Enhancement for the access and utilization of library resources using machine learning techniques

Abstract

The 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 to be 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.