Abstract
College students are under increasing competition pressure, which has a negative impact on their mental health, as the pace of learning and life accelerates, as well as the increasingly difficult employment situation. As a result, emphasizing the importance of college students’ mental health and fully addressing it has become a top priority in the work of colleges and universities. However, some students and even teachers are currently unconcerned about mental illness, making it difficult for students with psychological abnormalities to receive timely detection and effective treatment. As a result, it is the responsibility of student management for colleges and universities to identify and intervene early in the mental health problems of college students. Through the use of multimodal data and neural network models, it is now possible to evaluate and predict the mental state of college students in real time, thanks to the advancement of intelligent technology. Therefore, a novel multimodal neural network model is proposed in this paper. Our model is divided into two branches in particular. The traditional mental health assessment and prediction algorithm, which is based on the improved BP neural network and the International Mental Health Scale SCL-90, is one of the branches. Given how difficult it is to meet the requirements for the accuracy of college students’ mental health assessments using this method, our other branch is computer vision-based facial emotion recognition of college students, which is used to aid in the evaluation of mental health assessments. Our model demonstrates competitive performance through simulation and comparative experiments.
Highlights
With today’s fierce competition and increasing pressures in life, college students’ mental health problems [1,2,3] have become more visible, and their mental health conditions [4,5,6] are concerning
It is not uncommon to come across examples of exceptional college students who failed to deal with the final suicide due to emotional issues [14]
The main innovations of this article are as follows: (1) This paper proposes a novel dual-branch neural network model based on intelligent technology for college students’ mental health assessment
Summary
With today’s fierce competition and increasing pressures in life, college students’ mental health problems [1,2,3] have become more visible, and their mental health conditions [4,5,6] are concerning. One branch is based on improved BP neural network for mental health prediction, and the other branch is based on computer vision-based facial emotion recognition for college students (2) This paper proposes to combine neural networks with Bayesian methods, by constructing a suitable training model from selected typical samples, selecting the best training situation to grasp the internal relationship between input and output, and obtaining network rights about the knowledge of the problem. The obtained prior probability to the Bayesian formula and combine the conditional probability obtained by actual statistics to calculate the influence of various indicators on the mental health of college students (3) Small scale is the key to facial emotion recognition in complex environments. The rest of the paper is arranged as follows: the second section presents the related work, the third section proposes the methodology, the fourth section provides the experiments and results, and the fifth section proposes the conclusions
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