No abstract

The emotional well-being of college students is of utmost significance. The psychological states of college students who are on the verge of entering the social work field form the key factor that directly influences the quality of social construction because these students constitute the primary driving force in the field. On the other hand, the overwhelming amount of schoolwork, the intense level of competitiveness, and the undeveloped psychological qualities of college students are the primary contributors to their mental health problems. Currently, an increasing number of college students are struggling with mental health issues, which will have a significant impact on the growth of families and schools and the future construction of the nation. In this paper, deep features and a multiview fuzzy clustering technique are presented, as well as a mental health assessment model (CNN-MV-MEC) that is proposed for college students. The primary purpose of this research is to determine the mental state of the input sample by classifying and identifying an EEG that was acquired through the application of CNN-MV-MEC. If a certain number of samples are found to be in negative emotional states on a regular basis or for an extended period of time, this indicates that the sample most likely contains individuals who struggle with mental health issues. At this point in time, university officials are in a position to implement follow-up mental health management actions based on the outcomes of the model evaluation process. The primary contributions of this study are as follows. First, to extract the deep features from the given dataset, this paper makes use of a traditional convolutional neural network (CNN). In the second step, a classification model is trained using a multiview maximum entropy clustering (MV-MEC) technique. In the final step, the input test data are categorized by employing the trained classification model to determine the emotional state of the sample. The SEED dataset is used as the training data for the mental health assessment model proposed in this paper. Thus, the performance of the model can be evaluated. Model comparison experiments demonstrate that the proposed approach yields more accurate results than competing methods when assessing the mental health of college students.

Bayesian network is an effective tool for fault prognosis. Learning the Bayesian network structure from data is, however, a difficult problem for complex industrial chemical processes. This paper presents an idea of jointly using Pseudo Bond Graph model and Bayesian network for fault prognosis. Pseudo Bond Graph is used to determine the Bayesian network structure, and the network parameters are learned from process data. An illustrative example via a CSTR system is presented. The results can show the feasibility and effectiveness of the proposed fault prognosis scheme.