Special Sessions

The special sessions accepted until 26 July 2017 are listed below:

  • Current techniques to model, process and describe time series
    Today, there is a great interest on the research of time series since they are used in many situations in real life. Researchers are very interested in extracting the relevant information from data that can be modelled as time series. Initially, time series are represented as raw data, and usually, they are computed using mathematical methods to obtain information from them while other approaches develop models to represent series. When time series has been modelled, different techniques can be used to find patterns and/or to study trends in such series. Finally, another relevant line of research concerns the description of the series using natural language where researchers aims to extract information expressed in natural language. For all these reasons, the goal of this special session is to provide an international forum for the presentation of recent results in the research of this field.
    A non-exhaustive list of topics includes:
    • Methods for processing time series represented as raw data
    • Current models to represent time series
    • Efficient modelling of time series
    • Querying time series
    • Linguistic description of time series
    • Current techniques for extracting specific information from time series

  • Decision making modeling and applications
    Decision making is an inherent activity to mankind that can be seen as a process composed of different phases such as information gathering, analysis and selection based on different mental and reasoning processes that led to choose a suitable alternative among a set of possible alternatives in a given situation. Nowadays human beings daily face situations that rapidly change decision environments increasing its complexity.
    Moreover, Decision making is a core area in a wide range of disciplines such as Psychology, Economics, Political Sciences, Social Choice, Operations Research, Medicine, Artificial Intelligence, Engineering, etc. Because of this variety of disciplines, this special session aims at providing an opportunity for researchers working this research area to discuss in fundamental, approaches, methodologies, software systems, and applications, to share their novel ideas, original research results and practical experiences.

  • Discrete models and Computational Intelligence
    The topics include, but are not limited to:
    • Fuzzy graphs
    • Evolutionary search in graphs
    • fuzzy cognitive maps
    • Laszlo Koczy, Budapest University of Technology and Economics koczy@sze.hu

  • Formal concept analysis and uncertainty
    Formal Concept Analysis (FCA) is being recently adopted as a solid alternative to the process of information treatment to be used in real applications with some automated methods. More specifically, it has been appreciated with a significant growth of their use in a wide variety of areas: Biomedicine, Tourism, Education, Social Networks, etc.
    A great part of this recent interest is due to its unique and general framework which allows to develop from the beginning to the end all the stages involved in the way from information to knowledge and, moreover, to automatically reason about it.
    During the past years, the research on extending FCA theory to cope with imprecise and incomplete information made significant progress: Fuzzy Formal Concept Analysis, FCA with granular computing, interval-valued, possibility theory, triadic and more intend to handle the uncertainty and vagueness in data.
    This session aims to gather a number of reseachers concerning FCA and imprecision in data management. The topics include but not are limited to:
    • Theoretical foundations in FCA
    • Logic and fuzzy logic in FCA
    • Attribute implications,association rules and data dependencies
    • Redundancy and dimensionality reduction
    • Knowledge discovery and data analysis
    • Conceptual Exploration
    • Ontologies
    • Algorithms and applications

  • Fuzzy implication functions
    In recent years, fuzzy implication functions have become one of the main research lines of the fuzzy logic community. These logical connectives are the generalization of the classical two-valued implication to the infinite-valued setting. In addition to modelling fuzzy conditionals, they are also used to perform backward and forward inferences in different fuzzy rule based systems. Moreover, they have proved to be useful not only in fuzzy control and approximate reasoning, but also in many other fields like Multi-Valued Logic, Image Processing, Data Mining, Computing with Words and Rough Sets, among others.
    Due to this great variety of applications, fuzzy implication functions have attracted the efforts of many researchers also from the theoretical perspective focusing on problems whose solutions provide important insights from the point of view of their applications. Therefore, this special session seeks to bring together researchers interested in recent advances in the theory of fuzzy implication functions, concerning, among others, characterizations, representations, generalizations and their relationships with fuzzy negations, triangular norms, uninorms and other fuzzy logic connectives.

  • Fuzzy transforms: theory and applications to data analysis and processing
    Fuzzy (F)-transforms successfully link various transforms (Fourier, Laplace, integral, Wavelet, etc.) with fuzzy approximation models. The general idea is to bring an original model into a special space where succeeding computations are easier. In particular, the F-transform transforms an infinitary object (a real function) into a finitary one (a finite vector). Another specific feature of the F-transform consists in including a fuzzy partition in its formal representation.
    In the recent ten years, the theory of F-transform became an important constituent in the field of computational intelligence. It has a well justified theory and many sophisticated applications in image, signal and time series processing. Moreover, it can be successfully used in numerical methods for differential and integro-differential equations including the case when uncertainty is included in their formulation. The exceptional feature of the F-transform is that it successively and efficiently copes with classical problems as well as with problems that are affected by uncertainty or vagueness.
    In image and signal processing, the F-transform effectively solves problems connected with up- (down-) scaling, reconstruction, edge detection, fusion, registration, etc. In time series analysis, the F-transform is used for trend extraction. In big data area, it works as a successful method of pattern recognition.
    The F-transform propagates usefulness and effectiveness of fuzzy methods on all levels of data processing.
    The aim of this special session is to present recent developments and trends in the theory and applications of the F-transform, including all mentioned above. Beside theoretical aspects, the session will be focused on advanced applications in data analysis including handling big data.
    We invite contributions that extend traditional ways of data analysis and propose adequate methods for various kinds of data processing including, but not limited to the following topics:
    • Theoretical aspects of the F-transform and its higher degree versions
    • Inverse F-transform and how to improve its approximation quality
    • Numerical methods on the basis of F-transform
    • F-Transform and Aggregation Operators
    • Big data processing on the basis of the F-transform
    • Applications to Image Processing and Computer Vision
    • Time series analysis and forecasting

  • Imprecise probabilities: foundations and applications
    This session is devoted to Imprecise Probability Theory. This theory encompasses all the mathematical models that can be used as more flexible tools than usual Probability Theory when the available information is scarce, vague or incomplete. It includes lower previsions, n- monotone capacities, belief functions and possibility measures, among others.
    We would like to attract papers that discuss and solve foundational questions, or clearly demonstrate the usefulness of imprecise probabilistic models in an application. We would particularly welcome papers going from theoretical advances to the solution of an associated applied problem.
    Note also that Imprecise Probability Theory is connected to other topics within the scope of IPMU, such as Dempster-Shafer Evidence Theory, Fuzzy Measures and their connections to Game Theory. Papers in these topics emphasizing the role of Imprecise Probabilities are also welcome.

  • Mathematical fuzzy logic
    This special session is devoted to the most recent developments in the realm of fuzzy logic from a mathematical point of views, with particular emphasis on theoretical advances related to many-valued logics, algebraic semantics, combinatorial aspects, topological and categorical methods, proof theory and game theory, many-valued computation.
    A partial list of topics is the following:
    • 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
    • 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
    • Subjective probability approaches to many-valued logics and non-classical events

  • Measures of comparison and entropies for fuzzy sets and their extensions
    In the framework of fuzzy sets and their extensions, many different measures of comparison have been suggested in the literature. Some of them are based on the degree of equality as, for example, the similarities. Analogously, some of them are based on the degree of difference between two sets, like distances, dissimilarities and divergences. These measures is very important, mainly for applications. In particular, they can be used to measure the degree of imprecision of a set, that is, to measure the entropy of this set. The study of these measures of comparison and the measures of fuzziness or imprecision are the main topics of this special session.

  • Metaheuristics and machine learning
    In this special session, we look for innovative papers dealing with the use of computational intelligence techniques for the resolution of complex problems. Some techniques of interest are machine learning, evolutionary computation, and other metaheuristics to solve complex (discrete, continuous, or multi-objective) problems from different domains as telecommunications, engineering, bioinformatics, logistics, or scheduling, just to name a few.
    Related topics:
    • Metaheuristics for solving complex problems
Using machine learning for solving complex problems
    • Hybrid algorithms

    • Robust optimization

    • Dealing with uncertainty

    • Parallel algorithms

    • Multi-objective optimization
    • Dynamic optimization
Real world applications
    • Juan Carlos de la Torre, University of Cádiz, Spain
    • Patricia Ruiz, University of Cádiz, Spain
    • Bernabe Dorronsoro, University of Cádiz, Spain

  • Soft computing for decision making in uncertainty
    This special session aims to bring together researchers, engineers and practitioners to present the latest achievements and innovations in the field of Decision making in Uncertainty based on the theoretical foundation of soft computing, which can help (a) to decrease the level of input information uncertainty, (b) to improve the quality of decision-making processes by introduction new and modified fuzzy/neuro information processing algorithms and methods of structural- parametric optimization, (c) to discuss the main peculiarities of decision making systems’ applications taking into account specific uncertain data in different fields, and (d) to consider potential future directions in the area of Soft Computing for Decision Making in Uncertainty.
    Related topics
    • Soft computing methods and algorithms for Decision Making in Uncertainty
    • Methods for decreasing the level of input information uncertainty in decision-making
    • Fuzzy/neuro decision making and decision support systems
    • Optimization of intelligent systems using soft computing approach
    • New and modified soft-computing-methods of information processing for increasing
      efficiency of decision-making processes in uncertainty
    • The successful cases of the design and applications of the intelligent systems for decision-making in uncertainty

  • Soft computing in information retrieval and sentiment analysis
    In the current global Information Technology scenario, voluminous information from sources like webpages, blogs, social networks among many others, is available for processing. For this reason, new Information Retrieval Systems more and more powerful are necessary nowadays, and relatedto this issue, new fields are emerging to improve and complete the information provided by these
    Information Retrieval Systems. One of these trendiest topics may be Sentiment Analysis.
    Sentiment Analysis, also called Opinion Mining, studies the extraction of opinions or sentiments using mainly Information Retrieval, Natural Language Processing and Artificial Intelligence, especially due to the fact that the information treated is heterogeneous in nature and lack in precision and completeness. Traditional systems are incompetent to handle these data, for that reason, it is also necessary to use advance techniques like proposed by Soft Computing.
    Soft Computing refers to a family of several techniques (Fuzzy Logic, Neuro-computing, Probabilistic Reasoning, Evolutionary Computation, etc.) able to cope with lots of complex real-world problems. Therefore, this special session on “Soft Computing in Information Retrieval and Sentiment Analysis” provides a forum to the scientists, researchers, students and private sector parties to show original research works and real applications mainly related to possible uses of Soft Computing techniques for extracting, inferring, modeling, representing and handling information from heterogeneous sources like Internet.
    Potential topics of interest include but are not limited to:
    • Sentiment analysis/Opinion minig
    • Emotion detection
    • Search/meta search engines
    • Semi or unstructured information representation/modeling
    • Ontologies
    • Multi-Lingual and cross-Lingual Issues
    • Recommender systems

  • Uncertainty in video/image processing (UVIP)
    Fuzzy logic provides powerful tools in computer vision and video/image processing applications, where some data or results are uncertain due to inherent ambiguity and vagueness of video/image data. This uncertainty principle is one of the fundamental results in signal processing. Due to the recently increasing interest in using fuzzy set theory combined with computer vision techniques to create intelligent systems, this special session is designed to serve researchers and developers to publish their original, innovative and state-of-the-art works in these topics.
    This special session is aimed to cover a wide range of works and projects on computer vision, image processing, biometrics, neural networks, intelligent systems or related areas where the uncertainty is managed, providing a platform for academics and industry related researchers to discuss and share experiences. We hope that this session can provide a common forum to exchange ideas and the latest discoveries in the area.
    • Computer vision
    • Image processing
    • Image Segmentation
    • Biometric identification and recognition
    • Pattern recognition
    • Hardware implementations