Published Paper Details:

In recent years, Artificial Intelligence (AI) has been rapidly integrated into various domains, enabling systems to make informed decisions. AI advancements have the potential to transform the functioning of modern libraries. However, implementing AI in library operations poses significant challenges. This study examines how smart libraries can enhance user services through the application of machine learning (ML) techniques. The research explores various machine learning methods, including Random Forest (RF) and several boosting algorithms such as Light Gradient Boosting Machine (LGBM), Histogram-based Gradient Boosting (HGB), Extreme Gradient Boosting (XGB), CatBoost (CB), AdaBoost (AB), and Gradient Boosting (GB), for the task of identifying and categorizing favorite books. A detailed analysis using a publicly available dataset (Art Garfunkel's Library) demonstrates the proposed model's efficiency in identifying and classifying favorite books. The experimental findings indicate that LGBM achieves superior performance with an accuracy rate of 94.9367%, outperforming Random Forest and other boosting algorithms. This research highlights the potential of AI-driven solutions in modern libraries through the adoption of machine learning. To the best of our knowledge, this is the first intelligent system designed for automatically identifying and categorizing favorite books in a smart library context.