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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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Detection and Recognition of Food in Photo Galleries for Analysis of User Preferences

Ch. 9. P. 83–94.
Miasnikov E., Savchenko A.

Food analysis is one of the most important parts of user preference prediction engines for recommendation systems in the travel domain. In this paper, we describe and study the neural network method that allows you to recognize food in a gallery of photos taken with mobile devices. The described method consists of three main stages, including the classification of scenes, food detection, and subsequent classification. An essential feature of the developed method is the use of lightweight neural network models, which allows its usage on mobile devices. The development of the method was carried out using both known open data and a proprietary data set.

Language: English
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DOI
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Keywords: Deep Convolutional Neural Networksсверточные нейронные сетиscene recognitionраспознавание сценFood recognitionраспознавание еды на изображениях

In book

Proceedings of International Conference on Image Analysis and Recognition (ICIAR 2020)
Vol. 12131. , Cham: Springer, 2020.
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