Leveraging AI to Address Resource Allocation Challenges in Academic and Research Libraries

References

[1] Agupugo, C.P. and Tochukwu, M.F.C., 2021. A model to assess the economic viability of renewable energy microgrids: A case study of Imufu Nigeria.

[2] Ajie, I., 2019. Issues and prospects of knowledge sharing in academic libraries. Library Philosophy and Practice, pp.1-17.

[3] Akter, S., McCarthy, G., Sajib, S., Michael, K., Dwivedi, Y.K., D’Ambra, J. and Shen, K.N., 2021. Algorithmic bias in data-driven innovation in the age of AI. International Journal of Information Management, 60, p.102387.

[4] Ali, M.Y., Naeem, S.B. and Bhatti, R., 2020. Artificial intelligence tools and perspectives of university librarians: An overview. Business Information Review, 37(3), pp.116-124.

[5] Ari, M.R., 2019. Reading Between the Stacks: The Evolving Role of Librarians. IJRAR-International Journal of Research and Analytical Reviews (IJRAR), 6(2), pp.618-625.

[6] Ashiq, M., Rehman, S.U. and Mujtaba, G., 2021. Future challenges and emerging role of academic libraries in Pakistan: A phenomenology approach. Information Development, 37(1), pp.158-173.

[7] Bairagi, M. and Lihitkar, S., 2020. Empowering Libraries: AI-Driven Tools and Techniques for Digital Transformation and Sustainable Innovation. In One Day National Conference on ‘Preserving Indian Knowledge System and Envisioning the role of Libraries in NEP (pp. 388-396).

[8] Baxter, G., Beard, L., Beattie, G., Blake, M., Greenhall, M., Lingstadt, K., Nixon, W.J. and Reimer, T., 2021. Covid-19 and the future of the digital shift amongst research libraries: an RLUK perspective in context. New review of academic librarianship, 27(3), pp.322-348.

[9] Bello, O.A., Folorunso, A., Ogundipe, A., Kazeem, O., Budale, A., Zainab, F. and Ejiofor, O.E., 2022. Enhancing Cyber Financial Fraud Detection Using Deep Learning Techniques: A Study on Neural Networks and Anomaly Detection. International Journal of Network and Communication Research, 7(1), pp.90-113.

[10] Chatterjee, J. and Dethlefs, N., 2021. Scientometric review of artificial intelligence for operations & maintenance of wind turbines: The past, present and future. Renewable and Sustainable Energy Reviews, 144, p.111051.

[11] Cordell, R., 2020. Machine learning+ libraries. In Library of Congress, Washington, DC https://labs. loc. gov/static/labs/work/reports/Cordell-LOC-ML-report. pdf.

[12] Corrall, S. and Jolly, L., 2019. Innovations in learning and teaching in academic libraries: Alignment, collaboration, and the social turn. New Review of Academic Librarianship, 25(2-4), pp.113-128.

[13] Cullen, T. and Garcia, J.E., 2021. Data Mining, Data Analytics, and Bioinformatics: Leveraging Big Data to Identify the Most Vulnerable and to Reduce Health Disparities. In Innovations in Global Mental Health (pp. 455-488). Cham: Springer International Publishing.

[14] Das, A. and Banerjee, S., 2021. Optimising research support services through libraries: A review of practices. Library Philosophy and Practice, pp.1-43.

[15] Dedding, C., Goedhart, N.S., Broerse, J.E. and Abma, T.A., 2021. Exploring the boundaries of ‘good’Participatory Action Research in times of increasing popularity: dealing with constraints in local policy for digital inclusion. Educational Action Research, 29(1), pp.20-36.

[16] Ding, Y., Jin, M., Li, S. and Feng, D., 2021. Smart logistics based on the internet of things technology: an overview. International Journal of Logistics Research and Applications, 24(4), pp.323-345.

[17] Dwivedi, Y.K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A. and Galanos, V., 2021. Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International journal of information management, 57, p.101994.

[18] Fouad, N.S., 2021. Securing higher education against cyberthreats: from an institutional risk to a national policy challenge. Journal of Cyber Policy, 6(2), pp.137-154.

[19] Garoufallou, E. and Gaitanou, P., 2021. Big data: opportunities and challenges in libraries, a systematic literature review. College & Research Libraries, 82(3), p.410.

[20] Gul, S. and Bano, S., 2019. Smart libraries: an emerging and innovative technological habitat of 21st century. The Electronic Library, 37(5), pp.764-783.

[21] Gupta, S., Leszkiewicz, A., Kumar, V., Bijmolt, T. and Potapov, D., 2020. Digital analytics: Modeling for insights and new methods. Journal of Interactive Marketing, 51(1), pp.26-43.

[22] Jöhnk, J., Weißert, M. and Wyrtki, K., 2021. Ready or not, AI comes—an interview study of organizational AI readiness factors. Business & Information Systems Engineering, 63(1), pp.5-20.

[23] Kalusivalingam, A.K., Sharma, A., Patel, N. and Singh, V., 2020. Optimizing Decision-Making with AI-Enhanced Support Systems: Leveraging Reinforcement Learning and Bayesian Networks. International Journal of AI and ML, 1(2).

[24] Kuang, L., He, L.I.U., Yili, R.E.N., Kai, L.U.O., Mingyu, S.H.I., Jian, S.U. and Xin, L.I., 2021. Application and development trend of artificial intelligence in petroleum exploration and development. Petroleum Exploration and Development, 48(1), pp.1-14.

[25] Kush, R.D., Warzel, D., Kush, M.A., Sherman, A., Navarro, E.A., Fitzmartin, R., Pétavy, F., Galvez, J., Becnel, L.B., Zhou, F.L. and Harmon, N., 2020. FAIR data sharing: the roles of common data elements and harmonization. Journal of biomedical informatics, 107, p.103421.

[26] Langenwalter, G.A., 2020. Enterprise resources planning and beyond: integrating your entire organization. CRC Press.

[27] Lashitew, A.A., Bals, L. and van Tulder, R., 2020. Inclusive business at the base of the pyramid: The role of embeddedness for enabling social innovations. Journal of business ethics, 162(2), pp.421-448.

[28] Lee, P.C., 2021. Technological innovation in libraries. Library Hi Tech, 39(2), pp.574-601.

[29] Leone, D., Schiavone, F., Appio, F.P. and Chiao, B., 2021. How does artificial intelligence enable and enhance value co-creation in industrial markets? An exploratory case study in the healthcare ecosystem. Journal of Business Research, 129, pp.849-859.

[30] Leslie, D., 2020. Tackling COVID-19 through responsible AI innovation: Five steps in the right direction. Harvard Data Science Review, 10.

[31] Lim, C.H., Lim, S., How, B.S., Ng, W.P.Q., Ngan, S.L., Leong, W.D. and Lam, H.L., 2021. A review of industry 4.0 revolution potential in a sustainable and renewable palm oil industry: HAZOP approach. Renewable and Sustainable Energy Reviews, 135, p.110223.

[32] Lippincott, S., Kennedy, M.L., Lynch, C., Calvert, S. and Cozzo, J., 2021. Mapping the current landscape of research library engagement with emerging technologies in research and learning.

[33] Llewellyn, A., 2019. Innovations in learning and teaching in academic libraries: A literature review. New Review of Academic Librarianship, 25(2-4), pp.129-149.

[34] Lo, P., Allard, B., Anghelescu, H.G., Xin, Y., Chiu, D.K. and Stark, A.J., 2020. Transformational leadership practice in the world’s leading academic libraries. Journal of Librarianship and Information Science, 52(4), pp.972-999.

[35] Lynch, R., Young, J.C., Jowaisas, C., Rothschild, C., Garrido, M., Sam, J. and Boakye-Achampong, S., 2021. Data challenges for public libraries: African perspectives and the social context of knowledge. Information Development, 37(2), pp.292-306.

[36] Mahapatro, B., 2021. Human resource management. New Age International (P) ltd..

[37] Martín, G. A., Fernández-Isabel, A., Martín de Diego, I. and Beltrán, M., 2021. A survey for user behavior analysis based on machine learning techniques: current models and applications. Applied Intelligence, 51(8), pp.6029-6055.

[38] Martzoukou, K., 2021. Academic libraries in COVID-19: a renewed mission for digital literacy. Library management, 42(4/5), pp.266-276.

[39] Matizirofa, L., Soyizwapi, L., Siwela, A. and Khosie, M., 2021. Maintaining student engagement: The digital shift during the coronavirus pandemic a case of the library at the University of Pretoria. New Review of Academic Librarianship, 27(3), pp.364-379.

[40] Mhlanga, D., 2021. Artificial intelligence in the industry 4.0, and its impact on poverty, innovation, infrastructure development, and the sustainable development goals: Lessons from emerging economies?. Sustainability, 13(11), p.5788.

[41] Mutis, I., Ambekar, A. and Joshi, V., 2020. Real-time space occupancy sensing and human motion analysis using deep learning for indoor air quality control. Automation in Construction, 116, p.103237.

[42] Ng, K.K., Chen, C.H., Lee, C.K., Jiao, J.R. and Yang, Z.X., 2021. A systematic literature review on intelligent automation: Aligning concepts from theory, practice, and future perspectives. Advanced Engineering Informatics, 47, p.101246.

[43] Nyangon, J., 2020. Smart energy frameworks for smart cities: The need for polycentrism. Handbook of smart cities, pp.1-33.

[44] Olivetti, E.A., Cole, J.M., Kim, E., Kononova, O., Ceder, G., Han, T.Y.J. and Hiszpanski, A.M., 2020. Data-driven materials research enabled by natural language processing and information extraction. Applied Physics Reviews, 7(4).

[45] Ordu, M., Demir, E., Tofallis, C. and Gunal, M.M., 2021. A novel healthcare resource allocation decision support tool: A forecasting-simulation-optimization approach. Journal of the operational research society, 72(3), pp.485-500.

[46] Pedro, F., Subosa, M., Rivas, A. and Valverde, P., 2019. Artificial intelligence in education: Challenges and opportunities for sustainable development.

[47] Rai, A., 2020. Explainable AI: From black box to glass box. Journal of the Academy of Marketing Science, 48, pp.137-141.

[48] Ramachandran, R., Bugbee, K. and Murphy, K., 2021. From open data to open science. Earth and Space Science, 8(5), p.e2020EA001562.

[49] Safder, I., Hassan, S.U., Visvizi, A., Noraset, T., Nawaz, R. and Tuarob, S., 2020. Deep learning-based extraction of algorithmic metadata in full-text scholarly documents. Information processing & management, 57(6), p.102269.

[50] Schneider, A.W. and Sand, A.A., 2019. Green Open Access in Practice. In UKSG 42nd Annual Conference and Exhibition.

[51] Sey, A. and Mudongo, O., 2021. Case studies on AI skills capacity building and AI in workforce development in Africa. Research ICT Africa.

[52] Shiohira, K., 2021. Understanding the Impact of Artificial Intelligence on Skills Development. Education 2030. UNESCO-UNEVOC International Centre for Technical and Vocational Education and Training.

[53] Susan, S. and Kumar, A., 2021. The balancing trick: Optimized sampling of imbalanced datasets—A brief survey of the recent State of the Art. Engineering Reports, 3(4), p.e12298.

[54] Tian, H., Presa-Reyes, M., Tao, Y., Wang, T., Pouyanfar, S., Miguel, A., Luis, S., Shyu, M.L., Chen, S.C. and Iyengar, S.S., 2021. Data analytics for air travel data: a survey and new perspectives. ACM Computing Surveys (CSUR), 54(8), pp.1-35.

[55] Vijayakumar, S. and Sheshadri, K.N., 2019. Applications of artificial intelligence in academic libraries. International Journal of Computer Sciences and Engineering, 7(16), pp.136-140.

[56] Walker, C.C., Druckman, A. and Jackson, T., 2021. Welfare systems without economic growth: A review of the challenges and next steps for the field. Ecological Economics, 186, p.107066.

[57] Wang, X., Wang, Y., Guo, L., Xu, L., Gao, B., Liu, F. and Li, W., 2021. Exploring clustering-based reinforcement learning for personalized book recommendation in digital library. Information, 12(5), p.198.

[58] Witt, C., Bux, M., Gusew, W. and Leser, U., 2019. Predictive performance modeling for distributed batch processing using black box monitoring and machine learning. Information Systems, 82, pp.33-52.

[59] Yang, C.H., Lee, H.L., Tsai, W.H. and Chuang, S., 2020. Sustainable smart healthcare information portfolio strategy evaluation: an integrated activity-based costing decision model. Sustainability, 12(24), p.10662.

[60] Yu, P.K., 2020. The algorithmic divide and equality in the age of artificial intelligence. Fla. L. Rev., 72, p.331.

@article{1703369,
      author = {Ugochukwu Francis Ikwuanusi, Chima Azubuike, Chinekwu Somtochukwu Odionu, Aumbur Kwaghter Sule},
      title = {Leveraging AI to Address Resource Allocation Challenges in Academic and Research Libraries},
      journal = {Iconic Research And Engineering Journals},
      year = {2022},
      volume = {5},
      number = {10},
      pages = {311-322},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1703369.pdf},
      abstract = {The increasing complexity of resource allocation in academic and research libraries necessitates innovative solutions to address growing demands, budget constraints, and diverse user needs. Artificial Intelligence (AI) offers transformative capabilities to optimize resource management, ensuring equitable access to critical knowledge assets. This explores the application of AI-driven frameworks to tackle resource allocation challenges, focusing on predictive analytics, machine learning (ML), and natural language processing (NLP) technologies. AI enables libraries to analyze historical usage patterns and forecast future demands, facilitating data-driven decision-making for collection development and acquisition. Machine learning algorithms support personalized resource recommendations, aligning library offerings with user preferences and enhancing user satisfaction. NLP automates metadata creation and resource categorization, improving searchability and discovery across diverse collections. Additionally, AI-driven decision support systems assist in prioritizing investments based on resource impact metrics, ensuring efficient allocation of limited budgets. This research highlights the potential of AI in real-time monitoring of resource utilization, such as physical spaces, equipment, and digital content, enabling dynamic adjustments to meet changing needs. Case studies of successful AI implementations demonstrate best practices and collaborative approaches that can be scaled across institutions. However, the study also addresses critical challenges, including data privacy, algorithmic bias, and disparities in access to AI technologies, underscoring the need for ethical and transparent AI deployment. By leveraging AI, academic and research libraries can transform resource allocation strategies, promoting equity, efficiency, and sustainability. This review concludes with recommendations for future research and the development of open-source AI tools, aiming to empower libraries worldwide in their mission to support education, innovation, and knowledge dissemination.},
      keywords = {Artificial intelligence, Challenges, Academic, Research libraries},
      month = {April},
  }