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dc.contributor.authorMalugin, Vladimir
dc.contributor.authorSergeev, Akim
dc.contributor.authorSolomevich, Alexandr
dc.date.accessioned2023-12-12T12:42:14Z-
dc.date.available2023-12-12T12:42:14Z-
dc.date.issued2023
dc.identifier.citationPattern Recognition and Information Processing (PRIP’2023). Artificial Universe: New Horisont : Proceedings of the 16 th International Conference, Belarus, Minsk, October 17–19, 2023 / Belarusian State University : eds. A. Nedzved, A. Belotserkovsky. – Minsk : BSU, 2023. – Pp. 209-211.
dc.identifier.isbn978-985-881-522-6
dc.identifier.urihttps://elib.bsu.by/handle/123456789/306234-
dc.description.abstractThe paper presents the results of a multi-country analysis of the intensity typology of the COVID-19 pandemic in 30 countries of the European region based on publicly available and regularly updated panel data for the entire period 2020-2022 of high pandemic activity. In the generated space of classification features, using cluster analysis algorithms, all countries are divided into three classes, which differ in the intensity of the epidemic process. Based on the obtained country ratings, an integral statistical indicator of the COVID-19 pandemic is constructed. A set of discriminant analysis machine learning and neural network algorithms are used to estimate current as well predict the expected class of the epidemic state based on the newly acquiring data
dc.language.isoen
dc.publisherMinsk : BSU
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectЭБ БГУ::ЕСТЕСТВЕННЫЕ И ТОЧНЫЕ НАУКИ::Кибернетика
dc.subjectЭБ БГУ::ЕСТЕСТВЕННЫЕ И ТОЧНЫЕ НАУКИ::Математика
dc.titleMulti-country analysis of the COVID-19 pandemic typology using machine learning and neural network algorithms
dc.typeconference paper
Располагается в коллекциях:2023. Pattern Recognition and Information Processing (PRIP’2023). Artificial Intelliverse: Expanding Horizons

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