Special Sessions

Accepted Special Sessions

Manuscripts submitted to special sessions should be done through the paper submission website of Fuzz-IEEE 2017. All papers submitted to special sessions will be subject to the same peer-review procedure as the regular papers. Special sessions having fewer than four accepted papers will be combined with another ones with similar topic or will be cancelled and the accepted papers will be moved to regular sessions. Papers submitted to these special sessions (if accepted and presented) will be published in the FUZZ-IEEE proceedings.

In the nature there are many examples that could help humanity to develop new projects, to improve and to solve some real life complex problems. The Bio-inspired Fuzzy Systems have the ability to include both natural computing and real life coefficients of uncertainty to keep in balance the solutions of the large-scale static and dynamic problems. The strategies of natural organisms (as ants, bees, nano-bots, swarms, flocks etc.) include adaptation and learning based on environmental changes, incomplete input information and the presence of noise. That is why Artificial Intelligence uses bio-inspired techniques, like ant colonies, artificial immune systems, swarm intelligence, neural networks, evolutionary computation, and not at last fuzzy logic to solve difficult problems.
The aim of this special session is provide an opportunity for international researchers to share and review recent advances in the foundations, integration architectures, and applications of Bio-inspired Fuzzy Logic systems in Pattern Recognition, Bioinformatics and computational biology, Healthcare, Industry, Microelectronics, Transportation, Green Logistics, Social Network, Web services, Cloud Computing and other domains.

The topics include but are not limited to:

  • Fuzzy logic approaches in Evolutionary Computation, Swarm Intelligence, Genetic Algorithms, Particle Swarm Optimization, Ant Colony Optimization, Artificial Immune Systems and other natural computing systems
  • Uncertainty theory, programming, calculus and processes
  • Adaptive fuzzy pattern recognition
  • Learning based on Fuzzy Rule-Based Systems
  • Fuzzy-Neural and Hybrid schemes in adaptive estimation and control
  • Neuro-fuzzy technologies for medical and bioengineering applications
  • Bio-inspired fuzzy logic controllers for power system stabilizers
  • Agent based modeling and fuzzy logic
  • Multiobjective Bio-inspired Fuzzy Systems
  • Bio-inspired Fuzzy Clustering, Image Classification
  • Computational complexity
Rabie A. Ramadan
Cairo University
rabie@rabieramadan.org
Valentina E. Balas
University “Aurel Vlaicu” Arad , Romania
balas@drbalas.ro
Camelia Pintea
Universitatea Tehnica Cluj-Napoca Cluj-Napoca, Romania Join institution
dr.camelia.pintea@ieee.org
Mario Pavone
University of Catania
mpavone@dmi.unict.it
Nicolaie Popescu-Bodorin
University of S-E Europe LUMINA, Bucharest, RO
bodorin@ieee.org
Ahmad Taher Azar
Benha University
ahmad_t_azar@ieee.org

Recent developments in sensor and actuator technologies have stimulated much research in unmanned aerial vehicles (UAVs). On one hand, we are aware of the fact that quadrotors have been becoming a part of our daily life day to day; on the other hand, their control is still a challenging task as, unlike from the ground vehicles, they do not have enough friction forces to stabilize their motion. So far, model based controllers, in particular model predictive control and linear quadratic regulators, are widely implemented in aerial vehicles as they can deal with multi input multi output systems. However, modeling stage of complex aerial vehicles requires tremendous man power and expertise because of their highly nonlinear dynamics as well as complex inter couplings. In this case, their model free control is more than welcome.
The special session will aim to gather papers in practical guidance, navigation and control of aerial robotics by using intelligent control techniques with a mix of fundementals and real time applications. The special session will also try to present the state of the art aerial robot design, state and parameter estimation, perception and planning by using intelligent techniques.

The topics include but are not limited to:

  • UAV autonomy: perception, planning and control
  • UAV Guidance, Navigation and Control
  • Sense and Avoid
  • Aerial vehicle teams
  • Aerial manipulation
  • Modelling, learning and adaptation
  • Multi-drone operation planning, navigation, and motion control
  • Computer-vision and image processing system Sensors, fusion and perception
  • Ergonomic issues of UAVs, stability and robustness
  • Applications of UAVs in real-life
  • Neuro-fuzzy control of UAVs
  • Extreme learning applications for UAVs
Erdal KAYACAN
Nanyang Technological University
erdal@ntu.edu.sg
Mojtaba Ahmadieh Khanesar
K. N. Toosi University of Technology
mojtaba.ahmadieh@gmail.com
Mehmet Onder Efe
Hacettepe University
onderefe@gmail.com

Over the past few decades, research and applications of different learning algorithms for the training of the structure of neuro-fuzzy systems, type-1 or type-2 cases, has attracted growing attention from both scientific and industrial communities. With the ever increasing need for computational power particularly in areas of identification, pattern recognition, big data computing, fault detection, etc., different learning methods are proposed which offer important advantages such as fast learning speed, ease of implementation, rigorous stability analysis and fewer design parameters.
This special session aims to serve researchers and developers to publish original, innovative and state-of-the art algorithms and architectures for different training algorithms for neuro-fuzzy structures, type-1 or type-2 cases, and their real time applications.

The topics include but are not limited to:

  • Stability analysis of training methods
  • Recurent neuro-fuzzy systems and their training
  • Wavelet neuro-fuzzy systems and their training Self-organizing neuro-fuzzy systems
  • Multi-objective training of neuro-fuzzy systems
  • Neuro-fuzzy control design using novel training methods
  • Extreme learning machines
Erdal KAYACAN
Nanyang Technological University
erdal@ntu.edu.sg
Mojtaba Ahmadieh Khanesar
K. N. Toosi University of Technology
mojtaba.ahmadieh@gmail.com
Mohammad Biglarbegian
University of Waterloo
mbiglarbegian@gmail.com

The term Soft Computing, also widely known as Computational Intelligence, 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 techniques are not 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. 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. We want to offer an opportunity for researchers and practitioners to identify new promising research directions in this area.

The topics include but are not limited to:

  • 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
  • Data Science, Big Data, and High Performance Computing (Map-Reduce, GPGPU, etc.)
  • Applications
Jesús Alcalá-Fdez
University of Granada, Spain
jalcala@decsai.ugr.es
José M. Alonso
University of Santiago de Compostela, Spain
josemaria.alonso.moral@usc.es

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 increasing interest in these questions, the contribution of fuzzy logic sets and systems has been diverse depending on the area of application. On the one hand, considering the decoding of brain activity, 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. On the other hand, as regards neuroscience research, 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 an encounter and specialised forum for researchers interested in employing fuzzy sets, logic and systems for the analysis of brain signals and neuroimaging data, including related disciplines such as computational neuroscience, brain computer/machine interfaces, neuroscience, neuroinformatics, neuroergonomics, affective neuroscience, neurobiology, brain mapping, neuroengineering, and neurotechnology.

The topics include but are not limited to:

  • Application of fuzzy logic, sets and 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 brain computer/machine interfaces (BCI/BMI)
  • Fuzzy models for the simulation of brain processes in computational neuroscience
  • Fuzzy processing of brain microscope imaging
  • Application of fuzzy logic systems to neuropsychology
  • Neuroinformatic tools based on Fuzzy Sets, Fuzzy logic, and Fuzzy Systems
  • Fuzzy hardware architectures for neurotechnology.
Chin-Teng Lin
University of Technology Sydney
Chin-Teng.Lin@uts.edu.au
Javier Andreu-Perez
Imperial College London
javier.andreu@imperial.ac.uk

Although language, or linguistic expressions, undoubtedly contains fuzziness in nature, very little research has been conducted in related fields in recent years, as it was shown in “A Critical Survey on the use of Fuzzy Sets in Speech and Natural Language Processing”, Proc. of the IEEE WCCI 2012, Brisbane, Australia. This is partly because of the prevalence of probabilistic machine learning technologies in the natural language processing field. However, there has been a growing recognition that fuzziness found in every aspect of human language has to be adequately captured and that recent developments in the fields of computational intelligence such as computing with words can make a contribution. This session will follow on from the successful, special session entitled “Fuzzy Natural Language Processing” which was held at IEEE FUZZ 2015 in Istanbul and IEEE FUZZ 2013 in India and the hybrid special sessions held at the 2014 IEEE WCCI in Beijing and the 2016 IEEE WCCI in Vancouver.
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.
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.

The topics include but are 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
  • applications of fuzzy natural language processing techniques
Dr Keeley Crockett
Manchester Metropolitan University, UK
K.Crockett@mmu.ac.uk
Professor Joao Paulo Carvalho
Instituto Superior Técnico, Technical University of Lisbon, Portugal
joao.carvalho@inesc-id.pt

Type-2 fuzzy logic control is a technology which takes the fundamental concepts in control from type-1 fuzzy logic and expands upon them in order to deal with higher levels of uncertainty presented in many real-world control problems. A variety of control application areas have been addressed with type-2 fuzzy logic, from the control in steel production plants to the control of marine diesel engines and robotic control. For some engineering applications, there is evidence that type-2 fuzzy logic can provide benefits over both traditional forms of control as well as type-1 fuzzy logic.
It is the aim of this special session to attract a comprehensive selection of high quality current research in this area of type-2 control, motivating further collaboration and providing a platform for the discussion on future directions of type-2 fuzzy logic control by active researchers in the field. This special session will address advances in interval type-2 as well as general type-2 fuzzy logic control.

The topics include but are not limited to:

  • Interval Type-2 Fuzzy Logic Control
  • General Type-2 Fuzzy Logic Control
  • Type-2 TSK Fuzzy Logic Control
  • PID type Type-2 Fuzzy Logic Control
  • Model-Based Type-2 Fuzzy Logic Control
  • Adaptive / Self-Tuning Type-2 Fuzzy Control
  • Neuro-Fuzzy Type-2 Control
  • Real-time applications of Type-2 Fuzzy Controllers
  • Stability proof challenges for type-2 fuzzy logic controllers
Tufan Kumbasar
Istanbul Technical University
kumbasart@itu.edu.tr
Erdal Kayacan
Nanyang Technological University
erdal@ntu.edu.sg
Hao Ying
Wayne State University
hao.ying@wayne.edu

Evolving systems are modular systems that simultaneously develop their structure, functionality, and parameters in a continuous, self-organized, one pass adaptive way from data streams.
During the last 12-15 years, the concept of Evolving Fuzzy Systems (EFS) established as a useful and necessary methodology to address the problems of imprecision, incremental learning, adaptation and evolution of fuzzy systems in dynamic environments and during on-line/real-time operation modes. EFS are able to automatically and autonomously adapt themselves to new operating conditions and system states and hence guarantee a high process safety, especially in case of highly dynamic and time-variant systems. This is especially necessary when precise and sufficient training data is not available (e.g., because of high costs for data collection or annotation) in order to set up models which cover the whole range of possible system states. In other cases, drifts or shifts in the systems may appear (due to environmental changes or changes in system modes and interrelations), which cannot be appropriately handled with standard fuzzy systems learning methods. EFS significantly contributes in this field of research by assuring flexible models and robustly outweighing of older learned behaviors smoothly over time. Another major topic which can be addressed with EFS are the building of models from huge massive stream data or even from Big Data, and to serve as dynamically adaptable knowledge base within enriched human-machine interaction applications (learning and teaching).
The goal of the special session is to provide a broad picture of the recent developments and to explore further (open) research challenges in one or several specific research topics mentioned belonging to one of these areas: (1) novel adaptive, incremental methods in evolving fuzzy modeling tasks; (2) enhanced issues in dynamic fuzzy methods; (3) real-World applications of evolving fuzzy systems.

The topics include but are not limited to:

  • Evolving fuzzy classifiers (using different model architectures)
  • Evolving Takagi-Sugeno-Kang type fuzzy systems
  • Evolving neuro-fuzzy approaches
  • Evolving type-2 fuzzy systems and related architectures
  • Evolving modeling and control systems
  • Data stream fuzzy clustering (in various forms)
  • Adaptive fuzzy pattern recognition
  • Adaptive fuzzy regression and correlation techniques
  • Hybridizations of evolving fuzzy systems with incremental machine learning and data mining techniques
  • Issues on robustness, stability and process-safety in evolving fuzzy systems
  • Evolving techniques to address concept drift and shift
  • Evolving fuzzy models in soft sensing
  • On-line techniques to deal with model uncertainty and interpretability issues
  • Active and semi-supervised learning with fuzzy concepts
  • On-line and evolving design of experiments
  • On-line dimensionality reduction and feature selection
  • On-line complexity reduction and model transparency assurance issues
  • Dynamic split-and-merge techniques for fuzzy rules
  • Evolving granular modeling and control
  • Towards plug-and-play capability
  • Real-World applications of evolving fuzzy systems in on-line system identification
  • Real-World applications of evolving fuzzy systems in on-line fault detection and decision support diagnosis
  • Real-World applications of evolving fuzzy systems in data stream mining and adaptive knowledge discovery
  • Real-World applications of evolving fuzzy systems in database and web mining
  • Real-World applications of evolving fuzzy systems in Big Data
  • Real-World applications of evolving fuzzy systems in control and decision support systems
  • Real-World applications of evolving fuzzy systems in image classification and visual Inspection
  • Real-World applications of evolving fuzzy systems in automation and robotics
  • Real-World applications of evolving fuzzy systems in control systems
  • Real-World applications of evolving fuzzy systems in forecasting in financial domains and time-series prediction
  • Real-World applications of evolving fuzzy systems in on-line condition monitoring and predictive maintenance
Plamen Angelov
Lancaster University, UK
Fernando Gomide
University of Campinas, Brazil
Edwin Lughofer
Johannes Kepler University Linz, Austria
Igor Skrjanc
University of Ljubljana, Slovenia

The Session on “Cognitive Systems” provides an interdisciplinary forum for researchers and developers to present and discuss experiences, ideas, and research results in the Cognitive Computing area. This is an emerging research area, investigating the development of cognitive systems, able to learn, reason, and pervasively engage with humans in a natural, personalized, reactive and/or proactive way. This new generation of intelligent systems is mainly devised for specific application domains with the aim of providing decisional support for solving complex problems. Original contributions are sought, covering the whole range of theoretical and practical aspects, technologies and systems in such a research area. In particular, the Session will focus on methodologies, algorithms and techniques for the automatic learning and human-like reasoning, the identification of human activities and behaviors in specific contexts and the automatic adaptation in response to external dynamics, the natural human-computer interaction, the management and semantic integration of huge amounts of heterogeneous data. Submitted papers will be evaluated on the basis of significance, originality, technical quality, and exposition. Papers should clearly establish their research contribution and the relation to the goals of the Session.

The topics include but are not limited to:

  • Representation languages for Heterogeneous, Ambiguous and Textual knowledge
  • Ontologies and Linked Data for Cognitive Systems
  • Logic programming, Fuzzy logic and Neural Networks for Cognitive Systems
  • Sentiment Analysis and Opinion Mining
  • Natural Language Processing
  • Question-answering Systems
  • Semantic Information Retrieval
  • Deep Learning from Heterogeneous Data Sources
  • Human-like Reasoning and Neuromorphic Problem Solving
  • Cognitive Vision
  • Visual Recognition from Images and/or Videos
  • Spatial Human-Computer Interaction
  • Human Behavior Analysis in Cognitive Environments
  • Cognitive Apps, Agents and Multi-Agent Systems
  • Experiences in deploying Cognitive Systems in real application scenarios.
Massimo Esposito
Institute for High Performance Computing and Networking (ICAR) of National Research Council of Italy (CNR)
massimo.esposito@icar.cnr.it

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 with 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 underlying theory of type-2 fuzzy sets and systems. There are many open and unanswered questions about properties and nature of type-2 fuzzy sets and systems, this session is designed to provide a forum for the academic and industrial communities to report on advances in including, but are not limited to representations of type-2 fuzzy sets, approaches to defuzzification, fuzzy operators, fuzzy measures, interpretability, computational complexity, related extensions to type-1.

The topics include but are not limited to:

  • Representations of type-2 fuzzy sets
  • Approaches to defuzzification
  • Fuzzy operators
  • Fuzzy measures
  • Interpretability
  • Computational complexity
  • Related extensions to type-1
Robert John
University of Nottingham
robert.john@nottingham.ac.uk
Simon Coupland
Demontfort University
simonc@dmu.ac.uk
Josie McCulloch
University of Nottingham
josie.mcculloch@nottingham.ac.uk

Big Data has emerged as a hot topic in the recent years. It refers to those advantages, and also challenges, derived from collecting and processing vast amounts of data. The benefits from the management of these types of problems is clear: the larger the data, the higher the degree of knowledge that can be extracted from it. In addition to the former, the speed rate of incoming information is becoming higher and higher. Finally, the different sources that carry out the data recording also implies an heterogeneous structure.
We must emphasize the necessity of working in the scenario of Big Data. Clearly, having a larger amount of data can allow researchers and corporations to extract better and more useful knowledge from the applications they are working with. By achieving higher quality results from data, implies a stronger support for taking future decisions on this context.
The Big Data problem is a recent field of study, and therefore there are just few works which address this topic from the perspective of fuzzy modeling. Their ability to provide a better representation of the problem space by means of fuzzy labels and/or fuzzy sets, makes them a very interesting approach when both the volume and variety of the dataset increases. Additionally, experts can benefit from the interpretability associated to linguistic labels.
New research in the Big Data scenario for fuzzy modeling must be focused on re-designing the state of- the-art algorithms, as well as novel approaches for recent work scenarios.
This special session aims to provides innovative approaches to handle various fuzzy issues in big data presentation, processing and analysing by applying fuzzy sets, fuzzy logic and fuzzy systems.

The topics include but are not limited to:

  • Fuzzy rule-based knowledge representation in Big Data processing
  • Fuzzy models for large dimension problems
  • Fuzzy clustering, adaptive fuzzy systems, evolving fuzzy systems for Big Data
  • Tools and techniques for Big Data analytics in uncertain environments
  • Studies on scalability for fuzzy models
  • Distributed and parallel architectures for Fuzzy Modeling
  • Real world Big Data cases using fuzzy based approaches
Francisco Herrera
University of Granada, Spain
herrera@decsai.ugr.es
Alberto Fernández
University of Granada, Spain
alberto@decsai.ugr.es

Energy use is an increasing concern in data science. Recent research promote algorithms optimizing the number of computations per kilowatt-hour over the number of computations per second. In this respect, the concept of eco-efficiency brings together economic and environmental factors for a more efficient use of resources and lower emissions. Eco-efficiency is represented by the quotient between the service value and its environmental influence, that affects both energy consumption and greenhouse gas emissions.
In the context of data science, the environmental efficiency is accounted for through quality metrics that combine energy consumption and sustainability. Soft computing techniques are key in the design of these emerging quality metrics because, in many cases, partial preference orderings or analogous descriptions of uncertainty are involved when the different models are compared. Learning algorithms are being developed that are able to exploit these preferences, but many theoretical, methodological and practical issues about eco-efficiency in data science are still unsolved.

The topics include but are not limited to:

  • Soft-computing techniques for defining the eco-efficiency of systems and models
  • Preference orderings between eco-efficient models and other aspects of energy-efficient learning under uncertainty
  • Energy saving through an efficient exploitation of uncertain information
  • Joint optimization of service value and environmental influence in machine learning
  • Industrial applications and other success cases of soft computing-based eco-efficient designs.
Luciano Sánchez
Universidad de Oviedo
luciano@uniovi.es
José Ranilla
Universidad de Oviedo, Spain
Sebastien Destercke
Université de Technologie de Compiegne, France

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 with 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.

The topics include but are not limited to:

  • Type-2 Applications
  • Applications including similarity and distance measures for type-2 fuzzy sets
  • Data analysis using type-2 fuzzy sets and/or fuzzy systems
  • Robotics using type-2 fuzzy sets and/or fuzzy systems
  • Decision Making using type-2 fuzzy sets and/or fuzzy systems
  • Clustering and Classification using type-2 fuzzy sets and/or fuzzy systems
  • Modelling using type-2 fuzzy sets and/or fuzzy systems
  • Computing with words using type-2 fuzzy sets and/or fuzzy systems
  • Type-2 Fuzzy Agents
  • Any other application area that employs type-2 fuzzy sets
Christian Wagner
Michigan Technological University, USA and University of Nottingham, UK
chwagner@mtu.edu
Hani Hagras
University of Essex, UK
hani@essex.ac.uk
Jonathan M. Garibaldi
University of Nottingham, UK
jon.garibaldi@nottingham.ac.uk

Fuzzy inference is widely used in many aspects of problem solving, including data-mining, prediction, image and natural language processing. Here, it has been applied to a multitude of applications ranging from robotics to medicine and biology. While both type-1 and type-2 fuzzy logic systems have and are being developed, the vast majority of these systems are based on singleton Mamdani or TSK inference.
This special session focuses specifically on novel innovations which drive non-standard approaches to reasoning and fuzzy inference. Non-standard here refers to fuzzy inference systems which differ at a key stage from standard approaches to fuzzy inference. This includes differences in the fuzzification, rule combination, or defuzzification stages, as well as novel operators such as t-norms and t-conorms and their application.
The most prominent non-standard approach, which has recently seen a surge of innovative research, is non-singleton inference, which enables the direct capture of input uncertainties and their incorporation into the reasoning process. While these systems have been shown to deliver excellent performance, often superior to that of singleton systems, they also offer unique potential in developing design approaches for fuzzy inference systems where uncertainty at each stage of the system is modelled individually. Beyond non-singleton approaches at the system output stage, novel approaches which address the rule combination stage provide exciting ways of addressing both theoretical and real world challenges.
The aim of this special session is to bring such innovations around fuzzy inference and reasoning together and to provide a common forum for discussing the future development (and development needs) of fuzzy inference systems. Both theoretical advances in methods of inference and their applications will be of key interest.

The topics include but are not limited to:

  • Non-singleton type-1 fuzzy logic systems
  • Non-singleton interval type-2 fuzzy logic systems
  • Non-singleton general type-2 fuzzy logic systems
  • Non-standard t-norms and/or t-conorms and their application
  • Hybrid fuzzy systems
  • Adaptive / self-tuning fuzzy logic systems
  • Applications of non-standard fuzzy logic systems
Timothy C. Havens
Michigan Technological University, USA
thavens@mtu.edu
Christian Wagner
Michigan Technological University, USA and University of Nottingham, UK
christian.wagner@nottingham.ac.uk
Derek T. Anderson
Mississippi State University, USA
anderson@ece.msstate.edu

Data mining is a very active research field because there is a vast number of real-world problems that can be tackled using techniques encompassed by this area of research. In this field there can be found classical problems like classification, regression or clustering. Besides them, during the last years a great number of challenging problems have emerged like the problem of imbalanced data, multi-label and multi-instance problems, low quality and/or noisy instances or semi-supervised learning among others. Furthermore, data preprocessing techniques play a vital role for the subsequent success of machine learning techniques.
Learning methods based on soft computing techniques are widely used to face the aforementioned problems. Fuzzy systems have demonstrated the ability to provide an interpretable model understandable by human beings whereas the results obtained are accurate due to their ability to cope with the great uncertainty inherent to these new challenging problems. Evolutionary computation is a robust technique for optimization, learning and adaptation tasks. They can adapt the model parameters for each problem to obtain a highly accurate system. Their synergy implies a better capability for the design and optimization of fuzzy models. Moreover, in the era of Big Data new possibilities for fuzzy methods emerge in the context of data mining. There are new challenges regarding the scalability of fuzzy algorithms when addressing very large datasets.
The aim of the session is to provide a forum to disseminate and discuss recent and significant research efforts on soft computing techniques based on fuzzy logic to deal with data mining problems, in order to deal with the current challenges on this topic. The special session is therefore open to high quality submissions from researchers working in this research field.

The topics include but are not limited to:

  • Supervised / Unsupervised / Semi-supervised learning
  • Feature Selection / Extraction / Construction
  • Instance Selection / Generation
  • Data streams and concept drift
  • Big data mining
  • Imbalanced learning
  • Multi-label \ Multi-instance learning
  • Feature and label noise
  • Problems with low quality data and noise
  • Cost sensitive problems
  • Ensemble learning
  • Evolutionary fuzzy systems
  • One-class classification
  • Real-world applications
Mikel Galar
Public University of Navarre
mikel.galar@unavarra.es
José Antonio Sanz
Public University of Navarre
joseantonio.sanz@unavarra.es

There is a need to increase autonomy in modern security, defence, and sensing applications in order to reduce the burden on the operator and increase the spectral and geospatial coverage of modern high bandwidth and networked sensor systems. This need spans many applications, examples may include CCTV security systems, in-home monitoring sensors (e.g. falls detection), healthcare sensors (e.g. ECG monitors), indoor positioning and locating sensors (e.g. iBeacon systems) and many, many others. A common requirement across all of these sensing applications is how to ensure that the outputs of the sensors accurately and robustly replicate a decision that would otherwise be made by a human operator.
As an example, we have seen a considerable effort in recent years into increasing the autonomy of sensor-based surveillance systems, resulting in platforms that can recognise traffic infringements, unauthorised people entering areas, loitering, tailgating, etc. Performance of these is improving dramatically and, with the increased penetration of small, powerful, and embedded computing devices, holds the potential to further enhance the autonomy of sensor systems and provide accurate and robust decision support to human operators.
The aim of this special session is to present the latest research into the theory, application, and development of new computational methods that will lead to further increases in system autonomy. We welcome high quality, original research from both the academic community and industry, to report on recent advances in this area.

The topics include but are not limited to:

  • Algorithms for detection, identification or recognition (DIR)
  • Decision Support Systems
  • Sensor autonomy
  • Image analytics
  • Computational vision systems
  • Audio cognition
  • Adaptive and Learning systems
  • Human-Computer interaction
  • Edge computing
  • Distributed intelligence
  • Trust, Provenance and Security
  • Disparate sensor fusion
  • Data fusion
  • Behaviour analysis and detection
  • Anomaly recognition
  • Multi-layered decision systems
  • Tracking, hand-off and occlusion handling
  • Linguistic summarisation
Dr Jason J. Lepley
Leonardo
jason.lepley@leonardocompany.com

This special session aims to present highly technical papers describing new FCM models and methodologies addressing any of the following specific topics: theoretical aspects, learning algorithms, innovative applications and FCMs extensions. During the past decade, FCMs played a vital role in the applications of diverse scientific areas, such as social and political sciences, engineering, information technology, robotics, expert systems, medicine, education, prediction, environment etc.
Fuzzy Cognitive Map is an extension of cognitive maps for modeling complex causal relationships easily, both qualitatively and quantitatively. As a Soft Computing technique it is used for causal knowledge acquisition and providing causal knowledge reasoning process. FCMs have emerged as tools for representing and studying the behavior of systems and people. By combining the main aspects of fuzzy logic, neural networks, expert systems, semantic networks, they have gained considerable research interest and are widely used to analyze causal complex systems. FCMs can be constructed from raw data as well. FCMs model any real world system as a collection of concepts and causal relation among concepts. From an Artificial Intelligence perspective, FCMs are dynamic networks with learning capabilities, whereas more and more data is available to model the problem, the system becomes better at adapting itself and reaching a solution. They gained momentum due to their dynamic characteristics and learning capabilities. These capabilities make them essential for modeling and decision making tasks as they improve the performance of these tasks.
This Special Session is dedicated to providing presentaions with deep insights on fundamentals, modeling methodologies, learning algorithms, optimization, convergence issues for fuzzy cognitive maps (FCMs), supplemented with applications on real case studies using FCMs and their extensions, enhanced structures and dynamic capabilities.

The topics include but are not limited to:

  • Modeling Fuzzy Cognitive Maps
  • Approximate Reasoning
  • Knowledge Representation
  • Learning Algorithms for FCMs
  • Evolutionary Fuzzy Cognitive Maps
  • Granular Cognitive Maps
  • Rule Based Fuzzy Cognitive Map
  • Fuzzy Cognitive Agents
  • Dynamic Cognitive Networks
  • Fuzzy Grey Cognitive Maps
  • Rough Cognitive Map
  • Intuitionistic Fuzzy Cognitive Maps
  • Interval Fuzzy Cognitive Maps
  • Competitive Fuzzy Cognitive Maps
  • FCM Design Using Fuzzy Numbers
  • Hybrid FCM-based approaches
  • FCM extensions
  • FCMs in Engineering
  • FCMs for Stakeholders Analysis
  • FCMs in Biomedical Engineering
  • FCMs in Pattern Recognition
  • FCMs in Medical Decision Support
  • FCMs in Decision Making
  • FCMs in Control Systems
  • FCMs in Business Management
  • FCMs in Agricultural Systems
  • FCMs in Data Mining
  • FCMs in Computer Vision Tasks
Papageorgiou Elpiniki
University of Applied Science, Greece
epapageorgiou@teiste.gr
Engin Yesil
Getron Corp. Hoboken Riverfront Center, USA
engin.yesil@getron.com

Aggregation theory is a field in rapid progress. Apart from their obvious interest from a theoretical point of view, they are an essential tool for applications in many different fields, such as image processing, classification, machine learning or decision making among many other. For this reason, in recent years there is a growing interest in the topic, which is leading to the introduction of novel concepts and approaches.
This session will focus on recent theoretical developments in the fields of aggregation functions, and aims at bringing together leading researchers in the field in order to present their most recent developments and for discussing recent trends in this area, as well as to identify potential problems of interest for researchers. In particular, but not limited to, this special session will deal with research in fields such as t-norms and t-conorms, overlap and grouping functions, means, uninorms, and in general, any type of aggregation function both in the real setting and in lattices.

The topics include but are not limited to:

  • Aggregation functions
  • t-norms
  • t-conorms
  • overlap functions
  • means
  • uninorms
  • aggregation functions on extensions of fuzzy sets
Humberto Bustince
Public University of Navarra
bustince@unavarra.es
Radko Mesiar
Slovak University of Technology
mesiar@math.sk
Javier Montero
Complutense University of Madrid
monty@mat.ucm.edu
Javier Fernandez
Public University of Navarra
fcojavier.fernandez@unavarra.es

This session will deal with applications where aggregation functions play a crucial role. These applications may include, but are not limited to, image processing, data mining, big data, classification and decision making. The considered applications may make use either of real aggregation functions or of more general aggregation functions, as those defined over lattices or extensions of fuzzy sets. We hope to provide a forum to discuss up-to-date applications in which the usefulness of recent developments in aggregation functions is clear.
This special session is co-organized by the EUSFLAT working group on Soft Computing methods in Image processing. We want to put a bridge between aggregation and convolution/deconvolution in sampling/upsampling, denoising, fusion and other relevant problems.

The topics include but are not limited to:

  • Aggregation functions in Image processing
  • Machine learning
  • Data mining
  • Classification
  • decision making
  • Aggregation functions in extensions of fuzzy sets.
Humberto Bustince
Public University of Navarra
bustince@unavarra.es
Radko Mesiar
Slovak Univeristy of Technology
mesiar@math.sk
Javier Montero
Complutense University of Madrid
monty@mat.ucm.edu
Irina Perfilieva
University of Ostrava
irina.perfilieva@osu.cz
Daniel Paternain
Public Univeristy of Navarra
daniel.paternain@unavarra.es

This special session will consider generalizations of the notion of aggregation functions which have appeared in the literature in recent years and which lead to new classes of functions that encompass both classical aggregation functions and other functions which are relevant, specially from the point of view of the applications, but which do not fulfil all the conditions required to an aggregation. This session will be open to both theoretical work and applied works which may justify the use of the considered generalizations.

The topics include but are not limited to:

  • Monotonicity
  • generalized aggregation functions
  • applications of generalized monotonicity.
Humberto Bustince
Public University of Navarra
bustince@unavarra.es
Radko Mesiar
Slovak University of Technology
mesiar@math.sk
Javier Montero
Complutense University of Madrid
monty@mat.ucm.edu
Aranzazu Jurio
Public University of Navarra
aranzazu.jurio@unavarra.es

Real world decision problems are often defined under uncertain environments, with imprecise information, is straightforward the use of linguistic information due to the nature of different aspects of the decision problems. Fuzzy linguistic approach and Computing with Words (CW) provides the tools and methodology to deal with words or sentences defined in a natural or artificial language instead of numbers. However the complexity of decision problems make this topic to enhance previous models and methods constantly, rising new proposals such as the recent hesitant linguistic fuzzy sets, Linguistic Distribution Assessments, etc., for modelling linguistic information and their application to novel and classical problems with the view to improve the resolution of decision making under uncertainty.
All these models and their application to different complex decision real world problems have recently attracted much attention in which, novel mathematical foundations and new decision models risen to be applied in different decision fields such as multi-criteria decision making, decision analysis, evaluation processes, consensus reaching processes, emergency decision making, information fusion, etc.

The topics include but are not limited to:

  • Linguistic decision analysis
  • Linguistic expression domains to represent preferences
  • Linguistic hesitant and Linguistic Distribution Assessments for modeling preferences
  • Multi-criteria and group decision making
  • Consensus and selection models dealing with linguistic information
  • Information Fusion for Linguistic decision making
  • Linguistic decision making in emergency situations, engineering evaluation, resource management and transfer, Industry applications, sensory evaluation, evaluation and recommendation, investments applications and risk assessment, …
Luis Martínez
University of Jaén
martin@ujaen.es
Francisco Herrera
University of Granada
herrera@decsai.ugr.es

The aim of this special session is to present the recent advances in the theory and practice of the fuzzy-logic systems in solving complex aerospace problems. The main focus of this session is on modeling, identification, and control of aerospace vehicles including unmanned aerial vehicles (UAVs), aircraft, vertical take-off and landing (V/TOL), and spacecraft.

The topics include but are not limited to:

  • Applications of model-free and heuristic fuzzy systems
  • Applications of T-S and LMI-based fuzzy systems
  • Applications of type-II fuzzy systems
  • Applications of A-sum-of-squares approach with polynomial fuzzy systems
  • Applications of time-delay fuzzy model-based systems
  • Applications of fuzzy descriptor systems
  • Applications of T-S fuzzy systems for system identification
  • Applications of robust and optimal fuzzy control systems
  • Applications of fuzzy observers
  • Applications of Fuzzy-PID control systems
  • Applications of fuzzy adaptive control systems
  • Applications of adaptive neuro-fuzzy inference (ANFIS) systems
  • Applications of fuzzy sliding-mode control systems
  • Applications of genetic fuzzy systems
  • Validation and Verification (V&V) of fuzzy systems
Mohammad Ayoubi
Santa Clara University
maayoubi@scu.edu
Kelly Cohen
University of Cincinnati
cohenky@ucmail.uc.edu

For more than two decades, evolutionary computation and various meta-heuristics have frequently been used for fuzzy system design under the name of evolutionary fuzzy systems. Their learning and adaptation capabilities enable structure and parameter optimization of fuzzy systems for many kinds of machine learning tasks such as modeling, classification, and rule mining. Their flexible frameworks also enable to handle multiple objectives like accuracy and interpretability maximization and many kinds of data types like imbalanced, missing, and privacy-preserving data sets. Evolutionary fuzzy systems have frequently been used in a wide variety of applications. For example, the recent success of genetic fuzzy tree (ALPHA) proves the great potential of evolutionary fuzzy systems in AI.
The aim of this session is to provide a forum to disseminate and discuss recent and significant research efforts on evolutionary fuzzy systems in order to deal with current challenges on this topic.

The topics include but are not limited to:

  • Evolutionary Learning/Tuning of Fuzzy Rule-Based Systems
  • Evolutionary Selection of Fuzzy Rules
  • Interpretability-Accuracy Tradeoff
  • Multiobjective Evolutionary Fuzzy Systems
  • Evolutionary Fuzzy Tree
  • Evolutionary Fuzzy Neural Networks
  • Evolutionary Fuzzy Clustering
  • Swarm Intelligence for Fuzzy Systems
  • Preprocessing and Postprocessing for Evolutionary Fuzzy Systems
  • Applications of Evolutionary Fuzzy Systems to Real World Problems
Yusuke Nojima
Osaka Prefecture University
nojima@cs.osakafu-u.ac.jp
Rafael Alcalá
University of Granada
alcala@decsai.ugr.es
Hisao Ishibuchi
Osaka Prefecture University
hisaoi@cs.osakafu-u.ac.jp

This session aims to demonstrate how the foundations of many-valuedness, as enabling non-commutativity for its related operators as typically appearing in quantales and modules, provide a thematic and applicative aspect overlaying the formal treatment of many-valued order. This session and its scope opens up new avenues for approaches to many-valued mathematics, and the way related methodology becomes applicative in various areas of research. The foundations of many-valuedness, as enabling non-commutativity for its related operators, is another thematic and applicative aspect overlaying the formal algebraic treatment. The session indeed aims to exploit the benefit of using semigroups in complete lattices with focus on quantales and modules, and as it enables new approaches to many-valued mathematics, in particular as related to algebraic, logical and topological methodology.
Since the mid 1980's, quantales find an increasing interest in various areas of mathematics. This development has its origin in ideas coming from non-commutative geometry and is driven by the desire to create a non-commutative and nonidempotent set theory known as quantale sets. Since that time, an extensive literature has grown and is scattered over a diversity of journals. Furthermore, and as algebra at the same time became connect with developments using category theory, dedicated workshops and conferences emerge to focus on enriched category theory in further support of studying quantales.
The session also encourages application development even in situations where non-commutativity and many-valued order is used as a applicative mechanism in information structures as appearing in real applications. This ambition strives to demonstrate how complicated theory is required for solutions in complicated applications. The scope of this sessions has its focus on non-commutativity for the foundations of many-valuedness, on the one hand, as it typically appears in mathematical disciplines like algebra, logic, topology, and other related and similar disciplines, and, on the other hand, as it appears in computer and information sciences as e.g. related to structures of programming languages and analytics of typed information. The slogan is "many-valued and non-commutative structures are everywhere", i.e. going far beyond just saying "fuzzy sets are everywhere". As the ambition is that theory meets practice, the session invites to interdisciplinary thinking, yet building upon a comprehensive and coherent representation of non-commutativity and many-valued order in theory and practice.

The topics include but are not limited to:

  • Enriched categories and many-valued order theory
  • Modules on unital quantales and many-valued lattices
  • Many-valued algebra in non-classical logic
  • Many-valued and non-commutative topology
  • Applications in computer science.
Patrik Eklund
Umeå University
peklund@cs.umu.se
Ulrich Höhle
Bergische Universität Wuppertal
uhoehle@uni-wuppertal.de
Jari Kortelainen
Mikkeli University of Applied Sciences
jari.kortelainen@mamk.fi

Fuzzy set theory is the subject of deep investigation in fields such as engineering systems and related applications. Typically, these fields are interdisciplinary with a wide range of theory and methodologies that are being investigated and developed.
Ontologies have been started to be considered to represent the knowledge in many domains. Their fuzzy counterpart, i.e., fuzzy ontologies, are defined as an extension of crisp ontologies by adding a set of membership degrees to each concept of the domain ontology and adding fuzzy relations among these fuzzy concepts.
The main scope of this call is about fuzzy knowledge-based/ontology-based engineering systems and their application.

The topics include but are not limited to:

  • Fuzziness in Semantic Web Languages and Applications
  • Fuzziness and knowledge-based approaches in Web Search and Information Retrieval and Applications
  • Fuzziness and knowledge-based approaches in Decision Process, Decision Making and Applications
  • Fuzziness and knowledge-based approaches in Smart Societies, Smart Cities and Applications
  • Fuzziness and knowledge-based approaches in Data Mining, Machine Learning and Applications
  • Fuzzinness and Social Network Analysis
  • Fuzzinness and personalized interactive system
Hajer Baazaoui Zghal
University of Manouba, Tunis, Tunisia
hajerbaazaouizghal@gmail.com
Umberto Straccia
Istituto di Scienza e di Tecnologie dell’Informazione (ISTI) of the Italian National Council of Research (CNR)
umberto.straccia@isti.cnr.it
Mourad Abed
University of Valenciennes - LAMIH (Human- Computer Interaction and Automated Reasoning research group)
mourad.abed@univ-valencienne.fr

The aim of this special session is to discuss and disseminate the most recent advancements focused on interpretability of fuzzy systems. Many fuzzy algorithms and models are indeed aimed at extracting knowledge from data, and the acquired knowledge must be usually communicated to users. However, as far as such knowledge is difficult to understand by users, the acceptance of such methods may be seriously compromised. Interpretability must be the central point on system modeling. In fact, some of the hottest and most recent research topics like Precisiated Natural Language, Computing With Words, and Human Centric Computing strongly rely on the interpretability of the designed models. The challenge is to better exploit fuzzy logic techniques for improving the human-centric character of many intelligent systems.
Interpretability is therefore a key property of intelligent systems of practical use, but its human-centric character poses difficult challenges both in design and evaluation. The issue of interpretability is tackled in different ways by focusing on specific facets of system modeling, such as the formalization of interpretability in an algebraic structure, or the definition of constraints to be imposed during model design and tuning. Moreover, the evaluation of interpretability is a mandatory issue to validate and compare models.

The topics include but are not limited to:

  • Theoretical aspects of interpretability
  • Learning methods for interpretable systems and models
  • Interpretability evaluation and improvements
  • Relations between interpretability and other criteria (such as accuracy, stability, relevance, etc.)
  • Design issues
  • Successful applications of interpretable fuzzy systems
Ciro Castiello
University of Bari ''Aldo Moro Bari'', Italy
ciro.castiello@uniba.it
Corrado Mencar
University of Bari ''Aldo Moro'', Italy
corrado.mencar@uniba.it
José M. Alonso
University of Santiago de Compostela, Spain
josemaria.alonso.moral@usc.es

Geosciences are fields related to the usage of computer sciences to solve problems of Earth, atmospheric, oceanographic, and environmental sciences. The interdisciplinary technologies have been applied to many real-life problems in geosciences, including soft computing techniques. Today, computer and geosciences play an important role in bringing the advanced technologies to the life. It has been already demonstrated that fuzzy logic offers a wealth of effective techniques for solving many problems in geosciences.
Authors are invited to submit their original and unpublished work in the following areas including (but not limited to): fuzzy systems and hybrid systems for Geosciences, fuzzy clustering in remote sensing image processing, aAdvanced fuzzy sets (type-2/intuitionistic fuzzy sets, etc.) and systems for applications in geosciences, multi-spectral/Hyper-spectral satellite image analysis and Geography Information Systems.

The topics include but are not limited to:

  • Fuzzy systems and hybrid systems for Geosciences
  • Fuzzy clustering in remote sensing image processing
  • Advanced fuzzy sets (type-2/intuitionistic fuzzy sets, etc.) and systems for applications in geosciences
  • Multi-spectral/Hyper-spectral satellite image analysis
  • Geography Information Systems.
Long Thanh Ngo
Le Quy Don Technical University, Vietnam
ngotlong@gmail.com
Witold Pedrycz
University of Alberta, Canada
wpedrycz@ualberta.ca

Cluster analysis is a well-known set of techniques and algorithms which seeks to form groups of data with both strong internal similarity and strong external dissimilarity. In practice, this task is made harder by the presence of information in the data that is irrelevant to form the clusters, such as additional features, especially in the context of "big data" and high-dimensional data in general.
To face this difficulty, in the recent years there has been a growing interest for new clustering techniques which identify clusters by means of different subspaces from the original data space, for example by weighting these features, identifying linear subspaces where the data reside, or forming groups of correlated features which lead to clusters with high density. As for traditional clustering approaches, there exists a great diversity of choices and options regarding subspaces of interest. Various approaches to tackle this problem have been proposed by different communities of researchers. Some of the best known of these groups include "subspace clustering", "projected clustering", "coclustering" (or "biclustering"), and "correlation clustering".
This special session proposal aims to offer the opportunity for these communities to meet and exchange, with the specificities of soft clustering in mind.

The topics include but are not limited to:

  • Soft approaches to subspace clustering, coclustering, ...
  • Soft clustering with features or subspace identification in non-euclidean spaces
  • Soft approaches to "self-expressive models"
  • Unsupervised metric learning for clustering
  • Unsupervised subspace nearest neighbor search
  • Applications of soft subspace clustering to industrial and real-world problems
Arthur Guillon
University Pierre and Marie Curie
arthur.guillon@lip6.fr
Marie-Jeanne Lesot
University Pierre and Marie Curie
marie-jeanne.lesot@lip6.fr
Christophe Marsala
University Pierre and Marie Curie
christophe.marsala@lip6.fr
Nikhil R. Pal
Indian Statistical Institute
nikhil@isical.ac.in

In modern biomedical research, large sets of digital data are retrieved from a plethora of molecular, cellular and systemic experiments, which are possible thanks to the rapidly evolving high-throughput technologies, such as the next generation sequencing. Biomedical imaging informatics has become a crucial part of modern healthcare, clinical research and basic biomedical sciences. Rapid improvement of imaging technology and advancement of imaging modalities in recent years have resulted in a significant increase in the quantity and quality of such images. The identification of new strategies for analyzing such data is becoming more and more necessary since the large amount of data can sometimes represent a real obstacle to effectively identify the most relevant patterns and to build comprehensive models capable of explaining complex biological phenotypes.
Particularly, Soft Computing and Data Mining techniques are already emerged as powerful tools to mine and interpret this kind of data and promise to pave the way for ever more techniques to take on this challenge.
The aim of the special session is to host the recent research advances in the fields of Soft Computing methodologies concerning biomedical data.
Potential topics include Soft Computing methodologies (but are not limited to): Fuzzy Logic (i.e., algebras, norms, connectives, relations, reasoning, inferences, …); Rough-Sets; Neuro-Fuzzy; Granular Computing; Data Mining; when applied to: OMICs and high-throughput data in the broad context of genomics, epigenomics, transcriptomics and proteomics; models capable of explaining the pathogenesis or predicting the predisposition and/or the clinical outcome of human diseases; the evaluation of protein folding and/or protein-ligand interactions (where ligands are proteins, DNA, RNA and small molecules), also in the context of genetic variation; the identification of potential gene regulatory elements (i.e., binding of transcription factors, miRNAs, etc.); analysis of common genetic variants (i.e., SNPs, HLA genotypes, microsatellites); analysis of experimental data from next generation sequencing; analysis of gene expression data; analysis of medical imaging data; biomedical applications. Furthermore, software tools designed for addressing any of the above topics, might be also considered relevant for the special session.

The topics include but are not limited to:

  • Fuzzy Logic (i.e., algebras, norms, connectives, relations, reasoning, …)
  • Rough-Sets
  • Neuro-Fuzzy systems
  • Granular Computing
  • OMICs and high-throughput data in the broad context of genomics
  • epigenomics
  • transcriptomics
  • proteomics
  • pathogenesis
  • human diseases
  • protein folding
  • protein-ligand interactions (where ligands are proteins, DNA, RNA and small molecules)
  • genetic variation
  • gene regulatory elements (i.e., binding of transcription factors, miRNAs, etc.)
  • common genetic variants (i.e., SNPs, HLA genotypes, microsatellites)
  • next generation sequencing
  • gene expression data
  • medical imaging data
  • biomedical applications.
Angelo Ciaramella
Università degli studi di Napoli “Parthenope”, Italy
angelo.ciaramella@uniparthenope.it
Sushmita Mitra
Machine Intelligence Unit, Indian Statistical Institute, India
sushmita@isical.ac.in
Antonino Staiano
Università degli studi di Napoli “Parthenope”, Italy
antonino.staiano@uniparthenope.it

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).

The topics include but are not limited to:

  • 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
Daniel Sanchez
University of Granada, Spain
daniel@decsai.ugr.es
Nicolas Marin
University of Granada, Spain
nicm@decsai.ugr.es
Anna Wilbik
Eindhoven University of Technology
A.M.Wilbik@tue.nl
Rui-Jorge Almeida
Eindhoven University of Technology
rjalmeida@tue.nl

The problem of finding appropriate rankings to evaluate and/or sort different alternatives appears in a variety of disciplines such as Economics, Political Sciences, or Artificial Intelligence, among many others. In most decision making problems we aim at selecting the optimal alternative from a certain collection of them. Usually, the available information includes some evaluation of the different alternatives under different states of nature. Sometimes, this evaluation is based on human perceptions and/or expert knowledge. Different streams of the literature make different assumptions about the nature of this information: numerical utilities, qualitative evaluations, fuzzy-valued utilities, partial preferences, etc. We usually need some aggregation procedure in order to merge our information under the different states of nature in order to produce a single ordering (partial or total) over the whole set of alternatives. Such an aggregation method allows to select the optimal alternative, or the collection of non-dominated ones. The study of different aggregation methods under the above different assumptions is an ongoing research field. Researchers interested either in theoretical aspects or in applications related to this topic are encouraged to contribute to this session.

The topics include but are not limited to:

  • Qualitative decision theory
  • Partial preference modelling
  • Fuzzy preference relations
  • Orderings for random fuzzy quantities
  • Applications in Medical Diagnosis, Bioinformatics, Industrial Engineering, Finance, Insurance, Psychology, Social Choice, etc.
Gisella Facchinetti
University of Salento, Italy
gisella.facchinetti@unisalento.it
Inés Couso
Universidad de Oviedo, Spain
couso@uniovi.es
Bice Cavallo
University of Naples "Federico II", Italy
bice.cavallo@unina.it

Computer-Supported Collaborative Work has been defined as a generic term, which combines the understanding of the way people work in groups with diverse enabling technologies, such as computer networking, and associated hardware, software, services and techniques [1].
The recent increase on the use of mobile and wearable devices, and emerging technologies such as virtual reality, augmented reality, the internet-of-things, among others, have opened up possibilities for enhancing collaborative work experiences; fostering participation, collaboration, creativity and engagement; and creating huge opportunities for integration and research. This has stimulated the need of computational intelligence applications in CSCW. Particularly, Fuzzy Logic (FL) has been used to handle uncertainty, with the aim of mimic human reasoning; and when combined with other soft computing techniques and computational intelligence methods, it has been used as a base to model, simulate, and create hybrid dynamic systems for collaborative telework, learning and training environments for skills transfer, and knowledge management.
Moreover, when coupling machine learning with context-aware knowledge representation, possibly using Fuzzy Logic or Case-based Reasoning, new models can be designed; supporting approaches such as Contextual Learning; where Information is presented in a way that the learners are able to construct meaning on their own experience, sharing this meaning with others, as well as letting the machine the ability to auto-adapt according to the indicators of this meaning. The application areas are numerous; and when applied to real-time decision making support, they construct knowledge from unstructured uncertain information data, with the challenge of measuring knowledge due to an ever-changing environment.
Therefore, the aim of this Special Session is to explore recent advances and applications of fuzzy logic, machine learning, and computational intelligence in the field of computer-supported collaborative work, with the aim to provide a forum to disseminate and discuss recent and significant research efforts in this field.
[1] Wilson, P. (1991). Computer Supported Cooperative Work: An Introduction. Kluwer Academic Pub.

The topics include but are not limited to:

  • Collaboration systems based on emerging technologies, including (but not limited to) mobile and ubiquitous computing, virtual, augmented and mixed reality, context-aware computing, internet-of-things, cyber-physical systems, and HCI
  • Computer-supported collaborative learning (CSCL), including (but not limited to) personalised learning, adaptive educational systems, intelligent tutors, game-based learning, lifelong learning and transfer learning
  • Mining and Modelling studies, analyses and infrastructures for making use of large and small-scale data, including structured and unstructured type of data
  • Social and crowd computing. Studies, theories, designs, mechanisms, systems, and/or infrastructures addressing social media, social networking, wikis, blogs, online gaming, crowdsourcing, collective intelligence, virtual worlds or collaborative information behaviours
  • Domain-specific collaborative applications, including applications to manufacturing, scheduling, tele healthcare, tele homecare, transportation, sustainability, education, accessibility, global collaboration, facilities services, high security operations, or other domains
  • Innovations, challenges and advancement of solutions for real-time support, collaboration and training, knowledge discovery.
Anasol Pena-Rios
University of Essex
acpena@essex.ac.uk
Anne Liret
British Telecommunications / BT France
anne.liret@bt.com
George Panoutsos
University of Sheffield, UK
g.panoutsos@sheffield.ac.uk

The aim of this special session is to explore the uses of fuzzy systems in real world service operation applications. Fuzzy systems have demonstrated their capabilities in handling uncertainties. This makes them quite suitable for improving the performance and capabilities of real world applications. This uncertainty is particularly present in operational planning, where expected customer demand and expected available supply may not be easily predictable. The uncertainty is then also present from the types of customers to contact, to the delivery of the service, where that delivery relies on effective planning of resources.
The session will cover the latest developments in applications in service operations that utilise fuzzy logic systems. Applications should demonstrate a real world implementation. Potential benefits of the implementation of these systems should also be discussed.

The topics include but are not limited to:

  • Fuzzy Systems in Operational Planning
  • Fuzzy Systems in Scheduling
  • Fuzzy Systems in Logistics
  • Fuzzy Systems in Big Data for Industry
  • Fuzzy Systems in Employee Training
  • Fuzzy Systems in Customer Data Analysis
Gilbert Owusu
BT Plc
gilbert.owusu@bt.com
Sid Shakya
BT Plc / EBTIC
sid.shakya@bt.com
Andy Starkey
BT Plc
andrew.starkey@bt.com

Nowadays, robotics represents fields with a rapidly growing impact on a broad range of industrial and end users market sectors, including healthcare, agriculture, civil, commercial or consumer sectors, logistics, and transport. Nevertheless, its potential could be fully exploited only when robots have additional abilities such as reconfigurability, adaptability, interaction capability, dependability, decisional autonomy and cognitive capability. In this context, distributed reasoning capabilities provided by the integration of fuzzy logic and multi-agents methodologies may play a crucial role.
The aim of the Special Session is to provide a forum for experts in the development of multi-agent fuzzy systems to discuss the possible synergies between these research areas and open new perspectives for the development of cognitive robotics systems and applications.

The topics include but are not limited to:

  • Fuzzy Models for Autonomous Robots and Multi-robot Systems
  • Machine Learning for Cognitive Robotics
  • Human-robot Interaction
  • Coordination and Communication in Robotic Teams
  • Emergent and Adaptive Behavior in Robotics
  • Task Allocation, Cooperation and Planning
  • Cognitive Architectures for Robots
Silvia Rossi
Università degli Studi di Napoli Federico II
silvia.rossi@unina.it
Alessandro Di Nuovo
Sheffield Hallam University
a.dinuovo@shu.ac.uk
Mariacarla Staffa
Università degli Studi di Napoli Federico II
mariacarla.staffa@unina.it

The aim of this special session is to present the state-of-the-art results in the area of adaptive intelligent control theory and applications and to get together researchers in this area. Adaptive control is a technique of applying some methods to obtain a model of the process and using this model to design a controller. Especially, fuzzy adaptive control has been an important area of active research. Significant developments have been seen, including theoretical success and practical design. One of the reasons for the rapid growth of fuzzy adaptive control is its ability to control plants with uncertainties during its operation.
The papers in this special session present the most advanced techniques and algorithms of adaptive control. These include various robust techniques, performance enhancement techniques, techniques with less a-priori knowledge and nonlinear intelligent adaptive control techniques. This special session aims to provide an opportunity for international researchers to share and review recent advances in the foundations, integration architectures and applications of hybrid and adaptive systems.

The topics include but are not limited to:

  • Fuzzy Self-Organizing Controllers
  • Adaptive Fuzzy Control Design
  • Fuzzy Applications
  • Fuzzy Modeling and Simulation
  • Fuzzy Model Reference Learning Controller
  • Hybrid adaptive fuzzy control
  • Robust adaptive fuzzy control
  • Adaptive fuzzy sliding-mode control
  • Time-Delay Nonlinear Systems
  • Adaptive and learning control theory
  • Adaptive control of processes
  • Data based auto-tuning of the controller
  • Estimation and identification and its application to control design
  • Cooperative Control
  • Hybrid Intelligent Control
Valentina E. Balas
University “Aurel Vlaicu” Arad , Romania
balas@drbalas.ro
Tsung-Chih Lin
Feng-Chia University, Taiwan
tclin@fcu.edu.tw
Rajeeb Dey
CINVESTAV-IPN, Mexico
rajeeb_de@ieee.org
Yu-Chen Lin
Feng Chia University, Taiwan
yuchlin@fcu.edu.tw
Seshadhri Srinivasan
University of Sannio, Italy
seshucontrol@gmail.com

The aim of this special session is to provide a forum: (1) To disseminate and discuss contemporary and significant research efforts in the field of fuzzy logic, security and forensics; (2) To promote both theoretical and practical applications of fuzzy logic in security and forensics; (3) To foster the integration of the academic community and industry who have been working in the field of fuzzy logic, security and forensics.

The topics include but are not limited to:

  • Fuzzy logic in systems security
  • Fuzzy logic in cyber security
  • Fuzzy logic in forensics
  • Fuzzy logic in biometrics
  • Fuzzy logic in big data security
  • Fuzzy logic in cloud computing security
  • Fuzzy logic in IoT security
  • Fuzzy logic in web security
  • Fuzzy logic in software security
  • Fuzzy Logic in dependable systems
  • Fuzzy Logic in cyber-physical systems
Nitin Naik
Defence School of Communications and Information Systems, Ministry of Defence, United Kingdom
naikn@dscis.ac.uk
Paul Jenkins
Defence School of Communications and Information Systems, Ministry of Defence, United Kingdom
jenkinsp@dscis.ac.uk

The matrix factorization approach to fuzzy clustering is well documented in the literature. In 1974, Woodbury and Clive [WOO74] devised a method to estimate fuzzy partitions underlying multivariate categorical data, known as the grade of membership (GoM) model. They specifically focused on clinical data, and aimed to use the method for diagnostic and prognostic purposes. In 1990, Mirkin and Satarov [MIR90] proposed independently an extension of GoM analysis for modelling real-valued data as convex linear combinations of prototypes. Almost simultaneously, Cutler and Breiman [CUT94] presented their archetypal analysis, which received some popularity and following in the literature. The recent work by Ding, Li, and Jordan [DIN10], focusing on k-means clustering, gives another example of the application of matrix factorization approach to cluster analysis; see also [THU11] and particularly [SUL15] as an alternative way to perform fuzzy c-means clustering [BEZ81]. Therefore, we see the FUZZ-IEEE'2017 Conference as a good opportunity to integrate the matrix factorization within the mainstream of fuzzy data analysis. The matrix factorization approach to fuzzy clustering can be looked at from several perspectives. The potential contributors may wish to explore such research areas as: new approaches to prototypes, pattern recognition, computer vision, big data, cluster validation indices, estimation algorithms, software tools, and applications to any field of investigation where the fuzzy sets can be relevant.

[BEZ81] J.C. Bezdek Pattern Recognition with Fuzzy Objective Function Algorithms Plenum Press, 1981.
[CUT94] A. Cluter, L. Breiman Archetypal analysis Technometrics 36(4), 338-347, 1994.
[DIN10] C. Ding, T. Li, and M. Jordan Convex and semi-nonnegative matrix factorizations IEEE Transactions on Pattern Analysis and Machine Intelligence, 32, 1, January, 2010.
[MIR90] B. Mirkin, G. Satarov Method of fuzzy additive types for analysis of multidimensional data I Automation and Remote Control 51(5), 683-688, 1990.
[SUL15] A. Suleman A convex semi-nonnegative matrix factorisation approach to fuzzy c-means clustering Fuzzy Sets and Systems, 270, 90-110, 2015.
[THU11] C. Thurau, K. Kersting, M. Wahabzada, and C. Bauckhage Convex non-negative matrix factorization for massive datasets Knowledge and Information Systems 29 (2), 457-478, 2011.
[WOO74] M.A. Woodbury, J. Clive Clinical pure types as a fuzzy partition Journal of Cybernetics 11, 277-298, 1974.

The topics include but are not limited to:

  • Matrix factorization algorithms
  • New approaches to prototypes
  • Computer vision
  • Archetypal clustering
  • Cluster validation indices
  • Estimation algorithms
  • Software tools
Boris Mirkin
Higher School of Economics, Moscow RF and University of London, UK
bmirkin@hse.ru
mirkin@dcs.bbk.ac.uk
Abdul Suleman
Instituto Universitário de Lisboa (ISCTE-IUL), Portugal
abdul.suleman@iscte.pt

Historically, Fuzzy Logic has been successfully used to enable designers to model industrial controllers and decision making systems through simple linguistic rules. However, the design of a fuzzy system may be difficult if this must be implemented on different and heterogeneous hardware devices. As a consequence, it could be necessary to introduce standard and abstract languages for implementing fuzzy systems without considering technical constraints related to the real hardware platforms on which the fuzzy system will be run. In this context, on May 2016, IEEE Standards Association (IEEE-SA) published IEEE Std 1855™-2016, the first IEEE standard technology in the area of the Fuzzy Logic. IEEE Std 1855™-2016 is the IEEE Standard for Fuzzy Markup Language (FML), a markup language aimed at providing a unified and well-defined representation of Fuzzy Logic Systems (FLSs). This standard includes an extendable schema that natively defines the basic components of an FLS and enables the modeling of different categories of fuzzy inference engines, including Mamdani, Tsukamoto, Takagi-Sugeno- Kang (TSK), and AnYa. This standard allows fuzzy designers to simply code their ideas on heterogeneous hardware without need for a deep understanding of details related to the different platforms. In short, it makes it possible to model an FLS in a human-readable and hardware-independent way.
The aim of this session is to provide a forum to disseminate and discuss relevant researches in the area related to the modelling of FLSs for different domain applications by means of the IEEE Std 1855™-2016. Besides, this session aims to encourage the development of software to support the IEEE Std 1855™-2016 based modelling.

The topics include but are not limited to:

  • IEEE Std 1855™-2016 based Fuzzy Logic Systems for Healthy
  • IEEE Std 1855™-2016 based Fuzzy Logic Systems for brain computer interface
  • IEEE Std 1855™-2016 based Fuzzy Logic Systems for evolutionary optimization
  • IEEE Std 1855™-2016 based Fuzzy Logic Systems for neural networks
  • IEEE Std 1855™-2016 based Fuzzy Logic Systems for Ambient Intelligence
  • IEEE Std 1855™-2016 based Fuzzy Logic Systems for Power Systems
  • IEEE Std 1855™-2016 based Fuzzy Logic Systems for Web applications
  • IEEE Std 1855™-2016 based Fuzzy Logic Systems for software engineering
  • IEEE Std 1855™-2016 based Fuzzy Logic Systems for E-commerce
  • Programming Language (Java, C\C++, …) libraries for IEEE Std 1855™-2016 based modelling
  • Web technologies for IEEE Std 1855™-2016 based modelling
  • Graphical Interface for IEEE Std 1855™-2016 based modelling
  • Extension of IEEE Std 1855™-2016 for modelling additional kinds of fuzzy systems
Giovanni Acampora
University of Naples Federico II, Italy
giovanni.acampora@unina.it
Bruno Di Stefano
Nuptek Systems Ltd., Canada
bruno.distefano@gmail.com
Autilia Vitiello
University of Salerno, Italy
avitiello@unisa.it

The theory and applications of fuzzy logic have been developed and described in many research papers and books. Fuzzy logic has been applied in engineering, the natural sciences, computer science, medicine, the social sciences, statistics, uncertainty management and risk analysis. However, the place of fuzziness in mathematics is still in question. Some mathematicians readily use and work with fuzziness; others do not use fuzziness; and some believe that fuzziness has no place in mathematics.
The purpose of this special session is to begin openly to discuss these differing points of view. Given that this is a fuzzy conference, the special session organizers expect that many of the contributions to this special session will support fuzziness in mathematics. Thus, perhaps the working title for this special session should be What Fuzzy Sets Bring to Mathematics.
The purpose of this special session is to begin openly to discuss these differing points of view. Given that this is a fuzzy conference, the special session organizers expect that many of the contributions to this special session will support fuzziness in mathematics. Thus, perhaps the working title for this special session should be What Fuzzy Sets Bring to Mathematics.
In particular, with this special session, we hope to expand the fuzziness-in-mathematics or the many-valuedness-in-mathematics discussion to include examples of mathematical situations which naturally encompass fuzziness or many-valuednesss. A reason for using “many-valuedness” is the foundations of fuzziness are not mathematical. However, the usual implementations of fuzziness in mathematics involve many-valuedness, and many-valuedness can be expressed in precise mathematics.
Authors submitting papers with examples of fuzzy sets or many-valuedness should clearly describe their examples, explain the importance of their examples, and show why/how the fuzzy sets are an integral and important part of the examples.

The topics include but are not limited to:

  • The role of fuzziness in mathematics
  • Why mathematics needs many-valuedness
  • Examples of many-valuedness in mathematics
Austin Melton
Kent State University, USA
amelton@kent.edu
Didier Dubois
didier.dubois@irit.fr
Stephen E. Rodabaugh
serodabaugh@ysu.edu

Due to the increasing of the system complexity in different technology areas, such as transport systems, renewable energy systems, power network, electric smart grids… it is of significance to address several fundamental problems regarding high-level systems reliability, safety and health monitoring of such systems.
This special session aims to present state-of-the-art development in the areas of health monitoring, fault estimation, detection and diagnosis, prognosis and tolerant control for nonlinear systems. Both theoretical and technology-based aspects for practical engineering systems will be considered, and recent theoretic study and application progress will be reported by academics, scientists and engineers. Indeed, there are still places for developing and improving the existing results in order to propose new techniques for fuzzy systems control and their healthy.

The topics include but are not limited to:

  • Fault diagnosis for Takagi-Sugeno models
  • Fault diagnosis for Type-2 fuzzy systems
  • Estimation and optimization for complex fuzzy models
  • Robust fault detection for TS systems
  • Advanced fault tolerant control for complex fuzzy systems
  • Data-driven methods in diagnosis and prognosis
  • Real‐time implementation in practical applications
  • Safety and health monitoring of complex systems
Hamid Reza Karimi
Politecnico di Milano
hamidreza.karimi@polimi.it
Mohammed Chadli
Université de Picardie Jules Verne
mohammed.chadli@u-picardie.fr
Peng Shi
The University of Adelaide
peng.shi@adelaide.edu.au

It is clear that computers have revolutionized information processing throughout all aspects of society. This is particularly true in the military domain, where sensors and vast amounts of data allow an unprecedented potential for real-time situational awareness by all echelons of command. There are many tasks, however, that humans still perform much better than computers. Examples of such tasks include relationship extraction, sentiment analysis, and other natural language processing tasks; planning, reasoning, and other complex cognitive tasks; recognition, classification, and other perceptual tasks; and the like.
Research in the field of “human computation”, in which humans participate along with machines as computational elements, is rapidly expanding. The focus of this special session is to explore the challenges of, and propose solutions for, organizing and including humans in fuzzy-based intelligent systems to leverage the complimentary abilities of humans and machines in the military domain. We are particularly interested in applying human computation to allow for increased situational awareness by commanders on the battlefield. This focus includes, but is not limited to, areas such as information foraging by automated agents, information aggregation, reasoning with complimentary and contradictory information, decision support applications, and other related areas.

The topics include but are not limited to:

  • Fuzzy/human computation combinations for situational awareness
  • Fuzzy/human computation combinations for sense making
  • Fuzzy/human computation combinations for information foraging
  • Fuzzy/human computation combinations for reasoning with disparate information
  • Fuzzy/human computation combinations for information aggregation
  • Fuzzy/human computation combinations for information exploitation
  • Fuzzy/human computation combinations for decision science
Robert J. Hammell II
Towson University
rhammell@towson.edu
Timothy P. Hanratty
U.S. Army Research Laboratory
timothy.p.hanratty.civ@mail.mil

Business processes define how the work is performed within an organization and improve the collaboration among all the involved stakeholders in enterprises and administrative entities of today’s world. The area of business process management (design, modeling, representation, monitoring, execution and discovery) is quite wide and several approaches in the last years were proposed to provide effective solutions to the problems that it raises.
In particular, when processes are not (or can not) purposefully designed and optimized and quickly change over time or are loosely-structured, most business process management algorithms still give unsatisfactory results. In those cases Fuzzy logic and approximate rule-based reasoning can help explicitly representing uncertainty and incomplete information in processes. One interesting area of Business Process Management that can benefit from the application of fuzzy theories is Process mining. It consists of a set of techniques and methods that collectively aims to extract compact and understandable representations of business processes from enterprise systems logs.
To this aim, the existing Process Mining approaches (e.g.traditional process mining algorithms like the alpha algorithm or those based on structural analysis of control and data flow of Petri nets, BPMN, UML Activity Diagrams, or EPCs) are less useful since they are not capable to handle uncertainty and imprecision.
The fuzzy theory nowadays encompasses a well organized corpus of basic notions including aggregation operations, relations, measures of information content, a calculus of fuzzy numbers. For this reason fuzzy theories are becoming a key element in the development of data and process management approaches. In the field of business process management fuzzy theory could play an important role for clustering, data analysis, data fusion, pattern recognition, modeling, multi-criteria evaluation and, more in general, for several business intelligence approaches. Fuzzy theories can be also combined with other techniques such as neural nets and evolutionary computing and applied to both business process design and management approaches as well as to complex process analysis focused on the extraction and representation of hidden knowledge.
Basing on the above consideration, the objective of the special session is to provide a forum for the discussion of recent advances in the application of fuzzy set methodology and technology to business process management problems (e.g. adaptive and context-aware process execution, process and related data representation, management of process execution, effective analytics and visualization, process mining methods, compliance, data integration and quality, process modeling) and to offer an opportunity for researchers and practitioners to identify and discuss integration advances highlighting new promising research directions.

The topics include but are not limited to:

  • Business intelligence
  • Fuzzy models supporting business process management approaches
  • Offline and Online process mining approaches dealing with uncertainty
  • Fuzzy-based qualitative and quantitative process analysis
  • Using Fuzzy theories to for process querying, refactoring, searching and versioning
  • Fuzzy models to represent process data
  • Fuzzy models to perform process integration
  • Fuzzy models and data mining approaches for process management
  • Fuzzy-based frameworks specific for business process representation and modeling
  • Case studies and empirical evaluations
Mario Luca Bernardi
Giustino Fortunato University
m.bernardi@unifortunato.eu
Marta Cimitile
Unitelma Sapienza University
marta.cimitile@unitelma.it
Fabrizio Maria Maggi
University of Tartu
f.m.maggi@ut.ee

The constant growth of the Internet and introduction of such concepts 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 different pieces of information – it is inherently heterogeneous, imprecise, uncertain, incomplete and even inconsistent. The users are not able to constantly search the web and analyze obtained data and information. There is a need for systems supporting the users in their web-related activities.
Fuzzy Logic and Soft Computing 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, or representing information and knowledge using fuzzy ontology and fuzzy markup languages. It is anticipated, that applications of fuzziness and soft computing technologies to web systems will bring a new and human friendly way of interacting with the web, where many tasks related to web activities and information processing can be performed by systems on human behalf.
The 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 soft computing methods suitable for solving issues specific to web utilization, as well as to representation and processing of information and knowledge with special emphasis on fuzzy ontologies and their applications in various domains.

The topics include but are not limited to:

  • multi-criteria decision-making
  • approximate reasoning
  • case-based reasoning
  • fuzzy ontology and ontology-based systems
  • knowledge- and rule-based systems
  • hybrid intelligent systems
  • recommendation systems
  • context-aware systems
  • information retrieval and knowledge discovery
  • search engines
Marek Reformat
University of Alberta
marek.reformat@ualberta.ca
Chang-Shing Lee
National University of Tainan (NUTN), Tainan, Taiwan
changshing.lee@gmail.com
Trevor Martin
University of Bristol, UK
Trevor.Martin@bristol.ac.uk
Giovanni Acampora
University of Naples Federico II, Italy
giovanni.acampora@unina.it

Bloody crimes among strangers or family members are becoming more and more frequent in the society, resulting in an increasing feeling of fear in the population with related sociological and relational problems. These bloody events are very critical to investigate in the absence of eye witness. Indeed, when this is not found, investigation agencies have to reconstruct the criminal actions by only depending upon the evidences found on the crime scene. Unfortunately, during their activity, investigation agencies have to cope with a strong uncertainty related to the crime scene and the evidences found in it. Indeed, a crime scene is inherently imprecise due to high interaction occurring among victim, aggressor and surrounding environment. In this scenario, finding evidences is difficult and derivations from them are not obvious but they require an expertise-based human reasoning. Moreover, the investigation activity is performed manually by means of imprecise tools. As a consequence, each step related to the investigation activity adds more and more inaccuracies. All this imprecision makes the crime investigation an application domain particularly suitable to be addressed by fuzzy logic.
The aim of this session is to provide a forum to disseminate and discuss relevant researches in the area related to the application of fuzzy logic and computational intelligence techniques to the crime investigation, in order to support the resolution of complex cases and act as a deterrent to violent crimes.

The topics include but are not limited to:

  • Fuzzy logic for crime scene’s data analysis
  • Fuzzy logic for bloodstain pattern analysis
  • Fuzzy logic for crime scene reconstruction
  • Fuzzy logic approaches to support crime image processing
  • Fuzzy logic techniques for DNA profile matching
  • Fuzzy logic techniques for DNA Finger Printing
  • Fuzzy logic based decision making systems emulating investigator reasoning
Autilia Vitiello
University of Salerno, Italy
avitiello@unisa.it
Giovanni Acampora
University of Naples Federico II, Italy
giovanni.acampora@unina.it
Ciro Di Nunzio
University Magna Graecia of Catanzaro, Italy
dinunzio@unicz.it
Luciano Garofano
Italian Academy of Forensic Sciences, Italy
lugarofano@gmail.com
Maurizio Saliva
Azienda Sanitaria Locale ASL Napoli 3 Sud, Torre del Greco, Italy
dott.mauriziosaliva@gmail.com

Fuzzy logic influenced control and system theory under many aspects, from human-based decision making algorithms implementation to modeling and control of non-linear complex systems. Indeed, fuzzy logic techniques enable efficient and fast modelling and control of complex systems employed in industrial fields and human-shared environments where automated solutions are not allowed. Variable stiffness of the environment, deformable objects and component flexibilities are elements characterizing human-robot interaction in unstructured environments and even manufacturing hand-craft applications. The scope of this special section is to collect the state of the art of fuzzy controller applied to autonomous advanced robots that can be used in industries and in the every-days environments substituting or cooperating with humans in the execution of complex tasks where adaptive behavior is required. The main topics that will be addressed range from the classical well established applications of fault tolerance in Industrial Robotics to the control strategies for Human-Robot Interaction where standard industrial position-based control solutions are unsuitable in force-tracking or adaptive admittance/impedance applications. Other issues will be discussed such as trajectory planning for autonomous robots, modelling and identification for anthropomorphic manipulators, sensor fusion and image analysis for advanced control.

The topics include but are not limited to:

  • Fault Tolerance in Industrial Robotics
  • Multi-robot coordination and communication
  • Fuzzy control for robot navigation and manipulation
  • Modelling and identification for anthropomorphic manipulators
  • Sensor fusion and image analysis for advanced control
  • Adaptive fuzzy control methods for Human-Robot Interaction.
Fanny Ficuciello
University of Naples Federico II, Department of Electrical Engineering and Information Technology
fanny.ficuciello@unina.it
Alberto Finzi
University of Naples Federico II, Department of Electrical Engineering and Information Technology
albert.finz@gmail.com
Vincenzo Calabrò
Cybernetics R&D department of Kongsberg Maritime
vincenzo@calabrothers.com

The emergence of Data Science is radically changing the way products and services are conceived, made, delivered and operated. Thus, the data gathering became fast-paced, leading to high volumes of data to be collected and analysed. Fuzziness is called to play a relevant role in order to make the analysis of massive data feasible, effective and meaningful. This special session aims at sharing and discussing problems and applications where fuzzy techniques are employed to build models in different industrial and manufacturing domains.

The topics include but are not limited to:

  • Fuzzy Logic for Big Data in manufacturing
  • Fuzzy Logic for Data Science in industrial applications
  • Fuzzy Logic for Industry 4.0
  • Fuzzy Logic for Data Science in Finance
  • Fuzzy Logic in recommendation problems
  • Fuzzy Logic for the analysis of large social networks and graphs
  • Fuzzy Logic in Deep Learning architectures
Luigi Troiano
University of Sannio
troiano@unisannio.it
S. Irene Díaz
University of Oviedo, Spain
sirene@uniovi.es

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 real world big data applications 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 topics include but are not limited to:

  • 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
  • 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 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

Social Signal Processing (SSP) is a discipline that aims at giving an accurate interpretation and appropriate display of social signals using multimodal signal processing, which are inherently characterised by high levels of imprecisions and uncertainties, by means of artificial/computational intelligence techniques. Thanks to these techniques, SSP attempts to automatically capture non verbal behavioural cues (emotions, facial expressions, prosody, gestures, postures, etc.) that accompany any human-human (and human-machine) interaction. A peculiarity of social signals is that they can take the form of complex constellations of nonverbal behavioral cues. SSP involves a multidisciplinary collaboration between human sciences (psychology, anthropology, sociology, etc.) and technology (computer vision, speech analysis and synthesis, machine learning, signal processing, etc.), making the whole process a big challenge for computational intelligence community. The main goal of this session is to collect high-quality papers where computational methods based on fuzzy reasoning are used to face this tough challenge in SSP.

The topics include but are not limited to:

  • Applications of Fuzzy Logics to social psychology and social signals processing
  • Applications of Fuzzy Logics to facial behaviour analysis and synthesis in social interactions
  • Applications of Fuzzy Logics to expressive speech analysis and synthesis in social interactions
  • Applications of Fuzzy Logics to human gesture and action recognition and synthesis in social interactions
  • Applications of Fuzzy Logics to multimodal human behavior analysis and synthesis in social interactions
  • Applications of Fuzzy Logics to design of socially-adept embodied conversational agents
Franco Cutugno
University of Naples Federico II
cutugno@unina.it
Antonio Origlia
University of Naples Federico II
antori@gmail.com
Alessandro Vinciarelli
University of Glasgow and Idiap Research Institute
vincia@dcs.gla.ac.uk

Practical applications of Affective Computing and Physiological Computing based systems seek to achieve a positive impact on our everyday lives by monitoring, recognising and acting on our physiological signals, speech, facial expressions, gestures and emotive states. Integrating these sensing modalities into intelligent and pervasive computing systems can reveal a contextually richer understanding of how emotional responses, changing moods, and sensations (pain, touch, tastes and smells) are a reaction to or influence how we interact with the environment through increasingly connected computing artifacts.
The integration of AC and PC raise new challenges for signal processing, machine learning and Computational Intelligence (CI). Fuzzy systems provide a useful methodology for addressing research challenges in AC/PC, where data sources such as: external and internal body signals (e.g. heart rate, brain waves, skin conductance and respiration), facial features, speech and human kinematics are inherently noisy and uncertain. Fuzzy systems are well suited to model and represent vague and ambiguous linguistic notions of perceptions, impulses, feelings, desires and human cognitive states which can be both subject and context dependent. As we improve ways of integrating affective and physiological data more pervasively, it will create highly complex, dynamic and information rich scenarios. Here the use of hybrid fuzzy approaches combining other CI approaches such as evolutionary algorithms and deep learning techniques can be used to create novel self-learning affective computing systems that are able to more naturally interact and empathize with people, understand their physical and emotive states and automatically respond in beneficial and useful ways.
This special session aims to bring together researchers to discuss how fuzzy logic approaches can be used to help solve challenging AC/PC problems, and develop ways of modelling and using physiological and affect information to inspire new approaches and applications.

The topics include but are not limited to:

  • Models of emotion and physiological information
  • Classifiers for physiological information
  • Applications based on/around physiological information
  • Fuzzy set and system based architectures for processing emotions and other affective states
  • Automatic emotion recognition & synthesis from physiological signals, facial expressions, body language, speech, or neurocognitive performance
  • Multimodal sensor fusion for emotional recognition
  • Emotion mining from texts, images, or videos
  • Affective interaction with virtual agents and robots based on fuzzy systems
  • Physiological and emotion driven control
  • Applications of affective computing in interactive learning, affective gaming, personalized robotics, virtual reality, social networking, smart environments, healthcare and behavioral informatics
Faiyaz Doctor
Coventry University, UK
faiyaz.doctor@coventry.ac.uk
Christian Wagner
University of Nottingham, UK
christian.wagner@nottingham.ac.uk
Dongrui Wu
DataNova, NY, USA
drwu09@gmail.com
Marie-Jeanne Lesot
Université Pierre et Marie Curie, France
Marie-Jeanne.Lesot@lip6.fr

Decision Making is a common task related to intelligent and complex activities in which human beings face situations where they must choose among different alternatives by means of reasoning and mental processes. Such decision situations usually involve different types of uncertainty according to their nature.
Dealing with uncertainty in real life has been a long term challenging task, and different methodologies and theories have been proposed in the literature to deal with it. Recently V. Torra (2010) introduced the concept of hesitant fuzzy sets (HFSs) as an extension of fuzzy sets that models the uncertainty provoked by the hesitation that might appear when it is necessary to assign the membership degree of an element to a fuzzy set. In spite of youth of this theory, it has attracted attention of leading researchers to the study of analysis of multiple operations and properties of HFSs and it has been used to solve problems in different fields such as decision making.
This special session plans to focus on the recent advances in theory of HFS and its application on decision making providing an opportunity for researchers working in these areas to discuss and share new ideas and results. We expect contributions in the use of HFS in decision making.

The topics include but are not limited to:

  • HFSs foundation and recent advances
  • Multicriteria and group decision making methods dealing with HFS
  • Hesitant information fusion
  • Aggregation operators for HFS
  • Dealing with hesitant information to represent preferences
  • Handling incomplete HFS
  • Dealing with HFS in applications such as, engineering evaluation, recommender systems, sensory evaluation
Ismat Beg
Lahore School of Economics, Centre for Mathematics and Statistical Sciences
ibeg@lahoreschool.edu.pk
Rosa M. Rodríguez
University of Granada, Spain
rosam.rodriguez@decsai.ugr.es
Zeshui Xu
Sichuan University
xuzeshui@scu.edu.cn

Rough set theory is a new mathematical approach to imperfect knowledge. Rough sets have been proposed for a very wide variety of applications. In particular, the rough set approach seems to be important for Artificial Intelligence and cognitive sciences, especially in machine learning, knowledge discovery, data mining, expert systems, approximate reasoning and pattern recognition. The objective of this special session is to showcase the real-world applications of hybridization of rough sets and fuzzy rough sets with bio-inspired optimization techniques and other methods of data exploration and approximate computation. The special emphasis will be put on hybrid solutions combining rough sets with other tools, as well as the importance of utilization of domain knowledge in the data mining and data processing solutions.
The aim of this special session is provide an opportunity for international researchers to share and review recent advances in the foundations, integration architectures, and applications of bio-inspired optimization techniques with rough set and fuzzy rough. The special session aims to solicit original, full length original articles on new findings and developments from researchers, academicians and practitioners from industries, in the area of rough set theory, fuzzy rough, granular computing, knowledge discovery and data mining.

The topics include but are not limited to:

  • Rough set theory
  • Fuzzy Rough
  • Rough sets and near sets
  • Bio-inspired Rough set
  • Bio-inspired Fuzzy Rough set
  • Swarm Optimization
  • Data mining
  • Rough fuzzy hybridization
  • Granular computing theory and applications
  • Granular rough-fuzzy networks
  • Computing with words
  • Approximate reasoning
  • Machine learning
  • Evolutionary computing
  • Web intelligence and mining
Ahmad Taher Azar
Benha University
ahmad_t_azar@ieee.org
Valentina E. Balas
University “Aurel Vlaicu” Arad , Romania
balas@drbalas.ro
Camelia Pintea
Universitatea Tehnica Cluj-Napoca Cluj-Napoca, Romania Join institution
dr.camelia.pintea@ieee.org
Rabie A. Ramadan
Cairo University
rabie@rabieramadan.org
Mario Pavone
University of Catania
mpavone@dmi.unict.it
Nicolaie Popescu-Bodorin
University of S-E Europe LUMINA, Bucharest, RO
bodorin@ieee.org

The notions of near and far in mathematics can be traced back to works by F. Riesz, F. Hausdorff and V.A. Efremovich as well as later works by J.M. Smirnov, S. Leader, M. Lodato, S.A. Naimpally, J.F. Peters, A. Di Concilio, G. Di Maio, G. Beer, and many others. The related notions of resemblance and similarity can be traced back to J.H. Poincaré, who introduced sets of similar sensations (nascent tolerance classes) to represent the results of G.T. Fechner's sensation sensitivity experiments. F. Riesz introduced the concept of proximity or nearness of pairs of sets at the International Congress of Mathematicians (ICM) in 1908. Near sets have their origin in various forms of geometry and topology. Nearness of sets has led the solution of a number of problems rooted in rich mathematical structures and human perception in areas such as image processing, computer vision, object and pattern recognition. Near set theory provides a conceptual as well as practical framework that offers insights into several aspects of computer science (learning, social networks) and engineering (computer vision, pattern recognition) as well as pure and applied mathematics (proximity, topology, geometry and algebra).

The topics include but are not limited to:

  • Computational proximity
  • Nearness spaces
  • Nearness measures
  • Proximities
  • Nearness and uniformity and their applications in mathematics, science and engineering
Adel M. Alimi
University of Sfax, Tunisia
adel.alimi@ieee.org

Fuzzy set theory is the subject of intense investigation in areas like control theory, robotics, biomedical engineering, computing with words, knowledge discovery, remote sensing and socioeconomics, to name a few. In computer vision, other fields, e.g., “machine learning”, and communities, e.g., PAMI, ICCV, CVPR, ECCV, NIPS, are arguably state-of-the-art. In particular, the majority of top performing techniques on public datasets are steeped in probability (regardless of if they are the correct theory for the task at hand) or neural networks (e.g., the deep learning revolution).
Big questions to the fuzzy set community include the following. What is (should be) the role of fuzzy set theory in computer vision? Does fuzzy set theory make the most sense and biggest impact at low-, mid- or high-level computer vision? Furthermore, do current performance measures overly favor machine learning approaches? Last, is there additional benefit that fuzzy set theory brings, and if so, how is it measured?
This special session invites new fuzzy set theory research in computer vision. It is a follow up to the 2016 WCCI tutorial Computer Vision: A Computational Intelligence Perspective and 2016 special session Fuzzy Set Theory in Computer Vision.
In particular, we encourage authors to investigate their theory using public datasets and to compare results to fuzzy and non-fuzzy methods. Topics of interest include both theory and applications in computer vision

The topics include but are not limited to:

  • Detection and recognition
  • Categorization, classification, indexing and matching
  • Three dimensional computer vision
  • Computer vision in different sensors (e.g., radar, infrared, hyperspectral)
  • Image features and descriptors
  • Motion analysis and tracking
  • Linguistic descriptions (e.g., activity/behavior, color, spatial relations, etc.)
  • Video: events, activities and surveillance
  • Intelligent change detection
  • Face and gesture
  • Low-level, mid-level and high-level computer vision
  • Signal, spectrum, feature, data and decision level fusion for computer vision
  • Medical and biological image analysis
  • Vision for robotics
  • Deep learning
Derek T. Anderson
Mississippi State University, USA
anderson@ece.msstate.edu
Chee Seng Chan
University of Malaya, Malaysia
James M. Keller
Mississippi State University, USA

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.
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 the related researchers from a variety of fields including intelligent robotics, human-machine interface, Kansei engineering and so on.

The topics include but are not limited to:

  • 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
Tomohiro Yoshikawa
Nagoya University, Japan
yoshikawa@cse.nagoya-u.ac.jp
Yoichiro Maeda
Institute of Technologists, Japan
maeda@iot.ac.jp

Fuzzy interpolation provides a flexible means to perform reasoning in the presence of insufficient knowledge that is represented as sparse fuzzy rule bases. It enables approximate inferences to be carried out from a rule base that does not cover a given observation. Fuzzy interpolation also provides a way to simplify complex systems 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: (1) to disseminate and discuss recent and significant research efforts in the development of fuzzy interpolation and related techniques, 82) to promote both theoretical and practical applications of fuzzy interpolation, and (3) to foster integration of fuzzy interpolation with other computational intelligence techniques.

The topics 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 methods
Qiang Shen
Aberystwyth University, UK
qqs@aber.ac.uk
Laszlo Koczy
Szechenyi Istvan University, Hungary
koczy@tmit.bme.hu
Shyi-Ming Chen
National Taiwan University of Science and Technology, Taiwan
smchen@mail.ntust.edu.tw
Ying Li
Northwest Polytechnical University, China
lybyp@nwpu.edu.cn

Modern manufacturing environments are evolving considerably in order to adopt new ICT technologies and exploit their full potential to develop the so called factories of the future. Some of the main objectives consist on: (i) making sustainable manufacturing processes (highly efficient, productive, quality and accurate adaptive production processes), (ii) integrating human expert knowledge with the technology (iii) reducing the use of resources and generation of waste, (iv) opening new markets. The use of digital technologies throughout the manufacturing value chain plays a key role in order to achieve these goals.
Advanced Manufacturing implies an advanced degree of automation, autonomy and digitization within industrial processes and factories. Thus, advances in electronics and information technologies are considered key enabling technologies which are driving the transformation of current manufacturing systems towards the so called “Intelligent Factories”. The volume of data generated and archived in the manufacturing processes (design, simulation, monitoring, quality control, maintenance, etc.), represents a rich source of information which could potentially provide deep insight into the underlying physical processes and could also be used for process optimization. However, acquired process information is usually heterogeneous, complex, and with various degrees of uncertainty. Thus, intelligent data processing and analysis is an essential mechanism in order to extract useful knowledge models for their use in decision making.
With this special session, the organizers would like to continue the advanced manufacturing subject within FUZZ-IEEE, after its first edition introduced it in 2016 at WCCI in Canada. This time, organizers would like to give more emphasis on autonomous systems for manufacturing realized via soft computing methodologies.
The goal of this special session is to provide an insight into state of the art use of fuzzy logic based solutions in advanced manufacturing environments. These solutions should target mainly applications of: product design optimization, new manufacturing architectures for flexible manufacturing, product lifecycle management (PLM), zero-defect manufacturing, additive manufacturing, maintenance services, computer aided monitoring and quality non-destructive testing (NDT), collaborative manufacturing environments.

The topics include but are not limited to:

  • Fuzzy Control
  • Data Mining, classification and information fusion
  • Incremental learning - Self-Learning systems
  • Modeling, Control, and Optimization
  • Decision Support Systems
  • Autonomous systems
  • Fault-tolerant control
  • Human-Centric Systems
  • Machine to Machine (M2M) communications
  • Predictive Process Control
  • Fault/Anomaly Detection & Clasification
Luka Eciolaza
Mondragon University, Spain
leciolaza@lortek.es
George Panoutsos
University of Sheffield, UK
g.panoutsos@sheffield.ac.uk
Ander Muniategui
Intelligent Manufacturing at IK4-LORTEK, Spain
amuniategui@lortek.es

The development of formal mathematical models to support experts in making decisions is of great importance to assure the validity of the actions derived from a decision outcome is theoretically sound. This is of special relevance in decision contexts where the information on the problem at hand is not amenable to be modelled in a quantitative and precise way. Another issue to be addressed is that of inconsistency of information and the dynamic nature of the decision making process itself. This type of decision-making is now being described as decision-making under uncertainty in inconsistent and dynamic environments.
This special session aims at gathering researchers with an interest in the research area described above. Specifically, we are interested in contributions towards the development of consensus models for such decision-making problems, as well as formal approaches that are able to support incomplete or missing information.
Contributions to this special session are expected to pay special attention to the rigorous motivation of the approaches put forward and to support all aspects of the models developed with a corresponding theoretical sound framework. Straight approaches lacking such scientific approach are discouraged.

The topics include but are not limited to:

  • Consensus in group decision-making
  • Consistency in fuzz preference modeling
  • Missing preferences in fuzzy decision making
  • Aggregation of fuzzy preferences
  • Consensus measures
  • Consensus and fuzzy ontologies
  • Consensus software tools
  • Fuzzy decision in Web 2.0
Enrique Herrera-Viedma
University of Granada, Spain
viedma@decsai.ugr.es
Francisco Chiclana
De Montfort University, UK
chiclana@dmu.ac.uk
Yucheng Dong
Sichuan University, China,
ycdong@scu.edu.cn
Francisco Javier Cabrerizo
University of Granada, Spain,
cabrerizo@decsai.ugr.es

To enhance the robustness of the power grid, reduce greenhouse gas emission, and increase the penetration rate of renewable energy, the development of Smart Grid technologies is indispensable. Smart Grid technology uses information, communication, and automation technology to deploy an integrated power grid with smart power generation, transmission, distribution and users. Smart Grid emphasizes automation, safety, and the close cooperation between the users and suppliers to improve the operating efficiency of power system, to enhance power quality and to solidify grid reliability. From this perspective, a crucial issue is how to support the evolution of existing electrical grids from static hierarchical systems to self-organizing, high scalable and pervasive networks. 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.
The use of Smart Grid technology is also aligned with the policy goals of expanding the application of renewable energy, energy conservation, and carbon reduction. Renewable energies in general include wind, photovoltaic, hydroelectric, fuel cell and biomass power generation systems. They have been getting more attention recently due to cost competitiveness and environment friendly, as compared to fossil fuel and nuclear power generations. 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. Plus, to have a more reliable power supply, renewable power generation systems are usually interconnected with the power grid. As a result, modeling and controlling the power grid using Smart Grid techniques, such as smart meters, micro-grids, and distribution automations become very important issues.
Additionally, due to the highly nonlinear and time-varying nature with unmodeled dynamics, 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. Moreover, the large scale deployment of fuzzy-based technologies in Smart Grids could lead to a more efficient tasks distribution among the distributed energy resources and, consequently, to a sensible improvement of the electrical grid resiliency.
Hence, topics of interest of the special session would cover the whole range of researches and applications of fuzzy systems in renewable power generations and Smart Grid systems, with particular emphasis also on the emerging technologies and methodologies of Fuzzy logic and Computational Intelligence for resilient and proactive Smart Grids, ranging from methods for balancing resources to various control and security aspects. This special section not only focuses on technological breakthroughs and roadmaps in implementing the technology, but also presents the much needed sharing of best practices.

The topics include but are not limited to:

  • Fuzzy modeling of renewable power generation systems
  • Fuzzy energy management systems
  • Fuzzy control of renewable power generation systems
  • Fuzzy distribution systems automation
  • Prediction of renewable energy using fuzzy and neuro-fuzzy systems
  • Fuzzy power quality, protection and reliability analysis of power system
  • Hybrid systems of computational intelligence techniques in renewable power generation systems
  • Fuzzy Logic application for Smart Cities
  • Probabilistic and non-probabilistic paradigms for smart grids analysis in the presence of data uncertainty
  • Meta-heuristic algorithms for fast contingency analysis
  • Proactive paradigms for smart grid control and regulation
  • Decentralized and cooperative sensor networks for dynamical loading of power equipment
  • Fuzziness in optimal power flow analysis
  • Fuzziness in system restoration and smart restoration tools
  • Fuzzy Inference Systems for renewable power forecasting
  • Application of pervasive sensing systems for condition monitoring
Faa-Jeng Lin
National Central University, Taiwan
linfj@ee.ncu.edu.tw
Francesco Grimaccia
Polytechnic University of Milano, Italy
francesco.grimaccia@polimi.it
Marco Mussetta
Polytechnic University of Milano, Italy
marco.mussetta@polimi.it
Alfredo Vaccaro
University of Sannio, Italy
vaccaro@unisannio.it

This special session will provide a forum to consolidate the community of researchers in the area of Complex Fuzzy Sets and Logic, share our current ideas, reflect on future directions, and communicate our ideas and vision to the larger Computational Intelligence community. As such, we welcome submissions on all aspects of complex fuzzy sets or complex fuzzy logic.

The topics include but are not limited to:

  • Theory of complex fuzzy logic
  • Complex fuzzy sets
  • Complex fuzzy inferential systems
  • Elicitation of complex fuzzy rules
  • Machine learning for complex fuzzy inferential systems
  • Hybridizations of complex fuzzy sets and logic with other CI technologies
  • Data mining with complex fuzzy sets and logic
  • Applications of complex fuzzy sets and logic
  • Complex fuzzy logic hardware
Scott Dick
University of Alberta, Canada
dick@ece.ualberta.ca
Sarah Greenfield
De Montfort University, UK
s.greenfield@dmu.ac.uk
Francisco Chiclana
De Montfort University, UK
chiclana@dmu.ac.uk

The European Union's Framework Programme for Research and Innovation ''Horizon 2020'' and many national research initiatives, have identified a set of challenges our societies will have to face in the middle and long term. Among them we can cite: intelligent transport, health, safe societies, tourism, disaster and crisis management, etc. In each of these challenges it is possible to recognize decision and optimization problems leading to models and frameworks (be it mathematical, linguistic, computational...) requiring suitable solution methods or algorithms. Uncertainty, vagueness, imprecision are ubiquitous in the problems associated with such challenges. Thus fuzzy sets and systems concepts, methodologies and techniques, emerge as proper tools to model and solve them.
The aim of this special session is to serve as a meeting point and discussion forum for researchers and practitioners on the latest developments on FSS methodologies for engineering applications in the context of the challenges mentioned above. Departing from problems associated with intelligent transport, health, disaster and crisis management, safe societies, smart systems, tourism, etc. we will welcome both theoretical and more application oriented contributions addressing: (1) problems related with the societal challenges, where the use of FSS allows to obtain better problem models or solutions (the role of FSS should be clearly stated); (2) description of deployed FSS-based applications for solving challenging problems; (3) description of ongoing efforts to stablish FSS based solutions; (4) FSS ''Success Stories''.

The topics include but are not limited to:

    José Luis Verdegay
    University of Granada, Spain
    verdegay@ugr.es
    David Pelta
    University of Granada, Spain
    dpelta@ugr.es

    Many-valued logics have constituted for several decades key conceptual tools for the formal description and management of fuzzy, vague and uncertain information. In the last few years, the study of these logical systems has seen a bloom of new research related to the most diverse areas of mathematics and applied sciences. Relevant recent developments in this field are connected to the natural semantics of non-classical events. A nonclassical event is described by a formula in the language of a given manyvalued logic. A satisfying semantics for such events must account for their different aspects, in particular the "ontic" aspect, related to their vague nature, and the "epistemic" aspect, related to our ignorance, or approximate knowledge about them. The combination in a unique conceptual framework of the logic and the probability of a class of non-classical events, usually reached through the algebraic semantics and their topological or combinatorial dualities, provides both the theoreticians and the applicationoriented scholars with powerful tools to deal with this kind of events.
    This special session is devoted to the most recent development in the realm of many-valued logics, with particular emphasis on theoretical advances related to algebraic or alternative semantics, combinatorial aspects, topological and categorical methods, proof theory and game theory, manyvalued computation. In particular, results directed towards a better understanding of the natural semantics of non-classical events will be appreciated. Further, a special attention is also given to connections and synergies between many-valued logics and other different formal approaches to vague and approximate reasoning, such as Rough Sets, Formal Concept Analysis and Relational Methods.

    The topics include but are not limited to:

    • Algebraic semantics of many-valued logics
    • Applications of many-valued logics to Formal Concept Analysis and Relational Methods
    • Applications of many-valued logics to Fuzzy Sets and to Rough Sets
    • Combinatorial or topological dualities
    • Computational complexity of many-valued logics
    • Many-valued computational models
    • Modal logic approaches to probability and uncertainty in many-valued logics
    • Natural and alternative semantics for many-valued logics
    • Proof theory for many-valued logics
    • Representation theory
    • Subjective probability approaches to many-valued logics and nonclassical events
    Pietro Codara
    Institut d'Investigació en Intel.ligència Artificial (IIIA), Spain
    codara@di.unimi.it
    Brunella Gerla
    Università dell'Insubria, Italy
    brunella.gerla@uninsubria.it
    Diego Valota
    Università degli Studi di Milano, Italy
    valota@di.unimi.it

    Improving access and delivery of health care is a very actual topic in many countries. In order to meet the challenges posed by growing medical costs, aging population and limited resources, new technologies and new methods are being developed. Fuzzy systems play an important role in this context due to their ability to model nonlinear system behavior, to deal with non-probabilistic uncertainty and to describe model behavior in natural language, making communication with decision makers easier. This special session will bring together the contributions of prominent researchers in this area and will provide a platform to share, discuss and learn from the recent advances in the field. More specifically, the session is expected to attract papers regarding (clinical) decision support by using fuzzy set theory, process analysis with fuzzy sets, linguistic summarization of medical data and fuzzy systems-based support of healthy aging and wellbeing.

    The topics include but are not limited to:

    • Fuzzy set-based clinical decision support
    • fuzzy sets for patient planning
    • linguistic summarization of medical data
    • healthcare process analysis with fuzzy sets
    • fuzzy systems for predictive medical models
    • patient tracking and monitoring with fuzzy sets
    • fuzzy set-based health services.
    Susana M. Vieira
    IDMEC, Insttituto Superior Técnico, Universidade de Lisboa
    susana.vieira@tecnico.ulisboa.pt
    Anna Wilbik
    Industrial Engineering, Eindhoven University of Technology
    a.m.wilbik@tue.nl

    Ambient Intelligence has been adopted as a term referring to environments that are sensitive and responsive to the presence of people and it is a candidate to become the next wave of computing. Indeed, this novel computing approach is aimed to extend ubiquitous vision by incorporating intrinsic intelligence in pervasive systems. This idea enables the study, design and development of embodiments for smart environments that not only react to human events through sensing, interpretation and service provision, but also learn and adapt their operation and services to the users over time. These embodiments employ contextual information when available, and offer unobtrusive and intuitive interfaces to their users.
    The aim of this special session is to encompass valuable research in integration of Computational Intelligence and Ambient Intelligence. This special session is aimed at sharing latest progress, current challenges and potential applications of fuzzy logic, evolutionary computation and neural and learning systems in the scenario of ambient computing environment.
    This special session will be supported by all members of IEEE CIS Task Force on Ambient Intelligence. It is anticipated that members of this TF will contribute to this activity to promote the research of Computational Intelligence in the area of Ambient Intelligence. Details of this special session will be distributed publically through the task force web site.

    The topics include but are not limited to:

    • Adaptive Fuzzy Services
    • Assisted Ambient Intelligence
    • Ambient Intelligence for Healthcare
    • Ambient Intelligence Services
    • Ambient Intelligence Applications
    • Ambient Assisted Living
    • Assistive Robotics
    • Autonomous Robotic Systems
    • Human Behavioural Analysis
    • Elderly care Robots
    • Evolutionary Computation in Ambient Intelligence
    • Fuzzy Ambient Intelligence
    • Hybrid Intelligent Systems for Ubiquitous Computing
    • Intelligent Living Environments
    • Intelligent Fuzzy Agents
    • Intelligent Environments
    • Multi-Agent System for Ambient Intelligence
    • Neural Networks in Ambient Intelligence
    • RFID and Wireless Sensor Network Applications
    • Self-Organization in Ubiquitous Environments
    • Sensing and Reasoning Technology
    • Sensing Technologies and Measurements
    • Signal Fusion in Ubiquitous Environments
    • Situational/Context Awareness
    • Smart Evolving Sensors
    • Smart Homes
    • Social Sensor Networks
    • Soft Computing for Embedded Appliances
    • Well-being and Ambient Intelligence
    Ahmad Lotfi
    Nottingham Trent University, UK
    ahmad.lotfi@ntu.ac.uk
    Giovanni Acampora
    University of Naples Federico II, Italy
    giovanni.acampora@unina.it
    Caroline Langensiepen
    Nottingham Trent University, UK
    caroline.langensiepen@ntu.ac.uk