. Methods
PD SEP
PY 2010
VL 15
IS 3
BP 234
EP 249
DI 10.1037/a0019623
PG 16
WC Psychology, Multidisciplinary
WE Social Science Citation Index (SSCI)
SC Psychology
GA 645LP
UT WOS:000281459600002
PM 20822250
OA Green Accepted
DA 2024-09-05
ER
PT J
AU Okada, A
   Sheehy, K
AF Okada, Alexandra
   Sheehy, Kieron
TI Factors and Recommendations to Support Students' Enjoyment of Online
   Learning With Fun: A Mixed Method Study During COVID-19
SO FRONTIERS IN EDUCATION
LA English
DT Article
DE COVID-19; online learning; fun; higher education; academic performance;
   epistemic views; responsible research and innovation; recommendations
ID EPISTEMOLOGICAL BELIEFS; EPISTEMIC COGNITION; INDONESIAN TEACHERS;
   CURRICULUM; CREATIVITY; EDUCATION; SCHOOL; GAMES
AB Understanding components that influence students' enjoyment of distance higher education is increasingly important to enhance academic performance and retention. Although there is a growing body of research about students' engagement with online learning, a research gap exists concerning whether fun affect students' enjoyment. A contributing factor to this situation is that the meaning of fun in learning is unclear, and its possible role is controversial. This research is original in examining students' views about fun and online learning, and influential components and connections. This study investigated the beliefs and attitudes of a sample of 551 distance education students including pre-services and in-service teachers, consultants and education professionals using a mixed-method approach. Quantitative and Qualitative data were generated through a self-reflective instrument during the COVID-19 pandemic. The findings revealed that 88.77% of participants valued fun in online learning; linked to well-being, motivation and performance. However, 16.66% mentioned that fun within online learning could take the focus off their studies and result in distraction or loss of time. Principal component analysis revealed three groups of students who found (1) fun relevant in socio-constructivist learning (2) no fun in traditional transmissive learning and (3) disturbing fun in constructivist learning. This study also provides key recommendations extracted from participants' views supported by consensual review for course teams, teaching staff and students to enhance online learning experiences with enjoyment and fun.
C1 [Okada, Alexandra; Sheehy, Kieron] Open Univ, Fac Wellbeing Educ & Language Studies, Rumpus Res Grp, Milton Keynes, Bucks, England.
C3 Open University - UK
RP Okada, A (corresponding author), Open Univ, Fac Wellbeing Educ & Language Studies, Rumpus Res Grp, Milton Keynes, Bucks, England.
EM ale.okada@open.ac.uk
OI Okada, Alexandra/0000-0003-1572-5605; Sheehy, Kieron/0000-0001-7623-8400
FU Open University UK
FX This study was funded by the Open University UK and is part of the
   international project OLAF -Online Learning and Fun.
   http://www.open.ac.uk/blogs/rumpus/index.php/projects/olaf/.
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NR 77
TC 14
Z9 18
U1 1
U2 14
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 2504-284X
J9 FRONT EDUC
JI Front. Educ.
PD DEC 11
PY 2020
VL 5
AR 584351
DI 10.3389/feduc.2020.584351
PG 18
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA TW9FT
UT WOS:000682696900001
OA Green Accepted, gold
DA 2024-09-05
ER
PT J
AU Song, P
   Wang, X
AF Song, Pu
   Wang, Xiang
TI A bibliometric analysis of worldwide educational artificial intelligence
   research development in recent twenty years
SO ASIA PACIFIC EDUCATION REVIEW
LA English
DT Article
DE Bibliometric analysis; Educational artificial intelligence; Research
   development; Relationship between humans and machines
ID COGNITIVE TUTOR; STUDENT; MANAGEMENT; TRENDS
AB Educational artificial intelligence (EAI) refers to the use of artificial intelligence (AI) to support personalized and automated feedback and guidance in the educational field. Inevitably, it serves as a more important part of the educational system in the coming years. However, novel development in this field has been inadequately reviewed and conceptualized in a visualized, objective and comprehensive way. In this view, a bibliometric analysis was conducted to obtain an overview of its trends from publication outputs, countries' cooperation, cluster analysis, and research evolution. Around 8660 Scopus-published articles from 2000 to 2019 were gathered for analysis using CiteSpace and Alluvial generator. In the study, a growing interest in EAI research and deepening cooperation among countries was first identified, entailing favorable conditions for promoting globalization in this aspect. Afterward, five core clusters were established for the intellectual structure of EAI, including intelligent tutoring system, learning system, student, labeled training data, and pedagogy. The development of EAI research was further conceptualized as follows: (a) technological foundation; (b) technological breakthrough; (c) intelligent application; and (d) symbiotic integration. Finally, three prospective directions for future EAI research were suggested.
C1 [Song, Pu; Wang, Xiang] Guizhou Educ Univ, Sch Educ Sci, Guiyang 550018, Guizhou, Peoples R China.
   [Song, Pu; Wang, Xiang] Univ Putra Malaysia, Fac Educ Studies, Seri Kembangan, Malaysia.
   [Wang, Xiang] Mahidol Univ, Ctr China & Globalizing Asia Studies, Salaya, Nakhon Pathom, Thailand.
C3 Guizhou Education University; Universiti Putra Malaysia; Mahidol
   University
RP Wang, X (corresponding author), Guizhou Educ Univ, Sch Educ Sci, Guiyang 550018, Guizhou, Peoples R China.; Wang, X (corresponding author), Univ Putra Malaysia, Fac Educ Studies, Seri Kembangan, Malaysia.; Wang, X (corresponding author), Mahidol Univ, Ctr China & Globalizing Asia Studies, Salaya, Nakhon Pathom, Thailand.
EM 348634925@qq.com
RI Zhang, Yuqing/KSM-6924-2024
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NR 60
TC 49
Z9 50
U1 18
U2 186
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1598-1037
EI 1876-407X
J9 ASIA PAC EDUC REV
JI Asia Pac. Educ. Rev.
PD SEP
PY 2020
VL 21
IS 3
BP 473
EP 486
DI 10.1007/s12564-020-09640-2
EA AUG 2020
PG 14
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA ND3YO
UT WOS:000557313400001
OA Bronze
DA 2024-09-05
ER
PT J
AU Shi, C
   Wang, HX
   Chen, BJ
   Liu, YH
   Zhou, ZG
AF Shi, Chen
   Wang, Haoxuan
   Chen, Binjie
   Liu, Yuhua
   Zhou, Zhiguang
TI Visual Analysis of Citation Context-Based Article Influence Ranking
SO IEEE ACCESS
LA English
DT Article
DE Influence ranking; visualization; visual analysis; citation network;
   word2vec
ID HETEROGENEOUS ACADEMIC NETWORKS; SCHOLARLY DATA; VISUALIZATION;
   EXPLORATION; ANALYTICS; PAPER
AB Article influence ranking is an effective way to reduce information redundancy and improve the efficiency of article retrieval. A large number of ranking models for network items have been employed for the ranking of article influence, such as PageRank and Spamming-resistant Expertise Analysis and Ranking. However, the effectiveness of article influence ranking based on the models of PageRank and SPEAR declines with the rapid growth of academic datasets, because of the increasing complexity of citation network. In order to take a rich set of contextual structures of citation context into consideration, we propose a visualization system VAIR for the citation context-based article influence ranking. Firstly, the word2vec model, a renowned technique in the field of natural language processing, is applied to transform articles into vectorized representations according to citation context. Then, a novel citation context-based article influence ranking model is designed according to the complex relationships quantified in a semantic vectorised space. Several visual designs are implemented, allowing users to perceive and compare the ranking results visually and intuitively. A set of user-friendly interactions are provided in the visualization framework, enabling users to explore the desirable article influence and obtain deep insights into the ranking model. Moreover, a series of case studies and comparison experiments are carried out based on real-world datasets, which further demonstrate the effectiveness of our algorithm for article influence ranking.
C1 [Shi, Chen; Wang, Haoxuan; Chen, Binjie; Liu, Yuhua; Zhou, Zhiguang] Zhejiang Univ Finance & Econ, Informat Sch, Hangzhou 310018, Zhejiang, Peoples R China.
   [Zhou, Zhiguang] Zhejiang Univ, State Key Lab CAD&CG, Hangzhou 310058, Zhejiang, Peoples R China.
C3 Zhejiang University of Finance & Economics; Zhejiang University
RP Zhou, ZG (corresponding author), Zhejiang Univ Finance & Econ, Informat Sch, Hangzhou 310018, Zhejiang, Peoples R China.; Zhou, ZG (corresponding author), Zhejiang Univ, State Key Lab CAD&CG, Hangzhou 310058, Zhejiang, Peoples R China.
EM zhgzhou1983@163.com
FU National Natural Science Foundation of China [61872314, 61802339];
   Humanities and Social Sciences Foundation of Ministry of Education in
   China [18YJC910017]; Natural Science Foundation of Zhejiang Province
   [LY18F020024]; Major Humanities and Social Sciences Research Projects in
   Colleges of Zhejiang Province [2018QN021]; Open Project Program of the
   State Key Laboratory of CADAMP;CG of Zhejiang University [A1806]; First
   Class Discipline of Zhejiang-A (Zhejiang University of Finance and
   Economics-Statistics)
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61872314 and Grant 61802339, in part by
   the Humanities and Social Sciences Foundation of Ministry of Education
   in China under Grant 18YJC910017, in part by the Natural Science
   Foundation of Zhejiang Province under Grant LY18F020024, in part by the
   Major Humanities and Social Sciences Research Projects in Colleges of
   Zhejiang Province under Grant 2018QN021, in part by the Open Project
   Program of the State Key Laboratory of CAD& CG of Zhejiang University
   under Grant A1806, and in part by the First Class Discipline of
   Zhejiang-A (Zhejiang University of Finance and Economics-Statistics).
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NR 47
TC 3
Z9 4
U1 1
U2 15
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2019
VL 7
BP 113853
EP 113866
DI 10.1109/ACCESS.2019.2932051
PG 14
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering; Telecommunications
GA IT6YK
UT WOS:000483022100039
OA gold
DA 2024-09-05
ER
PT J
AU Kyebambe, MN
   Cheng, G
   Huang, YQ
   He, CH
   Zhang, ZY
AF Kyebambe, Moses Ntanda
   Cheng, Ge
   Huang, Yunqing
   He, Chunhui
   Zhang, Zhenyu
TI Forecasting emerging technologies: A supervised learning approach
   through patent analysis
SO TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
LA English
DT Article
DE Technology forecasting; Industrial technology roadmap; R & D planning;
   Patent analysis; Citation analysis
ID SCIENCE-AND-TECHNOLOGY; CITATION; KNOWLEDGE; NETWORKS; CLUSTERS; TOOL
AB Both private and public enterprises have great interest in prior knowledge of emerging technologies to enable them make strategic investments. Technology forecasting offers a relevant opportunity in this direction and is currently a hot upcoming area of research. However, accurate forecasting of emerging technologies is still problematic mainly due to absence labeled historical data to use in training of learners. Previous studies have approached the technological forecasting problem through unsupervised learning methods and, as such, are missing out on potential benefits of supervised learning approaches such as full automation. In this study, we propose a novel algorithm to automatically label data and then use the labeled data to train learners to forecast emerging technologies. As a case study, we used patent citation data provided by the United States Patent and Trademark Office to test and evaluate the proposed algorithm. The algorithm uses advanced patent citation techniques to derive useful predictors from patent citation data with a result of forecasting new technologies at least a year before they emerge. Our evaluation reveals that our proposed algorithm can retrieve as high as 70% of emerging technologies in a given year with high precision.
C1 [Kyebambe, Moses Ntanda; Huang, Yunqing; He, Chunhui; Zhang, Zhenyu] Xiangtan Univ, Dept Math & Computat Sci, Xiangtan, Hunan, Peoples R China.
   [Cheng, Ge] Xiangtan Univ, Coll Informat Engn, Xiangtan, Hunan, Peoples R China.
C3 Xiangtan University; Xiangtan University
RP Cheng, G (corresponding author), Xiangtan Univ, Coll Informat Engn, Xiangtan, Hunan, Peoples R China.
EM mntanda@cis.mak.ac.ug; chengge@xtu.edu.cn; huangyq@xtu.edu.cn;
   zhenyuzhang@smail.xtu.edu.cn
RI zhang, zhenyu/HOA-8440-2023; zhang, jinlu/KEE-9374-2024; Huang,
   YQ/JOK-7580-2023; He, chunhui/GWR-3689-2022
OI He, chunhui/0000-0003-1505-1620
CR ALBERT MB, 1991, RES POLICY, V20, P251, DOI 10.1016/0048-7333(91)90055-U
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NR 41
TC 84
Z9 92
U1 7
U2 187
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0040-1625
EI 1873-5509
J9 TECHNOL FORECAST SOC
JI Technol. Forecast. Soc. Chang.
PD DEC
PY 2017
VL 125
BP 236
EP 244
DI 10.1016/j.techfore.2017.08.002
PG 9
WC Business; Regional & Urban Planning
WE Social Science Citation Index (SSCI)
SC Business & Economics; Public Administration
GA FL3FY
UT WOS:000414109500020
DA 2024-09-05
ER
PT J
AU Jia, QF
   Wang, XF
   Zhou, RY
   Ma, BX
   Fei, FQ
   Han, H
AF Jia, Qianfang
   Wang, Xiaofang
   Zhou, Rongyi
   Ma, Bingxiang
   Fei, Fangqin
   Han, Hui
TI Systematic bibliometric and visualized analysis of research hotspots and
   trends in artificial intelligence in autism spectrum disorder
SO FRONTIERS IN NEUROINFORMATICS
LA English
DT Article
DE artificial intelligence; autism spectrum disorder; data visualization;
   bibliometric; CiteSpace; VOSviewer
AB BackgroundArtificial intelligence (AI) has been the subject of studies in autism spectrum disorder (ASD) and may affect its identification, diagnosis, intervention, and other medical practices in the future. Although previous studies have used bibliometric techniques to analyze and investigate AI, there has been little research on the adoption of AI in ASD. This study aimed to explore the broad applications and research frontiers of AI used in ASD.MethodsCitation data were retrieved from the Web of Science Core Collection (WoSCC) database to assess the extent to which AI is used in ASD. CiteSpace.5.8. R3 and VOSviewer, two online tools for literature metrology analysis, were used to analyze the data.ResultsA total of 776 publications from 291 countries and regions were analyzed; of these, 256 publications were from the United States and 173 publications were from China, and England had the largest centrality of 0.33; Stanford University had the highest H-index of 17; and the largest cluster label of co-cited references was machine learning. In addition, keywords with a high number of occurrences in this field were autism spectrum disorder (295), children (255), classification (156) and diagnosis (77). The burst keywords from 2021 to 2023 were infants and feature selection, and from 2022 to 2023, the burst keyword was corpus callosum.ConclusionThis research provides a systematic analysis of the literature concerning AI used in ASD, presenting an overall demonstration in this field. In this area, the United States and China have the largest number of publications, England has the greatest influence, and Stanford University is the most influential. In addition, the research on AI used in ASD mostly focuses on classification and diagnosis, and "infants, feature selection, and corpus callosum are at the forefront, providing directions for future research. However, the use of AI technologies to identify ASD will require further research.
C1 [Jia, Qianfang; Wang, Xiaofang] Hebei Univ Chinese Med, Shijiazhuang, Peoples R China.
   [Zhou, Rongyi; Ma, Bingxiang] Henan Univ Chinese Med, Affiliated Hosp 1, Childrens Brain Dis Diag Treatment & Rehabil Ctr, Zhengzhou, Peoples R China.
   [Zhou, Rongyi; Ma, Bingxiang] Henan Univ Chinese Med, Sch Pediat Med, Zhengzhou, Peoples R China.
   [Fei, Fangqin; Han, Hui] Huzhou Univ, Peoples Hosp Huzhou 1, Dept Nursing, Huzhou, Peoples R China.
C3 Hebei University of Chinese Medicine; Henan University of Traditional
   Chinese Medicine; Henan University of Traditional Chinese Medicine;
   Huzhou University
RP Ma, BX (corresponding author), Henan Univ Chinese Med, Affiliated Hosp 1, Childrens Brain Dis Diag Treatment & Rehabil Ctr, Zhengzhou, Peoples R China.; Ma, BX (corresponding author), Henan Univ Chinese Med, Sch Pediat Med, Zhengzhou, Peoples R China.; Fei, FQ; Han, H (corresponding author), Huzhou Univ, Peoples Hosp Huzhou 1, Dept Nursing, Huzhou, Peoples R China.
EM mbx1963@126.com; feifangqin@139.com; hanhui30@126.com
RI wang, xiaofang/HRD-7918-2023
OI wang, xiaofang/0000-0002-2457-3474
FU The 2023 Special Project for Scientific Research on the Creation of
   "Double-First-Class"Chinese Medicine in Henan Province
   [HSRP-DFCTCM-2023-3-06]; Construction Project of Chinese Medicine
   Discipline of Specialized Backbone Disciplines in Henan Province
   [STG-ZYX03-202129]; Construction Project of Chinese Medicine Discipline
   of Characteristic Disciplines in Henan Province [STG-ZYXKY-2020023]
FX This study was supported by the 2023 Special Project for Scientific
   Research on the Creation of "Double-First-Class"Chinese Medicine in
   Henan Province (HSRP-DFCTCM-2023-3-06), Construction Project of Chinese
   Medicine Discipline of Specialized Backbone Disciplines in Henan
   Province (STG-ZYX03-202129) and Construction Project of Chinese Medicine
   Discipline of Characteristic Disciplines in Henan Province
   (STG-ZYXKY-2020023).
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NR 48
TC 0
Z9 0
U1 13
U2 24
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 1662-5196
J9 FRONT NEUROINFORM
JI Front. Neuroinformatics
PD DEC 6
PY 2023
VL 17
AR 1310400
DI 10.3389/fninf.2023.1310400
PG 11
WC Mathematical & Computational Biology; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology; Neurosciences & Neurology
GA CY4G9
UT WOS:001128774000001
PM 38125308
OA Green Published, gold
DA 2024-09-05
ER
PT J
AU Wang, N
   Li, SQ
   Wang, CH
   Zhao, L
AF Wang, Nan
   Li, Suqi
   Wang, Chenhui
   Zhao, Li
TI Current Status and Emerging Trends of Generative Artificial Intelligence
   Technology: A Bibliometric Analysis
SO JOURNAL OF INTERNET TECHNOLOGY
LA English
DT Article
DE Generative artificial intelligence; ChatGPT; Generative adversarial
   network; Biblioshiny; Web of Science
AB With the widespread application of ChatGPT (Chat Generative Pre -trained Transformer), its superordinate concept, generative artificial intelligence (Gen AI), has received increasing attention from researchers. The current study attempted to explore the current status and emerging trends of Gen AI technology research by visualizing the relevant published articles using the Biblioshiny tool. A total of 1,902 academic articles in the Web of Science (WOS) database published between 2014-2022 were analyzed. Annual publications, most productive journals and countries, co-authors, co-occurring keywords, document co -citations, and emerging trends of Gen AI research were analyzed. The following are the main findings of the study: the current status of Gen AI research is reflected in the following aspects: (1) the volume of documents produced is increasing year on year; (2) the publication of practical applications such as ChatGPT has brought a high level of interest to related research exploring the application of Gen AI in various research fields, such as education, medicine, drug discovery, and so on; and (3) two influential co -citation clusters have been formed. The emerging trends of the application of Gen AI research are also summarized as follows: (1) Gen AI has powerful technical advantages; (2) the application of Gen AI has great potential in the field of medicine, education and so on for the future; and (3) the updating and development of relevant technologies will always be the focus of Gen AI research.
C1 [Wang, Nan; Li, Suqi; Wang, Chenhui; Zhao, Li] Nanjing Normal Univ, Sch Educ Sci, Nanjing, Peoples R China.
C3 Nanjing Normal University
RP Zhao, L (corresponding author), Nanjing Normal Univ, Sch Educ Sci, Nanjing, Peoples R China.
EM 220612032@njnu.edu.cn; 210602145@njnu.edu.cn; 210602154@njnu.edu.cn;
   li.zhao@njnu.edu.cn
FU National Social Science Fund of China "From Representation to
   Generation: A Study of the Symbolic Logic of Online Educational
   Resources" [BCA200093]
FX This research was supported by National Social Science Fund of China
   "From Representation to Generation: A Study of the Symbolic Logic of
   Online Educational Resources" (No. BCA200093) .
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NR 29
TC 0
Z9 0
U1 19
U2 19
PU LIBRARY & INFORMATION CENTER, NAT DONG HWA UNIV
PI HUALIEN
PA LIBRARY & INFORMATION CENTER, NAT DONG HWA UNIV, HUALIEN, 00000, TAIWAN
SN 1607-9264
EI 2079-4029
J9 J INTERNET TECHNOL
JI J. Internet Technol.
PD MAY
PY 2024
VL 25
IS 3
BP 477
EP 485
DI 10.53106/160792642024052503013
PG 9
WC Computer Science, Information Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA SY2U3
UT WOS:001237953600013
DA 2024-09-05
ER
PT C
AU James, M
   Palakkat, V
   Jones, GJF
AF James, Mathew
   Palakkat, Vikas
   Jones, Gareth J. F.
GP IEEE
TI Identifying Influential Citations in Scientific Papers
SO 2023 31ST IRISH CONFERENCE ON ARTIFICIAL INTELLIGENCE AND COGNITIVE
   SCIENCE, AICS
LA English
DT Proceedings Paper
CT 31st Irish Conference on Artificial Intelligence and Cognitive Science
   (AICS)
CY DEC 07-08, 2023
CL Letterkenny, IRELAND
DE citation influence; citation classification; sentiment analysis;
   citation analysis
AB The research described in academic papers builds on related existing work. This previous work can be considered as influential if it is used or extended in the current study or non-influential if it is only cited as background material. This study aims to identify influential citations, in particular examining the impact of sentiment associated with citations on citation influence. Our work leverages a set of statistical, contextual and metadata-based features to provide an assessment of the influence of cited works on the current work. Our findings show that sentiment analysis significantly enhances citation classification.
C1 [James, Mathew; Palakkat, Vikas; Jones, Gareth J. F.] Dublin City Univ, Sch Comp, Dublin, Ireland.
   [Jones, Gareth J. F.] Dublin City Univ, ADAPT Ctr, Dublin, Ireland.
C3 Dublin City University; Dublin City University
RP James, M (corresponding author), Dublin City Univ, Sch Comp, Dublin, Ireland.
EM mathew.james2@mail.dcu.ie; vikas.palakkat2@mail.dcu.ie;
   gareth.jones@dcu.ie
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NR 19
TC 0
Z9 0
U1 1
U2 1
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 979-8-3503-6021-9
PY 2023
DI 10.1109/AICS60730.2023.10470500
PG 4
WC Behavioral Sciences; Computer Science, Artificial Intelligence
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Behavioral Sciences; Computer Science
GA BW7SN
UT WOS:001195949100005
DA 2024-09-05
ER
PT J
AU Chen, YJ
   Zheng, N
AF Chen, Yanjie
   Zheng, Na
TI AI based research on exploration and innovation of development direction
   of piano performance teaching in university
SO JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
LA English
DT Article
DE AI; Piano teaching; new media
AB This paper investigates the cognition status of information piano education for teachers and students in a university, which mainly includes a summary of the piano teaching status in a university and make an analysis and summary of the investigation results. In addition, this paper puts forward the direction of the network information reform and construction for piano majors in Colleges and universities, mainly including three aspects, that is, taking piano "micro class" teaching to arm traditional classroom teaching, using the new media to build a networked piano learning environment, and building the piano teaching "MOOC" platform.
C1 [Chen, Yanjie] West Anhui Univ, Sch Art, Luan, Anhui, Peoples R China.
   [Zheng, Na] Shanghai Donghai Vocat & Tech Coll, Sch Educ, Shanghai, Peoples R China.
C3 West Anhui University
RP Chen, YJ (corresponding author), West Anhui Univ, Sch Art, Luan, Anhui, Peoples R China.
EM chen.yanjie@aol.com
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NR 16
TC 7
Z9 7
U1 5
U2 19
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1064-1246
EI 1875-8967
J9 J INTELL FUZZY SYST
JI J. Intell. Fuzzy Syst.
PY 2021
VL 40
IS 2
BP 3681
EP 3687
DI 10.3233/JIFS-189402
PG 7
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA QH1ZP
UT WOS:000618076700172
DA 2024-09-05
ER
PT J
AU GEAR, AE
   GILLESPI.JS
   ALLEN, JM
AF GEAR, AE
   GILLESPI.JS
   ALLEN, JM
TI APPLICATIONS OF DECISION TREES TO EVALUATION OF APPLIED RESEARCH
   PROJECTS
SO JOURNAL OF MANAGEMENT STUDIES
LA English
DT Article
C1 MANCHESTER BUSINESS SCH,MANCHESTER M15 6PB,ENGLAND.
C3 University of Manchester
CR [Anonymous], DECISION ANALYSIS
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NR 9
TC 2
Z9 2
U1 0
U2 2
PU BLACKWELL PUBL LTD
PI OXFORD
PA 108 COWLEY RD, OXFORD OX4 1JF, OXON, ENGLAND
SN 0022-2380
J9 J MANAGE STUD
JI J. Manage. Stud.
PY 1972
VL 9
IS 2
BP 172
EP 181
DI 10.1111/j.1467-6486.1972.tb00548.x
PG 10
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA M9990
UT WOS:A1972M999000004
DA 2024-09-05
ER
PT C
AU Liu, DW
   Liu, HM
   Tao, XC
   Lu, C
AF Liu Da-wei
   Liu Hong-mei
   Tao Xiao-chuang
   Lu Chen
BE Lee, G
TI Research on Performance Degradation Assessment for Hydraulic Servo
   System Based on Fault Observer and SOM Network
SO 2012 INTERNATIONAL CONFERENCE ON FUTURE INFORMATION TECHNOLOGY AND
   MANAGEMENT SCIENCE & ENGINEERING (FITMSE 2012)
SE Lecture Notes in Information Technology
LA English
DT Proceedings Paper
CT International Conference on Future Information Technology and Management
   Science and Engineering (FITMSE 2012)
CY APR 12-13, 2012
CL HONG KONG
DE fault observer; feature extraction; SOM; performance degradation
   assessment
AB Many researches on condition monitoring and fault diagnosis for hydraulic servo system have been carried on, but there are few researches on performance degradation assessment. As well as, there is no suitable method for performance degradation assessment of hydraulic servo system. So a novel method based on fault observer and SOM (Self-organizing Map) network is presented to assess the performance degradation of hydraulic servo system. The fault observer is adopted to generate residual error which can be used to assess the health state of hydraulic servo system. The feature of residual error is extracted in time domain, and then the feature vector is input into the SOM network to realize the performance degradation assessment. Finally, a simulation case is used to validate the effectiveness of the proposed method in accessing the performance degradation of hydraulic servo system.
C1 [Liu Da-wei; Liu Hong-mei; Tao Xiao-chuang; Lu Chen] Beihang Univ, Sch Reliabil & Syst Engn, Beijing 100191, Peoples R China.
C3 Beihang University
RP Liu, DW (corresponding author), Beihang Univ, Sch Reliabil & Syst Engn, Beijing 100191, Peoples R China.
EM zy1014114@dse.buaa.edu.cn; liuhongmei@buaa.edu.cn;
   taoxiaochuang1988@163.com; luchen@buaa.edu.cn
RI Lu, Chen/B-6310-2016; Liu, Dawei/AAD-3130-2019
FU National Nature Science Foundation of China [61074083, 50705005,
   51105019]; Technology Foundation Program of National Defense
   [Z132010B004]
FX This research is supported by the National Nature Science Foundation of
   China (Grant No.61074083, No.50705005 and No.51105019), as well as the
   Technology Foundation Program of National Defense (Grant
   No.Z132010B004).
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NR 6
TC 0
Z9 0
U1 0
U2 0
PU INFORMATION ENGINEERING RESEARCH INST, USA
PI NEWARK
PA 100 CONTINENTAL DR, NEWARK, DE 19713 USA
SN 2070-1918
BN 978-1-61275-012-5
J9 LECT NOTE INFORMTECH
PY 2012
VL 14
BP 89
EP 94
PG 6
WC Computer Science, Information Systems
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BG9MP
UT WOS:000393447400015
DA 2024-09-05
ER
PT J
AU Aytac, E
   Fombona-Pascual, A
   Lado, JJ
   Quismondo, EG
   Palma, J
   Khayet, M
AF Aytac, Ersin
   Fombona-Pascual, Alba
   Lado, Julio J.
   Quismondo, Enrique Garcia
   Palma, Jesus
   Khayet, Mohamed
TI Faradaic deionization technology: Insights from bibliometric, data
   mining and machine learning approaches
SO DESALINATION
LA English
DT Article
DE Biblioshiny; BIRCH clustering algorithm; Faradaic deionization; ISOMAP
   dimensionality reduction; SBERT; Text mining
ID MEMBRANE CAPACITIVE DEIONIZATION; ACTIVATED CARBON ELECTRODES;
   METAL-OXIDE COATINGS; LONG-TERM STABILITY; WASTE-WATER; DESALINATION
   PERFORMANCE; NICKEL HEXACYANOFERRATE; BRACKISH-WATER;
   ENERGY-CONSUMPTION; SELECTIVE REMOVAL
AB Faradaic deionization (FDI) is an emerging water treatment technology based on electrodes able to remove ionic species from water by charge transfer reactions. It is a young and promising technology that has attracted much attention due to its large capacity to store ions and the high selectivity of the faradaic electrode materials. This study reviews published papers on FDI from different angles: data mining, bibliometric and machine learning. Metrics such as annual growth rate, most important journals, relevant authors, collaborations maps, sentiment and subjectivity analysis, similarity and clustering analysis were performed. The results indicated that the strong interest in FDI really started in 2016, China is the most active country in FDI, and Desalination is the most important journal publishing FDI articles. The word cloud method showed that the most preferred adopted words are deionization, capacitive, electrode, material. Sentiment analysis results indicated that most of the researchers are optimistic about FDI technology. The title similarity method revealed that FDI researchers were successful in proposing unique and appropriate titles. The clustering approach stressed that FDI literature is concentrated on electrode material production, desalination application, lithium recovery and comparison with CDI.
C1 [Aytac, Ersin] Zonguldak Bulent Ecevit Univ, Dept Environm Engn, TR-67100 Zonguldak, Turkiye.
   [Aytac, Ersin; Khayet, Mohamed] Univ Complutense Madrid, Fac Phys, Dept Struct Matter Thermal Phys & Elect, Avda Complutense s-n, Madrid 28040, Spain.
   [Aytac, Ersin; Fombona-Pascual, Alba; Lado, Julio J.; Quismondo, Enrique Garcia; Palma, Jesus] IMDEA Energy Inst, Electrochem Proc Unit, Ave Ramon De La Sagra 3, Mostoles 28935, Madrid, Spain.
   [Khayet, Mohamed] IMDEA Water Inst, Madrid Inst Adv Studies Water, Calle Punto Net N 4, Madrid 28805, Spain.
C3 Zonguldak Bulent Ecevit University; Complutense University of Madrid;
   IMDEA Energy; IMDEA Water Institute
RP Khayet, M (corresponding author), Univ Complutense Madrid, Fac Phys, Dept Struct Matter Thermal Phys & Elect, Avda Complutense s-n, Madrid 28040, Spain.
EM khayetm@fis.ucm.es
RI Khayet, Mohamed/L-3814-2014; Palma, Jesus/G-6914-2015
OI Palma, Jesus/0000-0003-1022-0165; Aytac, Ersin/0000-0002-7124-4438
FU Scientific and Technological Research Council of Turkey (TUBITAK) at the
   University Complutense of Madrid (UCM) [2020-T1/AMB-19799]; Talento's
   program of the Community of Madrid;  [1059B191900618]
FX The basis of the graphical abstract was created with OpenAI's
   text-to-image-generation architecture, DALL-E 2. Upon generating the
   draft image, the Authors edited the image, and they took the ultimate
   responsibility for the content. The Authors would like to acknowledge
   OpenAI for their contribution to this article. Dr. Ersin Aytac would
   like to express his acknowledgment for the postdoctoral grant received
   from the Scientific and Technological Research Council of Turkey
   (TUBITAK) at the University Complutense of Madrid (UCM) with grant
   number 1059B191900618. Alba Fombona-Pascual and Julio J. Lado would like
   to thank the Talento's program of the Community of Madrid, which
   involves the project SELECTVALUE (2020-T1/AMB-19799).
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NR 328
TC 9
Z9 9
U1 22
U2 61
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0011-9164
EI 1873-4464
J9 DESALINATION
JI Desalination
PD OCT 1
PY 2023
VL 563
AR 116715
DI 10.1016/j.desal.2023.116715
EA JUN 2023
PG 24
WC Engineering, Chemical; Water Resources
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Water Resources
GA K2ZW5
UT WOS:001015182800001
OA hybrid
DA 2024-09-05
ER
PT J
AU Vieira, C
   Ortega-Alvarez, JD
   Magana, AJ
   Boutin, M
AF Vieira, Camilo
   Ortega-Alvarez, Juan D.
   Magana, Alejandra J.
   Boutin, Mireille
TI Beyond analytics: Using computer-aided methods in educational research
   to extend qualitative data analysis
SO COMPUTER APPLICATIONS IN ENGINEERING EDUCATION
LA English
DT Article; Early Access
DE assessment triangle; clustering algorithms; computer-aided methods; data
   analysis; education research; machine learning; qualitative methods;
   visualization
ID COMPUTATIONAL THINKING; ASSESSMENT TRIANGLE; SCIENCE; CONCEPTIONS;
   DEVELOP
AB This study proposes and demonstrates how computer-aided methods can be used to extend qualitative data analysis by quantifying qualitative data, and then through exploration, categorization, grouping, and validation. Computer-aided approaches to inquiry have gained important ground in educational research, mostly through data analytics and large data set processing. We argue that qualitative data analysis methods can also be supported and extended by computer-aided methods. In particular, we posit that computing capacities rationally applied can expand the innate human ability to recognize patterns and group qualitative information based on similarities. We propose a principled approach to using machine learning in qualitative education research based on the three interrelated elements of the assessment triangle: cognition, observation, and interpretation. Through the lens of the assessment triangle, the study presents three examples of qualitative studies in engineering education that have used computer-aided methods for visualization and grouping. The first study focuses on characterizing students' written explanations of programming code, using tile plots and hierarchical clustering with binary distances to identify the different approaches that students used to self-explain. The second study looks into students' modeling and simulation process and elicits the types of knowledge that they used in each step through a think-aloud protocol. For this purpose, we used a bubble plot and a k-means clustering algorithm. The third and final study explores engineering faculty's conceptions of teaching, using data from semi-structured interviews. We grouped these conceptions based on coding similarities, using Jaccard's similarity coefficient, and visualized them using a treemap. We conclude this manuscript by discussing some implications for engineering education qualitative research.
C1 [Vieira, Camilo] Univ Norte, Dept Educ, Barranquilla 080020, Colombia.
   [Ortega-Alvarez, Juan D.] Virginia Tech, Dept Engn Educ, Blacksburg, VA USA.
   [Ortega-Alvarez, Juan D.] Univ EAFIT, Escuela Ciencias Aplicadas & Ingn, Medellin, Colombia.
   [Magana, Alejandra J.] Purdue Univ, Dept Comp & Informat Technol, W Lafayette, IN USA.
   [Magana, Alejandra J.] Purdue Univ, Sch Engn Educ, W Lafayette, IN USA.
   [Boutin, Mireille] Eindhoven Univ Technol, Dept Math & Comp Sci, Eindhoven, Netherlands.
   [Boutin, Mireille] Purdue Univ, Dept Math, W Lafayette, IN USA.
C3 Universidad del Norte Colombia; Virginia Polytechnic Institute & State
   University; Universidad EAFIT; Purdue University System; Purdue
   University; Purdue University System; Purdue University; Eindhoven
   University of Technology; Purdue University System; Purdue University
RP Vieira, C (corresponding author), Univ Norte, Dept Educ, Barranquilla 080020, Colombia.
EM cvieira@uninorte.edu.co
RI Ortega-Alvarez, Juan David/AAN-1404-2020
OI Ortega-Alvarez, Juan David/0000-0001-6110-0791; Magana,
   Alejandra/0000-0001-6117-7502; Boutin, Mireille/0000-0002-0837-6577
FU National Science Foundation [DGE-2219271, EEC-1826099]; U.S. National
   Science Foundation
FX This research was supported in part by the U.S. National Science
   Foundation under awards No. DGE-2219271 and EEC-1826099. Any opinions,
   findings, and conclusions or recommendations expressed in this material
   are those of the author(s) and do not necessarily reflect the views of
   the National Science Foundation.
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NR 44
TC 0
Z9 0
U1 3
U2 3
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1061-3773
EI 1099-0542
J9 COMPUT APPL ENG EDUC
JI Comput. Appl. Eng. Educ.
PD 2024 MAY 23
PY 2024
DI 10.1002/cae.22749
EA MAY 2024
PG 15
WC Computer Science, Interdisciplinary Applications; Education, Scientific
   Disciplines; Engineering, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Education & Educational Research; Engineering
GA RR4W7
UT WOS:001229386200001
DA 2024-09-05
ER
PT C
AU Fan, G
   Peng, W
   Sun, S
   Li, PW
AF Fan, Ge
   Peng, Wei
   Sun, Shan
   Li, Peiwen
BE Yingying, S
   Guiran, C
   Zhen, L
TI A Research on National Sustainability Evaluation Model
SO PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON LOGISTICS, ENGINEERING,
   MANAGEMENT AND COMPUTER SCIENCE (LEMCS 2015)
SE Advances in Intelligent Systems Research
LA English
DT Proceedings Paper
CT International Conference on Logistics Engineering, Management and
   Computer Science (LEMCS)
CY JUL 29-31, 2015
CL Shenyang, PEOPLES R CHINA
DE mechine learning; k-means; logistic regression; neural network;
   sustainability
AB By applying an objective method to evaluate its sustainability, a certain country could make proper plans and policies for further development. However, subjective and complicated problems have been found in the current methods and index systems. Therefore, researchers set up a composite model that it can evaluate the sustainability for a certain country in a more objective way, in comparison with other methods. Researchers propose a method for evaluating sustainability for a certain country, which solves problems concerning strong subjectivity and complicity in current models. Researchers choose 17 representative core indicators based on the index system of UNCSD and divide them into two catalogues -- Nature Indicators and Operate Indicators. First, Means Clustering Algorithm of k-means (an unsupervised learning method) divides the data into three categories. Then, researchers obtain those indicators, using regression analysis, and build an objective evaluation model. When researchers make policies for a country to allocate resources reasonably, researchers maximize the improvement of the ability of sustainable development based on "along the gradient direction ascend the fastest". In this paper, researchers conduct simulations experiment, using data of 96 countries in the World Bank. After analyzing the deviations and sensitivity of the model, the theoretical results are verified experimentally.
C1 [Fan, Ge; Peng, Wei] Sichuan Agr Univ, Dept Business, Chengdu, Peoples R China.
   [Sun, Shan] Hunan Univ Commerce, Dept Econ & Trade, Changsha, Peoples R China.
   [Li, Peiwen] Donghua Univ, Dept Mech Engn, Shanghai, Peoples R China.
C3 Sichuan Agricultural University; Hunan University of Technology &
   Business; Donghua University
RP Peng, W (corresponding author), Sichuan Agr Univ, Dept Business, Chengdu, Peoples R China.
EM fange1122@gmail.com; pw7@163.com; sunshan920813@gmail.com;
   lipeiwen@me.com
RI Fan, Ge/GXM-8675-2022
OI Fan, Ge/0000-0001-5653-1626
CR [Anonymous], 2012, Living Planet Report 2012 - On the Road to Rio+20
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NR 17
TC 0
Z9 0
U1 0
U2 11
PU ATLANTIS PRESS
PI PARIS
PA 29 AVENUE LAVMIERE, PARIS, 75019, FRANCE
SN 1951-6851
BN 978-94-6252-102-5
J9 ADV INTEL SYS RES
PY 2015
VL 117
BP 524
EP 529
PG 6
WC Computer Science, Artificial Intelligence; Management; Operations
   Research & Management Science
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Business & Economics; Operations Research & Management
   Science
GA BE5OK
UT WOS:000373107000100
DA 2024-09-05
ER
PT C
AU Yang, W
   Lyu, S
   Hou, M
   Huang, C
AF Yang, W.
   Lyu, S.
   Hou, M.
   Huang, C.
BE Weng, CH
   He, Y
TI Research on the Transmission Performance of Multi-layer Simulated Mural
   Surface by Imaging Spectrum
SO INTERNATIONAL CONFERENCE ON ENVIRONMENTAL REMOTE SENSING AND BIG DATA
   (ERSBD 2021)
SE Proceedings of SPIE
LA English
DT Proceedings Paper
CT International Conference on Environmental Remote Sensing and Big Data
   (ERSBD)
CY OCT 29-31, 2021
CL Wuhan, PEOPLES R CHINA
DE Hyperspectral Imaging; Information Enhancement; Normalized Difference
   Index; Principal Component Analysis
AB Some ancient murals were found to be repainted on the surface of the original murals to form multi-layer murals. The patterns on the original layer are of great significance for studying the social and cultural behavior in that time. The hyperspectral imaging covers the visible and near-infrared bands, which has advantages for the information extraction of multi-layer murals. Therefore, a method to study the transmission performance of hyperspectral imaging on multi-layer simulated mural samples is proposed. By making mural samples, the mineral pigment painted on the surface is covered with 0-11 different layers of lime water. Then the samples were collected with hyperspectral images, and the method of principal component analysis and band calculation were used to analyze the enhancement effect of the mural patterns covered by different layers of lime water. The results show that hyperspectral imaging has certain transmittance to the interior of the mural and can enhance internal pigment information. The research results can support the information extraction of multi-layer murals to some extent.
C1 [Yang, W.; Lyu, S.; Hou, M.] Beijing Univ Civil Engn & Architecture, Sch Geomat & Urban Spatial Informat, 15 Yongyuan Rd, Beijing 102616, Peoples R China.
   [Huang, C.] Tsinghua Unigrp, 7 Zhichun Rd, Beijing, Peoples R China.
C3 Beijing University of Civil Engineering & Architecture
RP Hou, M (corresponding author), Beijing Univ Civil Engn & Architecture, Sch Geomat & Urban Spatial Informat, 15 Yongyuan Rd, Beijing 102616, Peoples R China.
EM 2108570020095@stu.bucea.edu.cn; lvshuqiang@bucea.edu.cn;
   houmiaole@bucea.edu.cn; 18510083275@163.com
CR [Anonymous], 2011, HYPERSPECTRAL REMOTE
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NR 12
TC 0
Z9 0
U1 5
U2 12
PU SPIE-INT SOC OPTICAL ENGINEERING
PI BELLINGHAM
PA 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA
SN 0277-786X
EI 1996-756X
BN 978-1-5106-5135-7; 978-1-5106-5134-0
J9 PROC SPIE
PY 2021
VL 12129
AR 1212906
DI 10.1117/12.2625588
PG 6
WC Engineering, Electrical & Electronic; Optics
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Engineering; Optics
GA BT1FE
UT WOS:000797324200005
DA 2024-09-05
ER
PT J
AU Roessger, KM
   Greenleaf, A
   Hoggan, C
AF Roessger, Kevin M.
   Greenleaf, Arie
   Hoggan, Chad
TI Using data collection apps and single-case designs to research
   transformative learning in adults
SO JOURNAL OF ADULT AND CONTINUING EDUCATION
LA English
DT Article
DE Transformative learning research; data collection apps; single-case
   design; causal inference; outcome research
ID INTERPERSONAL SENSITIVITY; INVENTORY; SCALE; SELF
AB To overcome situational hurdles when researching transformative learning in adults, we outline a research approach using single-case research designs and smartphone data collection apps. This approach allows researchers to better understand learners' current lived experiences and determine the effects of transformative learning interventions on demonstrable outcomes. We first discuss data collection apps and their features. We then describe how they can be integrated into single-case research designs to make causal inferences about a learning intervention's effects when limited by researcher access and learner retrospective reporting. Design controls for internal validity threats and visual and statistical data analysis are then discussed. Throughout, we highlight applications to transformative learning and conclude by discussing the approach's potential limitations.
C1 [Roessger, Kevin M.] Univ Arkansas, Fayetteville, AR 72701 USA.
   [Greenleaf, Arie] Seattle Univ, Seattle, WA 98122 USA.
   [Hoggan, Chad] North Carolina State Univ, Raleigh, NC USA.
C3 University of Arkansas System; University of Arkansas Fayetteville;
   Seattle University; North Carolina State University
RP Roessger, KM (corresponding author), Univ Arkansas, Coll Educ & Hlth Profess, Grad 102, Fayetteville, AR 72701 USA.
EM kmroessg@uark.edu
RI Greenleaf, Arie/HKE-8316-2023; Hoggan, Chad/AAL-7109-2021
OI Hoggan, Chad/0000-0001-7759-591X; Roessger, Kevin/0000-0001-6600-4731
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NR 53
TC 5
Z9 8
U1 0
U2 0
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 1477-9714
EI 1479-7194
J9 J ADULT CONTIN EDUC
JI J. Adult Contin. Educ.
PD NOV
PY 2017
VL 23
IS 2
BP 206
EP 225
DI 10.1177/1477971417732070
PG 20
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA GW9SK
UT WOS:000447339700006
DA 2024-09-05
ER
PT J
AU Finlay, JM
   Davila, H
   Whipple, MO
   McCreedy, EM
   Jutkowitz, E
   Jensen, A
   Kane, RA
AF Finlay, Jessica M.
   Davila, Heather
   Whipple, Mary O.
   McCreedy, Ellen M.
   Jutkowitz, Eric
   Jensen, Anne
   Kane, Rosalie A.
TI What we learned through asking about evidence: A model for
   interdisciplinary student engagement
SO GERONTOLOGY & GERIATRICS EDUCATION
LA English
DT Article
DE Graduate training; interdisciplinary collaboration; active learning;
   gerontology students; professional development; research in aging
ID PERFORMANCE; PROGRAM; CARE
AB Traditional university learning modalities of lectures and examinations do not prepare students fully for the evolving and complex world of gerontology and geriatrics. Students involved in more active, self-directed learning can develop a wider breadth of knowledge and perform better on practical examinations. This article describes the Evidence in Aging (EIA) study as a model of active learning with the aim of preparing students to be effective interdisciplinary researchers, educators, and leaders in aging. We focus particularly on the experiences and reflections of graduate students who collaborated with faculty mentors on study design, data collection, and analysis. Students acquired new methodological skills, gained exposure to diverse disciplines, built interdisciplinary understanding, and cultivated professional development. The EIA study is a model for innovative student engagement and collaboration, interactive learning, and critical scholarly development. Lessons learned can be applied to a range of collaborative research projects in gerontology and geriatrics education.
C1 [Finlay, Jessica M.] Univ Minnesota, Dept Geog Environm & Soc, 414 Social Sci,267 19th Ave S, Minneapolis, MN 55455 USA.
   [Davila, Heather] Univ Minnesota, Coll Educ & Human Dev, Evaluat Studies, Minneapolis, MN USA.
   [Whipple, Mary O.] Univ Minnesota, Sch Nursing, Minneapolis, MN 55455 USA.
   [McCreedy, Ellen M.] Brown Univ, Sch Publ Hlth, Ctr Gerontol & Healthcare Res, Providence, RI 02912 USA.
   [Jutkowitz, Eric] Brown Univ, Sch Publ Hlth, Dept Hlth Serv Policy & Practice, Providence, RI 02912 USA.
   [Jensen, Anne] Univ Minnesota, Carlson Sch Management, Sch Publ Hlth, Minneapolis, MN 55455 USA.
   [Jensen, Anne] Univ Minnesota, Carlson Sch Management, Minneapolis, MN 55455 USA.
   [Kane, Rosalie A.] Univ Minnesota, Sch Publ Hlth, Hlth Policy & Management, Minneapolis, MN USA.
C3 University of Minnesota System; University of Minnesota Twin Cities;
   University of Minnesota System; University of Minnesota Twin Cities;
   University of Minnesota System; University of Minnesota Twin Cities;
   Brown University; Brown University; University of Minnesota System;
   University of Minnesota Twin Cities; University of Minnesota System;
   University of Minnesota Twin Cities; University of Minnesota System;
   University of Minnesota Twin Cities
RP Finlay, JM (corresponding author), Univ Minnesota, Dept Geog Environm & Soc, 414 Social Sci,267 19th Ave S, Minneapolis, MN 55455 USA.
EM finla039@umn.edu
RI Whipple, Mary/AAK-3954-2020
OI Whipple, Mary/0000-0001-7073-3224; Finlay, Jessica/0000-0003-3427-8003;
   Davila, Heather/0000-0003-1832-7901
FU University of Minnesota Graduate School
FX The authors are indebted to Robert L. Kane, MD, who was a tireless
   advocate for critical learning and staunch supporter of emerging
   gerontologists. We are also grateful to the University of Minnesota
   Graduate School for providing startup funds to create the Aging Studies
   Interdisciplinary Graduate Group.
CR [Anonymous], 2013, National Journal of Physiology, Pharmacy and Pharmacology
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NR 40
TC 4
Z9 6
U1 2
U2 17
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0270-1960
EI 1545-3847
J9 GERONTOL GERIATR EDU
JI Gerontol. Geriatr. Educ.
PY 2019
VL 40
IS 1
SI SI
BP 90
EP 104
DI 10.1080/02701960.2018.1428578
PG 15
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA IA8WW
UT WOS:000469840300008
PM 29364792
OA Green Accepted
DA 2024-09-05
ER
PT C
AU Viet, NT
   Kravets, AG
AF Viet, Nguyen Thanh
   Kravets, Alla G.
BE Dwivedi, RK
   Saxena, AK
   Parygin, D
   Ather, D
   Yadav, V
TI Analyzing Recent Research Trends of Computer Science from Academic
   Open-access Digital Library
SO PROCEEDINGS OF THE 2019 8TH INTERNATIONAL CONFERENCE ON SYSTEM MODELING
   & ADVANCEMENT IN RESEARCH TRENDS (SMART-2019)
LA English
DT Proceedings Paper
CT 8th International Conference on System Modeling and Advancement in
   Research Trends (SMART)
CY NOV 22-23, 2019
CL Moradabad, INDIA
DE Digital Library; Data Mining; Data Crawling; Trend Prediction; Topic
   Modeling
AB The wider utilization of information and web technologies, database technologies, development of internet infrastructure has led to the evolution of digital libraries. In particular, digital libraries serve enormous number of various users and play an essential role as repositories and source of investigation and intelligence. With the emergence of IoT (Internet of Things) and different academic open-access digital libraries, the automatic extraction of advantageous knowledge from text data has been more and more a significant subject of research in data mining. In this paper, we perform web scraping system, statistical analyses from the arXiv repository and discuss the results of analyzing recent research trends in this academic open-access digital library.
C1 [Viet, Nguyen Thanh; Kravets, Alla G.] Volgograd State Tech Univ, Volgograd, Russia.
C3 Volgograd State Technical University
RP Viet, NT (corresponding author), Volgograd State Tech Univ, Volgograd, Russia.
EM vietqn1987@gmail.com; agk@gde.ru
RI Nguyễn, Thành Việt/HOH-4310-2023; Kravets, Alla/HGE-9499-2022; Kravets,
   Alla/JAC-9586-2023
OI Kravets, Alla/0000-0003-1675-8652; Nguyen, Thanh
   Viet/0000-0003-3805-5958
FU Russian Fund of Basic Research [19-07-01200]
FX This work was supported by Russian Fund of Basic Research (grant No.
   19-07-01200).
CR Chauhan N, 2018, PROCEEDINGS OF THE 2018 INTERNATIONAL CONFERENCE ON SYSTEM MODELING & ADVANCEMENT IN RESEARCH TRENDS (SMART), P44, DOI 10.1109/SYSMART.2018.8746945
   Clement Colin B., 2019, CORR
   Kravets A.G., 2013, WORLD APPL SCI J, V24, P98
   Kravets A.G., 2018, 2018 9 INT C INF INT
   Kravets A, 2017, COMM COM INF SC, V754, P37, DOI 10.1007/978-3-319-65551-2_3
   Manocha P, 2018, PROCEEDINGS OF THE 2018 INTERNATIONAL CONFERENCE ON SYSTEM MODELING & ADVANCEMENT IN RESEARCH TRENDS (SMART), P280, DOI 10.1109/SYSMART.2018.8746928
   Zhang Mei, 2011, J COMPUTERS, V6
NR 7
TC 1
Z9 1
U1 1
U2 4
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-7281-3245-7
PY 2019
BP 31
EP 36
DI 10.1109/smart46866.2019.9117215
PG 6
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Engineering
GA BQ6EE
UT WOS:000610825300008
DA 2024-09-05
ER
PT J
AU Lu, YY
   Chen, ZZ
   Chen, R
   Shi, YW
   Zheng, QY
AF Lu, Yuanyuan
   Chen, Zengzhao
   Chen, Rong
   Shi, Yawen
   Zheng, Qiuyu
TI Research on the Application Framework of Intelligent Technologies to
   Promote Teachers' Classroom Teaching Behavior Evaluation
SO FRONTIERS OF EDUCATION IN CHINA
LA English
DT Article
DE artificial intelligence (AI); teachers' classroom teaching behavior
   evaluation; teaching behavior recognition; speech emotion recognition
AB With the advantages of real-time analysis and visual evaluation results, intelligent technology-enabled teaching behavior evaluation has gradually become a powerful means to help teachers adjust teaching behaviors and improve teaching quality. However, at present, the evaluation of intelligent teachers' behaviors is still in the preliminary exploration stage, and the application research is not deep enough. This paper analyzes the application of intelligent technology in the evaluation of teachers' classroom teaching behaviors from the perspectives of evaluation data, methods, and results. Voice print recognition technology is used to recognize the teachers' identities and track the speech in the classroom videos, and the videos are segmented. Then, the evaluation framework of teachers' classroom teaching behaviors is constructed using three dimensions of emotion, posture, and position preference. Finally, evaluation results are presented to teachers in a more intuitive and easy - to-understand visual way, to help teachers reflect on teaching. This paper aims to promote the transformation of teachers' classroom teaching behavior evaluation toward an intelligent, efficient, and sustainable direction through current research.
C1 [Lu, Yuanyuan; Chen, Zengzhao; Chen, Rong; Shi, Yawen; Zheng, Qiuyu] Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan 430079, Peoples R China.
   [Lu, Yuanyuan] Wuhan Coll, Sch Informat Engn, Wuhan 430212, Peoples R China.
C3 Central China Normal University; Wuhan College
RP Lu, YY (corresponding author), Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan 430079, Peoples R China.; Lu, YY (corresponding author), Wuhan Coll, Sch Informat Engn, Wuhan 430212, Peoples R China.
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   Chen S., 2020, Unpublished doctoral dissertation
   Luo Z., 2018, Modern Educational Technology, P38
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   Yang J., 2020, China Educational Technology, P22
   Zhang D. J., 2015, Educational psychology, V3rd
   Zhang L., 2021, Modern Distance Education Research, P13
NR 14
TC 0
Z9 0
U1 8
U2 13
PU HIGHER EDUCATION PRESS
PI BEIJING
PA CHAOYANG DIST, 4, HUIXINDONGJIE, FUSHENG BLDG, BEIJING 100029, PEOPLES R
   CHINA
SN 1673-341X
EI 1673-3533
J9 FRONT EDUC CHINA
JI Front. Educ. China
PD JUN
PY 2023
VL 18
IS 2
BP 171
EP 186
DI 10.3868/s110-008-023-0012-8
PG 16
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA DO8V1
UT WOS:001133096100006
DA 2024-09-05
ER
PT J
AU Su, M
   Peng, H
   Li, SF
AF Su, Miao
   Peng, Hui
   Li, Shaofan
TI A visualized bibliometric analysis of mapping research trends of machine
   learning in engineering (MLE)
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Artificial intelligence; Engineering; Bibliometric analysis; VOSviewer;
   Research hotspots
ID SUPPORT VECTOR MACHINES; NEURAL-NETWORK; FEATURE-SELECTION; REGRESSION;
   ALGORITHM; MODEL; INTELLIGENCE; PREDICTION; FAILURE; ENSEMBLE
AB In this work, we conducted a visualized bibliometric analysis to map the research trends of machine learning in engineering (MLE) based on articles indexed in the Web of Science Core Collection published between 2000 and 2019. The research distributions, knowledge bases, research hotspots, and research frontiers for MLE studies are revealed by using VOSviewer software and visualization technology. The growth of the literature related to MLE averaged 24.3% in the past two decades. A total of 3057 peer-reviewed papers from 96 countries published in 1299 different journals were identified. The USA was the most productive country, with 23.73% of the overall articles and 32.25% of the overall citations. The most active research organization was MIT, with 41 publications and 1079 citations, and the Journal of Machine Learning Research had the largest number of citations in the field of MLE. In particular, our findings indicate that the research issues of "random forests", "support vector machine", "extreme learning machine", "deep learning", "statistical learning theory", and "Python machine learning" formed the knowledge bases of MLE from 2000 to 2019, while the research hotspots focused on applications of machine learning benchmark algorithms. Burst detection analysis results showed that more burst keywords emerged and had a higher frequency of change after 2010. This study provides an insight view of the overall research trends of MLE and may help researchers better understand this research field and predict its dynamic directions.
C1 [Su, Miao; Peng, Hui] Changsha Univ Sci & Technol, Sch Civil Engn, Changsha 410114, Hunan, Peoples R China.
   [Su, Miao; Li, Shaofan] Univ Calif Berkeley, Dept Civil & Environm Engn, Berkeley, CA 94720 USA.
C3 Changsha University of Science & Technology; University of California
   System; University of California Berkeley
RP Li, SF (corresponding author), Univ Calif Berkeley, 783 Davis Hall, Berkeley, CA 94720 USA.
EM sumiao@csust.edu.cn; shaofan@berkeley.edu
RI Li, Shaofan/G-8082-2011
OI Li, Shaofan/0000-0002-6950-1474
FU National Natural Science Foundation of China [51808056]; Research
   Project of Hunan Provincial Department of Education [19B012]; Hunan
   Provincial Natural Science Foundation of China [2020JJ5583]; China
   Scholarship Council [201808430232]
FX This work was conducted in the University of California, Berkeley. Dr.
   Su is supported by the National Natural Science Foundation of China
   (Grant No.51808056) , the Research Project of Hunan Provincial
   Department of Education (Grant NO. 19B012) , the Hunan Provincial
   Natural Science Foundation of China (Grant No. 2020JJ5583) , and the
   China Scholarship Council (Grant No.201808430232) .
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NR 90
TC 30
Z9 32
U1 5
U2 70
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0957-4174
EI 1873-6793
J9 EXPERT SYST APPL
JI Expert Syst. Appl.
PD DEC 30
PY 2021
VL 186
AR 115728
DI 10.1016/j.eswa.2021.115728
EA AUG 2021
PG 11
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering; Operations Research & Management Science
GA WC6EQ
UT WOS:000704349700008
DA 2024-09-05
ER
PT J
AU Ihsan, I
   Qadir, MA
AF Ihsan, Imran
   Qadir, Muhammad Abdul
TI CCRO: Citation's Context & Reasons Ontology
SO IEEE ACCESS
LA English
DT Article
DE Citation ontology; citation graphs; citation analysis; ontology
   development; natural language processing; computational linguistics
ID CLASSIFICATION; SCIENCES
AB Research papers can be visualized as a networked information space that contains a collection of information entities, inter-connected by directed links, commonly known as citation graph. There is a possibility to enrich the citation graph with meaningful relations using semantic tags. We have discovered and evaluated more than 150 citations' reasons from the existing published literature to be represented as citation tags. Many of these reasons have overlapped and diffused meanings. Annotating such a large volume of citation graphs with citation's reasons manually is nearly impossible, giving rise to a need to discover the citation's reasons automatically with high accuracy. The first step towards this is developing a minimal set of citation's context and reasons that are disjoint in nature. It would be a great help to the reasoning system if these reasons are represented in a formal way in the form of an ontology. By adopting a well-defined scientific methodology to formulate an ontology of citation reasons, we have reduced 150 reasons into only eight disjoint reasons, using an iterative process of sentiment analysis, collaborative meanings, and experts' opinions. Based on our findings and experiments, we have proposed a citation's context and reasons ontology (CCRO) that provides abstract conceptualization required to organize citations' relations. CCRO has been verified, validated, and assessed by using the well-defined procedures and tools proposed in the literature for ontology evaluation. The results show that the proposed ontology is concise, complete, and consistent. For the instantiation and mapping of ontology classes on real data, we have developed a mapping graph among the verbs with predicative complements in the English Language, the verbs extracted from the selected corpus using the NLP and CCRO classes. Using this mapping graph, the mapping of ontology classes in each citation's sentiment is explained with a complete mapping on the selected dataset.
C1 [Ihsan, Imran; Qadir, Muhammad Abdul] Capital Univ Sci & Technol, Ctr Res Data Sci Semant & Scientometr, Dept Comp Sci, Islamabad 45750, Pakistan.
C3 Capital University of Science & Technology
RP Ihsan, I (corresponding author), Capital Univ Sci & Technol, Ctr Res Data Sci Semant & Scientometr, Dept Comp Sci, Islamabad 45750, Pakistan.
EM iimranihsan@gmail.com
RI ihsan, imran/AAZ-6236-2021; ihsan, imran/ABA-7494-2021
OI ihsan, imran/0000-0002-3447-4576; Qadir, Muhammad
   Abdul/0000-0003-4634-9016
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NR 53
TC 8
Z9 8
U1 1
U2 27
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2019
VL 7
BP 30423
EP 30436
DI 10.1109/ACCESS.2019.2903450
PG 14
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering; Telecommunications
GA HR7EJ
UT WOS:000463314800001
OA gold
DA 2024-09-05
ER
PT J
AU Brizan, DG
   Gallagher, K
   Jahangir, A
   Brown, T
AF Brizan, David Guy
   Gallagher, Kevin
   Jahangir, Arnab
   Brown, Theodore
TI Predicting citation patterns: defining and determining influence
SO SCIENTOMETRICS
LA English
DT Article
DE Citation analysis; Bibliometrics; Big data; Machine learning
ID ARTICLE; INDEX
AB Definitions for influence in bibliometrics are surveyed and expanded upon in this work. On data composed of the union of DBLP and CiteSeer (x) , approximately 6 million publications, a relatively small number of features are developed to describe the set, including loyalty and community longevity, two novel features. These features are successfully used to predict the influential set of papers in a series of machine learning experiments. The most predictive features are highlighted and discussed.
C1 [Brizan, David Guy; Brown, Theodore] CUNY, Dept Comp Sci, 365 Fifth Ave, New York, NY 10016 USA.
   [Brizan, David Guy; Brown, Theodore] CUNY, Grad Ctr, 365 Fifth Ave, New York, NY 10016 USA.
   [Gallagher, Kevin] NYU, Tandon Sch Engn, Dept Comp Sci, 6 MetroTech Ctr, Brooklyn, NY 11201 USA.
   [Jahangir, Arnab] CUNY Hunter Coll, Dept Comp Sci, 695 Pk Ave, New York, NY 10065 USA.
C3 City University of New York (CUNY) System; City University of New York
   (CUNY) System; New York University; New York University Tandon School of
   Engineering; City University of New York (CUNY) System; Hunter College
   (CUNY)
RP Brizan, DG (corresponding author), CUNY, Dept Comp Sci, 365 Fifth Ave, New York, NY 10016 USA.; Brizan, DG (corresponding author), CUNY, Grad Ctr, 365 Fifth Ave, New York, NY 10016 USA.
EM dbrizan@gradcenter.cuny.edu
RI Gallagher, Kevin/GLS-0906-2022
OI Gallagher, Kevin/0000-0002-2714-7841
FU National Science Foundation [CNS-0958379, CNS-0855217, ACI-1126113];
   City University of New York High Performance Computing Center at the
   College of Staten Island
FX This research was supported, in part, under National Science Foundation
   Grants CNS-0958379, CNS-0855217, ACI-1126113 and the City University of
   New York High Performance Computing Center at the College of Staten
   Island. The authors also acknowledge the Office of Information
   Technology at The Graduate Center, CUNY for providing database and
   server resources that have contributed to the research results reported
   within this paper. URL: http://it.gc.cuny.edu/.
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NR 25
TC 13
Z9 13
U1 1
U2 38
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD JUL
PY 2016
VL 108
IS 1
BP 183
EP 200
DI 10.1007/s11192-016-1950-1
PG 18
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA DP8WB
UT WOS:000378777500009
DA 2024-09-05
ER
PT J
AU Kaur, M
   Kumar, A
   Mittal, AK
AF Kaur, Manpreet
   Kumar, Amit
   Mittal, Anil Kumar
TI Mapping the knowledge structure of artificial neural network research in
   the stock market: a bibliometric analysis and future research pathways
SO BENCHMARKING-AN INTERNATIONAL JOURNAL
LA English
DT Article; Early Access
DE Neural networks; Bibliometric; Stock market; Forecasting
ID DATA MINING TECHNIQUES; EARLY WARNING SYSTEM; FINANCIAL DISTRESS;
   BANKRUPTCY PREDICTION; PRICE INDEX; VOLATILITY; DEEP; INTEGRATION;
   COMPANIES; MODELS
AB Purpose - In past decades, artificial neural network (ANN) models have revolutionised various stock market operations due to their superior ability to deal with nonlinear data and garnered considerable attention from researchers worldwide. The present study aims to synthesize the research field concerning ANN applications in the stock market to a) systematically map the research trends, key contributors, scientific collaborations, and knowledge structure, and b) uncover the challenges and future research areas in the field. Design/methodology/approach - To provide a comprehensive appraisal of the extant literature, the study adopted the mixed approach of quantitative (bibliometric analysis) and qualitative (intensive review of influential articles) assessment to analyse 1,483 articles published in the Scopus and Web of Science indexed journals during 1992-2022. The bibliographic data was processed and analysed using VOSviewer and R software. Findings - The results revealed the proliferation of articles since 2018, with China as the dominant country, Wang J as the most prolific author, "Expert Systems with Applications" as the leading journal, "computer science" as the dominant subject area, and "stock price forecasting" as the predominantly explored research theme in the field. Furthermore, "portfolio optimization", "sentiment analysis", "algorithmic trading", and "crisis prediction" are found as recently emerged research areas. Originality/value - To the best of the authors' knowledge, the current study is a novel attempt that holistically assesses the existing literature on ANN applications throughout the entire domain of stock market. The main contribution of the current study lies in discussing the challenges along with the viable methodological solutions and providing application area-wise knowledge gaps for future studies.
C1 [Kaur, Manpreet; Kumar, Amit; Mittal, Anil Kumar] Kurukshetra Univ, Univ Sch Management, Kurukshetra, Haryana, India.
C3 Kurukshetra University
RP Kaur, M (corresponding author), Kurukshetra Univ, Univ Sch Management, Kurukshetra, Haryana, India.
EM manpreetkaur27oct@gmail.com
RI Kaur, Manpreet/HJZ-4398-2023
OI Kumar Mittal, Anil/0009-0007-3096-6008; Kumar, Amit/0000-0002-3055-1810;
   Kaur, Manpreet/0000-0001-8299-9986
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NR 78
TC 0
Z9 0
U1 1
U2 1
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 1463-5771
EI 1758-4094
J9 BENCHMARKING
JI Benchmarking
PD 2024 MAR 7
PY 2024
DI 10.1108/BIJ-06-2023-0373
EA MAR 2024
PG 30
WC Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA KH9I1
UT WOS:001179182800001
DA 2024-09-05
ER
PT J
AU Cheng, SY
   Zhang, JC
   Wang, GX
   Zhou, Z
   Du, J
   Wang, LJ
   Li, N
   Wang, JY
AF Cheng, Shiyuan
   Zhang, Jianchen
   Wang, Guangxia
   Zhou, Zheng
   Du, Jin
   Wang, Lijun
   Li, Ning
   Wang, Jiayao
TI Cartography and Neural Networks: A Scientometric Analysis Based on
   CiteSpace
SO ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
LA English
DT Article
DE cartography; neural network; CiteSpace; visual analysis; knowledge
   mapping
ID FUZZY COGNITIVE MAPS; LANDSLIDE SUSCEPTIBILITY ASSESSMENT; SUPPORT
   VECTOR MACHINE; SOIL ORGANIC-CARBON; FREQUENCY RATIO;
   LOGISTIC-REGRESSION; EMERGING TRENDS; DECISION TREE; MODELS;
   CLASSIFICATION
AB Propelled by emerging technologies such as artificial intelligence and deep learning, the essence and scope of cartography have significantly expanded. The rapid progress in neuroscience has raised high expectations for related disciplines, furnishing theoretical support for revealing and deepening the essence of maps. In this study, CiteSpace was used to examine the confluence of cartography and neural networks over the past decade (2013-2023), thus revealing the prevailing research trends and cutting-edge investigations in the field of machine learning and its application in mapping. In addition, this analysis included the systematic categorization of knowledge clusters arising from the fusion of cartography and neural networks, which was followed by the discernment of pivotal clusters in the field of knowledge mapping. Crucially, this study diligently identified the critical studies (milestones) that have made significant contributions to the development of these elucidated clusters. Timeline analysis was used to track these studies' origins, evolution, and current status. Finally, we constructed collaborative networks among the contributing authors, journals, institutions, and countries. This mapping aids in identifying and visualizing the primary contributing factors of the evolution of knowledge mapping encompassing cartography and neural networks, thus facilitating interdisciplinary and multidisciplinary research and investigations.
C1 [Cheng, Shiyuan; Zhang, Jianchen; Wang, Guangxia; Zhou, Zheng; Wang, Lijun; Li, Ning; Wang, Jiayao] Henan Univ, Coll Geog & Environm Sci, Kaifeng 475004, Peoples R China.
   [Cheng, Shiyuan; Zhang, Jianchen; Wang, Guangxia; Zhou, Zheng; Wang, Lijun; Li, Ning; Wang, Jiayao] Henan Univ, Henan Ind Technol Acad Spatial Temporal Big Data, Zhengzhou 450046, Peoples R China.
   [Cheng, Shiyuan; Zhang, Jianchen; Wang, Guangxia; Zhou, Zheng; Wang, Lijun; Li, Ning; Wang, Jiayao] Henan Univ, Key Lab Geospatial Technol Middle & Lower Yellow R, Minist Educ, Kaifeng 475004, Peoples R China.
   [Cheng, Shiyuan; Zhang, Jianchen; Wang, Guangxia; Zhou, Zheng; Wang, Lijun; Li, Ning; Wang, Jiayao] Henan Univ, Henan Technol Innovat Ctr Spatio Temporal Big Data, Zhengzhou 450046, Peoples R China.
   [Du, Jin] Henan Univ, Key Res Inst Yellow River Civilizat & Sustainable, Kaifeng 475001, Peoples R China.
   [Du, Jin] Henan Urban Planning Inst & Corp, Zhengzhou 450053, Peoples R China.
C3 Henan University; Henan University; Henan University; Henan University;
   Henan University
RP Wang, JY (corresponding author), Henan Univ, Coll Geog & Environm Sci, Kaifeng 475004, Peoples R China.; Wang, JY (corresponding author), Henan Univ, Henan Ind Technol Acad Spatial Temporal Big Data, Zhengzhou 450046, Peoples R China.; Wang, JY (corresponding author), Henan Univ, Key Lab Geospatial Technol Middle & Lower Yellow R, Minist Educ, Kaifeng 475004, Peoples R China.; Wang, JY (corresponding author), Henan Univ, Henan Technol Innovat Ctr Spatio Temporal Big Data, Zhengzhou 450046, Peoples R China.
EM shiyuan.cheng@henu.edu.cn; jczhang@vip.henu.edu.cn; wgx@henu.edu.cn;
   zzeduphd@henu.edu.cn; dujin@henu.edu.cn; henuwlj@henu.edu.cn;
   lining@henu.edu.cn; wjy@henu.edu.cn
OI Zhang, Jianchen/0000-0002-0093-0940
FU National Natural Science Foundation of China [U21A2014]; Natural Science
   Foundation of Henan Province [232300420436, 232300420432]; Open Fund of
   Key Laboratory of Urban Land Resources Monitoring and Simulation,
   Ministry of Natural Resources [KF-2022-07-020]; Key Laboratory of
   Geospatial Technology for Middle and Lower Yellow River Regions (Henan
   University) and the Ministry of Education open project [GTYR202203];
   Henan Collaborative Innovation Center of Geo-Information Technology for
   Smart Central Plains [2023C001]; Science and Technology Development
   Project of Henan Province [242102210175]
FX This work was supported by the National Natural Science Foundation of
   China under grant [number U21A2014]; the Natural Science Foundation of
   Henan Province under grant [number 232300420436, 232300420432]; the Open
   Fund of Key Laboratory of Urban Land Resources Monitoring and
   Simulation, Ministry of Natural Resources under grant [number
   KF-2022-07-020]; the Key Laboratory of Geospatial Technology for Middle
   and Lower Yellow River Regions (Henan University) and the Ministry of
   Education open project under grant [number GTYR202203]; Henan
   Collaborative Innovation Center of Geo-Information Technology for Smart
   Central Plains under grant [number 2023C001]; and the Science and
   Technology Development Project of Henan Province under grant [number
   242102210175].
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   Yan XF, 2024, CARTOGR GEOGR INF SC, V51, P79, DOI 10.1080/15230406.2023.2218106
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NR 150
TC 0
Z9 0
U1 9
U2 9
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2220-9964
J9 ISPRS INT J GEO-INF
JI ISPRS Int. J. Geo-Inf.
PD JUN
PY 2024
VL 13
IS 6
AR 178
DI 10.3390/ijgi13060178
PG 31
WC Computer Science, Information Systems; Geography, Physical; Remote
   Sensing
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Physical Geography; Remote Sensing
GA WO4M6
UT WOS:001255805000001
OA gold
DA 2024-09-05
ER
PT J
AU Fu, LD
   Aphinyanaphongs, Y
   Aliferis, CF
AF Fu, Lawrence D.
   Aphinyanaphongs, Yindalon
   Aliferis, Constantin F.
TI Computer models for identifying instrumental citations in the biomedical
   literature
SO SCIENTOMETRICS
LA English
DT Article
DE Bibliometrics; Citation analysis; Machine learning; Information
   retrieval
ID TEXT CATEGORIZATION; AGREEMENT; COUNTS
AB The most popular method for evaluating the quality of a scientific publication is citation count. This metric assumes that a citation is a positive indicator of the quality of the cited work. This assumption is not always true since citations serve many purposes. As a result, citation count is an indirect and imprecise measure of impact. If instrumental citations could be reliably distinguished from non-instrumental ones, this would readily improve the performance of existing citation-based metrics by excluding the non-instrumental citations. A citation was operationally defined as instrumental if either of the following was true: the hypothesis of the citing work was motivated by the cited work, or the citing work could not have been executed without the cited work. This work investigated the feasibility of developing computer models for automatically classifying citations as instrumental or non-instrumental. Instrumental citations were manually labeled, and machine learning models were trained on a combination of content and bibliometric features. The experimental results indicate that models based on content and bibliometric features are able to automatically classify instrumental citations with high predictivity (AUC = 0.86). Additional experiments using independent hold out data and prospective validation show that the models are generalizeable and can handle unseen cases. This work demonstrates that it is feasible to train computer models to automatically identify instrumental citations.
C1 [Fu, Lawrence D.; Aphinyanaphongs, Yindalon] NYU Med Ctr, Ctr Hlth Informat & Bioinformat, Dept Med, New York, NY 10016 USA.
   [Aliferis, Constantin F.] NYU Med Ctr, Ctr Hlth Informat & Bioinformat, Dept Pathol, New York, NY 10016 USA.
C3 New York University; New York University
RP Fu, LD (corresponding author), NYU Med Ctr, Ctr Hlth Informat & Bioinformat, Dept Med, 227 E 30th St,7th Floor, New York, NY 10016 USA.
EM lawrence.fu@nyumc.org
OI Aphinyanaphongs, Yin/0000-0001-8605-5392
FU  [R56 LM007948-04A1];  [1UL1RR029893]
FX The authors gratefully acknowledge support from R56 LM007948-04A1 and
   1UL1RR029893.
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NR 20
TC 4
Z9 4
U1 0
U2 25
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD DEC
PY 2013
VL 97
IS 3
BP 871
EP 882
DI 10.1007/s11192-013-0983-y
PG 12
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 255DP
UT WOS:000327219900020
DA 2024-09-05
ER
PT J
AU Chan, M
   Hu, PF
   Mak, MKF
AF Chan, Michael
   Hu, Panfeng
   K. F. Mak, Macau
TI Mediation Analysis and Warranted Inferences in Media and Communication
   Research: Examining Research Design in Communication Journals From 1996
   to 2017
SO JOURNALISM & MASS COMMUNICATION QUARTERLY
LA English
DT Article
DE mediation analysis; indirect effect; research design; causal inference;
   content analysis
ID SOCIAL IDENTITY; NEWS; SELF; EXPOSURE; INFORMATION; ATTITUDES; BELIEFS;
   INGROUP; HEALTH; ROLES
AB The number of studies employing mediation analysis has increased exponentially in the past two decades. Focusing on research design, this study examines 387 articles in theJournal of Communication,Human Communication Research,Communication Research,Journalism & Mass Communication Quarterly, andMedia Psychologybetween 1996 and 2017. Findings show that while most studies report statistically significant indirect effects, they are inadequate to make causal inferences. Authors also often infer that they uncovered the "true" mediator(s) while alternative models and mediators are rarely acknowledged. Future studies should pay more attention to the role of research design and its implications for making causal inferences.
C1 [Chan, Michael; Hu, Panfeng] Chinese Univ Hong Kong, Shatin, Hong Kong, Peoples R China.
   [K. F. Mak, Macau] Univ Wisconsin, Sch Journalism & Mass Commun, Madison, WI 53706 USA.
C3 Chinese University of Hong Kong; University of Wisconsin System;
   University of Wisconsin Madison
RP Chan, M (corresponding author), Chinese Univ Hong Kong, Sch Journalism & Commun, New Asia Coll, Shatin, Humanities Bldg, Hong Kong, Peoples R China.
EM mcmchan@cuhk.edu.hk
RI Mak, Macau K. F./ABC-3688-2022; Chan, Michael/A-9477-2013
OI Mak, Macau K. F./0000-0003-0819-1107; Hu, Panfeng/0000-0002-7820-2214;
   Chan, Michael/0000-0001-9911-593X
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NR 63
TC 32
Z9 36
U1 6
U2 35
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 1077-6990
EI 2161-430X
J9 J MASS COMMUN Q
JI Journal. Mass Commun. Q.
PD JUN
PY 2022
VL 99
IS 2
BP 463
EP 486
AR 1077699020961519
DI 10.1177/1077699020961519
EA OCT 2020
PG 24
WC Communication
WE Social Science Citation Index (SSCI)
SC Communication
GA 1X6BS
UT WOS:000575512500001
DA 2024-09-05
ER
PT J
AU Thakral, S
   Kamra, V
AF Thakral, Shruti
   Kamra, Vishal
TI Self-service technologies: A bibliometric analysis
SO INTERNATIONAL JOURNAL OF FINANCIAL ENGINEERING
LA English
DT Article; Early Access
DE Self-service technology; SSTs; bibliometric analyses; bibliographic
   coupling; topic modeling
ID CUSTOMER SATISFACTION; QUALITY; ANTHROPOMORPHISM; ENTREPRENEURSHIP;
   LOYALTY; TRUST
AB In this paper, we applied bibliometric methods to analyze the field of self-service technology (SST). Our goal was to identify the most impactful publications and journals, comprehend the key topics studied as well as the emerging trends and forecast future research trends in this domain. The Scopus database was used to extract 490 documents related to SST between 2000 and 2023. Our analyses include the most cited papers, sources of publications, bibliographic coupling, and topic modeling. As the body of literature continues to expand, we identified the most influential source "Journal of Service Marketing" which holds a prominent position in this area. The citation analysis revealed the highly influential papers that have made significant contributions to their respective fields. Notably, Meuter [Meuter, ML, AL Ostrom, RI Roundtree and MJ Bitner (2000). Self-Service Technologies: Understanding Customer Satisfaction with Technology-Based Service Encounters] in the realm of SST emerged as the most frequently cited paper, exerting substantial influence in academic literature. We applied bibliometric coupling in this field, which resulted in the discovery of seven clusters in SST. These clusters shed light on various aspects associated with these topics, offering valuable insights. The findings of this research offer valuable insights to study researchers by bringing clarity to this research field. Finally, the study presents the implications and recommendations for researchers. The findings of this paper offer valuable insights to study researchers by bringing clarity to the fields of the study.
C1 [Thakral, Shruti] Amity Univ, Amity Coll Commerce & Finance, Noida, Uttar Pradesh, India.
   [Kamra, Vishal] Amity Univ, Amity Sch Business, Noida, Uttar Pradesh, India.
C3 Amity University Noida; Amity University Noida
RP Thakral, S (corresponding author), Amity Univ, Amity Coll Commerce & Finance, Noida, Uttar Pradesh, India.
EM thakralshruti98@gmail.com; vishalkamra@ymail.com
OI shruti, thakral/0009-0004-1598-2788
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NR 47
TC 0
Z9 0
U1 0
U2 0
PU WORLD SCIENTIFIC PUBL CO PTE LTD
PI SINGAPORE
PA 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE
SN 2424-7863
EI 2424-7944
J9 INT J FINANC ENG
JI Int. J. Financ. Eng.
PD 2024 JUL 24
PY 2024
DI 10.1142/S2424786324420076
EA JUL 2024
PG 20
WC Business, Finance
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA ZK9B2
UT WOS:001275299500001
DA 2024-09-05
ER
PT J
AU Sadowski, A
   Sadowski, M
   Engelseth, P
   Galar, Z
   Skowron-Grabowska, B
AF Sadowski, Adam
   Sadowski, Michal
   Engelseth, Per
   Galar, Zbigniew
   Skowron-Grabowska, Beata
TI Using neural networks to examine trending keywords in Inventory Control
SO PRODUCTION ENGINEERING ARCHIVES
LA English
DT Article
DE Inventory control; Neural network; GAT; Bibliometric analysis; Keywords
ID OF-THE-ART; ROUTING PROBLEM; MANAGEMENT; STATE; COORDINATION;
   INTEGRATION
AB Inventory control is one of the key areas of research in logistics. Using the SCOPUS database, we have processed 9,829 articles on inventory control using triangulation of statistical methods and machine learning. We have proven the usefulness of the proposed statistical method and Graph Attention Network (GAT) architecture for determining trend-setting keywords in inventory control research. We have demonstrated the changes in the research conducted between 1950 and 2021 by presenting the evolution of keywords in articles. A novelty of our research is the applied approach to bibliometric analysis using unsupervised deep learning. It allows to identify the keywords that determined the high citation rate of the article. The theoretical framework for the intellectual structure of research proposed in the studies on inventory control is general and can be applied to any area of knowledge.
C1 [Sadowski, Adam] Univ Lodz, Fac Management, Ul Matejki 22-26, PL-90237 Lodz, Poland.
   [Sadowski, Michal] Jagiellonian Univ, Fac Math & Comp Sci, 6 Prof Stanislawa Lojasiewicza, PL-30348 Krakow, Poland.
   [Engelseth, Per] UiT Arctic Univ Norway, Tromso Univ, Tromso Sch Business & Econ, Narvik Campus,Lodve Langes Gate 2, N-8514 Narvik, Norway.
   [Galar, Zbigniew] Bayer, Al Jerozolimskie 158, PL-02326 Warsaw, Poland.
   [Skowron-Grabowska, Beata] Czestochowa Tech Univ, Fac Management, Ul Armii Krajowej 19b, PL-42200 Czestochowa, Poland.
C3 University of Lodz; Jagiellonian University; UiT The Arctic University
   of Tromso; Bayer AG; Technical University Czestochowa
RP Sadowski, A (corresponding author), Univ Lodz, Fac Management, Ul Matejki 22-26, PL-90237 Lodz, Poland.
EM adam.sadowski@uni.lodz.pl; sadowskimichal95@gmail.com;
   per.engelseth@uit.no; zbigniew.galar@gmail.com;
   b.skowron-grabowska@pcz.pl
OI Sadowski, Adam/0000-0002-8608-5118; Sadowski,
   Michal/0000-0003-3482-9733; Galar, Zbigniew/0000-0003-2629-0512
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NR 51
TC 0
Z9 0
U1 1
U2 4
PU WALTER DE GRUYTER GMBH
PI BERLIN
PA GENTHINER STRASSE 13, D-10785 BERLIN, GERMANY
SN 2353-5156
EI 2353-7779
J9 PROD ENG ARCH
JI Prod. Eng. Arch.
PD DEC 1
PY 2023
VL 29
IS 4
BP 474
EP 489
DI 10.30657/pea.2023.29.52
PG 16
WC Engineering, Industrial; Materials Science, Multidisciplinary
WE Emerging Sources Citation Index (ESCI)
SC Engineering; Materials Science
GA W9KB2
UT WOS:001094733600013
OA gold
DA 2024-09-05
ER
PT J
AU Arora, N
   Gupta, M
   Dharwal, M
   Agarwal, N
AF Arora, Nidhi
   Gupta, Manisha
   Dharwal, Mridul
   Agarwal, Nimmi
TI Luxury adapts to artificial intelligence & digital transformation - A
   case study of Burberry
SO JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES
LA English
DT Article
DE Artificial intelligence; Bibliometric analysis; Case study; Digital
   transformation; e-commerce; Luxury market; Technology
AB In the realm of luxury brands, the era of digital world and digital integration has seen a fundamental transformation. High-end clothing firms are demonstrating a strong interest in understanding digitalization in order to provide customers with an omni-channel buying experience. In this study, the main goal is to investigate the usage of Artificial Intelligence and its applications by luxury labels, as well as the impact that this technology has on the brand's performance in the market. Based on a case study, this article examines how luxury major retailers such as Burberry have successfully implemented an online strategy by repositioning themselves in fear of missing high-end customers and thus attempting to solve the existing trade between uniqueness and magnitude of economic system. Findings within the paper have been derived from a systematic bibliometric analysis which has taken into consideration a significant number of relevant publications such as conference proceedings and scholarly articles, as well as case studies of major global luxury fashion firms, among many other things. As the first study of its sort, this article will be useful to industry professionals in building their digital solutions; for scholars and academicians, the study will provide an ability to look at the issue in the context of consumer and technological innovation.
C1 [Arora, Nidhi; Gupta, Manisha] Sharda Univ, Sharda Sch Business Studies, Greater Noida, Uttar Pradesh, India.
   [Dharwal, Mridul] Sharda Univ, Dept Econ & Int Business, Greater Noida, Uttar Pradesh, India.
   [Agarwal, Nimmi] Sharda Univ, Sharda Sch Business Studies, Greater Noida, Uttar Pradesh, India.
C3 Sharda University; Sharda University; Sharda University
RP Arora, N (corresponding author), Sharda Univ, Sharda Sch Business Studies, Greater Noida, Uttar Pradesh, India.
EM nidhiarora30@outlook.com; manisha.gupta1@sharda.ac.in;
   mriduldharwal22@gmail.com; Nimmi.agarwal@sharda.ac.in
RI Gupta, Dr Manisha/AFP-7155-2022
OI Gupta, Dr Manisha/0000-0001-9326-0183
CR Alcouffe J, 2020, RESPONSIBLE MAR 0530
   [Anonymous], 2017, Forbes
   Chaffey D., 2013, SMART INSIGHT B 0216
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   Godey B, 2016, J BUS RES, V69, P5833, DOI 10.1016/j.jbusres.2016.04.181
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NR 14
TC 0
Z9 0
U1 6
U2 19
PU TARU PUBLICATIONS
PI NEW DELHI
PA G-159, PUSHKAR ENCLAVE, PASHCHIM VIHAR, NEW DELHI, 110 063, INDIA
SN 0252-2667
EI 2169-0103
J9 J INFORM OPTIM SCI
JI J. Inform. Optim. Science
PY 2023
VL 44
IS 1
SI SI
BP 41
EP 52
DI 10.47974/JIOS-1294
PG 12
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA M8ON3
UT WOS:001032755000005
DA 2024-09-05
ER
PT J
AU Choi, J
   Dekkers, OM
   le Cessie, S
AF Choi, Jungyeon
   Dekkers, Olaf M.
   le Cessie, Saskia
TI Tying research question and analytical strategy when variables are
   affected by medication use
SO PHARMACOEPIDEMIOLOGY AND DRUG SAFETY
LA English
DT Article
DE causal inference; estimand; medication effect; research question;
   well-defined question
ID IMMORTAL TIME BIAS; LONGITUDINAL DATA; CAUSAL INFERENCE; TARGET TRIAL;
   THERAPY; OBESITY; MODELS
AB Ill-defined research questions could be particularly problematic in an epidemiological setting where measurements fluctuate over time due to intercurrent events, such as medication use. When a research question fails to specify how medication use should be handled methodologically, arbitrary decisions may be made during the analysis phase, which likely leads to a mismatch between the intended question and the performed analysis. The mismatch can result in vastly different or meaningless interpretations of estimated effects. Thus, a research question such as "what is the effect of X on Y? " requires further elaboration, and it should consider whether and how medication use has affected the measurements of interest. In our study, we will discuss how well-defined questions can be formulated when medication use is involved in observational studies. We will distinguish between a situation where an exposure is affected by medication use and where the outcome of interest is affected by medication use. For each setting, we will give examples of different research questions that could be asked depending on how medication use is considered in the estimand and discuss methodological considerations under each question.
C1 [Choi, Jungyeon] Leiden Univ, Med Ctr, Dept Clin Epidemiol, Leiden, Netherlands.
   [Dekkers, Olaf M.] Leiden Univ, Med Ctr, Dept Clin Epidemiol, Dept Endocrinol & Metab, Leiden, Netherlands.
   [le Cessie, Saskia] Leiden Univ, Med Ctr, Dept Clin Epidemiol, Dept Biomed Data Sci, Leiden, Netherlands.
   [Choi, Jungyeon] Leiden Univ, Med Ctr, Dept Clin Epidemiol, Albinusdreef 2, C7-P, NL-2333 Leiden, Netherlands.
C3 Leiden University - Excl LUMC; Leiden University; Leiden University
   Medical Center (LUMC); Leiden University - Excl LUMC; Leiden University;
   Leiden University Medical Center (LUMC); Leiden University; Leiden
   University Medical Center (LUMC); Leiden University - Excl LUMC; Leiden
   University; Leiden University Medical Center (LUMC); Leiden University -
   Excl LUMC
RP Choi, J (corresponding author), Leiden Univ, Med Ctr, Dept Clin Epidemiol, Albinusdreef 2, C7-P, NL-2333 Leiden, Netherlands.
EM j.choi@lumc.nl
RI le+Cessie, Saskia/HGC-8966-2022
OI le Cessie, Saskia/0000-0003-2154-4923; Dekkers,
   Olaf/0000-0002-1333-7580; Choi, Jungyeon/0000-0002-1914-3488
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NR 35
TC 1
Z9 1
U1 0
U2 0
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1053-8569
EI 1099-1557
J9 PHARMACOEPIDEM DR S
JI Pharmacoepidemiol. Drug Saf.
PD JUN
PY 2023
VL 32
IS 6
BP 661
EP 670
DI 10.1002/pds.5599
EA FEB 2023
PG 10
WC Public, Environmental & Occupational Health; Pharmacology & Pharmacy
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Public, Environmental & Occupational Health; Pharmacology & Pharmacy
GA E5EE9
UT WOS:000933182000001
PM 36738180
OA hybrid
DA 2024-09-05
ER
PT J
AU Zheng, LL
   Cao, SY
   Ding, TQ
   Tian, J
   Sun, JH
AF Zheng, Lili
   Cao, Shiyu
   Ding, Tongqiang
   Tian, Jian
   Sun, Jinghang
TI Research on Active Safety Situation of Road Passenger Transportation
   Enterprises: Evaluation, Prediction, and Analysis
SO ENTROPY
LA English
DT Article
DE transportation enterprises; active safety situation; factor analysis;
   time series; model selection; statistical computing; feature selection;
   statistical inference
ID TRAFFIC ACCIDENTS; TIME-SERIES; RISK; SLEEPINESS; IMPACT
AB The road passenger transportation enterprise is a complex system, requiring a clear understanding of their active safety situation (ASS), trends, and influencing factors. This facilitates transportation authorities to promptly receive signals and take effective measures. Through exploratory factor analysis and confirmatory factor analysis, we delved into potential factors for evaluating ASS and extracted an ASS index. To predict obtaining a higher ASS information rate, we compared multiple time series models, including GRU (gated recurrent unit), LSTM (long short-term memory), ARIMA, Prophet, Conv_LSTM, and TCN (temporal convolutional network). This paper proposed the WDA-DBN (water drop algorithm-Deep Belief Network) model and employed DEEPSHAP to identify factors with higher ASS information content. TCN and GRU performed well in the prediction. Compared to the other models, WDA-DBN exhibited the best performance in terms of MSE and MAE. Overall, deep learning models outperform econometric models in terms of information processing. The total time spent processing alarms positively influences ASS, while variables such as fatigue driving occurrences, abnormal driving occurrences, and nighttime driving alarm occurrences have a negative impact on ASS.
C1 [Zheng, Lili; Cao, Shiyu; Ding, Tongqiang; Sun, Jinghang] Jilin Univ, Transportat Coll, Changchun 130022, Peoples R China.
   [Tian, Jian] China Acad Transportat Sci, Beijing 100029, Peoples R China.
C3 Jilin University; China Academy of Transportation Sciences
RP Ding, TQ (corresponding author), Jilin Univ, Transportat Coll, Changchun 130022, Peoples R China.
EM lilizheng@jlu.edu.cn; caosy22@mails.jlu.edu.cn; dingtq@jlu.edu.cn;
   tianjian@motcats.ac.cn; jhsun22@mails.jlu.edu.cn
OI Tong qiang, Ding/0000-0002-2212-961X
FU National Key R&D Program of China [2021YFC3001500]; Graduate
   InnovationFund of Jilin University [2024CX214]
FX This research was funded by the National Key R&D Program of China grant
   number 2021YFC3001500 and the Graduate InnovationFund of Jilin
   University grant number 2024CX214.
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NR 69
TC 0
Z9 0
U1 6
U2 6
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1099-4300
J9 ENTROPY-SWITZ
JI Entropy
PD JUN
PY 2024
VL 26
IS 6
AR 434
DI 10.3390/e26060434
PG 27
WC Physics, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Physics
GA WR7W7
UT WOS:001256676300001
PM 38920443
OA gold, Green Published
DA 2024-09-05
ER
PT C
AU Gong, R
   Yang, P
   Li, SJ
AF Gong, Rui
   Yang, Ping
   Li, Shijin
GP IEEE
TI Research on teaching quality evaluation model of online courses in
   Colleges and Universities
SO 2022 IEEE 6TH ADVANCED INFORMATION TECHNOLOGY, ELECTRONIC AND AUTOMATION
   CONTROL CONFERENCE (IAEAC)
SE IEEE Advanced Information Technology Electronic and Automation Control
   Conference-IAEAC
LA English
DT Proceedings Paper
CT 6th IEEE Advanced Information Technology, Electronic and Automation
   Control Conference (IEEE IAEAC)
CY OCT 03-05, 2022
CL Beijing, PEOPLES R CHINA
DE Keywords Evaluation Model; D D Evaluating Indicator; Deep learning
   Model; Text classification and recognition
AB Through the research on the classification of knowledge points of online courses, the current online teaching quality evaluation has some problems, such as unscientific evaluation index weight, imperfect evaluation system and so on, The deep learning model is applied to the research of text and image classification and recognition of online course evaluation indicators. By collecting the latest research results of big data analysis model for teaching quality evaluation of relevant online courses, By mapping big data models to research objectives, Using the analytic hierarchy process to preliminarily determine the weight index. At last, The big data analysis model is proposed to be applied to the online course evaluation system structure and the online course evaluation index system.
C1 [Gong, Rui] Informat & Traff Dept Yunnan Business Informat En, Kunming, Yunnan, Peoples R China.
   [Yang, Ping] Power China Kunming Engn Corp Ltd, Kunming, Yunnan, Peoples R China.
   [Li, Shijin] Yunnan Univ Finance & Econ, Off Acad Affairs, Kunming, Yunnan, Peoples R China.
   [Li, Shijin] Kunming Univ Sci & Technol, Fac Management & Econ, Kunming, Yunnan, Peoples R China.
C3 Yunnan University of Finance & Economics; Kunming University of Science
   & Technology
RP Li, SJ (corresponding author), Yunnan Univ Finance & Econ, Off Acad Affairs, Kunming, Yunnan, Peoples R China.; Li, SJ (corresponding author), Kunming Univ Sci & Technol, Fac Management & Econ, Kunming, Yunnan, Peoples R China.
EM grkm1988@l63.com; ypgr19870920@l63.com; shijin_lee@ynufe.edu.cn
RI li, shijin/HZL-6982-2023; Gong, Rui/JYO-4823-2024
FU Scientific research fund project of Yunnan Provincial Department of
   Education [2021J0593]
FX This work was supported by Scientific research fund project of Yunnan
   Provincial Department of Education (2021J0593)-Research on teaching
   quality evaluation system of online courses in University.
CR Chen Chunlian, 2020, CHINESE SCI PAPERS, V15, P137
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NR 10
TC 0
Z9 0
U1 0
U2 15
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2689-663X
EI 2689-6621
BN 978-1-6654-5864-1
J9 ADV INF TECHNOL ELEC
PY 2022
BP 1217
EP 1221
DI 10.1109/IAEAC54830.2022.9929986
PG 5
WC Automation & Control Systems; Computer Science, Information Systems
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Automation & Control Systems; Computer Science
GA BU2JX
UT WOS:000885103500227
DA 2024-09-05
ER
PT J
AU Song, S
   Li, SG
   Zhang, TJ
   Ma, L
   Zhang, L
   Pan, SB
AF Song, Shuang
   Li, Shugang
   Zhang, Tianjun
   Ma, Li
   Zhang, Lei
   Pan, Shaobo
TI Research on time series characteristics of the gas drainage evaluation
   index based on lasso regression
SO SCIENTIFIC REPORTS
LA English
DT Article
AB The evaluation of the coal mine gas drainage effect is affected by many factors, such as flow rate, wind speed, drainage negative pressure, concentration, and temperature. This paper starts from actual coal mine production monitoring data and based on the lasso regression algorithm, features selection of multiple parameters of the preprocessed gas concentration time series to construct gas concentration feature selection based on the algorithm. The three-time smoothing index method is used to fill in the missing values. Aiming at the problem of different dimensions in the gas concentration time series, the MinMaxScaler method is used to normalize the data. The lasso regression algorithm is used to perform feature selection on the multivariable gas concentration time series, and the gas concentration time series selected by the lasso feature and the gas concentration time series without feature selection are input. The performance of the ANN algorithm for gas concentration prediction is compared and analyzed. The optimal a value and L1 norm are selected based on the grid search method to determine the strong explanatory gas concentration time series feature set of the working face, and an experimental comparison of the gas concentration prediction results before and after the lasso feature selection is performed. We verify the effectiveness of the algorithm.
C1 [Song, Shuang; Zhang, Lei] Xian Univ Sci & Technol, Coll Energy, 58 Yanta Middle Rd, Xian 710054, Shaanxi, Peoples R China.
   [Li, Shugang; Zhang, Tianjun] Xian Univ Sci & Technol, Coll Safety Sci & Engn, 58 Yanta Middle Rd, Xian 710054, Shaanxi, Peoples R China.
   [Ma, Li; Pan, Shaobo] Xian Univ Sci & Technol, Coll Commun & Informat Engn, Xian 710054, Peoples R China.
C3 Xi'an University of Science & Technology; Xi'an University of Science &
   Technology; Xi'an University of Science & Technology
RP Song, S (corresponding author), Xian Univ Sci & Technol, Coll Energy, 58 Yanta Middle Rd, Xian 710054, Shaanxi, Peoples R China.; Zhang, TJ (corresponding author), Xian Univ Sci & Technol, Coll Safety Sci & Engn, 58 Yanta Middle Rd, Xian 710054, Shaanxi, Peoples R China.
EM songshuang@xust.edu.cn; tianjun_zhang@126.com
FU National Natural Science Foundation of China [52104215, 51734007,
   51804248]
FX This research was funded by "National Natural Science Foundation of
   China" Grant Nos. 52104215, 51734007, and 51804248.
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NR 19
TC 2
Z9 2
U1 5
U2 44
PU NATURE PORTFOLIO
PI BERLIN
PA HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY
SN 2045-2322
J9 SCI REP-UK
JI Sci Rep
PD OCT 18
PY 2021
VL 11
IS 1
AR 20593
DI 10.1038/s41598-021-00210-z
PG 11
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA YE8IG
UT WOS:000741362500004
PM 34663859
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU Chen, JC
   Cheng, SR
   Liu, YT
   Wang, HS
AF Chen, Juei-Chao
   Cheng, Shuenn-Ren
   Liu, Ya-Tzu
   Wang, Hsu-Sheng
TI RESEARCH ON THE FORECAST OF INTERNATIONAL EQUITY FUND PERFORMANCE
SO INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL
LA English
DT Article
DE Classification; Data mining; Intelligent data analysis; Logistic
   regression
ID MUTUAL FUNDS; INVESTMENT PERFORMANCE; SELECTION; RETURNS; MARKET
AB The purpose of this study is to establish a fund performance classification model that can forecast the probability of positive or negative return of the funds. The positive/negative return of fund performance considered by this study is the return of the ith fund subtracted by the risk free interest rate. A positive value represents a favorable fund performance while a negative value represents the otherwise. In. statistical analysis, correlation analysis was firstly used to search for the possible important factors of mutual fund performance. These factors provide a CHAID of Intelligence Data Analysis to ensure which important factors could possibly influence the fund's return on equity. With the interactions among important factors as independent variables, logistic regression was used to establish the classification model. Also, using the same regression method, another classification model was established with the important factors as the independent variables. The results discovered that the total correct classification rates of CHAID-logistic regression model are greater than the total correct classification rates of logistic regression model. The forecasted results can provide investors with more information about investments selection.
C1 [Chen, Juei-Chao; Liu, Ya-Tzu] Fu Jen Catholic Univ, Inst Appl Stat, Taipei, Taiwan.
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   [Wang, Hsu-Sheng] Chung Hua Univ, Inst Technol Management, Hsinchu, Taiwan.
C3 Fu Jen Catholic University; Chung Hua University
RP Chen, JC (corresponding author), Fu Jen Catholic Univ, Inst Appl Stat, Taipei, Taiwan.
EM 006884@mail.fju.edu.tw; tommy@csu.edu.tw; ya_122@hotmail.com;
   r92620@ms36.hinet.net
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NR 33
TC 1
Z9 1
U1 0
U2 2
PU ICIC INTERNATIONAL
PI KUMAMOTO
PA TOKAI UNIV, 9-1-1, TOROKU, KUMAMOTO, 862-8652, JAPAN
SN 1349-4198
EI 1349-418X
J9 INT J INNOV COMPUT I
JI Int. J. Innov. Comp. Inf. Control
PD JUN
PY 2009
VL 5
IS 6
BP 1515
EP 1525
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA 492UL
UT WOS:000269685400006
DA 2024-09-05
ER
PT J
AU Oyewola, DO
   Dada, EG
AF Oyewola, David Opeoluwa
   Dada, Emmanuel Gbenga
TI Exploring machine learning: a scientometrics approach using bibliometrix
   and VOSviewer
SO SN APPLIED SCIENCES
LA English
DT Article
DE Bibliometrix; VOSviewer; Coupling; Machine learning; Scientometrics
ID SEGMENTATION
AB Machine Learning has found application in solving complex problems in different fields of human endeavors such as intelligent gaming, automated transportation, cyborg technology, environmental protection, enhanced health care, innovation in banking and home security, and smart homes. This research is motivated by the need to explore the global structure of machine learning to ascertain the level of bibliographic coupling, collaboration among research institutions, co-authorship network of countries, and sources coupling in publications on machine learning techniques. The Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) was applied to clustering prediction of authors dominance ranking in this paper. Publications related to machine learning were retrieved and extracted from the Dimensions database with no language restrictions. Bibliometrix was employed in computation and visualization to extract bibliographic information and perform a descriptive analysis. VOSviewer (version 1.6.16) tool was used to construct and visualize structure map of source coupling networks of researchers and co-authorship. About 10,814 research papers on machine learning published from 2010 to 2020 were retrieved for the research. Experimental results showed that the highest degree of betweenness centrality was obtained from cluster 3 with 153.86 from the University of California and Harvard University with 24.70. In cluster 1, the national university of Singapore has the highest degree betweenness of 91.72. Also, in cluster 5, the University of Cambridge (52.24) and imperial college London (4.52) having the highest betweenness centrality manifesting that he could control the collaborative relationship and that they possessed and controlled a large number of research resources. Findings revealed that this work has the potential to provide valuable guidance for new perspectives and future research work in the rapidly developing field of machine learning.
C1 [Oyewola, David Opeoluwa] Fed Univ Kashere, Fac Sci, Dept Math & Comp Sci, PMB 0182, Gombe, Nigeria.
   [Dada, Emmanuel Gbenga] Univ Maiduguri, Fac Sci, Dept Math Sci, Maiduguri, Nigeria.
RP Oyewola, DO (corresponding author), Fed Univ Kashere, Fac Sci, Dept Math & Comp Sci, PMB 0182, Gombe, Nigeria.
EM davidakaprof01@yahoo.com; gbengadada@unimaid.edu.ng
RI Dada, Dr. Emmanuel Gbenga/CAA-0153-2022; DADA, EMMANUEL
   GBENGA/AAV-2728-2021
OI DADA, EMMANUEL GBENGA/0000-0002-1132-5447; oyewola,
   david/0000-0001-9638-8764
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NR 48
TC 37
Z9 39
U1 17
U2 108
PU SPRINGER INT PUBL AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 2523-3963
EI 2523-3971
J9 SN APPL SCI
JI SN Appl. Sci.
PD MAY
PY 2022
VL 4
IS 5
AR 143
DI 10.1007/s42452-022-05027-7
PG 18
WC Multidisciplinary Sciences
WE Emerging Sources Citation Index (ESCI)
SC Science & Technology - Other Topics
GA 0K9PC
UT WOS:000781118000003
PM 35434524
OA gold, Green Published
DA 2024-09-05
ER
PT C
AU Hu, ZW
   Lu, LF
   Liu, GJ
   Jin, Y
   Zhao, L
AF Hu Ziwei
   Lu Lifeng
   Liu Guojun
   Jin Yi
   Zhao Liang
BE Cao, J
   Wang, J
   Liu, W
   Xie, K
TI Research on Adaptability Evaluation Method of New Communication
   Technology Applied to Energy Internet Communication Network
SO 2017 FIRST IEEE INTERNATIONAL CONFERENCE ON ENERGY INTERNET (ICEI 2017)
LA English
DT Proceedings Paper
CT 1st IEEE International Conference on Energy Internet (ICEI)
CY APR 17-21, 2017
CL Beijing, PEOPLES R CHINA
DE principal component analysis; analytic hierarchy process; Energy
   Internet; evaluation method
AB Electricity is the major energy form of energy internet and the smart grid is the carrier. Compared with the traditional power grid, it puts forward higher requirements for communication. Introduction of new communication technology will effectively improve the performance of energy internet communication network, but how to evaluate the adaptability of new communication technologies is a key problem to be solved in the process of the development of energy internet communication network. The adaptability to the energy internet communication network should consider the development level of the regional power grid, the economy and other factors, and not only based on the single index, so we proposed an index system considered from four aspects: safety, economy, maturity and effectiveness. In addition, the evaluation method of the adaptability should be objective and reflect the intention and strategy of the evaluation subject. Based on these, an evaluation method of adaptability based on principal component analysis and analytic hierarchy process is proposed which focuses on the process of assessment and implementation plan. An example shows the proposed evaluation method by comparing the adaptability of PTN, POTN and enhanced MSTP. And the result demonstrates that POTN is more suitable for the communication network of energy internet.
C1 [Hu Ziwei; Lu Lifeng; Liu Guojun] Global Energy Interconnect Res Inst, Beijing 102209, Peoples R China.
   [Jin Yi] State Grid Jiangsu Elect Power Co, Nanjing 210024, Jiangsu, Peoples R China.
   [Zhao Liang] Beijing Univ Posts & Telecommun, Beijing 100876, Peoples R China.
C3 State Grid Corporation of China; Beijing University of Posts &
   Telecommunications
RP Hu, ZW (corresponding author), Global Energy Interconnect Res Inst, Beijing 102209, Peoples R China.
EM huziwei@geiri.sgcc.com.cn
RI Zhao, Liang/GLS-6320-2022
OI Zhao, Liang/0000-0003-1503-4708
FU State Grid RD project [SGRIXTKJ [2015]241]
FX This work was supported in part by State Grid R&D project (grant No.
   SGRIXTKJ [2015]241).
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NR 29
TC 1
Z9 1
U1 0
U2 2
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-5090-5759-7
PY 2017
BP 250
EP 255
DI 10.1109/ICEI.2017.51
PG 6
WC Computer Science, Information Systems; Computer Science,
   Interdisciplinary Applications; Energy & Fuels; Engineering, Electrical
   & Electronic; Telecommunications
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Energy & Fuels; Engineering; Telecommunications
GA BI2CZ
UT WOS:000408549200044
DA 2024-09-05
ER
PT J
AU Kumar, S
   Lim, WM
   Sivarajah, U
   Kaur, J
AF Kumar, Satish
   Lim, Weng Marc
   Sivarajah, Uthayasankar
   Kaur, Jaspreet
TI Artificial Intelligence and Blockchain Integration in Business: Trends
   from a Bibliometric-Content Analysis
SO INFORMATION SYSTEMS FRONTIERS
LA English
DT Article
DE Artificial intelligence; Blockchain; Business; Fourth industrial
   revolution; IR 4.0; Integration; Trends
ID INDUSTRIAL IOT; AI; ADOPTION; SYSTEMS; ARCHITECTURE; MANAGEMENT;
   FRAMEWORK
AB Artificial intelligence (AI) and blockchain are the two disruptive technologies emerging from the Fourth Industrial Revolution (IR4.0) that have introduced radical shifts in the industry. The amalgamation of AI and blockchain holds tremendous potential to create new business models enabled through digitalization. Although research on the application and convergence of AI and blockchain exists, our understanding of the utility of its integration for business remains fragmented. To address this gap, this study aims to characterize the applications and benefits of integrated AI and blockchain platforms across different verticals of business. Using bibliometric analysis, this study reveals the most influential articles on the subject based on their publications, citations, and importance in the intellectual network. Using content analysis, this study sheds light on the subject's intellectual structure, which is underpinned by four major thematic clusters focusing on supply chains, healthcare, secure transactions, and finance and accounting. The study concludes with 10 application areas in business that can benefit from these technologies.
C1 [Kumar, Satish; Kaur, Jaspreet] Malaviya Natl Inst Technol, Dept Management Studies, Jaipur 302017, Rajasthan, India.
   [Kumar, Satish; Lim, Weng Marc] Swinburne Univ Technol, Fac Business Design & Arts, Jalan Simpang Tiga, Sarawak 93350, Malaysia.
   [Lim, Weng Marc] Swinburne Univ Technol, Sch Business Law & Entrepreneurship, John St, Hawthorn, Vic 3122, Australia.
   [Sivarajah, Uthayasankar] Univ Bradford, Fac Management Law & Social Sci, Sch Management, Richmond Rd, Bradford BD7 1DP, W Yorkshire, England.
C3 National Institute of Technology (NIT System); Malaviya National
   Institute of Technology Jaipur; Swinburne University of Technology;
   Swinburne University of Technology Sarawak; Swinburne University of
   Technology; University of Bradford
RP Kumar, S (corresponding author), Malaviya Natl Inst Technol, Dept Management Studies, Jaipur 302017, Rajasthan, India.; Kumar, S (corresponding author), Swinburne Univ Technol, Fac Business Design & Arts, Jalan Simpang Tiga, Sarawak 93350, Malaysia.
EM skumar.dms@mnit.ac.in; lim@wengmarc.com; u.sivarajah@bradford.ac.uk;
   2019RBM9076@mnit.ac.in
RI Sivarajah, Uthayasankar/AAU-7065-2020; Lim, Weng Marc/I-1723-2019; Kaur,
   Jaspreet/HSG-1500-2023; Kumar, Satish/E-2103-2018; Kaur,
   Jaspreet/HSG-1545-2023; Kumar, Satish/M-8694-2017
OI Sivarajah, Uthayasankar/0000-0002-6401-540X; Lim, Weng
   Marc/0000-0001-7196-1923; Kumar, Satish/0000-0001-6788-0952; Kaur,
   Jaspreet/0000-0003-0118-5900; Kumar, Satish/0000-0001-5200-1476; Kaur,
   Jaspreet/0000-0001-6369-2428
FU CAUL
FX Open Access funding enabled and organized by CAUL and its Member
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NR 95
TC 61
Z9 62
U1 23
U2 64
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1387-3326
EI 1572-9419
J9 INFORM SYST FRONT
JI Inf. Syst. Front.
PD APR
PY 2023
VL 25
IS 2
SI SI
BP 871
EP 896
DI 10.1007/s10796-022-10279-0
EA APR 2022
PG 26
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA F6LR2
UT WOS:000781734200001
PM 35431617
OA Green Published, hybrid
DA 2024-09-05
ER
PT J
AU Duan, SY
   Zhao, Y
AF Duan, Siyu
   Zhao, Yang
TI Knowledge graph analysis of artificial intelligence application research
   in nursing field based on visualization technology
SO ALEXANDRIA ENGINEERING JOURNAL
LA English
DT Article
DE Nursing; Artificial intelligence; Bibliometrics; Visual analysis;
   CiteSpace; Map of knowledge
ID BIBLIOMETRIC ANALYSIS; EMERGING TRENDS
AB In order to assess the research status, hot issues of artificial intelligence in nursing to pro-vide reference for scholars. This work conduct a quantitative analysis on related literature in WOS from 2011 to 2022 by mathematical and statistical methods, including publication trend, journals, author, institution, national and regional, keyword, literature co-citation. The hotspots and trends were revealed. The results showed that: (1) scholars' attention to it showed a steady increasing. (2) There were 20 journals published more than 6 papers, published 205 papers, that's 30.83 % of the total, Journal of Healthcare Engineering published the most papers, that's 8.78 %. (2) Kendrick Cato, Lisiane Pruinelli published the most papers, that's 2.95 %, which have strong cooperation. (3) Harvard Medical School, Columbia University and University of Minnesota published 114 papers, that's 8.21 % of the total, were the core unit, Harvard Med Sch, Columbia Univ and Univ Penn had strong cooperation. (4) The United States, China, Japan, Australia and United Kingdom are the top five countries for publishing papers, that's 80.3 % of the total. The cooperation degree of the each are 0.51, 0.36, 0.07, 0.04, and 0.06. (6) "electronic health record", "risk prediction" and "supervised machine learning" are current research hotspots. & COPY; 2023 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/ 4.0/).
C1 [Duan, Siyu; Zhao, Yang] Panzhihua Univ, Sch Hlth & Wellness, Panzhihua 617000, Peoples R China.
C3 Panzhihua University
RP Zhao, Y (corresponding author), Panzhihua Univ, Sch Hlth & Wellness, Panzhihua 617000, Peoples R China.
EM zhaoyang@pzhu.edu.cn
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NR 38
TC 2
Z9 2
U1 13
U2 36
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1110-0168
EI 2090-2670
J9 ALEX ENG J
JI Alex. Eng. J.
PD AUG 1
PY 2023
VL 76
BP 651
EP 667
DI 10.1016/j.aej.2023.06.072
EA JUN 2023
PG 17
WC Engineering, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA M4KT2
UT WOS:001029917100001
OA gold
DA 2024-09-05
ER
PT J
AU Radanliev, P
   De Roure, D
   Maple, C
   Santos, O
AF Radanliev, Petar
   De Roure, David
   Maple, Carsten
   Santos, Omar
TI Forecasts on Future Evolution of Artificial Intelligence and Intelligent
   Systems
SO IEEE ACCESS
LA English
DT Article
DE Artificial intelligence; COVID-19; Medical services; Internet of Things;
   Bibliometrics; Ethics; Codes; Artificial intelligence; intelligent
   systems; future evolution; Covid-19
AB The field of artificial intelligence has gained a significant attention in the media. Some counties claim to be the leaders in the field, other countries claim to be winning in the race for leadership in artificial intelligence. This article conducts a statistical (i.e., bibliometric) analysis of research data records on artificial intelligence by year, country, language, and organisation. The results are clearly in favour of the USA on a national level, and English is clearly the dominant language for disseminating results. But in terms of leading organisation in the field of artificial intelligence creates more confusing result - e.g., between the Chinese Academy of Sciences and the University of California - in the leadership race. The forecasts from this study on future evolution of artificial intelligence is that it is unlikely that (in the next 60 years) AI 'superintelligence' would trigger a catastrophic event for humanity.
C1 [Radanliev, Petar; De Roure, David] Univ Oxford, Oxford E Res Ctr, Dept Engn Sci, Oxford OX1 2JD, England.
   [Maple, Carsten] Univ Warwick, WMG Cyber Secur Ctr, Coventry CV4 7AL, W Midlands, England.
   [Santos, Omar] Cisco Res Ctr, Res Triangle Pk, NC 27709 USA.
C3 University of Oxford; University of Warwick; Cisco Systems Inc
RP Radanliev, P (corresponding author), Univ Oxford, Oxford E Res Ctr, Dept Engn Sci, Oxford OX1 2JD, England.
EM petar.radanliev@oerc.ox.ac.uk
RI Radanliev, Petar/L-7509-2015
OI Radanliev, Petar/0000-0001-5629-6857; Maple, Carsten/0000-0002-4715-212X
FU Engineering and Physical Sciences Research Council (EPSRC)
   [EP/S035362/1]; Cisco Research Centre [CG1525381]; EPSRC [EP/V056883/1,
   EP/S021779/1, EP/R007195/1] Funding Source: UKRI; SPF [EP/S035362/1]
   Funding Source: UKRI
FX This work was supported in part by the Engineering and Physical Sciences
   Research Council (EPSRC) under Grant EP/S035362/1, and in part by the
   Cisco Research Centre under Grant CG1525381.
CR Adrien B., 2021, ARTIF INTELL REV, V54, P3849
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NR 22
TC 16
Z9 16
U1 9
U2 70
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2022
VL 10
BP 45280
EP 45288
DI 10.1109/ACCESS.2022.3169580
PG 9
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA 0Z0NQ
UT WOS:000790778100001
OA Green Submitted, gold
DA 2024-09-05
ER
PT J
AU Fijacko, N
   Creber, RM
   Abella, BS
   Kocbek, P
   Metlicar, S
   Greif, R
   Stiglic, G
AF Fijacko, Nino
   Creber, Ruth Masterson
   Abella, Benjamin S.
   Kocbek, Primoz
   Metlicar, Spela
   Greif, Robert
   Stiglic, Gregor
TI Using generative artificial intelligence in bibliometric analysis: 10
   years of research trends from the European Resuscitation Congresses
SO RESUSCITATION PLUS
LA English
DT Article
DE Emergency medicine; European Resuscitation Council; Congress;
   Bibliometrics analysis; Generative artificial intelligence
ID COUNCIL GUIDELINES; CARDIAC-ARREST; EMERGENCY
AB Aims: The aim of this study is to use generative artificial intelligence to perform bibliometric analysis on abstracts published at European Resuscitation Council (ERC) annual scientific congress and define trends in ERC guidelines topics over the last decade. Methods: In this bibliometric analysis, the WebHarvy software (SysNucleus, India) was used to download data from the Resuscitation journal's website through the technique of web scraping. Next, the Chat Generative Pre-trained Transformer 4 (ChatGPT-4) application programming interface (Open AI, USA) was used to implement the multinomial classification of abstract titles following the ERC 2021 guidelines topics. Results: From 2012 to 2022 a total of 2491 abstracts have been published at ERC congresses. Published abstracts ranged from 88 (in 2020) to 368 (in 2015). On average, the most common ERC guidelines topics were Adult basic life support (50.1%), followed by Adult advanced life support (41.5%), while Newborn resuscitation and support of transition of infants at birth (2.1%) was the least common topic. The findings also highlight that the Basic Life Support and Adult Advanced Life Support ERC guidelines topics have the strongest co-occurrence to all ERC guidelines topics, where the Newborn resuscitation and support of transition of infants at birth (2.1%; 52/2491) ERC guidelines topic has the weakest co-occurrence. Conclusion: This study demonstrates the capabilities of generative artificial intelligence in the bibliometric analysis of abstract titles using the example of resuscitation medicine research over the last decade at ERC conferences using large language models.
C1 [Fijacko, Nino; Kocbek, Primoz; Metlicar, Spela; Stiglic, Gregor] Univ Maribor, Fac Hlth Sci, Maribor 3000, Slovenia.
   [Fijacko, Nino; Greif, Robert] ERC Res Net, Niels, Belgium.
   [Fijacko, Nino] Univ Maribor, Med Ctr, Maribor, Slovenia.
   [Creber, Ruth Masterson] Columbia Univ, Sch Nursing, New York, NY USA.
   [Abella, Benjamin S.] Univ Penn, Ctr Resuscitat Sci, Philadelphia, PA USA.
   [Abella, Benjamin S.] Univ Penn, Dept Emergency Med, Philadelphia, PA USA.
   [Kocbek, Primoz] Univ Ljubljana, Fac Med, Ljubljana, Slovenia.
   [Metlicar, Spela] Univ Clin Ctr Ljubljana, Med Dispatch Ctr Maribor, Ljubljana, Slovenia.
   [Greif, Robert] Univ Bern, Bern, Switzerland.
   [Greif, Robert] Sigmund Freud Univ Vienna, Sch Med, Vienna, Austria.
   [Greif, Robert] Univ Maribor, Fac Elect Engn & Comp Sci, Maribor, Slovenia.
   [Stiglic, Gregor] Univ Edinburgh, Usher Inst, Edinburgh, Scotland.
C3 University of Maribor; University of Maribor; Columbia University;
   University of Pennsylvania; University of Pennsylvania; University of
   Ljubljana; University Medical Centre Ljubljana; University of Bern;
   University of Maribor; University of Edinburgh
RP Fijacko, N (corresponding author), Univ Maribor, Fac Hlth Sci, Maribor 3000, Slovenia.
EM nino.fijacko@um.si
OI Metlicar, Spela/0009-0007-4051-1736
FU Slovenian Research Agency [ARRS P2-0057, ARRS N3-0307, ARRS
   BI-US/22-24-138, C3330-22-953012]; National Institutes of Health
   [R01HL161458, R01NS123639, R01HL152021]; Department of Defense; Becton
   Dickinson; Zoll; Stryker; Patient-Centered Outcomes Research Insti tute
   (PCORI)
FX Nino Fija & ccaron;ko, Primo & zcaron; Kocbek, and Gregor Stiglic are
   supported by Slovenian Research Agency grants ARRS P2-0057, ARRS
   N3-0307, ARRS BI-US/22-24-138, NextGenerationEU and MVZI
   (C3330-22-953012) . Benjamin S. Abella has received research funding
   from the National Institutes of Health, the Department of Defense, and
   Becton Dickinson. He has served as a paid consultant to Becton
   Dickinson, Zoll and Stryker. He holds equity in MDAlly and VOCHealth.
   Ruth Masterson Creber receives research funding from the National
   Institutes of Health (R01HL161458, R01NS123639, R01HL152021) and the
   Patient-Centered Outcomes Research Insti tute (PCORI) .
CR [Anonymous], Generative Pre-trained Transformer 4 (GPT-4) application programming interface
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NR 28
TC 1
Z9 1
U1 4
U2 4
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2666-5204
J9 RESUSC PLUS
JI Resusc. Plus
PD JUN
PY 2024
VL 18
AR 100584
DI 10.1016/j.resplu.2024.100584
EA FEB 2024
PG 5
WC Critical Care Medicine; Emergency Medicine
WE Emerging Sources Citation Index (ESCI)
SC General & Internal Medicine; Emergency Medicine
GA MG0W0
UT WOS:001192365400001
PM 38420596
OA hybrid
DA 2024-09-05
ER
PT J
AU Bioglio, L
   Rho, V
   Pensa, RG
AF Bioglio, Livio
   Rho, Valentina
   Pensa, Ruggero G.
TI Ranking by inspiration: a network science approach
SO MACHINE LEARNING
LA English
DT Article
DE Information diffusion; Bibliographic indexes; Citation networks; Topic
   modeling
ID INDEX
AB Contagion processes have been widely studied in epidemiology and life science in general, but their implications are largely tangible in other research areas, such as in network science and computational social science. Contagion models, in particular, have proven helpful in the study of information diffusion, a very topical issue thanks to its applications to social media/network analysis, viral marketing campaigns, influence maximization and prediction. In bibliographic networks, for instance, an information diffusion process takes place when some authors, that publish papers in a given topic, influence some of their neighbors (coauthors, citing authors, collaborators) to publish papers in the same topic, and the latter influence their neighbors in their turn. This well-accepted definition, however, does not consider that influence in bibliographic networks is a complex phenomenon involving several scientific and cultural aspects. In fact, in scientific citation networks, influential topics are usually considered those ones that spread most rapidly in the network. Although this is generally a fact, this semantics does not consider that topics in bibliographic networks evolve continuously. In fact, knowledge, information and ideas are dynamic entities that acquire different meanings when passing from one person to another. Thus, in this paper, we propose a new definition of influence that captures the diffusion of inspiration within the network. We call it inspiration score, and show its effectiveness in detecting the most inspiring topics, authors, papers and venues in a citation network built upon two large bibliographic datasets. We show that the inspiration score can be used as an alternative or complementary bibliographic index in academic ranking applications.
C1 [Bioglio, Livio; Rho, Valentina; Pensa, Ruggero G.] Univ Turin, Dept Comp Sci, Turin, Italy.
C3 University of Turin
RP Bioglio, L (corresponding author), Univ Turin, Dept Comp Sci, Turin, Italy.
EM livio.bioglio@unito.it; valentina.rho@unito.it; ruggero.pensa@unito.it
RI Pensa, Ruggero G./B-5994-2011
OI Pensa, Ruggero G./0000-0001-5145-3438
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NR 46
TC 4
Z9 4
U1 0
U2 6
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0885-6125
EI 1573-0565
J9 MACH LEARN
JI Mach. Learn.
PD JUN
PY 2020
VL 109
IS 6
BP 1205
EP 1229
DI 10.1007/s10994-019-05828-9
PG 25
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA MB1CR
UT WOS:000542345400004
OA Green Submitted, Bronze
DA 2024-09-05
ER
PT J
AU Nicolas, G
   Bai, XCZ
   Fiske, ST
AF Nicolas, Gandalf
   Bai, Xuechunzi
   Fiske, Susan T.
TI Exploring Research-Methods Blogs in Psychology: Who Posts What About
   Whom, and With What Effect?
SO PERSPECTIVES ON PSYCHOLOGICAL SCIENCE
LA English
DT Article
DE research methods; blogs; social media; natural language processing;
   replicability
ID GENDER-DIFFERENCES
AB During the methods crisis in psychology and other sciences, much discussion developed online in forums such as blogs and other social media. Hence, this increasingly popular channel of scientific discussion itself needs to be explored to inform current controversies, record the historical moment, improve methods communication, and address equity issues. Who posts what about whom, and with what effect? Does a particular generation or gender contribute more than another? Do blogs focus narrowly on methods, or do they cover a range of issues? How do they discuss individual researchers, and how do readers respond? What are some impacts? Web-scraping and text-analysis techniques provide a snapshot characterizing 41 current research-methods blogs in psychology. Bloggers mostly represented psychology's traditional leaderships' demographic categories: primarily male, mid- to late career, associated with American institutions, White, and with established citation counts. As methods blogs, their posts mainly concern statistics, replication (particularly statistical power), and research findings. The few posts that mentioned individual researchers substantially focused on replication issues; they received more views, social-media impact, comments, and citations. Male individual researchers were mentioned much more often than female researchers. Further data can inform perspectives about these new channels of scientific communication, with the shared aim of improving scientific practices.
C1 [Nicolas, Gandalf; Bai, Xuechunzi; Fiske, Susan T.] Princeton Univ, Dept Psychol, 330 Peretsman Scully Hall, Princeton, NJ 08540 USA.
C3 Princeton University
RP Nicolas, G (corresponding author), Princeton Univ, Dept Psychol, 330 Peretsman Scully Hall, Princeton, NJ 08540 USA.
EM gnf@princeton.edu
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NR 34
TC 8
Z9 10
U1 0
U2 12
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 1745-6916
EI 1745-6924
J9 PERSPECT PSYCHOL SCI
JI Perspect. Psychol. Sci.
PD JUL
PY 2019
VL 14
IS 4
BP 691
EP 704
DI 10.1177/1745691619835216
PG 14
WC Psychology, Multidisciplinary
WE Social Science Citation Index (SSCI)
SC Psychology
GA IH1HT
UT WOS:000474242600012
PM 31199886
DA 2024-09-05
ER
PT J
AU Abdulkadiroglu, A
   Angrist, JD
   Narita, Y
   Pathak, PA
AF Abdulkadiroglu, Atila
   Angrist, Joshua D.
   Narita, Yusuke
   Pathak, Parag A.
TI Research Design Meets Market Design: Using Centralized Assignment for
   Impact Evaluation
SO ECONOMETRICA
LA English
DT Article
DE Causal inference; propensity score; instrumental variables; unified
   enrollment; charter schools
ID SCHOOL-CHOICE; PROPENSITY SCORE; SERIAL DICTATORSHIP; COLLEGE
   ADMISSIONS; BOSTON; ACHIEVEMENT; LOTTERIES; QUALITY; IDENTIFICATION;
   DISCONTINUITY
AB A growing number of school districts use centralized assignment mechanisms to allocate school seats in a manner that reflects student preferences and school priorities. Many of these assignment schemes use lotteries to ration seats when schools are oversubscribed. The resulting random assignment opens the door to credible quasi-experimental research designs for the evaluation of school effectiveness. Yet the question of how best to separate the lottery-generated randomization integral to such designs from non-random preferences and priorities remains open. This paper develops easily-implemented empirical strategies that fully exploit the random assignment embedded in a wide class of mechanisms, while also revealing why seats are randomized at one school but not another. We use these methods to evaluate charter schools in Denver, one of a growing number of districts that combine charter and traditional public schools in a unified assignment system. The resulting estimates show large achievement gains from charter school attendance. Our approach generates efficiency gains over ad hoc methods, such as those that focus on schools ranked first, while also identifying a more representative average causal effect. We also show how to use centralized assignment mechanisms to identify causal effects in models with multiple school sectors.
C1 [Abdulkadiroglu, Atila] Duke Univ, Dept Econ, Durham, NC 27706 USA.
   [Abdulkadiroglu, Atila; Angrist, Joshua D.; Pathak, Parag A.] NBER, Cambridge, MA 02138 USA.
   [Angrist, Joshua D.; Pathak, Parag A.] MIT, Dept Econ, Cambridge, MA 02139 USA.
   [Narita, Yusuke] Yale Univ, Dept Econ, New Haven, CT 06520 USA.
   [Narita, Yusuke] Yale Univ, Cowles Fdn, New Haven, CT 06520 USA.
C3 Duke University; National Bureau of Economic Research; Massachusetts
   Institute of Technology (MIT); Yale University; Yale University
RP Abdulkadiroglu, A (corresponding author), Duke Univ, Dept Econ, Durham, NC 27706 USA.; Abdulkadiroglu, A (corresponding author), NBER, Cambridge, MA 02138 USA.
EM atila.abdulkadiroglu@duke.edu; angrist@mit.edu; yusuke.narita@yale.edu;
   ppathak@mit.edu
RI Angrist, Josh/ADL-8782-2022
FU Laura and John Arnold Foundation; National Science Foundation
   [SES-1056325, SES-1426541]; Direct For Social, Behav & Economic Scie;
   Divn Of Social and Economic Sciences [1056325] Funding Source: National
   Science Foundation
FX We thank Alisha Chiarelli, Brian Eschbacher, Van Schoales, and the staff
   at Denver Public Schools for answering our questions and facilitating
   access to data. Nikhil Agarwal, Isaiah Andrews, Eduardo Azevedo, Gary
   Chamberlain, Victor Chernozhukov, Dean Eckles, Jerry Hausman, Peter
   Hull, Hide Ichimura, Guido Imbens, Rafael Lalive, Edward Lazear, Jacob
   Leshno, Anna Mikuscheva, Paul Rosenbaum, Chris Walters, and seminar
   participants at Harvard, SOLE, Stanford University, MIT, Duke
   University, the Fall 2015 NBER market design meeting, Kyoto, Osaka, and
   Tokyo provided helpful feedback. We are especially indebted to Mayara
   Felix, Ye Ji Kee, and Ignacio Rodriguez for expert research assistance
   and to MIT SEII program managers Annice Correia and Eryn Heying for
   invaluable administrative support. We gratefully acknowledge funding
   from the Laura and John Arnold Foundation and the National Science
   Foundation (under awards SES-1056325 and SES-1426541). Data from Denver
   Public Schools were made available to us through the Institute for
   Innovation in Public School Choice. Abdulkadiroglu and Pathak are
   Scientific Advisory Board members of the Institute for Innovation in
   Public School Choice. Angrist's daughter teaches at a Boston charter
   school.
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NR 84
TC 52
Z9 80
U1 4
U2 51
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0012-9682
EI 1468-0262
J9 ECONOMETRICA
JI Econometrica
PD SEP
PY 2017
VL 85
IS 5
BP 1373
EP 1432
DI 10.3982/ECTA13925
PG 60
WC Economics; Mathematics, Interdisciplinary Applications; Social Sciences,
   Mathematical Methods; Statistics & Probability
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Mathematics; Mathematical Methods In Social
   Sciences
GA FI6NA
UT WOS:000412112000002
OA Green Submitted, Green Published, Bronze
DA 2024-09-05
ER
PT J
AU Rose, RA
   Cosgrove, JA
   Lee, BR
AF Rose, Roderick A.
   Cosgrove, John A.
   Lee, Bethany R.
TI Directed Acyclic Graphs in Social Work Research and Evaluation: A Primer
SO JOURNAL OF THE SOCIETY FOR SOCIAL WORK AND RESEARCH
LA English
DT Article
DE directed acyclic graph; causal inference; confounding; quasi-experiment;
   observational data
ID CAUSAL INFERENCE; TOOL
AB Social work aspires to change social outcomes through policy and practice. Thus, researchers often use quantitative analysis to understand how social phenomena or policy and practice interventions change outcomes, but these causal questions cannot be answered using statistical association only. Directed acyclic graphs (DAGs), also called causal graphs, are a framework for representing assumptions about the causal relations between variables, imbuing statistical associations with causal meaning. This visual language may be more accessible than a math-based approach. We provide social work researchers with clear guidelines for using DAGs. First, we synthesize the current DAG literature and show how many statistical phenomena are represented in DAGs. Second, we describe an ordered process for building a DAG around a causal question about the impact of a treatment on an outcome. Third, we introduce a structured process called "clock and grid" for specifying the confounders that must be accounted for to estimate the unbiased causal effect. Throughout, we use working examples of social work evaluation scenarios to facilitate understanding of these concepts. We argue that DAGs represent an end-to-end conceptual framework for curating subject area knowledge that can advance social work research by informing design and analysis.
C1 [Rose, Roderick A.; Lee, Bethany R.] Univ Maryland, Sch Social Work, College Pk, MD USA.
   [Cosgrove, John A.] Westat Corp, Rockville, MD USA.
   [Rose, Roderick A.] 525 West Redwood St, Baltimore, MD 21201 USA.
C3 University System of Maryland; University of Maryland College Park;
   Westat
RP Rose, RA (corresponding author), 525 West Redwood St, Baltimore, MD 21201 USA.
EM rrose@ssw.umaryland.edu
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NR 17
TC 0
Z9 0
U1 0
U2 0
PU UNIV CHICAGO PRESS
PI CHICAGO
PA 1427 E 60TH ST, CHICAGO, IL 60637-2954 USA
SN 2334-2315
EI 1948-822X
J9 J SOC SOC WORK RES
JI J. Soc. Soc. Work Res.
PD JUN 1
PY 2024
VL 15
IS 2
BP 391
EP 415
DI 10.1086/723606
EA JUN 2024
PG 25
WC Social Work
WE Social Science Citation Index (SSCI)
SC Social Work
GA XZ6L9
UT WOS:001243418600001
DA 2024-09-05
ER
PT J
AU Ezugwu, AE
   Shukla, AK
   Nath, R
   Akinyelu, AA
   Agushaka, JO
   Chiroma, H
   Muhuri, PK
AF Ezugwu, Absalom E.
   Shukla, Amit K.
   Nath, Rahul
   Akinyelu, Andronicus A.
   Agushaka, Jeffery O.
   Chiroma, Haruna
   Muhuri, Pranab K.
TI Metaheuristics: a comprehensive overview and classification along with
   bibliometric analysis
SO ARTIFICIAL INTELLIGENCE REVIEW
LA English
DT Article
DE Metaheuristics; Bibliometric; Inspirational source; Classification;
   Taxonomy; Application areas
ID HEURISTIC OPTIMIZATION ALGORITHM; NUMERICAL FUNCTION OPTIMIZATION;
   SYMBIOTIC ORGANISMS SEARCH; NATURE-INSPIRED ALGORITHM; GLOBAL
   OPTIMIZATION; SWARM-INTELLIGENCE; EVOLUTIONARY ALGORITHM; EFFICIENT
   ALGORITHM; DESIGN; COLONY
AB Research in metaheuristics for global optimization problems are currently experiencing an overload of wide range of available metaheuristic-based solution approaches. Since the commencement of the first set of classical metaheuristic algorithms namely genetic, particle swarm optimization, ant colony optimization, simulated annealing and tabu search in the early 70s to late 90s, several new advancements have been recorded with an exponential growth in the novel proposals of new generation metaheuristic algorithms. Because these algorithms are neither entirely judged based on their performance values nor according to the useful insight they may provide, but rather the attention is given to the novelty of the processes they purportedly models, these area of study will continue to periodically see the arrival of several new similar techniques in the future. However, there is an obvious reason to keep track of the progressions of these algorithms by collating their general algorithmic profiles in terms of design inspirational source, classification based on swarm or evolutionary search concept, existing variation from the original design, and application areas. In this paper, we present a relatively new taxonomic classification list of both classical and new generation sets of metaheuristic algorithms available in the literature, with the aim of providing an easily accessible collection of popular optimization tools for the global optimization research community who are at the forefront in utilizing these tools for solving complex and difficult real-world problems. Furthermore, we also examined the bibliometric analysis of this field of metaheuristic for the last 30 years.
C1 [Ezugwu, Absalom E.; Agushaka, Jeffery O.] Univ KwaZulu Natal, Sch Math Stat & Comp Sci, King Edward Rd, ZA-3201 Pietermaritzburg, South Africa.
   [Shukla, Amit K.; Nath, Rahul; Muhuri, Pranab K.] South Asian Univ, Dept Comp Sci, New Delhi 110021, India.
   [Akinyelu, Andronicus A.] Univ Free State, Dept Comp Sci & Informat, ZA-9301 Bloemfontein, South Africa.
   [Chiroma, Haruna] Natl Yunlin Univ Sci & Technol, Dept Comp Sci & Informat Engn, Yuanlin, Taiwan.
   [Shukla, Amit K.] Univ Jyvaskyla, Fac Informat Technol, Box 35 Agora, Jyvaskyla 40014, Finland.
C3 University of Kwazulu Natal; South Asian University (SAU); University of
   the Free State; National Yunlin University Science & Technology;
   University of Jyvaskyla
RP Shukla, AK (corresponding author), South Asian Univ, Dept Comp Sci, New Delhi 110021, India.; Shukla, AK (corresponding author), Univ Jyvaskyla, Fac Informat Technol, Box 35 Agora, Jyvaskyla 40014, Finland.
EM amitkshukla@live.com
RI Shukla, Amit k./AAX-5624-2021; Ezugwu, Absalom El-Shamir/HTN-9866-2023;
   Agushaka, Ovre Jeffrey/AEI-6796-2022; MUHURI, PRANAB K./F-4301-2015;
   Haruna, Ph.D Chiroma/O-2934-2013; Ezugwu, Absalom
   El-Shamir/AIE-3466-2022
OI Ezugwu, Absalom El-Shamir/0000-0002-3721-3400; Agushaka, Ovre
   Jeffrey/0000-0001-8742-7522; MUHURI, PRANAB K./0000-0001-7122-7622;
   Ezugwu, Absalom El-Shamir/0000-0002-3721-3400
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NR 350
TC 118
Z9 122
U1 11
U2 62
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0269-2821
EI 1573-7462
J9 ARTIF INTELL REV
JI Artif. Intell. Rev.
PD AUG
PY 2021
VL 54
IS 6
BP 4237
EP 4316
DI 10.1007/s10462-020-09952-0
EA MAR 2021
PG 80
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA TS9CN
UT WOS:000629094800001
DA 2024-09-05
ER
PT J
AU Vartiainen, H
   Vuojärvi, H
   Saramäki, K
   Eriksson, M
   Ratinen, I
   Torssonen, P
   Vanninen, P
   Pöllänen, S
AF Vartiainen, Henriikka
   Vuojarvi, Hanna
   Saramaki, Kaija
   Eriksson, Miikka
   Ratinen, Ilkka
   Torssonen, Piritta
   Vanninen, Petteri
   Pollanen, Sinikka
TI Cross-boundary collaboration and knowledge creation in an online higher
   education course
SO BRITISH JOURNAL OF EDUCATIONAL TECHNOLOGY
LA English
DT Article
DE cross-boundary collaboration; design-based research; higher education;
   knowledge creation; online learning; pedagogical development
AB This study investigated an international, inter-university and multidisciplinary online course with the aim of helping higher education students develop competencies for solving complex problems in collaboration with their peers and stakeholders. The course design was informed by the knowledge creation framework and ideas about cross-boundary collaboration. We attempted to enrich perspectives on knowledge creation by investigating how higher education students (N = 42) from different fields of study and from 17 different nationalities perceived, built and regulated cross-boundary collaboration in the pursuit of real-life problems presented by companies or non-governmental organisations. Drawing on data from 11 in-depth group interviews and team reports of students who had completed this course, we showed the kinds of activities the students considered relevant for cross-boundary collaboration and knowledge creation online. Given this extended context for knowledge creation, the study contributes to the pedagogical development of online learning in higher education.
C1 [Vartiainen, Henriikka; Eriksson, Miikka; Pollanen, Sinikka] Univ Eastern Finland UEF, Sch Appl Educ Sci & Teacher Educ, POB 111, FI-80101 Joensuu, Finland.
   [Vuojarvi, Hanna; Ratinen, Ilkka] Univ Lapland, Fac Educ, Rovaniemi, Finland.
   [Saramaki, Kaija] Karelia Univ Appl Sci, Energy & Environm Technol, Joensuu, Finland.
   [Torssonen, Piritta] Univ Eastern Finland, Sch Forest Sci, Joensuu, Finland.
   [Vanninen, Petteri] Nat Resources Inst Finland Savonlinna, Savonlinna, Finland.
C3 University of Eastern Finland; University of Lapland; Karelia University
   of Applied Sciences; University of Eastern Finland; Natural Resources
   Institute Finland (Luke)
RP Vartiainen, H (corresponding author), Univ Eastern Finland UEF, Sch Appl Educ Sci & Teacher Educ, POB 111, FI-80101 Joensuu, Finland.
EM henriikka.vartiainen@uef.fi
OI Ratinen, Ilkka/0000-0001-7977-062X; Pollanen,
   Sinikka/0000-0002-0570-6032
FU Ministry of Education and Culture in Finland [2018--20]; 
   [OKM/262/523/2017]
FX The study presented here is a part of the research activities of the
   DigiCampus project (2018--20), which is funded by the Ministry of
   Education and Culture in Finland (Grant no. OKM/262/523/2017)
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NR 30
TC 8
Z9 8
U1 6
U2 41
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0007-1013
EI 1467-8535
J9 BRIT J EDUC TECHNOL
JI Br. J. Educ. Technol.
PD SEP
PY 2022
VL 53
IS 5
BP 1304
EP 1320
DI 10.1111/bjet.13186
EA FEB 2022
PG 17
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA 4N4RT
UT WOS:000751823000001
OA hybrid
DA 2024-09-05
ER
PT J
AU Sonk, M
   Tunger, D
AF Sonk, Matthias
   Tunger, Dirk
TI Trend mining with Orange - using topic modeling in futures research with
   the example of urban mobility
SO EUROPEAN JOURNAL OF FUTURES RESEARCH
LA English
DT Article
DE Text mining; Topic modeling; Bibliometric analysis; Trend mining; Mixed
   methods; Urban mobility; Flexible mobility
AB Today, assumptions about probable future developments (at least as far as they make use of quantifiable scientific methods and are not pure speculation) are generally based on data from the past. An interesting way to analyze the future through this type of data is text mining or individual methods out of the spectrum of text mining, such as topic modeling. Topic Modeling itself is a combination of quantitative and qualitative methodology and is based on the full spectrum of social science methodology. Therefore, the method is an interesting way for futures research to analyze futures. This publication addresses the question of how a combination of different methods can contribute to trend monitoring or trend mining. For this purpose, a set of scientific publications was first generated with the help of a search query in the Web of Science (WoS), which is the basis for all evaluations and statements and topics. In essence, the method considered here should be more fully integrated into the scientific practice of futures research because it can make a valuable contribution to estimating future development based on past development.
C1 [Sonk, Matthias] Free Univ Berlin, Berlin, Germany.
   [Tunger, Dirk] Forschungszentrum Julich, Inst Informat Management, Fac Informat Sci & Commun Studies, Ctr Excellence Anal Studies Strategy,TH Koln & Pro, Julich, Germany.
C3 Free University of Berlin; Helmholtz Association; Research Center Julich
RP Sonk, M (corresponding author), Free Univ Berlin, Berlin, Germany.
EM m.sonk@fu-berlin.de
FU Freie Universitt Berlin (1008)
FX Not applicable.
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NR 20
TC 0
Z9 0
U1 5
U2 5
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 2195-4194
EI 2195-2248
J9 EUR J FUTURES RES
JI Eur. J. Futures Res.
PD MAR 11
PY 2024
VL 12
IS 1
AR 6
DI 10.1186/s40309-024-00229-1
PG 7
WC Social Sciences, Interdisciplinary
WE Social Science Citation Index (SSCI)
SC Social Sciences - Other Topics
GA KL9R4
UT WOS:001180244400001
OA gold
DA 2024-09-05
ER
PT J
AU Zengul, FD
   Oner, N
   Byrd, JD
   Savage, A
AF Zengul, Ferhat D.
   Oner, Nurettin
   Byrd, James D.
   Savage, Arline
TI Revealing Research Themes and Trends in 30 Top-ranking Accounting
   Journals: A Text-mining Approach
SO ABACUS-A JOURNAL OF ACCOUNTING FINANCE AND BUSINESS STUDIES
LA English
DT Article
DE Text mining; Natural language processing; Accounting research; Research
   themes; Research trends
ID EXECUTIVE-COMPENSATION; PERFORMANCE; DISCLOSURE; ANALYSTS; AUDITOR;
   DETERMINANTS; REFLECTIONS; INVESTMENT; UNIVERSAL; PRIVATE
AB This study reveals themes and trends in accounting research over the past 20 years by utilizing natural language processing and text-mining techniques. We generated a corpus consisting of over 40,000 articles through multiple searches in EBSCOhost Business Source Premier, Scopus, and ScienceDirect to gather data from 30 highly ranked (A* and A) journals that were listed and categorized by the Australian Business Deans Council (ABDC) as the top accounting journals. Based upon predetermined inclusion and exclusion criteria, we eliminated 24,474 non-empirical articles and those with no abstracts, resulting in 16,449 abstracts. The text-mining analyses reveal 15 distinct clusters, with five clusters showing downward trends, six trending upward, and four maintaining stability. The downward trending clusters are: (1) capital markets; (2) financial reporting; (3) accounting education, careers, and diversity; (4) earnings/markets; and (5) accounting history and capitalism. Trending upward are: (1) critical accounting; (2) auditing; (3) corporate governance; (4) corporate social responsibility; (5) debt financing; and (6) financial markets and forecasting. Stable clusters are: (1) managerial accounting; (2) international accounting standards; (3) taxation; and (4) governmental accounting. This study introduces an innovative method for discerning themes and trends in accounting research and offers a guide to neophyte accounting faculty for determining publishing outlets for research. In utilizing our findings to drill down and provide more detailed knowledge, it also serves as a reference point for future text-mining studies.
C1 [Zengul, Ferhat D.; Oner, Nurettin; Byrd, James D.; Savage, Arline] Univ Alabama Birmingham, Birmingham, AL 35294 USA.
C3 University of Alabama System; University of Alabama Birmingham
RP Zengul, FD (corresponding author), Univ Alabama Birmingham, Birmingham, AL 35294 USA.
EM ferhat@uab.edu
RI Zengul, Ferhat Devrim/AFK-8074-2022; oner, nurettin/IQU-7736-2023
OI Zengul, Ferhat Devrim/0000-0002-8454-1335; Byrd,
   James/0000-0003-4916-3372; oner, nurettin/0000-0002-4761-7863
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NR 127
TC 10
Z9 10
U1 2
U2 59
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0001-3072
EI 1467-6281
J9 ABACUS
JI Abacus
PD SEP
PY 2021
VL 57
IS 3
BP 468
EP 501
DI 10.1111/abac.12214
EA MAR 2021
PG 34
WC Business, Finance
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA US4AX
UT WOS:000623661800001
DA 2024-09-05
ER
PT C
AU Lin, SJ
   Chen, JL
   Tsai, YC
   Chang, PC
   Yang, CS
AF Lin, Shou-Jen
   Chen, Jui-Le
   Tsai, Yu-Chen
   Chang, Ping-Chun
   Yang, Chu-Sing
BE Wang, JF
   Lau, R
TI Gesture Identification Research and Applications in Evaluation Systems
SO ADVANCES IN WEB-BASED LEARNING - ICWL 2013
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT 12th International Conference on Advances in Web-Based Learning (ICWL)
CY OCT 06-09, 2013
CL TAIWAN
DE Gesture identification; human-computer interaction; computer vision
ID SUBTRACTION
AB By introducing computer-assisted identification technique into the evaluation system design for elementary and junior high schools, the users gestures, acquired through cameras, are transformed into effective interactive information, which is further applied to the digital evaluation system. Such a new-styled interactive method is expected to enhance the communication model between the learners and the information system. Without touching any interactive devices, the users can communicate with the system through gestures, from which the messages are determined and compared through image processing to further control the system interaction. The integration with an evaluation system could promote the education popularity.
C1 [Lin, Shou-Jen; Chen, Jui-Le; Tsai, Yu-Chen; Chang, Ping-Chun; Yang, Chu-Sing] Natl Cheng Kung Univ, Inst Comp & Commun Engn, Tainan 70101, Taiwan.
C3 National Cheng Kung University
RP Lin, SJ (corresponding author), Natl Cheng Kung Univ, Inst Comp & Commun Engn, Tainan 70101, Taiwan.
EM lsr@tn.edu.tw; reler.chen@gmail.com; celia@tn.edu.tw;
   moneyhomeya@gmail.com; csyang@ee.ncku.edu.tw
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NR 25
TC 0
Z9 0
U1 0
U2 0
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 0302-9743
EI 1611-3349
BN 978-3-642-41174-8; 978-3-642-41175-5
J9 LECT NOTES COMPUT SC
PY 2013
VL 8167
BP 21
EP 30
PG 10
WC Computer Science, Information Systems; Computer Science, Software
   Engineering; Computer Science, Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BB0OX
UT WOS:000340556100003
DA 2024-09-05
ER
PT J
AU Ghosh, D
AF Ghosh, Debashis
TI Relaxed covariate overlap and margin-based causal effect estimation
SO STATISTICS IN MEDICINE
LA English
DT Article
DE average causal effect; comparative effectiveness research; convex
   optimization; counterfactual; covariate balance; support vector machines
ID PROPENSITY SCORE; MATCHING METHODS; INFERENCE; SELECTION; BALANCE
AB In most nonrandomized observational studies, differences between treatment groups may arise not only due to the treatment but also because of the effect of confounders. Therefore, causal inference regarding the treatment effect is not as straightforward as in a randomized trial. To adjust for confounding due to measured covariates, a variety of methods based on the potential outcomes framework are used to estimate average treatment effects. One of the key assumptions is treatment positivity, which states that the probability of treatment is bounded away from zero and one for any possible combination of the confounders. Methods for performing causal inference when this assumption is violated are relatively limited. In this article, we discuss a new balance-related condition involving the convex hulls of treatment groups, which I term relaxed covariate overlap. An advantage of this concept is that it can be linked to a concept from machine learning, termed the margin. Introduction of relaxed covariate overlap leads to an approach in which one can perform causal inference in a three-step manner. The methodology is illustrated with two examples.
C1 [Ghosh, Debashis] Univ Colorado, Dept Biostat & Informat, Sch Publ Hlth, Aurora, CO 80045 USA.
C3 Colorado School of Public Health; University of Colorado System;
   University of Colorado Anschutz Medical Campus
RP Ghosh, D (corresponding author), Univ Colorado, Dept Biostat & Informat, Sch Publ Hlth, Aurora, CO 80045 USA.
EM debashis.ghosh@ucdenver.edu
RI Ghosh, Debashis/KZT-8916-2024
OI Ghosh, Debashis/0000-0001-6618-1316
FU Data Science to Patient Value (D2V) Initiative from University of
   Colorado
FX Data Science to Patient Value (D2V) Initiative from University of
   Colorado
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NR 44
TC 4
Z9 6
U1 0
U2 7
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0277-6715
EI 1097-0258
J9 STAT MED
JI Stat. Med.
PD DEC 10
PY 2018
VL 37
IS 28
BP 4252
EP 4265
DI 10.1002/sim.7919
PG 14
WC Mathematical & Computational Biology; Public, Environmental &
   Occupational Health; Medical Informatics; Medicine, Research &
   Experimental; Statistics & Probability
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology; Public, Environmental &
   Occupational Health; Medical Informatics; Research & Experimental
   Medicine; Mathematics
GA HA2KR
UT WOS:000450067800011
PM 30168168
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Givi, ME
   Saberi, MK
   Talafidaryani, M
   Abdolhamid, M
   Nikandish, R
   Fattahi, A
AF Esmaeili Givi, Mohammadreza
   Saberi, Mohammad Karim
   Talafidaryani, Mojtaba
   Abdolhamid, Mahdi
   Nikandish, Rahim
   Fattahi, Abbas
TI Assessment of the history and trends of "The Journal of Intellectual
   Capital": a bibliometrics, altmetrics and text mining analysis
SO JOURNAL OF INTELLECTUAL CAPITAL
LA English
DT Article
DE Intellectual capital; Intellectual structure; Bibliometric analysis;
   Altmetric analysis; LDA; Topic modeling; JIC
ID KNOWLEDGE MANAGEMENT; INTANGIBLE ASSETS; BIG DATA; BUSINESS PERFORMANCE;
   EMPIRICAL-EVIDENCE; SOCIAL NETWORKS; MODERATING ROLE; IMPACT;
   INNOVATION; FIRMS
AB Purpose The Journal of Intellectual Capital (JIC) celebrated its 20th anniversary in 2020. Therefore, the present study aims to provide a general overview of the history and key trends in this journal during 2000-2019. Design/methodology/approach Two types of citation and textual data during a 20-year journal period were retrieved from the Scopus database. The citation structures and contents were explored based on a combination of bibliometric analysis, altmetric analysis and text mining. The journal themes and trends of their changes were analyzed through citation bursts, mapping and topic modeling. To make a better comparison, the text mining process for the topic modeling of the IC field was performed in addition to the topic modeling of JIC. Findings Bibliometric analysis indicated that JIC has experienced a remarkable growth in terms of the number of publications and citations over the last 20 years. The results indicated that JIC plays a significant role among IC researchers. Additionally, a large number of researchers, institutes and countries have made contributions to this journal and cited its research papers. Altmetric analysis showed that JIC has been shared in different social media such as Twitter, Facebook, Wikipedia, Mendeley, Citeulike, news and blogs. Text mining abstract of JIC articles indicated that "measurement," "financial performance" and "IC reporting" have the relative prevalence with increasing trends over the past 20 years. In addition, "research trends" and "national and international studies" had a stable trend with low thematic share. Research limitations/implications The findings have important implications for the JIC editorial team in order to make informed decisions about the further development of JIC as well as for IC researchers and practitioners to make more valuable contributions to the journal. Originality/value Using bibliometric analysis, altmetric analysis and text mining, this study provided a systematic and comprehensive analysis of JIC. The simultaneous use of these methods provides an interesting, unique and suitable capacity to analyze the journals by considering their various aspects.
C1 [Esmaeili Givi, Mohammadreza] Univ Tehran, Dept Publ Adm, Fac Management, Tehran, Iran.
   [Saberi, Mohammad Karim] Hamadan Univ Med Sci, Dept Med Lib & Informat Sci, Sch Paramed, Hamadan, Hamadan, Iran.
   [Talafidaryani, Mojtaba] Univ Tehran, Fac Management, Tehran, Iran.
   [Abdolhamid, Mahdi] Iran Univ Sci & Technol, Sch Management Econ & Progress Engn, Dept Management & Philosophy Sci & Technol, Tehran, Iran.
   [Nikandish, Rahim] Univ Tehran, Fac Management, Tehran, Iran.
   [Fattahi, Abbas] Hamadan Univ Med Sci, Student Res Comm, Hamadan, Hamadan, Iran.
C3 University of Tehran; Hamadan University of Medical Sciences; University
   of Tehran; Iran University Science & Technology; University of Tehran;
   Hamadan University of Medical Sciences
RP Saberi, MK (corresponding author), Hamadan Univ Med Sci, Dept Med Lib & Informat Sci, Sch Paramed, Hamadan, Hamadan, Iran.
EM s.givi@ut.ac.ir; mohamadsaberi@gmail.com; mojtabatalafi@ut.ac.ir;
   Mahdi_Abdolhamid@iust.ac.ir; nikandishrahim@ut.ac.ir;
   abbasumsha@gmail.com
RI fattahi, abbas/ADG-0979-2022; Saberi, Mohammad Karim/W-3331-2017;
   Abdolhamid, Mahdi/C-7444-2018
OI fattahi, abbas/0000-0002-8017-2069; Esmaeili Givi,
   Mohammadreza/0000-0002-9452-3616; Saberi, Mohammad
   Karim/0000-0002-2471-0408; Abdolhamid, Mahdi/0000-0003-4118-4937
FU Vice-chancellor for Research and Technology, Hamadan University of
   Medical Sciences [9903061309]
FX The study was funded by Vice-chancellor for Research and Technology,
   Hamadan University of Medical Sciences (No. 9903061309).
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NR 192
TC 3
Z9 3
U1 6
U2 71
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 1469-1930
EI 1758-7468
J9 J INTELLECT CAP
JI J. Intellect. Cap.
PD MAY 26
PY 2022
VL 23
IS 4
BP 864
EP 912
DI 10.1108/JIC-02-2020-0057
EA APR 2021
PG 49
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 1M1TC
UT WOS:000644797200001
DA 2024-09-05
ER
PT J
AU Spyroglou, O
   Yildirim, C
   Koumpis, A
AF Spyroglou, Odysseas
   Yildirim, Cagri
   Koumpis, Adamantios
TI Use of AI to Help Researchers Improve their Research Funding Capacities,
   Relevance, and Performance
SO INTERNATIONAL JOURNAL OF SEMANTIC COMPUTING
LA English
DT Article; Proceedings Paper
CT 3rd IEEE International Conference on Transdisciplinary Artificial
   Intelligence (TransAI) / 4th IEEE International Conference on Artificial
   Intelligence for Industries (IEEEAI4I)
CY 2021
CL ELECTR NETWORK
DE Research management; Horizon Europe; research fundraising;
   transdisciplinary AI; ethics
AB Researchers and scientists face globally, and parallel to their core research activities, increased pressure to successfully lead or participate in fundraising activities. The field has been experiencing fierce competition with success rates of proposals falling dramatically down, while the complexity of the funding instruments and the need for acquiring a wide understanding of issues related to impacts, research priorities in connection to wider national and transnational (e.g. EU-wide) policy aspects, increase discomfort levels for the individual researchers and scientists. In this paper, we suggest the use of transdisciplinary AI tools to support (semi-)- automation of several steps of the application and proposal preparation processes.
C1 [Spyroglou, Odysseas] Int Dev Ireland, Dublin, Ireland.
   [Yildirim, Cagri] TUBITAK Turkish Sci & Technol Res Council, Ankara, Turkey.
   [Koumpis, Adamantios] Rhein Westfal TH Aachen, Lehrstuhl Informat 5, Aachen, Germany.
C3 RWTH Aachen University
RP Spyroglou, O (corresponding author), Int Dev Ireland, Dublin, Ireland.
EM o.spyroglou@idi.ie; cagri.yildirim@tubitak.gov.tr;
   koumpis@dbis.rwth-aachen.de
RI Koumpis, Adamantios/HNP-1175-2023
OI Koumpis, Adamantios/0000-0003-2661-7749; Spyroglou,
   Odysseas/0000-0002-1885-4392
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NR 19
TC 0
Z9 0
U1 3
U2 10
PU WORLD SCIENTIFIC PUBL CO PTE LTD
PI SINGAPORE
PA 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE
SN 1793-351X
EI 1793-7108
J9 INT J SEMANT COMPUT
JI Int. J. Semant. Comput.
PD MAR
PY 2022
VL 16
IS 01
BP 93
EP 106
DI 10.1142/S1793351X22400050
PG 14
WC Computer Science, Artificial Intelligence
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA 0U0DX
UT WOS:000787331400006
DA 2024-09-05
ER
PT J
AU Hadhiatma, A
   Azhari, A
   Suyanto, Y
AF Hadhiatma, Agung
   Azhari, Azhari
   Suyanto, Yohanes
TI A Scientific Paper Recommendation Framework Based on Multi-Topic
   Communities and Modified PageRank
SO IEEE ACCESS
LA English
DT Article
DE Feature extraction; Filtering; Semantics; Recommender systems; Neural
   networks; Data mining; Collaborative filtering; Citation analysis;
   Ranking (statistics); Citation recommendation; academic citation
   network; serendipitous perspectives; multi-topic community; personalized
   PageRank
ID MEASURING ACADEMIC INFLUENCE; SERENDIPITY; SYSTEMS
AB Personalized PageRank is a variant of PageRank, widely developed for citation recommendation. However, the personalized PageRank that works with a vast amount and rich scholarly data still results in information overload. Sometimes, junior scholars still need help to arrange queries quickly because of limited domain knowledge. Senior researchers need reference papers regarding a similar topic they intend to search for and related topics as a new insight. In this research, scientific citation recommendation aims to find the most influential papers with similar and related topics. Related topic papers in serendipitous perspectives are reference papers that are novel, diversified and unexpected to a user. The unexpectedness of recommended papers can be papers with different topics to queries but still relevant. To accomplish these challenges, we propose a framework of scientific citation recommendation with serendipitous perspectives. The framework includes feature extraction of an academic citation network, selection of multi-topic communities, and ranking papers in the selected multi-topic communities by modified PageRank. Papers in the chosen communities tend to link to similar and related papers. Modified PageRank is an extension of personalized PageRank, which works on multi-topic communities and manuscript queries. The experiments reveal that the proposed models outperform some models of personalized PageRank and some models of Content-Based Filtering. The multi-topic communities-based models work more effectively than the baselines if they run in a large dataset since the topic communities become more cohesive.
C1 [Hadhiatma, Agung] Sanata Dharma Univ, Fac Sci & Technol, Dept Informat, Yogyakarta 55282, Indonesia.
   [Hadhiatma, Agung; Azhari, Azhari; Suyanto, Yohanes] Univ Gadjah Mada, Fac Math & Nat Sci, Dept Comp Sci & Elect, Yogyakarta 55281, Indonesia.
C3 Gadjah Mada University
RP Azhari, A (corresponding author), Univ Gadjah Mada, Fac Math & Nat Sci, Dept Comp Sci & Elect, Yogyakarta 55281, Indonesia.
EM arisn@ugm.ac.id
RI Suyanto, Yohanes/AFU-0693-2022
OI Suyanto, Yohanes/0000-0003-1670-8620; Hadhiatma,
   Agung/0009-0002-8952-3045
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NR 55
TC 1
Z9 3
U1 7
U2 19
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2023
VL 11
BP 25303
EP 25317
DI 10.1109/ACCESS.2023.3251189
PG 15
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA A3CL9
UT WOS:000953944100001
OA gold
DA 2024-09-05
ER
PT J
AU Wei, CL
   Li, J
   Shi, DB
AF Wei, Chunli
   Li, Jiang
   Shi, Dongbo
TI Quantifying revolutionary discoveries: Evidence from Nobel prize-winning
   papers
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
DE Revolutionary research; CD index; Citation count; Nobel prize-winning
   papers; Multivariate linear regression
ID CITATION ANALYSIS; H-INDEX; SCIENCE; IMPACT; JOURNALS; IDENTIFICATION;
   INSTITUTIONS; RESEARCHERS; PERFORMANCE; CREATIVITY
AB Numerous metrics have been developed to identify revolutionary science which is crucial for advancing science. However, these metrics have rarely successfully identified revolutionary dis-coveries. We propose a two-dimension metric to quantify revolutionary discoveries by combining the consolidation-or-destabilization (CD) index with the citation count. To verify the validity of the metric, we utilize multivariate linear regression to investigate the differences in the CD indices and citations between 164 Nobel prize-winning papers from 1976 to 2016 (i.e., revolu-tionary science) and 9,034 counterparts that are similar to the Nobel prize-winning papers in terms of bibliographic information. We find that our proposed metric successfully shows a sig-nificant and distinct difference between the Nobel prize-winning papers and their counterparts in that the former receive around 880 more citations and 0.07 higher CD indices than the latter. The reliability of our proposed measure is robust.
C1 [Wei, Chunli; Li, Jiang] Nanjing Univ, Sch Informat Management, Nanjing 210023, Peoples R China.
   [Shi, Dongbo] Shanghai Jiao Tong Univ, Sch Int & Publ Affairs, Shanghai 200230, Peoples R China.
C3 Nanjing University; Shanghai Jiao Tong University
RP Li, J (corresponding author), Nanjing Univ, Sch Informat Management, Nanjing 210023, Peoples R China.; Shi, DB (corresponding author), Shanghai Jiao Tong Univ, Sch Int & Publ Affairs, Shanghai 200230, Peoples R China.
EM lijiang@nju.edu.cn; shidongbo@sjtu.edu.cn
RI Shi, Dongbo/ISU-4223-2023; Li, Jiang/JHV-1585-2023; Li,
   Jiang/Z-1709-2019
OI Li, Jiang/0000-0001-5769-8647
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NR 100
TC 6
Z9 7
U1 30
U2 135
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0306-4573
EI 1873-5371
J9 INFORM PROCESS MANAG
JI Inf. Process. Manage.
PD MAY
PY 2023
VL 60
IS 3
AR 103252
DI 10.1016/j.ipm.2022.103252
EA JAN 2023
PG 16
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 8A0AE
UT WOS:000915910600001
DA 2024-09-05
ER
PT J
AU Ling, NH
   Chen, CJ
   Teh, CS
   John, DS
   Ch'ng, LC
   Lay, YF
AF Ling, Nie Hui
   Chen, Chwen Jen
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   Ch'ng, Looi Chin
   Lay, Yoon Fah
TI Global Trends of Educational Data Mining in Online Learning
SO INTERNATIONAL JOURNAL OF TECHNOLOGY IN EDUCATION
LA English
DT Article
DE Educational data mining; Online learning; Bibliometric analysis; Global
   trends
ID PREDICTING STUDENT PERFORMANCE; ACADEMIC-PERFORMANCE; ANALYTICS; SYSTEM
AB Educational data mining (EDM) in online learning involves data mining techniques to analyze data from online environments to gain insights into student behavior, performance, and engagement. This study explored EDM in online learning publication trends and focuses. It involved a bibliometric analysis of 615 scholarly works related to EDM in online learning as recorded in Scopus, the largest peer-reviewed citation database, on February 1, 2023. The study examined EDM in online learning publications regarding its evolution and distribution, key focus areas, impact and performance, and prominent authors and collaborations in the last decade, in which the timespan is the period from 2012 to 2022. This bibliometric analysis shows that EDM in online learning is a dynamic area of scientific research as related publications grow steadily throughout the years and involve worldwide collaborations. The study reveals current research trends, offering valuable insights for future researchers to guide their investigations in this field.
C1 [Ling, Nie Hui; Chen, Chwen Jen; Teh, Chee Siong; John, Dexter Sigan] Univ Malaysia Sarawak, Sarawak 94300, Malaysia.
   [Ch'ng, Looi Chin] Univ Teknol MARA Sarawak, Jalan Meranek, Sarawak 94300, Malaysia.
   [Lay, Yoon Fah] Univ Malaysia Sabah, Jalan UMS, Kota Kinabalu 88400, Sabah, Malaysia.
C3 University of Malaysia Sarawak; Universiti Malaysia Sabah
RP Chen, CJ (corresponding author), Univ Malaysia Sarawak, Sarawak 94300, Malaysia.
EM cjchen@unimas.my
OI John, Dexter/0000-0003-4096-101X; Lay, Yoon Fah/0000-0002-5219-6696
FU Universiti Malaysia Sarawak through the Malaysia Comprehensive
   University Network Grant Scheme [GL/F04/MCUN/20/2020]
FX The author acknowledges the support provided by Universiti Malaysia
   Sarawak through the Malaysia Comprehensive University Network Grant
   Scheme [GL/F04/MCUN/20/2020].
CR Abana EC, 2019, INT J ADV COMPUT SC, V10, P285
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NR 48
TC 0
Z9 0
U1 3
U2 5
PU INT SOC TECHNOLOGY EDUCATION & SCIENCE-ISTES
PI MONUMENT
PA 19723 LINDENMERE DR, MONUMENT, COLORADO, UNITED STATES
EI 2689-2758
J9 INT J TECHNOL EDUC
JI Int. J. Technol. Educ.
PY 2023
VL 6
IS 4
BP 656
EP 680
DI 10.46328/ijte.558
PG 25
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA W4GY6
UT WOS:001091237800007
OA gold
DA 2024-09-05
ER
PT J
AU Dutta, D
   Aruchamy, S
   Mandal, S
   Sen, S
AF Dutta, Debeshi
   Aruchamy, Srinivasan
   Mandal, Soumen
   Sen, Soumen
TI Poststroke Grasp Ability Assessment Using an Intelligent Data Glove
   Based on Action Research Arm Test: Development, Algorithms, and
   Experiments
SO IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
LA English
DT Article
DE Stroke (medical condition); Instruments; Force sensors; Medical
   services; Data gloves; Accelerometers; Thumb; ARAT; data glove;
   poststroke rehabilitation; ROC; SVC
ID REHABILITATION OUTCOMES; BLOCK TEST; STROKE; IMPAIRMENT; CLASSIFICATION;
   PERFORMANCE; DISABILITY; NETWORKS; MOVEMENT; BOX
AB Growing impact of poststroke upper extremity (UE) functional limitations entails newer dimensions in assessment methodologies. This has compelled researchers to think way beyond traditional stroke assessment scales during the out-patient rehabilitation phase. In concurrence with this, sensor-driven quantitative evaluation of poststroke UE functional limitations has become a fertile field of research. Here, we have emphasized an instrumented wearable for systematic monitoring of stroke patients with right-hemiparesis for evaluating their grasp abilities deploying intelligent algorithms. An instrumented glove housing 6 flex sensors, 3 force sensors, and a motion processing unit was developed to administer 19 activities of Action Research Arm Test (ARAT) while experimenting on 20 voluntarily participating subjects. After necessary signal conditioning, meaningful features were extracted, and subsequently the most appropriate ones were selected using the ReliefF algorithm. An optimally tuned support vector classifier was employed to classify patients with different degrees of disability and an accuracy of 92% was achieved supported by a high area under the receiver operating characteristic score. Furthermore, selected features could provide additional information that revealed the causes of grasp limitations. This would assist physicians in planning more effective poststroke rehabilitation strategies. Results of the one-way ANOVA test conducted on actual and predicted ARAT scores of the subjects indicated remarkable prospects of the proposed glove-based method in poststroke grasp ability assessment and rehabilitation.
C1 [Dutta, Debeshi] Acad Sci & Innovat Res AcSIR, Chennai, Tamil Nadu, India.
   [Aruchamy, Srinivasan; Mandal, Soumen] CSIR Cent Mech Engn Res Inst, Durgapur, India.
   [Sen, Soumen] CSIR Cent Mech Engn Res Inst, Robot & Automat Grp, Durgapur 713209, W Bengal, India.
C3 Academy of Scientific & Innovative Research (AcSIR); Council of
   Scientific & Industrial Research (CSIR) - India; CSIR - Central
   Mechanical Engineering Research Institute (CMERI); Council of Scientific
   & Industrial Research (CSIR) - India; CSIR - Central Mechanical
   Engineering Research Institute (CMERI)
RP Sen, S (corresponding author), CSIR Cent Mech Engn Res Inst, Robot & Automat Grp, Durgapur 713209, W Bengal, India.
EM soumen_sen@cmeri.res.in
RI MANDAL, SOUMEN/AAS-9672-2020
OI MANDAL, SOUMEN/0000-0002-7353-0067; Aruchamy,
   Srinivasan/0000-0001-7942-2377; Sen, Soumen/0000-0003-4906-7727
FU CSIR-Central Mechanical Engineering Research Institute; Department of
   Science and Technology, Govt. of India
FX This work was supported in part by CSIR-Central Mechanical Engineering
   Research Institute and Department of Science and Technology, Govt. of
   India, under Project `Indo-Korean joint network center on robotics'-Node
   3, under Grant INT/KOREA/JNC/Robotics dated April 12, 2018.
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NR 71
TC 13
Z9 14
U1 9
U2 36
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0018-9294
EI 1558-2531
J9 IEEE T BIO-MED ENG
JI IEEE Trans. Biomed. Eng.
PD FEB
PY 2022
VL 69
IS 2
BP 945
EP 954
DI 10.1109/TBME.2021.3110432
PG 10
WC Engineering, Biomedical
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Engineering
GA YK9HO
UT WOS:000745515000045
PM 34495824
DA 2024-09-05
ER
PT S
AU Rafik, M
AF Rafik, Meriem
BE Roumate, F
TI Artificial Intelligence and the Changing Roles in the Field of Higher
   Education and Scientific Research
SO ARTIFICIAL INTELLIGENCE IN HIGHER EDUCATION AND SCIENTIFIC RESEARCH:
   Future Development
SE Bridging Human and Machine- Future Education with Intelligence
LA English
DT Article; Book Chapter
DE Higher education; Scientific research; Bibliometric analysis; Academic
   world; Artificial intelligence; Pedagogy
AB The university as we know it today is going to die. Indeed, we are now seeing the chaos that precedes any change. The influence of new players, such as artificial intelligence, is incompatible with a university that is an essential element of the contemporary industrial, financial, and ideological apparatus (Deneault in La mediocratie. Lux Editeur, Canada, 2015). The revolution of the academic world is imperative, given that the relations between academia and knowledge reflect the evolution of societies. As a result, we will have to guide innovation, which in itself holds no particular moral value. Innovation is as good as what we decide to do with it. However, the absence of a social project prevents us from creating a transversal policy in the economic, social, and cultural fields. This is why the new university that we are going to invent will allow us to take up the immense challenge of serving us in a world soon to be saturated with artificial intelligence. The objective of this research is to analyze the addition of digital technology to the world of conservative universities and to propose an optimal way of orienting scientific research and higher education represented by professor-researchers, to adapt to a digital future that is certainly approaching. This article is organized into three main sections. The first section will expose the changes in the profession of academic professors in both their informational and financial capacities; the second section will focus on the changes in the profession of researchers, also in their informational and financial capacities; and the last section will offer some suggestions to optimize the profession of researchers and professors in the context of their interaction with artificial intelligence.
C1 [Rafik, Meriem] Univ Hassan 2, Casablanca, Morocco.
C3 Hassan II University of Casablanca
RP Rafik, M (corresponding author), Univ Hassan 2, Casablanca, Morocco.
EM miryame108@gmail.com
OI Rafik, Meriem/0009-0007-3985-6083
CR Alexandre L., 2017, La Guerre des Intelligences: comment l'intelligence artificielle va revolutionner l'education
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NR 23
TC 1
Z9 1
U1 1
U2 1
PU SPRINGER-VERLAG SINGAPORE PTE LTD
PI SINGAPORE
PA 152 BEACH ROAD, #21-01/04 GATEWAY EAST, SINGAPORE, 189721, SINGAPORE
SN 2662-5342
BN 978-981-19-8643-7; 978-981-19-8641-3; 978-981-19-8640-6
J9 Bridging Human and M
PY 2023
BP 35
EP 46
DI 10.1007/978-981-19-8641-3_3
D2 10.1007/978-981-19-8641-3
PG 12
WC Computer Science, Artificial Intelligence; Education & Educational
   Research
WE Book Citation Index – Social Sciences & Humanities (BKCI-SSH); Book Citation Index – Science (BKCI-S)
SC Computer Science; Education & Educational Research
GA BW5CA
UT WOS:001158874300004
DA 2024-09-05
ER
PT C
AU Hu, JL
   Nian, ZY
   Wang, XL
AF Hu, Jili
   Nian, Zhiyuan
   Wang, Xinle
BE Kocaoglu, DF
   Anderson, TR
   Kozanoglu, DC
   Niwa, K
   Steenhuis, HJ
TI Research on Financial Performance Evaluation on Artificial Intelligence
   Listed Companies in China Based on DEA Method
SO 2019 PORTLAND INTERNATIONAL CONFERENCE ON MANAGEMENT OF ENGINEERING AND
   TECHNOLOGY (PICMET)
SE Portland International Conference on Management of Engineering and
   Technology
LA English
DT Proceedings Paper
CT Portland International Conference on Management of Engineering and
   Technology (PICMET)
CY AUG 25-29, 2019
CL Portland, OR
ID EFFICIENCY
AB China's Artificial Intelligence (Al) industry has developed rapidly in recent years, with the State Council of China releasing a roadmap in July 2017 with a goal of creating a domestic industry worth 1 trillion Yuan and becoming a global Al powerhouse by 2030. This study evaluates the listed companies in China's Al industry from the perspective of financial performance and analyzes the development status of China's Al industry from a macro perspective. This study selects the more objective and appropriate DEA analysis as the evaluation method according to the characteristics of the Al industry. On the basis of summarizing the development status of the Al industry and Al listed companies, an empirical analysis is carried out. In the data sample, 34 Al listed companies in China's Shanghai and Shenzhen stock markets were selected, and the DEA model with output-orientation model was used to analyze the standard data. The result shows that in the different stock board the efficiency presents different development trends and distribution status.
C1 [Hu, Jili; Nian, Zhiyuan; Wang, Xinle] Jilin Univ, Sch Econ, JLU, Changchun, Peoples R China.
C3 Jilin University
RP Hu, JL (corresponding author), Jilin Univ, Sch Econ, JLU, Changchun, Peoples R China.
FU Jilin University project 'China High-tech Industry Innovation Efficiency
   Research' [2017ZZ046]
FX This research was supported by Dr. Tim Anderson from Department of
   Engineering and Technology Management (ETM) at Portland State
   University, who provided insight and expertise that greatly assisted the
   research, and with the help of his book Data Envelopment Analysis Using
   R'. This research was funded by the Jilin University project 'China
   High-tech Industry Innovation Efficiency Research' (project code:
   2017ZZ046).
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NR 14
TC 1
Z9 1
U1 0
U2 3
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2159-5100
BN 978-1-890843-40-3
J9 PORTL INT CONF MANAG
PY 2019
DI 10.23919/picmet.2019.8893931
PG 6
WC Engineering, Electrical & Electronic
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Engineering
GA BO5RJ
UT WOS:000518681200155
DA 2024-09-05
ER
PT J
AU Li, HX
   Di, HX
   Li, J
   Tian, SC
AF Li Hongxia
   Di Hongxi
   Li Jian
   Tian Shuicheng
TI Research on the application of the improved genetic algorithm in the
   electroencephalogram-based mental workload evaluation for miners
SO JOURNAL OF ALGORITHMS & COMPUTATIONAL TECHNOLOGY
LA English
DT Article
DE Genetic algorithm; particle swarm optimization; improved genetic
   algorithm; mental workload; electroencephalogram
AB Electroencephalogram is the electrical phenomena in the cerebral cortex or the scalp surface due to the electrophysiological activity of brain cells. Electroencephalogram has great theoretical and practical significance in measuring mental workload of people. More precise electroencephalographic is a precondition to study mental workload of miners. In this article, based on the actual situation of the electroencephalographic measurement of miners, the particle swarm optimization is introduced to improve the standard genetic algorithm, and put forward a combined method integrating the genetic algorithm with particle swarm optimization for achieving electroencephalogram-based measures of miners' mental workload. Moreover, the MATLAB simulation platform is used for simulation testing. Testing results prove the effectiveness of the combined method.
C1 [Li Hongxia; Di Hongxi] Xian Univ Sci & Technol, Sch Management, Xian, Shaanxi, Peoples R China.
   [Li Hongxia; Di Hongxi; Tian Shuicheng] Xian Univ Sci & Technol, Sch Energy Engn, Xian, Shaanxi, Peoples R China.
   [Li Hongxia; Di Hongxi; Tian Shuicheng] Xian Univ Sci & Technol, Key Lab Western Mine Exploitat & Hazard Prevent, Xian, Shaanxi, Peoples R China.
   [Li Jian] Shanxi Prov AuditOff, Xian, Shaanxi, Peoples R China.
C3 Xi'an University of Science & Technology; Xi'an University of Science &
   Technology; Xi'an University of Science & Technology
RP Li, HX (corresponding author), Xian Univ Sci & Technol, Sch Management, Xian, Shaanxi, Peoples R China.
EM hongxidi@sohu.com
FU National Natural Science Foundation of China [71271169, 71273208];
   Doctoral Program Foundation [20126121110004, 20116121110002]
FX The author(s) disclosed receipt of the following financial support for
   the research, authorship, and/or publication of this article: National
   Natural Science Foundation of China (71271169, 71273208) and The
   Doctoral Program Foundation (20126121110004, 20116121110002).
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NR 25
TC 2
Z9 2
U1 0
U2 4
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 1748-3018
EI 1748-3026
J9 J ALGORITHMS COMPUT
JI J. Algorithms Comput. Technol.
PD SEP
PY 2016
VL 10
IS 3
SI SI
BP 198
EP 207
DI 10.1177/1748301816649071
PG 10
WC Computer Science, Interdisciplinary Applications
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA FJ4HF
UT WOS:000412696900008
OA gold
DA 2024-09-05
ER
PT J
AU Shrivastava, R
   Mahajan, P
AF Shrivastava, Rishabh
   Mahajan, Preeti
TI Influence of social networking sites on scholarly communication: A study
   using literature in Artificial Intelligence
SO JOURNAL OF LIBRARIANSHIP AND INFORMATION SCIENCE
LA English
DT Article
DE Altmetrics; artificial intelligence; online reference managers;
   scholarly communication; social networking sites
ID CITATION COUNTS; MENDELEY; INFORMATION; INDICATORS; ALTMETRICS; SERVICES
AB With the advent of Web 2.0 tools and especially social media, researchers are increasingly active on the Web. This has resulted in a transformation in the scholarly communication process through which researchers share and bookmark research works in online platforms. In the present study, the influence of social networking sites on the field of Artificial Intelligence research was studied. The study analysed the influence of social networking sites on both conference papers and journal articles, as in a field like Artificial Intelligence both channels of research dissemination play important roles. The top 100 cited journal articles and conference papers in Artificial Intelligence published in 2009 and 2013 were analysed for their presence on social networking sites and online reference managers. It was found that amongst social networking sites, Mendeley had the greatest influence on research in Artificial Intelligence. Mendeley played the most remarkable role in transforming scholarly communication with the highest coverage of both journal articles and conference papers for both the years 2009 and 2013. It was found that the influence of social networking sites was greater for journal articles than conference papers, the latter still having a lower average Mendeley readership. The highest correlation between citation counts and Mendeley readership was found for journal articles published in 2009, followed by journal articles published in 2013, conference papers published in 2009 and conference papers published in 2013. The average Mendeley readership was also higher for journal articles than for conference papers. Mendeley readership was also found to be higher for journal articles and conference papers published earlier in time, indicating that research works published earlier in time were more popular in social networking sites and online reference managers.
C1 [Shrivastava, Rishabh; Mahajan, Preeti] Panjab Univ, Dept Lib & Informat Sci, Chandigarh 160014, India.
C3 Panjab University
RP Shrivastava, R (corresponding author), Panjab Univ, Dept Lib & Informat Sci, Chandigarh 160014, India.
EM rishabh3@outlook.com
RI Mahajan, Preeti/N-2176-2016
OI Shrivastava, Rishabh/0000-0003-3466-2590
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NR 38
TC 3
Z9 3
U1 4
U2 33
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 0961-0006
EI 1741-6477
J9 J LIBR INF SCI
JI J. Libr. Inf. Sci.
PD SEP
PY 2021
VL 53
IS 3
BP 522
EP 529
DI 10.1177/0961000616678309
PG 8
WC Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science
GA UK0SM
UT WOS:000691685200013
DA 2024-09-05
ER
PT J
AU Wang, DS
AF Wang, Dongsheng
TI Research on raw water quality assessment oriented to drinking water
   treatment based on the SVM model
SO WATER SCIENCE AND TECHNOLOGY-WATER SUPPLY
LA English
DT Article
DE chemical dosing process; drinking water treatment; raw water quality
   assessment; SVM
ID PARTICLE SWARM OPTIMIZATION; OXIDATION; SHALLOW; FATE; LAKE
AB Raw water quality variation has a great effect on drinking water treatment. To improve the adaptivity of drinking water treatment and stabilize the quality of treated water, a raw water quality assessment method, which is based upon the support vector machine (SVM), is developed in this study. Compared to existing raw water quality assessment methods, the assessment method studied herein is oriented to drinking water treatment and can directly be used for the control of the chemical (alum and ozone) dosing process. To this end, based upon the productive experiences and the analysis of the operating data of water supply, a raw water quality assessment standard oriented to drinking water treatment is proposed. A raw water quality model is set up to assess the raw water quality based upon the SVM technique. Based upon the raw water quality assessment results, a feedforward-feedback control scheme has been designed for the chemical dosing process control of drinking water treatment. Thus, the chemical dosage can be adjusted in time to cope with raw water quality variations and hence, the quality of the treated water is stabilized. Experimental results demonstrate the improved effectiveness of the proposed method of raw water quality assessment and the feedforward-feedback control scheme.
C1 [Wang, Dongsheng] Nanjing Univ Posts & Telecommun, Sch Automat, Nanjing 210003, Jiangsu, Peoples R China.
C3 Nanjing University of Posts & Telecommunications
RP Wang, DS (corresponding author), Nanjing Univ Posts & Telecommun, Sch Automat, Nanjing 210003, Jiangsu, Peoples R China.
EM wangdongsheng@njupt.edu.cn
FU Natural Science Foundation of Jiangsu Province [BK20150841]; NUPTSF
   [NY214019, NY214078]
FX This work was supported by Natural Science Foundation of Jiangsu
   Province (Grant No. BK20150841), NUPTSF (Grant No. NY214019) and NUPTSF
   (Grant No. NY214078).
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NR 39
TC 7
Z9 7
U1 7
U2 41
PU IWA PUBLISHING
PI LONDON
PA ALLIANCE HOUSE, 12 CAXTON ST, LONDON SW1H0QS, ENGLAND
SN 1606-9749
J9 WATER SCI TECH-W SUP
JI Water Sci. Technol.-Water Supply
PD JUN
PY 2016
VL 16
IS 3
BP 746
EP 755
DI 10.2166/ws.2015.186
PG 10
WC Engineering, Environmental; Environmental Sciences; Water Resources
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Environmental Sciences & Ecology; Water Resources
GA DO5GR
UT WOS:000377812100018
DA 2024-09-05
ER
PT J
AU Zhang, X
   Umeanowai, KO
AF Zhang, Xia
   Umeanowai, Kingsley Obiajulu
TI Exploring the transformative influence of artificial intelligence in EFL
   context: A comprehensive bibliometric analysis
SO EDUCATION AND INFORMATION TECHNOLOGIES
LA English
DT Article; Early Access
DE Bibliometric analysis; Artificial intelligence; English as a foreign
   language; EFL learners' writing skills; VOSviewer visualization
ID EDUCATION
AB This comprehensive bibliometric analysis examines the dynamic impact and influence of artificial intelligence (AI) within the domain of English as a Foreign Language (EFL) from 2013 to 2023. By analysing 3,300 documents from the Web of Science database, the study reveals a positive trend in AI integration, with notable growth attributed to various transformative factors such as the COVID-19 pandemic and increased academic funding. The result analysis identifies leading contributors, top authors, sources, and publishers, revealing China, the United States, and the United Kingdom as influential contributors. The co-occurrence analysis of keywords unveils five clusters representing trends in AI-enhanced language learning, spanning educational technology, EFL teaching factors, learner motivation, and assessment strategies. The study also highlights AI's impact on improving EFL writing skills through tools such as Chatgpt, Grammarly, and Quilbot. However, the study acknowledges limitations in database selection and language constraints. The findings offer valuable insights for researchers, educators, and policymakers, guiding interdisciplinary collaboration and innovative pedagogical approaches.
C1 [Zhang, Xia] Xinyang Agr & Forestry Univ, Dept Foreign language, Xinyang, Peoples R China.
   [Umeanowai, Kingsley Obiajulu] Zhengzhou Univ, Foreign Language Dept, Zhengzhou, Peoples R China.
C3 Xinyang Agriculture & Forestry University; Zhengzhou University
RP Umeanowai, KO (corresponding author), Zhengzhou Univ, Foreign Language Dept, Zhengzhou, Peoples R China.
EM xiahui1107@163.com; macleyscp@gmail.com
FU the Higher Education and Teaching Reform Research and Practice Project
   of Henan Province; Xinyang Agriculture and Forestry University
FX We would like to express our sincere gratitude to Xinyang Agriculture
   and Forestry University for its financial support, which made this
   research possible. We also extend our appreciation to the researchers
   and educators whose work laid the foundation for this study.DAS:The data
   supporting the findings of this article are available upon request from
   the corresponding author for researchers who meet the access criteria.
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NR 32
TC 0
Z9 0
U1 1
U2 1
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1360-2357
EI 1573-7608
J9 EDUC INF TECHNOL
JI Educ. Inf. Technol.
PD 2024 AUG 13
PY 2024
DI 10.1007/s10639-024-12937-z
EA AUG 2024
PG 16
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA D0T4R
UT WOS:001293394800004
OA hybrid
DA 2024-09-05
ER
PT J
AU Acar, S
AF Acar, Selcuk
TI Creativity Assessment, Research, and Practice in the Age of Artificial
   Intelligence
SO CREATIVITY RESEARCH JOURNAL
LA English
DT Article; Early Access
ID DIVERGENT THINKING; ORIGINALITY
AB Measurement tools and approaches have played a vital role in advancing creativity research, similar to their role in other scientific disciplines. Precise measurement is crucial for accurate hypothesis testing and comparing different theories. Recently, the field of creativity has reached a significant milestone with advancements in artificial intelligence (AI). AI has the potential to revolutionize creativity assessment methods, offering cost reduction, automation capabilities, and improved reliability compared to human raters. This advancement in measurement precision facilitated by AI can accelerate progress in creativity research. In this paper, I summarize the historical progression of advances in automated creativity assessment methods, present key findings from my recent and current collaborative research efforts and discuss the potential next steps of AI-related developments. Finally, I explore how these developments can support educational practices such as differentiation, enrichment, and identification of gifted and talented students.
C1 [Acar, Selcuk] Univ North Texas, Denton, TX 76203 USA.
   [Acar, Selcuk] Univ North Texas, Dept Educ Psychol, Matthews Hall 304E,1300 W Highland St, Denton, TX 76203 USA.
C3 University of North Texas System; University of North Texas Denton;
   University of North Texas System; University of North Texas Denton
RP Acar, S (corresponding author), Univ North Texas, Dept Educ Psychol, Matthews Hall 304E,1300 W Highland St, Denton, TX 76203 USA.
EM selcuk.acar@unt.edu
FU Institute of Education Sciences [R305A200519]
FX The work was supported by the Institute of Education Sciences
   [R305A200519].
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NR 43
TC 2
Z9 2
U1 39
U2 71
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1040-0419
EI 1532-6934
J9 CREATIVITY RES J
JI Creativ. Res. J.
PD 2023 OCT 27
PY 2023
DI 10.1080/10400419.2023.2271749
EA OCT 2023
PG 7
WC Psychology, Educational; Psychology, Multidisciplinary
WE Social Science Citation Index (SSCI)
SC Psychology
GA W3DM5
UT WOS:001090467100001
DA 2024-09-05
ER
PT C
AU Kim, IC
   Thoma, GR
AF Kim, In Cheol
   Thoma, George R.
GP IEEE
TI Automated Classification of Author's Sentiments in Citation Using
   Machine Learning Techniques: A Preliminary Study
SO 2015 IEEE CONFERENCE ON COMPUTATIONAL INTELLIGENCE IN BIOINFORMATICS AND
   COMPUTATIONAL BIOLOGY (CIBCB)
LA English
DT Proceedings Paper
CT IEEE Conference on Computational Intelligence in Bioinformatics and
   Computational Biology CIBCB
CY AUG 12-15, 2015
CL Honolulu, HI
DE Citation analysis; author's sentiments; "Comment-on"; support vector
   machine; n-grams word statistics; MEDLINE
AB Scientific papers generally include citations to external sources such as journal articles, books, or Web links to refer to works that are related in an important way to the research. The reason for the citation appears within the sentences surrounding the citation tag in the body text, and represents the relationship between the citation and cited works as supportive, contrastive, corrective, etc. This could be an important clue for researchers seeking relevant previous work or approaches for a certain research purpose. We propose to develop an automated method to identify the citing author's sentiments toward the cited external sources expressed in citation sentences using machine-learning techniques and linguistic cues. As a preliminary study, this paper presents a support vector machine (SVM)-based text categorization technique to classify the author's sentiments specifically toward Comment-on (CON) articles. CON, a MEDLINE citation field, indicates previously published articles commented on by authors of a given article expressing possibly complimentary or contradictory opinions. An SVM with a radial basis kernel function (RBF) is implemented, and Input feature vectors for the SVM are created based on n-grams word statistics representing the distribution of words in CON sentences. Experiments conducted on a set of CON sentences collected from 414 different online biomedical journal titles show that the SVM with a RBF yields the best result for an input feature vector combining uni-gram and bi-gram word statistics.
C1 [Kim, In Cheol; Thoma, George R.] Natl Lib Med, Lister Hill Natl Ctr Biomed Commun, 8600 Rockville Pike, Bethesda, MD 20894 USA.
C3 National Institutes of Health (NIH) - USA; NIH National Library of
   Medicine (NLM)
RP Kim, IC (corresponding author), Natl Lib Med, Lister Hill Natl Ctr Biomed Commun, 8600 Rockville Pike, Bethesda, MD 20894 USA.
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   [Anonymous], 2012, 2012 Conference of the North American Chapter of the Association for Computational Linguistics
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NR 20
TC 4
Z9 4
U1 0
U2 4
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-4799-6926-5
PY 2015
BP 488
EP 494
PG 7
WC Computer Science, Information Systems; Mathematical & Computational
   Biology
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Mathematical & Computational Biology
GA BF1SY
UT WOS:000380434200049
DA 2024-09-05
ER
PT J
AU TUHRIM, S
   HOROWITZ, DR
   SACHER, M
   GODBOLD, JH
AF TUHRIM, S
   HOROWITZ, DR
   SACHER, M
   GODBOLD, JH
TI VALIDATION AND COMPARISON OF MODELS PREDICTING SURVIVAL FOLLOWING
   INTRACEREBRAL HEMORRHAGE
SO CRITICAL CARE MEDICINE
LA English
DT Article
DE HEMORRHAGE, INTRACEREBRAL; LOGISTIC REGRESSION; PROGNOSIS; STROKE;
   OUTCOMES RESEARCH; PATIENT OUTCOME ASSESSMENT; NEUROLOGIC EMERGENCIES;
   CRITICAL ILLNESS
ID SUBARACHNOID HEMORRHAGE; INTRACRANIAL-PRESSURE; CEREBRAL-HEMORRHAGE;
   STROKE; OUTCOMES; RATES
AB Objective: To compare the performance of two previously reported logistic regression models using data independent from those data used to derive the models.
   Design: Prospective.
   Setting Acute stroke unit of a tertiary care hospital.
   Patients: One hundred twenty-nine patients with supratentorial intracerebral hemorrhage.
   Measurements and Main Results: Model 1 contains the initial Glasgow Coma Scale score, hemorrhage size, and pulse pressure. The more complex model 2 includes, in addition to those three variables, the presence or absence of intraventricular hemorrhage and a term representing the interaction of intraventricular hemorrhage and Glasgow Coma Scale score. The areas under the receiver operating characteristic curves generated for each model were statistically indistinguishable.
   Conclusions: Model 1 predicts 30-day patient status as well as the more complex model 2. Model 1 provides a valid, easy-to-use means of categorizing supratentorial intracerebral hemorrhage patients in terms of their probability of survival.
C1 MT SINAI SCH MED,DEPT NEUROL,NEW YORK,NY.
   MT SINAI SCH MED,DEPT RADIOL,NEW YORK,NY.
   MT SINAI SCH MED,DEPT COMMUNITY MED,NEW YORK,NY.
C3 Icahn School of Medicine at Mount Sinai; Icahn School of Medicine at
   Mount Sinai; Icahn School of Medicine at Mount Sinai
FU NINDS NIH HHS [NS 27924, NS 29762] Funding Source: Medline
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NR 30
TC 113
Z9 122
U1 0
U2 1
PU WILLIAMS & WILKINS
PI BALTIMORE
PA 351 WEST CAMDEN ST, BALTIMORE, MD 21201-2436
SN 0090-3493
J9 CRIT CARE MED
JI Crit. Care Med.
PD MAY
PY 1995
VL 23
IS 5
BP 950
EP 954
DI 10.1097/00003246-199505000-00026
PG 5
WC Critical Care Medicine
WE Science Citation Index Expanded (SCI-EXPANDED)
SC General & Internal Medicine
GA QX807
UT WOS:A1995QX80700026
PM 7736756
DA 2024-09-05
ER
PT J
AU Donner, P
AF Donner, Paul
TI Enhanced self-citation detection by fuzzy author name matching and
   complementary error estimates
SO JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY
LA English
DT Article
DE bibliometrics; author productivity; heuristics
ID MACRO
AB In this article I investigate the shortcomings of exact string match-based author self-citation detection methods. The contributions of this study are twofold. First, I apply a fuzzy string matching algorithm for self-citation detection and benchmark this approach and other common methods of exclusively author name-based self-citation detection against a manually curated ground truth sample. Near full recall can be achieved with the proposed method while incurring only negligible precision loss. Second, I report some important observations from the results about the extent of latent self-citations and their characteristics and give an example of the effect of improved self-citation detection on the document level self-citation rate of real data.
C1 [Donner, Paul] Inst Res Informat & Qual Assurance iFQ, Schutzenstr 6a, D-10117 Berlin, Germany.
RP Donner, P (corresponding author), Inst Res Informat & Qual Assurance iFQ, Schutzenstr 6a, D-10117 Berlin, Germany.
EM donner@forschungsinfo.de
RI Donner, Paul/AAT-7081-2020
OI Donner, Paul/0000-0001-5737-8483
FU Bundesministerium fur Forschung und Bildung (BMBF) [01PQ08004A]
FX Bundesministerium fur Forschung und Bildung (BMBF), Project 01PQ08004A,
   "Kompetenzzentrum Bibliometrie fur die deutsche Wissenschaft".
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NR 14
TC 4
Z9 4
U1 0
U2 28
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 2330-1635
EI 2330-1643
J9 J ASSOC INF SCI TECH
PD MAR
PY 2016
VL 67
IS 3
BP 662
EP 670
DI 10.1002/asi.23399
PG 9
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA DD9OQ
UT WOS:000370255700013
DA 2024-09-05
ER
PT J
AU Xu, ZY
   Liu, HJ
AF Xu, Zeyu
   Liu, Haijiang
TI Research on the DCT vehicle starting process evaluation based on LSTM
   neural network with attention mechanism
SO JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY
LA English
DT Article; Early Access
DE Attention mechanism; DCT vehicle; Dual-clutch transmission; Drivability;
   Evaluation; LSTM; Starting process
AB Currently, with the advancement of dual-clutch transmission (DCT) control systems and vehicle performance, it is necessary to develop better objective evaluation methods for DCT vehicles. The starting process is a critical element affecting the driving and riding experience of DCT vehicles. Therefore, it is crucial to establish and improve a starting process evaluation model for the objective evaluation to DCT vehicles and optimization to DCT control strategies. This paper proposes a new method to evaluate the DCT vehicle starting process objectively. The method analyzes and models the time-series signals of the driving data using the LSTM neural network and uses the attention mechanism to improve the evaluation performance and enhance the interpretability of the evaluation results. Taking the dynamic performance evaluation as an example, the evaluation results indicate that the proposed model is better than the conventional methods, showing notable efficacy and preponderance.
C1 [Xu, Zeyu; Liu, Haijiang] Tongji Univ, Sch Mech Engn, Shanghai 201804, Peoples R China.
C3 Tongji University
RP Xu, ZY (corresponding author), Tongji Univ, Sch Mech Engn, Shanghai 201804, Peoples R China.
EM xuzeyu@tongji.edu.cn
FU National Natural Science Foundation of China [U1764259]
FX This work was supported by the National Natural Science Foundation of
   China (No. U1764259).
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NR 32
TC 0
Z9 0
U1 1
U2 1
PU KOREAN SOC MECHANICAL ENGINEERS
PI SEOUL
PA KSTC NEW BLD. 7TH FLOOR, 635-4 YEOKSAM-DONG KANGNAM-KU, SEOUL 135-703,
   SOUTH KOREA
SN 1738-494X
EI 1976-3824
J9 J MECH SCI TECHNOL
JI J. Mech. Sci. Technol.
PD 2024 AUG 21
PY 2024
DI 10.1007/s12206-024-0811-8
EA AUG 2024
PG 14
WC Engineering, Mechanical
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA D4C6H
UT WOS:001295682800002
DA 2024-09-05
ER
PT J
AU Qayyum, F
   Jamil, H
   Jamil, F
   Kim, D
AF Qayyum, Faiza
   Jamil, Harun
   Jamil, Faisal
   Kim, Do-Hyeun
TI Towards Potential Content-Based Features Evaluation to Tackle Meaningful
   Citations
SO SYMMETRY-BASEL
LA English
DT Article
DE citation analysis; citation classification; binary classification;
   random forest; kernel logistic regression
ID H-INDEX; QUALITY; REASONS
AB The scientific community has presented various citation classification models to refute the concept of pure quantitative citation analysis systems wherein all citations are treated equally. However, a small number of benchmark datasets exist, which makes the asymmetric citation data-driven modeling quite complex. These models classify citations for varying reasons, mostly harnessing metadata and content-based features derived from research papers. Presently, researchers are more inclined toward binary citation classification with the belief that exploiting the datasets of incomplete nature in the best possible way is adequate to address the issue. We argue that contemporary ML citation classification models overlook essential aspects while selecting the appropriate features that hinder elutriating the asymmetric citation data. This study presents a novel binary citation classification model exploiting a list of potential natural language processing (NLP) based features. Machine learning classifiers, including SVM, KLR, and RF, are harnessed to classify citations into important and non-important classes. The evaluation is performed using two benchmark data sets containing a corpus of around 953 paper-citation pairs annotated by the citing authors and domain experts. The study outcomes exhibit that the proposed model outperformed the contemporary approaches by attaining a precision of 0.88.</p>
C1 [Qayyum, Faiza; Jamil, Faisal; Kim, Do-Hyeun] Jeju Natl Univ, Dept Comp Engn, Jeju Si 63243, South Korea.
   [Jamil, Harun] Jeju Natl Univ, Dept Elect Engn, Jeju Si 63243, South Korea.
   [Kim, Do-Hyeun] Jeju Natl Univ, Res Ctr Adv Technol, Jeju Si 63243, South Korea.
C3 Jeju National University; Jeju National University; Jeju National
   University
RP Kim, D (corresponding author), Jeju Natl Univ, Dept Comp Engn, Jeju Si 63243, South Korea.; Kim, D (corresponding author), Jeju Natl Univ, Res Ctr Adv Technol, Jeju Si 63243, South Korea.
EM harunjamil@hotmail.com
RI Jamil, Faisal/GSO-1371-2022; Jamil, Faisal/JHU-4465-2023
OI Jamil, Faisal/0000-0003-1994-6907; Qayyum, Faiza/0000-0001-9597-2387
FU National Research Foundation of Korea (NRF) - Ministry of Science, ICT
   [2019M3F2A1073387]; Institute for Information & communications
   Technology Planning & Evaluation (IITP) - Korea government (MSIT)
   [2018-0-01456]
FX This research was supported by Energy Cloud R & D Program through the
   National Research Foundation of Korea (NRF) funded by the Ministry of
   Science, ICT (2019M3F2A1073387),and this research was supported by
   Institute for Information & communications Technology Plan-ning &
   Evaluation (IITP) grant funded by the Korea government (MSIT)
   (No.2018-0-01456, AutoMaTa:Autonomous Management framework based on
   artificial intelligent Technology for adaptive and disposable IoT). Any
   correspondence related to this paper should be addressed to Dohyeun Kim.
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NR 33
TC 4
Z9 4
U1 2
U2 12
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2073-8994
J9 SYMMETRY-BASEL
JI Symmetry-Basel
PD OCT
PY 2021
VL 13
IS 10
AR 1973
DI 10.3390/sym13101973
PG 19
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics
GA WR9DK
UT WOS:000714793200001
OA gold
DA 2024-09-05
ER
PT J
AU Bayer, P
   Kennedy, R
   Yang, J
   Urpelainen, J
AF Bayer, Patrick
   Kennedy, Ryan
   Yang, Joonseok
   Urpelainen, Johannes
TI The need for impact evaluation in electricity access research
SO ENERGY POLICY
LA English
DT Article
DE Impact evaluation; Electricity access; Observational and experimental
   methods; Causal inference; Sustainable development
ID RURAL ELECTRIFICATION; WELFARE IMPACTS; PANEL-DATA; EMPLOYMENT;
   FERTILITY; BENEFITS; QUALITY; ENERGY
AB Universal household electrification is a key component of the United Nations Sustainable Development Goals, but the evidence base for social and economic impacts of electricity access remains unclear. Here we report results from a systematic review of impact evaluations of household electrification based on five key outcome measures. We only find 31 studies that conduct statistical hypothesis tests to assess impacts. Among these, seven draw on a randomized experiment designed for causal inference. The randomized experimental studies generate fewer positive results than observational or quasi-experimental studies, such as correlational, instrumental variable, and difference-in-differences designs. These results call for a reassessment of what we know about the impacts of household electrification. They also call for major investment in impact evaluation of electricity access using randomized controlled trials, with a particular focus on when and how energy access interventions can furnish large benefits to their intended beneficiaries. Large-scale impact evaluations using experimental methods will require close collaboration between policymakers and researchers.
C1 [Bayer, Patrick] Univ Strathclyde, Sch Govt & Publ Policy, McCance Bldg,16 Richmond St, Glasgow G1 1QX, Lanark, Scotland.
   [Kennedy, Ryan] Univ Houston, Dept Polit Sci, Philip Guthrie Hoffman Hall,3551 Cullen Blvd, Houston, TX 77204 USA.
   [Yang, Joonseok] Univ Calif Irvine, Dept Polit Sci, 4124 Social Sci Plaza A, Irvine, CA 92617 USA.
   [Urpelainen, Johannes] Johns Hopkins Univ, Sch Adv Int Studies, 1619 Massachusetts Ave NW, Washington, DC 20036 USA.
C3 University of Strathclyde; University of Houston System; University of
   Houston; University of California System; University of California
   Irvine; Johns Hopkins University
RP Bayer, P (corresponding author), Univ Strathclyde, Sch Govt & Publ Policy, McCance Bldg,16 Richmond St, Glasgow G1 1QX, Lanark, Scotland.
EM patrick.bayer@strath.ac.uk; rkennedy@uh.edu; joonsey1@uci.edu;
   johannes.u@jhu.edu
OI Bayer, Patrick/0000-0003-1731-1270
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   van de Walle D, 2017, WORLD BANK ECON REV, V31, P385, DOI 10.1093/wber/lhv057
NR 48
TC 22
Z9 24
U1 3
U2 25
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0301-4215
EI 1873-6777
J9 ENERG POLICY
JI Energy Policy
PD FEB
PY 2020
VL 137
AR 111099
DI 10.1016/j.enpol.2019.111099
PG 9
WC Economics; Energy & Fuels; Environmental Sciences; Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Energy & Fuels; Environmental Sciences & Ecology
GA KO3HQ
UT WOS:000515439900044
OA Green Accepted
DA 2024-09-05
ER
PT J
AU Ginde, G
   Saha, S
   Mathur, A
   Venkatagiri, S
   Vadakkepat, S
   Narasimhamurthy, A
   Sagar, BSD
AF Ginde, Gouri
   Saha, Snehanshu
   Mathur, Archana
   Venkatagiri, Sukrit
   Vadakkepat, Sujith
   Narasimhamurthy, Anand
   Sagar, B. S. Daya
TI ScientoBASE: a framework and model for computing scholastic indicators
   of non-local influence of journals via native data acquisition
   algorithms
SO SCIENTOMETRICS
LA English
DT Article
DE Journal Influence Score; Journal Internationality Modeling Index (JIMI);
   Web scraping; Feature extraction; Cobb-Douglas production function;
   Convex optimization; Supervised learning; Non-Local Influence Quotient
   (NLIQ); Source-Normalized Impact per Paper (SNIP); Other Citation
   Quotient (OCQ)
ID IMPACT
AB Defining and measuring internationality as a function of influence diffusion of scientific journals is an open problem. There exists no metric to rank journals based on the extent or scale of internationality. Measuring internationality is qualitative, vague, open to interpretation and is limited by vested interests. With the tremendous increase in the number of journals in various fields and the unflinching desire of academics across the globe to publish in "international'' journals, it has become an absolute necessity to evaluate, rank and categorize journals based on internationality. Authors, in the current work have defined internationality as a measure of influence that transcends across geographic boundaries. There are concerns raised by the authors about unethical practices reflected in the process of journal publication whereby scholarly influence of a select few are artificially boosted, primarily by resorting to editorial maneuvers. To counter the impact of such tactics, authors have come up with a new method that defines and measures internationality by eliminating such local effects when computing the influence of journals. A new metric, Non-Local Influence Quotient is proposed as one such parameter for internationality computation along with another novel metric, Other-Citation Quotient as the complement of the ratio of self-citation and total citation. In addition, SNIP and international collaboration ratio are used as two other parameters. As these journal parameters are not readily available in one place, algorithms to scrape these metrics are written and documented as a part of the current manuscript. Cobb-Douglas production function is utilized as a model to compute Journal Internationality Modeling Index. Current work elucidates the metric acquisition algorithms while delivering arguments in favor of the suitability of the proposed model. Acquired data is corroborated by different supervised learning techniques. As part of future work, the authors present a bigger picture, Reputation and Global Influence Score, that will be computed to facilitate the formation of clusters of journals of high, moderate and low internationality.
C1 [Ginde, Gouri; Saha, Snehanshu; Mathur, Archana; Venkatagiri, Sukrit; Vadakkepat, Sujith] PESIT South Campus, Dept Comp Sci & Engn, Bangalore, Karnataka, India.
   [Narasimhamurthy, Anand] BITS Pilani, Dept Comp Sci, Hyderabad Campus, Hyderabad, Andhra Pradesh, India.
   [Sagar, B. S. Daya] Indian Stat Inst, Syst Sci & Informat Unit, Bangalore, Karnataka, India.
C3 PES University; Birla Institute of Technology & Science Pilani (BITS
   Pilani); Indian Statistical Institute; Indian Statistical Institute
   Bangalore
RP Venkatagiri, S (corresponding author), PESIT South Campus, Dept Comp Sci & Engn, Bangalore, Karnataka, India.
EM gouri.ginde@gmail.com; snehanshusaha@pes.edu; 95sukrit@gmail.com
RI Sagar, BS Daya/A-2654-2012; Saha, Snehanshu/R-1028-2018
OI Sagar, BS Daya/0000-0002-6140-8742; Mathur, Archana/0000-0003-4522-6890;
   Saha, Snehanshu/0000-0002-8458-604X
CR Abrizah A, 2013, SCIENTOMETRICS, V94, P721, DOI 10.1007/s11192-012-0813-7
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NR 28
TC 17
Z9 17
U1 0
U2 20
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD SEP
PY 2016
VL 108
IS 3
BP 1479
EP 1529
DI 10.1007/s11192-016-2006-2
PG 51
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA DV4SA
UT WOS:000382914200027
DA 2024-09-05
ER
PT C
AU Zhai, MJ
   Cong, H
   Feng, FZ
   Wu, SJ
AF Zhai, MeiJie
   Cong, Hua
   Feng, FuZhou
   Wu, ShouJun
BE Liu, D
   Wang, S
   Liao, H
   Zhang, B
   Miao, Q
   Peng, Y
TI Research on Screening Method of Performance Index of PHM System for
   Armored Vehicles
SO 2017 PROGNOSTICS AND SYSTEM HEALTH MANAGEMENT CONFERENCE (PHM-HARBIN)
SE Prognostics and System Health Management Conference
LA English
DT Proceedings Paper
CT 2017 Prognostics and System Health Management Conference (PHM-Harbin)
CY JUL 09-12, 2017
CL Harbin, PEOPLES R CHINA
DE Armored vehicles; PHM system; Performance metrics; Principal Component
   Analysis (PCA); index screening
AB The comprehensive index system is a prerequisite and basis for ensuring the scientific performance of the product performance evaluation, so, the screening of the index is the necessary link in the construction of the index system. The paper regards the armored vehicle PHM system as a special product, aiming at the problem that the redundancy of the primary index set of the armored vehicle PHM system performance index system is high and the targeting is not strong. This paper uses Principal Component Analysis (PCA) to select the index, and then analysis of correlation between indicators and obtain the final optimal results. Finally, factor analysis is used to verify the feasibility and scientificity of the method, which lays a foundation for further evaluating the performance of PHM system.
C1 [Zhai, MeiJie; Cong, Hua; Feng, FuZhou; Wu, ShouJun] Acad Armored Force Engn, Dept Mech Engn, Beijing, Peoples R China.
C3 Academy of Armored Forces Engineering - China
RP Zhai, MJ (corresponding author), Acad Armored Force Engn, Dept Mech Engn, Beijing, Peoples R China.
EM 18222785009@163.com; fengfuzhou@tsinghua.org.cn
CR DU J. H., 2012, J N U CHINA, P81
   Li G. Z., 2014, PRINCIPAL COMPONENTS, P230
   Lu X. T., 2009, COOPERATIVE EC TECHN, P54
   SHI Y., 2011, CHINA BUSINESS, P103
   Yang Y., 2006, STAT DECISION, P17
   ZHANG H., 2013, J SHANDONG I FINANCE, P52
   Zhang Y. W., 2012, RES CONSTRUCTION PRO
NR 7
TC 4
Z9 4
U1 1
U2 2
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2166-5656
BN 978-1-5386-0370-3
J9 PROGNOST SYST HEALT
PY 2017
BP 1133
EP 1140
PG 8
WC Engineering, Multidisciplinary
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Engineering
GA BJ6MY
UT WOS:000426864600182
DA 2024-09-05
ER
PT C
AU Jung, S
   Datta, R
   Segev, A
AF Jung, Sukhwan
   Datta, Rituparna
   Segev, Aviv
BE Wu, XT
   Jermaine, C
   Xiong, L
   Hu, XH
   Kotevska, O
   Lu, SY
   Xu, WJ
   Aluru, S
   Zhai, CX
   Al-Masri, E
   Chen, ZY
   Saltz, J
TI An Automatic Classification of the Primary and the Corresponding Authors
   in Research Articles
SO 2020 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA)
SE IEEE International Conference on Big Data
LA English
DT Proceedings Paper
CT 8th IEEE International Conference on Big Data (Big Data)
CY DEC 10-13, 2020
CL ELECTR NETWORK
DE Byline Analysis; Machine Learning; Author Credit Measure; Citation
   Analysis; Scientometrics
ID K-NEAREST NEIGHBOR; CONSEQUENCES; PUBLICATIONS
AB Researchers often rely on the byline order in a publication to estimate relative contributions made by its authors, an assumption on which existing author contribution measures are based. This byline-based approach is, however, incompatible with the alphabetical author ordering, a practice still employed by many research fields. Manually requesting authors to state their contributions can overcome the limitation of the existing methods. Such approaches, however, require resource-intensive data acquisition and preprocessing, rendering them ungeneralizable to existing bodies of bibliographic records. The present paper proposed a possibility of order-independent automatic author contribution measure by focusing on distinguishing the main contributors from the rest of the authors using machine learning algorithms, bypassing the limitation of both the byline-based numerical author contribution methods and ungeneralizable manual approaches. The experiment validated the proposed approach by successfully classifying both the primary and the corresponding authors shown as the first and the last author without utilizing byline orders. The Random Forest classifier showed the best performances, successfully classifying the first author, the last author, and both with the accuracy of 0.90, 0.89, and 0.76 respectively.
C1 [Jung, Sukhwan; Datta, Rituparna; Segev, Aviv] Univ S Alabama, Dept Comp Sci, Mobile, AL 36688 USA.
C3 University of South Alabama
RP Jung, S (corresponding author), Univ S Alabama, Dept Comp Sci, Mobile, AL 36688 USA.
EM shjung@southalabama.edu; rdatta@southalabama.edu; segev@southalabama.edu
RI Segev, Aviv/C-2060-2011; Jung, Suk hwan/HIK-1039-2022
CR [Anonymous], 2016, HDB BIBLIOMETRIC IND, DOI DOI 10.1002/ANIE.201608447
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NR 33
TC 0
Z9 0
U1 1
U2 5
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2639-1589
BN 978-1-7281-6251-5
J9 IEEE INT CONF BIG DA
PY 2020
BP 4604
EP 4612
DI 10.1109/BigData50022.2020.9378455
PG 9
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BR6NZ
UT WOS:000662554704084
DA 2024-09-05
ER
PT J
AU Slater, A
AF Slater, Avery
TI Phantoms of Citation: AI and the Death of the Author-Function
SO POETICS TODAY
LA English
DT Article
DE author- function; large language models (LLMs); citation; hallucination;
   artificial intelligence
ID CHATGPT
C1 [Slater, Avery] Univ Toronto, Toronto, ON, Canada.
C3 University of Toronto
RP Slater, A (corresponding author), Univ Toronto, Toronto, ON, Canada.
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NR 26
TC 0
Z9 0
U1 0
U2 0
PU DUKE UNIV PRESS
PI DURHAM
PA 905 W MAIN ST, STE 18-B, DURHAM, NC 27701 USA
SN 0333-5372
J9 POETICS TODAY
JI Poetics Today
PD JUN 1
PY 2024
VL 45
IS 2
DI 10.1215/03335372-11092818
PG 9
WC Literature
WE Arts &amp; Humanities Citation Index (A&amp;HCI)
SC Literature
GA UO7O2
UT WOS:001249066600005
DA 2024-09-05
ER
PT J
AU Mohebbi, A
   Douzandegan, Y
AF Mohebbi, Alireza
   Douzandegan, Yousef
TI Linear Regression Analysis of Title Word Count and Article Time Cited
   using R
SO JOURNAL OF SCIENTOMETRIC RESEARCH
LA English
DT Article
DE Scientometric; Article Time Cited; Title Word Count; Linear Regression;
   R statistic
ID NUMBER
AB There is a common idea, that title variables like article title length would influence article citations. The aim of the present study was to investigating possible relationship between size of article title and number of article citations by minimizing scientometric variable biases. A dataset containing similar to 100,000 virological literatures was obtained from Web of Science InCiteTM database from 1997 to 2016. Variables, Title size (TWC), Year (YoP), Source (JS), and Publisher were selected. In addition number of times article is cited ` Time Cited' (TC) was retrieved from Web of Science InCiteTM. Linear regression analysis was performed between variables and TC using R for a possible prediction model for TC. Result has shown a robust standard error corrected linear regression with only 30.6% power of predictability. Furthermore, it was found that TC, YoP, and JS have meaningful potential in the linear model. Moreover, TC is negatively correlated with YoP, JS, and positively with TWC. As a result, size of article title, years passed since publication and the journal in which article accepted are good but not sufficient predictor of article citations. In addition, article is a multi-characteristic subject and other predictors can be supposed. However, we think that finding an efficient statistical linear predication model for TC, by increase of articles citation, is overwhelming.
C1 [Mohebbi, Alireza; Douzandegan, Yousef] Golestan Univ Med Sci, Sch Med, Student Res Comm, Gorgan, Iran.
C3 Golestan University of Medical Sciences
RP Mohebbi, A (corresponding author), Golestan Univ Med Sci, Sch Med, Student Res Comm, Gorgan, Iran.
EM Mohebbi-a@goums.ac.ir
RI Mohebbi, Alireza/M-5769-2016
OI Mohebbi, Alireza/0000-0003-2489-585X
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NR 13
TC 1
Z9 1
U1 0
U2 7
PU PHCOG NET
PI KARNATAKA
PA 17, 2ND FLR, BUDDHA VIHAR RD, NEAR SPORTS ZONE, COX TOWN, BENGALURU,
   KARNATAKA, 560005, INDIA
SN 2321-6654
EI 2320-0057
J9 J SCIENTOMETR RES
JI J. Scientometr. Res.
PD JAN-APR
PY 2017
VL 6
IS 1
BP 15
EP 22
DI 10.5530/jscires.6.1.3
PG 8
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA FI2WM
UT WOS:000411810300003
OA Green Submitted, hybrid
DA 2024-09-05
ER
PT J
AU MOREAU, DR
   DOMINICK, WD
AF MOREAU, DR
   DOMINICK, WD
TI OBJECT-ORIENTED GRAPHICAL INFORMATION-SYSTEMS - RESEARCH PLAN AND
   EVALUATION METRICS
SO JOURNAL OF SYSTEMS AND SOFTWARE
LA English
DT Article
C1 UNIV SW LOUISIANA,CTR ADV COMP STUDIES,LAFAYETTE,LA 70504.
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NR 23
TC 17
Z9 17
U1 0
U2 0
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA 655 AVENUE OF THE AMERICAS, NEW YORK, NY 10010
SN 0164-1212
J9 J SYST SOFTWARE
JI J. Syst. Softw.
PD JUL
PY 1989
VL 10
IS 1
BP 23
EP 28
DI 10.1016/0164-1212(89)90059-9
PG 6
WC Computer Science, Software Engineering; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA AK083
UT WOS:A1989AK08300004
DA 2024-09-05
ER
PT C
AU Bilal Zoric, A
   Miloloza, I
   Pejic Bach, M
AF Bilal Zoric, Alisa
   Miloloza, Ivan
   Pejic Bach, Mirjana
BE Erceg, A
   Pozega, Z
TI NEURAL NETWORKS FOR STUDENT PERFORMANCE IN HIGHER EDUCATION: PRELIMINARY
   BIBLIOMETRIC ANALYSIS
SO INTERDISCIPLINARY MANAGEMENT RESEARCH XVIII (IMR 2022)
SE Interdisciplinary Management Research-Interdisziplinare
   Managementforschung
LA English
DT Proceedings Paper
CT 18th Conference on Interdisciplinary Management Research (IMR)
CY MAY 05-07, 2022
CL Opatija, CROATIA
DE neural network; higher education; educational data mining; student
   performance
ID FUZZY INFERENCE SYSTEM; COST
AB Neural networks or artificial neural networks are a branch of machine learning, a technology based on brain and nervous system studies. Thanks to the fast-technological changes, growing computing power, advanced algorithms, and widely available digital data, the application of neural networks has spread tremendously in many fields of study, from health and medicine, accounting, finance, engineering, manufacturing, and marketing to natural language processing and robotics, wherever the analysis of big sets of data is needed. This paper focuses on using neural networks in higher educational institutions for student performance analysis and prediction, intending to investigate these researches' bibliometric and topical characteristics published in scientific papers. We have searched the indexing service Scopus to track the papers that present the applications of neural networks in higher education in the last five years. The research has been investigated based on the bibliometric characteristics (authors, publications, institutions, funding) as well as the topics of the research. The presented analysis is preliminary and can be relevant for future in-depth research on the applications of neural networks for the analysis of student performance in higher education.
C1 [Bilal Zoric, Alisa] Univ Appl Sci Baltazar Zapresic, Zapresic, Croatia.
   [Miloloza, Ivan] Josip Juraj Strossmayer Univ Osijek, Fac Dent Med & Hlth Osijek, Osijek, Croatia.
   [Pejic Bach, Mirjana] Univ Zagreb, Fac Econ & Business Zagreb, Zagreb, Croatia.
C3 University of JJ Strossmayer Osijek; University of Zagreb
RP Bilal Zoric, A (corresponding author), Univ Appl Sci Baltazar Zapresic, Zapresic, Croatia.
EM abilal@bak.hr; ivan.miloloza@fdmz.hr; mpejic@net.efzg.hr
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NR 59
TC 0
Z9 0
U1 0
U2 1
PU JOSIP JURAJ STROSSMAYER UNIV OSIJEK
PI OSIJEK
PA UNIV APPLIED SCIENCES, FAC ECONOMIC OSIJEK, HOCHSCHULE PFORZHEIM, TRG
   SV, TROJSTVA 3, OSIJEK, 31000, CROATIA
SN 1847-0408
J9 INTERDISC MANAG RES
PY 2022
BP 762
EP 781
PG 20
WC Business, Finance; Management
WE Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Business & Economics
GA BT5VO
UT WOS:000838680600041
DA 2024-09-05
ER
PT J
AU Das, RM
   Jv, M
AF Das, Runu Mani
   J.v., Madhusudan
TI Analysing the Community of Inquiry Model in the Context of Online
   Learning: A Bibliometric Study
SO TECHTRENDS
LA English
DT Article
DE Community of inquiry; Online learning; Bibliometric analysis
AB This paper presents a bibliometric analysis of the community of inquiry model in online learning. The study focuses on identifying the most trending topics, most impact authors, most relevant sources, most relevant countries and most cited articles in the community of inquiry online learning model. Another aim is to understand the literature's factorial analysis, co-occurrence mapping and productivity mapping and the importance of the community of inquiry model in online teaching and learning. A total of 405 studies published between 2015 and 2022 extracted from the Web of Science Core Collection for the study. The results show the extent of growth of research studies in the community of inquiry model. The analysis revealed trends of productive authors and journals, and also identified the top country in terms of publishing articles. It includes a deeper understanding of the intellectual structure and conceptual evolution of the CoI model. Future research should explore advanced bibliometric mapping for CoI dynamics and the factors influencing learner engagement within the community of inquiry model, the role of teaching, cognitive and social presences, and strategies to strengthen the three presences in online learning. The study will help educators and researchers to identify the trends in relation to the community of inquiry framework.
C1 [Das, Runu Mani; J.v., Madhusudan] Univ Hyderabad, Dept Educ & Educ Technol, Hyderabad, India.
C3 University of Hyderabad
RP Das, RM (corresponding author), Univ Hyderabad, Dept Educ & Educ Technol, Hyderabad, India.
EM runumanidas123@gmail.com
OI Jv, Madhusudan/0000-0002-6582-0152
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   Yu ZG, 2022, EDUC INF TECHNOL, V27, P11669, DOI 10.1007/s10639-022-11081-w
NR 22
TC 0
Z9 0
U1 7
U2 7
PU SPRINGER INT PUBL AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 8756-3894
EI 1559-7075
J9 TECHTRENDS
JI TechTrends
PD MAY
PY 2024
VL 68
IS 3
SI SI
BP 435
EP 447
DI 10.1007/s11528-024-00943-4
EA MAR 2024
PG 13
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA SD4A5
UT WOS:001186105500001
DA 2024-09-05
ER
PT J
AU Victor, BG
   Perron, BE
   Sokol, RL
   Fedina, L
   Ryan, JP
AF Victor, Bryan G.
   Perron, Brian E.
   Sokol, Rebeccah L.
   Fedina, Lisa
   Ryan, Joseph P.
TI Automated Identification of Domestic Violence in Written Child Welfare
   Records: Leveraging Text Mining and Machine Learning to Enhance Social
   Work Research and Evaluation
SO JOURNAL OF THE SOCIETY FOR SOCIAL WORK AND RESEARCH
LA English
DT Article
DE text mining; machine learning; data science; domestic violence; child
   welfare
ID TEST-RETEST RELIABILITY; ABUSE; MALTREATMENT; FAMILIES; SERVICES;
   NEGLECT
AB Objective: Child welfare agencies often lack information about the front-end service needs of the families they serve. Thus, the current study tests the feasibility of text mining and machine learning procedures for identifying problems related to domestic violence documented in child welfare investigation summaries. Method: We labeled child welfare investigation summaries (N = 1,402) for the presence or absence of an active domestic violence service need. Labeled documents were then used to develop text mining and machine learning models and test their accuracy and reliability. Results: Machine learning models achieved greater than 90% accuracy when compared with human coders. Fleiss kappa estimates of coding reliability between the top-performing model and human reviewers exceeded .80, indicating that our model could support human reviewers to complete this coding task. Conclusion: Results provide strong evidence that text mining and machine learning procedures can be a cost-effective solution for extracting meaningful insights from text data. Although unsuitable for case-level predictive analytics, insights derived from these procedures can be particularly useful for investigating the prevalence, temporal trends, and geographic distribution of domestic violence-related needs in the child welfare system. These methods could substantially enhance the use of text data in social work research and evaluation.
C1 [Victor, Bryan G.; Sokol, Rebeccah L.] Wayne State Univ, Sch Social Work, 5447 Woodward Ave, Detroit, MI 48202 USA.
   [Perron, Brian E.; Fedina, Lisa; Ryan, Joseph P.] Univ Michigan, Sch Social Work, Ann Arbor, MI 48109 USA.
   [Perron, Brian E.; Ryan, Joseph P.] Univ Michigan, Child & Adolescent DataLab, Ann Arbor, MI 48109 USA.
C3 Wayne State University; University of Michigan System; University of
   Michigan; University of Michigan System; University of Michigan
RP Victor, BG (corresponding author), Wayne State Univ, Sch Social Work, 5447 Woodward Ave, Detroit, MI 48202 USA.
EM bvictor@wayne.edu
RI Victor, Bryan/T-8349-2019; Perron, Brian E./AFW-1605-2022
OI Victor, Bryan/0000-0002-2092-912X; Sokol, Rebeccah/0000-0003-3892-2337
FU Casey Family Programs; Michigan Department of Health and Human Services
FX We would like to thank Cristina Garbacea from the Department of Computer
   Science and Engineering at the University of Michigan-Ann Arbor for her
   methodological guidance on data science methods. This study was
   supported by a grant from Casey Family Programs and the Michigan
   Department of Health and Human Services.
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NR 49
TC 11
Z9 11
U1 4
U2 20
PU UNIV CHICAGO PRESS
PI CHICAGO
PA 1427 E 60TH ST, CHICAGO, IL 60637-2954 USA
SN 2334-2315
EI 1948-822X
J9 J SOC SOC WORK RES
JI J. Soc. Soc. Work Res.
PD DEC 1
PY 2021
VL 12
IS 4
BP 631
EP 655
DI 10.1086/712734
EA DEC 2021
PG 25
WC Social Work
WE Social Science Citation Index (SSCI)
SC Social Work
GA XT0MX
UT WOS:000712780300001
DA 2024-09-05
ER
PT J
AU Ozyurt, H
   Ozyurt, O
   Mishra, D
AF Ozyurt, Hacer
   Ozyurt, Ozcan
   Mishra, Deepti
TI Exploring the Evolution of Educational Serious Games Research: A Topic
   Modeling Perspective
SO IEEE ACCESS
LA English
DT Article
DE Games; Education; Analytical models; Reviews; Systematics;
   Bibliometrics; Training; Data science; Serious games; Data science
   applications in education; research trend; serious games; topic modeling
ID VIRTUAL-REALITY; DESIGN
AB This study aims to reveal the dominant research interests and models in serious games research using topic modeling analysis. The dataset of this study covers a comprehensive collection of 2676 articles from the past to the end of 2022, indexed in the Scopus database. The study begins by presenting descriptive attributes of the articles, including their publication years, subject areas, and the journals in which they are published. Subsequently, employing topic modeling analysis, a form of unsupervised machine learning, the study identifies concealed themes, research interests, and tendencies within the literature. The findings indicate a notable surge in publications in this domain, particularly post-2009 and 2019. Furthermore, the study identifies eleven primary topics dominating the literature, with notable emphasis on "Training of STEM-related fields," "Programming learning," and "Medical education". To gauge the dynamics within these topics, the study calculates accelerations both within individual topics and in comparison to others over time. Remarkably, "Child and adolescent health" emerges as the topic with the highest self-acceleration, while "Medical education" stands out for its acceleration in comparison to other topics. In sum, the outcomes of this study, which provides a comprehensive overview of the serious games field, are anticipated to yield valuable insights for understanding the current landscape, guiding future research endeavors, and shaping the trajectory of this field.
C1 [Ozyurt, Hacer; Ozyurt, Ozcan] Karadeniz Tech Univ, Fac Technol, Dept Software Engn, TR-61830 Trabzon, Turkiye.
   [Mishra, Deepti] Norwegian Univ Sci & Technol, Dept Comp Sci IDI, Educ Technol Lab, N-2815 Gjovik, Norway.
C3 Karadeniz Technical University; Norwegian University of Science &
   Technology (NTNU)
RP Mishra, D (corresponding author), Norwegian Univ Sci & Technol, Dept Comp Sci IDI, Educ Technol Lab, N-2815 Gjovik, Norway.
EM deepti.mishra@ntnu.no
OI OZYURT, OZCAN/0000-0002-0047-6813
FU Norwegian University of Science and Technology, Gjvik, Norway
FX No Statement Available
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NR 81
TC 0
Z9 0
U1 4
U2 4
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2024
VL 12
BP 81827
EP 81841
DI 10.1109/ACCESS.2024.3411094
PG 15
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA UL3E9
UT WOS:001248167600001
OA gold
DA 2024-09-05
ER
PT J
AU Yeh, DY
   Cheng, CH
   Yio, HW
AF Yeh, Duen-Yian
   Cheng, Ching-Hsue
   Yio, Hwei-Wun
TI Empirical research of the principal component analysis and ordered
   weighted averaging integrated evaluation model on software projects
SO CYBERNETICS AND SYSTEMS
LA English
DT Article
ID AGGREGATION OPERATORS
AB With the restrictions of time and cost, the complexity of software project development implies that the software industry cannot develop high quality products to satisfy the customers' needs. The evaluation of software development cannot only assist the deciders with the prediction of feasibility and the impact of benefits in advance, but also offer excellent help for the improvement and developing strategy of management after software project is developed. This research proposed a principal component analysis (PCA) and ordered weighted averaging (OWA) integrated evaluation model to overcome the complexity adhered in appropriately evaluating the development of software projects. The distinguishing characteristic of this model lies in integrating the respective advantages of PCA and OWA operators to appropriately evaluate the development of software projects.
   In this model, a well-designed questionnaire was used to express the experts' opinions on the development of software projects with respect to each criterion. The amount of evaluation results was reduced by means of PCA with the aim at cutting down the number of criteria but the accumulated variance of the original ones was preserved. OWA was used to flexibly obtain the weights of resultant criteria under the consideration of information requirement. In empirical validation, three software projects belonging to one famous hospital in Taiwan were selected as the targets to examine the appropriateness of this model. A comparison was taken to reveal the superiority of this model. As expected, the model was more effective with the increased complexity the evaluation of software project development.
C1 Transworld Inst Technol, Dept Informat Management, Yunlin 640, Taiwan.
   Natl Yunlin Univ Sci & Technol, Dept Informat Management, Yunlin, Taiwan.
C3 National Yunlin University Science & Technology
RP Yeh, DY (corresponding author), Transworld Inst Technol, Dept Informat Management, 1221,Zheng Nan Rd, Yunlin 640, Taiwan.
EM yeh@tit.edu.tw
RI Cheng, Ching-Hsue/D-1785-2012
OI Cheng, Ching-Hsue/0000-0002-5509-6965
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NR 16
TC 5
Z9 6
U1 1
U2 3
PU TAYLOR & FRANCIS INC
PI PHILADELPHIA
PA 325 CHESTNUT ST, SUITE 800, PHILADELPHIA, PA 19106 USA
SN 0196-9722
J9 CYBERNET SYST
JI Cybern. Syst.
PY 2007
VL 38
IS 3
BP 289
EP 303
DI 10.1080/01969720601187347
PG 15
WC Computer Science, Cybernetics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA 145CJ
UT WOS:000244842600004
DA 2024-09-05
ER
PT C
AU Hamou-Lhadj, W
   Nayrolles, M
AF Hamou-Lhadj, Wahab
   Nayrolles, Mathieu
GP IEEE
TI A Project on Software Defect Prevention at Commit-Time: A Success Story
   of University-Industry Research Collaboration
SO PROCEEDINGS 2018 IEEE/ACM 5TH INTERNATIONAL WORKSHOP ON SOFTWARE
   ENGINEERING RESEARCH AND INDUSTRIAL PRACTICE (SER&IP)
LA English
DT Proceedings Paper
CT 5th ACM/IEEE International Workshop on Software Engineering Research and
   Industrial Practice
CY MAY 29, 2018
CL Gothenburg, SWEDEN
DE University-Industry Research Project; Bug Prevention at CommitTime;
   Machine Learning; Software Maintenance and Evolution
AB In this talk, we describe a research collaboration project between Concordia University and Ubisoft. The project consists of investigating techniques for defect prevention at commit-time for increased software quality. The outcome of this project is a tool called CLEVER (Combining Levels of Bug Prevention and Resolution techniques) that uses machine learning to automatically detect coding defects as programmers write code. The main novelty of CLEVER is that it relies on code matching techniques to detect coding mistakes based on a database of historical code defects found in multiple related projects. The tool also proposes fixes based on known patterns.
C1 [Hamou-Lhadj, Wahab] Concordia Univ, ECE, Montreal, PQ, Canada.
   [Nayrolles, Mathieu] Ubisoft, La Forge Res Lab, Montreal, PQ, Canada.
C3 Concordia University - Canada; Ubisoft Entertainment
RP Hamou-Lhadj, W (corresponding author), Concordia Univ, ECE, Montreal, PQ, Canada.
EM wahab.hamou-lhadj@concordia.ca; mathieu.nayrolles@ubisoft.com
RI Nayrolles, Mathieu/AAM-5981-2020
FU Natural Science and Engineering Research Council of Canada (NSERC)
FX We thank the teams at Ubisoft for their participation in this project,
   and acknowledge the role of the Natural Science and Engineering Research
   Council of Canada (NSERC) for funding partly this project.
CR Cordy JR, 2011, CONF PROC INT SYMP C, P219, DOI 10.1109/ICPC.2011.26
   Kamei Y, 2013, IEEE T SOFTWARE ENG, V39, P757, DOI 10.1109/TSE.2012.70
   Newman M., 2002, SOFTWARE ERRORS COST
   Rosen C, 2015, 2015 10TH JOINT MEETING OF THE EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND THE ACM SIGSOFT SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING (ESEC/FSE 2015) PROCEEDINGS, P966, DOI 10.1145/2786805.2803183
NR 4
TC 0
Z9 0
U1 0
U2 2
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-4503-5744-9
PY 2018
BP 24
EP 25
DI 10.1145/3195546.3206423
PG 2
WC Computer Science, Software Engineering
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BL7CY
UT WOS:000454852900006
DA 2024-09-05
ER
PT J
AU Choi, S
   Seo, J
AF Choi, Soyoung
   Seo, JooYoung
TI An Exploratory Study of the Research on Caregiver Depression: Using
   Bibliometrics and LDA Topic Modeling
SO ISSUES IN MENTAL HEALTH NURSING
LA English
DT Article
ID SCIENCE; ANXIETY; BURDEN; TRENDS; WELL; TOOL; WEB
AB Purpose: The purpose of this paper is to provide readers with a comprehensive overview of scholarly work on the depression of caregivers using bibliometrics and text mining.Methods: A total of 426 articles published between 2000 and 2018 were retrieved from the Clarivate Analytics Web of Science, and then, computer-aided bibliometric analysis as well as Latent Dirichlet Allocation (LDA) topic modeling were conducted on the collection of the data.Results: Descriptive statistics on the increasing number of publications, network analysis of scientific collaboration between countries, word co-occurrence analysis, conceptual structure, and six latent topics (k = 6) identified are discussed.Conclusions: Preventing or managing depression among caregivers is a growing field with the highest priority for the aging population. In the future, collaborating between countries and reflecting cultural backgrounds in caregiver depression research are needed. This study is expected to contribute to the field of psychological distress of caregivers in looking a big picture of the current position through data-driven analysis and moving forward towards a better direction.
C1 [Choi, Soyoung] Penn State Univ, Coll Nursing, University Pk, PA 16802 USA.
   [Seo, JooYoung] Penn State Univ, Learning Design & Technol, University Pk, PA 16802 USA.
C3 Pennsylvania Commonwealth System of Higher Education (PCSHE);
   Pennsylvania State University; Pennsylvania State University -
   University Park; Pennsylvania Commonwealth System of Higher Education
   (PCSHE); Pennsylvania State University; Pennsylvania State University -
   University Park
RP Choi, S (corresponding author), 307A Nursing Sci Bldg, University Pk, PA 16802 USA.
EM sxc940@psu.edu
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NR 43
TC 9
Z9 9
U1 7
U2 48
PU TAYLOR & FRANCIS INC
PI PHILADELPHIA
PA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA
SN 0161-2840
EI 1096-4673
J9 ISSUES MENT HEALTH N
JI Issues Ment. Health Nurs.
PD JUL 2
PY 2020
VL 41
IS 7
SI SI
BP 592
EP 601
DI 10.1080/01612840.2019.1705944
PG 10
WC Nursing; Psychiatry
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Nursing; Psychiatry
GA ME7BE
UT WOS:000544807700006
PM 32286089
DA 2024-09-05
ER
PT C
AU Teufel, S
AF Teufel, S
BE Shanahan, JG
   Qu, Y
   Wiebe, J
TI Argumentative zoning for improved citation indexing
SO COMPUTING ATTITUDE AND AFFECT IN TEXT: THEORY AND APPLICATIONS
SE Information Retrieval Series
LA English
DT Proceedings Paper
CT Symposium on Computing Attitude and Affect in Text
CY MAR, 2004
CL Stanford Univ, Stanford, CA
HO Stanford Univ
DE citation analysis; sentiment; machine learning; automatic summarisation
AB We address the problem of automatically classifying academic citations in scientific articles according to author affect. There are many ways how a citation might fit into the overall argumentation of the article: as part of the solution, as rival approach or as flawed approach that justifies the current research. Our motivation for this work is to improve citation indexing. The method we use for this task is machine learning from indicators of affect (such as "we follow X in assuming that... ", or "in contrast to Y, our system solves this problem") and of presentation of ownership of ideas (such as "We present a new methodfor... ", or "They claim that... "). Some of these features are borrowed from Argumentative Zoning (Teufel and Moens, 2002), a technique for determining the rhetorical status of each sentence in a scientific article. These features include the type of subject of the sentence, the citation type, the semantic class of main verb, and a list of indicator phrases. Evaluation will be both intrinsic and extrinsic, involving the measurement of human agreement on the task and a comparison of human and automatic evaluation, as well as a comparison of task-performance with our system versus task performance with a standard citation indexer (CiteSeer, Lawrence et al., 1999).
C1 Univ Cambridge, Comp Lab, Cambridge CB1 3PY, England.
C3 University of Cambridge
RP Teufel, S (corresponding author), Univ Cambridge, Comp Lab, JJ Thomson Ave, Cambridge CB1 3PY, England.
EM Simone.Teufel@cam.ac.uk
CR [Anonymous], P 3 ACM C DIG LIB
   [Anonymous], 2002, P 2002 C EMP METH NA
   [Anonymous], 1988, Non parametric Statistics for the Behavioral Sciences
   Ge N, 1998, 6 WORKSH VER LARG CO
   KUPIEC J, 1995, P 18 ANN INT C RES D
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NR 16
TC 6
Z9 7
U1 2
U2 11
PU SPRINGER
PI DORDRECHT
PA PO BOX 17, 3300 AA DORDRECHT, NETHERLANDS
SN 1387-5264
BN 1-4020-4026-1
J9 INFORM RETRIEVAL SER
PY 2006
VL 20
BP 159
EP 169
PG 11
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BDW67
UT WOS:000235869100013
DA 2024-09-05
ER
PT C
AU Lamiya, K
   Mohan, A
AF Lamiya, K.
   Mohan, Anuraj
BE Sa, PK
   Bakshi, S
   Hatzilygeroudis, IK
   Sahoo, MN
TI A Document Similarity Computation Method Based on Word Embedding and
   Citation Analysis
SO RECENT FINDINGS IN INTELLIGENT COMPUTING TECHNIQUES, VOL 3
SE Advances in Intelligent Systems and Computing
LA English
DT Proceedings Paper
CT 5th International Conference on Advanced Computing, Networking, and
   Informatics (ICACNI)
CY JUN 01-03, 2017
CL Natl Inst Technol Goa, Dept Comp Sci & Engn, Goa, INDIA
HO Natl Inst Technol Goa, Dept Comp Sci & Engn
DE Citation network; Word embedding; Word mover's distance; Word2vec
AB Document similarity is one among the most significant problems in knowledge discovery and information retrieval. Most of the works in document similarity only focus on textual content of the documents. However, these similarity measures do not provide an accurate measure. An alternative is to incorporate citation information into similarity measure. The content of a document can be improved by considering the content of cited documents, which is the key behind this alternative. In this work, citation network analysis is used to expand the content of citing document by including the information given in cited documents. The next issue is the representation of documents. A commonly used document representation is bag-of-words model. But it does not capture the meaning or semantics of the text as well as the ordering of the words. Hence, this proposed work uses word embedding representation. Word embedding represents a word as a dense vector with low dimensionality. Word2vec model is used to generate word embedding which can capture contextual similarity between words. The similarity between documents is measured using word mover's distance, which is based on the word embedding representation of words. The proposed work takes advantage of both textual similarity and contextual similarity. Experiments showed that the proposed method provides better results compared to other state-of-the-art methods.
C1 [Lamiya, K.; Mohan, Anuraj] NSS Coll Engn, Dept Comp Sci & Engn, Palakkad, Kerala, India.
C3 NSS College of Engineering Palakkad
RP Lamiya, K (corresponding author), NSS Coll Engn, Dept Comp Sci & Engn, Palakkad, Kerala, India.
EM lamiyalami04@gmail.com
RI Mohan, Anuraj/HLV-9215-2023; Mohan, Anuraj/ABB-6154-2021; Mohan,
   Anuraj/JCE-1702-2023
OI Mohan, Anuraj/0000-0002-1044-9368; Mohan, Anuraj/0000-0002-1044-9368
CR [Anonymous], 2002, P 8 ACM SIGKDD INT C, DOI DOI 10.1145/775047.775126
   Barrn-Cedeno A., 2009, P ICON 2009, P29
   Bengio Y, 2003, J MACH LEARN RES, V3, P1137, DOI 10.1162/153244303322533223
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NR 12
TC 0
Z9 0
U1 1
U2 4
PU SPRINGER-VERLAG SINGAPORE PTE LTD
PI SINGAPORE
PA 152 BEACH ROAD, #21-01/04 GATEWAY EAST, SINGAPORE, 189721, SINGAPORE
SN 2194-5357
EI 2194-5365
BN 978-981-10-8633-5; 978-981-10-8632-8
J9 ADV INTELL SYST COMP
PY 2018
VL 709
BP 161
EP 168
DI 10.1007/978-981-10-8633-5_17
PG 8
WC Computer Science, Artificial Intelligence; Computer Science, Hardware &
   Architecture; Computer Science, Information Systems; Computer Science,
   Software Engineering; Computer Science, Theory & Methods; Engineering,
   Electrical & Electronic
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Engineering
GA BQ4EY
UT WOS:000589275800017
DA 2024-09-05
ER
PT C
AU Yang, X
AF Yang, X.
GP Destech Publicat Inc
TI Research on Project Risk Evaluation Method Based on Bayesian Networks
SO INTERNATIONAL CONFERENCE ON ADVANCED EDUCATIONAL TECHNOLOGY AND
   INFORMATION ENGINEERING (AETIE 2015)
LA English
DT Proceedings Paper
CT International Conference on Advanced Educational Technology and
   Information Engineering (AETIE)
CY MAY 17-18, 2015
CL Beijing, PEOPLES R CHINA
DE IT project; risk management; Bayesian network
AB This paper identifies the risk factors and constructs risk management model based on the Bayesian network for IT project, and makes relevant evaluation on risk factors of IT project based on this model. Through the application on an IT project, the evaluation method could evaluate effectively the IT project risks and could provide us a new way to manage the risk of IT project.
C1 TUT, Zhonghuan Informat Technol Coll, Econ & Management Dept, Tianjin, Peoples R China.
RP Yang, X (corresponding author), TUT, Zhonghuan Informat Technol Coll, Econ & Management Dept, Tianjin, Peoples R China.
CR Bannerman P.L., 2010, J SYST SOFTWARE, P2118
   Chaos, 2005, PROJECT FAILURE SUCC
   Cooper G., 1992, MACH LEARN, V122, P309
   Fan Min, 2009, Computer Engineering and Applications, V45, P226, DOI 10.3778/j.issn.1002-8331.2009.21.065
   Fei Xiaoyan, 2010, CHINA MANAGEMENT INF, V24, P69
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   Wang Yanjun, 2010, CHINAS MANUFACTURING, V39, P63
   Yan Chao, 2009, PROJECT MANAGEMENT T, P507
   Yang Lin, 2009, BUSINESS EC, P97
NR 12
TC 0
Z9 0
U1 0
U2 1
PU DESTECH PUBLICATIONS, INC
PI LANCASTER
PA 439 DUKE STREET, LANCASTER, PA 17602-4967 USA
BN 978-1-60595-245-1
PY 2015
BP 1017
EP 1023
PG 7
WC Computer Science, Information Systems; Operations Research & Management
   Science
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Operations Research & Management Science
GA BD5UU
UT WOS:000361831300139
DA 2024-09-05
ER
PT J
AU Huang, SQ
   Yang, B
   Yan, SL
   Rousseau, R
AF Huang, Shuiqing
   Yang, Bo
   Yan, Sulan
   Rousseau, Ronald
TI Institution name disambiguation for research assessment
SO SCIENTOMETRICS
LA English
DT Article
DE Institution name disambiguation (IND); Rule-based system; Artificial
   intelligence; Informetrics
AB Research evaluation is a necessity for management of academic units (scientists, research groups, departments, institutes, universities) and for government decision making in science and technology. Yet, wrong conclusions may be drawn due to errors in assignments of authors to institutions. To improve existing techniques of institution name disambiguation (IND) based on word similarity or editing distance, a rule-based algorithm is proposed in this study. One-to-many relationships between an institution and many variant names under which it is referred to in bylines of publications are recognized with the aid of statistical methods and specific rules. The performance of the rule based IND algorithm is evaluated on large datasets in four fields. These experimental results demonstrate that the precision of the algorithm is high. Yet, recall should be improved.
C1 [Huang, Shuiqing; Yang, Bo; Yan, Sulan] Nanjing Agr Univ, Coll Informat Sci & Technol, Nanjing 210095, Jiangsu, Peoples R China.
   [Rousseau, Ronald] UA, Inst Educ & Informat Sci, IBW, B-2000 Antwerp, Belgium.
   [Rousseau, Ronald] Katholieke Univ Leuven, B-3000 Louvain, Belgium.
C3 Nanjing Agricultural University; University of Antwerp; KU Leuven
RP Yang, B (corresponding author), Nanjing Agr Univ, Coll Informat Sci & Technol, Nanjing 210095, Jiangsu, Peoples R China.
EM mail.boyang@gmail.com
FU National Social Science Fund of China [13CTQ031]
FX We would like to thank Qiuru Peng, Hui Lin, Xueqin Jiang, and Zengli She
   from the college of information science and technology for their work on
   data verification. The authors are supported by Grant No. 13CTQ031 of
   the National Social Science Fund of China.
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NR 28
TC 25
Z9 29
U1 3
U2 96
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD JUN
PY 2014
VL 99
IS 3
BP 823
EP 838
DI 10.1007/s11192-013-1214-2
PG 16
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA AH1TW
UT WOS:000335905000011
DA 2024-09-05
ER
PT J
AU Greenland, S
AF Greenland, Sander
TI For and Against Methodologies: Some Perspectives on Recent Causal and
   Statistical Inference Debates
SO EUROPEAN JOURNAL OF EPIDEMIOLOGY
LA English
DT Article
DE Bias; Causal inference; Causation; Counterfactuals; Potential outcomes;
   Effect estimation; Hypothesis testing; Intervention analysis; Modeling;
   Significance testing; Research synthesis; Statistical inference
ID P-VALUES; LUNG-CANCER; CONFIDENCE-INTERVALS; SIGNIFICANCE TESTS;
   EPIDEMIOLOGY; REGRESSION; RACE; BIAS; COUNTERFACTUALS; IDENTIFIABILITY
AB I present an overview of two methods controversies that are central to analysis and inference: That surrounding causal modeling as reflected in the "causal inference" movement, and that surrounding null bias in statistical methods as applied to causal questions. Human factors have expanded what might otherwise have been narrow technical discussions into broad philosophical debates. There seem to be misconceptions about the requirements and capabilities of formal methods, especially in notions that certain assumptions or models (such as potential-outcome models) are necessary or sufficient for valid inference. I argue that, once these misconceptions are removed, most elements of the opposing views can be reconciled. The chief problem of causal inference then becomes one of how to teach sound use of formal methods (such as causal modeling, statistical inference, and sensitivity analysis), and how to apply them without generating the overconfidence and misinterpretations that have ruined so many statistical practices.
C1 [Greenland, Sander] Univ Calif Los Angeles, Dept Epidemiol, Los Angeles, CA 90095 USA.
   [Greenland, Sander] Univ Calif Los Angeles, Dept Stat, Los Angeles, CA 90095 USA.
C3 University of California System; University of California Los Angeles;
   University of California System; University of California Los Angeles
RP Greenland, S (corresponding author), Univ Calif Los Angeles, Dept Epidemiol, Los Angeles, CA 90095 USA.; Greenland, S (corresponding author), Univ Calif Los Angeles, Dept Stat, Los Angeles, CA 90095 USA.
EM lesdomes@ucla.edu
CR Aalen OO, 2016, STAT METHODS MED RES, V25, P2294, DOI 10.1177/0962280213520436
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NR 186
TC 49
Z9 51
U1 0
U2 39
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0393-2990
EI 1573-7284
J9 EUR J EPIDEMIOL
JI Eur. J. Epidemiol.
PD JAN
PY 2017
VL 32
IS 1
BP 3
EP 20
DI 10.1007/s10654-017-0230-6
PG 18
WC Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Public, Environmental & Occupational Health
GA EM1ZI
UT WOS:000395115600002
PM 28220361
DA 2024-09-05
ER
PT J
AU Pei, ZG
AF Pei, Zhonggui
TI Framework design based on data-driven for evaluating the efficiency of
   group collaboration in scientific research teams
SO JOURNAL OF SUPERCOMPUTING
LA English
DT Article
DE Data driven; Group collaboration; Indicator construction; Machine
   learning; Adaptive enhancement algorithm; Tenfold cross; Hyperparameter
AB This paper presents a data-driven framework for evaluating collaboration efficiency within scientific research teams. The framework introduces a team efficiency evaluation system consisting of 40 specific indicators, which are analyzed and modeled using statistical methods. The adaptive enhancement algorithm model achieves the highest accuracy, recall, and F1 values, with scores of 0.852, 0.530, and 0.620, respectively. These findings demonstrate the feasibility of the proposed data-driven research team collaboration model, offering theoretical support for enhancing the effectiveness of group collaboration. Moreover, the study is significant for further research on group collaboration in diverse fields.
C1 [Pei, Zhonggui] Changzhou Vocat Inst Engn, Off Ind & Educ Integrat, Changzhou 213000, Peoples R China.
C3 Changzhou Vocational Institute of Engineering
RP Pei, ZG (corresponding author), Changzhou Vocat Inst Engn, Off Ind & Educ Integrat, Changzhou 213000, Peoples R China.
EM zgpei2023@126.com
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NR 30
TC 0
Z9 0
U1 4
U2 9
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0920-8542
EI 1573-0484
J9 J SUPERCOMPUT
JI J. Supercomput.
PD MAY
PY 2024
VL 80
IS 7
BP 10148
EP 10171
DI 10.1007/s11227-023-05815-x
EA DEC 2023
PG 24
WC Computer Science, Hardware & Architecture; Computer Science, Theory &
   Methods; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA PX0X9
UT WOS:001127073000001
DA 2024-09-05
ER
PT J
AU Coccia, M
AF Coccia, Mario
TI A Taxonomy of Public Research Bodies: A Systemic Approach<SUP>1</SUP>
SO PROMETHEUS
LA English
DT Article
DE public research sector; public research laboratory; research evaluation;
   cluster analysis; principal component analysis; type of research
   institutes; research policy
ID RESEARCH-AND-DEVELOPMENT; DEVELOPMENT PERFORMANCE; INNOVATION; MODELS
AB Nowadays the governments of industrialised countries, in the presence of reduced public resources, have to assign clear objectives to public research laboratories to increase the competitiveness of firms. The purpose of this article is to analyse the public research bodies of the National Research Council of Italy in order to pinpoint the main typologies operating in the national system of innovation (NSI). This research shows four main types of research institutes as drivers of NSI. The results can supply useful information to policy makers on the behaviour of these structures and on their strengths and weaknesses.
C1 [Coccia, Mario] Natl Res Council Italy, Ceris, Turin, Italy.
C3 Consiglio Nazionale delle Ricerche (CNR)
RP Coccia, M (corresponding author), Politecn Torino, Econ, Turin, Italy.
RI Coccia, Mario/F-9793-2015
OI Coccia, Mario/0000-0003-1957-6731
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NR 33
TC 29
Z9 30
U1 0
U2 0
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0810-9028
EI 1470-1030
J9 PROMETHEUS
JI Prometheus
PY 2005
VL 23
IS 1
BP 63
EP 82
DI 10.1080/0810902042000331322
PG 20
WC Social Sciences, Interdisciplinary
WE Emerging Sources Citation Index (ESCI)
SC Social Sciences - Other Topics
GA V60GW
UT WOS:000210820100004
DA 2024-09-05
ER
PT J
AU Murillo, AP
   Yoon, A
AF Murillo, Angela P.
   Yoon, Ayoung
TI A study of emerging trends in digital preservation literature: An
   analysis of journal articles presented in course syllabi
SO JOURNAL OF LIBRARIANSHIP AND INFORMATION SCIENCE
LA English
DT Article
DE Digital preservation; digital preservation education; syllabi analysis;
   citation analysis; topic modeling
ID INSTITUTIONAL REPOSITORIES; LIBRARY; EDUCATION; SCIENCE; SCHOOLS
AB The field of digital preservation education is evolving due to the rapid developments in the digital preservation field, and as educators and researchers respond to these developments. One way to understand trends in education is through the examination of course syllabi and through the assigned course readings, as instructors often utilize and integrate core and seminal literature in these courses. This study aims to understand the emerging topics and trends in digital preservation education through the examination of these course readings. This study examines these topics and trends through an analysis of the literature assigned digital preservation courses at North American ALA (American Library Association)-accredited Master's in Library and Information Science programs through bibliometric analysis, topic modeling, and visual analysis of the citation data.
C1 [Murillo, Angela P.; Yoon, Ayoung] Indiana Univ Purdue Univ, Indianapolis, IN 46202 USA.
C3 Purdue University System; Purdue University
RP Murillo, AP (corresponding author), Indiana Univ Purdue Univ, Indianapolis, IN 46202 USA.
EM apmurill@iu.edu
RI Murillo, Angela/V-2705-2018
OI Murillo, Angela/0000-0002-7213-7335
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NR 56
TC 2
Z9 2
U1 3
U2 34
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 0961-0006
EI 1741-6477
J9 J LIBR INF SCI
JI J. Libr. Inf. Sci.
PD DEC
PY 2021
VL 53
IS 4
BP 615
EP 629
AR 0961000620967714
DI 10.1177/0961000620967714
EA SEP 2021
PG 15
WC Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science
GA WL1LC
UT WOS:000691986500001
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Ren, ZD
   Yang, K
   Dong, W
AF Ren, Zhongda
   Yang, Kun
   Dong, Wen
TI Spatial Analysis and Risk Assessment Model Research of Arthritis Based
   on Risk Factors: China, 2011, 2013 and 2015
SO IEEE ACCESS
LA English
DT Article
DE Arthritis; Correlation; Analytical models; Diseases; Logistics; Risk
   management; Graphical models; Middle-aged and older adults; arthritis;
   spatial analysis; risk factors; logistic regression modeling; random
   forest modelling; risk assessment
ID RHEUMATOID-ARTHRITIS; FUNCTIONAL-DISABILITY; UNITED-STATES; DEPRESSION;
   PREVALENCE; ASSOCIATIONS; RESOLUTION; DISEASE; OBESITY; HEALTH
AB Arthritis is a public health issue that is of global concern. Arthritis is one of the chronic diseases with a high incidence of middle-aged and older adults. The patients have paid a heavy price for this and caused a substantial economic burden on society. In this study, we used spatial autocorrelation, spatial cluster analysis, multiple logistic regression, and random forest models to analyze the spatial distribution and possible risk factors for arthritis in elderly Chinese and assess arthritis risk. Global spatial autocorrelation analysis and significance test results show that Moran's I of arthritis spatial autocorrelation in 2011, 2013, and 2015 are statistically significant, so there is significant spatial autocorrelation three years. The results of local spatial autocorrelation and spatial clustering analysis show that the aggregation areas of arthritis patients are mainly in the southwest, northwest, and central China. Multivariate logistic regression analysis showed that gender, age, education level, Body Mass Index (BMI), Center for Epidemiologic Studies Depression Scale score (CES-D), altitude, region, weather temperature, hypertension, lung, liver, heart, stroke, digestive, and kidney disease were all arthritis affects factors (P < 0.05). Compared with the multi-factor Logistic regression model, the random forest model better assesses performance and higher fit. The fitting accuracy is 82.2% in the random forest model, which is better than the multi-factor Logistic regression model (66.6%). According to the assessment risk map generated by the random forest model, Northeast, Southwest, Northwest, South, and Central are high-risk areas for arthritis. These results provide benchmark data for the control and prevention of arthritis diseases.
C1 [Ren, Zhongda] Yunnan Normal Univ, Sch Comp Sci & Technol, Kunming 650500, Yunnan, Peoples R China.
   [Ren, Zhongda; Yang, Kun; Dong, Wen] Yunnan Normal Univ, Minist Educ, Engn Res Ctr GIS Technol Western China, Kunming 650500, Yunnan, Peoples R China.
   [Yang, Kun; Dong, Wen] Yunnan Normal Univ, Fac Geog, Kunming 650500, Yunnan, Peoples R China.
C3 Yunnan Normal University; Yunnan Normal University; Yunnan Normal
   University
RP Yang, K; Dong, W (corresponding author), Yunnan Normal Univ, Minist Educ, Engn Res Ctr GIS Technol Western China, Kunming 650500, Yunnan, Peoples R China.; Yang, K; Dong, W (corresponding author), Yunnan Normal Univ, Fac Geog, Kunming 650500, Yunnan, Peoples R China.
EM kmdcynu@163.com; dong_wen121@163.com
RI Yang, Kun/ISA-1094-2023
OI Yang, Kun/0000-0003-1335-3449; Ren, ZhongDa/0000-0002-1046-2220
FU National Natural Science Foundation of China [41661087]
FX This work was supported by the National Natural Science Foundation of
   China under Grant 41661087.
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NR 49
TC 2
Z9 2
U1 6
U2 32
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2020
VL 8
BP 206406
EP 206417
DI 10.1109/ACCESS.2020.3037912
PG 12
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering; Telecommunications
GA OZ6JF
UT WOS:000595029300001
OA gold
DA 2024-09-05
ER
PT J
AU Ng, DTK
   Ching, ACH
   Law, SW
AF Ng, Davy Tsz Kit
   Ching, Anthony Chun Hin
   Law, Sau Wai
TI Online learning in management education amid the pandemic: A
   bibliometric and content analysis
SO INTERNATIONAL JOURNAL OF MANAGEMENT EDUCATION
LA English
DT Article
DE Management education; Online learning; COVID; Information systems;
   Bibliometric analysis
ID TEACHER INPUT; BUSINESS; COVID-19; PERSPECTIVE; CHALLENGES; COMMUNITY;
   FUTURE
AB The COVID-19 pandemic (2020-2022) had triggered a global crisis which led to the suspension of colleges and universities. Management educators had digitally transformed their teaching to new modalities with digital technologies and adapted to technological solutions. The management students had experienced different online modes of learning and adjusted their methods to the new reality of content delivery. This study aims to discuss opportunities and challenges for management education and facilitate further investigation into the emerging trends on online learning by analyzing the characteristics of management education research and examining the most frequent research themes from 2020 to 2022. A bibliometric analysis is used to review 920 papers retrieved from the Scopus database for exploring key research themes of management education and online learning. The findings revealed that the publications are concentrated in developed countries while European countries had accounted for the largest proportion of the listed publications. Five sub themes are identified for receiving the most scholarly attention, such as pedagogy, technology, assessment methods, learning outcomes or skills, and challenges. After all, the bibliometric and thematic findings identified pivotal theoretical contributions, including fields of online or blended learning and management education converge, to extend the existing online learning theories.
C1 [Ching, Anthony Chun Hin] Univ Hong Kong, Fac Educ, Hong Kong, Peoples R China.
   [Law, Sau Wai] Bangor Univ, Business Sch, Bangor, England.
   [Ng, Davy Tsz Kit] Hong Kong Shue Yan Univ, Law & Business Dept, Hong Kong, Peoples R China.
   [Ng, Davy Tsz Kit] Univ Hong Kong, Fac Educ, Hong Kong Special Adm Reg China, Hong Kong, Peoples R China.
C3 University of Hong Kong; Bangor University; Hong Kong Shue Yan
   University; University of Hong Kong
RP Ng, DTK (corresponding author), Univ Hong Kong, Fac Educ, Hong Kong Special Adm Reg China, Hong Kong, Peoples R China.
EM davyngtk@connect.hku.hk; anthonychingchunhin@gmail.com
RI Ng, Tsz Kit Davy/ADD-3433-2022
OI Ng, Tsz Kit Davy/0000-0002-2380-7814; Law, Sau Wai/0000-0002-5231-2845
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NR 138
TC 11
Z9 11
U1 15
U2 35
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 1472-8117
EI 2352-3565
J9 INT J MANAG EDUC-OXF
JI Int. J. Manag. Educ.
PD JUL
PY 2023
VL 21
IS 2
AR 100796
DI 10.1016/j.ijme.2023.100796
EA MAR 2023
PG 18
WC Business; Education & Educational Research; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics; Education & Educational Research
GA C5DQ2
UT WOS:000962121800001
OA Bronze
DA 2024-09-05
ER
PT C
AU Wong, W
   Tomov, S
   Dongarra, J
AF Wong, Kwai
   Tomov, Stanimire
   Dongarra, Jack
BE Weiland, M
   Juckeland, G
   Alam, S
   Jagode, H
TI Hands-On Research and Training in High Performance Data Sciences, Data
   Analytics, and Machine Learning for Emerging Environments
SO HIGH PERFORMANCE COMPUTING: ISC HIGH PERFORMANCE 2019 INTERNATIONAL
   WORKSHOPS
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT 34th International Conference on High Performance Computing (ISC High
   Performance)
CY JUN 16-20, 2019
CL Frankfurt, GERMANY
DE Computational science; Educational outreach; Research Experiences for
   Undergraduates; Data analytics; Machine learning (ML); Hands-on
   experiences and education; HPC
AB This paper describes a hands-on Research Experiences for Computational Science, Engineering, and Mathematics (RECSEM) program in high-performance data sciences, data analytics, and machine learning on emerging computer architectures. RECSEM is a Research Experiences for Undergraduates (REU) site program supported by the USA National Science Foundation. This site program at the University of Tennessee (UTK) directs a group of ten undergraduate students to explore, as well as contribute to the emergent interdisciplinary computational science models and state-of-the-art HPC techniques via a number of cohesive compute and data intensive applications in which numerical linear algebra is the fundamental building block.
C1 [Wong, Kwai; Tomov, Stanimire; Dongarra, Jack] Univ Tennessee, Knoxville, TN 37996 USA.
   [Dongarra, Jack] Oak Ridge Natl Lab, Oak Ridge, TN 37831 USA.
C3 University of Tennessee System; University of Tennessee Knoxville;
   United States Department of Energy (DOE); Oak Ridge National Laboratory
RP Tomov, S (corresponding author), Univ Tennessee, Knoxville, TN 37996 USA.
EM kwong@utk.edu; tomov@icl.utk.edu; dongarra@icl.utk.edu
RI Dongarra, Jack/G-4199-2019
OI Dongarra, Jack/0000-0003-3247-1782; Tomov, Stanimire/0000-0002-5937-7959
FU National Science Foundation (NSF), through NSF REU Award [1262937,
   1659502]; University of Tennessee, Knoxville (UTK); National Institute
   for Computational Sciences (NICS); National Science Foundation
   [ACI-1548562]; XSEDE education allocation award [TG-ASC170031]; Office
   of Advanced Cyberinfrastructure (OAC); Direct For Computer & Info Scie &
   Enginr [1659502] Funding Source: National Science Foundation; Office of
   Advanced Cyberinfrastructure (OAC); Direct For Computer & Info Scie &
   Enginr [1262937] Funding Source: National Science Foundation
FX This work was conducted at the Joint Institute for Computational
   Sciences (JICS), sponsored by the National Science Foundation (NSF),
   through NSF REU Award #1262937 and #1659502, with additional Support
   from the University of Tennessee, Knoxville (UTK), and the National
   Institute for Computational Sciences (NICS). This work used the Extreme
   Science and Engineering Discovery Environment (XSEDE), which is
   supported by National Science Foundation grant number ACI-1548562.
   Computational Resources are available through a XSEDE education
   allocation award TG-ASC170031.
CR Betancourt F., 2019, PRACTICE EXPERIENCE
   Cheng L, 2018, OXID MED CELL LONGEV, V2018, DOI 10.1155/2018/9482018
   Cheng X., 2018, ACCELERATING 2D FFT
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NR 10
TC 0
Z9 0
U1 0
U2 1
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 0302-9743
EI 1611-3349
BN 978-3-030-34356-9; 978-3-030-34355-2
J9 LECT NOTES COMPUT SC
PY 2020
VL 11887
BP 643
EP 655
DI 10.1007/978-3-030-34356-9_49
PG 13
WC Computer Science, Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BQ6PM
UT WOS:000612971700049
DA 2024-09-05
ER
PT J
AU Liu, L
   Tang, J
   Han, JW
   Yang, SQ
AF Liu, Lu
   Tang, Jie
   Han, Jiawei
   Yang, Shiqiang
TI Learning influence from heterogeneous social networks
SO DATA MINING AND KNOWLEDGE DISCOVERY
LA English
DT Article
DE Social influence analysis; Social network analysis; Influence
   propagation; Topic modeling
ID CENTRALITY
AB Influence is a complex and subtle force that governs social dynamics and user behaviors. Understanding how users influence each other can benefit various applications, e.g., viral marketing, recommendation, information retrieval and etc. While prior work has mainly focused on qualitative aspect, in this article, we present our research in quantitatively learning influence between users in heterogeneous networks. We propose a generative graphical model which leverages both heterogeneous link information and textual content associated with each user in the network to mine topic-level influence strength. Based on the learned direct influence, we further study the influence propagation and aggregation mechanisms: conservative and non-conservative propagations to derive the indirect influence. We apply the discovered influence to user behavior prediction in four different genres of social networks: Twitter, Digg, Renren, and Citation. Qualitatively, our approach can discover some interesting influence patterns from these heterogeneous networks. Quantitatively, the learned influence strength greatly improves the accuracy of user behavior prediction.
C1 [Liu, Lu] Capital Med Univ, Beijing, Peoples R China.
   [Tang, Jie; Yang, Shiqiang] Tsinghua Univ, Beijing 100084, Peoples R China.
   [Han, Jiawei] Univ Illinois, Urbana, IL 61801 USA.
C3 Capital Medical University; Tsinghua University; University of Illinois
   System; University of Illinois Urbana-Champaign
RP Liu, L (corresponding author), Capital Med Univ, Beijing, Peoples R China.
EM lu-liu@mails.tsinghua.edu.cn
RI yang, shiqiang/AAH-5484-2019; Edgar, William D/J-8792-2013; tang,
   jie/KIE-8633-2024
OI Edgar, William D/0000-0002-7996-9273; 
FU National Natural Science Foundation of China [61103065, 61073073,
   61035004, 61003097, 60933013]; U.S. National Science Foundation
   [IIS-09-05215]; U.S. Army Research Laboratory [W911NF-09-2-0053]
FX The work was supported in part by the National Natural Science
   Foundation of China under grants 61103065, 61073073, 61035004, 61003097
   and 60933013, and by the U.S. National Science Foundation under grant
   IIS-09-05215 and the U.S. Army Research Laboratory under Cooperative
   Agreement Number W911NF-09-2-0053 (NS-CTA).
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NR 50
TC 67
Z9 86
U1 2
U2 84
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1384-5810
EI 1573-756X
J9 DATA MIN KNOWL DISC
JI Data Min. Knowl. Discov.
PD NOV
PY 2012
VL 25
IS 3
SI SI
BP 511
EP 544
DI 10.1007/s10618-012-0252-3
PG 34
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA 992GL
UT WOS:000307763500005
DA 2024-09-05
ER
PT J
AU Huang, L
   Ladikas, M
   Schippl, J
   He, GX
   Hahn, J
AF Huang, Lei
   Ladikas, Miltos
   Schippl, Jens
   He, Guangxi
   Hahn, Julia
TI Knowledge mapping of an artificial intelligence application scenario: A
   bibliometric analysis of the basic research of data-driven autonomous
   vehicles
SO TECHNOLOGY IN SOCIETY
LA English
DT Article
DE Artificial intelligence; Autonomous vehicles; Application scenarios;
   Bibliometrics; Basic research; Knowledge mapping
ID INNOVATION; DYNAMICS; SCIENCE; TRENDS; IMPACT; TRUST
AB With the rapid development and maturation of basic research in related fields such as deep learning and convolutional neural networks, artificial intelligence (AI) has become a policy hotspot of high interest in all major economies. Furthermore, the governance of application scenarios has become one of the important topics of AI governance policy research. The practice of AI governance policy depends on a change in the paradigm of relevant governance policy research theories, and there is an urgent need to empirically analyze the structural characteristics of the knowledge evolution of AI application scenarios. This study conducts a bibliometric analysis of the basic research trends in autonomous vehicles, which is one of the most important application scenarios of AI. The study empirically analyzes the relevant literature data from the Science Citation Index Expanded (SCI-Expanded) and Social Sciences Citation Index (SSCI) databases of Web of Science, based on both technical and social dimensions through the knowledge mapping analysis tools in Bibliometrix (in R environment). Based on the empirical analysis, the results show that the basic research on autonomous vehicles is characterized by strong data-driven innovation under the influence of AI. The fusion of AI and basic research on autonomous vehicles has become a major driver of knowledge innovation in this domain while there is a lack of the integration of social science research.
C1 [Huang, Lei; He, Guangxi] Chinese Acad Sci & Technol Dev, Beijing 100038, Peoples R China.
   [Huang, Lei; Schippl, Jens] Karlsruhe Inst Technol, Inst Technol Assessment & Syst Anal, D-76021 Karlsruhe, Germany.
   [Ladikas, Miltos; Hahn, Julia] Karlsruhe Inst Technol, Inst Technol Assessment & Syst Anal, D-10437 Berlin, Germany.
   [Huang, Lei] Yuyuantan South Rd 8th, Beijing 100038, Peoples R China.
C3 Helmholtz Association; Karlsruhe Institute of Technology; Helmholtz
   Association; Karlsruhe Institute of Technology
RP Huang, L (corresponding author), Yuyuantan South Rd 8th, Beijing 100038, Peoples R China.
EM lei.huang7601@foxmail.com; miltos.ladikas@kit.edu; jens.schippl@kit.edu;
   hegx@casted.org.cn; julia.hahn@kit.edu
RI Wang, Yuhan/KGL-5855-2024; wang, wang/KGW-2828-2024; wang,
   yue/KDO-9209-2024; Liu, Chang/KGL-6678-2024; Wang, YuHan/KGY-2933-2024;
   Sun, Yue/KHU-8159-2024; Li, Hongbo/KHV-4191-2024
OI Li, Hongbo/0000-0003-4495-0756; Huang, Lei/0000-0003-0989-5392
FU Helmholtz [2020025]; OCPC [2020025]
FX <STRONG> </STRONG>Supported by the International Postdoctoral Exchange
   Fellowship Program between Helmholtz and OCPC (Grant No. 2020025) .
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NR 67
TC 4
Z9 4
U1 17
U2 19
PU ELSEVIER SCI LTD
PI London
PA 125 London Wall, London, ENGLAND
SN 0160-791X
EI 1879-3274
J9 TECHNOL SOC
JI Technol. Soc.
PD NOV
PY 2023
VL 75
AR 102360
DI 10.1016/j.techsoc.2023.102360
EA OCT 2023
PG 12
WC Social Issues; Social Sciences, Interdisciplinary
WE Social Science Citation Index (SSCI)
SC Social Issues; Social Sciences - Other Topics
GA FS9T1
UT WOS:001147968900001
DA 2024-09-05
ER
PT J
AU Ke, ZT
   Ji, PS
   Jin, JS
   Li, WS
AF Ke, Zheng Tracy
   Ji, Pengsheng
   Jin, Jiashun
   Li, Wanshan
TI Recent Advances in Text Analysis
SO ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION
LA English
DT Article
DE BERT; journal ranking; knowledge graph; neural network; SCORE; Stigler's
   model; Topic-SCORE; topic weight
ID MODELS; STATISTICS
AB Text analysis is an interesting research area in data science and has various applications, such as in artificial intelligence, biomedical research, and engineering. We review popular methods for text analysis, ranging from topic modeling to the recent neural language models. In particular, we review Topic-SCORE, a statistical approach to topic modeling, and discuss how to use it to analyze the Multi-Attribute Data Set on Statisticians (MADStat), a data set on statistical publications that we collected and cleaned. The application of Topic-SCORE and other methods to MADStat leads to interesting findings. For example, we identified 11 representative topics in statistics. For each journal, the evolution of topic weights over time can be visualized, and these results are used to analyze the trends in statistical research. In particular, we propose a new statistical model for ranking the citation impacts of 11 topics, and we also build a cross-topic citation graph to illustrate how research results on different topics spread to one another. The results on MADStat provide a data-driven picture of the statistical research from 1975 to 2015, from a text analysis perspective.
C1 [Ke, Zheng Tracy] Harvard Univ, Dept Stat, Cambridge, MA 02138 USA.
   [Ji, Pengsheng] Univ Georgia, Dept Stat, Athens, GA USA.
   [Jin, Jiashun; Li, Wanshan] Carnegie Mellon Univ, Dept Stat & Data Sci, Pittsburgh, PA USA.
C3 Harvard University; University System of Georgia; University of Georgia;
   Carnegie Mellon University
RP Ke, ZT (corresponding author), Harvard Univ, Dept Stat, Cambridge, MA 02138 USA.
EM zke@fas.harvard.edu
RI Ji, Pengsheng/E-2634-2013
OI Ji, Pengsheng/0000-0003-1439-5819
FU National Science Foundation (NSF) CAREER grant [DMS-1943902]; NSF
   [DMS-2015469]
FX Z.T.K. was partially supported by National Science Foundation (NSF)
   CAREER grant DMS-1943902. J.J. was partially supported by NSF grant
   DMS-2015469. Z.T.K. thanks Russell Kunes and Xiao-Li Meng for helpful
   discussions.
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Z9 1
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PU ANNUAL REVIEWS
PI PALO ALTO
PA 4139 EL CAMINO WAY, PO BOX 10139, PALO ALTO, CA 94303-0139 USA
SN 2326-8298
EI 2326-831X
J9 ANNU REV STAT APPL
JI Annu. Rev. Stat. Application
PY 2024
VL 11
BP 347
EP 372
DI 10.1146/annurev-statistics-040522-022138
PG 26
WC Mathematics, Interdisciplinary Applications; Statistics & Probability
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematics
GA TC5R4
UT WOS:001239078200015
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Zhu, CQ
   Huang, DL
   Zuo, B
AF Zhu, Chengquan
   Huang, Donglin
   Zuo, Bin
TI The influence of AI robot threats on right-wing authoritarianism and the
   mitigating role of human uniqueness
SO CURRENT PSYCHOLOGY
LA English
DT Article; Early Access
DE Artificial intelligence; Perceived threats; Right-wing authoritarianism;
   Sense of control
ID COMPENSATORY CONTROL; SOCIAL IDENTITY; ANTHROPOMORPHISM; PERCEPTION;
   GOVERNMENT; ATTITUDES; SUPPORT; BELIEF; RISK; VIEW
AB The social implications of powerful Artificially Intelligent (AI) robots, specifically regarding the potential loss of sense of control they bring about, remain incompletely understood. This study investigated the relationship between people's perceptions of AI robots and their right-wing authoritarianism (RWA) through two studies (N = 2056, Mage=35.37, SDage=10.85). Study 1 revealed that when participants were exposed to advanced (mindful) AI robots they reported heightened levels of threat perception. This perceived threat engendered a sentiment of control erosion. Consequently, individuals exhibited heightened support for RWA as a means to regain the sense of control. Although RWA is considered to have adaptive significance, it may also bring many negative social impacts. Study 2 found that accentuating human uniqueness can mitigate perceptions of threat and reinstate a sense of control. This strategy appeared efficacious in curbing RWA inclinations. The inexorable integration of AI robots into society underscores the urgency of comprehending their potential societal implications.
C1 [Zhu, Chengquan; Zuo, Bin] Sun Yat Sen Univ, Dept Psychol, Guangzhou, Guangdong, Peoples R China.
   [Huang, Donglin] Xinxiang Inst Engn, Dept Student Affairs Off, Xinxiang, Henan, Peoples R China.
C3 Sun Yat Sen University
RP Zuo, B (corresponding author), Sun Yat Sen Univ, Dept Psychol, Guangzhou, Guangdong, Peoples R China.
EM zuobin@mail.sysu.edu.cn
FX DAS:Due to the protection of participants' privacy, the data that
   support the findings of this study are available from the corresponding
   author only upon reasonable request.
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   Yam KC, 2021, COMPUT HUM BEHAV, V125, DOI 10.1016/j.chb.2021.106945
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NR 67
TC 0
Z9 0
U1 1
U2 1
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1046-1310
EI 1936-4733
J9 CURR PSYCHOL
JI Curr. Psychol.
PD 2024 AUG 9
PY 2024
DI 10.1007/s12144-024-06347-0
EA AUG 2024
PG 12
WC Psychology, Multidisciplinary
WE Social Science Citation Index (SSCI)
SC Psychology
GA C2B6H
UT WOS:001287465400001
DA 2024-09-05
ER
PT C
AU Chantaranimi, K
   Sugunsil, P
   Natwichai, J
AF Chantaranimi, Kittayaporn
   Sugunsil, Prompong
   Natwichai, Juggapong
BE Barolli, L
   Chen, HC
   Miwa, H
TI An Approach to Enhance Academic Ranking Prediction with Augmented Social
   Perception Data
SO ADVANCES IN INTELLIGENT NETWORKING AND COLLABORATIVE SYSTEMS
   (INCOS-2021)
SE Lecture Notes in Networks and Systems
LA English
DT Proceedings Paper
CT 13th International Conference on Intelligent Networking and
   Collaborative Systems (INCoS)
CY SEP 01-03, 2021
CL Asia Univ, Taichung, TAIWAN
HO Asia Univ
DE Social perception data; Machine learning; Citation prediction;
   Altmetrics; Correlation; SciVal
AB Academic ranking prediction are indicators that have significant influences on the decision-making process of stakeholders of universities. In addition, we are in digital age with a pandemic situation, social media and technology have revolutionized the way scholars reach and disseminate academic outputs. Thus, the ranking consideration should be adjusted by augmented social perception data, e.g. Altmetrics. In this study, dataset of 1,752,494 research outputs from Altmetric.com and Scival.com which published between 2015-2020 are analyzed. This study assesses whether there are relationships between various scholarly output's social perception data and citations. Moreover, various machine learning models are constructed to predict the citations. Results show weak to moderate positive correlation between social perception data and citation. We have found that the outperforming prediction model is Random Forest regression. The finding in our study suggested that social perception data should be considered to enhance academic ranking prediction in conjunction with related features.
C1 [Chantaranimi, Kittayaporn] Chiang Mai Univ, Fac Engn, Data Sci Consortium, Chiang Mai, Thailand.
   [Sugunsil, Prompong] Chiang Mai Univ, Coll Art Media & Technol, Chiang Mai, Thailand.
   [Natwichai, Juggapong] Chiang Mai Univ, Fac Engn, Dept Comp Engn, Chiang Mai, Thailand.
C3 Chiang Mai University; Chiang Mai University; Chiang Mai University
RP Chantaranimi, K (corresponding author), Chiang Mai Univ, Fac Engn, Data Sci Consortium, Chiang Mai, Thailand.
EM kittayaporn_c@cmu.ac.th; prompong.sugunsil@cmu.ac.th;
   juggapong@eng.cmu.ac.th
RI Natwichai, Juggapong/HTO-0073-2023
OI Natwichai, Juggapong/0000-0001-6220-2589
CR Akella AP, 2021, J INFORMETR, V15, DOI 10.1016/j.joi.2020.101128
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   United Nations, Take action for sustainable development goals
   Wang DS, 2013, SCIENCE, V342, P127, DOI 10.1126/science.1237825
NR 13
TC 1
Z9 1
U1 3
U2 9
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 2367-3370
EI 2367-3389
BN 978-3-030-84910-8; 978-3-030-84909-2
J9 LECT NOTE NETW SYST
PY 2022
VL 312
BP 84
EP 95
DI 10.1007/978-3-030-84910-8_9
PG 12
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Interdisciplinary Applications; Computer
   Science, Theory & Methods; Telecommunications
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Telecommunications
GA BS2NO
UT WOS:000704003000009
DA 2024-09-05
ER
PT J
AU Solorio, T
AF Solorio, Thamar
TI Survey on Emerging Research on the Use of Natural Language Processing in
   Clinical Language Assessment of Children
SO LANGUAGE AND LINGUISTICS COMPASS
LA English
DT Article
ID NONWORD REPETITION; IMPAIRMENT; MEMORY; SLI
AB In the last decade, we have seen emerging research exploring the use of natural language processing (NLP) techniques for assisting in the identification of clinical conditions that affect language. One of these clinical conditions is language impairment, a disorder identified by delayed or disordered language patterns in an individual with normal intelligence with no neurological or other physiological conditions. In this article, we present a survey of this emerging line of research, which for the most part has focused on the task of discriminating the clinical from the non-clinical group by posing the task as an automated classification problem. The focus of this survey is on the types of features recent research has explored. We also discuss the many interesting open questions that this research has yet to explore.
C1 [Solorio, Thamar] Univ Alabama Birmingham, Dept Comp & Informat Sci, 1300 Univ Blvd, Birmingham, AL 35294 USA.
C3 University of Alabama System; University of Alabama Birmingham
RP Solorio, T (corresponding author), Univ Alabama Birmingham, Dept Comp & Informat Sci, 1300 Univ Blvd, Birmingham, AL 35294 USA.
EM solorio@cis.uab.edu
OI Solorio, Thamar/0000-0002-3541-9405
FU NSF [1018124]; Direct For Computer & Info Scie & Enginr; Div Of
   Information & Intelligent Systems [1018124] Funding Source: National
   Science Foundation
FX The author would like to thank the reviewers for the very constructive
   feedback received. Many thanks to Aquiles Iglesias, Yang Liu and Manuel
   Montes y Gomez for meaningful discussions and comments on previous
   versions of the paper. The author's research is currently supported by
   NSF grant No. 1018124.
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NR 55
TC 1
Z9 2
U1 0
U2 0
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1749-818X
J9 LANG LINGUIST COMPAS
JI Lang. Linguist. Compass
PD DEC
PY 2013
VL 7
IS 12
BP 633
EP 646
DI 10.1111/lnc3.12054
PG 14
WC Language & Linguistics
WE Emerging Sources Citation Index (ESCI)
SC Linguistics
GA V11CB
UT WOS:000214255200002
DA 2024-09-05
ER
PT J
AU Lalande, L
   Bourguignon, L
   Carlier, C
   Ducher, M
AF Lalande, Laure
   Bourguignon, Laurent
   Carlier, Chloe
   Ducher, Michel
TI Bayesian networks: a new method for the modeling of bibliographic
   knowledge
SO MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
LA English
DT Article
DE Bayesian network; Fall risk; Geriatrics
ID RISK-ASSESSMENT TOOL; FALL-RISK; OLDER-ADULTS; ELDERLY-PATIENTS;
   INJURIOUS FALLS; SCREENING TOOL; MEDICATION USE; PREDICTION; COMMUNITY;
   PEOPLE
AB Falls in geriatry are associated with important morbidity, mortality and high healthcare costs. Because of the large number of variables related to the risk of falling, determining patients at risk is a difficult challenge. The aim of this work was to validate a tool to detect patients with high risk of fall using only bibliographic knowledge. Thirty articles corresponding to 160 studies were used to modelize fall risk. A retrospective case-control cohort including 288 patients (88 +/- A 7 years) and a prospective cohort including 106 patients (89 +/- A 6 years) from two geriatric hospitals were used to validate the performances of our model. We identified 26 variables associated with an increased risk of fall. These variables were split into illnesses, medications, and environment. The combination of the three associated scores gives a global fall score. The sensitivity and the specificity were 31.4, 81.6, 38.5, and 90 %, respectively, for the retrospective and the prospective cohort. The performances of the model are similar to results observed with already existing prediction tools using model adjustment to data from numerous cohort studies. This work demonstrates that knowledge from the literature can be synthesized with Bayesian networks.
C1 [Lalande, Laure; Bourguignon, Laurent; Carlier, Chloe; Ducher, Michel] Hosp Civils Lyon, Grp Hosp Geriatrie, F-69340 Francheville, France.
C3 CHU Lyon
RP Ducher, M (corresponding author), Hosp Civils Lyon, Grp Hosp Geriatrie, Serv Pharm 40 Ave La Table de Pierre, F-69340 Francheville, France.
EM michel.ducher@chu-lyon.fr
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NR 45
TC 10
Z9 10
U1 0
U2 18
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 0140-0118
EI 1741-0444
J9 MED BIOL ENG COMPUT
JI Med. Biol. Eng. Comput.
PD JUN
PY 2013
VL 51
IS 6
BP 657
EP 664
DI 10.1007/s11517-013-1035-8
PG 8
WC Computer Science, Interdisciplinary Applications; Engineering,
   Biomedical; Mathematical & Computational Biology; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering; Mathematical & Computational Biology;
   Medical Informatics
GA 142MB
UT WOS:000318800400005
PM 23334773
DA 2024-09-05
ER
PT C
AU Cavalcanti, DC
   Prudêncio, RBC
   Pradhan, SS
   Shah, JY
   Pietrobon, RS
AF Cavalcanti, Diana C.
   Prudencio, Ricardo B. C.
   Pradhan, Shreyasee S.
   Shah, Jatin Y.
   Pietrobon, Ricardo S.
GP IEEE
TI GOOD TO BE BAD? DISTINGUISHING BETWEEN POSITIVE AND NEGATIVE CITATIONS
   IN SCIENTIFIC IMPACT
SO 2011 23RD IEEE INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL
   INTELLIGENCE (ICTAI 2011)
SE Proceedings-International Conference on Tools With Artificial
   Intelligence
LA English
DT Proceedings Paper
CT 23rd IEEE International Conference on Tools with Artificial Intelligence
   (ICTAI)
CY NOV 07-09, 2011
CL Boca Raton, FL
DE Impact Factor; Sentiment Analysis; SentiWordNet; Spearman Ranking
   Correlation
AB The impact of a publication is often measured by the number of citations it received, this number being taken as a proxy for the relevance of published work. However, a higher citation index does not necessarily mean that a publication necessarily had a positive feedback from citing authors, as a citation can represent a negative criticism. In order to overcome this limitation, we used sentiment analysis to rate citations as positive, neutral or negative. Adjectives are initially extracted from the citations, with the SentiWordNet lexicon being used to rate the degree of positivity and negativity for each adjective. Relevance scores were then computed to rank citations according to the sentiment expressed in the text corresponding to each citation. As expected for accurate information retrieval systems, higher precision rates were observed in the initial points of the curve. The SRC (0.6728) computed using number of raw citations is lower than the SRC (0.7397) observed by the ranking generated using sentiment scores (Table 3). Conclusion: This result indicates that child articles with higher values of relevance score were in general the ones expressing positive opinion about their parents. Therefore, the ranking generated by sentiment scores had an improved accuracy.
C1 [Cavalcanti, Diana C.; Prudencio, Ricardo B. C.] Univ Fed Pernambuco, Ctr Informat, UFPE, Recife, PE, Brazil.
   [Pradhan, Shreyasee S.; Shah, Jatin Y.; Pietrobon, Ricardo S.] Duke Univ, Dept Surg, Res Grp, Durham, NC USA.
C3 Universidade Federal de Pernambuco; Duke University
RP Cavalcanti, DC (corresponding author), Univ Fed Pernambuco, Ctr Informat, UFPE, Recife, PE, Brazil.
EM dcc2@cin.ufpe.br; rbcp@cin.ufpe.br; sp133@duke.edu; jys4@duke.edu;
   rpietro@duke.edu
RI Prudêncio, Ricardo BC/B-4632-2019; Shah, Jatin/B-6039-2011
OI Prudencio, Ricardo/0000-0001-7084-1233
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NR 23
TC 24
Z9 24
U1 0
U2 12
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA
SN 1082-3409
BN 978-0-7695-4596-7
J9 PROC INT C TOOLS ART
PY 2011
BP 156
EP 162
DI 10.1109/ICTAI.2011.32
PG 7
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Engineering
GA BYJ28
UT WOS:000299009900023
DA 2024-09-05
ER
PT J
AU Kim, EHJ
   Jeong, YK
   Kim, Y
   Song, M
AF Kim, Erin H. J.
   Jeong, Yoo Kyung
   Kim, YongHwan
   Song, Min
TI Exploring scientific trajectories of a large-scale dataset using
   topic-integrated path extraction
SO JOURNAL OF INFORMETRICS
LA English
DT Article
DE Citation analysis; Healthcare informatics; Longest path; Main path
   analysis; Topic modeling
ID CITATION NETWORK ANALYSIS; KNOWLEDGE DIFFUSION PATH; HEALTH-CARE; MAIN
   PATHS; SYSTEMS; INFORMATICS; PREFERENCES; FRAMEWORK; INTERNET; SUPPORT
AB Main path analysis (MPA) is the most widely accepted approach to tracing knowledge transfer in a research field. In this study, we extracted multiple longest paths from the multidisciplinary aca-demic field's citation network and integrating topic modeling to the extracted paths. We consider three main aspects of trajectory analysis when analyzing the represented documents through the extracted paths: emergence, authority, and topic dynamics. For path extraction, we adopt the longest path algorithm that consists of the following three steps: 1) topological sort, 2) edge re-laxation, and 3) multiple path extraction. For topic integration into multiple paths, we employ latent Dirichlet allocation (LDA) by utilizing the topic-document matrix that LDA derives to select an article's topic from the citation network, where each article is labeled with the topic that is assigned with the highest topical probability for that article. We conduct a series of experiments to examine the results on a dataset from the field of healthcare informatics that PubMed provides.
C1 [Kim, Erin H. J.] Kongju Natl Univ, Dept Lib & Informat Sci Educ, Coll Educ, Gongju 32588, South Korea.
   [Jeong, Yoo Kyung] Hannam Univ, Dept Lib & Informat Sci, Daejeon 34430, South Korea.
   [Kim, YongHwan] Cheongju Univ, Dept Lib & Informat Sci, Cheongju 28503, South Korea.
   [Song, Min] Yonsei Univ, Dept Lib & Informat Sci, Seoul 03722, South Korea.
C3 Kongju National University; Hannam University; Cheongju University;
   Yonsei University
RP Song, M (corresponding author), Yonsei Univ, Dept Lib & Informat Sci, Seoul 03722, South Korea.
EM erin.hj.kim@kongju.ac.kr; yk.jeong@hnu.kr; kimyonghwan@cju.ac.kr;
   min.song@yonsei.ac.kr
RI Kim, Erin/GXN-3556-2022; song, min/KPA-7030-2024
OI Jeong, Yoo Kyung/0000-0002-6571-6478
FU Ministry of Education of the Republic of Korea; National Research
   Foundation of Korea [NRF-2019S1A5A8033713]; Institute of Information and
   Communications Technology Planning and Evaluation (IITP) - Korean
   government (MSIT) (Artificial Intelligence Graduate School Program
   (Yonsei University)) [2020-0-01361]
FX This work was supported by the Ministry of Education of the Republic of
   Korea and the National Research Foundation of Korea
   (NRF-2019S1A5A8033713) . This work was partly supported by the Institute
   of Information and Communications Technology Planning and Evaluation
   (IITP) grant funded by the Korean government (MSIT) (No. 2020-0-01361,
   Artificial Intelligence Graduate School Program (Yonsei University) ) .
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NR 61
TC 9
Z9 10
U1 6
U2 39
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1751-1577
EI 1875-5879
J9 J INFORMETR
JI J. Informetr.
PD FEB
PY 2022
VL 16
IS 1
AR 101242
DI 10.1016/j.joi.2021.101242
PG 18
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 1H0CB
UT WOS:000796212300006
DA 2024-09-05
ER
PT C
AU Azcona, D
   Hsiao, IH
   Smeaton, AF
AF Azcona, David
   Hsiao, I-Han
   Smeaton, Alan F.
BE Rose, CP
   Martinez-Maldonado, R
   Hoppe, HU
   Luckin, R
   Mavrikis, M
   Porayska-Pomsta, K
   McLaren, B
   DuBoulay, B
TI Modelling Math Learning on an Open Access Intelligent Tutor
SO ARTIFICIAL INTELLIGENCE IN EDUCATION, PT II
SE Lecture Notes in Artificial Intelligence
LA English
DT Proceedings Paper
CT 19th International Conference on Artificial Intelligence in Education
   (AIED)
CY JUN 27-30, 2018
CL London, ENGLAND
DE Machine learning; Intelligent Tutoring Systems; Social network analysis;
   MOOC
AB This paper presents a methodology to analyze large amount of students' learning states on two math courses offered by Global Freshman Academy program at Arizona State University. These two courses utilised ALEKS (Assessment and Learning in Knowledge Spaces) Artificial Intelligence technology to facilitate massive open online learning. We explore social network analysis and unsupervised learning approaches (such as probabilistic graphical models) on these type of Intelligent Tutoring Systems to examine the potential of the embedding representations on students learning.
C1 [Azcona, David; Smeaton, Alan F.] Dublin City Univ, Insight Ctr Data Analyt, Dublin, Ireland.
   [Hsiao, I-Han] Arizona State Univ, Sch Comp Informat & Decis Syst Engn, Tempe, AZ USA.
C3 Dublin City University; Arizona State University; Arizona State
   University-Tempe
RP Azcona, D (corresponding author), Dublin City Univ, Insight Ctr Data Analyt, Dublin, Ireland.
EM david.azcona@insight-centre.org
RI Smeaton, Alan/F-8974-2011
OI Smeaton, Alan/0000-0003-1028-8389
FU Irish Research Council; National Forum for the Enhancement of Teaching
   and Learning in Ireland [GOIPG/2015/3497]; Science Foundation Ireland
   [SFI/12/RC/2289]; Fulbright Ireland; Irish Research Council (IRC)
   [GOIPG/2015/3497] Funding Source: Irish Research Council (IRC)
FX This research was supported by the Irish Research Council in association
   with the National Forum for the Enhancement of Teaching and Learning in
   Ireland under project number GOIPG/2015/3497, by Science Foundation
   Ireland under grant SFI/12/RC/2289, and by Fulbright Ireland. The
   authors are indebted to the Action Lab at EdPlus in Arizona State
   University for their help.
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NR 8
TC 2
Z9 2
U1 1
U2 2
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 0302-9743
EI 1611-3349
BN 978-3-319-93846-2; 978-3-319-93845-5
J9 LECT NOTES ARTIF INT
PY 2018
VL 10948
BP 36
EP 40
DI 10.1007/978-3-319-93846-2_7
PG 5
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications; Education & Educational Research
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Education & Educational Research
GA BU1FH
UT WOS:000877310400007
DA 2024-09-05
ER
PT J
AU Ha, T
AF Ha, Taehyun
TI An explainable artificial-intelligence-based approach to investigating
   factors that influence the citation of papers
SO TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
LA English
DT Article
DE Bibliometrics; Citations; Machine learning; Big data; SHAP
ID CITED PAPERS; DETERMINANTS; DRIVERS; NUMBER
AB The number of citations is often used to estimate the impact of a study. Previous studies have investigated what factors of publications affect citations and how they affect citations. However, the findings of the studies were unable to reach a consensus because of the limited sample size, domain, and measurement. This study reviewed previous studies that addressed factors influencing citations and then identified 14 measurable factors. Approximately 33 million publications from the Scopus database were used to train and validate a CatBoost model. A SHAP framework was used to interpret the trained model by focusing on how salient factors affect the number of citations. The results showed that the year is a significant factor affecting the citation but not the priority factor. A publication source was presented as the most important factor contributing to the citation. Several implications and strategic approaches to maximizing the impact of a study were discussed.
C1 [Ha, Taehyun] Korea Inst Sci & Technol Informat, Future Technol Anal Ctr, 66 Hoegiro,Dongdaemun Gu, Seoul 02456, South Korea.
C3 Korea Institute of Science & Technology Information (KISTI)
RP Ha, T (corresponding author), Korea Inst Sci & Technol Informat, Future Technol Anal Ctr, 66 Hoegiro,Dongdaemun Gu, Seoul 02456, South Korea.
EM taehyunha@kisti.re.kr
RI Ha, Taehyun/KXQ-6446-2024
OI Ha, Taehyun/0000-0003-3143-666X
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NR 35
TC 5
Z9 5
U1 10
U2 47
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0040-1625
EI 1873-5509
J9 TECHNOL FORECAST SOC
JI Technol. Forecast. Soc. Chang.
PD NOV
PY 2022
VL 184
AR 121974
DI 10.1016/j.techfore.2022.121974
EA AUG 2022
PG 9
WC Business; Regional & Urban Planning
WE Social Science Citation Index (SSCI)
SC Business & Economics; Public Administration
GA 4Y2TL
UT WOS:000861382600002
DA 2024-09-05
ER
PT C
AU Quille, K
   Nolan, K
AF Quille, Keith
   Nolan, Keith
GP ACM
TI Predicting Success in CS1-An Open Access Data Project
SO PROCEEDINGS OF THE 53RD ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE
   EDUCATION (SIGCSE 2022), VOL 2
LA English
DT Proceedings Paper
CT 53rd Annual ACM SIGCSE Technical Symposium on Computer Science Education
   (SIGCSE)
CY MAR 02-05, 2022
CL Providence, RI
DE EDM; Educational Data Mining; Metrics; Re-validation; Machine Learning
AB PreSS# is an online Machine Learning prediction model that aims to identify students at risk of failing or dropping out in an introductory programming course (typically called CS1). PreSS# has been developed over the past 16 years, where the model is capable of predicting at-risk students with an accuracy of approximate to 71%. There is, however, a need to re-validate the model using a larger international multi-jurisdictional multi-university data set, as up until now the data sets have been predominantly from a single jurisdiction. The goal of this study is to not only re-validate the model using a multijurisdictional data set, but, inline with a 2015 ITiCSE working group report's Grand Challenges, to openly publish the data set itself. This work timely to the CSEd community as other researchers can use this data to further their research, re-validate PreSS# and will be able to then contribute, by submitting their local PreSS# data sets to this global online repository.
C1 [Quille, Keith; Nolan, Keith] TU Dublin, Dublin, Ireland.
RP Quille, K (corresponding author), TU Dublin, Dublin, Ireland.
EM keith.quille@tudublin.ie; keith.nolan@tudublin.ie
OI Quille, Keith/0000-0002-1414-5142
CR Bergin S., 2005, SIGCSE Bulletin, V37, P411, DOI 10.1145/1047124.1047480
   Ihantola P, 2016, PROCEEDINGS OF THE 2015 ITICSE CONFERENCE ON WORKING GROUP REPORTS (ITICSE-WGP'15), P41, DOI 10.1145/2858796.2858798
   Quille K., 2015, INT C ENGUAGING PEDA, V10
   Quille K, 2018, ITICSE'18: PROCEEDINGS OF THE 23RD ANNUAL ACM CONFERENCE ON INNOVATION AND TECHNOLOGY IN COMPUTER SCIENCE EDUCATION, P15, DOI 10.1145/3197091.3197101
   Quille K, 2019, COMPUT SCI EDUC, V29, P254, DOI 10.1080/08993408.2019.1612679
NR 5
TC 2
Z9 2
U1 0
U2 2
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES
BN 978-1-4503-9070-5
PY 2022
BP 1126
EP 1126
DI 10.1145/3478432.3499092
PG 1
WC Computer Science, Theory & Methods; Education, Scientific Disciplines
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Education & Educational Research
GA BU8GR
UT WOS:000947124200080
DA 2024-09-05
ER
PT J
AU Amjad, T
   Daud, A
AF Amjad, Tehmina
   Daud, Ali
TI Indexing of authors according to their domain of expertise
SO MALAYSIAN JOURNAL OF LIBRARY & INFORMATION SCIENCE
LA English
DT Article
DE Indexing; Domain specific modeling; Topic modeling; Topic based ranking;
   Citation analysis
ID H-INDEX; IMPACT; MODEL
AB Measuring the impact and productivity of an author is an important, yet a challenging task. Most of the existing methods for ranking or indexing of authors are based on simple parameters such as publication counts, citation counts and their combinations. These methods are topic independent, hence ignoring the intra-field differences. This study introduces a specific method for indexing of researchers to measure their productivity in a given field of interest, believing that an author can be interested in more than one fields and can have different level of expertise in all these fields. This paper proposes Domain Specific Index (DSI), a novel method for indexing of authors with respect to their fields of interest. Latent Dirichlet Allocation (LDA) is applied to capture the latent topics within text corpora. DSI calculates the standing of an author in all topics of his or her interest by considering topic based citations instead of using overall citations like traditional methods. The citations received by a multi-authored paper are divided among all its co-authors on the basis of their topic probability in that particular field. Results show that instead of giving credit of received citations equally to all co-authors of a paper, if a weight is given with respect to their level of interest in that field, more specific authors in that field will be ranked as top authors.
C1 [Amjad, Tehmina; Daud, Ali] Int Islamic Univ, Dept Comp Sci & Software Engn, Islamabad 44000, Pakistan.
   [Daud, Ali] King Abdulaziz Univ, Fac Comp & Informat Technol, Jeddah, Saudi Arabia.
C3 International Islamic University, Pakistan; King Abdulaziz University
RP Amjad, T (corresponding author), Int Islamic Univ, Dept Comp Sci & Software Engn, Islamabad 44000, Pakistan.
EM tehminaamjad@iiu.edu.pk; ali.daud@iiu.edu.pk
RI Amjad, Tehmina/GLS-0209-2022; Daud, Ali/ABD-4485-2020; Daud,
   Adil/T-3079-2019; Daud, Ali/G-6568-2017
OI Daud, Adil/0000-0002-6617-8421; Daud, Ali/0000-0002-8284-6354; Amjad,
   Tehmina/0000-0003-1201-498X
FU Indigenous Ph.D. Fellowship Program of Higher Education Commission (HEC)
   Pakistan
FX The work is supported by the Indigenous Ph.D. Fellowship Program of
   Higher Education Commission (HEC) Pakistan.
CR Amjad T, 2016, INFORM PROCESS MANAG, V52, P374, DOI 10.1016/j.ipm.2015.12.001
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   Egghe L, 2008, INFORM PROCESS MANAG, V44, P770, DOI 10.1016/j.ipm.2007.05.003
   Hirsch JE, 2005, P NATL ACAD SCI USA, V102, P16569, DOI 10.1073/pnas.0507655102
   Jin BH, 2007, CHINESE SCI BULL, V52, P855, DOI 10.1007/s11434-007-0145-9
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NR 22
TC 17
Z9 18
U1 1
U2 21
PU UNIV MALAYA, FAC COMPUTER SCIENCE & INFORMATION TECH
PI KUALA LUMPUR
PA UNIV MALAYA, FAC COMPUTER SCIENCE & INFORMATION TECH, KUALA LUMPUR,
   50603, MALAYSIA
SN 1394-6234
J9 MALAYS J LIBR INF SC
JI Malays. J. Libr. Sci.
PY 2017
VL 22
IS 1
BP 69
EP 82
DI 10.22452/mjlis.vol22no1.6
PG 14
WC Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science
GA EM5ZG
UT WOS:000395392000006
OA Bronze
DA 2024-09-05
ER
PT J
AU Lundberg, L
   Boldt, M
   Borg, A
   Grahn, H
AF Lundberg, Lars
   Boldt, Martin
   Borg, Anton
   Grahn, Hakan
TI Bibliometric Mining of Research Trends in Machine Learning
SO AI
LA English
DT Article
DE bibliometrics; geographic regions; machine learning; research
   directions; research trends; Scopus database
ID REVIEWS; SYSTEM
AB We present a method, including tool support, for bibliometric mining of trends in large and dynamic research areas. The method is applied to the machine learning research area for the years 2013 to 2022. A total number of 398,782 documents from Scopus were analyzed. A taxonomy containing 26 research directions within machine learning was defined by four experts with the help of a Python program and existing taxonomies. The trends in terms of productivity, growth rate, and citations were analyzed for the research directions in the taxonomy. Our results show that the two directions, Applications and Algorithms, are the largest, and that the direction Convolutional Neural Networks is the one that grows the fastest and has the highest average number of citations per document. It also turns out that there is a clear correlation between the growth rate and the average number of citations per document, i.e., documents in fast-growing research directions have more citations. The trends for machine learning research in four geographic regions (North America, Europe, the BRICS countries, and The Rest of the World) were also analyzed. The number of documents during the time period considered is approximately the same for all regions. BRICS has the highest growth rate, and, on average, North America has the highest number of citations per document. Using our tool and method, we expect that one could perform a similar study in some other large and dynamic research area in a relatively short time.
C1 [Lundberg, Lars; Boldt, Martin; Borg, Anton; Grahn, Hakan] Blekinge Inst Technol, Dept Comp Sci, S-37179 Karlskrona, Sweden.
C3 Blekinge Institute Technology
RP Lundberg, L (corresponding author), Blekinge Inst Technol, Dept Comp Sci, S-37179 Karlskrona, Sweden.
EM lars.lundberg@bth.se; martin.boldt@bth.se; anton.borg@bth.se;
   hakan.grahn@bth.se
OI Boldt, Martin/0000-0002-9316-4842; Borg, Anton/0000-0002-8929-7220
FU Knowledge Foundation in Sweden through the project "Green Clouds-Load
   prediction and optimization in private cloud systems"
FX No Statement Available
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NR 64
TC 0
Z9 0
U1 5
U2 5
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2673-2688
J9 AI-BASEL
JI AI
PD MAR
PY 2024
VL 5
IS 1
BP 208
EP 236
DI 10.3390/ai5010012
PG 29
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA MC8C2
UT WOS:001191509100001
OA gold
DA 2024-09-05
ER
PT J
AU Figuerola, CG
   Marco, FJG
   Pinto, M
AF Figuerola, Carlos G.
   Garcia Marco, Francisco Javier
   Pinto, Maria
TI Mapping the evolution of library and information science (1978-2014)
   using topic modeling on LISA
SO SCIENTOMETRICS
LA English
DT Article
DE Library and Information Science; LISA; LDA; Evolution; Bibliometric
   studies
ID NORTH-AMERICAN LIBRARY; SEEKING BEHAVIOR; ABSTRACTS;
   INTERDISCIPLINARITY; RETRIEVAL; FRAMEWORK; TRACKING; ARTICLES; HISTORY;
   TRENDS
AB This paper offers an overview of the bibliometric study of the domain of library and information science (LIS), with the aim of giving a multidisciplinary perspective of the topical boundaries and the main areas and research tendencies. Based on a retrospective and selective search, we have obtained the bibliographical references (title and abstract) of academic production on LIS in the database LISA in the period 1978-2014, which runs to 92,705 documents. In the context of the statistical technique of topic modeling, we apply latent Dirichlet allocation, in order to identify the main topics and categories in the corpus of documents analyzed. The quantitative results reveal the existence of 19 important topics, which can be grouped together into four main areas: processes, information technology, library and specific areas of information application.
C1 [Figuerola, Carlos G.] Univ Salamanca, C Espejo S-N, Salamanca 37007, Spain.
   [Garcia Marco, Francisco Javier] Univ Zaragoza, C Pedro Zerbuna 12, E-50009 Zaragoza, Spain.
   [Pinto, Maria] Univ Granada, Campus Cartuja S-N, E-18071 Granada, Spain.
C3 University of Salamanca; University of Zaragoza; University of Granada
RP Figuerola, CG (corresponding author), Univ Salamanca, C Espejo S-N, Salamanca 37007, Spain.
EM figue@usal.es
RI Marco, Francisco Javier García/K-2316-2013; Figuerola, Carlos
   G./N-1459-2018
OI Marco, Francisco Javier García/0000-0002-6241-4060; Figuerola, Carlos
   G./0000-0001-6799-2874
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NR 87
TC 68
Z9 78
U1 6
U2 196
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD SEP
PY 2017
VL 112
IS 3
BP 1507
EP 1535
DI 10.1007/s11192-017-2432-9
PG 29
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA FB9ZF
UT WOS:000406497200018
DA 2024-09-05
ER
PT J
AU Tüselmann, H
   Sinkovics, RR
   Pishchulov, G
AF Tueselmann, Heinz
   Sinkovics, Rudolf R.
   Pishchulov, Grigory
TI Towards a consolidation of worldwide journal rankings - A classification
   using random forests and aggregate rating via data envelopment analysis
SO OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE
LA English
DT Article
DE Citation indices; Journal rankings; Journal lists; Research assessment;
   Data envelopment analysis
ID MISSING VALUES; IMPACT FACTOR; DEA; QUALITY; MANAGEMENT; IMPUTATION;
   EFFICIENCY; DISCRETE; BUSINESS; MODELS
AB The question of how to assess research outputs published in journals is now a global concern for academics. Numerous journal ratings and rankings exist, some featuring perceptual and peer-review-based journal ranks, some focusing on objective information related to citations, some using a combination of the two. This research consolidates existing journal rankings into an up-to-date and comprehensive list Existing approaches to determining journal rankings are significantly advanced with the application of a new classification approach, 'random forests', and data envelopment analysis. As a result, a fresh look at a publication's place in the global research community is offered. While our approach is applicable to all management and business journals, we specifically exemplify the relative position of 'operations research, management science, production and operations management' journals within the broader management field, as well as within their own subject domain. (C) 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/3.0/).
C1 [Tueselmann, Heinz] Manchester Metropolitan Univ, Sch Business, CIBI, Manchester M15 6BH, Lancs, England.
   [Sinkovics, Rudolf R.] Univ Manchester, Manchester Business Sch, Ctr Comparat & Int Business Res CIBER, Manchester M15 6PB, Lancs, England.
   [Sinkovics, Rudolf R.] Lappeenranta Univ Technol, Lappeenranta 53851, Finland.
   [Pishchulov, Grigory] TU Dortmund Univ, Fac Business Econ & Social Sci, D-44227 Dortmund, Germany.
C3 Manchester Metropolitan University; University of Manchester;
   Lappeenranta-Lahti University of Technology LUT; Dortmund University of
   Technology
RP Sinkovics, RR (corresponding author), Univ Manchester, Manchester Business Sch, Ctr Comparat & Int Business Res CIBER, Booth St West, Manchester M15 6PB, Lancs, England.
EM h.tuselman@mmu.ac.uk; Rudolf.Sinkovics@manchester.ac.uk;
   grigory.pishchulov@tu-dortmund.de
RI Sinkovics, Rudolf R./F-4092-2010; Pishchulov, Grigory/M-9405-2016
OI Sinkovics, Rudolf R./0000-0002-4471-5054; Pishchulov,
   Grigory/0000-0001-8787-1869; Tuselmann, Heinz/0000-0001-6628-1675
FU Economic and Social Research Council (ESRC), UK [RES-075-25-0028];
   Leverhulme Trust
FX We are grateful for comments received from participants of the session
   "Learning: Methods and Algorithms II" at the 26th EURO-INFORMS
   Conference on Operational Research in July 2013. We are also grateful to
   Editor-in-Chief Professor Ben Lev and three anonymous reviewers for
   constructive and highly insightful comments on the paper throughout the
   review process. Financial support from the Economic and Social Research
   Council (ESRC), UK, who funded part of Rudolf Sinkovics' time [Grant
   number RES-075-25-0028], is gratefully acknowledged. We also appreciate
   financial support from the Leverhulme Trust who funded the work of
   Grigory Pishchulov on this paper during his Leverhulme Overseas
   Fellowship at Manchester Metropolitan University.
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NR 78
TC 37
Z9 37
U1 0
U2 69
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0305-0483
EI 1873-5274
J9 OMEGA-INT J MANAGE S
JI Omega-Int. J. Manage. Sci.
PD MAR
PY 2015
VL 51
BP 11
EP 23
DI 10.1016/j.omega.2014.08.002
PG 13
WC Management; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Operations Research & Management Science
GA AW8ZX
UT WOS:000346548700002
OA Green Published, hybrid, Green Accepted
DA 2024-09-05
ER
PT J
AU Donlon, JJ
AF Donlon, James J.
TI The National Artificial Intelligence Research Institutes program and its
   significance to a prosperous future
SO AI MAGAZINE
LA English
DT Article
AB The U.S. National Artificial Intelligence (AI) Research Institutes program is introduced, and its significance is discussed relative to the guiding national AI research and development strategy. The future of the program is also discussed, including, the strategic priorities guiding the potential for new AI Institutes of the future, initiatives for building a broader ecosystem to connect Institutes into a strongly interconnected network, and the building of new AI capacity and fostering partnerships in minority-serving institutions.
C1 [Donlon, James J.] Natl Sci Fdn, Alexandria, VA 22314 USA.
C3 National Science Foundation (NSF)
RP Donlon, JJ (corresponding author), Natl Sci Fdn, Alexandria, VA 22314 USA.
EM jdonlon@nsf.gov
FU National Science Foundation (NSF); NSF currently funds 20 Institutes;
   U.S. government; NSF; U.S. Department of Education (ED) Institute of
   Education Sciences (IES), U.S. Department of Homeland Security (DHS)
   Science & Technology Directorate (ST); National Institute of Standards
   and Technology (NIST), Department of Defense (DOD) Office of the Under
   Secretary of Defense for Research and Engineering (OUSD); Accenture,
   Amazon; IBM Corporation; Intel Corporation; U.S. Department of
   Agriculture (USDA) National Institute of Food and Agriculture (NIFA); AI
   Institutes program: Capital One Financial Corporation; Simons Foundation
FX The National Artificial Intelligence Research Institutes Program is a
   joint government effort and multisector initiative in the U.S. led by
   the National Science Foundation (NSF). NSF currently funds 20
   Institutes, some of them with the support of other U.S. government
   agencies and U.S. industrial partners, as will be seen throughout this
   issue. NSF gratefully acknowledges the financial and intellectual
   contributions of its funding partners in these Institutes: U.S.
   Department of Education (ED) Institute of Education Sciences (IES), U.S.
   Department of Homeland Security (DHS) Science & Technology Directorate
   (S&T), National Institute of Standards and Technology (NIST), Department
   of Defense (DOD) Office of the Under Secretary of Defense for Research
   and Engineering (OUSD (R&E)), Accenture, Amazon, Google, IBM
   Corporation, and Intel Corporation. In addition, under this program, the
   U.S. Department of Agriculture (USDA) National Institute of Food and
   Agriculture (NIFA) fully funds an additional five AI Institutes. NSF
   also thanks the following new partners for joining in the current
   solicitation in the AI Institutes program: Capital One Financial
   Corporation and the Simons Foundation. The author thanks Dr. Michael
   Littman, Division Director of the NSF Division of Information and
   Intelligent Systems, for his leadership of NSF AI strategy and for his
   thoughts on the alignment of current Institute activities to the
   National AI R&D Strategic Plan.
CR AI Institutes Virtual Organization, ABOUT US
   Donlon J, 2023, AI MAG, V44, P345, DOI 10.1002/aaai.12107
   National Artificial Intelligence (AI) Research Institutes Accelerating Research, ABOUT US
   nitrd, NAT ART INT RES DEV
   nsf, 2022, EXP AI INN CAP BUILD
   nsf, 2023, ABOUT US
   nsf, NSF ANN 7 NEW NAT AR
NR 7
TC 0
Z9 0
U1 2
U2 2
PU AMER ASSOC ARTIFICIAL INTELL
PI MENLO PK
PA 445 BURGESS DRIVE, MENLO PK, CA 94025-3496 USA
SN 0738-4602
EI 2371-9621
J9 AI MAG
JI AI Mag.
PD MAR
PY 2024
VL 45
IS 1
SI SI
BP 6
EP 14
DI 10.1002/aaai.12153
EA FEB 2024
PG 9
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA LM0L4
UT WOS:001160649700001
OA hybrid
DA 2024-09-05
ER
PT J
AU Pinto, S
   Albanese, F
   Dorso, CO
   Balenzuela, P
AF Pinto, Sebastian
   Albanese, Federico
   Dorso, Claudio O.
   Balenzuela, Pablo
TI Quantifying time-dependent Media Agenda and public opinion by topic
   modeling
SO PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
LA English
DT Article
DE Mass media influence; Opinion formation; Topic detection; Agenda-setting
ID NEWS; DYNAMICS
AB The mass media plays a fundamental role in the formation of public opinion, either by defining the topics of discussion or by making an emphasis on certain issues. Directly or indirectly, people get informed by consuming news from the media. Naturally, two questions appear: What are the dynamics of the agenda and how the people become interested in their different topics? These questions cannot be answered without proper quantitative measures of agenda dynamics and public attention. In this work we study the agenda of newspapers in comparison with public interests by performing topic detection over the news. We define Media Agenda as the distribution of topic's coverage by the newspapers and Public Agenda as the distribution of public interest in the same topic space. We measure agenda diversity as a function of time using the Shannon entropy and differences between agendas using the Jensen-Shannon distance. We found that the Public Agenda is less diverse than the Media Agenda, especially when there is a very attractive topic and the audience naturally focuses only on this one. Using the same methodology we detect coverage bias in newspapers. Finally, it was possible to identify a complex agenda-setting dynamics within a given topic where the least sold newspaper triggered a public debate via a positive feedback mechanism with social networks discussions which install the issue in the Media Agenda. (C) 2019 Elsevier B.V. All rights reserved.
C1 [Pinto, Sebastian; Dorso, Claudio O.; Balenzuela, Pablo] Univ Buenos Aires, Fac Ciencias Exactas & Nat, Dept Fis, Av Cantilo S-N,Pabellon 1,Ciudad Univ, RA-1428 Buenos Aires, DF, Argentina.
   [Dorso, Claudio O.; Balenzuela, Pablo] Consejo Nacl Invest Cient & Tecn, Inst Fis Buenos Aires IFIBA, Av Cantilo S-N,Pabellon 1,Ciudad Univ, RA-1428 Buenos Aires, DF, Argentina.
   [Albanese, Federico] Consejo Nacl Invest Cient & Tecn, Inst Invest Ciencias Comp ICC, Av Cantilo S-N,Pabellon 1,Ciudad Univ, RA-1428 Buenos Aires, DF, Argentina.
C3 University of Buenos Aires; Consejo Nacional de Investigaciones
   Cientificas y Tecnicas (CONICET); University of Buenos Aires; Consejo
   Nacional de Investigaciones Cientificas y Tecnicas (CONICET); University
   of Buenos Aires
RP Pinto, S (corresponding author), Univ Buenos Aires, Fac Ciencias Exactas & Nat, Dept Fis, Av Cantilo S-N,Pabellon 1,Ciudad Univ, RA-1428 Buenos Aires, DF, Argentina.
EM spinto@df.uba.ar
OI Balenzuela, Pablo/0000-0002-8581-4892; Pinto,
   Sebastian/0000-0002-0441-9285
FU UBACyT [20020130100582BA, PICT-201-0215]
FX We thank Dr. A. Chernomoretz, Dr. M. Otero, Dra. V. Semeshenko, and Dr.
   M. Trevisan for bringing us a critical revision of the article. We
   thanks funding from UBACyT 20020130100582BA and PICT-201-0215.
CR Ali A. E., ARXIV180105802
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NR 49
TC 21
Z9 24
U1 3
U2 43
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0378-4371
EI 1873-2119
J9 PHYSICA A
JI Physica A
PD JUN 15
PY 2019
VL 524
BP 614
EP 624
DI 10.1016/j.physa.2019.04.108
PG 11
WC Physics, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Physics
GA IL0DK
UT WOS:000476966100049
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Ramya, GR
   Sivakumar, PB
AF Ramya, G. R.
   Bagavathi Sivakumar, P.
TI An incremental learning temporal influence model for identifying topical
   influencers on Twitter dataset
SO SOCIAL NETWORK ANALYSIS AND MINING
LA English
DT Article
DE Sentiment analysis; Influential user; Weighted partition around medoids;
   Artificial cooperative search; Fuzzy deep neural network; Incremental
   learning logistic regression
AB Sentiment analysis explores the views, perceptions and feelings of people concerning entities like subjects, goods, organizations, resources and individuals. The opinion of some people in social network influences the opinion behavior and thoughts of other people. They are known as influential user. In this article, both the sentiment analysis and identification of influential user are proposed. Initially, Twitter data are preprocessed by proposing weighted partition around medoids (WPAM) with artificial cooperative search (WPAM-ACS) which extracts topics from Twitter data through dynamic clustering (DC). For sentiment classification, NLP has been used in many works. The main issue of using NLP for sentiment classification is that many languages do not have the adequate resources to develop NLP models. So, a fuzzy deep neural network (FDNN) is proposed in this paper for sentiment classification, because FDNN effectively handles the uncertainties and noises in tweet data than other state of the arts. Emotional conformity is a metric that refers to how people from an emotional point of view agree with another person. It is given as additional input to FDNN along with the tweets for sentiment classification. Finally, influential users are detected by temporal influential model (TIM) formulated as likelihood function using incremental logistic regression (ILLR) in which user's opinion sequence is considered for identification of influential user. In the experimental results, sentiment analysis is evaluated in terms of precision, recall and F-measure and proved that the proposed DC-FDNN sentiment classification is better than fixed clustering and NLP (FC-NLP)-based sentiment classification. Influential user detection using TIM-ILLR on opinion sequences which are identified by DC-FDNN is evaluated in terms of accuracy and proved that TIM-ILLR is better than other methods such as maximum likelihood estimation (MLE), support vector regression (SVR) and logistic regression (LR).
C1 [Ramya, G. R.; Bagavathi Sivakumar, P.] Amrita Sch Engn, Dept Comp Sci & Engn, Coimbatore, Tamil Nadu, India.
C3 Amrita Vishwa Vidyapeetham; Amrita Vishwa Vidyapeetham Coimbatore
RP Ramya, GR (corresponding author), Amrita Sch Engn, Dept Comp Sci & Engn, Coimbatore, Tamil Nadu, India.
EM gr_ramya@cb.amrita.edu; pbsk@cb.amrita.edu
OI Saravanakumar GR, Ramya/0000-0001-8184-048X; P, Bagavathi
   Sivakumar/0000-0002-3763-9583
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NR 27
TC 5
Z9 5
U1 0
U2 12
PU SPRINGER WIEN
PI WIEN
PA SACHSENPLATZ 4-6, PO BOX 89, A-1201 WIEN, AUSTRIA
SN 1869-5450
EI 1869-5469
J9 SOC NETW ANAL MIN
JI Soc. Netw. Anal. Min.
PD MAR 9
PY 2021
VL 11
IS 1
AR 27
DI 10.1007/s13278-021-00732-4
PG 16
WC Computer Science, Information Systems
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA QU7JU
UT WOS:000627456300001
DA 2024-09-05
ER
PT C
AU Li, YX
   Chen, R
   Wu, J
AF Li, Yuxin
   Chen, Rui
   Wu, Juan
GP Assoc Computing Machinery
TI Research Status, Hotspots and Trends of International AI-assisted Second
   Language Learning
SO PROCEEDINGS OF 2023 6TH INTERNATIONAL CONFERENCE ON EDUCATIONAL
   TECHNOLOGY MANAGEMENT, ICETM 2023
LA English
DT Proceedings Paper
CT 6th International Conference on Educational Technology Management
   (ICETM)
CY NOV 03-05, 2023
CL S China Normal Univ, Guangzhou, PEOPLES R CHINA
HO S China Normal Univ
DE Artificial intelligence; Second language learning; Artificial
   intelligence-assisted language learning; Bibliometric analysis; Visual
   analysis
ID ENGLISH; TECHNOLOGIES; ELEMENTARY; COMPLEXITY; EDUCATION; TOOL
AB The widespread application of artificial intelligence has triggered important changes in second language learning. The study was conducted on 293 research articles cited in the Web of Science database from 2013 to 2022 using the Bibliometrix R-package and CiteSpace software. A comprehensive investigation and analysis in the field of international artificial intelligence-assisted second language learning in the past ten years was made in this study, specifically including the development trend, high-cited authors, high-yield regions, high-impact journals, core article topics and research hot topics. The study found that (1) the overall research in this field shows a booming trend; (2) the field has formed highly influential authors and countries and has formed a relatively concentrated core journal group; and (3) the core article topics focus on combination of corpus linguistics and computational linguistics. The research hotspots include the creation of second language learning situations, the development of multilingual skills, the accurate assessment and diagnosis of second language learning, and the automatic feedback system for second language learning. Based on the analysis of the research results, this paper sorts out the future research trends in this field, and provides reference and inspiration for the research and practice of second language learning.
C1 [Li, Yuxin; Chen, Rui] Beijing Normal Univ, Fac Educ, Sch Educ Technol, 19 XinJieKouWai St, Beijing, Peoples R China.
   [Wu, Juan] Beijing Normal Univ, Adv Innovat Ctr Future Educ, Beishahe West 3rd Rd, Beijing, Peoples R China.
C3 Beijing Normal University; Beijing Normal University
RP Wu, J (corresponding author), Beijing Normal Univ, Adv Innovat Ctr Future Educ, Beishahe West 3rd Rd, Beijing, Peoples R China.
EM 202221010184@mail.bnu.edu.cn; 202121010188@mail.bnu.edu.cn;
   wuj@bnu.edu.cn
OI Wu, Juan/0000-0001-5525-9026
FU Applied Study of Artificial Intelligence in Primary and Secondary School
   Teaching and Learning [CGEA23009]; BeiJing Office for Education Sciences
   Planning Project
FX This work was supported by the Applied Study of Artificial Intelligence
   in Primary and Secondary School Teaching and Learning (grant number
   CGEA23009), supported by BeiJing Office for Education Sciences Planning
   Project.
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NR 36
TC 0
Z9 0
U1 18
U2 18
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES
BN 979-8-4007-1667-6
PY 2023
BP 227
EP 234
DI 10.1145/3637907.3637978
PG 8
WC Computer Science, Interdisciplinary Applications; Education &
   Educational Research; Education, Scientific Disciplines
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Education & Educational Research
GA BW5FK
UT WOS:001159769900033
DA 2024-09-05
ER
PT J
AU Rodrigues, D
   Kreif, N
   Lawrence-Jones, A
   Barahona, M
   Mayer, E
AF Rodrigues, Daniela
   Kreif, Noemi
   Lawrence-Jones, Anna
   Barahona, Mauricio
   Mayer, Erik
TI Reflection on modern methods: constructing directed acyclic graphs
   (DAGs) with domain experts for health services research
SO INTERNATIONAL JOURNAL OF EPIDEMIOLOGY
LA English
DT Article
DE Causal inference; potential outcomes; directed acyclic graphs; policy
   evaluation; health services research
ID OBSERVATIONAL RESEARCH; SENSITIVITY-ANALYSIS; CAUSAL INFERENCE;
   KNOWLEDGE
AB Directed acyclic graphs (DAGs) are a useful tool to represent, in a graphical format, researchers' assumptions about the causal structure among variables while providing a rationale for the choice of confounding variables to adjust for. With origins in the field of probabilistic graphical modelling, DAGs are yet to be widely adopted in applied health research, where causal assumptions are frequently made for the purpose of evaluating health services initiatives. In this context, there is still limited practical guidance on how to construct and use DAGs. Some progress has recently been made in terms of building DAGs based on studies from the literature, but an area that has received less attention is how to create DAGs from information provided by domain experts, an approach of particular importance when there is limited published information about the intervention under study. This approach offers the opportunity for findings to be more robust and relevant to patients, carers and the public, and more likely to inform policy and clinical practice. This article draws lessons from a stakeholder workshop involving patients, health care professionals, researchers, commissioners and representatives from industry, whose objective was to draw DAGs for a complex intervention-online consultation, i.e. written exchange between the patient and health care professional using an online system-in the context of the English National Health Service. We provide some initial, practical guidance to those interested in engaging with domain experts to develop DAGs.
C1 [Rodrigues, Daniela; Lawrence-Jones, Anna; Mayer, Erik] Imperial Coll London, NIHR Imperial Patient Safety Translat Res Ctr, Inst Global Hlth Innovat Dept Surg & Canc, 10th Floor,Queen Elizabeth Queen Mother Wing QEQM, London W2 1NY, England.
   [Kreif, Noemi] Univ York, Ctr Hlth Econ, York, N Yorkshire, England.
   [Barahona, Mauricio] Imperial Coll London, Ctr Math Precis Healthcare, Dept Math, London, England.
C3 Imperial College London; University of York - UK; Imperial College
   London
RP Rodrigues, D (corresponding author), Imperial Coll London, NIHR Imperial Patient Safety Translat Res Ctr, Inst Global Hlth Innovat Dept Surg & Canc, 10th Floor,Queen Elizabeth Queen Mother Wing QEQM, London W2 1NY, England.
EM d.rodrigues@imperial.ac.uk
RI Barahona, Mauricio/C-3638-2008; Mayer, Erik/A-6207-2013
OI Barahona, Mauricio/0000-0002-1089-5675; Lawrence-Jones,
   Anna/0000-0002-7975-1346; Mayer, Erik/0000-0002-5509-4580; Kreif,
   Noemi/0000-0001-9008-5690; Rodrigues, Daniela/0000-0002-5791-5573
FU National Institute for Health Research (NIHR) Imperial Patient Safety
   Translational Research Centre [PSTRC-2016-004]; NIHR Imperial Biomedical
   Research Centre [IS-BRC-1215-20013]; Engineering and Physical Sciences
   Research Council (EPSRC) Centre for Mathematics of Precision Healthcare
   [EP/N014529/1]; EPSRC [EP/N014529/1] Funding Source: UKRI
FX The research was funded by the National Institute for Health Research
   (NIHR) Imperial Patient Safety Translational Research Centre
   (PSTRC-2016-004) and supported by the NIHR Imperial Biomedical Research
   Centre (IS-BRC-1215-20013) and the Engineering and Physical Sciences
   Research Council (EPSRC) Centre for Mathematics of Precision Healthcare
   (EP/N014529/1). The views expressed are those of the authors and not
   necessarily those of the NHS, the NIHR or the Department of Health and
   Social Care.
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NR 30
TC 10
Z9 11
U1 2
U2 8
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 0300-5771
EI 1464-3685
J9 INT J EPIDEMIOL
JI Int. J. Epidemiol.
PD AUG 10
PY 2022
VL 51
IS 4
BP 1339
EP 1348
DI 10.1093/ije/dyac135
EA JUN 2022
PG 10
WC Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Public, Environmental & Occupational Health
GA 3R3HX
UT WOS:000812338600001
PM 35713577
OA Green Published, hybrid
DA 2024-09-05
ER
PT J
AU Faupel-Badger, JM
   Vogel, AL
   Hussain, SF
   Austin, CP
   Hall, MD
   Ness, E
   Sanderson, P
   Terse, PS
   Xu, X
   Balakrishnan, K
   Patnaik, S
   Marugan, JJ
   Rudloff, U
   Ferrer, M
AF Faupel-Badger, Jessica M.
   Vogel, Amanda L.
   Hussain, Shadab F.
   Austin, Christopher P.
   Hall, Matthew D.
   Ness, Elizabeth
   Sanderson, Philip
   Terse, Pramod S.
   Xu, Xin
   Balakrishnan, Krishna
   Patnaik, Samarjit
   Marugan, Juan J.
   Rudloff, Udo
   Ferrer, Marc
TI Teaching principles of translational science to a broad scientific
   audience using a case study approach: A pilot course from the National
   Center for Advancing Translational Sciences
SO JOURNAL OF CLINICAL AND TRANSLATIONAL SCIENCE
LA English
DT Article
DE Education; research training; workforce; online learning; evaluation
AB There are numerous examples of translational science innovations addressing challenges in the translational process, accelerating progress along the translational spectrum, and generating solutions relevant to a wide range of human health needs. Examining these successes through an education lens can identify core principles and effective practices that lead to successful translational outcomes. The National Center for Advancing Translational Sciences (NCATS) is identifying and teaching these core principles and practices to a broad audience via online courses in translational science which teach from case studies of NCATS-led or supported research initiatives. In this paper, we share our approach to the design of these courses and offer a detailed description of our initial course, which focused on a preclinical drug discovery and development project spanning academic and government settings. Course participants were from a variety of career stages and institutions. Participants rated the course high in overall value to them and in providing a unique window into the translational science process. We share our model for course development as well as initial findings from the course evaluation with the goal of continuing to stimulate development of novel education activities teaching foundational principles in translational science to a broad audience.
C1 [Faupel-Badger, Jessica M.; Vogel, Amanda L.; Hussain, Shadab F.; Hall, Matthew D.; Sanderson, Philip; Terse, Pramod S.; Xu, Xin; Balakrishnan, Krishna; Patnaik, Samarjit; Marugan, Juan J.; Ferrer, Marc] NIH, Natl Ctr Adv Translat Sci, Bldg 10, Bethesda, MD 20892 USA.
   [Austin, Christopher P.] Flagship Pioneering, Cambridge, MA USA.
   [Ness, Elizabeth; Rudloff, Udo] NCI, NIH, Bethesda, MD 20892 USA.
C3 National Institutes of Health (NIH) - USA; NIH National Center for
   Advancing Translational Sciences (NCATS); National Institutes of Health
   (NIH) - USA; NIH National Cancer Institute (NCI)
RP Faupel-Badger, JM (corresponding author), Natl Ctr Adv Translat Sci, 6701 Democracy Blvd, Bethesda, MD 20892 USA.
EM badgerje@mail.nih.gov
RI Hall, Matthew D/B-2132-2010
OI Hussain, Shadab/0000-0003-3837-6225; Faupel-Badger,
   Jessica/0000-0001-9729-3660
FU National Center for Advancing Translational Sciences (NCATS) Education
   Branch at the National Institutes of Health; National Cancer Institute
   [ZIABC011267] Funding Source: NIH RePORTER
FX Course development as supported by the National Center for Advancing
   Translational Sciences (NCATS) Education Branch at the National
   Institutes of Health.
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NR 27
TC 3
Z9 3
U1 0
U2 0
PU CAMBRIDGE UNIV PRESS
PI CAMBRIDGE
PA EDINBURGH BLDG, SHAFTESBURY RD, CB2 8RU CAMBRIDGE, ENGLAND
EI 2059-8661
J9 J CLIN TRANSL SCI
JI J. Clin. Transl. Sci.
PD MAR 21
PY 2022
VL 6
IS 1
AR e66
DI 10.1017/cts.2022.374
PG 8
WC Medicine, Research & Experimental
WE Emerging Sources Citation Index (ESCI)
SC Research & Experimental Medicine
GA 1X3ZZ
UT WOS:000807397300001
PM 35754433
OA Green Published, gold
DA 2024-09-05
ER
PT J
AU Zhang, Z
   Zhang, RX
   Sun, JD
AF Zhang, Zhao
   Zhang, Ruixin
   Sun, Jiandong
TI Research on the Comprehensive Evaluation Method of Driving Behavior of
   Mining Truck Drivers in an Open-Pit Mine
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE open-pit mine; mining truck; driving behavior evaluation; principal
   component analysis
AB Trucking is an important production link in most open-pit mines, and its transportation cost accounts for more than 50% of the total production cost of open-pit mines. The quality of the driver's driving behavior plays a crucial role in the fine control of the production cost of transportation. Different from the previous evaluation studies of drivers' driving behavior in open-pit mines, which mainly took safety driving behavior index as a factor variable, this paper puts forward a comprehensive evaluation method of driving behavior of mining truck drivers, which takes both safety driving and transportation cost as factor variables. Taking the mining truck as the research object, firstly, a scientific and reasonable data collection scheme is established, and the data information characterizing the transport state of the mining truck is obtained through data collection and analysis. Secondly, the RKNN algorithm of time series prediction and the wavelet analysis method are used to achieve noise reduction and missing processing of the original data so as to obtain accurate sample data. Then, taking the principal component analysis method as the entry point, through constructing the principal component analysis theory model, the key index system representing safe driving behavior and transportation cost is established to realize the comprehensive evaluation of the driving behavior of mining truck drivers, and the evaluation system of "standard driving", "prudent driving" and "aggressive driving" of mining truck drivers is formulated. The results show that after noise reduction, the accuracy of mining car operation data can be improved by 7 similar to 12%, and the transportation cost can be reduced by about 5% after the driver's operation behavior is standardized.
C1 [Zhang, Zhao; Zhang, Ruixin] China Univ Min & Technol Beijing, Sch Energy & Min Engn, Beijing 100083, Peoples R China.
   [Zhang, Ruixin; Sun, Jiandong] North China Inst Sci & Technol, Mine Safety Inst, Langfang 065201, Peoples R China.
   [Sun, Jiandong] China Univ Min & Technol, State Key Lab Coal Resources & Safe Min, Beijing 100083, Peoples R China.
C3 China University of Mining & Technology; North China Institute Science &
   Technology; China University of Mining & Technology
RP Zhang, Z (corresponding author), China Univ Min & Technol Beijing, Sch Energy & Min Engn, Beijing 100083, Peoples R China.
EM zhangzhao821@126.com; zhangrx139@163.com; sjd_xx@126.com
RI Zhang, Ruixin/LBI-2317-2024
OI Jiandong, Sun/0000-0002-2909-7956
FU basic scientific research service fee of central universities
   [3142019007]
FX The work was supported by the basic scientific research service fee of
   central universities(funding number: 3142019007).
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NR 33
TC 1
Z9 1
U1 4
U2 13
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2076-3417
J9 APPL SCI-BASEL
JI Appl. Sci.-Basel
PD OCT
PY 2023
VL 13
IS 20
AR 11597
DI 10.3390/app132011597
PG 17
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA X6OH0
UT WOS:001099617500001
OA gold
DA 2024-09-05
ER
PT C
AU Hijazi, H
   Couceiro, R
   Castelhano, J
   Cruz, J
   Castelo-Branco, M
   de Carvalho, P
   Madeira, H
AF Hijazi, Haytham
   Couceiro, Ricardo
   Castelhano, Joao
   Cruz, Jose
   Castelo-Branco, Miguel
   de Carvalho, Paulo
   Madeira, Henrique
GP IEEE
TI Intelligent Biofeedback Comprehension Assessment: Theory, Research, and
   Tools
SO 2022 IEEE 21ST MEDITERRANEAN ELECTROTECHNICAL CONFERENCE (IEEE MELECON
   2022)
SE IEEE Mediterranean Electrotechnical Conference-MELECON
LA English
DT Proceedings Paper
CT 21st IEEE Mediterranean Electrotechnical Conference (IEEE MELECON)
CY JUN 14-16, 2022
CL Palermo, ITALY
DE Artificial Intelligence; Biofeedback; Cognitive Load; Code Review;
   Content Comprehension
AB The present paper describes the use of nonintrusive biofeedback sensors (e.g., ECG) and eye-tracker to study the cognitive load (CL) associated with two mental tasks: a) content reading and comprehension b) code review. The paper addresses the theoretical underpinnings of the comprehension assessment included in content reading (for understanding) and code review evaluation using biofeedback sensors and Artificial Intelligence (AI) techniques. Moreover, it demonstrates the current research directions that the authors developed in evaluating these two tasks. Finally, the paper presents the design of one of the tools being developed to use biofeedback sensors and AI to evaluate the code review quality by assessing the code reviewer's comprehension and engagement level.
C1 [Hijazi, Haytham; Couceiro, Ricardo; Cruz, Jose; de Carvalho, Paulo; Madeira, Henrique] Univ Coimbra, CISUC, Coimbra, Portugal.
   [Castelhano, Joao] Univ Coimbra, ICNAS, Coimbra, Portugal.
   [Castelo-Branco, Miguel] Univ Coimbra, ICNAS CIBIT, Coimbra, Portugal.
C3 Universidade de Coimbra; Universidade de Coimbra; Universidade de
   Coimbra
RP Hijazi, H (corresponding author), Univ Coimbra, CISUC, Coimbra, Portugal.
EM haytham@dei.uc.pt; rcouceir@dei.uc.pt; joaocastelhano@uc.pt;
   jpcruz@student.dei.uc.pt; mcbranco@fmed.uc.pt; carvalho@dei.uc.pt;
   henrique@dei.uc.pt
RI Castelhano, Joao/I-5090-2019; Hijazi, Haytham/AAZ-9425-2021;
   Castelo-Branco, Miguel/F-3866-2019; Madeira, Henrique/M-9392-2013
OI Castelhano, Joao/0000-0002-8996-1515; Hijazi,
   Haytham/0000-0002-4981-3649; Castelo-Branco, Miguel/0000-0003-4364-6373;
   Madeira, Henrique/0000-0001-8146-4664
FU VALU3S ("Verification and Validation of Automated Systems' Safety and
   Security") - ECSEL Joint Undertaking (JU) [876852]; FCT (Fundacao para a
   Ciencia e Tecnologia), Portugal; BASE project [POCI - 01-0145 -
   FEDER-031581]; Centro de Informatica e Sistemas da Universidade de
   Coimbra (CISUC)
FX The authors would like to thank all volunteers who took part in the
   controlled experiments. This study was partially supported by the VALU3S
   ("Verification and Validation of Automated Systems' Safety and
   Security"), funded by the ECSEL Joint Undertaking (JU) under grant
   agreement No 876852, and by national funds from partners, including FCT
   (Fundacao para a Ciencia e Tecnologia), Portugal. The work was also
   supported by the BASE project, POCI - 01-0145 - FEDER-031581, and also
   partially supported by the Centro de Informatica e Sistemas da
   Universidade de Coimbra (CISUC).
CR [Anonymous], 2017, THEORY APPL COGNITIV
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NR 25
TC 0
Z9 0
U1 0
U2 1
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2158-8481
BN 978-1-6654-4280-0
J9 IEEE MEDITERR ELECT
PY 2022
BP 414
EP 419
DI 10.1109/MELECON53508.2022.9843030
PG 6
WC Engineering, Electrical & Electronic
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Engineering
GA BT8NR
UT WOS:000854091100072
DA 2024-09-05
ER
PT J
AU Jones, S
   Alam, N
AF Jones, Stewart
   Alam, Nurul
TI A machine learning analysis of citation impact among selected Pacific
   Basin journals
SO ACCOUNTING AND FINANCE
LA English
DT Article
DE Machine learning; Citation impact; Pacific Basin accounting journals;
   Fields of research; Research methodology
ID CORPORATE SOCIAL-RESPONSIBILITY; FINANCIAL-REPORTING STANDARDS;
   ACCOUNTING RESEARCH; REAL-ESTATE; POLITICAL CONNECTIONS; DISCLOSURE
   COMPLIANCE; EARNINGS DISCLOSURES; CAPITAL STRUCTURE; BOARD DIVERSITY;
   AUDITOR CHOICE
AB This study uses a machine learning approach to identify and predict factors which influence citation impacts across five Pacific Basin journals: Abacus, Accounting & Finance, Australian Journal of Management, Australian Accounting Review and the Pacific Accounting Review from 2008 to 2018. The machine learning results indicate that citation impact is mostly influenced by: length of a journal article; the field of research (particularly environmental accounting), sample size; whether the sample is local or international; choice of research method (e.g., archival vs survey/interview); academic rank of the first author; institutional status of the first author; and number of authors of the article. The results may be useful for predicting future trends in citation impact as well as providing strategies for authors and editors to improve citation impact.
C1 [Jones, Stewart; Alam, Nurul] Univ Sydney, Discipline Accounting, Sch Business, Sydney, NSW, Australia.
C3 University of Sydney
RP Jones, S (corresponding author), Univ Sydney, Discipline Accounting, Sch Business, Sydney, NSW, Australia.
EM stewart.jones@sydney.edu.au
OI Jones, Stewart/0000-0003-1600-3030; Alam, Nurul/0000-0002-9956-2753
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   Zhong YX, 2017, AUST ACCOUNT REV, V27, P195, DOI 10.1111/auar.12107
   Zhou S, 2017, ABACUS, V53, P94, DOI 10.1111/abac.12104
NR 168
TC 7
Z9 8
U1 2
U2 30
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0810-5391
EI 1467-629X
J9 ACCOUNT FINANC
JI Account . Financ.
PD DEC
PY 2019
VL 59
IS 4
BP 2509
EP 2552
DI 10.1111/acfi.12584
EA NOV 2019
PG 44
WC Business, Finance
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA OI5XB
UT WOS:000499394100001
DA 2024-09-05
ER
PT C
AU Pektas, ST
AF Pektas, Sule Tasli
BE ACM
TI A Systematic Analysis of Machine Learning Studies in Education
SO PROCEEDINGS OF THE 15TH INTERNATIONAL CONFERENCE ON EDUCATION TECHNOLOGY
   AND COMPUTERS, ICETC 2023
LA English
DT Proceedings Paper
CT 15th International Conference on Education Technology and Computers
   (ICETC)
CY SEP 26-28, 2023
CL Univ Barcelona, Barcelona, SPAIN
HO Univ Barcelona
DE Machine Learning; Education; Bibliometric Analysis; Keyword
   Cooccurrence; Network
ID ANALYTICS
AB Machine learning has been transforming education and changing learning, teaching, and administration processes. However, studies analyzing the existing body of work and emerging research foci are lacking. To fill in the re-search gap, this paper presents a bibliometric analysis of articles on machine learning in education that were indexed byWeb of Science Core Citation In-dices from 1979 to 2023. The study investigates publication patterns (articles per year and journals) and key research areas. A keyword co-occurrence analysis was conducted to identify the clusters of keywords which often co-exist in articles. The analysis revealed six clusters which correspond to the main research themes: profiling and prediction, assessment, intelligent tutoring systems, MOOCs, natural language processing, and prediction in distance learning. It is discussed that the newly emerging and rapidly developing research area focuses merely on applications of the technology, while ethical, pedagogical, socio-cultural, and administrative is-sues regarding machine learning in education need further attention.
C1 [Pektas, Sule Tasli] OSTIM Tech Univ, Ankara, Turkiye.
C3 Ostim Technical University
RP Pektas, ST (corresponding author), OSTIM Tech Univ, Ankara, Turkiye.
EM sule.taslipektas@ostimteknik.edu.tr
RI Pektas, Sule Tasli/B-9453-2008
OI Pektas, Sule Tasli/0000-0003-0596-6405
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NR 33
TC 0
Z9 0
U1 4
U2 4
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES
BN 979-8-4007-0911-1
PY 2023
BP 451
EP 455
DI 10.1145/3629296.3629368
PG 5
WC Computer Science, Interdisciplinary Applications; Education &
   Educational Research; Education, Scientific Disciplines
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Education & Educational Research
GA BW5RO
UT WOS:001166851900069
DA 2024-09-05
ER
PT C
AU McCain, K
AF McCain, K
BE Ingwersen, P
   Larsen, B
TI Explorations in bibliometric historiography: The (re)emergence of neural
   networks, 1980-1991
SO ISSI 2005: Proceedings of the 10th International Conference of the
   International Society for Scientometrics and Informetrics, Vols 1 and 2
LA English
DT Proceedings Paper
CT 10th International Conference of the
   International-Society-for-Scientometrics-and-Informetrics
CY JUL 24-28, 2005
CL Stockholm, SWEDEN
ID COLLAGEN RESEARCH
C1 Drexel Univ, Coll Informat Sci & Technol, Philadelphia, PA 19104 USA.
C3 Drexel University
CR Garfield E, 2003, J AM SOC INF SCI TEC, V54, P400, DOI 10.1002/asi.10226
   Jain AK, 1996, COMPUTER, V29, P31, DOI 10.1109/2.485891
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   SMALL HG, 1977, SOC STUD SCI, V7, P139, DOI 10.1177/030631277700700202
NR 5
TC 0
Z9 0
U1 0
U2 4
PU KAROLINSKA UNIV PRESS AB
PI STOCKHOLM
PA BOX 200, STOCKHOLM, SE-171 77, SWEDEN
BN 91-7140-339-6
PY 2005
BP 668
EP 668
PG 1
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Information Science & Library Science
GA BDC93
UT WOS:000232759800090
DA 2024-09-05
ER
PT J
AU Kadiresan, N
   Singson, M
   Thiyagarajan, S
AF Kadiresan, N.
   Singson, Mangkhollen
   Thiyagarajan, S.
TI Examining the relationship between academic book citations and Goodreads
   reader opinion and rating
SO ANNALS OF LIBRARY AND INFORMATION STUDIES
LA English
DT Article
DE Sentiment analysis; Web scraping; Citation analysis; Goodreads; Scopus;
   Google Scholar
ID SOCIAL-SCIENCE; IMPACT; REVIEWS; RESEARCHERS; HUMANITIES
AB Although the traditional bibliometric citation database is an established academic impact assessment source, in this paper, we examine the role of social media impact on academic books. We identified the highly cited books in Scopus and compared the citations with ratings and reviews on the Goodreads website. R stat was used to extract the data from Goodreads website. We found that there is an uneven distribution of Goodreads rating and reviews. Social science books received the highest number of user's ratings, reviews and citations. The study finds that there is no relationship between citation counts and Goodreads ratings and reviews count in social science books. Although social science books generated the highest number of studies and engagement by the readers, there seems to be no evidence to suggest that this engagement results in an academic citation. Whereas, a correlation was observed between health science books citations and Goodreads overall rating, as with physical science book reviews and Google Scholar citation counts.
C1 [Kadiresan, N.; Singson, Mangkhollen] Pondicherry Univ, Dept Lib & Informat Sci, Pondicherry, India.
   [Thiyagarajan, S.] Pondicherry Univ, Dept Int Business, Pondicherry, India.
C3 Pondicherry University; Pondicherry University
RP Thiyagarajan, S (corresponding author), Pondicherry Univ, Dept Int Business, Pondicherry, India.
EM nkadiresan1992@gmail.com; manglien@gmail.com; sthiyags@yahoo.com
OI Singson, Mangkhollen/0000-0001-7671-9335
CR Amudhavalli A, 2008, 4 INT C WEB INF SCI
   Danell R, 2013, SCIENTOMETRICS, V94, P999, DOI 10.1007/s11192-012-0840-4
   FOSTER CL, 1951, ED RES B, P64
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NR 34
TC 1
Z9 1
U1 2
U2 7
PU NATL INST SCIENCE COMMUNICATION-NISCAIR
PI NEW DELHI
PA DR K S KRISHNAN MARG, PUSA CAMPUS, NEW DELHI 110 012, INDIA
SN 0972-5423
EI 0975-2404
J9 ANN LIBR INF STUD
JI Ann. Libr. Inf. Stud.
PD DEC
PY 2020
VL 67
IS 4
BP 215
EP 221
DI 10.56042/alis.v67i4.32597
PG 7
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA PR0VD
UT WOS:000606955800002
OA gold
DA 2024-09-05
ER
PT C
AU Lynch, G
AF Lynch, Grace
BE Balcaen, P
TI Increasing Student Participation and Success in Online Education
SO PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON E-LEARNING
LA English
DT Proceedings Paper
CT 6th International Conference on e-Learning
CY JUN 27-28, 2011
CL Univ British Columbia Okanagan, Kelowna, CANADA
HO Univ British Columbia Okanagan
DE eLearning; online learning excellence; open access; student retention;
   success
AB This short presentation will outline the work in progress for the establishment of a Centre of Online Learning Excellence (COLE) for the Open Universities Australia consortium and outcomes from initiatives to improve student participation and success through high quality learning environments. Open Universities Australia (OUA) provides access to 100 courses and 1000 units via twenty different Australian education providers and is Australia's fastest growing online education service with over 130,000 students having studied since its inception in 1993. By allowing potential candidates to make application to study without the provision of an entry score or previous study history, OUA endeavours to remove many of the barriers to university education such as distance, time and entry requirements. COLE is in a position to recommend, manage and evaluate for wider implementation trials across Providers in areas such as new assessment technologies, new ways of engaging with students and their mobile devices for learning and early alert systems for students at risk. OUA's history of harnessing expertise within its own community and in the general community provides a particularly advantageous position for providing forward-thinking, innovative and meaningful research and development. OUA has developed a strong understanding and solid expertise base in aspects relating to distance online: learning; teaching (pedagogical); delivery (educational technology); and instructional design issues. The five main areas of work that COLE undertakes are: ELearning research; educational and technological advice and consultancy; Professional Development; project management and educational design and educational design, development and production of units and resources.
C1 [Lynch, Grace] Open Univ Australia, Melbourne, Vic, Australia.
C3 Open Universities Australia
EM grace.lynch@open.edu.au
CR [Anonymous], 2009, ECONOMIST 1230
   De Fazio T., 2007, THESIS MONASH U MELB
   Herrington J., 2009, New technologies, new pedagogies: Mobile learning in higher education, P138, DOI DOI 10.11645/4.1.1478
   Lea M.R., 2000, STUDENT WRITING HIGH, P32
   LYNCH G, 2010, P WORLD C ED MULT HY, P2040
   Lynch G., 2010, CURRICULUM TECHNOLOG
   Shank P., 2010, LEARNING SOLUTI 0210
   Templer A., 2010, AUSTRALIAN
NR 8
TC 0
Z9 3
U1 0
U2 13
PU ACAD  CONFERENCES LTD
PI NR READING
PA CURTIS FARM, KIDMORE END, NR READING, RG4 9AY, ENGLAND
BN 978-1-908272-04-1
PY 2011
BP 500
EP 504
PG 5
WC Education & Educational Research
WE Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Education & Educational Research
GA BBT89
UT WOS:000308137800059
DA 2024-09-05
ER
PT C
AU Ramneet
   Gupta, D
   Madhukar, M
AF Ramneet
   Gupta, Deepali
   Madhukar, Mani
GP IEEE
TI Bibliometric Analysis of MOOC using Bibliometrix Package of R
SO PROCEEDINGS OF 2020 6TH IEEE INTERNATIONAL WOMEN IN ENGINEERING (WIE)
   CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING (WIECON-ECE 2020)
LA English
DT Proceedings Paper
CT 6th IEEE International Women in Engineering (WIE) Conference on
   Electrical and Computer Engineering (WIECON-ECE)
CY DEC 26-27, 2020
CL Bhubaneswar, INDIA
DE MOOC; MOOCS; E-learning; Artificial Intelligence; Machine Learning
AB As a growing academic approach, MOOCs have gained widespread attention through academic courses. There are many MOOC platforms available like Udemy, Udacity, Coursera, Swayam, Khan Academy and EdX. All these platforms provide free as well as paid courses. These types of certifications help in boosting the career of the learners. A strong recommendation system is the need of the day for these types of platforms that will guide learners in selecting the appropriate course according to their levels. In this paper, 1511 journal articles have been selected through the Scopus database from 2012 to 2020. This study uses a bibliometric analysis method to visualize the knowledge network analysis with the help of the Biblioshiny tool of R software.
C1 [Ramneet; Gupta, Deepali] Chitkara Univ, Chitkara Univ Inst Engn & Technol, Chandigarh, Punjab, India.
   [Madhukar, Mani] Univ Relat, IBM India Private Ltd, Greater Noida, India.
C3 Chitkara University, Punjab
RP Ramneet (corresponding author), Chitkara Univ, Chitkara Univ Inst Engn & Technol, Chandigarh, Punjab, India.
EM ramneet@chitkara.edu.in; deepali.gupta@chitkara.edu.in;
   manimad9@in.ibm.com
RI Gupta, Deepali/HJY-4480-2023
OI Gupta, Deepali/0000-0002-3207-5248; , Dr. Ramneet/0000-0002-4066-5416
CR Ayodele TO., 2010, NEW ADV MACHINE LEAR, V3, P19, DOI DOI 10.5772/9385
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NR 18
TC 0
Z9 0
U1 2
U2 31
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-6654-1917-8
PY 2020
BP 169
EP 173
DI 10.1109/WIECON-ECE52138.2020.9397952
PG 5
WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Engineering
GA BR8DZ
UT WOS:000671104100039
DA 2024-09-05
ER
PT C
AU Zhou, W
   Wang, SK
   Lao, DX
AF Zhou, Wei
   Wang, Shuke
   Lao, Danxue
GP IEEE
TI The field intersection of machine learning and intelligent decision
SO 2023 35TH CHINESE CONTROL AND DECISION CONFERENCE, CCDC
SE Chinese Control and Decision Conference
LA English
DT Proceedings Paper
CT 35th Chinese Control and Decision Conference (CCDC)
CY MAY 20-22, 2023
CL Yichang, PEOPLES R CHINA
DE machine learning; intelligent decision; bibliometric analysis;
   development track
AB Machine learning is an important tool for intelligent decision, the effective combination of the two is the current research hotspot, but fewer scholars have analyzed their development context. This paper provides a comprehensive analysis of the whole context from a scientometric perspective, aiming to help researchers understand the development of the two fields as they collide, and thus create new research results. We retrieved 2,218 documents from the Web of Science (WoS) database from 1990 to 2021 and reveals the research hotspot and research frontiers of the subject in the main path longitudinal comparison of the main path of the four sub-periods, which teases the research direction and its evolution route in the two fields. It is found that the field shows a trend of machine learning algorithms moving from single to multiple directions, and the application areas of intelligent decision are gradually widening.
C1 [Zhou, Wei; Wang, Shuke; Lao, Danxue] Yunnan Univ Finance & Econ, Sch Finance, Kunming, Yunnan, Peoples R China.
C3 Yunnan University of Finance & Economics
RP Zhou, W (corresponding author), Yunnan Univ Finance & Econ, Sch Finance, Kunming, Yunnan, Peoples R China.
EM zw453@163.com; lynnwang_9@163.com; 15123668128@163.com
RI Wang, Shuke/AAH-4843-2021
FU Natural Science Foundation of China [72071176]; Philosophy and Social
   Science Innovation Team Project of Yunnan Province [2022CX01]
FX This work was supported by the Natural Science Foundation of China (No.
   72071176) and the Philosophy and Social Science Innovation Team Project
   of Yunnan Province (No. 2022CX01)
CR AGhamdi M. A., ARABIAN J SCI ENG
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NR 57
TC 0
Z9 0
U1 3
U2 3
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 1948-9439
BN 979-8-3503-3472-2
J9 CHIN CONT DECIS CONF
PY 2023
BP 3952
EP 3957
DI 10.1109/CCDC58219.2023.10327500
PG 6
WC Automation & Control Systems; Operations Research & Management Science
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Automation & Control Systems; Operations Research & Management Science
GA BW2HE
UT WOS:001116704304030
DA 2024-09-05
ER
PT J
AU Siemens, G
   Matheos, K
AF Siemens, George
   Matheos, Kathleen
TI Systemic Changes in Higher Education
SO IN EDUCATION
LA English
DT Article
DE higher education; freedom of learners; open access; online learning
AB A power shift is occurring in higher education, driven by two trends: (a) the increased freedom of learners to access, create, and re-create content; and (b) the opportunity for learners to interact with each other outside of a mediating agent. Information access and dialogue, previously under control of the educator, can now be readily fulfilled by learners. When the essential mandate of universities is buffeted by global, social/political, technological, and educational change pressures, questions about the future of universities become prominent. The integrated university faces numerous challenges, including a decoupling of research and teaching functions. Do we still need physical classrooms? Are courses effective when information is fluid across disciplines and subject to continual changes? What value does a university provide society when educational resources and processes are open and transparent?
C1 [Siemens, George] Athabasca Univ, Athabasca, AB, Canada.
   [Matheos, Kathleen] Univ Manitoba, Winnipeg, MB, Canada.
C3 Athabasca University; University of Manitoba
RP Siemens, G (corresponding author), Athabasca Univ, Athabasca, AB, Canada.
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OI Siemens, George/0000-0002-9567-9794
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NR 66
TC 10
Z9 19
U1 0
U2 0
PU UNIV REGINA, FAC EDUCATION
PI REGINA
PA 3737 WASCANA PKWY, REGINA, SK S4S 0A2, CANADA
SN 1927-6117
J9 EDUCATION-CANADA
JI Education-Canada
PD SPR
PY 2010
VL 16
IS 1
SI SI
BP 3
EP 18
PN 2
PG 16
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA V4P1I
UT WOS:000219134900002
DA 2024-09-05
ER
PT J
AU COHEN, PR
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AF COHEN, PR
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TI HOW EVALUATION GUIDES AI RESEARCH
SO AI MAGAZINE
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NR 27
TC 48
Z9 50
U1 0
U2 5
PU AMER ASSOC ARTIFICIAL INTELL
PI MENLO PK
PA 445 BURGESS DRIVE, MENLO PK, CA 94025-3496
SN 0738-4602
J9 AI MAG
JI AI Mag.
PD WIN
PY 1988
VL 9
IS 4
BP 35
EP 43
PG 9
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA R7330
UT WOS:A1988R733000003
DA 2024-09-05
ER
PT J
AU Prasad, KDV
   Vaidya, R
   Srinivas, K
   Kumar, VA
AF Prasad, K. D. V.
   Vaidya, Rajesh
   Srinivas, K.
   Kumar, V. Anil
TI Evaluation of the Factors Influencing the Performance Appraisal System
   with Reference to Agriculture Research Sector, Hyderabad - A Multinomial
   Logistic Regression Approach
SO PACIFIC BUSINESS REVIEW INTERNATIONAL
LA English
DT Article
DE Performance Appraisal; Agriculture; Regression
ID PERSPECTIVE
AB In this research study we reported the results on the factors influencing the performance appraisal system using multinomial logistic regression analysis with reference to Agriculture Reearch Sector employees in Hyderabad Metro, India. The data collected from one of the critical factor of HR practices under Performance Management System -the performance appraisal forms of 400 employees working in the agriculture sector consisting of from 300 men and 100 women employees. The seven independent factors Job knowledge, Skill level, Job execution, Initiative, Client orientation, Team work, Compliance to policies and practices one dependent factor outcome of the Performance Appraisal System (PAS) the Rating were measured. The descriptive analysis and multinomial logistic regression analysis carried out to arrive at the conclusions. To measure the reliability of the scale used for this study, and internal consistencies of the instrument performance appraisal form, the reliability statistics Cronbach's alpha (C-Alpha) and Split-half reliability estimated. The overall C-Alpha value is 0.89, and the C-Alpha values for all the factors ranged 0.83 to 0.88, whereas overall Spearman Brown Split-half measured at 0.84. The multinomial logistic regression analysis was performed to estimate the likelyhood odds ratios (ORs) to explain the factors associated outcome of the performance appraisal system Rating, a dependent variable. It can be observed from the relative log odds ratios significant negative influence of independent variables, Job Knowledge (OR, 0.404, 95% 0.168-0.972) Job skill (OR 0.126, 95% CI 0.053-0.296), Job execution (OR 0.105. 95% CI 0.039-0.280), Initiative (OR 0.307, 95% CI 0.134-0.705), Team Work (OR 0.284, 95% CI 0.129-0.624), and Compliance to Policies and Practices (OR 0.260, 95% CI 0.117) for dependent variable Rating Excellent and Job knowledge (OR 0.320, 95% CI 0.113-0.907), Job skill (OR 0.066, 95% CI 0.024-0.178), Job Execution (OR 0.036, 95% CI 0.012-0.111), Initiative (OR 0.170, 95% CI 0.064, -0.453), Team work (OR 0.142, 95% CI 0.057-0.356), Compliance to policies (OR 0.083, 95% CI 0.032-0.215) for Rating Good verses Outstanding as reference variable.
C1 [Prasad, K. D. V.] Rashtrasant Tukdoji Maharaj Nagpur Univ, Fac Commerce, Nagpur, Maharashtra, India.
   [Vaidya, Rajesh] Shree Ramdeobaba Coll Engn & Management, Dept Management & Technol, Nagpur, Maharashtra, India.
   [Srinivas, K.] ICRISAT Asia Program, Patancheru, Andhra Prades, India.
   [Kumar, V. Anil] Int Crops Res Inst Semi Arid Trop, Biometr, Patancheru, Andhra Prades, India.
C3 Rashtrasant Tukadoji Maharaj Nagpur University; Shri Ramdeobaba College
   of Engineering & Management; Rashtrasant Tukadoji Maharaj Nagpur
   University; CGIAR; International Crops Research Institute for the
   Semi-Arid-Tropics (ICRISAT)
RP Prasad, KDV (corresponding author), Rashtrasant Tukdoji Maharaj Nagpur Univ, Fac Commerce, Nagpur, Maharashtra, India.
RI PRASAD, KDV/AAF-7097-2019; Kumar, Anil/AAF-4031-2020; Vaidya, Rajesh
   W/AAC-6790-2021
OI PRASAD, KDV/0000-0001-9921-476X; Vaidya, Rajesh W/0000-0002-7541-2187
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NR 48
TC 0
Z9 0
U1 1
U2 2
PU PACIFIC INST MANAGEMENT
PI RAJASTHAN
PA PACIFIC HILLS, PRATAP NAGAR EXTENSION, AIR PORT RD, UDAIPUR, RAJASTHAN,
   313 001, INDIA
SN 0974-438X
J9 PAC BUS REV INT
JI Pac. Bus. Rev. Int.
PD MAR
PY 2017
VL 9
IS 9
BP 7
EP 18
PG 12
WC Business
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA FY4DU
UT WOS:000426772600002
DA 2024-09-05
ER
PT C
AU Thijs, B
   Zhang, L
   Glänzel, W
AF Thijs, Bart
   Zhang, Lin
   Glanzel, Wolfgang
BE Gorraiz, J
   Schiebel, E
   Gumpenberger, C
   Horlesberger, M
   Moed, H
TI BIBLIOGRAPHIC COUPLING AND HIERARCHICAL CLUSTERING FOR THE VALIDATION
   AND IMPROVEMENT OF SUBJECT-CLASSIFICATION SCHEMES
SO 14TH INTERNATIONAL SOCIETY OF SCIENTOMETRICS AND INFORMETRICS CONFERENCE
   (ISSI)
SE Proceedings of the International Conference on Scientometrics and
   Informetrics
LA English
DT Proceedings Paper
CT 14th International-Society-of-Scientometrics-and-Informetrics Conference
   (ISSI)
CY JUL 15-20, 2013
CL Vienna, AUSTRIA
AB An attempt is made to apply bibliographic coupling to journal clustering of the complete Web of Science database. Since the sparseness of the underlying similarity matrix proved inappropriate for this exercise, second-order similarities have been used. Only 0.12% out of 8282 journals had to be removed from the classification as being singletons. The quality at three hierarchical levels with 6, 14 and 24 clusters substantiated the applicability of this method. Cluster labelling was made on the basis of the about 70 subfields of the Leuven-Budapest subject-classification scheme that also allowed the comparison with the existing two-level journal classification system developed in Leuven. The further comparison with the 22 field classification system of the Essential Science Indicators does, however, reveal larger deviations.
C1 [Thijs, Bart; Glanzel, Wolfgang] Katholieke Univ Leuven, ECOOM, Leuven, Belgium.
   [Thijs, Bart; Glanzel, Wolfgang] Katholieke Univ Leuven, Dept MSI, Leuven, Belgium.
   [Zhang, Lin] North China Univ Water Conservancy & Elect Power, Dept Management & Econ, Zhengzhou, Peoples R China.
   [Glanzel, Wolfgang] Lib Hungarian Acad Sci, Dept Sci Policy & Scientometr, Budapest, Hungary.
C3 KU Leuven; KU Leuven; North China University of Water Resources &
   Electric Power; Hungarian Academy of Sciences
RP Thijs, B (corresponding author), Katholieke Univ Leuven, ECOOM, Leuven, Belgium.
EM bart.thijs@kuleuven.be; zhanglin_1117@126.com;
   wolfgang.glanzel@kuleuven.be
RI Glanzel, Wolfgang/AAE-4395-2021; Zhang, Lin/M-3007-2017; Glanzel,
   Wolfgang/A-6280-2008; Thijs, Bart CM/C-2995-2008
OI Zhang, Lin/0000-0003-0526-9677; Glanzel, Wolfgang/0000-0001-7529-5198; 
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NR 18
TC 3
Z9 3
U1 0
U2 7
PU INT SOC SCIENTOMETRICS & INFORMETRICS-ISSI
PI LEUVEN
PA KATHOLIEKE UNIV LEUVEN, FACULTEIT E T E W, DEKENSTRAAT 2, LEUVEN,
   B-3000, BELGIUM
SN 2175-1935
BN 978-3-200-03135-7
J9 PRO INT CONF SCI INF
PY 2013
BP 237
EP 250
PG 14
WC Information Science & Library Science
WE Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Information Science & Library Science
GA BC6IG
UT WOS:000353961700019
DA 2024-09-05
ER
PT J
AU Mitsunaga, TM
   Garcia, BLN
   Pereira, LBR
   Costa, YCB
   da Silva, RF
   Delbem, ACB
   dos Santos, MV
AF Mitsunaga, Thatiane Mendes
   Garcia, Breno Luis Nery
   Pereira, Ligia Beatriz Rizzanti
   Costa, Yuri Campos Braga
   da Silva, Roberto Fray
   Delbem, Alexandre Claudio Botazzo
   dos Santos, Marcos Veiga
TI Current Trends in Artificial Intelligence and Bovine Mastitis Research:
   A Bibliometric Review Approach
SO ANIMALS
LA English
DT Article
DE mastitis; dairy cows; machine learning; detection; big data; milk
   production
ID SUBCLINICAL MASTITIS; CLINICAL MASTITIS; NEURAL-NETWORK; DAIRY-CATTLE;
   MILK; COWS; ASSOCIATIONS; FARMS; TOOL
AB Simple Summary Artificial intelligence has become essential for aiding in different knowledge domains by improving knowledge extraction from raw data and process automation. In dairy production, artificial intelligence offers promising applications in detecting and managing bovine mastitis, the most critical disease affecting the mammary gland in dairy cows, impacting milk production and profitability in dairy farms. This research evaluated the evolution of artificial intelligence applications in bovine mastitis between 2011 and 2021 using the Scopus database and the frequency of terms cited in titles, abstracts, and keywords. We selected the 62 papers that were the most relevant according to their citation index. Our results pointed out that the terms "machine learning" and "mastitis" were the most cited, with a significant increase between 2018 and 2021. There was an increase in artificial intelligence applications for bovine mastitis per country, showing applications primarily aimed at improving the current mastitis detection systems. The most cited model was artificial neural networks. We concluded that using artificial intelligence in bovine mastitis was related to mastitis detection as a vital tool to prevent this disease, considering its major impacts on dairy production and economic return.Abstract Mastitis, an important disease in dairy cows, causes significant losses in herd profitability. Accurate diagnosis is crucial for adequate control. Studies using artificial intelligence (AI) models to classify, identify, predict, and diagnose mastitis show promise in improving mastitis control. This bibliometric review aimed to evaluate AI and bovine mastitis terms in the most relevant Scopus-indexed papers from 2011 to 2021. Sixty-two documents were analyzed, revealing key terms, prominent researchers, relevant publications, main themes, and keyword clusters. "Mastitis" and "machine learning" were the most cited terms, with an increasing trend from 2018 to 2021. Other terms, such as "sensors" and "mastitis detection", also emerged. The United States was the most cited country and presented the largest collaboration network. Publications on mastitis and AI models notably increased from 2016 to 2021, indicating growing interest. However, few studies utilized AI for bovine mastitis detection, primarily employing artificial neural network models. This suggests a clear potential for further research in this area.
C1 [Mitsunaga, Thatiane Mendes] Univ Sao Paulo, Luiz Queiroz Coll Agr ESALQ, Av Padua Dias 11, BR-13418900 Piracicaba, SP, Brazil.
   [Garcia, Breno Luis Nery; Pereira, Ligia Beatriz Rizzanti; dos Santos, Marcos Veiga] Univ Sao Paulo, Sch Vet Med & Anim Sci, BR-13635900 Pirassununga, SP, Brazil.
   [Costa, Yuri Campos Braga] Sao Paulo State Technol Coll, BR-13469111 Americana, SP, Brazil.
   [da Silva, Roberto Fray] Univ Sao Paulo, Luiz Queiroz Coll Agr ESALQ, Biosyst Engn Dept, Av Padua Dias 11, BR-13418900 Piracicaba, SP, Brazil.
   [da Silva, Roberto Fray; Delbem, Alexandre Claudio Botazzo] Univ Sao Paulo, Ctr Artificial Intelligence C4AI, Av Prof Lucio Martins Rodrigues,370 Butanta, BR-05508020 Sao Paulo, SP, Brazil.
   [Delbem, Alexandre Claudio Botazzo] Univ Sao Paulo, Inst Math & Comp Sci, BR-13560970 Sao Carlos, SP, Brazil.
C3 Universidade de Sao Paulo; Universidade de Sao Paulo; Universidade de
   Sao Paulo; Universidade de Sao Paulo; Universidade de Sao Paulo
RP dos Santos, MV (corresponding author), Univ Sao Paulo, Sch Vet Med & Anim Sci, BR-13635900 Pirassununga, SP, Brazil.
EM thatiane.mitsunaga@usp.br; brenoluis.garcia@ucalgary.ca;
   ligiarizzanti@usp.br; yuri.costa4@fatec.sp.gov.br;
   roberto.fray.silva@gmail.com; acbd@icmc.usp.br; mveiga@usp.br
RI Santos, Marcos Veiga/D-6936-2012; Fray da Silva, Roberto/G-8929-2016
OI Santos, Marcos Veiga/0000-0002-4273-3494; Fray da Silva,
   Roberto/0000-0002-9792-0553
FU Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior-Brasil
   (CAPES) [001]; Sao Paulo Research Foundation (FAPESP) [2021/05400-3,
   2023/00286-3]; Vinnova [2023-00286] Funding Source: Vinnova; Forte
   [2023-00286] Funding Source: Forte
FX This study was financed in part by the Coordenacao de Aperfeicoamento de
   Pessoal de Nivel Superior-Brasil (CAPES)-finance code 001, and by Sao
   Paulo Research Foundation (FAPESP), grants 2021/05400-3 and
   2023/00286-3.
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NR 47
TC 0
Z9 0
U1 5
U2 5
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
SN 2076-2615
J9 ANIMALS-BASEL
JI Animals
PD JUL
PY 2024
VL 14
IS 14
AR 2023
DI 10.3390/ani14142023
PG 19
WC Agriculture, Dairy & Animal Science; Veterinary Sciences; Zoology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Agriculture; Veterinary Sciences; Zoology
GA ZP4Z6
UT WOS:001276500200001
PM 39061485
OA gold
DA 2024-09-05
ER
PT C
AU Charnine, M
   Klokov, A
   Kochiev, L
   Tishchenko, A
AF Charnine, Michael
   Klokov, Alexey
   Kochiev, Leon
   Tishchenko, Alexey
BE Sourin, A
   Rosenberger, C
   Sourina, O
TI Research Trending Topic Prediction as Cognitive Enhancement
SO 2021 INTERNATIONAL CONFERENCE ON CYBERWORLDS (CW 2021)
LA English
DT Proceedings Paper
CT 20th International Conference on Cyberworlds (CW)
CY SEP 28-30, 2021
CL Caen, FRANCE
DE long-term prediction; research trending topics; decision tree; CatBoost;
   scientific papers; dynamics of topic trends; Big Data; cognitive
   enhancement
AB The Internet has been identified in human enhancement scholarship as a powerful cognitive enhancement technology. Using the Internet as an external memory system has overall benefits if we have the skills to efficiently navigate, evaluate, and compare online information. Long-term prediction of research trending topics is a form of cognitive enhancement because it helps to efficiently navigate, evaluate scientific articles, identify promising directions, and focus efforts in these directions. This paper presents the results of a method designed to realize long-term prediction of research trending topics. Meaningful topics were identified among the words included in the titles of scientific articles. The title is the most important element of a scientific article and the main indication of the article's subject and topic. We treated the title words, which occur several times in cited articles of the analyzed collection, as the research trending topics. The longevity of the citation trend growth was the target for the machine learning algorithms. The CatBoost machine learning method, which is one of the best implementations of decision trees, was used. We conducted experiments on a scientific dataset including 5 million publications of top conferences in artificial intelligence and data mining areas to demonstrate the effectiveness of the proposed model. The accuracy rate of three-year forecasts for a number of experiments from 1997 to 2014 was about 60%.
C1 [Charnine, Michael; Klokov, Alexey; Kochiev, Leon] Russian Acad Sci, FRC CSC, Inst Informat Problems, Moscow, Russia.
   [Tishchenko, Alexey] Presidential Acad, RANEPA, Inst Appl Econ Res, Moscow, Russia.
C3 Federal Research Center "Computer Science & Control" of RAS; Institute
   of Informatics Problems of Russian Academy of Sciences; Russian Academy
   of Sciences; Russian Presidential Academy of National Economy & Public
   Administration
RP Charnine, M (corresponding author), Russian Acad Sci, FRC CSC, Inst Informat Problems, Moscow, Russia.
EM mc@keywen.com; alexeyseti82@yandex.ru
OI Klokov, Alexey/0000-0003-3311-2933
FU Russian Foundation for Basic Research
FX This work is supported by Russian Foundation for Basic Research, grant
   19-07-00857. We are grateful to the Russian Foundation for Basic
   Research for financial support of our projects.
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NR 11
TC 1
Z9 1
U1 1
U2 3
PU IEEE
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PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-6654-4065-3
PY 2021
BP 217
EP 220
DI 10.1109/CW52790.2021.00044
PG 4
WC Computer Science, Cybernetics; Computer Science, Interdisciplinary
   Applications; Computer Science, Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BS9SD
UT WOS:000783807600036
DA 2024-09-05
ER
PT J
AU Reiljan, J
   Paltser, I
AF Reiljan, Janno
   Paltser, Ingra
TI The influence of research and development policy: the case of Estonia in
   the EU
SO EUROPEAN JOURNAL OF INNOVATION MANAGEMENT
LA English
DT Article
DE Estonia; Innovation performance; Principal component analysis; Multiple
   regression modelling; R&D activities; R&D policy
ID DEVELOPMENT SUBSIDIES; PUBLIC SUBSIDIES; FEDERAL-SUPPORT; INNOVATION;
   GROWTH; ADDITIONALITY; PRODUCTIVITY; COMPLEMENT; INVESTMENT; STIMULATE
AB Purpose - The purpose of this paper is to evaluate the international position of Estonia among the member states of the EU and countries closely associated with the EU, from the perspective of the effect of research and development (R&D) policy on innovation activities in the business sector.
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   Practical implications - The assessment and comparative analysis of the influence of R&D policy components on business sector R&D activities and innovation performance in different countries makes it possible to identify directions for increasing the efficiency of R&D policy under the specific influence of the socioeconomic environment, especially in new member states of the EU.
   Originality/value - Using the PCA method significantly increased the robustness of the macroquantitative description of R&D policy dimensions. By combining the set of new synthetic R&D policy indicators created by the PCA with the multiple regression analysis method, a significant increase in the robustness of model coefficients (i.e. the assessments of influence intensity) was achieved. These robust models create the basis for reliable empirical assessment of the influence of R&D policy and a comparative analysis of the results.
C1 [Reiljan, Janno; Paltser, Ingra] Univ Tartu, Fac Econ & Business Adm, Tartu, Estonia.
C3 University of Tartu
RP Paltser, I (corresponding author), Univ Tartu, Fac Econ & Business Adm, Tartu, Estonia.
EM ingra.paltser@ut.ee
FU Ministry of Education and Research Foundation [SF0180037s08]
FX This paper is written with the support of the Ministry of Education and
   Research Foundation Project No. SF0180037s08 "The path dependent model
   of the innovation system: development and implementation in the case of
   a small country".
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NR 52
TC 0
Z9 0
U1 0
U2 13
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 1460-1060
EI 1758-7115
J9 EUR J INNOV MANAG
JI Eur. J. Innov. Manag.
PY 2015
VL 18
IS 3
BP 307
EP 329
DI 10.1108/EJIM-10-2013-0115
PG 23
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA V79IN
UT WOS:000212108800002
DA 2024-09-05
ER
PT J
AU Yin, C
   Xue, T
   Huang, XG
   Cheng, YH
   Dadras, S
   Dadras, O
AF Yin, Chun
   Xue, Ting
   Huang, Xuegang
   Cheng, Yu-Hua
   Dadras, Sara
   Dadras, Odden
TI Research on Damages Evaluation Method With Multi-Objective Feature
   Extraction Optimization Scheme for M/OD Impact Risk Assessment
SO IEEE ACCESS
LA English
DT Article
DE M/OD hypervelocity impact; impact risk assessment; damage evaluation;
   multi-objective optimization; evolution analysis
ID HYPERVELOCITY IMPACT; SPACE; THERMOGRAPHY; ALGORITHM
AB As the number of space debris (also called meteoroid/orbital debris-M/OD) increases in recent years, the hypervelocity-impact (HVI) events of M/OD on spacecrafts have become one of the most main risks threatening human activity in space. For the automatical M/OD risk assessment, some effective nondestructive testing (NDT) methods are critical to realizing the evaluation of the HVI damages. In this paper, a novel HVI damage evaluation method based on the active infrared thermal wave image detection technology with multi-objective feature extraction optimization (MO-FEO) is proposed to achieve the quantitative evaluation of M/OD HVI damages. For the precise selection of representative temperature point in thermal infrared image data, the proposed MO-FEO method has the advantage not only of considering the difference among temperature points in different thermal temperature categories but also considering the correlation among temperature points of each thermal temperature category. The multi-objective feature extraction problem decomposed by Tchebycheff aggregation is used to seek the representative temperature points through an evolution process brought the selection pressure and fitness value. In addition to the MO-FEO frame, the variable step search and classification of temperature points are also implemented in the HVI damage evaluation strategy to improve efficiency. Some experimental results of infrared detection for the real M/OD HVI test articles are proposed to illustrate the effectiveness of the proposed method.
C1 [Yin, Chun; Xue, Ting; Cheng, Yu-Hua] Univ Elect Sci & Technol China, Sch Automat Engn, Chengdu 611731, Sichuan, Peoples R China.
   [Huang, Xuegang] China Aerodynam Res & Dev Ctr, Hyperveloc Aerodynam Inst, Mianyang 621000, Sichuan, Peoples R China.
   [Dadras, Sara; Dadras, Odden] Utah State Univ, Elect & Comp Engn Dept, Logan, UT 84321 USA.
C3 University of Electronic Science & Technology of China; Utah System of
   Higher Education; Utah State University
RP Huang, XG (corresponding author), China Aerodynam Res & Dev Ctr, Hyperveloc Aerodynam Inst, Mianyang 621000, Sichuan, Peoples R China.
EM emei-126@126.com
RI Chen, YangQuan/A-2301-2008; Huang, Xuegang/O-2942-2019
OI Chen, YangQuan/0000-0002-7422-5988; Huang, Xuegang/0000-0002-9168-3040
FU National Basic Research Program of China [61873305, 61671109, U1830207];
   Sichuan Science and Technology Plan Project [2018JY0410, 2019YJ0199]
FX This work was supported in part by the National Basic Research Program
   of China under Grant 61873305, Grant 61671109, and Grant U1830207, and
   in part by the Sichuan Science and Technology Plan Project under Grant
   2018JY0410 and Grant 2019YJ0199.
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NR 31
TC 26
Z9 28
U1 1
U2 23
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2019
VL 7
BP 98530
EP 98545
DI 10.1109/ACCESS.2019.2930114
PG 16
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA IP8WF
UT WOS:000480326700029
OA gold
DA 2024-09-05
ER
PT J
AU Yu, ZG
   Li, M
AF Yu, Zhonggen
   Li, Ming
TI A bibliometric analysis of Community of Inquiry in online learning
   contexts over twenty-five years
SO EDUCATION AND INFORMATION TECHNOLOGIES
LA English
DT Article
DE Bibliometric analysis; Community of Inquiry; VOSviewer; CitNetExplorer;
   Online learning
ID SELF-EFFICACY; COGNITIVE PRESENCE; TEACHING PRESENCE; FRAMEWORK;
   CITNETEXPLORER; METACOGNITION; INSIGHTS; OUTCOMES
AB Since the outbreak of COVID-19, online learning has gained popularity among educators and learners, where Community of Inquiry (CoI) has caught researchers' attention. To bibliometrically analyze the framework of CoI over twenty-five years, we adopted both qualitative and quantitative research methods to examine the framework of CoI in online learning contexts. We concluded that teaching presence, social presence, cognitive presence, metacognition, and self-efficacy played important roles in the framework of CoI. This study also explored the top ten authors, sources, organizations, and countries using VOSviewer and established citation networks through the clustering techniques in CitNetExplorer. Future research could focus on how to motivate the educational institutes and educators to change their traditional educational methods and whether to include both metacognition and self-efficacy in the CoI framework.
C1 [Yu, Zhonggen; Li, Ming] Beijing Language & Culture Univ, Fac Foreign Studies, 15 Xueyuan Rd, Beijing 100083, Peoples R China.
C3 Beijing Language & Culture University
RP Yu, ZG; Li, M (corresponding author), Beijing Language & Culture Univ, Fac Foreign Studies, 15 Xueyuan Rd, Beijing 100083, Peoples R China.
EM 401373742@qq.com; 23743995@qq.com
RI Yu, Zhonggen/AAJ-3063-2020; Yu, Zhonggen/AAE-5514-2020
OI Yu, Zhonggen/0000-0002-3873-980X; Yu, Zhonggen/0000-0002-3873-980X
FU 2019 MOOC of Beijing Language and Culture University (Important)
   "Introduction to Linguistics" [MOOC201902]; "Introduction to
   Linguistics" of online and offline mixed courses in Beijing Language and
   Culture University in 2020; Special fund of Beijing Coconstruction
   Project-Research and reform of the "Undergraduate Teaching Reform and
   Innovation Project" of Beijing higher education in 2020-innovative
   "multilingual +" excellent talent training system [202010032003];
   research project of Graduate Students of Beijing Language and Culture
   University "Xi Jinping: The Governance of China" [SJTS202108]
FX This work is supported by 2019 MOOC of Beijing Language and Culture
   University (MOOC201902) (Important) "Introduction to Linguistics";
   "Introduction to Linguistics" of online and offline mixed courses in
   Beijing Language and Culture University in 2020; Special fund of Beijing
   Coconstruction Project-Research and reform of the "Undergraduate
   Teaching Reform and Innovation Project" of Beijing higher education in
   2020-innovative "multilingual +" excellent talent training system
   (202010032003); The research project of Graduate Students of Beijing
   Language and Culture University "Xi Jinping: The Governance of China"
   (SJTS202108).
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NR 75
TC 21
Z9 22
U1 15
U2 120
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1360-2357
EI 1573-7608
J9 EDUC INF TECHNOL
JI Educ. Inf. Technol.
PD SEP
PY 2022
VL 27
IS 8
BP 11669
EP 11688
DI 10.1007/s10639-022-11081-w
EA MAY 2022
PG 20
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA 5N0UV
UT WOS:000797755100004
PM 35610978
OA Green Published, Bronze
DA 2024-09-05
ER
PT J
AU Cuxac, P
   Lamirel, JC
   Bonvallot, V
AF Cuxac, Pascal
   Lamirel, Jean-Charles
   Bonvallot, Valerie
TI Efficient supervised and semi-supervised approaches for affiliations
   disambiguation
SO SCIENTOMETRICS
LA English
DT Article
DE Affiliation; Disambiguation; Data cleaning; Classification; Clustering;
   Semi-supervised; Bibliographic databases; K-means; Naive bayes
ID UNIVERSITIES; INFORMATION; ADDRESSES; RANKING
AB The disambiguation of named entities is a challenge in many fields such as scientometrics, social networks, record linkage, citation analysis, semantic web...etc. The names ambiguities can arise from misspelling, typographical or OCR mistakes, abbreviations, omissions... Therefore, the search of names of persons or of organizations is difficult as soon as a single name might appear in many different forms. This paper proposes two approaches to disambiguate on the affiliations of authors of scientific papers in bibliographic databases: the first way considers that a training dataset is available, and uses a Naive Bayes model. The second way assumes that there is no learning resource, and uses a semi-supervised approach, mixing soft-clustering and Bayesian learning. The results are encouraging and the approach is already partially applied in a scientific survey department. However, our experiments also highlight that our approach has some limitations: it cannot process efficiently highly unbalanced data. Alternatives solutions are possible for future developments, particularly with the use of a recent clustering algorithm relying on feature maximization.
C1 [Cuxac, Pascal; Bonvallot, Valerie] INIST CNRS, Vandoeuvre Les Nancy, France.
   [Lamirel, Jean-Charles] LORIA Synalp, Vandoeuvre Les Nancy, France.
C3 Centre National de la Recherche Scientifique (CNRS); Universite de
   Lorraine
RP Cuxac, P (corresponding author), INIST CNRS, Vandoeuvre Les Nancy, France.
EM pascal.cuxac@inist.fr; jean-charles.lamirel@loria.fr;
   valerie.bonvallot@inist.fr
RI cuxac, pascal/AAE-3002-2019
OI Cuxac, Pascal/0000-0002-6809-5654
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NR 31
TC 18
Z9 22
U1 1
U2 63
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD OCT
PY 2013
VL 97
IS 1
BP 47
EP 58
DI 10.1007/s11192-013-1025-5
PG 12
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 216IL
UT WOS:000324275600005
DA 2024-09-05
ER
PT J
AU Ching, FTS
   Cheah, TC
AF Ching, Felicia Tay Sue
   Cheah, Tan Chye
TI SENTIMENT ANALYSIS OF 10-K REPORTS USING TERM FREQUENCY COMPARATIVE
   APPROACH: A PRELIMINARY STUDY
SO INTERNATIONAL JOURNAL OF EARLY CHILDHOOD SPECIAL EDUCATION
LA English
DT Article
DE Textual content Evaluation; Phrase Rate of recurrence; 10K; Emotion
   Evaluation; Economic Statement Research
ID INFORMATION
AB Monetary 10K reviews consist of helpful info with regard to visitors to aid choice within expense. Because of the wealthy content material associated with 10K information, this is a really tiresome job to undergo the 10K are accountable to discover required details with regard to evaluation plus investment decision. The objective of this particular research would be to create a way regarding examining the particular emotions inside 10-K reviews, via assessment associated with phrase rate of recurrence (TF). Within Phase one execution, the primary function includes carrying out the test utilizing the current strategy associated with evaluating a listing of emotion conditions contrary to the conditions included inside the 10-K statement, also called typically the "Bag associated with Words" technique. The 2nd phase regarding execution is to create phrase eq from the group of 2 10-K reviews, to ensure that TF evaluation can be carried out upon both of these studies. Period one implies that there can be misunderstandings in case belief ratings were to become examined in encounter worth. It is because the particular ratings failed to reveal a definite partnership around the overall performance from the companies analysed. Level two pointed out that this comparison strategy allows typically the decrit from the are accountable to realize 12 months about 12 months modifications much better, since the TF variations in many cases are a sign involving modifications inside business instructions or even functional choices in the past year. This particular research plays a role in present books in the supporting technique associated with examining the particular 10-K record, that make utilization of relative expression eq and offers a good setup arrange for experts in addition to traders via procedure for drill down into the particular 10-K textual content.
C1 [Ching, Felicia Tay Sue; Cheah, Tan Chye] Asia Pacific Univ Technol & Innovat, Kuala Lumpur, Malaysia.
C3 Asia Pacific University of Technology & Innovation
RP Ching, FTS (corresponding author), Asia Pacific Univ Technol & Innovat, Kuala Lumpur, Malaysia.
EM tanchyecheah@yandex.com; feliciataysueching@yandex.com
FU APU Teachers Study Give plan [APURDG/01/20 19]
FX This particular function has been backed from the APU Teachers Study
   Give plan, task quantity: APURDG/01/20 19.
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NR 15
TC 0
Z9 0
U1 1
U2 1
PU ANADOLU UNIV
PI ESKISEHIR
PA INST FINE ARTS, ESKISEHIR, 26470, TURKEY
SN 1308-5581
J9 INT J EARLY CHILD SP
JI Int. J. Early Child. Spec. Educ.
PY 2022
VL 14
IS 3
BP 6425
EP 6434
DI 10.9756/INT-JECSE/V14I3.808
PG 10
WC Education, Special
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA 1U5KB
UT WOS:000805450400032
DA 2024-09-05
ER
PT J
AU Gu, TT
   Qian, XM
   Lou, PH
AF Gu, Tingting
   Qian, Xiaoming
   Lou, Peihuang
TI Research on Roundness Error Evaluation of Connecting Rod Journal in
   Crankshaft Journal Synchronous Measurement
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE synchronous measurement; connecting rod journal; non-equal interval
   sampling; particle swarm optimization; roundness error evaluation
AB Featured Application To solve the problem that the measurement data of connecting rod journal is not consistent with the measurement angle in the synchronous measurement of the crankshaft journal, improve the measurement accuracy. The crankshaft is the core part of an automobile engine, and the accuracy requirements of various shape and position errors are very high. On the basis of a synchronous measurement system, the connecting rod journal is deeply studied, including data processing and roundness evaluation. Firstly, according to the measuring processes of connecting rod journals, the real sampling angle distribution function was established, and the corresponding Gaussian weight function of each sampling angle was calculated. The weight function and the collected data corresponding to the angle were subjected to discrete cyclic convolution operation in the spatial domain to obtain the filtered effective circular contour data. Secondly, the particle swarm optimization algorithm was improved, and its inertia weight was set to decrease nonlinearly to speed up the convergence. A calculation process suitable for the evaluation of journal errors was designed. Then, the improved particle swarm optimization algorithm was used to evaluate the roundness of the corrected rod journal contour data. At last, through multiple measurement experiments, the feasibility and effectiveness of the synchronous measurement scheme and data processing method proposed in this paper are verified.
C1 [Gu, Tingting; Qian, Xiaoming; Lou, Peihuang] Nanjing Univ Aeronaut & Astronaut, Coll Mech & Elect Engn, 29 Yudao St, Nanjing 210016, Peoples R China.
   [Gu, Tingting] Nanjing Univ Aeronut & Astronaut, Jincheng Coll, 88 Hangjin Ave, Nanjing 211156, Peoples R China.
C3 Nanjing University of Aeronautics & Astronautics
RP Qian, XM; Lou, PH (corresponding author), Nanjing Univ Aeronaut & Astronaut, Coll Mech & Elect Engn, 29 Yudao St, Nanjing 210016, Peoples R China.
EM gutingting@nuaa.edu.cn; k.bamdad@westernsydney.edu.au;
   meephlou@nuaa.edu.cn
OI Gu, Tingting/0000-0003-1484-4178
FU Key R&D Project of Jiangsu Province [BE20160034]
FX FundingThis research was funded by the Key R&D Project of Jiangsu
   Province, grant number BE20160034.
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NR 27
TC 2
Z9 2
U1 6
U2 29
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2076-3417
J9 APPL SCI-BASEL
JI Appl. Sci.-Basel
PD FEB
PY 2022
VL 12
IS 4
AR 2214
DI 10.3390/app12042214
PG 15
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA ZR4LU
UT WOS:000767757400001
OA gold
DA 2024-09-05
ER
PT J
AU Avanesova, AA
   Shamliyan, TA
AF Avanesova, Anna A.
   Shamliyan, Tatyana A.
TI Comparative trends in research performance of the Russian universities
SO SCIENTOMETRICS
LA English
DT Article
DE Academic institutions; Productivity; Internationalization; Russian
   Federation; Artificial intelligence
ID PUBLICATION ACTIVITY; SCIENCE; INSTITUTIONS; INNOVATION; CAPACITY;
   STATE; CHINA
AB We analyzed comparative trends in research performance of the Russian institutions based on the quantitative and qualitative data available in the international SciVal, Scopus, web of science and the national Russian databases from 2012 to December 2017. Russian Federation represented 2.0% of the world population and 2% of the researchers while accounting to 2.2% of the world publications (14th place in the world scholarly output) with overall field-weighted citation impact of 0.75. Scholarly output of the Russian authors increased by 79%, field-weighted citation impact by 12% and outputs in top citation percentiles by 21% but without a statistically significant positive association between higher investment in research and development and the increase the national GDP. Scholarly output for the Russian publications in mathematics, physics and astronomy are among 5 top countries. However, field-weighted mass media impact, the number of citations per publication, citations per author and per publication, metrics of international collaboration and the academic-corporation collaboration and economic impact of the Russian research remain low. Routine analysis of the research performance and economic impact of R&D expenditure should be reflected in transparent distribution of state research funding. Legal aspects of the international research must be developed to ensure a complete integration of the Russian science into international research activities.
C1 [Avanesova, Anna A.] North Caucasian Fed Univ, Pushkina St 1, Stavropol 355009, Russia.
   [Shamliyan, Tatyana A.] Elsevier, Evidence Based Med Ctr, Qual Assurance, 1600 JFK Blvd, Philadelphia, PA 19103 USA.
C3 North Caucasus Federal University; Reed Elsevier; Elsevier
RP Shamliyan, TA (corresponding author), Elsevier, Evidence Based Med Ctr, Qual Assurance, 1600 JFK Blvd, Philadelphia, PA 19103 USA.
EM annavanesova@yandex.ru; t.shamliyan@elsevier.com
RI Shamliyan, Tatyana/AAU-8323-2020
OI Avanesova, Anna/0000-0003-2472-9616
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NR 41
TC 17
Z9 19
U1 3
U2 50
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD SEP
PY 2018
VL 116
IS 3
BP 2019
EP 2052
DI 10.1007/s11192-018-2807-6
PG 34
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA GQ8LT
UT WOS:000442007200028
DA 2024-09-05
ER
PT J
AU Weinberger, M
   Zhitomirsky-Geffet, M
AF Weinberger, Maor
   Zhitomirsky-Geffet, Maayan
TI Modeling a successful citation trajectory structure for scholar's impact
   evaluation in Israeli academia
SO HELIYON
LA English
DT Article
DE Scholarly impact; Universal impact measures; Citation trajectory
   structure; Logistic regression classifier; Web of Science; Google
   Scholar
ID RESEARCH PRODUCTIVITY; GENDER-DIFFERENCES; CAREER; FACULTY; SCIENCE;
   INDEX; UNIVERSALITY; PATTERNS; CHOICE
AB One of the main concerns of researchers and institutions is how to assess the future performance of scholars and identify their potential to become successful scientists. In this study, we model scholarly success in terms of the probability of a scholar belonging to a group of highly impactful scholars as determined by their citation trajectory structures. To this end, we developed a new set of impact measures based on a scholar's citation trajectory structure (rather than on absolute citation or h-index rates), that show a stable trend and scale for highly impactful scholars, independent of their field of study, seniority and citation index. These measures were then incorporated as influence factors into the logistic regression models and used as features for probabilistic classifiers based on these models to identify the successful scholars in the heterogeneous corpus of 400 of most and least cited professors from two Israeli universities. From the practical point of view, the study may yield useful insights and serve as an aid in making promotion decisions by institutions, as well as a self-assessment tool for researchers who strive to increase their academic influence and become leaders in their field.
C1 [Weinberger, Maor; Zhitomirsky-Geffet, Maayan] Bar Ilan Univ, Ramat Gan, Israel.
C3 Bar Ilan University
RP Weinberger, M (corresponding author), Bar Ilan Univ, Ramat Gan, Israel.
EM maor89@gmail.com
RI Weinberger, Maor/JGL-8627-2023
OI Weinberger, Maor/0000-0002-4943-1763
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NR 61
TC 1
Z9 1
U1 3
U2 6
PU CELL PRESS
PI CAMBRIDGE
PA 50 HAMPSHIRE ST, FLOOR 5, CAMBRIDGE, MA 02139 USA
EI 2405-8440
J9 HELIYON
JI Heliyon
PD MAY
PY 2023
VL 9
IS 5
AR e15673
DI 10.1016/j.heliyon.2023.e15673
EA APR 2023
PG 16
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA O2NV4
UT WOS:001042247800001
PM 37159699
OA Green Published, gold
DA 2024-09-05
ER
PT J
AU Guo, LF
   Cui, YW
   Wu, YM
   Ma, JQ
AF Guo Lifang
   Cui Yuwen
   Wu Yamin
   Ma Jiaqi
TI Research on the influence of relation embeddedness on innovation
   performance of manufacturing supply chain alliances using expert fuzzy
   rule -intermediary role of shared mental model
SO JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
LA English
DT Article
DE Artificial Intelligence (AI); relation embeddedness; shared mental
   model; innovation performance; structure equations; fuzzy rule
AB The innovation and development of manufacturing supply chain alliance is an important way for enterprises to meet the increasing market demand and maintain the competitive advantage. From the perspective of embeddedness, the research model of relation embeddedness on innovation performance of manufacturing supply chain was constructed based on AMOS. Shared mental model was selected as intermediary variable to study the influence of relation embeddedness, shared mental model and innovation performance of manufacturing supply chain alliances. Expert fuzzy rule based system is utilized for measuring the performance of manufacturing supply chain alliances. The conclusion shows that relation embeddedness is significantly positive shared mental model and innovation performance. Shared mental model is positively affects alliance innovation performance and plays a part of intermediary role between relational embedding and alliance innovation performance. Practice implicates that enhance the level of relation embeddedness can promote the formation of shared mental model and improve the innovation performance of manufacturing supply chain alliance.
C1 [Guo Lifang; Cui Yuwen; Wu Yamin; Ma Jiaqi] Taiyuan Univ Technol, Coll Econ & Management, Jinzhong 030600, Shanxi, Peoples R China.
C3 Taiyuan University of Technology
RP Guo, LF (corresponding author), Taiyuan Univ Technol, Coll Econ & Management, Jinzhong 030600, Shanxi, Peoples R China.
EM glfpapers@163.com
RI yang, yue/KCK-7870-2024
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NR 22
TC 0
Z9 0
U1 4
U2 27
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1064-1246
EI 1875-8967
J9 J INTELL FUZZY SYST
JI J. Intell. Fuzzy Syst.
PY 2021
VL 40
IS 4
BP 8287
EP 8294
DI 10.3233/JIFS-189651
PG 8
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA RN7PP
UT WOS:000640545600050
DA 2024-09-05
ER
PT J
AU Stelson, EA
   Bolenbaugh, M
   Woods-Jaeger, B
   Branch, C
   Ramirez, M
AF Stelson, Elisabeth A.
   Bolenbaugh, Mallory
   Woods-Jaeger, Briana
   Branch, Cassidy
   Ramirez, Marizen
TI Identifying research Participation effects through qualitative methods:
   Feedback from Research Engagement Consultants involved in a pediatric
   mental health comparative effectiveness trial
SO SSM-QUALITATIVE RESEARCH IN HEALTH
LA English
DT Article
DE Research participation effects; Causal inference; Qualitative; Hawthorne
   effect; Clinical trials; Community engagement
AB Research participation effects (RPEs)-effects due to study design rather than study intervention-have long been acknowledged as a research phenomenon. Identifying RPEs is crucial to understanding the true effects of an intervention and outcomes may change during dissemination. Few researchers systematically identify the potential role of RPEs on their research findings, despite recent calls to capture these effects. This study demonstrates the utility of using qualitative methodologies to detect RPEs in a clinical trial. Research Engagement Consultants (RECs) were parents and children (N1/419) who participated in a post-injury mental health trial in three Midwestern states. RECs were hired as part of the research team upon completion of their study participation. RECs participated in two semi-structured interviews detailing their experience in the study and their recovery process. Inter-coder reliability calculations were used iteratively to ensure coding consistency. Thematic analysis indicates that in addition to benefiting from the interventions being tested, RECs additionally reported benefitting from the study design in two primary ways: 1) contact with the research team provided social support; and 2) completion of specific data collection activities facilitated parent-child communication. Analysis of these interviews illustrates how embedding a qualitative evaluation process into clinical trials can help identify unintended RPEs and understand the potential effects of participating in the research process. Additionally, analysis of the qualitative data provides a quality improvement feedback loop to contextualize the results of the study; how the clinical trial environment may influence dissemination effectiveness; and how future research processes could potentially be strengthened. Trial registration number: NCT02323204.Trial registry: clinicaltrials.gov (US National Institutes of Health)
C1 [Stelson, Elisabeth A.] Harvard Univ, TH Chan Sch Publ Hlth, Dept Social & Behav Sci, 677 Huntington Ave, Boston, MA 02115 USA.
   [Bolenbaugh, Mallory] Univ Iowa, Coll Educ, Dept Psychol & Quantitat Fdn, Iowa City, IA USA.
   [Woods-Jaeger, Briana] Emory Univ, Rollins Sch Publ Hlth, Dept Behav Social & Hlth Educ Sci, Atlanta, GA USA.
   [Stelson, Elisabeth A.; Bolenbaugh, Mallory; Branch, Cassidy; Ramirez, Marizen] Univ Iowa, Coll Publ Hlth, Dept Occupat & Environm Hlth, Iowa City, IA USA.
   [Branch, Cassidy] Univ Iowa, Publ Policy Ctr, Iowa City, IA USA.
   [Ramirez, Marizen] Univ Minnesota, Sch Publ Hlth, Div Environm Hlth Sci, Minneapolis, MN USA.
C3 Harvard University; Harvard T.H. Chan School of Public Health;
   University of Iowa; Emory University; Rollins School Public Health;
   University of Iowa; University of Iowa; University of Minnesota System;
   University of Minnesota Twin Cities
RP Stelson, EA (corresponding author), Harvard Univ, TH Chan Sch Publ Hlth, Dept Social & Behav Sci, 677 Huntington Ave, Boston, MA 02115 USA.
EM estelson@g.harvard.edu
OI Stelson, Elisabeth/0000-0001-5176-2652
FU Patient-Centered Outcomes Research Institute (PCORI) Award
   [CER-1306-02918]; National Cancer Institute of the National Institutes
   of Health [R25CA057711, 2T32CA057711-26]; Harvard Univer-sity
FX This study was funded through a Patient-Centered Outcomes Research
   Institute (PCORI) Award (CER-1306-02918) to Principal Investigator
   Marizen Ramirez. The primary author was partially funded by the National
   Cancer Institute of the National Institutes of Health (R25CA057711 and
   2T32CA057711-26) , awarded to Harvard University. The funding
   organizations had no role in the design and conduct of the study;
   collection, management, analysis, and interpretation of the data;
   preparation, review, or approval of the manuscript; or decision to
   submit the manuscript for publication.
NR 0
TC 0
Z9 0
U1 0
U2 0
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2667-3215
J9 SSM-QUAL RES HEALTH
JI SSM-Qual. Res. Health
PD DEC
PY 2021
VL 1
AR 100023
DI 10.1016/j.ssmqr.2021.100023
PG 8
WC Public, Environmental & Occupational Health; Social Sciences, Biomedical
WE Emerging Sources Citation Index (ESCI)
SC Public, Environmental & Occupational Health; Biomedical Social Sciences
GA ES8T3
UT WOS:001141014500026
OA gold
DA 2024-09-05
ER
PT J
AU Zhang, RS
AF Zhang, Ruoshi
TI Research and evaluation on students' emotional attachment to campus
   landscape renewal coupling emotional attachment scale and public
   sentiment analysis: a case study of the "Heart of Forest" in Beijing
   Forestry University
SO FRONTIERS IN PSYCHOLOGY
LA English
DT Article
DE emotional attachment; campus landscape renewal; emotional attachment
   scale; public sentiment analysis; Heart of Forest
ID BENEFITS; GREENSPACE; DESIGN
AB In the era of stock renewal, the construction of university campuses in China's first-tier cities has shifted from demolition and construction to renewal and upgrading, in which public landscape space is the main environment for students' daily life, learning and entertainment. Especially during the outbreak of the recent COVID-19 epidemic, it has become an important way for students to interact with nature and obtain emotional healing. In the existing studies, there is a lack of discussion on the correlation between the spatial characteristics of the updated campus landscape and students' emotional attachment, and there are few quantitative studies. Based on this, this paper takes the "Heart of Forest" landscape space as an example, and integrates multi-dimensional quantitative methods including emotional attachment scale and public semantic analysis to study and evaluate the characteristics of landscape space that affect students' emotional attachment. The results show that: (1) Overall, the landscape space renewal of the Heart of Forest provides students with positive emotional experiences and effectively enhances students' emotional attachment as well as sense of belonging to the campus. (2) Among them, the material characteristics of the site including nature-related elements, materials, structures play a positive role in promoting the vast majority of students in the process of establishing emotional attachment, which is particularly obvious for students majoring in landscape, architecture and urban planning. (3) Whether the public social space can effectively provide students with a good emotional experience is closely related to the frequency and purpose of students' use of the space. (4) The interactive characteristics such as changeability and playability fail to promote emotional attachment because of lacking of management and maintenance. The renewal and transformation of the "Heart of Forest" landscape space is generally successful in promoting students' emotional attachment, and provides a reference for the future campus landscape renewal design from different angles. In addition, the quantitative study of emotional attachment constructed in this paper coupled with multi-dimensional data provides a method for the evaluation of students' emotional experience of campus landscape.
C1 [Zhang, Ruoshi] Beijing Forestry Univ, Sch Landscape Architecture, Beijing, Peoples R China.
C3 Beijing Forestry University
RP Zhang, RS (corresponding author), Beijing Forestry Univ, Sch Landscape Architecture, Beijing, Peoples R China.
EM zhang_rs@bjfu.edu.cn
OI Zhang, Ruoshi/0000-0001-8610-9151
FU This work was supported by the National Natural Science Foundation of
   China under Grant No. 52208005 and Beijing Social Science Foundation
   under Grant No. 22GLC063. [52208005]; National Natural Science
   Foundation of China [22GLC063]; Beijing Social Science Foundation
FX Thanks for Shujie Zhao's help with the scale measurement. Thanks for
   Yang Song, Weiyue Duan, Zhikai Zheng, Yuling Liu for their help of
   distributing and collecting scales. Thanks for Weiyue Duan and Zhikai
   Zhao for their photography of the Heart of Forest.r This work was
   supported by the National Natural Science Foundation of China under
   Grant No. 52208005 and Beijing Social Science Foundation under Grant No.
   22GLC063.
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NR 59
TC 2
Z9 2
U1 24
U2 40
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 1664-1078
J9 FRONT PSYCHOL
JI Front. Psychol.
PD SEP 25
PY 2023
VL 14
AR 1250441
DI 10.3389/fpsyg.2023.1250441
PG 20
WC Psychology, Multidisciplinary
WE Social Science Citation Index (SSCI)
SC Psychology
GA T4ZZ7
UT WOS:001078098800001
PM 37823071
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU Men, KP
   Cui, L
AF Men, Kepei
   Cui, Lei
TI Research on Evaluation Models and Empirical Analysis of Earthquake
   Disaster Losses in China
SO ZEITSCHRIFT FUR NATURFORSCHUNG SECTION A-A JOURNAL OF PHYSICAL SCIENCES
LA English
DT Article
DE Earthquake Disaster; Economic Loss Rating; Grey Relation-Cluster
   Analysis; Principal Component Analysis; Evaluation Models
ID GREAT EARTHQUAKES; NETWORK STRUCTURE; PREDICTION
AB Earthquake disasters occurred very frequently in China. As a result, to evaluate the losses has important social value and economic effect. This paper focuses on the assessment of economic losses of earthquake disasters which is divided into two parts: direct economic loss and indirect economic loss. First, the Kolmogorov-Smirnov (KS) test is used to determine the distribution of the earthquake losses per year in China, fitting the frequency of earthquake that happened per month in China. Second, the grey clustering method and principal component analysis (PCA) are applied, respectively, for direct economic loss rating and indirect economic loss rating. Finally, the economic loss generated by the earthquakes which happened from 2006 to 2009 in China is evaluated, and eight earthquakes are rated based on the comprehensive economic loss.
C1 [Men, Kepei; Cui, Lei] Nanjing Univ Informat Sci & Technol, Coll Math & Stat, Nanjing 210044, Jiangsu, Peoples R China.
C3 Nanjing University of Information Science & Technology
RP Men, KP (corresponding author), Nanjing Univ Informat Sci & Technol, Coll Math & Stat, Nanjing 210044, Jiangsu, Peoples R China.
EM menkepei@gmail.com
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NR 27
TC 1
Z9 1
U1 0
U2 21
PU WALTER DE GRUYTER GMBH
PI BERLIN
PA GENTHINER STRASSE 13, D-10785 BERLIN, GERMANY
SN 0932-0784
EI 1865-7109
J9 Z NATURFORSCH A
JI Z. Naturfors. Sect. A-J. Phys. Sci.
PD OCT-NOV
PY 2012
VL 67
IS 10-11
BP 534
EP 544
DI 10.5560/ZNA.2012-0059
PG 11
WC Chemistry, Physical; Physics, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Physics
GA 063BQ
UT WOS:000312971200002
OA hybrid
DA 2024-09-05
ER
PT B
AU Thomas, J
AF Thomas, Julia
BA Thomas, J
BF Thomas, J
TI Searchability
SO NINETEENTH-CENTURY ILLUSTRATION AND THE DIGITAL: STUDIES IN WORD AND
   IMAGE
SE Digital Nineteenth Century
LA English
DT Article; Book Chapter
DE Nineteenth century; Illustration; Digital; Computer vision; Keywording;
   Bibliographic metadata
AB This chapter focuses on methods for making the content of illustrations searchable online, including computer vision, which offers the possibility of automated image retrieval, the use of textual metadata (bibliographic information, captions, and the words that accompany the illustration), and keywording. Thomas contends that these methods are not detached from the material that they promise to make searchable, but are deeply implicated in the relation between word and image that characterises illustration. The chapter analyses the ways in which the search mechanisms in digital illustration resources engage with this problematic relation between the textal and the visual, from issues surrounding the instability of the vocabulary used to describe the presence of illustrations in books, to questions of whether an illustration 'reflects' the text it accompanies.
C1 [Thomas, Julia] Cardiff Univ, Cardiff, S Glam, Wales.
C3 Cardiff University
RP Thomas, J (corresponding author), Cardiff Univ, Cardiff, S Glam, Wales.
NR 0
TC 0
Z9 0
U1 0
U2 0
PU PALGRAVE
PI BASINGSTOKE
PA HOUNDMILLS, BASINGSTOKE RG21 6XS, ENGLAND
BN 978-3-319-58148-4; 978-3-319-58147-7
J9 DIG NINETEENTH CENT
PY 2017
BP 33
EP 64
DI 10.1007/978-3-319-58148-4_3
D2 10.1007/978-3-319-58148-4
PG 32
WC Humanities, Multidisciplinary; Information Science & Library Science
WE Book Citation Index – Social Sciences & Humanities (BKCI-SSH)
SC Arts & Humanities - Other Topics; Information Science & Library Science
GA BK0FR
UT WOS:000430506600003
DA 2024-09-05
ER
PT J
AU Panigrahi, RR
   Shrivastava, AK
   Qureshi, KM
   Mewada, BG
   Alghamdi, SY
   Almakayeel, N
   Almuflih, AS
   Qureshi, MRN
AF Panigrahi, Rashmi Ranjan
   Shrivastava, Avinash K.
   Qureshi, Karishma M.
   Mewada, Bhavesh G.
   Alghamdi, Saleh Yahya
   Almakayeel, Naif
   Almuflih, Ali Saeed
   Qureshi, Mohamed Rafik N.
TI AI Chatbot Adoption in SMEs for Sustainable Manufacturing Supply Chain
   Performance: A Mediational Research in an Emerging Country
SO SUSTAINABILITY
LA English
DT Article
DE artificial intelligence chatbot; emerging country; innovative
   capability; small and medium enterprises; supply chain visibility;
   sustainable supply chain performances; manufacturing sustainability
ID PLS-SEM; INDUSTRY 4.0; CAPABILITIES
AB AI chatbots (AICs) have the potential to increase the sustainability of a manufacturing supply chain (SC) through sales engagement and customer engagement to accomplish various activities related to logistics and SC in real time. Industry 4.0 (I4.0) has opened up several opportunities with internet-based technologies, along with challenges for small and medium enterprises (SMEs). SMEs are beginning to adopt such technologies for their competitive advantages and the required sustainability in the manufacturing supply chain. AICs may help in accomplishing supply chain visibility (SCV) to enhance sustainable supply chain performance (SSCP). Innovation capability (IC) is also due to disruptive technologies being adopted by SMEs. The present research investigates the role of AICs in SCV and IC, which lead to SSCP, by employing structural equation modeling (SEM). An empirical study based on dynamic capability (DC) theory was carried out using 246 responses, and later Smart PLS-4.0 was used for SEM. The analysis revealed that AICs positively influence SCV and IC to support SSCP. SCV and IC also partially mediate the relationship between the adoption of AICs and SSCP.
C1 [Panigrahi, Rashmi Ranjan] GITAM Deemed Univ, GITAM Sch Business, Visakhapatnam 530045, India.
   [Shrivastava, Avinash K.] Int Management Inst Kolkata, Kolkata 700027, India.
   [Qureshi, Karishma M.; Mewada, Bhavesh G.] Parul Univ, Parul Inst Technol, Dept Mech Engn, Waghodia 391760, India.
   [Alghamdi, Saleh Yahya; Almakayeel, Naif; Almuflih, Ali Saeed; Qureshi, Mohamed Rafik N.] King Khalid Univ, Coll Engn, Dept Ind Engn, Abha, Saudi Arabia.
C3 Gandhi Institute of Technology & Management (GITAM); International
   Management Institute (IMI) Kolkata; Parul University; King Khalid
   University
RP Shrivastava, AK (corresponding author), Int Management Inst Kolkata, Kolkata 700027, India.
EM rashmipanigrahi090@gmail.com; kavinash1987@gmail.com; kariq18@gmail.com;
   bmewada@paruluniversity.ac.in; syalghamdi@kku.edu.sa;
   halmakaeel@kku.edu.sa; asalmuflih@kku.edu.sa; mrnoor@kku.edu.sa
RI Panigrahi, Dr. Rashmi Ranjan/AAM-5236-2020; Almuflih, Ali/KLC-8563-2024;
   Mewada, Bhavesh/LCE-7475-2024; Almakayeel, Naif/ABA-4321-2022; Qureshi,
   Prof.(Dr.) M. N./L-4688-2016
OI Panigrahi, Dr. Rashmi Ranjan/0000-0002-2199-293X; Almuflih,
   Ali/0000-0001-5359-1519; Almakayeel, Naif/0000-0001-9461-5935; Qureshi,
   Prof.(Dr.) M. N./0000-0002-9508-8724; QURESHI, KARISHMA
   M/0000-0002-5843-2514; Almakayeel, Naif/0009-0008-8848-907X;
   Shrivastava, Avinash K/0000-0001-7794-7129; Mewada,
   Bhaveshkumar/0000-0003-0109-1795
FU We would like to express our gratitude to the Deanship of Scientific
   Research, King Khalid University, Kingdom of Saudi Arabia for funding
   this work, as well as to family, friends, and colleagues for their
   constant inspiration and encouragement. The infras; Deanship of
   Scientific Research, King Khalid University, Kingdom of Saudi Arabia;
   Parul University; GITAM School of Business and International Management
   Institute Kolkata
FX We would like to express our gratitude to the Deanship of Scientific
   Research, King Khalid University, Kingdom of Saudi Arabia for funding
   this work, as well as to family, friends, and colleagues for their
   constant inspiration and encouragement. The infrastructure support
   provided by Parul University, Vadodara, and GITAM School of Business and
   International Management Institute Kolkata is greatly appreciated.
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NR 62
TC 12
Z9 12
U1 35
U2 38
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD SEP
PY 2023
VL 15
IS 18
AR 13743
DI 10.3390/su151813743
PG 18
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA FJ3E2
UT WOS:001145345400001
OA gold
DA 2024-09-05
ER
PT J
AU Tontodimamma, A
   Nissi, E
   Sarra, A
   Fontanella, L
AF Tontodimamma, Alice
   Nissi, Eugenia
   Sarra, Annalina
   Fontanella, Lara
TI Thirty years of research into hate speech: topics of interest and their
   evolution
SO SCIENTOMETRICS
LA English
DT Article
DE Online hate speech; Bibliometrics analysis; Topic models; Latent
   Dirichlet allocation
ID SCIENCE
AB The exponential growth of social media has brought with it an increasing propagation of hate speech and hate based propaganda. Hate speech is commonly defined as any communication that disparages a person or a group on the basis of some characteristics such as race, colour, ethnicity, gender, sexual orientation, nationality, religion. Online hate diffusion has now developed into a serious problem and this has led to a number of international initiatives being proposed, aimed at qualifying the problem and developing effective counter-measures. The aim of this paper is to analyse the knowledge structure of hate speech literature and the evolution of related topics. We apply co-word analysis methods to identify different topics treated in the field. The analysed database was downloaded from Scopus, focusing on a number of publications during the last thirty years. Topic and network analyses of literature showed that the main research topics can be divided into three areas: "general debate hate speech versus freedom of expression","hate-speech automatic detection and classification by machine-learning strategies", and "gendered hate speech and cyberbullying". The understanding of how research fronts interact led to stress the relevance of machine learning approaches to correctly assess hatred forms of online speech.
C1 [Tontodimamma, Alice] Univ G dAnnunzio, Dept Neurosci Imaging & Clin Sci, Chieti, Italy.
   [Nissi, Eugenia] Univ G dAnnunzio, Dept Econ, Pescara, Italy.
   [Sarra, Annalina; Fontanella, Lara] Univ G dAnnunzio, Dept Legal & Social Sci, Pescara, Italy.
C3 G d'Annunzio University of Chieti-Pescara; G d'Annunzio University of
   Chieti-Pescara; G d'Annunzio University of Chieti-Pescara
RP Sarra, A (corresponding author), Univ G dAnnunzio, Dept Legal & Social Sci, Pescara, Italy.
EM alice.tontodimamma@unich.it; eugenia.nissi@unich.it; asarra@unich.it;
   lara.fontanella@unich.it
RI Fontanella, Lara/AAF-1334-2020; nissi, eugenia/IQS-0004-2023
OI Fontanella, Lara/0000-0002-5441-0035; nissi,
   eugenia/0000-0003-1943-0476; Sarra, Annalina/0000-0002-0974-0799
FU Universita degli Studi G. D'Annunzio Chieti Pescara within the CRUI-CARE
   Agreement
FX Open access funding provided by Universita degli Studi G. D'Annunzio
   Chieti Pescara within the CRUI-CARE Agreement. We are grateful to the
   reviewers for their useful comments and suggestions which have
   significantly improved the quality of the paper.
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NR 35
TC 46
Z9 50
U1 6
U2 69
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD JAN
PY 2021
VL 126
IS 1
BP 157
EP 179
DI 10.1007/s11192-020-03737-6
EA OCT 2020
PG 23
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA PU7XL
UT WOS:000585801800001
OA hybrid
DA 2024-09-05
ER
PT J
AU Yelenov, A
   Pak, AA
   Ziyaden, AA
   Akhmetov, I
   Gelbukh, A
   Gelbukh, I
AF Yelenov, Amir
   Pak, Alexandr A.
   Ziyaden, Atabay A.
   Akhmetov, Iskander
   Gelbukh, Alexander
   Gelbukh, Irina
TI Comprehensive Survey: Approaches to Emerging Technologies Detection
   within Scientific Publications
SO COMPUTACION Y SISTEMAS
LA English
DT Article
DE Citation prediction; emergent technology; neural networks;
   scientometrics
ID MATHEMATICAL APPROACH; SCIENCE; INDICATORS; TOPICS; GROWTH; TOOL
AB The identification of breakthrough topics and emerging technologies has been of interest to the governments of many countries and the scientific community since the last century. This study presents the status and trend of the research field through a comprehensive review of relevant publications, a new look at the problem of defining the term "emergent technologies, " defining boundaries between similar terms; and a modern baseline method on the citation prediction subtask for the discovery of emergent technologies. The outcomes of this technique have demonstrated the significance of features that characterize the preceding 1-year, 2-year, and 3-year citation counts, as well as their impact on the quality of neural network and random forest models. Our hypothesis, however, that author-specific measures may enhance prediction results was not supported. We ascribe this difficulty to the dimensionality curse. The authors examined methodological elements of research and technological development; consequently, it is important to note that, from a technical viewpoint, theoretical research is far from complete due to the vast variety of projects, outstanding challenges, research questions, and market assumptions. Finding more input characteristics to improve the quality of predictions and switching from classification to regression may also improve the precision of the suggested baseline model.
C1 [Yelenov, Amir; Pak, Alexandr A.; Ziyaden, Atabay A.; Akhmetov, Iskander] Inst Informat & Computat Technol, Alma Ata, Kazakhstan.
   [Yelenov, Amir; Pak, Alexandr A.; Ziyaden, Atabay A.; Akhmetov, Iskander] Kazakh British Tech Univ, Alma Ata, Kazakhstan.
   [Gelbukh, Alexander] Inst Politecn Nacl, Mexico City, Mexico.
C3 Institute of Information & Computational Technologies; Kazakh British
   Technical University; Instituto Politecnico Nacional - Mexico
RP Yelenov, A (corresponding author), Inst Informat & Computat Technol, Alma Ata, Kazakhstan.; Yelenov, A (corresponding author), Kazakh British Tech Univ, Alma Ata, Kazakhstan.
EM greamdesu@gmail.com; aa.pak83@gmail.com; iamdenay@gmail.com;
   iskander.akhmetov@gamil.com; gelbukh@gmail.com; ir.gelbukh@gmail.com
RI Pak, Alexandr/W-4002-2018; Yelenov, Amir/AAW-9817-2020; Yelenov,
   Amir/JEZ-9542-2023
OI Pak, Alexandr/0000-0002-8685-9355; Yelenov, Amir/0000-0003-0674-5460;
   Yelenov, Amir/0000-0003-0674-5460; Ziyaden, Atabay/0000-0002-4878-8971
FU Aerospace Committee of the Ministry of Digital Development, Innovations
   and Aerospace Industry of the Republic of Kazakhstan;  [BR11265420]
FX This research was funded by Aerospace Committee of the Ministry of
   Digital Development, Innovations and Aerospace Industry of the Republic
   of Kazakhstan grant number BR11265420.
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NR 57
TC 1
Z9 1
U1 3
U2 10
PU Inst Politecnico Nacional IPN, Centro Investigation Computacion
PI MEXICO CITY
PA AV JUAN DIOS BATIZ, S N ESQ M OTHON MENDIZABAL, UP ADOLFO LOPEZ MATEOS
   ZACATENCO, MEXICO CITY, 07738, MEXICO
SN 1405-5546
EI 2007-9737
J9 COMPUT SIST
JI Comput. Sist.
PY 2022
VL 26
IS 4
BP 1587
EP 1601
DI 10.13053/CyS-26-4-4424
PG 15
WC Computer Science, Information Systems
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA 7L0UM
UT WOS:000905691500013
DA 2024-09-05
ER
PT J
AU Jiang, CZ
   Xu, H
   Huang, CF
   Chen, YY
   Zou, RQ
   Wang, YX
AF Jiang, Chengzhi
   Xu, Hao
   Huang, Chuanfeng
   Chen, Yiyang
   Zou, Ruoqi
   Wang, Yixiu
TI Research on knowledge dissemination in smart cities environment based on
   intelligent analysis algorithms: a case study on online platform
SO MATHEMATICAL BIOSCIENCES AND ENGINEERING
LA English
DT Article
DE speech; relationship network; sentiment analysis; textual analysis;
   smart cities
AB In developing smart cities, the implementation of social connections, collaboration, innovation, exchange of views by observing, exploiting and integrating various types of knowledge is required. The smart cities concept that employs knowledge sharing mechanism can be defined as the concept of a city that utilizes information technology to increase citizens' awareness, intelligence as well as community's participation. The knowledge dissemination via online sharing platforms has been becoming more popular in recent years, especially during the epidemic of infectious diseases. Thus, the social network and emotional analysis method based on intelligent data analysis algorithms is proposed to study the speaker relationship and comment sentiment tendency of a Chinese popular speech (knowledge dissemination) platform: YiXi. In our research, 690 speakers' information and 23,685 comments' information are collected from YiXi website as the data source. The speaker relationship network construction algorithm and emotional analysis algorithm are designed in details respectively. Experiments show that speakers who have the same profession can deliver different types of speeches, indicating that selection of YiXi platform in the invitation of speakers is diversified. In addition, overall sentiment tendency of comments on speeches seem to be slightly positive and most of them are the personal feelings according to their experience after watching speech videos instead of the direct evaluations of speech quality. The research aims to gain an insight into the popular knowledge sharing phenomenon and is expected to provide reference for knowledge dissemination platforms in order to improve the knowledge sharing environment in smart cities.
C1 [Jiang, Chengzhi; Xu, Hao] Nanjing Univ, Sch Informat Management, Nanjing 210023, Peoples R China.
   [Jiang, Chengzhi; Xu, Hao; Huang, Chuanfeng] Sch Econ & Management, Nanjing Inst Technol, Nanjing 211167, Peoples R China.
   [Chen, Yiyang] Univ St Andrews, Sch Comp Sci, St Andrews KY16 9AJ, Fife, Scotland.
   [Zou, Ruoqi; Wang, Yixiu] Ping An Int Smart City Technol Co Ltd, Shenzhen 518002, Peoples R China.
C3 Nanjing University; Nanjing Institute of Technology; University of St
   Andrews
RP Xu, H (corresponding author), Nanjing Univ, Sch Informat Management, Nanjing 210023, Peoples R China.; Xu, H (corresponding author), Sch Econ & Management, Nanjing Inst Technol, Nanjing 211167, Peoples R China.
EM xhnju2014@163.com
RI Jiang, Cheng/JHU-0179-2023; Zhang, Ge/KGL-7634-2024
FU Nanjing Institute of Technology [YKJ201989]; Open Research Fund of NJIT
   Institute of Industrial Economy and Innovation Management [JGKB202001,
   JGKC 202003]; Innovation Fund General Project I of Nanjing institute of
   technology [CKJB202003]; University Philosophy and Social Science
   Research Project of Jiangsu province [2019SJA2274]; Major Project of
   Philosophy and Social Science Research in Universities of Jiangsu
   Province Education Department [2020SJZDA069]; ZHINIAO-ASKBOB Project of
   Vocational Education Department of Ping An International Smart City
   Technology Co., Ltd
FX The research is supported by the high level introduction of talent
   research startup fund of Nanjing Institute of Technology (Grant NO.:
   YKJ201989) ; Open Research Fund of NJIT Institute of Industrial Economy
   and Innovation Management (Grant NO.: JGKB202001, JGKC 202003) ;
   Innovation Fund General Project I of Nanjing institute of technology
   (Grant NO.: CKJB202003) ; University Philosophy and Social Science
   Research Project of Jiangsu province (Grant NO.: 2019SJA2274) ; Major
   Project of Philosophy and Social Science Research in Universities of
   Jiangsu Province Education Department (Grant NO.: 2020SJZDA069) .
   ZHINIAO-ASKBOB Project of Vocational Education Department of Ping An
   International Smart City Technology Co., Ltd
CR [Anonymous], 2009, [No title captured]
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   [Anonymous], 2018, [No title captured]
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NR 36
TC 1
Z9 1
U1 0
U2 17
PU AMER INST MATHEMATICAL SCIENCES-AIMS
PI SPRINGFIELD
PA PO BOX 2604, SPRINGFIELD, MO 65801-2604 USA
SN 1547-1063
EI 1551-0018
J9 MATH BIOSCI ENG
JI Math. Biosci. Eng.
PY 2021
VL 18
IS 3
BP 2632
EP 2653
DI 10.3934/mbe.2021134
PG 22
WC Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology
GA TD7LO
UT WOS:000669503800012
PM 33892564
OA gold
DA 2024-09-05
ER
PT J
AU Bentley, JP
   Ramachandran, S
   Salgado, TM
AF Bentley, John P.
   Ramachandran, Sujith
   Salgado, Teresa M.
TI Considerations when conducting moderation analysis with a binary
   outcome: Applications to clinical and social pharmacy research
SO RESEARCH IN SOCIAL & ADMINISTRATIVE PHARMACY
LA English
DT Article
DE Binary outcome; Moderation; Interaction; Additive interaction;
   Multiplicative interaction; Logistic regression
ID REGRESSION APPROACH; RISK; MEDIATION
AB Clinical and social pharmacy researchers often have questions regarding contingencies of effects (i.e., moderation) that are tested by including interactions in statistical models. Much of the available literature for estimating and testing effects that emanate from moderation models is based on extensions of the linear model with continuous outcomes. Binary (or dichotomous) outcome variables, such as prescription-medication misuse versus no misuse, are commonly encountered by clinical and social pharmacy researchers. In moderation analysis, binary outcomes have led to an increased focus on the fact that measures of interaction are scale-dependent; thus, researchers may need to consider both additive interaction and multiplicative interaction. Further complicating interpretation is that the statistical model chosen for an interaction can provide different answers to questions of moderation. This manuscript will: 1) identify research questions in clinical and social pharmacy that necessitate the use of these statistical methods, 2) review statistical models that can be used to estimate effects when the outcome of interest is binary, 3) review basic concepts of moderation, 4) describe the challenges inherent in conducting moderation analysis when modeling binary outcomes, and 5) demonstrate how to conduct such analyses and interpret relevant statistical output (including interpretations of interactions on additive and multiplicative scales with a focus on identifying which statistical models for binary outcomes lead to which measure of interaction). Although much of the basis for this paper comes from research in epidemiology, recognition of these issues has occurred in other disciplines.
C1 [Bentley, John P.; Ramachandran, Sujith] Univ Mississippi, Dept Pharm Adm, Sch Pharm, Faser Hall, University, MS 38677 USA.
   [Salgado, Teresa M.] Virginia Commonwealth Univ, Dept Pharmacotherapy & Outcomes Sci, Sch Pharm, 410 N 12th St,POB 980533, Richmond, VA 23298 USA.
C3 University of Mississippi; Virginia Commonwealth University
RP Bentley, JP (corresponding author), Ctr Pharmaceut Mkt & Management, Faser Hall 225, University, MS 38677 USA.; Bentley, JP (corresponding author), Pharm Adm, Faser Hall 225, University, MS 38677 USA.
EM phjpb@olemiss.edu
RI Salgado, Teresa M/M-7550-2017
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NR 37
TC 1
Z9 1
U1 1
U2 3
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 1551-7411
EI 1934-8150
J9 RES SOC ADMIN PHARM
JI Res. Soc. Adm. Pharm.
PD FEB
PY 2022
VL 18
IS 2
BP 2276
EP 2282
DI 10.1016/j.sapharm.2021.04.020
EA DEC 2021
PG 7
WC Public, Environmental & Occupational Health; Pharmacology & Pharmacy
WE Social Science Citation Index (SSCI)
SC Public, Environmental & Occupational Health; Pharmacology & Pharmacy
GA YU0XB
UT WOS:000751773000003
PM 34119445
DA 2024-09-05
ER
PT J
AU Teng, L
   Dong, FY
   Zhang, H
   Ding, HX
AF Teng, Ling
   Dong, Fangyun
   Zhang, Hui
   Ding, Huixia
TI Research on high-precision synchronous output technology of
   multi-reference source weighted synthesis in power system
SO IET CYBER-PHYSICAL SYSTEMS: THEORY & APPLICATIONS
LA English
DT Article
DE genetic algorithms; global positioning system; power electronics;
   synchronisation
AB The massive perception data based on efficient analysis and intelligent decision have put forward higher requirements for high-precision time synchronisation with the construction and development of smart power grid. However, multi-reference source time-frequency synchronisation of power system only selects the best method after comparison, which cannot make the most efficient use of the existing resources. It also cannot meet the need for high-precision time synchronisation of future power system. The existing multi-reference source synthesis algorithms cannot take into account both long-term stability and high-precision synchronous output. This article presents a multi-reference source weighted improved noise model and the high-precision output method. The multi-reference source error after classification is eliminated by leading into classification vector and classification coefficient. The synthesised frequency offset or the time precision of output can be optimised as the objective function by weighted classification algorithm and genetic algorithm. A simulation example based on the synthesis of two satellite system clock sources and three local caesium reference sources shows that the peak value of long-term output accuracy is controlled within 10 ns after classification weighted synthesis and optimisation, which is better than that of any single reference source.
C1 [Teng, Ling; Dong, Fangyun; Zhang, Hui; Ding, Huixia] China Elect Power Res Inst, Beijing, Peoples R China.
RP Teng, L (corresponding author), China Elect Power Res Inst, Beijing, Peoples R China.
EM tengling@epri.sgcc.com.cn
FU China Electric Power Research Institute; National Key R&D Program of
   China [2020YFB0905900]; Science and Technology Project of SGCC; Key
   Technologies for Electric Internet of Things [SGTJDK00DWJS2100223]
FX China Electric Power Research Institute; the National Key R&D Program of
   China, Grant/Award Number: 2020YFB0905900; the Science and Technology
   Project of SGCC, Grant/Award Number: State Grid Corporation of China;
   Key Technologies for Electric Internet of Things, Grant/Award Number:
   SGTJDK00DWJS2100223
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NR 21
TC 0
Z9 0
U1 1
U2 1
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
EI 2398-3396
J9 IET CYBER PHYS SYST
JI IET Cyber Phys. Syst. Theory Appl.
PD DEC
PY 2023
VL 8
IS 4
SI SI
BP 247
EP 256
DI 10.1049/cps2.12051
EA MAY 2023
PG 10
WC Computer Science, Information Systems; Computer Science,
   Interdisciplinary Applications; Engineering, Electrical & Electronic
WE Emerging Sources Citation Index (ESCI)
SC Computer Science; Engineering
GA CE9J8
UT WOS:000988363200001
OA gold
DA 2024-09-05
ER
PT J
AU Casagrande, E
   Woldeamlak, S
   Woon, WL
   Zeineldin, HH
   Svetinovic, D
AF Casagrande, Erik
   Woldeamlak, Selamawit
   Woon, Wei Lee
   Zeineldin, H. H.
   Svetinovic, Davor
TI NLP-KAOS for Systems Goal Elicitation: Smart Metering System Case Study
SO IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
LA English
DT Article
DE Requirements engineering; goal elicitation; NLP; data mining;
   bibliometrics
ID REQUIREMENTS; ONTOLOGY
AB This paper presents a computational method that employs Natural Language Processing (NLP) and text mining techniques to support requirements engineers in extracting and modeling goals from textual documents. We developed a NLP-based goal elicitation approach within the context of KAOS goal-oriented requirements engineering method. The hierarchical relationships among goals are inferred by automatically building taxonomies from extracted goals. We use smart metering system as a case study to investigate the proposed approach. Smart metering system is an important subsystem of the next generation of power systems (smart grids). Goals are extracted by semantically parsing the grammar of goal-related phrases in abstracts of research publications. The results of this case study show that the developed approach is an effective way to model goals for complex systems, and in particular, for the research-intensive complex systems.
C1 [Casagrande, Erik; Woldeamlak, Selamawit; Woon, Wei Lee; Zeineldin, H. H.; Svetinovic, Davor] Masdar Inst Sci & Technol, Dept Elect Engn & Comp Sci, Abu Dhabi, U Arab Emirates.
C3 Khalifa University of Science & Technology
RP Casagrande, E (corresponding author), Masdar Inst Sci & Technol, Dept Elect Engn & Comp Sci, Abu Dhabi, U Arab Emirates.
EM dsvetinovic@masdar.ac.ae
RI Svetinovic, Davor/AAQ-9433-2020; Svetinovic, Davor/IUQ-2141-2023
OI Svetinovic, Davor/0000-0002-3020-9556; Svetinovic,
   Davor/0000-0002-3020-9556; Woon, Wei Lee/0000-0002-6155-1741; Gifalli,
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NR 56
TC 26
Z9 29
U1 5
U2 24
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 0098-5589
EI 1939-3520
J9 IEEE T SOFTWARE ENG
JI IEEE Trans. Softw. Eng.
PD OCT
PY 2014
VL 40
IS 10
BP 941
EP 956
DI 10.1109/TSE.2014.2339811
PG 16
WC Computer Science, Software Engineering; Engineering, Electrical &
   Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA AR9ML
UT WOS:000343899100001
DA 2024-09-05
ER
PT J
AU Jensen, MJ
   Chen, TC
AF Jensen, Michael J.
   Chen, Titus C.
TI Illiberal Media in a Liberal Democracy: Examining Identity in
   Australia's Mandarin Language News
SO ISSUES & STUDIES
LA English
DT Article
DE China; WeChat; topic modeling; foreign influence; identity narratives
AB The regime of censorship in the People's Republic of China (PRC) extends beyond its borders through the extraterritorial application of its media regulations to popular social media platforms like WeChat. This research investigates the effects of the PRC's extraterritorial control of online content on the identity narratives and norms communicated by comparing Australia's Special Broadcast Service (SBS) Mandarin language news and the news targeting Australian audiences published on popular WeChat Official Accounts (OAs). We find significant differences in the news content between these two platforms: SBS provides more political content and a focus on political and cultural integration, while WeChat pages tend to avoid political topics that are not otherwise press releases from the PRC and they encourage strong cultural ties with Mainland China. Finally, SBS tends to both inform and cultivate democratic political identities and identification with the Australian political system, whereas WeChat tends to differentiate the Chinese diaspora from the wider Australian community. We situate these findings within a wider understanding of PRC's national security strategies and doctrine. Whether by requirement or practice, not only the WeChat OAs in Australia implement PRC's communication controls, but the content on these pages also challenges the liberal democratic practices and norms and supports foreign influence and espionage in Australia.
C1 [Jensen, Michael J.] Univ Canberra, Inst Governance & Policy Anal, Canberra, ACT, Australia.
   [Chen, Titus C.] Natl Sun Yat Sen Univ, Inst Polit, Taipei, Taiwan.
C3 University of Canberra; National Sun Yat Sen University
RP Jensen, MJ (corresponding author), Univ Canberra, Inst Governance & Policy Anal, Canberra, ACT, Australia.
EM Michael.Jensen@canberra.edu.au; tituschen@mail.nsysu.edu.tw
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NR 71
TC 0
Z9 0
U1 1
U2 6
PU WORLD SCIENTIFIC PUBL CO PTE LTD
PI SINGAPORE
PA 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE
SN 1013-2511
EI 2529-802X
J9 ISSUES STUD
JI Issues Stud.
PD JUN
PY 2021
VL 57
IS 2
AR 2150005
DI 10.1142/S1013251121500053
PG 35
WC Area Studies; International Relations; Political Science
WE Emerging Sources Citation Index (ESCI)
SC Area Studies; International Relations; Government & Law
GA TZ3ME
UT WOS:000684379000004
DA 2024-09-05
ER
PT J
AU Pradhan, T
   Sahoo, S
   Singh, U
   Pal, S
AF Pradhan, Tribikram
   Sahoo, Suchit
   Singh, Utkarsh
   Pal, Sukomal
TI A proactive decision support system for reviewer recommendation in
   academia
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Reviewer recommendation; Topic modeling; Clustering; Citation analysis;
   Random walk with restart (RWR)
ID INFORMATION; INDEX
AB Peer review is an essential part of scientific communications to ensure the quality of publications and a healthy scientific evaluation process. Assigning appropriate reviewers poses a great challenge for program chairs and journal editors for many reasons, including relevance, fair judgment, no conflict of interest, and qualified reviewers in terms of scientific impact. With a steady increase in the number of research domains, scholarly venues, researchers, and papers in academia, manually selecting and accessing adequate reviewers is becoming a tedious and time-consuming task. Traditional approaches for reviewer selection mainly focus on the matching of research relevance by keywords or disciplines. However, in real-world systems, various factors are often needed to be considered. Therefore, we propose a multilayered approach integrating Topic Network, Citation Network, and Reviewer Network into a reviewer Recommender System (TCRRec). We explore various aspects, including relevance between reviewer candidates and submission, authority, expertise, diversity, and conflict of interest and integrate them into the proposed framework TCRRec. The paper also addresses cold start issues for researchers having unique areas of interest or for isolated researchers. Experiments based on the NIPS and AMiner dataset demonstrate that the proposed TCRRec outperforms state-of-the-art recommendation techniques in terms of standard metrics of precision@k, MRR, nDCG@k, authority, expertise, diversity, and coverage.
C1 [Pradhan, Tribikram; Pal, Sukomal] Indian Inst Technol BHU, Dept Comp Sci & Engn, Varanasi, Uttar Pradesh, India.
   [Pradhan, Tribikram] MAHE, Manipal Inst Technol, Dept Informat & Commun Technol, Manipal, Karnataka, India.
   [Sahoo, Suchit; Singh, Utkarsh] Indian Inst Technol BHU, Dept Elect Engn, Varanasi, Uttar Pradesh, India.
C3 Indian Institute of Technology System (IIT System); Indian Institute of
   Technology BHU Varanasi (IIT BHU Varanasi); Manipal Academy of Higher
   Education (MAHE); Indian Institute of Technology System (IIT System);
   Indian Institute of Technology BHU Varanasi (IIT BHU Varanasi)
RP Pradhan, T (corresponding author), Indian Inst Technol BHU, Dept Comp Sci & Engn, Varanasi, Uttar Pradesh, India.; Pradhan, T (corresponding author), MAHE, Manipal Inst Technol, Dept Informat & Commun Technol, Manipal, Karnataka, India.
EM tpradhan.rs.cse16@itbhu.ac.in; suchit.sahoo.eee15@itbhu.ac.in;
   utkarsh.singh.eee15@itbhu.ac.in; spal.cse@iitbhu.ac.in
RI PRADHAN, TRIBIKRAM/AAY-1283-2021
OI PRADHAN, TRIBIKRAM/0000-0001-5458-2286
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NR 50
TC 12
Z9 13
U1 1
U2 18
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0957-4174
EI 1873-6793
J9 EXPERT SYST APPL
JI Expert Syst. Appl.
PD MAY 1
PY 2021
VL 169
AR 114331
DI 10.1016/j.eswa.2020.114331
EA FEB 2021
PG 20
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Operations Research & Management Science
GA SV3FJ
UT WOS:000663708000016
DA 2024-09-05
ER
PT J
AU Li, ZM
AF Li, ZhengMin
TI Research on Brand Image Evaluation Method Based on Consumer Sentiment
   Analysis
SO COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
LA English
DT Article
ID SYSTEM; ONTOLOGY; MODEL
AB Brand image assessment is a key step to reasonably quantify the value of a brand and has far-reaching significance for improving the competitiveness of an enterprise. With the rapid development of Internet technology, traditional questionnaires can no longer meet the current needs of brand image assessment. In this environment, the huge amount of fragmented consumer topic data provides a rich data resource and new research ideas for brand image assessment. Therefore, a brand image assessment method based on consumer sentiment analysis is proposed. First, a topic-based brand image cognitive label extraction method is proposed by setting language rules, aggregation rules, and ranking rules according to the characteristics of online topic data. Then, the fusion of cognitive labels and deep features is performed by fusing the deep features extracted from word vectors. Finally, a supervised learning support vector machine is selected as the sentiment classification model. The experimental results show that based on the obtained important cognitive labels, enterprises are able to better understand the unique attributes that consumers have for the brand; the feature fusion approach is better evaluated and can accurately reflect consumers' views on brand image and quantified as brand score.
C1 [Li, ZhengMin] Wuhan Univ, Sch Journalism & Commun, Wuhan 430072, Hubei, Peoples R China.
C3 Wuhan University
RP Li, ZM (corresponding author), Wuhan Univ, Sch Journalism & Commun, Wuhan 430072, Hubei, Peoples R China.
EM 2015101030022@whu.edu.cn
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NR 22
TC 0
Z9 1
U1 4
U2 14
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1687-5265
EI 1687-5273
J9 COMPUT INTEL NEUROSC
JI Comput. Intell. Neurosci.
PD MAY 27
PY 2022
VL 2022
AR 2647515
DI 10.1155/2022/2647515
PG 8
WC Mathematical & Computational Biology; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology; Neurosciences & Neurology
GA 1Y0AU
UT WOS:000807810000001
PM 35669638
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU Bhattacharya, S
AF Bhattacharya, Sujit
TI Some Salient Aspects of Machine Learning Research: A Bibliometric
   Analysis
SO JOURNAL OF SCIENTOMETRIC RESEARCH
LA English
DT Article
DE Machine Learning; Social Network Analysis; Bibliometrics; Co-word
   analysis; Intellectual Structure
AB Machine learning has emerged as an important and distinct area of research closely related to and often overlaps with various domains within computer science, computational statistics, artificial intelligence, cognitive science. One can observe connections with these fields at the cognitive level (in terms of theoretical framework), and on methodological levels (drawing from tools and techniques of these fields). The evolution of the field has taken a very directed and operational approach with basic tenet of machine learning being 'teaching computers how to learn from data to make decisions or predictions'. As we move into systems that increasingly need to exploit data, we find the research in this area getting more application oriented, expansive in scope with loci of research and innovation dispersed across academia, research institutions and industry. It is thus becoming a challenging as well as useful exercise to know the structure and dynamics of this field. The paper is centered on this issue; it tries to capture the intellectual structure of this field and research trends from quantitative and statistical analysis of research publications. Conceptual connections are constructed from linkages among keywords using tools and techniques of Social network Analysis. It also acts as a conceptual framework for the study. Some indications from patent statistics are also drawn to provide some insights of the technological trends.
C1 [Bhattacharya, Sujit] CSIR Natl Inst Sci Technol & Dev Studies, NISTADS Campus,KS Krishnan Marg,Pusa Campus, New Delhi 110012, India.
   [Bhattacharya, Sujit] AcSIR, NISTADS Campus,KS Krishnan Marg,Pusa Campus, New Delhi 110012, India.
C3 Council of Scientific & Industrial Research (CSIR) - India; CSIR -
   National Institute of Science Communication & Policy Research (NIScPR);
   Academy of Scientific & Innovative Research (AcSIR)
RP Bhattacharya, S (corresponding author), CSIR Natl Inst Sci Technol & Dev Studies, NISTADS Campus,KS Krishnan Marg,Pusa Campus, New Delhi 110012, India.; Bhattacharya, S (corresponding author), AcSIR, NISTADS Campus,KS Krishnan Marg,Pusa Campus, New Delhi 110012, India.
EM sujit_academic@yahoo.com
RI Bhattacharya, Sujit/AAH-2660-2020
OI Bhattacharya, Sujit/0000-0002-6769-8293
CR [Anonymous], 2009, Social Network Analysis. Methods and Applications
   [Anonymous], STRUCTURAL ANAL SOCI
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NR 13
TC 11
Z9 11
U1 2
U2 9
PU PHCOG NET
PI KARNATAKA
PA 17, 2ND FLR, BUDDHA VIHAR RD, NEAR SPORTS ZONE, COX TOWN, BENGALURU,
   KARNATAKA, 560005, INDIA
SN 2321-6654
EI 2320-0057
J9 J SCIENTOMETR RES
JI J. Scientometr. Res.
PD MAY-AUG
PY 2019
VL 8
IS 2
SI SI
BP S85
EP S92
DI 10.5530/jscires.8.2.26
PG 8
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA KA8FQ
UT WOS:000506037200007
OA hybrid
DA 2024-09-05
ER
PT C
AU Jing, M
   Fang, FY
AF Jing, Ma
   Fang, Fu Yan
GP IEEE
TI Research on the Dynamic Assessment of Battlefield Forces Based on
   Bayesian Networks
SO 2012 INTERNATIONAL CONFERENCE ON INDUSTRIAL CONTROL AND ELECTRONICS
   ENGINEERING (ICICEE)
LA English
DT Proceedings Paper
CT International Conference on Industrial Control and Electronics
   Engineering (ICICEE)
CY AUG 23-25, 2012
CL Xian, PEOPLES R CHINA
DE situation battlefield forces; Bayesian network; dynamic assessment
AB Two sides of the battlefield forces contrast is an important influence factor of battlefield situation. Accurately judge it is a preconditions of situation assessment. This paper researches on the battlefield forces assessment and the results of the dynamic changes. In order to achieve it, we build two Bayesian networks, one analyses situation battlefield forces, the other analyses dynamic changes with the nodes. The nodes of first Bayesian network contains enemy air-to-ground attack force, our air-to-ground attack force, enemy air-to-air attack force, our air-to-air attack force, enemy ground-to-air attack force, our ground-to-air attack force, enemy ground-to-ground attack force, our ground-to-ground attack force, enemy ground defenses, our ground defenses, etc. The second Bayesian network reflects the changes of nodes of first Bayesian network. Through the Bayesian network, we can get the current situation of battlefield forces, and find the change between original result and now. Finally, the process of how to use the Bayesian networks for situation assessment was showed by an example.
C1 [Jing, Ma; Fang, Fu Yan] Xian Technol Univ, Xian 710032, Shaanxi, Peoples R China.
C3 Xi'an Technological University
RP Jing, M (corresponding author), Xian Technol Univ, Xian 710032, Shaanxi, Peoples R China.
CR BARNARD GF, 1993, CANCER RES, V53, P4048
   BJOERN T, 2006, CANCER, P792
   Determan J, 1999, P 1999 C EV COMP PIS, P2101
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   Wang M.-L, 2010, J WUYI U NATURAL SCI, V24, P44
NR 5
TC 1
Z9 1
U1 0
U2 6
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA
BN 978-0-7695-4792-3
PY 2012
BP 321
EP 323
DI 10.1109/ICICEE.2012.92
PG 3
WC Engineering, Electrical & Electronic
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Engineering
GA BCJ89
UT WOS:000310339200085
DA 2024-09-05
ER
PT J
AU Manakhova, AM
   Lagutina, NS
AF Manakhova, A. M.
   Lagutina, N. S.
TI Analysis of the Influence of Mixed-Level Stylometric Characteristics on
   the Verification of Authors of Literary Works
SO AUTOMATIC CONTROL AND COMPUTER SCIENCES
LA English
DT Article
DE stylometry; stylometric characteristics; authorship verification;
   natural language processing
AB This article analyses the influence of various combinations of mixed-level stylometric characteristics on the quality of verification of the authorship of Russian, English and French prose texts. The study is carried out both for low-level stylometric characteristics based on words and characters, and for higher-level structure ones. All stylometric characteristics are calculated automatically using the ProseRhythmDetector program. This approach provides the analyses of works of a large volume and many writers at the same time. In the course of the work, character-level, word-level, and structure-level stylometric vectors are associated with each text. During the experiments, the sets of parameters of these three levels were combined with each other in all possible ways. The resulting vectors of stylometric characteristics were submitted to the input of various classifiers to perform verification and identify the most suitable classifier for solving the problem. The best results were obtained using the AdaBoost classifier. The average F-measure for all languages was over 92%. Detailed verification quality assessments are given for each author and analyzed. The use of high-level stylometric characteristics, in particular, the frequency of using N-grams of POS tags, opens the prospect of a more detailed analysis of author's styles. The results of the experiments show that when combining the characteristics of the structure level with the characteristics of the word level and/or character level, the most accurate results of authorship verification for literary texts in Russian, English, and French are obtained. Additionally, the authors concluded that stylometric characteristics have different degrees of influence on the quality of authorship verification for different languages.
C1 [Manakhova, A. M.; Lagutina, N. S.] Yaroslavl State Univ, Yaroslavl 150003, Russia.
C3 Yaroslavl State University
RP Manakhova, AM (corresponding author), Yaroslavl State Univ, Yaroslavl 150003, Russia.
EM al.mnkhv@yandex.ru; lagutinans@rambler.ru
OI Lagutina, Nadezhda/0000-0002-6137-8643
CR Adamovic S, 2019, J ASSOC INF SCI TECH, V70, P858, DOI 10.1002/asi.24163
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NR 22
TC 0
Z9 0
U1 1
U2 2
PU PLEIADES PUBLISHING INC
PI NEW YORK
PA PLEIADES HOUSE, 7 W 54 ST, NEW YORK,  NY, UNITED STATES
SN 0146-4116
EI 1558-108X
J9 AUTOM CONTROL COMPUT
JI Autom. Control Comp. Sci.
PD DEC
PY 2022
VL 56
IS 7
BP 744
EP 761
DI 10.3103/S0146411622070148
PG 18
WC Automation & Control Systems
WE Emerging Sources Citation Index (ESCI)
SC Automation & Control Systems
GA 9D9NI
UT WOS:000936422500013
DA 2024-09-05
ER
PT J
AU Habib, E
   Deshotel, M
   Lai, GL
   Miller, R
AF Habib, Emad
   Deshotel, Matthew
   Lai, Guolin
   Miller, Robert
TI Student Perceptions of an Active Learning Module to Enhance Data and
   Modeling Skills in Undergraduate Water Resources Engineering Education
SO INTERNATIONAL JOURNAL OF ENGINEERING EDUCATION
LA English
DT Article
DE water resources engineering education; active-learning; numerical
   modeling; data analysis; project-based; improvement-focused evaluation;
   design-based research; open-source tools
ID CURRICULUM
AB This article describes the design, development, and evaluation of an undergraduate learning module that builds student's skills on how data analysis and numerical modeling can be used to analyze and design water resources engineering projects. The module follows a project-based approach by using a hydrologic restoration project in a coastal basin in south Louisiana, USA. The module has two main phases, a feasibility analysis phase and a hydraulic design phase, and follows an active learning approach where students perform a set of quantitative learning activities that involve extensive data and modeling analyses. The module is designed using open resources, including online datasets, hydraulic simulation models and geographical information system software that are typically used by the engineering industry and research communities. Upon completing the module, students develop skills that involve model formulation, parameter calibration, sensitivity analysis, and the use of data and models to assess and design a hydrologic a proposed hydrologic engineering project. Guided by design-based research framework, the implementation and evaluation of the module focused primarily on assessing students' perceptions of the module usability and its design attributes, their perceived contribution of the module to their learning, and their overall receptiveness of the module and how it impacts their interest in the subject and future careers. Following an improvement-focused evaluation approach, design attributes that were found most critical to students included the use of user-support resources and self-checking mechanisms. These aspects were identified as key features that facilitate students' self-learning and independent completion of tasks, while still enriching their learning experiences when using data and modeling-rich applications. Evaluation data showed that the following attributes contributed the most to students' learning and potential value for future careers: application of modern engineering data analysis; use of real-world hydrologic datasets; and appreciation of uncertainties and challenges imposed by data scarcity. The evaluation results were used to formulate a set of guiding principles on how to design effective and conducive undergraduate learning experiences that adopt technology-enhanced and data and modeling-based strategies, on how to enhance users' experiences with free and open-source engineering analysis tools, and on how to strike a pedagogical balance between module complexity, student engagement, and flexibility to fit within existing curricula limitations.
C1 [Habib, Emad; Deshotel, Matthew; Miller, Robert] Univ Louisiana Lafayette, Dept Civil Engn, POB 70503, Lafayette, LA 70504 USA.
   [Habib, Emad; Miller, Robert] Univ Louisiana Lafayette, Louisiana Watershed Flood Ctr, POB 70503, Lafayette, LA 70504 USA.
   [Lai, Guolin] Univ Louisiana Lafayette, Dept Management, POB 43930, Lafayette, LA 70504 USA.
C3 University of Louisiana Lafayette; University of Louisiana Lafayette;
   University of Louisiana Lafayette
RP Habib, E (corresponding author), Univ Louisiana Lafayette, Dept Civil Engn, POB 70503, Lafayette, LA 70504 USA.; Habib, E (corresponding author), Univ Louisiana Lafayette, Louisiana Watershed Flood Ctr, POB 70503, Lafayette, LA 70504 USA.
EM habib@louisiana.edu; mdeshot@gmail.com; glai@louisiana.edu;
   robert.miller@louisiana.edu
FU National Science Foundation [1122898, 1726965]; Louisiana Sea Grant
   College Program (LSG) under NOAA [R/EMD-03, NA14OAR4170099]; Division Of
   Undergraduate Education; Direct For Education and Human Resources
   [1726965, 1122898] Funding Source: National Science Foundation
FX This material is based upon work supported by the National Science
   Foundation Collaborative Award No. 1122898 and Award No. 1726965, and by
   Program Project ID: R/EMD-03 through the Louisiana Sea Grant College
   Program (LSG) under NOAA Award #NA14OAR4170099.
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NR 23
TC 12
Z9 12
U1 1
U2 11
PU TEMPUS PUBLICATIONS
PI DURRUS, BANTRY
PA IJEE , ROSSMORE,, DURRUS, BANTRY, COUNTY CORK 00000, IRELAND
SN 0949-149X
J9 INT J ENG EDUC
JI Int. J. Eng. Educ
PY 2019
VL 35
IS 5
BP 1353
EP 1365
PG 13
WC Education, Scientific Disciplines; Engineering, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Education & Educational Research; Engineering
GA IV6FK
UT WOS:000484364100010
DA 2024-09-05
ER
PT J
AU Khayet, M
   Aytaç, E
   Matsuura, T
AF Khayet, Mohamed
   Aytac, Ersin
   Matsuura, Takeshi
TI Bibliometric and sentiment analysis with machine learning on the
   scientific contribution of Professor Srinivasa Sourirajan
SO DESALINATION
LA English
DT Article
DE Biblioshiny; Exploratory Tool; Text mining; VADER; Word cloud
ID SEPARATION; PRESSURE; FLOW
AB Prof. Srinivasa Sourirajan is remembered by the desalination and membrane community as the "Father of Reverse Osmosis ". He passed away at the age of 98 peacefully in his beloved city Ottawa (Canada). His legacy will be remembered by the scientific community "membrane science, membrane processes, desalination and engineering ". His research studies were not only novel, but also very creative and even visionary. He offered a priceless gift to humanity by bringing clean water to all those in need through the presentation of reverse osmosis technology together with its appropriate membranes for water treatment, including desalination. This technology has now gained worldwide interest as it is able to produce clean water at a lower cost compared to other separation processes. His scientific contribution also pioneered other research areas. He developed novel research methodologies in geophysics while in catalysis he produced unleaded gasoline to help with the smog issue. He was nominated for the Nobel Prize three times. Prof. Sourirajan had also an exceptional humanitarian attribute. He played a significant role in bringing the Indian community to Ottawa. In the present paper we apply machine learning for his extraordinary and original scientific contribution. The results reveal how influential scientist he was.
C1 [Khayet, Mohamed; Aytac, Ersin] Univ Complutense Madrid, Fac Phys, Dept Struct Matter Thermal Phys & Elect, Avda Complutense S-N, Madrid 28040, Spain.
   [Khayet, Mohamed] Madrid Inst Adv Studies Water, IMDEA Water Inst, Calle Punto Net 4, Madrid 28805, Spain.
   [Aytac, Ersin] Zonguldak Bulent Ecevit Univ, Dept Environm Engn, TR-67100 Zonguldak, Turkey.
   [Matsuura, Takeshi] Univ Ottawa, Dept Chem & Biol Engn, Ottawa, ON K1N 6N5, Canada.
C3 Complutense University of Madrid; IMDEA Water Institute; Zonguldak
   Bulent Ecevit University; University of Ottawa
RP Khayet, M (corresponding author), Univ Complutense Madrid, Fac Phys, Dept Struct Matter Thermal Phys & Elect, Avda Complutense S-N, Madrid 28040, Spain.
EM khayetm@fis.ucm.es
RI ; Khayet, Mohamed/L-3814-2014
OI Aytac, Ersin/0000-0002-7124-4438; Khayet, Mohamed/0000-0002-5117-2975
FU Scientific and Technological Research Council of Turkey (TUBITAK) at the
   University Complutense of Madrid (UCM) [1059B191900618]
FX Acknowledgements Dr. Ersin Ayta? would like to express his
   acknowledgement for the postdoctoral grant received from The Scientific
   and Technological Research Council of Turkey (TUBITAK) at the University
   Complutense of Madrid (UCM) with the grant number of 1059B191900618.
CR Abu Sayed M, 2021, ACCIDENT ANAL PREV, V159, DOI 10.1016/j.aap.2021.106211
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   Sourirajan S, 2022, THE OTTAWA CITIZEN
   Suardi S, 2022, INT REV ECON FINANC, V79, P289, DOI 10.1016/j.iref.2022.02.017
   Tamala J.K., 2022, CLEAN ENG TECH, V7
   Tseng H.-H., 2022, 60 YEARS LOEB SOURIR, pxxvii
   Zhan Q, 2022, PHOTODIAGN PHOTODYN, V38, DOI 10.1016/j.pdpdt.2022.102860
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   Zheng ZY, 2022, NANO TODAY, V44, DOI 10.1016/j.nantod.2022.101453
NR 47
TC 7
Z9 7
U1 2
U2 24
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0011-9164
EI 1873-4464
J9 DESALINATION
JI Desalination
PD DEC 1
PY 2022
VL 543
AR 116095
DI 10.1016/j.desal.2022.116095
EA SEP 2022
PG 12
WC Engineering, Chemical; Water Resources
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Water Resources
GA 4V1OU
UT WOS:000859254300003
DA 2024-09-05
ER
PT C
AU Rong, CH
   Hu, GL
AF Rong Cuihong
   Hu Guoliang
BE Duysters, G
   DeHoyos, A
   Kaminishi, K
TI Innovation Mechanism of Academic Journals
SO PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON INNOVATION AND
   MANAGEMENT
LA English
DT Proceedings Paper
CT 9th International Conference on Innovation and Management
CY NOV 14-16, 2012
CL Eindhoven Univ Technol, Eindhoven, NETHERLANDS
HO Eindhoven Univ Technol
DE Academic journals; Management; Multiple linear regression model; Impact
   factor; Circulation
AB The present article deals with the issue of the quality of academic journals. After we have analyzed the current situation of the problems, we propose some solutions. Academic journals should try to attract the best articles by making an arrangement in advance with the writer or by contributions solicitation. At the same time, the amount of subscription should be enlarged and the academic quality of the editorial team should be improved. It is also important for striving for more financial support. We conclude that the coordination of the different aspects involved is the key to the improvement of the quality of academic journals.
C1 [Rong Cuihong] Wuhan Univ Technol, Wuhan 430070, Peoples R China.
   [Hu Guoliang] Cent China Normal Univ, Dept Sports, Wuhan 430079, Peoples R China.
C3 Wuhan University of Technology; Central China Normal University
RP Rong, CH (corresponding author), Wuhan Univ Technol, Wuhan 430070, Peoples R China.
EM rongyan5878@vip.sina.com; 510522042@qq.com
CR Kelman H.C. Compliance, 1968, J CONFLICT RESOLUT, P51
   Powers Willian, 2002, NATL J, V12
   Xiao Tanghua, 2007, CHINESE J SCI TECHNI
   Zhang Yaoming, 2006, J TSINGHUA U PHILOS
NR 4
TC 0
Z9 0
U1 0
U2 3
PU WUHAN UNIV TECHNOLOGY PRESS
PI WUHAN
PA 122 LUOSHI RD, WUHAN 430070, PEOPLES R CHINA
BN 978-7-5629-3854-5
PY 2012
BP 1107
EP +
PG 2
WC Economics; Management; Regional & Urban Planning
WE Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Business & Economics; Public Administration
GA BDF49
UT WOS:000313020500176
DA 2024-09-05
ER
PT C
AU Indrianti, Y
   Sasmoko
   Manalu, SR
   Waromi, QLK
AF Indrianti, Yasinta
   Sasmoko
   Manalu, Sonya Rapinta
   Waromi, Queensy Lovenia Kerrin
GP ACM
TI Literature Review Profiles of Specialization in Education and Profession
   as the basis for the development of Artificial Intelligence Website
SO AICCC 2021: 2021 4TH ARTIFICIAL INTELLIGENCE AND CLOUD COMPUTING
   CONFERENCE
LA English
DT Proceedings Paper
CT 4th Artificial Intelligence and Cloud Computing Conference (AICCC)
CY DEC 17-19, 2021
CL ELECTR NETWORK
DE Literature Review; Specialization in Education; Specialization in
   Profession; Artificial Intelligence Website; Bibliometric
ID FRAMEWORK; KNOWLEDGE; SKILLS; WILL
AB Profiles of specialization in education and profession are currently heavily influenced by the development of industry 4.0. The era of disruption is marked by massive changes due to innovations that change business systems and arrangements to newer levels. In various studies, there will be many professions that are extinct but there will also be many new professions that will be born. This study aims to conduct a literature review of profiles of specialization in education and profession that are relevant to industry 4.0 trends which will then be developed in a measurement through an artificial intelligence-based website. The research method used is a bibliometric approach through scientific studies of various library sources using VOSViewer with cartographic overlay techniques and density visualization maps, to represent sequences and their relationships. While the method used to develop website profiles of specialization in education and profession based on artificial intelligence is to use the waterfall method. The results of the literature review found 10 words with a high level of bibliometric correlation as the basis for developing artificial intelligence-based websites.
C1 [Indrianti, Yasinta] Univ Agung Podomoro, Entrepreneurship Dept, West Jakarta, Indonesia.
   [Sasmoko] Bina Nusantara Univ, Primary Teacher Educ Dept, Fac Humanities, Jakarta, Indonesia.
   [Manalu, Sonya Rapinta] Bina Nusantara Univ, Dept Comp Sci, Jakarta, Indonesia.
   [Waromi, Queensy Lovenia Kerrin] Bina Nusantara Univ, Dept Management, Binus Business Sch, Undergrad Program, Jakarta, Indonesia.
C3 Universitas Bina Nusantara; Universitas Bina Nusantara; Universitas Bina
   Nusantara
RP Indrianti, Y (corresponding author), Univ Agung Podomoro, Entrepreneurship Dept, West Jakarta, Indonesia.
EM yasinta.indrianti@podomorouniversity.ac.id; sasmoko@binus.edu;
   smanalu@binus.edu; queensy.waromi@binus.ac.id
FU Research and Technology Transfer Office, Bina Nusantara University as a
   part of the Multi-Year Grant Ministry of Research and Technology/BRIN
   2020 entitled Student Interest Preferences in Education and Profession
   Era 4.0: Artificial Intelligence
FX This work is supported by the Research and Technology Transfer Office,
   Bina Nusantara University as a part of the Multi-Year Grant Ministry of
   Research and Technology/BRIN 2020 entitled Student Interest Preferences
   in Education and Profession Era 4.0: Artificial Intelligence. We also
   want to say thanks to the Podomoro University for the participation in
   this research.
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NR 25
TC 0
Z9 0
U1 6
U2 16
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES
BN 978-1-4503-8416-2
PY 2021
BP 216
EP 220
DI 10.1145/3508259.3508296
PG 5
WC Computer Science, Artificial Intelligence; Computer Science, Theory &
   Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BU6VJ
UT WOS:000929714300030
DA 2024-09-05
ER
PT J
AU Zhao, QY
   Liang, ZQ
AF Zhao, Qianyi
   Liang, Zhiqiang
TI Research on multimodal based learning evaluation method in smart
   classroom
SO LEARNING AND MOTIVATION
LA English
DT Article
DE Artificial intelligence; Smart classrooms; Multi -modal information
   fusion
ID INFORMATION FUSION; RECOGNITION; EDUCATION; EMOTION
AB In traditional learning contexts, teachers primarily assess students' behavior, emotional changes, and assignment completion to ensure teaching quality. Currently, there are challenges in evalu-ating students, such as assessments being insufficiently comprehensive and timely, a singular evaluation perspective that hinders the holistic consideration of factors affecting learning as-sessments, and a weak correlation among evaluation criteria, resulting in suboptimal evaluation outcomes. In recent years, with the rapid development and widespread application of artificial intelligence and information technology, the era of smart classrooms has arrived. New technol-ogies like image processing and artificial intelligence offer opportunities for personalized support services and enhancing teaching quality. Therefore, to provide a more comprehensive and objective reflection of teaching quality, this paper proposes a multi-modal information fusion learning assessment model. This model is achieved by determining the weight values of three dimensions, cognitive attention, emotional attitude, and course acceptance along with their corresponding attributes. Subsequently, through a fusion strategy, it calculates the learning assessment score by integrating information from these three dimensions. A series of experi-mental data confirms the effectiveness of this approach.
C1 [Zhao, Qianyi] Jinan Univ, Sch Journalism & Commun, Guangzhou 510000, Peoples R China.
   [Liang, Zhiqiang] Guangzhou Univ, Sch Civil Engn, Guangzhou 510000, Peoples R China.
C3 Jinan University; Guangzhou University
RP Zhao, QY (corresponding author), Jinan Univ, Sch Journalism & Commun, Guangzhou 510000, Peoples R China.
EM zhao_qianyi89@outlook.com
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NR 41
TC 1
Z9 1
U1 11
U2 20
PU ACADEMIC PRESS INC ELSEVIER SCIENCE
PI SAN DIEGO
PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA
SN 0023-9690
EI 1095-9122
J9 LEARN MOTIV
JI Learn. Motiv.
PD NOV
PY 2023
VL 84
AR 101943
DI 10.1016/j.lmot.2023.101943
EA NOV 2023
PG 19
WC Psychology, Biological; Psychology, Experimental
WE Social Science Citation Index (SSCI)
SC Psychology
GA CF0G5
UT WOS:001123712600001
DA 2024-09-05
ER
PT J
AU Ren, YS
   Ma, CQ
   Kong, XL
   Baltas, K
   Zureigat, Q
AF Ren, Yi-Shuai
   Ma, Chao-Qun
   Kong, Xiao-Lin
   Baltas, Konstantinos
   Zureigat, Qasim
TI Past, present, and future of the application of machine learning in
   cryptocurrency research
SO RESEARCH IN INTERNATIONAL BUSINESS AND FINANCE
LA English
DT Article
DE Cryptocurrency; Machine learning; Blockchain; Bibliometric analysis
ID NEURAL-NETWORKS; PRICE PREDICTION; SAFE HAVEN; BITCOIN; VOLATILITY;
   BLOCKCHAIN; CURRENCIES; HEDGE; ALGORITHMS; SCIENCE
AB Cryptocurrency has captured the interest of financial scholars and become a major research topic in blockchain. In cryptocurrency research, the use of machine learning algorithms is enabled by the presence of many types of data and abundant resources. However, there is currently no comprehensive review on cryptocurrencies using machine learning. Therefore, we collect papers on cryptocurrency-related using machine learning in the web of science database, and summarize these papers according to the algorithm, and draw the following conclusions: (1) The application of machine learning for cryptocurrencies research is increasing year over year; (2) Predicting cryptocurrency price trends and income fluctuations is the most relevant research topic; (3) The machine learning algorithm utilized in cryptocurrency research is not unique, and the practise of combining multiple machine learning approaches has emerged; (4) Concerns such as overfitting and interpretability still persist with machine learning methods. Finally, we suggest future research directions.
C1 [Ren, Yi-Shuai] Hunan Univ, Sch Publ Adm, Hunan, Peoples R China.
   [Ma, Chao-Qun; Kong, Xiao-Lin] Hunan Univ, Sch Business, Hunan, Peoples R China.
   [Ren, Yi-Shuai; Ma, Chao-Qun; Kong, Xiao-Lin] Hunan Univ, Res Inst Digital Soc & BlockChain, Hunan, Peoples R China.
   [Ren, Yi-Shuai; Ma, Chao-Qun] Hunan Univ, Ctr Resource & Environm Management, Hunan, Peoples R China.
   [Ren, Yi-Shuai] Univ Auckland, Energy Ctr, 12 Grafton Rd, Auckland 1010, New Zealand.
   [Baltas, Konstantinos] Univ Essex, Essex Business Sch, Colchester, Essex, England.
   [Zureigat, Qasim] Sulaiman Al Rajhi Univ, Dept Accounting & Informat Syst, Al Bukayriyah, Saudi Arabia.
C3 Hunan University; Hunan University; Hunan University; Hunan University;
   University of Auckland; University of Essex; Sulaiman AlRajhi University
RP Kong, XL (corresponding author), Hunan Univ, Sch Business, Hunan, Peoples R China.
EM renyishuai1989@126.com; cqma1998@126.com; kongxiaolin@hnu.edu.cn;
   k.baltas@essex.ac.uk; q.zureigat@sr.edu.sa
RI Ren, Yishuai/AAB-9519-2019
FU National Natural Science Foundation of China [71850012, 72104075,
   72274056, 72192800]; National Social Science Fund of China [19AZD014,
   21ZD125]; Major Special Projects of the Department of Science and
   Technology of Hunan province [2018GK1020]; Natural Science Foundation of
   Hunan Province [2022JJ40106]; Hunan Social Science Achievement Review
   Committee [XSP21YBC087]; Hunan University Youth Talent Program
FX This research is supported by the National Natural Science Foundation of
   China (No. 71850012, 72104075, 72274056, 72192800), the National Social
   Science Fund of China (No. 19AZD014, 21&ZD125), the Major Special
   Projects of the Department of Science and Technology of Hunan province
   (No. 2018GK1020), the Natural Science Foundation of Hunan Province (No.
   2022JJ40106), the Hunan Social Science Achievement Review Committee (No.
   XSP21YBC087), and Hunan University Youth Talent Program.
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NR 162
TC 17
Z9 18
U1 7
U2 28
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0275-5319
EI 1878-3384
J9 RES INT BUS FINANC
JI Res. Int. Bus. Financ.
PD DEC
PY 2022
VL 63
AR 101799
DI 10.1016/j.ribaf.2022.101799
PG 21
WC Business, Finance
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA H1NY5
UT WOS:000993711000001
OA Green Accepted
DA 2024-09-05
ER
PT J
AU Xue, XL
   Yang, XW
   Deng, ZY
   Tu, H
   Kong, DZ
   Li, N
   Xu, F
AF Xue, Xiali
   Yang, Xinwei
   Deng, Zhongyi
   Tu, Huan
   Kong, Dezhi
   Li, Ning
   Xu, Fan
TI Global Trends and Hotspots in Research on Rehabilitation Robots: A
   Bibliometric Analysis From 2010 to 2020
SO FRONTIERS IN PUBLIC HEALTH
LA English
DT Article
DE machine learning; bibliometric analysis; CiteSpace; trend; artificial
   intelligence; rehabilitation robot
ID EXOSKELETON; THERAPY; STROKE
AB Background: In recent years, with the development of medical science and artificial intelligence, research on rehabilitation robots has gained more and more attention, for nearly 10 years in the Web of Science database by journal of rehabilitation robot-related research literature analysis, to parse and track rehabilitation robot research hotspot and front, and provide some guidance for future research.Methods: This study employed computer retrieval of rehabilitation robot-related research published in the core data collection of the Web of Science database from 2010 to 2020, using CiteSpace 5.7 visualization software. The hotspots and frontiers of rehabilitation robot research are analyzed from the aspects of high-influence countries or regions, institutions, authors, high-frequency keywords, and emergent words.Results: A total of 3,194 articles were included. In recent years, the research on rehabilitation robots has been continuously hot, and the annual publication of relevant literature has shown a trend of steady growth. The United States ranked first with 819 papers, and China ranked second with 603 papers. Northwestern University ranked first with 161 publications. R. Riener, a professor at the University of Zurich, Switzerland, ranked as the first author with 48 articles. The Journal of Neural Engineering and Rehabilitation has the most published research, with 211 publications. In the past 10 years, research has focused on intelligent control, task analysis, and the learning, performance, and reliability of rehabilitation robots to realize the natural and precise interaction between humans and machines. Research on neural rehabilitation robots, brain-computer interface, virtual reality, flexible wearables, task analysis, and exoskeletons has attracted more and more attention.Conclusions: At present, the brain-computer interface, virtual reality, flexible wearables, task analysis, and exoskeleton rehabilitation robots are the research trends and hotspots. Future research should focus on the application of machine learning (ML), dimensionality reduction, and feature engineering technologies in the research and development of rehabilitation robots to improve the speed and accuracy of algorithms. To achieve wide application and commercialization, future rehabilitation robots should also develop toward mass production and low cost. We should pay attention to the functional needs of patients, strengthen multidisciplinary communication and cooperation, and promote rehabilitation robots to better serve the rehabilitation medical field.
C1 [Xue, Xiali; Deng, Zhongyi; Tu, Huan; Kong, Dezhi; Li, Ning] Chengdu Sport Univ, Inst Sports Med & Hlth, Chengdu, Peoples R China.
   [Yang, Xinwei] Chengdu Sport Univ, Sch Sports Med & Hlth, Chengdu, Peoples R China.
   [Xu, Fan] Chengdu Med Coll, Sch Publ Hlth, Chengdu, Peoples R China.
C3 Chengdu Sport University; Chengdu Sport University; Chengdu Medical
   College
RP Li, N (corresponding author), Chengdu Sport Univ, Inst Sports Med & Hlth, Chengdu, Peoples R China.; Xu, F (corresponding author), Chengdu Med Coll, Sch Publ Hlth, Chengdu, Peoples R China.
EM lining@cdsu.edu.cn; xufan@cmc.edu.cn
RI XU, FAN/HLW-1600-2023; xue, xiali/KUF-2917-2024; LI, Ning/AGU-3177-2022
OI XU, FAN/0000-0001-9984-2854; xue, xiali/0000-0003-0727-6459; LI,
   Ning/0000-0002-0394-933X
FU Key Laboratory of Sports Medicine of Sichuan Province, Institute of
   Sports Medicine and Health, Chengdu Sport University [2021-A030];
   National Key Research and Development Program of China [2018YFF0300904]
FX & nbsp;This work was supported by the Key Laboratory of Sports Medicine
   of Sichuan Province, Institute of Sports Medicine and Health, Chengdu
   Sport University (No. 2021-A030) and the National Key Research and
   Development Program of China (No. 2018YFF0300904).
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NR 67
TC 9
Z9 11
U1 6
U2 89
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 2296-2565
J9 FRONT PUBLIC HEALTH
JI Front. Public Health
PD JAN 11
PY 2022
VL 9
AR 806723
DI 10.3389/fpubh.2021.806723
PG 17
WC Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Public, Environmental & Occupational Health
GA ZE3AP
UT WOS:000758759800001
PM 35087788
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU Walstad, WB
AF Walstad, WB
TI Improving assessment in university economics
SO JOURNAL OF ECONOMIC EDUCATION
LA English
DT Article
DE active learning; assessment; economics courses; research; testing
ID STUDENT; COURSES
AB The author discusses the following seven issues affecting assessment of undergraduates in universities: decisionmaking and the selection of tests, the use of written and oral assignments to measure learning, the characteristics of grades and portfolios for evaluating students, opportunities for self-assessment and feedback to instructors, retention of learning and the testing for higher-ordered thinking, the psychology of students in the economics classroom, and the development of new tests as public goods. The author suggests ways that economics faculty can add new dimensions to their assessment practices, improve their understanding of assessment choices, use assessment to enhance the quality of student thinking, and conduct research studies on assessment questions.
C1 Univ Nebraska, Natl Ctr Res Econ Educ, Lincoln, NE 68588 USA.
C3 University of Nebraska System; University of Nebraska Lincoln
RP Walstad, WB (corresponding author), Univ Nebraska, Natl Ctr Res Econ Educ, Lincoln, NE 68588 USA.
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NR 31
TC 51
Z9 59
U1 0
U2 9
PU HELDREF PUBLICATIONS
PI WASHINGTON
PA 1319 EIGHTEENTH ST NW, WASHINGTON, DC 20036-1802 USA
SN 0022-0485
J9 J ECON EDUC
JI J. Econ. Educ.
PD SUM
PY 2001
VL 32
IS 3
BP 281
EP 294
DI 10.2307/1183385
PG 14
WC Economics; Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Business & Economics; Education & Educational Research
GA 447RL
UT WOS:000169586200008
DA 2024-09-05
ER
PT C
AU Yao, QP
   Guo, L
AF Yao, Qipeng
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SO DATA SCIENCE
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT 2nd International Conference on Data Science (ICDS)
CY AUG 08-09, 2015
CL Univ Technol Sydney, Sydney, AUSTRALIA
HO Univ Technol Sydney
DE Influence minimization; Blocking nodes; Social networks
ID DIFFUSION; NODES
AB In this paper, we address the problem of minimizing the negative influence of undesirable things in a network by blocking a limited number of nodes from a topic modeling perspective. When undesirable thing such as a rumor or an infection emerges in a social network and part of users have already been infected, our goal is to minimize the size of ultimately infected users by blocking k nodes outside the infected set. We first employ the HDP-LDA and KL divergence to analysis the influence and relevance from a topic modeling perspective. Then two topic-aware heuristics based on betweenness and out-degree for finding approximate solutions to this problem are proposed. Using two real networks, we demonstrate experimentally the high performance of the proposed models and learning schemes.
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C3 Chinese Academy of Sciences; Institute of Information Engineering, CAS;
   Beijing University of Posts & Telecommunications
RP Yao, QP (corresponding author), Chinese Acad Sci, Inst Informat Engn, Beijing 100093, Peoples R China.
EM yaoqipeng0706@gmail.com; guoli@iie.ac.cn
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NR 35
TC 3
Z9 3
U1 0
U2 7
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 0302-9743
EI 1611-3349
BN 978-3-319-24474-7; 978-3-319-24473-0
J9 LECT NOTES COMPUT SC
PY 2015
VL 9208
BP 6
EP 15
DI 10.1007/978-3-319-24474-7_2
PG 10
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Software Engineering; Computer Science,
   Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BE0HB
UT WOS:000366018100002
DA 2024-09-05
ER
PT J
AU Akrami, NE
   Hanine, M
   Flores, ES
   Aray, DG
   Ashraf, I
AF Akrami, Nouhaila El
   Hanine, Mohamed
   Flores, Emmanuel Soriano
   Aray, Daniel Gavilanes
   Ashraf, Imran
TI Unleashing the Potential of Blockchain and Machine Learning: Insights
   and Emerging Trends From Bibliometric Analysis
SO IEEE ACCESS
LA English
DT Article
DE Blockchain; machine learning; bibliometric analysis; network
   visualization
ID ARTIFICIAL-INTELLIGENCE; SMART; AI; CRYPTOCURRENCIES; INTERNET; SYSTEM;
   HEALTH; IOT
AB Blockchain and machine learning (ML) has garnered growing interest as cutting-edge technologies that have witnessed tremendous strides in their respective domains. Blockchain technology provides a decentralized and immutable ledger, enabling secure and transparent transactions without intermediaries. Alternatively, ML is a sub-field of artificial intelligence (AI) that empowers systems to enhance their performance by learning from data. The integration of these data-driven paradigms holds the potential to reinforce data privacy and security, improve data analysis accuracy, and automate complex processes. The confluence of blockchain and ML has sparked increasing interest among scholars and researchers. Therefore, a bibliometric analysis is carried out to investigate the key focus areas, hotspots, potential prospects, and dynamical aspects of the field. This paper evaluates 700 manuscripts drawn from the Web of Science (WoS) core collection database, spanning from 2017 to 2022. The analysis is conducted using advanced bibliometric tools (e.g., Bibliometrix R, VOSviewer, and CiteSpace) to assess various aspects of the research area regarding publication productivity, influential articles, prolific authors, the productivity of academic countries and institutions, as well as the intellectual structure in terms of hot topics and emerging trends. The findings suggest that upcoming research should focus on blockchain technology, AI-powered 5G networks, industrial cyber-physical systems, IoT environments, and autonomous vehicles. This paper provides a valuable foundation for both academic scholars and practitioners as they contemplate future projects on the integration of blockchain and ML.
C1 [Akrami, Nouhaila El; Hanine, Mohamed] Chouaib Doukkali Univ, Natl Sch Appl Sci, Lab Informat Technol, El Jadida 24002, Morocco.
   [Flores, Emmanuel Soriano; Aray, Daniel Gavilanes] Univ Europea Atlantico, Santander 39011, Spain.
   [Flores, Emmanuel Soriano] Univ Int Iberoamer, Dept Informat & Commun Engn, Campeche 24560, Mexico.
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   [Aray, Daniel Gavilanes] Univ Int Cuanza, Kuito, Bie, Angola.
   [Aray, Daniel Gavilanes] Fdn Univ Int Colombia, Bogota 111311, Colombia.
   [Ashraf, Imran] Yeungnam Univ, Dept Informat & Commun Engn, Gyongsan 38541, South Korea.
C3 Chouaib Doukkali University of El Jadida; Yeungnam University
RP Hanine, M (corresponding author), Chouaib Doukkali Univ, Natl Sch Appl Sci, Lab Informat Technol, El Jadida 24002, Morocco.; Ashraf, I (corresponding author), Yeungnam Univ, Dept Informat & Commun Engn, Gyongsan 38541, South Korea.
EM hanine.m@ucd.ac.ma; ashrafimran@live.com
RI Hanine, Mohamed/AAC-1879-2021
OI Hanine, Mohamed/0000-0001-5981-2511; Ashraf, Imran/0000-0002-8271-6496
FU European University of Atlantics
FX This work was supported by the European University of Atlantics.
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NR 78
TC 10
Z9 10
U1 5
U2 20
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2023
VL 11
BP 78879
EP 78903
DI 10.1109/ACCESS.2023.3298371
PG 25
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA O2CS4
UT WOS:001041956300001
OA gold
DA 2024-09-05
ER
PT C
AU Makawana, PR
   Jhaveri, RH
AF Makawana, Pooja R.
   Jhaveri, Rutvij H.
BE Hu, YC
   Tiwari, S
   Mishra, KK
   Trivedi, MC
TI A Bibliometric Analysis of Recent Research on Machine Learning for Cyber
   Security
SO INTELLIGENT COMMUNICATION AND COMPUTATIONAL TECHNOLOGIES
SE Lecture Notes in Networks and Systems
LA English
DT Proceedings Paper
CT International Conference on Internet of Things for Technological
   Development (IoT4TD)
CY APR 01-02, 2017
CL Gandhinagar, INDIA
DE Machine learning; Cybersecurity; Trend analysis; Graphical
   interpretation
AB In today's world a huge amount of information is shared around the globe using internet. While connected to cyber word, cybersecurity is an increasing problem. Nowadays, various machine learning techniques are used to deal with cybersecurity threats. To enlighten the researchers about recent trends in this research area, we have analyzed 149 research papers from January 2015 to December 2016 and present a graphical and organized view of the referred research works. We observe that machine learning for cybersecurity has a great potential in carrying out further research. We have carried out bibliometric analysis by categorizing the referred papers using the method of implementation, article type, publishers and article efficiency. This analysis will provide insights for researchers, students, publishers and experts to study current research trends in the area of machine learning for cybersecurity.
C1 [Makawana, Pooja R.; Jhaveri, Rutvij H.] Shri Sad Vidya Mandal Inst Technol, Dept Informat Technol, Bharuch, India.
RP Makawana, PR (corresponding author), Shri Sad Vidya Mandal Inst Technol, Dept Informat Technol, Bharuch, India.
EM poojakatariya1408@gmail.com; rhj_svmit@yahoo.com
RI Jhaveri, Rutvij H./A-5354-2018
OI Jhaveri, Rutvij H./0000-0002-3285-7346; makawana,
   pooja/0000-0001-6705-0393
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NR 8
TC 9
Z9 11
U1 0
U2 19
PU SPRINGER-VERLAG SINGAPORE PTE LTD
PI SINGAPORE
PA 152 BEACH ROAD, #21-01/04 GATEWAY EAST, SINGAPORE, 189721, SINGAPORE
SN 2367-3370
EI 2367-3389
BN 978-981-10-5523-2; 978-981-10-5522-5
J9 LECT NOTE NETW SYST
PY 2018
VL 19
BP 213
EP 226
DI 10.1007/978-981-10-5523-2_20
PG 14
WC Computer Science, Artificial Intelligence; Telecommunications
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Telecommunications
GA BL1UF
UT WOS:000448431100020
DA 2024-09-05
ER
PT J
AU Pereira, RA
   Puga, MED
   Atallah, AN
   Macedo, EC
   Macedo, CR
AF Pereira, Rogerio Aparecido
   dos Santos Puga, Maria Eduarda
   Atallah, Alvaro Nagib
   Macedo, Elizeu Coutinho
   Macedo, Cristiane Rufino
TI lilacs search strategy for systematic reviews of diagnostic test
   accuracy studies
SO HEALTH INFORMATION AND LIBRARIES JOURNAL
LA English
DT Article
DE bibliographic databases; precision; recall; search strategies
ID DA-SAUDE LILACS; TRIALS
AB Background There are few publications on search strategies to identify diagnostic test accuracy (DTA) studies in lilacs. Objective To translate and customise medline search strategies for use in lilacs and assess their retrieval of studies in Cochrane DTA systematic reviews. Method We developed a six-step process to translate and customise medline search strategies for use in lilacs (iAHx interface). We identified medline search strategies of published Cochrane DTA reviews, translated/customised them for use in lilacs, ran searches in lilacs and compared the retrieval results of our translated search strategy versus the one used in the published reviews. Results Our lilacs search strategies translated/customised from the medline strategies retrieved studies in 70 Cochrane DTA reviews. Only 29 of these reviews stated that they had searched the lilacs database and 21 published their lilacs search strategies. Few had used the lilacs database search tools, none exploded the subject headings, and 86% used only English terms. Conclusion Translating and tailoring a medline search strategy for the lilacs database resulted in the retrieval of DTA studies that would have been missed otherwise.
C1 [Pereira, Rogerio Aparecido] Univ Fed Sao Paulo, Inst Fed Sao Paulo, Evidence Based Dept, Sao Paulo, Brazil.
   [Pereira, Rogerio Aparecido] Univ Fed Sao Paulo, Leforte Hosp, Sao Paulo, Brazil.
   [dos Santos Puga, Maria Eduarda] Univ Fed Sao Paulo, Unifesp, Brazilian Cochrane Ctr, Sao Paulo, Brazil.
   [Atallah, Alvaro Nagib; Macedo, Cristiane Rufino] Univ Fed Sao Paulo, Unifesp, EPM, Brazilian Cochrane Ctr, Sao Paulo, Brazil.
   [Macedo, Elizeu Coutinho] Univ Prebiteriana Mackenzie, Ctr Hlth & Biol Sci, Social & Cognit Neurosci Lab, Sao Paulo, Brazil.
   [Macedo, Elizeu Coutinho] Univ Prebiteriana Mackenzie, Ctr Hlth & Biol Sci, Dev Disorders Program, Sao Paulo, Brazil.
C3 Universidade Federal de Sao Paulo (UNIFESP); Instituto Federal de Sao
   Paulo (IFSP); Universidade Federal de Sao Paulo (UNIFESP); Universidade
   Federal de Sao Paulo (UNIFESP); Universidade Federal de Sao Paulo
   (UNIFESP); Universidade Presbiteriana Mackenzie; Universidade
   Presbiteriana Mackenzie
RP Puga, MED (corresponding author), Rua Borges Lagoa 564,Cj 63, BR-04003800 Sao Paulo, SP, Brazil.
EM mespuga@yahoo.com.br
RI macedo, cristiane R/A-8987-2013; Macedo, Elizeu C/N-4387-2015; Macedo,
   Elizeu Coutinho/AAF-1775-2020
OI Macedo, Elizeu Coutinho/0000-0003-1412-3450
FU CNPQ [311479/2015-4] Funding Source: Medline
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   Whiting P, 2008, J CLIN EPIDEMIOL, V61, P357, DOI 10.1016/j.jclinepi.2007.05.013
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NR 96
TC 1
Z9 1
U1 0
U2 11
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1471-1834
EI 1471-1842
J9 HEALTH INFO LIBR J
JI Heatlth Info. Libr. J.
PD SEP
PY 2019
VL 36
IS 3
BP 223
EP 243
DI 10.1111/hir.12263
PG 21
WC Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science
GA IZ1ZQ
UT WOS:000486886900003
PM 31271504
OA Bronze
DA 2024-09-05
ER
PT J
AU Montejo-Ráez, A
   Ureña-López, LA
   Steinberger, R
AF Montejo-Raez, Arturo
   Alfonso Urena-Lopez, L.
   Steinberger, Ralf
TI Text Categorization using bibliographic records: beyond document content
SO PROCESAMIENTO DEL LENGUAJE NATURAL
LA English
DT Article
DE text categorization; machine learning; digital libraries
AB This paper studies the use of direrent sources of information for performing a text classiffcation task. The growing number of digital libraries imposes a review of the available data from those databases. Some experiments applying diffrerent base classiffers for a multi-label classffer in the domain of High Energy Physics on several of these possible sources have been carried out. Results show that the use of metadata is almost as good as the full-text version of papers.
C1 [Montejo-Raez, Arturo; Alfonso Urena-Lopez, L.] Univ Jaen, Dept Comp Sci, Jaen, Spain.
   [Steinberger, Ralf] European Commiss, IPSC, Joint Res Ctr, Ispra, Italy.
C3 Universidad de Jaen; European Commission Joint Research Centre; EC JRC
   ISPRA Site
RP Montejo-Ráez, A (corresponding author), Univ Jaen, Dept Comp Sci, Jaen, Spain.
EM amontejo@ujaen.es; laurena@ujaen.es; ralf.steinberger@jrc.it
FU Spanish Minister of Science and Technology [TIC2003-07158-004-04]
FX This work is partially ffnanced by the Spanish Minister of Science and
   Technology, by means of project TIC2003-07158-004-04.
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NR 20
TC 3
Z9 3
U1 0
U2 0
PU SOC ESPANOLA PROCESAMIENTO LENGUAJE NATURAL-SEPLN
PI ALICANTE
PA DEPT LENGUAJES & SISTEMAS INFORMATICOS, UNIV ALICANTE, APDO 99,
   ALICANTE, 03080, SPAIN
SN 1135-5948
EI 1989-7553
J9 PROCES LENG NAT
JI Proces. Leng. Nat.
PY 2005
IS 35
BP 119
EP 126
PG 8
WC Computer Science, Artificial Intelligence; Linguistics
WE Emerging Sources Citation Index (ESCI)
SC Computer Science; Linguistics
GA V29FZ
UT WOS:000215482200015
DA 2024-09-05
ER
PT J
AU Ebert-May, D
   Batzli, J
   Lim, H
AF Ebert-May, D
   Batzli, J
   Lim, H
TI Disciplinary research strategies for assessment of learning
SO BIOSCIENCE
LA English
DT Article
DE assessment; introductory biology; carbon cycle; active learning;
   misconceptions
ID RESPIRATION
AB Science faculty who want to improve instructional strategies need to design appropriate methods for assessing and analyzing classroom data to determine the effectiveness of their approaches to learning. We used systematic strategies derived from methods of discipline-based science research to design problems to assess students' understanding of the carbon cycle in two introductory biology courses for science majors. Among typical misconceptions are the ideas that gaseous carbon dioxide is not respired during decomposition by organisms in the soil and that plants acquire carbon from the soil rather than from the air through leaves during photosynthesis. Diagnostic problems provided data on students' understanding and misconceptions. In-class instruction, problems, and laboratories were designed to focus on student misconceptions and provided formative assessment. After two semesters, results indicated that the majority of students responded accurately; however, 20 to 40 percent of the students maintained misconceptions even after instruction. Assessment strategies enabled us to collect, analyze, and report data that will influence future instruction.
C1 Michigan State Univ, Dept Plant Biol, E Lansing, MI 48824 USA.
   Michigan State Univ, Div Sci & Math Educ, E Lansing, MI 48824 USA.
   Univ Wisconsin, Biol Core Curriculum, Madison, WI 53706 USA.
C3 Michigan State University; Michigan State University; University of
   Wisconsin System; University of Wisconsin Madison
RP Michigan State Univ, Dept Plant Biol, E Lansing, MI 48824 USA.
EM ebertmay@msu.edu
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NR 21
TC 52
Z9 66
U1 1
U2 16
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 0006-3568
EI 1525-3244
J9 BIOSCIENCE
JI Bioscience
PD DEC
PY 2003
VL 53
IS 12
BP 1221
EP 1228
DI 10.1641/0006-3568(2003)053[1221:DRSFAO]2.0.CO;2
PG 8
WC Biology
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Life Sciences & Biomedicine - Other Topics
GA 754NA
UT WOS:000187322200011
OA Bronze
DA 2024-09-05
ER
PT J
AU Halverson, LR
   Graham, CR
   Spring, KJ
   Drysdale, JS
AF Halverson, Lisa R.
   Graham, Charles R.
   Spring, Kristian J.
   Drysdale, Jeffery S.
TI An analysis of high impact scholarship and publication trends in blended
   learning
SO DISTANCE EDUCATION
LA English
DT Article
DE blended learning; hybrid learning; publication impact and trends; online
   learning; scholarship
ID FACE-TO-FACE; INSTRUCTIONAL-MODEL; ONLINE; STUDENTS; EDUCATION;
   OUTCOMES; COURSES; ENVIRONMENTS; SATISFACTION; PERCEPTIONS
AB Blended learning is a diverse and expanding area of design and inquiry that combines face-to-face and online modalities. As blended learning research matures, numerous voices enter the conversation. This study begins the search for the center of this emerging area of study by finding the most cited scholarship on blended learning. Using Harzing's Publish or Perish software (http://www.harzing.com/pop.htm), we determined the most frequently cited books, book chapters, and articles on the subject of blended learning, as well as the journals in which these highly cited articles appeared. Through these findings we offer some conclusions about where the conversations about blended learning are happening, which scholars are at the forefront of these conversations, and other emerging trends in blended learning scholarship.
C1 [Halverson, Lisa R.; Spring, Kristian J.; Drysdale, Jeffery S.] Brigham Young Univ, Instruct Psychol & Technol Program, Provo, UT 84602 USA.
C3 Brigham Young University
RP Halverson, LR (corresponding author), Brigham Young Univ, Instruct Psychol & Technol Program, Provo, UT 84602 USA.
EM lisa.halverson@byu.edu
OI Halverson, Lisa/0000-0001-8867-6598; Graham, Charles
   R./0000-0001-8598-2602
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NR 141
TC 64
Z9 138
U1 1
U2 37
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0158-7919
EI 1475-0198
J9 DISTANCE EDUC
JI Distance Educ.
PY 2012
VL 33
IS 3
BP 381
EP 413
DI 10.1080/01587919.2012.723166
PG 33
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA 034SW
UT WOS:000310894200007
DA 2024-09-05
ER
PT J
AU Vangumalli, DR
   Nikolopoulos, K
   Litsiou, K
AF Vangumalli, Dinesh Reddy
   Nikolopoulos, Konstantinos
   Litsiou, Konstantia
TI Aggregate Selection, Individual Selection, and Cluster Selection: An
   Empirical Evaluation and Implications for Systems Research
SO CYBERNETICS AND SYSTEMS
LA English
DT Article
DE Business analytics; clustering; forecasting; method selection; nearest
   neighbors; random forests
ID TIME; CLASSIFICATION; INFORMATION; REGRESSION; TRENDS
AB Data analysts when forecasting large number of time series, they regularly employ one of the following methodological approaches: either select a single forecasting method for the entire dataset (aggregate selection), or use the best forecasting method for each time series (individual selection). There is evidence in the predictive analytics literature that the former is more robust than the latter, as in individual selection you tend to overfit models to the data. A third approach is to first identify homogeneous clusters within the dataset, and then select a single forecasting method for each cluster (cluster selection). To that end, we examine three machine learning clustering methods: k-medoids, k-NN and random forests. The evaluation is performed in the 645 yearly series of the M3 competition. The empirical evidence suggests: (a) random forests provide the best clusters for the sequential forecasting task, and (b) cluster selection has the potential to outperform aggregate selection.
C1 [Vangumalli, Dinesh Reddy] Resolut Life, Washington, DC USA.
   [Nikolopoulos, Konstantinos] Univ Durham, Business Sch, Durham, England.
   [Litsiou, Konstantia] Manchester Metropolitan Univ, Dept Mkt Retail & Tourism, Business Sch, Manchester, Lancs, England.
C3 Durham University; Manchester Metropolitan University
RP Nikolopoulos, K (corresponding author), Univ Durham, Business Sch, Durham, England.
EM kostas.nikolopoulos@durham.ac.uk
OI Litsiou, Konstantia/0009-0009-6157-7683
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NR 43
TC 0
Z9 0
U1 0
U2 4
PU TAYLOR & FRANCIS INC
PI PHILADELPHIA
PA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA
SN 0196-9722
EI 1087-6553
J9 CYBERNET SYST
JI Cybern. Syst.
PD JUL 16
PY 2021
VL 52
IS 7
BP 553
EP 578
DI 10.1080/01969722.2021.1902049
EA APR 2021
PG 26
WC Computer Science, Cybernetics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA TJ3PG
UT WOS:000661315100001
OA Green Accepted
DA 2024-09-05
ER
PT J
AU Sarbay, I
   Berikol, GB
   Özturan, IU
AF Sarbay, Ibrahim
   Berikol, Goeksu Bozdereli
   Ozturan, Ibrahim Ulas
TI Performance of emergency triage prediction of an open access natural
   language processing based chatbot application (ChatGPT): A preliminary,
   scenario-based cross-sectional study
SO TURKISH JOURNAL OF EMERGENCY MEDICINE
LA English
DT Article
DE Chatbot; ChatGPT; emergency severity index; triage
ID CHIEF COMPLAINT
AB OBJECTIVES: Artificial intelligence companies have been increasing their initiatives recently to improve the results of chatbots, which are software programs that can converse with a human in natural language. The role of chatbots in health care is deemed worthy of research. OpenAI's ChatGPT is a supervised and empowered machine learning-based chatbot. The aim of this study was to determine the performance of ChatGPT in emergency medicine (EM) triage prediction. METHODS: This was a preliminary, cross-sectional study conducted with case scenarios generated by the researchers based on the emergency severity index (ESI) handbook v4 cases. Two independent EM specialists who were experts in the ESI triage scale determined the triage categories for each case. A third independent EM specialist was consulted as arbiter, if necessary. Consensus results for each case scenario were assumed as the reference triage category. Subsequently, each case scenario was queried with ChatGPT and the answer was recorded as the index triage category. Inconsistent classifications between the ChatGPT and reference category were defined as over-triage (false positive) or under-triage (false negative). RESULTS: Fifty case scenarios were assessed in the study. Reliability analysis showed a fair agreement between EM specialists and ChatGPT (Cohen's Kappa: 0.341). Eleven cases (22%) were over triaged and 9 (18%) cases were under triaged by ChatGPT. In 9 cases (18%), ChatGPT reported two consecutive triage categories, one of which matched the expert consensus. It had an overall sensitivity of 57.1% (95% confidence interval [CI]: 34-78.2), specificity of 34.5% (95% CI: 17.9-54.3), positive predictive value (PPV) of 38.7% (95% CI: 21.8-57.8), negative predictive value (NPV) of 52.6 (95% CI: 28.9-75.6), and an F1 score of 0.461. In high acuity cases (ESI-1 and ESI-2), ChatGPT showed a sensitivity of 76.2% (95% CI: 52.8-91.8), specificity of 93.1% (95% CI: 77.2-99.2), PPV of 88.9% (95% CI: 65.3-98.6), NPV of 84.4 (95% CI: 67.2-94.7), and an F1 score of 0.821. The receiver operating characteristic curve showed an area under the curve of 0.846 (95% CI: 0.724-0.969, P < 0.001) for high acuity cases. CONCLUSION: The performance of ChatGPT was best when predicting high acuity cases (ESI-1 and ESI-2). It may be useful when determining the cases requiring critical care. When trained with more medical knowledge, ChatGPT may be more accurate for other triage category predictions.
C1 [Sarbay, Ibrahim] Kesan State Hosp, Dept Emergency Med, Asagi Zaferiye Mahallesi Evrese Caddesi, Edirne, Turkiye.
   [Berikol, Goeksu Bozdereli] Bakirkoy Dr Sadi Konuk Training & Res Hosp, Dept Emergency Med, Istanbul, Turkiye.
   [Ozturan, Ibrahim Ulas] Kocaeli Univ, Fac Med, Dept Emergency Med, Kocaeli, Turkiye.
   [Ozturan, Ibrahim Ulas] Acibadem Univ, Inst Hlth Sci, Dept Med Educ, Istanbul, Turkiye.
C3 Bakirkoy Dr. Sadi Konuk Research & Training Hospital; Kocaeli
   University; Acibadem University
RP Sarbay, I (corresponding author), Kesan State Hosp, Dept Emergency Med, Asagi Zaferiye Mahallesi Evrese Caddesi, Edirne, Turkiye.
EM ibrahimsar@gmail.com
RI Sarbay, İbrahim/JDD-3003-2023; Berikol, Goksu/ABM-3605-2022
OI Sarbay, İbrahim/0000-0001-8804-2501; Berikol, Goksu/0000-0002-4529-3578
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NR 30
TC 15
Z9 15
U1 3
U2 9
PU WOLTERS KLUWER MEDKNOW PUBLICATIONS
PI MUMBAI
PA WOLTERS KLUWER INDIA PVT LTD , A-202, 2ND FLR, QUBE, C T S  NO 1498A-2
   VILLAGE MAROL, ANDHERI EAST, MUMBAI, Maharashtra, INDIA
SN 2452-2473
J9 TURK J EMERG MED
JI Turk. J. Emerg. Med.
PD JUL-SEP
PY 2023
VL 23
IS 3
BP 156
EP +
DI 10.4103/tjem.tjem_79_23
PG 9
WC Emergency Medicine
WE Emerging Sources Citation Index (ESCI)
SC Emergency Medicine
GA L9QI8
UT WOS:001026537900004
PM 37529789
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU Hohmann, E
   Wetzler, MJ
   D'Agostino, RB
AF Hohmann, Erik
   Wetzler, Merrick J.
   D'Agostino, Ralph B.
TI Research Pearls: The Significance of Statistics and Perils of Pooling.
   Part 2: Predictive Modeling
SO ARTHROSCOPY-THE JOURNAL OF ARTHROSCOPIC AND RELATED SURGERY
LA English
DT Article
ID SURVIVAL ANALYSIS; CLINICAL-TRIALS; DESCRIPTIVE STATISTICS; REGRESSION;
   PROGNOSIS; IMPACT; PRIMER; FIT
AB The focus of predictive modeling or predictive analytics is to use statistical techniques to predict outcomes and/or the results of an intervention or observation for patients that are conditional on a specific set of measurements taken on the patients prior to the outcomes occurring. Statistical methods to estimate these models include using such techniques as Bayesian methods; data mining methods, such as machine learning; and classical statistical models of regression such as logistic (for binary outcomes), linear (for continuous outcomes), and survival (Cox proportional hazards) for time-to-event outcomes. A Bayesian approach incorporates a prior estimate that the outcome of interest is true, which is made prior to data collection, and then this prior probability is updated to reflect the information provided by the data. In principle, data mining uses specific algorithms to identify patterns in data sets and allows a researcher to make predictions about outcomes. Regression models describe the relations between 2 or more variables where the primary difference among methods concerns the form of the outcome variable, whether it is measured as a binary variable (i.e., success/failure), continuous measure (i.e., pain score at 6 months postop), or time to event (i.e., time to surgical revision). The outcome variable is the variable of interest, and the predictor variable(s) are used to predict outcomes. The predictor variable is also referred to as the independent variable and is assumed to be something the researcher can modify in order to see its impact on the outcome (i.e., using one of several possible surgical approaches). Survival analysis investigates the time until an event occurs. This can be an event such as failure of a medical device or death. It allows the inclusion of censored data, meaning that not all patients need to have the event (i.e., die) prior to the study's completion.
C1 [Hohmann, Erik] Univ Queensland, Med Sch, Brisbane, Qld, Australia.
   [Hohmann, Erik] Univ Pretoria, Med Sch, Pretoria, South Africa.
   [Wetzler, Merrick J.] South Jersey Orthoped, Voorhees, NJ USA.
   [D'Agostino, Ralph B.] Wake Forest Sch Med, Winston Salem, NC USA.
C3 University of Queensland; University of Pretoria; Wake Forest University
RP Hohmann, E (corresponding author), Valiant Healthcare Houston Methodist Grp, POB 414296, Dubai, U Arab Emirates.
EM ehohmann@hotmail.com
RI Hohmann, Erik/B-1922-2012; Dagostino Jr, Ralph/C-4060-2017
OI Dagostino Jr, Ralph/0000-0002-3550-8395
FU Arthroscopy: The Journal of Arthroscopic and Related Surgery and Storz
FX The authors report the following potential conflicts of interest or
   sources of funding: M.J.W. received support from Arthroscopy: The
   Journal of Arthroscopic and Related Surgery and Storz.
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NR 25
TC 15
Z9 16
U1 1
U2 6
PU W B SAUNDERS CO-ELSEVIER INC
PI PHILADELPHIA
PA 1600 JOHN F KENNEDY BOULEVARD, STE 1800, PHILADELPHIA, PA 19103-2899 USA
SN 0749-8063
EI 1526-3231
J9 ARTHROSCOPY
JI Arthroscopy
PD JUL
PY 2017
VL 33
IS 7
BP 1423
EP 1432
DI 10.1016/j.arthro.2017.01.054
PG 10
WC Orthopedics; Sport Sciences; Surgery
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Orthopedics; Sport Sciences; Surgery
GA FB2OH
UT WOS:000405982600028
PM 28457678
OA Green Submitted, Green Accepted
DA 2024-09-05
ER
PT J
AU Dorta-González, P
AF Dorta-Gonzalez, Pablo
TI A Multiple Linear Regression Analysis to Measure the Journal
   Contribution to the Social Attention of Research
SO AXIOMS
LA English
DT Article
DE altmetrics; social mentions; multiple linear regression; public
   attention to research; socially influential journal
ID RESEARCH COLLABORATION; CITATION COUNTS; IMPACT; ALTMETRICS; METRICS;
   MEDIA; WEB
AB This paper proposes a three-year average of social attention as a more reliable measure of the social impact of journals since the social attention of research can vary widely among scientific articles, even within the same journal. The proposed measure is used to evaluate a journal's contribution to social attention in comparison to other bibliometric indicators. This study uses Dimensions as a data source and examines research articles from 76 disciplinary libraries and information science journals through multiple linear regression analysis. This study identifies socially influential journals whose contribution to social attention is twice that of scholarly impact, as measured by citations. In addition, this study finds that the number of authors and open access have a moderate effect on social attention, while the journal impact factor has a negative effect and funding has a small effect.
C1 [Dorta-Gonzalez, Pablo] Univ Las Palmas Gran Canaria, Inst Tourism & Sustainable Econ Dev TIDES, Campus Tafira, Las Palmas Gran Canaria 35017, Spain.
C3 Universidad de Las Palmas de Gran Canaria
RP Dorta-González, P (corresponding author), Univ Las Palmas Gran Canaria, Inst Tourism & Sustainable Econ Dev TIDES, Campus Tafira, Las Palmas Gran Canaria 35017, Spain.
EM pablo.dorta@ulpgc.es
RI Dorta-González, Pablo/C-6425-2009
OI Dorta-González, Pablo/0000-0003-0494-2903
CR [Anonymous], IS ALTM ATT SCOR CAL
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NR 27
TC 0
Z9 0
U1 4
U2 15
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2075-1680
J9 AXIOMS
JI Axioms
PD APR
PY 2023
VL 12
IS 4
AR 337
DI 10.3390/axioms12040337
PG 13
WC Mathematics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematics
GA E7KE2
UT WOS:000977281900001
OA Green Published, Green Submitted, gold
DA 2024-09-05
ER
PT J
AU Xu, LH
   Ding, K
   Lin, Y
AF Xu, Linhong
   Ding, Kun
   Lin, Yuan
TI Do negative citations reduce the impact of cited papers?
SO SCIENTOMETRICS
LA English
DT Article
DE Negative citation; Citation analysis; Citation polarity; SVM
ID REFERENCES; BEHAVIOR; QUALITY
AB Citation is an important process of scientific activities, reflecting the inheritance and development of knowledge. However, citations representing different sentiment polarities function differently in knowledge construction, especially negative citations holding critical views, which deserve more in-depth study. This paper selected papers on SVM from 1995 to 2020, and used the stratified random sampling method to obtain 3,337 citation sentences from 46,157 citations, coding several attributes such as citation polarity, to analyze the relationship between negative citation and the impact of cited paper and the role of negative citation in the development of SVM technology. The results of the study found that negative citations do not reduce the literature impact; papers with a certain negative citation ratio would have a higher impact; and the impact of those partially dismissed papers would be even higher. In addition, negative citation presents different characteristics in different periods of the development of SVM, which has a certain promotion effect on the improvement of this technology.
C1 [Xu, Linhong; Ding, Kun; Lin, Yuan] Dalian Univ Technol, Inst Sci Sci & Technol Management, WISE Lab, Dalian 116024, Peoples R China.
   [Xu, Linhong] Dalian Univ Foreign Languages, Software Inst, Dalian 116044, Peoples R China.
C3 Dalian University of Technology; Dalian University of Foreign Languages
RP Ding, K; Lin, Y (corresponding author), Dalian Univ Technol, Inst Sci Sci & Technol Management, WISE Lab, Dalian 116024, Peoples R China.
EM qingniao1203@163.com; dingk@dlut.edu.cn; zhlin@dlut.edu.cn
RI DING, KUN/HNJ-1709-2023
FU Natural Science Foundation of China [61976036]; Ministry of Education
   Humanities and Social Science Project [18YJCZH208]
FX This work is partially supported by Grant from the Natural Science
   Foundation of China (Nos. 61772103, 61806038), Ministry of Education
   Humanities and Social Science Project (Nos. 18YJCZH208), Natural Science
   Foundation of China (Nos. 61976036). We also thank the anonymous
   reviewers for their constructive comments and suggestions.
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NR 35
TC 6
Z9 6
U1 7
U2 48
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD FEB
PY 2022
VL 127
IS 2
BP 1161
EP 1186
DI 10.1007/s11192-021-04214-4
EA JAN 2022
PG 26
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA YY7GU
UT WOS:000749747600005
DA 2024-09-05
ER
PT J
AU Zhu, YB
   Chen, XY
   Wang, G
   Zhong, ZC
   Zhuang, M
AF Zhu, Yuanbing
   Chen, Xueying
   Wang, Gang
   Zhong, Zuchang
   Zhuang, Meier
TI Research on the impact of home country patent level on outward foreign
   direct investment: Empirical analysis via equal part linear regression
   model and Grey Computing (Publication with Expression of Concern)
SO INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING EDUCATION
LA English
DT Article; Early Access; Publication with Expression of Concern
DE Patent level; outward foreign direct investment; equal part linear
   regression; Grey Computing
ID DETERMINANTS
AB From the practice of developed countries, countries with higher patent applications and PCT patent applications (such as the United States, China, Japan, the United Kingdom, Germany, etc.) have relatively higher outward foreign direct investment, and the actual data of provinces in China also show that with the improvement of the patent level in various provinces and cities, the intensity of outward foreign direct investment in each province and city has also increased. At present, there are relatively few research data and the research method is relatively single. Therefore, collecting panel data on China's 31 provinces from 2003 to 2016, this paper conducts an empirical analysis on the influence of patent level on outward foreign direct investment via analytical method of equal part linear regression and Grey Computing. By comparing analysis results with the model and the results with conventional linear regression model, the difference of different regression models is observed. Furthermore, the impact of China's patent level on China's inter-provincial outward foreign direct investment is further analyzed.
C1 [Zhu, Yuanbing] Jinan Univ, Sch Int Studies, Guangzhou, Peoples R China.
   [Chen, Xueying; Wang, Gang; Zhong, Zuchang; Zhuang, Meier] Guangdong Univ Foreign Studies, Sch Business, Guangzhou, Peoples R China.
C3 Jinan University; Guangdong University of Foreign Studies
RP Wang, G (corresponding author), Guangdong Univ Foreign Studies, Sch Business, Guangzhou, Peoples R China.
EM 13570105916@163.com
RI Li, Huizhen/JPX-2563-2023; Guo, yongqing/KDS-5864-2024
FU Humanities and Social Sciences Planning Project of the Ministry of
   Education [15YJA630067]; Soft Science Project of the Science and
   Technology Program of Guangdong Province [2015A070704055]; 12th
   Five-year Plan Program of Philosophy and Social Science in Guangdong
   Province [GD14CGL11]; National Natural Science Foundation of China
   [71673064, 71974039]; National Natural Science Foundation of Guangdong
   [2019A1515011475]; Innovation team project (HUMANITIES AND SOCIAL
   SCIENCES) of universities in Guangdong [2017WCXTD003]; Guangdong
   University of Foreign Studies [6]
FX The author(s) disclosed receipt of the following financial support for
   the research, authorship, and/or publication of this article: The
   research is supported by the Humanities and Social Sciences Planning
   Project of the Ministry of Education (Grant: 15YJA630067), Soft Science
   Project of the Science and Technology Program of Guangdong Province
   (Grant: 2015A070704055), and the 12th Five-year Plan Program of
   Philosophy and Social Science in Guangdong Province (Grant: GD14CGL11),
   the National Natural Science Foundation of China (Grant:
   71673064;71974039), the National Natural Science Foundation of Guangdong
   (Grant: 2019A1515011475), Innovation team project (HUMANITIES AND SOCIAL
   SCIENCES) of universities in Guangdong (Grant: 2017WCXTD003), Regional
   and national projects of Guangdong University of Foreign Studies in 2018
   (Grant: 6).
CR Chang SC, 2014, GLOBAL ECON REV, V43, P244, DOI 10.1080/1226508X.2014.930670
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NR 12
TC 2
Z9 2
U1 1
U2 16
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 0020-7209
EI 2050-4578
J9 INT J ELEC ENG EDUC
JI Int. J. Elec. Eng. Educ.
PD 2020 MAY 31
PY 2020
AR 0020720920922517
DI 10.1177/0020720920922517
EA MAY 2020
PG 15
WC Education, Scientific Disciplines; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Education & Educational Research; Engineering
GA LT6NC
UT WOS:000537184500001
DA 2024-09-05
ER
PT J
AU Liu, LJ
   Wu, H
   Wang, JW
   Yang, TY
AF Liu, Lanjun
   Wu, Han
   Wang, Junwu
   Yang, Tingyou
TI Research on the evaluation of the resilience of subway station projects
   to waterlogging disasters based on the projection pursuit model
SO MATHEMATICAL BIOSCIENCES AND ENGINEERING
LA English
DT Article
DE subway station project; waterlogging disasters; resilience capability;
   projection pursuit model; quantum particle swarm optimization
ID PARTICLE SWARM OPTIMIZATION; RISK-ASSESSMENT; ALGORITHM; POWER
AB To improve sustainable development, increasingly more attention has been paid to the evaluation of the resilience to waterlogging disasters. This paper proposed a projection pursuit model (PPM) improved by quantum particle swarm optimization (QPSO) for the evaluation of the resilience of subway station projects to waterlogging disasters. In view of the lack of research results related to the evaluation of the resilience of subway station projects to waterlogging disasters, 16 secondary indicators that affected the ability of subway station projects to recover from waterlogging disasters were identified from defense, recovery, and adaptability, for the first time. A PPM improved by QPSO was then proposed to effectively deal with the high-dimensional data about the resilience of subway station projects to waterlogging disasters. The QPSO was used to solve the best projection vector of the PPM, and interpolation algorithm was used to construct the mathematical model of evaluation. Finally, four station projects of Chengdu Metro Line 11 in China were selected for a case study analysis. The case study revealed that, among the secondary indicators, the emergency plan of construction order, the exercise frequency of emergency plans, and relief supplies had the greatest weights. The recovery was found to be the most important in the primary indicators. The values of the resilience of Lushan Avenue Station, Miaoeryan Station, Shenyang Road Station, and Tianfu CBD North Station to waterlogging disasters were found to be 2, 1.6571, 2.8318, and 3 respectively. This resilience ranking was consistent with the actual disaster situation in the flood season of 2019. In addition, the case study results showed that QPSO had the advantages of fewer parameter settings and a faster convergence speed as compared with PSO and the genetic algorithm.
C1 [Liu, Lanjun] Wuhan Inst Technol, Sch Civil Engn & Architecture, Wuhan 430070, Peoples R China.
   [Wu, Han; Wang, Junwu] Wuhan Univ Technol, Sch Civil Engn & Architecture, Wuhan 430070, Peoples R China.
   [Yang, Tingyou] CCTEB Infrastruct Construct Investment Co Ltd, Wuhan 430070, Peoples R China.
C3 Wuhan Institute of Technology; Wuhan University of Technology
RP Wu, H (corresponding author), Wuhan Univ Technol, Sch Civil Engn & Architecture, Wuhan 430070, Peoples R China.
EM wu.han@whut.edu.cn
RI Wu, Han/JXN-2469-2024
OI wu, han/0000-0001-5827-0159
FU Science and Technology Project of Wuhan Urban and Rural Construction
   Bureau, China [201943]
FX This paper is supported by the Science and Technology Project of Wuhan
   Urban and Rural Construction Bureau, China (201943).
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NR 58
TC 21
Z9 23
U1 13
U2 107
PU AMER INST MATHEMATICAL SCIENCES-AIMS
PI SPRINGFIELD
PA PO BOX 2604, SPRINGFIELD, MO 65801-2604 USA
SN 1547-1063
EI 1551-0018
J9 MATH BIOSCI ENG
JI Math. Biosci. Eng.
PY 2020
VL 17
IS 6
BP 7302
EP 7331
DI 10.3934/mbe.2020374
PG 30
WC Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology
GA QL6KW
UT WOS:000621193600009
PM 33378898
OA gold
DA 2024-09-05
ER
PT J
AU Boffa, E
   Maffei, A
AF Boffa, Eleonora
   Maffei, Antonio
TI How does Manufacturing Strategy Impact the Goals of a Firm? A Relational
   Framework Characterizing the Related Business Models' Components
SO MANAGEMENT AND PRODUCTION ENGINEERING REVIEW
LA English
DT Article
DE Business models; Manufacturing model; Scientometric analysis; Topic
   modelling; Latent Dirichlet Allocation
ID INDUSTRY 4.0; INNOVATION; PATTERNS
AB The fourth industrial revolution has resulted in technology advancements in the manufacturing industry. However, the innovation potential embedded in these technologies should be unlocked by a viable application, i.e., the business model (BM). The BM as a holistic concept featuring different interacting elements is thus emerging as a promising vehicle for innovation. Current BM research describes the entire domain but lacks depth in the characterization of its individual components. This paper investigates the available manufacturing literature through the lens of the BM concept performing a scientometric analysis. The results are presented in a relational framework that provides an in-depth characterization of the manufacturing element of the BM and highlights identified connections that link the BM components. This is the basis for tools that will support firms in developing manufacturing portfolios aligned with their strategic goals.
C1 [Boffa, Eleonora; Maffei, Antonio] KTH Royal Inst Technol, Prod Engn, Stockholm, Sweden.
   [Boffa, Eleonora] KTH royal Inst technol, Stockholm, Sweden.
C3 Royal Institute of Technology; Royal Institute of Technology
RP Boffa, E (corresponding author), KTH royal Inst technol, Stockholm, Sweden.
EM boffa@kth.se
OI Boffa, Eleonora/0000-0003-4847-3723; /0000-0002-0723-1712
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NR 43
TC 0
Z9 0
U1 0
U2 0
PU POLSKA AKAD NAUK, POLISH ACAD SCIENCES
PI WARSZAWA
PA PL DEFILAD 1, WARSZAWA, 00-901, POLAND
SN 2080-8208
EI 2082-1344
J9 MANAG PROD ENG REV
JI Manag. Prod. Eng. Rev.
PD JUN
PY 2023
VL 14
IS 2
BP 18
EP 36
DI 10.24425/mper.2023.146020
PG 19
WC Engineering, Industrial
WE Emerging Sources Citation Index (ESCI)
SC Engineering
GA O6OA6
UT WOS:001044966600002
OA gold
DA 2024-09-05
ER
PT J
AU Sharma, A
   Koohang, A
   Rana, NP
   Abed, SS
   Dwivedi, YK
AF Sharma, Anuj
   Koohang, Alex
   Rana, Nripendra P.
   Abed, Salma S.
   Dwivedi, Yogesh K.
TI Journal of Computer Information Systems: Intellectual and Conceptual
   Structure
SO JOURNAL OF COMPUTER INFORMATION SYSTEMS
LA English
DT Article
DE Scientometric analysis; topic modeling; structural topic models;
   co-citation analysis; keyword co-occurrence analysis
ID TECHNOLOGY ACCEPTANCE MODEL; USER ACCEPTANCE; CONSUMER-BEHAVIOR;
   PERCEIVED EASE; E-COMMERCE; COCITATION; KNOWLEDGE; CITATION; TRUST;
   USAGE
AB This study examines the intellectual and conceptual structure of the Journal of Computer Information Systems (JCIS) from 1995 to 2021. The evolution of the key topics and the performance of different actors like the key publications, authors, institutions, countries, etc., are reported using a hybrid methodology based upon scientometrics and topic modeling. The latent topics are discovered using structural topic models, and the temporal deviation in the topic prevalences from 1995 to 2021 is visualized. Further, this study reports the most prominent articles, themes, and collaboration patterns using co-citation network analysis, assessment of keywords co-occurrences, and exploration of coauthorship patterns. Finally, the disciplinary influences and knowledge exchange across disciplines are reported. The most significant findings from the study reveal that themes such as "Information Security and Privacy," "Social Commerce and Social Networking Sites," "Social Media, Web Search and User Satisfaction," "Big Data Analytics and Cloud Computing, and "ICT for Economic Development and Empowerment" may become the hotspot for future research. The social exchange of knowledge reveals intra-disciplinarity, where JCIS gets most of the knowledge from the information systems domain itself. However, closest associations with the general business domain, computer science, marketing, organization science, and psychology for knowledge inflows make JCIS a net knowledge receiver.
C1 [Sharma, Anuj] Chandragupt Inst Management Patna, Patna, Bihar, India.
   [Koohang, Alex] Middle Georgia State Univ, Macon, GA USA.
   [Rana, Nripendra P.] Qatar Univ, Coll Business & Econ, Doha, Qatar.
   [Abed, Salma S.] King Abdulaziz Univ, Rabigh, Saudi Arabia.
   [Dwivedi, Yogesh K.] Swansea Univ, Swansea, W Glam, Wales.
   [Dwivedi, Yogesh K.] Pune & Symbiosis Int Deemed Univ, Symbiosis Inst Business Management, Pune, Maharashtra, India.
C3 Qatar University; King Abdulaziz University; Swansea University;
   Symbiosis International University; Symbiosis Institute of Business
   Management (SIBM) Pune
RP Dwivedi, YK (corresponding author), Swansea Univ, Sch Management, Emerging Markets Res Ctr Emarc, Bay Campus, Swansea SA1 8EN, W Glam, Wales.
EM y.k.dwivedi@swansea.ac.uk
RI Dwivedi, Yogesh Kumar/A-5362-2008; Rana, Nripendra P./ABA-4719-2020;
   Abed, Salma S./AAU-7092-2021; Sharma, Anuj/AAE-5767-2020; Sharma,
   Anuj/JTS-4887-2023
OI Dwivedi, Yogesh Kumar/0000-0002-5547-9990; Rana, Nripendra
   P./0000-0003-1105-8729; Sharma, Anuj/0000-0001-6602-9285; Sharma,
   Anuj/0000-0002-6281-6115; Koohang, Alex/0000-0002-4565-0408; Shodmonov,
   Ruslan/0000-0002-8723-2378
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NR 61
TC 14
Z9 14
U1 1
U2 28
PU TAYLOR & FRANCIS INC
PI PHILADELPHIA
PA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA
SN 0887-4417
EI 2380-2057
J9 J COMPUT INFORM SYST
JI J. Comput. Inf. Syst.
PD JAN 2
PY 2023
VL 63
IS 1
BP 37
EP 67
DI 10.1080/08874417.2021.2021114
EA JAN 2022
PG 31
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 8I3KY
UT WOS:000744778600001
OA Green Submitted
DA 2024-09-05
ER
PT C
AU Amerika, M
   Kim, LH
   Gallagher, B
AF Amerika, Mark
   Kim, Laura Hyunjhee
   Gallagher, Brad
GP Assoc Comp Machinery
TI Fatal Error: Artificial Creative Intelligence (ACI)
SO CHI'20: EXTENDED ABSTRACTS OF THE 2020 CHI CONFERENCE ON HUMAN FACTORS
   IN COMPUTING SYSTEMS
LA English
DT Proceedings Paper
CT ACM CHI Conference on Human Factors in Computing Systems (CHI)
CY APR 25-30, 2020
CL Honolulu, HI
DE Artificial intelligence; Arts; Poetry; Persona; Performance;
   Practice-based Research
AB Computer-generated algorithmic art has undergone significant developments since its emergence in the 1960s. With further integration of art and technology in the 21st century, artists continue to respond, take risks and challenge the ways computers can be thought of as a creative medium. This project specifically addresses speculative forms of artificial intelligence particularly the possibilities for creative collaboration between human and machine-generated embodiments of poetic expression. The Artificial Creative Intelligence (ACI) is a fictional AI Poet whose spoken word poetry signals the horizon of a new type of authorship that questions the philosophical implications of artificial intelligence for creative practitioners.
C1 [Amerika, Mark] Univ Colorado, Art & Art Hist, Boulder, CO 80309 USA.
   [Kim, Laura Hyunjhee; Gallagher, Brad] Univ Colorado, Intermedia Art Writing & Performance, Boulder, CO 80309 USA.
C3 University of Colorado System; University of Colorado Boulder;
   University of Colorado System; University of Colorado Boulder
RP Amerika, M (corresponding author), Univ Colorado, Art & Art Hist, Boulder, CO 80309 USA.
EM mark.amerika@colorado.edu; laura.h.kim@colorado.edu;
   jonathan.gallagher@colorado.edu
FU University of Colorado's College of Media, Communication and
   Information; College of Arts and Sciences; Professor of Distinction Fund
FX The University of Colorado's College of Media, Communication and
   Information, the College of Arts and Sciences and the Professor of
   Distinction Fund have all provided support for this project.
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NR 13
TC 2
Z9 3
U1 2
U2 19
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES
BN 978-1-4503-6819-3
PY 2020
AR ALT10
DI 10.1145/3334480.3381815
PG 9
WC Computer Science, Cybernetics
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BQ9OG
UT WOS:000626317801035
DA 2024-09-05
ER
PT J
AU Zuccala, A
   van Someren, M
   van Bellen, M
AF Zuccala, Alesia
   van Someren, Maarten
   van Bellen, Maurits
TI A Machine-Learning Approach to Coding Book Reviews as Quality
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SO JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY
LA English
DT Article
DE bibliometrics; book reviews; machine learning
ID CITATION CHARACTERISTICS; HUMANITIES; SCIENCE; SCHOLARLINESS;
   PUBLISHERS; MONOGRAPHS; BEHAVIOR; COUNTS; IMPACT
AB A theory of megacitation is introduced and used in an experiment to demonstrate how a qualitative scholarly book review can be converted into a weighted bibliometric indicator. We employ a manual human-coding approach to classify book reviews in the field of history based on reviewers' assessments of a book author's scholarly credibility (SC) and writing style (WS). In total, 100 book reviews were selected from the American Historical Review and coded for their positive/negative valence on these two dimensions. Most were coded as positive (68% for SC and 47% for WS), and there was also a small positive correlation between SC and WS (r=0.2). We then constructed a classifier, combining both manual design and machine learning, to categorize sentiment-based sentences in history book reviews. The machine classifier produced a matched accuracy (matched to the human coding) of approximately 75% for SC and 64% for WS. WS was found to be more difficult to classify by machine than SC because of the reviewers' use of more subtle language. With further training data, a machine-learning approach could be useful for automatically classifying a large number of history book reviews at once. Weighted megacitations can be especially valuable if they are used in conjunction with regular book/journal citations, and libcitations (i.e., library holding counts) for a comprehensive assessment of a book/monograph's scholarly impact.
C1 [Zuccala, Alesia] Univ Amsterdam, Inst Log Language & Computat, Fac Humanities, NL-1090 GE Amsterdam, Netherlands.
   [van Someren, Maarten; van Bellen, Maurits] Univ Amsterdam, Fac Sci, Inst Informat, NL-1098 XG Amsterdam, Netherlands.
C3 University of Amsterdam; University of Amsterdam
RP Zuccala, A (corresponding author), Univ Amsterdam, Inst Log Language & Computat, Fac Humanities, Sci Pk 105, NL-1090 GE Amsterdam, Netherlands.
EM a.a.zuccala@uva.nl; m.w.vansomeren@uva.nl; mauritsvanbellen@gmail.com
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NR 80
TC 25
Z9 25
U1 1
U2 67
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 2330-1635
EI 2330-1643
J9 J ASSOC INF SCI TECH
PD NOV
PY 2014
VL 65
IS 11
BP 2248
EP 2260
DI 10.1002/asi.23104
PG 13
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA AS4EE
UT WOS:000344225200007
DA 2024-09-05
ER
PT C
AU Khor, KA
   Ko, G
   Theseira, W
   Cai, XQ
   Goh, YC
AF Khor, Khiam Aik
   Ko, Giovanni
   Theseira, Walter
   Cai, Xin Qing
   Goh, Yeow Chong
BE Catalano, G
   Daraio, C
   Gregori, M
   Moed, HF
   Ruocco, G
TI Evaluating Human Versus Machine Learning Performance in Classifying
   Research Abstracts
SO 17TH INTERNATIONAL CONFERENCE ON SCIENTOMETRICS & INFORMETRICS
   (ISSI2019), VOL II
SE Proceedings of the International Conference on Scientometrics and
   Informetrics
LA English
DT Proceedings Paper
CT 17th International Conference of the
   International-Society-for-Scientometrics-and-Informetrics (ISSI) on
   Scientometrics and Informetrics
CY SEP 02-05, 2019
CL Sapienza Univ Rome, Rome, ITALY
HO Sapienza Univ Rome
AB Machine Learning (ML) methods are now applied to many problems in Scientometrics. Given sufficiently large training datasets, ML can efficiently complete natural language processing tasks such as classifying research abstracts and outputs, which otherwise require extensive manpower. But what are the relative strengths and limitations of ML methods versus human research assistance when training data is limited? Our study compares the performance of 63 student research assistants to that of an ML model. The task is classifying a research grant abstract into one of nineteen scientific funding areas in physical and life sciences defined by the European Research Council. We find that ML models, even trained on relatively small datasets, outperform the average human research assistant. While some research assistants perform at levels just below that of the ML models, the research assistants display lower inter-rater reliability. Crucially, human classification performance and reliability appears fixed over moderate levels of training and task exposure, suggesting that selecting research assistants based on pre-existing ability could be superior to relying on task-specific training. These results suggest ML classification may be superior to human research assistance for natural language processing tasks even when training datasets are limited.
C1 [Khor, Khiam Aik] Nanyang Technol Univ, Talent Recruitment & Career Support TRACS Off & B, 50 Nanyang Ave, Singapore 639798, Singapore.
   [Ko, Giovanni] Singapore Management Univ, Sch Econ, 90 Stamford Rd, Singapore 178903, Singapore.
   [Theseira, Walter] Singapore Univ Social Sci, Sch Business, 463 Clementi Rd, Singapore 599494, Singapore.
C3 Nanyang Technological University; Singapore Management University;
   Singapore University of Social Sciences (SUSS)
RP Ko, G (corresponding author), Singapore Management Univ, Sch Econ, 90 Stamford Rd, Singapore 178903, Singapore.
EM mkakhor@ntu.edu.sg; giovanniko@smu.edu.sg; waltertheseira@suss.edu.sg;
   xqcai@ntu.edu.sg; ycgoh@ntu.edu.sg
RI Khor, Michael/B-6929-2009; Theseira, Walter/AAW-2209-2020
OI Theseira, Walter/0000-0002-8738-2341
CR BUCKLAND M, 1994, J AM SOC INFORM SCI, V45, P12, DOI 10.1002/(SICI)1097-4571(199401)45:1<12::AID-ASI2>3.0.CO;2-L
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NR 15
TC 0
Z9 0
U1 0
U2 2
PU INT SOC SCIENTOMETRICS & INFORMETRICS-ISSI
PI LEUVEN
PA KATHOLIEKE UNIV LEUVEN, FACULTEIT E T E W, DEKENSTRAAT 2, LEUVEN,
   B-3000, BELGIUM
SN 2175-1935
BN 978-88-3381-118-5
J9 PRO INT CONF SCI INF
PY 2019
BP 2157
EP 2162
PG 6
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Information Science & Library Science
GA BO2SO
UT WOS:000508227200085
DA 2024-09-05
ER
PT J
AU SCHIMINOVICH, S
AF SCHIMINOVICH, S
TI AUTOMATIC CLASSIFICATION OF BIBLIOGRAPHIC DATA BASES - MODEL FOR
   COGNITIVE BIOLOGICAL PROCESSES AND ARTIFICIAL INTELLIGENCE
SO BIOSCIENCES COMMUNICATIONS
LA English
DT Article
C1 AMER INST PHYS,355 E 45 ST,NEW YORK,NY 10017.
CR FEINMAN RD, 1973, J AM SOC INFORM SCI, V24, P382, DOI 10.1002/asi.4630240510
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NR 7
TC 1
Z9 1
U1 0
U2 3
PU KARGER
PI BASEL
PA ALLSCHWILERSTRASSE 10, CH-4009 BASEL, SWITZERLAND
SN 0378-9845
J9 BIOSCI COMMUN
PY 1975
VL 1
IS 1
BP 24
EP 39
PG 16
WC Communication; Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Communication; Information Science & Library Science
GA AT159
UT WOS:A1975AT15900003
DA 2024-09-05
ER
PT J
AU Kseoglu, MA
AF Koseoglu, Mehmet Ali
TI Identifying the intellectual structure of fields: introduction of the
   MAK approach
SO SCIENTOMETRICS
LA English
DT Article
DE Co-citation analysis; Text-net analysis; LDA; Strategic management
ID STRATEGIC MANAGEMENT RESEARCH; INFORMATION-SYSTEMS; EVOLUTION; FUTURE
AB This study introduces MAK approach to investigate intellectual structure of fields which combines text-net analysis (TNA), latent dirichlet allocation (LDA), and co-citation analysis. Researchers have previously deployed co-citation analysis to reveal the intellectual structure of fields. However, in these applications, the research has two technical limitations-small representativeness in datasets analyzed and the primary consideration for dated documents-towards the co-citation analysis. These limitations impede the formation of a larger picture in the structure. The present study seeks to eliminate these limitations by utilizing TNA and LDA methods as topic modeling approaches for 38,368 journal articles as references with 125,154 appearances in 2680 articles published between 1980 and 2019 in theStrategic Management Journal(SMJ). We suggest researchers should embrace MAK approach as complementary approach to research, with its focus on the intellectual structures of the field. We provide a workflow to show potential research applications and address advantages and limitations associated with the two new methods.
C1 [Koseoglu, Mehmet Ali] Hong Kong Polytech Univ, Sch Hotel & Tourism Management, Kowloon, Hong Kong, Peoples R China.
C3 Hong Kong Polytechnic University
RP Kseoglu, MA (corresponding author), Hong Kong Polytech Univ, Sch Hotel & Tourism Management, Kowloon, Hong Kong, Peoples R China.
EM MehmetAli.Koseoglu@polyu.edu.hk
RI Koseoglu, Mehmet Ali/AAF-1401-2019
OI Koseoglu, Mehmet Ali/0000-0001-9369-1995
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NR 43
TC 20
Z9 20
U1 4
U2 4
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD DEC
PY 2020
VL 125
IS 3
BP 2169
EP 2197
DI 10.1007/s11192-020-03719-8
EA SEP 2020
PG 29
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA PE1YL
UT WOS:000571691800002
DA 2024-09-05
ER
PT C
AU Brown, AO
   Crawford, RH
   Jensen, DD
   Rencis, JJ
   Liu, JC
   Watson, KA
   Jackson, KS
   Hackett, RK
   Schimpf, PH
   Chen, CC
   Orabi, II
   Akasheh, F
   Wood, JJ
   Dunlap, BU
   Sargent, ER
AF Brown, Ashland O.
   Crawford, Richard H.
   Jensen, Daniel D.
   Rencis, Joseph J.
   Liu, Jiancheng
   Watson, Kyle A.
   Jackson, Kathy Schmidt
   Hackett, Rachelle Kisst
   Schimpf, Paul Henry
   Chen, Chuan-Chiang
   Orabi, Ismail I.
   Akasheh, Firas
   Wood, John J.
   Dunlap, Brock U.
   Sargent, Ella R.
GP ASEE
TI Assessment of Active Learning Modules: An Update of Research Findings
SO 2013 ASEE ANNUAL CONFERENCE
SE ASEE Annual Conference & Exposition
LA English
DT Proceedings Paper
CT ASEE Annual Conference
CY JUN 23-26, 2013
CL Atlanta, GA
ID CURRICULUM
AB The landscape of contemporary engineering education is ever changing, adapting and evolving. As an example, finite element theory and application has often been included in graduate-level courses in engineering programs; however, current industry needs bachelor's-level engineering graduates with skills in applying this essential analysis and design technique. Engineering education is also changing to include more active learning. In response to the need to introduce undergrads to the finite element method as well as the need for engineering curricula to include more active learning, we have developed, implemented and assessed a suite of Active Learning Module (ALMs). The ALMs are designed to improve student learning of difficult engineering concepts while students gain essential knowledge of finite element analysis. We have used the Kolb Learning Cycle as a conceptual framework to guide our design of the ALMs.
   Originally developed using MSC Nastran, followed by development efforts in SolidWorks Simulation, ANSOFT, ANSYS, and other commercial FEA software packages, a team of researchers, with National Science Foundation support, have created over twenty-eight active learning modules. We will discuss the implementation of these learning modules which have been incorporated into undergraduate courses that cover topics such as machine design, mechanical vibrations, heat transfer, bioelectrical engineering, electromagnetic field analysis, structural fatigue analysis, computational fluid dynamics, rocket design, chip formation during manufacturing, and large scale deformation in machining.
   This update on research findings includes statistical results for each module which compare performance on pre-and post-learning module quizzes to gauge change in student knowledge related to the difficult engineering concepts that each module addresses. Statistically significant student performance gains provide evidence of module effectiveness. In addition, we present statistical comparisons between different personality types (based on Myers-Briggs Type Indicator, MBTI, subgroups) and different learning styles (based on Felder-Solomon ILS subgroups) in regards to the average gains each group of students have made on quiz performance. Although exploratory, and generally based on small sample sizes at this point in our multi-year effort, the modules for which subgroup differences are found are being carefully reviewed in an attempt to determine whether modifications should be made to better ensure equitable impact of the modules across students from specific personality and /or learning styles subgroups (e.g., MBTI Intuitive versus Sensing; ILS Sequential versus Global).
C1 [Brown, Ashland O.] Univ Pacific, Mech Engn, Sch Engn & Comp Sci, Stockton, CA 95211 USA.
   [Crawford, Richard H.] Univ Texas Austin, Mech Engn, Austin, TX 78712 USA.
   [Jensen, Daniel D.] US Air Force Acad, Engn Mech, Colorado Springs, CO 80840 USA.
   [Rencis, Joseph J.] Tennessee Technol Univ, Clay N Hixson Chair Engn Leadership, Cookeville, TN 38505 USA.
   [Rencis, Joseph J.] Tennessee Technol Univ, Mech Engn, Cookeville, TN 38505 USA.
   [Liu, Jiancheng] Univ Pacific, Dept Mech Engn, Stockton, CA 95211 USA.
   [Watson, Kyle A.] Univ Pacific, Mech Engn, Stockton, CA 95211 USA.
   [Jackson, Kathy Schmidt] Penn State Univ, Schreyer Inst Teaching Excellence, University Pk, PA 16802 USA.
   [Hackett, Rachelle Kisst] Univ Pacific, Stockton, CA 95211 USA.
   [Schimpf, Paul Henry] Eastern Washington Univ, Cheney, WA 99004 USA.
   [Chen, Chuan-Chiang] Calif State Polytech Univ Pomona, Dept Mech Engn, Pomona, CA 91768 USA.
   [Orabi, Ismail I.] Univ New Haven, Dept Mech Engn, West Haven, CT 06516 USA.
   [Akasheh, Firas] Tuskegee Univ, Tuskegee, AL 36088 USA.
   [Wood, John J.] US Air Force Acad, Engn Mech, Colorado Springs, CO 80840 USA.
   [Dunlap, Brock U.] Univ Texas Austin, Act Learning & Prototyping Methodol, Austin, TX 78712 USA.
   [Sargent, Ella R.] Univ Pacific, Benerd Sch Educ, Stockton, CA 95211 USA.
C3 University of the Pacific; University of Texas System; University of
   Texas Austin; United States Department of Defense; United States Air
   Force; United States Air Force Academy; Tennessee Technological
   University; Tennessee Technological University; University of the
   Pacific; University of the Pacific; Pennsylvania Commonwealth System of
   Higher Education (PCSHE); Pennsylvania State University; Pennsylvania
   State University - University Park; University of the Pacific; Eastern
   Washington University; California State University System; California
   State Polytechnic University Pomona; University New Haven; Tuskegee
   University; United States Department of Defense; United States Air
   Force; United States Air Force Academy; University of Texas System;
   University of Texas Austin; University of the Pacific
RP Brown, AO (corresponding author), Univ Pacific, Mech Engn, Sch Engn & Comp Sci, Stockton, CA 95211 USA.
CR [Anonymous], EFF EV 2012 2013 ACC
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NR 31
TC 0
Z9 0
U1 0
U2 8
PU AMER SOC ENGINEERING EDUCATION
PI WASHINGTON
PA 1818 N STREET, NW SUITE 600, WASHINGTON, DC 20036 USA
SN 2153-5965
J9 ASEE ANNU CONF EXPO
PY 2013
PG 26
WC Education & Educational Research; Education, Scientific Disciplines;
   Engineering, Multidisciplinary
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Education & Educational Research; Engineering
GA BE7HB
UT WOS:000375256301018
DA 2024-09-05
ER
PT J
AU Goh, YC
   Cai, XQ
   Theseira, W
   Ko, G
   Khor, KA
AF Goh, Yeow Chong
   Cai, Xin Qing
   Theseira, Walter
   Ko, Giovanni
   Khor, Khiam Aik
TI Evaluating human versus machine learning performance in classifying
   research abstracts
SO SCIENTOMETRICS
LA English
DT Article
DE Discipline classification; Text classification; Supervised
   classification
ID COMBINED COCITATION; WORD ANALYSIS; SCIENCE
AB We study whether humans or machine learning (ML) classification models are better at classifying scientific research abstracts according to a fixed set of discipline groups. We recruit both undergraduate and postgraduate assistants for this task in separate stages, and compare their performance against the support vectors machine ML algorithm at classifying European Research Council Starting Grant project abstracts to their actual evaluation panels, which are organised by discipline groups. On average, ML is more accurate than human classifiers, across a variety of training and test datasets, and across evaluation panels. ML classifiers trained on different training sets are also more reliable than human classifiers, meaning that different ML classifiers are more consistent in assigning the same classifications to any given abstract, compared to different human classifiers. While the top five percentile of human classifiers can outperform ML in limited cases, selection and training of such classifiers is likely costly and difficult compared to training ML models. Our results suggest ML models are a cost effective and highly accurate method for addressing problems in comparative bibliometric analysis, such as harmonising the discipline classifications of research from different funding agencies or countries.
C1 [Goh, Yeow Chong; Cai, Xin Qing] Nanyang Technol Univ, Sch Mech & Aerosp Engn, Singapore, Singapore.
   [Theseira, Walter] Singapore Univ Social Sci, Sch Business, Singapore, Singapore.
   [Ko, Giovanni] Singapore Management Univ, Sch Econ, Singapore, Singapore.
   [Khor, Khiam Aik] Nanyang Technol Univ, Talent Recruitment & Career Support TRACS Off & B, Singapore, Singapore.
C3 Nanyang Technological University; Singapore University of Social
   Sciences (SUSS); Singapore Management University; Nanyang Technological
   University
RP Khor, KA (corresponding author), Nanyang Technol Univ, Talent Recruitment & Career Support TRACS Off & B, Singapore, Singapore.
EM mkakhor@ntu.edu.sg
RI Khor, Michael/B-6929-2009; Theseira, Walter/AAW-2209-2020
OI Theseira, Walter/0000-0002-8738-2341
FU Singapore National Research Foundation [NRF2014-NRF-SRIE001-027]
FX The study was partially funded by the Singapore National Research
   Foundation, Grant No. NRF2014-NRF-SRIE001-027. A portion of this study
   used the resources of the ASPIRE1 supercomputer hosted at the National
   Supercomputing Centre (NSCC) Singapore (https://www.nscc.sg/).
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NR 36
TC 24
Z9 25
U1 1
U2 18
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD NOV
PY 2020
VL 125
IS 2
BP 1197
EP 1212
DI 10.1007/s11192-020-03614-2
EA JUL 2020
PG 16
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA OT3PI
UT WOS:000549661600001
PM 32836529
OA Green Published, hybrid
DA 2024-09-05
ER
PT J
AU Di Zio, S
   Tontodimamma, A
   del Gobbo, E
   Fontanella, L
AF Di Zio, Simone
   Tontodimamma, Alice
   del Gobbo, Emiliano
   Fontanella, Lara
TI Exploring the research dynamics of futures studies: An analysis of six
   top journals
SO FUTURES
LA English
DT Article
DE Futures studies; Bibliometrics analysis; Dynamic topic models; Latent
   Dirichlet Allocation; GIS
ID EVOLUTION; SCIENCE; TRENDS; TOOL
AB This paper focuses on the global literature on Futures Studies and foresight over the last thirty years by using a bibliographic dataset from the Scopus and using an integrated statistical methodological approach. Bibliometric measures, knowledge mapping tools, topic modelling, Geographical Information Systems and network analysis are used to understand the scholarly literature's evolution, main research areas, temporal evolution, geographical differences, and fragmentation. This allows to outline a separation between research areas and understand the dynamics of the main topics. The aim of this research is to fill the gap in the literature regarding the mapping of research themes in Futures Studies and foresight, as well as their temporal evolution and geographical distribution. Results showed a notable growth in the number of published articles in the last 32 years and identified (through Latent Dirichlet Allocation) 21 topics, which summarize the most important research themes in the context of Future Studies and foresight. A dynamic topic model helped to understand the evolution of topics, while the network analysis provided quantitative measures on the interactions between the topics as well as the international collaborations. Finally, a geographical analysis of both authors and topics highlighted the global distribution of research on Futures Studies.
C1 [Di Zio, Simone; Tontodimamma, Alice; Fontanella, Lara] G dAnnunzio Univ Chieti Pescara, Dept Legal & Social Sci, Pescara, Italy.
   [del Gobbo, Emiliano] Univ Foggia, Dept Econ Management & Terr, Foggia, Italy.
C3 G d'Annunzio University of Chieti-Pescara; University of Foggia
RP Di Zio, S (corresponding author), G dAnnunzio Univ Chieti Pescara, Dept Legal & Social Sci, Pescara, Italy.
EM simone.dizio@unich.it; alice.tontodimamma@unich.it;
   emiliano.delgobbo@unifg.it; lara.fontanella@unich.it
OI del Gobbo, Emiliano/0000-0003-1088-7306; Di Zio,
   Simone/0000-0002-9139-1451
CR Alghamdi R, 2015, INT J ADV COMPUT SC, V6, P147
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   Wang C, 2008, P 24 C UNCERTAINTY A, P579
   Wang Xuerui., P 12 ACM SIGKDD INT, P424
   Zhang JL, 2017, ADV INTEL SYS RES, V132, P300
NR 48
TC 2
Z9 2
U1 5
U2 15
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0016-3287
EI 1873-6378
J9 FUTURES
JI Futures
PD OCT
PY 2023
VL 153
AR 103232
DI 10.1016/j.futures.2023.103232
EA AUG 2023
PG 22
WC Economics; Regional & Urban Planning
WE Social Science Citation Index (SSCI)
SC Business & Economics; Public Administration
GA Q1LC0
UT WOS:001055191300001
OA Bronze
DA 2024-09-05
ER
PT C
AU Yu, TB
   Jiang, XY
   Wang, JR
   Su, YY
   Yu, G
   Wang, WS
AF Yu, Tianbiao
   Jiang, Xingyu
   Wang, Jianrong
   Su, Yingying
   Yu, Ge
   Wang, Wanshan
GP IEEE
TI Research on project evaluation system based on "Black Box" technology
SO 2007 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS, VOLS 1-5
LA English
DT Proceedings Paper
CT IEEE International Conference on Robotics and Biomimetics (ROBIO 2007)
CY DEC 15-18, 2007
CL Sanya, PEOPLES R CHINA
DE black box; project evaluation; export performance evaluation; artificial
   intelligence
AB To assure justice and science of scientific and technological project evaluation, avoiding the corrupt transaction in the process of project evaluation, scientific and technological project evaluation management model based on "black box" technology is presented, and the architecture of evaluation "black box" is established based on artificial intelligence, experts' selection based on knowledge reasoning is analyzed, system architecture and work workflow are studied, and evaluation model of experts' performance based on analytic hierarchy process and fuzzy comprehensive evaluation is established. Based on these a prototype system is developed, results of the system running prove the correctness of theory study and feasibility of technology research. The study works provides a scientific and reliable method of scientific and technological project evaluation.
C1 [Yu, Tianbiao; Jiang, Xingyu; Wang, Jianrong; Su, Yingying; Yu, Ge; Wang, Wanshan] Northeastern Univ, Sch Mech Engn & Automat, Shenyang, Liaoning Prov, Peoples R China.
C3 Northeastern University - China
RP Yu, TB (corresponding author), Northeastern Univ, Sch Mech Engn & Automat, Shenyang, Liaoning Prov, Peoples R China.
EM tbyu@me.neu.edu.cn
RI Jiang, Xingyu/AGB-8636-2022; Yu, Ge/AAN-8191-2021; Wang,
   Shuai/JAZ-0277-2023; Wang, Jianrong/C-2222-2008
OI Yu, Tianbiao/0000-0002-6161-8838
CR DAN ZP, 1999, COMPUTER ENG, V24, P92
   DONG J, 2007, COMPUTER APPL SOFTWA, V24, P145
   FANG Y, 2006, CONTROL THEORY APPL, V23
   Jiang Zhizhong, 2007, Computer Engineering, V33, P278
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   Yang Jian-chi, 2007, Journal of System Simulation, V19, P1199
   Zhang Xin-Liang, 2007, Journal of Software, V18, P574, DOI 10.1360/jos180574
NR 8
TC 0
Z9 0
U1 0
U2 6
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-4244-1761-2
PY 2007
BP 2250
EP 2255
PG 6
WC Automation & Control Systems; Computer Science, Artificial Intelligence;
   Engineering, Biomedical; Engineering, Electrical & Electronic; Robotics
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Automation & Control Systems; Computer Science; Engineering; Robotics
GA BHW67
UT WOS:000257065801229
DA 2024-09-05
ER
PT J
AU Roy, J
AF Roy, Jason
TI Randomized treatment-belief trials
SO CONTEMPORARY CLINICAL TRIALS
LA English
DT Article
DE Causal inference; Comparative effectiveness research; Placebo effect;
   Potential outcomes; Pragmatic trials; Randomized trials
ID CLINICAL-TRIALS
AB It is widely recognized that traditional randomized controlled trials (RCTs) have limited generalizability due to the numerous ways in which conditions of RCTs differ from those experienced each day by patients and physicians. As a result, there has been a recent push towards pragmatic trials that better mimic real-world conditions. One way in which RCTs differ from normal everyday experience is that all patients in the trial have uncertainty about what treatment they were assigned. Outside of the RCT setting, if a patient is prescribed a drug then there is no reason for them to wonder if it is a placebo. Uncertainty about treatment assignment could affect both treatment and placebo response. We use a potential outcomes approach to define relevant causal effects based on combinations of treatment assignment and belief about treatment assignment. We show that traditional RCTs are designed to estimate a quantity that is typically not of primary interest. We propose a new study design that has the potential to provide information about a wider range of interesting causal effects. (C) 2011 Elsevier Inc. All rights reserved.
C1 Univ Penn, Ctr Clin Epidemiol & Biostat, Philadelphia, PA 19104 USA.
C3 University of Pennsylvania
RP Roy, J (corresponding author), Univ Penn, Ctr Clin Epidemiol & Biostat, Philadelphia, PA 19104 USA.
EM jaroy@upenn.edu
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NR 13
TC 10
Z9 10
U1 0
U2 7
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 1551-7144
EI 1559-2030
J9 CONTEMP CLIN TRIALS
JI Contemp. Clin. Trials
PD JAN
PY 2012
VL 33
IS 1
BP 172
EP 177
DI 10.1016/j.cct.2011.09.011
PG 6
WC Medicine, Research & Experimental; Pharmacology & Pharmacy
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Research & Experimental Medicine; Pharmacology & Pharmacy
GA 889KL
UT WOS:000300072500025
PM 21989161
DA 2024-09-05
ER
PT J
AU Zhao, WD
   Yu, ZX
   Wu, R
AF Zhao, Weidong
   Yu, Zhaoxin
   Wu, Ran
TI A citation recommendation method based on context correlation
SO INTELLIGENT DATA ANALYSIS
LA English
DT Article
DE Citation recommendation; context correlation; neural networks; citation
   network; authority
AB Researchers need to formulate their achievements as research papers. Representative references are essential to high-quality papers. Academic citation recommendation refers to providing the recommendation of citations for the author of papers when they write. With the help of citation recommendation, researchers can improve the efficiency of writing academic papers and reduce the omission of important related literature. To achieve this goal, some methods were proposed. Many of them used citation networks to learn the representation of papers and chose references, they tended to ignore the content properties of papers. There are also some methods used partial properties to recommend citation. But their performance can be further improved. In this paper, we propose a citation recommendation method based on context correlation. We use two neural network models to learn the representations of papers and their references, then calculate the context similarity of them. Besides, we also introduce the publishing time and authority of papers, two key properties of papers for citation evaluation. In the experiment section, we compare our method with other methods and evaluate the performance of different properties choice in our method, it shows that our method outperforms some baselines and the combination of the dimensions including time, authority and context performs better.
C1 [Zhao, Weidong; Yu, Zhaoxin; Wu, Ran] Fudan Univ, Sch Software, Shanghai Key Lab Data Sci, 220 Handan Rd, Shanghai 200433, Peoples R China.
C3 Fudan University
RP Yu, ZX (corresponding author), Fudan Univ, Sch Software, Shanghai Key Lab Data Sci, 220 Handan Rd, Shanghai 200433, Peoples R China.
EM 18212010047@fudan.edu.cn
RI li, fei/JYP-3334-2024
FU National Nature Science Foundation of China [61671157]
FX Third, this work was supported by the National Nature Science Foundation
   of China [grant number 61671157], we want to give our heartiest thanks
   for the National Nature Science Foundation of China.
CR [Anonymous], 2010, P 19 INT C WORLD WID, DOI DOI 10.1145/1772690.1772731
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NR 34
TC 4
Z9 4
U1 1
U2 38
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1088-467X
EI 1571-4128
J9 INTELL DATA ANAL
JI Intell. Data Anal.
PY 2021
VL 25
IS 1
BP 225
EP 243
DI 10.3233/IDA-195041
PG 19
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA QH1VI
UT WOS:000618065600013
DA 2024-09-05
ER
PT J
AU Huo, Q
   Luo, X
   Xu, ZC
   Yang, XY
AF Huo, Qing
   Luo, Xu
   Xu, Zu-Cai
   Yang, Xiao-Yan
TI Machine learning applied to epilepsy: bibliometric and visual analysis
   from 2004 to 2023
SO FRONTIERS IN NEUROLOGY
LA English
DT Article
DE machine learning; epilepsy; VOSviewer; CiteSpace; visual analysis
ID SEIZURE ONSET; CLASSIFICATION; CONNECTIVITY; ALGORITHM; PATTERNS; TRENDS
AB Background: Epilepsy is one of the most common serious chronic neurological disorders, which can have a serious negative impact on individuals, families and society, and even death. With the increasing application of machine learning techniques in medicine in recent years, the integration of machine learning with epilepsy has received close attention, and machine learning has the potential to provide reliable and optimal performance for clinical diagnosis, prediction, and precision medicine in epilepsy through the use of various types of mathematical algorithms, and promises to make better parallel advances. However, no bibliometric assessment has been conducted to evaluate the scientific progress in this area. Therefore, this study aims to visually analyze the trend of the current state of research related to the application of machine learning in epilepsy through bibliometrics and visualization. Methods: Relevant articles and reviews were searched for 2004-2023 using Web of Science Core Collection database, and bibliometric analyses and visualizations were performed in VOSviewer, CiteSpace, and Bibliometrix (R-Tool of R-Studio). Results: A total of 1,284 papers related to machine learning in epilepsy were retrieved from the Wo SCC database. The number of papers shows an increasing trend year by year. These papers were mainly from 1,957 organizations in 87 countries/regions, with the majority from the United States and China. The journal with the highest number of published papers is EPILEPSIA. Acharya, U. Rajendra (Ngee Ann Polytechnic, Singapore) is the authoritative author in the field and his paper "Deep Convolutional Neural Networks for Automated Detection and Diagnosis of Epileptic Seizures Using EEG Signals" was the most cited. Literature and keyword analysis shows that seizure prediction, epilepsy management and epilepsy neuroimaging are current research hotspots and developments. Conclusions: This study is the first to use bibliometric methods to visualize and analyze research in areas related to the application of machine learning in epilepsy, revealing research trends and frontiers in the field. This information will provide a useful reference for epilepsy researchers focusing on machine learning.
C1 [Huo, Qing] Zunyi Med Univ, Sch Nursing, Zunyi, Peoples R China.
   [Luo, Xu] Zunyi Med Univ, Sch Med Informat Engn, Zunyi, Peoples R China.
   [Xu, Zu-Cai; Yang, Xiao-Yan] Zunyi Med Univ, Affiliated Hosp, Dept Neurol, Zunyi, Peoples R China.
C3 Zunyi Medical University; Zunyi Medical University; Zunyi Medical
   University
RP Luo, X (corresponding author), Zunyi Med Univ, Sch Med Informat Engn, Zunyi, Peoples R China.
EM luoxu@zmu.edu.cn
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NR 59
TC 0
Z9 0
U1 20
U2 20
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 1664-2295
J9 FRONT NEUROL
JI Front. Neurol.
PD APR 2
PY 2024
VL 15
AR 1374443
DI 10.3389/fneur.2024.1374443
PG 18
WC Clinical Neurology; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Neurosciences & Neurology
GA NX9N5
UT WOS:001203871200001
PM 38628694
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU Valencia-Arias, A
   González-Ruiz, JD
   Flores, LV
   Vega-Mori, L
   Rodríguez-Correa, P
   Santos, GS
AF Valencia-Arias, Alejandro
   Gonzalez-Ruiz, Juan David
   Flores, Lilian Verde
   Vega-Mori, Luis
   Rodriguez-Correa, Paula
   Santos, Gustavo Sanchez
TI Machine Learning and Blockchain: A Bibliometric Study on Security and
   Privacy
SO INFORMATION
LA English
DT Article
DE Internet of Things; 5G networks; artificial intelligence; PRISMA-2020;
   cloud computing; intrusion detection; smart contracts
ID IOT; AI
AB Machine learning and blockchain technology are fast-developing fields with implications for multiple sectors. Both have attracted a lot of interest and show promise in security, IoT, 5G/6G networks, artificial intelligence, and more. However, challenges remain in the scientific literature, so the aim is to investigate research trends around the use of machine learning in blockchain. A bibliometric analysis is proposed based on the PRISMA-2020 parameters in the Scopus and Web of Science databases. An objective analysis of the most productive and highly cited authors, journals, and countries is conducted. Additionally, a thorough analysis of keyword validity and importance is performed, along with a review of the most significant topics by year of publication. Co-occurrence networks are generated to identify the most crucial research clusters in the field. Finally, a research agenda is proposed to highlight future topics with great potential. This study reveals a growing interest in machine learning and blockchain. Topics are evolving towards IoT and smart contracts. Emerging keywords include cloud computing, intrusion detection, and distributed learning. The United States, Australia, and India are leading the research. The research proposes an agenda to explore new applications and foster collaboration between researchers and countries in this interdisciplinary field.
C1 [Valencia-Arias, Alejandro; Flores, Lilian Verde] Univ Senor Sipan, Escuela Ingn Ind, Chiclayo 14001, Peru.
   [Gonzalez-Ruiz, Juan David] Univ Nacl Colombia, Dept Econ, Medellin 050001, Colombia.
   [Vega-Mori, Luis; Santos, Gustavo Sanchez] Univ Ricardo Palma, Inst Invest & Estudios Mujer, Lima 15074, Peru.
   [Rodriguez-Correa, Paula] Inst Univ Escolme, Inst Universitaria Escolme, Ctr Invest Escolme CIES, Medellin 050001, Colombia.
C3 Universidad Senor de Sipan; Universidad Nacional de Colombia;
   Universidad Ricardo Palma
RP Valencia-Arias, A (corresponding author), Univ Senor Sipan, Escuela Ingn Ind, Chiclayo 14001, Peru.
EM valenciajho@uss.edu.pe; jdgonza3@unal.edu.co;
   lilianverde@crece.uss.edu.pe; iepvlvega@gmail.com; cies4@escolme.edu.co;
   sanchezsantosgustavo1@gmail.com
RI Arias, Alejandro Valencia/I-9436-2019
OI Arias, Alejandro Valencia/0000-0001-9434-6923; Verde Flores, Lilian
   Janet/0000-0003-0496-853X; Gonzalez Ruiz, Juan
   David/0000-0003-4425-7687; Vega Mori, Luis Angel/0000-0002-3825-7720
FU Universidad Seor de Sipn-USS
FX No Statement Available
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NR 54
TC 0
Z9 0
U1 4
U2 4
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2078-2489
J9 INFORMATION
JI Information
PD JAN
PY 2024
VL 15
IS 1
AR 65
DI 10.3390/info15010065
PG 25
WC Computer Science, Information Systems
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA GD9N1
UT WOS:001150846700001
OA gold
DA 2024-09-05
ER
PT J
AU Galanti, TM
   Baker, CK
   Morrow-Leong, K
   Kraft, T
AF Galanti, Terrie McLaughlin
   Baker, Courtney Katharine
   Morrow-Leong, Kimberly
   Kraft, Tammy
TI Enriching TPACK in mathematics education: using digital interactive
   notebooks in synchronous online learning environments
SO INTERACTIVE TECHNOLOGY AND SMART EDUCATION
LA English
DT Article
DE Research; Mathematics; Distance learning; Higher education; Teaching
   methods; Assessment and e-assessment
ID PEDAGOGICAL CONTENT KNOWLEDGE; TEACHERS; TECHNOLOGY
AB Purpose - In spring 2020, educators throughout the world abruptly shifted to emergency remote teaching in response to an emerging pandemic. The instructors of a graduate-level synchronous online geometry and measurement course for practicing school teachers redesigned their summative assessments. Their goals were to reduce outside-of-class work and to model the integration of content, pedagogy and technology. This paper aims to describe the development of a digital interactive notebook (dINB) assignment using online presentation software, dynamic geometry tools and mathematical learning trajectories. Broader implications for dINBs as assessments in effective distance learning are presented.
   Design/methodology/approach - The qualitative analysis in this study consists of a sequence of first-cycle coding of mid-semester surveys and second-cycle thematic categorizations of mid-semester surveys and end-of-course reflections. Descriptive categorization counts along with select quotations from open-ended participant responses provided a window on evolving participant experiences with the dINB across the course.
   Findings - Modifications to the dINB design based on teacher mid-semester feedback created a flexible assessment tool aligned with the technological pedagogical content knowledge (TPACK) framework. The teachers also constructed their own visions for adapting the dINB for student-centered instructional technology integration in their own virtual classrooms.
   Originality/value - The development of the dINB enriched the TPACK understandings of the instructors in this study. It also positioned teachers to facilitate innovative synchronous and blended learning in their own school communities. Further analysis of dINB artifacts in future studies will test the hypothesis that practicing teachers' experiences as learners increased their TPACK knowledge.
C1 [Galanti, Terrie McLaughlin] Univ North Florida, Dept Teaching Learning & Curriculum, Jacksonville, FL 32224 USA.
   [Baker, Courtney Katharine; Morrow-Leong, Kimberly; Kraft, Tammy] George Mason Univ, Math Educ Leadership, Fairfax, VA 22030 USA.
C3 State University System of Florida; University of North Florida; George
   Mason University
RP Galanti, TM (corresponding author), Univ North Florida, Dept Teaching Learning & Curriculum, Jacksonville, FL 32224 USA.
EM terrie.galanti@unf.edu
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NR 44
TC 10
Z9 10
U1 4
U2 51
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 1741-5659
EI 1758-8510
J9 INTERACT TECHNOL SMA
JI Interact. Technol. Smart Educ.
PD OCT 4
PY 2021
VL 18
IS 3
SI SI
BP 345
EP 361
DI 10.1108/ITSE-08-2020-0175
EA DEC 2020
PG 17
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA WC0FE
UT WOS:000603426600001
DA 2024-09-05
ER
PT J
AU De Block, A
   Conix, S
AF De Block, Andreas
   Conix, Stijn
TI Responsible Dissemination in Sexual Orientation Research: The Case of
   the AI "Gaydar"
SO PHILOSOPHY OF SCIENCE
LA English
DT Article
AB A recent controversy about neural networks allegedly capable of detecting a person's sexual orientation raises the question of whether all research on homosexuality should be permitted. This paper considers two arguments for limits to such research, and concludes that there are good reasons to limit at least the dissemination of applied research on the etiology of homosexuality. The paper then briefly sketches how this could work, and looks at three objections against these limitations.
C1 [De Block, Andreas; Conix, Stijn] Katholieke Univ Leuven, Inst Philosophy, Ctr L & Philosophy Sci, Leuven, Belgium.
C3 KU Leuven
RP De Block, A (corresponding author), Katholieke Univ Leuven, Inst Philosophy, Ctr L & Philosophy Sci, Leuven, Belgium.
EM andreas.deblock@kuleuven.be
RI Conix, Stijn/HKO-8315-2023
OI Conix, Stijn/0000-0002-1487-0213; De Block, Andreas/0000-0002-7927-8210
FU Research Council - Flanders [3H200026]
FX The authors would like to thank Jacob Stegenga for organizing the
   symposium on "the sciences of sexual desire," and the attendees of that
   symposium for their interesting comments and suggestions on this paper.
   Stijn Conix gratefully acknowledges funding from the Research Council -
   Flanders (grant 3H200026).
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NR 25
TC 0
Z9 0
U1 0
U2 5
PU CAMBRIDGE UNIV PRESS
PI NEW YORK
PA 32 AVENUE OF THE AMERICAS, NEW YORK, NY 10013-2473 USA
SN 0031-8248
EI 1539-767X
J9 PHILOS SCI
JI Philos. Sci.
PD DEC
PY 2022
VL 89
IS 5
BP 1075
EP 1084
DI 10.1017/psa.2022.44
PG 10
WC History & Philosophy Of Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI); Arts &amp; Humanities Citation Index (A&amp;HCI)
SC History & Philosophy of Science
GA 8O7XM
UT WOS:000926046700020
OA Bronze, Green Accepted
DA 2024-09-05
ER
PT J
AU Kerekes, J
   King, KP
AF Kerekes, Judit
   King, Kathleen P.
TI THE KING'S CARPET: DRAMA PLAY IN TEACHER EDUCATION
SO INTERNATIONAL JOURNAL OF INSTRUCTION
LA English
DT Article
DE teacher education; collaboration; co-teaching; drama; role playing;
   active learning; critical thinking and research; inquiry based learning
AB Trying to develop new perspectives of teaching is never easy, but trying to cultivate ownership and initiative among teacher education students is a still greater aspiration that is infrequently realized. This article addresses each of these highly valued goals for teacher educators as a case study reveals the impact of involving teacher candidates in interdisciplinary focused, constructivist and reflective models and planning for teaching, and then student teaching, which reaffirms this approach. Most significant is the phenomenon of several teacher candidates continuing their development and study of innovative drama play projects with their classes after the semester finishes. The resulting transformations in professional identity development, self-efficacy and studentteacher relationships confirm the value of the teacher education model which has developed over a decade of practice (Lyublinskaya & Kerekes, 2009).
C1 [Kerekes, Judit] CUNY, 2800 Victory Blvd, Staten Isl, NY 10314 USA.
   [King, Kathleen P.] Fordham Univ, New York, NY 10023 USA.
C3 City University of New York (CUNY) System; Fordham University
RP Kerekes, J (corresponding author), CUNY, 2800 Victory Blvd, Staten Isl, NY 10314 USA.
EM Kerekes@mail.csi.cuny.edu; kpking@fordham.edu
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   Laszlo Kaposi, 2005, A DRAMA TANITASA
   Lyublinskaya I., 2009, INTEGRATING MATH SCI
   Merriam S.B., 2007, QUALITATIVE RES CASE
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   Schn D. A., 1987, Educating the reflexive practitioner
NR 21
TC 10
Z9 13
U1 0
U2 0
PU ESKISEHIR OSMANGAZI UNIV, FAC EDUCATION
PI ESKISEHIR
PA ESKISEHIR OSMANGAZI UNIV, FAC EDUCATION, ESKISEHIR, 26480, TURKEY
SN 1694-609X
EI 1308-1470
J9 INT J INSTR
JI Int. J. Instr.
PD JAN
PY 2010
VL 3
IS 1
BP 39
EP 60
PG 22
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA VA3AT
UT WOS:000409779800004
DA 2024-09-05
ER
PT J
AU Ye, ZW
   Li, JL
   Wang, WJ
   Qin, FZ
   Li, KT
   Tan, H
   Zhang, C
AF Ye, Zhiwei
   Li, Jialing
   Wang, Wenjun
   Qin, Fanzhi
   Li, Keteng
   Tan, Hao
   Zhang, Chen
TI Data-driven visualization of the dynamics of machine learning in
   materials research
SO JOURNAL OF CLEANER PRODUCTION
LA English
DT Article
DE Materials; Machine learning; Visualization; Bibliometrics analysis;
   Environmental sustainability
ID HIGH ENTROPY ALLOYS; PHASE PREDICTION; DESIGN; MODELS; SCIENCE
AB The intricate interplay between material structure and properties lies at the heart of modern materials research. Understanding and manipulating this relationship is essential for the development of advanced materials with tailored properties for a wide range of applications. Machine learning (ML) has been intensively employed for prediction purposes. This trend of research into new insights, techniques, and research paradigms is gaining great popularity and demonstrating its promising potential for materials research. This study aims to conduct a bibliometric analysis of ML applications in materials, offering researchers, particularly those in the green energy sector, insights to incorporate into their future research plans. Here, the dataset was retrieved from the Web of Science Core Collection and the earliest related publication was recorded in 1998. Metrics based on retrieved data were extracted, including publication evaluations, countries, journals, and authors. Keywords temporal variations and citation-based scientific landscapes were constructed. The findings underscore the embryonic nature of machine learning's deployment in materials research but also highlight its significance as an emerging field that has captured the attention of scholars across multiple domains. Specifically, ongoing research efforts are directed towards optimizing ML models and algorithms, as well as refining data handling techniques to glean insights into complex structure-property relationships. The findings will provide novices with a data-driven visualization summary about the dynamics of this field, and its inspiration to environmental sustainability, and benefit a wide range of stakeholders to enhance their informed decisions on research funding and policy.
C1 [Ye, Zhiwei; Qin, Fanzhi; Li, Keteng; Zhang, Chen] Hunan Univ, Coll Environm Sci & Engn, Changsha 410082, Peoples R China.
   [Li, Jialing; Tan, Hao] Hunan Univ, Sch Design, Changsha 410082, Hunan, Peoples R China.
   [Wang, Wenjun] Hunan Univ Technol & Business, Sch Resources & Environm, Changsha 410205, Peoples R China.
C3 Hunan University; Hunan University; Hunan University of Technology &
   Business
RP Zhang, C (corresponding author), Hunan Univ, Coll Environm Sci & Engn, Changsha 410082, Peoples R China.; Li, JL (corresponding author), Hunan Univ, Sch Design, Changsha 410082, Hunan, Peoples R China.
EM zhangchen@hnu.edu.cn
RI Zhang, Chen/AAY-8585-2021
OI Zhang, Chen/0000-0002-3579-6980
FU National Natural Science Foundation of China [52170162, 51809090];
   Natural Science Foundation of Hunan Province, China [2022JJ10016];
   Science and Technology Innovation Program of Hunan Province
   [2021RC3049]; Fundamental Research Funds for the Central Universities
   [531118010114]
FX This study was financially supported by the program for the National
   Natural Science Foundation of China (52170162, 51809090) , the Natural
   Science Foundation of Hunan Province, China (2022JJ10016) , the Science
   and Technology Innovation Program of Hunan Province (2021RC3049) , and
   the Fundamental Research Funds for the Central Universities
   (531118010114) .
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NR 66
TC 1
Z9 1
U1 12
U2 12
PU ELSEVIER SCI LTD
PI London
PA 125 London Wall, London, ENGLAND
SN 0959-6526
EI 1879-1786
J9 J CLEAN PROD
JI J. Clean Prod.
PD APR 10
PY 2024
VL 449
AR 141410
DI 10.1016/j.jclepro.2024.141410
EA MAR 2024
PG 13
WC Green & Sustainable Science & Technology; Engineering, Environmental;
   Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics; Engineering; Environmental Sciences
   & Ecology
GA QI2Z5
UT WOS:001220195100001
DA 2024-09-05
ER
PT J
AU Zabezhailo, MI
AF Zabezhailo, M. I.
TI Three Comprehension Test Questions for Fellow Members
SO PATTERN RECOGNITION AND IMAGE ANALYSIS
LA English
DT Article
DE artificial intelligence; intelligent data analysis; research and
   developments; quality evaluation
AB The work concerns the quality of intelligent-data-analysis results. Learning-sample quality for precedents of knowledge representation language and means for intelligent data analysis are considered. Several problems are formulated, acceptable answers to which will make it possible to increase the efficiency and quality of intelligent data analysis.
C1 [Zabezhailo, M. I.] Russian Acad Sci, Fed Res Ctr Comp Sci & Control, Moscow 119333, Russia.
C3 Russian Academy of Sciences; Federal Research Center "Computer Science &
   Control" of RAS
RP Zabezhailo, MI (corresponding author), Russian Acad Sci, Fed Res Ctr Comp Sci & Control, Moscow 119333, Russia.
EM m.zabezhailo@yandex.ru
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NR 8
TC 0
Z9 0
U1 0
U2 0
PU SPRINGERNATURE
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND
SN 1054-6618
EI 1555-6212
J9 PATTERN RECOGN IMAGE
JI Pattern Recogn. Image Anal.
PD SEP
PY 2023
VL 33
IS 3
SI SI
BP 555
EP 559
DI 10.1134/S1054661823030501
PG 5
WC Computer Science, Interdisciplinary Applications
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA T0NU8
UT WOS:001075049100047
DA 2024-09-05
ER
PT J
AU Podestà, F
AF Podesta, Federico
TI Combining process tracing and synthetic control method: Bridging two
   ways of making causal inference in evaluation research
SO EVALUATION
LA English
DT Article
DE causation; process tracing; single-case study; Synthetic control method
ID MECHANISMS
AB This article discusses potential ways of combining two methods of evaluation in single-case studies: the synthetic control method and the process tracing method. Both are designed to examine certain events/programmes that take place in given cases but view these events/programmes from different causal perspectives. Seeing an event/programme as a cause, synthetic control estimates its impact on one or more outcomes. Conversely, starting from a certain outcome, process tracing uncovers the causes responsible. One can start from the causal explanation reached via one of the two methods and then proceed to examine that explanation through the other method. Once the causes of an outcome are traced via a process tracing analysis, that account can be validated by estimating the effects of those causes via synthetic control. Equally, once the impact of a certain event is estimated through synthetic control, causal mechanisms traceable via process tracing can be exploited to refine that impact evaluation.
C1 [Podesta, Federico] Bruno Kessler Fdn, Trento, Italy.
C3 Fondazione Bruno Kessler
RP Podestà, F (corresponding author), Bruno Kessler Fdn, Res Inst Evaluat Publ Policies IRVAPP, Via S Croce 77, I-38122 Trento, Italy.
EM podesta@irvapp.it
OI Podesta, Federico/0000-0003-0307-8041
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NR 41
TC 0
Z9 0
U1 3
U2 11
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 1356-3890
EI 1461-7153
J9 EVALUATION-US
JI Evaluation
PD JAN
PY 2023
VL 29
IS 1
BP 50
EP 66
DI 10.1177/13563890221139511
EA DEC 2022
PG 17
WC Social Sciences, Interdisciplinary
WE Social Science Citation Index (SSCI)
SC Social Sciences - Other Topics
GA 7X3LH
UT WOS:000893653200001
DA 2024-09-05
ER
PT J
AU Frandsen, TF
   Carlsen, AMF
   Eriksen, MB
AF Frandsen, Tove Faber
   Carlsen, Anne-Marie Fiala
   Eriksen, Mette Brandt
TI The use of subject headings varied in Embase and MEDLINE: An analysis of
   indexing across six subject areas
SO JOURNAL OF INFORMATION SCIENCE
LA English
DT Article
DE Bibliographic databases; Embase; linear regression; MEDLINE; subject
   headings; thesaurus
ID SYSTEMATIC REVIEWS; ABSTRACTS; LENGTH; CINAHL; MESH
AB Many bibliographic databases describe the content of a publication using a thesaurus. The vocabularies vary and the extent to which the databases apply them may also differ significantly. The aim of this study is to empirically explore the number of subject headings assigned to publications in two databases over time and to determine if publication characteristics are associated with the number of subject headings. Articles and reviews in MEDLINE and Embase from 1990 to 2019 assigned with one of the subject headings from six subject areas are included in this study. Each of the retrieved publications in Embase is matched with a similar publication in MEDLINE. Furthermore, multivariable linear regressions are used to explore the association of the number of subject headings in MEDLINE and Embase with six prespecified publication characteristics. The average number of assigned subject headings in MEDLINE is stable or even slightly decreasing over time. In Embase, the average number of assigned subject headings was stable until about 2000 where the average number increased dramatically during the next 3 years. Furthermore, linear regressions show that the average number of subject headings in MEDLINE and Embase is higher for publications in English, publications with longer abstract, recent publications and if it belongs to specific subject areas. However, reviews are assigned with more subject headings in Embase and fewer in MEDLINE. The implications of the results are discussed.
C1 [Frandsen, Tove Faber] Univ Southern Denmark, Dept Design & Commun, Univ Sparken 1, DK-6000 Odence, Denmark.
   [Carlsen, Anne-Marie Fiala] UCL Univ Coll, Svendborg, Denmark.
   [Eriksen, Mette Brandt] Univ Southern Denmark, Univ Lib Southern Denmark, Cochrane Denmark, Odense, Denmark.
   [Eriksen, Mette Brandt] Univ Southern Denmark, Ctr Evidence Based Med Odense CEBMO, Odense, Denmark.
C3 University of Southern Denmark; University of Southern Denmark;
   University of Southern Denmark
RP Frandsen, TF (corresponding author), Univ Southern Denmark, Dept Design & Commun, Univ Sparken 1, DK-6000 Odence, Denmark.
EM t.faber@sdu.dk
RI Frandsen, Tove Faber/A-6185-2012
OI Frandsen, Tove Faber/0000-0002-8983-5009; Brandt Eriksen,
   Mette/0000-0001-6785-261X
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NR 49
TC 0
Z9 0
U1 2
U2 7
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 0165-5515
EI 1741-6485
J9 J INF SCI
JI J. Inf. Sci.
PD AUG
PY 2024
VL 50
IS 4
BP 851
EP 860
DI 10.1177/01655515221107335
EA AUG 2022
PG 10
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA C3E8I
UT WOS:000835912300001
DA 2024-09-05
ER
PT J
AU Nazarovets, S
   Teixeira da Silva, JA
AF Nazarovets, Serhii
   Teixeira da Silva, Jaime A.
TI ChatGPT as an "author": Bibliometric analysis to assess the validity of
   authorship
SO ACCOUNTABILITY IN RESEARCH-ETHICS INTEGRITY AND POLICY
LA English
DT Article; Early Access
DE Artificial intelligence (AI); authorship principles; ethics;
   responsibility; transparency
AB Background: Following the 2023 surge in popularity of large language models like ChatGPT, significant ethical discussions emerged regarding their role in academic authorship. Notable ethics organizations, including the ICMJE and COPE, alongside leading publishers, have instituted ethics clauses explicitly stating that such models do not meet the criteria for authorship due to accountability issues.Objective: This study aims to assess the prevalence and ethical implications of listing ChatGPT as an author on academic papers, in violation of existing ethical guidelines set by the ICMJE and COPE.Methods: We conducted a comprehensive review using databases such as Web of Science and Scopus to identify instances where ChatGPT was credited as an author, co-author, or group author.Results: Our search identified 14 papers featuring ChatGPT in such roles. In four of those papers, ChatGPT was listed as an "author" alongside the journal's editor or editor-in-chief. Several of the ChatGPT-authored papers have accrued dozens, even hundreds of citations according to Scopus, Web of Science, and Google Scholar.Discussion: The inclusion of ChatGPT as an author on these papers raises critical questions about the definition of authorship and the accountability mechanisms in place for content produced by artificial intelligence. Despite the ethical guidelines, the widespread citation of these papers suggests a disconnect between ethical policy and academic practice.Conclusion: The findings suggest a need for corrective measures to address these discrepancies. Immediate review and amendment of the listed papers is advised, highlighting a significant oversight in the enforcement of ethical standards in academic publishing.
C1 [Nazarovets, Serhii] Borys Grinchenko Kyiv Metropolitan Univ, Lib, 18-2 Bulvarno Kudriavska Str, UA-04053 Kiev, Ukraine.
C3 Ministry of Education & Science of Ukraine; Borys Grinchenko Kyiv
   Metropolitan University
RP Nazarovets, S (corresponding author), Borys Grinchenko Kyiv Metropolitan Univ, Lib, 18-2 Bulvarno Kudriavska Str, UA-04053 Kiev, Ukraine.
EM sergiy.nazarovets@gmail.com
RI Nazarovets, Serhii/I-1680-2012
OI Nazarovets, Serhii/0000-0002-5067-4498
CR Abalkina A, 2023, LEARN PUBL, V36, P689, DOI 10.1002/leap.1574
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   ICMJE (International Committee of Medical Journal Editors), 2024, REC
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   Springer Nature, 2024, AUTHORSHIP
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NR 30
TC 1
Z9 1
U1 11
U2 11
PU TAYLOR & FRANCIS INC
PI PHILADELPHIA
PA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA
SN 0898-9621
EI 1545-5815
J9 ACCOUNT RES
JI Account. Res.
PD 2024 MAY 3
PY 2024
DI 10.1080/08989621.2024.2345713
EA MAY 2024
PG 11
WC Medical Ethics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Medical Ethics
GA PS2Z6
UT WOS:001216019800001
PM 38693669
DA 2024-09-05
ER
PT J
AU Rosenkrantz, AB
   Doshi, AM
   Ginocchio, LA
   Aphinyanaphongs, Y
AF Rosenkrantz, Andrew B.
   Doshi, Ankur M.
   Ginocchio, Luke A.
   Aphinyanaphongs, Yindalon
TI Use of a Machine-learning Method for Predicting Highly Cited Articles
   Within General Radiology Journals
SO ACADEMIC RADIOLOGY
LA English
DT Article
DE Radiology; bibliometrics; biomedical journals; machine learning
ID DATA SYSTEM; NODULES
AB Rationale and Objectives: This study aimed to assess the performance of a text classification machine-learning model in predicting highly cited articles within the recent radiological literature and to identify the model's most influential article features.
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   Conclusions: Machine learning can be successfully applied to create specific feature-based models for predicting articles likely to achieve high influence within the radiological literature.
C1 [Rosenkrantz, Andrew B.; Doshi, Ankur M.; Ginocchio, Luke A.] NYU, Langone Med Ctr, Dept Radiol, 660 First Ave,3rd Floor, New York, NY 10016 USA.
   [Aphinyanaphongs, Yindalon] NYU, Ctr Healthcare Innovat & Delivery Sci, Langone Med Ctr, New York, NY USA.
C3 New York University; NYU Langone Medical Center; NYU Langone Medical
   Center; New York University
RP Rosenkrantz, AB (corresponding author), NYU, Langone Med Ctr, Dept Radiol, 660 First Ave,3rd Floor, New York, NY 10016 USA.
EM Andrew.Rosenkrantz@nyumc.org
OI Ginocchio, Luke/0000-0002-0183-7246; Aphinyanaphongs,
   Yin/0000-0001-8605-5392; Doshi, Ankur/0000-0002-9415-2763; Rosenkrantz,
   Andrew/0000-0002-1558-5350
CR [Anonymous], INCITES J CIT REP
   [Anonymous], MULTINOMIAL NAIVE BA
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NR 25
TC 2
Z9 3
U1 2
U2 42
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 1076-6332
EI 1878-4046
J9 ACAD RADIOL
JI Acad. Radiol.
PD DEC
PY 2016
VL 23
IS 12
BP 1573
EP 1581
DI 10.1016/j.acra.2016.08.011
PG 9
WC Radiology, Nuclear Medicine & Medical Imaging
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Radiology, Nuclear Medicine & Medical Imaging
GA EC8ZJ
UT WOS:000388431300015
PM 27692588
DA 2024-09-05
ER
PT J
AU Aristovnik, A
   Karampelas, K
   Umek, L
   Ravselj, D
AF Aristovnik, Aleksander
   Karampelas, Konstantinos
   Umek, Lan
   Ravselj, Dejan
TI Impact of the COVID-19 pandemic on online learning in higher education:
   a bibliometric analysis
SO FRONTIERS IN EDUCATION
LA English
DT Article
DE online learning; e-learning; higher education; bibliometrics; mapping;
   visualization; VOSviewer; COVID-19
ID GOOGLE-SCHOLAR; SCIENCE; SCOPUS; WEB
AB The outbreak of the COVID-19 pandemic significantly disrupted higher education by forcing the transition to online learning, which became a mandatory teaching process during the lockdowns. Although the epidemiological situation has gradually improved since then, online learning is becoming ever more popular as it provides new learning opportunities. Therefore, the paper aims to present recent research trends concerning online learning in higher education during the COVID-19 pandemic by using selected bibliometric approaches. The bibliometric analysis is based on 8,303 documents from the Scopus database published between January 2020 and March 2022, when repeated lockdowns meant most countries were experiencing constant disruptions to the educational process. The results show that the COVID-19 pandemic increased interest in online learning research, notably in English-speaking and Asian countries, with most research being published in open-access scientific journals. Moreover, the topics most frequently discussed in the online learning research during the COVID-19 pandemic were ICT and pedagogy, technology-enhanced education, mental health and well-being, student experience and curriculum and professional development. Finally, the COVID-19 pandemic encouraged explorations of emergency remote learning approaches like e-learning, distance learning and virtual learning, which are intended to limit physical contact between teachers and students, where the specific requirements of a given field of study often guide which online learning approach is the most suitable. The findings add to the existing body of scientific knowledge and support the evidence-based policymaking needed to ensure sustainable higher education in the future.
C1 [Aristovnik, Aleksander; Umek, Lan; Ravselj, Dejan] Univ Ljubljana, Fac Publ Adm, Ljubljana, Slovenia.
   [Karampelas, Konstantinos] Univ Aegean, Dept Primary Level Educ, Rhodes, Greece.
C3 University of Ljubljana; University of Aegean
RP Aristovnik, A; Ravselj, D (corresponding author), Univ Ljubljana, Fac Publ Adm, Ljubljana, Slovenia.
EM aleksander.aristovnik@fu.uni-lj.si; dejan.ravselj@fu.uni-lj.si
RI Karampelas, Konstantinos/AFA-6504-2022; Ravšelj, Dejan/JMA-8751-2023
OI Karampelas, Konstantinos/0000-0001-6631-1408; Ravšelj,
   Dejan/0000-0003-0426-820X
FU Slovenian Research Agency [P5-0093, Z5-4569]
FX This research and the APC were funded by the Slovenian Research Agency
   under grant numbers P5-0093 and Z5-4569.
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NR 90
TC 10
Z9 11
U1 5
U2 18
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 2504-284X
J9 FRONT EDUC
JI Front. Educ.
PD AUG 3
PY 2023
VL 8
AR 1225834
DI 10.3389/feduc.2023.1225834
PG 13
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA P3ZH4
UT WOS:001050058100001
OA gold
DA 2024-09-05
ER
PT C
AU Hou, W
   Huang, Y
   Zhang, K
AF Hou, Wei
   Huang, Yuan
   Zhang, Kao
BE Ge, N
   Lu, J
   Wang, Y
   Howard, N
   Chen, P
   Tao, X
   Zhang, B
   Zadeh, LA
TI Research of Micro-blog Diffusion Effect Based on Analysis of retweet
   Behavior
SO PROCEEDINGS OF 2015 IEEE 14TH INTERNATIONAL CONFERENCE ON COGNITIVE
   INFORMATICS & COGNITIVE COMPUTING (ICCI*CC)
LA English
DT Proceedings Paper
CT 14th IEEE International Conference on Cognitive Informatics and
   Cognitive Computing (ICCI*CC)
CY JUL 06-08, 2015
CL Beijing, PEOPLES R CHINA
DE Micro-Blog; Behavior Prediction; Retweet Scale; Diffusion Depth;
   Logistic Regression
AB Research on the diffusion effect of micro-blog plays an important role in improving marketing efficiency, strengthening monitoring public opinion and accurately discovering hotspot etc. To solve the problems not taking users' differences into consideration in the previous research, this paper proposes an algorithm to predict scale and depth of retweet massages based on analysis of retweet behavior. With the combination of LR algorithm and nine related features extracted from micro-blog users themselves, their relationships and micro-blog contents, we proposes a prediction model of retweet behavior. Based on this model, we proposes an algorithm to predict the diffusion effect, which considers the character of information spreading along users and does statistical analysis of adjacent users iteratively. Experimental results on Sina micro-blog dataset show that the algorithm has a prediction accuracy of 87.1 % and 81.6% in scale and depth respectively, which indicates the model works well.
C1 [Hou, Wei; Huang, Yuan] Natl Comp Network & Informat Secur Management Ctr, Beijing 100038, Peoples R China.
   [Zhang, Kao] Natl Digital Switching Syst Engn & Technol R&D Ct, Zhengzhou 450001, Peoples R China.
C3 PLA Information Engineering University
RP Hou, W (corresponding author), Natl Comp Network & Informat Secur Management Ctr, Beijing 100038, Peoples R China.
EM 1004790227@qq.com
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NR 18
TC 5
Z9 7
U1 0
U2 2
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-4673-7290-9
PY 2015
BP 255
EP 261
PG 7
WC Computer Science, Hardware & Architecture; Computer Science, Theory &
   Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BF2GR
UT WOS:000380466100040
DA 2024-09-05
ER
PT C
AU Rochd, E
   Quafafou, M
   Aznag, M
AF Rochd, El Mehdi
   Quafafou, Mohamed
   Aznag, Mustapha
GP IEEE
TI Encoding local correspondence in Topic Models
SO 2013 IEEE 25TH INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL
   INTELLIGENCE (ICTAI)
SE Proceedings-International Conference on Tools With Artificial
   Intelligence
LA English
DT Proceedings Paper
CT 25th IEEE International Conference on Tools with Artificial Intelligence
   (ICTAI)
CY NOV 04-06, 2013
CL Washington, DC
DE Topic Models; Automatic Image Annotation; Local Influence; Probabilistic
   Graphical Models
AB Exploiting label correlations is a challenging and crucial problem especially in multi-label learning context. Labels correlations are not necessarily shared by all instances and have generally a local definition. This paper introduces LOC-LDA, which is a latent variable model that adresses the problem of modeling annotated data by locally exploiting correlations between annotations. In particular, we represent explicitly local dependencies to define the correspondence between specific objects, i.e. regions of images and their annotations. We conducted experiments on a collection of pictures provided by the Wikipedia "Picture of the day" website (1), and evaluated our model on the task of "automatic image annotation". The results validate the effectiveness of our approach.
C1 [Rochd, El Mehdi; Quafafou, Mohamed; Aznag, Mustapha] Aix Marseille Univ, LSIS, UMR 7296, Marseille, France.
C3 Aix-Marseille Universite
RP Rochd, E (corresponding author), Aix Marseille Univ, LSIS, UMR 7296, Marseille, France.
EM el-mehdi.rochd@univ-amu.fr; mohamed.quafafou@univ-amu.fr;
   mustapha.aznag@univ-amu.fr
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TC 1
Z9 1
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U2 0
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 1082-3409
BN 978-1-4799-2971-9
J9 PROC INT C TOOLS ART
PY 2013
BP 602
EP 609
DI 10.1109/ICTAI.2013.95
PG 8
WC Computer Science, Artificial Intelligence; Computer Science, Theory &
   Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BN4OO
UT WOS:000482633400069
DA 2024-09-05
ER
PT J
AU Ning, T
   Duan, XD
   An, L
   Gou, T
AF Ning, Tao
   Duan, Xiaodong
   An, Lu
   Gou, Tao
TI Research on disruption management of urgent arrival in job shop with
   deteriorating effect
SO JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
LA English
DT Article
DE Flexible job-shop scheduling; deteriorating effect; emergency order
   insertion; disruption management; multi-phase quantum particle swarm
   optimization
ID OPTIMIZATION; ALGORITHM; RESOURCE; ROBUST; CLOUD
AB A disruption management method based on cumulative prospect theory is proposed for the urgent with deteriorating effect arrival in flexible job shop scheduling problem (FJSP). First, the mathematical model of problem is established with minimizing the completion time of urgent order, minimizing the total process time of the system and minimizing the total cost as the target. Then, the cumulative prospect theory equation of the urgent arrival in job shop scheduling process is induced designed. Based on the selected model, an optimized multi-phase quantum particle swarm algorithm (MQPSO) is proposed for selecting processing route. Finally, using Solomon example simulation and company Z riveting shop example as the study object, the performance of the proposed method is analyzed. It is compared with the current common rescheduling methods, and the results verify that the method proposed in this paper not only meets the goal of the optimized objects, but improves the practical requirements for the stability of production and processing system during urgent arrival. Lastly, the optimized multiphase quantum particle swarm algorithm is used to solve disruption management of urgent arrival problem. Through instance analysis and comparison, the effectiveness and efficiency of urgent arrival disruption management method with deteriorating effect are verified.
C1 [Ning, Tao; Duan, Xiaodong] Dalian Minzu Univ, Inst Comp Sci & Engn, Dalian, Peoples R China.
   [Ning, Tao; Duan, Xiaodong] Big Data Applicat Technol Key Lab State Ethn Affa, Dalian, Peoples R China.
   [An, Lu; Gou, Tao] Dalian Jiaotong Univ, Inst Software, Dalian, Peoples R China.
C3 Dalian Minzu University; Dalian Jiaotong University
RP Duan, XD (corresponding author), Dalian Minzu Univ, Inst Comp Sci & Engn, Dalian, Peoples R China.; Duan, XD (corresponding author), Big Data Applicat Technol Key Lab State Ethn Affa, Dalian, Peoples R China.
EM daliannt@126.com
RI An, Lu/V-1548-2018
FU Liaoning Provincial Natural Science Foundation [20180550499,
   2019-ZD-0109]; Education Department Project of Liaoning Province
   [JDL2019022]; Science and technology innovation fund program of Dalian
   [2021JJ13SN81]
FX Foundation items: Project supported by Liaoning Provincial Natural
   Science Foundation (20180550499, 2019-ZD-0109) and Education Department
   Project of Liaoning Province (JDL2019022). Science and technology
   innovation fund program of Dalian (2021JJ13SN81).
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NR 33
TC 2
Z9 2
U1 2
U2 47
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1064-1246
EI 1875-8967
J9 J INTELL FUZZY SYST
JI J. Intell. Fuzzy Syst.
PY 2021
VL 41
IS 1
BP 1247
EP 1259
DI 10.3233/JIFS-210166
PG 13
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA UB5PA
UT WOS:000685896700075
DA 2024-09-05
ER
PT J
AU Kusumastuti, R
   Silalahi, M
   Sambodo, MT
   Juwono, V
AF Kusumastuti, Retno
   Silalahi, Mesnan
   Sambodo, Maxensius Tri
   Juwono, Vishnu
TI Understanding rural context in the social innovation knowledge structure
   and its sector implementations
SO MANAGEMENT REVIEW QUARTERLY
LA English
DT Article
DE Rural social innovation; knowledge structure; Topic modeling; Co-word
   analysis; Co-citation analysis
ID CHALLENGES
AB The concept of social innovation is increasingly being discussed to pursue sustainable development. New terms and keywords are created to cope with new ideas in various contexts. How these terms are developed in the current structure of knowledge and how we can reinterpret the semantic networks with the empirical context are the primary motivation of this paper. The rural social innovation knowledge structure is constructed to understand the phenomena better and cope with future needs. A multi-methods methodology is applied to construct the knowledge structure with the primary method being topic modeling. The results from topic modeling, co-word analysis, and co-citation are combined to co-construct the knowledge structure. The narratives for the built knowledge structure are then developed in the context of rural social innovation to enhance our understanding. This study found three findings. First, the trend of keywords "community", "governance", and "rural" have increased significantly in the field of social innovation. Second, an investigation of the intensity of the topics found six dominant groups of topics, namely actor, business model, natural resources, food security, governance, and urban. Third, the co-word analysis shows that the word innovation is closely related to the terms: sustainable development, social entrepreneurship, social enterprise, rural community, electronic commerce, co-design, and social behavior. The mapping of key terms shows that the structure of the global social innovation research landscape is quite complex. However, it can be broken down into five main parts: objectives, inputs, transformations, outputs, and outcomes.
C1 [Kusumastuti, Retno; Juwono, Vishnu] Univ Indonesia, Fac Adm Sci, Jl Prof DR Selo Soemardjan, Depok 16424, ID, Indonesia.
   [Silalahi, Mesnan] Indonesian Inst Sci, Ctr Policy Res & Management Sci Technol & Innovat, Jl Gatot Subroto 10, Jakarta 12042, ID, Indonesia.
   [Sambodo, Maxensius Tri] Indonesian Inst Sci, Econ Res Ctr, Jl Gatot Subroto 10, Jakarta 12042, ID, Indonesia.
C3 University of Indonesia; National Research & Innovation Agency of
   Indonesia (BRIN); Indonesian Institute of Sciences (LIPI); National
   Research & Innovation Agency of Indonesia (BRIN); Indonesian Institute
   of Sciences (LIPI)
RP Silalahi, M (corresponding author), Indonesian Inst Sci, Ctr Policy Res & Management Sci Technol & Innovat, Jl Gatot Subroto 10, Jakarta 12042, ID, Indonesia.
EM r.kusumastuti@ui.ac.id; mesnans@yahoo.com; smaxensius@yahoo.com;
   vjuwono@ui.ac.id
OI Kusumastuti, Retno/0000-0001-9290-4952; Sambodo,
   Maxensius/0000-0003-3705-2245
FU Universitas Indonesia [NKB - 480/UN2, RST/HKP.05.00/2021]; University of
   Indonesia in the World Class University Scheme
FX The process to produce this study and its publication is supported by
   funding from University of Indonesia in the World Class University
   Scheme, as outlined in the Assignment Agreement for the Publication of
   Review Article (PRA) of 2021 Number: NKB - 480/UN2.RST/HKP.05.00/2021.
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NR 70
TC 2
Z9 2
U1 5
U2 5
PU SPRINGERNATURE
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND
SN 2198-1620
EI 2198-1639
J9 MANAG REV Q
JI Manag. Rev. Q.
PD DEC
PY 2023
VL 73
IS 4
BP 1873
EP 1901
DI 10.1007/s11301-022-00288-3
PG 29
WC Business; Business, Finance; Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA SI8D0
UT WOS:001233907400008
OA Bronze
DA 2024-09-05
ER
PT J
AU Chen, Y
   Si, F
   Lu, XY
   Li, X
AF Chen, Yan
   Si, Fan
   Lu, Xiying
   Li, Xin
TI Research on the Influence Mechanism of the Across-Industrial-Chain
   Investment Speed on Innovation Performance of AI Enterprises:
   Improvement Path of Artificial Intelligence Technology Application
SO MOBILE INFORMATION SYSTEMS
LA English
DT Article
ID MANAGEMENT; IMPACT; TIME
AB This paper presents a regression analysis by using the system generalized method of moments (SYS-GMM) model as the main regression model and combining it with the fixed effect of panel data and acquires the basic empirical research data from Wind database. The research shows that the speed of cross-industrial-chain investment can improve the innovation ability of AI enterprises, and AI enterprises with deep technology accumulation can improve their innovation performance in the rapid across-industrial-chain investment. In this paper, an across-industrial-chain investment decision path model for AI enterprises is proposed for the first time, suggesting that AI enterprises should pay attention to the related factors of industry and AI enterprises when making across-industrial-chain investment decisions. This helps to express the determination of investment, integration, and reconstruction to the target AI enterprises, and it can also facilitate fast across-industrial-chain investment and improve the innovation performance of AI enterprises.
C1 [Chen, Yan; Si, Fan; Lu, Xiying] Beijing Univ Posts & Telecommun, Sch Econ & Management, Beijing, Peoples R China.
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C3 Beijing University of Posts & Telecommunications; Civil Aviation
   University of China
RP Li, X (corresponding author), Civil Aviat Univ China, Sch Econ & Management, Tianjin, Peoples R China.
EM 2019071091@cauc.edu.cn
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NR 35
TC 0
Z9 0
U1 13
U2 52
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1574-017X
EI 1875-905X
J9 MOB INF SYST
JI Mob. Inf. Syst.
PD NOV 29
PY 2021
VL 2021
AR 6149746
DI 10.1155/2021/6149746
PG 12
WC Computer Science, Information Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Telecommunications
GA YS4BN
UT WOS:000750624400003
OA gold
DA 2024-09-05
ER
PT J
AU Sampatrao, GS
   Dey, SR
   Bansal, A
   Saha, S
AF Sampatrao, Gambhire Swati
   Dey, Sudeepa Roy
   Bansal, Abhishek
   Saha, Sriparna
TI Analyzing the Common Wisdom of Binarization Doctrine in Internationality
   Classification of Journals: A Machine Learning Approach
SO JOURNAL OF SCIENTOMETRIC RESEARCH
LA English
DT Article
DE Journal Internationality; Binary classification; Bayesian Error rate
   function; web scraping; supervised learning; Non-Local Influence
   Quotient (NLIQ); Source-Normalized Impact per Paper (SNIP); Other
   Citation Quotient (OCQ); Unified Granular Neural Network (UGNN).
AB Evaluating and identifying "Internationality" of peer reviewed journals is a hotly debated topic. The problem broadly focuses on whether a journal is international or not, indicating a strong tilt toward binary classification doctrine. The manuscript investigates the doctrine, for the first time. The authors have validated their study further by using minimum error rate classifier, investigated theoretical lower and upper bounds of classification error in the context of internationality. The novel approach has rich ramifications in Scientometrics. Further, we propose a new principle of classification that results in greater accuracy fortifying the assertion.
C1 [Sampatrao, Gambhire Swati; Dey, Sudeepa Roy] PESIT BSC, Dept Comp Sci & Engn, Bangalore 560100, Karnataka, India.
   [Sampatrao, Gambhire Swati; Dey, Sudeepa Roy] Visvesvarya Tech Univ, Belagavi, Karnataka, India.
   [Bansal, Abhishek; Saha, Sriparna] IIT Patna, Dept Comp Sci, Patna, Bihar, India.
C3 PES University; Visvesvaraya Technological University; Indian Institute
   of Technology System (IIT System); Indian Institute of Technology (IIT)
   - Patna
RP Dey, SR (corresponding author), PESIT BSC, Dept Comp Sci & Engn, Bangalore 560100, Karnataka, India.; Dey, SR (corresponding author), Visvesvarya Tech Univ, Belagavi, Karnataka, India.
EM sudeepar@gmail.com
CR [Anonymous], 2017, S SAHAS MACHINE LEAR
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NR 16
TC 1
Z9 1
U1 0
U2 0
PU PHCOG NET
PI KARNATAKA
PA 17, 2ND FLR, BUDDHA VIHAR RD, NEAR SPORTS ZONE, COX TOWN, BENGALURU,
   KARNATAKA, 560005, INDIA
SN 2321-6654
EI 2320-0057
J9 J SCIENTOMETR RES
JI J. Scientometr. Res.
PD MAY-AUG
PY 2019
VL 8
IS 2
SI SI
BP S7
EP S38
DI 10.5530/jscires.8.2.22
PG 32
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA KA8FQ
UT WOS:000506037200003
DA 2024-09-05
ER
PT J
AU Shi, X
   Li, JN
   Xiong, QY
   Wu, YF
   Yuan, YP
AF Shi, Xin
   Li, Jiannan
   Xiong, Qingyu
   Wu, Yinfang
   Yuan, Yupeng
TI Research of uniformity evaluation model based on entropy clustering in
   the microwave heating processes
SO NEUROCOMPUTING
LA English
DT Article
DE Spectral clustering; Maximum entropy; Uniformity evaluation model;
   Microwave heating
ID HIGH-DIMENSIONAL DATA; SELECTION; CONSTRAINT; ALGORITHM; OVENS
AB This paper proposes a uniformity evaluation method based on Spectral Clustering and Maximum Information Entropy (ECUEM) for clustering the simulation results in the microwave heating system. The proposed method can effectively evaluate the dataset of the electric field E, the magnetic field H, the temperature field T, and analyze the non-uniformity phenomenon in the microwave heating processes. Compared with other clustering algorithms, the ECUEM can get better clustering results for the dataset in simulation of microwave heating. In particular, in the resonant cavity, the experimental results show that the minimum the evaluation results, the better the materials heating uniformity. In addition, when the ECUEM method is used to analyze the experiment of waveguide moving, the best position (0, 11/20*do, 3/14*ho) of waveguide can be obtained; at the same time, the uniformity or efficiency of materials microwave heating is the best. Moreover, other rules have been obtained in the microwave heating processes. Thus, the proposed method would provide a new method to guide the researchers who are working in the area of dataset clustering in the microwave heating. (C) 2015 Published by Elsevier B.V.
C1 [Shi, Xin; Li, Jiannan; Yuan, Yupeng] Chongqing Univ, Dept Automat, Chongqing 630044, Peoples R China.
   [Xiong, Qingyu] Chongqing Univ, Dept Software, Chongqing 630044, Peoples R China.
   [Wu, Yinfang] Guangxi Normal Univ, Dept Foreign Studies, Guilin, Guangxi, Peoples R China.
C3 Chongqing University; Chongqing University; Guangxi Normal University
RP Shi, X (corresponding author), Chongqing Univ, Dept Automat, Chongqing 630044, Peoples R China.
EM shixin@cqu.edu.cn; cdljn2011@126.com; xiong03@cqu.edu.cn;
   Wuyinfang77@163.com; yup@cqu.edu.cn
FU National Basic Research Program of China [2013CB328903]; National
   Natural Science Foundation of China [61473050]
FX The authors thank the editors and the anonymous reviewers for their
   helpful comments and suggestions. This research is supported by the
   National Basic Research Program of China (Grant no. 2013CB328903), the
   National Natural Science Foundation of China (Grant no. 61473050).
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NR 30
TC 8
Z9 10
U1 0
U2 23
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD JAN 15
PY 2016
VL 173
BP 562
EP 572
DI 10.1016/j.neucom.2015.07.034
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA CZ1QG
UT WOS:000366879800009
DA 2024-09-05
ER
PT J
AU Wing, C
   Bello-Gomez, RA
AF Wing, Coady
   Bello-Gomez, Ricardo A.
TI Regression Discontinuity and Beyond: Options for Studying External
   Validity in an Internally Valid Design
SO AMERICAN JOURNAL OF EVALUATION
LA English
DT Article
DE regression discontinuity design; external validity; extrapolation;
   causal inference; research design; program evaluation; quasi-experiments
ID STUDENT-ACHIEVEMENT; VARIABLES; SCHOOL
AB Treatment effect estimates from a regression discontinuity design (RDD) have high internal validity. However, the arguments that support the design apply to a subpopulation that is narrower and usually different from the population of substantive interest in evaluation research. The disconnect between RDD population and the evaluation population of interest suggests that RDD evaluations lack external validity. New methodological research offer strategies for studying and sometimes improving external validity in RDDs. This article examines four techniques: comparative RDD, covariate matching RDD, treatment effect derivatives, and statistical tests for local selection bias. The goal of the article is to help evaluators understand the logic, assumptions, data requirements, and reach of the new methods.
C1 [Wing, Coady; Bello-Gomez, Ricardo A.] Indiana Univ, Sch Publ & Environm Affairs, 1315 East Tenth St,Room 339A, Bloomington, IN 47405 USA.
C3 Indiana University System; Indiana University Bloomington
RP Wing, C (corresponding author), Indiana Univ, Sch Publ & Environm Affairs, 1315 East Tenth St,Room 339A, Bloomington, IN 47405 USA.
EM cwing@indiana.edu
RI Bello-Gomez, Ricardo/U-1146-2019
OI Bello-Gomez, Ricardo/0000-0001-6479-4979
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NR 31
TC 11
Z9 16
U1 0
U2 12
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 1098-2140
EI 1557-0878
J9 AM J EVAL
JI Am. J. Eval.
PD MAR
PY 2018
VL 39
IS 1
BP 91
EP 108
DI 10.1177/1098214017736155
PG 18
WC Social Sciences, Interdisciplinary
WE Social Science Citation Index (SSCI)
SC Social Sciences - Other Topics
GA FW1JZ
UT WOS:000425054900007
OA Bronze
DA 2024-09-05
ER
PT C
AU Zhang, JS
   Liu, XZ
AF Zhang, Jinsong
   Liu, Xiaozhong
GP ACM
TI Full-text and Topic Based AuthorRank and Enhanced Publication Ranking
SO JCDL'13: PROCEEDINGS OF THE 13TH ACM/IEEE-CS JOINT CONFERENCE ON DIGITAL
   LIBRARIES
SE ACM-IEEE Joint Conference on Digital Libraries JCDL
LA English
DT Proceedings Paper
CT 13th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL)
CY JUL 22-26, 2013
CL Indianapolis, IN
DE AuthorRank; Paper Ranking; Full-text; Labeled-LDA; PageRank with Priors;
   Citation Analysis
AB The idea behind AuthorRank is that a content created by more popular authors should rank higher than the content created by less popular authors. This paper brings this idea into scientific publications analysis to test whether the optimized topical AuthorRank can replace or enhance topical PageRank for publication ranking. First, the PageRank with Priors (PRP) algorithm was employed to rank topic-based publications and authors. Second, the first author's reputation was used for generating an AuthorRank score. Additionally, linear combination method of topical AuthorRank and PageRank were compared with several baselines. Finally, as shown in our evaluation results, the performance of topical AuthorRank combined with topic-based PageRank is better than other baselines for publication ranking.
C1 [Zhang, Jinsong] Dalian Maritime Univ, Coll Transportat Management, Dalian 116026, Peoples R China.
   [Liu, Xiaozhong] Indiana Univ, Sch Lib & Informat Sci, Bloomington, IN 47405 USA.
C3 Dalian Maritime University; Indiana University System; Indiana
   University Bloomington
RP Zhang, JS (corresponding author), Dalian Maritime Univ, Coll Transportat Management, Dalian 116026, Peoples R China.
EM zhangjinsong85@163.com; liu237@indiana.edu
CR [Anonymous], 1999, WWW 1999
   [Anonymous], 2012, P 21 ACM INT C INFOR
   Dom B., 2003, Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery, DMKD '03, ACM, New York, NY, USA, P42, DOI DOI 10.1145/882082.882093
   Fang H, 2007, LECT NOTES COMPUT SC, V4425, P418
   GARFIELD E, 1972, SCIENCE, V178, P471, DOI 10.1126/science.178.4060.471
   Haveliwala TH, 2003, IEEE T KNOWL DATA EN, V15, P784, DOI 10.1109/TKDE.2003.1208999
   Ramage Daniel., 2009, EMNLP
   Rodriguez M. A, 2006, SIMULATING NETWORK I
NR 8
TC 3
Z9 3
U1 0
U2 2
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA
SN 2575-7865
EI 2575-8152
BN 978-1-4503-2076-4
J9 ACM-IEEE J CONF DIG
PY 2013
BP 393
EP 394
PG 2
WC Information Science & Library Science
WE Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Information Science & Library Science
GA BL7RG
UT WOS:000455376400064
DA 2024-09-05
ER
PT C
AU Hassany, M
   Ke, JZ
   Brusilovsky, P
   Narayanan, ABL
   Akhuseyinoglu, K
AF Hassany, Mohammad
   Ke, Jiaze
   Brusilovsky, Peter
   Narayanan, Arun Balajiee Lekshmi
   Akhuseyinoglu, Kamil
GP ASSOC COMPUTING MACHINERY
TI Authoring Worked Examples for Java Programming with Human-AI
   Collaboration
SO 39TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING, SAC 2024
LA English
DT Proceedings Paper
CT 39th Annual ACM Symposium on Applied Computing (SAC)
CY APR 08-12, 2024
CL Univ Salamanca, Avila, SPAIN
HO Univ Salamanca
DE Code Examples; Authoring Tool; Human-AI Collaboration
AB Worked examples are among the most popular types of learning content in programming classes. However, instructors rarely have time to provide line-by-line explanations for a large number of examples typically used in a programming class. In this paper, we explore and assess a human-AI collaboration approach to authoring worked examples for Java programming. We introduce an authoring system for creating Java worked examples that generates a starting version of code explanations and presents it to the instructor to edit if necessary. We also present a study that assesses the quality of explanations created with this approach.
C1 [Hassany, Mohammad; Brusilovsky, Peter; Narayanan, Arun Balajiee Lekshmi; Akhuseyinoglu, Kamil] Univ Pittsburgh, Pittsburgh, PA 15260 USA.
   [Ke, Jiaze] Carnegie Mellon Univ, Pittsburgh, PA USA.
C3 Pennsylvania Commonwealth System of Higher Education (PCSHE); University
   of Pittsburgh; Carnegie Mellon University
RP Hassany, M (corresponding author), Univ Pittsburgh, Pittsburgh, PA 15260 USA.
EM moh70@pitt.edu; jiazek@andrew.cmu.edu; peterb@pitt.edu; arl122@pitt.edu;
   kaa108@pitt.edu
OI Akhuseyinoglu, Kamil/0000-0002-7761-9755; Brusilovsky,
   Peter/0000-0002-1902-1464
CR Brusilovsky Peter, 2009, Journal of Educational Multimedia and Hypermedia, V18, P267
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NR 9
TC 0
Z9 0
U1 0
U2 0
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES
BN 979-8-4007-0243-3
PY 2024
BP 101
EP 103
DI 10.1145/3605098.3636160
PG 3
WC Computer Science, Interdisciplinary Applications; Computer Science,
   Software Engineering; Computer Science, Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BX0OH
UT WOS:001236958200015
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Kumar, P
   Prakash, K
   Dimri, A
   Khulbe, M
   Mishra, SC
AF Kumar, Pankaj
   Prakash, Karuna
   Dimri, Anjali
   Khulbe, Manjula
   Mishra, Satish Chandra
TI Using bibliometric analysis to determine the role of cutting-edge
   technologies in the development of future performance management system
SO BENCHMARKING-AN INTERNATIONAL JOURNAL
LA English
DT Article; Early Access
DE Performance management system; Artificial intelligence; Machine
   learning; Bibliometric analysis; VOS viewer
ID BALANCED SCORECARD; PUBLIC-SECTOR; INFORMATION-SYSTEMS; SUPPLY CHAINS;
   WORK; KNOWLEDGE; FRAMEWORK; SCIENCE; INCENTIVES; EVOLUTION
AB PurposePerformance management system (PMS) is a crucial element of strategic human resource practices in any organization. This research aims to provide a concise overview of how bibliometric analysis is employed to assess the influence and significance of cutting-edge technologies in shaping of PMS. This study seeks to identify key trends, emerging technologies and their impact on the evolution of performance management practices, contributing valuable insights for researchers, practitioners and policymakers in this field.Design/methodology/approachThis investigation is carried out utilizing total of eight research questions, which are examined through VOS Viewer and Biblioshiny software. The research offers visual diagrams and tables depicting the data extracted from the Scopus Database.FindingsThe study's results underscore a noticeable increase in research literature pertaining to PMS, indicating a shift from conventional methods to a strategic, technology-driven approach. These findings cover the way for further investigation across various disciplines, offering opportunities to enhance the efficacy and productivity of PMS.Practical implicationsThe implementation of new technologies such as Artificial intelligence (AI), machine learning and robotics etc. in PMS have also been analysed to give a sneak peak of the bigger future picture of AI and strategic human resource integration.Originality/valueTo the best of the authors' understanding, this analysis represents the inaugural application of bibliometric techniques to evaluate the advancement of research on Performance Management System (PMS) dating back to 1978, utilizing academic literature sourced from the Scopus database.
C1 [Kumar, Pankaj; Prakash, Karuna; Dimri, Anjali; Khulbe, Manjula; Mishra, Satish Chandra] DIT Univ, Dept Management Studies, Dehra Dun, India.
C3 DIT University
RP Kumar, P (corresponding author), DIT Univ, Dept Management Studies, Dehra Dun, India.
EM pankaj.kumar@dituniversity.edu.in
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NR 112
TC 0
Z9 0
U1 3
U2 3
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 1463-5771
EI 1758-4094
J9 BENCHMARKING
JI Benchmarking
PD 2024 JUN 17
PY 2024
DI 10.1108/BIJ-07-2023-0477
EA JUN 2024
PG 29
WC Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA UP9A2
UT WOS:001249368000001
DA 2024-09-05
ER
PT C
AU Maiorino, A
   Padgett, Z
   Wang, C
   Yakubovskiy, M
   Jiang, P
AF Maiorino, Antonio
   Padgett, Zoe
   Wang, Chun
   Yakubovskiy, Misha
   Jiang, Peng
GP ACM
TI Application and Evaluation of Large Language Models for the Generation
   of Survey Questions
SO PROCEEDINGS OF THE 32ND ACM INTERNATIONAL CONFERENCE ON INFORMATION AND
   KNOWLEDGE MANAGEMENT, CIKM 2023
LA English
DT Proceedings Paper
CT 32nd ACM International Conference on Information and Knowledge
   Management (CIKM)
CY OCT 21-25, 2023
CL Birmingham, ENGLAND
DE Generative AI; Survey Research; Text Evaluation
AB Generative Language Models have shown promising results in various domains, and some of the most successful applications are related to "concept expansion", which is the task of generating extensive text based on concise instructions provided through a "seed" prompt. In this presentation we will discuss the recent work conducted by the Data Science team at SurveyMonkey, where we have recently introduced a new feature that harnesses Generative AI models to streamline the survey design process. With this feature users can effortlessly initiate this process by specifying their desired objectives through a prompt, allowing them to automate the creation of surveys that include the critical aspects they wish to investigate.
   We will share our findings regarding some of the challenges encountered during the development of this feature. These include techniques for conditioning the model outputs, integrating generated text with industry-standard questions, fine-tuning Language Models using semi-synthetic Data Generation techniques, and more. Moreover, we will showcase the Evaluation Methodology that we have developed to measure the quality of the generated surveys across several dimensions. This evaluation process is crucial in ensuring that the generated surveys align well with user expectations and serve their intended purpose effectively. Our goal is to demonstrate the promising potential of Generative Language Models in the context of Survey Research, and we believe that sharing our learnings on these challenges and how we addressed them will be useful for practitioners working with Language Models on similar problems.
C1 [Maiorino, Antonio] SurveyMonkey, Milan, Italy.
   [Padgett, Zoe] SurveyMonkey, Gaithersburg, MD USA.
   [Wang, Chun] SurveyMonkey, Ottawa, ON, Canada.
   [Yakubovskiy, Misha] SurveyMonkey, Bellevue, WA USA.
   [Jiang, Peng] SurveyMonkey, San Mateo, CA USA.
RP Maiorino, A (corresponding author), SurveyMonkey, Milan, Italy.
EM amaiorino@surveymonkey.com; zpadgett@surveymonkey.com;
   chunw@surveymonkey.com; myakubovskiy@surveymonkey.com;
   pjiang@surveymonkey.com
NR 0
TC 0
Z9 0
U1 5
U2 5
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES
BN 979-8-4007-0124-5
PY 2023
BP 5244
EP 5245
DI 10.1145/3583780.3615506
PG 2
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BW5IO
UT WOS:001161549505056
DA 2024-09-05
ER
PT J
AU Shahzad, M
   Alhoori, H
   Freedman, R
   Rahman, SA
AF Shahzad, Murtuza
   Alhoori, Hamed
   Freedman, Reva
   Rahman, Shaikh Abdul
TI Quantifying the online long-term interest in research
SO JOURNAL OF INFORMETRICS
LA English
DT Article
DE Long-term research interest; Online scholarly impact; Aging of articles;
   Social media; Altmetrics; Machine learning
ID CITED OLD PAPERS; SOCIAL MEDIA; SOCIETAL IMPACT; OBSOLESCENCE; CITATION;
   ALTMETRICS; GROWTH; ARTICLE; PUBLICATION; CONTINUE
AB Research articles are being shared in increasing numbers on multiple online platforms. Although the scholarly impact of these articles has been widely studied, the online interest determined by how long the research articles are shared online remains unclear. Being cognizant of how long a research article is mentioned online could be valuable information to the researchers. In this paper, we analyzed multiple social media platforms on which users share and/or discuss scholarly articles. We built three clusters for papers, based on the number of yearly online mentions having publication dates ranging from the year 1920 to 2016. Using the online social media metrics for each of these three clusters, we built machine learning models to predict the long-term online interest in research articles. We addressed the prediction task with two different approaches: regression and classification. For the regression approach, the Multi-Layer Perceptron model performed best, and for the classification approach, the tree-based models performed better than other models. We found that old articles are most evident in the contexts of economics and industry (i.e., patents). In contrast, recently published articles are most evident in research platforms (i.e., Mendeley) followed by social media platforms (i.e., Twitter).
C1 [Shahzad, Murtuza; Alhoori, Hamed; Freedman, Reva; Rahman, Shaikh Abdul] Northern Illinois Univ, De Kalb, IL 60115 USA.
C3 Northern Illinois University
RP Shahzad, M (corresponding author), Northern Illinois Univ, De Kalb, IL 60115 USA.
EM msyed1@niu.edu; alhoori@niu.edu; freedman@cs.niu.edu; ashaikh2@niu.edu
RI Alhoori, Hamed/B-8106-2009
OI Alhoori, Hamed/0000-0002-4733-6586; Freedman, Reva/0000-0002-0823-6961;
   Syed, Mohammed Murtuza Shahzad/0000-0001-7630-1617
FU NSF [2022443]; SBE Off Of Multidisciplinary Activities; Direct For
   Social, Behav & Economic Scie [2022443] Funding Source: National Science
   Foundation
FX This work is supported in part by NSF Grant No. 2022443.
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NR 81
TC 3
Z9 5
U1 3
U2 31
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1751-1577
EI 1875-5879
J9 J INFORMETR
JI J. Informetr.
PD MAY
PY 2022
VL 16
IS 2
AR 101288
DI 10.1016/j.joi.2022.101288
EA APR 2022
PG 16
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 2S3GW
UT WOS:000821684900008
OA Green Submitted
DA 2024-09-05
ER
PT C
AU Yang, H
   Ji, CJ
   Wu, QY
AF Yang, Hua
   Ji, Chao-jun
   Wu, Qiao-yun
BE Qi, E
   Shen, J
   Dou, R
TI Research on Risk Assessment of the Equipment Maintenance Contractor
   Support
SO 19TH INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING
   MANAGEMENT: MANAGEMENT SYSTEM INNOVATION
LA English
DT Proceedings Paper
CT 19th International Conference on Industrial Engineering and Engineering
   Management
CY OCT 27-29, 2012
CL Changsha, PEOPLES R CHINA
DE Bayesian networks; Contractor support; Equipment maintenance; Risk
   identification; Risk assessment
AB With regard to many uncertain factors which the process of military equipment maintenance support undertaken by contractors exists, this paper focuses on analysis and assessment of contractor support risks. Firstly, the whole procedure of the contractor support is formal described. Eight risk factors are identified through three aspects, including the army, the contractor, and both concerned parties. Secondly, a Bayesian Network diagram of these risk factors is constructed. Through an example, it has demonstrated the forward reasoning process of equipment maintenance contractor support risks, with using Bayesian Networks rational arithmetic. In order to control the most influencing risk factors, the sensitivity of different risk factors is finally analyzed. This method to identify, classify, and assess risks is useful for the army to prevent risks from occurring.
C1 [Yang, Hua; Ji, Chao-jun; Wu, Qiao-yun] Univ Mil Transportat, Dept Equipment Support, Tianjin, Peoples R China.
RP Yang, H (corresponding author), Univ Mil Transportat, Dept Equipment Support, Tianjin, Peoples R China.
EM liuzesheng_1999@sina.com
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NR 15
TC 0
Z9 0
U1 0
U2 0
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
BN 978-3-642-38427-1; 978-3-642-38426-4
PY 2013
BP 1035
EP 1045
DI 10.1007/978-3-642-38427-1_109
PG 11
WC Management; Operations Research & Management Science
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Business & Economics; Operations Research & Management Science
GA BG8UK
UT WOS:000392719200109
DA 2024-09-05
ER
PT J
AU Brown, P
   Brody, JG
   Morello-Frosch, R
   Tovar, J
   Zota, AR
   Rudel, RA
AF Brown, Phil
   Brody, Julia Green
   Morello-Frosch, Rachel
   Tovar, Jessica
   Zota, Ami R.
   Rudel, Ruthann A.
TI Measuring the Success of Community Science: The Northern California
   Household Exposure Study
SO ENVIRONMENTAL HEALTH PERSPECTIVES
LA English
DT Article
DE breast cancer; community-based participatory research; environmental
   justice; evaluation metrics; exposure science
ID ENDOCRINE-DISRUPTING COMPOUNDS; BREAST-CANCER; PARTICIPATORY RESEARCH;
   AIR; EXPERIENCE; INDOOR; DUST
AB BACKGROUND: Environmental health research involving community participation has increased substantially since the National Institute of Environmental Health Sciences (NIEHS) environmental justice and community-based participatory research (CBPR) partnerships began in the mid-1990s. The goals of these partnerships are to inform and empower better decisions about exposures, foster trust, and generate scientific knowledge to reduce environmental health disparities in low-income, minority communities. Peer-reviewed publication and clinical health outcomes alone are inadequate criteria to judge the success of projects in meeting these goals; therefore, new strategies for evaluating success are needed.
   OBJECTIVES: We reviewed the methods used to evaluate our project, "Linking Breast Cancer Advocacy and Environmental Justice," to help identify successful CBPR methods and to assist other teams in documenting effectiveness. Although our project precedes the development of the NIEHS Evaluation Metrics Manual, a schema to evaluate the success of projects funded through the Partnerships in Environmental Public Health (PEPH), our work reported here illustrates the record keeping and self-reflection anticipated in NIEHS's PEPH.
   DISCUSSION: Evaluation strategies should assess how CBPR partnerships meet the goals of all partners. Our partnership, which included two strong community-based organizations, produced a team that helped all partners gain organizational capacity. Environmental sampling in homes and reporting the results of that effort had community education and constituency-building benefits. Scientific results contributed to a court decision that required cumulative impact assessment for an oil refinery and to new policies for chemicals used in consumer products. All partners leveraged additional funding to extend their work.
   CONCLUSIONS: An appropriate evaluation strategy can demonstrate how CBPR projects can advance science, support community empowerment, increase environmental health literacy, and generate individual and policy action to protect health.
C1 [Brown, Phil] Brown Univ, Dept Sociol, Providence, RI 02912 USA.
   [Brown, Phil] Brown Univ, Ctr Environm Studies, Providence, RI 02912 USA.
   [Brody, Julia Green; Rudel, Ruthann A.] Silent Spring Inst, Newton, MA USA.
   [Morello-Frosch, Rachel] Univ Calif Berkeley, Sch Publ Hlth, Berkeley, CA 94720 USA.
   [Morello-Frosch, Rachel] Univ Calif Berkeley, Dept Environm Sci Policy & Management, Berkeley, CA 94720 USA.
   [Tovar, Jessica] Commun Better Environm, Oakland, CA USA.
   [Zota, Ami R.] Univ Calif San Francisco, Program Reprod Hlth & Environm, Oakland, CA USA.
C3 Brown University; Brown University; University of California System;
   University of California Berkeley; University of California System;
   University of California Berkeley; University of California System;
   University of California San Francisco
RP Brown, P (corresponding author), Brown Univ, Dept Sociol, Box 1916, Providence, RI 02912 USA.
EM phil_brown@brown.edu
RI Rudel, Ruthann/AAJ-5956-2021
OI Rudel, Ruthann/0000-0002-1809-4127; Zota, Ami/0000-0003-0710-354X;
   Newman, Gregory/0000-0003-0503-5782; Morello-Frosch,
   Rachel/0000-0003-1153-7287
FU National Institute of Environmental Health Sciences [R25 ES013258];
   National Science Foundation [SES 0450837]; breast cancer organizations
FX This research was supported by the National Institute of Environmental
   Health Sciences (R25 ES013258) and the National Science Foundation (SES
   0450837). J.G.B. and R.A.R. are employed at Silent Spring Institute, a
   scientific research organization dedicated to studying environmental
   factors in women's health. The institute is a 501(c)(3) public charity
   funded by federal grants and contracts, foundation grants, and private
   donations, including from breast cancer organizations. J.T. is employed
   by Communities for a Better Environment, Oakland, CA.
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NR 37
TC 65
Z9 71
U1 1
U2 57
PU US DEPT HEALTH HUMAN SCIENCES PUBLIC HEALTH SCIENCE
PI RES TRIANGLE PK
PA NATL INST HEALTH, NATL INST ENVIRONMENTAL HEALTH SCIENCES, PO BOX 12233,
   RES TRIANGLE PK, NC 27709-2233 USA
SN 0091-6765
EI 1552-9924
J9 ENVIRON HEALTH PERSP
JI Environ. Health Perspect.
PD MAR
PY 2012
VL 120
IS 3
BP 326
EP 331
DI 10.1289/ehp.1103734
PG 6
WC Environmental Sciences; Public, Environmental & Occupational Health;
   Toxicology
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology; Public, Environmental & Occupational
   Health; Toxicology
GA 907CF
UT WOS:000301394700017
PM 22147336
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU Muthusi, J
   Mwalili, S
   Young, P
AF Muthusi, Jacques
   Mwalili, Samuel
   Young, Peter
TI %<i>svy</i>_<i>logistic</i>_<i>regression</i>: A generic SAS macro for
   simple and multiple logistic regression and creating quality
   publication-ready tables using survey or non-survey data
SO PLOS ONE
LA English
DT Article
ID REPRODUCIBLE RESEARCH; HIV; MEN; SEX
AB Introduction
   Reproducible research is increasingly gaining interest in the research community. Automating the production of research manuscript tables from statistical software can help increase the reproducibility of findings. Logistic regression is used in studying disease prevalence and associated factors in epidemiological studies and can be easily performed using widely available software including SAS, SUDAAN, Stata or R. However, output from these software must be processed further to make it readily presentable. There exists a number of procedures developed to organize regression output, though many of them suffer limitations of flexibility, complexity, lack of validation checks for input parameters, as well as inability to incorporate survey design.
   Methods
   We developed a SAS macro, % svy_logistic_regression, for fitting simple and multiple logistic regression models. The macro also creates quality publication-ready tables using survey or non-survey data which aims to increase transparency of data analyses. It further significantly reduces turn-around time for conducting analysis and preparing output tables while also addressing the limitations of existing procedures. In addition, the macro allows for user-specific actions to handle missing data as well as use of replication-based variance estimation methods.
   Results
   We demonstrate the use of the macro in the analysis of the 2013-2014 National Health and Nutrition Examination Survey (NHANES), a complex survey designed to assess the health and nutritional status of adults and children in the United States. The output presented here is directly from the macro and is consistent with how regression results are often presented in the epidemiological and biomedical literature, with unadjusted and adjusted model results presented side by side.
   Conclusions
   The SAS code presented in this macro is comprehensive, easy to follow, manipulate and to extend to other areas of interest. It can also be incorporated quickly by the statistician for immediate use. It is an especially valuable tool for generating quality, easy to review tables which can be incorporated directly in a publication.
C1 [Muthusi, Jacques; Mwalili, Samuel; Young, Peter] US Ctr Dis Control & Prevent, Div Global HIV & TB, Nairobi, Kenya.
RP Muthusi, J (corresponding author), US Ctr Dis Control & Prevent, Div Global HIV & TB, Nairobi, Kenya.
EM mwj6@cdc.gov
RI Mwalili, Samuel/AAV-6758-2020; Muthusi, Jacques/ABB-1863-2021
OI Mwalili, Samuel/0000-0002-9703-6514; Muthusi,
   Jacques/0000-0003-4092-3486
FU President's Emergency Plan for AIDS Relief (PEPFAR) through the U.S.
   Centers for Disease Control and Prevention (CDC)
FX This work was supported by the President's Emergency Plan for AIDS
   Relief (PEPFAR) through the U.S. Centers for Disease Control and
   Prevention (CDC). The funder had no role in study design, data
   collection and analysis, decision to publish, or preparation of the
   manuscript.
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NR 37
TC 2
Z9 2
U1 0
U2 13
PU PUBLIC LIBRARY SCIENCE
PI SAN FRANCISCO
PA 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA
SN 1932-6203
J9 PLOS ONE
JI PLoS One
PD SEP 3
PY 2019
VL 14
IS 9
AR e0214262
DI 10.1371/journal.pone.0214262
PG 14
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA IY3OS
UT WOS:000486302400001
PM 31479445
OA Green Published, gold, Green Submitted
DA 2024-09-05
ER
PT C
AU Tang, J
   Sun, JM
   Wang, C
   Yang, Z
AF Tang, Jie
   Sun, Jimeng
   Wang, Chi
   Yang, Zi
GP ACM
TI Social Influence Analysis in Large-scale Networks
SO KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA
   MINING
LA English
DT Proceedings Paper
CT 15th ACM SIGKDD International Conference on Knowledge Discovery and Data
   Mining
CY JUN 28-JUL 01, 2009
CL Paris, FRANCE
DE Social Influence Analysis; Topical Affinity Propagation; Large-scale
   Network; Social Networks
AB In large social networks, nodes (users, entities) are influenced by others for various reasons. For example, the col leagues have strong influence on one's work, while the friends have strong influence on one's daily life. How to differentiate the social influences from different angles(topics)? How to quantify the strength of those social influences? How to estimate the model on real large networks?
   To address these fundamental questions, we propose Topical Affinity Propagation (TAP) to model the topic-level social influence on large networks. In particular, TAP can take results of any topic modeling and the existing network structure to perform topic-level influence propagation. With the help of the influence analysis, we present several important applications on real data sets such as 1) what are the representative nodes on a given topic? 2) how to identify the social influences of neighboring nodes on a particular node?
   To scale to real large networks, TAP is designed with efficient distributed learning algorithms that is implemented and tested under the Map-Reduce framework. We further present the common characteristics of distributed learning algorithms for Map-Reduce. Finally, we demonstrate the effectiveness and efficiency of TAP on real large data sets.
C1 [Tang, Jie; Wang, Chi; Yang, Zi] Tsinghua Univ, Dept Comp Sci, Beijing, Peoples R China.
C3 Tsinghua University
RP Tang, J (corresponding author), Tsinghua Univ, Dept Comp Sci, Beijing, Peoples R China.
EM jietang@tsinghau.edu.cn; jimeng@us.ibm.com; sonicive@gmail.com;
   yz@keg.cs.tsinghua.edu.cn
RI tang, jie/KIE-8633-2024
OI Sun, Jimeng/0000-0003-1512-6426
CR Albert R., 2002, REV MODERN PHYS, V74, DOI arXivcond-mat/0106096v1
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NR 27
TC 546
Z9 650
U1 1
U2 26
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA
BN 978-1-60558-495-9
PY 2009
BP 807
EP 815
PG 9
WC Computer Science, Artificial Intelligence
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BLS23
UT WOS:000270922000080
DA 2024-09-05
ER
PT J
AU Mauro, DM
   Ellis, JA
   Lilly, JF
   Dallaghan, GLB
   Jordan, SG
AF Mauro, David M.
   Ellis, Joshua A.
   Lilly, John F., III
   Dallaghan, Gary L. Beck
   Jordan, Sheryl G.
TI Creating an Open-Access Educational Radiology Website for Medical
   Students: A Guide for Radiology Educators
SO ACADEMIC RADIOLOGY
LA English
DT Article
DE Website; Active learning; Medical student education; Clinician educator;
   Radiology
AB Rationale and objectives: Studies of medical school clerkship websites have reported efficient time management, resource utilization, and hands-on activities. We built a website devoted to medical student education in radiology to address student, educator, and school curricular needs and surveyed students to assess their satisfaction with the site. Materials and methods: The website was created using an easily-recalled name, no-cost institutional software, and no-cost enterprise-level university hardware. The main menu links to the student formal didactic lecture calendar, custom-built health sciences library e-resources in radiology, American College of Radiology Appropriateness Criteria, each radiology course page, and teaching files. Each course tab includes faculty-curated content from course lectures, supplemental articles and educational modules. At 6, 12, and 24 months, website analytics were assessed. At 12 and 24 months postimplementation, data were evaluated to include student assessment and satisfaction surveys and student course comments. This project was IRB-exempted. Results: At 6 months, the website had received 5792 views, at 12 months 10,022 views and at 24 months 19,478 views. The website homepage with the formal didactic lecture calendar received 7156 views, the general clerkship page 4233 views, the teaching file page 3884, and thereafter subspecialty pages as follows: breast 1478, body 633, pediatrics 361, neuro 346, cardiothoracic 291, musculoskele-tal 249, vascular interventional 178. One hundred fifty-two of 240 (63.3%) of students surveyed replied. Of students who utilized the web-site on the satisfaction survey, 80 of 97 (82.5%) indicated ratings of "extremely informative" and "very informative" to the question "How would you rate the website?." Students indicated convenience and structure as website strengths in their postcourse evaluations. Conclusion: The radiology medical student website incorporates demands and needs of today's students, faculty, and our medical school. A radiology clerkship website for medical students centralizes access to course resources and promotes an active learning experi-ence with high satisfaction. Instructions on setting up a website are offered to today's radiology educators, including pearls and pitfalls.
C1 [Mauro, David M.; Lilly, John F., III; Jordan, Sheryl G.] UNC Hlth, Dept Radiol, 101 Manning Dr Campus Box 7510, Chapel Hill, NC 27514 USA.
   [Ellis, Joshua A.] Univ N Carolina, Sch Med, Chapel Hill, NC USA.
   [Dallaghan, Gary L. Beck] Univ N Carolina, Sch Med, Dept Pediat, Chapel Hill, NC USA.
C3 University of North Carolina School of Medicine; University of North
   Carolina; University of North Carolina Chapel Hill; University of North
   Carolina School of Medicine; University of North Carolina; University of
   North Carolina Chapel Hill
RP Mauro, DM (corresponding author), UNC Hlth, Dept Radiol, 101 Manning Dr Campus Box 7510, Chapel Hill, NC 27514 USA.
EM david_mauro@med.unc.edu
OI Ellis, Joshua/0000-0003-3284-2731; Jordan, Sheryl/0000-0002-6686-2100;
   Mauro, David/0000-0003-3088-1809
CR [Anonymous], 2011, ACR appropriateness criteria
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   Hopkins L, 2018, AM J OBSTET GYNECOL, V218, P188, DOI 10.1016/j.ajog.2017.06.001
   Liaison Committee on Medical Education, FUNCT STRUCT MED SCH
   Ramnanan CJ, 2017, ADV MED EDUC PRACT, V8, P63, DOI 10.2147/AMEP.S109037
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NR 9
TC 8
Z9 8
U1 2
U2 4
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 1076-6332
EI 1878-4046
J9 ACAD RADIOL
JI Acad. Radiol.
PD NOV
PY 2021
VL 28
IS 11
BP 1631
EP 1636
DI 10.1016/j.acra.2020.08.021
EA NOV 2021
PG 6
WC Radiology, Nuclear Medicine & Medical Imaging
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Radiology, Nuclear Medicine & Medical Imaging
GA WZ3MU
UT WOS:000719875900018
PM 32972840
DA 2024-09-05
ER
PT J
AU Wang, CH
AF Wang, Chien-hsing
TI An Exploratory Study of Student Self-Assessment in an Online Learning
   Context
SO INTERNATIONAL JOURNAL OF ONLINE PEDAGOGY AND COURSE DESIGN
LA English
DT Article
DE Action Research; Classroom Management; Self-Assessment; Student
   Reflection; Teacher Education
ID HIGHER-EDUCATION
AB This paper reports the investigation of the application of self-assessment in an online learning setting based on action research. The reset:irk participants were students who completed their self-assessment when taking the course on Classroom Management taught by the teacher researcher. Although the analytic results show the lack of critical reflection in student self-assessment, the teacher researcher learned the following lessons: a) self-assessment helps the students to articulate their learning results in specific; (b) self-assessment can be a means to cultivate students' abilities in information-integration; (c) using multiple evaluative tools for assessing self-assessment is recommended to better describe the levels of student reflection; and (d) effective and efficient implementation of self-assessment requires a redesigning of the learning management system. Finally, further research can focus on the possibility of promoting the level of student reflection by encouraging students to use evaluative tools to assess their self-assessment.
C1 [Wang, Chien-hsing] Natl Changhua Univ Educ, Grad Inst Educ, Changhua, Taiwan.
C3 National Changhua University of Education
RP Wang, CH (corresponding author), Natl Changhua Univ Educ, Grad Inst Educ, Changhua, Taiwan.
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NR 48
TC 1
Z9 1
U1 0
U2 2
PU IGI GLOBAL
PI HERSHEY
PA 701 E CHOCOLATE AVE, STE 200, HERSHEY, PA 17033-1240 USA
SN 2155-6873
EI 2155-6881
J9 INT J ONLINE PEDAGOG
JI Int. J. Online Pedagog. Course Des.
PD OCT-DEC
PY 2011
VL 1
IS 4
BP 50
EP 61
DI 10.4018/ijopcd.2011100104
PG 12
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA VF9OZ
UT WOS:000444154700004
DA 2024-09-05
ER
PT J
AU Dalavi, AM
   Gomes, A
   Husain, AJ
AF Dalavi, Amol M.
   Gomes, Alyssa
   Husain, Aaliya Javed
TI Bibliometric analysis of nature inspired optimization techniques
SO COMPUTERS & INDUSTRIAL ENGINEERING
LA English
DT Article
DE Nature inspired optimization algorithms; Optimization; Bibliometric
   analysis; Citation analysis; Evolutionary algorithms; Bio-inspired
   algorithms
ID SEARCH ALGORITHM; EVOLUTIONARY ALGORITHM; HEURISTIC OPTIMIZATION; GLOBAL
   OPTIMIZATION; GENETIC ALGORITHMS; DESIGN; BEHAVIOR; SCOPUS
AB Nature-inspired optimization has gained immense popularity over the past six decades and has been extensively used across various disciplines. This paper aims to statistically evaluate the impact and importance of natureinspired optimization by presenting an analysis of works published between 2016 and 2020. The data is obtained from Scopus and focuses on metrics like the total number of publications, citations, average citations per publication, and the h-index. Graphical and statistical analysis was carried out using Excel, Python, RAWGraphs, and Tableau Public. All the data in the present work was accessed on 11th August 2021. A total of 91,507 publications were analysed. China, India, and the US are the highest contributors with 27045, 12129, and 8947 publications respectively. The Ministry of Education China has contributed the most to this field, followed by the Chinese Academy of Sciences. The National Natural Science Foundation of China has funded the highest number of works (14.72% publications). Zhang M. is the most productive author with 224 publications. Lecture Notes in Computer Science, Advances in Intelligent Systems and Computing, and IEEE Access are the most productive journals. The top disciplines contributing to research include Computer Science (55.22%), Engineering (48.06%), and Mathematics (27.30%), and the top application areas include optimization, artificial intelligence, and decision sciences. The most popular algorithms include Genetic Algorithms, Simulated Annealing, and Particle Swarm Optimization. This data could prove beneficial to scholars looking for an overview of nature-inspired algorithms to determine future research directions.
C1 [Dalavi, Amol M.; Gomes, Alyssa; Husain, Aaliya Javed] Symbiosis Int Univ, Symbiosis Inst Technol, Dept Mech Engn, Pune 412115, India.
C3 Symbiosis International University; Symbiosis Institute of Technology
   (SIT)
RP Dalavi, AM (corresponding author), Symbiosis Int Univ, Symbiosis Inst Technol, Dept Mech Engn, Pune 412115, India.
EM amol.dalavi@sitpune.edu.in
RI Gomes, Alyssa/HKV-2935-2023
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NR 174
TC 7
Z9 7
U1 2
U2 20
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0360-8352
EI 1879-0550
J9 COMPUT IND ENG
JI Comput. Ind. Eng.
PD JUL
PY 2022
VL 169
AR 108161
DI 10.1016/j.cie.2022.108161
EA APR 2022
PG 19
WC Computer Science, Interdisciplinary Applications; Engineering,
   Industrial
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA 1J5GJ
UT WOS:000797946900009
DA 2024-09-05
ER
PT C
AU Palen-Michel, C
   Kim, J
   Lignos, C
AF Palen-Michel, Chester
   Kim, June
   Lignos, Constantine
BA Mariani, J
BF Mariani, J
BE Calzolari, N
   Bechet, F
   Blache, P
   Choukri, K
   Cieri, C
   Declerck, T
   Goggi, S
   Isahara, H
   Maegaard, B
   Mazo, H
   Odijk, H
   Piperidis, S
TI Multilingual Open Text Release 1: Public Domain News in 44 Languages
SO LREC 2022: THIRTEEN INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND
   EVALUATION
LA English
DT Proceedings Paper
CT 13th International Conference on Language Resources and Evaluation
   (LREC)
CY JUN 20-25, 2022
CL Marseille, FRANCE
DE multilingual corpora; text data; low resource NLP; open access text
AB We present a Multilingual Open Text (MOT), a new multilingual corpus containing text in 44 languages, many of which have limited existing text resources for natural language processing. The first release of the corpus contains over 2.8 million news articles and an additional 1 million short snippets (photo captions, video descriptions, etc.) published between 2001-2022 and collected from Voice of America's news websites. We describe our process for collecting, filtering, and processing the data. The source material is in the public domain, our collection is licensed using a creative commons license (CC BY 4.0), and all software used to create the corpus is released under the MIT License. The corpus will be regularly updated as additional documents are published.
C1 [Palen-Michel, Chester; Kim, June; Lignos, Constantine] Brandeis Univ, Michtom Sch Comp Sci, Waltham, MA 02254 USA.
C3 Brandeis University
RP Palen-Michel, C (corresponding author), Brandeis Univ, Michtom Sch Comp Sci, Waltham, MA 02254 USA.
EM cpalenmichel@brandeis.edu; junekim@brandeis.edu; lignos@brandeis.edu
FU 2021 Brandeis University Provost Research Grant
FX We thank the early adopters of our resource-many of whom are members of
   the Masakhane communitywho used preliminary releases and offered
   feedback. This work was supported by a 2021 Brandeis University Provost
   Research Grant.
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NR 31
TC 2
Z9 3
U1 1
U2 2
PU EUROPEAN LANGUAGE RESOURCES ASSOC-ELRA
PI PARIS
PA 55-57, RUE BRILLAT-SAVARIN, PARIS, 75013, FRANCE
BN 979-10-95546-72-6
PY 2022
BP 2080
EP 2089
PG 10
WC Computer Science, Interdisciplinary Applications; Linguistics
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Linguistics
GA BU2ZO
UT WOS:000889371702021
DA 2024-09-05
ER
PT J
AU Long, ZH
   Zhao, GJ
   Wang, J
   Zhang, MT
   Zhou, SY
   Zhang, L
   Huang, ZX
AF Long, Zehai
   Zhao, Guojing
   Wang, Jing
   Zhang, Mengting
   Zhou, Shaoyu
   Zhang, Ling
   Huang, Zhaoxin
TI Research on the Drivers of Entrepreneurship Education Performance of
   Medical Students in the Digital Age
SO FRONTIERS IN PSYCHOLOGY
LA English
DT Article
DE medical students; entrepreneurship education performance; driving
   factors; digital economy; ridge regression model
ID RIDGE REGRESSION; INNOVATION; TEACHER; GENDER; INTENTIONS; BUSINESS;
   POLICY
AB COVID-19 has made the entire society pay more attention to medical students training. Medicine development is inseparable from the spirit of innovation, focusing on cultivating medical students' innovative awareness and improving entrepreneurship education performance, which has an irreplaceable effect on both the students themselves and the society. This study is based on the ridge regression model to study the driving factors of the entrepreneurship education performance of medical students. Compared with traditional multiple regression, it can improve the consistency of parameter estimation and obtain more realistic results. Based on a large sample of empirical survey data of 24,677 medical students in China, this study analyzed the driving factors of the entrepreneurship education performance of medical students and found that medical students of different genders have differences in entrepreneurship education performance; the digital economy impacts entrepreneurship education performance of medical students; entrepreneurship course, entrepreneurship faculty, entrepreneurship competition, entrepreneurship practice, and entrepreneurship policy have a driving effect on the entrepreneurship education performance of medical students. Meanwhile, the impact of entrepreneurship policy is the most obvious, followed by entrepreneurship practice and entrepreneurship competition, followed by entrepreneurship course and entrepreneurship faculty.
C1 [Long, Zehai; Zhao, Guojing; Wang, Jing; Zhang, Mengting; Zhou, Shaoyu; Huang, Zhaoxin] Wenzhou Med Univ, Inst China Innovat & Entrepreneurship Educ, Wenzhou, Peoples R China.
   [Zhang, Ling] Hangzhou Normal Univ, Sch Educ, Hangzhou, Peoples R China.
C3 Wenzhou Medical University; Hangzhou Normal University
RP Zhao, GJ; Zhou, SY; Huang, ZX (corresponding author), Wenzhou Med Univ, Inst China Innovat & Entrepreneurship Educ, Wenzhou, Peoples R China.; Zhang, L (corresponding author), Hangzhou Normal Univ, Sch Educ, Hangzhou, Peoples R China.
EM 13600641869@126.com; shaowzmc@qq.com; 1046814422@qq.com;
   25732880@wmu.edu.cn
RI Tang, Hao/HHN-2620-2022
OI Zhao, Guojing/0000-0003-1920-0769
FU National Social Science Fund of China
FX Funding This work was supported by the key project of the National
   Social Science Fund of Chinathe Research on Barriers and Policy Support
   Mechanisms for Female Entrepreneurship in the Digital Era (20ASH012).
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NR 73
TC 8
Z9 8
U1 9
U2 85
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 1664-1078
J9 FRONT PSYCHOL
JI Front. Psychol.
PD OCT 29
PY 2021
VL 12
AR 733301
DI 10.3389/fpsyg.2021.733301
PG 17
WC Psychology, Multidisciplinary
WE Social Science Citation Index (SSCI)
SC Psychology
GA WW3FM
UT WOS:000717806900001
PM 34777115
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU Tahiru, F
   Parbanath, S
   Agbesi, S
AF Tahiru, Fati
   Parbanath, Steven
   Agbesi, Samuel
TI Machine Learning-based Predictive Systems in Higher Education: A
   Bibliometric Analysis
SO JOURNAL OF SCIENTOMETRIC RESEARCH
LA English
DT Article
DE Machine Learning; Predictive System; Data Analytics; Learning Analytics;
   Higher Education
AB This paper aims to comprehensively review the present state and research trends in predictive systems in higher education. It also addresses the research contribution of countries in Machine Learning-based predictive systems in higher education to depict the research landscape given the growing number of related publications. A bibliometric analysis of publications on predictive systems in education published in the Scopus Database from 2015 to 2022 was conducted. The dataset obtained covered the contribution of authors, affiliations, countries, themes and trends in the field of Machine Learning-based predictive systems in higher education. A total of 72 publications with 3408 cited references were collected from Scopus for the bibliometric analysis. The technique used for the bibliometric analysis included performance analysis and science mapping. Research on Machine Learning-based predictive systems has been widely published from 2020 to 2022. Researchers in China, Belgium, Spain, India, and Korea were most active in researching Machine Learning-based predictive systems in education. However, international collaborations have remained infrequent except for the few involving Australia, Belgium, and Canada. There is a lack of research in the subject area in Africa. This study illustrates the intellectual landscape of Machine Learning-based predictive systems in higher education and the field's evolution and emerging trends. The findings highlight the area of research concentration and the most recent developments and suggest future research collaborations on a larger scale as well as additional research on the implementation of predictive systems in education in Africa.
C1 [Tahiru, Fati; Parbanath, Steven] Durban Univ Technol, Dept Informat Technol, Kwa Zulu, South Africa.
   [Agbesi, Samuel] IT Univ Copenhagen, Dept Comp Sci, Copenhagen, Denmark.
C3 Durban University of Technology; IT University Copenhagen
RP Tahiru, F (corresponding author), Durban Univ Technol, Dept Informat Technol, Kwa Zulu, South Africa.
EM 22176488@dut4life.ac.za
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NR 30
TC 0
Z9 0
U1 6
U2 6
PU PHCOG NET
PI KARNATAKA
PA 17, 2ND FLR, BUDDHA VIHAR RD, NEAR SPORTS ZONE, COX TOWN, BENGALURU,
   KARNATAKA, 560005, INDIA
SN 2321-6654
EI 2320-0057
J9 J SCIENTOMETR RES
JI J. Scientometr. Res.
PD MAY-AUG
PY 2023
VL 12
IS 2
BP 436
EP 447
DI 10.5530/jscires.12.2.040
PG 12
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA HF6A6
UT WOS:001158105300020
OA hybrid
DA 2024-09-05
ER
PT J
AU Kim, J
   Kim, J
AF Kim, Jinseok
   Kim, Jenna
TI ANDez: An open-source tool for author name disambiguation using machine
   learning
SO SOFTWAREX
LA English
DT Article
DE Author name disambiguation; Authority control; Machine learning; Science
   of science; Scientometrics; Bibliometrics
AB Author name disambiguation in bibliographic data is challenging due to the same names of different authors and name variations of authors. Various machine learning (ML) methods address this, but a unified framework for comparing them is lacking. This study introduces ANDez, an open-source tool that integrates top-performing ML techniques for author name disambiguation. Developed in Python using popular ML libraries, ANDez provides a transparent system, merging complex procedures from different ML approaches. This promotes the assessment, modification, and benchmarking of ML techniques in author name disambiguation. ANDez's user-friendly design also helps researchers analyze ambiguous bibliographic data without needing advanced ML coding expertise.
C1 [Kim, Jinseok] Univ Michigan, Inst Social Res, 330 Packard St, Ann Arbor, MI 48104 USA.
   [Kim, Jinseok] Univ Michigan, Sch Informat, 330 Packard St, Ann Arbor, MI 48104 USA.
   [Kim, Jenna] Univ Illinois, Sch Informat Sci, 501 E Daniel St, Champaign, IL 61820 USA.
C3 University of Michigan System; University of Michigan; University of
   Michigan System; University of Michigan; University of Illinois System;
   University of Illinois Urbana-Champaign
RP Kim, J (corresponding author), Univ Michigan, Inst Social Res, 330 Packard St, Ann Arbor, MI 48104 USA.; Kim, J (corresponding author), Univ Michigan, Sch Informat, 330 Packard St, Ann Arbor, MI 48104 USA.
EM jinseokk@umich.edu; jkim682@illinois.edu
OI Kim, Jinseok/0000-0001-6481-2065
FU National Science Foundation (NSF NCSES) [1917663]; NSF; University of
   Michigan Institute for Data Science
FX ANDez was created with support from the National Science Foundation (NSF
   NCSES Award #1917663: Creating a Data Quality Control Framework for
   Producing New Personnel-Based S & E Indicators) grant and additional NSF
   funding through the Research Experiences for Undergraduates (REU)
   program. A PODS grant from the University of Michigan Institute for Data
   Science also supported the development of ANDez. We thank Dr. Jason D.
   Owen-Smith (University of Michigan) for his valuable feedback and
   recommendations that have enhanced ANDez's accessibility and utility for
   a diverse group of social scientists.
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NR 37
TC 0
Z9 0
U1 0
U2 0
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2352-7110
J9 SOFTWAREX
JI SoftwareX
PD MAY
PY 2024
VL 26
AR 101719
DI 10.1016/j.softx.2024.101719
EA APR 2024
PG 7
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA QQ9P4
UT WOS:001222456100001
OA gold
DA 2024-09-05
ER
PT C
AU Zhang, QA
   Liu, TN
AF Zhang, Qian
   Liu, Tongna
BE Xu, H
TI Research on Performance Evaluation of Project Management Based on
   Support Vector Machine and Fuzzy Rules
SO 2ND IEEE INTERNATIONAL CONFERENCE ON ADVANCED COMPUTER CONTROL (ICACC
   2010), VOL. 5
LA English
DT Proceedings Paper
CT 2nd IEEE International Conference on Advanced Computer Control
CY MAR 27-29, 2010
CL Shenyang, PEOPLES R CHINA
DE SVM; project management; performance evaluation; fuzzy rules
AB The principle and step of performance evaluation of project management based on SVM and fuzzy rules are studied. The index system of performance evaluation of project management is set up. Then we built up the evaluation model on SVM and fuzzy rules. Finally, take some samples of project for an example, we carry on this model to instance. It can take a preferably evaluation, so that it is a viable method.
C1 [Zhang, Qian] North China Elect Power Univ, Dept Econ Management, Baoding 071000, Hebei, Peoples R China.
   [Liu, Tongna] North China Elect Power Univ, Dept Elect & Commun ENgn, Baoding, Hebei, Peoples R China.
C3 North China Electric Power University; North China Electric Power
   University
RP Zhang, QA (corresponding author), North China Elect Power Univ, Dept Econ Management, Baoding 071000, Hebei, Peoples R China.
EM hdzhq@yeah.net; hdltn@yeah.net
FU Hebei Natural Science Fund [G2009001410]
FX This research was supported by Hebei Natural Science Fund.!G2009001410"
CR [Anonymous], INF SCI
   Hu Jun, 2005, Modular Machine Tool & Automatic Manufacturing Technique, P97
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   Liu Shihong, 2005, AGR INFORM TECHNOLOG
   Machlup Fritz., 1962, The Production and Distribution of Knowledge in the United States
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NR 11
TC 1
Z9 1
U1 0
U2 1
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-4244-5847-9
PY 2010
BP 397
EP 400
DI 10.1109/ICACC.2010.5487081
PG 4
WC Automation & Control Systems; Engineering, Electrical & Electronic
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Automation & Control Systems; Engineering
GA BUG32
UT WOS:000289207500093
DA 2024-09-05
ER
PT J
AU Wang, ZY
   Dong, W
   Yang, K
AF Wang, Zhenyi
   Dong, Wen
   Yang, Kun
TI Spatiotemporal Analysis and Risk Assessment Model Research of Diabetes
   among People over 45 Years Old in China
SO INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
LA English
DT Article
DE spatiotemporal analysis; risk factors; binary logistic regression;
   random forest model
ID GEOGRAPHIC INFORMATION-SYSTEMS; SPATIAL-ANALYSIS; PREVALENCE; HEALTH;
   HYPERTENSION; MELLITUS; SMOKING; INDEX
AB Diabetes, which is a chronic disease with a high prevalence in people over 45 years old in China, is a public health issue of global concern. In order to explore the spatiotemporal patterns of diabetes among people over 45 years old in China, to find out diabetes risk factors, and to assess its risk, we used spatial autocorrelation, spatiotemporal cluster analysis, binary logistic regression, and a random forest model in this study. The results of the spatial autocorrelation analysis and the spatiotemporal clustering analysis showed that diabetes patients are mainly clustered near the Beijing-Tianjin-Hebei region, and that the prevalence of diabetes clusters is waning. Age, hypertension, dyslipidemia, and smoking history were all diabetes risk factors (p < 0.05), but the spatial heterogeneity of these factors was weak. Compared with the binary logistic regression model, the random forest model showed better accuracy in assessing diabetes risk. According to the assessment risk map generated by the random forest model, the northeast region and the Beijing-Tianjin-Hebei region are high-risk areas for diabetes.
C1 [Wang, Zhenyi; Dong, Wen; Yang, Kun] Yunnan Normal Univ, Fac Geog, Kunming 650500, Yunnan, Peoples R China.
   [Wang, Zhenyi; Dong, Wen; Yang, Kun] Yunnan Normal Univ, GIS Technol Engn Res Ctr, West China Resources & Environm Educ Minist, Kunming 650500, Yunnan, Peoples R China.
C3 Yunnan Normal University; China Resources Group; Yunnan Normal
   University
RP Dong, W; Yang, K (corresponding author), Yunnan Normal Univ, Fac Geog, Kunming 650500, Yunnan, Peoples R China.; Dong, W; Yang, K (corresponding author), Yunnan Normal Univ, GIS Technol Engn Res Ctr, West China Resources & Environm Educ Minist, Kunming 650500, Yunnan, Peoples R China.
EM kmynnu_gis@163.com; kmdcynu@163.com
RI Yang, Kun/ISA-1094-2023; yang, kun/JGM-4169-2023
OI Yang, Kun/0000-0003-1335-3449; Wang, Zhenyi/0009-0001-4552-7207
FU National Natural Science Foundation of China [42161071, 42071381,
   41661087]
FX This research was supported by National Natural Science Foundation of
   China (Grants Nos. 42161071, 42071381, 41661087).
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NR 64
TC 1
Z9 1
U1 7
U2 37
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1660-4601
J9 INT J ENV RES PUB HE
JI Int. J. Environ. Res. Public Health
PD AUG
PY 2022
VL 19
IS 16
AR 9861
DI 10.3390/ijerph19169861
PG 26
WC Environmental Sciences; Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology; Public, Environmental & Occupational
   Health
GA 4B3JQ
UT WOS:000845678700001
PM 36011493
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU Purnell, PJ
AF Purnell, Philip J.
TI A comparison of different methods of identifying publications related to
   the United Nations Sustainable Development Goals: Case study of SDG
   13-Climate Action
SO QUANTITATIVE SCIENCE STUDIES
LA English
DT Article
DE artificial intelligence; bibliometrics; climate action; machine
   learning; sustainable development goal
ID DIMENSIONS; WEB; SCOPUS
AB As sustainability becomes an increasing priority throughout global society, academic and research institutions are assessed on their contribution to relevant research publications. This study compares four methods of identifying research publications related to United Nations Sustainable Development Goal 13-Climate Action (SDG 13). The four methods (Elsevier, STRINGS, SIRIS, and Dimensions) have each developed search strings with the help of subject matter experts, which are then enhanced through distinct methods to produce a final set of publications. Our analysis showed that the methods produced comparable quantities of publications but with little overlap between them. We visualized some difference in topic focus between the methods and drew links with the search strategies used. Differences between publications retrieved are likely to come from subjective interpretation of the goals, keyword selection, operationalizing search strategies, AI enhancements, and selection of bibliographic database. Each of the elements warrants deeper investigation to understand their role in identifying SDG-related research. Before choosing any method to assess the research contribution to SDGs, end users of SDG data should carefully consider their interpretation of the goal and determine which of the available methods produces the closest data set. Meanwhile, data providers might customize their methods for varying interpretations of the SDGs.
C1 [Purnell, Philip J.] Leiden Univ, Ctr Sci & Technol Studies, Leiden, Netherlands.
   [Purnell, Philip J.] United Arab Emirates Univ, Al Ain, U Arab Emirates.
C3 Leiden University; Leiden University - Excl LUMC; United Arab Emirates
   University
RP Purnell, PJ (corresponding author), Leiden Univ, Ctr Sci & Technol Studies, Leiden, Netherlands.; Purnell, PJ (corresponding author), United Arab Emirates Univ, Al Ain, U Arab Emirates.
EM p.j.purnell@cwts.leidenuniv.nl
RI Purnell, Philip/JQX-0944-2023; Purnell, Philip/ITU-3369-2023; Purnell,
   Philip/A-3080-2009
OI Purnell, Philip/0000-0003-3146-2737
FU GCRF [AH/S011501/1] Funding Source: UKRI
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NR 45
TC 6
Z9 7
U1 7
U2 16
PU MIT PRESS
PI CAMBRIDGE
PA ONE ROGERS ST, CAMBRIDGE, MA 02142-1209 USA
EI 2641-3337
J9 QUANT SCI STUD
JI Quant. Sci. Stud.
PD DEC 20
PY 2022
VL 3
IS 4
BP 976
EP 1002
DI 10.1162/qss_a_00215
PG 27
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA 9B9MZ
UT WOS:000935055300005
OA gold, Green Submitted
DA 2024-09-05
ER
PT J
AU Pan, X
   Zuo, DJ
   Zhang, WJ
   Hu, LH
   Wang, HX
   Jiang, J
AF Pan, Xing
   Zuo, Dujun
   Zhang, Wenjin
   Hu, Lunhu
   Wang, Huixiong
   Jiang, Jing
TI Research on Human Error Risk Evaluation Using Extended Bayesian Networks
   with Hybrid Data
SO RELIABILITY ENGINEERING & SYSTEM SAFETY
LA English
DT Article
DE Bayesian Network; Human Reliability Analysis; Hybrid Data
ID HUMAN RELIABILITY-ANALYSIS; EPISTEMIC UNCERTAINTY; CREDAL NETWORKS;
   PROBABILITY; ALGORITHM; TANKER; SETS; 2U
AB Bayesian networks (BNs) play an important role in performing uncertainty analysis. BNs, as a sort of directed acyclic graph with probabilities, can establish causality and clarify complex uncertain relationships to benefit risk analyze. A large number of accurate data must be obtained for precisely reasoning, but it is often difficult in human reliability analysis (HRA). Inadequate data on space launch sites make it necessary to utilize different types of data in engineering. This paper studies the uncertainty in BNs and classifies the using data. Besides, the concept of Extended BNs containing the most likely probabilities and probability boundaries is proposed to address the hybrid data problem in BNs. Accordingly, the mathematical model and usage of the Extended BNs are also developed to fuse different types of data in HRA. To verify the rationality and accuracy of this method, the Extended BN with hybrid data is applied to HRA for fueling task in space launch sites. Finally, the case study shows the validity of the uncertainty expression in Extended BNs, and the Extended BNs perform well in risk prediction and risk avoidance.
C1 [Pan, Xing; Zuo, Dujun; Zhang, Wenjin; Hu, Lunhu; Wang, Huixiong; Jiang, Jing] Beihang Univ, Sch Reliabil & Syst Engn, Beijing, Peoples R China.
   [Jiang, Jing] China Astronauts Res & Training Ctr, Natl Key Lab Human Factors Engn, Beijing, Peoples R China.
C3 Beihang University
RP Zhang, WJ (corresponding author), 37 Xueyuan Rd, Beijing 100191, Peoples R China.
EM panxing@buaa.edu.cn; zuodujun@buaa.edu.cn; zwjok@buaa.edu.cn;
   hulunhu@buaa.edu.cn; wanghuixiong@buaa.edu.cn; jiangjingbuaa@buaa.edu.cn
RI Wang, Huixiong/M-3891-2019; XIONG, LIU/JOK-5886-2023; Zuo,
   Dujun/ABA-1204-2020; zhu, yujie/KBC-4009-2024
OI Wang, Huixiong/0000-0002-8954-1967; 
FU National Natural Science Foundation of China [72071011, 71571004]; Open
   Funding Project of National Key Laboratory of Human Factors Engineering
   [6142222190307]
FX This work is supported by the National Natural Science Foundation of
   China under Grants No. 72071011/No. 71571004, and the Open Funding
   Project of National Key Laboratory of Human Factors Engineering under
   Grant No. 6142222190307.
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NR 54
TC 9
Z9 9
U1 5
U2 40
PU ELSEVIER SCI LTD
PI London
PA 125 London Wall, London, ENGLAND
SN 0951-8320
EI 1879-0836
J9 RELIAB ENG SYST SAFE
JI Reliab. Eng. Syst. Saf.
PD MAY
PY 2021
VL 209
AR 107336
DI 10.1016/j.ress.2020.107336
EA JAN 2021
PG 13
WC Engineering, Industrial; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Operations Research & Management Science
GA SV6DB
UT WOS:000663909200002
DA 2024-09-05
ER
PT J
AU Leoni, RA
   Alves-Silva, L
   De Araujo , HI Jr
AF Leoni, Ronaldo A.
   Alves-Silva, Lais
   De Araujo-Junior, Herminio Ismael
TI Overview of computational methods in taphonomy based on the combination
   of bibliometric analysis and natural language
SO ANAIS DA ACADEMIA BRASILEIRA DE CIENCIAS
LA English
DT Article
DE artificial intelligence; google scholar; machine learning; paleontology;
   web scraping; scopus
ID CUT MARKS; BONE
AB Artificial intelligence tools are new in taphonomy and are growing fast. They are being used mainly to investigate bone surface marks. In order to investigate this subject, a bibliometric study was made to understand the growing rate of this intersectional field, the future, and gaps in the field until now. From Scopus and Google Scholar metadata, graphs were made to describe the data, and inferential statistics were made by regression with the Ordinary Least Squares method. Exploratory analysis with word clouds, topic modeling, and natural language processing with Latent Dirichlet Allocation as a method were also made using the entire corpus from the papers. From the first register until 2023, we found eight articles in Scopus and 32 in Google Scholar; the majority of the studies and the most cited were from Spain. The studies are growing fast from 2016 to 2018, and the regression shows that growth can be maintained in the coming years. Exploratory analysis shows the most frequent words are marks, models, data, and bone. Topic modeling shows that the studies are highly concentrated on similar problems and the tools to solve them, revealing that there is much more to explore with computational tools in taphonomy and paleontology as well.
C1 [Leoni, Ronaldo A.; Alves-Silva, Lais; De Araujo-Junior, Herminio Ismael] Univ Estado Rio de Janeiro, Programa Posgrad Geociencias, Rua Sao Francisco Xavier 524, BR-20950000 Rio De Janeiro, RJ, Brazil.
C3 Universidade do Estado do Rio de Janeiro
RP Leoni, RA (corresponding author), Univ Estado Rio de Janeiro, Programa Posgrad Geociencias, Rua Sao Francisco Xavier 524, BR-20950000 Rio De Janeiro, RJ, Brazil.
EM ronaldoaleoni@gmail.com
RI ; Araujo-Junior, Herminio/E-5832-2013
OI Alves Silva, Lais/0000-0001-9692-9989; Araujo-Junior,
   Herminio/0000-0003-4371-0611
FU Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior - CAPES
   [88887.683402/2022-00, 88887.602327/2021-00]; Fundaco de Amparo a
   Pesquisa do Estado do Rio de Janeiro [E-26/201.371/2021,
   E-26/010.002218/2019]; Conselho Nacional de Desenvolvimento Cientifico e
   Tecnologico -CNPq [305576/2021-6]; UERJ
FX We thank Mr. Eroaldo G. S. Junior for his support on the final
   development of the web scraping technique and useful discussions in the
   early stages of this work, and Mr. Zainal Salam for his several valuable
   suggestions and elucidations to improve the writing during the final
   stage of writing this manuscript. The authors are grateful to
   Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior - CAPES for
   funding RAL [CNPq; grants: 88887.683402/2022-00] and LAS (process number
   88887.602327/2021-00) ; HIAJR thanks to Fundac & atilde;o de Amparo a
   Pesquisa do Estado do Rio de Janeiro (processes n degrees
   E-26/201.371/2021 and E-26/010.002218/2019) , Conselho Nacional de
   Desenvolvimento Cientifico e Tecnologico -CNPq (process n degrees
   305576/2021-6) and UERJ (Prociencia grant) .
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NR 42
TC 0
Z9 0
U1 0
U2 0
PU ACAD BRASILEIRA DE CIENCIAS
PI RIO JANEIRO
PA RUA ANFILOFIO DE CARVALHO, 29, 3 ANDAR, 20030-060 RIO JANEIRO, BRAZIL
SN 0001-3765
EI 1678-2690
J9 AN ACAD BRAS CIENC
JI An. Acad. Bras. Cienc.
PY 2024
VL 96
IS 3
AR e20230789
DI 10.1590/0001-3765202420230789
PG 15
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA A9J4F
UT WOS:001285627100001
PM 39109751
DA 2024-09-05
ER
PT C
AU Juric, M
   Sandic, A
   Brcic, M
AF Juric, Mislav
   Sandic, Agneza
   Brcic, Mario
BE Koricic, M
   Skala, K
   Car, Z
   CincinSain, M
   Sruk, V
   Skvorc, D
   Ribaric, S
   Jerbic, B
   Gros, S
   Vrdoljak, B
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   Pale, P
   Grbac, TG
   Fijan, NF
   Boukalov, A
   Cisic, D
   Gradisnik, V
TI AI safety: state of the field through quantitative lens
SO 2020 43RD INTERNATIONAL CONVENTION ON INFORMATION, COMMUNICATION AND
   ELECTRONIC TECHNOLOGY (MIPRO 2020)
LA English
DT Proceedings Paper
CT 43rd International Convention on Information, Communication and
   Electronic Technology (MIPRO)
CY SEP 28-OCT 02, 2020
CL Opatija, CROATIA
DE AI safety; technical AI safety; research; surveys; bibliometrics
ID INTELLIGENCE; SECURITY
AB Last decade has seen major improvements in the performance of artificial intelligence which has driven wide-spread applications. Unforeseen effects of such mass-adoption has put the notion of AI safety into the public eye. AI safety is a relatively new field of research focused on techniques for building AI beneficial for humans. While there exist survey papers for the field of AI safety, there is a lack of a quantitative look at the research being conducted. The quantitative aspect gives a data-driven insight about the emerging trends, knowledge gaps and potential areas for future research. In this paper, bibliometric analysis of the literature finds significant increase in research activity since 2015. Also, the field is so new that most of the technical issues are open, including: explainability and its long-term utility, and value alignment which we have identified as the most important long-term research topic. Equally, there is a severe lack of research into concrete policies regarding AI. As we expect AI to be the one of the main driving forces of changes, AI safety is the field under which we need to decide the direction of humanity's future.
C1 [Juric, Mislav; Sandic, Agneza; Brcic, Mario] Univ Zagreb, Fac Elect Engn & Comp, Zagreb, Croatia.
C3 University of Zagreb
RP Juric, M (corresponding author), Univ Zagreb, Fac Elect Engn & Comp, Zagreb, Croatia.
EM mislav.juric2@fer.hr; agneza.sandic@fer.hr; mario.brcic@fer.hr
RI Brčić, Mario/L-3415-2019
OI Brčić, Mario/0000-0002-7564-6805
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NR 56
TC 11
Z9 12
U1 3
U2 7
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-953-233-099-1
PY 2020
BP 1254
EP 1259
PG 6
WC Engineering, Electrical & Electronic; Telecommunications
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Engineering; Telecommunications
GA BT0NT
UT WOS:000790326400227
DA 2024-09-05
ER
PT J
AU Dunkel, J
   Dominguez, D
   Borzdynski, OG
   Sanchez, A
AF Dunkel, Juergen
   Dominguez, David
   Borzdynski, oscar G.
   Sanchez, Angel
TI Solid Waste Analysis Using Open-Access Socio-Economic Data
SO SUSTAINABILITY
LA English
DT Article
DE solid waste management; OECD datasets; machine learning; forecasting
   models on countries; clustering on countries; smart cities
ID DEVELOPING-COUNTRIES; NEURAL-NETWORKS; GENERATION; MANAGEMENT;
   PREDICTION
AB Nowadays, problems related with solid waste management become a challenge for most countries due to the rising generation of waste, related environmental issues, and associated costs of produced wastes. Effective waste management systems at different geographic levels require accurate forecasting of future waste generation. In this work, we investigate how open-access data, such as provided from the Organisation for Economic Co-operation and Development (OECD), can be used for the analysis of waste data. The main idea of this study is finding the links between socio-economic and demographic variables that determine the amounts of types of solid wastes produced by countries. This would make it possible to accurately predict at the country level the waste production and determine the requirements for the development of effective waste management strategies. In particular, we use several machine learning data regression (Support Vector, Gradient Boosting, and Random Forest) and clustering models (k-means) to respectively predict waste production for OECD countries along years and also to perform clustering among these countries according to similar characteristics. The main contributions of our work are: (1) waste analysis at the OECD country-level to compare and cluster countries according to similar waste features predicted; (2) the detection of most relevant features for prediction models; and (3) the comparison between several regression models with respect to accuracy in predictions. Coefficient of determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), respectively, are used as indices of the efficiency of the developed models. Our experiments have shown that some data pre-processings on the OECD data are an essential stage required in the analysis; that Random Forest Regressor (RFR) produced the best prediction results over the dataset; and that these results are highly influenced by the quality of available socio-economic data. In particular, the RFR model exhibited the highest accuracy in predictions for most waste types. For example, for "municipal " waste, it produced, respectively, R-2 = 1 and MAPE=4.31 global error values for the test set; and for "household " waste, it, respectively, produced R-2 = 1 and MAPE=3.03. Our results indicate that the considered models (and specially RFR) all are effective in predicting the amount of produced wastes derived from input data for the considered countries.
C1 [Dunkel, Juergen] Hsch Hannover, Comp Sci Dept, D-30459 Hannover, Germany.
   [Dominguez, David; Borzdynski, oscar G.] Univ Autonoma Madrid, Comp Engn Dept, Madrid 28049, Spain.
   [Sanchez, Angel] Univ Rey Juan Carlos, Comp Sci & Stat Dept, Mostoles 28933, Spain.
C3 Hochschule Hannover-University of Applied Sciences & Arts; Autonomous
   University of Madrid; Universidad Rey Juan Carlos
RP Dunkel, J (corresponding author), Hsch Hannover, Comp Sci Dept, D-30459 Hannover, Germany.
EM juergen.dunkel@hs-hannover.de; david.dominguez@uam.es;
   oscar.gomezb@estudiante.uam.es; angel.sanchez@urjc.es
RI Dominguez Carreta, David Renato/L-8715-2014; Sanchez, Angel/B-8271-2012
OI Dominguez Carreta, David Renato/0000-0003-0911-1834; Gomez Borzdynski,
   Oscar/0000-0001-6598-3448; Dunkel, Jurgen/0000-0003-3567-1173; Sanchez,
   Angel/0000-0001-9069-6985
FU Spanish Ministry of Science and Innovation [RTI2018-098019-B-I00,
   PID2020-114867RB-I00]; CYTED Network "IberoAmerican Thematic Network on
   ICT Applications for Smart Cities" [518RT0559]
FX This work was funded by the Spanish Ministry of Science and Innovation
   projects with Grants No.: RTI2018-098019-B-I00 and PID2020-114867RB-I00;
   and by the CYTED Network "IberoAmerican Thematic Network on ICT
   Applications for Smart Cities" with Grant No.: 518RT0559.
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NR 32
TC 5
Z9 5
U1 1
U2 17
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD FEB
PY 2022
VL 14
IS 3
AR 1233
DI 10.3390/su14031233
PG 24
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA YZ0BC
UT WOS:000755147600001
OA Green Published, gold
DA 2024-09-05
ER
PT J
AU Morris, V
AF Morris, Victoria
TI Automated Language Identification of Bibliographic Resources
SO CATALOGING & CLASSIFICATION QUARTERLY
LA English
DT Article
DE Language identification; machine learning; automatic metadata
   generation; metadata; legacy record enhancement
AB This article describes experiments in the use of machine learning techniques at the British Library to assign language codes to catalog records, in order to provide information about the language of content of the resources described. In the first phase of the project, language codes were assigned to 1.15 million records with 99.7% confidence. The automated language identification tools developed will be used to contribute to future enhancement of over 4 million legacy records.
C1 [Morris, Victoria] British Lib, Collect Metadata, Boston Spa, Wetherby, England.
RP Morris, V (corresponding author), British Lib, Collect Metadata, Boston Spa, Wetherby, England.
EM victoria.morris@bl.uk
OI Morris, Victoria/0000-0002-5954-6994
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NR 13
TC 3
Z9 3
U1 0
U2 9
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0163-9374
EI 1544-4554
J9 CAT CLASSIF Q
JI Cat. Classif. Q.
PY 2020
VL 58
IS 1
BP 1
EP 27
DI 10.1080/01639374.2019.1700201
PG 27
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA LD2IA
UT WOS:000525854000001
DA 2024-09-05
ER
PT J
AU Phillips, JL
   Heneka, N
   Hickman, L
   Lam, L
   Shaw, T
AF Phillips, Jane L.
   Heneka, Nicole
   Hickman, Louise
   Lam, Lawrence
   Shaw, Tim
TI Impact of a novel online learning module on specialist palliative care
   nurses' pain assessment competencies and patients' reports of pain:
   Results from a quasi-experimental pilot study
SO PALLIATIVE MEDICINE
LA English
DT Article
DE Palliative care; nurses; pain assessment; learning;
   education-professional; intervention; translational medical research;
   teaching materials; patient-centred outcome research; inpatient
ID SPACED EDUCATION; ASSESSMENT TOOLS; MANAGEMENT; BEHAVIOR; OUTCOMES;
   STANDARDS; PROGRAM; HEALTH
AB Background: Pain is a complex multidimensional phenomenon moderated by consumer, provider and health system factors. Effective pain management cuts across professional boundaries, with failure to screen and assess contributing to the burden of unrelieved pain.
   Aim: To test the impact of an online pain assessment learning module on specialist palliative care nurses' pain assessment competencies, and to determine whether this education impacted positively on palliative care patients' reported pain ratings.
   Design: A quasi-experimental pain assessment education pilot study utilising 'Qstream (c)', an online methodology to deliver II case-based pain assessment learning scenarios, developed by an interdisciplinary expert panel and delivered to participants' work emails over a 28-day period in mid-2012. The 'Self-Perceived Pain Assessment Competencies' survey and chart audit data, including patient-reported pain intensity ratings, were collected pre-intervention (TI) and post-intervention (T2) and analysed using inferential statistics to determine key outcomes.
   Setting/participants: Nurses working at two Australian inpatient specialist palliative care services in 2012.
   Results: The results reported conform to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Guidelines. Participants who completed the education intervention (n = 34) increased their pain assessment knowledge, assessment tool knowledge and confidence to undertake a pain assessment (p < 0.001). Participants were more likely to document pain intensity scores in patients' medical records than non-participants (95% confidence interval = 7.3%-22.7%, p = 0.021). There was also a significant reduction in the mean patient-reported pain ratings between the admission and audit date at post-test of 1.5 (95% confidence interval = 0.7-2.3) units in pain score.
   Conclusion: This pilot confers confidence of the education interventions capacity to improve specialist palliative care nurses' pain assessment practices and to reduce patient-rated pain intensity scores.
C1 [Phillips, Jane L.] Univ Notre Dame, Sch Nursing, Sydney, NSW 2007, Australia.
   [Phillips, Jane L.] Cunningham Ctr Palliat Care, POB 944, Sydney, NSW 2007, Australia.
   [Hickman, Louise] Univ Technol Sydney, Fac Hlth, Sydney, NSW 2007, Australia.
   [Lam, Lawrence] Hong Kong Inst Educ, Dept Hlth & Phys Educ, Hong Kong, Hong Kong, Peoples R China.
   [Lam, Lawrence] Univ Sydney, Sydney Med Sch, Sydney, NSW 2006, Australia.
   [Shaw, Tim] Univ Sydney, Sydney Med Sch, WEDG, Sydney, NSW 2006, Australia.
C3 The University of Notre Dame Australia; University of Technology Sydney;
   Education University of Hong Kong (EdUHK); University of Sydney;
   University of Sydney
RP Phillips, JL (corresponding author), Cunningham Ctr Palliat Care, POB 944, Sydney, NSW 2007, Australia.
EM jane.phillips@nd.edu.au
RI Hickman, Louise D/AAV-1449-2020; Phillips, Jane/A-7780-2015; Lam,
   Lawrence/HTP-2419-2023; Heneka, Nicole/AAP-1807-2021
OI Hickman, Louise D/0000-0002-5116-6559; Phillips,
   Jane/0000-0002-3691-8230; Lam, Lawrence/0000-0001-6183-6854; Heneka,
   Nicole/0000-0001-8102-1871; shaw, tim/0000-0003-0783-1918
FU Curran Foundation; St Vincent's Clinic Multidisciplinary Research Grant;
   Cancer Institute New South Wales Academic Chairs Program
FX This research was undertaken, in part, with funding support from the
   Curran Foundation, St Vincent's Clinic Multidisciplinary Research Grant
   and the Cancer Institute New South Wales Academic Chairs Program.
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   Therapeutic Guidelines, 2010, THER GUID PALL CAR V
NR 40
TC 23
Z9 23
U1 1
U2 13
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 0269-2163
EI 1477-030X
J9 PALLIATIVE MED
JI Palliat. Med.
PD JUN
PY 2014
VL 28
IS 6
BP 521
EP 529
DI 10.1177/0269216314527780
PG 9
WC Health Care Sciences & Services; Public, Environmental & Occupational
   Health; Medicine, General & Internal
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Health Care Sciences & Services; Public, Environmental & Occupational
   Health; General & Internal Medicine
GA AI9NH
UT WOS:000337258800008
PM 24685649
DA 2024-09-05
ER
PT C
AU Salatino, AA
   Osborne, F
   Thanapalasingam, T
   Motta, E
AF Salatino, Angelo A.
   Osborne, Francesco
   Thanapalasingam, Thiviyan
   Motta, Enrico
BE Doucet, A
   Isaac, A
   Golub, K
   Aalberg, T
   Jatowt, A
TI The CSO Classifier: Ontology-Driven Detection of Research Topics in
   Scholarly Articles
SO DIGITAL LIBRARIES FOR OPEN KNOWLEDGE, TPDL 2019
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT 23rd International Conference on Theory and Practice of Digital
   Libraries (TPDL)
CY SEP 09-12, 2019
CL Oslo Metropolitan Univ, Oslo, NORWAY
HO Oslo Metropolitan Univ
DE Scholarly data; Digital libraries; Bibliographic data; Ontology; Text
   mining; Topic detection; Word embeddings; Science of science
AB Classifying research papers according to their research topics is an important task to improve their retrievability, assist the creation of smart analytics, and support a variety of approaches for analysing and making sense of the research environment. In this paper, we present the CSO Classifier, a new unsupervised approach for automatically classifying research papers according to the Computer Science Ontology (CSO), a comprehensive ontology of research areas in the field of Computer Science. The CSO Classifier takes as input the metadata associated with a research paper (title, abstract, keywords) and returns a selection of research concepts drawn from the ontology. The approach was evaluated on a gold standard of manually annotated articles yielding a significant improvement over alternative methods.
C1 [Salatino, Angelo A.; Osborne, Francesco; Thanapalasingam, Thiviyan; Motta, Enrico] Open Univ, Knowledge Media Inst, Milton Keynes MK7 6AA, Bucks, England.
C3 Open University - UK
RP Salatino, AA (corresponding author), Open Univ, Knowledge Media Inst, Milton Keynes MK7 6AA, Bucks, England.
EM angelo.salatino@open.ac.uk; francesco.osborne@open.ac.uk;
   thiviyan.thanapalasingam@open.ac.uk; enrico.motta@open.ac.uk
RI Salatino, Angelo A./AAD-7423-2022; Osborne, Francesco/HGU-2673-2022
OI Salatino, Angelo A./0000-0002-4763-3943; Osborne,
   Francesco/0000-0001-6557-3131; Thanapalasingam,
   Thiviyan/0000-0002-0170-9105; Motta, Enrico/0000-0003-0015-1952
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NR 28
TC 39
Z9 41
U1 0
U2 3
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 0302-9743
EI 1611-3349
BN 978-3-030-30760-8; 978-3-030-30759-2
J9 LECT NOTES COMPUT SC
PY 2019
VL 11799
BP 296
EP 311
DI 10.1007/978-3-030-30760-8_26
PG 16
WC Information Science & Library Science
WE Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Information Science & Library Science
GA BP4BL
UT WOS:000550576600026
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Urooj, A
   Khan, HU
   Iqbal, S
   Alghobiri, M
AF Urooj, Amber
   Khan, Hikmat Ullah
   Iqbal, Saqib
   Alghobiri, Mohammed
TI Exploring Author, Article, and Venue Feature Sets for Rising Star
   Prediction in Academic Network
SO JOURNAL OF SCHOLARLY PUBLISHING
LA English
DT Article
DE machine learning; scientometrics; rising star prediction; feature
   engineering; academic ranking
AB Rising stars are the researchers who are relatively new to the research area and have published fewer research articles, but their research work is of such standard that they have the potential to be top researchers in near future. Research work on the evaluation of researchers and prediction of rising stars is getting attention because it can be useful for selecting capable candidates for the jobs, hiring young faculty members for institutes, and seeking reviewers for journals and conferences and members for different committees. In this research study, the authors address the research problem of finding rising stars and propose novel features in diverse feature sets of three categories: article, author, and venue. The real-world data set has been extracted, preprocessed, and used from the Web of Science for empirical analysis. Several diverse supervised machine learning, ensemble learning algorithms, and deep learning are applied to the data set. The results, using classifiers, are compared based on standard performance evaluation measures to reveal the significance of the proposed as well as existing features. It also shows that the novel features play a significant role in finding rising stars. The ensemble-based machine learning classifier generalized linear model outperforms all other classifiers and gives the highest accuracy and F-measure compared to other models and the existing studies in the relevant literature.
C1 [Urooj, Amber; Khan, Hikmat Ullah] COMATS Univ Islamabad, Dept Comp Sci, Wah Campus, Wah Cantt, Pakistan.
   [Khan, Hikmat Ullah] Namal Univ Mianwali, Dept Comp Sci, Mianwali, Pakistan.
   [Iqbal, Saqib] Al Ain Univ, Coll Engn, Al Ain, U Arab Emirates.
   [Alghobiri, Mohammed] King Khalid Univ, Dept informat Syst, Abha, Asir, Saudi Arabia.
C3 King Khalid University
RP Urooj, A (corresponding author), COMATS Univ Islamabad, Dept Comp Sci, Wah Campus, Wah Cantt, Pakistan.
RI Khan, Hikmat Ullah/GZG-2251-2022
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NR 47
TC 0
Z9 0
U1 4
U2 13
PU UNIV TORONTO PRESS INC
PI TORONTO
PA JOURNALS DIVISION, 5201 DUFFERIN ST, DOWNSVIEW, TORONTO, ON M3H 5T8,
   CANADA
SN 1198-9742
EI 1710-1166
J9 J SCHOLARLY PUBL
JI J. Sch. Publ.
PD JUL 1
PY 2023
VL 54
IS 3
BP 445
EP 473
DI 10.3138/jsp-2022-0025
EA JUN 2023
PG 29
WC Humanities, Multidisciplinary; Information Science & Library Science
WE Social Science Citation Index (SSCI); Arts &amp; Humanities Citation Index (A&amp;HCI)
SC Arts & Humanities - Other Topics; Information Science & Library Science
GA N4XJ2
UT WOS:001033650100001
DA 2024-09-05
ER
PT J
AU Li, XC
   Wu, Q
   Liu, YY
AF Li, Xingchen
   Wu, Qiang
   Liu, Yuanyuan
TI A quantitative analysis of researcher citation personal display
   considering disciplinary differences and influence factors
SO SCIENTOMETRICS
LA English
DT Article
DE Personal website; Citation personal display; Bibliometric indicators;
   Citation analysis; Binary logistic regression; Disciplinary differences;
   Influence factors
ID HIGHLY CITED RESEARCHERS; BIBLIOMETRIC INDICATORS; SCIENTIFIC IMPACT;
   WEB; INDEX; VISIBILITY; HOMEPAGES; RANK
AB Personal websites are a good place not only for the scientists to show a wealth of content, but also for researchers to excavate some useful information related to quantitative evaluation. Based on researchers' personal websites this study aims to investigate the degree of citation personal display (CPD) in three major disciplines (chemistry, mathematics, and physics), as well as disciplinary differences in CPD. This paper also studies the factors which have influences on CPD by using binary logistic regression. The datasets studied consisted of 5771 researchers in 39 U.S. universities. Results show that CPD varies significantly by discipline, with chemistry researchers having the highest CPD (15.3%), followed by physics researchers (12.7%), and mathematics researchers (7.1%). The binary logistic models indicate that total citations, h-index, and citations per publication have significantly positive effects on CPD in chemistry; for mathematics, total citations and h-index do; and for physics, only total citations does (p < .05). The significantly positive influence of publication counts occurs in chemistry and mathematics, and significantly negative influences of scientific age and academic rank only appear in chemistry (p < .05).
C1 [Li, Xingchen; Wu, Qiang; Liu, Yuanyuan] Univ Sci & Technol China, Sch Management, 96 Jinzhai Rd, Hefei 230026, Anhui, Peoples R China.
C3 Chinese Academy of Sciences; University of Science & Technology of
   China, CAS
RP Wu, Q (corresponding author), Univ Sci & Technol China, Sch Management, 96 Jinzhai Rd, Hefei 230026, Anhui, Peoples R China.
EM qiangwu@ustc.edu.cn
RI Wu, Qiang/HLX-9353-2023; liu, yuanyuan/IQS-2755-2023; Wu,
   Qiang/JAC-9731-2023
OI Wu, Qiang/0000-0002-1308-1669
FU National Natural Science Foundation of China [71273250]
FX This research was supported by the National Natural Science Foundation
   of China (Grant No. 71273250). We would like to thank the editor and
   anonymous reviewers for their constructive comments and suggestions,
   which helped us to improve the paper.
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NR 29
TC 4
Z9 4
U1 1
U2 68
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD NOV
PY 2017
VL 113
IS 2
BP 1093
EP 1112
DI 10.1007/s11192-017-2501-0
PG 20
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA FJ7EN
UT WOS:000412920200021
DA 2024-09-05
ER
PT J
AU Mota, FB
   Braga, LAM
   Cabral, BP
AF Mota, Fabio Batista
   Braga, Luiza Amara Maciel
   Cabral, Bernardo Pereira
TI Alternative Dispute Resolution Research Landscape from 1981 to 2022
SO GROUP DECISION AND NEGOTIATION
LA English
DT Article
DE Alternative dispute resolution; Scientific publications; Bibliometrics;
   Network analysis; Latent Dirichlet allocation analysis
ID MEDICAL MALPRACTICE; MEDIATION; LITIGATION; ADR
AB Alternative dispute resolution (ADR) is an important means of resolving disputes outside of traditional legal frameworks. It is usually adopted because of its flexibility, cost-effectiveness, and ability to preserve relationships that a contentious court battle might damage. This study aims to evaluate the scientific publication related to ADR. To do so, we used metadata of ADR-related articles from 1981 to 2022 collected in the Web of Science Core Collection and carried out a bibliometric, network, and latent Dirichlet allocation analysis. Our results indicate that ADR research is concentrated in North America, with research organizations from the United States accounting for most publications. At the same time, recent years have seen a shift to a more diverse group of countries, with the Chinese City University of Hong Kong and the Australian Victoria University leading in the last five years. The five main topics in ADR research include online dispute resolution for consumer protection, mediation for family law, arbitration in medical malpractice cases, ADR in construction projects, and ADR in employment law. The most frequent research areas assigned to ADR publications are Government & Law and Business & Economics. Network results show keywords ADR, Mediation, and Arbitration as central nodes, while the Chinese and North American organizations established the most frequent collaborations, addressing ADR applications in various sectors. The findings underscore the interdisciplinary nature of ADR research, its adaptability across industries, and the importance of cross-cultural research partnerships.
C1 [Mota, Fabio Batista; Braga, Luiza Amara Maciel; Cabral, Bernardo Pereira] Fundacao Oswaldo Cruz, Oswaldo Cruz Inst, Lab Cellular Commun, Rio De Janeiro, Brazil.
   [Cabral, Bernardo Pereira] Univ Fed Bahia, Dept Econ, Salvador, Brazil.
C3 Fundacao Oswaldo Cruz; Universidade Federal da Bahia
RP Mota, FB (corresponding author), Fundacao Oswaldo Cruz, Oswaldo Cruz Inst, Lab Cellular Commun, Rio De Janeiro, Brazil.
EM fabio.mota@fiocruz.br; luiza.braga@fiocruz.br;
   bernardo.cabral@fiocruz.br
RI Mota, Fabio Batista/G-3164-2015; Braga, Luiza Amara Maciel/KUF-1504-2024
OI Mota, Fabio Batista/0000-0003-2401-7336; Braga, Luiza Amara
   Maciel/0000-0002-1726-2643
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NR 86
TC 0
Z9 0
U1 5
U2 15
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0926-2644
EI 1572-9907
J9 GROUP DECIS NEGOT
JI Group Decis. Negot.
PD DEC
PY 2023
VL 32
IS 6
BP 1415
EP 1435
DI 10.1007/s10726-023-09848-8
EA AUG 2023
PG 21
WC Management; Social Sciences, Interdisciplinary
WE Social Science Citation Index (SSCI)
SC Business & Economics; Social Sciences - Other Topics
GA U8BG5
UT WOS:001044846500001
DA 2024-09-05
ER
PT C
AU Baron, G
AF Baron, Grzegorz
BE Jedrzejowicz, P
   Czarnowski, I
   Howlett, RJ
   Jain, LC
TI Influence of data discretization on efficiency of Bayesian classifier
   for authorship attribution
SO KNOWLEDGE-BASED AND INTELLIGENT INFORMATION & ENGINEERING SYSTEMS 18TH
   ANNUAL CONFERENCE, KES-2014
SE Procedia Computer Science
LA English
DT Proceedings Paper
CT 18th Annual International Conference on Knowledge-Based and Intelligent
   Information and Engineering Systems (KES)
CY SEP 15-17, 2014
CL Pomeranian Sci & Technol, Gdynia, POLAND
HO Pomeranian Sci & Technol
DE Bayesian classifier; Naive Bayes; stylometry; authorship attribution;
   text analysis; classification; discretization; binarization
ID DECISION TREE; NAIVE
AB Authorship attribution is one of the research areas in data mining domain and various methods can be employed for performing that task. The paper presents results of research on influence of data discretization on efficiency of Naive Bayes classifier. The analysis has been carried on datasets founded on texts of two male and two female authors using the WEKA data mining software framework. The binary classification was performed separately for both datasets for wide range of parameters of discretization process in order to investigate dependency between ways of discretization and quality of classification using Naive Bayes method. The numerical results of tests have been compared and discussed and some observations and conclusions formulated. (C) 2014 The Authors. Published by Elsevier B. V.
C1 Silesian Tech Univ, PL-44100 Gliwice, Poland.
C3 Silesian University of Technology
RP Baron, G (corresponding author), Silesian Tech Univ, PL-44100 Gliwice, Poland.
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NR 29
TC 16
Z9 18
U1 0
U2 4
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA SARA BURGERHARTSTRAAT 25, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 1877-0509
J9 PROCEDIA COMPUT SCI
PY 2014
VL 35
BP 1112
EP 1121
DI 10.1016/j.procs.2014.08.201
PG 10
WC Computer Science, Artificial Intelligence; Computer Science, Software
   Engineering; Computer Science, Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BB7DO
UT WOS:000345394100115
OA gold
DA 2024-09-05
ER
PT J
AU Green, S
   Bevan, A
   Shapland, M
AF Green, Susie
   Bevan, Andrew
   Shapland, Michael
TI A comparative assessment of structure from motion methods for
   archaeological research
SO JOURNAL OF ARCHAEOLOGICAL SCIENCE
LA English
DT Article
DE Structure from motion; Computer vision; Bundler; Open source;
   Photogrammetry; Multi-view stereo
AB This paper addresses the use of open source, structure from motion methods for creating 3d pointclouds from photographs and compares these with alternative workflows in other software, and relative accuracy compared to other 3D modelling methods. It describes a series of case studies that use structure from motion to record standing buildings and create digital elevation models. Looking at other recording techniques it finds that structure from motion can produce better results than traditional techniques such as plan drawing, topographic survey and photogrammetry, and is cheaper and more accessible than new techniques such as laser scanning and LiDAR, although it is less accurate in some regards. It demonstrates that good accuracy can be achieved if careful measurements are made, and concludes that it has great potential for widespread archaeological application. (C) 2014 Elsevier Ltd. All rights reserved.
C1 [Green, Susie; Bevan, Andrew] UCL, Inst Archaeol, London WC1H 0PY, England.
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C3 University of London; University College London
RP Green, S (corresponding author), UCL, Inst Archaeol, 31-34 Gordon Sq, London WC1H 0PY, England.
EM susan.green.10@ucl.ac.uk
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NR 19
TC 107
Z9 118
U1 1
U2 31
PU ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
PI LONDON
PA 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND
SN 0305-4403
EI 1095-9238
J9 J ARCHAEOL SCI
JI J. Archaeol. Sci.
PD JUN
PY 2014
VL 46
BP 173
EP 181
DI 10.1016/j.jas.2014.02.030
PG 9
WC Anthropology; Archaeology; Geosciences, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI); Arts &amp; Humanities Citation Index (A&amp;HCI)
SC Anthropology; Archaeology; Geology
GA AI6UZ
UT WOS:000337013800016
DA 2024-09-05
ER
PT J
AU Din, EU
   Hua, L
   Lu, ZY
AF Din, Ejaz ud
   Hua, Long
   Lu, Zhongyu
TI Research on Performance of the Classifying Models Based on Chinese,
   Pakistani, and Other Genres
SO INTERNATIONAL JOURNAL OF INFORMATION RETRIEVAL RESEARCH
LA English
DT Article
DE Audio Classification; Classifiers; Feature Extraction; MFCC;
   Pre-Processing
ID CLASSIFICATION
AB In recent years, with the increase in the amount of audio on the internet, the demand for audio classification is increasing. This paper focuses on finding the performance of the classifiers, uses Python for the simulation part, compares the performance, and finds the best classifier. Two experiments are performed for this paper; for the first part of the experiment, Pakistan and Chinese music samples are considered, and classifiers are used to classify these music samples. It is found that the artificial neural network (ANN) has lowest accuracy of 81.4%; additionally, support vector machine (SVM), k-nearest neighbor (KNN), and convolutional (CNN) accuracies remain between 82% to 86% based on the dataset. Random forest model has the highest accuracy of 94.3%. It is considered to be the best classifier. For the second part of the experiment, other genres such as classical, country, and pop music were added to the previous dataset. After adding these genres, performance of the classifying models varies slightly; it fluctuates between 75% to 84%. These results can be used for music recommendation applications.
C1 [Din, Ejaz ud; Hua, Long] Kunming Univ Sci & Technol, Fac Informat Engn & Automat, Kunming, Yunnan, Peoples R China.
   [Lu, Zhongyu] Univ Huddersfield, Huddersfield, W Yorkshire, England.
C3 Kunming University of Science & Technology; University of Huddersfield
RP Din, EU (corresponding author), Kunming Univ Sci & Technol, Fac Informat Engn & Automat, Kunming, Yunnan, Peoples R China.
RI Lu, Zhongyu/ABC-4006-2020
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NR 30
TC 0
Z9 0
U1 0
U2 1
PU IGI GLOBAL
PI HERSHEY
PA 701 E CHOCOLATE AVE, STE 200, HERSHEY, PA 17033-1240 USA
SN 2155-6377
EI 2155-6385
J9 INT J INF RETR RES
JI Int. J. Inf. Retr. Res.
PD OCT-DEC
PY 2021
VL 11
IS 4
BP 61
EP 79
DI 10.4018/IJIRR.2021100104
PG 19
WC Computer Science, Information Systems
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA UE9ZL
UT WOS:000688240900004
DA 2024-09-05
ER
PT C
AU Wang, XJ
   Guo, JF
AF Wang Xuejun
   Guo Jianfang
BA Zhou, Q
BF Zhou, Q
BE Luo, J
TI Application Research of SVM in The Evaluation of Scientific Research
   Project
SO 2008 INTERNATIONAL SYMPOSIUM ON INTELLIGENT INFORMATION TECHNOLOGY
   APPLICATION WORKSHOP: IITA 2008 WORKSHOPS, PROCEEDINGS
LA English
DT Proceedings Paper
CT 2nd International Symposium on Intelligent Information Technology
   Application
CY DEC 21-22, 2008
CL Shanghai, PEOPLES R CHINA
AB In the management of scientific research project, the evaluation of scientific research project is one of important processes. Traditional evaluation methods can not suffice the increasing need in the evaluation of scientific research project. In order to improve the efficiency of the evaluation of scientific research Project., this paper on the basis of support vector machines(SVM) based on statistical learning theory, especially analyzes SVM for classification theory, and proposes a binary tree multi-class method based on two-class SVM algorithm and applies this method in the evaluation of scientific research Project.
C1 [Wang Xuejun; Guo Jianfang] Shijiazhuang Railway Inst, Shijiazhuang 050043, Hebei, Peoples R China.
C3 Shijiazhuang Tiedao University
RP Wang, XJ (corresponding author), Shijiazhuang Railway Inst, Shijiazhuang 050043, Hebei, Peoples R China.
EM wangxj@sjzri.edu.cn; guo_jian_fang@sina.com
CR [Anonymous], P IEEE INT C AUT FAC
   [Anonymous], 1998, Encyclopedia of Biostatistics
   [Anonymous], 2000, NATURE STAT LEARNING, DOI DOI 10.1007/978-1-4757-3264-1
   Cristianini N., 2000, Support Vector Machines and other kernel-based learning methods
   Guo GD, 2003, IEEE T NEURAL NETWOR, V14, P209, DOI 10.1109/TNN.2002.806626
   QIU DH, 2003, MINIMICROSYSTENS, V11, P2004
NR 6
TC 0
Z9 0
U1 0
U2 1
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA
BN 978-0-7695-3505-0
PY 2008
BP 364
EP 367
DI 10.1109/IITA.Workshops.2008.160
PG 4
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Engineering
GA BIY43
UT WOS:000263690400087
DA 2024-09-05
ER
PT C
AU Liu, JZ
   Shao, XK
AF Liu Jianzhou
   Shao Xiongkai
BE Wang, XL
TI Research on Automated Assessment of Chinese Subjective Questions
SO 2011 INTERNATIONAL CONFERENCE ON COMPUTER, ELECTRICAL, AND SYSTEMS
   SCIENCES, AND ENGINEERING (CESSE 2011)
LA English
DT Proceedings Paper
CT International Conference on Computer, Electrical, and Systems Sciences,
   and Engineering
CY APR 10-11, 2011
CL Wuhan, PEOPLES R CHINA
DE semantic similarity; machine learning; HowNet; vector space model
AB In this paper, based on the analysis of previous automated assessment methods of Chinese subjective questions, we have proposed a novel automated assessment algorithm using sentence semantic similarity in Natural Language Processing. At the same time, we make a full comparative study on two different similarity methods. The results of the research indicate that our algorithm is robust.
C1 [Liu Jianzhou; Shao Xiongkai] Hubei Univ Technol, Sch Comp Sci, Wuhan, Hubei Province, Peoples R China.
C3 Hubei University of Technology
RP Liu, JZ (corresponding author), Hubei Univ Technol, Sch Comp Sci, Wuhan, Hubei Province, Peoples R China.
EM xxzzsoft@163.com; shao_xk@163.com
RI Liu, Jianzhou/ADN-7696-2022
OI Liu, Jianzhou/0000-0002-3721-6871
CR BING H, 2005, COMPUTER NETWORK, P50
   Dong Zhendong, ABOUT HOWNET
   Hu Bin, 2005, COMPUTER NETWORK, P50
   LIANG Z, 2001, COMPUTER ENG APPL, V37, P108
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   SUJIAN L, 2002, COMPUTER ENG APPL, V38, P75
   XIUBO Y, 2003, MODERN DISTANCE ED, P1
NR 7
TC 0
Z9 0
U1 0
U2 0
PU INFORMATION ENGINEERING RESEARCH INST, USA
PI NEWARK
PA 100 CONTINENTAL DR, NEWARK, DE 19713 USA
BN 978-0-615-42292-3
PY 2011
BP 318
EP 321
PG 4
WC Automation & Control Systems; Computer Science, Information Systems;
   Computer Science, Interdisciplinary Applications
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Automation & Control Systems; Computer Science
GA BWO35
UT WOS:000294390700079
DA 2024-09-05
ER
PT J
AU Sánchez-Núñez, P
   Cobo, MJ
   De las Heras-Pedrosa, C
   Peláez, JI
   Herrera-Viedma, E
AF Sanchez-Nunez, Pablo
   Cobo, Manuel J.
   De las Heras-Pedrosa, Carlos
   Ignacio Pelaez, Jose
   Herrera-Viedma, Enrique
TI Opinion Mining, Sentiment Analysis and Emotion Understanding in
   Advertising: A Bibliometric Analysis
SO IEEE ACCESS
LA English
DT Article
DE Advertising; Sentiment analysis; Bibliometrics; Organizations;
   Registers; Indexes; Computer science; Advertising research;
   bibliometrics; communication; consumer behavior; emotion understanding;
   opinion mining; science mapping analysis; SciMAT; sentiment analysis;
   VOSviewer; Web of Science (WoS)
ID PREDICTION
AB In the last decade, the advertising industry has experienced a quantum leap, powered by recent advances in neuroscience, a large investment in artificial intelligence, and a high degree of consumer expertise. Within this context, opinion mining, sentiment analysis, and emotion understanding bring us closer to one of the most sought-after objectives of advertising: to offer relevant ads at scale. The importance of studies about opinion mining, sentiment analysis, and emotion understanding in advertising has been rising exponentially over the last years. The peak of this new situation has been the interest of the research community in studying the relationship between such innovations and the spread of smart and contextual advertising. This article analyzes those works that address the relationship between sentiment analysis, opinion mining, and emotion understanding in advertising. The main objective is to clarify the current state of these studies, explore issues, methods, findings, themes, and gaps as well as to define their significance within the current convergence advertising research scenario. To reach such objectives, a bibliometric analysis was conducted, retrieving and analyzing 919 research works published between 2010 and 2019 based on results from Web of Science (WoS).
C1 [Sanchez-Nunez, Pablo] Univ Malaga, Joint PhD Programme Commun, Malaga 29071, Spain.
   [Cobo, Manuel J.] Univ Cadiz, Dept Comp Sci & Engn, Cadiz 11202, Spain.
   [De las Heras-Pedrosa, Carlos] Univ Malaga, Fac Commun Sci, Dept Audiovisual Commun & Advertising, Malaga 29071, Spain.
   [Ignacio Pelaez, Jose] Univ Malaga, Higher Tech Sch Comp Engn, Dept Languages & Comp Sci, Malaga 29071, Spain.
   [Herrera-Viedma, Enrique] Univ Granada, Andalusian Res Inst Data Sci & Computat Intellige, Granada 18071, Spain.
C3 Universidad de Malaga; Universidad de Cadiz; Universidad de Malaga;
   Universidad de Malaga; University of Granada
RP De las Heras-Pedrosa, C (corresponding author), Univ Malaga, Fac Commun Sci, Dept Audiovisual Commun & Advertising, Malaga 29071, Spain.
EM cheras@uma.es
RI de las Heras-Pedrosa, Carlos/M-4492-2015; Cobo Martí­n, Manuel
   Jesús/C-5581-2011; HERRERA-VIEDMA, ENRIQUE/C-2704-2008; Pelaez Sanchez,
   Jose Ignacio/O-9450-2016
OI de las Heras-Pedrosa, Carlos/0000-0002-2738-4177; Cobo Martí­n, Manuel
   Jesús/0000-0001-6575-803X; HERRERA-VIEDMA, ENRIQUE/0000-0002-7922-4984;
   Sanchez Nunez, Pablo/0000-0001-7845-9506; Pelaez Sanchez, Jose
   Ignacio/0000-0002-2606-3849
FU Programa Operativo FEDER Andalucia [UMA 18-FEDERJA-148]
FX This work was supported by the Programa Operativo FEDER Andalucia
   2014-2020 under Grant UMA 18-FEDERJA-148.
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NR 40
TC 41
Z9 46
U1 9
U2 64
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2020
VL 8
BP 134563
EP 134576
DI 10.1109/ACCESS.2020.3009482
PG 14
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering; Telecommunications
GA MS6CG
UT WOS:000554360900001
OA gold, Green Submitted
DA 2024-09-05
ER
PT J
AU D'Angelo, CA
   van Eck, NJ
AF D'Angelo, Ciriaco Andrea
   van Eck, Nees Jan
TI Collecting large-scale publication data at the level of individual
   researchers: a practical proposal for author name disambiguation
SO SCIENTOMETRICS
LA English
DT Article
DE Authorship disambiguation; Bibliometrics; Precision-recall; Publication
   oeuvre; Research evaluation
ID CITATIONS
AB The disambiguation of author names is an important and challenging task in bibliometrics. We propose an approach that relies on an external source of information for selecting and validating clusters of publications identified through an unsupervised author name disambiguation method. The application of the proposed approach to a random sample of Italian scholars shows encouraging results, with an overall precision, recall, and F-measure of over 96%. The proposed approach can serve as a starting point for large-scale census of publication portfolios for bibliometric analyses at the level of individual researchers.
C1 [D'Angelo, Ciriaco Andrea] Univ Roma Tor Vergata, Dept Engn & Management, Rome, Italy.
   [van Eck, Nees Jan] Leiden Univ, Ctr Sci & Technol Studies, Leiden, Netherlands.
C3 University of Rome Tor Vergata; Leiden University; Leiden University -
   Excl LUMC
RP D'Angelo, CA (corresponding author), Univ Roma Tor Vergata, Dept Engn & Management, Rome, Italy.
EM dangelo@dii.uniroma2.it; ecknjpvan@cwts.leidenuniv.nl
RI D'Angelo, Ciriaco Andrea/J-8162-2012; van Eck, Nees Jan/B-6042-2008
OI D'Angelo, Ciriaco Andrea/0000-0002-6977-6611; van Eck, Nees
   Jan/0000-0001-8448-4521
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NR 54
TC 32
Z9 35
U1 1
U2 41
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD MAY
PY 2020
VL 123
IS 2
BP 883
EP 907
DI 10.1007/s11192-020-03410-y
PG 25
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA LJ0VN
UT WOS:000529892100015
DA 2024-09-05
ER
PT J
AU Drummond, C
AF Drummond, Chris
TI Reproducible research: aminority opinion
SO JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE
LA English
DT Article
DE machine learning; reproducible research; scientific evaluation;
   scientific communication
AB Reproducible research, a growing movement within many scientific fields, including machine learning, would require the code, used to generate the experimental results, be published along with any paper. Probably the most compelling argument for this is that it is simply following good scientific practice, established over the years by the greats of science. The implication is that failure to follow such a practice is unscientific, not a label any machine learning researchers would like to carry. It is further claimed that misconduct is causing a growing crisis of confidence in science. That, without this practice being enforced, science would inevitably fall into disrepute. This viewpoint is becoming ubiquitous but here I offer a differing opinion. I argue that far from being central to science, what is being promulgated is a narrow interpretation of how science works. I contend that the consequences are somewhat overstated. I would also contend that the effort necessary to meet the movement's aims, and the general attitude it engenders would not serve well any of the research disciplines, including our own.
C1 [Drummond, Chris] Natl Res Council Canada, Informat & Commun Technol, Ottawa, ON, Canada.
C3 National Research Council Canada
RP Drummond, C (corresponding author), Natl Res Council Canada, Informat & Commun Technol, Ottawa, ON, Canada.
EM Chris.Drummond@nrc-cnrc.gc.ca
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Z9 11
U1 0
U2 4
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 0952-813X
EI 1362-3079
J9 J EXP THEOR ARTIF IN
JI J. Exp. Theor. Artif. Intell.
PY 2018
VL 30
IS 1
BP 1
EP 11
DI 10.1080/0952813X.2017.1413140
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA FZ8SH
UT WOS:000427879800001
OA Bronze
DA 2024-09-05
ER
PT J
AU Kinnear, G
   Jones, I
   Sangwin, C
   Alarfaj, M
   Davies, B
   Fearn, S
   Foster, C
   Heck, A
   Henderson, K
   Hunt, T
   Iannone, P
   Kontorovich, I
   Larson, N
   Lowe, T
   Meyer, JC
   O'Shea, A
   Rowlett, PJ
   Sikurajapathi, I
   Wong, T
AF Kinnear, George
   Jones, Ian
   Sangwin, Chris
   Alarfaj, Maryam
   Davies, Ben
   Fearn, Sam
   Foster, Colin
   Heck, Andre
   Henderson, Karen
   Hunt, Tim
   Iannone, Paola
   Kontorovich, Igor'
   Larson, Niclas
   Lowe, Tim
   Meyer, John Christopher
   O'Shea, Ann
   Rowlett, Peter James
   Sikurajapathi, Indunil
   Wong, Thomas
TI A Collaboratively-Derived Research Agenda for E-assessment in
   Undergraduate Mathematics
SO INTERNATIONAL JOURNAL OF RESEARCH IN UNDERGRADUATE MATHEMATICS EDUCATION
LA English
DT Article
DE Online learning; Assessment; Feedback; Mathematics education; Delphi
   methods
ID COMPUTER-AIDED ASSESSMENT; PEER-ASSESSMENT; EDUCATION; FEEDBACK; TESTS;
   PERFORMANCE; LESSONS; IMPACT
AB This paper describes the collaborative development of an agenda for research on e-assessment in undergraduate mathematics. We built on an established approach to develop the agenda from the contributions of 22 mathematics education researchers, university teachers and learning technologists interested in this topic. The resulting set of 55 research questions are grouped into 5 broad themes: errors and feedback, student interactions with e-assessment, design and implementation choices, affordances offered by e-assessment tools, and mathematical skills. This agenda gives a framework for a programme of research aligned with practical concerns that will contribute to both theoretical and practical development.
C1 [Kinnear, George; Sangwin, Chris; Alarfaj, Maryam] Univ Edinburgh, Sch Math, Edinburgh, Midlothian, Scotland.
   [Jones, Ian; Foster, Colin; Iannone, Paola] Loughborough Univ, Loughborough, Leics, England.
   [Davies, Ben] UCL, London, England.
   [Fearn, Sam] Univ Durham, Durham, England.
   [Heck, Andre] Univ Amsterdam, Amsterdam, Netherlands.
   [Henderson, Karen; Sikurajapathi, Indunil] Univ West England, Bristol, Avon, England.
   [Hunt, Tim; Lowe, Tim] Open Univ, Milton Keynes, Bucks, England.
   [Kontorovich, Igor'] Univ Auckland, Auckland, New Zealand.
   [Larson, Niclas] Univ Agder, Kristiansand, Norway.
   [O'Shea, Ann] Maynooth Univ, Maynooth, Kildare, Ireland.
   [Rowlett, Peter James] Sheffield Hallam Univ, Sheffield, S Yorkshire, England.
   [Wong, Thomas] Heriot Watt Univ, Edinburgh, Midlothian, Scotland.
   [Meyer, John Christopher] Univ Birmingham, Birmingham, W Midlands, England.
   [Alarfaj, Maryam] Saudi Elect Univ, Riyadh, Saudi Arabia.
C3 University of Edinburgh; Loughborough University; University of London;
   University College London; Durham University; University of Amsterdam;
   University of West England; Open University - UK; University of
   Auckland; University of Agder; Maynooth University; Sheffield Hallam
   University; Heriot Watt University; University of Birmingham; Saudi
   Electronic University
RP Kinnear, G (corresponding author), Univ Edinburgh, Sch Math, Edinburgh, Midlothian, Scotland.
EM G.Kinnear@ed.ac.uk
RI Kontorovich, Igor'/AFH-4311-2022; OShea, Ann/IUQ-3104-2023; Rowlett,
   Peter/R-2612-2017
OI Kontorovich, Igor'/0000-0002-3353-5445; OShea, Ann/0000-0001-8504-2290;
   Rowlett, Peter/0000-0003-1917-7458; Lowe, Tim/0000-0001-7154-0168;
   Meyer, John Christopher/0000-0003-0708-1088; Heck,
   Andre/0000-0002-7923-6360
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NR 100
TC 3
Z9 3
U1 0
U2 3
PU SPRINGER INT PUBL AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 2198-9745
EI 2198-9753
J9 INT J RES UN MATH ED
JI Int. J. Res. Undergrad. Math. Educ.
PD APR
PY 2024
VL 10
IS 1
BP 201
EP 231
DI 10.1007/s40753-022-00189-6
EA SEP 2022
PG 31
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA TV2J1
UT WOS:000852922600001
OA Green Accepted, Green Published, hybrid
DA 2024-09-05
ER
PT C
AU Liu, HJ
   Xu, HZ
   Yan, Y
   Li, W
AF Liu, Hong Jie
   Xu, Hong Zhe
   Yan, Yu
   Li, Wen
BE Getov, V
   Gaudiot, JL
   Yamai, N
   Cimato, S
   Chang, M
   Teranishi, Y
   Yang, JJ
   Leong, HV
   Shahriar, H
   Takemoto, M
   Towey, D
   Takakura, H
   Elci, A
   Susumu
   Puri, S
TI Research on Evaluation Function of Clustering Algorithm Based on Duty
   Cycle
SO 2019 IEEE 43RD ANNUAL COMPUTER SOFTWARE AND APPLICATIONS CONFERENCE
   (COMPSAC), VOL 2
SE Proceedings International Computer Software and Applications Conference
LA English
DT Proceedings Paper
CT 43rd IEEE-Computer-Society Annual International Computers, Software and
   Applications Conference (COMPSAC)
CY JUL 15-19, 2019
CL Marquette Univ, Milwaukee, WI
HO Marquette Univ
DE DBSCAN; Validity dex; Density-based Clustering; Trajectory Clustering
AB Density-based clustering (DBSCAN) is one of the most effective methods for trajectory data mining, but density based clustering algorithms are often limited by the choice of input parameters. In the trajectory data mining, clustering results are not only affected by the within-class distance and between-class distance, but also by the number of coordinate points in the cluster. Therefore, this paper proposes a novel cluster validity index based on the internal and external duty cycle to balance the three factors. In this way, the parameters of density clustering can be automatically selected, and effective clustering can be formed on different datasets. Then the clustering method is applied to the depth analysis and mining of travelers' behavior trajectories. The experiment proves that compared with the traditional Validity index, the evaluation function proposed in this paper can optimize input parameters and get better user location information clustering results.
C1 [Liu, Hong Jie; Xu, Hong Zhe; Yan, Yu; Li, Wen] Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Xian, Peoples R China.
C3 Xi'an Jiaotong University
RP Liu, HJ (corresponding author), Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Xian, Peoples R China.
EM hj_popel@stu.xjtu.edu.cn; xuhz@xjtu.edu.cn; yanyu94@stu.xjtu.edu.cn;
   leewhen@xjtu.edu.cn
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NR 13
TC 1
Z9 1
U1 1
U2 4
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA
SN 0730-3157
BN 978-1-7281-2607-4
J9 P INT COMP SOFTW APP
PY 2019
BP 48
EP 54
DI 10.1109/COMPSAC.2019.10182
PG 7
WC Computer Science, Interdisciplinary Applications; Computer Science,
   Software Engineering
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BP1DH
UT WOS:000538781300009
DA 2024-09-05
ER
PT J
AU Alemdag, E
AF Alemdag, Ecenaz
TI The effect of chatbots on learning: a meta-analysis of empirical
   research
SO JOURNAL OF RESEARCH ON TECHNOLOGY IN EDUCATION
LA English
DT Article; Early Access
DE Chatbot; conversational agent; learning; meta-analysis; artificial
   intelligence
ID INTELLIGENT TUTORING SYSTEMS; CONVERSATIONAL AGENTS; FEEDBACK PRACTICE;
   TEACHING-ENGLISH; SKILLS; PERCEPTIONS; EDUCATION; GUIDANCE
AB This meta-analysis aimed to comprehensively review empirical studies on the effect of chatbots on learning and quantitatively synthesize their findings to produce an overall effect size. Searching several databases yielded 28 eligible reports with 31 individual effect sizes. The results revealed a significant and medium effect (g = .48) of chatbots on learning. Further analyses indicated four significant moderators: the type of instruction in the comparison group, experimental duration, chatbot type, and chatbot tasks. The highest effect sizes emerged when the comparison group had no specific support, the experiment lasted only one session, and chatbots were task-focused and took care of frequently asked questions. These results suggest that chatbots can be more effective in certain cases within their overall contribution area to learning.
C1 [Alemdag, Ecenaz] Tech Univ Dresden, Dresden, Germany.
   [Alemdag, Ecenaz] Tech Univ Dresden, Psychol Learning & Instruct, Dresden, Germany.
C3 Technische Universitat Dresden; Technische Universitat Dresden
RP Alemdag, E (corresponding author), Tech Univ Dresden, Psychol Learning & Instruct, Dresden, Germany.
EM ecenazalemdag@gmail.com
OI Alemdag, Ecenaz/0000-0003-2645-4732
FU The author completed this research during her postdoctoral fellowship
   funded by the Alexander von Humboldt Foundation. She is grateful to this
   foundation for providing financial support for her studies. She also
   expresses her gratitude to Dr. Merve Basdoga; Alexander von Humboldt
   Foundation
FX The author completed this research during her postdoctoral fellowship
   funded by the Alexander von Humboldt Foundation. She is grateful to this
   foundation for providing financial support for her studies. She also
   expresses her gratitude to Dr. Merve Basdogan, who provided enormous
   help in the analysis stage of this study.
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NR 87
TC 9
Z9 9
U1 39
U2 99
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1539-1523
EI 1945-0818
J9 J RES TECHNOL EDUC
JI J. Res. Technol. Educ.
PD 2023 SEP 4
PY 2023
DI 10.1080/15391523.2023.2255698
EA SEP 2023
PG 23
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA R5BL3
UT WOS:001064500700001
DA 2024-09-05
ER
PT C
AU Chen, C
   Zhang, SM
   Zhang, H
   Li, XJ
   He, ZC
AF Chen, Chong
   Zhang, Shimin
   Zhang, Hang
   Li, Xiaojun
   He, Zichen
BE Reich, A
   Cluever, J
TI RESEARCH ON RISK ASSESSMENT METHOD OF STICK-SLIP VIBRATION OF THE BIT
   BASED ON BP NEURAL NETWORK ALGORITHM
SO PROCEEDINGS OF THE ASME PRESSURE VESSELS AND PIPING CONFERENCE, 2018,
   VOL 7
LA English
DT Proceedings Paper
CT ASME Pressure Vessels and Piping Conference (PVP 2018)
CT COFOLA INTERNATIONAL 2017: Resolution of International Disputes
   Conference
CY JUL 15-20, 2018
CY APR 27-29, 2017
CL Prague, CZECH REPUBLIC
CL Masaryk Univ, Telc, CZECH REPUBLIC
HO Masaryk Univ
DE stick-slip vibration; time and frequency domain; kernel principal
   component analysis; backpropagation neural network; drilling risk
AB During the drilling process, the non-linear contacts between the bit and the bottom hole, the drill string and the borehole wall can cause the bit's stick-slip vibration, which will shorten the life of the bit and even endanger the safety of the drill string. The severity of stick-slip vibration of a bit can be identified by the rotary speed of a bit, the triaxial accelerations of the drill string, the wellhead torque and other parameters measured by the measuring while drilling (MWD) tools in the downhole and devices on the surface. To evaluate the level of stick-slip vibration, this paper proposes a risk assessment method of sick-slip vibration based on backpropagation neural network (BPNN). According to the time and frequency domain analysis of the data collected from simulation, the feature parameters of the time and frequency domains of signals are extracted, and then the kernel principal component analysis (KPCA) is applied to reduce dimensions. Consequently, the feature vectors can be obtained, which become the input parameters of the BPNN. Based on BPNN algorithm, the stick slip vibration of the bit is determined, and the classification of stick-slip vibration strength is carried out. The results show that this method can effectively identify the severity of stick-slip vibration of a bit. Therefore, this method is valid to evaluate the stick-slip vibration of a bit, which will help drillers adjust the drilling parameters practically according to the severity of vibration, so as to reduce the risks of stick-slip vibration during drilling and improve the efficiency and safety of drilling operation.
C1 [Chen, Chong; Zhang, Shimin; Zhang, Hang; He, Zichen] China Univ Petr, Beijing, Peoples R China.
   [Li, Xiaojun] CNPC Xibu Drilling Engn Co Ltd, Urumqi, Xinjiang, Peoples R China.
C3 China University of Petroleum; China National Petroleum Corporation
RP Chen, C (corresponding author), China Univ Petr, Beijing, Peoples R China.
FU CNPC Xibu Drilling Engineering Company Limited
FX The author would like to thank CNPC Xibu Drilling Engineering Company
   Limited, for sponsoring this study, and providing the field data.
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NR 13
TC 0
Z9 0
U1 0
U2 0
PU AMER SOC MECHANICAL ENGINEERS
PI NEW YORK
PA THREE PARK AVENUE, NEW YORK, NY 10016-5990 USA
BN 978-0-7918-5170-8
PY 2019
AR UNSP V007T07A026
PG 6
WC Engineering, Mechanical; Nuclear Science & Technology
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Engineering; Nuclear Science & Technology
GA BM2GO
UT WOS:000460999100026
DA 2024-09-05
ER
PT J
AU Xu, M
AF Xu, Man
TI Research on Evaluation and Improvement of Government Short Video
   Communication Effect Based on Big Data Statistics
SO INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS
LA English
DT Article
DE Big data statistics; short videos of government affairs; communication
   effect; linear regression; mainstream media
ID FUEL MOISTURE-CONTENT; DATA SCIENCE; ERA
AB Mainstream media is no longer the only way for people to obtain information, and the official media no longer has absolute control. People can choose the form and content of receiving information according to their preferences, which poses a new challenge to the government departments that have always been serious. From the beginning of short video to its prosperity, the government has shown great interest in its characteristics and functions. It has started to layout short video of government affairs on platforms such as Tiktok and Kwai, opened accounts one after another, and actively participated in the production and dissemination of content. Through the continuous launch of well-designed "hot money", the popularity of government affairs short videos on Tiktok and other platforms continued to rise, harvested a large number of fans, attracted social attention, and also brought good results and repercussions. This paper proposes an optimization design scheme for the evaluation and improvement of the dissemination effect of government short video based on big data statistics. The basic situation of government video is obtained through content analysis, and then the judgment coefficient and linear regression in big data statistics are used to extract common factors to improve the dissemination effect of government short video, so as to improve the dissemination influence of government short video. Finally, simulation test and analysis are carried out. Simulation results show that the proposed algorithm has certain accuracy, which is 8.24% higher than the traditional algorithm. Carrying out the research on the promotion planning and design with the dissemination of short videos of government affairs as the core has important practical guiding significance for guiding local grass-roots governments to build public services and public feedback.
C1 [Xu, Man] Commun Univ China, Dept Journalism, Beijing 100020, Peoples R China.
   [Xu, Man] Qiqihar Univ, Qiqihar 161000, Heilongjiang, Peoples R China.
C3 Communication University of China; Qiqihar University
RP Xu, M (corresponding author), Commun Univ China, Dept Journalism, Beijing 100020, Peoples R China.; Xu, M (corresponding author), Qiqihar Univ, Qiqihar 161000, Heilongjiang, Peoples R China.
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NR 26
TC 0
Z9 0
U1 4
U2 4
PU SCIENCE & INFORMATION SAI ORGANIZATION LTD
PI WEST YORKSHIRE
PA 19 BOLLING RD, BRADFORD, WEST YORKSHIRE, 00000, ENGLAND
SN 2158-107X
EI 2156-5570
J9 INT J ADV COMPUT SC
JI Int. J. Adv. Comput. Sci. Appl.
PD JAN
PY 2024
VL 15
IS 1
BP 1073
EP 1083
PG 11
WC Computer Science, Theory & Methods
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA UV6A2
UT WOS:001250863900033
DA 2024-09-05
ER
PT J
AU Ni, Z
   Peng, ML
   Balakrishnan, V
   Tee, V
   Azwa, I
   Saifi, R
   Nelson, LE
   Vlahov, D
   Altice, FL
AF Ni, Zhao
   Peng, Mary L.
   Balakrishnan, Vimala
   Tee, Vincent
   Azwa, Iskandar
   Saifi, Rumana
   Nelson, LaRon E.
   Vlahov, David
   Altice, Frederick L.
TI Implementation of Chatbot Technology in Health Care: Protocol for a
   Bibliometric Analysis
SO JMIR RESEARCH PROTOCOLS
LA English
DT Article
DE artificial intelligence; AI; bibliometric analysis; chatbots; health
   care; health promotion
AB Background: Chatbots have the potential to increase people's access to quality health care. However, the implementation of chatbot technology in the health care system is unclear due to the scarce analysis of publications on the adoption of chatbot in health and medical settings. Objective: This paper presents a protocol of a bibliometric analysis aimed at offering the public insights into the current state and emerging trends in research related to the use of chatbot technology for promoting health. Methods: In this bibliometric analysis, we will select published papers from the databases of CINAHL, IEEE Xplore, PubMed, Scopus, and Web of Science that pertain to chatbot technology and its applications in health care. Our search strategy includes keywords such as "chatbot," "virtual agent," "virtual assistant," "conversational agent," "conversational AI," "interactive agent," "health," and "healthcare." Five researchers who are AI engineers and clinicians will independently review the titles and abstracts of selected papers to determine their eligibility for a full-text review. The corresponding author (ZN) will serve as a mediator to address any discrepancies and disputes among the 5 reviewers. Our analysis will encompass various publication patterns of chatbot research, including the number of annual publications, their geographic or institutional distribution, and the number of annual grants supporting chatbot research, and further summarize the methodologies used in the development of health-related chatbots, along with their features and applications in health care settings. Software tool VOSViewer (version 1.6.19; Leiden University) will be used to construct and visualize bibliometric networks. Results: The preparation for the bibliometric analysis began on December 3, 2021, when the research team started the process of familiarizing themselves with the software tools that may be used in this analysis, VOSViewer and CiteSpace, during which they consulted 3 librarians at the Yale University regarding search terms and tentative results. Tentative searches on the aforementioned databases yielded a total of 2340 papers. The official search phase started on July 27, 2023. Our goal is to complete the screening of papers and the analysis by February 15, 2024. Conclusions: Artificial intelligence chatbots, such as ChatGPT (OpenAI Inc), have sparked numerous discussions within the health care industry regarding their impact on human health. Chatbot technology holds substantial promise for advancing health care systems worldwide. However, developing a sophisticated chatbot capable of precise interaction with health care consumers, delivering personalized care, and providing accurate health-related information and knowledge remain considerable challenges.
C1 [Ni, Zhao; Nelson, LaRon E.; Vlahov, David] Yale Univ, Sch Nursing, Orange, CT USA.
   [Ni, Zhao; Nelson, LaRon E.; Vlahov, David; Altice, Frederick L.] Yale Univ, Ctr Interdisciplinary Res AIDS, New Haven, CT USA.
   [Peng, Mary L.] Harvard Univ, Harvard Med Sch, Dept Global Hlth & Social Med, Boston, MA USA.
   [Balakrishnan, Vimala] Univ Malaya, Fac Comp Sci & Informat Technol, Dept Informat Syst, Kuala Lumpur, Malaysia.
   [Tee, Vincent; Azwa, Iskandar; Saifi, Rumana; Altice, Frederick L.] Univ Malaya, Fac Med, Ctr Excellence Res AIDS, Kuala Lumpur, Malaysia.
   [Azwa, Iskandar] Univ Malaya, Fac Med, Infect Dis Unit, Kuala Lumpur, Malaysia.
   [Altice, Frederick L.] Yale Sch Med, Dept Internal Med, Sect Infect Dis, New Haven, CT USA.
   [Altice, Frederick L.] Yale Sch Publ Hlth, Div Epidemiol Microbial Dis, New Haven, CT USA.
   [Ni, Zhao] Yale Univ, Sch Nursing, 400 West Campus Dr, Orange, CT 06477 USA.
C3 Yale University; Yale University; Harvard University; Harvard Medical
   School; Universiti Malaya; Universiti Malaya; Universiti Malaya; Yale
   University; Yale University; Yale University
RP Ni, Z (corresponding author), Yale Univ, Sch Nursing, 400 West Campus Dr, Orange, CT 06477 USA.
EM zhao.ni@yale.edu
RI Ni, Zhao/HNB-9508-2023; Balakrishnan, Vimala/F-4037-2011; Azwa,
   Iskandar/N-9186-2017; Tee, Vincent/AAU-1296-2021
OI Ni, Zhao/0000-0002-9185-9894; Balakrishnan, Vimala/0000-0002-6859-4488;
   Azwa, Iskandar/0000-0003-1977-6709; Tee, Vincent/0000-0002-6562-2666;
   Peng, Lihong/0000-0001-9360-5487; Nelson, LaRon/0000-0002-2630-602X;
   Altice, Frederick L/0000-0002-7860-693X
FU National Institutes of Health [R21TW011663, R33TW011663, D43TW011324];
   Yale University School of Nursing
FX This work was supported by grants from the National Institutes of Health
   (R21TW011663 and R33TW011663 for ZN and FLA; and D43TW011324 for FLA and
   RS) and faculty research funds from Yale University School of Nursing
   for ZN.
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NR 39
TC 0
Z9 0
U1 18
U2 18
PU JMIR PUBLICATIONS, INC
PI TORONTO
PA 130 QUEENS QUAY East, Unit 1100, TORONTO, ON M5A 0P6, CANADA
SN 1929-0748
J9 JMIR RES PROTOC
JI JMIR RES. Protoc.
PY 2024
VL 13
AR e54349
DI 10.2196/54349
PG 7
WC Health Care Sciences & Services; Public, Environmental & Occupational
   Health
WE Emerging Sources Citation Index (ESCI)
SC Health Care Sciences & Services; Public, Environmental & Occupational
   Health
GA JQ5Z9
UT WOS:001174657500001
PM 38228575
OA Green Published, gold
DA 2024-09-05
ER
PT J
AU Pradhan, T
   Pal, S
AF Pradhan, Tribikram
   Pal, Sukomal
TI A hybrid personalized scholarly venue recommender system integrating
   social network analysis and contextual similarity
SO FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
LA English
DT Article
DE Recommender system; Social network analysis; Citation analysis; Topic
   modeling; Factorization model; Main path analysis
ID PATH-ANALYSIS; DATA FUSION; JOURNALS; COLLABORATION; COCITATION;
   CENTRALITY; WALK
AB Rapidly developing academic venues throw a challenge to researchers in identifying the most appropriate ones that are in-line with their scholarly interests and of high relevance. Even a high-quality paper is sometimes rejected due to a mismatch between the area of the paper, and the scope of the journal attempted to. Recommending appropriate academic venues can, therefore, enable researchers to identify and take part in relevant conferences and to publish in impactful journals. Although a researcher may know a few leading high-profile venues for her specific field of interest, a venue recommender system becomes particularly helpful when one explores a new field or when more options are needed. We propose DISCOVER: A Diversified yet Integrated Social network analysis and COntextual similarity-based scholarly VEnue Recommender system. Our work provides an integrated framework incorporating social network analysis, including centrality measure calculation, citation and co-citation analysis, topic modeling based contextual similarity, and key-route identification based main path analysis of a bibliographic citation network. The paper also addresses cold start issues for a new researcher and a new venue along with a considerable reduction in data sparsity, computational costs, diversity, and stability problems. Experiments based on the Microsoft Academic Graph (MAG) dataset show that the proposed DISCOVER outperforms state-of-the-art recommendation techniques using standard metrics of precision@k, nDCG@k, accuracy, MRR, F - measure(macro), diversity, stability, and average venue quality. (C) 2019 Elsevier B.V. All rights reserved.
C1 [Pradhan, Tribikram; Pal, Sukomal] Indian Inst Technol BHU, Dept Comp Sci & Engn, Varanasi, Uttar Pradesh, India.
C3 Indian Institute of Technology System (IIT System); Indian Institute of
   Technology BHU Varanasi (IIT BHU Varanasi)
RP Pradhan, T (corresponding author), Indian Inst Technol BHU, Dept Comp Sci & Engn, Varanasi, Uttar Pradesh, India.
EM tpradhan.rs.cse16@itbhu.ac.in; spal.cse@itbhu.ac.in
RI PRADHAN, TRIBIKRAM/AAY-1283-2021
OI PRADHAN, TRIBIKRAM/0000-0001-5458-2286
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NR 86
TC 32
Z9 36
U1 1
U2 39
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0167-739X
EI 1872-7115
J9 FUTURE GENER COMP SY
JI Futur. Gener. Comp. Syst.
PD SEP
PY 2020
VL 110
BP 1139
EP 1166
DI 10.1016/j.future.2019.11.017
PG 28
WC Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA LZ3UK
UT WOS:000541153400091
DA 2024-09-05
ER
PT J
AU Traymbak, S
   Sharma, M
   Anand, A
   Shukla, A
AF Traymbak, Shruti
   Sharma, Meghna
   Anand, Aastha
   Shukla, Anju
TI Chatbot technology in the education sector: a bibliometrics analysis
   using VOS viewer
SO INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT
LA English
DT Article; Early Access
DE Artificial intelligence; Natural language processing; Chatbot; Education
   sector; Harzing's publish or perish software; Bibliometrics; VOS viewer;
   C8; C88; I2; I21
AB Chatbots that are powered by artificial intelligence and natural language processing, have found a significant foothold in the sphere of education. In this research, VOS viewer software was employed to conduct a comprehensive bibliometric analysis, focusing on 49 research papers and conference papers published between 2005 and 2023, with the keyword "chatbot in education." The analysis revealed an average of 30.76 citations per year and 10.67 citations per author, indicating a sustained and growing interest in the subject. Notably, the paper titled "An Overview of Chatbot Technology," (Adamopoulou and Moussiades in An overview of chatbot technology, Springer, Cham, 2020a), emerged as the most highly cited work with 573 citations, underscoring the robust recognition of chatbot research in the academic community. The recent increase in research serves to underscore the growing significance of this subject in educational discussions. The analysis suggested that the authors, Ahmed Tlili, Boulus Shehata, Michael Adarkwah, and Aras Bozkurt emerged as highly cited contributors, accumulating an annual mean of 209 citations. This study underscores the profound interest and influence of chatbots in the educational sector, as underscored by citation metrics and author analysis. The findings illuminate influential publications, eminent authors, and the interplay of keywords and research themes within this dynamic field.
C1 [Traymbak, Shruti; Shukla, Anju] Jagannath Int Management Sch, New Delhi, India.
   [Sharma, Meghna; Anand, Aastha] Amity Univ, AIBS, Noida, India.
C3 Amity University Noida
RP Anand, A (corresponding author), Amity Univ, AIBS, Noida, India.
EM shruti.traymbak@jagannath.org; msharma9@amity.edu;
   anand.aastha11@gmail.com; anju.shukla@jagannath.org
OI traymbak, shruti/0000-0003-2306-7999
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NR 47
TC 0
Z9 0
U1 15
U2 23
PU SPRINGER INDIA
PI NEW DELHI
PA 7TH FLOOR, VIJAYA BUILDING, 17, BARAKHAMBA ROAD, NEW DELHI, 110 001,
   INDIA
SN 0975-6809
EI 0976-4348
J9 INT J SYST ASSUR ENG
JI Int. J. Syst. Assur. Eng. Manag.
PD 2024 JAN 3
PY 2024
DI 10.1007/s13198-023-02230-6
EA JAN 2024
PG 12
WC Engineering, Multidisciplinary
WE Emerging Sources Citation Index (ESCI)
SC Engineering
GA DV4U2
UT WOS:001134851900001
DA 2024-09-05
ER
PT C
AU Scozzi, MV
   Iacovides, I
   Linehan, C
AF Scozzi, Monica Visani
   Iacovides, Ioanna
   Linehan, Conor
GP Assoc Comp Machinery
TI A Mixed Method Approach for Evaluating and Improving the Design of
   Learning in Puzzle Games
SO CHI PLAY'17: PROCEEDINGS OF THE ANNUAL SYMPOSIUM ON COMPUTER-HUMAN
   INTERACTION IN PLAY
LA English
DT Proceedings Paper
CT Annual Symposium on Computer-Human Interaction in Play (CHI PLAY)
CY OCT 15-18, 2017
CL Amsterdam, NETHERLANDS
DE Games; Learning Curves; Breakdowns; Player Experience; Evaluation
   Methods; Games User Research
AB Despite the acknowledgment that learning is a necessary part of all gameplay, the area of Games User Research lacks an established evidence based method through which designers and researchers can understand, assess, and improve how commercial games teach players game-specific skills and information. In this paper, we propose a mixed method procedure that draws together both quantitative and experiential approaches to examine the extent to which players are supported in learning about the game world and mechanics. We demonstrate the method through presenting a case study of the game Portal involving 14 participants, who differed in terms of their gaming expertise. By comparing optimum solutions to puzzles against observed player performance, we illustrate how the method can indicate particular problems with how learning is structured within a game. We argue that the method can highlight where major breakdowns occur and yield design insights that can improve the player experience with puzzle games.
C1 [Scozzi, Monica Visani] UCL, London, England.
   [Iacovides, Ioanna] Open Univ, Milton Keynes, Bucks, England.
   [Linehan, Conor] Univ Coll Cork, Cork, Ireland.
C3 University of London; University College London; Open University - UK;
   University College Cork
RP Scozzi, MV (corresponding author), UCL, London, England.
EM monica.visani@gmail.com; jo.iacovides@open.ac.uk; conor.linehan@ucc.ie
RI ; Iacovides, Ioanna/C-5124-2016
OI Linehan, Conor/0000-0002-7654-6697; Iacovides,
   Ioanna/0000-0001-9674-8440
FU EPSRC [EP/G059063/1]
FX We thank all the volunteers who took part in both studies. This research
   was supported by the EPSRC funded CHI+ MED project (EP/G059063/1).
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NR 43
TC 4
Z9 4
U1 0
U2 2
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA
BN 978-1-4503-4898-0
PY 2017
BP 217
EP 228
DI 10.1145/3116595.3116628
PG 12
WC Computer Science, Cybernetics; Computer Science, Software Engineering
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BL7OX
UT WOS:000455267200019
OA Green Accepted
DA 2024-09-05
ER
PT J
AU Burgelman, JC
   Pascu, C
   Szkuta, K
   Von Schomberg, R
   Karalopoulos, A
   Repanas, K
   Schouppe, M
AF Burgelman, Jean-Claude
   Pascu, Corina
   Szkuta, Katarzyna
   Von Schomberg, Rene
   Karalopoulos, Athanasios
   Repanas, Konstantinos
   Schouppe, Michel
TI Open Science, Open Data, and Open Scholarship: European Policies to Make
   Science Fit for the Twenty-First Century
SO FRONTIERS IN BIG DATA
LA English
DT Article
DE open science; open data; open access; open scholarship; European science
   policies; artificial intelligence
AB Open science will make science more efficient, reliable, and responsive to societal challenges. The European Commission has sought to advance open science policy from its inception in a holistic and integrated way, covering all aspects of the research cycle from scientific discovery and review to sharing knowledge, publishing, and outreach. We present the steps taken with a forward-looking perspective on the challenges laying ahead, in particular the necessary change of the rewards and incentives system for researchers (for which various actors are co-responsible and which goes beyond the mandate of the European Commission). Finally, we discuss the role of artificial intelligence (AI) within an open science perspective.
C1 [Burgelman, Jean-Claude; Pascu, Corina; Szkuta, Katarzyna; Von Schomberg, Rene; Karalopoulos, Athanasios; Repanas, Konstantinos; Schouppe, Michel] European Commiss, DG Res & Innovat, Open Sci, Brussels, Belgium.
RP Burgelman, JC; Pascu, C (corresponding author), European Commiss, DG Res & Innovat, Open Sci, Brussels, Belgium.
EM jean-claude.burgelman@ec.europa.eu; corina.pascu@ec.europa.eu
RI Burgelman, Jean-Claude/KEH-1053-2024; Von Schomberg, Rene/J-5376-2014
OI Burgelman, Jean-Claude/0000-0003-4817-8425; Von Schomberg,
   Rene/0000-0003-1768-806X; Pascu, Corina/0000-0002-9068-5271; repanas,
   kostas/0000-0002-7848-2834
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NR 38
TC 81
Z9 83
U1 12
U2 118
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 2624-909X
J9 FRONT BIG DATA
JI Front. Big Data
PD DEC 10
PY 2019
VL 2
AR 43
DI 10.3389/fdata.2019.00043
PG 6
WC Computer Science, Information Systems; Computer Science,
   Interdisciplinary Applications; Multidisciplinary Sciences
WE Emerging Sources Citation Index (ESCI)
SC Computer Science; Science & Technology - Other Topics
GA TV4WA
UT WOS:000681724000001
PM 33693366
OA Green Published, gold
DA 2024-09-05
ER
PT J
AU Zhai, MM
   Li, WH
   Tie, P
   Wang, XC
   Xie, T
   Ren, H
   Zhang, Z
   Song, WM
   Quan, DC
   Li, MC
   Chen, LM
   Qiu, LX
AF Zhai, Mengmeng
   Li, Wenhan
   Tie, Ping
   Wang, Xuchun
   Xie, Tao
   Ren, Hao
   Zhang, Zhuang
   Song, Weimei
   Quan, Dichen
   Li, Meichen
   Chen, Limin
   Qiu, Lixia
TI Research on the predictive effect of a combined model of ARIMA and
   neural networks on human brucellosis in Shanxi Province, China: a time
   series predictive analysis
SO BMC INFECTIOUS DISEASES
LA English
DT Article
DE Human brucellosis; ARIMA-ERNN model; ARIMA-BPNN model; Predictive effect
AB BackgroundBrucellosis is a major public health problem that seriously affects developing countries and could cause significant economic losses to the livestock industry and great harm to human health. Reasonable prediction of the incidence is of great significance in controlling brucellosis and taking preventive measures.MethodsOur human brucellosis incidence data were extracted from Shanxi Provincial Center for Disease Control and Prevention. We used seasonal-trend decomposition using Loess (STL) and monthplot to analyse the seasonal characteristics of human brucellosis in Shanxi Province from 2007 to 2017. The autoregressive integrated moving average (ARIMA) model, a combined model of ARIMA and the back propagation neural network (ARIMA-BPNN), and a combined model of ARIMA and the Elman recurrent neural network (ARIMA-ERNN) were established separately to make predictions and identify the best model. Additionally, the mean squared error (MAE), mean absolute error (MSE) and mean absolute percentage error (MAPE) were used to evaluate the performance of the model.ResultsWe observed that the time series of human brucellosis in Shanxi Province increased from 2007 to 2014 but decreased from 2015 to 2017. It had obvious seasonal characteristics, with the peak lasting from March to July every year. The best fitting and prediction effect was the ARIMA-ERNN model. Compared with those of the ARIMA model, the MAE, MSE and MAPE of the ARIMA-ERNN model decreased by 18.65, 31.48 and 64.35%, respectively, in fitting performance; in terms of prediction performance, the MAE, MSE and MAPE decreased by 60.19, 75.30 and 64.35%, respectively. Second, compared with those of ARIMA-BPNN, the MAE, MSE and MAPE of ARIMA-ERNN decreased by 9.60, 15.73 and 11.58%, respectively, in fitting performance; in terms of prediction performance, the MAE, MSE and MAPE decreased by 31.63, 45.79 and 29.59%, respectively.ConclusionsThe time series of human brucellosis in Shanxi Province from 2007 to 2017 showed obvious seasonal characteristics. The fitting and prediction performances of the ARIMA-ERNN model were better than those of the ARIMA-BPNN and ARIMA models. This will provide some theoretical support for the prediction of infectious diseases and will be beneficial to public health decision making.
C1 [Zhai, Mengmeng; Li, Wenhan; Wang, Xuchun; Ren, Hao; Zhang, Zhuang; Song, Weimei; Quan, Dichen; Li, Meichen; Qiu, Lixia] Shanxi Med Univ, Sch Publ Hlth, Dept Hlth Stat, Taiyuan, Shanxi, Peoples R China.
   [Tie, Ping] Shanxi Ctr Dis Control & Prevent, Endem Dis Prevent & Control Sect, Taiyuan, Shanxi, Peoples R China.
   [Xie, Tao] Jiangxi Univ Finance & Econ, Sch Stat, Dept Math Stat, Nanchang, Jiangxi, Peoples R China.
   [Chen, Limin] Shanxi Prov Peoples Hosp, Taiyuan, Shanxi, Peoples R China.
C3 Shanxi Medical University; Jiangxi University of Finance & Economics;
   Shanxi People's Hospital
RP Qiu, LX (corresponding author), Shanxi Med Univ, Sch Publ Hlth, Dept Hlth Stat, Taiyuan, Shanxi, Peoples R China.; Chen, LM (corresponding author), Shanxi Prov Peoples Hosp, Taiyuan, Shanxi, Peoples R China.
EM sxchenlimin@163.com; qlx_1126@163.com
RI Chen, Limin/X-5598-2019
OI Chen, Limin/0000-0003-4937-2756; Xie, Tao/0000-0003-2930-2742
FU Key Research and Development Projects of Shanxi Province [201803D31066]
FX This study was supported by Key Research and Development Projects of
   Shanxi Province [grant No. 201803D31066]. The funders had no role in
   design of the study and collection, analysis, and interpretation of data
   and in writing the manuscript.
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NR 47
TC 19
Z9 22
U1 6
U2 47
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
EI 1471-2334
J9 BMC INFECT DIS
JI BMC Infect. Dis.
PD MAR 19
PY 2021
VL 21
IS 1
AR 280
DI 10.1186/s12879-021-05973-4
PG 12
WC Infectious Diseases
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Infectious Diseases
GA RA6CF
UT WOS:000631504200004
PM 33740904
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU Chen, XL
   Zou, D
   Cheng, G
   Xie, HR
   Jong, M
AF Chen, Xieling
   Zou, Di
   Cheng, Gary
   Xie, Haoran
   Jong, Morris
TI Blockchain in smart education: Contributors, collaborations,
   applications and research topics
SO EDUCATION AND INFORMATION TECHNOLOGIES
LA English
DT Article
DE Smart education; Blockchain; Topic modeling; Bibliometrics;
   Contributors; Collaborations
ID INTERNET
AB Researchers and practitioners are paying increasing attention to blockchain's potential for resolving trust, privacy, and transparency-related issues in smart education. Research on educational blockchain is also becoming an active field of research. Based on 206 studies published from 2017 to 2020, we identify contributors, collaborators, applications, and research topics using topic modeling and bibliometrics. Results indicate that currently, studies on the educational blockchain are mostly published in computer science conferences, with few in educational technology journals. Asian countries/regions (e.g., China and India) and institutions (e.g., University of Raharja) are active in this field. Countries/regions and institutions from the same regions tend to have close collaborations. Blockchain is mainly used to facilitate online testing and learning, education data mining and analytics, resource sharing, educational record verification and authentication, data management and storing, administration management, education/discipline of children, and college crowdfunding. Based on the results, we highlight the need to integrate artificial intelligence to enhance scalability and security and to facilitate justification and personalization of blockchain solutions based on accumulated big data to capture abnormal behaviors. This study assists researchers and practitioners in understanding educational blockchain research and becoming aware of the hot topics and future directions.
C1 [Chen, Xieling] South China Normal Univ, Sch Informat Technol Educ, Guangzhou, Peoples R China.
   [Zou, Di] Educ Univ Hong Kong, Dept English Language Educ, Hong Kong, Peoples R China.
   [Xie, Haoran] Educ Univ Hong Kong, Dept Math & Informat Technol, Hong Kong, Peoples R China.
   [Jong, Morris] Lingnan Univ, Dept Comp & Decis Sci, Hong Kong, Peoples R China.
   [Cheng, Gary] Chinese Univ Hong Kong, Dept Curriculum & Instruct, Hong Kong, Peoples R China.
C3 South China Normal University; Education University of Hong Kong
   (EdUHK); Education University of Hong Kong (EdUHK); Lingnan University;
   Chinese University of Hong Kong
RP Zou, D (corresponding author), Educ Univ Hong Kong, Dept English Language Educ, Hong Kong, Peoples R China.
EM xielingchen0708@gmail.com; dizoudaisy@gmail.com; chengks@eduhk.hk;
   hrxie2@gmail.com; mjong@cuhk.edu.hk
RI Xie, Haoran/AFS-3515-2022
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NR 62
TC 8
Z9 8
U1 12
U2 48
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1360-2357
EI 1573-7608
J9 EDUC INF TECHNOL
JI Educ. Inf. Technol.
PD APR
PY 2023
VL 28
IS 4
BP 4597
EP 4627
DI 10.1007/s10639-022-11399-5
EA OCT 2022
PG 31
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA F7WX6
UT WOS:000870636400003
DA 2024-09-05
ER
PT C
AU Fiallos, A
   Jimenes, K
   Vaca, C
   Ochoa, X
AF Fiallos, Angel
   Jimenes, Karina
   Vaca, Carmen
   Ochoa, Xavier
BE Teran, L
   Meier, A
TI Scientific Communities Detection and Analysis in the Bibliographic
   Database: Scopus
SO 2017 FOURTH INTERNATIONAL CONFERENCE ON EDEMOCRACY & EGOVERNMENT
   (ICEDEG)
SE International Conference on eDemocracy and eGovernment ICEDEG
LA English
DT Proceedings Paper
CT 4th International Conference on eDemocracy and eGovernment (ICEDEG)
CY APR 19-21, 2017
CL Quito, ECUADOR
DE LDA; TF-IDF; SNA; Scopus; authors; communities; Ecuador
AB In recent years, Governments of many countries had promoted higher education in exclusive areas and disciplines to improve the quality of inhabitant's life, economy, and public management. However, there is no an effective way to help governments to verify if scientific works and research centers are aligned to a country priority research area. A dataset of 4552 scientific works associated to 29 research areas in Ecuador was collected from a bibliographic database SCOPUS. This work is focused on detection of the most important Ecuadorian research areas based on scientific collaboration in scientific publications in such areas through a proposed methodology. The methodology is subject to Social Network Analysis and Natural Language Processing to identify collaboration communities and the most relevant topics on densely connected communities. Finally, an exploratory analysis of a case study thought this methodology is presented to demonstrate that is possible to know which scientific areas are the most collaborative and which topics were the most popular in Ecuador.
C1 [Fiallos, Angel; Jimenes, Karina; Vaca, Carmen; Ochoa, Xavier] Escuela Super Politecn Litoral, ESPOL8, Fac Ingn Elect & Computac, Campus Gustavo Galindo Km 30-5,Via Perimetral, Guayaquil, Ecuador.
C3 Escuela Superior Politecnica del Litoral
RP Fiallos, A (corresponding author), Escuela Super Politecn Litoral, ESPOL8, Fac Ingn Elect & Computac, Campus Gustavo Galindo Km 30-5,Via Perimetral, Guayaquil, Ecuador.
EM anfiallos@fiec.espol.edu.ec; kbjimene@fiec.espol.edu.ec;
   cvaca@fiec.espol.edu.ec; xochoa@fiec.espol.edu.ec
RI Fiallos, Angel/CAE-9290-2022; Vaca, Carmen/AAL-4156-2021; Ochoa,
   Xavier/AAJ-8085-2021
OI Ochoa, Xavier/0000-0002-4371-7701; Vaca Ruiz, Carmen
   Karina/0000-0002-0474-1901; Fiallos, Angel/0000-0002-7828-1207
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TC 5
Z9 5
U1 0
U2 6
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2573-2005
EI 2573-1998
BN 978-1-5090-4830-4
J9 INT CONF EDEMOC EGOV
PY 2017
BP 118
EP 124
PG 7
WC Computer Science, Interdisciplinary Applications; Computer Science,
   Theory & Methods; Political Science
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Government & Law
GA BM4XR
UT WOS:000464415600020
DA 2024-09-05
ER
PT J
AU Zhang, W
   Bai, L
   Zhang, FY
   Zhao, FF
   Liu, ZL
   Xiong, XC
AF Zhang, Wei
   Bai, Lu
   Zhang, Fengyi
   Zhao, Feifan
   Liu, Zilong
   Xiong, Xingchuang
TI A comprehensive research on measurement and evaluation of intelligent
   meter reading systems
SO ENGINEERING RESEARCH EXPRESS
LA English
DT Article
DE meter; intelligent reading system; artificial intelligence; measurement
   and evaluation; metrology
ID ROBUST
AB Based on the machine vision and artificial intelligence technologies, the reading and acquisition of meter values have become the mainstream technical solutions for real-time monitoring of production data in the current industrial field. At the same time, the number of intelligent instruments and meters is increasing, and the quality varies. There is a lack of scientifically measurement methods and quality trust for intelligent meter reading algorithms. To promote their reliable and widespread application in various fields, it is necessary to conduct in-depth research on the assessment system of artificial intelligence in intelligent meter reading systems. This paper comprehensively studies various aspects involved in the emerging measurement field of artificial intelligence evaluation at the current stage, providing scientific measurement and evaluation references for the quality trust of artificial intelligence in the field of meters.
C1 [Zhang, Wei; Bai, Lu; Zhang, Fengyi; Zhao, Feifan; Liu, Zilong; Xiong, Xingchuang] Natl Inst Metrol, Beijing 100029, Peoples R China.
   [Zhang, Wei; Bai, Lu; Zhang, Fengyi; Zhao, Feifan; Liu, Zilong; Xiong, Xingchuang] Key Lab Metrol Digitalizat & Digital Metrol State, Beijing 100029, Peoples R China.
   [Zhang, Fengyi] China Jiliang Univ, Coll Informat Engn, Hangzhou 310018, Zhejiang, Peoples R China.
   [Zhao, Feifan] Zhengzhou Univ, Sch Comp & Artificial Intelligence, Zhengzhou 450001, Peoples R China.
C3 National Institute of Metrology China; China Jiliang University;
   Zhengzhou University
RP Xiong, XC (corresponding author), Natl Inst Metrol, Beijing 100029, Peoples R China.; Xiong, XC (corresponding author), Key Lab Metrol Digitalizat & Digital Metrol State, Beijing 100029, Peoples R China.
EM xiongxch@nim.ac.cn
FU National Key R&D Program of China
FX No Statement Available
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NR 37
TC 0
Z9 0
U1 2
U2 2
PU IOP Publishing Ltd
PI BRISTOL
PA TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
SN 2631-8695
J9 ENG RES EXPRESS
JI Eng. Res. Express
PD JUN 1
PY 2024
VL 6
IS 2
AR 025212
DI 10.1088/2631-8695/ad45b5
PG 16
WC Engineering, Multidisciplinary
WE Emerging Sources Citation Index (ESCI)
SC Engineering
GA QP4E9
UT WOS:001222053600001
DA 2024-09-05
ER
PT J
AU Jiang, T
   Liu, XP
   Zhang, C
   Yin, CAH
   Liu, HZ
AF Jiang, Tian
   Liu, Xiaoping
   Zhang, Chao
   Yin, Chuanhao
   Liu, Huizhou
TI Overview of Trends in Global Single Cell Research Based on Bibliometric
   Analysis and LDA Model (2009-2019)
SO JOURNAL OF DATA AND INFORMATION SCIENCE
LA English
DT Article
DE LDA model; Topic evolution; Bibliometric analysis; Post-discretized;
   Single-cell
AB Purpose: This article aims to describe the global research profile and the development trends of single cell research from the perspective of bibliometric analysis and semantic mining.
   Design/methodology/approach: The literatures on single cell research were extracted from Clarivate Analytic's Web of Science Core Collection between 2009 and 2019. Firstly, bibliometric analyses were performed with Thomson Data Analyzer (TDA). Secondly, topic identification and evolution trends of single cell research was conducted through the LDA topic model. Thirdly, taking the post-discretized method which is used for topic evolution analysis for reference, the topics were also be dispersed to countries to detect the spatial distribution.
   Findings: The publication of single cell research shows significantly increasing tendency in the last decade. The topics of single cell research field can be divided into three categories, which respectively refers to single cell research methods, mechanism of biological process, and clinical application of single cell technologies. The different trends of these categories indicate that technological innovation drives the development of applied research. The continuous and rapid growth of the topic strength in the field of cancer diagnosis and treatment indicates that this research topic has received extensive attention in recent years. The topic distributions of some countries are relatively balanced, while for the other countries, several topics show significant superiority.
   Research limitations: The analyzed data of this study only contain those were included in the Web of Science Core Collection.
   Practical implications: This study provides insights into the research progress regarding single cell field and identifies the most concerned topics which reflect potential opportunities and challenges. The national topic distribution analysis based on the post-discretized analysis method extends topic analysis from time dimension to space dimension.
   Originality/value: This paper combines bibliometric analysis and LDA model to analyze the evolution trends of single cell research field. The method of extending post-discretized analysis from time dimension to space dimension is distinctive and insightful.
C1 [Jiang, Tian; Liu, Xiaoping; Zhang, Chao; Liu, Huizhou] Chinese Acad Sci, Natl Sci Lib, Beijing 100190, Peoples R China.
   [Yin, Chuanhao] Chinese Inst Elect, Beijing 100036, Peoples R China.
C3 Chinese Academy of Sciences; National Science Library, CAS; Chinese
   Academy of Sciences; Institute of Electronics, CAS
RP Liu, HZ (corresponding author), Chinese Acad Sci, Natl Sci Lib, Beijing 100190, Peoples R China.
EM jiangtian@mail.las.ac.cn; liuxp@mail.las.ac.cn; zhangch@mail.las.ac.cn;
   cieyinchuanhao@163.com; liuhz@mail.las.ac.cn
FU  [E290001]
FX This study was supported by the Chinese Academy of Sciences literature
   information capability construction project of 2020 "Construction of
   strategic information research and consultation system in science and
   technology field" (Grant No. E290001).
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NR 18
TC 1
Z9 1
U1 2
U2 48
PU SCIENDO
PI WARSAW
PA BOGUMILA ZUGA 32A, WARSAW, MAZOVIA, POLAND
SN 2096-157X
EI 2543-683X
J9 J DATA INFO SCI
JI J. Data Info. Sci.
PD APR
PY 2021
VL 6
IS 2
BP 163
EP 178
DI 10.2478/jdis-2021-0008
PG 16
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA RV9WI
UT WOS:000646175200008
OA gold
DA 2024-09-05
ER
PT J
AU Alon, I
   Chebance, Z
   Massucci, FA
   Bounartzi, T
   Ravitsky, V
AF Alon, Ido
   Chebance, Zacharie
   Massucci, Francesco Alessandro
   Bounartzi, Theofano
   Ravitsky, Vardit
TI Mapping international research output within ethical, legal, and social
   implications (ELSI) of assisted reproductive technologies
SO JOURNAL OF ASSISTED REPRODUCTION AND GENETICS
LA English
DT Article
DE Assisted reproductive technologies; Ethical; social; and legal
   implications; Mapping; Topic modeling; Geographic distribution of
   research; International research
ID MITOCHONDRIAL REPLACEMENT TECHNIQUES; COMMERCIAL SURROGACY;
   INEQUALITIES; INFERTILITY
AB PurposeResearch about ethical, legal, and social implications (ELSI) of assisted reproductive technologies (ART) is influenced by cultural and value-based perspectives. It impacts regulations, funding, and clinical practice, and shapes the perception of ART in society. We analyze trends in the global literature on ELSI of ART between 1999 and 2019. As most output is produced by North America, Western Europe, and Australia, we focus on international research, i.e., academic articles studying a different country than that of the corresponding author.MethodsThe corpus, extracted from PubMed, Web of Science, and Scopus, includes 7714 articles, of which 1260 involved international research. Analysis is based on titles, abstracts and keywords, classification into ART fields and Topic Modeling, the countries of corresponding author, and countries mentioned in abstracts.ResultsAn absolute increase in the number of international studies, and their relative proportion. Trends of decentralization are apparent, yet geographic centralization remains, which reflects an unequal distribution of research funds across countries and may result in findings that do not reflect global diversity of norms and values. Preference for studying conceptual challenges through philosophical analysis, and for fields that concern only a portion of ART cycles. Less attention was dedicated to economic analysis and barriers to access, or to knowledge of and attitudes. International studies provide an opportunity to expand and diversify the scope of ELSI research.ConclusionWe call on the research community to promote international collaborations, focus on less explored regions, and divert more attention to questions of cost, access, knowledge, and attitudes.
C1 [Alon, Ido] Autonomous Univ Madrid, Dept Dev Econ, Madrid, Spain.
   [Alon, Ido; Ravitsky, Vardit] Univ Montreal, Montreal, PQ, Canada.
   [Chebance, Zacharie] Mines Paristech, Paris, France.
   [Massucci, Francesco Alessandro] SIRIS Acad, Res Div, SIRIS Lab, Barcelona, Spain.
   [Bounartzi, Theofano] Univ Thessaly, Fac Med, Sch Hlth Sci, Dept Obstet & Gynaecol, Larisa, Greece.
   [Ravitsky, Vardit] Harvard Med Sch, Boston, MA USA.
C3 Autonomous University of Madrid; Universite de Montreal; Universite PSL;
   MINES ParisTech; University of Thessaly; Harvard University; Harvard
   Medical School
RP Alon, I (corresponding author), Autonomous Univ Madrid, Dept Dev Econ, Madrid, Spain.; Alon, I (corresponding author), Univ Montreal, Montreal, PQ, Canada.
EM idoalon77@gmail.com
OI Alon, Ido/0000-0001-6603-7496
FU CRUE-CSIC; Springer Nature; Autonomous University of Madrid; Centre de
   recherche en ethique (CRE) at the University of Montreal
FX & nbsp;Open Access funding provided thanks to the CRUE-CSIC agreement
   with Springer Nature. This work received financial support provided by
   both the Margarita Salas Fellowship from the Autonomous University of
   Madrid and the Centre de recherche en ethique (CRE) at the University of
   Montreal which have both provided invaluable financial assistance,
   enabling us to conduct our research.
CR Alon I, 2021, INT J EQUITY HEALTH, V20, DOI 10.1186/s12939-021-01438-x
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   Präg P, 2017, HUM REPROD, V32, P2305, DOI 10.1093/humrep/dex298
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   White, 2019, PUBLICATIONS OUTPUT
   Wyns C, 2020, HUM REPROD OPEN, V2020, DOI 10.1093/hropen/hoaa032
NR 37
TC 4
Z9 4
U1 3
U2 7
PU SPRINGER/PLENUM PUBLISHERS
PI NEW YORK
PA 233 SPRING ST, NEW YORK, NY 10013 USA
SN 1058-0468
EI 1573-7330
J9 J ASSIST REPROD GEN
JI J. Assist. Reprod. Genet.
PD SEP
PY 2023
VL 40
IS 9
BP 2023
EP 2043
DI 10.1007/s10815-023-02834-8
EA JUN 2023
PG 21
WC Genetics & Heredity; Obstetrics & Gynecology; Reproductive Biology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Genetics & Heredity; Obstetrics & Gynecology; Reproductive Biology
GA P6OS5
UT WOS:001020140100002
PM 37382788
OA Green Published, hybrid
DA 2024-09-05
ER
PT C
AU Shang, WY
   Cordell, R
   Downie, JS
AF Shang, Wenyi
   Cordell, Ryan
   Downie, J. Stephen
GP ACM
TI Book Size, Book Format: Historical Relationship in the HathiTrust
   Digital Library Metadata (1500-1799)
SO 2023 ACM/IEEE JOINT CONFERENCE ON DIGITAL LIBRARIES, JCDL
SE ACM-IEEE Joint Conference on Digital Libraries JCDL
LA English
DT Proceedings Paper
CT 23rd ACM/IEEE Joint Conference on Digital Libraries (JCDL)
CY JUN 26-30, 2023
CL Santa Fe, NM
DE Digital library; Bibliographic metadata; Book history; Machine learning
AB Digital libraries create new scholarly possibilities for investigating hook format at scale, providing a valuable perspective for hook history. This study evaluates the historical relationship between hook format and book size using the HathiTrust Digital Library metadata of 133,268 books published between 1500 and 1799. We found that: (1) the size of books generally decreased; (2) smaller hook formats gradually replaced larger hook formats; and, (3) book size can predict book format relatively accurately. Our findings suggest possible automated improvements to digital library metadata where information about book size is better represented than format, enabling book historians to estimate the prevalence of formats and analyze publication trends.
C1 [Shang, Wenyi; Cordell, Ryan; Downie, J. Stephen] Univ Illinois, Sch Informat Sci, Champaign, IL 61820 USA.
C3 University of Illinois System; University of Illinois Urbana-Champaign
RP Shang, WY (corresponding author), Univ Illinois, Sch Informat Sci, Champaign, IL 61820 USA.
EM wenyis3@illinois.edu; rcordell@illinois.edu; jdownie@illinois.edu
CR McGill ML, 2018, EARLY AM STUD, V16, P671, DOI 10.1353/eam.2018.0033
   Pettegree Andrew, 2010, The Book in the Renaissance, P354
   Stallybrass P, 2004, PMLA, V119, P1347, DOI 10.1632/003081204X17914
NR 3
TC 0
Z9 0
U1 2
U2 3
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES
SN 2575-7865
EI 2575-8152
BN 979-8-3503-9931-8
J9 ACM-IEEE J CONF DIG
PY 2023
BP 279
EP 281
DI 10.1109/JCDL57899.2023.00059
PG 3
WC Computer Science, Information Systems; Computer Science,
   Interdisciplinary Applications
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BW0PC
UT WOS:001098971300049
DA 2024-09-05
ER
PT J
AU Maatouk, Y
AF Maatouk, Yasser
TI AI-SPedia: a novel ontology to evaluate the impact of research in the
   field of artificial intelligence
SO PEERJ COMPUTER SCIENCE
LA English
DT Article
DE Altmetrics; Bibliometrics; Semantic web; Ontology
ID BIBLIOMETRIC INDICATORS; ALTMETRIC SCORE; SCIENCE; WEB
AB Background. Sharing knowledge such as resources, research results, and scholarly documents, is of key importance to improving collaboration between researchers worldwide. Research results from the field of artificial intelligence (AI) are vital to share because of the extensive applicability of AI to several other fields of research. This has led to a significant increase in the number of AI publications over the past decade. The metadata of AI publications, including bibliometrics and altmetrics indicators, can be accessed by searching familiar bibliographical databases such as Web of Science (WoS), which enables the impact of research to be evaluated and identify rising researchers and trending topics in the field of AI. Problem description. In general, bibliographical databases have two limitations in terms of the type and form of metadata we aim to improve. First, most bibliographical databases, such as WoS, are more concerned with bibliometric indicators and do not offer a wide range of altmetric indicators to complement traditional bibliometric indi-cators. Second, the traditional format in which data is downloaded from bibliographical databases limits users to keyword-based searches without considering the semantics of the data. Proposed solution. To overcome these limitations, we developed a repository, named AI-SPedia. The repository contains semantic knowledge of scientific publications concerned with AI and considers both the bibliometric and altmetric indicators. Moreover, it uses semantic web technology to produce and store data to enable semantic-based searches. Furthermore, we devised related competency questions to be answered by posing smart queries against the AI-SPedia datasets. Results. The results revealed that AI-SPedia can evaluate the impact of AI research by exploiting knowledge that is not explicitly mentioned but extracted using the power of semantics. Moreover, a simple analysis was performed based on the answered questions to help make research policy decisions in the AI domain. The end product, AI-SPedia, is considered the first attempt to evaluate the impacts of AI scientific publications using both bibliometric and altmetric indicators and the power of semantic web technology.
C1 [Maatouk, Yasser] King Abdulaziz Univ, Fac Comp & Informat Technol, Jeddah, Saudi Arabia.
C3 King Abdulaziz University
RP Maatouk, Y (corresponding author), King Abdulaziz Univ, Fac Comp & Informat Technol, Jeddah, Saudi Arabia.
EM ymaatouk@kau.edu.sa
RI Maatouk, Yasser/E-6548-2016
OI Maatouk, Yasser/0000-0001-9333-5907
FU Deanship of Scientific Research (DSR) , King Abdulaziz University,
   Jeddah;  [DF-771-611-1441]
FX Funding This project was funded by the Deanship of Scientific Research
   (DSR) , King Abdulaziz University, Jeddah, under Grant No.
   DF-771-611-1441. The funders had no role in study design, data
   collection and analysis, decision to publish, or preparation of the
   manuscript.
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NR 32
TC 0
Z9 0
U1 3
U2 15
PU PEERJ INC
PI LONDON
PA 341-345 OLD ST, THIRD FLR, LONDON, EC1V 9LL, ENGLAND
EI 2376-5992
J9 PEERJ COMPUT SCI
JI PeerJ Comput. Sci.
PD SEP 22
PY 2022
VL 8
AR e1099
DI 10.7717/peerj-cs.1099
PG 24
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 5A7EM
UT WOS:000863046700001
PM 37346315
OA Green Published, gold
DA 2024-09-05
ER
PT J
AU Yang, SC
   Guo, CY
   Ren, W
AF Yang, Shucai
   Guo, Chaoyang
   Ren, Wei
TI Research on optimization of milling performance of V-groove
   micro-texture ball-end milling cutter
SO JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY
LA English
DT Article
DE V-groove micro-texture; Ball-end milling cutter; Titanium alloy;
   Performance optimization; Simulated annealing algorithm
ID WEAR
AB Titanium alloys generally have problems such as sticking, large cutting force, and poor heat dissipation during the cutting process., research shows that processing microtextures on the surface of the tool can effectively improve the above problems. Therefore, this paper designs the V-groove micro-textures based on the principle of bionics. Use simulation software to analyze the milling behavior of different V-groove micro-texture ball-end milling cutters for milling titanium alloys. Build a test platform to study the milling performance of the V-groove micro-texture ball-end milling cutter for milling titanium alloys, obtain the prior choice range of V-groove micro-texture parameters, optimize the parameters based on the simulated annealing algorithm and conduct experimental verification. The results of the optimal tool parameters are that the opening angle of the V-groove micro-textures is 79 degrees, the V-groove micro-texture spacing is 170 mu m, and the V-groove micro-texture width is 30 mu m, the distance from blade of the V-groove micro-textures is 90 mu m.
C1 [Yang, Shucai; Guo, Chaoyang; Ren, Wei] Harbin Univ Sci & Technol, Sch Mech & Power Engn, Harbin 150080, Peoples R China.
C3 Harbin University of Science & Technology
RP Yang, SC (corresponding author), Harbin Univ Sci & Technol, Sch Mech & Power Engn, Harbin 150080, Peoples R China.
EM yangshucai@hrbust.edu.cn
FU National Natural Science Foundation of China, China [51875144]
FX This work was funded by the National Natural Science Foundation of China
   (Micro-texture preparation of cemented carbide ball end milling tool and
   its dynamic evolution of milling behavior [51875144]), China.
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NR 18
TC 3
Z9 3
U1 3
U2 49
PU KOREAN SOC MECHANICAL ENGINEERS
PI SEOUL
PA KSTC NEW BLD. 7TH FLOOR, 635-4 YEOKSAM-DONG KANGNAM-KU, SEOUL 135-703,
   SOUTH KOREA
SN 1738-494X
EI 1976-3824
J9 J MECH SCI TECHNOL
JI J. Mech. Sci. Technol.
PD JUN
PY 2022
VL 36
IS 6
BP 2849
EP 2860
DI 10.1007/s12206-022-0517-8
PG 12
WC Engineering, Mechanical
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA 2G8CH
UT WOS:000813815300017
DA 2024-09-05
ER
PT C
AU Shivakumar, R
   Diddi, S
   Maitra, S
AF Shivakumar, R.
   Diddi, Sreelakshmi
   Maitra, Samita
GP IEEE
TI Utilization of Unisim Design Tool for Enhanced Learning and Assessment
   in Engineering Education
SO 2017 5TH IEEE INTERNATIONAL CONFERENCE ON MOOCS, INNOVATION AND
   TECHNOLOGY IN EDUCATION (MITE)
LA English
DT Proceedings Paper
CT 5th IEEE International Conference on MOOCs, Innovation and Technology in
   Education (MITE)
CY OCT 27-28, 2017
CL Bangalore, INDIA
DE Virtual Experiments; Assessment tool; Active learning; Student Centered
   Learning; Open Ended Research and Self-Evaluation
AB Virtual and augmented tools have revolutionized the changes in teaching styles and curriculum design of engineering education irrespective of the disciplines. These tools aims at improving their learning skills, bridge the gap between instructional laboratory experiments & open ended research experiments and initiate for student centred learning. Many initiatives for development of virtual labs from different universities across the world have proved that virtual reality can be one of the important tools in engineering education by bringing experience based learning in students. This article aims to project the applicability of virtual experiments as an important tool for learning and assessment of self-study component. Unisim Design tool is used to develop virtual experiments for core courses. These virtual experiments can be used as one of the self-assessment tools for all laboratory based courses in engineering education. A methodology for the assessment is proposed.
C1 [Shivakumar, R.; Diddi, Sreelakshmi; Maitra, Samita] BMS Coll Engn, Dept Chem Engn, Bengaluru 19, India.
C3 BMS College of Engineering
RP Shivakumar, R (corresponding author), BMS Coll Engn, Dept Chem Engn, Bengaluru 19, India.
RI R, Shivakumar/HLH-4693-2023; Diddi, Sreelakshmi/F-1370-2018; Maitra,
   Samita/ABA-8195-2020; R, Shivakumar/O-9688-2015
OI R, Shivakumar/0000-0002-1806-1878; Diddi,
   Sreelakshmi/0000-0002-5657-2564; R, Shivakumar/0000-0001-6729-4574;
   Maitra, Samita/0000-0003-0196-1762
FU TEQIP-II, MHRD, Govt. of India; BMS College of engineering
FX The authors wish to acknowledge the funding under TEQIP-II, MHRD, Govt.
   of India for procuring UNISIM Simulation Software. Thanks to Principal,
   BMS College of engineering for the support. Our gratitude to Honeywell
   Automations Ltd., for seeding the idea to develop virtual experiments
   for chemical engineering using Unisim
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NR 28
TC 2
Z9 2
U1 0
U2 3
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-5386-3189-8
PY 2017
BP 108
EP 114
DI 10.1109/MITE.2017.00025
PG 7
WC Computer Science, Interdisciplinary Applications; Education &
   Educational Research
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Education & Educational Research
GA BM0AA
UT WOS:000458537800021
DA 2024-09-05
ER
PT J
AU Gil, EL
AF Gil, Esther L.
TI Maximizing and assessing a one-shot information literacy session: A case
   study
SO JOURNAL OF BUSINESS & FINANCE LIBRARIANSHIP
LA English
DT Article
DE Active learning; assessment; bibliographic instruction; business
   education; information literacy
ID BUSINESS STUDENTS; LIBRARY; INSTRUCTION; COLLABORATION; FACULTY
AB This case study describes collaboration between an instructor and business librarian to maximize a one-shot information literacy session for a required marketing class for business majors. It includes an active-learning exercise where groups use three key business databases to conduct a company and industry analysis and an environmental scan for a company. The instructor then guides them in developing a strengths, weaknesses, opportunities, threats (SWOT) analysis. This session prepares students for the SWOT analysis they will have to submit for their actual projects. This article includes assessment of groups' in-class exercises as well as citation analysis of the assignments they turned in for their actual projects.
C1 [Gil, Esther L.] Univ Denver, 2150 E Evans Ave, Denver, CO 80208 USA.
C3 University of Denver
RP Gil, EL (corresponding author), Univ Denver, Univ Lib, 2150 E Evans Ave, Denver, CO 80208 USA.
EM egil@du.edu
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NR 21
TC 9
Z9 10
U1 0
U2 4
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0896-3568
EI 1547-0644
J9 J BUS FINANC LIBR
JI J. Bus. Financ. Libr.
PY 2017
VL 22
IS 2
BP 97
EP 110
DI 10.1080/08963568.2017.1285748
PG 14
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA FV5KO
UT WOS:000424620400002
DA 2024-09-05
ER
PT C
AU de Cerqueira, JAS
   de Almeida, PS
   Canedo, ED
   Alves, GD
   Giozza, WF
   de Mendonça, FLL
   de Sousa, RT
AF Siqueira de Cerqueira, Jose Antonio
   de Almeida, Paulo Santos
   Canedo, Edna Dias
   Alves, Gabriel de Oliveira
   Giozza, William Ferreira
   Lopes de Mendonca, Fabio Lucio
   de Sousa Jr, Rafael T.
BE Rocha, A
   Perez, BE
   Penalvo, FG
   Miras, MD
   Goncalves, R
TI Exploratory Overview on Breaking CAPTCHAs Using the Theory of the
   Consolidated Meta-Analytic Approach
SO 2020 15TH IBERIAN CONFERENCE ON INFORMATION SYSTEMS AND TECHNOLOGIES
   (CISTI'2020)
SE Iberian Conference on Information Systems and Technologies
LA English
DT Proceedings Paper
CT 15th Iberian Conference on Information Systems and Technologies (CISTI)
CY JUN 24-27, 2020
CL ELECTR NETWORK
DE CAPTCHA; CAPTCHA Breaking; Bibliographic Research; State-of-the-art;
   Artificial Intelligence
ID COCITATION
AB This study sought to provide an integrating model of the main contributions of the literature on CAPTCHAs with an impact in this field. With the expansion of internet access, there is an increasing need for a mechanism to protect websites from attacks, although there are situations where it is interesting to be able to automate some activities. This work consisted of identifying the most influential CAPTCHA-related academic works and trends in the field, which could serve as a metric on what approaches to take when developing new studies. Data such as main authors, current lines of research and more prolific research centers are arrived at using the Theory of the Consolidated Meta-analytic Approach. Inputting the keyword "captcha" in the Web of Science database, 539 records were found, from 2001 to 2020. The main classes retrieved are: (a) Captcha in Security Context (31.9%), (b) Usability in Captcha Design (28%), (c) Captcha Recognition by AI (21.8%), (d) Captcha Approaches and Novel Implementation Proposals (18.2%).
C1 [Siqueira de Cerqueira, Jose Antonio; Canedo, Edna Dias] Univ Brasilia UnB, Dept Comp Sci, POB 4466, BR-70910900 Brasilia, DF, Brazil.
   [de Almeida, Paulo Santos; Alves, Gabriel de Oliveira; Giozza, William Ferreira; Lopes de Mendonca, Fabio Lucio; de Sousa Jr, Rafael T.] Univ Brasilia UnB, Technol Coll, Elect Engn Dept ENE, Decis Technol Lab LATITUDE, Brasilia, DF, Brazil.
C3 Universidade de Brasilia; Universidade de Brasilia
RP de Cerqueira, JAS (corresponding author), Univ Brasilia UnB, Dept Comp Sci, POB 4466, BR-70910900 Brasilia, DF, Brazil.
EM jose.cerqueira@redes.unb.br; paulo.almeida@redes.unb.br;
   ednacanedo@unb.br; gabriel.alves@redes.unb.br; giozza@unb.br;
   fabio.mendonca@redes.unb.br; desousa@unb.br
RI Canedo, Edna Dias/D-5674-2015; de Sousa Júnior, Rafael/V-3293-2019;
   Giozza, William/AAH-4838-2019; Canedo, Edna Dias/AGR-0318-2022
OI Canedo, Edna Dias/0000-0002-2159-339X; de Sousa Júnior,
   Rafael/0000-0003-1101-3029; Giozza, William/0000-0002-3003-3458; Canedo,
   Edna Dias/0000-0002-2159-339X; Lopes de Mendonca, Fabio
   Lucio/0000-0001-7100-7304; Siqueira de Cerqueira, Jose
   Antonio/0000-0002-8143-1042
FU CAPES [23038.007604/2014-69 FORTE, 88887.144009/201700 PROBRAL]; CNPq
   [312180/2019-5 PQ-2, BRICS2017-591 LargEWiN, 465741/2014-2 INCT]; FAP-DF
   [0193.001366/2016 UIoT, 0193.001365/2016 SSDDC]; Ministry of the Economy
   [DIPLA 005/2016, ENAP 083/2016]; Institutional Security Office of the
   Presidency of the Republic [ABIN 002/2017]; Administrative Council for
   Economic Defense [CADE 08700.000047/2019-14]; General Attorney of the
   Union [AGU 697.935/2019]
FX The authors would like to thank the support of the Brazilian research,
   development and innovation agencies CAPES (grants 23038.007604/2014-69
   FORTE and 88887.144009/201700 PROBRAL), CNPq (grants 312180/2019-5 PQ-2,
   BRICS2017-591 LargEWiN, and 465741/2014-2 INCT in Cybersecurity) and
   FAP-DF (grants 0193.001366/2016 UIoT and 0193.001365/2016 SSDDC), as
   well as the cooperation projects with the Ministry of the Economy
   (grants DIPLA 005/2016 and ENAP 083/2016), the Institutional Security
   Office of the Presidency of the Republic (grant ABIN 002/2017), the
   Administrative Council for Economic Defense (grant CADE
   08700.000047/2019-14) and the General Attorney of the Union (grant AGU
   697.935/2019).
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NR 21
TC 0
Z9 0
U1 0
U2 0
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2166-0727
BN 978-989-54659-0-3
J9 IBER CONF INF SYST
PY 2020
PG 6
WC Computer Science, Information Systems
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BQ6NN
UT WOS:000612720600182
DA 2024-09-05
ER
PT J
AU Lyutov, A
   Uygun, Y
   Hütt, MT
AF Lyutov, Alexey
   Uygun, Yilmaz
   Huett, Marc-Thorsten
TI Machine learning misclassification of academic publications reveals
   non-trivial interdependencies of scientific disciplines
SO SCIENTOMETRICS
LA English
DT Article
DE Machine learning; Scientometrics; Maps of science; Classification
   algorithms; Interdisciplinary research
ID SCIENCE; PAPER; MAP
AB Exploring the production of knowledge with quantitative methods is the foundation of scientometrics. In an application of machine learning to scientometrics, we here consider the classification problem of the mapping of academic publications to the subcategories of a multidisciplinary journal-and hence to scientific disciplines-based on the information contained in the abstract. In contrast to standard classification tasks, we are not interested in maximizing the accuracy, but rather we ask, whether the failures of an automatic classification are systematic and contain information about the system under investigation. These failures can be represented as a 'misclassification network' inter-relating scientific disciplines. Here we show that this misclassification network (1) gives a markedly different pattern of interdependencies among scientific disciplines than common 'maps of science', (2) reveals a statistical association between misclassification and citation frequencies, and (3) allows disciplines to be classified as 'method lenders' and 'content explorers', based on their in-degree out-degree asymmetry. On a more general level, in a wide range of machine learning applications misclassification networks have the potential of extracting systemic information from the failed classifications, thus allowing to visualize and quantitatively assess those aspects of a complex system, which are not machine learnable.
C1 [Lyutov, Alexey; Uygun, Yilmaz] Jacobs Univ, Dept Math & Logist, Campus Ring 1, D-28759 Bremen, Germany.
   [Huett, Marc-Thorsten] Jacobs Univ, Dept Life Sci & Chem, Campus Ring 1, D-28759 Bremen, Germany.
C3 Jacobs University; Jacobs University
RP Lyutov, A (corresponding author), Jacobs Univ, Dept Math & Logist, Campus Ring 1, D-28759 Bremen, Germany.
EM a.lyutov@jacobs-university.de
OI /0000-0003-1271-9672; Lyutov, Alexey/0000-0002-0733-2783
FU Projekt DEAL
FX Open Access funding enabled and organized by Projekt DEAL.
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NR 44
TC 6
Z9 7
U1 2
U2 8
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD FEB
PY 2021
VL 126
IS 2
BP 1173
EP 1186
DI 10.1007/s11192-020-03789-8
PG 14
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 3U7TI
UT WOS:000841168800001
OA hybrid
DA 2024-09-05
ER
PT C
AU Mosans, G
   Kampars, J
AF Mosans, Guntis
   Kampars, Janis
GP IEEE
TI Big Data-based Solutions for Sustainable Digital Services: Evaluation of
   Research Methods
SO 2022 63RD INTERNATIONAL SCIENTIFIC CONFERENCE ON INFORMATION TECHNOLOGY
   AND MANAGEMENT SCIENCE OF RIGA TECHNICAL UNIVERSITY (ITMS)
LA English
DT Proceedings Paper
CT 63rd International Scientific Conference on
   Information-Technology-and-Management-Science of
   Riga-Technical-University (ITMS)
CY OCT 06-07, 2022
CL Riga, LATVIA
DE digital services; graphs; machine learning
AB Modern information technology infrastructure is highly complex, and its monitoring requires the integration of different monitoring tools and management systems. This is especially important for businesses that must be able to provide their digital services in crisis situations, such as the COVID-19 pandemic. This paper identifies research methods suitable to evaluate algorithms for integrated processing of graphs and vertex metrics in data streams. They are identified by means of a literature review. The research finding will serve as an input for further research activities on methods and technological solutions that enable the creation of resilient digital services that are able to adapt to changing contexts and crises, combining big data analysis, knowledge management, business data, and knowledge ecosystems.
C1 [Mosans, Guntis; Kampars, Janis] Riga Tech Univ, Riga, Latvia.
C3 Riga Technical University
RP Mosans, G (corresponding author), Riga Tech Univ, Riga, Latvia.
EM guntis.mosans@rtu.lv; janis.kampars@rtu.lv
RI Mosans, Guntis/LDG-2421-2024
OI Mosans, Guntis/0000-0001-9373-4000
FU Riga Technical University's Doctoral Grant program
FX This research/publication was supported by Riga Technical University's
   Doctoral Grant program.
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NR 28
TC 0
Z9 0
U1 0
U2 2
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 979-8-3503-9985-1
PY 2022
DI 10.1109/ITMS56974.2022.9937095
PG 6
WC Computer Science, Information Systems; Operations Research & Management
   Science
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Operations Research & Management Science
GA BU4DE
UT WOS:000895918800004
DA 2024-09-05
ER
PT C
AU Dubson, M
   Johnsen, E
   Lieberman, D
   Olsen, J
   Finkelstein, N
AF Dubson, Michael
   Johnsen, Ed
   Lieberman, David
   Olsen, Jack
   Finkelstein, Noah
BE Engelhardt, PV
   Churukian, AD
   Jones, DL
TI Apples vs. Oranges: Comparison of Student Performance in a MOOC vs. a
   Brick-and-Mortar Course
SO 2014 PHYSICS EDUCATION RESEARCH CONFERENCE
SE Physics Education Research Conference
LA English
DT Proceedings Paper
CT Physics Education Research (PER) Conference on Outpacing New
   Technologies with Novel Pedagogies - The Role of PER in the Transforming
   Landscape of Higher Education
CY JUL 30-31, 2014
CL Minneapolis, MN
DE MOOC; online learning; FMCE; PER; education research; exam performance
AB In the fall of 2013, we taught the calculus-based introductory physics course at the University of Colorado at Boulder and, at the same time we taught a MOOC version of the same course, through Coursera. Students in both courses received identical lectures, homework assignments, and timed exams. We present data on participation rates and exam performance for the two groups. We find that the MOOC is like a drug targeted at a very specific population. When it works, it works well, but it works for very few. This MOOC worked well for older, well-educated students, who already have a good understanding of Newtonian mechanics.
C1 [Dubson, Michael; Johnsen, Ed; Olsen, Jack; Finkelstein, Noah] Univ Colorado, Dept Phys, UCB 390, Boulder, CO 80309 USA.
   [Lieberman, David] CUNY Queensborough Community Coll, Dept Phys, Bayside, NY 11364 USA.
C3 University of Colorado System; University of Colorado Boulder; City
   University of New York (CUNY) System
RP Dubson, M (corresponding author), Univ Colorado, Dept Phys, UCB 390, Boulder, CO 80309 USA.
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   [Anonymous], 2013, RES PRACTICE ASSESSM, V8, P1
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NR 8
TC 0
Z9 1
U1 0
U2 4
PU AMER ASSOC PHYSICS TEACHERS
PI COLLEGE PARK
PA ONE PHYSICS ELLIPSE, COLLEGE PARK, MD 20740-3845 USA
SN 2377-2379
BN 978-1-931024-23-5
J9 PHYS EDUC RES CONF
PY 2014
BP 9
EP 12
DI 10.1119/perc.2014.plenary.001
PG 4
WC Education & Educational Research; Education, Scientific Disciplines;
   Physics, Multidisciplinary
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Education & Educational Research; Physics
GA BE7ED
UT WOS:000375145900001
DA 2024-09-05
ER
PT J
AU Solstad, SM
   Kleiven, GS
   Castonguay, LG
   Moltu, C
AF Solstad, Stig Magne
   Kleiven, Goril Solberg
   Castonguay, Louis Georges
   Moltu, Christian
TI Clinical dilemmas of routine outcome monitoring and clinical feedback: A
   qualitative study of patient experiences
SO PSYCHOTHERAPY RESEARCH
LA English
DT Article
DE psychotherapy; outcome research; qualitative research; ROM; routine
   outcome monitoring; clinical feedback; IPR; interpersonal process recall
ID MENTAL-HEALTH; PSYCHOTHERAPY; PERCEPTIONS; VIEWS
AB Purpose:Routine outcome monitoring (ROM) and clinical feedback systems (CFS) are becoming prevalent in mental health services, but there are several challenges to successful implementation. ROM/CFS seem to be helpful for some patients, but not for others. To investigate this, we explored patients' experiences with ROM/CFS as an interpersonal and psychotherapeutic process, in naturalistic settings.Method:We used video-assisted interpersonal process recall interviews to investigate the experiences of 12 patients using ROM/CFS in a Norwegian mental health outpatient clinic. Data were analyzed through systematic text condensation.Results:Our analysis resulted in three pairs of experiences with ROM/CFS: (1) Explicit vs. implicit use of information, (2) Directing focus towards or away from therapeutic topics, and (3) Giving and receiving feedback. These experiences could be helpful or hindering, depending on participants' needs and preferences. All participants needed to know that the CFS was used in a meaningful way. If not, it could be detrimental to the therapeutic process.Conclusion:In order to be helpful for patients, ROM/CFS should be used in a way that is flexible, meaningful to patients, and sensitive to individual needs and preferences. Future research should further explore this how-to aspect of ROM/CFS with different CFS and populations.
C1 [Solstad, Stig Magne; Kleiven, Goril Solberg; Moltu, Christian] Dist Gen Hosp Forde, Sunnfjord, Norway.
   [Castonguay, Louis Georges] Penn State Univ, Dept Psychol, University Pk, PA 16802 USA.
   [Moltu, Christian] Western Norway Univ Appl Sci, Dept Hlth & Caring Sci, Sunnfjord, Norway.
C3 Pennsylvania Commonwealth System of Higher Education (PCSHE);
   Pennsylvania State University; Pennsylvania State University -
   University Park; Western Norway University of Applied Sciences
RP Solstad, SM (corresponding author), Dist Gen Hosp Forde, POB 1000, N-6807 Forde, Norway.
EM stig.magne.solstad@helse-forde.no
FU Helse Vest [913032]
FX This work was supported by Helse Vest [grant number 913032].
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NR 45
TC 18
Z9 20
U1 0
U2 5
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1050-3307
EI 1468-4381
J9 PSYCHOTHER RES
JI Psychother. Res.
PD FEB 17
PY 2021
VL 31
IS 2
SI SI
BP 200
EP 210
DI 10.1080/10503307.2020.1788741
EA JUL 2020
PG 11
WC Psychology, Clinical
WE Social Science Citation Index (SSCI)
SC Psychology
GA QA4JE
UT WOS:000547012500001
PM 32635834
DA 2024-09-05
ER
PT J
AU Cornford, R
   Millard, J
   González-Suárez, M
   Freeman, R
   Johnson, TF
AF Cornford, Richard
   Millard, Joseph
   Gonzalez-Suarez, Manuela
   Freeman, Robin
   Johnson, Thomas Frederick
TI Automated synthesis of biodiversity knowledge requires better tools and
   standardised research output
SO ECOGRAPHY
LA English
DT Article
DE data extraction; ecology; literature synthesis; machine learning;
   population trends; text mining
AB As the impact of anthropogenic activity on the environment has grown, research into biodiversity change and associated threats has also accelerated. Synthesising this vast literature is important for understanding the drivers of biodiversity change and identifying those actions that will mitigate further ecological losses. However, keeping pace with an ever-increasing publication rate presents a substantial challenge to efficient syntheses, an issue which could be partly addressed by increasing levels of automation in the synthesis pipeline. Here, we evaluate the potential for automated tools to extract ecologically important information from the abstracts of articles compiled in the Living Planet Database. Specifically, we focused on extracting key information on taxonomy (studied species names), geographic location and estimated population trend, assessing the accuracy of automated versus manual information extraction, the potential for automated tools to introduce biases into syntheses, and evaluating if synthesising abstracts was enough to capture the key information from the full article. Taxonomic and geographic extraction tools performed reasonably well, although information on studied species was sometimes limited in the abstract (compared to the main text) preventing fast extraction. In contrast, extraction of trends was less successful, highlighting the challenges involved in automating information extraction from abstracts, such as deficiencies in the algorithms, linguistic complexity associated with ecological findings, and limited information when compared to the main text. In light of these results, we cautiously advocate for a wider use of automated taxonomic and geographic parsing tools for ecological synthesis. Additionally, to further the use of automated synthesis within ecology, we recommend a dual approach: development of improved computational tools to reduce biases; and enhanced protocols for abstracts (and associated metadata) to ensure key information is included in a format that facilitates machine-readability.
C1 [Cornford, Richard] Imperial Coll London, Dept Life Sci, London, England.
   [Cornford, Richard; Freeman, Robin] Zool Soc London, Inst Zool, London, England.
   [Cornford, Richard] Nat Hist Museum, Dept Life Sci, London, England.
   [Millard, Joseph] UCL, Dept Genet Evolut & Environm, London, England.
   [Millard, Joseph] Univ Oxford, Leverhulme Ctr Demog Sci, Oxford, England.
   [Gonzalez-Suarez, Manuela] Univ Reading, Sch Biol Sci, Reading, Berks, England.
   [Johnson, Thomas Frederick] Univ Sheffield, Dept Anim & Plant Sci, Sheffield, S Yorkshire, England.
C3 Imperial College London; Zoological Society of London; Natural History
   Museum London; University of London; University College London;
   University of Oxford; University of Reading; University of Sheffield
RP Cornford, R (corresponding author), Imperial Coll London, Dept Life Sci, London, England.; Cornford, R (corresponding author), Zool Soc London, Inst Zool, London, England.; Cornford, R (corresponding author), Nat Hist Museum, Dept Life Sci, London, England.
EM richard.cornford16@imperial.ac.uk
RI Freeman, Robin/JOZ-4393-2023; González-Suárez, Manuela/B-9740-2008
OI Freeman, Robin/0000-0002-0560-8942; González-Suárez,
   Manuela/0000-0001-5069-8900; Cornford, Richard/0000-0002-9963-3603;
   Millard, Joseph/0000-0002-3025-3565; Johnson, Thomas/0000-0002-6363-1825
FU NERC [NE/R012229/1]
FX RC was supported by the QMEE CDT, funded by NERC grant no. NE/R012229/1.
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NR 64
TC 2
Z9 3
U1 2
U2 14
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0906-7590
EI 1600-0587
J9 ECOGRAPHY
JI Ecography
PD MAR
PY 2022
VL 2022
IS 3
AR e06068
DI 10.1111/ecog.06068
EA FEB 2022
PG 9
WC Biodiversity Conservation; Ecology
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Biodiversity & Conservation; Environmental Sciences & Ecology
GA ZK0BM
UT WOS:000757297100001
OA Green Published, gold, Green Accepted
DA 2024-09-05
ER
PT J
AU Gurcan, F
   Cagiltay, NE
AF Gurcan, Fatih
   Cagiltay, Nergiz Ercil
TI Exploratory Analysis of Topic Interests and Their Evolution in
   Bioinformatics Research Using Semantic Text Mining and Probabilistic
   Topic Modeling
SO IEEE ACCESS
LA English
DT Article
DE Bioinformatics; Market research; Biology; Analytical models; Genomics;
   Proteins; Computational modeling; Bioinformatics corpus; probabilistic
   topic modeling; textual content analysis; scientometric analysis;
   bioinformatics topics and trends
ID TRENDS; FIELD; DYNAMICS; IMPACT; LDA
AB Bioinformatics, which has developed rapidly in recent years with the collaborative contributions of the fields of biology and informatics, provides a deeper perspective on the analysis and understanding of complex biological data. In this regard, bioinformatics has an interdisciplinary background and a rich literature in terms of domain-specific studies. Providing a holistic picture of bioinformatics research by analyzing the major topics and their trends and developmental stages is critical for an understanding of the field. From this perspective, this study aimed to analyze the last 50 years of bioinformatics studies (a total of 71,490 articles) by using an automated text-mining methodology based on probabilistic topic modeling to reveal the main topics, trends, and the evolution of the field. As a result, 24 major topics that reflect the focuses and trends of the field were identified. Based on the discovered topics and their temporal tendencies from 1970 until 2020, the developmental periods of the field were divided into seven phases, from the "newborn" to the "wisdom" stages. Moreover, the findings indicated a recent increase in the popularity of the topics "Statistical Estimation", "Data Analysis Tools", "Genomic Data", "Gene Expression", and "Prediction". The results of the study revealed that, in bioinformatics studies, interest in innovative computing and data analysis methods based on artificial intelligence and machine learning has gradually increased, thereby marking a significant improvement in contemporary analysis tools and techniques based on prediction.
C1 [Gurcan, Fatih] Karadeniz Tech Univ, Fac Engn, Dept Comp Engn, TR-61080 Trabzon, Turkey.
   [Cagiltay, Nergiz Ercil] Atilim Univ, Fac Engn, Dept Software Engn, TR-06830 Ankara, Turkey.
C3 Karadeniz Technical University; Atilim University
RP Gurcan, F (corresponding author), Karadeniz Tech Univ, Fac Engn, Dept Comp Engn, TR-61080 Trabzon, Turkey.
EM fgurcan@ktu.edu.tr
RI Cagiltay, Nergiz/O-3082-2019; GURCAN, Fatih/AAJ-7503-2021
OI Cagiltay, Nergiz/0000-0003-0875-9276; GURCAN, Fatih/0000-0001-9915-6686
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NR 44
TC 17
Z9 17
U1 5
U2 32
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2022
VL 10
BP 31480
EP 31493
DI 10.1109/ACCESS.2022.3160795
PG 14
WC Computer Science, Information Systems; Engineering, Electrical &
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WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA ZZ4FZ
UT WOS:000773228200001
OA gold
DA 2024-09-05
ER
PT J
AU Roda-Segarra, J
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AF Roda-Segarra, Jacobo
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TI History of Education Meets Digital Humanities: A Field-Specific Finding
   Aid to Review Past and Present Research
SO HISTORY OF EDUCATION
LA English
DT Article
DE Digital humanities/digital history; data sources; bibliographic data;
   artificial intelligence; Hecumen
ID AHR-EXCHANGE; MANIFESTO; NETWORKS
AB Research in the field of History of Education has experienced a remarkable increase in recent decades. Resulting publications are referenced in generalist databases that do not catalogue academic works according to the specific characteristics of History of Education. Seeking to give response to this bibliographic gap, we are developing a database catered for historians of education that aims to map out present, past, and future research. Conceived within the framework of Digital Humanities/Digital History, Hecumen is being designed, with the aid of Artificial Intelligence, as an open access finding aid that permits (1) conducting specific and multilevel complex engine searches, (2) having a panoramic view of publications; (3) mapping out relevant/missing areas of research, and, ultimately, (4) keeping up to date with the research produced by historians of education. This paper presents, contextualises, and problematises Hecumen - a digital tool that will facilitate and boost History of Education research.
C1 [Roda-Segarra, Jacobo; Rico, Andres Paya] Univ Valencia, Dept Comparat Educ & Educ Hist, Ave Blasco Ibanez 30, Valencia 46010, Spain.
   [Simon-Martin, Meritxell] Univ Lleida, Dept Pedag, Lleida, Spain.
   [Huerta, Jose Luis Hernandez] Univ Valladolid, Dept Philosophy, Valladolid, Spain.
C3 University of Valencia; Universitat de Lleida; Universidad de Valladolid
RP Roda-Segarra, J (corresponding author), Univ Valencia, Dept Comparat Educ & Educ Hist, Ave Blasco Ibanez 30, Valencia 46010, Spain.
EM jacobo.roda@uv.es
OI Roda-Segarra, Jacobo/0000-0002-4717-6295; Paya Rico,
   Andres/0000-0001-7646-4539; Simon-Martin, Meritxell/0000-0002-9486-3020
FU MCIN/AEI [PID2019-105328GB-I00]; ESF Investing in Your Future
   [PRE2020-093276]
FX This work was supported by the MCIN/AEI/10.13039/501100011033 and by
   "ESF Investing in Your Future" under Grant [PRE2020-093276];
   MCIN/AEI/10.13039/501100011033 under Grant [PID2019-105328GB-I00].
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   Roda-Segarra J., 2023, THESIS U VALENCIA
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   Simon-Martin M., 2023, CONECTANDO HIST EDUC, P231
   Somogyvári L, 2023, PAEDAGOG HIST, V59, P837, DOI 10.1080/00309230.2021.1962927
   Staring J, 2013, HIST EDUC REV, V42, P85, DOI 10.1108/08198691311317714
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   Zaagsma G, 2013, BMGN, V128, P3
NR 51
TC 1
Z9 1
U1 5
U2 5
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0046-760X
EI 1464-5130
J9 HIST EDUC
JI Hist. Educ.
PD SEP 2
PY 2024
VL 53
IS 5
BP 893
EP 913
DI 10.1080/0046760X.2024.2337896
EA APR 2024
PG 21
WC Education & Educational Research; History Of Social Sciences
WE Social Science Citation Index (SSCI)
SC Education & Educational Research; Social Sciences - Other Topics
GA A4N8L
UT WOS:001208161500001
DA 2024-09-05
ER
PT C
AU Brown, AO
   Watson, KA
   Liu, JC
   Orabi, II
   Rencis, JJ
   Chen, CC
   Akasheh, F
   Wood, JJ
   Jackson, KS
   Hackett, RK
   Sargent, ER
   Dunlap, B
   Wejmar, CA
   Crawford, RH
   Jensen, DD
AF Brown, Ashland O.
   Watson, Kyle A.
   Liu, Jiancheng
   Orabi, Ismail I.
   Rencis, Joseph J.
   Chen, Chuan-Chiang
   Akasheh, Firas
   Wood, John J.
   Jackson, Kathy Schmidt
   Hackett, Rachelle Kisst
   Sargent, Ella R.
   Dunlap, Brock
   Wejmar, Christopher Allen
   Crawford, Richard H.
   Jensen, Daniel D.
GP ASEE
TI Assessment of Finite Element Active Learning Modules: An Update in
   Research Findings
SO 2014 ASEE ANNUAL CONFERENCE
SE ASEE Annual Conference & Exposition
LA English
DT Proceedings Paper
CT ASEE Annual Conference
CY JUN 15-18, 2014
CL Indianapolis, IN
ID CURRICULUM
AB The landscape of contemporary engineering education is ever changing, adapting and evolving. As an example, finite element theory and application has often been included in graduate-level courses in engineering programs; however, current industry needs bachelor's-level engineering graduates with skills in applying this essential analysis and design technique. Engineering education is also changing to include more active learning. In response to the need to introduce undergrads to the finite element method as well as the need for engineering curricula to include more active learning, we have developed, implemented and assessed a suite of Active Learning Module (ALMs). The ALMs are designed to improve student learning of difficult engineering concepts while students gain essential knowledge of finite element analysis. We have used the Kolb Learning Cycle as a conceptual framework to guide our design of the ALMs.
   Originally developed using MSC Nastran, followed by development efforts in SolidWorks Simulation, ANSOFT, ANSYS, and other commercial FEA software packages, a team of researchers, with National Science Foundation support, have created over twenty-eight active learning modules. We will discuss the implementation of these learning modules which have been incorporated into undergraduate courses that cover topics such as machine design, mechanical vibrations, heat transfer, bioelectrical engineering, electromagnetic field analysis, structural fatigue analysis, computational fluid dynamics, rocket design, and chip formation during manufacturing, and large scale deformation in machining.
   This update on research findings includes statistical results for each module which compare performance on pre-and post-learning module quizzes to gauge change in student knowledge related to the difficult engineering concepts that each module addresses. Statistically significant student performance gains provide evidence of module effectiveness. In addition, we present statistical comparisons between different personality types (based on Myers-Briggs Type Indicator, MBTI, subgroups) and different learning styles (based on Felder-Solomon ILS subgroups) in regards to the average gains each group of students have made on quiz performance. Although exploratory, and generally based on small sample sizes at this point in our multi-year effort, the modules for which subgroup differences are found are being carefully reviewed in an attempt to determine whether modifications should be made to better ensure equitable impact of the modules across students from specific personality and / or learning styles subgroups (e.g., MBTI Intuitive versus Sensing; ILS Sequential versus Global).
C1 [Brown, Ashland O.] Univ Pacific, Mech Engn, Sch Engn & Comp Sci, Stockton, CA 95211 USA.
   [Brown, Ashland O.] Univ Pacific, Engn, Stockton, CA 95211 USA.
   [Brown, Ashland O.; Watson, Kyle A.; Hackett, Rachelle Kisst; Sargent, Ella R.] Univ Pacific, Stockton, CA 95211 USA.
   [Liu, Jiancheng] Univ Pacific, Mech Engn, Stockton, CA 95211 USA.
   [Orabi, Ismail I.] Univ New Haven, West Haven, CT USA.
   [Rencis, Joseph J.] Tennessee Technol Univ, Mech Engn, Cookeville, TN 38505 USA.
   [Chen, Chuan-Chiang] Tuskegee Univ, Tuskegee, AL 36088 USA.
   [Akasheh, Firas] Tuskegee Univ, Tuskegee, AL 36088 USA.
   [Wood, John J.; Jensen, Daniel D.] US Air Force Acad, Engn Mech, Colorado Springs, CO 80840 USA.
   [Jackson, Kathy Schmidt] Penn State Univ, Schreyer Inst Teaching Excellence, University Pk, PA 16802 USA.
   [Dunlap, Brock] Univ Texas Austin, Austin, TX 78712 USA.
   [Wejmar, Christopher Allen] Univ Pacific, Sch Engn & Comp Sci, Stockton, CA 95211 USA.
   [Crawford, Richard H.] Univ Texas Austin, Mech Engn, Austin, TX 78712 USA.
C3 University of the Pacific; University of the Pacific; University of the
   Pacific; University of the Pacific; University New Haven; Tennessee
   Technological University; Tuskegee University; Tuskegee University;
   United States Department of Defense; United States Air Force; United
   States Air Force Academy; Pennsylvania Commonwealth System of Higher
   Education (PCSHE); Pennsylvania State University; Pennsylvania State
   University - University Park; University of Texas System; University of
   Texas Austin; University of the Pacific; University of Texas System;
   University of Texas Austin
RP Brown, AO (corresponding author), Univ Pacific, Mech Engn, Sch Engn & Comp Sci, Stockton, CA 95211 USA.
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   Belytschko T, 1997, INT J ENG EDUC, V13, P457
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   Kaufman K., 2011, INT J ENG ED UNPUB
   Kolb D.A., 2014, Experiential Learning: Experience as a source of learning and development
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NR 31
TC 0
Z9 0
U1 0
U2 4
PU AMER SOC ENGINEERING EDUCATION
PI WASHINGTON
PA 1818 N STREET, NW SUITE 600, WASHINGTON, DC 20036 USA
SN 2153-5965
J9 ASEE ANNU CONF EXPO
PY 2014
PG 31
WC Education & Educational Research; Education, Scientific Disciplines;
   Engineering, Multidisciplinary
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Education & Educational Research; Engineering
GA BF6XT
UT WOS:000383779702062
DA 2024-09-05
ER
PT C
AU García-Sánchez, P
   Mora, AM
   Castillo, PA
   Pérez, IJ
AF Garcia-Sanchez, Pablo
   Mora, Antonio M.
   Castillo, Pedro A.
   Perez, Ignacio J.
BE HerreraViedma, E
   Shi, Y
   Berg, D
   Tien, J
   Cabrerizo, FJ
   Li, J
TI A bibliometric study of the research area of videogames using
   Dimensions.ai database
SO 7TH INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND QUANTITATIVE
   MANAGEMENT (ITQM 2019): INFORMATION TECHNOLOGY AND QUANTITATIVE
   MANAGEMENT BASED ON ARTIFICIAL INTELLIGENCE
SE Procedia Computer Science
LA English
DT Proceedings Paper
CT 7th International Conference on Information Technology and Quantitative
   Management (ITQM) - Information Technology and Quantitative Management
   Based on Artificial Intelligence
CY NOV 03-06, 2019
CL Granada, SPAIN
DE videogames; Dimensions.ai; bibliometrics; scientometrics
ID GAMES
AB Videogames are a very interesting area of research for fields as diverse as computer science, health, psychology or even social sciences. Every year a growing number of articles are published in different topics inside this field, so it is very convenient to study the different bibliometric data in order to consolidate the research efforts.
   Thus, the aim of this work is to conduct a study on the distribution of articles related to videogames in the different fields of research, as well as to measure their interest over time, to identify the sources, countries and authors with the highest scientific production. In order to carry out this analysis, the information system Dimensions.ai has been considered, since it covers a large number of documents and allows for easy downloading and analysis of datasets.
   According to the study, three countries are the most prolific in this area: USA, Canada and UK. The obtained results also indicate that the fields with the highest number of publications are Information and Computer Sciences, Medical and Health Sciences, and Psychology and Cognitive Sciences, in this order. With regard to the impact of the publications, differences between the number of citations, and the number of Altmetric Attention Score, have been found. (C) 2020 The Authors. Published by Elsevier B.V.
C1 [Garcia-Sanchez, Pablo; Perez, Ignacio J.] Univ Cadiz, Dept Comp Sci & Engn, Escuela Super Ingn, Avda Univ 10, Cadiz 11519, Spain.
   [Mora, Antonio M.] Univ Granada, Dept Signal Theory Telemat & Commun, ETSIIT CITIC, Calle Periodista Daniel Saucedo Aranda S-N, Granada, Spain.
   [Castillo, Pedro A.] Univ Granada, Dept Comp Sci & Comp Architecture, ETSIIT CITIC, Calle Periodista Daniel Saucedo Aranda S-N, Granada, Spain.
C3 Universidad de Cadiz; University of Granada; University of Granada
RP García-Sánchez, P (corresponding author), Univ Cadiz, Dept Comp Sci & Engn, Escuela Super Ingn, Avda Univ 10, Cadiz 11519, Spain.
EM pablo.garciasanchez@uca.es
RI Castillo-Valdivieso, Pedro A./KBQ-6381-2024; Perez, Ignacio
   javier/M-2437-2015; García, Antonio M. M. Mora/M-1127-2014;
   García-Sánchez, Pablo/G-2166-2010
OI Castillo-Valdivieso, Pedro A./0000-0002-5258-0620; Perez, Ignacio
   javier/0000-0003-4253-8629; García-Sánchez, Pablo/0000-0003-4644-2894
FU Spanish Ministry of Economy and Competitiveness [TIN2016-75850-R,
   11E2015-68752, TIN2017-85727-C4-2-P]; Program of Promotion and
   Development of Research Activity of the University of Cadiz (Programa de
   Fomento e Impulso de la actividad Investigadora de la Universidad de
   Cadiz); Junta de Andalucia [B-TIC-402-UGR18]; FEDER [B-TIC-402-UGR18]
FX This contribution has been made possible thanks to Dimensions.ai
   database. Also, the authors would like to acknowledge FEDER funds
   provided by the Spanish Ministry of Economy and Competitiveness under
   grants TIN2016-75850-R, 11E2015-68752, TIN2017-85727-C4-2-P and Program
   of Promotion and Development of Research Activity of the University of
   Cadiz (Programa de Fomento e Impulso de la actividad Investigadora de la
   Universidad de Cadiz); and B-TIC-402-UGR18 (FEDER and Junta de
   Andalucia).
CR [Anonymous], 2002, Education and Health
   Carbonell X, 2009, J MED LIBR ASSOC, V97, P102, DOI 10.3163/1536-5050.97.2.006
   Ciftci S., 2017, J ED TRAINING STUDIE, V6
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NR 15
TC 10
Z9 10
U1 2
U2 15
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA SARA BURGERHARTSTRAAT 25, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 1877-0509
J9 PROCEDIA COMPUT SCI
PY 2019
VL 162
BP 737
EP 744
DI 10.1016/j.procs.2019.12.045
PG 8
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BO4FC
UT WOS:000514081500092
OA gold, Green Submitted, Green Published
DA 2024-09-05
ER
PT J
AU Ding, X
   Wang, BZ
   He, GQ
AF Ding, Xiao
   Wang, Bing-Zhong
   He, Guo-Qiang
TI Research on a Millimeter-Wave Phased Array With Wide-Angle Scanning
   Performance
SO IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION
LA English
DT Article
DE Genetic algorithms; pattern reconfigurable antenna; phased array;
   wide-angle scanning
ID ANTENNA
AB In order to extend the scanning range of a phased array and maintain the scanning gain flatness, a novel millimeter-wave phased array with four linearly-arranged pattern reconfigurable elements is presented in this communication. The phased array has been designed, fabricated, measured and analyzed. The active patterns of each reconfigurable element are measured at different reconfigurable modes and the pattern scanning performance of the phased array is synthesized by using these active patterns. Furthermore, a genetic algorithm is used to lower the level of side lobes of the proposed phased array. The results show that the phased array can scan its main lobe from -75 degrees to + 75 degrees in the elevation plane with a gain fluctuation less than 3 dB.
C1 [Ding, Xiao; Wang, Bing-Zhong; He, Guo-Qiang] Univ Elect Sci & Technol China, Inst Appl Phys, Chengdu 610054, Peoples R China.
C3 University of Electronic Science & Technology of China
RP Ding, X (corresponding author), Univ Elect Sci & Technol China, Inst Appl Phys, Chengdu 610054, Peoples R China.
EM xiaoding.antenna@gmail.com; bzwang@uestc.edu.cn; guoqianghe22@gmail.com
RI Wang, Bing/IAQ-0291-2023; ding, xiao/KAM-4458-2024; He,
   Guoqiang/AAV-7800-2021; Wang, Bing/IAP-6059-2023
OI ding, xiao/0000-0003-0804-6444; Wang, Bing-Zhong/0000-0003-0679-8925
FU Doctoral Program of Higher Education of China [20100185110021,
   20120185130001]; National Natural Science Foundation of China
   [61071031];  [ITR1113]
FX This work was supported in part by the Research Fund for the Doctoral
   Program of Higher Education of China (No. 20100185110021, No.
   20120185130001), in part by the National Natural Science Foundation of
   China (No. 61071031), and in part by the Project ITR1113.
CR Bai YY, 2011, IEEE T ANTENN PROPAG, V59, P4071, DOI 10.1109/TAP.2011.2164176
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   Toshev AG, 2008, IEEE T ANTENN PROPAG, V56, P3330, DOI 10.1109/TAP.2008.929527
NR 11
TC 116
Z9 126
U1 2
U2 56
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0018-926X
EI 1558-2221
J9 IEEE T ANTENN PROPAG
JI IEEE Trans. Antennas Propag.
PD OCT
PY 2013
VL 61
IS 10
BP 5319
EP 5324
DI 10.1109/TAP.2013.2275247
PG 7
WC Engineering, Electrical & Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Telecommunications
GA 250DR
UT WOS:000326831000051
DA 2024-09-05
ER
PT J
AU Hasumi, T
   Chiu, MS
AF Hasumi, Toshiyuki
   Chiu, Mei-Shiu
TI Online mathematics education as bio-eco-techno process: bibliometric
   analysis using co-authorship and bibliographic coupling
SO SCIENTOMETRICS
LA English
DT Article
DE Bibliometric analysis; Ecological technology theories; Mathematics
   education; Online learning; Flipped learning
ID E-LEARNING SYSTEM; CLASSROOM; PATTERNS; DESIGN; PERFORMANCE; PRINCIPLES;
   COCITATION; CITATION; SCIENCE; TRENDS
AB Under the COVID-19 pandemic, mathematics education has moved completely online. To tackle this new norm based on bio-eco-techno theories, this study aims to provide educators an overview of the research landscape for envisioning educational practices through bibliometric analysis of 319 articles and reviews published in peer-reviewed journals from 1993 to 2020. Country and institutional co-authorship depicts the social network structure of the field to identify top productive contributors. Bibliographic coupling of publications forms the conceptual structure, revealing research themes. Together, the results are mapped according to the bio-eco-techno perspective. The bioecological system highlights student achievement as the central concerns. The microsystem emphasizes techno-subsystems for supporting flipped learning. The exosystem and mesosystem require institution support for teacher pedagogical design, digital competencies, and collaboration. The macrosystem raises the issue of distribution or centralization in the strengths of online mathematics education and calls for greater cross-national boundary digital use and collaboration. The chronosystem asks: Does Covid-19 force the popularity of blended or flipped learning into online education? Based on the bio-eco-techno perspective, further recommendations are provided.
C1 [Hasumi, Toshiyuki] Ming Chuan Univ, Int Coll, 250 Zhong Shan N Rd,Sec 5, Taipei 111, Taiwan.
   [Chiu, Mei-Shiu] Natl Chengchi Univ, Dept Educ, 64 Zhinan Rd,Sec 2, Taipei 11605, Taiwan.
C3 Ming Chuan University; National Chengchi University
RP Hasumi, T (corresponding author), Ming Chuan Univ, Int Coll, 250 Zhong Shan N Rd,Sec 5, Taipei 111, Taiwan.
EM 107152521@g.nccu.edu.tw; chium@nccu.edu.tw
RI Hasumi, Toshiyuki/IQV-0095-2023; Chiu, Mei-Shiu/Q-1116-2019
OI Hasumi, Toshiyuki/0000-0003-4259-5607; Chiu,
   Mei-Shiu/0000-0002-2929-5151
FU Ministry of Science and Technology, Taiwan [MOST 109-2629H-004-002]
FX This work was supported by the Ministry of Science and Technology,
   Taiwan (MOST 109-2629H-004-002). An early part of this study was orally
   presented, without any written publication, as "Hasumi, T., & Chiu,
   M.-S. (2021, June 9). Online mathematics education: A bibliometric
   analysis [Conference session]. Global Conference on Education and
   Research, virtually hosted by University of South Florida, FL, United
   States. https://glocer.org/schedule/2/".
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NR 99
TC 6
Z9 6
U1 5
U2 38
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD AUG
PY 2022
VL 127
IS 8
BP 4631
EP 4654
DI 10.1007/s11192-022-04441-3
EA JUL 2022
PG 24
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 3R1UC
UT WOS:000820549600005
PM 35813407
OA Bronze, Green Published
DA 2024-09-05
ER
PT C
AU Wu, KY
   Hu, SS
AF Wu, Kunya
   Hu, Shuaishuai
BE Liu, Z
   Wang, L
TI Research on the Upgrade of Evaluation System Design of Vegetables Safety
   of Shenzhen
SO 2017 INTERNATIONAL CONFERENCE ON FRONTIERS IN EDUCATIONAL TECHNOLOGIES
   AND MANAGEMENT SCIENCES (FETMS 2017)
LA English
DT Proceedings Paper
CT International Conference on Frontiers in Educational Technologies and
   Management Sciences (FETMS)
CY OCT 07-08, 2017
CL Nanjing, PEOPLES R CHINA
DE Risk evaluation; Hierarchy analysis; Cluster analysis; Linear regression
   model
AB Food safety issues are national security issues. Any food safety event they not only brings people great harm, but also hurts the credibility of the government. At present, many kinds of fresh vegetables are being imported into consumer market of Shenzhen via many channels. Different habitats and production management patterns pose a new challenge to the traditional vegetable safety supervision. This paper improves the traditional method of vegetable safety supervision through modeling and analysis of the factors directly related to vegetable safety.
C1 [Wu, Kunya; Hu, Shuaishuai] BOHAI Univ, Coll Math & Phys, Jinzhou 121000, Peoples R China.
C3 Bohai University
RP Wu, KY (corresponding author), BOHAI Univ, Coll Math & Phys, Jinzhou 121000, Peoples R China.
CR Cao Na, 2016, SCI TECHNOLOGY FOOD, V37
   Wang Huimin, 2011, ISSUES AGR EC
   Zhou JH, 2015, J INTEGR AGR, V14, P2189, DOI 10.1016/S2095-3119(15)61115-7
   Zhu Feng, 2015, CHINESE J HLTH LAB T, V25, P274
NR 4
TC 0
Z9 0
U1 0
U2 1
PU FRANCIS ACAD PRESS
PI LONDON
PA 35 IVOR PL, LOWER GROUND, LONDON, NW1 6EA, ENGLAND
BN 978-1-912407-67-5
PY 2017
BP 422
EP 426
PG 5
WC Education & Educational Research; Management
WE Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Education & Educational Research; Business & Economics
GA BL5HH
UT WOS:000451521400098
DA 2024-09-05
ER
PT J
AU Maphosa, M
   Doorsamy, W
   Paul, BS
AF Maphosa, Mfowabo
   Doorsamy, Wesley
   Paul, Babu Sena
TI Factors Influencing Students' Choice of and Success in STEM: A
   Bibliometric Analysis and Topic Modeling Approach
SO IEEE TRANSACTIONS ON EDUCATION
LA English
DT Article
DE STEM; Engineering profession; Education; Analytical models; Citation
   analysis; Data visualization; Systematics; Choice of qualifications;
   social cognitive career theory (SCCT); science; technology; engineering;
   and mathematics (STEM) students; student retention
ID ENGINEERING STUDENTS; COLLEGE MAJOR; SELF-EFFICACY; SCIENCE; CAREER;
   MATHEMATICS; IMPACT; PREDICTORS; INDICATORS; KNOWLEDGE
AB Contribution: This article lends empirical evidence to this research area of factors influencing students' choice of and success in science, technology, engineering, and mathematics (STEM). Background: Understanding these factors is crucial as it informs recruitment and support interventions provided to students and constitutes a premise to improving graduation rates. The social cognitive career theory (SCCT) was used as a theoretical framework to provide insight regarding factors influencing students' choice of qualifications. Research Questions: What is the state of research on the factors influencing students' choice of and success in STEM programmes? Which of these factors have interested most researchers? What research themes are covered in articles investigating these factors? Methodology: This study followed the general bibliometric analysis workflow--study design, data collection, data analysis, data visualization, and interpretation. Data collection followed the preferred reporting items for systematic review and metaanalysis (PRISMA) guidelines. From an initial set of 408 articles, 179 related to the theme and were published in the Web of Science between 2004 and 2020. These articles were analyzed using the standard bibliometric metrics. Findings: Findings indicate that this research field is still growing. Thirty-two factors were identified and rated based using an objective assessment criterion. In addition, a classification of the factors is presented based on the SCCT. This study provides a theoretical reference for improving success rates for STEM qualifications and better understanding the theme. The study proposes a research agenda of what future research in the field should focus on, based on current gaps.
C1 [Maphosa, Mfowabo; Doorsamy, Wesley; Paul, Babu Sena] Univ Johannesburg, Inst Intelligent Syst, ZA-2193 Johannesburg, South Africa.
C3 University of Johannesburg
RP Maphosa, M (corresponding author), Univ Johannesburg, Inst Intelligent Syst, ZA-2193 Johannesburg, South Africa.
EM 201312940@student.uj.ac.za; wdoorsamy@uj.ac.za; bspaul@uj.ac.za
RI Zandonade, Viviane/JKI-1817-2023
OI Doorsamy, Wesley/0000-0001-9043-9882; Maphosa,
   Mfowabo/0000-0003-3702-6821
FU U.S. Department of Commerce [BS123456]
FX This work was supported in part by the U.S. Department of Commerce under
   Grant BS123456.
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NR 63
TC 8
Z9 8
U1 5
U2 30
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0018-9359
EI 1557-9638
J9 IEEE T EDUC
JI IEEE Trans. Educ.
PD NOV
PY 2022
VL 65
IS 4
BP 657
EP 669
DI 10.1109/TE.2022.3160935
EA MAR 2022
PG 13
WC Education, Scientific Disciplines; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Education & Educational Research; Engineering
GA 5T5CV
UT WOS:000778627200001
DA 2024-09-05
ER
PT C
AU Zhang, YF
   Wang, Y
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AF Zhang Yafei
   Wang Yong
   Wen Jingxuan
BE Kuek, M
   Cheng, H
   Zhao, R
TI Construction of Comprehensive Evaluation Model of Credit Rating of Small
   and Medium Enterprises and Empirical Research
SO PROCEEDINGS OF 2013 INTERNATIONAL CONFERENCE - WTO & FINANCIAL
   ENGINEERING
LA English
DT Proceedings Paper
CT International Conference on WTO and Financial Engineering
CY SEP 15-16, 2013
CL Hangzhou, PEOPLES R CHINA
DE Big data; Small and medium enterprises; Credit rating; RESSET database;
   Principal component analysis; Entropy method; Panel exponential
   smoothing
AB From the perspective of analysis of big data, based on RESSET database of financial research, authors of this article choose quarterly financial index data of 60 companies in 20 quarters on small and medium board, combining with the selected non financial indicators and use principal component analysis, entropy method, panel exponential smoothing and the analytic hierarchy process (AHP) to construct the comprehensive evaluation model. The survey and empirical evidence have demonstrated that credit rating system designed in this paper can preferably reflect and predict the enterprise's credit rating, and the distribution of credit rating of sample enterprises basically agrees with actual situation in morphology and grade center.
C1 [Zhang Yafei; Wang Yong; Wen Jingxuan] Nanjing Univ Finance & Econ, Sch Finance, Nanjing 210036, Jiangsu, Peoples R China.
C3 Nanjing University of Finance & Economics
CR Bian Ning, 2008, THESIS WUHAN U TECHN
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   Chi Guotai, 2012, FINANCIAL PROBLEM, P63
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NR 10
TC 0
Z9 0
U1 0
U2 9
PU ST PLUM-BLOSSOM PRESS PTY LTD
PI HAWTHORN EAST
PA STE 4, LEVEL 3, 695 BURKE RD, HAWTHORN EAST, VC 3123, AUSTRALIA
BN 978-0-9874593-5-0
PY 2013
BP 39
EP 47
PG 9
WC Business; Business, Finance; Economics; Management
WE Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Business & Economics
GA BB2XU
UT WOS:000342522500007
DA 2024-09-05
ER
PT J
AU Shi, XQ
   Long, W
   Li, YY
   Deng, DS
   Wei, YL
AF Shi, Xiaoqiu
   Long, Wei
   Li, Yanyan
   Deng, Dingshan
   Wei, Yonglai
TI Research on the performance of multi-population genetic algorithms with
   different complex network structures
SO SOFT COMPUTING
LA English
DT Article
DE Complex network; Network structure; Multi-population; Genetic algorithm;
   Flexible job shop scheduling problem
ID EVOLUTIONARY ALGORITHMS; OPTIMIZATION; COOPERATION; MODELS
AB Genetic algorithm is a frequently used evolutionary algorithm that cannot avoid premature convergence. Multi-population is usually used to overcome this disadvantage, obtaining multi-population genetic algorithm (MGA). If sub-populations and communications among them are considered as nodes and edges, respectively, an MGA can be represented as a complex network. After reviewing previous researches, we find that the network structures used to design MGAs are limited and some parameters (SPS, sub-population size, and SPN, sub-population number) under a certain total individual number (TIN) are always ignored. Using seven network structures (BAnet, BDnet, CTnet, ERnet, HAnet, LCnet, and SWnet) to design MGAs that are used to solve some flexible job shop scheduling problems, how the network structures and parameters affect the performances of MGAs is addressed. The simulation results indicate that: (i) the MGA with ERnet rather than the famous BAnet often performs well although their performances are problem-dependent; (ii) the Hamming distance index proposed here can properly capture the phenomenon that the smaller the average path length, the higher the propagation rate; and (iii) under a certain TIN, their performances first increase and then decrease gradually as SPN increases, and their performances first increase rapidly and then remain almost unchanged as SPS increases.
C1 [Shi, Xiaoqiu] Southwest Univ Sci & Technol, Sch Mfg Sci & Engn, Mianyang 621000, Sichuan, Peoples R China.
   [Long, Wei; Li, Yanyan; Deng, Dingshan; Wei, Yonglai] Sichuan Univ, Sch Mfg Sci & Engn, Chengdu 610000, Peoples R China.
C3 Southwest University of Science & Technology - China; Sichuan University
RP Shi, XQ (corresponding author), Southwest Univ Sci & Technol, Sch Mfg Sci & Engn, Mianyang 621000, Sichuan, Peoples R China.
EM shixiaoqiu_scu@163.com
RI 邓, 丁山/ABF-7645-2020; SHI, Xingqiang/D-8625-2011
OI 邓, 丁山/0000-0001-7553-8655; SHI, Xingqiang/0000-0003-2029-1506
FU National Green Manufacturing System Plan [[2017]327]
FX This research was partially supported by the National Green
   Manufacturing System Plan ([2017]327).
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NR 54
TC 16
Z9 18
U1 1
U2 35
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1432-7643
EI 1433-7479
J9 SOFT COMPUT
JI Soft Comput.
PD SEP
PY 2020
VL 24
IS 17
BP 13441
EP 13459
DI 10.1007/s00500-020-04759-1
EA FEB 2020
PG 19
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA MT2NR
UT WOS:000516032500002
DA 2024-09-05
ER
PT J
AU Yaghtin, M
   Sotudeh, H
   Nikseresht, A
   Mirzabeigi, M
AF Yaghtin, Maryam
   Sotudeh, Hajar
   Nikseresht, Alireza
   Mirzabeigi, Mahdieh
TI Modeling the co-citation dependence on semantic layers of co-cited
   documents
SO ONLINE INFORMATION REVIEW
LA English
DT Article
DE Co-citation; Co-opinionatedness; MeSH; Content-based citation analysis;
   Natural language processing; Citation proximity index; Semantic
   similarity; Syntactic similarity
ID SCIENTIFIC LITERATURE; CITATION; SIMILARITY
AB Purpose - Co-citation frequency, defined as the number of documents co-citing two articles, is considered as a quantitative, and thus, an efficient proxy of subject relatedness or prestige of the co-cited articles.
   Despite its quantitative nature, it is found effective in retrieving and evaluating documents, signifying its linkage with the related documents' contents. To better understand the dynamism of the citation network, the present study aims to investigate various content features giving rise to the measure. Design/methodology/approach - The present study examined the interaction of different co-citation features in explaining the co-citation frequency. The features include the co-cited works' similarities in their full-texts, Medical Subject Headings (MeSH) terms, co-citation proximity, opinions and co-citances. A test collection is built using the CITREC dataset. The data were analyzed using natural language processing (NLP) and opinion mining techniques. A linear model was developed to regress the objective and subjective contentbased co-citation measures against the natural log of the co-citation frequency.
   Findings - The dimensions of co-citation similarity, either subjective or objective, play significant roles in predicting co-citation frequency. The model can predict about half of the co-citation variance. The interaction of co-opinionatedness and non-co-opinionatedness is the strongest factor in the model.
   Originality/value - It is the first study in revealing that both the objective and subjective similarities could significantly predict the co-citation frequency. The findings re-confirm the citation analysis assumption claiming the connection between the cognitive layers of cited documents and citation measures in general and the co-citation frequency in particular.
C1 [Yaghtin, Maryam; Sotudeh, Hajar; Nikseresht, Alireza; Mirzabeigi, Mahdieh] Shiraz Univ, Sch Educ & Psychol, Dept Knowledge & Informat Sci, Shiraz, Iran.
C3 Shiraz University
RP Sotudeh, H (corresponding author), Shiraz Univ, Sch Educ & Psychol, Dept Knowledge & Informat Sci, Shiraz, Iran.
EM sotudeh@shirazu.ac.ir
RI Nikseresht, Alireza/GLN-3972-2022; Yaghtin, Maryam/ABE-6954-2021;
   Sotudeh, Hajar/D-5718-2016; Mirzabeigi, mahdieh/W-6831-2018
OI Nikseresht, Alireza/0000-0002-4516-0409; Yaghtin,
   Maryam/0000-0001-5806-3942; Sotudeh, Hajar/0000-0002-7949-7165;
   Mirzabeigi, mahdieh/0000-0002-3256-3153
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NR 79
TC 2
Z9 2
U1 5
U2 42
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 1468-4527
EI 1468-4535
J9 ONLINE INFORM REV
JI Online Inf. Rev.
PD JAN 25
PY 2022
VL 46
IS 1
BP 59
EP 78
DI 10.1108/OIR-04-2020-0126
EA MAY 2021
PG 20
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA YT5WW
UT WOS:000649782500001
DA 2024-09-05
ER
PT S
AU Vaaler, PM
   Aguilera, RV
   Flores, R
AF Vaaler, Paul M.
   Aguilera, Ruth V.
   Flores, Ricardo
BE Ketchen, DJ
   Bergh, DD
TI NEW METHODS FOR <i>EX POST</i> EVALUATION OF REGIONAL GROUPING SCHEMES
   IN INTERNATIONAL BUSINESS RESEARCH: A SIMULATED ANNEALING APPROACH
SO RESEARCH METHODOLOGY IN STRATEGY AND MANAGEMENT, VOL 4
SE Research Methodology in Strategy and Management
LA English
DT Article; Book Chapter
ID MULTINATIONAL-ENTERPRISES; NATIONAL CULTURE; OPTIMIZATION; GLOBE;
   GLOBALIZATION; CONSEQUENCES; PERFORMANCE; INVESTMENT; COUNTRIES;
   STRATEGY
AB International business research has long acknowledged the importance of regional factors for foreign direct investment (FDI) by multinational corporations (MNCs). However, significant differences when defining these regions obscure the analysis about how and why regions matter. In response, we develop and empirically document support for a framework to evaluate alternative regional grouping schemes. We demonstrate application of this evaluative framework using data on the global location decisions by US-based MNCs from 1980 to 2000 and two alternative regional grouping schemes. We conclude with discussion of implications for future academic research related to understanding the impact of country groupings on MNC FDI decisions.
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RP Vaaler, PM (corresponding author), Coll Business, Dept Business Adm, Champaign, IL USA.
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   Paul/H-9703-2017
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NR 70
TC 2
Z9 2
U1 0
U2 1
PU EMERALD GROUP PUBLISHING LIMITED
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY, W YORKSHIRE BD16 1WA, ENGLAND
SN 1479-8387
BN 978-0-7623-1404-1
J9 RES METHOD STRAT MAN
PY 2007
VL 4
BP 161
EP 190
DI 10.1016/S1479-8387(07)04007-6
PG 30
WC Management
WE Book Citation Index – Social Sciences & Humanities (BKCI-SSH)
SC Business & Economics
GA BLR18
UT WOS:000270845100008
DA 2024-09-05
ER
PT J
AU Schwenke, T
   Holzkämper, E
AF Schwenke, Theresa
   Holzkamper, Eike
TI Social (-Ecological) Network Analysis in Environmental Governance:
   Central Publications, Important Concepts, and Areas of Application
SO HUMAN ECOLOGY REVIEW
LA English
DT Article
DE bibliometric network analysis; environmental management; Latent
   Dirichlet Allocation; social network analysis; topic detection
ID STAKEHOLDER ANALYSIS; WATER GOVERNANCE; CLIMATE-CHANGE; INSIGHTS;
   COLLABORATION; MANAGEMENT; COASTAL; POLICY; FOREST; TOOL
AB Social and social-ecological network analysis (S(E)NA) have recently emerged as new methods in the environmental governance (EG) literature. By investigating networks of connections between actors, S(E)NA advances the understanding ofwho is involved in EG and how. We provide an overview of the EG literature applying S(E)NA and map (1) the citation network emerging from cross-references and (2) the similarity network emerging from word similarities between publications. We show that S(E)NA application in EG is in the process of developing into a field of research where publications frequently cite each other. We identify 20 publications which occupy positions as sources, storers, or bridges of knowledge in the citation network. While we see S(E)NA applied in diverse resource contexts, these are mainly discussed on the local spatial level, with a focus on "policy" or "collaboration." We discover that "power structures" and "the production of knowledge" are themes influencing the whole field.
C1 [Schwenke, Theresa; Holzkamper, Eike] Leibniz Ctr Trop Marine Res, Social Ecol Syst Anal Working Grp, Dept Social Sci, Bremen, Germany.
C3 Leibniz Zentrum fur Marine Tropenforschung (ZMT)
RP Schwenke, T (corresponding author), Leibniz Ctr Trop Marine Res, Social Ecol Syst Anal Working Grp, Dept Social Sci, Bremen, Germany.
EM theresa.schwenke@leibniz-zmt.de
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NR 73
TC 2
Z9 2
U1 8
U2 30
PU ANU PRESS
PI ACTON
PA RG MENZIES LIBRARY, BLDG 2, AUSTRALIAN NATL UNIV, ACTON, ACT 2601 AP,
   AUSTRALIA
SN 1074-4827
EI 2204-0919
J9 HUM ECOL REV
JI Hum. Ecol. Rev.
PY 2020
VL 26
IS 2
BP 103
EP 145
PG 43
WC Environmental Studies; Sociology
WE Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology; Sociology
GA XN0CO
UT WOS:000729183300006
DA 2024-09-05
ER
PT J
AU Van Eck, NJ
   Waltman, L
AF Van Eck, Nees Jan
   Waltman, Ludo
TI Bibliometric mapping of the computational intelligence field
SO INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED
   SYSTEMS
LA English
DT Article
DE bibliometrics; bibliometric mapping; computational intelligence; neural
   networks; fuzzy systems; evolutionary computation
ID DISCIPLINE; JACKKNIFE; SCIENCE; MAPS
AB In this paper, a bibliometric study of the computational intelligence field is presented. Bibliometric maps showing the associations between the main concepts in the field are provided for the periods 1996-2000 and 2001-2005. Both the current structure of the field and the evolution of the field over the last decade are analyzed. In addition, a number of emerging areas in the fild are identified. It turns out that the computational intelligence can best be seen as a field that is structured around four important types of problems, namely control problems, classification problems, regression problemsm and optimization problems. Within the computational intelligence field, the neural networks and fuzzy systems sudfields are fairly intertwined, whereas the evolutionary computation subfield has a relatively independent postion.
C1 Erasmus Univ, Erasmus Sch Econ, Inst Econometr, NL-3000 DR Rotterdam, Netherlands.
C3 Erasmus University Rotterdam; Erasmus University Rotterdam - Excl
   Erasmus MC
RP Van Eck, NJ (corresponding author), Erasmus Univ, Erasmus Sch Econ, Inst Econometr, PO Box 1738, NL-3000 DR Rotterdam, Netherlands.
EM nvaneck@few.eur.nl; waltman@few.eur.nl
RI van Eck, Nees Jan/B-6042-2008; White, Howard D./A-7034-2009; Waltman,
   Ludo/B-5561-2008
OI van Eck, Nees Jan/0000-0001-8448-4521; Waltman, Ludo/0000-0001-8249-1752
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NR 24
TC 176
Z9 184
U1 5
U2 69
PU WORLD SCIENTIFIC PUBL CO PTE LTD
PI SINGAPORE
PA 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE
SN 0218-4885
EI 1793-6411
J9 INT J UNCERTAIN FUZZ
JI Int. J. Uncertainty Fuzziness Knowl.-Based Syst.
PD OCT
PY 2007
VL 15
IS 5
BP 625
EP 645
DI 10.1142/S0218488507004911
PG 21
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA 238DG
UT WOS:000251426900008
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Zhang, H
   Yang, S
   Guo, L
   Zhao, Y
   Shao, F
   Chen, F
AF Zhang, Hui
   Yang, Sheng
   Guo, Li
   Zhao, Yang
   Shao, Fang
   Chen, Feng
TI Comparisons of isomiR patterns and classification performance using the
   rank-based MANOVA and 10-fold cross-validation
SO GENE
LA English
DT Article
DE Differential expression; IsomiR pattems; Classification performance;
   miRNA sequencing research
ID DIFFERENTIAL EXPRESSION ANALYSIS; ARM SELECTION; RNA-SEQ; MICRORNAS;
   EVENTS; MIRNAS
AB Next generation sequencing technology has identified a series of miRNA variants (named "isomiRs"), which might be associated with cancer progression. We provide a new strategy to reanalyze the miR-seq datasets through a view of the isomiR spectrum. Firstly, differentially expressed (DE) isomiRs were detected with the DESeq algorithm based on negative binomial distribution. Secondly, the rank-based MANOVA was adopted to compare the isomiR patterns between normal and tumor tissues. Moreover, a comprehensive survey on classification performance of three features was conducted, including the logistic regression, k-nearest neighbors and Random Forest. Finally, functional enrichment analysis was performed with the putative targets of specific isomiRs to elucidate their biological functions. Furthermore, the methods were applied to the downloaded miR-seq datasets of breast invasive carcinoma from TCGA. We found that the expression levels of multiple isomiRs derived from the same miRNA locus showed significant inconsistency between normal and tumor samples. In most cases, logistic regression with multiple DE isomiRs was superior to the others, with highest ADC and lowest AIC. Similarly, DE isomiRs performed best in the average accuracy of standard classifiers. Integrated targets were significantly enriched in some cancer-related pathways, including MAPK signaling pathway, and focal adhesion. Collectively, we could recommend the rank-based MANOVA for comparing different isomiR patterns, and further investigation on isomiRs needs to be considered in miRNA sequencing research. (C) 2014 Elsevier B.V. All rights reserved.
C1 [Zhang, Hui; Yang, Sheng; Guo, Li; Zhao, Yang; Shao, Fang; Chen, Feng] Nanjing Med Univ, Sch Publ Hlth, Dept Epidemiol & Biostat, Nanjing 211166, Jiangsu, Peoples R China.
C3 Nanjing Medical University
RP Chen, F (corresponding author), Nanjing Med Univ, Sch Publ Hlth, Dept Epidemiol & Biostat, Nanjing 211166, Jiangsu, Peoples R China.
EM fengchen@njmu.edu.cn
OI Chen, Feng/0000-0002-2699-7190; Zhang, Hui/0000-0002-5172-4039
FU National Natural Science Foundation of China [61301251, 81473070,
   81373102]; Research Fund for the Doctoral Program of Higher Education of
   China [211323411002, 20133234120009]; National Natural Science
   Foundation of Jiangsu [BK20130885]; Natural Science Foundation of the
   Jiangsu Higher Education Institutions [12KJB310003, 13KJB330003,
   14KJA310002]; Priority Academic Program Development of Jiangsu Higher
   Education Institutions (PAPD); Research and Innovation Project for
   College Graduates of Jiangsu Province [944]
FX This work was supported by the National Natural Science Foundation of
   China (Nos. 61301251, 81473070 and 81373102), the Research Fund for the
   Doctoral Program of Higher Education of China (Nos. 211323411002 and
   20133234120009), the National Natural Science Foundation of Jiangsu (No.
   BK20130885), the Natural Science Foundation of the Jiangsu Higher
   Education Institutions (Nos.12KJB310003, 13KJB330003, and 14KJA310002),
   the Priority Academic Program Development of Jiangsu Higher Education
   Institutions (PAPD) and the Research and Innovation Project for College
   Graduates of Jiangsu Province (No. 944).
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NR 25
TC 25
Z9 29
U1 0
U2 20
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0378-1119
EI 1879-0038
J9 GENE
JI Gene
PD SEP 10
PY 2015
VL 569
IS 1
BP 21
EP 26
DI 10.1016/j.gene.2014.11.026
PG 6
WC Genetics & Heredity
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Genetics & Heredity
GA CN4IS
UT WOS:000358394400004
PM 25447923
DA 2024-09-05
ER
PT C
AU Novotná, T
AF Novotna, Tereza
BE Schweighofer, E
TI Human Evaluation Experiment of Legal Information Retrieval Methods
SO LEGAL KNOWLEDGE AND INFORMATION SYSTEMS
SE Frontiers in Artificial Intelligence and Applications
LA English
DT Proceedings Paper
CT 34th Annual International Conference on Legal Knowledge and Information
   Systems (JURIX)
CY DEC 08-10, 2021
CL Mykolas Romeris Univ, ELECTR NETWORK
HO Mykolas Romeris Univ
DE human evaluation; court decisions retrieval; doc2vec; citation analysis;
   LDA; multilayered approach
AB In this article, I present the results of the human evaluation experiment of three commonly used methods in legal information retrieval and a new "multilayered" approach. I use the doc2vec model, citation network analysis and two topic modelling algorithms for the Czech Supreme Court decisions retrieval and evaluate their performance. To improve the accuracy of the results of these methods, I combine the methods in a "multilayered" way and perform the subsequent evaluation. Both evaluation experiments are conducted with a group of legal experts to assess the applicability and usability of the methods for legal information retrieval. The combination of the doc2vec and citations is found satisfactory accurate for practical use for the Czech court decisions retrieval.
C1 [Novotna, Tereza] Masaryk Univ, Inst Law & Technol, Brno, Czech Republic.
C3 Masaryk University Brno
RP Novotná, T (corresponding author), Masaryk Univ, Inst Law & Technol, Brno, Czech Republic.
EM tereza.novotna@law.muni.cz
RI Novotná, Tereza/JNR-9159-2023
OI Novotná, Tereza/0000-0002-1426-4547
FU ERDF project "Internal grant agency of Masaryk University"
   [CZ.02.2.69/0.0/0.0/19 073/0016943]
FX I acknowledge the support of the ERDF project "Internal grant agency of
   Masaryk University" (No. CZ.02.2.69/0.0/0.0/19 073/0016943). I would
   like to thank Jakub Hara.sta for consultations and ideas for this
   research.
CR Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
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NR 13
TC 0
Z9 0
U1 0
U2 0
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 0922-6389
EI 1879-8314
BN 978-1-64368-253-2; 978-1-64368-252-5
J9 FRONT ARTIF INTEL AP
PY 2021
VL 346
BP 131
EP 137
DI 10.3233/FAIA210328
PG 7
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Information Science & Library Science; Law
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Information Science & Library Science; Government &
   Law
GA BW6PH
UT WOS:001180102000018
OA hybrid
DA 2024-09-05
ER
PT J
AU Kolakoti, A
   Tadros, M
   Ambati, VK
   Gudlavalleti, VNS
AF Kolakoti, Aditya
   Tadros, Mina
   Ambati, Vijay Kumar
   Gudlavalleti, Venkata Naga Sai
TI Optimization of biodiesel production, engine exhaust emissions, and
   vibration diagnosis using a combined approach of definitive screening
   design (DSD) and artificial neural network (ANN)
SO ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
LA English
DT Article
DE Machine learning; Research operation; Biofuel production; Engine
   performance; Engine vibration; Palm oil; Feed forward back propagation
   algorithm; Correlation coefficient
ID OIL; PERFORMANCE; MICROALGAE; BIOFUELS
AB In this study, definitive screening design (DSD) optimization and artificial neural network (ANN) modelling techniques are applied for the production of palm oil biodiesel (POBD). These techniques are implemented to examine the vital contributing factors in achieving maximum POBD yield. For this purpose, seventeen experiments are conducted randomly by varying the four contributing factors. The results of DSD optimization reveal that a biodiesel yield of 96.06% is achieved. Also, the experimental results are trained in ANN for predicting the biodiesel yield. The results proved that the prediction capability of ANN is superior, with a high correlation coefficient (R-2) and low mean square error (MSE). Furthermore, the obtained POBD is characterized by significant fuel properties and fatty acid compositions and observed within the standards (ASTM-D675). Finally, the neat POBD is examined for exhaust emissions and engine cylinder vibration analysis. The emissions results confirm a significant drop in NOx (32.46%), HC (40.57%), CO (44.44%), and exhaust smoke (39.65%) compared to diesel fuel at 100% load. Likewise, the engine cylinder vibration measured on top of the cylinder head reveals a low spectral density with low amplitude vibrations observed for POBD at measured loads.
C1 [Kolakoti, Aditya; Ambati, Vijay Kumar; Gudlavalleti, Venkata Naga Sai] Raghu Engn Coll A, Dept Mech Engn, Visakhapatnam, India.
   [Tadros, Mina] Univ Lisbon, Ctr Marine Technol & Ocean Engn CENTEC, Inst Super Tecn, Lisbon, Portugal.
   [Tadros, Mina] Alexandria Univ, Fac Engn, Naval Architecture & Marine Engn, Alexandria, Egypt.
C3 Universidade de Lisboa; Egyptian Knowledge Bank (EKB); Alexandria
   University
RP Tadros, M (corresponding author), Univ Lisbon, Ctr Marine Technol & Ocean Engn CENTEC, Inst Super Tecn, Lisbon, Portugal.; Tadros, M (corresponding author), Alexandria Univ, Fac Engn, Naval Architecture & Marine Engn, Alexandria, Egypt.
EM mina.tadros@centec.tecnico.ulisboa.pt
RI Tadros, Mina/D-1800-2018; Kolakoti, Aditya/AAO-8216-2020; Ambati, Vijay
   Kumar/GVS-3304-2022
OI Tadros, Mina/0000-0001-9065-3803; Kolakoti, Aditya/0000-0002-7515-8318;
   Ambati, Vijay Kumar/0000-0002-4902-0737
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NR 47
TC 2
Z9 2
U1 1
U2 3
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 0944-1344
EI 1614-7499
J9 ENVIRON SCI POLLUT R
JI Environ. Sci. Pollut. Res.
PD AUG
PY 2023
VL 30
IS 37
BP 87260
EP 87273
DI 10.1007/s11356-023-28619-1
EA JUL 2023
PG 14
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology
GA HH5V7
UT WOS:001024269500006
PM 37421526
DA 2024-09-05
ER
PT J
AU Dommeyer, CJ
AF Dommeyer, Curt J.
TI Lecture capturing: Its effects on students' absenteeism, performance,
   and impressions in a traditional marketing research course
SO JOURNAL OF EDUCATION FOR BUSINESS
LA English
DT Article
DE Distance learning; lecture capture; online learning
ID IMPACT; WONT; IF
AB A quasiexperiment was conducted among marketing research students to determine the effects of lecture capturing (LC). One group of students (the LC group) was allowed access to video recordings of the class lectures whereas another group of students in a parallel class (the control group) was not given access to the recordings. When both groups were compared on their absentee rate and performance variables, the LC group had a lower absentee rate and higher scores on all of the performance variables. Moreover, survey data revealed that the LC group made fewer visits to the instructor than the control group did.
C1 [Dommeyer, Curt J.] Calif State Univ Northridge, David Nazarian Coll Business & Econ, Northridge, CA 91330 USA.
C3 California State University System; California State University
   Northridge
RP Dommeyer, CJ (corresponding author), Calif State Univ Northridge, David Nazarian Coll Business & Econ, Mkt, 18111 Nordhoff St, Northridge, CA 91330 USA.
EM vcmkt001@csun.edu
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NR 28
TC 15
Z9 16
U1 0
U2 15
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0883-2323
EI 1940-3356
J9 J EDUC BUS
JI J. Educ. Bus.
PY 2017
VL 92
IS 8
BP 388
EP 395
DI 10.1080/08832323.2017.1398129
PG 8
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA FU5TE
UT WOS:000423915100003
DA 2024-09-05
ER
PT J
AU Dahl, AA
   Bowling, J
   Krinner, LM
   Brown, CS
   Shaw, G 
   Lewis, JB
   Moore-Harrison, T
   Clinton, SM
   Gartlan, SR
AF Dahl, Alicia A.
   Bowling, Jessamyn
   Krinner, Lisa M.
   Brown, Candace S.
   Shaw, George, Jr.
   Lewis, Janaka B.
   Moore-Harrison, Trudy
   Clinton, Sandra M.
   Gartlan, Scott R.
TI "If we can do it, anyone can!": Evaluating a virtual "Paper Chase"
   collaborative writing model for rapid research dissemination
SO ACTIVE LEARNING IN HIGHER EDUCATION
LA English
DT Article
DE active learning; collaborative writing; dissemination; higher education;
   Paper Chase; science communication
ID GROUP WORK; PERCEPTIONS; EXPERIENCE; ATTITUDES
AB The Paper Chase model is a synchronous collaborative approach to manuscript development. Through a structured and team-based design, authors participate in a "marathon" of writing, editing, revising, and submitting their publications within a specified period. This active-learning approach is considered a high-impact practice by engaging students in research dissemination through a collaborative project. This study sought to evaluate the feasibility and acceptability of a virtual Paper Chase exercise. We conducted the Paper Chase with six teams led by multidisciplinary faculty (with 24 undergraduate students and four graduate students). All participants were given pre-and post-surveys, with both open- and closed-ended questions. Results indicated that the process increased cooperative and problem-solving components of group work attitudes, increased participants' confidence in writing skills, increased understanding of research processes and that participants appreciated putting their skills immediately into practice. Participants identified strengths as well as opportunities for improvement in online modules and facilitation. The process was effective in that half of the manuscripts were submitted to peer-reviewed outlets within 90 days of the event. The positive evidence for learning in the virtual Paper Chase model supports future applications and may strengthen the involvement of students in research dissemination. Additional research may expand upon the findings by assessing group work dynamics, quality of final products, and conducting the process in a hybrid model.
C1 [Dahl, Alicia A.; Bowling, Jessamyn] Univ N Carolina, Publ Hlth Sci, Charlotte, NC USA.
   [Krinner, Lisa M.] Univ N Carolina, Abacus, Chapel Hill, NC USA.
   [Brown, Candace S.] Univ N Carolina, Gerontol, Charlotte, NC USA.
   [Shaw, George, Jr.] Univ N Carolina, Dept Publ Hlth Sci, Charlotte, NC USA.
   [Lewis, Janaka B.] Univ N Carolina, English, Charlotte, NC USA.
   [Moore-Harrison, Trudy] Univ N Carolina, Dept Appl Physiol Hlth & Clin Sci, Charlotte, NC USA.
   [Clinton, Sandra M.] Univ N Carolina, Geog & Earth Sci, Charlotte, NC USA.
   [Gartlan, Scott R.] Univ N Carolina, Charlotte Teachers Inst, Charlotte, NC USA.
C3 University of North Carolina; University of North Carolina Charlotte;
   University of North Carolina; University of North Carolina Chapel Hill;
   University of North Carolina; University of North Carolina Charlotte;
   University of North Carolina; University of North Carolina Charlotte;
   University of North Carolina; University of North Carolina Charlotte;
   University of North Carolina; University of North Carolina Charlotte;
   University of North Carolina; University of North Carolina Charlotte;
   University of North Carolina; University of North Carolina Charlotte
RP Dahl, AA (corresponding author), Univ N Carolina, Dept Publ Hlth Sci, 9201 Univ City Blvd, Charlotte, NC 28223 USA.
EM adahl3@uncc.edu; jessamyn.bowling@uncc.edu; lisa_krinner@med.unc.edu;
   cbrow342@uncc.edu; j.lewis@uncc.edu; tlmoore2@uncc.edu;
   sclinto1@uncc.edu; scott.gartlan@uncc.edu
RI Dahl, Alicia/ITV-8301-2023; Brown, Candace S/KVB-3413-2024; Bowling,
   Jessamyn/ABI-5661-2020; Clinton, Sandra/V-8925-2019
OI Dahl, Alicia/0000-0002-6229-0926; Bowling, Jessamyn/0000-0001-7410-4433;
   Clinton, Sandra/0000-0002-8042-6671; Brown, Candace/0000-0001-6102-7483
FU Office of Undergraduate Research at the University of North Carolina at
   Charlotte; Women + Girls Research Alliance Seed Grant
FX The author(s) disclosed receipt of the following financial support for
   the research, authorship, and/or publication of this article: This work
   was supported by the Office of Undergraduate Research at the University
   of North Carolina at Charlotte; and the Women + Girls Research Alliance
   Seed Grant.
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NR 56
TC 0
Z9 0
U1 3
U2 14
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 1469-7874
EI 1741-2625
J9 ACT LEARN HIGH EDUC
JI Act. Learn. High. Educ.
PD MAR
PY 2024
VL 25
IS 1
BP 115
EP 134
AR 14697874221099011
DI 10.1177/14697874221099011
EA JUN 2022
PG 20
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA KF5B9
UT WOS:000808554300001
DA 2024-09-05
ER
PT S
AU Leetch, A
   Hauk, M
AF Leetch, Amanda
   Hauk, Marna
BE Filho, WL
   Mifsud, M
   Shiel, C
   Pretorius, R
TI A Decade of Earth in the Mix: A Bibliometric Analysis of Emergent
   Scholarly Research on Sustainability Education and Ecopsychology in
   Higher Education
SO HANDBOOK OF THEORY AND PRACTICE OF SUSTAINABLE DEVELOPMENT IN HIGHER
   EDUCATION, VOL 3
SE World Sustainability Series
LA English
DT Article; Book Chapter
DE Sustainability education; Ecopsychology; Resilience; Latent dirichlet
   allocation; Bayesian network; Bibliometric analysis; Post-DESD
   sustainability education
AB As part of understanding methodological approaches which aim to integrate the topic of sustainability in the curriculum of universities, the intersection of ecopsychology with education for sustainable development marks an emergent methodological and curricular bridge. Using Bayesian probablistic modeling of related topics, called latent Dirichlet allocation, an analysis of theses and dissertations published since the advent of the UN Decade of Education for Sustainable Development revealed a substantial volume of higher education research regarding ecopsychology and sustainability education in higher education (using Proquest Database). Within that larger field, several hundred works existed at the intersection of ecopsychology and education for sustainable development/sustainability education. This research reported on findings from analyzing titles, abstracts and keywords of this data set of theses and dissertations to identify emergent trends in topics within the research scholarship. Initial findings indicated directions for viable inter-and transdisciplinary collaboration in order to extend and integrate the reach of sustainability education across the university curriculum. Research from the field suggested such inter-and transdisciplinary curricular approaches to the "wicked" problems of sustainability are requisite to cultivate the next generation of sustainability innovators and educators.
C1 [Leetch, Amanda] Prescott Coll, Dept Educ, Prescott, AZ 86301 USA.
   [Hauk, Marna] Prescott Coll, Dept Sustainabil Educ, Prescott, AZ USA.
RP Leetch, A (corresponding author), Prescott Coll, Dept Educ, Prescott, AZ 86301 USA.
EM amanda.leetch@student.prescott.edu; earthregenerative@gmail.com
CR Adomssent M., 2007, International Journal of Sustainability in Higher Education, V8, P385, DOI DOI 10.1108/14676370710823564
   Agyeman Julian., 2013, Introducing Just Sustainabilities: Policy, Planning, and Practice
   [Anonymous], 2008, PSYCHOL LIBERATION
   [Anonymous], THESIS
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   Bigelow Bill., 2014, PEOPLES CURRICULUM E
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   Chalquist Craig., 2010, RebEarths: Conversations with a world ensouled
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   Corral-Verdugo V., 2013, PSYCHOL APPROACHES S
   Doherty T. J., 2011, EC ENV FOC PSYCHOL A
   Edwards S., 2009, ECOTHERAPY HEALING N, P126
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   Ziegler B., 2009, 200910 CISL MASS I
NR 49
TC 3
Z9 4
U1 0
U2 5
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 2199-7373
EI 2199-7381
BN 978-3-319-47895-1; 978-3-319-47894-4
J9 WORLD SUSTAIN SER
PY 2017
BP 291
EP 306
DI 10.1007/978-3-319-47895-1_18
D2 10.1007/978-3-319-47895-1
PG 16
WC Green & Sustainable Science & Technology; Education & Educational
   Research
WE Book Citation Index – Social Sciences & Humanities (BKCI-SSH)
SC Science & Technology - Other Topics; Education & Educational Research
GA BI5WY
UT WOS:000412935300019
DA 2024-09-05
ER
PT J
AU Bodea, C
AF Bodea, Constanta
TI Artificial intelligence techniques applied to the evaluation of the
   research and technology development projects and programmes
SO ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH
LA English
DT Article
DE KPI; RTD project; RTD programme; data mining; project portfolio;
   indicator template
AB The paper presents the specific ways in which indicators and artificial intelligence methods and tools can be used for the evaluation of research projects and programmes. The author's research purpose is to improve the programme ex post evaluation and ex ante impact assessment thought the development of a improved set of strong integrated research performance indicators, structured according to the results chains and comprehensively described using a standard indicator template; the development of data sets and databases for projects and programmes evaluation and, finally the development of projects and programmes evaluation techniques, based on database and machine learning technologies.
   Using these methods a new and better understanding of the scientific, technological, human resources, structuring, economic, social, environmental etc impacts of national and European research programmes is possible. The research is financed by the Minister of Education and Research, IDEI programme.
C1 Acad Econ Studies, Econ Informat Dept, Bucharest, Romania.
C3 Bucharest University of Economic Studies
RP Bodea, C (corresponding author), Acad Econ Studies, Econ Informat Dept, Bucharest, Romania.
RI Bodea, Constanta-Nicoleta/GXV-2034-2022
OI Bodea, Constanta-Nicoleta/0000-0001-5542-8133
CR [Anonymous], 2002, Proposed standard practice for surveys on research and experimental development
   Fahrenkrog G., 2002, RTD EVALUATION TOOLB
   GHEORGIOU L, 2002, ASSESSING SOCIO EC I
   2002, USE EVALUATION COMMI
   1999, MANAGING NATL INNOVA
NR 5
TC 3
Z9 3
U1 0
U2 26
PU ACAD ECONOMIC STUDIES
PI BUCHAREST
PA 15-17 CALEA DOROBANTI, SECTOR 1, BUCHAREST, 00000, ROMANIA
SN 0424-267X
EI 1842-3264
J9 ECON COMPUT ECON CYB
JI Econ. Comput. Econ. Cybern. Stud.
PY 2007
VL 41
IS 3-4
BP 141
EP 150
PG 10
WC Economics; Mathematics, Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Mathematics
GA 339KE
UT WOS:000258575700012
DA 2024-09-05
ER
PT J
AU Chen, XL
   Zou, D
   Xie, HR
   Chen, GL
   Lin, JH
   Cheng, GR
AF Chen, Xieling
   Zou, Di
   Xie, Haoran
   Chen, Guanliang
   Lin, Jionghao
   Cheng, Gary
TI Exploring contributors, collaborations, and research topics in
   educational technology: A joint analysis of mainstream conferences
SO EDUCATION AND INFORMATION TECHNOLOGIES
LA English
DT Article
DE Educational technology; Mainstream conferences; Bibliometrics;
   Structural topic modeling
ID CITATION ANALYSIS; DECADES; SYSTEMS; IMPACT
AB The diversity and advance of information, communication, and analytical technologies and their increasing adoption to assist instruction and learning give rise to various technology-driven conferences (e.g., artificial intelligence in education) in educational technology. Previous reviews on educational technology commonly focused on journal articles while seldom including mainstream conference papers which also contribute to an important part of scientific output in computer science and emerging disciplines like educational technology and are equally and even more important than articles in knowledge transmission. Hence, conference papers should also be included in bibliometric studies to produce a complete and precise picture of scientific production concerning educational technology. This study, therefore, uses bibliometrics and topic modeling to analyze papers from mainstream conferences, including Artificial Intelligence in Education, Learning Analytics and Knowledge, Educational Data Mining, Intelligent Tutoring System, and Learning at Scale, focusing on contributors, collaborations, and particularly research topics and topic evolutions to inform relevant stakeholders about educational technology's development and its future. Results indicate promising areas like affective computing and behavior mining for adaptive instruction, recommender systems in personalized learning recommendations, eye-tracking for cognitive process diagnosis, videos for feedback provision, and natural language processing in discourse analysis and language education.
C1 [Chen, Xieling] South China Normal Univ, Sch Informat Technol Educ, Guangzhou, Peoples R China.
   [Zou, Di] Educ Univ Hong Kong, Dept English Language Educ, Hong Kong, Peoples R China.
   [Xie, Haoran] Lingnan Univ, Dept Comp & Decis Sci, Tuen Mun, Hong Kong, Peoples R China.
   [Chen, Guanliang; Lin, Jionghao] Monash Univ, Fac Informat Technol, Melbourne, Vic, Australia.
   [Cheng, Gary] Educ Univ Hong Kong, Dept Math & Informat Technol, Hong Kong, Peoples R China.
C3 South China Normal University; Education University of Hong Kong
   (EdUHK); Lingnan University; Monash University; Education University of
   Hong Kong (EdUHK)
RP Zou, D (corresponding author), Educ Univ Hong Kong, Dept English Language Educ, Hong Kong, Peoples R China.; Chen, GL (corresponding author), Monash Univ, Fac Informat Technol, Melbourne, Vic, Australia.
EM xielingchen0708@gmail.com; dizoudaisy@gmail.com; hrxie2@gmail.com;
   guanliang.chen@monash.edu; jionghao.lin@monash.edu; chengks@eduhk.hk
RI Lin, Jionghao/JEO-6478-2023; Xie, Haoran/AFS-3515-2022; Xie,
   Haoran/AAW-8845-2020
OI Lin, Jionghao/0000-0003-3320-3907; Xie, Haoran/0000-0003-0965-3617; ZOU,
   Di/0000-0001-8435-9739; PV, THAYYIB/0000-0001-8929-0398
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NR 69
TC 2
Z9 2
U1 13
U2 63
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1360-2357
EI 1573-7608
J9 EDUC INF TECHNOL
JI Educ. Inf. Technol.
PD FEB
PY 2023
VL 28
IS 2
BP 1323
EP 1358
DI 10.1007/s10639-022-11209-y
EA JUL 2022
PG 36
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA 9Q6HD
UT WOS:000829709100002
DA 2024-09-05
ER
PT J
AU Ghaffari, M
   Aliahmadi, A
   Khalkhali, A
   Zakery, A
   Daim, TU
   Yalcin, H
AF Ghaffari, Mohsen
   Aliahmadi, Alireza
   Khalkhali, Abolfazl
   Zakery, Amir
   Daim, Tugrul U.
   Yalcin, Haydar
TI Topic-based technology mapping using patent data analysis: A case study
   of vehicle tires
SO TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
LA English
DT Article
DE Bibliometrics; Patent analysis; Technology mapping; Tire Industry;
   Machine learning; Topic modeling; Emerging technologies
ID FORECASTING TECHNOLOGY; EMERGING TECHNOLOGIES; TRANSPORTATION;
   CLASSIFICATION; IDENTIFICATION; PREDICTION; INNOVATION; SUCCESS;
   SCIENCE; AREAS
AB The analysis of patent certificates for the purpose of determining the technologies of an industry is a method that has been used by experts and researchers of technology management and technology forecasting for nearly two decades. Meanwhile, using different techniques and software and completing the experiences of past researches have increased the speed, accuracy, and practicality of the relevant reports. In this study, the tire industry has been investigated with regard to its prominent role in the future automobile and transportation industry. All tirerelated patent certificates in the last 20 years were extracted from the Derwent Innovation Index database using a search string and IPC codes, and with the help of Latent Dirichlet Allocation (LDA) which is an unsupervised machine learning method, the relevant technology areas were extracted. The analysis of technologies and forecasting future technology areas were conducted regarding the share and growth rate of each technology in two 10-year periods (2000-2009 and 2010-2019) and the study of trends and technical indicators related to the industry and value chain. The analysis of nine technology areas considered by tire industry innovators during the last 20 years, as well as the analysis of trends and effective factors on these technologies indicated that the fields of airless tires and intelligent tires technology areas would be highly welcomed in the future and become the dominant and extensively-used technologies of the tire industry in the future.
C1 [Ghaffari, Mohsen; Aliahmadi, Alireza; Khalkhali, Abolfazl; Zakery, Amir] Iran Univ Sci & Technol IUST, Tehran, Iran.
   [Daim, Tugrul U.] Portland State Univ, Portland, OR 97201 USA.
   [Daim, Tugrul U.; Yalcin, Haydar] Ege Univ, Izmir, Turkiye.
   [Daim, Tugrul U.] Chaoyang Univ Technol, Taichung, Taiwan.
C3 Iran University Science & Technology; Portland State University; Ege
   University; Chaoyang University of Technology
RP Daim, TU (corresponding author), Portland State Univ, Portland, OR 97201 USA.; Daim, TU (corresponding author), Ege Univ, Izmir, Turkiye.; Daim, TU (corresponding author), Chaoyang Univ Technol, Taichung, Taiwan.
EM tugrul.u.daim@pdx.edu
RI Ghaffari, Mohsen/IAQ-7348-2023; Zakery, Amir/R-8017-2018; Daim,
   Tugrul/JFJ-5740-2023; Yalcin, Haydar/HHN-1057-2022
OI Ghaffari, Mohsen/0009-0003-9109-2616; Zakery, Amir/0000-0002-6530-8933; 
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NR 120
TC 11
Z9 11
U1 47
U2 115
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0040-1625
EI 1873-5509
J9 TECHNOL FORECAST SOC
JI Technol. Forecast. Soc. Chang.
PD AUG
PY 2023
VL 193
AR 122576
DI 10.1016/j.techfore.2023.122576
EA MAY 2023
PG 15
WC Business; Regional & Urban Planning
WE Social Science Citation Index (SSCI)
SC Business & Economics; Public Administration
GA I7TR5
UT WOS:001004779200001
DA 2024-09-05
ER
PT J
AU Alani, F
   Geng, F
   Toribio, M
   Grewal, R
AF Alani, Faiez
   Geng, Fei
   Toribio, Mae
   Grewal, Rehmat
TI Effect of Case-Based Learning (CBL) on Student Performance in
   Engineering Biotechnology Education
SO INTERNATIONAL JOURNAL OF ENGINEERING EDUCATION
LA English
DT Article
DE CBL; active learning; pedagogical research; student performance;
   engineering biotechnology
ID CASE-BASED INSTRUCTION
AB Case-based learning is a method that has been used increasingly in a variety of disciplines. However, in the engineering technology education, this method is still underutilized. The goal of this study was to evaluate the effect of case-based learning in the performance of engineering technology students. Students enrolled in an undergraduate biotechnology course answered an anonymous survey about the effects of CBL on different factors that are linked to improving their performance. The results demonstrate that CBL had a positive effect on the students' learning experience, concept understanding, and deep understanding for the course which contributed to the effectiveness of CBL in improving the students' performance. Furthermore, this study found that having more cases reviewed per term increased the student performance based on their final marks on the course, clearly indicating the positive impact of CBL on student performance.
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U2 7
PU TEMPUS PUBLICATIONS
PI DURRUS, BANTRY
PA IJEE , ROSSMORE,, DURRUS, BANTRY, COUNTY CORK 00000, IRELAND
SN 0949-149X
J9 INT J ENG EDUC
JI Int. J. Eng. Educ
PY 2022
VL 38
IS 2
BP 543
EP 548
PG 6
WC Education, Scientific Disciplines; Engineering, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Education & Educational Research; Engineering
GA ZE1PN
UT WOS:000758662400022
DA 2024-09-05
ER
PT J
AU Tran, HN
   Phan, GTT
   Do, QB
   Tran, VP
AF Tran, Hoai-Nam
   Phan, Giang T. T.
   Do, Quang Binh
   Tran, Viet-Phu
TI Comparative evaluation of the performance of improved genetic algorithms
   and differential evolution for in-core fuel management of a research
   reactor
SO NUCLEAR ENGINEERING AND DESIGN
LA English
DT Article
DE Genetic algorithm; Differential evolution; In-core fuel management; DNRR
ID MULTIOBJECTIVE OPTIMIZATION; LOADING PATTERN
AB This paper presents a comparative evaluation of the performance of Genetic Algorithm (GA) and Differential Evolution (DE) algorithm applied to in-core fuel management of the DNRR research reactor. Two GA variants corresponding to two selection operators, i.e., tournament (GA1) and roulette wheel (GA2) selections, respectively, with two-point crossover and scramble mutation were implemented for the ICFM problem. A comprehensive survey of the GA control parameters such as population size, crossover-type, mutation probability, and elitist archive size has been conducted to optimize the performance of the GAs. The basic DE was implemented with a standard mutation strategy DE/rand/1/bin. Numerical computations were performed based on the DNRR research reactor core loaded with 100 highly enriched uranium fuel (HEU) bundles for evaluating the performance of the GA and DE algorithms. Two main objectives were included in the fitness function to maximize the fuel cycle length and flatten the power distribution. The performance of the two GA variants and the basic DE was investigated with the same population size, fitness function, and convergence criterion. Each method was performed with 50 independent runs, and the best fitness values were collected for statistical analysis using Kruskal-Wallis and Mann-Whitney tests in comparison among the three methods. The statistical analysis shows that the performance of GA1 with tournament selection and DE are not significantly different and are better than GA2 with roulette wheel selection. DE is stable and efficient in exploring the search space to approach the global optimal solution in most runs. While, GA1 and GA2 were trapped at local optima by about 26% and 38%, respectively. However, the best solutions obtained with GA1 and GA2 after 50 independent runs are better than that obtained with DE in term of fitness values. This suggests an improvement of the basic DE is needed to maintain the potential good solutions during the evolution process.
C1 [Tran, Hoai-Nam] PHENIKAA Univ, Fac Fundamental Sci, Hanoi 12116, Vietnam.
   [Phan, Giang T. T.] Duy Tan Univ, Inst Fundamental & Appl Sci, Ho Chi Minh City 700000, Vietnam.
   [Phan, Giang T. T.] Duy Tan Univ, Fac Nat Sci, Da Nang 550000, Vietnam.
   [Do, Quang Binh] Sai Gon Univ, Inst Environm Energy Technol, 230 An Duong Vuong,Dist 5, Ho Chi Minh City 700000, Vietnam.
   [Tran, Viet-Phu] VINATOM, Inst Nucl Sci & Technol, 179 Hoang Quoc Viet, Hanoi 100000, Vietnam.
C3 Duy Tan University; Duy Tan University; Saigon University
RP Tran, HN (corresponding author), PHENIKAA Univ, Fac Fundamental Sci, Hanoi 12116, Vietnam.; Phan, GTT (corresponding author), Duy Tan Univ, Inst Fundamental & Appl Sci, Ho Chi Minh City 700000, Vietnam.
EM nam.tranhoai@phenikaa-uni.edu.vn; phantthuygiang@duytan.edu.vn
FU National Foundation for Science and Technology Development (NAFOSTED) ,
   Vietnam;  [103.04-2020.06]
FX Acknowledgment This research was funded by National Foundation for
   Science and Technology Development (NAFOSTED) , Vietnam under grant
   103.04-2020.06.
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NR 26
TC 3
Z9 4
U1 3
U2 9
PU ELSEVIER SCIENCE SA
PI LAUSANNE
PA PO BOX 564, 1001 LAUSANNE, SWITZERLAND
SN 0029-5493
EI 1872-759X
J9 NUCL ENG DES
JI Nucl. Eng. Des.
PD NOV
PY 2022
VL 398
AR 111953
DI 10.1016/j.nucengdes.2022.111953
EA SEP 2022
PG 14
WC Nuclear Science & Technology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Nuclear Science & Technology
GA 5A0FZ
UT WOS:000862573900003
DA 2024-09-05
ER
PT J
AU Kim, D
   Kim, E
   Cha, SK
   Son, S
   Kim, Y
AF Kim, Dongkwan
   Kim, Eunsoo
   Cha, Sang Kil
   Son, Sooel
   Kim, Yongdae
TI Revisiting Binary Code Similarity Analysis Using Interpretable Feature
   Engineering and Lessons Learned
SO IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
LA English
DT Article
DE Benchmark testing; Computer architecture; Binary codes; Syntactics;
   Semantics; Licenses; Market research; Binary code similarity analysis;
   similarity measures; feature evaluation and selection; benchmark
ID SEMANTICS; ACCURATE; GRAPH; SOFTWARE; SEARCH
AB Binary code similarity analysis (BCSA) is widely used for diverse security applications, including plagiarism detection, software license violation detection, and vulnerability discovery. Despite the surging research interest in BCSA, it is significantly challenging to perform new research in this field for several reasons. First, most existing approaches focus only on the end results, namely, increasing the success rate of BCSA, by adopting uninterpretable machine learning. Moreover, they utilize their own benchmark, sharing neither the source code nor the entire dataset. Finally, researchers often use different terminologies or even use the same technique without citing the previous literature properly, which makes it difficult to reproduce or extend previous work. To address these problems, we take a step back from the mainstream and contemplate fundamental research questions for BCSA. Why does a certain technique or a certain feature show better results than the others? Specifically, we conduct the first systematic study on the basic features used in BCSA by leveraging interpretable feature engineering on a large-scale benchmark. Our study reveals various useful insights on BCSA. For example, we show that a simple interpretable model with a few basic features can achieve a comparable result to that of recent deep learning-based approaches. Furthermore, we show that the way we compile binaries or the correctness of underlying binary analysis tools can significantly affect the performance of BCSA. Lastly, we make all our source code and benchmark public and suggest future directions in this field to help further research.
C1 [Kim, Dongkwan; Kim, Eunsoo; Cha, Sang Kil; Son, Sooel; Kim, Yongdae] Korea Adv Inst Sci & Technol, Daejeon 34141, South Korea.
C3 Korea Advanced Institute of Science & Technology (KAIST)
RP Cha, SK (corresponding author), Korea Adv Inst Sci & Technol, Daejeon 34141, South Korea.
EM dkay@kaist.ac.kr; hahah@kaist.ac.kr; sangkilc@kaist.ac.kr;
   sl.son@kaist.ac.kr; yongdaek@kaist.ac.kr
OI Kim, Yongdae/0000-0003-4879-1262; Son, Sooel/0000-0003-0904-2875; Cha,
   Sang Kil/0000-0002-6012-7228
FU Institute of Information & Communications Technology Planning &
   Evaluation (IITP) Grant, Korea Government (MSIT) [2021-0-01332]
FX This work was supported by the Institute of Information & Communications
   Technology Planning & Evaluation (IITP) Grant, Korea Government
   (MSIT)under Grant 2021-0-01332, Developing Next-Generation Binary
   Decompiler.
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NR 166
TC 16
Z9 19
U1 1
U2 7
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 0098-5589
EI 1939-3520
J9 IEEE T SOFTWARE ENG
JI IEEE Trans. Softw. Eng.
PD APR 1
PY 2023
VL 49
IS 4
BP 1661
EP 1682
DI 10.1109/TSE.2022.3187689
PG 22
WC Computer Science, Software Engineering; Engineering, Electrical &
   Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA E9NM9
UT WOS:000978723600015
OA hybrid, Green Submitted
DA 2024-09-05
ER
PT J
AU Deka, M
   Buragohain, D
   Kumar, A
AF Deka, Manashjyoti
   Buragohain, Dibanjyoti
   Kumar, Amit
TI Two decades of research on Online Learning published by Springer Link: A
   bibliometric analysis
SO QUALITATIVE & QUANTITATIVE METHODS IN LIBRARIES
LA English
DT Article
DE Online Learning; SpringerLink; Annual Growth Rate; Relative Growth Rate;
   Degree of Collaboration and Collaboration Coefficient
ID COLLABORATION
AB The emergence of online learning has acquired the attention of learners and educators in the 21st century. The need has arisen especially when the world has suffered from the Covid-19 pandemic, the institutions of higher learning have come up with the latest technologies to combat the learning environment scenario. The present study mainly analysis the existing works of literature published on the topic of online learning which is abstracted from SpringerLink. The bibliometrics method of analyzing the data was adopted for the study. The study analysis the components such as the growth of literature,year-wise distribution, annual growth rate (AGR), compound annual growth rate (CAGR), relative growth rate (RGR), and doubling time (DT) of the publications authorship pattern and authors productivity of literature, Study the citation, altmetrics, download and access pattern of literature, the most prominent journals, publisher -wise, country -wise distribution of literature, most productive institutions and top -cited paper in the published literature on "online learning". The study shows that the highest number of publications can be noticed from the conference papers and there is a frequent rise in publications. The annual growth rate of the published literature is 533.34 in 2003 and the number of authors per publication on average was highest in 2017. The study shall contribute to examining the existing scenario of the published literature measurement on online learning and shall encourage more exciting researchers and academicians to work more in this specific field.
C1 [Deka, Manashjyoti; Buragohain, Dibanjyoti] Mizoram Univ, Aizawl, Mizoram, India.
   [Kumar, Amit] Cent Univ Gujarat, Gandhinagar, Gujarat, India.
C3 Mizoram University; Central University of Gujarat
RP Deka, M (corresponding author), Mizoram Univ, Aizawl, Mizoram, India.
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NR 21
TC 0
Z9 0
U1 3
U2 3
PU INT SOC ART SCIENCE & TECHNOLOGY-ISAST
PI ATHINA
PA INT SOC ART SCIENCE & TECHNOLOGY-ISAST, ATHINA, 00000, GREECE
SN 2241-1925
J9 QUAL QUANT METHODS L
JI Qual. Quant. Methods Libr.
PD MAR
PY 2024
VL 13
IS 1
BP 1
EP 34
PG 34
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA MX4O7
UT WOS:001196924600002
DA 2024-09-05
ER
PT J
AU Sánchez-Núñez, P
   de las Heras-Pedrosa, C
   Peláez, JI
AF Sanchez-Nunez, Pablo
   de las Heras-Pedrosa, Carlos
   Ignacio Pelaez, Jose
TI Opinion Mining and Sentiment Analysis in Marketing Communications: A
   Science Mapping Analysis in Web of Science (1998-2018)
SO SOCIAL SCIENCES-BASEL
LA English
DT Article
DE sentiment analysis; opinion mining; advertising; marketing; science
   mapping analysis; Web of Science (WoS); bibliometric indicators;
   scientific collaboration
ID SCIENTIFIC COLLABORATION; DECISION-MAKING
AB Opinion mining and sentiment analysis has become ubiquitous in our society, with applications in online searching, computer vision, image understanding, artificial intelligence and marketing communications (MarCom). Within this context, opinion mining and sentiment analysis in marketing communications (OMSAMC) has a strong role in the development of the field by allowing us to understand whether people are satisfied or dissatisfied with our service or product in order to subsequently analyze the strengths and weaknesses of those consumer experiences. To the best of our knowledge, there is no science mapping analysis covering the research about opinion mining and sentiment analysis in the MarCom ecosystem. In this study, we perform a science mapping analysis on the OMSAMC research, in order to provide an overview of the scientific work during the last two decades in this interdisciplinary area and to show trends that could be the basis for future developments in the field. This study was carried out using VOSviewer, CitNetExplorer and InCites based on results from Web of Science (WoS). The results of this analysis show the evolution of the field, by highlighting the most notable authors, institutions, keywords, publications, countries, categories and journals.
C1 [Sanchez-Nunez, Pablo] Univ Malaga, Univ Huelva, Univ Cadiz, Doctorate Program Commun, Malaga 29071, Spain.
   [Sanchez-Nunez, Pablo] Univ Seville, Malaga 29071, Spain.
   [de las Heras-Pedrosa, Carlos] Univ Malaga, Fac Commun Sci, Dept Audiovisual Commun & Advertising, Malaga 29071, Spain.
   [Ignacio Pelaez, Jose] Univ Malaga, Higher Tech Sch Comp Engn, Dept Languages & Comp Sci, Malaga 29071, Spain.
C3 Universidad de Huelva; Universidad de Cadiz; Universidad de Malaga;
   University of Sevilla; Universidad de Malaga; Universidad de Malaga
RP Sánchez-Núñez, P (corresponding author), Univ Malaga, Univ Huelva, Univ Cadiz, Doctorate Program Commun, Malaga 29071, Spain.; Sánchez-Núñez, P (corresponding author), Univ Seville, Malaga 29071, Spain.
EM psancheznunez@uma.es; cheras@uma.es; jipelaez@uma.es
RI de las Heras-Pedrosa, Carlos/M-4492-2015; Pelaez Sanchez, Jose
   Ignacio/O-9450-2016
OI de las Heras-Pedrosa, Carlos/0000-0002-2738-4177; Sanchez Nunez,
   Pablo/0000-0001-7845-9506; Pelaez Sanchez, Jose
   Ignacio/0000-0002-2606-3849
FU Programa Operativo FEDER Andalucia 2014-2020 [UMA18-FEDERJA-148]
FX The research was funded by Programa Operativo FEDER Andalucia 2014-2020,
   grant number "La reputacion de las organizaciones en una sociedad
   digital. Elaboracion de una Plataforma Inteligente para la Localizacion,
   Identificacion y Clasificacion de Influenciadores en los Medios Sociales
   Digitales (UMA18-FEDERJA-148)" and The APC was funded by the same
   research grant.
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TC 13
Z9 13
U1 3
U2 13
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2076-0760
J9 SOC SCI-BASEL
JI Soc. Sci.-Basel
PD MAR
PY 2020
VL 9
IS 3
AR 23
DI 10.3390/socsci9030023
PG 20
WC Social Sciences, Interdisciplinary
WE Emerging Sources Citation Index (ESCI)
SC Social Sciences - Other Topics
GA TY3DZ
UT WOS:000683665600002
OA gold, Green Published
DA 2024-09-05
ER
PT C
AU Hauffa, J
   Bräu, W
   Groh, G
AF Hauffa, Jan
   Braeu, Wolfgang
   Groh, Georg
BE Spezzano, F
   Chen, W
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TI Detection of Topical Influence in Social Networks via Granger-Causal
   Inference: A Twitter Case Study
SO PROCEEDINGS OF THE 2019 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN
   SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM 2019)
LA English
DT Proceedings Paper
CT IEEE/ACM International Conference on Advances in Social Networks
   Analysis and Mining (ASONAM)
CY AUG 27-30, 2019
CL Vancouver, CANADA
ID LINEAR-DEPENDENCE; FEEDBACK
AB With the ever-increasing importance of computer-mediated communication in our everyday life, understanding the effects of social influence in online social networks has become a necessity. In this work, we argue that cascade models of information diffusion do not adequately capture attitude change, which we consider to be an essential element of social influence. To address this concern, we propose a topical model of social influence and attempt to establish a connection between influence and Granger-causal effects on a theoretical and empirical level. While our analysis of a social media dataset finds effects that are consistent with our model of social influence, evidence suggests that these effects can be attributed largely to external confounders. The dominance of external influencers, including mass media, over peer influence raises new questions about the correspondence between objectively measurable information diffusion and social influence as perceived by human observers.
C1 [Hauffa, Jan; Braeu, Wolfgang; Groh, Georg] Tech Univ Munich, Dept Informat, Garching, Germany.
C3 Technical University of Munich
RP Hauffa, J (corresponding author), Tech Univ Munich, Dept Informat, Garching, Germany.
EM hauffa@in.tum.de; braeuw@in.tum.de; grohg@in.tum.de
OI Hauffa, Jan/0000-0001-5785-8698
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TC 2
Z9 2
U1 0
U2 4
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA
BN 978-1-4503-6868-1
PY 2019
BP 969
EP 977
DI 10.1145/3341161.3345024
PG 9
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Telecommunications
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Telecommunications
GA BP5IK
UT WOS:000555683800164
DA 2024-09-05
ER
PT C
AU Cui, JF
   Wang, HM
   Wang, CY
   Wan, JZ
   Mu, G
AF Cui, Jiefan
   Wang, Hemin
   Wang, Chengyuan
   Wan, Junzhu
   Mu, Gang
GP IEEE
TI Research on High Performance Direct Torque Control System Based on DSP
SO 2008 7TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-23
LA English
DT Proceedings Paper
CT 7th World Congress on Intelligent Control and Automation
CY JUN 25-27, 2008
CL Chongqing, PEOPLES R CHINA
DE DSP; DTC; PMSM; SVM
ID MOTOR-DRIVES
AB To minimize the ripples of the electromagnetic torque and flux linkage, fix the variable switching frequency, produced in the conventional direct torque control (DTC) system for permanent magnet synchronous motor (PMSM), a space vector modulation (SVM) DTC strategy was introduced based on PI predictive controller. This strategy and the conventional DTC were simulated by the Matlab/Simulink toolbox, the conventional DTC and SVM-DTC PMSM drive system was implemented based on TMS320LF2407 digital signal processor (DSP), the steady state and dynamic performance of the two schemes was analyzed. The results show that the steady state performance is improved in the SVM DTC system while preserving the dynamic performance of conventional DTC system and achieving constant switching frequency.
C1 [Cui, Jiefan; Wang, Hemin; Wang, Chengyuan; Wan, Junzhu; Mu, Gang] Shenyang Univ Technol, Sch Elect Engn, Shenyang, Liaoning Prov, Peoples R China.
C3 Shenyang University of Technology
RP Cui, JF (corresponding author), Shenyang Univ Technol, Sch Elect Engn, Shenyang, Liaoning Prov, Peoples R China.
EM mugangsmart@126.com
RI Mu, Gang/E-7851-2013
OI Mu, Gang/0000-0002-3907-1856
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PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-4244-2113-8
PY 2008
BP 1494
EP 1497
DI 10.1109/WCICA.2008.4594456
PG 4
WC Automation & Control Systems; Computer Science, Artificial Intelligence;
   Computer Science, Cybernetics; Engineering, Electrical & Electronic;
   Robotics
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Automation & Control Systems; Computer Science; Engineering; Robotics
GA BIJ02
UT WOS:000259965701053
DA 2024-09-05
ER
PT J
AU Wu, JF
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   Chi, CH
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AF Wu, Junfeng
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   Huang, Guang-Li
   Cai, Borui
   Chi, Chi-Hung
   He, Jing
TI Emerging Scientific Topic Discovery by Analyzing Reliable Patterns of
   Infrequent Synonymous Biterms
SO IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
LA English
DT Article
DE Reliability; Collaboration; Market research; Linguistics; Computational
   intelligence; Resource management; Linear regression; Emerging
   scientific topics; trend analysis; synonymous terms; text mining;
   clustering; adaptivity
AB Emerging scientific topics are those topics that the number of related articles was small in the past but has grown dramatically in recent years. Automatic discovery of emerging scientific topics has become increasingly necessary because of the exponentially increasing of research papers. Such discovery enables broad applications, such as optimizing resource allocations for promising research areas, predicting future technology trends, finding knowledge gaps and new concepts, and recommending personalized research directions. In this paper, we provide a framework of emerging topic discovery methods using Infrequent Synonymous Biterm (ISB), which automatically extracts the dedicated knowledge from the infrequent patterns of synonymous biterms in a corpus (e.g., paper titles); each term in a synonymous biterm represents a collaborating supertopic, whose collaboration originates an emerging topic. In particular, we propose an Analyzing Reliable Patterns of Infrequent Synonymous Biterms (ARPISB) method, which guarantees the quality of the result emerging topics by adaptively giving larger weights to more reliable ISB. Extensive experiments on five subfields' scholarly papers demonstrate the significant and robust improvement of the accuracy of emerging scientific topic discovery.
C1 [Wu, Junfeng; Huang, Guangyan; Huang, Guang-Li; Cai, Borui] Deakin Univ, Sch Informat Technol, Blackburn South 3130, Australia.
   [Zheng, Hui] CSIRO, Data61, Clayton, Vic 3168, Australia.
   [Chi, Chi-Hung] Nanyang Technol Univ, Singapore 639798, Singapore.
   [He, Jing] Univ Oxford, Nuffield Dept Clin Neurosci, Oxford OX1 2JD, England.
C3 Deakin University; Commonwealth Scientific & Industrial Research
   Organisation (CSIRO); Nanyang Technological University; University of
   Oxford
RP Huang, GY (corresponding author), Deakin Univ, Sch Informat Technol, Blackburn South 3130, Australia.
EM wujunfeng@vip.163.com; guangyan.huang@deakin.edu.au;
   hui.zheng@data61.csiro.au; guangli.huang@qq.com; b.cai@deakin.edu.au;
   chihungchi@gmail.com; jing.he@ndcn.ox.ac.uk
RI Zhang, Yuyao/KEH-7175-2024; Huang, Guang-Li/AED-6392-2022; Wu,
   Junfeng/C-7246-2019
OI Huang, Guang-Li/0000-0001-8698-2946; Wu, Junfeng/0000-0003-1263-3051
FU Australian Research Council [DP190100587]
FX This work was supported by the Australian Research Council under Grant
   DP190100587.(Corresponding author: Guangyan Huang.).
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PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2471-285X
J9 IEEE T EM TOP COMP I
JI IEEE Trans. Emerg. Top. Comput. Intell.
PD FEB
PY 2024
VL 8
IS 1
BP 752
EP 761
DI 10.1109/TETCI.2023.3266944
EA APR 2023
PG 10
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA KA1I7
UT WOS:000980472900001
DA 2024-09-05
ER
PT J
AU Thomas, J
   Zaytseva, A
AF Thomas, John
   Zaytseva, Anna
TI Mapping complexity/Human knowledge as a complex adaptive system
SO COMPLEXITY
LA English
DT Article
DE knowledge architecture dynamics; scientometrics; knowledge as a complex
   adaptive system; emergence; Latent Dirichlet Allocation
AB Cartography is the art of map-making that integrates science, technology, and visual aesthetics for the purpose of rendering the domain of interest, navigable. The science could aid the cartographer if it were to inform about the underlying process. Thus, Mendeleev's periodic table was informed by insights about the atomic mass periodicity. Likewise, Harvey's work on the circulatory system map was informed by his theoretical insights on Galen's errors. Mapping of human knowledge dates back at least to Porphyry who laid out the first tree-of-knowledge. Modern knowledge-cartographers use a wide array of scientometric techniques capable of rendering appealing visuals of massive scientific corpuses. But what has perhaps been lacking is a sound theoretical basis for rendering legible the adaptive dynamics of knowledge creation and accumulation. Proposed is a theoretical framework, knowledge as a complex adaptive system (CAS) patterned on Holland's work on CAS, as well as the view that knowledge is a hierarchically heterarchic dynamical system. As a first leg in the conjoining experimental phase, we extract terms from approximately 1400 complexity science papers published at the Santa Fe Institute, deduce the topic distribution using Latent Dirichlet Allocation, capture the underlying dynamics, and show how to navigate the corpus visually. (c) 2016 Wiley Periodicals, Inc. Complexity 21: 207-234, 2016
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C3 University of Oslo
RP Thomas, J (corresponding author), Cognit Tools Ltd LLC, POB 695,255 North Ave, New Rochelle, NY 10801 USA.
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U2 25
PU WILEY-HINDAWI
PI LONDON
PA ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON, WIT 5HE, ENGLAND
SN 1076-2787
EI 1099-0526
J9 COMPLEXITY
JI Complexity
PD NOV-DEC
PY 2016
VL 21
IS S2
BP 207
EP 234
DI 10.1002/cplx.21799
PG 28
WC Mathematics, Interdisciplinary Applications; Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematics; Science & Technology - Other Topics
GA EC7ND
UT WOS:000388325600020
DA 2024-09-05
ER
PT J
AU Briggs, DC
AF Briggs, Derek C.
TI Strive for Measurement, Set New Standards, and Try Not to Be Evil
SO JOURNAL OF EDUCATIONAL AND BEHAVIORAL STATISTICS
LA English
DT Article; Early Access
DE artificial intelligence; assessment; language comprehension/development;
   measurement; psychology; research methodology; validity/reliability
AB I consider recent attempts to establish standards, principles, and goals for artificial intelligence (AI) through the lens of educational measurement. Distinctions are made between generative AI and AI-adjacent methods and applications of AI in formative versus summative assessment contexts. While expressing optimism about its possibilities, I caution that the examples of truly generative AI in educational testing have the potential to be overexaggerated, that efforts to establish standards for AI should complement the Standards for Educational and Psychological Testing and focus attention on the issues of fairness and social responsibility, and that scientific advance and transparency in the development and application of AI in educational assessment may be incompatible with the competitive marketplace that is funding this development.
C1 [Briggs, Derek C.] Univ Colorado, Educ, Boulder, CO 80309 USA.
C3 University of Colorado System; University of Colorado Boulder
RP Briggs, DC (corresponding author), Univ Colorado, Educ, Boulder, CO 80309 USA.
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NR 18
TC 1
Z9 1
U1 6
U2 6
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 1076-9986
EI 1935-1054
J9 J EDUC BEHAV STAT
JI J. Educ. Behav. Stat.
PD 2024 APR 7
PY 2024
DI 10.3102/10769986241238479
EA APR 2024
PG 8
WC Education & Educational Research; Social Sciences, Mathematical Methods;
   Psychology, Mathematical
WE Social Science Citation Index (SSCI)
SC Education & Educational Research; Mathematical Methods In Social
   Sciences; Psychology
GA NE5V5
UT WOS:001198799200001
DA 2024-09-05
ER
PT C
AU Rao, SG
   Rao, PV
   Rambabu, R
   Reddy, PCS
AF Rao, Govinda S.
   Rao, Varaprasada P.
   Rambabu, R.
   Reddy, Chandra Sekahar P.
BE Goswami, A
TI A Novel Approach in Clustering Algorithm to Evaluate the Performance of
   Regression Analysis
SO PROCEEDINGS OF THE 2018 IEEE 8TH INTERNATIONAL ADVANCE COMPUTING
   CONFERENCE (IACC 2018)
SE IEEE International Advance Computing Conference
LA English
DT Proceedings Paper
CT IEEE 8th International Advance Computing Conference (IACC)
CY DEC 14-15, 2018
CL Bennett Univ, Greater Noida, INDIA
HO Bennett Univ
DE Modified K Means Clustering Algorithm; Regression Analysis; Cluster;
   SCImago; Bibliometric Index
AB This paper, introduced a new methodology to raise the metric of a journal's impact. This method is depending on finding clusters from SC Imago database and creates datasets utilizing a modified k-means clustering algorithm. Farther, developing of linear regression analysis on these datasets is perplexed by seeing index values are dependent variables and citation parameters as independent variables result in assessing contributing factors to increase bibliometric index of any journal. next step, cluster quality metrics enforced to evaluate the perfectness of fit of the cluster such as homogeneity score, completeness score, V measure, accommodated rand score and silhouette coefficient. The output of modified k-means algorithm on a dataset of 1445 journals resulted in 3 clusters (k=3). Each cluster data clustered depending on the title.
   The regression analysis states that the publisher who desires to enhance his journal bibliometric indexes should deliberate the advice conferred, in this work, bring large number of paper submissions to their journal especially. Almost four indices which are of main importance in the publisher industry having been used this. The analysis ensure in strong advantage as the testing of output produced including regression parameters clarified with the identification of outliers by the inclusion of relative error calculation. Accordingly, seeing the suggestive features with increase or decrease in TD3, TC3, CD3, CD2 and RD values, the publisher would profit from raising their respective bibliometric index.
C1 [Rao, Govinda S.; Rao, Varaprasada P.; Reddy, Chandra Sekahar P.] GRIET, CSE, Hyderabad, Telangana, India.
   [Rambabu, R.] RIET, CSE, Rajamahendravaram, India.
C3 Gokaraju Rangaraju Institute of Engineering & Technology
RP Rao, SG (corresponding author), GRIET, CSE, Hyderabad, Telangana, India.
EM govind.griet@gmail.com; prasadp.griet@gmail.com;
   rambabureddy.rampatruni@gmail.com; pchandureddy@yahoo.com
RI s, govinda rao/AAF-9720-2020; REDDY, P CHANDRA Sekhar/AAD-6388-2020; P,
   Varaprasada Rao/AAG-2920-2020
OI s, govinda rao/0000-0002-1331-1786; REDDY, P CHANDRA
   Sekhar/0000-0001-8666-2619; 
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NR 10
TC 0
Z9 0
U1 0
U2 2
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2164-8263
BN 978-1-5386-6678-4
J9 IEEE INT ADV COMPUT
PY 2018
BP 47
EP 52
PG 6
WC Computer Science, Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BO1AG
UT WOS:000494356000008
DA 2024-09-05
ER
PT J
AU Wang, DL
   Guo, XW
AF Wang, Daliang
   Guo, Xiaowen
TI Research on Evaluation Model of Music Education Informatization System
   Based on Machine Learning
SO SCIENTIFIC PROGRAMMING
LA English
DT Article
AB Music education informatization system can promote music teaching; in addition, due to the characteristics of music disciplines such as the audiovisual nature of music, the influence of informatization on music teaching is self-evident. With the rapid development of the human ability to obtain information, machine learning algorithms have been widely used in various fields of scientific research and engineering, involving chemical production statistical process control, archeology text recognition, social and criminal investigation field fingerprint and image recognition, and genomic information research in the field of biomedicine. In order to correctly evaluate the music education information system based on machine learning, through the comparison of four models, it is concluded that the construction of the GBDT model is optimal.
C1 [Wang, Daliang; Guo, Xiaowen] Henan Polytech, Mus Acad, Zhengzhou, Peoples R China.
C3 Henan Polytechnic
RP Wang, DL (corresponding author), Henan Polytech, Mus Acad, Zhengzhou, Peoples R China.
EM daliangwang@hnzj.edu.cn; padoca@163.com
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NR 22
TC 3
Z9 3
U1 0
U2 9
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1058-9244
EI 1875-919X
J9 SCI PROGRAMMING-NETH
JI Sci. Program.
PD FEB 24
PY 2022
VL 2022
AR 9658735
DI 10.1155/2022/9658735
PG 12
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZZ0WL
UT WOS:000772997100005
OA gold
DA 2024-09-05
ER
PT J
AU RAITT, DI
AF RAITT, DI
TI RECALL AND PRECISION DEVICES IN INTERACTIVE BIBLIOGRAPHIC SEARCH AND
   RETRIEVAL-SYSTEMS
SO ASLIB PROCEEDINGS
LA English
DT Article
RP RAITT, DI (corresponding author), EUROPEAN SPACE AGCY,LIB & INFORMAT SERV,NL-2200 AG NOORDWIJK,NETHERLANDS.
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NR 8
TC 5
Z9 5
U1 0
U2 1
PU ASLIB
PI LONDON
PA 20-24 OLD ST, LONDON, ENGLAND EC1V 9AP
SN 0001-253X
J9 ASLIB PROC
JI Aslib Proc.
PY 1980
VL 32
IS 7-8
BP 281
EP 301
DI 10.1108/eb050747
PG 21
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA JZ642
UT WOS:A1980JZ64200001
DA 2024-09-05
ER
PT J
AU Qi, RH
   Wei, J
   Shao, Z
   Li, ZG
   Chen, H
   Sun, YH
   Li, SH
AF Qi, Ruihua
   Wei, Jia
   Shao, Zhen
   Li, Zhengguang
   Chen, Heng
   Sun, Yunhao
   Li, Shaohua
TI Multi-task learning model for citation intent classification in
   scientific publications
SO SCIENTOMETRICS
LA English
DT Article
DE Citation intent classification; Multi-task; Pretrained language model;
   Heterogeneous features
AB Citations play a significant role in the evaluation of scientific literature and researchers. Citation intent analysis is essential for academic literature understanding. Meanwhile, it is useful for enriching semantic information representation for the citation intent classification task because of the rapid growth of publicly accessible full-text literature. However, some useful information that is readily available in citation context and facilitates citation intent analysis has not been fully explored. Furthermore, some deep learning models may not be able to learn relevant features effectively due to insufficient training samples of citation intent analysis tasks. Multi-task learning aims to exploit useful information between multiple tasks to help improve learning performance and exhibits promising results on many natural language processing tasks. In this paper, we propose a joint semantic representation model, which consists of pretrained language models and heterogeneous features of citation intent texts. Considering the correlation between citation intents, citation section and citation worthiness classification tasks, we build a multi-task citation classification framework with soft parameter sharing constraint and construct independent models for multiple tasks to improve the performance of citation intent classification. The experimental results demonstrate that the heterogeneous features and the multi-task framework with soft parameter sharing constraint proposed in this paper enhance the overall citation intent classification performance.
C1 [Qi, Ruihua; Li, Zhengguang; Chen, Heng; Sun, Yunhao; Li, Shaohua] Dalian Univ Foreign Languages, Sch Software, Dalian, Liaoning, Peoples R China.
   [Qi, Ruihua; Wei, Jia; Shao, Zhen] Dalian Univ Foreign Languages, Res Ctr Language Intelligence, Dalian, Liaoning, Peoples R China.
C3 Dalian University of Foreign Languages; Dalian University of Foreign
   Languages
RP Qi, RH (corresponding author), Dalian Univ Foreign Languages, Sch Software, Dalian, Liaoning, Peoples R China.; Qi, RH (corresponding author), Dalian Univ Foreign Languages, Res Ctr Language Intelligence, Dalian, Liaoning, Peoples R China.
EM rhqi@dlufl.edu.cn; lizhengguang2004@163.com
FU This work is partially supported by grant from the Applied Basic
   Research Project of Liaoning Province (No. 2022JH2/101300270), the
   Scientific Research Innovation Team Project of Dalian University of
   Foreign Languages (No. 2016CXTD06) [2022JH2/101300270]; Applied Basic
   Research Project of Liaoning Province [2016CXTD06]; Scientific Research
   Innovation Team Project of Dalian University of Foreign Languages
FX This work is partially supported by grant from the Applied Basic
   Research Project of Liaoning Province (No. 2022JH2/101300270), the
   Scientific Research Innovation Team Project of Dalian University of
   Foreign Languages (No. 2016CXTD06)
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NR 36
TC 1
Z9 1
U1 6
U2 14
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD DEC
PY 2023
VL 128
IS 12
BP 6335
EP 6355
DI 10.1007/s11192-023-04858-4
EA OCT 2023
PG 21
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA AH4D9
UT WOS:001097747800001
DA 2024-09-05
ER
PT J
AU Li, L
   Wang, JZ
AF Li, Lin
   Wang, Juzhen
TI Research on feature importance evaluation of wireless signal recognition
   based on decision tree algorithm in cognitive computing
SO COGNITIVE SYSTEMS RESEARCH
LA English
DT Article
DE Cognitive computing; Modulation recognition; Feature evaluation
ID NETWORKS
AB Cognitive computing is an important method in the field of wireless signal processing, analysis and recognition. How to select features to complete the cognitive computing quickly and effectively is an important role in real application. In this paper, three kinds of features are extracted from six communication signals: power spectrum entropy, singular spectrum entropy and wavelet energy entropy. And the importance of the features is evaluated. Box-diagram and recognition rate are used for the evaluation of single feature. The visual boundaries of feature classification are used to evaluate two features. Meanwhile, the confusion matrix and the visualization model of decision tree are given for more detailed evaluation. The evaluation results show that the combination of power spectrum entropy and singular spectrum entropy can get the best recognition performance. (C) 2018 Elsevier B.V. All rights reserved.
C1 [Li, Lin] State Key Lab Complex Electromagnet Environm Effe, Luoyang 471003, Henan, Peoples R China.
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RP Wang, JZ (corresponding author), Zhongxing Telecommun Equipment Corp ZTE Corp, 55 Keji Rd South, Shenzhen 518057, Peoples R China.
EM wjzheu@163.com
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NR 33
TC 6
Z9 6
U1 0
U2 13
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2214-4366
EI 1389-0417
J9 COGN SYST RES
JI Cogn. Syst. Res.
PD DEC
PY 2018
VL 52
BP 882
EP 890
DI 10.1016/j.cogsys.2018.09.007
PG 9
WC Computer Science, Artificial Intelligence; Neurosciences; Psychology,
   Experimental
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Neurosciences & Neurology; Psychology
GA HB2GA
UT WOS:000450854400089
DA 2024-09-05
ER
PT C
AU Kosch, O
   Szarucki, M
AF Kosch, Oskar
   Szarucki, Marek
BE Glanzel, W
   Heeffer, S
   Chi, PS
   Rousseau, R
TI A model for Custom Bibliographic Databases Creation: Machine Learning
   Approach for Analogue Documents Inclusion
SO 18TH INTERNATIONAL CONFERENCE ON SCIENTOMETRICS & INFORMETRICS
   (ISSI2021)
SE Proceedings of the International Conference on Scientometrics and
   Informetrics
LA English
DT Proceedings Paper
CT 18th International Conference on Scientometrics and Informetrics (ISSI)
CY JUL 12-15, 2021
CL KU Leuven, ELECTR NETWORK
HO KU Leuven
ID SCOPUS; ERRORS/HORRORS; COVERAGE; SCIENCE; MUSEUM; WEB
AB To fully take advantage of rigorous literature-based study (e.g., bibliometrics or systematic literature reviews), databases that enable the inclusion of all relevant documents are needed. As many currently available bibliographic databases inherit characteristics of the core-periphery model of scientific production or are unbalanced in their thematic structure, the need for a model for custom bibliographic databases creation procedure arises. At the same time, the adoption of machine learning is apparent throughout science, technology and business, as we learn how to maximise its usefulness. We investigate what model for custom bibliographic database could be adopted, especially taking into consideration the inclusion of analogue publications. Such documents need to be converted into digital form, hence we propose machine learning algorithms and then we explore their accuracy in analogue documents inclusion into the custom bibliographic database for the case of Polish management sciences methodology. We explore the application of neural networks for image preprocessing and optical character recognition, and subsequent application of conditional random fields to obtain a bibliographic database. Our results are highly suggestive and reveal the applicability of the proposed model consisting of database search and snowballing.
C1 [Kosch, Oskar; Szarucki, Marek] Cracow Univ Econ, Rakowicka 27, PL-31510 Krakow, Poland.
C3 Cracow University of Economics
RP Kosch, O (corresponding author), Cracow Univ Econ, Rakowicka 27, PL-31510 Krakow, Poland.
EM koscho@uek.krakow.pl; szaruckm@uek.krakow.pl
RI Kosch, Oskar/AAD-3183-2021; Szarucki, Marek/T-2662-2018
OI Kosch, Oskar/0000-0003-2697-1393
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   Web of Knowledge, J TITL ABBR
NR 46
TC 0
Z9 0
U1 0
U2 1
PU INT SOC SCIENTOMETRICS & INFORMETRICS-ISSI
PI LEUVEN
PA KATHOLIEKE UNIV LEUVEN, FACULTEIT E T E W, DEKENSTRAAT 2, LEUVEN,
   B-3000, BELGIUM
SN 2175-1935
BN 978-90-803282-2-8
J9 PRO INT CONF SCI INF
PY 2021
BP 573
EP 584
PG 12
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Information Science & Library Science
GA BS3CO
UT WOS:000709638700065
DA 2024-09-05
ER
PT J
AU Badia, G
AF Badia, Giovanna
TI Identifying "best bets" for searching in chemical engineering Comparing
   database content and performance for information retrieval
SO JOURNAL OF DOCUMENTATION
LA English
DT Article
DE Information retrieval; Searching; Comparative tests; Citation analysis;
   Online databases; Search recall
ID BIBLIOGRAPHIC DATABASES; GOOGLE SCHOLAR; H-INDEX; COVERAGE; JOURNALS;
   EIGENFACTOR(TM); RECALL
AB Purpose - Performing efficient literature searches and subscribing to the most comprehensive databases for interdisciplinary fields can be challenging since the literature is typically indexed in numerous databases to different extents. Comparing databases will help information professionals make appropriate choices when teaching, literature searching, creating online subject guides, and deciding which databases to renew when faced with fiscal challenges. The purpose of this paper is to compare databases for searching the chemical engineering literature.
   Design/methodology/approach - This paper compares journal indexing and search recall across seven databases that cover the chemical engineering literature in order to determine which database and database pair provide the most comprehensive coverage in this area. It also summarizes published, database comparison methods to aid information professionals in undertaking their own comparative assessments.
   Findings - SciFinder, Scopus, and Web of Science, listed alphabetically, were the leading databases for searching the chemical engineering literature. SciFinder-Scopus and SciFinder-Web of Science were the top two database pairs. No single database or pair provided 100 percent complete coverage of the literature examined. Searching a second database increased the recall of results by an average of 17.6 percent.
   Practical implications - The findings are useful since they identify "best bets" for performing an efficient search of the chemical engineering literature. Information professionals can also use the methods discussed to compare databases for any discipline or search topic.
   Originality/value - This paper builds on the previous literature by using a dual approach to compare the coverage of the chemical engineering literature across multiple databases. To the author's knowledge, comparing databases in the field of chemical engineering has not been reported in the literature thus far.
C1 [Badia, Giovanna] McGill Univ, Schulich Lib Phys Sci Life Sci & Engn, Montreal, PQ, Canada.
C3 McGill University
RP Badia, G (corresponding author), McGill Univ, Schulich Lib Phys Sci Life Sci & Engn, Montreal, PQ, Canada.
EM giovanna.badia@mcgill.ca
RI Badia, Giovanna/A-5829-2009
OI Badia, Giovanna/0000-0003-1544-2728
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NR 46
TC 2
Z9 3
U1 0
U2 31
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 0022-0418
EI 1758-7379
J9 J DOC
JI J. Doc.
PY 2018
VL 74
IS 1
BP 80
EP 98
DI 10.1108/JD-09-2016-0112
PG 19
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA FR6QP
UT WOS:000419191300005
DA 2024-09-05
ER
PT J
AU de Waal, A
AF de Waal, Andre
TI Evaluating High Performance the Evidence-Based Way: The Case of the
   Swagelok Transformers
SO SAGE OPEN
LA English
DT Article
DE high performance organizations; HPO framework; long-term research; North
   America; organizational performance
ID ORGANIZATIONAL PERFORMANCE; SUBJECTIVE MEASURES; IMPACT; INNOVATION;
   EFFICIENCY; UNIVERSITY; RESOURCES; TURNOVER; GREAT; FOCUS
AB Many of the publications on achieving high performance have been written by North American researchers and consultants, and the case companies they described originate mainly from the United States. However, there is a lack of long-term studies that subject the described techniques to rigorous evidence-based management research in North American companies, to test the ideas in practice over a period of time to evaluate their relevance to managerial practice. In this article, we evaluate the high performance organization (HPO) Framework, a scientifically validated technique for helping organizations become high performing, in the North American context. This framework evaluates the strengths and weaknesses of the internal organization of a company, using a questionnaire. This questionnaire was applied in 2013 at seven Swagelok locations in the United States and Canada. From the questionnaire improvement opportunities were identified on which the locations subsequently worked. In 2015, the questionnaire was repeated to evaluate the effects of these improvements on the locations' performance and to identify the most effective interventions. The study results show that the application of the HPO Framework had different outcomes depending on local circumstances. Some locations experienced a growth while other locations used the framework to battle the consequences of adverse economic circumstances. All locations agreed that the HPO Framework had been instrumental, in a positive way, to the development of their organization and its people.
C1 [de Waal, Andre] HPO Ctr, Havenstr 29, NL-1211 KG Hilversum, Netherlands.
RP de Waal, A (corresponding author), HPO Ctr, Havenstr 29, NL-1211 KG Hilversum, Netherlands.
EM andredewaal@planet.nl
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NR 60
TC 2
Z9 3
U1 0
U2 2
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 2158-2440
J9 SAGE OPEN
JI SAGE Open
PD OCT 12
PY 2017
VL 7
IS 4
AR 2158244017736801
DI 10.1177/2158244017736801
PG 15
WC Social Sciences, Interdisciplinary
WE Social Science Citation Index (SSCI)
SC Social Sciences - Other Topics
GA FJ7PE
UT WOS:000412950400001
OA gold
DA 2024-09-05
ER
PT C
AU Li, XL
   Xue, HA
AF Li, Xueliang
   Xue, Haian
BE Marcus, A
   Rosenzweig, E
   Soares, MM
TI Cultivating Researcher-Sensibility in Novice Designers: Exploring
   Genre-Specific Heuristics for Game Evaluation in a Design Studio
SO DESIGN, USER EXPERIENCE, AND USABILITY, DUXU 2023, PT II
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT 12th International Conference on Design, User Experience, and Usability
   (DUXU) Held as Part of the 25th International Conference on
   Human-Computer Interaction (HCII)
CY JUL 23-28, 2023
CL Copenhagen, DENMARK
DE Design Education; Studio; Design Research Methodology; Design Evaluation
AB This paper presents an eight-day design studio that teaches heuristic evaluation of games to third-year bachelor students at the School of Design, Southern University of Science and Technology. Through this course, students gain the first-hand experiences of developing heuristics for games through online survey and using them in idea generation and game evaluation. 13 students (working in groups of two or individually) developed 88 heuristics for 8 game genres by analyzing 349 quotes of game reviews collected from online. The heuristics were further developed into questionnaires and tested with invited 51 game players, followed up by post-interviews. The heuristics were also used as inspirational tools to help the students generate design ideas in an ideation exercise. Results of the students' work indicate usefulness of the heuristics as evaluative and inspirational tools. In the discussion, we reflected on the challenges encountered by the students over the course and how dealing with these challenges could reveal further directions of teaching research methods in HCI studios.
C1 [Li, Xueliang] Southern Univ Sci & Technol, Sch Design, Chuangyuan Bldg 6,1088 Xueyuan Ave, Shenzhen, Guangdong, Peoples R China.
   [Xue, Haian] Delft Univ Technol, Fac Ind Design Engn, Landbergstr 15, NL-2628 CE Delft, Netherlands.
C3 Southern University of Science & Technology; Delft University of
   Technology
RP Li, XL (corresponding author), Southern Univ Sci & Technol, Sch Design, Chuangyuan Bldg 6,1088 Xueyuan Ave, Shenzhen, Guangdong, Peoples R China.
EM lixl6@sustech.edu.cn; h.xue@tudelft.nl
OI Xue, Haian/0000-0002-2351-9070
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NR 27
TC 0
Z9 0
U1 0
U2 0
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 0302-9743
EI 1611-3349
BN 978-3-031-35695-7; 978-3-031-35696-4
J9 LECT NOTES COMPUT SC
PY 2023
VL 14031
BP 483
EP 496
DI 10.1007/978-3-031-35696-4_35
PG 14
WC Computer Science, Cybernetics; Computer Science, Software Engineering;
   Computer Science, Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BW8RY
UT WOS:001206155200035
DA 2024-09-05
ER
PT J
AU Liang, K
   Wu, P
   Zhang, R
AF Liang, Kun
   Wu, Peng
   Zhang, Rui
TI Research on the Evaluation of Regional Scientific and Technological
   Innovation Capabilities Driven by Big Data
SO SUSTAINABILITY
LA English
DT Article
DE RSTI; big data; LDA; AHP-SMO
ID DEVELOPMENT PROJECT-SELECTION; SYSTEM
AB Scientific and technological innovation (STI) is an important internal driver of social and economic development. Reasonable evaluation of regional scientific and technological innovation (RSTI) capability helps discover shortcomings in the development of urban development and guides the allocation of scientific and technological resources and the formulation of policies to promote innovation. This paper analyzes new opportunities created by big data and artificial intelligence for the evaluation of RSTI capability, and based on this analysis, the collaborative evaluation schemes of multi-entity participation are investigated. In addition, considering the important value of unstructured data in evaluating STI, the Latent Dirichlet Allocation (LDA) topic model and sentiment analysis method are employed to analyze the construction of an evaluation indicator system that integrates scientific and technological news data. To fully utilize the respective advantages of human experts and machine learning in the field of complex issue evaluation, this paper proposes an RSTI capability evaluation model based on AHP-SMO human-machine fusion. This study promotes the integration of science and technology and economy and has theoretical and practical significance.
C1 [Liang, Kun; Wu, Peng; Zhang, Rui] Anhui Univ, Sch Business, Hefei 230601, Peoples R China.
C3 Anhui University
RP Liang, K (corresponding author), Anhui Univ, Sch Business, Hefei 230601, Peoples R China.
EM 17014@ahu.edu.cn; pengwu@ahu.edu.cn; 05073@ahu.edu.cn
FU University Scientific Research Project of Anhui Province
FX No Statement Available
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NR 46
TC 0
Z9 0
U1 17
U2 17
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD FEB
PY 2024
VL 16
IS 4
AR 1379
DI 10.3390/su16041379
PG 22
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA JJ0D2
UT WOS:001172670700001
OA gold
DA 2024-09-05
ER
PT J
AU Mulhearn, TJ
   Watts, LL
   Todd, EM
   Medeiros, KE
   Connelly, S
   Mumford, MD
AF Mulhearn, Tyler J.
   Watts, Logan L.
   Todd, E. Michelle
   Medeiros, Kelsey E.
   Connelly, Shane
   Mumford, Michael D.
TI Validation and Use of a Predictive Modeling Tool: Employing Scientific
   Findings to Improve Responsible Conduct of Research Education
SO ACCOUNTABILITY IN RESEARCH-ETHICS INTEGRITY AND POLICY
LA English
DT Article
DE Education; ethics; evaluation; path model; RCR; responsible conduct of
   research; tool; training
ID TEACHING MEDICAL-ETHICS; SENSEMAKING APPROACH; STUDENTS; INSTRUCTION
AB Although recent evidence suggests ethics education can be effective, the nature of specific training programs, and their effectiveness, varies considerably. Building on a recent path modeling effort, the present study developed and validated a predictive modeling tool for responsible conduct of research education. The predictive modeling tool allows users to enter ratings in relation to a given ethics training program and receive instantaneous evaluative information for course refinement. Validation work suggests the tool's predicted outcomes correlate strongly (r=0.46) with objective course outcomes. Implications for training program development and refinement are discussed.
C1 [Mulhearn, Tyler J.; Watts, Logan L.; Todd, E. Michelle; Connelly, Shane; Mumford, Michael D.] Univ Oklahoma, Dept Psychol, 201 Stephenson Pkwy,Ste 4100, Norman, OK 73019 USA.
   [Medeiros, Kelsey E.] Univ Texas Arlington, Dept Psychol, Arlington, TX 76019 USA.
C3 University of Oklahoma System; University of Oklahoma - Norman;
   University of Texas System; University of Texas Arlington
RP Mumford, MD (corresponding author), Univ Oklahoma, Dept Psychol, 201 Stephenson Pkwy,Ste 4100, Norman, OK 73019 USA.
EM mmumford@ou.edu
RI Watts, Logan L./I-5332-2019
OI Watts, Logan L./0000-0001-7629-0188; Medeiros,
   Kelsey/0000-0002-7388-3970
FU Office of Research Integrity [ORIIR140010-01-00]
FX The project described was supported by grant number ORIIR140010-01-00
   from the Office of Research Integrity. The contents of this publication
   are solely the responsibility of the authors and do not necessarily
   represent the official views of the Department of Health and Human
   Services or the Office of Research Integrity.
CR Al-Jalahma Mariam, 2004, Educ Health (Abingdon), V17, P62, DOI 10.1080/13576280310001656187
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NR 46
TC 4
Z9 4
U1 0
U2 5
PU TAYLOR & FRANCIS INC
PI PHILADELPHIA
PA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA
SN 0898-9621
EI 1545-5815
J9 ACCOUNT RES
JI Account. Res.
PY 2017
VL 24
IS 4
BP 195
EP 210
DI 10.1080/08989621.2016.1274886
PG 16
WC Medical Ethics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Medical Ethics
GA EN0MS
UT WOS:000395704700001
PM 28005407
DA 2024-09-05
ER
PT J
AU Basilio, MP
   Pereira, V
   de Oliveira, MWCM
   Neto, AFD
   de Moraes, OCR
   Siqueira, SCB
AF Basilio, Marcio Pereira
   Pereira, Valdecy
   de Oliveira, Max William Coelho Moreira
   da Costa Neto, Antonio Fernandes
   de Moraes, Orlinda Claudia Rosa
   Siqueira, Samya Cotta Brandao
TI Knowledge discovery in research on domestic violence: an overview of the
   last fifty years
SO DATA TECHNOLOGIES AND APPLICATIONS
LA English
DT Article
DE Domestic violence; Intimate partner violence; Bibliometric analysis;
   Latent Dirichlet allocation; Domestic abuse; Family violence
AB Purpose The database of the Web of Science (WoS) was searched for publications from January 1945-May 7, 2020 on the topic of domestic violence in titles, abstracts and keywords. The references were analyzed using the R bibliometrix package, and abstracts were analyzed using latent Dirichlet allocation (LDA) with collapsed Gibbs sampling to obtain topics related to domestic violence. Design/methodology/approach The aim of the study is to explore and provide an overview of research carried out on domestic violence, in its various aspects, over the past fifty years. Findings As a result of the research, the authors can assert that in the last fifty years, 32,298 authors have produced 19,495 documents on the theme of policing strategy and related subjects in 111 countries. Scientific production in this area grows at a rate of 12.81 per year. The United States of America is the leading country in publications with 48.14%, followed by the United Kingdom with 7.57% and Australia with 6.05%. Regarding universities, the highlight is the University of California with 664 publications, followed by the University of London with 515 and the University of North Carolina with 484. As for journals, the highlight is the Journal of Interpersonal Violence, Journal of Family Violence and Violence Against Women, which account for more than 14.32% of all indexed literature. Regarding the authors, the highlight is Campbell J.C and Feder G. Probabilistic topic modeling revealed that 18% of the topics concentrate 90% of all tokens. Topic 1 accounts for 27.9% of the sample and conducts research related to intimate partner violence. Practical implications As a practical implication of using the LDA in the bibliographic review, we infer that its capacity to explore large masses of data allows the researcher to explore an infinitely greater amount than the traditional methods of systematic literature review. Originality/value The value of these studies is summarized in the presentation of an overview on the theme in the last fifty years, offering the opportunity for other researchers to use this research as a starting point for other analyses.
C1 [Basilio, Marcio Pereira] Fed Fluminense Univ, Dept Prod Engn, Niteroi, RJ, Brazil.
   [Basilio, Marcio Pereira; da Costa Neto, Antonio Fernandes; Siqueira, Samya Cotta Brandao] Mil Police State Rio de Janeiro, Rio De Janeiro, Brazil.
   [da Costa Neto, Antonio Fernandes] Getulio Vargas Fdn, Rio De Janeiro, Brazil.
C3 Universidade Federal Fluminense; Getulio Vargas Foundation
RP Basilio, MP (corresponding author), Fed Fluminense Univ, Dept Prod Engn, Niteroi, RJ, Brazil.; Basilio, MP (corresponding author), Mil Police State Rio de Janeiro, Rio De Janeiro, Brazil.
EM marcio_basilio@id.uff.br; valdecy.pereira@gmail.com;
   mwcoliveira@yahoo.com.br; antoniocostaneto@gmail.com;
   orlindamoraes@gmail.com; samyacotta@gmail.com
RI Pereira, Valdecy/I-7493-2017; Basilio, Marcio Pereira/L-4363-2016
OI Pereira, Valdecy/0000-0003-0599-8888; Basilio, Marcio
   Pereira/0000-0002-9453-741X; Fernandes da Costa Neto,
   Antonio/0000-0002-6920-586X
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NR 69
TC 12
Z9 12
U1 2
U2 47
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 2514-9288
EI 2514-9318
J9 DATA TECHNOL APPL
JI Data Technol. Appl.
PD AUG 5
PY 2021
VL 55
IS 4
BP 480
EP 510
DI 10.1108/DTA-08-2020-0179
EA MAR 2021
PG 31
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA TX9NL
UT WOS:000625455900001
DA 2024-09-05
ER
PT J
AU Steinfeldt, C
   Mihaljevic, H
AF Steinfeldt, Christian
   Mihaljevic, Helena
TI A machine learning approach to quantify gender bias in collaboration
   practices of mathematicians
SO FRONTIERS IN BIG DATA
LA English
DT Article
DE collaboration networks; machine learning; gender in mathematics;
   regression-based analysis; authorship; scientific publishing;
   single-authored publications; coauthorship
AB Collaboration practices have been shown to be crucial determinants of scientific careers. We examine the effect of gender on coauthorship-based collaboration in mathematics, a discipline in which women continue to be underrepresented, especially in higher academic positions. We focus on two key aspects of scientific collaboration-the number of different coauthors and the number of single authorships. A higher number of coauthors has a positive effect on, e.g., the number of citations and productivity, while single authorships, for example, serve as evidence of scientific maturity and help to send a clear signal of one's proficiency to the community. Using machine learning-based methods, we show that collaboration networks of female mathematicians are slightly larger than those of their male colleagues when potential confounders such as seniority or total number of publications are controlled, while they author significantly fewer papers on their own. This confirms previous descriptive explorations and provides more precise models for the role of gender in collaboration in mathematics.
C1 [Steinfeldt, Christian; Mihaljevic, Helena] Univ Appl Sci, Hsch Tech & Wirtschaft Berlin, Dept Comp Sci Commun & Econ 4, Berlin, Germany.
RP Mihaljevic, H (corresponding author), Univ Appl Sci, Hsch Tech & Wirtschaft Berlin, Dept Comp Sci Commun & Econ 4, Berlin, Germany.
EM helena.mihaljevic@htw-berlin.de
RI Mihaljevic, Helena/L-8928-2013
OI Mihaljevic, Helena/0000-0003-0782-5382
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NR 46
TC 1
Z9 1
U1 0
U2 4
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 2624-909X
J9 FRONT BIG DATA
JI Front. Big Data
PD JAN 18
PY 2023
VL 5
AR 989469
DI 10.3389/fdata.2022.989469
PG 17
WC Computer Science, Information Systems; Computer Science,
   Interdisciplinary Applications; Multidisciplinary Sciences
WE Emerging Sources Citation Index (ESCI)
SC Computer Science; Science & Technology - Other Topics
GA 8G0DS
UT WOS:000920023500001
PM 36743404
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU Chen, X
   Zhang, QS
   Liu, RT
   Zhao, DK
   Li, HZ
   Pan, XD
   He, WL
   Shi, LL
   Guo, SX
AF Chen, Xin
   Zhang, Qing-Song
   Liu, Ren-Tai
   Zhao, Du-Kun
   Li, Hong-Zhao
   Pan, Xu-Dong
   He, Wan-Li
   Shi, Le-Le
   Guo, Shao-Xuan
TI Research on the impact of underground excavation metro on surface
   traffic safety and assessment method
SO JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS
LA English
DT Article
DE Ou; Yu-Chen; Risk assessment; underground engineering; traffic safety;
   ensemble learning
ID TUNNEL CONSTRUCTION; SETTLEMENT; CHINA
AB This study focuses on a problem that has been seriously troubled urban traffic managers: the impact of underground excavation metros on surface traffic safety during construction. In underground construc-tion, surface subsidence and road collapse due to concealed underground construction constantly occur, resulting in casualties property damage and seriously impairing normal urban road traffic. Urban road managers urgently need a quick, objective, and easy-to-use method for the early warning and prevention of such risks. Therefore, this study collates relevant accident cases that have occurred in China over the last 20 years and summarises the impact mechanisms of concealed underground works affecting surface road traffic capacity through a detailed study of natural factors, engineering geology, types of structures, and types of workmanship. Two indices, Rank(S-c) and Cr, are proposed to describe the impact of concealed underground works on surface traffic capacity, while an early warning method based on data on the impact of urban road capacity by concealed underground works is established using integrated learning methods and is well applied in practical projects. The results of this study are important for helping city managers quickly assess urban road traffic risks.
C1 [Chen, Xin; Zhang, Qing-Song; Liu, Ren-Tai; Zhao, Du-Kun; Li, Hong-Zhao; Pan, Xu-Dong; Guo, Shao-Xuan] Shandong Univ, Geotech & Struct Engn Res Ctr, Jinan, Shandong, Peoples R China.
   [He, Wan-Li] Qingdao Metro Grp Co, Survey & Mapping Ctr, Qingdao, Shandong, Peoples R China.
   [Shi, Le-Le] Qingdao Survey & Mapping Inst, Operat Branch, Qingdao, Shandong, Peoples R China.
C3 Shandong University
RP Chen, X (corresponding author), Shandong Univ, Geotech & Struct Engn Res Ctr, Jinan, Shandong, Peoples R China.
EM chenxinsdu@mail.sdu.edu.cn
RI Zhou, Qi/JTT-3417-2023; pan, xu/KJL-1993-2024; Zhang,
   Qing/IZQ-5273-2023; Yang, Fei/JLM-3367-2023; Zhao, Xuan/JMR-2135-2023;
   GUO, SHAOXUAN/GWV-5154-2022; Wang, Xingyi/KHT-7171-2024; zhang,
   ly/JMB-7214-2023; qin, cheng/KHC-3344-2024; Yan, Miaochen/JLL-5061-2023
FU Key research and development project of Shandong ProvinceThe Major
   Science and Technology Innovation Project of Shandong Province
   [2019JZZY010427]; National Key research and development program Plan of
   China [2018YFB1600104,2020YFB1600504,2021YFB2600800]
FX This work was supported by the Key research and development project of
   Shandong ProvinceThe Major Science and Technology Innovation Project of
   Shandong Province [2019JZZY010427]; The National Key research and
   development program Plan of China
   [2018YFB1600104,2020YFB1600504,2021YFB2600800].
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NR 50
TC 3
Z9 3
U1 13
U2 39
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 0253-3839
EI 2158-7299
J9 J CHIN INST ENG
JI J. Chin. Inst. Eng.
PD APR 3
PY 2023
VL 46
IS 3
BP 267
EP 281
DI 10.1080/02533839.2023.2170928
EA FEB 2023
PG 15
WC Engineering, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA 9G3LR
UT WOS:000929529100001
DA 2024-09-05
ER
PT C
AU Dias, GP
   Gomes, H
AF Dias, G. P.
   Gomes, H.
BE Chova, LG
   Martinez, AL
   Torres, IC
TI COMBINING RESEARCH AND LEARNING: AN EXAMPLE USING LOCAL E-GOVERNMENT
   EVALUATION
SO 12TH INTERNATIONAL TECHNOLOGY, EDUCATION AND DEVELOPMENT CONFERENCE
   (INTED)
SE INTED Proceedings
LA English
DT Proceedings Paper
CT 12th International Technology, Education and Development Conference
   (INTED)
CY MAR 05-07, 2018
CL Valencia, SPAIN
DE research-based learning; active learning; higher education; e-government
AB Between 2010 and 2015, the authors implemented a set of research-based learning activities in the context of two curricular units offered at the University of Aveiro in Portugal. Although initially designed with strictly pedagogical purposes and to familiarize students with research, these activities have formed the basis of several studies that have been published in journals and international conference proceedings. In general, the activities developed with students aimed at evaluating the maturity of municipal websites, under different perspectives of analysis, at different dates, and in different geographies, using content analysis and, as appropriate, maturity models or conceptual analysis. In this article, the motivations, methods, and results achieved are described. The experiment demonstrates that, in appropriate contexts, the link between teaching and learning and research can contribute to student motivation and the development of relevant learning and simultaneously be an added value for research.
C1 [Dias, G. P.] Univ Aveiro, ESTGA GOVCOPP, Aveiro, Portugal.
   [Gomes, H.] Univ Aveiro, ESTGA IEETA, Aveiro, Portugal.
C3 Universidade de Aveiro; Universidade de Aveiro
RP Dias, GP (corresponding author), Univ Aveiro, ESTGA GOVCOPP, Aveiro, Portugal.
RI Dias, Gonçalo Paiva/B-7461-2008; Gomes, Helder/JCD-7431-2023; Gomes,
   Helder/AAC-4495-2019
OI Dias, Gonçalo Paiva/0000-0002-8599-3798; 
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   Dias G. P., 2011, INF SYST TECHN CISTI, P1
   Dias GP, 2013, ADV INTELL SYST, V206, P87, DOI 10.1007/978-3-642-36981-0_9
   Healey M, 2005, J GEOGR HIGHER EDUC, V29, P183, DOI 10.1080/03098260500130387
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   Paiva Dias Goncalo, 2016, Electronic Government, V12, P301
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NR 12
TC 0
Z9 0
U1 0
U2 2
PU IATED-INT ASSOC TECHNOLOGY EDUCATION & DEVELOPMENT
PI VALENICA
PA LAURI VOLPI 6, VALENICA, BURJASSOT 46100, SPAIN
SN 2340-1079
BN 978-84-697-9480-7
J9 INTED PROC
PY 2018
BP 5628
EP 5633
PG 6
WC Education & Educational Research
WE Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Education & Educational Research
GA BL1ZA
UT WOS:000448704000088
DA 2024-09-05
ER
PT C
AU Gao, LC
   Tang, Z
   Lin, XF
AF Gao, Liangcai
   Tang, Zhi
   Lin, Xiaofan
GP ACM
TI CEBBIP: A Parser of Bibliographic Information in Chinese Electronic
   Books
SO JCDL 09: PROCEEDINGS OF THE 2009 ACM/IEEE JOINT CONFERENCE ON DIGITAL
   LIBRARIES
SE ACM-IEEE Joint Conference on Digital Libraries JCDL
LA English
DT Proceedings Paper
CT 9th Annual International ACM/IEEE Joint Conference on Digital Libraries
CY JUN 15-19, 2009
CL Austin, TX
DE Metadata extraction; Digital Library; Chinese Electronic Book;
   Bibliography; Machine learning
AB Bibliographic information is essential for many digital library applications, such as citation analysis, academic searching and topic discovery. And bibliographic data extraction has attracted a great deal of attention in recent years. In this paper, we address the problem of automatic extraction of bibliographic data in Chinese electronic book and propose a tool called CEBBIP. for the task, which includes three main systems: data preprocessing, data parsing and data postprocessing. In the data preprocessing system, the tool adopts a rules-based method to locate citation data in a book and to segment citation data into citation strings of individual referencing literature. And a learning-based approach, Conditional Random Fields (CRF), is employed to parse citation strings in the data parsing system. Finally, the tool takes advantage of document intrinsic local format consistency to enhance citation data segmentation and parsing through clustering techniques. CEBBIP has been used in a commercial E-book production system. Experimental results show that CEBBIP's precision rate is very high. More specially, adopting the document intrinsic local format consistency obviously improves the citation data segmenting and parsing accuracy.
C1 [Gao, Liangcai; Tang, Zhi] Peking Univ, Inst Comp Sci & Technol, Beijing 100871, Peoples R China.
C3 Peking University
RP Gao, LC (corresponding author), Peking Univ, Inst Comp Sci & Technol, Beijing 100871, Peoples R China.
EM gaoliangcai@icst.pku.edu.cn; tangzhi@icst.pku.edu.cn;
   xiaofan@vobileinc.com
RI gao, liangcai/P-8338-2017
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NR 15
TC 4
Z9 4
U1 0
U2 3
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA
SN 2575-7865
EI 2575-8152
BN 978-1-60558-697-7
J9 ACM-IEEE J CONF DIG
PY 2009
BP 73
EP 76
PG 4
WC Computer Science, Hardware & Architecture; Computer Science, Information
   Systems
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BWY55
UT WOS:000295296700010
DA 2024-09-05
ER
PT J
AU Krange, I
   Segaran, M
   Gamlem, S
   Moltudal, S
   Engeness, I
AF Krange, Ingeborg
   Segaran, Meerita
   Gamlem, Siv
   Moltudal, Synnove
   Engeness, Irina
TI A Triple Challenge: Students' Identification, Interpretation, and Use of
   Individualized Automated Feedback in Learning to Write English as a
   Foreign Language.
SO INTERACTION DESIGN AND ARCHITECTURES
LA English
DT Article
DE Assessment for learning; sociocultural interpretation of learning and
   teaching; design-based research; interaction analysis; frequency
   analysis; artificial intelligence (AI); automatic essay assessment;
   junior high school
ID SUPPORT; TEACHERS; QUALITY
AB The aim of this study was to investigate eighth-grade students' assessment literacy and writing skills in English as a foreign language using an AI-based automated essay assessment tool (EAT). Data were gathered from a design-based research initiative where the EAT was designed, developed, and tested in naturalistic school settings. Fifty-six eighth-grade students wrote individual essays, for which they received automatic feedback. The feedback was discussed with their teachers and peers. Both the writing process and teacher and peer interactions were video recorded. The video data were analyzed using an interaction analysis. The improvements made on the essay based on the feedback logs registered by the EAT for each student's writing trajectory and the different versions of the essay were examined using frequency analyses. The findings demonstrate that automated essay assessment might be useful for fostering students' writing skills if teachers help students get started, identify errors, and share interpretations.
C1 [Krange, Ingeborg; Segaran, Meerita; Engeness, Irina] Univ Coll Ostfold, Dept Pedag ICT & Learning, POB 700, NO-1757 Halden, Norway.
   [Gamlem, Siv; Moltudal, Synnove] Univ Coll Volda, Dept Humanities & Teacher Educ, POB 500, NO-6101 Volda, Norway.
C3 Volda University College
RP Krange, I (corresponding author), Univ Coll Ostfold, Dept Pedag ICT & Learning, POB 700, NO-1757 Halden, Norway.
EM ingeborg.h.krange@hiof.no
OI Gamlem, Siv M./0000-0002-6523-0486; Krange,
   Ingeborg/0009-0005-5946-0612; Kunna Segaran,
   Meerita/0000-0001-5592-038X; Engeness, Irina/0000-0001-5948-4992
FU Norwegian Research Council [326607]
FX For their comments and helpful feedback at various stages of manuscript
   preparation, we thank Professor Halla Holmarsdottir, Oslo Metropolitan
   University; Associate Professor Sima Caspari-Sadeghi, Q stfold
   University College; and our research group RIDE (Research in Digital
   Education) . For their participation in this study, we thank the
   teachers and students. For the design and development of the essay
   assessment tool, we thank our interdisciplinary research team from Q
   stfold University College, Volda University College, Hypatia Learning,
   and Halden Municipality. For providing grant No. 326607, we thank the
   Norwegian Research Council.
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NR 42
TC 1
Z9 1
U1 4
U2 4
PU INTERACTION DESIGN & ARCHITECTURES
PI ROMA
PA INTERACTION DESIGN & ARCHITECTURES, ROMA, 00000, ITALY
SN 1826-9745
EI 2283-2998
J9 INTERACT DES ARCHIT
JI Interact. Des. Archit.
PD WIN
PY 2023
IS 59
SI SI
BP 37
EP 61
DI 10.55612/s-5002-059-001
PG 25
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA WC9L0
UT WOS:001252786900003
OA gold
DA 2024-09-05
ER
PT J
AU Leiva, V
   Castro, C
   Vila, R
   Saulo, H
AF Leiva, Victor
   Castro, Cecilia
   Vila, Roberto
   Saulo, Helton
TI Unveiling patterns and trends in research on cumulative damage models
   for statistical and reliability analyses: Bibliometric and thematic
   explorations with data analytics
SO CHILEAN JOURNAL OF STATISTICS
LA English
DT Article
DE Bibliometrical analysis; Birnbaum-Saunders distribution; cumulative
   damage; fatigue life prediction; Gaussian inverse distribution; latent
   Dirichlet allocation
ID BIRNBAUM-SAUNDERS DISTRIBUTION; TRUNCATED LIFE TESTS; ACCEPTANCE
   SAMPLING PLANS; FATIGUE DAMAGE; FAILURE; DISTRIBUTIONS; PREDICTION;
   DIAGNOSTICS; CORROSION; FAMILY
AB This study comprehensively explores the research landscape within statistical and reliability studies, focusing on the Birnbaum-Saunders distribution, Gaussian inverse distribution, cumulative damage models, and fatigue life prediction. Using a combination of bibliometric analysis, network visualization, thematic mapping, and latent Dirichlet allocation, we analyze 465 articles from the ISI Web of Science database. These articles were selected for their relevance based on a targeted search strategy. Our analysis identifies key trends, collaboration networks, and emerging research themes. Notable growth in scholarly activity was observed from 2015 to 2021, with a peak around 2021, followed by a decline in the number of publications. Relevant contributions were noted from countries such as Brazil, Canada, Chile, China, Iran, Japan, and the United States. The thematic analysis of keywords reveals influential motor themes like the Birnbaum-Saunders distribution and expectation-maximization algorithm; specialized niche areas such as producer risk; emerging or declining themes like the generalized Birnbaum-Saunders distribution; and foundational themes including cumulative damage and fatigue life distributions. A cluster analysis states key focus areas, such as material durability and advanced statistical methods. Integrating latent Dirichlet allocation, six main topics are derived, capturing broad thematic structures. However, some niche areas do not align directly due to their specialized nature and limited cross-field impact. These findings map the current research on this thematic and suggest future research directions, including deeper exploration of niche themes, integration of advanced statistical methods in practical applications, and increased collaboration across diverse research areas to enhance the robustness and applicability of reliability models.
C1 [Leiva, Victor] Pontificia Univ Catolica Valparaiso, Escuela Ingn Ind, Valparaiso, Chile.
   [Castro, Cecilia] Univ Minho, Ctr Math, Braga, Portugal.
   [Vila, Roberto; Saulo, Helton] Univ Brasilia, Dept Estat, Brasilia, DF, Brazil.
C3 Pontificia Universidad Catolica de Valparaiso; Universidade do Minho;
   Universidade de Brasilia
RP Leiva, V (corresponding author), Pontificia Univ Catolica Valparaiso, Escuela Ingn Ind, Valparaiso, Chile.
EM victorleivasanchez@gmail.com
RI Costa e Castro, Cecilia Maria Vasconcelos/ACU-7420-2022
OI Costa e Castro, Cecilia Maria Vasconcelos/0000-0001-9897-8186
FU FONDECYT, Chile [1200525]; National Agency for Research and Development
   (ANID) of the Chilean government under the Ministry of Science,
   Technology; Portuguese funds through the CMAT-Research Centre of
   Mathematics of University of Minho, Portugal [UIDB/00013/2020,
   UIDP/00013/2020]; Brazilian agency Conselho Nacional de Desenvolvimento
   Cientifico e Tecnologico (CNPq) [309674/2020-4, 304716/2023-5]
FX This research was partially supported by FONDECYT, Chile, grant number
   1200525 (V.L.) , from the National Agency for Research and Development
   (ANID) of the Chilean government under the Ministry of Science,
   Technology, Knowledge, and Innovation; by Portuguese funds through the
   CMAT-Research Centre of Mathematics of University of Minho, Portugal,
   within projects UIDB/00013/2020
   (https://doi.org/10.54499/UIDB/00013/2020) and UIDP/00013/2020
   (https://doi.org/10.54499/UIDP/00013/2020) (C.C.) ; and funds provided
   by the Brazilian agency Conselho Nacional de Desenvolvimento Cientifico
   e Tecnologico (CNPq) through grant numbers 309674/2020-4 and
   304716/2023-5 (H.S.) .
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NR 74
TC 2
Z9 2
U1 1
U2 1
PU SOC CHILENA ESTADISTICA-SOCHE
PI SANTIAGO
PA CASILLA 306, CORREO 22, SANTIAGO, 00000, CHILE
SN 0718-7912
EI 0718-7920
J9 CHIL J STAT
JI Chil. J. Stat.
PD JUN
PY 2024
VL 15
IS 1
BP 81
EP 109
DI 10.32372/chjs.15-01-05
PG 29
WC Statistics & Probability
WE Emerging Sources Citation Index (ESCI)
SC Mathematics
GA XZ9N1
UT WOS:001265620900005
DA 2024-09-05
ER
PT J
AU Weinand, JM
   Sörensen, K
   San Segundo, P
   Kleinebrahm, M
   McKenna, R
AF Weinand, Jann Michael
   Sorensen, Kenneth
   San Segundo, Pablo
   Kleinebrahm, Max
   McKenna, Russell
TI Research trends in combinatorial optimization
SO INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH
LA English
DT Article
DE combinatorial optimization; bibliometric analysis; metaheuristics;
   genetic algorithms; exact algorithms; OR in energy
ID HARMONY SEARCH ALGORITHM; ANT COLONY OPTIMIZATION; BIG DATA;
   METAHEURISTICS; NETWORK; SYSTEM; UNCERTAINTY; DESIGN; MODELS;
   COMPUTATION
AB Real-world problems are becoming highly complex and therefore have to be solved with combinatorial optimization (CO) techniques. Motivated by the strong increase in publications on CO, 8393 articles from this research field are subjected to a bibliometric analysis. The corpus of literature is examined using mathematical methods and a novel algorithm for keyword analysis. In addition to the most relevant countries, organizations, and authors as well as their collaborations, the most pertinent CO problems, solution methods, and application areas are presented. Publications on CO focus mainly on the development or enhancement of metaheuristics like genetic algorithms. The increasingly problem-oriented studies deal particularly with real-world applications within the energy sector, production sector, or data management, which are of increasing relevance due to various global developments. The demonstration of global research trends in CO can support researchers in identifying the relevant issues regarding this expanding and transforming research area.
C1 [Weinand, Jann Michael; Kleinebrahm, Max] Karlsruhe Inst Technol, Inst Ind Prod, Chair Energy Econ, D-76131 Karlsruhe, Germany.
   [Sorensen, Kenneth] Univ Antwerp, Dept Engn Management, B-2000 Antwerp, Belgium.
   [San Segundo, Pablo] Univ Politecn Madrid, Ctr Automat & Robot, Madrid 28040, Spain.
   [McKenna, Russell] Univ Aberdeen, Sch Engn, Chair Energy Transit, Aberdeen AB24 3FX, Scotland.
C3 Helmholtz Association; Karlsruhe Institute of Technology; University of
   Antwerp; Consejo Superior de Investigaciones Cientificas (CSIC);
   Universidad Politecnica de Madrid; CSIC-UPM - Centro de Automatica y
   Robotica; University of Aberdeen
RP Weinand, JM (corresponding author), Karlsruhe Inst Technol, Inst Ind Prod, Chair Energy Econ, D-76131 Karlsruhe, Germany.
EM jann.weinand@kit.edu; kenneth.sorensen@uantwerpen.be;
   pablo.sansegundo@upm.es; max.kleinebrahm@kit.edu;
   russell.mckenna@abdn.ac.uk
RI Weinand, Jann Michael/AAH-4400-2020; Weinand, Jann/GNH-5511-2022
OI Kleinebrahm, Max/0000-0002-6957-2379; Weinand, Jann
   Michael/0000-0003-2948-876X; McKenna, Russell/0000-0001-6758-482X
FU Spanish Ministry of Science, Innovation, and Universities through the
   project COGDRIVE [DPI2017-86915-C3-3-R]; Projekt DEAL
FX This work has been partially funded by the Spanish Ministry of Science,
   Innovation, and Universities through the project COGDRIVE
   (DPI2017-86915-C3-3-R). In this context, we would also like to thank the
   Karlsruhe Institute of Technology.; Open access funding enabled and
   organized by Projekt DEAL.
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NR 121
TC 15
Z9 15
U1 0
U2 14
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0969-6016
EI 1475-3995
J9 INT T OPER RES
JI Int. Trans. Oper. Res.
PD MAR
PY 2022
VL 29
IS 2
BP 667
EP 705
DI 10.1111/itor.12996
EA MAY 2021
PG 39
WC Management; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Operations Research & Management Science
GA WI8YH
UT WOS:000655468200001
OA Green Published
DA 2024-09-05
ER
PT J
AU Pudasaini, B
   Shahandashti, M
AF Pudasaini, Binaya
   Shahandashti, Mohsen
TI Seismic Rehabilitation Optimization of Water Pipe Networks Considering
   Spatial Variabilities of Demand Criticalities and Seismic Ground Motion
   Intensities
SO JOURNAL OF INFRASTRUCTURE SYSTEMS
LA English
DT Article
DE Critical infrastructure; Water pipe networks; Rehabilitation;
   Optimization; Simulated annealing; Operations research; Seismic
   vulnerability assessment; Spatial analyses
ID RISK-ASSESSMENT; EARTHQUAKE; PIPELINES; FRAMEWORK
AB Operation of critical infrastructure facilities such as hospitals, firefighting stations, and disaster shelters are critical during a postearthquake scenario. The serviceability of many such facilities is, in turn, dependent on the proper operation of water-supply systems providing water to these facilities. Due to such dependency of disaster relief systems on water supply, having a resilient water-supply system is even more critical in a postearthquake scenario as compared to a normal operating condition. Extant pertinent literature ignores spatial variabilities of the water demand priorities. It assumes that the water demand originating from critical facilities, such as hospitals and the water demand originating from less critical facilities such as golf courses and temporary storage facilities, are of equal importance. This oversimplification has made existing models practically limited, especially in a postearthquake scenario. The objective of this study is to create a methodology to identify optimized proactive seismic rehabilitation policy for water pipe networks considering spatial variabilities of demand criticalities and seismic ground motion intensities. A novel approach based on proximity analysis was created to determine the criticality of each node where the criticality was established based on the spatial distribution of water demand type in the neighborhood of the node. The spatial variabilities of demand criticalities along with the spatial variabilities of the seismic ground motion intensities integrated into the formulation of a stochastic combinatorial optimization problem to identify economical rehabilitation policies for enhancing seismic resilience of the water-supply network. A purpose-built simulated-annealing algorithm integrated with Monte Carlo simulation was then used to solve the optimization problem. A city-scale water pipe network was used as a testbed to demonstrate the effectiveness of the created methodology. The results of this study and their comparison with results from existing methods showed that the created methodology was highly effective in identifying economical proactive seismic rehabilitation policies for preventive intervention when the rehabilitation budget is limited. Furthermore, the results showed that the consideration of spatial variability in water demand type leads to the identification of rehabilitation policies that ensure higher postearthquake serviceability in nodes supplying water to critical facilities.
C1 [Pudasaini, Binaya; Shahandashti, Mohsen] Univ Texas Arlington, Dept Civil Engn, 416 S Yates St, Arlington, TX 76019 USA.
C3 University of Texas System; University of Texas Arlington
RP Shahandashti, M (corresponding author), Univ Texas Arlington, Dept Civil Engn, 416 S Yates St, Arlington, TX 76019 USA.
EM binaya.pudasaini@mavs.uta.edu; mohsen@uta.edu
OI Shahandashti, Mohsen/0000-0002-2373-7596
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NR 58
TC 6
Z9 7
U1 3
U2 17
PU ASCE-AMER SOC CIVIL ENGINEERS
PI RESTON
PA 1801 ALEXANDER BELL DR, RESTON, VA 20191-4400 USA
SN 1076-0342
EI 1943-555X
J9 J INFRASTRUCT SYST
JI J. Infrastruct. Syst.
PD DEC 1
PY 2021
VL 27
IS 4
AR 04021028
DI 10.1061/(ASCE)IS.1943-555X.0000638
PG 12
WC Engineering, Civil
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Engineering
GA WI1JZ
UT WOS:000708123600016
DA 2024-09-05
ER
PT J
AU Watts, FM
   Dood, AJ
   Shultz, GV
   Rodriguez, JMG
AF Watts, Field M.
   Dood, Amber J.
   Shultz, Ginger V.
   Rodriguez, Jon-Marc G.
TI Comparing Student and Generative Artificial Intelligence Chatbot
   Responses to Organic Chemistry Writing-to-Learn Assignments
SO JOURNAL OF CHEMICAL EDUCATION
LA English
DT Article
DE Second-Year Undergraduate; Organic Chemistry; Writing; Problem Solving;
   Assessment; Mechanisms of Reactions; Internet/Web-Based Learning;
   Chemical Education Research
ID MECHANISMS; FRAMEWORK; THINKING
AB Chemistry education research demonstrates the value of open-ended writing tasks, such as writing-to-learn (WTL) assignments, for supporting students' learning with topics including reasoning about reaction mechanisms. The emergence of generative artificial intelligence (AI) technology, such as chatbots ChatGPT and Bard, raises concerns regarding the value of open-ended writing tasks in the classroom; one concern involves academic integrity and whether students will use these chatbots to produce sufficient responses to open-ended writing tasks. The present study investigates the degree to which generative AI chatbots exhibit mechanistic reasoning in response to organic chemistry WTL assignments. We produced responses from three generative AI chatbots (ChatGPT-3.5, ChatGPT-4, and Bard) to two WTL assignments developed to elicit students' mechanistic reasoning. Using previously reported machine learning models for analyzing student writing in response to the WTL assignments, we analyzed the chatbot responses for the inclusion of features pertinent to mechanistic reasoning. Herein, we report quantitative analyses of (1) the differences between chatbot responses on the two assignments and (2) the differences between chatbot and authentic student responses. Findings indicate that chatbots respond differently to different WTL assignments. Additionally, the chatbots rarely incorporated the discussion of electron movement, a key feature of mechanistic reasoning. Furthermore, the chatbots, in general, do not engage in mechanistic reasoning at the same level as students. We contextualize the results by considering academic integrity with the assumption that students' intentions are to engage in academically honest behavior, and we focus on understanding the ethical uses of generative AI for classroom assignments.
C1 [Dood, Amber J.; Shultz, Ginger V.] Univ Michigan, Dept Chem, Ann Arbor, MI 48109 USA.
   [Watts, Field M.; Rodriguez, Jon-Marc G.] Univ Wisconsin, Dept Chem & Biochem, Milwaukee, WI 53211 USA.
C3 University of Michigan System; University of Michigan; University of
   Wisconsin System; University of Wisconsin Milwaukee
RP Watts, FM (corresponding author), Univ Wisconsin, Dept Chem & Biochem, Milwaukee, WI 53211 USA.
EM wattsf@uwm.edu
RI Watts, Field/ABA-1960-2021
OI Dood, Amber/0000-0003-4572-1402; Rodriguez, Jon-Marc
   G./0000-0001-6949-6823; Watts, Field/0000-0002-1800-1816
FU University of Michigan Provost's Third Century Initiative
FX The authors would like to thank the University of Michigan Provost's
   Third Century Initiative for funding. We would like to thank the
   participating students, as well as Solaire Finkenstaedt-Quinn, Ina
   Zaimi, and Michael Petterson for their assistance in developing the two
   WTL assignments. The authors would additionally like to thank Solaire
   FinkenstaedtQuinn for discussions related to the preparation of this
   manuscript.
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NR 75
TC 19
Z9 19
U1 47
U2 123
PU AMER CHEMICAL SOC
PI WASHINGTON
PA 1155 16TH ST, NW, WASHINGTON, DC 20036 USA
SN 0021-9584
EI 1938-1328
J9 J CHEM EDUC
JI J. Chem. Educ.
PD SEP 7
PY 2023
VL 100
IS 10
BP 3806
EP 3817
DI 10.1021/acs.jchemed.3c00664
EA SEP 2023
PG 12
WC Chemistry, Multidisciplinary; Education, Scientific Disciplines
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Education & Educational Research
GA T5DC4
UT WOS:001070107700001
DA 2024-09-05
ER
PT J
AU Bian, YJ
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AF Bian, Yijie
   Lu, Yanchi
   Li, Jingqi
TI Research on an Artificial Intelligence-Based Professional Ability
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SO SCIENTIFIC PROGRAMMING
LA English
DT Article
AB The rapid development of artificial intelligence technology demands higher requirements for employment and talent training. The integration of industry and education is an important way to solve the mismatch between industrial demand and talent supply. Therefore, this study starts from the perspective of the integration of industry and education. We collect recruitment texts from the perspective of "industry" and mine the specific requirements of the artificial intelligence post system through the LDA topic model and the combination of Word2Vec and K-means. We then conduct expert consultations and adjust the selected indicators from the perspective of "education." Finally, we construct a four-dimensional vocational ability grade evaluation index system, including basic vocational skills of artificial intelligence, database, network skills, algorithm and design skills, and research and practice skills. The intuitionistic fuzzy analytic hierarchy process, which can eliminate the subjective uncertainty of experts in the scoring process, is applied to calculate the index weights. We find that the weight of algorithm and design skill is the highest, which is an important criterion for artificial intelligence professional ability evaluation. Among the second-level indicators, practical indicators such as team spirit, innovation ability, and communication ability are the focus of investigation from the perspective of industry, while in education, the cultivation of knowledge and skills such as programming ability, applied mathematics ability, data structures, and algorithms are more important.
C1 [Bian, Yijie; Lu, Yanchi; Li, Jingqi] Hohai Univ, Business Sch, Nanjing 211100, Peoples R China.
C3 Hohai University
RP Lu, YC (corresponding author), Hohai Univ, Business Sch, Nanjing 211100, Peoples R China.
EM byj@hhu.edu.cn; 1019238072@qq.com; lijingqi@hhu.edu.cn
FU special key project "research on vocational skill level evaluation
   system of artificial intelligence integrated with industry and
   education" of the Modern Educational Technology Research Smart Campus in
   Jiangsu Province;  [2020-R-84366]
FX AcknowledgmentsThis work was supported by the special key project
   "research on vocational skill level evaluation system of artificial
   intelligence integrated with industry and education" of the Modern
   Educational Technology Research Smart Campus in Jiangsu Province
   (2020-R-84366).
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TC 3
Z9 3
U1 23
U2 101
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1058-9244
EI 1875-919X
J9 SCI PROGRAMMING-NETH
JI Sci. Program.
PD AUG 24
PY 2022
VL 2022
AR 4478115
DI 10.1155/2022/4478115
PG 20
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 4O8IC
UT WOS:000854936200002
OA gold
DA 2024-09-05
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PT J
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Z9 4
U1 0
U2 0
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1069-1316
EI 1545-2530
J9 COLL UNDERGRAD LIBR
JI Coll. Undergrad. Libr.
PY 2007
VL 14
IS 1
BP 39
EP 56
DI 10.1300/J106v14n01_04
PG 18
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA VC3DR
UT WOS:000433665900004
DA 2024-09-05
ER
PT J
AU Smith, MK
   Wood, WB
AF Smith, Michelle K.
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TI Teaching Genetics: Past, Present, and Future
SO GENETICS
LA English
DT Article
DE active learning; assessment; discipline-based education research;
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ID STUDENT PERFORMANCE; INVENTORY; INSTRUMENT; SCIENCE; COURSES; PEER; TOOL
AB Genetics teaching at the undergraduate level has changed in many ways over the past century. Compared to those of 100 years ago, contemporary genetics courses are broader in content and are taught increasingly differently, using instructional techniques based on educational research and constructed around the principles of active learning and backward design. Future courses can benefit from wider adoption of these approaches, more emphasis on the practice of genetics as a science, and new methods of assessing student learning.
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RP Smith, MK (corresponding author), Univ Maine, Sch Biol & Ecol, 5751 Murray Hall, Orono, ME 04469 USA.
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U1 1
U2 25
PU GENETICS SOCIETY AMERICA
PI BETHESDA
PA 9650 ROCKVILLE AVE, BETHESDA, MD 20814 USA
SN 0016-6731
EI 1943-2631
J9 GENETICS
JI Genetics
PD SEP
PY 2016
VL 204
IS 1
BP 5
EP 10
DI 10.1534/genetics.116.187138
PG 6
WC Genetics & Heredity
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Genetics & Heredity
GA DW9RD
UT WOS:000383998500003
PM 27601614
OA Green Published, Bronze
DA 2024-09-05
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PT J
AU COHEN, PR
   HOWE, AE
AF COHEN, PR
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TI TOWARD AI RESEARCH METHODOLOGY - 3 CASE STUDIES IN EVALUATION
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   Waltz D.L., 1975, GENERATING SEMANTIC, P19
NR 30
TC 30
Z9 33
U1 1
U2 2
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017-2394
SN 0018-9472
J9 IEEE T SYST MAN CYB
JI IEEE Trans. Syst. Man Cybern.
PD MAY-JUN
PY 1989
VL 19
IS 3
BP 634
EP 646
DI 10.1109/21.31069
PG 13
WC Computer Science, Cybernetics; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA AM016
UT WOS:A1989AM01600021
DA 2024-09-05
ER
PT C
AU Shtekh, G
   Kazakova, P
   Nikitinsky, N
   Skachkov, N
AF Shtekh, Gennady
   Kazakova, Polina
   Nikitinsky, Nikita
   Skachkov, Nikolay
BE Bodrunova, SS
TI Exploring Influence of Topic Segmentation on Information Retrieval
   Quality
SO INTERNET SCIENCE (INSCI 2018)
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT 5th International Conference on Internet Science (INSCI)
CY OCT 24-26, 2018
CL St. Petersburg, RUSSIA
DE Information retrieval; Text segmentation; Topic modeling; Querying by
   example
AB In the present paper we address the issue of how an information retrieval system might be improved via text segmentation and to what extent. We assume that topic text segmentation allows one to better model text structure and therefore language itself, which influences the quality of text representation. We propose a search pipeline based on text segmentation by means of BigARTM tool and TopicTiling algorithm. We test the initial hypothesis by conducting experiments with several baseline models on two textual collections. The results are rather contradictory: while one collection showed that segmentation does improve the quality of retrieval, the other one demonstrated that segmentation does not influence the quality significantly.
C1 [Shtekh, Gennady; Kazakova, Polina] Natl Univ Sci & Technol MISIS, Leninsky Ave 4, Moscow 119049, Russia.
   [Nikitinsky, Nikita] Integrated Syst, Vorontsovskaya St,35B Bldg 3,Room 413, Moscow 109147, Russia.
   [Skachkov, Nikolay] Lomonosov Moscow State Univ, Leninskie Gory 1, Moscow 119991, Russia.
C3 National University of Science & Technology (MISIS); Lomonosov Moscow
   State University
RP Kazakova, P (corresponding author), Natl Univ Sci & Technol MISIS, Leninsky Ave 4, Moscow 119049, Russia.
EM kazakova1537@gmail.com
RI Nikitinsky, Nikita/AAE-4248-2022
FU Ministry of Education and Science of the Russian Federation
   [RFMEFI57917X0143]
FX This research was supported by the Ministry of Education and Science of
   the Russian Federation under the unique research id RFMEFI57917X0143.
CR [Anonymous], 2015, DOCUMENT EMBEDDING P
   [Anonymous], 2017, BILLION SCALE SIMILA
   [Anonymous], 2018, NAACL
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NR 20
TC 1
Z9 1
U1 0
U2 3
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 0302-9743
EI 1611-3349
BN 978-3-030-01437-7; 978-3-030-01436-0
J9 LECT NOTES COMPUT SC
PY 2018
VL 11193
BP 131
EP 140
DI 10.1007/978-3-030-01437-7_11
PG 10
WC Computer Science, Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BN2UQ
UT WOS:000477766000011
DA 2024-09-05
ER
PT J
AU Liu, XW
   Doboli, A
   MacCarthy, T
   Doboli, S
AF Liu, Xiaowei
   Doboli, Alex
   MacCarthy, Tom
   Doboli, Simona
TI Understanding the Significance of Mid-Tier Research Teams in Idea Flow
   Through a Community
SO IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
LA English
DT Article
DE Measurement; Magnetic heads; Ecology; Clustering algorithms; Testing;
   Mathematical models; Transforms; Experimental study; groups; impact of
   ideas; research community; role
ID NETWORK; COLLABORATION; CENTRALITY; CREATIVITY; KNOWLEDGE; PATENTS
AB Studying the dynamics of research communities has been an important yet tedious topic. It is important for devising management policies that optimize the quality of research outputs by promoting a certain composition, structure, and interaction patterns for a community. While there has been significant attention to understanding the roles of individuals, there has been less focus on studying the roles that research groups perform and how roles relate to the characteristics of the community. This article presents an experimental study on the roles that research groups have in the idea flow through a community. The study analyzes two facets: the contribution of different kinds of groups to the impact research ideas make on other groups and the importance of specific groups to connect a community together. The results showed that research groups can be classified into four categories depending on the number of received citations. Groups of the mid-tier categories (called categories B and C in this article) are important in tying a community together as they improve the idea impact and bridging between groups. Policies that aggressively eliminate such groups reduce the effectiveness of idea flow, even though research communities include a certain amount of robustness for moderate levels of group removals.
C1 [Liu, Xiaowei; Doboli, Alex] SUNY Stony Brook, Dept Elect & Comp Engn, Stony Brook, NY 11794 USA.
   [MacCarthy, Tom] SUNY Stony Brook, Dept Appl Math & Stat, Stony Brook, NY 11794 USA.
   [Doboli, Simona] Hofstra Univ, Dept Comp Sci, Hempstead, NY 11549 USA.
C3 State University of New York (SUNY) System; State University of New York
   (SUNY) Stony Brook; State University of New York (SUNY) System; State
   University of New York (SUNY) Stony Brook; Hofstra University
RP Doboli, A (corresponding author), SUNY Stony Brook, Dept Elect & Comp Engn, Stony Brook, NY 11794 USA.
EM alex.doboli@stonybrook.edu; thomas.maccarthy@stonybrook.edu;
   simona.doboli@hofstra.edu
OI Doboli, Alex/0000-0003-2472-4014; Doboli, Simona/0000-0003-0158-4725
FU National Science Foundation [1247971]; Division Of Behavioral and
   Cognitive Sci; Direct For Social, Behav & Economic Scie [1247971]
   Funding Source: National Science Foundation
FX This work was supported in part by the National Science Foundation under
   Grant 1247971.
CR Agarwal P, 2008, BRIEF BIOINFORM, V9, P479, DOI 10.1093/bib/bbn035
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NR 32
TC 0
Z9 0
U1 0
U2 2
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2329-924X
J9 IEEE T COMPUT SOC SY
JI IEEE Trans. Comput. Soc. Syst.
PD DEC
PY 2023
VL 10
IS 6
BP 3422
EP 3432
DI 10.1109/TCSS.2022.3205279
EA OCT 2022
PG 11
WC Computer Science, Cybernetics; Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA CE5H8
UT WOS:000865079000001
DA 2024-09-05
ER
PT C
AU Xu, YH
   Jiang, WJ
   Xu, YS
AF Xu, YH
   Jiang, WJ
   Xu, YS
GP IEEE
TI Research on reliability evaluation of series systems with optimization
   algorithm
SO PROCEEDINGS OF 2005 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND
   CYBERNETICS, VOLS 1-9
LA English
DT Proceedings Paper
CT 4th International Conference on Machine Learning and Cybernetics
CY AUG 18-21, 2005
CL Canton, PEOPLES R CHINA
DE system reliability; First Order Second Moment; optimization algorithms;
   Monte Carlo method; importance sampling
ID STRUCTURAL RELIABILITY; CODE
AB The failure probability of a system can be expressed as an integral of the joint probability density function within the failure domain defined by the limit state functions of the system. Generally, it is very difficult to solve this integral directly. The evaluation of system reliability has been the active research area during the recent decades. Some methods were developed to solve system reliability analysis, such as Monte Carlo method, importance sampling method, bounding techniques and Probability Network Evaluation Technique (PNET). This paper presents the implementation of several optimization algorithms, modified Method of Feasible Direction (MFD), Sequential Linear Programming (SLP) and Sequential Quadratic programming (SQP), in order to demonstrate the convergence abilities and robust nature of the optimization technique when applied to series system reliability analysis. Examples taken from the published references were calculated and the results were compared with the answers of various other methods and the exact solution. Results indicate the optimization technique has a wide range of application with good convergence ability and robustness, and handle problems under generalized conditions or cases.
C1 Zhuzhou Inst Technol, Dept Informat & Comp Sci, Zhuzhou 412008, Peoples R China.
RP Zhuzhou Inst Technol, Dept Informat & Comp Sci, Zhuzhou 412008, Peoples R China.
EM jwj3666@163.com; yshxu520@163.com
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NR 10
TC 0
Z9 0
U1 0
U2 1
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 0-7803-9091-1
PY 2005
BP 3438
EP 3442
PG 5
WC Computer Science, Artificial Intelligence; Computer Science,
   Cybernetics; Computer Science, Information Systems
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BDT94
UT WOS:000235325605031
DA 2024-09-05
ER
PT C
AU Block, BM
AF Block, Brit-Maren
GP IEEE
TI Digitalization in Engineering Education Research and Practice
SO PROCEEDINGS OF 2018 IEEE GLOBAL ENGINEERING EDUCATION CONFERENCE
   (EDUCON) - EMERGING TRENDS AND CHALLENGES OF ENGINEERING EDUCATION
SE IEEE Global Engineering Education Conference
LA English
DT Proceedings Paper
CT IEEE Global Engineering Education Conference (EDUCON) - Emerging Trends
   and Challenges of Engineering Education
CY APR 17-20, 2018
CL Santa Cruz de Tenerife, SPAIN
DE digitalization; engineering education research; e-assessment; active
   learning; students competences
AB Digitalization is the ongoing trend of recent years. It covers all areas of business, society and research. This paper makes a contribution to that discourse focusing on engineering education research and practice. The contribution aims at generating new insights in a threefold way: (I) by analyzing the significance of digitalization in the research area, (2) by describing an example of the implementation of digital methods in education practice, and (3) by presenting the theoretical and methodological framework of a course that covers the digital transformation and backlash effects on society. This approach generates empirically grounded knowledge on the state-of-the-art and contributes to the translation of engineering education research to practice.
C1 [Block, Brit-Maren] Leuphana Univ Lueneburg, Inst Prod & Proc Innovat, Luneburg, Germany.
C3 Leuphana University Luneburg
RP Block, BM (corresponding author), Leuphana Univ Lueneburg, Inst Prod & Proc Innovat, Luneburg, Germany.
CR [Anonymous], EUROPEAN J ENG ED
   [Anonymous], 2012, GIEE 2011 GENDER INT
   Block BM, 2014, IEEE GLOB ENG EDUC C, P23, DOI 10.1109/EDUCON.2014.6826062
   Block BM, 2013, IEEE GLOB ENG EDUC C, P213, DOI 10.1109/EduCon.2013.6530108
   DECI EL, 1993, Z PADAGOGIK, V39, P223
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   Erpenbeck J., 2007, HDB COMPETENCE ASSES
   Hakala I, 2016, IEEE GLOB ENG EDUC C, P409, DOI 10.1109/EDUCON.2016.7474586
   Harteis Ch., 2018, PROFESSIONAL PRACTIC, V21
   Krapp A., 2006, ED PSYCHOL
   Kruse D, 2016, IEEE GLOB ENG EDUC C, P469, DOI 10.1109/EDUCON.2016.7474595
   Lauer P., 2017, GLOBAL CURRICULUM PR
   Mayring Philipp., 2008, GRUNDLAGEN TECHNIKEN
   Mills J., 2010, GENDER INCLUSIVE ENG
   Ortelt TR, 2016, IEEE GLOB ENG EDUC C, P602, DOI 10.1109/EDUCON.2016.7474612
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   Tsironis A, 2016, IEEE GLOB ENG EDUC C, P608, DOI 10.1109/EDUCON.2016.7474613
NR 17
TC 9
Z9 9
U1 0
U2 8
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2165-9567
BN 978-1-5386-2957-4
J9 IEEE GLOB ENG EDUC C
PY 2018
BP 1024
EP 1028
PG 5
WC Education, Scientific Disciplines
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Education & Educational Research
GA BK3EO
UT WOS:000434866100143
DA 2024-09-05
ER
PT J
AU Wang, Y
   Li, F
   Dou, JL
AF Wang, Yong
   Li, Fang
   Dou, Jia-Li
TI Research on the Evaluation Model of the Overall Benefit of the Nonlinear
   Rural Pension Based on Particle Swarm Optimization Algorithm: From the
   Perspective of Migrant Workers Returning
SO MATHEMATICAL PROBLEMS IN ENGINEERING
LA English
DT Article
ID INFORMATION; COSTS
AB In this paper, based on the existing research, the rural talent supply situation is analyzed. Rural talent relative to the existing rural labor force has a higher human capital of the labor force. We also discuss the part of moving workers who can be considered as talents. Financial dimension is mostly through literature collection and statistical data finding and computational calculation, according to the results of communicate indicators, to reflect its performance level. The performance analysis of organizational management dimension and institutional development dimension mainly focuses on qualitative analysis and comparative analysis, and then we summarize and evaluate the performance status of Shanghai basic endowment insurance system for urban and rural residents. Therefore, all countries in the world are constantly exploring and trying to improve the performance management model of their social security institutions. Through the evaluation and analysis of the above four dimensions, it can be found that the overall performance level of urban and rural residential insurance in Shanghai is relatively high, but there are still some problems in the operation process.
C1 [Wang, Yong] Anhui Sci & Technol Univ Finance, Econ College1, Bengbu 233100, Anhui, Peoples R China.
   [Wang, Yong; Li, Fang] Nanjing Agr Univ, Sch Publ Adm, Nanjing 210095, Jiangsu, Peoples R China.
   [Dou, Jia-Li] Anhui Sci & Technol Univ Adm Coll, Bengbu 233100, Anhui, Peoples R China.
C3 Nanjing Agricultural University
RP Dou, JL (corresponding author), Anhui Sci & Technol Univ Adm Coll, Bengbu 233100, Anhui, Peoples R China.
EM n3480646duxiezi56@163.com; bjwje204@163.com; doujl@ahstu.edu.cn
FU Philosophy and Social Science Planning Project of Anhui Province, China
   [ahsky2016d18]; Industry university research innovation fund project of
   science and technology development center of the Ministry of Education
   [2018a01011]
FX This study was supported by Philosophy and Social Science Planning
   Project of Anhui Province, China: Research on rural old-age security
   from the perspective of the return of elderly migrant workers--Based on
   the survey of Fuyang, Anhui Province (ahsky2016d18), and Industry
   university research innovation fund project of science and technology
   development center of the Ministry of Education: Research on influencing
   factors and mechanism of purchase intention of Internet personal
   financial products based on big data technology (2018a01011).
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NR 16
TC 0
Z9 0
U1 2
U2 7
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1024-123X
EI 1563-5147
J9 MATH PROBL ENG
JI Math. Probl. Eng.
PD APR 12
PY 2022
VL 2022
AR 6554300
DI 10.1155/2022/6554300
PG 8
WC Engineering, Multidisciplinary; Mathematics, Interdisciplinary
   Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Mathematics
GA 1V7CZ
UT WOS:000806244400007
OA gold
DA 2024-09-05
ER
PT J
AU Sasaki, H
   Hara, T
   Sakata, I
AF Sasaki, Hajime
   Hara, Tadayoshi
   Sakata, Ichiro
TI Identifying Emerging Research Related to Solar Cells Field using a
   Machine Learning Approach
SO JOURNAL OF SUSTAINABLE DEVELOPMENT OF ENERGY WATER AND ENVIRONMENT
   SYSTEMS-JSDEWES
LA English
DT Article
DE Solar cells; Photovoltaic; Emerging research; Technology prediction;
   Citation network; Machine learning; Scientometrics; Innovation
   management
ID SCIENCE-AND-TECHNOLOGY; RECENT PROGRESS; POLYMER; CENTRALITY; LANDSCAPE;
   EFFICIENT; NETWORKS; GRAPHENE; DESIGN; TIO2
AB The number of research papers related to solar cells field is increasing rapidly. It is hard to grasp research trends and to identify emerging research issues because of exponential growth of publications, and the field's subdivided knowledge structure. Machine learning techniques can be applied to the enormous amounts of data and subdivided research fields to identify emerging researches. This paper proposed a prediction model using a machine learning approach to identify emerging solar cells related academic research, i.e. papers that might be cited very frequently within three years. The proposed model performed well and stable. The model highlighted some articles published in 2015 that will be emerging in the future. Research related to vegetable-based dye-sensitized solar cells was identified as the one of the promising researches by the model. The proposed prediction model is useful to gain foresight into research trends in science and technology, facilitating decision-making processes.
C1 [Sasaki, Hajime; Sakata, Ichiro] Univ Tokyo, Policy Alternat Res Inst, Bunkyo Ku, 7-3-1 Hongo, Tokyo, Japan.
   [Hara, Tadayoshi; Sakata, Ichiro] Univ Tokyo, Innovat Policy Res Ctr, Inst Engn Innovat, Sch Engn,Bunkyo Ku, Yayoi 2-11-16, Tokyo, Japan.
C3 University of Tokyo; University of Tokyo
RP Sasaki, H (corresponding author), Univ Tokyo, Policy Alternat Res Inst, Bunkyo Ku, 7-3-1 Hongo, Tokyo, Japan.
EM sasaki@pari.u-tokyo.ac.jp; t.hara@ipr-ctr.t.u-tokyo.ac.jp;
   isakata@ipr-ctr.t.u-tokyo.ac.jp
RI Sasaki, Hajime/AHA-8512-2022; Sasaki, Hajime/AEB-0480-2022
OI Sasaki, Hajime/0000-0003-0026-4076; SAKATA, ICHIRO/0000-0001-5881-3790
FU Project of the NARO Bio-oriented Technology Research Advancement
   Institution
FX This research was supported by grants from the Project of the NARO
   Bio-oriented Technology Research Advancement Institution (Integration
   research for agriculture and interdisciplinary fields).
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NR 45
TC 11
Z9 11
U1 0
U2 20
PU INT CENTRE SUSTAINABLE DEV ENERGY WATER & ENV SYSTEMS-SDEWES
PI ZAGREB
PA IVANA LUCICA 5, ZAGREB, 10000, CROATIA
SN 1848-9257
J9 J SUSTAIN DEV ENERGY
JI J. Sustain. Dev. Energy Water Environ. Syst.-JSDEWES
PD DEC
PY 2016
VL 4
IS 4
BP 418
EP 429
DI 10.13044/j.sdewes.2016.04.0032
PG 12
WC Environmental Sciences
WE Emerging Sources Citation Index (ESCI)
SC Environmental Sciences & Ecology
GA FG1KH
UT WOS:000409554200008
OA gold, Green Submitted
DA 2024-09-05
ER
PT J
AU Sahni, S
   Verma, S
   Kaurav, RPS
AF Sahni, Shalini
   Verma, Sushma
   Kaurav, Rahul Pratap Singh
TI Understanding digital transformation challenges for online learning and
   teaching in higher education institutions: a review and research
   framework
SO BENCHMARKING-AN INTERNATIONAL JOURNAL
LA English
DT Article; Early Access
DE HEIs; Digital transformation; Online teaching; Online learning;
   Bibliometrics; TCM framework; Sustainable higher education
ID STUDENT SATISFACTION; MENTAL-HEALTH; COVID-19; ENGAGEMENT; EXPERIENCES;
   UNIVERSITIES; PERFORMANCE; MOTIVATION; IMPROVE; IMPACT
AB PurposeThe widespread uptake of digital technology tools for online teaching and learning reached its peak during the nationwide lockdown triggered by the COVID-19 pandemic. It transformed the higher education institutions (HEIs) marketplace both in developed and developing countries. However, in this process of digital transformation, several HEIs, specifically from developing countries, faced major challenges. That threatened to affect their sustainability and performance. In this vein, this study conducts a bibliometric review to map the challenges during the COVID-19 pandemic and suggest strategies for HEIs to cope with post-pandemic situations in the future.Design/methodology/approachThis comprehensive review encompasses 343 papers published between 2020 and 2023, employing a systematic approach that combines bibliometrics and content analysis to thoroughly evaluate the articles.FindingsThe investigation revealed a lack of published work addressing the specific challenges faced by the faculty members affecting their well-being. The study underscores the importance of e-learning technology adoption for higher education sustainability by compelling both students and teachers to rely heavily on social media platforms to maintain social presence and facilitate remote learning. The reduced interpersonal interaction during the pandemic has had negative consequences for academic engagement and professional advancement for both educators and students.Practical implicationsThis has implications for policymakers and the management of HEIs, as it may prove useful in reenvisioning and redesigning future curricula. The paper concludes by developing a sustainable learning framework using a blended approach. Additionally, we also provide directions for future research to scholars.Originality/valueThis study has implications for policymakers and HEI management to rethink the delivery of future courses with a focus on education and institute sustainability. Finally, the research also proposes a hybrid learning framework for sustainability and forms a robust foundation for scholars in future research.
C1 [Sahni, Shalini] Koach Scholar, New Delhi, India.
   [Verma, Sushma] Vivekanand Educ Soc, Inst Management Studies & Res, Mumbai, Maharashtra, India.
   [Kaurav, Rahul Pratap Singh] FORE Sch Management, New Delhi, India.
C3 FORE School of Management
RP Kaurav, RPS (corresponding author), FORE Sch Management, New Delhi, India.
EM drshalini2532@gmail.com; sushma.verma@ves.ac.in; rsinghkaurav@gmail.com
RI Kaurav, Rahul Pratap Singh/K-8505-2015; Verma, Sushma/KGL-2188-2024
OI Kaurav, Rahul Pratap Singh/0000-0001-9851-6854; Verma,
   Sushma/0000-0003-2027-380X; sahni, Shalini/0000-0002-9096-6524
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NR 192
TC 0
Z9 0
U1 3
U2 3
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 1463-5771
EI 1758-4094
J9 BENCHMARKING
JI Benchmarking
PD 2024 MAY 10
PY 2024
DI 10.1108/BIJ-04-2022-0245
EA MAY 2024
PG 35
WC Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA PU1L8
UT WOS:001216504800001
DA 2024-09-05
ER
PT J
AU Wang, KQ
   Wang, T
   Wang, TY
   Cai, ZQ
AF Wang, Keqin
   Wang, Ting
   Wang, Tianyi
   Cai, Zhiqiang
TI Research on Evaluation Methods for Sustainable Enrollment Plan
   Configurations in Chinese Universities Based on Bayesian Networks
SO SUSTAINABILITY
LA English
DT Article
DE enrollment plan configurations; sustainable enrollment policies;
   advanced education rate prediction; importance ranking; indicator
   combination strategy
ID BIRNBAUM IMPORTANCE; EDUCATION
AB Evaluation methods based on data-driven techniques and artificial intelligence for the sustainable enrollment plan configurations of Chinese universities have become a research hotspot in the field of higher education teaching reform. Enrollment, education, and employment constitute the three key pillars of talent cultivation in universities. However, due to an unclear understanding of their interconnection, universities have yet to establish robust quantitative relationship models, hindering the formation of an evaluation mechanism for sustainable enrollment plan configurations. This study begins by constructing a relevant indicator system and utilizing real enrollment data from a specific university. Through statistical methods such as correlation analysis, it systematically sorts out key variables and identifies seven effective indicators, including average admission score and first-time graduation rate. Subsequently, by using the increase or decrease in enrollment quotas for each major as the experimental target, evaluation models for sustainable enrollment plan configurations aimed at enhancing the advanced education rate are constructed using naive Bayes networks and tree-augmented Bayesian networks; these are compared with three other classic machine learning methods. The accuracy of these models is evaluated through confusion matrices and receiver operating characteristic curves. Additionally, the Birnbaum importance analysis method is utilized to prioritize remaining variables, ultimately identifying the optimal combination strategy of indicators conducive to the sustainable development of the advanced education rate. The results indicate that the average admission score, transfer rate, and student/teacher ratio are the top 3 prognostic factors affecting the advanced education rate, with the TAN model achieving an accuracy of 96.49%, thus demonstrating good reliability.
C1 [Wang, Keqin] Northwestern Polytech Univ, Undergrad Acad Affairs Off, Xian 710072, Peoples R China.
   [Wang, Keqin] Northwestern Polytech Univ, Sch Management, Xian 710072, Peoples R China.
   [Wang, Ting; Wang, Tianyi; Cai, Zhiqiang] Northwestern Polytech Univ, Dept Ind Engn, Xian 710072, Peoples R China.
C3 Northwestern Polytechnical University; Northwestern Polytechnical
   University; Northwestern Polytechnical University
RP Cai, ZQ (corresponding author), Northwestern Polytech Univ, Dept Ind Engn, Xian 710072, Peoples R China.
EM keqinwang@nwpu.edu.cn; wt3377@mail.nwpu.edu.cn;
   wangtianyi@mail.nwpu.edu.cn; caizhiqiang@nwpu.edu.cn
OI Cai, Zhiqiang/0000-0002-7380-8110; Wang, Keqin/0000-0001-5248-7672
FU Key Research Project on Undergraduate and Higher Continuing Education
   Teaching Reform in Shaanxi Province
FX No Statement Available
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NR 32
TC 0
Z9 0
U1 8
U2 8
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD APR
PY 2024
VL 16
IS 7
AR 2998
DI 10.3390/su16072998
PG 18
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA NN8D5
UT WOS:001201212000001
OA gold
DA 2024-09-05
ER
PT J
AU Wan, M
AF Wan, Min
TI Research on economic system based on fuzzy set comprehensive evaluation
   model
SO JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
LA English
DT Article
DE Fuzzy set; machine learning; comprehensive evaluation; economic system
ID BIG DATA; PREDICTION; ANALYTICS
AB The development of the economic system is affected by many factors, and the stability of the traditional economic analysis model is difficult to maintain. In order to explore the efficient and stable economic system evaluation and analysis model, based on machine learning ideas, this study uses rough set algorithm as the basic algorithm, and applies the related methods of rough set and catastrophe model theory to the evaluation of ecological economic development level. Moreover, this study reduces the redundant index of the index system and calculates the importance of the index after reduction. Based on the catastrophe set model, this study uses MATLAB software programming to comprehensively quantify the ecological economy, and finally divides the ecological economic grade. In addition, this study combines rough set theory with fuzzy mathematics, and initially establishes a two-branch fuzzy evaluation model. Finally, this study combines the actual situation to use the established model to evaluate the regional eco-economic system. The research results show that the method proposed in this paper has a certain effect, which can provide a reference for subsequent related research.
C1 [Wan, Min] East China Jiaotong Univ, Inst Technol, Nanchang, Jiangxi, Peoples R China.
C3 East China Jiaotong University
RP Wan, M (corresponding author), East China Jiaotong Univ, Inst Technol, Nanchang, Jiangxi, Peoples R China.
EM wanmin516@163.com
RI wan, min/KLC-3833-2024
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NR 30
TC 2
Z9 2
U1 1
U2 10
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1064-1246
EI 1875-8967
J9 J INTELL FUZZY SYST
JI J. Intell. Fuzzy Syst.
PY 2021
VL 40
IS 4
BP 7471
EP 7481
DI 10.3233/JIFS-189569
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA RN7FL
UT WOS:000640518000150
DA 2024-09-05
ER
PT C
AU Gomez, MJ
   Ruipérez-Valiente, JA
   Clemente, FJG
AF Gomez, Manuel J.
   Ruiperez-Valiente, Jose A.
   Garcia Clemente, Felix J.
BE DeLaet, T
   Klemke, R
   AlarioHoyos, C
   Hilliger, I
   OrtegaArranz, A
TI Bibliometric Analysis of the Last Ten Years of the European Conference
   on Technology-Enhanced Learning
SO TECHNOLOGY-ENHANCED LEARNING FOR A FREE, SAFE, AND SUSTAINABLE WORLD,
   EC-TEL 2021
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT 16th European Conference on Technology Enhanced Learning (EC-TEL)
CY SEP 20-24, 2021
CL ELECTR NETWORK
DE Technology-enhanced learning; Bibliometrics; Natural language
   processing; Education
ID EDUCATION
AB Over the last decade, we have seen a large amount of research being performed in technology-enhanced learning. The European Conference on Technology-enhanced Learning (EC-TEL) is one of the conferences with the most extended trajectory in this area. The goal of this paper is to provide an overview of the last ten years of the conference. We collected all papers from the last ten years of the conference, along with the metadata, and used their keywords to find the most important ones across the papers. We also parsed papers' full text automatically, and used it to extract information about this year's conference topic. These results will shed some light on the latest trends and evolution of EC-TEL.
C1 [Gomez, Manuel J.; Ruiperez-Valiente, Jose A.; Garcia Clemente, Felix J.] Univ Murcia, Fac Comp Sci, Murcia, Spain.
C3 University of Murcia
RP Gomez, MJ (corresponding author), Univ Murcia, Fac Comp Sci, Murcia, Spain.
EM manueljesus.gomezm@um.es
RI Gomez Moratilla, Manuel Jesus/HMW-0780-2023; Clemente, Félix Jesús
   Garcia/AAM-8396-2020; Ruiperez-Valiente, Jose A./U-8795-2018
OI Clemente, Félix Jesús Garcia/0000-0001-6181-5033; Ruiperez-Valiente,
   Jose A./0000-0002-2304-6365; Gomez, Manuel J./0000-0003-0571-2923
CR [Anonymous], 2009, COMP STUDY METHODOLO
   Elsevier, 2021, About Scopus
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NR 11
TC 1
Z9 1
U1 0
U2 1
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 0302-9743
EI 1611-3349
BN 978-3-030-86436-1; 978-3-030-86435-4
J9 LECT NOTES COMPUT SC
PY 2021
VL 12884
BP 337
EP 341
DI 10.1007/978-3-030-86436-1_31
PG 5
WC Computer Science, Information Systems; Computer Science, Software
   Engineering; Computer Science, Theory & Methods; Education & Educational
   Research
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Education & Educational Research
GA BT0SL
UT WOS:000791071400031
DA 2024-09-05
ER
PT J
AU El-Taliawi, OG
   Goyal, N
   Howlett, M
AF El-Taliawi, Ola G.
   Goyal, Nihit
   Howlett, Michael
TI Holding out the promise of Lasswell's dream: Big data analytics in
   public policy research and teaching(sic)(sic)(sic)Palabras Clave
SO REVIEW OF POLICY RESEARCH
LA English
DT Article
DE bibliometric review; big data analytics; machine learning; pedagogy;
   policy sciences; public policy; topic modeling
ID SUPPLY CHAIN; BIOMEDICAL TEXT; SCIENCE; INFORMATION; PERFORMANCE;
   COMPLEXITY; ONTOLOGIES; DISCOVERY; STRATEGY; BEHAVIOR
AB While the emergence of big data raises concerns regarding governance and public policy, it also creates opportunities for diversifying the toolkit for analysis for the policy sciences as a whole, i.e., research concerning policy analysis as well as policy studies. Further, it opens avenues for practice, which together with research requires adaptation in teaching curricula if policy education were to remain relevant. However, it is not clear to what extent this opportunity is being realized in public policy research and teaching. In this study, we examine the prevalence of big data analytics in public policy research and pedagogy using bibliometric analysis and topic modeling for the former, and content analysis of course titles and descriptions for the latter. We find that despite significant scope for application of various big data techniques, the use of these analytic techniques in public policy has been largely limited to select institutions in a few countries. Further, data science has received limited attention in policy pedagogy, once again with significant geographic variation in its prevalence. We conclude that, to stay relevant, the policy sciences need to pay more attention to the integration of big data techniques in policy research, pedagogy, and thereby practice.
C1 [Goyal, Nihit] Delft Univ Technol, Fac Technol Policy & Management, Delft, Netherlands.
   [El-Taliawi, Ola G.] Carleton Univ, Fac Publ Affairs, Dept Polit Sci, Ottawa, ON, Canada.
   [Howlett, Michael] Simon Fraser Univ, Dept Polit Sci, Burnaby, BC, Canada.
C3 Delft University of Technology; Carleton University; Simon Fraser
   University
RP Goyal, N (corresponding author), Delft Univ Technol TU Delft, Fac Technol Policy & Management, Jaffalaan 5, NL-2628 BX Delft, Netherlands.
EM nihit.goyal@tudelft.nl
RI Howlett, Michael/W-7544-2019
OI Howlett, Michael/0000-0003-4689-740X; Goyal, Nihit/0000-0002-1025-7585;
   El-Taliawi, Ola G./0000-0002-1615-6021
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NR 94
TC 14
Z9 15
U1 2
U2 44
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1541-132X
EI 1541-1338
J9 REV POLICY RES
JI Rev. Policy Res.
PD NOV
PY 2021
VL 38
IS 6
BP 640
EP 660
DI 10.1111/ropr.12448
EA SEP 2021
PG 21
WC Political Science; Public Administration
WE Social Science Citation Index (SSCI)
SC Government & Law; Public Administration
GA WN8JA
UT WOS:000694642700001
OA hybrid
DA 2024-09-05
ER
PT C
AU Song, HM
   Cao, ZX
AF Song, Huaming
   Cao, Zhexiu
GP IEEE
TI Research on Product Quality Evaluation Based on Big Data Analysis
SO 2017 IEEE 2ND INTERNATIONAL CONFERENCE ON BIG DATA ANALYSIS (ICBDA)
LA English
DT Proceedings Paper
CT 2nd IEEE International Conference on Big Data Analysis (ICBDA)
CY MAR 10-12, 2017
CL Beijing, PEOPLES R CHINA
DE quality evaluation; online reviews; big data analysis; machine learning
ID SENTIMENT ANALYSIS
AB In order to evaluate product quality from nonnumerical data, we propose the product quality evaluation model based on big data analysis including data collecting, data preprocessing, quality feature extraction, vector quantization and quality classification. Quality feature word extension algorithm, reviews quantization algorithm and machine learning algorithm are applied. We finally obtain the qualified rate(88.94%) and 7 features that most concerned by consumers through the analysis of 184,967 effective product reviews of wooden toys. In the end, we compare the SVM machine learning algorithm with decision tree and naive bayes, and discuss the credibility of the results. Our research on product quality evaluation extends the application of big data analysis, and also presents a new method to evaluate product quality in the field of manufacture.
C1 [Song, Huaming; Cao, Zhexiu] Nanjing Univ Sci & Technol, Sch Econ Management, Nanjing, Jiangsu, Peoples R China.
C3 Nanjing University of Science & Technology
RP Song, HM (corresponding author), Nanjing Univ Sci & Technol, Sch Econ Management, Nanjing, Jiangsu, Peoples R China.
EM huaming@njust.edu.cn; caozhexiu@126.com
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NR 14
TC 0
Z9 0
U1 0
U2 9
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-5090-3619-6
PY 2017
BP 178
EP 182
PG 5
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Operations Research & Management Science
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Operations Research & Management Science
GA BJ6KQ
UT WOS:000426794900037
DA 2024-09-05
ER
PT C
AU Omae, Y
   Mitsui, T
   Takahashi, H
AF Omae, Yuto
   Mitsui, Takako
   Takahashi, Hirotaka
GP IEEE
TI Rubric Evaluation for Project Research as Active Learning in Super
   Science High School
SO 2016 IEEE/SICE INTERNATIONAL SYMPOSIUM ON SYSTEM INTEGRATION (SII)
SE IEEE/SICE International Symposium on System Integration
LA English
DT Proceedings Paper
CT IEEE/SICE International Symposium on System Integration (SII)
CY DEC 13-15, 2016
CL Sapporo, JAPAN
AB In Super Science High School's (SSH's) educational activities, the students at Yamanashi Eiwa Senior and Junior high school are working on the project research as active learning. In this paper, we report on the results of rubric evaluation and questionnaire survey of motivation for the project research. By using the rubric evaluation's items and questionnaire for motivation between two points of time, we measured the rubric score and motivation of the students in SSH class (n = 17). Moreover, to measure the rubric score as others evaluation, the committee consisted of the researchers and teachers also evaluated at the same time. The results of analysis of the rubric score showed that the average scores were improved significantly and the students and committee could evaluate consistently. The results of analysis of the questionnaire for motivations showed the motivation of interesting, importance and confidence were improved. From the results of the detailed analysis of the relation between the rubric score of others evaluation and motivation, we found that the rubric score of others evaluation was improved by promoting the motivation of interesting. These results suggested that the project research as active learning had an effect to increase the motivation of research activity. As the result, the students worked on the research activity strenuously.
C1 [Omae, Yuto] Japan Inst Sports Sci, Tokyo, Japan.
   [Mitsui, Takako] Yamanashi Eiwa Jr & Senior High Sch, Yamanashi, Japan.
   [Takahashi, Hirotaka] Nagaoka Univ Technol, Niigata, Japan.
C3 Nagaoka University of Technology
RP Omae, Y (corresponding author), Japan Inst Sports Sci, Tokyo, Japan.
EM yuto.omae@gmail.com; hirotaka@kjs.nagaokaut.ac.jp
FU JSPS [16K04672]; Grants-in-Aid for Scientific Research [16K04672]
   Funding Source: KAKEN
FX We would like to thank the teachers at Yamanashi Eiwa Junior and Senior
   High School for cooperating on our research. This work was supported in
   part by JSPS Grant-in-Aid for Scientific Research (C) (Grant Number
   16K04672; H. Takahashi).
CR Ichihara M, 2006, JPN J EDUC PSYCHOL, V54, P199, DOI 10.5926/jjep1953.54.2_199
   Japan Science and Technology Agency, SUP SCI HIGH SCH
   Omae Y., 2016, J YAMANASHI EI UNPUB, V15
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   Yamanashi Eiwa Junior and Senior High School, 2016, 3 ANN REP RES DEV SU
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NR 7
TC 0
Z9 0
U1 0
U2 2
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2474-2317
BN 978-1-5090-3329-4
J9 IEEE/SICE I S SYS IN
PY 2016
BP 827
EP 831
PG 5
WC Computer Science, Interdisciplinary Applications; Engineering,
   Electrical & Electronic
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Engineering
GA BH6MF
UT WOS:000401914000135
DA 2024-09-05
ER
PT J
AU Avram, S
   Velter, V
   Dumitrache, I
AF Avram, Sorin
   Velter, Victor
   Dumitrache, Ioan
TI Semantic Analysis Applications in Computational Bibliometrics
SO CONTROL ENGINEERING AND APPLIED INFORMATICS
LA English
DT Article
DE bibliometrics; citation weighting; natural language processing; text
   similarity
AB Continuing a previous theoretical research in bibliometrics, this study aims to conclude a bibliometric endeavor, in the quest of finding an adapted impact measure for scientific papers. Its main objective is to define a technological solution capable to interpret both citations and papers' content, in an integrative approach. The solution employs natural language processors, similarity measures and graph computation algorithms, while integrating them in a software prototype. Describing the design and implementation phases, the research underlines specific solutions and optimizations for relevance computing in citation networks.
C1 [Avram, Sorin; Dumitrache, Ioan] Univ Politehn Bucuresti, Bucharest 060042, Romania.
   [Velter, Victor] Execut Agcy Higher Educ, Bucharest 010362, Romania.
C3 National University of Science & Technology POLITEHNICA Bucharest
RP Avram, S (corresponding author), Univ Politehn Bucuresti, Bucharest 060042, Romania.
EM avram.sorin@gmail.com; victor.velter@uefiscdi.ro;
   ioan.dumitrache@acse.pub.ro
RI VELTER, Victor/E-9135-2017; VELTER, Victor/J-3536-2013
OI VELTER, Victor/0000-0003-0566-3789; VELTER, Victor/0000-0003-0566-3789
CR ACETIC CYBERLEX, 2013, SEM SEARCH ENG TEXT
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NR 28
TC 3
Z9 4
U1 0
U2 21
PU ROMANIAN SOC CONTROL TECH INFORMATICS
PI BUCHAREST
PA 313 SPLAIUL INDEPENDENTEI, BUCHAREST, 060042, ROMANIA
SN 1454-8658
J9 CONTROL ENG APPL INF
JI Control Eng. Appl. Inform.
PD MAR
PY 2014
VL 16
IS 1
BP 62
EP 69
PG 8
WC Automation & Control Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Automation & Control Systems
GA AE1JB
UT WOS:000333724500007
DA 2024-09-05
ER
PT C
AU Bugajska, M
AF Bugajska, Malgorzata
BE Ackerman, M
   DiengKuntz, R
   Simone, C
   Wulf, V
TI "KT" CarePacks - Collaboration patterns for knowledge transfer: Learning
   from IS/IT-outsourcing case at a Swiss financial institution
SO KNOWLEDGE MANAGEMENT IN ACTION
SE International Federation for Information Processing
LA English
DT Proceedings Paper
CT 20th World Computer Congress
CY SEP 07-10, 2008
CL Milan, ITALY
DE knowledge transfer; IS/IT outsourcing; patterns
AB Organizations now more than ever focus on fostering team work in their daily activities to secure better results for their stakeholders. Team work and collaboration are especially important for inter-organizational outsourcing relationships where these qualities are crucial for the successful knowledge transfer conducted throughout all phases of outsourcing relationship. Knowledge workers involved in such complex, inter-organizational collaboration processes require support to secure structured and well managed collaboration. Consequently, there is a strong need of service receiver organizations to use sustainable approaches for the knowledge transfer to satisfy recurring transfer processes in forthcoming sourcing activities. Idea of "pattern" offers encapsulated approach for describing solutions for recurring problems and is already successfully used within the IT domain. In this paper we present the concept of patterns for the sustainable knowledge transfer for outsourcing relationships. We introduce CarePacks - reusable patterns for supporting act of the collaborative knowledge transfer and present lessons learned from introducing them at a Swiss financial - institution while conducting six knowledge transfer pilots in three consecutive trials.
C1 [Bugajska, Malgorzata] Univ Zurich, Dept Informat, CH-8006 Zurich, Switzerland.
C3 University of Zurich
EM bugajska@gmail.coni
CR Alexander C., 1977, PATTERN LANGUAGE TOW
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   BUGAJSKA M, 2007, PILOTING KNOWLEDGE T
   Carmel E., 2005, Offshoring Information Technology: Sourcing and Outsourcing to a Global Workforce
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   Collison C., 2004, LEARNING FLY PRACTIC
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   ENGLISH W, 2006, WINNING KNOWLEDGE TR
   FOWLER B, 2006, IS INSOURCING NEW OU
   Gamma E., 1994, Design Patterns: Elements of Reusable Object-Oriented Software
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NR 41
TC 0
Z9 0
U1 0
U2 4
PU SPRINGER
PI NEW YORK
PA 233 SPRING STREET, NEW YORK, NY 10013, UNITED STATES
SN 1571-5736
BN 978-0-387-09658-2
J9 INT FED INFO PROC
PY 2008
VL 270
BP 17
EP 36
PG 20
WC Computer Science, Information Systems; Management; Operations Research &
   Management Science
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Business & Economics; Operations Research & Management
   Science
GA BIC05
UT WOS:000258322500003
DA 2024-09-05
ER
PT C
AU Wang, Z
   Zhang, XC
AF Wang, Zheng
   Zhang, Xianchao
BE Zhao, C
TI Research on real-time and intelligent learning performance testing
   method
SO PROCEEDINGS OF 2008 INTERNATIONAL COLLOQUIUM ON ARTIFICIAL INTELLIGENCE
   IN EDUCATION
LA English
DT Proceedings Paper
CT International Colloquium on Artificial Intelligence in Education
CY OCT 17-18, 2008
CL Wuhan, PEOPLES R CHINA
DE online learning; learning performance; real-time and intelligent testing
   method; distance learning
ID TUTORING SYSTEMS
AB Based on the results of online exams, a tree-like representation method for course resources and a knowledge-based testing method for students' online learning performance are presented in the paper, by introducing the methodology of artificial intelligence, knowledge engineering and fuzzy mathematics. A real-time and intelligent testing system is realized based on the testing method, which tests whether students have mastered the course's knowledge. The application and its feedback from students prove the scientificity and validity of the testing method.
C1 [Wang, Zheng] Dalian Univ Technol, Software Sch, Dalian, Peoples R China.
C3 Dalian University of Technology
CR Akhras FN, 2002, INSTR SCI, V30, P1, DOI 10.1023/A:1013544300305
   Hwang GJ, 2003, COMPUT EDUC, V40, P217, DOI 10.1016/S0360-1315(02)00121-5
   MYMIC LLC, 2004, OUTSTANDING RES ISSU
   WANG QH, 2006, RES DESIGN ANAL SYST
   YACEF K, 2003, AIED 2003 WORKSH P I
NR 5
TC 0
Z9 0
U1 0
U2 4
PU WORLD ACAD UNION-WORLD ACAD PRESS
PI LIVERPOOL
PA 113, ACADEMIC HOUSE, MILL LANE, WAVERTREE TECHNOLOGY PARK, LIVERPOOL,
   L13 4 AH, ENGLAND
BN 978-1-84626-173-2
PY 2008
BP 37
EP 41
PG 5
WC Computer Science, Artificial Intelligence; Education & Educational
   Research; Education, Scientific Disciplines
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Education & Educational Research
GA BIM65
UT WOS:000260861200008
DA 2024-09-05
ER
PT J
AU Glänzel, W
   Thijs, B
AF Glanzel, Wolfgang
   Thijs, Bart
TI Using hybrid methods and 'core documents' for the representation of
   clusters and topics: the astronomy dataset
SO SCIENTOMETRICS
LA English
DT Article
DE Astronomy; Astrophysics; Clustering; NLP; Bibliographic coupling; Hybrid
   clustering; Core documents
ID COCITATION ANALYSIS; INFORMATION; NETWORK; TOOL
AB Based on a dataset on Astronomy and Astrophysics, hybrid cluster analyses have been conducted. In order to obtain an optimum solution and to analyse possible issues resulting from the bibliometric methodologies used, we have systematically studied three models and, within these models, two scenarios each. The hybrid clustering was based on a combination of bibliographic coupling and textual similarities using the Louvain method at two resolution levels. The procedure resulted in three clearly hierarchical structures with six and thirteen, seven and thirteen and finally five and eleven clusters, respectively. These structures are analysed with the help of a concordance table. The statistics reflect a high quality of classification. The results of these three models are presented, discussed and compared with each other. For labelling and interpreting clusters, core documents representing the obtained clusters are used. Furthermore, these core documents help depict the internal structure of the complete network and the clusters. This work has been done as part of the international project 'Measuring the Diversity of Research' and in the framework a special workshop on the comparative analysis of algorithms for the identification of topics in science organised in Berlin in August 2014.
C1 [Glanzel, Wolfgang; Thijs, Bart] Katholieke Univ Leuven, ECOOM, Louvain, Belgium.
   [Glanzel, Wolfgang; Thijs, Bart] Katholieke Univ Leuven, Dept MSI, Louvain, Belgium.
   [Glanzel, Wolfgang] Lib Hungarian Acad Sci, Dept Sci Policy & Scientometr, Budapest, Hungary.
C3 KU Leuven; KU Leuven; Hungarian Academy of Sciences
RP Glänzel, W (corresponding author), Katholieke Univ Leuven, ECOOM, Louvain, Belgium.; Glänzel, W (corresponding author), Katholieke Univ Leuven, Dept MSI, Louvain, Belgium.; Glänzel, W (corresponding author), Lib Hungarian Acad Sci, Dept Sci Policy & Scientometr, Budapest, Hungary.
EM wolfgang.glanzel@kuleuven.be
RI Glanzel, Wolfgang/AAE-4395-2021
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NR 24
TC 51
Z9 53
U1 2
U2 114
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD MAY
PY 2017
VL 111
IS 2
BP 1071
EP 1087
DI 10.1007/s11192-017-2301-6
PG 17
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA ES9JC
UT WOS:000399871500024
OA Green Accepted
DA 2024-09-05
ER
PT J
AU Panchendrarajan, R
   Saxena, A
AF Panchendrarajan, Rrubaa
   Saxena, Akrati
TI Topic-based influential user detection: a survey
SO APPLIED INTELLIGENCE
LA English
DT Article
DE Topic-based influential user detection; Topic modeling; Influence
   mining; Online social network
ID LEVEL INFLUENCERS; SOCIAL NETWORKS; OPINION LEADER; TWITTER;
   RECOMMENDATION; REGRESSION; MODEL; BERT
AB Online Social networks have become an easy means of communication for users to share their opinion on various topics, including breaking news, public events, and products. The content posted by a user can influence or affect other users, and the users who could influence or affect a high number of users are called influential users. Identifying such influential users has a wide range of applications in the field of marketing, including product advertisement, recommendation, and brand evaluation. However, the users' influence varies in different topics, and hence a tremendous interest has been shown towards identifying topic-based influential users over the past few years. Topic-level information in the content posted by the users can be used in various stages of the topic-based influential user detection (IUD) problem, including data gathering, construction of influence network, quantifying the influence between two users, and analyzing the impact of the detected influential user. This has opened up a wide range of opportunities to utilize the existing techniques to model and analyze the topic-level influence in online social networks. In this paper, we perform a comprehensive study of existing techniques used to infer the topic-based influential users in online social networks. We present a detailed review of these approaches in a taxonomy while highlighting the challenges and limitations associated with each technique. Moreover, we perform a detailed study of different evaluation techniques used in the literature to overcome the challenges that arise in evaluating topic-based IUD approaches. Furthermore, closely related research topics and open research questions in topic-based IUD are discussed to provide a deep understanding of the literature and future directions.
C1 [Panchendrarajan, Rrubaa] Sri Lanka Inst Informat Technol, Fac Comp, Colombo, Sri Lanka.
   [Saxena, Akrati] Eindhoven Univ Technol, Dept Math & Comp Sci, Eindhoven, Netherlands.
C3 Sri Lanka Institute of Information Technology (SLIIT); Eindhoven
   University of Technology
RP Saxena, A (corresponding author), Eindhoven Univ Technol, Dept Math & Comp Sci, Eindhoven, Netherlands.
EM rrubaa.p@sliit.lk; a.saxena@tue.nl
OI Saxena, Akrati/0000-0002-7151-6309
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NR 132
TC 7
Z9 8
U1 5
U2 20
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0924-669X
EI 1573-7497
J9 APPL INTELL
JI Appl. Intell.
PD MAR
PY 2023
VL 53
IS 5
BP 5998
EP 6024
DI 10.1007/s10489-022-03831-7
EA JUL 2022
PG 27
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA A3ZV8
UT WOS:000822010200013
OA hybrid
DA 2024-09-05
ER
PT J
AU Argoubi, M
   Jammeli, H
   Masri, H
AF Argoubi, Majdi
   Jammeli, Haifa
   Masri, Hatem
TI The intellectual structure of the waste management field
SO ANNALS OF OPERATIONS RESEARCH
LA English
DT Article
DE Waste management; Intellectual structure; Optimization; Heuristics;
   Co-citation analysis; Literature mapping
ID REVERSE LOGISTICS NETWORK; DECISION-SUPPORT-SYSTEM; LANDFILL SITE
   SELECTION; RIO-GRANDE VALLEY; PROGRAMMING APPROACH; ENVIRONMENTAL
   ASSESSMENT; OPTIMIZATION MODEL; ALGORITHM; GREY; DESIGN
AB Waste management is an important issue in the field of green logistics. It has consequently drawn the attention of the scientific community and has been extensively investigated over the past few years. Through an analysis of the existing waste management literature, we attempt in this paper to better understand past developments in this area as well as emerging trends and recent developments. Emphasis will be put mainly on Operations Research and Management Science techniques when dealing with waste management problems. To reach this target, we follow bibliometric-based methods, specifically Co-citation Analysis, Betweenness Centrality and Burst Detection combined with network visualization. After identifying the research papers published between 1990 and 2018 within the Thomson Reuters Web of Science database, a Co-citation network has been constructed. We propose an algorithm for modularity-based clustering in small networks that iteratively solves a sequence of Mixed Integer Non-linear Programming problems to maximize the modularity therefore providing a non-overlapping partition of the network. A display of the principal research areas and landmark articles that shape the intellectual structure of the waste management problems during the last 30 years is reported.
C1 [Argoubi, Majdi] Univ Sousse, Rue Abdlaaziz Behi, Sousse, Tunisia.
   [Jammeli, Haifa] Univ Tunis, Rue Liberte, Le Bardo, Tunisia.
   [Jammeli, Haifa] HIGHFI, HF LAB, Paris, France.
   [Masri, Hatem] Univ Bahrain, POB 32038, Sakhir, Bahrain.
C3 Universite de Sousse; Universite de Tunis; University of Bahrain
RP Masri, H (corresponding author), Univ Bahrain, POB 32038, Sakhir, Bahrain.
EM mejdiargoubi@yahoo.fr; haifa.echaarika@gmail.com; hmasri@uob.edu.bh
RI ARGOUBI, Majdi/AEI-8735-2022; Masri, Hatem/M-9133-2015; jammeli,
   haifa/P-3141-2017
OI Masri, Hatem/0000-0002-2750-129X; Jammeli, Haifa/0000-0003-0326-1321
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NR 75
TC 8
Z9 9
U1 3
U2 22
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0254-5330
EI 1572-9338
J9 ANN OPER RES
JI Ann. Oper. Res.
PD NOV
PY 2020
VL 294
IS 1-2
SI SI
BP 655
EP 676
DI 10.1007/s10479-020-03570-3
EA MAR 2020
PG 22
WC Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Operations Research & Management Science
GA OP9XW
UT WOS:000564403200001
DA 2024-09-05
ER
PT J
AU Kusch, JD
   Nelson, DA
   Simpson, D
   Gerrits, R
   Glass, L
AF Kusch, Jennifer D.
   Nelson, David A.
   Simpson, Deborah
   Gerrits, Ronald
   Glass, Laurie
TI Using AI to Understand Key Success Features in Evolving CTSAs
SO CTS-CLINICAL AND TRANSLATIONAL SCIENCE
LA English
DT Article
DE clinical translational science; appreciative inquiry; research;
   qualitative evaluation; organizational change
AB A vital role for Clinical and Translational Science Award (CTSA) evaluators is to first identify and then articulate the necessary change processes that support the research infrastructures and achieve synergies needed to improve health through research. The use of qualitative evaluation strategies to compliment quantitative tracking measures (e.g., number of grants/publications) is an essential but under-utilized approach in CTSA evaluations. The Clinical and Translational Science Institute of Southeast Wisconsin implemented a qualitative evaluation approach using appreciative inquiry (AI) that has revealed three critical features associated with CTSA infrastructure transformation success: developing open communication, creating opportunities for proactive collaboration, and ongoing attainment of milestones at the key function group level. These findings are consistent with Bolman & Deal's four interacting hallmarks of successful organizations: structural (infrastructure), political (power distribution; organizational politics), human resource (facilitating change among humans necessary for continued success), and symbolic (visions and aspirations). Data gathered through this longitudinal AI approach illuminates how these change features progress over time as CTSA funded organizations successfully create the multiinstitutional infrastructures to connect laboratory discoveries with the diagnosis and treatment of human disease.
C1 [Kusch, Jennifer D.] Med Coll Wisconsin, Clin & Translat Sci Inst Southeast Wisconsin, Milwaukee, WI 53226 USA.
   [Nelson, David A.; Simpson, Deborah] Med Coll Wisconsin, Milwaukee, WI 53226 USA.
   [Simpson, Deborah] Aurora Hlth Care, Milwaukee, WI USA.
   [Gerrits, Ronald] Milwaukee Sch Engn, Milwaukee, WI USA.
   [Glass, Laurie] Univ Wisconsin, Milwaukee, WI 53201 USA.
C3 Medical College of Wisconsin; Medical College of Wisconsin; Milwaukee
   School Engineering; University of Wisconsin System; University of
   Wisconsin Milwaukee
RP Kusch, JD (corresponding author), Med Coll Wisconsin, Clin & Translat Sci Inst Southeast Wisconsin, Milwaukee, WI 53226 USA.
EM jkusch@mcw.edu
RI Nelson, David A/A-8306-2008
FU National Center for Advancing Translational Sciences, National
   Institutes of Health [8UL1TR000055]
FX This publication was supported by the National Center for Advancing
   Translational Sciences, National Institutes of Health, through Grant
   Number 8UL1TR000055. Its Contents are solely the responsibility of the
   authors and do not necessarily represent the official views of the NIH.
CR Bolman L.G., 2008, Reframing organizations, artistry, choice and leadership, VFourth
   Bushe G.R., 2005, J APPL BEHAV SCI, V41, P161, DOI [10.1177/0021886304270337, DOI 10.1177/0021886304270337]
   Preskill H., 2006, REFRAMING EVALUATION
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NR 6
TC 1
Z9 1
U1 0
U2 11
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1752-8054
EI 1752-8062
J9 CTS-CLIN TRANSL SCI
JI CTS-Clin. Transl. Sci.
PD AUG
PY 2013
VL 6
IS 4
BP 314
EP 316
DI 10.1111/cts.12027
PG 3
WC Medicine, Research & Experimental
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Research & Experimental Medicine
GA 196GR
UT WOS:000322762300013
PM 23919368
OA Green Published, Green Accepted, hybrid
DA 2024-09-05
ER
PT C
AU Hou, JB
AF Hou, Jinbiao
BE Luo, Q
   Wang, T
TI Research on Design of an Automatic Evaluation System of Search Engine
SO 2009 ETP INTERNATIONAL CONFERENCE ON FUTURE COMPUTER AND COMMUNICATION
   (FCC 2009)
LA English
DT Proceedings Paper
CT 1st International Conference on Future Computer and Communication (FCC
   2009)
CY JUN 06-07, 2009
CL Wuhan, PEOPLES R CHINA
DE search engine; evaluation system; recall rate; accuracy rate
AB At present, the search engine is getting more and more important. The frequency of use is getting higher and higher. In order to help users to choose a highly effective search engine, C# is used as a development tool. It researches and implements an automatic evaluation system of search engine which is accurate, highly effective, highly automatic, safe. The structure of the system is simple. It has four modules. Its functions are powerful. It can implement evaluation to search engines on the web. It has the good promotion and application value.
C1 Dezhou Univ, Dept Comp Sci & Technol, Dezhou, Peoples R China.
C3 Dezhou University
RP Hou, JB (corresponding author), Dezhou Univ, Dept Comp Sci & Technol, Dezhou, Peoples R China.
EM houjinb@126.com
CR LI CL, 2006, ASP NET SQL SERVER D, P63
   LIBERTY J, 2001, PROGRAMMING C, P86
   LIU GQ, 2006, DESIGN RES TOPIC SPE, P8
   Liu Y, 2007, CYTOKINE, V39, P25, DOI 10.1016/j.cyto.2007.07.096
   MEN FC, 2008, MODERN INFORM, P21
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NR 8
TC 3
Z9 3
U1 0
U2 0
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA
BN 978-0-7695-3676-7
PY 2009
BP 16
EP 18
DI 10.1109/FCC.2009.11
PG 3
WC Computer Science, Hardware & Architecture; Engineering, Electrical &
   Electronic; Telecommunications
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Engineering; Telecommunications
GA BVF72
UT WOS:000291396200004
DA 2024-09-05
ER
PT J
AU Li, XX
   Bai, Y
   Kang, YF
AF Li, Xixi
   Bai, Yun
   Kang, Yanfei
TI Exploring the social influence of the Kaggle virtual community on the M5
   competition
SO INTERNATIONAL JOURNAL OF FORECASTING
LA English
DT Article
DE Forecasting competition; Virtual community; Social influence; Topic
   modeling; Social network analysis; M5
ID LEADERSHIP; ROLES
AB One of the most significant differences of M5 over previous forecasting competitions is that it was held on Kaggle, an online platform for data scientists and machine learning practitioners. Kaggle provides a gathering place, or virtual community, for web users who are interested in the M5 competition. Users can share code, models, features, and loss functions through online notebooks and discussion forums. Here, we study the social influence of this virtual community on user behavior in the M5 competition. We first research the content of the M5 virtual community by topic modeling and trend analysis. Further, we perform social media analysis to identify the potential relationship network of the virtual community. We study the roles and characteristics of some key participants who promoted the diffusion of information within the M5 virtual community. Overall, this study provides in-depth insights into the mechanism of the virtual community's influence on the participants and has potential implications for future online competitions.(c) 2021 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
C1 [Li, Xixi] Univ Manchester, Dept Math, Manchester, England.
   [Bai, Yun; Kang, Yanfei] Beihang Univ, Sch Econ & Management, Beijing, Peoples R China.
C3 University of Manchester; Beihang University
RP Kang, YF (corresponding author), Beihang Univ, Sch Econ & Management, Beijing, Peoples R China.
EM xixi.li@manchester.ac.uk; baiyun12138@buaa.edu.cn;
   yanfeikang@buaa.edu.cn
RI Kang, Yanfei/ITT-3438-2023
OI Kang, Yanfei/0000-0001-8769-6650; Bai, Yun/0000-0003-4237-7589; Li,
   Xixi/0000-0001-5846-3460
FU National Natural Science Foundation of China; National Key Re-search and
   Development Program;  [72171011];  [72021001];  [2019YFB1404600]
FX Acknowledgments The authors are grateful to the editors and two
   anony-mous reviewers for their helpful comments that improved the
   content of the paper. Yanfei Kang is supported by the National Natural
   Science Foundation of China (No. 72171011 and No. 72021001) and the
   National Key Re-search and Development Program (No. 2019YFB1404600) .
   This research was supported by the high-performance computing (HPC)
   resources at Beihang University.
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NR 42
TC 1
Z9 1
U1 9
U2 16
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0169-2070
EI 1872-8200
J9 INT J FORECASTING
JI Int. J. Forecast.
PD OCT-DEC
PY 2022
VL 38
IS 4
SI SI
BP 1507
EP 1518
DI 10.1016/j.ijforecast.2021.10.001
EA OCT 2022
PG 12
WC Economics; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 5U0AG
UT WOS:000876216700022
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Arguello, J
   Callan, J
   Shulman, S
AF Arguello, Jaime
   Callan, Jamie
   Shulman, Stuart
TI Recognizing Citations in Public Comments
SO JOURNAL OF INFORMATION TECHNOLOGY & POLITICS
LA English
DT Article
DE Citation analysis; public comments; e-rulemaking; text mining;
   information extraction; machine learning
ID ALGORITHM
AB Notice and comment rulemaking is central to how U.S. federal agencies craft new regulation. E-rulemaking, the process of soliciting and considering public comments that are submitted electronically, poses a challenge for agencies. The large volume of comments received makes it difficult to distill and address the most substantive concerns of the public. This work attempts to alleviate this burden by applying existing machine learning techniques to the problem of recognizing citation sentences. A citation in this context is defined as a statement in which the author of the public comment references an external source of factual information that is associated with a specific person or organization. The problem is formulated as a binary classification problem: Is a specific person or organization mentioned in a sentence being referenced as an external source of information? We show that our definition of a citation is reproducible by human judges and that citations can be detected using machine learning techniques with some success. Casting this as a machine learning problem requires selecting an appropriate representation of the sentence. Several feature sets are evaluated individually and in combination. Superior results are obtained by combining feature sets. Syntactic features, which characterize the structure of the sentence rather than its content, significantly improve accuracy when combined with other features, but not when used in isolation. Although prediction error rate is adequate, coverage could be improved. An error analysis enumerates short-term and long-term challenges that must be overcome to improve recall.
C1 [Arguello, Jaime] Carnegie Mellon Univ, Language Technol Inst, Pittsburgh, PA 15213 USA.
   [Callan, Jamie] Carnegie Mellon Univ, Sch Comp Sci, Language Technol Inst, Grad Dept, Pittsburgh, PA 15213 USA.
   [Shulman, Stuart] Univ Pittsburgh, Sch Informat Sci, Sara Fine Inst, Pittsburgh, PA 15260 USA.
   [Shulman, Stuart] Univ Pittsburgh, Ctr Social & Urban Res, Qualitat Data Anal Program, Pittsburgh, PA 15260 USA.
   [Shulman, Stuart] DARPA, Natl Inst Hlth, Natl Sci Fdn, Arlington, VA 22203 USA.
C3 Carnegie Mellon University; Carnegie Mellon University; Pennsylvania
   Commonwealth System of Higher Education (PCSHE); University of
   Pittsburgh; Pennsylvania Commonwealth System of Higher Education
   (PCSHE); University of Pittsburgh; National Science Foundation (NSF);
   National Institutes of Health (NIH) - USA; United States Department of
   Defense; Defense Advanced Research Projects Agency (DARPA)
RP Arguello, J (corresponding author), Carnegie Mellon Univ, Pittsburgh, PA 15213 USA.
EM jaime@cs.cmu.edu
RI Shulman, Stuart/JQW-7521-2023
CR [Anonymous], 2007, HUMAN LANGUAGE TECHN
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NR 23
TC 2
Z9 3
U1 1
U2 5
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1933-1681
EI 1933-169X
J9 J INF TECHNOL POLITI
JI J. Inf. Technol. Politics
PY 2008
VL 5
IS 1
SI SI
BP 49
EP 71
DI 10.1080/19331680802153683
PG 23
WC Communication; Political Science
WE Emerging Sources Citation Index (ESCI)
SC Communication; Government & Law
GA V92JK
UT WOS:000212989900005
DA 2024-09-05
ER
PT J
AU Fitzgerald, L
   Wong, P
   Hannon, J
   Tokerud, MS
   Lyons, J
AF Fitzgerald, Les
   Wong, Pauline
   Hannon, John
   Tokerud, Marte Solberg
   Lyons, Judith
TI Curriculum learning designs: Teaching health assessment skills for
   advanced nursing practitioners through sustainable flexible learning
SO NURSE EDUCATION TODAY
LA English
DT Article
DE Action research; Clinical practice; Curriculum development; Nursing;
   Online learning; Health assessment
AB Background: Innovative curriculum designs are vital for effective learning in contemporary nursing education where traditional modes of delivery are not adequate to meet the learning needs of postgraduate students. This instance of postgraduate teaching in a distributed learning environment offered the opportunity to design a flexible learning model for teaching advanced clinical skills.
   Aim: To present a sustainable model for flexible learning that enables specialist nurses to gain postgraduate qualifications without on-campus class attendance by teaching and assessing clinical health care skills in an authentic workplace setting.
   Methods: An action research methodology was used to gather evidence and report on the process of curriculum development of a core unit, Comprehensive Health Assessment (CHA), within 13 different postgraduate speciality courses. Qualitative data was collected from 27 teaching academics, 21 clinical specialist staff, and 7 hospital managers via interviews, focus groups and journal reflections. Evaluations from the initial iteration of CHA from 36 students were obtained. Data was analyzed to develop and evaluate the curriculum design of CHA.
   Results: The key factors indicated by participants in the curriculum design process were coordination and structuring of teaching and assessment; integration of content development; working with technologies, balancing specialities and core knowledge; and managing induction and expectations.
   Conclusions: A set of recommendations emerged as a result of the action research process. These included: a constructive alignment approach to curriculum design; the production of a facilitator's guide that specifies expectations and unit information for academic and clinical education staff; an agreed template for content authors; and the inclusion of synchronous communication for real-time online tutoring. The highlight of the project was that it built curriculum design capabilities of clinicians and students which can sustain this alternative model of online learning. (C) 2012 Elsevier Ltd. All rights reserved.
C1 [Fitzgerald, Les; Hannon, John; Lyons, Judith] La Trobe Univ, La Trobe Rural Hlth Sch, Bundoora, Vic 3086, Australia.
   [Wong, Pauline] La Trobe Univ, Alfred Hlth Clin Sch, Bundoora, Vic 3086, Australia.
   [Tokerud, Marte Solberg] La Trobe Univ, Sch Nursing & Midwifery, Bundoora, Vic 3086, Australia.
C3 La Trobe University; La Trobe University; La Trobe University
RP Fitzgerald, L (corresponding author), La Trobe Univ, La Trobe Rural Hlth Sch, Bundoora, Vic 3086, Australia.
EM L.Fitzgerald@latrobe.edu.au; P.Wong@latrobe.edu.au;
   J.Hannon@latrobe.edu.au; M.SolbergTokerud@latrobe.edu.au;
   Judith.Lyons@latrobe.edu.au
RI Wong, Pauline/X-2488-2019; Hannon, John/AAZ-4722-2021; Wong,
   Pauline/JCE-4682-2023; Wong, Pauline/AAQ-1526-2020
OI Lyons, Judith/0000-0002-3184-173X; Hannon, John/0000-0002-1790-0860;
   Wong, Pauline/0000-0002-6396-0338
CR [Anonymous], 2004, THEORY PRACTICE ONLI
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NR 14
TC 8
Z9 11
U1 0
U2 33
PU CHURCHILL LIVINGSTONE
PI EDINBURGH
PA JOURNAL PRODUCTION DEPT, ROBERT STEVENSON HOUSE, 1-3 BAXTERS PLACE,
   LEITH WALK, EDINBURGH EH1 3AF, MIDLOTHIAN, SCOTLAND
SN 0260-6917
EI 1532-2793
J9 NURS EDUC TODAY
JI Nurse Educ. Today
PD OCT
PY 2013
VL 33
IS 10
BP 1230
EP 1236
DI 10.1016/j.nedt.2012.05.029
PG 7
WC Education, Scientific Disciplines; Nursing
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Education & Educational Research; Nursing
GA 244SP
UT WOS:000326410800025
PM 22749437
DA 2024-09-05
ER
PT J
AU Liang, ZT
   Mao, J
   Lu, K
   Ba, ZC
   Li, G
AF Liang, Zhentao
   Mao, Jin
   Lu, Kun
   Ba, Zhichao
   Li, Gang
TI Combining deep neural network and bibliometric indicator for emerging
   research topic prediction
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
DE Emerging topic prediction; Time series forecasting; Neural network;
   Bibliometric indicator
ID SCIENTIFIC LITERATURES; IDENTIFICATION; EVOLUTION; IMPACT; TECHNOLOGIES;
   EMERGENCE; SCIENCE; TRENDS
AB Predicting emerging research topics is important to researchers and policymakers. In this study, we propose a two-step solution to the problem of emerging topic prediction. The first step forecasts the future popularity score, a novel indicator reflecting the impact and growth, of candidate topics in a time-series manner. The second step selects novel topics from the candidates predicted to be popular in the first step. Terms with domain characteristics are used as candidate topics. Deep neural networks, specifically LSTM and NNAR, are applied with nine features of topics to predict popularity score. We evaluated the models and five baselines on two datasets from two perspectives, i.e., the ability to (1) predict the correct indicator value and (2) reconstruct the optimal ranking order. Two types of training strategies were compared, including a global strategy that trains a model with all topics and two local strategies that train separate models with different groups of topics. Our results show that LSTM and NNAR outperform other models in predicting the value of popularity score measured by MAE and RMSE, while LightGBM is a competitive baseline in ranking the topics in terms of NDCG@20. The performance difference of global and local strategies is not significant. Emerging topics predicted by our approach are compared with those by other methods. A qualitative assessment on nominated emerging topics suggests topics nominated by machine learning methods are more alike than those by the rulebased model. Some important topics are nominated according to a preliminary literature analysis. This study exploited the strengths of both machine learning and bibliometric indicator approaches for emerging topic prediction. Deep neural networks are applied where objective optimization target can be defined and measured. Bibliometric indicator offers an efficient way to select novel topics from candidates. The hybrid approach shows promise in considering various characteristics of emerging topics when making predictions.
C1 [Liang, Zhentao; Mao, Jin; Li, Gang] Wuhan Univ, Ctr Studies Informat Resources, Bayi Rd 299, Wuhan 430072, Peoples R China.
   [Liang, Zhentao; Mao, Jin] Wuhan Univ, Sch Informat Management, Bayi Rd 299, Wuhan 430072, Peoples R China.
   [Lu, Kun] Univ Oklahoma, Sch Lib & Informat Studies, Norman, OK 73019 USA.
   [Ba, Zhichao] Nanjing Univ Sci & Technol, Dept Informat Management, Xiaolingwei St 200, Nanjing 210094, Peoples R China.
C3 Wuhan University; Wuhan University; University of Oklahoma System;
   University of Oklahoma - Norman; Nanjing University of Science &
   Technology
RP Mao, J (corresponding author), Wuhan Univ, Ctr Studies Informat Resources, Bayi Rd 299, Wuhan 430072, Peoples R China.; Mao, J (corresponding author), Wuhan Univ, Sch Informat Management, Bayi Rd 299, Wuhan 430072, Peoples R China.
EM maojin@whu.edu.cn
RI Ba, Zhichao/IAR-0606-2023; Liang, Zhentao/AAP-7103-2020; Lu,
   Kun/G-2416-2015
OI Liang, Zhentao/0000-0003-2927-3523; Lu, Kun/0000-0001-5614-7042; ba, zhi
   chao/0000-0001-7005-8265
FU National Natural Science Foundation of China (NSFC) [71804135, 71921002,
   71790612]; world class discipline project of the Ministry of Education
   "Library, Information, and Data Science" in China
FX This study was funded by the National Natural Science Foundation of
   China (NSFC) Grant Nos. 71804135, 71921002, and 71790612. This research
   was also supported by the world class discipline project of the Ministry
   of Education "Library, Information, and Data Science" in China. In
   addition, we are very grateful to the anonymous reviewers for their
   helpful comments.
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NR 53
TC 34
Z9 39
U1 20
U2 220
PU ELSEVIER SCI LTD
PI London
PA 125 London Wall, London, ENGLAND
SN 0306-4573
EI 1873-5371
J9 INFORM PROCESS MANAG
JI Inf. Process. Manage.
PD SEP
PY 2021
VL 58
IS 5
AR 102611
DI 10.1016/j.ipm.2021.102611
EA APR 2021
PG 18
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA TU6HG
UT WOS:000681134700007
DA 2024-09-05
ER
PT J
AU Marrone, M
   Lemke, S
   Kolbe, LM
AF Marrone, Mauricio
   Lemke, Sascha
   Kolbe, Lutz M.
TI Entity linking systems for literature reviews
SO SCIENTOMETRICS
LA English
DT Article
DE Word-sense disambiguation; Entity annotation; Science mapping;
   Bibliometric methods; Systematic mapping; Systematic literature review;
   Named entity recognition
ID SCIENCE; MANAGEMENT; TOOL; KNOWLEDGE; LANGUAGE
AB Computer-assisted methods and tools can help researchers automate the coding process of literature reviews and accelerate the literature review process. However, existing approaches for coding textual data do not account for lexical ambiguity; that is, instances in which individual words have multiple meanings. To counter this, we developed a method to conduct rapid and comprehensive analyses of diverse literature types. Our method uses entity linking and keyword analysis and is embedded into a literature review framework. Next, we apply the framework to review the literature on digital disruption and digital transformation. We outline the method's advantages and its applicability to any research topic.
C1 [Marrone, Mauricio] Macquarie Univ, Dept Accounting & Corp Governance, Sydney, NSW 2109, Australia.
   [Lemke, Sascha; Kolbe, Lutz M.] Univ Goettingen, Chair Informat Syst, Gottingen, Germany.
C3 Macquarie University; University of Gottingen
RP Marrone, M (corresponding author), Macquarie Univ, Dept Accounting & Corp Governance, Sydney, NSW 2109, Australia.
EM mauricio.marrone@mq.edu.au
OI Marrone, Mauricio/0000-0003-3896-6049
FU CAUL
FX Open Access funding enabled and organized by CAUL and its Member
   Institutions.
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NR 87
TC 3
Z9 3
U1 3
U2 26
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD JUL
PY 2022
VL 127
IS 7
BP 3857
EP 3878
DI 10.1007/s11192-022-04423-5
EA JUN 2022
PG 22
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 3L2ZJ
UT WOS:000817847200005
OA hybrid
DA 2024-09-05
ER
PT C
AU Gabureanu, S
   Istrate, O
AF Gabureanu, Simona
   Istrate, Olimpius
BE Soare, E
TI The effects of using intelligent tutoring systems for language learning
   - findings of a research evaluation report
SO 5TH INTERNATIONAL CONFERENCE EDU-WORLD 2012 - EDUCATION FACING
   CONTEMPORARY WORLD ISSUES
SE Procedia Social and Behavioral Sciences
LA English
DT Proceedings Paper
CT 5th International Conference EDU-WORLD - Education Facing Contemporary
   World Issues
CY NOV 29-DEC 01, 2012
CL Pitesti, ROMANIA
DE language learning; intelligent tutoring system; online learning platform
AB The present paper aims to present the pilot project "ICE3- Integrating CALL in early education environments" - developed in Romania, Spain and Germany and the impact of the ICE3 platform usage - a state-of-the-art online platform for learning English or German as a foreign language - on students learning, on teaching, on development of educational situations using ICT. The evaluation research developed within the project comprised data from a sample of 513 students which have participated in the instructional activities performed within the project. The article is describing the outcomes of the evaluation: (1) the effects of using an innovative didactic pathway on students' learning motivation, (2) learners preferences for different language learning activities such as individual study and communication, (3) increasing the learners' preference for practicing different language skills such as understanding a written text, writing a text, (4) increase in students' digital competences used for learning, (5) the degree to which students better understand the content after using intelligent feed-back provided on the AutoTutor exercises. (c) 2013 The Authors. Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the University of Pitesti, Romania
C1 [Gabureanu, Simona] Univ Politehn Bucuresti, Bucharest 060042, Romania.
   [Istrate, Olimpius] Univ Bucharest, Bucharest 050107, Romania.
C3 National University of Science & Technology POLITEHNICA Bucharest;
   University of Bucharest
RP Gabureanu, S (corresponding author), Univ Politehn Bucuresti, Bucharest 060042, Romania.
RI Gabureanu, Simona/AAY-2267-2020; Gabureanu, Simona N./IQS-7425-2023;
   Istrate, Olimpius/E-8553-2011
OI Istrate, Olimpius/0000-0002-1940-6284
CR [Anonymous], ICVL P 6 INT C VIRT
   Badia T, 2011, P E SOC IADIS INT C, P1354
   Estrada Mariona, 2009, P E SOC IADIS INT C, VII, P95
NR 3
TC 0
Z9 0
U1 0
U2 17
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA SARA BURGERHARTSTRAAT 25, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 1877-0428
J9 PROCD SOC BEHV
PY 2013
VL 76
BP 351
EP 355
DI 10.1016/j.sbspro.2013.04.126
PG 5
WC Education & Educational Research
WE Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Education & Educational Research
GA BFH50
UT WOS:000319885300064
OA gold
DA 2024-09-05
ER
PT J
AU Wang, XB
   Dai, ZY
   Li, H
   Yang, JF
AF Wang, Xibin
   Dai, Zhenyu
   Li, Hui
   Yang, Jianfeng
TI Research on Hybrid Collaborative Filtering Recommendation Algorithm
   Based on the Time Effect and Sentiment Analysis
SO COMPLEXITY
LA English
DT Article
AB In this study, we focus on the problem of information expiration when using the traditional collaborative filtering algorithm and propose a new collaborative filtering algorithm by integrating the time factor (ITWCF). This algorithm considers information influence attenuation over time, introduces an information retention period based on the information half-value period, and proposes a time-weighted function, which is applied to the nearest neighbor selection and score prediction to assign different time weights to the scores. In addition, to further improve the quality of the nearest neighbor selection and alleviate the problem of data sparsity, a method of calculating users' sentiment tendency by analysis of user review features is proposed to mine users' attitudes about the reviewed items, which expands the score matrix. The time factor and sentiment tendency are then integrated into the K-means clustering algorithm to select the nearest neighbor. A hybrid collaborative filtering model (TWCHR) based on the improved K-means clustering algorithm is then proposed, by combining item-based and user-based collaborative filtering. Finally, the experimental results show that the proposed algorithm can address the time effect and sentiment analysis in recommendations and improve the predictive performance of the model.
C1 [Wang, Xibin; Yang, Jianfeng] Guizhou Inst Technol, Sch Data Sci, Guiyang 550003, Guizhou, Peoples R China.
   [Wang, Xibin; Yang, Jianfeng] Special Key Lab Artificial Intelligence & Intelli, Guiyang 550003, Guizhou, Peoples R China.
   [Dai, Zhenyu; Li, Hui] Guizhou Univ, Coll Comp Sci & Technol, Guiyang 550025, Guizhou, Peoples R China.
C3 Guizhou Institute of Technology; Guizhou University
RP Li, H (corresponding author), Guizhou Univ, Coll Comp Sci & Technol, Guiyang 550025, Guizhou, Peoples R China.
EM cse.huili@gzu.edu.cn
RI Yang, Jianfeng/AAC-5521-2021
OI Yang, Jianfeng/0000-0003-3486-9604; Wang, Xibin/0000-0003-1831-9872
FU Technology Foundation of Guizhou Province [QianKeHeJiChu[2020]1Y269];
   New Academic Seedling Cultivation and Exploration Innovation Project
   [QianKeHe Platform Talents[2017]5789-21]; Program for Innovative Talent
   of Guizhou Province [QianCaiJiao[2018]190]; National Natural Science
   Foundation of China [71901078, 71964009]; High-Level Talent Project of
   Guizhou Institute of Technology [XJGC20190929]; Special Key Laboratory
   of Artificial Intelligence and Intelligent Control of Guizhou Province
   [KY[2020]001]
FX This work was partially supported by the Technology Foundation of
   Guizhou Province (Grant no. QianKeHeJiChu[2020]1Y269), New Academic
   Seedling Cultivation and Exploration Innovation Project (Grant no.
   QianKeHe Platform Talents[2017]5789-21), Program for Innovative Talent
   of Guizhou Province (Grant no. QianCaiJiao[2018]190), National Natural
   Science Foundation of China (Grant nos. 71901078 and 71964009),
   High-Level Talent Project of Guizhou Institute of Technology (Grant no.
   XJGC20190929), and Special Key Laboratory of Artificial Intelligence and
   Intelligent Control of Guizhou Province (Grant no. KY[2020]001).
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NR 20
TC 7
Z9 7
U1 3
U2 24
PU WILEY-HINDAWI
PI LONDON
PA ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON, WIT 5HE, ENGLAND
SN 1076-2787
EI 1099-0526
J9 COMPLEXITY
JI Complexity
PD MAR 8
PY 2021
VL 2021
AR 6635202
DI 10.1155/2021/6635202
PG 11
WC Mathematics, Interdisciplinary Applications; Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematics; Science & Technology - Other Topics
GA RB1UK
UT WOS:000631901200006
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU Li, X
   Chen, HC
   Zhang, Z
   Li, JX
   Nunamaker, JF
AF Li, Xin
   Chen, Hsinchun
   Zhang, Zhu
   Li, Jiexun
   Nunamaker, Jay F., Jr.
TI Managing Knowledge in Light of Its Evolution Process: An Empirical Study
   on Citation Network-Based Patent Classification
SO JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
LA English
DT Article
DE citation analysis; classification; kernel-based method; knowledge
   management; machine learning; patent management
ID ECONOMICS; WEB
AB Knowledge management is essential to modem organizations. Due to the information overload problem, managers are facing critical challenges in utilizing the data in organizations. Although several automated tools have been applied, previous applications often deem knowledge items independent and use solely contents, which may limit their analysis abilities. This study focuses on the process of knowledge evolution and proposes to incorporate this perspective into knowledge management tasks. Using a patent classification task as an example, we represent knowledge evolution processes with patent citations and introduce a labeled citation graph kernel to classify patents under a kernel-based machine learning framework. In the experimental study, our proposed approach shows more than 30 percent improvement in classification accuracy compared to traditional content-based methods. The approach can potentially affect the existing patent management procedures. Moreover, this research ends strong support to considering knowledge evolution processes in other knowledge management tasks.
C1 [Li, Xin] City Univ Hong Kong, Dept Informat Syst, Hong Kong, Hong Kong, Peoples R China.
   [Zhang, Zhu] Univ Arizona, Dept MIS, Tucson, AZ 85721 USA.
   [Li, Jiexun] Drexel Univ, Coll Informat Sci & Technol, Philadelphia, PA USA.
   [Nunamaker, Jay F., Jr.] Univ Arizona, Ctr Management Informat, Tucson, AZ 85721 USA.
C3 City University of Hong Kong; University of Arizona; Drexel University;
   University of Arizona
RP Li, X (corresponding author), City Univ Hong Kong, Dept Informat Syst, Hong Kong, Hong Kong, Peoples R China.
RI Li, Xin/K-8045-2015
OI Li, Xin/0000-0002-0041-3134
FU NSF [IIS-0311652, DMI-0533749]
FX This research is supported by the NSF: IIS-0311652 "Intelligent Patent
   Analysis for Nanoscale Science and Engineering" and DMI-0533749
   "NanoMap: Mapping Nanotechnology Development." The authors thank the
   USPTO for making their data available for research purposes.
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NR 56
TC 19
Z9 21
U1 7
U2 106
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0742-1222
EI 1557-928X
J9 J MANAGE INFORM SYST
JI J. Manage. Inform. Syst.
PD SUM
PY 2009
VL 26
IS 1
BP 129
EP 153
DI 10.2753/MIS0742-1222260106
PG 25
WC Computer Science, Information Systems; Information Science & Library
   Science; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science; Business &
   Economics
GA 477LL
UT WOS:000268520500007
DA 2024-09-05
ER
PT J
AU Willis, M
   Duckworth, P
   Coulter, A
   Meyer, ET
   Osborne, M
AF Willis, Matthew
   Duckworth, Paul
   Coulter, Angela
   Meyer, Eric T.
   Osborne, Michael
TI The Future of Health Care: Protocol for Measuring the Potential of Task
   Automation Grounded in the National Health Service Primary Care System
SO JMIR RESEARCH PROTOCOLS
LA English
DT Article
DE qualitative research; supervised machine learning; automation;
   interdisciplinary research; task performance and analysis
ID TIME; JOBS
AB Background: Recent advances in technology have reopened an old debate on which sectors will be most affected by automation. This debate is ill served by the current lack of detailed data on the exact capabilities of new machines and how they are influencing work. Although recent debates about the future of jobs have focused on whether they are at risk of automation, our research focuses on a more fine-grained and transparent method to model task automation and specifically focus on the domain of primary health care.
   Objective: This protocol describes a new wave of intelligent automation, focusing on the specific pressures faced by primary care within the National Health Service (NHS) in England. These pressures include staff shortages, increased service demand, and reduced budgets. A critical part of the problem we propose to address is a formal framework for measuring automation, which is lacking in the literature. The health care domain offers a further challenge in measuring automation because of a general lack of detailed, health care-specific occupation and task observational data to provide good insights on this misunderstood topic.
   Methods: This project utilizes a multimethod research design comprising two phases: a qualitative observational phase and a quantitative data analysis phase; each phase addresses one of the two project aims. Our first aim is to address the lack of task data by collecting high-quality, detailed task-specific data from UK primary health care practices. This phase employs ethnography, observation, interviews, document collection, and focus groups. The second aim is to propose a formal machine learning approach for probabilistic inference of task- and occupation-level automation to gain valuable insights. Sensitivity analysis is then used to present the occupational attributes that increase/decrease automatability most, which is vital for establishing effective training and staffing policy.
   Results: Our detailed fieldwork includes observing and documenting 16 unique occupations and performing over 130 tasks across six primary care centers. Preliminary results on the current state of automation and the potential for further automation in primary care are discussed. Our initial findings are that tasks are often shared amongst staff and can include convoluted workflows that often vary between practices. The single most used technology in primary health care is the desktop computer. In addition, we have conducted a large-scale survey of over 156 machine learning and robotics experts to assess what tasks are susceptible to automation, given the state-of-the-art technology available today. Further results and detailed analysis will be published toward the end of the project in early 2019.
   Conclusions: We believe our analysis will identify many tasks currently performed manually within primary care that can be automated using currently available technology. Given the proper implementation of such automating technologies, we expect considerable staff resources to be saved, alleviating some pressures on the NHS primary care staff.
C1 [Willis, Matthew; Meyer, Eric T.] Univ Oxford, Oxford Internet Inst, 1 St Giles, Oxford OX1 3JS, England.
   [Duckworth, Paul; Osborne, Michael] Univ Oxford, Dept Engn Sci, Machine Learning Res Grp, Oxford, England.
   [Coulter, Angela] Univ Oxford, Nuffield Dept Populat Hlth, Hlth Serv Res Unit, Oxford, England.
   [Meyer, Eric T.] Univ Texas Austin, Sch Informat, Austin, TX 78712 USA.
C3 University of Oxford; University of Oxford; University of Oxford;
   University of Texas System; University of Texas Austin
RP Willis, M (corresponding author), Univ Oxford, Oxford Internet Inst, 1 St Giles, Oxford OX1 3JS, England.
EM mwillis@syr.edu
RI Coulter, Angela/N-6998-2019; Meyer, Eric T./C-1029-2011
OI Coulter, Angela/0000-0002-6308-8375; Frey, Carl
   Benedikt/0000-0002-0034-6293; Osborne, Michael/0000-0003-1959-012X
FU Health Foundation [7559]; Oxford Martin Programme on Technology and
   Employment; Rhodes Trust
FX We thank The Health Foundation (award #7559), the Oxford Martin
   Programme on Technology and Employment, and the Rhodes Trust.
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NR 30
TC 6
Z9 6
U1 0
U2 6
PU JMIR PUBLICATIONS, INC
PI TORONTO
PA 59 WINNERS CIRCLE, TORONTO, ON M4L 3Y7, CANADA
SN 1929-0748
J9 JMIR RES PROTOC
JI JMIR RES. Protoc.
PD APR
PY 2019
VL 8
IS 4
AR e11232
DI 10.2196/11232
PG 9
WC Health Care Sciences & Services; Public, Environmental & Occupational
   Health
WE Emerging Sources Citation Index (ESCI)
SC Health Care Sciences & Services; Public, Environmental & Occupational
   Health
GA HW2EW
UT WOS:000466496800019
PM 30964437
OA Green Published, Green Submitted, gold
DA 2024-09-05
ER
PT J
AU Peng, Z
   Zhang, H
   Tang, HT
   Feng, Y
   Yin, WM
AF Peng, Zhao
   Zhang, Huan
   Tang, Hongtao
   Feng, Yue
   Yin, Weiming
TI Research on flexible job-shop scheduling problem in green sustainable
   manufacturing based on learning effect
SO JOURNAL OF INTELLIGENT MANUFACTURING
LA English
DT Article
DE Green sustainable development; Man&#8211; machine dual resource
   constraint mechanism; FJSP; Learning effect; HDMICA; Improved simulated
   annealing
ID SEARCH ALGORITHM; MACHINE; OPTIMIZATION; NOISE; TIME
AB As one of the manufacturing industries with high energy consumption and high pollution, sand casting is facing major challenges in green manufacturing. In order to balance production and green sustainable development, this paper puts forward man-machine dual resource constraint mechanism. In addition, a multi-objective flexible job shop scheduling problem model constrained by job transportation time and learning effect is constructed, and the goal is to minimize processing time energy consumption and noise. Subsequently, a hybrid discrete multi-objective imperial competition algorithm (HDMICA) is developed to solve the model. The global search mechanism based on the HDMICA improves two aspects: a new initialization method to improve the quality of the initial population, and the empire selection method based on Pareto non-dominated solution to balance the empire forces. Then, the improved simulated annealing algorithm is embedded in imperial competition algorithm (ICA), which overcomes the premature convergence problem of ICA. Therefore, four neighborhood structures are designed to help the algorithm jump out of the local optimal solution. Finally, an example is used to verify the feasibility of the proposed algorithm. By comparing with the original ICA and other four algorithms, the effectiveness of the proposed algorithm in the quality of the first frontier solution is verified.
C1 [Peng, Zhao; Zhang, Huan; Tang, Hongtao; Feng, Yue; Yin, Weiming] Wuhan Univ Technol, Hubei Key Lab Digital Mfg, Sch Mech & Elect Engn, Wuhan 430070, Peoples R China.
C3 Wuhan University of Technology
RP Tang, HT (corresponding author), Wuhan Univ Technol, Hubei Key Lab Digital Mfg, Sch Mech & Elect Engn, Wuhan 430070, Peoples R China.
EM pz@whut.edu.cn; 2932532969@qq.com; tanghongtaozc@163.com;
   2662133749@qq.com; 528566385@qq.com
RI 殷, 伟铭/HJY-3493-2023
OI 殷, 伟铭/0009-0002-9074-1699
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NR 39
TC 41
Z9 43
U1 14
U2 227
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0956-5515
EI 1572-8145
J9 J INTELL MANUF
JI J. Intell. Manuf.
PD AUG
PY 2022
VL 33
IS 6
BP 1725
EP 1746
DI 10.1007/s10845-020-01713-8
EA MAR 2021
PG 22
WC Computer Science, Artificial Intelligence; Engineering, Manufacturing
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA 2O2EH
UT WOS:000631323400001
DA 2024-09-05
ER
PT C
AU Wei, ZJ
   Han, BY
   Zhou, EX
AF Wei, Zhongji
   Han, Baiyang
   Zhou, Enxian
GP IOP
TI Research on Text Emotion Analysis and Product Performance based on NLP
   and VAR Model
SO 2020 ASIA CONFERENCE ON GEOLOGICAL RESEARCH AND ENVIRONMENTAL TECHNOLOGY
SE IOP Conference Series-Earth and Environmental Science
LA English
DT Proceedings Paper
CT Asia Conference on Geological Research and Environmental Technology
   (GRET)
CY OCT 10-11, 2020
CL ELECTR NETWORK
AB Customers' evaluations, in terms of text or ratings, are important sources of information for online shops to decide products and marketing strategies. In this paper, we discuss methods to conclude and predict merchants' performance and characteristics of customers' behavior of target products based on Amazon dataset. First, we design a Natural Language Processing (NLP) model, Amazon-BERT, to predict text to rating based on transfer learning. We improve the model accuracy by 20% compared to the original pre-trained BERT, and we use the predicted rating in our following steps of modeling. Second, we use PCA to extract 5 principle components, which estimate review's popularity, product's reputation and so on. And then, for pacifier, we find that vector auto regression (VAR) model fits better, we apply it to the regression and doing a Granger causality test. Finally, we looked at the impulse response and variance decomposition. All 3 factors included do have a small impact.
C1 [Wei, Zhongji; Han, Baiyang; Zhou, Enxian] Tsinghua Univ, Sch Econ & Management, Beijing 100084, Peoples R China.
C3 Tsinghua University
RP Wei, ZJ (corresponding author), Tsinghua Univ, Sch Econ & Management, Beijing 100084, Peoples R China.
EM zhongjiwei@tsinghua.edu.cn
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NR 6
TC 0
Z9 0
U1 0
U2 6
PU IOP PUBLISHING LTD
PI BRISTOL
PA DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND
SN 1755-1307
J9 IOP C SER EARTH ENV
JI IOP Conf. Ser. Earth Envir. Sci.
PY 2021
VL 632
AR 052077
DI 10.1088/1755-1315/632/5/052077
PG 7
WC Green & Sustainable Science & Technology; Energy & Fuels; Engineering,
   Civil; Engineering, Geological; Environmental Sciences
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Science & Technology - Other Topics; Energy & Fuels; Engineering;
   Environmental Sciences & Ecology
GA BS1EX
UT WOS:000688420702092
OA gold
DA 2024-09-05
ER
PT C
AU Wahle, JP
   Ruas, T
   Mohammad, SM
   Gipp, B
AF Wahle, Jan Philip
   Ruas, Terry
   Mohammad, Saif M.
   Gipp, Bela
BA Mariani, J
BF Mariani, J
BE Calzolari, N
   Bechet, F
   Blache, P
   Choukri, K
   Cieri, C
   Declerck, T
   Goggi, S
   Isahara, H
   Maegaard, B
   Mazo, H
   Odijk, H
   Piperidis, S
TI D3: A Massive Dataset of Scholarly Metadata for Analyzing the State of
   Computer Science Research
SO LREC 2022: THIRTEEN INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND
   EVALUATION
LA English
DT Proceedings Paper
CT 13th International Conference on Language Resources and Evaluation
   (LREC)
CY JUN 20-25, 2022
CL Marseille, FRANCE
DE Computer Science; Scientometrics; Research Trends; NLP; DBLP; AI
AB DBLP is the largest open-access repository of scientific articles on computer science and provides metadata associated with publications, authors, and venues. We retrieved more than 6 million publications from DBLP and extracted pertinent metadata (e.g., abstracts, author affiliations, citations) from the publication texts to create the DBLP Discovery Dataset (D3). D3 can be used to identify trends in research activity, productivity, focus, bias, accessibility, and impact of computer science research. We present an initial analysis focused on the volume of computer science research (e.g., number of papers, authors, research activity), trends in topics of interest, and citation patterns. Our findings show that computer science is a growing research field (approximate to 15% annually), with an active and collaborative researcher community. While papers in recent years present more bibliographical entries in comparison to previous decades, the average number of citations has been declining. Investigating papers' abstracts reveals that recent topic trends are clearly reflected in D3. Finally, we list further applications of D3 and pose supplemental research questions. The D3 dataset, our findings, and source code are publicly available for research purposes.
C1 [Wahle, Jan Philip; Ruas, Terry; Gipp, Bela] Univ Wuppertal, Wuppertal, Germany.
   [Mohammad, Saif M.] Natl Res Council Canada, Ottawa, ON, Canada.
C3 University of Wuppertal; National Research Council Canada
RP Wahle, JP (corresponding author), Univ Wuppertal, Wuppertal, Germany.
EM wahle@uni-wuppertal.de; ruas@uni-wuppertal.de;
   saif.mohammad@nrc-cnrc.gc.ca; gipp@uni-wuppertal.de
OI Lima Ruas, Terry/0000-0002-9440-780X
FU DBLP, ACL Anthology, Semantic Scholar and teams
FX This work would not be possible without the great resources offered by
   DBLP, ACL Anthology, Semantic Scholar and teams, to whom we are very
   thankful. We also thank Lennart K <spacing diaeresis>ull who helped us
   during the initial phase of the project.
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NR 13
TC 1
Z9 1
U1 0
U2 1
PU EUROPEAN LANGUAGE RESOURCES ASSOC-ELRA
PI PARIS
PA 55-57, RUE BRILLAT-SAVARIN, PARIS, 75013, FRANCE
BN 979-10-95546-72-6
PY 2022
BP 2642
EP 2651
PG 10
WC Computer Science, Interdisciplinary Applications; Linguistics
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Linguistics
GA BU2ZO
UT WOS:000889371702080
DA 2024-09-05
ER
PT J
AU Taheri, S
   Aliakbary, S
AF Taheri, Soroush
   Aliakbary, Sadegh
TI Research trend prediction in computer science publications: a deep
   neural network approach
SO SCIENTOMETRICS
LA English
DT Article
DE Scientometrics; Research trends; Time-series prediction; Deep learning;
   Computer science
AB Thousands of research papers are being published every day, and among all these research works, one of the fastest-growing fields is computer science (CS). Thus, learning which research areas are trending in this particular field of study is advantageous to a significant number of scholars, research institutions, and funding organizations. Many scientometric studies have been done focusing on analyzing the current CS trends and predicting future ones from different perspectives as a consequence. Despite the large datasets from this vast number of CS publications and the power of deep learning methods in such big data problems, deep neural networks have not yet been used to their full potential in this area. Therefore, the objective of this paper is to predict the upcoming years' CS trends using long short-term memory neural networks. Accordingly, CS papers from 1940 and their corresponding fields of study from the microsoft academic graph dataset have been exploited for solving this research trend prediction problem. The prediction accuracy of the proposed method is then evaluated using RMSE and coefficient of determination (R-2) metrics. The evaluations show that the proposed method outperforms the baseline approaches in terms of the prediction accuracy in all considered time periods. Subsequently, adopting the proposed method's predictions, we investigate future trending areas in computer science research from various viewpoints.
C1 [Taheri, Soroush; Aliakbary, Sadegh] Shahid Beheshti Univ, Fac Comp Sci & Engn, Tehran, Iran.
C3 Shahid Beheshti University
RP Aliakbary, S (corresponding author), Shahid Beheshti Univ, Fac Comp Sci & Engn, Tehran, Iran.
EM so.taheri@mail.sbu.ac.ir; s_aliakbary@sbu.ac.ir
RI Taheri, Soroush/GVU-0703-2022
OI Taheri, Soroush/0000-0002-5885-3036
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NR 46
TC 10
Z9 10
U1 6
U2 49
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD FEB
PY 2022
VL 127
IS 2
BP 849
EP 869
DI 10.1007/s11192-021-04240-2
EA JAN 2022
PG 21
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA YY7GU
UT WOS:000744421100005
DA 2024-09-05
ER
PT J
AU Wang, R
AF Wang, Ran
TI Legal technology in contemporary USA and China
SO COMPUTER LAW & SECURITY REVIEW
LA English
DT Article
DE Legal technology; Big data; Artificial intelligence; Intelligent legal
   research; Risk assessment; Judicial system
AB The US and China are two typical models that present legal tech trends that are common world over. In China, robust regional models of intelligent judicial systems have emerged alongside some common applications that include the same-type case referencing, auto-mated sentencing decision, uniform standards of evidence, and judges' data profiling systems. In the US, legal tech refers to artificial intelligence in domains such as innovative legal research, predictive litigation analysis, e-discovery, and contract review.
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C1 [Wang, Ran] Tianjin Univ TJU, Law Sch, Tianjin, Peoples R China.
   [Wang, Ran] TJU, Inst Intelligent Rule Law, Tianjin, Peoples R China.
RP Wang, R (corresponding author), Tianjin Univ TJU, Law Sch, Tianjin, Peoples R China.; Wang, R (corresponding author), TJU, Inst Intelligent Rule Law, Tianjin, Peoples R China.
EM ran.wang@tju.edu.cn
FU Chinese Scholarship Council of the Ministry of Education; Youth Project
   of the National Social Science Fund of China "Research on Big Data
   Evidence" [18CFX036]
FX This project was supported by the Chinese Scholarship Council of the
   Ministry of Education and the Youth Project of the National Social
   Science Fund of China "Research on Big Data Evidence"(18CFX036).
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   Wang Lusheng, 2020, J COMP LAW, V2, P133
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NR 85
TC 13
Z9 13
U1 5
U2 31
PU ELSEVIER ADVANCED TECHNOLOGY
PI OXFORD
PA OXFORD FULFILLMENT CENTRE THE BOULEVARD, LANGFORD LANE, KIDLINGTON,
   OXFORD OX5 1GB, OXON, ENGLAND
SN 0267-3649
J9 COMPUT LAW SECUR REV
JI Comput. Law Secur. Rev.
PD NOV
PY 2020
VL 39
AR 105459
DI 10.1016/j.clsr.2020.105459
PG 20
WC Law
WE Social Science Citation Index (SSCI)
SC Government & Law
GA OP8LV
UT WOS:000588342700003
DA 2024-09-05
ER
PT J
AU Balbuena, LD
AF Balbuena, Lloyd D.
TI The UK Research Excellence Framework and the Matthew effect: Insights
   from machine learning
SO PLOS ONE
LA English
DT Article
ID ASSESSMENT EXERCISE RATINGS; CITATION COUNTS; H-INDEX; SCIENCE; IMPACT;
   PRODUCTIVITY; UNIVERSITIES; KNOWLEDGE; SYSTEM; SCOPUS
AB With the high cost of the research assessment exercises in the UK, many have called for simpler and less time-consuming alternatives. In this work, we gathered publicly available REF data, combined them with library-subscribed data, and used machine learning to examine whether the overall result of the Research Excellence Framework 2014 could be replicated. A Bayesian additive regression tree model predicting university grade point average (GPA) from an initial set of 18 candidate explanatory variables was developed. One hundred and nine universities were randomly divided into a training set (n = 79) and test set (n = 30). The model "learned" associations between GPA and the other variables in the training set and was made to predict the GPA of universities in the test set. GPA could be predicted from just three variables: the number of Web of Science documents, entry tariff, and percentage of students coming from state schools (r-squared = .88). Implications of this finding are discussed and proposals are given.
C1 [Balbuena, Lloyd D.] Univ Saskatchewan, Dept Psychiat, Saskatoon, SK, Canada.
C3 University of Saskatchewan
RP Balbuena, LD (corresponding author), Univ Saskatchewan, Dept Psychiat, Saskatoon, SK, Canada.
EM lloyd.balbuena@usask.ca
RI Balbuena, Lloyd/H-5658-2013
OI Balbuena, Lloyd/0000-0002-3745-5426
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NR 58
TC 3
Z9 3
U1 0
U2 10
PU PUBLIC LIBRARY SCIENCE
PI SAN FRANCISCO
PA 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA
SN 1932-6203
J9 PLOS ONE
JI PLoS One
PD NOV 26
PY 2018
VL 13
IS 11
AR e0207919
DI 10.1371/journal.pone.0207919
PG 13
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics
GA HB8GM
UT WOS:000451325700088
PM 30475868
OA gold, Green Published, Green Submitted
DA 2024-09-05
ER
PT J
AU Hidalgo, RC
   Elias, RP
   Torres-Moreno, JM
   Villegas, OOV
   Salgado, GR
   Salazar, AM
AF Cuellar Hidalgo, Rodrigo
   Pinto Elias, Raul
   Torres-Moreno, Juan-Manuel
   Vergara Villegas, Osslan Osiris
   Reyes Salgado, Gerardo
   Magadan Salazar, Andrea
TI Neural Architecture Comparison for Bibliographic Reference Segmentation:
   An Empirical Study
SO DATA
LA English
DT Article
DE reference mining; BiLSTM; transformers; byte-pair encoding; Conditional
   Random Fields
ID EXTRACTION
AB In the realm of digital libraries, efficiently managing and accessing scientific publications necessitates automated bibliographic reference segmentation. This study addresses the challenge of accurately segmenting bibliographic references, a task complicated by the varied formats and styles of references. Focusing on the empirical evaluation of Conditional Random Fields (CRF), Bidirectional Long Short-Term Memory with CRF (BiLSTM + CRF), and Transformer Encoder with CRF (Transformer + CRF) architectures, this research employs Byte Pair Encoding and Character Embeddings for vector representation. The models underwent training on the extensive Giant corpus and subsequent evaluation on the Cora Corpus to ensure a balanced and rigorous comparison, maintaining uniformity across embedding layers, normalization techniques, and Dropout strategies. Results indicate that the BiLSTM + CRF architecture outperforms its counterparts by adeptly handling the syntactic structures prevalent in bibliographic data, achieving an F1-Score of 0.96. This outcome highlights the necessity of aligning model architecture with the specific syntactic demands of bibliographic reference segmentation tasks. Consequently, the study establishes the BiLSTM + CRF model as a superior approach within the current state-of-the-art, offering a robust solution for the challenges faced in digital library management and scholarly communication.
C1 [Cuellar Hidalgo, Rodrigo] Biblioteca Daniel Cosio Villegas, Colegio Mexico, Carretera Picacho Ajusco 20, Mexico City 14110, Mexico.
   [Pinto Elias, Raul; Magadan Salazar, Andrea] Tecnol Nacl Mexico CENIDET, Cuernavaca 62490, Mexico.
   [Torres-Moreno, Juan-Manuel] Univ Avignon, Lab Informat Avignon, 339 Chemin Meinajaries, F-84911 Avignon 9, France.
   [Vergara Villegas, Osslan Osiris] Univ Autonoma Ciudad Juarez, Ind & Mfg Engn Dept, Ciudad Juarez 32310, Mexico.
   [Reyes Salgado, Gerardo] Univ Rey Juan Carlos, Dept Informat & Estadist, Ave Alcalde de Mostoles, Madrid 28933, Spain.
C3 Colegio de Mexico; Avignon Universite; Universidad Autonoma de Ciudad
   Juarez; Universidad Rey Juan Carlos
RP Torres-Moreno, JM (corresponding author), Univ Avignon, Lab Informat Avignon, 339 Chemin Meinajaries, F-84911 Avignon 9, France.
EM rcuellar@colmex.mx; raul.pe@cenidet.tecnm.mx;
   juan-manuel.torres@univ-avignon.fr; overgara@uacj.mx;
   gerardo.reyes@urjc.es; andrea.ms@cenidet.tecnm.mx
RI Reyes Salgado, Gerardo/U-4717-2018; Torres-Moreno,
   Juan-Manuel/K-5137-2012; Vergara Villegas, Osslan Osiris/N-1898-2016
OI Reyes Salgado, Gerardo/0000-0001-7942-2967; Torres-Moreno,
   Juan-Manuel/0000-0002-4392-1825; Vergara Villegas, Osslan
   Osiris/0000-0002-6572-6596
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NR 28
TC 0
Z9 0
U1 1
U2 1
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2306-5729
J9 DATA-BASEL
JI Data
PD MAY
PY 2024
VL 9
IS 5
AR 71
DI 10.3390/data9050071
PG 24
WC Computer Science, Information Systems; Multidisciplinary Sciences
WE Emerging Sources Citation Index (ESCI)
SC Computer Science; Science & Technology - Other Topics
GA SC3Q1
UT WOS:001232224300001
OA gold
DA 2024-09-05
ER
PT J
AU Bartolomé, E
   Benítez, P
AF Bartolome, Elena
   Benitez, Paula
TI Failure mode and effect analysis (FMEA) to improve collaborative
   project-based learning: Case study of a Study and Research Path in
   mechanical engineering
SO INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING EDUCATION
LA English
DT Article
DE Failure mode and effect analysis; active learning; collaborative and
   project-based learning; Study and Research Paths; Mechanical
   Engineering; Theory of Machines and Mechanisms
ID RISK-MANAGEMENT; OPPORTUNITIES; WEAKNESSES; STRENGTHS; THREATS; TOOL
AB Failure Mode and Effect Analysis (FMEA) is a powerful quality tool, widely used in industry, for the identification of failure modes, their effects and causes. In this work, we investigated the utility of FMEA in the education field to improve active learning processes. In our case study, the FMEA principles were adapted to assess the risk of failures in a Mechanical Engineering course on "Theory of Machines and Mechanisms" conducted through a project-based, collaborative "Study and Research Path (SRP)" methodology. The SRP is an active learning instruction format which is initiated by a generating question that leads to a sequence of derived questions and answers, and combines moments of study and inquiry. By applying the FMEA, the teaching team was able to identify the most critical failures of the process, and implement corrective actions to improve the SRP in the subsequent year. Thus, our work shows that FMEA represents a simple tool of risk assesment which can serve to identify criticality in educational process, and improve the quality of active learning.
C1 [Bartolome, Elena; Benitez, Paula] Escola Univ Salesiana Sarria EUSS, Mech Engn Dept, Passeig St Joan Bosco 74, Barcelona 08017, Spain.
RP Bartolomé, E (corresponding author), Escola Univ Salesiana Sarria EUSS, Mech Engn Dept, Passeig St Joan Bosco 74, Barcelona 08017, Spain.
EM ebartolome@euss.es
RI Bartolomé, Elena/AFS-0763-2022; Bartolome, Elena/K-9014-2017; Bartolome,
   Elena/R-5486-2019
OI Bartolome, Elena/0000-0001-5108-0977; Bartolome,
   Elena/0000-0001-5108-0977
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NR 60
TC 8
Z9 8
U1 3
U2 18
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 0306-4190
EI 2050-4586
J9 INT J MECH ENG EDUC
JI Int. J. Mech. Eng. Educ.
PD APR
PY 2022
VL 50
IS 2
BP 291
EP 325
DI 10.1177/0306419021999046
PG 35
WC Education, Scientific Disciplines; Engineering, Mechanical
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research; Engineering
GA ZZ5IQ
UT WOS:000773302700005
DA 2024-09-05
ER
PT C
AU Obaideen, K
   AlShabi, M
   Bettayeb, M
   Faroukh, Y
   Bonny, T
AF Obaideen, Khaled
   AlShabi, Mohammad
   Bettayeb, Maamar
   Faroukh, Yousuf
   Bonny, Talal
BE Schwartz, PJ
   Jensen, B
   Hohil, ME
TI The confluence of PSO and MDO: a bibliometric perspective
SO ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FOR MULTI-DOMAIN OPERATIONS
   APPLICATIONS VI
SE Proceedings of SPIE
LA English
DT Proceedings Paper
CT Conference on Artificial Intelligence and Machine Learning for
   Multi-Domain Operations Applications VI
CY APR 22-26, 2024
CL National Harbor, MD
DE Particle Swarm Optimization; Multi-Disciplinary Design Optimization;
   VOSviewer; Bibliometric; MDO
ID PARTICLE SWARM OPTIMIZATION; NEURAL-NETWORKS; DESIGN
AB This paper presents a thorough investigation into the convergence of Particle Swarm Optimization (PSO) and Multi-Disciplinary Design Optimization (MDO), two pivotal methodologies in the realm of computational optimization. By harnessing the strengths of PSO's heuristic search capabilities and MDO's integrative design approach, this study explores the synergistic potential of combining these methods to tackle complex optimization challenges. Through a systematic literature review and bibliometric analysis, we delve into the evolution, methodologies, applications, and outcomes of this interdisciplinary integration, drawing from a diverse array of scholarly works. Our analysis reveals a growing trend in the application of PSO within MDO frameworks, highlighting significant advancements, identifying gaps in the current literature, and suggesting fruitful directions for future research. The findings underscore the robustness and adaptability of PSO-MDO integration across various domains, offering insights into its potential to enhance optimization practices and contribute to the advancement of engineering and technology. This study not only charts the current landscape of PSO and MDO convergence but also sets the groundwork for future explorations in this promising research domain.
C1 [Obaideen, Khaled] Univ Sharjah, Biosensing & Biosensors Grp, Smart Automat & Commun Technol, RISE, Sharjah, U Arab Emirates.
   [AlShabi, Mohammad] Univ Sharjah, Coll Engn, Dept Mech & Nucl Engn, Sharjah, U Arab Emirates.
   [Bettayeb, Maamar] Univ Sharjah, Coll Engn, Dept Elect Engn, Sharjah, U Arab Emirates.
   [Faroukh, Yousuf] Univ Sharjah, Sharjah Acad Astron Space Sci & Technol, CubeSat Lab, Sharjah, U Arab Emirates.
   [Bonny, Talal] Univ Sharjah, Coll Comp & Informat, Sharjah, U Arab Emirates.
C3 University of Sharjah; University of Sharjah; University of Sharjah;
   University of Sharjah; University of Sharjah
RP Obaideen, K (corresponding author), Univ Sharjah, Biosensing & Biosensors Grp, Smart Automat & Commun Technol, RISE, Sharjah, U Arab Emirates.
OI Obaideen, Khaled/0000-0002-6472-2753
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NR 53
TC 0
Z9 0
U1 0
U2 0
PU SPIE-INT SOC OPTICAL ENGINEERING
PI BELLINGHAM
PA 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA
SN 0277-786X
EI 1996-756X
BN 978-1-5106-7421-9; 978-1-5106-7420-2
J9 PROC SPIE
PY 2024
VL 13051
AR 130511P
DI 10.1117/12.3013817
PG 7
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BX2FO
UT WOS:001259449700049
DA 2024-09-05
ER
PT J
AU Ribeiro, VM
AF Ribeiro, Vitor Miguel
TI Pioneering paradigms: unraveling niche opportunities in green finance
   through bibliometric analysis of nation brands and brand culture
SO GREEN FINANCE
LA English
DT Article
DE bibliometric analysis; latent Dirichlet allocation; multinomial discrete
   choice analysis; nation brand; brand culture; green economy; green
   finance
ID COUNTRY-OF-ORIGIN; EXPRESSING HERSELF; GOOGLE-SCHOLAR; SOCIAL MEDIA;
   SCIENCE; IMPACT; IDENTITY; CHINA; MANAGEMENT; INFRASTRUCTURE
AB This study reviews the literature focused on nation brands and brand culture through the innovative combination of latent Dirichlet allocation with a multinomial and unordered discrete choice analysis. Unlike a narrow perspective of bibliometric work, which confines itself to reviewing existing literature within a specific research domain, a broader viewpoint leverages bibliometric analysis to pinpoint potential research opportunities indicative of emerging trends in related fields. Adopting this comprehensive paradigm, the current study scrutinizes 60 articles spanning the timeframe from 1992 to 2021. The analysis discerns six prospective marketing strategies instrumental in propelling a country to global brand prominence: the synergistic integration of country -of -origin and city brands, consumption branding, materialistic branding, green branding, ideological branding, and scientific branding. Notably, environmental branding has assumed a pivotal global role post-2015, while ideological branding represents a more recent trend centered on diligent e fforts to invigorate national identity systems. Empirical insights underscore the need of a multidisciplinary approach in the creation of nation brands, suggesting that distinct strategies need not be mutually exclusive. Quantitatively, it is found evidence that covering one additional environmental topic in a study increases (decreases) its likelihood of belonging to the consumption (ideology) cluster by 50.8 (50.6) percentage points, respectively. Strategic recommendations for future national endeavors emphasize the significance of becoming a Stackelberg leader in the race to generate added value. Collectively, these findings underscore that the bibliometric analysis employed to elucidate the evolution of nation brands and brand culture, typically associated with international marketing, unveils two promising niche areas for future research in green finance: green nation brands and green brand culture . The former pertains to asset allocations within green enterprises and environmental sectors, enhancing a country's symbolic commitment to the burgeoning green paradigm. Meanwhile, the later delves into the internalization of fintech development's growth and intermediary e ffects, fostering green innovation, energy e fficiency, and green supply chains. This bottom -up approach is geared towards meeting community -based needs and presents valuable avenues for future exploration in the field of green finance.
C1 [Ribeiro, Vitor Miguel] Univ Porto, Fac Econ, Dept Econ, Econometr Grp, Rua Dr Roberto Frias, P-4200464 Porto, Portugal.
C3 Universidade do Porto
RP Ribeiro, VM (corresponding author), Univ Porto, Fac Econ, Dept Econ, Econometr Grp, Rua Dr Roberto Frias, P-4200464 Porto, Portugal.
EM vsribeiro@fep.up.pt
OI Ribeiro, Vitor Miguel/0000-0002-7223-9841
FU FCT [DSAIPA-CS-0086-2020]
FX V. M. Ribeiro acknowledges financial support from the FCT project
   DSAIPA-CS-0086-2020. Any necessary material for replication of this
   study can be provided upon request to vsribeiro@fep.up.pt. V. M. Ribeiro
   also appreciates the comments and suggestions from two anonymous
   reviewers, the Editor -in -Chief, the Editor -in -Charge, the Assistant
   Editor, and the English Editor, which substantially improved the quality
   of the final version of this manuscript.
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NR 153
TC 0
Z9 0
U1 1
U2 1
PU AMER INST MATHEMATICAL SCIENCES-AIMS
PI SPRINGFIELD
PA PO BOX 2604, SPRINGFIELD, MO 65801-2604, UNITED STATES
SN 2643-1092
J9 GREEN FINANC
JI Green Financ.
PY 2024
VL 6
IS 2
BP 287
EP 347
DI 10.3934/GF.2024012
PG 61
WC Business, Finance; Green & Sustainable Science & Technology
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics; Science & Technology - Other Topics
GA UL8R0
UT WOS:001248310400001
OA gold
DA 2024-09-05
ER
PT J
AU Altuntas, S
   Dereli, T
AF Altuntas, Serkan
   Dereli, Turkay
TI A Regression-Based "Patent Data Analysis" Approach: A Case Study for
   "Weapon Technology" Evaluation Process
SO IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
LA English
DT Article
DE Patents; Weapons; Defense industry; Data analysis; Linear regression;
   Companies; Market research; Linear regression model; patent analysis;
   technology evaluation; weapon technology
ID SOCIAL NETWORK ANALYSIS; DEFENSE INDUSTRY; MILITARY TECHNOLOGY;
   KNOWLEDGE; FUTURE; CHINA; FIRMS; PERFORMANCE; INNOVATION; DOCUMENTS
AB Technology evaluation is one of the most essential tasks for today's companies to make correct decisions that will increase their competitiveness. To date, numeroushave been made to address technology evaluation in the literature. Nevertheless, none of them have built a technology map or classified technologies based on their effects on the competitive power of a company in practice. To overcome the limitations of previous studies, a regression-based patent data analysis is proposed here for technology evaluation in this article. To this end, the proposed approach builds a technology map and classifies the technologies into four classes with respect to the technology effect ratio, namely 1) very high effect class, 2) high effect class, 3) medium effect class, and 4) low effect class. The technology evaluation of weapon technology in the defense industry has been conducted to show how the proposed approach works in real life. A good technology management strategy can be selected among available alternatives by decision makers and managers using the proposed approach. The results show that the proposed technology evaluation process can be used effectively in practice.
C1 [Altuntas, Serkan] Yildiz Tech Univ, Dept Ind Engn, TR-34349 Istanbul, Turkey.
   [Dereli, Turkay] Hasan Kalyoncu Univ, Off President, TR-27010 Gaziantep, Turkey.
C3 Yildiz Technical University; Hasan Kalyoncu University
RP Altuntas, S (corresponding author), Yildiz Tech Univ, Dept Ind Engn, TR-34349 Istanbul, Turkey.
EM serkan@yildiz.edu.tr; turkay.dereli@hku.edu.tr
RI Altuntas, Serkan/ABA-3083-2020
OI Altuntas, Serkan/0000-0003-4383-4710; Dereli, Turkay/0000-0002-2130-5503
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NR 75
TC 3
Z9 3
U1 6
U2 39
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0018-9391
EI 1558-0040
J9 IEEE T ENG MANAGE
JI IEEE Trans. Eng. Manage.
PD DEC
PY 2022
VL 69
IS 6
BP 3874
EP 3886
DI 10.1109/TEM.2021.3088804
EA JUN 2021
PG 13
WC Business; Engineering, Industrial; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Engineering
GA 5Y1MO
UT WOS:000732640600001
DA 2024-09-05
ER
PT C
AU Kim, J
   Le, DX
   Thoma, GR
AF Kim, Jongwoo
   Le, Daniel X.
   Thoma, George R.
GP IEEE
TI Identification of Investigator Name Zones using SVM Classifiers and
   Heuristic Rules
SO 2013 12TH INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION
   (ICDAR)
SE Proceedings of the International Conference on Document Analysis and
   Recognition
LA English
DT Proceedings Paper
CT 12th International Conference on Document Analysis and Recognition
   (ICDAR)
CY AUG 25-28, 2013
CL Washington, DC
DE Investigator Names; MEDLINE; Support Vector Machine; heuristic rules;
   labeling; bibliographic information
AB The research reported in biomedical articles often involves large numbers of investigators at different institutions. To properly credit these investigators, an article's authors frequently name them together in some part of the article. These Investigator Names (IN) now constitute a required field in the MEDLINE (R) citation for the article. The automated extraction of these names is implemented in a system developed by a research group at the U. S. National Library of Medicine, consisting of three modules based on Support Vector Machine (SVM) classifiers and heuristic rules. The SVM classifiers label text blocks ("zones") that possibly contain Investigator Names, and the heuristic rules identify the actual zones. We collect eleven sets of word lists to train and test the classifiers, each set containing 100 to 56,000 words. Experimental results on online biomedical articles show a Precision of 0.90, 0.95 Recall, 0.92 F-Measure, and 0.99 Accuracy.
C1 [Kim, Jongwoo; Le, Daniel X.; Thoma, George R.] Natl Lib Med, Bethesda, MD 20894 USA.
C3 National Institutes of Health (NIH) - USA; NIH National Library of
   Medicine (NLM)
RP Kim, J (corresponding author), Natl Lib Med, 8600 Rockville Pike, Bethesda, MD 20894 USA.
EM jongkim@mail.nih.gov
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NR 17
TC 2
Z9 2
U1 0
U2 1
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 1520-5363
J9 PROC INT CONF DOC
PY 2013
BP 140
EP 144
DI 10.1109/ICDAR.2013.35
PG 5
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Engineering
GA BB4XX
UT WOS:000343489100026
DA 2024-09-05
ER
PT C
AU Dong, HQ
   Cao, GH
   Huang, L
AF Dong, Huan Qing
   Cao, Gao Hui
   Huang, Lu
GP ASSOC COMPUTING MACHINERY
TI Research on evaluation system of human-computer interaction system in
   university library under the background of artificial intelligence
SO 2024 6TH ASIA PACIFIC INFORMATION TECHNOLOGY CONFERENCE, APIT 2024
LA English
DT Proceedings Paper
CT 6th Asia Pacific Information Technology Conference (APIT)
CY JAN 29-31, 2024
CL Bangkok, THAILAND
DE university library; human-computer interaction system; evaluation index
   system; analytic hierarchy process
AB The human-computer interaction system of university library plays a vital role in the digital age. In order to comprehensively evaluate the quality and performance of the system, the optimization of human-computer interaction system in the field of university library is promoted. Through literature investigation and expert investigation, this study screens the factors that affect the human-computer interaction system, constructs the evaluation index system of the human-computer interaction system of university library, and uses the analytic hierarchy process to analyze. The evaluation index system consists of 4 primary indicators and 14 secondary indicators. The results show that user experience is the most important index in the evaluation system of university library human-computer interaction system, followed by intelligent interaction performance, and finally system performance and application technology performance. Through the evaluation index system constructed, it is expected to provide an effective evaluation tool for the human-computer interaction system of university library and provide a reference for improving the quality of the human-computer interaction system of university library.
C1 [Dong, Huan Qing; Cao, Gao Hui] Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.
   [Huang, Lu] Cent China Normal Univ, Natl Res Ctr Cultural Ind, Wuhan, Peoples R China.
C3 Central China Normal University; Central China Normal University
RP Dong, HQ (corresponding author), Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.
EM donghuanqing13@163.com; ghcao@mail.ccnu.edu.cn; huanglu433123@163.com
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NR 23
TC 0
Z9 0
U1 1
U2 1
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES
BN 979-8-4007-1621-8
PY 2024
BP 69
EP 77
DI 10.1145/3651623.3651634
PG 9
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Interdisciplinary Applications
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BX0CD
UT WOS:001228306500011
DA 2024-09-05
ER
PT J
AU Ingwersen, P
   Serrano-López, AE
AF Ingwersen, Peter
   Eleazar Serrano-Lopez, Antonio
TI Smart city research 1990-2016
SO SCIENTOMETRICS
LA English
DT Article
DE Smart city research; Publication and citation analysis; Median age of
   references; Median age of citations; Topic modeling; Social network
   analysis; Clustering
AB This scientometric analysis of the area of smart city(ies)' research covers 1990-2016, divided into three nine year periods: 1990-1998; 1999-2007; and 2008-2016. The methodology is partly based on the issue management' approach by Lancaster and Lee (J Assoc Inf Sci Technol 36(6):389-397, 1985) partly on common publication and citation analysis of the set of source documents (n=4725), the set of their references (n=27,099) and the set of publications (n=7863) citing the source documents. Median age analyses are included for the sets of references and citations to the source documents. DIVA-like diagrams (Database Information Visualization and Analysis system) are used to demonstrate the distribution of source documents over document types, time and volume of citations obtained. Social Network Analysis (SNA) is applied to topic modeling of the top-100 central WoS Categories of smart city(ies)' research and to the set of references. Findings show that the first mention of the concept smart city(ies)' in publication titles takes place in 1999. The research area demonstrates a strong multidisciplinary nature and an exponential growth of research publications (in WoS) 2008-2016 dominated by China, Italy, USA, Spain and England. The same five countries are also among the most citing and cited countries. Aside from a constantly strong ICT (Information and Communication Technology) and Electrical/Electronic Engineering presence sustainability' elements (Energy, Transport, Environment) are also vital, in particular during the first and third analysis period. The references from the source documents have more distinct topical clusters than the source documents. Artificial Intelligence (AI) appears as a novel field among the source documents 2008-2016, but disappears from the top-25 list in the citing documents. Instead Economics, Water Resources and Meteorology and Atmospheric Sciences move into the list. Proceedings papers, as in many other engineering and technology based research fields, are the dominant document type (70%) but have small citation impact (0.6c/p), thus decreasing the overall impact of the area to 3.6c/p. Journal articles are the most cited type with 76% of all citations received (impact 2008-2016: 7.5c/p). Most citations to journal articles derive from journal articles themselves (76%).
C1 [Ingwersen, Peter] Aalborg Univ, Copenhagen, Denmark.
   [Eleazar Serrano-Lopez, Antonio] Carlos III Univ Madrid, Madrid, Spain.
C3 Aalborg University
RP Ingwersen, P (corresponding author), Aalborg Univ, Copenhagen, Denmark.
EM ingwersen@id.aau.dk
RI Lopez, Antonio Eleazar Serrano/D-4726-2013; Ingwersen,
   Peter/AEC-1759-2022
OI Lopez, Antonio Eleazar Serrano/0000-0003-1261-386X; Ingwersen,
   Peter/0000-0001-7964-5896
FU Spanish Ministry of Economy and Competitiveness [CSO
   2014-51,916-C2-1-R]; Titled "La investigacion en eficiencia energetica y
   transporte sostenibles en el medio urbano
FX This research was funded by the Spanish Ministry of Economy and
   Competitiveness under the Project CSO 2014-51,916-C2-1-R. Titled "La
   investigacion en eficiencia energetica y transporte sostenibles en el
   medio urbano: analisis del desarrollo cientifico y la percepcion social
   del tema desde la perspectiva de los estudios metricos de la
   informacion" "(Research on energy efficiency and sustainable transport
   in the urban environment: analysis of the scientific development and the
   social perception of the topic from the perspective of the metric
   studies of information)"
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NR 29
TC 38
Z9 39
U1 4
U2 129
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD NOV
PY 2018
VL 117
IS 2
BP 1205
EP 1236
DI 10.1007/s11192-018-2901-9
PG 32
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA HC4EA
UT WOS:000451754300024
DA 2024-09-05
ER
PT J
AU Wang, J
   Zhu, L
   Dai, T
   Wang, YB
AF Wang, Jie
   Zhu, Li
   Dai, Tao
   Wang, Yabin
TI Deep memory network with Bi-LSTM for personalized context-aware citation
   recommendation
SO NEUROCOMPUTING
LA English
DT Article
DE Context-aware citation recommendation; Memory network; Bi-LSTM;
   Personalized author; Citation relationship
ID SYSTEM
AB The explosive growth of data leads researchers to waste time and energy to search for papers they need. Context-aware citation recommendation aims to solve this problem by analyzing a citation context and provides a list of recommended papers. In this paper, we propose a context-aware citation recommendation model based on end to end memory network. The model learns the representations of papers and citation contexts respectively based on bidirectional long short-term memory (Bi-LSTM). In particular, we jointly integrate author information and citation relationship in the distributed vector representations of citation contexts and papers. Then calculates the continuous relevance between them based on a computational multilayers memory network. We also conduct experiments on three real-world datasets to evaluate the performance of our model. (C) 2020 Elsevier B.V. All rights reserved.
C1 [Wang, Jie; Zhu, Li; Dai, Tao; Wang, Yabin] Xi An Jiao Tong Univ, Sch Software Engn, Xian, Shaanxi, Peoples R China.
C3 Xi'an Jiaotong University
RP Zhu, L (corresponding author), Xi An Jiao Tong Univ, Sch Software Engn, Xian, Shaanxi, Peoples R China.
EM zhuli@xjtu.edu.cn
OI wang, jie/0009-0005-7117-8580; wang, Yabin/0000-0003-2931-572X
FU National Key Research and Development Project [2019YFB2102500]
FX This research is supported by National Key Research and Development
   Project (No. 2018AAA0101100) and National Key Research and Development
   Project (No. 2019YFB2102500).
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NR 54
TC 35
Z9 36
U1 5
U2 37
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29a, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-2312
EI 1872-8286
J9 NEUROCOMPUTING
JI Neurocomputing
PD OCT 14
PY 2020
VL 410
BP 103
EP 113
DI 10.1016/j.neucom.2020.05.047
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA OD4CB
UT WOS:000579799300010
DA 2024-09-05
ER
PT J
AU Song, YH
   Cao, JY
AF Song, Yanhui
   Cao, Jiayi
TI An ARIMA-based study of bibliometric index prediction
SO ASLIB JOURNAL OF INFORMATION MANAGEMENT
LA English
DT Article
DE ARIMA model; Literature indicators; Indicator forecasting; Time series
   forecasting
ID CITATION COUNTS; H-INDEX; IMPACT; MODELS; TIME
AB Purpose The purpose of this paper is to predict bibliometric indicators based on ARIMA models and to study the short-term trends of bibliometric indicators. Design/methodology/approach This paper establishes a non-stationary time series ARIMA (p, d, q) model for forecasting based on the bibliometric index data of 13 journals in the library intelligence category selected from the Chinese Social Sciences Citation Index (CSSCI) as the data source database for the period 1998-2018, and uses ACF and PACF methods for parameter estimation to predict the development trend of the bibliometric index in the next 5 years. The predicted model was also subjected to error analysis. Findings ARIMA models are feasible for predicting bibliometric indicators. The model predicted the trend of the four bibliometric indicators in the next 5 years, in which the number of publications showed a decreasing trend and the H-value, average citations and citations showed an increasing trend. Error analysis of the model data showed that the average absolute percentage error of the four bibliometric indicators was within 5%, indicating that the model predicted well. Research limitations/implications This study has some limitations. 13 Chinese journals were selected in the field of Library and Information Science as the research objects. However, the scope of research based on bibliometric indicators of Chinese journals is relatively small and cannot represent the evolution trend of the entire discipline. Therefore, in the future, the authors will select different fields and different sources for further research. Originality/value This study predicts the trend changes of bibliometric indicators in the next 5 years to understand the trend of bibliometric indicators, which is beneficial for further in-depth research. At the same time, it provides a new and effective method for predicting bibliometric indicators.
C1 [Song, Yanhui; Cao, Jiayi] Hangzhou Dianzi Univ, Hangzhou, Peoples R China.
C3 Hangzhou Dianzi University
RP Song, YH (corresponding author), Hangzhou Dianzi Univ, Hangzhou, Peoples R China.
EM syh687@163.com
RI lan, xueyao/JZD-4201-2024
OI song, yan hui/0000-0003-2456-222X
FU major project of National Social Science Foundation of China [19ZDA348];
   Fundamental Research Funds for the Provincial Universities of Zhejiang
   [GK209907299001-201]
FX This study was funded by major project of National Social Science
   Foundation of China (19ZDA348), and the Fundamental Research Funds for
   the Provincial Universities of Zhejiang (GK209907299001-201).
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NR 34
TC 2
Z9 2
U1 6
U2 56
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 2050-3806
EI 1758-3748
J9 ASLIB J INFORM MANAG
JI Aslib J. Inf. Manag.
PD JAN 3
PY 2022
VL 74
IS 1
BP 94
EP 109
DI 10.1108/AJIM-03-2021-0072
EA OCT 2021
PG 16
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA XW8ED
UT WOS:000710628000001
DA 2024-09-05
ER
PT J
AU Talafidaryani, M
   Jalali, SMJ
   Moro, S
AF Talafidaryani, Mojtaba
   Jalali, Seyed Mohammad Jafar
   Moro, Sergio
TI Tracing the evolution of digitalisation research in business and
   management fields: Bibliometric analysis, topic modelling and deep
   learning trend forecasting
SO JOURNAL OF INFORMATION SCIENCE
LA English
DT Article; Early Access
DE bibliometric analysis; business and management; digital transformation;
   digital X; digitalization; topic modelling; trend forecasting
ID INFORMATION-TECHNOLOGY; INTELLECTUAL STRUCTURE; OPEN INNOVATION; IMPACT;
   ENTREPRENEURSHIP; CAPABILITIES; INTERNET; DETERMINANTS; HOSPITALITY;
   GENERATION
AB Research on digitalisation trends and digital topics has become one of the most prolific streams of research within the fields of business and management during the course of the past few years. The purpose of this study is to provide a general picture of the intellectual structure and the conceptual space of this research realm. To this purpose, 6067 publications related to digital topics, indexed in the business and management categories of Web of Science (WoS), and dated from 1990 to 2020 are explored based on the approaches of bibliometric analysis, topic modelling and trend forecasting. The results of the bibliometric analysis comprise insights into the publication and citation structure, the most productive authors, the most productive universities, the most productive countries, the most productive journals, the most cited studies and the most prevalent themes and sub-themes on digitalisation in business and management. In addition, the outcomes of the topic modelling give new knowledge on the latent topical structure along with the rising, falling and fluctuating trends of this literature. In addition, the results of the trend forecasting enable readers to have a glimpse of how the underlying trends of the literature will probably change within the next years until 2025. These results provide guidance and orientation for both academics and practitioners who are initiating or currently developing their efforts in this discipline.
C1 [Talafidaryani, Mojtaba] Univ Tehran, Fac Management, Tehran, Iran.
   [Jalali, Seyed Mohammad Jafar] Deakin Univ, Inst Intelligent Syst Res & Innovat, Burwood, Australia.
   [Moro, Sergio] Inst Univ Lisboa ISCTE IUL, ISTAR, Lisbon, Portugal.
C3 University of Tehran; Deakin University; Instituto Universitario de
   Lisboa
RP Talafidaryani, M (corresponding author), Univ Tehran, Fac Management, Tehran, Iran.
EM mojtabatalafi@ut.ac.ir
FU Fundacxao para a Ci<^>encia e Tecnologia (FCT) [UIDB/04466/2020,
   UIDP/04466/2020]
FX The author(s) disclosed receipt of the following financial support for
   the research, authorship, and/or publication of this article: The work
   by Sergio Moro was supported by the Fundacxao para a Ciencia e
   Tecnologia (FCT) within the following Projects: UIDB/04466/2020 and
   UIDP/04466/2020.
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NR 138
TC 0
Z9 0
U1 9
U2 63
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 0165-5515
EI 1741-6485
J9 J INF SCI
JI J. Inf. Sci.
PD 2023 JAN 17
PY 2023
DI 10.1177/01655515221148365
EA JAN 2023
PG 29
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 7X7WV
UT WOS:000914408700001
OA Green Accepted
DA 2024-09-05
ER
PT J
AU Zhou, XJ
   Wang, Y
   Tsafnat, G
   Coiera, E
   Bourgeois, FT
   Dunn, AG
AF Zhou, Xujuan
   Wang, Ying
   Tsafnat, Guy
   Coiera, Enrico
   Bourgeois, Florence T.
   Dunn, Adam G.
TI Citations alone were enough to predict favorable conclusions in reviews
   of neuraminidase inhibitors
SO JOURNAL OF CLINICAL EPIDEMIOLOGY
LA English
DT Article
DE Neuraminidase inhibitors; Bibliometrics; Evidence synthesis; Reviews as
   a topic; Citation analysis; Supervised machine learning
ID NETWORK ANALYSIS SEBRINA; STATISTICALLY SIGNIFICANT; SYSTEMATIC REVIEWS;
   INFLUENZA; OSELTAMIVIR; BIAS; CLASSIFICATION; METAANALYSIS; SELECTION;
   ADULTS
AB Objectives: To examine the use of supervised machine learning to identify biases in evidence selection and determine if citation information can predict favorable conclusions in reviews about neuraminidase inhibitors.
   Study Design and Setting: Reviews of neuraminidase inhibitors published during January 2005 to May 2013 were identified by searching PubMed. In a blinded evaluation, the reviews were classified as favorable if investigators agreed that they supported the use of neuraminidase inhibitors for prophylaxis or treatment of influenza. Reference lists were used to identify all unique citations to primary articles. Three classification methods were tested for their ability to predict favorable conclusions using only citation information.
   Results: Citations to 4,574 articles were identified in 152 reviews of neuraminidase inhibitors, and 93 (61%) of these reviews were graded as favorable. Primary articles describing drug resistance were among the citations that were underrepresented in favorable reviews. The most accurate classifier predicted favorable conclusions with 96.2% accuracy, using citations to only 24 of 4,574 articles.
   Conclusion: Favorable conclusions in reviews about neuraminidase inhibitors can be predicted using only information about the articles they cite. The approach highlights how evidence exclusion shapes conclusions in reviews and provides a method to evaluate citation practices in a corpus of reviews. (C) 2015 Elsevier Inc. All rights reserved.
C1 [Zhou, Xujuan; Wang, Ying; Tsafnat, Guy; Coiera, Enrico; Dunn, Adam G.] Univ New S Wales, Australian Inst Hlth Innovat, Ctr Hlth Informat, Sydney, NSW 2052, Australia.
   [Bourgeois, Florence T.] Boston Childrens Hosp, Div Emergency Med, Boston, MA USA.
   [Bourgeois, Florence T.] Harvard Univ, Sch Med, Dept Pediat, Boston, MA 02115 USA.
   [Bourgeois, Florence T.] Boston Childrens Hosp, Childrens Hosp Informat Program, Boston, MA USA.
C3 University of New South Wales Sydney; Harvard University; Boston
   Children's Hospital; Harvard University; Harvard Medical School; Harvard
   University; Boston Children's Hospital
RP Dunn, AG (corresponding author), Univ New S Wales, Australian Inst Hlth Innovat, Ctr Hlth Informat, Sydney, NSW 2052, Australia.
EM a.dunn@unsw.edu.au
RI Wang, Xuejun/K-8874-2013; Dunn, Adam/H-4425-2019; Tsafnat,
   Guy/C-2292-2008; Bourgeois, Florence/H-6710-2016; Zhou,
   Xujuan/HIZ-7836-2022
OI Wang, Xuejun/0000-0001-9267-1343; Dunn, Adam/0000-0002-1720-8209;
   Bourgeois, Florence/0000-0001-7798-4560; Coiera,
   Enrico/0000-0002-6444-6584; Wang, Ying/0000-0001-8537-3954; Zhou,
   Xujuan/0000-0002-1736-739X
FU National Health and Medical Research Council [568612, 1045065]
FX This study was supported by National Health and Medical Research Council
   Program Grant 568612 and Project Grant 1045065. The sponsors had no role
   in the study.
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NR 46
TC 9
Z9 9
U1 0
U2 28
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA
SN 0895-4356
EI 1878-5921
J9 J CLIN EPIDEMIOL
JI J. Clin. Epidemiol.
PD JAN
PY 2015
VL 68
IS 1
BP 87
EP 93
DI 10.1016/j.jclinepi.2014.09.014
PG 7
WC Health Care Sciences & Services; Public, Environmental & Occupational
   Health
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Health Care Sciences & Services; Public, Environmental & Occupational
   Health
GA AX1EL
UT WOS:000346690800011
PM 25450452
DA 2024-09-05
ER
PT C
AU Yang, P
   Xia, HH
   Liu, WY
   Li, ZS
AF Yang, Ping
   Xia, Huanhuan
   Liu, Wangyang
   Li, Zesong
GP IEEE
TI Research on Government Integrity Evaluation Based on Big Data
SO 2019 2ND INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND BIG
   DATA (ICAIBD 2019)
LA English
DT Proceedings Paper
CT 2nd International Conference on Artificial Intelligence and Big Data
   (ICAIBD)
CY MAY 25-28, 2019
CL Chengdu, PEOPLES R CHINA
DE government integrity; evaluation; big data; internet public opinion;
   credit scorecard model; NLP
AB By studying the international advanced practices of government integrity evaluation represented by Standard & Poor's, Global Integrity, Transparency International and Korea, this paper found that most of the evaluations are based on perceptual surveys. Although this method of data acquisition is simple and easy to implement, it has inherent defects, such as strong subjectivity, lack of details, prone to cause sample deviation, etc. Based on the goal of improving government capacity in integrity, this paper put forward a new idea of government integrity evaluation based on big data: Firstly, collect the behavior data and complaint reporting data in the government business systems, combine with the public opinion data on the Internet. Then, use the big data technology to get the evaluation indicator data of government integrity. Finally, construct a scientific evaluation model through the credit scorecard method. Compared with the traditional measure of evaluation, the government integrity evaluation based on big data in this paper not only can obtain more accurate evaluation results, but also has great value in its evaluation process. A large amount of objective and quantitative - rather than subjective and perceived data - can better support government decision-making and reform. The scientific evaluation indicator system can also guide the specific work of the government.
C1 [Yang, Ping; Li, Zesong] CETC Big Data Res Inst Co Ltd, Chengdu Branch, Chengdu, Peoples R China.
   [Xia, Huanhuan; Liu, Wangyang] CETC Big Data Res Inst Co Ltd, Gen Technol Res Ctr, Guiyang, Peoples R China.
RP Yang, P (corresponding author), CETC Big Data Res Inst Co Ltd, Chengdu Branch, Chengdu, Peoples R China.
EM yangping@cetcbigdata.com; xiahuanhuan@cetcbigdata.com;
   liuwangyang@cetcbigdata.com; lizesongcd@cetcbigdata.com
FU China Electronics Technology Group Corporation [KJ1805002]
FX This paper is the research achievement of Technology Innovation Fund
   Project "Research on Key Technologies and Systems of Intelligent
   Governance System for Promoting Government Executive Power" (KJ1805002)
   of China Electronics Technology Group Corporation.
CR Alvarez-Diaz M., 2018, Corruption perceptions index 2017 statistical assessment
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   [Anonymous], 2012, GLOBAL INTEGRITY REP
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NR 15
TC 3
Z9 3
U1 0
U2 5
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-7281-0831-5
PY 2019
BP 28
EP 35
PG 8
WC Computer Science, Artificial Intelligence; Computer Science, Theory &
   Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BQ4OL
UT WOS:000591586200006
DA 2024-09-05
ER
PT C
AU Kim, J
   Le, DX
   Thoma, GR
AF Kim, Jongwoo
   Le, Daniel X.
   Thoma, George R.
BE ViardGaudin, C
   Zanibbi, R
TI Combining SVM Classifiers to Identify Investigator Name Zones in
   Biomedical Articles
SO DOCUMENT RECOGNITION AND RETRIEVAL XIX
SE Proceedings of SPIE
LA English
DT Proceedings Paper
CT Conference on Document Recognition and Retrieval XIX (DRR)/Electronic
   Imaging Symposium
CY JAN 25-26, 2012
CL Burlingame, CA
DE Investigator Names; MEDLINE; Support Vector Machine; labeling; text
   classification; bibliographic information
AB This paper describes an automated system to label zones containing Investigator Names (IN) in biomedical articles, a key item in a MEDLINE (R) citation. The correct identification of these zones is necessary for the subsequent extraction of IN from these zones. A hierarchical classification model is proposed using two Support Vector Machine (SVM) classifiers. The first classifier is used to identify an IN zone with highest confidence, and the other classifier identifies the remaining IN zones. Eight sets of word lists are collected to train and test the classifiers, each set containing collections of words ranging from 100 to 1,200. Experiments based on a test set of 105 journal articles show a Precision of 0.88, 0.97 Recall, 0.92 F-Measure, and 0.99 Accuracy.
C1 [Kim, Jongwoo; Le, Daniel X.; Thoma, George R.] Natl Lib Med, Bethesda, MD 20894 USA.
C3 National Institutes of Health (NIH) - USA; NIH National Library of
   Medicine (NLM)
RP Kim, J (corresponding author), Natl Lib Med, 8600 Rockville Pike, Bethesda, MD 20894 USA.
EM jongkim@mail.nih.gov
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NR 13
TC 1
Z9 1
U1 0
U2 1
PU SPIE-INT SOC OPTICAL ENGINEERING
PI BELLINGHAM
PA 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA
SN 0277-786X
EI 1996-756X
BN 978-0-81948-944-9
J9 PROC SPIE
PY 2012
VL 8297
AR 829704
DI 10.1117/12.910517
PG 8
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic; Optics
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Engineering; Optics
GA BYT89
UT WOS:000300251700003
DA 2024-09-05
ER
PT J
AU Kunoe, G
AF Kunoe, Gorm
TI Using persuasive marketing methods and mobile phones as active learning
   devices to enhance students' learning
SO JOURNAL OF PEDAGOGIC DEVELOPMENT
LA English
DT Article
DE Mobile learning parcels; a four-factor pedagogy model; learning
   evaluation model; action research
AB One of the latest additions to the educator's toolbox are learning parcels sent to the students' mobile phones. We used a four factor sales and marketing model from previous research to produce and evaluate the contents of the learning parcels and the mobile phone concept. The four factors are: Relevance, timeliness, responsibility and value. In an action research program during the first semester of 2015, we tested to which degree the students felt their curriculum knowledge improved through the use of learning parcels on their mobile phones. We learned that the novelty effect of using mobile phones as a medium quickly evaporated. The use of the learning parcels depends to a high degree on the instant feeling of high usefulness in relation to the four factors and is decisive for students' use of the learning parcels and the concept as such. A significant number of the students liked the concept, and were sure of its positive learning outcome.
   Purpose
   The purpose of this paper is to present the development and effectiveness test of a series of learning packets sent to the mobile phones of students taking a Bachelor's degree. A four-factor research model is presented that can be used for accessing new teaching tools and the contents of learning parcels.
   Design/methodology/approach
   We tested the four-factor model and the use of learning parcels on one class of Norwegian students. 135 students established an account, and 57 student made use of the APP and downloaded in total ten learning parcels. These were weekly send to their mobile phones, assisting them in understanding specific parts of the curriculum.
   Findings - Students experienced improved learning outcomes through the use of learning parcels on their mobile phones. The contents of the learning parcels should be optimized by the use of our four-factor model. The universal model can be used to judge learning parcels in industrial teaching and training programs. The contents are more engaging when the medium is a mobile phone and the length of the contents is short and engaging.
   Research limitations/implications
   It would be rewarding to measure the effect on the outcome of the mobile learning parcels on the results of the students. We have only their own opinion on the effect. The results from the action research will be used in a new round of action research this year and next year. It will then be possible to compare the alterations made from the first round of research presented here.
   Originality/value
   This paper appears to be the first, which simultaneously examines the use of a marketing model to target contents of learning parcels and the use of mobile phones as learning media.
C1 [Kunoe, Gorm] Norwegian Business Sch, Inst Mkt, Oslo, Norway.
C3 BI Norwegian Business School
RP Kunoe, G (corresponding author), Norwegian Business Sch, Inst Mkt, Oslo, Norway.
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NR 8
TC 0
Z9 0
U1 0
U2 1
PU UNIV BEDFORDSHIRE, CENTRE LEARNING EXCELLENCE
PI BEDS
PA UNIVERSITY SQ, LUTON, BEDS, LU1 3JU, ENGLAND
SN 2047-3257
EI 2047-3265
J9 J PEDAGOG DEV
JI J. Pedagog. Dev.
PY 2016
VL 6
IS 1
BP 64
EP 70
PG 7
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA EL5CM
UT WOS:000394639200008
DA 2024-09-05
ER
PT J
AU Prasad, KDV
   Vaidya, R
AF Prasad, K. D., V
   Vaidya, Rajesh
TI Causes and Effect of Job Stress and Coping on Performance and
   Psychological Well-being among the Agricultural Research Sector
   Employees: An Empirical Study Using Multinomial Logistic Regression
   Approach
SO HELIX
LA English
DT Article
DE Job Stress; Agricultural Research Sector (ARS); Psychological
   Well-being; Coping; Gender
AB This manuscript reports the results of our study to assess the influence of stress due to job, strategies adopted for coping the stress, their association, and effect on performance and psychological well-being of agricultural research sector (ARS) employees. A survey of 700 staff working in the ARS in Hyderabad Metro, consisting of 360 women and 340 men were carried out. The 14 independent job stress components, harassment, role ambiguity, psychological factors, peer support, workload, co-workers, role conflict, career, physiological factors, behavioral factors, job control and social support, strategies of coping both the approach & avoidance strategies on dependent factors Performance and Psychological well-being of the agricultural research sector employees were estimated. The Cronbach's alpha value for the whole sample is 0.87, and C-alpha values for the job stress components and outcome factors performance and psychological well-being ranged between 0.70-0.81. The odds ratios (ORs) were measured to calculate the level of relation of job stress, coping methods, relationship with performance and psychological well-being of ARS staff. The age and gender differences were also studied.
C1 [Prasad, K. D., V] Int Crops Res Inst Semi Arid Trop, Hyderabad, Telangana, India.
   [Vaidya, Rajesh] Shri Ramdeobaba Coll Engn & Management, Dept Management Technol, Katol Rd, Nagpur 444013, Maharashtra, India.
C3 CGIAR; International Crops Research Institute for the Semi-Arid-Tropics
   (ICRISAT); Rashtrasant Tukadoji Maharaj Nagpur University; Shri
   Ramdeobaba College of Engineering & Management
RP Prasad, KDV (corresponding author), Int Crops Res Inst Semi Arid Trop, Hyderabad, Telangana, India.
EM k.d.prasad@cgiar.org; rwvaidya@gmail.com
RI PRASAD, KDV/AAF-7097-2019; Vaidya, Rajesh W/AAC-6790-2021
OI PRASAD, KDV/0000-0001-9921-476X; Vaidya, Rajesh W/0000-0002-7541-2187
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NR 22
TC 1
Z9 1
U1 1
U2 12
PU BIOAXIS DNA RESEARCH CENTRE PRIVATE LIMITED
PI HYDERBAD
PA 13-51, SRI LAKSHMI NAGAR COLONY, BESIDES BLG BAZAR, NEAR KAMINENI
   HOSPITALS, GSI POST BANDAGUDA, HYDERBAD, 500068, INDIA
SN 2277-3495
EI 2319-5592
J9 HELIX
JI Helix
PY 2018
VL 8
IS 6
BP 4114
EP 4119
DI 10.29042/2018-4114-4119
PG 6
WC Biotechnology & Applied Microbiology
WE Emerging Sources Citation Index (ESCI)
SC Biotechnology & Applied Microbiology
GA HN2LK
UT WOS:000460016500002
OA Bronze
DA 2024-09-05
ER
PT C
AU Anupkant, S
   Kumar, PVMS
   Sateesh, N
   Mahesh, DB
AF Anupkant, S.
   Kumar, P. V. M. Seravana
   Sateesh, Nayani
   Mahesh, D. Bhanu
BE Niranjan, SK
TI Opinion mining on author's citation characteristics of scientific
   publications
SO PROCEEDINGS OF THE 2017 INTERNATIONAL CONFERENCE ON BIG DATA ANALYTICS
   AND COMPUTATIONAL INTELLIGENCE (ICBDAC)
LA English
DT Proceedings Paper
CT International Conference on Big Data Analytics and Computational
   Intelligence (ICBDAC)
CY MAR 23-25, 2017
CL Chirala Engn Coll, Chirala, INDIA
HO Chirala Engn Coll
DE Opinion mining; citation; publications; egression; sentiment analysis
ID INDEX
AB Opinion mining of authors opinions on scientific papers in citations is an important feature of scientific publications. Opinion mining aims to determine the defiance of a topic with respect to the overall polarity of a document. The main engine that drives opinion mining is the processing of subjective information. A dataset in the form of sentence-based collection of over 785 citations were collected. After excluding neutral citations, the dataset of 234 opinion citations were analyzed for the presence of positive and negative features. It was observed that majority (91.6 %) were found to be positive, the fraction of citations with negative orientation amounted to 8.4% and nearly 6% of the citations contained opinion terms of both positive and negative polarity. Logistic regression applied on training set resulted in 0.98 precision obtained on a classification model; hence the obtained regression model was applied on test set data to reveal positive and negative opinions.
C1 [Anupkant, S.; Kumar, P. V. M. Seravana; Sateesh, Nayani; Mahesh, D. Bhanu] CVR Coll Engn, Dept IT, Hyderabad 501510, Andhra Pradesh, India.
RP Anupkant, S (corresponding author), CVR Coll Engn, Dept IT, Hyderabad 501510, Andhra Pradesh, India.
EM anupkant@gmail.com; seravanakumar@gmail.com; nayanisateesh@gmail.com;
   dbhanumahesh@gmail.com
RI D, BHANU MAHESH/IAL-9086-2023; NAYANI, SATEESH/HHM-7838-2022
OI D, BHANU MAHESH/0000-0003-3082-0598; N, Sateesh/0000-0003-0094-1057;
   NAYANI, SATEESH/0000-0001-8216-1809
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NR 30
TC 1
Z9 2
U1 0
U2 6
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-5090-6400-7
PY 2017
BP 348
EP 351
PG 4
WC Computer Science, Artificial Intelligence; Computer Science, Hardware &
   Architecture; Computer Science, Software Engineering
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BJ5EI
UT WOS:000425843000064
DA 2024-09-05
ER
PT J
AU Min, J
   Han, YX
   Sun, Y
   Jin, FX
   Li, T
AF Min, Ji
   Yunxiu, Han
   Yong, Sun
   Fengxiang, Jin
   Ting, Li
TI Research on Analysis of Evaluation Influence Factors of Air Quality
   Opinions Sentiment Value and Quantification Method
SO WATER AIR AND SOIL POLLUTION
LA English
DT Article
DE Air quality perception; Social media; Opinion mining; Sentiment
   analysis; Public perceptions
ID MORTALITY; HEALTH
AB More and more people make opinions on air quality on social media, as science develops and the seriousness of air quality issues. Studies on sentiment analysis keyed to this kind of public opinions will do help in the introduction and perfection of policy of air quality. The evaluation of opinions sentiment is still a hard point in at this stage. There are only a few researches about the factors of the value of opinions sentiment. This article uses opinions related to air quality from Sina Weibo (Chinese Twitter) users, by online questionnaire surveys, and studies the results of opinions sentiment value about air quality in different groups, aiming at doing a research on the evaluation influence factors of opinions sentiment value on air quality. Furthermore, we came up with a solution synthesizing the influential level to calculate opinions sentiment value. We found amounts of respondents had a negative attitude to the opinions. We can now say with some confidence that education is the most significant influence factor on the public to tell the degree of sentiment level of air quality opinions. The sentiment calculation method where we come up could reduce the one-sidedness of different individuals' evaluations of the same opinion to some extent. The research could be reference for air quality management from the perspective of public opinion sentiment analysis.
C1 [Min, Ji; Yunxiu, Han; Ting, Li] Shandong Univ Sci & Technol, Coll Geodesy & Geomat, Qingdao 266590, Peoples R China.
   [Yong, Sun; Fengxiang, Jin] Shandong Jianzhu Univ, Sch Surveying & Geoinformat, Jinan 250000, Peoples R China.
C3 Shandong University of Science & Technology; Shandong Jianzhu University
RP Han, YX (corresponding author), Shandong Univ Sci & Technol, Coll Geodesy & Geomat, Qingdao 266590, Peoples R China.
EM HanYunxiu1997@163.com
FU Major Science and Technology Innovation Projects of Shandong Province
   [2019JZZY020103]
FX This research was funded by the Major Science and Technology Innovation
   Projects of Shandong Province (2019JZZY020103).
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EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS C EMPIRICAL METHODS
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NR 34
TC 1
Z9 1
U1 1
U2 31
PU SPRINGER INT PUBL AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 0049-6979
EI 1573-2932
J9 WATER AIR SOIL POLL
JI Water Air Soil Pollut.
PD JUL
PY 2021
VL 232
IS 7
AR 256
DI 10.1007/s11270-021-05197-x
PG 17
WC Environmental Sciences; Meteorology & Atmospheric Sciences; Water
   Resources
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology; Meteorology & Atmospheric Sciences;
   Water Resources
GA UJ8XE
UT WOS:000691561700004
DA 2024-09-05
ER
PT J
AU Zeng, ST
   Cai, YF
   Zhang, RS
   Lyu, X
AF Zeng, Shaoting
   Cai, Yifei
   Zhang, Renshui
   Lyu, Xin
TI Research on Human-Machine Collaborative Aesthetic Decision-Making and
   Evaluation Methods in Automotive Body Design: Based on DCGAN and ANN
   Models
SO IEEE ACCESS
LA English
DT Article
DE Automobiles; Generators; Training; Collaboration; Human-machine systems;
   Solid modeling; Noise measurement; Machine learning; Design for
   manufacture; Automotive components; Human-machine collaboration; human
   calculating and machine computing; machine learning; aesthetic
   evaluation and optimization; automotive body design
AB The main content of this study is the human-machine collaborative design research, taking the car body design as the carrier. The research framework focused on two phases of car body design process, that of design ideation and evaluation. In the ideation stage, we trained an imperfect Deep Convolutional Generative Adversarial Network (DCGAN) model that just could generate blur automobile images as the blur design motherboards for the iterative sketching, which had design uncertainties and blanks, thus activating designers' subjective initiative and aesthetic intuition to provide more creative deepen sketches. We leveraged motherboards to address uncertainty through sketching and aesthetic intuition, refining options and ultimately selecting an optimal design. In the evaluation phase, we initially constructed a parametric 3D model with 20 parameters based on the optimal design, and invited 32 designers conducting participatory design experiments, getting 1024 human-designed schemes. Following this, we administered an online survey to assess the aesthetic qualities of a total of 1024 design schemes. Leveraging the collected score data (The first round of surveys engaged 279 participants, while the second round involved 73 participants), we trained an Artificial Neural Network (ANN) model to serve as an aesthetic evaluation score predictor for unknown parameter configurations. The machine could evaluate designs autonomously, thus selecting best design from 20,000 schemes generated randomly by machine. We utilized the parametric design converting sketching images to the numeric parameters, switching the qualitative ideation to the quantitative evaluation, thus achieving aesthetic evaluation and optimization. This study explores the relationship between human cognitive intuition and machine intelligence and how they can collaborate with each other.
C1 [Zeng, Shaoting; Cai, Yifei; Zhang, Renshui; Lyu, Xin] Beijing Univ Technol, Coll Art & Design, Beijing 100124, Peoples R China.
C3 Beijing University of Technology
RP Zeng, ST (corresponding author), Beijing Univ Technol, Coll Art & Design, Beijing 100124, Peoples R China.
EM sjmjzst@gmail.com
RI Lyu, Xin/IVH-1329-2023
OI Lyu, Xin/0009-0008-0118-2834; Zeng, Shaoting/0000-0003-4720-7351
FU R&D Program of Beijing Municipal Education Commission [SM202310005009]
FX This work was supported by the R&D Program of Beijing Municipal
   Education Commission under Grant SM202310005009. This work involved
   human subjects or animals in its research. Approval of all ethical and
   experimental procedures and protocols was granted by the Science and
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NR 56
TC 0
Z9 0
U1 1
U2 1
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2024
VL 12
BP 91575
EP 91589
DI 10.1109/ACCESS.2024.3422134
PG 15
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA YQ0V0
UT WOS:001269840500001
OA gold
DA 2024-09-05
ER
PT C
AU Wiechetek, L
AF Wiechetek, Lukasz
BE Chova, LG
   Martinez, AL
   Torres, IC
TI EDUCATORS AND ACADEMICS IN SPECIALIZED SOCIAL NETWORKS. COMPARISON OF
   GOOGLE SCHOLAR AND RESEARCHGATE USAGE BY BUSINESS RESEARCHERS OF MCSU
SO EDULEARN19: 11TH INTERNATIONAL CONFERENCE ON EDUCATION AND NEW LEARNING
   TECHNOLOGIES
SE EDULEARN Proceedings
LA English
DT Proceedings Paper
CT 11th International Conference on Education and New Learning Technologies
   (EDULEARN)
CY JUL 01-03, 2019
CL Palma, SPAIN
DE social network; specialized social network; SNS; ResearchGate; Google
   Scholar; business scientist; comparative analysis; WEB 2.0; research
   evaluation; research evaluation metrics; researcher reputation
ID WEB-OF-SCIENCE; SCOPUS
AB Social networks play significant role not only in entertainment but also can improve the effectiveness of business and scientific processes. IT tools accelerate the knowledge dissemination, promote provided services or goods but also scientific achievements. At present researchers and scientists not only have to find interesting, up to date and significant topic of research, plan and perform the research in accordance with the methodology. They have to analyze and clearly visualize collected data and describe the final outcomes, prepare the scientific articles, but also effectively manage the research teams and widely promote the research and achievements. In today's fast changing and digital world, single researcher is not able to perform the whole complex research process on global scale and in a short time. Also, after performing the hard research work, the outcomes should by widely disseminated to promote the results, the author and the research organization. Research results have to reach a large, global audience, otherwise they could be not noticed or quickly forgotten. In this case very useful can be specialized social network sites like ResearchGate or Google Scholar. They integrate researchers with similar or supplementary interests, allow for building scientific portfolio by adding papers, research data, projects description. They can be also successfully used for improving communication, give the possibility of worldwide promotion of research outcomes and offer useful mechanisms for evaluation and comparison of educators, academics and research centers.
   The aim of the article is to present and compare the usage of specialized social network platforms by MCSU business researchers. The author compares two popular platforms Google Scholar and ResearchGate. The article contains the characteristics of analyzed platforms, main profits and problems related to their usage and comparison of offered functionalities. The main part of the paper is the quantitative analysis of business researchers' profiles on ResearchGate and Google Scholar. The data collected in April 2018 were used to answer the following research questions: If SNS platforms are widely used by the researchers?, What are the values of main metrics provided by both systems? and What are the differences between profiles created on explored platforms by various groups of users?
   The literature research, analysis of services offered by the explored portals and performed quantitative analysis indicate that possessing SNS profile is not very popular among analyzed researchers. Metrics presented by Google Scholar are higher than available on ResearchGate. However, RG presents more metrics which allow for a more in-depth achievements analysis. The author experiences and opinions of active SNS users indicate that use of specialized social networks is easy and intuitive. The main initial barrier is manual profile creating. Many activities like metrics calculation, development of the publication list is performed automatically without the extra effort of the researchers. Therefore, it is worth to create the profile in social network sites for researchers to be more recognizable and boost the development of scientific career.
C1 [Wiechetek, Lukasz] Marie Curie Sklodowska Univ, Fac Econ, Lublin, Poland.
C3 Maria Curie-Sklodowska University
RP Wiechetek, L (corresponding author), Marie Curie Sklodowska Univ, Fac Econ, Lublin, Poland.
RI Ločičnik, Aleksandra/ABE-7348-2021; Wiechetek, Łukasz/AAH-7309-2021
OI Wiechetek, Łukasz/0000-0001-7755-2282
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NR 15
TC 4
Z9 4
U1 0
U2 2
PU IATED-INT ASSOC TECHNOLOGY EDUCATION & DEVELOPMENT
PI VALENICA
PA LAURI VOLPI 6, VALENICA, BURJASSOT 46100, SPAIN
SN 2340-1117
BN 978-84-09-12031-4
J9 EDULEARN PROC
PY 2019
BP 8039
EP 8051
PG 13
WC Education & Educational Research
WE Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Education & Educational Research
GA BP4NG
UT WOS:000553304902092
DA 2024-09-05
ER
PT J
AU Bukowski, M
   Geisler, S
   Schmitz-Rode, T
   Farkas, R
AF Bukowski, Mark
   Geisler, Sandra
   Schmitz-Rode, Thomas
   Farkas, Robert
TI Feasibility of activity-based expert profiling using text mining of
   scientific publications and patents
SO SCIENTOMETRICS
LA English
DT Article
DE Supervised learning; Biomedical engineering domain model; Translational
   value chain; Research evaluation; Author contribution; Domain-specific
   recommendation; Self; and external-assessment
ID SLEEPING BEAUTIES; GOOGLE-SCHOLAR; CLASSIFICATION; COLLABORATION;
   TECHNOLOGY; AUTHORSHIP; INNOVATION; PRODUCTIVITY; NETWORKS; JOURNALS
AB Research and development (R&D) in many technological areas is characterized by growing complexity. In biomedical engineering, too, interdisciplinary collaboration is regarded as a promising way to master this challenge. Therefore, identifying suitable experts becomes crucial, which is currently being researched, amongst others, by analyzing semantic data. However, previous approaches lack clarity and traceability of the mechanisms for compiling top-n lists of recommended experts, as domain specificity in profiling is insufficient. Moreover, these recommenders are mainly based on scientific publications, while patents are rarely considered as an important outcome of R&D. Thus, we study the feasibility of profiling 16 biomedical engineering experts using both publications and patents. These documents are automatically labeled according to a three-dimensional domain model by machine learning-based classifiers. On this basis, we created various activity-based representations, including author-contribution-weighting. We evaluated the profiling through self- and external-assessments and tested the recommendation compared to scientometric measures in three case studies. All interviewed experts identify themselves among 10 pseudonymous profiles and 96% of all 51 external-assignments are correct. The recommendation over three case studies reaches a high mean average precision of 89% and contrasts with the use of scientometric measures (41%). Moreover, the activity based on patents primarily corresponds to that of publications but patents also introduce new activities. The author-contribution-weighting improves the performance. In conclusion, our findings show that exploiting publications and patents enables comprehensible profiling of biomedical engineering experts that allows visual comparisons and clear selection and ranking of potential R&D collaboration partners along the translational value chain.
C1 [Bukowski, Mark; Farkas, Robert] Rhein Westfal TH Aachen, Univ Hosp Aachen, Inst Appl Med Engn, Helmholtz Inst,Dept Sci Management, Pauwelsstr 20, D-52074 Aachen, Germany.
   [Geisler, Sandra] Fraunhofer Inst Appl Informat Technol FIT, Schloss Birlinghoven, D-53754 St Augustin, Germany.
   [Schmitz-Rode, Thomas] Rhein Westfal TH Aachen, Univ Hosp Aachen, Inst Appl Med Engn, Helmholtz Inst, Pauwelsstr 20, D-52074 Aachen, Germany.
C3 Helmholtz Association; RWTH Aachen University; RWTH Aachen University
   Hospital; Fraunhofer Gesellschaft; RWTH Aachen University; RWTH Aachen
   University Hospital; Helmholtz Association
RP Bukowski, M (corresponding author), Rhein Westfal TH Aachen, Univ Hosp Aachen, Inst Appl Med Engn, Helmholtz Inst,Dept Sci Management, Pauwelsstr 20, D-52074 Aachen, Germany.
EM bukowski@ame.rwth-aachen.de
RI Bukowski, Mark/M-4621-2019
OI Bukowski, Mark/0000-0003-4563-1159; Farkas, Robert/0000-0002-8199-6764;
   Geisler, Sandra/0000-0002-8970-6282; Schmitz-Rode,
   Thomas/0000-0002-1181-2165
FU Klaus Tschira Stiftung gGmbH [00.263.2015]
FX This study was funded by the Klaus Tschira Stiftung gGmbH (Grant No.
   00.263.2015).
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NR 98
TC 6
Z9 6
U1 3
U2 69
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD MAY
PY 2020
VL 123
IS 2
BP 579
EP 620
DI 10.1007/s11192-020-03414-8
EA MAR 2020
PG 42
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA LJ0VN
UT WOS:000520818400002
DA 2024-09-05
ER
PT C
AU Hou, L
   Li, JZ
   Li, XL
   Su, Y
AF Hou, Lei
   Li, Juanzi
   Li, Xiao-Li
   Su, Yu
BE Renz, M
   Shahabi, C
   Zhou, X
   Cheema, MA
TI Measuring the Influence from User-Generated Content to News via
   Cross-Dependence Topic Modeling
SO DATABASE SYSTEMS FOR ADVANCED APPLICATIONS, PT1
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT 20th International Conference on Database Systems for Advanced
   Applications (DASFAA)
CY APR 20-23, 2015
CL Hanoi, VIETNAM
DE News stream; User-generated content; Cross dependence; Influence;
   Response
AB Online news has become increasingly prevalent as it helps the public access timely information conveniently. Meanwhile, the rapid proliferation of Web 2.0 applications has enabled the public to freely express opinions and comments over news (user-generated content, or UGC for short), making the current Web a highly interactive platform. Generally, a particular event often brings forth two correlated streams from news agencies and the public, and previous work mainly focuses on the topic evolution in single or multiple streams. Studying the inter-stream influence poses a new research challenge. In this paper, we study the mutual influence between news and UGC streams (especially the UGC-to-news direction) through a novel three-phase framework. In particular, we first propose a cross-dependence temporal topic model (CDTTM) for topic extraction, then employ a hybrid method to discover short and long term influence links across streams, and finally introduce four measures to quantify how the unique topics from one stream affect or influence the generation of the other stream (e.g. UGC to news). Extensive experiments are conducted on five actual news datasets from Sina, New York Times and Twitter, and the results demonstrate the effectiveness of the proposed methods. Furthermore, we observe that not only news triggers the generation of UGC, but also UGC conversely drives the news reports.
C1 [Hou, Lei; Li, Juanzi] Tsinghua Univ, Tsinghua Natl Lab Informat Sci & Technol, Dept Comp Sci & Technol, Beijing 100084, Peoples R China.
   [Li, Xiao-Li] ASTAR, Inst Infocomm Res, Singapore 138632, Singapore.
   [Su, Yu] Xinhua News Agcy, Commun Technol Bur, Beijing 100803, Peoples R China.
C3 Tsinghua University; Agency for Science Technology & Research (A*STAR);
   A*STAR - Institute for Infocomm Research (I2R)
RP Hou, L (corresponding author), Tsinghua Univ, Tsinghua Natl Lab Informat Sci & Technol, Dept Comp Sci & Technol, Beijing 100084, Peoples R China.
EM houl10@mails.tsinghua.edu.cn; lijuanzi@tsinghua.edu.cn;
   xlli@i2r.a-star.edu.sg; suyu@xinhua.org
RI Li, Zhiyuan/AAT-1121-2020; Li, Xiaoli/C-9739-2012; Li,
   Xiaoli/GYQ-7384-2022
OI Li, Xiaoli/0000-0002-0762-6562; Hou, Lei/0000-0002-8907-3526
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NR 32
TC 0
Z9 0
U1 0
U2 4
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 0302-9743
EI 1611-3349
BN 978-3-319-18120-2; 978-3-319-18119-6
J9 LECT NOTES COMPUT SC
PY 2015
VL 9049
BP 125
EP 141
DI 10.1007/978-3-319-18120-2_8
PG 17
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BD5PI
UT WOS:000361697600008
DA 2024-09-05
ER
PT J
AU Traag, VA
AF Traag, V. A.
TI Inferring the causal effect of journals on citations
SO QUANTITATIVE SCIENCE STUDIES
LA English
DT Article
DE Bayesian model; causal inference; citations; journal effects; science of
   science
ID IMPACT; PERFORMANCE; BIAS
AB Articles in high-impact journals are, on average, more frequently cited. But are they cited more often because those articles are somehow more "citable"? Or are they cited more often simply because they are published in a high-impact journal? Although some evidence suggests the latter, the causal relationship is not clear. We here compare citations of preprints to citations of the published version to uncover the causal mechanism. We build on an earlier model of citation dynamics to infer the causal effect of journals on citations. We find that high-impact journals select articles that tend to attract more citations. At the same time, we find that high-impact journals augment the citation rate of published articles. Our results yield a deeper understanding of the role of journals in the research system. The use of journal metrics in research evaluation has been increasingly criticized in recent years and article-level citations are sometimes suggested as an alternative. Our results show that removing impact factors from evaluation does not negate the influence of journals. This insight has important implications for changing practices of research evaluation.
C1 [Traag, V. A.] Leiden Univ, Ctr Sci & Technol Studies CWTS, Leiden, Netherlands.
C3 Leiden University - Excl LUMC; Leiden University
RP Traag, VA (corresponding author), Leiden Univ, Ctr Sci & Technol Studies CWTS, Leiden, Netherlands.
EM v.a.traag@cwts.leidenuniv.nl
RI Traag, Vincent/A-1897-2011
OI Traag, Vincent/0000-0003-3170-3879
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NR 36
TC 16
Z9 16
U1 4
U2 22
PU MIT PRESS
PI CAMBRIDGE
PA ONE ROGERS ST, CAMBRIDGE, MA 02142-1209 USA
EI 2641-3337
J9 QUANT SCI STUD
JI Quant. Sci. Stud.
PD JUL 15
PY 2021
VL 2
IS 2
BP 496
EP 504
DI 10.1162/qss_a_00128
PG 9
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA YX2LG
UT WOS:000753939000005
OA Green Published, gold, Green Submitted
DA 2024-09-05
ER
PT J
AU Aljohani, NR
   Fayoumi, A
   Saeed-Ul Hassan
AF Aljohani, Naif Radi
   Fayoumi, Ayman
   Saeed-Ul Hassan
TI Bot prediction on social networks of Twitter in altmetrics using deep
   graph convolutional networks
SO SOFT COMPUTING
LA English
DT Article
DE Social media; Twitter; Information spread; Smart cities; Bots;
   Prediction; Altmetrics; Deep learning
ID CITIES; MEDIA; ALGORITHM; INNOVATION; QUALITY; TWEETS
AB In the context of smart cities, it is crucial to filter out falsified information spread on social media channels through paid campaigns or bot-user accounts that significantly influence communication networks across the social communities and may affect smart decision-making by the citizens. In this paper, we focus on two major aspects of the Twitter social network associated with altmetrics: (a) to analyze the properties of bots on Twitter networks and (b) to distinguish between bots and human accounts. Firstly, we employed state-of-the-art social network analysis techniques that exploit Twitter's social network properties in novel altmetrics data. We found that 87% of tweets are affected by bots that are involved in the network's dominant communities. We also found that, to some extent, community size and the degree of distribution in Twitter's altmetrics network follow a power-law distribution. Furthermore, we applied a deep learning model, graph convolutional networks, to distinguish between organic (human) and bot Twitter accounts. The deployed model achieved the promising results, providing up to 71% classification accuracy over 200 epochs. Overall, the study concludes that bot presence in altmetrics-associated social media platforms can artificially inflate the number of social usage counts. As a result, special attention is required to eliminate such discrepancies when using altmetrics data for smart decision-making, such as research assessment either independently or complementary along with traditional bibliometric indices.
C1 [Aljohani, Naif Radi; Fayoumi, Ayman] King Abdulaziz Univ, Fac Comp & Informat Technol, Jeddah, Saudi Arabia.
   [Saeed-Ul Hassan] Informat Technol Univ, Dept Comp Sci, 346-B,Ferozepur Rd, Lahore, Pakistan.
C3 King Abdulaziz University
RP Saeed-Ul Hassan (corresponding author), Informat Technol Univ, Dept Comp Sci, 346-B,Ferozepur Rd, Lahore, Pakistan.
EM nraljohani@kau.edu.sa; afayoumi@kau.edu.sa; saeed-ul-hassan@itu.edu.pk
RI Fayoumi, Ayman/E-7236-2014; Aljohani, Naif R/S-1109-2017; Hassan,
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OI Fayoumi, Ayman/0000-0002-4160-3305; Hassan, Saeed-Ul/0000-0002-6509-9190
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NR 76
TC 11
Z9 12
U1 4
U2 39
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1432-7643
EI 1433-7479
J9 SOFT COMPUT
JI Soft Comput.
PD AUG
PY 2020
VL 24
IS 15
SI SI
BP 11109
EP 11120
DI 10.1007/s00500-020-04689-y
PG 12
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA MI8VP
UT WOS:000547678300009
DA 2024-09-05
ER
PT J
AU Sun, C
   Li, XJ
AF Sun, Chan
   Li, Xiaojuan
TI Research on the Relationship between Human Resource Management
   Activities and Enterprise Performance Based on the Supervised Learning
   Model
SO DISCRETE DYNAMICS IN NATURE AND SOCIETY
LA English
DT Article
ID PERSPECTIVE
AB HRMS is a very critical tool for companies. The recruitment text contains rich information that can provide strong information support for the company's recruitment work and also improve the efficiency of job seekers in finding job opportunities. To this end, for the problem of multilabel text classification of recruitment information, this paper provides two algorithms for multilayer classification based on supported SVM. First, the same learning subclass method is used for text sorting subclass acquisition, and then, the class of the text is determined. Second, the hemispherical support SVM is used to find the smallest hypersphere in the feature space that contains the most text of that class and segment the text of that class from other texts. For the text to be classified, the distance from it to the center of each hypersphere is used to determine the class of the text. Experimental results on recruitment data demonstrate that the algorithm in this paper has a high check-all rate, check-accuracy rate, and F1. And, the relationship between HRM activities and corporate performance is discussed.
C1 [Sun, Chan] Hunan Int Econ Univ, Sch Business, Changsha 410000, Hunan, Peoples R China.
   [Li, Xiaojuan] Hunan Univ Finance & Econ, Sch Business Adm, Changsha 410205, Hunan, Peoples R China.
C3 Hunan University of Finance & Economics
RP Li, XJ (corresponding author), Hunan Univ Finance & Econ, Sch Business Adm, Changsha 410205, Hunan, Peoples R China.
EM lixiaojuan@hufe.edu.cn
FU National Social Science Fund Project: Research on Inter-organizational
   Knowledge Sharing Behavior and Its Dynamic Incentive Mechanism in
   Differentiation Context [20BGL126]
FX This work was supported by National Social Science Fund Project:
   Research on Inter-organizational Knowledge Sharing Behavior and Its
   Dynamic Incentive Mechanism in Differentiation Context, under Grant no.
   20BGL126.
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NR 36
TC 1
Z9 1
U1 5
U2 15
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1026-0226
EI 1607-887X
J9 DISCRETE DYN NAT SOC
JI Discrete Dyn. Nat. Soc.
PD NOV 28
PY 2021
VL 2021
AR 4094704
DI 10.1155/2021/4094704
PG 7
WC Mathematics, Interdisciplinary Applications; Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematics; Science & Technology - Other Topics
GA 2T8VP
UT WOS:000822746100001
OA gold
DA 2024-09-05
ER
PT S
AU Devyatkin, D
   Suvorov, R
   Tikhomirov, I
   Grigoriev, O
AF Devyatkin, D.
   Suvorov, R.
   Tikhomirov, I.
   Grigoriev, O.
BE Sgurev, V
   Jotsov, V
   Kacprzyk, J
TI Scientific Research Funding Criteria: An Empirical Study of Peer Review
   and Scientometrics
SO PRACTICAL ISSUES OF INTELLIGENT INNOVATIONS
SE Studies in Systems Decision and Control
LA English
DT Article; Book Chapter
DE Decision making; Decision analysis; Research funding; R&D support; Peer
   review; Feature selection; Criteria importance; Machine learning; Random
   forest; ReliefF; Gini importance; Linear SVM
ID INDICATORS
AB In this paper we investigated the problem of scientific research funding from the perspective of data-mining. The object was to conduct versatile retrospective analysis of decisions made by the Russian Foundation for Basic Research regarding scientific research funding. The central task of the analysis was to compare the impact of various items of information on final decision making. In other words, we tried to answer two questions: (a) what does an evaluation committee mainly look at when it selects projects for funding; (b) are scientometric indicators (or science metrics) useful in decision analysis? To achieve this, we built predictive models (classifiers), performed introspection (extracted feature importance) and compared them. The input data was a set of review forms (questionnaires) from the Russian Foundation for Basic Research completed in by peer reviewers. Final decision is made by the foundation board (an evaluation committee). Finally, we concluded that the available input (project proposals, expert assessments and scientometric data) was not enough to explain all the decisions. We showed that scientometric data does not have any significant influence on project proposals assessment. It also means that h-index, mean impact factor, publication and citation number cannot supersede the peer review procedure.
C1 [Devyatkin, D.; Suvorov, R.; Tikhomirov, I.; Grigoriev, O.] Russian Acad Sci, Fed Res Ctr Comp Sci & Control, Moscow, Russia.
C3 Federal Research Center "Computer Science & Control" of RAS; Russian
   Academy of Sciences
RP Devyatkin, D (corresponding author), Russian Acad Sci, Fed Res Ctr Comp Sci & Control, Moscow, Russia.
EM devyatkin@isa.ru; rsuvorov@isa.ru; tih@isa.ru; oleggpolikvart@yandex.ru
RI Grigoriev, Oleg/AAO-5552-2021; Tikhomirov, Ilya A/I-3771-2016;
   Devyatkin, Dmitry/R-1809-2019
OI Devyatkin, Dmitry/0000-0002-0811-725X; Grigoriev,
   Oleg/0000-0001-9660-2396
CR ATANASSOV KT, 1986, FUZZY SET SYST, V20, P87, DOI 10.1016/S0165-0114(86)80034-3
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NR 33
TC 1
Z9 1
U1 0
U2 13
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 2198-4182
EI 2198-4190
BN 978-3-319-78437-3; 978-3-319-78436-6
J9 STUD SYST DECIS CONT
PY 2018
VL 140
BP 277
EP 292
DI 10.1007/978-3-319-78437-3_12
D2 10.1007/978-3-319-78437-3
PG 16
WC Automation & Control Systems; Computer Science, Artificial Intelligence;
   Engineering, Electrical & Electronic
WE Book Citation Index – Science (BKCI-S)
SC Automation & Control Systems; Computer Science; Engineering
GA BM6GS
UT WOS:000466552900013
DA 2024-09-05
ER
PT J
AU Poggi, F
   Ciancarini, P
   Gangemi, A
   Nuzzolese, AG
   Peroni, S
   Presutti, V
AF Poggi, Francesco
   Ciancarini, Paolo
   Gangemi, Aldo
   Nuzzolese, Andrea Giovanni
   Peroni, Silvio
   Presutti, Valentina
TI Predicting the results of evaluation procedures of academics
SO PEERJ COMPUTER SCIENCE
LA English
DT Article
DE Predictive Models; Scientometrics; Research Evaluation; Data Processing;
   ASN; Machine Learning; National Scientific Habilitation; Academic
   assessment; Science of Science; Informetrics
ID BIBLIOMETRIC INDICATORS; CITATION COUNTS; H-INDEX; PUBLICATIONS;
   INFORMATION; APPLICANTS; EXCELLENCE; DECISIONS; QUALITY; WORK
AB Background. The 2010 reform of the Italian university system introduced the National Scientific Habilitation (ASN) as a requirement for applying to permanent professor positions. Since the CVs of the 59,149 candidates and the results of their assessments have been made publicly available, the ASN constitutes an opportunity to perform analyses about a nation-wide evaluation process.
   Objective. The main goals of this paper are: (i) predicting the ASN results using the information contained in the candidates' CVs; (ii) identifying a small set of quantitative indicators that can be used to perform accurate predictions.
   Approach. Semantic technologies are used to extract, systematize and enrich the information contained in the applicants' CVs, and machine learning methods are used to predict the ASN results and to identify a subset of relevant predictors.
   Results. For predicting the success in the role of associate professor, our best models using all and the top 15 predictors make accurate predictions (F-measure values higher than 0.6) in 88% and 88.6% of the cases, respectively. Similar results have been achieved for the role of full professor.
   Evaluation. The proposed approach outperforms the other models developed to predict the results of researchers' evaluation procedures.
   Conclusions. Such results allow the development of an automated system for supporting both candidates and committees in the future ASN sessions and other scholars' evaluation procedures.
C1 [Poggi, Francesco; Ciancarini, Paolo] Univ Bologna, Dept Comp Sci & Engn DISI, Bologna, Italy.
   [Ciancarini, Paolo] Innopolis Univ, Inst Data Sci & Artificial Intelligence, Innopolis, Russia.
   [Gangemi, Aldo; Peroni, Silvio] Univ Bologna, Dept Class Philol & Italian Studies, Bologna, Italy.
   [Nuzzolese, Andrea Giovanni; Presutti, Valentina] CNR, Inst Cognit Sci & Technol, STLab, Rome, Italy.
C3 University of Bologna; Innopolis University; University of Bologna;
   Consiglio Nazionale delle Ricerche (CNR); Istituto di Scienze e
   Tecnologie della Cognizione (ISTC-CNR)
RP Poggi, F (corresponding author), Univ Bologna, Dept Comp Sci & Engn DISI, Bologna, Italy.
EM fpoggi@cs.unibo.it
RI Poggi, Francesco/AGK-3974-2022; Nuzzolese, Andrea
   Giovanni/AAC-8369-2020; Gangemi, Aldo/C-7420-2013; Nuzzolese, Andrea
   Giovanni/S-2298-2016; Ciancarini, Paolo/ABA-8413-2020
OI Poggi, Francesco/0000-0001-6577-5606; Gangemi, Aldo/0000-0001-5568-2684;
   Ciancarini, Paolo/0000-0002-7958-9924
FU Italian National Agency for the Assessment of Universities and Research
   (ANVUR); CINI (ENAV project); CNR-ISTC
FX This research has been supported by the Italian National Agency for the
   Assessment of Universities and Research (ANVUR) within the Uniform
   Representation of Curricular Attributes (URCA) project (see articolo 4
   of the `Concorso Pubblico di Idee di Ricerca' bando ANVUR, 12 February
   2015). Paolo Ciancarini was also supported by CINI (ENAV project) and by
   CNR-ISTC. There was no additional external funding received for this
   study. The funders had no role in study design, data collection and
   analysis, decision to publish, or preparation of the manuscript.
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NR 40
TC 7
Z9 7
U1 0
U2 9
PU PEERJ INC
PI LONDON
PA 341-345 OLD ST, THIRD FLR, LONDON, EC1V 9LL, ENGLAND
EI 2376-5992
J9 PEERJ COMPUT SCI
JI PeerJ Comput. Sci.
PD JUN 21
PY 2019
AR e199
DI 10.7717/peerj-cs.199
PG 28
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA OH0RK
UT WOS:000582279900001
PM 33816852
OA Green Submitted, Green Published, gold
DA 2024-09-05
ER
PT J
AU Wilson, AJ
   Ekti, AR
   Follum, J
   Biswas, S
   Annalicia, C
   Joo, JY
   Aziz, O
   Lian, JM
AF Wilson, Aaron J.
   Ekti, Ali Riza
   Follum, Jim
   Biswas, Shuchismita
   Annalicia, Christabella
   Joo, Jhi-Young
   Aziz, Omer
   Lian, Jamie
TI The Grid Event Signature Library: An Open-Access Repository of Power
   System Measurement Signatures
SO IEEE ACCESS
LA English
DT Article
DE Phasor measurement units; Taxonomy; Current measurement; Voltage
   measurement; Labeling; Frequency measurement; Streams; Climate change;
   Artificial intelligence; Data models; Power systems; Power grids;
   data-driven applications; power systems; signatures
AB The power grid is undergoing massive changes, driven by the need to improve both reliability and resiliency, as well as meeting goals intended to combat climate change. Many solutions to such problems will require vast amounts of data. Almost all measurement, control - and in the future, artificial intelligence (AI) - systems utilize sensing mechanisms designed to capture, transmit, or even act on voltage and/or current measurement parsing and characterization. In this paper, a free, open-access online repository of such grid signatures is presented with the intent of encouraging open sharing of power grid data for the development of artificial intelligence and data-driven applications to meet the goals of tomorrow's grid. Known as the Grid Event Signature Library, or GESL, this Department of Energy-funded endeavour has seen a growth of over 200 users worldwide since its inception.
C1 [Wilson, Aaron J.; Ekti, Ali Riza; Aziz, Omer; Lian, Jamie] Oak Ridge Natl Lab, Oak Ridge, TN 37830 USA.
   [Follum, Jim; Biswas, Shuchismita] Pacific Northwest Natl Lab, Richland, WA 99354 USA.
   [Annalicia, Christabella; Joo, Jhi-Young] Lawrence Livermore Natl Lab, Livermore, CA 94550 USA.
C3 United States Department of Energy (DOE); Oak Ridge National Laboratory;
   United States Department of Energy (DOE); Pacific Northwest National
   Laboratory; United States Department of Energy (DOE); Lawrence Livermore
   National Laboratory
RP Wilson, AJ (corresponding author), Oak Ridge Natl Lab, Oak Ridge, TN 37830 USA.
EM wilsonaj@ornl.gov
OI Biswas, Shuchismita/0000-0003-2090-6830
FU U.S. Department of Energy (DOE), Office of Electricity, through
   UT-Battelle, LLC
FX No Statement Available
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NR 27
TC 0
Z9 0
U1 2
U2 2
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2024
VL 12
BP 76207
EP 76218
DI 10.1109/ACCESS.2024.3404886
PG 12
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA SW0D8
UT WOS:001237359900001
OA gold
DA 2024-09-05
ER
PT C
AU Packer, S
   Seals, C
   Dozier, G
AF Packer, Sadaira
   Seals, Cheryl
   Dozier, Gerry
BE Moallem, A
TI Towards the Improvement of UI/UX of a Human-AI Adversarial Authorship
   System
SO HCI FOR CYBERSECURITY, PRIVACY AND TRUST, HCI-CPT 2022
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT 4th International Conference on HCI for Cybersecurity, Privacy and Trust
   (HCI-CPT) Held as Part of the 24th International Conference on
   Human-Computer Interaction (HCII)
CY JUN 26-JUL 01, 2022
CL ELECTR NETWORK
DE Human-AI collaboration; Adversarial authorship; AuthorCAAT; Usability
AB AuthorCAAT (Author Cyber Analysis & Advisement Tool) is a tool created to aid users in protecting their online privacy by assisting them in altering their writing style through Adversarial Authorship. There have been several iterations of AuthorCAAT that were all tested for efficiency, but not for usability. In this work, we conduct a preliminary study on AuthorCAAT-V to determine any features that need improvement to inform our iterative design decisions for an Adversarial Authorship framework JohariMAA. We plan to develop JohariMAA to adapt to the evolving authorship attribution field and be accessible to a broad audience. Our usability assessment of AuthorCAAT reveals issues involving task complexity, usability, user experience, and user interface efficiency. We discuss potential design decisions for JohariMAA planned to alleviate these issues.
C1 [Packer, Sadaira; Seals, Cheryl; Dozier, Gerry] Auburn Univ, Auburn, AL 36849 USA.
C3 Auburn University System; Auburn University
RP Packer, S (corresponding author), Auburn Univ, Auburn, AL 36849 USA.
EM smp0043@auburn.edu; sealscd@auburn.edu; doziegv@auburn.edu
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NR 39
TC 0
Z9 0
U1 3
U2 4
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 0302-9743
EI 1611-3349
BN 978-3-031-05563-8; 978-3-031-05562-1
J9 LECT NOTES COMPUT SC
PY 2022
VL 13333
BP 194
EP 205
DI 10.1007/978-3-031-05563-8_13
PG 12
WC Computer Science, Cybernetics; Computer Science, Information Systems;
   Computer Science, Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BU5CT
UT WOS:000911440600013
DA 2024-09-05
ER
PT C
AU Mariani, J
   Francopoulo, G
   Paroubek, P
AF Mariani, Joseph
   Francopoulo, Gil
   Paroubek, Patrick
BA Declerck, T
BF Declerck, T
BE Calzolari, N
   Choukri, K
   Cieri, C
   Hasida, K
   Isahara, H
   Maegaard, B
   Mariani, J
   Moreno, A
   Odijk, J
   Piperidis, S
   Tokunaga, T
   Goggi, S
   Mazo, H
TI Measuring Innovation in Speech and Language Processing Publications
SO PROCEEDINGS OF THE ELEVENTH INTERNATIONAL CONFERENCE ON LANGUAGE
   RESOURCES AND EVALUATION (LREC 2018)
LA English
DT Proceedings Paper
CT 11th International Conference on Language Resources and Evaluation
   (LREC)
CY MAY 07-12, 2018
CL Miyazaki, JAPAN
DE Speech Processing; Natural Language Processing; Text Analytics;
   Bibliometrics; Scientometrics
AB The goal of this paper is to propose measures of innovation through the study of publications in the field of speech and language processing. It is based on the NLP4NLP corpus, which contains the articles published in major conferences and journals related to speech and language processing over 50 years (1965-2015). It represents 65,003 documents from 34 different sources, conferences and journals, published by 48,894 different authors in 558 events, for a total of more than 270 million words and 324,422 bibliographical references. The data was obtained in textual form or as an image that had to be converted into text. This resulted in a lower quality for the most ancient papers, that we measured through the computation of an unknown word ratio. The multi-word technical terms were automatically extracted after parsing, using a set of general language text corpora. The occurrences, frequencies, existences and presences of the terms were then computed overall, for each year and for each document. It resulted in a list of 3.5 million different terms and 24 million term occurrences. The evolution of the research topics over the year, as reflected by the terms presence, was then computed and we propose a measure of the topic popularity based on this computation. The author(s) who introduced the terms were searched for, together with the year when the term was first introduced and the publication where it was introduced. We then studied the global and evolutional contributions of authors to a given topic. We also studied the global and evolutional contributions of the various publications to a given topic. We finally propose a measure of innovativeness for authors and publications.
C1 [Mariani, Joseph; Paroubek, Patrick] Univ Paris Saclay, CNRS, LIMSI, Rue John von Neumann, F-91400 Orsay, France.
   [Francopoulo, Gil] Tagmatica, 126 Rue Picpus, F-75012 Paris, France.
C3 Universite Paris Cite; Centre National de la Recherche Scientifique
   (CNRS); Universite Paris Saclay
RP Mariani, J (corresponding author), Univ Paris Saclay, CNRS, LIMSI, Rue John von Neumann, F-91400 Orsay, France.
EM Joseph.Mariani@limsi.fr; gil.francopoulo@wanadoo.fr; pap@limsi.fr
CR Banchs R. E., 2012, P ACL 2012 SPEC WORK
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NR 13
TC 0
Z9 0
U1 0
U2 0
PU EUROPEAN LANGUAGE RESOURCES ASSOC-ELRA
PI PARIS
PA 55-57, RUE BRILLAT-SAVARIN, PARIS, 75013, FRANCE
BN 979-10-95546-00-9
PY 2018
BP 1890
EP 1895
PG 6
WC Computer Science, Interdisciplinary Applications; Linguistics; Language
   & Linguistics
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Linguistics
GA BS5BI
UT WOS:000725545001150
DA 2024-09-05
ER
PT J
AU Rizvi, STR
   Dengel, A
   Ahmed, S
AF Rizvi, Syed Tahseen Raza
   Dengel, Andreas
   Ahmed, Sheraz
TI A Hybrid Approach and Unified Framework for Bibliographic Reference
   Extraction
SO IEEE ACCESS
LA English
DT Article
DE Layout; Feature extraction; Task analysis; Metadata; Tools; Portable
   document format; Libraries; Reference extraction; layout detection;
   image-based reference detection; bibliography
ID METADATA
AB Publications are an integral part of a scientific community. Bibliographic reference extraction from scientific publication is a challenging task due to diversity in referencing styles and document layout. Existing methods perform sufficiently on one dataset however, applying these solutions to a different dataset proves to be challenging. Therefore, a generic solution was anticipated which could overcome the limitations of the previous approaches. The contribution of this paper is three-fold. First, it presents a novel approach called DeepBiRD which is inspired by human visual perception and exploits layout features to identify individual references in a scientific publication. Second, we release a large dataset for image-based reference detection with 2401 scans containing 38863 references, all manually annotated for individual reference. Third, we present a unified and highly configurable end-to-end automatic bibliographic reference extraction framework called BRExSys which employs DeepBiRD along with state-of-the-art text-based models to detect and visualize references from a bibliographic document. Our proposed approach pre-processes the images in which a hybrid representation is obtained by processing the given image using different computer vision techniques. Then, it performs layout driven reference detection using Mask R-CNN on a given scientific publication. DeepBiRD was evaluated on two different datasets to demonstrate the generalization of this approach. The proposed system achieved an AP50 of 98.56% on our dataset. DeepBiRD significantly outperformed the current state-of-the-art approach on their dataset. Therefore, suggesting that DeepBiRD is significantly superior in performance, generalized, and independent of any domain or referencing style.
C1 [Rizvi, Syed Tahseen Raza; Dengel, Andreas; Ahmed, Sheraz] German Res Ctr Artificial Intelligence, D-67663 Kaiserslautern, Germany.
   [Rizvi, Syed Tahseen Raza] Tech Univ Kaiserslautern, Dept Comp Sci, D-67663 Kaiserslautern, Germany.
C3 University of Kaiserslautern
RP Rizvi, STR (corresponding author), German Res Ctr Artificial Intelligence, D-67663 Kaiserslautern, Germany.; Rizvi, STR (corresponding author), Tech Univ Kaiserslautern, Dept Comp Sci, D-67663 Kaiserslautern, Germany.
EM syed_tahseen_raza.rizvi@dfki.de
OI Dengel, Andreas/0000-0002-6100-8255; Rizvi, Syed Tahseen
   Raza/0000-0002-4359-4772
FU BMBF Project DeFuseNN [01IW17002]; JSPS KAKENHI [JP17H06100]
FX This work was supported in part by the BMBF Project DeFuseNN under Grant
   01IW17002, and in part by JSPS KAKENHI under Grant JP17H06100.
CR Ahmed MW, 2020, IEEE ACCESS, V8, P99458, DOI 10.1109/ACCESS.2020.2997907
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NR 31
TC 4
Z9 4
U1 1
U2 3
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2020
VL 8
BP 217231
EP 217245
DI 10.1109/ACCESS.2020.3042455
PG 15
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA PE3AH
UT WOS:000598239200001
OA Green Submitted, gold
DA 2024-09-05
ER
PT J
AU Du, YF
   You, SB
   Zhang, MY
   Song, Z
   Liu, WS
   Li, DJ
AF Du, Yuanfang
   You, Shibing
   Zhang, Mengyu
   Song, Ze
   Liu, Weisheng
   Li, Dongju
TI Analysis of Correlation between Quality of Life and Subjective
   Evaluation of Air Quality-Empirical Research Based on CHARLS 2018 Data
SO ATMOSPHERE
LA English
DT Article
DE air quality satisfaction; quality of life; binomial logistic regression;
   health utility value; experienced utility
AB This paper mainly focuses on the relationship between the subjective evaluation of air quality and the quality of life (QOL) of middle-aged and elderly residents in China. The 2018 China Health and Retirement Longitudinal Study (CHARLS) project database is the key sources of data, from which 16,736 valid samples were used in our research. Multivariate linear regression analysis and binomial logistic regression model were applied to detect the impact of the subjective evaluation of air quality on QOL, which was evaluated in two dimensions, which are health utility and experienced utility, using the health utility EQ-5D score and the experienced utility of life satisfaction score. Our results show that there is a significant positive correlation between the subjective evaluation of air quality and the two dimensions of QOL. Age, education, marital status and sleep status also have a relatively great impact on the QOL of residents. This worked studied the overall QOL of middle-aged and elderly residents in China, while policy suggestions regarding high-quality air public goods are also given in the paper.
C1 [Du, Yuanfang; You, Shibing; Zhang, Mengyu; Song, Ze] Wuhan Univ, Sch Econ & Management, Wuhan 430072, Peoples R China.
   [Du, Yuanfang] Tibet Univ, Math Dept, Lhasa 850000, Peoples R China.
   [Liu, Weisheng] Jiangxi Univ Finance & Econ, Sch Econ, Nanchang 330013, Peoples R China.
   [Li, Dongju] Henan Univ Econ & Law, Sch Stat & Big Data, Zhengzhou 450046, Peoples R China.
C3 Wuhan University; Tibet University; Jiangxi University of Finance &
   Economics; Henan University of Economics & Law
RP Du, YF (corresponding author), Wuhan Univ, Sch Econ & Management, Wuhan 430072, Peoples R China.; Du, YF (corresponding author), Tibet Univ, Math Dept, Lhasa 850000, Peoples R China.
EM ruyier521@whu.edu.cn; 00001839@whu.edu.cn; 2019201050157@whu.edu.cn;
   songze@whu.edu.cn; liu.wilson.edu@gmail.com; 20100452@huel.edu.cn
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NR 13
TC 2
Z9 2
U1 12
U2 59
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2073-4433
J9 ATMOSPHERE-BASEL
JI Atmosphere
PD DEC
PY 2021
VL 12
IS 12
AR 1551
DI 10.3390/atmos12121551
PG 15
WC Environmental Sciences; Meteorology & Atmospheric Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology; Meteorology & Atmospheric Sciences
GA XW0EH
UT WOS:000735303000001
OA gold
DA 2024-09-05
ER
PT J
AU Jung, JH
   Lee, JHY
   Choi, S
   Baek, W
AF Jung, Jihoo
   Lee, Jehyun
   Choi, Sangjin
   Baek, Woonho
TI Information Analysis on Foreign Institution for International R&D
   Collaboration Using Natural Language Processing
SO ENERGIES
LA English
DT Article
DE open API; international cooperation; data analysis; R&D planning; text
   mining
ID PYROLYSIS
AB The number of international collaborations in research and development (R&D) has been increasing in the energy sector to solve global environmental problems-such as climate change and the energy crisis-and to reduce the time, cost, and risk of failure. Successful international project planning requires the analysis of research fields and the technology expertise of cooperative partner institutions or countries, but this takes time and resources. In this study, we developed a method to analyze the information on research organizations and topics, taking advantage of data analysis as well as deep learning natural language processing (NLP) models. A method to evaluate the relative superiority of efficient international collaboration was suggested, assuming international collaboration of the National Renewable Energy Laboratory (NREL) and the Korea Institute of Energy Research (KIER). Additionally, a workflow of an automated executive summary and a translation of tens of web-posted articles is also suggested for a quick glance. The valuation of the suggested methodology is estimated as much as the annual salary of an experienced employee.
C1 [Jung, Jihoo; Choi, Sangjin; Baek, Woonho] Korea Inst Energy Res, Global Strategy Team, Daejeon 34129, South Korea.
   [Lee, Jehyun] Korea Inst Energy Res, Computat Sci & Engn Lab, Daejeon 34129, South Korea.
C3 Korea Institute of Energy Research (KIER); Korea Institute of Energy
   Research (KIER)
RP Lee, JHY (corresponding author), Korea Inst Energy Res, Computat Sci & Engn Lab, Daejeon 34129, South Korea.
EM jehyunlee@kier.re.kr
OI Lee, Jehyun/0000-0003-1752-3564
FU Korea Institute of Energy Research (KIER) [C2-2444, C2-2447]
FX This work was conducted under the framework of Research and Development
   Program of the Korea Institute of Energy Research (KIER) (C2-2444 and
   C2-2447).
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NR 85
TC 0
Z9 0
U1 2
U2 5
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1996-1073
J9 ENERGIES
JI Energies
PD JAN
PY 2023
VL 16
IS 1
AR 33
DI 10.3390/en16010033
PG 17
WC Energy & Fuels
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Energy & Fuels
GA 7R6QF
UT WOS:000910194700001
OA gold
DA 2024-09-05
ER
PT J
AU Kim, M
   Park, Y
   Yoon, J
AF Kim, Mujin
   Park, Youngjin
   Yoon, Janghyeok
TI Generating patent development maps for technology monitoring using
   semantic patent-topic analysis
SO COMPUTERS & INDUSTRIAL ENGINEERING
LA English
DT Article
DE Patent development map; Technology monitoring; Patent map;
   Bibliometrics; Latent Dirichlet allocation; Topic analysis; 3D printing
   technology
ID SYSTEM; TRENDS
AB Patent development maps (PDMs) are a useful visual and monitoring tool for technology-trend identification, and therefore proper technology planning, because they provide an overall understanding of a technology's historical development and current stage. The rapid increase in technical data, however, has made it costly and time-consuming to monitor the technology development progress manually. Although some studies have suggested how to identify development paths among patents, little attention has been paid to synthetic consideration of the two core factors for PDMs: (1) the succession relationship among patents in terms of technological content and (2) the technological taxonomies of individual patents. Therefore, this paper suggests a semantic patent topic analysis-based bibliometric method for PDM generation.
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C1 [Kim, Mujin; Park, Youngjin; Yoon, Janghyeok] Konkuk Univ, Dept Ind Engn, Seoul, South Korea.
C3 Konkuk University
RP Yoon, J (corresponding author), Konkuk Univ, Dept Ind Engn, Seoul, South Korea.
EM janghyoon@konkuk.ac.kr
FU Konkuk University
FX This paper was supported by Konkuk University in 2014.
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NR 35
TC 38
Z9 39
U1 6
U2 114
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0360-8352
EI 1879-0550
J9 COMPUT IND ENG
JI Comput. Ind. Eng.
PD AUG
PY 2016
VL 98
BP 289
EP 299
DI 10.1016/j.cie.2016.06.006
PG 11
WC Computer Science, Interdisciplinary Applications; Engineering,
   Industrial
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering
GA DU1EA
UT WOS:000381949800025
DA 2024-09-05
ER
PT J
AU Rodríguez-Sabiote, C
   Ubeda-Sánchez, AM
   Alvarez-Rodríguez, J
   Alvarez-Ferrándiz, D
AF Rodriguez-Sabiote, Clemente
   Ubeda-Sanchez, Alvaro Manuel
   Alvarez-Rodriguez, Jose
   Alvarez-Ferrandiz, Daniel
TI Active Learning in an Environment of Innovative Training and
   Sustainability. Mapping of the Conceptual Structure of Research Fronts
   through a Bibliometric Analysis
SO SUSTAINABILITY
LA English
DT Article
DE active learning; teacher training; digital learning environments;
   information and communication technologies; bibliometric analysis
ID EDUCATION; CENTRALITY; NETWORKS
AB The present study seeks to map and visualize up-to-date perspectives of the topic of active learning by analyzing and interpreting the different elements that make up learning ecosystems within the European Higher Education Area. With this aim, scientometric methods were employed to analyze a sample of 474 articles recovered from Web of Science (WoS) during the three-year period between 2018 and 2020. All articles examined the topic of active learning. Keywords (authors' keywords and 'keywords plus') from the manuscripts were examined through co-occurrence analysis in order to establish the conceptual structure of active learning. Among the different trends and emerging topics identified, there is an important presence of topics related to technology applied to the field of education, where digital contexts acquire a preponderant role in current education. These innovative changes focused on the digital updating and exploitation of technology represent a methodological challenge that requires an involvement and commitment to this new space for educational practice by teachers and students.
C1 [Rodriguez-Sabiote, Clemente; Ubeda-Sanchez, Alvaro Manuel] Univ Granada, Dept Res Methods & Diagnost Educ, Granada 18071, Spain.
   [Alvarez-Rodriguez, Jose] Univ Granada, Dept Pedag, Granada 18071, Spain.
   [Alvarez-Ferrandiz, Daniel] Univ Granada, Fac Educ Sci, Granada 18071, Spain.
C3 University of Granada; University of Granada; University of Granada
RP Rodríguez-Sabiote, C (corresponding author), Univ Granada, Dept Res Methods & Diagnost Educ, Granada 18071, Spain.
EM clerosa@ugr.es; amsu@correo.ugr.es; alvarez@ugr.es;
   ferrandiz98@correo.ugr.es
RI Rodríguez-Sabiote, Clemente/R-5941-2017
OI Ubeda-Sanchez, Alvaro Manuel/0000-0001-8948-8767; Alvarez Rodriguez,
   Jose/0000-0002-8411-9265; Alvarez Ferrandiz, Daniel/0000-0003-4924-1334;
   Rodriguez Sabiote, Clemente/0000-0003-3094-9199
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NR 62
TC 14
Z9 14
U1 0
U2 13
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD OCT
PY 2020
VL 12
IS 19
AR 8012
DI 10.3390/su12198012
PG 18
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA ON1YE
UT WOS:000586504700001
OA Green Published, gold
DA 2024-09-05
ER
PT J
AU Manju, G
   Kavitha, V
   Geetha, TV
AF Manju, G.
   Kavitha, V
   Geetha, T., V
TI Influential Researcher Identification in Academic Network Using Rough
   Set Based Selection of Time-Weighted Academic and Social Network
   Features
SO INTERNATIONAL JOURNAL OF INTELLIGENT INFORMATION TECHNOLOGIES
LA English
DT Article
DE Academic Influence; Feature Selection; Influential Researcher; Map
   Reduce Paradigm; Recommendation System; Rough Set Theory; Spreading
   Activation; Time-Weighted Relation
ID SIMILARITY
AB Researchers entering into a new research area are interested in knowing the current research trends, popular publications and influential (popular) researchers in that area in order to initiate their research. In this work, we attempt to determine the influential researcher for a specific topic. The active participation of the researchers in both the academic and social network activities signifies the researchers' influence level across time. The content and frequency of social interaction to a researcher reflects his or her influence. In our system, appropriate time-based social and academic features are selected using entropy based feature selection approach of rough set theory. A three layer model comprising semantically related concepts, researcher and social relations is developed based on the appropriate (influential) features. The researchers' topic trajectories are identified and recommended using Spreading activation algorithm. To cope up with the scalable academic network, map reduce paradigm has been employed in the spreading activation algorithm.
C1 [Manju, G.; Kavitha, V; Geetha, T., V] Anna Univ, Dept Comp Sci, Madras, Tamil Nadu, India.
C3 Anna University; Anna University Chennai
RP Manju, G (corresponding author), Anna Univ, Dept Comp Sci, Madras, Tamil Nadu, India.
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NR 50
TC 4
Z9 4
U1 0
U2 7
PU IGI GLOBAL
PI HERSHEY
PA 701 E CHOCOLATE AVE, STE 200, HERSHEY, PA 17033-1240 USA
SN 1548-3657
EI 1548-3665
J9 INT J INTELL INF TEC
JI Int. J. Intell. Inf. Technol.
PD JAN-MAR
PY 2017
VL 13
IS 1
BP 1
EP 25
DI 10.4018/IJIIT.2017010101
PG 25
WC Computer Science, Information Systems
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA EO5GQ
UT WOS:000396721700001
DA 2024-09-05
ER
PT J
AU García-Pineda, V
   Valencia-Arias, A
   Patino-Vanegas, JC
   Cueto, JJF
   Arango-Botero, D
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   Rodríguez-Correa, PA
AF Garcia-Pineda, Vanessa
   Valencia-Arias, Alejandro
   Patino-Vanegas, Juan Camilo
   Flores Cueto, Juan Jose
   Arango-Botero, Diana
   Rojas Coronel, Angel Marcelo
   Rodriguez-Correa, Paula Andrea
TI Research Trends in the Use of Machine Learning Applied in Mobile
   Networks: A Bibliometric Approach and Research Agenda
SO INFORMATICS-BASEL
LA English
DT Article
DE mobile networks; mobile communication systems; 5G; PRISMA; machine
   learning; internet of things
ID MASSIVE MIMO; 5G NETWORKS; 6G; FRAMEWORK; SYSTEMS; ACCESS; ENERGY;
   INTELLIGENCE; CHALLENGES; MANAGEMENT
AB This article aims to examine the research trends in the development of mobile networks from machine learning. The methodological approach starts from an analysis of 260 academic documents selected from the Scopus and Web of Science databases and is based on the parameters of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Quantity, quality and structure indicators are calculated in order to contextualize the documents' thematic evolution. The results reveal that, in relation to the publications by country, the United States and China, who are competing for fifth generation (5G) network coverage and are responsible for manufacturing devices for mobile networks, stand out. Most of the research on the subject focuses on the optimization of resources and traffic to guarantee the best management and availability of a network due to the high demand for resources and greater amount of traffic generated by the many Internet of Things (IoT) devices that are being developed for the market. It is concluded that thematic trends focus on generating algorithms for recognizing and learning the data in the network and on trained models that draw from the available data to improve the experience of connecting to mobile networks.
C1 [Garcia-Pineda, Vanessa] Corp Univ Amer, Fac Ingn, Medellin 055428, Colombia.
   [Valencia-Arias, Alejandro] Univ Senor Sipan, Escuela Ingn Ind, Chiclayo 14001, Peru.
   [Patino-Vanegas, Juan Camilo; Arango-Botero, Diana] Inst Tecnol Metropolitano, Fac Ciencias Econ & Adm, Medellin 050034, Colombia.
   [Flores Cueto, Juan Jose] Univ San Martin Porres, Unidad Virtualizac Acad, Santa Anita 15011, Peru.
   [Rojas Coronel, Angel Marcelo] Univ Senor Sipan, Escuela Ingn Mecan, Chiclayo 14001, Peru.
   [Rodriguez-Correa, Paula Andrea] Inst Univ Escolme, Ctr Invest, Medellin 050012, Colombia.
C3 Universidad Senor de Sipan; Universidad de San Martin de Porres;
   Universidad Senor de Sipan
RP Valencia-Arias, A (corresponding author), Univ Senor Sipan, Escuela Ingn Ind, Chiclayo 14001, Peru.
EM vgarcia@americana.edu.co; valenciajho@crece.uss.edu.pe;
   juanpatino@itm.edu.co; jfloresc@usmp.pe; dianaarangob@itm.edu.co;
   rmarcelo@crece.uss.edu.pe; cies4@escolme.edu.co
RI Arango-Botero, Diana/W-6231-2018; Arias, Alejandro Valencia/I-9436-2019
OI Arias, Alejandro Valencia/0000-0001-9434-6923; Rojas Coronel, Angel
   Marcelo/0000-0002-2720-9707; Rodriguez Correa,
   Paula/0000-0002-9748-0148; Garcia Pineda, Vanessa/0000-0003-3418-8956
FU Corporacion Universitaria Americana (Colombia); Universidad Senor de
   Sipan (Peru)
FX This research was funded by the Corporacion Universitaria Americana
   (Colombia) and the Universidad Senor de Sipan (Peru). The APC was funded
   by Universidad Senor de Sipan (Peru).
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NR 96
TC 1
Z9 1
U1 0
U2 3
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2227-9709
J9 INFORMATICS-BASEL
JI Informatics-Basel
PD SEP
PY 2023
VL 10
IS 3
AR 73
DI 10.3390/informatics10030073
PG 24
WC Computer Science, Interdisciplinary Applications
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA S8OC5
UT WOS:001073697200001
OA gold
DA 2024-09-05
ER
PT C
AU Feng, WW
   Wang, P
   Zhou, C
   Hu, Y
   Guo, L
AF Feng, Weiwei
   Wang, Peng
   Zhou, Chuan
   Hu, Yue
   Guo, Li
BE Lu, Y
   Wu, X
   Zhang, X
TI Nonparametric Topic-Aware Sparsification of Influence Networks
SO TRUSTWORTHY COMPUTING AND SERVICES (ISCTCS 2014)
SE Communications in Computer and Information Science
LA English
DT Proceedings Paper
CT International Standard Conference on Trustworthy Computing and Services
   (ISCTCS)
CY NOV 28-29, 2014
CL Beijing, PEOPLES R CHINA
DE Social network; Sparsification; HDP-LDA
AB In the last decade social networks are becoming denser and denser, which makes analyzing their structures and properties very difficult. However, for certain task, if we can remove the inactive users and irrelevant links, the network will be amazingly sparse and tractable. In this paper we propose the Nonparametric Topic-aware Sparsification (NTAS) algorithm, which can simplify social networks for a specific task. To determine whether a link is relevant to the task, we adopt nonparametric topic model to analyze the topic distribution of links and the task. We empirically demonstrate that our algorithm can return a more sparse network compared with other state-of-the-art methods in the task of network monitoring.
C1 [Feng, Weiwei; Zhou, Chuan; Hu, Yue; Guo, Li] Chinese Acad Sci, Inst Informat Engn, Beijing, Peoples R China.
   [Wang, Peng] Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China.
C3 Chinese Academy of Sciences; Institute of Information Engineering, CAS;
   Chinese Academy of Sciences; Institute of Computing Technology, CAS
RP Feng, WW (corresponding author), Chinese Acad Sci, Inst Informat Engn, Beijing, Peoples R China.
EM fengweiwei@iie.ac.cn; peng860215@gmail.com; zhouchuan@iie.ac.cn;
   huyue@iie.ac.cn; guoli@iie.ac.cn
RI WANG, Peng/GSN-5263-2022; Zhou, Chuan/JFS-4721-2023; Hu,
   Yue/HGE-1673-2022
OI Zhou, Chuan/0000-0001-9958-8673; 
CR [Anonymous], 2008, Proceedings of the 17th international conference on World Wide Web
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NR 12
TC 0
Z9 0
U1 0
U2 1
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 1865-0929
EI 1865-0937
BN 978-3-662-47401-3; 978-3-662-47400-6
J9 COMM COM INF SC
PY 2015
VL 520
BP 83
EP 90
DI 10.1007/978-3-662-47401-3_11
PG 8
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BE1QX
UT WOS:000368404900011
DA 2024-09-05
ER
PT J
AU Liu, J
   Wang, C
   Liu, ZL
   Gao, MH
   Xu, YH
   Chen, JY
   Cheng, YC
AF Liu, Jun
   Wang, Cong
   Liu, Zile
   Gao, Minghui
   Xu, Yanhua
   Chen, Jiayu
   Cheng, Yichun
TI A bibliometric analysis of generative AI in education: current status
   and development
SO ASIA PACIFIC JOURNAL OF EDUCATION
LA English
DT Article
DE Generative AI; education; bibliometric analysis; visualization;
   CiteSpace; VOSviewer
ID INTELLIGENCE; EVOLUTION
AB The rapid advancement of generative AI technology offers new opportunities for the innovation and transformation of education. However, this also brings forth risks and challenges, including the potential to exacerbate educational inequality and integrity. This study aims to address the extensive controversies surrounding the application of generative AI technology in education by providing an objective and comprehensive understanding of its current state, development in educational contexts. Using the CiteSpace and VOSviewer software, we conducted visual analyses of relevant literature from the Web of Science core collection pertaining to the application of generative AI in education.Subsequently, we identified productive journals, productive articles, collaboration patterns, article hotspots, and prevalent topics in this field.This study will facilitate the promotion of in-depth research and practical implementation of AI in education.
C1 [Liu, Jun; Wang, Cong; Liu, Zile] Capital Normal Univ, Coll Educ, Beijing, Peoples R China.
   [Gao, Minghui; Chen, Jiayu; Cheng, Yichun] Capital Normal Univ, Coll Teacher Educ, Beijing, Peoples R China.
   [Xu, Yanhua] Jiangxi Normal Univ, Sch Geog & Environm, Nanchang 330022, Jiangxi, Peoples R China.
C3 Capital Normal University; Capital Normal University; Jiangxi Normal
   University
RP Xu, YH (corresponding author), Jiangxi Normal Univ, Sch Geog & Environm, Nanchang 330022, Jiangxi, Peoples R China.
EM yanhuaxuedu@foxmail.com
RI guo, yi/KHC-4669-2024; ren, jun/KHG-7717-2024; liu, qi/KHC-7509-2024;
   li, cheng/KCZ-0615-2024; zhu, hao/KHW-3813-2024; Zhang,
   Lu/KHE-5879-2024; liu, qi/KFA-4047-2024; Liu, Yu/KFS-0769-2024; zhang,
   yan/KHC-3163-2024; Chen, Yang/KHD-8849-2024; su, lin/KHC-5034-2024; li,
   jing/KHC-8303-2024
OI Wang, Cong/0000-0002-6976-0433
FU Major Educational Project of National Social Science Fund [VHA220005];
   The 2023 Open Research Project "Artificial Intelligence in Education" in
   the College of Education in Capital Normal University
FX This work was supported by Major Educational Project of National Social
   Science Fund under the Grant [number VHA220005], and 2023 Open Research
   Project "Artificial Intelligence in Education" in the College of
   Education in Capital Normal University.
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NR 91
TC 4
Z9 4
U1 152
U2 179
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0218-8791
EI 1742-6855
J9 ASIA PAC J EDUC
JI Asia Pac. J. Educ.
PD JAN 2
PY 2024
VL 44
IS 1
SI SI
BP 156
EP 175
DI 10.1080/02188791.2024.2305170
EA JAN 2024
PG 20
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA JG4I1
UT WOS:001151191800001
DA 2024-09-05
ER
PT J
AU Wang, CS
   Zhou, J
   Wu, HR
   Li, JX
   Zhao, CJ
   Liu, R
AF Wang, Chunshan
   Zhou, Ji
   Wu, Huarui
   Li, Jiuxi
   Chunjiang, Zhao
   Liu, Rong
TI Research on the Evaluation Method of Eggshell Dark Spots Based on
   Machine Vision
SO IEEE ACCESS
LA English
DT Article
DE Object segmentation; Image segmentation; Manuals; Clustering algorithms;
   Production; Lighting; eggshell; dark spot; evaluation; machine vision;
   K-means
ID CRACK DETECTION; PENETRATION; SALMONELLA
AB Dark spots, which are widely present in different species of eggs, not only significantly affect the appearance and reduce the commercial value of eggs, but also increase the safety hazards of edible eggs in view of that Salmonella can easily penetrate the eggshell at the location of dark spots. During the first 5 days after egg production, it is difficult to identify and evaluate dark spots on the eggshell surface under natural lighting conditions. Therefore, it is a great challenge to automatically classify commercial eggs according to the amount of dark spots at the initial stage. In this paper, a method based on machine vision was proposed for identifying and evaluating eggshell dark spots. First, the K-means clustering algorithm was used to segment the individual egg image on the production line in order to obtain the complete eggshell surface area; then, the unsharp masking method was used to enhance the dark-spot features so as to realize the recognition of dark spots; and finally, quantitative evaluation was conducted according to the amount of dark spots on the eggshell surface and the ratio of the dark-spot projected area. Our experimental results show that the proposed method is able to quickly and accurately calculate the distribution of dark spots and the ratio of the dark-spot projected area. Specifically, the processing speed of dark-spot image is 1 frame/0.5s, which is 960 times faster than the speed of manual marking (1 frame/480s), and the detection capacity of the experimental device is 3600 eggs/h. It provides an automated method for quantitatively examining dark spots on eggshells, a scientific tool for conducting further research on the formation mechanism of dark spots, as well as a technical means for the high-throughput online examination of egg quality.
C1 [Wang, Chunshan; Zhou, Ji] Hebei Agr Univ, Sch Informat Sci & Technol, Baoding 071001, Peoples R China.
   [Wang, Chunshan; Wu, Huarui; Chunjiang, Zhao] Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China.
   [Wang, Chunshan; Wu, Huarui; Chunjiang, Zhao] Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China.
   [Li, Jiuxi; Liu, Rong] Hebei Agr Univ, Sch Mech & Elect Engn, Baoding 071001, Peoples R China.
C3 Hebei Agricultural University; Beijing Academy of Agriculture & Forestry
   Sciences (BAAFS); Beijing Academy of Agriculture & Forestry Sciences
   (BAAFS); Hebei Agricultural University
RP Zhao, CJ (corresponding author), Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China.; Zhao, CJ (corresponding author), Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China.
EM zhaocj@nercita.org.cn
RI wang, chunshan/ADR-4786-2022
OI wang, chunshan/0000-0002-0222-8397
FU National Natural Science Foundation of China [61871041]; Beijing
   Municipal Science and Technology Project [Z191100004019007]; Hebei
   Province Key Research and Development Project [20326630D]; Project of
   Introducing Overseas Students in Hebei Province [C20190340]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61871041, in part by the Beijing
   Municipal Science and Technology Project under Grant Z191100004019007,
   in part by the Hebei Province Key Research and Development Project under
   Grant 20326630D, and in part by the Project of Introducing Overseas
   Students in Hebei Province under Grant C20190340. They respectively
   provided equipment development, experimental location, experimental
   conditions and technical support for the research work of this paper.
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   [Anonymous], 2019, CHINA POULTRY, DOI DOI 10.3969/J.ISSN.1674-0858.2019.05.12
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NR 29
TC 0
Z9 0
U1 3
U2 23
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2020
VL 8
BP 160116
EP 160125
DI 10.1109/ACCESS.2020.3020260
PG 10
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA NP3QU
UT WOS:000570094400001
OA gold
DA 2024-09-05
ER
PT J
AU Mendes, AM
   Tonin, FS
   Buzzi, MF
   Pontarolo, R
   Fernandez-Llimos, F
AF Mendes, Antonio M.
   Tonin, Fernanda S.
   Buzzi, Maiko F.
   Pontarolo, Roberto
   Fernandez-Llimos, Fernando
TI Mapping pharmacy journals: A lexicographic analysis
SO RESEARCH IN SOCIAL & ADMINISTRATIVE PHARMACY
LA English
DT Article
DE Pharmacy; Periodicals as topic; Bibliometrics; Principal component
   analysis
ID SCIENCE; KNOWLEDGE; ARTICLES
AB Background: Pharmacy journals constitute a heterogeneous group that can be map to identify Pharmacy scientific subareas.
   Objective: This study aimed to objectively map Pharmacy journals by means of a lexicographic analysis of the titles of published articles.
   Methods: Active journals between 2006 and 2016 containing any of the terms 'pharmacy', 'pharmacist*', 'pharmaceut*', 'pharmacol*', or 'pharmacotherap*' in their titles were searched in four databases (01/15/2018): Medline, PubMed Central, Science Citation Index expanded/Social Sciences Citation Index expanded (SCIe/SSCIe), and Scopus CiteScore Metrics. The titles of all the articles (Jan-2006 to Dec-2016) in the identified journals were gathered into a single text corpus. The following analyses were performed (Iramuteq 0.7): lexicographic analysis to determine the number, frequency and distribution of active words; descending hierarchical classification (DHC) to categorize active words and journals into lexical classes; factorial correspondence analyses (FCA) to obtain bi- and tri-dimensional graphs.
   Results: A total of 285 journals comprising 316,089 articles (median 70.4 articles [IQR 34.0-141.0] per journal per year) were included for the analyses. The journals were indexed in Scopus (90.2%) with a median CiteScore of 1.16 (IQR 0.28-2.55); in SCIe/SSCIe (44.6%) with a median impact factor of 2.410 (IQR 1.629-3.316); and in PubMed (65.7%). The DHC of active words produced three major groups (A, B, C) with two lexical classes each, representing six Pharmacy subareas depicted by the FCA as: Group A comprising 'Cell Pharmacology' (20 journals) and 'Molecular Pharmacology' (46 journals), Group B with 'Clinical Pharmacology' (57 journals) and 'Pharmacy Practice' (67 journals), and Group C with 'Pharmaceutics' (35 journals) and 'Pharmaceutical Analysis' (60 journals). Coverage of the classes in bibliographic databases and impact metrics is unbalanced.
   Conclusions: Pharmacy journals that can be objectively classified into six different classes that represent different research subareas with uneven coverage in bibliographic databases.
C1 [Mendes, Antonio M.; Tonin, Fernanda S.] Univ Fed Parana, Pharmaceut Sci Postgrad Programme, Curitiba, Parana, Brazil.
   [Buzzi, Maiko F.] Univ Tecnol Fed Parana, Dept Math, Curitiba, Parana, Brazil.
   [Pontarolo, Roberto] Univ Fed Parana, Dept Pharm, Curitiba, Parana, Brazil.
   [Fernandez-Llimos, Fernando] Univ Lisbon, Dept Social Pharm, Fac Pharm, Res Inst Med iMed ULisboa, Av Prof Gama Pinto, P-1649003 Lisbon, Portugal.
C3 Universidade Federal do Parana; Universidade Tecnologica Federal do
   Parana; Universidade Federal do Parana; Pontificia Universidade Catolica
   do Parana; Universidade Federal do Parana; Universidade de Lisboa
RP Fernandez-Llimos, F (corresponding author), Univ Lisbon, Dept Social Pharm, Fac Pharm, Res Inst Med iMed ULisboa, Av Prof Gama Pinto, P-1649003 Lisbon, Portugal.
EM mmendesantonio@gmail.com; stumpf.tonin@ufpr.br; maikobuzzi@hotmail.com;
   pontarolo@ufpr.br; f-llimos@ff.ulisboa.pt
RI Tonin, Fernanda S/O-2050-2017; Tonin, Fernanda S./AAE-3435-2022;
   Fernandez-Llimos, Fernando/B-8931-2008; Pontarolo, Roberto/G-6948-2014
OI Tonin, Fernanda S/0000-0003-4262-8608; Tonin, Fernanda
   S./0000-0003-4262-8608; Fernandez-Llimos, Fernando/0000-0002-8529-9595;
   Matoso Mendes, Antonio Eduardo/0000-0002-5752-349X; Pontarolo,
   Roberto/0000-0002-7049-4363
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NR 48
TC 31
Z9 33
U1 0
U2 10
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 1551-7411
EI 1934-8150
J9 RES SOC ADMIN PHARM
JI Res. Soc. Adm. Pharm.
PD DEC
PY 2019
VL 15
IS 12
BP 1464
EP 1471
DI 10.1016/j.sapharm.2019.01.011
PG 8
WC Public, Environmental & Occupational Health; Pharmacology & Pharmacy
WE Social Science Citation Index (SSCI)
SC Public, Environmental & Occupational Health; Pharmacology & Pharmacy
GA JS8TO
UT WOS:000500574500012
PM 30683600
DA 2024-09-05
ER
PT C
AU Schedl, M
   Seyerlehner, K
   Schnitzer, D
   Widmer, G
   Schiketanz, C
AF Schedl, Markus
   Seyerlehner, Klaus
   Schnitzer, Dominik
   Widmer, Gerhard
   Schiketanz, Cornelia
BE Chen, HH
   Efthimiadis, EN
   Savoy, J
   Crestani, F
   MarchandMaillet, S
TI Three Web-based Heuristics to Determine a Person's or Institution's
   Country of Origin
SO SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR
   CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL
LA English
DT Proceedings Paper
CT 33rd Annual International ACM SIGIR Conference on Research and
   Development in Information Retrieval
CY JUL 19-23, 2010
CL Geneva, SWITZERLAND
DE information extraction; country of origin detection; term weighting;
   music information research; evaluation
AB We propose three heuristics to determine the country of origin of a person or institution via text-based IE from the Web. We evaluate all methods on a collection of music artists and bands, and show that some heuristics outperform earlier work on the topic by terms of coverage, while retaining similar precision levels. We further investigate an extension using country-specific synonym lists.
C1 [Schedl, Markus; Seyerlehner, Klaus; Schnitzer, Dominik; Widmer, Gerhard; Schiketanz, Cornelia] Johannes Kepler Univ Linz, Dept Computat Percept, A-4040 Linz, Austria.
C3 Johannes Kepler University Linz
RP Schedl, M (corresponding author), Johannes Kepler Univ Linz, Dept Computat Percept, A-4040 Linz, Austria.
EM markus.schedl@jku.at; klaus.seyerlehner@jku.at;
   dominik.schnitzer@ofai.at; gerhard.widmer@jku.at; music@jku.at
RI Widmer, Gerhard/B-8218-2017
OI Widmer, Gerhard/0000-0003-3531-1282
CR [Anonymous], 1979, INFORM RETRIEVAL
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NR 7
TC 0
Z9 0
U1 1
U2 1
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA
BN 978-1-60558-896-4
PY 2010
BP 801
EP 802
PG 2
WC Computer Science, Information Systems
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BTG52
UT WOS:000286904100146
DA 2024-09-05
ER
PT C
AU Wen, L
   Li, JF
AF Wen, Lei
   Li, Junfei
BE Chen, Y
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TI Research of credit grade assessment for suppliers based on multi-layer
   SVM classifier
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   AND APPLICATIONS, VOL 1
LA English
DT Proceedings Paper
CT 6th International Conference on Intelligent Systems Design and
   Applications (ISDA 2006)
CY OCT 16-18, 2006
CL Jinan Univ, Jinan, PEOPLES R CHINA
HO Jinan Univ
AB It is very important to choose swift, powerful and compatible suppliers in supply chain management (SCM). According to the credit grade assessment for suppliers in practice and the related theories, a set of index system which assesses a supplier's credit grade is established. Basing on the index system, a multi-layer support vector machines (SVM) classifier is established to assess and classify the suppliers' credit grade in SC. In order to verify the effectiveness of the method, a real case is given and BP neural network is also used assess the same data. The experimental results show that multi-layer SVM classifier is effective in credit level assessment and achieves better performance than BP neural network.
C1 [Wen, Lei; Li, Junfei] North China Elect Power Univ, Dept Econ Management, Baoding 071003, Hebei, Peoples R China.
C3 North China Electric Power University
RP Wen, L (corresponding author), North China Elect Power Univ, Dept Econ Management, Baoding 071003, Hebei, Peoples R China.
EM leejfgood@126.com
FU Doctor Foundation of North China Electric Power University [20041205];
   nature science planning project of hebei provence education bureau
   [z2005117]
FX This paper was supported by Doctor Foundation of North China Electric
   Power University. number:20041205 And the nature science planning
   project of hebei provence education bureau, number :z2005117.
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PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA
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BP 207
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WC Computer Science, Artificial Intelligence
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GA BFK66
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PT J
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NR 32
TC 14
Z9 16
U1 0
U2 8
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0138-9130
J9 SCIENTOMETRICS
JI Scientometrics
PD JAN
PY 1990
VL 18
IS 1-2
BP 5
EP 20
DI 10.1007/BF02019159
PG 16
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA CP071
UT WOS:A1990CP07100001
DA 2024-09-05
ER
PT C
AU Zhang, CX
AF Zhang, Chenxiang
BE Chen, T
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TI Research on recognition algorithm of network public opinion in view of
   evaluation
SO PROCEEDINGS OF THE 2015 2ND INTERNATIONAL CONFERENCE ON ELECTRICAL,
   COMPUTER ENGINEERING AND ELECTRONICS (ICECEE 2015)
SE ACSR-Advances in Comptuer Science Research
LA English
DT Proceedings Paper
CT 2nd International Conference on Electrical, Computer Engineering and
   Electronics (ICECEE)
CY MAY 29-31, 2015
CL Jinan, PEOPLES R CHINA
DE Internet public opinion; Online review; Opinion analysis; Feature
   presentation; Ensemble learning; Combination optimization; Review spam
   detection
AB In This paper, firstly, discusses the theoretical background, the existed research achievements and commercial products of Internet public opinion monitoring and analysis, according to preceding context we find the needs of public opinion analysis and the deficiency in the existed systems, and then raise the prototype of Internet public opinion mining with the function of opinion tracking. Secondly, decomposes the prototype to figure out the technology points, then corroding to them, gives a detailed introduction of their theories. Thirdly, describes the overall system design and detail works in each module of the Internet public opinion monitoring and analysis system.
C1 Suzhou Ind Pk Inst Serv Outsourcing, Suzhou 215123, Jiangsu, Peoples R China.
RP Zhang, CX (corresponding author), Suzhou Ind Pk Inst Serv Outsourcing, Suzhou 215123, Jiangsu, Peoples R China.
EM kylinbaby@163.com
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NR 5
TC 0
Z9 0
U1 0
U2 10
PU ATLANTIS PRESS
PI PARIS
PA 29 AVENUE LAVMIERE, PARIS, 75019, FRANCE
SN 2352-538X
BN 978-94-62520-81-3
J9 ACSR ADV COMPUT
PY 2015
VL 24
BP 178
EP 181
PG 4
WC Engineering, Electrical & Electronic
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Engineering
GA BD3KZ
UT WOS:000359820600040
DA 2024-09-05
ER
PT J
AU Cappelletti-Montano, B
   Cherchi, G
   Manca, B
   Montaldo, S
   Musio, M
AF Cappelletti-Montano, Beniamino
   Cherchi, Gianmarco
   Manca, Benedetto
   Montaldo, Stefano
   Musio, Monica
TI How to Measure the Researcher Impact with the Aid of its Impactable
   Area: A Concrete Approach Using Distance Geometry
SO JOURNAL OF CLASSIFICATION
LA English
DT Article; Early Access
DE Bibliometrics; Dimensionality reduction; Linear programming
ID INDIVIDUAL RESEARCHERS; SINGLE RESEARCHERS; LOCALIZATION
AB Assuming that the subject of each scientific publication can be identified by one or more classification entities, we address the problem of determining a similarity function (distance) between classification entities based on how often two classification entities are used in the same publication. This similarity function is then used to obtain a representation of the classification entities as points of an Euclidean space of a suitable dimension by means of optimization and dimensionality reduction algorithms. This procedure allows us also to represent the researchers as points in the same Euclidean space and to determine the distance between researchers according to their scientific production. As a case study, we consider as classification entities the codes of the American Mathematical Society Classification System.
C1 [Cappelletti-Montano, Beniamino; Cherchi, Gianmarco; Manca, Benedetto; Montaldo, Stefano; Musio, Monica] Univ Cagliari, Dept Math & Comp Sci, Via Osped 72, I-09124 Cagliari, Italy.
C3 University of Cagliari
RP Manca, B (corresponding author), Univ Cagliari, Dept Math & Comp Sci, Via Osped 72, I-09124 Cagliari, Italy.
EM b.cappellettimontano@unica.it; g.cherchi@unica.it; bmanca@unica.it;
   montaldo@unica.it; mmusio@unica.it
FU PRIN 2022 - EUD4XR End User Development for eXtended Reality
FX No Statement AvailableDAS:The datasets generated by the survey research
   during and/or analyzed during the current study are available in the
   Zentralblatt MATH repository, https://zbmath.org.
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NR 39
TC 0
Z9 0
U1 0
U2 0
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 0176-4268
EI 1432-1343
J9 J CLASSIF
JI J. Classif.
PD 2024 AUG 26
PY 2024
DI 10.1007/s00357-024-09490-2
EA AUG 2024
PG 29
WC Mathematics, Interdisciplinary Applications; Psychology, Mathematical
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Mathematics; Psychology
GA D7F0C
UT WOS:001297789800001
OA hybrid
DA 2024-09-05
ER
PT J
AU Nguyen, VA
   Boyd-Graber, J
   Resnik, P
   Cai, DA
   Midberry, JE
   Wang, YX
AF Viet-An Nguyen
   Boyd-Graber, Jordan
   Resnik, Philip
   Cai, Deborah A.
   Midberry, Jennifer E.
   Wang, Yuanxin
TI Modeling topic control to detect influence in conversations using
   nonparametric topic models
SO MACHINE LEARNING
LA English
DT Article
DE Bayesian nonparametrics; Influencer detection; Topic modeling; Topic
   segmentation; Gibbs sampling
ID DISCOURSE; DOMINANCE; TEXT; LEADERSHIP; MEETINGS; TURNS; TIME
AB Identifying influential speakers in multi-party conversations has been the focus of research in communication, sociology, and psychology for decades. It has been long acknowledged qualitatively that controlling the topic of a conversation is a sign of influence. To capture who introduces new topics in conversations, we introduce SITS-Speaker Identity for Topic Segmentation-a nonparametric hierarchical Bayesian model that is capable of discovering (1) the topics used in a set of conversations, (2) how these topics are shared across conversations, (3) when these topics change during conversations, and (4) a speaker-specific measure of "topic control". We validate the model via evaluations using multiple datasets, including work meetings, online discussions, and political debates. Experimental results confirm the effectiveness of SITS in both intrinsic and extrinsic evaluations.
C1 [Viet-An Nguyen] Univ Maryland, Dept Comp Sci, College Pk, MD 20742 USA.
   [Boyd-Graber, Jordan] Univ Maryland, ISch, College Pk, MD 20742 USA.
   [Boyd-Graber, Jordan; Resnik, Philip] Univ Maryland, UMIACS, College Pk, MD 20742 USA.
   [Resnik, Philip] Univ Maryland, Dept Linguist, College Pk, MD 20742 USA.
   [Cai, Deborah A.; Midberry, Jennifer E.; Wang, Yuanxin] Temple Univ, Sch Media & Commun, Philadelphia, PA 19122 USA.
C3 University System of Maryland; University of Maryland College Park;
   University System of Maryland; University of Maryland College Park;
   University System of Maryland; University of Maryland College Park;
   University System of Maryland; University of Maryland College Park;
   Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple
   University
RP Nguyen, VA (corresponding author), Univ Maryland, Dept Comp Sci, College Pk, MD 20742 USA.
EM vietan@cs.umd.edu; jbg@umiacs.umd.edu; resnik@umd.edu;
   debcai@temple.edu; jmidberry@gmail.com; yuanxin@gmail.com
RI Midberry, Jennifer/KIC-3279-2024; Boyd-Graber, Jordan/A-9976-2016; Cai,
   Deborah A/N-8816-2018
OI Midberry, Jennifer/0000-0003-0709-2406; Boyd-Graber,
   Jordan/0000-0002-7770-4431
FU Army Research Laboratory through ARL [W911NF-09-2-0072]; Office of the
   Director of National Intelligence (ODNI), Intelligence Advanced Research
   Projects Activity (IARPA), through the Army Research Laboratory; US
   National Science Foundation Grant NSF [1018625, IIS1211153]; Direct For
   Computer & Info Scie & Enginr; Div Of Information & Intelligent Systems
   [1211153] Funding Source: National Science Foundation; Division of
   Computing and Communication Foundations; Direct For Computer & Info Scie
   & Enginr [1018625] Funding Source: National Science Foundation
FX We would like to thank the reviewers for their insightful comments. We
   are grateful to Eric Hardisty, Pranav Anand, Craig Martell, Douglas W.
   Oard, Earl Wagner, and Marilyn Walker for helpful discussions. This
   research was funded in part by the Army Research Laboratory through ARL
   Cooperative Agreement W911NF-09-2-0072 and by the Office of the Director
   of National Intelligence (ODNI), Intelligence Advanced Research Projects
   Activity (IARPA), through the Army Research Laboratory. Jordan
   Boyd-Graber and Philip Resnik are supported by US National Science
   Foundation Grant NSF #1018625. Viet-An Nguyen and Philip Resnik are also
   supported by US National Science Foundation Grant NSF #IIS1211153. Any
   opinions, findings, conclusions, or recommendations expressed are the
   authors' and do not necessarily reflect those of the sponsors.
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NR 121
TC 16
Z9 27
U1 1
U2 14
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0885-6125
EI 1573-0565
J9 MACH LEARN
JI Mach. Learn.
PD JUN
PY 2014
VL 95
IS 3
SI SI
BP 381
EP 421
DI 10.1007/s10994-013-5417-9
PG 41
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA AH3OO
UT WOS:000336034000006
OA Bronze
DA 2024-09-05
ER
PT J
AU Street, JN
   Santhanakrishnan, M
AF Street, Jeffrey N.
   Santhanakrishnan, Mukunthan
TI Real options logic in R&D project valuation A useful tool for decision
   making through the lens of heuristics
SO JOURNAL OF STRATEGY AND MANAGEMENT
LA English
DT Article
DE Decision making; Research and development; Return on investment; Project
   evaluation
AB Purpose - Decision making for acceptance of an R&D project occurs under uncertainty and may involve predominantly quantitative analyses, such as net-present value, predominantly intuitive analyses, such as real options logic, or some combination thereof. This paper attempts to bring together two concepts of decision theory, i.e. heuristics and framing, and real options logic into one integrated view relative to R&D project valuation. It is believed that the integration of theory helps explain expected and unexpected decisions resulting from the R&D project valuation process.
   Design/methodology/approach - It is proposed here that, under a typical R&D project review, aspects of two theoretical concepts integrate to aid project valuation and decision making. The aim of this paper is to develop a research framework leading to advancement in the understanding of the relationship of heuristic principles from decision theory and the valuation methodology of real options logic.
   Findings - As a conceptual paper, propositions and a research model representing the conceptual framework are presented.
   Research limitations/implications - Stemming from the propositions and research model, it is believed that the degree of influence that heuristics potentially exhibit on real options logic can be successfully measured. Confirming the degree of influence is a matter for future empirical research.
   Originality/value - The originality of this paper is to develop a research framework leading to advancement in the understanding of the relationship of heuristic principles from decision theory and the valuation methodology of real options logic. In this framework, heuristics has been positioned as a moderator affecting project valuation derived by real options logic.
C1 [Street, Jeffrey N.; Santhanakrishnan, Mukunthan] Idaho State Univ, Coll Business, Pocatello, ID 83209 USA.
C3 Idaho; Idaho State University
RP Street, JN (corresponding author), Idaho State Univ, Coll Business, Pocatello, ID 83209 USA.
EM strejeff@isu.edu
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NR 53
TC 1
Z9 2
U1 0
U2 1
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 1755-425X
J9 J STRATEGY MANAG
JI J. Strategy Manag.
PY 2011
VL 4
IS 2
BP 155
EP 171
DI 10.1108/17554251111128628
PG 17
WC Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA V15MD
UT WOS:000214551800004
DA 2024-09-05
ER
PT J
AU VOIGT, K
   PEPPING, T
   KOTCHETOVA, E
   MUCKE, W
AF VOIGT, K
   PEPPING, T
   KOTCHETOVA, E
   MUCKE, W
TI TESTING OF ONLINE DATABASES IN THE INFORMATION-SYSTEM FOR ENVIRONMENTAL
   CHEMICALS WITH A TESTSET OF 68 CHEMICALS
SO CHEMOSPHERE
LA English
DT Article
DE DATA-BANKS; DATABASES; BIBLIOGRAPHIC DATABASES; NUMERIC DATABASES;
   ENVIRONMENTAL CHEMICALS; EXISTING CHEMICALS; TEST SET; HOSTS
ID FOOD
AB As part of the research project "Information system for environmental chemicals' we tested 265 databases with the help of a testset of 68 environmentally relevant chemicals. The different groups of chemicals in the testset and their number of hits in our Databank of Databases on Environmental Chemicals (DADB) are shown. On top of the general difficulties in searching for information on chemicals there are considerable differences in using either bibliographic or numeric databases. The differences searching by chemical name or by CAS-Number are illustrated and discussed.
C1 MV LOMONOSOV STATE UNIV,DEPT ORGAN CHEM,INFORMAT SERV,MOSCOW 117234,USSR.
   TECH UNIV MUNICH,INST TOXIKOL & UMWELTHYG,W-8000 MUNICH 19,GERMANY.
C3 Lomonosov Moscow State University; Technical University of Munich
RP VOIGT, K (corresponding author), GSF FORSCHUNGSZENTRUM UMWELT & GESUNDHEIT GMBH,W-8042 NEUHERBERG,GERMANY.
CR MUCKE W, 1986, TOXICOL ENVIRON CHEM, V13, P129, DOI 10.1080/02772248609357175
   MUCKE W, 1989, Z LEBENSM UNTERS FOR, V189, P99, DOI 10.1007/BF01332939
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   1991, BEDIENUNGSANLEITUNG
   1986, EXISTING CHEM SYSTEM
NR 9
TC 5
Z9 5
U1 0
U2 0
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB
SN 0045-6535
J9 CHEMOSPHERE
JI Chemosphere
PD APR
PY 1992
VL 24
IS 7
BP 857
EP 866
DI 10.1016/0045-6535(92)90005-C
PG 10
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology
GA HM303
UT WOS:A1992HM30300004
DA 2024-09-05
ER
PT J
AU Raman, R
   Aljafari, R
   Venkatesh, V
   Richardson, V
AF Raman, Raji
   Aljafari, Ruba
   Venkatesh, Viswanath
   Richardson, Vernon
TI Mixed-methods research in the age of analytics, an exemplar leveraging
   sentiments from news articles to predict firm performance
SO INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT
LA English
DT Article
DE Sentiments; Machine learning; Abnormal returns; Econometrics
ID INFORMATION-CONTENT; STOCK; IMPACT; MEDIA; TEXT
AB Investors and companies have always aspired to make informed investment decisions by using diverse information sources. With the explosion of information sources on the web and emergence of predictive analytics, many investors moved beyond traditional financial measures, as key predictors of firm performance, to textual content from analysts' reports. Empirical research suggests that these information sources complement each other by providing a clear picture of firm performance, but remains silent on the role of additional textual content that continues to emerge and reach more potential investors on the web. We build on this line of research to examine the effect of textual content from business journals in conjunction with summary measures on cumulative abnormal returns. We use sentiment analysis with machine learning and econometrics methods to examine content extracted from textual articles about S&P 500 index companies that are published in the Wall Street Journal (years 2013-2016). Textual analysis of business journals in conjunction with quantitative measures revealed direct and interaction effects on abnormal returns over time. We also tested for robustness by replicating the analysis with different variable operationalization and observe consistent patterns. Relative to positive sentiments, negative sentiments have more profound effects on cumulative abnormal returns. The effect of positive sentiments becomes weaker when past quantitative measures are high. As information sources continue to emerge on the web, this work makes key contributions to the practice of sentiment analysis in financial markets.
C1 [Raman, Raji; Venkatesh, Viswanath] Virginia Tech, Pamplin Coll Business, Blacksburg, VA 24061 USA.
   [Aljafari, Ruba] Univ Pittsburgh, Katz Grad Sch Business, Pittsburgh, PA USA.
   [Richardson, Vernon] Univ Arkansas, Walton Coll Business, Fayetteville, AR USA.
C3 Virginia Polytechnic Institute & State University; Pennsylvania
   Commonwealth System of Higher Education (PCSHE); University of
   Pittsburgh; University of Arkansas System; University of Arkansas
   Fayetteville
RP Venkatesh, V (corresponding author), Virginia Tech, Pamplin Coll Business, Blacksburg, VA 24061 USA.
EM rajiraman@vt.edu; raljafari@katz.pitt.edu; vvenkatesh@vvenkatesh.us;
   vrichardson@walton.uark.edu
RI Venkatesh, Viswanath/ABD-9343-2020
OI Venkatesh, Viswanath/0000-0001-8473-376X
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NR 42
TC 5
Z9 5
U1 5
U2 39
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0268-4012
EI 1873-4707
J9 INT J INFORM MANAGE
JI Int. J. Inf. Manage.
PD JUN
PY 2022
VL 64
AR 102451
DI 10.1016/j.ijinfomgt.2021.102451
EA FEB 2022
PG 11
WC Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science
GA 0Z8FW
UT WOS:000791308400008
OA Green Accepted, hybrid
DA 2024-09-05
ER
PT J
AU Bhatt, PC
   Lu, TC
AF Bhatt, Priyanka C. C.
   Lu, Tzu-Chuen
TI Identifying Firm Significance and Positions in the Patent Innovation
   Based on Centrality Measures' Clustering Approach
SO IEEE ACCESS
LA English
DT Article
DE Patents; Technological innovation; Organizations; Knowledge engineering;
   Position measurement; Trajectory; Research and development; Patent
   analysis; innovation assessment; k-means clustering; patent centrality
   analysis; social network analysis
ID TECHNOLOGICAL CONVERGENCE; NETWORK
AB Organizations strive to achieve technological competence in the current era of inevitable technological progress. One way to measure the adaptability of firms to huge technological shifts is through various parameters, including patenting activities. This study presents a method for identifying the significance of firms in an innovation network using patent citation analysis and centrality measures. Specifically, the study employs k-means clustering to classify firms into similar clusters based on network-based centrality measures such as betweenness, closeness, and eigenvector centrality. The study then develops a cluster relational network by establishing a cluster adjacency network and identifying firm positions within and between clusters. By examining the relationship between clusters, the cluster network identifies the significance of firms. The study identifies four positions, namely, leader, follower, knowledge inertia, and significantly emerging, that align with the status of firms in patenting innovation capability. The method is implemented using blockchain technology as a case study. The novelty of the study lies in the structured approach to identifying firm significance by adding another layer of adjacency network to existing patent citation analysis techniques.
C1 [Bhatt, Priyanka C. C.; Lu, Tzu-Chuen] Chaoyang Univ Technol, Dept Informat Management, Taichung 413, Taiwan.
C3 Chaoyang University of Technology
RP Lu, TC (corresponding author), Chaoyang Univ Technol, Dept Informat Management, Taichung 413, Taiwan.
EM tclu@cyut.edu.tw
OI Lu, Tzu-Chuen/0000-0001-7305-4622; Bhatt, Priyanka
   Chand/0000-0001-5638-6844
FU Ministry of Science and Technology (MOST), Taiwan, Republic of China
   [MOST 109-2221-E-324-025-MY3]
FX This work was supported in part by the Ministry of Science and
   Technology (MOST), Taiwan, Republic of China, under Grant MOST
   109-2221-E-324-025-MY3.
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TC 1
Z9 1
U1 11
U2 36
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2023
VL 11
BP 30515
EP 30528
DI 10.1109/ACCESS.2023.3261331
PG 14
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA D0TP5
UT WOS:000965940000001
OA gold
DA 2024-09-05
ER
PT J
AU Saha, S
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   Mathur, A
   Narsimhamurthy, AM
AF Saha, Snehanshu
   Jangid, Neelam
   Mathur, Archana
   Narsimhamurthy, Anand M.
TI DSRS: Estimation and forecasting of journal influence in the science and
   technology domain via a lightweight quantitative approach
SO COLLNET JOURNAL OF SCIENTOMETRICS AND INFORMATION MANAGEMENT
LA English
DT Article
DE Journal Influence Score (JIS); Downselection with Regression and
   Significance scheme (DSRS); Multiple Linear Regression (MLR);
   Clustering; Significance test; Internationality; Principal
   representative features
ID IMPACT; INTERNATIONALITY
AB The evaluation of journals based on their influence is of interest for numerous reasons. Various methods of computing a score have been proposed for measuring the scientific influence of scholarly journals. Typically the computation of any of these scores involves compiling the citation information pertaining to the journal under consideration. This involves significant overhead since the article citation information of not only the journal under consideration but also that of other journals for the recent few years need to be stored. Our work is motivated by the idea of developing a computationally lightweight approach that does not require any data storage, yet yields a score which is useful for measuring the importance of journals. In this paper, a regression analysis based method is proposed to calculate Journal Influence Score. Proposed model is validated using historical data from the SCImago portal. The results show that the error is small between rankings obtained using the proposed method and the SCImago Journal Rank, thus proving that the proposed approach is a feasible and effective method of calculating scientific impact of journals.
C1 [Saha, Snehanshu; Jangid, Neelam; Mathur, Archana] PESIT South Campus, Dept Comp Sci & Engn, Bangalore 560100, Karnataka, India.
   [Narsimhamurthy, Anand M.] BITS Hyderabad, Hyderabad, Andhra Pradesh, India.
C3 PES University; Birla Institute of Technology & Science Pilani (BITS
   Pilani)
RP Saha, S (corresponding author), PESIT South Campus, Dept Comp Sci & Engn, Bangalore 560100, Karnataka, India.
EM snehanshusaha@pes.edu; neelu.jangid88@gmail.com; archanamathur@pes.edu;
   anand@hyderabad.bits-pilani.ac.in
RI /AAD-2304-2020
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DA 2024-09-05
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PT J
AU Patkar, V
AF Patkar, Vivek
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LA English
DT Article
DE Artificial intelligence; bibliographic control; information
   architecture; knowledge representation; ontology evaluation; semantic
   Web; social tagging
AB To facilitate access to relevant documents and information has been the core of the library and information science (LIS) profession. In this regard tools like classification, cataloguing, and indexing formed the basis of library practice for a long time. These served particularly well for the material that was predominantly in the print form and required physical location for storage. New information sources, however, in contrast are increasingly in the electronic or digital form and stored on medium like computer hard disks requiring completely different strategy for access and management. Extension of the traditional bibliographic control tools as well as construction of new tools has therefore become pertinent. Ontology is one of the latest tools in this context. The paper discusses progress of information organising tools culminating in ontology, highlights the commonality of the concept of ontology and its applications among the fields of philosophy, computer science and LIS. It also discusses the select features of ontology development in practice and directions for features of ontology development in practice and directions for further
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NR 41
TC 4
Z9 4
U1 0
U2 1
PU DEFENCE SCIENTIFIC INFORMATION DOCUMENTATION CENTRE
PI DELHI
PA METCALFE HOUSE, DELHI 110054, INDIA
SN 0974-0643
EI 0976-4658
J9 DESIDOC J LIB INF TE
JI DESIDOC J. Lib. Inf. Technol.
PD MAR
PY 2011
VL 31
IS 2
SI SI
BP 90
EP 102
DI 10.14429/djlit.31.2.861
PG 13
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA V9N1L
UT WOS:000422463200004
DA 2024-09-05
ER
PT J
AU Bryda, G
   Costa, AP
AF Bryda, Grzegorz
   Costa, Antonio Pedro
TI Qualitative Research in Digital Era: Innovations, Methodologies and
   Collaborations
SO SOCIAL SCIENCES-BASEL
LA English
DT Article
DE CAQDAS; artificial intelligence; digital methods; digital skills;
   collaborative analysis
ID COMPETENCE; STRATEGIES; PROJECT; SKILLS
AB The differentiation of contemporary approaches to qualitative data analysis can seem daunting even for experienced social science researchers. Especially when they move forward in the data analysis process from general analytical strategies used in qualitative research to more specific approaches for different types of qualitative data, including interviews, text, audio, images, videos, and so-called virtual data, by discovering the domain ontology of the qualitative research field, we see that there are more than twice as many different classes of data analysis methods as qualitative research methods. This article critically reflects on qualitative research and the qualitative computer data analysis process, emphasising its significance in harnessing digital opportunities and shaping collaborative work. Using our extensive analytical and research project experience, the last research results, and a literature review, we try to show the impact of new technologies and digital possibilities on our thinking. We also try to do the qualitative data analysis. The essence of this procedure is a dialectical interplay between the new world of digital technology and the classic methodology. The use of digital possibilities in qualitative research practices shapes the researcher's identity and their analytical and research workshop. Moreover, it teaches collaborative thinking and teamwork and fosters the development of new analytical, digital, and Information Technology (IT) skills. Imagining contemporary qualitative research and data analysis in the humanities and social sciences is difficult. Opening to modern technologies in computer-based qualitative data analysis shapes our interpretation frameworks and changes the optics and perception of research problems.
C1 [Bryda, Grzegorz] Jagiellonian Univ, Inst Sociol, CAQDAS TM Lab, PL-31007 Krakow, Poland.
   [Costa, Antonio Pedro] Univ Aveiro, Res Ctr Didact & Technol Educ Trainers, Dept Educ & Psychol, P-3810193 Aveiro, Portugal.
C3 Jagiellonian University; Universidade de Aveiro; Centro Investigacao
   Didatica Tecnologia Formacao Formadores (CIDTFF)
RP Bryda, G (corresponding author), Jagiellonian Univ, Inst Sociol, CAQDAS TM Lab, PL-31007 Krakow, Poland.
EM grzegorz.bryda@uj.edu.pl; pcosta@ua.pt
RI Costa, António Pedro/M-4494-2016
OI Costa, António Pedro/0000-0002-4644-5879; Bryda,
   Grzegorz/0000-0002-8892-099X
FU Narodowe Centrum Nauki (Poland) [2016/23/B/HS6/00301]; national funds
   through FCT-Fundacao para a Ciencia e a Tecnologia (Portugal)
   [CDL-CTTRI-248-SGRH/2022]; CIDTFF [UIDB/00194/2020, UIDP/00194/2020]
FX The work of the first author was entitled "The domain ontology as a
   model of knowledgerepresentation about the contemporary field of
   qualitative research" and financed from 2017 to 2021 by Narodowe Centrum
   Nauki (Poland, Project No. 2016/23/B/HS6/00301). The work of the
   secondauthor is funded by national funds through FCT-Fundacao para a
   Ciencia e a Tecnologia (Portugal), under the Scientific Employment
   Stimulus-Institutional Call-[CDL-CTTRI-248-SGRH/2022] and the CIDTFF
   (projects UIDB/00194/2020 and UIDP/00194/2020).
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NR 61
TC 3
Z9 3
U1 8
U2 14
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2076-0760
J9 SOC SCI-BASEL
JI Soc. Sci.-Basel
PD OCT
PY 2023
VL 12
IS 10
AR 570
DI 10.3390/socsci12100570
PG 17
WC Social Sciences, Interdisciplinary
WE Emerging Sources Citation Index (ESCI)
SC Social Sciences - Other Topics
GA X6EZ8
UT WOS:001099375600001
OA gold
DA 2024-09-05
ER
PT J
AU Dzikowicz, DJ
   Carey, MG
AF Dzikowicz, Dillon J.
   Carey, Mary G.
TI The Use of Interactive Technology to Improve Student Accuracy on
   Electrocardiographic Interpretation
SO NURSING EDUCATION PERSPECTIVES
LA English
DT Article
DE Electrocardiography (ECG) Instruction; Online Learning; Research
   Evaluation; Teaching and Learning; Technology in Education
AB Electrocardiography (ECG) instruction relies heavily on memorization of interpretation rules and lacks opportunities for hands-on practice. Consequently, nursing students struggle with ECG interpretation. In an online undergradute nursing course, we implemented interactive technology to facilitate kinesthetic pedagogy. Accuracy was evaluated at midterm and during final assessments by two experts using a standardized rubric. Students who engaged with interactive technology at both assessments demonstrated consistent accuracy of ECG interpretation; students who did not failed to demonstrate consistent accuracy with ECG interpretation. Incorporating interactive technology to facilitate psychomotor learning may be essential in improving the accuracy of ECG interpretation.
C1 [Dzikowicz, Dillon J.] Univ Rochester Med Ctr, Rochester, NY 14627 USA.
   [Dzikowicz, Dillon J.; Carey, Mary G.] Univ Rochester, Sch Nursing, Rochester, NY 14627 USA.
C3 University of Rochester; University of Rochester
RP Dzikowicz, DJ (corresponding author), Univ Rochester Med Ctr, Rochester, NY 14627 USA.; Dzikowicz, DJ (corresponding author), Univ Rochester, Sch Nursing, Rochester, NY 14627 USA.
EM illon_dzikowicz@urmc.rochester.edu; Mary_Carey@URMC.Rochester.edu
OI Carey, Mary/0000-0002-5013-3464
CR Brooks CA, 2016, AM J CRIT CARE, V25, P61, DOI 10.4037/ajcc2016556
   Dzikowicz D. J., 2020, MEDSURG NURSING, V29, P263
   Funk M, 2017, CIRC-CARDIOVASC QUAL, V10, DOI 10.1161/CIRCOUTCOMES.116.003132
   Rubenstein CD, 2017, NURS EDUC, V42, P85, DOI 10.1097/NNE.0000000000000293
   Schwimer D, 2022, CRIT CARE NURSE, V42, P33, DOI 10.4037/ccn2022465
   Suba S, 2022, AM J CRIT CARE, V31, P83, DOI 10.4037/ajcc2022748
   Viljoen CA, 2021, BMC MED EDUC, V21, DOI 10.1186/s12909-021-02854-x
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   Weeks KW, 2019, NURSE EDUC PRACT, V37, P29, DOI 10.1016/j.nepr.2019.04.010
NR 9
TC 0
Z9 0
U1 0
U2 0
PU LIPPINCOTT WILLIAMS & WILKINS
PI PHILADELPHIA
PA TWO COMMERCE SQ, 2001 MARKET ST, PHILADELPHIA, PA 19103 USA
SN 1536-5026
EI 1943-4685
J9 NURS EDUC PERSPECT
JI Nurs. Educ. Perspect.
PD JUL-AUG
PY 2023
VL 44
IS 4
BP 247
EP 249
DI 10.1097/01.NEP.0000000000001078
PG 3
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA L7SL8
UT WOS:001025220600011
PM 36729816
DA 2024-09-05
ER
PT J
AU Wang, R
   Li, SJ
   Yin, Q
   Zhang, J
   Yao, RJ
   Wu, O
AF Wang, Rui
   Li, Shijie
   Yin, Qing
   Zhang, Ji
   Yao, Rujing
   Wu, Ou
TI Improved PageRank and New Indices for Academic Impact Evaluation Using
   AI Papers as Case Studies
SO JOURNAL OF INFORMATION SCIENCE
LA English
DT Article
DE Group evaluation; PageRank; Paper evaluation
ID H-INDEX; CITATION; PERFORMANCE; INDICATOR; ARTICLES; SCIENCE
AB Evaluating academic papers and groups is important in scholar evaluation and literature retrieval. However, current evaluation indices, which pay excessive attention to the citation number rather than the citation importance and unidirectionality, are relatively simple. This study proposes new evaluation indices for papers and groups. First, an improved PageRank (PR) algorithm introducing citation importance is proposed to obtain a new citation-based paper index (CPI) via a pre-ranking and fine-tuning strategy. Second, to evaluate the paper's influence inside and outside its research field, the focus citation-based paper index (FCPI) and diversity citation-based paper index (DCPI) are proposed based on topic similarity and diversity, respectively. Third, aside from the statistical indices for academic papers, we propose a foreign academic degree of dependence (FAD) to characterise the dependence between two academic groups. Finally, artificial intelligence (AI) papers from 2005 to 2019 are utilised for a case study.
C1 [Wang, Rui; Li, Shijie; Yin, Qing; Yao, Rujing; Wu, Ou] Tianjin Univ, Tianjin, Peoples R China.
   [Zhang, Ji] Zhejiang Lab, Hangzhou, Peoples R China.
C3 Tianjin University; Zhejiang Laboratory
RP Wu, O (corresponding author), Tianjin Univ, Ctr Appl Math, 92 Weijin Rd, Tianjin 300072, Peoples R China.
EM wuou@tju.edu.cn
FU ZJFund [2019KB0AB03]; NSFC [62076178]; TJ-NSF [19JCZDJC31300,
   19ZXAZNGX00050]
FX The author(s) disclosed receipt of the following financial support for
   the research, authorship, and/or publication of this article: This study
   is supported by ZJFund 2019KB0AB03, NSFC 62076178, TJ-NSF
   (19JCZDJC31300, 19ZXAZNGX00050).
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   Yuan J., P 24 INT C WORLD WID
   Zhang F, 2020, J INFORMETR, V14, DOI 10.1016/j.joi.2020.101035
NR 39
TC 2
Z9 2
U1 7
U2 51
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 0165-5515
EI 1741-6485
J9 J INF SCI
JI J. Inf. Sci.
PD JUN
PY 2024
VL 50
IS 3
BP 690
EP 702
DI 10.1177/01655515221105038
EA JUL 2022
PG 13
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA SS2E6
UT WOS:000828846900001
DA 2024-09-05
ER
PT J
AU Brand, C
   Ward, F
   MacDonagh, N
   Cunningham, S
   Timulak, L
AF Brand, Charles
   Ward, Fiona
   MacDonagh, Niamh
   Cunningham, Sharon
   Timulak, Ladislav
TI A national evaluation of the Irish public health counselling in primary
   care service- examination of initial effectiveness data
SO BMC PSYCHIATRY
LA English
DT Article
DE Psychotherapy research; Primary care counselling; Practice-based
   evidence; National counselling service evaluation; Logistic regression;
   Multi-level modelling
ID DOSE-EFFECT RELATIONS; PSYCHOLOGICAL THERAPY; CORE-OM; PSYCHOTHERAPY;
   RECOVERY; OUTCOMES; DURATION
AB Background The Counselling in Primary Care service (CIPC) is the first and only nationally available public counselling service in the Republic of Ireland. This study provides initial data for the effectiveness of short-term psychotherapy delivered in a primary care setting in Ireland for the first time. Method A practice-based observational research approach was employed to examine outcome data from 2806 clients receiving therapy from 130 therapists spread over 150 primary care locations throughout Ireland. Pre-post outcomes were assessed using the CORE-OM and reliable and clinically significant change proportions. Binary logistic regression examined the effect of pre therapy symptom severity on the log odds of recovering. Six and 12 month follow up data from a subsample of 276 clients were also analysed using growth curve analysis. Results Of 14,156 referred clients, 5356 presented for assessment and 52.3% (N = 2806) consented to participate. Between assessment and post-therapy a large reduction in severity of symptoms was observed- Cohen's d = 0.98. Furthermore, 47% of clients achieved recovery,a further 15.5% reliably improved, 2.7% reliably deteriorated and34.7% showed no reliable improvement. Higher initial severity was associated with less chance of recovering at post-therapy. Significant gains were maintained between assessment and12 months after therapy- Cohen's d = 0.50. Conclusions Outcomes for clients in the CIPC service compared favourably with large scale counselling and psychotherapy services in jurisdictions in the U.K., the U.S.A., Norway and Sweden. This study expands the international primary care psychotherapy research base to include the entire Republic of Ireland jurisdiction.
C1 [Brand, Charles; Timulak, Ladislav] Trinity Coll Dublin, Sch Psychol, Dublin 2, Ireland.
   [Brand, Charles; Timulak, Ladislav] Counselling Primary Care Natl Evaluat, Hlth Serv Execut, 19 Upper Ormond Quay, Dublin 2, Ireland.
   [Ward, Fiona] Hlth Serv Execut, 34 Brews Hill, Navan, Meath, Ireland.
   [MacDonagh, Niamh] Primary Care Ctr, Hlth Serv Execut, 1st Floor Junct House,Airton Rd, Dublin, Ireland.
   [Cunningham, Sharon] Hlth Serv Execut, Unit 8A Brulington Business Pk,Srah Ave, Tullamore, Offaly, Ireland.
C3 Trinity College Dublin
RP Brand, C (corresponding author), Trinity Coll Dublin, Sch Psychol, Dublin 2, Ireland.; Brand, C (corresponding author), Counselling Primary Care Natl Evaluat, Hlth Serv Execut, 19 Upper Ormond Quay, Dublin 2, Ireland.
EM brandc@tcd.ie
RI Timulak, Ladislav/P-6881-2017
OI Timulak, Ladislav/0000-0003-2785-0753; BRAND,
   CHARLES/0000-0001-9831-1189
FU Irish Research Council [EBPPG12015, 124]
FX This work was supported by the Irish Research Council (Project ID:
   EBPPG12015 vertical bar 124). The funding body was not involved in study
   design, data collection, analyses, data interpretation, or manuscript
   preparation.
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NR 40
TC 4
Z9 4
U1 0
U2 0
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
EI 1471-244X
J9 BMC PSYCHIATRY
JI BMC Psychiatry
PD MAY 3
PY 2021
VL 21
IS 1
AR 227
DI 10.1186/s12888-021-03226-x
PG 10
WC Psychiatry
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Psychiatry
GA SK3SS
UT WOS:000656139500006
PM 33941127
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU SLATER, PB
AF SLATER, PB
TI HIERARCHICAL-CLUSTERING OF MATHEMATICAL JOURNALS BASED UPON CITATION
   MATRICES
SO SCIENTOMETRICS
LA English
DT Article
RP SLATER, PB (corresponding author), UNIV CALIF SANTA BARBARA,COMMUNITY & ORG RES INST,SANTA BARBARA,CA 93106, USA.
RI Slater, Paul B/U-1847-2018
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NR 9
TC 12
Z9 12
U1 0
U2 1
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0138-9130
J9 SCIENTOMETRICS
JI Scientometrics
PY 1983
VL 5
IS 1
BP 55
EP 58
DI 10.1007/BF02097177
PG 4
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Social Science Citation Index (SSCI); Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Information Science & Library Science
GA QD335
UT WOS:A1983QD33500003
DA 2024-09-05
ER
PT C
AU Tsuji, K
   Takizawa, N
   Sato, S
   Ikeuchi, U
   Ikeuchi, A
   Yoshikane, F
   Itsumura, H
AF Tsuji, Keita
   Takizawa, Nobuya
   Sato, Sho
   Ikeuchi, Ui
   Ikeuchi, Atsushi
   Yoshikane, Fuyuki
   Itsumura, Hiroshi
BE Giannakopoulos, G
   Sakas, DP
   Vlachos, DS
   KyriakiManessi, D
TI Book Recommendation Based on Library Loan Records and Bibliographic
   Information
SO 3RD INTERNATIONAL CONFERENCE ON INTEGRATED INFORMATION (IC-ININFO)
SE Procedia Social and Behavioral Sciences
LA English
DT Proceedings Paper
CT 3rd International Conference on Integrated Information (IC-ININFO)
CY SEP 05-09, 2013
CL Prague, CZECH REPUBLIC
DE Book Recommendation; Recommender System; Library Loan Records; Support
   Vector Machine; SVM
AB In order to show the effectiveness of using (a) library loan records and (b) information about book contents as a basis for book recommendations, we entered various data into a support vector machine (SVM), used it to recommend books to subjects, and asked them for evaluations of the recommendations that were given. The data that we used were (1) confidence and support with an association rule that was based on the loan records, (2) similarities between book titles, (3) matches/mismatches between the Nippon Decimal Classification (NDC) categories of the books, and (4) similarities between the outlines of the books in the BOOK Database. The subjects were 32 students who belonged to T University. The books that we recommended and the loan records that we used were obtained from the T University Library. The results showed that the combinations of (1), (2), (3) and (1), (2) were rated more favorably by the subjects than the other combinations. However, the books that were recommended by Amazon were rated even more favorably by the subjects. This is a topic for further research. (C) 2014 Elsevier Ltd. This is an open under the CC BY-NC-ND license (http://creavativecommons.org/licenses/by-nc-nd/3.0/). Selection and peer-review under responsibility of the 3rd International Conference on Integrated Information.
C1 [Tsuji, Keita; Ikeuchi, Atsushi; Yoshikane, Fuyuki; Itsumura, Hiroshi] Univ Tsukuba, Fac Lib Informat & Media Sci, Tsukuba, Ibaraki 3058550, Japan.
   [Takizawa, Nobuya] Univ Tsukuba, Coll Knowledge & Lib Sci, Sch Informat, Tsukuba, Ibaraki 3058550, Japan.
   [Sato, Sho] Doshisha Univ, Fac Social Studies, Kamigyo Ku, Kyoto 6028580, Japan.
   [Ikeuchi, Ui] Univ Tsukuba, Grad Sch Lib Informat & Media Studies, Tsukuba, Ibaraki 3058550, Japan.
C3 University of Tsukuba; University of Tsukuba; Doshisha University;
   University of Tsukuba
RP Tsuji, K (corresponding author), Univ Tsukuba, Fac Lib Informat & Media Sci, 1-2 Kasuga, Tsukuba, Ibaraki 3058550, Japan.
EM keita@slis.tsukuba.ac.jp
RI Ikeuchi, Ui/M-5494-2019; Ikeuchi, Ui/N-8436-2015
OI Ikeuchi, Ui/0000-0002-5680-1881; Ikeuchi, Ui/0000-0002-5680-1881
CR Chen CC, 2007, ELECTRON LIBR, V25, P711, DOI 10.1108/02640470710837137
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   Harada T., 2009, DIGITAL LIB, V36, P22
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   Whitney C., 2006, D LIB MAGAZINE, V12
   Yongcheng Luo, 2009, Proceedings of the 2009 Second International Workshop on Computer Science and Engineering (WCSE 2009), P323, DOI 10.1109/WCSE.2009.822
NR 9
TC 13
Z9 16
U1 2
U2 17
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA SARA BURGERHARTSTRAAT 25, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 1877-0428
J9 PROCD SOC BEHV
PY 2014
VL 147
BP 478
EP 486
DI 10.1016/j.sbspro.2014.07.142
PG 9
WC Information Science & Library Science
WE Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Information Science & Library Science
GA BD4GO
UT WOS:000360703000065
OA gold
DA 2024-09-05
ER
PT C
AU Wilson, M
AF Wilson, Marianne
BE Kamps, J
   Goeuriot, L
   Crestani, F
   Maistro, M
   Joho, H
   Davis, B
   Gurrin, C
   Kruschwitz, U
   Caputo, A
TI Designing Useful Conversational Interfaces for Information Retrieval in
   Career Decision-Making Support
SO ADVANCES IN INFORMATION RETRIEVAL, ECIR 2023, PT III
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT 45th European Conference on Information Retrieval (ECIR)
CY APR 02-06, 2023
CL Dublin, IRELAND
DE Conversational information retrieval; Applied NLP;
   Research-through-design; Social impact; Evaluation Ethics
AB The proposal is an interdisciplinary problem-focused study to explore the usefulness of conversational information retrieval (CIR) in a complex domain. Aresearch-through-design methodology will be used to identify the informational, practical, affective, and ethical requirements for a CIR system in the specific context of Career Education, Information, Advice & Guidance (CEIAG) services for young people in Scotland. Later phases of the research will use these criteria to identify appropriate techniques in the literature, and design and evaluate artefacts intended to meet these. This research will use an interdisciplinary approach to further understanding on the use and limitations of dialogue systems as intermediaries for information retrieval where there are awide range of possible information tasks and specific users' needs may be ambiguous.
C1 [Wilson, Marianne] Edinburgh Napier Univ, 10 Colinton Rd, Edinburgh EH10 5DT, Scotland.
C3 Edinburgh Napier University
RP Wilson, M (corresponding author), Edinburgh Napier Univ, 10 Colinton Rd, Edinburgh EH10 5DT, Scotland.
EM m.wilson2@napier.ac.uk
OI Wilson, Marianne Clare/0000-0002-4780-2401
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NR 58
TC 0
Z9 0
U1 0
U2 0
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 0302-9743
EI 1611-3349
BN 978-3-031-28240-9; 978-3-031-28241-6
J9 LECT NOTES COMPUT SC
PY 2023
VL 13982
BP 482
EP 488
DI 10.1007/978-3-031-28241-6_56
PG 7
WC Computer Science, Information Systems; Computer Science, Software
   Engineering; Computer Science, Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BV1RO
UT WOS:000995495200056
DA 2024-09-05
ER
PT C
AU Kang, H
   Yoo, SJ
   Han, D
AF Kang, Hanhoon
   Yoo, Seong Joon
   Han, Dongil
BE Ko, FIS
   DalKwack, K
   Hwang, S
   Kawata, S
   Chen, YW
TI Social Ranking of Medical Institutions Based on Social Information
SO 2011 6TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCES AND CONVERGENCE
   INFORMATION TECHNOLOGY (ICCIT)
LA English
DT Proceedings Paper
CT 6th International Conference on Computer Sciences and Convergence
   Information Technology (ICCIT)
CY NOV 29-DEC 01, 2011
CL Jeju Island, SOUTH KOREA
DE medical institutions; ranking; sentiment analysis
AB In this paper we propose a method with which to rank medical institutions by utilizing the social information provided by social network services (SNS). Furthermore, the gap between the rankings proposed in this paper and the rankings based on the existing method provided by USNews.com is analyzed, and a need for social marketing by medical institutions is proposed. USNews.com ranks medical institutions each year and publishes the data through its site. Here, the rankings are determined based on the information generated offline, based on the ranking criteria determined by a specific group, in order to provide the applicable information. Consequently, the rankings perceived by the patients who actually visit the medical institutions may be somewhat different from the rankings provided by USNews.com. For this paper the social information related to medical institutions was crawled from the SNS sites in order to rank the medical institutions based on the information generated by many users in the social media, and these rankings were compared to the ranking results provided by USNews.com. The data used for the experiment comprised the basic information on the 892 medical institutions crawled from USNews.com and the social information crawled from the SNS sites. Social information was available for only 443 institutions. The rankings were computed for these institutions based on the method proposed in this paper, so as to compare to the existing results. According to the ranking results for 316 institutions, the rankings improved from one place to 846 places, with the average improvement being 330 places.
C1 [Kang, Hanhoon; Yoo, Seong Joon; Han, Dongil] Sejong Univ, Dept Comp Engn, Seoul 143747, South Korea.
C3 Sejong University
RP Yoo, SJ (corresponding author), Sejong Univ, Dept Comp Engn, 98 Gunja, Seoul 143747, South Korea.
EM sjyoo@sejong.ac.kr
RI YOO, SEONG JOON/AAB-3791-2021
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NR 31
TC 0
Z9 0
U1 0
U2 1
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-89-88678-55-8
PY 2012
BP 155
EP 162
PG 8
WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Engineering
GA BCC83
UT WOS:000309767600031
DA 2024-09-05
ER
PT J
AU Zwilling, M
   Eckhaus, E
AF Zwilling, Moti
   Eckhaus, Eyal
TI Do managers learn more about successful project management methods from
   articles in high impact factor journals?
SO HUMAN SYSTEMS MANAGEMENT
LA English
DT Article
DE Project management; project procedures; prospect theory; project
   management decision making; machine learning
ID PERFORMANCE; BENCHMARKING; FRAMEWORK; EDUCATION; SYSTEM; IMPROVEMENT;
   RISKS
AB BACKGROUND: In recent years, the need to develop performance-based measurement systems to improve project management outcomes has dramatically increased. Managers still take various risks during the course of managing projects which lead to ineffective decision making. A range of theories discuss such behaviors. These theories demonstrate that the discussion of risk embedded in non-optimal decision-making processes is based on theory rather than practical knowledge. However, various components of project management can be derived from academic best practices for decision making.
   OBJECTIVE: The study aims to explore whether articles in high impact journals tend to embody practical, rather than theoretical, knowledge thus closing the gap between academia and industry. The study is based on SEM and various machine learning classification methods.
   METHOD: The study was conducted using an NLP analysis of 1461 academic journals in the field of project management.
   RESULTS: Results show a significant positive relationship between the success of projects and the impact of new practical procedures. In contrast, a negative correlation was found between theories that use non-practical processes of effective project management.
   CONCLUSION: Managers can learn about newmethods for project management from articles in high impact factor journals.
C1 [Zwilling, Moti; Eckhaus, Eyal] Ariel Univ, Dept Econ & Business Adm, Ramat Hagolan 65 St, Ariel, Israel.
C3 Ariel University
RP Zwilling, M (corresponding author), Ariel Univ, Dept Econ & Business Adm, Ramat Hagolan 65 St, Ariel, Israel.
EM motiz@ariel.ac.il
RI Eckhaus, Eyal/AAX-2557-2020; Zwilling, Moti/AAD-3965-2020
OI Eckhaus, Eyal/0000-0002-1815-0045; Zwilling, Moti/0000-0001-7628-8889
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NR 64
TC 0
Z9 0
U1 0
U2 21
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 0167-2533
EI 1875-8703
J9 HUM SYST MANAGE
JI Hum. Syst. Manag.
PY 2022
VL 41
IS 1
BP 119
EP 141
DI 10.3233/HSM-211194
PG 23
WC Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA YU8KK
UT WOS:000752285400009
DA 2024-09-05
ER
PT C
AU Kehoe, AK
   Torvik, VI
AF Kehoe, Adam K.
   Torvik, Vetle I.
GP IEEE
TI Predicting Medical Subject Headings Based on Abstract Similarity and
   Citations to MEDLINE Records
SO 2016 IEEE/ACM JOINT CONFERENCE ON DIGITAL LIBRARIES (JCDL)
SE ACM-IEEE Joint Conference on Digital Libraries JCDL
LA English
DT Proceedings Paper
CT 16th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL)
CY JUN 19-23, 2016
CL Newark, NJ
DE Controlled vocabularies; Medical subject headings; Machine Learning;
   Curation of bibliographic databases
ID MESH; ASSIGNMENT
AB We describe a classifier-enhanced nearest neighbor approach to assigning Medical Subject Headings (MeSH (R)) to unlabeled documents using a combination of abstract similarities and direct citations to labeled MEDLINE records. The approach frames the classification problem by decomposing it into sets of siblings in the MeSH hierarchy (e.g., training a classifier for predicting "Heterocyclic Compounds, 2-Ring" vs. other "Heterocyclic Compounds"). Preliminary experiments using a small but diverse set of MeSH terms shows the highest performance when using both abstracts and citations compared to each alone, and coupled with a non-naive classifier: 90+% precision and recall with 10-fold cross-validation. NLM's Medical Text Indexer (MTI) tool achieves similar overall performance but varies more across the terms tested. For example, MTI performs better on "Heterocyclic Compounds, 2-Ring", while our approach performs better on Alzheimer Disease and Neuroimaging. Our approach can be applied broadly to documents with abstracts that are similar to (or cite) MEDLINE abstracts, which would help linking and searching across bibliographic databases beyond MEDLINE.
C1 [Kehoe, Adam K.; Torvik, Vetle I.] Univ Illinois, Grad Sch Lib & Informat Sci, Champaign, IL 61801 USA.
C3 University of Illinois System; University of Illinois Urbana-Champaign
RP Kehoe, AK (corresponding author), Univ Illinois, Grad Sch Lib & Informat Sci, Champaign, IL 61801 USA.
EM kehoe2@illinois.edu; vtorvik@illinois.edu
RI Torvik, Vetle I/A-2327-2008
OI Torvik, Vetle/0000-0002-0035-1850
CR Agarwal S., Patci: A probabilistic citation matcher
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   Wahle Manuel, 2012, AMIA Annu Symp Proc, V2012, P940
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NR 11
TC 2
Z9 3
U1 0
U2 6
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2575-7865
EI 2575-8152
BN 978-1-4503-4229-2
J9 ACM-IEEE J CONF DIG
PY 2016
BP 167
EP 170
DI 10.1145/2910896.2910920
PG 4
WC Computer Science, Interdisciplinary Applications
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BG5HY
UT WOS:000389502300026
DA 2024-09-05
ER
PT C
AU Zhang, HY
   Zhang, J
   Liu, XC
   Yan, GH
   Liu, YZ
AF Zhang, Hongyuan
   Zhang, Jing
   Liu, Xuecheng
   Yan, Guohui
   Liu, Yanzhao
BE Jinhui, L
   Hualong, H
TI Research on method of health assessment about the destruction equipment
   for high-risk hazardous chemical waste
SO SEVENTH INTERNATIONAL CONFERENCE ON WASTE MANAGEMENT AND TECHNOLOGY
   (ICWMT 7)
SE Procedia Environmental Sciences
LA English
DT Proceedings Paper
CT 7th International Conference on Waste Management and Technology (ICWMT)
CY SEP 05-07, 2012
CL Beijing, PEOPLES R CHINA
DE health status; destruc*tion equipment; high-risk hazardous chemical
   wastes; Bayesian Networks; health assessment
AB The destroying tasks of high-risk hazardous chemical waste have a strict request to the health status of destruction equipment. The paper proposes the health status classification method based on time between failures for the destruction of equipment, set up health status assessment model based on Time-varying Bayesian Networks and the time slice, which can take advantage of history fault information and health status monitoring indicator information to health status assessment for the destruction equipment, and which provides a reliable and safe evaluation method. (C) 2012 Selection and/or peer-review under responsibility of Basel Convention Coordinating Centre for Asia and the Pacific and National Center of Solid Waste Management, Ministry of Environmental Protection of China.
C1 [Zhang, Hongyuan; Zhang, Jing; Liu, Xuecheng; Yan, Guohui; Liu, Yanzhao] Inst Chem Def CPLA, Beijing 102205, Peoples R China.
   [Zhang, Hongyuan] Sch Automat Sci & Elect Engn BUAA, Beijing 100083, Peoples R China.
C3 Research Institute of Chemical Defense - China; Beihang University
RP Zhang, J (corresponding author), Inst Chem Def CPLA, Beijing 102205, Peoples R China.
EM zhybeijing@sina.com
RI Chen, Liang/JXX-7887-2024
CR Bing Guangfu, 2010, THEORY PRACTICE SYST, V30
   Bobbio A, 1999, LECT NOTES COMPUT SC, V1698, P310
   Chen Xiaotong, 2005, REL PRACT GUID, P7
   Jia Yunxian, RELIABILITY BASED MA
   People's Liberation Army General Armament Department, 1999, GJB Z108 NONW STAT E
   People's Liberation Army General Armament Department, 1999, GJB Z 299B 98 EXP RE
   Shukla CS, 2001, INT J ADV MANUF TECH, V18, P422, DOI 10.1007/s001700170052
   Wang Guangyan, 2004, THEORY PRACTICE SYST, P76
   Wang Guoping, 2004, Journal of System Simulation, V16, P963
   [吴波 Wu BO], 2009, [计算机测量与控制, Computer Measurement & Control], V17, P2345
NR 10
TC 2
Z9 4
U1 1
U2 9
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA SARA BURGERHARTSTRAAT 25, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 1878-0296
J9 PROCEDIA ENVIRON SCI
PY 2012
VL 16
BP 192
EP 201
DI 10.1016/j.proenv.2012.10.027
PG 10
WC Environmental Sciences
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Environmental Sciences & Ecology
GA BDN49
UT WOS:000314024400026
OA gold
DA 2024-09-05
ER
PT J
AU Ortega, JL
AF Luis Ortega, Jose
TI Disciplinary differences of the impact of altmetric
SO FEMS MICROBIOLOGY LETTERS
LA English
DT Article
DE altmetrics; disciplinary differences; principal component analysis
   (PCA); PlumX; Crossref; citations
ID CITATION; MENDELEY; READERSHIP; TWITTER; METRICS; TWEETS; USAGE
AB The main objective of this work was to group altmetric indicators according to their relationships and detect disciplinary differences with regard to altmetric impact in a set of 3793 research articles published in 2013. Three of the most representative altmetric providers (Altmetric, PlumX and Crossref Event Data) and Scopus were used to extract information about these publications and their metrics. Principal component analysis was used to summarize the information on these metrics and detect groups of indicators. The results show that these metrics can be grouped into three components: social media, gathering metrics from social networks and online media; usage, including metrics on downloads and views; and citations and saves, grouping metrics related to research impact and saves in bookmarking sites. With regard to disciplinary differences, articles in the General category attract more attention from social media, Social Sciences articles have higher usage than Physical Sciences, and General articles are more cited and saved than Health Sciences and Social Sciences articles.
C1 [Luis Ortega, Jose] CSIC, Cybermetr Lab, Serrano 113, Madrid 2006, Spain.
C3 Consejo Superior de Investigaciones Cientificas (CSIC)
RP Ortega, JL (corresponding author), CSIC, Cybermetr Lab, Serrano 113, Madrid 2006, Spain.
EM jortega@orgc.csic.es
OI Ortega, Jose Luis/0000-0001-9857-1511
CR [Anonymous], METRICS 2011
   [Anonymous], RESEARCHGATE
   [Anonymous], BIBLIOMETRIE PRAXIS
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NR 31
TC 25
Z9 25
U1 2
U2 49
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 0378-1097
EI 1574-6968
J9 FEMS MICROBIOL LETT
JI FEMS Microbiol. Lett.
PD APR
PY 2018
VL 365
IS 7
AR fny049
DI 10.1093/femsle/fny049
PG 6
WC Microbiology
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Microbiology
GA GP7TA
UT WOS:000441110500013
PM 29518193
OA Bronze, Green Published
DA 2024-09-05
ER
PT C
AU Ma, YJ
   Hou, YY
   Liu, YS
   Xue, YH
AF Ma, Yongjun
   Hou, Yangyang
   Liu, Yushan
   Xue, Yonghao
GP IEEE
TI Research of Food Safety Risk Assessment Methods based on Big Data
SO PROCEEDINGS OF 2016 IEEE INTERNATIONAL CONFERENCE ON BIG DATA ANALYSIS
   (ICBDA)
LA English
DT Proceedings Paper
CT IEEE International Conference on Big Data Analysis (ICBDA)
CY MAR 12-14, 2016
CL Hangzhou, PEOPLES R CHINA
DE Big Data; Food Safety Risk Assessment; The cascade SVM
AB The risk of food safety is serious in China, there are more and more food safety problems involving complex application of big data. The food safety risk assessment under the background of big data is an important issue. The paper takes dairy productions as an example to explain a method of dairy productions risk assessment using parallel support vector machine in the big data platform. We establish big data platform firstly, parallel the cascade support vector machine in the big data platform to obtain the assessment model secondly and get assessment results by data process according to the features of real-time data at last. The results of experiments show that big data platform can process dairy productions big data real-time. The accuracy of dairy productions safety assessment has improved.
C1 [Ma, Yongjun; Hou, Yangyang; Liu, Yushan; Xue, Yonghao] Tianjin Univ Sci & Technol, Coll Comp Sci & Informat Engn, Tianjin, Peoples R China.
C3 Tianjin University Science & Technology
RP Ma, YJ (corresponding author), Tianjin Univ Sci & Technol, Coll Comp Sci & Informat Engn, Tianjin, Peoples R China.
EM yjma@tust.edu.cn
RI Liu, YuShan/KRO-9598-2024
OI Liu, YuShan/0009-0001-6470-7879
CR [Anonymous], 2012, Hadoop: The definitive guide
   Chongming Wu, 2009, Proceedings of the 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2009), P551, DOI 10.1109/FSKD.2009.784
   Collobert R, 2002, NEURAL COMPUT, V14, P1105, DOI 10.1162/089976602753633402
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   Li Z.W., 2005, J HARBIN ENG U, V26, P643
   Qi Li, 2010, Proceedings 10th International Conference on Intelligent Systems Design and Applications (ISDA 2010), P1131, DOI 10.1109/ISDA.2010.5687033
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   Zhang P.X., 2014, RES SANDSTORM METEOR
   [张巍 Zhang Wei], 2013, [计算机科学, Computer Science], V40, P69
NR 10
TC 1
Z9 1
U1 1
U2 13
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-4673-9591-5
PY 2016
BP 142
EP 146
PG 5
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Engineering
GA BG6HN
UT WOS:000390299100028
DA 2024-09-05
ER
PT J
AU Qin, W
   Lin, QN
AF Qin, Wei
   Lin, Qingna
TI Research on the Fusion Model of Professional Vocal Music Performance
   Voice Care and Artificial Intelligence Technology in Intelligent Medical
   Treatment
SO WIRELESS COMMUNICATIONS & MOBILE COMPUTING
LA English
DT Article
AB Intelligent medical treatment is an important research field in today's world. Artificial intelligence technology is the key factor to construct intelligent medical treatment. In the development of artificial intelligence technology, it is necessary to establish a scientific, systematic, and comprehensive system analysis model, inevitably with certain professional characteristics. At present, in the research of vocal health care in professional vocal music performance, the application of intelligent medical care and vocal health care in professional vocal music performance is studied. According to the DEMATEL-ISM research method, this paper constructs 4 internal and external factors and 16 influencing factors to build a comprehensive and systematic weight analysis model, which provides a theoretical and practical basis for the scientific construction of AI technology algorithms. The aim is to improve the value and research significance of intelligent medical artificial intelligence technology in professional vocal music performance sound care.
C1 [Qin, Wei; Lin, Qingna] Chongqing Univ Posts & Telecommun, Chongqing South Bank, Chongqing 400065, Peoples R China.
C3 Chongqing University of Posts & Telecommunications
RP Lin, QN (corresponding author), Chongqing Univ Posts & Telecommun, Chongqing South Bank, Chongqing 400065, Peoples R China.
EM qinwei@cqupt.edu.cn; linqn@cqupt.edu.cn
OI qin, wei/0000-0001-6598-327X
FU Chongqing Natural Science Foundation (Postdoctoral Fund) Project:
   Development and Research of Interactive Accurate Evaluation Platform for
   Professional Solfeggio Training Based on Reinforcement Learning
   [cstc2021jcyj-bsh0202]
FX This work was financially supported by the Chongqing Natural Science
   Foundation (Postdoctoral Fund) Project: Development and Research of
   Interactive Accurate Evaluation Platform for Professional Solfeggio
   Training Based on Reinforcement Learning (No. cstc2021jcyj-bsh0202,
   host: Qin Wei).
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   Devadas U, 2020, J VOICE, V34, DOI 10.1016/j.jvoice.2018.09.013
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   Flavia Z.A., 2021, J VOICE, V35, P17
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   Gebhardt R. M., 2016, VOICE YELLOW RIVER
   Gunjawate DR, 2017, J VOICE, V31, DOI 10.1016/j.jvoice.2016.06.022
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NR 13
TC 1
Z9 1
U1 4
U2 17
PU WILEY-HINDAWI
PI LONDON
PA ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON, WIT 5HE, ENGLAND
SN 1530-8669
EI 1530-8677
J9 WIREL COMMUN MOB COM
JI Wirel. Commun. Mob. Comput.
PD MAY 9
PY 2022
VL 2022
AR 2947554
DI 10.1155/2022/2947554
PG 7
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA 1Q6SK
UT WOS:000802814700031
OA gold
DA 2024-09-05
ER
PT C
AU Mariani, J
   Francopoulo, G
   Paroubek, P
   Vernier, F
AF Mariani, Joseph
   Francopoulo, Gil
   Paroubek, Patrick
   Vernier, Frederic
GP IEEE
TI REDISCOVERING 50 YEARS OF DISCOVERIES IN SPEECH AND LANGUAGE PROCESSING:
   A SURVEY.
SO 2017 20TH CONFERENCE OF THE ORIENTAL CHAPTER OF THE INTERNATIONAL
   COORDINATING COMMITTEE ON SPEECH DATABASES AND SPEECH I/O SYSTEMS AND
   ASSESSMENT (O-COCOSDA)
LA English
DT Proceedings Paper
CT 20th Conference of the
   Oriental-Chapter-of-the-International-Coordinating-Committee-on-Speech-D
   atabases-and-Speech-I/O-Systems-and-Assessment (O-COCOSDA)
CY NOV 01-03, 2017
CL Seoul, SOUTH KOREA
DE Speech Processing; Natural Language Processing; Text Analytics;
   Bibliometrics; Scientometrics; Informetrics
AB We have created the NLP4NLP corpus to study the content of scientific publications in the field of speech and natural language processing. It contains articles published in 34 major conferences and journals in that field over a period of 50 years (1965-2015). comprising 65.000 documents. gathering 50.000 authors. including 325.000 references and representing approximately 270 million words. Most of these publications are in English. some are in French. German or Russian. Some are open access. others have been provided by the publishers. In order to constitute and analyze this corpus several tools have been used or developed. Some of them use Natural Language Processing methods that have been published in the corpus. hence its name. Numerous manual corrections were necessary. which demonstrated the importance of establishing standards for uniquely identifying authors. publications or resources. We have conducted various studies: evolution over time of the number of articles and authors. collaborations between authors. citations between papers and authors. evolution of research themes and identification of the authors who introduced them. measure of innovation and detection of epistemological ruptures. use of language resources. reuse of articles and plagiarism in the context of a global or comparative analysis between sources.
C1 [Mariani, Joseph; Paroubek, Patrick; Vernier, Frederic] CNRS, LIMSI, Paris, France.
   [Francopoulo, Gil] Tagmatica, Paris, France.
C3 Centre National de la Recherche Scientifique (CNRS); Universite Paris
   Saclay
RP Mariani, J (corresponding author), CNRS, LIMSI, Paris, France.
EM Joseph.Mariani@limsi.fr; gil.francopoulo@wanadoo.fr; pap@limsi.fr;
   frederic.vernier@limsi.fr
CR [Anonymous], 2012, P ACL 2012 SPECIAL W
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   Francopoulo G., 2013, LMF Lexical Markup Framework
   Francopoulo Gil, 2015, WORKSH MIN COMP LING
   Francopoulo Gil, 2007, ICGL ING C GLOB INT
   Francopoulo Gil, 2015, 4 INT WORKSH MIN SCI
   Francopoulo Gil, 2015, D LIB, V21
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   Gollapalli Sujatha Das, 2015, EMNLP VERSUS ACL ANA
   Hall David, 2008, P 2008 C EMP METH NA, P363, DOI DOI 10.3115/1613715.1613763
   Ide Nancy, 2010, P 7 C INT LANG RES E
   Jha R, 2017, NAT LANG ENG, V23, P93, DOI 10.1017/S1351324915000443
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   Li H., 2006, P 15 INT C WORLD WID
   Mariani J, 2018, INT J DIGIT LIBRARIE, V19, P113, DOI 10.1007/s00799-017-0211-0
   Mariani J, 2016, LANG RESOUR EVAL, V50, P165, DOI 10.1007/s10579-016-9352-9
   Mariani Joseph, 1990, NOTES DOCUMENTS LIMS
   Mariani Joseph, 2014, P LREC 2014 26 31 MA
   Mariani Joseph, 2015, P L TC 2015 27 29 NO
   Mariani Joseph, 2013, P INT 2013 26 29 AUG
   Moro Andrea, 2014, T ACL
   Osborne F, 2013, INT SEM WEB C SYDN A
   Paul M, 2009, RECENT ADV NATURAL L
   Perin Charles, 2016, IEEE COMPUTER GRAPHI
   Radev DR, 2013, LANG RESOUR EVAL, V47, P919, DOI 10.1007/s10579-012-9211-2
   Rochat Y., 2009, ASNA
   Tang J, 2008, P 14 INT C KNOWL DIS
   Witten IH, 2011, MOR KAUF D, P1
NR 35
TC 0
Z9 0
U1 0
U2 3
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-5386-3333-5
PY 2017
PG 23
WC Computer Science, Interdisciplinary Applications; Engineering,
   Multidisciplinary; Engineering, Electrical & Electronic
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Engineering
GA BM4XH
UT WOS:000464400300001
DA 2024-09-05
ER
PT J
AU Thijs, B
   Glänzel, W
AF Thijs, Bart
   Glanzel, Wolfgang
TI The contribution of the lexical component in hybrid clustering, the case
   of four decades of "Scientometrics"
SO SCIENTOMETRICS
LA English
DT Article
DE Science mapping; Lexical similarity; Hybrid clustering; Natural Language
   Processing; Scientometrics
ID INFORMATION
AB The introduction of textual analysis and the use of lexical similarities already proved an important asset in science mapping. Earlier research showed the added value of hybrid document networks over link-based ones through the reduction of the extreme sparseness. However, it was only after the application of Natural Language Processing and phrase extraction that networks purely based on lexical similarities could be used as input for topic detection in quantitative science studies. This study investigates the contribution of the lexical component in hybrid cluster on a set of articles published in the journal Scientometrics since its foundation during four decades. Shifting the weight of the lexical components generates changes in the structure of the underlying hybrid network, which can be detected through clustering techniques. We show that these changes are not moving documents randomly, but in fact identify small groups of papers either at the borderline between different topics or combining those. In addition, the analysis substantiates that the lexical component adopts the structure of the network rather than amplifies hidden structures of the link-based network.
C1 [Thijs, Bart; Glanzel, Wolfgang] Katholieke Univ Leuven, ECOOM, Louvain, Belgium.
   [Glanzel, Wolfgang] Katholieke Univ Leuven, Dept MSI, Louvain, Belgium.
   [Glanzel, Wolfgang] Lib Hungarian Acad Sci, Dept Sci Policy & Scientometr, Budapest, Hungary.
C3 KU Leuven; KU Leuven; Hungarian Academy of Sciences
RP Thijs, B (corresponding author), Katholieke Univ Leuven, ECOOM, Louvain, Belgium.
EM bart.thijs@kuleuven.be; wolfgang.glanzel@kuleuven.be
RI Glanzel, Wolfgang/AAE-4395-2021; Thijs, Bart CM/C-2995-2008; Glanzel,
   Wolfgang/A-6280-2008
OI Glanzel, Wolfgang/0000-0001-7529-5198
CR Blondel VD, 2008, J STAT MECH-THEORY E, DOI 10.1088/1742-5468/2008/10/P10008
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   Glenisson P, 2005, INFORM PROCESS MANAG, V41, P1548, DOI 10.1016/j.ipm.2005.03.021
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   Thijs B, 2012, BIBLIOMETRIE PRAXIS, V1
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NR 13
TC 7
Z9 7
U1 3
U2 51
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD APR
PY 2018
VL 115
IS 1
BP 21
EP 33
DI 10.1007/s11192-018-2659-0
PG 13
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA FY4PT
UT WOS:000426807700002
DA 2024-09-05
ER
PT J
AU Yao, JX
   Shepperd, M
AF Yao, Jingxiu
   Shepperd, Martin
TI The impact of using biased performance metrics on software defect
   prediction research
SO INFORMATION AND SOFTWARE TECHNOLOGY
LA English
DT Article
DE Software engineering; Machine learning; Software defect prediction;
   Computational experiment; Classification metrics
ID CLASSIFICATION; REVIEWS
AB Context: Software engineering researchers have undertaken many experiments investigating the potential of software defect prediction algorithms. Unfortunately some widely used performance metrics are known to be problematic, most notably F1, but nevertheless F1 is widely used. Objective: To investigate the potential impact of using F1 on the validity of this large body of research. Method: We undertook a systematic review to locate relevant experiments and then extract all pairwise comparisons of defect prediction performance using F1 and the unbiased Matthews correlation coefficient (MCC). Results: We found a total of 38 primary studies. These contain 12,471 pairs of results. Of these comparisons, 21.95% changed direction when the MCC metric is used instead of the biased F1 metric. Unfortunately, we also found evidence suggesting that F1 remains widely used in software defect prediction research. Conclusion: We reiterate the concerns of statisticians that the F1 is a problematic metric outside of an information retrieval context, since we are concerned about both classes (defect-prone and not defect-prone units). This inappropriate usage has led to a substantial number (more than one fifth) of erroneous (in terms of direction) results. Therefore we urge researchers to (i) use an unbiased metric and (ii) publish detailed results including confusion matrices such that alternative analyses become possible.
C1 [Yao, Jingxiu] Beihang Univ, Beijing, Peoples R China.
   [Shepperd, Martin] Brunel Univ London, London, England.
C3 Beihang University; Brunel University
RP Shepperd, M (corresponding author), Brunel Univ London, London, England.
EM JingxiuYao@buaa.edu.cn; martin.shepperd@brunel.ac.uk
RI Shepperd, Martin/F-9683-2013; Yao, Jingxiu/HKE-8358-2023
OI Shepperd, Martin/0000-0003-1874-6145; Yao, Jingxiu/0000-0003-3742-9612
FU China Scholarship Council
FX The authors wish to thank the reviewers and the editor for their helpful
   and constructive comments. They also thank the authors of the 38 primary
   studies included for providing sufficient information to make this
   analysis possible. We also wish to stress that our criticism of F1 does
   not mean we are criticising their papers. On the contrary, their
   foresight that alternative metrics to F1 are needed, has been
   invaluable. Jingxiu Yao wishes to acknowledge the support of the China
   Scholarship Council.
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NR 78
TC 32
Z9 33
U1 1
U2 7
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-5849
EI 1873-6025
J9 INFORM SOFTWARE TECH
JI Inf. Softw. Technol.
PD NOV
PY 2021
VL 139
AR 106664
DI 10.1016/j.infsof.2021.106664
EA JUN 2021
PG 14
WC Computer Science, Information Systems; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA US8LY
UT WOS:000697678300007
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Wang, MY
   Jiao, SJ
   Zhang, JQ
   Zhang, XR
   Zhu, N
AF Wang, Mingyang
   Jiao, Shijia
   Zhang, Jiaqi
   Zhang, Xiangrong
   Zhu, Na
TI Identification High Influential Articles by Considering the Topic
   Characteristics of Articles
SO IEEE ACCESS
LA English
DT Article
DE Feature extraction; Bibliometrics; Task analysis; Semantics; Physics;
   Oncology; Neurology; High influential articles; topic characteristics;
   LDA; feature extraction
ID CITATION COUNTS MEASURE; OPEN ACCESS; RESEARCH PERFORMANCE; SCHOLARLY
   ARTICLES; IMPACT; PUBLICATION; JOURNALS; USAGE; FIELD; COLLABORATION
AB The topic of one article reflects its main semantic content, which is also the main guidance for researchers to choose reference literature. In order to explore whether the topic of an article will affect its citation trend in future, this paper establishes a machine learning framework to study the role of topic characteristics in the prediction of future high influential articles. Articles from four different disciplines are collected as experimental samples to verify whether the framework proposed in this paper can be applied to the prediction task in different disciplines. The Latent Dirichlet Allocation (LDA) is used to determine the topic characteristics of sample articles. LDA can map sample articles to current hot topics and generate the mapping probability of sample articles under different hot topics. The maximum mapping probability of the sample article under the hot topics is extracted as the topic feature of the article. Then the feature space for the prediction task is constructed by combining the topic feature and some bibliometrics indices of articles. Three feature selection algorithms, Fisher Score, Relief-F and Spectral Feature Selection (SPEC), are taken to select the important features in the feature space. The prediction performance of these features is finally tested by three classifiers, SVM, KNN and Bagging. The experimental results show that the topic characteristics of article, the early citation characteristics of article, and the reputation of the author are the key factors that determine whether an article can grow into a highly influential one. The important value of topic characteristics in articles' citation activities shows that the content of the article is an important factor in attracting more citations.
C1 [Wang, Mingyang; Jiao, Shijia; Zhang, Jiaqi] Northeast Forestry Univ, Coll Informat & Comp Engn, Harbin 150040, Peoples R China.
   [Zhang, Xiangrong] Heilongjiang Inst Technol, Coll Econ & Business Adm, Harbin 150040, Peoples R China.
   [Zhu, Na] Harbin Univ, Lib, Harbin 150086, Peoples R China.
C3 Northeast Forestry University - China; Heilongjiang Institute of
   Technology; Harbin University
RP Wang, MY (corresponding author), Northeast Forestry Univ, Coll Informat & Comp Engn, Harbin 150040, Peoples R China.
EM wmynefu@163.com
RI wang, mingyang/KSM-1989-2024; zhang, jiaqi/JNR-7443-2023
OI zhang, jiaqi/0000-0001-8888-9542
FU National Natural Science Foundation of China [71473034, 717D1063];
   Heilongjiang Provincial Natural Science Foundation of China
   [LH2019G001]; Financial Assistance from Postdoctoral Scientific Research
   Developmental Fund of Heilongjiang Province [LBH-Q16003]; Heilongjiang
   Province Art Planning Project: Research on Discipline Theme Evolution
   Based on Multi-source Data Fusion [2019C027]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 71473034 and Grant 717D1063, in part by
   the Heilongjiang Provincial Natural Science Foundation of China under
   Grant LH2019G001, in part by the Financial Assistance from Postdoctoral
   Scientific Research Developmental Fund of Heilongjiang Province under
   Grant LBH-Q16003, and in part by the Heilongjiang Province Art Planning
   Project: Research on Discipline Theme Evolution Based on Multi-source
   Data Fusion under Grant 2019C027.
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NR 128
TC 5
Z9 6
U1 17
U2 59
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2020
VL 8
BP 107887
EP 107899
DI 10.1109/ACCESS.2020.3001190
PG 13
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering; Telecommunications
GA MD5WH
UT WOS:000544042700021
OA gold
DA 2024-09-05
ER
PT J
AU Gharaibeh, MK
AF Gharaibeh, Malik Khlaif
TI Measuring student satisfaction of Microsoft teams as an online learning
   platform in Jordan: An application of UTAUT2 model
SO HUMAN SYSTEMS MANAGEMENT
LA English
DT Article
DE Microsoft teams; UTAUT2; ministry of higher education and scientific
   research; performance expectancy; effort expectancy
ID INFORMATION-TECHNOLOGY; USER ACCEPTANCE; INTENTION
AB BACKGROUND: As a result of the Covid-19 pandemic, educational institutions have shifted to electronic education at the global level, after face-to-face education was common in most countries of the world. From this aspect, assessing students' satisfaction with the platforms used in e-learning is very important. In this study, students' satisfaction with Microsoft Teams was measured, as it is one of the most important programs used in the educational process in various educational institutions. OBJECTIVE: This study uses five variables from the UTAUT2 model namely; performance expectancy, effort expectancy, facilitating conditions, social influence, price value, as well as two new variables which include student satisfaction, and flexibility to study the learning satisfaction with Microsoft Teams. METHODS: 520 questionnaires were distributed to Yarmouk and Ajloun National Universities students to collect the required data, and the data was analyzed using Smart PLS. RESULTS: The results showed that performance expectancy, effort expectancy, social influence, price value, facilitating conditions, student confidence, and flexibility are important indicators of satisfaction with Microsoft Teams. CONCLUSIONS: This study adds to the body of knowledge by building a conceptual model capable of effectively predicting student satisfaction with the Microsoft Teams platform. It concluded that the expected benefit from using Microsoft Teams will increase student satisfaction.
C1 [Gharaibeh, Malik Khlaif] Ajloun Natl Univ, Management Informat Syst, Ajloun, Jordan.
RP Gharaibeh, MK (corresponding author), Ajloun Natl Univ, Management Informat Syst, Ajloun, Jordan.
EM malik.gharaibeh@anu.edu.jo
OI Gharaibeh, Malik/0000-0002-8462-9599
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NR 58
TC 2
Z9 2
U1 0
U2 5
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 0167-2533
EI 1875-8703
J9 HUM SYST MANAGE
JI Hum. Syst. Manag.
PY 2023
VL 42
IS 2
BP 121
EP 130
DI 10.3233/HSM-220032
PG 10
WC Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA C8ZC2
UT WOS:000964726300002
DA 2024-09-05
ER
PT J
AU Xiong, H
   Li, QY
   Liu, JZ
AF Xiong, Hui
   Li, Qingyu
   Liu, Jinzhen
TI Performance Optimization and Simulation Research of New Coil for
   Transcranial Magnetic Stimulation Based on Improved Particle Swarm
   Optimizer
SO IEEE TRANSACTIONS ON MAGNETICS
LA English
DT Article
DE Optimization; Focusing; Magnetic heads; Convergence; Electric fields;
   Particle swarm optimization; Attenuation; Particle swarm optimizer (PSO)
   algorithm; three-layer multicoil (TLMC); transcranial magnetic
   stimulation (TMS); von-Neumann topology
AB Transcranial magnetic stimulation (TMS) is a noninvasive technology used to treat certain brain disorders. As an important part of the TMS system, the stimulation coil induces the electric field (EF) in the brain, which can change the excitability of nerve tissue. First, a three-layer multicoil (TLMC) is proposed on the basis of the principle of superposition and cancellation of magnetic field (MF). The simulation results in COMSOL show that this structure can significantly improve focality and stimulation strength, but it reduces the depth of stimulation. Second, a novel improved particle swarm optimizer (PSO) is proposed with three improved strategies. The novel improved PSO has higher convergence accuracy and stability in the test function. Finally, we utilized the novel improved PSO to optimize the current configuration and rotation angle of the TLMC three times. The numerical results show that the TLMC after the second optimization can increase stimulation strength by 19.10% on the basis of satisfying the stimulation depth of 15 mm. Compared with the unoptimized, the optimized TLMC can better balance the focality and stimulation depth.
C1 [Xiong, Hui] Tiangong Univ, Sch Control Sci & Engn, Tianjin 300387, Peoples R China.
   Tiangong Univ, Key Lab Intelligent Control Elect Equipment, Tianjin 300387, Peoples R China.
C3 Tiangong University; Tiangong University
RP Xiong, H (corresponding author), Tiangong Univ, Sch Control Sci & Engn, Tianjin 300387, Peoples R China.
EM xionghui@tiangong.edu.cn
OI Li, Qingyu/0000-0002-7820-1908
FU National Natural Science Foundation of China [62071329]; Natural Science
   Foundation Applying System of Tianjin [18JCYBJC90400, 18JCQNJC84000];
   Science and Technology Development Fund of Tianjin Education Commission
   for Higher Education [2019KJ014]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 62071329, in part by the Natural Science
   Foundation Applying System of Tianjin under Grant 18JCYBJC90400 and
   Grant 18JCQNJC84000, and in part by the Science and Technology
   Development Fund of Tianjin Education Commission for Higher Education
   under Grant 2019KJ014.
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NR 27
TC 3
Z9 3
U1 1
U2 24
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0018-9464
EI 1941-0069
J9 IEEE T MAGN
JI IEEE Trans. Magn.
PD DEC
PY 2021
VL 57
IS 12
AR 5800711
DI 10.1109/TMAG.2021.3121338
PG 11
WC Engineering, Electrical & Electronic; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Physics
GA XA2XX
UT WOS:000720517600019
DA 2024-09-05
ER
PT J
AU Druskat, S
AF Druskat, Stephan
TI Software and Dependencies in Research Citation Graphs
SO COMPUTING IN SCIENCE & ENGINEERING
LA English
DT Article
DE Software; Publishing; Stakeholders; Metadata; Sociotechnical systems;
   Computational modeling; Digital systems; Citation analysis;
   Bibliometrics; Graphical models; Scholarship; software citation;
   citation graphs; transitive credit
ID SCIENCE; LIGHT
AB Following the widespread digitalization of scholarship, software has become essential for research, but the current sociotechnical system of citation does not reflect this sufficiently. Citation provides context for research, but the current model for the respective research citation graphs does not integrate software. In this article, I develop a directed graph model to alleviate this, describe challenges for its instantiation, and give an outlook of useful applications of research citation graphs, including transitive credit.
C1 [Druskat, Stephan] German Aerosp Ctr DLR, Cologne, Germany.
   [Druskat, Stephan] Humboldt Univ, Dept Comp Sci, Berlin, Germany.
   [Druskat, Stephan] Friedrich Schiller Univ Jena, Jena, Germany.
C3 Helmholtz Association; German Aerospace Centre (DLR); Humboldt
   University of Berlin; Friedrich Schiller University of Jena
RP Druskat, S (corresponding author), German Aerosp Ctr DLR, Cologne, Germany.; Druskat, S (corresponding author), Humboldt Univ, Dept Comp Sci, Berlin, Germany.; Druskat, S (corresponding author), Friedrich Schiller Univ Jena, Jena, Germany.
RI Druskat, Stephan/AAS-5131-2021
OI Druskat, Stephan/0000-0003-4925-7248
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NR 49
TC 7
Z9 7
U1 0
U2 9
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1521-9615
EI 1558-366X
J9 COMPUT SCI ENG
JI Comput. Sci. Eng.
PD MAR-APR
PY 2020
VL 22
IS 2
BP 8
EP 21
DI 10.1109/MCSE.2019.2952840
PG 14
WC Computer Science, Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA KU4RW
UT WOS:000519698500002
OA Green Submitted, Green Accepted
DA 2024-09-05
ER
PT C
AU Alsukhni, M
   Zhu, Y
AF Alsukhni, Mohammad
   Zhu, Ying
BE Zhang, C
   Joshi, J
   Bertino, E
   Thuraisingham, B
TI Interactive Visualization of the Social Network of Research
   Collaborations
SO 2012 IEEE 13TH INTERNATIONAL CONFERENCE ON INFORMATION REUSE AND
   INTEGRATION (IRI)
LA English
DT Proceedings Paper
CT 13th IEEE International Conference on Information Reuse and Integration
   (IEEE IRI) / DIM / WICSOC / IEEE EM- RITE / IRI-HI
CY AUG 08-10, 2012
CL Las Vegas, NV
DE Social network; co-authorships network; Multidimensional scaling;
   digital bibliography and library project; information retrieval;
   gradient descent; graphs
AB Social networks have been evolving over the past few years, leading to a rapid increase in the number and complexity of relationships among their entities. In this paper, we focus on a large scale dataset known as the Digital Bibliography and Library Project (DBLP), which contains information on all publications that have been published in computer and information science related journals and conference proceedings. We model the DBLP dataset as a social network of research collaborations. DBLP is a structured and dynamic dataset stored in the XML file format; it contains over 850,000 authors and 2 million publications and the resulting collaboration social network is a scale-free network. We define DBLP collaboration social network as a graph that consists of researchers as nodes and links representing the collaboration among the researchers. In this work, we implement a data analysis algorithm called Multidimensional Scaling (MDS) to represent the degree of collaboration among the DBLP authors as Euclidean distances in order to analyze, mine and understand the relational information in this large scale network in a visual way. MDS requires a highly computational complexity for large scale graphs such as the DBLP graph. Therefore, we propose different solutions to overcome this problem, and improve the MDS performance. In addition, as the quality of the MDS result is measured by a metric known as the stress value, we use the steepest descent method to minimize the stress in an iterative process called stress optimization in order to generate the best geometric layout of the graph. We also propose a solution to further enhance the graph visualization by partitioning the graph into sub-graphs and using repelling forces among nodes within the same sub-graph.
C1 [Alsukhni, Mohammad; Zhu, Ying] Univ Ontario, Inst Technol, Fac Engn & Appl Sci, Oshawa, ON, Canada.
C3 Ontario Tech University
RP Alsukhni, M (corresponding author), Univ Ontario, Inst Technol, Fac Engn & Appl Sci, Oshawa, ON, Canada.
EM mohammad.alsukhni@uoit.ca; ying.zhu@uoit.ca
CR Alsukhni Mohammad, 2012, THESIS
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NR 12
TC 1
Z9 1
U1 0
U2 4
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-4673-2284-3
PY 2012
BP 247
EP 254
PG 8
WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Engineering
GA BDC08
UT WOS:000312540300039
DA 2024-09-05
ER
PT C
AU Yi, XT
AF Yi, Xitian
BE Lee, LK
   Wang, FL
   Kato, Y
   Hui, YK
   Sato, S
TI Research on the Cognitive Input Evaluation Model of MOOC Online Learning
   from the Perspective of Learning Analysis
SO 2021 INTERNATIONAL SYMPOSIUM ON EDUCATIONAL TECHNOLOGY (ISET 2021)
SE International Symposium on Educational Technology
LA English
DT Proceedings Paper
CT International Symposium on Educational Technology (ISET)
CY AUG 10-13, 2021
CL Nihon Fukushi Univ, ELECTR NETWORK
HO Nihon Fukushi Univ
DE learning cognitive input; evalution model; learing analysis
AB Learning cognitive input is one of the most direct and continuous issues in educational practice. As a key element of online learning quality, effective evaluation of students' cognitive input in online learning can encourage students to actively participate in the online learning process. However, it is currently unclear which indicators can reflect the cognitive input of learners in the online learning process. Therefore, from the perspective of learning analysis, this research explored the relationship between online learning behavior and online cognitive input, clarified evaluation indicators that can represent online cognitive input, and attempted to build a learning analysis model for online cognitive input evaluation.
C1 [Yi, Xitian] South China Normal Univ, Sch Informat Technol Educ, Guangzhou, Peoples R China.
C3 South China Normal University
RP Yi, XT (corresponding author), South China Normal Univ, Sch Informat Technol Educ, Guangzhou, Peoples R China.
EM 1019672875@qq.com
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NR 16
TC 1
Z9 1
U1 5
U2 55
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA
SN 2766-2144
EI 2766-2128
BN 978-1-6654-2859-0
J9 INT SYMP EDUC TECH
PY 2021
BP 211
EP 215
DI 10.1109/ISET52350.2021.00051
PG 5
WC Computer Science, Interdisciplinary Applications; Education &
   Educational Research; Education, Scientific Disciplines
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Education & Educational Research
GA BS6XE
UT WOS:000754450200041
DA 2024-09-05
ER
PT J
AU Geng, CX
   Wen, BH
   Liu, R
AF Geng, Chengxuan
   Wen, Bihan
   Liu, Rui
TI Research on Financing Environment Evaluation of Scientific Innovation
   Industry Based on the Bayesian Network Model under the Background of
   Green Economy
SO POLISH JOURNAL OF ENVIRONMENTAL STUDIES
LA English
DT Article
DE environmental crisis; green economy; scientific innovation industry;
   financing environment; bayesian networks
AB As the global environmental crisis evolves, China's traditional industries have serious problems of high energy consumption, high emission and low energy efficiency. Developing green industries has become an important direction of China's industrial transformation and upgrading. This paper selects scientific innovation industry as a typical representative of green industry and studies its financing environment assessment. In order to solve the financing dilemma faced by the science and technology innovation industry, this paper puts forward the evaluation method of the financing environment of science and technology innovation industry based on Bayesian network from the Angle of industry particularity. In this paper, Netica software is used to construct a Bayesian network model of the financing environment of the science and technology innovation industry, and the financing environment of the science and technology innovation industry in 2016-2020 is inferred according to the annual probability distribution. Then, the sensitivity analysis is carried out, and the hierarchical policy simulation is used to simulate the conditional probability of the initial node and the intermediate node respectively, so as to determine the impact of each node on the financing environment, and finally obtain the optimization path. The research results can be of great value for improving the financing environment of scientific innovation industry and promoting the development of green industry.
C1 [Geng, Chengxuan; Wen, Bihan; Liu, Rui] Nanjing Univ Aeronaut & Astronaut, Coll Econ & Management, Nanjing 211106, Jiangsu, Peoples R China.
C3 Nanjing University of Aeronautics & Astronautics
RP Wen, BH; Liu, R (corresponding author), Nanjing Univ Aeronaut & Astronaut, Coll Econ & Management, Nanjing 211106, Jiangsu, Peoples R China.
EM Wenbihan98@163.com; Ruiliu_68@163.com
FU project "Research on the Driving Mechanism and Promotion Path of the
   market-oriented Allocation of Scientific Innovation Industry Elements
   under the Background of Digital Economy" [22BJL140]
FX y This paper is supported by the project "Research on the Driving
   Mechanism and Promotion Path of the market-oriented Allocation of
   Scientific Innovation Industry Elements under the Background of Digital
   Economy". The project approval number is 22BJL140.
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NR 42
TC 0
Z9 0
U1 10
U2 12
PU HARD
PI OLSZTYN 5
PA POST-OFFICE BOX, 10-718 OLSZTYN 5, POLAND
SN 1230-1485
EI 2083-5906
J9 POL J ENVIRON STUD
JI Pol. J. Environ. Stud.
PY 2023
VL 32
IS 6
BP 5047
EP 5060
DI 10.15244/pjoes/169619
PG 14
WC Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology
GA EJ2S6
UT WOS:001138499200038
OA gold
DA 2024-09-05
ER
PT C
AU Taxitari, L
   Cappa, C
   Ferro, M
   Marzi, C
   Nadalini, A
   Pirrelli, V
AF Taxitari, Loukia
   Cappa, Claudia
   Ferro, Marcello
   Marzi, Claudia
   Nadalini, Andrea
   Pirrelli, Vito
BE Elmohajir, M
   AlAchhab, M
   Elmohajir, BE
   Ane, BK
   Jellouli, I
TI Using mobile technology for reading assessment
SO 2020 6TH IEEE CONGRESS ON INFORMATION SCIENCE AND TECHNOLOGY (IEEE
   CIST'20)
SE Colloquium in Information Science and Technology
LA English
DT Proceedings Paper
CT 6th IEEE International Congress on Information Science and Technology
   (IEEE CiSt)
CY JUN 05-12, 2021
CL Innov.org, Agadir, MOROCCO
HO Innov.org
DE reading assessment; reading research; mobile technology; NLP; cloud
   computing; special education needs
ID COGNITIVE SKILLS; WORD RECOGNITION; SIMPLE VIEW; COMPREHENSION;
   LANGUAGE; DIFFICULTIES
AB The enormous potential of Information and Communication Technologies (ICT) for addressing critical educational issues is generally acknowledged, but its use in the assessment of the complex skills of reading and understanding a text has been very limited to date. The paper contrasts traditional reading assessment protocols with ReadLet, an ICT platform with a tablet front-end, designed to support online monitoring of silent and oral reading abilities in early graders. ReadLet makes use of cloud computing and mobile technology for large-scale data collection and allows the time alignment of the child's reading behaviour with texts tagged using Natural Language Processing (NLP) tools. Initial findings replicate established benchmarks from the psycholinguistic literature on reading in both typically and atypically developing children, making the application a new ground-breaking approach in the evaluation of reading skills.
C1 [Taxitari, Loukia; Ferro, Marcello; Marzi, Claudia; Nadalini, Andrea; Pirrelli, Vito] CNR, Inst Computat Linguist Antonio Zampolli, Pisa, Italy.
   [Cappa, Claudia] CNR, Inst Clin Physiol, Pisa, Italy.
C3 Consiglio Nazionale delle Ricerche (CNR); Istituto di Linguistica
   Computazionale "A. Zampolli" (ILC-CNR); Consiglio Nazionale delle
   Ricerche (CNR); Istituto di Fisiologia Clinica (IFC-CNR)
RP Taxitari, L (corresponding author), CNR, Inst Computat Linguist Antonio Zampolli, Pisa, Italy.
EM loukia.taxitari@ilc.cnr.it; claudia.cappa@ilc.cnr.it;
   marcello.ferro@ilc.cnr.it; claudia.marzi@ilc.cnr.it;
   andrea.nadalini@ilc.cnr.it; vito.pirrelli@ilc.cnr.it
RI ; MARZI, CLAUDIA/C-8034-2012
OI Nadalini, Andrea/0000-0001-8859-9449; MARZI, CLAUDIA/0000-0002-3427-2827
FU Italian Ministry of University and Research [2017W8HFRX]
FX We gratefully acknowledge the financial support of PRIN grant 2017W8HFRX
   "ReadLet: reading to understand. An ICT-driven, large-scale
   investigation of early grade children's reading strategies" (2020-2022),
   from the Italian Ministry of University and Research.
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NR 34
TC 4
Z9 4
U1 1
U2 10
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2327-185X
BN 978-1-7281-6646-9
J9 COLLOQ INF SCI TECH
PY 2020
BP 302
EP 307
DI 10.1109/CIST49399.2021.9357173
PG 6
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Information Science & Library Science
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Information Science & Library Science
GA BR5TS
UT WOS:000657322100052
DA 2024-09-05
ER
PT C
AU Zhang, LN
   Yang, B
AF Zhang, Li-na
   Yang, Bo
BE Liu, HW
   Wang, G
   Zhang, GW
TI Research on the Evaluation of Students' Development Based on Support
   Vector Machine
SO MATERIAL SCIENCE, CIVIL ENGINEERING AND ARCHITECTURE SCIENCE, MECHANICAL
   ENGINEERING AND MANUFACTURING TECHNOLOGY II
SE Applied Mechanics and Materials
LA English
DT Proceedings Paper
CT 3rd International Conference on Advanced Engineering Materials and
   Architecture Science (ICAEMAS)
CY JUL 26-27, 2014
CL Huhhot, PEOPLES R CHINA
DE Development of the students; Index of evaluation; SVM
AB This paper applies SVM theory to the evaluation of student development system, and study on the content of students' development contains, at the same time establish the evaluation index system of students' development. Through the use of sample data on the evaluation system for a certain amount of training, we get a trained model, then evaluate and analyze the students' data to be measured. This approach can make students' assessment more accurate and reasonable, and evaluation results can be used in scientific. It can be seen from the development point of view, evaluation of all aspects of students' quality, and reduce the error caused by subjective on evaluation process.
C1 [Zhang, Li-na] Jilin Agr Univ, Changchun 130118, Jilin, Peoples R China.
   [Yang, Bo] Changchun Univ Finance & Econ, Changchun 130122, Jilin, Peoples R China.
C3 Jilin Agricultural University; Jilin University of Finance & Economics
RP Zhang, LN (corresponding author), Jilin Agr Univ, Changchun 130118, Jilin, Peoples R China.
EM mail_zhangln@163.com; mail_yangbo@163.com
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NR 5
TC 2
Z9 2
U1 0
U2 3
PU TRANS TECH PUBLICATIONS LTD
PI DURNTEN-ZURICH
PA KREUZSTRASSE 10, 8635 DURNTEN-ZURICH, SWITZERLAND
SN 1660-9336
BN 978-3-03835-267-9
J9 APPL MECH MATER
PY 2014
VL 651-653
BP 2502
EP +
DI 10.4028/www.scientific.net/AMM.651-653.2502
PG 2
WC Construction & Building Technology; Engineering, Civil; Engineering,
   Mechanical; Materials Science, Multidisciplinary
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Construction & Building Technology; Engineering; Materials Science
GA BC0UL
UT WOS:000349617700530
DA 2024-09-05
ER
PT C
AU Hooper, CJ
   Neves, B
   Bordea, G
AF Hooper, Clare J.
   Neves, Bruna
   Bordea, Georgeta
BE Tiropanis, T
   Vakali, A
   Sartori, L
   Burnap, P
TI A Disciplinary Analysis of Internet Science
SO INTERNET SCIENCE (INSCI 2015)
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT 2nd International Conference on Internet Science - Societies, Governance
   and Innovation (INSCI)
CY MAY 27-29, 2015
CL Brussels, BELGIUM
DE Internet Science; Disciplinary analysis; Interdisciplinarity;
   Bibliometrics; Natural language processing
AB Internet Science is an interdisciplinary field. Motivated by the unforeseen scale and impact of the Internet, it addresses Internet-related research questions in a holistic manner, incorporating epistemologies from a broad set of disciplines. Nonetheless, there is little empirical evidence of the levels of disciplinary representation within this field.
   This paper describes an analysis of the presence of different disciplines in Internet Science based on techniques from Natural Language Processing and network analysis. Key terms from Internet Science are identified, as are nine application contexts. The results are compared with a disciplinary analysis of Web Science, showing a surprisingly low amount of overlap between these two related fields. A practical use of the results within Internet Science is described. Finally, next steps are presented that will consolidate the analysis regarding representation of less technologically-oriented disciplines within Internet Science.
C1 [Hooper, Clare J.; Neves, Bruna] Univ Southampton, IT Innovat Ctr, Southampton, Hants, England.
   [Bordea, Georgeta] Natl Univ Ireland Univ Coll Galway, Insight, Galway, Ireland.
C3 University of Southampton; Ollscoil na Gaillimhe-University of Galway
RP Hooper, CJ (corresponding author), Univ Southampton, IT Innovat Ctr, Southampton, Hants, England.
EM cjh@it-innovation.soton.ac.uk; georgeta.bordea@insight-centre.org
RI Bordea, Georgeta/T-5762-2019
OI Bordea, Georgeta/0000-0001-9921-8234
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   Nagel T., 2011, 22 ACM C HYP HYP DEM
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   Zhang Z., 2008, P 6 INT C LANG RES E
NR 29
TC 1
Z9 1
U1 0
U2 8
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 0302-9743
EI 1611-3349
BN 978-3-319-18609-2; 978-3-319-18608-5
J9 LECT NOTES COMPUT SC
PY 2015
VL 9089
BP 63
EP 77
DI 10.1007/978-3-319-18609-2_5
PG 15
WC Computer Science, Artificial Intelligence; Computer Science, Hardware &
   Architecture; Computer Science, Information Systems; Computer Science,
   Theory & Methods; Robotics
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Robotics
GA BD6MA
UT WOS:000362365800005
DA 2024-09-05
ER
PT C
AU David, P
   Hawes, T
AF David, Peter
   Hawes, Timothy
BE Hanratty, TP
   Llinas, J
TI Quantity and Unit Extraction for Scientific and Technical Intelligence
   Analysis
SO NEXT-GENERATION ANALYST V
SE Proceedings of SPIE
LA English
DT Proceedings Paper
CT 5th Conference on Next-Generation Analyst
CY APR 10-11, 2017
CL Anaheim, CA
DE Natural Language Processing; Quantity Extraction; Unit of Measure;
   Scientometrics
AB Scientific and Technical (S&T) intelligence analysts consume huge amounts of data to understand how scientific progress and engineering efforts affect current and future military capabilities. One of the most important types of information S&T analysts exploit is the quantities discussed in their source material. Frequencies, ranges, size, weight, power, and numerous other properties and measurements describing the performance characteristics of systems and the engineering constraints that define them must be culled from source documents before quantified analysis can begin. Automating the process of finding and extracting the relevant quantities from a wide range of S&T documents is difficult because information about quantities and their units is often contained in unstructured text with ad hoc conventions used to convey their meaning. Currently, even simple tasks, such as searching for documents discussing RF frequencies in a band of interest, is a labor intensive and error prone process. This research addresses the challenges facing development of a document processing capability that extracts quantities and units from S&T data, and how Natural Language Processing algorithms can be used to overcome these challenges.
C1 [David, Peter; Hawes, Timothy] Decis Analyt Corp, 1400 Crystal Dr,Suite 1400, Arlington, VA 22202 USA.
RP David, P (corresponding author), Decis Analyt Corp, 1400 Crystal Dr,Suite 1400, Arlington, VA 22202 USA.
FU Army Research Laboratory [W911QX-15-C-0031]; Air Force Research
   Laboratory [FA8750-14-C-0135]
FX The research reported in this document/presentation was performed under
   the sponsorship the DoD Rapid Innovation Fund in conjunction with the
   Army Research Laboratory under contract No W911QX-15-C-0031, and the Air
   Force Research Laboratory under contract No. FA8750-14-C-0135. The views
   and conclusions contained in this document/presentation are those of the
   author and should not be interpreted as presenting the official policies
   or position, either expressed or implied, of the Army Research
   Laboratory, the Air Force Research Laboratory or the U.S. Government.
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NR 14
TC 0
Z9 0
U1 0
U2 2
PU SPIE-INT SOC OPTICAL ENGINEERING
PI BELLINGHAM
PA 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA
SN 0277-786X
EI 1996-756X
BN 978-1-5106-0915-0; 978-1-5106-0916-7
J9 PROC SPIE
PY 2017
VL 10207
AR UNSP 102070F
DI 10.1117/12.2266039
PG 7
WC Computer Science, Artificial Intelligence; Computer Science,
   Cybernetics; Computer Science, Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BJ2TD
UT WOS:000422621500014
DA 2024-09-05
ER
PT J
AU Rokach, L
   Kalech, M
   Blank, I
   Stern, R
AF Rokach, Lior
   Kalech, Meir
   Blank, Ido
   Stern, Rami
TI Who Is Going to Win the Next Association for the Advancement of
   Artificial Intelligence Fellowship Award? Evaluating Researchers by
   Mining Bibliographic Data
SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY
LA English
DT Article
ID H-INDEX; SCIENCE; WEB; SCOPUS
AB Accurately evaluating a researcher and the quality of his or her work is an important task when decision makers have to decide on such matters as promotions and awards. Publications and citations play a key role in this task, and many previous studies have proposed using measurements based on them for evaluating researchers. Machine learning techniques as a way of enhancing the evaluating process have been relatively unexplored. We propose using a machine learning approach for evaluating researchers. In particular, the proposed method combines the outputs of three learning techniques (logistics regression, decision trees, and artificial neural networks) to obtain a unified prediction with improved accuracy. We conducted several experiments to evaluate the model's ability to: (a) classify researchers in the field of artificial intelligence as Association for the Advancement of Artificial Intelligence (AAAI) fellows and (b) predict the next AAAI fellowship winners. We show that both our classification and prediction methods are more accurate than are previous measurement methods, and reach a precision rate of 96% and a recall of 92%.
C1 [Rokach, Lior; Kalech, Meir; Blank, Ido] Ben Gurion Univ Negev, Dept Informat Syst Engn, POB 653, IL-84105 Beer Sheva, Israel.
   [Rokach, Lior; Stern, Rami] Ben Gurion Univ Negev, Deutsch Telekom Labs, IL-84105 Beer Sheva, Israel.
C3 Ben Gurion University; Deutsche Telekom AG; Ben Gurion University
RP Rokach, L (corresponding author), Ben Gurion Univ Negev, Dept Informat Syst Engn, POB 653, IL-84105 Beer Sheva, Israel.
EM liorrk@bgu.ac.il; kalech@bgu.ac.il; blanki@bgu.ac.il; sternr@bgu.ac.il
RI Rokach, Lior/F-8247-2010; Kalech, Meir/AAC-5476-2019
OI Kalech, Meir/0000-0001-7394-4713
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NR 42
TC 12
Z9 12
U1 1
U2 30
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1532-2882
EI 1532-2890
J9 J AM SOC INF SCI TEC
JI J. Am. Soc. Inf. Sci. Technol.
PD DEC
PY 2011
VL 62
IS 12
BP 2456
EP 2470
DI 10.1002/asi.21638
PG 15
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 851WT
UT WOS:000297303100014
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Li, K
   Rollins, J
   Yan, E
AF Li, Kai
   Rollins, Jason
   Yan, Erjia
TI Web of Science use in published research and review papers 1997-2017: a
   selective, dynamic, cross-domain, content-based analysis
SO SCIENTOMETRICS
LA English
DT Article
DE Web of Science; Scientometrics; Natural language processing; Eugene
   Garfield
ID IMPACT FACTOR; SCIENTOMETRICS; CITATIONS; BIBLIOMETRICS; TRENDS; TOOL
AB Clarivate Analytics's Web of Science (WoS) is the world's leading scientific citation search and analytical information platform. It is used as both a research tool supporting a broad array of scientific tasks across diverse knowledge domains as well as a dataset for large-scale data-intensive studies. WoS has been used in thousands of published academic studies over the past 20 years. It is also the most enduring commercial legacy of Eugene Garfield. Despite the central position WoS holds in contemporary research, the quantitative impact of WoS has not been previously examined by rigorous scientific studies. To better understand how this key piece of Eugene Garfield's heritage has contributed to science, we investigated the ways in which WoS (and associated products and features) is mentioned in a sample of 19,478 English-language research and review papers published between 1997 and 2017, as indexed in WoS databases. We offered descriptive analyses of the distribution of the papers across countries, institutions and knowledge domains. We also used natural language processingtechniques to identify the verbs and nouns in the abstracts of these papers that are grammatically connected to WoS-related phrases. This is the first study to empirically investigate the documentation of the use of the WoS platform in published academic papers in both scientometric and linguistic terms.
C1 [Li, Kai; Yan, Erjia] Drexel Univ, 30N 33rd St, Philadelphia, PA 19104 USA.
   [Rollins, Jason] Clarivate Analyt, 50 Calif St, San Francisco, CA 94111 USA.
C3 Drexel University; Clarivate
RP Li, K (corresponding author), Drexel Univ, 30N 33rd St, Philadelphia, PA 19104 USA.
EM kl696@drexel.edu; Jason.rollins@clarivate.com; Erjia.yan@drexel.edu
RI Li, Kai/N-3209-2013; Yan, Erjia/E-7887-2011
OI Li, Kai/0000-0002-7264-365X; Yan, Erjia/0000-0002-0365-9340; Rollins,
   Jason/0000-0001-6201-1133
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NR 73
TC 310
Z9 325
U1 28
U2 242
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD APR
PY 2018
VL 115
IS 1
BP 1
EP 20
DI 10.1007/s11192-017-2622-5
PG 20
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA FY4PT
UT WOS:000426807700001
PM 29527070
OA Green Published, hybrid
HC Y
HP N
DA 2024-09-05
ER
PT C
AU Cosh, K
   Ramingwong, S
   Eiamkanitchat, N
   Ramingwong, L
AF Cosh, Kenneth
   Ramingwong, Sakgasit
   Eiamkanitchat, Narissara
   Ramingwong, Lachana
GP IEEE
TI Automatically Identifying Themes and Trends in Software Engineering
   Research
SO 2018 10TH INTERNATIONAL CONFERENCE ON KNOWLEDGE AND SMART TECHNOLOGY
   (KST 2018) - CYBERNETICS IN THE NEXT DECADES
SE International Conference on Knowledge and Smart Technology
LA English
DT Proceedings Paper
CT 10th International Conference on Knowledge and Smart Technology (KST) -
   Cybernetics in the Next Decades
CY JAN 31-FEB 03, 2018
CL Chiangmai, THAILAND
DE Bibliometric Analysis; Natural Language Processing; Information
   Extraction; Software Engineering
AB Understanding the ways that research topics are evolving in a research domain is important when considering research proposals. Bibliometric analysis provides a variety of tools for exploring publication data, but often involves manual effort. This paper presents an automatic method for extracting and examining key research themes by using natural language processing to parse a large collection of papers. The method was applied to over 8,000 papers published in the software engineering field over the past 20 years. Key research themes were identified and visualized, so that trends could be highlighted. Some research fields that are in decline are identified, along with newly popular research topics such as fuzzy set membership, cloud computing, feature selection and agile development teams.
C1 [Cosh, Kenneth; Ramingwong, Sakgasit; Eiamkanitchat, Narissara; Ramingwong, Lachana] Chiang Mai Univ, Comp Engn Dept, Chiang Mai, Thailand.
C3 Chiang Mai University
RP Cosh, K (corresponding author), Chiang Mai Univ, Comp Engn Dept, Chiang Mai, Thailand.
EM drkencosh@gmail.com
RI Eiamkanitchat, Narissara/GRX-3360-2022
OI Eiamkanitchat, Narissara/0000-0001-7473-9762
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NR 20
TC 2
Z9 2
U1 0
U2 2
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2374-314X
BN 978-1-5386-4015-9
J9 INT CONF KNOWL SMART
PY 2018
BP 106
EP 111
PG 6
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Engineering
GA BO1RK
UT WOS:000502134300021
DA 2024-09-05
ER
PT J
AU Cueva, K
   Cueva, M
   Revels, L
   Hensel, M
   Dignan, M
AF Cueva, Katie
   Cueva, Melany
   Revels, Laura
   Hensel, Michelle
   Dignan, Mark
TI Culturally Relevant Online Cancer Education Supports Tribal Primary Care
   Providers to Reduce Their Cancer Risk and Share Information About Cancer
SO HEALTH PROMOTION PRACTICE
LA English
DT Article
DE Alaska Native; Indigenous; community-based participatory research;
   online learning; evaluation; survey; distance education; cancer
ID COMMUNITY-HEALTH AIDES/PRACTITIONERS
AB Background. Culturally relevant education is an opportunity to reduce health disparities, and online learning is an emerging avenue for health promotion. In 2014-2019, a team based at the Alaska Native Tribal Health Consortium developed, implemented, and evaluated culturally relevant online cancer education modules with, and for, Alaska's tribal primary care providers. The project was guided by Indigenous Ways of Knowing and the principles of community-based participatory action research and was evaluated in alignment with empowerment theory. About 265 unique learners completed 1,898 end-of-module evaluation surveys between March 2015 and August 2019, and 13 people completed a follow-up survey up to 28 months post module completion. Key Findings. Learners described the modules as culturally respectful and informative and reported feeling more knowledgeable and comfortable talking about cancer as a result of the modules. About 98% of the learners planned to reduce their cancer risk because of the modules, and all follow-up survey respondents had reduced their risk, including by quitting smoking, getting screened for cancer, eating healthier, and exercising more. About 98% of the learners planned to share information with their patients, families, friends, and community members because of the modules, with all follow-up survey respondents indicating that they had shared information about cancer from the modules. Implications for Practice and Further Research. Culturally relevant online modules have the capacity for positive behavioral change and relatively high correlations between intent and behavior change. Future research could determine which aspects of the modules catalyzed reduced cancer risk and increased dissemination of cancer information.
C1 [Cueva, Katie] Univ Alaska Anchorage, Anchorage, AK USA.
   [Cueva, Melany; Revels, Laura; Hensel, Michelle] Alaska Native Tribal Hlth Consortium, Anchorage, AK USA.
   [Dignan, Mark] Univ Kentucky, Lexington, KY USA.
C3 University of Alaska System; University of Alaska Anchorage; Alaska
   Native Tribal Health Consortium; University of Kentucky
RP Cueva, K (corresponding author), Univ Alaska Anchorage, Inst Social & Econ Res Anchorage, 3211 Providence Dr, Anchorage, AK 99508 USA.
EM kcueva@alaska.edu
OI Cueva, Katie/0000-0002-8013-9680
CR Alaska Community Health Aide Program, 2017, AL COMM HLTH AID
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   Blake I., 2016, Alaska native mortality update: 2009-2013
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NR 26
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Z9 1
U1 1
U2 2
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 1524-8399
EI 1552-6372
J9 HEALTH PROMOT PRACT
JI Health Promot. Pract.
PD JUL
PY 2022
VL 23
IS 4
BP 631
EP 639
DI 10.1177/15248399211027827
EA AUG 2021
PG 9
WC Public, Environmental & Occupational Health
WE Emerging Sources Citation Index (ESCI)
SC Public, Environmental & Occupational Health
GA 3W6UR
UT WOS:000686964900001
PM 34416831
DA 2024-09-05
ER
PT J
AU Wang, YM
   Liu, ZF
   Zhao, YS
   Cheng, Q
   Cai, LG
AF Wang, Yumo
   Liu, Zhifeng
   Zhao, Yongsheng
   Cheng, Qiang
   Cai, Ligang
TI Research on an ANN system for monitoring hydrostatic turntable
   performance based on ODNE training
SO TRIBOLOGY INTERNATIONAL
LA English
DT Article
DE Hydrostatic turntable; Artificial neural network training; Intelligent
   monitoring system; Performance evaluation
ID OPTIMIZATION; PREDICTION; BEARING; DESIGN; IMPACT; FORCE; WEAR
AB Reliable operating conditions of hydrostatic turntables are prerequisite to ensuring machine tool performance. The hydrostatic turntable is affected by multiple working conditions, therefore, methods for evaluating turntable load-carrying capacity has become research hotspot. In this paper, by analyzing bearing capacity, parameters closely related to the support performance of turntables are selected as recognition features and an artificial neural network (ANN) training method is proposed. The ANN method is based on numerical solutions of over-determined nonlinear equations (ODNE) to intelligently evaluate turntable performance. In this study, ANN and ODNE training are applied to evaluate the performance of hydrostatic turntables. Finally, to verify the feasibility of the method, an intelligent monitoring system is established to collect data on machine tools.
C1 [Wang, Yumo; Liu, Zhifeng; Zhao, Yongsheng; Cheng, Qiang; Cai, Ligang] Beijing Univ Technol, Inst Adv Mfg & Intelligent Technol, Beijing 100124, Peoples R China.
   [Wang, Yumo; Liu, Zhifeng; Zhao, Yongsheng; Cheng, Qiang; Cai, Ligang] Beijing Univ Technol, Mech Ind Lab Heavy Machine Tool Digital Design &, Beijing 100124, Peoples R China.
C3 Beijing University of Technology; Beijing University of Technology
RP Liu, ZF (corresponding author), Beijing Univ Technol, Inst Adv Mfg & Intelligent Technol, Beijing 100124, Peoples R China.
EM lzfeng1@126.com
RI Wang, Yumo/G-2572-2017; Liu, Zhifeng/AGZ-4638-2022
FU National Natural Science Fund [51575009]; National Science and
   Technology Major Project [2018ZX04043001]; Jing-Hua Talents Project of
   Beijing University of Technology
FX This research was supported by the National Natural Science Fund (grant
   no. 51575009), National Science and Technology Major Project (grant no.
   2018ZX04043001), and Jing-Hua Talents Project of Beijing University of
   Technology.
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PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0301-679X
EI 1879-2464
J9 TRIBOL INT
JI Tribol. Int.
PD MAY
PY 2019
VL 133
BP 21
EP 31
DI 10.1016/j.triboint.2018.12.041
PG 11
WC Engineering, Mechanical
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA HL7TI
UT WOS:000458943500003
DA 2024-09-05
ER
PT J
AU Obaideen, K
   Albasha, L
   Iqbal, U
   Mir, H
AF Obaideen, Khaled
   Albasha, Lutfi
   Iqbal, Usama
   Mir, Hasan
TI Wireless power transfer: Applications, challenges, barriers, and the
   role of AI in achieving sustainable development goals - A bibliometric
   analysis
SO ENERGY STRATEGY REVIEWS
LA English
DT Article
DE Wireless power transfer; Wireless charging; Sustainable development
   goals; Electric vehicle; Artificial intelligence; Sustainable cities
ID ENERGY MANAGEMENT; DESIGN; SYSTEM; NETWORKS; COMMUNICATION;
   ARCHITECTURE; AGRICULTURE; INTERNET; TRENDS; THINGS
AB This study presents a comprehensive bibliometric analysis of 19,235 publications on Wireless Power Transfer (WPT) from 2015 to 2023, underlining its critical role in advancing the Sustainable Development Goals (SDGs). Focusing on SDG 7 (Affordable and Clean Energy) and SDG 3 (Good Health and Well-being), the research highlights WPT ' s significant contributions to sustainability across economic, environmental, and social realms. Utilizing Biblioshiny and VOSviewer, we extract and visualize key insights into WPT ' s recent progress, technological applications, top keywords, publication trends, geographical distribution, and thematic clusters. Our findings indicate a strong emphasis on sustainable energy solutions, with 1589 publications directly related to SDG 7. WPT also supports SDG 9 (Industry, Innovation, and Infrastructure) with 56 publications and SDG 11 (Sustainable Cities and Communities) with 171 publications, contributing to resilient infrastructure and sustainable urban development. Moreover, the study uncovers WPT ' s potential in environmental conservation, with notable attention to SDG 14 (Life Below Water) and SDG 15 (Life on Land). The synergy of Artificial Intelligence (AI) with WPT is emphasized for enhancing efficiency and broad application in areas such as affordable energy, agricultural yields, health standards, digital education, and water purification. Despite challenges like financial constraints, technical hurdles, and environmental concerns, the paper suggests innovative solutions through funding, research, environmental assessments, and collaborative policymaking. Highlighting the promise of state -of -the -art WPT techniques, this analysis advocates for democratizing technology access in marginalized regions, presenting WPT as a pivotal tool for a sustainable, equitable future aligned with the SDGs.
C1 [Obaideen, Khaled; Albasha, Lutfi; Iqbal, Usama; Mir, Hasan] Amer Univ Sharjah, Dept Elect Engn, Sharjah, U Arab Emirates.
C3 American University of Sharjah
RP Obaideen, K (corresponding author), Amer Univ Sharjah, Dept Elect Engn, Sharjah, U Arab Emirates.
EM Khaled.obaideen@gmail.com; lalbasha@aus.edu; b00063610@alumni.aus.edu;
   hmir@aus.edu
RI Kudrina, Olha/KHU-2090-2024; Adenidji, Eriola/ACX-8694-2022
OI Obaideen, Khaled/0000-0002-6472-2753
FU American University of Sharjah-UAE research fund [FRG20-L- E76,
   FRG21-M-E80]
FX The authors would like to acknowledge the Energy, Water, and Sustainable
   Environment Research Center at the American University of Sharjah-UAE,
   for their essential support. This work has been funded by the American
   University of Sharjah-UAE research fund no. FRG20-L- E76 and fund no.
   FRG21-M-E80.
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NR 200
TC 1
Z9 1
U1 11
U2 11
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2211-467X
EI 2211-4688
J9 ENERGY STRATEG REV
JI Energy Strateg. Rev.
PD MAY
PY 2024
VL 53
AR 101376
DI 10.1016/j.esr.2024.101376
EA APR 2024
PG 25
WC Energy & Fuels
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Energy & Fuels
GA RK9P0
UT WOS:001227679000001
OA gold
DA 2024-09-05
ER
PT J
AU Miner-Romanoff, K
   Sweetland, Y
   Yang, Y
   Fennema, B
AF Miner-Romanoff, Karen
   Sweetland, Yuerong
   Yang, Yi
   Fennema, Barbara
TI Assessment of Professional Development and Research-Based Instructional
   Strategies for Instructors of Online Undergraduate STEM Courses
SO INTERNATIONAL JOURNAL OF ONLINE PEDAGOGY AND COURSE DESIGN
LA English
DT Article
DE Active Learning; Observations; Online Faculty; Professional Development;
   Research-Based Instructional Strategies; STEM
ID HIGHER-EDUCATION; SCIENCE; FACULTY
AB Professional development (PD) programs for faculty are critical for improvement of STEM instruction. Little research exists on the impact of such programs in the online environment. This article reports the pilot study results of an observation protocol (OP) on the development of an online PD program for STEM faculty grounded in research-based instructional strategies (RBIS) and the development plan for the program. The RBIS-based OP in place at Franklin University will be used to identify and assess online STEM instructors' teaching practices before and after the PD program. Pilot study results suggested that the OP yields valid and reliable evidence of STEM faculty's RBIS usage. Approximately 80 STEM course sections will be observed using the OP with data collected pre- and post-PD (3 year period). The mixed-method data will be analyzed by university researchers in conjunction with a community research partner. This project will test the success of an online professional development program with RBIS for higher education STEM faculty, aid determination of which RBIS can contribute most effectively to improving student outcomes and produce the first robust evidence of the impact of an online PD for STEM faculty.
C1 [Miner-Romanoff, Karen] NYU, Acad Qual, Sch Profess Studies, New York, NY 10003 USA.
   [Miner-Romanoff, Karen] NYU, Sch Profess Studies, Ctr Acad Excellence & Support, New York, NY 10003 USA.
   [Sweetland, Yuerong] Franklin Univ, Assessment, Columbus, OH USA.
   [Sweetland, Yuerong] Franklin Univ, Columbus, OH USA.
   [Yang, Yi] Franklin Univ, Doctor Profess Studies, Instruct Design Leadership Program, Columbus, OH USA.
   [Fennema, Barbara] Franklin Univ, Int Inst Innovat Instruct, Columbus, OH USA.
C3 New York University; New York University; University System of Ohio;
   Franklin University; University System of Ohio; Franklin University;
   University System of Ohio; Franklin University; University System of
   Ohio; Franklin University
RP Miner-Romanoff, K (corresponding author), NYU, Acad Qual, Sch Profess Studies, New York, NY 10003 USA.; Miner-Romanoff, K (corresponding author), NYU, Sch Profess Studies, Ctr Acad Excellence & Support, New York, NY 10003 USA.
CR American Association for the Advancement of Science, 2013, TRANSF UND STEM ED L
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TC 2
Z9 3
U1 1
U2 19
PU IGI GLOBAL
PI HERSHEY
PA 701 E CHOCOLATE AVE, STE 200, HERSHEY, PA 17033-1240 USA
SN 2155-6873
EI 2155-6881
J9 INT J ONLINE PEDAGOG
JI Int. J. Online Pedagog. Course Des.
PD JAN-MAR
PY 2019
VL 9
IS 1
BP 51
EP 61
DI 10.4018/IJOPCD.2019010104
PG 11
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA HN1FS
UT WOS:000459933300004
DA 2024-09-05
ER
PT J
AU Melnychuk, T
   Galke, L
   Seidlmayer, E
   Broring, S
   Forstner, KU
   Tochtermann, K
   Schultz, C
AF Melnychuk, Tetyana
   Galke, Lukas
   Seidlmayer, Eva
   Broring, Stefanie
   Forstner, Konrad U.
   Tochtermann, Klaus
   Schultz, Carsten
TI Development of Similarity Measures From Graph-Structured Bibliographic
   Metadata: An Application to Identify Scientific Convergence
SO IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
LA English
DT Article; Early Access
DE Data enrichment; machine learning; network analysis; science dynamics;
   scientific convergence; similarity indicator
ID INTERNATIONAL JOINT RESEARCH; TECHNOLOGY CONVERGENCE; INDUSTRY
   CONVERGENCE; SCIENCE CONVERGENCE; KNOWLEDGE DIFFUSION; SEARCH REGIMES;
   IDENTIFICATION; INNOVATION; DYNAMICS; FIELDS
AB Scientific convergence is a phenomenon where the distance between hitherto distinct scientific fields narrows and the fields gradually overlap over time. It is creating important potential for research, development, and innovation. Although scientific convergence is crucial for the development of radically new technology, the identification of emerging scientific convergence is particularly difficult since the underlying knowledge flows are rather fuzzy and unstable in the early convergence stage. Nevertheless, novel scientific publications emerging at the intersection of different knowledge fields may reflect convergence processes. Thus, in this article, we exploit the growing number of research and digital libraries providing bibliographic metadata to propose an automated analysis of science dynamics. We utilize and adapt machine-learning methods (DeepWalk) to automatically learn a similarity measure between scientific fields from graphs constructed on bibliographic metadata. With a time-based perspective, we apply our approach to analyze the trajectories of evolving similarities between scientific fields. We validate the learned similarity measure by evaluating it within the well-explored case of cholesterol-lowering ingredients in which scientific convergence between the distinct scientific fields of nutrition and pharmaceuticals has partially taken place. Our results confirm that the similarity trajectories learned by our approach resemble the expected behavior, indicating that our approach may allow researchers and practitioners to detect and predict scientific convergence early.
C1 [Melnychuk, Tetyana; Schultz, Carsten] Univ Kiel, Kiel Inst Responsible Innovat, D-24118 Kiel, Germany.
   [Galke, Lukas] Leibniz Informat Ctr Econ ZBW, D-24105 Kiel, Germany.
   [Galke, Lukas] Max Planck Inst Psycholinguist, NL-6525 XD Nijmegen, Netherlands.
   [Seidlmayer, Eva; Forstner, Konrad U.] Informat Ctr Life Sci ZB Med, D-50931 Cologne, Germany.
   [Broring, Stefanie] Ruhr Univ Bochum, Fac Management & Econ, D-44780 Bochum, Germany.
   [Tochtermann, Klaus] Leibniz Informat Ctr Econ ZBW, D-24105 Kiel, Germany.
C3 University of Kiel; Deutsche Zentralbibliothek fur
   Wirtschaftswissenschaften (ZBW); Max Planck Society; Ruhr University
   Bochum; Deutsche Zentralbibliothek fur Wirtschaftswissenschaften (ZBW)
RP Melnychuk, T (corresponding author), Univ Kiel, Kiel Inst Responsible Innovat, D-24118 Kiel, Germany.
EM melnychuk@bwl.uni-kiel.de; lukas.galke@mpi.nl; seidlmayer@zbmed.de;
   stefanie.broering@ruhr-uni-bochum.de; foerstner@zbmed.de;
   k.tochtermann@zbw-online.eu; schultz@bwl.uni-kiel.de
RI Schultz, Carsten/G-5554-2016
OI Schultz, Carsten/0000-0002-5984-9872; Galke, Lukas/0000-0001-6124-1092;
   Tochtermann, Klaus/0000-0003-2471-2697; Broring,
   Stefanie/0000-0003-2014-2586; Seidlmayer, Eva/0000-0001-7258-0532;
   Melnychuk, Tetyana/0000-0002-7258-2842; Forstner, Konrad
   U./0000-0002-1481-2996
FU German Federal Ministry of Education and Research (BMBF) [01PU17013A,
   01PU17013B];  [01PU17013C]
FX This work was supported by the German Federal Ministry of Education and
   Research (BMBF) represented by the executing agency the German Aerospace
   Center (DLR) within the framework of the Research Project Q-AKTIV under
   Grant 01PU17013A, Grant 01PU17013B,and Grant 01PU17013C within the
   funding line "Quantitative Research on the Science Sector."
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NR 88
TC 1
Z9 1
U1 11
U2 24
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0018-9391
EI 1558-0040
J9 IEEE T ENG MANAGE
JI IEEE Trans. Eng. Manage.
PD 2023 SEP 12
PY 2023
DI 10.1109/TEM.2023.3308008
EA SEP 2023
PG 17
WC Business; Engineering, Industrial; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Engineering
GA S1PN6
UT WOS:001068962400001
OA Green Accepted, hybrid
DA 2024-09-05
ER
PT J
AU Menekse, M
   Chi, MTH
AF Menekse, Muhsin
   Chi, Michelene T. H.
TI The role of collaborative interactions versus individual construction on
   students? learning of engineering concepts
SO EUROPEAN JOURNAL OF ENGINEERING EDUCATION
LA English
DT Article
DE Engineering education research; teamwork; assessment of learning
   outcomes; active learning; peer learning; teaching
ID TEAMS; INQUIRY; EXPLANATIONS; INSTRUCTION; MATHEMATICS; TECHNOLOGY;
   ENGAGEMENT; FRAMEWORK; WORK
AB This study primarily investigated the role of interactional factors in an unstructured face-to-face collaborative learning environment with challenging engineering activities. We explored dialogue patterns in terms of quality of interaction, students? scaffolding instances, and discourse moves for productive interactions of collaborative dyads in the context of the Interactive-Constructive-Active-Passive (ICAP) framework. The sample included 72 engineering students for the interactive and constructive conditions. Students? understanding of material science and engineering concepts were measured using pre and posttest design. Results showed students in the interactive condition performed significantly better than students in the constructive condition. Verbal analysis of approximately 12 hours video recordings and 210 pages of transcriptions for students? dialogue in the interactive condition indicated a strong relation between the quality of interaction, scaffolding instances, and individual learning gains. In addition, a verbal analysis examining each utterance based on the discourse moves revealed that the certain moves are significantly linked with learning outcomes.
C1 [Menekse, Muhsin] Purdue Univ, Sch Engn Educ, W Lafayette, IN 47907 USA.
   [Menekse, Muhsin] Purdue Univ, Dept Curriculum & Instruct, W Lafayette, IN 47907 USA.
   [Chi, Michelene T. H.] Arizona State Univ, Mary Lou Fulton Teachers Coll, Tempe, AZ USA.
C3 Purdue University System; Purdue University; Purdue University System;
   Purdue University; Arizona State University; Arizona State
   University-Tempe
RP Menekse, M (corresponding author), Purdue Univ, Sch Engn Educ, W Lafayette, IN 47907 USA.; Menekse, M (corresponding author), Purdue Univ, Dept Curriculum & Instruct, W Lafayette, IN 47907 USA.
EM menekse@purdue.edu
OI Menekse, Muhsin/0000-0002-5547-5455
FU USA National Science Foundation [0935235]; Div Of Engineering Education
   and Centers; Directorate For Engineering [0935235] Funding Source:
   National Science Foundation
FX This material is based upon work supported by the USA National Science
   Foundation under Grant No. 0935235.
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NR 62
TC 31
Z9 38
U1 3
U2 29
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 0304-3797
EI 1469-5898
J9 EUR J ENG EDUC
JI Eur. J. Eng. Educ.
PD SEP 3
PY 2019
VL 44
IS 5
BP 702
EP 725
DI 10.1080/03043797.2018.1538324
PG 24
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA JB3PR
UT WOS:000488469700006
DA 2024-09-05
ER
PT J
AU Li, WD
   Yigitcanlar, T
   Liu, A
   Erol, I
AF Li, Wenda
   Yigitcanlar, Tan
   Liu, Aaron
   Erol, Isil
TI Mapping two decades of smart home research: A systematic scientometric
   analysis
SO TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
LA English
DT Article
DE Smart home; Home automation; Home innovation; Domotics;
   Internet-of-things (IoT); Artificial intelligence (AI)
ID ARTIFICIAL-INTELLIGENCE; ENERGY MANAGEMENT; PRESENT STATE; HEALTH-CARE;
   INTERNET; THINGS; URBAN; CHALLENGES; SECURITY; MODEL
AB Technological advancements such as information and communication technologies (ICTs), artificial intelligence (AI), internet-of-things (IoT), and the increasing popularity of the smart city and smart living movements during the last couple of decades boosted the developments in the smart home domain. Although the number of smart home related research has been expanding rapidly, there is still a lack of systematic analysis of the evolution of this research domain. This study helps to generate an understanding of the historical vicissitude, state-of-the-art and emerging trends, and the existing smart home research clusters. The study applies a scientometric method to analyse the published scholarly research (n = 17,153) over the last two decades, from 2000 to 2021. The scientometric analysis findings reveal that: Smart home literature has experienced steady growth during the last two decades; Smart home research has mainly clustered around ICT for home automation, home information management, AI for home automation, domestic energy management, IoT for home automation, and home-based healthcare areas; IoT is seen as the most popular technology to realise fully functioning smart homes; Limited evidence exists on the urban perspective and social issues of smart home technology; Smart homes are seen potentially as a strong driver of the smart city agenda.
C1 [Li, Wenda; Yigitcanlar, Tan; Liu, Aaron] Queensland Univ Technol, Sch Architecture & Built Environm, 2 George St, Brisbane, Qld 4000, Australia.
   [Erol, Isil] Univ Reading, Henley Business Sch, Reading RG6 6UD, Berks, England.
C3 Queensland University of Technology (QUT); University of Reading
RP Yigitcanlar, T (corresponding author), Queensland Univ Technol, Sch Architecture & Built Environm, 2 George St, Brisbane, Qld 4000, Australia.
EM tan.yigitcanlar@qut.edu.au
RI Liu, Aaron/T-7759-2019; Li, Wenda/ISB-6315-2023; Yigitcanlar,
   Tan/J-1142-2012
OI Liu, Aaron/0000-0001-7690-6608; Li, Wenda/0000-0002-4430-7405;
   Yigitcanlar, Tan/0000-0001-7262-7118; Erol, Isil/0000-0001-8125-9118
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NR 65
TC 19
Z9 19
U1 28
U2 142
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0040-1625
EI 1873-5509
J9 TECHNOL FORECAST SOC
JI Technol. Forecast. Soc. Chang.
PD JUN
PY 2022
VL 179
AR 121676
DI 10.1016/j.techfore.2022.121676
EA APR 2022
PG 13
WC Business; Regional & Urban Planning
WE Social Science Citation Index (SSCI)
SC Business & Economics; Public Administration
GA 1C1XP
UT WOS:000792920900004
OA Green Published, Green Accepted
DA 2024-09-05
ER
PT J
AU Klein, SA
   Baiocchi, M
   Rodu, J
   Baker, H
   Rosemond, E
   Doyle, JM
AF Klein, Sean A.
   Baiocchi, Michael
   Rodu, Jordan
   Baker, Heather
   Rosemond, Erica
   Doyle, Jamie Mihoko
TI An analysis of the Clinical and Translational Science Award pilot
   project portfolio using data from Research Performance Progress Reports
SO JOURNAL OF CLINICAL AND TRANSLATIONAL SCIENCE
LA English
DT Article
DE Portfolio analysis; CTSA; evaluation; machine learning; networks;
   collaboration
AB Introduction: Pilot projects ("pilots") are important for testing hypotheses in advance of investing more funds for full research studies. For some programs, such as Clinical and Translational Science Awards (CTSAs) supported by the National Center for Translational Sciences, pilots also make up a significant proportion of the research projects conducted with direct CTSA support. Unfortunately, administrative data on pilots are not typically captured in accessible databases. Though data on pilots are included in Research Performance Progress Reports, it is often difficult to extract, especially for large programs like the CTSAs where more than 600 pilots may be reported across all awardees annually. Data extraction challenges preclude analyses that could provide valuable information about pilots to researchers and administrators. Methods: To address those challenges, we describe a script that partially automates extraction of pilot data from CTSA research progress reports. After extraction of the pilot data, we use an established machine learning (ML) model to determine the scientific content of pilots for subsequent analysis. Analysis of ML-assigned scientific categories reveals the scientific diversity of the CTSA pilot portfolio and relationships among individual pilots and institutions. Results: The CTSA pilots are widely distributed across a number of scientific areas. Content analysis identifies similar projects and the degree of overlap for scientific interests among hubs. Conclusion: Our results demonstrate that pilot data remain challenging to extract but can provide useful information for communicating with stakeholders, administering pilot portfolios, and facilitating collaboration among researchers and hubs.
C1 [Klein, Sean A.] US Dept HHS, Off Sci & Data Policy, Off Assistant Secretary Nanning & Evaluat, Washington, DC 20201 USA.
   [Baiocchi, Michael] Stanford Univ, Dept Epidemiol & Populat Hlth, Stanford, CA 94305 USA.
   [Rodu, Jordan] Univ Virginia, Dept Stat, Charlottesville, VA USA.
   [Baker, Heather; Rosemond, Erica; Doyle, Jamie Mihoko] NIH, Div Clin Innovat, Natl Ctr ForAdv Translat Sci, Bldg 10, Bethesda, MD 20892 USA.
C3 Stanford University; University of Virginia; National Institutes of
   Health (NIH) - USA
RP Klein, SA (corresponding author), US Dept HHS, Planning & Evaluat, Room 440F1,200 Independence Ave SW, Washington, DC 20201 USA.
EM sean.klein@hhs.gov
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   US
NR 32
TC 0
Z9 0
U1 0
U2 1
PU CAMBRIDGE UNIV PRESS
PI CAMBRIDGE
PA EDINBURGH BLDG, SHAFTESBURY RD, CB2 8RU CAMBRIDGE, ENGLAND
EI 2059-8661
J9 J CLIN TRANSL SCI
JI J. Clin. Transl. Sci.
PD AUG 18
PY 2022
VL 6
IS 1
AR e113
DI 10.1017/cts.2022.444
PG 8
WC Medicine, Research & Experimental
WE Emerging Sources Citation Index (ESCI)
SC Research & Experimental Medicine
GA 4P9QY
UT WOS:000855724800001
PM 36285022
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU Bhullar, PS
   Joshi, M
   Chugh, R
AF Bhullar, Pritpal Singh
   Joshi, Mahesh
   Chugh, Ritesh
TI ChatGPT in higher education - a synthesis of the literature and a future
   research agenda
SO EDUCATION AND INFORMATION TECHNOLOGIES
LA English
DT Article; Early Access
DE ChatGPT; Artificial Intelligence; Generative AI; Higher education;
   Plagiarism; Academic integrity; Systematic review; Bibliometric analysis
ID RESEARCH-FRONT; PERFORMANCE
AB ChatGPT has emerged as a significant subject of research and exploration, casting a critical spotlight on teaching and learning practices in the higher education domain. This study examines the most influential articles, leading journals, and productive countries concerning citations and publications related to ChatGPT in higher education, while also shedding light on emerging thematic and geographic clusters within research on ChatGPT's role and challenges in teaching and learning at higher education institutions. Forty-seven research papers from the Scopus database were shortlisted for bibliometric analysis. The findings indicate that the use of ChatGPT in higher education, particularly issues of academic integrity and research, has been studied extensively by scholars in the United States, who have produced the largest volume of publications, alongside the highest number of citations. This study uncovers four distinct thematic clusters (academic integrity, learning environment, student engagement, and scholarly research) and highlights the predominant areas of focus in research related to ChatGPT in higher education, including student examinations, academic integrity, student learning, and field-specific research, through a country-based bibliographic analysis. Plagiarism is a significant concern in the use of ChatGPT, which may reduce students' ability to produce imaginative, inventive, and original material. This study offers valuable insights into the current state of ChatGPT in higher education literature, providing essential guidance for scholars, researchers, and policymakers.
C1 [Bhullar, Pritpal Singh] Maharaja Ranjit Singh Punjab Tech Univ, Univ Business Sch, Bathinda, Punjab, India.
   [Joshi, Mahesh] RMIT Univ, Sch Accounting Informat Syst & Supply Chain, Dept Financial Planning & Tax, Melbourne, Australia.
   [Chugh, Ritesh] Cent Queensland Univ, CML NET & CREATE Res Ctr, Sch Engn & Technol, Rockhampton, Qld, Australia.
C3 Royal Melbourne Institute of Technology (RMIT); Central Queensland
   University
RP Chugh, R (corresponding author), Cent Queensland Univ, CML NET & CREATE Res Ctr, Sch Engn & Technol, Rockhampton, Qld, Australia.
EM mgtpritpal@mrsptu.ac.in; mahesh.joshi@rmit.edu.au; r.chugh@cqu.edu.au
OI Chugh, Ritesh/0000-0003-0061-7206
FU Central Queensland University
FX No Statement Available
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NR 75
TC 2
Z9 2
U1 81
U2 81
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1360-2357
EI 1573-7608
J9 EDUC INF TECHNOL
JI Educ. Inf. Technol.
PD 2024 MAY 2
PY 2024
DI 10.1007/s10639-024-12723-x
EA MAY 2024
PG 22
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA PV9O8
UT WOS:001216978200002
OA hybrid
DA 2024-09-05
ER
PT J
AU Hoekstra, RC
   van Arkel, H
   Leurs, B
AF Hoekstra, Rinke C.
   van Arkel, Henk
   Leurs, Bas
TI MODELING LOCAL MONETARY FLOWS IN POOR REGIONS: A RESEARCH SETUP TO
   SIMULATE THE MULTIPLIER EFFECT IN LOCAL ECONOMIES
SO INTERDISCIPLINARY DESCRIPTION OF COMPLEX SYSTEMS
LA English
DT Article
DE multiplier effect; simulation; multi-agent based simulation; social
   accounting matrix; artificial intelligence techniques
AB In poor regions, lack of local monetary circulation is one of the key elements causing underdevelopment. The more incoming money is passed from hand to hand, the more the local economy will be stimulated. However, in most poor areas money is spent outside the community before circulating locally, reducing the effectiveness of money inflow dramatically.
   Development programs would increase their effectiveness if knowledge was available on how spending money could lead to optimized and prolonged local circulation. To gain this knowledge a simulation tool will be created, which is able to analyze financial flows, to evaluate the potency of specific actions aimed on local development, and to monitor a development scheme during the execution phase.
   The basic model will be developed through a multi-agent approach, where each agent represents one (or more) family/households belonging to one of several socio-economic groups. A Social Accounting Matrix (SAM) of the local economy will be used as a basis to set up a spendings matrix for each agent, defining its spending priorities. Artificial Intelligence techniques will be used to give the agent the possibility to make decisions on how to satisfy these spending priorities. Also, social dynamics, the simulation of strategic planning behavior, learning, and exchange in limited networks will be addressed. The simulation application will consist of a common user interface allowing the user to "play" the simulation. This user interface layer will be "pluggable" with the underlying programming layer responsible for the calculations on the simulation, so that different plug-ins may be used for different simulation techniques.
C1 [Hoekstra, Rinke C.; van Arkel, Henk; Leurs, Bas] STRO Fdn, Utrecht, Netherlands.
RP Hoekstra, RC (corresponding author), Stichting STROhalm, Oude Gracht 42, NL-3511 AR Utrecht, Netherlands.
EM rinke@strohalm.nl
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NR 12
TC 1
Z9 1
U1 0
U2 0
PU CROATIAN INTERDISCIPLINARY SOC
PI SESVETE
PA SIMUNCEVECKA 38B, SESVETE, HR-10360, CROATIA
SN 1334-4684
EI 1334-4676
J9 INTERDISCIP DESCR CO
JI Interdiscip. Descr. Complex Syst.
PD OCT
PY 2007
VL 5
IS 2
BP 138
EP 150
PG 13
WC Social Sciences, Interdisciplinary
WE Emerging Sources Citation Index (ESCI)
SC Social Sciences - Other Topics
GA VA6YI
UT WOS:000410261300005
DA 2024-09-05
ER
PT C
AU Mojzes, M
   Kukal, J
AF Mojzes, Matej
   Kukal, Jaromir
BE Prazak, P
TI Bayesian Study on When to Restart Heuristic Search
SO MATHEMATICAL METHODS IN ECONOMICS (MME 2017)
LA English
DT Proceedings Paper
CT 35th International Conference Mathematical Methods in Economics (MME)
CY SEP 13-15, 2017
CL Hradec Kralove, CZECH REPUBLIC
DE Heuristics; search process; performance measure; restart strategy;
   Bayesian analysis; operational research
ID ALGORITHMS
AB Heuristic algorithm performance measures assess the quality of a search process by statistically analyzing its performance data, typically the number of objective function evaluations before optimal or acceptable solution is found. Such criteria are not only intended to provide the verdict on which algorithm is better for what task, but also to help make the best possible use of a given algorithm on a given task. This target may be achieved by an appropriate restart strategy of the search process. In our paper we formulate axiomatic approach which also describes existing performance measures. Novelty of this paper consist in performance measure analysis via Marko-ian chain calculation and its direct Bayesian estimation based on Monte Carlo simulations. Practical results are demonstrated on combinatorial optimization problems and are applicable e.g. to NP-hard problems from the field of operational research.
C1 [Mojzes, Matej; Kukal, Jaromir] FNSPE CTU, Dept Software Engn, Trojanova 13, Prague, Czech Republic.
C3 Czech Technical University Prague
RP Mojzes, M (corresponding author), FNSPE CTU, Dept Software Engn, Trojanova 13, Prague, Czech Republic.
EM mojzemat@fjfi.cvut.cz; jaromir.kukal@fjfi.cvut.cz
FU Czech Technical University in Prague [SGS17/196/OHK4/3T/14]
FX This paper was created under the support of grant SGS17/196/OHK4/3T/14
   Czech Technical University in Prague.
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NR 26
TC 0
Z9 0
U1 1
U2 3
PU UNIV HRADEC KRALOVE
PI HRADEC KRALOVE 3
PA ROKITANSKEHO 62, HRADEC KRALOVE 3, 500 03, CZECH REPUBLIC
BN 978-80-7435-678-0
PY 2017
BP 480
EP 485
PG 6
WC Economics; Operations Research & Management Science; Mathematics,
   Interdisciplinary Applications; Social Sciences, Mathematical Methods
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Business & Economics; Operations Research & Management Science;
   Mathematics; Mathematical Methods In Social Sciences
GA BJ7ED
UT WOS:000427151400082
DA 2024-09-05
ER
PT J
AU Gou, W
   Ren, ZT
   Chen, HX
   Xing, L
   Zhou, ZX
   Xia, XX
   Shi, JF
AF Gou, Wei
   Ren, Zhaoting
   Chen, Huanxin
   Xing, Lu
   Zhou, Zhenxin
   Xia, Xingxiang
   Shi, Jingfeng
TI Experimental research on the performance and parameters sensitivity
   analysis of variable refrigerant flow system with common faults imposed
   in heating mode
SO ENERGY AND BUILDINGS
LA English
DT Article
DE Variable refrigerant flow systems; Fault impacts; Sensitivity analysis;
   Feature selection; Fault decoupling variables
ID AIR-CONDITIONER; PUMP; CHARGE; IMPACT; CONDENSER
AB Due to improper installation, operation, and maintenance, many common faults inevitably occur in the air conditioning systems. However, the same fault could demonstrate different characteristics under dif-ferent load rates and fault levels; different faults may present similar fault phenomena. This paper reveals the quantitative impact of faults on system performance of VRF system with common faults imposed in heating mode through fault experiments. The system parameters sensitivity study has been carried out to understand the internal impact mechanism related to system parameters. This provides a reference for selecting characteristic parameters and fault decoupling variables and developing interpretable, profes-sional, and reliable fault detection and diagnosis models. The results show that the outdoor unit fouling fault can cause the heating capacity to drop by 73.2%, and the indoor unit fouling can cause the system power to drop by 65.5%. The coefficient of performance drop is as high as 48.6%. The sensitivity study result shows that the system load rate is an influential parameter; it needs to be considered when char-acteristic parameters and fault decoupling variables are selected.(c) 2022 Elsevier B.V. All rights reserved.
C1 [Gou, Wei; Chen, Huanxin; Zhou, Zhenxin] Huazhong Univ Sci & Technol, Sch Energy & Power Engn, Dept Refrigerant & Cryogen Engn, Wuhan 430074, Peoples R China.
   [Ren, Zhaoting; Xia, Xingxiang; Shi, Jingfeng] Qingdao Hisense Hitachi Air Conditioning Syst Co L, Qingdao 266510, Peoples R China.
   [Xing, Lu] Northumbria Univ, Mech & Construct Engn, Newcastle Upon Tyne NE1 8ST, England.
C3 Huazhong University of Science & Technology; Northumbria University
RP Chen, HX (corresponding author), Huazhong Univ Sci & Technol, Sch Energy & Power Engn, Dept Refrigerant & Cryogen Engn, Wuhan 430074, Peoples R China.
EM chenhuanxin@tsinghua.org.cn
RI Xing, Lu/ABH-4014-2022
OI Xing, Lu/0000-0002-5561-7417
FU National Natural Science Foundation of China;  [51876070]
FX Acknowledgments The authors gratefully acknowledge the support of the
   National Natural Science Foundation of China (Grant number 51876070) .
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NR 34
TC 4
Z9 4
U1 3
U2 6
PU ELSEVIER SCIENCE SA
PI LAUSANNE
PA PO BOX 564, 1001 LAUSANNE, SWITZERLAND
SN 0378-7788
EI 1872-6178
J9 ENERG BUILDINGS
JI Energy Build.
PD JAN 1
PY 2023
VL 278
AR 112624
DI 10.1016/j.enbuild.2022.112624
EA NOV 2022
PG 14
WC Construction & Building Technology; Energy & Fuels; Engineering, Civil
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Construction & Building Technology; Energy & Fuels; Engineering
GA 6V3QN
UT WOS:000894967000002
DA 2024-09-05
ER
PT J
AU Casey, M
   Raynor, M
   Jacob, C
   Sharp, S
   McFarlane, E
AF Casey, Monica
   Raynor, Michael
   Jacob, Catherine
   Sharp, Stephen
   McFarlane, Emma
TI Improving the precision of search strategies for guideline surveillance
SO RESEARCH SYNTHESIS METHODS
LA English
DT Article
DE databases; bibliographic; information storage and retrieval; search
   precision; search recall; search strategy
ID HEALTH
AB Introduction and aim NICE guideline surveillance determines whether previously published guidelines need updating. The surveillance process must balance time constraints with methodological rigor. It includes a rapid review to identify new evidence to contradict, reinforce or clarify guideline recommendations. Despite this approach, the screening burden can still be high. Applying additional search techniques may increase the precision of the database searches.
   Methods A retrospective analysis was conducted on five surveillance reviews with less than 2% of the studies included after screening. Modified searches were run in MEDLINE, Embase and PsycINFO (where appropriate) to test the impact of additional search techniques: focused subject headings, subheadings, frequency operators and title only searches. Modified searches were compared to original search results to determine: the retrieval of included studies, the precision of the search and the number needed to read. Studies not retrieved by the modified search were checked to determine if the surveillance decision would have been affected.
   Results The additional search techniques tested indicated that a combination of focused subject headings and frequency operators could improve the precision of surveillance searches. The modified search retrieved all the original studies included in the surveillance review for three of the reviews tested. Some of the original included studies were not retrieved for two reviews but the missing studies would not have affected the surveillance decision.
   Conclusions Combining focused subject headings and frequency operators is a viable option for improving the precision of surveillance searches without compromising recall and without impacting the surveillance decision.
C1 [Casey, Monica; Jacob, Catherine] Natl Inst Hlth & Care Excellence, Informat Serv, London, England.
   [Raynor, Michael; Sharp, Stephen; McFarlane, Emma] Natl Inst Hlth & Care Excellence, Ctr Guidelines, London, England.
C3 National Institute for Health & Care Excellence; National Institute for
   Health & Care Excellence
RP Casey, M (corresponding author), Natl Inst Hlth & Care Excellence, Informat Serv, London, England.
EM monica.casey@nice.org.uk
RI Jacob, Catherine/IXD-2619-2023
OI Casey, Monica/0000-0002-9483-9012
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NR 17
TC 6
Z9 6
U1 0
U2 1
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1759-2879
EI 1759-2887
J9 RES SYNTH METHODS
JI Res. Synth. Methods
PD NOV
PY 2020
VL 11
IS 6
BP 903
EP 912
DI 10.1002/jrsm.1461
PG 10
WC Mathematical & Computational Biology; Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology; Science & Technology - Other
   Topics
GA OR4HP
UT WOS:000589433800014
PM 32985071
DA 2024-09-05
ER
PT C
AU Schilling, K
AF Schilling, Katherine
BE Beldhuis, H
TI The Efficacy of eLearning for Information-Retrieval Skills in Medical
   Education
SO PROCEEDINGS OF THE 11TH EUROPEAN CONFERENCE ON E-LEARNING
LA English
DT Proceedings Paper
CT 11th European Conference on E-Learning (ECEL)
CY OCT 26-27, 2012
CL Univ Groningen, Groningen, NETHERLANDS
HO Univ Groningen
DE online learning; distance education; literature searching; bibliographic
   retrieval; information-retrieval skills; evidence-based practice
ID FACE-TO-FACE; STUDENTS; LITERACY; MEDLINE; ONLINE; CURRICULUM; OUTCOMES;
   SEEKING
AB A randomised, blinded study addressed the extent to which the training method used to deliver information literacy skills instruction impacted on students' information-retrieval skills and other variables. First-year medical students at a major U. S. university (N = 128) were randomly assigned to a control or intervention group for information-retrieval skills training on searching the MEDLINE database for the best evidence on common patient problems. The control group (n = 63) participated in traditional, instructor-led information and MEDLINE searching skills training, and the intervention group (n = 65) participated in the same instruction via a web-based tutorial. Data was gathered from multiple sources including a) pre- and post-training surveys, skills self-assessments, and written skills tests; b) the evaluation of students' MEDLINE literature searches; and c) follow-up surveys administered at the end of the semester measuring students' use of information resources for evidence-based practice. Students' MEDLINE literature searches were evaluated by expert searchers, allowing for a comprehensive analysis of students' literature searching skills for identifying the best evidence in the biomedical journals. Intervention group (e-learning) students earned slightly higher MEDLINE searching scores. Data analysis showed no statistically significant differences (P = 0.065) between the training groups, however, illustrating that e-learning methods and face-to-face training were equally effective. Study results provide a picture of students' MEDLINE searching skills, information usage patterns and behaviours, and attitudes regarding library and information services and resources. Research findings are important for assessing the viability of self-paced, online tutorials for teaching and promoting effective information skills, particularly in evidence-based practice environments in which literature searching is routinely required.
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NR 44
TC 6
Z9 6
U1 0
U2 38
PU ACAD  CONFERENCES LTD
PI NR READING
PA CURTIS FARM, KIDMORE END, NR READING, RG4 9AY, ENGLAND
BN 978-1-908272-74-4
PY 2012
BP 515
EP 522
PG 8
WC Education & Educational Research
WE Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Education & Educational Research
GA BFW04
UT WOS:000321613000062
DA 2024-09-05
ER
PT J
AU Leiva, R
   Kimber, D
AF Leiva, Ricardo
   Kimber, David
TI A Persistent Gender Bias in Chilean Press: The Influence of Journalist's
   Gender and Editor's Gender
SO JOURNALISM & MASS COMMUNICATION QUARTERLY
LA English
DT Article
DE gatekeeping; newspaper and online news; newspaper; Chile; logistic
   regression; content analysis; gender
ID INFORMATION-SOURCES; SOURCE SELECTION; NEWS COVERAGE; WOMEN; NEWSPAPER;
   MEDIA; US; AMERICAN; MODEL
AB There is a lot of evidence about gender bias in the media, but not clear evidence about its causes. In this article, we study the influence of journalist's gender and editor's gender on gender bias in Chilean press through time. Based on content analysis of 2,645 news articles from Chilean leading newspapers and logistics regression, results confirm the relevance of the gender of both, journalists and editors, on the presence of gender bias in Chilean press, being a permanent behavior through time. Our research supports that the more women in the newsrooms, the greater women's representation by the news media.
C1 [Leiva, Ricardo] Univ Los Andes, Sch Commun, Monsenor Alvaro del Portillo 12455, Santiago 7550000, Chile.
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C3 Universidad de los Andes - Chile; Universidad de los Andes - Chile
RP Leiva, R (corresponding author), Univ Los Andes, Sch Commun, Monsenor Alvaro del Portillo 12455, Santiago 7550000, Chile.
EM rleiva@uandes.cl
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NR 112
TC 9
Z9 9
U1 3
U2 23
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 1077-6990
EI 2161-430X
J9 J MASS COMMUN Q
JI Journal. Mass Commun. Q.
PD MAR
PY 2022
VL 99
IS 1
BP 156
EP 182
AR 1077699020958753
DI 10.1177/1077699020958753
EA SEP 2020
PG 27
WC Communication
WE Social Science Citation Index (SSCI)
SC Communication
GA YX7ZR
UT WOS:000572397500001
DA 2024-09-05
ER
PT J
AU Cooper, C
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AF Cooper, Chris
   Snowsill, Tristan
   Worsley, Christine
   Prowse, Amanda
   O'Mara-Eves, Alison
   Greenwood, Helen
   Boulton, Emma
   Strickson, Amanda
TI Handsearching had best recall but poor efficiency when exporting to a
   bibliographic tool: case study
SO JOURNAL OF CLINICAL EPIDEMIOLOGY
LA English
DT Article
ID RANDOMIZED CONTROLLED-TRIALS; SYSTEMATIC REVIEWS; PUBLIC-HEALTH;
   ABSTRACTS; SEARCHES; JOURNALS
C1 [Cooper, Chris] UCL, Dept Clin Educ & Hlth Psychol, London WC1E 7HB, England.
   [Snowsill, Tristan] Univ Exeter, Med Sch, Hlth Econ Grp, Exeter, Devon, England.
   [Worsley, Christine; Prowse, Amanda; Boulton, Emma; Strickson, Amanda] Tolley Hlth Econ, Buxton, England.
   [O'Mara-Eves, Alison] UCL, EPPi Ctr, London, England.
   [Greenwood, Helen] Royal Coll Psychiatrists, London, England.
C3 University of London; University College London; University of Exeter;
   University of London; University College London; UCL Institute of
   Education
RP Cooper, C (corresponding author), UCL, 1-19 Torrington Pl, London WC1E 7HB, England.
EM ucjucc4@ucl.ac.uk
RI Cooper, Chris/C-9318-2012
OI Cooper, Chris/0000-0003-0864-5607; O'Mara-Eves,
   Alison/0000-0002-0359-6423
FU Takeda Pharmaceuticals, Cambridge MA, USA
FX The systematic review related to this study was sponsored by Takeda
   Pharmaceuticals, Cambridge MA, USA. The case study presented here was
   undertaken without any specific sponsorship or funding.
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NR 49
TC 6
Z9 6
U1 0
U2 4
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0895-4356
EI 1878-5921
J9 J CLIN EPIDEMIOL
JI J. Clin. Epidemiol.
PD JUL
PY 2020
VL 123
BP 39
EP 48
DI 10.1016/j.jclinepi.2020.03.013
PG 10
WC Health Care Sciences & Services; Public, Environmental & Occupational
   Health
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Health Care Sciences & Services; Public, Environmental & Occupational
   Health
GA LY4AU
UT WOS:000540472200006
PM 32229252
OA Green Accepted, Green Published
DA 2024-09-05
ER
PT J
AU Thorburn, D
AF Thorburn, D
TI Significance testing, interval estimation or Bayesian inference:
   Comments to "Extracting a maximum of useful information from statistical
   research data" by S. Sohlberg and G. Andersson
SO SCANDINAVIAN JOURNAL OF PSYCHOLOGY
LA English
DT Article
DE Bayesian paradigm; information building; model selection; posterior
   distribution; scientific knowledge
AB Statistical inference plays an important part in the formation of scientific knowledge in psychology. Starting from a paper by Sohlberg and Andersson (2005; Scandinavian Journal of Psychology, 46, 69-77) these issues are discussed. It is argued that interval estimates are easy to understand and that they are more suitable than significance testing for most problems. Bayesian inference is a coherent description of the information building process. With some examples it is shown that null hypothesis significance testing is full of contradictions. Finally, some other important issues like convenience sampling and model selection are shortly mentioned.
C1 Stockholm Univ, Dept Stat, SE-10691 Stockholm, Sweden.
C3 Stockholm University
RP Thorburn, D (corresponding author), Stockholm Univ, Dept Stat, SE-10691 Stockholm, Sweden.
EM Daniel.Thorburn@stat.su.se
CR [Anonymous], 2009, Bayesian Theory
   [Anonymous], 1979, MEASURES ASS CROSS C
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NR 9
TC 0
Z9 0
U1 0
U2 3
PU BLACKWELL PUBL LTD
PI OXFORD
PA 108 COWLEY RD, OXFORD OX4 1JF, OXON, ENGLAND
SN 0036-5564
J9 SCAND J PSYCHOL
JI Scand. J. Psychol.
PD FEB
PY 2005
VL 46
IS 1
BP 79
EP 82
DI 10.1111/j.1467-9450.2005.00437.x
PG 4
WC Psychology, Multidisciplinary
WE Social Science Citation Index (SSCI)
SC Psychology
GA 888MJ
UT WOS:000226378100009
PM 15660636
DA 2024-09-05
ER
PT J
AU Ortega, JL
   Lopez-Romero, E
   Fernández, I
AF Luis Ortega, Jose
   Lopez-Romero, Elena
   Fernandez, Ines
TI Multivariate approach to classify research institutes according to their
   outputs: The case of the CSIC's institutes
SO JOURNAL OF INFORMETRICS
LA English
DT Article
DE Scientometrics; Principal component analysis; Linear discriminant
   analysis; Research centres classification
ID PRINCIPAL COMPONENT ANALYSIS; CLASSIFICATION; UNIVERSITIES; PERFORMANCE
AB This paper attempts to build a classification model according to the research products created by those institutes and hence to design specific evaluation processes. Several scientific input/output indicators belonging to 109 research institutes from the Spanish National Research Council (CSIC) were selected. A multidimensional approach was proposed to resume these indicators in various components. A clustering analysis was used to classify the institutes according to their scores with those components (principal component analysis). Moreover, the validity of the a priori classification was tested and the most discriminant variables were detected (linear discriminant analysis). Results show that there are three types of institutes according to their research outputs: Humanistic, Scientific and Technological. It is argue that these differences oblige to design more precise assessment exercises which focus on the particular results of each type of institute. We conclude that this method permits to build more precise research assessment exercises which consider the varied nature of the scientific activity. (C) 2011 Elsevier Ltd. All rights reserved.
C1 [Luis Ortega, Jose; Lopez-Romero, Elena; Fernandez, Ines] CSIC, R&DUnit, E-28006 Madrid, Spain.
C3 Consejo Superior de Investigaciones Cientificas (CSIC)
RP Ortega, JL (corresponding author), CSIC, R&DUnit, Serrano 113, E-28006 Madrid, Spain.
EM jortega@orgc.csic.es; elena.lopez@orgc.csic.es;
   ines.fernandez@orgc.csic.es
RI FERNANDEZ PINTADO, INES/E-3209-2014
OI Ortega, Jose Luis/0000-0001-9857-1511
CR [Anonymous], INT J INNOVATION REG
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NR 29
TC 17
Z9 17
U1 0
U2 37
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1751-1577
EI 1875-5879
J9 J INFORMETR
JI J. Informetr.
PD JUL
PY 2011
VL 5
IS 3
BP 323
EP 332
DI 10.1016/j.joi.2011.01.004
PG 10
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 769QF
UT WOS:000291032500001
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Zou, J
   Le, D
   Thoma, GR
AF Zou, Jie
   Le, Daniel
   Thoma, George R.
TI Locating and parsing bibliographic references in HTML medical articles
SO INTERNATIONAL JOURNAL ON DOCUMENT ANALYSIS AND RECOGNITION
LA English
DT Article; Proceedings Paper
CT 16th Document Recognition and Retrieval Conference
CY JAN, 2009
CL San Jose, CA
DE HTML document analysis; Document Object Model (DOM); Reference parsing;
   Support Vector Machine (SVM); Conditional Random Field (CRF)
ID EXTRACTION; REPRESENTATION; METADATA
AB The set of references that typically appear toward the end of journal articles is sometimes, though not always, a field in bibliographic (citation) databases. But even if references do not constitute such a field, they can be useful as a preprocessing step in the automated extraction of other bibliographic data from articles, as well as in computer-assisted indexing of articles. Automation in data extraction and indexing to minimize human labor is key to the affordable creation and maintenance of large bibliographic databases. Extracting the components of references, such as author names, article title, journal name, publication date and other entities, is therefore a valuable and sometimes necessary task. This paper describes a two-step process using statistical machine learning algorithms, to first locate the references in HTML medical articles and then to parse them. Reference locating identifies the reference section in an article and then decomposes it into individual references. We formulate this step as a two-class classification problem based on text and geometric features. An evaluation conducted on 500 articles drawn from 100 medical journals achieves near-perfect precision and recall rates for locating references. Reference parsing identifies the components of each reference. For this second step, we implement and compare two algorithms. One relies on sequence statistics and trains a Conditional Random Field. The other focuses on local feature statistics and trains a Support Vector Machine to classify each individual word, followed by a search algorithm that systematically corrects low confidence labels if the label sequence violates a set of predefined rules. The overall performance of these two reference-parsing algorithms is about the same: above 99% accuracy at the word level, and over 97% accuracy at the chunk level.
C1 [Zou, Jie; Le, Daniel; Thoma, George R.] Natl Lib Med, Lister Hill Natl Ctr Biomed Commun, NIH, Bethesda, MD 20894 USA.
C3 National Institutes of Health (NIH) - USA; NIH National Library of
   Medicine (NLM)
RP Zou, J (corresponding author), Natl Lib Med, Lister Hill Natl Ctr Biomed Commun, NIH, 8600 Rockville Pike, Bethesda, MD 20894 USA.
EM jzou@mail.nlm.nih.gov
CR [Anonymous], P INT C VER LARG DAT
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NR 40
TC 16
Z9 18
U1 0
U2 11
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1433-2833
EI 1433-2825
J9 INT J DOC ANAL RECOG
JI Int. J. Doc. Anal. Recognit.
PD JUN
PY 2010
VL 13
IS 2
SI SI
BP 107
EP 119
DI 10.1007/s10032-009-0105-9
PG 13
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA 613UJ
UT WOS:000279007000004
PM 20640222
OA Green Accepted
DA 2024-09-05
ER
PT J
AU Tompos, A
   Margitfalvi, JL
   Tfirst, E
   Végvári, L
AF Tompos, A
   Margitfalvi, JL
   Tfirst, E
   Végvári, L
TI Evaluation of catalyst library optimization algorithms:: Comparison of
   the Holographic Research Strategy and the Genetic Algorithm in virtual
   catalytic experiments
SO APPLIED CATALYSIS A-GENERAL
LA English
DT Article
DE catalyst library design; combinatorial catalysis; Holographic Research
   Strategy; Genetic Algorithm; visualization
ID ARTIFICIAL NEURAL-NETWORKS; DESIGN; OXIDATION; PROPANE; METHANE
AB In this study two catalyst library optimization methods, the Holographic Research Strategy (HRS) and the Genetic Algorithm (GA) were compared based on their ability to find the optimum compositions in a given multi-dimensional experimental space. Results obtained in three different case studies were used to investigate both the rate and the certainty of the optimum search. In these case studies the activity-composition relationships were established using Artificial Neural Networks (ANNs) trained with catalytic data published earlier. The above relationships were used in "virtual optimization experiments" using both HRS and GA for catalyst library optimization. Upon using the stochastic GA its exceedingly divers mode of sampling often resulted in poor catalytic materials in the next catalyst generation. This fact resulted in a decreased rate of convergence to the optimum. In contrast, in HRS, which is a deterministic optimization algorithm, a moderate level of diversity in the catalyst library can easily be achieved. In this way an acceptable rate in optimum search can be accomplished. The visualization ability of HRS allows the illustration of all virtually tested compositions in a two-dimensional form regardless the optimization algorithm used. Upon using HRS a structured arrangement of experimental points in the virtual holograms was observed. However, when GA was applied for virtual optimization "starry sky"-like arrangement of compositions in the virtual holograms was obtained. Therefore based on virtual holograms, upon using HRS the relationship between the composition of catalytic materials and their performance can be qualitatively revealed, while no similar correlation can be obtained using GA. (c) 2006 Elsevier B.V. All rights reserved.
C1 Hungarian Acad Sci, Chem Res Ctr, Inst Surface Chem & Catalysis, H-1525 Budapest, Hungary.
   Meditor Gen Innovat Bur, H-2623 Kismaros, Hungary.
C3 Hungarian Academy of Sciences; Hungarian Research Network; HUN-REN
   Research Centre for Natural Sciences
RP Hungarian Acad Sci, Chem Res Ctr, Inst Surface Chem & Catalysis, POB 17, H-1525 Budapest, Hungary.
EM joemarg@chemres.hu
RI Tompos, Andras/K-8462-2019
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NR 12
TC 20
Z9 22
U1 0
U2 7
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0926-860X
EI 1873-3875
J9 APPL CATAL A-GEN
JI Appl. Catal. A-Gen.
PD APR 18
PY 2006
VL 303
IS 1
BP 72
EP 80
DI 10.1016/j.apcata.2006.01.028
PG 9
WC Chemistry, Physical; Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Environmental Sciences & Ecology
GA 034GM
UT WOS:000236917100010
DA 2024-09-05
ER
PT J
AU Wu, H
   Jiao, HZ
   Yu, Y
   Li, ZG
   Peng, ZH
   Liu, LB
   Zeng, Z
AF Wu, Hao
   Jiao, Hongzan
   Yu, Yang
   Li, Zhigang
   Peng, Zhenghong
   Liu, Lingbo
   Zeng, Zheng
TI Influence Factors and Regression Model of Urban Housing Prices Based on
   Internet Open Access Data
SO SUSTAINABILITY
LA English
DT Article
DE regression model; housing prices; geographically weighted regression;
   influence factor; hedonic model; artificial neural network (ANN);
   geographically weighted regression (GWR); urban planning
ID GEOGRAPHICALLY WEIGHTED REGRESSION; ACCESSIBILITY; IMPACTS; CHOICE
AB With the commercialization of housing and the deepening of urbanization in China, housing prices are having increasing influence on the land market, and thus indirectly affecting urban development. As various spatial features of an urban housing property directly affect its price, the study of this connection has significance for urban planning. The present study uses mainly open internet data of housing prices, supplemented by other data sources, to identify the spatial features of housing prices and the influence factors in a case study city, Wuhan. Methods employed in the study include the hedonic linear regression model, the geographically weighted regression (GWR) model and the artificial neural network (ANN) model, etc. Progress is made in the following two aspects: first, when calculating the influence factors, hierarchical values for accessibility variables of certain public facilities are used instead of simple Euclidean distance and the results shows a better model fit; second, the ANN model shows the best fit in the study, and while the three models all show respective strengths, the combined use of all models offers the possibility of a more comprehensive analysis of the influence factors of housing prices.
C1 [Wu, Hao; Jiao, Hongzan; Peng, Zhenghong] Wuhan Univ, Sch Urban Design, Dept Graph & Digital Technol, Wuhan 430072, Hubei, Peoples R China.
   [Yu, Yang; Li, Zhigang; Liu, Lingbo] Wuhan Univ, Sch Urban Design, Dept Urban Planning, Wuhan 430072, Hubei, Peoples R China.
   [Zeng, Zheng] China Univ Geosci, Dept Sch Arts & Commun, Wuhan 430074, Hubei, Peoples R China.
C3 Wuhan University; Wuhan University; China University of Geosciences
RP Yu, Y (corresponding author), Wuhan Univ, Sch Urban Design, Dept Urban Planning, Wuhan 430072, Hubei, Peoples R China.
EM wh79@whu.edu.cn; jiaohongzan@whu.edu.cn; yuyang1@whu.edu.cn;
   zhigangli@whu.edu.cn; pengzhenghong@whu.edu.cn; lingbo.liu@whu.edu.cn;
   zeng_cug@hotmail.com
RI li, zhigang/AFT-5267-2022; Liu, Lingbo/AAH-8240-2019; Wu,
   Hao/HJG-9487-2022
OI Liu, Lingbo/0000-0002-9876-8506; Wu, Hao/0000-0001-7107-8081; Yu,
   Yang/0000-0001-8776-0157
FU China Postdoctoral Science Foundation [2016M600609, 2016M602357];
   National Science Fund for Young Scholars [51708425, 51708426]; English
   Course Program of Wuhan University [209411800012]
FX The study is funded by the China Postdoctoral Science Foundation (No.
   2016M600609); National Science Fund for Young Scholars (No. 51708425);
   China Postdoctoral Science Foundation (No. 2016M602357); National
   Science Fund for Young Scholars (No. 51708426); and English Course
   Program of Wuhan University (No. 209411800012).
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NR 38
TC 29
Z9 33
U1 12
U2 90
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD MAY
PY 2018
VL 10
IS 5
AR 1676
DI 10.3390/su10051676
PG 17
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA GJ7RP
UT WOS:000435587100372
OA gold
DA 2024-09-05
ER
PT J
AU Bach, MP
   Ivec, A
   Hrman, D
AF Bach, Mirjana Pejic
   Ivec, Arian
   Hrman, Danijela
TI Industrial Informatics: Emerging Trends and Applications in the Era of
   Big Data and AI
SO ELECTRONICS
LA English
DT Article
DE industrial informatics; Scopus; bibliometrics; VosViewer; Big Data; AI
ID GOOGLE-SCHOLAR; SCIENCE; CITATION; BIBLIOMETRICS; WEB
AB Industrial informatics is a rapidly developing scientific field that deals with the knowledge-based automation of industrial design and manufacturing processes. In the last decade, industrial informatics has been strongly influenced by the rapid rise of data-based technologies such as Data Science, Big Data, and artificial intelligence. The goal of this paper is to provide a literature review of academic research analyzing the extensive spectrum of industrial informatics. Articles indexed in Scopus with the term "Industrial Informatics" in the title, abstract, or keywords were extracted since the term emerged in the 1990s, over a period of 29 years. The main journals, conferences, authors and countries were studied using bibliometric analysis. Text mining using VosViewer was used to extract the thematic groups of research related to industrial informatics, which are as follows: (i) Internet of Things, (ii) machine learning, (iii) engineering education, (iv) cyber-physical systems, and (v) embedded systems. We also found that China, Germany, and Brazil dominate research in industrial computing. The results showed that research in industrial informatics is related to the emergence of new methods and tools, and is nowadays shifting towards the application of intelligent methods such as machine learning and Big Data.
C1 [Bach, Mirjana Pejic] Univ Zagreb, Fac Econ & Business, Dept Informat, Zagreb 10000, Croatia.
   [Ivec, Arian] Univ Zagreb, Fac Sci, Dept Phys, Zagreb 10000, Croatia.
   [Hrman, Danijela] Probotica D o o, Zagreb 10000, Croatia.
C3 University of Zagreb; University of Zagreb
RP Bach, MP (corresponding author), Univ Zagreb, Fac Econ & Business, Dept Informat, Zagreb 10000, Croatia.
EM mpejic@net.efzg.hr
RI Pejic Bach, Mirjana/E-7313-2012
OI Pejic Bach, Mirjana/0000-0003-3899-6707
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NR 57
TC 3
Z9 3
U1 7
U2 24
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2079-9292
J9 ELECTRONICS-SWITZ
JI Electronics
PD MAY 15
PY 2023
VL 12
IS 10
AR 2238
DI 10.3390/electronics12102238
PG 16
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Physics
GA H5SG1
UT WOS:000996553400001
OA gold
DA 2024-09-05
ER
PT J
AU DAVIDSON, WN
   WORRELL, DL
AF DAVIDSON, WN
   WORRELL, DL
TI Research notes and communications: The effect of product recall
   announcements on shareholder wealth
SO STRATEGIC MANAGEMENT JOURNAL
LA English
DT Article
DE PRODUCT RECALLS; SHAREHOLDER WEALTH
ID MODERN FINANCIAL THEORY; ORGANIZATIONAL ECONOMICS; CORPORATE-STRATEGY;
   PUBLIC-POLICY; IMPACT
AB Previous research has found that product recall announcements in the automobile industry are associated with negative abnormal returns. We extend this research by examining announcements of product recalls and products taken off the market outside the automobile industry. We find negative abnormal returns for these announcements and that the returns are significantly more negative when products are replaced (or the purchase price is returned) than when the products are checked and repaired. We find only limited evidence that government-ordered recalls produce more negative returns than voluntary recalls.
C1 APPALACHIAN STATE UNIV, DEPT MANAGEMENT, BOONE, NC 28608 USA.
C3 University of North Carolina; Appalachian State University
RP SO ILLINOIS UNIV, DEPT FINANCE, CARBONDALE, IL 62901 USA.
RI Sharma, Kulwant Kumar/AAE-7849-2022
OI Sharma, Kulwant Kumar/0000-0002-3564-6130
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NR 18
TC 140
Z9 189
U1 1
U2 37
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0143-2095
EI 1097-0266
J9 STRATEGIC MANAGE J
JI Strateg. Manage. J.
PD SEP
PY 1992
VL 13
IS 6
BP 467
EP 473
DI 10.1002/smj.4250130606
PG 7
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA JK648
UT WOS:A1992JK64800005
DA 2024-09-05
ER
PT J
AU Gu, SS
   Wang, KY
   Gao, LY
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AF Gu, Suishan
   Wang, Kangyu
   Gao, Lianyue
   Liu, Jun
TI Research on express service defect evaluation based on semantic network
   diagram and SERVQUAL model
SO FRONTIERS IN PUBLIC HEALTH
LA English
DT Article
DE express service defects; text mining; semantic network diagram; SERVQUAL
   model; LDA topic model
ID BIG DATA; FAILURE
AB This paper constructs a defect evaluation model of express service, uses the text mining methods of web crawler, SVM (Support Vector Machine) emotion analysis and LDA (Linear Discriminant Analysis) topic model to capture and clean up the online negative comment data of express service, establishes a semantic network diagram, and uses LDA topic model to extract the characteristic words of defect topic. Based on SERVQUAL model, it can classify the subject characteristic words of express service defects from the dimensions of tangibility, reliability, responsiveness, assurance, empathy and economy, etc., calculate the degree value and attention value of express service defects, and establish IPA model for defect mapping and identify the improvement direction. The evaluation model constructed in this paper has reference value for evaluating the defects of service industry and improving service quality. It is found that the "responsiveness" defect is the primary improvement direction, and the reliability, assurance and economy are the secondary improvement defects. Among them, the "responsiveness" defect has five improvement detail defects. The evaluation model constructed in this paper has reference value for evaluating the defects of service industry and improving service quality.
C1 [Gu, Suishan; Wang, Kangyu; Gao, Lianyue] Jilin Univ, Sch Business & Management, Changchun, Jilin, Peoples R China.
   [Liu, Jun] China Shipbldg Ind Grp Co Ltd, Res Inst 716, Lianyungang, Jiangsu, Peoples R China.
C3 Jilin University
RP Gu, SS (corresponding author), Jilin Univ, Sch Business & Management, Changchun, Jilin, Peoples R China.
EM guss@jlu.edu.cn
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NR 45
TC 0
Z9 0
U1 16
U2 71
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 2296-2565
J9 FRONT PUBLIC HEALTH
JI Front. Public Health
PD DEC 2
PY 2022
VL 10
AR 1056575
DI 10.3389/fpubh.2022.1056575
PG 11
WC Public, Environmental & Occupational Health
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Public, Environmental & Occupational Health
GA 7B1GC
UT WOS:000898889800001
PM 36530722
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU Hamilton, L
   Elliott, D
   Quick, A
   Smith, S
   Choplin, V
AF Hamilton, Leah
   Elliott, Desha
   Quick, Aaron
   Smith, Simone
   Choplin, Victoria
TI Exploring the Use of AI in Qualitative Analysis: A Comparative Study of
   Guaranteed Income Data
SO INTERNATIONAL JOURNAL OF QUALITATIVE METHODS
LA English
DT Article
DE methods in qualitative inquiry; phenomenology; qualitative evaluation;
   social justice; community based research
AB This study explores the potential of the AI chatbot ChatGPT to supplement human-centered tasks such as qualitative research analysis. The study compares the emergent themes in human and AI-generated qualitative analyses of interviews with guaranteed income pilot recipients. The results reveal that there are similarities and differences between human and AI-generated analyses, with the human coders recognizing some themes that ChatGPT did not and vice versa. The study concludes that AI like ChatGPT provides a powerful tool to supplement complex human-centered tasks, and predicts that such tools will become an additional tool to facilitate research tasks. Future research could explore feeding raw interview transcripts into ChatGPT and incorporating AI-generated themes into triangulation discussions to help identify oversights, alternative frames, and personal biases.
C1 [Hamilton, Leah; Choplin, Victoria] Appalachian State Univ, Dept Social Work, ASU Box 32155, 1179 State Farm Rd, Boone, NC 28608 USA.
   [Elliott, Desha; Quick, Aaron; Smith, Simone] Clark Atlanta Univ, Dept Social Work, Atlanta, GA USA.
C3 University of North Carolina; Appalachian State University; Clark
   Atlanta University
RP Hamilton, L (corresponding author), Appalachian State Univ, Dept Social Work, ASU Box 32155, 1179 State Farm Rd, Boone, NC 28608 USA.
EM hamiltonl@appstate.edu
OI Hamilton, Leah/0000-0002-1253-171X
FU Give Directly
FX The author(s) disclosed receipt of the following financial support
   forthe research, authorship, and/or publication of this article: This
   workwas supported by the Give Directly.
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NR 20
TC 5
Z9 9
U1 15
U2 31
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 1609-4069
J9 INT J QUAL METH
JI Int. J. Qual. Meth.
PD SEP
PY 2023
VL 22
AR 16094069231201504
DI 10.1177/16094069231201504
PG 13
WC Social Sciences, Interdisciplinary
WE Social Science Citation Index (SSCI)
SC Social Sciences - Other Topics
GA R1AC1
UT WOS:001061726100001
OA gold
DA 2024-09-05
ER
PT J
AU Zhang, WS
   Ma, LP
AF Zhang, Wangshu
   Ma, Liping
TI Research and application of second-hand commodity price evaluation
   methods on B2C platform: take the used car platform as an example
SO ANNALS OF OPERATIONS RESEARCH
LA English
DT Article
DE Second-hand product price; Business to customer (B2C) platform; Support
   vector machine regression (SVR) model; Gradient boosting decision tree
   (GBDT) algorithm; Boruta algorithm
ID MODEL
AB Taking the business to customer used car trading platform as an example, two feature selection algorithms, namely, gradient boosting decision tree (GBDT) and Boruta, have been used for optimizing the support vector machine regression (SVR) model in order to explore efficient and accurate second-hand commodity price evaluation methods. On comparing the prediction accuracy of the original SVR model, the GBDT-SVR model, and the Boruta-SVR model, it has been found that the error between the price predicted by the Boruta-SVR model and the actual given price is the smallest among the three models. Thus, this model can provide an accurate evaluation of the prices of used cars. This method can also be extended to other second-hand goods by incorporating their respective attributes and the price evaluation link of the second-hand trading platform in the consumer to business to consumer model, which will help in improving the efficiency of the merchant price evaluation and the merchant evaluation process.
C1 [Zhang, Wangshu; Ma, Liping] Capital Univ Econ & Business, Sch Stat, Beijing 100071, Peoples R China.
C3 Capital University of Economics & Business
RP Zhang, WS (corresponding author), Capital Univ Econ & Business, Sch Stat, Beijing 100071, Peoples R China.
EM zws19971009@126.com
CR Chi XC, 2015, OPT MATER, V46, P350, DOI 10.1016/j.optmat.2015.04.043
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NR 15
TC 3
Z9 3
U1 8
U2 84
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0254-5330
EI 1572-9338
J9 ANN OPER RES
JI Ann. Oper. Res.
PD JUL
PY 2023
VL 326
IS SUPPL 1
SU 1
BP 37
EP 37
DI 10.1007/s10479-021-04332-5
EA OCT 2021
PG 1
WC Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Operations Research & Management Science
GA N0LM4
UT WOS:000712497400002
DA 2024-09-05
ER
PT J
AU Jing, WX
   Zhen, LM
   Wei, SY
   Wang, X
AF Jing, Wang X.
   Zhen, Liu M.
   Wei, Sun Y.
   Wang, Xin
TI Research on low-speed performance of continuous rotary electro-hydraulic
   servo motor based on robust control with Adaboost prediction
SO JOURNAL OF ENGINEERING-JOE
LA English
DT Article
AB In order to improve the robustness and low-speed performance of continuous rotary electro-hydraulic servo system under influences of dynamic uncertainties, parametric perturbation, friction, other non-linear properties, and uncertainties, the robust control strategy was proposed with Adaboost prediction. Firstly, basing on the system mathematic model, the model with structured uncertainty and generalised state equation was established with parametric perturbation and external disturbances, and then the robust controller was developed by adopting $H_\infty $H infinity theory. Furthermore, Adaboost algorithm based on radial basis function (RBF) neural network was applied to design the system feedback mechanism, so the multiple weak neural network learners were obtained by using Adaboost algorithm to train system actual output and input. Also, these weak neural network learners constituted a strong learner to predict the electro-hydraulic servo system output and calculate the predictive error so as to adjust the system robust control output, so the real-time control was carried out by the robust controller. Some comparative simulated results are obtained to verify the proposed controller guarantees performances of low speed, tracking accuracy, and ability of anti-interference, which greatly expands the band of frequency response and improve the system robustness.
C1 [Jing, Wang X.; Zhen, Liu M.; Wei, Sun Y.] Harbin Univ Sci & Technol, Sch Mech & Power Engn, Harbin, Heilongjiang, Peoples R China.
   [Wang, Xin] Jilin Univ, Sch Mech & Aerosp Engn, Changchun, Jilin, Peoples R China.
C3 Harbin University of Science & Technology; Jilin University
RP Zhen, LM (corresponding author), Harbin Univ Sci & Technol, Sch Mech & Power Engn, Harbin, Heilongjiang, Peoples R China.
EM mzliu94@163.com
FU National Natural Science Foundation of China [51305108]; Post Doctoral
   Researchers Settled in Heilongjiang Research Start Funding Projects
   [LBH-Q15069]
FX This project was supported by National Natural Science Foundation of
   China (grant no. 51305108), and Post Doctoral Researchers Settled in
   Heilongjiang Research Start Funding Projects (no. LBH-Q15069).
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NR 21
TC 1
Z9 2
U1 0
U2 7
PU INST ENGINEERING TECHNOLOGY-IET
PI HERTFORD
PA MICHAEL FARADAY HOUSE SIX HILLS WAY STEVENAGE, HERTFORD SG1 2AY, ENGLAND
SN 2051-3305
J9 J ENG-JOE
JI J. Eng.-JOE
PD JAN
PY 2019
IS 13
BP 60
EP 67
DI 10.1049/joe.2018.8970
PG 8
WC Engineering, Multidisciplinary
WE Emerging Sources Citation Index (ESCI)
SC Engineering
GA HN0ZX
UT WOS:000459918000009
OA gold
DA 2024-09-05
ER
PT J
AU Doloreux, D
   de la Puerta, JG
   Pastor-López, I
   Gómez, IP
   Sanz, B
   Zabala-Iturriagagoitia, JM
AF Doloreux, David
   Gaviria de la Puerta, Jose
   Pastor-Lopez, Iker
   Porto Gomez, Igone
   Sanz, Borja
   Mikel Zabala-Iturriagagoitia, Jon
TI Territorial innovation models: to be or not to be, that's the question
SO SCIENTOMETRICS
LA English
DT Article
DE Territorial innovation models; Bibliometric analysis; Natural language
   processing; Regional development
ID RESEARCH-AND-DEVELOPMENT; INDUSTRIAL DISTRICTS; KEYWORD COOCCURRENCE;
   LEARNING REGION; CLUSTERS; SCIENCE; SYSTEMS; POLICY; DYNAMICS; KNOWLEDGE
AB Industrial agglomerations are key in explaining the development paths followed by territories, particularly at sub-national levels. This field of research has received increasing attention in the last decades, what has led to the emergence of a variety of models intended to characterize innovation at the regional level. Moulaert and Sekia (Reg Stud 37:289-302, 2003) introduced the concept of 'Territorial Innovation Models' (TIMs) as a generic name that embraced these conceptual models of regional innovation in the literature. However, the literature does not help to assess the extent to which convergence or divergence is found across TIMs. In this paper we aim to clarify if there are clear boundaries across TIMs, so each TIM has particular characteristics that make it conceptually different from others, and hence, justify its introduction in the literature. Based on natural language processing methodologies, we extract the key terms of a large volume of academic papers published in peer review journals indexed in the Web of Science for the following TIMS: industrial districts, innovative milieu, learning regions, clusters, regional innovation systems, local production systems and new industrial spaces. We resort to Rapid Automatic Keyword Extraction to identify the associations between the topics extracted from the previous corpus. Finally, a configuration to visualise the results of the methodology followed is also proposed. Our results evidence that the previous models do not have a unique flavour but are rather similar in their taste. We evidence that there is quite little that is truly new in the different TIMs in terms of theory-building and the concepts being used in each model.
C1 [Doloreux, David] HEC Montreal, Dept Int Business, Montreal, PQ, Canada.
   [Gaviria de la Puerta, Jose; Pastor-Lopez, Iker; Sanz, Borja] Univ Deusto, Fac Engn, Ave Universidades 24, Bilbao 48007, Spain.
   [Porto Gomez, Igone] Univ Deusto, Deusto Business Sch, Ave Universidades 24, Bilbao 48007, Spain.
   [Mikel Zabala-Iturriagagoitia, Jon] Univ Deusto, Deusto Business Sch, Camino Mundaiz 50, Donostia San Sebastian 20012, Spain.
C3 Universite de Montreal; HEC Montreal; University of Deusto; University
   of Deusto; University of Deusto
RP Gómez, IP (corresponding author), Univ Deusto, Deusto Business Sch, Ave Universidades 24, Bilbao 48007, Spain.
EM igone.porto@deusto.es
RI Gomez, Igone Porto/J-4501-2014; Zabala-Iturriagagoitia, Jon
   Mikel/L-9297-2013; Sanz, Borja/L-8365-2014; Doloreux, David/Y-7368-2019
OI Gomez, Igone Porto/0000-0003-2865-4818; Zabala-Iturriagagoitia, Jon
   Mikel/0000-0003-1975-2555; Sanz, Borja/0000-0003-2039-7773; Doloreux,
   David/0000-0001-7101-2170
FU Eusko Jaurlaritza [IT885-16, H2020-700367]
FX The funding was provided by Eusko Jaurlaritza (Grand No. IT885-16) and
   H2020 Societal Challenges (Grand No. H2020-700367).
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TC 13
Z9 16
U1 2
U2 44
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD SEP
PY 2019
VL 120
IS 3
BP 1163
EP 1191
DI 10.1007/s11192-019-03181-1
PG 29
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA IR4WE
UT WOS:000481434200010
DA 2024-09-05
ER
PT J
AU Zope, B
   Mishra, S
   Shaw, K
   Vora, DR
   Kotecha, K
   Bidwe, RV
AF Zope, Bhushan
   Mishra, Sashikala
   Shaw, Kailash
   Vora, Deepali Rahul
   Kotecha, Ketan
   Bidwe, Ranjeet Vasant
TI Question Answer System: A State-of-Art Representation of Quantitative
   and Qualitative Analysis
SO BIG DATA AND COGNITIVE COMPUTING
LA English
DT Article
DE question answering system; bibliometric analysis; natural language
   processing; machine comprehension
ID WEB; CATEGORIZATION; SIMILARITY; QUERIES; MODELS
AB Question Answer System (QAS) automatically answers the question asked in natural language. Due to the varying dimensions and approaches that are available, QAS has a very diverse solution space, and a proper bibliometric study is required to paint the entire domain space. This work presents a bibliometric and literature analysis of QAS. Scopus and Web of Science are two well-known research databases used for the study. A systematic analytical study comprising performance analysis and science mapping is performed. Recent research trends, seminal work, and influential authors are identified in performance analysis using statistical tools on research constituents. On the other hand, science mapping is performed using network analysis on a citation and co-citation network graph. Through this analysis, the domain's conceptual evolution and intellectual structure are shown. We have divided the literature into four important architecture types and have provided the literature analysis of Knowledge Base (KB)-based and GNN-based approaches for QAS.
C1 [Zope, Bhushan; Mishra, Sashikala; Shaw, Kailash; Vora, Deepali Rahul; Bidwe, Ranjeet Vasant] Symbiosis Int Deemed Univ SIU, Symbiosis Inst Technol, Pune 412115, India.
   [Kotecha, Ketan] Symbiosis Int Deemed Univ SIU, Symbiosis Inst Technol, Symbiosis Ctr Appl Artificial Intelligence SCAAI, Pune 412115, India.
C3 Symbiosis International University; Symbiosis Institute of Technology
   (SIT); Symbiosis International University; Symbiosis Institute of
   Technology (SIT)
RP Zope, B (corresponding author), Symbiosis Int Deemed Univ SIU, Symbiosis Inst Technol, Pune 412115, India.
EM bhushan.zope.phd2021@sitpune.edu.in
RI Vora, Deepali/AAB-8430-2019; Zope, Bhushan/GQP-7499-2022; Shaw,
   Kailash/AHC-6550-2022; Bidwe, Ranjeet/AGD-7872-2022; Shaw,
   Kailash/AAG-4625-2021; Kotecha, K/U-3927-2017
OI Vora, Deepali/0000-0003-3969-9800; Zope, Bhushan/0000-0003-2636-223X;
   Bidwe, Ranjeet/0000-0002-6801-3102; Shaw, Kailash/0000-0001-5677-7423;
   Kotecha, K/0000-0003-2653-3780
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NR 157
TC 4
Z9 4
U1 2
U2 17
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2504-2289
J9 BIG DATA COGN COMPUT
JI Big Data Cogn. Comput.
PD DEC
PY 2022
VL 6
IS 4
AR 109
DI 10.3390/bdcc6040109
PG 33
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA 7D2TR
UT WOS:000900349900001
OA gold
DA 2024-09-05
ER
PT J
AU Cumiskey, KM
   Humphreys, L
AF Cumiskey, Kathleen M.
   Humphreys, Lee
TI Social, seamless, just, and open: Advancing mobile communication
   research
SO NEW MEDIA & SOCIETY
LA English
DT Article
DE AI; diversity; inclusivity; mobile phones; open access; open research;
   seamless; smartphones; social; social justice
ID PHONE; TECHNOLOGY; INFORMATION; MIDWIVES; ACCESS; AGE
AB Through integrating the research featured in this issue, this article describes generative areas for future research and the means to advance the impact of our field. Reflective practices related to field building and knowledge access for which Rich Ling helped to lay the groundwork are highlighted. Ling's work in mobile media and telecommunications has influenced the theoretical, methodological, and empirical opportunities for mobile communication research. Four themes for future mobile communication research have emerged: social, seamless, just, and open. These themes align with the work featured in this issue and with Ling's promotion of practices that enhance our field to develop relevancy, integrity, and ecological validity. This article places special focus on global and social justice as leading to a better understanding of mobile communication in the world.
C1 [Cumiskey, Kathleen M.] CUNY, Coll Staten Isl, Dept Psychol, New York, NY USA.
   [Humphreys, Lee] Cornell Univ, Dept Commun, Ithaca, NY USA.
   [Humphreys, Lee] Cornell Univ, Qualitat & Interpret Res Inst, Ithaca, NY USA.
   [Cumiskey, Kathleen M.] CUNY, Coll Staten Isl, 2800 Victory Blvd,Bldg 4S-108, Staten Isl, NY 10314 USA.
C3 City University of New York (CUNY) System; College of Staten Island
   (CUNY); Cornell University; Cornell University; City University of New
   York (CUNY) System; College of Staten Island (CUNY)
RP Cumiskey, KM (corresponding author), CUNY, Coll Staten Isl, 2800 Victory Blvd,Bldg 4S-108, Staten Isl, NY 10314 USA.
EM Katie.cumiskey@csi.cuny.edu
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NR 57
TC 0
Z9 0
U1 1
U2 8
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 1461-4448
EI 1461-7315
J9 NEW MEDIA SOC
JI New Media Soc.
PD APR
PY 2023
VL 25
IS 4
SI SI
BP 833
EP 848
DI 10.1177/14614448231158642
EA APR 2023
PG 16
WC Communication
WE Social Science Citation Index (SSCI)
SC Communication
GA E2SW2
UT WOS:000973458000001
DA 2024-09-05
ER
PT C
AU Rao, Y
   Zhong, XH
   Lu, SM
AF Rao, Yuan
   Zhong, Xuhui
   Lu, Shumin
GP IEEE
TI Research on News Topic-driven Market Flucatuation and Predication
SO 2016 INTERNATIONAL CONFERENCE ON IDENTIFICATION, INFORMATION AND
   KNOWLEDGE IN THE INTERNET OF THINGS (IIKI)
LA English
DT Proceedings Paper
CT International Conference on Identification, Information and Knowledge in
   the Internet of Things (IIKI)
CY OCT 20-21, 2016
CL Beijing, PEOPLES R CHINA
DE News Topic Recognition; LP-based LDA; Degree of Influence; Price
   Movement
AB In order to forecast the price movement of stock with the correlated news events, an enhanced Topic-driven model with the positional weight of feature words and label of stocks, named LP-LDA model, is proposed to represent and analyze the intrinsic mechanism in financial market. The experiment results show that LP-LDA has a better performance than traditional LDA model. Especially, when the number of topics are increasing, the running time of LP-LDA model are 0.69s, 0.78 s and 1.15s at 100, 200 and 300 topics, respectively, which are better than LDA. Furthermore, Degree of Influence (DoI) is defined to describe the considerable influence about the news events on the price movement of certain stock, which provides a new mechanism to measure the fluctuating price. The experiment results shown that the coefficient of correlation between news topic and return rate of stock is 0.9137, which is much higher than other results of experiment.
C1 [Rao, Yuan; Zhong, Xuhui] Xi An Jiao Tong Univ, Sch Software Engn, Lab Social Intelligence & Complex Data Proc, Xian 710049, Peoples R China.
   [Lu, Shumin] Xi An Jiao Tong Univ, Sch Humanity Social Sci, Lab Social Intelligence & Complex Data Proc, Xian 710049, Peoples R China.
C3 Xi'an Jiaotong University; Xi'an Jiaotong University
RP Rao, Y (corresponding author), Xi An Jiao Tong Univ, Sch Software Engn, Lab Social Intelligence & Complex Data Proc, Xian 710049, Peoples R China.
EM yuanrao@163.com; 2998999684@qq.com; Shuminlu@126.com
FU Science and Technology Project of Xi'an City in China [CXY1514(5)]; Key
   Science and Technology Project of YanTa District of Xi'an [QX1404-2];
   Key Collaborative Innovation Project of Shanxi Province in China
   [2015XT-21]
FX This paper is joint supported by "2015 Key Collaborative Innovation
   Project of Shanxi Province in China (2015XT-21)", "2015 Science and
   Technology Project of Xi'an City in China(CXY1514(5))" and "2014 Key
   Science and Technology Project of YanTa District of Xi'an (QX1404-2)".
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NR 11
TC 2
Z9 2
U1 0
U2 4
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-5090-5952-2
PY 2016
BP 559
EP 562
DI 10.1109/IIKI.2016.93
PG 4
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Engineering
GA BJ6WP
UT WOS:000426969900103
DA 2024-09-05
ER
PT J
AU Anghelescu, A
   Ciobanu, I
   Munteanu, C
   Anghelescu, LAM
   Onose, G
AF Anghelescu, Aurelian
   Ciobanu, Ilinca
   Munteanu, Constantin
   Anghelescu, Lucia Ana Maria
   Onose, Gelu
TI ChatGPT: "To be or not to be"... in academic research. The human mind's
   analytical rigor and capacity to discriminate between AI bots' truths
   and hallucinations
SO BALNEO AND PRM RESEARCH JOURNAL
LA English
DT Article
DE ChatGPT; academic writing; bibliographic resources; metha-analyse
AB Background. ChatGPT can generate increasingly realistic language, but the correctness and integrity of implementing these models in scientific papers remain unknown.
   Recently published literature emphasized the "three faces of the coin" of ChatGPT: the negative impact on academic writing, limitations in analyzing and conducting extensive searches of references across multiple databases, and the superiority of the human mind.
   Method. The present study assessed the chatbot's ability for improvement and its propensity for self-correction at various points in 2023.
   Starting from previous papers published in our clinic, the authors repeatedly challenged the ChatGPT to conduct extensive searches for references across multiple databases at different time intervals (in March and September 2023). The bot was asked to find recent meta-analyses on a particular topic.
   Results. The replies (print screens) generated in March and September 2023 serve as evidence of the OpenAI platform's qualitative development and improvement.
   During the first contact with ChatGPT-3, one noticed significant content flows and drawbacks. ChatGPT provided references and short essays, but none of them were real, despite ChatGPT's clear affirmative response. When searching PubMed IDs, all DOI numbers indicated by the chatbot correlated to various unconnected manuscripts.
   After a few months, the authors repeated the same interrogative provocations and observed a significant shift in the replies. The ChatGPT-3.5 delivered balanced responses, emphasizing the superiority of the human intellect and advocating traditional academic research techniques and methods.
   Discussion. A recent comparative systematic analysis using the PRISMA method using the same keyword syntactic correlations to search for systematic literature or open sources has revealed the superiority of the classical scholarly method of research.
   In contrast, every document (title, authors, doi) that ChatGPT-3 initially delivered was erroneous and associated with a different field or topic.
   Literature published during the first trimester of 2023 emphasized ChatGPT`s hallucinatory tendency to supply fake "bibliographic resources" and confabulatory attempts to paraphrase nonexistent "research papers" presented as authentic articles.
   A second inquiry was realized six months later generated reserved and cautious solutions, indicating the researcher should analyze and carefully verify the information from specialized academic databases.
   Conclusions. The paper succinctly describes the flows and initial limitations of the ChatGPT-3 version and the process of updating and improving the GPT-3.5 system during 2023.
   ChatGPT might be a possible adjunct to academic writing and scientific research, considering any limitations that might jeopardize the study.
   The new perspective from ChatGPT claims that human intelligence and thought must thoroughly assess any AI information.
C1 [Anghelescu, Aurelian; Onose, Gelu] Carol Davila Univ Med & Pharm, Bucharest, Romania.
   [Anghelescu, Aurelian; Ciobanu, Ilinca; Munteanu, Constantin; Onose, Gelu] Teaching Emergency Hosp Bagdasar Arseni TEHBA, Bucharest, Romania.
   [Munteanu, Constantin] Univ Med & Pharm Grigore T Popa Iasi, Iasi, Romania.
C3 Carol Davila University of Medicine & Pharmacy; Grigore T Popa
   University of Medicine & Pharmacy
RP Anghelescu, A (corresponding author), Carol Davila Univ Med & Pharm, Bucharest, Romania.; Anghelescu, A (corresponding author), Teaching Emergency Hosp Bagdasar Arseni TEHBA, Bucharest, Romania.
EM aurelian.anghelescu@umfcd.ro
RI Munteanu, Constantin/ACM-9541-2022
OI Munteanu, Constantin/0000-0002-1084-7710
CR Alkaissi H, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.35179
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   opengenus, About us
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NR 28
TC 0
Z9 0
U1 11
U2 11
PU ROMANIAN ASSOC BALNEOLOGY
PI BUCHAREST
PA SECTOR 2, ALEEA DOBRINA NO 7, BL D10, SC A, AP 4, BUCHAREST, ROMANIA
SN 2734-844X
EI 2734-8458
J9 BALNEO PRM RES J
JI Balneo PRM Res. J.
PD DEC
PY 2023
VL 14
IS 4
AR 614
DI 10.12680/balneo.2023.614
PG 9
WC Rehabilitation
WE Emerging Sources Citation Index (ESCI)
SC Rehabilitation
GA IN9F3
UT WOS:001167118000019
OA gold
DA 2024-09-05
ER
PT J
AU Mateo, FW
   Redchuk, A
AF Walas Mateo, Federico
   Redchuk, Andres
TI Artificial Intelligence as a Process Optimization Driver under Industry
   4.0 Framework and the Role of IIoT, a Bibliometric Analysis
SO JOURNAL OF INDUSTRIAL INTEGRATION AND MANAGEMENT-INNOVATION AND
   ENTREPRENEURSHIP
LA English
DT Article; Early Access
DE Industry digitalization; Industry 4.0; industrial Internet of Things
   (IIoT); AI/ML; data-driven culture; people empowerment; data-driven
   process optimization
AB Connected products generate data that are being seen as a key source of competitive advantage, and the management and processing of that data are generating new challenges in the industrial environment. This paper proposes a conceptual framework and preliminary findings to go deeper into a bibliometric analysis around the idea of artificial intelligence and machine learning (AI/ML) as a tool for the optimization of processes within the Industry 4.0 model. Methodologically, a technological mapping was carried out through an exercise on Scopus indexed database, the results of which were analyzed using bibliometric indicators. The bibliometric study is completed with a screening of relevant papers searching for linking among the industrial Internet of Thing (IIoT) or Internet of Thing (IoT), IA/ML and process optimization. Finally, this paper gives information about the state-of-the-art AI/ML applied to the optimization of industrial processes and presents a novel Canadian startup that has a business model that aims to make AI/ML easy to use in the industrial world towards a lean processes strategy.
C1 [Walas Mateo, Federico] Univ Nacl Arturo Jauretche, RA-1888 Buenos Aires, DF, Argentina.
   [Redchuk, Andres] Univ Nacl Lomas de Zamora, Fac Ingn, Buenos Aires, DF, Argentina.
   [Redchuk, Andres] Univ Rey Juan Carlos, ETSII, Madrid, Spain.
C3 National University of Lomas de Zamora; Universidad Rey Juan Carlos
RP Mateo, FW (corresponding author), Univ Nacl Arturo Jauretche, RA-1888 Buenos Aires, DF, Argentina.
EM fedewalas@gmail.com
RI Redchuk, Andrés/ABF-6067-2021
OI Redchuk, Andrés/0000-0001-5903-166X; WALAS MATEO,
   FEDERICO/0000-0001-5437-5789
FU Resolution Engineering Faculty of University of Lomas de Zamora, Buenos
   Aires, Argentine [148-18-UNAJ]
FX The authors wish to thank for the support received from two research
   projects. The projects are "Analisis del abordaje de herramientas de
   Produccion 4.0 en PyMEs locales," approved by Resolution (R) No.
   148-18-UNAJ INVESTIGA 2017 Program, and "Mejora de Procesos,
   Optimizacion y Data Analytics," approved by Resolution Engineering
   Faculty of University of Lomas de Zamora, Buenos Aires, Argentine.
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TC 7
Z9 7
U1 8
U2 43
PU WORLD SCIENTIFIC PUBL CO PTE LTD
PI SINGAPORE
PA 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE
SN 2424-8622
EI 2424-8630
J9 J IND INTEGR MANAG
JI J. Ind. Integr. Manag.
PD 2022 SEP 17
PY 2022
DI 10.1142/S2424862222500130
EA SEP 2022
PG 16
WC Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA 4P0VC
UT WOS:000855112700001
DA 2024-09-05
ER
PT J
AU Semushin, IV
   Tsyganova, JV
   Ugarov, VV
   Afanasova, AI
AF Semushin, I. V.
   Tsyganova, J. V.
   Ugarov, V. V.
   Afanasova, A. I.
TI The WHATs and HOWs of maturing computational and software engineering
   skills in Russian higher education institutions
SO EUROPEAN JOURNAL OF ENGINEERING EDUCATION
LA English
DT Article
DE Active learning; assessment of learning outcomes; engineering education
   research; mathematics education; project organised learning
ID LEARNING APPROACH; MOTIVATION; MODEL
AB Russian higher education institutions' tradition of teaching large-enrolled classes is impairing student striving for individual prominence, one-upmanship, and hopes for originality. Intending to converting these drawbacks into benefits, a Project-Centred Education Model (PCEM) has been introduced to deliver Computational Mathematics and Information Science courses. The model combines a Frontal Competitive Approach and a Project-Driven Learning (PDL) framework. The PDL framework has been developed by stating and solving three design problems: (i) enhance the diversity of project assignments on specific computation methods algorithmic approaches, (ii) balance similarity and dissimilarity of the project assignments, and (iii) develop a software assessment tool suitable for evaluating the technological maturity of students' project deliverables and thus reducing instructor's workload and possible overlook. The positive experience accumulated over 15 years shows that implementing the PCEM keeps students motivated to strive for success in rising to higher levels of their computational and software engineering skills.
C1 [Semushin, I. V.; Tsyganova, J. V.; Ugarov, V. V.; Afanasova, A. I.] Ulyanovsk State Univ, Math Informat & Aviat Technol Fac, Ulyanovsk, Russia.
C3 Ulyanovsk State University
RP Semushin, IV (corresponding author), Ulyanovsk State Univ, Math Informat & Aviat Technol Fac, Ulyanovsk, Russia.
EM i.v.semushin@ieee.org
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NR 110
TC 1
Z9 1
U1 0
U2 3
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 0304-3797
EI 1469-5898
J9 EUR J ENG EDUC
JI Eur. J. Eng. Educ.
PY 2018
VL 43
IS 3
SI SI
BP 446
EP 472
DI 10.1080/03043797.2017.1385594
PG 27
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA GA9QF
UT WOS:000428675200009
DA 2024-09-05
ER
PT J
AU Chen, HS
   Wang, XM
   Pan, SR
   Xiong, F
AF Chen, Hongshu
   Wang, Ximeng
   Pan, Shirui
   Xiong, Fei
TI Identify Topic Relations in Scientific Literature Using Topic Modeling
SO IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
LA English
DT Article
DE Bibliometrics; tech mining; text mining; topic analysis
ID BIG DATA; CITATION; TEXT; INFORMATION; TECHNOLOGY; SCIENCE; EMERGENCE;
   MAP
AB Over the past five years, topic models have been applied to bibliometrics research as an efficient tool for discovering latent and potentially useful content. The combination of topic modeling algorithms and bibliometrics has generated new challenges of interpreting and understanding the outcome of topic modeling. Motivated by these new challenges, this paper proposes a systematic methodology for topic analysis in scientific literature corpora to face the concerns of conducting post topic modeling analysis. By linking the corpus metadata with the discovered topics, we feature them with a number of topic-based analytic indices to explore their significance, developing trend, and received attention. A topic relation identification approach is then presented to quantitatively model the relations among the topics. To demonstrate the feasibility and effectiveness of our methodology, we present two case studies, using big data and dye-sensitized solar cell publications derived from searches in World of Science. Possible application of the methodology in telling good stories of a target corpus is also explored to facilitate further research management and opportunity discovery.
C1 [Chen, Hongshu] Beijing Inst Technol, Sch Management & Econ, Beijing 100081, Peoples R China.
   [Wang, Ximeng; Xiong, Fei] Beijing Jiaotong Univ, Sch Elect & Informat Engn, Beijing 100044, Peoples R China.
   [Wang, Ximeng] Univ Technol Sydney, Fac Engn & Informat Techno Logy, Ctr Artificial Intelligence, Ultimo, NSW 2007, Australia.
   [Pan, Shirui] Monash Univ, Fac Informat & Technol, Clayton, Vic 3800, Australia.
C3 Beijing Institute of Technology; Beijing Jiaotong University; University
   of Technology Sydney; Monash University
RP Xiong, F (corresponding author), Beijing Jiaotong Univ, Sch Elect & Informat Engn, Beijing 100044, Peoples R China.
EM Hongsue1114@hotmail.com; wangxm@bjtu.edu.cn; shirui.pan@monash.edu;
   xiongf@bjtu.edu.cn
RI Pan, Shirui/K-6763-2018; Chen, Hongshu/O-2926-2017
OI Pan, Shirui/0000-0003-0794-527X; Chen, Hongshu/0000-0002-0893-1817;
   Xiong, Fei/0000-0002-1610-335X; Wang, Ximeng/0000-0002-2445-6737
FU National Natural Science Foundation of China [61872033]; Humanity and
   Social Science Youth Foundation of Ministry of Education of China
   [18YJCZH204]; Beijing Natural Science Foundation [4184084]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61872033, in part by the Humanity and
   Social Science Youth Foundation of Ministry of Education of China under
   Grant 18YJCZH204, and in part by the Beijing Natural Science Foundation
   under Grant 4184084.
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NR 52
TC 14
Z9 16
U1 6
U2 57
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0018-9391
EI 1558-0040
J9 IEEE T ENG MANAGE
JI IEEE Trans. Eng. Manage.
PD OCT
PY 2021
VL 68
IS 5
BP 1232
EP 1244
DI 10.1109/TEM.2019.2903115
PG 13
WC Business; Engineering, Industrial; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Engineering
GA TK4EZ
UT WOS:000674114900003
DA 2024-09-05
ER
PT J
AU Sang, NM
AF Sang, Nguyen Minh
TI "Bibliometric insights into the evolution of digital marketing trends
SO INNOVATIVE MARKETING
LA English
DT Article
DE artificial intelligence; content marketing; customer engagement; digital
   transformation; machine learning; social media marketing; technology
   adoption; virtual reality
ID SOCIAL MEDIA; STRATEGIES; PERSONALIZATION; PERCEPTIONS; ACCEPTANCE;
   RICHNESS; CHANNELS; BEHAVIOR; WORD
AB This bibliometric analysis aims to delineate the progression of research in the domain of digital marketing by examining 513 English -language articles published in Scopus during the period of 2003-2024. An examination of scholarly productivity indicates an upward trend, as evidenced by the increase in publications from one in 2003 to 115 in 2022 and citations from 79 in 2003 to 1131 in 2021, as determined by keyword, citation, and authorship analyses. A review of citation patterns reveals that publications with significant impact are primarily found in prestigious academic journals, such as Industrial Marketing Management and International Journal of Research in Marketing. Prominent contributors hail from Jordan, Finland, Spain, the United Arab Emirates, and Saudi Arabia; among other regions - the United States, the Middle East, Europe, and Asia. Keyword analysis revealed an emphasis on emerging technologies such as artificial intelligence and traditional digital marketing techniques (e.g., social media, content marketing, internet marketing). Co -occurrence theme analysis highlighted digital marketing strategy, digital marketing audiences, the digital transformation of business and marketing, and the acceleration of digital adoption as a result of COVID-19. Further areas of investigation encompass optimizing the utilization of emergent social media platforms, implementing virtual and augmented reality technologies to enhance the customer experience, and capitalizing on the potential of artificial intelligence and machine learning to augment the efficacy of digital marketing. By utilizing data -driven insights, this study offers guidance for curricular enhancements, scholarly agendas, and digital marketing practice.
C1 [Sang, Nguyen Minh] Ho Chi Minh Univ Banking, Fac Int Econ, Ho Chi Minh City, Vietnam.
RP Sang, NM (corresponding author), Ho Chi Minh Univ Banking, Fac Int Econ, Ho Chi Minh City, Vietnam.
OI Sang, Nguyen Minh/0000-0002-4272-0247
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NR 61
TC 0
Z9 0
U1 15
U2 15
PU LLC CPC BUSINESS PERSPECTIVES
PI SUMY
PA HRYHORII SKOVORODA LN, 10, SUMY, 40022, UKRAINE
SN 1814-2427
EI 1816-6326
J9 INNOV MARKET
JI Innovation Marketing
PY 2024
VL 20
IS 2
DI 10.21511/im.20(2).2024.01
PG 15
WC Business
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA RV4B4
UT WOS:001230411600012
OA gold
DA 2024-09-05
ER
PT J
AU Hahn, J
AF Hahn, Jim
TI Semi-Automated Methods for BIBFRAME Work Entity Description
SO CATALOGING & CLASSIFICATION QUARTERLY
LA English
DT Article
DE RDF; BIBFRAME; work entity description; bibliographic entities; machine
   learning; RDF editors; linked data
ID MARC
AB This paper reports an investigation of machine learning methods for the semi-automated creation of a BIBFRAME Work entity description within the RDF linked data editor Sinopia (https://sinopia.io). The automated subject indexing software Annif was configured with the Library of Congress Subject Headings (LCSH) vocabulary from the Linked Data Service at https://id.loc.gov/. The training corpus was comprised of 9.3 million titles and LCSH linked data references from the IvyPlus POD project (https://pod.stanford. edu/) and from Share-VDE (https://wiki.share-vde.org). Semi-automated processes were explored to support and extend, not replace, professional expertise.
C1 [Hahn, Jim] Univ Penn, Univ Lib, Philadelphia, PA 19104 USA.
C3 University of Pennsylvania
RP Hahn, J (corresponding author), Univ Penn, Univ Lib, Philadelphia, PA 19104 USA.
EM jimhahn@upenn.edu
OI Hahn, Jim/0000-0001-7924-5294
CR Aizawa A, 2003, INFORM PROCESS MANAG, V39, P45, DOI 10.1016/S0306-4573(02)00021-3
   Clemente, DATA SCI
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   Gebru T, 2021, COMMUN ACM, V64, P86, DOI 10.1145/3458723
   GitHub/jimfhahn, ANN TUT DAT
   Github/Kubeflow, KUB KUB
   GitHub/lcnetdev, LCNETD BFE
   GitHub/LD4P, SIN ED
   GitHub/NatLibFi, ANN TUT
   GitHub/samvera, QUEST AUTH
   Haas Konstantinos, P 25 ACM SIGKDD INT, DOI 10.1145/3292500.3340408
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NR 43
TC 3
Z9 3
U1 1
U2 10
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0163-9374
EI 1544-4554
J9 CAT CLASSIF Q
JI Cat. Classif. Q.
PD DEC 4
PY 2021
VL 59
IS 8
SI SI
BP 853
EP 867
DI 10.1080/01639374.2021.2014011
EA DEC 2021
PG 15
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA YU1BN
UT WOS:000734206800001
OA hybrid
DA 2024-09-05
ER
PT B
AU Fister, B
AF Fister, Barbara
BA Flaspohler, MR
BF Flaspohler, MR
TI Pragmatic pedagogical approaches
SO ENGAGING FIRST-YEAR STUDENTS IN MEANINGFUL LIBRARY RESEARCH: A PRACTICAL
   GUIDE FOR TEACHING FACULTY
SE Chandos Information Professional Series
LA English
DT Article; Book Chapter
DE novice researchers; information landscape; librarian advice; assignment
   design; staggered approach; faculty assumptions; topic generation;
   higher-order research skills; Internet; research projects; research
   process; outcomes; constructivist theory; Bloom's Taxonomy; Taxonomy for
   Information Literacy; information literacy; curricular planning; library
   programs; collaboration; pedagogy; active learning; faculty buy-in;
   semantics
AB This chapter offers practical advice to faculty who plan to include research projects in their work with first-year students. After briefly discussing constructivist learning theory and Bloom's Taxonomy of Educational Outcomes, the author suggests using an Information Literacy Taxonomy as a framework for developing increasingly complex library competencies among novice researchers. Additionally, the value of student-based, active learning is described and a number of guidelines for effective assignment design are provided. Finally, this chapter concludes by offering suggestions for moving information literacy beyond the first year.
C1 Gustavus Adolphus Coll, St Peter, MN 56082 USA.
C3 Gustavus Adolphus College
RP Fister, B (corresponding author), Gustavus Adolphus Coll, St Peter, MN 56082 USA.
NR 0
TC 0
Z9 0
U1 0
U2 3
PU CHANDOS PUBL
PI SAWSTON
PA 80 HIGH ST, SAWSTON, CAMBRIDGE CB22 3HJ, ENGLAND
BN 978-1-78063-294-0
J9 CHANDOS INF PROF SER
PY 2012
BP 99
EP 158
PG 60
WC Information Science & Library Science
WE Book Citation Index – Social Sciences & Humanities (BKCI-SSH)
SC Information Science & Library Science
GA BYV76
UT WOS:000300620800005
DA 2024-09-05
ER
PT J
AU Cherkaoui, A
   Aliat, M
AF Cherkaoui, Adil
   Aliat, Marouane
TI Mapping the field of responsible sourcing: Topic modeling through
   bibliometric analysis
SO BUSINESS STRATEGY AND DEVELOPMENT
LA English
DT Article
DE bibliometric analysis; co-wording analysis; responsible sourcing;
   sustainability; sustainable procurement
ID SUSTAINABLE PROCUREMENT; PUBLIC PROCUREMENT; ORGANIZATIONS; FUTURE;
   SECTOR
AB Academic interest concerning inclusion of sustainability principles in the field of procurement has been in steady increase over the last 20 years. However, there is an apparent void in bibliometric analyses in this field. To address this gap, VosViewer is used in this study to examine the academic literature on responsible sourcing. 575 peer-reviewed papers from Scopus were analyzed to reveal important topics, authors, and publications connected to the subject in hand. The first part of the analysis analyzed publication activity and listed all high impact journals covering the topic. Geographical distribution of findings showed that developed countries are the most productive in the field, in addition to an increasing interest from developing countries over the last decade. Keyword analysis generated from Vosviewer yielded to four main clusters covering the topic and shaping the intellectual structure of the study: (1) sustainable development & supply chain management, (2) environmental impact of procurement, (3) lifecycle analysis, (4) social responsibility and ethical sourcing. The article discusses the four main clusters and ends with concluding remarks encompassing six knowledge gaps and potential future research fields.
C1 [Cherkaoui, Adil; Aliat, Marouane] Hassan II Univ Casablanca, Fac Legal Econ & Social Sci, Res Lab Dev Econ & Governance Org LAREDGO, Casablanca, Morocco.
   [Cherkaoui, Adil] Hassan II Univ Casablanca, Fac Legal Econ & Social Sci, Res Lab Dev Econ & Governance Org LAREDGO, Km 8,Route El Jadida,BP 8110, Oasis, Casablanca, Morocco.
C3 Hassan II University of Casablanca; Hassan II University of Casablanca
RP Cherkaoui, A (corresponding author), Hassan II Univ Casablanca, Fac Legal Econ & Social Sci, Res Lab Dev Econ & Governance Org LAREDGO, Km 8,Route El Jadida,BP 8110, Oasis, Casablanca, Morocco.
EM cherkaoui.adil.casa@gmail.com
RI CHERKAOUI, Adil/CAH-1494-2022
OI CHERKAOUI, Adil/0000-0002-9857-3629
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NR 102
TC 1
Z9 1
U1 2
U2 16
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
EI 2572-3170
J9 BUS STRATEGY DEV
JI Bus. Strategy Dev.
PD SEP
PY 2023
VL 6
IS 3
BP 397
EP 410
DI 10.1002/bsd2.246
EA MAY 2023
PG 14
WC Business; Environmental Studies
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics; Environmental Sciences & Ecology
GA Q9YT2
UT WOS:000988483600001
DA 2024-09-05
ER
PT J
AU Kennedy, JV
   Arendale, DR
AF Kennedy, Jolie V.
   Arendale, David R.
TI Investigating Metacognitive Strategies and Exam Performance: A
   Cross-Sectional Survey Research Study
SO EDUCATION SCIENCES
LA English
DT Article
DE exam preparation; history; active learning; cooperative learning;
   metacognitive learning strategies; Universal Design for Learning;
   constructivism; self-regulated learning
AB This investigation used cross-sectional survey research methods in a high-enrollment undergraduate history course, setting out to examine test performance and metacognitive strategies that subjects self-selected prior to class, during class, and during the exam. This study examined the differences in exam scores between one group of students who self-reported completing specific metacognitive strategies and one group of students who self-reported not completing them. An online survey instrument was used to collect data from 121 students about the frequency of occurrence of specific behaviors. Frequencies and an Independent Samples T-Test were used to analyze metacognitive strategies and exam performance. The results showed the following strategies were statistically significant at the 0.05 alpha level: (1) read or listened to assigned readings and audio files before they were discussed during class; (2) frequently took part in small group discussion at the table during the class session; (3) created outlines for each of the potential essay questions to prepare for the examination; and (4) made an outline of the essay question before beginning to write while taking the exam. Limitations of the study, implications of the results, and recommendations for future research are provided. With the challenges of supporting students to earn higher grades and persist toward graduation, faculty members need to join the rest of the campus to be active agents in supporting students through simple learning strategies and effective student behaviors embedded into their courses. This may require extra time and effort to engage in professional development to learn how to embed practice with metacognitive strategies during class sessions.
C1 [Kennedy, Jolie V.] Empire State Univ, Educ Technol & Learning Design, Saratoga Springs, NY 12866 USA.
   [Arendale, David R.] Univ Minnesota Twin Cities, Curriculum & Instruct, Minneapolis, MN 55455 USA.
C3 University of Minnesota System; University of Minnesota Twin Cities
RP Arendale, DR (corresponding author), Univ Minnesota Twin Cities, Curriculum & Instruct, Minneapolis, MN 55455 USA.
EM jolie.kennedy@sunyempire.edu; arendale@umn.edu
RI Kennedy, Jolie V./KFS-7391-2024; Arendale, David/GNW-5740-2022
OI Kennedy, Jolie V./0000-0002-7317-5173; Arendale,
   David/0000-0003-1928-9310
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NR 43
TC 0
Z9 0
U1 2
U2 3
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2227-7102
J9 EDUC SCI
JI Educ. Sci.
PD NOV
PY 2023
VL 13
IS 11
AR 1132
DI 10.3390/educsci13111132
PG 17
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA Y8VQ3
UT WOS:001107988600001
OA gold
DA 2024-09-05
ER
PT J
AU Fang, KL
   Li, L
   Wu, YF
AF Fang, Kailun
   Li, Li
   Wu, Yifei
TI Research on student engagement in distance learning in sustainability
   science to design an online intelligent assessment system
SO FRONTIERS IN PSYCHOLOGY
LA English
DT Article
DE student engagement; engagement assessment system; distance learning;
   higher education; machine learning algorithms
ID HIGHER-EDUCATION
AB Distance learning programs in sustainability science provide a structured curriculum that covers various aspects of sustainability. Despite the growing recognition of distance learning in higher education, existing literature has primarily focused on specific and detailed factors, without a comprehensive summary of the global themes, especially neglecting in-depth exploration of poor engagement factors. This study bridged this gap by not only examining detailed factors but also synthesizing the overarching themes that influenced student engagement. The aim of this study was to investigate the factors that impact student engagement in distance learning within higher education institutions across different countries. By developing a theoretical framework, three key aspects of student engagement in higher education were identified. A total of 42 students and 2 educators affiliated with universities participated in semi-structured interviews. The findings of this paper indicated that sociocultural, infrastructure, and digital equity factors were the main influencing factors of student engagement. Furthermore, a student engagement assessment system was developed using machine learning algorithms to identify students with low levels of engagement and conduct further analysis that considers the three aforementioned factors. The proposed automated approach holds the potential to enhance and revolutionize digital learning methodologies.
C1 [Fang, Kailun] Guangzhou Urban Planning & Design Co Ltd, Guangzhou, Peoples R China.
   [Li, Li] Shanghai Baiaoheng New Mat Co Ltd, Shanghai, Peoples R China.
   [Wu, Yifei] Shenzhen Polytech Univ, Sch Architectural Engn, Shenzhen, Peoples R China.
C3 Shenzhen Polytechnic University
RP Wu, YF (corresponding author), Shenzhen Polytech Univ, Sch Architectural Engn, Shenzhen, Peoples R China.
EM wuyifei@szpt.edu.cn
OI WU, YIFEI/0000-0001-7574-9479
FU Teaching and Research Program of Shenzhen Polytechnic University
   [7023310200]
FX The author(s) declare financial support was received for the research,
   authorship, and/or publication of this article. The authors gratefully
   acknowledge financial support from Teaching and Research Program of
   Shenzhen Polytechnic University (grant no. 7023310200).
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NR 47
TC 0
Z9 0
U1 5
U2 11
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 1664-1078
J9 FRONT PSYCHOL
JI Front. Psychol.
PD NOV 23
PY 2023
VL 14
AR 1282386
DI 10.3389/fpsyg.2023.1282386
PG 13
WC Psychology, Multidisciplinary
WE Social Science Citation Index (SSCI)
SC Psychology
GA AA0N8
UT WOS:001115614700001
PM 38078210
OA gold
DA 2024-09-05
ER
PT C
AU Mohammad, SM
AF Mohammad, Saif M.
BE Calzolari, N
   Bechet, F
   Blache, P
   Choukri, K
   Cieri, C
   Declerck, T
   Goggi, S
   Isahara, H
   Maegaard, B
   Mariani, J
   Mazo, H
   Moreno, A
   Odijk, J
   Piperidis, S
TI NLP Scholar: A Dataset for Examining the State of NLP Research
SO PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES
   AND EVALUATION (LREC 2020)
LA English
DT Proceedings Paper
CT 12th International Conference on Language Resources and Evaluation
   (LREC)
CY MAY 11-16, 2020
CL Marseille, FRANCE
DE Scientometrics; Trends in Research; Google Scholar; ACL Anthology;
   Citations
ID CITATIONS
AB Google Scholar is the largest web search engine for academic literature that also provides access to rich metadata associated with the papers. The ACL Anthology (AA) is the largest repository of articles on Natural Language Processing (NLP). We extracted information from AA for about 44 thousand NLP papers and identified authors who published at least three papers in AA. We then extracted citation information from Google Scholar for all their papers (not just their AA papers). This resulted in a dataset of 1.1 million papers and associated Google Scholar information. We aligned the information in the AA and Google Scholar datasets to create the NLP Scholar Dataset-a single unified source of information (from both AA and Google Scholar) for tens of thousands of NLP papers. NLP Scholar can be used to identify broad trends in productivity, focus, and impact of NLP research. We present here initial work on analyzing the volume of research in NLP over the years and identifying the most cited papers in NLP. We also list a number of additional potential applications.
C1 [Mohammad, Saif M.] Natl Res Council Canada, Ottawa, ON, Canada.
C3 National Research Council Canada
RP Mohammad, SM (corresponding author), Natl Res Council Canada, Ottawa, ON, Canada.
EM saif.mohammad@nrc-cnrc.gc.ca
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NR 30
TC 10
Z9 10
U1 0
U2 0
PU EUROPEAN LANGUAGE RESOURCES ASSOC-ELRA
PI PARIS
PA 55-57, RUE BRILLAT-SAVARIN, PARIS, 75013, FRANCE
BN 979-10-95546-34-4
PY 2020
BP 868
EP 877
PG 10
WC Computer Science, Interdisciplinary Applications; Linguistics; Language
   & Linguistics
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Linguistics
GA BS4ZZ
UT WOS:000724697200109
DA 2024-09-05
ER
PT J
AU Phillips, F
AF Phillips, Fred
TI 50 years of TF&SC
SO TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
LA English
DT Article
DE Technology assessment; Technology forecasting; Technology management;
   Artificial intelligence; Complexity; Operations research; Technology
   diffusion; Multiple perspectives; Technology transitions
ID TRANSITIONS; INNOVATION
AB On Technological Forecasting & Social Change's 50th birthday, the journal's second and current Editor-in-Chief remarks on TF&SC's progress, the changes in the technological, cultural, and geopolitical environments in which the journal operates, TF&SC articles' changing topics and origins, and where future TF&SC volumes may lead.
C1 [Phillips, Fred] Univ New Mexico, Albuquerque, NM 87131 USA.
   [Phillips, Fred] SUNY Stony Brook, Stony Brook, NY 11794 USA.
C3 University of New Mexico; State University of New York (SUNY) System;
   State University of New York (SUNY) Stony Brook
RP Phillips, F (corresponding author), Univ New Mexico, Albuquerque, NM 87131 USA.
EM fred.phillips@stonybrook.edu
OI Phillips, Fred/0000-0002-1409-6889
FU FITE Department of the University of New Mexico's Anderson School of
   Management
FX Appreciation goes to the FITE Department of the University of New
   Mexico's Anderson School of Management for providing a research
   assistant to support the preparation of this special anniversary issue.
   The Editor-in-Chief is grateful also to Professor U.N. Umesh for taking
   on guest editor responsibilities attending to this issue.
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NR 34
TC 8
Z9 8
U1 0
U2 23
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0040-1625
EI 1873-5509
J9 TECHNOL FORECAST SOC
JI Technol. Forecast. Soc. Chang.
PD JUN
PY 2019
VL 143
BP 125
EP 131
DI 10.1016/j.techfore.2019.03.004
PG 7
WC Business; Regional & Urban Planning
WE Social Science Citation Index (SSCI)
SC Business & Economics; Public Administration
GA IB1RT
UT WOS:000470043000012
DA 2024-09-05
ER
PT C
AU Chandrasekaran, MK
   Jaidka, K
   Mayr, P
AF Chandrasekaran, Muthu Kumar
   Jaidka, Kokil
   Mayr, Philipp
GP ACM/SIGIR
TI Joint Workshop on Bibliometric-enhanced Information Retrieval and
   Natural Language Processing for Digital Libraries (BIRNDL 2018)
SO ACM/SIGIR PROCEEDINGS 2018
LA English
DT Proceedings Paper
CT 41st Annual International ACM SIGIR Conference on Research and
   Development in Information Retrieval (SIGIR)
CY JUL 08-12, 2018
CL Univ Michigan, Ann Arbor, MI
HO Univ Michigan
AB The large scale of scholarly publications poses a challenge for scholars in information seeking and sensemaking. Information retrieval (IR), bibliometric and natural language processing (NLP) techniques could enhance scholarly search, retrieval and user experience but are not yet widely used. To this purpose, we propose the third iteration of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL) [1, 3]. The workshop is intended to stimulate IR, NLP researchers and Digital Library professionals to elaborate on new approaches in natural language processing, information retrieval, scientometrics, text mining and recommendation techniques that can advance the state-of-the-art in scholarly document understanding, analysis, and retrieval at scale. The BIRNDL workshop will incorporate multiple invited talks, paper sessions, a poster session and the 4th edition of the Computational Linguistics (CL) Scientific Summarization Shared Task.
C1 [Chandrasekaran, Muthu Kumar] Natl Univ Singapore, Sch Comp, Singapore, Singapore.
   [Jaidka, Kokil] Univ Penn, Sch Arts & Sci, Philadelphia, PA 19104 USA.
   [Mayr, Philipp] GESIS Leibniz Inst Social Sci, Mannheim, Germany.
C3 National University of Singapore; University of Pennsylvania; Leibniz
   Institut fur Sozialwissenschaften (GESIS)
RP Chandrasekaran, MK (corresponding author), Natl Univ Singapore, Sch Comp, Singapore, Singapore.
EM muthu.chandra@comp.nus.edu.sg; jaidka@sas.upenn.edu;
   philipp.mayr@gesis.org
RI Jaidka, Kokil/AAK-2618-2020
OI Jaidka, Kokil/0000-0002-8127-1157; Chandrasekaran, Muthu
   Kumar/0000-0001-6479-5904
FU Microsoft Research Asia
FX We thank Microsoft Research Asia for their generous support in funding
   the development, dissemination and organization of the CL-SciSumm
   dataset and the Shared Task. We immensely thank Prof. Dragomir Radev and
   Michihiro Yasunaga from Yale University for co-organising CL-Scisumm
   with us this time. We are also grateful to the co-organizers of the 1st
   BIRNDL workshop - Guillaume Cabanac, Ingo Frommholz, Min-Yen Kan and
   Dietmar Wolfram, for their continued support and involvement.
CR Cabanac G., 2017, SIGIR FORUM, V50, P36, DOI [10.1145/3053408.3053417, DOI 10.1145/3053408.3053417]
   Jaidka Kokil, 2014, The computational linguistics summarization pilot task
   Mayr P, 2018, INT J DIGIT LIBRARIE, V19, P107, DOI 10.1007/s00799-017-0230-x
   Mayr Philipp, 2017, SIGIR FORUM, V51, P29
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   Philipp Mayr, 2017, SIGIR FORUM, V51, P107
   Teufel S., 2017, P 2 JOINT WORKSHOP B, P7
   Wolfram D, 2016, P JOINT WORKSH BIBL, P6
NR 8
TC 1
Z9 1
U1 1
U2 5
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA
BN 978-1-4503-5657-2
PY 2018
BP 1415
EP 1418
DI 10.1145/3209978.3210194
PG 4
WC Computer Science, Information Systems
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BL4TH
UT WOS:000450784600232
OA Green Submitted
DA 2024-09-05
ER
PT C
AU Baran, M
   Kulakowski, K
   Ligeza, A
AF Baran, Mateusz
   Kulakowski, Konrad
   Ligeza, Antoni
BE Rutkowski, L
   Korytkowski, M
   Scherer, R
   Tadeusiewicz, R
   Zadeh, LA
   Zurada, JM
TI A Note on Machine Learning Approach to Analyze the Results of Pairwise
   Comparison Based Parametric Evaluation of Research Units
SO ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, ICAISC 2014, PT II
SE Lecture Notes in Artificial Intelligence
LA English
DT Proceedings Paper
CT 13th International Conference on Artificial Intelligence and Soft
   Computing (ICAISC)
CY JUN 01-05, 2014
CL Zakopane, POLAND
ID CONSISTENCY; PRIORITIES
AB This paper presents an attempt at an analysis of parametric evaluation of research units with machine learning toolkit. The main goal was to investigate if the rules of evaluation can be expressed in a readable, transparent, and easy to interpret way. A further attempt was made at investigating consistency of the applied procedure and presentation of some observed anomalies.
C1 [Baran, Mateusz; Kulakowski, Konrad; Ligeza, Antoni] AGH Univ Sci & Technol, PL-30059 Krakow, Poland.
C3 AGH University of Krakow
RP Baran, M (corresponding author), AGH Univ Sci & Technol, Al Mickiewicza 30, PL-30059 Krakow, Poland.
EM mateusz.baran@agh.edu.pl; konrad.kulakowski@agh.edu.pl;
   antoni.ligeza@agh.edu.pl
RI Ligeza, Antoni/E-2422-2012; Baran, Mateusz/U-9571-2019; Kulakowski,
   Konrad/C-1784-2013
OI Ligeza, Antoni/0000-0002-6573-4246; Baran, Mateusz/0000-0001-9667-5579;
   Kulakowski, Konrad/0000-0002-2857-0916
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NR 24
TC 1
Z9 1
U1 0
U2 4
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 0302-9743
EI 1611-3349
BN 978-3-319-07175-6; 978-3-319-07176-3
J9 LECT NOTES ARTIF INT
PY 2014
VL 8468
BP 27
EP 39
PG 13
WC Computer Science, Artificial Intelligence
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BB1EQ
UT WOS:000341055600003
DA 2024-09-05
ER
PT J
AU Chen, F
AF Chen, Feng
TI Research on the Performance of Network Propagation by Using the Machine
   Learning and Internet-of-Things Technology Integrating Model
SO COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
LA English
DT Article
ID CONTEXT
AB We combine machine learning with Internet of Things technology to study the performance of network propagation model. This paper first introduces the construction environment of the business push system and then realizes user clustering and active business push by using the experimental data. Experimental results show that the active service push system constructed in this paper is feasible and effective. The experiment also compares and analyzes the influence of different clustering methods on the accuracy of service push. The results show that the clustering effect of the multi-Markov chain model (m-MCM) method is superior to that of the K-means method, a commonly used machine learning method, and the accuracy of user-service push obtained by the m-MCM method is superior to that obtained by the K-means method. Finally, on the basis of the existing experimental results, the shortcomings of the service push system are summarized, the future improvement direction and specific implementation measures are proposed, and new requirements for the future update of the service push system are put forward.
C1 [Chen, Feng] Zhejiang Coll Secur Technol, Coll Artificial Intelligence, Wenzhou 325016, Zhejiang, Peoples R China.
RP Chen, F (corresponding author), Zhejiang Coll Secur Technol, Coll Artificial Intelligence, Wenzhou 325016, Zhejiang, Peoples R China.
EM chenfeng@zjcst.edu.cn
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TC 0
Z9 0
U1 0
U2 4
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1687-5265
EI 1687-5273
J9 COMPUT INTEL NEUROSC
JI Comput. Intell. Neurosci.
PD SEP 19
PY 2022
VL 2022
AR 5480015
DI 10.1155/2022/5480015
PG 13
WC Mathematical & Computational Biology; Neurosciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology; Neurosciences & Neurology
GA 5E5FF
UT WOS:000865649400012
PM 36172310
OA Green Published, gold
DA 2024-09-05
ER
PT C
AU Raval, MS
   Kaya, T
AF Raval, Mehul S.
   Kaya, Tolga
BE Cardoso, A
   Alves, GR
   Restivo, MT
TI Effect of Multinational Projects on Engineering Students through a
   Summer Exposure Research Program
SO PROCEEDINGS OF THE 2020 IEEE GLOBAL ENGINEERING EDUCATION CONFERENCE
   (EDUCON 2020)
SE IEEE Global Engineering Education Conference
LA English
DT Proceedings Paper
CT IEEE Global Engineering Education Conference (IEEE EDUCON)
CY APR 27-30, 2020
CL ELECTR NETWORK
DE active learning; global competencies; engineering education;
   multinational projects
ID EXPERIENCE
AB This paper studies and quantifies the impact of active learning experienced through multinational projects. The hypothesis was engineering education delivered through Active Learning in multicultural environment improves student competencies. The investigation captures the impact of international exposure program in developing global competencies of the modern engineer. The paper shows positive trends in the development of domain and life skills of engineering students. Post-survey after six months of completion of the program revealed that the program was valuable to students and their motivation increased.
C1 [Raval, Mehul S.] Pandit Deendayal Petr Univ, Sch Technol, Gandhinagar, India.
   [Kaya, Tolga] Sacred Heart Univ, Sch Comp Sci & Engn, Fairfield, CT USA.
C3 Pandit Deendayal Energy University; Sacred Heart University
RP Raval, MS (corresponding author), Pandit Deendayal Petr Univ, Sch Technol, Gandhinagar, India.
EM mehul.raval@sot.pdpu.ac.in; kayat@sacredheart.edu
OI Kaya, Tolga/0000-0003-3425-4298; Raval, Mehul/0000-0002-3895-1448
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NR 22
TC 1
Z9 1
U1 0
U2 4
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2165-9567
BN 978-1-7281-0930-5
J9 IEEE GLOB ENG EDUC C
PY 2020
BP 51
EP 55
DI 10.1109/educon45650.2020.9125095
PG 5
WC Education, Scientific Disciplines; Engineering, Multidisciplinary
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Education & Educational Research; Engineering
GA BQ7NB
UT WOS:000617739900013
DA 2024-09-05
ER
PT J
AU Gomez, MJ
   Ruipérez-Valiente, JA
   Clemente, FGJ
AF Gomez, Manuel J.
   Ruiperez-Valiente, Jose A.
   Garcia Clemente, Felix J.
TI Analyzing Trends and Patterns Across the Educational Technology
   Communities Using Fontana Framework
SO IEEE ACCESS
LA English
DT Article
DE Bibliometrics; Market research; Task analysis; Metadata; Social
   networking (online); Databases; Network analyzers; EdTech; data mining;
   bibliometrics; NLP; network analysis; topic modeling
ID NETWORK ANALYSIS
AB Nowadays, the use of technology in continuously increasing, making a significant impact in almost every area, including education. New areas have gained much popularity in the last years in educational technology (EdTech), such as Massive Open Online Courses (MOOCs) or computer-supported collaborative learning. In addition, research and interest in this area have also been growing over the years. The quantity of research and scientific publications in EdTech is constantly increasing, and trying to analyze and extract information from a set of research papers is often a very time-consuming task. To make this process easier and solve these limitations, we present Fontana, a framework that can quickly perform trend and social network analysis using any corpus of documents and its metadata. Specifically, the framework can: 1) Discover the latest trends given any corpus of documents, using Natural Language Processing (NLP) analysis and keywords (bibliometric approach); 2) Discover the evolution of the trends previously identified over the years; 3) Discover the primary authors and papers, along with hidden relationships between existing communities. To test its functionality, we evaluated the framework using a corpus of papers from the EdTech research field. We also followed an open science methodology making the entire framework available in Open Science Framework (OSF) easy to access and use. The case study successfully proved the capabilities of the framework, revealing some of the most frequent topics in the area, such as "EDM," "learning analytics," or "collaborative learning." We expect our work to help identifying trends and patterns in the EdTech area, using natural language processing and social network analysis to objectively process large amounts of research.
C1 [Gomez, Manuel J.; Ruiperez-Valiente, Jose A.; Garcia Clemente, Felix J.] Univ Murcia, Fac Comp Sci, Murcia 30003, Spain.
C3 University of Murcia
RP Gomez, MJ (corresponding author), Univ Murcia, Fac Comp Sci, Murcia 30003, Spain.
EM manueljesus.gomezm@um.es
RI Clemente, Félix Jesús Garcia/AAM-8396-2020; Gomez Moratilla, Manuel
   Jesus/HMW-0780-2023; Ruiperez-Valiente, Jose A./U-8795-2018
OI Clemente, Félix Jesús Garcia/0000-0001-6181-5033; Ruiperez-Valiente,
   Jose A./0000-0002-2304-6365; Gomez, Manuel J./0000-0003-0571-2923
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NR 66
TC 2
Z9 2
U1 3
U2 17
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2022
VL 10
BP 35336
EP 35351
DI 10.1109/ACCESS.2022.3163253
PG 16
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering; Telecommunications
GA 0H6YD
UT WOS:000778877900001
OA gold
DA 2024-09-05
ER
PT J
AU Cabrera, PR
   Bolívar, CR
   Ramírez, T
AF Cabrera, Pablo Rios
   Bolivar, Carlos Ruiz
   Ramirez, Tulio
TI Online Course Evaluation for the Development of Research Competences
   under a Socio-constructivist Pedagogical Approach
SO REVISTA EDUCACION
LA English
DT Article
DE Investigative Competencies; Socio-Constructivist Approach; Project-Based
   Teaching; Pedagogical Innovation; Active Learning; Teamwork; Formative
   Evaluation
AB The results of the evaluation of an online course are presented to develop investigative skills under a socio-constructivist pedagogical approach. Eleven subjects of both genders, university professionals from Latin America, and members of a cohort of the International Diploma in Investigative Competences participated in the course. Moreover, a mixed research design was used. For the quantitative analysis, a self-assessment scale (pretest and posttest) was used, which served to measure the acquisition of investigative skills. For the qualitative analysis, the focus group, a self-assessment rubric, and a final evaluation questionnaire of the course were used. The results indicate that there were statistically significant differences between the pretest and posttest of investigative skills (p < .001). 73% of the participants consider that they fully achieved the investigative competencies, while 27% partially achieved them and there were no participants in the not achieved category. In addition, the participants reached their achievement expectations in 90.6% of the cases and 63.6% of the individuals exceeded their achievement expectations. Therefore, it is concluded that the socio-constructivist pedagogical approach used was effective and efficient in the development of investigative skills, so its use in teaching research at the university is highly recommended. This research highlights the importance of the mediating work of teachers in the integration of research theory and practice, as well as the use of digital technologies to achieve active and collaborative learning. Thus, this study offers an innovative pedagogical perspective for the teaching of research at the university. However, the small sample size is recognized as a limitation. Hence, it is suggested that in future research the study be extended to a greater number of participants in different university contexts.
C1 [Cabrera, Pablo Rios; Ramirez, Tulio] Univ Catolica Andres Bello, Caracas, Venezuela.
   [Bolivar, Carlos Ruiz] Nova Southeastern Univ, Ft Lauderdale, FL USA.
C3 Nova Southeastern University
RP Cabrera, PR (corresponding author), Univ Catolica Andres Bello, Caracas, Venezuela.
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NR 40
TC 0
Z9 0
U1 3
U2 7
PU UNIV COSTA RICA, EDITORIAL
PI SAN JOSE
PA SEDE RODRIGO FACIO BRENES, MONTES DE OCA, SAN JOSE, 00000, COSTA RICA
SN 0379-7082
EI 2215-2644
J9 REV EDUC-COSTA RICA
JI Rev. Educ.
PD JUL-DEC
PY 2023
VL 47
IS 2
AR 53856
DI 10.15517/revedu.v47i2.53856
PG 20
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA O0MF9
UT WOS:001040842100002
OA gold
DA 2024-09-05
ER
PT C
AU Rehs, A
AF Rehs, Andreas
BE Glanzel, W
   Heeffer, S
   Chi, PS
   Rousseau, R
TI The scientific productivity of German PhD graduates: A machine
   learning-based author name disambiguation and record linkage approach
SO 18TH INTERNATIONAL CONFERENCE ON SCIENTOMETRICS & INFORMETRICS
   (ISSI2021)
SE Proceedings of the International Conference on Scientometrics and
   Informetrics
LA English
DT Proceedings Paper
CT 18th International Conference on Scientometrics and Informetrics (ISSI)
CY JUL 12-15, 2021
CL KU Leuven, ELECTR NETWORK
HO KU Leuven
C1 [Rehs, Andreas] Univ Kassel, Int Ctr Higher Educ Res, Monchebergstr 17, D-34109 Kassel, Germany.
C3 Universitat Kassel
RP Rehs, A (corresponding author), Univ Kassel, Int Ctr Higher Educ Res, Monchebergstr 17, D-34109 Kassel, Germany.
EM andreasrehs@googlemail.com
RI Rehs, Andreas/AAG-3062-2022; Rehs, Andreas/AFK-4297-2022
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NR 7
TC 0
Z9 0
U1 0
U2 1
PU INT SOC SCIENTOMETRICS & INFORMETRICS-ISSI
PI LEUVEN
PA KATHOLIEKE UNIV LEUVEN, FACULTEIT E T E W, DEKENSTRAAT 2, LEUVEN,
   B-3000, BELGIUM
SN 2175-1935
BN 978-90-803282-2-8
J9 PRO INT CONF SCI INF
PY 2021
BP 1531
EP 1532
PG 2
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Information Science & Library Science
GA BS3CO
UT WOS:000709638700205
DA 2024-09-05
ER
PT C
AU Tran, HN
   Huynh, T
   Do, T
AF Hung Nghiep Tran
   Tin Huynh
   Tien Do
BE Nguyen, NT
   Attachoo, B
   Trawinski, B
   Somboonviwat, K
TI Author Name Disambiguation by Using Deep Neural Network
SO INTELLIGENT INFORMATION AND DATABASE SYSTEMS, PT 1
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT 6th Asian Conference on Intelligent Information and Database Systems
   (ACIIDS)
CY APR 07-09, 2014
CL Bangkok, THAILAND
DE Digital Library; Bibliographic Data; Author Name Disambiguation; Machine
   Learning; Feature Learning; Deep Neural Network
AB Author name ambiguity is one of the problems that decrease the quality and reliability of information retrieved from digital libraries. Existing methods have tried to solve this problem by predefining a feature set based on expert's knowledge for a specific dataset. In this paper, we propose a new approach which uses deep neural network to learn features automatically for solving author name ambiguity. Additionally, we propose the general system architecture for author name disambiguation on any dataset. We evaluate the proposed method on a dataset containing Vietnamese author names. The results show that this method significantly outperforms other methods that use predefined feature set. The proposed method achieves 99.31% in terms of accuracy. Prediction error rate decreases from 1.83% to 0.69%, i.e., it decreases by 1.14%, or 62.3% relatively compared with other methods that use predefined feature set (Table 3).
C1 [Hung Nghiep Tran; Tin Huynh; Tien Do] Univ Informat Technol Vietnam, Linh Trung Ward, Ho Chi Minh City, Vietnam.
RP Tran, HN (corresponding author), Univ Informat Technol Vietnam, Linh Trung Ward, Ho Chi Minh City, Vietnam.
EM nghiepth@uit.edu.vn; tinhn@uit.edu.vn; tiendv@uit.edu.vn
RI Do, Tien/IVU-9134-2023
CR [Anonymous], 2007, NIPS
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NR 15
TC 36
Z9 44
U1 0
U2 9
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 0302-9743
EI 1611-3349
BN 978-3-319-05476-6; 978-3-319-05475-9
J9 LECT NOTES COMPUT SC
PY 2014
VL 8397
BP 123
EP 132
PG 10
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BA6XT
UT WOS:000337302600013
DA 2024-09-05
ER
PT C
AU Ghosh, S
   Das, D
   Chakraborty, T
AF Ghosh, Souvick
   Das, Dipankar
   Chakraborty, Tanmoy
BE Gelbukh, A
TI Determining Sentiment in Citation Text and Analyzing Its Impact on the
   Proposed Ranking Index
SO COMPUTATIONAL LINGUISTICS AND INTELLIGENT TEXT PROCESSING, (CICLING
   2016), PT II
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT 17th International Conference on Intelligent Text Processing and
   Computational Linguistics (CICLing)
CY APR 03-09, 2016
CL Mevlana Univ, Konya, TURKEY
HO Mevlana Univ
DE Sentiment analysis; Citation; Citation sentiment analysis; Citation
   polarity; Ranking; Bibliometrics
AB Whenever human beings interact with each other, they exchange or express opinions, emotions and sentiments. These opinions can be expressed in text, speech or images. Analysis of these sentiments is one of the popular research areas of present day researchers. Sentiment analysis, also known as opinion mining tries to identify or classify these sentiments or opinions into two broad categories - positive and negative. Much work on sentiment analysis has been done on social media conversations, blog posts, newspaper articles and various narrative texts. However, when it came to identifying emotions from scientific papers, researchers used to face difficulties due to the implicit and hidden natures of opinions or emotions. As the citation instances are considered inherently positive in emotion, popular ranking and indexing paradigms often neglect the opinion present while citing. Therefore in the present paper, we deployed a system of citation sentiment analysis to achieve three major objectives. First, we identified sentiments in the citation text and assigned a score to each of the instances. We have used a supervised classifier for this purpose. Secondly, we have proposed a new index (we shall refer to it hereafter as M-index) which takes into account both the quantitative and qualitative factors while scoring a paper. Finally, we developed a ranking of research papers based on the M-index. We have also shown the impacts of M-index on the ranking of scientific papers.
C1 [Ghosh, Souvick; Das, Dipankar] Jadavpur Univ, Kolkata 700032, India.
   [Chakraborty, Tanmoy] Univ Maryland, College Pk, MD 20742 USA.
C3 Jadavpur University; University System of Maryland; University of
   Maryland College Park
RP Ghosh, S (corresponding author), Jadavpur Univ, Kolkata 700032, India.
EM souvick.gh@gmail.com; dipankar.dipnil2005@gmail.com;
   tanchak@umiacs.umd.edu
OI Ghosh, Souvick/0000-0003-1610-9038; CHAKRABORTY,
   TANMOY/0000-0002-0210-0369
CR Abu-Jbara A., 2011, P ACL
   Abu-Jbara A., 2013, NAACL, P596
   [Anonymous], 2012, 2012 Conference of the North American Chapter of the Association for Computational Linguistics
   [Anonymous], 2005, PROC 14 INT C WORLD
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NR 19
TC 5
Z9 5
U1 0
U2 10
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 0302-9743
EI 1611-3349
BN 978-3-319-75487-1; 978-3-319-75486-4
J9 LECT NOTES COMPUT SC
PY 2018
VL 9624
BP 292
EP 306
DI 10.1007/978-3-319-75487-1_23
PN II
PG 15
WC Computer Science, Artificial Intelligence; Computer Science, Software
   Engineering; Computer Science, Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BP1NL
UT WOS:000540377700023
DA 2024-09-05
ER
PT C
AU Mao, CH
AF Mao, Chunhua
BE Zhang, H
   Jin, D
   Zhao, XJ
TI Research on Information System for Teaching Quality Evaluation Model of
   Business English Translation Based on SVM
SO ADVANCED RESEARCH ON MATERIAL SCIENCE, ENVIROMENT SCIENCE AND COMPUTER
   SCIENCE III
SE Advanced Materials Research
LA English
DT Proceedings Paper
CT 3rd International Conference on Material Science, Environment Science
   and Computer Science (MSESCS 2014)
CY JAN 11-12, 2014
CL Wuhan, PEOPLES R CHINA
DE Business English Translation; Teaching Quality Evaluation; Support
   Vector Machine; Information Applied Technology
ID INNOVATION
AB The teaching quality evaluation of business English translation is a key basis to discover the teaching problems of business English translation and to promote the teaching quality. Compared with the traditional teaching quality evaluation method, support vector machine which is a type of information applied technology has many unique advantages, such as high accuracy, easily operation and fast implementation. This paper studies the current teaching quality on the basis of business English translation, and establishes the teaching quality evaluation model of business English translation based on SVM, and the experimental results show the superiority and validity of this method in the teaching quality assessment of business English translation.
C1 Hunan Vocat Coll Commerce, Changsha 410205, Hunan, Peoples R China.
RP Mao, CH (corresponding author), Hunan Vocat Coll Commerce, Changsha 410205, Hunan, Peoples R China.
EM 465756969@qq.com
CR ABERNATHY WJ, 1978, TECHNOL REV, V80, P40
   Gang Fu Yong, 2004, COMPUTER ENG, V30, P15
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NR 6
TC 4
Z9 4
U1 0
U2 10
PU TRANS TECH PUBLICATIONS LTD
PI STAFA-ZURICH
PA LAUBLSRUTISTR 24, CH-8717 STAFA-ZURICH, SWITZERLAND
SN 1022-6680
BN 978-3-03835-011-8
J9 ADV MATER RES-SWITZ
PY 2014
VL 886
BP 552
EP 555
DI 10.4028/www.scientific.net/AMR.886.552
PG 4
WC Materials Science, Multidisciplinary
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Materials Science
GA BA7XQ
UT WOS:000337849000122
DA 2024-09-05
ER
PT J
AU Asatani, K
   Takeda, H
   Yamano, H
   Sakata, I
AF Asatani, Kimitaka
   Takeda, Haruo
   Yamano, Hiroko
   Sakata, Ichiro
TI Scientific Attention to Sustainability and SDGs: Meta-Analysis of
   Academic Papers
SO ENERGIES
LA English
DT Article
DE bibliometrics; network analysis; SDGs; natural language processing;
   information retrieval; scientific foresight
ID ENERGY-STORAGE; LANDSCAPE; EVOLUTION; TOURISM; BATTERY
AB Scientific research plays an important role in the achievement of a sustainable society. However, grasping the trends in sustainability research is difficult because studies are not devised and conducted in a top-down manner with Sustainable Development Goals (SDGs). To understand the bottom-up research activities, we analyzed over 300,000 publications concerned with sustainability by using citation network analysis and natural language processing. The results suggest that sustainability science's diverse and dynamic changes have been occurring over the last few years; several new topics, such as nanocellulose and global health, have begun to attract widespread scientific attention. We further examined the relationship between sustainability research subjects and SDGs and found significant correspondence between the two. Moreover, we extracted SDG topics that were discussed following a convergent approach in academic studies, such as "inclusive society" and "early childhood development", by observing the convergence of terms in the citation network. These results are valuable for government officials, private companies, and academic researchers, empowering them to understand current academic progress along with research attention devoted to SDGs.
C1 [Asatani, Kimitaka; Yamano, Hiroko; Sakata, Ichiro] Univ Tokyo, Grad Sch Engn, Tokyo 1138654, Japan.
   [Takeda, Haruo] Hitachi Ltd, Tokyo 1008280, Japan.
C3 University of Tokyo; Hitachi Limited
RP Asatani, K (corresponding author), Univ Tokyo, Grad Sch Engn, Tokyo 1138654, Japan.
EM asatani@ipr-ctr.t.u-tokyo.ac.jp; haruo.takeda.vp@hitachi.com;
   yamano@pari.u-tokyo.ac.jp; isakata@ipr-ctr.t.u-tokyo.ac.jp
OI SAKATA, ICHIRO/0000-0001-5881-3790; Asatani,
   Kimitaka/0000-0003-3595-8940
FU NEDO (New Energy and Industrial Technology Development Organization),
   the funding agency of the Japan Ministry of Economy, Trade and Industry
   (METI)
FX This project was funded by NEDO (New Energy and Industrial Technology
   Development Organization), the funding agency of the Japan Ministry of
   Economy, Trade and Industry (METI).
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NR 42
TC 18
Z9 18
U1 3
U2 42
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1996-1073
J9 ENERGIES
JI Energies
PD FEB
PY 2020
VL 13
IS 4
AR 975
DI 10.3390/en13040975
PG 21
WC Energy & Fuels
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Energy & Fuels
GA KY3TB
UT WOS:000522492700203
OA Green Submitted, gold
DA 2024-09-05
ER
PT C
AU Liu, D
   Li, GG
   Zeng, W
   Sun, M
   Li, CB
AF Liu Ding
   Li Gege
   Zeng Wei
   Sun Min
   Li Cunbin
GP IOP
TI Research on Comprehensive Benefit Post Evaluation of Photovoltaic
   Poverty Alleviation Projects Based on FCM and SVM
SO 2019 3RD INTERNATIONAL CONFERENCE ON ENVIRONMENTAL AND ENERGY
   ENGINEERING (IC3E 2019)
SE IOP Conference Series-Earth and Environmental Science
LA English
DT Proceedings Paper
CT 3rd International Conference on Environmental and Energy Engineering
   (IC3E)
CY MAR 14-16, 2019
CL Shanghai, PEOPLES R CHINA
AB Photovoltaic (PV) Poverty Alleviation makes full use of the solar energy in poverty-stricken areas so as to achieve sTable incomes increase for the poor households for 25 years. It is an advanced mode integrating new energy development, emission reduction and accurate poverty alleviation. Post evaluation of PV poverty alleviation project is of great guiding significance for new energy development planning, poverty alleviation promoting and construction and operation of PV power stations. Under the guidance of the practical experience of PV poverty alleviation in Jiangxi province, China, this paper firstly builds a comprehensive evaluation index system with 7 second-level targets based on the post evaluation theory. Then, considering that projects with different sizes, construction and operation mode have different features in the evaluation, this paper uses pattern recognition method based on fuzzy C-means clustering algorithm and support vector machine to classify the projects. Then comparative analysis is carried out within each class to achieve comprehensive benefits evaluation. The method can reduce the information loss of multi-index weighted aggregation of traditional post evaluation methods. The features of PV poverty alleviation projects are highlighted to help to find the weak points of the projects. So the evaluation results are more scientific and reasonable.
C1 [Liu Ding; Li Gege; Li Cunbin] North China Elect Power Univ, Beijing Key Lab New Energy & Low Carbon Dev, Beijing 102206, Peoples R China.
   [Zeng Wei; Sun Min] State Grid Jiangxi Elect Power Co Elect Power Res, Nanchang 330039, Jiangxi, Peoples R China.
C3 North China Electric Power University
RP Liu, D (corresponding author), North China Elect Power Univ, Beijing Key Lab New Energy & Low Carbon Dev, Beijing 102206, Peoples R China.
EM liuding@163.com
RI Lau, Dean/IZP-7679-2023
FU Natural Science Foundation of China [71671065]; Science and Technology
   Project of State Grid Corporation of China "Research and Application of
   Improving the Accommodation Capacity and Guarantee Technologies of Power
   Grid in PV Poverty Alleviation Areas" [52182017000W]
FX This study is supported by the Natural Science Foundation of China
   (71671065) and the Science and Technology Project of State Grid
   Corporation of China "Research and Application of Improving the
   Accommodation Capacity and Guarantee Technologies of Power Grid in PV
   Poverty Alleviation Areas" (52182017000W).
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NR 10
TC 5
Z9 5
U1 0
U2 23
PU IOP PUBLISHING LTD
PI BRISTOL
PA DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND
SN 1755-1307
J9 IOP C SER EARTH ENV
JI IOP Conf. Ser. Earth Envir. Sci.
PY 2019
VL 281
AR 012034
DI 10.1088/1755-1315/281/1/012034
PG 9
WC Energy & Fuels; Engineering, Environmental; Environmental Sciences
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Energy & Fuels; Engineering; Environmental Sciences & Ecology
GA BO1EL
UT WOS:000495364600034
OA gold
DA 2024-09-05
ER
PT J
AU Gomes, RC
   de Azevedo, CB
AF Gomes, Ricardo Correa
   de Azevedo, Clovis Bueno
TI Balanced Scorecard: A Literature Review to Trace its Trajectory in the
   Public Administration Domain
SO INTERNATIONAL JOURNAL OF PUBLIC ADMINISTRATION
LA English
DT Article; Early Access
DE Balanced scorecard; performance management; public administration;
   bibliometric studies; natural language processing
ID PERFORMANCE-MEASUREMENT; MANAGEMENT; SERVICE; SECTOR; IMPLEMENTATION;
   DEPARTMENTS; CONTEXT; ISSUES; CITY; TOOL
AB The Balanced Scorecard (BSC) has been a strategic and performance management tool for 30 years, initially for for-profit organizations but later adopted by other types. This study reviews its use by public administration scholars, analyzing articles from the Web of Science database with bibliometric tools and Artificial Intelligence for lexical and semantic analysis. The analysis concluded that BSC is mainly used in local government for performance management and measurement. The study enhances theoretical understanding by exploring BSC's application in the public sector, its effectiveness, distinctions from for-profit use, and identifying areas for further research.
C1 [Gomes, Ricardo Correa] Res Degree Program Publ Adm, FGV EAESP, Rua Ingleses,484,Apto 83, BR-1329000 Sao Paulo, SP, Brazil.
   [de Azevedo, Clovis Bueno] FGV EAESP, Publ Management, Sao Paulo, Brazil.
C3 Getulio Vargas Foundation; Getulio Vargas Foundation
RP Gomes, RC (corresponding author), Res Degree Program Publ Adm, FGV EAESP, Rua Ingleses,484,Apto 83, BR-1329000 Sao Paulo, SP, Brazil.
EM ricardo.gomes@fgv.br
RI Gomes, Ricardo/D-8311-2017
OI Gomes, Ricardo/0000-0002-4164-5986
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NR 76
TC 0
Z9 0
U1 4
U2 4
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0190-0692
EI 1532-4265
J9 INT J PUBLIC ADMIN
JI Int. J. Public Adm.
PD 2024 JUL 19
PY 2024
DI 10.1080/01900692.2024.2376053
EA JUL 2024
PG 17
WC Public Administration
WE Emerging Sources Citation Index (ESCI)
SC Public Administration
GA YW0I3
UT WOS:001271399400001
DA 2024-09-05
ER
PT J
AU Watrianthos, R
   Ahmad, ST
   Muskhir, M
AF Watrianthos, Ronal
   Ahmad, Selamat Triono
   Muskhir, Mukhlidi
TI CHARTING THE GROWTH AND STRUCTURE OF EARLY CHATGPT-EDUCATION RESEARCH: A
   BIBLIOMETRIC STUDY
SO JOURNAL OF INFORMATION TECHNOLOGY EDUCATION-INNOVATIONS IN PRACTICE
LA English
DT Article
DE artificial intelligence; bibliometric; ChatGPT; education
AB Aim/Purpose The purpose of this article is to provide an overview and analysis of the emerging research landscape surrounding the integration of ChatGPT into education. The main problem appears to be that this is a new, rapidly developing research area for which there is no comprehensive synthesis of the current literature. The aim of the article is to fill this gap by conducting a timely bibliometric study to map publication trends, influential works, themes, and opportunities, thus representing the growth and structure of ChatGPT educational research.Background This article addresses the issue of the lack of a comprehensive synthesis of the new research on ChatGPT in education by conducting a bibliometric analysis. Specifically, the authors use statistical and network analysis techniques to examine the patterns of publication, citation, and keywords and map the growth, contributions, themes, structure, and opportunities in this evolving field. The bibliometric approach provides a comprehensive, evidence-based overview of the current state of the literature to uncover trends and gaps and help researchers improve their understanding of appropriate and effective applications of ChatGPT in educational contexts. Methodology The authors used bibliometric analysis as the primary method to summarize the new research on ChatGPT in education. We searched the database of the Web of Science Core Collection to find 51 relevant documents from 2023 that included ChatGPT in the title and were classified as 'educational research.' The sample consisted of these 51 documents, including articles, early access articles, editorials, reviews, and letters. Statistical techniques examined publication, citation, and keyword patterns. Network analysis visualized citation and co -occurrence networks to reveal intellectual structure. The multifaceted bibliometric approach allowed a comprehensive study of the sample from a productive, conceptual, and intellectual perspective. Contribution This article conducts comprehensive bibliometric analysis of this emerging research area and synthesizes publication, citation, and keyword data to map the growth and structure of the literature. The results reveal important trends, such as the rapid growth of publications since the release of ChatGPT, initial authorship patterns, the focus on higher education applications, and distinct research clusters around pedagogical, ethical, and assessment issues. Visualizing citation networks identifies seminal studies while mapping co-occurrence clarifies conceptual relationships between topics. The comparative analysis highlights the differences between document types, topics, and time periods. Knowledge mapping highlights gaps in the literature, such as lack of focus on K-12 contexts, and highlights opportunities for further research.Findings Key findings from this bibliometric analysis of the emerging research landscape surrounding ChatGPT integration in education include the following:center dot Since ChatGPT was released in late 2022, the number of releases has increased significantly, indicating rapid growth in this emerging space.center dot The most cited authors initially came primarily from Anthropic, but over time, the citations spread throughout the research community. center dot The topics focused primarily on higher education applications, with a clear focus on pedagogical strategies, ethical risks, and implications for assessment.center dot Citation networks visualized seminal studies, while the co-occurrence of keywords clarified conceptual connections.
   center dot Gaps such as applications in the K-12 context were uncovered, and opportunities for further research were highlighted.center dot The literature is rapidly evolving and requires ongoing monitoring of the development of this field. In general, the analysis presents the productivity, contributors, themes, structure, and opportunities in this emerging area around the integration of ChatGPT in education based on current scientific evidence. The key findings focus on the growing early interest, gaps and developments that can provide insight for researchers and educators.Recommendations for Practitioners Practitioners should carefully integrate ChatGPT into education based on new evidence, carefully assess contextual applicability, and proactively develop guidelines for ethical and equitable implementation. Ongoing advice, impact monitoring, and research partnerships are crucial to informing best practices. Educators must be vigilant for risks such as privacy, student well-being, and competence impairment while staying abreast of advances in knowledge to dynamically adapt integration strategies. The introduction should empower diverse learners through measured, integrative approaches based on continuous contextual analysis and ethical principles.Recommendations for Researchers This article recommends that researchers conduct more studies in under- researched contexts, use multiple methods to capture nuanced impacts, increase focus on responsible integration strategies, develop tailored assessments, conduct interdisciplinary collaborations, monitor long-term adoption, mix with interactive explain and publish open access technologies, help guide adoption pathways through actionable studies, and synthesize the exponentially growing literature through updated systematic reviews.Impact on Society The rapid publication growth and prevailing optimism suggest that the integration of ChatGPT into education will accelerate, increasing the need for rigorous research that guides ethical, responsible innovations that avoid risks and improve outcomes in all educational contexts. The findings have broader implications for guiding adoption trajectories through ongoing evidence synthesis and expanded investigations in under-researched areas to address knowledge gaps. Ultimately, continued monitoring and updated guidance are critical to ensure that ChatGPT's educational penetration progresses carefully by maximizing benefits and minimizing harms in rapidly evolving AI-powered learning ecosystems.Future Research Based on the basic mapping provided by this paper, recommended research directions include longitudinal impact studies, research tailored to under- researched contexts such as K-12, qualitative research to capture stakeholder perspectives, development and testing of AI-calibrated assessments as well as explorations that combine conversational and interactive learning technologies, updated systematic reviews, and co-designed implementation research that explain pedagogical strategies that ethically unlock learning potential while mitigating risks in diverse educational environments. Such multilayered tracking can provide critical insights to guide context-specific, responsible ChatGPT integration and monitor impact within rapidly evolving AI-powered education ecosystems.
C1 [Watrianthos, Ronal] Univ Negeri Padang, Fac Engn, Tech Vocat Educ, Padang, Indonesia.
   [Ahmad, Selamat Triono] Univ Negeri Medan, Fac Engn, Medan, Indonesia.
   [Ahmad, Selamat Triono] Univ Negeri Medan, Vocat & Tech Educ, Medan, Indonesia.
   [Muskhir, Mukhlidi] Univ Negeri Padang, Dept Elect Engn, Padang, Indonesia.
C3 Universitas Negeri Padang; Universitas Negeri Medan; Universitas Negeri
   Medan; Universitas Negeri Padang
RP Watrianthos, R (corresponding author), Univ Negeri Padang, Fac Engn, Tech Vocat Educ, Padang, Indonesia.
EM ronal.watrianthos@gmail.com; striono.ahmad@gmail.com;
   muskhir@ft.unp.ac.id
RI Watrianthos, Ronal/AEQ-0522-2022
OI Watrianthos, Ronal/0000-0003-3475-7266
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NR 47
TC 1
Z9 1
U1 37
U2 53
PU INFORMING SCIENCE INST
PI SANTA ROSA
PA 131 BROOKHILL CT, SANTA ROSA, CA 95409 USA
SN 2165-3151
EI 2165-316X
J9 J INF TECHNOL EDUC-I
JI J. Inf. Technol. Educ.-Innov. Pract.
PY 2023
VL 22
BP 235
EP 253
DI 10.28945/5221
PG 19
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA CE5Q2
UT WOS:001123588900001
OA gold
DA 2024-09-05
ER
PT J
AU Lamirel, JC
   Chen, Y
   Cuxac, P
   Al Shehabi, S
   Dugue, N
   Liu, ZY
AF Lamirel, Jean-Charles
   Chen, Yue
   Cuxac, Pascal
   Al Shehabi, Shadi
   Dugue, Nicolas
   Liu, Zeyuan
TI An overview of the history of Science of Science in China based on the
   use of bibliographic and citation data: a new method of analysis based
   on clustering with feature maximization and contrast graphs
SO SCIENTOMETRICS
LA English
DT Article
DE Science of Science; China; World; Topic tracking; Feature maximization;
   Unsupervised learning; Diachronic analysis
AB In the first part of this paper, we shall discuss the historical context of Science of Science both in China and at world level. In the second part, we use the unsupervised combination of GNG clustering with feature maximization metrics and associated contrast graphs to present an analysis of the contents of selected academic journal papers in Science of Science in China and the construction of an overall map of the research topics' structure during the last 40 years. Furthermore, we highlight how the topics have evolved through analysis of publication dates and also use author information to clarify the topics' content. The results obtained have been reviewed and approved by 3 leading experts in this field and interestingly show that Chinese Science of Science has gradually become mature in the last 40 years, evolving from the general nature of the discipline itself to related disciplines and their potential interactions, from qualitative analysis to quantitative and visual analysis, and from general research on the social function of science to its more specific economic function and strategic function studies. Consequently, the proposed novel method can be used without supervision, parameters and help from any external knowledge to obtain very clear and precise insights about the development of a scientific domain. The output of the topic extraction part of the method (clustering + feature maximization) is finally compared with the output of the well-known LDA approach by experts in the domain which serves to highlight the very clear superiority of the proposed approach.
C1 [Lamirel, Jean-Charles] INRIA Nancy Grand Est, SYNALP Team LORIA, Vandoeuvre Les Nancy, France.
   [Chen, Yue; Liu, Zeyuan] Dalian Univ Technol, WISELAB, Dalian, Peoples R China.
   [Cuxac, Pascal] INIST CNRS, Vandoeuvre Les Nancy, France.
   [Al Shehabi, Shadi] Univ Turkish Aeronaut Assoc, Ankara, Turkey.
   [Dugue, Nicolas] Univ Mans, LIUM, Le Mans, France.
C3 Universite de Lorraine; Dalian University of Technology; Centre National
   de la Recherche Scientifique (CNRS); Turk Hava Kurumu University;
   Turkish Aeronautical Association; Le Mans Universite
RP Lamirel, JC (corresponding author), INRIA Nancy Grand Est, SYNALP Team LORIA, Vandoeuvre Les Nancy, France.
EM jean-charles.lamirel@loria.fr; chenyue@dlut.edu.cn;
   pascal.cuxac@inist.fr; shadialshehabi@gmail.com;
   nicolas.dugue@univ-lemans.fr; liuzy@dlut.edu.cn
RI cuxac, pascal/AAE-3002-2019; Shehabi, Shadi Al/X-4322-2019
OI Shehabi, Shadi Al/0000-0003-0545-9104; Cuxac, Pascal/0000-0002-6809-5654
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NR 36
TC 7
Z9 8
U1 3
U2 50
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD DEC
PY 2020
VL 125
IS 3
BP 2971
EP 2999
DI 10.1007/s11192-020-03503-8
EA MAY 2020
PG 29
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA PE1YL
UT WOS:000534949200002
DA 2024-09-05
ER
PT J
AU Lascialfari, M
   Magrini, MB
   Cabanac, G
AF Lascialfari, Matteo
   Magrini, Marie-Benoit
   Cabanac, Guillaume
TI Unpacking research lock-in through a diachronic analysis of topic
   cluster trajectories in scholarly publications
SO SCIENTOMETRICS
LA English
DT Article
DE Bibliometric data; Path-dependency; Natural language processing; Science
   mapping; Sustainability concern
ID SCIENCE; NETWORKS; EVOLUTION; SYSTEMS; FUTURE; SHIFTS; FIELD; PATH;
   MAPS; FOOD
AB Lock-in and path-dependency are well-known concepts in economics dealing with unbalanced development of alternative options. Lock-in was studied in various sectors, considering production or consumption sides. Lock-in in academic research went little addressed. Yet, science develops through knowledge accumulation and cross-fertilisation of research topics, that could lead to similar phenomena when some topics do not sufficiently benefit from accumulation mechanisms, reducing innovation opportunities from the concerned field consequently. We introduce an original method to explore these phenomena by comparing topic trajectories in research fields according to strong or weak accumulative processes over time. We combine the concepts of 'niche' and 'mainstream' from transition studies with scientometric tools to revisit Callon's strategic diagram with a diachronic perspective of topic clusters over time. Considering the trajectories of semantic clusters, derived from titles and authors' keywords extracted from scholarly publications in the Web of Science, we applied our method to two competing research fields in food sciences and technology related to pulses and soya over the last 60 years worldwide. These highly interesting species for the sustainability of agrifood systems experienced unbalanced development and thus is under-debated. Our analysis confirms that food research for soya was more dynamic than for pulses: soya topic clusters revealed a stronger accumulative research path by cumulating mainstream positions while pulses research did not meet the same success. This attempt to unpack research lock-in for evaluating the competition dynamics of scientific fields over time calls for future works, by strengthening the method and testing it on other research fields.
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C1 [Lascialfari, Matteo; Magrini, Marie-Benoit] Univ Toulouse, INRAE, AGIR, Castanet Tolosan, France.
   [Cabanac, Guillaume] Univ Toulouse, CNRS, IRIT, Toulouse, France.
C3 INRAE; Centre National de la Recherche Scientifique (CNRS); Universite
   Federale Toulouse Midi-Pyrenees (ComUE); Universite de Toulouse;
   Institut National Polytechnique de Toulouse; Universite Toulouse III -
   Paul Sabatier
RP Magrini, MB (corresponding author), Univ Toulouse, INRAE, AGIR, Castanet Tolosan, France.
EM mtt.lascialfari@gmail.com; Marie-benoit.magrini@inrae.fr;
   guillaume.cabanac@univ-tlse3.fr
RI Cabanac, Guillaume/C-5913-2011
OI Cabanac, Guillaume/0000-0003-3060-6241; Lascialfari,
   Matteo/0000-0001-8128-572X; MAGRINI, Marie-Benoit/0000-0001-8027-7496
FU European Union [727672 LEGVALUE]; Agence Nationale de la Recherche (ANR)
   [ANR-11-LABX-0066]; Occitanie region in France; Agence Nationale de la
   Recherche (ANR) [ANR-11-LABX-0066] Funding Source: Agence Nationale de
   la Recherche (ANR)
FX This work was supported by funding from the European Union's Horizon
   2020 research and innovation program under grant agreement No. 727672
   LEGVALUE (Fostering sustainable legume-based farming systems and
   agri-feed and food chains in the EU); from the Agence Nationale de la
   Recherche (ANR) under grant number ANR-11-LABX-0066; and the Occitanie
   region in France.
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U2 26
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD NOV
PY 2022
VL 127
IS 11
BP 6165
EP 6189
DI 10.1007/s11192-022-04514-3
EA SEP 2022
PG 25
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 5U5FU
UT WOS:000860397400003
DA 2024-09-05
ER
PT J
AU Dana, LP
   Crocco, E
   Culasso, F
   Giacosa, E
AF Dana, Leo-Paul
   Crocco, Edoardo
   Culasso, Francesca
   Giacosa, Elisa
TI Mapping the field of digital entrepreneurship: a topic modeling approach
SO INTERNATIONAL ENTREPRENEURSHIP AND MANAGEMENT JOURNAL
LA English
DT Article
DE Digital entrepreneurship; Enterprise; Bibliometric review; Digital
   platforms
ID BIBLIOMETRIC ANALYSIS; EFFECTUATION; BUSINESS; WEB
AB In the evolving landscape of entrepreneurship, digital technologies have ushered in new possibilities, attracting considerable academic attention. Despite the burgeoning research in Digital Entrepreneurship, the field remains fragmented, warranting a synthesized overview and structured research agenda. Consistently with the above, the paper presents a quantitative mapping of Digital Entrepreneurship through a bibliometric analysis of its publications. The research aims to address the need for a comprehensive, bibliometric overview of the topic, which has been echoed in recently published papers. In order to achieve this goal, we collected data from the Web of Science database, a common and scientifically sound choice in entrepreneurship research. The data were analyzed by applying Latent Dirichlet Allocation and topic modeling, thus providing a unique approach to bibliometric mapping. Topic modeling allows for the processing and analysis of significant amounts of scientific data, thus making it an ideal tool for bibliometric research. We find the field of Digital Entrepreneurship to be rather lively and in rapid development, with several publication outlets, affiliations, and countries contributing to it. We found four main topics to be extracted: the implications of Digital Entrepreneurship for innovation, Digital Entrepreneurship as an enabler for empowerment, the transformation of business models through digitalization, and the surge of digital platforms as entrepreneurial ecosystems. Additionally, we have provided a comprehensive overview of the theoretical lenses used amid the sample and a structured research agenda built upon extant gaps. From a theoretical perspective, the article serves as a starting point for future research on the topic and a comprehensive analysis of its present and past. From a practical perspective, the study is of interest to digital entrepreneurs willing to learn more about the opportunities and challenges provided by the digital landscape.
C1 [Dana, Leo-Paul] ICD Business Sch, Lappeenranta, Finland.
   [Dana, Leo-Paul] Lappeenranta Univ Technol, Lappeenranta, Finland.
   [Crocco, Edoardo; Culasso, Francesca; Giacosa, Elisa] Univ Turin, Turin, Italy.
C3 Lappeenranta-Lahti University of Technology LUT; University of Turin
RP Crocco, E (corresponding author), Univ Turin, Turin, Italy.
EM edoardo.crocco@unito.it
OI Dana, Leo-Paul/0000-0002-0806-1911; Culasso,
   Francesca/0000-0001-8357-1914; GIACOSA, Elisa/0000-0002-0445-3176
FU Universit degli Studi di Torino
FX No Statement Available
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NR 103
TC 0
Z9 0
U1 22
U2 34
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1554-7191
EI 1555-1938
J9 INT ENTREP MANAG J
JI Int. Entrep. Manag. J.
PD MAR
PY 2024
VL 20
IS 1
BP 55
EP 88
DI 10.1007/s11365-023-00926-6
EA DEC 2023
PG 34
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA JD4G8
UT WOS:001120224200003
OA hybrid
DA 2024-09-05
ER
PT J
AU Hao, TY
   Chen, XL
   Li, GZ
   Yan, J
AF Hao, Tianyong
   Chen, Xieling
   Li, Guozheng
   Yan, Jun
TI A bibliometric analysis of text mining in medical research
SO SOFT COMPUTING
LA English
DT Article
DE Text mining; Medical; Bibliometric analysis; Topic modeling
ID MODEL APPROACH; TOPIC MODEL; SIMULATION; PARENTS; SPEECH
AB Text mining has become an increasingly significant role in processing medical information. The research of text mining enhanced medical has attracted much attention in view from the substantial expansion of literature. This study aims to systematically review the existing academic research outputs of the field from Web of Science and PubMed by using techniques such as geographic visualization, collaboration degree, social network analysis, and topic modeling analysis. Specifically, publication statistical characteristics, geographical distribution, collaboration relations, and research topic are quantitatively analyzed. This study contributes to the text mining enhanced medical research field in a number of ways. First, it provides the latest research status for researchers who are interested in the field through literature analysis. Second, it helps scholars become more aware of the research subfields through hot topic identification. Third, it provides insights to researchers engaging in the field and motivates attention on the relevant research.
C1 [Hao, Tianyong] South China Normal Univ, Sch Comp Sci, Guangzhou, Guangdong, Peoples R China.
   [Chen, Xieling] Jinan Univ, Coll Econ, Guangzhou, Guangdong, Peoples R China.
   [Li, Guozheng] China Acad Chinese Med Sci, Natl Data Ctr Tradit Chinese Med, Beijing, Peoples R China.
   [Yan, Jun] Yidu Cloud Beijing Technol Co Ltd, AI Lab, Beijing, Peoples R China.
C3 South China Normal University; Jinan University; China Academy of
   Chinese Medical Sciences
RP Chen, XL (corresponding author), Jinan Univ, Coll Econ, Guangzhou, Guangdong, Peoples R China.
EM haoty@126.com; shaylyn_chen@163.com; gzli@ndctcm.cn;
   jun.yan@yiducloud.cn
RI Hao, Tianyong/HJH-2742-2023; Li, Guo-Zheng/D-5744-2011; Yang,
   Jing/JFK-4046-2023
OI Hao, Tianyong/0000-0002-9792-3949; Yang, Jing/0009-0004-8274-9863; PV,
   THAYYIB/0000-0001-8929-0398; Chen, Xieling/0000-0003-3417-7421
FU National Natural Science Foundation of China [61772146]; Guangzhou
   Science Technology and Innovation Commission [201803010063]
FX The work was funded by the grant from National Natural Science
   Foundation of China (No. 61772146) and Guangzhou Science Technology and
   Innovation Commission (No. 201803010063).
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NR 68
TC 66
Z9 68
U1 5
U2 85
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1432-7643
EI 1433-7479
J9 SOFT COMPUT
JI Soft Comput.
PD DEC
PY 2018
VL 22
IS 23
SI SI
BP 7875
EP 7892
DI 10.1007/s00500-018-3511-4
PG 18
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA HC0FI
UT WOS:000451472000019
DA 2024-09-05
ER
PT C
AU Zheng, H
   Chen, XY
   Duan, X
AF Zheng, Han
   Chen, Xiaoyu
   Duan, Xu
BE Jatowt, A
   Maeda, A
   Syn, SY
TI An Overview of Altmetrics Research: A Typology Approach
SO DIGITAL LIBRARIES AT THE CROSSROADS OF DIGITAL INFORMATION FOR THE
   FUTURE, ICADL 2019
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT 21st International Conference on Asia-Pacific Digital Libraries (ICADL)
CY NOV 04-07, 2019
CL Kuala Lumpur, MALAYSIA
DE Altmetrics research; Typology; Topic modeling; Text mining
AB Altmetrics, novel metrics based on social media, have received much attention from scholars in recent years. As an emerging research area in information science, it is essential to understand the overview of altmetrics research. We extracted 731 altmetrics-related articles from the Scopus databases and adopted a text mining method (i.e., topic modeling) to develop a typology of altmetrics research. Six major themes were identified in our analysis, including altmetrics research in general, bibliometric and altmetrics, measuring research impact, metrics application, social media use, and performance evaluation. We interpreted the meaning of the six themes and their associations with altmetrics research. This paper is a first step in mapping the landscape of altmetrics research through uncovering the core topics discussed by scholars. Limitations and future work are also discussed.
C1 [Zheng, Han; Chen, Xiaoyu; Duan, Xu] Nanyang Technol Univ, Wee Kim Wee Sch Commun & Informat, Singapore, Singapore.
C3 Nanyang Technological University
RP Zheng, H (corresponding author), Nanyang Technol Univ, Wee Kim Wee Sch Commun & Informat, Singapore, Singapore.
EM hn019@e.ntu.edu.sg; xiaoyu001@e.ntu.edu.sg; xu007@e.ntu.edu.sg
RI Chen, Xiaoyu/AGX-8179-2022; Zheng, Han/AAD-6949-2020; Duan,
   Xu/JDD-4522-2023
OI Chen, Xiaoyu/0000-0003-1741-0674; Zheng, Han/0000-0003-4032-4299; 
CR [Anonymous], 2015, Incentives and Performance
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NR 20
TC 1
Z9 1
U1 3
U2 38
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 0302-9743
EI 1611-3349
BN 978-3-030-34058-2; 978-3-030-34057-5
J9 LECT NOTES COMPUT SC
PY 2019
VL 11853
BP 33
EP 39
DI 10.1007/978-3-030-34058-2_4
PG 7
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods; Information Science &
   Library Science; Imaging Science & Photographic Technology
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Information Science & Library Science; Imaging Science
   & Photographic Technology
GA BQ3BB
UT WOS:000583740100004
DA 2024-09-05
ER
PT J
AU Sheller, MJ
   Edwards, B
   Reina, GA
   Martin, J
   Pati, S
   Kotrotsou, A
   Milchenko, M
   Xu, WL
   Marcus, D
   Colen, RR
   Bakas, S
AF Sheller, Micah J.
   Edwards, Brandon
   Reina, G. Anthony
   Martin, Jason
   Pati, Sarthak
   Kotrotsou, Aikaterini
   Milchenko, Mikhail
   Xu, Weilin
   Marcus, Daniel
   Colen, Rivka R.
   Bakas, Spyridon
TI Federated learning in medicine: facilitating multi-institutional
   collaborations without sharing patient data
SO SCIENTIFIC REPORTS
LA English
DT Article
AB Several studies underscore the potential of deep learning in identifying complex patterns, leading to diagnostic and prognostic biomarkers. Identifying sufficiently large and diverse datasets, required for training, is a significant challenge in medicine and can rarely be found in individual institutions. Multi-institutional collaborations based on centrally-shared patient data face privacy and ownership challenges. Federated learning is a novel paradigm for data-private multi-institutional collaborations, where model-learning leverages all available data without sharing data between institutions, by distributing the model-training to the data-owners and aggregating their results. We show that federated learning among 10 institutions results in models reaching 99% of the model quality achieved with centralized data, and evaluate generalizability on data from institutions outside the federation. We further investigate the effects of data distribution across collaborating institutions on model quality and learning patterns, indicating that increased access to data through data private multi-institutional collaborations can benefit model quality more than the errors introduced by the collaborative method. Finally, we compare with other collaborative-learning approaches demonstrating the superiority of federated learning, and discuss practical implementation considerations. Clinical adoption of federated learning is expected to lead to models trained on datasets of unprecedented size, hence have a catalytic impact towards precision/personalized medicine.
C1 [Sheller, Micah J.; Edwards, Brandon; Reina, G. Anthony; Martin, Jason; Xu, Weilin] Intel Corp, 2200 Mission Coll Blvd, Santa Clara, CA 95052 USA.
   [Pati, Sarthak; Bakas, Spyridon] Univ Penn, Richards Med Res Labs, Ctr Biomed Image Comp & Analyt CBICA, Floor 7,3700 Hamilton Walk, Philadelphia, PA 19104 USA.
   [Pati, Sarthak; Bakas, Spyridon] Univ Penn, Richards Med Res Labs, Perelman Sch Med, Dept Radiol, Floor 7,3700 Hamilton Walk, Philadelphia, PA 19104 USA.
   [Kotrotsou, Aikaterini; Colen, Rivka R.] Univ Texas MD Anderson Canc Ctr, Dept Diagnost Radiol, 1400 Pressler St, Houston, TX 77030 USA.
   [Kotrotsou, Aikaterini; Colen, Rivka R.] Univ Texas MD Anderson Canc Ctr, Dept Canc Syst Imaging, 1881 East Rd,3SCRB4, Houston, TX 77054 USA.
   [Milchenko, Mikhail; Marcus, Daniel] Washington Univ, Sch Med, Dept Radiol, St Louis, MO 63110 USA.
   [Colen, Rivka R.] Univ Pittsburgh, Med Ctr, Hillman Canc Ctr, Pittsburgh, PA 15232 USA.
   [Colen, Rivka R.] Univ Pittsburgh, Dept Radiol, Pittsburgh, PA 15213 USA.
   [Bakas, Spyridon] Univ Penn, Richards Med Res Labs, Perelman Sch Med, Dept Pathol & Lab Med, Floor 7,3700 Hamilton Walk, Philadelphia, PA 19104 USA.
C3 Intel Corporation; University of Pennsylvania; University of
   Pennsylvania; University of Texas System; UTMD Anderson Cancer Center;
   University of Texas System; UTMD Anderson Cancer Center; Washington
   University (WUSTL); Pennsylvania Commonwealth System of Higher Education
   (PCSHE); University of Pittsburgh; Pennsylvania Commonwealth System of
   Higher Education (PCSHE); University of Pittsburgh; University of
   Pennsylvania
RP Bakas, S (corresponding author), Univ Penn, Richards Med Res Labs, Ctr Biomed Image Comp & Analyt CBICA, Floor 7,3700 Hamilton Walk, Philadelphia, PA 19104 USA.; Bakas, S (corresponding author), Univ Penn, Richards Med Res Labs, Perelman Sch Med, Dept Radiol, Floor 7,3700 Hamilton Walk, Philadelphia, PA 19104 USA.; Bakas, S (corresponding author), Univ Penn, Richards Med Res Labs, Perelman Sch Med, Dept Pathol & Lab Med, Floor 7,3700 Hamilton Walk, Philadelphia, PA 19104 USA.
EM sbakas@upenn.edu
RI Pati, Sarthak/AAL-1334-2020
OI Pati, Sarthak/0000-0003-2243-8487; Reina, G Anthony/0000-0001-9623-9259
FU National Institutes of Health (NIH) [NCI:U01CA242871, NINDS:R01NS042645,
   NCI:U24CA189523, NCI:U24CA204854, UPMC CCSG P30CA047904]
FX The authors would like to thank Dr. Christos Davatzikos for his
   insightful comments during writing of this manuscript. Research reported
   in this publication was partly supported by the National Institutes of
   Health (NIH) under Award Numbers NCI:U01CA242871, NINDS:R01NS042645,
   NCI:U24CA189523, NCI:U24CA204854, and UPMC CCSG P30CA047904. The content
   of this publication is solely the responsibility of the authors and does
   not necessarily represent the official views of the NIH.
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NR 36
TC 460
Z9 486
U1 10
U2 63
PU NATURE PORTFOLIO
PI BERLIN
PA HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY
SN 2045-2322
J9 SCI REP-UK
JI Sci Rep
PD JUL 28
PY 2020
VL 10
IS 1
AR 12598
DI 10.1038/s41598-020-69250-1
PG 12
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA MV5LE
UT WOS:000556398500021
PM 32724046
OA gold, Green Published
HC Y
HP N
DA 2024-09-05
ER
PT C
AU Kergosien, E
   Farvardin, A
   Teisseire, M
   Bessagnet, MN
   Schöpfel, J
   Chaudiron, S
   Jacquemin, B
   Lacayrelle, A
   Roche, M
   Sallaberry, C
   Tonneau, JP
AF Kergosien, Eric
   Farvardin, Amin
   Teisseire, Maguelonne
   Bessagnet, Marie-Noelle
   Schopfel, Joachim
   Chaudiron, Stephane
   Jacquemin, Bernard
   Lacayrelle, Annig
   Roche, Mathieu
   Sallaberry, Christian
   Tonneau, Jean Philippe
BA Declerck, T
BF Declerck, T
BE Calzolari, N
   Choukri, K
   Cieri, C
   Hasida, K
   Isahara, H
   Maegaard, B
   Mariani, J
   Moreno, A
   Odijk, J
   Piperidis, S
   Tokunaga, T
   Goggi, S
   Mazo, H
TI Automatic Identification of Research Fields in Scientific Papers
SO PROCEEDINGS OF THE ELEVENTH INTERNATIONAL CONFERENCE ON LANGUAGE
   RESOURCES AND EVALUATION (LREC 2018)
LA English
DT Proceedings Paper
CT 11th International Conference on Language Resources and Evaluation
   (LREC)
CY MAY 07-12, 2018
CL Miyazaki, JAPAN
DE text mining; natural language processing; geographical information
   retrieval; scientometrics; document analysis
AB The TERRE-ISTEX project aims to identify scientific research dealing with specific geographical territories areas based on heterogeneous digital content available in scientific papers. The project is divided into three main work packages: (1) identification of the periods and places of empirical studies, and which reflect the publications resulting from the analyzed text samples, (2) identification of the themes which appear in these documents, and (3) development of a web-based geographical information retrieval tool (GIR). The first two actions combine Natural Language Processing patterns with text mining methods. The integration of the spatial, thematic and temporal dimensions in a GIR contributes to a better understanding of what kind of research has been carried out, of its topics and its geographical and historical coverage. Another originality of the TERRE-ISTEX project is the heterogeneous character of the corpus, including PhD theses and scientific articles from the ISTEX digital libraries and the CIRAD research center.
C1 [Kergosien, Eric; Schopfel, Joachim; Chaudiron, Stephane; Jacquemin, Bernard] Univ Lille, GERiiCO, EA 4073, F-59000 Lille, France.
   [Bessagnet, Marie-Noelle; Lacayrelle, Annig; Sallaberry, Christian] Univ Pau & Pays Adour, LIUPPA, Pau, France.
   [Teisseire, Maguelonne; Roche, Mathieu; Tonneau, Jean Philippe] Univ Montpellier, TETIS, APT, Cirad,CNRS,Irstea, Montpellier, France.
   [Roche, Mathieu; Tonneau, Jean Philippe] Cirad, Montpellier, France.
   [Farvardin, Amin] Univ Paris 09, LAMSADE, Paris, France.
   [Schopfel, Joachim] ANRT, Lille, France.
C3 Universite de Lille; Universite de Pau et des Pays de l'Adour;
   Universite de Montpellier; Centre National de la Recherche Scientifique
   (CNRS); CIRAD; INRAE; AgroParisTech; CIRAD; Universite PSL; Universite
   Paris-Dauphine
RP Kergosien, E (corresponding author), Univ Lille, GERiiCO, EA 4073, F-59000 Lille, France.
EM eric.kergosien@univ-lille.fr; MohammadAmin.Farvardin@dauphine.eu;
   maguelonne.teisseire@teledetection.fr;
   marie-noelle.bessagnet@univ-pau.fr; joachim.schopfel@univ-lille.fr;
   stephane.chaudiron@univ-lille.fr; bernard.jacquemin@univ-lille.fr;
   annig.lacayrelle@univ-pau.fr; mathieu.roche@teledetection.fr;
   christian.sallaberry@univ-pau.fr; jean.tonneau@teledetection.fr
RI Jacquemin, Bernard/D-9532-2016
OI Jacquemin, Bernard/0000-0001-9274-7424
FU ISTEX; SONGES project (Occitanie Fund); SONGES project (European
   Regional Development Fund); DYNAMITEF project (CNES)
FX This work is funded by ISTEX (https://www.istex.fr/), SONGES project
   (Occitanie and European Regional Development Funds), and DYNAMITEF
   project (CNES).
CR [Anonymous], 2013, P 7 INT WORKSH SEM E
   [Anonymous], 2013, P 3 INT C WEB INT MI
   KERGOSIEN E, 2015, P 7 INT C KNOWL DISC, P301
   Lossio-Ventura JA, 2016, INFORM RETRIEVAL J, V19, P59, DOI 10.1007/s10791-015-9262-2
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NR 10
TC 1
Z9 1
U1 0
U2 0
PU EUROPEAN LANGUAGE RESOURCES ASSOC-ELRA
PI PARIS
PA 55-57, RUE BRILLAT-SAVARIN, PARIS, 75013, FRANCE
BN 979-10-95546-00-9
PY 2018
BP 1902
EP 1907
PG 6
WC Computer Science, Interdisciplinary Applications; Linguistics; Language
   & Linguistics
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Linguistics
GA BS5BI
UT WOS:000725545001152
DA 2024-09-05
ER
PT J
AU Xue, Y
AF Xue, Yi
TI Towards automated writing evaluation: A comprehensive review with
   bibliometric, scientometric, and meta-analytic approaches
SO EDUCATION AND INFORMATION TECHNOLOGIES
LA English
DT Article; Early Access
DE Natural language processing; Automated writing evaluation; Self-set
   corpus; Writing proficiency; Moderator effects
ID WRITTEN CORRECTIVE FEEDBACK; FOREIGN-LANGUAGE; SYNTACTIC COMPLEXITY;
   STUDENTS; IMPACT; EFFICIENCY; EVOLUTION; ACCURACY; ENGLISH; INDEXES
AB The new era of generative artificial intelligence has sparked the blossoming academic fireworks in the realm of education and information technologies. Driven by natural language processing (NLP), automated writing evaluation (AWE) tools become a ubiquitous practice in intelligent computer-assisted language learning (CALL) environments. Based on the self-set corpus of the plain text file encompassing 1524 documents from the Web of Science core collection, the current study adopts quantitative and qualitative methods and integrates bibliometric, scientometric, and meta-analytic approaches aiming to comprehensively review automated writing evaluation (AWE) over fifteen years from 2008 to 2023. Feedback literacy is the theoretical framework of automated written corrective feedback (AWCF). Through VOSviewer, this study bibliographically visualized AWE-relevant keywords, documents, authors, organizations, and regions at a macro level. Science mapping analysis (SMA), mapping knowledge domain (MKD), and author co-citation analysis (ACA) are the theoretical foundations of visualization on VOSviewer. Through Stata/SE 16 and SPSS 29, this study meta-analytically investigated moderator effects of various AWE tools, feedback types, intervention duration, target language learners, educational levels, genres of writing, regions, document types, and publication year at a micro level. It is concluded that AWE tools could facilitate writing proficiency at a statistical significance level (SMD = 0.422, p < 0.001) based on 29 experimental studies. The findings illuminate future research directions and provide heuristic implications for practitioners, researchers, and AWE technology developers.
C1 [Xue, Yi] Beijing Language & Culture Univ, Fac Foreign Studies, 15 Xueyuan Rd, Beijing 100083, Peoples R China.
C3 Beijing Language & Culture University
RP Xue, Y (corresponding author), Beijing Language & Culture Univ, Fac Foreign Studies, 15 Xueyuan Rd, Beijing 100083, Peoples R China.
EM 3252393470@qq.com
RI Yi, Xue/JRY-7660-2023
OI Yi, Xue/0009-0003-8573-0845
FU Beijing Language and Culture University
FX The author would like to extend her heartfelt gratitude to anonymous
   reviewers and editors. The author sincerely appreciates the constructive
   and thorough feedback provided by editors and reviewers. The author
   sincerely appreciates Doctor Zhang Qi, Professor Liu Linjun, Professor
   Zhu Erqian, Professor Xu Hongchen, beloved family, kind friends, and
   Beijing Language and Culture University. The process of writing academic
   articles is not only a self-actualization, but also a panacea.
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NR 134
TC 2
Z9 2
U1 42
U2 42
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1360-2357
EI 1573-7608
J9 EDUC INF TECHNOL
JI Educ. Inf. Technol.
PD 2024 MAR 23
PY 2024
DI 10.1007/s10639-024-12596-0
EA MAR 2024
PG 42
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA LY1G5
UT WOS:001190274400002
DA 2024-09-05
ER
PT C
AU Drago, C
   Hoxhalli, G
AF Drago, Carlo
   Hoxhalli, Gentian
BE Schiuma, G
TI Bibliometric Big Data Analysis in Economics
SO 15TH INTERNATIONAL FORUM ON KNOWLEDGE ASSET DYNAMICS (IFKAD 2020):
   KNOWLEDGE IN DIGITAL AGE
SE Proceedings IFKAD
LA English
DT Proceedings Paper
CT 15th International Forum on Knowledge Asset Dynamics (IFKAD) - Knowledge
   in Digital Age
CY SEP 09-11, 2020
CL ELECTR NETWORK
DE Bibliometric Analysis; Big Data; Symbolic Data Analysis; Regression
   Discontinuity; Causal Inference
ID NETWORKS; SCIENCE
AB Over the last years there has been a relevant increase of large scale data. "Volume", Velocity", "Variety", "Veracity" and, more importantly today, "Value" are the dimensions that characterize Big Data. Big data are increasingly important in the real world. In this paper, we deal with bibliometric datasets which represent relevant information and value for applications, usually difficult to identify. In economic analyses, bibliographic networks are useful to represent the data and provide relevant insights on research findings. In this work we will propose to apply a framework based on symbolic data and, in particular, one based on data-based symbolic observation interval which represent relevant patterns and information from complex data. These results are important because they consider a relevant case of complex information which is transformed into a representation useful to be analysed as network data. From these network representations of the information (considering a co-occurrence network from the relevant concepts of the studied literature of "regression discontinuity") we are able to identify the most relevant patterns in data, as "communities" of concepts maximally connected to each other. From the communities we are able to represent the semantic cores of the literature.
C1 [Drago, Carlo; Hoxhalli, Gentian] Univ Niccolo Cusano, Via Don Carlo Gnocchi 3, I-00166 Rome, Italy.
   [Drago, Carlo] NCI Univ, Northern & Shell Tower,4 Selsdon Way, London E14 9GL, England.
   [Hoxhalli, Gentian] Luarasi Univ, Rruga Dritan Hoxha 127-1, Tirana, Albania.
C3 Niccolo Cusano Online University
RP Drago, C (corresponding author), Univ Niccolo Cusano, Via Don Carlo Gnocchi 3, I-00166 Rome, Italy.; Drago, C (corresponding author), NCI Univ, Northern & Shell Tower,4 Selsdon Way, London E14 9GL, England.
RI Drago, Carlo/Y-3357-2019
OI Drago, Carlo/0000-0002-3920-0267
CR Aguero C, 2019, RSDA R SYMBOLIC DATA
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NR 30
TC 0
Z9 0
U1 0
U2 3
PU IKAM-INST KNOWLEDGE ASSET MANAGEMENT
PI MATERA
PA VIA D SCHIAVONE 1, MATERA, MT 75100, ITALY
SN 2280-787X
BN 978-88-96687-13-0
J9 PR IFKAD
PY 2020
BP 148
EP 155
PG 8
WC Business; Computer Science, Interdisciplinary Applications; Information
   Science & Library Science; Management; Operations Research & Management
   Science
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Business & Economics; Computer Science; Information Science & Library
   Science; Operations Research & Management Science
GA BS4CI
UT WOS:000717224200010
DA 2024-09-05
ER
PT J
AU Chen, C
   Du, GL
   Tong, DN
   Lv, GD
   Lv, XY
   Si, RM
   Tang, J
   Li, HY
   Ma, HB
   Mo, JQ
AF Chen, Cheng
   Du, Guoli
   Tong, Dongni
   Lv, Guodong
   Lv, Xiaoyi
   Si, Rumeng
   Tang, Jun
   Li, Hongyi
   Ma, Hongbing
   Mo, Jiaqing
TI Exploration research on the fusion of multimodal spectrum technology to
   improve performance of rapid diagnosis scheme for thyroid dysfunction
SO JOURNAL OF BIOPHOTONICS
LA English
DT Article
DE PCA; serum; spectral fusion; SVM; thyroid dysfunction
ID RAMAN-SPECTROSCOPY
AB The spectral fusion by Raman spectroscopy and Fourier infrared spectroscopy combined with pattern recognition algorithms is utilized to diagnose thyroid dysfunction serum, and finds the spectral segment with the highest sensitivity to further advance diagnosis speed. Compared with the single infrared spectroscopy or Raman spectroscopy, the proposal can improve the detection accuracy, and can obtain more spectral features, indicating greater differences between thyroid dysfunction and normal serum samples. For discriminating different samples, principal component analysis (PCA) was first used for feature extraction to reduce the dimension of high-dimension spectral data and spectral fusion. Then, support vector machine (SVM), back propagation neural network, extreme learning machine and learning vector quantization algorithms were employed to establish the discriminant diagnostic models. The accuracy of spectral fusion of the best analytical model PCA-SVM, single Raman spectral accuracy and single infrared spectral accuracy is 83.48%, 78.26% and 80%, respectively. The accuracy of spectral fusion is higher than the accuracy of single spectrum in five classifiers. And the diagnostic accuracy of spectral fusion in the range of 2000 to 2500cm(-1) is 81.74%, which greatly improves the sample measure speed and data analysis speed than analysis of full spectra. The results from our study demonstrate that the serum spectral fusion technique combined with multivariate statistical methods have great potential for the screening of thyroid dysfunction.
C1 [Chen, Cheng; Lv, Xiaoyi; Si, Rumeng; Mo, Jiaqing] Xinjiang Univ, Coll Informat Sci & Engn, Urumqi 830046, Xinjiang, Peoples R China.
   [Du, Guoli; Tong, Dongni] Xinjiang Med Univ, Affiliated Hosp 1, Urumqi, Peoples R China.
   [Lv, Guodong] Xinjiang Med Univ, Affiliated Hosp 1, State Key Lab Pathogenesis Prevent & Treatment Ce, Urumqi, Peoples R China.
   [Tang, Jun] Xinjiang Univ, Phys & Chem Detecting Ctr, Urumqi 830046, Xinjiang, Peoples R China.
   [Li, Hongyi] Qual Prod Supervis & Inspect Inst, Urumqi, Peoples R China.
   [Ma, Hongbing] Tsinghua Univ, Dept Elect Engn, Beijing, Peoples R China.
C3 Xinjiang University; Xinjiang Medical University; Xinjiang Medical
   University; Xinjiang University; Tsinghua University
RP Lv, XY (corresponding author), Xinjiang Univ, Coll Informat Sci & Engn, Urumqi 830046, Xinjiang, Peoples R China.; Tang, J (corresponding author), Xinjiang Univ, Phys & Chem Detecting Ctr, Urumqi 830046, Xinjiang, Peoples R China.
EM xiaoz813@163.com; tangjunwq@163.com
RI ARSLAN, Okan/AAA-3232-2020
OI Chen, Cheng/0000-0002-6739-1937
FU National Science Foundation of China [61765014]; Urumqi Science and
   Technology Project [P161310002]
FX the National Science Foundation of China, Grant/Award Number: 61765014;
   the Urumqi Science and Technology Project, Grant/Award Number:
   P161310002
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NR 21
TC 40
Z9 40
U1 3
U2 133
PU WILEY-V C H VERLAG GMBH
PI WEINHEIM
PA POSTFACH 101161, 69451 WEINHEIM, GERMANY
SN 1864-063X
EI 1864-0648
J9 J BIOPHOTONICS
JI J. Biophotonics
PD FEB
PY 2020
VL 13
IS 2
AR e201900099
DI 10.1002/jbio.201900099
EA NOV 2019
PG 9
WC Biochemical Research Methods; Biophysics; Optics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Biophysics; Optics
GA KM1BB
UT WOS:000497056500001
PM 31593625
DA 2024-09-05
ER
PT J
AU Yik, BJ
   Dood, AJ
AF Yik, Brandon J.
   Dood, Amber J.
TI ChatGPT Convincingly Explains Organic Chemistry Reaction Mechanisms
   Slightly Inaccurately with High Levels of Explanation Sophistication
SO JOURNAL OF CHEMICAL EDUCATION
LA English
DT Article
DE Second-Year Undergraduate; Upper-DivisionUndergraduate; Organic
   Chemistry; Testing and Assessment; Mechanistic Reasoning; Generative
   Artificial Intelligence; Chemistry Education Research
ID FEATURES; MODES
AB The chemistry education research community values and emphasizes the role of constructing explanations and mechanistic reasoning to support students' learning of organic chemistry. Emerging large language model (LLM) and generative artificial intelligence (GAI) technologies are uniquely equipped to advance the teaching and learning of chemistry. GAI-based chatbots, such as ChatGPT, have the potential to help students learn mechanistic reasoning through their generated responses. This study investigates the extent to which 255 ChatGPT-generated responses are accurate explanations of 85 different reaction mechanisms and exhibit mechanistic reasoning as categorized by the levels of explanation sophistication framework. The study also explores the effects of prompt engineering on mechanism accuracy and explanation sophistication through three types of prompt cueing. Study findings show that (1) a quarter of responses are fully accurate explanations of reaction mechanisms and the majority contain predominantly accurate explanations of chemical phenomena and identification of nucleophiles and electrophiles, (2) responses exhibit high levels of explanation sophistication, and (3) prompt engineering plays a significant role in eliciting high levels of explanation sophistication but not mechanism description accuracy. Results are situated in mechanistic reasoning and prompt engineering frameworks with a focus on how these new technologies can be integrated into the chemistry classroom.
C1 [Yik, Brandon J.] Univ Virginia, Dept Chem, Charlottesville, VA 22904 USA.
   [Dood, Amber J.] Univ Michigan, Dept Chem, Ann Arbor, MI 48109 USA.
C3 University of Virginia; University of Michigan System; University of
   Michigan
RP Yik, BJ (corresponding author), Univ Virginia, Dept Chem, Charlottesville, VA 22904 USA.
EM byik@virginia.edu
RI Yik, Brandon/AAS-6477-2021
OI Yik, Brandon/0000-0001-8124-8451; Dood, Amber/0000-0003-4572-1402
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NR 76
TC 3
Z9 3
U1 34
U2 34
PU AMER CHEMICAL SOC
PI WASHINGTON
PA 1155 16TH ST, NW, WASHINGTON, DC 20036 USA
SN 0021-9584
EI 1938-1328
J9 J CHEM EDUC
JI J. Chem. Educ.
PD APR 25
PY 2024
VL 101
IS 5
BP 1836
EP 1846
DI 10.1021/acs.jchemed.4c00235
EA APR 2024
PG 11
WC Chemistry, Multidisciplinary; Education, Scientific Disciplines
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Education & Educational Research
GA RE6I2
UT WOS:001208324000001
OA hybrid
DA 2024-09-05
ER
PT J
AU Danesh, F
   Ghavidel, S
AF Danesh, Farshid
   Ghavidel, Somayeh
TI A longitudinal study on knowledge organization publications: using
   hierarchical clustering and multidimensional scaling
SO GLOBAL KNOWLEDGE MEMORY AND COMMUNICATION
LA English
DT Article
DE Knowledge organization (KO); Scientometrics; Co-word; Strategic diagram;
   Multidimensional scaling; Bibliometrics
ID CO-WORD ANALYSIS; INFORMATION-SCIENCE; INTELLECTUAL STRUCTURE; LIBRARY;
   SCIENTOMETRICS; DISSERTATIONS; COCITATION; EVOLUTION; NETWORKS; KEYWORD
AB PurposeThe purpose of this study was a longitudinal study on knowledge organization (KO) realm structure and cluster concepts and emerging KO events based on co-occurrence analysis. Design/methodology/approachThis longitudinal study uses the co-occurrence analysis. This research population includes keywords of articles indexed in the Web of Science Core Collection 1975-1999 and 2000-2018. Hierarchical clustering, multidimensional scaling and co-occurrence analysis were used to conduct the present research. SPSS, UCINET, VOSviewer and NetDraw were used to analyze and visualize data. FindingsThe "Information Technology" in 1975-1999 and the "Information Literacy" in 2000-2018, with the highest frequency, were identified as the most widely used keywords of KO in the world. In the first period, the cluster "Knowledge Management" had the highest centrality, the cluster "Strategic Planning" had the highest density in 2000-2018 and the cluster "Information Retrieval" had the highest centrality and density. The two-dimensional map of KO's thematic and clustering of KO topics by cluster analysis method indicates that in the periods examined in this study, thematic clusters had much overlap in terms of concept and content. Originality/valueThe present article uses a longitudinal study to examine the KO's publications in the past half-century. This paper also uses hierarchical clustering and multidimensional scaling methods. Studying the concepts and thematic trends in KO can impact organizing information as the core of libraries, museums and archives. Also, it can scheme information organizing and promote knowledge management. Because the results obtained from this article can help KO policymakers determine and design the roadmap, research planning, and micro and macro budgeting processes.
C1 [Danesh, Farshid] Reg Informat Ctr Sci & Technol, Informat Management Dept, Shiraz, Iran.
   [Ghavidel, Somayeh] Iran Publ Lib Fdn, Tehran, Iran.
RP Ghavidel, S (corresponding author), Iran Publ Lib Fdn, Tehran, Iran.
EM farshiddanesh@ricest.ac.ir; ghavidel62@gmail.com
RI Danesh, Farshid/AAG-5286-2020; Danesh, Farshid/D-3829-2011
OI Danesh, Farshid/0000-0003-2581-7052; Danesh, Farshid/0000-0001-5481-3988
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   Zou Q., 2018, INT J LIBRARIANSHIP, V3, P67
NR 51
TC 2
Z9 2
U1 7
U2 16
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 2514-9342
EI 2514-9350
J9 GLOB KNOWL MEM COMMU
JI Glob. Knowl. Mem. Commun.
PD JUL 23
PY 2024
VL 73
IS 6/7
BP 929
EP 955
DI 10.1108/GKMC-05-2022-0111
EA DEC 2022
PG 27
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA ZD4P5
UT WOS:000899121500001
DA 2024-09-05
ER
PT J
AU Llewellyn, A
   Whittington, C
   Stewart, G
   Higgins, JPT
   Meader, N
AF Llewellyn, Alexis
   Whittington, Craig
   Stewart, Gavin
   Higgins, Julian P. T.
   Meader, Nick
TI The Use of Bayesian Networks to Assess the Quality of Evidence from
   Research Synthesis: 2. Inter-Rater Reliability and Comparison with
   Standard GRADE Assessment
SO PLOS ONE
LA English
DT Article
AB Background
   The grades of recommendation, assessment, development and evaluation (GRADE) approach is widely implemented in systematic reviews, health technology assessment and guideline development organisations throughout the world. We have previously reported on the development of the Semi-Automated Quality Assessment Tool (SAQAT), which enables a semi-automated validity assessment based on GRADE criteria. The main advantage to our approach is the potential to improve inter-rater agreement of GRADE assessments particularly when used by less experienced researchers, because such judgements can be complex and challenging to apply without training. This is the first study examining the inter-rater agreement of the SAQAT.
   Methods
   We conducted two studies to compare: a) the inter-rater agreement of two researchers using the SAQAT independently on 28 meta-analyses and b) the inter-rater agreement between a researcher using the SAQAT (who had no experience of using GRADE) and an experienced member of the GRADE working group conducting a standard GRADE assessment on 15 meta-analyses.
   Results
   There was substantial agreement between independent researchers using the Quality Assessment Tool for all domains (for example, overall GRADE rating: weighted kappa 0.79; 95% CI 0.65 to 0.93). Comparison between the SAQAT and a standard GRADE assessment suggested that inconsistency was parameterised too conservatively by the SAQAT. Therefore the tool was amended. Following amendment we found fair-to-moderate agreement between the standard GRADE assessment and the SAQAT (for example, overall GRADE rating: weighted kappa 0.35; 95% CI 0.09 to 0.87).
   Conclusions
   Despite a need for further research, the SAQAT may aid consistent application of GRADE, particularly by less experienced researchers.
C1 [Llewellyn, Alexis; Meader, Nick] Univ York, Ctr Reviews & Disseminat, York YO10 5DD, N Yorkshire, England.
   [Whittington, Craig] UCL, Dept Clin Educ & Hlth Psychol, Ctr Outcomes Res & Effectiveness Res, London, England.
   [Stewart, Gavin] Newcastle Univ, Sch Agr Food & Rural Dev, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England.
   [Higgins, Julian P. T.] Univ Bristol, Sch Social & Community Med, Bristol, Avon, England.
C3 University of York - UK; University of London; University College
   London; Newcastle University - UK; University of Bristol
RP Stewart, G (corresponding author), Newcastle Univ, Sch Agr Food & Rural Dev, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England.
EM gavin.stewart@newcastle.ac.uk
RI Higgins, Julian/H-4008-2011; Whittington, Craig/B-1380-2008
OI Higgins, Julian/0000-0002-8323-2514; Stewart, Gavin/0000-0001-5684-1544;
   Meader, Nick/0000-0001-9332-6605; Whittington, Craig/0000-0002-1950-0334
FU National Institute for Health Research; MRC [MR/K025643/1] Funding
   Source: UKRI
FX This work was funded by the National Institute for Health Research.
CR [Anonymous], GRADEPRO
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NR 28
TC 7
Z9 7
U1 0
U2 13
PU PUBLIC LIBRARY SCIENCE
PI SAN FRANCISCO
PA 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA
SN 1932-6203
J9 PLOS ONE
JI PLoS One
PD DEC 30
PY 2015
VL 10
IS 12
AR e0123511
DI 10.1371/journal.pone.0123511
PG 11
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA DA0TN
UT WOS:000367510500001
PM 26716874
OA gold, Green Published, Green Submitted
DA 2024-09-05
ER
PT J
AU Bogdanovich, B
   Shah, PR
   Patel, PA
   Bui, T
   Boyd, CJ
AF Bogdanovich, Brennan
   Shah, Pearl
   Patel, Parth A.
   Bui, Tommy
   Boyd, Carter J.
TI Altmetric Analysis of Artificial Intelligence Articles in Plastic
   Surgery
SO ARCHIVES OF PLASTIC SURGERY-APS
LA English
DT Article
C1 [Bogdanovich, Brennan; Shah, Pearl; Patel, Parth A.; Bui, Tommy] Augusta Univ, Med Coll Georgia, Augusta, GA USA.
   [Boyd, Carter J.] NYU Langone Hlth, Hansjorg Wyss Dept Plast Surg, New York, NY USA.
   [Boyd, Carter J.] NYU, Hansjorg Wyss Dept Plast Surg, Langone Hlth, 305 East 47th St,Suite 1A, New York, NY 10017 USA.
C3 University System of Georgia; Augusta University; NYU Langone Medical
   Center; New York University
RP Boyd, CJ (corresponding author), NYU, Hansjorg Wyss Dept Plast Surg, Langone Hlth, 305 East 47th St,Suite 1A, New York, NY 10017 USA.
EM Carterjosephboyd@gmail.com
OI Shah, Pearl/0009-0000-7949-2586; Patel, Parth/0000-0001-6001-8462; Bui,
   Tommy/0000-0002-7389-125X; Bogdanovich, Brennan/0009-0006-8252-4255;
   Boyd, Carter/0000-0002-1421-6852
CR [Anonymous], 2018, GLOBAL GENDER GAP RE
   Bui T, 2023, ASIA-PAC J OPHTHALMO, V12, P625, DOI 10.1097/APO.0000000000000587
   Collier M., ACCENTURE
   Elmore SA, 2018, TOXICOL PATHOL, V46, P252, DOI 10.1177/0192623318758294
   Silvestre J, 2016, PLAST RECONSTR SURG, V138, p136E, DOI 10.1097/PRS.0000000000002308
NR 5
TC 0
Z9 0
U1 0
U2 2
PU GEORG THIEME VERLAG KG
PI STUTTGART
PA RUDIGERSTR 14, D-70469 STUTTGART, GERMANY
SN 2234-6163
EI 2234-6171
J9 ARCH PLAST SURG-APS
JI Arch. Plast. Surg.-APS
PD MAR
PY 2024
VL 51
IS 02
DI 10.1055/a-2223-5458
EA JAN 2024
PG 2
WC Surgery
WE Emerging Sources Citation Index (ESCI)
SC Surgery
GA NC9C9
UT WOS:001153227300001
PM 38596152
OA Green Published, gold
DA 2024-09-05
ER
PT J
AU Rothut, S
   Schulze, H
   Hohner, J
   Rieger, D
AF Rothut, Sophia
   Schulze, Heidi
   Hohner, Julian
   Rieger, Diana
TI Ambassadors of ideology: A conceptualization and computational
   investigation of far-right influencers, their networking structures, and
   communication practices
SO NEW MEDIA & SOCIETY
LA English
DT Article; Early Access
DE Computational; mobilization; network analysis; parasocial opinion
   leadership; political influencers; radicalization; social influence;
   Telegram; topic modeling; far-right extremism
ID HATE
AB Increasingly, influencers are employed to market not only products but also ideas and beliefs. The far right has recognized the strategic potential of influencer communication to tap into new target groups and mobilize supporters. This paper provides insights into the little-explored field of far-right influencers. We conceptualize them as individual actors characterized by far-right ideology, positioned as political influencers, actively advocating for their ideological aims. Employing a multi-layered computational approach to explore communication practices and networking structures of 243 German-speaking far-right influencers on Telegram, we derive a typology and observe the emergence of a functionally differentiated influencer collective. In this collective, each community has specific functions and characteristics that emphasize different ideological aspects, mobilization modes, and influencer practices. Despite the decentralized organization, we find high efficiency in information dissemination. The results corroborate the assumed potential of far-right influencers as disseminators of ideological content who can be particularly persuasive through their role as parasocial opinion leaders.
C1 [Rothut, Sophia; Schulze, Heidi; Hohner, Julian; Rieger, Diana] Ludwig Maximilian Univ Munich, Dept Media & Commun, Munich, Germany.
   [Rothut, Sophia] Ludwig Maximilian Univ Munich, Dept Media & Commun, Oettingenstr 67, D-80538 Munich, Germany.
C3 University of Munich; University of Munich
RP Rothut, S (corresponding author), Ludwig Maximilian Univ Munich, Dept Media & Commun, Oettingenstr 67, D-80538 Munich, Germany.
EM sophia.rothut@ifkw.lmu.de; heidi.schulze@ifkw.lmu.de;
   julian.hohner@ifkw.lmu.de; diana.rieger@ifkw.lmu.de
OI Hohner, Julian/0000-0002-5872-0954; Rothut, Sophia/0000-0003-0990-8034;
   Rieger, Diana/0000-0002-2417-0480
FU German Federal Ministry of Education and Research [MOTRA-13N15223]
FX The author(s) disclosed receipt of the following financial support for
   the research, authorship, and/or publication of this article: This study
   was supported by grants from the German Federal Ministry of Education
   and Research within the framework of the program "Research for Civil
   Security" of the Federal Government (grant no. MOTRA-13N15223).
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NR 86
TC 6
Z9 6
U1 10
U2 17
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 1461-4448
EI 1461-7315
J9 NEW MEDIA SOC
JI New Media Soc.
PD 2023 MAY 31
PY 2023
DI 10.1177/14614448231164409
EA MAY 2023
PG 28
WC Communication
WE Social Science Citation Index (SSCI)
SC Communication
GA U3LM4
UT WOS:001083846700001
OA hybrid
DA 2024-09-05
ER
PT S
AU McKillop, CA
AF McKillop, Christena A.
BE Sengupta, E
   Blessinger, P
   Cox, MD
TI DESIGNING EFFECTIVE LIBRARY LEARNING SPACES-STUDENT2SCHOLAR: A CASE
   STUDY
SO DESIGNING EFFECTIVE LIBRARY LEARNING SPACES IN HIGHER EDUCATION
SE Innovations in Higher Education Teaching and Learning
LA English
DT Article; Book Chapter
DE Student2Scholar; online learning; e-resource; information literacy;
   academic literacies; research skills; graduate students; higher
   education; academic librarians; Canadian universities; Association of
   College and Research Libraries ACRL; Queens University; University of
   Toronto Ontario Institute for Studies in Education; University of
   Western Ontario; pedagogy; collaboration; learning outcomes; teaching;
   learning design; feedback; testing; assessment
AB In this chapter, the author examines Student2Scholar (S2S), an online e-learning resource for graduate students in the social sciences, as a case study that coalesces around effective learning design, innovation, and collaboration to meet and overcome the changes, challenges, and opportunities that have arisen in the twenty-first century. The author provides an overview of the S2S project, including an examination of the key design choices and pedagogy which were both strategic and critical in setting the foundation for effective learning in an online environment. This chapter also examines different elements of the project with a focus on the structure, purpose, and goals specific to a limited budget and a tight project timeline. A unique aspect of the project was the collaboration in and across three Canadian universities. The diverse project group of experts and important contributions by the team members played a significant role in creating a richer and more innovative product. These elements combined in such a way that led to the successful creation and launch of S2S, an award-winning e-learning resource.
C1 [McKillop, Christena A.] Univ Calgary, Taylor Family Digital Lib, Prestigious Arch & Special Collect, Calgary, AB, Canada.
C3 University of Calgary
RP McKillop, CA (corresponding author), Univ Calgary, Taylor Family Digital Lib, Prestigious Arch & Special Collect, Calgary, AB, Canada.
FU Government of Ontario, Ministry of Training, Colleges and Universities'
   Shared Online Course Fund, the Ontario Online Initiative
FX The author wishes to acknowledge the contributions of all the S2S team
   members over the course of the project. As experts in library and
   information services, graduate research, and online learning for adults,
   the team members' dedication and passion for the project was essential.
   Dr Elan Paulson as the Primary Investigator inspired and encouraged the
   team throughout the project. The author gratefully acknowledges the
   Government of Ontario, Ministry of Training, Colleges and Universities'
   Shared Online Course Fund, the Ontario Online Initiative's funding
   support, and eCampus Ontario which have made creating and freely sharing
   S2S possible.
CR ACRL, 2015, Framework for Information Literacy for Higher Education
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   [Anonymous], 2021, STUDENT2SCHOLAR S2S
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NR 24
TC 0
Z9 0
U1 0
U2 1
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY, W YORKSHIRE BD16 1WA, ENGLAND
SN 2055-3641
BN 978-1-83909-782-9; 978-1-83909-783-6
J9 INNOV HIGH EDUC TEAC
PY 2020
VL 29
BP 45
EP 59
DI 10.1108/S2055-364120200000029005
D2 10.1108/S2055-3641202029
PG 15
WC Education & Educational Research; Information Science & Library Science
WE Book Citation Index – Social Sciences & Humanities (BKCI-SSH)
SC Education & Educational Research; Information Science & Library Science
GA BS8YK
UT WOS:000778450500005
DA 2024-09-05
ER
PT J
AU Bohn, S
   Braun, T
AF Bohn, Stephan
   Braun, Timo
TI Field-configuring projects: How projects shape the public reflection of
   electric mobility in Germany
SO INTERNATIONAL JOURNAL OF PROJECT MANAGEMENT
LA English
DT Article
DE Temporary organising; Field-level influence; Field-configuring events
   (FCE); Electric mobility; Topic modeling; Framing; Industry;
   Configuration; Mixed-method
ID MANAGEMENT RESEARCH; INSTITUTIONAL WORK; TRANSFORMATION; CULTURE;
   EVENTS; ORGANIZATIONS; CONSTRUCTION; CONFERENCES; DISCOURSE; DYNAMICS
AB Organising in the context of projects has been extensively investigated with a focus on project-level processes. However, the industry and market impact, i.e. the field influencing potential of this temporary form of organizing, is still poorly understood. By using concepts derived from the literature on field-configuring events (FCE), we analyse the influence of two projects on the field of electric mobility in Germany. To capture the role of projects within fields, we apply a longitudinal mixed-methods approach combining interpretative qualitative methods with a topic modeling machine learning research strategy. The results highlight how projects can shape fields by opening and closing public debates and narratives in the sense of discursive institutional work. Additionally, we offer concepts that enable a more complete understanding of the sequential entanglement between projects and events, based on three scenarios: the project-by-itself, project-follows-event, and event-follows-project scenarios. With these insights, our empirical study contributes to the project management and institutional literature.
C1 [Bohn, Stephan] Humboldt Inst Internet & Soc HIIG, Berlin, Germany.
   [Bohn, Stephan; Braun, Timo] Univ Appl Sci Darmstadt, Business Sch, Haardtring 100, D-64295 Darmstadt, Germany.
C3 Hochschule Darmstadt
RP Braun, T (corresponding author), Univ Appl Sci Darmstadt, Business Sch, Haardtring 100, D-64295 Darmstadt, Germany.
EM stephan.bohn@hiig.de; timo.braun@h-da.de
OI Braun, Timo/0000-0002-4427-2644; Bohn, Stephan/0000-0003-2404-4617
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NR 60
TC 5
Z9 5
U1 3
U2 23
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0263-7863
EI 1873-4634
J9 INT J PROJ MANAG
JI Int. J. Proj. Manag.
PD AUG
PY 2021
VL 39
IS 6
BP 605
EP 619
DI 10.1016/j.ijproman.2021.04.006
EA SEP 2021
PG 15
WC Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA UR8PC
UT WOS:000697003100003
DA 2024-09-05
ER
PT C
AU Sykownik, P
   Masuch, M
   Emmerich, K
   Peketz, J
AF Sykownik, Philipp
   Masuch, Maic
   Emmerich, Katharina
   Peketz, Jochen
GP Assoc Comp Machinery
TI Blending Science and Practice: A Collaborative Approach for Evaluating
   the Value of Heart Rate Measurement
SO CHI PLAY'19: EXTENDED ABSTRACTS OF THE ANNUAL SYMPOSIUM ON
   COMPUTER-HUMAN INTERACTION IN PLAY
LA English
DT Proceedings Paper
CT 6th ACM SIGCHI Annual Symposium on Computer-Human Interaction in Play
   (CHI PLAY)
CY OCT 22-25, 2019
CL Barcelona, SPAIN
DE playtests; game user research; game industry; evaluation methods;
   biometrics; heart rate monitoring; stimulated recall; interviews
AB We report on our collaboration between the user research team of Ubisoft Blue Byte (UBB) Dusseldorf and the Entertainment Computing Research Group (ECG) of the University of Duisburg-Essen. Based on a shared interest in exploring the use of biometrics for evaluating the player experience, we opted for heart rate (HR) monitoring in order to gain initial experience with general requirements for psychophysiological methods. We conducted two subsequent studies: the first at the university to evaluate the method in general, the second at UBB to gain insights into the particular requirements of playtests in practice. This two-stage process was chosen to reduce resource requirements on the company's side and to refine the procedure before testing it in the field. Experiences with guidelines to use biometrics are shared and discussed regarding opportunities for collaboration between academics and practitioners.
C1 [Sykownik, Philipp; Masuch, Maic; Emmerich, Katharina] Univ Duisurg Essen, D-47058 Duisburg, Germany.
   [Peketz, Jochen] Ubisoft Blue Byte, D-40211 Dusseldorf, Germany.
RP Sykownik, P (corresponding author), Univ Duisurg Essen, D-47058 Duisburg, Germany.
EM philipp.sykownik@uni-due.de; maic.masuch@uni-due.de;
   katharina.emmerich@uni-due.de; jochen.peketz@ubisoft.de
CR [Anonymous], 2012, P CHI 2012
   Blue Byte, 2019, ANN 1800 GAM PC
   Cacioppo JT, 2007, HANDBOOK OF PSYCHOPHYSIOLOGY, 3RD EDITION, P1, DOI 10.2277/ 0521844711
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   Drachen A, 2010, P 5 ACM SIGGRAPH S V, P49, DOI DOI 10.1145/1836135.1836143
   Drachen Anders, 2018, Games User Research
   Fairclough Stephen H., 2011, BIOMETRICS GAME EVAL
   IJsselsteijn W., 2013, Eindhoven: Technische Universiteit Eindhoven
   Isbister K., 2008, Using biometric measurement to help develop emotionally compelling games, P187, DOI DOI 10.1016/B978-0-12-374447-0.00013-5
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NR 19
TC 0
Z9 0
U1 0
U2 1
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA
BN 978-1-4503-6871-1
PY 2019
BP 211
EP 222
DI 10.1145/3341215.3354644
PG 12
WC Computer Science, Cybernetics; Computer Science, Interdisciplinary
   Applications; Computer Science, Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BO5NK
UT WOS:000518428000027
DA 2024-09-05
ER
PT C
AU Port, D
   Korte, M
AF Port, Dan
   Korte, Marcel
GP ACM
TI Comparative Studies of the Model Evaluation Criterions MMRE and PRED in
   Software Cost Estimation Research
SO ESEM'08: PROCEEDINGS OF THE 2008 ACM-IEEE INTERNATIONAL SYMPOSIUM ON
   EMPIRICAL SOFTWARE ENGINEERING AND MEASUREMENT
LA English
DT Proceedings Paper
CT ACM/IEEE International Symposium on Empirical Software Engineering and
   Measurement
CY OCT 09-10, 2008-2009
CL Kaiserslautern, GERMANY
DE Cost Estimation; Cost Model; Standard Error; Confidence; MMRE; PRED;
   Model Selection; Parameters; Calibration; Bootstrapping; Confidence
   Interval
AB Software cost model research results depend on model accuracy criteria such as MMRE and PRIED. Despite criticism, MMRE has emerged as the de facto standard criterion. Many alternatives have been proposed and studied, surprisingly however PRED, the second most popular criterion, has not been extensively studied. This work attempts to fill this gap in the literature and expand the understanding and use of evaluation criterion in general. The majority of this work is empirically based, applying MMRE and PRED to a number of COCOMO model variations with respect to a simulated data set and four publicly available cost estimation data sets. We replicate a number of results based on MMRE and extend them to PRED. We study qualities of MMRE and PRED as sample estimator statistics for parameters of a cost model error distribution. Standard error is used to ensure greater confidence in replicated and new results based on sample data.
C1 [Port, Dan] Univ Hawaii Manoa, 2404 Maile Way,E303, Honolulu, HI 96822 USA.
   [Korte, Marcel] Univ Appl Sci & Arts, Dortmund, Germany.
C3 University of Hawaii System; University of Hawaii Manoa; Hochschule
   Hannover-University of Applied Sciences & Arts
RP Port, D (corresponding author), Univ Hawaii Manoa, 2404 Maile Way,E303, Honolulu, HI 96822 USA.
EM dport@hawaii.edu; marcel.korte@stud.fh-dortmund.de
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   BESTWEB BETTER ESTIM
   2007, DESHARNIS DATASET
   2007, NASA 93 DATASET
   2007, COCOMO81 DATASET
   2008, COCOMONASA DATASET
NR 32
TC 49
Z9 50
U1 0
U2 0
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA
BN 978-1-59593-971-5
PY 2008
BP 51
EP +
PG 2
WC Computer Science, Software Engineering
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BJJ30
UT WOS:000266371500006
DA 2024-09-05
ER
PT J
AU Glänzel, W
   Heeffer, S
   Thijs, B
AF Glanzel, Wolfgang
   Heeffer, Sarah
   Thijs, Bart
TI Lexical analysis of scientific publications for nano-level
   scientometrics
SO SCIENTOMETRICS
LA English
DT Article; Proceedings Paper
CT 6th Global Tech Mining Conference
CY SEP, 2016
CL Valencia, SPAIN
DE Quantitative linguistics; Word-frequency; Waring distribution; Natural
   language processing; Nano-level analysis
AB In earlier studies (e.g. Glanzel and Thijs in Scientometrics, 2017) we have used components of text analysis in combination with link-based techniques to cluster documents spaces and to detect emerging research topics on the large scale. Taking up now the objectives of evaluative scientometrics, we attempt to link the textual analysis of small sets of individual scientific papers to evaluative bibliometrics. The objective is, however, quite similar. We focus on the detection of similarities and on monitoring structural changes but this time on the small scale. We proceed from earlier approaches used in quantitative linguistics applied to bibliometrics (Telcs et al. in Math Soc Sci; 10(2):169-178, 1985). In the present pilot study we have selected 18 papers by Andras Schubert and published in three different periods with 6 papers each: 1983-1985, 1993-1998 and 2010-2013. The objective is twofold: We first try only to detect linguistic regularities in the scientometric text by applying a Waring model to the analysis of Schubert's vocabulary on the basis of all words and nouns. The second goal refers to the identification of changes in the used vocabulary over a period of three decades. The main findings are discussed along with future research tasks, which arise from these result in the context of the analysis of dynamics and emergence of research topics at the micro and nano level.
C1 [Glanzel, Wolfgang; Heeffer, Sarah; Thijs, Bart] Katholieke Univ Leuven, ECOOM, Naamsestr 61, B-3000 Leuven, Belgium.
   [Glanzel, Wolfgang; Heeffer, Sarah; Thijs, Bart] Katholieke Univ Leuven, Dept MSI, Naamsestr 61, B-3000 Leuven, Belgium.
   [Glanzel, Wolfgang] Lib Hungarian Acad Sci, Dept Sci Policy & Scientometr, Arany Janos U 1, H-1051 Budapest, Hungary.
C3 KU Leuven; KU Leuven; Hungarian Academy of Sciences
RP Glänzel, W (corresponding author), Katholieke Univ Leuven, ECOOM, Naamsestr 61, B-3000 Leuven, Belgium.; Glänzel, W (corresponding author), Katholieke Univ Leuven, Dept MSI, Naamsestr 61, B-3000 Leuven, Belgium.; Glänzel, W (corresponding author), Lib Hungarian Acad Sci, Dept Sci Policy & Scientometr, Arany Janos U 1, H-1051 Budapest, Hungary.
EM Wolfgang.Glanzel@kuleuven.be; Sarah.Heeffer@kuleuven.be;
   Bart.Thijs@kuleuven.be
RI Glanzel, Wolfgang/AAE-4395-2021; Glanzel, Wolfgang/A-6280-2008; Thijs,
   Bart CM/C-2995-2008
OI Glanzel, Wolfgang/0000-0001-7529-5198; 
CR Braun T, 2016, REV ROUM CHIM, V61, P231
   Gelbukh A, 2001, LECT NOTES COMPUT SC, V2004, P332
   Glänzel W, 2014, SCIENTOMETRICS, V101, P939, DOI 10.1007/s11192-014-1247-1
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NR 16
TC 4
Z9 4
U1 1
U2 45
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD JUN
PY 2017
VL 111
IS 3
BP 1897
EP 1906
DI 10.1007/s11192-017-2336-8
PG 10
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH); Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Information Science & Library Science
GA EV4RE
UT WOS:000401747900037
DA 2024-09-05
ER
PT J
AU Schwemmer, C
   Wieczorek, O
AF Schwemmer, Carsten
   Wieczorek, Oliver
TI The Methodological Divide of Sociology: Evidence from Two Decades of
   Journal Publications
SO SOCIOLOGY-THE JOURNAL OF THE BRITISH SOCIOLOGICAL ASSOCIATION
LA English
DT Article
DE natural language processing; research methodology; scientometrics;
   sociology of science; sociology of Sociology
ID RESEARCH COLLABORATION; SCIENCE; BOUNDARIES; CRISIS; MODEL
AB Past research indicates that Sociology is a low-consensus discipline, where different schools of thought have distinct expectations about suitable scientific practices. This division of Sociology into different subfields is to a large extent related to methodology and choices between qualitative or quantitative research methods. Relying on theoretical constructs of the academic prestige economy, boundary demarcation and taste for research, we examine the methodological divide in generalist Sociology journals. Using automated text analysis for 8737 abstracts of articles published between 1995 and 2017, we discover evidence of this divide, but also of an entanglement between methodological choices and different research topics. Moreover, our results suggest a marginally increasing time trend for the publication of quantitative research in generalist journals. We discuss how this consolidation of methodological practices could enforce the entrenchment of different schools of thought, which ultimately reduces the potential for innovative and effective sociological research.
C1 [Schwemmer, Carsten] Univ Bamberg, Field Computat Social Sci, Bamberg, Germany.
   [Schwemmer, Carsten] Univ Bamberg, Chair Polit Sociol, Feldkirchenstr 21, D-96052 Bamberg, Germany.
   [Wieczorek, Oliver] Univ Bamberg, Chair Sociol, Especially Sociol Theory, Bamberg, Germany.
C3 Otto Friedrich University Bamberg; Otto Friedrich University Bamberg;
   Otto Friedrich University Bamberg
RP Schwemmer, C (corresponding author), Univ Bamberg, Chair Polit Sociol, Feldkirchenstr 21, D-96052 Bamberg, Germany.
EM c.schwem2er@gmail.com
RI Wieczorek, Oliver/ABC-8053-2020
OI Wieczorek, Oliver/0000-0002-6504-0965; Schwemmer,
   Carsten/0000-0001-9084-946X
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NR 62
TC 35
Z9 37
U1 5
U2 19
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 0038-0385
EI 1469-8684
J9 SOCIOLOGY
JI Sociol.-J. Brit. Sociol. Assoc.
PD FEB
PY 2020
VL 54
IS 1
BP 3
EP 21
DI 10.1177/0038038519853146
PG 19
WC Sociology
WE Social Science Citation Index (SSCI)
SC Sociology
GA KF0YQ
UT WOS:000508978500001
OA hybrid, Green Submitted
DA 2024-09-05
ER
PT J
AU Zhao, XL
   Zhang, YM
   Xue, JF
   Shan, C
   Liu, Z
AF Zhao, Xiaolin
   Zhang, Yiman
   Xue, Jingfeng
   Shan, Chun
   Liu, Zhen
TI Research on Network Risk Evaluation Method Based on a Differential
   Manifold
SO IEEE ACCESS
LA English
DT Article
DE Security; Measurement; Communication networks; Standards; Analytic
   hierarchy process; Machine learning; Manifolds; Network risk evaluation;
   differential manifold; Riemannian manifold; analytic hierarchy process;
   common vulnerability scoring system
AB With the rapid development of networks, network security is a serious problem. To evaluate a network accurately, this paper proposes a network risk evaluation method based on a differential manifold (DM) and research on traditional methods. The DM divides the network risk evaluation into network structure risk and network behavior risk evaluations. Network structure risk evaluates the network identity, and network behavior risk evaluates the attack and defense of the network. Network assets and asset vulnerabilities characterize a network, and the analytic hierarchy process (AHP) and the Common Vulnerability Scoring System (CVSS) are combined to evaluate the network identity. Network behavior causes high-dimensional indicator changes, and DMs are used to measure network behavior. To examine the effectiveness and accuracy of DMs, two experiments were performed. The experimental results show that the DM method is valid and accurate for evaluating network risk.
C1 [Zhao, Xiaolin; Xue, Jingfeng; Shan, Chun; Liu, Zhen] Beijing Inst Technol, Beijing 100081, Peoples R China.
   [Zhang, Yiman] China Ship Dev & Design Ctr, Wuhan 430064, Peoples R China.
C3 Beijing Institute of Technology
RP Xue, JF (corresponding author), Beijing Inst Technol, Beijing 100081, Peoples R China.
EM xuejf@bit.edu.cn
RI liu, zhen/GZK-9546-2022
OI Zhao, Xiaolin/0000-0002-9741-2954; LIU, ZHEN/0000-0003-2473-0086
FU National Key Research and Development Program of China [2016YFB0800700]
FX This work was supported by the National Key Research and Development
   Program of China under Grant 2016YFB0800700.
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NR 33
TC 1
Z9 2
U1 1
U2 22
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2020
VL 8
BP 66315
EP 66326
DI 10.1109/ACCESS.2020.2985547
PG 12
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA LF4US
UT WOS:000527415200029
OA gold
DA 2024-09-05
ER
PT J
AU Li, X
   Shen, YF
   Cheng, HL
   Yuan, F
   Huang, LC
AF Li, Xin
   Shen, Yuanfei
   Cheng, Haolun
   Yuan, Fei
   Huang, Lucheng
TI Identifying the Development Trends and Technological Competition
   Situations for Digital Twin: A Bibliometric Overview and Patent
   Landscape Analysis
SO IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
LA English
DT Article
DE Patents; Market research; Hidden Markov models; Analytical models;
   Digital twin; Bibliometrics; Industries; Bibliometric analysis;
   competitive situation; digital twin; evolutionary trends; patent
   analysis
ID MODEL
AB Digital twin is increasingly prominent for realizing the digital and intelligent transformation of various industries as an emerging technological means to connect the physical and virtual world. While there has been a recent growth of interest in digital twin in industry, finance, and academia, most relevant studies lack a systematic analysis of the status quo, development trends, and technological competition situations for digital twin. In this article, we used bibliometrics and patent analysis to conduct comprehensive and in-depth research of digital twin by reviewing the current status of academic research and technological development, distribution of countries and institutions, and technological competition situations. We found that academic research and technological development in digital twin are currently in the early stages of rapid growth, which is radiating from applications in smart manufacturing to other scenarios such as medical and health, smart cities, energy, transportation, public emergency, and agricultural food. Artificial intelligence technology, digital twin integrated architecture and system, intelligent real-time control have gradually become the key topics of academic research and technology research and development in the field of digital twin in recent years. The digital framework, sustainable digital twin, deep learning and neural network algorithms, and full lifecycle management have the potential to become technology development trends. USA and Germany are the technology leaders and occupy first-mover advantage at present, while China, the U.K., and South Korea are the powerful chasers in the future.
C1 [Li, Xin; Shen, Yuanfei; Yuan, Fei; Huang, Lucheng] Beijing Univ Technol, Coll Management & Econ, Beijing 100124, Peoples R China.
   [Cheng, Haolun] Beijing Inst Graph Commun, Coll New Media, Beijing 102600, Peoples R China.
C3 Beijing University of Technology
RP Li, X (corresponding author), Beijing Univ Technol, Coll Management & Econ, Beijing 100124, Peoples R China.
EM lixinyz@bjut.edu.cn; yuan@bjut.edu.cn; haoluncheng1997@163.com;
   18810601577@163.com; hlch@bjut.edu.cn
RI Yuan, Fei/D-5734-2011
OI Yuan, Fei/0000-0001-7633-0573
FU National Natural Science Foundation of China [72174017, 71673018];
   Social Science Program of Beijing Municipal Education Commission
   [SM202110005012]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 72174017 and Grant 71673018 and in part
   by the Social Science Program of Beijing Municipal Education Commission
   under Grant SM202110005012.
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NR 51
TC 5
Z9 5
U1 30
U2 226
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0018-9391
EI 1558-0040
J9 IEEE T ENG MANAGE
JI IEEE Trans. Eng. Manage.
PY 2024
VL 71
BP 1998
EP 2021
DI 10.1109/TEM.2022.3166794
EA MAY 2022
PG 24
WC Business; Engineering, Industrial; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Engineering
GA EO4E7
UT WOS:000791727000001
DA 2024-09-05
ER
PT J
AU Rejeb, A
   Rejeb, K
   Appolloni, A
   Treiblmaier, H
AF Rejeb, Abderahman
   Rejeb, Karim
   Appolloni, Andrea
   Treiblmaier, Horst
TI Foundations and knowledge clusters in TikTok (Douyin) research: evidence
   from bibliometric and topic modelling analyses
SO MULTIMEDIA TOOLS AND APPLICATIONS
LA English
DT Article
DE TikTok; Public health; Body image; Bibliometrics; Topic modeling
ID SOCIAL MEDIA; COVID-19 INFORMATION; SHORT VIDEO; MANAGEMENT; DOMAIN;
   VISUALIZATION; OPPORTUNITIES; ENGAGEMENT; BUSINESS; SCIENCE
AB The goal of this study is to comprehensively analyze the dynamics and structure of TikTok research since its initial development. The scholarly composition of articles dealing with TikTok was dissected via a bibliometric study based on a corpus of 542 journal articles from the Scopus database. The results show that TikTok research has flourished in recent years and also demonstrate that the authors' collaboration networks are disjointed, indicating a lack of cooperation among TikTok researchers. Furthermore, the analysis reveals that research collaboration among academic institutions reflects the North-South divide, also highlighting a limited research collaboration between institutions in developed and developing countries. Based on the keyword co-occurrence network and topic modeling, TikTok research revolves mainly around five thematic areas, including public health, health communication and education, platform governance, body image, and its impact on children and students. Based on these findings, numerous suggestions for further research are offered. As far as the authors are aware, this is the first application of bibliometrics and topic modeling to assess the growth of TikTok research and reveal the intellectual base of this knowledge domain.
C1 [Rejeb, Abderahman] Univ Roma Tor Vergata, Fac Econ, Dept Management & Law, Via Columbia 2, I-00133 Rome, Italy.
   [Rejeb, Karim] Univ Carthage, Fac Sci Bizerte, Zarzouna 7021, Bizerte, Tunisia.
   [Appolloni, Andrea] Univ Roma Tor Vergata, Fac Econ, Dept Management & Law, Via Columbia 2, I-00133 Rome, Italy.
   [Treiblmaier, Horst] Modul Univ Vienna, Sch Int Management, Kahlenberg 1, A-1190 Vienna, Austria.
C3 University of Rome Tor Vergata; Universite de Carthage; University of
   Rome Tor Vergata
RP Treiblmaier, H (corresponding author), Modul Univ Vienna, Sch Int Management, Kahlenberg 1, A-1190 Vienna, Austria.
EM horst.treiblmaier@modul.ac.at
RI Appolloni, Andrea/L-3607-2014
OI Appolloni, Andrea/0000-0001-5741-398X
FU MODUL University Vienna GmbH
FX No Statement Available
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NR 155
TC 1
Z9 1
U1 16
U2 21
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1380-7501
EI 1573-7721
J9 MULTIMED TOOLS APPL
JI Multimed. Tools Appl.
PD MAR
PY 2024
VL 83
IS 11
BP 32213
EP 32243
DI 10.1007/s11042-023-16768-x
EA SEP 2023
PG 31
WC Computer Science, Information Systems; Computer Science, Software
   Engineering; Computer Science, Theory & Methods; Engineering, Electrical
   & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA KF5H9
UT WOS:001070386400002
OA hybrid
DA 2024-09-05
ER
PT J
AU Hake, J
   Crowley, M
   Coy, A
   Shanks, D
   Eoff, A
   Kirmer-Voss, K
   Dhanda, G
   Parente, DJ
AF Hake, Joel
   Crowley, Miles
   Coy, Allison
   Shanks, Denton
   Eoff, Aundria
   Kirmer-Voss, Kalee
   Dhanda, Gurpreet
   Parente, Daniel J.
TI Quality, Accuracy, and Bias in ChatGPT-Based Summarization of Medical
   Abstracts
SO ANNALS OF FAMILY MEDICINE
LA English
DT Article
DE artificial intelligence; large language models; ChatGPT; primary care
   research; critical assessment of scientific literature; bias; text
   mining; text analysis
AB PURPOSE Worldwide clinical knowledge is expanding rapidly, but physicians have sparse time to review scientific literature. Large language models (eg, Chat Generative Pretrained Transformer [ChatGPT]), might help summarize and prioritize research articles to review. However, large language models sometimes "hallucinate" incorrect information. METHODS We evaluated ChatGPT's ability to summarize 140 peer -reviewed abstracts from 14 journals. Physicians rated the quality, accuracy, and bias of the ChatGPT summaries. We also compared human ratings of relevance to various areas of medicine to ChatGPT relevance ratings. RESULTS ChatGPT produced summaries that were 70% shorter (mean abstract length of 2,438 characters decreased to 739 characters). Summaries were nevertheless rated as high quality (median score 90, interquartile range [IQR] 87.0-92.5; scale 0-100), high accuracy (median 92.5, IQR 89.0-95.0), and low bias (median 0, IQR 0-7.5). Serious inaccuracies and hallucinations were uncommon. Classification of the relevance of entire journals to various fields of medicine closely mirrored physician classifications (nonlinear standard error of the regression [SER] 8.6 on a scale of 0-100). However, relevance classification for individual articles was much more modest (SER 22.3). CONCLUSIONS Summaries generated by ChatGPT were 70% shorter than mean abstract length and were characterized by high quality, high accuracy, and low bias. Conversely, ChatGPT had modest ability to classify the relevance of articles to medical specialties. We suggest that ChatGPT can help family physicians accelerate review of the scientific literature and have developed software (pyJournalWatch) to support this application. Life -critical medical decisions should remain based on full, critical, and thoughtful evaluation of the full text of research articles in context with clinical guidelines.
C1 [Hake, Joel; Crowley, Miles; Coy, Allison; Shanks, Denton; Eoff, Aundria; Kirmer-Voss, Kalee; Dhanda, Gurpreet; Parente, Daniel J.] Univ Kansas, Dept Family Med & Community Hlth, Med Ctr, Kansas City, KS USA.
   [Parente, Daniel J.] 3901 Rainbow Blvd,MS 4010, Kansas City, KS 66160 USA.
C3 University of Kansas; University of Kansas Medical Center
RP Parente, DJ (corresponding author), 3901 Rainbow Blvd,MS 4010, Kansas City, KS 66160 USA.
EM dparente@kumc.edu
FU REDCap data management platform at the University of Kansas Medical
   Center - National Center for Advancing Translational Sciences (NCATS)
   [UL1TR002366]
FX This work was not directly funded but used the REDCap data management
   platform at the University of Kansas Medical Center, which was supported
   by a Clinical and Translational Science Awards grant from the National
   Center for Advancing Translational Sciences (NCATS) awarded to the
   University of Kansas for Frontiers: University of Kansas Clinical and
   Translational Science Institute (#UL1TR002366) . This work is solely the
   responsibility of the authors and does not necessarily represent the
   official views of the National Institutes of Health or NCATS. This
   agency had no role in the design and conduct of the study; collection,
   management, analysis, and interpretation of the data; preparation,
   review, or approval of the manuscript; or decision to submit the
   manuscript for publication.
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NR 33
TC 0
Z9 0
U1 16
U2 16
PU ANNALS FAMILY MEDICINE
PI LEAWOOD
PA 11400 TOMAHAWK CREEK PARKWAY, LEAWOOD, KS 66211-2672 USA
SN 1544-1709
EI 1544-1717
J9 ANN FAM MED
JI Ann. Fam. Med.
PD MAR-APR
PY 2024
VL 22
IS 2
BP 113
EP 120
DI 10.1370/afm.3075
PG 8
WC Primary Health Care; Medicine, General & Internal
WE Science Citation Index Expanded (SCI-EXPANDED)
SC General & Internal Medicine
GA NA2N2
UT WOS:001197653100009
PM 38527823
OA gold
DA 2024-09-05
ER
PT J
AU Vieira, ES
   Gomes, JANF
AF Vieira, Elizabeth S.
   Gomes, Jose A. N. F.
TI The bibliometric indicators as predictors of the final decision of the
   peer review
SO RESEARCH EVALUATION
LA English
DT Article
DE peer review; bibliometric indicators; logistic regression; auxiliary
   instrument; margins
ID ASSESSMENT EXERCISE RATINGS; CITATION COUNTS; COMPUTER-SCIENCE;
   UNIVERSITY; INDEX; INFORMATION; ARCHAEOLOGY; VALIDATION; EXPERIENCE;
   METRICS
AB Peer review of candidates' proposals for research position is generally used as the best method available to select the most promising researchers, but it is very costly and has its limitations. This article analyzes to what extent bibliometric indicators can predict the results of the peer review exercise using the example of a particular selection process. Two composite indicators are found to be strongly correlated with peer review-based decisions. We calculated that the probability of the estimated prediction, as determined by the composite indicators, for a selected applicant to be higher than the estimated prediction determined for a rejected applicant is about 75%.
C1 [Vieira, Elizabeth S.; Gomes, Jose A. N. F.] Univ Porto, Fac Ciencias, Dept Quim & Bioquim, REQUIMTE, Rua Campo Alegre,687, P-4169007 Oporto, Portugal.
C3 Universidade do Porto
RP Vieira, ES (corresponding author), Univ Porto, Fac Ciencias, Dept Quim & Bioquim, REQUIMTE, Rua Campo Alegre,687, P-4169007 Oporto, Portugal.
EM elizabeth.vieira@fc.up.pt
RI de Sousa Vieira, Elizabeth/A-6820-2010
OI de Sousa Vieira, Elizabeth/0000-0002-2240-110X
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NR 38
TC 5
Z9 5
U1 2
U2 63
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 0958-2029
EI 1471-5449
J9 RES EVALUAT
JI Res. Evaluat.
PD APR
PY 2016
VL 25
IS 2
BP 170
EP 183
DI 10.1093/reseval/rvv037
PG 14
WC Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science
GA DT1XB
UT WOS:000381274300006
DA 2024-09-05
ER
PT J
AU Kim, H
AF Kim, Heesop
TI Retrieval effectiveness of controlled and uncontrolled index terms in
   INSPEC database
SO MALAYSIAN JOURNAL OF LIBRARY & INFORMATION SCIENCE
LA English
DT Article
DE Bibliographic Databases; Controlled vocabulary; Uncontrolled vocabulary;
   INSPEC database; Information retrieval; Index terms; Precision and
   recall
ID INFORMATION-RETRIEVAL; CONTROLLED VOCABULARY; FREE-TEXT;
   NATURAL-LANGUAGE; RELEVANCE; SEARCH; SCIENCE
AB The purpose of this empirical study is to assess the retrieval effectiveness of controlled and uncontrolled index terms in bibliographic database. Two types of index terms were tested in a web-based environment using the operational large-scale INSPEC database. 15 query types used in the study were both controlled terms and uncontrolled index terms derived from inverse document frequency weights. The retrieval effectiveness was evaluated using Precision. The main finding indicates there are statistically significant differences in precision arising from the two types of index terms; the uncontrolled index terms demonstrate better precision than the controlled index terms.
C1 Kyungpook Natl Univ, Dept Lib & Informat Sci, Taegu 702701, South Korea.
C3 Kyungpook National University (KNU)
RP Kim, H (corresponding author), Kyungpook Natl Univ, Dept Lib & Informat Sci, Taegu 702701, South Korea.
EM heesop@knu.ac.kr
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NR 38
TC 6
Z9 6
U1 0
U2 21
PU UNIV MALAYA, FAC COMPUTER SCIENCE & INFORMATION TECH
PI KUALA LUMPUR
PA UNIV MALAYA, FAC COMPUTER SCIENCE & INFORMATION TECH, KUALA LUMPUR,
   50603, MALAYSIA
SN 1394-6234
J9 MALAYS J LIBR INF SC
JI Malays. J. Libr. Sci.
PY 2014
VL 19
IS 2
BP 103
EP 117
PG 15
WC Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science
GA AO3MH
UT WOS:000341233600007
DA 2024-09-05
ER
PT J
AU Yang, AM
   Choi, IM
   Abeliuk, A
   Saffer, A
AF Yang, Aimei
   Choi, Ian Myoungsu
   Abeliuk, Andres
   Saffer, Adam
TI The Influence of Interdependence in Networked Publics Spheres: How
   Community-Level Interactions Affect the Evolution of Topics in Online
   Discourse
SO JOURNAL OF COMPUTER-MEDIATED COMMUNICATION
LA English
DT Article
DE Networked Public Spheres; Cyberbalkanization; Twitter Discourse;
   Community-Level Interaction; Network Analysis; Dynamic Topic Modeling
AB Investigations of networked public spheres often examine the structures of online platforms by studying users' interactions. These works suggest that users' interactions can lead to cyberbalkanization when interlocutors form homophilous communities that typically have few connections to others with opposing ideologies. Yet, rather than assuming communities are isolated, this study examines community-level interactions to reveal how communities in online discourses are more interdependent than previously theorized. Specifically, we examine how such interactions influence the evolution of topics overtime in source and target communities. Our analysis found that (a) the size of a source community (the community that initiates interactions) and a target community (the community that receives interactions), (b) the stability of the source community, and (c) the volume of mentions from a source community to a target community predicts the level of influence one community has on another's discussion topics. We argue this has significant theoretical and practical implications.
   Lay Summary
   Political discussions online, especially those in the United States, seem to range between harmonious discussions of likeminded people and heated debates that end with few, if any, who have changed their minds. Researchers have often examined these balkanized/polarized situations by studying online communities as isolated echo chambers of opinion. Our study focuses on the interactions between online communities who have different worldviews. We examine communities engaged in the global refugee crisis. We consider how the inter-community interactions influence the agenda of the respective communities. Our longitudinal analysis on the one hand confirms previous studies, namely that intra-community interactions indeed resemble echo chambers. On the other hand, we also find that there is interdependence in the inter-community discussion topics, albeit some communities had greater influence on other communities' discussion topics. For example, larger, more stable communities command more influence.
C1 [Yang, Aimei] Univ Southern Calif, Annenberg Sch Commun & Journalism, Los Angeles, CA 90089 USA.
   [Choi, Ian Myoungsu] Univ Southern Calif, Viterbi Sch Engn, Informat Sci Inst, Marina Del Rey, CA 90292 USA.
   [Abeliuk, Andres] Univ Chile, Dept Comp Sci, Santiago, Chile.
   [Saffer, Adam] Univ Minnesota, Hubbard Sch Journalism & Mass Commun, Minneapolis, MN 55455 USA.
C3 University of Southern California; University of Southern California;
   Universidad de Chile; University of Minnesota System; University of
   Minnesota Twin Cities
RP Yang, AM (corresponding author), Univ Southern Calif, Annenberg Sch Commun & Journalism, Los Angeles, CA 90089 USA.
EM aimei.yang@usc.edu
RI Saffer, Adam/GRY-0838-2022
OI Saffer, Adam/0000-0001-8032-4256
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NR 25
TC 4
Z9 5
U1 7
U2 77
PU OXFORD UNIV PRESS INC
PI CARY
PA JOURNALS DEPT, 2001 EVANS RD, CARY, NC 27513 USA
SN 1083-6101
J9 J COMPUT-MEDIAT COMM
JI J. Comput.-Mediat. Commun.
PD MAY
PY 2021
VL 26
IS 3
BP 148
EP 166
DI 10.1093/jcmc/zmab002
EA MAY 2021
PG 19
WC Communication; Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Communication; Information Science & Library Science
GA UE2WK
UT WOS:000687753900002
OA gold
DA 2024-09-05
ER
PT J
AU Mustapha, KB
   Yap, EH
   Abakr, YA
AF Mustapha, Khameel B.
   Yap, Eng Hwa
   Abakr, Yousif Abdalla
TI Bard, ChatGPT and 3DGPT: a scientometric analysis of generative AI tools
   and assessment of implications for mechanical engineering education
SO INTERACTIVE TECHNOLOGY AND SMART EDUCATION
LA English
DT Article; Early Access
DE Generative AI; ChatGPT; Bard; 3DGPT; Mechanical engineering; Engineering
   education
ID ARTIFICIAL-INTELLIGENCE; COMMUNICATION; MATHEMATICS; PERCEPTIONS;
   CHALLENGES; CURRICULUM; MODELS
AB PurposeFollowing the recent rise in generative artificial intelligence (GenAI) tools, fundamental questions about their wider impacts have started to reverberate around various disciplines. This study aims to track the unfolding landscape of general issues surrounding GenAI tools and to elucidate the specific opportunities and limitations of these tools as part of the technology-assisted enhancement of mechanical engineering education and professional practices.Design/methodology/approachAs part of the investigation, the authors conduct and present a brief scientometric analysis of recently published studies to unravel the emerging trend on the subject matter. Furthermore, experimentation was done with selected GenAI tools (Bard, ChatGPT, DALL.E and 3DGPT) for mechanical engineering-related tasks.FindingsThe study identified several pedagogical and professional opportunities and guidelines for deploying GenAI tools in mechanical engineering. Besides, the study highlights some pitfalls of GenAI tools for analytical reasoning tasks (e.g., subtle errors in computation involving unit conversions) and sketching/image generation tasks (e.g., poor demonstration of symmetry).Originality/valueTo the best of the authors' knowledge, this study presents the first thorough assessment of the potential of GenAI from the lens of the mechanical engineering field. Combining scientometric analysis, experimentation and pedagogical insights, the study provides a unique focus on the implications of GenAI tools for material selection/discovery in product design, manufacturing troubleshooting, technical documentation and product positioning, among others.
C1 [Mustapha, Khameel B.; Abakr, Yousif Abdalla] Univ Nottingham Malaysia, Dept Mech Mat & Mfg Engn, Semenyih, Malaysia.
   [Yap, Eng Hwa] Xian Jiaotong Liverpool Univ, XJTLU Entrepreneur Coll Taicang, Sch Robot, Taicang, Greater Suzhou, Peoples R China.
C3 University of Nottingham Malaysia; Xi'an Jiaotong-Liverpool University
RP Mustapha, KB (corresponding author), Univ Nottingham Malaysia, Dept Mech Mat & Mfg Engn, Semenyih, Malaysia.
EM Khameelb.Mustapha@nottingham.edu.my; Eng.Hwa@xjtlu.edu.cn;
   Yousif.Ab@nottingham.edu.my
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NR 146
TC 0
Z9 0
U1 19
U2 24
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 1741-5659
EI 1758-8510
J9 INTERACT TECHNOL SMA
JI Interact. Technol. Smart Educ.
PD 2024 FEB 16
PY 2024
DI 10.1108/ITSE-10-2023-0198
EA FEB 2024
PG 37
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA HS1H6
UT WOS:001161398500001
DA 2024-09-05
ER
PT J
AU Farhat, F
   Sohail, SS
   Madsen, DO
AF Farhat, Faiza
   Sohail, Shahab Saquib
   Madsen, Dag Oivind
TI How trustworthy is ChatGPT? The case of bibliometric analyses
SO COGENT ENGINEERING
LA English
DT Article
DE ChatGpt; bibliometrics; trustworthiness; artificial intelligence;
   chatbots
AB The introduction of the AI-powered chatbot ChatGPT by OpenAI has sparked much interest and debate among academic researchers. Commentators from different scientific disciplines have raised many concerns and issues, especially related to the ethics of using these tools in scientific writing and publications. In addition, there has been discussions about whether ChatGPT is trustworthy, effective, and useful in increasing researchers' productivity. Therefore, in this paper, we evaluate ChatGPT's performance on tasks related to bibliometric analysis, by comparing the output provided by the chatbot with a recently conducted bibliometric study on the same topic. The findings show that there are large discrepancies and ChatGPT's trustworthiness is low in this particular area. Therefore, researchers should exercise caution when using ChatGPT as a tool in bibliometric studies.
C1 [Farhat, Faiza] Aligarh Muslim Univ, Dept Zool, Aligarh, India.
   [Sohail, Shahab Saquib] Jamia Hamdard, Dept Comp Sci & Engn, New Delhi, India.
   [Madsen, Dag Oivind] Univ South Eastern Norway, USN Sch Business, Dept Business Mkt & Law, Honefoss, Norway.
C3 Aligarh Muslim University; Jamia Hamdard University; University College
   of Southeast Norway
RP Madsen, DO (corresponding author), Univ South Eastern Norway, USN Sch Business, Dept Business Mkt & Law, Honefoss, Norway.
EM dag.oivind.madsen@usn.no
RI FARHAT, FAIZA/KIK-8175-2024; Madsen, Dag Øivind/I-1587-2016; sohail,
   shahab/O-3263-2019
OI Madsen, Dag Øivind/0000-0001-8735-3332; sohail,
   shahab/0000-0002-5944-7371
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NR 30
TC 16
Z9 16
U1 17
U2 123
PU TAYLOR & FRANCIS AS
PI OSLO
PA KARL JOHANS GATE 5, NO-0154 OSLO, NORWAY
SN 2331-1916
J9 COGENT ENG
JI Cogent Eng.
PD DEC 31
PY 2023
VL 10
IS 1
AR 2222988
DI 10.1080/23311916.2023.2222988
PG 8
WC Engineering, Multidisciplinary
WE Emerging Sources Citation Index (ESCI)
SC Engineering
GA K4RV1
UT WOS:001016335100001
OA Green Published, gold
DA 2024-09-05
ER
PT J
AU Lin, QN
   Zhuo, LZ
AF Lin, Qingna
   Zhuo, Lizheng
TI Research on the Evaluation and Optimization Method of the Impact of
   Chorus Education on University Culture Based on Coevolution Model in the
   Background of Artificial Intelligence
SO SCIENTIFIC PROGRAMMING
LA English
DT Article
AB The development of artificial intelligence technology is a field where all walks of life need to carry out in-depth research in the future, and the introduction of artificial intelligence technology in the field of university evaluation has become an inevitable trend. Through the collection and collation of the literature at home and abroad, the influence of chorus education on college culture in China has long remained in qualitative and experiential judgment and the significance and value of chorus education to colleges and universities are relatively single. Therefore, it is of great innovative value and practical significance to establish a scientific, systematic, and comprehensive evaluation mechanism for the impact of chorus education on university culture and to scientifically analyze key issues, establish evaluation criteria, and inject new research perspectives into the promotion of chorus education in colleges and universities in China, combining with the mature coevolution theoretical model of management science. It is of great innovative value and significance to combine the DEMATEL research method with the current practice of promoting chorus education in China's colleges and universities and to systematically and comprehensively construct the evaluation system and research paradigm in line with chorus education by using the qualitative and quantitative methods.
C1 [Lin, Qingna; Zhuo, Lizheng] Krirk Univ, Int Coll, Bangkok 10220, Thailand.
C3 Krirk University
RP Zhuo, LZ (corresponding author), Krirk Univ, Int Coll, Bangkok 10220, Thailand.
EM linqn@cqupt.edu.cn; 845902916@qq.com
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NR 45
TC 0
Z9 0
U1 2
U2 30
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1058-9244
EI 1875-919X
J9 SCI PROGRAMMING-NETH
JI Sci. Program.
PD OCT 31
PY 2021
VL 2021
AR 9261934
DI 10.1155/2021/9261934
PG 10
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA XM9ZL
UT WOS:000729175200004
OA gold
DA 2024-09-05
ER
PT C
AU Tran, HMT
   Anvari, F
AF Hien Minh Thi Tran
   Anvari, Farshid
BE Blooma, J
   Nkhoma, M
   Leung, N
TI How Reflective Professionals Design and Evaluate Financial Information
   Management Systems Courses
SO PROCEEDINGS OF THE 4TH INTERNATIONAL CONFERENCE ON INFORMATION SYSTEMS
   MANAGEMENT AND EVALUATION (ICIME 2013)
LA English
DT Proceedings Paper
CT 4th International Conference on Information Systems Management and
   Evaluation (ICIME)
CY MAY 13-14, 2013
CL RMIT Univ Vietnam, Ho Chi Minh City, VIETNAM
HO RMIT Univ Vietnam
DE active learning; reflective practice strategies; action-research;
   evaluation; financial information management systems (FIMS); accounting
   information systems (AIS)
ID PRACTITIONER PERSPECTIVE
AB Financial Information Management Systems (FIMS) or Accounting Information Systems (AIS) is a cross-discipline subject, often taught by Computing and Accounting disciplines. In recent years, demand for this subject has grown. However, educators have lamented high failure rates among accounting information systems students; professional bodies have reported that graduates lack sufficient meta-cognitive knowledge of information systems to perform their tasks. Quality teaching of FIMS or AIS requires instructors to actively update their knowledge of accounting systems and information technology as well as to reflect on teaching techniques. Reflection and reflective practices are taught within the education discipline, and have grown in popularity among many other disciplines. Yet little has been written about how accounting and IT professionals reflect on their practice and how they apply their reflections to their teaching. This paper explores the research question: how can reflective professionals assist computing and accounting academics in the design and delivery of the FIMS or AIS courses? Through our case study at an Australian university, we provide insights into the application of constructivist theory and reflective practice strategies in teaching FIMS courses. We discuss (1) the rationale for the importance of constructivist theory, cognitive load theory, reflective and action-research in teaching and learning, (2) Bloom's Revised Taxonomy, (3) the applications of Bloom and the reflective concept to design and deliver FIMS courses, (4) reflection on our teaching strategies in applying these concepts and, (5) conclusions on how reflective professionals can assist computing and accounting academics in the design and delivery of FIMS or AIS courses. Our study supports the view that reflection is a strategy; the Bloom's Revised Taxonomy and the PEER Model are tools to assist instructors in designing and delivering courses that enhance participant's learning abilities. We propose the five dimensional reflective cycle to facilitate reflective practice among academic and professional instructors for designing and delivering FIMS and AIS courses.
C1 [Hien Minh Thi Tran] Macquarie Univ, Off Financial Serv, N Ryde, NSW 2109, Australia.
C3 Macquarie University
EM hien.tran@mq.edu.au
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NR 41
TC 0
Z9 0
U1 0
U2 1
PU ACAD  CONFERENCES LTD
PI NR READING
PA CURTIS FARM, KIDMORE END, NR READING, RG4 9AY, ENGLAND
BN 978-1-909507-20-3
PY 2013
BP 263
EP 271
PG 9
WC Business; Computer Science, Information Systems; Information Science &
   Library Science; Management
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Business & Economics; Computer Science; Information Science & Library
   Science
GA BFY80
UT WOS:000321909400032
DA 2024-09-05
ER
PT J
AU Zan, P
   Zhao, YT
   Zhong, H
   Yu, Y
   Wang, YB
   Shao, YJ
   Li, CY
AF Zan, Peng
   Zhao, Yutong
   Zhong, Hua
   Yu, Yang
   Wang, Yuanbo
   Shao, Yijia
   Li, Chunyong
TI Research on the evaluation of rectal function after LAR based on
   CEEMDAN-Fast-ICA algorithm
SO IET SCIENCE MEASUREMENT & TECHNOLOGY
LA English
DT Article
DE CEEMDAN; FAST-ICA; multi-sensor information fusion; PSO-SVM; rectal
   function assessment
ID INDEPENDENT COMPONENT ANALYSIS; ANTERIOR RESECTION SYNDROME; CANCER;
   MANOMETRY; SURGERY
AB Rectal cancer is one of the most common lower gastrointestinal diseases worldwide. Currently, the common treatment is low anterior resection (LAR) of the rectum, which preserves the anus of the patient. However, it is easy to cause low anterior resection syndrome after surgery, which has a significant negative impact on the life of patients, and there is no unified evaluation standard for postoperative rectal function. To solve this problem, a multi-sensor fusion rectal information acquisition system is designed in this paper, and a rectal signal processing method is proposed to theoretically evaluate the rectal function of postoperative patients. The method uses the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to decompose the one-dimensional rectal signal to solve the underdetermined ICA problem, uses the Fast independent component analysis (Fast-ICA) to separate the pure rectal signal, uses the wavelet packet to extract features, and uses the particle swarm optimization optimizes support vector machine (PSO-SVM) to classify and evaluate postoperative function. According to the experimental results, the rectal signal preprocessing effect is good, the evaluation prediction rate is 99.5565%, and the algorithm classification results are accurate, which provides a certain preliminary theoretical basis and reference value for the evaluation of rectal function after LAR.
C1 [Zan, Peng; Zhao, Yutong; Zhong, Hua; Yu, Yang; Wang, Yuanbo] Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai Key Lab Power Stn Automat Technol, Shanghai, Peoples R China.
   [Shao, Yijia] Southeast Univ, Sch Elect Sci & Engn, Nanjing, Jiangsu, Peoples R China.
   [Li, Chunyong] Beijing Inst Radiat Med, Beijing, Peoples R China.
   [Li, Chunyong] Beijing Inst Radiat Med, 27 Taiping Rd, Beijing 100850, Peoples R China.
C3 Shanghai University; Southeast University - China; Academy of Military
   Medical Sciences - China; Academy of Military Medical Sciences - China
RP Li, CY (corresponding author), Beijing Inst Radiat Med, 27 Taiping Rd, Beijing 100850, Peoples R China.
EM lcy07@tsinghua.org.cn
RI li, chunyong/JJE-0805-2023; zhong, hua/JRW-4786-2023
FU Science and Technology Commission of Shanghai Municipality; Development
   Fund for Shanghai Talents;  [22xtcx00300];  [2020010]
FX ACKNOWLEDGEMENTS This work was sponsored by Science and Technology
   Commission of Shanghai Municipality (No. 22xtcx00300) and the
   Development Fund for Shanghai Talents (No. 2020010).
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NR 46
TC 1
Z9 1
U1 3
U2 14
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1751-8822
EI 1751-8830
J9 IET SCI MEAS TECHNOL
JI IET Sci. Meas. Technol.
PD JUN
PY 2023
VL 17
IS 4
BP 167
EP 182
DI 10.1049/smt2.12140
EA JAN 2023
PG 16
WC Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA H7BW9
UT WOS:000909425200001
OA gold
DA 2024-09-05
ER
PT C
AU Lei, YF
   Liu, ZB
AF Lei, Yufei
   Liu, Zhongbao
GP IOP
TI The development of artificial intelligence: a bibliometric analysis,
   2007-2016
SO 2018 INTERNATIONAL CONFERENCE ON COMPUTER INFORMATION SCIENCE AND
   APPLICATION TECHNOLOGY
SE Journal of Physics Conference Series
LA English
DT Proceedings Paper
CT International Conference on Computer Information Science and Application
   Technology (CISAT)
CY DEC 07-09, 2018
CL NE Petr Univ, Daqing, PEOPLES R CHINA
HO NE Petr Univ
AB The aim of this study is to research the development of artificial intelligence in bibliometrics perspective. Bibliometrics, as one sub-field of scientometric, is an effective tool to evaluating research trends in different fields. The total number of 1188 publications between 1st January 2007 to 31, December 2016 was identified from an academic database Web of Science. In this study, yearly research output, distribution of publication by countries, most productive publication institutions, most productive authors, distribution of research field, artificial intelligence-related researches were analyzed based on bibliometrics. This study tries to provide a valuable reference for researchers to understand the development of artificial intelligence in multiple perspectives.
C1 [Lei, Yufei; Liu, Zhongbao] Quanzhou Univ Informat Engn, Sch Software, Quanzhou 362000, Fujian, Peoples R China.
   [Liu, Zhongbao] North Univ China, Sch Software, Taiyuan 030051, Shanxi, Peoples R China.
C3 North University of China
RP Liu, ZB (corresponding author), Quanzhou Univ Informat Engn, Sch Software, Quanzhou 362000, Fujian, Peoples R China.; Liu, ZB (corresponding author), North Univ China, Sch Software, Taiyuan 030051, Shanxi, Peoples R China.
EM liuzb@nuc.edu.cn
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NR 14
TC 14
Z9 14
U1 5
U2 40
PU IOP PUBLISHING LTD
PI BRISTOL
PA DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND
SN 1742-6588
EI 1742-6596
J9 J PHYS CONF SER
PY 2019
VL 1168
AR 022027
DI 10.1088/1742-6596/1168/2/022027
PG 6
WC Computer Science, Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BM9MB
UT WOS:000471120700027
OA gold
DA 2024-09-05
ER
PT C
AU Shubankar, K
   Singh, AP
   Pudi, V
AF Shubankar, Kumar
   Singh, Aditya Pratap
   Pudi, Vikram
GP IEEE
BE Famili, FA
   Osman, IH
   Kendall, G
   Hamdan, AR
   Othman, Z
   Sarim, HM
   Abdullah, S
TI A Frequent Keyword-Set Based Algorithm for Topic Modeling and Clustering
   of Research Papers
SO 2011 3RD CONFERENCE ON DATA MINING AND OPTIMIZATION (DMO)
SE IEEE International Conference on Data Mining
LA English
DT Proceedings Paper
CT 3rd Conference on Data Mining and Optimization (DMO)/1st Multi
   Conference on Artificial Intelligence Technology (MCAIT)
CY JUN 28-29, 2011
CL Putrajaya, MALAYSIA
DE Closed Frequent Keyword-set; Topic Detection; Graph Mining; Citation
   Network; Authoritative Score
AB In this paper we introduce a novel and efficient approach to detect topics in a large corpus of research papers. With rapidly growing size of academic literature, the problem of topic detection has become a very challenging task. We present a unique approach that uses closed frequent keyword-set to form topics. Our approach also provides a natural method to cluster the research papers into hierarchical, overlapping clusters using topic as similarity measure. To rank the research papers in the topic cluster, we devise a modified PageRank algorithm that assigns an authoritative score to each research paper by considering the sub-graph in which the research paper appears. We test our algorithms on the DBLP dataset and experimentally show that our algorithms are fast, effective and scalable.
C1 [Shubankar, Kumar; Singh, Aditya Pratap; Pudi, Vikram] IIIT Hyderabad, Ctr Data Engn, Hyderabad, Andhra Pradesh, India.
C3 International Institute of Information Technology Hyderabad
RP Shubankar, K (corresponding author), IIIT Hyderabad, Ctr Data Engn, Hyderabad, Andhra Pradesh, India.
EM shubankar@students.iiit.ac.in; aditya_pratap@students.iiit.ac.in;
   vikram@iiit.ac.in
RI Pudi, Vikram/O-8981-2017
OI Pudi, Vikram/0000-0002-0589-6366
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NR 25
TC 12
Z9 12
U1 0
U2 6
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 1550-4786
BN 978-1-61284-212-7
J9 IEEE DATA MINING
PY 2011
BP 96
EP 102
PG 7
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BYP33
UT WOS:000299559300016
DA 2024-09-05
ER
PT J
AU Blair, BL
   Slagle, DR
   Williams, AM
AF Blair, Bruce L.
   Slagle, Derek R.
   Williams, Adam M.
TI Institutional and programmatic determinants for graduate public affairs'
   online education: Assessing the influence of faculty workload
SO TEACHING PUBLIC ADMINISTRATION
LA English
DT Article
DE Public Affairs Education; Higher Education; FacultyWorkload; Online
   Learning; COVID-19
ID DISTANCE EDUCATION
AB The research explores why some public affairs graduate programs choose to develop fully online degree offerings while others do not. The study attempts to address questions surrounding how different institutions and programs are pursuing degree offerings and the potential influence of faculty workload. The research utilizes a quantitative, cross-sectional design analyzing results from a survey on institutional and programmatic practices in workload, hiring, and degree offerings administered to primary points of contact within public affairs academic units from all institutions found in the US News World Report Graduate Programs in Public Affairs Rankings from 2019. Survey data is paired with program information from the accrediting body institutional member database. Findings indicate differences from both institutional and programmatic groupings do demonstrate workload measures have unique characteristics depending upon the type of institution and rank of the program. Further analysis discusses the influence of the COVID-19 pandemic on future public affairs programming.
C1 [Blair, Bruce L.] Clayton State Univ, Morrow, GA USA.
   [Slagle, Derek R.] Univ Arkansas, Little Rock, AR USA.
C3 University System of Georgia; Clayton State University; University of
   Arkansas System; University of Arkansas Little Rock; University of
   Arkansas Fayetteville
RP Williams, AM (corresponding author), Univ Illinois, One Univ Plaza,MS PAC 420, Springfield, IL 62704 USA.
EM adamwilliams1986@yahoo.com
OI Slagle, Derek/0000-0002-0367-3185; Williams, Adam/0000-0001-5307-5370
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NR 32
TC 1
Z9 1
U1 1
U2 4
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 0144-7394
EI 2047-8720
J9 TEACH PUBLIC ADMIN
JI Teach. Public Admin.
PD JUL
PY 2022
VL 40
IS 2
BP 181
EP 198
DI 10.1177/01447394211017326
EA MAY 2021
PG 18
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA 2G4CL
UT WOS:000652700900001
OA Bronze
DA 2024-09-05
ER
PT C
AU Chagas, RRFF
   Modesti, PH
   Borsato, M
AF Chagas, Ruan R. F. F.
   Modesti, Paulo H.
   Borsato, Milton
BE Pokojski, J
   Gil, M
   Newnes, L
   Stjepandic, J
   Wognum, N
TI Bibliometric and Systemic Analysis of Production Planning Optimization
SO TRANSDISCIPLINARY ENGINEERING FOR COMPLEX SOCIO-TECHNICAL SYSTEMS -
   REAL-LIFE APPLICATIONS
SE Advances in Transdisciplinary Engineering
LA English
DT Proceedings Paper
CT 27th ISTE International Conference on Transdisciplinary Engineering (TE)
   - Transdisciplinary Engineering for Complex Socio-Technical Systems in
   Perspective of Real-Life Application
CY JUL 01-10, 2020
CL ELECTR NETWORK
DE Artificial intelligence; optimization; production planning
ID SEQUENCE-DEPENDENT SETUPS
AB Having good production planning is essential to companies who need to maximize the use of their resources and boost their profits. However, to formulate efficient production planning is necessary to consider many variables. That makes analytical solutions almost impossible, forcing companies to use computational methods to solve this kind of problem. Even so, because of the complexity of the problems, much computational effort is needed. In that sense, using 4.0 industry concepts, like artificial intelligence, has been helping companies formulate optimal, or near-optimal, production plans for their process in a feasible time. Since each company has different characteristics and variables, the possibilities to formulate and optimize production planning are diverse. Thus, many case studies can be carried out. Generating a huge range of research opportunities. So, this study is a survey attempting to find some of these gaps through a systemic and bibliometric analysis. To achieve this goal the methodological procedure Knowledge Development Process - Constructivist (ProKnow - C) was used. This method aims to minimize the amount of content out of alignment with the research subject. In the first search, 44,609 articles were found, and after a filtering process that prioritized scientific recognized articles and journals, only 15 articles remained. Finally, common themes among the articles and opportunities for future work were highlighted.
C1 [Chagas, Ruan R. F. F.; Modesti, Paulo H.; Borsato, Milton] Univ Tecnol Fed Parana UTFPR, Curitiba, Parana, Brazil.
C3 Universidade Tecnologica Federal do Parana
RP Chagas, RRFF (corresponding author), Univ Tecnol Fed Parana UTFPR, Curitiba, Parana, Brazil.
EM ruan@alunos.utfpr.edu.br
RI Borsato, Milton/H-5937-2012
OI Borsato, Milton/0000-0002-3607-8315
FU Fundacao de Apoio a Universidade Tecnologica Federal do Parana
FX The authors wish to thank Fundacao de Apoio a Universidade Tecnologica
   Federal do Parana for supporting the present research.
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NR 20
TC 0
Z9 1
U1 1
U2 2
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 2352-7528
BN 978-1-64368-111-5; 978-1-64368-110-8
J9 ADV TRANSDISCIPL ENG
PY 2020
VL 12
BP 661
EP 669
DI 10.3233/ATDE200128
PG 9
WC Engineering, Industrial
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Engineering
GA BR3OM
UT WOS:000648613100067
OA gold
DA 2024-09-05
ER
PT C
AU Mathew, G
   Agrawal, A
   Menzies, T
AF Mathew, George
   Agrawal, Amritanshu
   Menzies, Tim
GP IEEE
TI Trends in Topics at SE Conferences (1993-2013)
SO PROCEEDINGS OF THE 2017 IEEE/ACM 39TH INTERNATIONAL CONFERENCE ON
   SOFTWARE ENGINEERING COMPANION (ICSE-C 2017)
SE Proceedings of the IEEE-ACM International Conference on Software
   Engineering Companion
LA English
DT Proceedings Paper
CT IEEE/ACM 39th International Conference on Software Engineering Companion
   (ICSE-C)
CY MAY 20-28, 2017
CL Buenos Aires, ARGENTINA
DE Software Engineering; Bibliometrics; Topic Modeling; Text Mining
AB Using topic modeling, we analyse the titles and abstracts of nearly 10,000 papers from 20 years published in 11 top-ranked Software Engineering(SE) conferences between 1993 to 2013. Seven topics are identified as the dominant themes in modern software engineering. We show that these topics are not static; rather, some of them are becoming decidedly less prominent over time (modeling) while others are become very prominent indeed (defect analysis).
   By clustering conferences according to the topics they publish, we identify four large groups of SE conferences; e. g. ASE, FSE and ICSE publish mostly the same work (exceptions: there are more program analysis results in FSE than in ASE or ICSE).
   Using these results, we offer numerous recommendations including how to plan an individuals research program; when to make or merge conferences; and how to encourage a broader range of topics at SE conferences. An extended version of this paper, that analyzes more conferences and papers, is available on https://goo. gl/mVdyfj
C1 [Mathew, George; Agrawal, Amritanshu; Menzies, Tim] NCSU, Comp Sci, Raleigh, NC 27695 USA.
C3 North Carolina State University
RP Mathew, G (corresponding author), NCSU, Comp Sci, Raleigh, NC 27695 USA.
EM george.meg91@gmail.com; aagrawa8@ncsu.edu; tim@menzies.us
RI Agrawal, Amritanshu/R-7093-2019; Menzies, Tim/X-7680-2019
OI Agrawal, Amritanshu/0000-0002-1220-8533; Menzies,
   Tim/0000-0002-5040-3196
CR AGRAWAL A, IST UNPUB
   Tang J., 2008, KDD, P990, DOI DOI 10.1145/1401890.1402008
   Vasilescu B, 2013, IEEE WORK CONF MIN S, P373, DOI 10.1109/MSR.2013.6624051
NR 3
TC 11
Z9 11
U1 2
U2 2
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2574-1926
BN 978-1-5386-1589-8
J9 PROC IEEE ACM INT C
PY 2017
BP 397
EP 398
DI 10.1109/ICSE-C.2017.52
PG 2
WC Computer Science, Software Engineering; Engineering, Electrical &
   Electronic
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Engineering
GA BJ5GY
UT WOS:000425916900125
DA 2024-09-05
ER
PT J
AU Kumar, S
   Sharma, D
   Rao, SD
   Lim, WM
   Mangla, SK
AF Kumar, Satish
   Sharma, Dipasha
   Rao, Sandeep
   Lim, Weng Marc
   Mangla, Sachin Kumar
TI Past, present, and future of sustainable finance: insights from big data
   analytics through machine learning of scholarly research
SO ANNALS OF OPERATIONS RESEARCH
LA English
DT Article; Early Access
DE Sustainable finance; Sustainable development goals; Socially responsible
   investing; Climate financing; Green financing; Impact investing; Carbon
   financing; Energy financing; Governance; Big data analytics; Machine
   learning; Bibliometric analysis; Systematic literature review
ID CLIMATE FINANCE; CARBON FINANCE; SOCIAL-RESPONSIBILITY; BIBLIOMETRIC
   ANALYSIS; PERFORMANCE EVIDENCE; POLITICAL-ECONOMY; GREEN FINANCE;
   IMPACT; INVESTMENT; BONDS
AB Sustainable finance is a rich field of research. Yet, existing reviews remain limited due to the piecemeal insights offered through a sub-set rather than the entire corpus of sustainable finance. To address this gap, this study aims to conduct a large-scale review that would provide a state-of-the-art overview of the performance and intellectual structure of sustainable finance. To do so, this study engages in a review of sustainable finance research using big data analytics through machine learning of scholarly research. In doing so, this study unpacks the most influential articles and top contributing journals, authors, institutions, and countries, as well as the methodological choices and research contexts for sustainable finance research. In addition, this study reveals insights into seven major themes of sustainable finance research, namely socially responsible investing, climate financing, green financing, impact investing, carbon financing, energy financing, and governance of sustainable financing and investing. To drive the field forward, this study proposes several suggestions for future sustainable finance research, which include developing and diffusing innovative sustainable financing instruments, magnifying and managing the profitability and returns of sustainable financing, making sustainable finance more sustainable, devising and unifying policies and frameworks for sustainable finance, tackling greenwashing of corporate sustainability reporting in sustainable finance, shining behavioral finance on sustainable finance, and leveraging the power of new-age technologies such as artificial intelligence, blockchain, internet of things, and machine learning for sustainable finance.
C1 [Kumar, Satish] Malaviya Natl Inst Technol Jaipur, Dept Management Studies, Jaipur 302017, Rajasthan, India.
   [Kumar, Satish; Lim, Weng Marc] Swinburne Univ Technol, Sch Business, Jalan Simpang Tiga, Sarawak 93350, Malaysia.
   [Sharma, Dipasha] Symbiosis Int, Symbiosis Ctr Management & Human Resource Dev, Pune, Maharashtra, India.
   [Rao, Sandeep] Dublin City Univ, DCU Business Sch, Dublin 09, Ireland.
   [Lim, Weng Marc] Swinburne Univ Technol, Swinburne Business Sch, John St, Hawthorn, Vic 3122, Australia.
   [Mangla, Sachin Kumar] OP Jindal Global Univ, Jindal Global Business Sch, Sonipat, Haryana, India.
C3 National Institute of Technology (NIT System); Malaviya National
   Institute of Technology Jaipur; Swinburne University of Technology
   Sarawak; Swinburne University of Technology; Symbiosis International
   University; Symbiosis Centre for Management & Human Resource Development
   (SCMHRD); Dublin City University; Swinburne University of Technology;
   O.P. Jindal Global University
RP Mangla, SK (corresponding author), OP Jindal Global Univ, Jindal Global Business Sch, Sonipat, Haryana, India.
EM skumar.dms@mnit.ac.in; dipasha_sharma@scmhrd.edu;
   sandeep.keshavarao@dcu.ie; lim@wengmarc.com; smangla@jgu.edu.in
RI Sharma, Dipasha/AAK-3480-2020; Lim, Weng Marc/I-1723-2019; Rao,
   Sandeep/AAU-1452-2020; Kumar, Satish/M-8694-2017
OI Sharma, Dipasha/0000-0002-6804-0055; Lim, Weng Marc/0000-0001-7196-1923;
   Rao, Sandeep/0000-0001-7752-4492; Kumar, Satish/0000-0001-5200-1476;
   KUMAR MANGLA, SACHIN/0000-0001-7166-5315
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NR 195
TC 166
Z9 170
U1 215
U2 1156
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0254-5330
EI 1572-9338
J9 ANN OPER RES
JI Ann. Oper. Res.
PD 2022 JAN 4
PY 2022
DI 10.1007/s10479-021-04410-8
EA JAN 2022
PG 44
WC Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Operations Research & Management Science
GA XZ5SE
UT WOS:000737710700001
PM 35002001
OA Green Published, Bronze, Green Accepted
HC Y
HP N
DA 2024-09-05
ER
PT J
AU Zhang, LP
   Qiu, HH
   Chen, JY
   Zhou, WH
   Li, HL
AF Zhang, Liping
   Qiu, Hanhui
   Chen, Jinyi
   Zhou, Wenhao
   Li, Hailin
TI How Do Heterogeneous Networks Affect a Firm's Innovation Performance? A
   Research Analysis Based on Clustering and Classification
SO ENTROPY
LA English
DT Article
DE innovation performance; IUR collaboration network; IF collaboration
   network; decision rules; machine learning algorithms; entropy weight
ID INBOUND OPEN INNOVATION; QUALITY
AB Based on authorized patents of China's artificial intelligence industry from 2013 to 2022, this paper constructs an Industry-University-Research institution (IUR) collaboration network and an Inter-Firm (IF) collaboration network and used the entropy weight method to take both the quantity and quality of patents into account to calculate the innovation performance of firms. Through the hierarchical clustering algorithm and classification and regression trees (CART) algorithm, in-depth analysis has been conducted on the intricate non-linear influence mechanisms between multiple variables and a firm's innovation performance. The findings indicate the following: (1) Based on the network centrality (NC), structural hole (SH), collaboration breadth (CB), and collaboration depth (CD) of both IUR and IF collaboration networks, two types of focal firms are identified. (2) For different types of focal firms, the combinations of network characteristics affecting their innovation performance are various. (3) In the IUR collaboration network, focal firms with a wide range of heterogeneous collaborative partners can obtain high innovation performance. However, focal firms in the IF collaboration network can achieve the same aim by maintaining deep collaboration with other focal firms. This paper not only helps firms make scientific decisions for development but also provides valuable suggestions for government policymakers.
C1 [Zhang, Liping; Qiu, Hanhui; Chen, Jinyi; Zhou, Wenhao; Li, Hailin] Huaqiao Univ, Coll Business Adm, Quanzhou 362021, Peoples R China.
   [Zhang, Liping; Li, Hailin] Huaqiao Univ, Res Ctr Appl Stat & Big Data, Xiamen 361021, Peoples R China.
C3 Huaqiao University; Huaqiao University
RP Li, HL (corresponding author), Huaqiao Univ, Coll Business Adm, Quanzhou 362021, Peoples R China.; Li, HL (corresponding author), Huaqiao Univ, Res Ctr Appl Stat & Big Data, Xiamen 361021, Peoples R China.
EM zhanglp@hqu.edu.cn; 2016411039@stu.hqu.edu.cn;
   2116104003@stu.hqu.edu.cn; wenhaoz2021@stu.hqu.edu.cn; hailin@hqu.edu.cn
RI Chen, Yinglong/D-2104-2011
OI Chen, Yinglong/0000-0003-0798-8683; Zhou, Wenhao/0000-0001-9421-8526;
   Li, Hailin/0000-0001-6924-9689
FU Social Science Foundation Project of Fujian Province of China
FX No Statement Available
CR Aghagolzadeh M, 2011, ENTROPY-SWITZ, V13, P450, DOI 10.3390/e13020450
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NR 45
TC 1
Z9 1
U1 8
U2 15
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1099-4300
J9 ENTROPY-SWITZ
JI Entropy
PD NOV
PY 2023
VL 25
IS 11
AR 1560
DI 10.3390/e25111560
PG 18
WC Physics, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Physics
GA AT5Z4
UT WOS:001120733100001
PM 37998252
OA gold
DA 2024-09-05
ER
PT J
AU Obrenovic, B
   Gu, X
   Wang, GY
   Godinic, D
   Jakhongirov, I
AF Obrenovic, Bojan
   Gu, Xiao
   Wang, Guoyu
   Godinic, Danijela
   Jakhongirov, Ilimdorjon
TI Generative AI and human-robot interaction: implications and future
   agenda for business, society and ethics
SO AI & SOCIETY
LA English
DT Article; Early Access
DE Generative AI; HRI; Human-robot interaction; Anthropomorphism; Humanoid
   robot; Robot; ChatGPT; Scientometric analyses
ID ANTHROPOMORPHISM; INTELLIGENCE; INTERFACE; TRUST
AB The revolution of artificial intelligence (AI), particularly generative AI, and its implications for human-robot interaction (HRI) opened up the debate on crucial regulatory, business, societal, and ethical considerations. This paper explores essential issues from the anthropomorphic perspective, examining the complex interplay between humans and AI models in societal and corporate contexts. We provided a comprehensive review of existing literature on HRI, with a special emphasis on the impact of generative models such as ChatGPT. The scientometric study posits that due to their advanced linguistic capabilities and ability to mimic human-like behavior, generative AIs like ChatGPT will continue to grow in popularity in pair with human rational empathy, tendency for personification and their advanced linguistic capabilities and ability to mimic human-like behavior. As they blur the boundaries between humans and robots, these models introduce fresh moral and philosophical dilemmas. Our research aims to extrapolate key trends and unique factors in HRI and to elucidate the technical aspects of generative AI that enhance its effectiveness in this field compared to traditional rule-based AI systems. We further discuss the challenges and limitations of applying generative AI in HRI, providing a future research agenda for AI optimization in diverse applications, including education, entertainment, and healthcare.
C1 [Obrenovic, Bojan] Zagreb Sch Econ & Management, Zagreb 10000, Croatia.
   [Gu, Xiao; Wang, Guoyu] Commun Univ Zhejiang, Media Literacy Res Inst, Hangzhou 310018, Zhejiang, Peoples R China.
   [Godinic, Danijela] Univ Zagreb, Fac Humanities & Social Sci, Zagreb 10000, Croatia.
   [Jakhongirov, Ilimdorjon] Ferghana Polytech Inst, Ferghana 150107, Uzbekistan.
C3 Communication University of Zhejiang; University of Zagreb; Fergana
   Polytechnic Institute
RP Gu, X (corresponding author), Commun Univ Zhejiang, Media Literacy Res Inst, Hangzhou 310018, Zhejiang, Peoples R China.
EM bojan@inovatus-usluge.hr; guxiao0705@163.com; wangguoyu0323@126.com;
   danijela.godinic5@gmail.com; jahongirov@inbox.ru
RI Gu, Xiao/HLX-4953-2023
OI Gu, Xiao/0000-0002-8346-4964
FU National Social Science Fund of China
FX No Statement Available
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NR 106
TC 1
Z9 1
U1 55
U2 55
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 0951-5666
EI 1435-5655
J9 AI SOC
JI AI Soc.
PD 2024 MAR 15
PY 2024
DI 10.1007/s00146-024-01889-0
EA MAR 2024
PG 14
WC Computer Science, Artificial Intelligence
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA LA8H8
UT WOS:001184143100001
DA 2024-09-05
ER
PT C
AU Bertin, M
   Atanassova, I
AF Bertin, Marc
   Atanassova, Iana
BE Reyes, E
   Szoniecky, S
   Mkadmi, A
   Kembellec, G
   Fournier-S'niehotta, R
   Siala-Kallel, F
   Ammi, M
   Labelle, S
TI Recommending Scientific Papers The Role of Citation Contexts
SO DTUC'18: PROCEEDINGS OF THE 1ST INTERNATIONAL CONFERENCE ON DIGITAL
   TOOLS & USES CONGRESS
LA English
DT Proceedings Paper
CT 1st International Conference on Digital Tools and Uses (DTU)
CY OCT 03-05, 2018
CL Maison Sci Homme Paris Nord, Saint Denis, FRANCE
HO Maison Sci Homme Paris Nord
DE Citation Context Analysis; Bibliometrics; Natural Language Processing;
   Recommender Systems; Information Retrieval; Scientific Papers
ID SYSTEMS
AB This paper addresses the problem of building recommender systems for scientific papers based on the linguistic and contextual analysis of citation contexts. We explain the importance of taking into consideration citation contexts and the different methodologies that exist as well as the ways that citations impact recommender systems. We also discuss the limits of using citation contexts to generate recommendations.
C1 [Bertin, Marc] Univ Claude Bernard Lyon 1, ELICO, Villeurbanne, France.
   [Atanassova, Iana] Univ Franche Comte, CRIT, Besancon, France.
C3 Universite Claude Bernard Lyon 1; Universite de Franche-Comte
RP Bertin, M (corresponding author), Univ Claude Bernard Lyon 1, ELICO, Villeurbanne, France.
EM marc.bertin@univ-lyon1.fr; iana.atanassova@univ-fcomte.fr
RI Atanassova, Iana/ABC-5986-2020; Bertin, Marc/U-7606-2019; Bertin,
   Marc/GQB-3671-2022; Bertin, Marc/ACT-2020-2022
OI Atanassova, Iana/0000-0003-3571-4006; Bertin, Marc/0000-0003-1803-6952;
   Bertin, Marc/0000-0003-1803-6952; Bertin, Marc/0000-0003-1803-6952
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NR 55
TC 1
Z9 1
U1 3
U2 12
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA
BN 978-1-4503-6451-5
PY 2018
DI 10.1145/3240117.3240123
PG 4
WC Computer Science, Interdisciplinary Applications; Computer Science,
   Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BO0IC
UT WOS:000491157600006
DA 2024-09-05
ER
PT J
AU Zhang, ZB
   Wang, JY
   Li, JW
   Wang, Y
   Yin, K
   Fei, XC
AF Zhang, Zhibo
   Wang, Jingyi
   Li, Jiuwei
   Wang, Yao
   Yin, Ke
   Fei, Xunchang
TI Impacts of regional socioeconomic statuses and global events on solid
   waste research reflected in six waste-focused journals
SO WASTE MANAGEMENT
LA English
DT Article
DE Solid waste research; Bibliometric analysis; Natural language
   processing; Socioeconomic status; Global environmental events
ID BIBLIOMETRIC ANALYSIS; CO-AUTHORSHIP; MANAGEMENT; TRENDS; GENERATION;
   ENERGY; INCINERATION; REUSE; LONG
AB The research pertaining to solid waste is undergoing extensive advancement, thereby necessitating a consolidation and analysis of its research trajectories. The existing biblio-studies on solid waste research (SWR) lack thorough analyses of the factors influencing its trends. This article presents an innovative categorization framework that categorizes publications from six SWR journals utilizing Source Latent Dirichlet Allocation. First analyse changes in publication numbers across main categories, subcategories, journals, and regions, providing a macro-level study of SWR. Temporal analysis of keywords supplements a micro-level study of SWR, which highlights that emerging technologies with low Technology Readiness Level receive significant attention, while studies on widespread technologies are diminishing. Additionally, this study demonstrates the substantial influence of socioeconomic factors and previous SWR publications on current and future SWR trends. Finally, the article confirms the impact of global events on SWR trends by examining the structural breakpoints of SWR and their correlation with global events.
C1 [Zhang, Zhibo; Li, Jiuwei; Wang, Yao; Fei, Xunchang] Nanyang Technol Univ, Sch Civil & Environm Engn, 50 Nanyang Ave, Singapore 639798, Singapore.
   [Wang, Jingyi] Natl Univ Singapore, Dept Stat & Data Sci, Sci Dr 2, Singapore 117546, Singapore.
   [Li, Jiuwei; Fei, Xunchang] Nanyang Environm & Water Res Inst, Residues & Resource Reclamat Ctr, 1 Cleantech Loop, Singapore 637141, Singapore.
   [Yin, Ke] Nanjing Forestry Univ, Sch Biol & Environm, Dept Environm Engn, Nanjing 210037, Peoples R China.
   [Fei, Xunchang] Nanyang Technol Univ, Sch Civil & Environm Engn, NI Bldg 01C-70,50 Nanyang Ave, Singapore 639798, Singapore.
C3 Nanyang Technological University; National University of Singapore;
   Nanyang Technological University; Nanjing Forestry University; Nanyang
   Technological University
RP Fei, XC (corresponding author), Nanyang Technol Univ, Sch Civil & Environm Engn, NI Bldg 01C-70,50 Nanyang Ave, Singapore 639798, Singapore.
EM xcfei@ntu.edu.sg
OI Wang, Yao/0000-0003-2373-0201; Wang, Jingyi/0009-0003-4100-813X; Zhang,
   Zhibo/0000-0002-7239-4833; Fei, Xunchang/0000-0002-7435-9011
FU Nanyang Technological Uni- versity (Singapore)
FX Yao Wang would like to acknowledge Nanyang Technological Uni- versity
   (Singapore) for a scholarship support.
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NR 73
TC 0
Z9 0
U1 7
U2 7
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0956-053X
EI 1879-2456
J9 WASTE MANAGE
JI Waste Manage.
PD JUN 15
PY 2024
VL 182
BP 113
EP 123
DI 10.1016/j.wasman.2024.04.028
EA APR 2024
PG 11
WC Engineering, Environmental; Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Environmental Sciences & Ecology
GA SC7V0
UT WOS:001232333200001
PM 38648689
DA 2024-09-05
ER
PT C
AU Beaudoin, BL
   Nusinovich, GS
   Turner, C
   Karakkad, JA
   Narayan, AH
   Thomson, C
   Antonsen, TM
AF Beaudoin, Brian L.
   Nusinovich, Gregory S.
   Turner, Charles
   Karakkad, Jayakrishnan A.
   Narayan, Amith H.
   Thomson, Connor
   Antonsen, Thomas M., Jr.
GP IEEE
TI Novel High-Power Radio-Frequency Sources for Ionospheric Heating
SO 2016 IEEE INTERNATIONAL VACUUM ELECTRONICS CONFERENCE (IVEC)
SE IEEE International Vacuum Electronics Conference IVEC
LA English
DT Proceedings Paper
CT 17th IEEE International Vacuum Electronics Conference (IVEC)
CY APR 19-21, 2016
CL Monterey, CA
DE Inductive output tube; Ionospher; Radio frequency; Space-charge;
   Inductive adders; Transformers; High-frequency Active Auroral Research
   Program (HAARP)
AB Development of Mobile Ionospheric Heating sources (MIHs) would allow investigators to conduct needed research at different latitudes without building permanent and costly installations. As part of an Air Force Multi-University Research Initiative (MURI), the University of Maryland is designing a prototype of a powerful Radio Frequency (RF) source utilizing Inductive Output Tube (IOT) technology operating in class-D with a mod-anode controlled electron gun [1]. This technology was chosen because it has the potential to operate at efficiencies exceeding 90% [2].
C1 [Beaudoin, Brian L.; Nusinovich, Gregory S.; Turner, Charles; Karakkad, Jayakrishnan A.; Narayan, Amith H.; Thomson, Connor; Antonsen, Thomas M., Jr.] Univ Maryland, Inst Res Elect & Appl Phys, College Pk, MD 20742 USA.
C3 University System of Maryland; University of Maryland College Park
RP Beaudoin, BL (corresponding author), Univ Maryland, Inst Res Elect & Appl Phys, College Pk, MD 20742 USA.
RI Nusinovich, Gregory S/C-1314-2017; Nusinovich, Gregory/B-4751-2017;
   Beaudoin, Brian/D-5174-2017; Antonsen, Thomas/D-8791-2017
OI Nusinovich, Gregory/0000-0002-8641-5156; Beaudoin,
   Brian/0000-0001-9935-4658; Antonsen, Thomas/0000-0002-2362-2430
CR Beaudoin B. L., 2015, IPAC2015, P3398
   Nusinovich G., 2016, IVEC2016
   Pedersen T, 2015, PHYS TODAY, V68, P72, DOI 10.1063/PT.3.3032
   Petillo JJ, 2005, IEEE T ELECTRON DEV, V52, P742, DOI 10.1109/TED.2005.845800
NR 4
TC 0
Z9 0
U1 0
U2 0
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-4673-9217-4
J9 IEEE INT VAC ELECT C
PY 2016
PG 2
WC Engineering, Electrical & Electronic
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Engineering
GA BG0DO
UT WOS:000386185700013
DA 2024-09-05
ER
PT J
AU Jha, D
   Alam, S
   Pyun, JY
   Lee, KH
   Kwon, GR
AF Jha, Debesh
   Alam, Saruar
   Pyun, Jae-Young
   Lee, Kun Ho
   Kwon, Goo-Rak
TI Alzheimer's Disease Detection Using Extreme Learning Machine, Complex
   Dual Tree Wavelet Principal Coefficients and Linear Discriminant
   Analysis
SO JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS
LA English
DT Article
DE Alzheimer's Disease; Computer-Aided Diagnosis; Dual-Tree Complex Wavelet
   Transform; Principal Component Analysis; Linear Discriminant Analysis;
   Extreme Learning Machine; Alzheimer's Disease Neuroimaging Initiative;
   Open Access Series of Imaging Studies
ID SUPPORT VECTOR MACHINE; MILD COGNITIVE IMPAIRMENT; CLASS IMBALANCE;
   CLASSIFICATION; BRAIN; PREDICTION; DIAGNOSIS; SCANS; MRI
AB The early detection and classification of Alzheimer's disease (AD) are important clinical support tasks for medical practitioners in customizing patient treatment programs to have better manage the development and progression of these diseases. Efforts are being made to diagnose these neurodegenerative disorders in the early stages. Efficient early categorization of the AD and mild Cognitive Impairment (MCI) from HC is necessary as prompt preventive care could assist to mitigate risk factors. For analysis and prognosis of disease, Magnetic resonance imaging (MRI). In this paper, we proposed a novel computer-aided diagnosis (CAD) cascade model to discriminate patients with the AD from healthy controls using dual-tree complex wavelet transforms (DTCWT), principal component analysis, linear discriminant analysis, and extreme learning machine (ELM). The proposed method obtained accuracy of 90.26 +/- 1.17, a specificity of 90.20 +/- 1.56 and sensitivity of 90.27 +/- 1.29 on the Alzheimer's disease Neuroimaging Initiative (ADNI) dataset and accuracy of 95.72 +/- 1.54, a sensitivity of 96.59 +/- 2.34 and specificity of 93.03 +/- 1.67 on the Open Access Series of Imaging Studies (OASIS) dataset. The proposed method is effective and superior to the existing models.
C1 [Jha, Debesh; Alam, Saruar; Pyun, Jae-Young; Kwon, Goo-Rak] Chosun Univ, Dept Informat & Commun Engn, 375 Seosuk Dong, Gwangju 501759, South Korea.
   [Lee, Kun Ho] Chosun Univ, Natl Res Ctr Dementia, 375 Seosuk Dong, Gwangju 501759, South Korea.
   [Lee, Kun Ho] Chosun Univ, Dept Biomed Sci, 375 Seosuk Dong, Gwangju 501759, South Korea.
C3 Chosun University; Chosun University; Chosun University
RP Kwon, GR (corresponding author), Chosun Univ, Dept Informat & Commun Engn, 375 Seosuk Dong, Gwangju 501759, South Korea.
RI Jha, Debesh/M-2526-2019
OI Jha, Debesh/0000-0002-8078-6730
FU Brain Research Program through the National Research Foundation of Korea
   - Ministry of Science, ICT and Future Planning [NRF-2014M3C7A1046050];
   National Research Foundation of Korea Grant - Korean Government
   [NRF-2017R1A2B4006533]; Alzheimer's Disease Neuroimaging Initiative
   (ADNI) (National Institutes of Health) [U01 AG024904]; DOD ADNI
   (Department of Defense) [W81XWH-12-2-0012]
FX This research was supported by the Brain Research Program through the
   National Research Foundation of Korea funded by the Ministry of Science,
   ICT and Future Planning (NRF-2014M3C7A1046050). And this work was
   supported by the National Research Foundation of Korea Grant funded by
   the Korean Government (NRF-2017R1A2B4006533). Data collection and
   sharing for this project was funded by the Alzheimer's Disease
   Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01
   AG024904) and DOD ADNI (Department of Defense award number
   W81XWH-12-2-0012). The funding details of ADNI can be found at:
   http://adni.loni.usc.edu/about/funding/.
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NR 36
TC 19
Z9 20
U1 0
U2 19
PU AMER SCIENTIFIC PUBLISHERS
PI VALENCIA
PA 26650 THE OLD RD, STE 208, VALENCIA, CA 91381-0751 USA
SN 2156-7018
EI 2156-7026
J9 J MED IMAG HEALTH IN
JI J. Med. Imaging Health Inform.
PD JUN
PY 2018
VL 8
IS 5
BP 881
EP 890
DI 10.1166/jmihi.2018.2381
PG 10
WC Mathematical & Computational Biology; Radiology, Nuclear Medicine &
   Medical Imaging
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mathematical & Computational Biology; Radiology, Nuclear Medicine &
   Medical Imaging
GA GJ1AF
UT WOS:000434985100003
DA 2024-09-05
ER
PT J
AU Baier-Fuentes, H
   Cascón-Katchadourian, J
   Martínez, MA
   Herrera-Viedma, E
   Merigó, JM
AF Baier-Fuentes, Hugo
   Cascon-Katchadourian, Jesus
   Martinez, M. A.
   Herrera-Viedma, Enrique
   Merigo, Jose M.
TI A Bibliometric Overview of the International Journal of Interactive
   Multimedia and Artificial Intelligence
SO INTERNATIONAL JOURNAL OF INTERACTIVE MULTIMEDIA AND ARTIFICIAL
   INTELLIGENCE
LA English
DT Article
DE Bibliometrics; Web Of Science; VOS Viewer
ID MANAGEMENT; UNIVERSITY; ECONOMICS; HISTORY; SCIENCE; FIELD
AB The International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI) published its first issue ten years ago. Currently, IJIMAI is indexed in the important database Emerging Sources Citation Index. This paper aims to identify, through a mapping of science, those most relevant aspects of the structure of publications made during the first 10 years of IJIMAI. Using VOSviewer software, the structural maps of the IJIMAI publications are analysed according to techniques such as bibliographic coupling, co-citations and co-occurrence of keywords. In addition, the evolution of the publications, citations and an analysis of the most cited papers of the journal are presented. The results show that IJIMAI has experienced a remarkable growth of both publications and citations in the last five years. We also observe that IJIMAI does not only capture the attention of the Spanish scientific community, but also of emerging countries such as India and Iran and emerging Latin American countries such as Colombia. With a such increasing behaviour, it is expected in the coming years that IJIMAI will position itself among the best journals with similar scientific scope.
C1 [Baier-Fuentes, Hugo] Univ Catolica Santisima Concepcion, Dept Business Adm, Av Alonso de Ribera 2850, Concepcion, Chile.
   [Cascon-Katchadourian, Jesus] Colegio Maximo Cartuja, Fac Commun & Documentat, Dept Informat & Commun, Granada 18071, Spain.
   [Martinez, M. A.] Univ Granada, Dept Social Work & Social Serv, Granada, Spain.
   [Herrera-Viedma, Enrique] Univ Granada, Dept Comp Sci & Artificial Intelligence, Av Periodista Daniel Saucedo S-N, Granada, Spain.
   [Merigo, Jose M.] Univ Technol Sydney, Fac Engn & Informat Technol, Sch Syst Management & Leadership, 81 Broadway, Ultimo, NSW 2007, Australia.
   [Merigo, Jose M.] Univ Chile, Sch Econ & Business, Dept Management Control & Informat Syst, Av Diagonal Paraguay 257, Santiago 8330015, Chile.
C3 Universidad Catolica de la Santisima Concepcion; University of Granada;
   University of Granada; University of Technology Sydney; Universidad de
   Chile
RP Merigó, JM (corresponding author), Univ Technol Sydney, Fac Engn & Informat Technol, Sch Syst Management & Leadership, 81 Broadway, Ultimo, NSW 2007, Australia.; Merigó, JM (corresponding author), Univ Chile, Sch Econ & Business, Dept Management Control & Informat Syst, Av Diagonal Paraguay 257, Santiago 8330015, Chile.
EM hbaier@ucsc.cl; jesuscascon@gmail.com; mundodesilencio@ugr.es;
   viedma@decsai.ugr.es; jmerigo@fen.uchile.cl
RI Merigó, José M./K-1500-2019; Sánchez, Maria Angeles
   Martínez/AAB-7403-2019; Katchadourian, Jesús Daniel Cascón/E-7704-2016;
   Baier-Fuentes, Hugo/AAP-5413-2020; HERRERA-VIEDMA, ENRIQUE/C-2704-2008
OI Merigó, José M./0000-0002-4672-6961; Katchadourian, Jesús Daniel
   Cascón/0000-0002-3388-7862; Baier-Fuentes, Hugo/0000-0002-6436-1222;
   HERRERA-VIEDMA, ENRIQUE/0000-0002-7922-4984
FU FEDER [TIN2016-75850-P]; Spanish Ministry of Science, Innovation and
   Universities; Fondecyt Regular program [1160286]; Chilean Government
   through Conicyt
FX This project was funded by the FEDER financial support from the Project
   TIN2016-75850-P provided by the Spanish Ministry of Science, Innovation
   and Universities. The last author acknowledges support from the Fondecyt
   Regular program (project number 1160286) of the Chilean Government
   through Conicyt.
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NR 38
TC 16
Z9 16
U1 1
U2 35
PU UNIV INT RIOJA-UNIR
PI LOGRONO
PA RECTORADO, AVENIDA DE LA PAZ, 137, LOGRONO, 26006, SPAIN
SN 1989-1660
J9 INT J INTERACT MULTI
JI Int. J. Interact. Multimed. Artif. Intell.
PD DEC
PY 2018
VL 5
IS 3
BP 9
EP 16
DI 10.9781/ijimai.2018.12.003
PG 8
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA HC5WZ
UT WOS:000451874400002
OA gold, Green Submitted
DA 2024-09-05
ER
PT J
AU Levin, G
   Brezinov, Y
   Meyer, R
AF Levin, Gabriel
   Brezinov, Yoav
   Meyer, Raanan
TI Exploring the use of ChatGPT in OBGYN: a bibliometric analysis of the
   first ChatGPT-related publications
SO ARCHIVES OF GYNECOLOGY AND OBSTETRICS
LA English
DT Article
DE Artificial intelligence; Bibliometrics; ChatGPT; OBGYN literature;
   Research
AB PurposeLittle is known about the scientific literature regarding the new revolutionary tool, ChatGPT. We aim to perform a bibliometric analysis to identify ChatGPT-related publications in obstetrics and gynecology (OBGYN).Study designA bibliometric study through PubMed database. We mined all ChatGPT-related publications using the search term "ChatGPT". Bibliometric data were obtained from the iCite database. We performed a descriptive analysis. We further compared IF among publications describing a study vs. other publications.ResultsOverall, 42 ChatGPT-related publications were published across 26 different journals during 69 days. Most publications were editorials (52%) and news/briefing (22%), with only one (2%) research article identified. Five (12%) publications described a study performed. No ChatGPT-related publications in OBGYN were found. The leading journal by the number of publications was Nature (24%), followed by Lancet Digital Health and Radiology (7%, for both). The main subjects of publications were ChatGPT's scientific writing quality (26%) and a description of ChatGPT (26%) followed by tested performance of ChatGPT (14%), authorship and ethical issues (10% for both topics).In a comparison of publications describing a study performed (n = 5) vs. other publications (n = 37), mean IF was lower in the study-publications (mean 6.25 +/- 0 vs. 25.4 +/- 21.6, p < .001).ConclusionsThe study highlights main trends in ChatGPT-related publications. OBGYN is yet to be represented in this literature.
C1 [Levin, Gabriel] Hadassah Hebrew Univ, Med Ctr, Dept Gynecol Oncol, Jerusalem, Israel.
   [Levin, Gabriel] McGill Univ, Jewish Gen Hosp, Lady Davis Inst Canc Res, Quebec City, PQ, Canada.
   [Brezinov, Yoav] McGill Univ, Expt Surg, Quebec City, PQ, Canada.
   [Meyer, Raanan] Cedars Sinai Med Ctr, Dept Obstet & Gynecol, Div Minimally Invas Gynecol Surg, Los Angeles, CA USA.
   [Meyer, Raanan] Sheba Med Ctr, Dr Pinchas Bornstein Talpiot Med Leadership Progra, Ramat Gan, Israel.
C3 Hebrew University of Jerusalem; Hadassah University Medical Center;
   McGill University; McGill University; Cedars Sinai Medical Center; Chaim
   Sheba Medical Center
RP Levin, G (corresponding author), Hadassah Hebrew Univ, Med Ctr, Dept Gynecol Oncol, Jerusalem, Israel.; Levin, G (corresponding author), McGill Univ, Jewish Gen Hosp, Lady Davis Inst Canc Res, Quebec City, PQ, Canada.
EM levin.gaby@gmail.com
RI Levin, Gabriel/AAF-2239-2020
OI Brezinov, Yoav/0000-0001-6452-9868
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NR 15
TC 11
Z9 11
U1 23
U2 135
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 0932-0067
EI 1432-0711
J9 ARCH GYNECOL OBSTET
JI Arch. Gynecol. Obstet.
PD DEC
PY 2023
VL 308
IS 6
BP 1785
EP 1789
DI 10.1007/s00404-023-07081-x
EA MAY 2023
PG 5
WC Obstetrics & Gynecology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Obstetrics & Gynecology
GA U5KK2
UT WOS:000994086600001
PM 37222839
DA 2024-09-05
ER
PT J
AU Bornmann, L
   Stefaner, M
   Anegon, FD
   Mutz, R
AF Bornmann, Lutz
   Stefaner, Moritz
   de Moya Anegon, Felix
   Mutz, Ruediger
TI Excellence networks in science: A Web-based application based on
   Bayesian multilevel logistic regression (BMLR) for the identification of
   institutions collaborating successfully
SO JOURNAL OF INFORMETRICS
LA English
DT Article
DE Citation network; Best paper rate; Co-authorship; Collaboration
ID GEOGRAPHICAL PROXIMITY; CITATION; PERFORMANCE; IMPACT; RANK; MAP
AB In this study we present an application which can be accessed via www.excellence-networks.net and which represents networks of scientific institutions worldwide. The application is based on papers (articles, reviews and conference papers) published between 2007 and 2011. It uses (network) data, on which the SCImago Institutions Ranking is based (Scopus data from Elsevier). Using this data, institutional networks have been estimated with statistical models (Bayesian multilevel logistic regression, BMLR) for a number of Scopus subject areas. Within single subject areas, we have investigated and visualized how successfully overall an institution (reference institution) has collaborated (compared to all the other institutions in a subject area), and with which other institutions (network institutions) a reference institution has collaborated particularly successfully. The "best paper rate" (statistically estimated) was used as an indicator for evaluating the collaboration success of an institution. This gives the proportion of highly cited papers from an institution, and is considered generally as an indicator for measuring impact in bibliometrics. (C) 2016 Elsevier Ltd. All rights reserved.
C1 [Bornmann, Lutz] Max Planck Gesell, Div Sci & Innovat Studies, Adm Headquarters, Munich, Germany.
   [Stefaner, Moritz] Eickedorfer Damm 35, D-28865 Lilienthal, Germany.
   [de Moya Anegon, Felix] CSIC, Inst Publ Goods & Policies IPP, Madrid, Spain.
   [Mutz, Ruediger] ETH, Social Psychol & Res Higher Educ, Zurich, Switzerland.
C3 Max Planck Society; Consejo Superior de Investigaciones Cientificas
   (CSIC); CSIC - Instituto de Politicas y Bienes Publicos (IPP); Swiss
   Federal Institutes of Technology Domain; ETH Zurich
RP Bornmann, L (corresponding author), Max Planck Gesell, Div Sci & Innovat Studies, Adm Headquarters, Munich, Germany.
EM bornmann@gv.mpg.de
RI Mutz, Rüdiger/AAA-9629-2021; de Moya Anegón, Félix/C-4004-2009; Mutz,
   Ruediger/A-2226-2009; Bornmann, Lutz/A-3926-2008; de Moya Anegón,
   Félix/V-3678-2019
OI Mutz, Rüdiger/0000-0003-3345-6090; de Moya Anegón,
   Félix/0000-0002-0255-8628
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NR 59
TC 13
Z9 13
U1 1
U2 44
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1751-1577
EI 1875-5879
J9 J INFORMETR
JI J. Informetr.
PD FEB
PY 2016
VL 10
IS 1
BP 312
EP 327
DI 10.1016/j.joi.2016.01.005
PG 16
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA DG2ZF
UT WOS:000371938600026
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Wang, JN
   Li, J
   Ji, Y
AF Wang, Jiani
   Li, Jian
   Ji, Yong
TI Mendelian randomization as a cornerstone of causal inference for gut
   microbiota and related diseases from the perspective of bibliometrics
SO MEDICINE
LA English
DT Article
DE bibliometric analysis; causality; CiteSpace; gut microbiota; Mendelian
   randomization; VOSviewer
ID SCIENCE
AB Gut microbiota, a special group of microbiotas in the human body, contributes to health in a way that can't be ignored. In recent years, Mendelian randomization, which is a widely used and successful method of analyzing causality, has been investigated for the relationship between the gut microbiota and related diseases. Unfortunately, there seems to be a shortage of systematic bibliometric analysis in this field. Therefore, this study aims to investigate the research progress of Mendelian randomization for gut microbiota through comprehensive bibliometric analysis. In this study, publications about Mendelian randomization for gut microbiota were gathered from 2013 to 2023, utilizing the Web of Science Core Collection as our literature source database. The search strategies were as follows: TS = (intestinal flora OR gut flora OR intestinal microflora OR gut microflora OR intestinal microbiota OR gut microbiota OR bowel microbiota OR bowel flora OR gut bacteria OR intestinal tract bacteria OR bowel bacteria OR gut metabolites OR gut microbiota) and TS = (Mendelian randomization). VOSviewer (version 1.6.18), CiteSpace (version 6.1.R1), Microsoft Excel 2021, and Scimago Graphica were employed for bibliometric and visualization analysis. According to research, from January 2013 to August 2023, 154 publications on Mendelian randomization for gut microbiota were written by 1053 authors hailing from 332 institutions across 31 countries and published in 86 journals. China had the highest number of publications, with 109. Frontiers in Microbiology is the most prolific journal, and Lei Zhang has published the highest number of significant articles. The most popular keywords were "Mendelian randomization," "gut microbiota," "instruments," "association," "causality," "gut microbiome," "risk," "bias," "genome-wide association," and "causal relationship." Moreover, the current research hotspots in this field focus on utilizing a 2-sample Mendelian randomization to investigate the relationship between gut microbiota and associated disorders. This research systematically reveals a comprehensive overview of the literature that has been published over the last 10 years about Mendelian randomization for gut microbiota. Moreover, the knowledge of key information in the field from a bibliometric perspective may greatly facilitate future research in the field.
C1 [Wang, Jiani] Shanxi Med Univ, Dept Pediat, Taiyuan, Peoples R China.
   [Li, Jian] Shanxi Med Univ, Shanxi Bethune Hosp, Shanxi Acad Med Sci, Dept Orthoped,Hosp 3, Taiyuan, Peoples R China.
   [Ji, Yong] Matern Hosp Shanxi Prov, Childrens Hosp Shanxi Prov, Maternal & Child Heath Hosp Shanxi Prov, Dept Neonatal Intens Care Unit, Taiyuan, Peoples R China.
C3 Shanxi Medical University; Shanxi Medical University; Shanxi Medical
   University
RP Ji, Y (corresponding author), Matern Hosp Shanxi Prov, Childrens Hosp Shanxi Prov, Maternal & Child Heath Hosp Shanxi Prov, Dept Neonatal Intens Care Unit, Taiyuan, Peoples R China.
EM wjn_aoliao_lj@163.com; jianlisyd@163.com; jiyongnicu@163.com
FU National Clinical Key Specialty Neonatal Construction Funding
FX This work was supported by National Clinical Key Specialty Neonatal
   Construction Funding.
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NR 49
TC 0
Z9 0
U1 9
U2 9
PU LIPPINCOTT WILLIAMS & WILKINS
PI PHILADELPHIA
PA TWO COMMERCE SQ, 2001 MARKET ST, PHILADELPHIA, PA 19103 USA
SN 0025-7974
EI 1536-5964
J9 MEDICINE
JI Medicine (Baltimore)
PD JUN 28
PY 2024
VL 103
IS 26
AR e38654
DI 10.1097/MD.0000000000038654
PG 13
WC Medicine, General & Internal
WE Science Citation Index Expanded (SCI-EXPANDED)
SC General & Internal Medicine
GA YD9H4
UT WOS:001266661100006
PM 38941393
OA gold
DA 2024-09-05
ER
PT J
AU Massonnaud, CR
   Kerdelhué, G
   Grosjean, J
   Lelong, R
   Griffon, N
   Darmoni, SJ
AF Massonnaud, Clement R.
   Kerdelhue, Gaetan
   Grosjean, Julien
   Lelong, Romain
   Griffon, Nicolas
   Darmoni, Stefan J.
TI Identification of the Best Semantic Expansion to Query PubMed Through
   Automatic Performance Assessment of Four Search Strategies on All
   Medical Subject Heading Descriptors: Comparative Study
SO JMIR MEDICAL INFORMATICS
LA English
DT Article
DE bibliographic database; information retrieval; literature search;
   Medical Subject Headings; MEDLINE; PubMed; precision; recall; search
   strategy; thesaurus
ID KNOWLEDGE
AB Background: With the continuous expansion of available biomedical data, efficient and effective information retrieval has become of utmost importance. Semantic expansion of queries using synonyms may improve information retrieval.
   Objective: The aim of this study was to automatically construct and evaluate expanded PubMed queries of the form "preferred term"[MH] OR "preferred term"[TIAB] OR "synonym 1"[TIAB] OR "synonym 2"[TIAB] OR..., for each of the 28,313 Medical Subject Heading (MeSH) descriptors, by using different semantic expansion strategies. We sought to propose an innovative method that could automatically evaluate these strategies, based on the three main metrics used in information science (precision, recall, and F-measure).
   Methods: Three semantic expansion strategies were assessed. They differed by the synonyms used to build the queries as follows: MeSH synonyms, Unified Medical Language System (UMLS) mappings, and custom mappings (Catalogue et Index des Sites Medicaux de langue Francaise [CISMeF]). The precision, recall, and F-measure metrics were automatically computed for the three strategies and for the standard automatic term mapping (ATM) of PubMed. The method to automatically compute the metrics involved computing the number of all relevant citations (A), using National Library of Medicine indexing as the gold standard ("preferred term"[MH]), the number of citations retrieved by the added terms ("synonym 1"[TIAB] OR "synonym 2"[TIAB] OR...) (B), and the number of relevant citations retrieved by the added terms (combining the previous two queries with an "AND" operator) (C). It was possible to programmatically compute the metrics for each strategy using each of the 28,313 MeSH descriptors as a "preferred term," corresponding to 239,724 different queries built and sent to the PubMed application program interface. The four search strategies were ranked and compared for each metric.
   Results: ATM had the worst performance for all three metrics among the four strategies. The MeSH strategy had the best mean precision (51%, SD 23%). The UMLS strategy had the best recall and F-measure (41%, SD 31% and 36%, SD 24%, respectively). CISMeF had the second best recall and F-measure (40%, SD 31% and 35%, SD 24%, respectively). However, considering a cutoff of 5%, CISMeF had better precision than UMLS for 1180 descriptors, better recall for 793 descriptors, and better F-measure for 678 descriptors.
   Conclusions: This study highlights the importance of using semantic expansion strategies to improve information retrieval. However, the performances of a given strategy, relatively to another, varied greatly depending on the MeSH descriptor. These results confirm there is no ideal search strategy for all descriptors. Different semantic expansions should be used depending on the descriptor and the user's objectives. Thus, we developed an interface that allows users to input a descriptor and then proposes the best semantic expansion to maximize the three main metrics (precision, recall, and F-measure).
C1 [Massonnaud, Clement R.; Kerdelhue, Gaetan; Grosjean, Julien; Lelong, Romain; Griffon, Nicolas; Darmoni, Stefan J.] Rouen Univ Hosp, Dept Biomed Informat, 1 Rue Germont, Rouen, France.
   [Massonnaud, Clement R.; Kerdelhue, Gaetan; Grosjean, Julien; Lelong, Romain; Griffon, Nicolas; Darmoni, Stefan J.] Sorbonne Univ, Lab Informat Med & Ingn Connaissances & Sante, INSERM, U1142, Paris, France.
C3 Universite de Rouen Normandie; CHU de Rouen; Institut National de la
   Sante et de la Recherche Medicale (Inserm); Sorbonne Universite
RP Massonnaud, CR (corresponding author), Rouen Univ Hosp, Dept Biomed Informat, 1 Rue Germont, Rouen, France.
EM clement.massonnaud@gmail.com
RI Kerdelhué, Gaétan/J-6933-2019
OI Kerdelhué, Gaétan/0000-0001-5803-5554; Massonnaud,
   Clement/0000-0003-0292-9668; Grosjean, Julien/0000-0002-7446-644X;
   LELONG, Romain/0000-0003-1865-8786; Griffon, Nicolas/0000-0002-9602-6429
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NR 26
TC 3
Z9 3
U1 0
U2 7
PU JMIR PUBLICATIONS, INC
PI TORONTO
PA 130 QUEENS QUAY E, STE 1102, TORONTO, ON M5A 0P6, CANADA
EI 2291-9694
J9 JMIR MED INF
JI JMIR Med. Inf.
PD JUN
PY 2020
VL 8
IS 6
AR e12799
DI 10.2196/12799
PG 9
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Medical Informatics
GA MG9YS
UT WOS:000546388900001
PM 32496201
OA Green Submitted, Green Published, gold
DA 2024-09-05
ER
PT J
AU Abdelfattah, F
   Al Alawi, AM
   Dahleez, KA
   El Saleh, A
AF Abdelfattah, Fadi
   Al Alawi, Abrar Mohammed
   Dahleez, Khalid Abed
   El Saleh, Ayman
TI Reviewing the critical challenges that influence the adoption of the
   e-learning system in higher educational institutions in the era of the
   COVID-19 pandemic
SO ONLINE INFORMATION REVIEW
LA English
DT Article
DE COVID-19; E-Learning; Online learning; System; Obstacles; Challenges
ID STUDENTS; ACCEPTANCE; MODEL
AB Purpose - This paper aims to review the critical challenges and factors influencing the successful adoption of electronic learning (e-learning) systems in higher educational institutions before and during the current propagation of the coronavirus disease 2019 (COVID-19) pandemic.Design/methodology/approach - This study undertook a literature review concerning the in-depth revision of previous studies published in 2020 and 2021. A total of 100 out of 170 selected research papers were adopted to identify and recognise the factors restricting the application of e-learning systems.Findings - The findings determine and illuminate the most challenging factors that impact the successful application of online learning, particularly during the wide propagation of the COVID-19 pandemic. The review of the literature provides evidence that technological, organisational and behavioural issues constitute significant drivers that frontier the facilitation of the e-learning process in higher educational institutions.Practical implications - The current paper suggests a guide for managers and scholars in educational institutions and acts as a roadmap for practitioners and academics in the educational field and policymakers as this research spotlights the significant factors challenging the e-learning process before and during the pandemic crisis.Originality/value - The provided in-depth literature review in this research will support the researchers and system designers with a comprehensive review and recent studies conducted before and during the COVID-19 pandemic considering the factors limiting the e-learning process. This paper formulates a valuable contribution to the body of knowledge that will assist the stakeholders in the higher educational institutions' context.Peer review - The peer review history for this article is available at: .
C1 [Abdelfattah, Fadi] Modern Coll Business & Sci, Business & Econ Dept, Al Khuwair, Oman.
   [Al Alawi, Abrar Mohammed] Univ Nizwa, Entrepreneurship Ctr, Nizwa, Oman.
   [Dahleez, Khalid Abed] ASharqiyah Univ, Coll Business Adm, Ibra, Oman.
   [El Saleh, Ayman] ASharqiyah Univ, Coll Engn, Ibra, Oman.
C3 University of Nizwa
RP Abdelfattah, F (corresponding author), Modern Coll Business & Sci, Business & Econ Dept, Al Khuwair, Oman.
EM fadi_fattah@yahoo.com; abraralalawi@unizwa.edu.om;
   khalid.dahleez@asu.edu.om; ayman.elsaleh@asu.edu.om
RI AbdelFattah, Fadi/L-7441-2014; El-Saleh, Ayman A A./B-3732-2010;
   Dahleez, Khalid Abed/M-6157-2017
OI AbdelFattah, Fadi/0000-0002-4665-4777; Dahleez, Khalid
   Abed/0000-0002-1526-8750
FU Research Council (TRC) of the Sultanate of Oman under the Block Funding
   Program [TRC/BFP/ASU/01/2018]
FX The research leading to these results has received fund from The
   Research Council (TRC) of the Sultanate of Oman under the Block Funding
   Program (No: TRC/BFP/ASU/01/2018).
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NR 114
TC 11
Z9 11
U1 0
U2 8
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 1468-4527
EI 1468-4535
J9 ONLINE INFORM REV
JI Online Inf. Rev.
PD NOV 8
PY 2023
VL 47
IS 7
BP 1225
EP 1247
DI 10.1108/OIR-02-2022-0085
EA APR 2023
PG 23
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA W6CS8
UT WOS:000962019700001
DA 2024-09-05
ER
PT J
AU Guo, H
AF Guo, Hao
TI Research on the Construction of the Quality Evaluation Model System for
   the Teaching Reform of Physical Education Students in Colleges and
   Universities under the Background of Artificial Intelligence
SO SCIENTIFIC PROGRAMMING
LA English
DT Article
ID CHALLENGES
AB With the continuous progress of the times, the reform of physical education teaching in colleges and universities has to be promoted day by day. The most important task in the process of reform is how to improve the quality of physical education teaching. Only by reforming colleges and universities can we transport outstanding talents into the society. It is very important to improve the teaching quality by improving the physical education quality evaluation system. As artificial intelligence technology has been more and more widely used in different fields, various educational administration systems based on information management have been established in various colleges and universities. On the one hand, it has brought great convenience to the management of physical education in colleges and universities and improvement of the efficiency of sports education management, but on the other hand, there are many shortcomings in the process of practical application. For example, the application of the database does not fully reflect its function and convenience, and it is only used at the level of query and statistics. Therefore, a better evaluation system of physical education teaching quality has become the common expectation of all colleges and universities. This paper makes a powerful analysis of the current quality evaluation of physical education in colleges and universities and proposes a method of establishing a basic framework through expert systems, filling in details with the idea of knowledge base and fuzzy sets, and further using a three-layer B/S framework model to design universal teaching quality assessment system. When discussing the requirements, functional framework, and actual development of the teaching evaluation system, the characteristics of the traditional physical education evaluation model are deeply analyzed, and the system's interactivity, flexibility, accuracy, and fairness are emphasized in the implementation process. Object-oriented design and analysis are carried out on the requirements of the system, and finally, black-box testing is carried out to ensure the reliability and correctness of the system logic.
C1 [Guo, Hao] Chongqing Coll Humanities, Sci & Technol, Chongqing 401524, Peoples R China.
RP Guo, H (corresponding author), Chongqing Coll Humanities, Sci & Technol, Chongqing 401524, Peoples R China.
EM ym520@swu.edu.cn
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NR 19
TC 0
Z9 0
U1 11
U2 35
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1058-9244
EI 1875-919X
J9 SCI PROGRAMMING-NETH
JI Sci. Program.
PD MAY 9
PY 2022
VL 2022
AR 6556631
DI 10.1155/2022/6556631
PG 9
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1S3JN
UT WOS:000803950700011
OA gold
DA 2024-09-05
ER
PT C
AU Tang, L
   Zhou, CY
   He, L
   Zhang, SH
AF Tang, Li
   Zhou, Caiyun
   He, Li
   Zhang, Shuhua
BE Liu, C
   Cheung, KS
TI Research on Evaluation of Morality and Ability of Teachers in
   Universities Based on the Perspective of Data Mining
SO PROCEEDINGS OF THE 2017 INTERNATIONAL CONFERENCE ON MANAGEMENT,
   EDUCATION AND SOCIAL SCIENCE (ICMESS 2017)
SE Advances in Social Science Education and Humanities Research
LA English
DT Proceedings Paper
CT International Conference on Management, Education and Social Science
   (ICMESS)
CY JUN 23-25, 2017
CL Qingdao, PEOPLES R CHINA
DE Support Vector Machine (SVM); Morality of Teachers; Ability of teachers;
   Data Mining
AB The evaluation of professional morality and ability of teachers is an important part of management in the colleges and universities. Focusing on the evaluation of teachers in Chinese universities, this paper designs an evaluation model, and constructs an evaluation metrics from the perspective of data mining. Furthermore, a new evaluation method for teachers based on Support Vector Machine (SVM) is proposed. The prediction levels of the morality and ability of teachers will be given by SVM. Finally, it gives the relative policies to promote the morality and ability of teachers. It will efficiently improve the quality of teaching and bring benefit to the teaching reform.
C1 [Tang, Li; He, Li] Tianjin Univ Finance & Econ, Dept Informat Sci & Technol, Tianjin, Peoples R China.
   [Zhou, Caiyun] Tianjin Univ Finance & Econ, Dept Econ, Tianjin, Peoples R China.
   [Zhang, Shuhua] Tianjin Univ Finance & Econ, Coordinated Innovat Ctr Computable Modeling Manag, Tianjin, Peoples R China.
C3 Tianjin University of Finance & Economics; Tianjin University of Finance
   & Economics; Tianjin University of Finance & Economics
RP Tang, L (corresponding author), Tianjin Univ Finance & Econ, Dept Informat Sci & Technol, Tianjin, Peoples R China.
EM tangli0831@tjufe.edu.cn; zhoucaiyun_0119@163.com; renkeheli@163.com;
   shuhua55@126.com
FU Tianjin Social Science Foundation of China [TJYY15-017]
FX This research is supported by the Tianjin Social Science Foundation of
   China (TJYY15-017).
CR Chang C.-C., LIBSVM: A library for support vector machines
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   Zhang J., 2014, RES EVALUATION MODEL
NR 8
TC 1
Z9 1
U1 1
U2 4
PU ATLANTIS PRESS
PI PARIS
PA 29 AVENUE LAVMIERE, PARIS, 75019, FRANCE
SN 2352-5398
BN 978-94-6252-348-7
J9 ADV SOC SCI EDUC HUM
PY 2017
VL 72
BP 167
EP 170
PG 4
WC Social Sciences, Interdisciplinary
WE Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Social Sciences - Other Topics
GA BI9GX
UT WOS:000416086100040
DA 2024-09-05
ER
PT J
AU Persson, RAX
AF Persson, Rasmus A. X.
TI Bibliometric author evaluation through linear regression on the coauthor
   network
SO JOURNAL OF INFORMETRICS
LA English
DT Article
DE Multiple authorship; Statistical method; Coauthor contribution
ID CITATION IMPACT INDICATORS; H-INDEX; ARTICLES; CREDIT; PUBLICATION;
   BIOMEDICINE; COUNTS
AB The rising trend of coauthored academic works obscures the credit assignment that is the basis for decisions of funding and career advancements. In this paper, a simple model based on the assumption of an unvarying "author ability" is introduced. With this assumption, the weight of author contributions to a body of coauthored work can be statistically estimated. The method is tested on a set of some more than five-hundred authors in a coauthor network from the CiteSeerX database. The ranking obtained agrees fairly well with that given by total fractional citation counts for an author, but noticeable differences exist. (C) 2017 Elsevier Ltd. All rights reserved.
C1 [Persson, Rasmus A. X.] Univ Gothenburg, Dept Chem & Mol Biol, SE-41296 Gothenburg, Sweden.
C3 University of Gothenburg
RP Persson, RAX (corresponding author), Univ Gothenburg, Dept Chem & Mol Biol, SE-41296 Gothenburg, Sweden.
EM rasmus.a.persson@gmail.com
RI Persson, Rasmus A. X./AAN-5310-2021; Persson, Rasmus/A-6436-2011
OI Persson, Rasmus A. X./0000-0001-6587-5287; Persson,
   Rasmus/0000-0001-6587-5287
CR [Anonymous], 2007, International Society for Scientometrics and Informetrics newsletter
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NR 30
TC 8
Z9 9
U1 1
U2 43
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 1751-1577
EI 1875-5879
J9 J INFORMETR
JI J. Informetr.
PD FEB
PY 2017
VL 11
IS 1
BP 299
EP 306
DI 10.1016/j.joi.2017.01.003
PG 8
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA EO3SM
UT WOS:000396614600022
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Grodzinski, N
   Grodzinski, B
   Davies, BM
AF Grodzinski, Noah
   Grodzinski, Ben
   Davies, Benjamin M.
TI Can co-authorship networks be used to predict author research impact? A
   machine-learning based analysis within the field of degenerative
   cervical myelopathy research
SO PLOS ONE
LA English
DT Article
ID SCIENTIFIC COLLABORATION; SPONDYLOTIC MYELOPATHY; SURGICAL DECOMPRESSION
AB Introduction Degenerative Cervical Myelopathy (DCM) is a common and disabling condition, with a relatively modest research capacity. In order to accelerate knowledge discovery, the AO Spine RECODE-DCM project has recently established the top priorities for DCM research. Uptake of these priorities within the research community will require their effective dissemination, which can be supported by identifying key opinion leaders (KOLs). In this paper, we aim to identify KOLs using artificial intelligence. We produce and explore a DCM co-authorship network, to characterise researchers' impact within the research field.
   Methods Through a bibliometric analysis of 1674 scientific papers in the DCM field, a co-authorship network was created. For each author, statistics about their connections to the co-authorship network (and so the nature of their collaboration) were generated. Using these connectedness statistics, a neural network was used to predict H-Index for each author (as a proxy for research impact). The neural network was retrospectively validated on an unseen author set.
   Results DCM research is regionally clustered, with strong collaboration across some international borders (e.g., North America) but not others (e.g., Western Europe). In retrospective validation, the neural network achieves a correlation coefficient of 0.86 (p<0.0001) between the true and predicted H-Index of each author. Thus, author impact can be accurately predicted using only the nature of an author's collaborations.
   Discussion Analysis of the neural network shows that the nature of collaboration strongly impacts an author's research visibility, and therefore suitability as a KOL. This also suggests greater collaboration within the DCM field could help to improve both individual research visibility and global synergy.
C1 [Grodzinski, Noah] Univ Cambridge, St Johns Coll, Cambridge, England.
   [Grodzinski, Ben] Univ Cambridge, Sch Clin Med, Cambridge, England.
   [Davies, Benjamin M.] Univ Cambridge, Dept Clin Neurosci, Div Neurosurg, Cambridge, England.
C3 University of Cambridge; University of Cambridge; University of
   Cambridge
RP Davies, BM (corresponding author), Univ Cambridge, Dept Clin Neurosci, Div Neurosurg, Cambridge, England.
EM bd375@cam.ac.uk
OI Grodzinski, Ben/0000-0001-8839-4718
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NR 42
TC 15
Z9 15
U1 1
U2 18
PU PUBLIC LIBRARY SCIENCE
PI SAN FRANCISCO
PA 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA
SN 1932-6203
J9 PLOS ONE
JI PLoS One
PD SEP 2
PY 2021
VL 16
IS 9
AR e0256997
DI 10.1371/journal.pone.0256997
PG 14
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics
GA WG5QV
UT WOS:000707050100085
PM 34473796
OA Green Published, gold
DA 2024-09-05
ER
PT J
AU Massonnaud, C
   Lelong, R
   Kerdelhué, G
   Lejeune, E
   Grosjean, J
   Griffon, N
   Darmoni, SJ
AF Massonnaud, Clement
   Lelong, Romain
   Kerdelhue, Gaetan
   Lejeune, Emeline
   Grosjean, Julien
   Griffon, Nicolas
   Darmoni, Stefan J.
TI Performance evaluation of three semantic expansions to query PubMed
SO HEALTH INFORMATION AND LIBRARIES JOURNAL
LA English
DT Article
DE bibliographic databases; information retrieval; literature searching;
   medical subject headings (MeSH); MEDLINE; precision; PubMed; recall;
   search strategies; thesaurus
ID INFORMATION; BEHAVIOR
AB Background PubMed is one of the most important basic tools to access medical literature. Semantic query expansion using synonyms can improve retrieval efficacy. Objective The objective was to evaluate the performance of three semantic query expansion strategies. Methods Queries were built for forty MeSH descriptors using three semantic expansion strategies (MeSH synonyms, UMLS mappings, and mappings created by the CISMeF team), then sent to PubMed. To evaluate expansion performances for each query, the first twenty citations were selected, and their relevance were judged by three independent evaluators based on the title and abstract. Results Queries built with the UMLS expansion provided new citations with a slightly higher mean precision (74.19%) than with the CISMeF expansion (70.28%), although the difference was not significant. Inter-rater agreement was 0.28. Results varied greatly depending on the descriptor selected. Discussion The number of citations retrieved by the three strategies and their precision varied greatly according to the descriptor. This heterogeneity could be explained by the quality of the synonyms. Optimal use of these different expansions would be through various combinations of UMLS and CISMeF intersections or unions. Conclusion Information retrieval tools should propose different semantic expansions depending on the descriptor and the search objectives.
C1 [Massonnaud, Clement; Lelong, Romain; Kerdelhue, Gaetan; Lejeune, Emeline; Grosjean, Julien; Griffon, Nicolas; Darmoni, Stefan J.] Rouen Univ Hosp, Dept Biomed Informat, 1 Rue Germont, F-76000 Rouen, Normandy, France.
   [Massonnaud, Clement; Lelong, Romain; Kerdelhue, Gaetan; Lejeune, Emeline; Grosjean, Julien; Griffon, Nicolas; Darmoni, Stefan J.] Sorbonne Univ, LIMICS, U1142, Paris, France.
C3 Universite de Rouen Normandie; CHU de Rouen; Sorbonne Universite
RP Massonnaud, C (corresponding author), Rouen Univ Hosp, Dept Biomed Informat, 1 Rue Germont, F-76000 Rouen, Normandy, France.
EM clement.massonnaud@gmail.com
RI LEJEUNE, Emeline/KAM-4290-2024; Stefan, Darmoni J/H-4554-2016;
   Kerdelhué, Gaétan/J-6933-2019
OI LEJEUNE, Emeline/0000-0001-6177-2125; Kerdelhué,
   Gaétan/0000-0001-5803-5554; Darmoni, Stefan/0000-0002-7162-318X;
   Massonnaud, Clement/0000-0003-0292-9668; Griffon,
   Nicolas/0000-0002-9602-6429; Grosjean, Julien/0000-0002-7446-644X;
   LELONG, Romain/0000-0003-1865-8786
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NR 27
TC 2
Z9 2
U1 0
U2 10
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1471-1834
EI 1471-1842
J9 HEALTH INFO LIBR J
JI Heatlth Info. Libr. J.
PD JUN
PY 2021
VL 38
IS 2
BP 113
EP 124
DI 10.1111/hir.12291
PG 12
WC Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science
GA TB9RL
UT WOS:000668281300004
PM 31837099
DA 2024-09-05
ER
PT J
AU Verma, MK
   Yuvaraj, M
AF Verma, Manoj Kumar
   Yuvaraj, Mayank
TI What's up in WhatsApp research: a comprehensive analysis of 12,947
   papers indexed in Dimensions.ai
SO LIBRARY HI TECH
LA English
DT Article; Early Access
DE WhatsApp; Instant messaging platform; Bibliometrics; Dimensions.ai;
   Performance analysis; Science mapping; Cluster analysis
AB PurposeIn recent years, instant messaging platforms like WhatsApp have gained substantial popularity in both academic and practical domains. However, despite this growth, there is a lack of a comprehensive overview of the literature in this field. The primary purpose of this study is to bridge this gap by analyzing a substantial dataset of 12,947 articles retrieved from the Dimensions.ai, database spanning from 2011 to March 2023.Design/methodology/approachTo achieve the authors' objective, the authors employ bibliometric analysis techniques. The authors delve into various bibliometric networks, including citation networks, co-citation networks, collaboration networks, keywords and bibliographic couplings. These methods allow for the uncovering of the social and conceptual structures within the academic discourse surrounding WhatsApp.FindingsThe authors' analysis reveals several significant findings. Firstly, the authors observe a remarkable and continuous growth in the number of academic studies dedicated to WhatsApp over time. Notably, two prevalent themes emerge: the impact of coronavirus disease 2019 (COVID-19) and the role of WhatsApp in the realm of social media. Furthermore, the authors' study highlights diverse applications of WhatsApp, including its utilization in education and learning, as a communication tool, in medical education, cyberpsychology, security, psychology and behavioral learning.Originality/valueThis paper contributes to the field by offering a comprehensive overview of the scholarly research landscape related to WhatsApp. The findings not only illuminate the burgeoning interest in WhatsApp among researchers but also provide insights into the diverse domains where WhatsApp is making an impact. The analysis of bibliometric networks offers a unique perspective on the social and conceptual structures within this field, shedding light on emerging trends and influential research. This study thus serves as a valuable resource for scholars, practitioners and policymakers seeking to navigate the evolving landscape of WhatsApp research. The study will also be useful for researchers interested in conducting bibliometric analysis using Dimensions.ai, a free database.
C1 [Verma, Manoj Kumar] Mizoram Univ, Dept Lib & Informat Sci, Aizawl, India.
   [Yuvaraj, Mayank] Cent Univ South Bihar, Rajarshi Janak Cent Lib, Gaya, India.
C3 Mizoram University; Central University of South Bihar
RP Yuvaraj, M (corresponding author), Cent Univ South Bihar, Rajarshi Janak Cent Lib, Gaya, India.
EM mayank.yuvaraj@gmail.com
RI Verma, Manoj Kumar/ABE-4906-2020
OI Verma, Prof. Manoj Kumar/0000-0002-3009-3258
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NR 213
TC 0
Z9 0
U1 4
U2 13
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 0737-8831
J9 LIBR HI TECH
JI Libr. Hi Tech
PD 2023 DEC 5
PY 2023
DI 10.1108/LHT-11-2023-0525
EA DEC 2023
PG 36
WC Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science
GA Z5JB3
UT WOS:001112425400001
DA 2024-09-05
ER
PT J
AU Hassan, SU
   Aljohani, NR
   Tarar, UI
   Safder, I
   Sarwar, R
   Alelyani, S
   Nawaz, R
AF Hassan, Saeed-Ul
   Aljohani, Naif Radi
   Tarar, Usman Iqbal
   Safder, Iqra
   Sarwar, Raheem
   Alelyani, Salem
   Nawaz, Raheel
TI Exploiting tweet sentiments in altmetrics large-scale data
SO JOURNAL OF INFORMATION SCIENCE
LA English
DT Article
DE Altmetrics; aspect-based sentiment analysis; lexicon; Twitter
ID RESEARCH EXCELLENCE; IMPACT; AGREEMENT; OPINIONS
AB This article aims to exploit social exchanges on scientific literature, specifically tweets, to analyse social media users' sentiments towards publications within a research field. First, we employ the SentiStrength tool, extended with newly created lexicon terms, to classify the sentiments of 6,482,260 tweets associated with 1,083,535 publications provided by Altmetric.com. Then, we propose harmonic means-based statistical measures to generate a specialised lexicon, using positive and negative sentiment scores and frequency metrics. Next, we adopt a novel article-level summarisation approach to domain-level sentiment analysis to gauge the opinion of social media users on Twitter about the scientific literature. Last, we propose and employ an aspect-based analytical approach to mine users' expressions relating to various aspects of the article, such as tweets on its title, abstract, methodology, conclusion or results section. We show that research communities exhibit dissimilar sentiments towards their respective fields. The analysis of the field-wise distribution of article aspects shows that in Medicine, Economics, Business and Decision Sciences, tweet aspects are focused on the results section. In contrast, in Physics and Astronomy, Materials Sciences and Computer Science, these aspects are focused on the methodology section. Overall, the study helps us to understand the sentiments of online social exchanges of the scientific community on scientific literature. Specifically, such a fine-grained analysis may help research communities in improving their social media exchanges about the scientific articles to disseminate their scientific findings effectively and to further increase their societal impact.
C1 [Hassan, Saeed-Ul] Informat Technol Univ, Dept Comp Sci, Lahore, Pakistan.
   [Aljohani, Naif Radi] King Abdulaziz Univ, Fac Comp & Informat Technol, Jeddah, Saudi Arabia.
   [Tarar, Usman Iqbal] Informat Technol Univ, Dept Comp Sci, Lahore, Pakistan.
   [Safder, Iqra] FAST NU Lahore, FAST Sch Comp, Lahore, Pakistan.
   [Sarwar, Raheem] Manchester Metropolitan Univ, Dept Operat Technol Events & Hospitality Managemen, Manchester, England.
   [Alelyani, Salem] King Khalid Univ, Ctr Artificial Intelligence CAI, Abha, Saudi Arabia.
   [Alelyani, Salem] King Khalid Univ, Coll Comp Sci, Abha, Saudi Arabia.
   [Nawaz, Raheel] Staffordshire Univ, Stoke On trent, England.
C3 King Abdulaziz University; Manchester Metropolitan University; King
   Khalid University; King Khalid University; Staffordshire University
RP Sarwar, R (corresponding author), Manchester Metropolitan Univ, Dept Operat Technol Events & Hospitality Managemen, Manchester, England.
EM R.Sarwar@mmu.ac.uk
RI Nawaz, Raheel/AAX-5293-2021; Aljohani, Naif R/S-1109-2017; Safder,
   Iqra/JXN-8069-2024; Hassan, Saeed-Ul/G-1889-2016
OI Nawaz, Raheel/0000-0001-9588-0052; /0000-0002-0640-807X; Hassan,
   Saeed-Ul/0000-0002-6509-9190
CR Bornmann L, 2019, J INFORMETR, V13, P325, DOI 10.1016/j.joi.2019.01.008
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NR 34
TC 1
Z9 1
U1 5
U2 32
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 0165-5515
EI 1741-6485
J9 J INF SCI
JI J. Inf. Sci.
PD OCT
PY 2023
VL 49
IS 5
BP 1229
EP 1245
DI 10.1177/01655515211043713
EA NOV 2022
PG 17
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA R5HB5
UT WOS:000888554300001
OA Green Submitted, Green Accepted
DA 2024-09-05
ER
PT C
AU Bernadt, J
   Soh, LK
AF Bernadt, J
   Soh, LK
BE Arabnia, HR
   Mun, Y
TI Authoritative citation KNN learning with noisy training datasets
SO IC-AI '04 & MLMTA'04 , VOL 1 AND 2, PROCEEDINGS
LA English
DT Proceedings Paper
CT International Conference on Artificial Intelligence/International
   Conference on Machine Learning, Models, Technologies and Applications
CY JUN 21-24, 2004
CL Las Vegas, NV
AB In this paper, we investigate the effectiveness of Citation K-Nearest Neighbors (KNN) learning with noisy training datasets. We devise an authority measure associated with each training instance that changes based on the outcome of Citation KNN classification. We show that by modifying only the authority measures, the classification accuracy by Citation KNN improves significantly in a variety of datasets with different noise levels. Also, by analyzing the authority measures, we are able to identify and correct noisy training instances.
C1 Univ Nebraska, Dept Comp Sci & Engn, Lincoln, NE 68588 USA.
C3 University of Nebraska System; University of Nebraska Lincoln
RP Bernadt, J (corresponding author), Univ Nebraska, Dept Comp Sci & Engn, Lincoln, NE 68588 USA.
CR Aha David, 1997, Lazy learning
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NR 9
TC 0
Z9 0
U1 0
U2 0
PU C S R E A PRESS
PI ATHENS
PA 115 AVALON DR, ATHENS, GA 30606 USA
BN 1-932415-33-5
PY 2004
BP 916
EP 921
PG 6
WC Computer Science, Artificial Intelligence
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BBL81
UT WOS:000226030400139
DA 2024-09-05
ER
PT J
AU Carson, S
   Kanchanaraksa, S
   Gooding, I
   Mulder, F
   Schuwer, R
AF Carson, Stephen
   Kanchanaraksa, Sukon
   Gooding, Ira
   Mulder, Fred
   Schuwer, Robert
TI Impact of OpenCourseWare Publication on Higher Education Participation
   and Student Recruitment
SO INTERNATIONAL REVIEW OF RESEARCH IN OPEN AND DISTRIBUTED LEARNING
LA English
DT Article
DE Distance education; open learning; open universities; distance
   universities; higher education; e-learning; online learning
AB The free and open publication of course materials (OpenCourseWare or OCW) was initially undertaken by Massachusetts Institute of Technology (MIT) and other universities primarily to share educational resources among educators (Abelson, 2007). OCW, however, and more in general open educational resources (OER),(1) have also provided well-documented opportunities for all learners, including the so-called "informal learners" and "independent learners" (Carson, 2005; Mulder, 2006, p. 35). Universities have also increasingly documented clear benefits for specific target groups such as secondary education students and lifelong learners seeking to enter formal postsecondary education programs.
   In addition to benefitting learners, OCW publication has benefitted the publishing institutions themselves by providing recruiting advantages. Finally enrollment figures from some institutions indicate that even in the case of the free and open publication of materials from online programs, OCW does not negatively affect enrollment. This paper reviews evaluation conducted at Massachusetts Institute of Technology, Johns Hopkins Bloomberg School of Public Health (JHSPH), and Open Universiteit Nederland (OUNL) concerning OCW effects on higher education participation and student recruitment.
C1 [Carson, Stephen] MIT, Cambridge, MA 02139 USA.
C3 Massachusetts Institute of Technology (MIT)
RP Carson, S (corresponding author), MIT, 77 Massachusetts Ave, Cambridge, MA 02139 USA.
RI Schuwer, Robert/IZE-0549-2023
OI Schuwer, Robert/0000-0001-5756-5406
CR Abelson H., 2007, J SCI ED TECHNOLOGY
   [Anonymous], CASE STUDIES I OPEN
   Butcher N., 2012, Exploring the business case for Open Educational Resources
   Carson S., 2003, PROGRAM EVALUA UNPUB
   CARSON S, 2005, PROGRAM EVALUATION F
   Janssen B., 2012, C P CAMBR 2012 INN I
   Mulder F., 2006, PRESENTATIONS DIES N
   Schuwer R, 2009, OPEN LEARN, V24, P67, DOI 10.1080/02680510802627852
   Wiley D, 2012, INT REV RES OPEN DIS, V13, P263, DOI 10.19173/irrodl.v13i3.1153
NR 9
TC 19
Z9 21
U1 0
U2 30
PU ATHABASCA UNIV PRESS
PI ATHABASCA
PA 1 UNIVERSITY DR, ATHABASCA, AB T9S 3A3, CANADA
SN 1492-3831
J9 INT REV RES OPEN DIS
JI Int. Rev. Res. Open Distrib. Learn.
PY 2012
VL 13
IS 4
BP 19
EP 32
DI 10.19173/irrodl.v13i4.1238
PG 14
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA 072HK
UT WOS:000313660500003
OA Green Submitted, gold
DA 2024-09-05
ER
PT C
AU Guo, LJ
   Yan, HJ
   Gao, WS
   Chen, Y
   Hao, YQ
AF Guo, Lijuan
   Yan, Haijun
   Gao, Wensheng
   Chen, Yun
   Hao, Yongqi
GP IOP
TI Research on big data risk assessment of major transformer defects and
   faults fusing power grid, equipment and environment based on SVM
SO 2017 3RD INTERNATIONAL CONFERENCE ON ENVIRONMENTAL SCIENCE AND MATERIAL
   APPLICATION (ESMA2017), VOLS 1-4
SE IOP Conference Series-Earth and Environmental Science
LA English
DT Proceedings Paper
CT 3rd International Conference on Environmental Science and Material
   Application (ESMA)
CY NOV 25-26, 2017
CL Chongqing, PEOPLES R CHINA
ID PARTIAL-DISCHARGE; PD
AB With the development of power big data, considering the wider power system data, the appropriate large data analysis method can be used to mine the potential law and value of power big data. On the basis of considering all kinds of monitoring data and defects and fault records of main transformer, the paper integrates the power grid, equipment as well as environment data and uses SVM as the main algorithm to evaluate the risk of the main transformer. It gets and compares the evaluation results under different modes, and proves that the risk assessment algorithms and schemes have certain effectiveness. This paper provides a new idea for data fusion of smart grid, and provides a reference for further big data evaluation of power grid equipment.
C1 [Guo, Lijuan; Yan, Haijun] Guangxi Elect Power Res Inst, Nanning, Peoples R China.
   [Gao, Wensheng; Chen, Yun] Tsinghua Univ, Sch Elect Engn, Beijing, Peoples R China.
   [Hao, Yongqi] Southwest Jiaotong Univ, Sch Elect Engn, Chengdu, Sichuan, Peoples R China.
C3 Tsinghua University; Southwest Jiaotong University
RP Hao, YQ (corresponding author), Southwest Jiaotong Univ, Sch Elect Engn, Chengdu, Sichuan, Peoples R China.
EM haoyongqi001@163.com
CR [Anonymous], 2016, Automation of Electric Power Systems, DOI DOI 10.7500/AEPS20151208005
   Bai Cuifen, 2013, ADV ENG SCI, V2
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   Ji Xuan, 2016, HEILONGJIANG SCI TEC, P143
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   [孙莹 Sun Ying], 2016, [高电压技术, High Voltage Engineering], V42, P2054
   Wang Dewen, 2016, P CSEE, P5111
   Wang Yiping, 2016, ELECTROTECHNICS ELEC, P24
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NR 20
TC 1
Z9 1
U1 2
U2 19
PU IOP PUBLISHING LTD
PI BRISTOL
PA DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND
SN 1755-1307
J9 IOP C SER EARTH ENV
JI IOP Conf. Ser. Earth Envir. Sci.
PY 2018
VL 108
AR 052027
DI 10.1088/1755-1315/108/5/052027
PG 5
WC Engineering, Environmental; Environmental Sciences
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Engineering; Environmental Sciences & Ecology
GA BL0RY
UT WOS:000446447200314
OA gold
DA 2024-09-05
ER
PT J
AU King, CH
   Yoon, N
   Wang, XX
   Lo, NC
   Alsallaq, R
   Ndeffo-Mbah, M
   Li, E
   Gurarie, D
AF King, Charles H.
   Yoon, Nara
   Wang, Xiaoxia
   Lo, Nathan C.
   Alsallaq, Ramzi
   Ndeffo-Mbah, Martial
   Li, Emily
   Gurarie, David
TI Application of Schistosomiasis Consortium for Operational Research and
   Evaluation Study Findings to Refine Predictive Modeling of
   <i>Schistosoma mansoni</i> and <i>Schistosoma haematobium</i> Control in
   Sub-Saharan Africa
SO AMERICAN JOURNAL OF TROPICAL MEDICINE AND HYGIENE
LA English
DT Article
ID SOIL-TRANSMITTED HELMINTHIASIS; DRUG ADMINISTRATION STRATEGIES;
   PERSISTENT HOTSPOTS; MATHEMATICAL-MODELS; TRANSMISSION; DYNAMICS;
   PRAZIQUANTEL; IMPACT; CHEMOTHERAPY; INFECTIONS
AB An essential mission of the Schistosomiasis Consortium for Operational Research and Evaluation (SCORE) was to help inform global health practices related to the control and elimination of schistosomiasis. To provide more accurate, evidence-based projections of the most likely impact of different control interventions, whether implemented alone or in combination, SCORE supported mathematical modeling teams to provide simulations of community-level Schistosoma infection outcomes in the setting of real or hypothetical programs implementing multiyear mass drug administration (MDA) for parasite control. These models were calibrated using SCORE experience with Schistosoma mansoni and Schistosoma haematobium gaining and sustaining control studies, and with data from comparable programs that used community-based or school-based praziquantel MDA in other parts of sub-Saharan Africa. From 2010 to 2019, models were developed and refined, first to project the likely SCORE control outcomes, and later to more accurately reflect impact of MDA across different transmission settings, including the role of snail ecology and the impact of seasonal rainfall on snail abundance. Starting in 2014, SCORE modeling projections were also compared with the models of colleagues in the Neglected Tropical Diseases Modelling Consortium. To explore further possible improvement to program-based control, later simulations examined the cost-effectiveness of combining MDA with environmental snail control, and the utility of early impact assessment to more quickly identify persistent hot spots of transmission. This article provides a nontechnical summary of the 11 SCORE-related modeling projects and provides links to the original open-access articles describing model development and projections relevant to schistosomiasis control policy.
C1 [King, Charles H.; Alsallaq, Ramzi; Gurarie, David] Case Western Reserve Univ, Ctr Global Hlth & Dis, Cleveland, OH 44106 USA.
   [King, Charles H.] Univ Georgia, Ctr Trop & Emerging Global Dis, Schistosomiasis Consortium Operat Res & Evaluat, Athens, GA 30602 USA.
   [Yoon, Nara; Wang, Xiaoxia; Gurarie, David] Case Western Reserve Univ, Dept Math Appl Math & Stat, Cleveland, OH 44106 USA.
   [Lo, Nathan C.] Univ Calif San Francisco, Dept Med, San Francisco, CA 94143 USA.
   [Ndeffo-Mbah, Martial] Yale Univ, Yale Sch Publ Hlth, New Haven, CT USA.
   [Li, Emily] Case Western Reserve Univ, Sch Med, Cleveland, OH USA.
C3 University System of Ohio; Case Western Reserve University; University
   System of Georgia; University of Georgia; University System of Ohio;
   Case Western Reserve University; University of California System;
   University of California San Francisco; Yale University; University
   System of Ohio; Case Western Reserve University
RP King, CH (corresponding author), CWRU Sch Med, Ctr Global Hlth & Dis, 2109 Adelbert Rd, Cleveland, OH 44106 USA.
EM chk@cwru.edu; nyoon@adelphi.edu; xiaoxiawang248@gmail.com;
   nathan.lo@ucsf.edu; ramzi.alsallaq@gmail.com; mndeffo@cvm.tamu.edu;
   yel3@case.edu; dxg5@cwru.edu
OI Ndeffo Mbah, Martial/0000-0003-4158-7613; King,
   Charles/0000-0001-8349-9270
FU University of Georgia Research Foundation, Inc. - Bill AMP; Melinda
   Gates Foundation; Children's Investment Fund Foundation (UK) ("CIFF")
FX These studies received financial support from the University of Georgia
   Research Foundation, Inc., which was funded by the Bill & Melinda Gates
   Foundation for the SCORE project. Schistosomiasis modeling at the NTD
   Modelling Consortium was also was funded by The Children's Investment
   Fund Foundation (UK) ("CIFF") through a grant to the Neglected Tropical
   Diseases Modelling Consortium at Warwick University, United Kingdom.
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NR 54
TC 3
Z9 3
U1 0
U2 2
PU AMER SOC TROP MED & HYGIENE
PI MCLEAN
PA 8000 WESTPARK DR, STE 130, MCLEAN, VA 22101 USA
SN 0002-9637
EI 1476-1645
J9 AM J TROP MED HYG
JI Am. J. Trop. Med. Hyg.
PD JUL
PY 2020
VL 103
IS 1
SU S
BP 97
EP 104
DI 10.4269/ajtmh.19-0852
PG 8
WC Public, Environmental & Occupational Health; Tropical Medicine
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Public, Environmental & Occupational Health; Tropical Medicine
GA MS9QP
UT WOS:000554606800013
PM 32400357
OA Green Published, Bronze
DA 2024-09-05
ER
PT J
AU Shackelford, J
   Thompson, DS
   James, MB
AF Shackelford, J
   Thompson, DS
   James, MB
TI Teaching strategy and assignment design - Assessing the quality and
   validity of information via the Web
SO SOCIAL SCIENCE COMPUTER REVIEW
LA English
DT Article; Proceedings Paper
CT Annual Conference of the CSS / IASSIST on Global Access, Local Support -
   Social Science Computing in the Age of the World Wide Web
CY MAY 19-22, 1998
CL YALE UNIV, NEW HAVEN, CONNECTICUT
HO YALE UNIV
DE information resources; active learning; pedagogy; Web-based information;
   critical thinking skills; research strategies; Web assignments;
   evaluation of Internet sites
AB As students increasingly use Internet technology to access information, new educational issues and opportunities arise. This article examines ways in which instructors and teaching professionals might work with students in guiding their research to establish "good practices" in both citing and using resources. The authors report on the process of crafting, administering, and assessing an assignment that might serve as a model for research-based papers and projects in a variety of disciplines. This assignment employs active learning strategies to encourage students to become better editors and critics as well as to teach them skills that they will be able to employ as this technology changes and others emerge.
C1 Bucknell Univ, Dept Econ, Lewisburg, PA 17837 USA.
   Bucknell Univ, User Educ Serv, Bertrand Lib, Lewisburg, PA 17837 USA.
   Bucknell Univ, Comp & Commun Serv, Lewisburg, PA 17837 USA.
C3 Bucknell University; Bucknell University; Bucknell University
RP Shackelford, J (corresponding author), Bucknell Univ, Dept Econ, Lewisburg, PA 17837 USA.
CR Bonwell James., 1991, Active Learning: Creating Excitement in the Classroom AEHE-ER1C Higher Education Report No. 1
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   [No title captured]
NR 12
TC 7
Z9 8
U1 0
U2 4
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 0894-4393
J9 SOC SCI COMPUT REV
JI Soc. Sci. Comput. Rev.
PD SUM
PY 1999
VL 17
IS 2
BP 196
EP 208
DI 10.1177/089443939901700206
PG 13
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science; Social Sciences, Interdisciplinary
WE Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH); Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science; Social Sciences
   - Other Topics
GA 183WT
UT WOS:000079577700006
DA 2024-09-05
ER
PT J
AU Vázquez-Cano, E
   Parra-González, ME
   Segura-Robles, A
   López-Meneses, E
AF Vazquez-Cano, Esteban
   Parra-Gonzalez, M. Elena
   Segura-Robles, Adrian
   Lopez-Meneses, Eloy
TI The Negative Effects of Technology on Education: A Bibliometric and
   Topic Modeling Mapping Analysis (2008-2019)
SO INTERNATIONAL JOURNAL OF INSTRUCTION
LA English
DT Article
DE disadvantages; problems; technology; education; bibliometrics
ID ACADEMIC-ACHIEVEMENT; UNIVERSITY-STUDENTS; INFORMATION; TEACHERS;
   PRIVACY; IMPACT; MEDIA; ICT
AB This study aims to analyze the scientific research that has addressed the negative impact of technology in the educational field. The research is implemented from a methodological approach based on the bibliometric mapping of the scientific production registered in the Web of Science in the period 2008-2019. To do this, indicators of growth, production, impact, topics, keywords, journal references, and analysis of co-citations of authors and co-authors are analyzed. This bibliometric approach is complemented by the analysis of the density, frequency and degree of centrality of the main terms associated with the difficulties and problems of technology in education located in the abstracts and discussion of results in the period 2016-2019. For this purpose, graph theory is developed, using the sigma, cytoscape and graphology libraries. The results show that, among the most common disadvantages linked to the use of technology in education, are: privacy problems, discerning reliable and relevant information, the time required for the preparation of educational materials, the negative impact on academic performance of the students, the lack of resources for its implementation in the classrooms and the infoxication. Finally, it should be noted that in the last three years, the negative impact of technology in the psychosocial field and its impact on teaching-learning processes are beginning to be analyzed in greater depth.
C1 [Vazquez-Cano, Esteban] Univ Nacl Educ Distancia, Madrid, Spain.
   [Parra-Gonzalez, M. Elena; Segura-Robles, Adrian] Univ Granada, Granada, Spain.
   [Lopez-Meneses, Eloy] Univ Pablo de Olavide, Seville, Spain.
C3 Universidad Nacional de Educacion a Distancia (UNED); University of
   Granada; Universidad Pablo de Olavide
RP Vázquez-Cano, E (corresponding author), Univ Nacl Educ Distancia, Madrid, Spain.
EM evazquez@edu.uned.es; elenaparra@ugr.es; adrianseg@ugr.es;
   elopmen@upo.es
RI Segura, Adrián/B-4963-2019; López-Meneses, Eloy/G-1307-2011;
   Vazquez-Cano, Esteban/K-5424-2014
OI Segura, Adrián/0000-0003-0753-7129; López-Meneses,
   Eloy/0000-0003-0741-5367; Vazquez-Cano, Esteban/0000-0002-6694-7948
FU I+D+I Project entitled: "Gamification and ubiquitous learning in Primary
   Education [RTI2018099764-B-100]; FEDER (European Regional Development
   Fund); Ministry of Science, Innovation and Universities of Spain
FX This research has been developed with the support of the I+D+I Project
   entitled: "Gamification and ubiquitous learning in Primary Education.
   Development of a map of teaching, learning and parental competences and
   resources "GAUBI". (RTI2018099764-B-100) (MICINN/FEDER) financed by
   FEDER (European Regional Development Fund) and Ministry of Science,
   Innovation and Universities of Spain.
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NR 71
TC 9
Z9 11
U1 7
U2 27
PU GATE ASSOC TEACHING & EDUCATION-GATE, SWITZERLAND
PI BASEL
PA GATE ASSOC TEACHING & EDUCATION-GATE, SWITZERLAND, BASEL, SWITZERLAND
SN 1694-609X
EI 1308-1470
J9 INT J INSTR
JI Int. J. Instr.
PD APR
PY 2022
VL 15
IS 2
BP 37
EP 60
DI 10.29333/iji.2022.1523a
PG 24
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA 0U3GZ
UT WOS:000787542400005
OA Green Published, gold
DA 2024-09-05
ER
PT J
AU Rokach, L
   Mitra, P
AF Rokach, Lior
   Mitra, Prasenjit
TI Parsimonious citer-based measures: The artificial intelligence domain as
   a case study
SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY
LA English
DT Article
DE bibliometric scatter; citation indexes
ID CITATION ANALYSIS; IMPACT; AUTHORS
AB This article presents a new Parsimonious Citer-Based Measure for assessing the quality of academic papers. This new measure is parsimonious as it looks for the smallest set of citing authors (citers) who have read a certain paper. The Parsimonious Citer-Based Measure aims to address potential distortion in the values of existing citer-based measures. These distortions occur because of various factors, such as the practice of hyperauthorship. This new measure is empirically compared with existing measures, such as the number of citers and the number of citations in the field of artificial intelligence (AI). The results show that the new measure is highly correlated with those two measures. However, the new measure is more robust against citation manipulations and better differentiates between prominent and nonprominent AI researchers than the above-mentioned measures.
C1 [Rokach, Lior] Ben Gurion Univ Negev, Dept Informat Syst Engn, IL-84105 Beer Sheva, Israel.
   [Mitra, Prasenjit] Penn State Univ, Coll Informat Sci & Technol, University Pk, PA 16802 USA.
C3 Ben Gurion University; Pennsylvania Commonwealth System of Higher
   Education (PCSHE); Pennsylvania State University; Pennsylvania State
   University - University Park
RP Rokach, L (corresponding author), Ben Gurion Univ Negev, Dept Informat Syst Engn, POB 653, IL-84105 Beer Sheva, Israel.
EM liorrk@bgu.ac.il; pmitra@ist.psu.edu
RI Rokach, Lior/F-8247-2010
FU U.S. National Science Foundation [0845487]; Direct For Computer & Info
   Scie & Enginr; Div Of Information & Intelligent Systems [0845487]
   Funding Source: National Science Foundation
FX This paper is based on work supported partially by the U.S. National
   Science Foundation under grant no. 0845487.
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NR 19
TC 1
Z9 2
U1 4
U2 37
PU WILEY-BLACKWELL
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1532-2882
J9 J AM SOC INF SCI TEC
JI J. Am. Soc. Inf. Sci. Technol.
PD SEP
PY 2013
VL 64
IS 9
BP 1951
EP 1959
DI 10.1002/asi.22887
PG 9
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 197SG
UT WOS:000322870300017
DA 2024-09-05
ER
PT J
AU Gao, F
   Jia, XF
   Zhao, ZY
   Chen, CC
   Xu, F
   Geng, Z
   Song, XT
AF Gao, Fang
   Jia, Xiaofeng
   Zhao, Zhiyun
   Chen, Chih-Cheng
   Xu, Feng
   Geng, Zhe
   Song, Xiaotong
TI Bibliometric analysis on tendency and topics of artificial intelligence
   over last decade
SO MICROSYSTEM TECHNOLOGIES-MICRO-AND NANOSYSTEMS-INFORMATION STORAGE AND
   PROCESSING SYSTEMS
LA English
DT Article
ID CO-WORD ANALYSIS; DEEP; EXPLORATION
AB Artificial intelligence (AI), together with its applications, has received world-wide attentions and is expected to exert force on the development of global economy and society in the future. By means of bibliometric method, the study aims at providing an overview on the research tendency and the most concerned topics of AI during the past decade. The database of Web of Science was chosen and the articles published in AI journals were retrieved. Top 10% of the yearly high-citation articles (12,301 articles) published since the year of 2008 were selected as sampling articles for the analysis. The bibliographic records were used for the overall analysis, and the core keywords were studied and classified into three categories (algorithm, general technology and application technology) for topics analysis. As results, number of articles in AI by year and country, the country collaboration and well-known institutes and researchers in AI were presented. Also we proposed and concluded the five most concerned topics, which are perception intelligence (1st), human mind simulated intelligence (2nd), classical model based machine learning (3rd), bio-inspired intelligence (4th), and big-data based intelligence (5th). It is the authors' wish that the study were helpful for researchers to have an overall grasp of the recent status of AI development.
C1 [Gao, Fang; Jia, Xiaofeng; Zhao, Zhiyun; Xu, Feng; Geng, Zhe; Song, Xiaotong] Inst Sci & Tech Informat China ISTIC, Beijing 100038, Peoples R China.
   [Gao, Fang; Jia, Xiaofeng; Zhao, Zhiyun; Xu, Feng; Geng, Zhe; Song, Xiaotong] Minist Sci & Technol Peoples Republ China Most, New Generat Artificial Intelligence Dev Res Ctr, Beijing 100038, Peoples R China.
   [Chen, Chih-Cheng] Jimei Univ, Sch Informat Engn, Xiamen 361021, Fujian, Peoples R China.
C3 Jimei University
RP Jia, XF (corresponding author), Inst Sci & Tech Informat China ISTIC, Beijing 100038, Peoples R China.; Jia, XF (corresponding author), Minist Sci & Technol Peoples Republ China Most, New Generat Artificial Intelligence Dev Res Ctr, Beijing 100038, Peoples R China.; Chen, CC (corresponding author), Jimei Univ, Sch Informat Engn, Xiamen 361021, Fujian, Peoples R China.
EM jiaxiaofeng@istic.ac.cn; 201761000018@jmu.edu.cn
RI jia, xiaofeng/KVB-5084-2024; zhao, zhiyun/E-6560-2011
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NR 31
TC 22
Z9 22
U1 2
U2 29
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 0946-7076
EI 1432-1858
J9 MICROSYST TECHNOL
JI Microsyst. Technol.
PD APR
PY 2021
VL 27
IS 4
SI SI
BP 1545
EP 1557
DI 10.1007/s00542-019-04426-y
PG 13
WC Engineering, Electrical & Electronic; Nanoscience & Nanotechnology;
   Materials Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Engineering; Science & Technology - Other Topics; Materials Science;
   Physics
GA RY3FC
UT WOS:000647800200047
DA 2024-09-05
ER
PT J
AU Guns, R
   Rousseau, R
AF Guns, Raf
   Rousseau, Ronald
TI Recommending research collaborations using link prediction and random
   forest classifiers
SO SCIENTOMETRICS
LA English
DT Article; Proceedings Paper
CT 14th International-Society-of-Scientometrics-and-Informetrics Conference
   (ISSI)
CY JUL 15-20, 2013
CL Vienna, AUSTRIA
DE Collaboration; Networks; Link prediction; Machine learning; Random
   forest classifiers; Recommendation; Facilitator cities
ID NETWORKS
AB We introduce a method to predict or recommend high-potential future (i.e., not yet realized) collaborations. The proposed method is based on a combination of link prediction and machine learning techniques. First, a weighted co-authorship network is constructed. We calculate scores for each node pair according to different measures called predictors. The resulting scores can be interpreted as indicative of the likelihood of future linkage for the given node pair. To determine the relative merit of each predictor, we train a random forest classifier on older data. The same classifier can then generate predictions for newer data. The top predictions are treated as recommendations for future collaboration. We apply the technique to research collaborations between cities in Africa, the Middle East and South-Asia, focusing on the topics of malaria and tuberculosis. Results show that the method yields accurate recommendations. Moreover, the method can be used to determine the relative strengths of each predictor.
C1 [Guns, Raf; Rousseau, Ronald] Univ Antwerp, IBW, Inst Educ & Informat Sci, B-2000 Antwerp, Belgium.
   [Rousseau, Ronald] Katholieke Univ Leuven, B-3000 Leuven, Belgium.
C3 University of Antwerp; KU Leuven
RP Guns, R (corresponding author), Univ Antwerp, IBW, Inst Educ & Informat Sci, Venusstr 35, B-2000 Antwerp, Belgium.
EM raf.guns@uantwerpen.be; ronald.rousseau@uantwerpen.be
RI Guns, Raf/D-6762-2012
OI Guns, Raf/0000-0003-3129-0330
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NR 27
TC 59
Z9 75
U1 2
U2 112
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD NOV
PY 2014
VL 101
IS 2
BP 1461
EP 1473
DI 10.1007/s11192-013-1228-9
PG 13
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH); Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Information Science & Library Science
GA AR5FF
UT WOS:000343609900034
DA 2024-09-05
ER
PT J
AU Wu, TT
   Yu, ZG
AF Wu, Tiantian
   Yu, Zhonggen
TI Bibliometric and Systematic Analysis of Artificial Intelligence
   Chatbots' Use for Language Education
SO JOURNAL OF UNIVERSITY TEACHING AND LEARNING PRACTICE
LA English
DT Article
ID CONTINUANCE INTENTION; EXPERIENCES; ANALYTICS; LEARNERS
AB This research aimed to comprehensively analyze the use of chatbots in language education and its potential for advancing educational development. Through a bibliometric and systematic approach, the study identified influential authors, references, organizations, and countries in the field of chatbot application in language education using VOSviewer. A total of 26 peer-reviewed publications were selected for a systematic review. The findings of the study indicated that chatbots have a positive impact on language learning, although they are limited in terms of facilitating listening and writing practice. The study extended the Human-Organization-Technology (HOT) fit framework for chatbots' use for language education and discussed the factors that frustrate learners' use of chatbots for language education from human, organization, and technology dimensions. Furthermore, the author further discussed the suggestions for chatbots' better application for language education based on the three dimensions.
C1 [Wu, Tiantian; Yu, Zhonggen] Beijing Language & Cultural Univ, Beijing, Peoples R China.
C3 Beijing Language & Culture University
RP Wu, TT (corresponding author), Beijing Language & Cultural Univ, Beijing, Peoples R China.
FU Fundamental Research Funds for the Central Universities; Research Funds
   of Beijing Language and Culture University [23YCX021]
FX Funding This work is supported by the Fundamental Research Funds for the
   Central Universities, and the Research Funds of Beijing Language and
   Culture University, grant number 23YCX021.
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NR 70
TC 0
Z9 0
U1 0
U2 0
PU Open Access Publishing Assoc
PI Launceston
PA 28a Brisbane St, Launceston, Tasmania, AUSTRALIA
SN 1449-9789
J9 J UNIV TEACH LEARN P
JI J. Univ. Teach. Learn. Pract.
PY 2024
VL 21
IS 6
AR 54
PG 25
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA D5J4K
UT WOS:001296541300001
DA 2024-09-05
ER
PT J
AU Archambault, PM
   Thanh, J
   Blouin, D
   Gagnon, S
   Poitras, J
   Fountain, RM
   Fleet, R
   Bilodeau, A
   de Belt, THV
   Légaré, F
AF Archambault, Patrick M.
   Thanh, Jasmine
   Blouin, Danielle
   Gagnon, Susie
   Poitras, Julien
   Fountain, Renee-Marie
   Fleet, Richard
   Bilodeau, Andrea
   de Belt, Tom H. van
   Legare, France
TI Emergency medicine residents' beliefs about contributing to an online
   collaborative slideshow
SO CANADIAN JOURNAL OF EMERGENCY MEDICINE
LA English
DT Article
DE online learning; collaborative writing applications; medical education;
   Theory of Planned Behaviour; free and open access medical education (#
   FOAMed); knowledge translation; evidence-based medicine
ID HEALTH-CARE PROFESSIONALS; WEB 2.0 TOOLS; PLANNED BEHAVIOR;
   CLINICAL-RESEARCH; NET GENERATION; SOCIAL MEDIA; EDUCATION; WIKIS;
   UNDERGRADUATE; TECHNOLOGIES
AB Objective: Collaborative writing applications (CWAs), such as the Google Docs (TM) platform, can improve skill acquisition, knowledge retention, and collaboration in medical education. Using CWAs to support the training of residents offers many advantages, but stimulating them to contribute remains challenging. The purpose of this study was to identify emergency medicine (EM) residents' beliefs about their intention to contribute summaries of landmark articles to a Google Docs (TM) slideshow while studying for their Royal College of Physicians and Surgeons of Canada (RCPSC) certification exam.
   Method: Using the Theory of Planned Behavior, the authors interviewed graduating RCPSC EM residents about contributing to a slideshow. Residents were asked about behavioral beliefs (advantages/disadvantages), normative beliefs (positive/ negative referents), and control beliefs (barriers/facilitators). Two reviewers independently performed qualitative content analysis of interview transcripts to identify salient beliefs in relation to the defined behaviors.
   Results: Of 150 eligible EM residents, 25 participated. The main reported advantage of contributing to the online slideshow was learning consolidation (n = 15); the main reported disadvantage was information overload (n = 3). The most frequently reported favorable referents were graduating EM residents writing the certification exam (n = 16). Few participants (n = 3) perceived any negative referents. The most frequently reported facilitator was peerreviewed high-quality scientific information (n = 9); and the most frequently reported barrier was time constraints (n = 22).
   Conclusion: Salient beliefs exist regarding EM residents' intention to contribute content to an online collaborative writing project using a Google Docs (TM) slideshow. Overall, participants perceived more advantages than disadvantages to contributing and believed that this initiative would receive wide support. However, participants reported several barriers that need to be addressed to increase contributions. Our intention is for the beliefs identified in this study to contribute to the design of a theory-based questionnaire to explore determinants of residents' intentions to contribute to an online collaborative writing project. This will help develop implementation strategies for increasing contributions to other CWAs in medical education.
C1 [Archambault, Patrick M.; Fleet, Richard; Legare, France] Univ Laval, Dept Family Med & Emergency Med, Quebec City, PQ G1K 7P4, Canada.
   [Archambault, Patrick M.; Gagnon, Susie; Fleet, Richard] CHAU Levis, CSSS Alphonse Desjardins, Levis, ON G6V 3Z1, Canada.
   [Thanh, Jasmine; Poitras, Julien] Univ Laval, Fac Med, Quebec City, PQ G1K 7P4, Canada.
   [Blouin, Danielle] Queens Univ, Dept Emergency Med, Kingston, ON, Canada.
   [Fountain, Renee-Marie] Univ Laval, Fac Sci Educ, Ste Foy, PQ G1K 7P4, Canada.
   [Bilodeau, Andrea] Minist Sante & Serv Sociaux, Quebec City, PQ, Canada.
   [de Belt, Tom H. van] Radboud Univ Nijmegen, Med Ctr, Radboud Reshape & Innovat Ctr, NL-6525 ED Nijmegen, Netherlands.
   [Legare, France] Univ Laval, Canada Res Chair Implementat Shared Decis Making, Ste Foy, PQ G1K 7P4, Canada.
   [Legare, France] Univ Quebec, Ctr Hosp, Quebec City, PQ, Canada.
C3 Laval University; Laval University; Queens University - Canada; Laval
   University; Radboud University Nijmegen; Laval University; Laval
   University; University of Quebec
RP Archambault, PM (corresponding author), CHAU Levis, CSSS Alphonse Desjardins, 143 Rue Wolfe, Levis, ON G6V 3Z1, Canada.
EM patrick.m.archambault@gmail.com
RI Blouin, Danielle/AAF-9083-2019; van de Belt, Tom H/AAN-4345-2020
OI van de Belt, Tom H/0000-0002-5401-8973; Legare,
   France/0000-0002-2296-6696; Archambault, Patrick M/0000-0002-5090-6439
FU Gilles Cormier Fund (Fonds Gilles Cormier) [FO099395]; Faculty of
   Medicine at Universite Laval; Fonds de Recherche du Quebec - Sante
   [FQ102051]
FX The principal author (PA) has received honoraria for presentations at
   the National Emergency Medicine Review course at Queen's University. All
   other authors declare that they have no competing interests. None of the
   authors have a financial interest in the free online collaborative tool
   discussed, and no patents are pending for this tool. This study was
   supported by a grant from the Gilles Cormier Fund (Fonds Gilles Cormier
   reference number: FO099395). This fund provided by the Faculty of
   Medicine at Universite Laval supports research projects in medical
   education. The funding organization did not influence the design of the
   study or content of the manuscript. Patrick Archambault holds a career
   scientist award from the Fonds de Recherche du Quebec - Sante (reference
   number: FQ102051). France Legare holds a Canada Research Chair in
   Implementation of Shared Decision Making in Primary Care.
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NR 60
TC 9
Z9 11
U1 1
U2 14
PU CAMBRIDGE UNIV PRESS
PI NEW YORK
PA 32 AVENUE OF THE AMERICAS, NEW YORK, NY 10013-2473 USA
SN 1481-8035
EI 1481-8043
J9 CAN J EMERG MED
JI Can. J. Emerg. Med.
PD JUL
PY 2015
VL 17
IS 4
BP 374
EP 386
DI 10.1017/cem.2014.49
PG 13
WC Emergency Medicine
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Emergency Medicine
GA CL9XM
UT WOS:000357332400005
PM 26134054
OA Bronze
DA 2024-09-05
ER
PT J
AU Wang, YF
   Fan, LP
   Wu, L
AF Wang, Yuefen
   Fan, Lipeng
   Wu, Lei
TI A validation test of the Uzzi et al. novelty measure of innovation and
   applications to collaboration patterns between institutions
SO SCIENTOMETRICS
LA English
DT Article
DE Novelty; Atypical combination; Institutional collaborative pattern;
   Artificial intelligence
ID COMBINATIONS; IMPACT
AB Exploring a robust and universal appeal bibliometric indicator for assessing creativity is essential but challenging. The novelty measure of innovation proposed by Uzzi et al. (NoveltyU) has sparked considerable interest and debate. Thus, further validation and understanding of its portfolio form of novelty and scope of application are necessary. This paper delves into the calculation and application of the NoveltyU method to shed light on its effectiveness and scope. Analysis of the calculation process reveals that journal pairs with higher novelty often span independent fundamental areas, while those with lower novelty tend to focus on similar and applied fields. Utilizing collaboration patterns between institutions, as discussed in our prior study (Fan et al., Scientometrics 125:1179-1196, 2020), offers insight into the method's performance in real-world contexts. Results consistently show higher mean NoveltyU values in MM pattern over time, affirming the method's validity. Categorizing papers into high conventional, low conventional, low novel, and high novel categories unveils higher overlap degree of terms among different patterns in high novel papers. Moreover, leading terms in MM pattern exhibit specific information, while delay terms tend to be more general, and simultaneous terms are even more so. These findings offer valuable insights into identifying hot and frontier topics, bolstering the credibility and application potential of the NoveltyU method, aligning with the broader objective of establishing valid measures of innovativeness in research.
C1 [Wang, Yuefen; Fan, Lipeng] Tianjin Normal Univ, Tianjin, Peoples R China.
   [Wu, Lei] Shandong Normal Univ, Jinan, Shandong, Peoples R China.
C3 Tianjin Normal University; Shandong Normal University
RP Fan, LP (corresponding author), Tianjin Normal Univ, Tianjin, Peoples R China.
EM yuefen163@163.com; funnypower@126.com; ccnustone@yeah.net
OI , Lipeng/0000-0003-4884-511X
FU National Social Science of China [16ZDA224]; National Natural Science
   Foundation of China [62307026]
FX This study is supported by the National Social Science of China
   (16ZDA224) and National Natural Science Foundation of China (62307026).
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NR 33
TC 0
Z9 0
U1 5
U2 5
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD JUL
PY 2024
VL 129
IS 7
BP 4379
EP 4394
DI 10.1007/s11192-024-05071-7
EA JUN 2024
PG 16
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA ZU5Q7
UT WOS:001258044800003
DA 2024-09-05
ER
PT C
AU Bernadt, J
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AF Bernadt, J
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BE Kantardzic, M
   Nasraoui, O
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TI Authoritative citation KNN learning in multiple-instance problems
SO PROCEEDINGS OF THE 2004 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND
   APPLICATIONS (ICMLA'04)
LA English
DT Proceedings Paper
CT 3rd International Conference on Machine Learning and Applications
CY DEC 16-18, 2004
CL Louisville, KY
AB In this paper, we propose an authoritative citation K-nearest neighbor (ACKNN) algorithm for learning and classification in multiple-instance problems. We devise an authority measure for each instance or each bag of instances. This authority measure records how well an instance or a bag of instances has contributed to a correct classification, thus documenting how well an instance or a bag has been cited as a nearest neighbor. Based on our experiments with the Musk1 and Musk2 datasets, by learning the authority measures, the ACKNN algorithm outperforms Most other algorithms in Musk1 classification accuracy, but only performs reasonably well in Musk2 classification accuracy.
C1 Univ Nebraska, Dept Comp Sci & Engn, Lincoln, NE 68588 USA.
C3 University of Nebraska System; University of Nebraska Lincoln
RP Bernadt, J (corresponding author), Univ Nebraska, Dept Comp Sci & Engn, Lincoln, NE 68588 USA.
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NR 17
TC 0
Z9 0
U1 0
U2 0
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 0-7803-8823-2
PY 2004
BP 410
EP 417
PG 8
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BBR87
UT WOS:000227493000058
DA 2024-09-05
ER
PT J
AU Lei, Y
   Qiu, XD
AF Lei, Yi
   Qiu, Xiaodong
TI Research on the Evaluation of Cross-Border E-Commerce Overseas Strategic
   Climate Based on Decision Tree and Adaptive Boosting Classification
   Models
SO FRONTIERS IN PSYCHOLOGY
LA English
DT Article
DE cross-border e-commerce; strategic climate; "Belt and Road" countries;
   machine learning; Decision Tree; Adaptive Boosting
ID CHINA BELT; LOGISTICS
AB At present, China's cross-border e-commerce has ushered in a golden period of development. When developing cross-border e-commerce, enterprises should first assess the market climate of the target country and reasonably select the target country. Based on the PESTEL theory, an evaluation index system is established for China's cross-border e-commerce overseas strategic climate. Taking "One Belt, One Road" as the opportunity and background, the overseas strategic climate of cross-border e-commerce in 62 countries along the "One Belt, One Road" is selected as the research object, and the Decision Tree and Adaptive Boosting classification methods in machine learning are applied to train and predict the established index system. Finally an overall picture of the overseas strategic climate of the 62 countries is obtained. The results are compared and analysed in depth to identify the most suitable countries for cross-border e-merchants and to provide reference for cross-border e-merchants investors.
C1 [Lei, Yi; Qiu, Xiaodong] Beijing Jiaotong Univ, Sch Econ & Management, Beijing, Peoples R China.
C3 Beijing Jiaotong University
RP Lei, Y; Qiu, XD (corresponding author), Beijing Jiaotong Univ, Sch Econ & Management, Beijing, Peoples R China.
EM 18113082@bjtu.edu.cn; xdqiu@bjtu.edu.cn
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NR 52
TC 3
Z9 3
U1 3
U2 52
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 1664-1078
J9 FRONT PSYCHOL
JI Front. Psychol.
PD DEC 23
PY 2021
VL 12
AR 803989
DI 10.3389/fpsyg.2021.803989
PG 10
WC Psychology, Multidisciplinary
WE Social Science Citation Index (SSCI)
SC Psychology
GA YK5EU
UT WOS:000745236600001
PM 35002896
OA Green Published, gold
DA 2024-09-05
ER
PT J
AU Li, SY
   Yu, TZ
   Cao, X
   Pei, Z
   Yi, WC
   Chen, Y
   Lv, RF
AF Li, Shiyun
   Yu, Tianzong
   Cao, Xu
   Pei, Zhi
   Yi, Wenchao
   Chen, Yong
   Lv, Ruifeng
TI Machine learning-based scheduling: a bibliometric perspective
SO IET COLLABORATIVE INTELLIGENT MANUFACTURING
LA English
DT Article
AB In recent years, the rapid development of artificial intelligence and data science has given rise to the study of data driven algorithms in highly volatile systems. The scheduling of complex shop floor resources falls into such a category, which is often non-linear in nature, time varying, multi-objective, and subject to interruptions. Ergo, the machine learning-based scheduling, has become a research hotspot and attracted the attention of many scholars. In the literature, the research methods employed in solving scheduling problems are based on various perspectives, such as mathematical programming, combinatorial optimization, and heuristic rules. However, due to the inherent complexity of the problem, many issues remain to be addressed. In particular, with the availability of production data, the progress of computing power, and the breakthrough in intelligent algorithms, a novel branch of data driven algorithms present great potential, for example, the deep learning and reinforcement learning-based algorithms. To reveal the value of machine learning-based scheduling methods, bibliometric analysis was conducted to analyse the relevant articles and documents from the year 1980 to 2019. Finally, the future research trend in the domain of machine learning-based scheduling is considered and tips are provided for researchers as well as practitioners to find leading scientists for collaborations.
C1 [Li, Shiyun; Yu, Tianzong; Cao, Xu; Pei, Zhi; Yi, Wenchao; Chen, Yong; Lv, Ruifeng] Zhejiang Univ Technol, Coll Mech Engn, Hangzhou, Peoples R China.
C3 Zhejiang University of Technology
RP Pei, Z (corresponding author), Zhejiang Univ Technol, Coll Mech Engn, Hangzhou, Peoples R China.
EM peizhi@zjut.edu.cn
RI Pei, Zhi/AFO-6324-2022
OI Pei, Zhi/0000-0001-6808-1490
FU Natural Science Foundation of China [71871203, 52005447, 51305400,
   L1924063]; Zhejiang Provincial Natural Science Foundation of China
   [LY18G010017, LY18G010020]
FX Natural Science Foundation of China, Grant/Award Number: 71871203,
   52005447, 51305400, L1924063; Zhejiang Provincial Natural Science
   Foundation of China, Grant/Award Number: LY18G010017, LY18G010020
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NR 82
TC 7
Z9 7
U1 7
U2 10
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
EI 2516-8398
J9 IET COLL INTEL MANUF
JI IET Collab. Intell. Manufact.
PD JUN
PY 2021
VL 3
IS 2
BP 131
EP 146
DI 10.1049/cim2.12004
PG 16
WC Engineering, Industrial; Engineering, Manufacturing
WE Emerging Sources Citation Index (ESCI)
SC Engineering
GA VM0RA
UT WOS:000937711000004
OA gold
DA 2024-09-05
ER
PT J
AU Chen, XL
   Zou, D
   Xie, HR
AF Chen, Xieling
   Zou, Di
   Xie, Haoran
TI Fifty years of <i>British Journal of Educational Technology</i>: A topic
   modeling based bibliometric perspective
SO BRITISH JOURNAL OF EDUCATIONAL TECHNOLOGY
LA English
DT Article
ID LEARNING-EXPERIENCES; DECADES
AB The British Journal of Educational Technology (BJET) has been active in the field of educational technology since 1970. To celebrate its 50th anniversary and to demonstrate a comprehensive overview of the field, we conducted a bibliometric analysis of the 3710 publications in this journal from 1971 to 2018 as indexed in the Web of Science with full bibliographic information. This study aimed to (1) identify the publication and citation trends, (2) explore the distribution of paper types, (3) recognize the most relevant countries/regions, affiliations and authors, and (4) reveal relevant thematic features by analyzing publication abstracts and titles with the use of word cloud analysis and topic modeling analysis. The results highlighted several research hotspots and emerging topics such as Technology-enhanced classroom pedagogy, Blended learning, Online social communities, Mobile assisted language learning, Game-based learning and Socialized e-learning.
C1 [Chen, Xieling; Zou, Di] Educ Univ Hong Kong, Hong Kong, Peoples R China.
   [Xie, Haoran] Lingnan Univ, Hong Kong, Peoples R China.
C3 Education University of Hong Kong (EdUHK); Lingnan University
RP Zou, D (corresponding author), Educ Univ Hong Kong, Dept English Language Educ, Hong Kong, Peoples R China.
EM dizoudaisy@gmail.com
RI Xie, Haoran/AAW-8845-2020; Xie, Haoran/AFS-3515-2022
OI Xie, Haoran/0000-0003-0965-3617; ZOU, Di/0000-0001-8435-9739; PV,
   THAYYIB/0000-0001-8929-0398
FU Standing Committee on Language Education and Research
   [EDB(LE)/PR/EL/175/2]; Education Bureau of the Hong Kong Special
   Administrative Region; Interdisciplinary Research Scheme of the Dean's
   Research Fund 2018-19 [FLASS/DRF/IDS-3]; Departmental Collaborative
   Research Fund 2019 of The Education University of Hong Kong
   [MIT/DCRF-R2/18-19];  [RG93/2018-2019R];  [RG 1/2019-2020R]
FX This research received grants from the Standing Committee on Language
   Education and Research (EDB(LE)/P&R/EL/175/2), the Education Bureau of
   the Hong Kong Special Administrative Region, the Internal Research Grant
   (RG93/2018-2019R), the Internal Research Fund (RG 1/2019-2020R),
   Interdisciplinary Research Scheme of the Dean's Research Fund 2018-19
   (FLASS/DRF/IDS-3) and Departmental Collaborative Research Fund 2019
   (MIT/DCRF-R2/18-19) of The Education University of Hong Kong.
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NR 28
TC 91
Z9 94
U1 24
U2 121
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0007-1013
EI 1467-8535
J9 BRIT J EDUC TECHNOL
JI Br. J. Educ. Technol.
PD MAY
PY 2020
VL 51
IS 3
SI SI
BP 692
EP 708
DI 10.1111/bjet.12907
EA FEB 2020
PG 17
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA LI2BT
UT WOS:000511218300001
DA 2024-09-05
ER
PT C
AU Meng, QX
   Kennedy, PJ
AF Meng, Qinxue
   Kennedy, Paul J.
GP IEEE
TI Using field of research codes to discover research groups from
   co-authorship networks
SO 2012 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS
   ANALYSIS AND MINING (ASONAM)
LA English
DT Proceedings Paper
CT IEEE/ACM International Conference on Advances in Social Networks
   Analysis and Mining
CY AUG 26-29, 2012
CL Kadir Has Univ, Istanbul, TURKEY
HO Kadir Has Univ
DE academic networks; academic collaboration; co-authorship; academic
   network; spectral clustering
AB Nowadays, academic collaboration has become more prevalent and crucial than ever before and many studies of academic collaboration analysis are implemented based on co-authorship networks. This paper aims to build a novel co-authorship network by importing field of research codes based on Newman's model, and then analyze and extract research groups via spectral clustering. In order to explain the effectiveness of this revised network, we take the academic collaboration at the University of Technology, Sydney (UTS) as an example. The result of this study advances methods for selecting the most prolific research groups and individuals in research institutions, and provides scientific evidence for policymakers to manage laboratories and research groups more efficiently in the future.
C1 [Meng, Qinxue; Kennedy, Paul J.] Univ Technol Sydney, Fac Engn & Informat Technol, Ctr Quantum Computat & Intelligent Syst, Sydney, NSW 2007, Australia.
C3 University of Technology Sydney
RP Meng, QX (corresponding author), Univ Technol Sydney, Fac Engn & Informat Technol, Ctr Quantum Computat & Intelligent Syst, Sydney, NSW 2007, Australia.
EM Qinxue.Meng@student.uts.edu.au; Paul.Kennedy@uts.edu.au
RI Kennedy, Paul/GPC-6789-2022
OI Kennedy, Paul/0000-0001-7837-3171
CR Abbasi A, 2011, J INFORMETR, V5, P594, DOI 10.1016/j.joi.2011.05.007
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NR 15
TC 2
Z9 3
U1 1
U2 2
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-0-7695-4799-2
PY 2012
BP 289
EP 293
PG 5
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BFL86
UT WOS:000320443500042
DA 2024-09-05
ER
PT J
AU Sokil, JP
   Osorio, L
AF Pablo Sokil, Juan
   Osorio, Laura
TI Scientific p roduction i n the fi eld of gender studies: analysis of se
   lected Web of Science journals (2008-2018)
SO REVISTA ESPANOLA DE DOCUMENTACION CIENTIFICA
LA English
DT Article
DE gender studies; gender gap; bibliometrics; topic modeling; sex
   prediction
AB The following research analyzes the scientific production of gender studies worldwide indexed in the Web of Science (WOS) between 2008 and 2018. The objectives are to analyze the dynamics of participation of women and men within this area and investigate what topics they investigate (separately and together) using sex classification and topic modeling algorithms. The results show that gender studies are one of the most feminized research areas, with the greatest gender gap and that there were no changes throughout the selected period. The identified topics segment the preferences between the authors: female authors specialize in feminism, politics, violence, male authors have a distributed production and mixed authorship specializes in medicine/ health and statistics/ methodology.
C1 [Pablo Sokil, Juan; Osorio, Laura] OCTS OEI, Org Estados Iberoamer, Observ Iberoamer Ciencia Tecnol & Soc, Madrid, Spain.
RP Sokil, JP (corresponding author), OCTS OEI, Org Estados Iberoamer, Observ Iberoamer Ciencia Tecnol & Soc, Madrid, Spain.
EM juanpablosokil@gmail.com; losorio.oei@gmail.com
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NR 26
TC 4
Z9 4
U1 0
U2 7
PU CONSEJO SUPERIOR INVESTIGACIONES CIENTIFICAS-CSIC
PI MADRID
PA VITRUVIO 8, 28006 MADRID, SPAIN
SN 0210-0614
EI 1988-4621
J9 REV ESP DOC CIENT
JI Rev. Esp. Doc. Cient.
PD JAN-MAR
PY 2022
VL 45
IS 1
AR e320
DI 10.3989/redc.2022.1.1849
PG 14
WC Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science
GA YR7KU
UT WOS:000750172500003
OA gold
DA 2024-09-05
ER
PT J
AU Zhou, X
   Guo, Y
   Li, FS
   Wang, J
   Wei, HA
   Yu, MM
   Chen, SL
AF Zhou, Xiao
   Guo, Ying
   Li, Fangshun
   Wang, Jin
   Wei, Huanan
   Yu, Miaomiao
   Chen, Siliang
TI Identifying and Assessing Innovation Pathways for Emerging Technologies:
   A Hybrid Approach Based on Text Mining and Altmetrics
SO IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
LA English
DT Article
DE Altmetrics; gold nanoparticles; sentiment analysis; technological
   innovation pathways
ID SENTIMENT ANALYSIS; EXTRACTION; REVIEWS
AB Accurately identifying core technological innovation pathways (TIPs) and evaluating opportunities in emerging technologies is important. In this article, we present an integrated framework that combines sentiment analysis, subject-action-object (SAO) analysis, machine learning, altmetrics, and expert judgments to extract technical intelligence, chart technological innovation pathways, and analyze which research avenues have the most future promise. For industry stakeholders, our approach provides a comprehensive, time-efficient, and future-oriented evaluation system to support decision making. For researchers, our methodology should inspire new ways of thinking about technological opportunity analysis-particularly, exploring the merits of finding new and advantageous combinations of existing bibliometric and nonbibliometric techniques, rather than reinventing the wheel. For example, this methodology is to integrate bibliometric analysis with sentiment analysis to gauge the attitudes of domain experts toward a topic's potential value. We combine SAO analysis and machine learning to identify relationships between topics from tremendous short texts. Beyond traditional techniques, we have also drawn on altmetrics to further validate the findings our of analysis. A case study on gold nanoparticles demonstrates the merits of our framework, revealing anti-cancer therapies and dye-sensitized solar cells (DSSCs) as the two applications with most future potential and societal impact in this field.
C1 [Zhou, Xiao; Chen, Siliang] Xidian Univ, Sch Econ & Management, Xian 710000, Peoples R China.
   [Guo, Ying] China Univ Polit Sci & Law, Business Sch, Beijing 100000, Peoples R China.
   [Li, Fangshun] Univ Elect Sci & Technol China, Sch Econ & Management, Chengdu 610000, Peoples R China.
   [Wang, Jin; Wei, Huanan; Yu, Miaomiao] Beijing Inst Thchnol, Sch Management & Econ, Beijing 100000, Peoples R China.
C3 Xidian University; China University of Political Science & Law;
   University of Electronic Science & Technology of China
RP Guo, Y (corresponding author), China Univ Polit Sci & Law, Business Sch, Beijing 100000, Peoples R China.
EM belinda1214@126.com; guoying_cup1@126.com; lifangshunlfs@163.com;
   wangjin_bit@163.com; hnwei97@163.com; 18165268237@163.com;
   siliang_chen547@163.com
FU Chinese National Science Foundation for Young Scholars Award [71704139];
   Chinese National Science Foundation Award [71874013]; National Science
   Foundation of Shaanxi Province Award [2019JQ-661]; Basic Research
   Foundation of Xidian University Award [RW180171, JB190603]; Program for
   Qian Duansheng Excellent Researcher in China University of Political
   Science and Law
FX This work was supported in part by the Chinese National Science
   Foundation for Young Scholars Award 71704139, in part by the Chinese
   National Science Foundation Award 71874013, in part by the Program for
   Qian Duansheng Excellent Researcher in China University of Political
   Science and Law, in part by the National Science Foundation of Shaanxi
   Province Award 2019JQ-661, and in part by the Basic Research Foundation
   of Xidian UniversityAward RW180171 and JB190603.
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TC 6
Z9 7
U1 8
U2 134
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0018-9391
EI 1558-0040
J9 IEEE T ENG MANAGE
JI IEEE Trans. Eng. Manage.
PD OCT
PY 2021
VL 68
IS 5
BP 1360
EP 1371
DI 10.1109/TEM.2020.2994049
PG 12
WC Business; Engineering, Industrial; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Engineering
GA TK4EZ
UT WOS:000674114900013
OA hybrid
DA 2024-09-05
ER
PT C
AU Jung, SW
   Datta, R
   Segev, A
AF Jung, Sukhwan
   Datta, Rituparna
   Segev, Aviv
BE Wu, XT
   Jermaine, C
   Xiong, L
   Hu, XH
   Kotevska, O
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   Xu, WJ
   Aluru, S
   Zhai, CX
   Al-Masri, E
   Chen, ZY
   Saltz, J
TI Identification and Prediction of Emerging Topics through Their
   Relationships to Existing Topics
SO 2020 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA)
SE IEEE International Conference on Big Data
LA English
DT Proceedings Paper
CT 8th IEEE International Conference on Big Data (Big Data)
CY DEC 10-13, 2020
CL ELECTR NETWORK
DE Topic Evolution; Topic Prediction; Network-based Topic Modeling;
   Scientometric
ID TRENDS; EVOLUTION
AB Understanding the current research topics and their histories allow researchers to focus their capabilities on the current research trends. The field of topic evolution helps the understanding by automatically model and detect the set of shared research fields in the academic papers as topics. The authors propose a novel topic evolution method for identifying and predicting the emergence of new topics under the assumption that neighborhoods of new topics in the future have distinguishable structural features. Eight journals were selected from the Microsoft Academic Graph dataset, each representing topics networks with varying size, history, and research domains. Both retrospective classification and prospective prediction showed promising performance with classifications above 0.89 for six journals and coefficients of determination exceeding 0.95 for five journals. The result showed both the retrospective identification and the prospective prediction can be done, validating the assumption that topic evolution events can be predicted with a network-based approach.
C1 [Jung, Sukhwan; Datta, Rituparna; Segev, Aviv] Univ S Alabama, Dept Comp Sci, Mobile, AL 36688 USA.
C3 University of South Alabama
RP Jung, SW (corresponding author), Univ S Alabama, Dept Comp Sci, Mobile, AL 36688 USA.
EM shjung@southalabama.edu; rdatta@southalabama.edu; segev@southalabama.edu
RI Segev, Aviv/C-2060-2011; Jung, Suk hwan/HIK-1039-2022
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NR 38
TC 7
Z9 7
U1 2
U2 24
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2639-1589
BN 978-1-7281-6251-5
J9 IEEE INT CONF BIG DA
PY 2020
BP 5078
EP 5087
DI 10.1109/BigData50022.2020.9378277
PG 10
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BR6NZ
UT WOS:000662554705020
DA 2024-09-05
ER
PT J
AU Li, DC
   Okamoto, J
   Liu, HF
   Leischow, S
AF Li, Dingcheng
   Okamoto, Janet
   Liu, Hongfang
   Leischow, Scott
TI A bibliometric analysis on tobacco regulation investigators
SO BIODATA MINING
LA English
DT Article
DE Author topic modeling; Bibliometric analysis; Tobacco regulation
   science; FDA; Principle investigators
ID SCIENCE
AB Background: To facilitate the implementation of the Family Smoking Prevention and Tobacco Control Act of 2009, the Federal Drug Agency (FDA) Center for Tobacco Products (CTP) has identified research priorities under the umbrella of tobacco regulatory science (TRS). As a newly integrated field, the current boundaries and landscape of TRS research are in need of definition. In this work, we conducted a bibliometric study of TRS research by applying author topic modeling (ATM) on MEDLINE citations published by currently-funded TRS principle investigators (PIs).
   Results: We compared topics generated with ATM on dataset collected with TRS PIs and topics generated with ATM on dataset collected with a TRS keyword list. It is found that all those topics show a good alignment with FDA's funding protocols. More interestingly, we can see clear interactive relationships among PIs and between PIs and topics. Based on those interactions, we can discover how diverse each PI is, how productive they are, which topics are more popular and what main components each topic involves. Temporal trend analysis of key words shows the significant evaluation in four prime TRS areas.
   Conclusions: The results show that ATM can efficiently group articles into discriminative categories without any supervision. This indicates that we may incorporate ATM into author identification systems to infer the identity of an author of articles using topics generated by the model. It can also be useful to grantees and funding administrators in suggesting potential collaborators or identifying those that share common research interests for data harmonization or other purposes. The incorporation of temporal analysis can be employed to assess the change over time in TRS as new projects are funded and the extent to which new research reflects the funding priorities of the FDA.
C1 [Li, Dingcheng; Liu, Hongfang] Mayo Clin, Dept Biomed Stat & Informat, Rochester, MN 55905 USA.
   [Okamoto, Janet; Leischow, Scott] Mayo Clin, Dept Hematol Oncol, Scottsdale, AZ USA.
C3 Mayo Clinic; Mayo Clinic; Mayo Clinic Phoenix
RP Li, DC (corresponding author), Mayo Clin, Dept Biomed Stat & Informat, Rochester, MN 55905 USA.
EM Li.Dingcheng@mayo.edu
RI Liu, Hongfang/ISU-9369-2023
OI Liu, Hongfang/0000-0002-4352-2533
FU NLM NIH HHS [R01 LM009959] Funding Source: Medline
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NR 23
TC 7
Z9 7
U1 0
U2 14
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 1756-0381
J9 BIODATA MIN
JI BioData Min.
PD MAR 21
PY 2015
VL 8
AR 11
DI 10.1186/s13040-015-0043-7
PG 20
WC Mathematical & Computational Biology
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Mathematical & Computational Biology
GA CI1NA
UT WOS:000354509700001
PM 25984237
OA Green Published, gold
DA 2024-09-05
ER
PT J
AU DeStefano, I
   Oey, LA
   Brockbank, E
   Vul, E
AF DeStefano, Isabella
   Oey, Lauren A.
   Brockbank, Erik
   Vul, Edward
TI Integration by Parts: Collaboration and Topic Structure in the CogSci
   Community
SO TOPICS IN COGNITIVE SCIENCE
LA English
DT Article
DE Co&#8208; authorship networks; Topic modeling; Interdisciplinarity;
   Multidisciplinarity; Scientometrics
ID COGNITIVE SCIENCE; NETWORK; INFORMATION; GROWTH
AB Is cognitive science interdisciplinary or multidisciplinary? We contribute to this debate by examining the authorship structure and topic similarity of contributions to the Cognitive Science Society from 2000 to 2019. Our analysis focuses on graph theoretic features of the co-authorship network-edge density, transitivity, and maximum subgraph size-as well as clustering within the space of scientific topics. We also combine structural and semantic information with an analysis of how authors choose their collaborators based on their interests and prior collaborations. We compare findings from CogSci to abstracts from the Vision Science Society over the same time frame and validate our approach by predicting new collaborations in the 2020 CogSci proceedings. Our results suggest that collaboration across authors and topics within cognitive science has become increasingly integrated in the last 19 years. More broadly, we argue that a formal quantitative approach which combines structural co-authorship information and semantic topic analysis provides inroads to questions about the level of interdisciplinary collaboration in a scientific community.
C1 [DeStefano, Isabella; Oey, Lauren A.; Brockbank, Erik; Vul, Edward] Univ Calif San Diego, Dept Psychol, 9500 Gilman Dr, La Jolla, CA 92093 USA.
C3 University of California System; University of California San Diego
RP DeStefano, I; Oey, LA (corresponding author), Univ Calif San Diego, Dept Psychol, 9500 Gilman Dr, La Jolla, CA 92093 USA.
EM idestefa@ucsd.edu; loey@ucsd.edu
OI Oey, Lauren/0000-0002-4959-5135; Brockbank, Erik/0000-0001-8702-239X
FU UCSD Research Grant [RG095178]; NSF Graduate Research Fellowship
   [DGE-1650112]
FX We thank Rafael Nunez, Carson Miller Rigoli, Michael Allen, and Richard
   Gao for helpful discussion. We also thank Jamal Williams and Hayden
   Schill for their assistance with an earlier version of the analyses
   presented here. Finally, we thank the CogSci 2020 organizers for
   graciously sharing an early list of the authors published in the 2020
   proceedings. This material is based upon work supported by a UCSD
   Research Grant to I.D., the NSF Graduate Research Fellowship under Grant
   No. DGE-1650112 to L.A.O. and UCSD Research Grant No. RG095178 to E.B.
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NR 30
TC 4
Z9 5
U1 0
U2 5
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1756-8757
EI 1756-8765
J9 TOP COGN SCI
JI Top. Cogn. Sci.
PD APR
PY 2021
VL 13
IS 2
SI SI
BP 399
EP 413
DI 10.1111/tops.12526
EA MAR 2021
PG 15
WC Psychology, Experimental
WE Social Science Citation Index (SSCI)
SC Psychology
GA RU2QQ
UT WOS:000630752600001
PM 33742776
OA Bronze
DA 2024-09-05
ER
PT J
AU Dantu, R
   Dissanayake, I
   Nerur, S
AF Dantu, Ramakrishna
   Dissanayake, Indika
   Nerur, Sridhar
TI Exploratory Analysis of Internet of Things (IoT) in Healthcare: A Topic
   Modelling & Co-citation Approaches
SO INFORMATION SYSTEMS MANAGEMENT
LA English
DT Article
DE Internet of things; healthcare; topic modeling; author co-citation;
   smart health
ID MONITORING-SYSTEM; BIG DATA; SENSOR; INTEGRATION; MEDICINE; CITATION
AB Internet of Things (IoT) have made a significant impact in healthcare domain. The purpose of this study is to unravel key themes latent in the academic literature on IoT applications in healthcare. Using topic modeling and author co-citation techniques, we identified five dominant clusters of research. Our results show that research in healthcare IoT has mainly focused on the technical aspects with little attention to social concerns. Our paper provides directions for future research.
C1 [Dantu, Ramakrishna] Calif State Univ Sacramento, Informat Syst & Business Analyt Dept, Sacramento, CA 95819 USA.
   [Dissanayake, Indika] Univ N Carolina, Dept Informat Syst & Supply Chain Management, Greensboro, NC USA.
   [Nerur, Sridhar] Univ Texas Arlington, Dept Informat Syst & Operat Management, Arlington, TX 76019 USA.
C3 California State University System; California State University
   Sacramento; University of North Carolina; University of North Carolina
   Greensboro; University of Texas System; University of Texas Arlington
RP Dantu, R (corresponding author), Calif State Univ Sacramento, Coll Business Adm, Informat Syst & Business Analyt Dept, 6000 J St, Sacramento, CA 95819 USA.
EM Ramakrishna.dantu@csus.edu
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NR 100
TC 25
Z9 29
U1 0
U2 27
PU TAYLOR & FRANCIS INC
PI PHILADELPHIA
PA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA
SN 1058-0530
EI 1934-8703
J9 INFORM SYST MANAGE
JI Inf. Syst. Manage.
PD JAN 2
PY 2021
VL 38
IS 1
BP 62
EP 78
DI 10.1080/10580530.2020.1746982
EA APR 2020
PG 17
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA PC3BG
UT WOS:000528370200001
DA 2024-09-05
ER
PT J
AU Dobos, I
   Halmai, P
   Sasvári, P
AF Dobos, Imre
   Halmai, Peter
   Sasvari, Peter
TI Compilation of an International Journal List in the HAS IX. Section of
   the Doctoral Committee for Economics and Management
SO PUBLIC FINANCE QUARTERLY-HUNGARY
LA English
DT Article
DE scientometrics; list of journals; linear regression
AB Only some of the Sections of the Hungarian Academy of Sciences (HAS) compile a list of journals. One of these is the IX Section of Economics and Law of the MTA. The section(sic)s doctoral committees evaluate candidates for the title of Doctor of the Academy of Sciences on the basis of eight lists of journals. The lists are generally stable in the sense that they remain unchanged for about five years, but renewal of the lists becomes necessary from time to time. In this publication, we describe the process of renewing the list of journals of the Qualification Committee for Doctoral Candidates in Economics and Management, Section IX of the Academy of Sciences, from the method of compiling the list of journals to the statistical methods used to determine the A, B, C, and D.
C1 [Dobos, Imre; Halmai, Peter] Budapest Univ Technol & Econ, Fac Econ & Social Sci, Budapest, Hungary.
   [Halmai, Peter] Hungarian Acad Sci, Budapest, Hungary.
   [Halmai, Peter; Sasvari, Peter] Univ Publ Serv, Fac Publ Governance & Int Stud, Budapest, Hungary.
   [Sasvari, Peter] Univ Miskolc, Fac Mech Engn & Informat, Miskolc, Hungary.
C3 Budapest University of Technology & Economics; Hungarian Academy of
   Sciences; Ludovika University of Public Service; University of Miskolc
RP Sasvári, P (corresponding author), Univ Publ Serv, Fac Publ Governance & Int Stud, Budapest, Hungary.; Sasvári, P (corresponding author), Univ Miskolc, Fac Mech Engn & Informat, Miskolc, Hungary.
EM dobos.imre@gtk.bme.hu; halmai.peter@gtk.bme.hu; sasvari.peter@uni-nke.hu
RI Dobos, Imre/A-5180-2013; Sasvari, Peter/B-5149-2013
OI Dobos, Imre/0000-0001-6248-2920; Sasvari, Peter/0000-0002-4031-4843
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   Clarivate, J CIT IND
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NR 11
TC 0
Z9 0
U1 0
U2 2
PU CORVINUS UNIV BUDAPEST
PI BUDAPEST
PA VILLANYI UT 29/43, BUDAPEST, H-1118, HUNGARY
SN 0031-496X
EI 2064-8278
J9 PUBLIC FINANC Q-HUNG
JI Public Financ. Q.-Hung.
PY 2023
VL 69
IS 2
BP 67
EP 80
DI 10.35551/PFQ_2023_2_4
PG 14
WC Business, Finance
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA P6FB1
UT WOS:001051602100007
OA Green Accepted, Bronze
DA 2024-09-05
ER
PT J
AU Jung, S
   Segev, A
AF Jung, Sukhwan
   Segev, Aviv
TI Analyzing the generalizability of the network-based topic emergence
   identification method
SO SEMANTIC WEB
LA English
DT Article
DE Topic evolution; topic prediction; network-based topic modeling;
   scientometrics
ID TRENDS
AB Topic evolution helps the understanding of current research topics and their histories by automatically modeling and detecting the set of shared research fields in academic publications as topics. This paper provides a generalized analysis of the topic evolution method for predicting the emergence of new topics, which can operate on any dataset where the topics are defined as the relationships of their neighborhoods in the past by extrapolating to the future topics. Twenty sample topic networks were built with various fields-of-study keywords as seeds, covering domains such as business, materials, diseases, and computer science from the Microsoft Academic Graph dataset. The binary classifier was trained for each topic network using 15 structural features of emerging and existing topics and consistently resulted in accuracy and F1 over 0.91 for all twenty datasets over the periods of 2000 to 2019. Feature selection showed that the models retained most of the performance with only one-third of the tested features. Incremental learning was tested within the same topic over time and between different topics, which resulted in slight performance improvements in both cases. This indicates there is an underlying pattern to the neighbors of new topics common to research domains, likely beyond the sample topics used in the experiment. The result showed that network-based new topic prediction can be applied to various research domains with different research patterns.
C1 [Jung, Sukhwan; Segev, Aviv] Univ S Alabama, Dept Comp Sci, 150 Student Serv Dr, Mobile, AL 36608 USA.
C3 University of South Alabama
RP Jung, S (corresponding author), Univ S Alabama, Dept Comp Sci, 150 Student Serv Dr, Mobile, AL 36608 USA.
EM shjung@southalabama.edu
RI Jung, Suk hwan/HIK-1039-2022; Segev, Aviv/C-2060-2011
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NR 43
TC 6
Z9 6
U1 1
U2 19
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1570-0844
EI 2210-4968
J9 SEMANT WEB
JI Semant. Web
PY 2022
VL 13
IS 3
BP 423
EP 439
DI 10.3233/SW-212951
PG 17
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 0J3YH
UT WOS:000780041500007
OA hybrid
DA 2024-09-05
ER
PT J
AU Li, R
   Wang, XD
   Lei, L
   Wu, CM
AF Li, Rui
   Wang, Xiaodan
   Lei, Lei
   Wu, Chongming
TI Representation learning by hierarchical ELM auto-encoder with double
   random hidden layers
SO IET COMPUTER VISION
LA English
DT Article
DE learning (artificial intelligence); pattern classification; random
   hidden mapping layers; output layer; decoding feature; stacking DELM-AE;
   hierarchical structure; H-DELM model; feature representation; original
   input information; original input data; expressive feature; compact
   feature; deep learning algorithms; extreme learning machine; multilayer
   network architecture lead; extremely fast training speed; expressive
   representation learning method; hierarchical ELM; H-ELM; novel
   architectural unit; double random hidden layers ELM; auto-encoder; novel
   DELM-AE; research developments; relevant multilayer ELM
ID EXTREME; MACHINE; NETWORKS
AB Recent research developments of extreme learning machine (ELM) with multilayer network architecture lead to a promising high performance with extremely fast training speed for representation learning. In this work, the authors are dedicated to develop an efficient and expressive representation learning method with hierarchical ELM, and proposing a novel architectural unit named as double random hidden layers ELM auto-encoder (DELM-AE). The novel DELM-AE consists of one input layer, two random hidden mapping layers for encoding feature, and one output layer for decoding feature. When stacking DELM-AE in the hierarchical structure, they can construct an H-DELM model, where the input of the current AE is the feature representation learned by the previous one, but the output is identical to the original input information and is not the input. Hence, the H-DELM can reproduce the original input data as much as possible to learn more expressive and compact feature. They validate their method on various widely public datasets, and the results demonstrate that H-DELM can bring significant performance improvements in terms of classification accuracy and robustness compared with existing relevant multilayer ELM and other deep learning algorithms at a slight computational cost.
C1 [Li, Rui; Wang, Xiaodan; Lei, Lei] Air Force Engn Univ, Coll Air & Missile Def, Xian 710051, Shanxi, Peoples R China.
   [Wu, Chongming] Xijing Univ, Coll Business, Xian 710123, Shanxi, Peoples R China.
C3 Air Force Engineering University; Xijing University
RP Wang, XD (corresponding author), Air Force Engn Univ, Coll Air & Missile Def, Xian 710051, Shanxi, Peoples R China.
EM afeu_wang@163.com
RI Li, June/JEF-1173-2023; wang, xiao/HZI-9156-2023; Wu,
   Chongming/AAQ-9085-2021
OI Li, Rui/0000-0003-1770-906X
FU National Natural Science Foundation of China [61876189, 61806219,
   61503407, 61703426, 61273275]
FX This work was supported by the National Natural Science Foundation of
   China under Grants nos. 61876189, 61806219, 61503407, 61703426, and
   61273275.
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NR 33
TC 9
Z9 9
U1 0
U2 24
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1751-9632
EI 1751-9640
J9 IET COMPUT VIS
JI IET Comput. Vis.
PD JUN
PY 2019
VL 13
IS 4
BP 411
EP 419
DI 10.1049/iet-cvi.2018.5590
PG 9
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA IG7TO
UT WOS:000474000800007
OA Bronze
DA 2024-09-05
ER
PT J
AU Kawamura, T
   Egami, S
AF Kawamura, Takahiro
   Egami, Shusaku
TI Bilingual Textual Similarity in Scientific Documents
SO IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
LA English
DT Article
DE Artificial intelligence; bibliometrics; semantic web; text mining
ID INFORMATION
AB Y Maps of science visualizing the structure of science help us analyze the current spread of science, technology, and innovation (ST&I). ST&I enterprises can use the maps of science as competitive technical intelligence to anticipate changes, especially those initiated in their immediate vicinity. Research laboratories and universities can understand their environmental changes and use the map for their research management. However, traditional maps based on bibliometrics, such as citation and cocitation, have difficulty in representing recently published papers and ongoing projects that have few or no references; thus, maps based on contents, i.e., text-mining, have been developed in recent years for locating research papers/projects, for example, using word and paragraph vectors. The content-based maps, however, still pose difficulty in comparing documents in different languages. Therefore, aiming to construct a bilingual (English and Japanese) content-based map of science for the analyses of ST&I information resources in different languages, this article proposes a method for creating word and paragraph vectors corresponding to bilingual textual information in the same multidimensional space. In a comparison of 11 methods for generating document vectors, we confirmed that the best method achieved 87% accuracy of the bilingual content matching based on 10 000 IEEE papers. Finally, we published a map of approximately 150 000 funding projects of the National Science Foundation, Japan Society for the Promotion of Science, and Japan Science and Technology agency from 2013 to 2017.
C1 [Kawamura, Takahiro] Natl Agr & Food Res Org, Tokyo 1000013, Japan.
   [Kawamura, Takahiro] Japan Sci & Technol Agcy, Tokyo 1028666, Japan.
   [Egami, Shusaku] Natl Inst Maritime Port & Aviat Technol, Tokyo 1820012, Japan.
C3 National Agriculture & Food Research Organization - Japan; Japan Science
   & Technology Agency (JST)
RP Kawamura, T (corresponding author), Natl Agr & Food Res Org, Tokyo 1000013, Japan.
EM takahiro.kawamura@affrc.go.jp; s-egami@mpat.go.jp
RI Egami, Shusaku/R-6180-2019
OI Egami, Shusaku/0000-0002-3821-6507; Kawamura,
   Takahiro/0000-0002-2765-6232
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NR 43
TC 0
Z9 0
U1 3
U2 21
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0018-9391
EI 1558-0040
J9 IEEE T ENG MANAGE
JI IEEE Trans. Eng. Manage.
PD OCT
PY 2021
VL 68
IS 5
BP 1299
EP 1308
DI 10.1109/TEM.2019.2946886
PG 10
WC Business; Engineering, Industrial; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Engineering
GA TK4EZ
UT WOS:000674114900008
OA hybrid
DA 2024-09-05
ER
PT J
AU Gurcan, F
   Dalveren, GGM
   Cagiltay, NE
   Soylu, A
AF Gurcan, Fatih
   Dalveren, Gonca Gokce Menekse
   Cagiltay, Nergiz Ercil
   Soylu, Ahmet
TI Detecting Latent Topics and Trends in Software Engineering Research
   Since 1980 Using Probabilistic Topic Modeling
SO IEEE ACCESS
LA English
DT Article
DE Market research; Systematics; Software engineering; Software;
   Bibliometrics; Text mining; Licenses; Corpus creation; research trends
   and topics; software engineering; text mining; topic model
ID SYSTEMATIC LITERATURE-REVIEWS; CITED ARTICLES; EMPIRICAL-RESEARCH;
   JOURNALS; SCHOLARS
AB The landscape of software engineering research has changed significantly from one year to the next in line with industrial needs and trends. Therefore, today's research literature on software engineering has a rich and multidisciplinary content that includes a large number of studies; however, not many of them demonstrate a holistic view of the field. From this perspective, this study aimed to reveal a holistic view that reflects topics, trends, and trajectories in software engineering research by analyzing the majority of domain-specific articles published over the last 40 years. This study first presents an objective and systematic method for corpus creation through major publication sources in the field. A corpus was then created using this method, which includes 44 domain-specific conferences and journals and 57,174 articles published between 1980 and 2019. Next, this corpus was analyzed using an automated text-mining methodology based on a probabilistic topic-modeling approach. As a result of this analysis, 24 main topics were found. In addition, topical trends in the field were revealed. Finally, three main developmental stages of the field were identified as: the programming age, the software development age, and the software optimization age.
C1 [Gurcan, Fatih] Karadeniz Tech Univ, Fac Engn, Dept Comp Engn, TR-61080 Trabzon, Turkey.
   [Dalveren, Gonca Gokce Menekse; Cagiltay, Nergiz Ercil] Atilim Univ, Fac Engn, Dept Software Engn, TR-06830 Ankara, Turkey.
   [Soylu, Ahmet] Norwegian Univ Sci & Technol, Dept Comp Sci, N-2815 Gjovik, Norway.
C3 Karadeniz Technical University; Atilim University; Norwegian University
   of Science & Technology (NTNU)
RP Soylu, A (corresponding author), Norwegian Univ Sci & Technol, Dept Comp Sci, N-2815 Gjovik, Norway.
EM ahmet.soylu@ntnu.no
RI Cagiltay, Nergiz/O-3082-2019; GURCAN, Fatih/AAJ-7503-2021; Soylu,
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   Stefano/I-3083-2018
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NR 64
TC 11
Z9 11
U1 3
U2 10
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2022
VL 10
BP 74638
EP 74654
DI 10.1109/ACCESS.2022.3190632
PG 17
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA 3Q9KV
UT WOS:000838542600001
OA Green Submitted, gold
DA 2024-09-05
ER
PT J
AU Yin, ZL
   Wang, HY
   Chen, B
   Zhang, X
   Lin, XG
   Sun, HL
   Li, AJ
   Zhou, CY
AF Yin, Zilong
   Wang, Haoyu
   Chen, Bin
   Zhang, Xin
   Lin, Xiaogang
   Sun, Hangling
   Li, Anji
   Zhou, Chenyu
TI Federated semi-supervised representation augmentation with
   cross-institutional knowledge transfer for healthcare collaboration
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Healthcare collaboration; Vertical federated learning; Knowledge
   transfer; Semi-supervised representation augmentation
ID BIG DATA; SECURE; CHALLENGES; ATTACK
AB In the healthcare field, cross-institutional collaboration can fasten medical research progress. Vertical federated learning (VFL) addresses data heterogeneity across multiple medical institutions while ensuring medical data privacy, thereby enhancing the accuracy of disease diagnoses and treatments. However, traditional VFL only benefits from aligned samples, thereby limiting its applicability due to constrained sample sizes, and a large amount of non-aligned data remains untapped, resulting in wasted data. To exert full leverage on the value of all data obtained from medical institutions, this paper proposes a federated healthcare collaborative framework based on semi-supervised representation augmentation mechanism with cross-institutional knowledge transfer (CrossKT-FRA). Specifically, the developed method comprises three steps. First, the federated representations of shared data (aligned data) among medical institutions are extracted through efficient vertical federated representation learning (FRL) methods. Second, the federated knowledge contained in federated representations and potential labels derived through recurrent learning assist local shared data representations in performing supervised augmented learning. Finally, the federated knowledge is transferred indirectly from the representation augmentation module for shared data to the unsupervised representation augmentation module for local private data (non-aligned data). The experimental results show the effectiveness of the proposed knowledge transfer mechanism, whether applied independently or used to enhance VFL on medical datasets. Our findings contribute to a deeper theoretical understanding of VFL, further facilitating the utilization of high- value medical data. By promoting cross-institutional and cross-disciplinary collaboration in healthcare data sharing, our study enhances the quality efficiency of medical services, thereby accelerating the development of interdisciplinary medical research. Code is available at https://github.com/LieLieLieLieLie/CrossKT-FRA.
C1 [Yin, Zilong; Wang, Haoyu; Chen, Bin] Univ Shanghai Sci & Technol, Sch Mech Engn, Shanghai, Peoples R China.
   [Sun, Hangling] Hengtu Imalligent Technol Shanghai Co Ltd, Shanghai, Peoples R China.
   [Li, Anji] Abbott Labs Shanghai Co Ltd, Shanghai, Peoples R China.
   [Zhang, Xin] Tianjin Univ Technol, Tianjin, Peoples R China.
   [Lin, Xiaogang] North Univ China, Taiyuan, Peoples R China.
   [Zhou, Chenyu] Xinjiang Univ, Sch Comp Sci & Technol, Urumqi, Peoples R China.
   [Chen, Bin] Shanghai Jiao Tong Univ, Sch Mech Engn, Shanghai, Peoples R China.
   [Zhou, Chenyu] Tsinghua Univ, Beijing, Peoples R China.
C3 University of Shanghai for Science & Technology; Tianjin University of
   Technology; North University of China; Xinjiang University; Shanghai
   Jiao Tong University; Tsinghua University
RP Chen, B (corresponding author), Univ Shanghai Sci & Technol, Sch Mech Engn, Shanghai, Peoples R China.
EM zilong_yin@163.com; ambityuki@gmail.com; chenbin1933@163.com;
   kxamanda@163.com; lxg2630782125@163.com; sunnyhl2000@gmail.com;
   anjicake@gmail.com; Zhou_cy@stu.xju.edu.cn
OI Yin, Zilong/0009-0002-7994-2772
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NR 76
TC 0
Z9 0
U1 1
U2 1
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD SEP 27
PY 2024
VL 300
AR 112208
DI 10.1016/j.knosys.2024.112208
EA JUL 2024
PG 21
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZK3H4
UT WOS:001275148500001
DA 2024-09-05
ER
PT J
AU Basilio, MP
   Pereira, V
   de Oliveira, MWCM
AF Basilio, Marcio Pereira
   Pereira, Valdecy
   Oliveira, Max William Coelho Moreira de
TI Knowledge discovery in research on policing strategies: an overview of
   the past fifty years
SO JOURNAL OF MODELLING IN MANAGEMENT
LA English
DT Article
DE Community policing; Bibliometric analysis; Policing strategies;
   Problem-oriented policing; Predictive policing; Latent Dirichlet
   allocation; Focused policing; Police crackdown; Repeat offenders; Hot
   spot policing; Foot patrol; Radio patrol
ID FOOT PATROL; BIBLIOMETRIC ANALYSIS; CRIME REDUCTION; HOT-SPOTS; BIG
   DATA; COMMUNITY; IMPACT; PERCEPTIONS; COCITATION; EVOLUTION
AB Purpose The insecurity generated, today in various parts of the planet, by the various conflicts that arise in the violence in large cities, has motivated the academy to research the solutions and strategies adopted by local governments in the fight against crime. The volume of data generated by several universities over the past 50 years has increased exponentially. Consequently, researchers struggle to process essential data in today's competitive world. The aim of this study is to explore and provide an overview of the studies carried out in the field of action to combat crime in different countries. Design/methodology/approach The Web of Science and Scopus databases were searched for publications from January 1945 to September 3, 2020 on the topic of policing strategies in titles, abstracts and keywords. References were analyzed using the R bibliometrix package, and abstracts were analyzed using latent Dirichlet allocation (LDA) with collapsed Gibbs sampling for topics related to policing and related subjects. Findings As a result of the research, this paper can assert that in the last 50 years, 3,361 authors have produced 2,085 documents on the theme of policing strategy and related subjects in 58 countries. Scientific production in this area grows at a rate of 5.10 per year. The USA is the leading country in publications with 42.58%, followed by the UK with 8.39% and Canada with 4.07%. As for journals, the highlight is Policing, Policing and Society and Police Quarterly, which account for more than 15.44% of all indexed literature. Regarding the authors, the highlight is Weisburd and Braga. As a result, the LDA grouped the latent words in the articles analyzed by themes studied and presented the list of articles by themes. The thematic map identifies the following themes as basic research subjects: community policing, problem-oriented policing, predictive policing, fear of crime and social control. Practical implications As the main implication between the combination of the bibliometric analysis method with the probabilistic topic modelling, is the emergence of a primordial step in the systematic literature review process, as this method allows to explore and group a large volume of data. Another practical implication that is intended is to provide the beginning researcher or any other reader with a panoramic view of the main authors who study the themes that impact police activity in any city in the world, which are the countries and reference centers of the study on the subject and, finally, the evolution of the main themes researched in the police area. Originality/value The value of these studies is summarized in the presentation of an overview on the theme in the last 50 years, offering the opportunity for other researchers to use this research as a starting point for other analyses.
C1 [Basilio, Marcio Pereira; Pereira, Valdecy] Fed Fluminense Univ, Prod Engn Dept, Niteroi, RJ, Brazil.
   [Basilio, Marcio Pereira] Mil Police State Rio de Janeiro, Rio De Janeiro, Brazil.
   [Oliveira, Max William Coelho Moreira de] Secretariat State Mil Police, Coordinat Special Affairs, Rio De Janeiro, Brazil.
C3 Universidade Federal Fluminense
RP Basilio, MP (corresponding author), Fed Fluminense Univ, Prod Engn Dept, Niteroi, RJ, Brazil.; Basilio, MP (corresponding author), Mil Police State Rio de Janeiro, Rio De Janeiro, Brazil.
EM marciopbasilio@gmail.com; valdecy.pereira@gmail.com;
   mwcoliveira@yahoo.com.br
RI Basilio, Marcio Pereira/L-4363-2016; Pereira, Valdecy/I-7493-2017
OI Basilio, Marcio Pereira/0000-0002-9453-741X; Pereira,
   Valdecy/0000-0003-0599-8888
FU Federal Fluminense University; Military Police of the State of Rio de
   Janeiro; coordination for the improvement of Higher Education Personnel
   - Brazil (CAPES)
FX The authors thank the Federal Fluminense University and the Military
   Police of the State of Rio de Janeiro for the unrestricted support
   received for the research. In addition to the coordination for the
   improvement of Higher Education Personnel - Brazil (CAPES), for partial
   funding of the research. Finally, the authors would like to thank the
   contributions of the reviewers and editors that resulted in this
   publication.
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NR 88
TC 14
Z9 14
U1 4
U2 25
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 1746-5664
EI 1746-5672
J9 J MODEL MANAG
JI J. Model. Manag.
PD NOV 29
PY 2022
VL 17
IS 4
BP 1372
EP 1409
DI 10.1108/JM2-10-2020-0268
EA AUG 2021
PG 38
WC Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA 6R1FP
UT WOS:000681522800001
DA 2024-09-05
ER
PT J
AU Jing, YH
   Zhao, LY
   Zhu, KK
   Wang, HM
   Wang, CL
   Xia, Q
AF Jing, Yuhui
   Zhao, Leying
   Zhu, Keke
   Wang, Haoming
   Wang, Chengliang
   Xia, Qi
TI Research Landscape of Adaptive Learning in Education: A Bibliometric
   Study on Research Publications from 2000 to 2022
SO SUSTAINABILITY
LA English
DT Article
DE adaptive learning; artificial intelligence; bibliometrics; intelligent
   technology; learning system
ID FUZZY-NEURAL-NETWORK; SYSTEM; MODEL; PATH; FRAMEWORK; SCIENCE; ONLINE;
   PERSONALIZATION; CLASSIFICATION; ENVIRONMENT
AB Adaptive learning is an approach toward personalized learning and places the concept of "learner-centered education" into practice. With the rapid development of artificial intelligence and other technologies in recent years, there have been many breakthroughs in adaptive learning. Thus, it is important to gain insight into the evolution of related research and to track the research frontiers to further promote its development. This study used CiteSpace and VOSviewer to conduct a bibliometric analysis of 644 adaptive learning journal papers indexed in the WoS database from 2000 to 2022. This study presented a general view of the field of adaptive learning research over the last two decades using quantitative analysis. Currently, adaptive learning research is rapidly developing. In terms of the major research forces, a core group of authors including Qiao J. F., Han H. G. and Song Q has been formed; the major publishing country in this field is China; the core publishing journals include IEEE Transactions on Neural Networks and Learning Systems. Four major research topics in this field were identified using cluster analysis, namely the application of deep learning in educational data analysis, the development and application of adaptive learning model in AI education, the development and application of intelligent tutoring system in tutoring and teaching, cutting-edge modeling technology for feature modeling and knowledge tracing. Through evolution analyses, the logic of adaptive learning research's development was determined; that is, technological changes have played a key role in the development of this field. Following the logic, we presented three frontiers of adaptive learning with burst terms: feature extraction, adaptation model and computational modeling. Adaptive learning is a core research topic for both computer science and educational technology disciplines, and it is also an important field where emerging technologies empowering education and teaching can play a part. The findings of the study clearly presented the current research status, evolutionary logic and research frontiers of this topic, which can provide references for the further development of this research field.
C1 [Jing, Yuhui; Zhao, Leying; Wang, Haoming; Wang, Chengliang] Zhejiang Univ Technol, Coll Educ Sci & Technol, Hangzhou 310023, Peoples R China.
   [Zhu, Keke] Zhejiang Univ Technol, Coll Foreign Languages, Hangzhou 310023, Peoples R China.
   [Xia, Qi] Chinese Univ Hong Kong, Instruction Fac Educ, Dept Curriculum, Hongkong 999077, Peoples R China.
C3 Zhejiang University of Technology; Zhejiang University of Technology;
   Chinese University of Hong Kong
RP Zhao, LY; Wang, CL (corresponding author), Zhejiang Univ Technol, Coll Educ Sci & Technol, Hangzhou 310023, Peoples R China.
EM 202005720434@zjut.edu.cn; 201906120404@zjut.edu.cn
RI Wang, Henry/HMD-3336-2023; Wang, Chengliang/JEZ-9556-2023; XIA,
   Qi/KRO-3142-2024
OI Wang, Henry/0000-0003-3624-2127; Wang, Chengliang/0000-0003-2208-3508;
   XIA, Qi/0000-0003-0538-7665; Jing, Yuhui/0000-0002-8593-2725
FU National Social Science Foundation Education Youth Project "Research on
   the Strategy of Creating Learning Space Value and Empowering Classroom
   Teaching under the background of Double Reduction'" [CCA220319]
FX This research was supported by the 2022 National Social Science
   Foundation Education Youth Project "Research on the Strategy of Creating
   Learning Space Value and Empowering Classroom Teaching under the
   background of Double Reduction'" (Grant No. CCA220319).
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NR 113
TC 10
Z9 10
U1 21
U2 82
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD FEB
PY 2023
VL 15
IS 4
AR 3115
DI 10.3390/su15043115
PG 21
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA 9K0PW
UT WOS:000940578200001
OA gold
DA 2024-09-05
ER
PT J
AU Luo, FS
   Li, RYM
   Crabbe, MJC
   Pu, RH
AF Luo, Fansong
   Li, Rita Yi Man
   Crabbe, M. James C.
   Pu, Ruihui
TI Economic development and construction safety research: A bibliometrics
   approach
SO SAFETY SCIENCE
LA English
DT Article
DE Construction industry; Reconstruction; Bibliometrics; Safety management
   and artificial intelligence
ID INTELLECTUAL STRUCTURE; MANAGEMENT; CLIMATE; HEALTH; FATALITIES;
   ACCIDENTS; BEHAVIOR; COUNTRY; WORKERS; IMPACT
AB The construction industry contributes significantly to economic development worldwide, yet it is one of the most hazardous industries where numerous accidents and fatalities happen every year. Little research to date has shed light on the impact of economic development on construction safety research. In this paper, we conduct an analysis of construction safety articles published in the 21st century via a bibliometrics approach. We have analysed: (1) construction safety in developed and developing countries; (2) the major organisations that have conducted construction safety research; (3) authors and territories of the research and (4) topics in construction safety and future research directions. The largest number of published construction safety documents were published by scholars from the US and China; the total number of published articles by these two countries was 1,125, at 56% of the 2000 articles that were published. Both countries showed high levels of research collaboration. While our results suggest that economic development may drive academic construction safety research, there has been an increase in construction safety research conducted by developing countries in recent years, probably due to an improvement in their economic development. While authors' keywords evidenced the popularity of research on safety management and climate, the network analysis on all keywords, i.e. keywords given by Web of Science and authors, suggest that construction safety research focused on three areas: construction safety management, the relationship between people and construction safety, and the protection and health of workers' impact on construction safety. We found that there is a new interdisciplinary research trend where construction safety combines with digital technologies, with the largest number involving deep learning. Other trends focus on machine learning, Building Information Modelling, machine learning and visualisation.
C1 [Luo, Fansong; Li, Rita Yi Man] Hong Kong Shue Yan Univ, Sustainable Real Estate Res Ctr, Hong Kong, Peoples R China.
   [Crabbe, M. James C.] Univ Oxford, Wolfson Coll, Oxford OX2 6UD, England.
   [Crabbe, M. James C.] Univ Bedfordshire, Inst Biomed & Environm Sci & Technol, Luton LU1 3JU, Beds, England.
   [Crabbe, M. James C.] Shanxi Univ, Sch Life Sci, Taiyuan 030006, Peoples R China.
   [Pu, Ruihui] Srinakharinwirot Univ, Fac Econ, Bangkok 10110, Thailand.
C3 Hong Kong Shue Yan University; University of Oxford; University of
   Bedfordshire; Shanxi University; Srinakharinwirot University
RP Pu, RH (corresponding author), Srinakharinwirot Univ, Fac Econ, Bangkok 10110, Thailand.
EM 179034@hksyu.edu.hk; ymli@hksyu.edu; crabbe@wolfson.ox.ac.uk;
   ruihui@g.swu.ac.th
RI LUO, Fansong/AFJ-9215-2022
OI Pu, Ruihui/0000-0002-8523-242X
FU Research Grants Council of the Hong Kong Special Administrative Region,
   China [UGC/FDS15/E01/18]
FX This work described in this paper was fully supported by a grant from
   the Research Grants Council of the Hong Kong Special Administrative
   Region, China (Project No. UGC/FDS15/E01/18).
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NR 59
TC 48
Z9 49
U1 10
U2 136
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0925-7535
EI 1879-1042
J9 SAFETY SCI
JI Saf. Sci.
PD JAN
PY 2022
VL 145
AR 105519
DI 10.1016/j.ssci.2021.105519
EA OCT 2021
PG 10
WC Engineering, Industrial; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Engineering; Operations Research & Management Science
GA WH6TY
UT WOS:000707808600018
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Alipour, O
   Soheili, F
   Khasseh, AA
AF Alipour, Omid
   Soheili, Faramarz
   Khasseh, Ali Akbar
TI A Co-Word Analysis of Global Research on Knowledge Organization:
   1900-2019
SO KNOWLEDGE ORGANIZATION
LA English
DT Article
DE co-word analysis; bibliometric networks; strategic diagram;
   scientometrics; knowledge structure; hierarchical clustering
ID INTELLECTUAL STRUCTURE; INFORMATION-SCIENCE; LIBRARY; COOCCURRENCE
AB The study's objective is to analyze the structure of knowledge organization studies conducted worldwide. This applied research has been conducted with a scientometrics approach using the co-word analysis. The research records consisted of all articles published in the journals of Knowledge Organization and Cataloging & Classification Quarterly and keywords related to the field of knowledge organization indexed in Web of Science from 1900 to 2019, in which 17,950 records were analyzed entirely with plain text format. The total number of keywords was 25,480, which was reduced to 12,478 keywords after modifications and removal of duplicates. Then, 115 keywords with a frequency of at least 18 were included in the final analysis, and finally, the co-word network was drawn. BibExcel, UCINET, VOSviewer, and SPSS software were used to draw matrices, analyze co-word networks, and draw dendrograms. Furthermore, strategic diagrams were drawn using Excel software. The keywords "information retrieval," "classification," and "ontology" are among the most frequently used keywords in knowledge organization articles. Findings revealed that "Ontology*Semantic Web", "Digital Library*Information Retrieval" and "Indexing*Information Retrieval" are highly frequent co-word pairs, respectively. The results of hierarchical clustering indicated that the global research on knowledge organization consists of eight main thematic clusters; the largest is specified for the topic of "classification, indexing, and information retrieval." The smallest clusters deal with the topics of "data processing" and "theoretical concepts of information and knowledge organization" respectively. Cluster 1 (cataloging standards and knowledge organization) has the highest density, while Cluster 5 (classification, indexing, and information retrieval) has the highest centrality. According to the findings of this research, the keyword "information retrieval" has played a significant role in knowledge organization studies, both as a keyword and co-word pair. In the co-word section, there is a type of related or general topic relationship between co-word pairs. Results indicated that information retrieval is one of the main topics in knowledge organization, while the theoretical concepts of knowledge organization have been neglected. In general, the co-word structure of knowledge organization research indicates the multiplicity of global concepts and topics studied in this field globally.
C1 [Alipour, Omid] Guilan Univ, Fac Nat Resources, Lib Serv, Guilan, Iran.
   [Soheili, Faramarz; Khasseh, Ali Akbar] Payame Noor Univ, Dept Lib & Informat Sci, POB 19395-4697, Tehran, Iran.
C3 University of Guilan; Payame Noor University
RP Alipour, O (corresponding author), Guilan Univ, Fac Nat Resources, Lib Serv, Guilan, Iran.
EM alipour.omid@gmail.com; soheili@pnu.ac.ir; khasseh@pnu.ac.ir
RI Khasseh, Ali Akbar/W-4434-2017; soheili, faramarz/ABE-4978-2020
OI Khasseh, Ali Akbar/0000-0001-5664-4671; 
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NR 40
TC 1
Z9 1
U1 8
U2 19
PU NOMOS VERLAGSGESELLSCHAFT MBH & CO KG
PI BADEN-BADEN
PA WALDSEESTR 3 5, BADEN-BADEN, 76530, GERMANY
SN 0943-7444
J9 KNOWL ORGAN
JI Knowl. Organ.
PY 2022
VL 49
IS 5
BP 303
EP 315
DI 10.5771/0943-7444-2022-5-303
PG 13
WC Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science
GA G1FR9
UT WOS:000986704600001
DA 2024-09-05
ER
PT J
AU Millimouno, TM
   Delvaux, T
   Kolié, JM
   Kourouma, K
   Van Bastelaere, S
   Tsunami, CK
   Béavogui, AH
   Garcia, M
   Van Damme, W
   Delamou, A
AF Millimouno, Tamba Mina
   Delvaux, Therese
   Kolie, Jean Michel
   Kourouma, Karifa
   Van Bastelaere, Stefaan
   Tsunami, Carlos Kiyan
   Beavogui, Abdoul Habib
   Garcia, Marlon
   Van Damme, Wim
   Delamou, Alexandre
TI Evaluation of Three Blended Learning Courses to Strengthen Health
   Professionals' Capacity in Primary Health Care, Management of Sexual and
   Reproductive Health Services and Research Methods in Guinea
SO FRONTIERS IN DIGITAL HEALTH
LA English
DT Article
DE blended learning (BL); e-learning; online learning (OL); distance
   learning; training; health professionals (HPs); medical education;
   Guinea (Conakry)
ID EDUCATION; IMPLEMENTATION
AB BackgroundThree blended courses on Primary Health Care (eSSP), Management of Sexual and Reproductive Health Services (eSSR), and Research Methods (eMR) were developed and implemented between 2017 and 2021 by the Maferinyah National Training and Research Center in Rural Health, a training and research institution of the Ministry of Health in Guinea. The study objectives were to evaluate the reasons for dropout and abstention, the learners' work behavior following the training, and the impact of the behavior change on the achievements of learners' organizations or services. MethodsWe evaluated the three implemented courses in 2021, focusing on levels 3 and 4 of the Kirkpatrick training evaluation model. Quantitative and qualitative data were collected through an open learning platform (Moodle), via an electronic questionnaire, during the face-to-face component of the courses (workshops), and at learners' workplaces. Descriptive statistics and thematic analysis were performed for quantitative and qualitative data, respectively. ResultsOut of 1,016 applicants, 543 including 137 (25%) women were enrolled in the three courses. Over the three courses, the completion rates were similar (67-69%) along with 20-29% dropout rates. Successful completion rates were 72% for eSSP, 83% for eMR and 85% for eSSR. Overall success rate (among all enrollees) ranged from 50% (eSSP) to 58% (eSSR). The majority (87%) of the learners reported applying the knowledge and skills they acquired during the courses through activities such as supervision (22%), service delivery (20%), and training workshops (14%). A positive impact of the training on utilization/coverage of services and increased revenues for their health facilities were also reported by some trainees. ConclusionThese findings showed fair success rates and a positive impact of the training on learners' work behavior and the achievements of their organizations.
C1 [Millimouno, Tamba Mina; Kolie, Jean Michel; Kourouma, Karifa; Beavogui, Abdoul Habib; Delamou, Alexandre] Maferinyah Natl Training & Res Ctr Rural Hlth, Forecariah, Guinea.
   [Delvaux, Therese; Garcia, Marlon; Van Damme, Wim] Inst Trop Med, Dept Publ Hlth, Antwerp, Belgium.
   [Van Bastelaere, Stefaan] Belgian Dev Agcy Enabel, Brussels, Belgium.
   [Tsunami, Carlos Kiyan] Inst Trop Med, Dept Clin Sci, Antwerp, Belgium.
   [Delamou, Alexandre] Gamal Abdel Nasser Univ, Africa Ctr Excellence Prevent & Control Transmiss, Conakry, Guinea.
C3 Institute of Tropical Medicine (ITM); Institute of Tropical Medicine
   (ITM)
RP Millimouno, TM (corresponding author), Maferinyah Natl Training & Res Ctr Rural Hlth, Forecariah, Guinea.
EM minamillimouno@gmail.com
RI Van Damme, Wim/F-7404-2011
OI Van Damme, Wim/0000-0002-6344-3007; KOUROUMA,
   KARIFA/0000-0001-6375-262X; Millimouno, Tamba Mina/0000-0002-4628-1296;
   Kiyan Tsunami, Carlos/0009-0008-3215-4078
FU Belgian Development Agency (Enabel)
FX This evaluation was funded by the Belgian Development Agency (Enabel).
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NR 43
TC 2
Z9 2
U1 0
U2 2
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 2673-253X
J9 FRONT DIGIT HEALTH
JI Front. Digit. Health
PD JUN 27
PY 2022
VL 4
AR 911089
DI 10.3389/fdgth.2022.911089
PG 10
WC Health Care Sciences & Services; Medical Informatics
WE Emerging Sources Citation Index (ESCI)
SC Health Care Sciences & Services; Medical Informatics
GA M4TV3
UT WOS:001030161100001
PM 35832657
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU Pei, B
   Xing, WL
   Wang, MJ
AF Pei, Bo
   Xing, Wanli
   Wang, Minjuan
TI Academic development of multimodal learning analytics: a bibliometric
   analysis
SO INTERACTIVE LEARNING ENVIRONMENTS
LA English
DT Article
DE Multimodal learning analytics; bibliometric analysis; learning
   analytics; social network analysis; topic modeling
ID KNOWLEDGE
AB Multimodal Learning Analytics (MMLA) has huge potential for extending the work beyond traditional learning analytics for the capabilities of leveraging multiple data modalities (e.g. physiological data, digital tracing data). To shed a light on its applications and academic development, a systematic bibliometric analysis was conducted in this paper. Specifically, we examine the following aspects: (1) Analyzing the yearly publication and citation trends since the year 2010; (2) Recognizing the most prolific countries, institutions, and authors in this field; (3) Identifying the collaborative patterns among countries, institutions, and authors, respectively; (4) Tracing the evolving procedure of the applied keywords and development of the research topics during the last decade. These analytic tasks were conducted on 194 carefully selected articles published since 2010. The analytical results revealed an increasing trend in the number of publications and citations, identified the prominent institutions and scholars with significant academic contributions to the area, and detected the topics (e.g. characterizing learning processes using multimodal data, implementing ubiquitous learning platforms) that received the most attention. Finally, we also highlighted the current research hotspots attempting to initiate potential interdisciplinary collaborations to promote further progress in the area of MMLA.
C1 [Pei, Bo; Xing, Wanli] Univ Florida, Coll Educ, Sch Teaching & Learning, Gainesville, FL 32611 USA.
   [Wang, Minjuan] San Diego State Univ, Learning Design & Technol, San Diego, CA 92182 USA.
C3 State University System of Florida; University of Florida; California
   State University System; San Diego State University
RP Xing, WL (corresponding author), Univ Florida, Coll Educ, Sch Teaching & Learning, Gainesville, FL 32611 USA.
EM wanli.xing@coe.ufl.edu
OI Pei, Bo/0000-0002-6328-6929; Xing, Wanli/0000-0002-1446-889X
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NR 25
TC 11
Z9 11
U1 8
U2 91
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1049-4820
EI 1744-5191
J9 INTERACT LEARN ENVIR
JI Interact. Learn. Environ.
PD AUG 18
PY 2023
VL 31
IS 6
BP 3543
EP 3561
DI 10.1080/10494820.2021.1936075
EA JUN 2021
PG 19
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA P6MO6
UT WOS:000658203800001
DA 2024-09-05
ER
PT J
AU van den Besselaar, P
   Leydesdorff, L
AF van den Besselaar, P
   Leydesdorff, L
TI Mapping change in scientific specialties: A scientometric reconstruction
   of the development of artificial intelligence
SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE
LA English
DT Article
ID CITATION ANALYSIS; JOURNALS; SCIENCE
AB Has an identifiable core of activities called AI been established, during the AI-boom in the eighties? Is AI already in a ''paradigmatic'' phase? There has been a lot of disagreement among commentators and specialists about the nature of Artificial Intelligence as a specialty, This makes AI an interesting case of an emerging specialty, We use aggregated journal-journal citations for describing Artificial Intelligence as sets of journals; factor analytic techniques are used to analyze the development of AI in terms of (an emerging) stability and coherency of the journal-sets during the period 1982-1992, The analysis teaches us that AI has emerged as a set of journals with the characteristics of a discipline only since 1988, The thereafter relatively stable set of journals includes both fundamental and applied AI-journals, and journals with a focus on expert systems, Additionally, specialties related to artificial intelligence (like pattern analysis, computer science, cognitive psychology) are identified, Neural network research is a part neither of AI nor of its direct citation environment, Information science is related to AI only in the early eighties, The citation environment of AI is more stable than AI itself.
C1 UNIV AMSTERDAM, DEPT SCI & TECHNOL DYNAM, 1018 WV AMSTERDAM, NETHERLANDS.
C3 University of Amsterdam
RP van den Besselaar, P (corresponding author), UNIV AMSTERDAM, DEPT SOCIAL SCI INFORMAT, ROETERSSTR 15, 1018 WB AMSTERDAM, NETHERLANDS.
RI van den Besselaar, Peter A A/E-5938-2013; Leydesdorff, Loet/E-2903-2010;
   van den Besselaar, Peter A.A./A-8945-2011
OI van den Besselaar, Peter A A/0000-0002-8304-8565; Leydesdorff,
   Loet/0000-0002-7835-3098; 
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NR 44
TC 77
Z9 83
U1 1
U2 47
PU JOHN WILEY & SONS INC
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN, NJ 07030 USA
SN 0002-8231
J9 J AM SOC INFORM SCI
JI J. Am. Soc. Inf. Sci.
PD JUN
PY 1996
VL 47
IS 6
BP 415
EP 436
DI 10.1002/(SICI)1097-4571(199606)47:6<415::AID-ASI3>3.0.CO;2-Y
PG 22
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA UM121
UT WOS:A1996UM12100003
DA 2024-09-05
ER
PT J
AU Porter, LA
AF Porter, Lon A., Jr.
TI Active Learning and Student Engagement via 3D Printing and Design:
   Integrating Undergraduate Research, Service Learning, and
   Cross-Disciplinary Collaborations
SO MRS ADVANCES
LA English
DT Article
AB In order to provide students with the training required to meet the substantial and diverse challenges of the 21st Century, effective programs in engineering, science, and technology must continue to take the lead in developing high-impact educational practices. Over the past year, faculty across several departments collaborated in the establishment of a campus 3D printing and fabrication center. This facility was founded to offer opportunities for exploring innovative active learning strategies in order to enhance the lives of Wabash College students and serve as a model to other institutions of higher education. This campus resource provides the infrastructure that will empower faculty and staff to explore diverse and meaningful cross-disciplinary collaborations related to teaching and learning across campus. New initiatives include the development of courses on design and fabrication, collaborative cross-disciplinary projects that bridge courses in the arts and sciences, 3D printing and fabrication-based undergraduate research internships, and entrepreneurial collaborations with local industry. These innovative approaches are meant to open the door to greater active learning experiences that empower and prepare students for creative and practical problem solving. Furthermore, service learning projects, community-based opportunities, and global outreach initiatives provide students with a sense of social responsibility, ethical awareness, leadership, and teamwork. This paper shares initial successes of this effort and goals for future enrichment of student learning.
C1 [Porter, Lon A., Jr.] Wabash Coll, Dept Chem, 301 W Wabash Ave, Crawfordsville, IN 47933 USA.
RP Porter, LA (corresponding author), Wabash Coll, Dept Chem, 301 W Wabash Ave, Crawfordsville, IN 47933 USA.
FU Wabash College and the Department of Chemistry; Ball Brothers Foundation
   Venture Fund
FX The authors gratefully acknowledge financial support of this work by
   Wabash College and the Department of Chemistry. The Wabash College 3D
   Printing and Fabrication Center, supported through a Ball Brothers
   Foundation Venture Fund Grant, is thanked for facilities and
   instrumentation support. Roland Morin and the Wabash Center for
   Innovation, Business & Entrepreneurship (CIBE) are acknowledged for
   student intern support.
CR Barnatt C., 2013, 3D PRINTING
   Gross BC, 2014, ANAL CHEM, V86, P3240, DOI 10.1021/ac403397r
   Hofmann M, 2014, ACS MACRO LETT, V3, P382, DOI 10.1021/mz4006556
   Hutchings P., 2011, SCHOLARSHIP TEACHING
   Kuh G.D., 2008, Excerpt from "High-Impact Educational Practices: What They Are, Who Has Access to Them, and Why They Matter
   Lipson H., 2014, FABRICATED NEW WORLD
   Porter Jr L. A., 2016, J CHEM ED IN PRESS
   Sousa DavidThomas Pilecki., 2013, From Stem to Steam: Using Brain-Compatible Strategies to Integrate the Arts
   Violante LEH, 2014, ACS SYM SER, V1180, P125
NR 9
TC 4
Z9 4
U1 0
U2 13
PU CAMBRIDGE UNIV PRESS
PI NEW YORK
PA 32 AVENUE OF THE AMERICAS, NEW YORK, NY 10013-2473 USA
SN 2059-8521
J9 MRS ADV
JI MRS Adv.
PY 2016
VL 1
IS 56
BP 3703
EP 3708
DI 10.1557/adv.2016.82
PG 6
WC Materials Science, Multidisciplinary
WE Emerging Sources Citation Index (ESCI)
SC Materials Science
GA FJ4GQ
UT WOS:000412695200001
DA 2024-09-05
ER
PT J
AU Sarol, MJ
   Ming, SF
   Radhakrishna, S
   Schneider, J
   Kilicoglu, H
AF Sarol, Maria Janina
   Ming, Shufan
   Radhakrishna, Shruthan
   Schneider, Jodi
   Kilicoglu, Halil
TI Assessing citation integrity in biomedical publications: corpus
   annotation and NLP models
SO BIOINFORMATICS
LA English
DT Article
ID COUNTS MEASURE; ACCURACY
AB Motivation Citations have a fundamental role in scholarly communication and assessment. Citation accuracy and transparency is crucial for the integrity of scientific evidence. In this work, we focus on quotation errors, errors in citation content that can distort the scientific evidence and that are hard to detect for humans. We construct a corpus and propose natural language processing (NLP) methods to identify such errors in biomedical publications.Results We manually annotated 100 highly-cited biomedical publications (reference articles) and citations to them. The annotation involved labeling citation context in the citing article, relevant evidence sentences in the reference article, and the accuracy of the citation. A total of 3063 citation instances were annotated (39.18% with accuracy errors). For NLP, we combined a sentence retriever with a fine-tuned claim verification model to label citations as ACCURATE, NOT_ACCURATE, or IRRELEVANT. We also explored few-shot in-context learning with generative large language models. The best performing model-which uses citation sentences as citation context, the BM25 model with MonoT5 reranker for retrieving top-20 sentences, and a fine-tuned MultiVerS model for accuracy label classification-yielded 0.59 micro-F1 and 0.52 macro-F1 score. GPT-4 in-context learning performed better in identifying accurate citations, but it lagged for erroneous citations (0.65 micro-F1, 0.45 macro-F1). Citation quotation errors are often subtle, and it is currently challenging for NLP models to identify erroneous citations. With further improvements, the models could serve to improve citation quality and accuracy.Availability and implementation We make the corpus and the best-performing NLP model publicly available at https://github.com/ScienceNLP-Lab/Citation-Integrity/.
C1 [Sarol, Maria Janina] Univ Illinois, Informat Programs, Champaign, IL 61820 USA.
   [Ming, Shufan; Schneider, Jodi; Kilicoglu, Halil] Univ Illinois, Sch Informat Sci, 501 E Daniel St, Champaign, IL 61820 USA.
   [Radhakrishna, Shruthan] Univ Illinois, Dept Comp Sci, Champaign, IL 61801 USA.
C3 University of Illinois System; University of Illinois Urbana-Champaign;
   University of Illinois System; University of Illinois Urbana-Champaign;
   University of Illinois System; University of Illinois Urbana-Champaign
RP Kilicoglu, H (corresponding author), Univ Illinois, Sch Informat Sci, 501 E Daniel St, Champaign, IL 61820 USA.
EM halil@illinois.edu
RI Schneider, Jodi/AAK-2236-2020
OI Schneider, Jodi/0000-0002-5098-5667
FU Office of Research Integrity (ORI) of the US Department of Health and
   Human Services (HHS) [ORIIR220073]
FX This study was supported by the Office of Research Integrity (ORI) of
   the US Department of Health and Human Services (HHS) (grant number:
   ORIIR220073). The contents are those of the authors and do not
   necessarily represent the official views of, nor an endorsement, by
   ORI/OASH/HHS, or the US Government.
CR Agarwal Shashank, 2010, AMIA Annu Symp Proc, V2010, P11
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NR 49
TC 0
Z9 0
U1 1
U2 1
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1367-4803
EI 1367-4811
J9 BIOINFORMATICS
JI Bioinformatics
PD JUL 9
PY 2024
VL 40
IS 7
AR btae420
DI 10.1093/bioinformatics/btae420
PG 9
WC Biochemical Research Methods; Biotechnology & Applied Microbiology;
   Computer Science, Interdisciplinary Applications; Mathematical &
   Computational Biology; Statistics & Probability
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology;
   Computer Science; Mathematical & Computational Biology; Mathematics
GA XV4F6
UT WOS:001264431900001
PM 38924508
OA gold
DA 2024-09-05
ER
PT J
AU Xu, ZY
   Archambault, É
AF Xu, Ziyun
   Archambault, Eric
TI Chinese interpreting studies: structural determinants of MA students'
   career choices
SO SCIENTOMETRICS
LA English
DT Article
DE Scientometrics; Chinese interpreting studies; Targeted maximum
   likelihood estimation; MA theses; Career choices; Causal inference
ID MENTOR
AB During the last 30 years, the growth of the interpreting industry in China has been outstanding. Increasing economic and political collaboration has driven the demand for interpreters to bridge the linguistic and cultural divides that exist between China and the West. With the creation of master's and bachelor's degrees in interpreting and translation all over China, hundreds of graduates from various universities have since undertaken distinctly different career paths. Using an exhaustive corpus of Masters' theses and a combination of logistic regression and Targeted Maximum Likelihood Estimation to establish causalities, this paper focuses on some of the structural determinants of graduate students' career choices. The paper examines to what extent university affiliations, thesis advisors, research methodology and thesis content influence the choice to pursue an academic career. The research reveals that graduating from a top university makes students less likely to become academics, and studying under a top advisor does not necessarily increase an individual's chances of securing an academic post. By contrast, writers of empirical theses or ones that are about training are more likely to enter the academic sphere.
C1 [Xu, Ziyun] Univ Rovira & Virgili, Intercultural Studies Grp, E-43007 Tarragona, Spain.
   [Archambault, Eric] Sci Metrix, Montreal, PQ, Canada.
C3 Universitat Rovira i Virgili
RP Xu, ZY (corresponding author), Univ Rovira & Virgili, Intercultural Studies Grp, E-43007 Tarragona, Spain.
EM xuziyun@gmail.com
RI Archambault, Eric JA/G-5808-2019
OI Archambault, Eric JA/0000-0002-4422-1054
CR AIIC, 2013, DIR INT SCH PROGR
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   Zhao Z., 2012, THESIS XIAMEN U
NR 53
TC 2
Z9 3
U1 4
U2 46
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD NOV
PY 2015
VL 105
IS 2
BP 1041
EP 1058
DI 10.1007/s11192-015-1717-0
PG 18
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA CU1ED
UT WOS:000363261600021
DA 2024-09-05
ER
PT J
AU Nah, S
   Luo, J
   Akçakir, G
   Wu, XL
   Nam, G
   Kim, S
AF Nah, Seungahn
   Luo, Jun
   Akcakir, Gulsah
   Wu, Xinlei
   Nam, Gwiwon
   Kim, Seungbae
TI Revisiting citizen journalism scholarship in the Web Era (1994-2023):
   Past, present, and prospect
SO JOURNALISM
LA English
DT Article; Early Access
DE Citizen journalism; topic modeling; bibliometric analysis; and manual
   content analysis
ID USER-GENERATED CONTENT; SOCIAL MEDIA; PARTICIPATORY JOURNALISM; NEWS;
   COMMUNICATION; INITIATIVES; INFORMATION; NEWSROOMS; NETWORKS; INTERNET
AB The study revisits citizen journalism scholarship spanning 30 years of journal articles published in the fields of journalism, communication, media, technology studies, and beyond. Previous studies in this domain have endeavored to evaluate the landscape of citizen journalism research since the inception of the Internet and its associated web technologies in 1994. Nonetheless, it remains fully unexplored concerning the topology and knowledge network structure of citizen journalism scholarship. The study assesses the landscape of citizen journalism scholarship over the past 30 years by employing a variety of mixed methods, including topic modeling, bibliometric analysis, and manual content analysis. This study provides an exploratory examination of the realm of citizen journalism within the context of journalism and democracy and further discusses the past, present, and prospects for future directions of this field. The study aims to advance citizen journalism scholarship in terms of theory, research, practice, and policy implications with a focus on English language articles. While the study contributes to the existing body of knowledge on citizen journalism scholarship, it serves as a catalyst for continuous intellectual inquiry in an international and interdisciplinary environment.
C1 [Nah, Seungahn] Univ Florida, Coll Journalism & Commun, Polit Commun & Journalism, Gainesville, FL USA.
   [Nah, Seungahn] Univ Florida, Coll Journalism & Commun, Media Trust, Gainesville, FL USA.
   [Luo, Jun; Akcakir, Gulsah] Univ Calif Los Angeles, Dept Commun, Los Angeles, CA USA.
   [Wu, Xinlei; Nam, Gwiwon] Univ Florida, Coll Journalism & Commun, Gainesville, FL USA.
   [Kim, Seungbae] Univ S Florida, Dept Comp Sci & Engn, Tampa, FL USA.
C3 State University System of Florida; University of Florida; State
   University System of Florida; University of Florida; University of
   California System; University of California Los Angeles; State
   University System of Florida; University of Florida; State University
   System of Florida; University of South Florida
RP Nah, S (corresponding author), Univ Florida, Dept Journalism, Coll Journalism & Commun, 1885 Stadium Rd, Gainesville, FL 32611 USA.
EM snah@ufl.edu
OI Kim, Seungbae/0000-0001-5667-3560; AKCAKIR, GULSAH/0000-0001-9572-9352
CR Ambala AT, 2016, AFR JOURNAL STUD, V37, P45, DOI 10.1080/23743670.2016.1256056
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   Yeo TED, 2016, J MASS COMMUN Q, V93, P609, DOI 10.1177/1077699016628823
NR 68
TC 0
Z9 0
U1 0
U2 0
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 1464-8849
EI 1741-3001
J9 JOURNALISM
JI Journalism
PD 2024 JUN 6
PY 2024
DI 10.1177/14648849241247972
EA JUN 2024
PG 26
WC Communication
WE Social Science Citation Index (SSCI)
SC Communication
GA TN7V7
UT WOS:001242017100001
DA 2024-09-05
ER
PT J
AU Citron, DT
   Way, SF
AF Citron, Daniel T.
   Way, Samuel F.
TI Network assembly of scientific communities of varying size and
   specificity
SO JOURNAL OF INFORMETRICS
LA English
DT Article
DE Collaboration networks; Network assembly; Social network analysis; Topic
   modeling; Scientometrics
ID DYNAMICS
AB How does the collaboration network of researchers coalesce around a scientific topic? What sort of social restructuring occurs as a new field develops? Previous empirical explorations of these questions have examined the evolution of co-authorship networks associated with several fields of science, each noting a characteristic shift in network structure as fields develop. Historically, however, such studies have tended to rely on manually annotated datasets and therefore only consider a handful of disciplines, calling into question the universality of the observed structural signature. To overcome this limitation and test the robustness of this phenomenon, we use a comprehensive dataset of over 189,000 scientific articles and develop a framework for partitioning articles and their authors into coherent, semantically related groups representing scientific fields of varying size and specificity. We then use the resulting population of fields to study the structure of evolving co-authorship networks. Consistent with earlier findings, we observe a global topological transition as the co-authorship networks coalesce from a disjointed aggregate into a dense giant connected component that dominates the network. We validate these results using a separate, complimentary corpus of scientific articles, and, overall, we find that the previously reported characteristic structural evolution of a scientific field's associated co-authorship network is robust across a large number of scientific fields of varying size, scope, and specificity. Additionally, the framework developed in this study may be used in other scientometric contexts in order to extend studies to compare across a larger range of scientific disciplines. (C) 2017 Elsevier Ltd. All rights reserved.
C1 [Citron, Daniel T.] Cornell Univ, Ithaca, NY 14853 USA.
   [Way, Samuel F.] Univ Colorado, Boulder, CO 80309 USA.
C3 Cornell University; University of Colorado System; University of
   Colorado Boulder
RP Citron, DT (corresponding author), Cornell Univ, Ithaca, NY 14853 USA.
EM dtc65@cornell.edu; samuel.way@colorado.edu
RI way, S/JRY-7985-2023
FU National Science Foundation Graduate Research Fellowship [DGE-1144153];
   NSF [SMA 1633747]
FX This material is based upon work supported by the National Science
   Foundation Graduate Research Fellowship under Grant No. DGE-1144153, and
   NSF award SMA 1633747. Any opinion, findings, and conclusions or
   recommendations expressed in this material are those of the authors and
   do not necessarily reflect the views of the National Science Foundation.
   The authors would also like to acknowledge Michael W. Macy, Paul H.
   Ginsparg, Alexandra Schofield, and Haofei Wei, as well as Brent
   Schneeman, Laurence Brandenberger, Richard Barnes, and the other
   attendees of the Santa Fe Institute's 2015 Complex Systems Summer School
   for helpful discussions.
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NR 31
TC 8
Z9 8
U1 0
U2 28
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1751-1577
EI 1875-5879
J9 J INFORMETR
JI J. Informetr.
PD FEB
PY 2018
VL 12
IS 1
BP 181
EP 190
DI 10.1016/j.joi.2017.12.008
PG 10
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA FZ3ID
UT WOS:000427479800013
OA Bronze, Green Submitted
DA 2024-09-05
ER
PT J
AU Amjad, T
   Shahid, N
   Daud, A
   Khatoon, A
AF Amjad, Tehmina
   Shahid, Nafeesa
   Daud, Ali
   Khatoon, Asma
TI Citation burst prediction in a bibliometric network
SO SCIENTOMETRICS
LA English
DT Article
DE Journal; Conference; Citation burst; Citations analysis; Features;
   Correlation; Multiple linear regression
ID IMPACT; JOURNALS; AUTHOR; COUNT
AB In the field of computer science, both journal and conference publications are considered valuable. The popularity of an author is mostly determined by the paper's high citations in a short time. Features that can help to attract higher visibility are not yet thoroughly investigated in the literature. This study aims to investigate the impact of the several features on received citations, for articles published in both journals or conferences. The correlation analysis and multiple linear regression models are applied to explore the strength of all related features. The study helps in finding the impact of the individual features on the number of citations both for journals and conferences, and to predict future citations. AMiner citation dataset has been used for experimental analysis. The findings of the study show that in the case of journal publications, "author first-year citations" and "author total citation" are the most important features. While, in the case of conference publications, "author total citation" is more effective as compared to other features. In the case of journal publications, the multiple linear regression model shows the coefficient of determination (R-2) is 0.975 and accuracy 0.846. For the conference publications, the R-2 value and accuracy are 0.877 and 0.846, respectively.
C1 [Amjad, Tehmina; Shahid, Nafeesa; Khatoon, Asma] IIU, Dept Comp Sci & Software Engn, Islamabad, Pakistan.
   [Daud, Ali] Zhejiang Ocean Univ, Sch Informat Engn, Zhoushan 316022, Peoples R China.
   [Daud, Ali] Univ Jeddah, Dept Comp Sci & Artificial Intelligence, Jeddah, Saudi Arabia.
C3 Zhejiang Ocean University; University of Jeddah
RP Daud, A (corresponding author), Zhejiang Ocean Univ, Sch Informat Engn, Zhoushan 316022, Peoples R China.; Daud, A (corresponding author), Univ Jeddah, Dept Comp Sci & Artificial Intelligence, Jeddah, Saudi Arabia.
EM alimsdb@gmail.com
RI Shahid, Nafeesa/LDF-5707-2024; Amjad, Tehmina/GLS-0209-2022; Daud,
   Ali/ABA-8422-2020; Khatoon, Asma/V-4893-2019
OI Daud, Ali/0000-0002-8284-6354; Khatoon, Asma/0000-0001-9871-1844; Amjad,
   Tehmina/0000-0003-1201-498X
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NR 41
TC 18
Z9 19
U1 8
U2 81
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD MAY
PY 2022
VL 127
IS 5
BP 2773
EP 2790
DI 10.1007/s11192-022-04344-3
EA MAR 2022
PG 18
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 1I1KM
UT WOS:000773181400002
DA 2024-09-05
ER
PT J
AU Kravets, LG
AF Kravets, L. G.
TI Fifty years of patent information centres in Russia
SO WORLD PATENT INFORMATION
LA English
DT Article
DE Machine translation; Automatic indexing; Subject matter search;
   Bibliographic data processing; Machine-readable information;
   International exchange; State patent information system; Competitive
   business information support; Soviet Union
AB The article contains a brief historical review of developing patent information centres in the structure of the State Committee on Inventions and Discoveries (Rospatent) and their participation in establishing the national patent information system. The process is divided into three stages resulting from, first, changes in demands made by the new generations of users of information products and services and, second, renewal of methods, technologies and organizational forms of information support of innovation processes. (C) 2011 Elsevier Ltd. All rights reserved.
C1 [Kravets, L. G.] Patent Informat Today Magazine, Druzhinnikovskaia Str 11A, Moscow 123995, Russia.
RP Kravets, LG (corresponding author), Patent Informat Today Magazine, Druzhinnikovskaia Str 11A, Moscow 123995, Russia.
EM kravets27@yandex.ru
CR Alferova I. V., 1981, THESIS
   Budnikova L. F., 2010, PATENTNAIA INFORM SE
   Kedrovski OV, 1984, WORLD PATENT INFORMA, V6, P18
   Kedrovsky O. V., 1980, VOPROSY IZOBRETATELS
   Kedrovsky OV, 1986, WORLD PATENT INFORM, V8, P273
   Kravets L. G., 1966, 6 ANN M ICIREPAT HAG
   Kravets L. G., THESIS
   Morozov A. N., 1981, World Patent Information, V3, P50, DOI 10.1016/0172-2190(81)90002-8
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NR 11
TC 2
Z9 2
U1 0
U2 2
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0172-2190
EI 1874-690X
J9 WORLD PAT INF
JI World Pat. Inf.
PD SEP
PY 2011
VL 33
IS 3
BP 282
EP 285
DI 10.1016/j.wpi.2011.04.003
PG 4
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA V6Z1L
UT WOS:000420747200010
DA 2024-09-05
ER
PT J
AU Jeong, B
   Yoon, J
AF Jeong, Byeongki
   Yoon, Janghyeok
TI Competitive Intelligence Analysis of Augmented Reality Technology Using
   Patent Information
SO SUSTAINABILITY
LA English
DT Article
DE competitive intelligence analysis; patent analysis; Topic modeling;
   bibliometrics; augmented reality
ID FORECASTING EMERGING TECHNOLOGIES; MOBILE
AB Augmented reality has recently achieved a rapid growth through its applications in various industries, including education and entertainment. Despite the growing attraction of augmented reality, trend analyses in this emerging technology have relied on qualitative literature review, failing to provide comprehensive competitive intelligence analysis using objective data. Therefore, tracing industrial competition trends in augmented reality will provide technology experts with a better understanding of evolving competition trends and insights for further technology and sustainable business planning. In this paper, we apply a topic modeling approach to 3595 patents related to augmented reality technology to identify technology subjects and their knowledge stocks, thereby analyzing industrial competitive intelligence in light of technology subject and firm levels. As a result, we were able to obtain some findings from an inventional viewpoint: technological development of augmented reality will soon enter a mature stage, technologies of infrastructural requirements have been a focal subject since 2001, and several software firms and camera manufacturing firms have dominated the recent development of augmented reality.
C1 [Jeong, Byeongki; Yoon, Janghyeok] Konkuk Univ, Dept Ind Engn, 120 Neungdong Ro, Seoul 05029, South Korea.
C3 Konkuk University
RP Yoon, J (corresponding author), Konkuk Univ, Dept Ind Engn, 120 Neungdong Ro, Seoul 05029, South Korea.
EM byeongkij@konkuk.ac.kr; janghyoon@konkuk.ac.kr
OI Jeong, Byeongki/0000-0003-2701-4245
FU Konkuk University
FX This paper was supported by Konkuk University in 2016.
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NR 56
TC 18
Z9 18
U1 8
U2 93
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
SN 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD APR
PY 2017
VL 9
IS 4
AR 497
DI 10.3390/su9040497
PG 22
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA EV9FE
UT WOS:000402090300020
OA gold, Green Submitted
DA 2024-09-05
ER
PT J
AU Cichowicz, E
   Rollnik-Sadowska, E
AF Cichowicz, Ewa
   Rollnik-Sadowska, Ewa
TI Inclusive Growth in CEE Countries as a Determinant of Sustainable
   Development
SO SUSTAINABILITY
LA English
DT Article
DE inclusive growth; CEE countries; sustainable development; globalization;
   cohesion; public policy; factor analysis; principal component analysis;
   bibliometric analysis
AB Pursuant to the concept of inclusive growth, the authors analyze the transition economies of Central and Eastern European countries, which have become EU members (Bulgaria, Croatia, the Czech Republic, Estonia, Latvia, Lithuania, Hungary, Poland, Romania, Slovakia, and Slovenia). CEE countries characterized by comparable historic and economic backgrounds now seem to reach diversified stages of development. The objective of the study is to identify the level of inclusive growth among CEE countries by taking into account indicators assigned to its seven pillars. The article's thesis is that CEE countries represent social and economic heterogeneity as well as varied levels of sustainable development. Research methods included the application of the principal components analysis and the multivariate analysis. For a literature review, the bibliometric analysis was conducted with the visualization prepared by the VOSviewer software. The main findings suggest that Estonia, Slovenia, and the Czech Republic seem to exhibit the highest level of inclusive growth while Bulgaria and Romania represent the lowest level of indicators measured.
C1 [Cichowicz, Ewa] Warsaw Sch Econ, Coll Socioecon, Inst Social Econ, Al Niepodleglosci 162, PL-02554 Warsaw, Poland.
   [Rollnik-Sadowska, Ewa] Bialystok Tech Univ, Fac Engn Management, Div Managerial Econ, 45AWiejska St, PL-15351 Bialystok, Poland.
C3 Warsaw School of Economics; Bialystok University of Technology
RP Rollnik-Sadowska, E (corresponding author), Bialystok Tech Univ, Fac Engn Management, Div Managerial Econ, 45AWiejska St, PL-15351 Bialystok, Poland.
EM ewa.cichowicz@sgh.waw.pl; e.rollnik@pb.edu.pl
RI Rollnik-Sadowska, Ewa/AAV-4280-2020
OI Rollnik-Sadowska, Ewa/0000-0002-4896-1199; Cichowicz,
   Ewa/0000-0002-9379-9127
FU Ministry of Science and Higher Education of Poland [KES/BMN18/01/18,
   S/WZ/4//2015]
FX The research for this paper has been conducted in the framework of
   projects no. KES/BMN18/01/18 and S/WZ/4//2015 financed from the funds of
   the Ministry of Science and Higher Education of Poland.
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NR 46
TC 23
Z9 23
U1 1
U2 16
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD NOV
PY 2018
VL 10
IS 11
AR 3973
DI 10.3390/su10113973
PG 23
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA HC1AQ
UT WOS:000451531700147
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU Yin, B
   Dridi, M
   El Moudni, A
AF Yin, Biao
   Dridi, Mahjoub
   El Moudni, Abdellah
TI Forward search algorithm based on dynamic programming for real-time
   adaptive traffic signal control
SO IET INTELLIGENT TRANSPORT SYSTEMS
LA English
DT Article; Proceedings Paper
CT Vienna Congress on How to Enhance the Sustainable Use of ITS
CY 2014
CL Vienna, AUSTRIA
DE search problems; dynamic programming; adaptive control; road traffic
   control; scheduling; artificial intelligence; decision trees; optimal
   control; forward search algorithm; traffic signal scheduling; artificial
   intelligence system; decision tree; real-time adaptive traffic signal
   control policy; fixed phase sequence; variable phase sequence; forward
   research dynamic programming; process optimisation; FSDP algorithm;
   online algorithm; labelled position method; rolling horizon approach;
   optimal fixed-time control; adaptive control; traffic delay evaluation;
   asymmetrical traffic flow scenarios
ID FUNCTION APPROXIMATION; ARCHITECTURE; SYSTEM
AB The scheduling of traffic signal at intersections is involved in an application of artificial intelligence system. This study presents a new forward search algorithm based on dynamic programming (FSDP) under a decision tree, and explores an efficient solution for real-time adaptive traffic signal control policy. Traffic signal control with cases of fixed phase sequence and variable phase sequence are both considered in the algorithm. Owing to the properties of forward research dynamic programming and the process optimisation of repeated or invalid traffic states the authors proposed, FSDP algorithm reduces the number of states and saves much computation time. Consequently, FSDP is certain to be an on-line algorithm through its application to a complicated traffic control problem. Moreover, the labelled position method is firstly proposed in the author's study to search the optimal policy after reaching the goal state. For practical operations, this new algorithm is extended by adding the rolling horizon approach, and some derived methods are compared with the optimal fixed-time control and adaptive control on the evaluation of traffic delay. Experimental results obtained by the simulations of symmetrical and asymmetrical traffic flow scenarios show that the FSDP method can perform quite well with high efficiency and good qualities in traffic control.
C1 [Yin, Biao; Dridi, Mahjoub; El Moudni, Abdellah] Univ Technol Belfort Montbeliard, Lab Syst & Transports, F-90000 Belfort, France.
C3 Universite de Technologie de Belfort-Montbeliard (UTBM)
RP Yin, B (corresponding author), Univ Technol Belfort Montbeliard, Lab Syst & Transports, F-90000 Belfort, France.
EM biao.yin@utbm.fr
RI University of Technology of Belfort-Montbeliard, Mahjoub
   DRIDI./AAS-2243-2021; Gomaa, Ahmed/I-6442-2017; Yin, Biao/AAN-7652-2021
OI Yin, Biao/0000-0001-8087-5939
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NR 24
TC 14
Z9 19
U1 1
U2 23
PU INST ENGINEERING TECHNOLOGY-IET
PI HERTFORD
PA MICHAEL FARADAY HOUSE SIX HILLS WAY STEVENAGE, HERTFORD SG1 2AY, ENGLAND
SN 1751-956X
EI 1751-9578
J9 IET INTELL TRANSP SY
JI IET Intell. Transp. Syst.
PD SEP
PY 2015
VL 9
IS 7
SI SI
BP 754
EP 764
DI 10.1049/iet-its.2014.0156
PG 11
WC Engineering, Electrical & Electronic; Transportation Science &
   Technology
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Engineering; Transportation
GA CQ5XW
UT WOS:000360680000013
OA Bronze
DA 2024-09-05
ER
PT J
AU Zyoud, SH
   Zyoud, AH
AF Zyoud, Shaher H.
   Zyoud, Ahed H.
TI Visualization and Mapping of Knowledge and Science Landscapes in
   <i>Expert Systems With Applications</i> Journal: A 30 Years'
   Bibliometric Analysis
SO SAGE OPEN
LA English
DT Article
DE artificial intelligence; expert systems; visualization maps;
   bibliometric indicators; content analysis
ID SUPPORT VECTOR MACHINES; DECISION-MAKING TECHNIQUES; DATA MINING
   TECHNIQUES; SUPPLIER SELECTION; NEURAL-NETWORKS; SCIENTIFIC-RESEARCH;
   RISK-EVALUATION; GOOGLE-SCHOLAR; CLIMATE-CHANGE; FUZZY DEMATEL
AB The Expert Systems With Applications (ESWA) is a leading journal in the fields of computer science and engineering. Motivated by its outstanding performance, this paper seeks to develop a comprehensive overview of research activities in ESWA since its inception in 1990. In this regard, bibliometric techniques have been utilized to characterize the status quo, dynamics, and development trends of research patterns in ESWA. In doing so, the work used Scopus database as a source of data required. A data visualization software, visualization of similarities (VOS) viewer, was used to map the bibliographic material. The Scopus database yielded 12,254 documents published in ESWA from 105 countries with an average of 408 documents/year. The most productive country was Taiwan (2,069 documents; 17.0%). National Cheng Kung University, Taiwan, was the most productive institution (219 documents; 1.8%). The major topics which will continue to be active include genetic algorithms, data mining, neural networks, support vector machines, classification and machine learning, feature selection, particle swarm optimization, fuzzy logic, and clustering. The outcomes underline the significant growth of ESWA through time. The vitality of topics addressed in ESWA to solve real-world problems boosts the progress and advancements of knowledge in this journal.
C1 [Zyoud, Shaher H.] Palestine Tech Univ Kadoorei, Tulkarem, Palestine.
   [Zyoud, Ahed H.] An Najah Natl Univ, Nablus, Palestine.
C3 An Najah National University
RP Zyoud, SH (corresponding author), Palestine Tech Univ Kadoorei, Dept Bldg Engn & Environm, Fac Engn & Technol, POB Tulkarem Jaffa St 7, Tulkarem 00970, Palestine.
EM shaher.zyoud@ptuk.edu.ps
RI Zyoud, Shaher/AAB-4345-2022; Zyoud, Ahed/R-6263-2016
OI Zyoud, Shaher/0000-0002-2832-1047; Zyoud, Ahed/0000-0001-5812-5955
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NR 94
TC 3
Z9 3
U1 3
U2 21
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 2158-2440
J9 SAGE OPEN
JI SAGE Open
PD APR
PY 2021
VL 11
IS 2
AR 21582440211027574
DI 10.1177/21582440211027574
PG 23
WC Social Sciences, Interdisciplinary
WE Social Science Citation Index (SSCI)
SC Social Sciences - Other Topics
GA UL2ZC
UT WOS:000692524200001
OA gold
DA 2024-09-05
ER
PT J
AU Yalcinkaya, T
   Yucel, SC
AF Yalcinkaya, Turgay
   Yucel, Sebnem Cinar
TI Bibliometric and content analysis of ChatGPT research in nursing
   education: The rabbit hole in nursing education
SO NURSE EDUCATION IN PRACTICE
LA English
DT Article
DE ChatGPT; OpenAI; Artificial intelligence; Bibliometrics analysis;
   Nursing education; Nursing students
AB Aim: This study was conducted to perform the bibliometric and content analysis of ChatGPT studies in nursing education. Background: ChatGPT is an artificial intelligence -based chatbot developed by OpenAI. The benefits and limitations of the use of ChatGPT in nursing education are still discussed; however, it is a tool having potential to be used in nursing education. Design: Bibliometric and content analysis. Methods: The study data were scanned through Scopus and Web of Science. Bibliometric analysis was carried out with VOSViewer and Bibliometrix software. In the bibliometric analysis, science mapping and performance analysis techniques were used. Various bibliometric data, including most cited publications, journals and countries, were analyzed and visualized. The synthetic knowledge synthesis method was used in content analysis. Results: We analyzed 53 publications to which 151 authors contributed. The publications had been published in 29 different journals. The average number of citations of publications is 8.2. It was determined that most of the articles were published in Nurse Education Today and Nurse Educator journals and that the leading countries were the USA and Canada. It was observed that international cooperation on the issue was weak. The most frequently mentioned keywords in the publications were "ChatGPT", "artificial intelligence" and "nursing". The following three themes emerged after the content analysis: (1) Integration of ChatGPT into nursing education; (2) Potential benefits and limitations of ChatGPT; and (3) Stepping down the rabbit hole. Conclusions: We expect that the results of the study can give nursing faculties and academics ideas about the current status of ChatGPT in nursing education and enable them to make inferences for the future.
C1 [Yalcinkaya, Turgay] Sinop Univ, Fac Hlth Sci, Dept Nursing, Sinop, Turkiye.
   [Yucel, Sebnem Cinar] Ege Univ, Dept Fundamentals Nursing, Nursing Fac, Izmir, Turkiye.
C3 Sinop University; Ege University
RP Yalcinkaya, T (corresponding author), Sinop Univ, Fac Hlth Sci, Dept Nursing, Sinop, Turkiye.
EM tyalcinkaya@sinop.edu.tr
RI Yalcinkaya, Turgay/HFZ-8650-2022
OI Yalcinkaya, Turgay/0000-0002-0115-295X
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NR 63
TC 1
Z9 1
U1 35
U2 35
PU ELSEVIER SCI LTD
PI London
PA 125 London Wall, London, ENGLAND
SN 1471-5953
EI 1873-5223
J9 NURSE EDUC PRACT
JI Nurse Educ. Pract.
PD MAY
PY 2024
VL 77
AR 103956
DI 10.1016/j.nepr.2024.103956
EA APR 2024
PG 10
WC Nursing
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Nursing
GA SB9R6
UT WOS:001232121800001
PM 38653086
DA 2024-09-05
ER
PT J
AU Wang, YQ
   Luo, H
   Shi, YY
AF Wang, Yinqiu
   Luo, Hui
   Shi, Yunyan
TI Complex network analysis for international talent mobility based on
   bibliometrics
SO INTERNATIONAL JOURNAL OF INNOVATION SCIENCE
LA English
DT Article
DE Bibliometrics; Multiple linear regression; International talent
   mobility; Complex network
ID GLOBAL COMPETITION; MIGRATION; MIGRANTS
AB Purpose This paper aims to explore international talent mobility and identify its negative/positive factors. Design/methodology/approach Bibliometric data from Scopus are explicated to model the mobility network and providing a more comprehensive posture. In addition, by using indicators of complex network, significant features of international talent mobility are described quantitatively. After that, by introducing a kind of improved gravity model with multiple linear regression, the authors identify factors to explain international talent mobility flows. Findings With the analysis of international talent mobility in complex network, the overall network is not balanced. A small part of developed countries and developing countries with good emergency attract and drain a lot of talents and talents usually moving between these countries, the amount of talents leaving or entering into other countries is very limited. Furthermore, according to multiple linear regression, it is found that the share of migrants in population is the major negative factor for international talent mobility, and the factors of destination countries is more significant than original countries. Originality/value The result of this paper may support further research studies and political suggestions for cultivating, attracting and retaining scientific and technological talents in the world.
C1 [Wang, Yinqiu; Luo, Hui; Shi, Yunyan] Natl Acad Innovat Strategy, Beijing, Peoples R China.
RP Wang, YQ (corresponding author), Natl Acad Innovat Strategy, Beijing, Peoples R China.
EM wh6509@yahoo.com; HuiLuo@sina.com; shiyunyan@cast.org.cn
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NR 35
TC 9
Z9 11
U1 6
U2 55
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 1757-2223
EI 1757-2231
J9 INT J INOV SCI
JI Int. J. Innov. Sci.
PD OCT 11
PY 2019
VL 11
IS 3
BP 419
EP 435
DI 10.1108/IJIS-04-2019-0044
PG 17
WC Business
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA JC0JR
UT WOS:000488965800006
DA 2024-09-05
ER
PT J
AU Akhil, MP
   Lathabhavan, R
   Mathew, AM
AF Akhil, M. P.
   Lathabhavan, Remya
   Mathew, Aparna Merin
TI Exploring research trends of metaverse in education: a bibliometric
   analysis
SO HIGHER EDUCATION SKILLS AND WORK-BASED LEARNING
LA English
DT Article; Early Access
DE Metaverse; Education; Virtual reality; Augmented reality; Artificial
   intelligence
AB Purpose - By a thorough bibliometric examination of the area through time, this paper analyses the research landscape of metaverse in education. It is an effort that is focused on the metaverse research trends, academic production and conceptual focus of scientific publications.Design/methodology/approach - The Web of Science (WoS) database was explored for information containing research articles and associated publications that met the requirements. For a thorough analysis of the trend, thematic focus and scientific output in the subject of metaverse in education, a bibliometric technique was used to analyse the data. The bibliometrix package of R software, specifically the biblioshiny interface of R-studio, was used to conduct the analysis. Findings - The analysis of the metaverse in education spanning from 1995 to the beginning of 2023 reveals a dynamic and evolving landscape. Notably, the field has experienced robust annual growth, with a peak of publications in 2022. Citation analysis highlights seminal works, with Dionisio et al. (2013) leading discussions on the transition of virtual worlds into intricate digital cultures. Thematic mapping identifies dominant themes such as "system," "augmented reality" and "information technology," indicating a strong technological focus. Surprisingly, China emerges as a leading contributor with significant citation impact, emphasising the global nature of metaverse research. The thematic map suggests ongoing developments in performance and future aspects, emphasising the essential role of emerging technologies like artificial intelligence and virtual reality. Overall, the findings depict a vibrant and multidimensional metaverse in education, poised for continued exploration and innovation. Originality/value - The study is among the pioneers that provide a comprehensive bibliometric analysis in the area of metaverse in education which will guide the novice researchers to identify the unexplored areas.
C1 [Akhil, M. P.] Alliance Univ, Bengaluru, India.
   [Lathabhavan, Remya] IIM Bodh Gaya, Dept OB & HRM, Bodh Gaya, India.
   [Mathew, Aparna Merin] All St Coll Trivandrum, Trivandrum, India.
C3 Alliance University; Indian Institute of Management (IIM System); Indian
   Institute of Management Bodh Gaya
RP Lathabhavan, R (corresponding author), IIM Bodh Gaya, Dept OB & HRM, Bodh Gaya, India.
EM remya.l@iimbg.ac.in
RI MP, Akhil/IQS-7253-2023
OI MP, Akhil/0000-0002-2409-0747; Lathabhavan, Remya/0000-0002-4666-4748
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NR 60
TC 0
Z9 0
U1 11
U2 14
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 2042-3896
EI 2042-390X
J9 HIGH EDUC SKILL WORK
JI High Educ. Skills Work-based Learn
PD 2024 JAN 19
PY 2024
DI 10.1108/HESWBL-06-2023-0156
EA JAN 2024
PG 21
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA FK3K5
UT WOS:001145630900001
DA 2024-09-05
ER
PT J
AU da Silva, RF
   Pantoja, MJ
AF da Silva, Ricardo Freitas
   Pantoja, Maria Julia
TI Active learning: a new look at learning organizations
SO REVISTA DE GESTAO E SECRETARIADO-GESEC
LA English
DT Article
DE Distance education; Learning; Bibliometric Review; Covid-19
ID KNOWLEDGE; REFLECTIONS; MANAGEMENT; CLASSROOM; CONTEXT; WORK
AB This article aims to analyze the national scientific production on andragogy, active learning and organizational learning, in the period from 2015 to 2021, in order to identify the contribution of distance education (DL) in the learning process to overcome the challenges faced by organizations today. To this end, a systematic literature review was conducted based on the protocol for selecting and analyzing sources defined by Cronnin, et al. (2008). The qualitative and quantitative research was carried out in journals classified as Qualis Capes A1 to B2, in the Web of Science database. Bibliometric mapping was used to analyze the publications and Iramuteq software was used to process the lexical data of the abstracts of the selected articles. The results of the research point to distance education as a potential model for the educational process capable of working on different organizational competencies.
C1 [da Silva, Ricardo Freitas] Minist Gestao & Inovacao Serv Publ, Gestao Publ, Brasilia, Brazil.
   [Pantoja, Maria Julia] Univ Brasilia UNB, Psicol Socialdo Trabalho & Org, Foco Proc Aprendizagem Humana Trabalho, Programa Posgrad Gestao Publ, BR-70910900 Brasilia, DF, Brazil.
C3 Universidade de Brasilia
RP da Silva, RF (corresponding author), Minist Gestao & Inovacao Serv Publ, Gestao Publ, Brasilia, Brazil.
EM ricardo.freitas@economia.gov.br; jpantoja@unb.br
CR Almeida B. G., 2020, EDUCA O UFSM, V45, P64, DOI [10.5902/1984644437035, DOI 10.5902/1984644437035]
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NR 31
TC 0
Z9 0
U1 2
U2 6
PU SINDICATO SECRETARIAS ESTADO SAO PAULO
PI SAO PAULO
PA RUA TUPI 118, SAO PAULO, 01233-000, BRAZIL
SN 2178-9010
J9 REV GEST SECR-GESEC
JI Rec. Gest. Secr.-GeSeC
PD JAN-APR
PY 2023
VL 14
IS 1
BP 174
EP 195
DI 10.7769/gesec.v14i1.1507
PG 22
WC Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA F3YD0
UT WOS:000981726300003
OA gold
DA 2024-09-05
ER
PT J
AU Lahman, MKE
   D'Amato, RC
   Stecker, S
   McGrain, E
AF Lahman, Maria K. E.
   D'Amato, Rik Carl
   Stecker, Steffanie
   McGrain, Elizabeth
TI Addressing the shortage of rural school psychologists via technology -
   Using candidate qualitative interviews to inform practice
SO SCHOOL PSYCHOLOGY INTERNATIONAL
LA English
DT Article
DE curriculum; distance education; e-learning; interactive video; online
   learning; program evaluation; qualitative approaches; qualitative
   research; school psychology jobs; school psychology training; shortage;
   technology
AB For decades Colorado, USA, has had a shortage of school psychologists, especially in rural areas. The program 'Giving Rural Areas Access to School Psychologist' (GRAASP) was developed to meet this need by offering a technology based distance education program that trained candidates from remote sites across the state. Through the use of in-depth interviews the authors sought to answer the following question: What are the student-candidate perceptions of the GRAASP program? Students' perceptions of their experiences added significantly to our understanding of the effectiveness of the GRAASP program. This article offers insight to educators of school psychologists and others who are considering the use of technology to train learners in remote areas to meet current shortages of qualified personnel in education and related areas.
C1 Univ No Colorado, Ctr Collaborat Res Educ, Coll Educ & Behav Sci, Greeley, CO 80639 USA.
C3 University of Northern Colorado
RP D'Amato, RC (corresponding author), Univ No Colorado, Ctr Collaborat Res Educ, Coll Educ & Behav Sci, McKee Hall Educ 125, Greeley, CO 80639 USA.
EM rik.damato@unco.edu
CR Badri A, 1998, SCHOOL PSYCHOL INT, V19, P121, DOI 10.1177/0143034398192002
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NR 26
TC 6
Z9 13
U1 1
U2 7
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 0143-0343
J9 SCHOOL PSYCHOL INT
JI Sch. Psychol. Int.
PD OCT
PY 2006
VL 27
IS 4
BP 439
EP 461
DI 10.1177/0143034306070429
PG 23
WC Psychology, Educational
WE Social Science Citation Index (SSCI)
SC Psychology
GA 109XH
UT WOS:000242342400005
DA 2024-09-05
ER
PT J
AU Yang, DH
   Wang, Y
   Yu, T
   Liu, XY
AF Yang, Dong-Hui
   Wang, Yan
   Yu, Tian
   Liu, Xueyu
TI Macro-level collaboration network analysis and visualization with
   Essential Science Indicators: A case of social sciences
SO MALAYSIAN JOURNAL OF LIBRARY & INFORMATION SCIENCE
LA English
DT Article
DE International collaboration; Scientometrics; Social network analysis;
   Hierarchical clustering; Essential Science Indicators
ID INTERNATIONAL RESEARCH COLLABORATION; SCIENTIFIC COLLABORATION;
   PREFERENTIAL ATTACHMENT; PUBLICATIONS; CONVERGENCE; PATTERNS; GROWTH;
   KEY
AB Cross-national collaboration has been shaped by internationalization of scientific relationships. To study the synergic network of high quality research patterns, this paper collects a total of 300 top 50 items, in each indicator from the big database, Essential Science Indicators, which lists top-ranking papers, scientists and institutions from 2005 to 2015. First, the country level relations of co-authorship addresses in five indicator variables are extracted in the field of social sciences to build international collaboration networks. The social network analysis (SNA) method was applied to calculate the metrics of vertices, edges, average degree, average shortest path, diameter, clustering coefficient and betweenness centrality to illuminate the structural characters and collaboration patterns. Based on the international collaboration similarities, this paper also visualizes the endemic clustering groups of six networks, as cluster dendrograms, using Hierarchical Clustering (HC) method. Findings illustrate that USA, England and Canada are outstanding countries in the international collaboration networks of five indicators. There are geographical groups in European countries in the collaboration networks of scientists, institutes and countries/territories. It is also found that international collaboration contributes to both highly cited papers in the recent 10 years and hot papers in the recent 2 years in this field, rather than geographical similarity does. Those conclusions are critical for policy makers to produce guidelines on how to encourage researchers to build collaboration networks with high-level scholars in different countries.
C1 [Yang, Dong-Hui; Wang, Yan; Liu, Xueyu] Southeast Univ, Sch Econ & Management, Nanjing 210096, Peoples R China.
   [Yu, Tian] Harbin Engn Univ, Sch Econ & Management, Harbin 150001, Peoples R China.
C3 Southeast University - China; Harbin Engineering University
RP Yang, DH (corresponding author), Southeast Univ, Sch Econ & Management, Nanjing 210096, Peoples R China.; Yu, T (corresponding author), Harbin Engn Univ, Sch Econ & Management, Harbin 150001, Peoples R China.
EM dhyang@seu.edu.cn; happyyanyan003@163.com; yutian@hrbeu.edu.cn;
   xyliu@gmail.com
FU National Natural Science Foundation of China [71871053]; China
   Scholarship Council; Fundamental Research Funds for the Central
   Universities
FX Many thanks to two reviewers for giving useful suggestions to refine
   this paper. This work was supported by the National Natural Science
   Foundation of China (Grant No. 71871053), the China Scholarship Council
   and the Fundamental Research Funds for the Central Universities.
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NR 51
TC 13
Z9 13
U1 1
U2 55
PU UNIV MALAYA, FAC COMPUTER SCIENCE & INFORMATION TECH
PI KUALA LUMPUR
PA UNIV MALAYA, FAC COMPUTER SCIENCE & INFORMATION TECH, KUALA LUMPUR,
   50603, MALAYSIA
SN 1394-6234
J9 MALAYS J LIBR INF SC
JI Malays. J. Libr. Sci.
PY 2020
VL 25
IS 1
BP 121
EP 138
DI 10.22452/mjlis.vol25no1.7
PG 18
WC Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science
GA NF0BS
UT WOS:000562968500007
OA Bronze
DA 2024-09-05
ER
PT J
AU Mostafa, MM
AF Mostafa, Mohamed M.
TI Two decades of Wikipedia research: a PubMed bibliometric network
   analysis
SO GLOBAL KNOWLEDGE MEMORY AND COMMUNICATION
LA English
DT Article
DE Topic modeling; Wikipedia; Collaboration networks; Bibliometric
   networks; Keywords co-occurrence network; Scholarly publications
ID CO-WORD ANALYSIS; INFORMATION-SCIENCE; KNOWLEDGE DOMAIN; DOCTORAL
   DISSERTATIONS; INTELLECTUAL STRUCTURE; SCIENTOMETRIC ANALYSIS; CITATION
   ANALYSIS; TOURISM RESEARCH; EMERGING TRENDS; RESEARCH THEMES
AB Purpose - This paper aims to examine the structure and dynamics of scholarly publications dealing with Wikipedia. The research also aims to investigate how such research evolved since its launch in 2001.
   Design/methodology/approach - Wikipedia has grown to be the biggest online encyclopedia in terms of comprehensiveness, reach and coverage. Based on 1,040 PubMed Wikipedia documents written by 5,280 authors over two decades (2001-2021), this paper conducts a bibliometric review of the intellectual structure of scholarly publications dealing withWikipedia.
   Findings - Results show that annual scholarly publications on Wikipedia growth rate is 13.26. Major outlets publishing Wikipedia's research are PloS One, the Journal of Medical Internet Research, Nucleic Acids Research, Studies in Health Technology and Informatics, Bioinformatics and the International Journal of Medical Informatics. Results also show that the author collaboration network is very sparse, signifying rather negligible collaboration among the authors. Furthermore, results reveal that the Wikipedia research institutions' collaboration network reflects what is sometimes termed Wikipedia's "North-South divide," indicating limited collaboration between rich and poor nations' institutions. Finally, the multiple correspondence analysis applied to obtain the Wikipedia research conceptual map and its intellectual structure reveals the intellectual thrust and the diversity of the scholarly publications dealing withWikipedia.
   Originality/value - To the best of the author's knowledge, this research represents the first application of bibliometric methods to investigate two decades of scholarly publications dealing with Wikipedia based on the PubMed database.
C1 [Mostafa, Mohamed M.] Gulf Univ Sci & Technol, West Mishref, Kuwait.
RP Mostafa, MM (corresponding author), Gulf Univ Sci & Technol, West Mishref, Kuwait.
EM mostafa@usa.com
OI Mostafa, Mohamed/0000-0002-1145-4919
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NR 140
TC 1
Z9 1
U1 0
U2 17
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 2514-9342
EI 2514-9350
J9 GLOB KNOWL MEM COMMU
JI Glob. Knowl. Mem. Commun.
PD DEC 5
PY 2022
VL 71
IS 8/9
BP 947
EP 971
DI 10.1108/GKMC-03-2021-0056
EA NOV 2021
PG 25
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA 6S6FF
UT WOS:000713737600001
DA 2024-09-05
ER
PT C
AU Roopashree, N
   Umadevi, V
AF Roopashree, N.
   Umadevi, V
BE Chandrasekaran, K
   Tahiliani, MP
   Mathew, J
TI Future Collaboration Prediction in Co-authorship Network
SO 2014 3RD INTERNATIONAL CONFERENCE ON ECO-FRIENDLY COMPUTING AND
   COMMUNICATION SYSTEMS (ICECCS 2014)
LA English
DT Proceedings Paper
CT 3rd International Conference on Eco-Friendly Computing and Communication
   Systems (ICECCS)
CY DEC 18-21, 2014
CL Natl Inst Technol, Mangalore, INDIA
HO Natl Inst Technol
DE SVM; Co-authorship network; Future collaboration
AB The advent of proliferation of social networking is high on use in present era. A co-authorship network which shows research collaborations, are an important class of social networks. Research collaborations often yield good results but organizing a research group is a tedious task. Every researcher is concerned to collaborate with the best expertise complimenting him. Although there was abundant research conducted to find future collaborators or links, very few of them are able to find out effective relationship among them. In this article, we propose a method that makes link predictions in co-authorship networks using supervised approach. The model extracts the features from the networks node and topological structure which can be good indicators of future collaborations. The proposed method was evaluated on synthetic as well as real social networks such as NetScience. Our experiment corroborated the results, and demonstrated the efficiency of the method.
C1 [Roopashree, N.; Umadevi, V] BMS Coll Engn, Dept CSE, Bangalore, Karnataka, India.
C3 BMS College of Engineering
RP Roopashree, N (corresponding author), BMS Coll Engn, Dept CSE, Bangalore, Karnataka, India.
EM roops.gowda@gmail.com; umav.77@gmail.com
RI , Dr. Umadevi V/E-6454-2017
OI , Dr. Umadevi V/0000-0001-5265-3925
CR Al Hasan M, 2011, SOCIAL NETWORK DATA ANALYTICS, P243
   AlHasan M., 2006, P SDM 06 WORKSH LINK
   [Anonymous], P 7 WORKSH SOC NETW
   Backstrom L., 2011, WSDM, P635
   Chang CC, 2011, ACM T INTEL SYST TEC, V2, DOI 10.1145/1961189.1961199
   Gong NZ., 2011, JOINTLY PREDICTING L
   Liben-Nowell D, 2007, J AM SOC INF SCI TEC, V58, P1019, DOI 10.1002/asi.20591
   Newman MEJ, 2006, PHYS REV E, V74, DOI 10.1103/PhysRevE.74.036104
NR 8
TC 1
Z9 1
U1 0
U2 1
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-4799-7002-5
PY 2014
BP 183
EP 188
DI 10.1109/Eco-friendly.2014.45
PG 6
WC Computer Science, Interdisciplinary Applications; Engineering,
   Electrical & Electronic
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Engineering
GA BI2ZA
UT WOS:000410569900035
DA 2024-09-05
ER
PT J
AU Zhao, XT
   Langlois, K
   Furst, J
   An, Y
   Hu, XH
   Gualdron, DG
   Uribe-Romo, F
   Greenberg, J
AF Zhao, Xintong
   Langlois, Kyle
   Furst, Jacob
   An, Yuan
   Hu, Xiaohua
   Gualdron, Diego Gomez
   Uribe-Romo, Fernando
   Greenberg, Jane
TI Research evolution of metal organic frameworks: A scientometric approach
   with human-in-the-loop
SO JOURNAL OF DATA AND INFORMATION SCIENCE
LA English
DT Article
DE Scientometric; Metal-Organic Frameworks (MOFs); Network Analysis; Topic
   Modeling; Human in the Loop
ID BIBLIOMETRIC ANALYSIS; CRYSTAL-STRUCTURE; DESIGN; CHEMISTRY
AB Purpose This paper reports on a scientometric analysis bolstered by human-in-the-loop, domain experts, to examine the field of metal-organic frameworks (MOFs) research. Scientometric analyses reveal the intellectual landscape of a field. The study engaged MOF scientists in the design and review of our research workflow. MOF materials are an essential component in next-generation renewable energy storage and biomedical technologies. The research approach demonstrates how engaging experts, via human-in-the-loop processes, can help develop a comprehensive view of a field's research trends, influential works, and specialized topics.Design/methodology/approach A scientometric analysis was conducted, integrating natural language processing (NLP), topic modeling, and network analysis methods. The analytical approach was enhanced through a human-in-the-loop iterative process involving MOF research scientists at selected intervals. MOF researcher feedback was incorporated into our method. The data sample included 65,209 MOF research articles. Python3 and software tool VOSviewer were used to perform the analysis.Findings The findings demonstrate the value of including domain experts in research workflows, refinement, and interpretation of results. At each stage of the analysis, the MOF researchers contributed to interpreting the results and method refinements targeting our focus on MOF research. This study identified influential works and their themes. Our findings also underscore four main MOF research directions and applications.Research limitations This study is limited by the sample (articles identified and referenced by the Cambridge Structural Database) that informed our analysis.Practical implications Our findings contribute to addressing the current gap in fully mapping out the comprehensive landscape of MOF research. Additionally, the results will help domain scientists target future research directions.Originality/value To the best of our knowledge, the number of publications collected for analysis exceeds those of previous studies. This enabled us to explore a more extensive body of MOF research compared to previous studies. Another contribution of our work is the iterative engagement of domain scientists, who brought in-depth, expert interpretation to the data analysis, helping hone the study.
C1 [Zhao, Xintong; An, Yuan; Hu, Xiaohua; Greenberg, Jane] Drexel Univ, 3141 Chestnut St, Philadelphia, PA 19104 USA.
   [Langlois, Kyle; Furst, Jacob; Uribe-Romo, Fernando] Univ Cent Florida, 4000 Cent Florida Blvd, Orlando, FL USA.
   [Gualdron, Diego Gomez] Colorado Sch Mines, 1500 Illinois St, Golden, CO USA.
C3 Drexel University; State University System of Florida; University of
   Central Florida; Colorado School of Mines
RP Zhao, XT (corresponding author), Drexel Univ, 3141 Chestnut St, Philadelphia, PA 19104 USA.
EM xz485@drexel.edu
FU NSF OAC [2118201]
FX This work is funded by NSF OAC # 2118201.
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NR 64
TC 0
Z9 0
U1 0
U2 0
PU SCIENDO
PI WARSAW
PA BOGUMILA ZUGA 32A, WARSAW, MAZOVIA, POLAND
SN 2096-157X
EI 2543-683X
J9 J DATA INFO SCI
JI J. Data Info. Sci.
PD JUN 1
PY 2024
VL 9
IS 3
BP 44
EP 64
DI 10.2478/jdis-2024-0019
EA JUL 2024
PG 21
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA A4S8X
UT WOS:001272122700001
OA gold
DA 2024-09-05
ER
PT J
AU Yasar, MY
   Kaya, M
AF Yasar, Mehmet Yasar
   Kaya, Mehmet
TI Author-Profile-Based Journal Recommendation for a Candidate Article:
   Using Hybrid Semantic Similarity and Trend Analysis
SO IEEE ACCESS
LA English
DT Article
DE Market research; Bibliometrics; Recommender systems; Deep learning;
   Databases; Collaborative filtering; Semantics; Journal suggester;
   ontological similarity; trend analyses; venue selection; user-profile
   recommender
AB Finding the right journal for a manuscript to be submitted is difficult and often time-consuming because authors take into account some criteria while searching for the appropriate journal for their manuscript. One of the most important criteria is the content similarity of the journals and manuscript. For this purpose, the subject of the manuscript should be in accordance with the scope of the journal. Also, the manuscript content should be closed to the journals' trend for higher chance of acceptance. Second criterion is to take into account the impact-factor, acceptance-rate, review-time and publishing houses of the journal, which are suitable for the author's past publication profile. In this study, a novel method is proposed in which both the content of the article and the author / authors profile are considered together to find the appropriate journal. To the best of our knowledge, this is the first effort in this direction. Experimental results conducted on real data sets have shown that the proposed method is applicable and performs high accuracy values.
C1 [Yasar, Mehmet Yasar] Bingol Univ, Muhendislik Fakult, Bilgisayar Muhendisligi, Bingol, Turkiye.
   [Kaya, Mehmet] Firat Univ, Muhendislik Fakult, Bilgisayar Muhendisligi, Elazig, Turkiye.
C3 Bingol University; Firat University
RP Yasar, MY (corresponding author), Bingol Univ, Muhendislik Fakult, Bilgisayar Muhendisligi, Bingol, Turkiye.
EM mybayraktar@bingol.edu.tr
RI Bayraktar, Mehmet Yasar/JRX-8798-2023; Kaya, Mehmet/D-4459-2013
OI Bayraktar, Mehmet Yasar/0000-0003-3182-120X; Kaya,
   Mehmet/0000-0003-2995-8282
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NR 43
TC 0
Z9 0
U1 5
U2 11
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2023
VL 11
BP 45826
EP 45837
DI 10.1109/ACCESS.2023.3271732
PG 12
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA G8KY0
UT WOS:000991592900001
OA gold
DA 2024-09-05
ER
PT S
AU Cirillo, B
   Tzabbar, D
   Seo, D
AF Cirillo, Bruno
   Tzabbar, Daniel
   Seo, Donghwi
BE Tzabbar, D
   Cirillo, B
TI A BIBLIOMETRIC AND TOPIC MODELING ANALYSIS OF THE STRUCTURAL DIVIDE IN
   THE MULTIDISCIPLINARY RESEARCH ON EMPLOYEE MOBILITY
SO EMPLOYEE INTER- AND INTRA-FIRM MOBILITY: TAKING STOCK OF WHAT WE KNOW,
   IDENTIFYING NOVEL INSIGHTS AND SETTING A THEORETICAL AND EMPIRICAL
   AGENDA
SE Advances in Strategic Management-A Research Annual
LA English
DT Article; Book Chapter
ID INTERGENERATIONAL OCCUPATIONAL-MOBILITY; JOB EMBEDDEDNESS;
   GREAT-BRITAIN; UNITED-STATES; TURNOVER; METAANALYSIS; PERFORMANCE;
   ANTECEDENTS; PREDICTOR; SEARCH
AB Research on employee mobility has proliferated in the past four decades across four research traditions: Economics, sociology, management, and organizational behavior/human resource management. Despite significant overlap in interest and focus, these four streams of research have evolved independent from each other, resulting in a structural divide. We provide a detailed account of the research on employee mobility and the structural divide across disciplines. We document that the payoff from this profusion of research and increasing interest has been disappointing, as reflected in the limited number of cross-disciplinary citations, even among common topics of interest. However, our analysis also provides some encouraging signs in the form of specific journals and individuals who provide a bridge for cross-disciplinary fertilization.
C1 [Cirillo, Bruno] Univ Cote Azur GREDEG, SKEMA Business Sch, Sophia Antipolis, France.
   [Tzabbar, Daniel; Seo, Donghwi] Drexel Univ, LeBow Coll Business, Philadelphia, PA 19104 USA.
C3 SKEMA Business School; Universite Cote d'Azur; Drexel University
RP Cirillo, B (corresponding author), Univ Cote Azur GREDEG, SKEMA Business Sch, Sophia Antipolis, France.
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NR 30
TC 1
Z9 1
U1 0
U2 8
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY, W YORKSHIRE BD16 1WA, ENGLAND
SN 0742-3322
BN 978-1-78973-549-9; 978-1-78973-550-5
J9 ADV STRATEG MANAGE
JI Adv. Strat. M.
PY 2020
VL 41
BP 15
EP 36
DI 10.1108/S0742-332220200000041001
D2 10.1108/S0742-3322202041
PG 22
WC Management
WE Book Citation Index – Social Sciences & Humanities (BKCI-SSH); Social Science Citation Index (SSCI)
SC Business & Economics
GA BS1MN
UT WOS:000693396900002
DA 2024-09-05
ER
PT J
AU Frohardt, RJ
AF Frohardt, Russell J.
TI Engaging Community College Students in Publishable Research
SO FRONTIERS IN PSYCHOLOGY
LA English
DT Article
DE extramural funding; guided pathway; active learning; community college;
   experiential learning; co-curricular activities; STEM - science
   technology engineering mathematics; hispanic serving institution (HSI)
C1 [Frohardt, Russell J.] Northwest Vista Coll, Acad Success, San Antonio, TX 78251 USA.
RP Frohardt, RJ (corresponding author), Northwest Vista Coll, Acad Success, San Antonio, TX 78251 USA.
EM rfrohardt@alamo.edu
CR Altman W. S, 1995, PSYCHOL RES CAPSTONE
   American Association of Community Colleges, 2018, MEMB DAT
   American Association of Community Colleges, 2018, AACC PATHW PROJ
   American Psychological Association, 2018, GRANTS AW FUND PAG
   [Anonymous], DEV HISP SERV I TITL
   Association for Psychological Science, 2018, GRANTS AW S
   Bailey T.R., 2015, Redesigning America's community colleges: A clearer path to student success
   Bowen J., 2012, Teaching naked: How moving technology out of your classromm will improve student learning, VFirst
   *COLL BOARD, 2017, TRENDS COLL PRIC 201
   Community College Research Center and American Association of Community Colleges, 2018, PATHW MOD DESCR
   Davidson N., 2014, J EXCELLENCE COLL TE, V25, P1
   Lucas D, 2018, SW PSYCHOL, V11, P3
   National Center for Educational Statistics, 2018, IPEDS FALL 2016 ENR
   National Science Foundation, 2018, IMPR UND STEM ED HIS
   Psi Beta, 2019, AW OV DEADL
   Society for Neuroscience, 2017, NEUR CAR ADV UND RES
   Stevens C., 2016, SCHOLARSHIP TEACHING, V2, P245, DOI [https://doi.org/10.1037/stl0000070, DOI 10.1037/STL0000070]
NR 17
TC 2
Z9 4
U1 0
U2 3
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 1664-1078
J9 FRONT PSYCHOL
JI Front. Psychol.
PD APR 23
PY 2019
VL 10
AR 882
DI 10.3389/fpsyg.2019.00882
PG 4
WC Psychology, Multidisciplinary
WE Social Science Citation Index (SSCI)
SC Psychology
GA HU6OD
UT WOS:000465399200001
PM 31065248
OA Green Published, gold
DA 2024-09-05
ER
PT C
AU Muhamedyev, RI
   Amirgaliyev, YN
   Kalimoldayev, MN
   Khamitov, AN
   Abdilmanova, A
AF Muhamedyev, Ravil I.
   Amirgaliyev, Yedilkhan N.
   Kalimoldayev, Maksat N.
   Khamitov, Alim N.
   Abdilmanova, Ainur
BE Guvercin, S
   Zhaparov, M
   Sagandykova, A
TI Selection of the most prominent lines of research in ICT domain
SO 2015 TWELVE INTERNATIONAL CONFERENCE ON ELECTRONICS COMPUTER AND
   COMPUTATION (ICECCO)
LA English
DT Proceedings Paper
CT 12th International Conference on Electronics Computer and Computation
   (ICECCO)
CY SEP 27-30, 2015
CL Suleyman Demirel Univ, Almaty, KAZAKHSTAN
HO Suleyman Demirel Univ
DE ICT domain; taxonomy; scientometric databases; Big Data; Bioinformatics;
   Cloud computing; Cyber-Physical systems; Embedded systems; Information
   Security; Internet of Things; Human-machine systems; Mobile computing;
   Machine Learning; Machine to Machine; Multi agent systems; Neural
   Networks; Robotics; Visualization; Augmented Reality; SDN; 5G;
   e-Governance
AB The paper is devoted to selection of the most crucial directions of research in ICT domain that could be implemented in the Republic of Kazakhstan. In the paper we evaluated the dynamics of the annual changes in the number of publications and convergence of ICT sub-domains based on data of Scopus, EBSCO (Information Science & Technology Abstracts, Academic Search Complete) and Google Scholar. To analyze the place of Kazakhstan, we considered indexes shown in the Global Competitiveness Report. As a result, the most rapidly developing areas of research were revealed (big data, machine learning, 5G, augmented reality, and etc.). The semantic network of the most modern concepts of the ICT domain was constructed that visualizes the binary relationship between the components and their relative importance. By using comparative analysis of the number of publications in the leading countries and some other countries including Kazakhstan, we selected some key domains which need to be seriously improved onto the way of development science in RK.
C1 [Muhamedyev, Ravil I.; Kalimoldayev, Maksat N.] Minist Educ & Sci Republ Kazakhstan, IICT, 125 Pushkina St, Alma Ata 050010, Kazakhstan.
   [Amirgaliyev, Yedilkhan N.] Suleyman Demirel Univ, Kaskelen 040900, Kazakhstan.
   [Muhamedyev, Ravil I.] ISMA Univ, LV-1019 Riga, Latvia.
   [Khamitov, Alim N.; Abdilmanova, Ainur] Int Informat Technol Univ, Alma Ata 050010, Kazakhstan.
C3 Suleyman Demirel University - Kazakhstan; International Information
   Technology University
RP Muhamedyev, RI (corresponding author), Minist Educ & Sci Republ Kazakhstan, IICT, 125 Pushkina St, Alma Ata 050010, Kazakhstan.
EM ravil.muhamedyev@gmail.com; amir_ed@mail.ru; mnk@ipic.kz;
   alikhamt@umail.iu.edu; abdilmanovaa@gmail.com
RI Kozbakova, Ainur/K-5077-2018; Kalimoldayev, Maksat/ABE-8607-2021;
   Abdildayeva, Assel/O-4374-2017; Amirgaliyev, Yedilkhan N/C-6963-2015;
   Mukhamediev, Ravil I./X-1461-2019
OI Kozbakova, Ainur/0000-0002-5213-4882; Mukhamediev, Ravil
   I./0000-0002-3727-043X
CR Abdilmanova A., 2015, P 13 INT SCI C INF T, P106
   Adolph M., 2014, P INT SCI PRACT C SM, P7
   Alkaras Christina, 2014, OPEN SYSTEMS
   [Anonymous], BIG DATA SURVEY MOBI
   Ardito L, 2013, ENERGIES, V6, P251, DOI 10.3390/en6010251
   Chernyak L., 2014, OPEN SYSTEMS, P12
   Golyshko A.V., 2013, TELECOMMUNICATIONS, P4
   Gubbi J, 2013, FUTURE GENER COMP SY, V29, P1645, DOI 10.1016/j.future.2013.01.010
   Larsson EG, 2014, IEEE COMMUN MAG, V52, P186, DOI 10.1109/MCOM.2014.6736761
   López G, 2014, ENERGIES, V7, P3453, DOI 10.3390/en7053453
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NR 17
TC 2
Z9 2
U1 0
U2 9
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
PY 2015
BP 36
EP 42
PG 7
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BF1IW
UT WOS:000380400900035
DA 2024-09-05
ER
PT J
AU Song, M
   Heo, GE
   Lee, D
AF Song, Min
   Heo, Go Eun
   Lee, Dahee
TI Identifying the landscape of Alzheimer's disease research with network
   and content analysis
SO SCIENTOMETRICS
LA English
DT Article
DE Alzheimer's disease (AD); Bibliometrics; Document representation;
   Concept graph; Topic modeling
ID BIBLIOMETRIC ANALYSIS; DEMENTIA; RISK; PRODUCTIVITY; INFORMATION;
   DECLINE; IMPACT; WOMEN; AD
AB Alzheimer's disease (AD) is one of degenerative brain diseases, whose cause is hard to be diagnosed accurately. As the number of AD patients has increased, researchers have strived to understand the disease and develop its treatment, such as medical experiments and literature analysis. In the area of literature analysis, several traditional studies analyzed the literature at the macro level like author, journal, and institution. However, analysis of the literature both at the macro level and micro level will allow for better recognizing the AD research field. Therefore, in this study we adopt a more comprehensive approach to analyze the AD literature, which consists of productivity analysis (year, journal/proceeding, author, and Medical Subject Heading terms), network analysis (co-occurrence frequency, centrality, and community) and content analysis. To this end, we collect metadata of 96,081 articles retrieved from PubMed. We specifically perform the concept graph-based network analysis applying the five centrality measures after mapping the semantic relationship between the UMLS concepts from the AD literature. We also analyze the time-series topical trend using the Dirichlet multinomial regression topic modeling technique. The results indicate that the year 2013 is the most productive year and Journal of Alzheimer's Disease the most productive journal. In discovery of the core biological entities and their relationships resided in the AD related PubMed literature, the relationship with glycogen storage disease is founded most frequently mentioned. In addition, we analyze 16 main topics of the AD literature and find a noticeable increasing trend in the topic of transgenic mouse.
C1 [Song, Min; Heo, Go Eun; Lee, Dahee] Yonsei Univ, Dept Lib & Informat Sci, Seoul 120749, South Korea.
C3 Yonsei University
RP Song, M (corresponding author), Yonsei Univ, Dept Lib & Informat Sci, 50 Yonsei Ro, Seoul 120749, South Korea.
EM min.song@yonsei.ac.kr
RI song, min/KPA-7030-2024
OI Song, Min/0000-0003-3255-1600
FU Bio-Synergy Research Project of the Ministry of Science, ICT and Future
   Planning through the National Research Foundation [2013M3A9C4078138]
FX This work was supported by the Bio-Synergy Research Project
   (2013M3A9C4078138) of the Ministry of Science, ICT and Future Planning
   through the National Research Foundation.
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NR 43
TC 23
Z9 25
U1 1
U2 63
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD JAN
PY 2015
VL 102
IS 1
BP 905
EP 927
DI 10.1007/s11192-014-1372-x
PG 23
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA AY0OZ
UT WOS:000347297400044
DA 2024-09-05
ER
PT J
AU Tyni, J
   Tarkiainen, A
   Lopez-Pernas, S
   Saqr, M
   Kahila, J
   Bednarik, R
   Tedre, M
AF Tyni, Janne
   Tarkiainen, Anni
   Lopez-Pernas, Sonsoles
   Saqr, Mohammed
   Kahila, Juho
   Bednarik, Roman
   Tedre, Matti
TI Games and Rewards: A Scientometric Study of Rewards in Educational and
   Serious Games
SO IEEE ACCESS
LA English
DT Article
DE Games; Education; Bibliometrics; Collaboration; Licenses; Clustering
   algorithms; Vocabulary; Scientometric analysis; bibliometrics; rewards;
   educational games; serious games
ID VIDEO GAMES; GAMIFICATION; SYSTEMS; FUTURE; SUPPORT; SCIENCE; TOOLS
AB In this study we provide a new viewpoint on the body of literature regarding rewards in serious and educational games. The study includes a quantitative bibliometric analysis of literature in this context from 1969 to 2020. The dataset from the Scopus abstract and citation database was analyzed with the Bibliometrix R library. The data set was manually cleaned to contain only the relevant articles and conference papers. The data was then categorized to match the common themes. From the remaining documents, the amount of annual numbers of publications is presented and the most contributing countries are shown. The most frequent terms from the abstracts and keywords set by the authors are presented, and a co-occurrence network is drawn from the same data. The results of this study reveal that the most occurring topics in this dataset are gamification, physical activity, health, game design, and game-based learning. New directions for research are provided as the most commonly used media appear to be video games and mobile devices in addition to the literature being mostly focused on theory and not practical application.
C1 [Tyni, Janne; Tarkiainen, Anni; Saqr, Mohammed; Bednarik, Roman; Tedre, Matti] Univ Eastern Finland, Sch Comp, Joensuu 80101, Finland.
   [Lopez-Pernas, Sonsoles] Univ Politecn Madrid, Dept Sistemas Informat, ETSI Sistemas Informat, E-28040 Madrid, Spain.
   [Kahila, Juho] Univ Eastern Finland, Sch Appl Educ Sci & Teacher Educ, Joensuu 80101, Finland.
C3 University of Eastern Finland; Universidad Politecnica de Madrid;
   University of Eastern Finland
RP Tyni, J (corresponding author), Univ Eastern Finland, Sch Comp, Joensuu 80101, Finland.
EM janne.tyni@uef.fi
RI Saqr, Mohammed/AAH-2520-2020; López-Pernas, Sonsoles/M-7375-2019
OI Saqr, Mohammed/0000-0001-5881-3109; López-Pernas,
   Sonsoles/0000-0002-9621-1392; Tyni, Janne/0000-0003-4512-1570; Kahila,
   Juho/0000-0002-9913-0627
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NR 36
TC 5
Z9 5
U1 4
U2 35
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2022
VL 10
BP 31578
EP 31585
DI 10.1109/ACCESS.2022.3160230
PG 8
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering; Telecommunications
GA 0J8OU
UT WOS:000780360500001
OA gold
DA 2024-09-05
ER
PT J
AU Vital, A
   Amancio, DR
AF Vital, Adilson
   Amancio, Diego R.
TI A comparative analysis of local similarity metrics and machine learning
   approaches: application to link prediction in author citation networks
SO SCIENTOMETRICS
LA English
DT Article
DE Link prediction; Citation networks; Network similarity; Science of
   science; Authors citation networks
ID COMPLEX NETWORKS; COLLABORATION; EVOLUTION; SCIENCE
AB Understanding the evolution of paper and author citations is of paramount importance for the design of research policies and evaluation criteria that can promote and accelerate scientific discoveries. Recently many studies on the evolution of science have been conducted in the context of the emergent Science of Science field. While many studies have probed the link problem in citation networks, only a few works have analyzed the temporal nature of link prediction in author citation networks. In this study we compared the performance of 10 well-known local network similarity measurements with four machine learning models to predict future links in author citations networks. Differently from traditional link prediction methods, the temporal nature of the predict links is relevant for our approach. Our analysis revealed that the Jaccard coefficient was found to be among the most relevant measurements. The preferential attachment measurement, conversely, displayed the worst performance. We also found that the extension of local measurements to their weighted version do not significantly improved the performance of predicting citations. Finally, we also found that a XGBoost and neural network approach summarizing the information from all 10 considered similarity measurements was able to provide the highest AUC performance and competitive precision values.
C1 [Vital, Adilson; Amancio, Diego R.] Univ Sao Paulo, Inst Math & Comp Sci, Dept Comp Sci, Sao Carlos, SP, Brazil.
C3 Universidade de Sao Paulo
RP Amancio, DR (corresponding author), Univ Sao Paulo, Inst Math & Comp Sci, Dept Comp Sci, Sao Carlos, SP, Brazil.
EM diego@icmc.usp.br
RI Amancio, Diego Raphael/I-1071-2012
FU Sao Paulo Research Foundation (FAPESP) [2020/06271-0]; CNPq-Brazil
   [304026/2018-2, 311074/2021-9]; Coordenacao de Aperfeicoamento de
   Pessoal de Nivel Superior -Brasil (CAPES) [001]
FX A preprint version of this manuscript is available at arXiv (Vital and
   Amancio 2021). D.R.A. acknowledges financial support from Sao Paulo
   Research Foundation (FAPESP Grant No. 2020/06271-0) and CNPq-Brazil
   (Grant No. 304026/2018-2 and 311074/2021-9). This study was financed in
   part by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior
   -Brasil (CAPES) -Finance Code 001.
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NR 62
TC 6
Z9 7
U1 5
U2 43
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD OCT
PY 2022
VL 127
IS 10
BP 6011
EP 6028
DI 10.1007/s11192-022-04484-6
EA AUG 2022
PG 18
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 4P1FY
UT WOS:000840040600001
DA 2024-09-05
ER
PT J
AU Ardia, D
   Bluteau, K
   Meghani, MA
AF Ardia, David
   Bluteau, Keven
   Meghani, Mohammad-Abbas
TI Thirty years of academic finance
SO JOURNAL OF ECONOMIC SURVEYS
LA English
DT Article; Early Access
DE finance literature; structural topic model (STM); scientometrics; topic
   modeling; textual analysis
ID BETA REGRESSION; TEXT; REVEAL; GENDER; MODEL
AB We study how the financial literature has evolved in scale, research team composition, and article topicality across finance-focused academic journals from 1992 to 2021. We document that the field has vastly expanded regarding outlets and published articles. Teams have become larger, and the proportion of women participating in research has increased significantly. Using the Structural Topic Model, we identify 45 topics discussed in the literature. We investigate the topic coverage of individual journals and can identify highly specialized and generalist outlets, but our analyses reveal that most journals have covered more topics over time, thus becoming more generalist. Finally, we find that articles with at least one woman author focus more on topics related to social and governance aspects of corporate finance. We also find that teams with at least one top-tier institution scholar tend to focus more on theoretical aspects of finance.
C1 [Ardia, David; Meghani, Mohammad-Abbas] HEC Montreal, GERAD, Montreal, PQ, Canada.
   [Ardia, David; Meghani, Mohammad-Abbas] HEC Montreal, Dept Decis Sci, Montreal, PQ, Canada.
   [Bluteau, Keven] Univ Sherbrooke, Dept Finance, Sherbrooke, PQ, Canada.
   [Ardia, David] HEC Montreal, GERAD, 3000 Chemin Cote Sainte Catherine, Montreal, PQ H3T 2A7, Canada.
   [Ardia, David] HEC Montreal, Dept Decis Sci, 3000 Cheminde Cote Sainte Catherine, Montreal, PQ H3T 2A7, Canada.
C3 Universite de Montreal; HEC Montreal; Universite de Montreal; HEC
   Montreal; University of Sherbrooke; Universite de Montreal; HEC
   Montreal; Universite de Montreal; HEC Montreal
RP Ardia, D (corresponding author), HEC Montreal, GERAD, 3000 Chemin Cote Sainte Catherine, Montreal, PQ H3T 2A7, Canada.; Ardia, D (corresponding author), HEC Montreal, Dept Decis Sci, 3000 Cheminde Cote Sainte Catherine, Montreal, PQ H3T 2A7, Canada.
EM david.ardia@hec.ca
RI Ardia, David/E-4920-2019; Bluteau, Keven/JUB-1302-2023
OI Bluteau, Keven/0000-0003-2990-4807
FU Institut de Valorisation des Donnees; Schweizerischer Nationalfonds zur
   Forderung der Wissenschaftlichen Forschung [179281, 191730]; Natural
   Sciences and Engineering Research Council of Canada [RGPIN-2022-03767]
FX Institut de Valorisation des Donnees, Grant/Award Number: Professorship;
   Schweizerischer Nationalfonds zur Forderung der Wissenschaftlichen
   Forschung, Grant/Award Numbers: 179281, 191730; Natural Sciences and
   Engineering Research Council of Canada, Grant/Award Number:
   RGPIN-2022-03767
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NR 48
TC 0
Z9 0
U1 1
U2 3
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0950-0804
EI 1467-6419
J9 J ECON SURV
JI J. Econ. Surv.
PD 2023 JUN 1
PY 2023
DI 10.1111/joes.12571
EA JUN 2023
PG 35
WC Economics
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA H8NE9
UT WOS:000998455800001
OA Green Submitted, hybrid
DA 2024-09-05
ER
PT C
AU Lund, K
   Chen, BD
   Grauwin, S
AF Lund, Kristine
   Chen, Bodong
   Grauwin, Sebastian
GP ACM
TI The Potential of Interdisciplinarity in MOOC Research: How Do Education
   and Computer Science Intersect?
SO PROCEEDINGS OF THE FIFTH ANNUAL ACM CONFERENCE ON LEARNING AT SCALE
   (L@S'18)
LA English
DT Proceedings Paper
CT 5th Annual ACM Conference on Learning at Scale (L at S)
CY JUN 26-28, 2018
CL London, ENGLAND
DE MOOC; online learning; interdisciplinary research; bibliometrics
ID BIBLIOMETRIC ANALYSIS
AB Given that both computer scientists and educational researchers publish on the topic of massive open online courses (MOOCs), the research community should analyze how these disciplines approach the same topic. In order to promote productive dialogue within the community, we report on a bibliometrics study of the growing MOOC literature and examine the potential interdisciplinarity of this research space. Drawing from 3,380 bibliographic items retrieved from Scopus, we conducted descriptive analyses on publication years, publication sources, disciplinary categories of publication sources, frequent keywords, leading authors, and cited references. We applied bibliographic coupling and network analysis to further investigate clusters of research topics in the MOOC literature. We found balanced representation of education and computer science within most topic clusters. However, integration could be further improved on, for example, by enhancing communication between the disciplines and broadening the scope of methods in specific studies.
C1 [Lund, Kristine] Univ Lyon, Ctr Natl Rech Sci CNRS, Ecole Normale Super Lyon, Lyon, France.
   [Chen, Bodong] Univ Minnesota, Coll Educ & Human Dev, Minneapolis, MN USA.
   [Grauwin, Sebastian] Univ Lyon, Ecole Normale Super Lyon, Lab Phys, Lyon, France.
C3 Centre National de la Recherche Scientifique (CNRS); Ecole Normale
   Superieure de Lyon (ENS de LYON); University of Minnesota System;
   University of Minnesota Twin Cities; Ecole Normale Superieure de Lyon
   (ENS de LYON); Universite Paris Cite
RP Lund, K (corresponding author), Univ Lyon, Ctr Natl Rech Sci CNRS, Ecole Normale Super Lyon, Lyon, France.
EM kristine.lund@ens-lyon.fr; chenbd@umn.edu; sebgrauwin@gmail.com
OI Chen, Bodong/0000-0003-4616-4353
FU French Centre National de la Recherche Scientifique; Ecole Normale
   SupAl'rieure de Lyon through the Laboratoire de l'Education [UMS 3773];
   Aslan of UniversitAl' de Lyon within the program Investissements
   d'Avenir of the French government [ANR-10-LABX-0081, ANR-11-IDEX-0007]
FX We gratefully acknowledge financing by the French Centre National de la
   Recherche Scientifique and Ecole Normale SupAl'rieure de Lyon through
   the Laboratoire de l'Education (UMS 3773), as well as Aslan
   (ANR-10-LABX-0081) of UniversitAl' de Lyon, for its financial support
   within the program Investissements d'Avenir (ANR-11-IDEX-0007) of the
   French government operated by the National Research Agency (ANR).
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NR 34
TC 3
Z9 3
U1 0
U2 5
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES
BN 978-1-4503-5886-6
PY 2018
DI 10.1145/3231644.3231661
PG 10
WC Computer Science, Interdisciplinary Applications; Education &
   Educational Research; Education, Scientific Disciplines
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Education & Educational Research
GA BP3ER
UT WOS:000546308900056
DA 2024-09-05
ER
PT J
AU Amarathunga, B
AF Amarathunga, Buddhini
TI ChatGPT in education: unveiling frontiers and future directions through
   systematic literature review and bibliometric analysis
SO ASIAN EDUCATION AND DEVELOPMENT STUDIES
LA English
DT Article; Early Access
DE Artificial intelligence; Bibliometric analysis; ChatGPT; Education;
   Future directions; Systematic literature review
ID INDEX
AB Purpose - This is a dual-focused study that anticipates qualitatively and quantitatively examining the literature on the recently initiated revolutionizing concept of ChatGPT in education by performing a Systematic Literature Review (SLR) and bibliometric analysis. Current study analyzed eight research questions: (1) the main information and annual scientific publications on ChatGPT in education, (2) the pioneer authors and collaborative authors exploring ChatGPT in education, (3) the authors' productivity through Lotka's Law of Authors' Scientific Productivity, (4) the most pertinent sources on ChatGPT in education and how are sources clustered through Bradford's Law of Scattering, (5) the most related, cited countries and the nature of international collaborations exploring ChatGPT in education, (6) the most relevant publications exploring ChatGPT in education, (7) the most occurring and trending keywords in the empirical studies on ChatGPT in education, and (8) the themes and areas for future investigations on ChatGPT in education. Design/methodology/approach - The current study was designed as a SLR and bibliometric analysis, extracting articles from the Scopus database and utilizing both Biblioshiny and VOSviewer software for advanced scientific mapping and visualizations via quantitative and qualitative analysis approaches. Findings - The results indicated that ChatGPT in education is a progressively evolving worldwide concept generating 45 scientific publications from 2023 to 2024 (May). The USA, China, and Indonesia are the most productive countries that have published articles on ChatGPT in education. The education systems, AI, students, educational computing, human experiments, teaching, educational status, chatbots, generative AI, academic integrity, educational technology, worldwide education, and technology acceptance are the pertinent future directions in the field of ChatGPT in education. Originality/value - The analysis's outcomes will enhance the area of study with theoretical and practical implications and benefit students, teachers, policymakers, regulators of educational and higher educational sectors, government, and the general public worldwide with effective utilization of ChatGPT in education.
C1 [Amarathunga, Buddhini] Wayamba Univ Sri Lanka, Dept Business Management, Kuliyapitiya, Sri Lanka.
C3 Wayamba University of Sri Lanka
RP Amarathunga, B (corresponding author), Wayamba Univ Sri Lanka, Dept Business Management, Kuliyapitiya, Sri Lanka.
EM buddhini@wyb.ac.lk
RI Amarathunga, Buddhini/GMX-4063-2022
OI Amarathunga, Buddhini/0000-0003-3837-9979
FX I am grateful to the editor-in-chief, the editorial board of the Asian
   Education and Development Studies Journal, and Emerald Publishing for
   their valuable cooperation in the article publication process. Moreover,
   I thank anonymous reviewers for constructive comments to enhance the
   quality of the article. This research did not receive any specific grant
   from funding agencies in the public, commercial, or not-for-profit
   sectors.
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NR 30
TC 0
Z9 0
U1 27
U2 27
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 2046-3162
EI 2046-3170
J9 ASIAN EDUC DEV STUD
JI Asian Educ. Dev. Stud.
PD 2024 JUL 9
PY 2024
DI 10.1108/AEDS-05-2024-0101
EA JUL 2024
PG 20
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA XS0I0
UT WOS:001263544600001
DA 2024-09-05
ER
PT J
AU Zhu, YP
   Park, HW
AF Zhu, Yu-Peng
   Park, Han-Woo
TI Use of Triangulation in Comparing the Blockchain Knowledge Structure
   between China and South Korea: Scientometric Network, Topic Modeling,
   and Prediction Technique
SO SUSTAINABILITY
LA English
DT Article
DE triangulation; scientometric; network analysis; blockchain
ID BIG DATA; COLLABORATION; UNIVERSITY; CRITERIA; SCIENCE
AB Blockchain, as a new innovative technology, has become a popular topic in many fields in recent years. In this study, triangulation was used to investigate the development of knowledge structures. First, scientometric network analysis was employed to identify the cooperation of knowledge networks. It was found that the structure of blockchain knowledge networks in China is relatively more complex and diverse than in South Korea. Since increased teamwork in blockchain is conducive to the creation of high-quality knowledge products, the Chinese government appears to strongly promote diversified cooperation on blockchain technology through centralized policies. Second, machine-learning topic modeling was used to analyze the content exchanged via a collaborative network. As a result, it was found that both countries lacked the societal and commercial aspects of blockchain technology. Finally, we developed a prediction technique based on the Ernie model to automatically categorize the nature of blockchain research.
C1 [Zhu, Yu-Peng] Yeungnam Univ, Cyber Emot Res Inst, Blockchian Policy Res Ctr, Gyongsan 38541, South Korea.
   [Zhu, Yu-Peng; Park, Han-Woo] Yeungnam Univ, Dept Media & Commun, Gyongsan 38541, South Korea.
   [Park, Han-Woo] Yeungnam Univ, Interdisciplinary Grad Programs Digital Convergen, Gyongsan 38541, South Korea.
   [Park, Han-Woo] Yeungnam Univ, Interdisciplinary Grad Programs East Asian Cultur, Gyongsan 38541, South Korea.
C3 Yeungnam University; Yeungnam University; Yeungnam University; Yeungnam
   University
RP Zhu, YP (corresponding author), Yeungnam Univ, Cyber Emot Res Inst, Blockchian Policy Res Ctr, Gyongsan 38541, South Korea.; Zhu, YP; Park, HW (corresponding author), Yeungnam Univ, Dept Media & Commun, Gyongsan 38541, South Korea.; Park, HW (corresponding author), Yeungnam Univ, Interdisciplinary Grad Programs Digital Convergen, Gyongsan 38541, South Korea.; Park, HW (corresponding author), Yeungnam Univ, Interdisciplinary Grad Programs East Asian Cultur, Gyongsan 38541, South Korea.
EM zhuyupeng@ynu.ac.kr; hanpark@ynu.ac.kr
RI Zhu, Yu Peng/AAM-6683-2021
OI Zhu, Yu Peng/0000-0003-0544-3911; Park, Han Woo/0000-0002-1378-2473
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NR 54
TC 0
Z9 0
U1 5
U2 19
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD FEB
PY 2022
VL 14
IS 4
AR 2326
DI 10.3390/su14042326
PG 16
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA ZT2OV
UT WOS:000768993700001
OA gold
DA 2024-09-05
ER
PT J
AU Oliski, M
   Krukowski, K
   Siecinski, K
AF Olinski, Marian
   Krukowski, Krzysztof
   Siecinski, Kacper
TI Bibliometric Overview of ChatGPT: New Perspectives in Social Sciences
SO PUBLICATIONS
LA English
DT Article
DE ChatGPT; artificial intelligence; bibliometric analysis; ethical
   implications; educational technology; interdisciplinary research
AB This study delves into a bibliometric analysis of ChatGPT, an AI tool adept at analysing and generating text, highlighting its influence in the realm of social sciences. By harnessing data from the Scopus database, a total of 814 relevant publications were selected and scrutinised through VOSviewer, focusing on elements such as co-citations, keywords and international collaborations. The objective is to unearth prevailing trends and knowledge gaps in scholarly discourse regarding ChatGPT's application in social sciences. Concentrating on articles from the year 2023, this analysis underscores the rapid evolution of this research domain, reflecting the ongoing digital transformation of society. This study presents a broad thematic picture of the analysed works, indicating a diversity of perspectives-from ethical and technological to sociological-regarding the implementation of ChatGPT in the fields of social sciences. This reveals an interest in various aspects of using ChatGPT, which may suggest a certain openness of the educational sector to adopting new technologies in the teaching process. These observations make a contribution to the field of social sciences, suggesting potential directions for future research, policy or practice, especially in less represented areas such as the socio-legal implications of AI, advocating for a multidisciplinary approach.
C1 [Olinski, Marian; Krukowski, Krzysztof; Siecinski, Kacper] Univ Warmia & Mazury, Inst Management & Qual Sci, Fac Econ Sci, PL-10719 Olsztyn, Poland.
C3 University of Warmia & Mazury
RP Oliski, M (corresponding author), Univ Warmia & Mazury, Inst Management & Qual Sci, Fac Econ Sci, PL-10719 Olsztyn, Poland.
EM olinski@uwm.edu.pl; kkruk@uwm.edu.pl; kacper.siecinski@uwm.edu.pl
RI ; Olinski, Marian/X-2066-2018; Krukowski, Krzysztof/T-2278-2018
OI Siecinski, Kacper/0000-0001-8484-0741; Olinski,
   Marian/0000-0002-1707-0553; Krukowski, Krzysztof/0000-0002-1614-4397
CR [Anonymous], SIMILARWEB CHAT OPEN
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NR 61
TC 2
Z9 2
U1 25
U2 25
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2304-6775
J9 PUBLICATIONS-BASEL
JI Publications
PD MAR
PY 2024
VL 12
IS 1
AR 9
DI 10.3390/publications12010009
PG 16
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA MH7V5
UT WOS:001192807500001
OA gold
DA 2024-09-05
ER
PT J
AU Huang, XY
   Zou, D
   Cheng, GRY
   Chen, XL
   Xie, HR
AF Huang, Xinyi
   Zou, Di
   Cheng, Gary
   Chen, Xieling
   Xie, Haoran
TI A bibliometric analysis of the trends, topics, and findings of research
   publications on asynchronous and synchronous online language learning
   over three decades
SO KNOWLEDGE MANAGEMENT & E-LEARNING-AN INTERNATIONAL JOURNAL
LA English
DT Article
DE Synchronous learning; Online learning; Language learning; Bibliometric
   analysis
ID FOREIGN-LANGUAGE; CLASSROOM; ENGLISH; FUTURE
AB Since the first study on computer-mediated communication tools in support of language learning was published in 1992, asynchronous and synchronous tools have been widely adopted; however, few reviews have been conducted to explore the research status in this field. As COVID-19 has increased the use of online tools in education, the need to understand how asynchronous and synchronous tools are being used in language education has grown. In this bibliometric analysis, we reviewed asynchronous and synchronous online language learning (ASOLL) by analyzing the trends, topics, and findings of 319 articles on ASOLL. The results indicate that interest in ASOLL has increased over the past three decades with ASOLL for oral proficiency development and collaborative ASOLL being the two main research issues. Interest in three topics collaborative ASOLL, emotions, and corrective feedback - was especially apparent. The review contributes to the understanding of ASOLL while providing practical implications for using information communication technologies to enhance language learning.
C1 [Huang, Xinyi; Cheng, Gary; Chen, Xieling] Educ Univ Hong Kong, Dept Math & Informat Technol, Hong Kong, Peoples R China.
   [Zou, Di] Educ Univ Hong Kong, Dept English Language Educ, Hong Kong, Peoples R China.
   [Xie, Haoran] Lingnan Univ, Dept Comp & Decis Sci, Hong Kong, Peoples R China.
C3 Education University of Hong Kong (EdUHK); Education University of Hong
   Kong (EdUHK); Lingnan University
RP Zou, D (corresponding author), Educ Univ Hong Kong, Dept English Language Educ, Hong Kong, Peoples R China.
EM hxinyicara@gmail.com; dizoudaisy@gmail.com; chengks@eduhk.hk;
   xielingchen0708@gmail.com; hrxie2@gmail.com
RI Huang, Xinyi/AFI-7092-2022; Xie, Haoran/AFS-3515-2022
OI Huang, Xinyi/0000-0001-9777-7905; Xie, Haoran/0000-0003-0965-3617; ZOU,
   Di/0000-0001-8435-9739
FU University Grant Committee; Strategic Development of Virtual Teaching
   and Learning, The Education University of Hong Kong
FX Dr Di Zou's work is supported by the University Grant Committee, Special
   Grant for Strategic Development of Virtual Teaching and Learning, The
   Education University of Hong Kong, Hong Kong.
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NR 45
TC 0
Z9 0
U1 6
U2 10
PU LABORATORY KNOWLEDGE MANAGEMENT & E-LEARNING UNIV
PI HONG KONG
PA RM 212, RUNME SHAW BLDG, FAC EDUCATION, UNIV HONG KONG, HONG KONG,
   00000, HONG KONG
SN 2073-7904
J9 KNOWL MANAG E-LEARN
JI Knowl. Manag. E-Learn.
PD JUN
PY 2023
VL 15
IS 2
BP 153
EP 173
DI 10.34105/j.kmel.2023.15.009
PG 22
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA H5FS2
UT WOS:000996225100002
OA gold
DA 2024-09-05
ER
PT J
AU Vorohobovs, V
   Kleinhofs, M
AF Vorohobovs, Vladimirs
   Kleinhofs, Martins
TI DEFICIENCY-SURPLUS TRANSITION FUNCTION (DeSuTra) IN SEMI-EMPIRICAL
   FORMULAS FOR TUMBLING OF FREELY FALLING CARD
SO COMPOSITES THEORY AND PRACTICE
LA English
DT Article
DE phenomenology; heuristics; semi-empirical research; tumbling cards;
   Magnus effect; autorotation; lift force; bifurcation; efficiency
   reducer; efficiency diminisher; DeSuTra function; singularity
ID FLUTTER; ZIGZAG; FLUID; WINGS
AB A new phenomenological method for composing analytical formulae to describe dynamic systems using the DeSuTra function as a building block is introduced. Based on heuristic considerations, it is possible to write a correct formula with several unknown coefficients. Next, these coefficients are tuned such a way that the result coincides with the experimental data. To illustrate the viability of such a method, a simple but not trivial aerodynamic system was chosen: the autorotation of a rectangular piece of paper that falls in air. Three correction coefficients (diminishers) were introduced to calculate its rotation frequency. Then a simple expression for the Magnus effect and drag force was used. All the obtained formulae were experimentally proved and the coefficients calculated. The conclusions drawn confirm the usefulness of the presented calculation procedure for the design of composites with chaotically distributed reinforcements.
C1 [Vorohobovs, Vladimirs; Kleinhofs, Martins] Riga Tech Univ, Inst Aeronaut, Fac Mech Engn Transport & Aeronaut, 6B Kipsalas St, LV-1048 Riga, Latvia.
C3 Riga Technical University
RP Vorohobovs, V (corresponding author), Riga Tech Univ, Inst Aeronaut, Fac Mech Engn Transport & Aeronaut, 6B Kipsalas St, LV-1048 Riga, Latvia.
EM riga2006@inbox.lv
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NR 32
TC 0
Z9 0
U1 0
U2 0
PU POLISH SOC COMPOSITE MATERIALS
PI CZESTOCHOWA
PA AL ARMII KRAJOWEJ 19, CZESTOCHOWA, 42-200, POLAND
SN 2084-6096
EI 2299-128X
J9 COMPOS THEORY PRACT
JI Compos. Theory Pract.
PY 2022
VL 22
IS 2
BP 92
EP 98
PG 7
WC Materials Science, Composites
WE Emerging Sources Citation Index (ESCI)
SC Materials Science
GA 2X3WD
UT WOS:000825137100005
DA 2024-09-05
ER
PT J
AU Yang, L
   Sun, TT
   Liu, YL
AF Yang, Lie
   Sun, Tiantian
   Liu, Yanli
TI A Bibliometric Investigation of Flipped Classroom Research during
   2000-2015
SO INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING
LA English
DT Article
DE flipped classroom; active learning; blended learning; bibliometric
ID NURSING-EDUCATION; CONFCHEM CONFERENCE; STUDENT ENGAGEMENT; STRATEGIES
AB The paper analyzed the global growth and development of flipped classroom research productivity in terms of publication output as reflected in SCI/SSCI for the period 2000-2015. Publication types and languages, characteristics of articles outputs, countries, subject categories and journals, and the frequency of keywords were analyzed using bibliometric methods. There are 149 articles in 78 journals listed in 41 SCI/SSCI subject categories. A sharp growth trend of publication output was observed during 2011-2015. USA played a predominant role in flipped classroom research. Education educational research, chemistry and medical were the top 3 categories and "active learning" and "blended learning" recent major topics of flipped classroom research during the past 16 years. The results could help researchers understand the characteristics of research output and search hot spots of flipped education field.
C1 [Yang, Lie; Sun, Tiantian; Liu, Yanli] Wuhan Univ Technol, Sch Resources & Environm Engn, Wuhan, Hubei, Peoples R China.
C3 Wuhan University of Technology
RP Yang, L (corresponding author), Wuhan Univ Technol, Sch Resources & Environm Engn, Wuhan, Hubei, Peoples R China.
EM yanglie612@whut.edu.cn; stt313352667@163.com; liuyanli_l@163.com
RI Yang, Lie/S-9216-2019
OI Yang, Lie/0000-0002-2814-5311
FU Program of National Natural Science Foundation of China [51508430]
FX This work was supported by Program of National Natural Science
   Foundation of China (No. 51508430).
CR Betihavas V, 2016, NURS EDUC TODAY, V38, P15, DOI 10.1016/j.nedt.2015.12.010
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NR 30
TC 26
Z9 30
U1 2
U2 27
PU KASSEL UNIV PRESS GMBH
PI KASSEL
PA DIAGONALE 10, D-34127 KASSEL, GERMANY
SN 1863-0383
J9 INT J EMERG TECHNOL
JI Int. J. Emerg. Technol. Learn.
PY 2017
VL 12
IS 6
BP 178
EP 186
DI 10.3991/ijet.v12i06.7095
PG 9
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA FB9KC
UT WOS:000406457000014
OA gold
DA 2024-09-05
ER
PT J
AU Qian, Y
   Gou, XJ
   Xu, ZS
AF Qian, Yu
   Gou, Xunjie
   Xu, Zeshui
TI The Development and Progress of Engineering Economics: A Retrospect and
   Prospect Based on Visual Analysis
SO INZINERINE EKONOMIKA-ENGINEERING ECONOMICS
LA English
DT Article
DE Engineering Economics; Bibliometric Analysis; Software Engineering;
   Artificial Intelligence; Environment.
ID ARTIFICIAL-INTELLIGENCE TECHNIQUES; PRESCRIBING EFFICIENCY; CARBON
   CAPTURE; OPTIMIZATION; MODEL; DESIGN; EUROPE
AB Engineering economics is a cross subject with a wide range of applications, and it has taken on different characteristics with the changing times. The aim of this paper is to depict a sufficiently elaborate and vivid knowledge map of this field, with further discussion and outlook on the hotspots presented therein. Based on the principles and methods of bibliometrics, we use several visualization tools, mainly Vosviewer, to present the characteristics of the published literature within the field of engineering economics from multiple perspectives. Specifically, we collect 624 engineering economics documents published in the Web of Science core collection database between 1915 and 2021, and quantitatively analyze them in the following three aspects: (1) basic data characteristics, including annual publications, annual citations, research directions, and highly cited publications; (2) outstanding performers and cooperations in the four levels of country/region, institution, source and author, including co-authorship, bibliographic coupling, co-citation and co-occurrence analyses; and (3) keyword analyses, including co-occurrence analyses, burst detection analyses, and high-frequency word clouds. In addition, we further explore important topics within the field represented by intelligent and green transformation.
C1 [Qian, Yu; Gou, Xunjie; Xu, Zeshui] Sichuan Univ, Business Sch, 29 Jiuyanqiao Wangjiang Rd, Chengdu 610064, Peoples R China.
C3 Sichuan University
RP Qian, Y (corresponding author), Sichuan Univ, Business Sch, 29 Jiuyanqiao Wangjiang Rd, Chengdu 610064, Peoples R China.
EM yuqian_echo@163.com; gou_xunjie@163.com; xuzeshui@263.net
RI Gou, Xunjie/KEJ-2735-2024; Xu, Zeshui/N-8908-2013
OI Gou, Xunjie/0000-0003-1963-0451; 
FU National Natural Science Foundation of China [72071135, 72271173];
   National Social Science Fund of China [22FGLB005]; Ministry of education
   of Humanities and Social Science project [21YJC630030]; China
   Postdoctoral Science Foundation [2020M680151, 2023T160459]
FX Acknowledgments The work was supported by the National Natural Science
   Foundation of China (Nos. 72071135 and 72271173) , the National Social
   Science Fund of China (22FGLB005) , the Ministry of education of
   Humanities and Social Science project (No. 21YJC630030) , and the China
   Postdoctoral Science Foundation (No. 2020M680151, 2023T160459) .
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NR 97
TC 0
Z9 0
U1 8
U2 8
PU KAUNAS UNIV TECHNOL
PI KAUNAS
PA LAISVES AL 55, KAUNAS, 44309, LITHUANIA
SN 1392-2785
EI 2029-5839
J9 INZ EKON
JI Inz. Ekon.
PY 2024
VL 35
IS 1
BP 4
EP 24
DI 10.5755/j01.ee.35.1.32448
PG 21
WC Economics
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA JL1W8
UT WOS:001173240600005
OA gold
DA 2024-09-05
ER
PT C
AU Liao, HT
   Pan, CL
   Huang, JQ
AF Liao, Han-Teng
   Pan, Chung-Lien
   Huang, Jieqi
BE Singh, M
   Kang, DK
   Lee, JH
   Tiwary, US
   Singh, D
   Chung, WY
TI A Scientometric Review of Digital Economy for Intelligent Human-Computer
   Interaction Research
SO INTELLIGENT HUMAN COMPUTER INTERACTION, PT I
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT 12th International Conference on Intelligent Human Computer Interaction
   (IHCI)
CY NOV 24-26, 2020
CL Daegu, SOUTH KOREA
DE Human-Computer Interaction; Digital economy; Platform economy;
   Artificial intelligence
ID PLATFORMS; LABOR; GIG
AB As the economy goes digital, Human-Computer Interaction (HCI) professionals have been helping companies to better understand the usability, experience, and thus profitability issues, suggesting the contribution ofHCI professionals to the digital economy. However, there is no comprehensive review or theorydriven work that comes from the research area of the digital economy itself. This exploratory study, based on a scientometric analysis of digital economy literature, aims to outline the possibilities and application areas for future research and policy development for HCI research and its intelligent applications. By identifying and analyzing top key authors from 2,778 articles and their more than 100,000 citations, collected from the Web of Science database, the study reveals a dense network with a few clusters of concepts and research work.
C1 [Liao, Han-Teng; Pan, Chung-Lien; Huang, Jieqi] Sun Yat Sen Univ, Higher Educ Impact Assessment Ctr, Nanfang Coll, Guangzhou, Peoples R China.
C3 Sun Yat Sen University; Nanfang College, Guangzhou
RP Pan, CL (corresponding author), Sun Yat Sen Univ, Higher Educ Impact Assessment Ctr, Nanfang Coll, Guangzhou, Peoples R China.
EM peter5612@gmail.com
RI Liao, Han-Teng/AAC-5793-2019; Pan, Chung-Lien/ACJ-6686-2022
OI Liao, Han-Teng/0000-0003-1081-5599; Pan, Chung-Lien/0000-0001-9488-5329;
   huang, jieqi/0000-0003-0814-2495
FU project of Smart AppDesign Innovation Research in the Age of New
   Business, Arts and Engineering Disciplines under the 2019 Guangdong
   Education Grants, China [2019GXJK186]
FX The research is funded by a project of Smart AppDesign Innovation
   Research in the Age of New Business, Arts and Engineering Disciplines
   (2019GXJK186), under the 2019 Guangdong Education Grants, China.
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   van Dijck J., 2018, The platform society: Public values in a connective world, DOI DOI 10.1093/OSO/9780190889760.001.0001
   Wood AJ, 2019, WORK EMPLOY SOC, V33, P56, DOI 10.1177/0950017018785616
NR 31
TC 0
Z9 0
U1 1
U2 13
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 0302-9743
EI 1611-3349
BN 978-3-030-68449-5; 978-3-030-68448-8
J9 LECT NOTES COMPUT SC
PY 2021
VL 12615
BP 469
EP 480
DI 10.1007/978-3-030-68449-5_45
PG 12
WC Computer Science, Artificial Intelligence; Computer Science,
   Cybernetics; Computer Science, Interdisciplinary Applications
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BT5BZ
UT WOS:000835686200045
DA 2024-09-05
ER
PT J
AU Lauer, MS
   Danthi, NS
   Kaltman, J
   Wu, CL
AF Lauer, Michael S.
   Danthi, Narasimhan S.
   Kaltman, Jonathan
   Wu, Colin
TI Predicting Productivity Returns on Investment Thirty Years of Peer
   Review, Grant Funding, and Publication of Highly Cited Papers at the
   National Heart, Lung, and Blood Institute
SO CIRCULATION RESEARCH
LA English
DT Article
DE bibliometrics; National Institutes of Health (US); National Heart, Lung,
   and Blood Institute (US); peer review; ROC curve
ID CITATION IMPACT; PERCENTILE RANKING; SCIENCE; TRIALS; PEOPLE
AB There are conflicting data about the ability of peer review percentile rankings to predict grant productivity, as measured through publications and citations. To understand the nature of these apparent conflicting findings, we analyzed bibliometric outcomes of 6873 de novo cardiovascular R01 grants funded by the National Heart, Lung, and Blood Institute (NHLBI) between 1980 and 2011. Our outcomes focus on top-10% articles, meaning articles that were cited more often than 90% of other articles on the same topic, of the same type (eg, article, editorial), and published in the same year. The 6873 grants yielded 62468 articles, of which 13507 (or 22%) were top-10% articles. There was a modest association between better grant percentile ranking and number of top-10% articles. However, discrimination was poor (area under receiver operating characteristic curve [ROC], 0.52; 95% confidence interval, 0.51-0.53). Furthermore, better percentile ranking was also associated with higher annual and total inflation-adjusted grant budgets. There was no association between grant percentile ranking and grant outcome as assessed by number of top-10% articles per $million spent. Hence, the seemingly conflicting findings on peer review percentile ranking of grants and subsequent productivity largely reflect differing questions and outcomes. Taken together, these findings raise questions about how best National Institutes of Health (NIH) should use peer review assessments to make complex funding decisions.
C1 [Lauer, Michael S.] NHLBI, Off Director, Div Cardiovasc Sci, Bethesda, MD 20892 USA.
   [Danthi, Narasimhan S.] NHLBI, Adv Technol & Surg Branch, Div Cardiovasc Sci, Bethesda, MD 20892 USA.
   [Kaltman, Jonathan] NHLBI, Heart Dev & Struct Dis Branch, Div Cardiovasc Sci, Bethesda, MD 20892 USA.
   [Wu, Colin] NHLBI, Off Biostat Res, Div Cardiovasc Sci, Bethesda, MD 20892 USA.
C3 National Institutes of Health (NIH) - USA; NIH National Heart Lung &
   Blood Institute (NHLBI); National Institutes of Health (NIH) - USA; NIH
   National Heart Lung & Blood Institute (NHLBI); National Institutes of
   Health (NIH) - USA; NIH National Heart Lung & Blood Institute (NHLBI);
   National Institutes of Health (NIH) - USA; NIH National Heart Lung &
   Blood Institute (NHLBI)
RP Lauer, MS (corresponding author), 6701 Rockledge Dr,Room 8128, Bethesda, MD 20892 USA.
EM lauerm@nhlbi.nih.gov
RI Lauer, Michael S/L-9656-2013
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NR 25
TC 32
Z9 34
U1 0
U2 30
PU LIPPINCOTT WILLIAMS & WILKINS
PI PHILADELPHIA
PA TWO COMMERCE SQ, 2001 MARKET ST, PHILADELPHIA, PA 19103 USA
SN 0009-7330
EI 1524-4571
J9 CIRC RES
JI Circ.Res.
PD JUL 17
PY 2015
VL 117
IS 3
BP 239
EP 243
DI 10.1161/CIRCRESAHA.115.306830
PG 5
WC Cardiac & Cardiovascular Systems; Hematology; Peripheral Vascular
   Disease
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Cardiovascular System & Cardiology; Hematology
GA CM9QZ
UT WOS:000358045300005
PM 26089369
OA Green Accepted
DA 2024-09-05
ER
PT J
AU Cao, LM
   Yang, Q
AF Cao, Limei
   Yang, Qi
TI Interaction With Cutting-Edge Technologies: A Bibliometric Analysis and
   a Theoretical Framework
SO JOURNAL OF HOSPITALITY & TOURISM RESEARCH
LA English
DT Article; Early Access
DE cutting-edge technologies; artificial intelligence; virtual reality;
   robot; interaction
ID VIRTUAL-REALITY; CULTURAL-DIFFERENCES; TOURISM; EXPERIENCE; SERVICE;
   ACCEPTANCE; LESSONS; TRENDS; ROBOTS
AB In hospitality and tourism research, cutting-edge technologies (CETs) are receiving growing attention. However, most reviews on CETs focus on specific types of CETs and are devoid of theories focusing on the stakeholders-CETs interaction. Consequently, a comprehensive review and an integrated theoretical framework from an interaction perspective is necessary. To address this gap, we conducted a bibliometric analysis of 554 articles published between 2003 and 2023 to identify three research clusters and key entities of CETs. Moreover, drawing on stimulus-organism-response (SOR) theory and media equation theory, we built a new integrated theoretical framework for understanding the stakeholders' interaction with the CETs. The focus of this new framework centers on how CETs' representations and CETs' types directly and indirectly interact to affect stakeholders' outcomes. This study represents the first attempt to combine bibliometric and qualitative analysis, contributing to a forward-thinking review and theoretical building that accelerates and enhances research in CETs.
C1 [Cao, Limei] Sun Yat Sen Univ, Sch Business, Guangzhou, Guangdong, Peoples R China.
   [Yang, Qi] Macao Inst Tourism Studies, Sch Tourism Management, Macau, Peoples R China.
   [Yang, Qi] Guangdong Univ Sci & Technol, Management Sch, Dongguan, Guangdong, Peoples R China.
   [Yang, Qi] Macao Inst Tourism Studies, Sch Tourism Management, Xu Rishengyin Rd, Taipa 999078, Macao, Peoples R China.
C3 Sun Yat Sen University; Macao University of Tourism; Guangdong
   University of Science & Technology; Macao University of Tourism
RP Yang, Q (corresponding author), Macao Inst Tourism Studies, Sch Tourism Management, Xu Rishengyin Rd, Taipa 999078, Macao, Peoples R China.
EM yeungkei@foxmail.com
OI Yang, Qi/0000-0002-2527-1283
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NR 91
TC 0
Z9 0
U1 7
U2 32
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 1096-3480
EI 1557-7554
J9 J HOSP TOUR RES
JI J. Hosp. Tour. Res.
PD 2023 JUN 30
PY 2023
DI 10.1177/10963480231182965
EA JUN 2023
PG 14
WC Hospitality, Leisure, Sport & Tourism
WE Social Science Citation Index (SSCI)
SC Social Sciences - Other Topics
GA K8TQ3
UT WOS:001019111000001
DA 2024-09-05
ER
PT J
AU Valizadeh, M
   Moezzi, F
   Khavassi, Z
   Movahedinia, M
   Mazloomzadeh, S
   Mehran, L
AF Valizadeh, Majid
   Moezzi, Farzaneh
   Khavassi, Zohreh
   Movahedinia, Mohammad
   Mazloomzadeh, Seideh
   Mehran, Ladan
TI Influence of topical iodine-containing antiseptics used during delivery
   on recall rate of congenital hypothyroidism screening program
SO JOURNAL OF PEDIATRIC ENDOCRINOLOGY & METABOLISM
LA English
DT Article
DE congenital hypothyroidism; endocrinology-pediatric; iodine; thyroid
   function
ID PROVINCE
AB Background: The proportion of newborns recalled during neonatal screening programs for congenital hypothyroidism (CH) varies substantially by country and may be higher in settings where povodine iodine (PVP-I) is used during delivery. We assessed this hypothesis by substituting PVP-I for chlorhexidine (CHL) and evaluated the reduction in the recall rate of the Irainian newborn screening program.
   Methods: This study investigated 2282 neonates of mothers admitted to a local hospital for delivery between December 2012 and October 2013. We measured thyorid stimulating hormone (TSH) levels in heel-prick blood specimens of infants, aged between 3 and 5 days, born to mothers who received PVP-I (phase I) and those who received CHL after withdrawal of PVP-I from obstetric procedures (phase II). Then we compared the median TSH levels and the recall rate based on a TSH level >= 5 mU/L.
   Results: Of 2282 cases, 1094 infants were born to mothers exposed to PVP-I during phase I (PVP-I group) and 1188 ones were born to mothers exposed to chlorhexidine in phase II (CHL group); 6.56% of the PVP-I group and 1.91% of the CHL group were recalled later during screening (p < 0.001). The median TSH level was significantly higher in the PVP-I group compared to the CHL group (1.35 vs. 1.00, p < 0.001).
   Conclusions: Replacement of iodine-containing antiseptics by iodine-free ones, during delivery resulted in a significant reduction in the recall rate of the Iranian screening program for CH.
C1 [Movahedinia, Mohammad] Shaheed Beheshti Univ Med Sci, Velenjak St,Shahid Chamran Highway, Tehran 193954719, Iran.
   [Valizadeh, Majid; Mehran, Ladan] Shaheed Beheshti Univ Med Sci, Res Inst Endocrine Sci, Tehran, Iran.
   [Moezzi, Farzaneh; Khavassi, Zohreh] Zanjan Univ Med Sci, Mosavi Hosp Zanjan, Zanjan, Iran.
   [Mazloomzadeh, Seideh] Zanjan Univ Med Sci, Dept Publ Hlth Zanjan, Zanjan, Iran.
C3 Shahid Beheshti University Medical Sciences; Shahid Beheshti University
   Medical Sciences
RP Movahedinia, M (corresponding author), Shaheed Beheshti Univ Med Sci, Velenjak St,Shahid Chamran Highway, Tehran 193954719, Iran.
EM pudo_72@yahoo.com
RI Movahedinia, Mohammad/IUP-6790-2023; Mazloomzadeh, Saeideh/H-4062-2016
OI Movahedinia, Mohammad/0000-0001-7889-8015; Mazloomzadeh,
   Saeideh/0000-0001-6325-0662; Valizadeh, Majid/0000-0002-3155-1951
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NR 19
TC 6
Z9 7
U1 0
U2 4
PU WALTER DE GRUYTER GMBH
PI BERLIN
PA GENTHINER STRASSE 13, D-10785 BERLIN, GERMANY
SN 0334-018X
EI 2191-0251
J9 J PEDIATR ENDOCR MET
JI J. Pediatr. Endocrinol. Metab.
PD SEP
PY 2017
VL 30
IS 9
BP 973
EP 978
DI 10.1515/jpem-2016-0164
PG 6
WC Endocrinology & Metabolism; Pediatrics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Endocrinology & Metabolism; Pediatrics
GA FF3LB
UT WOS:000408795400009
PM 28809751
DA 2024-09-05
ER
PT J
AU Hoes, AC
   Regeer, BJ
   Bunders, JFG
AF Hoes, Anne-Charlotte
   Regeer, Barbara J.
   Bunders, Joske F. G.
TI TransFormers in knowledge production: building science-practice
   collaborations
SO ACTION LEARNING
LA English
DT Article
DE action learning; system innovation; inter-institutional collaboration;
   science and society
AB This article places action learning in the context of system innovation, as it studies the potential use of action learning for system change. In order to effect such system change, collaboration between actors from different institutional backgrounds is essential. To gain insight into if and how action learning can be applied for system change, we study three system change projects in Dutch agriculture. We focus specifically on the approaches developed by the project leaders for collaboration between the scientists and the entrepreneurs and analyse how the interaction between these two contributed to the learning process within the project. This article concludes with guiding concepts for action learning for system change in the field of sustainable development of agriculture and beyond.
C1 [Hoes, Anne-Charlotte; Regeer, Barbara J.] Vrije Univ Amsterdam, Athena Inst Res Commun & Innovat Hlth & Life Sci, Athena Inst, Amsterdam, Netherlands.
   [Bunders, Joske F. G.] Vrije Univ Amsterdam, Athena Inst Res Commun & Innovat Hlth & Life Sci, Athena Inst, Biol & Soc, Amsterdam, Netherlands.
C3 Vrije Universiteit Amsterdam; Vrije Universiteit Amsterdam
RP Hoes, AC (corresponding author), Vrije Univ Amsterdam, Athena Inst Res Commun & Innovat Hlth & Life Sci, Athena Inst, Amsterdam, Netherlands.
EM ahoes@falw.vu.nl
RI Regeer, Barbara J/JCE-1493-2023; Regeer, Barbara J/M-1207-2018
OI Regeer, Barbara J/0000-0002-9044-9367
FU TransForum
FX We are grateful for the support of TransForum, the co-funder of our
   research project 'Networked Learning, Learning from Networks', of which
   the three case studies discussed in this article form a part. TransForum
   is a national programme that supports collaboration between science and
   practice in order to realise the sustainable development of agribusiness
   and rural areas. In addition we would also like to thank the project
   leaders and other interviewees, without whose openness this research
   would not have been possible.
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NR 29
TC 25
Z9 27
U1 0
U2 0
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1476-7333
EI 1476-7341
J9 ACTION LEARN
JI Action Learn.
PY 2008
VL 5
IS 3
BP 207
EP 220
DI 10.1080/14767330802461298
PG 14
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA V99JE
UT WOS:000213462500002
DA 2024-09-05
ER
PT J
AU Hiragi, S
   Tamura, H
   Goto, R
   Kuroda, T
AF Hiragi, Shusuke
   Tamura, Hiroshi
   Goto, Rei
   Kuroda, Tomohiro
TI The effect of model selection on cost-effectiveness research: a
   comparison of kidney function-based microsimulation and disease
   grade-based microsimulation in chronic kidney disease modeling
SO BMC MEDICAL INFORMATICS AND DECISION MAKING
LA English
DT Article
DE Chronic kidney disease; Health economics; Cost effectiveness analysis;
   Disease modeling
ID QUALITY-OF-LIFE; GLOMERULAR-FILTRATION-RATE; STAGE RENAL-DISEASE; IGA
   NEPHROPATHY; DECLINE; PROGRESSION; POPULATION; TRIAL; CARE; CKD
AB BackgroundCost effectiveness research is emerging in the chronic kidney disease (CKD) research field. Especially, an individual-level state transition model (microsimulation) is widely used for these researches. Some researchers set CKD grades as discrete health states, and the transition probabilities between these states were dependent on the CKD grades (disease grade-based microsimulation, MSM-dg), while others set estimated glomerular filtration rate value which determines the severity of CKD as a main variable describing patients' continuous status (kidney function-based microsimulation, MSM-kf). MSM-kf seems to reflect the real world more precisely but is more difficult to implement. We compared the calculation results of these two microsimulation models to evaluate the effect of model selection on CKD cost-effectiveness analysis.MethodsWe implemented simplified MSM-dg and MSM-kf emulating natural course of CKD in general, and compared models using parameters derived from an IgA nephropathy cohort. After checking these models' overall behavior, life-years, utilities, and thresholds regarding intervention costs below which the intervention is thought as dominant (V0) or cost-effective (V1) were calculated. In addition, one-way and probabilistic sensitivity analyses were performed.ResultsWith base-case parameters, the calculated life-years was shorter in MSM-dg (73.89 vs. 75.80years) while the thresholds were almost equal (86.87 vs. 90.43 (V0), 132.29 vs. 146.25 [V1 in 1000 USD]) compared to MSM-kf. Sensitivity analyses showed a tendency of the MSM-dg to show shorter results in life-years. V0 and V1 were distributed by approximately 100,000 USD (V0) and +/- 150,000 USD (V1) between models.Conclusions p id= Par4 Estimated cost-effectiveness thresholds by both models were not the same and its difference distributed too wide to be ignored. This result indicated that model selection in CKD cost-effectiveness research has large effect on their conclusions.
C1 [Hiragi, Shusuke; Tamura, Hiroshi; Kuroda, Tomohiro] Kyoto Univ Hosp, Div Med Informat Technol & Adm Planning, Sakyo Ku, 54 Kawaharacho, Kyoto 6068507, Japan.
   [Hiragi, Shusuke] Kyoto Univ Hosp, Dept Nephrol, Sakyo Ku, 54 Kawaharacho, Kyoto 6068507, Japan.
   [Goto, Rei] Keio Univ, Grad Sch Business Adm, Kohoku Ku, 2-33-28 Hiyoshi Honcho, Yokohama, Kanagawa 2238526, Japan.
   [Goto, Rei] Keio Univ, Keio Business Sch, Kohoku Ku, 2-33-28 Hiyoshi Honcho, Yokohama, Kanagawa 2238526, Japan.
C3 Kyoto University; Kyoto University; Keio University; Keio University
RP Hiragi, S (corresponding author), Kyoto Univ Hosp, Div Med Informat Technol & Adm Planning, Sakyo Ku, 54 Kawaharacho, Kyoto 6068507, Japan.; Hiragi, S (corresponding author), Kyoto Univ Hosp, Dept Nephrol, Sakyo Ku, 54 Kawaharacho, Kyoto 6068507, Japan.
EM hiragi.shusuke.4x@kyoto-u.ac.jp
RI Tamura, Hiroshi/H-1855-2011; Kuroda, Tomohiro/F-7041-2010; Hiragi,
   Shusuke/AAF-4310-2019; HIRAGI, Shusuke/GYA-3171-2022
OI Tamura, Hiroshi/0000-0002-7740-2732; Kuroda,
   Tomohiro/0000-0003-1472-7203; Hiragi, Shusuke/0000-0003-1629-6195; 
FU Kyoto University; Advanced Science, Technology & Management Research
   Institute of KYOTO (ASTEM RI / KYOTO)
FX Financial support for this study was provided in part by a grant from
   Kyoto University and Advanced Science, Technology & Management Research
   Institute of KYOTO (ASTEM RI / KYOTO). The funding agreement ensured the
   authors' independence in designing the study, interpreting the data,
   writing, and publishing the report.
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NR 40
TC 7
Z9 7
U1 0
U2 2
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 1472-6947
J9 BMC MED INFORM DECIS
JI BMC Med. Inform. Decis. Mak.
PD NOV 9
PY 2018
VL 18
AR 94
DI 10.1186/s12911-018-0678-7
PG 11
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Medical Informatics
GA GZ8IZ
UT WOS:000449734700001
PM 30413200
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU Maria, MR
   Ballini, R
   Souza, RF
AF Maria, Mariana Reis
   Ballini, Rosangela
   Souza, Roney Fraga
TI Evolution of Green Finance: A Bibliometric Analysis through Complex
   Networks and Machine Learning
SO SUSTAINABILITY
LA English
DT Article
DE green finance; bibliometric analysis; literature review; topic modelling
ID RENEWABLE ENERGY-CONSUMPTION; JUSTICE ADAPTATION FINANCE; CARBON
   EMISSIONS EVIDENCE; EMERGING RESEARCH FRONTS; CLIMATE-CHANGE;
   ECONOMIC-GROWTH; CITATION NETWORKS; CO2 EMISSIONS; COUNTRIES; AID
AB A fundamental structural transformation that must occur to break global temperature rise and advance sustainable development is the green transition to a low-carbon system. However, dismantling the carbon lock-in situation requires substantial investment in green finance. Historically, investments have been concentrated in carbon-intensive technologies. Nonetheless, green finance has blossomed in recent years, and efforts to organise this literature have emerged, but a deeper understanding of this growing field is needed. For this goal, this paper aims to delineate this literature's existing groups and explore its heterogeneity. From a bibliometric coupling network, we identified the main groups in the literature; then, we described the characteristics of these articles through a novel combination of complex network analysis, topological measures, and a type of unsupervised machine learning technique called structural topic modelling (STM). The use of computational methods to explore literature trends is increasing as it is expected to be compatible with a large amount of information and complement the expert-based knowledge approach. The contribution of this article is twofold: first, identifying the most relevant articles in the network related to each group and, second, the most prestigious topics in the field and their contributions to the literature. A final sample of 3275 articles shows three main groups in the literature. The more mature is mainly related to the distribution of climate finance from the developed to the developing world. In contrast, the most recent ones are related to climate financial risks, green bonds, and the insertion of financial development in energy-emissions-economics models. Researchers and policy-makers can recognise current research challenges and make better decisions with the help of the central research topics and emerging trends identified from STM. The field's evolution shows a clear movement from an international perspective to a nationally-determined discussion on finance to the green transition.
C1 [Maria, Mariana Reis; Ballini, Rosangela] Univ Estadual Campinas, Inst Econ, BR-13083857 Campinas, SP, Brazil.
   [Souza, Roney Fraga] Univ Fed Mato Grosso, Fac Econ, BR-78060900 Cuiaba, MT, Brazil.
C3 Universidade Estadual de Campinas; Universidade Federal de Mato Grosso
RP Maria, MR (corresponding author), Univ Estadual Campinas, Inst Econ, BR-13083857 Campinas, SP, Brazil.
EM marianareismaria@gmail.com
RI Ballini, Rosangela/B-1560-2015; Maria, Mariana/CAJ-5582-2022; Fraga
   Souza, Roney/M-5661-2013
OI Maria De Lana, Mariana Reis/0000-0002-0036-3667; Fraga Souza,
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NR 99
TC 6
Z9 6
U1 8
U2 61
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD JAN
PY 2023
VL 15
IS 2
AR 967
DI 10.3390/su15020967
PG 23
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA 8Q7KD
UT WOS:000927380500001
OA gold
DA 2024-09-05
ER
PT J
AU Ikram, MT
   Afzal, MT
AF Ikram, Muhammad Touseef
   Afzal, Muhammad Tanvir
TI Aspect based citation sentiment analysis using linguistic patterns for
   better comprehension of scientific knowledge
SO SCIENTOMETRICS
LA English
DT Article
DE Citation analytics; Aspect detection; n-grams; n-gram after; n-gram
   before; n-gram around; Scientometrics
ID FEATURE-SELECTION; CONTEXT; EXTRACTION; INDEX
AB An almost unrestrained access to research plethora has emerged with a potential drawback: extracting relevant scientific publications is not a straightforward task anymore. The best way is to search on citation indexes, which also provide large number of pertinent papers and when a paper is focused even then it ascertains thousands of citations. In such a scenario, citation text could be a quintessential indicator in determining the importance and relevancy of paper for the researcher based on different aspects of the cited work such as technique, corpus, method, task, concept, measure, model and tool etc. This paper presents a novel approach to identify aspect level sentiments to reveal the hidden patterns from scholarly big data. The proposed methodology comprises of two levels. At first level, it extracts the aspects from the citation sentences using the pattern of opinionated phrases around the aspect. At the second level, it detects the sentiment polarity of the identified aspect considering nearby words and associates it with the corresponding aspect category based on a linguistic rule-based approach. We consider the words before, after and around the aspect using n-gram based features: N-gram after', N-gram before' and N-gram around'. Our results reveal that N-gram around' feature performed better than other features and the SVM outperformed other considered classifiers for all N-gram models.
C1 [Ikram, Muhammad Touseef; Afzal, Muhammad Tanvir] Capital Univ Sci & Technol, Comp Sci Dept, Islamabad, Pakistan.
C3 Capital University of Science & Technology
RP Ikram, MT (corresponding author), Capital Univ Sci & Technol, Comp Sci Dept, Islamabad, Pakistan.
EM touseefgrw@hotmail.com; tanvirqau@hotmail.com
RI Afzal, Muhammad/D-3741-2019
OI Afzal, Muhammad/0000-0002-7851-2327; Afzal, Muhammad
   Tanvir/0000-0002-9765-8815
CR Abirami AM, 2016, J UNIVERS COMPUT SCI, V22, P650
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NR 37
TC 23
Z9 27
U1 4
U2 53
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD APR
PY 2019
VL 119
IS 1
BP 73
EP 95
DI 10.1007/s11192-019-03028-9
PG 23
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA HS1QS
UT WOS:000463637600004
DA 2024-09-05
ER
PT C
AU Belz, A
   Graddy-Reed, A
   Shweta, F
   Giga, A
   Murali, SM
AF Belz, Andrea
   Graddy-Reed, Alexandra
   Shweta, Fnu
   Giga, Aleksandar
   Murali, Shivesh Meenakshi
GP IEEE
TI Deterministic bibliometric disambiguation challenges in company names
SO 2023 IEEE 17TH INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING, ICSC
LA English
DT Proceedings Paper
CT 17th IEEE International Conference on Semantic Computing (ICSC)
CY FEB 01-03, 2023
CL Laguna Hills, CA
DE disambiguation; names; patents; NLP; bibliometric; NASA; SBIR
ID GOVERNMENT
AB Peer-reviewed publications and patents serve as important signatures of knowledge generation, and therefore the authors and their organizations can represent agents of intellectual transformation. Accurate tracking of these players enables scholars to follow knowledge evolution. However, while author name disambiguation has been discussed extensively, less is known about the impact of organization name on bibliometric studies. We expand here on the recently defined phenomenon of "onomastic profusion," high-frequency words used in organization names for semantic reasons, and thus contributing a non-random source of error to bibliographic studies. We use the Small Business Innovation Research (SBIR) Phase I awardees of the National Aeronautics and Space Administration (NASA) as a use case in the field of engineering innovation. We find that firms in California or Massachusetts experience a six percent decrease in the likelihood of using the word "Technologies" in their names. Furthermore, use of the words "Research" and "Science" is linked to doubling the number of awards. We illustrate that, in aggregate, firms executing rational strategic naming decisions can create deterministic bibliometric challenges.
C1 [Belz, Andrea; Shweta, Fnu; Murali, Shivesh Meenakshi] Univ Southern Calif, Viterbi Sch Engn, Los Angeles, CA 90007 USA.
   [Graddy-Reed, Alexandra] Univ Southern Calif, Sol Price Sch Publ Policy, Los Angeles, CA USA.
   [Giga, Aleksandar] Delft Univ Technol, Fac Technol Policy & Management, Delft, Netherlands.
C3 University of Southern California; University of Southern California;
   Delft University of Technology
RP Belz, A (corresponding author), Univ Southern Calif, Viterbi Sch Engn, Los Angeles, CA 90007 USA.
EM abelz@usc.edu; graddyre@usc.edu; s779682@usc.edu; A.Giga@tudelft.nl;
   smeenaks@usc.edu
FU National Science Foundation (NSF) I-Corps [1444080, 1740721]
FX This research was funded in part by the National Science Foundation
   (NSF) I-Corps awards 1444080 and 1740721. Any opinions, findings, or
   recommendations expressed in this paper are those of the authors and do
   not necessarily reflect the views of the aforementioned organizations.
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NR 42
TC 2
Z9 2
U1 0
U2 1
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA
BN 978-1-6654-8263-9
PY 2023
BP 239
EP 243
DI 10.1109/ICSC56153.2023.00047
PG 5
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications; Computer Science, Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BV0YC
UT WOS:000981661600039
OA Green Published
DA 2024-09-05
ER
PT J
AU Argueta-Guzman, M
   West, M
   Gaiarsa, MP
   Allen, CW
   Cecala, JM
   Gedlinske, L
   McFrederick, QS
   Murillo, AC
   Sankovitz, M
   Rankin, EEW
AF Argueta-Guzman, Magda
   West, Mari
   Gaiarsa, Marilia P.
   Allen, Christopher W.
   Cecala, Jacob M.
   Gedlinske, Lauren
   McFrederick, Quinn S.
   Murillo, Amy C.
   Sankovitz, Madison
   Rankin, Erin E. Wilson
TI Words matter: how ecologists discuss managed and non-managed bees and
   birds
SO SCIENTOMETRICS
LA English
DT Article
DE Invasion biology; Sentiment analysis; Wild; Native; Introduced;
   Bibliometric analysis
ID INVASION BIOLOGY; FERAL CHICKENS; HONEY-BEES; SCIENCE; WILD;
   CONSERVATION; LANGUAGE
AB Effectively promoting the stability and quality of ecosystem services involves the successful management of domesticated species and the control of introduced species. In the pollinator literature, interest and concern regarding pollinator species and pollinator health dramatically increased in recent years. Concurrently, the use of loaded terms when discussing domesticated and non-native species may have increased. As a result, pollinator ecology has inherited both the confusion associated with invasion biology's lack of a standardized terminology to describe native, managed, or introduced species as well as loaded terms with very strong positive or negative connotations. The recent explosion of research on native bees and alternative pollinators, coupled with the use of loaded language, has led to a perceived divide between native bee and managed bee researchers. In comparison, the bird literature discusses the study of managed (poultry) and non-managed (all other birds) species without an apparent conflict with regard to the use of terms with strong connotations or sentiment. Here, we analyze word usage when discussing non-managed and managed bee and bird species in 3614 ecological and evolutionary biology papers published between 1990 and 2019. Using time series analyses, we demonstrate how the use of specific descriptor terms (such as wild, introduced, and exotic) changed over time. We then conducted co-citation network analyses to determine whether papers that share references have similar terminology and sentiment. We predicted a negative language bias towards introduced species and positive language bias towards native species. We found an association between the term invasive and bumble bees and we observed significant increases in the usage of more ambiguous terms to describe non-managed species, such as wild. We detected a negative sentiment associated with the research area of pathogen spillover in bumble bees, which corroborates the subjectivity that language carries. We recommend using terms that acknowledge the role of human activities on pathogen spillover and biological invasions. Avoiding the usage of loaded terms when discussing managed and non-managed species will advance our understanding and promote effective and productive communication across scientists, general public, policy makers and other stake holders in our society.
C1 [Argueta-Guzman, Magda; West, Mari; Gaiarsa, Marilia P.; Allen, Christopher W.; Cecala, Jacob M.; Gedlinske, Lauren; McFrederick, Quinn S.; Murillo, Amy C.; Sankovitz, Madison; Rankin, Erin E. Wilson] Univ Calif Riverside, Dept Entomol, Riverside, CA 92521 USA.
   [Gaiarsa, Marilia P.] Univ Calif, Dept Life & Environm Sci, Merced, CA 95343 USA.
   [Gaiarsa, Marilia P.] Univ Zurich, Dept Evolutionary Biol & Environm Studies, CH-8057 Zurich, Switzerland.
   [Cecala, Jacob M.] Univ Calif Davis, Dept Entomol & Nematol, Davis, CA 95616 USA.
   [Gedlinske, Lauren] Montana State Univ, Dept Ecol, Bozeman, MT 59715 USA.
C3 University of California System; University of California Riverside;
   University of California System; University of California Merced;
   University of Zurich; University of California System; University of
   California Davis; Montana State University System; Montana State
   University Bozeman
RP Rankin, EEW (corresponding author), Univ Calif Riverside, Dept Entomol, Riverside, CA 92521 USA.
EM e.wilson.rankin@gmail.com
RI McFrederick, Quinn/AAD-2858-2019; Wilson Rankin, E E/GLS-9381-2022;
   Gedlinske, Lauren/HMV-3667-2023
OI Wilson Rankin, E E/0000-0001-7741-113X; West, Mari/0000-0001-5828-7639;
   McFrederick, Quinn S./0000-0003-0740-6954; Cecala,
   Jacob/0000-0002-6224-8517; Murillo, Amy/0000-0002-2467-2747; Argueta
   Guzman, Magda Paola/0000-0003-1758-3129; Palumbo Gaiarsa,
   Marilia/0000-0003-4414-472X
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NR 83
TC 0
Z9 0
U1 3
U2 36
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD MAR
PY 2023
VL 128
IS 3
BP 1745
EP 1764
DI 10.1007/s11192-022-04620-2
EA JAN 2023
PG 20
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 9M3IZ
UT WOS:000916678200001
OA hybrid
DA 2024-09-05
ER
PT J
AU Piryani, R
   Gupta, V
   Singh, VK
   Pinto, D
AF Piryani, Rajesh
   Gupta, Vedika
   Singh, Vivek Kumar
   Pinto, David
TI Book impact assessment: A quantitative and text-based exploratory
   analysis
SO JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
LA English
DT Article
DE Altmetrics; book impact; citation impact; review mining; sentiment
   analysis
ID ALTERNATIVE METRICS; CITATION ANALYSIS; REVIEWS; LEVEL
AB Books are an important source of knowledge to disseminate information. Researchers and academicians write books to propagate their innovative research or teachings amongst academic as well as non-academic audience. The number of books written every year is increasing rapidly. According to International Publisher Association (IPA) annual report 2015-2016, around 150 million different books were published worldwide in 2014-2015. Many e-commerce websites are also involved in selling books. A recent addition to book publishing world is e-books, which have really made it very simple to publish. While, availability of large number of books is good for readers, at the same time it is challenging to find a good book, particularly in scholarly settings. Researchers in the area of Scientometrics have attempted to view assessment of goodness of a scholarly book by measuring citations that a book receive. However, citations alone are not a true measure of a book's impact. Many a times people use the knowledge in a book without actually citing it. Also use of books in classroom settings or for general reading often is not reflected in terms of citations. Therefore, it is important to obtain users's opinion about a book from other forms of data. Fortunately, we have now some data of this sort available in form of reviews, downloads and social media mentions etc. Amazon and Goodreads, both of which provide the readers' views about a book, are two good examples. This paper presents an exploratory research work on using these non-traditional data about books to assess impact of a book. A set of Scopus-indexed computer science books with good citations as well as some other popular books in computer science domain are used for analysis. The reviews of books have been crawled in an automated fashion from Amazon and Goodreads. Thereafter sentiment analysis is carried out the text of reviews. Results of sentiment analysis are compared and correlated with traditional impact assessment metrics. The experimental analysis does not show a coherent relationship between citation and online reviews. Also, majority of the online reviews are found to be positive for large number of books in the dataset. As a related exercise, the Scopus citation data and Google scholar citation data for books are also compared. A high value of correlation is observed in these two. Overall the exploratory analysis provides a useful insight into the problem of book impact assessment.
C1 [Piryani, Rajesh] South Asian Univ, Dept Comp Sci, New Delhi, India.
   [Gupta, Vedika] Natl Inst Technol Delhi, Dept Comp Sci & Engn, Delhi, India.
   [Singh, Vivek Kumar] Banaras Hindu Univ, Dept Comp Sci, Varanasi 221005, Uttar Pradesh, India.
   [Pinto, David] Benemerita Univ Autonoma Puebla, Puebla, Mexico.
C3 South Asian University (SAU); National Institute of Technology (NIT
   System); National Institute of Technology Delhi; Banaras Hindu
   University (BHU); Benemerita Universidad Autonoma de Puebla
RP Singh, VK (corresponding author), Banaras Hindu Univ, Dept Comp Sci, Varanasi 221005, Uttar Pradesh, India.
EM vivek@bhu.ac.in
RI Piryani, Rajesh/AAF-8148-2020; Singh, Vivek Kumar/O-5699-2019; gupta,
   vedika/ABD-1934-2021; Gupta, Vedika/JXL-2328-2024; pinto,
   david/C-2797-2019
OI Piryani, Rajesh/0000-0003-3374-0657; Singh, Vivek
   Kumar/0000-0002-7348-6545; gupta, vedika/0000-0002-8109-498X; Pinto,
   David/0000-0002-8516-5925
FU Indo-German (DST-DAAD) Joint Research Project
   [DST/INT/FRG/DAAD/P-28/2017]
FX This project is partly funded by an Indo-German (DST-DAAD) Joint
   Research Project, Grant No.: DST/INT/FRG/DAAD/P-28/2017.
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NR 25
TC 8
Z9 8
U1 2
U2 55
PU IOS PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 1064-1246
EI 1875-8967
J9 J INTELL FUZZY SYST
JI J. Intell. Fuzzy Syst.
PY 2018
VL 34
IS 5
BP 3101
EP 3110
DI 10.3233/JIFS-169494
PG 10
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA GH2AV
UT WOS:000433204800025
DA 2024-09-05
ER
PT J
AU Fuentealba, D
   Flores-Fernández, C
   Carrasco, R
AF Fuentealba, Diego
   Flores-Fernandez, Cherie
   Carrasco, Raul
TI Analisis bibliometrico y de contenido sobre VUCA
SO REVISTA ESPANOLA DE DOCUMENTACION CIENTIFICA
LA English
DT Article
DE VUCA; Bibliometrics; content analysis; LDA; K-Means
ID LEADERSHIP; CHALLENGES; INNOVATION; WORLD
AB analysis Abstract: VUCA is an acronym for volatility, uncertainty, complexity, and ambiguity, used to describe an environment that defies confident predictions. An example of this environment is the Covid-19 pandemic, which has created uncer-tainty worldwide because it is an unknown and highly contagious disease that neither society nor institutions were pre-pared to face. This article aims to describe the scientific production of VUCA to understand its main research focus. This research analyzes 105 documents from the Web of Science (WoS) database using Bibliometrics and Content Analysis. The bibliometric analysis reported several production indexes: annual, personal, national, institutional, and journal productiv-ity. The content analysis analyzed 95 article abstracts in nineteen clusters selected by comparing two clustering methods, Latent Dirichlet Allocation and K-Means, using the coherence and silhouette indices, respectively. VUCA is an emerging topic with increased scientific production in the last four years. However, there are no major producers to date. The most frequent topics are management, leadership, and change, where several works emphasize the role of the leader in deal-ing with change. The literature has focused on understanding the skills needed to cope with a VUCA environment and how to teach them. In addition, the use of two methods based on machine learning techniques to estimate the number of clusters of scientific papers is highlighted as an alternative to splitting articles into topics when the dataset is small.
C1 [Fuentealba, Diego] Univ Tecnol Metropolitana, Dept Informat & Comp, Santiago, Chile.
   [Flores-Fernandez, Cherie] Univ Tecnol Metropolitana, Dept Gest Informac, Santiago, Chile.
   [Carrasco, Raul] Univ Amer, Fac Ingn & Negocios, Providencia, Chile.
C3 Universidad Tecnologica Metropolitana; Universidad Tecnologica
   Metropolitana; Universidad de Las Americas - Chile
RP Fuentealba, D (corresponding author), Univ Tecnol Metropolitana, Dept Informat & Comp, Santiago, Chile.
EM d.fuentealba@utem.cl; cflores@utem.cl; rcarrasco@udla.cl
RI Flores, Cherie/AAC-6868-2020; Fuentealba, Diego/AAM-8509-2020; Carrasco,
   Raúl/G-5440-2015
OI Flores, Cherie/0000-0001-5294-7157; Fuentealba,
   Diego/0000-0001-5284-0448; Carrasco, Raúl/0000-0002-5023-9349
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NR 67
TC 2
Z9 2
U1 8
U2 29
PU CONSEJO SUPERIOR INVESTIGACIONES CIENTIFICAS-CSIC
PI MADRID
PA Editorial CSIC, C/VITRUVIO 8, 28006 MADRID, SPAIN
SN 0210-0614
EI 1988-4621
J9 REV ESP DOC CIENT
JI Rev. Esp. Doc. Cient.
PD APR-JUN
PY 2023
VL 46
IS 2
AR 1968
DI 10.3989/redc.2023.2.1968
PG 13
WC Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science
GA H0VN0
UT WOS:000993225500003
OA gold
DA 2024-09-05
ER
PT J
AU Di Vaio, A
   Hassan, R
   Alavoine, C
AF Di Vaio, Assunta
   Hassan, Rohail
   Alavoine, Claude
TI Data intelligence and analytics: A bibliometric analysis of
   human-Artificial intelligence in public sector decision-making
   effectiveness
SO TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
LA English
DT Article
DE Ambidexterity; Industry 4; 0; Business intelligence; Big data;
   Intellectual capital; Human intellect; Accountability and performance
ID BIG DATA ANALYTICS; FIRM PERFORMANCE; SUPPLY CHAIN; BUSINESS
   INTELLIGENCE; PREDICTIVE ANALYTICS; INSTITUTIONAL THEORY; VALUE
   CREATION; MEDIATING ROLE; HEALTH-CARE; MANAGEMENT
AB This study investigates the literary corpus of the role and potential of data intelligence and analytics through the lenses of artificial intelligence (AI), big data, and the human-AI interface to improve overall decision-making processes. It investigates how data intelligence and analytics improve decision-making processes in the public sector. A bibliometric analysis of a database containing 161 English-language articles published between 2017 and 2021 is performed, providing a map of the knowledge produced and disseminated in previous studies. It provides insights into key topics, citation patterns, publication activities, the status of collaborations between contributors over past studies, aggregated data intelligence, and analytics research contributions. The study provides a retrospective review of published content in the field of data intelligence and analytics. The findings indicate that field research has been concentrated mainly on emerging technologies' intelligence capabilities rather than on human-artificial intelligence in decision-making performance in the public sector. This study extends an ambidexterity theory in decision support, which enlightens how this ambidexterity can be encouraged and how it affects decision outcomes. The study emphasises the importance of the public sector adoption of data intelligence and analytics, as well as its efficiency. Furthermore, this study expands how researchers and practitioners interpret and understand data intelligence and analytics, AI, and big data for effective public sector decision-making.
C1 [Di Vaio, Assunta] Univ Naples Parthenope, Dept Law, Via G Parisi 13, I-80132 Naples, Italy.
   [Hassan, Rohail] Univ Utara Malaysia UUM, Othman Yeop Abdullah Grad Sch Business OYAGSB, Kuala Lumpur 50300, Malaysia.
   [Alavoine, Claude] IPAG Business Sch, 4 Bd Carabacel, F-06000 Nice, France.
C3 Parthenope University Naples; Universiti Utara Malaysia; IPAG Business
   School
RP Di Vaio, A (corresponding author), Univ Naples Parthenope, Dept Law, Via G Parisi 13, I-80132 Naples, Italy.
EM susy.divaio@uniparthenope.it; rohail.hassan@uum.edu.my;
   c.alavoine@ipag.fr
RI Hassan, Rohail/G-1213-2015; Di Vaio, Assunta/N-2259-2019
OI Hassan, Rohail/0000-0002-7825-0283; Di Vaio, Assunta/0000-0002-0498-1630
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NR 107
TC 55
Z9 56
U1 53
U2 275
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0040-1625
EI 1873-5509
J9 TECHNOL FORECAST SOC
JI Technol. Forecast. Soc. Chang.
PD JAN
PY 2022
VL 174
AR 121201
DI 10.1016/j.techfore.2021.121201
EA SEP 2021
PG 17
WC Business; Regional & Urban Planning
WE Social Science Citation Index (SSCI)
SC Business & Economics; Public Administration
GA UY7AO
UT WOS:000701672300012
DA 2024-09-05
ER
PT J
AU Ding, ZJ
   Ji, YR
   Gan, Y
   Wang, YW
   Xia, YK
AF Ding, Zijie
   Ji, Yingrui
   Gan, Yan
   Wang, Yuwen
   Xia, Yukun
TI Current status and trends of technology, methods, and applications of
   Human-Computer Intelligent Interaction (HCII): A bibliometric research
SO MULTIMEDIA TOOLS AND APPLICATIONS
LA English
DT Article; Early Access
DE Human-Computer Intelligent Interaction; Human-Computer Interaction;
   Artificial intelligence; Bibliometric Research; CiteSpace
ID TRACKING; NETWORK
AB This study delves into Human-Computer Intelligent Interaction (HCII), a burgeoning interdisciplinary field that builds upon traditional Human-Computer Interaction (HCI) by integrating advanced technologies like Natural Language Processing (NLP) and Machine Learning (ML). In this paper, we scrutinize 5,781 HCII papers published between 2000 and 2023, narrowing our focus to 803 most relevant articles to construct co-citation and interdisciplinary networks based on the CiteSpace Software. Our findings reveal that the publications of the United States and China are relatively high with 558 and 616 publications respectively. Furthermore, we found that machine learning and deep learning have emerged as the prevalent methodologies in HCII, which currently emphasizes multimodal emotion recognition, facial expression recognition, and NLP. We predict that HCII will be integrated into advanced applications such as neural-based interactive games and multi-sensory environments. In sum, our analysis underscores HCII's role in advancing artificial intelligence, facilitating more intuitive and efficient human-computer interactions, and its prospective societal impact. We hope that our review and analysis may guide the efforts of researchers aiming to contribute to HCII and develop more powerful and intelligent methods, tools, and applications.
C1 [Ding, Zijie; Gan, Yan; Wang, Yuwen; Xia, Yukun] Huazhong Univ Sci & Technol, Sch Mech Sci & Engn, Wuhan 430074, Peoples R China.
   [Ji, Yingrui] Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100190, Peoples R China.
   [Ji, Yingrui] Univ Chinese Acad Sci, Beijing 100190, Peoples R China.
C3 Huazhong University of Science & Technology; Chinese Academy of
   Sciences; Aerospace Information Research Institute, CAS; Chinese Academy
   of Sciences; University of Chinese Academy of Sciences, CAS
RP Gan, Y (corresponding author), Huazhong Univ Sci & Technol, Sch Mech Sci & Engn, Wuhan 430074, Peoples R China.
EM ganyan@hust.edu.cn
FU National Laboratory of Mechanical Systems and Vibrations of China
FX No Statement Available
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U2 31
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1380-7501
EI 1573-7721
J9 MULTIMED TOOLS APPL
JI Multimed. Tools Appl.
PD 2024 JAN 30
PY 2024
DI 10.1007/s11042-023-18096-6
EA JAN 2024
PG 34
WC Computer Science, Information Systems; Computer Science, Software
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   & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA HA6S6
UT WOS:001156811100010
DA 2024-09-05
ER
PT J
AU Nielsen, MW
   Börjeson, L
AF Nielsen, Mathias Wullum
   Borjeson, Love
TI Gender diversity in the management field: Does it matter for research
   outcomes?
SO RESEARCH POLICY
LA English
DT Article
DE Gender diversity; Research outcomes; Management research; Citations;
   Topic modeling; Bibliometrics; Research questions
ID RESEARCH PRODUCTIVITY; SCIENTIFIC PRODUCTIVITY; COMBINED COCITATION; SEX
   SEGREGATION; WORD ANALYSIS; SCIENCE; IMPACT; PERFORMANCE; FACULTY;
   DETERMINANTS
AB This study examines the relationship between gender diversity and research outcomes. Existing research on the topic primarily focuses on how team gender diversity influences scholarly productivity in terms of citations and publication rates. Far less attention has been devoted to the question of how the intellectual contents of research disciplines change as they become more gender diverse. Drawing on a global sample of more than 25,000 management papers, we use natural language processing techniques, correspondence analysis and regression models to illuminate impact-, content- and status-related dimensions of gender diversity in management research. In regression models adjusting for geographical setting, institutional prestige and collaboration patterns, we find no discernable effects of team gender diversity on per-paper scientific impact. In contrast, our analyses converge to yield a broadly consistent pattern of gender-related variations in research focus: women are well represented in social- and human-centered areas of management, while men comprise the vast majority in areas addressing more technical and operational aspects. Our findings corroborate recent sociological research suggesting that cultural norms and expectations are channeling women and men towards different areas of work and study. We argue that the broadened repertoire of perspectives, values and questions resulting from gender diversity may render management research more responsive to the full gamut of societal needs and expectations.
C1 [Nielsen, Mathias Wullum] Aarhus Univ, Dept Polit Sci, Danish Ctr Studies Res & Res Policy, Bartholins Alle 7, DK-8000 Aarhus, Denmark.
   [Borjeson, Love] Stockholm Sch Econ, Stockholm Sch Econ Inst Res SIR, Box 6501, S-11383 Stockholm, Sweden.
C3 Aarhus University; Stockholm School of Economics
RP Nielsen, MW (corresponding author), Aarhus Univ, Dept Polit Sci, Danish Ctr Studies Res & Res Policy, Bartholins Alle 7, DK-8000 Aarhus, Denmark.
EM mwn@ps.au.dk
RI Nielsen, Mathias/KUC-8621-2024
OI Borjeson, Love/0000-0003-1328-4164; Nielsen, Mathias
   Wullum/0000-0001-8759-7150
FU Aarhus University Research Foundation [AUFF-F-2018-7-5]
FX Bibliometric indices (CS, NCS, PP top-10%, JS, self-citation rates,
   institutional collaboration, international collaboration and WoS
   coverage) were generously provided by the Centre for Science and
   Technology Studies (CWTS) at Leiden University. We thank Jesper Wiborg
   Schneider and Sergiy Prostiv for help with data acquisition and
   processing, and useful comments on the manuscript. We also received
   valuable feedback from audiences at SCANCOR, Stanford University, the
   Editor Paul Nightingale and three anonymous reviewers. This project was
   generously funded by the Aarhus University Research Foundation [Award
   AUFF-F-2018-7-5].
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NR 115
TC 42
Z9 44
U1 4
U2 119
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0048-7333
EI 1873-7625
J9 RES POLICY
JI Res. Policy
PD SEP
PY 2019
VL 48
IS 7
BP 1617
EP 1632
DI 10.1016/j.respol.2019.03.006
PG 16
WC Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA IC4RC
UT WOS:000470952000002
DA 2024-09-05
ER
PT C
AU Zou, D
   Han, Y
AF Zou, Dan
   Han, Yi
GP ISSI
BE Atanassova, I
   Bertin, M
   Mayr, P
TI An Altmetrics Study of TOP100 Samples in 2016
SO 16TH INTERNATIONAL CONFERENCE ON SCIENTOMETRICS & INFORMETRICS (ISSI
   2017)
SE Proceedings of the International Conference on Scientometrics and
   Informetrics
LA English
DT Proceedings Paper
CT 16th International Conference on Scientometrics and Informetrics (ISSI)
CY OCT 16-20, 2017
CL Wuhan Univ, Wuhan, PEOPLES R CHINA
HO Wuhan Univ
DE Altmetrics; Altmetrics scores; citation counting; correlation analysis;
   multiple linear regression analysis
AB This paper takes the TOP100 literatures with the highest Altmetrics Scores in the Altmetric.com in 2016 as samples. Taking the advantage of SPSS19.0, the correlation analysis between Altmetrics score and citation counting was presented, and the Pearson correlation coefficient is 0.036, which means that the Altmetrics score does not correlate with the citation counting and the Altmetrics indicator maybe independent variable to assess the literature impact. Meanwhile, the multiple linear regression analysis was conducted, which the Altmetric scores was viewed as dependent variable and some component indicators as independent ones. The fitness between the independent variable and the dependent variable is very good, and the significance test of the variable coefficient is also verified.
C1 [Zou, Dan; Han, Yi] Southwest Univ, Coll Comp & Informat Sci, Chongqing, Peoples R China.
C3 Southwest University - China
RP Zou, D (corresponding author), Southwest Univ, Coll Comp & Informat Sci, Chongqing, Peoples R China.
EM 531479222@qq.com; hanyi72@swu.edu.cn
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NR 13
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Z9 1
U1 0
U2 7
PU INT SOC SCIENTOMETRICS & INFORMETRICS-ISSI
PI LEUVEN
PA KATHOLIEKE UNIV LEUVEN, FACULTEIT E T E W, DEKENSTRAAT 2, LEUVEN,
   B-3000, BELGIUM
SN 2175-1935
J9 PRO INT CONF SCI INF
PY 2017
BP 1710
EP 1718
PG 9
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Information Science & Library Science
GA BO2SP
UT WOS:000508234900188
DA 2024-09-05
ER
PT J
AU Sharmiladevi, JC
   Khandelwal, NK
   Mishra, PK
   Dutt, I
   Raees, S
   Mathur, AR
   Sharma, A
AF Sharmiladevi, J. C.
   Khandelwal, Nishant Kumar
   Mishra, Punit Kumar
   Dutt, Ishita
   Raees, Sameera
   Mathur, Ashutosh Rajendera
   Sharma, Adya
TI Changes and Current Trends in Higher Education in Management and Allied
   Disciplines: A Bibliometric Study
SO INTERNATIONAL JOURNAL OF EDUCATIONAL SCIENCES
LA English
DT Article
DE Online Learning; Research Trends; Sustainable Higher Education;
   Management; Scientometrics
ID SCIENTIFIC LITERATURE; COMPETENCES; COCITATION; IMPACT
AB Changes in the higher education domain are essential for the enhancement of the future generation. It must be embraced successfully to face the dynamics and accept new developments with their inherent outcomes. Sustaining changes will equip policymakers to understand the wheel of knowledge central to success. This domain has undergone many changes recently due to multiple global events. Using bibliometric analysis, authors tried to understand these changes in management and allied fields by extracting 2503 documents from the Scopus database from January 2020 to April 2023. Studies in Higher Education and International Journal of Sustainability in Higher Education are the most prominent sources. Mishra L, Gupta T, and Shree A are the highest-cited authors. Braun, Marginson, and Bourdieu are the most co-cited authors. Change management, online collaborative learning, and digital competence are the themes that emerged from thematic analysis. The results of this study are significant in decision-making for scholars, researchers, policymakers, and institutions in the higher education sector.
C1 [Sharmiladevi, J. C.; Khandelwal, Nishant Kumar; Mishra, Punit Kumar; Dutt, Ishita; Raees, Sameera; Mathur, Ashutosh Rajendera; Sharma, Adya] Symbiosis Int Deemed Univ, Symbiosis Ctr Management Studies, Pune, Maharashtra, India.
C3 Symbiosis International University; Symbiosis Centre for Management
   Studies Pune
RP Sharmiladevi, JC (corresponding author), Symbiosis Int Deemed Univ, Symbiosis Ctr Management Studies, Pune, Maharashtra, India.
EM sharmiladevi@scmspune.ac.in
RI J.C, Sharmiladevi/U-3983-2017; Sharma, Adya/HLV-8669-2023; Dutt,
   Ishita/GLT-8249-2022; RAEES, Dr. SAMEERA/GHD-8110-2022; Mishra, Punit
   Kumar/HDL-9155-2022; Mathur, Ashutosh/HJP-2278-2023
OI Mishra, Punit Kumar/0000-0002-2912-4977; 
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NR 124
TC 0
Z9 0
U1 4
U2 9
PU KAMLA-RAJ ENTERPRISES
PI GURUGRAM
PA C210, NIRVANA COURTYARD SOUTH CITY 2, GURUGRAM, HARYANA 122 018, INDIA
SN 0975-1122
J9 INT J EDUC SCI
JI Int. J. Educ. Sci.
PD JUL-SEP
PY 2023
VL 42
IS 1-3
BP 6
EP 20
DI 10.31901/24566322.2023/42.1-3.1292
PG 15
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA T9CX3
UT WOS:001080901500002
DA 2024-09-05
ER
PT J
AU Cunillera, T
   Guilera, G
AF Cunillera, Toni
   Guilera, Georgina
TI Twenty years of <i>statistical learning</i>: from language, back to
   machine learning
SO SCIENTOMETRICS
LA English
DT Article
DE Statistical learning; Language; Psychology; Bibliometrics
ID WORD SEGMENTATION; CHILDREN
AB Twenty years ago, Saffran et al. (Science 274:1926-1928, 1996) published a paper in the prestigious journal Science, proposing statistical learning as a key learning process to explain how infants acquire their first words. The current paper presents an overview of how this publication has impacted the scientific community under a bibliometric perspective. Documents citing that paper were searched on the Web of Science Core Collection. Its evolution over time has been analyzed, most productive journals and subject areas have been identified, and a keywords co-occurrence map has been created. Results show that statistical learning has spread widely around scientific areas out of Linguistics and Psychology, and has aroused the interest of researchers from other related areas such as Rehabilitation or Education and Educational Research.
C1 [Cunillera, Toni] Univ Barcelona, Fac Psicol, Dept Cognit Dev & Educ Psychol, Pg Vall dHebron 171, Barcelona 08035, Spain.
   [Guilera, Georgina] Univ Barcelona, Fac Psychol, Dept Social Psychol & Quantitat Psychol, Barcelona, Spain.
C3 University of Barcelona; University of Barcelona
RP Cunillera, T (corresponding author), Univ Barcelona, Fac Psicol, Dept Cognit Dev & Educ Psychol, Pg Vall dHebron 171, Barcelona 08035, Spain.
EM tcunillera@ub.edu
RI Guilera, Georgina/A-4253-2009; Cunillera, Toni/I-4212-2015
OI Guilera, Georgina/0000-0002-4941-2511; Cunillera,
   Toni/0000-0002-1768-5910
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NR 23
TC 5
Z9 6
U1 1
U2 109
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD OCT
PY 2018
VL 117
IS 1
BP 1
EP 8
DI 10.1007/s11192-018-2856-x
PG 8
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA GR6EH
UT WOS:000442737700001
DA 2024-09-05
ER
PT J
AU O'Leary, DE
AF O'Leary, Daniel E.
TI INTELLIGENT SYSTEMS IN <i>ACCOUNTING, FINANCE AND MANAGEMENT</i>: ISI
   JOURNAL AND PROCEEDING CITATIONS, AND RESEARCH ISSUES FROM MOST- CITED
   PAPERS
SO INTELLIGENT SYSTEMS IN ACCOUNTING FINANCE & MANAGEMENT
LA English
DT Article
DE naming a journal; ISI proceeding citations; ISI journal citations;
   Google citations; most-cited papers; research topics in artificial
   intelligence
ID INDEX
AB This paper analyses the citations from Intelligent Systems in Accounting, Finance and Management that have occurred in ISI's Web of Knowledge in February 2010. I found roughly 1000 citations to the journal under 10 different journal name abbreviations, with roughly 25% of the citations occurring during 2008-2009, associated with 27 of the more frequently cited papers. Using that citation data, the H-index and the 40 (42 with ties) mostcited papers are presented. I found that ISI's new proceedings data appear to have a different citation pattern than ISI's journal citation data, resulting in citations to more sources, but fewer citations per source. I also examine the research methodologies and applications of the most-cited papers in an attempt to determine what areas have been cited most and where there are potential gaps in the research. Copyright (C) 2010 John Wiley & Sons, Ltd.
C1 [O'Leary, Daniel E.] Univ South Calif, Marshall Sch Business, 3660 Trousdale Pkwy, Los Angeles, CA 90089 USA.
C3 University of Southern California
RP O'Leary, DE (corresponding author), Univ South Calif, Marshall Sch Business, 3660 Trousdale Pkwy, Los Angeles, CA 90089 USA.
EM oleary@usc.edu
RI O'Leary, Daniel E/B-6469-2008
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NR 55
TC 4
Z9 4
U1 0
U2 6
PU JOHN WILEY & SONS LTD
PI CHICHESTER
PA THE ATRIUM, SOUTHERN GATE, CHICHESTER PO19 8SQ, W SUSSEX, ENGLAND
SN 1055-615X
EI 1099-1174
J9 INTELL SYST ACCOUNT
JI Intell. Syst. Account. Financ. Manag.
PD JAN-MAR
PY 2010
VL 17
IS 1
BP 41
EP 58
DI 10.1002/isaf.312
PG 18
WC Business, Finance
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA VA3PS
UT WOS:000409823400003
DA 2024-09-05
ER
PT J
AU Willbur, JF
   Vail, JD
   Mitchell, LN
   Jakeman, DL
   Timmons, SC
AF Willbur, Jaime F.
   Vail, Justin D.
   Mitchell, Lindsey N.
   Jakeman, David L.
   Timmons, Shannon C.
TI Expression, Purification, and Characterization of a Carbohydrate-Active
   Enzyme: A Research-Inspired Methods Optimization Experiment for the
   Biochemistry Laboratory
SO BIOCHEMISTRY AND MOLECULAR BIOLOGY EDUCATION
LA English
DT Article
DE active learning; curriculum assessment; curriculum design; curriculum
   development; curriculum implementation; enzymes and catalysis; gene
   expression; glycobiology; integration of research into undergraduate
   teaching; laboratory exercises
ID RESEARCH EXPERIENCES; MECHANISMS; BENEFITS
AB The development and implementation of research-inspired, discovery-based experiences into science laboratory curricula is a proven strategy for increasing student engagement and ownership of experiments. In the novel laboratory module described herein, students learn to express, purify, and characterize a carbohydrate-active enzyme using modern techniques and instrumentation commonly found in a research laboratory. Unlike in a traditional cookbook-style experiment, students generate their own hypotheses regarding expression conditions and quantify the amount of protein isolated using their selected variables. Over the course of three 3-hour laboratory periods, students learn to use sterile technique to express a protein using recombinant DNA in E. coli, purify the resulting enzyme via affinity chromatography and dialysis, analyze the success of their purification scheme via SDS-PAGE, assess the activity of the enzyme via an HPLC-based assay, and quantify the amount of protein isolated via a Bradford assay. Following the completion of this experiment, students were asked to evaluate their experience via an optional survey. All students strongly agreed that this laboratory module was more interesting to them than traditional experiments because of its lack of a predetermined outcome and desired additional opportunities to participate in the experimental design process. This experiment serves as an example of how research-inspired, discovery-based experiences can benefit both the students and instructor; students learned important skills necessary for real-world biochemistry research and a more concrete understanding of the research process, while generating new knowledge to enhance the scholarly endeavors of the instructor. (C) 2015 by The International Union of Biochemistry and Molecular Biology, 44: 75-85, 2016.
C1 [Willbur, Jaime F.; Vail, Justin D.; Mitchell, Lindsey N.; Timmons, Shannon C.] Lawrence Technol Univ, Dept Nat Sci, 21000 West Ten Mile Rd, Southfield, MI 48075 USA.
   [Jakeman, David L.] Dalhousie Univ, Coll Pharm, Halifax, NS B3H 4R2, Canada.
C3 Dalhousie University
RP Timmons, SC (corresponding author), Lawrence Technol Univ, Dept Nat Sci, 21000 West Ten Mile Rd, Southfield, MI 48075 USA.
EM stimmons@ltu.edu
RI Jakeman, David/X-7624-2018; Timmons, Shannon/IQS-9336-2023
OI Timmons, Shannon/0000-0003-0178-1286; Jakeman, David/0000-0003-3002-3388
FU Lawrence Tech Faculty Seed Grant; Lawrence Tech Quest co-curricular
   experiential learning program; Department of Natural Sciences
FX The development of this laboratory module was supported by funding from
   a Lawrence Tech Faculty Seed Grant, the Lawrence Tech Quest
   co-curricular experiential learning program, and the Department of
   Natural Sciences. Special thanks to Dr. Nicole Villeneuve for HPLC
   assistance and to the CHM 3411 students who completed this experiment
   and the corresponding surveys.
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NR 18
TC 4
Z9 4
U1 0
U2 22
PU WILEY-BLACKWELL
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1470-8175
EI 1539-3429
J9 BIOCHEM MOL BIOL EDU
JI Biochem. Mol. Biol. Educ.
PD JAN-FEB
PY 2016
VL 44
IS 1
BP 75
EP 85
DI 10.1002/bmb.20928
PG 11
WC Biochemistry & Molecular Biology; Education, Scientific Disciplines
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Education & Educational Research
GA DH7YN
UT WOS:000373010200009
PM 26710673
OA Bronze
DA 2024-09-05
ER
PT J
AU Chan, CH
   Grill, C
AF Chan, Chung-hong
   Grill, Christiane
TI The Highs in Communication Research: Research Topics With High Supply,
   High Popularity, and High Prestige in High-Impact Journals
SO COMMUNICATION RESEARCH
LA English
DT Article
DE research topics; foci of scientific interest; topic modeling; citation
   counts; citation networks; high-impact journals
ID TECHNOLOGY RESEARCH; CITATION COUNTS; MODEL; INTERNET; FUTURE; SCHOLARS;
   NETWORK; TRENDS
AB More and more scholarly attention is paid to dissecting discipline of communication research under the microscope thereby aiming at revealing foci of scientific interest. The lion's share of research has hereby focused either on the supply side of research examining what topics scholars write about or at the popularity side of research shedding light on what scientific publications receive the most citations. Building up on this, we argue that these research strands are inadequate to the task of exhaustively identifying foci of scientific interest. Tailoring for the fragmented topical landscape of communication research, we propose an integrative combination of three metrics: supply, popularity, and prestige of research topics. By means of topic modeling, citation counts and citation networks, our study showcases how our approach is able to reveal the intellectual architecture of our discipline in order to identify relevant paths for further scientific inquiry.
C1 [Chan, Chung-hong] Univ Mannheim, Mannheim, Germany.
   [Grill, Christiane] Univ Mannheim, Mannheim Ctr European Social Res MZES, MZES A5,6 Bauteil A, D-68131 Mannheim, Germany.
C3 University of Mannheim; University of Mannheim
RP Chan, CH (corresponding author), Univ Mannheim, Mannheim Ctr European Social Res MZES, MZES A5,6 Bauteil A, D-68131 Mannheim, Germany.
EM Chung-hong.chan@mzes.uni-mannheim.de
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NR 70
TC 12
Z9 12
U1 4
U2 42
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 0093-6502
EI 1552-3810
J9 COMMUN RES
JI Commun. Res.
PD JUL
PY 2022
VL 49
IS 5
SI SI
BP 599
EP 626
AR 0093650220944790
DI 10.1177/0093650220944790
EA JUL 2020
PG 28
WC Communication
WE Social Science Citation Index (SSCI)
SC Communication
GA 1U4RA
UT WOS:000556225200001
OA Green Published
DA 2024-09-05
ER
PT J
AU Hintzen, RE
   Papadopoulou, M
   Mounce, R
   Banks-Leite, C
   Holt, RD
   Mills, M
   Knight, A
   Leroi, AM
   Rosindell, J
AF Hintzen, Rogier E.
   Papadopoulou, Marina
   Mounce, Ross
   Banks-Leite, Cristina
   Holt, Robert D.
   Mills, Morena
   Knight, Andrew
   Leroi, Armand M.
   Rosindell, James
TI Relationship between conservation biology and ecology shown through
   machine reading of 32,000 articles
SO CONSERVATION BIOLOGY
LA English
DT Article
DE bibliometrics; ecological applications; ecological theory;
   interdisciplinary; latent Dirichlet allocation; aplicaciones ecologicas;
   asignacion latente Dirichlet; bibliometria; interdisciplinario; teoria
   ecologica; (sic)(sic)(sic)(sic)(sic)(sic)(sic); (sic)(sic)(sic);
   (sic)(sic)(sic)(sic)(sic); (sic)(sic)(sic)(sic)(sic);
   (sic)(sic)(sic)(sic)(sic)
ID OVERESTIMATE EXTINCTION RATES; HABITAT LOSS; COEXISTENCE; SCIENCE;
   BIODIVERSITY; DYNAMICS; DESIGN
AB Conservation biology was founded on the idea that efforts to save nature depend on a scientific understanding of how it works. It sought to apply ecological principles to conservation problems. We investigated whether the relationship between these fields has changed over time through machine reading the full texts of 32,000 research articles published in 16 ecology and conservation biology journals. We examined changes in research topics in both fields and how the fields have evolved from 2000 to 2014. As conservation biology matured, its focus shifted from ecology to social and political aspects of conservation. The 2 fields diverged and now occupy distinct niches in modern science. We hypothesize this pattern resulted from increasing recognition that social, economic, and political factors are critical for successful conservation and possibly from rising skepticism about the relevance of contemporary ecological theory to practical conservation.
C1 [Hintzen, Rogier E.; Papadopoulou, Marina; Banks-Leite, Cristina; Mills, Morena; Knight, Andrew; Leroi, Armand M.; Rosindell, James] Imperial Coll London, Dept Life Sci, Silwood Pk Campus,Buckhurst Rd, Ascot SW7 2AZ, Berks, England.
   [Papadopoulou, Marina] Univ Groningen, Groningen Inst Evolutionary Life Sci, NL-9747 AG Groningen, Netherlands.
   [Holt, Robert D.] Univ Florida, Dept Biol, Gainesville, FL 32611 USA.
   [Mounce, Ross] Arcadia Fund, Sixth Floor,5 Young St, London W8 6EH, England.
C3 Imperial College London; University of Groningen; State University
   System of Florida; University of Florida
RP Rosindell, J (corresponding author), Imperial Coll London, Dept Life Sci, Silwood Pk Campus,Buckhurst Rd, Ascot SW7 2AZ, Berks, England.
EM j.rosindell@imperial.ac.uk
RI Papadopoulou, Marina/AGG-3307-2022; Mounce, Ross/A-2538-2010;
   Papadopoulou, Marina/GQP-2573-2022; Knight, Andrew Thomas/C-8394-2009;
   Banks-Leite, Cristina/D-3075-2011
OI Mounce, Ross/0000-0002-3520-2046; Papadopoulou,
   Marina/0000-0002-6478-8365; Knight, Andrew Thomas/0000-0002-6563-0500;
   Banks-Leite, Cristina/0000-0002-0091-2857; Leroi,
   Armand/0000-0002-5603-0351; Rosindell, James/0000-0002-5060-9346
FU Natural Environment Research Council (NERC) doctoral training
   scholarship (Science and Solutions for a Changing Planet DTP); NERC
   [NE/L011611/1]; NERC [NE/L011611/1] Funding Source: UKRI
FX The authors thank attendees of the Tansley Workshops at Imperial College
   for valuable discussion. R.H. was funded by a Natural Environment
   Research Council (NERC) doctoral training scholarship (Science and
   Solutions for a Changing Planet DTP). J.R. was funded by a NERC
   fellowship (NE/L011611/1). This study is a contribution to Imperial
   College's Grand Challenges in Ecosystems and the Environment initiative.
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NR 61
TC 23
Z9 24
U1 0
U2 37
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0888-8892
EI 1523-1739
J9 CONSERV BIOL
JI Conserv. Biol.
PD JUN
PY 2020
VL 34
IS 3
BP 721
EP 732
DI 10.1111/cobi.13435
EA DEC 2019
PG 12
WC Biodiversity Conservation; Ecology; Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biodiversity & Conservation; Environmental Sciences & Ecology
GA LQ6QH
UT WOS:000501791200001
PM 31702070
OA hybrid, Green Published
DA 2024-09-05
ER
PT C
AU Pishtari, G
   Sarmiento-Márquez, EM
   Tammets, K
   Aru, J
AF Pishtari, Gerti
   Sarmiento-Marquez, Edna Milena
   Tammets, Kairit
   Aru, Jaan
BE Hilliger, I
   Munoz-Merino, PJ
   DeLaet, T
   Ortega-Arranz, A
   Farrell, T
TI The Evaluation of One-to-One Initiatives: Exploratory Results from a
   Systematic Review
SO EDUCATING FOR A NEW FUTURE: MAKING SENSE OF TECHNOLOGY-ENHANCED LEARNING
   ADOPTION, EC-TEL 2022
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT 17th European Conference on Technology Enhanced Learning (EC-TEL)
CY SEP 12-16, 2022
CL Inst Rech Informatique Toulouse, Toulouse, FRANCE
HO Inst Rech Informatique Toulouse
DE One-to-one computing; Evaluation; Bibliometric network analysis; Topic
   modeling; Systematic literature review
ID FRAMEWORK
AB While one-to-one initiatives (that equip each student and teacher with digital devices) have been widely implemented, no systematic review has explored how they are being evaluated. The contribution of this paper is twofold. First, we present exploratory insights from a systematic review on the evaluation of one-to-one initiatives. We focus on the relations inside the related research community and explore the relevant research topics that they have considered, through bibiliometric network analyses and topic modeling. Second, this paper contributes to existing guidelines about systematic reviews with an example that applies the mentioned analyses after the manual in-depth review of the papers (usually they are applied in parallel, or afterwards). Results depict a fragmented community, with little explicit collaborations among the research groups, but that shares a common body of literature providing good practices that can inform future one-to-one implementations. This community has considered a common set of topics (including, the implementation of educational technologies, mobile learning and classroom orchestration). Future evaluations of one-to-one initiatives would benefit if grounded in pedagogical theories and informed by learning analytics. Our approach enabled us to understand the dynamics of the related community, identify the core literature, and define guiding questions for future qualitative analyses.
C1 [Pishtari, Gerti] Danube Univ Krems, Univ Continuing Educ Krems, Dr Karl Dorrek 30, A-3500 Krems An Der Donau 3500, Austria.
   [Sarmiento-Marquez, Edna Milena; Tammets, Kairit] Tallinn Univ, Narva Mnt 25, EE-10120 Tallinn, Estonia.
   [Aru, Jaan] Univ Tartu, Ulikooli 18, EE-50090 Tartu, Estonia.
C3 Danube University Krems; Tallinn University; University of Tartu
RP Pishtari, G (corresponding author), Danube Univ Krems, Univ Continuing Educ Krems, Dr Karl Dorrek 30, A-3500 Krems An Der Donau 3500, Austria.
EM gerti.pishtari@donau-uni.ac.at; msm@t1u.ee; kairit@tlu.ee;
   jaan.aru@ut.ee
RI Aru, Jaan/H-3967-2015
OI Aru, Jaan/0000-0003-3927-452X; Sarmiento-Marquez, Edna
   Milena/0000-0002-8186-9028
FU ETAG [PRG1634]; HarNothe; European Social Fund through the IT Academy
   Programme; Estonian Research Council [PSG728]
FX This research was supported by the ETAG project PRG1634 and HarNothe, as
   well as the European Social Fund through the IT Academy Programme and
   the Estonian Research Council grant PSG728.
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NR 19
TC 2
Z9 2
U1 0
U2 3
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 0302-9743
EI 1611-3349
BN 978-3-031-16290-9; 978-3-031-16289-3
J9 LECT NOTES COMPUT SC
PY 2022
VL 13450
BP 310
EP 323
DI 10.1007/978-3-031-16290-9_23
PG 14
WC Computer Science, Interdisciplinary Applications; Education &
   Educational Research
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Education & Educational Research
GA BU0US
UT WOS:000871898800023
DA 2024-09-05
ER
PT J
AU Drongstrup, D
   Malik, S
   Aljohani, NR
   Alelyani, S
   Safder, I
   Hassan, SU
AF Drongstrup, Dorte
   Malik, Shafaq
   Aljohani, Naif Radi
   Alelyani, Salem
   Safder, Iqra
   Hassan, Saeed-Ul
TI Can social media usage of scientific literature predict journal indices
   of AJG, SNIP and JCR? An altmetric study of economics
SO SCIENTOMETRICS
LA English
DT Article
DE Altmetrics; Economics; AJG; SNIP; JCR; Machine learning
ID IMPACT; DOCUMENTS; CITATIONS
AB Altmetrics are often praised as an alternative or complement to classic bibliometric metrics, especially in the social sciences discipline. However, empirical investigations of altmetrics concerning the social sciences are scarce. This study investigates the extent to which economic research is shared on social media platforms with an emphasis on mentions in policy documents in addition to other mentions such as Twitter or Facebook. Moreover, this study explores machine learning models to predict the likelihood of a research article being classified into the top-quality tier of a journal ranking based on the altmetric mentions. The included journal rankings are the academic journal guide (AJG), source normalized impact per paper (SNIP) and journal citation reports (JCR). The investigated journals have been selected based on the AJG list and extracted from Altmetric.com data. After applying extensive data cleaning on the extracted data, a final set of 55,560 journal article records is obtained. The results indicate that the average number of policy mentions of the publications of economics journals is higher than the other subject areas included in the AJG list. Moreover, the publications in top-ranking economic journals are more likely to have a higher average number of policy mentions. Policy and Twitter mentions are presented as the most significant and informative social media mentions in demonstrating the broader impact and dissemination of Economics discipline followed by Blogs, Facebook, Wikipedia, and News. The results show that Support Vector Machine and Logistic Regression performed best in classifying the journal ranking tiers i.e. SNIP-based with 77% accuracy, JCR-based with 71% accuracy, and AJG-based with 66% accuracy. The models classified the ranking tier AJG18 with lower accuracy than SNIP and JCR. This might be because the AJG18 rankings are based on expert opinion, whereas SNIP and JCR are based on citations.
C1 [Drongstrup, Dorte] Univ Lib Southern Denmark, Res & Anal Sect, Campusvej 55, DK-5230 Odense, Denmark.
   [Malik, Shafaq; Safder, Iqra; Hassan, Saeed-Ul] Informat Technol Univ, 346-B,Ferozepur Rd, Lahore, Pakistan.
   [Aljohani, Naif Radi] King Abdulaziz Univ, Fac Comp & Informat Technol, Jeddah, Saudi Arabia.
   [Alelyani, Salem] King Khalid Univ, Ctr Artificial Intelligence CAI, POB 9004, Abha 61413, Saudi Arabia.
   [Alelyani, Salem] King Khalid Univ, Coll Comp Sci, POB 9004, Abha 61413, Saudi Arabia.
C3 King Abdulaziz University; King Khalid University; King Khalid
   University
RP Hassan, SU (corresponding author), Informat Technol Univ, 346-B,Ferozepur Rd, Lahore, Pakistan.
EM shafaq.malik@itu.edu.pk; nraljohani@kau.edu.sa; s.alelyani@kku.edu.sa;
   iqra.safder@itu.edu.pk; saeed-ul-hassan@itu.edu.pk
RI Safder, Iqra/JXN-8069-2024; Alelyani, Salem/AAT-8273-2020; Aljohani,
   Naif R/S-1109-2017; Drongstrup, Dorte/I-7395-2018; Hassan,
   Saeed-Ul/G-1889-2016
OI Alelyani, Salem/0000-0002-4571-9073; Drongstrup,
   Dorte/0000-0002-2541-3819; Hassan, Saeed-Ul/0000-0002-6509-9190
FU King Khalid University [239]
FX This article is an extended version of research in progress presented at
   the 17th International Conference on Scientometrics and Informetrics,
   Rome (Italy), 2-5 September 2019 (Drongstrup et al. 2019). The authors
   (Saeed-Ul Hassan and Salem Alelyani) are grateful for the financial
   support received from King Khalid University for this research under
   Grant No. 239, 2019.
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NR 34
TC 13
Z9 14
U1 10
U2 71
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD NOV
PY 2020
VL 125
IS 2
BP 1541
EP 1558
DI 10.1007/s11192-020-03613-3
EA JUL 2020
PG 18
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA OT3PI
UT WOS:000547804200011
DA 2024-09-05
ER
PT J
AU Basu, A
   Hazra, AK
   Chaudhury, S
   Ross, AB
   Balachandran, S
AF Basu, Aman
   Hazra, Amit Kumar
   Chaudhury, Shibani
   Ross, Andrew B.
   Balachandran, Srinivasan
TI State of the Art Research on Sustainable Use of Water Hyacinth: A
   Bibliometric and Text Mining Analysis
SO INFORMATICS-BASEL
LA English
DT Article
DE water hyacinth; cluster analysis; sentiment analysis; text-mining;
   network analysis
ID CRASSIPES MART SOLMS; EICHHORNIA-CRASSIPES; AQUEOUS-SOLUTION; GROWTH
   DYNAMICS; ETHANOL; REMOVAL; BIOMASS; FOCUS; PHYTOREMEDIATION;
   SACCHARIFICATION
AB This study aims to present a systematic data-driven bibliometric analysis of the water hyacinth (Eichhornia crassipes) infestation problem around the globe. As many solutions are being proposed in academia for its management, mitigation, and utilization, it requires investigation through a systematic scrutinizing lens. In this study, literature records from 1977 to June 2020 concerning research on water hyacinth are taken from Scopus for text analysis. Trends in the publication of different article types, dynamics of publication, clustering, correlation, and co-authoring patterns between different countries are observed. The cluster analysis indicated four clusters viz. (i) ecological works related to species, (ii) pollutant removal process and methods, (iii) utilization of biofuels for biogas production, and (iv) modelling works. It is clear from the networking analysis that most of the publications regarding water hyacinth are from India, followed by China and the United States. Sentiment analysis with the AFINN lexicon showed that the negative sentiment towards the aquatic weed has intensified over time. An exploratory analysis was performed using a bigram network plot, depicting and outlining different important domains of water hyacinth research. Water hyacinth research has passed the pioneering phase and is now at the end of a steady growth phase or at the beginning of an acceleration phase. In this article, an overview is given for the entirety of water hyacinth research, with an indication of future trends and possibilities.
C1 [Basu, Aman] York Univ, Dept Biol, 4700 Keele St, Toronto, ON M3J 1P3, Canada.
   [Hazra, Amit Kumar] Visva Bharati Univ, Dept Environm Studies, Visva Bharati 731235, W Bengal, India.
   [Chaudhury, Shibani; Balachandran, Srinivasan] Inst Rural Reconstruct, Dept Lifelong Learning & Extens, Visva Bharati 731235, W Bengal, India.
   [Ross, Andrew B.] Univ Leeds, Sch Chem & Proc Engn, Leeds LS2 9JT, W Yorkshire, England.
C3 York University - Canada; Visva Bharati University; University of Leeds
RP Balachandran, S (corresponding author), Inst Rural Reconstruct, Dept Lifelong Learning & Extens, Visva Bharati 731235, W Bengal, India.
EM amanbasu@yorku.ca; amit.hazra@visva-bharati.ac.in;
   shibani.chaudhury@visva-bharati.ac.in; A.B.Ross@leeds.ac.uk;
   s.balachandran@visva-bharati.ac.in
RI Balachandran, Srinivasan/AAW-7223-2020
OI Balachandran, Srinivasan/0000-0003-4247-408X; Basu,
   Aman/0000-0001-6814-4615
FU Biotechnology and Biological Sciences Research Council (BBSRC)
   [BB/S011439/1]; BBSRC [BB/S011439/1] Funding Source: UKRI
FX This research was funded by the Biotechnology and Biological Sciences
   Research Council (BBSRC; grant number BB/S011439/1) via the GCRF project
   BEFWAM (Bioenergy, Fertilizer, and Clean Water from Invasive Aquatic
   Macrophytes).
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NR 48
TC 6
Z9 6
U1 1
U2 6
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2227-9709
J9 INFORMATICS-BASEL
JI Informatics-Basel
PD JUN
PY 2021
VL 8
IS 2
AR 38
DI 10.3390/informatics8020038
PG 14
WC Computer Science, Interdisciplinary Applications
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA SX4KE
UT WOS:000665175000001
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU Rober, R
   Hall, B
   Daubner, C
   Goodman, A
   Pikaart, M
   Sikora, A
   Craig, P
AF Rober, Rebecca
   Hall, Bonnie
   Daubner, Colette
   Goodman, Anya
   Pikaart, Michael
   Sikora, Arthur
   Craig, Paul
TI Flexible Implementation of the BASIL CURE
SO BIOCHEMISTRY AND MOLECULAR BIOLOGY EDUCATION
LA English
DT Article
DE active learning; assessment of educational activities; computers in
   research and teaching; curriculum design development and implementation;
   enzymes and catalysis; genomics proteomics bioinformatics; inquiry based
   teaching
ID EDUCATION; DATABASE; DOCKING
AB Course-based Undergraduate Research Experiences (CUREs) can be a very effective means to introduce a large number of students to research. CUREs are often an extension of the instructor's research, which may make them difficult to replicate in other settings because of differences in expertise or facilities. The BASIL (Biochemistry Authentic Scientific Inquiry Lab) CURE has evolved over the past 4 years as faculty members with different backgrounds, facilities, and campus cultures have all contributed to a robust curriculum focusing on enzyme function prediction that is suitable for implementation in a wide variety of academic settings. (c) 2019 International Union of Biochemistry and Molecular Biology, 00(00):1-8, 2019.
C1 [Rober, Rebecca] Ursinus Coll, Dept Biol, Collegeville, PA 19426 USA.
   [Hall, Bonnie] Grand View Univ, Dept Chem, Des Moines, IA USA.
   [Daubner, Colette] St Marys Univ, Dept Biol Sci, San Antonio, TX USA.
   [Goodman, Anya] Cal Poly San Luis Obispo, Dept Chem & Biochem, San Luis Obispo, CA USA.
   [Pikaart, Michael] Hope Coll, Dept Chem & Biochem, Holland, MI 49423 USA.
   [Sikora, Arthur] Nova Southeastern Univ, Dept Chem & Phys, Ft Lauderdale, FL 33314 USA.
   [Craig, Paul] Rochester Inst Technol, Head Sch Chem & Mat Sci, Rochester, NY 14623 USA.
C3 California State University System; California Polytechnic State
   University San Luis Obispo; Hope College; Nova Southeastern University;
   Rochester Institute of Technology
RP Craig, P (corresponding author), Rochester Inst Technol, Head Sch Chem & Mat Sci, Rochester, NY 14623 USA.
EM paul.craig@rit.edu
RI Pikaart, Mike/ABE-3086-2020
OI Roberts, Rebecca/0000-0002-3855-0871; Craig, Paul/0000-0002-2085-7816;
   Pikaart, Michael/0000-0002-3771-4942; Sikora,
   Arthur/0000-0001-6295-9928; Goodman, Anya/0000-0003-1385-9992; Hall,
   Bonnie/0000-0002-7431-0349
FU Division of Undergraduate Education [1709170]; Division Of Undergraduate
   Education; Direct For Education and Human Resources [1709170] Funding
   Source: National Science Foundation
FX Grant sponsor: Division of Undergraduate Education; Grant number:
   1709170
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NR 30
TC 16
Z9 17
U1 0
U2 11
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1470-8175
EI 1539-3429
J9 BIOCHEM MOL BIOL EDU
JI Biochem. Mol. Biol. Educ.
PD SEP
PY 2019
VL 47
IS 5
BP 498
EP 505
DI 10.1002/bmb.21287
EA AUG 2019
PG 8
WC Biochemistry & Molecular Biology; Education, Scientific Disciplines
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Education & Educational Research
GA IW9HU
UT WOS:000479286300001
PM 31381264
OA Bronze
DA 2024-09-05
ER
PT J
AU Xu, ZS
   Yu, DJ
   Wang, XZ
AF Xu, Zeshui
   Yu, Dejian
   Wang, Xizhao
TI A bibliometric overview of International Journal of Machine Learning and
   Cybernetics between 2010 and 2017
SO INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
LA English
DT Article
DE Bibliometric; Citation and co-citation; IJMLC; CiteSpace; Vosviewer
ID PARTICLE SWARM OPTIMIZATION; FUZZY DECISION TREES; INFORMATION-SCIENCE;
   EMERGING TRENDS; NEURAL-NETWORK; SCIENTOMETRICS; ENSEMBLE; MEDICINE;
   ENERGY
AB International Journal of Machine Learning and Cybernetics (IJMLC) is one of the influential journals in the area of computer science, and it published its first issue in 2010. On the one hand, taking the 544 IJMLC publications between 2010 and 2017 as the research object, this paper uses bibliometric methods to study the citation characteristics, international cooperation and institutional cooperation, the author's cooperation rate and cooperation degree, geographical distribution of the IJMLC publications. On the other hand, CiteSpace and Vosviewer, two data visualization software tools, are used to make the comprehensive analysis of the co-occurrence of the author keywords of the IJMLC publications. The document co-citation clusters visualization and burst detection of keywords are also presented to explore the development of the research trends. The research results in this paper provide a basis for further improving the academic level and quality of the IJMLC.
C1 [Xu, Zeshui] Sichuan Univ, Business Sch, Chengdu 610064, Sichuan, Peoples R China.
   [Yu, Dejian] Nanjing Audit Univ, Business Sch, Nanjing 211815, Jiangsu, Peoples R China.
   [Wang, Xizhao] Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Peoples R China.
C3 Sichuan University; Nanjing Audit University; Shenzhen University
RP Xu, ZS (corresponding author), Sichuan Univ, Business Sch, Chengdu 610064, Sichuan, Peoples R China.
EM xuzeshui@263.net; yudejian62@126.com; xizhaowang@ieee.org
RI Wang, Xizhao/ABG-7225-2020; Xu, Zeshui/N-8908-2013
FU China National Natural Science Foundation [71771155, 71571123]
FX The work was supported in part by the China National Natural Science
   Foundation (nos. 71771155, 71571123).
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NR 55
TC 16
Z9 16
U1 4
U2 124
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1868-8071
EI 1868-808X
J9 INT J MACH LEARN CYB
JI Int. J. Mach. Learn. Cybern.
PD SEP
PY 2019
VL 10
IS 9
BP 2375
EP 2387
DI 10.1007/s13042-018-0875-9
PG 13
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA IR4QF
UT WOS:000481418600011
DA 2024-09-05
ER
PT C
AU Bhoiwala, J
   Jhaveri, RH
AF Bhoiwala, Jaina
   Jhaveri, Rutvij H.
BE Sa, PK
   Bakshi, S
   Hatzilygeroudis, IK
   Sahoo, MN
TI A Bibliometric Analysis of Recent Research on Machine Learning for
   Medical Science
SO RECENT FINDINGS IN INTELLIGENT COMPUTING TECHNIQUES, VOL 1
SE Advances in Intelligent Systems and Computing
LA English
DT Proceedings Paper
CT 5th International Conference on Advanced Computing, Networking, and
   Informatics (ICACNI)
CY JUN 01-03, 2017
CL Natl Inst Technol Goa, Dept Comp Sci & Engn, Goa, INDIA
HO Natl Inst Technol Goa, Dept Comp Sci & Engn
DE Wireless sensor networks; Trend analysis; Graphical interpretation
ID CLASSIFICATION
AB Machine learning is a system capable of the independent acquisition and integration of knowledge. Machine learning is a chosen approach to speech recognition, natural language processing computer vision, medical outcome analysis, and computational biology. In this paper we carry out bibliometric analysis of 150 papers from January 2015 to September 2016 in order to recognize various aspects of machine learning when used for medical science. We have considered large number of objectives and top rated publishers for analyzing the papers. For carrying out further research in the machine learning for medical science, our analysis would assist students, researchers, publishers, and experts to study the recent trends.
C1 [Bhoiwala, Jaina; Jhaveri, Rutvij H.] Shri Sad Vidya Mandal Inst Technol, Dept Comp Engn, Bharuch, India.
RP Bhoiwala, J (corresponding author), Shri Sad Vidya Mandal Inst Technol, Dept Comp Engn, Bharuch, India.
EM jainabhoiwala@gmail.com; rhj_symit@yahoo.com
RI Jhaveri, Rutvij H./A-5354-2018
OI Jhaveri, Rutvij H./0000-0002-3285-7346
CR Bhardwaj A., 2015, EXPERT SYST APPL
   Jain R., RECENT MACHINE LEARN, P1
   Kalaiselvi T., 2015, RAPID AUTOMATIC BRAI
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NR 11
TC 0
Z9 0
U1 2
U2 9
PU SPRINGER-VERLAG SINGAPORE PTE LTD
PI SINGAPORE
PA 152 BEACH ROAD, #21-01/04 GATEWAY EAST, SINGAPORE, 189721, SINGAPORE
SN 2194-5357
EI 2194-5365
BN 978-981-10-8639-7; 978-981-10-8638-0
J9 ADV INTELL SYST
PY 2019
VL 707
BP 225
EP 233
DI 10.1007/978-981-10-8639-7_23
PG 9
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods; Engineering, Electrical &
   Electronic
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Engineering
GA BQ4EW
UT WOS:000589271900023
DA 2024-09-05
ER
PT J
AU Myers, CT
   O'Brien, SP
AF Myers, Christine Teeters
   O'Brien, Shirley Peganoff
TI Teaching Interprofessional Collaboration: Using Online Education Across
   Institutions
SO OCCUPATIONAL THERAPY IN HEALTH CARE
LA English
DT Article
DE Distance learning; e-Learning; Interprofessional education; Online
   learning; Social presence
AB Interdisciplinary courses among students in occupational therapy, physical therapy, and speech-language pathology are important for addressing teamwork, communication, and understanding of professional roles, especially in pre-service training for early intervention and school-based practice where collaboration is essential. Although interprofessional education (IPE) as a part of higher education in the health sciences has been strongly encouraged, IPE courses are difficult to schedule and implement. This article discusses the challenges of developing and delivering two IPE courses in an online format, specifically the innovation that addresses logistics, time factors, and social presence for the IPE courses across two institutions.
C1 [Myers, Christine Teeters; O'Brien, Shirley Peganoff] Eastern Kentucky Univ, Dept Occupat Sci & Occupat Therapy, Richmond, KY 40475 USA.
C3 Eastern Kentucky University
RP Myers, CT (corresponding author), Eastern Kentucky Univ, Dept Occupat Sci & Occupat Therapy, Dizney 103,521 Lancaster Ave, Richmond, KY 40475 USA.
EM christine.myers@eku.edu
OI Myers, Christine/0000-0003-1492-9008
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NR 26
TC 13
Z9 18
U1 0
U2 10
PU TAYLOR & FRANCIS INC
PI PHILADELPHIA
PA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA
SN 0738-0577
EI 1541-3098
J9 OCCUP THER HEALTH CA
JI Occup. Ther. Health Care
PD APR
PY 2015
VL 29
IS 2
SI SI
BP 178
EP 185
DI 10.3109/07380577.2015.1017789
PG 8
WC Rehabilitation
WE Emerging Sources Citation Index (ESCI)
SC Rehabilitation
GA CH8WQ
UT WOS:000354317300008
PM 25821890
DA 2024-09-05
ER
PT J
AU Tang, L
   Walsh, JP
AF Tang, Li
   Walsh, John P.
TI Bibliometric fingerprints: name disambiguation based on approximate
   structure equivalence of cognitive maps
SO SCIENTOMETRICS
LA English
DT Article
DE Name disambiguation; Common names; Cognitive map; Approximate structural
   equivalence; Knowledge homogeneity score; Hierarchical clustering
ID CITATION ANALYSIS; PUBLICATIONS; AUTHORSHIP; IMPACT; WEB
AB Authorship identity has long been an Achilles' heel in bibliometric analyses at the individual level. This problem appears in studies of scientists' productivity, inventor mobility and scientific collaboration. Using the concepts of cognitive maps from psychology and approximate structural equivalence from network analysis, we develop a novel algorithm for name disambiguation based on knowledge homogeneity scores. We test it on two cases, and the results show that this approach outperforms other common authorship identification methods with the ASE method providing a relatively simple algorithm that yields higher levels of accuracy with reasonable time demands.
C1 [Tang, Li; Walsh, John P.] Georgia Inst Technol, Sch Publ Policy, Atlanta, GA 30332 USA.
C3 University System of Georgia; Georgia Institute of Technology
RP Walsh, JP (corresponding author), Georgia Inst Technol, Sch Publ Policy, Atlanta, GA 30332 USA.
EM litang@gatech.edu; jwalsh6@mail.gatech.edu
RI Tang, Li/B-6182-2011; Walsh, John/GQI-2785-2022; Tang, Li/AAG-8045-2021
OI Tang, Li/0000-0003-4971-6192; Walsh, John/0000-0002-2510-8734; Tang,
   Li/0000-0003-4971-6192
FU Direct For Social, Behav & Economic Scie; Divn Of Social and Economic
   Sciences [0937591] Funding Source: National Science Foundation
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NR 46
TC 83
Z9 90
U1 2
U2 70
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD SEP
PY 2010
VL 84
IS 3
BP 763
EP 784
DI 10.1007/s11192-010-0196-6
PG 22
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 630LK
UT WOS:000280274400014
DA 2024-09-05
ER
PT J
AU Ganjavi, C
   Eppler, MB
   Pekcan, A
   Biedermann, B
   Abreu, A
   Collins, GS
   Gill, IS
   Cacciamani, GE
AF Ganjavi, Conner
   Eppler, Michael B.
   Pekcan, Asli
   Biedermann, Brett
   Abreu, Andre
   Collins, Gary S.
   Gill, Inderbir S.
   Cacciamani, Giovanni E.
TI Publishers' and journals' instructions to authors on use of generative
   artificial intelligence in academic and scientific publishing:
   bibliometric analysis
SO BMJ-BRITISH MEDICAL JOURNAL
LA English
DT Article
ID CHATGPT
AB OBJECTIVES To determine the extent and content of academic publishers' and scientific journals' guidance for authors on the use of generative artificial intelligence (GAI). DESIGN Cross sectional, bibliometric study. SETTING Websites of academic publishers and scientific journals, screened on 19-20 May 2023, with the search updated on 8-9 October 2023. PARTICIPANTS Top 100 largest academic publishers and top 100 highly ranked scientific journals, regardless of subject, language, or country of origin. Publishers were identified by the total number of journals in their portfolio, and journals were identified through the Scimago journal rank using the Hirsch index (H index) as an indicator of journal productivity and impact. MAIN OUTCOME MEASURES The primary outcomes were the content of GAI guidelines listed on the websites of the top 100 academic publishers and scientific journals, and the consistency of guidance between the publishers and their affiliated journals. RESULTS Among the top 100 largest publishers, 24% provided guidance on the use of GAI, of which 15 (63%) were among the top 25 publishers. Among the top 100 highly ranked journals, 87% provided guidance on GAI. Of the publishers and journals with guidelines, the inclusion of GAI as an author was prohibited in 96% and 98%, respectively. Only one journal (1%)explicitly prohibited the use of GAI in the generation of a manuscript, and two (8%) publishers and 19 (22%) journals indicated that their guidelines exclusively applied to the writing process. When disclosing the use of GAI, 75% of publishers and 43% of journals included specific disclosure criteria. Where to disclose the use of GAI varied, including in the methods or acknowledgments, in the cover letter, or in a new section. Variability was also found in how to access GAI guidelines shared between journals and publishers. GAI guidelines in 12 journals directly conflicted with those developed by the publishers. The guidelines developed by top medical journals were broadly similar to those of academic journals. CONCLUSIONS Guidelines by some top publishers and journals on the use of GAI by authors are lacking. Among those that provided guidelines, the allowable uses of GAI and how it should be disclosed varied substantially, with this heterogeneity persisting in some instances among affiliated publishers and journals. Lack of standardization places a burden on authors and could limit the effectiveness of the regulations. As GAI continues to grow in popularity, standardized guidelines to protect the integrity of scientific output are needed.
C1 [Ganjavi, Conner; Eppler, Michael B.; Pekcan, Asli; Biedermann, Brett; Abreu, Andre; Gill, Inderbir S.; Cacciamani, Giovanni E.] Univ Southern Calif, Keck Sch Med, Los Angeles, CA 90007 USA.
   [Ganjavi, Conner; Eppler, Michael B.; Pekcan, Asli; Biedermann, Brett; Abreu, Andre; Gill, Inderbir S.; Cacciamani, Giovanni E.] Univ Southern Calif, USC Inst Urol, Los Angeles, CA 90007 USA.
   [Ganjavi, Conner; Eppler, Michael B.; Pekcan, Asli; Biedermann, Brett; Abreu, Andre; Gill, Inderbir S.; Cacciamani, Giovanni E.] Univ Southern Calif, Keck Sch Med, Catherine & Joseph Aresty Dept Urol, Los Angeles, CA 90007 USA.
   [Ganjavi, Conner; Eppler, Michael B.; Pekcan, Asli; Biedermann, Brett; Abreu, Andre; Gill, Inderbir S.; Cacciamani, Giovanni E.] Univ Southern Calif, USC Inst Urol, Artificial Intelligence Ctr, USC Urol, Los Angeles, CA 90007 USA.
   [Collins, Gary S.] Univ Oxford, UK EQUATOR Ctr, Ctr Stat Med, Nuffield Dept Orthopaed Rheumatol & Musculoskeleta, Oxford, England.
C3 University of Southern California; University of Southern California;
   University of Southern California; University of Southern California;
   University of Oxford
RP Cacciamani, GE (corresponding author), Univ Southern Calif, Keck Sch Med, Los Angeles, CA 90007 USA.; Cacciamani, GE (corresponding author), Univ Southern Calif, USC Inst Urol, Los Angeles, CA 90007 USA.; Cacciamani, GE (corresponding author), Univ Southern Calif, Keck Sch Med, Catherine & Joseph Aresty Dept Urol, Los Angeles, CA 90007 USA.; Cacciamani, GE (corresponding author), Univ Southern Calif, USC Inst Urol, Artificial Intelligence Ctr, USC Urol, Los Angeles, CA 90007 USA.
EM Giovanni.cacciamani@med.usc.edu
RI Collins, Gary Stephen/A-2258-2014
OI Collins, Gary Stephen/0000-0002-2772-2316; Cacciamani,
   Giovanni/0000-0002-8892-5539; Pekcan, Asli/0000-0002-8826-3184
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NR 28
TC 16
Z9 16
U1 36
U2 36
PU BMJ PUBLISHING GROUP
PI LONDON
PA BRITISH MED ASSOC HOUSE, TAVISTOCK SQUARE, LONDON WC1H 9JR, ENGLAND
SN 0959-535X
EI 1756-1833
J9 BMJ-BRIT MED J
JI BMJ-British Medical Journal
PD JAN 31
PY 2024
VL 384
AR e077192
DI 10.1136/bmj-2023-077192
PG 12
WC Medicine, General & Internal
WE Science Citation Index Expanded (SCI-EXPANDED)
SC General & Internal Medicine
GA NE0G5
UT WOS:001198653600005
PM 38296328
OA hybrid
HC Y
HP N
DA 2024-09-05
ER
PT J
AU Wu, DH
   Zhou, DH
   Chen, MY
   Zhu, JF
   Yan, F
   Zheng, SM
   Guo, ET
AF Wu, Dehao
   Zhou, Donghua
   Chen, Maoyin
   Zhu, Jifeng
   Yan, Fei
   Zheng, Shuiming
   Guo, Entao
TI Output-Relevant Common Trend Analysis for KPI-Related Nonstationary
   Process Monitoring With Applications to Thermal Power Plants
SO IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
LA English
DT Article
DE Market research; Power generation; Process monitoring; Coal; Temperature
   measurement; Power measurement; Informatics; Anomaly detection; fault
   diagnosis; common trend analysis; key performance indicator (KPI);
   nonstationary process monitoring; power plant; thermal efficiency
ID PARTIAL LEAST-SQUARES; FAULT-DETECTION; DIAGNOSIS; COINTEGRATION;
   COMPONENTS; REGRESSION; PROJECTION; ALGORITHM
AB Operation safety and efficiency are two main concerns in power plants. It is important to detect the anomalies in power plants, and further judge whether they affect key performance indicators (KPIs), such as the thermal efficiency. These two goals can be achieved by KPI-related nonstationary process monitoring. Although the thermal efficiency cannot be accurately measured online, it can be strongly characterized by some online measurable variables, including the exhaust gas temperature and oxygen content of flue gas. These critical variables closely related to the thermal efficiency are termed as output variables. Inspired from nonstationary common trends between input and output variables in thermal power plants, the output-relevant common trend analysis (OCTA) method is proposed, in this article, to model the input-output relationship. In OCTA, input and output variables are decomposed into nonstationary common trends and stationary residuals, and the model parameters are estimated by solving an optimization problem. It is pointed out that OCTA is a generalized form of partial least squares (PLS). The superior monitoring performance of OCTA is illustrated by case studies on a real power plant in Zhejiang Provincial Energy Group of China. Compared with the other PLS-based recursive algorithms, OCTA can effectively detect the anomalies in power plants and accurately determine whether they have an impact on the thermal efficiency or not.
C1 [Wu, Dehao; Zhou, Donghua; Chen, Maoyin] Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China.
   [Zhou, Donghua] Shandong Univ Sci & Technol, Coll Elect Engn & Automat, Qingdao 266590, Peoples R China.
   [Zhu, Jifeng; Yan, Fei; Zheng, Shuiming; Guo, Entao] Zhejiang Zheneng Zhongmei Zhoushan Coal & Elect C, Zhejiang Prov Energy Grp, Zhoushan 316000, Peoples R China.
C3 Tsinghua University; Shandong University of Science & Technology
RP Zhou, DH; Chen, MY (corresponding author), Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China.; Zhou, DH (corresponding author), Shandong Univ Sci & Technol, Coll Elect Engn & Automat, Qingdao 266590, Peoples R China.
EM wudh16@mails.tsinghua.edu.cn; zdh@tsinghua.edu.cn;
   mychen@tsinghua.edu.cn; 61030737@qq.com; 451995139@qq.com;
   316643967@qq.com; guoentao@126.com
RI Wu, Dehao/GZH-1054-2022
OI Wu, Dehao/0000-0003-0649-1085
FU National Natural Science Foundation of China [61751307, 61873143,
   62033008]; Research Fund for the Taishan Scholar Project of Shandong
   Province of China [LZB2015-162, TII-20-3264]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61751307, Grant 61873143, and Grant
   62033008 and in part by the Research Fund for the Taishan Scholar
   Project of Shandong Province of China under Grant LZB2015-162. Paper no.
   TII-20-3264.
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TC 8
Z9 9
U1 5
U2 13
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 1551-3203
EI 1941-0050
J9 IEEE T IND INFORM
JI IEEE Trans. Ind. Inform.
PD OCT
PY 2021
VL 17
IS 10
BP 6664
EP 6675
DI 10.1109/TII.2020.3041516
PG 12
WC Automation & Control Systems; Computer Science, Interdisciplinary
   Applications; Engineering, Industrial
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Automation & Control Systems; Computer Science; Engineering
GA TJ3VW
UT WOS:000673414500011
DA 2024-09-05
ER
PT C
AU Nasiar, N
   Baker, RS
   Li, J
   Gong, WY
AF Nasiar, Nidhi
   Baker, Ryan S.
   Li, Jillian
   Gong, Weiyi
BE Rodrigo, MM
   Matsuda, N
   Cristea, AI
   Dimitrova, V
TI How do A/B Testing and Secondary Data Analysis on AIED Systems Influence
   Future Research?
SO ARTIFICIAL INTELLIGENCE IN EDUCATION, PT I
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT 23rd International Conference on Artificial Intelligence in Education
   (AIED)
CY JUL 27-31, 2022
CL Durham Univ, Durham, ENGLAND
HO Durham Univ
DE Scientometrics; A/B testing; Online learning; AIED systems
AB Recent years have seen a surge in research conducted on intelligent online learning platforms, with a particular expansion of research conducting A/B testing to decide which design to use, and research using secondary platform data in analyses. This scientometric study aims to investigate how scholarship builds on these two different types of research. We collected papers for both categories - A/B testing, and educational data mining (EDM) on log data- in the context of the same learning platform. We then collected a randomized stratified sample of papers citing those A/B and EDM papers, and coded the reason for each citation. Oncomparing the frequency of citation categories between the two types of papers, we found that A/B test papers were cited more often to provide background and context for a study, whereas the EDM papers were cited to use past specific core ideas, theories, and findings in the field. This paper establishes a method to compare the contribution of different types of research on AIED systems such as interactive learning platforms.
C1 [Nasiar, Nidhi; Baker, Ryan S.; Li, Jillian; Gong, Weiyi] Univ Penn, Grad Sch Educ, Philadelphia, PA 19104 USA.
C3 University of Pennsylvania
RP Nasiar, N (corresponding author), Univ Penn, Grad Sch Educ, Philadelphia, PA 19104 USA.
EM nasiar@upenn.edu
RI Baker, Ryan/IWE-2102-2023
OI Nasiar, Nidhi/0009-0006-7063-5433
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NR 41
TC 0
Z9 0
U1 0
U2 2
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 0302-9743
EI 1611-3349
BN 978-3-031-11644-5; 978-3-031-11643-8
J9 LECT NOTES COMPUT SC
PY 2022
VL 13355
BP 115
EP 126
DI 10.1007/978-3-031-11644-5_10
PG 12
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications; Education & Educational Research
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Education & Educational Research
GA BU1FW
UT WOS:000877435100010
DA 2024-09-05
ER
PT J
AU Li, BJ
   Lee, HM
AF Li, Benjamin (Benjy) J.
   Lee, Hui Min
TI Emotional Personalization in Immersive Journalism: Exploring the
   Influence of Emotional Testimonies and Modality on Emotional Valence,
   Presence, Empathy, and Recall
SO PRESENCE-VIRTUAL AND AUGMENTED REALITY
LA English
DT Article
ID VIRTUAL-REALITY; EXPERIENCE; NEWS; PERCEPTIONS; CITIZENS; IMPACT
AB Immersive journalism (IJ), where individuals engage in a news story from a first-person perspective using interactive technologies, has become increasingly popular in recent years. Such stories may improve the impact of journalism on the audience by enhancing feelings and emotions associated with the news content. Studies have shown that rather than undermining rationality, emotion could increase engagement toward news pieces, and improve knowledge of social issues. Emotional personalization (EP), a strategy where the production of news content involves the emotional testimony of ordinary citizens at the heart of the story, is therefore increasingly employed. This study explores how EP, as well as the modality of IJ content, influences our perceptions and cognitions with regards to an IJ piece on war and conflict. In our study, 193 participants took part in a 2 (EP: present vs. absent) x 2 (modality: VR vs. desktop) experiment. Participants in the EP-present condition reported stronger feelings of presence and greater story recall, while those in the VR condition experienced lower emotional valence and stronger feelings of empathy. Our results support current literature on IJ and EP and suggest that with the rising interest in immersive technologies, sustained investigation on the implications of EP strategies in IJ is crucial.
C1 [Li, Benjamin (Benjy) J.; Lee, Hui Min] Nanyang Technol Univ, Wee Kim Wee Sch Commun & Informat, Singapore, Singapore.
C3 Nanyang Technological University
RP Li, BJ (corresponding author), Nanyang Technol Univ, Wee Kim Wee Sch Commun & Informat, Singapore, Singapore.
EM benjyli@ntu.edu.sg
FU Nanyang Technological University
FX This project was supported by a Start-up Grant from Nanyang
   Technological University. The authors thank Carynn Chung for her
   assistance in data collection.
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NR 43
TC 3
Z9 3
U1 2
U2 30
PU MIT PRESS
PI CAMBRIDGE
PA ONE ROGERS ST, CAMBRIDGE, MA 02142-1209 USA
SN 1054-7460
EI 1531-3263
J9 PRESENCE-VIRTUAL AUG
JI PRESENCE-Virtual Augmented Reality
PD JAN 1
PY 2019
VL 28
BP 281
EP 292
DI 10.1162/pres_a_00352
PG 12
WC Computer Science, Cybernetics; Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 1F2AR
UT WOS:000794975800003
DA 2024-09-05
ER
PT J
AU Hu, YH
   Tai, CT
   Liu, KE
   Cai, CF
AF Hu, Ya-Han
   Tai, Chun-Tien
   Liu, Kang Ernest
   Cai, Cheng-Fang
TI Identification of highly-cited papers using topic-model-based and
   bibliometric features: the consideration of keyword popularity
SO JOURNAL OF INFORMETRICS
LA English
DT Article
DE highly-cited papers; keyword popularity; supervised learning; binary
   classification; topic model
ID CITATION IMPACT; COUNTS; EVOLUTION; ARTICLES; PREDICT; FIELDS
AB The number of received citations have been used as an indicator of the impact of academic publications. Developing tools to find papers that have the potential to become highly-cited has recently attracted increasing scientific attention. Topics of concern by scholars may change over time in accordance with research trends, resulting in changes in received citations. Author-defined keywords, title and abstract provide valuable information about a research article. This study performs a latent Dirichlet allocation technique to extract topics and keywords from articles; five keyword popularity (KP) features are defined as indicators of emerging trends of articles. Binary classification models are utilized to predict papers that were highly-cited or less highly-cited by a number of supervised learning techniques. We empirically compare KP features of articles with other commonly used journal-related and author-related features proposed in previous studies. The results show that, with KP features, the prediction models are more effective than those with journal and/or author features, especially in the management information system discipline. (C) 2019 Elsevier Ltd. All rights reserved.
C1 [Hu, Ya-Han] Natl Cent Univ, Dept Informat Management, Taoyuan 320, Taiwan.
   [Hu, Ya-Han] Natl Chung Cheng Univ, Ctr Innovat Res Aging Soc, Chiayi 621, Taiwan.
   [Hu, Ya-Han] Natl Cheng Kung Univ, MOST AI Biomed Res Ctr, Tainan 701, Taiwan.
   [Tai, Chun-Tien; Cai, Cheng-Fang] Natl Chung Cheng Univ, Dept Informat Management, Chiayi 621, Taiwan.
   [Tai, Chun-Tien] Chiayi Chang Gung Mem Hosp, Chiayi 613, Taiwan.
   [Liu, Kang Ernest] Natl Taiwan Univ Taipei, Dept Agr Econ, Taipei 106, Taiwan.
C3 National Central University; National Chung Cheng University; National
   Cheng Kung University; National Chung Cheng University; Chang Gung
   Memorial Hospital; National Taiwan University
RP Liu, KE (corresponding author), Natl Taiwan Univ, Dept Agr Econ, 1,Sect 4,Roosevelt Rd, Taipei 10617, Taiwan.
EM yahan.hu@mis.ccu.edu.tw; flamquit@hotmail.com; kangernestliu@ntu.edu.tw;
   u9933116@gmail.com
FU Ministry of Science and Technology of the Republic of China [MOST
   107-2410-H-194 -054 -MY2]; Center for Innovative Research on Aging
   Society from The Featured Areas Research Center Program within Ministry
   of Education (MOE) in Taiwan
FX This research was supported in part by the Ministry of Science and
   Technology of the Republic of China (grant number MOST 107-2410-H-194
   -054 -MY2) and the Center for Innovative Research on Aging Society from
   The Featured Areas Research Center Program within the framework of the
   Higher Education Sprout Project by the Ministry of Education (MOE) in
   Taiwan.
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NR 52
TC 33
Z9 36
U1 5
U2 78
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1751-1577
EI 1875-5879
J9 J INFORMETR
JI J. Informetr.
PD FEB
PY 2020
VL 14
IS 1
AR 101004
DI 10.1016/j.joi.2019.101004
PG 14
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA LH7EI
UT WOS:000528948000006
DA 2024-09-05
ER
PT C
AU Saier, T
   Krause, J
   Färber, M
AF Saier, Tarek
   Krause, Johan
   Faerber, Michael
GP ACM
TI unarXive 2022: All arXiv Publications Pre-Processed for NLP, Including
   Structured Full-Text and Citation Network
SO 2023 ACM/IEEE JOINT CONFERENCE ON DIGITAL LIBRARIES, JCDL
SE ACM-IEEE Joint Conference on Digital Libraries JCDL
LA English
DT Proceedings Paper
CT 23rd ACM/IEEE Joint Conference on Digital Libraries (JCDL)
CY JUN 26-30, 2023
CL Santa Fe, NM
DE scholarly data; information extraction; citation network; LATEX
AB Large-scale data sets on scholarly publications are the basis for a variety of bibliometric analyses and natural language processing (NLP) applications. Especially data sets derived from publication's full-text have recently gained attention. While several such data sets already exist, we see key shortcomings in terms of their domain and time coverage, citation network completeness, and representation of full-text content. To address these points, we propose a new version of the data set unarXive. We base our data processing pipeline and output format on two existing data sets, and improve on each of them. Our resulting data set comprises 1.9 M publications spanning multiple disciplines and 32 years. It furthermore has a more complete citation network than its predecessors and retains a richer representation of document structure as well as non-textual publication content such as mathematical notation. In addition to the data set, we provide ready-to-use training/test data for citation recommendation and IMRaD classification. All data and source code is publicly available at https://github.com/IllDepence/unarXive.
C1 [Saier, Tarek; Krause, Johan; Faerber, Michael] Karlsruhe Inst Technol, Karlsruhe, Germany.
C3 Helmholtz Association; Karlsruhe Institute of Technology
RP Saier, T (corresponding author), Karlsruhe Inst Technol, Karlsruhe, Germany.
EM tarek.saier@kit.edu; johan.krause@student.kit.edu;
   michael.faerber@kit.edu
RI Färber, Michael/AAA-4789-2021
OI Färber, Michael/0000-0001-5458-8645
FU German Federal Ministry of Education and Research (BMBF); Software
   Campus project [01IS17042]; state of Baden-Wurttemberg through bwHPC
FX This work was partially supported by the German Federal Ministry of
   Education and Research (BMBF) via [KOM,BI], a Software Campus project
   (01IS17042). The authors acknowledge support by the state of
   Baden-Wurttemberg through bwHPC. We thank Johannes Reber for supporting
   early stages of the software development.
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NR 27
TC 1
Z9 1
U1 0
U2 1
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES
SN 2575-7865
EI 2575-8152
BN 979-8-3503-9931-8
J9 ACM-IEEE J CONF DIG
PY 2023
BP 66
EP 70
DI 10.1109/JCDL57899.2023.00020
PG 5
WC Computer Science, Information Systems; Computer Science,
   Interdisciplinary Applications
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BW0PC
UT WOS:001098971300010
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Sulastri, S
   Rahayu, A
   Sulaksana, RDIZF
AF Sulastri, S.
   Rahayu, Agus
   Sulaksana, Ratu Dintha Insyani Zukhruf Firdausi
TI MODEL OF TECHNOLOGY ACCEPTANCE USING ONLINE LEARNING SYSTEMS AND ITS
   IMPACT ON LEARNING EFFECTIVENESS
SO JOURNAL OF ENGINEERING SCIENCE AND TECHNOLOGY
LA English
DT Article
DE Bibliometric; Inquiry-based learning; Scopus; Technology; VOSviewer
AB This study presents a bibliometric and bibliographic review of the studies of technology-assisted inquiry-based learning (IBL). A bibliometric approach using VOSviewer was used to conduct this study. 191 eligible and included documents published between 1999 - 2022 from the Scopus database were analysed using some main analyses such as performance, citation, co-authorship, and co-word supported by visualization and clustering analysis. The results revealed that the development of publications regarding technology-assisted IBL studies relatively soared from 1999 to 2020 while the development of citations related to the studies of technology-assisted IBL relatively fluctuated between 1999 and 2022. Furthermore, the United States relatively had the dominant influence in which most influential documents, authors, sources, and institutions affiliated with the became the leaders in conducting the studies of technology-assisted IBL in the social interactions among authors and also countries. This study implies that the use of technologies such as websites, computers, robots, and video conferencing extremely supports educational practitioners such as teachers and lecturers in implementing IBL. Additionally, this study suggests exploring the trending research topic related to critical digital literacy, an essential cognitive skill.
C1 [Sulastri, S.; Rahayu, Agus; Sulaksana, Ratu Dintha Insyani Zukhruf Firdausi] Univ Pendidikan Indonesia, Jl Dr Setiabudi 229, Bandung, Indonesia.
C3 Universitas Pendidikan Indonesia
RP Sulastri, S (corresponding author), Univ Pendidikan Indonesia, Jl Dr Setiabudi 229, Bandung, Indonesia.
EM sulastri@upi.edu
RI Sulastri, Sulastri/O-3540-2019
OI Sulastri, Sulastri/0000-0001-6091-4451; Dintha IZFS,
   Ratu/0009-0003-3125-0985
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NR 59
TC 0
Z9 0
U1 1
U2 1
PU TAYLORS UNIV SDN BHD
PI SELANGOR
PA 1 JALAN SS15-8, SUBANG JAYA, SELANGOR, 47500, MALAYSIA
EI 1823-4690
J9 J ENG SCI TECHNOL
JI J. Eng. Sci. Technol.
PD JAN
PY 2023
VL 18
SI SI
BP 39
EP 52
PG 14
WC Engineering, Multidisciplinary
WE Emerging Sources Citation Index (ESCI)
SC Engineering
GA L7BK2
UT WOS:001024772800004
DA 2024-09-05
ER
PT J
AU Klein, JJ
   Baker, NC
   Foil, DH
   Zorn, KM
   Urbina, F
   Puhl, AC
   Ekins, S
AF Klein, Jennifer J.
   Baker, Nancy C.
   Foil, Daniel H.
   Zorn, Kimberley M.
   Urbina, Fabio
   Puhl, Ana C.
   Ekins, Sean
TI Using Bibliometric Analysis and Machine Learning to Identify Compounds
   Binding to Sialidase-1
SO ACS OMEGA
LA English
DT Article
ID ENZYME REPLACEMENT THERAPY; DRUG DISCOVERY; GENE-THERAPY; DISEASES;
   DATABASE; AGREEMENT
AB Rare diseases impact hundreds of millions of individuals worldwide. However, few therapies exist to treat the rare disease population because financial resources are limited, the number of patients affected is low, bioactivity data is often nonexistent, and very few animal models exist to support preclinical development efforts. Sialidosis is an ultrarare lysosomal storage disorder in which mutations in the NEU1 gene result in the deficiency of the lysosomal enzyme sialidase-1. This enzyme catalyzes the removal of sialic acid moieties from glycoproteins and glycolipids. Therefore, the defective or deficient protein leads to the buildup of sialylated glycoproteins as well as several characteristic symptoms of sialidosis including visual impairment, ataxia, hepatomegaly, dysostosis multiplex, and developmental delay. In this study, we used a bibliometric tool to generate links between lysosomal storage disease (LSD) targets and existing bioactivity data that could be curated in order to build machine learning models and screen compounds in silico. We focused on sialidase as an example, and we used the data curated from the literature to build a Bayesian model which was then used to score compound libraries and rank these molecules for in vitro testing. Two compounds were identified from in vitro testing using microscale thermophoresis, namely sulfameter (K-d 2.15 +/- 1.02 mu M) and mexenone (K-d 8.88 +/- 4.02 mu M), which validated our approach to identifying new molecules binding to this protein, which could represent possible drug candidates that can be evaluated further as potential chaperones for this ultrarare lysosomal disease for which there is currently no treatment. Combining bibliometric and machine learning approaches has the ability to assist in curating small molecule data and model building, respectively, for rare disease drug discovery. This approach also has the capability to identify new compounds that are potential drug candidates.
C1 [Klein, Jennifer J.; Foil, Daniel H.; Zorn, Kimberley M.; Urbina, Fabio; Puhl, Ana C.; Ekins, Sean] Collaborat Pharmaceut Inc, Raleigh, NC 27606 USA.
   [Baker, Nancy C.] ParlezChem, Hillsborough, NC 27278 USA.
RP Puhl, AC; Ekins, S (corresponding author), Collaborat Pharmaceut Inc, Raleigh, NC 27606 USA.
EM ana@collaborationspharma.com; sean@collaborationspharma.com
RI Baker, Nancy/ABE-7415-2021; Foil, Daniel/AAJ-1809-2021
OI Foil, Daniel/0000-0003-0512-8997; Puhl, Ana/0000-0002-1456-8882
FU NIH [DP7OD020317]; NIGMS [R44GM122196-02A1]; NINDS [1R43NS107079-01,
   3R43NS107079-01S1]
FX We acknowledge Dr. Alexander Tropsha and Dr. Anthony Hickey for rare
   disease discussions and Dr. Alex M. Clark (Molecular Materials
   Informatics, Inc.) for Assay Central support. We thank Dr. Dinorah Leyva
   for helping with the analysis of MST data. We kindly acknowledge NIH
   funding to develop the software from NIGMS R44GM122196-02A1 as well as
   support from NINDS 1R43NS107079-01, NINDS 3R43NS107079-01S1. F.U. was
   partially supported by the NIH award number DP7OD020317.
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NR 46
TC 10
Z9 10
U1 1
U2 13
PU AMER CHEMICAL SOC
PI WASHINGTON
PA 1155 16TH ST, NW, WASHINGTON, DC 20036 USA
SN 2470-1343
J9 ACS OMEGA
JI ACS Omega
PD FEB 2
PY 2021
VL 6
IS 4
BP 3186
EP 3193
DI 10.1021/acsomega.0c05591
EA JAN 2021
PG 8
WC Chemistry, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry
GA QH2DO
UT WOS:000618087400074
PM 33553934
OA Green Published, gold
DA 2024-09-05
ER
PT J
AU Niu, CL
   Gutierrez, G
   Cossette, L
   Sadeghi, S
   Portugal, M
   Zeng, S
   Zhang, P
AF Niu, Chunling
   Gutierrez, Grace
   Cossette, Loren
   Sadeghi, Soheila
   Portugal, Missy
   Zeng, Shuang
   Zhang, Peng
TI Introducing Bibliometric Analysis: A Methodological Tutorial Using Adult
   Online Learning Motivation Literature (2000-2022)
SO AMERICAN JOURNAL OF DISTANCE EDUCATION
LA English
DT Article; Early Access
ID STUDENT MOTIVATION; DISTANCE EDUCATION; LEARNERS; PARTICIPATION;
   PERFORMANCE; INSTRUCTOR
AB This paper introduces an innovative and robust methodology for bibliometric analyses, utilizing the adult online learning motivation literature from 2000 to 2022 as its exemplar. Capitalizing on this comprehensive dataset, the methodology adeptly identifies emerging research trends, influential contributors, and thematic interconnections. The United States emerges as a dominant research contributor, and "Computers & Education" is spotlighted as an influential journal shaping discourse. From an overarching perspective, the study delves into the multifaceted dynamics of the field, spotlighting the rise of themes such as Self-Directed/Self-Regulated Learning (SDL), Transformative Learning (TL), gamification, and the profound influence of global events like the COVID-19 pandemic on online learning trajectories. However, the manuscript's central value lies in its methodological prowess, offering scholars a blueprint to rigorously interpret and dissect voluminous literatures across varied academic terrains.
C1 [Niu, Chunling; Gutierrez, Grace; Cossette, Loren; Sadeghi, Soheila; Portugal, Missy] Univ Incarnate Word, San Antonio, TX USA.
   [Zeng, Shuang] Univ Shanghai Sci & Technol, Shanghai, Peoples R China.
   [Zhang, Peng] Sichuan Int Studies Univ, Chongqing, Peoples R China.
   [Niu, Chunling] Univ Incarnate Word, Dreeben Sch Educ, 4301 Broadway, San Antonio, TX 78209 USA.
C3 University Incarnate Word; University of Shanghai for Science &
   Technology; Sichuan International Studies University; University
   Incarnate Word
RP Niu, CL (corresponding author), Univ Incarnate Word, Dreeben Sch Educ, 4301 Broadway, San Antonio, TX 78209 USA.
EM chunling.niu@gmail.com
RI Cossette, Loren/KIH-9449-2024; Sadeghi, Soheila/HMV-2123-2023
OI Sadeghi, Soheila/0000-0002-7062-9002; Niu, Chunling/0000-0002-9106-0417
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NR 50
TC 0
Z9 0
U1 4
U2 5
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0892-3647
EI 1538-9286
J9 AM J DISTANCE EDUC
JI Am. J. Distance Educ.
PD 2024 JAN 21
PY 2024
DI 10.1080/08923647.2024.2303324
EA JAN 2024
PG 32
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA FI8C8
UT WOS:001145214000001
DA 2024-09-05
ER
PT J
AU Arana-Barbier, PJ
AF Arana-Barbier, Pablo Jose
TI The Relationship Between Scientific Production and Economic Growth
   Through R&D Investment: A Bibliometric Approach
SO JOURNAL OF SCIENTOMETRIC RESEARCH
LA English
DT Article
DE Bibliometrics; Economic growth; Multiple linear regression; Research and
   development; Scientific production; SDG 8
ID INNOVATION; SCIENCE; IMPACT; CHINA
AB This quantitative bibliometric research measures the efficiency of investment in R&D for the 17 more relevant countries investing in R&D through a novel indicator based on the number of scientific articles (associated with stock markets), produced for every 1% of investment in R&D in terms of GDP. The study is justified by the need to deepen the relationship between investment in R&D and economic growth, and was conducted for developed and emerging countries separately, so that the understanding of which countries or regions' investment in R&D and its consequent scientific production has the greatest impact over the size of their economies through innovation. Our findings indicate clearly that R&D investment strongly correlates to the economy's size of the studied countries. In addition to finding our novel indicator statistically significant with respect to economic growth through a series of multiple linear regressions and proposing economic growth not statically, but as a dynamic cumulative effect over time, this becomes more relevant for emerging countries (represented in this study by China, Brazil, India, Russia and Turkey, or BRIC + Turkey) compared to developed ones, which decants into an opportunity for scholars and particularly governments to design or restructure their R&D policies towards innovation.
C1 [Arana-Barbier, Pablo Jose] Ctr Catolica Grad Business Sch CCGBS, Lima, Peru.
   [Arana-Barbier, Pablo Jose] Pontificia Univ Catolica Peru PUCP, Lima, Peru.
   [Arana-Barbier, Pablo Jose] Jr Daniel Alomia Robles 125, Lima 15023, Peru.
C3 Pontificia Universidad Catolica del Peru
RP Arana-Barbier, PJ (corresponding author), Ctr Catolica Grad Business Sch CCGBS, Lima, Peru.; Arana-Barbier, PJ (corresponding author), Pontificia Univ Catolica Peru PUCP, Lima, Peru.; Arana-Barbier, PJ (corresponding author), Jr Daniel Alomia Robles 125, Lima 15023, Peru.
EM pablo.arana@pucp.pe
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NR 41
TC 1
Z9 1
U1 5
U2 5
PU PHCOG NET
PI KARNATAKA
PA 17, 2ND FLR, BUDDHA VIHAR RD, NEAR SPORTS ZONE, COX TOWN, BENGALURU,
   KARNATAKA, 560005, INDIA
SN 2321-6654
EI 2320-0057
J9 J SCIENTOMETR RES
JI J. Scientometr. Res.
PD SEP-DEC
PY 2023
VL 12
IS 3
BP 596
EP 602
DI 10.5530/jscires.12.3.057
PG 7
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA JE8W2
UT WOS:001171589100008
OA hybrid
DA 2024-09-05
ER
PT J
AU Bansal, R
   Shrivastava, P
   Kumar, A
AF Bansal, Ramita
   Shrivastava, Preeti
   Kumar, Amar
TI Impact of goods and services tax (GST) on Indian economy
SO INTERNATIONAL JOURNAL OF FINANCIAL ENGINEERING
LA English
DT Article
DE GST; goods and services tax bill; sentiment analysis; bibliometric
   visualization; India
ID SAP-LAP; INNOVATION
AB India has implemented the Goods and Services Tax, an indirect tax, to help and promote the nation's economic expansion. The Goods and Services Tax Bill has been enacted in the majority of developed nations. In India, GST was established in 1999. A committee was set up to design the model of GST. But GST was re-launched on 1 July 2017 by the Indian government. There was a lot of uproar in favor of its introduction. All of the different taxes levied by the federal and state governments were replaced with the GST. The phrase "One Nation, One Tax" refers to the fact that all taxes must be paid in one location nationwide. The report thoroughly examines the effects of GST in India. The study offers sentiment analysis and bibliometric visualization of GST. It was discovered that the government implemented the GST in order to tax everyone in the nation and stop the flow of illicit money. However, it was noted that many Indian residents' feelings were conflicted. Therefore, it is advised to review the structure and maintain a focus on ongoing development.
C1 [Bansal, Ramita; Shrivastava, Preeti; Kumar, Amar] IIMT Coll Management, Greater Noida, India.
RP Kumar, A (corresponding author), IIMT Coll Management, Greater Noida, India.
EM theamarkumar.ak@gmail.com
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NR 18
TC 0
Z9 0
U1 2
U2 2
PU WORLD SCIENTIFIC PUBL CO PTE LTD
PI SINGAPORE
PA 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE
SN 2424-7863
EI 2424-7944
J9 INT J FINANC ENG
JI Int. J. Financ. Eng.
PD JUN
PY 2024
VL 11
IS 02
DI 10.1142/S2424786323500457
EA MAR 2024
PG 10
WC Business, Finance
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA WD9K9
UT WOS:001180825700001
DA 2024-09-05
ER
PT C
AU Kumar, D
   Karwasra, K
   Soni, G
AF Kumar, Devesh
   Karwasra, Kritika
   Soni, Gunjan
TI Bibliometric analysis of artificial neural network applications in
   materials and engineering
SO MATERIALS TODAY-PROCEEDINGS
LA English
DT Proceedings Paper
CT International Conference on Aspects of Materials Science and Engineering
   (ICAMSE)
CY MAY 29-30, 2020
CL ELECTR NETWORK
DE Bibliometric analysis; ANN; Statistics; Materials; Machining
AB Manufacturing industries in world are under pressure to adopt new technologies in order to sustain their market reputation and increase their performance. ANN is the widely used approach using in the manufacturing industries in various machining processes which results in improving the performance of manufacturing industries. There is need to identify the relationship in cutting parameters in various machining processes which results in the improving the quality and productivity. These relationships can be identified with various mathematical modelling techniques in which ANN is widely used at present time. An Artificial neural network (ANN) is the collection of nodes called artificial neurons, which is modelled according to neurons in biological brain. ANN approach is used because it has an ability to learn, can be used to model complex patterns and prediction problems. Here, Scopus database is used for collection of data with the keywords "Artificial neural network" and also "Artificial Neural Network and machining" is used to determine the trend on machining area, based on collected data bibliometric analysis has been performed. This analysis is used to determine the popularity, impact of publications and use of ANN on machining of different materials. This method is to explore the impact of ANN, the impact of different research areas. A thorough study of statistics of ANN publications by years, research areas, document types, countries, source titles and authors are conducted in this paper. This paper is for research evaluation only. (C) 2020 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Aspects of Materials Science and Engineering.
C1 [Kumar, Devesh; Karwasra, Kritika; Soni, Gunjan] Malaviya Natl Inst Technol Jaipur, Dept Mech Engn, Jaipur 302017, Rajasthan, India.
C3 National Institute of Technology (NIT System); Malaviya National
   Institute of Technology Jaipur
RP Kumar, D (corresponding author), Malaviya Natl Inst Technol Jaipur, Dept Mech Engn, Jaipur 302017, Rajasthan, India.
EM deveshkumar1993@gmail.com
RI Kumar, Devesh/HLX-4332-2023
OI Kumar, Devesh/0000-0002-4888-5173; Karwasra, Kritika/0000-0003-2044-0073
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NR 20
TC 10
Z9 10
U1 0
U2 4
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2214-7853
J9 MATER TODAY-PROC
JI Mater. Today-Proc.
PY 2020
VL 28
BP 1629
EP 1634
DI 10.1016/j.matpr.2020.04.855
PN 2
PG 6
WC Materials Science, Multidisciplinary
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Materials Science
GA MJ4RE
UT WOS:000548078600028
DA 2024-09-05
ER
PT J
AU Jankowski, R
   Sienkiewicz, J
AF Jankowski, R.
   Sienkiewicz, J.
TI Determining Crucial Factors for the Popularity of Scientific Articles
SO ACTA PHYSICA POLONICA A
LA English
DT Article; Proceedings Paper
CT 10th Polish Symposium of Physics in Economy and Social Sciences (FENS)
CY JUL 03-05, 2019
CL Natl Ctr Nucl Res, Otwock Swierk, POLAND
HO Natl Ctr Nucl Res
DE scientometrics; scientific data; predictions; machine learning
AB Using a set of over 70.000 records from PLOS One journal consisting of 37 lexical, sentiment and bibliographic variables we perform analysis backed with machine learning methods to predict the class of popularity of scientific papers defined by the number of times they have been viewed. Our study shows correlations among the features and recovers a threshold for the number of views that results in the best prediction outcomes in terms of Matthew's correlation coefficient. Moreover, by creating a variable importance plot for random forest classifier, we are able to reduce the number of features while keeping similar predictability and determine crucial factors responsible for the popularity.
C1 [Jankowski, R.; Sienkiewicz, J.] Warsaw Univ Technol, Fac Phys, Koszykowa 75, PL-00662 Warsaw, Poland.
C3 Warsaw University of Technology
RP Sienkiewicz, J (corresponding author), Warsaw Univ Technol, Fac Phys, Koszykowa 75, PL-00662 Warsaw, Poland.
EM julian.sienkiewicz@pw.edu.pl
RI Sienkiewicz, Julian/AAB-4900-2020
OI Sienkiewicz, Julian/0000-0003-2097-1499
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NR 28
TC 0
Z9 0
U1 1
U2 10
PU POLISH ACAD SCIENCES INST PHYSICS
PI WARSAW
PA AL LOTNIKOW 32-46, PL-02-668 WARSAW, POLAND
SN 0587-4246
EI 1898-794X
J9 ACTA PHYS POL A
JI Acta Phys. Pol. A
PD JUL
PY 2020
VL 138
IS 1
BP 41
EP 47
DI 10.12693/APhysPolA.138.41
PG 7
WC Physics, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)
SC Physics
GA NX2JT
UT WOS:000575541700007
OA gold, Green Submitted
DA 2024-09-05
ER
PT J
AU Mashroofa, MM
   Jusoh, M
   Chinna, K
AF Mashroofa, Mohamed Majeed
   Jusoh, Mazuki
   Chinna, Karuthan
TI Research trend on the application of "E-learning adoption theory" : A
   scientometric study during 2000-2019, based on Web of Science and SCOPUS
SO COLLNET JOURNAL OF SCIENTOMETRICS AND INFORMATION MANAGEMENT
LA English
DT Article
DE Scientometric study; Learning theories; Educational Technology;
   Technology Acceptance Theories; Online learning
ID TECHNOLOGY ACCEPTANCE MODEL; BEHAVIOR
AB Bibliometric study of the research trend on e-learning adoption theory is essential to investigate the existing literature to identify a suitable theory for new research. Objective of this study is to explore the research trend in scientific literature on e-learning adoption theory. Search of Web of Science and SCOPUS were carried out to find papers dealing with the contours of e-learning adoption theory. This study retrieved 84 research papers from Web of Science and 324 from SCOPUS on the "e-learning adoption theory." USA and Taiwan accounted for the highest number of publications with 39 papers (12.03%) and 19 papers (22.62%) in SCOPUS and Web of Science respectively. Computer Science has the highest number of papers in SCOPUS whilst Education and Educational Research is the top in Web of Science. China Agricultural University and Universiti Teknologi Malaysia have the highest number of publications four and seven in Web of Science and SCOPUS respectively. National Central University and China Agricultural University are the most productive organization as they have received 321 and 69 citations on e-learning adoption theory in Web of Science and SCOPUS respectively. Among the authors, Tarhini Ali of Brunel University, London has published the highest number of papers (five) on e-learning adoption theory in Web of Science and received 124 citations. Despites, Giannakos, M.N. and Pappas, I.O. have published the same quantity of papers (five) in SCOPUS, they have received only 47 and 22 citations respectively. Journal namely Computers and Education have published the highest number of papers in both databases at 13 (16.05%) and 14 (4.32%) received the highest number of citations at 996 (53.04%) and 1487 (39.37%) for those papers in Web of Science and SCOPUS respectively. Among the technology adoption theories, the Decomposed Theory of Planned Behaviour is more suitable to study e-learning behavior of individuals.
C1 [Mashroofa, Mohamed Majeed] South Eastern Univ Sri Lanka, Sci Lib, Oluvil, Sri Lanka.
   [Mashroofa, Mohamed Majeed] Management & Sci Univ, Shah Alam, Selangor, Malaysia.
   [Jusoh, Mazuki] Management & Sci Univ, Postgrad Ctr, Shah Alam, Selangor, Malaysia.
   [Chinna, Karuthan] Taylors Univ, Fac Hlth & Med Sci, Sch Med, Subang Jaya, Selangor, Malaysia.
C3 South Eastern University of Sri Lanka; Management Science University;
   Management Science University; Taylor's University
RP Mashroofa, MM (corresponding author), South Eastern Univ Sri Lanka, Sci Lib, Oluvil, Sri Lanka.; Mashroofa, MM (corresponding author), Management & Sci Univ, Shah Alam, Selangor, Malaysia.
EM mashroof@seu.ac.lk
RI Mashroofa, Mohamed Majeed/GNM-7789-2022; Mashroofa, Mohamed
   Majeed/AAP-2633-2020
OI Mashroofa, Mohamed Majeed/0000-0002-2250-2445; Mashroofa, Mohamed
   Majeed/0000-0002-2250-2445
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NR 28
TC 9
Z9 9
U1 0
U2 13
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0973-7766
EI 2168-930X
J9 COLLNET J SCIENTOMET
JI Collnet J. Scientometr. Inf. Manag.
PD JUL 3
PY 2019
VL 13
IS 2
BP 387
EP 408
DI 10.1080/09737766.2020.1729072
PG 22
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA KY9GO
UT WOS:000522883100011
DA 2024-09-05
ER
PT J
AU Pozsgai-Alvarez, J
   Sanz, IP
AF Pozsgai-Alvarez, Joseph
   Pastor Sanz, Ivan
TI Mapping the (anti-)corruption field: key topics and changing trends,
   1968-2020
SO JOURNAL OF COMPUTATIONAL SOCIAL SCIENCE
LA English
DT Article
DE Corruption; Corpus; Machine learning; Bibliometrics; Interdisciplinary
ID FOREIGN DIRECT-INVESTMENT; MORAL DISENGAGEMENT; CORRUPTION; IMPACT;
   GROWTH; INSTITUTIONS; ENFORCEMENT; PERFORMANCE; GOVERNANCE; GOVERNMENT
AB As research on (anti-)corruption continues to accelerate, the heterogeneity of perspectives that have emerged in the field complicates the identification of key topics and trends, limiting our capacity to set meaningful research priorities, risking the waste of time and funds, and potentially broadening the gap between scholarly production and policy necessities. To help elucidate this morass, we use the Latent Dirichlet Allocation (LDA) algorithm to classify a dataset of 5417 publications listed in the Global Anticorruption Blog's (GAB) Anticorruption Bibliography. The results allow us to recognize eight main topics in the literature, as well as their evolution over the past 2 decades in terms of relative attention (as measured by citation count) and publication rates. The topics and trends found here invite us to reflect on the current structure of the (anti-)corruption field, and to draw attention to persistent-and emerging-gaps.
C1 [Pozsgai-Alvarez, Joseph] Kyoto Univ, Ctr Southeast Asian Studies, 46 Shimoadachi Cho, Kyoto 6068501, Japan.
   [Pastor Sanz, Ivan] Univ Valladolid, Sch Business & Econ, Avda Valle de Esgueva 6, Valladolid 47011, Spain.
C3 Kyoto University; Universidad de Valladolid
RP Pozsgai-Alvarez, J (corresponding author), Kyoto Univ, Ctr Southeast Asian Studies, 46 Shimoadachi Cho, Kyoto 6068501, Japan.
EM jpozsgai@dailycorruption.info; ivanpastorsanz@gmail.com
RI Pozsgai-Alvarez, Joseph/AAL-8789-2021
OI Pozsgai-Alvarez, Joseph/0000-0002-9338-2583
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NR 110
TC 3
Z9 3
U1 1
U2 8
PU SPRINGERNATURE
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND
SN 2432-2717
EI 2432-2725
J9 J COMPUT SOC SCI
JI J. Comput. Soc. Sci.
PD NOV
PY 2021
VL 4
IS 2
BP 851
EP 881
DI 10.1007/s42001-021-00110-2
PG 31
WC Social Sciences, Mathematical Methods
WE Emerging Sources Citation Index (ESCI)
SC Mathematical Methods In Social Sciences
GA WH4ET
UT WOS:000707633900018
DA 2024-09-05
ER
PT J
AU Baker, RS
   Nasiar, N
   Gong, WY
   Porter, C
AF Baker, Ryan S.
   Nasiar, Nidhi
   Gong, Weiyi
   Porter, Chelsea
TI The impacts of learning analytics and A/B testing research: a case study
   in differential scientometrics
SO INTERNATIONAL JOURNAL OF STEM EDUCATION
LA English
DT Article
DE Scientometrics; A; B testing; Learning analytics; Online learning; STEM
   education platform
ID SCIENCE
AB Background In recent years, research on online learning platforms has exploded in quantity. More and more researchers are using these platforms to conduct A/B tests on the impact of different designs, and multiple scientific communities have emerged around studying the big data becoming available from these platforms. However, it is not yet fully understood how each type of research influences future scientific discourse within the broader field. To address this gap, this paper presents the first scientometric study on how researchers build on the contributions of these two types of online learning platform research (particularly in STEM education). We selected a pair of papers (one using A/B testing, the other conducting learning analytics (LA), on platform data of an online STEM education platform), published in the same year, by the same research group, at the same conference. We then analyzed each of the papers that cited these two papers, coding from the paper text (with inter-rater reliability checks) the reason for each citation made. Results After statistically comparing the frequency of each category of citation between papers, we found that the A/B test paper was self-cited more and that citing papers built on its work directly more frequently, whereas the LA paper was more often cited without discussion. Conclusions Hence, the A/B test paper appeared to have had a larger impact on future work than the learning analytics (LA) paper, even though the LA paper had a higher count of total citations with a lower degree of self-citation. This paper also established a novel method for understanding how different types of research make different contributions in learning analytics, and the broader online learning research space of STEM education.
C1 [Baker, Ryan S.; Nasiar, Nidhi; Gong, Weiyi; Porter, Chelsea] Univ Penn, Grad Sch Educ, Philadelphia, PA 19104 USA.
C3 University of Pennsylvania
RP Baker, RS (corresponding author), Univ Penn, Grad Sch Educ, Philadelphia, PA 19104 USA.
EM ryanshaunbaker@gmail.com
RI Baker, Ryan/IWE-2102-2023
OI Nasiar, Nidhi/0009-0006-7063-5433; Porter, Chelsea/0009-0005-0246-8509
FU Schmidt Futures Foundation
FX The project is funded by the Schmidt Futures Foundation, in an award
   initially given to Worcester Polytechnic Institute. This article does
   not necessarily represent the perspectives of either of these
   organizations.
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NR 51
TC 0
Z9 0
U1 0
U2 18
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 2196-7822
J9 INT J STEM EDUC
JI Int. J. STEM Educ.
PD FEB 14
PY 2022
VL 9
IS 1
AR 16
DI 10.1186/s40594-022-00330-6
PG 10
WC Education & Educational Research; Education, Scientific Disciplines
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Education & Educational Research
GA YY9UO
UT WOS:000755130600002
PM 35194544
OA gold, Green Published
DA 2024-09-05
ER
PT C
AU Kuznetsova, A
   Pozdniakov, S
   Musabirov, I
AF Kuznetsova, Anastasiya
   Pozdniakov, Stanislav
   Musabirov, Ilya
BE Alexandrov, DA
   Boukhanovsky, AV
   Chugunov, AV
   Kabanov, Y
   Koltsova, O
TI Analyzing Web Presence of Russian Universities in a Scientometric
   Context
SO DIGITAL TRANSFORMATION AND GLOBAL SOCIETY (DTGS 2017)
SE Communications in Computer and Information Science
LA English
DT Proceedings Paper
CT 2nd International Conference on Digital Transformation and Global
   Society (DTGS)
CY JUN 21-23, 2017
CL ITMO Univ, St. Petersburg, RUSSIA
HO ITMO Univ
DE Russian universities; Northwestern region; Webometrics; Altmetrics; LDA;
   Topic modelling
AB In this paper, we analyse the strategies and stratification of Russian universities in the Northwestern region. By enriching traditional social network analysis scientometric tools, we developed web presence indicators focused on the contexts in which universities are linked with businesses and are mentioned in media. We treat resulting groups in terms of Gouldner's cosmopolitans versus locals theory, based on differences in their publication strategies, and embeddedness in business connections and media contexts.
C1 [Kuznetsova, Anastasiya; Pozdniakov, Stanislav; Musabirov, Ilya] Natl Res Univ Higher Sch Econ, St Petersburg, Russia.
C3 HSE University (National Research University Higher School of Economics)
RP Kuznetsova, A (corresponding author), Natl Res Univ Higher Sch Econ, St Petersburg, Russia.
EM adkuznetsova13@gmail.com; pozdniakov.stanislav@gmail.com;
   ilya@musabirov.info
RI Musabirov, Ilya/K-3905-2015
FU Academic Fund Program at the National Research University Higher School
   of Economics (HSE) in 2017 2018 [17-05-0024]
FX The article was prepared within the framework of the Academic Fund
   Program at the National Research University Higher School of Economics
   (HSE) in 2017 2018 (grant No. 17-05-0024) and by the Russian Academic
   Excellence Project "5-100".
CR Altbach P., 2017, ANARCHY EXPLOITATION
   Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
   Dyachenko E., 2017, HIGH ED RUSSIA, V11, P7
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NR 12
TC 0
Z9 0
U1 0
U2 1
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 1865-0929
EI 1865-0937
BN 978-3-319-69784-0; 978-3-319-69783-3
J9 COMM COM INF SC
PY 2017
VL 745
BP 113
EP 119
DI 10.1007/978-3-319-69784-0_9
PG 7
WC Computer Science, Theory & Methods; Telecommunications
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Telecommunications
GA BO4XN
UT WOS:000515664100009
DA 2024-09-05
ER
PT J
AU Li, SQ
   Zhang, YP
   Ding, M
   Dai, PC
AF Li, Shuqin
   Zhang, Yipeng
   Ding, Meng
   Dai, Pengcheng
TI Research on integrated computer game algorithm for dots and boxes
SO JOURNAL OF ENGINEERING-JOE
LA English
DT Article; Proceedings Paper
CT 3rd Asian Conference on Artificial Intelligence Technology (ACAIT)
CY JUN 07-09, 2019
CL Chongqing, PEOPLES R CHINA
DE C++ language; tree searching; Monte Carlo methods; artificial
   intelligence; neural nets; computer games; game theory; research on
   integrated computer game algorithm; dots; computer game research; game
   progresses; famous chess game; Monte Carlo tree search algorithm;
   Alpha-Beta algorithm; deep convolution neural network; computer game
   problem; deep convolutional neural network model; deep value network;
   situation assessment; strategy recommendation; Monte Carlo Tree Search
   algorithm; deep strategy network integrated MCTS algorithm; integrated
   models; Alpha-Beta complete search; Monte Carlo simulation process;
   integrated algorithm game systems
ID GO
AB Situation assessment and search are two key problems in computer game research. In general, as the game progresses, the difficulty of evaluating the situation of the game is significantly reduced, and the accuracy of the evaluation is significantly increased. Based on the famous chess game, this article proposes and implements a new scheme that combines the Monte Carlo tree search algorithm, the Alpha-Beta algorithm and the model based on the deep convolution neural network (CNN) to solve the computer game problem. This article first proposes a deep convolutional neural network model based on dots and boxes, including deep value network and deep strategy network, focusing on situation assessment and strategy recommendation, respectively. Then, using the Monte Carlo Tree Search (MCTS) algorithm as a framework, deep value network integrated MCTS algorithm and deep strategy network integrated MCTS algorithm are proposed. In both integrated models, Alpha-Beta complete search is used to truncate the Monte Carlo simulation process and improve simulation efficiency. Through competition with human players, the results show that the two integrated algorithm game systems have reached much higher intelligence level than ordinary humans in solving the problem of dots and boxes.
C1 [Li, Shuqin; Zhang, Yipeng; Ding, Meng; Dai, Pengcheng] Beijing Informat Sci & Technol Univ, Comp Acad, Beijing, Peoples R China.
   [Li, Shuqin; Zhang, Yipeng; Ding, Meng; Dai, Pengcheng] Percept & Computat Intelligence Joint Lab, 35 North Fourth Ring Rd, Beijing, Peoples R China.
C3 Beijing Information Science & Technology University
RP Li, SQ (corresponding author), Beijing Informat Sci & Technol Univ, Comp Acad, Beijing, Peoples R China.; Li, SQ (corresponding author), Percept & Computat Intelligence Joint Lab, 35 North Fourth Ring Rd, Beijing, Peoples R China.
EM lishuqin_de@126.com
RI Zhang, Yipeng/HGD-1556-2022
OI Zhang, Yipeng/0000-0003-2869-4692
FU Key potential projects of Promoting Research Level program at Beijing
   Information Science and Technology University [5211910927]; Normal
   projects of General Science and Technology research program
   [KM201911232002]; Science & Technology Innovation program for graduated
   students at Beijing Information Science and Technology University
   [5121911044]; Normal projects of promoting graduated education program
   at Beijing Information Science and Technology University [5121911019]
FX This work is supported by Key potential projects of Promoting Research
   Level program at Beijing Information Science and Technology University
   (NO. 5211910927), by Normal projects of General Science and Technology
   research program (NO. KM201911232002), by Science & Technology
   Innovation program for graduated students at Beijing Information Science
   and Technology University (NO. 5121911044), by Normal projects of
   promoting graduated education program at Beijing Information Science and
   Technology University (NO. 5121911019).
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NR 14
TC 1
Z9 1
U1 0
U2 3
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
EI 2051-3305
J9 J ENG-JOE
JI J. Eng.-JOE
PD JUL
PY 2020
VL 2020
IS 13
BP 601
EP 606
DI 10.1049/joe.2019.1185
PG 6
WC Engineering, Multidisciplinary
WE Emerging Sources Citation Index (ESCI)
SC Engineering
GA NI3QK
UT WOS:000565270600060
OA gold
DA 2024-09-05
ER
PT C
AU Antonakaki, D
   Polakis, I
   Athanasopoulos, E
   Ioannidis, S
   Fragopoulou, P
AF Antonakaki, Despoina
   Polakis, Iasonas
   Athanasopoulos, Elias
   Ioannidis, Sotiris
   Fragopoulou, Paraskevi
BE Aiello, LM
   McFarland, D
TI Think Before RT: An Experimental Study of Abusing Twitter Trends
SO SOCIAL INFORMATICS
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT SocInfo International Workshops
CY NOV 10, 2014
CL Barcelona, SPAIN
DE Spam; Twitter; Microblogging; Social influence; Spammer; Spam campaign;
   Trending topic; Machine learning; Classification; Regression trees; Gain
   more followers campaign; Online social networks; Privacy
AB Twitter is one of the most influential Online Social Networks (OSNs), adopted not only by hundreds of millions of users but also by public figures, organizations, news media, and official authorities. One of the factors contributing to this success is the inherent property of the platform for spreading news - encapsulated in short messages that are tweeted from one user to another - across the globe. Today, it is sufficient to just inspect the trending topics in Twitter for figuring out what is happening around the world. Unfortunately, the capabilities of the platform can be also abused and exploited for distributing illicit content or boosting false information, and the consequences of such actions can be really severe: one false tweet was enough for making the stock-market crash for a short period of time in 2013.
   In this paper, we analyze a large collection of tweets and explore the dynamics of popular trends and other Twitter features in regards to deliberate misuse. We identify a specific class of trend-exploiting campaigns that exhibits a stealthy behavior and hides spam URLs within Google search-result links. We build a spam classifier for both users and tweets, and demonstrate its simplicity and efficiency. Finally, we visualize these spam campaigns and reveal their inner structure.
C1 [Antonakaki, Despoina; Athanasopoulos, Elias; Ioannidis, Sotiris; Fragopoulou, Paraskevi] FORTH ICS, Iraklion, Greece.
   [Polakis, Iasonas] Columbia Univ, New York, NY USA.
C3 Columbia University
RP Antonakaki, D (corresponding author), FORTH ICS, Iraklion, Greece.
EM despoina@ics.forth.gr; polakis@cs.columbia.edu; elathan@ics.forth.gr;
   sotiris@ics.forth.gr; fragopou@ics.forth.gr
OI Antonakaki, Despoina/0000-0001-9081-6115
CR [Anonymous], 1954, TIME
   [Anonymous], 2011, ICWSM
   [Anonymous], 2012, 19 ANN NETWORK DISTR
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NR 16
TC 1
Z9 1
U1 0
U2 2
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 0302-9743
EI 1611-3349
BN 978-3-319-15168-7; 978-3-319-15167-0
J9 LECT NOTES COMPUT SC
PY 2015
VL 8852
BP 402
EP 413
DI 10.1007/978-3-319-15168-7_49
PG 12
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BF3NP
UT WOS:000380559900049
DA 2024-09-05
ER
PT J
AU Blanco, FJ
AF Blanco, Jose F.
TI Fashion at the museum: successful experiences with student curators
SO MUSEUM MANAGEMENT AND CURATORSHIP
LA English
DT Article
DE fashion exhibition; student curators; active learning; institutional
   collaboration; museum exhibitions
AB This paper describes the development of two fashion-centered exhibitions organized by non-museum studies college students in collaboration with a university museum and a community arts center and historic house. The work was the result of a new course, 'Museum Issues in Historic Clothing and Textiles,' offered in the Department of Textiles, Merchandising and Interiors at the University of Georgia. The department holds a large historic clothing and textiles collection, but has limited exhibition space. This paper describes the steps involved in the collaborative project, the active learning process in the classroom, the exact nature and process of the collaboration, and provides information on the final product - the two exhibitions created by the class. The paper aims to encourage similar collaborations between museum and fashion design, or fashion merchandizing programs housing historic clothing and textiles collections.
C1 [Blanco, Jose F.] Univ Georgia, Coll Familyand Consumer Sci, Dept Text Merchandising & Interiors, 303 Dawson Hall, Athens, GA 30602 USA.
C3 University System of Georgia; University of Georgia
RP Blanco, FJ (corresponding author), Univ Georgia, Coll Familyand Consumer Sci, Dept Text Merchandising & Interiors, 303 Dawson Hall, Athens, GA 30602 USA.
EM jblanco@uga.edu
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NR 23
TC 3
Z9 5
U1 0
U2 1
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0964-7775
EI 1872-9185
J9 MUS MANAGE CURATOR
JI Mus. Manag. Curatorship
PY 2010
VL 25
IS 2
BP 199
EP 217
DI 10.1080/09647771003737323
PG 19
WC Humanities, Multidisciplinary
WE Emerging Sources Citation Index (ESCI)
SC Arts & Humanities - Other Topics
GA V73AY
UT WOS:000211683500005
DA 2024-09-05
ER
PT J
AU Antonakaki, D
   Polakis, I
   Athanasopoulos, E
   Ioannidis, S
   Fragopoulou, P
AF Antonakaki, Despoina
   Polakis, Iasonas
   Athanasopoulos, Elias
   Ioannidis, Sotiris
   Fragopoulou, Paraskevi
TI Exploiting abused trending topics to identify spam campaigns in Twitter
SO SOCIAL NETWORK ANALYSIS AND MINING
LA English
DT Article
DE Spam; Twitter; Microblogging; Social influence; Spammer; Spam campaign;
   Trending topic; Machine learning; Classification; Regression trees;
   Gain-more-followers campaign; Online social networks; Privacy
AB Twitter is an online social network (OSN) with approximately 650 million users. It has been fairly characterized as one of the most influential OSNs since it includes public figures, organizations, news media and official authorities. Twitter has an inherent simple philosophy with short messages, friendship relations, hashtags and support for media sharing such as photos and short videos. Popular hashtags that emerge from users' activity are displayed prominently in the platform as Popular Trends. Unfortunately, the capabilities of the platform can be also abused and exploited for distributing illicit content or boosting false information, and the consequences of such actions can be really severe: one false tweet was enough for making the stock market crash for a short period of time in 2013. In this study, we make an experimental analysis on a large dataset containing 150 million tweets. We delve into the dynamics of the popular trends as well as other Twitter features in regard to deliberate misuse. We investigate traditional spam techniques as well as an obfuscated way of spam campaigns that exploit trending topics and hides malicious URLs within Google search result links. We implement a simple and lightweight classifier for indentifying spam users as well as spam tweets. Finally, we visualize these spam campaigns and investigate their inner properties.
C1 [Antonakaki, Despoina; Athanasopoulos, Elias; Ioannidis, Sotiris; Fragopoulou, Paraskevi] FORTH ICS, Iraklion, Greece.
   [Polakis, Iasonas] Columbia Univ, New York, NY USA.
C3 Columbia University
RP Antonakaki, D (corresponding author), FORTH ICS, Iraklion, Greece.
EM despoina@ics.forth.gr; polakis@cs.columbia.edu; elathan@ics.forth.gr;
   sotiris@ics.forth.gr; fragopou@ics.forth.gr
OI Antonakaki, Despoina/0000-0001-9081-6115
CR [Anonymous], 2010, ANN COLL EL MESS ANT
   [Anonymous], 2011, ICWSM
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NR 23
TC 13
Z9 14
U1 0
U2 55
PU SPRINGER WIEN
PI WIEN
PA SACHSENPLATZ 4-6, PO BOX 89, A-1201 WIEN, AUSTRIA
SN 1869-5450
EI 1869-5469
J9 SOC NETW ANAL MIN
JI Soc. Netw. Anal. Min.
PD DEC
PY 2016
VL 6
IS 1
DI 10.1007/s13278-016-0354-9
PG 11
WC Computer Science, Information Systems
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA DT1CY
UT WOS:000381220500048
DA 2024-09-05
ER
PT J
AU López-Robles, JR
   Cobo, MJ
   Gutiérrez-Salcedo, M
   Martínez-Sánchez, MA
   Gamboa-Rosales, NK
   Herrera-Viedma, E
AF Lopez-Robles, J. R.
   Cobo, M. J.
   Gutierrez-Salcedo, M.
   Martinez-Sanchez, M. A.
   Gamboa-Rosales, N. K.
   Herrera-Viedma, E.
TI 30th Anniversary of <i>Applied Intelligence</i>: A combination of
   bibliometrics and thematic analysis using <i>SciMAT</i>
SO APPLIED INTELLIGENCE
LA English
DT Article
DE Science mapping; Journal conceptual structure; Artificial intelligence;
   Intelligent systems; Applied intelligence; SciMAT
ID MANAGEMENT; EVOLUTION; BUSINESS
AB Applied Intelligence is one of the most important international scientific journals in the field of artificial intelligence. From 1991, Applied Intelligence has been oriented to support research advances in new and innovative intelligent systems, methodologies, and their applications in solving real-life complex problems. In this way, Applied Intelligence hosts more than 2,400 publications and achieves around 31,800 citations. Moreover, Applied Intelligence is recognized by the industrial, academic, and scientific communities as a source of the latest innovative and advanced solutions in intelligent manufacturing, privacy-preserving systems, risk analysis, knowledge-based management, modern techniques to improve healthcare systems, methods to assist government, and solving industrial problems that are too complex to be solved through conventional approaches. Bearing in mind that Applied Intelligence celebrates its 30th anniversary in 2021, it is appropriate to analyze its bibliometric performance, conceptual structure, and thematic evolution. To do that, this paper conducts a bibliometric performance and conceptual structure analysis of Applied Intelligence from 1991 to 2020 using SciMAT. Firstly, the performance of the journal is analyzed according to the data retrieved from Scopus, putting the focus on the productivity of the authors, citations, countries, organizations, funding agencies, and most relevant publications. Finally, the conceptual structure of the journal is analyzed with the bibliometric software tool SciMAT, identifying the main thematic areas that have been the object of research and their composition, relationship, and evolution during the period analyzed.
C1 [Lopez-Robles, J. R.] Autonomous Univ Zacatecas, Acad Unit Accounting & Management, Zacatecas, Zacatecas, Mexico.
   [Lopez-Robles, J. R.] Univ Cadiz, Dept Comp Sci & Engn, Cadiz, Spain.
   [Cobo, M. J.] Univ Cadiz, Andalusian Res Inst Data Sci & Computat Intellige, Dept Comp Sci & Engn, Cadiz, Spain.
   [Gutierrez-Salcedo, M.] Univ Jaen, Dept Management Mkt & Sociol, Jaen, Spain.
   [Martinez-Sanchez, M. A.] Univ Granada, Dept Social Work, Granada, Spain.
   [Gamboa-Rosales, N. K.] Autonomous Univ Zacatecas, CONACYT Acad Unit Elect Engn, Zacatecas, Zacatecas, Mexico.
   [Herrera-Viedma, E.] Univ Granada, Andalusian Res Inst Data Sci & Computat Intellige, Dept Comp Sci & Artificial Intelligence, Granada, Spain.
C3 Universidad Autonoma de Zacatecas; Universidad de Cadiz; Universidad de
   Cadiz; Universidad de Jaen; University of Granada; Universidad Autonoma
   de Zacatecas; University of Granada
RP Cobo, MJ (corresponding author), Univ Cadiz, Andalusian Res Inst Data Sci & Computat Intellige, Dept Comp Sci & Engn, Cadiz, Spain.
EM manueljesus.cobo@uca.es
RI Cobo Martí­n, Manuel Jesús/C-5581-2011; HERRERA-VIEDMA,
   ENRIQUE/C-2704-2008; Gutiérrez-Salcedo, María/E-9633-2013
OI Cobo Martí­n, Manuel Jesús/0000-0001-6575-803X; HERRERA-VIEDMA,
   ENRIQUE/0000-0002-7922-4984; Gutiérrez-Salcedo,
   María/0000-0002-8874-8069
FU CONACYT-Consejo Nacional de Ciencia y Tecnologia (Mexico);
   COZCYT-Consejo Zacatecano de Ciencia, Tecnologia e Innovacion (Mexico);
   Spanish State Research Agency
   [PID2019-105381GA-I00/AEI/10.13039/501100011033,
   PID2019-103880RB-I00/AEI/10.13039/501100011033]
FX The authors acknowledge the support of the CONACYT-Consejo Nacional de
   Ciencia y Tecnologia (Mexico) and COZCYT-Consejo Zacatecano de Ciencia,
   Tecnologia e Innovacion (Mexico) to carry out this study. Additionally,
   this work has been supported by the Spanish State Research Agency
   through the project PID2019-105381GA-I00/AEI/10.13039/501100011033
   (iScience) and PID2019-103880RB-I00/AEI/10.13039/501100011033.
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NR 30
TC 43
Z9 43
U1 13
U2 112
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0924-669X
EI 1573-7497
J9 APPL INTELL
JI Appl. Intell.
PD SEP
PY 2021
VL 51
IS 9
SI SI
BP 6547
EP 6568
DI 10.1007/s10489-021-02584-z
EA JUL 2021
PG 22
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science
GA UC7PL
UT WOS:000671766700001
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Park, S
AF Park, Sungmin
TI Differences in technology innovation R&D performance creation behavior
   between for-profit institutions and not-for-profit institutions
SO SPRINGERPLUS
LA English
DT Article
DE Data splitting; For-profit institutions; Not-for-profit institutions;
   R&D collaboration; Stepwise performance creation; Successive binary
   logistic regression
ID DEVELOPMENT EFFICIENCY; NETWORKS; PRODUCTIVITY; DEA
AB The present study compares the performance creation behavior between for-profit institutions and not-for-profit institutions within a national technology innovation research and development (R&D) program. Based on the stepwise performance creation chain structure of typical R&D logic models, a series of successive binary logistic regression models is newly proposed. Using the models, a sample of n = 2076 completed government-sponsored R&D projects was analyzed. For each institution type, its distinctive behavior is diagnosed, and relevant implications are suggested for improving the R&D performance.
C1 [Park, Sungmin] Baekseok Univ, Dept Business Adm, Cheonan 330704, South Korea.
C3 Baekseok University
RP Park, S (corresponding author), Baekseok Univ, Dept Business Adm, Cheonan 330704, South Korea.
EM smpark99@bu.ac.kr
FU Baekseok University Research Grant
FX The author acknowledges the contribution of the Korea Institute for
   Advancement of Technology (KIAT) and the Korea Evaluation Institute of
   Industrial Technology (KEIT), who permits the data available in the
   present study. This study was partially supported by Baekseok University
   Research Grant.
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NR 74
TC 3
Z9 3
U1 4
U2 24
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 2193-1801
J9 SPRINGERPLUS
JI SpringerPlus
PD APR 14
PY 2016
VL 5
AR 451
DI 10.1186/s40064-016-2092-x
PG 23
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics
GA DL5TA
UT WOS:000375698600007
PM 27119055
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU Hua, GB
AF Hua, Goh Bee
TI The state of applications of quantitative analysis techniques to
   construction economics and management (1983 to 2006)
SO CONSTRUCTION MANAGEMENT AND ECONOMICS
LA English
DT Article
DE Artificial intelligence; statistical method; quantitative analysis
   technique; bibliometrics; construction economics; construction
   management
ID OPTIMIZING RESOURCE UTILIZATION; ARTIFICIAL NEURAL-NETWORK; TIME-COST
   OPTIMIZATION; GENETIC ALGORITHMS; SITE LAYOUT; SCHEDULING MODEL;
   SIMULATION-MODEL; DYNAMICS MODEL; PERFORMANCE; PROJECTS
AB With increasing complexity of construction industry problems, researchers are experimenting with computationally rigorous techniques with the aim of seeking innovative solutions. In order to trace the applications of quantitative analysis techniques to research in the two fields of construction economics and construction management for both conventional and AI techniques, the methodology involves compiling all the relevant papers from the top two ranking construction management journals, namely, Construction Management and Economics and ASCE's Journal of Construction Engineering and Management. The period is from 1983 to 2006. The compiled papers are classified by field, area (or topic), technique applied and year of publication to enable time series and cross-sectional analyses of the data. Mainly, the results are depicted as trends when the patterns of distribution of the papers are plotted over time. The three findings are: (1) for construction economics, the overall increasing trend is higher for papers that have applied conventional techniques; (2) for construction management, there is a clear positive trend for papers that have applied AI techniques which starts from 1995; and (3) the areas (or topics) of construction management that have increasingly higher growth in the application of AI techniques are optimization of site operations and optimization of project time, cost and resources allocation. Two broad recommendations are made that relate to advancing the fields of construction economics and construction management with the view that researchers must better enable themselves to build tools that incorporate intelligence as innovative solutions for increasingly complex problems.
C1 [Hua, Goh Bee] Natl Univ Singapore, Sch Design & Environm, Dept Bldg, Singapore, Singapore.
C3 National University of Singapore
RP Hua, GB (corresponding author), Natl Univ Singapore, Sch Design & Environm, Dept Bldg, Singapore, Singapore.
EM bdggohbh@nus.edu.sg
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NR 119
TC 10
Z9 10
U1 0
U2 4
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0144-6193
EI 1466-433X
J9 CONSTR MANAG ECON
JI Constr. Manag. Econ.
PY 2008
VL 26
IS 5
BP 485
EP 497
DI 10.1080/01446190801998716
PG 13
WC Business
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA V96DW
UT WOS:000213245900006
DA 2024-09-05
ER
PT J
AU Kumari, P
   Kumar, R
AF Kumari, Priti
   Kumar, Rajeev
TI Clustering Scientometrics of Computer Science Journals for Subarea
   Decomposition
SO JOURNAL OF SCIENTOMETRIC RESEARCH
LA English
DT Article
DE Scientometrics; Bibliometrics; Publications; K-means; Clustering;
   Computer Science; Subarea Indicators; Machine Learning
ID BIBLIOMETRIC INDICATORS; IMPACT; CITATIONS; METRICS; INDEX
AB Scientometrics indicators vary widely across subareas of the Computer Science (CS) discipline. Most researchers have previously analyzed scientometrics data specific to a particular subfield or a few subfields. More popular subareas lead to high scientometrics, and others have lower values. This work considers seven diversified CS subareas and six commonly used scientometrics indicators. First, we study the varying range of chosen scientometrics indicators of various subareas of the CS discipline. We explore the correlation patterns of these six indicators. Then, we consider a few combinations of these indicators and apply K-means clustering to decompose the pattern space. Correlation findings indicate that though the highly correlated indicators vary for most subfields, no single indicator can be considered equally suitable for all the subareas. The K-means clustering results show distinctive patterns across subfields, which are stable across K. The clustered subfield-specific indicators are quite distinct across subfields. This knowledge can be used as a signature for partitioning the subarea-specific indicators.
C1 [Kumari, Priti; Kumar, Rajeev] Jawaharlal Nehru Univ, Sch Comp & Syst Sci, Data Knowledge D2K Lab, New Delhi 110067, India.
C3 Jawaharlal Nehru University, New Delhi
RP Kumari, P (corresponding author), Jawaharlal Nehru Univ, Sch Comp & Syst Sci, Data Knowledge D2K Lab, New Delhi 110067, India.
EM priti08.1993@gmail.com
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NR 32
TC 0
Z9 0
U1 2
U2 2
PU PHCOG NET
PI KARNATAKA
PA 17, 2ND FLR, BUDDHA VIHAR RD, NEAR SPORTS ZONE, COX TOWN, BENGALURU,
   KARNATAKA, 560005, INDIA
SN 2321-6654
EI 2320-0057
J9 J SCIENTOMETR RES
JI J. Scientometr. Res.
PD MAY-AUG
PY 2023
VL 12
IS 2
BP 383
EP 394
DI 10.5530/jscires.12.2.034
PG 12
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA HF6A6
UT WOS:001158105300014
OA hybrid
DA 2024-09-05
ER
PT J
AU Kolahi, J
   Khazaei, S
   Bidram, E
   Kelishadi, R
   Iranmanesh, P
   Khademi, A
   Nekoofar, MH
   Dummer, PMH
AF Kolahi, Jafar
   Khazaei, Saber
   Bidram, Elham
   Kelishadi, Roya
   Iranmanesh, Pedram
   Khademi, Abbasali
   Nekoofar, Mohammad H.
   Dummer, Paul M. H.
TI Science Map of Cochrane Systematic Reviews Receiving the Most Altmetric
   Attention Score: A Network Analysis
SO JOURNAL OF SCIENTOMETRIC RESEARCH
LA English
DT Article
DE Cochrane systematic review; Altmetric; Bibliometric; Twitter; Machine
   learning; Network analysis; Random forest
ID DENTAL LITERATURE; SCIENTIFIC LANDSCAPE; IMPACT; CITATIONS; ARTICLES;
   JOURNALS; TWITTER
AB The present study aimed to analyze and visualize the science map of Cochrane systematic reviews (CSRs) with high Altmetric attention score (AAS). On 2020-07-29, the altmetric data of the Cochrane Database of Systematic Reviews were obtained from the Altmetric database (Altmetric LLP, London, UK). Bibliometric data of the top 5% AAS of CSRs were extracted from the Web of Science. Keyword co-occurrence, co-authorship and co-citation network analyses were then employed using VOSviewer software. The random forest model was used to rank the importance of the altmetric resource. A total of 11222 CSRs with AAS were found (Total mentions: 305265), with Twitter being the most popular Altmetric resource. Consequently, the top 5% AAS (649 articles, mean AAS: 204.95, 95% confidence level: 18.95, mean citations: 123.68, 95% confidence level: 13.9) were included. Density mapping revealed female, adult and child as the most popular author keywords. According to network visualization, Helen V. Worthington (University of Manchester, Manchester, UK), the University of Oxford and UK had the greatest impact on the network at the author, organization and country levels respectively. AAS were weekly correlated with citations (r(s)=0.21) although citations were moderately correlated with policy document and blog mentions (r(s)=0.46 and r(s)=0.43). Cochrane systematic reviews received high levels of online attention, particularly in the Twittersphere and mostly from the UK. However, CSRs were rarely publicized and discussed using recently developed academic tools. such as F1000 prime, Publons and PubPeer.
C1 [Kolahi, Jafar] Dent Hypotheses, Esfahan, Iran.
   [Khazaei, Saber] Kermanshah Univ Med Sci, Sch Dent, Dept Endodont, Kermanshah, Iran.
   [Bidram, Elham] Isfahan Univ Med Sci, Sch Adv Technol Med, Dept Biomat Nanotechnol & Tissue Engn, Biosensor Res Ctr, Esfahan, Iran.
   [Kelishadi, Roya] Isfahan Univ Med Sci, Child Growth & Dev Res Ctr, Res Inst Primordial Prevent Noncommunicable Dis, Dept Pediat, Esfahan, Iran.
   [Iranmanesh, Pedram; Khademi, Abbasali] Isfahan Univ Med Sci, Dent Res Ctr, Dent Res Inst, Dept Endodont, Esfahan, Iran.
   [Nekoofar, Mohammad H.] Univ Tehran Med Sci, Sch Dent, Dept Endodont, Tehran, Iran.
   [Nekoofar, Mohammad H.; Dummer, Paul M. H.] Cardiff Univ, Coll Biomed & Life Sci, Sch Dent, Cardiff, Wales.
C3 Kermanshah University of Medical Sciences; Isfahan University Medical
   Science; Isfahan University Medical Science; Isfahan University Medical
   Science; Tehran University of Medical Sciences; Cardiff University
RP Iranmanesh, P (corresponding author), Isfahan Univ Med Sci, Sch Dent, Dept Endodont, Hezar Jerib Ave, Esfahan, Iran.
EM pedram.iranmanesh@yahoo.com
RI Dummer, Paul M. H./F-3063-2012; Khazaei, Saber/B-7654-2011; Iranmanesh,
   Pedram/M-5673-2017
OI Dummer, Paul M. H./0000-0002-0726-7467; Khazaei,
   Saber/0000-0002-9085-1292; Iranmanesh, Pedram/0000-0002-4813-4097;
   Bidram, Elham/0000-0002-6319-3204
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NR 50
TC 1
Z9 1
U1 1
U2 11
PU PHCOG NET
PI KARNATAKA
PA 17, 2ND FLR, BUDDHA VIHAR RD, NEAR SPORTS ZONE, COX TOWN, BENGALURU,
   KARNATAKA, 560005, INDIA
SN 2321-6654
EI 2320-0057
J9 J SCIENTOMETR RES
JI J. Scientometr. Res.
PD SEP-DEC
PY 2020
VL 9
IS 3
BP 293
EP 299
DI 10.5530/jscires.9.3.36
PG 7
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA PP1WG
UT WOS:000605658100006
OA hybrid, Green Submitted
DA 2024-09-05
ER
PT J
AU Kashnitsky, Y
   Roberge, G
   Mu, JW
   Kang, K
   Wang, WW
   Vanderfeesten, M
   Rivest, M
   Chamezopoulos, S
   Jaworek, R
   Vignes, M
   Jayabalasingham, B
   Boonen, F
   James, C
   Doornenbal, M
   Labrosse, I
AF Kashnitsky, Yury
   Roberge, Guillaume
   Mu, Jingwen
   Kang, Kevin
   Wang, Weiwei
   Vanderfeesten, Maurice
   Rivest, Maxime
   Chamezopoulos, Savvas
   Jaworek, Robert
   Vignes, Maeva
   Jayabalasingham, Bamini
   Boonen, Finne
   James, Chris
   Doornenbal, Marius
   Labrosse, Isabelle
TI Evaluating approaches to identifying research supporting the United
   Nations Sustainable Development Goals
SO QUANTITATIVE SCIENCE STUDIES
LA English
DT Article
DE benchmarking; bibliometrics; machine learning; scientometrics;
   sustainability; Sustainable Development Goals
AB The United Nations (UN) Sustainable Development Goals (SDGs) challenge the global community to build a world where no one is left behind. Recognizing that research plays a fundamental part in supporting these goals, attempts have been made to classify research publications according to their relevance in supporting each of the UN's SDGs. In this paper, we outline the methodology that we followed when mapping research articles to SDGs and which is adopted by Times Higher Education in its Social Impact rankings. We compare our solution with other existing queries and models mapping research papers to SDGs. We also discuss various aspects in which the methodology can be improved and generalized to other types of content apart from research articles. The results presented in this paper are the outcome of the SDG Research Mapping Initiative, which was established as a partnership between the University of Southern Denmark, the Aurora European Universities Alliance (represented by Vrije Universiteit Amsterdam), the University of Auckland, and Elsevier to bring together broad expertise and share best practices on identifying research contributions to UN's Sustainable Development Goals.
C1 [Kashnitsky, Yury; Chamezopoulos, Savvas; Jayabalasingham, Bamini; Boonen, Finne; James, Chris; Doornenbal, Marius] Elsevier BV, Amsterdam, Netherlands.
   [Roberge, Guillaume; Rivest, Maxime; Labrosse, Isabelle] Elsevier BV, Montreal, PQ, Canada.
   [Mu, Jingwen; Kang, Kevin; Wang, Weiwei] Univ Auckland, Fac Sci, Auckland, New Zealand.
   [Vanderfeesten, Maurice] Vrije Univ Amsterdam, Amsterdam, Netherlands.
   [Rivest, Maxime] McGill Univ, Montreal, PQ, Canada.
   [Jaworek, Robert] Palacky Univ Olomouc, Olomouc, Czech Republic.
   [Vignes, Maeva] Univ Southern Denmark, Odense, Denmark.
   [Jayabalasingham, Bamini] Elsevier BV, New York, NY USA.
C3 Reed Elsevier; Elsevier; University of Auckland; Vrije Universiteit
   Amsterdam; McGill University; Palacky University Olomouc; University of
   Southern Denmark; Reed Elsevier; Elsevier
RP Kashnitsky, Y (corresponding author), Elsevier BV, Amsterdam, Netherlands.
EM y.kashnitskiy@elsevier.com
OI Doornenbal, Marius/0000-0001-6319-850X
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NR 24
TC 0
Z9 0
U1 2
U2 2
PU MIT PRESS
PI CAMBRIDGE
PA ONE ROGERS ST, CAMBRIDGE, MA 02142-1209 USA
EI 2641-3337
J9 QUANT SCI STUD
JI Quant. Sci. Stud.
PD MAY 1
PY 2024
VL 5
IS 2
BP 408
EP 425
DI 10.1162/qss_a_00304
PG 18
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA XG0J5
UT WOS:001260409900004
OA Green Submitted, gold
DA 2024-09-05
ER
PT J
AU Wang, MY
   Wang, ZY
   Chen, GS
AF Wang, Mingyang
   Wang, Zhenyu
   Chen, Guangsheng
TI Which can better predict the future success of articles? Bibliometric
   indices or alternative metrics
SO SCIENTOMETRICS
LA English
DT Article
DE Highly-cited papers; Bibliometric index; Alternative metrics; Machine
   learning
ID CITATION IMPACT; SCIENTIFIC PAPERS; MENDELEY READERS; JOURNALS; NUMBER;
   ALTMETRICS; COLLABORATION; DETERMINANTS; PUBLICATIONS; CORRELATE
AB In this paper, we made a survey on the prediction capability of bibliometric indices and alternative metrics on the future success of articles by establishing a machine learning framework. Twenty-three bibliometric and alternative indices were collected to establish the feature space for the predication task. In order to eliminate the possible redundancy in feature space, three feature selection techniques of Relief-F, principal component analysis and entropy weighted method were used to rank the features according to their contribution to the original data set. Combining the fractal dimension of the data set, the intrinsic features which can better represent the original feature space were extracted. Three classifiers of Naive Bayes, KNN and random forest were performed to detect the classification performance of these features. Experimental results show that both bibliometric indices and alternative metrics are beneficial to articles' growth. Early citation features, early Web usage statistics, as well as the reputation of the first author are the most valuable indicators in making an article more influential in the future.
C1 [Wang, Mingyang; Wang, Zhenyu; Chen, Guangsheng] Northeast Forestry Univ, Coll Informat & Comp Engn, Harbin 150040, Heilongjiang, Peoples R China.
C3 Northeast Forestry University - China
RP Chen, GS (corresponding author), Northeast Forestry Univ, Coll Informat & Comp Engn, Harbin 150040, Heilongjiang, Peoples R China.
EM kjc_chen@163.com
RI wang, mingyang/KSM-1989-2024
FU National Natural Science Foundation of China [71473034]; Postdoctoral
   Scientific Research Developmental Fund of Heilongjiang Province
   [LBH-Q16003]; national undergraduate training programs for innovation
   [201510225167]
FX This work was supported by the National Natural Science Foundation of
   China (Grant No. 71473034), the financial assistance from Postdoctoral
   Scientific Research Developmental Fund of Heilongjiang Province (Grant
   No. LBH-Q16003), and the national undergraduate training programs for
   innovation (Grant No. 201510225167).
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NR 121
TC 29
Z9 34
U1 8
U2 116
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD JUN
PY 2019
VL 119
IS 3
BP 1575
EP 1595
DI 10.1007/s11192-019-03052-9
PG 21
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA HZ7UB
UT WOS:000469058000012
DA 2024-09-05
ER
PT C
AU Ray, JB
   Maitra, S
AF Ray, Julie Basu
   Maitra, Samita
GP IEEE
TI Collaborative Interdisciplinary Teaching and Learning across Borders,
   Using Mobile Technologies and Smart Devices
SO 2017 5TH IEEE INTERNATIONAL CONFERENCE ON MOOCS, INNOVATION AND
   TECHNOLOGY IN EDUCATION (MITE)
LA English
DT Proceedings Paper
CT 5th IEEE International Conference on MOOCs, Innovation and Technology in
   Education (MITE)
CY OCT 27-28, 2017
CL Bangalore, INDIA
DE Collaboration; Undergraduate Institutions; Active learning strategies;
   Heavy Metals in Biology; Fermentation
ID METALS
AB Collaborative teaching today, is one of the best practices followed globally to foster active learning. As a variety of teaching styles emerge to address more student learning preferences, collaboration across disciplines and across borders, using mobile technologies, allows a cultural and academic exchange of ideas, improving teaching and learning pedagogy. The authors at the Department of Biology, Dillard University (DU) in New Orleans, Louisiana, USA and the Department of Chemical Engineering, BMS College of Engineering in Bangalore (BMSCE), India collaborated in Fall 2015 and 2016 to develop a teaching module where DU Human Physiology students and BMSCE Chemical Engineering students participated. This paper chronicles our experience in the interdisciplinary study across borders, focusing on critical and creative thinking; writing and communication using smart devices and collaboration and shared leadership.
C1 [Ray, Julie Basu] Dillard Univ, Dept Biol, New Orleans, LA 70122 USA.
   [Maitra, Samita] BMS Coll Engn, Dept Chem Engn, Bangalore, Karnataka, India.
C3 Dillard University; BMS College of Engineering
RP Ray, JB (corresponding author), Dillard Univ, Dept Biol, New Orleans, LA 70122 USA.
RI Maitra, Samita/ABA-8195-2020
OI Maitra, Samita/0000-0003-0196-1762
CR [Anonymous], MILL DEV GOALS REP 2
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U2 3
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-5386-3189-8
PY 2017
BP 77
EP 82
DI 10.1109/MITE.2017.00020
PG 6
WC Computer Science, Interdisciplinary Applications; Education &
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WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Education & Educational Research
GA BM0AA
UT WOS:000458537800016
DA 2024-09-05
ER
PT J
AU Jung, SK
   Segev, A
AF Jung, Sukhwan
   Segev, Aviv
TI Identifying a common pattern within ancestors of emerging topics for
   pan-domain topic emergence prediction
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE Topic prediction; Scientometrics; Knowledge management; Machine learning
ID EVOLUTION
AB The shared interest among existing research topics matures over time until it emerges as a topic of its own. This paper detects emerging topics as well as general predictor models spanning multiple research domains through the network-based topic evolution approach, which offers additional topic evolution capabilities such as extrapolation of data and separation of topic transition and correlation. Topics are represented as their neighbors in the past, or ancestors, and their structural properties are used to train binary classification models in capturing the materialization of such topics. The entirety of 197 million publications within the Microsoft Academic Graph was used to build multiple datasets, where machine learning algorithms were trained with structural features resulting in over 0.98 area under the precision-recall curve. General topic emergence predictor equations are then proposed based on the models trained specifically for each domain, which were able to capture a common pattern shared by emerging topics in general.(c) 2022 Elsevier B.V. All rights reserved.
C1 [Jung, Sukhwan; Segev, Aviv] Univ S Alabama, Dept Comp Sci, 150 Student Serv Dr, Mobile, AL 36688 USA.
C3 University of South Alabama
RP Jung, SK (corresponding author), Univ S Alabama, Dept Comp Sci, 150 Student Serv Dr, Mobile, AL 36688 USA.
EM shjung@southalabama.edu
RI Jung, Suk hwan/AAW-3977-2020; Jung, Suk hwan/HIK-1039-2022; Segev,
   Aviv/C-2060-2011
OI Jung, Suk hwan/0000-0003-1758-1211; 
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TC 2
Z9 2
U1 5
U2 17
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0950-7051
EI 1872-7409
J9 KNOWL-BASED SYST
JI Knowledge-Based Syst.
PD DEC 22
PY 2022
VL 258
AR 110020
DI 10.1016/j.knosys.2022.110020
EA OCT 2022
PG 9
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA 6F9QZ
UT WOS:000884396500005
DA 2024-09-05
ER
PT J
AU Pooja
   Sood, SK
AF Pooja
   Sood, Sandeep Kumar
TI Scientometric analysis of quantum-inspired metaheuristic algorithms
SO ARTIFICIAL INTELLIGENCE REVIEW
LA English
DT Article
DE Quantum-inspired optimization algorithms; Quantum computing;
   Quantum-inspired evolutionary algorithms; CiteSpace
ID GRAVITATIONAL SEARCH ALGORITHM; HARMONIC-OSCILLATOR ALGORITHM; PARTICLE
   SWARM OPTIMIZATION; GENETIC ALGORITHM; EVOLUTIONARY ALGORITHM
AB Quantum algorithms, based on the principles of quantum mechanics, offer significant parallel processing capabilities with a wide range of applications. Nature-inspired stochastic optimization algorithms have long been a research hotspot. The fusion of quantum mechanics with optimization methods can potentially address NP-hard problems more efficiently and exponentially faster. The potential advantages provided by the ground-breaking paradigm have expedited the scientific output of quantum-inspired optimization algorithms locale. Consequently, a pertinent investigation is required to explain how ground-breaking scientific advancements have evolved. The scientometric approach utilizes quantitative and qualitative techniques to analyze research publications to evaluate the structure of scientific knowledge. Henceforth, the current research presents a scientometric and systematic analysis of quantum-inspired metaheuristic algorithms (QiMs) literature from the Scopus database since its inception. The scientometric implications of the article offer a detailed exploration of the publication patterns, keyword co-occurrence network analysis, author co-citation analysis and country collaboration analysis corresponding to each opted category of QiMs. The analysis reveals that QiMs solely account to 26.66% of publication share in quantum computing and have experienced an impressive 42.59% growth rate in the past decade. Notably, power management, adiabatic quantum computation, and vehicle routing are prominent emerging application areas. An extensive systematic literature analysis identifies key insights and research gaps in the QiMs knowledge domain. Overall, the findings of the current article provide scientific cues to researchers and the academic fraternity for identifying the intellectual landscape and latest research trends of QiMs, thereby fostering innovation and informed decision-making.
C1 [Pooja; Sood, Sandeep Kumar] Natl Inst Technol Kurukshetra, Dept Comp Applicat, Kurukshetra 136119, Haryana, India.
C3 National Institute of Technology (NIT System); National Institute of
   Technology Kurukshetra
RP Pooja (corresponding author), Natl Inst Technol Kurukshetra, Dept Comp Applicat, Kurukshetra 136119, Haryana, India.
EM insanpooja777@gmail.com
OI , Pooja/0000-0002-3504-9694
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TC 2
Z9 2
U1 11
U2 11
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0269-2821
EI 1573-7462
J9 ARTIF INTELL REV
JI Artif. Intell. Rev.
PD JAN 30
PY 2024
VL 57
IS 2
AR 22
DI 10.1007/s10462-023-10659-1
PG 30
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA HQ8C6
UT WOS:001161054000002
OA hybrid
DA 2024-09-05
ER
PT C
AU Sun, XL
   Ding, K
   Lin, Y
   Lin, HF
AF Sun, Xiaoling
   Ding, Kun
   Lin, Yuan
   Lin, Hongfei
BE Catalano, G
   Daraio, C
   Gregori, M
   Moed, HF
   Ruocco, G
TI Open Science Behavior of AI Industry: Collaboration Patterns and Topics
   from the Perspective of Cross-Institutional Authors
SO 17TH INTERNATIONAL CONFERENCE ON SCIENTOMETRICS & INFORMETRICS
   (ISSI2019), VOL I
SE Proceedings of the International Conference on Scientometrics and
   Informetrics
LA English
DT Proceedings Paper
CT 17th International Conference of the
   International-Society-for-Scientometrics-and-Informetrics (ISSI) on
   Scientometrics and Informetrics
CY SEP 02-05, 2019
CL Sapienza Univ Rome, Rome, ITALY
HO Sapienza Univ Rome
ID UNIVERSITY; PUBLICATIONS; TECHNOLOGY; LEAD
AB In this study, the open science behaviour of industry is analyzed in terms of publications co-authored by at least one author from industry. Firstly, authors are classified into five types according to affiliations, and collaboration network is built to investigate the role of different types of authors. Then, topic evolution of papers related to industry is studied using Hierarchical Dirichelet Process and topic mapping algorithm. Artificial intelligence research area is taken as an example. Results show cross-university-industry type authors play an important bridge role in the collaboration network. And research topics that industries are concerned about are revealed and analyzed. This research could provide reference for formulating science and technology policy and promoting scientific research innovation in both universities and industries.
C1 [Sun, Xiaoling; Ding, Kun; Lin, Yuan; Lin, Hongfei] Dalian Univ Technol, Dalian, Peoples R China.
C3 Dalian University of Technology
RP Sun, XL (corresponding author), Dalian Univ Technol, Dalian, Peoples R China.
EM xlsun@dlut.edu.cn; dingk@dlut.edu.cn; zhlin@dlut.edu.cn;
   hflin@dlut.edu.cn
RI DING, KUN/HNJ-1709-2023
FU National Natural Science Foundation of China [71704019]; Planning Fund
   for Liaoning Social Science [L17CGL009]
FX This work is partially supported by grant from National Natural Science
   Foundation of China (No. 71704019) and the Planning Fund for Liaoning
   Social Science (No. L17CGL009).
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PU INT SOC SCIENTOMETRICS & INFORMETRICS-ISSI
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PA KATHOLIEKE UNIV LEUVEN, FACULTEIT E T E W, DEKENSTRAAT 2, LEUVEN,
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EP 338
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WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Information Science & Library Science
GA BO2SN
UT WOS:000508217900033
DA 2024-09-05
ER
PT J
AU Capizzi, V
   Paltrinieri, A
   Pattnaik, D
   Kumar, S
AF Capizzi, Vincenzo
   Paltrinieri, Andrea
   Pattnaik, Debidutta
   Kumar, Satish
TI Retrospective overview of the journal <i>venture capital</i> using
   bibliometric approach
SO VENTURE CAPITAL
LA English
DT Article
DE Venture capital; entrepreneurial finance; bibliometrics; NLP;
   co-occurrence analysis; VOSviewer; Gephi
ID BUSINESS ANGELS; ENTREPRENEURS; MODEL; MANAGEMENT; LEGITIMACY; NETWORKS;
   FINANCE
AB The journal Venture Capital (VC) is a well-established highly reputed academic outlet specializing in research on entrepreneurial finance conducted from various methodological standpoints, on a global basis. This study uses bibliometrics to analyze the journal's impact, prominent topics, most frequent authors, and their affiliated institutions. Between 1999 and 2021, VC published 385 documents receiving 9,892 citations. About 62% of VC papers have more than 10 citations each. Some of the notable themes which may offer future scope for publications include crowdfunding platforms, equity crowdfunding, government venture capital, private equity firm and investment, entrepreneurial finance, market failure, and female entrepreneurship.
C1 [Capizzi, Vincenzo] Univ Piemonte Orientale Amedeo Avogadro, Dipartimento Studi Econ & Impresa, I-28100 Novara, Italy.
   [Paltrinieri, Andrea] Univ Cattolica Sacro Cuore, Dept Econ & Business Admin, Campus Roma, Rome, Italy.
   [Pattnaik, Debidutta] Woxsen Sch Business, Sch Business, Hyderabad, India.
   [Kumar, Satish] Malaviya Natl Inst Technol, Dept Management Studies, Jaipur, Rajasthan, India.
C3 University of Eastern Piedmont Amedeo Avogadro; Catholic University of
   the Sacred Heart; IRCCS Policlinico Gemelli; National Institute of
   Technology (NIT System); Malaviya National Institute of Technology
   Jaipur
RP Kumar, S (corresponding author), Univ Piemonte Orientale Amedeo Avogadro, Dipartimento Studi Econ & Impresa, I-28100 Novara, Italy.
EM skumar.dms@mnit.ac.in
RI Pattnaik, Debidutta/Q-2125-2019; Kumar, Satish/M-8694-2017; Pattnaik,
   Debidutta/GWU-6164-2022; Capizzi, Vincenzo/M-7251-2016
OI Pattnaik, Debidutta/0000-0001-6180-0499; Kumar,
   Satish/0000-0001-5200-1476; Capizzi, Vincenzo/0000-0003-3761-9942;
   PALTRINIERI, Andrea/0000-0002-8172-9199
CR Acedo FJ, 2006, J MANAGE STUD, V43, P957, DOI 10.1111/j.1467-6486.2006.00625.x
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   [Anonymous], 2001, VENTURE CAPITAL INT
   [Anonymous], 2013, VENTURE CAPITAL PRIV
   Baker HK, 2021, INT REV FINANC ANAL, V78, DOI 10.1016/j.irfa.2021.101946
   Baker HK, 2021, J FUTURES MARKETS, V41, P1027, DOI 10.1002/fut.22211
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   Baker HK, 2021, J CORP FINANC, V66, DOI 10.1016/j.jcorpfin.2020.101572
   Baker HK, 2020, FINANC REV, V55, P7, DOI 10.1111/fire.12228
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   Belleflamme P, 2013, VENTUR CAP, V15, P313, DOI 10.1080/13691066.2013.785151
   Bianchini R., 2022, REV CORPORATE FINANC, V2
   Block J, 2009, VENTUR CAP, V11, P295, DOI 10.1080/13691060903184803
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   Bygrave W.D., 2003, VENTUR CAP, V5, P101, DOI DOI 10.1080/1369106032000097021
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   Donthu N, 2021, J INT MARKETING, V29, P1, DOI 10.1177/1069031X211004234
   Donthu N, 2021, J BUS IND MARK, V36, P1435, DOI 10.1108/JBIM-04-2020-0214
   Donthu N, 2021, INT J INFORM MANAGE, V57, DOI 10.1016/j.ijinfomgt.2020.102307
   Feeney L., 1999, Venture Capital, V1, P121, DOI DOI 10.1080/136910699295938
   Freear J., 2002, VENTUR CAP, V4, P275
   Frydrych D, 2014, VENTUR CAP, V16, P247, DOI 10.1080/13691066.2014.916512
   Gabrielsson J., 2002, Venture Capital An International Journal of Entrepreneurial Finance, V4, P125
   Goodell JW, 2021, J BEHAV EXP FINANC, V32, DOI 10.1016/j.jbef.2021.100577
   Greene PG., 2001, Venture Capital, V3, P63, DOI [DOI 10.1080/13691060118175, 10.1080/13691060118175]
   Harrison R., 2000, Venture Capital: An international journal of entrepreneurial finance, V2, P223
   Jones O, 2010, VENTUR CAP, V12, P127, DOI 10.1080/13691061003658886
   Kenney M, 2019, VENTUR CAP, V21, P35, DOI 10.1080/13691066.2018.1517430
   Khan MA, 2021, J BUS RES, V125, P295, DOI 10.1016/j.jbusres.2020.12.015
   Kumar S, 2022, CROSS CULT STRATEG M, V29, P171, DOI 10.1108/CCSM-08-2021-0147
   Kumar S, 2022, INT J BANK MARK, V40, P341, DOI 10.1108/IJBM-07-2021-0351
   Kumar S, 2022, SOC INDIC RES, V162, P413, DOI 10.1007/s11205-021-02847-9
   Kumar S, 2021, ELECTRON COMMER RES, V21, P1, DOI 10.1007/s10660-021-09464-1
   Lehner OM, 2014, VENTUR CAP, V16, P271, DOI 10.1080/13691066.2014.925305
   Lehner OM, 2013, VENTUR CAP, V15, P289, DOI 10.1080/13691066.2013.782624
   Lockett A., 1999, Venture Capital, V1, P303, DOI DOI 10.1080/136910699295839
   Madill JJ, 2005, VENTUR CAP, V7, P107, DOI 10.1080/1369106042000316341
   Manigart S., 2002, Venture Capital, V4, P103, DOI [https://doi.org/10.1080/13691060110103233, DOI 10.1080/13691060110103233]
   Mason CM, 2008, VENTUR CAP, V10, P309, DOI 10.1080/13691060802380098
   Maula M., 2003, VENTUR CAP, V5, P117, DOI DOI 10.1080/1369106032000087275
   Maula M, 2005, VENTUR CAP, V7, P3, DOI 10.1080/1369106042000316332
   Murray G., 1999, VENTUR CAP, V1, P351
   Pattnaik D, 2022, ECON MODEL, V107, DOI 10.1016/j.econmod.2021.105712
   Pattnaik D, 2021, AUST ACCOUNT REV, V31, P150, DOI 10.1111/auar.12332
   Pattnaik D, 2020, QUAL RES FINANC MARK, V12, P367, DOI 10.1108/QRFM-09-2019-0103
   Pattnaik D, 2020, RES INT BUS FINANC, V54, DOI 10.1016/j.ribaf.2020.101287
   Paul S, 2007, VENTUR CAP, V9, P107, DOI 10.1080/13691060601185425
   Politis D, 2008, VENTUR CAP, V10, P127, DOI 10.1080/13691060801946147
   Shepherd D. A., 1999, Venture Capital: An International Journal of Entrepreneurial Finance, V1, P197, DOI DOI 10.1080/136910699295866
   Sohl J.E., 1999, VENTUR CAP, V1s, P101, DOI [10.1080/136910699295929, DOI 10.1080/136910699295929]
   Stremersch S, 2007, J MARKETING, V71, P171, DOI 10.1509/jmkg.71.3.171
   Tomczak A, 2013, VENTUR CAP, V15, P335, DOI 10.1080/13691066.2013.847614
   Tranfield D, 2003, BRIT J MANAGE, V14, P207, DOI 10.1111/1467-8551.00375
   Treichel MZ, 2006, VENTUR CAP, V8, P51, DOI 10.1080/13691060500453726
   Valtakoski A, 2020, J SERV MARK, V34, P8, DOI 10.1108/JSM-12-2018-0359
   Van Osnabrugge Mark., 2000, VENTUR CAP, V2, P91, DOI [10.1080/136910600295729, DOI 10.1080/136910600295729]
NR 62
TC 6
Z9 6
U1 2
U2 34
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1369-1066
EI 1464-5343
J9 VENTUR CAP
JI Ventur. Cap.
PD JAN 2
PY 2022
VL 24
IS 1
BP 1
EP 23
DI 10.1080/13691066.2022.2051769
EA MAR 2022
PG 23
WC Business, Finance
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 1B5OO
UT WOS:000771824000001
DA 2024-09-05
ER
PT J
AU Paris, N
   Lamer, A
   Parrot, A
AF Paris, Nicolas
   Lamer, Antoine
   Parrot, Adrien
TI Transformation and Evaluation of the MIMIC Database in the OMOP Common
   Data Model: Development and Usability Study
SO JMIR MEDICAL INFORMATICS
LA English
DT Article
DE data reuse; open data; OMOP; common data model; critical care; machine
   learning; big data; health informatics; health data; health database;
   electronic health records; open access database; digital health;
   intensive care; health care
ID CRITICALLY-ILL; VALIDATION; MANAGEMENT; RECORDS; CARE
AB Background: In the era of big data, the intensive care unit (ICU) is likely to benefit from real-time computer analysis and modeling based on close patient monitoring and electronic health record data. The Medical Information Mart for Intensive Care (MIMIC) is the first open access database in the ICU domain. Many studies have shown that common data models (CDMs) improve database searching by allowing code, tools, and experience to be shared. The Observational Medical Outcomes Partnership (OMOP) CDM is spreading all over the world. Objective: The objective was to transform MIMIC into an OMOP database and to evaluate the benefits of this transformation for analysts. Methods: We transformed MIMIC (version 1.4.21) into OMOP format (version 5.3.3.1) through semantic and structural mapping. The structural mapping aimed at moving the MIMIC data into the right place in OMOP, with some data transformations. The mapping was divided into 3 phases: conception, implementation, and evaluation. The conceptual mapping aimed at aligning the MIMIC local terminologies to OMOP's standard ones. It consisted of 3 phases: integration, alignment, and evaluation. A documented, tested, versioned, exemplified, and open repository was set up to support the transformation and improvement of the MIMIC community's source code. The resulting data set was evaluated over a 48-hour datathon. Results: With an investment of 2 people for 500 hours, 64% of the data items of the 26 MIMIC tables were standardized into the OMOP CDM and 78% of the source concepts mapped to reference terminologies. The model proved its ability to support community contributions and was well received during the datathon, with 160 participants and 15,000 requests executed with a Conclusions: The resulting MIMIC-OMOP data set is the first MIMIC-OMOP data set available free of charge with real disidentified data ready for replicable intensive care research. This approach can be generalized to any medical field.
C1 [Paris, Nicolas; Lamer, Antoine; Parrot, Adrien] InterHop, 30 Ave Maine, F-75015 Paris, France.
   [Lamer, Antoine] Univ Lille, ULR 2694 METRICS Evaluat Technol Sante & Prat Med, CHU Lille, Lille, France.
C3 Universite de Lille; CHU Lille
RP Paris, N (corresponding author), InterHop, 30 Ave Maine, F-75015 Paris, France.
EM nicolas.paris@riseup.net
RI Lamer, Antoine/AAL-2936-2020; Lamer, Antoine/JEP-7710-2023
OI Lamer, Antoine/0000-0002-9546-1808; Lamer, Antoine/0000-0002-9546-1808;
   PARROT, Adrien/0000-0002-9862-1408
FU Massachusetts Institute of Technology; Observational Health Data
   Sciences and Informatics community
FX We acknowledge the Massachusetts Institute of Technology and the
   Observational Health Data Sciences and Informatics community for their
   support.
CR Aboab J, 2016, SCI TRANSL MED, V8, DOI 10.1126/scitranslmed.aad9072
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   hl7, WELC FHIR
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   Karwin B., KEEPING IT SIMPLE RE
   Klungel OH, 2016, PHARMACOEPIDEM DR S, V25, P156, DOI 10.1002/pds.3968
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   Observational Health Data Sciences and Informatics, OHDSI ACH
   Observational Health Data Sciences and Informatics, OHDSI US
   Observational Health Data Sciences and Informatics, OHDSI FOR
   Observational Health Data Sciences and Informatics, 2021, OHDSI OBS HLTH DAT S
   OMOPonFHIR, FHIR PROJ GEORG TECH
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   U.S. National Library of Medicine, ICD 9 900 DIAGN COD
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   Zhang Y, 2016, CHINESE MED J-PEKING, V129, P731, DOI 10.4103/0366-6999.178019
NR 42
TC 10
Z9 11
U1 0
U2 2
PU JMIR PUBLICATIONS, INC
PI TORONTO
PA 130 QUEENS QUAY E, STE 1102, TORONTO, ON M5A 0P6, CANADA
EI 2291-9694
J9 JMIR MED INF
JI JMIR Med. Inf.
PD DEC
PY 2021
VL 9
IS 12
AR e30970
DI 10.2196/30970
PG 14
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Medical Informatics
GA YA8SQ
UT WOS:000738596600015
PM 34904958
OA Green Submitted, Green Published, gold
DA 2024-09-05
ER
PT J
AU Edelaar-Peeters, Y
   Putter, H
   Snoek, GJ
   Sluis, TAR
   Smit, CAJ
   Post, MWM
   Stiggelbout, AM
AF Edelaar-Peeters, Yvette
   Putter, Hein
   Snoek, Govert J.
   Sluis, Tebbe A. R.
   Smit, Christof A. J.
   Post, Marcel W. M.
   Stiggelbout, Anne M.
TI The Influence of Time and Adaptation on Health State Valuations in
   Patients With Spinal Cord Injury
SO MEDICAL DECISION MAKING
LA English
DT Article
DE affect and emotion; heuristics and biases; utility assessment; utility
   measurement; Health-Related Quality of Life; cognitive psychology;
   EQ-5D; health state preferences; utilities; valuations; outcomes
   research
ID QUALITY-OF-LIFE; SATISFACTION; REHABILITATION; PREFERENCES; ADJUSTMENT;
   EXPERIENCE; PREDICTORS; UTILITIES; IMPACT
AB Objectives: One of the explanations for the difference between health state utilities elicited from patients and the public-often provided but seldom studied-is adaptation. The influence of adaptation on utilities was investigated in patients with spinal cord injury. Methods: Interviews were held at 3 time points (T1, after admission to the rehabilitation center; T2, during active rehabilitation; T3, at least half a year after discharge). At T1, 60 patients were interviewed; 10 patients withdrew at T2 and T3. At all time points, patients were asked to value their own health and a health state description of rheumatoid arthritis on a time trade-off and a visual analogue scale. The Barthel Index, a measure of independence from help in activities of daily living, and the adjustment ladder were filled out. Main analyses were performed using mixed linear models taking the time-dependent covariates (Barthel Index and adjustment ladder) into account. Results: Time trade-off valuations for patients' own health changed over time, even after correction for gain in independence from help in activities of daily living, F(2, 59) = 8.86, P < 0.001. This change was related to overall adaptation. Both a main effect for adaptation, F(87, 1) = 10.05; P = 0.002, and an interaction effect between adaptation and time, F(41, 1)= 4.10, P = 0.024, were seen for time trade-off valuations. Valuations given for one's own health on the visual analogue scale did not significantly change over time, nor did the valuations for the hypothetical health state. Conclusion: Patients' health state valuations change over time, over and above the change expected by the rehabilitation process, and this change is partly explained by adaptation. Experience with a chronic illness did not lead to change in valuations of hypothetical health states.
C1 [Edelaar-Peeters, Yvette; Stiggelbout, Anne M.] Leiden Univ, Med Ctr, Dept Med Decis Making, NL-2300 RC Leiden, Netherlands.
   [Putter, Hein] Leiden Univ, Med Ctr, Dept Med Stat & Bioinformat, NL-2300 RC Leiden, Netherlands.
   [Snoek, Govert J.] Rehabil Ctr Het Roessingh & Roessingh Res & Dev, Enschede, Netherlands.
   [Sluis, Tebbe A. R.] Rijndam Rehabil Ctr Rotterdam, Rotterdam, Netherlands.
   [Smit, Christof A. J.] Reade, Ctr Rehabil & Rheumatol, Amsterdam, Netherlands.
   [Post, Marcel W. M.] Univ Med Ctr, Rudolf Magnus Inst Neurosci, Utrecht, Netherlands.
   [Post, Marcel W. M.] Univ Med Ctr, Ctr Excellence Rehabil Med, Utrecht, Netherlands.
C3 Leiden University - Excl LUMC; Leiden University; Leiden University
   Medical Center (LUMC); Leiden University; Leiden University Medical
   Center (LUMC); Leiden University - Excl LUMC; Utrecht University;
   Utrecht University Medical Center; Utrecht University; Utrecht
   University Medical Center
RP Edelaar-Peeters, Y (corresponding author), Leiden Univ, Med Ctr, Dept Med Decis Making, POB 9600, NL-2300 RC Leiden, Netherlands.
EM y.peeters@lumc.nl
RI Post, Marcel/AAS-2502-2021; Stiggelbout, Anne/D-2293-2018; Putter,
   Hein/C-2244-2018
OI Stiggelbout, Anne/0000-0002-6293-4509; Post, Marcel/0000-0002-2205-9404;
   Putter, Hein/0000-0001-5395-1422
FU VIDI award of the Netherlands Organization for Scientific Research,
   Innovational Research Incentives Scheme [917.56.356]
FX Y. Peeters and A. M. Stiggelbout were entirely supported by a VIDI award
   of the Netherlands Organization for Scientific Research, Innovational
   Research Incentives Scheme (grant 917.56.356). This study was presented
   at the 12th biennial meeting of the Society for Medical Decision Making,
   Europe, 2008. Revision accepted for publication 1 February 2012.
CR Centraal Bureau voor de statistiek, 2007, OV NAAR LEEF TIJD GE
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NR 28
TC 5
Z9 5
U1 0
U2 8
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 0272-989X
J9 MED DECIS MAKING
JI Med. Decis. Mak.
PD NOV-DEC
PY 2012
VL 32
IS 6
BP 805
EP 814
DI 10.1177/0272989X12447238
PG 10
WC Health Care Sciences & Services; Health Policy & Services; Medical
   Informatics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Health Care Sciences & Services; Medical Informatics
GA 046YV
UT WOS:000311802700007
PM 22622845
DA 2024-09-05
ER
PT J
AU Mathew, G
   Agrawal, A
   Menzies, T
AF Mathew, George
   Agrawal, Amritanshu
   Menzies, Tim
TI Finding Trends in Software Research
SO IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
LA English
DT Article
DE Software engineering; Conferences; Software; Analytical models; Data
   models; Predictive models; Testing; bibliometrics; topic modeling; text
   mining
ID RESEARCH TOPICS; INSTITUTIONS; EVOLUTION; RANKING; GENDER
AB Text mining methods can find large scale trends within research communities. For example, using stable Latent Dirichlet Allocation (a topic modeling algorithm) this study found 10 major topics in 35,391 SE research papers from 34 leading SE venues over the last 25 years (divided, evenly, between conferences and journals). Out study also shows how those topics have changed over recent years. Also, we note that (in the historical record) mono-focusing on a single topic can lead to fewer citations than otherwise. Further, while we find no overall gender bias in SE authorship, we note that women are under-represented in the top-most cited papers in our field. Lastly, we show a previously unreported dichotomy between software conferences and journals (so research topics that succeed at conferences might not succeed at journals, and vice versa). An important aspect of this work is that it is automatic and quickly repeatable (unlike prior SE bibliometric studies that used tediously slow and labor intensive methods). Automation is important since, like any data mining study, its conclusions are skewed by the data used in the analysis. The automatic methods of this paper make it far easier for other researchers to re-apply the analysis to new data, or if they want to use different modeling assumptions.
C1 [Mathew, George; Agrawal, Amritanshu; Menzies, Tim] North Carolina State Univ NCSU, Dept Comp Sci CS, Raleigh, NC 27695 USA.
C3 North Carolina State University
RP Menzies, T (corresponding author), North Carolina State Univ NCSU, Dept Comp Sci CS, Raleigh, NC 27695 USA.
EM george.meg91@gmail.com; aagrawa8@ncsu.edu; timm@ieee.org
RI Menzies, Tim/X-7680-2019; Agrawal, Amritanshu/R-7093-2019
OI Menzies, Tim/0000-0002-5040-3196; Agrawal,
   Amritanshu/0000-0002-1220-8533
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NR 58
TC 13
Z9 13
U1 7
U2 27
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 0098-5589
EI 1939-3520
J9 IEEE T SOFTWARE ENG
JI IEEE Trans. Softw. Eng.
PD APR 1
PY 2023
VL 49
IS 4
BP 1397
EP 1410
DI 10.1109/TSE.2018.2870388
PG 14
WC Computer Science, Software Engineering; Engineering, Electrical &
   Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA E9NM9
UT WOS:000978723600001
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Zhang, YY
   Jin, SX
   Li, DG
   Chen, GQ
   Chen, YL
   Xia, QM
   Mao, QJ
   Li, YL
   Yang, J
   Fan, XX
   Lin, H
AF Zhang, Yiyin
   Jin, Shengxi
   Li, Duguang
   Chen, Guoqiao
   Chen, Yongle
   Xia, Qiming
   Mao, Qijiang
   Li, Yiling
   Yang, Jing
   Fan, Xiaoxiao
   Lin, Hui
TI A Machine-Learning-Based Bibliometric Analysis of Cell Membrane-Coated
   Nanoparticles in Biomedical Applications over the Past Eleven Years
SO GLOBAL CHALLENGES
LA English
DT Article
DE bibliometrics; cell membrane encapsulation; medical applications;
   nanoparticles
ID ERYTHROCYTE-MEMBRANE
AB Cell membrane encapsulation is a growing concept in nanomedicine, for it achieves the purpose of camouflage nanoparticles, realizing the convenience for drug delivery, bio-imaging, and detoxification. Cell membranes are constructed by bilayer lipid phospholipid layers, which have unique properties in cellular uptake mechanism, targeting ability, immunomodulation, and regeneration. Current medical applications of cell membranes include cancers, inflammations, regenerations, and so on. In this article, a general bibliometric overview is conducted of cell membrane-coated nanoparticles covering 11 years of evolution in order to provide researchers in the field with a comprehensive view of the relevant achievements and trends. The authors analyze the data from Web of Science Core Collection database, and extract the annual publications and citations, most productive countries/regions, most influential scholars, the collaborations of journals and institutions. The authors also divided cell membranes into several subgroups to further understand the application of different cell membranes in medical scenarios. This study summarizes the current research overview in cell membrane-coated nanoparticles and intuitively provides a direction for future research.
C1 [Zhang, Yiyin; Jin, Shengxi; Li, Duguang; Chen, Guoqiao; Chen, Yongle; Xia, Qiming; Mao, Qijiang; Li, Yiling; Yang, Jing; Fan, Xiaoxiao; Lin, Hui] Zhejiang Univ, Sir Run Run Shaw Hosp, Sch Med, Dept Gen Surg, Hangzhou 310016, Peoples R China.
   [Lin, Hui] Zhejiang Univ, Sir Run Run Shaw Hosp, Zhejiang Engn Res Ctr Cognit Healthcare, Sch Med, Hangzhou 310016, Peoples R China.
C3 Zhejiang University; Zhejiang University
RP Fan, XX; Lin, H (corresponding author), Zhejiang Univ, Sir Run Run Shaw Hosp, Sch Med, Dept Gen Surg, Hangzhou 310016, Peoples R China.; Lin, H (corresponding author), Zhejiang Univ, Sir Run Run Shaw Hosp, Zhejiang Engn Res Ctr Cognit Healthcare, Sch Med, Hangzhou 310016, Peoples R China.
EM fanxx_gs@zju.edu.cn; 369369@zju.edu.cn
RI Liu, Yuan/JFB-4766-2023; li, yang/IQV-3559-2023; li,
   chunyuan/IQW-1618-2023; wang, yingying/JSK-6741-2023; WANG,
   YANG/JFA-8821-2023; Yang, Jing/HZJ-2451-2023; jing, yang/JDV-8487-2023;
   Wang, lili/IXD-9828-2023; zhang, xiao/JCN-8822-2023; wang,
   yu/IUQ-6654-2023; li, jing/JEF-8436-2023; Wang, Yanlin/JGC-6782-2023;
   liu, jiaming/IWE-3196-2023; lu, yang/IWE-3635-2023; liu,
   bing/JJD-5566-2023; liu, huan/JKI-3764-2023; .., What/IXW-6776-2023; LI,
   XIAO/JCE-6169-2023; Zhang, Yunyi/JHS-3626-2023; wang, wei/JBS-7400-2023
FU National Natural Science Foundation of China [81874059, 82102105];
   Natural Science Foundation of Zhejiang Province [LQ22H160017]; China
   Postdoctoral Science Foundation [2021M702825]
FX Acknowledgements Thanks for the help of Figdraw developed by HOME for
   Researchers. Table of contents image drawn with assistance from Figdraw.
   This work was supported by the National Natural Science Foundation of
   China (81874059 and 82102105), the Natural Science Foundation of
   Zhejiang Province (LQ22H160017), and the China Postdoctoral Science
   Foundation (2021M702825).
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NR 42
TC 0
Z9 0
U1 4
U2 17
PU WILEY-V C H VERLAG GMBH
PI WEINHEIM
PA POSTFACH 101161, 69451 WEINHEIM, GERMANY
EI 2056-6646
J9 GLOB CHALL
JI Glob. Chall.
PD APR
PY 2023
VL 7
IS 4
DI 10.1002/gch2.202200206
EA FEB 2023
PG 13
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA J7NA2
UT WOS:000935244400001
PM 37020629
OA gold, Green Published
DA 2024-09-05
ER
PT C
AU Aparicio, G
   Catalán, E
AF Aparicio, Gloria
   Catalan, Elena
BE Chova, LG
   Martinez, AL
   Torres, IC
TI THE CURRENT STATE OF STUDENT ENGAGEMENT AT UNIVERSITIES. REVIEW OF IATED
   CONGRESS PAPERS (2011-2015)
SO EDULEARN16: 8TH INTERNATIONAL CONFERENCE ON EDUCATION AND NEW LEARNING
   TECHNOLOGIES
SE EDULEARN Proceedings
LA English
DT Proceedings Paper
CT 8th International Conference on Education and New Learning Technologies
   (EDULEARN)
CY JUL 04-06, 2016
CL Barcelona, SPAIN
DE Student Engagement; Active Learning and Teaching Methodologies;
   Bibliometric Review
ID HIGHER-EDUCATION
AB The impact of recent generations who have grown in up in an information society is displacing the concept of teachers' roles in conveying and transferring knowledge. Neither students nor instructors have ever had access to such accurate, diverse and multidisciplinary means for the teaching-learning process. However, never has there been such a large distance separating them or such unease.
   Globalisation has changed the structure of production since the 1990s, creating the need for people with high capacity to adapt to changes that occur in companies and the economy in general. Simultaneously, the information society has drastically revolutionised interpersonal paradigms as well as access to knowledge, causing deep social, political and economic transformations. In this context of rapid change, the university as an institution, and the various levels of its organisation have also been plunged into a paradigm adaption process in order to integrate into the system.
   In the particular case of adaptation of the Spanish university system to the EHEA (European Higher Education Area), lecturers' direct involvement in the process due to their relationship with students has led them to rethink education. This has required shifting from a lecturer-centred model to a student-centred one. In view of the challenge involving such a profound cultural change for universities, which are organisationally complex, endeavours have been made to give the new model more flexible guidelines by earmarking resources for training to transform methodology. However, this entire process has taken place in a context of economic crisis. Teachers have therefore often had to assume major changes as per the time and effort devoted while also working with facilities that have not been adapted to the new methodologies due to lack of investment in infrastructures. In effect, it has been necessary to accept changes without any budgetary support, above all in public education where constant cutbacks have limited possibilities for action.
   Involving, motivating, making students participate and ultimately achieving their academic and emotional engagement in their own learning process and recognition of the university are the challenges that today's universities must face. This takes place within a broader movement with deep international implications, the so called Student Engagement, which is still somewhat only tentatively seen on the Spanish university panorama. However, it is becoming increasingly important from the perspective of research, education, theory and debate on its content as well as teaching praxis due to the growing evidence of its critical role in students' success and learning.
   The main contribution of this work consists of analysis of the content of papers given at IATED congresses in the last five years. The texts are organised by year, county, and theme to draw conclusions on the main ideas we observe, focusing on an overall view of each group of studies conducted on Student Engagement. We consider this as the current state of the issue or roadmap of the topic in the leading congresses on education innovation in Spain where the events were organised and also on the international scene.
C1 [Aparicio, Gloria; Catalan, Elena] Univ Basque Country UPV EHU, Leioa, Spain.
C3 University of Basque Country
RP Aparicio, G (corresponding author), Univ Basque Country UPV EHU, Leioa, Spain.
RI Aparicio, Gloria/L-1866-2017
OI Aparicio, Gloria/0000-0001-6878-3353
CR ASTIN AW, 1984, J COLL STUDENT DEV, V25, P297
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NR 8
TC 0
Z9 0
U1 0
U2 3
PU IATED-INT ASSOC TECHNOLOGY EDUCATION A& DEVELOPMENT
PI VALENICA
PA LAURI VOLPI 6, VALENICA, BURJASSOT 46100, SPAIN
SN 2340-1117
BN 978-84-608-8860-4
J9 EDULEARN PROC
PY 2016
BP 5889
EP 5894
PG 6
WC Education & Educational Research; Education, Scientific Disciplines
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Education & Educational Research
GA BH7TE
UT WOS:000402955905149
DA 2024-09-05
ER
PT C
AU Rúbio, TRPM
   Gulo, CASJ
AF Rubio, Thiago R. P. M.
   Gulo, Carlos A. S. J.
BE Rocha, A
   Reis, LP
   Cota, MP
   Suarez, OS
   Goncalves, R
TI Enhancing Academic Literature Review through Relevance Recommendation
   Using Bibliometric and Text-based Features for Classification
SO 2016 11TH IBERIAN CONFERENCE ON INFORMATION SYSTEMS AND TECHNOLOGIES
   (CISTI)
SE Iberian Conference on Information Systems and Technologies
LA English
DT Proceedings Paper
CT 11th Iberian Conference on Information Systems and Technologies (CISTI)
CY JUN 15-18, 2016
CL SPAIN
DE Systematic Literature Review (SLR); Machine Learning; Classification;
   Text Mining; Bibliometric
AB The growing number of scientific publications and the availability of information in online repositories enable researchers to discover, analyze and maintain an updated state of the art bibliography. Indeed, few works explore this scenario in order to support researchers on the literature review step. Literature reviewing comprises a fundamental part of the scientific writing, in which publications are evaluated and selected by relevance. Different approaches for relevance are possible, whether a more qualitative (semantic) approach with text-based techniques either more quantitative (numerical) approaches that use article's metadata, such as bibliometric measures. Bibliometrics provide direct evidences of relevance and could represent good attributes for automatic classification. Our insight is that if a bibliometric-based cannot outperform text-based approaches, a hybrid model using both could benefit from it enhancing the classification performance (in terms of accuracy, precision and recall). In this paper we presented a novel approach, using Machine Learning (ML), namely the ID3 algorithm for a classification model that learn from specialist annotated data and recommend relevant papers for a specific research. Experiments showed good results on learning performance when using a hybrid approach, increasing testing performance in 12%, achieving 89.05% in accuracy when classifying a paper as relevant.
C1 [Rubio, Thiago R. P. M.] Univ Porto, Fac Engn, DEI, LIACC Artificial Intelligence & Comp Sci Lab, Oporto, Portugal.
   [Gulo, Carlos A. S. J.] Univ Porto, Fac Engn, UNEMAT Brazil, PIXEL Res Grp, Oporto, Portugal.
C3 Universidade do Porto; Universidade do Porto
RP Rúbio, TRPM (corresponding author), Univ Porto, Fac Engn, DEI, LIACC Artificial Intelligence & Comp Sci Lab, Oporto, Portugal.
EM reis.thiago@fe.up.pt; sander@unemat.br
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NR 19
TC 0
Z9 0
U1 0
U2 9
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2166-0727
BN 978-989-98434-6-2
J9 IBER CONF INF SYST
PY 2016
PG 6
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Engineering
GA BF6BA
UT WOS:000382923300256
DA 2024-09-05
ER
PT J
AU Cheng, CH
   Holsapple, CW
   Lee, A
AF Cheng, CH
   Holsapple, CW
   Lee, A
TI Citation-based journal rankings for AI research - A business perspective
SO AI MAGAZINE
LA English
DT Article
AB A significant and growing area of business-computing research is concerned with AI. Knowledge about which journals are the most influential forums for disseminating AI research is important for business school faculty, students, administrators, and librarians. To date, there has been only one study attempting to rank AI journals from a business-computing perspective. It used a subjective methodology, surveying opinions of business faculty about a prespecified list of 30 journals. Here, we report the results of a more objective study. We conducted a citation analysis covering a time period of 5 years to compile 15,600 citations to 1,244 different journals. Based on these data, the journals are ranked in two ways involving the magnitude and the duration of scientific impact each has had in the field of AI.
C1 UNIV KENTUCKY,LEXINGTON,KY 40506.
C3 University of Kentucky
RP Cheng, CH (corresponding author), CHINESE UNIV HONG KONG,SHATIN,HONG KONG.
RI Cheng, Chun-Hung/F-6113-2011; holsapple, c w/A-6342-2013; Holsapple,
   Clyde W/A-2338-2008
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Z9 12
U1 0
U2 5
PU AMER ASSOC ARTIFICIAL INTELL
PI MENLO PK
PA 445 BURGESS DRIVE, MENLO PK, CA 94025-3496
SN 0738-4602
J9 AI MAG
JI AI Mag.
PD SUM
PY 1996
VL 17
IS 2
BP 87
EP 97
PG 11
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA UT195
UT WOS:A1996UT19500005
DA 2024-09-05
ER
PT J
AU Lathabai, HH
   Nandy, A
   Singh, VK
AF Lathabai, Hiran H.
   Nandy, Abhirup
   Singh, Vivek Kumar
TI <i>x</i>-index: Identifying core competency and thematic research
   strengths of institutions using an NLP and network based ranking
   framework
SO SCIENTOMETRICS
LA English
DT Article
DE Core competency; Expertise indices; Research strength; Thematic
   strength; x-index
ID UNIVERSITIES
AB The currently prevailing international ranking systems for institutions are limited in their assessment as they only provide assessments either at an overall level or at very broad subject levels such as Science, Engineering, Medicine, etc. While these rankings have their own usage, they cannot be used to identify best institutions in a specific subject (say Computer Science) by taking into account their performance in different thematic areas of research of the given subject (say Artificial Intelligence or Machine Learning or Computer Vision etc. for the subject Computer Science). This paper tries to bridge this gap by proposing a framework that uses the NLP and Network approach for identifying the core competency of institutions and their thematic research strengths. The core competency can be viewed as a measure of breadth of research capability of an institution in a given subject, whereas thematic research strength can be viewed as depth of research of the institution in a specific theme of a subject. The working of the framework is demonstrated in the area of Computer Science for 195 Indian institutions. The framework can be useful for institutions and the scientometrics research community as a system providing a detailed assessment of the core competency and the research strengths of institutions in different thematic areas. The framework and outcomes can also be useful for funding agencies in devising programs for 'performance-based funding' in 'thrust areas' or 'national priority areas'.
C1 [Lathabai, Hiran H.; Nandy, Abhirup; Singh, Vivek Kumar] Banaras Hindu Univ, Dept Comp Sci, Varanasi 221005, Uttar Pradesh, India.
C3 Banaras Hindu University (BHU)
RP Singh, VK (corresponding author), Banaras Hindu Univ, Dept Comp Sci, Varanasi 221005, Uttar Pradesh, India.
EM vivek@bhu.ac.in
RI Lathabai, Hiran H/AAF-4552-2019; Nandy, Abhirup/AFR-0690-2022; Singh,
   Vivek Kumar/O-5699-2019; Nandy, Abhirup/GQA-4428-2022
OI Nandy, Abhirup/0000-0001-8618-0847; Singh, Vivek
   Kumar/0000-0002-7348-6545; lathabai, hiran/0000-0002-5633-9842
FU DST-NSTMIS [DST/NSTMIS/05/04/2019-20]
FX The authors would like to acknowledge the support provided by the
   DST-NSTMIS funded project-'Design of a Computational Framework for
   Discipline-wise and Thematic Mapping of Research Performance of Indian
   Higher Education Institutions (HEIs)', bearing Grant No.
   DST/NSTMIS/05/04/2019-20, for this work.
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NR 31
TC 7
Z9 7
U1 3
U2 24
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD DEC
PY 2021
VL 126
IS 12
BP 9557
EP 9583
DI 10.1007/s11192-021-04188-3
EA NOV 2021
PG 27
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA XF4WA
UT WOS:000715664900002
DA 2024-09-05
ER
PT J
AU Schöbel, S
   Schmitt, A
   Benner, D
   Saqr, M
   Janson, A
   Leimeister, JM
AF Schoebel, Sofia
   Schmitt, Anuschka
   Benner, Dennis
   Saqr, Mohammed
   Janson, Andreas
   Leimeister, Jan Marco
TI Charting the Evolution and Future of Conversational Agents: A Research
   Agenda Along Five Waves and New Frontiers
SO INFORMATION SYSTEMS FRONTIERS
LA English
DT Article
DE Bibliometric analysis; Chatbot; Conversational agent; Voice assistant;
   ChatGPT; Large language models; Generative artificial intelligence
ID PERSPECTIVE; SYSTEMS; SCIENCE
AB Conversational agents (CAs) have come a long way from their first appearance in the 1960s to today's generative models. Continuous technological advancements such as statistical computing and large language models allow for an increasingly natural and effortless interaction, as well as domain-agnostic deployment opportunities. Ultimately, this evolution begs multiple questions: How have technical capabilities developed? How is the nature of work changed through humans' interaction with conversational agents? How has research framed dominant perceptions and depictions of such agents? And what is the path forward? To address these questions, we conducted a bibliometric study including over 5000 research articles on CAs. Based on a systematic analysis of keywords, topics, and author networks, we derive "five waves of CA research" that describe the past, present, and potential future of research on CAs. Our results highlight fundamental technical evolutions and theoretical paradigms in CA research. Therefore, we discuss the moderating role of big technologies, and novel technological advancements like OpenAI GPT or BLOOM NLU that mark the next frontier of CA research. We contribute to theory by laying out central research streams in CA research, and offer practical implications by highlighting the design and deployment opportunities of CAs.
C1 [Schoebel, Sofia] Univ Osnabruck, Informat Syst, Osnabruck, Germany.
   [Schmitt, Anuschka; Janson, Andreas; Leimeister, Jan Marco] Univ St Gallen, Inst Informat Management IWI HSG, St Gallen, Switzerland.
   [Benner, Dennis; Leimeister, Jan Marco] Univ Kassel, Informat Syst Res Ctr IS Design ITeG, Kassel, Germany.
   [Saqr, Mohammed] Univ Eastern Finland, Sch Comp, Joensuu, Finland.
C3 University Osnabruck; University of St Gallen; Universitat Kassel;
   University of Eastern Finland
RP Schöbel, S (corresponding author), Univ Osnabruck, Informat Syst, Osnabruck, Germany.
EM sofia.schoebel@uni-osnabrueck.de; anuschka.schmitt@unisg.ch;
   benner@uni-kassel.de; mohammed.saqr@uef.fi; andreas.janson@unisg.ch;
   leimeister@uni-kassel.de
RI Saqr, Mohammed/AAH-2520-2020
OI Saqr, Mohammed/0000-0001-5881-3109; Leimeister, Jan
   Marco/0000-0002-1990-2894; Benner, Dennis/0000-0001-6535-1643; Janson,
   Andreas/0000-0003-3149-0340
FU Stiftung Innovation in der Hochschullehre"; Swiss National Science
   Foundation [100013_192718]; Academy of Finland (Suomen Akatemia)
   Research Council for Natural Sciences and Engineering [350560]; Basic
   Research Fund (GFF) of the University of St. Gallen; Swiss National
   Science Foundation (SNF) [100013_192718] Funding Source: Swiss National
   Science Foundation (SNF)
FX The authors acknowledge partial funding of the following funding bodies:
   "Stiftung Innovation in der Hochschullehre" within the project
   "Universitaet Kassel digital: Universitaere Lehre neu gestalten", Swiss
   National Science Foundation (project ID: 100013_192718), Academy of
   Finland (Suomen Akatemia) Research Council for Natural Sciences and
   Engineering for the project Towards precision education: Idiographic
   learning analytics (TOPEILA), Decision Number 350560, and the fifth
   author acknowledges personal funding from the Basic Research Fund (GFF)
   of the University of St. Gallen.
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NR 95
TC 15
Z9 15
U1 17
U2 79
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1387-3326
EI 1572-9419
J9 INFORM SYST FRONT
JI Inf. Syst. Front.
PD APR
PY 2024
VL 26
IS 2
SI SI
BP 729
EP 754
DI 10.1007/s10796-023-10375-9
EA APR 2023
PG 26
WC Computer Science, Information Systems; Computer Science, Theory &
   Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA MX9W3
UT WOS:000972807600001
OA Green Accepted, hybrid
DA 2024-09-05
ER
PT J
AU Haghighat, M
   Hayatdavoudi, J
AF Haghighat, Mansour
   Hayatdavoudi, Javad
TI How hot are hot papers? The issue of prolificacy and self-citation
   stacking
SO SCIENTOMETRICS
LA English
DT Article
DE Self-citation; Hot paper; Web of science; Co-citation; Co-author;
   Co-word
ID SCIENCE
AB The nature of self-citation is not unequivocal as it fluctuates across the borders of approbation and condemnation. While it is tenable that scholars tend to build upon and thus appeal to their previous work, excessive self-citation is considered as a likely strategic tool to showcase one's achievement, inflate citations, and distort bibliometric indices. The present study aimed to explore how self-citation may affect hot paper designation in Web of Science (WoS) in a short-term citation window. To this end, we studied the self-citation behavior of the authors contributing a sample of hot papers in a select number of journals over two consecutive periods. The cited and citing papers were analyzed in terms of synchronous and diachronous self-citations as well as co-authorship and co-citation networks. The results showed that self-citation evidently proved problematic in as short a citation window of hot papers as two months. The results also suggested that including too many cited references in a given article might be a potential strategy to inflate citations. Thus, we suggest that hot paper designation should assume sensitivity to self-citation, or at least, excessive self-citations by either ruling them out or setting limits on how often an author can reasonably cite earlier works. Still, this is not an attempt at policing excessive self-citation practice of a group of authors and by no means intends to criticize the authors; rather, we aimed to cite an example of how excessive self-citation practice may distort the original agenda of a bibliometric designation in WoS, hot papers.
C1 [Haghighat, Mansour] Shiraz Univ, Sch Sci, Dept Phys, Shiraz, Iran.
   [Haghighat, Mansour] ISC, Deputy Res Affairs, Shiraz, Iran.
   [Hayatdavoudi, Javad] ISC, Dept Anal Resources, Shiraz, Iran.
C3 Shiraz University
RP Hayatdavoudi, J (corresponding author), ISC, Dept Anal Resources, Shiraz, Iran.
EM javad.hayatdavoudi@gmail.com
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NR 34
TC 4
Z9 4
U1 5
U2 42
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD JAN
PY 2021
VL 126
IS 1
BP 565
EP 578
DI 10.1007/s11192-020-03749-2
EA OCT 2020
PG 14
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA PU7XL
UT WOS:000579442800006
DA 2024-09-05
ER
PT J
AU Grobbelaar, S
   Oosthuizen, R
AF Grobbelaar, S.
   Oosthuizen, R.
TI A reflection on <i>Southern Forests: a Journal of Forest Science</i>
   using bibliometrics
SO SOUTHERN FORESTS-A JOURNAL OF FOREST SCIENCE
LA English
DT Article
DE text classification; supervised machine learning; sustainability;
   network analysis
AB Bibliometrics is used to determine patterns in published research. The aim of this paper is to illustrate the observ- able bibliometric patterns in the journal Southern Forests: a Journal of Forest Science. Frequency analysis and co-occurrence network analysis were performed to identify patterns. Natural Language Processing and Supervised Machine Learning were used to perform text classification. The objective of the text classification was to classify articles into 15 themes. Each article was categorised in terms of the two main themes associated with the article. The analysis included 1 574 publications from 1941 to 2020 and confirmed that the journal's change in name and aims were successful in increasing the number of international researchers publishing in the journal. The research institute co-occurrence network diagram illustrates that there are two main research collaboration clusters. The one surrounds Stellenbosch University, and the other encompasses several South African universities and research institutes. Mondi and Sappi were the companies that collaborated the most with independent research institutes. The keywords and theme analysis confirmed that the journal's aim and scope were supported in the publications. The theme analysis also identified themes or aspects with very few publications. The methods illustrated in this paper can be used to identify research strengths and weaknesses and may assist in strategic planning for future research prioritisation.
C1 [Grobbelaar, S.; Oosthuizen, R.] Univ Pretoria, Dept Engn & Technol Management, Fac Engn Built Environm & IT, Hatfield, South Africa.
C3 University of Pretoria
RP Grobbelaar, S (corresponding author), Univ Pretoria, Dept Engn & Technol Management, Fac Engn Built Environm & IT, Hatfield, South Africa.
EM schalk.grobbelaar@up.ac.za
RI Oosthuizen, Rudolph/AAH-9253-2021; Grobbelaar, Schalk/AFE-2046-2022
OI Oosthuizen, Rudolph/0000-0002-2333-6995; Grobbelaar,
   Schalk/0000-0001-8148-2440
CR Ackerman PA, 2017, SOUTH FORESTS, V79, P329, DOI 10.2989/20702620.2016.1255380
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NR 35
TC 1
Z9 1
U1 0
U2 4
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 2070-2620
EI 2070-2639
J9 SOUTH FORESTS
JI South. Forests-A J. Forest Sci.
PD APR 3
PY 2022
VL 84
IS 2
BP 93
EP 100
DI 10.2989/20702620.2022.2084353
EA AUG 2022
PG 8
WC Forestry
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Forestry
GA 5F3TU
UT WOS:000842800200001
OA Green Submitted
DA 2024-09-05
ER
PT C
AU Anchan, J
   Laube, D
AF Anchan, John
   Laube, Donna
BE Chova, LG
   Belenguer, DM
   Torres, IC
TI A CONSORTIUM OF COLLABORATION: GOVERNMENT AND POST SECONDARY
   INSTITUTIONS IN COMMUNITY OUTREACH
SO 3RD INTERNATIONAL CONFERENCE OF EDUCATION, RESEARCH AND INNOVATION
   (ICERI2010)
LA English
DT Proceedings Paper
CT 3rd International Conference of Education, Research and Innovation
   (ICERI)
CY NOV 15-17, 2010
CL Madrid, SPAIN
DE University/Industry/Government partnership; Digital Divide; Community
   Development; Emerging Technologies; Online Learning; Community Outreach
AB Universities in Canada and around the world are in a crisis of conflict. With higher tuition, dropping enrollments, increasing class sizes, constrained budgets, aging faculty, increasing salaries, systemic use of sessional and stipendiary instructors, and added demand for corollary revenues and bottom lines, universities are being stretched beyond their limits. Increasingly, the ivory towers have lost their gleam and are under constant pressure to publish or perish, react to grade inflation, increase revenues even as they produce knowledge, conduct quality research, and respond to a Net generation demanding employable credentials (Cote & Allahar, 2007). Thus, educational institutions are under siege. Many of these issues are not recent as we see scholars having raised the alarm bells even in the sixties (Coombs, 1985; Dore, 1976).
   Nevertheless, with the advent of Toffler's Third Wave, we have emerging technologies challenging our notion of relevant education in relation to pursuit of knowledge. Considerable debate exists on the role and influence of computers in particular and technology in general. While for some like Papert and Zuboff, the computer is a tool, which implies a neutral "social and political content," others like Weizenbaum, Hubert, Dreyfus, and Sullivan question these assumptions (Anchan, 2000; Aronowitz & Giroux, 1993). With some of the greatest criticisms of computer technology having emerged from writers in technology and literature (Roszak, 1986; Stoll, 1995; Hislop, 1995), the influence of technology in the way we learn and interact is undeniable. Conversely, these very same institutions have become isolated ivory towers distanced from their immediate communities that are supposed to be the recipients of progress and development. With the disconnect that has plagued higher institutions of learning, traditional universities have begun to revisit the notion of production of knowledge for its own sake. This is especially true in Canada - where the taxpayers highly subsidize education without having maximized benefit of returns. Amidst the phenomenon of globalization, digital divide and the evolution of McCluhan's the global village, the immediate indigenous communities remain alienated and marginalized (Anchan, 2003a).
   Yet, not all is lost. Universities have begun to recognize and acknowledge this disconnect; a wave of sweeping reforms and best practices have started to respond to communities and other stakeholders.
   Many initiatives attempt to bridge the chasm of disconnect between the university and the surrounding communities even as they open access to the remote and marginalized populations (Anchan, 2003b). For instance, on a large global scale, and as the first institution in Canada to become the headquarters for international aboriginal programming affiliated with the prestigious MacArthur Foundation network, the University of Winnipeg will be using the latest technologies from Cisco (TelePresence and WebEx systems) to connect across the country and the around the world. This multimillion dollar project will not only establish world class cutting edge research centre in Canada's newest and most environmentally friendly, Science Complex and Richardson College for the Environment, but also be a project that will integrate many of the community needs across the province and beyond. Online/Distance Education using asynchronous and synchronous deliveries will complement state of the art studios to allow collaborative technologies including both urban bureaucrats and remote communities.
C1 [Anchan, John; Laube, Donna] Univ Winnipeg, Winnipeg, MB R3B 2E9, Canada.
C3 University of Winnipeg
EM j.anchan@uwinnipeg.ca; d.laube@uwinnipeg.ca
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NR 18
TC 0
Z9 0
U1 0
U2 5
PU IATED-INT ASSOC TECHNOLOGY EDUCATION A& DEVELOPMENT
PI VALENICA
PA LAURI VOLPI 6, VALENICA, BURJASSOT 46100, SPAIN
BN 978-84-614-2439-9
PY 2010
PG 7
WC Education & Educational Research
WE Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Education & Educational Research
GA BEZ06
UT WOS:000318797405031
DA 2024-09-05
ER
PT J
AU Küçük-Avci, S
   Topal, M
   Istanbullu, A
AF Kucuk-Avci, Sirin
   Topal, Murat
   Istanbullu, Aslihan
TI The Effects of the Covid-19 Pandemic on Distance Education in Higher
   Education: A Bibliometric Analysis Study
SO CROATIAN JOURNAL OF EDUCATION-HRVATSKI CASOPIS ZA ODGOJ I OBRAZOVANJE
LA English
DT Article
DE COVID-19; distance education; e-learning; higher education; online
   learning; pandemic
ID PUBLICATIONS; TRENDS
AB This study aimed to examine the COVID-19 effect on distance education in higher education in the pre-COVID-19 pandemic (January to November 2019) and postCOVID-19 pandemic periods (December 2019 and January to December 2020). Two different meta-data sets, consisting of 580 articles for the pre-COVID-19 period and 746 for the post-COVID-19 period, obtained by querying the Web of Science database, were used for analysis. SciMAT and Vosviewer software were used for bibliometric analysis. Publications from the two different periods were compared according to keywords, words from abstracts, based on the criteria using co-occurrence and co-word analysis. The results of the keyword co-occurrence analysis show that the keywords "e-learning" and "online learning" were used more in the post-COVID- 19 period compared to the pre-pandemic period. In the pre-pandemic period, the thematic trend of academic studies largely aligned with students and satisfaction. However, in the post-pandemic period, the research trend was mostly toward such themes as video lectures and web 2.0 technologies. The research results show that the impact of COVID-19 was reflected in the research published in the post-pandemic period, with the interest in e-learning and online learning increasing in higher education, alongside a trend towards investigating the delivery of instruction rather than conducting student-centered studies.
C1 [Kucuk-Avci, Sirin] Akdeniz Univ, Fac Educ, Dept Curriculumn & Instruct, Dumlupinar Bulvari,Kampus Antalya, TR-07058 Antalya, Turkey.
   [Topal, Murat] Sakarya Univ, Fac Educ, Dept Comp Educ & Instruct Technol, TR-54300 Hendek, Province Sakary, Turkey.
   [Istanbullu, Aslihan] Amasya Univ, Vocat Sch Tech Sci, Dept Comp Technol, Seyhcui Mah Kemal Nehrozoglu Cad 92 B, TR-05100 Merkez, Amasya, Turkey.
C3 Akdeniz University; Sakarya University; Amasya University
RP Küçük-Avci, S (corresponding author), Akdeniz Univ, Fac Educ, Dept Curriculumn & Instruct, Dumlupinar Bulvari,Kampus Antalya, TR-07058 Antalya, Turkey.
EM sirinavci@akdeniz.edu.tr; mtopal@sakarya.edu.tr;
   aslihan.babur@amasya.edu.tr
RI Topal, Murat/AAB-1651-2022; Kucuk-Avci, Sirin/IQU-6674-2023
OI Topal, Murat/0000-0001-5270-426X; Kucuk-Avci, Sirin/0000-0002-5518-0542
CR Almaiah Mohammed Amin, 2019, Education and Information Technologies, V24, P885, DOI 10.1007/s10639-018-9810-7
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NR 39
TC 5
Z9 5
U1 2
U2 50
PU FAC TEACHER EDUCATION
PI ZAGREB
PA UNIV ZAGREB, SAVSKA CESTA 77, ZAGREB, 00000, CROATIA
SN 1848-5189
EI 1848-5197
J9 CROAT J EDUC
JI Croat. J. Educ.
PY 2022
VL 24
IS 2
BP 457
EP 488
DI 10.15516/cje.v24i2.4534
PG 32
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA 3F6KW
UT WOS:000830776800004
OA Green Published
DA 2024-09-05
ER
PT C
AU Nakatoh, T
   Nagatani, K
   Minami, T
   Hirokawa, S
   Nanri, T
   Funamori, M
AF Nakatoh, Tetsuya
   Nagatani, Kenta
   Minami, Toshiro
   Hirokawa, Sachio
   Nanri, Takeshi
   Funamori, Miho
BE Yamamoto, S
TI Analysis of the Quality of Academic Papers by the Words in Abstracts
SO HUMAN INTERFACE AND THE MANAGEMENT OF INFORMATION: SUPPORTING LEARNING,
   DECISION-MAKING AND COLLABORATION, HCI INTERNATIONAL 2017, PT II
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT 19th International Conference on Human-Computer Interaction (HCI
   International)
CY JUL 09-14, 2017
CL Vancouver, CANADA
DE Bibliometrics; Research investigation; SVM; Citation
ID PUBLICATIONS
AB The investigation of related research is very important for research activities. However, it is not easy to choose an appropriate and important academic paper from among the huge number of possible papers. The researcher searches by combining keywords and then selects an paper to be checked because it uses an index that can be evaluated. The citation count is commonly used as this index, but information about recently published papers cannot be obtained. This research attempted to identify good papers using only the words included in the abstract. We constructed a classifier by machine learning and evaluated it using cross validation. As a result, it was found that a certain degree of discrimination is possible.
C1 [Nakatoh, Tetsuya; Hirokawa, Sachio; Nanri, Takeshi] Kyushu Univ, Res Inst Informat Technol, Nishi Ku, 744 Motooka, Fukuoka, Fukuoka 8190395, Japan.
   [Nagatani, Kenta] Kyushu Univ, Grad Sch, Fukuoka, Fukuoka, Japan.
   [Nagatani, Kenta] Kyushu Univ, Fac Informat Sci & Elect Engn, Fukuoka, Fukuoka, Japan.
   [Minami, Toshiro] Kyushu Inst Informat Sci, Fukuoka, Fukuoka, Japan.
   [Funamori, Miho] Natl Inst Informat, Tokyo, Japan.
C3 Kyushu University; Kyushu University; Kyushu University; Research
   Organization of Information & Systems (ROIS); National Institute of
   Informatics (NII) - Japan
RP Nakatoh, T (corresponding author), Kyushu Univ, Res Inst Informat Technol, Nishi Ku, 744 Motooka, Fukuoka, Fukuoka 8190395, Japan.
EM nakatoh@cc.kyushu-u.ac.jp
RI Hirokawa, Sachio/S-3526-2018
OI Hirokawa, Sachio/0000-0002-8050-1109
FU JSPS KAKENHI [24500176]; Grants-in-Aid for Scientific Research
   [24500176] Funding Source: KAKEN
FX This work was partially supported by JSPS KAKENHI Grant Number 24500176.
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   Otani S, 2014, 2014 IIAI 3RD INTERNATIONAL CONFERENCE ON ADVANCED APPLIED INFORMATICS (IIAI-AAI 2014), P216, DOI 10.1109/IIAI-AAI.2014.53
   Schulte J, 2015, HOMEOPATHY, V104, P311, DOI 10.1016/j.homp.2015.06.004
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NR 18
TC 0
Z9 0
U1 2
U2 5
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 0302-9743
EI 1611-3349
BN 978-3-319-58524-6; 978-3-319-58523-9
J9 LECT NOTES COMPUT SC
PY 2017
VL 10274
BP 434
EP 443
DI 10.1007/978-3-319-58524-6_34
PN II
PG 10
WC Computer Science, Information Systems; Computer Science, Software
   Engineering; Computer Science, Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BL6NA
UT WOS:000454446400034
DA 2024-09-05
ER
PT J
AU Polat, H
   Topuz, AC
   Yildiz, M
   Taslibeyaz, E
   Kursun, E
AF Polat, Hamza
   Topuz, Arif Cem
   Yildiz, Mine
   Taslibeyaz, Elif
   Kursun, Engin
TI A Bibliometric Analysis of Research on ChatGPT in Education
SO INTERNATIONAL JOURNAL OF TECHNOLOGY IN EDUCATION
LA English
DT Article
DE ChatGPT; Educational technologies; Bibliometric analysis; Generative AI
ID ARTIFICIAL-INTELLIGENCE
AB ChatGPT has become a prominent tool for fostering personalized and interactive learning with the advancements in AI technology. This study analyzes 212 academic research articles indexed in the Scopus database as of July 2023. It maps the trajectory of educational studies on ChatGPT, identifying primary themes, influential authors, and contributing institutions. By employing bibliometric indicators and network analysis, the study explores collaboration patterns, citation trends, and the evolution of research interests. The findings show the exponential growth of interest in leveraging ChatGPT for educational purposes and provide insights into the specific educational domains and contexts that have garnered the most attention. Furthermore, the study reveals the collaborative dynamics and intellectual foundations shaping the field by examining co-authorship and citation networks. This bibliometric analysis contributes to a comprehensive understanding of the current state of ChatGPT research in education, offering researchers and practitioners valuable insights into evolving trends and potential future directions for this innovative aspect of AI and learning.
C1 [Polat, Hamza] Ataturk Univ, Fac Sci Appl, Dept Informat Syst & Technol, Erzurum, Turkiye.
   [Topuz, Arif Cem] Ardahan Univ, Fac Engn, Dept Comp Engn, TR-75002 Ardahan, Turkiye.
   [Yildiz, Mine] Ataturk Univ, Kazim Karabekir Educ Fac, Dept Foreign Language Educ, TR-25240 Erzurum, Turkiye.
   [Taslibeyaz, Elif] Erzincan Binali Yildirim Univ, Fac Educ, Dept Comp Educ & Instruct Technol, Erzincan, Turkiye.
   [Kursun, Engin] Ataturk Univ, Kazim Karabekir Educ Fac, Dept Comp Educ & Instruct, TR-25240 Erzurum, Turkiye.
C3 Ataturk University; Ardahan University; Ataturk University; Erzincan
   Binali Yildirim University; Ataturk University
RP Polat, H (corresponding author), Ataturk Univ, Fac Sci Appl, Dept Informat Syst & Technol, Erzurum, Turkiye.
EM hamzapolat@atauni.edu.tr
RI Polat, Hamza/HWQ-4414-2023
OI Polat, Hamza/0000-0002-9646-7507; Topuz, Arif Cem/0000-0002-5110-5334;
   Kursun, Engin/0000-0002-5649-8595
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NR 81
TC 4
Z9 4
U1 47
U2 47
PU INT SOC TECHNOLOGY EDUCATION & SCIENCE-ISTES
PI MONUMENT
PA 19723 LINDENMERE DR, MONUMENT, COLORADO, UNITED STATES
EI 2689-2758
J9 INT J TECHNOL EDUC
JI Int. J. Technol. Educ.
PY 2024
VL 7
IS 1
SI SI
BP 59
EP 85
DI 10.46328/ijte.606
PG 27
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA NB4F0
UT WOS:001197961000004
OA gold
DA 2024-09-05
ER
PT J
AU Kushairi, N
   Ahmi, A
AF Kushairi, Norliza
   Ahmi, Aidi
TI Flipped classroom in the second decade of the Millenia: a Bibliometrics
   analysis with Lotka's law
SO EDUCATION AND INFORMATION TECHNOLOGIES
LA English
DT Article
DE Active learning; Bibliometrics analysis; Lotka's law; Blended learning;
   Flipped classroom; Flipped learning
ID STUDENT PERFORMANCE; PRODUCTIVITY; SCIENCE; ENGAGEMENT; EDUCATION;
   HIV/AIDS; NIGERIA; AUTHORS; TRENDS; FIELD
AB This paper aims to examine the current dynamics of the flipped classroom studies and to propose a direction for future research for the field. Using a bibliometric approach, we observe a sample of 1557 documents from the Scopus database to identify research activity on the flipped classroom. The keywords "flipped classroom" and "flipped learning" have been executed in the search query. We presented the earlier stage of research in the flipped classroom, the subsequent trends, publications status based on source title, country and institution and examined citations pattern of the publication. We also discuss the themes based on the occurrences and terms of the keywords, title and abstract of the documents. This paper also predicts the future study in the flipped classroom using Lotka's law. We found that the pattern distribution of the author's contribution fits with the law. We conclude by suggesting a few potential research directions on the flipped classroom. Research on flipped classroom focuses on approaches, strategies and effectiveness perceived by practitioners and learners with relatively less attention on author's contribution and the prediction on their future and sustainable contribution and networking in guaranteeing the survival and expansion of flipped classroom approach for the coming decades.
C1 [Kushairi, Norliza] Univ Utara Malaysia, Sch Educ, Sintok 06010, Kedah, Malaysia.
   [Ahmi, Aidi] Univ Utara Malaysia, Tunku Puteri Intan Safinaz Sch Accountancy, Sintok 06010, Kedah, Malaysia.
C3 Universiti Utara Malaysia; Universiti Utara Malaysia
RP Kushairi, N (corresponding author), Univ Utara Malaysia, Sch Educ, Sintok 06010, Kedah, Malaysia.
EM drnk@uum.edu.my; aidi@uum.edu.my
RI Ahmi, Aidi/F-2858-2013
OI Ahmi, Aidi/0000-0002-8488-6966
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NR 55
TC 54
Z9 55
U1 14
U2 80
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1360-2357
EI 1573-7608
J9 EDUC INF TECHNOL
JI Educ. Inf. Technol.
PD JUL
PY 2021
VL 26
IS 4
BP 4401
EP 4431
DI 10.1007/s10639-021-10457-8
EA MAR 2021
PG 31
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA TL6AD
UT WOS:000626920500002
PM 33686330
OA Green Published, Bronze
DA 2024-09-05
ER
PT J
AU Tu, K
   Sun, A
   Levin, DM
AF Tu, Kevin
   Sun, Angela
   Levin, Daniel M.
TI Using memes to promote student engagement and classroom community during
   remote learning
SO BIOCHEMISTRY AND MOLECULAR BIOLOGY EDUCATION
LA English
DT Article
DE active learning; assessment of educational activities; distance
   learning; integration of research into undergraduate teaching; memes;
   molecular biology; teaching and learning techniques methods and
   approaches; using simulation and internet resources for teaching
AB As colleges moved to online teaching during the COVID-19 pandemic, many instructors found it difficult to maintain student engagement and classroom community in the virtual environment. We developed a semester-long activity for a molecular biology research methodology course where students created, and shared original memes related to course content with peers through group chat. Surveys and semi-structured interviews revealed that the exercise was effective in promoting student engagement, a sense of community, and relieving stress.
C1 [Tu, Kevin; Sun, Angela; Levin, Daniel M.] Univ Maryland, Dept Teaching & Learning Policy & Leadership, College Pk, MD USA.
   [Tu, Kevin] Univ Maryland, Dept Cell Biol & Mol Genet, College Pk, MD USA.
   [Sun, Angela] Univ Maryland, Fischell Dept Bioengn, College Pk, MD USA.
   [Tu, Kevin] Univ Maryland, Dept Teaching & Learning Policy & Leadership, College Pk, MD 20742 USA.
C3 University System of Maryland; University of Maryland College Park;
   University System of Maryland; University of Maryland College Park;
   University System of Maryland; University of Maryland College Park;
   University System of Maryland; University of Maryland College Park
RP Tu, K (corresponding author), Univ Maryland, Dept Teaching & Learning Policy & Leadership, College Pk, MD 20742 USA.
EM ktu@umd.edu
OI Tu, Kevin/0000-0002-0089-3932
CR Holton JA, 2008, GROUNDED THEORY REV, V7, P67
   Liu X., 2007, Q REV DISTANCE ED, V8, P9
   Means B., 2020, Unmasking inequality: STEM course experiences during the COVID-19 pandemic
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NR 7
TC 1
Z9 1
U1 2
U2 8
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1470-8175
EI 1539-3429
J9 BIOCHEM MOL BIOL EDU
JI Biochem. Mol. Biol. Educ.
PD MAR
PY 2023
VL 51
IS 2
BP 202
EP 205
DI 10.1002/bmb.21700
EA DEC 2022
PG 4
WC Biochemistry & Molecular Biology; Education, Scientific Disciplines
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Education & Educational Research
GA A0KF1
UT WOS:000894584100001
PM 36479805
OA hybrid
DA 2024-09-05
ER
PT J
AU Goulding, L
AF Goulding, Lauren
TI Spotlights: A Bibliometric Analysis of Lab Safety, Machine Learning
   Predictions of Nanomaterial Toxicity, and Creating Great Titles
SO ACS CHEMICAL HEALTH & SAFETY
LA English
DT Editorial Material; Early Access
NR 0
TC 0
Z9 0
U1 0
U2 3
PU AMER CHEMICAL SOC
PI WASHINGTON
PA 1155 16TH ST, NW, WASHINGTON, DC 20036 USA
EI 1878-0504
J9 ACS CHEM HEALTH SAFE
JI ACS Chem. Health Saf.
PD 2022 JUN 22
PY 2022
DI 10.1021/acs.chas.2c00049
EA JUN 2022
PG 2
WC Public, Environmental & Occupational Health
WE Emerging Sources Citation Index (ESCI)
SC Public, Environmental & Occupational Health
GA 2Q2DG
UT WOS:000820237600001
DA 2024-09-05
ER
PT J
AU Zhou, X
   Huang, L
   Zhang, Y
   Yu, MM
AF Zhou, Xiao
   Huang, Lu
   Zhang, Yi
   Yu, Miaomiao
TI A hybrid approach to detecting technological recombination based on text
   mining and patent network analysis
SO SCIENTOMETRICS
LA English
DT Article
DE Patent network analysis; The structure of science revolutions;
   Bibliometrics; Text mining; Technological recombination; Artificial
   intelligence
ID TECHNICAL INTELLIGENCE; KNOWLEDGE STRUCTURES; INNOVATION; SCIENCE;
   IMPACT; SIMILARITY; EVOLUTION; PATHWAYS; DYNAMICS; INTERNET
AB Detecting promising technology groups for recombination holds the promise of great value for R&D managers and technology policymakers, especially if the technologies in question can be detected before they have been combined. However, predicting the future is always easier said than done. In this regard, Arthur's theory (The nature of technology: what it is and how it evolves, Free Press, New York, 2009) on the nature of technologies and how science evolves, coupled with Kuhn's theory of scientific revolutions (Kuhn in The structure of scientific revolutions, 1st edn, University of Chicago Press, Chicago, p 3, 1962), may serve as the basis of a shrewd methodological framework for forecasting recombinative innovation. These theories help us to set out quantifiable criteria and decomposable steps to identify research patterns at each stage of a scientific revolution. The first step in the framework is to construct a conceptual model of the target technology domain, which helps to refine a reasonable search strategy. With the model built, the landscape of a field-its communities, its technologies, and their interactions-is fleshed out through community detection and network analysis based on a set of quantifiable criteria. The aim is to map normal patterns of research in the domain under study so as to highlight which technologies might contribute to a structural deepening of technological recombinations. Probability analysis helps to detect and group candidate technologies for possible recombination and further manual analysis by experts. To demonstrate how the framework works in practice, we conducted an empirical study on AI research in China. We explored the development potential of recombinative technologies by zooming in on the top patent assignees in the field and their innovations. In conjunction with expert analysis, the results reveal the cooperative and competitive relationships among these technology holders and opportunities for future innovation through technological recombinations.
C1 [Zhou, Xiao] Xidian Univ, Sch Econ & Management, 266 Xinglong Sect,Xifeng Rd, Xian 710126, Shaanxi, Peoples R China.
   [Huang, Lu; Yu, Miaomiao] Beijing Inst Technol, Sch Management & Econ, 5 South Zhong Guan Cun St, Beijing 100081, Peoples R China.
   [Zhang, Yi] Univ Technol Sydney, Fac Engn & Informat Technol, Ctr Artificial Intelligence, Ultimo, NSW 2007, Australia.
C3 Xidian University; Beijing Institute of Technology; University of
   Technology Sydney
RP Huang, L (corresponding author), Beijing Inst Technol, Sch Management & Econ, 5 South Zhong Guan Cun St, Beijing 100081, Peoples R China.
EM belinda1214@126.com; huanglu628@163.com; yi.zhang@uts.edu.au;
   18165268237@163.com
RI Zhang, Yi/AAT-6945-2021
OI Zhang, Yi/0000-0002-7731-0301
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NR 100
TC 24
Z9 24
U1 7
U2 171
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD NOV
PY 2019
VL 121
IS 2
BP 699
EP 737
DI 10.1007/s11192-019-03218-5
PG 39
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA JF2FJ
UT WOS:000491201000006
DA 2024-09-05
ER
PT C
AU Freeman, C
   Roy, MK
   Fattoruso, M
   Alhoori, H
AF Freeman, Cole
   Roy, Mrinal Kanti
   Fattoruso, Michele
   Alhoori, Hamed
BE Bonn, M
   Wu, D
   Downie, SJ
   Martaus, A
TI Shared Feelings: Understanding Facebook Reactions to Scholarly Articles
SO 2019 ACM/IEEE JOINT CONFERENCE ON DIGITAL LIBRARIES (JCDL 2019)
SE ACM-IEEE Joint Conference on Digital Libraries JCDL
LA English
DT Proceedings Paper
CT 19th ACM/IEEE Joint Conference on Digital Libraries (JCDL)
CY JUN 02-06, 2019
CL IL
DE Facebook Reactions; Altmetrics; Data Collection; Social Clicks; Re-
   search Community; Social Media Analytics; Supervised Learning
AB Research on social-media platforms has tended to rely on textual analysis to perform research tasks. While text-based approaches have significantly increased our understanding of online behavior and social dynamics, they overlook features on these platforms that have grown in prominence in the past few years: click-based responses to content. In this paper, we present a new dataset of Facebook Reactions to scholarly content. We give an overview of its structure, analyze some of the statistical trends in the data, and use it to train and test two supervised learning algorithms. Our preliminary tests suggest the presence of stratification in the number of users following pages, divisions that seem to fall in line with distinctions in the subject matter of those pages.
C1 [Freeman, Cole; Roy, Mrinal Kanti; Fattoruso, Michele; Alhoori, Hamed] Northern Illinois Univ, De Kalb, IL 60115 USA.
C3 Northern Illinois University
RP Freeman, C (corresponding author), Northern Illinois Univ, De Kalb, IL 60115 USA.
EM cole.freeman9@gmail.com; mkantiroy@niu.edu; z1840898@students.niu.edu;
   alhoori@niu.edu
RI Alhoori, Hamed/B-8106-2009
OI Alhoori, Hamed/0000-0002-4733-6586
CR Alhoori H, 2014, ACM-IEEE J CONF DIG, P375, DOI 10.1109/JCDL.2014.6970193
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NR 12
TC 6
Z9 11
U1 0
U2 6
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2575-7865
EI 2575-8152
BN 978-1-7281-1547-4
J9 ACM-IEEE J CONF DIG
PY 2019
BP 301
EP 304
DI 10.1109/JCDL.2019.00050
PG 4
WC Computer Science, Information Systems; Computer Science,
   Interdisciplinary Applications; Computer Science, Theory & Methods;
   Information Science & Library Science
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Information Science & Library Science
GA BP5KE
UT WOS:000555928200043
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Corrin, L
   Thompson, K
   Lodge, JM
AF Corrin, Linda
   Thompson, Kate
   Lodge, Jason M.
TI AJET in 2023: Reflections on educational technology, people, and
   bibliometrics
SO AUSTRALASIAN JOURNAL OF EDUCATIONAL TECHNOLOGY
LA English
DT Article
DE educational technology; generative AI; academic publishing; bibliometric
   data
AB In this editorial we reflect on the last three years of AJET achievements, challenges, and opportunities as we reach a time of transition in the lead editorial team. We also reflect on the key themes of 2023, especially the impact that the growing availability of generative artificial intelligence has had on research and practice in the tertiary education sector. We present our annual round up of bibliometrics, thank our hardworking editorial team, and acknowledge the contributions of those who are ending their service with AJET in 2023. In conclusion, we look ahead by outlining our goals for 2024 and discussing the themes and technologies that will be a focus for AJET in the new year.
C1 [Corrin, Linda] Deakin Univ, Geelong, Australia.
   [Thompson, Kate] Queensland Univ Technol, Brisbane, Australia.
   [Lodge, Jason M.] Univ Queensland, Brisbane, Australia.
C3 Deakin University; Queensland University of Technology (QUT); University
   of Queensland
RP Corrin, L (corresponding author), Deakin Univ, Geelong, Australia.
EM linda.corrin@deakin.edu.au
RI ; Corrin, Linda/AAD-8545-2019
OI Lodge, Jason/0000-0001-6330-6160; Corrin, Linda/0000-0002-1593-3271
CR Conroy G, 2023, NATURE, V622, P234, DOI 10.1038/d41586-023-03144-w
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   D'Andrea R, 2017, PLOS ONE, V12, DOI 10.1371/journal.pone.0186111
   Goodyear P, 2023, AUSTRALAS J EDUC TEC, V39, P1, DOI 10.14742/ajet.9082
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   Thompson K, 2023, AUSTRALAS J EDUC TEC, V39, DOI 10.14742/ajet.9251
NR 9
TC 0
Z9 0
U1 7
U2 10
PU AUSTRALASIAN SOC COMPUTERS LEARNING TERTIARY EDUCATION-ASCILITE
PI TUGUN
PA UNIT 5, 202 COODE ST, PO BOX 350, TUGUN, 4224, AUSTRALIA
SN 1449-3098
EI 1449-5554
J9 AUSTRALAS J EDUC TEC
JI Australas. J. Educ. Technol.
PY 2023
VL 39
IS 6
AR 9277
DI 10.14742/ajet.9277
PG 8
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA EE9E6
UT WOS:001137353900005
OA Green Submitted, gold
DA 2024-09-05
ER
PT C
AU Shaw, RA
   Fleming, SW
   Levay, K
   Thompson, R
   Koekemoer, AM
   Tseng, SA
   Forshay, P
   Hargis, JR
   McLean, B
   Marston, A
   Mullally, SE
   Peek, JEG
   Shiao, B
   White, RL
AF Shaw, Richard A.
   Fleming, Scott W.
   Levay, Karen
   Thompson, Randy
   Koekemoer, Anton M.
   Tseng, Shui-Ay
   Forshay, Peter
   Hargis, Jonathan R.
   McLean, Brian
   Marston, Anthony
   Mullally, Susan E.
   Peek, J. E. G.
   Shiao, Bernie
   White, Richard L.
BE Peck, AB
   Seaman, RL
   Benn, CR
TI Enabling new science with MAST community contributed data collections
SO OBSERVATORY OPERATIONS: STRATEGIES, PROCESSES, AND SYSTEMS VII
SE Proceedings of SPIE
LA English
DT Proceedings Paper
CT Conference on Observatory Operations - Strategies, Processes, and
   Systems VII
CY JUN 11-15, 2018
CL Austin, TX
DE data archive; bibliometrics; machine learning; process improvement;
   science impact
AB The Mikulski Archive for Space Telescopes' (MAST), a multi-mission archive that hosts science data products for several NASA missions, has since 2003 solicited collections of processed data, termed High-Level Science Products (HLSPs), from investigators with observing and archive science programs. As of early 2018 there were nearly 130 contributed collections, and the growth rate is expected to accelerate with the start of the TESSc and JWST(d) missions. While the data volume of all HLSP collections is only about 1% of the total volume hosted by MAST, they have an outsized impact on science. The aggregate downloaded volume for a given HLSP collection is typically about 40 times the collection size, and the citation rates for HLSP collections are significantly higher than that for typical observing programs. Yet hosting HLSPs presents special challenges for long-term archives. It is often problematic to obtain sufficient metadata to specify fully the data products without requiring work from potential contributors that may discourage them from sharing their collections. Historically, preparing an HLSP collection for distribution via MAST has been quite time-consuming and often required substantial interaction with the collection contributors. We are creating a more automated workflow and using new technologies for HLSP collection management to improve collection discoverability, simplify the process for the investigator, ease the burden for MAST staff, and shorten the timeframe for publishing HLSPs. This work will also help MAST staff better assess the impact of HLSP collections on science outcomes for hosted mission data.
C1 [Shaw, Richard A.; Fleming, Scott W.; Levay, Karen; Thompson, Randy; Koekemoer, Anton M.; Tseng, Shui-Ay; Forshay, Peter; Hargis, Jonathan R.; McLean, Brian; Marston, Anthony; Mullally, Susan E.; Peek, J. E. G.; Shiao, Bernie; White, Richard L.] Space Telescope Sci Inst, 3700 San Martin Dr, Baltimore, MD 21218 USA.
   [Marston, Anthony] European Space Agcy, 3400 N Charles St, Baltimore, MD 21218 USA.
   [Peek, J. E. G.] Johns Hopkins Univ, Dept Phys & Astron, 3400 N Charles St, Baltimore, MD 21218 USA.
C3 Space Telescope Science Institute; Johns Hopkins University
RP Shaw, RA (corresponding author), Space Telescope Sci Inst, 3700 San Martin Dr, Baltimore, MD 21218 USA.
EM shaw@stsci.edu
RI White, Richard L/A-8143-2012; Koekemoer, Anton M./F-8400-2014
OI Koekemoer, Anton M./0000-0002-6610-2048; Shaw,
   Richard/0000-0003-4058-5202; Marston, Anthony/0000-0001-5788-5258;
   White, Richard/0000-0002-9194-2807
FU NASA [NAS 5-26555]
FX MAST is operated by the Space Telescope Science Institute for NASA under
   contract NAS 5-26555. This research has made use of NASA's Astrophysics
   Data System Bibliographic Services. We are grateful for the contribution
   of InSight Data Science Fellow Alicia Shen, who built a machine-learning
   model for evaluating whether HLSP data were employed in citing papers.
CR FITS, 2017, DEF FLEX IM TRANSP S
   Giavalisco M, 2004, ASTROPHYS J, V600, pL93, DOI 10.1086/379232
   Hirsch JE, 2005, P NATL ACAD SCI USA, V102, P16569, DOI 10.1073/pnas.0507655102
   Pepe A, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0104798
NR 4
TC 1
Z9 1
U1 0
U2 1
PU SPIE-INT SOC OPTICAL ENGINEERING
PI BELLINGHAM
PA 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA
SN 0277-786X
EI 1996-756X
BN 978-1-5106-1962-3
J9 PROC SPIE
PY 2018
VL 10704
AR UNSP 1070414
DI 10.1117/12.2312810
PG 13
WC Engineering, Aerospace; Remote Sensing; Optics; Imaging Science &
   Photographic Technology
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Engineering; Remote Sensing; Optics; Imaging Science & Photographic
   Technology
GA BL5SA
UT WOS:000452635900036
DA 2024-09-05
ER
PT J
AU Santos, BS
   Silva, I
   Costa, DG
AF Santos, Breno Santana
   Silva, Ivanovitch
   Costa, Daniel G.
TI Symmetry in Scientific Collaboration Networks: A Study Using Temporal
   Graph Data Science and Scientometrics
SO SYMMETRY-BASEL
LA English
DT Article
DE graph data science; symmetry properties; machine learning; graph
   embedding; temporal analysis; scientometrics
AB This article proposes a novel approach that leverages graph theory, machine learning, and graph embedding to evaluate research groups comprehensively. Assessing the performanceand impact of research groups is crucial for funding agencies and research institutions, but many traditional methods often fail to capture the complex relationships between the evaluated elements.In this sense, our methodology transforms publication data into graph structures, allowing the visualization and quantification of relationships between researchers, publications, and institutions.By incorporating symmetry properties, we offer a more in-depth evaluation of research groups cohesiveness and structure over time. This temporal evaluation methodology bridges the gap between unstructured scientometrics networks and the evaluation process, making it a valuable tool for decision-making procedures. A case study is defined to demonstrate the potential to providevaluable insights into the dynamics and limitations of research groups, which ultimately reinforces the feasibility of the proposed approach when supporting decision making for funding agencies andresearch institutions.
C1 [Santos, Breno Santana; Silva, Ivanovitch] Univ Fed Rio Grande do Norte, Postgrad Program Elect & Comp Engn, BR-59078970 Natal, Brazil.
   [Santos, Breno Santana] Univ Fed Sergipe, Informat Syst Dept, BR-49506036 Itabaiana, Brazil.
   [Costa, Daniel G.] Univ Porto, Fac Engn, INEGI, P-4200465 Porto, Portugal.
C3 Universidade Federal do Rio Grande do Norte; Universidade Federal de
   Sergipe; Universidade do Porto
RP Santos, BS; Silva, I (corresponding author), Univ Fed Rio Grande do Norte, Postgrad Program Elect & Comp Engn, BR-59078970 Natal, Brazil.; Santos, BS (corresponding author), Univ Fed Sergipe, Informat Syst Dept, BR-49506036 Itabaiana, Brazil.
EM breno.santos.038@ufrn.edu.br; breno1005@hotmail.com
RI Silva, Ivanovitch/N-8075-2019; Santos, Breno Santana/AAQ-2189-2020;
   Costa, Daniel G./I-4928-2013
OI Silva, Ivanovitch/0000-0002-0116-6489; Santos, Breno
   Santana/0000-0002-8790-2546; Costa, Daniel G./0000-0003-3988-8476
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NR 38
TC 1
Z9 1
U1 2
U2 14
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2073-8994
J9 SYMMETRY-BASEL
JI Symmetry-Basel
PD MAR
PY 2023
VL 15
IS 3
AR 601
DI 10.3390/sym15030601
PG 17
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA A9FA6
UT WOS:000958091800001
OA gold
DA 2024-09-05
ER
PT J
AU Velasco, ED
   Hirumi, A
   Chen, BY
AF de la Mora Velasco, Efren
   Hirumi, Atsusi
   Chen, Baiyun
TI Improving Instructional Videos with Background Music and Sound Effects:
   A Design-Based Research Approach
SO JOURNAL OF FORMATIVE DESIGN IN LEARNING
LA English
DT Article
DE Background music; Music; Sound; Sound effects; Instructional videos;
   Animations; Motivation; Engagement; Learning; Knowledge retention;
   Attention; Relevance; Confidence; Satisfaction; Working memory;
   Cognitive load; Multimedia learning; Multimedia theory; Online learning;
   Design-based&#160; research; Formative evaluation
ID ENGAGEMENT; MOOD
AB There is a lack of empirical studies incorporating background music (BM) and sounds (SFX) to instructional videos to facilitate students' learning. A design-based research study was conducted to enhance student engagement and motivation including four iterative formative evaluations. In the first iteration, graduate students' reactions were examined to understand the potential influences of BM and SFX in the learning experience. The second iteration consisted of a small-group session focused on describing students' levels of engagement, motivation, and learning while using videos that included BM and SFX. In the third iteration, a field test was conducted to explore the effects of BM and SFX on students' engagement, motivation, and learning. The fourth iteration collected experts' feedback to inform future research and practice. Results showed beneficial effects of BM and SFX for motivation, engagement, and potential learning. Practical guidelines are suggested for faculty and instructional designers to implement BM and SFX on instructional videos. The paper concludes by posing an emergent theory: the design and integration of BM and SFX need to balance the motivational improvements with the challenges of cognitive dissonance and overload.
C1 [de la Mora Velasco, Efren; Hirumi, Atsusi; Chen, Baiyun] Univ Cent Florida, 4000 Cent Florida Blvd, Orlando, FL 32816 USA.
C3 State University System of Florida; University of Central Florida
RP Velasco, ED (corresponding author), Univ Cent Florida, 4000 Cent Florida Blvd, Orlando, FL 32816 USA.
EM efren@ucf.edu
RI de la mora velasco, Efren/AAE-4618-2021
OI de la mora velasco, Efren/0000-0001-7485-9510
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NR 50
TC 5
Z9 6
U1 4
U2 28
PU SPRINGER INT PUBL AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
EI 2509-8039
J9 J FORMATIVE DES LEAR
JI J. Formative Des. Learn.
PD JUN
PY 2021
VL 5
IS 1
BP 1
EP 15
DI 10.1007/s41686-020-00052-4
EA JAN 2021
PG 15
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA TS8JB
UT WOS:000607759100001
DA 2024-09-05
ER
PT J
AU Wahid, R
   Ahmi, A
   Alam, ASAF
AF Wahid, Ratnaria
   Ahmi, Aidi
   Alam, A. S. A. Ferdous
TI Growth and Collaboration in Massive Open Online Courses: A Bibliometric
   Analysis
SO INTERNATIONAL REVIEW OF RESEARCH IN OPEN AND DISTRIBUTED LEARNING
LA English
DT Article
DE massive open online courses; MOOCs; distance education; online learning;
   collaborative research; inclusiveness
ID MOOCS
AB Massive open online courses (MOOCs) are an important approach for achieving UNESCO's aim of open and accessible education. However, there are concerns regarding fragmentation or bias of MOOCs toward certain disciplines or countries. This study sought to: (a) examine how MOOCs research has evolved and is distributed, (b) determine what key areas are discussed in MOOCs research, and (c) identify the major players in MOOCs research and their collaborations. This study conducted a bibliometric analysis of 3,118 scholarly works related to MOOCs as recorded in the Scopus database in July, 2019. Specifically, we analyzed the evolution of MOOCs research by examining (a) published studies, (b) source titles, (c) types of sources and documents, as well as (d) the languages in which the documents were written in. We further analyzed the key areas of MOOCs research by looking into common subject areas, keywords used most often, and title analysis. Finally, we sought to increase our understanding of the major players in MOOCs research and their collaborations by examining (a) which countries contributed most to MOOCs research, (b) the main institutions involved, as well as (c) authorship and citation analysis. Findings indicated that in their early development starting in 2009, MOOCs caught the attention of scholars from both the East and the West, and the number of publications grew consistently over the 10 years after that. MOOCs research has been well distributed but has yet to adequately encourage inclusiveness. There has been healthy cross-country collaboration, but there is a gap in MOOCs research originating from certain countries as compared to the rest of the world. Our findings provide important input towards improving the inclusivity and global reach of MOOCs.
C1 [Wahid, Ratnaria; Ahmi, Aidi; Alam, A. S. A. Ferdous] Univ Utara Malaysia, Bukit Kayu Hitam, Kedah, Malaysia.
C3 Universiti Utara Malaysia
RP Wahid, R (corresponding author), Univ Utara Malaysia, Bukit Kayu Hitam, Kedah, Malaysia.
RI Ahmi, Aidi/F-2858-2013; Alam, A. S. A. Ferdous/I-4594-2014; Wahid,
   Ratnaria/K-7066-2015
OI Ahmi, Aidi/0000-0002-8488-6966; Alam, A. S. A.
   Ferdous/0000-0003-2413-3046; Wahid, Ratnaria/0000-0001-9424-9285
FU Ministry of Education Malaysia [13049]
FX Y This work was supported in part by the Ministry of Education Malaysia,
   under the Malaysian Fundamental Research Grant Scheme [S/O Code 13049].
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NR 55
TC 22
Z9 23
U1 0
U2 9
PU ATHABASCA UNIV PRESS
PI ATHABASCA
PA 1 UNIVERSITY DR, ATHABASCA, AB T9S 3A3, CANADA
SN 1492-3831
J9 INT REV RES OPEN DIS
JI Int. Rev. Res. Open Distrib. Learn.
PD NOV
PY 2020
VL 21
IS 4
BP 292
EP 322
PG 31
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA PA7QU
UT WOS:000595826000016
DA 2024-09-05
ER
PT J
AU Xie, Z
AF Xie, Zheng
TI Assessing the Attractions of MOOCs From the Perspective of
   Scientometrics
SO IEEE ACCESS
LA English
DT Article
DE Data science applications in education; distance education and online
   learning; evaluation methodologies
ID IMPACT FACTORS; COAUTHORSHIP; PERFORMANCE; INDICATORS; MOTIVATION;
   EDUCATION; STUDENTS; MODEL
AB A range of empirical observations show that the right-skewed phenomenon emerges in the citation distributions of papers. The phenomenon also exists in the length distributions of learners' time spent on viewing the videos of a massive open online course. The mechanisms underlying this phenomenon in both cases are the same, namely cumulative advantage, known as the Matthew effect and the Lindy effect respectively. These similarities make it possible to apply the theories and methods in scientometrics to attraction assessments of massive open online courses. Massive data of learning behaviors are recorded on the MOOC platforms with high measurability and wide coverage, setting the precondition for this study. Based on the data derived from the log records of viewing behavior, we showed how to utilize the ideas of typical scientometric indicators, such as the impact factor and h-index, to assess courses' attraction. Our work adumbrates not only the practicability but also the limitation of the provided indicators.
C1 [Xie, Zheng] Natl Univ Def Technol, Coll Liberal Arts & Sci, Changsha 410000, Hunan, Peoples R China.
C3 National University of Defense Technology - China
RP Xie, Z (corresponding author), Natl Univ Def Technol, Coll Liberal Arts & Sci, Changsha 410000, Hunan, Peoples R China.
EM xiezheng81@nudt.edu.cn
OI Xie, Zheng/0000-0003-0391-8725
FU National Education Science Foundation of China [DIA180383]; National
   Natural Science Foundation of China [61773020]
FX This work was supported in part by the National Education Science
   Foundation of China under Grant DIA180383, and in part by the National
   Natural Science Foundation of China under Grant 61773020.
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NR 47
TC 1
Z9 1
U1 2
U2 16
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2019
VL 7
BP 136409
EP 136418
DI 10.1109/ACCESS.2019.2942835
PG 10
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering; Telecommunications
GA JQ1DH
UT WOS:000498693400002
OA gold
DA 2024-09-05
ER
PT J
AU Aguilar-Soto, M
   Robinson-García, N
   Vargas-Quesada, B
AF Aguilar-Soto, Maria
   Robinson-Garcia, Nicolas
   Vargas-Quesada, Benjamin
TI Altmetrics for the identification of scientific controversies: The case
   of <i>NeuroGenderings</i> and neurosexism
SO PROFESIONAL DE LA INFORMACION
LA English
DT Article
DE Neurosexism; Scientific controversies; Altmetrics; Sentiment analysis;
   News; Blogs; Social media; Wikipedia; Reddit; Facebook; Science mapping;
   Neuroscience; Gender; Sex; Neurogenderings
ID GENDER SIMILARITIES; NEUROSCIENCE; LINKING; SEX
AB This work presents a methodological proposal for the analysis of social controversies related to scientific literature. This methodology consists of three clearly differentiated parts. First, we identify the cognitive structure of a set of scientific works. To do this, a historiogram is created through the analysis of references cited by seminal works. This allows us to expand the set of works to work with, subsequently conducting a co-word analysis to identify the cognitive structure of the scientific field to be explored. Secondly, we obtain social mentions of this scientific literature using so-called altmetrics. This allows us to extract mentions made to each scientific document from non-academic environments. Finally, we apply sentiment analysis techniques to these mentions to identify focal points of negative sentiment. We test this methodology on the case study of NeuroGenderings, a movement in the field of neuroscience that denounces the lack of scientific evidence in works that claim the existence of brain differences driven by the biological sex of the subjects. Our results confirm the viability of these types of approaches that enable the identification of research areas with greater controversy. Although our study is limited to the analysis of controversies in news, blogs, Facebook, Wikipedia, and Reddit, the methodology can be applied to other domains and social platforms.
C1 [Aguilar-Soto, Maria; Vargas-Quesada, Benjamin] Univ Granada, Unit Computat Humanities & Social Sci, Colegio Maximo Cartuja S-N, E-18071 Granada, Spain.
   [Robinson-Garcia, Nicolas] Univ Granada, Unit Computat Humanities & Social Sci, EC3 Res Grp, Colegio Maximo Cartuja S-N, E-18071 Granada, Spain.
C3 University of Granada; University of Granada
RP Robinson-García, N (corresponding author), Univ Granada, Unit Computat Humanities & Social Sci, EC3 Res Grp, Colegio Maximo Cartuja S-N, E-18071 Granada, Spain.
EM m_aguilar@ugr.es; elrobin@ugr.es; benjamin@ugr.es
RI Robinson-Garcia, Nicolas/B-3590-2012; Vargas-Quesada,
   Benjamin/L-7222-2014
OI Robinson-Garcia, Nicolas/0000-0002-0585-7359; Vargas-Quesada,
   Benjamin/0000-0001-5115-7460; Aguilar-Soto, Maria/0000-0002-8772-8832
FU Spanish Ministry of Science and Innovation, reference MCIN/AEI; European
   Social Fund "FSE invierte en tu futuro"
FX This work has been financed by the Spanish Ministry of Science and
   Innovation, reference MCIN/AEI /10.13039/501100011033, and by the
   European Social Fund "FSE invierte en tu futuro". Nicolas
   Robinson-Garcia is a Ramon y Cajal researcher (REF: RYC2019-027886-I).
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NR 52
TC 0
Z9 0
U1 9
U2 10
PU  EDICIONES PROFESIONALES INFORMACION SL-EPI
PI BARCELONA
PA MISTRAL, 36, BARCELONA, ALBOLOTE, SPAIN
SN 1386-6710
EI 1699-2407
J9 PROF INFORM
JI Prof. Inf.
PY 2023
VL 32
IS 6
AR e320610
DI 10.3145/epi.2023.nov.10
PG 11
WC Communication; Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Communication; Information Science & Library Science
GA GZ7F9
UT WOS:001156562800014
OA hybrid
DA 2024-09-05
ER
PT J
AU Chen, XL
   Zou, D
   Xie, HR
   Wang, FL
AF Chen, Xieling
   Zou, Di
   Xie, Haoran
   Wang, Fu Lee
TI Metaverse in Education: Contributors, Cooperations, and Research Themes
SO IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES
LA English
DT Article
DE Metaverse; Education; Bibliometrics; Analytical models; Solid modeling;
   Collaboration; Databases; Educational Metaverse (Edu-Metaverse); social
   network visualization; structural topic modeling
ID AUGMENTED REALITY; COLLABORATIONS; ENVIRONMENT; EVOLUTION; COMMUNITY;
   PATTERNS; NETWORK; IMPACT; TIME
AB Research on Educational Metaverse (Edu-Metaverse) has developed into an active research field. Based on 310 academic papers published from 2004 to 2022, this study identifies contributors, scientific cooperations, and research themes using bibliometrics, social network analysis, topic modeling, and keyword analysis. Results suggest that Edu-Metaverse has been gaining increasing attention in academia since 2019. Countries/affiliations located in the same regions are close partners in scientific cooperation. By jointly interpreting topic modeling and keyword analysis results, this study reveals eight main themes in the field of Edu-Metaverse: 1) Metaverse-based physical education; 2) Metaverse-supported simulations for collaborative problem-based learning (PBL) in health/medical education; 3) 3-D virtual learning environment-supported art appreciation and creation in art education; 4) Metaverse-enabled laboratories for STEM education; 5) language and 21st century skill development through Metaverse-supported immersive language learning; 6) Metaverse for developing autism children' social communication abilities; 7) virtual world Metaverse-supported gameful experience-based education; and 8) quantitative research on Edu-Metaverse focusing on learners' experience. We also identified challenges and directions needing further attention: 1) data security and privacy protection; 2) balance between the real world and virtual world identities; 3) preparing instructors for Edu-Metaverse; and 4) assessment of higher-order thinking competencies in Edu-Metaverse-based PBL. This work helps facilitate researchers' and practitioners' understanding of Edu-Metaverse research and raises their awareness of research frontiers and future directions.
C1 [Chen, Xieling] Guangzhou Univ, Guangzhou 510006, Peoples R China.
   [Zou, Di] Educ Univ Hong Kong, Hong Kong, Peoples R China.
   [Xie, Haoran] Lingnan Univ, Hong Kong, Peoples R China.
   [Wang, Fu Lee] Hong Kong Metropolitan Univ, Hong Kong, Peoples R China.
C3 Guangzhou University; Education University of Hong Kong (EdUHK); Lingnan
   University; Hong Kong Metropolitan University
RP Zou, D (corresponding author), Educ Univ Hong Kong, Hong Kong, Peoples R China.
EM xielingchen0708@gmail.com; dzou@eduhk.hk; hrxie2@gmail.com;
   pwang@hkmu.edu.hk
RI Xie, Haoran/AFS-3515-2022
OI Xie, Haoran/0000-0003-0965-3617; Wang, Fu Lee/0000-0002-3976-0053; ZOU,
   Di/0000-0001-8435-9739
FU Special Grant for Strategic Development of Virtual Teaching and
   Learning, University Grant Committee, Hong Kong
FX No Statement Available
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NR 89
TC 13
Z9 13
U1 70
U2 78
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1939-1382
J9 IEEE T LEARN TECHNOL
JI IEEE Trans. Learn. Technol.
PD DEC
PY 2023
VL 16
IS 6
BP 1111
EP 1129
DI 10.1109/TLT.2023.3277952
PG 19
WC Computer Science, Interdisciplinary Applications; Education &
   Educational Research
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Education & Educational Research
GA EY5S1
UT WOS:001142515100011
OA hybrid
DA 2024-09-05
ER
PT J
AU Vaicondam, Y
   Sikandar, H
   Irum, S
   Khan, N
   Qureshi, MI
AF Vaicondam, Yamunah
   Sikandar, Huma
   Irum, Sobia
   Khan, Nohman
   Qureshi, Muhammad Imran
TI Research Landscape of Digital Learning Over the Past 20 Years: A
   Bibliometric and Visualisation Analysis
SO INTERNATIONAL JOURNAL OF ONLINE AND BIOMEDICAL ENGINEERING
LA English
DT Article
DE digital learning; e-learning; m-learning; bibliometric analysis;
   visualisation; online learning; research trend analysis; covid-19
AB The concept of digital learning has grown in popularity significantly over the last few decades especially in the past couple of years due to covid-19. Digital learning is defined as any type of learning that integrated Information and communication technology in its conduct. This study aims to presents a research landscape of digital learning research published in the past 20 years. We conducted a bibliometric analysis to determine the pattern of digital learning published literature from 2002 to 2021. The search for the relevant articles was made on the basis of keywords linked with digital learning in the article's title, abstract, and keywords. As a result, we retrieved 1361 papers from Scopus for bibliometric analysis. The review identifies the publication growth trend, most cited articles, top journals, productive authors, and the leading countries and institutions and major subject areas. According to the findings of our analysis, the United States is the most productive country in terms oof publications and citations. Computers and Education is the leading journal. Through the co-occurrence of keywords analysis, we determined that the most significant keywords associated with digital learning are covid-19, online learning, e-learning and digital learning environment, higher education, digital technologies and so on. The highest number of digital learning articles are published under social science domain. The publication growth trend is consistently rising and is projected to continue in the following years, indicating the importance of digital learning in different domain. The study provides a roadmap for future researchers to follow, where they can focus on key areas where success is possible.
C1 [Vaicondam, Yamunah] Taylors Univ, Sch Accounting & Finance, Subang Jaya, Malaysia.
   [Sikandar, Huma] Univ Teknol Malaysia UTM, Azman Hashim Int Business Sch AHIBS, Johor Baharu 81310, Johor, Malaysia.
   [Irum, Sobia] Univ Bahrain, Dept Management & Mkt, Coll Business Adm, Zallaq, Bahrain.
   [Khan, Nohman] Univ Kuala Lumpur, UniKL Business Sch, Kuala Lumpur, Malaysia.
   [Qureshi, Muhammad Imran] Teesside Univ, Int Business Sch, Clarendon Bldg, Middlesbrough TS1 3BX, Cleveland, England.
C3 Taylor's University; Universiti Teknologi Malaysia; University of
   Bahrain; University of Kuala Lumpur; University of Teesside
RP Sikandar, H (corresponding author), Univ Teknol Malaysia UTM, Azman Hashim Int Business Sch AHIBS, Johor Baharu 81310, Johor, Malaysia.
EM Huma.sikandar@gmail.com; nohman.khan@s.unikl.edu.my;
   m.qureshi@tees.ac.uk
RI Khan, Nohman/AAR-2414-2020; Qureshi, Muhammad Imran/I-4390-2016;
   Sikandar, Huma/AAA-1479-2022; Vaicondam, Yamunah/KTI-6691-2024
OI Khan, Nohman/0000-0001-9714-6273; Qureshi, Muhammad
   Imran/0000-0001-8861-0628; Sikandar, Huma/0000-0001-7777-2314;
   Vaicondam, Yamunah/0000-0003-2658-8319
CR Albort-Morant G, 2016, J BUS RES, V69, P4912, DOI 10.1016/j.jbusres.2016.04.052
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NR 31
TC 7
Z9 7
U1 0
U2 17
PU Int Federation Engineering Education Societies-IFEES
PI Fairfax
PA 4400 University Drive, MS 4A3, Fairfax, VA, UNITED STATES
EI 2626-8493
J9 INT J ONLINE BIOMED
JI Int. J. Online Biomed. Eng.
PY 2022
VL 18
IS 8
BP 4
EP 22
DI 10.3991/ijoe.v18i08.31963
PG 19
WC Computer Science, Interdisciplinary Applications
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA 2R3GA
UT WOS:000820998600001
OA gold, Green Submitted
DA 2024-09-05
ER
PT J
AU Morsink, MC
   Dekter, HE
   Dirks-Mulder, A
   van Leeuwen, WB
AF Morsink, M. C.
   Dekter, H. E.
   Dirks-Mulder, A.
   van Leeuwen, W. B.
TI Molecular diagnostic analysis of outbreak scenarios
SO BIOCHEMISTRY AND MOLECULAR BIOLOGY EDUCATION
LA English
DT Article
DE Assessment of educational activities; molecular biology; molecular
   medicine; active learning; emerging diseases; inquiry-based teaching;
   integration of courses; integration of research into undergraduate
   teaching; laboratory exercises; teaching and learning techniques methods
   and approaches
AB In the current laboratory assignment, technical aspects of the polymerase chain reaction (PCR) are integrated in the context of six different bacterial outbreak scenarios. The Enterobacterial Repetitive Intergenic Consensus Sequence (ERIC) PCR was used to analyze different outbreak scenarios. First, groups of 24 students determined optimal ERIC-PCR conditions to validate the protocol and subsequently applied ERIC-PCR to identify genetic relatedness among bacterial strains. Based on these genetic fingerprints, students selected the outbreak cases from the patient samples and assessed the risk factors for the outbreak scenario. Finally, students presented their findings during a classroom presentation. The results indicated that the assignment successfully facilitated student learning on the technical aspects of (ERIC) PCR and clearly demonstrated the practical application of PCR in a clinical diagnostic setting. Additionally, the assignment was highly appreciated by the students.
C1 [Morsink, M. C.; Dekter, H. E.; Dirks-Mulder, A.; van Leeuwen, W. B.] Univ Appl Sci Leiden, Dept Innovat Mol Diagnost, Hogesch Leiden, Leiden, Netherlands.
RP Morsink, MC (corresponding author), Univ Appl Sci Leiden, Dept Innovat Mol Diagnost, Hogesch Leiden, Leiden, Netherlands.
EM maarten.morsink@gmail.com
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TC 0
Z9 0
U1 0
U2 4
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1470-8175
EI 1539-3429
J9 BIOCHEM MOL BIOL EDU
JI Biochem. Mol. Biol. Educ.
PD MAR-APR
PY 2012
VL 40
IS 2
BP 112
EP 120
DI 10.1002/bmb.20562
PG 9
WC Biochemistry & Molecular Biology; Education, Scientific Disciplines
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Education & Educational Research
GA 908JK
UT WOS:000301486500006
PM 22419592
OA Bronze
DA 2024-09-05
ER
PT J
AU Jain, J
   Walia, N
   Singh, S
   Jain, E
AF Jain, Jinesh
   Walia, Nidhi
   Singh, Simarjeet
   Jain, Esha
TI Mapping the field of behavioural biases: a literature review using
   bibliometric analysis
SO MANAGEMENT REVIEW QUARTERLY
LA English
DT Article
DE Heuristics; Prospect; Herding; Bibliometric analysis; Content analysis;
   Behavioural biases; G40; G41
ID MYOPIC LOSS AVERSION; INVESTMENT DECISION-MAKING; HERD BEHAVIOR;
   INTELLECTUAL STRUCTURE; CAPITAL-MARKETS; PROSPECT-THEORY; RISK-TAKING;
   INFORMATION; INVESTORS; OVERCONFIDENCE
AB Research on behavioural biases has witnessed a momentous growth in the last two decades, supported by rising interest and publication thrust shown by academic scholars. Present study maps the academic literature on the role of behavioural biases in investment decision-making. With the help of bibliometric tools, the paper highlights the current state-of-the-art and identifies significant gaps in the existing literature on behavioural biases. Through keyword and reference searching approaches, the study retrieved 212 research papers from the Scopus database. Application of performance analysis techniques has helped in identification of influential journals, prolific authors, countries and affiliations enriching the literature on behavioural biases. Scientific mapping approaches such as bibliographic coupling and thematic mapping has provided valuable insights about the conceptual and intellectual structure of the field. Finally, the research directions proposed in this review will provide a roadmap for future research.
C1 [Jain, Jinesh; Jain, Esha] Sri Aurobindo Coll Commerce & Management, Ludhiana, India.
   [Walia, Nidhi; Singh, Simarjeet] Punjabi Univ, Univ Sch Appl Management, Patiala, India.
C3 Punjabi University
RP Singh, S (corresponding author), Punjabi Univ, Univ Sch Appl Management, Patiala, India.
EM jineshjain81@gmail.com; nidhiwalia79@gmail.com;
   jeetsimarkamal93@gmail.com; ejain2028@gmail.com
RI Singh, Simarjeet/ABB-5409-2020; jain, Jinesh/AAM-6220-2021
OI Singh, Simarjeet/0000-0003-3497-2177; jain, Jinesh/0000-0003-1774-8704
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NR 163
TC 35
Z9 35
U1 0
U2 0
PU SPRINGERNATURE
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND
SN 2198-1620
EI 2198-1639
J9 MANAG REV Q
JI Manag. Rev. Q.
PD SEP
PY 2022
VL 72
IS 3
BP 823
EP 855
DI 10.1007/s11301-021-00215-y
PG 33
WC Business; Business, Finance; Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA TG4L8
UT WOS:001240099300005
DA 2024-09-05
ER
PT C
AU Johanes, P
AF Johanes, Petr
GP Assoc Comp Machinery
TI Start of a Science: An Epistemological Analysis of Learning at Scale
SO L@S '19: PROCEEDINGS OF THE SIXTH (2019) ACM CONFERENCE ON LEARNING @
   SCALE
LA English
DT Proceedings Paper
CT 6th ACM Conference on Learning @ Scale (L@S)
CY JUN 24-25, 2019
CL Chicago, IL
DE Epistemology; Philosophy of Science; Citation Network Analysis;
   Bibliometrics; Knowledge Modeling; Online Learning; MOOCs
ID DESIGN-BASED RESEARCH
AB The Learning at Scale (L@S) conference has brought together researchers from diverse scholarly communities to design and study technologies that are explicitly meant to scale to a large number and variety of learners. Over the last three years, the L@S community has published a thematic, methodological, and bibliometric analysis to reflect on its own interests, challenges, and foundations. This paper continues the wider reflection effort and complements these two prior analyses with an epistemological analysis of the way the papers employ learning theory, evaluate evidence, and deploy statistical models. The epistemological analysis uses two methodologies: coding the full papers from the first four years for epistemological markers of interest and analyzing the network of citations from all of the full papers for dominant institutional and epistemological traditions. By combining these two methods, the present analysis reveals that most papers explicitly show their theoretical commitments, target a narrow slice of available learning theory, draw on varied academic fields in different proportions, and showcase epistemological practices in line with what philosophers of computational science observe in communities using similar model-based methods. The paper then situates these claims in wider conversations occurring in the learning sciences and philosophy of science to provide theoretical insights as well as practical recommendations for how the community can more consciously conduct and communicate its scientific endeavor.
C1 [Johanes, Petr] Stanford Univ, Stanford, CA 94305 USA.
C3 Stanford University
RP Johanes, P (corresponding author), Stanford Univ, Stanford, CA 94305 USA.
EM pjohanes@stanford.edu
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NR 31
TC 1
Z9 1
U1 1
U2 2
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES
BN 978-1-4503-6804-9
PY 2019
DI 10.1145/3330430.3333631
PG 10
WC Computer Science, Interdisciplinary Applications; Education &
   Educational Research; Education, Scientific Disciplines
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Education & Educational Research
GA BO2RA
UT WOS:000507611000001
DA 2024-09-05
ER
PT J
AU Cauthen, K
   Rai, P
   Hale, N
   Freeman, L
   Ray, J
AF Cauthen, Katherine
   Rai, Prashant
   Hale, Nicholas
   Freeman, Laura
   Ray, Jaideep
TI Detecting technological maturity from bibliometric patterns
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Technology life cycle; Machine learning; Artificial neural network; Data
   augmentation
ID LIFE-CYCLE; S-CURVE; INNOVATION; DYNAMICS; GOMPERTZ; PATHS; MODEL
AB The capability to identify emergent technologies based upon easily accessed open-source indicators, such as publications, is important for decision-makers in industry and government. The scientific contribution of this work is the proposition of a machine learning approach to the detection of the maturity of emerging technologies based on publication counts. Time-series of publication counts have universal features that distinguish emerging and growing technologies. We train an artificial neural network classifier, a supervised machine learning algo-rithm, upon these features to predict the maturity (emergent vs. growth) of an arbitrary technology. With a training set comprised of 22 technologies we obtain a classification accuracy ranging from 58.3% to 100% with an average accuracy of 84.6% for six test technologies. To enhance classifier performance, we augmented the training corpus with synthetic time-series technology life cycle curves, formed by calculating weighted averages of curves in the original training set. Training the classifier on the synthetic data set resulted in improved ac-curacy, ranging from 83.3% to 100% with an average accuracy of 90.4% for the test technologies. The perfor-mance of our classifier exceeds that of competing machine learning approaches in the literature, which report an average classification accuracy of only 85.7% at maximum. Moreover, in contrast to current methods our approach does not require subject matter expertise to generate training labels, and it can be automated and scaled.
C1 [Cauthen, Katherine] Sandia Natl Labs, 1515 Eubank Blvd SE, Albuquerque, NM 87123 USA.
   [Rai, Prashant; Ray, Jaideep] Sandia Natl Labs, 7011 East Ave, Livermore, CA 94550 USA.
   [Hale, Nicholas; Freeman, Laura] Virginia Tech Appl Res Corp, 900 N Glebe Rd, Arlington, VA 22203 USA.
   [Cauthen, Katherine] Sandia Natl Labs, 1515 Eubank Blvd SE,Mail Stop 1137, Albuquerque, NM 87123 USA.
C3 United States Department of Energy (DOE); Sandia National Laboratories;
   United States Department of Energy (DOE); Sandia National Laboratories;
   United States Department of Energy (DOE); Sandia National Laboratories
RP Cauthen, K (corresponding author), Sandia Natl Labs, 1515 Eubank Blvd SE,Mail Stop 1137, Albuquerque, NM 87123 USA.
EM kcauthe@sandia.gov; laura.freeman@vt.edu; jairay@sandia.gov
OI Ray, Jaideep/0009-0000-9908-7035
FU U.S. Department of Energys National Nuclear Security Administration
   [DE-NA0003525]; Army Research Laboratory, U.S. Army [W911NF-15-3-0001]
FX Sandia National Laboratories is a multimission laboratory managed and
   operated by National Technology & Engineering Solutions of San-dia, LLC,
   a wholly owned subsidiary of Honeywell International Inc., for the U.S.
   Department of Energys National Nuclear Security Administration under
   contract DE-NA0003525. This paper describes objective technical results
   and analysis. Any subjective views or opinions that might be expressed
   in the paper do not necessarily represent the views of the U.S.
   Department of Energy or the United States Government. Effort sponsored
   in whole or in part by the Army Research Laboratory, U.S. Army, under
   Partnership Intermediary Agreement No. W911NF-15-3-0001. The U.S.
   Government is authorized to reproduce and distribute reprints for
   Governmental purposes notwithstanding any copyright notation thereon.
   The views and conclusions contained herein are those of the authors and
   should not be interpreted as necessarily representing the official
   policies or endorsements, either expressed or implied, of the Army
   Research Laboratory.
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NR 39
TC 6
Z9 6
U1 4
U2 24
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0957-4174
EI 1873-6793
J9 EXPERT SYST APPL
JI Expert Syst. Appl.
PD SEP 1
PY 2022
VL 201
AR 117177
DI 10.1016/j.eswa.2022.117177
EA MAY 2022
PG 12
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering; Operations Research & Management Science
GA 1L1DO
UT WOS:000799032800001
OA Bronze
DA 2024-09-05
ER
PT J
AU Sonkor, MS
   de Soto, BG
AF Sonkor, Muammer Semih
   Garcia de Soto, Borja
TI Using ChatGPT in construction projects: unveiling its cybersecurity
   risks through a bibliometric analysis
SO INTERNATIONAL JOURNAL OF CONSTRUCTION MANAGEMENT
LA English
DT Article; Early Access
DE ChatGPT; construction 4.0; cybersecurity; large language model; machine
   learning
AB ChatGPT, a large language model chatbot by OpenAI, has increasingly become a part of employees' day-to-day activities in numerous industries, including construction, and researchers have looked into this tool since its first release in late 2022 to assist in different fields. One of the benefits of such tools can be related to improved efficiency; however, it raises data privacy and security concerns. Considering the increasing reliance on information technology and operational technology for enhanced productivity, accuracy and quality in projects, these concerns also affect the construction sector. This study presents an overview of the existing literature on the applications of ChatGPT in the construction sector, highlighting its potential to revolutionize various resource-intensive tasks in projects and the related cybersecurity risks. VOSviewer is used for bibliometric analysis of academic publications and to identify the relevant cybersecurity problems. The identified issues are categorized into three main groups and discussed in the context of construction applications. Suggestions are provided to address identified concerns. This paper highlights the importance of ensuring the secure deployment of ChatGPT in the construction sector, a subject that has not been explored in the existing literature.
C1 [Sonkor, Muammer Semih; Garcia de Soto, Borja] New York Univ Abu Dhabi NYUAD, Div Engn, SMART Construct Res Grp, Expt Res Bldg,Saadiyat Isl,POB 129188, Abu Dhabi, U Arab Emirates.
RP Sonkor, MS (corresponding author), New York Univ Abu Dhabi NYUAD, Div Engn, SMART Construct Res Grp, Expt Res Bldg,Saadiyat Isl,POB 129188, Abu Dhabi, U Arab Emirates.
EM semih.sonkor@nyu.edu
FU Center for Cyber Security (CCS) - Tamkeen under the NYUAD Research
   Institute Award [G1104]; Tamkeen under the NYUAD Research Institute
   Award [CG001]
FX This work was supported by the Center for Cyber Security (CCS), funded
   by Tamkeen under the NYUAD Research Institute Award G1104 and in
   collaboration with the NYUAD Center for Interacting Urban Networks
   (CITIES), funded by Tamkeen under the NYUAD Research Institute Award
   CG001.
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NR 66
TC 0
Z9 0
U1 10
U2 10
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 1562-3599
EI 2331-2327
J9 INT J CONSTR MANAG
JI Int. J. Constr. Manag.
PD 2024 MAY 15
PY 2024
DI 10.1080/15623599.2024.2355782
EA MAY 2024
PG 9
WC Construction & Building Technology; Engineering, Civil; Management
WE Emerging Sources Citation Index (ESCI)
SC Construction & Building Technology; Engineering; Business & Economics
GA ST5J1
UT WOS:001236707800001
OA hybrid
DA 2024-09-05
ER
PT J
AU Tattershall, E
   Nenadic, G
   Stevens, RD
AF Tattershall, E.
   Nenadic, G.
   Stevens, R. D.
TI Detecting bursty terms in computer science research
SO SCIENTOMETRICS
LA English
DT Article
DE Computer science; Bibliometrics; Term life cycles; Machine learning;
   DBLP; MACD
ID TOPICS
AB Research topics rise and fall in popularity over time, some more swiftly than others. The fastest rising topics are typically called bursts; for example "deep learning", "internet of things" and "big data". Being able to automatically detect and track bursty terms in the literature could give insight into how scientific thought evolves over time. In this paper, we take a trend detection algorithm from stock market analysis and apply it to over 30 years of computer science research abstracts, treating the prevalence of each term in the dataset like the price of a stock. Unlike previous work in this domain, we use the free text of abstracts and titles, resulting in a finer-grained analysis. We report a list of bursty terms, and then use historical data to build a classifier to predict whether they will rise or fall in popularity in the future, obtaining accuracy in the region of 80%. The proposed methodology can be applied to any time-ordered collection of text to yield past and present bursty terms and predict their probable fate.
C1 [Tattershall, E.; Nenadic, G.; Stevens, R. D.] Univ Manchester, Dept Comp Sci, Oxford Rd, Manchester M13 9PL, Lancs, England.
C3 University of Manchester
RP Tattershall, E (corresponding author), Univ Manchester, Dept Comp Sci, Oxford Rd, Manchester M13 9PL, Lancs, England.
EM emma.tattershall@postgrad.manchester.ac.uk
RI Tattershall, Emma/HGV-0693-2022
OI Tattershall, Emma/0000-0001-5616-4002
FU Manchester Centre for Doctoral Training in Computer Science
   [EP/I028099/1]
FX This research was supported by the Manchester Centre for Doctoral
   Training in Computer Science, EP/I028099/1
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NR 34
TC 13
Z9 15
U1 2
U2 51
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD JAN
PY 2020
VL 122
IS 1
BP 681
EP 699
DI 10.1007/s11192-019-03307-5
EA NOV 2019
PG 19
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA KQ7OX
UT WOS:000498738600001
OA hybrid
DA 2024-09-05
ER
PT J
AU Carballo-Meilan, A
   McDonald, L
   Pragot, W
   Starnawski, LM
   Saleemi, AN
   Afzal, W
AF Carballo-Meilan, Ara
   McDonald, Lewis
   Pragot, Wanawan
   Starnawski, Lukasz Michal
   Saleemi, Ali Nauman
   Afzal, Waheed
TI Development of a data-driven scientific methodology: From articles to
   chemometric data products
SO CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
LA English
DT Article
DE Scientific method; Data mining; Meta-methodology; Chemometrics;
   Scientometrics; Machine learning; Scientific method; Data mining;
   Meta-methodology; Chemometrics; Scientometrics; Machine learning
ID CLASSIFICATION
AB Information and data science algorithms were combined to predict the outcome of an experiment in chemical engineering. Using the Scientific Method workflow, we started the journey with the formulation of a specific question. At the research stage, the common process of querying and reading articles on scientific databases was substituted by a systematic review with a built-in recursive data mining method. This procedure identifies a specific community of knowledge with the key concepts and experiments that are necessary to address the formulated question. A small subset of relevant articles from a very specific topic among thousands of papers was identified while assuring the loss of the least amount of information through the process. The secondary dataset was bigger than a common individual study. The process revealed the main ideas currently under study and identified optimal synthesis conditions to produce a chemical substance. Once the research step was finished, the experimental information was compiled and prepared for metaanalysis using a supervised learning algorithm. This is a hypothesis generation stage whereby the secondary dataset was transformed into experimental knowledge about a particular chemical reaction. Finally, the predicted sets of optimal conditions to produce the desired chemical compound were validated in the laboratory.
C1 [Carballo-Meilan, Ara; McDonald, Lewis; Pragot, Wanawan; Starnawski, Lukasz Michal; Afzal, Waheed] Univ Aberdeen, Kings Coll, Sch Engn, Aberdeen AB24 3UE, Scotland.
   [Saleemi, Ali Nauman] GlaxoSmithKline, Stevenage SG1 2NY, Herts, England.
C3 University of Aberdeen; GlaxoSmithKline
RP Afzal, W (corresponding author), Univ Aberdeen, Kings Coll, Sch Engn, Aberdeen AB24 3UE, Scotland.
EM waheed@abdn.ac.uk
OI McDonald, Lewis/0000-0001-6185-7444; McDonald,
   Lewis/0000-0002-7635-4732; Afzal, Waheed/0000-0002-2927-0114
CR [Anonymous], 2011, Causality in the Sciences
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NR 53
TC 3
Z9 3
U1 0
U2 6
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0169-7439
EI 1873-3239
J9 CHEMOMETR INTELL LAB
JI Chemometrics Intell. Lab. Syst.
PD JUN 15
PY 2022
VL 225
AR 104555
DI 10.1016/j.chemolab.2022.104555
EA APR 2022
PG 13
WC Automation & Control Systems; Chemistry, Analytical; Computer Science,
   Artificial Intelligence; Instruments & Instrumentation; Mathematics,
   Interdisciplinary Applications; Statistics & Probability
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Automation & Control Systems; Chemistry; Computer Science; Instruments &
   Instrumentation; Mathematics
GA 1Q6BI
UT WOS:000802769800004
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Goodell, JW
   Kumar, S
   Li, X
   Pattnaik, D
   Sharma, A
AF Goodell, John W.
   Kumar, Satish
   Li, Xiao
   Pattnaik, Debidutta
   Sharma, Anuj
TI Foundations and research clusters in investor attention: Evidence from
   bibliometric and topic modelling analysis
SO INTERNATIONAL REVIEW OF ECONOMICS & FINANCE
LA English
DT Article
DE Investor attention; Systematic literature review; Bibliometric analysis;
   Machine learning; Structural topic modelling; Cluster analysis
ID LIMITED ATTENTION; EARNINGS ANNOUNCEMENTS; CROSS-SECTION; SEARCH;
   RETURNS; PREDICT; RETAIL; NEWS; UNDERREACTION; INFORMATION
AB Investor attention is an emergent area in financial scholarship. However, no review till date offers a comprehensive retrospection of this research domain. To address this gap, we present an overview of investor-attention research in finance. Using both co-citation and structural topic modelling analyses, we infer the knowledge and thematic structure of investor-attention research for 1994-2021. By uncovering four co-citation clusters and eight specific topics, we conclude that investor-attention research in the areas of "underreaction to earnings announcements", "sector and financial risk", and "investors' attention and firm disclosures" is likely to soon expand rapidly. Our survey also identifies the most prominent scholars, author affiliations, and scientific outlets publishing on investor-attention research topics. We also explore the various theories examined or discussed in investor-attention scholarship, as well as identify research gaps.
C1 [Goodell, John W.] Univ Akron, Coll Business, 302 Buchtel Common, Akron, OH 44325 USA.
   [Kumar, Satish] Malaviya Natl Inst Technol Jaipur, Jaipur, Rajasthan, India.
   [Li, Xiao] Nankai Univ, Sch Finance, Tianjin, Peoples R China.
   [Li, Xiao] Nankai Univ, Inst Digital Finance, Tianjin, Peoples R China.
   [Pattnaik, Debidutta] Int Management Inst, Bhubaneswar, India.
   [Sharma, Anuj] Chandragupt Inst Management Patna, Patna, Bihar, India.
   [Kumar, Satish] Swinbume Univ Technol, Fac Business Design & Arts, Sarawak, Malaysia.
C3 University System of Ohio; University of Akron; National Institute of
   Technology (NIT System); Malaviya National Institute of Technology
   Jaipur; Nankai University; Nankai University; International Management
   Institute (IMI) Bhubaneswar
RP Goodell, JW (corresponding author), Univ Akron, Coll Business, 302 Buchtel Common, Akron, OH 44325 USA.
EM johngoo@uakron.edu; skumar.dms@mnit.ac.in; xiaoli@nankai.edu.cn;
   2018rbm9086@mnit.ac.in; anuj@cimp.ac.in
RI Pattnaik, Debidutta/Q-2125-2019; Kumar, Satish/M-8694-2017; Kumar,
   Satish/E-2103-2018; Sharma, Anuj/JTS-4887-2023; Pattnaik,
   Debidutta/GWU-6164-2022
OI Pattnaik, Debidutta/0000-0001-6180-0499; Kumar,
   Satish/0000-0001-5200-1476; Kumar, Satish/0000-0001-6788-0952; Sharma,
   Anuj/0000-0002-6281-6115; Sharma, Anuj/0000-0001-6602-9285; Goodell,
   John W./0000-0003-4126-9244
FU National Natural Science Foundation of China [72171125]; Fundamental
   Research Funds for the Central Universities [63222068]
FX This work is supported by the National Natural Science Foundation of
   China (72171125) and the Fundamental Research Funds for the Central
   Universities (63222068).
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NR 125
TC 18
Z9 18
U1 9
U2 47
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1059-0560
EI 1873-8036
J9 INT REV ECON FINANC
JI Int. Rev. Econ. Financ.
PD NOV
PY 2022
VL 82
BP 511
EP 529
DI 10.1016/j.iref.2022.06.020
EA JUL 2022
PG 19
WC Business, Finance; Economics
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 4L8TR
UT WOS:000852903200030
DA 2024-09-05
ER
PT C
AU Makarov, I
   Gerasimova, O
AF Makarov, Ilya
   Gerasimova, Olga
GP IEEE
TI Predicting Collaborations in Co-authorship Network
SO 2019 14TH INTERNATIONAL WORKSHOP ON SEMANTIC AND SOCIAL MEDIA ADAPTATION
   AND PERSONALIZATION (SMAP)
LA English
DT Proceedings Paper
CT 14th International Workshop on Semantic and Social Media Adaptation and
   Personalization (SMAP)
CY JUN 09-SEP 10, 2019
CL Larnaka, CYPRUS
DE Co-authorship Networks; Co-occurrence Networks; Recommender Systems;
   Network Embedding; Link Prediction; Machine Learning
ID LINK-PREDICTION
AB In this paper, we study the problem of predicting collaborations in co-authorship network. We formulated our task in terms of link prediction problem on weighted co-authorship network, in which authors play the role of nodes, and weighted edges connecting two authors are formed by storing either a number or quality metric of research papers co-authored by these authors. Our task is then formulated as regression machine learning model based on network features constructed using network embedding. We evaluate our edge embeddings on large AMiner co-authorship network for (un)weighted node2vec network embeddings and also on the dataset containing temporal information on National Research University Higher School of Economics (HSE) over twenty five years of research articles indexed in Russian Science Citation Index and Scopus for predicting the quality of future research publications measures in terms of quartiles corresponding to published journals indexed in Scopus. We showed that our model of network edge representation has better performance for stated regression task on both, AMiner and HSE co-authorship networks.
C1 [Makarov, Ilya] Univ Ljubljana, Fac Comp & Informat Sci, Vena Pot 113, SI-1000 Ljubljana, Slovenia.
   [Gerasimova, Olga] Natl Res Univ Higher Sch Econ, Sch Data Anal & Artificial Intelligence, 3 Kochnovskiy Proezd, Moscow 125319, Russia.
C3 University of Ljubljana; HSE University (National Research University
   Higher School of Economics)
RP Makarov, I (corresponding author), Univ Ljubljana, Fac Comp & Informat Sci, Vena Pot 113, SI-1000 Ljubljana, Slovenia.
EM iamakarov@hse.ru; ogerasimova@hse.ru
RI Gerasimova, Olga/P-1560-2016; Makarov, Ilya/ABC-6521-2021
OI Makarov, Ilya/0000-0002-3308-8825
FU Russian Science Foundation [17-11-01294] Funding Source: Russian Science
   Foundation
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NR 63
TC 8
Z9 8
U1 0
U2 0
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-7281-3634-9
PY 2019
BP 72
EP 77
PG 6
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BS3KM
UT WOS:000712164100014
DA 2024-09-05
ER
PT J
AU Natarajan, R
   Verma, MK
   Muthuraj, S
AF Natarajan, Rajkumar
   Verma, Manoj Kumar
   Muthuraj, Surulinathi
TI Mapping the Global Academic Support for Sustainable Development Goal 7:
   A Bibliometric Analysis and Topic Modelling Approach
SO JOURNAL OF SCIENTOMETRIC RESEARCH
LA English
DT Article
DE SDG 7; Affordable and Clean Energy; Scientometrics; Text Analysis;
   Machine Learning; Latent Semantic Analysis
ID ENERGY; SCIENCE
AB The Sustainable Development Goal 7 (SDG-7) promises to ensure the affordable and clean energy to the world. The United Nations (UN) has set a target for 2030, which can only be achieved through academic excellence. The present study aims to analyze the academic research support of SDG 7 from a global perspective by using bibliometric analysis and topic modelling approaches using Orange Python -based software. The present study extracts the scholarly publications from the lens database from 2015 to 2022 and the dataset consisted of 918 publications with 18,377 citations related to the SDG 7. These including 121 single -author and 797 multiple -authors publications. Most of the papers have been published in open -access journals. Environmental Science and Pollution Research International (5343 citations; 225 publications and CPP 23.74) was the most impactful journal, Muntasir Murshed (13 publications, 421 citations, CPP 32.3) was the most influential author, and China was the most productive country. Under co -occurrence analysis, Clean Energy, Environmental Economics, Health, Affordable Energy, Climate Change, and Business, six different denoted clusters were found, while in the topic modeling approach, six key topics were identified, in which three topics were related to economics and the other were energy -related and climate change. Environmental, renewable energy, and economics were the top words used in SDG 7, and six key documents on each topic were identified according to the distribution and weighting of the topics. The Implications of the research findings and addressing research gaps can inform researchers, policymakers, and funding agencies involved in advancing SDG 7 to help accelerate the achievement of the SDGs in the decision -making process.
C1 [Natarajan, Rajkumar; Verma, Manoj Kumar; Muthuraj, Surulinathi] Bharathidasan Univ, Dept Lib & Informat Sci, Tiruchirappalli, Tamil Nadu, India.
   [Natarajan, Rajkumar] Mizoram Univ, Dept Lib & Informat Sci, Mizoram, India.
C3 Bharathidasan University; Mizoram University
RP Natarajan, R (corresponding author), Bharathidasan Univ, Dept Lib & Informat Sci, Tiruchirappalli, Tamil Nadu, India.
EM rajkumarnataraj19@gmail.com
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NR 71
TC 0
Z9 0
U1 2
U2 2
PU PHCOG NET
PI KARNATAKA
PA 17, 2ND FLR, BUDDHA VIHAR RD, NEAR SPORTS ZONE, COX TOWN, BENGALURU,
   KARNATAKA, 560005, INDIA
SN 2321-6654
EI 2320-0057
J9 J SCIENTOMETR RES
JI J. Scientometr. Res.
PD JAN-APR
PY 2024
VL 13
IS 1
BP 285
EP 297
DI 10.5530/jscires.13.1.24
PG 13
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA TZ7J6
UT WOS:001245143700024
OA hybrid
DA 2024-09-05
ER
PT J
AU Luong, DH
   Nguyen, XA
   Ngo, TT
   Tran, MN
   Nguyen, HL
AF Dinh-Hai Luong
   Xuan-An Nguyen
   Thanh-Thuy Ngo
   My-Ngoc Tran
   Hong-Lien Nguyen
TI Social Media in General Education: A Bibliometric Analysis of Web of
   Science from 2005-2021
SO JOURNAL OF SCIENTOMETRIC RESEARCH
LA English
DT Article
DE COVID-19; ICT integration; Teachers' beliefs; Online learning; Distance
   learning; Social; media; General education.
ID PROFESSIONAL-DEVELOPMENT; PRIOR-KNOWLEDGE; TEACHERS; STUDENTS; FACEBOOK;
   BELIEFS; IMPACT; ONLINE; SITES; TOOL
AB Social media plays an increasingly important role in school activities. The study analysised 2,122 eligibility bibliographic records from 2005 to 2021 were extracted from the Web of Science database. This study employs a bibliometric method to analyze the use of social media on K-12 education worldwide. We concerned the following issues: the annual publication of Social Media in General Education (SMGE), the main characteristics of the SMGE research community, the primary sources in the field, the leading research themes and the new research topics in the field of SMGE. The results represented an annual growth trend of 17.15%. Countries with the highest number of publications were the US, England, Australia, China, and Turkey. The research community consisted of small groups; and Valcke M from the University of Ghent (Belgium) was one of the leading authors with large number of publications and citations. Sources focused on four scopes: Language Education, Educational Technology, Teacher and Teaching Education, Science Education. Furthermore, six themes were developed: SMGE's environment, ICT integration, teachers' beliefs and teaching practice, students' learning, teachers' motivation and engagement, SMGE's learning approach. Two prominent topics were COVID-19-related, online and distance learning. The findings represent the basic information of the SMGE knowledge base considered as a source of reference for teachers, school managers, and policymakers interested in SMGE research and suggest further research directions.
C1 [Dinh-Hai Luong; Xuan-An Nguyen; Thanh-Thuy Ngo; My-Ngoc Tran; Hong-Lien Nguyen] Vietnam Natl Inst Educ Sci, Hanoi 100000, Vietnam.
RP Nguyen, HL (corresponding author), Vietnam Natl Inst Educ Sci, Hanoi 100000, Vietnam.
EM honglien2601@gmail.com
RI Luong, Dinh-Hai/AAH-7107-2021
OI Luong, Dinh-Hai/0000-0003-0167-2645
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NR 92
TC 1
Z9 1
U1 0
U2 0
PU PHCOG NET
PI KARNATAKA
PA 17, 2ND FLR, BUDDHA VIHAR RD, NEAR SPORTS ZONE, COX TOWN, BENGALURU,
   KARNATAKA, 560005, INDIA
SN 2321-6654
EI 2320-0057
J9 J SCIENTOMETR RES
JI J. Scientometr. Res.
PD SEP-DEC
PY 2023
VL 12
IS 3
BP 680
EP 690
DI 10.5530/jscires.12.3.066
PG 11
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA JE8W2
UT WOS:001171589100017
OA hybrid
DA 2024-09-05
ER
PT J
AU Barrot, JS
AF Barrot, Jessie S.
TI Scientific Mapping of Social Media in Education: A Decade of Exponential
   Growth
SO JOURNAL OF EDUCATIONAL COMPUTING RESEARCH
LA English
DT Article
DE bibliometric analysis; informal learning; media in education; online
   learning; social media; technology-enhanced learning
ID RESEARCH PERFORMANCE; H-INDEX; FACEBOOK; SCHOOL; WEB; LEADERSHIP;
   INDICATORS; UNIVERSITY; STUDENTS
AB Given the increasing number of research on social media for educational purposes, few studies have examined the scientific literature in this field of interest. However, reviews that comprehensively mapped this research landscape in a broader view remain very limited. It is on this premise that the current study identifies the growth trajectory, distribution, and topical foci of scientific literature on social media in education published between 2007 and 2019. A total of 2,215 documents from Scopus-indexed journals were analysed. Using a bibliometric approach, the findings show a steady growth of scientific output and citations and the expansion of the topical foci in the past decade. Of the 15 examined social media platforms, Facebook, Twitter, and YouTube have attracted the greatest attention, while the rest remained underexplored or unexplored. The popularity of certain platforms among scholars was attributed to three factors: the number of active users, the pedagogical affordances, and the geographical scope. Implications for future studies are discussed.
C1 [Barrot, Jessie S.] Natl Univ, Coll Educ Arts & Sci, Manila, Philippines.
C3 National University Philippines
RP Barrot, JS (corresponding author), Natl Univ, 551 MF Jhocson St, Manila 1008, Philippines.
EM jessiebarrot@yahoo.com
RI Barrot, Jessie/AAE-8566-2020
OI Barrot, Jessie/0000-0001-8517-4058
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NR 72
TC 36
Z9 37
U1 3
U2 38
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 0735-6331
EI 1541-4140
J9 J EDUC COMPUT RES
JI J. Educ. Comput. Res.
PD JUL
PY 2021
VL 59
IS 4
BP 645
EP 668
AR 0735633120972010
DI 10.1177/0735633120972010
EA NOV 2020
PG 24
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA RZ5LI
UT WOS:000600152400001
DA 2024-09-05
ER
PT J
AU Coavoux, S
   Boutet, M
   Zabban, V
AF Coavoux, Samuel
   Boutet, Manuel
   Zabban, Vinciane
TI What We Know About Games: A Scientometric Approach to Game Studies in
   the 2000s
SO GAMES AND CULTURE
LA English
DT Article
DE topic modeling; lexicometric analysis; multidisciplinarity; sociology of
   science; epistemic culture; game studies; path dependence; trading zone;
   World of Warcraft (WoW); interdisciplinarity
AB This article proposes a reflexive approach on the scientific production in the field of game studies in recent years. It relies on a sociology of science perspective to answer the question: What are game studies really about? Relying on scientometric and lexicometric tools, we analyze the metadata and content of a corpus of articles from the journals Games Studies and Games & Culture and of Digital Games Research Association (DiGRA) proceedings. We show that published researches have been studying only a limited set of game genres and that they especially focus on online games. We then expose the different ways game studies are talking about games through a topic model analysis of our corpus. We test two hypotheses to explain the concentration of research on singular objects: path dependence and trading zone. We describe integrative properties of the focus on common objects but stress also the scientific limits met by this tendency.
C1 [Coavoux, Samuel] Ecole Normale Super Lyon, Ctr Max Weber, Lyon, France.
   [Coavoux, Samuel] Ecole Normale Super, Dept Sci Sociales, Paris, France.
   [Boutet, Manuel] Univ Cote dAzur, GREDEG Lab, Nice, France.
   [Zabban, Vinciane] Paris 13 Univ, EXPERICE Lab, Villetaneuse, France.
C3 Centre National de la Recherche Scientifique (CNRS); Ecole Normale
   Superieure de Lyon (ENS de LYON); Universite Jean Monnet; Universite
   Lyon 2; Universite PSL; Ecole Normale Superieure (ENS); Universite Cote
   d'Azur
RP Boutet, M (corresponding author), Maison Sci Homme & Soc Sud Est, 24 Ave Diables Bleus,Pole Univ St Jean dAngely 3, F-06357 Nice, France.
EM manuel.boutet@unice.fr
RI Coavoux, Samuel/AAV-5125-2021
OI Coavoux, Samuel/0000-0001-7991-3555
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NR 25
TC 13
Z9 14
U1 1
U2 30
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 1555-4120
EI 1555-4139
J9 GAMES CULT
JI Games Cult.
PD SEP
PY 2017
VL 12
IS 6
SI SI
BP 563
EP 584
DI 10.1177/1555412016676661
PG 22
WC Cultural Studies; Communication
WE Social Science Citation Index (SSCI); Arts &amp; Humanities Citation Index (A&amp;HCI)
SC Cultural Studies; Communication
GA FD8YG
UT WOS:000407809400005
OA Green Submitted
DA 2024-09-05
ER
PT C
AU Bunga, M
   Joshi, S
AF Bunga, Manisha
   Joshi, Sujata
GP IEEE
TI A Bibliometric Analysis of Blockchain and its applications in IOT and ML
   for Improved Decision Making
SO 2022 INTERNATIONAL CONFERENCE ON DECISION AID SCIENCES AND APPLICATIONS
   (DASA)
LA English
DT Proceedings Paper
CT International Conference on Decision Aid Sciences and Applications
   (DASA)
CY MAR 23-25, 2022
CL Chiangrai, THAILAND
DE Blockchain; Banking; Decision Making; Security; Supplychain; Internet of
   Things; Machine Learning
ID INTERNET; THINGS; DIRECTIONS; CHALLENGES
AB The fundamental goal of this research is to examine the evolution of Blockchain technology, particularly in the areas of IoT and machine learning. Decision-making has become more complex in the modern era as there are more options to pick from. This research explains how blockchain is used in the decision-making process. Blockchain stores the data where it is highly impossible to hack or edit this data. The research methodology used is Bibliometric Analysis. Bibliometric analysis is the analysis of books, articles, and other publications using statistical methods. There were a total of 18,978 publications available in Scopus from 2012 to 2021 on Blockchain. There were a total of 18,994 publications on Blockchain overall. Country-wise, year-wise, topic-wise, journal-wise, institution-wise and research field-wise analysis is done in this study. By the obtained data, the performance of Blockchain will be analysed. According to the study, Blockchain has a wide range of applications in IoT and ML, all of which improve decision-making. This analysis provides valuable insights and adds benefit for researchers to understand the overall development of Blockchain.
C1 [Bunga, Manisha; Joshi, Sujata] Symbiosis Inst Digital & Telecom Management, Pune, Maharashtra, India.
C3 Symbiosis International University; Symbiosis Institute of Digital &
   Telecom Management (SIDTM)
RP Bunga, M (corresponding author), Symbiosis Inst Digital & Telecom Management, Pune, Maharashtra, India.
EM bunga.manisha2022@sidtm.edu.in; sjoshi@sidtm.edu.in
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NR 30
TC 1
Z9 1
U1 0
U2 11
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-6654-9501-1
PY 2022
BP 416
EP 420
DI 10.1109/DASA54658.2022.9764994
PG 5
WC Computer Science, Artificial Intelligence; Operations Research &
   Management Science
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Operations Research & Management Science
GA BT5YM
UT WOS:000839386600221
DA 2024-09-05
ER
PT J
AU Chen, CF
   Li, Q
   Deng, ZQ
   Chiu, K
   Wang, P
AF Chen, Chuanfu
   Li, Qiao
   Deng, Zhiqing
   Chiu, Kuei
   Wang, Ping
TI The preferences of Chinese LIS journal articles in citing works outside
   the discipline
SO JOURNAL OF DOCUMENTATION
LA English
DT Article
DE Bibliometrics; Interdisciplinary studies; Machine learning; Chinese LIS;
   Citation preference; Knowledge import
ID INFORMATION-SCIENCE; CITATION ANALYSIS; KNOWLEDGE DISCOVERY;
   SUBJECT-MATTER; LIBRARY; INTERDISCIPLINARITY; CLASSIFICATION; SYSTEM;
   COMMUNICATION; EVOLUTION
AB Purpose - The purpose of this paper is to understand how Chinese library and information science (LIS) journal articles cite works from outside the discipline (WOD) to identify the impact of knowledge import from outside the discipline on LIS development.
   Design/methodology/approach - This paper explores the Chinese LIS' preferences in citing WOD by employing bibliometrics and machine learning techniques.
   Findings - Chinese LIS citations to WOD account for 29.69 percent of all citations, and they rise over time. Computer science, education and communication are the most frequently cited disciplines. Under the categorization of Biglan model, Chinese LIS prefers to cite WOD from soft science, applied science or nonlife science. In terms of community affiliation, the cited authors are mostly from the academic community, but rarely from the practice community. Mass media has always been a citation source that is hard to ignore. There is a strong interest of Chinese LIS in citing emerging topics.
   Practical implications - This paper can be implemented in the reformulation of Chinese LIS knowledge system, the promotion of interdisciplinary collaboration, the development of LIS library collection and faculty advancement. It may also be used as a reference to develop strategies for the global LIS.
   Originality/value - This paper fills the research gap in analyzing citations to WOD from Chinese LIS articles and their impacts on LIS, and recommends that Chinese LIS should emphasize on knowledge both on technology and people as well as knowledge from the practice community, cooperate with partners from other fields, thus to produce knowledge meeting the demands from library and information practice as well as users.
C1 [Chen, Chuanfu; Li, Qiao; Deng, Zhiqing] Wuhan Univ, Sch Informat Management, Dept Lib Sci, Wuhan, Hubei, Peoples R China.
   [Chiu, Kuei] Univ Calif Riverside, Univ Lib, Riverside, CA 92521 USA.
   [Wang, Ping] Wuhan Univ, Sch Informat Management, Dept Arch & Govt Informat Management, Wuhan, Hubei, Peoples R China.
C3 Wuhan University; University of California System; University of
   California Riverside; Wuhan University
RP Chen, CF (corresponding author), Wuhan Univ, Sch Informat Management, Dept Lib Sci, Wuhan, Hubei, Peoples R China.
EM cfchen@whu.edu.cn
RI lan, xueyao/JZD-4201-2024
OI chen, chuan fu/0000-0003-1163-7691; Li, Qiao/0000-0002-9265-8808
FU National Natural Science Foundation of China [91546124]
FX The authors would like to acknowledge reviewers and all participants for
   their contribution to the improvement of this study. This study is
   supported by a grant from the National Natural Science Foundation of
   China under the agreement 91546124.
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NR 88
TC 11
Z9 12
U1 4
U2 100
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 0022-0418
EI 1758-7379
J9 J DOC
JI J. Doc.
PY 2018
VL 74
IS 1
BP 99
EP 118
DI 10.1108/JD-04-2017-0057
PG 20
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA FR6QP
UT WOS:000419191300006
DA 2024-09-05
ER
PT J
AU Klarin, A
   Inkizhinov, B
   Nazarov, D
   Gorenskaia, E
AF Klarin, Anton
   Inkizhinov, Boris
   Nazarov, Dashi
   Gorenskaia, Elena
TI International business education: What we know and what we have yet to
   develop
SO INTERNATIONAL BUSINESS REVIEW
LA English
DT Article
DE International business education; Curriculum; Systems research;
   Systematic review; Scientometrics; Bibliometric mapping; COVID-19;
   Business schools; Language; Online learning
ID CORPORATE SOCIAL-RESPONSIBILITY; ENTREPRENEURSHIP EDUCATION; ACADEMIC
   LIFE; MANAGEMENT; STUDENTS; MBA; KNOWLEDGE; LANGUAGE; PEDAGOGY; CULTURE
AB International business education (IBE) scholarship is extensive and is continuously growing. Nevertheless, to date there is no systems perspective overview of the literature dedicated to this topic. Using latest advancements in scientometric analysis, this study structures and visualizes the entire IBE scholarship, which allows to identify gaps in research and propose a number of future research directions. Data extracted from 894 peer-reviewed documents made available through the Scopus database allows to map the scholarship across five identified research directions in IBE - IB, political economy environment, and education; student learning and experience; the lingua franca and communication; interrelationship of IBE and the ecosystem; and business school curricula and internationalization. The scholarship was also compared to the Academy of Management Learning and Education and to the Journal of International Business Studies together with the Journal of World Business journal scholarships to recommend further prospective directions for the future development of IBE.
C1 [Klarin, Anton] Edith Cowan Univ, Sch Business & Law, 270 Joondalup Dr, Joondalup, WA 6027, Australia.
   [Inkizhinov, Boris] Univ Surrey, Guildford GU2 7XH, Surrey, England.
   [Nazarov, Dashi] Queen Mary Univ, London, England.
   [Gorenskaia, Elena] Russian Acad Sci, Siberian Branch, Baikal Inst Nat Management, Ulan Ude 670047, Russia.
C3 Edith Cowan University; University of Surrey; University of London;
   Queen Mary University London; Russian Academy of Sciences; Baikal
   Institute of Nature Management SB RAS
RP Klarin, A (corresponding author), Edith Cowan Univ, Sch Business & Law, 270 Joondalup Dr, Joondalup, WA 6027, Australia.
EM a.klarin@ecu.edu.au
RI Klarin, Anton/AAB-3031-2019; Klarin, Anton/IQS-6054-2023
OI Klarin, Anton/0000-0002-5597-4027; Klarin, Anton/0000-0002-5597-4027
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NR 160
TC 21
Z9 22
U1 4
U2 50
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0969-5931
EI 1873-6149
J9 INT BUS REV
JI Int. Bus. Rev.
PD OCT
PY 2021
VL 30
IS 5
AR 101833
DI 10.1016/j.ibusrev.2021.101833
EA AUG 2021
PG 15
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA UD4VR
UT WOS:000687206200009
OA Green Published, hybrid
DA 2024-09-05
ER
PT J
AU Gowanlock, M
   Gazan, R
AF Gowanlock, Michael
   Gazan, Rich
TI Assessing researcher interdisciplinarity: a case study of the University
   of Hawaii NASA Astrobiology Institute
SO SCIENTOMETRICS
LA English
DT Article
DE Astrobiology; Bibliometrics; Information bottleneck method;
   Interdisciplinary science; Machine learning; Text mining
ID SCIENCE; CITATION
AB In this study, we combine bibliometric techniques with a machine learning algorithm, the sequential information bottleneck, to assess the interdisciplinarity of research produced by the University of Hawaii NASA Astrobiology Institute (UHNAI). In particular, we cluster abstract data to evaluate Thomson Reuters Web of Knowledge subject categories as descriptive labels for astrobiology documents, assess individual researcher interdisciplinarity, and determine where collaboration opportunities might occur. We find that the majority of the UHNAI team is engaged in interdisciplinary research, and suggest that our method could be applied to additional NASA Astrobiology Institute teams in particular, or other interdisciplinary research teams more broadly, to identify and facilitate collaboration opportunities.
C1 [Gowanlock, Michael; Gazan, Rich] Univ Hawaii, Dept Informat & Comp Sci, NASA Astrobiol Inst, Lib & Informat Sci Program, Honolulu, HI 96822 USA.
C3 University of Hawaii System; National Aeronautics & Space Administration
   (NASA)
RP Gowanlock, M (corresponding author), Univ Hawaii, Dept Informat & Comp Sci, NASA Astrobiol Inst, Lib & Informat Sci Program, POST 310,East West Rd, Honolulu, HI 96822 USA.
EM gowanloc@hawaii.edu
OI Gazan, Rich/0000-0003-0741-9050
FU National Aeronautics and Space Administration through NASA Astrobiology
   Institute through Office of Space Science [NNA08DA77A]
FX We thank David Schanzenbach for devising scripts, and Mahdi Belcaid and
   the anonymous reviewers for insightful comments. This material is based
   upon work supported by the National Aeronautics and Space Administration
   through the NASA Astrobiology Institute under Cooperative Agreement No.
   NNA08DA77A issued through the Office of Space Science.
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   Zhang L, 2010, J INFORMETR, V4, P185, DOI 10.1016/j.joi.2009.11.005
NR 31
TC 19
Z9 23
U1 1
U2 94
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD JAN
PY 2013
VL 94
IS 1
BP 133
EP 161
DI 10.1007/s11192-012-0765-y
PG 29
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 063RA
UT WOS:000313016300008
DA 2024-09-05
ER
PT J
AU Berninger, M
   Kiesel, F
   Schiereck, D
   Gaar, E
AF Berninger, Marc
   Kiesel, Florian
   Schiereck, Dirk
   Gaar, Eduard
TI Citations and the readers' information-extracting costs of finance
   articles
SO JOURNAL OF BANKING & FINANCE
LA English
DT Article
DE Information extracting costs; Readability; Textual analysis; Finance
   literature; Machine learning; Scientometrics
ID CO-AUTHORSHIP; READABILITY; IMPACT; PRODUCTIVITY; DETERMINANTS;
   INSTITUTIONS; ABSTRACTS; JOURNALS; NUMBER
AB This paper focuses on the relationship between the reader's information-extracting costs of finance articles and the article's number of citations. The reader's information-extracting costs are measured using three metrics: (i) the Flesch-Kincaid readability score, ( ii ) the article's length, and ( iii ) the number of complex words. Based on a sample of more than 14,0 0 0 full text articles published between 20 0 0 and 2016 in 16 finance journals, we show that the information-extracting costs of finance journals have significantly increased over time, while the topics of these articles, determined by machine-learning topic modeling, remained relatively constant. We find a positive correlation between the reader's information extracting costs and the number of citations achieved by a paper for articles that are published in the top-three finance journals (JF, JFE, RFS), but do not observe this pattern for articles published in other major finance journals. (c) 2021 Elsevier B.V. All rights reserved.
C1 [Berninger, Marc; Schiereck, Dirk; Gaar, Eduard] Tech Univ Darmstadt, Dept Business Adm Econ & Law, D-64289 Darmstadt, Germany.
   [Kiesel, Florian] Grenoble Ecole Management, F-38000 Grenoble, France.
C3 Technical University of Darmstadt; Grenoble Ecole Management
RP Berninger, M (corresponding author), Tech Univ Darmstadt, Dept Business Adm Econ & Law, D-64289 Darmstadt, Germany.
EM berninger@bwl.tu-darmstadt.de
OI Gaar, Eduard/0000-0003-2499-3855; Kiesel, Florian/0000-0001-7380-5190
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NR 60
TC 4
Z9 4
U1 3
U2 23
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0378-4266
EI 1872-6372
J9 J BANK FINANC
JI J. Bank Financ.
PD OCT
PY 2021
VL 131
AR 106188
DI 10.1016/j.jbankfin.2021.106188
EA JUN 2021
PG 34
WC Business, Finance; Economics
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA TC8YY
UT WOS:000668925400003
DA 2024-09-05
ER
PT S
AU Umeki, H
   Takase, H
AF Umeki, H.
   Takase, H.
BE Ahn, J
   Apted, MJ
TI Application of knowledge management systems for safe geological disposal
   of radioactive waste
SO GEOLOGICAL REPOSITORY SYSTEMS FOR SAFE DISPOSAL OF SPENT NUCLEAR FUELS
   AND RADIOACTIVE WASTE
SE Woodhead Publishing Series in Energy
LA English
DT Article; Book Chapter
DE knowledge management system (KMS); knowledge engineering (KE); strategic
   environmental assessment (SEA); research and development R&D; quality
   management system (QMS); argumentation model (AM); knowledge acquisition
   design system (KADS); artificial neural network (ANN)
AB Information overload caused by the rate at which data can be produced and the ease with which it can be accessed poses a significant challenge to those charged with maintaining an overview of large, complex, multidisciplinary projects. Geological disposal of long-lived radioactive waste is a technical area characterised by a breadth of multidisciplinary knowledge wider than almost any other industry. The particular challenges for radwaste require the development of a system that pushes the current limits of information technology (IT). This chapter illustrates an approach to the problem that focuses on a formal knowledge management system (KMS) and associated knowledge base that utilise state-of-the-art tools, developed in the field of knowledge engineering (KE) and IT. It considers whether advanced KM tools can contribute to the development, review, communication and control of the iterative evolution of safety cases.
C1 [Umeki, H.] Japan Atom Energy Agcy, Chiyoda Ku, Tokyo 1008577, Japan.
   [Takase, H.] Quintessa KK, Nishi Ku, Yokohama, Kanagawa 2206007, Japan.
C3 Japan Atomic Energy Agency
RP Umeki, H (corresponding author), Japan Atom Energy Agcy, Chiyoda Ku, 2-1-8 Uchisaiwaicho, Tokyo 1008577, Japan.
EM umeki.hiroyuki@jaea.go.jp; htakase@quintessa.co.jp
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NR 40
TC 1
Z9 1
U1 0
U2 0
PU WOODHEAD PUBL LTD
PI CAMBRIDGE
PA ABINGTON HALL ABINGTON, CAMBRIDGE CB1 6AH, CAMBS, ENGLAND
SN 2044-9372
EI 2044-9364
BN 978-1-84569-978-9; 978-1-84569-542-2
J9 WOODHEAD PUBL SER EN
PY 2010
IS 9
BP 610
EP 638
D2 10.1533/9781845699789
PG 29
WC Engineering, Environmental; Nuclear Science & Technology
WE Book Citation Index – Science (BKCI-S)
SC Engineering; Nuclear Science & Technology
GA BRQ28
UT WOS:000283424700020
DA 2024-09-05
ER
PT J
AU Chai, S
   D'Amour, A
   Fleming, L
AF Chai, Sen
   D'Amour, Alexander
   Fleming, Lee
TI Explaining and predicting the impact of authors within a community: an
   assessment of the bibliometric literature and application of machine
   learning
SO INDUSTRIAL AND CORPORATE CHANGE
LA English
DT Article
ID KNOWLEDGE; INNOVATION; NETWORKS; PRODUCTIVITY; MARGINALITY; COMPETITION;
   STRENGTH; CREATION; RETURNS; QUALITY
AB Following widespread availability of computerized databases, much research has correlated biblio-metric measures from papers or patents to subsequent success, typically measured as the number of publications or citations. Building on this large body of work, we ask the following questions: given available bibliometric information in one year, along with the combined theories on sources of creative breakthroughs from the literatures on creativity and innovation, how accurately can we explain the impact of authors in a given research community in the following year? In particular, who is most likely to publish, publish highly cited work, and even publish a highly cited outlier? And, how accurately can these existing theories predict breakthroughs using only contemporaneous data? After reviewing and synthesizing (often competing) theories from the literatures, we simultaneously model the collective hypotheses based on available data in the year before RNA interference was discovered. We operationalize author impact using publication count, forward citations, and the more stringent definition of being in the top decile of the citation distribution. Explanatory power of current theories altogether ranges from less than 9% for being top cited to 24% for productivity. Machine learning (ML) methods yield similar findings as the explanatory linear models, and tangible improvement only for non-linear Support Vector Machine models. We also perform predictions using only existing data until 1997, and find lower predictability than using explanatory models. We conclude with an agenda for future progress in the bibliometric study of creativity and look forward to ML research that can explain its models.
C1 [Chai, Sen] ESSEC Business Sch, Dept Management, F-95021 Cergy Pontoise, France.
   [D'Amour, Alexander] Univ Calif Berkeley, Dept Stat, Berkeley, CA 94720 USA.
   [Fleming, Lee] Univ Calif Berkeley, Coll Engn, Coleman Fung Inst Engn Leadership, Berkeley, CA 94720 USA.
C3 ESSEC Business School; University of California System; University of
   California Berkeley; University of California System; University of
   California Berkeley
RP Chai, S (corresponding author), ESSEC Business Sch, Dept Management, F-95021 Cergy Pontoise, France.
EM chai@esse-c.edu; alex-damour@berkeley.edu; lfleming@ieor.berkeley.edu
RI Chai, Sen/JYP-5197-2024
OI Chai, Sen/0000-0003-1243-3404
FU ESSEC Business School; NSF [1536022]; Coleman Fung Institute for
   Engineering Leadership; SBE Off Of Multidisciplinary Activities; Direct
   For Social, Behav & Economic Scie [1536022] Funding Source: National
   Science Foundation
FX The authors gratefully acknowledge support from the ESSEC Business
   School, NSF grant 1536022, and the Coleman Fung Institute for
   Engineering Leadership. All opinions and errors remain the authors'.
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NR 47
TC 1
Z9 1
U1 1
U2 26
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 0960-6491
EI 1464-3650
J9 IND CORP CHANGE
JI Ind. Corp. Change
PD FEB
PY 2020
VL 29
IS 1
BP 61
EP 80
DI 10.1093/icc/dtz042
PG 20
WC Business; Economics; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA LD3KE
UT WOS:000525930700004
DA 2024-09-05
ER
PT J
AU Fleerackers, A
   Nehring, L
   Maggio, LA
   Enkhbayar, A
   Moorhead, L
   Alperin, JP
AF Fleerackers, Alice
   Nehring, Lise
   Maggio, Lauren A.
   Enkhbayar, Asura
   Moorhead, Laura
   Alperin, Juan Pablo
TI Identifying science in the news: An assessment of the precision and
   recall of Altmetric.com news mention data
SO SCIENTOMETRICS
LA English
DT Article
DE Altmetric; Data quality; News; Accuracy; Scholarly communication;
   Journalism
AB The company Altmetric is often used to collect mentions of research in online news stories, yet there have been concerns about the quality of this data. This study investigates these concerns. Using a manual content analysis of 400 news stories as a comparison method, we analyzed the precision and recall with which Altmetric identified mentions of research in 8 news outlets. We also used logistic regression to identify the characteristics of research mentions that influence their likelihood of being successfully identified. We find that, for a predefined set of outlets, Altmetric's news mention data were relatively accurate (F-score = 0.80), with very high precision (0.95) and acceptable recall (0.70), although recall is below 0.50 for some news outlets. Altmetric is more likely to successfully identify mentions of research that include a hyperlink to the research item, an author name, and/or the title of a publication venue. This data source appears to be less reliable for mentions of research that provide little or no bibliometric information, as well as for identifying mentions of scholarly monographs, conference presentations, dissertations, and non-English research articles. Our findings suggest that, with caveats, scholars can use Altmetric news mention data as a relatively reliable source to identify research mentions across a range of outlets with high precision and acceptable recall, offering scholars the potential to conserve resources during data collection. Our study does not, however, offer an assessment of completeness or accuracy of Altmetric news data overall.
C1 [Fleerackers, Alice; Enkhbayar, Asura] Simon Fraser Univ, Interdisciplinary Studies, Vancouver, BC, Canada.
   [Nehring, Lise] Univ Victoria, Ctr Forest Biol, Victoria, BC, Canada.
   [Maggio, Lauren A.] Uniformed Serv Univ Hlth Sci, Dept Med, Bethesda, MD USA.
   [Moorhead, Laura] San Francisco State Univ, Coll Liberal & Creat Arts, Journalism, San Francisco, CA USA.
   [Alperin, Juan Pablo] Simon Fraser Univ, Publishing Program, Vancouver, BC, Canada.
C3 Simon Fraser University; University of Victoria; Uniformed Services
   University of the Health Sciences - USA; California State University
   System; San Francisco State University; Simon Fraser University
RP Fleerackers, A (corresponding author), Simon Fraser Univ, Interdisciplinary Studies, Vancouver, BC, Canada.; Alperin, JP (corresponding author), Simon Fraser Univ, Publishing Program, Vancouver, BC, Canada.
EM afleerac@sfu.ca; juan@alperin.ca
OI Fleerackers, Alice/0000-0002-7182-4061; Maggio,
   Lauren/0000-0002-2997-6133; Moorhead, Laura/0000-0001-9185-6290;
   Alperin, Juan Pablo/0000-0002-9344-7439
FU Social Sciences and Humanities Research Council of Canada (SSHRC)
   insight grant, Sharing health research [453-2020-0401]; Social Sciences
   and Humanities Research Council Joseph Bombardier Doctoral Fellowship
   [767-2019-0369]
FX This research is supported by a Social Sciences and Humanities Research
   Council of Canada (SSHRC) insight grant, Sharing health research
   (#453-2020-0401). AF is supported by a Social Sciences and Humanities
   Research Council Joseph Bombardier Doctoral Fellowship (#767-2019-0369).
CR ALPERIN JP, 2022, ZENODO
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NR 51
TC 6
Z9 7
U1 2
U2 27
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD NOV
PY 2022
VL 127
IS 11
BP 6109
EP 6123
DI 10.1007/s11192-022-04510-7
EA OCT 2022
PG 15
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA 5U5FU
UT WOS:000862527700001
PM 36212767
OA Green Published, Bronze, Green Submitted
DA 2024-09-05
ER
PT C
AU Obaideen, K
   AlShabi, M
   Bettayeb, M
   Gadsden, SA
   Bonny, T
AF Obaideen, Khaled
   AlShabi, Mohammad
   Bettayeb, Maamar
   Gadsden, S. Andrew
   Bonny, Talal
BE Blowers, M
   Wysocki, BT
TI The Convergence of Control and Cognition: A Bibliometric Overview of UKF
   in AI-Infused Robotics
SO DISRUPTIVE TECHNOLOGIES IN INFORMATION SCIENCES VIII
SE Proceedings of SPIE
LA English
DT Proceedings Paper
CT Conference on Disruptive Technologies in Information Sciences VIII
CY APR 22-25, 2024
CL National Harbor, MD
DE Unscented Kalman Filter; UKF; Robotics; VOSviewer; Bibliometric
ID ARTIFICIAL-INTELLIGENCE TECHNIQUES; UNMANNED AERIAL VEHICLES; UNSCENTED
   KALMAN FILTER; ROBUST; STRATEGIES; PREDICTION; NAVIGATION; FUSION;
   MODELS; AUV
AB This paper gives a bibliometric summary of Unscented Kalman Filter (UKF) in AI-infused robotics, highlighting its role in unifying control and cognition. Using a systematic approach that includes literature collection from IEEE Xplore, Web of Science and Google Scholar, rigorous screening and selection, and VOSviewer for a comprehensive bibliometric analysis. This analysis reports major trends, primary contributors and central themes, highlighting UKF's pivotal role in improving robotics cognitive and control capacities. The study emphasizes the universally used UKF in many fields of robotics, i.e. in navigation and mapping, sensor fusion, and state estimation, as one of its principal developers, which illustrates its vital role in promoting robotic autonomy and intelligence. The integration of findings from the bibliometric analysis thus not only presents the current state of research but also identifies possible future research directions, highlighting the increasing unification of control theories and cognitive processes in robotics. This research adds to the body of knowledge by delivering a comprehensive map of the UKF application. In this light, the UKF will be able to penetrate AI-infused robotics, the future of robotic developments will rely on the deep fusion of control and cognition facilitated by UKF and alike.
C1 [Obaideen, Khaled] RISE, Smart Automat & Commun Technol, Bio Sensing & Bio Sensors Grp, POB 27272, Sharjah 27272, U Arab Emirates.
   [AlShabi, Mohammad] Univ Sharjah, Dept Mech & Nucl Engn, POB 27272, Sharjah, U Arab Emirates.
   [Bettayeb, Maamar] Univ Sharjah, Dept Elect Engn, POB 27272, Sharjah, U Arab Emirates.
   [Gadsden, S. Andrew] McMaster Univ, Dept Mech Engn, Hamilton, ON L8S 4L8, Canada.
   [Bonny, Talal] Univ Sharjah, Dept Comp Engn, POB 27272, Sharjah, U Arab Emirates.
C3 University of Sharjah; University of Sharjah; McMaster University;
   University of Sharjah
RP AlShabi, M (corresponding author), Univ Sharjah, Dept Mech & Nucl Engn, POB 27272, Sharjah, U Arab Emirates.
EM malshabi@sharjah.ac.ae
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NR 104
TC 0
Z9 0
U1 0
U2 0
PU SPIE-INT SOC OPTICAL ENGINEERING
PI BELLINGHAM
PA 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA
SN 0277-786X
EI 1996-756X
BN 978-1-5106-7435-6; 978-1-5106-7434-9
J9 PROC SPIE
PY 2024
VL 13058
AR 1305817
DI 10.1117/12.3013841
PG 8
WC Computer Science, Information Systems
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BX1WH
UT WOS:001253884500036
DA 2024-09-05
ER
PT J
AU Li, JJ
   Yu, J
   Yang, D
   Tian, WY
   Zhao, LL
   Hu, JF
AF Li, Jianjun
   Yu, Jie
   Yang, Dan
   Tian, Wanyong
   Zhao, Lulu
   Hu, Junfeng
TI A Novel Semantic Segmentation Algorithm Using a Hierarchical Adjacency
   Dependent Network
SO IEEE ACCESS
LA English
DT Article
DE Semantics; Correlation; Feature extraction; Image segmentation;
   Convolution; Decoding; Bibliometrics; Semantic segmentation;
   hierarchical adjacency dependent network; adjacency dependency module
AB Recent semantic segmentation networks mainly focus on how to fuse multi-level features from classification networks to improve segmentation accuracy. Some researches evenly emphasize the correlation of pixels in a global region, such as conditional random field (CRF). However, the strong correlation feature of pixels in a limited region is less considered in the previous researches and the remedy ability of the correlation of local pixels in semantic segmentation is severely ignored. To deal with this problem, we introduce a hierarchical adjacency dependent network (HadNet), in which an adjacency dependency module (ADM) is constructed by calculating and utilizing the impact fact of the pixel in different directions to classify the pixel. We explored the correlation of adjacent pixels and feature coverage in different feature levels to improve the segmentation accuracy. We evaluate our method on the popular Pascal VOC 2012 test set, and achieve a comparable result of mIOU accuracy of 79.8 with the state of art methods, such as DeepLabv3 and Exfuse. Further, we discuss and analyze the data distribution of COCO dataset for deeply understanding the feature correlation and coverage in semantic segmentation.
C1 [Li, Jianjun; Yu, Jie; Yang, Dan] Hangzhou Dianzi Univ, Sch Comp Sci & Technol, Hangzhou 310018, Zhejiang, Peoples R China.
   [Tian, Wanyong; Zhao, Lulu; Hu, Junfeng] CETC Key Lab Data Link Technol, Xian 710000, Shaanxi, Peoples R China.
C3 Hangzhou Dianzi University; China Electronics Technology Group
RP Li, JJ (corresponding author), Hangzhou Dianzi Univ, Sch Comp Sci & Technol, Hangzhou 310018, Zhejiang, Peoples R China.
EM jianjun.li@hdu.edu.cn
OI Li, Jianjun/0000-0001-6658-9709
FU National Science Fund of China [61871170]; National Defense Basic
   Research Program [JCKY2017210A001]
FX This work was supported in part by the National Science Fund of China
   under Grant 61871170, and in part by the National Defense Basic Research
   Program under Grant JCKY2017210A001.
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NR 39
TC 1
Z9 1
U1 0
U2 6
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2019
VL 7
BP 150444
EP 150452
DI 10.1109/ACCESS.2019.2944219
PG 9
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA JN8TB
UT WOS:000497163000006
OA gold
DA 2024-09-05
ER
PT J
AU Riekki, J
   Mämmela, Ä
AF Riekki, Jukka
   Mammela, Aarne
TI Research and Education Towards Smart and Sustainable World
SO IEEE ACCESS
LA English
DT Article
DE Smart world vision; sustainable development goals; Internet of Things
   (IoT); artificial intelligence (AI); computational intelligence (CI);
   reductive view; systems view; emergence; experimental-inductive method;
   hypothetico-deductive method; functionality; basic resources;
   performance; energy efficiency; dependability; availability;
   reliability; safety; security; constraints; optimization; decision
   making; hierarchy; open-loop control; closed-loop feedback control;
   degree of centralization; distributed systems; education; integrative
   learning; research; innovation; history
ID SYSTEM DYNAMICS; HEALTH RESEARCH; BIG DATA; INTERNET; THINGS;
   INTELLIGENT; VISION; MULTIDISCIPLINARITY; TRANSDISCIPLINARITY;
   INTERDISCIPLINARITY
AB We propose a vision for directing research and education in the field of information and communications technology (ICT). Our Smart and Sustainable World vision targets prosperity for the people and the planet through better awareness and control of both human-made and natural environments. The needs of society, individuals, and industries are fulfilled with intelligent systems that sense their environment, make proactive decisions on actions advancing their goals, and perform the actions on the environment. We emphasize artificial intelligence, feedback loops, human acceptance and control, intelligent use of basic resources, performance parameters, mission-oriented interdisciplinary research, and a holistic systems view complementing the conventional analytical reductive view as a research paradigm, especially for complex problems. To serve a broad audience, we explain these concepts and list the essential literature. We suggest planning research and education by specifying, in a step-wise manner, scenarios, performance criteria, system models, research problems, and education content, resulting in common goals and a coherent project portfolio as well as education curricula. Research and education produce feedback to support evolutionary development and encourage creativity in research. Finally, we propose concrete actions for realizing this approach.
C1 [Riekki, Jukka] Univ Oulu, Ctr Ubiquitous Comp, Oulu 90570, Finland.
   [Mammela, Aarne] VTT Tech Res Ctr Finland Ltd, Oulu 90570, Finland.
C3 University of Oulu; VTT Technical Research Center Finland
RP Riekki, J (corresponding author), Univ Oulu, Ctr Ubiquitous Comp, Oulu 90570, Finland.
EM jukka.riekki@oulu.fi
OI Mammela, Aarne/0000-0002-6659-4126; Riekki, Jukka/0000-0002-1694-9152
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NR 243
TC 11
Z9 11
U1 1
U2 29
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2021
VL 9
BP 53156
EP 53177
DI 10.1109/ACCESS.2021.3069902
PG 22
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering; Telecommunications
GA RM7YC
UT WOS:000639873400001
OA gold, Green Submitted
DA 2024-09-05
ER
PT C
AU Zheng, YP
   Zhou, ZQ
   Blikstein, P
AF Zheng, Yipu
   Zhou, Zhuqian
   Blikstein, Paulo
BE Rodrigo, MM
   Matsuda, N
   Cristea, AI
   Dimitrova, V
TI Towards an Inclusive and Socially Committed Community in Artificial
   Intelligence in Education: A Social Network Analysis of the Evolution of
   Authorship and Research Topics over 8 Years and 2509 Papers
SO ARTIFICIAL INTELLIGENCE IN EDUCATION, PT I
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT 23rd International Conference on Artificial Intelligence in Education
   (AIED)
CY JUL 27-31, 2022
CL Durham Univ, Durham, ENGLAND
HO Durham Univ
DE Co-authorship network; Social network analysis; Keyword analysis;
   Bibliometric analysis
AB This paper presents an overviewof the last decade of research on Artificial Intelligence in Education by conducting keyword and social network analysis on the time-evolving co-authorship networks in four major research conferences: the International Conference on Artificial Intelligence in Education, the International Conference on Educational Data Mining, the International Conference on Learning Analytics and Knowledge, and the ACM Conference on Learning at Scale. Time-evolving co-authorship networks were used as a proxy for the collaboration dynamic in the field, while keyword analysis was conducted to supplement the social network analysis in order to pinpoint foci of individuals and cliques. Recent research foci and the level of openness of the four research communities were examined to inform strategies on how to promote diverse ideas and further collaborations within the field of AI in Education.
C1 [Zheng, Yipu; Zhou, Zhuqian; Blikstein, Paulo] Columbia Univ, Teachers Coll, New York, NY 10027 USA.
C3 Columbia University Teachers College; Columbia University
RP Zheng, YP (corresponding author), Columbia Univ, Teachers Coll, New York, NY 10027 USA.
EM yz3204@tc.columbia.edu; zz2404@tc.columbia.edu; paulob@tc.columbia.edu
RI ; Blikstein, Paulo/F-2396-2019
OI Zhou, Zhuqian/0000-0002-8045-6213; Blikstein, Paulo/0000-0003-3941-1088
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NR 14
TC 1
Z9 1
U1 4
U2 10
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 0302-9743
EI 1611-3349
BN 978-3-031-11644-5; 978-3-031-11643-8
J9 LECT NOTES COMPUT SC
PY 2022
VL 13355
BP 414
EP 426
DI 10.1007/978-3-031-11644-5_34
PG 13
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications; Education & Educational Research
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Education & Educational Research
GA BU1FW
UT WOS:000877435100034
DA 2024-09-05
ER
PT J
AU Pan, XQ
   Gan, ZD
AF Pan, Xiaoquan
   Gan, Zhengdong
TI Perceiving Technology-Based Professional Development Practices for
   Teachers: Accounts From English as a Foreign Language (EFL) Teachers in
   China
SO INTERNATIONAL JOURNAL OF COMPUTER-ASSISTED LANGUAGE LEARNING AND
   TEACHING
LA English
DT Article
DE Collaboration; Community of Practice; Institutional Culture;
   Interaction; Online Learning Community; Teacher Professional
   Development; Teachers' Belief; Technology-Based Professional Development
ID COMMUNITY; IDENTITY; AGENCY; MODEL
AB This study explored how 26 Chinese EFL teachers perceived community-based, technology-supported professional development practices. The methods of data collection in this study blend quantitative and qualitative techniques: 1) questionnaire survey of teachers' satisfaction about community-based technology-supported professional development practices; 2) online teacher discussion postings; 3) teacher self-reflection journals; and 4) semi-structured interviews. Data analysis revealed a generally positive attitude and empowering feelings in these Chinese EFL teachers who viewed technology-facilitated practices as affording constructive functions for their professional development. Results also revealed a range of factors that apparently mediated/limited EFL teachers' participation in the professional development activities. This study thus contributes to the understanding of the reality in relation to actual utilization of technological resources in second-language teacher development in the context of a developing country such as China.
C1 [Pan, Xiaoquan] Zhejiang Normal Univ, Xingzhi Coll, Jinhua, Zhejiang, Peoples R China.
   [Gan, Zhengdong] Univ Macau, Fac Educ, Macau, Peoples R China.
C3 Zhejiang Normal University; University of Macau
RP Pan, XQ (corresponding author), Zhejiang Normal Univ, Xingzhi Coll, Jinhua, Zhejiang, Peoples R China.
FU Department of Education of Zhejiang Province [kg20160564]
FX This research was supported by the Department of Education of Zhejiang
   Province [grant number kg20160564].
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NR 55
TC 7
Z9 7
U1 3
U2 33
PU IGI GLOBAL
PI HERSHEY
PA 701 E CHOCOLATE AVE, STE 200, HERSHEY, PA 17033-1240 USA
SN 2155-7098
EI 2155-7101
J9 INT J COMPUT-ASSIST
JI Int. J. Comput.-Assist. Lang. Learn. Teach.
PD APR-JUN
PY 2020
VL 10
IS 2
BP 40
EP 58
DI 10.4018/IJCALLT.2020040103
PG 19
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA PP3BA
UT WOS:000605740200003
DA 2024-09-05
ER
PT J
AU Nti, IK
   Bawah, FU
   Quarcoo, JA
   Kalos, F
AF Nti, Isaac Kofi
   Bawah, Faiza Umar
   Quarcoo, Juanita Ahia
   Kalos, Favour
TI A Bibliometric Analysis of Soft Computing Technology Applications Trends
   and Characterisation in Educational Research: Africa
SO AFRICA EDUCATION REVIEW
LA English
DT Article
DE Africa education; artificial intelligence in education; educational
   analytics; educational data mining; education technology; soft computing
   technology applications in education
ID STUDENTS; PERFORMANCE; METAANALYSIS
AB Computers in education, along with soft-computing technology applications, have revolutionised global interconnectedness and the need for a well-educated workforce. Many studies worldwide explore technology in education, often relying on systematic reviews, though concerns about selection bias have emerged. This article takes a different approach, employing bibliometric analysis to delve into the trends, key authors, institutions, and themes of soft-computing technology applications in education (SCTAE) research in Africa. Initially, 7 435 papers were downloaded from Scopus and then narrowed down to 1 358 using the PRISMA model and defined criteria. Utilising the VOSViewer text mining tool, the article maps out prolific authors, institutions, and thematic networks. It provides detailed findings and outlines opportunities, challenges, and future research prospects in SCTAE in the African context.
C1 [Nti, Isaac Kofi] Univ Cincinnati, Cincinnati, ND 45221 USA.
   [Bawah, Faiza Umar; Kalos, Favour] Univ Energy & Nat Resources, Sunyani, Ghana.
   [Quarcoo, Juanita Ahia] Sunyani Tech Univ, Sunyani, Ghana.
RP Nti, IK (corresponding author), Univ Cincinnati, Cincinnati, ND 45221 USA.
EM isaac.nti@uc.edu
RI NTI, ISAAC KOFI/E-2004-2017
OI NTI, ISAAC KOFI/0000-0001-9257-4295; Ahia Quarcoo,
   Juanita/0000-0001-7702-755X
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NR 49
TC 0
Z9 0
U1 5
U2 5
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1814-6627
EI 1753-5921
J9 AFR EDUC RE
JI Afr. Educ. Rev.
PD MAY 4
PY 2022
VL 19
IS 3
BP 55
EP 77
DI 10.1080/18146627.2023.2284744
PG 23
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA CF0E1
UT WOS:001123710200001
DA 2024-09-05
ER
PT J
AU Limaymanta, CH
   Apaza-Tapia, L
   Vidal, E
   Gregorio-Chaviano, O
AF Limaymanta, Cesar H.
   Apaza-Tapia, Ludgarda
   Vidal, Elizabeth
   Gregorio-Chaviano, Orlando
TI Flipped Classroom in Higher Education: A Bibliometric Analysis and
   Proposal of a Framework for its Implementation
SO INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING
LA English
DT Article
DE Flipped classroom; Bibliometrics; Higher education; Active learning;
   Framework; Scientific collaboration; Co-occurrence; Scientific
   production; Information and communication technology
ID UNIVERSITY; SCIENCE; COLLABORATION
AB The flipped classroom as an educational model is perfectly aligned with the current demands of higher education. Therefore, the objectives of this article were to carry out a bibliometric analysis of the scientific production of the flipped classroom in higher education (2012-2020) and to propose a framework for its implementation in face-to-face, blended or online learning modalities. The records were recovered from the Web of Science Core Collection and Scopus, from which, after a five-phase methodological process, a consolidated dataset of 782 documents was obtained. The results showed the importance of the subject matter as scientific production reflected a continuous growth during the period of study. For their part, the most productive authors come from various institutions worldwide with an H index of over 50. The collaboration indicators show the growth trend of these indexes over the years, which reflects the capacity to generate national and international impact in the documents published in collaboration. The keywords co-occurrence analysis showed that the flipped classroom as a technological and innovative approach is complemented by active learning, blended learning, e-learning, ICT, teaching method, among others. Finally, a framework with five components was proposed as a basic guide for the implementation of the flipped classroom in higher education.
C1 [Limaymanta, Cesar H.] Univ Nacl Mayor San Marcos, Lima, Peru.
   [Limaymanta, Cesar H.] Univ Peruana Ciencias Aplicadas, Lima, Peru.
   [Apaza-Tapia, Ludgarda] Univ Nacl San Agustin Arequipa, Sch Accounting & Financial Sci, Arequipa, Peru.
   [Vidal, Elizabeth] Univ Nacl San Agustin Arequipa, Comp & Syst Engn Dept, Arequipa, Peru.
   [Gregorio-Chaviano, Orlando] Pontificia Univ Javeriana, Bibliometr, Bogota, Colombia.
C3 Universidad Nacional Mayor de San Marcos; Universidad Peruana de
   Ciencias Aplicadas (UPC); Universidad Nacional de San Agustin de
   Arequipa; Universidad Nacional de San Agustin de Arequipa; Pontificia
   Universidad Javeriana
RP Limaymanta, CH (corresponding author), Univ Nacl Mayor San Marcos, Lima, Peru.; Limaymanta, CH (corresponding author), Univ Peruana Ciencias Aplicadas, Lima, Peru.
EM climaymantaa@unmsm.edu.pe
RI Limaymanta, Cesar H/IUM-1770-2023; Limaymanta, Cesar H./AAC-2537-2019;
   Gregorio-Chaviano, Orlando/B-5480-2018; Vidal, Elizabeth/HMV-4001-2023
OI Limaymanta, Cesar H./0000-0002-8797-4275; Gregorio-Chaviano,
   Orlando/0000-0002-3064-8639; Vidal, Elizabeth/0000-0002-8367-9439;
   Apaza-Tapia, Ludgarda/0000-0001-8894-3879
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NR 44
TC 14
Z9 16
U1 2
U2 26
PU INT ASSOC ONLINE ENGINEERING
PI WIEN
PA KIRCHENGASSE 10-200, WIEN, A-1070, AUSTRIA
SN 1863-0383
J9 INT J EMERG TECHNOL
JI Int. J. Emerg. Technol. Learn.
PY 2021
VL 16
IS 9
BP 133
EP 149
DI 10.3991/ijet.v16i09.21267
PG 17
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA RZ0OU
UT WOS:000648300600009
OA Green Submitted, gold
DA 2024-09-05
ER
PT C
AU García-Sánchez, P
   Cobo, MJ
AF Garcia-Sanchez, P.
   Cobo, M. J.
BE Yin, H
   Camacho, D
   Novais, P
   TallonBallesteros, AJ
TI Measuring the Impact of the International Relationships of the
   Andalusian Universities Using Dimensions Database
SO INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING (IDEAL 2018), PT II
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT 19th International Conference on Intelligent Data Engineering and
   Automated Learning (IDEAL)
CY NOV 21-23, 2018
CL Univ Autonoma Madrid, Polytechn Sch, Madrid, SPAIN
HO Univ Autonoma Madrid, Polytechn Sch
DE Bibliometric analysis; International collaboration; Andalusian
   universities; Dimensions.ai
ID COLLABORATION
AB Researchers usually have been inclined to publish papers with close collaborators: same University, region or even country. However, thanks to the advancements in communication technologies, members of international research networks can cooperate almost seamlessly. These networks usually tend to publish works with more impact than the local counterparts. In this paper, we try to demonstrate if this assumption is also valid in the region of Andalusia (Spain). The Dimensions.ai database is used to obtain the articles where at least one author is from an Andalusian University. The publication list is divided into 4 geographical areas: local (only one affiliation), regional (only Andalusian affiliations), national (only Spanish affiliations) and International (any affiliation). Results show that the average number of citations per paper increases as the author collaboration networks increases geographically.
C1 [Garcia-Sanchez, P.; Cobo, M. J.] Univ Cadiz, Dept Comp Sci & Engn, Cadiz, Spain.
C3 Universidad de Cadiz
RP García-Sánchez, P (corresponding author), Univ Cadiz, Dept Comp Sci & Engn, Cadiz, Spain.
EM pablo.garciasanchez@uca.es; manueljesus.cobo@uca.es
RI Cobo Martí­n, Manuel Jesús/C-5581-2011; García-Sánchez,
   Pablo/G-2166-2010
OI Cobo Martí­n, Manuel Jesús/0000-0001-6575-803X; García-Sánchez,
   Pablo/0000-0003-4644-2894
FU FEDER funds [TIN2016-75850-R, TIN2017-85727-C4-2-P]; Program of
   Promotion and Development of Research Activity of the University of
   Cadiz (Programa de Fomento e Impulso de la actividad Investigadora de la
   Universidad de Cadiz)
FX This contribution has been made possible thanks to Dimensions.ai
   database. Also, the authors would like to acknowledge FEDER funds under
   grants TIN2016-75850-R and TIN2017-85727-C4-2-P and Program of Promotion
   and Development of Research Activity of the University of Cadiz
   (Programa de Fomento e Impulso de la actividad Investigadora de la
   Universidad de Cadiz).
CR Adams J, 2013, NATURE, V497, P557, DOI 10.1038/497557a
   [Anonymous], 2018, APPL INTELL, DOI [10.1007/s10489-017-1105-y., DOI 10.1007/s10489-017-1105-y, DOI 10.1007/S10489-017-1105-Y]
   Cobo MJ, 2011, J AM SOC INF SCI TEC, V62, P1382, DOI 10.1002/asi.21525
   Fortunato S, 2018, SCIENCE, V359, DOI 10.1126/science.aao0185
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   Sugimoto CR, 2017, NATURE, V550, P29, DOI 10.1038/550029a
   Wagner CS, 2005, RES POLICY, V34, P1608, DOI 10.1016/j.respol.2005.08.002
NR 8
TC 6
Z9 6
U1 0
U2 0
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 0302-9743
EI 1611-3349
BN 978-3-030-03496-2; 978-3-030-03495-5
J9 LECT NOTES COMPUT SC
PY 2018
VL 11315
BP 138
EP 144
DI 10.1007/978-3-030-03496-2_16
PG 7
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BQ2MP
UT WOS:000582459900016
DA 2024-09-05
ER
PT C
AU Apanovich, Z
   Marchuk, A
AF Apanovich, Zinaida
   Marchuk, Alexander
BE Allen, RB
   Hunter, J
   Zeng, ML
TI Experiments on Russian-English Identity Resolution
SO DIGITAL LIBRARIES: PROVIDING QUALITY INFORMATION
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT 17th International Conference on Asia-Pacific Digital Libraries (ICADL)
CY DEC 09-12, 2015
CL Seoul, SOUTH KOREA
DE Linked open data; Cross-language identity resolution; Authorship
   attribution; Self-citation network; Tf-idf; LDA; Jaro-Winkler
AB The focus of this paper is on Russian-English identity resolution when English names of entities have been created by a transliteration or translation of Russian names. A new approach combining attribute-based identity resolution with the text analysis of publications attributed to these entities has been proposed. The dataset of the Open Archive of the Russian Academy of Sciences and digital library SpringerLink are used as test examples.
C1 [Apanovich, Zinaida; Marchuk, Alexander] Russian Acad Sci, Siberian Branch, AP Ershov Inst Informat Syst, Novosibirsk, Russia.
   [Apanovich, Zinaida; Marchuk, Alexander] Novosibirsk State Univ, Novosibirsk 630090, Russia.
C3 Russian Academy of Sciences; Ershov Institute of Informatics Systems;
   Novosibirsk State University
RP Apanovich, Z (corresponding author), Russian Acad Sci, Siberian Branch, AP Ershov Inst Informat Syst, Novosibirsk, Russia.
EM apanovich@iis.nsk.su; mag@iis.nsk.su
RI Apanovich, Zinaida V./K-5339-2018
OI Apanovich, Zinaida/0000-0002-5767-284X
CR [Anonymous], 2007, Probabilistic Topic Models
   Apanovich Z, 2013, COMM COM INF SC, V394, P1
   Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
   COHEN WW, 2003, 2 WEB, P73
   Ferreira A. A., 2013, LARG BIBL REP C INT
   Godby C. J., COMMON GROUND EXPLOR
   Hickey T. B., 2014, D LIB MAGAZINE   JUL, V20
   Isele Robert, 2010, COLD, V665
   Kukushkina O. V., 2001, Problems of Information Transmission, V37, P172, DOI 10.1023/A:1010478226705
   Ley M, 2009, PROC VLDB ENDOW, V2, P1493, DOI 10.14778/1687553.1687577
   Marchuk A. G., 2010, P RCDL 2010 C, P19
   Rogov A. A., 2001, P 6 INT C SEPT 10 14, V2, P187
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   Song Y, 2007, ACM-IEEE J CONF DIG, P342, DOI 10.1145/1255175.1255243
   Stamatatos E, 2009, J AM SOC INF SCI TEC, V60, P538, DOI 10.1002/asi.21001
NR 15
TC 0
Z9 0
U1 0
U2 2
PU SPRINGER INT PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 0302-9743
BN 978-3-319-27974-9; 978-3-319-27973-2
J9 LECT NOTES COMPUT SC
PY 2015
VL 9469
BP 12
EP 21
DI 10.1007/978-3-319-27974-9_2
PG 10
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications; Computer Science, Theory & Methods;
   Robotics
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science; Robotics
GA BE7RG
UT WOS:000375767400002
DA 2024-09-05
ER
PT J
AU Gehanno, JF
   Rollin, L
   Le Jean, T
   Louvel, A
   Darmoni, S
   Shaw, W
AF Gehanno, Jean-Francois
   Rollin, Laetitia
   Le Jean, Tony
   Louvel, Alexandre
   Darmoni, Stefan
   Shaw, William
TI Precision and Recall of Search Strategies for Identifying Studies on
   Return-To-Work in Medline
SO JOURNAL OF OCCUPATIONAL REHABILITATION
LA English
DT Article
DE Return to work; Medline; Bibliometrics
ID RANDOMIZED CONTROLLED-TRIALS; MEDICAL SUBJECT-HEADINGS; BIBLIOGRAPHIC
   DATABASES; OCCUPATIONAL-HEALTH; TEXT-WORD; PUBLICATIONS; RELEVANT; READ;
   CARE
AB Introduction The purpose of this study was to report on the qualities of various search strategies and keywords to find return to work (RTW) studies in the Medline bibliographic database. Methods We searched Medline for articles on RTW published in 2003, using multiple search strings, and hand searched 16 major periodicals of rehabilitation or occupational medicine. Among the retrieved articles, those considered to be relevant, were pooled in a Gold Standard Database. From this database, we identified candidate text words or MeSH terms for search strategies using a word frequency analysis of the abstracts and a MEDLINE categorization algorithm. According to the frequency of identified terms, searches were run for each term independently and in combination. We computed Recall, Precision, and number needed to read (NNR = 1/Precision) of each keyword or combination of keywords. Results Among the 8,073 articles examined, 314 (3.9%) were considered relevant and included in the Gold Standard Database. The search strings ("Rehabilitation, Vocational'' [MeSH]), ("Return to work''[All]) and ("Back to work''[All]) had Recall/Precision ratio of 30.46/19.11, 59.55/87.38 and 3.18/90.91%, respectively. Their combination with the Boolean operator OR yielded to a Recall/Precision ratio of 73.89/58.44% and a NNR of 1.7. For the end user requiring comprehensive literature search, the best string was ("Return to work'' OR "Back to work'' OR "Rehabilitation, vocational''[MeSH] OR "rehabilitation''[Subheading]), with a Recall of 88.22% and a NNR of 18. Conclusions No single MeSH term is available to help the physician to identify relevant studies on RTW in Medline. Locating these types of studies requires the use of various MeSH and non-MeSH terms in combination to obtain a satisfactory Recall. Nevertheless, enhancing the Recall of search strategies may lead to lower Precision, and higher NNR, although with a non linear trend. This factor must be taken into consideration by the end user in order to improve the cost-effectiveness ratio of the search in Medline.
C1 [Gehanno, Jean-Francois; Rollin, Laetitia; Louvel, Alexandre] Rouen Univ Hosp, Inst Occupat Hlth, F-76000 Rouen, France.
   [Le Jean, Tony] Gen Hosp Dieppe, Dept Rehabil Med, Dieppe, France.
   [Gehanno, Jean-Francois; Darmoni, Stefan] Rouen Univ Hosp, GCSIS Lab, F-76000 Rouen, France.
   [Shaw, William] Liberty Mutual Res Inst Safety, Hopkinton, MA USA.
C3 Universite de Rouen Normandie; CHU de Rouen; Universite de Rouen
   Normandie; CHU de Rouen; Liberty Mutual Research Institute for Safety
RP Gehanno, JF (corresponding author), Rouen Univ Hosp, Inst Occupat Hlth, 1 Rue Germont, F-76000 Rouen, France.
EM jean-francois.gehanno@chu-rouen.fr
RI Shaw, William/Q-3013-2019; Stefan, Darmoni J/H-4554-2016
OI Shaw, William/0000-0002-6830-6415; Gehanno,
   jean-francois/0000-0002-2309-7322; Darmoni, Stefan/0000-0002-7162-318X;
   ROLLIN, Laetitia/0000-0002-5454-4340
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NR 25
TC 31
Z9 33
U1 0
U2 11
PU SPRINGER/PLENUM PUBLISHERS
PI NEW YORK
PA 233 SPRING ST, NEW YORK, NY 10013 USA
SN 1053-0487
J9 J OCCUP REHABIL
JI J. Occup. Rehabil.
PD SEP
PY 2009
VL 19
IS 3
BP 223
EP 230
DI 10.1007/s10926-009-9177-0
PG 8
WC Rehabilitation; Social Issues
WE Social Science Citation Index (SSCI)
SC Rehabilitation; Social Issues
GA 479SA
UT WOS:000268680000001
PM 19381789
DA 2024-09-05
ER
PT J
AU Maisonobe, M
AF Maisonobe, Marion
TI The future of urban models in the Big Data and AI era: a bibliometric
   analysis (2000-2019)
SO AI & SOCIETY
LA English
DT Article
DE Bibliometrics; Urban modelling; Research dynamics; Science studies
ID SMART CITIES; CHALLENGES; GOVERNMENT; SCIENCE; HELIX
AB This article questions the effects on urban research dynamics of the Big Data and AI turn in urban management. Increasing access to large datasets collected in real time could make certain mathematical models developed in research fields related to the management of urban systems obsolete. These ongoing evolutions are the subject of numerous works whose main angle of reflection is the future of cities rather than the transformations at work in the academic field. Our article proposes grasp the scientific dynamics in areas of research related to two urban systems: transportation and water. The article demonstrates the importance of grasping these dynamics if we want to be able to apprehend what the urban management of tomorrow's cities will be like. To analyse these research areas' dynamics, we use two complementary materials: bibliometric data and interviews. The interviews conducted in 2018 with academics and higher education officials in Paris and Edinburgh suggest avenues for hybridization between traditional modelling approaches and research in machine learning, artificial intelligence and Big Data. The bibliometric analysis highlight the trends at work: it shows that traffic flow as well as transportation studies are focussing more and more on AI and Big Data and that traffic flow studies are arousing a growing interest among computer scientists, while, so far, this interest is less pronounced in the water research area, and more especially regarding water quality. The differences observed between research on transportation and that on water confirm the multifaceted nature of the developments at work and encourage us to reject overly hasty and simplistic generalisations about the transformations underway.
C1 [Maisonobe, Marion] CNRS, Geog Cites UMR 8504 CNRS, Paris, France.
C3 Centre National de la Recherche Scientifique (CNRS); Universite Paris
   Cite
RP Maisonobe, M (corresponding author), CNRS, Geog Cites UMR 8504 CNRS, Paris, France.
EM marion.maisonobe@cnrs.fr
OI Maisonobe, Marion/0000-0002-2968-9038
FU "City and Digital" working group of the "Urban Futures" Labex within the
   I-SITE FUTURE [16-IDEX-0003]; joint research unit UMR LATTS in 2018;
   IASH fellowship
FX This work was carried out within the framework of the "City and Digital"
   working group of the "Urban Futures" Labex within the I-SITE FUTURE
   (16-IDEX-0003). It was funded through a post-doctoral contract within
   the joint research unit UMR LATTS in 2018. The fieldwork carried out in
   Edinburgh was supported by an IASH fellowship. I would like to thank the
   interviewees for their time and the qualitative insights they brought to
   this exploratory work. I would also like to thank P. Tubaro, A. Casilli
   and E. Ollion for organising an inspiring research day about "the Big
   Data moment in Social Sciences" on the 21 February 2019 in Paris.
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NR 52
TC 5
Z9 5
U1 2
U2 21
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 0951-5666
EI 1435-5655
J9 AI SOC
JI AI Soc.
PD MAR
PY 2022
VL 37
IS 1
BP 177
EP 194
DI 10.1007/s00146-021-01166-4
EA MAR 2021
PG 18
WC Computer Science, Artificial Intelligence
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA YY9XL
UT WOS:000629071300002
DA 2024-09-05
ER
PT J
AU Zennayi, Y
   Bourzeix, F
   Guennoun, Z
AF Zennayi, Yahya
   Bourzeix, Francois
   Guennoun, Zouhair
TI Analyzing the Scientific Evolution of Face Recognition Research and Its
   Prominent Subfields
SO IEEE ACCESS
LA English
DT Article
DE Face recognition; Performance analysis; Databases; Lighting; Indexes;
   Feature extraction; Statistical analysis; Bibliometric studies; co-word
   analysis; face recognition; performance analysis; science mapping;
   thematic evolution
ID REPRESENTATION BASED CLASSIFICATION; LINEAR DISCRIMINANT-ANALYSIS;
   DISCRETE COSINE TRANSFORM; LOCAL BINARY PATTERNS; SPARSE REPRESENTATION;
   FEATURE-EXTRACTION; PRESERVING PROJECTIONS; 2-DIMENSIONAL PCA; VISUAL
   TRACKING; NEURAL-NETWORK
AB This paper presents a science mapping approach to analyze thematic evolution of face recognition research. For this reason, different bibliometric tools are combined (performance analysis, science mapping and Co-word analysis) in order to identify the most important, productive and the highest-impact subfields. Moreover, different visualization tools are used to display a graphical vision of face recognition field to determine the thematic domains and their evolutionary behavior. Finally, this study proposes the most relevant lines of research for the face recognition field. Findings indicate a huge increase in face recognition research since 2014. Mixed approaches revealed a great interest compared to local and global approaches. In terms of algorithms, the use of deep learning methods is the new trend. On the other hand, the illumination variation impact on face recognition algorithms performances is nowadays, the most important and impacting challenge for the face recognition field.
C1 [Zennayi, Yahya; Guennoun, Zouhair] Mohammed V Univ, Smart Commun ERSC Team, Res Ctr E3S, Rabat 10090, Morocco.
   [Zennayi, Yahya; Bourzeix, Francois] Moroccan Fdn Adv Sci Innovat & Res, Embedded Syst & AI Dept, Rabat 10010, Morocco.
C3 Mohammed V University in Rabat; Moroccan Foundation for Advanced Science
   Innovation & Research (MASCIR)
RP Zennayi, Y (corresponding author), Mohammed V Univ, Smart Commun ERSC Team, Res Ctr E3S, Rabat 10090, Morocco.; Zennayi, Y (corresponding author), Moroccan Fdn Adv Sci Innovat & Res, Embedded Syst & AI Dept, Rabat 10010, Morocco.
EM zennayi.yahya@gmail.com
OI guennoun, zouhair/0000-0002-7142-0550; ZENNAYI,
   Yahya/0000-0002-9561-7622
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NR 271
TC 3
Z9 3
U1 2
U2 8
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2022
VL 10
BP 68175
EP 68201
DI 10.1109/ACCESS.2022.3185137
PG 27
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA 2P5ZZ
UT WOS:000819820100001
OA gold
DA 2024-09-05
ER
PT C
AU Kumar, MS
   Gupta, S
   Baskaran, S
   Na, JC
AF Kumar, Mahalakshmi Suresh
   Gupta, Shreya
   Baskaran, Subashini
   Na, Jin-Cheon
BE Jatowt, A
   Maeda, A
   Syn, SY
TI User Motivation Classification and Comparison of Tweets Mentioning
   Research Articles in the Fields of Medicine, Chemistry and Environmental
   Science
SO DIGITAL LIBRARIES AT THE CROSSROADS OF DIGITAL INFORMATION FOR THE
   FUTURE, ICADL 2019
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT 21st International Conference on Asia-Pacific Digital Libraries (ICADL)
CY NOV 04-07, 2019
CL Kuala Lumpur, MALAYSIA
DE Altmetrics; Machine learning; Support Vector Machine; Medicine; Twitter;
   User motivation; Chemistry; Environmental science
ID TWITTER
AB Modern metrics like Altmetrics help researchers and scientists to gauge the impact of their research findings through social media discussions. Twitter holds more scholarly and scientific discussions than other social media platforms and is extensively used to discuss and share research articles by domain experts as well as by the general public. In this study, we have analyzed the motivations of people using Twitter as a medium to propagate the research works. Tweets and the publication details from the field of medicine are collected from altmetric.com for journals with high impact factors and a Support Vector Machine classifier is developed with 85.2% accuracy to categorize the tweets into one of the six motivation classes. The model is then extended to observe the pattern of user motivations in chemistry and environmental science. Medicine and environmental science were found to have similar patterns in user motivations as they directly impact the general public. Chemistry, on the other hand, showed a peculiar pattern with a high percentage of self-citation and promotion. From this study, the domain is also found to play a vital role in measuring research impacts when alternate metrics are used.
C1 [Kumar, Mahalakshmi Suresh; Gupta, Shreya; Baskaran, Subashini; Na, Jin-Cheon] Nanyang Technol Univ, Wee Kim Wee Sch Commun & Informat, 31 Nanyang Link, Singapore 637718, Singapore.
C3 Nanyang Technological University
RP Baskaran, S (corresponding author), Nanyang Technol Univ, Wee Kim Wee Sch Commun & Informat, 31 Nanyang Link, Singapore 637718, Singapore.
EM mahalaks002@e.ntu.edu.sg; shreya015@e.ntu.edu.sg;
   subashin001@e.ntu.edu.sg; tjcna@ntu.edu.sg
OI Na, Jin-Cheon/0000-0002-2211-9382
CR [Anonymous], 2014, Eighth Int. AAAI Conf. Weblogs Soc. Media
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NR 24
TC 1
Z9 1
U1 0
U2 10
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 0302-9743
EI 1611-3349
BN 978-3-030-34058-2; 978-3-030-34057-5
J9 LECT NOTES COMPUT SC
PY 2019
VL 11853
BP 40
EP 53
DI 10.1007/978-3-030-34058-2_5
PG 14
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods; Information Science &
   Library Science; Imaging Science & Photographic Technology
WE Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Computer Science; Information Science & Library Science; Imaging Science
   & Photographic Technology
GA BQ3BB
UT WOS:000583740100005
DA 2024-09-05
ER
PT J
AU Tran, J
   Meller, L
   Le, V
   Tam, J
   Nicholas, A
AF Tran, Joanne
   Meller, Leo
   Le, Vy
   Tam, Jasmine
   Nicholas, Andrea
TI Behavioral assessment of soft skill development in a highly structured
   pre-health biology course for undergraduates
SO JOURNAL OF MICROBIOLOGY & BIOLOGY EDUCATION
LA English
DT Article
DE pre-health undergraduate education; active learning; soft-skill
   assessment; biology education; patient simulations; collaborative
   learning; videotaped simulations; pre-health skill development; critical
   thinking analysis; behavioral analysis; scientific literacy; case-based
   education; group work; design-based research; Multiple Mini Exams (MME)
ID QUALITIES; EDUCATION
AB In this study, we assessed a highly structured, yearlong, case-based course designed for undergraduate pre-health students. We incorporated both content learning assessments and developed a novel method called Multiple Mini Exams for assessing course impact on the development of skills that professional schools often seek in pre-health students, focusing on students' abilities to collaborate with others, display bedside manners, synthesize patient case details, appropriately use scientific and medical language, and effectively attain patients' medical histories. This novel method utilized a rubric based on desired medical student skills to score videotaped behaviors and interactions of students role playing as doctors in a hypothetical patient case study scenario. Overall, our findings demonstrate that a highly structured course, incorporating weekly student performance and presentation of patient cases encompassing history taking, diagnosis, and treatment, can result in content learning, as well as improve desired skills specific for success in medical fields.
C1 [Tran, Joanne; Meller, Leo; Le, Vy; Tam, Jasmine] Univ Calif Irvine, Dept Biol Sci, Irvine, CA USA.
   [Nicholas, Andrea] Univ Calif Irvine, Dept Neurosci, Irvine, CA 92697 USA.
C3 University of California System; University of California Irvine;
   University of California System; University of California Irvine
RP Nicholas, A (corresponding author), Univ Calif Irvine, Dept Neurosci, Irvine, CA 92697 USA.
EM acnichol@uci.edu
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   Nicholas A., DIS RIVER CASE STUDY
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NR 19
TC 0
Z9 0
U1 0
U2 0
PU AMER SOC MICROBIOLOGY
PI WASHINGTON
PA 1752 N ST NW, WASHINGTON, DC 20036-2904 USA
SN 1935-7877
EI 1935-7885
J9 J MICROBIOL BIOL EDU
JI J. Microbiol. Biol. Educ.
PD AUG 29
PY 2024
VL 25
IS 2
DI 10.1128/jmbe.00192-23
EA JUN 2024
PG 13
WC Education, Scientific Disciplines
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA E0R9E
UT WOS:001243431200001
PM 38860778
OA gold
DA 2024-09-05
ER
PT J
AU Gehanno, JF
   Thaon, I
   Pelissier, C
   Rollin, L
AF Gehanno, Jean-Francois
   Thaon, Isabelle
   Pelissier, Carole
   Rollin, Laetitia
TI Precision and Recall of Search Strategies for Identifying Studies on
   Work-Related Psychosocial Risk Factors in PubMed
SO JOURNAL OF OCCUPATIONAL REHABILITATION
LA English
DT Article
DE Psychosocial risk factors; MEDLINE; Bibliometrics
ID OCCUPATIONAL-HEALTH; BIBLIOGRAPHIC DATABASES; SYSTEMATIC REVIEWS;
   MEDLINE; PERFORMANCE; SUFFICIENT; FILTERS
AB PurposeThis study aims to report on the effectiveness of various search strategies and keywords to find studies on work-related psychosocial risk factors (PRF) in the PubMed bibliographic database.MethodsWe first selected by hand-searching 191articles published on PRF and indexed in PubMed. We extracted 30 relevant MeSH terms and 38 additional textwords. We then searched PubMed combining these 68 keywords and 27 general keywords on work-related factors. Among the 2953 articles published in January 2020, we identified 446 articles concerning exposure to PRF, which were gathered in a Gold Standard database. We then computed the Recall, Precision, and Number Needed to Read of each keyword or combination of keywords.ResultsOverall, 189 search-words alone or in combination were tested. The highest Recall with a single MeSH term or textword was 43% and 35%, respectively. Subsequently, we developed two different search strings, one optimizing Recall while keeping Precision acceptable (Recall 98.2%, Precision 5.9%, NNR 16.9) and one optimizing Precision while keeping Recall acceptable (Recall 73.1%, Precision 25.5%, NNR 9.7).ConclusionsNo single MeSH term is available to identify relevant studies on PRF in PubMed. Locating these types of studies requires the use of various MeSH and non-MeSH terms in combination to obtain a satisfactory Recall. Nevertheless, enhancing the Recall of search strategies may lead to lower Precision, and higher NNR, although with a non-linear trend. This factor must be taken into consideration when searching PubMed.
C1 [Gehanno, Jean-Francois; Rollin, Laetitia] Rouen Univ Hosp, Inst Occupat Med, 1 Rue Germont, F-76000 Rouen, France.
   [Gehanno, Jean-Francois; Rollin, Laetitia] Univ Paris 13, Rouen Univ, Sorbonne Univ, Lab Med Informat & Knowledge Engn E Hlth,Inserm,LI, Paris, France.
   [Thaon, Isabelle] CHRU Nancy, Ctr Consultat Pathol Profess, Vandoeuvre Les Nancy, France.
   [Pelissier, Carole] Univ Gustave Eiffel IFSTTAR, Hosp Univ Ctr St Etienne, Univ Lyon 1, Univ St Etienne, F-42005 St Etienne, France.
   [Pelissier, Carole] UMRESTTE UMR T9405, F-42005 St Etienne, France.
C3 Universite de Rouen Normandie; CHU de Rouen; Universite Paris 13;
   Universite de Rouen Normandie; Sorbonne Universite; Institut National de
   la Sante et de la Recherche Medicale (Inserm); CHU de Nancy; Universite
   Jean Monnet; Universite Claude Bernard Lyon 1
RP Gehanno, JF (corresponding author), Rouen Univ Hosp, Inst Occupat Med, 1 Rue Germont, F-76000 Rouen, France.; Gehanno, JF (corresponding author), Univ Paris 13, Rouen Univ, Sorbonne Univ, Lab Med Informat & Knowledge Engn E Hlth,Inserm,LI, Paris, France.
EM jf.gehanno@chu-rouen.fr
RI Thaon, Isabelle M/AAE-8650-2020
OI Thaon, Isabelle M/0000-0002-1462-3722; Gehanno,
   jean-francois/0000-0002-2309-7322
CR Brun Emmanuelle., 2007, EXPERT FORECAST EMER
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NR 25
TC 1
Z9 1
U1 0
U2 8
PU SPRINGER/PLENUM PUBLISHERS
PI NEW YORK
PA 233 SPRING ST, NEW YORK, NY 10013 USA
SN 1053-0487
EI 1573-3688
J9 J OCCUP REHABIL
JI J. Occup. Rehabil.
PD DEC
PY 2023
VL 33
IS 4
BP 776
EP 784
DI 10.1007/s10926-023-10110-w
EA MAR 2023
PG 9
WC Rehabilitation; Social Issues
WE Social Science Citation Index (SSCI)
SC Rehabilitation; Social Issues
GA AH2I3
UT WOS:000953888300001
PM 36941513
DA 2024-09-05
ER
PT J
AU Aljohani, NR
   Fayoumi, A
   Saeed-Ul Hassan
AF Aljohani, Naif Radi
   Fayoumi, Ayman
   Saeed-Ul Hassan
TI An in-text citation classification predictive model for a scholarly
   search system
SO SCIENTOMETRICS
LA English
DT Article
DE Citation classification; Machine learning; In-text citations; Scholarly
   search systems; Bibliometric-enhanced information retrieval
ID SCIENTIFIC ARTICLES; CONTEXT; IDENTIFICATION; RETRIEVAL; DOCUMENTS;
   KNOWLEDGE
AB We argue that citations in scholarly documents do not always perform equivalent functions or possess equal importance. To address this problem, we worked with a corpus of over 21 k citations from the Association for Computational Linguistics, from which 465 citations were randomly annotated by experts as either important or unimportant. We used an array of machine-learning models on these annotated citations: Random Forest (RF); Support Vector Machine (SVM); and Decision Tree (DT). For the classification task, the selected models employed 15 novel features: contextual; quantitative; and qualitative. We show that the RF model outperformed the comparative model by 9.52%, achieving a 92% precision-recall area under the curve. We present a prototype of a scientific publication search system based on the RF prediction model for feature engineering. This was used on a dataset of 4138 full-text articles indexed by PLOS ONE that consists of 31,839 unique references. The empirical evaluation shows that the proposed search system improves visibility of a given scientific document by including, along with its index terms, terms from the works that it cites that are predicted to be important. Overall, this yields improved search results against the queries by the user.
C1 [Aljohani, Naif Radi; Fayoumi, Ayman] King Abdulaziz Univ, Fac Comp & Informat Technol, Jeddah, Saudi Arabia.
   [Saeed-Ul Hassan] Informat Technol Univ, 346-B Ferozepur Rd, Lahore, Pakistan.
C3 King Abdulaziz University
RP Saeed-Ul Hassan (corresponding author), Informat Technol Univ, 346-B Ferozepur Rd, Lahore, Pakistan.
EM nraljohani@kau.edu.sa; afayoumi@kau.edu.sa; saeed-ul-hassan@itu.edu.pk
RI Aljohani, Naif R/S-1109-2017; Fayoumi, Ayman/E-7236-2014; Hassan,
   Saeed-Ul/G-1889-2016
OI Fayoumi, Ayman/0000-0002-4160-3305; Hassan, Saeed-Ul/0000-0002-6509-9190
FU Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah
   [RG-14-611-40]
FX This project was funded by the Deanship of Scientific Research (DSR),
   King Abdulaziz University, Jeddah, under grant No. RG-14-611-40. The
   authors, therefore, gratefully acknowledge DSR technical and financial
   support.
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NR 59
TC 6
Z9 7
U1 6
U2 39
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0138-9130
EI 1588-2861
J9 SCIENTOMETRICS
JI Scientometrics
PD JUL
PY 2021
VL 126
IS 7
SI SI
BP 5509
EP 5529
DI 10.1007/s11192-021-03986-z
EA APR 2021
PG 21
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA SY3VS
UT WOS:000644380300024
DA 2024-09-05
ER
PT J
AU Liu, JY
   Xia, F
   Feng, X
   Ren, J
   Liu, H
AF Liu, Jiaying
   Xia, Feng
   Feng, Xu
   Ren, Jing
   Liu, Huan
TI Deep Graph Learning for Anomalous Citation Detection
SO IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
LA English
DT Article
DE Anomaly detection; Image edge detection; Bibliometrics; Task analysis;
   Semantics; Network analyzers; Representation learning; Anomalous
   citation; deep graph learning; network representation; scholarly network
   analysis (SNA)
ID SELF-CITATION; NETWORKS
AB Anomaly detection is one of the most active research areas in various critical domains, such as healthcare, fintech, and public security. However, little attention has been paid to scholarly data, that is, anomaly detection in a citation network. Citation is considered as one of the most crucial metrics to evaluate the impact of scientific research, which may be gamed in multiple ways. Therefore, anomaly detection in citation networks is of significant importance to identify manipulation and inflation of citations. To address this open issue, we propose a novel deep graph learning model, namely graph learning for anomaly detection (GLAD), to identify anomalies in citation networks. GLAD incorporates text semantic mining to network representation learning by adding both node attributes and link attributes via graph neural networks (GNNs). It exploits not only the relevance of citation contents, but also hidden relationships between papers. Within the GLAD framework, we propose an algorithm called Citation PUrpose (CPU) to discover the purpose of citation based on citation context. The performance of GLAD is validated through a simulated anomalous citation dataset. Experimental results demonstrate the effectiveness of GLAD on the anomalous citation detection task.
C1 [Liu, Jiaying] Dalian Univ Technol, Sch Econ & Management, Dalian 116024, Peoples R China.
   [Xia, Feng; Ren, Jing] Federat Univ Australia, Sch Engn IT & Phys Sci, Ballarat, Vic 3353, Australia.
   [Feng, Xu] Dalian Univ Technol, Sch Software, Dalian 116620, Peoples R China.
   [Liu, Huan] Arizona State Univ, Sch Comp Informat & Decis Syst Engn, Tempe, AZ 85281 USA.
C3 Dalian University of Technology; Federation University Australia; Dalian
   University of Technology; Arizona State University; Arizona State
   University-Tempe
RP Xia, F (corresponding author), Federat Univ Australia, Sch Engn IT & Phys Sci, Ballarat, Vic 3353, Australia.
EM f.xia@ieee.org
RI Liu, JY/GYJ-0138-2022; liu, huan/JEO-4705-2023; Xia, Feng/Y-2859-2019;
   liu, huan/JKI-3764-2023; Wang, Luyao/JLL-2001-2023
OI Xia, Feng/0000-0002-8324-1859; Feng, Xu/0000-0002-3923-5590; Liu,
   Jiaying/0000-0001-9090-6305; Ren, Jing/0000-0003-0169-1491
FU National Natural Science Foundation of China [61872054]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant 61872054.
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PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2162-237X
EI 2162-2388
J9 IEEE T NEUR NET LEAR
JI IEEE Trans. Neural Netw. Learn. Syst.
PD JUN
PY 2022
VL 33
IS 6
BP 2543
EP 2557
DI 10.1109/TNNLS.2022.3145092
EA FEB 2022
PG 15
WC Computer Science, Artificial Intelligence; Computer Science, Hardware &
   Architecture; Computer Science, Theory & Methods; Engineering,
   Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA 1T4KK
UT WOS:000754285500001
PM 35143402
OA Green Submitted
DA 2024-09-05
ER
PT J
AU Nahr, N
   Heikkilä, M
AF Nahr, Nora
   Heikkila, Marikka
TI Uncovering the identity of Electronic Markets research through text
   mining techniques
SO ELECTRONIC MARKETS
LA English
DT Article
DE Electronic Markets journal; Core identity; Text mining analysis; Cluster
   analysis; Unsupervised machine learning; Bibliometric analysis
ID VALUE CO-CREATION; FINANCIAL SERVICES; RISK; TECHNOLOGIES; MANAGEMENT;
   IMPACT; INNOVATIVENESS; METAANALYSIS; EXPERIENCES; ALGORITHMS
AB As an established academic journal in the e-commerce and digital platforms fields, Electronic Markets (EM) features a diverse range of topics and occupies a significant role in the information systems field. The study investigates EM's topic diversity over the time period 2009-2020 using a text mining analysis and a bibliometric analysis and identifies 28 cluster groups. The analysis reveals that the top three topics are 1) service quality, 2) blockchain and other shared trust building solutions, their impact and credibility, as well as 3) consumer buying behavior and interactions. EM's core identity lies in a balanced set of core themes that bring technological, business or human/ social perspectives to the research of networked business and digital economy. This includes research on digital and smart services, applications, consumer behavior and business models, as well as technology and e-commerce data. Ethical and sustainability related topics are however still less present in EM.
C1 [Nahr, Nora] Univ Passau, Chair Informat Syst Informat & IT Serv Management, Innstr 43, D-94032 Passau, Germany.
   [Heikkila, Marikka] Univ Turku, Sch Econ, Rehtorinpellonkatu 3, Turku 20500, Finland.
C3 University of Passau; University of Turku
RP Nahr, N (corresponding author), Univ Passau, Chair Informat Syst Informat & IT Serv Management, Innstr 43, D-94032 Passau, Germany.
EM nora.nahr@uni-passau.de; marikka.heikkila@utu.fi
RI Heikkilä, Marikka/AAU-4000-2021
OI Heikkilä, Marikka/0000-0002-7298-7217; Nahr, Nora/0000-0002-2859-4088
FU Projekt DEAL
FX Open Access funding enabled and organized by Projekt DEAL.
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NR 87
TC 4
Z9 4
U1 8
U2 36
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1019-6781
EI 1422-8890
J9 ELECTRON MARK
JI Electron. Mark.
PD SEP
PY 2022
VL 32
IS 3
SI SI
BP 1257
EP 1277
DI 10.1007/s12525-022-00560-0
EA JUL 2022
PG 21
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 6G4VT
UT WOS:000824995600001
OA hybrid
DA 2024-09-05
ER
PT J
AU Aagaard, T
   Lund, H
   Juhl, C
AF Aagaard, Thomas
   Lund, Hans
   Juhl, Carsten
TI Optimizing literature search in systematic reviews - are MEDLINE, EMBASE
   and CENTRAL enough for identifying effect studies within the area of
   musculoskeletal disorders?
SO BMC MEDICAL RESEARCH METHODOLOGY
LA English
DT Article
DE Information retrieval; Bibliometric; MECIR guidelines; Evidence-based
   medicine; Relative recall; Literature searching; Systematic review;
   Musculoskeletal area
ID RANDOMIZED CONTROLLED-TRIALS; MENTAL-HEALTH; DATABASES; INTERVENTIONS;
   ARTICLES; INFORMATION; PREVENTION; RELEVANT; NETWORK; DISEASE
AB Background: When conducting systematic reviews, it is essential to perform a comprehensive literature search to identify all published studies relevant to the specific research question. The Cochrane Collaborations Methodological Expectations of Cochrane Intervention Reviews (MECIR) guidelines state that searching MEDLINE, EMBASE and CENTRAL should be considered mandatory. The aim of this study was to evaluate the MECIR recommendations to use MEDLINE, EMBASE and CENTRAL combined, and examine the yield of using these to find randomized controlled trials (RCTs) within the area of musculoskeletal disorders.
   Methods: Data sources were systematic reviews published by the Cochrane Musculoskeletal Review Group, including at least five RCTs, reporting a search history, searching MEDLINE, EMBASE, CENTRAL, and adding reference-and hand-searching. Additional databases were deemed eligible if they indexed RCTs, were in English and used in more than three of the systematic reviews. Relative recall was calculated as the number of studies identified by the literature search divided by the number of eligible studies i.e. included studies in the individual systematic reviews. Finally, cumulative median recall was calculated for MEDLINE, EMBASE and CENTRAL combined followed by the databases yielding additional studies.
   Results: Deemed eligible was twenty-three systematic reviews and the databases included other than MEDLINE, EMBASE and CENTRAL was AMED, CINAHL, HealthSTAR, MANTIS, OT-Seeker, PEDro, PsychINFO, SCOPUS, SportDISCUS and Web of Science. Cumulative median recall for combined searching in MEDLINE, EMBASE and CENTRAL was 88.9% and increased to 90.9% when adding 10 additional databases.
   Conclusion: Searching MEDLINE, EMBASE and CENTRAL was not sufficient for identifying all effect studies on musculoskeletal disorders, but additional ten databases did only increase the median recall by 2%. It is possible that searching databases is not sufficient to identify all relevant references, and that reviewers must rely upon additional sources in their literature search. However further research is needed.
C1 [Aagaard, Thomas] Holbaek Univ Hosp, Dept Physiotherapy, Holbaek, Denmark.
   [Aagaard, Thomas; Lund, Hans; Juhl, Carsten] Univ Southern Denmark, Inst Sports Sci & Clin Biomech, Res Unit Musculoskeletal Funct & Physiotherapy, Odense, Denmark.
   [Lund, Hans] Bergen Univ Coll, Ctr Evidence Based Practice, Bergen, Norway.
   [Juhl, Carsten] Copenhagen Univ Hosp, Dept Rehabil, Gentofte, Denmark.
C3 University of Southern Denmark; Western Norway University of Applied
   Sciences; University of Copenhagen
RP Aagaard, T (corresponding author), Holbaek Univ Hosp, Dept Physiotherapy, Holbaek, Denmark.; Aagaard, T (corresponding author), Univ Southern Denmark, Inst Sports Sci & Clin Biomech, Res Unit Musculoskeletal Funct & Physiotherapy, Odense, Denmark.
EM tvaagaard@gmail.com
RI Juhl, Carsten/ISA-2180-2023; Lund, Hans Aage/HWT-2338-2023; Aagaard,
   Thomas/O-6439-2018
OI Lund, Hans Aage/0000-0001-6847-8324; Aagaard,
   Thomas/0000-0002-5098-5982; Juhl, Carsten/0000-0001-8456-5364
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NR 81
TC 36
Z9 36
U1 0
U2 15
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
EI 1471-2288
J9 BMC MED RES METHODOL
JI BMC Med. Res. Methodol.
PD NOV 22
PY 2016
VL 16
AR 161
DI 10.1186/s12874-016-0264-6
PG 11
WC Health Care Sciences & Services
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Health Care Sciences & Services
GA ED0EP
UT WOS:000388516100002
PM 27875992
OA gold, Green Published
DA 2024-09-05
ER
PT J
AU Bragge, J
   Thavikulwat, P
   Töyli, J
AF Bragge, Johanna
   Thavikulwat, Precha
   Toyli, Juuso
TI Profiling 40 Years of Research in <i>Simulation & Gaming</i>
SO SIMULATION & GAMING
LA English
DT Article
DE affinity propagation; bibliometrics; clusters of authors; conventional
   thinking; descriptors; hidden patterns; hot topics; knowledge creation;
   literature review; mapping; new thinking; profiling; prolific authors;
   research articles; research profiling; statistical methods; text mining;
   topic evolution; visualization; work division
AB The authors apply the research profiling method to review all the research that has been published in Simulation & Gaming since the journal's inauguration in 1970. The data include 2,096 articles, of which 1,046 are research articles. The authors identify the prolific authors and their institutional affiliations. They tally referenced articles, title phrases, and descriptors. They find that the most prolific authors neither engage in more work division nor author more conventional thinking articles than less prolific authors and that the 51 prolific authors fall into 7 to 11 clusters.
C1 [Bragge, Johanna] Aalto Univ, Helsinki, Finland.
   [Thavikulwat, Precha] Towson Univ, Towson, MD USA.
   [Toyli, Juuso] Turku Sch Econ & Business Adm, Turku, Finland.
C3 Aalto University; University System of Maryland; Towson University;
   University of Turku
RP Bragge, J (corresponding author), Aalto Univ, Dept Business Technol, Sch Econ, POB 21220, Aalto 00076, Finland.
EM johanna.bragge@aalto.fi; pthavikulwat@towson.edu; juuso.toyli@gmail.com
RI Bragge, Johanna/C-6227-2008; Töyli, Juuso/B-7305-2009
OI Bragge, Johanna/0000-0002-4084-3104
FU Jenny and Antti Wihuri Foundation
FX The author(s) disclosed receipt of the following financial support for
   the research and/or authorship of this article:; The first author
   received funding for authorship of this article from the Jenny and Antti
   Wihuri Foundation, which is gratefully acknowledged.
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NR 24
TC 14
Z9 18
U1 0
U2 0
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 1046-8781
EI 1552-826X
J9 SIMULAT GAMING
JI Simul. Gaming
PD DEC
PY 2010
VL 41
IS 6
BP 869
EP 897
DI 10.1177/1046878110387539
PG 29
WC Education & Educational Research; Psychology, Social; Social Sciences,
   Interdisciplinary
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research; Psychology; Social Sciences - Other
   Topics
GA VG2QN
UT WOS:000445913100004
DA 2024-09-05
ER
PT C
AU Kernerman, VY
   Koenig, MED
AF Kernerman, VY
   Koenig, MED
BE Williams, ME
TI HyMARC-based CIP IS for the Net/Web (General architecture and
   characteristics)
SO 19TH ANNUAL NATIONAL ONLINE MEETING, PROCEEDINGS-1998
LA English
DT Proceedings Paper
CT 19th Annual National Online Meeting
CY MAY 12-14, 1998
CL NEW YORK, NY
DE CIP IS (Cataloging in Publication Information System); DTD (Document
   Type Definition); DSR CAPI (Dewey Speech Recognition Client Application
   Programming Interface); Dublin Core Metadata format; hyperdocument
   (hypertext/hypermedia document); HyMARC (hyperdocument machine readable
   caraloging bibliographic format); Net; NC (network computer); OCLC
   (Online computer library center, inc.); SGML (Standard generalized
   markup language); Web; WebZ (HTTP-Z39 50) server; Z39.50 server
AB Historically, it was the USMARC format that mapped the way from the first library filing systems to modern Electronic CIP (ECIP) and bibliographic online IS. Similarly, the HyMARC may map a way to the CIP IS for the Net/Web. The idea behind this presentation is that not only the requirements of the existing Net/Web systems should determine the specifications of the HyMARC (HDOF) format as it was presented at the 17th NOM (Ref. 1) but these specifications, in turn, would shape the overall architecture of the future CIP IS and general characteristics of its elements modules.
   The suggested architecture and general characteristics of the CIP IS are presented, A comparison of the CIP IS architecture (Fig. 1) with the OCLC Web Spectrum System led to the conclusion that the SGML Grammar Builder, WebZ server, Z39.50 Server, and other software developed and implemented by OCLC can be assembled in accordance with the HyMARC structuring/linking specifications and then utilized in the CIP IS architecture.
C1 Truman Coll, Chicago, IL USA.
NR 0
TC 0
Z9 0
U1 0
U2 0
PU INFORMATION TODAY INC
PI MEDFORD
PA 143 OLD MARLTON PIKE, MEDFORD, NJ 08055 USA
PY 1998
BP 197
EP 204
PG 8
WC Information Science & Library Science
WE Conference Proceedings Citation Index - Social Science &amp; Humanities (CPCI-SSH)
SC Information Science & Library Science
GA BL14G
UT WOS:000074436800022
DA 2024-09-05
ER
PT J
AU Pooja
   Sood, SK
AF Pooja
   Sood, Sandeep Kumar
TI Scientometric Analysis of Quantum Algorithms for VANET Optimization
SO IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
LA English
DT Article; Early Access
DE Quantum computing; Optimization; Vehicular ad hoc networks; Machine
   learning algorithms; Quantum mechanics; Market research; Bibliometrics;
   CiteSpace; quantum algorithm taxonomy; quantum optimization; vehicular
   ad-hoc network (VANET)
ID INSPIRED EVOLUTIONARY ALGORITHM; DIFFERENTIAL EVOLUTION; DESIGN; UAV
AB The rapid proliferation of quantum information technologies, spanning theoretical investigations to practical experiments, has generated a number of research papers and documents in quantum algorithms. Consequently, the current research serves as a gateway for interested readers to comprehend the status quo of quantum algorithms, with a specific focus on vehicular network optimization. It aims to explore the research patterns and latest trends by analyzing the dataset sourced from the Scopus and Web of Science databases. The scientometric implications offer valuable insights into publication patterns, keyword co-occurrence, author co-citation, country collaboration, and burst reference. These analyses delineate the temporal progression, prominent research topics, emerging research areas, leading collaborative nations, prolific authors, and research trends within this knowledge domain. The results reveal that smart power grids, traveling salesman problem, electric vehicle charging, battery life estimation, and air traffic control are emerging research areas. Similarly, quantum approximate optimization algorithms, adiabatic quantum computing, quantum-inspired evolutionary algorithms, and quantum annealing emerge as prominent quantum algorithms employed for vehicular network optimization problems. In addition, systematic literature analysis is objectively conducted to discern key insights, research challenges and future research directions in the current knowledge domain.
C1 [Pooja; Sood, Sandeep Kumar] Natl Inst Technol Kurukshetra, Dept Comp Applicat, Kurukshetra 136119, Haryana, India.
C3 National Institute of Technology (NIT System); National Institute of
   Technology Kurukshetra
RP Pooja (corresponding author), Natl Inst Technol Kurukshetra, Dept Comp Applicat, Kurukshetra 136119, Haryana, India.
EM insanpooja777@gmail.com; san1198@gmail.com
OI K. Sood, Sandeep/0000-0002-8196-5503
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NR 86
TC 0
Z9 0
U1 2
U2 2
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2168-2216
EI 2168-2232
J9 IEEE T SYST MAN CY-S
JI IEEE Trans. Syst. Man Cybern. -Syst.
PD 2024 AUG 2
PY 2024
DI 10.1109/TSMC.2024.3428707
EA AUG 2024
PG 11
WC Automation & Control Systems; Computer Science, Cybernetics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Automation & Control Systems; Computer Science
GA A6R6M
UT WOS:001283791300001
DA 2024-09-05
ER
PT J
AU Savin, IV
   Teplyakov, NS
AF Savin, Ivan V.
   Teplyakov, Nikita S.
TI Using Computational Linguistics to Analyse Main Research Directions in
   Economy of Regions
SO ECONOMY OF REGION
LA English
DT Article
DE topic modelling; machine learning; computational linguistics; text
   mining; literature review; academic journal; spatial economics;
   environmental economics; scientometrics; third-party funding
ID TOPICS; DECADE
AB Over the past decades, the process of knowledge generation has accelerated, producing a lot of scientific publications, which makes reviewing even a relatively narrow subject area very demanding, if not impossible. However, recent text data mining tools can assist researchers in conducting such analysis in an objective and time-efficient way. We conduct such a literature review on 1307 articles published in the journal Economy of Regions from 2010 to 2021 using advanced topic modelling techniques. This analysis aims to describe the main research areas in the journal over time, the dynamics of their popularity and the relationship with key quantitative indicators. We identified 22 topics ranging from "Agriculture" and "Economic Geography" to "Fiscal Policy" and "Entrepreneurship". We estimate how popularity of these topics was changing over time and find topics that gained the most popularity from 2010 to 2021 (+17.61 %, "Spatial Economics") or lost it (-14.58 %, "Economics of Innovation"). The topic of environmental economics collects the largest number of citations per article (3.64, on average), and the topics on monetary policy and poverty are the most popular among manuscripts in English, which is also true for articles written by authors with foreign affiliation. Papers with third-party funding are concentrated the most in "Spatial Economics" (around 11 %), and the least - in "Agriculture". Our results can help to understand the evolution in scope of research of Economy of Regions and serve researchers to find promising directions for future studies.
C1 [Savin, Ivan V.] Ural Fed Univ, Acad Dept Econ, 19 Mira St, Ekaterinburg 620002, Russia.
   [Savin, Ivan V.] Univ Autonoma Barcelona, Inst Environm Sci & Technol ICTA, Cerdanyola Del Valles, Barcelona, Spain.
   [Teplyakov, Nikita S.] Ural Fed Univ, 19 Mira St, Ekaterinburg 620002, Russia.
C3 Ural Federal University; Autonomous University of Barcelona; Ural
   Federal University
RP Teplyakov, NS (corresponding author), Ural Fed Univ, 19 Mira St, Ekaterinburg 620002, Russia.
EM nekit_teplykov@mail.ru
RI Savin, Ivan/P-9035-2016
OI Savin, Ivan/0000-0002-9469-0510
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NR 33
TC 3
Z9 3
U1 1
U2 1
PU RUSSIAN ACAD SCIENCES, URAL BRANCH, INST ECONOMICS
PI EKATERINBURG
PA UL MOSKOVSKAYA 29, EKATERINBURG, 620014, RUSSIA
SN 2072-6414
EI 2411-1406
J9 EKON REG
JI Ekon. Reg.
PY 2022
VL 18
IS 2
BP 338
EP 352
DI 10.17059/ekon.reg.2022-2-3
PG 15
WC Area Studies
WE Emerging Sources Citation Index (ESCI)
SC Area Studies
GA F1DJ0
UT WOS:000979818500003
OA gold, Green Published, Green Submitted
DA 2024-09-05
ER
PT J
AU Ray, M
   Ramasubramanian, V
   Singh, KN
   Rathod, S
   Shekhawat, RS
AF Ray, Mrinmoy
   Ramasubramanian, V
   Singh, K. N.
   Rathod, Santosha
   Shekhawat, Ravindra Singh
TI Technology Forecasting for Envisioning Bt Technology Scenario in Indian
   Agriculture
SO AGRICULTURAL RESEARCH
LA English
DT Article
DE Scientometrics; Grey model; Cross impact analysis (CIA) technique;
   Activity Index (AI); Genetic Algorithm (GA); TOPSIS; MICMAC; CIAT
ID IMPACT
AB For scoping the future prospects of Bacillus thuringiensis (Bt) technology in Indian agricultural scenario, case studies of three quantitative/quasi-quantitative techniques of Technology Forecasting tools viz., activity index (AI)-based scientometric analysis, Grey modeling and cross impact analysis (CIA) techniques have been done. Under AI-based scientometric analysis, information relating to abstract, keywords, authors, affiliation, etc., relevant to research publication on applications of Bt technology in India vis-a-vis three other competing country regions-China, USA cum Canada and European countries were collected from ScienceDirect database for the period 1997-2017. AI has been constructed for seven domains viz. Bt Cotton, Bt Maize, Bt Mustard, Bt Brinjal, Bt Soybean, Bt Sunflower, Bt Rice, and 'Bt related but not crop specific' under these four regions considered. From the values of AI, it has been found that India's research effort is higher only in Bt Cotton and Bt Mustard than the other regions considered. Secondly, for Grey modeling, its conventional version as well as Grey model improved by Genetic Algorithm (GA) were fitted using yearly Bt cotton yield of India (2002-2003 to 2016-2017) obtained from Cotton Advisory Board of India. Only the first 11 years were utilized for model fitting and the rest were utilized for validation purposes. The results revealed that Grey model improved by GA performed better. Lastly, for employing CIA technique to study the direct as well as indirect cross impacts of Bt technology, 14 factors were considered. Three types of CIA techniques viz., Direct Classification, Cross-Impact Matrix Multiplication Applied to Classification, and CIA with Time Consideration have been attempted. The ranking of the factors obtained by three methods was combined using Technique for Order Preference by Similarity to an Ideal Solution approach. The analysis suggested that factors viz., Government policy, Bt seed sector, and technological interventions came out to be mainly responsible for future prospects of Bt technology in India.
C1 [Ray, Mrinmoy; Ramasubramanian, V; Singh, K. N.; Shekhawat, Ravindra Singh] ICAR Indian Agr Stat Res Inst, New Delhi 110012, India.
   [Rathod, Santosha] ICAR Indian Inst Rice Res, Hyderabad 500030, India.
C3 Indian Council of Agricultural Research (ICAR); ICAR - Indian
   Agricultural Statistics Research Institute; Indian Council of
   Agricultural Research (ICAR); ICAR - Indian Institute of Rice Research
RP Ray, M (corresponding author), ICAR Indian Agr Stat Res Inst, New Delhi 110012, India.
EM mrinmoy.ray@icar.gov.in
RI Ray, Mrinmoy/IAM-4275-2023
OI Rathod, Santosha/0000-0001-9820-149X; Ray, Mrinmoy/0000-0002-1337-0348
CR Abramo G, 2018, J INFORMETR, V12, P590, DOI 10.1016/j.joi.2018.05.001
   [Anonymous], 2011, ANN LIBR INF STUD
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NR 12
TC 1
Z9 1
U1 0
U2 6
PU SPRINGER INDIA
PI NEW DELHI
PA 7TH FLOOR, VIJAYA BUILDING, 17, BARAKHAMBA ROAD, NEW DELHI, 110 001,
   INDIA
SN 2249-720X
EI 2249-7218
J9 AGR RES
JI Agric. Res.
PD DEC
PY 2022
VL 11
IS 4
BP 747
EP 757
DI 10.1007/s40003-022-00612-z
EA FEB 2022
PG 11
WC Agronomy
WE Emerging Sources Citation Index (ESCI)
SC Agriculture
GA 6I1EQ
UT WOS:000755402100003
DA 2024-09-05
ER
PT J
AU Choudhary, V
   Tanwar, S
   Choudhury, T
AF Choudhary, Vandana
   Tanwar, Sarvesh
   Choudhury, Tanupriya
TI Evaluation of contemporary intrusion detection systems for internet of
   things environment
SO MULTIMEDIA TOOLS AND APPLICATIONS
LA English
DT Article
DE Intrusion Detection System (IDS); Internet of Things (IoT); Bibliometric
   Analysis; Convolutional Neural Network (CNN); Aquila Optimization (AO)
ID IOT; ANALYTICS
AB Internet of Things (IoT) involves wide-ranging devices connected through the Internet with an aim to enable coherent communication amongst them without human intervention to realize profuse smart applications which inherently makes our life a lot easier and furthermore productive. These connected devices continuously sense and gather information from surroundings, thereby producing an immense amount of data that cater for big data analytics. In the current era, number of smart devices are increasing rapidly due to the magnificent features they offer. Moreover, public access to the Internet makes the system even more vulnerable to intrusions. Catastrophically, this has fascinated numerous cybercriminals who have turned the IoT ecosystem into a hotbed of illicit activities. Thereupon, implication of Intrusion Detection System (IDS) in IoT is apparent. The literature suggests a number of IDS to address intrusions/attacks in the discipline of IoT. In the current paper, besides Systematic Literature Review of the IDS for IoT environment, a deep learning model with aquila optimization is proposed to predict anomaly using IoTID20, UNSW-NB15-1 and UNSW_2018_IoT_Botnet_Full5pc_4 datasets. The hybrid model that we have developed, uses a combined network structure of convolutional neural network and aquila optimization algorithm. In all of the studies that were carried out, the swarm intelligence-driven deep learning strategy outperformed other, comparable approaches. Based on current findings, it is reasonable to draw the conclusion that the suggested technique offers an efficient method for early anomaly detection and contributes to viable control of anomaly in the IoT environment.
C1 [Choudhary, Vandana; Tanwar, Sarvesh] Amity Univ, Amity Inst Informat Technol, Noida 201301, Uttar Pradesh, India.
   [Choudhury, Tanupriya] Univ Petr & Energy Studies, Sch Comp Sci, Informat Cluster, Dehra Dun 248007, Uttarakhand, India.
C3 Amity University Noida; University of Petroleum & Energy Studies (UPES)
RP Choudhary, V; Tanwar, S (corresponding author), Amity Univ, Amity Inst Informat Technol, Noida 201301, Uttar Pradesh, India.
EM vandana.choudhary@s.amity.edu; s.tanwar1521@gmail.com;
   tanupriya1986@gmail.com
OI Choudhury, Tanupriya/0000-0002-9826-2759; Tanwar,
   Sarvesh/0000-0003-0136-0182
CR Abu Al-Haija Q, 2022, ELECTRONICS-SWITZ, V11, DOI 10.3390/electronics11040556
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NR 59
TC 3
Z9 3
U1 1
U2 3
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1380-7501
EI 1573-7721
J9 MULTIMED TOOLS APPL
JI Multimed. Tools Appl.
PD JAN
PY 2024
VL 83
IS 3
BP 7541
EP 7581
DI 10.1007/s11042-023-15918-5
EA JUN 2023
PG 41
WC Computer Science, Information Systems; Computer Science, Software
   Engineering; Computer Science, Theory & Methods; Engineering, Electrical
   & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA GY5L7
UT WOS:001005496300002
DA 2024-09-05
ER
PT J
AU Ehlers, C
   Wiesener, N
   Teichgräber, U
   Guntinas-Lichius, O
AF Ehlers, Claudia
   Wiesener, Nadine
   Teichgraeber, Ulf
   Guntinas-Lichius, Orlando
TI Reformed conventional curriculum promoting the professional interest
   orientation of students of medicine: JENOS
SO GMS JOURNAL FOR MEDICAL EDUCATION
LA English
DT Article
DE JENa professional interest-Oriented Studies (JENOS); Ambulatory-oriented
   medicine (AoM); bottom-up strategy; Canadian Medical Education
   Directions of Specialist (CanMEDS) rolls; Clinic-oriented medicine
   (KoM); constructive alignment; costs; curriculum; deep learning;
   evaluations; Flexner model; identification; incentives; interactivity;
   JUH-specific lecturer and student information system (DOSIS); learning
   portfolio; longitudinal curriculum; mapping; Master Plan 2020; medical
   didactic programmes; mentoring; organisational difficulties;
   performance-based compensation; practical orientation; professional
   interest orientation; reduction of the curriculum; reform; reinforcement
   of ambulatory and general medicine; research-oriented Medicine (FoM);
   resources; scientific orientation; small group modules; student centered
   learning
AB Introduction: In the last ten years, the medical faculty at Friedrich Schiller University Jena has reformed its traditional curriculum for human medicine. The reformed JENa professional interest-Oriented Studies (JEnaer Neigungs-Orientiertes Studium, JENOS) - with the objective to facilitate career entry through a professional interest-oriented practical approach - emerged due to the stipulation of cost neutrality.
   Methods: Report on the process sequence of JENOS from the reform idea to implementation: the initial processes, the development and assessment process with accompanying dialogue and dispute of the reform process within the faculty shall be discussed. The 17 objectives of the JENOS reformed traditional curriculum shall be presented and the current level of fulfilment assessed.
   Results: The structural link of the professional interest-oriented proposals was achieved through the recognition by the "Landesprufungsamt" (State Examination Board) as elective subjects with 21 semester hours (SH). Feedback and evaluations were conducted using lecturer and student information systems that were implemented in parallel. Eleven of 17 objectives have been achieved, three are still in process and three have not been achieved.
   Discussion: A professional interest orientation could be achieved through the reform. The weaknesses are found primarily in the links between teaching content. These are currently undergoing a mapping process in order to be optimised.
   Conclusions: Despite cost neutrality, JENOS is the successful result of reforming the curriculum. The academic reform complied with some requirements for the Master Plan 2020 for Medical Studies in order to be able to implement future changes.
C1 [Ehlers, Claudia; Wiesener, Nadine; Guntinas-Lichius, Orlando] Friedrich Schiller Univ Jena, Med Fac, Studies, Jena, Germany.
   [Teichgraeber, Ulf] Jena Univ Hosp, Inst Diagnost & Intervent Radiol, Jena, Germany.
   [Guntinas-Lichius, Orlando] Jena Univ Hosp, ENT Dept, Jena, Germany.
C3 Friedrich Schiller University of Jena; Friedrich Schiller University of
   Jena; Friedrich Schiller University of Jena
RP Ehlers, C (corresponding author), Friedrich Schiller Univ, Med Fak, Studiendekanat, Bachstr 18, D-07743 Jena, Germany.
EM claudia.ehlers@med.uni-jena.de
RI Teichgräber, med. Ulf/H-8562-2019; Guntinas-Lichius, Orlando/L-1871-2016
OI Teichgräber, med. Ulf/0000-0002-4048-3938; Guntinas-Lichius,
   Orlando/0000-0001-9671-0784
CR [Anonymous], 2017, MAST MED 2020
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NR 19
TC 6
Z9 6
U1 0
U2 3
PU GERMAN MEDICAL SCIENCE-GMS
PI DUESSELDORF
PA UBIERSTRASSE 20, DUESSELDORF, 40223, GERMANY
SN 2366-5017
J9 GMS J MED EDU
JI GMS J. Med. Educ.
PY 2019
VL 36
IS 5
SI SI
AR Doc50
DI 10.3205/zma001258
PG 22
WC Education, Scientific Disciplines
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA JE0XH
UT WOS:000490417000004
PM 31815160
DA 2024-09-05
ER
PT C
AU Siddike, MAK
   Kohda, Y
AF Siddike, Md Abul Kalam
   Kohda, Youji
GP IEEE
TI Service Innovation Research in the World: A Bibliometric Analysis
SO 2013 INTERNATIONAL CONFERENCE ON SERVICE SCIENCES (ICSS 2013)
SE International Conference on Service Sciences
LA English
DT Proceedings Paper
CT International Conference on Service Science (ICSS)
CY APR 11-13, 2013
CL Shenzhen, PEOPLES R CHINA
DE activity index (AI); attractive index (AII); productive author index
   (PAI); productive institutions index (PII); productive journal index
   (PJI); publication proficiency index (PEI); service innovation (SI)
ID MANAGEMENT
AB The purpose of the study is to find out the growth and development of research productivity in service innovation (SI) in the world during the period of 2001-2011. Firstly, we explore the overall growth of SI research, and then investigate cross-country comparisons in its research performances, with the focus on the world share, relative research effort, impact and quality of top ten productive countries. Furthermore, we develop productive institution index, productive author index and productive journal index in the field of SI. The data was retrieved using Web of Science (WOS) database. The cross-country comparisons show that USA is the leading country and has the biggest world share of SI research.
C1 [Siddike, Md Abul Kalam; Kohda, Youji] Japan Adv Inst Sci & Technol, Sch Knowledge Sci, Kanazawa, Ishikawa, Japan.
C3 Japan Advanced Institute of Science & Technology (JAIST)
RP Siddike, MAK (corresponding author), Japan Adv Inst Sci & Technol, Sch Knowledge Sci, Kanazawa, Ishikawa, Japan.
EM siddike@jaist.ac.jp; kohda@jaist.ac.jp
RI Siddike, Abul Kalam/AAT-6157-2021
OI Siddike, Abul Kalam/0000-0003-0541-0183
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NR 28
TC 0
Z9 0
U1 0
U2 10
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2165-3828
BN 978-0-7695-4972-9; 978-1-4673-6258-0
J9 INT CONF SERVICE SCI
PY 2013
BP 34
EP 39
DI 10.1109/ICSS.2013.15
PG 6
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Interdisciplinary Applications
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Computer Science
GA BHD52
UT WOS:000325086200007
DA 2024-09-05
ER
EF