ACCEPTED SPECIAL SESSIONS

SS1: Interval Uncertainty


Abstract: Interval uncertainty is closely related to fuzzy techniques: indeed, if we want to know how the fuzzy uncertainty of the inputs propagates through the data processing algorithm, then the usual Zadeh’s extension principle is equivalent to processing alpha-cuts (intervals) for each level alpha.
This relation between intervals and fuzzy computations is well known, but often, fuzzy researchers are unaware of the latest most efficient interval techniques and thus use outdated less efficient methods. One of the objectives of the proposed session is to help fuzzy community by explaining the latest interval techniques and to help interval community to better understand the related interval computation problems.
Yet another relation between interval and fuzzy techniques is that the traditional fuzzy techniques implicitly assume that experts can describe their degree of certainty in different statements by an exact number. In reality, it is more reasonable to expect experts to provide only a rage (interval) of possible values — leading to interval-valued fuzzy techniques that, in effect, combine both types of uncertainty.

Organizers: Martine Ceberio, Christoph Lauter, Vladik Kreinovich.

SS1 webpage: TBA

SS2: Information fusion techniques based on aggregation functions, preaggregation functions and their generalizations


Abstract: The search of new information fusion techniques under uncertainty is currently a hot topic in almost every research field, from image processing, classification, data stream clustering, brain computer interfaces, decision making to deep learning, transforms and adaptive neuro fuzzy inference systems. This interest has led to new analysis of the notion of aggregation function and the introduction of new concepts that go beyond usual aggregation functions, either by considering more general definitions (e.g., considering weaker forms of monotonicity), or by extending them to other frameworks different from that of the unit interval (e.g., intervals, lattices). The aim of this section is to promote the discussion of the up-to-date theoretical research in the topic, as well as their applications, in total connection with the interests of FUZZ-IEEE community, related to the theoretical and applied subjects covered by conference.
The special issue focuses on mathematical foundations, models and techniques for data fusion for Artificial Intelligence under uncertainty, aiming at disclosing the most recent and innovate developments in the field, including, but not limited to:
– Theoretical results in aggregation functions, pre-aggregation functions, and fusion functions with other kinds of weaker monotonicity;
– Theoretical results in common aggregation functions, pre-aggregation functions, and fusion functions on many-valued status (including, e.g., interval and lattice-valued);
– Theoretical and applied results in the controlling of the uncertainty in interval-valued data fusion;
– Fuzzy measures, fuzzy integrals and their generalizations;
– Other fusion functions, models and techniques for data fusion under uncertainty;
– (Adaptative) Neuro-fuzzy models and systems;
– Deep (fuzzy) learning;
– Fuzzy data stream;
– Applications in decision making (including, e.g. multi-criteria decision making), image processing, classification and multi-label classification, machine learning, data stream clustering, and data flow prediction.

Organizers: Humberto Bustince, Graçaliz Pereira Dimuro, Javier Fernández, Tiago da Cruz Asmus, Helida Santos.

SS2 webpage: TBA

SS3: Software for Soft Computing


Abstract: The term Soft Computing is usually used in reference to a family of several preexisting techniques (Fuzzy Logic, Neuro-computing, Probabilistic Reasoning, Evolutionary Computation, etc.) able to work in a cooperative way, taking profit from the main advantages of each individual technique, in order to solve lots of complex real-world problems for which other classical techniques are not quite well suited. In the last few years, many software tools have been developed for Soft Computing. Although a lot of them are commercially distributed, unfortunately only a few tools are available as open source software (see the webpage http://sci2s.ugr.es/fss). In the field of evolutionary computation, JCLEC (Java Class Library for Evolutionary Computation), KEEL (Knowledge Extraction based on Evolutionary Learning), and JMetal (Metaheuristic Algorithms in Java) provide nice examples of frameworks for both evolutionary and multi-objective optimization. JavaNNS (Java version of Stuttgart Neural Network Simulator) is probably the best free suite for neural networks. Regarding fuzzy modeling, JFML (the first library in the world that allows to develop fuzzy systems according to the new IEEE Std 1855 published and sponsored by the Standards Committee of the IEEE Computational Intelligence Society), Xfuzzy (a development environment for fuzzy-inference-based systems), FisPro (Fuzzy Inference System Professional), and GUAJE (Generating Understandable and Accurate fuzzy models in a Java Environment) represent very useful tools. Regarding neuro-fuzzy algorithms we can point out to NEFCLASS (Neuro-Fuzzy Classification). Finally, FrIDA (Free Intelligent Data Analysis Toolbox) and KNIME (Konstanz Information Miner) are examples of user-friendly open-source software which offer several individual tools for data processing, analysis and exploration/visualization. Please, notice that such open tools have recently reached a high level of development. As a result, they are ready to play an important role for industry and academia research.

The aim of this session is to provide a forum to disseminate and discuss Software for Soft Computing, with special attention to Fuzzy Systems Software thus offering an opportunity for researchers and practitioners to identify new promising research in this area. Potential topics of interest are:

  • Data Preprocessing
  • Data Mining and Evolutionary Knowledge Extraction
  • Modeling, Control, and Optimization
  • System Validation, Verification, and Exploratory Analysis
  • Knowledge Extraction and Linguistic/Graphical Representation
  • Visualization of results
  • Languages for Soft Computing Software
  • Interoperability and Standards • Data Science, Big Data, and High-Performance Computing, (Map-Reduce, GPGPU, Quantum Computing, etc.)
  • Explainable and Trustworthy Artificial Intelligence
  • Educational Software
  • Free and Open Source Software

As we did in FUZZIEEE2024, the session will end with an Open Discussion Slot where the session chairs will lead a panel with wider discussion around the special session topic in the context of the papers presented and their alignment with the mid-term strategic goals of the IEEE-CIS FSTC Task Force on Fuzzy Systems Software which is considered as one of the main planned activities of this Task Force in 2025.

Organizers: Jose Manuel Soto-Hidalgo, Jesús Alcalá-Fdez.

SS3 webpage: TBA

SS4: Advancements in Fuzzy Modeling for Edge Analysis and Processing Method


Abstract: Fuzzy modeling methods are built for efficient, fast and accurate data processing and analysis in edge computing architectures. Traditional methods often do not allow for the analysis of uncertainty and noise, which is the reason for modeling new solutions in fuzzy logic. This consequently allows for handling imprecision and offers a robust framework for more flexible and reliable edge analysis. The importance of new fuzzy models in this context is important to enable data analysis, feature extraction, or even integration with other processing techniques. The session aims to draw attention to the latest achievements in new fuzzy models that allow for data processing on end devices such as sensors, IoT devices and smartphones.

Aims and Scope: The purpose of this special session is to encourage research and innovation in the fields of fuzzy modeling for edge computing. We are looking for ideas that focus on the use and implications of fuzzy modeling for data processing enabling more accurate, faster and more efficient analysis in edge computing models. The call includes, but is not limited to, the following topics:
● Fuzzy models of I, II or III type,
● Data clustering techniques on the end-user side
● Architectures and models of fuzzy controllers operating on data obtained from IoT system sensors,
● Data visualization and interpretation using fuzzy processing tools,
● Applications of fuzzy models in mobile applications, automation control, federated learning, etc.
● Interoperability and integration of fuzzy controllers,
● Fuzzy models of clustering techniques, feature extractors in processing and analyzing multimodal data,
● Agent operations in edge computing based on fuzzy models.

Organizers: Dawid Połap, Stefania Tomasiello, Robertas Damasevicius.

SS4 webpage: TBA

SS5: Fuzzy Rule Base Transformation


Abstract: Fuzzy rule base transformation provides a flexible and effective means to perform reasoning in the presence of imprecise knowledge. It enables approximate inference with a sparse rule base that may not cover a given observation and offers an approach to simplify complex system models, reducing the number of rules needed while enhancing parameter optimisation and runtime efficiency. Topics of this special session include but are not limited to: fuzzy rule interpolation and extrapolation; fuzzy systems simplification; fuzzy set and function representation and transformation; and hybrid fuzzy interpolative learning and reasoning systems.

The aim of this special session is to provide a forum to address the following objectives: 1) To disseminate and discuss novel and significant research efforts in the development of fuzzy rule and rule base transformation techniques; and 2) To promote both theoretical and practical applications of fuzzy transformation.

This special session is a continuation of similar sessions (e.g., those on fuzzy rule interpolation) that have been successfully and consecutively held over several past FUZZ-IEEE conferences.

Organizers: Qiang Shen, Laszlo T. Koczy, Shyi-Ming Chen.

SS5 webpage: TBA

SS6: Evolving and learning fuzzy systems


Abstract: Evolving fuzzy systems represent a dynamic approach to fuzzy logic, integrating learning mechanisms that adapt to new data and changing environments. These systems enhance traditional fuzzy models by incorporating real-time learning, enabling them to refine their rules and membership functions as they process incoming information. This adaptability not only improves performance in uncertain and complex scenarios but also fosters resilience to noise and variability in data. In this special session, we present recent advancements in evolving fuzzy systems, including algorithmic innovations, applications across various domains, and future research directions. Our findings highlight the potential of these systems to enhance decision-making processes in fields such as control systems, pattern recognition, and data mining.

Organizers: Igor Škrjanc, Fernando Gomide.

SS6 webpage: TBA

SS7: Ethical, Legal, Social Implications of Computational Intelligence


Abstract: This special session focuses on novel technical contributions to the field of Artificial Intelligence Ethics (including fairness, explainability, risk, accountability and responsibility). We will also consider novel research in the field of data-driven guidelines and recommendations on responsible AI policies, standards, and methodologies; social science studies and recommendations related to the impact of AI on society, as well as surveys of the state-of-the-art in the space of AI ethics.

The aim of the proposed special session is to discuss the ethical and moral principles that govern the behaviour of AI/CI technology, as well as the operator, user and other stakeholders who are impacted by decisions informed by such technologies. These principles should cover the following: balancing the ecological footprint of technologies against the economic benefits; managing the impact of automation on the workforce; ensuring privacy is not adversely affected; and dealing with the legal implications of embodying AI/CI technologies in autonomous systems.

Research Topics – Topics of interest include, but are not limited to:

  • AI/CI on data privacy
  • Safety of AI/CI systems embedded in autonomous and automated systems
  • Human-machine Trust in AI/CI Systems
  • Specific applications of AI/CI and the potential ethical/social benefits
  • Legal implications of AI/CI (e.g., legal liabilities when things go wrong; how do you certify systems that can ‘learn’ from their environment etc)
  • Citizen perceptions of AI and its impact
  • Empirical research into the ethical impacts of AI/CI systems, including but not limited to impacts of AI/CI on human workforce and distribution of wealth, data privacy, business, economics or manufacturing and politics, human cognition and social relatedness, and security
  • Applications of AI and the potential ethical/social benefits and risks
  • Technical research into the representation, acquisition, and use of ethical knowledge by AI/CI systems.
  • Technical research and human-centred solutions for AI/CI, such as bias, fairness, explainability, accountability, responsibility, risk
  • Sustainable AI
  • Data-driven guidelines and recommendations, standard developments.

Novel Interdisciplinary research and industry submissions are welcome.

IEEE CIS Support: This special session is supported by the IEEE Technical Committee on Ethical, Legal, Social, Environmental and Human Dimensions of AI/CI (SHIELD).

Organizers: Keeley Crockett, Robert Reynolds, Naomi Adel, Christian Wagner.

SS7 webpage: TBA

SS8: Fuzzy Federated Learning: Theoretical advances and novel applications (FL-A)


Abstract: This special session will consider and discuss contributions with a particular focus on Federated Learning. This session will address the discussion on Federated Learning approaches that extend, further enquire, or improve the existing Fuzzy Federated Learning theory. We will discuss the uses of Federated Learning models for new tasks in data science, artificial intelligence, deep learning, explainable/interpretable artificial intelligence, machine learning, decision-making, robotics, control, and information science. We will also consider contributions to novel applications of Federated Fuzzy models in real-world scenarios and research questions.

The term Federated Learning was introduced in 2017, since then, the field has had a significant growth, especially in the Neural Network community. The fuzzy community has also picked it up, in fact, there is a trend in works presented on the 3 last FUZZ-IEEE, which has had 2, 6 and 8 works respectively, specifically related to Federated Learning. The topic has relevance in the main Journals of the field, in the last 3 years TFS published 9 papers, 6 of them in 2024, and IEEE Xplore shows around 30 results for the same period. The community of aggregator operators can be important in this area, as one of the key components of the field is the aggregation of the information of the different clients. The goal of this session is to raise the awareness of the research challenges and opportunities that Federated Learning bring to the fuzzy community.

Organizers: Javier Andreu-Perez, Barbara Pękala, Asier Urio-Larrea, Anna Wilbik.

SS8 webpage: TBA

SS9: Recent Advances in Fuzzy Control for the 40 Years of Takagi-Sugeno Models


Abstract: The aim of this special session is to present the state-of-the-art results in the area of theory and applications of fuzzy-model-based control design and analysis, and to get together well-known and potential researchers in this area. Fuzzy-model-based control provides a systematic and efficient approach to controlling of nonlinear plants and analysis of nonlinear control systems. It has been employed to deal with a wide range of nonlinear control systems such as continuous-time, discrete-time, hybrid, sampled-data, time-delay, switching, adaptive control systems and so on. A number of results on this area have appeared in the literature. However, there is still room for improvement of the existing results in order to propose new techniques for control of nonlinear systems. In the proposed special session, the focus is mainly on the fuzzy-model-based control systems and analysis with emphasis on the theory and applications. The important problems and difficulties on the fuzzy-model-based control systems will be addressed, its concepts will be provided and methodologies will be proposed to take care of the nonlinear systems using the fuzzy-model-based control approaches.

The main topics of this special session include, but are not limited to:

• Takagi-Sugeno fuzzy control systems
• Membership functions
• Uncertain fuzzy systems
• Type 2 fuzzy systems
• Fuzzy hybrid systems
• Fuzzy switching systems
• Fuzzy time-delay systems
• Fuzzy stochastic systems
• Fuzzy polynomial systems
• Stability analysis of fuzzy systems
• Nonlinear control design based on fuzzy systems
• Predictive control
• Robust control
• Sampled-data control
• Observer and filter design

Organizers: Jun Yoneyama, Zsófia Lendek, Tadanari Taniguchi, Tufan Kumbasar, Anh-Tu Nguyen, Pedro Henrique Silva Coutinho.

SS9 webpage: TBA

SS10: Uncertainty Modeling for Engineering Applications


Abstract: The session aims at exchanging the experiences of researchers and practitioners that use fuzzy methods and their extensions to cope with uncertainty in solving industrial problems and discussing possible future developments in this area. Research papers are of interest in the following topics:

• Imprecise information modeling with interval, fuzzy, rough, and other methods;
• Federated learning;
• Image processing and computer vision;
• Information retrieval;
• Knowledge representation and engineering;
• Decision-making models;
• Expert systems;
• Intelligent data analysis and data mining;
• Approximate reasoning.

Potential areas of application, among others, include:
• Production engineering;
• Medical and Healthcare systems;
• Business Process Modeling;
• Social and economic models.

The session will allow for the exchange of experiences of researchers, engineers, and practitioners using fuzzy methods and their extension concerning various types of uncertainties in solving industrial problems, and thus will focus on significant issues, as improper management of uncertainty is a threat to the effectiveness of decision-makings in many aspects.

Organizers: Barbara Pękala, Krzysztof Dyczkowski, Przemysław Grzegorzewski, Marek Reformat.

SS10 webpage: TBA

SS11: Emerging Trends in Soft Computing for Data, Web, and Social Media Mining in the Age of Generative AI


Abstract: The integration of soft computing techniques with generative AI is opening new horizons in data, web, and social media mining. Soft computing approaches, including fuzzy logic, neural networks, evolutionary algorithms, and probabilistic reasoning, are particularly well-suited for handling uncertainty, imprecision, and high-dimensional data. In conjunction with generative AI models, these methods enable more adaptable and robust solutions for knowledge extraction from complex data sources, especially in dynamic online environments. This session aims to explore recent advancements in applying soft computing to the challenges and opportunities presented in the fields of data, web, and social media mining with special interest in LLMs and generative AI models.

We invite contributions that cover theoretical, methodological, and applied aspects of soft computing and generative AI in the following areas:

  • Adaptive models that combine neural networks and fuzzy logic to enable real-time, responsive analysis of social media trends and events.
  • Generative AI in Social Media Analysis:
  • Application of generative models (e.g., LLMs, GANs) in creating synthetic data, enhancing sentiment analysis, and performing content generation in social media contexts.
  • Combining Soft Computing with LLMs for Enhanced Text Mining:
  • Innovative applications that leverage both soft computing and large language models to improve understanding and classification of unstructured text data.
  • Soft Computing Techniques for Web and Social Media Mining: Utilization of fuzzy logic, evolutionary algorithms, and neural networks to process and analyze data from social platforms and web sources.
  • Hybrid Models for Improved Knowledge Discovery:
  • Development of hybrid soft computing and generative AI models for discovering patterns in high-dimensional, complex, or sparse data from web and social sources.
  • Fuzzy Logic and Probabilistic Reasoning in Data Mining:
  • Methods that use soft computing to manage ambiguity and uncertainty in mining processes, enhancing decision-making across various domains.
  • Ethics and Transparency in Soft Computing-Enhanced AI: Addressing ethical and transparency issues arising from the use of generative AI and soft computing in data mining, with a focus on data privacy and explainability.

Organizers: Dolores Ruiz, Maria J. Martin-Bautista.

SS11 webpage: TBA

SS12: Fuzzy Modeling for Explainable Artificial Intelligence (XAI)rative AI


Abstract: The machine learning (ML) changed its goal from the symbolic approaches it had inherited from AI, and toward methods and models borrowed from statistics, fuzzy modeling, and probability theory.

Currently, theoretical ML is focused on problems oriented towards explainable artificial intelligence (XAI). In this context, we plan to approach XAI based on the knowledge of a chain of interrelated steps, starting with formulating the problem to be solved and proceeding through modeling, computation, and analysis of the results. In this chain, we focus on fuzzy approximation-based modeling and restrict the computational step to that implemented by a neural network (NN) with a specific architecture.

With this chain in mind, we plan to discuss different neural network architectures in terms of their underlying computational models (including but not limited to fuzzy models) and, hence, the practical aspects of learning the model parameters.

We solicit theoretical contributions that discuss, but are not limited to, the following aspects of new learning paradigms related to the chosen neural network model:

· General theory on connection between NN architectures and various (fuzzy) approximation models
· Feedforward NNs and their learning based on classical and fuzzy models
· Convolutional neural networks and their (fuzzy) approximation models
· NNs and their computational models based on hypercalculus
· Graph NNs and their fuzzy graph extension
· Curriculum training procedures
· Role of recurrence

Organizers: Irina Perfilieva, Radoslaw Kycia, Agnieszka Niemczynowicz, Jan Hula.

SS12 webpage: TBA

SS13: Explainable Artificial Intelligence Methods in Healthcare


Abstract: This special session aims to create an international forum specifically focusing on eXplainable Artificial Intelligence methods in healthcare. In recent years, Artificial Intelligence algorithms have become integral to our daily lives and applied in diverse contexts, including healthcare, economics, and law. The adoption of these systems conceals significant challenges. Undoubtedly, these data-driven systems create highly accurate models, but their ‘democratic’ fairness is questionable. Mitigating these issues is essential to increase trust in using these models, especially in the medical field. Transparent AI models with explanations boost users’ understanding and trust. Two main approaches are employed: post-hoc techniques applied after model training and ante-hoc approaches that integrate explainability from the outset. In the second case, explainability is integrated into a model immediately.

This special session aims to gather experts, practitioners, industry, and international authorities contributing to assessing, developing, and deploying explainability techniques in the healthcare domain.

Organizers: Gabriella Casalino, Giovanna Castellano, Katarzyna Kaczmarek-Majer, Uzay Kaymak, Gianluca Zaza.

SS13 webpage: TBA

SS14: Quantum Computational Intelligence


Abstract: Quantum computing is an exciting field that bridges disciplines like computer science, physics, and engineering. Leveraging superposition and entanglement, quantum computing inherently exhibits a level of parallelism that is key to surpassing the performance of classical computers. Quantum devices have the potential to deliver unprecedented efficiency in a wide range of applications, with artificial intelligence being one of the most significantly impacted areas. On the other hand, AI techniques can also play a supportive role in advancing quantum computing. They can help address challenges such as the limited reliability of quantum outcomes, often affected by issues like quantum decoherence.

The special session will focus on the current research trends in the integration between quantum computing and computational intelligence techniques.

The topics of particular interest to the session include but are not limited to:

• Quantum algorithms for fuzzy reasoning.
• Quantum evolutionary computation.
• Quantum machine learning.
• Approximate reasoning for quantum error correction and mitigation.
• Evolutionary computation in designing variational quantum circuits.
• Machine learning for quantum circuits compilers.

Special Session Outline (importance, impact, and relevance to the conference and community at large)

Quantum computing is a fascinating research area at the intersection of computer science, physics, and engineering, which is catching the attention of both the academic and corporate worlds by promising a revolution in computing performance, due to a massive and intrinsic parallelism enabled by “interfering, super-positioning, and entangling” different pieces of information. This research is particularly significant because quantum computers are no longer a theoretical utopia and, currently, they can be accessed via cloud. Quantum computers will be able to yield performance never seen before in several application domains, and the area of artificial intelligence may be the one most affected by this revolution. Indeed, the intrinsic parallelism provided by quantum computers could support the design of efficient algorithms for artificial intelligence such as, for example, the training algorithms of machine learning models, and bio-inspired optimization algorithms. Moreover, on other point of view, artificial intelligence techniques could be useful to quantum computing to address some problems such as, for example, the not so high reliability of quantum outcomes due to the negative effect of quantum decoherence. Indeed, in this context, neural networks, evolutionary computation and fuzzy logic can be exploited for reducing quantum noise and, consequently, enhancing quantum computation reliability. All research efforts involving the integration between quantum computing and artificial intelligence can be enclosed in an emerging research area denoted as quantum artificial intelligence.

Computational intelligence is a consolidate research area. The integration with this innovative research area, i.e., the quantum computing, will lead new life to researches in computational intelligence. In detail, quantum technologies could be used to realize innovative computational intelligence-based approaches, whereas traditional computational intelligence approaches could be used for emerging applications for example, error mitigation in quantum computing.

Organizers: Giovanni Acampora, Amir Pourabdollah, Samuel Yen-Chi Chen, Prayag Tiwari, Autilia Vitiello.

SS14 webpage: TBA

SS15: Fuzzy systems for modeling and control of renewable energy systems and smart grids


Abstract: The main aim of this session is to provide a forum for researchers covering the whole range of fuzzy systems applications to Smart Grid systems and renewable power generation and use.

Smart Grid technology employ information, communication, and automation technology to deploy an integrated power grid with smart power generation, transmission, distribution and the integration of renewable energy sources.

Owing to the relatively higher investment cost of renewable power generation systems, it is important to operate the systems near their maximum power output point, especially for the wind and solar PV generation systems. In addition, since the wind and solar PV power resources are intermittent, accurate predictions and modeling of wind speed and solar radiation are necessary. Moreover, Smart Grid integrated with smart meters, EV charging stations and home (building) energy management system are the key enabling factor toward the Smart City concept.

As a result, effective uses of computational intelligence techniques such as fuzzy systems for the controlling and modeling of renewable power generation in a smart-grid system turn out to be very crucial for successful operations of the systems.

The session continues the series of special sessions organized in past conferences (FUZZ-IEEE 2011, IEEE WCCI 2012, FUZZ-IEEE 2013, IEEE WCCI 2014, IEEE WCCI 2016, FUZZ-IEEE 2017, IEEE WCCI 2018, FUZZ-IEEE 2019, IEEE WCCI 2020, FUZZ-IEEE 2021, IEEE WCCI 2022, , FUZZ-IEEE 2023, IEEE WCCI 2024) and is supported by the IEEE CIS Task Force on “Fuzzy Systems in Renewable Energy and Smart Grid”.

The main topics include but are not limited to:

• Fuzzy modeling of renewable power generation systems.
• Fuzzy control of renewable power generation systems.
• Prediction of renewable energy using fuzzy and neuro-fuzzy systems.
• Hybrid systems of computational intelligence techniques in Smart Grids and Virtual Power Plants
• Fuzzy energy management systems.
• Fuzzy distribution systems automation.
• Fuzzy power quality, protection and reliability analysis of power system.
• Fuzzy Logic application for Demand-Response and Smart Buildings.
• Fuzzy Logic application for Smart Grids and Smart Cities.
• Novel applications in electric energy market.

Organizers: Marco Mussetta, Faa-Jeng Lin, Horst Schulte, Francesco Grimaccia.

SS15 webpage: TBA

SS16: Real world applications of fuzzy systems


Abstract: Fuzzy logic has been introduced by Zadeh 60 years ago. Many works have derived from his first papers and lead to great tools to capture vagueness and uncertainty. This special session is dedicated to industrial applications of fuzzy logic. It is a session where experts in the field can share their work on the integration of fuzzy logic in various sectors, such as health, automotive, robotics and security. The presentations may highlight real-world cases where fuzzy approaches have been successful. Papers may show how fuzzy logic facilitates the treatment of uncertainty and improves the accuracy of decisions. Lively discussions will follow and enrich the exchanges, highlighting the challenges and future prospects for this promising technology. This session clearly demonstrated the growing importance of fuzzy logic in industrial innovation, paving the way for new collaborations and research.

Organizers: Laurence Boudet, Jean-Philippe Poli.

SS16 webpage: TBA

SS17: Challenges and New Trends in Fuzzy Intelligent Decision-making


Abstract: As data complexity, volume, and ambiguity increase across domains, intelligent decision-making systems face unprecedented challenges. Fuzzy set theory, with its ability to handle imprecision and ambiguity, provides a flexible and resilient framework for multi-criteria decision-making (MCDM) in complex environments. Modern advancements in artificial intelligence (AI) and data science further enhance fuzzy decision-making models, enabling more accurate, adaptive, and context-aware decisions in dynamic settings.
This Special Session aims to address the latest challenges and new trends in fuzzy intelligent decision-making, including advancements in theoretical frameworks, integration with AI, and application-oriented innovations. We aim to bring together experts and practitioners to share breakthroughs, innovative methodologies, and successful case studies in fields as diverse as engineering, healthcare, finance, environmental management, and social sciences among others.

The session will cover both foundational research and applied contributions, with a special focus on hybrid models, interpretability, scalability, and real-world implementation of fuzzy decision-making systems in a data-rich world. We invite papers covering, but not limited to, the following topics:

• Theoretical Foundations in Fuzzy MCDM: Innovations in fuzzy set theory, including type-2, hesitant fuzzy sets, intuitionistic fuzzy sets, and other extensions; together their application to a multi-criteria evaluation in uncertain contexts. Also, new models and extensions to address challenges in ambiguity, preference modeling, and linguistic assessment for nuanced decision support.
• Emerging Models and Frameworks in Fuzzy Intelligent Decision-Making: Development of cutting-edge fuzzy MCDM models tailored to high-dimensional, unstructured, or multi-source data, with applications in real-time decision support. As well as, hybrid decision-making frameworks that combine fuzzy logic with probabilistic, rough set, or Bayesian approaches for complex decision environments.
• Aggregation Operators and Multi-Source Information Fusion: Advances in aggregation techniques for integrating diverse data types, including multi-source and multi-criteria fusion.
• Next-Generation Fuzzy Decision Support Systems (DSS): Design and evaluation of DSS incorporating fuzzy MCDM, enhancing DSS usability through interpretability, transparency, trustability and user-centered design. Particularly those targeting high-impact fields like healthcare, finance, environmental science, and engineering.
• Integration of Fuzzy Logic with AI and Machine Learning: New methodologies combining fuzzy MCDM with machine learning, neural networks, and deep learning for adaptable decision-making and enhanced predictive capabilities.
• Real-World Applications and Case Studies: Practical applications of fuzzy MCDM in domains such as risk assessment, resource allocation, supply chain management, and policy-making. Case studies showcasing the adaptability, effectiveness, and scalability of fuzzy decision-making in industries like cybersecurity, disaster response, and urban planning.
• Interdisciplinary and Emerging Applications: Innovative uses of fuzzy decision-making in areas such as big data analytics, blockchain, social network analysis, and the Internet of Things (IoT). Cross-disciplinary research bridging fuzzy MCDM with cognitive science, human-computer interaction, and social sciences to foster new approaches in decision-making.

Organizers: Diego García-Zamora, Bapi Dutta, Luis Martínez.

SS17 webpage: TBA

SS18: Fuzzy machine learning


Abstract: The traditional machine learning models lack the ability to handle real-world uncertainty, provide interpretable models, and offer a robust mechanism to support dynamic environment. Fuzzy sets, fuzzy logic and fuzzy systems are well renowned for their capability to model uncertainty, enhance models’ interpretability and offer an efficient and flexible way of representing data and navigating prediction models. Thus, the integration of machine learning and fuzzy techniques is prevailing and has gained great success in many areas. This special session aims to provide a forum for researchers to share the latest results in integrating fuzzy techniques and machine learning methods.

Keywords: Fuzzy systems, fuzzy classification, machine learning, fuzzy trees, and fuzzy data processing.

Rationale: The integration of machine learning and fuzzy techniques has gained great popularity in many areas resulting in fuzzy neural networks, fuzzy clustering, and fuzzy transfer learning. In this session, we aim to study the theories, models, algorithms and applica on of fuzzy machine learning and provide a pla orm to host novel ideas. The main topics of this special session include, but are not limited to, the following:

  • Fuzzy technique-based feature selection and extraction
  • Fuzzy rule-based knowledge representation in machine learning
  • Fuzzy classifica on, fuzzy regression, and fuzzy clustering
  • Fuzzy transfer learning
  • Fuzzy concept dri
  • Fuzzy cross-domain recommendation
  • Fuzzy neural networks to modelling complex problems
  • Fuzzy support vector machine, fuzzy decision trees
  • Fuzzy modelling for handling uncertainties in machine learning models
  • Methods to improve models’ interpretability using fuzzy techniques
  • Methods to enhance models’ robustness using fuzzy techniques
  • Granular clustering, modelling and control
  • Fuzzy techniques for aggregation, combina on and informa on fusion in machine learning
    models
  • Fuzzy machine learning based decision support
  • Applications in transport, ICT, healthcare, business intelligence and more

Organizers: Keqiuyin Li, Hua Zuo, Witold Pedrycz, Guangquan Zhang, Jie Lu.

SS18 webpage: TBA

SS19: Type-2 Fuzzy Sets and Systems: Theoretical advances and novel applications (T2-A)


Abstract: This special session will consider and discuss contributions with a particular focus on Type-2 fuzzy sets and systems. This session will address the discussion on Type-2 approaches that extend, further enquire or improve the existing Type-2 fuzzy theory. We will discuss the uses of type-2 fuzzy models for new tasks in data science, artificial intelligence, deep learning, explainable/interpretable artificial intelligence, machine learning, decision-making, robotics, control, and information science. We will also consider contributions to novel applications of type-2 fuzzy models in real-world scenarios and research questions.

Organizers: Javier Andreu-Perez, Javier Fumanal-Idocin, Pedro Huidobro Fernández.

SS19 webpage: TBA

SS20: Fuzzy Natural Language Processing and Applications


Abstract: This special session is relevant to IEEE-FUZZ, as novel solutions are needed across the computational intelligence landscape to solve many of the challenges associated with NLP; for example, a deeper machine understanding of human subjectivity in language in given contexts. The session will provide a forum to disseminate and discuss recent and significant research efforts in fuzzy methods for natural language processing in addition to hybrid and emerging computational intelligence paradigms. It invites researchers from different related fields and gathers the most recent studies including but not limited to Fuzzy Set models of human language; Fuzzy applications to human language processing; Fuzzy approaches to text mining and simulations of language use; Fuzzy ontologies for human language; Computing with words; Real world computational intelligence inspired natural language processing applications; Fuzzy methodologies, tools and techniques for mining and interpretation of social media textual data.

The session will provide a forum to disseminate and discuss recent and significant research efforts in fuzzy methods for natural language processing in addition to hybrid and emerging computational intelligence paradigms. It also seeks to present novel applications of fuzzy technologies within the field of natural language processing.

Topics including but not limited to:

• Fuzzy set models of human language
• Fuzzy applications to human language processing
• Fuzzy finger printing
• Large Language Models
• Fuzzy approaches to text mining
• Fuzzy simulations of language use
• Fuzzy ontologies for human language
• Computing with words within natural language processing
• Real world fuzzy inspired natural language processing applications
• Fuzzy founded methodologies, tools and techniques for mining and interpretation of social media textual data

Organizers: Keeley Crockett, Joao Paulo Carvalho, Naomi Adel.

SS20 webpage: TBA