EXPLORING GROWTH OF RESEARCH TRENDS IN ARTIFICIAL INTELLIGENCE: A BIBLIOMETRIC STUDY
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Abstract
Bibliometric analysis is one of the major techniques applied to measure the literature output on research in any subject. This study identifies the global literature output on ‘Artificial intelligence or AI-related’ research based on data retrieved from ‘DOAJ’ indexing database over a longitudinal period 1950-2024. Classification of data has been done using a spreadsheet package. Quantitative analysis to evaluate the trends in AI-related research has been undertaken using bibliometric statistics of open access publications. It is observed from the research output that the AI-related research began with research in Robotics followed by growth in Neural Network(s) and Natural Language Processing. The exponential growth in AI-related research is observed in the last ten years. The significant findings show that out of total 134025 Artificial Intelligence and AI-related open access publications available during the period 1950-2024, the publications of AI research with focus on Machine Learning are the highest (28.97%) followed by Neural Networks (27.86%) and Deep Learning (22.13%). Deep Learning has been the fastest growing research area since its origin as compared to other AI-related areas of research.
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