An Intelligent Evaluation Method of Information Course Teaching Effect Based on Image Analysis

Due to its own limitations, the traditional teaching quality evaluation method has been unable to adapt to the development of information-based curriculum teaching. Therefore, the establishment of a scientific and intelligent teaching effect evaluation method will help to improve the teaching qualit...

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Autores principales:	HaiDong Chen, JuFang Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/c6607d22191a4e64bfe9c2e3cc5e61e2
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id	oai:doaj.org-article:c6607d22191a4e64bfe9c2e3cc5e61e2
record_format	dspace
spelling	oai:doaj.org-article:c6607d22191a4e64bfe9c2e3cc5e61e22021-11-22T01:11:06ZAn Intelligent Evaluation Method of Information Course Teaching Effect Based on Image Analysis1875-919X10.1155/2021/3200865https://doaj.org/article/c6607d22191a4e64bfe9c2e3cc5e61e22021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/3200865https://doaj.org/toc/1875-919XDue to its own limitations, the traditional teaching quality evaluation method has been unable to adapt to the development of information-based curriculum teaching. Therefore, the establishment of a scientific and intelligent teaching effect evaluation method will help to improve the teaching quality of college teachers. To solve the above problems, a student fatigue state evaluation method based on the quantum particle swarm optimization artificial neural network is proposed. Firstly, face detection is realized by adding three Haar-like feature blocks and improving the AdaBoost algorithm of a weak classifier connection. Secondly, in order to effectively improve the image imbalance, the MSR algorithm is used to enhance the face data image, which is effectively suitable for network training. Then, by readjusting the connection mode, the DenseNet is improved to fully reflect the local detail feature information of the low level. Finally, quantum particle swarm optimization (QPSO) is used to optimize the DenseNet structure, which makes the optimization of network structure more automatic and solves the uncertainty of manual selection. The experimental results show that the proposed method has a good detection effect and prove the effectiveness and correctness of the proposed method.HaiDong ChenJuFang ZhangHindawi LimitedarticleComputer softwareQA76.75-76.765ENScientific Programming, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer software QA76.75-76.765
spellingShingle	Computer software QA76.75-76.765 HaiDong Chen JuFang Zhang An Intelligent Evaluation Method of Information Course Teaching Effect Based on Image Analysis
description	Due to its own limitations, the traditional teaching quality evaluation method has been unable to adapt to the development of information-based curriculum teaching. Therefore, the establishment of a scientific and intelligent teaching effect evaluation method will help to improve the teaching quality of college teachers. To solve the above problems, a student fatigue state evaluation method based on the quantum particle swarm optimization artificial neural network is proposed. Firstly, face detection is realized by adding three Haar-like feature blocks and improving the AdaBoost algorithm of a weak classifier connection. Secondly, in order to effectively improve the image imbalance, the MSR algorithm is used to enhance the face data image, which is effectively suitable for network training. Then, by readjusting the connection mode, the DenseNet is improved to fully reflect the local detail feature information of the low level. Finally, quantum particle swarm optimization (QPSO) is used to optimize the DenseNet structure, which makes the optimization of network structure more automatic and solves the uncertainty of manual selection. The experimental results show that the proposed method has a good detection effect and prove the effectiveness and correctness of the proposed method.
format	article
author	HaiDong Chen JuFang Zhang
author_facet	HaiDong Chen JuFang Zhang
author_sort	HaiDong Chen
title	An Intelligent Evaluation Method of Information Course Teaching Effect Based on Image Analysis
title_short	An Intelligent Evaluation Method of Information Course Teaching Effect Based on Image Analysis
title_full	An Intelligent Evaluation Method of Information Course Teaching Effect Based on Image Analysis
title_fullStr	An Intelligent Evaluation Method of Information Course Teaching Effect Based on Image Analysis
title_full_unstemmed	An Intelligent Evaluation Method of Information Course Teaching Effect Based on Image Analysis
title_sort	intelligent evaluation method of information course teaching effect based on image analysis
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/c6607d22191a4e64bfe9c2e3cc5e61e2
work_keys_str_mv	AT haidongchen anintelligentevaluationmethodofinformationcourseteachingeffectbasedonimageanalysis AT jufangzhang anintelligentevaluationmethodofinformationcourseteachingeffectbasedonimageanalysis AT haidongchen intelligentevaluationmethodofinformationcourseteachingeffectbasedonimageanalysis AT jufangzhang intelligentevaluationmethodofinformationcourseteachingeffectbasedonimageanalysis
_version_	1718418306153578496

An Intelligent Evaluation Method of Information Course Teaching Effect Based on Image Analysis

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