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News
July 9, 2026
HSE Economists Use Search Queries to Forecast Birth Rates
Researchers from the HSE Faculty of Economic Sciences have shown that the accuracy of birth rate forecasts for Russia can be improved by almost 50% by incorporating the dynamics of online search queries related to pregnancy and childbirth into forecasting models. In the best-performing models, the forecasting error fell from 4.6% to 3.2%. The findings have been published in Populations and Economics.
July 8, 2026
HSE Researchers Discover Who Eats Out in Russia-And Why
Around one-third of Russians (31.3%) rarely eat out or buy ready-made meals. The core group of active consumers—those who eat out or purchase prepared food almost every day or several times a week—accounts for only about 9% of the population. These are the findings of a study conducted by the HSE Institute for Social Policy. According to the researchers eating out is no longer a marker of high social status in Russia.
July 8, 2026
HSE University and RREDA Join Forces to Support 2026 Renewable Energy of the Planet Competition
HSE University and the Russia Renewable Energy Development Association (RREDA) have signed a partnership and information cooperation agreement to support Renewable Energy of the Planet—2026, a national competition with international participation for students and early-career researchers. Applications are open on the competition's website until September 20, 2026.

 

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Диагностика тяжести симптомов депрессии при помощи объяснимого искусственного интеллекта

Доклады Российской академии наук. Математика, информатика, процессы управления (ранее - Доклады Академии Наук. Математика). 2023. Т. 514. № 2. С. 242–249.
Shalileh S., Koptseva A., Shishkovskaya T., Khudyakova M., Dragoy O.

This paper represents our research to (i) propose an artificial intelligence, AI-based solution to identify depression and (ii) investigate our psychiatric knowledge. Concerning the first objective, we collected and annotated a new audio data set, and scrutinized the performance of eight regression approaches. Our studies showed that k-nearest neighbor and random forest form the group having the most acceptable results. Regarding our second objective, we determined the importance of the features of our best model using the SHapley Additive exPlanations approach: our findings showed that the fourth Mel-frequency cepstral coefficients, harmonic difference, and shimmer are the most important features.

Research target: Medical Technologies Health Studies Psychology
Language: Russian
DOI
Text on another site
Keywords: explainable artificial intelligenceобъяснимый искусственный интеллект artificial intelligencedepression recognition acoustic features regressionраспознавание депрессииакустические признаки регрессия
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