Introduction

Fuzzy Data Analysis (FDA) tackles a fundamental challenge in Data Science: making sense of data that is inherently uncertain, imprecise, or ambiguous. By leveraging Fuzzy Set Theory and Fuzzy Logic, FDA provides a powerful framework for analyzing, processing, and extracting insights from data where traditional methods struggle-particularly in scenarios that defy crisp categorizations and exact values.
This track aims to foster discussions and disseminate cutting-edge research in FDA methodologies and applications. We welcome contributions that explore innovative approaches to handling heterogeneous, high-dimensional, noisy, or uncertain data in real-world settings.

Topics of interest

Topics of interest include, but are not limited to:

• Fuzzy Clustering, Fuzzy Classification and Fuzzy Regression • Fuzzy Intelligent Decision-making
• Fuzzy Logic in Deep Learning
• Fuzzy Modeling for ExplainableAI (XAI)
• Fuzzy Optimization in AI
• Fuzzy Systems in Natural Language Processing
• Hybrid Fuzzy-AI Systems
• Interval-value Fuzzy Logic in AI
• Methods to improve models’interpretability using fuzzy techniques • Neurocomputing,Fuzzy Neural Nets, and Deep Learning
• Real-Time Decision Making with Fuzzy Systems
• Type-2 Fuzzy Systems
• Real-world applications

We invite researchers, practitioners, and industry experts to submit their latest findings, case studies, and theoretical advancements in Fuzzy Data Analysis.

Organizing committee

• Susana Nascimento, Universidade Nova de Lisboa, Portugal
• Gozde Ulutagay, Ege University,Turkey
• João Paulo Carvalho, INESC-ID /Instituto Superior Técnico, Universidade de Lisboa, Portugal