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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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Lecture Notes in Networks and Systems. Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016

Vol. 2. L. : Springer, 2018.
Editor-in-chief: Y. Bi, S. Kapoor, R. Bhatia

Presents recent research in intelligent systems and applications Gathers the proceedings of the SAI Intelligent Systems Conference 2016 (IntelliSys 2016) held in London, UK from 21 to 22 September 2016 Focuses on various areas of intelligent systems and artificial intelligence and how they apply to the real world These proceedings of the SAI Intelligent Systems Conference 2016 (IntelliSys 2016) offer a remarkable collection of papers on a wide range of topics in intelligent systems, and their applications to the real world. Authors hailing from 56 countries on 5 continents submitted 404 papers to the conference, attesting to the global importance of the conference’s themes. After being reviewed, 222 papers were accepted for presentation, and 168 were ultimately selected for these proceedings. Each has been reviewed on the basis of its originality, novelty and rigorousness. The papers not only present state-of-the-art methods and valuable experience from researchers in the related research areas; they also outline the field’s future development.

Chapters
Strategic Development and Dynamic Models of Supply Chains: Search for Effective Model Constructions
Lychkina N. N., , in: Lecture Notes in Networks and Systems. Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016Vol. 2.: L.: Springer, 2018. Ch. 2 P. 175–185.
In this research the author studies the approaches to simulation of strategic development of supply chains and formation of cooperation strategies on the basis of the methods of ontology engineering, complex system dynamics simulation and agent-based simulation. ...
Added: September 4, 2017
Research target: Economics and Management Computer Science
Priority areas: management IT and mathematics business informatics
Language: English
DOI
Text on another site
Keywords: artificial intelligenceComputational Intelligence and Complexity
Lecture Notes in Networks and Systems. Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016
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Business Creativity and Circular Economy
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Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 3: System Demonstrations)
Rabat: Association for Computational Linguistics, 2026.
Added: May 19, 2026
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Added: April 28, 2026
Natural hazard database from Internet publications: text mining with a large language model
Derkacheva A., Sakirkina M., Kraev G. et al., /. 2026.
Comprehensive data on natural hazards and their consequences are crucial for effective for risk assessment, adaptation planning, and emergency response. However, many countries face challenges with fragmented, inconsistent, and inaccessible data, particularly regarding local-scale events. To address this data gap in Russia, we developed an end-to-end processing pipeline that scrapes news from various online sources, ...
Added: April 28, 2026
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