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Exploration of the Educational Utility of National Film Using Deep Learning From the Positive Psychology Perspective
The research focuses on the application of positive psychology theory, and studies the educational utility of national films by using deep learning (DL) algorithm. As an art form leading China's film and TV industry, national films have attracted the interest of many domestic scholars. Meanwhil...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9218536/ https://www.ncbi.nlm.nih.gov/pubmed/35756315 http://dx.doi.org/10.3389/fpsyg.2022.804447 |
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author | Zhaxi, Yangzhen Xiang, Yueting Zou, Jilin Zhang, Fengrui |
author_facet | Zhaxi, Yangzhen Xiang, Yueting Zou, Jilin Zhang, Fengrui |
author_sort | Zhaxi, Yangzhen |
collection | PubMed |
description | The research focuses on the application of positive psychology theory, and studies the educational utility of national films by using deep learning (DL) algorithm. As an art form leading China's film and TV industry, national films have attracted the interest of many domestic scholars. Meanwhile, researchers have employed various science and technologies to conduct in-depth research on national films to improve film artistic levels and EDU-UTL. Accordingly, this paper comprehensively studies the EDU-UTL of national films using quality learning (Q-Learning) combined with DL algorithms and educational psychology. Then, a deep Q-Learning psychological model is proposed based on the convolutional neural network (CNN). Specifically, the CNN uses the H-hop matrix to represent each node, and each hop indicates the neighborhood information. The experiment demonstrates that CNN has a good effect on local feature acquisition, and the representation ability of the obtained nodes is also powerful. When K = 300, the psychological factor Recall of Probability Matrix Decomposition Factorization, Collaborative DL, Stack Denoising Automatic Encoder, and CNN-based deep Q-Learning algorithm is 0.35, 0.71, 0.76, and 0.78, respectively. The results suggest that CNN-based deep Q-Learning psychological model can enhance the EDU-UTL of national films and improve the efficiency of film education from the Positive Psychology perspective. |
format | Online Article Text |
id | pubmed-9218536 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92185362022-06-24 Exploration of the Educational Utility of National Film Using Deep Learning From the Positive Psychology Perspective Zhaxi, Yangzhen Xiang, Yueting Zou, Jilin Zhang, Fengrui Front Psychol Psychology The research focuses on the application of positive psychology theory, and studies the educational utility of national films by using deep learning (DL) algorithm. As an art form leading China's film and TV industry, national films have attracted the interest of many domestic scholars. Meanwhile, researchers have employed various science and technologies to conduct in-depth research on national films to improve film artistic levels and EDU-UTL. Accordingly, this paper comprehensively studies the EDU-UTL of national films using quality learning (Q-Learning) combined with DL algorithms and educational psychology. Then, a deep Q-Learning psychological model is proposed based on the convolutional neural network (CNN). Specifically, the CNN uses the H-hop matrix to represent each node, and each hop indicates the neighborhood information. The experiment demonstrates that CNN has a good effect on local feature acquisition, and the representation ability of the obtained nodes is also powerful. When K = 300, the psychological factor Recall of Probability Matrix Decomposition Factorization, Collaborative DL, Stack Denoising Automatic Encoder, and CNN-based deep Q-Learning algorithm is 0.35, 0.71, 0.76, and 0.78, respectively. The results suggest that CNN-based deep Q-Learning psychological model can enhance the EDU-UTL of national films and improve the efficiency of film education from the Positive Psychology perspective. Frontiers Media S.A. 2022-06-09 /pmc/articles/PMC9218536/ /pubmed/35756315 http://dx.doi.org/10.3389/fpsyg.2022.804447 Text en Copyright © 2022 Zhaxi, Xiang, Zou and Zhang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Psychology Zhaxi, Yangzhen Xiang, Yueting Zou, Jilin Zhang, Fengrui Exploration of the Educational Utility of National Film Using Deep Learning From the Positive Psychology Perspective |
title | Exploration of the Educational Utility of National Film Using Deep Learning From the Positive Psychology Perspective |
title_full | Exploration of the Educational Utility of National Film Using Deep Learning From the Positive Psychology Perspective |
title_fullStr | Exploration of the Educational Utility of National Film Using Deep Learning From the Positive Psychology Perspective |
title_full_unstemmed | Exploration of the Educational Utility of National Film Using Deep Learning From the Positive Psychology Perspective |
title_short | Exploration of the Educational Utility of National Film Using Deep Learning From the Positive Psychology Perspective |
title_sort | exploration of the educational utility of national film using deep learning from the positive psychology perspective |
topic | Psychology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9218536/ https://www.ncbi.nlm.nih.gov/pubmed/35756315 http://dx.doi.org/10.3389/fpsyg.2022.804447 |
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