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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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Разработка и применение комплексных нейросетевых моделей массовой оценки и прогнозирования стоимости жилых объектов на примере рынков недвижимости Екатеринбурга и Перми

Имущественные отношения в Российской Федерации. 2017. Т. 186. № 3. С. 68–84.
Yasnitsky L., Ясницкий В. Л.

Developed complex economic and mathematical models of mass valuation of residential real estate of cities of Yekaterinburg and Perm, which take into account both construction and operating parameters of the apartments, and the changing economic situation in the country and the world. Study models showed that the increase in new housing in Yekaterinburg, 4% in the short term can lead to market saturation and the subsequent decrease in the value of residential properties, in contrast to the Perm market, where the increase of housing to the same 4% to the saturation effect of the market does not.

Research target: Mathematics Economics and Management
Priority areas: economics management business informatics
Language: Russian
Full text
Keywords: недвижимостьоценка недвижимостипрогнозированиеartificial neural networks real estateискусственная нейронная сетьestate appraisal
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