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Abnormal Behavior Determination Model of Multimedia Classroom Students Based on Multi-task Deep Learning

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Abstract

The abnormal behavior of students in the multimedia classroom is not significant, which leads to the difficulty in determining abnormal behavior. Therefore, the abnormal behavior determination model of multimedia classroom students based on multi-task deep learning is constructed. The eigenimage filtering algorithm is used to denoise the captured multimedia classroom student images. The multimedia classroom student images are denoised using an adaptive histogram equalization algorithm to enhance the denoised multimedia classroom student images. The multimedia classroom student images are segmented using the Renyi entropy method, and the student behavioral characteristics are determined based on the image segmentation results. Student behavioral characteristics are determined based on image segmentation results. A multi-task deep learning model is built based on convolutional neural networks. The model mainly uses convolutional neural networks and students' behavioral features to classify students' abnormal behaviors in multimedia classrooms, achieve the determination of abnormal behaviors of multimedia classroom students, and obtain relevant determination results. The experimental results show that the model can effectively determine the abnormal behaviors of students in multimedia classrooms, such as looking to the right and looking left, playing with mobile phones, etc. The accuracy of the determination of abnormal behavior is higher than 98%, and the practical application is good.

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Data will be made available on reasonable request.

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Funding

The paper was funded by Cooperative education project of the Ministry of Education; Research On Improving the Comprehensive Ability of Young Teachers in Police Security Colleges and Universities under the OBE Guidance with No. 220503924263429, 2020 Henan Provincial Philosophy and Social Sciences Planning Project: Non direct non reality of Henan in the new era Research on Social Contradictions with No. 2020BZX009, Research and Practice Project on School level Education and teaching Reform of Henan Police College; Research on General Education in Public Security Colleges under the Target Orientation of Police Core Literacy Goals with No. JY 2022003 and 2022 Undergraduate Research Teaching Series Project of Henan Provincial Department of Education: Application of Research-Based Teaching Mode in General Education in Public Security Colleges with No. 2022SYJXLX100.

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Authors and Affiliations

  1. Faculty of Education, Henan Normal University, Zhengzhou, 453007, China

    Jing Zhou

  2. Department of Public Basic Teaching, Henan Police College, Zhengzhou, 450046, China

    Jing Zhou

  3. Department of Telecommunications, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 12, 616 00, Brno, Czechia

    Norbert Herencsar

Authors
  1. Jing Zhou
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  2. Norbert Herencsar
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Contributions

Jing Zhou contributed to Writing—Original Draft, Methodology, and Conceptualization; Norbert Herencsar contributed to Conceptualization and Writing—Review and Editing.

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Correspondence to Norbert Herencsar.

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Zhou, J., Herencsar, N. Abnormal Behavior Determination Model of Multimedia Classroom Students Based on Multi-task Deep Learning. Mobile Netw Appl 28, 900–913 (2023). https://doi.org/10.1007/s11036-023-02187-7

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