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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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Выявление знаний в демографических последовательностях

С. 9601–9615.
Muratova A., Gizdatullin D., Ignatov D. I., Mitrofanova E.

In this paper, we summarize the results of recent studies on the application of pattern mining and machine learning to the analysis of demographic sequences. The main goal is the demonstration of demographers’ needs, including next-event prediction and the extraction of interesting patterns from substantial datasets of demographic data, which cannot be handled by conventional demographic techniques. We use decision trees as a technique for demographic event prediction, and emerging sequential patterns and pattern structures for discovering relevant interpretable sequences. The emerging problem statements and positive prospects of the usage of pattern mining in the demography domain are worth dissemination in the data mining community.

Language: Russian
Full text
Keywords: decision treespattern structuresанализ последовательностейEmerging patternsDemographic sequencesSequence mining деревья решенийдемографические последовательности
Publication based on the results of:
Разработка и апробация методик анализа демографических последовательностей (2016)

In book

Социология и общество: социальное неравенство и социальная справедливость (Екатеринбург , 19-21 октября 2016 года). Материалы V Всероссийского социологического конгресса
Социология и общество: социальное неравенство и социальная справедливость (Екатеринбург , 19-21 октября 2016 года). Материалы V Всероссийского социологического конгресса
М.: Российское общество социологов, 2016.
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