Published Paper Details:

In today's fast-paced digital era, the Information Technology (IT) sector standsas one of the most dynamic and demanding industries globally. With the ever-evolving technological landscape and relentless project deadlines, IT employees often find themselves subjected to high levels of stress. Recognizing the critical importance of employee well- being,organizationsareincreasinglyturningtoinnovativesolutionstomonitorandmanage stress levels among their workforce. In this context, the integration of machine learning techniques with live face monitoring using image processing, particularly through MATLAB, presents a promising avenue for stress detection and mitigation. Machine learning algorithms offer a powerful toolset for analysing complex data patterns and extractingactionableinsights. Byleveragingthesealgorithmsinconjunctionwithlivefacemonitoring, psychological states in real-time. Through the analysis of facial expressions, physiological signals, and other relevant indicators, such systemscan provide valuable feedback on individuals' stress levels, enabling timely interventions and support mechanisms. By implementing stress detection systems based on machine learning algorithmscapturelivefacemonitoringusingthecameraofthesystem,wecollectthefacial expression ofthe employee for a particular time period. Further, pre-process the collected imageand extractthe featuresand fineturningtheimage capturedusingRegion ofInterest (ROI)model. Movingontonext step,processtheextracted featuresbyusingclassification ML algorithm like Random Forest and Light GBM to classify the captured image among the given category of emotions. It will display theresult by voiceand notification will pop up on the screen. The integration of machine learning with live face monitoring using image processing and MATLAB represents a transformative approach to stress detection and management in the IT