Please submit proposals (up to 1 page) in MS Word or RTF format by email to the official email address of the conference firstname.lastname@example.orgThe proposal should include the following information:
Important: IFSA-EUSFLAT Conference is open to any topics related to the fuzzy set theory from the theoretical as well as application point of view, other topics from the area of the computational intelligence, or topics from machine learning, computational linguistics, rough sets or quantum structures etc. are highly welcome and encouraged to build special sessions or even special tracks.Deadline for the special session proposals: October 31, 2020
The special session seeks to bring together researchers and talks in the areas of decision making and optimization in which uncertainty, fuzzy, and/or possibility analysis is an inherent part of talk. In particular, this special session, invites talks dealing with decision making, fuzzy/interval/uncertainty decision making, interval, uncertainty, fuzzy, and possibilistic optimization. Talks with theoretical content, algorithms, state-of–the–art surveys, and applications in the above-mentioned areas, are welcome.
The topics associated with session include at least the following areas: decision making, fuzzy decision making, decision making under uncertainty, Analytic Hierarchy Processes (AHP), Interval AHP, Fuzzy AHP, optimization under uncertainty, interval optimization, fuzzy optimization, possibilistic optimization.
Interval uncertainty is closely related to fuzzy: e.g., Zadeh's extension principle is equivalent to applying interval analysis to alpha-cuts (intervals).
This relation between intervals and fuzzy computations is well known, but often, fuzzy researchers are unaware of the latest most efficient interval techniques and thus use outdated less efficient methods. One of the objectives of the proposed session is to help the fuzzy community by explaining the latest interval techniques and to help the interval community to better understand the related interval computation problems. Another relation is interval-valued fuzzy techniques that combine both uncertainties.
For more than a decade now, 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 twoag-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 such as 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 from the points of view of both theory and applications. Indeed, the theoretical perspective focuses 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 and the applications of fuzzy implication functions, concerning, among others, characterizations, representations, generalizations and their relationships with fuzzy negations, triangular norms, uninorms and other fuzzy logic connectives.
Data stream mining and modeling is a recent methodology that deals with the analysis of potentially large volumes of ordered sequences of data samples. Sensor networks, e-mails, online transactions, network traffic, weather forecasting, health monitoring, industrial process monitoring, and social networks are just some of the most common sources of this kind of data. Stream data arrive continuously. They dynamically change over the time, and need to be processed as soon as they arrive, in a finite amount of time. The idea is to capture the essence of the information within the data, and represent it in the parameters and structure of a model. Thus, special-purpose evolving data analysis methods, which are able to identify patterns in data in quite a real time, are needed to address major challenges such as nonstationarity (concept change) and large datasets (Big data).
This special session is intended to collect novel ideas and share different experiences in the field of soft computing for evolving data streams. Submission of papers covering theoretical and application aspects are encouraged. Topics of interest include, but are not limited to:
Internet of Things (IoT) is a paradigm that connects multiple and diverse smart objects via Internet. Nowadays, this paradigm is receiving a momentous interest in a number of real-life fields including industry, transport, healthcare and smart cities. This interest will be more and more growing in the future due to the unprecedented number of objects/devices that will be connected in the world. In www.statista.com, this number has been estimated to more than 75 billion devices by 2025. It is expected to rich 125 billion by 2030. Interconnected smart objects will then become the major data producers and consumers instead of humans and they generate tremendous amounts of data using their sensors every single of second. Such IoT data are inherently uncertain, erroneous and noisy on the one hand, and voluminous, distributed and continuous on the other hand.
On the other hand, a smart environment is a connected small world where sensor-enabled connected devices work collaboratively to make the lives of persons comfortable, the business of enterprises much big and flourishing, and so on. It is capable of obtaining knowledge and applying it to satisfy more complex users’ needs.
Recent research efforts have been conducted to integrate IoT with smart environments. This integration allows extending the capabilities of smart objects by enabling the user to monitor the environment from remote sites. IoT-based smart environments have two main and unique characteristics: the prediction capabilities and the decision-making capabilities. Such environments can collect a variety of data from different sources (i.e., objects/devices) and apply data fusion and mining/learning techniques to leverage and analyse data gathered. Hence, data are then the base for making intelligent decisions and providing new services.
Nowadays, in the context of IoT-based smart environments, data management constitutes a modern and a hot topic and has raised many challenging research tasks. New solutions and revisited existing ones are proposed to address such tasks. The main goal of this Special Session is to provide an international forum for researchers from academia and industry to exchange ideas and experiences regarding current and future solutions for managing IoT data. Especially, solutions that leverage techniques borrowed from computational Intelligence field (Soft computing, Fuzzy logic, Uncertainty models, Neural networks, Evolutionary computing, …).
Works in this field should address business, governmental and societal needs. With a holistic objective, it places humans at the epicentre of the digital transformation to unleash our collective potential, thus taking biological as well as artificial intelligence into account. Humans and institutions should have benefits of explained knowledge from the IoT data in the understandable ways, i.e., linguistically. Next, the promising field is focusing on nature inspired approaches in machine learning and computational intelligence for sustainability reasons, as nature-inspired approaches are not only more natural towards humanity but also more energy-efficient than today’s technology, which is the key factor when analysing huge amounts of IoT data. Our emerging, symbiotic technology thus should intend people to support a life in harmony with each other as well as with Nature.
Remark: Note that the authors may choose between submitting to IJCRS proceedings (choice a): Springer) or to IFSA-EUSFLAT proceedings (choice b): Atlantis Press).
Follow the instructions when preparing the manuscript.
The notion of uncertainty has been extensively analysed in the last decades by philosophers, logicians and computer scientists. Here we are interested in the uncertainty originated by different characteristics and flaws in information: incompleteness, imprecision, graduality, granularity, contradiction between agents, etc. For each of these aspects one (or more) specific tool has been introduced in literature: fuzzy sets, rough sets, formal concept analysis, possibility theory, Dempster-Shafer theory, interval analysis, compound objects comparators, etc. Further, when more than one form of uncertainty are present at the same time, it seems natural to fuse such tools, as in the fuzzy rough set case. The special session is devoted to collect all contributions that deal with scenarios leading to a form of uncertainty and tools to represent and manage it. In particular, all critical discussions, comparisons among two or more forms of uncertainty and/or comparisons and fusion of two or more tools are welcome.
The not exhaustive list of topics includes:
The study of logical, algebraic and proof theoretical tools for the management of imprecision and uncertainty is a well-established line of research, whose development has significantly influenced many areas of applied research, from Economics and Game Theory to Artificial Intelligence. This special session, expression of the EUSFLAT working group of mathematical fuzzy logic, aims at collecting papers about formal approaches to the theories of vagueness, uncertainty and imprecise information management that can be treated in the realm of fuzzy or many-valued logics, and the possible applications in modeling different types of reasoning.
It is focused on (but is not limited to) the following topics:
The rapidly growing stream of diverse data, increasing complexity of systems explored in science and engineering and new challenges data analysts face reveal the need for more flexible uncertainty modeling tools. Such challenges and needs require soft modeling and computing that are less rigid than traditional approaches and techniques, and therefore easier to adapt to the real nature of the information. The desired methodology should combine different possible types of uncertainty, including randomness, imprecision, ambiguity, etc. By integrating fuzzy sets and probability theory, more robust and interpretable models and tools can be developed that better capture all kinds of information contained in the data.
The aim of this special session is to bring together theorists and practitioners who apply fuzzy methods in statistical reasoning and data analysis to exchange ideas and discuss new trends that enrich traditional approaches and tools.
Topics of interest include but are not limited to: