Different numerical dimensions data flows: machine learning instead of correlation methods

Leonid A. Slavutskii; Nadezhda M. Lazareva; Mikhail S. Portnov; Elena V. Slavutskaya

doi:10.1117/12.3024919

20 February 2024 Different numerical dimensions data flows: machine learning instead of correlation methods

Leonid A. Slavutskii, Nadezhda M. Lazareva, Mikhail S. Portnov, Elena V. Slavutskaya

Proceedings Volume 13065, Third International Conference on Optics, Computer Applications, and Materials Science (CMSD-III 2023); 1306509 (2024) https://doi.org/10.1117/12.3024919
Event: Third International Conference on Optics, Computer Applications, and Materials Science (CMSD-III 2023), 2023, Dushanbe, Tajikistan

Abstract

System analysis of multidimensional random data is one of the most important tasks of modern information technologies. Traditional statistical methods for analyzing such data do not always allow one to obtain adequate results. In this article, the joint use of the decision tree method and the artificial neural networks is proposed for data classification. It is shown that this approach can be an alternative to multiple factorization of multidimensional data and has a number of significant advantages over traditional statistical methods. Even when the data has limited and different digital scales, the use of machine learning makes it possible to conduct a hierarchical classification of intra-system data relationships, to reduce the number of parameters significant for analysis, and significantly reduce the size of the learning sample required. For the technical applications, this allows for real-time data analysis using a microprocessor implementation of neural network algorithms.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Leonid A. Slavutskii, Nadezhda M. Lazareva, Mikhail S. Portnov, and Elena V. Slavutskaya "Different numerical dimensions data flows: machine learning instead of correlation methods", Proc. SPIE 13065, Third International Conference on Optics, Computer Applications, and Materials Science (CMSD-III 2023), 1306509 (20 February 2024); https://doi.org/10.1117/12.3024919

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