Keynote speakers ESCIM 2019:
Prof. José Luis Verdegay received the M.S. degree in mathematics and the Ph.D. degree in sciences from the University of Granada, Granada, Spain, in 1975 and 1981, respectively. He is a full Professor at Department of Computer Science and Artificial Intelligence (DECSAI), University of Granada, Spain, and director of the Models of Decision and Optimization (MODO) Research Group. He has published 29 books and more than 350 scientific and technical papers in leading scientific journals, and has been Advisor of 21 Ph.D. dissertations. He has been Principal Researcher in a variety of national and international research and educational projects, and currently is leading a research project on “Computational Intelligence based Mobility and Renewable Energies Models: Applications in Sustainable Cities” and coordinating the Ibero-American research network in Decision and Optimization models (iMODA). He is also a member of the Editorial Board of several international leading journals.
Professor Verdegay has been Chairman of DECSAI (1990–1994), President (founder) of the Spanish Association for Fuzzy Logic and Technologies (1990–1996), Advisor for Intelligent Technologies of the Spanish Science Inter-Ministry Commission (1995–1996), Director of International Affairs at the University of Granada (1996–2000) and Delegate of the Rector for ICT in University of Granada (2008-2015). In July 2015 he was appointed Regional Director of the Postgrade Iberoamerican Universities Association.
Professor Verdegay is an IFSA fellow, IEEE Senior member and Honorary member of the Cuban Academy of Mathematics and Computation. Besides he has the Featured Position of “Invited Professor” at the Technical University of Havana (CUJAE, Cuba), Central University of Las Villas (Santa Clara, Cuba) and University of Holguín (Cuba). He is also a “Distinguished Guest” of the National University of Trujillo (Perú). His current scientific interests are on Soft Computing, fuzzy sets and systems, decision support systems, metaheuristic algorithms, nature inspired systems and all their applications to real world problems.
Title: Guidelines to solve Decision Making Problems
Abstract: To build Intelligent Systems that act in daily life like people do, it is very important to know in depth the mechanisms that govern the decision processes that human beings follow. The context in which a decision process is developed is a key aspect that needs to be known in depth. In this talk, this aspect will be approached and studied. Accordingly the definition of General Decision Problem will be modified and, by means of some examples, a set of guidelines to solve decision making problems will be proposed.
Prof. João Paulo Carvalho has a PhD (2002), a MSc (1996) and an Electrical and Computer Engineer (1992) degree from Instituto Superior Técnico, University of Lisbon, Portugal, where he is currently a Tenured Associate Professor at the Department of Electrical Engineering and Computers. He has lectured courses on Computational Intelligence, Distributed Systems, Entrepreneurship and Technology Transfer, Computer Architectures and Digital Circuits since 1999. He is in the Board of Directors of INESC-ID, where he is a senior researcher and has coordinated 5 funded research projects and participated in more than a dozen national and European funded projects.
His current main research interest involves applying Computational Intelligence techniques in natural language processing, text mining, social network analysis, social sciences and earth sciences. He has authored over 130 papers in international scientific Journals, books and peer-reviewed conferences. He is Area Editor of Fuzzy Sets and Systems and was Associate Editor of 2 other international Journals. He will be the General Chair of IPMU2020, was program co-chair and organizer of IFSA-EUSFLAT2009, Webchair of the 2010 IEEE World Congress on Computational Computation, Fuzz-IEEE2015 and FUZZ-IEEE2017 Publicity-chair, IPMU2016 program-chair, IEEE-WCCI2017 PR and Publicity-chair, and is PC member of more than 30 international conferences.
Title: Recommender Systems: Using Fuzzy Fingerprints for “Proper” Recommendations
Abstract: Most Recommender Systems rely exclusively on ratings and are known as Memory-based Collaborative Filtering systems. This is the currently dominant approach outside of academia due to the low implementation effort and service maintenance, when compared with more complex Model-based approaches. Traditional Memory-based systems have as their main goal to predict ratings, using similarity metrics to determine similarities between the users’ (or items) rating patterns. In this talk, we propose item and user-based Fuzzy Collaborative Filtering approaches that do not rely on rating prediction, instead leveraging on Fuzzy Fingerprints to create a novel similarity based recommendation approach. Fuzzy Fingerprints provide a concise and compact representation of users allowing the reduction of the dimensionality usually associated with user-based collaborative filtering.
Dr Andreja Tepavcevic is a full professor at Department of Mathematics and Informatics, Faculty of Sciences, University of Novi Sad and also a researcher at Mathematical Institute of Serbian Academy of Sciences and Arts in Belgrade, Serbia. She is the head of Applied Algebra Chair at Faculty of Sciences. She graduated two tracks in Informatics and Mathematics and defended a PhD thesis in Algebra at University of Novi Sad. She was a visiting professor, gave courses and seminar lectures at more than 25 universities throughout the world. She gave tutorials at FUZZ-IEEE conferences and was a plenary and invited speaker at several conferences. She supervised six PhD, several master and graduate thesis. Her main research is in lattice theory and theory of ordered sets; fuzzy sets theory and applications, with about 150 refereed publications in journals, chapters in monographs, international conferences; one research monograph and 10 university textbooks. She is an associate editor of several journals, coordinator of several research projects (national and international) and the head or a member of organizing and program committees of several conferences.
Title: Special lattice valued structures and approximate solutions of linear equations
Equations and solution of equations are mostly based on different types of algebraic structures (coefficients and solutions are usually elements of algebras). A common algebraic structure of this type is the structure of real numbers (which is a field). The topic of this talk will be equations and (approximate) solutions of equations in a different framework. The underlying structures are lattice valued algebras and relational systems, which are classical structures equipped with a generalized (lattice valued) equality replacing the classical equality. The theoretical basics originate in universal algebra (weak congruences), in general algebra (algebraic structures, quasigroups), in logic (Boolean and Heyting valued models) and in fuzzy mathematics (fuzzy algebra).
The valuating structure is a complete lattice Omega, and main ingredients of the theory are different types of Omega-algebras, which are classical algebras equipped with an Omega-valued equality replacing the ordinary one. In these new structures identities hold as appropriate lattice-theoretic formulas. Identities hold in such algebra if and only if they hold on all particular cut-factor algebras, i.e., cut subalgebras over cut-equalities. Algebraic structures that will be exploited in the framework of equations are groups, quasigroups, rings, fields, vector spaces and modules.
Applying general results to mentioned algebraic structures, we give answers to existence of approximate solutions of various types of linear equations with respect to a fuzzy equality, and we describe solving procedures. Some potential applications in coding theory and cryptology are proposed.
Prof. Juan Moreno-Garcia received the B.E. and Ph.D. degrees from the University of Castilla-La Mancha, Spain, in 1992 and 2002, respectively, and the M.S. degree from the University of Murcia, Spain, in 1996. He is an Associate Professor of industrial engineering at the University of Castilla-La Mancha. He is a member of the ORETO research group.
Prof. Juan Moreno-Garcia has published more than 150 papers on International Journals and Books, and on the Proceedings of International Conferences. He has been principal researcher in a variety of national projects. He is a member of the Editorial Board of the International Journal of Computational Intelligence Systems. He is member of the Spanish Association for Fuzzy Logic and Technologies, and Spanish Association of Artificial Intelligence. His main fields of interest are fuzzy and linguistic modeling, time series and linguistic description. He tries to develop models for real applications.
Title: Generating linguistic descriptions using Linguistic Petri Nets
Abstract: The generation of linguistic descriptions from raw data is a current line of research. These kinds of descriptions are used, for instance, in virtual assistants such as Alexa, Google Home or SIRI. The generation of linguistic descriptions of data that has been acquired by all kind of sensors, technologies and observations over a time period differs from classical techniques based on segmentation, forecasting and, pattern recognition and extraction. The use of Fuzzy Logic and Petri Nets to generate linguistic descriptions from raw data is an interesting possibility. On one side, the relevance of fuzzy sets, protoforms and computation with words to generate linguistic descriptions has been proved. On the other hand, Petri Nets (PNs) can detect events and manage the input flow, thus providing the necessary tools to synchronize and coordinate the system to describe. In this talk, a new method to generate linguistic descriptions with an operation similar to PNs is presented. The presented approach maintains the operation of PNs, while adding the necessary mechanism to generate linguistic descriptions. The different linguistics elements are added to the places and transitions of the PNs, thereby maintaining the operation of the PNs. This extension is called linguistic Linguistic Petri Nets (LPNs). It is a language to generate linguistic descriptions of systems. Some examples using datasets from real applications will be presented during the talk.