Article ; Online: Impact Analysis of Basketball Exercise Strength Based on Machine Learning in the Mental Health of College Students.
Computational intelligence and neuroscience
2022 Volume 2022, Page(s) 9628446
Abstract: In the current environment of globalization, the communication between people is gradually getting closer, and the society is becoming more and more complex. With the continuous development and progress of science and technology, people are more skilled ... ...
Abstract | In the current environment of globalization, the communication between people is gradually getting closer, and the society is becoming more and more complex. With the continuous development and progress of science and technology, people are more skilled in applying science and technology to their own concerns. College students are about to enter the society, will feel multiple pressure from family, school, and society, study and life problems will gradually convert into mental health problems, and we need to use machine learning basketball exercise to positively affect the mental health quality of college students. The improvement of living conditions makes people pay more attention to their physical and mental health, and learn to use machine learning sports reasonably, not only basketball exercise, to improve mental health diseases. However, we need to use machine learning to identify the different effects of different basketball exercise intensity on mental health, in order to ensure that the most appropriate basketball exercise intensity brings good aspects to the mental health of college students. Through the investigation and data sampling, it can be concluded that the machine learning-based basketball exercise intensity has a positive impact on the mental health of college students. |
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MeSH term(s) | Basketball/psychology ; Exercise ; Humans ; Machine Learning ; Mental Health ; Students/psychology |
Language | English |
Publishing date | 2022-09-27 |
Publishing country | United States |
Document type | Journal Article |
ZDB-ID | 2388208-6 |
ISSN | 1687-5273 ; 1687-5273 |
ISSN (online) | 1687-5273 |
ISSN | 1687-5273 |
DOI | 10.1155/2022/9628446 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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