Stress is a growing worry for IT personnel due to their rigorous workloads and constant pressure to meet corporate requirements. A person's general well-being, productivity, and mental health can all suffer from prolonged stress. There are promising non-invasive techniques for early stress detection thanks to advancements in artificial intelligence (AI) and machine learning (ML). This research presents a deep learning and facial image analysis AI-powered system that can detect stress levels in IT workers. Businesses can lessen workplace burnout and enhance employee welfare by implementing the proposed system, which provides real-time stress monitoring. Facial picture datasets with both stressed and non-stressed expressions are gathered and preprocessed as part of the research. Relevant features are extracted using a variety of image processing approaches, including micro-expression analysis, facial landmark identification, and histogram analysis. Convolutional neural networks (CNN), support vector machines (SVM), and random forests are among the machine learning models that use the retrieved features for categorisation. Hyperparameter tuning and model optimization are employed to improve accuracy and robustness. The proposed system is evaluated using metrics such as accuracy, precision, recall, and F1-score. The results demonstrate a high accuracy rate of 92% in detecting stress levels, with CNN models outperforming other classifiers. Furthermore, the system is integrated into a web-based application to provide real-time stress monitoring and analysis. According to this study, AI-driven stress detection tools have the potential to increase IT workers' productivity, lower burnout, and promote workplace wellness. Enhancing the system's real-time capabilities, adding more physiological markers, and boosting model generalisation will be the main goals of future research.
AI-Powered Stress Detection, Machine Learning, IT Professionals, Facial Recognition, Deep Learning, Convolutional Neural Networks (CNNS), Stress Monitoring, Real-Time Analysis, Mental Health, Workplace Wellness.
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