Subjects -> LIBRARY AND INFORMATION SCIENCES (Total: 392 journals)
    - DIGITAL CURATION AND PRESERVATION (13 journals)
    - LIBRARY ADMINISTRATION (1 journals)
    - LIBRARY AND INFORMATION SCIENCES (378 journals)

LIBRARY AND INFORMATION SCIENCES (378 journals)                  1 2 | Last

Showing 1 - 200 of 379 Journals sorted alphabetically
027.7 Zeitschrift für Bibliothekskultur / Journal for Library Culture     Open Access   (Followers: 61)
Access     Full-text available via subscription   (Followers: 23)
Acervo : Revista do Arquivo Nacional     Open Access   (Followers: 1)
African Journal of Library, Archives and Information Science     Full-text available via subscription   (Followers: 67)
Against the Grain     Partially Free   (Followers: 127)
AIB Studi     Full-text available via subscription   (Followers: 10)
Alexandría : Revista de Ciencias de la Información     Open Access   (Followers: 11)
Alexandria : The Journal of National and International Library and Information Issues     Full-text available via subscription   (Followers: 56)
Alsic : Apprentissage des Langues et Systèmes d'Information et de Communication     Open Access   (Followers: 12)
American Archivist     Hybrid Journal   (Followers: 135)
American Libraries     Partially Free   (Followers: 195)
Anales de Documentacion     Open Access   (Followers: 14)
Anuari de l'Observatori de Biblioteques, Llibres i Lectura     Open Access   (Followers: 2)
ANZTLA EJournal     Full-text available via subscription  
Archeion Online     Open Access   (Followers: 3)
Archimag     Full-text available via subscription   (Followers: 4)
Archival Science     Hybrid Journal   (Followers: 64)
Archivaria     Open Access   (Followers: 33)
Archives     Full-text available via subscription   (Followers: 7)
Archives and Manuscripts     Hybrid Journal   (Followers: 51)
Archives and Museum Informatics     Hybrid Journal   (Followers: 97)
Ariadne Magazine     Open Access   (Followers: 154)
Art Libraries Journal     Hybrid Journal   (Followers: 10)
Aslib Journal of Information Management     Hybrid Journal   (Followers: 33)
Aslib Proceedings     Hybrid Journal   (Followers: 152)
AtoZ : novas práticas em informação e conhecimento     Open Access  
Australasian Journal of Information Systems     Open Access   (Followers: 17)
Australasian Public Libraries and Information Services     Full-text available via subscription   (Followers: 31)
Australian Academic & Research Libraries     Full-text available via subscription   (Followers: 92)
Australian Library Journal     Full-text available via subscription   (Followers: 157)
Baca : Jurnal Dokumentasi dan Informasi     Open Access   (Followers: 2)
Bangladesh Journal of Library and Information Science     Open Access   (Followers: 45)
Behavioral & Social Sciences Librarian     Hybrid Journal   (Followers: 150)
Berkala Ilmu Perpustakaan dan Informasi     Open Access  
Biblios     Open Access   (Followers: 11)
Biblioteca Escolar em Revista     Open Access  
Biblioteca Universitaria     Open Access   (Followers: 14)
Bibliotecas : Revista de la Escuela de Bibliotecología, Documentación e Información     Open Access   (Followers: 3)
Bibliotecas Universitárias : pesquisas, experiências e perspectivas     Open Access   (Followers: 1)
Bibliotecas. Anales de Investigacion     Open Access   (Followers: 1)
Biblioteka     Open Access   (Followers: 2)
Biblioteka i Edukacja     Open Access   (Followers: 5)
Bibliotheca Orientalis     Full-text available via subscription   (Followers: 14)
BIBLIOTIKA : Jurnal Kajian Perpustakaan dan Informasi     Open Access  
BIBLOS - Revista do Departamento de Biblioteconomia e História     Open Access   (Followers: 7)
BiD : textos universitaris de biblioteconomia i documentació     Open Access   (Followers: 10)
Bilgi Dünyası     Open Access   (Followers: 5)
Biodiversity Information Science and Standards     Open Access   (Followers: 2)
Bioinformatics     Hybrid Journal   (Followers: 226)
Biuletyn EBIB     Open Access  
Boletín Cultural y Bibliográfico     Open Access   (Followers: 2)
Book History     Full-text available via subscription   (Followers: 121)
Bridgewater Review     Open Access   (Followers: 4)
Bulletin des bibliotheques de France     Full-text available via subscription   (Followers: 7)
Bulletin of the Association for Information Science and Technology     Open Access   (Followers: 25)
Bulletin of the John Rylands Library     Hybrid Journal   (Followers: 21)
Canadian Journal of Academic Librarianship     Open Access   (Followers: 20)
Canadian Journal of Information and Library Science     Full-text available via subscription   (Followers: 254)
Cataloging & Classification Quarterly     Hybrid Journal   (Followers: 179)
CERN IdeaSquare Journal of Experimental Innovation     Open Access  
Children and Libraries : The Journal of the Association for Library Service to Children     Full-text available via subscription   (Followers: 16)
CIC. Cuadernos de Informacion y Comunicacion     Open Access   (Followers: 5)
Ciência da Informação em Revista     Open Access   (Followers: 2)
Code4Lib Journal     Open Access   (Followers: 182)
Collaborative Librarianship     Open Access   (Followers: 52)
Collection and Curation     Hybrid Journal   (Followers: 11)
College & Research Libraries     Open Access   (Followers: 464)
College & Research Libraries News     Partially Free   (Followers: 256)
College & Undergraduate Libraries     Hybrid Journal   (Followers: 231)
Communicate : Journal of Library and Information Science     Full-text available via subscription   (Followers: 64)
Communication Booknotes Quarterly     Hybrid Journal   (Followers: 15)
Communications in Information Literacy     Open Access   (Followers: 203)
Community & Junior College Libraries     Hybrid Journal   (Followers: 44)
Cuadernos de Gestión de Información     Open Access   (Followers: 1)
Data Curation Profiles Directory     Open Access   (Followers: 6)
Data Technologies and Applications     Hybrid Journal   (Followers: 217)
DESIDOC Journal of Library & Information Technology     Open Access   (Followers: 98)
Digital Library Perspectives     Hybrid Journal   (Followers: 40)
Digital Platform: Information Technologies in Sociocultural Sphere     Open Access   (Followers: 1)
Documentación de las Ciencias de la Información     Open Access   (Followers: 1)
Documentation et bibliothèques     Full-text available via subscription   (Followers: 9)
e & i Elektrotechnik und Informationstechnik     Hybrid Journal   (Followers: 8)
e-Ciencias de la Información     Open Access   (Followers: 1)
Eastern Librarian     Open Access   (Followers: 11)
EDUCAUSE Review     Full-text available via subscription   (Followers: 141)
Edulib : Journal of Library and Information Science     Open Access   (Followers: 27)
Egyptian Informatics Journal     Open Access   (Followers: 5)
El Profesional de la Informacion     Full-text available via subscription   (Followers: 18)
eLucidate     Open Access   (Followers: 7)
Emerging Library & Information Perspectives     Open Access   (Followers: 30)
Encontros Bibli : revista eletrônica de biblioteconomia e ciência da informação     Open Access   (Followers: 3)
Ethics and Information Technology     Hybrid Journal   (Followers: 64)
European Journal of Information Systems     Hybrid Journal   (Followers: 86)
European Science Editing     Open Access   (Followers: 1)
Evidence Based Library and Information Practice     Open Access   (Followers: 393)
Florida Libraries     Open Access   (Followers: 1)
Folia Bibliologica     Open Access  
Forensic Science International: Digital Investigation     Full-text available via subscription   (Followers: 319)
Foundations and Trends® in Information Retrieval     Full-text available via subscription   (Followers: 30)
Georgia Library Quarterly     Open Access   (Followers: 21)
Ghana Library Journal     Full-text available via subscription   (Followers: 16)
Global Knowledge, Memory and Communication     Hybrid Journal   (Followers: 819)
GSI Journals Serie C : Advancements in Information Sciences and Technologies     Open Access   (Followers: 1)
Health Information Management Journal     Hybrid Journal   (Followers: 24)
Hipertext.net : Anuario Académico sobre Documentación Digital y Comunicación Interactiva     Open Access   (Followers: 1)
HLA News     Full-text available via subscription   (Followers: 2)
IASSIST Quarterly     Open Access  
Idaho Librarian     Free   (Followers: 8)
IFLA Journal     Hybrid Journal   (Followers: 229)
In Monte Artium     Full-text available via subscription   (Followers: 1)
In the Library with the Lead Pipe     Open Access   (Followers: 124)
InCID : Revista de Ciência da Informação e Documentação     Open Access  
InCite     Full-text available via subscription   (Followers: 19)
Informaatiotutkimus     Open Access   (Followers: 4)
Informação & Informação     Open Access   (Followers: 2)
Informação em Pauta     Open Access  
Informacijos mokslai     Open Access  
Información, Cultura y Sociedad     Open Access   (Followers: 2)
Informatio. Revista del Instituto de Información de la Facultad de Información y Comunicación     Open Access  
Information     Open Access   (Followers: 30)
Information & Culture : A Journal of History     Full-text available via subscription   (Followers: 30)
Information Discovery and Delivery     Hybrid Journal   (Followers: 44)
Information Manager (The)     Open Access   (Followers: 29)
Information Processing & Management     Hybrid Journal   (Followers: 145)
Information Retrieval     Hybrid Journal   (Followers: 206)
Information Sciences     Hybrid Journal   (Followers: 187)
Information Systems Frontiers     Hybrid Journal   (Followers: 27)
Information Systems Research     Full-text available via subscription   (Followers: 146)
Information Technologies & International Development     Open Access   (Followers: 82)
Information Technologist (The)     Full-text available via subscription   (Followers: 17)
Information Technology and Libraries     Open Access   (Followers: 312)
Information Today     Full-text available via subscription   (Followers: 35)
Informationspraxis     Open Access   (Followers: 12)
Informationswissenschaft : Theorie, Methode und Praxis     Open Access   (Followers: 4)
iNFOTEZY     Open Access  
Insaniyat : Journal of Islam and Humanities     Open Access   (Followers: 1)
Insights : the UKSG journal     Open Access   (Followers: 62)
InterActions: UCLA Journal of Education and Information     Open Access   (Followers: 11)
Interdisciplinary Journal of e-Skills and Lifelong Learning     Open Access   (Followers: 3)
Interdisciplinary Journal of Information, Knowledge, and Management     Open Access   (Followers: 12)
International Association of School Librarianship Conference Proceedings     Open Access  
International Information & Library Review     Hybrid Journal   (Followers: 404)
International Journal of Bibliometrics in Business and Management     Hybrid Journal   (Followers: 2)
International Journal of Business Information Systems     Hybrid Journal   (Followers: 14)
International Journal of Cooperative Information Systems     Hybrid Journal   (Followers: 4)
International Journal of Digital Curation     Open Access   (Followers: 81)
International Journal of Digital Library Systems     Full-text available via subscription   (Followers: 73)
International Journal of Doctoral Studies     Open Access   (Followers: 6)
International Journal of Information and Decision Sciences     Hybrid Journal   (Followers: 10)
International Journal of Information Management     Hybrid Journal   (Followers: 163)
International Journal of Information Privacy, Security and Integrity     Hybrid Journal   (Followers: 25)
International Journal of Information Retrieval Research     Full-text available via subscription   (Followers: 28)
International Journal of Information Science and Management     Open Access   (Followers: 6)
International Journal of Information Technology, Communications and Convergence     Hybrid Journal   (Followers: 14)
International Journal of Information, Diversity, & Inclusion     Open Access   (Followers: 3)
International Journal of Intellectual Property Management     Hybrid Journal   (Followers: 26)
International Journal of Intercultural Information Management     Hybrid Journal   (Followers: 12)
International Journal of Legal Information     Full-text available via subscription   (Followers: 50)
International Journal of Librarianship     Open Access   (Followers: 25)
International Journal of Library and Information Science     Open Access   (Followers: 248)
International Journal of Library Science     Open Access   (Followers: 269)
International Journal of Library Science     Full-text available via subscription   (Followers: 55)
International Journal of Multicriteria Decision Making     Hybrid Journal   (Followers: 8)
International Journal of Multimedia Information Retrieval     Partially Free   (Followers: 8)
International Journal of Organisational Design and Engineering     Hybrid Journal   (Followers: 3)
International Journal of Web Portals     Full-text available via subscription   (Followers: 16)
International Journal on Digital Libraries     Hybrid Journal   (Followers: 550)
InULA Notes : Indiana University Librarians Association     Open Access  
Investigación Bibliotecológica     Open Access   (Followers: 4)
IRIS - Revista de Informação, Memória e Tecnologia     Open Access  
Issues in Informing Science and Information Technology     Open Access   (Followers: 2)
Issues in Science and Technology Librarianship     Open Access   (Followers: 2)
JISTEM : Journal of Information Systems and Technology Management     Open Access   (Followers: 6)
JLIS.it     Open Access   (Followers: 7)
JMIR Medical Informatics     Open Access   (Followers: 10)
Journal of Academic Librarianship     Hybrid Journal   (Followers: 1024)
Journal of Access Services     Hybrid Journal   (Followers: 39)
Journal of Advancements in Library Sciences     Open Access   (Followers: 47)
Journal of Adventist Libraries and Archives     Open Access  
Journal of Altmetrics     Open Access   (Followers: 7)
Journal of Archival Organization     Hybrid Journal   (Followers: 29)
Journal of Copyright in Education & Librarianship     Open Access   (Followers: 29)
Journal of Creative Library Practice     Open Access   (Followers: 98)
Journal of Data Mining and Digital Humanities     Open Access   (Followers: 39)
Journal of Documentation     Hybrid Journal   (Followers: 169)
Journal of East Asian Libraries     Open Access   (Followers: 7)
Journal of Education in Library and Information Science - JELIS     Full-text available via subscription   (Followers: 71)
Journal of Educational Media & Library Sciences     Open Access   (Followers: 9)
Journal of Educational Media, Memory, and Society     Full-text available via subscription   (Followers: 13)
Journal of Electronic Publishing     Open Access   (Followers: 76)
Journal of Electronic Resources Librarianship     Hybrid Journal   (Followers: 231)
Journal of eScience Librarianship     Open Access   (Followers: 115)
Journal of Global Information Management     Full-text available via subscription   (Followers: 9)
Journal of Health & Medical Informatics     Open Access   (Followers: 50)
Journal of Hospital Librarianship     Hybrid Journal   (Followers: 160)
Journal of Information & Knowledge Management     Hybrid Journal   (Followers: 150)
Journal of Information and Data Management     Open Access   (Followers: 14)
Journal of Information Engineering and Applications     Open Access   (Followers: 10)
Journal of Information Literacy     Open Access   (Followers: 790)
Journal of Information Science     Hybrid Journal   (Followers: 1031)

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Similar Journals
Journal Cover
Data Technologies and Applications
Journal Prestige (SJR): 0.355
Citation Impact (citeScore): 1
Number of Followers: 217  
 
Hybrid Journal Hybrid journal   * Containing 2 Open Access Open Access article(s) in this issue *
ISSN (Print) 2514-9288 - ISSN (Online) 2514-9288
Published by Emerald Homepage  [360 journals]
  • A boosting-based transfer learning method to address absolute-rarity in
           skin lesion datasets and prevent weight-drift for melanoma detection

    • Free pre-print version: Loading...

      Authors: Lokesh Singh , Rekh Ram Janghel , Satya Prakash Sahu
      Abstract: Automated skin lesion analysis plays a vital role in early detection. Having relatively small-sized imbalanced skin lesion datasets impedes learning and dominates research in automated skin lesion analysis. The unavailability of adequate data poses difficulty in developing classification methods due to the skewed class distribution. Boosting-based transfer learning (TL) paradigms like Transfer AdaBoost algorithm can compensate for such a lack of samples by taking advantage of auxiliary data. However, in such methods, beneficial source instances representing the target have a fast and stochastic weight convergence, which results in “weight-drift” that negates transfer. In this paper, a framework is designed utilizing the “Rare-Transfer” (RT), a boosting-based TL algorithm, that prevents “weight-drift” and simultaneously addresses absolute-rarity in skin lesion datasets. RT prevents the weights of source samples from quick convergence. It addresses absolute-rarity using an instance transfer approach incorporating the best-fit set of auxiliary examples, which improves balanced error minimization. It compensates for class unbalance and scarcity of training samples in absolute-rarity simultaneously for inducing balanced error optimization. Promising results are obtained utilizing the RT compared with state-of-the-art techniques on absolute-rare skin lesion datasets with an accuracy of 92.5%. Wilcoxon signed-rank test examines significant differences amid the proposed RT algorithm and conventional algorithms used in the experiment. Experimentation is performed on absolute-rare four skin lesion datasets, and the effectiveness of RT is assessed based on accuracy, sensitivity, specificity and area under curve. The performance is compared with an existing ensemble and boosting-based TL methods.
      Citation: Data Technologies and Applications
      PubDate: 2022-06-20
      DOI: 10.1108/DTA-10-2021-0296
      Issue No: Vol. ahead-of-print , No. ahead-of-print (2022)
       
  • Construction of public security indicators based on characteristics of
           shared group behavior patterns

         This is an Open Access Article Open Access Article

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      Authors: XiYue Deng , Xiaoming Li , Zhenzhen Chen , Mengli Zhu , Naixue Xiong , Li Shen
      Abstract: Human group behavior is the driving force behind many complex social and economic phenomena. Few studies have integrated multi-dimensional travel patterns and city interest points to construct urban security risk indicators. This paper combines traffic data and urban alarm data to analyze the safe travel characteristics of the urban population. The research results are helpful to explore the diversity of human group behavior, grasp the temporal and spatial laws and reveal regional security risks. It provides a reference for optimizing resource deployment and group intelligence analysis in emergency management. Based on the dynamics index of group behavior, this paper mines the data of large shared bikes and ride-hailing in a big city of China. We integrate the urban interest points and travel dynamic characteristics, construct the urban traffic safety index based on alarm behavior and further calculate the urban safety index. This study found significant differences in the travel power index among ride-sharing users. There is a positive correlation between user shared bike trips and the power-law bimodal phenomenon in the logarithmic coordinate system. It is closely related to the urban public security index. Based on group-shared dynamic index integrated alarm, we innovatively constructed an urban public safety index and analyzed the correlation of travel alarm behavior. The research results fully reveal the internal mechanism of the group behavior safety index and provide a valuable supplement for the police intelligence analysis.
      Citation: Data Technologies and Applications
      PubDate: 2022-06-03
      DOI: 10.1108/DTA-12-2021-0389
      Issue No: Vol. ahead-of-print , No. ahead-of-print (2022)
       
  • Analyzing the structure of tourism destination network based on digital
           footprints: taking Guilin, China as a case

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      Authors: Caihua Yu , Tonghui Lian , Hongbao Geng , Sixin Li
      Abstract: This paper gathers tourism digital footprint from online travel platforms, choosing social network analysis method to learn the structure of destination networks and to probe into the features of tourist flow network structure and flow characteristics in Guilin of China. The digital footprint of tourists can be applied to study the behaviors and laws of digital footprint. This research contributes to improving the understanding of demand-driven network relationships among tourist attractions in a destination. (1) Yulong River, Yangshuo West Street, Longji Terraced Fields, Silver Rock and Four Lakes are the divergent and agglomerative centers of tourist flow, which are the top tourist attractions for transiting tourists. (2) The core-periphery structure of the network is clearly stratified. More specifically, the core nodes in the network are prominent and the core area of the network has weak interaction with the peripheral area. (3) There are eight cohesive subgroups in the network structure, which contains certain differences in the radiation effects. This research aims at exploring the spatial network structure characteristics of tourism flows in Guilin by analyzing the online footprints of tourists. It takes a good try to analyze the application of network footprint with the research of tourism flow characteristics, and also provides a theoretical reference for the design of tourist routes and the cooperative marketing among various attractions.
      Citation: Data Technologies and Applications
      PubDate: 2022-05-23
      DOI: 10.1108/DTA-09-2021-0240
      Issue No: Vol. ahead-of-print , No. ahead-of-print (2022)
       
  • Privacy-preserving techniques in recommender systems: state-of-the-art
           review and future research agenda

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      Authors: Dhanya Pramod
      Abstract: This study explores privacy challenges in recommender systems (RSs) and how they have leveraged privacy-preserving technology for risk mitigation. The study also elucidates the extent of adopting privacy-preserving RSs and postulates the future direction of research in RS security. The study gathered articles from well-known databases such as SCOPUS, Web of Science and Google scholar. A systematic literature review using PRISMA was carried out on the 41 papers that are shortlisted for study. Two research questions were framed to carry out the review. It is evident from this study that privacy issues in the RS have been addressed with various techniques. However, many more challenges are expected while leveraging technology advancements for fine-tuning recommenders, and a research agenda has been devised by postulating future directions. The study unveils a new comprehensive perspective regarding privacy preservation in recommenders. There is no promising study found that gathers techniques used for privacy protection. The study summarizes the research agenda, and it will be a good reference article for those who develop privacy-preserving RSs.
      Citation: Data Technologies and Applications
      PubDate: 2022-05-04
      DOI: 10.1108/DTA-02-2022-0083
      Issue No: Vol. ahead-of-print , No. ahead-of-print (2022)
       
  • A deep neural networks-based fusion model for COVID-19 rumor detection
           from online social media

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      Authors: Heng-yang Lu , Jun Yang , Wei Fang , Xiaoning Song , Chongjun Wang
      Abstract: The COVID-19 has become a global pandemic, which has caused large number of deaths and huge economic losses. These losses are not only caused by the virus but also by the related rumors. Nowadays, online social media are quite popular, where billions of people express their opinions and propagate information. Rumors about COVID-19 posted on online social media usually spread rapidly; it is hard to analyze and detect rumors only by artificial processing. The purpose of this paper is to propose a novel model called the Topic-Comment-based Rumor Detection model (TopCom) to detect rumors as soon as possible. The authors conducted COVID-19 rumor detection from Sina Weibo, one of the most widely used Chinese online social media. The authors constructed a dataset about COVID-19 from January 1 to June 30, 2020 with a web crawler, including both rumor and non-rumors. The rumor detection task is regarded as a binary classification problem. The proposed TopCom model exploits the topical memory networks to fuse latent topic information with original microblogs, which solves the sparsity problems brought by short-text microblogs. In addition, TopCom fuses comments with corresponding microblogs to further improve the performance. Experimental results on a publicly available dataset and the proposed COVID dataset have shown superiority and efficiency compared with baselines. The authors further randomly selected microblogs posted from July 1–31, 2020 for the case study, which also shows the effectiveness and application prospects for detecting rumors about COVID-19 automatically. The originality of TopCom lies in the fusion of latent topic information of original microblogs and corresponding comments with DNNs-based models for the COVID-19 rumor detection task, whose value is to help detect rumors automatically in a short time.
      Citation: Data Technologies and Applications
      PubDate: 2022-04-22
      DOI: 10.1108/DTA-06-2021-0160
      Issue No: Vol. ahead-of-print , No. ahead-of-print (2022)
       
  • Hybrid data analytic technique for grading fairness

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      Authors: Thepparit Banditwattanawong , Arnon Marco Polo Jankasem , Masawee Masdisornchote
      Abstract: Fair grading produces learning ability levels that are understandable and acceptable to both learners and instructors. Norm-referenced grading can be achieved by several means such as z score, K-means and a heuristic. However, these methods typically deliver the varied degrees of grading fairness depending on input score data. To attain the fairest grading, this paper proposes a hybrid algorithm that integrates z score, K-means and heuristic methods with a novel fairness objective function as a decision function. Depending on an experimented data set, each of the algorithm's constituent methods could deliver the fairest grading results with fairness degrees ranging from 0.110 to 0.646. We also pointed out key factors in the fairness improvement of norm-referenced achievement grading. The main contributions of this paper are four folds: the definition of fair norm-referenced grading requirements, a hybrid algorithm for fair norm-referenced grading, a fairness metric for norm-referenced grading and the fairness performance results of the statistical, heuristic and machine learning methods.
      Citation: Data Technologies and Applications
      PubDate: 2022-04-20
      DOI: 10.1108/DTA-01-2022-0047
      Issue No: Vol. ahead-of-print , No. ahead-of-print (2022)
       
  • Utility optimization-based multi-stakeholder personalized recommendation
           system

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      Authors: Rahul Shrivastava , Dilip Singh Sisodia , Naresh Kumar Nagwani
      Abstract: In a multi-stakeholder recommender system (MSRS), stakeholders are the multiple entities (consumer, producer, system, etc.) benefited by the generated recommendations. Traditionally, the exclusive focus on only a single stakeholders' (for example, only consumer or end-user) preferences obscured the welfare of the others. Two major challenges are encountered while incorporating the multiple stakeholders' perspectives in MSRS: designing a dedicated utility function for each stakeholder and optimizing their utility without hurting others. This paper proposes multiple utility functions for different stakeholders and optimizes these functions for generating balanced, personalized recommendations for each stakeholder. The proposed methodology considers four valid stakeholders user, producer, cast and recommender system from the multi-stakeholder recommender setting and builds dedicated utility functions. The utility function for users incorporates enhanced side-information-based similarity computation for utility count. Similarly, to improve the utility gain, the authors design new utility functions for producer, star-cast and system to incorporate long-tail and diverse items in the recommendation list. Next, to balance the utility gain and generate the trade-off recommendation solution, the authors perform the evolutionary optimization of the conflicting utility functions using NSGA-II. Experimental evaluation and comparison are conducted over three benchmark data sets. The authors observed 19.70% of average enhancement in utility gain with improved mean precision, diversity and novelty. Exposure, hit, reach and target reach metrics are substantially improved. A new approach considers four stakeholders simultaneously with their respective utility functions and establishes the trade-off recommendation solution between conflicting utilities of the stakeholders.
      Citation: Data Technologies and Applications
      PubDate: 2022-04-15
      DOI: 10.1108/DTA-07-2021-0182
      Issue No: Vol. ahead-of-print , No. ahead-of-print (2022)
       
  • An Argentine ant system algorithm for partial set covering problem

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      Authors: Xiaofan Liu , Yupeng Zhou , Minghao Yin , Shuai Lv
      Abstract: The paper aims to provide an efficient meta-heuristic algorithm to solve the partial set covering problem (PSCP). With rich application scenarios, the PSCP is a fascinating and well-known non-deterministic polynomial (NP)-hard problem whose goal is to cover at least k elements with as few subsets as possible. In this work, the authors present a novel variant of the ant colony optimization (ACO) algorithm, called Argentine ant system (AAS), to deal with the PSCP. The developed AAS is an integrated system of different populations that use the same pheromone to communicate. Moreover, an effective local search framework with the relaxed configuration checking (RCC) and the volatilization-fixed weight mechanism is proposed to improve the exploitation of the algorithm. A detailed experimental evaluation of 75 instances reveals that the proposed algorithm outperforms the competitors in terms of the quality of the optimal solutions. Also, the performance of AAS gradually improves with the growing instance size, which shows the potential in handling complex practical scenarios. Finally, the designed components of AAS are experimentally proved to be beneficial to the whole framework. Finally, the key components in AAS have been demonstrated. At present, there is no heuristic method to solve this problem. The authors present the first implementation of heuristic algorithm for solving PSCP and provide competitive solutions.
      Citation: Data Technologies and Applications
      PubDate: 2022-04-13
      DOI: 10.1108/DTA-08-2021-0205
      Issue No: Vol. ahead-of-print , No. ahead-of-print (2022)
       
  • A collaborative trend prediction method using the crowdsourced wisdom of
           web search engines

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      Authors: Ze-Han Fang , Chien Chin Chen
      Abstract: The purpose of this paper is to propose a novel collaborative trend prediction method to estimate the status of trending topics by crowdsourcing the wisdom in web search engines. Government officials and decision makers can take advantage of the proposed method to effectively analyze various trending topics and make appropriate decisions in response to fast-changing national and international situations or popular opinions. In this study, a crowdsourced-wisdom-based feature selection method was designed to select representative indicators showing trending topics and concerns of the general public. The authors also designed a novel prediction method to estimate the trending topic statuses by crowdsourcing public opinion in web search engines. The authors’ proposed method achieved better results than traditional trend prediction methods and successfully predict trending topic statuses by using the crowdsourced wisdom of web search engines. This paper proposes a novel collaborative trend prediction method and applied it to various trending topics. The experimental results show that the authors’ method can successfully estimate the trending topic statuses and outperform other baseline methods. To the best of the authors’ knowledge, this is the first such attempt to predict trending topic statuses by using the crowdsourced wisdom of web search engines.
      Citation: Data Technologies and Applications
      PubDate: 2022-03-28
      DOI: 10.1108/DTA-08-2021-0209
      Issue No: Vol. ahead-of-print , No. ahead-of-print (2022)
       
  • Credit default swap prediction based on generative adversarial networks

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      Authors: Shu-Ying Lin , Duen-Ren Liu , Hsien-Pin Huang
      Abstract: Financial price forecast issues are always a concern of investors. However, the financial applications based on machine learning methods mainly focus on stock market predictions. Few studies have explored credit risk predictions. Understanding credit risk trends can help investors avoid market risks. The purpose of this study is to investigate the prediction model that can effectively predict credit default swaps (CDS). A novel generative adversarial network (GAN) for CDS prediction is proposed. The authors take three features into account that are highly relevant to the future trends of CDS: historical CDS price, news and financial leverage. The main goal of this model is to improve the existing GAN-based regression model by adding finance and news feature extraction approaches. The proposed model adopts an attentional long short-term memory network and convolution network to process historical CDS data and news information, respectively. In addition to enhancing the effectiveness of the GAN model, the authors also design a data sampling strategy to alleviate the overfitting issue. The authors conduct an experiment with a real dataset and evaluate the performance of the proposed model. The components and selected features of the model are evaluated for their ability to improve the prediction performance. The experimental results show that the proposed model performs better than other machine learning algorithms and traditional regression GAN. There are very few studies on prediction models for CDS. With the proposed novel approach, the authors can improve the performance of CDS predictions. The proposed work can thereby increase the commercial value of CDS predictions to support trading decisions.
      Citation: Data Technologies and Applications
      PubDate: 2022-03-24
      DOI: 10.1108/DTA-09-2021-0260
      Issue No: Vol. ahead-of-print , No. ahead-of-print (2022)
       
  • Ranking the ontology development methodologies using the weighted decision
           matrix

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      Authors: Prashant Kumar Sinha , Biswanath Dutta , Udaya Varadarajan
      Abstract: The current work provides a framework for the ranking of ontology development methodologies (ODMs). The framework is a step-by-step approach reinforced by an array of ranking features and a quantitative tool, weighted decision matrix. An extensive literature investigation revealed a set of aspects that regulate ODMs. The aspects and existing state-of-the-art estimates facilitated in extracting the features. To determine weight to each of the features, an online survey was implemented to secure evidence from the Semantic Web community. To demonstrate the framework, the authors perform a pilot study, where a collection of domain ODMs, reported in 2000–2019, is used. State-of-the-art research revealed that ODMs have been accumulated, surveyed and assessed to prescribe the best probable ODM for ontology development. But none of the prevailing studies provide a ranking mechanism for ODMs. The recommended framework overcomes this limitation and gives a systematic and uniform way of ranking the ODMs. The pilot study yielded NeOn as the top-ranked ODM in the recent two decades. There is no work in the literature that has investigated ranking the ODMs. Hence, this is a first of its kind work in the area of ODM research. The framework supports identifying the topmost ODMs from the literature possessing a substantial amount of features for ontology development. It also enables the selection of the best possible ODM for the ontology development.
      Citation: Data Technologies and Applications
      PubDate: 2022-03-18
      DOI: 10.1108/DTA-05-2021-0123
      Issue No: Vol. ahead-of-print , No. ahead-of-print (2022)
       
  • 3MO-AHP: an inconsistency reduction approach through mono-, multi- or
           many-objective quality measures

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      Authors: Carla Martins Floriano , Valdecy Pereira , Brunno e Souza Rodrigues
      Abstract: Although the multi-criteria technique analytic hierarchy process (AHP) has successfully been applied in many areas, either selecting or ranking alternatives or to derive priority vector (weights) for a set of criteria, there is a significant drawback in using this technique if the pairwise comparison matrix (PCM) has inconsistent comparisons, in other words, a consistency ratio (CR) above the value of 0.1, the final solution cannot be validated. Many studies have been developed to treat the inconsistency problem, but few of them tried to satisfy different quality measures, which are minimum inconsistency (fMI), the total number of adjusted pairwise comparisons (fNC), original rank preservation (fKT), minimum average weights adjustment (fWA) and finally, minimum L1 matrix norm between the original PCM and the adjusted PCM (fLM). The approach is defined in four steps: first, the decision-maker should choose which quality measures she/he wishes to use, ranging from one to all quality measures. In the second step, the authors encode the PCM to be used in a many-objective optimization algorithm (MOOA), and each pairwise comparison can be adjusted individually. The authors generate consistent solutions from the obtained Pareto optimal front that carry the desired quality measures in the third step. Lastly, the decision-maker selects the most suitable solution for her/his problem. Remarkably, as the decision-maker can choose one (mono-objective), two (multi-objective), three or more (many-objectives) quality measures, not all MOOAs can handle or perform well in mono- or multi-objective problems. The unified non-sorting algorithm III (U-NSGA III) is the most appropriate MOOA for this type of scenario because it was specially designed to handle mono-, multi- and many-objective problems. The use of two quality measures should not guarantee that the adjusted PCM is similar to the original PCM; hence, the decision-maker should consider using more quality measures if the objective is to preserve the original PCM characteristics. For the first time, a many-objective approach reduces the CR to consistent levels with the ability to consider one or more quality measures and allows the decision-maker to adjust each pairwise comparison individually.
      Citation: Data Technologies and Applications
      PubDate: 2022-02-18
      DOI: 10.1108/DTA-11-2021-0315
      Issue No: Vol. ahead-of-print , No. ahead-of-print (2022)
       
  • Modular framework for similarity-based dataset discovery using external
           knowledge

         This is an Open Access Article Open Access Article

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      Authors: Martin Nečaský , Petr Škoda , David Bernhauer , Jakub Klímek , Tomáš Skopal
      Abstract: Semantic retrieval and discovery of datasets published as open data remains a challenging task. The datasets inherently originate in the globally distributed web jungle, lacking the luxury of centralized database administration, database schemes, shared attributes, vocabulary, structure and semantics. The existing dataset catalogs provide basic search functionality relying on keyword search in brief, incomplete or misleading textual metadata attached to the datasets. The search results are thus often insufficient. However, there exist many ways of improving the dataset discovery by employing content-based retrieval, machine learning tools, third-party (external) knowledge bases, countless feature extraction methods and description models and so forth. In this paper, the authors propose a modular framework for rapid experimentation with methods for similarity-based dataset discovery. The framework consists of an extensible catalog of components prepared to form custom pipelines for dataset representation and discovery. The study proposes several proof-of-concept pipelines including experimental evaluation, which showcase the usage of the framework. To the best of authors’ knowledge, there is no similar formal framework for experimentation with various similarity methods in the context of dataset discovery. The framework has the ambition to establish a platform for reproducible and comparable research in the area of dataset discovery. The prototype implementation of the framework is available on GitHub.
      Citation: Data Technologies and Applications
      PubDate: 2022-02-15
      DOI: 10.1108/DTA-09-2021-0261
      Issue No: Vol. ahead-of-print , No. ahead-of-print (2022)
       
  • Text mining the mission statements of the most ethical companies

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      Authors: Tuncay Bayrak
      Abstract: This paper explores and examines the mission statements of the most ethical companies across the globe in terms of their main purposes, values, goals, and objective, and what they say about their vision and goals. This study is based on the data published by the Ethisphere Institute, the global leader in defining and advancing the standards of ethical business practices. Having compiled the mission statements into a text file, the authors conducted text mining using a commercially available text mining tool SAS Enterprise Miner to survey if the most ethical companies have valued the same vision and mission such as social responsibility and ethics. A review of their mission statements indicated that some of the most ethical companies surveyed in this study such as 3M and Voya strive to be “socially responsible and ethical,” support their “societies” and respect and protect the “nature,” “planet” and “environment.” The world's most ethical companies that stress these weighted terms in their mission statements may do so to show their commitment by being socially responsible and ethical, and delivering sustainable business solutions to their customers. This study provides a systematic and comprehensive exploration of mission statements of the most ethical companies in an attempt to identify patterns of differences and similarities within these statements.
      Citation: Data Technologies and Applications
      PubDate: 2022-02-15
      DOI: 10.1108/DTA-10-2021-0280
      Issue No: Vol. ahead-of-print , No. ahead-of-print (2022)
       
  • Social recruiting: an application of social network analysis for
           preselection of candidates

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      Authors: Stevan Milovanović , Zorica Bogdanović , Aleksandra Labus , Marijana Despotović-Zrakić , Svetlana Mitrović
      Abstract: The paper aims to studiy social recruiting for finding suitable candidates on social networks. The main goal is to develop a methodological approach that would enable preselection of candidates using social network analysis. The research focus is on the automated collection of data using the web scraping method. Based on the information collected from the users' profiles, three clusters of skills and interests are created: technical, empirical and education-based. The identified clusters enable the recruiter to effectively search for suitable candidates. This paper proposes a new methodological approach for the preselection of candidates based on social network analysis (SNA). The defined methodological approach includes the following phases: Social network selection according to the defined preselection goals; Automatic data collection from the selected social network using the web scraping method; Filtering, processing and statistical analysis of data. Data analysis to identify relevant information for the preselection of candidates using attributes clustering and SNA. Preselection of candidates is based on the information obtained. It is possible to contribute to candidate preselection in the recruiting process by identifying key categories of skills and interests of candidates. Using a defined methodological approach allows recruiters to identify candidates who possess the skills and interests defined by the search. A defined method automates the verification of the existence, or absence, of a particular category of skills or interests on the profiles of the potential candidates. The primary intention is reflected in the screening and filtering of the skills and interests of potential candidates, which contributes to a more effective preselection process. A small sample of the participants is present in the preliminary evaluation. A manual revision of the collected skills and interests is conducted. The recruiters should have basic knowledge of the SNA methodology in order to understand its application in the described method. The reliability of the collected data is assessed, because users provide data themselves when filling out their social network profiles. The presented method could be applied on different social networks, such as GitHub or AngelList for clustering profile skills. For a different social network, only the web scraping instructions would change. This method is composed of mutually independent steps. This means that each step can be implemented differently, without changing the whole process. The results of a pilot project evaluation indicate that the HR experts are interested in the proposed method and that they would be willing to include it in their practice. The social implication should be the determination of relevant skills and interests during the preselection phase of candidates in the process of social recruitment. In contrast to previous studies that were discussed in the paper, this paper defines a method for automatic data collection using the web scraper tool. The described method allows the collection of more data in a shorter period. Additionally, it reduces the cost of creating an initial data set by removing the cost of hiring interviewers, questioners and people who collect data from social networks. A completely automated process of data collection from a particular social network stands out from this model from currently available solutions. Considering the method of data collection implemented in this paper, the proposed method provides opportunities to extend the scope of collected data to implicit data, which is not possible using the tools presented in other papers.
      Citation: Data Technologies and Applications
      PubDate: 2022-02-14
      DOI: 10.1108/DTA-01-2021-0021
      Issue No: Vol. ahead-of-print , No. ahead-of-print (2022)
       
  • Exploring the effectiveness of word embedding based deep learning model
           for improving email classification

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      Authors: Deepak Suresh Asudani , Naresh Kumar Nagwani , Pradeep Singh
      Abstract: Classifying emails as ham or spam based on their content is essential. Determining the semantic and syntactic meaning of words and putting them in a high-dimensional feature vector form for processing is the most difficult challenge in email categorization. The purpose of this paper is to examine the effectiveness of the pre-trained embedding model for the classification of emails using deep learning classifiers such as the long short-term memory (LSTM) model and convolutional neural network (CNN) model. In this paper, global vectors (GloVe) and Bidirectional Encoder Representations Transformers (BERT) pre-trained word embedding are used to identify relationships between words, which helps to classify emails into their relevant categories using machine learning and deep learning models. Two benchmark datasets, SpamAssassin and Enron, are used in the experimentation. In the first set of experiments, machine learning classifiers, the support vector machine (SVM) model, perform better than other machine learning methodologies. The second set of experiments compares the deep learning model performance without embedding, GloVe and BERT embedding. The experiments show that GloVe embedding can be helpful for faster execution with better performance on large-sized datasets. The experiment reveals that the CNN model with GloVe embedding gives slightly better accuracy than the model with BERT embedding and traditional machine learning algorithms to classify an email as ham or spam. It is concluded that the word embedding models improve email classifiers accuracy.
      Citation: Data Technologies and Applications
      PubDate: 2022-02-02
      DOI: 10.1108/DTA-07-2021-0191
      Issue No: Vol. ahead-of-print , No. ahead-of-print (2022)
       
  • Techniques to detect terrorists/extremists on the dark web: a review

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      Authors: Hanan Alghamdi , Ali Selamat
      Abstract: With the proliferation of terrorist/extremist websites on the World Wide Web, it has become progressively more crucial to detect and analyze the content on these websites. Accordingly, the volume of previous research focused on identifying the techniques and activities of terrorist/extremist groups, as revealed by their sites on the so-called dark web, has also grown. This study presents a review of the techniques used to detect and process the content of terrorist/extremist sites on the dark web. Forty of the most relevant data sources were examined, and various techniques were identified among them. Based on this review, it was found that methods of feature selection and feature extraction can be used as topic modeling with content analysis and text clustering. At the end of the review, present the current state-of-the- art and certain open issues associated with Arabic dark Web content analysis.
      Citation: Data Technologies and Applications
      PubDate: 2022-01-06
      DOI: 10.1108/DTA-07-2021-0177
      Issue No: Vol. ahead-of-print , No. ahead-of-print (2022)
       
  • Feature distillation and accumulated selection for automated fraudulent
           publisher classification from user click data of online advertising

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      Authors: Deepti Sisodia , Dilip Singh Sisodia
      Abstract: The problem of choosing the utmost useful features from hundreds of features from time-series user click data arises in online advertising toward fraudulent publisher's classification. Selecting feature subsets is a key issue in such classification tasks. Practically, the use of filter approaches is common; however, they neglect the correlations amid features. Conversely, wrapper approaches could not be applied due to their complexities. Moreover, in particular, existing feature selection methods could not handle such data, which is one of the major causes of instability of feature selection. To overcome such issues, a majority voting-based hybrid feature selection method, namely feature distillation and accumulated selection (FDAS), is proposed to investigate the optimal subset of relevant features for analyzing the publisher's fraudulent conduct. FDAS works in two phases: (1) feature distillation, where significant features from standard filter and wrapper feature selection methods are obtained using majority voting; (2) accumulated selection, where we enumerated an accumulated evaluation of relevant feature subset to search for an optimal feature subset using effective machine learning (ML) models. Empirical results prove enhanced classification performance with proposed features in average precision, recall, f1-score and AUC in publisher identification and classification. The FDAS is evaluated on FDMA2012 user-click data and nine other benchmark datasets to gauge its generalizing characteristics, first, considering original features, second, with relevant feature subsets selected by feature selection (FS) methods, third, with optimal feature subset obtained by the proposed approach. ANOVA significance test is conducted to demonstrate significant differences between independent features.
      Citation: Data Technologies and Applications
      PubDate: 2022-01-06
      DOI: 10.1108/DTA-09-2021-0233
      Issue No: Vol. ahead-of-print , No. ahead-of-print (2022)
       
  • Artificial intelligence technologies for more flexible recommendation in
           uniforms

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      Authors: Chih-Hao Wen , Chih-Chan Cheng , Yuh-Chuan Shih
      Abstract: This research aims to collect human body variables via 2D images captured by digital cameras. Based on those human variables, the forecast and recommendation of the Digital Camouflage Uniforms (DCU) for Taiwan's military personnel are made. A total of 375 subjects are recruited (male: 253; female: 122). In this study, OpenPose converts the photographed 2D images into four body variables, which are compared with those of a tape measure and 3D scanning simultaneously. Then, the recommendation model of the DCU is built by the decision tree. Meanwhile, the Euclidean distance of each size of the DCU in the manufacturing specification is calculated as the best three recommendations. The recommended size established by the decision tree is only 0.62 and 0.63. However, for the recommendation result of the best three options, the DCU Fitting Score can be as high as 0.8 or more. The results of OpenPose and 3D scanning have the highest correlation coefficient even though the method of measuring body size is different. This result confirms that OpenPose has significant measurement validity. That is, inexpensive equipment can be used to obtain reasonable results. In general, the method proposed in this study is suitable for applications in e-commerce and the apparel industry in a long-distance, non-contact and non-pre-labeled manner when the world is facing Covid-19. In particular, it can reduce the measurement troubles of ordinary users when purchasing clothing online.
      Citation: Data Technologies and Applications
      PubDate: 2022-01-04
      DOI: 10.1108/DTA-09-2021-0230
      Issue No: Vol. ahead-of-print , No. ahead-of-print (2022)
       
  • Data Technologies and Applications

    • Free pre-print version: Loading...

       
 
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