Prediction Analysis of College Students’ Physical Activity Behavior by Improving Gray Wolf Algorithm and Support Vector Machine
Physical exercise for college students is an important means to build a healthy standard of college students and an important way to a healthy campus. In addition to creating good physical fitness, physical exercise has significant effects on improving psychological stress and alleviating psychologi...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Advances in Multimedia |
| Online Access: | http://dx.doi.org/10.1155/2022/4943510 |
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| author | Minjian Wang |
| author_facet | Minjian Wang |
| author_sort | Minjian Wang |
| collection | DOAJ |
| description | Physical exercise for college students is an important means to build a healthy standard of college students and an important way to a healthy campus. In addition to creating good physical fitness, physical exercise has significant effects on improving psychological stress and alleviating psychological problems and mental illnesses among college students. It is important to predict and analyze the physical exercise behavior of college students and explore the positive value of physical exercise for college education. In order to overcome the problem of low accuracy of traditional algorithms in prediction, this paper uses the improved gray wolf algorithm (IGWO) and support vector machine (SVM) for predictive analysis of college students' physical exercise behavior. A nonlinear decreasing convergence factor strategy and an inertia weight strategy are introduced to improve the gray wolf optimization algorithm, which is used to determine the SVM parameters for the purpose of improving the model accuracy. Then, the college students' physical exercise data are input into the model for validation. By constructing a campus behavior data set of college students and conducting experiments, the algorithm achieves 90.45% behavior prediction accuracy, which is better than that of typical prediction models. Finally, individual growth monitoring of college students is targeted to warn students with abnormal behaviors. At the same time, the higher-order information such as physical exercise behavior habits of college students is explored to provide meaningful reference for constructing personalized training. |
| format | Article |
| id | doaj-art-4f8d029cd5e34f99ae8a09a8ba444181 |
| institution | OA Journals |
| issn | 1687-5699 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Multimedia |
| spelling | doaj-art-4f8d029cd5e34f99ae8a09a8ba4441812025-08-20T02:05:39ZengWileyAdvances in Multimedia1687-56992022-01-01202210.1155/2022/4943510Prediction Analysis of College Students’ Physical Activity Behavior by Improving Gray Wolf Algorithm and Support Vector MachineMinjian Wang0Jiaxing Vocational and Technical CollegePhysical exercise for college students is an important means to build a healthy standard of college students and an important way to a healthy campus. In addition to creating good physical fitness, physical exercise has significant effects on improving psychological stress and alleviating psychological problems and mental illnesses among college students. It is important to predict and analyze the physical exercise behavior of college students and explore the positive value of physical exercise for college education. In order to overcome the problem of low accuracy of traditional algorithms in prediction, this paper uses the improved gray wolf algorithm (IGWO) and support vector machine (SVM) for predictive analysis of college students' physical exercise behavior. A nonlinear decreasing convergence factor strategy and an inertia weight strategy are introduced to improve the gray wolf optimization algorithm, which is used to determine the SVM parameters for the purpose of improving the model accuracy. Then, the college students' physical exercise data are input into the model for validation. By constructing a campus behavior data set of college students and conducting experiments, the algorithm achieves 90.45% behavior prediction accuracy, which is better than that of typical prediction models. Finally, individual growth monitoring of college students is targeted to warn students with abnormal behaviors. At the same time, the higher-order information such as physical exercise behavior habits of college students is explored to provide meaningful reference for constructing personalized training.http://dx.doi.org/10.1155/2022/4943510 |
| spellingShingle | Minjian Wang Prediction Analysis of College Students’ Physical Activity Behavior by Improving Gray Wolf Algorithm and Support Vector Machine Advances in Multimedia |
| title | Prediction Analysis of College Students’ Physical Activity Behavior by Improving Gray Wolf Algorithm and Support Vector Machine |
| title_full | Prediction Analysis of College Students’ Physical Activity Behavior by Improving Gray Wolf Algorithm and Support Vector Machine |
| title_fullStr | Prediction Analysis of College Students’ Physical Activity Behavior by Improving Gray Wolf Algorithm and Support Vector Machine |
| title_full_unstemmed | Prediction Analysis of College Students’ Physical Activity Behavior by Improving Gray Wolf Algorithm and Support Vector Machine |
| title_short | Prediction Analysis of College Students’ Physical Activity Behavior by Improving Gray Wolf Algorithm and Support Vector Machine |
| title_sort | prediction analysis of college students physical activity behavior by improving gray wolf algorithm and support vector machine |
| url | http://dx.doi.org/10.1155/2022/4943510 |
| work_keys_str_mv | AT minjianwang predictionanalysisofcollegestudentsphysicalactivitybehaviorbyimprovinggraywolfalgorithmandsupportvectormachine |