Adolescent mental health state assessment framework by combining YOLO with random forest

IF 7.2 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Applied Soft Computing Pub Date : 2024-11-23 DOI:10.1016/j.asoc.2024.112497

Min Wan , Sai Zou

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引用次数: 0

Abstract

The problems of adolescent mental health are becoming increasingly prominent. The house-tree-person test (HTPT) method can map the psychological state of subjects and has been widely used in clinical testing. However, the HTPT method requires an amount of time for professionals to assess. Based on the HTPT method, how to use artificial intelligence technology to quickly, objectively, and automatically complete mental state assessments has become a new trend. In this paper, a Hybrid of Enhanced YOLO and Random Forest algorithm for adolescent mental health assessment is proposed. Because of the dependence between HTPT feature positions, Bayesian theory is used to enhance YOLO to improve detection accuracy. Among the many features detected by the enhanced YOLO algorithm, RF is used to automatically assess mental state. The method is validated by simulation experiments and actual measurements of university students. Moreover, the simulation results show that the recognition accuracy can reach 92% and the recognition speed can reach the second level. The measured results show that this method can quickly and accurately assess mental state.

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将 YOLO 与随机森林相结合的青少年心理健康状况评估框架

青少年心理健康问题日益突出。房屋--三人测试法（HTPT）可以反映受试者的心理状态，已被广泛应用于临床测试。然而，HTPT 法需要专业人员花费大量时间进行评估。在 HTPT 方法的基础上，如何利用人工智能技术快速、客观、自动地完成心理状态评估已成为一种新趋势。本文提出了一种混合增强 YOLO 算法和随机森林算法的青少年心理健康评估方法。由于 HTPT 特征位置之间存在依赖关系，因此采用贝叶斯理论来增强 YOLO，以提高检测精度。在增强型 YOLO 算法检测到的众多特征中，RF 被用于自动评估心理状态。该方法通过模拟实验和对大学生的实际测量进行了验证。仿真结果表明，识别准确率达到 92%，识别速度达到二级水平。实测结果表明，该方法可以快速、准确地评估心理状态。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Applied Soft Computing 工程技术-计算机：跨学科应用

CiteScore

15.80

自引率

6.90%

发文量

874

审稿时长

10.9 months

期刊介绍： Applied Soft Computing is an international journal promoting an integrated view of soft computing to solve real life problems.The focus is to publish the highest quality research in application and convergence of the areas of Fuzzy Logic, Neural Networks, Evolutionary Computing, Rough Sets and other similar techniques to address real world complexities. Applied Soft Computing is a rolling publication: articles are published as soon as the editor-in-chief has accepted them. Therefore, the web site will continuously be updated with new articles and the publication time will be short.