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Please use this identifier to cite or link to this item: http://dspace.bsu.edu.ru/handle/123456789/43868
Title: Content adaptation neural network method cause-specific the state of users
Authors: Khlivnenko, L. V.
Pyatakovich, F. A.
Yakunchenko, T. I.
Keywords: technique
cybernetics
neural network
autonomic nervous system
educational content
sympathetic nervous system
Issue Date: 2020
Publisher: Khlivnenko L.V. Content adaptation neural network method cause-specific the state of users / L.V. Khlivnenko, F.A. Pyatakovich, T.I. Yakunchenko // Journal of Physics: Conference Series. - 2020. - Vol.1679, №1.-Art. 032084. - (Applied Physics, Information technologies and Engineering : II international Scientific, Krasnoyarsk, Russian Federation, 25 September - 4 October 2020). - Doi: 10.1088/1742-6596/1679/3/032084.
Citation: Khlivnenko, L.V. Content adaptation neural network method cause-specific the state of users / L.V. Khlivnenko, F.A. Pyatakovich, T.I. Yakunchenko // Journal of Physics: Conference Series. - 2020. - Vol.1679, №1.-Art. 032084. - (Applied Physics, Information technologies and Engineering : II international Scientific, Krasnoyarsk, Russian Federation, 25 September - 4 October 2020). - Doi: 10.1088/1742-6596/1679/3/032084.
Abstract: The aim of the study is to develop recommendations for adapting educational content based on the use of a neural network method for recognizing the human autonomic nervous system degree of activity. The initial data for decision making are the vectors of cardiointervals obtained with the help of the pulse sensor. The state of the autonomic nervous system is monitored by a two-layer artificial neural network of direct propagation. The artificial neural network was trained by combining gradient and stochastic training methods
URI: http://dspace.bsu.edu.ru/handle/123456789/43868
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