October 30, 2017

FUZZ Sessions

FUZZ-IEEE-01 Linguistic Summarization and Description of Data
FUZZ-IEEE-02 Recent trends in many-valued logic and fuzziness
FUZZ-IEEE-03 Fuzzy Models for Data Science and Big Data
FUZZ-IEEE-04 Fuzzy Logic Systems for Security and Forensics
FUZZ-IEEE-05 Fuzzy Brain Analysis and Interfaces
FUZZ-IEEE-06 Type-2 Fuzzy Sets in Emerging Systems
FUZZ-IEEE-07 Fuzzy Systems for Data Mining: data wrangling, machine learning and real-world applications
FUZZ-IEEE-08 Fuzzy Interpolation
FUZZ-IEEE-09 Software for Soft Computing
FUZZ-IEEE-10 Methods and Applications of Fuzzy Cognitive Maps
FUZZ-IEEE-11 Interpretable Deep Learning Classifiers
FUZZ-IEEE-12 Handling Uncertainties in Big Data by Fuzzy Systems
FUZZ-IEEE-13 Advances to Type-2 Fuzzy Logic Control
FUZZ-IEEE-14 Special Session on “Fuzzy Systems in Renewable Energy and Smart Grid”
FUZZ-IEEE-15 Special Session on Complex Fuzzy Sets and Logic
FUZZ-IEEE-16 Uncertainty in Learning from Data
FUZZ-IEEE-17 Inter-Relation Between Interval and Fuzzy Techniques
FUZZ-IEEE-18 Advances and applications of Rough sets and Fuzzy Rough
FUZZ-IEEE- 19 Ambient Computational Intelligence
FUZZ-IEEE- 20 Recent Advances in Fuzzy Control System Design and Analysis
FUZZ-IEEE- 21 Human Symbiotic Systems
FUZZ-IEEE- 22 Predictive models for medical applications of Fuzzy Logic
FUZZ-IEEE- 23 Fuzzy Cognitive Maps theory
FUZZ-IEEE- 24 The Theory of Type-2 Fuzzy Sets and Systems
FUZZ-IEEE- 25 Innovations in Fuzzy Inference
FUZZ-IEEE- 26 Cyber Security and Fuzzy Logic
FUZZ-IEEE- 27 Type-2 Fuzzy Sets and Systems Applications (T2-A)
FUZZ-IEEE- 28- Business Processes and Fuzzy Logic (BPFL)
FUZZ-IEEE- 29 Fuzzy Technologies for Web Intelligence and Internet of Things
FUZZ-IEEE- 30 Recent Advances in Lattice Computing
FUZZ-IEEE- 31 Adaptive fuzzy control for nonlinear systems

FUZZ-IEEE- 01 Linguistic Summarization and Description of Data

Organized by Anna Wilbik (a.m.wilbik@tue.nl ) Daniel Sanchez, Nicolas Marin

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 byhuman 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.

Scope and Topics

The aim 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, IEEE WCCI 2016, FUZZ-IEEE 2017) and is supported by IEEE CIS task force on Linguistic Summaries and Description of Data.
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

 

FUZZ-IEEE-02 Recent trends in many-valued logic and fuzziness

Organized by Diego Valota (valota@di.unimi.it), Brunella Gerla, Pietro Codara

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 non classical event is described by a formula in the language of a given many valued 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 application oriented scholars with powerful tools to deal with this kind of events.

Scope and Topics

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, many valued 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 non classical events

 

FUZZ-IEEE-03 Fuzzy Models for Data Science and Big Data

Organized by Francisco Herrera, Alberto Fernández, (alberto@decsai.ugr.es ) Mikel Galar

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 are 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 a 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 that 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.

Scope and Topics

The main aim of this session is to provide a forum to provide innovative approaches to handle various fuzzy issues in Big Data presentation, processing and analyzing by applying fuzzy sets, fuzzy logic and fuzzy systems. 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:

  • 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

 

FUZZ-IEEE-04  Fuzzy Logic Systems for Security and ForensicsOrganized by Longzhi Yang (longzhi.yang@northumbria.ac.uk), Nitin Naik,  Paul Jenkins

Computational Intelligence has taken the centre of the research in cyber security and digital forensics. As one of the three most important parts in Computational Intelligence, fuzzy logic has been successfully applied in many applications in this field. Thanks to its ability to provide human comprehensible solutions to cyber security and digital forensics problems under uncertainty environment and also to the fuzziness of security and forensics problems themselves, fuzzy logic is expected to have a more significant impact in such field.

Scope and Topics

The aim of this special session is to provide a forum: (1) to disseminate and discuss the advances and significant research efforts in the field of fuzzy logic systems, security and forensics, (2) to promote both theoretical development and practical applications of fuzzy logic systems in the field of security, privacy and forensics, (3) to foster the integration of communities from academic, industry, and other organisations who have been working in the field of fuzzy logic, security and forensics.

The topics include but are not limited to:

  • Fuzzy systems for cyber security, privacy and digital forensics
  • Uncertainty representation and processing in the cyber space for security, privacy, and forensics
  • Fuzzy algorithms and models for cyber-attack detection, privacy preservation, and forensics investigation
  • Fuzzy approaches for the security of big data, cloud, computer networks, IoT, cyber-physical systems and other hardware and software systems in or involved in the cyber space
  • Fuzzy hardware architectures for cyber security, privacy and digital forensics
  • Fuzzy software systems for cyber security, privacy and digital forensics
  • Application of fuzzy logic systems to cyber security, privacy and forensics

 

FUZZ-IEEE-05 Fuzzy Brain Analysis and InterfacesOrganized by Chin-Teng Lin (Chin-Teng.Lin@uts.edu.au), Javier Andreu-Perez, Mukesh Prasad

Given the important challenges associated with the processing of brain signals obtained from neuroimaging modalities, fuzzy sets and systems have been proposed as a useful and effective framework for the analysis of brain activity as well as to enable a direct communication pathway between the brain and external devices (brain computer/machine interfaces). While there has been 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.

Scope and Topics

This special session aims to bring together original or preliminary research about applications 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.).

The topics include but are not limited to:

  • Fuzzy models for the simulation of brain processes in computational neuroscience
  • Fuzzy brain computer/machine interfaces (BCI/BMI)
  • 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

FUZZ-IEEE-06  Type-2 Fuzzy Sets in Emerging SystemsOrganized by Oscar Castillo  (ocastillo@tectijuana.mx), Pranab K. Muhuri

In 1965, Prof. L. Zadeh introduced the concept of Fuzzy Sets (FSs) to represent the uncertain system parameters. However, in many real-world systems, uncertainty appears due to multiple reasons. In such a scenario, uncertainty modelling capabilities of the Type 1 (T1) or traditional FSs are quite limited. Due to which, Zadeh himself brought the concept of higher order or Type-m FSs in 1975. Even then, for more than a decade, these types of FSs got very little attention from the scientific community. Interestingly starting from the late 1980´s, researchers have started investigating the T2 FSs, or more specifically Interval Type-2 (IT2) FSs and successfully applied the same for realistic uncertainty modelling in a number of applications. Very recently, a new research trend has been noticed, where researchers have shifted their focus from the IT2 FSs to the General Type 2 (GT2) FSs and exploring better results in many applications. This has further been motivated by some of Prof. J. M. Mendel´s recent works, where he has nicely shown that if proper care is taken during the designing phase, an IT2 Fuzzy Logic System (FLS) shall always produce better (or at least equal) performance than  a T1 FLS. Similarly, a GT2 FLS has the capability to give better (or at least equal) performance than a IT2 FLS. Nevertheless, growth of research carried out on the T2 FSs and T2 FLSs are far less than the volume of research conducted on T1 FSs. This warrants more and more research attention more from the scientific community on this important topic especially since everyday newer and newer systems are emerging across all the domains of science and engineering, e.g. social networks, big data analytics, cyber security, cyber-physical systems, cloud computing etc.

Scope and Topics

Therefore, this special session aims to introduce cutting edge research concepts of the possible of applications of the T2 FSs and the related theory in a number of emerging systems.

The topics include but are not limited to:

  • T2 FS based uncertainty modelling in Cyber-Physical Systems
  • Social Network Analysis under T2 Fuzzy Uncertainty
  • T2 FLSs in Cyber Security
  • Secure Communication
  • T2 FS based uncertainty modelling in Big Data Analytics
  • Multi-media Applications with Fuzzy Uncertainty
  • CWW (Computing with words)
  • T2 FSs for Image Processing
  • T2 FSs in Evolutionary Optimization
  • T2 FSs and T2 FLSs in Machine Learning
  • T2 FSs and T2 FLSs Deep Learning
  • T2 FLSs for Power Systems
  • T2 FSs for Energy Optimization
  • T2 FSs and T2 FLSs Green Computing
  • T2 FS based uncertainty modelling Vehicle Routing Problem
  • And other application areas with T2 FS based uncertainty modelling

 

FUZZ-IEEE-07 Fuzzy Systems for Data Mining: data wrangling, machine learning and real-world applicationsOrganized by Mikel Galar, José Antonio Sanz (joseantonio.sanz@unavarra.es), Alberto Fernández

Data mining is an active research field due to the large number of real-world problems that can be addressed using techniques of this area of research. The canonical data mining problems are classification, regression and clustering. However, in 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. Additionally, data wrangling techniques are a key element for the subsequent success of machine learning techniques.To deal with the aforementioned problems, the usage of learning methods based on soft computing techniques is widely applied. Among them, fuzzy systems have proven to be a powerful solution due to their ability to provide both an interpretable model and accurate results, since these systems cope with the great uncertainty inherent to new challenging problems. Finally, the synergy between fuzzy techniques and evolutionary computation has led to a better capability for the design and optimization of the former when tackling real-world problems.

Scope and Topics

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
  • 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

 

FUZZ-IEEE-08  Fuzzy InterpolationOrganized by Qiang Shen (qqs@aber.ac.uk), Laszlo Koczy, Shyi-Ming Chen

Fuzzy interpolation provides a flexible means to perform reasoning in the presence of insufficient knowledge that is represented as a sparse fuzzy rule base. It enables approximate inference to be carried out from a rule base that does not cover a given observation. Fuzzy interpolation also provides a way to simplify complex 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.

Scope and Topics

The aim of this special session is to provide a forum too disseminate and discuss recent and significant research efforts in the development of fuzzy interpolation and related techniques, to promote both theoretical and practical applications of fuzzy interpolation, and to foster integration of fuzzy interpolation with other computational intelligence techniques.

The topics 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

 

FUZZ-IEEE-09  Software for Soft ComputingOrganized by Jesús Alcalá-Fernandez (jalcala@decsai.ugr.es), Jose M. Alonso, Jose Manuel Soto-Hidalgo.

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. Most of these tools are available as open source software (see the webpagehttp://sci2s.ugr.es/fss). Please, notice that they are ready to play an important role in both industry and academia.

Scope and Topics

The aim of this session is to provide a forum to disseminate and discuss Software for Soft Computing, with special attention to Fuzzy Systems Software. We want to offer an opportunity for researchers 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 and Standards
  • Data Science, Big Data, and High Performance Computing (Map-Reduce, GPGPU, etc.)Applications

 

FUZZ-IEEE-10  Methods and Applications of Fuzzy Cognitive MapsOrganized by Elpiniki I. Papageorgiou (epapageorgiou@teiste.gr), Giovanni Acampora, Engin Yesil

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 modeling approach resembles human reasoning; it relies on the human expert knowledge for a domain, making associations along generalized relationships between domain descriptors, concepts and conclusions. 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. They combine fuzzy logic and recurrent neural networks inheriting their main advantages. 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

Scope and Topics

This special session aims to present highly technical papers describing new fuzzy cognitive maps (FCM) models and methodologies addressing any of the following specific topics: theoretical aspects, learning algorithms, innovative applications and FCMs extensions. It is also dedicated to providing participants with new and deep insights on fundamentals, modeling methodologies, learning algorithms, optimization and application issues for FCMs, accompanied with available open source tools and libraries for them

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
  • Computing with words
  • Type-II Fuzzy Cognitive Maps
  • FCM Design Using Fuzzy Numbers
  • Hybrid FCM-based approaches
  • Optimization algorithms
  • FCM in Big Data Analytics
  • 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
  • Open source software tools

 

FUZZ-IEEE-011  Interpretable Deep Learning ClassifiersOrganized by Plamen P. Angelov (p.angelov@lancaster.ac.uk), Jose C. Principe.

Deep Learning is becoming a synonym of highly precise (reaching or surpassing capabilities of a human) computational intelligence technique. Very interesting and important results were reported recently in both scientific literature and also grabbed the imagination of the wider public and industry helping propel the interest towards AI, neural networks, machine learning. It was applied mostly to solve classification problems in image processing, but also for predictive tasks in speech processing and other problems. Despite the undoubted success in achieving high precision and avoiding handcrafting in feature selection a number of issues remain unresolved, such as: i) transparency and interpretability; ii) the requirement for extremely large training data set, computational resources and time; iii) overfitting and catastrophic failures with high confidence in some cases; iv) convergence proof for the case of reinforcement learning; v) rigid structure unable to be adapted/to dynamically evolve with new samples and/or new classes; vi) repeatability of the results.

Methodologically, the vast majority of the techniques of this hot and quickly developing area are based exclusively on neural networks (convolutional, belief based, etc.). Very recently publications appear where the deep learning (multi-layer) architecture with different levels of abstraction is build based on fuzzy rule-based systems or fuzzy sets are used to represent coefficients/weights in Restricted Bolzman Machines, etc.

Scope and Topics

The aim of the special session is to address the bottleneck issues listed above and discuss and represent alternative and most recent methods, techniques and approaches that can help resolve these issues

The topics include but are not limited to:

  • Interpretable/Transparent Deep Learning
  • Computational and time complexity/efficiency of Deep Learning Methods
  • Repeatability of the results of Deep Learning Methods
  • Degree of confidence in the results of Deep Learning
  • Highly Parallelisable Deep Learning Methods
  • Deep Learning with proven convergence
  • Re-trainability and dynamically evolving structures/architectures for Deep Learning
  • Ensembles of Deep Learning Classifiers
  • Fuzzy Deep Rule-based Classifiers
  • Self-adaptive and Self-organising Deep Learning Architectures

Also applications to:

  • Computer Vision
  • Image Classification
  • Robotics
  • Remote Sensing
  • Biology and Tomography
  • Surveillance and Defense
  • Industry 4.0
  • Assistive Technologies and Digital Health

 

FUZZ-IEEE-12 Handling Uncertainties in Big Data by Fuzzy Systems

Organized by Guangquan Zhang (Guangquan.Zhang@uts.edu.au), Dianshuang Wu, Jie Lu

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.

Scope and Topics

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
FUZZ-IEEE- 13 Advances to Type-2 Fuzzy Logic Control

 

Organized by Tufan KUMBASAR (kumbasart@itu.edu.tr), Erdal Kayacan, Hao YING

 

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 present a comprehensive selection of high-quality, representative current research in the area of type-2 control, motivating further collaboration and providing a platform for the discussion on future directions of type-2 fuzzy logic control. This special session will focus on advances in the interval type-2 as well as general type- 2 fuzzy logic control.<br>

Scope and Topics

 

This special session will address advances in interval type-2 as well as general type-2 fuzzy logic control, including different types of fuzzy logic controllers such as PID type, Fuzzy model, Neuro- Fuzzy and TSK based Fuzzy controllers. As such, the session aims to provide both an overview of the current top quality research in the area, as well as a window into the future of type-2 fuzzy logic control. 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
  • Interpretability of Type-2 Fuzzy Controllers
  • Real-time applications of Type-2 Fuzzy Controllers
FUZZ-IEEE- 14 Special Session on “Fuzzy Systems in Renewable Energy and Smart Grid”

 

Organized by Marco Mussetta (marco.mussetta@polimi.it), Faa-Jeng Lin and Francesco Grimaccia

 

The recent developments in Smart Grid technology employ information, communication, and automation technology to deploy an integrated power grid with smart power generation, transmission, distribution and users. The Smart Grid paradigm is aligned with the policy goals of expanding the application of renewable energy, energy conservation, and carbon reduction.

 

Renewable energy sources recently got more attention due to cost competitiveness and environmental sustainability, 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 thir 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.

 

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. Moreover, Smart Grid integrated with smart meters, EV charging stations and home (building) energy management system are the key enabling factor toward the Smart City concept.

 

As a result, modeling and controlling the power grid using Smart Grid techniques, such as smart meters, micro-grids, and distribution automations become very important issues. Additionally, 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.

 

Scope and Topics

 

The main aim of this session is to provide a forum for researchers covering the whole range of fuzzy systems applications to Smart Grid systems and renewable power generation and use. The session continues the series of special sessions on the topic organized by some of the organizers of this session in past conferences (FUZZ-IEEE 2011, WCCI 2012, FUZZ-IEEE 2013, WCCI 2014, WCCI 2016, FUZZ-IEEE 2017) and is supported by the IEEE CIS Task Force on “Fuzzy Systems in Renewable Energy and Smart Grid”.
The topics include but are not limited to:

  • Fuzzy modeling of renewable power generation systems.
  • Fuzzy control of renewable power generation systems.
  • Prediction of renewable energy using fuzzy and neuro-fuzzy systems.
  • Hybrid systems of computational intelligence techniques in Smart Grid and renewable power
    generation systems.
  • Neuro-Fuzzy system for oil and gas integration with renewable sources.
  • Fuzzy energy management systems.
  • Fuzzy distribution systems automation.
  • Fuzzy power quality, protection and reliability analysis of power system.
  • Fuzzy Logic application for Demand-Response and Smart Buildings.
  • Fuzzy Logic application for Smart Grid and Smart Cities.
  • Novel applications in electric energy market.

FUZZ-IEEE- 15 Special Session on Complex Fuzzy Sets and Logic

Organized by Scott Dick (dick@ece.ualberta.ca)

Complex fuzzy sets are an extension to type-1 fuzzy sets in which membership grades are complex- valued. Likewise, complex fuzzy logic is an isomorphic family of multi-valued logics whose truth values are complex numbers. In the ten years since these concepts were first proposed, further theoretical investigations and a number of applications have made complex fuzzy sets and logic a lively and growing research area. This special session will provide a forum to consolidate the community of researchers in this area, share our current ideas, reflect on future directions, and
communicate our ideas and vision to the larger Computational Intelligence community.

Scope and Topics

We welcome submissions on all aspects of complex fuzzy sets or complex fuzzy logic, including but 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

FUZZ-IEEE- 16 Uncertainty in Learning from Data

Organized by Xizhao Wang (xizhaowang@ieee.org), Qinghua Hu, Dr. Ran Wang

Modeling uncertainty in learning from data is to show that the representation, measure, and handling of uncertainty have a significant impact on the performance of learning algorithms. Usually the modeling/handling of uncertainty is associated with the feature-type and volume of dada. Recent research shows that making clear the change/adaptation of uncertainty with feature-type and volume of data is a very difficult issue. This difficulty is significantly increasing if we deal with the big data. This SS is mainly about how to model the uncertainty and how the learning performance is improved through uncertainty handling.

Scope and Topics

This special session is aiming at showing some recent advances of study on uncertainty modeling and processing in learning from data (especially from big data). It covers but is not limited to the following topics:

  • Machine learning with uncertainty
  • Learning from examples with fuzzy representation
  • Fuzzy techniques in learning
  • Fuzzy clustering
  • Fuzzy optimization
  • Active learning based uncertainty
  • Multiple instance learning with uncertainty
  • Learning with/from uncertainty
  • Fuzzy neural networks
  • Fuzzy rough approaches
  • Decision-making related to hesitant or intuitionistic fuzzy sets
  • Cognitive model related to concept lattice
  • Uncertain information systems
  • Bayesian model based on non-additive measures
  • Domain applications of uncertainty learning
  • Learning with Noise,
  • Open set learning and new label learning

FUZZ-IEEE- 17 Inter-Relation Between Interval and Fuzzy Techniques

Organized by Martine Ceberio (mceberio@utep.edu) and Vldik Kreinovich (vladik@utep.edul)

The relation between fuzzy and interval techniques is well known; e.g., due to the fact that a fuzzy number can be represented as a nested family of intervals (alpha-cuts), level-by- level interval techniques are often used to process fuzzy data.

At present, researchers in fuzzy data processing mainly used interval techniques originally designed for non- fuzzy applications, techniques which are often taken from textbooks and are, therefore, already outperformed by more recent and more efficient methods.

Scope and Topics

One of the main objectives of the proposed special session is to make the fuzzy community at-large better acquainted with the latest, most efficient interval techniques, especially with techniques specifically developed for solving fuzzy-related problems.

Another objective is to combine fuzzy and interval techniques, so that we will be able to use the combined techniques in (frequent) practical situations where both types of uncertainty are present: for example, when some quantities are known with interval uncertainty (e.g., coming from measurements), while other quantities are known with fuzzy uncertainty (coming from expert estimates).

The topics include but are not limited to:

  • interval computations, especially topic of potential and actual interest to fuzzy
    community
  • interval uncertainty
  • interval-valued fuzzy sets
FUZZ-IEEE 18: Advances and applications of Rough sets and Fuzzy Rough

 

Organized by Ahmad Taher Azar, (ahmad_t_azar@ieee.org), Valentina E. Balas, Camelia Pintea

 

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 rough sets and fuzzy rough sets.

 

Scope and Topics

The aim of this special session is to 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, knowledge discovery and data mining.The topics of interest 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

FUZZ-IEEE- 19 Ambient Computational IntelligenceOrganized by Ahmad Lotfi (ahmad.lotfi@ntu.ac.uk), Amir Pourabdollah, Giovanni Acampora

Ambient Intelligence, as a candidate to become the next wave of computing, has been adopted as a term referring to environments that are sensitive and responsive to the presence of people. 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.

Scope and Topics

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, neural networks and machine learning in the wider scenario of ambient computing intelligence.

The topics include but are not limited to:

  • Adaptive Fuzzy Services
  • Assisted Ambient Intelligence
  • Ambient Intelligence for Healthcare
  • 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
  • Fuzzy Mark-up Language
  • 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

FUZZ-IEEE- 20 Recent Advances in Fuzzy Control System Design and AnalysisOrganized by Jun Yoneyama*(yoneyama@ee.ayama.ac.jp) and Kevin Guelton

Fuzzy control system has been employed to deal with a wide range of nonlinear control systems design and analysis. Fuzzy control system design and analysis provide a systematic and efficient approach to controlling of nonlinear plants and analysis of nonlinear control systems. A number of results on this area have appeared in the literature. However, there is still room for improvement of the existing results in order to propose new techniques for control of nonlinear systems.

Scope and Topics

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

The topics include but are not limited to:

  • Takagi-Sugeno fuzzy control system
  • Uncertain fuzzy system
  • Fuzzy hybrid system
  • Fuzzy switching system
  • Fuzzy time-delay system
  • Fuzzy stochastic system
  • Fuzzy polynomial system
  • Type-2 fuzzy control system
  • Stability analysis of Takagi-Sugeno fuzzy system
  • Nonlinear control design based on Takagi-Sugeno fuzzy system
  • Predictive control
  • Robust control
  • Sampled-data control
  • Filtering
  • Sliding mode control and observer

FUZZ-IEEE- 21 Human Symbiotic Systems

Organized by Tomohiro Yoshikawa (yoshikawa@cse.nagoya-u.ac.jp), Yoichiro Maeda

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

Scope and Topics

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

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

FUZZ-IEEE- 22 Predictive models for medical applications of Fuzzy Logic

Organized by Marco POTA, Massimo ESPOSITO (massimo.esposito@icar.cnr.it)

Fuzzy Logic keeps its research interest for developing predictive models, in order to solve problems in a wide range of application fields, especially in medicine, where data are typically affected by measurement errors, and where the chance of presenting prediction results together with a clear explanation and with a measure of the associated uncertainty is highly appealing. However, designing a fuzzy system is a thorny process, requiring many steps, from the knowledge extraction and representation, to the inference process, until the presentation of results, which should be paid particular careful, in order to accomplish special requirements of medical applications, like robustness, interpretability, and confidence-weighted presentation of results.

Scope and Topics

This Session provides an interdisciplinary forum for researchers and developers to present and discuss experiences, ideas, and research results in the application of Fuzzy Logic to develop predictive models in medical field. Original contributions are sought, covering the whole range of theoretical and practical aspects, technologies and systems in such a research area.
The topics include but are not limited to:

  • Fuzzy knowledge extraction;
  • Robustness to noise of fuzzy logic predictive models;
  • Interpretability of fuzzy models;
  • Medical applications of Fuzzy Logic.

FUZZ-IEEE- 23 Fuzzy Cognitive Maps theoryOrganized by Chrysostomos Stylios (stylios@teiep.gr)

Since Fuzzy Cognitive Maps (FCMs) first appearance in 1986, they have attracted the interest of many researchers who have contributed to both FCM structural improvement and applying FCMs to a variety of areas. Kosko (1986) based on cognitive maps introduced partial causality among concepts allowing degrees of causality and he also suggested Hebbian Learning as the most suitable approach for FCMs training.
Since then, Fuzzy Cognitive Maps (FCMs) have presented a growth and broaden interest in research community mainly as a modeling methodology based on knowledge able to model any complex system. Fuzzy Cognitive Maps (FCMs) is characterized as a soft computing approach gathering causal knowledge representation and reasoning process. FCMs development is based on human experts who describe a domain using concepts along with causal interconnections among them.

Scope and Topics

This special session aims to present new approaches on how to enhance Fuzzy Cognitive Maps, mainly by contributions investigating structural changes in FCMs, proposing new inference processes, proposing
hybridization with other methods and generally contributing to FCMs theory.
The topics include but are not limited to:

  • Temporal (Timed) Fuzzy Cognitive Maps
  • Evolutionary Fuzzy Cognitive Maps
  • Competitive Fuzzy Cognitive Maps
  • Complementary models of Fuzzy Cognitive Maps
  • Rough Cognitive Map
  • Intuitionistic Fuzzy Cognitive Maps
  • Granular Cognitive maps
  • Rule Based Fuzzy Cognitive Map
  • Fuzzy Cognitive Agents
  • Interval Fuzzy Cognitive Maps
  • Hebbian Learning based Methods for FCMs
  • Evolutionary Learning algorithms for FCM
  • Particle Swarm Optimization Algorithms for FCMs
  • Simulated Annealing Algorithms for FCMs
  • Genetic Algorithms for FCMs

FUZZ-IEEE- 24 The Theory of Type-2 Fuzzy Sets and SystemsOrganized by Jon Garibaldi, Josie McCulloch (Josie.McCulloch@nottingham.ac.uk ) and Erdal Kayacan

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).

Scope and Topics

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:
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
FUZZ-IEEE- 25 Innovations in Fuzzy InferenceOrganized by Christian Wagner (Christian.Wagner@nottingham.ac.uk), Timothy C. Havens, Derek T.Anderson
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.

Scope and Topics

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

FUZZ-IEEE- 26 Cyber Security and Fuzzy LogicOrganized by Francesco Mercaldo (francesco.mercaldo@iit.cnr.it), Marioluca Bernardi, Marta Cimitile

The widespread diffusion of computational capabilities in a plethora of aspect of every-day life (for instance mobile devices, cars, smart houses, critical infrastructures, e-health), and the penetration of computers and software in enterprises of every dimension have led to an enormous number of victims of cyber attacks.
Malware writers are targeting not only end-users, but also companies and public services: the last trend is represented by the so-called ransomware that has reached epidemic proportions globally. Global ransomware
damage cost exceeds $5 billion in 2017, up from $325 million in 2015. Furthermore, ransomware damages
increased up to 15X in only 2 years.

Research community in last years has provided several methodologies  to detect threats, but the number of false positive is usually too high to consider their solutions in the real-world environment. In addition, scientific literature lacks of methods used to prevent zero-day attacks and to mitigate the consequences of infections.

Fuzzy logic is based on fuzzy set theory to deal with reasoning that is, by its nature, fluid, approximate or uncertain rather than exact. Fuzzy logic variables have truth values continuously ranging in degree between 0 and 1 and which can handle partial truth and uncertainty.

Techniques exploiting Fuzzy theory have been successfully used in many real world applications on a wide spectrum of engineering problems. Looking at the security context, these approaches can also be used to protect the privacy and security of the users of information and communication systems and technologies. Such techniques can be useful for  the verification and  mitigation of malicious threats in order and to thoroughly verify security properties.

Topics of interest in this areas include: denial of service attacks, forensics, intrusion detection systems, homeland security, critical infrastructures protection, sensitive information leakage, access control and malware detection and tracking.

Thus, fuzzy logic offers the potential to develop secure systems . From these considerations, the objective of this special session is to encourage the integration between these cyber-security and fuzzy logic communities towards the development of secure and resistant systems.

Scope and Topics

The main aim of this session is to provide a forum to provide innovative approaches to handle various fuzzy issues in Big Data presentation, processing and analyzing by applying fuzzy sets, fuzzy logic and fuzzy systems. 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:

  • New principles for qualitative and quantitative security analysis
  • Analysis, Design and Assessment of secure systems
  • Malware economics and black market studies
  • Security and privacy in Internet of Things (IoT)
  • Securing private data on mobile devices
  • Security in Smart Grid and in Cloud Computing environments
  • Security in Social Networks
  • Intrusion Detection
  • Cyber Insurance
  • Fraud detection and forensics
  • Big Data security
  • Network security and Verification and Validation of Critical Infrastructures
  • Design and validation of malware detection approaches and systems
  • Botnet detection, tracking and defense approaches
  • Security issues in  Complex System and Environment.

FUZZ-IEEE- 27 Type-2 Fuzzy Sets and Systems Applications (T2-A)Organized by Christian Wagner (Christian.Wagner@nottingham.ac.uk) and Hani Hagras

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)

Scope and Topics

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*
  • Robotics*
  • Decision Making*
  • Clustering and Classification*
  • Modelling*
  • Computing with words*
  • Type-2 Fuzzy Agents
  • Any other application area that employs type-2 fuzzy sets

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

FUZZ-IEEE- 28- Business Processes and Fuzzy Logic (BPFL)Organized by Mario Luca Bernardi, Marta Cimitile (marta.cimitile@unitelma.it ), Autilia Vitiello

Business processes are a formal and well-defined representation of enterprise activities, capable of highlighting, in a structured way, how multiple complex tasks are performed within a given organization. This representation enables a more clear understanding of interactions occurring within a company with a consequent and improved collaboration among all the stakeholders involved in a given technical or administrative process. In the last years, several techniques for business process management have been introduced to efficiently discovery, monitor and execute business processes in different enterprise scenarios. However, these techniques could fail when processes are not purposefully designed and optimized, they quickly change over time or when they refer to highly uncertain and vague enterprise contexts. Consequently, fuzzy logic and approximate rule-based reasoning can efficiently support tool for business process management in facing aforementioned challenges, mainly in the area of process mining. Precisely, 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.

Scope and Topics

The objective of this special session is to provide a forum for the discussion of recent research trends in the application of fuzzy set methodology and technology to business process management problems and to offer
an opportunity for researchers and practitioners to identify and discuss about new promising research
directions in this challenging scenario.

The topics include but are not limited to:

● Business intelligence;
● Fuzzy Logic for Adaptive and Context-Aware process execution;
● Fuzzy Logic for effective analytics and visualization of enterprise processes;
● 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.

FUZZ-IEEE- 29 Fuzzy Technologies for Web Intelligence and Internet of ThingsOrganized by Giovanni Acampora, Chang-Shing Lee, Marek Reformat (reformat@ualberta.ca )

The constant growth of the Internet and introduction of such concepts as the Semantic Web and Internet of Things create challenges as well as opportunities to transform the internet into an environment providing the users and any internet enabled devices with the abilities to utilize and explore efficient way.

The Internet is a huge collection of services, different pieces of information, and data generated devices – it is inherently heterogeneous, imprecise, uncertain, incomplete and inconsistent. There is a need for techniques, methods and algorithms supporting processing symbolic and numerical data.

Fuzzy Logic and Soft Computing provide important and non-trivial approaches, techniques and methods suitable for dealing with imprecision, fusing information from multiple sources, selecting best among multiple alternatives, or representing information and knowledge using. It is anticipated, that applications of fuzziness and soft computing technologies to internet systems will bring a new way of performing tasks related to web activities and information processing.

Scope and Topics

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 addressing issues specific to the internet of things as well as representation and processing of information and knowledge existing on the web.

The topics include but are not limited to:

  • multi-criteria decision-making
  • internet of things
  • information fusion
  • semantic-based processing of data
  • approximate 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
FUZZ-IEEE- 30 Recent Advances in Lattice ComputingOrganized by Peter Sussner (sussner@ime.unicamp.br), Manuel Graña, and Vassilis Kaburlasos

Ever since its inception, the area of fuzzy sets and systems has explicitly or implicitly benefited from lattice theory that draws on both order theory and universal algebra. Many types of information granules such as such as truth values, numbers, intervals, sets, symbols, graphs, possibility and probability distributions as well as fuzzy and L-fuzzy sets can be lattice ordered. Note that L-fuzzy sets include type-2, interval-valued, bipolar, and intuitionistic fuzzy sets.

These and other classes of information granules yield complete lattices that have instrumentally been used in different domains including non-classical logics, formal concept analysis, automated decision making, computing with words, computer vision as well as image processing and analysis, in particular mathematical morphology. Notions and facts drawn from lattice theory have also enabled some researchers to propose novel extensions of computational intelligence paradigms such as fuzzy inference systems, fuzzy associative memories, artificial neural networks, fuzzy adaptive resonance theory, and self-organizing maps.

All the aforementioned tools and mathematical models for processing lattice-ordered data can be characterized as techniques of lattice computing. One of the major benefits of lattice computing is its capability of processing diverse and/or disparate types of data. Hence, lattice computing gives rise to a number of mathematically rigorous approaches towards granular computing

Scope and Topics

This special session is meant as a forum for researchers who are interested in lattice computing. The objective is to present high-quality, state-of- the-art research results. An array of novel mathematical tools, design practices and real world applications will be presented. We are welcoming contributions that are potentially related to all theoretical and practical aspects of lattice computing.

The topics include but are not limited to:

  • Theoretical aspects of extended fuzzy sets such as complex, type-2, interval-valued, and
    intuitionistic fuzzy sets as well as fuzzy multisets, rough sets, and shadowed sets;
  • L-fuzzy sets and systems;
  • Image algebra;
  • Minimax, a.k.a. max-plus algebra or tropical linear algebra, and its applications;
  • Non-classical logics;
  • Formal concept analysis;
  • Lattice fuzzy transforms;
  • Mathematical morphology on complete lattices and semilattices;
  • Fuzzy and L-fuzzy mathematical morphology;
  • Lattice computing methods for computer vision, image/signal processing and analysis;
  • Granular computing;
  • Computing with words;
  • Fuzzy lattice reasoning;
  • Fuzzy, morphological, and lattice associative memories;
  • Morphological neural networks;
  • Distance, similarity, subsethood and inclusion measures;
  • Aggregation functions;
  • Automated decision making;
  • Approximate reasoning;
  • Spatial/temporal reasoning;
  • Data mining;
  • Disparate data fusion;
  • Semantic web;
  • Knowledge representation;
  • Applications in pattern recognition and time series prediction.
FUZZ-IEEE- 31 Adaptive fuzzy control for nonlinear systems

Organized by Valentina E. Balas, (balas@drbalas.ro), Tsung-Chih Lin, Rajeeb Dey

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.

Scope and Topics

The main aim of this session is to provide a forum to provide innovative approaches to handle various fuzzy controllers, adaptive control strategies, time-delay nonlinear systems, cooperative control, hybrid intelligent control. 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:

  • 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