Comprehensive Study

Comprehensive studies across diverse fields are increasingly leveraging machine learning and deep learning models to address complex problems. Current research focuses on improving model performance and robustness, exploring various architectures like convolutional neural networks (CNNs), transformers, and graph neural networks (GNNs), and investigating techniques such as knowledge distillation, transfer learning, and quantization. These advancements have significant implications for various applications, including medical diagnosis, natural language processing, and image analysis, by improving accuracy, efficiency, and reliability. Furthermore, research emphasizes addressing challenges like class imbalance, adversarial attacks, and bias in model outputs.