Automatic Classification

Automatic classification uses machine learning to categorize data across diverse fields, aiming to improve efficiency and accuracy compared to manual methods. Current research emphasizes deep learning models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs, including LSTMs), and transformers (including BERT), often enhanced with attention mechanisms or ensemble techniques, to classify data ranging from medical images and text to audio and sensor readings. These advancements have significant implications for various sectors, including healthcare (e.g., disease diagnosis), agriculture (e.g., seed sorting), and information retrieval (e.g., news categorization), by automating time-consuming tasks and potentially improving decision-making. The development of robust and explainable models remains a key focus.