The rapid advancement of Artificial Intelligence (AI) has revolutionized various domains, with pattern recognition emerging as one of the most transformative areas. This research presents an investigative exploration into AI informatics within the context of pattern recognition, delving into the computational techniques, algorithms, and frameworks that drive its evolution. The research focuses on how AI informatics, characterized by its ability to manage vast datasets, facilitates more accurate and efficient pattern recognition systems. Key areas of exploration include supervised, unsupervised, and self-supervised learning models, alongside the application of neural networks, deep learning, and reinforcement learning in developing robust recognition frameworks. The exploration investigations also highlight challenges such as scalability, real-time processing, and data privacy, while investigating future directions in AI-driven pattern recognition. Through a comprehensive analysis of existing available knowledge and the examination of emerging trends, this research provides insights into the intersection of AI informatics and pattern recognition, contributing to the field’s advancement by offering novel perspectives and identifying areas for further research.
Keywords: Artificial Intelligence (AI), AI Informatics, Computer Vision, Cognitive Computing, Computational Neuroscience, Deep Learning, Machine Learning, Pattern Recognition (PR), Robotics.
Citation: Akhtar, Z. B. (2025). Artificial Intelligence (AI) Informatics within Pattern Recognition An Investigative Exploration. Int J Math Expl & Comp Edu.2(2):1-15.