From Classical Machine Learning to Deep Neural Networks: A Simplified Scientometric Review

• Published in: Applied sciences 2021-06, Vol.11 (12), p.5541
• Main Authors: Mukhamediev, Ravil I., Symagulov, Adilkhan, Kuchin, Yan, Yakunin, Kirill, Yelis, Marina
• Format: Article
• Language: English
• Subjects: Algorithms; Archives & records; Artificial intelligence; Bibliometrics; Citation indexes; Classification; convolution neural networks; Data acquisition; Deep learning; Domains; explainable machine learning; Growth rate; Hirsch index; Indicators; Learning algorithms; Literature reviews; Machine learning; Natural language processing; Neural networks; Principal components analysis; Productivity; Robotics; Scientific papers; Scientometrics; Software; transfer learning
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app11125541

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.