Artificial Intelligence Framework

Artificial intelligence (AI) frameworks are being developed to address diverse challenges across various domains, from healthcare diagnostics and data quality management to optimizing satellite communications and accelerating scientific discovery. Current research emphasizes the development of robust and adaptable frameworks, often incorporating deep learning models like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer networks, alongside techniques for data augmentation and bias mitigation. These frameworks aim to improve efficiency, accuracy, and interpretability in AI applications, ultimately impacting fields ranging from medical diagnosis and scientific computing to education and resource management.