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May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
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A Hybrid Approach to the Maximum Clique Problem in the Domain of Information Management

P. 323–336.
Demidovskij A., Babkina T. S., Babkin E.

In this paper we observe the opportunity to offer new methods of solving NP-hard problems which frequently arise in the domain of information management, including design of database structures and big data processing. In our research we are focusing on the Maximum Clique Problem (MCP) and propose a new approach to solving that problem. The approach combines the artificial neuro-network paradigm and genetic programming. For boosting the convergence of the Hopfield Neural Network (HNN) we propose the genetic algorithm as the selection mechanism for terms of energy function. As a result, we demonstrate the proposed approach on experimental graphs and formulate two hypotheses for further research.

Language: English
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Keywords: information managementGenetic algorithmHopfield Neural networks

In book

Advances in Intelligent Systems and Computing Proceedings of the Fifth International Conference on Fuzzy and Neuro Computing (FANCCO - 2015)
Advances in Intelligent Systems and Computing Proceedings of the Fifth International Conference on Fuzzy and Neuro Computing (FANCCO - 2015)
Vol. 415. , Switzerland: Springer, 2015.
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