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 |
Appears in Collections: | Статьи из периодических изданий и сборников (на иностранных языках) = Articles from periodicals and collections (in foreign languages) |
File | Description | Size | Format | |
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Yakinchenko_Content.pdf | 735.36 kB | Adobe PDF | View/Open |
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