DETECTION OF IT EMPLOYEE STRESS USING CNN MODEL ENGINEERING
Title: Detection of IT Employee Stress Using CNN Model Engineering
Permalink: http://www.ijetjournal.org/it-employee-stress-detection-cnn
Description: This paper presents a deep learning-based approach for detecting stress in IT professionals using CNN Model Engineering. It integrates live detection and periodic analysis to assess physical and mental stress levels, ensuring healthier workplace environments.
Keywords: IT employee stress detection, CNN model engineering, deep learning in workplace wellness, stress management technology, IJET Journal
International Journal of Engineering and Techniques – Volume 10 Issue 2, April 2024
Dr. D J Samatha Naidu, B. Bhargavi
Annamacharya PG College of Computer Studies, Rajampet, India
Email: samramana44@gmail.com, bathalabhargavi2002@gmail.com
Abstract
IT professionals often experience high stress levels due to demanding work environments. This paper introduces a CNN-based model to detect stress by analyzing images submitted by employees through live detection and periodic surveys. By classifying emotional states using image processing techniques, the system assists in stress management strategies for organizations. The study enhances workplace wellness through AI-driven solutions.
Keywords
IT employee stress detection, CNN model engineering, deep learning stress analysis, workplace wellness technology, IJET Journal
How to Cite
Dr. D J Samatha Naidu, B. Bhargavi, “Detection of IT Employee Stress Using CNN Model Engineering,” International Journal of Engineering and Techniques, Volume 10, Issue 2, April 2024. ISSN 2395-1303
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