Art Therapy to Promote College Students’ Mental Health Based on a Hierarchical Clustering Algorithm

心理健康 心理学 感觉 聚类分析 艺术疗法 过程(计算) 晋升(国际象棋) 多级模型 创造力 心理治疗师 人工智能 计算机科学 应用心理学 机器学习 临床心理学 社会心理学 政治 政治学 法学 操作系统
作者
Jun Zheng
出处
期刊:International Journal of Maritime Engineering [Royal Institution of Naval Architects]
卷期号:1 (1): 713-726
标识
DOI:10.5750/ijme.v1i1.1398
摘要

Art therapy is a therapeutic approach that utilizes the creative process of making art to improve mental, emotional, and physical well-being. Art therapy is a form of expressive therapy that utilizes the creative process of making art to improve mental, emotional, and psychological well-being. It provides individuals with a non-verbal outlet for self-expression and exploration, allowing them to communicate and process their thoughts, feelings, and experiences in a safe and supportive environment. This paper proposed an efficient Weighted Hierarchical Clustering Deep Neural Network (WH-CDNN) to promote the mental health of college students. The proposed WH-CDNN model extracts the features of the art therapy to promote the mental health of students. The features considered for the analysis are color palette, texture, and therapy for the promotion of mental health assessment of students. The features associated with the weighted model are computed for the college student mental health assessment. The features with the WH-CDNN model use the hierarchical clustering model for the computation of the features in art therapy based on the assessment of mental health. The examination is based on the consideration of 10 features for the estimation with the 5 clusters for the evaluation of the mental health assessment. Experimental analysis of the results demonstrated that the proposed WH-CDNN model achieves significant improvement in the after the art therapy of the students with the mental health assessment. Through simulation and analysis, the study demonstrates the effectiveness of art therapy in improving mental health outcomes, with significant reductions observed in anxiety and depression levels post-therapy. Moreover, the WH-CDNN model accurately predicts students' mental health states and evaluates the efficacy of art therapy interventions. The findings highlight the potential of integrating advanced computational techniques with art therapy to support student well-being and inform targeted mental health interventions in educational settings.

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