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 Cognitive Computing and Digital Twin Section is dedicated to the evolution of intelligent virtual representation and autonomous reasoning. We invite original research, review articles, and case studies that explore Cognitive Computing as a driver for human-like intelligence, Digital Twin technology as the backbone of physical-virtual synchronization, and the Convergence of these two domains. This section focuses on the transition from 'Passive Twins' to 'Cognitive Twins', systems that do not merely mirror physical reality but perceive, learn, and reason about it to provide proactive interventions. By integrating sensory perception with deep reasoning, we aim to redefine how complex systems are monitored, managed, and optimized across global industries.

The scope of this section is defined by, but not limited to, the following technical pillars; we warmly welcome submissions of all original research and review articles that address broader innovations and emerging trends related to these domains.

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