Indian Research on Artificial Neural Networks: A Bibliometric Assessment of Publications Output during 1999-2018

Abstract

The paper describes quantitative and qualitative dimensions of  artificial neural networks (ANN) in India in global context. The study is based on  research publications data (8260) as covered in Scopus database during 1999-18. ANN research in India registered 24.52% growth, averaged 11.95 citations per paper and contributed 9.77%  share to the global ANN research. ANN research is skewed as top 10 countries account for 75.15% of global output. India ranks as the third most productive country in the world. The distribution of research by type of ANN networks reveal that Feed Forward Neural Network type accounted for the highest share (10.18% share), followed by Adaptive Weight Neural Network (5.38% share), Feed Backward Neural Network (2.54% share), etc.  ANN research applications across subjects were the largest in medical science and environment science (11.82% and 10.84% share respectively), followed by materials science, energy, chemical engineering and water resources (from 6.36%  to 9.12%), etc. Indian Institute of Technology, Kharagpur and Indian Institute of Technology, Roorkee lead the country as the most productive organizations (with 289 and 264 papers). Besides, Indian Institute of Technology, Kanpur (33.04 and 2.76) and Indian Institute of Technology, Madras (24.26 and 2.03) lead the country as the most impactful organizations in terms of citation per paper and relative citation index. P. Samui and T.N. Singh have have been the most productive authors and G.P.S.Raghava (86.21 and 7.21) and K.P. Sudheer (84.88 and 7.1) have been the most impactful authors. Neurocomputing, International Journal of Applied Engineering Research and Applied Soft Computing topped the list of most productive journals.

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