M.V. Dobrina1

1 Financial University under the Government of the Russian Federation (Moscow, Russia)

Problem setting. At present, the constant development of technologies and data management systems is noticeable. This trend leads to an increase in the volume of information storage. In practice, mining technology is often used to process a significant amount of data.

Target. To evaluate the prospects of using neural networks as a data analysis tool.

Results. The prospects of using neural networks as a data analysis tool are analyzed. The concept of a neural network is considered, its structure is described, and an algorithm for data analysis using artificial neural networks is proposed. The work contains a description of a single-layer and multi-layer perceptron, a direct propagation network, an activation function, as well as LSTM and recurrent neural networks.

Practical significance. The materials discussed in the article can be used to train specialists in the development of various intelligent systems using artificial neural networks. The developed theory and methods of constructing artificial neural networks in the form of methodological materials can be used in the educational process.

Dobrina M.V. Methods of data analysis using artificial neural networks. Neurocomputers. 2023. V. 25. № 4. Р. 45-53. DOI: https://doi.org/10.18127/j19998554-202304-06 (In Russian)

1. McCulloch W.S., Pitts W. A logical Calculus of Ideas Immanent in Nervous Activity. The bulletin of mathematical biophysics. 1943. V. 5. № 4. P. 115–133. DOI 10.1007/BF02478259.
2. Karpov I.G., Proskurin D.K. Generalized distributions of mathematical statistics in data processing problems. Radiotechnika. 2010. № 8. P. 109–112. (In Russian)
3. Frawley W., Piatetsky-Shapiro G., Matheus C. Knowledge Discovery in Databases: An Overview. AI Magazine. 1992. V. 13. № 3.
   Р. 57–70. DOI 10.1609/aimag.v13i3.1011.
4. Ivanova G.S., Martynyuk P.A. Analysis of neural network language models for solving problems of text data processing. Neurocomputers. 2023. V. 25. № 2. Р. 5-20. DOI 10.18127/j19998554-202302-01. (In Russian)
5. Dobrina M.V., Alekseenko M.D., Cesko E.E. The ecommerce market: the essence and directions of improvement. Materials of the XVII All-Russian Scientific and Practical. Internet conference "Electronic business: problems, development and prospects". Ed. by V.V. Davnis. Voronezh. 2019. P. 119–122. (In Russian)
6. Dobrina M.V., Strekalova D.S. Forecasting the closing price of stocks in Python. Materials of the XVII International Scientific and Practical Conference "Economic forecasting: models and methods". Ed. by V.V. Davnis. Voronezh: Voronezh State University. 2022.
   P. 109–112. (In Russian)
7. Romanov V.P. Intellectual information systems in economics: textbook. Ed. by N.P. Tikhomirov. M.: Exam. 2003. 496 p. (In Russian)
8. Dyuk V.A., Flegontov A.V., Fomina I.K. Application of data mining technologies in natural science, technical and humanitarian fields. Izvestia of the A.I. Herzen Russian State Pedagogical University. 2011. № 138. P. 77–84. (In Russian)
9. Makarychev P.P., Mekhanov V.B., Afonin A.Yu. Operational and intellectual data analysis: a textbook. Penza: Penza State University. 2010. 156 p. (In Russian)
10. Mulyukova K.V. Comparative analysis of modern Data Mining tools. Young scientist. 2019. № 1(239). P. 19–21. (In Russian)
11. Yasnitskiy L.N. Introduction to artificial intelligence: textbook. manual for universities. M.: Publishing center "Academy". 2005. 176 p. (In Russian)