Methodology of Artificial Intelligence
This study examines the key issues, developments, technological strategies, and artificial intelligence techniques created by new researchers and experts in the field of machine learning, with a focus on the most notable and pertinent works to date. This review of the literature rates machine learning's major methodological contributions to artificial intelligence. Content analysis was the method used to analyse the documents, and the key terms used in the study between 2017 and 2021 are big data, artificial intelligence, and machine learning. 120 of the 181 references we chose for this study are included in the literature review. Four groups, eight subgroups, and twelve categories make up the conceptual framework. The four machine learning groups—supervised learning, unsupervised learning, semi-supervised learning, and reinforced learning—show symmetry in the study of data management using AI methodologies. In addition, artificial neural networks, Support Vector Machines, K-means, and Bayesian Methods are the artificial intelligence techniques with the most symmetry across all groups. Finally, five research directions are presented to enhance machine learning prediction.
Reference:
Serey, J. et al. (2021) Artificial Intelligence methodologies for Data Management, researchgate. Available at: https://www.researchgate.net/publication/355887448_Artificial_Intelligence_Methodologies_for_Data_Management
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