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- 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 SystemsOrganized 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