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Issue title: Complex evolutionary artificial intelligence in cognitive digital twinning
Guest editors: Neal Wagner, Sundhararajan, Le Hoang Son and Meng Joo
Article type: Research Article
Authors: Jingchao, Hua | Zhang, Haiyingb; *
Affiliations: [a] Women’s Education Institute, Hebei Women’s Vocational College, Shijiazhuang, China | [b] Department of Foundation, Hebei Women’s Vocational College, Shijiazhang, China
Correspondence: [*] Corresponding author. Haiying Zhang, Department of Foundation, Hebei Women’s Vocational College, Shijiazhang, China. E-mail: [email protected].
Abstract: The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features.
Keywords: Deep learning, machine learning, student state, online recognition, feature recognition
DOI: 10.3233/JIFS-189232
Journal: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 2, pp. 2361-2372, 2021
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