Theoretical Framework

Theoretical frameworks provide structured approaches for understanding complex systems, guiding research and enabling the development of effective models and algorithms. Current research focuses on developing frameworks for diverse applications, including humor detection, reservoir computing, AI adoption in education, and optimization of neural networks, often leveraging techniques like normalizing flows, reinforcement learning, and various neural network architectures. These frameworks aim to improve model interpretability, prediction accuracy, and efficiency, ultimately advancing both theoretical understanding and practical applications across numerous scientific disciplines.