Real Time Risk

Real-time risk assessment focuses on rapidly evaluating and mitigating potential hazards in dynamic systems, aiming to improve safety and efficiency. Current research emphasizes the use of artificial neural networks and machine learning algorithms, often combined with model-based approaches, to analyze real-time sensor data and predict risks across diverse applications, including autonomous vehicles and power grids. This field is crucial for enhancing safety in complex systems, particularly those with high stakes and unpredictable environments, and is driving advancements in both theoretical understanding and practical applications.