[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125225-en":3,"doc-seo-125225-105":30,"detail-sidebar-cat-0-en-105":95},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125225,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Enhancement for the Access and Utilization of Library Resources Using Machine Learning Techniques","Rising demand for online information drives the need for more effective digital library (DL) resource tools, especially for improving information search and retrieval where label relevance and feature correlation are critical. This study addresses limitations of prior approaches that relied on unbalanced multi-label data by using machine learning techniques to enhance access and utilization of library resources. Questionnaire data were collected from NM-AIST, Mzumbe University, and UDSM and analyzed with Python and MAXQDA. Results show 1,217 (73%) awareness of electronic information resources but persistent accessibility constraints. The proposed ensemble model achieves the highest precision (95%) and the developed resource discovery tool attains strong WCAG 2.1 compliance (90%).","ENHANCEMENT FOR THE ACCESS AND UTILIZATION OF LIBRARY RESOURCES USING MACHINE LEARNING TECHNIQUES  \nAgrey Kato  \nA Thesis Submitted in Fulfilment of the Requirements for the Degree of Doctor of Philosophy in Information and Communication Science and Engineering of the Nelson Mandela African Institution of Science and Technology  \nArusha, Tanzania  \nABSTRACT  \nThe growing demands for online information have motivated researchers to explore the most effectively use of digital library (DL) resource tools. The main challenges of online DL are information search and retrieval attributes related to label relevance and feature correlation segments. Previous research mainly relied on unbalanced multi-label data and therefore could not develop a reliable tool to access online information. To improve availability and usefulness of online DL, this work uses machine learning techniques to enhance the access and utilization of library resources. The research data were collected at The Nelson Mandela African Institution of Science and Technology (NM-AIST), Mzumbe University (MU), and the University of Dar es Salaam (UDSM) through questionnaire and purposeful sampling technique were then analysed with python and MAXQDA tools respectively. The survey found that 1,217 (73%) of respondents were aware of electronic information resources (EIRs) but faced accessibility limitations due to social and technical issues. Then, the proposed ensemble model (PEM) for machine learning (ML) methods was used to develop a resource discovery tool (RDT) . The effectiveness of the PEM was then evaluated by comparing the accuracy of the PEM, logistic regression (LR), support vector machine (SVM), and knearest neighbor (kNN) algorithms. The experimental results reveal that PEM offers the highest precision of 95%, as compared to LR's 84%, SVM's 65%, and kNN's 57% . The Web Content Accessibility Guidelines (WCAG) 2.1 standards had been successfully used to test the four digital library tools, the developed RDT, NM-AIST, MU, and UDSM to see how well the developed system performs. The developed RDT had the highest established compliance score for online content accessibility, which is 90% with only one violation, compared to NM-AIST's 80% with 16 violations, MU's 55% with 12 violations, and UDSM's inability tobe evaluated because of the excessive number of infractions. Therefore, the results of this study show the need to regularly check the accessibility of an online resources as well as optimization of the digital libraries.  \nDECLARATION  \nI, Agrey Kato do hereby declare to the Senate of Nelson Mandela African Institution of Science and Technology that this thesis is my own original work and that it has neither been submitted nor being concurrently submitted for degree award in anyother institution.  \nAgrey Kato Akato 10/12/2023  \nCandidate name and Signature Date  \nThe above declaration is confirmed  \nProf. Michael Kisangiri  \nName and Signature of Supervisor Date  \nProf. Shubi Kaijage  \nName and Signature of Supervisor Date  \nCOPYRIGHT  \nThis thesis is copyright material protected under the Berne Convention, the Copyright Act of 1999 and other international and national enactments, in that behalf, on intellectual property. It must not be reproduced by any means, in full or in part, except for short extracts in fair dealing; for researcher private study, critical scholarly review or discourse with an acknowledgement, without the written permission of the office of Deputy Vice Chancellor (Academic, Research and Innovation), on behalf of both the author and the Nelson Mandela African Institution of Science and Technology.  \nCERTIFICATION  \nThe undersigned certify that they have read and hereby recommend for submission to the Nelson Mandela Institution of Science and Technology (NM-AIST) a thesis titled Enhancing the access and utilization of library resources using machine learning techniques, in fulfillment of the requirements for the degree of Doctor of Philosophy","cbCaiiT0a5jBpM1Y","https://ap.wps.com/l/cbCaiiT0a5jBpM1Y","pdf",2229957,1,126,"English","en",105,"# Abstract\n# Declaration\n# Copyright\n# Certification\n# Acknowledgements","[{\"question\":\"What problem does the thesis address in online digital libraries?\",\"answer\":\"It targets challenges in information search and retrieval related to label relevance and feature correlation, which are worsened when prior work depends on unbalanced multi-label data.\"},{\"question\":\"What data sources and analysis tools are used?\",\"answer\":\"Questionnaire responses were collected from NM-AIST, Mzumbe University, and the University of Dar es Salaam, then analyzed using Python and MAXQDA.\"},{\"question\":\"How does the proposed ensemble model perform compared with other algorithms?\",\"answer\":\"The ensemble model (PEM) achieves the highest precision at 95%, outperforming logistic regression (84%), support vector machine (65%), and k-nearest neighbor (57%).\"},{\"question\":\"How is the accessibility of digital library tools evaluated?\",\"answer\":\"The study uses Web Content Accessibility Guidelines (WCAG) 2.1 to test the developed resource discovery tool and compare compliance scores across NM-AIST, Mzumbe University, and UDSM.\"}]","Enhancement for the Access and Utilization of Library Resources Using Machine Learning Techniques | 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problem does the thesis address in online digital libraries?","Question",{"text":75,"@type":76},"It targets challenges in information search and retrieval related to label relevance and feature correlation, which are worsened when prior work depends on unbalanced multi-label data.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data sources and analysis tools are used?",{"text":80,"@type":76},"Questionnaire responses were collected from NM-AIST, Mzumbe University, and the University of Dar es Salaam, then analyzed using Python and MAXQDA.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed ensemble model perform compared with other algorithms?",{"text":84,"@type":76},"The ensemble model (PEM) achieves the highest precision at 95%, outperforming logistic regression (84%), support vector machine (65%), and k-nearest neighbor (57%).",{"name":86,"@type":73,"acceptedAnswer":87},"How is the accessibility of digital library tools 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