Research Article
Employee Stress Detection System Using Deep Learning and Cloud Technologies
@INPROCEEDINGS{10.4108/eai.28-4-2025.2358079, author={Bandaru Naga Joshnika and Kodela Lakshmi Meghana and Varshini Priya P J and N. Kathirvel}, title={Employee Stress Detection System Using Deep Learning and Cloud Technologies}, proceedings={Proceedings of the 4th International Conference on Information Technology, Civil Innovation, Science, and Management, ICITSM 2025, 28-29 April 2025, Tiruchengode, Tamil Nadu, India, Part II}, publisher={EAI}, proceedings_a={ICITSM PART II}, year={2025}, month={10}, keywords={tensorflow cnn amazon webservice open cv excel haar cascade classifier}, doi={10.4108/eai.28-4-2025.2358079} }- Bandaru Naga Joshnika
Kodela Lakshmi Meghana
Varshini Priya P J
N. Kathirvel
Year: 2025
Employee Stress Detection System Using Deep Learning and Cloud Technologies
ICITSM PART II
EAI
DOI: 10.4108/eai.28-4-2025.2358079
Abstract
Face reading is the most important aspect in understanding human behaviour. The words can't convey the expression that does. The viewpoints and mental states of humans are reflected in their facial expressions. This project aims to recognize faces from images, extract expressions from them, and categorize them into different emotional categories such as neutral, happy, angry, and sad. This project explores a method called convolutional neural networks (FERC) for facial emotion recognition. Convolution neural networks (CNNs) have two components: initially, they remove background from images, and then they extract characteristics of faces. This program is utilized in the fields of medicine, education, law enforcement, and human- robot interface.
