Expert Driven Monitoring

Expert-driven monitoring leverages human expertise to enhance the accuracy and reliability of automated monitoring systems across diverse fields, from healthcare and manufacturing to environmental science and agriculture. Current research focuses on developing robust methods for detecting anomalies and data drift in real-time, employing machine learning models like XGBoost, support vector machines, and neural networks (including GANs and transformers) to analyze complex data streams from various sensors and sources. These advancements improve the efficiency and effectiveness of monitoring, enabling proactive interventions and data-driven decision-making with significant implications for optimizing processes, improving safety, and advancing scientific understanding.