SAFE-Net: Secure AI-driven Forecasting and Efficiency Network for Smart Helmet Systems

Worker safety in hazardous environments is paramount, and the integration of artificial intelligence (AI) and Internet of Things (IoT) technologies into smart helmets can significantly enhance safety protocols. This paper proposes SAFE-Net, a novel algorithm combining predictive analytics, IoT-integrated sensors, and cloud computing to predict hazard likelihood and optimize worker schedules in real-time. SAFE-Net leverages a Recurrent Neural Network (RNN) for time-series forecasting, integrating historical hazard data, worker schedules, and environmental conditions to provide actionable insights. The proposed model outperformed existing methods by achieving higher accuracy in hazard prediction and optimizing schedules to minimize risks. Experimental results demonstrate that SAFE-Net reduces hazard occurrences by 30% and improves worker productivity by 20%, demonstrating its efficiency and effectiveness in ensuring worker safety and operational efficiency.