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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 10 Issue 1, 2019.
Abstract: In the modern era where the use of computer systems in software houses is mandatory and in various organizations has increased, it has given rise to the level of stress of employees working for hours at the system as well. Employees working in software houses are prone to have increased stress and anxiety level. It is important to detect the stress level of the employees so that various solutions can be applied in the working environment to get a better output. This paper would be beneficial for detecting the stress level of employees working on the computer using various inputs i.e. heart rate, pupil contraction, facial expressions, skin temperature, blood pressure, age and number of hours working on the computer. This research would indicate the raised level of stress of employees and this indication can be used to increase the yield of the quality of work and satisfaction of employees working in a particular organization. According to the levels of stress, within the working environment, during break hours various steps can be taken as a solution and applied during break hours of employees to ensure maximum satisfaction and the improved quality of work.
Rabia Abid, Nageen Saleem, Hafiza Ammaraa Khalid, Fahad Ahmad, Muhammad Rizwan, Jaweria Manzoor and Kashaf Junaid, “Stress Detection of the Employees Working in Software Houses using Fuzzy Inference” International Journal of Advanced Computer Science and Applications(IJACSA), 10(1), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100129
@article{Abid2019,
title = {Stress Detection of the Employees Working in Software Houses using Fuzzy Inference},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100129},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100129},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {1},
author = {Rabia Abid and Nageen Saleem and Hafiza Ammaraa Khalid and Fahad Ahmad and Muhammad Rizwan and Jaweria Manzoor and Kashaf Junaid}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.