ENHANCING UNIVERSITY ELECTRONIC BOOK ACQUISITION STRATEGY USING A DEEP FOREST FUSION APPROACH

Authors

  • Sanjai Kumar
  • Mohammad Sameer Aloun Department Faculty of Science and Information Technology Irbid National University https://orcid.org/0009-0001-1571-110X

Abstract

This study presents a new approach, LHGCAT-XDF, to improve the efficiency of electronic book procurement in university settings by combining the strengths of the LightGBM and CatBoost algorithms. This innovative model benefits from the LightGBM's minimal memory usage and CatBoost's reduced time complexity. Through testing, it's shown that LHGCAT-XDF surpasses standard machine learning models in overall effectiveness, successfully addressing the shortcomings of conventional procurement strategies in terms of accuracy and efficiency. Thus, it offers dependable guidance for the selection of electronic books in university libraries.

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Published

2024-04-02

Issue

Vol. 2 No. 1 (2024): Journal of Intelligent Systems and applied data science (JISADS)

Section

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