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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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Прогнозирование вероятности банкротства предприятий с учетом изменения финансовых показателей в динамике

Бизнес-информатика. 2011. № 1. С. 50–60.
Bogdanova T., Алексеева Ю. А.

System of logistic regression models is suggested which takes into account variation of financial coefficients in dynamics. Besides it comparative analysis of predictive accuracy is performed for the system of models on the basis of learning and control samples of manufacturing companies.

Priority areas: economics business informatics
Language: Russian
Full text
Keywords: logistic regression modelfinancial performance of the companyprediction of bankruptcy probabilitylearning samplecontrol samplevariation of financial coefficients in dynamicsлогистическая регрессионная модельфинансовое состояние предприятияпрогнозирование вероятности банкротстваобучающая выборкаконтрольная выборкадинамика изменения финансовых показателей
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