The Evaluation Model of College Students' Mental Health in the Environment of Independent Entrepreneurship Using Neural Network Technology

Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches

As mental health (MH) disorders become increasingly prevalent, their multifaceted symptoms and comorbidities with other conditions introduce complexity to diagnosis, posing a risk of underdiagnosis. While machine learning (ML) has been explored to mitigate these challenges, we hypothesized that multiple data modalities support more comprehensive detection and that non-intrusive collection approaches better capture natural behaviors. To understand the current trends, we systematically reviewed 184 studies to assess feature extraction, feature fusion, and ML methodologies applied to detect MH disorders from passively sensed multimodal data, including audio and video recordings, social media, smartphones, and wearable devices. Our findings revealed varying correlations of modality-specific features in individualized contexts, potentially influenced by demographics and personalities. We also observed the growing adoption of neural network architectures for model-level fusion and as ML algorithms, which have demonstrated promising efficacy in handling high-dimensional features while modeling within and cross-modality relationships. This work provides future researchers with a clear taxonomy of methodological approaches to multimodal detection of MH disorders to inspire future methodological advancements. The comprehensive analysis also guides and supports future researchers in making informed decisions to select an optimal data source that aligns with specific use cases based on the MH disorder of interest.

Retracted: The Evaluation Model of College Students' Mental Health in the Environment of Independent Entrepreneurship Using Neural Network Technology

[This retracts the article DOI: 10.1155/2021/4379623.].

Construction and Application of Dynamic Evaluation System and Educational Model of College Students' Mental Health

The aim of the paper is a dynamic evaluation system that provides support to the students' mental health. Mental health contains students' feelings, psychological, and social well-being. Extracurricular activities, professional and innovative skills, self-awareness, emotional management, cultural norms, and personality development are the essential factors in a dynamic evaluation system for students' mental health. Extracurricular activities support learning additional things except for the subjects. Thinking skill is being an optimistic idea to mental health. Through professional and innovative skills, students can express their thoughts and ideas with others. Understanding one's feelings are considered self-awareness. It supports finding the good and uniqueness of a particular person. Emotional management accepts and controls the feelings of the students. Virtue, ethics, honesty, loyalty, responsibility, positive thoughts, relational skills are the important factors in cultural norms. Feelings and ways of behavior affect mental health in personality development. Self-confidence is the main element in personality development. This article engaged to adopt a descriptive research method to present a perfect model for this research. The probability method (random sampling method) and non-probability method (purposive sampling method) were utilized for this research. A total of 349 sample sizes of college students participated in the questionnaire survey and 12 sample sizes of college instructors responded to the face-to-face interview from the priority areas of the university. Finally, Thus, in this research, extracurricular activities and professional and innovative skills are dominant factors when examining other factors. These leading factors are expressed clearly by students to protect their mental health of the students.