Stress Classification using Deep Learning with 1D Convolutional Neural Networks

Abdulrazak Yahya, Saleh and Khai Xian, Lau (2021) Stress Classification using Deep Learning with 1D Convolutional Neural Networks. Knowledge Engineering and Data Science, 4 (2). pp. 145-152. ISSN 2597-4637

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Stress has been a major problem impacting people in various ways, and it gets serious every day. Identifying whether someone is suffering from stress is crucial before it becomes a severe illness. Artificial Intelligence (AI) interprets external data, learns from such data, and uses the learning to achieve specific goals and tasks. Deep Learning (DL) has created an impact in the field of Artificial Intelligence as it can perform tasks with high accuracy. Therefore, the primary purpose of this paper is to evaluate the performance of 1D Convolutional Neural Networks (1D CNNs) for stress classification. A Psychophysiological stress (PS) dataset is utilized in this paper. The PS dataset consists of twelve features obtained from the expert. The 1D CNNs are trained and tested using 10-fold cross-validation using the PS dataset. The algorithm performance is evaluated based on accuracy and loss matrices. The 1D CNNs outputs 99.7% in stress classification, which outperforms the Backpropagation (BP), only 65.57% in stress classification. Therefore, the findings yield a promising outcome that the 1D CNNs effectively classify stress compared to BP. Further explanation is provided in this paper to prove the efficiency of 1D CNN for the classification of stress.

Item Type: Article
Uncontrolled Keywords: 1D Convolutional Neural Networks, Artificial Intelligence, Classification, Deep Learning, Stress.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Academic Faculties, Institutes and Centres > Faculty of Cognitive Sciences and Human Development
Depositing User: Saleh Al-Hababi
Date Deposited: 16 Mar 2022 00:36
Last Modified: 16 Mar 2022 00:36

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