Cognitive Computing simulates human thought, enabling systems to learn, reason, and self-correct from unstructured data using AI. Digital Twins are real-time virtual replicas of physical systems, providing monitoring, simulation, and optimization. The powerful convergence creates "Cognitive Digital Twins," where virtual models gain intelligence to autonomously learn, predict failures, and self-optimize their physical counterparts.

Scope and Key Topics

• Architectures and frameworks for cognitive digital twins
• AI/ML for predictive modeling and optimization
• Real-time data integration and sensor fusion
• Edge AI for localized cognitive processing
• Security, privacy, and trust in twin ecosystems
• Smart city digital twins for urban planning
• Performance evaluation and systems metrics
• Ethical considerations and Explainable AI (XAI)
• ML and DL for advanced pattern recognition
• Reinforcement learning for autonomous control
• NLP and speech recognition for human interaction
• Knowledge representation and reasoning
• Cognitive robotics and autonomous systems
• Interoperability standards for digital twins
• Data management and lifecycle operation
• Storage and security for twin ecosystems