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June 2, 2026
HSE Study Reveals Imbalance in the Generative AI Market
Researchers at HSE University analysed how effectively the global generative artificial intelligence market converts investment into real revenue, concluding that AI is currently developing faster than it is paying off. The results have been published in the journal Foresight and STI Governance.
June 2, 2026
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On May 23, 2026, the V International Scientific and Practical Conference ‘Discovering the World of Science’ took place in Kazan at the Preparatory Faculty for International Students of Kazan Federal University. Four students of the HSE International Preparatory Year took part in the event: two delivered their presentations in person, while two participated online. Their work was supervised by Acting Director of the International Prep Year Irina Isaeva and lecturer Ekaterina Kozhemyakova.
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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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Отраслевые особенности применения моделей прогнозирования банкротства предприятия

Стратегические решения и риск-менеджмент. 2018. № 1 (104). С. 64–71.
Fedorova E., Khrustova L., Чекризов Д. В.

The aim of the research is to develop the methodology of bankruptcy prediction applying the specified statutory values of the existing models with a glance to company’s industry and developing the author’s prediction model. Initially authors estimated the forecast accuracy of the existing models for the enterprises of 8 industries. Using CART (Classification And Regression Tree) methodology the original statutory values of the models were specified for every industry under research. The calculated statutory values demonstrated the high level of prediction accuracy and balanced the indicators of accuracy for bankrupt and non-bankrupt companies. The indicators with the maximum level of significance for bankruptcy prediction were selected from all the models. They formed a basis for a new developed model, which has demonstrated the high level of prediction accuracy on a sample under research. The statutory values for the new model were also developed. The implementation of the research’s results will increase the efficiency of bank ruptcy prediction and low the number of bank rupt companies.

Priority areas: economics
Language: Russian
Keywords: банкротствопрогнозирование банкротства предприятиямодели прогнозированияклассическая модель
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