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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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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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Towards Stable Significant Subgroup Discovery

P. 287–292.
Jyoti -., Buzmakov Aleksey, Kailasam S.
Language: English
Full text
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Keywords: анализ формальных понятийformal concept analysissubgroup discoveryStable significant pattern miningпоиск подгрупп

In book

The 15th International Conference on Concept Lattices and Their Applications CLA2020
Issue 2668. , CEUR-WS, 2020.
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