Educational Behaviour Analysis Using Convolutional Neural Network and Particle Swarm Optimization Algorithm
With the continuous development of online technology, online education has become a trend. To improve the quality of online education, a comprehensive and effective analysis of educational behaviour is necessary. In this paper, we proposed a network model based on the ResNet50 network fused with a b...
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| Vydané v: | Advances in Multimedia Ročník 2022; s. 1 - 10 |
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| Médium: | Journal Article |
| Jazyk: | English |
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New York
Hindawi
07.07.2022
John Wiley & Sons, Inc Wiley |
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| ISSN: | 1687-5680, 1687-5699 |
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| Abstract | With the continuous development of online technology, online education has become a trend. To improve the quality of online education, a comprehensive and effective analysis of educational behaviour is necessary. In this paper, we proposed a network model based on the ResNet50 network fused with a bilinear hybrid attention mechanism and proposed an adaptive pooling weight algorithm based on the average pooling algorithm for the problems of image feature extraction caused by traditional pooling algorithm such as mutilation and blurring. At the same time, the hyperparameters of the convolutional neural network model are adaptively adjusted based on the particle swarm algorithm to improve the model recognition accuracy further. Through the experimental validation on NTU-RGB + D and NTU-RGB + D120 data set, the recognition accuracy of this paper is 88.8% for cross-subject (CS), 94.7% for cross-view (CV), 82.8% for cross-subject (CSub) 83.2%, and 84.3% for cross-setup (CSet), respectively. The experimental results show that the algorithm in this paper is an effective method for educational behaviur recognition. |
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| AbstractList | With the continuous development of online technology, online education has become a trend. To improve the quality of online education, a comprehensive and effective analysis of educational behaviour is necessary. In this paper, we proposed a network model based on the ResNet50 network fused with a bilinear hybrid attention mechanism and proposed an adaptive pooling weight algorithm based on the average pooling algorithm for the problems of image feature extraction caused by traditional pooling algorithm such as mutilation and blurring. At the same time, the hyperparameters of the convolutional neural network model are adaptively adjusted based on the particle swarm algorithm to improve the model recognition accuracy further. Through the experimental validation on NTU-RGB + D and NTU-RGB + D120 data set, the recognition accuracy of this paper is 88.8% for cross-subject (CS), 94.7% for cross-view (CV), 82.8% for cross-subject (CSub) 83.2%, and 84.3% for cross-setup (CSet), respectively. The experimental results show that the algorithm in this paper is an effective method for educational behaviur recognition. |
| Audience | Academic |
| Author | Dong, Zhenjiang |
| Author_xml | – sequence: 1 givenname: Zhenjiang orcidid: 0000-0002-2540-391X surname: Dong fullname: Dong, Zhenjiang organization: Hebi PolytechnicHebi 458030China |
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| CitedBy_id | crossref_primary_10_1080_2331186X_2025_2485508 crossref_primary_10_1016_j_eswa_2023_120621 crossref_primary_10_4018_IJWLTT_336851 |
| Cites_doi | 10.12973/eurasia.2017.00744a 10.1002/bin.1508 10.1080/02680939.2020.1748722 10.1109/tpami.2016.2560816 10.1088/1742-6596/1882/1/012127 10.1080/00461520.2017.1281747 10.1049/cje.2021.00.080 10.17509/ijal.v8i3.15251 10.3390/su13052888 10.24059/olj.v22i1.1175 10.1007/s00371-019-01630-9 10.1109/TMM.2018.2844085 10.1016/j.neucom.2019.11.048 10.1109/TCSVT.2020.3015051 10.3390/s18124270 10.1371/journal.pone.0219019 10.1109/tpami.2018.2858826 10.1177/0014402917690729 10.3390/rs13122285 10.1007/s10639-019-09892-5 10.1016/j.neucom.2020.10.016 10.1109/lra.2019.2927954 10.1109/TPAMI.2017.2771306 10.1049/iet-ipr.2018.0009 10.1109/tpami.2019.2913372 10.1177/0162643417690255 |
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| Copyright | Copyright © 2022 Zhenjiang Dong. COPYRIGHT 2022 John Wiley & Sons, Inc. Copyright © 2022 Zhenjiang Dong. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 |
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| DOI | 10.1155/2022/9449328 |
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| References | 22 23 24 25 28 B. Tan (16) 2018; 35 Y. F. Song (27) 2020; 31 Y. Wei (14) 2019; 29 S. Xie (26) 2018; 21 11 12 13 15 17 18 19 J. Liu (10) 2017; 40 1 2 3 4 5 6 7 8 9 20 21 |
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| SubjectTerms | Adaptive algorithms Algorithms Analysis Artificial neural networks Blurring CAI Computer assisted instruction Decision making Design Education Feature extraction Mathematical optimization Model accuracy Online education Particle swarm optimization Recognition Students Teaching |
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| Title | Educational Behaviour Analysis Using Convolutional Neural Network and Particle Swarm Optimization Algorithm |
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