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April 30, 2026
HSE Researchers Compile Scientific Database for Studying Childrens Eating Habits
The database created at HSE University can serve as a foundation for studying children’s eating habits. This is outlined in the study ‘The Influence of Age, Gender, and Social-Role Factors on Children’s Compliance with Age-Based Nutritional Norms: An Experimental Study Using the Dish-I-Wish Web Application.’ The work has been carried out as part of the HSE Basic Research Programme and was presented at the XXVI April International Academic Conference named after Evgeny Yasin.
April 30, 2026
New Foresight Centre Study Identifies the Most Destructive Global Trends for Humankind
A team of researchers from the HSE International Research and Educational Foresight Centre has examined how global trends affect the quality of human life—from life expectancy to professional fulfilment. The findings of the study titled ‘Human Capital Transformation under the Influence of Global Trends’ were published in Foresight.
April 28, 2026
Scientists Develop Algorithm for Accurate Financial Time Series Forecasting
Researchers at the HSE Faculty of Computer Science benchmarked more than 200,000 model configurations for predicting financial asset prices and realised volatility, showing that performance can be improved by filtering out noise at specific frequencies in advance. This technique increased accuracy in 65% of cases. The authors also developed their own algorithm, which achieves accuracy comparable to that of the best models while requiring less computational power. The study has been published in Applied Soft Computing.

 

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Классификация текстов по жанрам при помощи алгоритмов машинного обучения

Научно-техническая информация. Серия 2: Информационные процессы и системы. 2018. № 8. С. 34–38.
Builova N.

The problem of documents classification by genre was examined in this review. The main characteristics of the text used to recognize the genre of text were highlighted, and the most widely used algorithms of machine learning were described. The methods considered serve for the classification of scientific, technical, journalistic and artistic texts.

Research target: Philology and Linguistics
Priority areas: humanitarian
Language: Russian
Full text
Keywords: машинное обучениеgenreslinguistic classificationжанрообразующие признакиmachine learningклассификация текстов
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