Student Behavior Prediction of Mental Health Based on Two-Stream Informer Network
Source abstract
Abstract
Students’ mental health has always been the focus of social attention, and mental health prediction can be regarded as a time-series classification task. In this paper, an informer network based on a two-stream structure (TSIN) is proposed to calculate the interdependence between students’ behaviors and the trend of time cycle, and the intermediate features are integrated layer by layer to realize the prediction of mental health by a gating mechanism. Through experiments on a real campus environment dataset (STU) and an open dataset (MTS), it is verified that the proposed algorithm can obtain higher accuracy than existing methods.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Time Series Analysis and Forecasting · Mental Health Research Topics · Anomaly Detection Techniques and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Computer Science, Sichuan University, Chengdu 610065, China
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