Themes and Topics

Researchers and practitioners are encouraged to submit original, unpublished contributions via the Online Submission System. The topics of interest include but are not limited to:

Fuzzy Set Theory, Algorithm and System
Mathematical foundations of fuzzy systems
Multi-granular cognitive computation
Fuzzy optimization and modeling
Fuzzy decision and support systems
Fuzzy system architectures and hardware
Fuzzy classification
Fuzzy complex systems
Fuzzy control and robotics system
Fuzzy electronics systems
Fuzzy systems in machine learning
Fuzzy systems in video games
Evolutionary and hybrid systems
Rough set theory
Three-way decisions

Interdisciplinary Field of Fuzzy Logic and Data Mining
Applications in electrical engineering
Applications in manufacturing engineering
Applications in industrial engineering
Applications in chemical engineering
Applications in mechanical engineering
Applications in civil engineering
Applications in engineering management
Applications in bioengineering
Applications in biomedical engineering
Applications in environmental issues
Applications in economic and statistic issues
Fuzzy Application
Fuzzy databases and information retrieval
Fuzzy pattern recognition and image processing
Fuzzy sets and logic in ontology, web, and social networks
Fuzzy sets in operations research and manufacturing
Fuzzy financial forecasting
Fuzzy preference modeling
Fuzzy neural networks
Intelligent agents and ambient intelligence
Learning, adaptive, and evolvable fuzzy systems
Explainable AI based on fuzzy logic





Data Mining
Scalable data preprocessing
Big data and data stream mining Parallel and distributed data mining algorithms
Graph and subgraph mining
Text, video, multimedia data mining
Web mining
High performance data mining algorithms
Data mining visualization
Security and privacy issues
Competitive analysis of mining algorithms
Data mining systems in finance and e-commerce
Massive data mining

Special Session on "Fine-Tuning and Optimization of Large Language Models (FTOLM)"
  • Advanced Fine-Tuning Techniques: Supervised Fine-Tuning (SFT), Instruction Tuning, Reinforcement Learning from Human/AI Feedback (RLHF/RLAIF), and novel optimization algorithms for LLMs.
  • Parameter-Efficient Fine-Tuning (PEFT): Innovations in LoRA, QLoRA, Adapter layers, prompt tuning, and other methods for efficient model adaptation.
  • Domain-Specific Adaptation & Specialization: Strategies for fine-tuning LLMs in specialized fields (biomedical, legal, scientific, financial) with limited or proprietary data.
  • Alignment & Safety Optimization: Methods for steering model behavior, improving factual accuracy (reducing hallucination), enhancing safety guardrails, and ensuring ethical outputs post-tuning.
  • Optimization for Deployment: Quantization, pruning, distillation, and compilation techniques to optimize LLMs for latency, memory, and energy efficiency on edge devices or in production environments.
  • Data-Centric Optimization: Curation, synthesis, and engineering of high-quality datasets for effective fine-tuning; managing bias and data provenance.
  • Evaluation & Benchmarking: Novel frameworks and metrics for assessing the performance, robustness, efficiency, and safety of fine-tuned LLMs.
  • Theoretical Foundations & Challenges: Understanding catastrophic forgetting, overfitting, transfer learning limits, and the stability of the fine-tuning process.
  • Full-Stack & MLOps for LLMs: Workflow tools, platforms, and best practices for managing the end-to-end lifecycle of fine-tuning and deploying LLMs.
  • Societal & Economic Implications: Cost-benefit analyses, environmental impact of training/fine-tuning, and the accessibility democratization of advanced LLM customization.
Special Session on "Artificial Intelligence and Big Data in Education (AIBigEdu)"
• AI-powered personalized learning systems and adaptive educational pathways.
• Big data analytics for student performance prediction, behavioral analysis, and dropout prevention.
• Ethical considerations and privacy challenges in educational data collection and usage.
• AI applications in automated grading, feedback generation, and intelligent tutoring.
• Data-driven strategies for optimizing institutional resource allocation and policy-making.
• Integration of generative AI (e.g., ChatGPT) in curriculum design and interactive learning experiences.
• Case studies on AI-enhanced virtual/augmented reality (VR/AR) in immersive education.
• Cross-disciplinary collaborations between AI, neuroscience, and pedagogy to improve cognitive learning models.

Special Session on "Applied Mathematics and Intelligent Algorithms for Modern Industry (AMIAMI)"
• Development and application of intelligent algorithms in industries such as manufacturing, logistics, healthcare, finance, energy, insurance, and telecommunications.
• Case studies showcasing successful implementation of mathematics models and algorithms in solving real-world industrial problems.
• Advances in computational methods, machine learning, and artificial intelligence that contribute to industrial applications.
• Theoretical and practical challenges in applying mathematics and algorithmic solutions in the industry.
• Future trends and emerging technologies in the field of applied mathematics and intelligent algorithms for industry.
• Ethical, legal, and societal implications of deploying algorithmic solutions in an industrial context.

Submission Online