Implementing a Machine Learning-Based Library Information Management System: A CATALYST-Based Framework Integration

Abstract

This research proposes using machine learning as a foundational element for enhancing information retrieval procedures in university libraries. This initiative will enhance students' comprehension of the topic and improve the integration of instructional resources. To determine which method is the most effective, the performance of each methodology is compared. The author utilizes two separate methodologies in machine learning. The efficacy of inventory management in university libraries is enhanced by the use of forecasting algorithms. The implementation of these two algorithms was conducted within the framework of the CATALYST technology platform. This strategy enhances the efficacy of information retrieval for diverse book needs.