Special Sessions

Open Discussion Slots: At Fuzz-IEEE 2021, select Special Sessions are organizing an ‘Open Discussion Slot’ (ODS). An Open Discussion Slot usually takes the shape of a standard (20 minutes) paper presentation slot where proposers lead an open discussion on the topic of the special session with both presenting authors and session attendees. Open Discussion Slots will usually be timetabled at the end of a special session to allow wider discussion around the special session topic in the context of the papers presented. For details on the ODS program put forward as part of the specific special sessions, please review the special session information for the respective special sessions below.

List of Special Sessions running an Open Discussion Slot:

Submission of Papers to Special Sessions: Manuscripts submitted to special sessions should be submitted through the standard paper submission website of Fuzz-IEEE 2021, selecting the topic of the special session as the primary topic. All papers submitted to special sessions will be subject to the same peer-review procedure as regular papers. Papers submitted to Special Sessions will be handled by both the Special Session and the Conference Program Chairs. The review of submissions of Special Session chairs and co-chairs to their own special session will be handled by the Conflict-of-Interest or Program Chairs. Note that special sessions with a very small number of accepted papers will either be merged with compatible special sessions or be cancelled and the accepted papers will be moved to regular sessions. All papers submitted to special sessions (if accepted and presented) will be published as part of the regular FUZZ-IEEE proceedings.

List of Special Sessions

Advances on Explainable Artificial Intelligence

Computational Web Intelligence

Computing with Words (CWW)

Fuzzy and Uncertain Intelligent Knowledge Engineering Systems

Fuzzy Interpolation

Fuzzy Natural Language Processing

Fuzzy System for Renewable Energy and Smart Grid

Fuzzy Systems for Brain Sciences & Interfaces

Fuzzy-based Methods for Knowledge Integration

Handling Uncertainty in Big Data by Fuzzy Systems

Human Symbiotic Systems

Information fusion techniques based on aggregation functions, preaggregation functions and their generalization

Linguistic Summarization and Description of Data

Multiagent/Swarm Fuzzy and Intelligent Systems

Recent Advances in Fuzzy Control System Design and Analysis

Scalable Incremental Fuzzy Learning Techniques for Big Data and Genome Sequence Analysis

Software for Soft Computing

Type-2 Fuzzy Sets and Systems Applications

Uncertainty Modeling for Engineering Applications

More Details

Advances on Explainable Artificial Intelligence

Organized by Jose M. Alonso (<josemaria.alonso.moral@usc.es>), Ciro Castiello (<ciro.castiello@uniba.it>), Corrado Mencar (<corrado.mencar@uniba.it>), Luis Magdalena (<luis.magdalena@upm.es>), and Shang-Ming Zhou (<smzhou@ieee.org>), website.

In the era of the Internet of Things and Big Data, data scientists are required to extract valuable knowledge from the given data. They first analyze, cure and pre-process data. Then, they apply Artificial Intelligence (AI) techniques to automatically extract knowledge from data. Our focus is on knowledge representation and how to enhance human-machine interaction. As remarked in the last challenge stated by the USA Defense Advanced Research Projects Agency (DARPA), “even though current AI systems offer many benefits in many applications, their effectiveness is limited by a lack of explanation ability when interacting with humans”. Accordingly, users without a strong background on AI, require a new generation of eXplainable AI (XAI in short) systems. They are expected to naturally interact with humans, thus providing comprehensible explanations of decisions automatically made. Thus, the goal of this special session is to discuss and disseminate the most recent advancements focused on XAI. The session goes a step ahead in the way towards XAI and it is supported by the IEEE-CIS Task Force on Explainable Fuzzy Systems and the H2020 MSCA-ITN-2019 NL4XAI project.

The aim of this session is to offer an opportunity for researchers and practitioners to identify new promising research directions on XAI and to provide a forum to disseminate and discuss XAI, with special attention to Explainable Fuzzy Systems. Three main XAI challenges are to be addressed: (1) designing explainable models; (2) building explanation interfaces; and (3) measuring explainability.

Scope and Topics:

  • Explainable Computational Intelligence
  • Theoretical Aspects of Interpretability
  • Dimensions of Interpretability: Readability versus
  • Understandability
  • Learning Methods for Interpretable Systems and Models
  • Interpretability Evaluation and Improvements
  • Models for Explainable Recommendations
  • Design Issues
  • Applications of XAI Systems
  • Interpretable Machine Learning
  • Interpretable Fuzzy Systems
  • Relations between Interpretability and other Criteria (such as Accuracy, Stability, Relevance, etc.)
  • Explainable Agents
  • Self-explanatory Decision-Support Systems
  • Argumentation Theory for XAI
  • Natural Language Generation for XAI
  • Human-Machine Interaction for XAI
  • Explainable Computational Intelligence
  • Theoretical Aspects of Interpretability
  • Dimensions of Interpretability: Readability versus
  • Understandability
  • Learning Methods for Interpretable Systems and Models
  • Interpretability Evaluation and Improvements
  • Models for Explainable Recommendations
  • Design Issues
  • Applications of XAI Systems
  • Interpretable Machine Learning
  • Interpretable Fuzzy Systems
  • Relations between Interpretability and other Criteria (such as Accuracy, Stability, Relevance, etc.)
  • Explainable Agents
  • Self-explanatory Decision-Support Systems
  • Argumentation Theory for XAI
  • Natural Language Generation for XAI
  • Human-Machine Interaction for XAI

Computational Web Intelligence

Organized by Marek Reformat (<marek.reformat@ualberta.ca>), Chang-Shing Lee (<changshing.lee@gmail.com,>), Giovanni Acampora (<giovanni.acampora@unina.it,>), Amir Pourabdollah (<amir.pourabdollah@ntu.ac.uk>), and Autilia Vitiello (<autilia.vitiello@unina.it>).

The constant growth of the Internet and the introduction of concepts such as the Semantic Web and Linked Data create challenges as well as opportunities to transform the web into an environment providing the users with the abilities to utilize and explore it in a different “human-like” and efficient way.

The Internet is a huge collection of services, documents and data that are inherently heterogeneous, imprecise, uncertain, incomplete and even inconsistent. Moreover, searching data is not the same as obtaining information. Indeed, the web users have often difficulty to search the web, analyze obtained data and extract information. Hence, there is a need for intelligent systems supporting the users in their web-related activities.

Computational intelligence can provide important and non-trivial approaches, techniques and methods ensuring a human-like way of dealing with imprecision, fusing information from multiple sources, selecting best among multiple alternatives, extracting knowledge and managing massive data. It is anticipated, that applications of fuzziness, learning and evolutionary approaches to web systems will bring a new and human friendly way of interacting with the web and an efficient tool to extract useful information.

This special session will focus on the current research trends in the area of theory and practical aspects of intelligent systems equipped with fuzzy and other computational intelligence methods suitable for solving issues specific to web utilization, as well as the representation and processing of information and knowledge.

Scope and Topics:

  • fuzzy ontology and ontology-based systems
  • IEEE Std. 1855-2016 fuzzy systems
  • knowledge- and rule-based systems
  • hybrid intelligent systems
  • recommendation systems
  • multi-criteria decision-making
  • context-aware systems
  • information retrieval and knowledge discovery
  • learning approaches for web service
  • learning methods for web attack detection
  • fuzzy ontology and ontology-based systems
  • IEEE Std. 1855-2016 fuzzy systems
  • knowledge- and rule-based systems
  • hybrid intelligent systems
  • recommendation systems
  • multi-criteria decision-making
  • context-aware systems
  • information retrieval and knowledge discovery
  • learning approaches for web service
  • learning methods for web attack detection

Computing with Words (CWW)

Organized by Prashant Gupta (<guptaprashant1986@gmail.com>), Deepak Sharma (<deepak.btg@gmail.com>), Andreu Javier (<javier.andreu@essex.ac.uk>), Luis Martínez López (<martin@ujaen.es>), website.

Computing with Words (CWW) is a remarkable methodology which aims to provide the computing machines with the ability to interpret and process linguistic information in a manner similar to human beings. Since its inception, various CWW methodologies as well as the linguistic computational models have been developed, over the years. These include the extension principle based linguistic computational model, symbolic method based linguistic computational model, 2-tuple based linguistic computational model together its extensions and the very popular Perceptual Computing. Further, the use of different fuzzy sets, for linguistic semantic representation, like type-1, interval type-2, etc., has attracted a lot of attention for modelling the semantics of the linguistic information in CWW.

There have been numerous theoretical as well as the application oriented contributions in the field of CWW. On the theoretical side, the works have focused on modeling linguistic uncertainty using higher order fuzzy sets, advocating the use of correct uncertainty models, user perception modeling, etc. On the other hand, the application oriented contributions have been made across a variety of spectrum such as investment decision making, hierarchical decision making, power optimization, selection based problems, E-Health, etc.

With the ongoing research in the field of CWW, we can be sure that it will see more robust and better uncertainty models as well as be successfully applied to larger set of application areas in the future.

Scope and Topics:

The aim of this special session is to provide a platform to the scientists and researchers of these fields to exchange ideas about the latest trends and research directions. Researchers are invited to submit original and unpublished works that deal with various aspects of research in CWW. Specific topics of interest include, but are not limited to:

  • Uncertainty modeling in CWW
  • Interpretable models using CWW methodology
  • CWW for data driven decision making
  • Fuzzy data Analytics
  • Industrial applications of CWW
  • CWW applied to E-Health
  • CWW in Security
  • Personalized as well as group decision making using CWW
  • Fuzzy Machine learning in CWW
  • Neuro-fuzzy approach for CWW
  • CWW for design of recommender systems
  • Internet of Things (IoT) and CWW
  • Image processing and CWW
  • CWW for Human-Computer Interaction
  • Advanced CWW models and real-life applications

Fuzzy and Uncertain Intelligent Knowledge Engineering Systems

Organized by Jerry Chun-Wei Lin (<jerrylin@ieee.org>), Gautam Srivastava (<SRIVASTAVAG@brandonu.ca>), Uttam Ghosh (<ghosh.uttam@ieee.org>), website.

Intelligent knowledge engineering systems have become an emerging research topic in recent
years since it applies discovered information and knowledge into making further effective decisions. Compared to the traditional data analytic techniques, the representation of linguistic terms based on fuzzy-set theory provides more understandable, alternative ways for decision-making. Concurrently, as the amount of collected data from IoT environment and mobile devices rapidly grows, data uncertainty is also considered as an important factor in knowledge discovery involved in intelligent knowledge engineering systems. Designing an efficient and effective intelligent knowledge engineering system is an emerging topic and issue in recent decades, especially focusing on considering the uncertain and fuzzy factors for data analytics, prediction and pattern mining. The main contents of intelligent knowledge engineering system are mining, retrieving, and analysing the novel, interesting, useful and surprising patterns from data for further decision-making. Popular techniques from the field of artificial intelligence such as machine learning and optimization techniques are also often used to make predictable decisions in a limited amount of time. Many frameworks and platforms such as Hadoop or Spark are also adapted to efficiently handle large databases and streams, commonly found in this big data era. This special session of FUZZ-IEEE 2020 focuses on issues regarding intelligent knowledge engineering under the uncertain and fuzzy-set concepts, including analytics and knowledge and pattern mining from data collected in real-world applications (fuzzy and uncertainty), that can be used to provide decision and strategy making abilities to intelligent systems. We welcome the innovative, creative, original, cutting-edge and state-of-the-art theoretical and applied contributions on this and relevant issues. Topics of interest include but are not limited to:

Scope and Topics:

  • Intelligent systems based on fuzzy-set theory
  • Fuzzy data analytics and mining
  • Uncertain data mining and decision-making
  • Fuzzy knowledge prediction
  • Theoretical methodology for fuzzy intelligent systems
  • Fuzzy machine learning techniques
  • Fuzzy big data mining and prediction
  • Knowledge engineering for fuzzy decision making
  • Security and privacy issues based on fuzzy intelligent systems
  • Fuzzy image recognition and prediction
  • AI for fuzzy-set systems
  • Optimization for fuzzy knowledge engineering
  • Fuzzy granularities
  • Fuzzy bigdata mining based on the Hadoop or Spark platforms
  • Fuzzy knowledge integration and fusion
  • Applications based on fuzzy-set theory
  • Fuzzy visualization models

Fuzzy Interpolation

Organized by Qiang Shen (<qqs@aber.ac.uk>) , Kóczy T. László (<koczy@tmit.bme.hu>),
Shyi-Ming Chen (<smchen@mail.ntust.edu.tw>).

Fuzzy interpolation provides a flexible means to perform reasoning in the presence of insufficient knowledge that is represented as a sparse fuzzy rule base. It enables approximate inference to be carried out from a rule base that does not cover a given observation. Fuzzy interpolation also provides a way to simplify complex-system models and/or the process of fuzzy rule generation. It allows the reduction of the number of rules needed, thereby speeding up parameter optimisation and runtime efficiency.

The aim of this special session is to provide a forum:

  • To disseminate and discuss recent and significant research efforts in the development of fuzzy interpolation and related techniques,
  • To promote both theoretical and practical applications of fuzzy interpolation, and
  • To foster integration of fuzzy interpolation with other computational intelligence techniques.

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

  • Fuzzy interpolation
  • Fuzzy extrapolation
  • Fuzzy interpolative learning
  • Fuzzy systems simplification
  • Fuzzy set transformation
  • Fuzzy set representation
  • Fuzzy interpolation application
  • Fuzzy function approximation
  • Hybrid fuzzy interpolation systems
  • Comparative studies of interpolation method

Fuzzy Natural Language Processing

Organized by Keeley Crockett (<K.Crockett@mmu.ac.uk>), Joao Paulo Carvalho (<joao.carvalho@inesc-id.pt>).

Although language, or linguistic expressions, undoubtedly contains fuzziness in nature, the area of computational intelligence inspired natural language processing is still a relatively emerging research area that provides novel CI disruptions to traditional natural language processing. Initial work in “A Critical Survey on the use of Fuzzy Sets in Speech and Natural Language Processing”, Proc. of the IEEE WCCI 2012, Brisbane, Australia, led to growing recognition that fuzziness is naturally found in every aspect of human language. The degree of fuzziness within human discourse needs to be captured, modelled and used in context for a machine to understand the nature of the “conversation” or request actuality means.This session will follow on from the successful, special sessions entitled “Fuzzy Natural Language Processing” which were successfully held at IEEE FUZZ 2019 in New Orleans, FUZZ 2017 in Naples, IEEE FUZZ 2015 in Istanbul and IEEE FUZZ 2013 in India and the hybrid special  sessions held at both the 2014 IEEE WCCI in Beijing, 2016 IEEE WCCI in Vancouver, 2018 IEEE WCCI in Rio and 2020 IEEE WCCI in Glasgow. The aim of this Special Session is therefore to explore new techniques and applications in the field of fuzzy natural language processing which capture the fuzzy nature of human language.

Scope and Topics:

The session will provide a forum to disseminate and discuss recent and significant research efforts in fuzzy paradigms and applications in the field of fuzzy natural language processing. It also seeks to present novel applications of “fuzzy” technologies within the field of natural language processing. 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 logic applications to human language processing
  • fuzzy machine learning approach to human language
  • fuzzy text and social media mining
  • fuzzy simulations of language use
  • fuzzy ontology for human language
  • fuzzy applications to the semantic web
  • Computing with words within natural language processing
  • Real world computational intelligence inspired natural language processing applications
  • Computational intelligence founded methodologies, tools and techniques for mining and interpretation of social media textual data.

Fuzzy System for Renewable Energy and Smart Grid

Organized by Marco Mussetta (<marco.mussetta@polimi.it>), Faa-Jeng Lin (<linfj@ee.ncu.edu.tw>), Horst Schulte (<schulte@htw-berlin.de>), and Francesco Grimaccia (<francesco.grimaccia@polimi.it>), website.

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 insolation 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, WCCI 2012, FUZZ-IEEE 2013, WCCI 2014, WCCI 2016, FUZZ-IEEE 2017, WCCI 2018, FUZZ-IEEE 2019, WCCI 2020) and is supported by the
IEEE CIS Task Force on “Fuzzy Systems in Renewable Energy and Smart Grid”.

Scope and Topics:

  • Fuzzy modelling 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 Grid and renewable power generation systems.
  • Neuro-Fuzzy system for oil and gas integration with renewable sources.
  • 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 Grid and Smart Cities.
  • Novel applications in electric energy market.

Fuzzy Systems for Brain Sciences & Interfaces

Organized by Chin-Teng Li (<Chin-Teng.Lin@uts.edu.au>), Javier Andreu-Perez (<javier.andreu@essex.ac.uk>), and Mukesh Prasad.

Given the important challenges associated with the processing of brain signals obtained from neuroimaging modalities, fuzzy sets and systems have been proposed as a useful and effective framework for the analysis of brain activity as well as to enable a direct communication pathway between the brain and external devices (brain computer/machine interfaces). While there has been an increasing interest in these questions, the contribution of fuzzy systems has been diverse depending on the area of application. On the one hand, considering the decoding of brain activity, advanced computational intelligence methods that handles uncertainty such as fuzzy sets and systems, represent an excellent tool to overcome the challenge of processing extremely noisy signals that are very likely to be affected by non-stationarities, invariants and  poor generalisation. On the other hand, as regards neuroscience research, possibility and fuzziness has equally been employed for the measurement of smooth integration between synapses, neurons, and brain regions or areas. In this context, the proposed special session aims at providing a specialised forum for researchers interested in employing advanced methods of computational intelligence and fuzzy systems to model and represent uncertainty for the analysis of brain signals and neuroimaging data. Contributions can be on any related
disciplines such as computational neuroscience, brain computer/machine interfaces, neuroscience, neuroinformatics, neuroergonomics, computational cognitive neuroscience, affective neuroscience, neurobiology, brain mapping, neuro-engineering, and neurotechnology.

Scope and Topics:

  • Application of fuzzy systems for the analysis of brain signals from any functional or structural neuroimaging modalities (fMRI /MRI, PET/SPECT, EEG, MEG, fNIRS, DOI, EROS, etc.)
  • Fuzzy Systems for uncertain modelling of Brain computer/Machine interfaces (BCI/BMI).
  • Brain computer/machine interfaces (all paradigms, transfer learning, multi-modal BCI, Neural Prostheses) powered by Fuzzy Systems.
  • Fuzzy systems the simulation of brain processes in computational neuroscience.
  • Fuzzy systems for Neuroscience applications and the understanding of brain processes.
  • Neuroinformatic tools based on fuzzy systems.
  • Edge-technologies for neurotechnology.

Fuzzy-based Methods for Knowledge Integration

Organized by Ngoc-Thanh Nguyen (<Ngoc-Thanh.Nguyen@pwr.edu.pl>), David Camacho (<david.camacho@upm.es>), Adrianna Kozierkiewicz (<adrianna.kozierkiewicz@pwr.edu.pl>), Loan Thuy Thi Nguyen (<nttloan@hcmiu.edu.vn>), website.

In recent years we can observe a rapid growth of information, its sources, and methods of representation. It has caused the necessity of developing methods for storing and processing. Modern companies are characterized by the increasing complexity of used systems and the amount of data which is stored in not only one, central database, but frequently in several distributed collocations. Furthermore, sometimes the same data is replicated among multiple databases to ensure its safety. To properly manage a company that has to deal with such diversity, the effective methods of knowledge integration from distributed and autonomous sources are needed. However, the problem becomes more complex if the knowledge included in these resources is inconsistent. The question is: How to manage data (knowledge) inconsistency for an effective integration process?

The FMKI 2021 Special Session at 2021 IEEE International Conference on Fuzzy Systems is devoted to the ensemble methods for handling fuzzy-based methods for inconsistency processing and knowledge integration. Especially we will focus on the application of fuzzy theories to knowledge representation, processing, and analysis. Fuzzy techniques can help to extend well know methods from artificial intelligence, machine learning, multi-agent system and data mining field to handling knowledge integration.

Our aim is to offer an opportunity for researchers and practitioners to identify new promising research directions as well as to publish recent advances in this area. The scope of the FMKI 2021 includes, but is not limited to the following topics:

Scope and Topics:

  • Uncertainty in knowledge representation
  • Knowledge integration
  • Conflict resolution
  • Handling knowledge inconsistency
  • Managing evolution in fuzzy systems
  • Ontology integration
  • Data Fusion
  • Multi-agent systems
  • Data Mining

Handling Uncertainty in Big Data by Fuzzy Systems

Organized by Hua Zuo (<Hua.Zuo@uts.edu.au>) ,Jie Lu (<Jie.Lu@uts.edu.au>), Feng Liu (<Feng.Liu@uts.edu.au>), Guangquan Zhang (<Guangquan.Zhang@uts.edu.au>).

The volume, variety, velocity, veracity and value of data and data communication are increasing exponentially. The “Five Vs” are the key features of big data, and also the causes of inherent uncertainties in the representation, processing, and analysis of big data. Also, big data often contain a significant amount of unstructured, uncertain and imprecise data. Fuzzy sets, logic and systems enable us to efficiently and flexibly handle uncertainties in big data in a transparent way, thus enabling it to better satisfy the needs of big data applications in real world and improve the quality of organizational data-based decisions. Successful developments in this area have appeared in many different aspects, such as fuzzy data analysis technique, fuzzy data inference methods and fuzzy machine learning. In particular, the linguistic representation and processing power of fuzzy sets is a unique tool for bridging symbolic intelligence and numerical intelligence gracefully. Hence, fuzzy techniques can help to extend machine learning in big data from the numerical data level to the knowledge rule level. It is therefore instructive and vital to gather current trends and provide a high-quality forum for the theoretical research results and practical development of fuzzy techniques in handling uncertainties in big data.

This special session aims to offer a systematic overview of this new field and provides innovative approaches to handle various uncertainty issues in big data presentation, processing and analysing by applying fuzzy sets, fuzzy logic, fuzzy systems, and other computational intelligent techniques. The scope of this special session includes, but not limited to, fuzzy rule-based knowledge representation in big data processing, granular modelling, fuzzy transfer learning, uncertain data presentation and modelling in cloud computing, and realworld cases of uncertainties in big data, etc.

Scope and topics:
The main topics of this special session include, but are not limited to, the following:

  • Fuzzy rule-based knowledge representation in big data processing
  • Information uncertainty handling in big data processing
  • Unstructured big data visualization
  • Uncertain data presentation and fuzzy knowledge modelling in big data sets
  • Tools and techniques for big data analytics in uncertain environments
  • Computational intelligence methods for big data analytics
  • Techniques to address concept drifts in big data
  • Fuzzy transfer learning in big data
  • Methods to deal with model uncertainty and interpretability issues in big data processing
  • Feature selection and extraction techniques for big data processing
  • Granular modelling, classification and control
  • Fuzzy clustering, modelling and fuzzy neural networks in big data
  • Evolving and adaptive fuzzy systems in big data
  • Uncertain data presentation and modelling in data-driven decision support systems
  • Information uncertainty handling in recommender systems
  • Uncertain data presentation and modelling in cloud computing
  • Information uncertainty handling in social network and web services
  • Real world cases of uncertainties in big data

Human Symbiotic Systems 

Organized by Tsuyoshi Nakamura (<tnaka@nitech.ac.jp>), Masayoshi Kanoh (<mkanoh@sist.chukyo-u.ac.jp>), Tomohiro Yoshikawa (<yoshi@suzuka-u.ac.jp>).

This special session aims at discussing the basic principles and methods of designing intelligent interaction with the bidirectional communication based on the effective collaboration and symbiosis between the human and the artifact, i.e. robots, agents, computer and so on relating to fuzzy theory.

We aims at encouraging the academic and industrial discussion about the research on Human‐Agent Interaction (HAI), Human‐Robot Interaction (HRI), and Human‐Computer Interaction (HCI) concerning Symbiotic Systems. Reflecting the fact that this society covers a wide range of topics, in this session we invite not only fuzzy researchers but also the related researchers from a variety of fields including intelligent robotics, human‐machine interface, Kansei engineering and so on.

Scope and Topics:

  • Human‐Agent Interaction (HAI)
  • Human‐Robot Interaction (HRI)
  • Human‐Computer Interaction (HCI)
  • Social Communication or Interaction
  • Partner or Communication Robots
  • Hospitality Robots
  • Human Interface Systems
  • Cooperative Intelligence
  • Kansei Engineering

Information fusion techniques based on aggregation functions, preaggregation functions and their generalization

Organized by Humberto Bustince (<bustince@unavarra.es>), Graçaliz Dimuro (<gracaliz@gmail.com>), Javier Fernández (<fcojavier.fernandez@unavarra.es>), Radko Mesiar (<mesiar@math.sk>).

The search of new information fusion techniques is currently a hot topic in almost every research field, from image processing and decision making to deep learning. This interest has led to new analysis of the notion of aggregation function, as well as to the introduction of new concepts that go beyond usual aggregation functions, either by considering more general definitions or by extending them to other frameworks different from that of the unit interval (e.g., intervals, lattices, etc.) The ongoing study of these concepts as well as of their applications has already  been displayed in many special sessions in previous FUZZ-IEEE conferences. The aim of this FUZZ-IEEE special session is to follow this longstanding tradition and to present a forum for researchers to discuss the most up-to-date research in the field of fusion techniques using aggregation functions, preaggregation functions and their generalizations, as well as their possible applications.

This special session covers the study of fusion functions which are either aggregation functions or a generalization of the latter, as it is the case of pre-aggregation functions. The session is opened to the discussion of both theoretical research on the topic, as well as applications in any field of artificial intelligence and computer science, including, but not limited to, image processing, classification, deep learning, big data, approximate reasoning or decision making.

Linguistic Summarization and Description of Data

Organized by Anna Wilbik (<a.wilbik@maastrichtuniversity.nl>), Daniel Sánchez Fernández (<daniel@decsai.ugr.es>), Nicolás Marín (<nicm@decsai.ugr.es>).

The development of human–computer interaction systems based on natural language, already
important in the last decades, is growing in importance nowadays. Particularly, data-to-text systems are intended to obtain a text describing the most relevant aspects of data for a certain  user in a specific context. Such texts, called linguistic summaries and descriptions of data, are comprised of a collection of natural language sentences, and must be as close as possible to those generated by human experts. In this realm, not only specialized users (e.g. in decision support systems) are interested in this type of approach, but nonspecialized users also show interest in receiving understandable information that is supported by data.

Linguistic summaries commonly use fuzzy set theory to model linguistic variables and incorporate different forms of imprecision in a collection of natural language sentences. In many approaches they can be considered as quantifier based sentences, hence linguistic summaries constitute a perfect application for new developments in the domain of fuzzy quantifiers. Furthermore, linguistic summaries have been related to fuzzy rule systems.

Linguistic summaries and description of data is related to other research areas such as knowledge discovery in databases and intelligent data analysis, flexible query answering systems for data, human-machine interaction, uncertainty management, heuristics and metaheuristics, and natural language generation and processing. More recently, this field has been related to the linguistic description of complex phenomena and computing with words paradigms.

The objective of this special session is to provide a forum for researchers, from the above indicated areas, to present recent developments in linguistic summarizes and description of data as well as discuss how these different approaches can complement each other for the task of building such systems.


The session continues the series of special sessions on the topic organized by some of the organizers of this session in past conferences (IFSA 2015, FUZZ-IEEE 2015, FUZZ-IEEE 2016, FUZZ-IEEE 2017, FUZZ-IEEE 2018, FUZZ-IEEE 2019, FUZZ-IEEE 2020).

Scope and Topics:

  • Protoforms and fuzzy concepts for the linguistic summaries and fuzzy description
  • Referring expression generation with fuzzy properties
  • Quality assessment of linguistic summaries and fuzzy description
  • Techniques and algorithms for generating linguistic summaries and descriptions of data
  • Ontologies for data summarization
  • Logical approaches for modeling linguistic expressions
  • Modeling uncertainty for linguistic summaries and fuzzy description
  • User preference/interest modeling for linguistic summaries and fuzzy description
  • Applications of linguistic summaries and fuzzy description
  • Natural language generation for data summarization
  • Machine Learning applied to data summarization
  • Linguistic information extraction from visual information
  • Context-awareness in data summarization and description, and natural language generation

Multiagent/Swarm Fuzzy and Intelligent Systems

Organized by Mohammad-R. Akbarzadeh-T. (<akbazar0@gmail.com>), (<akbarzadeh@ieee.org>), Fahimeh Baghbani (<fahimehbaghbani@gmail.com>), and Nasibeh Rady Raz (<radyraz@yahoo.com>).

The development of technology in different domains such as computing, sensing, and information processing, together with reliable communication, has made it possible to employ collaborative autonomous systems that allow for greater efficiency, flexibility, reliability, and capacity. It has also become possible to deploy multiple satellites, robots, crewless vehicles, and even many nanoscaled machines for automation, monitoring, healthcare, space research, and even targeted drug delivery and precision medicine.


The above advancements necessitate further research on efficient distributed structures and collaborative control of multiagent or swarm systems. The multiagent systems are associated with significant challenges such as handling uncertainties and complexities of the operating environment, uncertainties of dynamics of possibly heterogeneous agents, and limited interactions between the agents. Swarm systems carry similar challenges to multiagent systems, with the difference being in their design simplicity and limitations on their sensing, cognition, and actuation. Swarm systems make up for these challenges with their sheer numbers and taking inspiration in nature’s way of handling complexity and uncertainty. Nevertheless, we face further challenges when the multiagent/swarm systems are also human-centered.


This special session will highlight the latest development in this rapidly growing research  area, emphasizing the new trends in fuzzy and intelligent multiagent/distributed/swarm systems. All researchers, academics, and experts are invited to submit their original works to this special session.

Scope and Topics:

  • Multiagent control systems such as formation, flocking, and cooperative control
  • Multiagent decision-making systems
  • Synchronization of multiagent systems
  • Cooperation and coordination
  • Social animals behavioral inspiration for swarms
  • Distributed urban traffic control
  • Cognitive swarm agents
  • Swarm medical intelligence and well-being
  • Bioinspired swarms such as
  • Sensing, perception, and reasoning swarm technology
  • Evolving swarms
  • Synchronization of multiagent systems
  • Autonomous control of crewless vehicles
  • Coordinated control of multi-vehicle systems
  • Intelligent robotic systems
  • Swarm robotic and artificial intelligence
  • Fuzzy and intelligent agents
  • Swarm intelligence for learning, decision
  • making, and control
  • Human-swarm interaction
  • Distributed energy management
  • Other real-world applications such as in engineering, physics, medicine, mathematics, chemistry, biology, cosmology, agriculture, geology, sociology, literature, and art.

Recent Advances in Fuzzy Control System Design and Analysis

Organized by Zsofia Lendek (<zsofia@lendek.net>), Tufan Kumbasar (<kumbasart@itu.edu.tr>), Kevin Guelton (<kevin.guelton@univ-reims.fr>), website.

Fuzzy models have been employed for the analysis and design of a wide range of nonlinear control systems. As confirmed by the large number of results being published in the literature, fuzzy control systems provide a systematic and efficient approach to the analysis and control of nonlinear processes. However, new techniques and the improvement of current ones are constantly required, fuelled by the need for more and more efficient control of nonlinear systems.

This special session proposal is supported by the IEEE CIS Task Force on Fuzzy Control Theory and Application (http://kguelton.free.fr/FCTA.html).

Aim and scope:
The aim of this special session is to present the state-of-the-art results in the area of
theory and applications of fuzzy control system design and analysis, and to get together well-known and potential researchers in this area, from both the academia and industry. In the proposed special session, the focus is mainly on the fuzzy control system design and analysis with emphasis on the theory and applications. The important problems and difficulties on the fuzzy control systems will be addressed, their concepts will be provided, and methodologies will be proposed to take care of the nonlinear systems using the fuzzy control system approaches.

The topics include but are not limited to:

Scope and Topics:

  • Takagi-Sugeno (T-S) fuzzy modelling,
  • Stability analysis of T-S fuzzy systems,
  • T-S model-based control of nonlinear systems,
  • Type-1/Type-2 Fuzzy systems,
  • Adaptive / Self-Tuning Type-2 Fuzzy Control,
  • Interpretability of Type-2 Fuzzy Controllers,
  • Neuro-Fuzzy Type-2 Control,
  • Deep Learning based Type-2 Fuzzy Controllers,
  • Fuzzy hybrid systems,
  • Fuzzy switching systems,
  • Fuzzy time-delay systems,
  • Fuzzy stochastic systems,
  • Fuzzy polynomial systems,
  • Predictive control,
  • Sampled-data/Networked fuzzy control,
  • Observer design,
  • Filtering,
  • etc.

Scalable Incremental Fuzzy Learning Techniques for Big Data and Genome Sequence Analysis

Organized by Neha Bharill (<neha.bharill@mahindrauniversity.edu.in>),  , Om Prakash Patel (<omprakash.patel@mahindrauniversity.edu.in>), Aruna Tiwari (<artiwari@iiti.ac.in>), Milind Ratnaparkhe (<ratnaparkhe.milind@gmail.com>), website.

Big Data Analytics has become increasingly popular, not only in academia, but also in industrial and government applications. This attributed towards the fact that Big Data analytics offers huge promises as well as imposes grand challenges in large number of critical real-world applications such as Bioinformatics field dealing with genome sequence analysis, healthcare, finance, and business intelligence. The main challenges in handling Big Data lie not only in “Five Vs” namely volume, variety, velocity, veracity, and value but also in the approach of understanding the data. Therefore, it is expected to come up with the powerful tools for addressing Big Data challenges such as scalable incremental fuzzy based learning methodologies that are inherently capable of handling various amount of uncertainty from  it Data. Thus, there is an urgent need to innovate the advance scalable fuzzy based machine learning approaches. These approaches can address the important issues of Big data analysis, data pre-processing of computational bioinformatics through various scalable fuzzy learning techniques such as clustering, classification, feature selection, fuzzy data analysis technique and fuzzy data interference methods. Hence, fuzzy techniques can help to extend machine learning in big data from the numerical data level to the knowledge rule level and applied to the nucleotides of genomics data. It is therefore instructive and vital to gather current trends and provide a high-quality forum for the theoretical research results and practical development of scalable fuzzy techniques for handling big data.

Scope and Topics:

The main aim of this special session is to provide a forum to the scientists and researchers with a systematic overview of this field to exchange the latest advances in theories and experiments in this field of research. Researchers are invited to submit original and unpublished works that deal with theoretical and experimental results of advanced scalable machine learning techniques in the following and other related areas. The main topics of this special session include, but not limited to, the following:

  • Scalable machine learning methods to handle big data
  • Fuzzy rule-based knowledge representation in big data processing
  • Unstructured big data visualization
  • Computational intelligence methods for big data
  • Feature extractions and selection techniques for big data processing
  • Pattern recognition for genomics
  • Big Data analytics for bioinformatics applications
  • Scalable fuzzy clustering, modelling and fuzzy neural networks in big data
  • Evolving and adaptive fuzzy systems in big data
  • Scalable multi-objective optimization approaches for Big Data.
  • Scalable feature selection approach for Genomics.
  • Techniques to address concept drifts in big data.
  • Scalable fuzzy transfer learning in big data

Software for Soft Computing

Organized by Jose Manuel Soto Hidalgo (<jmsoto@ugr.es>) , Jesus Alcala Fernandez (<jalcala@decsai.ugr.es>), Alonso Moral Jose Maria (<josemaria.alonso.moral@usc.es>), website.

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 complex real-world problems for which other techniques are not well suited.

There are many software tools for Soft Computing. Most of these tools are available as open source software (see the webpage http://sci2s.ugr.es/fss). For example, regarding fuzzy modeling, JFML is 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. There are stand-alone tools but also useful libraries in Java, Python, R, etc. Please, notice that these software tools are
ready to play an important role in both industry and academia.

This session is supported by the IEEE-CIS FSTC Task Force on Fuzzy Systems Software and it is considered as one of the main planned activities of this TF in 2021.

Scope and Topics:

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. We want to offer an opportunity for researchers to identify new promising research directions in this area. 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
  • Explainable Artificial Intelligence
  • Applications
  • 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
  • Explainable Artificial Intelligence
  • Applications

Type-2 Fuzzy Sets and Systems Applications (T2-A)

Organized by Javier Andreu-Perez (<javier.andreu@essex.ac.uk>), Dongrui Wu (<drwu@hust.edu.cn>).

Type-2 fuzzy sets and systems are paradigms which seek to realize computationally efficient
fuzzy systems with the ability to give excellent performance in the face of highly uncertain
conditions.

Specifically, type-2 fuzzy sets provide a framework for the comprehensive capturing and
modelling of uncertain data, which, together with approaches such as clustering and similarity measures (to name but two) provides strong capability for reasoning about and wit uncertain information sources in a variety of contexts and applications.

Type-2 fuzzy systems combine the potential of type-2 fuzzy sets with the strengths of rule-based inference in order to provide highly capable inference systems over uncertain data which remain white-box systems (i.e. interpretable).

The aim of this special session is to present and focus top quality research in the areas related
to the practical aspects and applications of type-2 fuzzy sets and systems. The session will also
provide a forum for the academic community and industry to report on recent advances
within the type-2 fuzzy sets and systems research.

Scope and Topics:

  • Type-2 Fuzzy systems, logic, and sets.
  • Applications including similarity and distance measures for type-2 fuzzy sets.
  • Mechatronics*
  • Robotics*
  • Actuators*
  • Industrial systems*
  • Decision Making*
  • Cybernetics*
  • Human-computer interaction*
  • Fuzzy Agents*
  • Any other application area that employs type-2 fuzzy sets

*using type-2 fuzzy sets and/or fuzzy systems

Uncertainty Modeling for Engineering Applications

Organized by Barbara Pękala (<bpekala@ur.edu.pl>), Krzysztof Dyczkowski  (<chris@amu.edu.pl>), Przemyslaw Grzegorzewski (<pgrzeg@ibspan.waw.pl>), Marek Reformat (<marek.reformat@ualberta.ca>), Patryk Żywica (<bikol@amu.edu.pl>).

The session aims at exchanging the experiences of researchers, engineers, 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.

Scope and Topics:

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

Potential areas of application include but are not limited to:

  • Knowledge based economy,
  • Production engineering,
  • Medical and Healthcare systems,
  • Business Process Modeling,
  • Automated and autonomous vehicles,
  • Computer-aided and continuous auditing,
  • Social and economic models