(The following topics include but are not limited to)
Track 1: Intelligent Information Processing
Multimodal information perception, collection and preprocessing technologies
Image / video information understanding, recognition and content analysis
Natural language processing and text information mining
Time series signal processing and spatio-temporal information analysis
Remote sensing and multi-dimensional sensing information processing
Big data information noise reduction, feature extraction and representation learning
Privacy and security information transmission and information desensitization technologies
Knowledge graph construction, reasoning and information fusion
Information modeling methods under small samples and weak labels
Heterogeneous information processing in industrial / medical / financial fields
Information retrieval, recommendation systems and user behavior mining
Lightweight information parsing and real-time processing at the edge
Multi-source heterogeneous information fusion and uncertainty information modeling
Track 2: Algorithm Optimization Theory and Technology
Meta-heuristic optimization algorithm design and improvement
Convex optimization, non-convex optimization and stochastic optimization theory
Multi-objective optimization, multi-constraint optimization decision methods
Distributed optimization and parallel optimization algorithms
Dynamic optimization, robust optimization and uncertainty optimization
Reinforcement learning-driven optimization solution methods
Efficient solution strategies for combinatorial optimization problems
Large-scale sparse optimization and dimensionality reduction optimization algorithms
Convergence, complexity theory analysis of optimization algorithms
Implementation and verification of optimization algorithms under engineering constraints
Game optimization, double-layer optimization and collaborative optimization
Adaptive parameter optimization and automatic tuning algorithms
Hybrid optimization framework of global search and local search
Track 3: Computational Intelligence Theory and Applications
Artificial neural networks, deep learning models and architecture design
Fuzzy systems, rough set theory and granular computing
Evolutionary computation, swarm intelligence and bionic computing
Neural network interpretability, reliability and robustness
Transfer learning, federated learning and continuous learning
Generative intelligent models and AIGC basic theory
Intelligent decision-making systems and autonomous intelligent agents
Application of computational intelligence in intelligent manufacturing
Application of computational intelligence in smart transportation, energy systems
Biological-inspired computing and brain-inspired intelligent models
Chaos computing, quantum computing intelligent basic methods
Intelligent fault diagnosis, prediction and health management
Compression, acceleration and hardware deployment of computational intelligence models
...