Paper Contents
Abstract
Stress is a mental condition that develops over time and has a detrimental impact on health as well as other nervous system-related difficulties. Excessive stress can also cause troubles with the muscles. Everybody experiences mental stress because of the fast-paced nature of today's environment, and this issue has spread widely. The individual is stressed as a result of the pressure on their mind and body, and as we all know, stress is a major problem in today's society. Every aspect of society is under pressure, and this has an impact on the mental health of the social people. Even many corporate sectors offer a range of mental health-related programmes in an effort to lessen stress-related problems in the workplace. Due to the heavy workload, long hours and poor management most of the employees are facing immense stress and there are several changes in the lifestyle of the employee resulting in incrementation of stress in the minds of jobholders. There is a need to show a way for coming out of the stressed state. Employee stress from work has a negative effect on their health, their conduct, and even how well they perform for the company. To trigger these negative effects, stress needs to be well controlled.The easiest technique to identify stress and avoid interfering with working conditions is to analyze facial expressions. The stress levels are recognized by the facial expressions and movements of parts of the face and facial recognition is handled using deep learning which is a part of artificial intelligence. The feelings and expressions of the employees are considered as the input of the system and the stress is predicted which helps the organization for proper work adjustment among the employees. The idea deals with the usage of deep learning algorithms such as Convolutional Neural Networks, image processing and determining the amount of stress on an employee working in corporate sectors.
Copyright
Copyright © 2023 RAYAVARAPU JAHNAVI PRAMODHANI. This is an open access article distributed under the Creative Commons Attribution License.
