Novel Framework

Research on novel frameworks spans diverse applications, aiming to improve efficiency, robustness, and explainability across various domains. Current efforts focus on developing federated learning architectures for distributed data analysis, leveraging large language models for tasks like summarization and reasoning, and employing advanced algorithms such as transformers and XGBoost for improved model performance and interpretability. These advancements hold significant potential for enhancing healthcare, manufacturing, and AI-driven applications by enabling more efficient data processing, more accurate predictions, and more reliable systems.