| Abstrakt: |
Nowadays, a stress classification system is essential to classify the psychological stress that impairs a person's socioeconomic life. Several Deep Learning (DL) models have been developed in recent years to classify stress using physiological signals, including electrodermal activity (EDA) and electrocardiography (ECG). However, those models cannot handle concept drift during the training phase, which may struggle to adapt to changing data patterns, leading to unreliable predictions. Concept drift refers to changes in the characteristics or patterns of physiological signals used for stress classification. These changes could be due to various factors, including shifts in the data distribution, environmental conditions, or the subjects' behavior. Therefore, this article develops a novel Deep Transfer Learning with Organic Computing (DTLOC) model by integrating the Deep Convolutional Neural Network (DCNN) with the TL and OC mechanisms to handle concept drift and improve the accuracy of stress classification. The TL brings prior knowledge about EDA and ECG features, which enhances the model's initial capabilities and shortens the learning curve. Additionally, the OC provides a self-management system that oversees the structure and operation of the model. It dynamically adapts the DCNN in response to changing data patterns, ensuring that the model remains accurate and effective in classifying stress, even in the presence of concept drift. The experimental results demonstrate that the DTLOC model, utilizing EDA and ECG data from the WESAD dataset, achieves an accuracy of 93.53%. This is a significant improvement compared to the LIBSVM, LSTM, DNN, and CNN models, with increases of 15.63%, 13.15%, 10.37%, and 5.03% respectively. Thus, this model can enhance individuals' quality of life and safety by detecting stress-related illnesses at an earlier stage. [ABSTRACT FROM AUTHOR] |
