A new artificial intelligence-based approach is proposed by developing a deep learning (DL) model for identifying the people who violate the face mask protocol in public places. To achieve this goal, a private dataset was created, including different face images with and without masks. The proposed model was trained to detect face masks from real-time surveillance videos. The proposed face mask detection (FMDNet) model achieved a promising detection of 99.0% in terms of accuracy for identifying violations (no face mask) in public places. The model presented a better detection capability compared to other recent DL models such as FSA-Net, MobileNet V2, and ResNet by 24.03%, 5.0%, and 24.10%, respectively. Meanwhile, the model is lightweight and had a confidence score of 99.0% in a resource-constrained environment. The model can perform the detection task in real-time environments at 41.72 frames per second (FPS). Thus, the developed model can be applicable and useful for governments to maintain the rules of the SOP protocol.

Stretching the integration of renewable energy, energy storage, electric vehicles (EVs) and smart-grid technologies at an unprecedented rate will produce increasingly complex and dynamic power systems that defy conventional optimization and control methods. This paper offers a systematic engineering review of the state of the art and future potential for the application of artificial intelligence (AI) in sustainable and resilient electric power systems, which includes machine learning, deep learning, reinforcement learning (RL), hybrid AI, explainable artificial intelligence, federated learning (FL), and digital twins (DTs). The review covers their use in renewable energy and load forecasting, energy management, predictive maintenance, demand response, adaptive control, energy storage, smart grid and EV/Vehicle-to-Grid coordination. A single framework based on the grid is presented that integrates predictive intelligence, optimization, adaptive control, cyber-physical systems, explainability, cyber-security, and sustainability in a closed-loop system. The analysis underscores the transformative possibilities of AI in enhancing the use of renewable energy, its efficiency, resilience of the grid and smart decision making. But, deployment needs to address the uncertainties, data quality, computational costs, latency, Cybersecurity, interoperability, scalability, model drift, and regulatory needs. The review also highlights Green AI to tackle the energy and carbon footprint of increasingly large AI models. Lastly, physics-informed and uncertainty-aware AI, safe RL, DT-assisted validation, edge intelligence, FL and real-world benchmarking are identified as key directions for next-generation power systems to remain trustworthy and sustainable.