Deep Recurrent Neural Network Assisted Stress Detection System for Working Professionals
Source abstract
Abstract
Predicting the stress levels of working professionals is one of the most time-consuming and difficult research topics of current day. As a result, estimating working professionals’ stress levels is critical in order to assist them in growing and developing professionally. Numerous machine learning and deep learning algorithms have been developed for this purpose in previous papers. They do, however, have some disadvantages, including increased design complexity, a high rate of misclassification, a high rate of errors, and decreased efficiency. To address these concerns, the purpose of this research is to forecast the stress levels of working professionals using a sophisticated deep learning model called the Deep Recurrent Neural Network (DRNN). The model proposed here comprises dataset preparation, feature extraction, optimal feature selection, and classification using DRNNs. Preprocessing the original dataset removes duplicate attributes and fills in missing values.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Emotion and Mood Recognition · Smart Systems and Machine Learning · Internet of Things and AI
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Uttaranchal School of Computing Sciences, Uttaranchal University, Dehradun 248007, India
Uttaranchal Institute of Technology, Uttaranchal University, Dehradun 248007, India
Department of Project Management, Universidad Internacional Iberoamericana, Campeche 24560, Mexico
Department of Electrical Engineering, JIS College of Engineering, Kolkata 741235, India
Digital Transformation Portfolio, Tshwane University of Technology, Staatsartillerie Rd, Pretoria West, Pretoria 0183, South Africa
Funding
Tshwane University of Technology, South Africa
References (27)
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