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FSDM 2026: International Conference on Fuzzy Systems and Data Mining

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Submission Date:
2026-09-15 Due in 13 days
Notification Date:
Conference Date:
2026-11-20
Location:
Macao, China
Years:
Viewed: 20922   Tracked: 2   Attend: 0

Conference Partner Index (CP-I)

51.8 / 100
Ranked #1,170 of 5,647 conferences · Top 21%

#60 of 336 in Data Mining & Databases #102 of 734 in Artificial Intelligence & Machine Learning

Academic recognition (35%) No data - scored at the neutral baseline of 50 —
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%)
69
Community attention (10%)
23
Public record completeness (15%)
55

Inputs used: Editions on record: 12 · Researchers following it here: 2 · Researchers who opened this page in the past 24 months: 4

Missing from the public record: Historical acceptance rates (+4.5) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 45% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-09-02

Call For Papers

FSDM 2026 (International Conference on Fuzzy Systems and Data Mining) is an academic conference held in Macao, China on 2026-11-20. The paper submission deadline is 2026-09-15.

Themes and Topics Researchers and practitioners are encouraged to submit original, unpublished contributions via the Online Submission System.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, Algorithms and Systems Mathematical foundations of fuzzy modeling 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/vehicles 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 Applications of Fuzzy Modeling 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 Time series processing 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. Special Session on Digital Transformation from Omics to Primary Care in Decentralized Health Systems (Omics2Care) AI in bioinformatics and precision medicine • Robust biomarker discovery and feature selection for omics and multi-omics • Patient subtyping and phenotype discovery from multi-modal data (omics + clinical + lifestyle) • Translational modeling linking molecular signals to clinical outcomes and risk trajectories Holistic healthcare intelligence • Causal inference from real-world data (RWD) for treatment effect estimation and personalized care pathways • Multimorbidity modeling, patient similarity learning, and longitudinal trajectory mining • Early risk detection for chronic conditions using sparse primary care signals • Behavioral adherence modeling and patient engagement analytics Primary healthcare management and smart strategies • Decision support for screening, triage, referral, and care planning • Pathway analytics and workflow-aware AI for front-line primary care settings • Digital twin in healthcare for patient pathway simulation, care planning, and service optimization Decentralization healthcare management and policy analytics • Health Technology Assessment (HTA) for decentralized services, resource allocation, and planetary health considerations • Analytics for devolved service performance monitoring (district/province comparisons and trends) • Referral network mining and optimization across decentralized facilities • Resource allocation and capacity planning under uncertainty (workforce, supplies, service access) • Equity and access intelligence using geospatial and socioeconomic signals Trustworthy and deployable health AI • Methodological developments for integrating AI and evidence synthesis into clinical practice guideline formulation • Uncertainty quantification and calibration for clinical risk prediction • Explainability and interpretable modeling for clinical and system-level decisions • Fairness and bias auditing and robustness under domain shift and data fragmentation • Digital twin in healthcare for interpretable simulation and decision support
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