From Classical Machine Learning to Deep Neural Networks: A Simplified Scientometric Review
Abstract
:1. Introduction
- Systematization of AI&ML sections according to literature data.
- Development of methods for collecting data from open sources and assessing changes in publication activity using differential indicators.
- Assessment of changes in publication activity in AI&ML using differential indicators to identify fast-growing and “fading” research domains.
2. Literature Review
- Machine learning (ML) is a subset of artificial intelligence techniques that allow computer systems to learn from previous experience (i.e., from observations of data) and improve their behavior to perform a particular task. ML methods include support vector methods (SVMs), decision trees, Bayesian learning, k-means clustering, association rule learning, regression, neural networks, and more.
- Neural networks (NN) or artificial NNs are a subset of ML methods with some indirect relationship to biological neural networks. They are usually described as a set of connected elements called artificial neurons, in organized layers.
- Deep learning (DL) is a subset of NN that provides computation for multilayer NN. Typical DL architectures are deep neural networks (DNN), convolutional neural networks (CNN), recurrent neural networks (RNN), generating adversarial networks (GAN), and more.
- Standard feed forward neural network—NN.
- Recurrent neural network—RNN.
- Convolution neural network—CNN.
- Hybrid architectures that include elements of 1, 2, and 3 basic architectures, such as Siamese networks and transformers.
- DNA analysis [66], in which the nucleotide sequence determines the meaning of the gene.
- Classifications of the emotional coloring of the text or tone (sentiment analysis [67]). The tone of the text is determined not only by specific words, but also by their combinations.
- Name entity recognition [68], that is, proper names, days of the week and months, locations, dates, etc.
3. Method
- —actual value,
- —calculated value (hypothesis function value) for the i-th example,
- —part of the training sample (sets of marked objects).
- For each search query, the regression order is chosen individually, starting from n = 3 to ensure r2_score ≥ 0.7. As soon as the specified boundary is reached, n is fixed and the selection process stops.
- Since we are most interested in the values of dynamic indicators for the last year, we used the last value (number of publications for 2020) and the value equal to half of the growth of articles achieved at the end of 2020, which we conventionally associate with the middle of the year, as a test set on which r2_score is determined.
4. Results and Discussion
- When both values (D1, D2) are positive, it indicates an accelerated growth of the number of articles in this domain.
- When D1 is negative and D2 is positive, this indicates a slowdown in the number of items.
- When D1 is positive and D2 is negative, it indicates a slowdown in the growth of the number of items.
- When both values are negative, it indicates an accelerated decrease in the number of items.
- In general, the number of publications in the AI&ML domain will decrease.
- New domains such as applications of transformers and explainable machine learning will see rapid growth.
- Classic machine learning models such as SVM, k-NN, and logistic regression will attract less attention from researchers.
- The number of articles on clustering models will continue to increase.
5. Conclusions
- For all the depth of the informal analysis, the set of terms is still set by the researcher. Consequently, some of the articles that are part of the section under study may be left out, and, conversely, some publications may be incorrectly attributed to the topic in question. We also cannot guarantee the exhaustive completeness and consistency of the empirical review performed.
- This analysis does not take into account the fact that the importance of a particular scientific topic is determined not only by the number of articles, but also by the volume of citations, the “weight” of the individual characteristics of the authors, the quality of the journals, and so on.
- The method does not evaluate term change processes and semantic proximity of scientific domains.
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Appendix B
Appendix C
Appendix D
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Mukhamediev, R.I.; Symagulov, A.; Kuchin, Y.; Yakunin, K.; Yelis, M. From Classical Machine Learning to Deep Neural Networks: A Simplified Scientometric Review. Appl. Sci. 2021, 11, 5541. https://doi.org/10.3390/app11125541
Mukhamediev RI, Symagulov A, Kuchin Y, Yakunin K, Yelis M. From Classical Machine Learning to Deep Neural Networks: A Simplified Scientometric Review. Applied Sciences. 2021; 11(12):5541. https://doi.org/10.3390/app11125541
Chicago/Turabian StyleMukhamediev, Ravil I., Adilkhan Symagulov, Yan Kuchin, Kirill Yakunin, and Marina Yelis. 2021. "From Classical Machine Learning to Deep Neural Networks: A Simplified Scientometric Review" Applied Sciences 11, no. 12: 5541. https://doi.org/10.3390/app11125541
APA StyleMukhamediev, R. I., Symagulov, A., Kuchin, Y., Yakunin, K., & Yelis, M. (2021). From Classical Machine Learning to Deep Neural Networks: A Simplified Scientometric Review. Applied Sciences, 11(12), 5541. https://doi.org/10.3390/app11125541