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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 1, 2024.
Abstract: This paper investigates the integration of Artificial Intelligence (AI) into systematic literature reviews (SLRs), aiming to address the challenges associated with the manual review process. SLRs, a crucial aspect of scholarly research, often prove time-consuming and prone to errors. In response, this work explores the application of AI techniques, including Natural Language Processing (NLP), machine learning, data mining, and text analytics, to automate various stages of the SLR process. Specifically, we focus on paper identification, information extraction, and data synthesis. The study delves into the roles of NLP and machine learning algorithms in automating the identification of relevant papers based on defined criteria. Researchers now have access to a diverse set of AI-based tools and platforms designed to streamline SLRs, offering automated search, retrieval, text mining, and analysis of relevant publications. The dynamic field of AI-driven SLR automation continues to evolve, with ongoing exploration of new techniques and enhancements to existing algorithms. This shift from manual efforts to automation not only enhances the efficiency and effectiveness of SLRs but also marks a significant advancement in the broader research process.
Kirtirajsinh Zala, Biswaranjan Acharya, Madhav Mashru, Damodharan Palaniappan, Vassilis C. Gerogiannis, Andreas Kanavos and Ioannis Karamitsos, “Transformative Automation: AI in Scientific Literature Reviews” International Journal of Advanced Computer Science and Applications(IJACSA), 15(1), 2024. http://dx.doi.org/10.14569/IJACSA.2024.01501122
@article{Zala2024,
title = {Transformative Automation: AI in Scientific Literature Reviews},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.01501122},
url = {http://dx.doi.org/10.14569/IJACSA.2024.01501122},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {1},
author = {Kirtirajsinh Zala and Biswaranjan Acharya and Madhav Mashru and Damodharan Palaniappan and Vassilis C. Gerogiannis and Andreas Kanavos and Ioannis Karamitsos}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.