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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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Affiliations based bibliometric analysis of publications on parkinson’s disease

Computational Management Science. 2024. Vol. 21. Article 13.
Fuad Aleskerov, Khutorskaya O., Viacheslav Yakuba, Stepochkina A., Zinovyeva K.

Parkinson’s disease is the second most common neurodegenerative disorder in the world. Thousands of scientific works are published every year. We have analyzed more than 3 thousand organizations, who have published works on various aspects of parkinson’s disease in the period from 2015 to 2021. We have evaluated 4 classical centrality indices (In-degree, Eigenvector, Pagerank and Betweenness) and 2 new centrality indices. The new indices allow to take into account group influence and to identify pivotal nodes. Using the method, we have extracted the most influential organizations in the scientific area of parkinson’s disease. Stability analysis allows us to measure the value of dynamic changes in the network during the period under consideration.

Research target: Mathematics
Language: English
DOI
Text on another site
Keywords: network analysisParkinson’s diseasecitations
Publication based on the results of:
Models of data analysis and decision making in the socio-economic sphere (2024)
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