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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.
May 25, 2026
'The Humanities Serve as a Conscience'
Maria Mizernaia studies Soviet literature and the history of book publishing. In this interview for the HSE Young Scientists project, she discusses plans to publish a novel about besieged Leningrad, AI-provoked reflections on what it means to be human, and how novels can help satisfy our dopamine hunger.
May 25, 2026
Is It Possible to Predict a Citys Life Based on the Shape of Its Neighbourhoods?
Is it possible to predict, based on the configuration of streets and buildings, where a café will open or where traffic congestion will occur? Participants in the Spatial Analysis and Modelling of Urban Processes research and study group use open data and machine learning to identify universal patterns. Alexander Sheludkov and Eduard Somov discuss the purpose of comparing cities, the need for new forms of urban statistics, and how open data is transforming approaches to urban studies.

 

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?

Neural networks to solve modern artificial intelligence tasks

P. 1–7.
Gruzenkin D. V., Sukhanova A. V., Novikov O. S., Grishina G. V., Rutskiy V., Tsarev R., Zhigalov K. Y.

Today the information technologies are increasing and improving by leaps and bounds becoming smarter and faster. And artificial intelligence is penetrating deeper and deeper in everyday life. So, artificial intelligence field is becoming more interesting for modern scientists and engineers. Artificial neural networks are used all over the world as one of the approaches that can provide with the high relevance level of resolving results of badly formalized or unformalized tasks. In this article a review of artificial neural networks development tools of different kinds is presented. Also there is a detailed description of all of them and the main features are emphasized. As a result, the table is presented that allows to make a quick compare of described tools and decide if any of them are suitable for a reader or not.

Language: English
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Keywords: искусственный интеллектнейронные сетиneural networkscomplex systemsartificial intelligenceкомплексные системыneural networks softwareпрограммы нейронных сетей

In book

Journal of Physics: Conference Series
Journal of Physics: Conference Series
Vol. 1399: International Scientific Conference "Conference on Applied Physics, Information Technologies and Engineering – APITECH-2019" 25–27 September 2019, Krasnoyarsk, Russian Federation. Issue 3. , Institute of Physics Publishing (IOP), 2019.
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