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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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?

Efficient Mining of Subsample-Stable Graph Patterns

Ch. 89. P. 757–762.
Buzmakov A. V., Kuznetsov S., Napoli A.

A scalable method for mining graph patterns stable under subsampling is proposed.
The existing subsample stability and robustness measures are not antimonotonic according to definitions known so far.
We study a broader notion of antimonotonicity for graph patterns, so that measures of subsample stability become antimonotonic. Then we propose gSOFIA for mining the most subsample-stable graph patterns.
The experiments on numerous graph datasets show that gSOFIA is very efficient for discovering subsample-stable graph patterns.
 

Language: English
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Keywords: устойчивостьstabilityанализ формальных понятийdata miningFCA (Formal Concept Analysis)pattern structuresузорные структуры

In book

2017 IEEE 17th International Conference on Data Mining (ICDM)
New Orleans: IEEE, 2017.
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