The Annual IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) is one of the premier international conferences in the field of fuzzy sets and systems.
FUZZ-IEEE 2027 will represent a unique meeting point for scientists and engineers, both from academia and industry, to interact and discuss the latest enhancements and innovations in the field. The topic of the conference will cover all the aspects of theory and applications in fuzzy sets theory and hybridizations with other artificial and computational intelligence techniques. In particular, FUZZ-IEEE 2027 topics include, but are not limited to:
• Fuzzy machine learning
• Fuzzy human-computer interaction
• Fuzzy sets and logic fundamental development
• Fuzzy reasoning and knowledge representation
• Fuzzy image, speech and signal processing, vision and multimedia
• Fuzzy systems design, modeling, identification, fault detection
• Fuzzy data analysis, clustering and classifiers, pattern recognition, bio-informatics
• Fuzzy optimization, decision analysis and decision making
• Fuzzy information extraction and retrieval, fusion, text mining
• Fuzzy systems with big data and cloud computing
• Fuzzy data analytics and visualization
• Soft computing and rough sets
• Fuzzy systems in social sciences and natural language processing
• Adaptive, hierarchical and hybrid (neuro- and evolutionary-) fuzzy systems
• Hybrid systems of computational intelligence techniques
• Fuzzy system applications in finance, health, transportation, manufacturing, agriculture and other industry sectors
• Trustworthy human-machine interface and brain-computer interface
• Trustworthy machine learning under uncertainty
• Reasoning under uncertainty
• Machine learning in dynamic environments
• Explainable AI in non-stationary environments
• Causal learning and reasoning in complex systems
• Continual (lifelong) learning in evolving systems
• Safe and reliable AI systems in real-world settings
• Robust optimisation for real-time intelligent systems
• Uncertainty quantification in machine learning and AI models
• Multi-agent learning in stochastic environments
• Resilient AI for real-time and mission-critical applications
• Computational intelligence for resilient and adaptive cybersecurity in evolving cyber-physical systems