Abstract
Nowadays, Stress has repercussions on the mental, physical and psychological health for many persons in life especially students in universities. Stressful life has many negative consequences such as anxiety disorders and depression. The negative influences of stress can be seen in several areas such as mental health.
The prediction of stress can compensate for the negative effects and does not lead to an advanced state. This prediction can be made through smartphones.
In this paper, we aim to classify psychological students state on two classes “stressed” and “not stressed” using smartphones by analyzing extracted features from heterogenous smartphone sensors input information. Indeed, we suggest hybrid deep learning method using both attentional model and Long Short-Term Memory (LSTM) recurrent network. The obtained result achieves 93% accuracy on the test set and comparing to other studies.
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Kadri, N., Turki, S.H., Ellouze, A., Ksantini, M. (2022). An Hybrid Deep Learning Approach for Prediction and Binary Classification of Student’s Stress. In: Bennour, A., Ensari, T., Kessentini, Y., Eom, S. (eds) Intelligent Systems and Pattern Recognition. ISPR 2022. Communications in Computer and Information Science, vol 1589. Springer, Cham. https://doi.org/10.1007/978-3-031-08277-1_26
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