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Киреев, Василий Сергеевич

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Организационные подразделения
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Институт интеллектуальных кибернетических систем
Цель ИИКС и стратегия развития - это подготовка кадров, способных противостоять современным угрозам и вызовам, обладающих знаниями и компетенциями в области кибернетики, информационной и финансовой безопасности для решения задач разработки базового программного обеспечения, повышения защищенности критически важных информационных систем и противодействия отмыванию денег, полученных преступным путем, и финансированию терроризма.
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Киреев
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Василий Сергеевич
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2019 - 20203
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Автор: Filippov, S. A. ×

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  • Уменьшенное изображение
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    Association Rules Mining for Predictive Analytics in IoT Cloud System
    (2019) Guseva, A. I.; Bochkaryov, P. V.; Kuznetsov, I. A.; Kireev, V. S.; Filippov, S. A.; Киреев, Василий Сергеевич; Факультет бизнес-информатики и управления комплексными системами
    © 2019, Springer Nature Switzerland AG. The Internet of Things is one of the fastest growing areas of research currently. A promising area for the introduction of this technology is housing and communal services, for which the reduction of accidents, increasing efficiency, in general, focus on transparency, personalization of services and payments for the end user are relevant. This article is devoted to the development and testing of predictive algorithm for predicting the need for repair of various units within the smart home, such as heating, ventilation and air conditioning. The basis for the algorithm is the association rules mining. The paper presents the results of experiments and the directions for further improvement of the algorithm.
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    Monitoring System for the Housing and Utility Services Based on the Digital Technologies IIoT, Big Data, Data Mining, Edge and Cloud Computing
    (2019) Kireev, V. S.; Bochkaryov, P. V.; Guseva, A. I.; Kuznetsov, I. A.; Filippov, S. A.; Киреев, Василий Сергеевич; Бочкарёв, Пётр Владимирович; Гусева, Анна Ивановна; Кузнецов, Игорь Александрович; Факультет бизнес-информатики и управления комплексными системами
    © 2019, Springer Nature Switzerland AG.This article discusses the development of an information system for monitoring, remote control and support of technical conditions in a housing infrastructure engineering system. The monitoring system is based on the concept of edge computing - cloud computing and is designed to collect and analyze Big data procured via the Industrial Internet of Things (IIoT). Author acknowledges support from the MEPhI Academic Excellence Project (Contract No. 02.a03.21.0005).
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    Development of Text Data Processing Pipeline for Scientific Systems
    (2020) Guseva, A. I.; Kuznetsov, I. A.; Bochkaryov, P. V.; Filippov, S. A.; Kireev, V. S.; Гусева, Анна Ивановна; Кузнецов, Игорь Александрович; Бочкарёв, Пётр Владимирович; Киреев, Василий Сергеевич; Факультет бизнес-информатики и управления комплексными системами
    © 2020, Springer Nature Switzerland AG.The aim of this work was to develop pipeline processing of scientific texts, including articles and abstracts, for their further categorization, identify patterns and build recommendations to users of scientific systems. The authors proposed a number of methods of pre-processing of texts, the method of cluster and classification analysis of texts, developed a software system of recommendations to users of scientific publications. To solve the problem of data preprocessing it is proposed to use parametrical approach to retrieve new – semantic – feature from textual publications – the type of scientific result. Scientific result type extraction is built just based on user’s need for content having specific property. To solve the problem of users’ profile clustering it is proposed to use ensemble method with distance metric change. For classification, ensemble method based on entropy is used. Evaluation of proposed methods and algorithms employment efficiency was carried out as applied to operation of search module of “Technologies in Education” International Congress of Conferences information system. Author acknowledges support from the MEPhI Academic Excellence Project (Contract No. 02.a03.21.0005).