Favorite Book Prediction System Using Machine Learning Algorithms
DOI:
https://doi.org/10.37385/jaets.v4i2.1925Keywords:
Machine Learning, artificial intelligence (AI), Smart Library, Light Gradient Boosting Machine (LGBM), Boosting ModelAbstract
Recent years have seen the rapid deployment of Artificial Intelligence (AI) which allows systems to take intelligent decisions. AI breakthroughs could radically change modern libraries' operations. However, introducing AI in modern libraries is a challenging task. This research explores the potential for smart libraries to improve the caliber of user services through the use of machine learning (ML) techniques. The proposed work investigates machine learning methods such as Random Forest (RF) and boosting algorithms, including 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 classifying Favorite books and compares their performances. Comprehensive experiments performed on the publicly available dataset (Art Garfunkel's Library) show that the proposed model can effectively handle the task of identifying and classifying Favorite books. Experimental results show that LGBM has achieved outstanding performance with an accuracy rate of 94.9367% than Random Forest and other boosting ML algorithms. This empirical research work takes advantage of AI adoption in libraries using machine learning techniques. To the best of our knowledge, we are the first to develop an intelligent application for the modern library to automatically identify and classify Favorite books
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References
A, C. (2022). How artificial intelligence might change academic library work: Applying the competencies literature and the theory of the professions. Journal of the Association for Information Science and Technology, 74(3), 367-380. doi:https://doi.org/10.1002/asi.24635
Abayomi, O., Adenekan, F., & Adeleke, O. (2020). Awareness and Perception of the Artificial Intelligence in the Management of University Libraries in Nigeria. Journal of Interlibrary Loan, Document Delivery & Electronic Reserve, 29(2). doi:https://doi.org/10.1080/1072303X.2021.1918602
Andrea Alessandro, G., & Heli, K. (2022). Understanding Artificial Intelligence in Research Libraries: An Extensive Literature Review. Te Journal of European Research Libraries, 32(1), 1-36. doi:https://doi.org/10.53377/lq.10934
Antevski, K., E. C. Redondi, A., & Pitic, R. (2016). A hybrid BLE and Wi-Fi localization system for the creation of study groups in smart libraries. 9th IFIP Wireless and Mobile Networking Conference (WMNC). Colmar, France. doi:10.1109/WMNC.2016.7543928
Bai, Y. (2022). Construction of a Smart Library Subject Precise Service Platform Based on User Needs. Mathematical Problems in Engineering, 1-8. doi:DOI: 10.1155/2022/5675291
Bi , S., Wang, C., Zhang , J., & Huang , W. (2022). A Survey on Artificial Intelligence Aided Internet-of-Things Technologies in Emerging Smart Libraries. sensors, MDPI, 22(8), 2991. doi:https://doi.org/10.3390/s22082991
Daimari, D., Narzary, M., Mazumdar, N., & Nag, A. (2021). A Machine Learning Based Book Availability Prediction Model for Library Management System. Library Philosophy and Practice (e-journal).
Emmanuel Ajakaye, J. (2022). Applications of Artificial Intelligence (AI) in Libraries. Handbook of Research on Emerging Trends and Technologies in Librarianship, 1-18. doi:10.4018/978-1-7998-9094-2.ch006
Folorunsho, A., & Omeiza Momoh, E. (2020). Application of Artificial Intelligence and Robotics in Libraries : A Review of Literature. ILIS Journal of Librarianship and Informatics, 3(2), 93 – 98.
Garg, A., & Mago, V. (2021). Role of machine learning in medical research: A survey. Computer Science Review, 40. doi:https://doi.org/10.1016/j.cosrev.2021.100370
Geetha, K., Srinivasa Rao, G., Kaur, C., & Kumar, K. (2022). Machine learning based library management system. 6th International Conference on Electronics, Communication and Aerospace Technology, (pp. 1031-1034). Coimbatore, India. doi:10.1109/ICECA55336.2022.10009423
Hamad, F., Al-Fadel, M., & Khafaga Shehata, A. (2023). The level of digital competencies for the provision of smart information service at academic libraries in Jordan. Global Knowledge, Memory and Communication. doi:https://doi.org/10.1108/GKMC-06-2022-0131
Harisanty , D., Variant Anna, N., Eranti Putr, T., & Firdaus, A. (2022). Leaders, practitioners and scientists' awareness of artificial intelligence in libraries: a pilot study. Library Hi Tech. doi:https://doi.org/10.1108/LHT-10-2021-0356
Hassan Azimi, M., Mohammadi, Z., & Rafieinasab, F. (2022). A Survey of Academic Librarians' Perceptions of Artificial Intelligence Technology: A Case Study (Librarians of Shahid Chamran University of Ahvaz and Jundishapur University of Medical Sciences). Library and Information Sciences, 24(4), 154-177. doi:10.30481/LIS.2021.286969.1831
He, C., Li, S., So, J., & Zeng, X. (2020). FedML: A Research Library and Benchmark for Federated Machine Learning. Machine Learning. doi:https://doi.org/10.48550/arXiv.2007.13518
Hervieux, S., & Wheatley, A. (2021). Perceptions of artificial intelligence: A survey of academic librarians in Canada and the United States. The Journal of Academic Librarianship, 47(1). doi:https://doi.org/10.1016/j.acalib.2020.102270
Hussain, A. (2023). Use of artificial intelligence in the library services: prospects and challenges. Library Hi Tech News, 40(2), 15-17. doi:https://doi.org/10.1108/LHTN-11-2022-0125
Irawan, Y., Sabna, E., Fauzan Azim, A., & Wahyuni, R. (2022). Automatic Chili Plant Watering Based On Internet Of Things (IoT). Journal of Applied Engineering and Technological Science (JAETS), 3(2), 77–83. doi: https://doi.org/10.37385/jaets.v3i2.532
Iskandar, . D. M., Yel, M. B., & Maheswara, E. (2022). Sign Language Detection System Using Adaptive Neuro Fuzzy Inference System (ANFIS) Method. Journal of Applied Engineering and Technological Science (JAETS), 4(1), 158–167. https://doi.org/10.37385/jaets.v4i1.967
Jayawardena, C., Reyal, S., Kekirideniya, K., & Wijayawardhana, G. (2021). Artificial Intelligence Based Smart Library Management System. 6th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE) (pp. 1-6). Kedah, Malaysia: IEEE. doi:10.1109/ICRAIE52900.2021.9703998
Li, J., Liu, Y., & Wang, L. (2021). Design and Development of Promotion APP of University Smart Library Service Platform Based on Network Teaching. Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) (pp. 1344-1347). Palladam, India: IEEE. doi:10.1109/I-SMAC52330.2021.9640779
Tang, J., Wang, Z., & Lei, L. (2018). Book title recognition for smart library with deep learning. Mobile Multimedia/Image Processing, Security, and Applications. doi: https://doi.org/10.1117/12.2312245
Tsabedze, V., Mathabela, N., & Ademola, S. (2022). "A Framework for Integrating Artificial Intelligence into Library and Information Science Curricula. Innovative Technologies for Enhancing Knowledge Access in Academic Libraries, 1-14. doi:DOI: 10.4018/978-1-6684-3364-5.ch014
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