Publication detail

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

ZHOU, J. HERENCSÁR, N.

Original Title

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

Type

journal article in Web of Science

Language

English

Original 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.

Keywords

Multi-task Deep Learning; Multimedia Classroom; Aberrant Student Behavior; Determination Model; Adaptive Histogram Equilibrium; Renyi Entropy

Authors

ZHOU, J.; HERENCSÁR, N.

Released

19. 8. 2023

Publisher

SPRINGER

Location

NEW YORK

ISBN

1383-469X

Periodical

MOBILE NETWORKS & APPLICATIONS

Year of study

neuvedeno

Number

neuvedeno

State

Kingdom of the Netherlands

Pages count

14

URL

https://link.springer.com/article/10.1007/s11036-023-02187-7

BibTex

@article{BUT184542,
  author="Jing {Zhou} and Norbert {Herencsár}",
  title="Abnormal Behavior Determination Model of Multimedia Classroom Students Based on Multi-task Deep Learning",
  journal="MOBILE NETWORKS & APPLICATIONS",
  year="2023",
  volume="neuvedeno",
  number="neuvedeno",
  pages="14",
  doi="10.1007/s11036-023-02187-7",
  issn="1383-469X",
  url="https://link.springer.com/article/10.1007/s11036-023-02187-7"
}