From classical machine learning to deep neural networks: A simplified scientometric review

Mukhamediev R.I. Symagulov A. Kuchin Y. Yakunin K. Yelis M.
2 June 2021 MDPI AG

Applied Sciences (Switzerland)
2021 #11 Issue 12

There are promising prospects on the way to widespread use of AI, as well as problems that need to be overcome to adapt AI&ML technologies in industries. The paper systematizes the AI sections and calculates the dynamics of changes in the number of scientific articles in machine learning sections according to Google Scholar. The method of data acquisition and calculation of dynamic indicators of changes in publication activity is described: growth rate (D1) and acceleration of growth (D2) of scientific publications. Analysis of publication activity, in particular, showed a high interest in modern transformer models, the development of datasets for some industries, and a sharp increase in interest in methods of explainable machine learning. Relatively small research domains are receiving increasing attention, as evidenced by the negative correlation between the number of articles and D1 and D2 scores. The results show that, despite the limitations of the method, it is possible to (1) identify fast-growing areas of research regardless of the number of articles, and (2) predict publication activity in the short term with satisfactory accuracy for practice (the average prediction error for the year ahead is 6%, with a standard deviation of 7%). This paper presents results for more than 400 search queries related to classified research areas and the application of machine learning models to industries. The proposed method evaluates the dynamics of growth and the decline of scientific domains associated with certain key terms. It does not require access to large bibliometric archives and allows to relatively quickly obtain quantitative estimates of dynamic indicators.

Artificial intelligence , Bibliometric indicators , Convolution neural networks , Deep learning , Explainable machine learning , Machine learning , Recurrent neural networks , Scientometrics , Transfer learning , Transformers

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Institute of Cybernetics and Information Technology, Satbayev University (KazNRTU), Satpayev str., 22A, Almaty, 050013, Kazakhstan
Institute of Information and Computational Technologies MES RK, Pushkin str., 125, Almaty, 050010, Kazakhstan

Institute of Cybernetics and Information Technology
Institute of Information and Computational Technologies MES RK

10 лет помогаем публиковать статьи Международный издатель

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