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.
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.
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.
Muratova A., Ignatov D. I., В кн.: Программа секций XVIII Апрельской конференции. Сессия P-04. Исследование демографических последовательностей.: НИУ ВШЭ, 2017. С. 1–13.
Анализ демографических последовательностей становится все более востребованным в последние годы. В рамках данной работы был осуществлен анализ демографических последовательностей с помощью деревьев решений. Для демографических и социоэкономических событий были определены (предсказаны) первые и последние значимые события в жизни человека с учетом всех его признаков и уже произошедших событий. Также выявлены взаимосвязи событий для признаков пол ...
Gizdatullin D., Baixeries J., Ignatov D. I. et al., , in: Intelligent Data Processing 11th International Conference, IDP 2016, Barcelona, Spain, October 10–14, 2016, Revised Selected PapersVol. 794.: Switzerland: Springer, 2019. Ch. 6 P. 74–91.
There are many different methods for computing relevant
patterns in sequential data and interpreting the results. In this paper,
we compute emerging patterns (EP) in demographic sequences using
sequence-based pattern structures, along with different algorithmic solutions.
The purpose of this method is to meet the following domain
requirement: the obtained patterns must be (closed) frequent contiguous
prefixes of the input sequences. ...