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大数据监测环境下短视频应用对大学生心理健康的影响。

Influence of Short Video Application on College Students' Mental Health under Big Data Monitoring Environment.

机构信息

Chengdu University of Technology, College of Communication Science and art, Chengdu 610059, China.

Si Chuan Radio and Television Station, Omnimedia Center, Chengdu, 610000, China.

出版信息

J Environ Public Health. 2022 Sep 10;2022:1732573. doi: 10.1155/2022/1732573. eCollection 2022.

Abstract

Short videos are increasingly being consumed by college students as crucial content in the age of big data since they are a perfect fit for this medium. Therefore, college students should place a high value on the utilization of short movies. In this study, a neural network is utilized to create a mental health prediction model for college students. The neural network is trained using its self-learning capability to map out the relationships between different elements and mental health. The enhanced algorithm minimizes the production of candidate item sets to some amount, as well as the algorithm's time and space requirements, significantly decreasing the initialization time of the transaction set. According to the research, the test sample's pattern recognition accuracy was 81.29%, whereas the training sample's accuracy for pattern recognition was 83.37%. The analysis's finding is that the enhanced mining algorithm offers a fresh approach to educating college students about their health.

摘要

短视频作为大数据时代的重要内容,越来越受到大学生的青睐,非常适合这种媒介。因此,大学生应该高度重视短视频的利用。本研究利用神经网络为大学生创建心理健康预测模型。该神经网络利用其自学能力来映射不同元素与心理健康之间的关系。增强算法将候选项目集的生成量最小化到一定程度,以及算法的时间和空间要求,大大减少了事务集的初始化时间。根据研究,测试样本的模式识别准确率为 81.29%,而训练样本的模式识别准确率为 83.37%。分析的结果是,增强挖掘算法为大学生健康教育提供了一种新方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd13/9482513/b56378ffdc3f/JEPH2022-1732573.001.jpg

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