(Include but not limited to)
Track 1: Machine Learning
Machine learning methods and applications
Deep learning and reinforcement learning
Neural networks
Computational learning theory
AI/ML method optimization
Pattern recognition and classification
Data mining and knowledge discovery
Hybrid intelligent systems
Soft computing theory and applications
Experimental evaluation of AI/ML algorithms
Distributed and parallel AI/ML algorithms
AI/ML algorithm tools
Track 2: Intelligent Computing Power
High-performance computing
Distributed and parallel computing
Cloud computing
Edge and fog computing
Heterogeneous and accelerated computing
Resource scheduling and management
Large-scale data center systems
Data center networks
High-speed interconnection technologies
Storage systems and distributed file systems
Optimization of hybrid cloud storage
Energy efficiency and carbon-aware computing
Computational power optimization for AI tasks
AI-driven system optimization
Virtualization and container computing
AI and computing power integration systems
Track 3: Computational Science
Machine learning applications
Data mining techniques
High-performance computing
Cloud computing optimization
Quantum computing research
Complex system modeling
Parallel algorithm design
Network security technology
Natural language processing
Image processing technology
Algorithm complexity analysis
Deep learning models
Computer vision innovation
Human-computer interaction technology
Big data analysis
Simulation and modeling
Computational science theory ......