Novel Application

Novel applications of machine learning and other advanced computational methods are rapidly expanding across diverse scientific and engineering domains. Current research focuses on improving model accuracy, efficiency, and explainability, employing techniques like deep learning (including EfficientNet and transformer networks), global sensitivity analysis for feature selection, and normalizing flows for probabilistic modeling. These advancements are driving progress in areas ranging from medical diagnosis (e.g., COVID-19 detection from CT scans and breast cancer classification from ultrasound) and environmental monitoring (e.g., wind speed prediction) to more efficient resource allocation and improved understanding of complex systems (e.g., the HPA axis and stellar age inference).