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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F23%3APU148924" target="_blank" >RIV/00216305:26220/23:PU148924 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/article/10.1007/s11036-023-02187-7" target="_blank" >https://link.springer.com/article/10.1007/s11036-023-02187-7</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11036-023-02187-7" target="_blank" >10.1007/s11036-023-02187-7</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20200 - Electrical engineering, Electronic engineering, Information engineering

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

MOBILE NETWORKS & APPLICATIONS

ISSN

1383-469X

e-ISSN

1572-8153

Volume of the periodical

neuvedeno

Issue of the periodical within the volume

neuvedeno

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

14

Pages from-to

„“-„“

UT code for WoS article

001050927300003

EID of the result in the Scopus database

2-s2.0-85168327067