Brief Review

Recent research focuses on mini-reviews across diverse fields, synthesizing existing knowledge and identifying gaps in various areas of machine learning and its applications. Current efforts concentrate on improving model explainability, addressing multicollinearity, and optimizing training speed for deep neural networks, including transformers and U-Net architectures. These reviews highlight the need for improved evaluation methodologies, standardized interfaces for integrating AI into existing systems (like OPC UA), and the development of more robust and reliable datasets for training and evaluating models across domains such as healthcare, e-commerce, and astrophysics. The ultimate goal is to enhance the accuracy, efficiency, and trustworthiness of AI systems for a wide range of practical applications.