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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM25211

Stress Detection in IT Employees Using CNN

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM25211

Volume:

7 |

8 |

Aug 2023

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

DATV

Dr. Aravinda T V

PN

Poral Nagaraj

DKKR

Dr. Krishnareddy K R

RBE

Ramesh B E

NTT

Neelakantappa T T

S

Suresh

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Abstract

In contemporary society, Stress has emerged as a pervasive issue affecting a majority of individuals. Despite their material prosperity, people often find themselves dissatisfied due to pressures of stress. Stress is a difficult phenomenon that can manifest in emotional, physical, and mental forms. The objective of this project is to be identifying indicators of stress in IT professionals over the application of sophisticated image processing and machine learning methods. This technology represents an improvement over previous approaches for stress detection that didn’t account for the subjective experiences of employees or real-time detection. The current model incorporates both periodic and live detection of employee emotions. Automated stress detection serves to mitigate health risks and enhance the well-being of both the IT Company and its employees. By understanding the emotional states of IT professionals, businesses can provide appropriate guidance and achieve better outcomes.

KEYWORDS

Stress Machine learning and IT professionals.

Cite This Article

Dr. Aravinda T V, Poral Nagaraj, Dr. Krishnareddy K R, Ramesh B E, Neelakantappa T T, Suresh, "Stress Detection in IT Employees Using CNN," International Journal of Scientific Research in Engineering and Management, vol. 7, no. 8, Aug 2023. https://doi.org/10.55041/IJSREM25211

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Publication Date

8/16/2023

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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No funding information available for this research.

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