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Construction of Correlation Analysis Model of College Students' Sports Performance Based on Convolutional Neural Network

This paper proposes a network model recurrent fully connected network (RFC-Net) based on recurrent full convolution and polarization change. RFC-Net enriches the network by reconstructing and fine-tuning the fully convolutional network and adding recurrent convolutions to it. By studying the data mi...

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Autor principal: Jiang, Guangtao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9167112/
https://www.ncbi.nlm.nih.gov/pubmed/35669652
http://dx.doi.org/10.1155/2022/3621316
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author Jiang, Guangtao
author_facet Jiang, Guangtao
author_sort Jiang, Guangtao
collection PubMed
description This paper proposes a network model recurrent fully connected network (RFC-Net) based on recurrent full convolution and polarization change. RFC-Net enriches the network by reconstructing and fine-tuning the fully convolutional network and adding recurrent convolutions to it. By studying the data mining technology of multidimensional association rules, based on the existing algorithms, this paper improves the shortcomings of the algorithms and realizes an efficient and practical method for data mining based on interdimensional multidimensional association rules. On the basis of mastering the actual student information, the effectiveness of the method is tested, and an employment analysis system based on association rules is established. Aiming at the fact that traditional grade prediction methods ignore the different influences of different behavioral characteristics on grades, and considering that behavioral data in different periods have different influences on student grades, the grade prediction problem is abstracted into a time series classification problem. The mechanism is combined with long short-term memory neural network to construct a performance prediction model based on Attention-BiLSTM. Experiments show that the prediction model proposed in this paper improves the accuracy and effectively improves the prediction quality compared with the logistic regression model with a better prediction effect in the traditional benchmark model and the long short-term memory neural network model without the introduction of the attention mechanism. Research shows that physical performance and academic performance are not contradictory. We must face up to the status of physical exercise in schools; as long as physical exercise is properly arranged, it can inspire students to form a spirit of unity, interaction, positivity, and perseverance in cultural studies.
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spelling pubmed-91671122022-06-05 Construction of Correlation Analysis Model of College Students' Sports Performance Based on Convolutional Neural Network Jiang, Guangtao Comput Intell Neurosci Research Article This paper proposes a network model recurrent fully connected network (RFC-Net) based on recurrent full convolution and polarization change. RFC-Net enriches the network by reconstructing and fine-tuning the fully convolutional network and adding recurrent convolutions to it. By studying the data mining technology of multidimensional association rules, based on the existing algorithms, this paper improves the shortcomings of the algorithms and realizes an efficient and practical method for data mining based on interdimensional multidimensional association rules. On the basis of mastering the actual student information, the effectiveness of the method is tested, and an employment analysis system based on association rules is established. Aiming at the fact that traditional grade prediction methods ignore the different influences of different behavioral characteristics on grades, and considering that behavioral data in different periods have different influences on student grades, the grade prediction problem is abstracted into a time series classification problem. The mechanism is combined with long short-term memory neural network to construct a performance prediction model based on Attention-BiLSTM. Experiments show that the prediction model proposed in this paper improves the accuracy and effectively improves the prediction quality compared with the logistic regression model with a better prediction effect in the traditional benchmark model and the long short-term memory neural network model without the introduction of the attention mechanism. Research shows that physical performance and academic performance are not contradictory. We must face up to the status of physical exercise in schools; as long as physical exercise is properly arranged, it can inspire students to form a spirit of unity, interaction, positivity, and perseverance in cultural studies. Hindawi 2022-05-27 /pmc/articles/PMC9167112/ /pubmed/35669652 http://dx.doi.org/10.1155/2022/3621316 Text en Copyright © 2022 Guangtao Jiang. 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
Jiang, Guangtao
Construction of Correlation Analysis Model of College Students' Sports Performance Based on Convolutional Neural Network
title Construction of Correlation Analysis Model of College Students' Sports Performance Based on Convolutional Neural Network
title_full Construction of Correlation Analysis Model of College Students' Sports Performance Based on Convolutional Neural Network
title_fullStr Construction of Correlation Analysis Model of College Students' Sports Performance Based on Convolutional Neural Network
title_full_unstemmed Construction of Correlation Analysis Model of College Students' Sports Performance Based on Convolutional Neural Network
title_short Construction of Correlation Analysis Model of College Students' Sports Performance Based on Convolutional Neural Network
title_sort construction of correlation analysis model of college students' sports performance based on convolutional neural network
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9167112/
https://www.ncbi.nlm.nih.gov/pubmed/35669652
http://dx.doi.org/10.1155/2022/3621316
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