Adetayo, A. J. (2024). Reimagining learning through AI art: The promise of DALL-E and MidJourney for education and libraries. Library Hi Tech News. Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/LHTN-01-2024-0005
Armano, G., Manconi, A. & Vargiu, E. (2007). A multiagent
system for retrieving bioinformatics publications from web sources. IEEE Transactions on Nanobioscience, 6(2), 104-109. https://doi.org/10.1109/TNB.2007.897452
Asim, M., Arif, M., Rafiq, M. & Ahmad, R. (2023). Investigating applications of artificial intelligence in university libraries of Pakistan: An empirical study. The Journal of Academic Librarianship, 49(6), 102803. https://doi.org/10.1016/j.acalib.2023.102803
Bellika, J. G., Hartvigsen, G. & Widding, R. A. (1998). The virtual library secretary: A user model-based software agent. Personal Technologies, 2(3), 162-187. https://doi.org/10.1007/BF01321174
Bender, E. M., Gebru, T., McMillan-Major, A. & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610–623). ACM. https://doi.org/10.1145/3442188.3445922
Bin Makhashen, G. M. & Al-Jamimi, H. A. (2023). An intelligent prediction of the next highly cited paper using machine learning. Journal of Scientometric Research, 12(1), 44-53. https://doi.org/10.5530/jscires.12.1.008
Borovič, M., Ojsteršek, M. & Strnad, D. (2022). A hybrid approach to recommending universal decimal classification codes for cataloguing in Slovenian digital libraries. IEEE Access, 10, 85595-85605. https://doi.org/10.1109/ACCESS.2022.3198706
Cao, J. (2021). Design and implementation of a human-computer
interaction system in a
parallel digital library system based on a
neural network. Scientific Programming, 2021, 9921551. https://doi.org/10.1155/2021/9921551
Cho, Y. I. (2007). Implementation of an intelligent personalized digital library system based on improved negotiation mobile multiagents. Artificial Life and Robotics, 11(1), 71-75. https://doi.org/10.1007/s10015-006-0403-y
Daradkeh, M., Abualigah, L., Atalla, S. & Mansoor, W. (2022). Scientometric analysis and classification of research using convolutional neural networks: A case study in data science and analytics. Electronics, 11(13), 2066. https://doi.org/10.3390/electronics11132066
Das, R. K. & Islam, M. S. U. (2021). Application of artificial intelligence and machine learning in libraries: A systematic review. Library Philosophy and Practice (e-Journal), 6762. Retrieved from https://digitalcommons.unl.edu/libphilprac/6762
Desai, P., Pujari, J., Sujatha, C., Kamble, A. P. & Jain, S. (2021). Hybrid approach for content-based image retrieval using VGG16 layered architecture and SVM: An application of deep learning. SN Computer Science, 2(3). https://doi.org/10.1007/s42979-021-00529-4
Dhal, P. & Azad, C. (2024). Fine-tuning deep learning with multi-objective-based feature selection for text classification. Neural Computing and Applications, 36(7), 3525-3553. https://doi.org/10.1007/s00521-023-09225-1
Eykens, J., Guns, R. & Engels, T. C. E. (2021). Fine-grained classification of social science journal articles using textual data. Quantitative Science Studies, 2(1), 89-110. https://doi.org/10.1162/qss_a_00106
Fox, R. (2016). Digital libraries: The systems analysis perspective. Digital Library Perspectives, 32(2), 62–67. https://doi.org/10.1108/DLP-02-2016-0006
Gao, K., Wang, Y. C. & Wang, Z. Q. (2005). Similar interest clustering and partial back-propagation-based recommendation in a
digital library. Library Hi Tech, 23(4), 587-597. https://doi.org/10.1108/07378830510636364
Ghanadi Nezhad, F., Osareh, F. & Ghane, M. R. (2022). Forecasting the subject trend of international library and information science research by 2030 using the deep learning approach. International Journal of Information Science and Management (IJISM), 20(1), 471-492. https://ijism.isc.ac/article_698379.html
Giannopoulou, E. & Mitrou, N. (2020). An AI-based methodology for automatic classification of a multiclass ebook collection. IEEE Access, 8, 218658–218675. https://doi.org/10.1109/ACCESS.2020.3041651
Jomsri, P., Prangchumpol, D., Poonsilp, K. & Panityakul, T. (2024). Hybrid recommender system model for digital library from multiple online publishers. F1000Research, 12, 1140. https://doi.org/10.12688/f1000research.133013.3
Kandimalla, B., Rohatgi, S., Wu, J. & Giles, C. L. (2021). Large scale subject category classification with deep attentive neural networks. Frontiers in Research Metrics and Analytics, 5, 600382. https://doi.org/10.3389/frma.2020.600382
Kong, Y. (2024). Enhancing library digitalization: A heterogeneous network embedding approach for personalized book recommendations. IEEE Access, 12, 188723–188738. https://doi.org/10.1109/ACCESS.2024.10571949
Kumar, S., Lim, W. M., Sivarajah, U. & Kaur, J. (2023). Artificial intelligence and blockchain integration in business: Trends from a bibliometric-content analysis. Information Systems Frontiers, 25(2), 871–896. https://doi.org/10.1007/s10796-022-10279-0
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-T., Rocktäschel, T., Riedel, S. & Kiela, D. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. In NIPS'20: Proceedings of the 34th International Conference on Neural Information Processing Systems. 793. (pp. 9459 – 947).
Liao, J. & Li, T. (2024). Leveraging deep learning for personalized book recommendations. Journal of Computing and Information Technology, 32(3), 177-194. https://doi.org/10.20532/cit.2024.1005845
Liu, G. & Zhao, X. (2018). Recommender system for books in university library with implicit data. Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018) (pp. 164-168). https://doi.org/10.2991/ncce-18.2018.28
Manzoor, A., Asghar, S. & Amjad, T. (2022). Toward a new paradigm for author name disambiguation. IEEE Access, 10, 76055-76068. https://doi.org/10.1109/ACCESS.2022.3190088
Mary Harin Fernandez, F., Venkata Ramana, T., Shabana, M., Kannagi, V. & Nalini, M. (2023). Personalized ontology and deep training tree‐based optimal gated recurrent unit‐recurrent neural network for prediction of students' behavior. Concurrency and Computation: Practice and Experience, 35(1), e7420. https://doi.org/10.1002/cpe.7420
Metaxiotis, K., Psarras, J. & Papastefanatos, S. (2002). Knowledge and information management in e‐learning environments:
The user agent architecture. Information Management & Computer Security, 10(4), 165-170. https://doi.org/10.1108/09685220210436958
Nicholson, S. (2003). Bibliomining for automated collection development. Journal of the American Society for Information Science and Technology, 54(12), 1081–1090. https://doi.org/10.1002/asi.10313
Ni, P., Wang, X., Lv, B. & Wu, L. (2024). GTR: An explainable graph topic-aware recommender. Electronic Commerce Research and Applications, 67, 101439. https://doi.org/10.1016/j.elerap.2024.101439
Omri, S., Avshalom, E. & Maayan, Z. (2021). Text analysis using deep neural networks. Journal of the Association for Information Science and Technology, 73(2), 268-287. https://doi.org/10.1002/asi.24544
Pal, J. K. (2011). Usefulness and applications of data mining in extracting information from different perspectives. Annals of Library and Information Studies, 58(1), 7-16. http://nopr.niscpr.res.in/handle/123456789/11552
Panda, S. & Chakravarty, R. (2022). Adapting intelligent information services in libraries: a case of smart AI chatbots. Library Hi Tech News, 39(1), 12–15. https://doi.org/10.1108/LHTN-11-2021-0081
Pang, N. (2022). A personalized recommendation algorithm for semantic classification of new book recommendation services for university libraries.
Mathematical Problems in Engineering, 2022(1), 8740207.
https://doi.org/10.1155/2022/8740207
Papavlasopoulos, S. & Poulos, M. (2012). Neural network design and evaluation for classifying library indicators using the
personal opinion of an
expert. Library Management, 33(4/5), 261-271. https://doi.org/10.1108/01435121211242308
Parian-Scherb, M., Uhrig, P., Rossetto, L., Dupont, S. & Schuldt, H. (2024). Gesture retrieval and its application to the study of multimodal communication. International Journal on Digital Libraries, 25(4), 689–702. https://doi.org/10.1007/s00799-023-00367-0
Rather, I. H., Kumar, S. & Gandomi, A. H. (2024). Breaking the data barrier:
A review of deep learning techniques for democratizing AI with small datasets Artificial Intelligence Review, 57(9), 226. https://doi.org/10.1007/s10462-024-10859-3
Rauber, A. & Merkl, D. (2003). Text mining in the SOMLib digital library system: The representation of topics and genres. Applied Intelligence, 18(3), 271-293. https://doi.org/10.1023/A:1023297920966
Semeler, A., Pinto, A., Koltay, T., Dias, T., Oliveira, A., González, J. & Rozados, H. B. F. (2024). Algorithmic literacy: Generative artificial intelligence technologies for data librarians. EAI Endorsed Transactions on Scalable Information Systems, 11(2). https://doi.org/10.4108/eetsis.4067
Sharma, R., Gulati, S., Kaur, A. & Chakravarty, R. (2021). Users' sentiment analysis toward National Digital Library of India: A quantitative approach for understanding user perception. Library Philosophy and Practice (e-journal), 6372. https://digitalcommons.unl.edu/libphilprac/6372
Shi, X., Hao, C., Yue, D. & Lu, H. (2024). Library book recommendation with CNN-FM deep learning approach. Library Hi Tech, 42(5), 1559-1578. https://doi.org/10.1108/LHT-08-2022-0400
Shi, Y. & Zhu, Y. (2020). Research on aided reading system of digital library based on text image features and edge computing. IEEE Access, 8, 205980-205988. https://doi.org/10.1109/ACCESS.2020.3037349
Shorin, O. N. (2023). Possible areas of application of artificial intelligence in libraries and information centers. Scientific and Technical Information Processing, 50(4), 259-263. https://doi.org/10.3103/S0147688223040093
Silva, M. (2015). A systematic review of foresight in project management literature. Procedia Computer Science, 64, 792–799. https://doi.org/10.1016/j.procs.2015.08.630
Son, Y. J., Jones, A. T. & Wysk, R. A. (2003). Component based simulation modeling from neutral component libraries. Computers & Industrial Engineering, 45(1), 141-165. https://doi.org/10.1016/S0360-8352(03)00023-8
Stergiopoulos, V., Vassilakopoulos, M., Tousidou, E. & Corral, A. (2024). An academic recommender system on large citation data based on clustering, graph modeling and deep learning
. Knowledge and Information Systems, 66(8), 4463-4496. https://doi.org/10.1007/s10115-024-02094-7
Sun, Y. (2024). Research on digital book resource recommendation algorithm based on knowledge graph. Procedia Computer Science, 247, 953-962. https://doi.org/10.1016/j.procs.2024.10.115
Unnisa, S. & Akshaya, N. S. (2024). Personalized mood-centric book recommendation integrating machine learning with content-based
filtering. Journal homepage: https://ijitra.comInternational Journal of Information Technology, Research and Applications, 3(3), 15-22. https://doi.org/10.59461/ijitra.v3i3.100
Uzwyshyn, R. J. (2022). Steps towards building library AI infrastructures: Research data repositories, scholarly research ecosystems,
and AI scaffolding. In New Horizons in Artificial Intelligence in Libraries (Satellite Meeting of IFLA WLIC 2022). International Federation of Library Associations and Institutions. Retrieved from https://repository.ifla.org/handle/20.500.14598/2062
VandenBosch, A., Schmidt, B. M., Matusiak, K. K. & Organisciak, P. (2021). Moving past metadata: Improving digital libraries with content‐based methods. Proceedings of the Association for Information Science and Technology, 58(1), 849-851. https://doi.org/10.1002/pra2.585
Verma, M. & Patnaik, P. K. (2024). An automatic college library book recommendation system using optimized Hidden Markov based weighted fuzzy ranking model. Engineering Applications of Artificial Intelligence, 130, 107664. https://doi.org/10.1016/j.engappai.2023.107664
Wu, Y.,Wang, X., Yu, P. & Huang, Y. (2022). ALBERT-BPF: A book purchase forecast model for university library by using ALBERT for text feature extraction. Aslib Journal of Information Management, 74(4), 673–687. https://doi.org/10.1108/AJIM-04-2021-0114
Zhang, F. (2024). Research on library resource management. Journal of Cases on Information Technology, 26(1), 1–12. https://doi.org/10.4018/JCIT.349134
Zhang, S., Yao, L., Sun, A. & Tay, Y. (2019). Deep learning-based recommender systems: A Survey and New Perspectives. ACM Computing Surveys, 52(1), 5, 1–38. https://doi.org/10.1145/3285029
