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News
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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Возможности моделирования предрасположенности к наркозависимости методами искусственного интеллекта

Вестник Пермского университета. Философия. Психология. Социология. 2015. № 1. С. 61–71.
Yasnitsky L., Грацилев В. И., Куляшова Ю. С., Черепанов Ф. М.

A computer program designed to determine the degree human predisposition to drug addiction. The program is based neural network trained on the results of sociological surveys. Error of neural network model was less than 1%. With the help of neural network model evaluated the importance of factors that can influence the predisposition to drug addiction. The most important factors were: the level of education, having friends who use drugs, temperament type, number of children in the family, financial situation. Neural network model allows to evaluate the effect of varying the parameters characterizing the man and his predisposition to addiction and select the optimal combination of these parameters for each individual and thus receive individual recommendations for reducing drug addiction.

Research target: Psychology Computer Science
Priority areas: sociology
Language: Russian
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Keywords: нейронная сетьпрогнозэкономико-математическое моделированиеmathematical modelingartificial neural networksdrug addictionpredictionartificial intelligence
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