Facial Recognition Intelligent System based on Convolutional Neural Network

Detection and control of human stress are challenging tasks because of individual variability, intricate psychological mechanisms, and the impact of dynamic environments. Conventional stress assessment techniques are mostly based on questionnaires, self-reporting, and manual observation, which tend to produce inconsistent and biased results. Current automated methods using statistical or rule-based models cannot capture the non-linear, multi-dimensional nature of stress responses. To overcome these shortcomings, this study presents a holistic AI-based framework that leverages sophisticated machine learning methods, wearable sensor information, and behavioral analytics to identify, model, and control stress with high accuracy. The suggested model combines physiological signals, contextual information, and psychological patterns to offer real-time adaptive stress tracking and customized interventions. Experimental assessments illustrate better accuracy and generalizability in various populations with a 95.6% classification accuracy and outperforming traditional models in robustness and responsiveness. The intelligent system sets the foundation for scalable, privacy-preserving, and user-oriented solutions for healthcare, education, and workplace settings.