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The Cognitive Transformation of Japanese Language Education by Artificial Intelligence Technology in the Wireless Network Environment
This study aims to solve the multiscale problems faced by the current classroom student behavior target detection based on the convolutional neural network (CNN) in the wireless network environment. Firstly, the recent reform of Japanese language education is introduced. Secondly, the multiscale pro...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9283021/ https://www.ncbi.nlm.nih.gov/pubmed/35845870 http://dx.doi.org/10.1155/2022/7886369 |
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author | Zhang, Su |
author_facet | Zhang, Su |
author_sort | Zhang, Su |
collection | PubMed |
description | This study aims to solve the multiscale problems faced by the current classroom student behavior target detection based on the convolutional neural network (CNN) in the wireless network environment. Firstly, the recent reform of Japanese language education is introduced. Secondly, the multiscale problem research of classroom student behavior target detection is discussed. A CNN-based new extraction network is designed based on dilated convolution and pyramid features. An anchor reconstruction algorithm based on improved K-means clustering is presented for the self-made student behavior dataset. Finally, the performance of the designed algorithm is tested. The anchor reconstruction algorithm's mean average precision is 83.2%, and the average intersection over union is 73.7%. The experimental results of this scheme outperform the original single-shot multibox detector and K-means algorithms. Compared with other algorithms, the designed multiscale detection algorithm of classroom student behavior has the best detection effect on Pascal visual object classes (VOC) dataset. The detection accuracy of the entire dataset is 79.8%. Overall, the multiscale detection algorithm for classroom student behavior has a better detection effect on the Pascal VOC dataset and has good generalization ability and robustness. This research can guide students to recognize their class status and make corresponding adjustments to improve their learning efficiency, which has essential research significance and application value. |
format | Online Article Text |
id | pubmed-9283021 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-92830212022-07-15 The Cognitive Transformation of Japanese Language Education by Artificial Intelligence Technology in the Wireless Network Environment Zhang, Su Comput Intell Neurosci Research Article This study aims to solve the multiscale problems faced by the current classroom student behavior target detection based on the convolutional neural network (CNN) in the wireless network environment. Firstly, the recent reform of Japanese language education is introduced. Secondly, the multiscale problem research of classroom student behavior target detection is discussed. A CNN-based new extraction network is designed based on dilated convolution and pyramid features. An anchor reconstruction algorithm based on improved K-means clustering is presented for the self-made student behavior dataset. Finally, the performance of the designed algorithm is tested. The anchor reconstruction algorithm's mean average precision is 83.2%, and the average intersection over union is 73.7%. The experimental results of this scheme outperform the original single-shot multibox detector and K-means algorithms. Compared with other algorithms, the designed multiscale detection algorithm of classroom student behavior has the best detection effect on Pascal visual object classes (VOC) dataset. The detection accuracy of the entire dataset is 79.8%. Overall, the multiscale detection algorithm for classroom student behavior has a better detection effect on the Pascal VOC dataset and has good generalization ability and robustness. This research can guide students to recognize their class status and make corresponding adjustments to improve their learning efficiency, which has essential research significance and application value. Hindawi 2022-07-07 /pmc/articles/PMC9283021/ /pubmed/35845870 http://dx.doi.org/10.1155/2022/7886369 Text en Copyright © 2022 Su Zhang. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Zhang, Su The Cognitive Transformation of Japanese Language Education by Artificial Intelligence Technology in the Wireless Network Environment |
title | The Cognitive Transformation of Japanese Language Education by Artificial Intelligence Technology in the Wireless Network Environment |
title_full | The Cognitive Transformation of Japanese Language Education by Artificial Intelligence Technology in the Wireless Network Environment |
title_fullStr | The Cognitive Transformation of Japanese Language Education by Artificial Intelligence Technology in the Wireless Network Environment |
title_full_unstemmed | The Cognitive Transformation of Japanese Language Education by Artificial Intelligence Technology in the Wireless Network Environment |
title_short | The Cognitive Transformation of Japanese Language Education by Artificial Intelligence Technology in the Wireless Network Environment |
title_sort | cognitive transformation of japanese language education by artificial intelligence technology in the wireless network environment |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9283021/ https://www.ncbi.nlm.nih.gov/pubmed/35845870 http://dx.doi.org/10.1155/2022/7886369 |
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