Transparent Peer Review By Scholar9
STRESS DETECTION IN IT PROFESSIONALS USING IMAGE PROCESSING AND MACHINE LEARNING
Abstract
Stress is intuited because the maximum crucial element inside the character lifestyles. The World Health Organization is defined as pressure is a intellectual health problem that impacts the residents. In world such a lot of human beings are affected by strain. Stress is one of the predominant symptoms in all the man or women for the mental health. Stress can affect all of the elements of our life along with our feelings, wondering capability, our behavior and so on. So we must manage the stress. Stress is a subjective phenomenon this is tough to degree comprehensively. However, we are able to classify and quantify pressure and the way it affects one's private fitness, together with diverse organic and mental vulnerabilities. If we most effective listen what an person says and ignore what the face of that man or woman is telling us, then we just have half the story. The predominant motive of our venture is to discover pressure inside the IT experts the use of shiny Machine studying and Image processing strategies. Our system is an upgraded model of the old strain detection systems which excluded the stay detection and the private counseling however this system incorporates of stay detection and periodic evaluation of employees and detecting bodily as well as mental stress ranges in his/her via presenting them with right remedies for managing pressure by presenting survey shape periodically. Our device in particular makes a specialist of managing pressure and making the working environment wholesome and spontaneous for the employees and to get the great out of them at some stage in working hours.
Chandrasekhara (Samba) Mokkapati Reviewer
24 Sep 2024 05:39 PM
Approved
Relevance and Originality
This research addresses a critical issue in contemporary workplaces, particularly in the IT sector, by exploring stress management through innovative machine learning and image processing techniques. The focus on real-time stress detection and personalized counseling presents an original approach to a widely recognized problem, making it highly relevant as organizations increasingly prioritize employee mental health.
Methodology
The methodology should clearly outline the research design, including the specific machine learning algorithms and image processing techniques used for stress detection. It would be beneficial to describe how data is collected, including sample size, the demographics of participants, and the types of surveys administered. Additionally, detailing how the effectiveness of the system is evaluated will provide insights into its reliability and validity.
Validity & Reliability
To enhance validity, the study should discuss how results are verified, perhaps by comparing findings with established stress assessment tools or through participant feedback. Information on the reliability of data collection methods, including the consistency of stress indicators, will strengthen the overall credibility of the research. Addressing potential biases in self-reported data and the accuracy of machine learning predictions is also essential.
Clarity and Structure
The paper should be organized into clear sections, such as introduction, methodology, results, and discussion, allowing for easy navigation. Each section should logically flow into the next, ensuring that readers can follow the progression of ideas. Using accessible language while avoiding excessive jargon will make the content understandable for a broader audience, enhancing engagement with the important topic of stress management.
Result Analysis
The analysis should delve into how effectively the proposed system detects stress levels in real time, comparing its performance to traditional methods. It should also evaluate the impact of periodic assessments and interventions on employee well-being and productivity. Discussing potential limitations and challenges encountered during implementation will provide a balanced view and suggest areas for future improvement in stress management strategies.
IJ Publication Publisher
Thank You Sir
Chandrasekhara (Samba) Mokkapati Reviewer