Citation :

Dr. M.Sarathkumar, Veeramani M.K, Abiram A,Prasannakumar S, NareshKrishnan L, 2025. "Face Stress Detection Using CNN And XG Boost" ESP International Journal of Communication Engineering & Electronics Technology (ESP-IJCEET)  Volume 3, Issue 1: 1-9.

Abstract :

The increasing adoption of electric vehicles (EVs) presents significant challenges to power grid management, particularly in balancing demand and supply while ensuring energy efficiency. This project explores a smart Power Allocation and EV Charging System designed to optimize energy distribution, enhance grid stability, and reduce charging costs. The proposed system integrates advanced algorithms for demand response, renewable energy utilization, and predictive analytics to dynamically allocate power to EV charging stations based on real-time data. By incorporating machine learning techniques, the system predicts peak usage times and adjusts charging schedules to minimize grid stress. A cloud-based monitoring and control system for real-time data collection and decision-making. An adaptive charging algorithm to prioritize critical loads and optimize charging rates. Integration of renewable energy sources like solar and wind to promote sustainability. This project aims to contribute to a scalable, cost-effective, and environmentally sustainable solution for managing the growing energy demands of EVs while ensuring grid reliability.

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Keywords :

Face Stress Detection, Convolutional Neural Networks (CNN), XGBoost, Facial Expression Recognition, Stress Classification, Emotion Detection, Machine Learning, Deep Learning, Image Processing, Emotion Analysis, Computer Vision, Facial Landmark Detection, Stress Recognition Models, Classification Algorithms, Neural Networks, Biometric Stress Detection, Predictive Modeling.