Evaluation of stress based on multiple distinct modalities using machine learning techniques - Pradhan, Samarendra Narayana; Shankar, Reddy Shiva; Barik, Shekharesh; Mohanty, Bhabodeepika; Rao, Venkata Rama Maheswara (Intelektual Pustaka Media Utama, 2024)
Nowadays, one of the most time-consuming and complex study subjects is predicting working professionals' stress levels. It is thus crucial to estimate active professionals' stress levels to aid their professional development. Several machine learning (ML) and deep learning (DL) methods have been created in earlier articles for this goal. But they also have drawbacks, such as increased design complexity, a high rate of misclassification, a high incidence of mistakes, and reduced efficiency. Considering these issues, the objective of this study is to make a prognosis about the stress levels experienced by working professionals by using a cutting-edge deep learning model known as the convolutional neural networks (CNN). In this paper, we offer a model that combines CNN-based classification with dataset preprocessing, feature extraction, and optimum feature selection using principal component analysis (PCA). When the raw data is preprocessed, duplicate characteristics are eliminated, and missing values are filled.