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.
Aravind Ayyagari Reviewer
24 Sep 2024 05:22 PM
Not Approved
Relevance and Originality
The research article addresses a crucial issue in the workplace by focusing on stress management among IT professionals, an area of growing concern due to mental health awareness. Integrating machine learning and image processing for real-time stress detection presents a novel approach, distinguishing this study from existing literature. By targeting a specific professional group and employing innovative solutions, the research highlights the importance of mental health in the workplace while contributing original methodologies to the field.
Methodology
The methodology section should provide clearer details about the specific machine learning algorithms and image processing techniques used. Additionally, outlining the participant selection process, including criteria for a representative sample of IT professionals, is essential. Elaborating on these aspects would enhance transparency and help readers understand the framework and processes employed in the study.
Validity & Reliability
To strengthen the findings, the article should discuss methods for establishing the validity and reliability of the stress detection system. This could include pilot studies or comparisons with established stress measurement tools. Addressing potential biases in data collection and interpretation would enhance the credibility of the research, ensuring that the conclusions drawn are well-founded and applicable.
Clarity and Structure
The clarity and structure of the research article could benefit from improved organization, ensuring smooth transitions between sections. Some sentences are overly complex, which may obscure key points. Simplifying language and maintaining consistent terminology would significantly enhance readability, making it easier for the audience to grasp the study's objectives and findings.
Result Analysis
A more thorough analysis of the results is needed, including statistical significance and implications of the findings on stress management practices. Presenting visual aids, such as graphs, to illustrate stress levels before and after the system's implementation would clarify its effectiveness. A comprehensive evaluation of the results would more effectively convey the research's impact and contribute to discussions on improving workplace mental health.
IJ Publication Publisher
Done Sir
Aravind Ayyagari Reviewer