Manuscript Topics

In the age of rapid technological evolution, the landscape of Artificial Intelligence (AI) has witnessed unparalleled transformation. Within the span of a few years, AI has transitioned from being a theoretical construct to an omnipresent entity, seamlessly integrating into various sectors, from healthcare to education, transportation to emergency security. However, the maturity of AI has been punctuated by intricate challenges, including limited computing power, complex system integration, and data privacy. Extensive advanced techniques have been developed in recent years, which enabled the innovation of AI systems and facilitated many successful applications. For instance, the automatic data clustering and generative models have been demonstrated to play an important role in understanding complex data patterns and generating new data samples. Moreover, the Edge AI has been proposed to address certain limitations that cloud computing models might encounter, such as network latency, bandwidth constraints, and data privacy concerns. However, there remain many theoretical and practical challenges, and open questions remain in terms of how to enable robots to deal with real-world applications.

This special issue is therefore poised at the results related to the recent advancements of AI applications and systems. The authors from different backgrounds (such as artificial intelligence, robotics, computer science, and control engineering) are welcome to share their theoretical and experimental solutions or frameworks to enhance AI systems and broaden AI applications. Review articles are also encouraged.

The primary list of topics covers, but not limited to the followings:
• Advanced Neural Network Architectures and Applications
• AI in Healthcare Diagnostics and Prognostics
• Quantum Computing in AI Model Optimization
• Robotics and Autonomous Systems Integration
• Challenges in Real-time AI Deployment
• Reinforcement Learning in Real-world Scenarios
• Multi-modal Data Fusion in AI Systems
• Virtual Reality and AI Convergence
• Evolution of Edge AI and Distributed Learning

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