Entropy Based

Entropy-based methods are increasingly used to analyze complex systems and improve machine learning models, focusing on quantifying uncertainty and optimizing performance. Current research explores applications ranging from analyzing biosignal synchronization and detecting anomalies in crowd dynamics to enhancing electrocardiogram analysis and improving the efficiency of large language models through sparsity techniques. These approaches offer valuable tools for understanding system behavior, improving model reliability, and addressing challenges in semi-supervised learning and long-tailed data distributions, ultimately impacting diverse fields from healthcare to artificial intelligence.