ABSTRACT: APPROXIMATE REASONING UNDER BIMODAL Z-INFORMATION.

In decision analysis, control systems, prediction problems and other related areas the great attention is paid to the development of approximate reasoning using fuzzy conditional inference rules. It is related with logical inference in which the preconditions and conclusions contain fuzzy concepts. Unfortunately, these approaches did not take into account reliability of existing information. As a formal construct to deal with bimodal information, a Z-number concept was introduced by Zadeh. To the best of our knowledge there is no study on Z-number valued conditional inference rules. In this study we formulate some inference rules in which the logical preconditions and consequences are conditional propositions including fuzzy Z-extension concepts. To do this it is needed to formulate Z-valued implications here we suggest different types of fuzzy Z-implications.

The following Z-conditional inference is considered:

Proposition 1: IF x is Z₁(A₁, B₁) THEN y is Z₂(A₂, B₂)

Proposition 2: x is Z₁'(A₁', B₁')

Conclusion is Z₂'(A₂, B₂').

Here Z₁, Z₁' and Z₂, Z₂' are fuzzy Z-concepts, represented as Z-sets. The logical consequence Z₂' is derived by using different type of Z-set composition operators. The obtained theoretical results are tested using control system for chemical reactor described by Z-number valued IF-THEN rules.

SHORT BIO ABOUT THE AUTHOR :

Rafik A. Aliev received the Ph.D. and Doctorate degrees from the Institute of Control Problems, Moscow, Russia, in 1967 and 1975, respectively. His major fields of study are decision theory with imperfect information, fuzzy logic, soft computing and control theory. He is a Professor and the Head of the Department of the joint MBA Program between the Georgia State University (Atlanta, GA, USA) and the Azerbaijan State Oil Academy. His current research is focused on generalized theory of stability, recurrent fuzzy neural networks, fuzzy type-2 systems, evolutionary computation, decision theory with imperfect information, calculus with Z-numbers, and fuzzy economics. He has over 350 scientific publications including 55 books, 15 editor volumes and more than 280 research papers. Dr. Aliev is a regular Chairman of the International Conferences on Applications of Fuzzy Systems and Soft Computing and International Conferences on Soft Computing and Computing with Words. He is an Editor of the Journal of Advanced Computational Intelligence and Intelligent Informatics (Japan), associate editor of the Information Sciences journal, a member of Editorial Boards of International Journal of Information Technology and Decision Making, International Journal of Web-based Communities (The Netherlands), Iranian Journal of Fuzzy Systems (Iran), International Journal of Advances in Fuzzy Mathematics (Italy), and International Journal “Intelligent Automation and Soft Computing.” He is series editor of “Advances in Uncertain Computation”, “World Scientific”. He was awarded USSR State Prize in field of Science (1983), USA Fulbright Award (1997), and Lifetime Achievement Award in Science (2014). He was a supervisor of more than 150 PhD Students and over 30 Doctorates.

ABSTRACT: MULTISTAGE FUZZY DECISION MAKING AND CONTROL WITH AN IMPLICITLY SPECIFIED TERMINATION TIME: NEW PERSPECTIVES AND CHALLENGES FOR THE USE OF Z-NUMBERS.

The classic Bellman and Zadeh’s (1970) problem of multistage decision making and control under fuzzy constraints on inputs (decisions or controls) and fuzzy goals on the outputs (states, for simplicity) is considered (cf. Kacprzyk, 1997, for a full account of all problems and approaches). The termination time is assumed to be finite and implicitly specified by the moment when the state enters for the first time a specified set of termination states. The system under control is deterministic, for simplicity. To account for a partial reliability, the fuzzy constraints and goals are represented by Z-number defined in finite states, and the system under control is represented by a Z-relation. We show how the Z-number based basic approach with a fixed and specified termination time by Aliev, Pedrycz, Guirimov, Huseynov and Aliyev (2024) can be extended to the impicit termination time. We briefly consider the main approaches to solving the problem: the classic, functional equation based approach by Bellman and Zadeh, the graph theoretic approach by Komolov et al., and the branch-and-bound approach by Kacprzyk - cf. Kacprzyk (1997) for details. We comment upon main challenges and difficulties implied by the use of the Z-numbers in these different approaches which are mainly related to difficulties in the formulation and solution of respective functional equations. Some examples are mentioned.

SHORT BIO ABOUT THE AUTHOR :

Janusz Kacprzyk is a professor of Computer Science at the Systems Research Institute, Polish Academy of Sciences, WIT – Warsaw School of Information Technology, and Chongqing Three Gorges University, Wanzhou, Chongqing, China, and Professor of Automatic Control at PIAP – Industrial Institute of Automation and Measurements in Warsaw, Poland. He is Honorary Foreign Professor at the Department of Mathematics, Yli Normal University, Xinjiang, China. He is Full Member of the Polish Academy of Sciences, Member of Academia Europaea, European Academy of Sciences and Arts, European Academy of Sciences, Foreign Member of the: Bulgarian Academy of Sciences, Spanish Royal Academy of Economic and Financial Sciences (RACEF), Finnish Society of Sciences and Letters, Flemish Royal Academy of Belgium of Sciences and the Arts (KVAB), National Academy of Sciences of Ukraine and Lithuanian Academy of Sciences. He was awarded with 6 honorary doctorates. He is Fellow of IEEE, IET, IFSA, EurAI, IFIP, AAIA, I2CICC, and SMIA.
His main research interests include the use of modern computation computational and artificial intelligence tools, notably fuzzy logic, in systems science, decision making, optimization, control, data analysis and data mining, with applications in mobile robotics, systems modeling, ICT etc.
He authored 7 books, (co)edited more than 150 volumes, (co)authored more than 650 papers, including ca. 150 in journals indexed by the WoS. He is listed in 2020 and 2021 ”World’s 2% Top Scientists” by Stanford University, Elsevier (Scopus) and ScieTech Strategies and published in PLOS Biology Journal.
He is the editor in chief of 8 book series at Springer, and of 2 journals, and is on the editorial boards of ca. 40 journals. He is President of the Polish Operational and Systems Research Society and Past President of International Fuzzy Systems Association.

ABSTRACT: DATA – KNOWLEDGE ENVIRONMENT AND KNOWLEDGE LANDMARKS IN MACHINE LEARNING.

The unpreceded progress in Machine Learning (ML) can be attributed to an efficient use of masses of data as being recently exemplified through numerous constructs of LLMs and foundation models. It becomes intriguing, though, that while exhibiting a heavy reliance on data, a role of knowledge in ML has not been clearly considered. In this talk, we advocate an ultimate importance of synthesizing a unified design knowledge-data (KD) of Machine Learning or KD-ML, for brief. As a new paradigm, KD-ML focuses on a prudent and orchestrated engagement of data and knowledge in the design practices in the area. The fundamentals of the KD environment are formulated along with a historical perspective and the key highlights are identified. The issues of origin of problem-oriented knowledge, taxonomy of knowledge and the and its main features are discussed. Data and knowledge arise at very different levels of abstraction with knowledge being formalized and represented at symbolic level. This constitutes a genuine challenge as data are predominantly numeric. We stress that in the development of a cohesive and unified framework of coping with data and knowledge in learning processes, one needs to reconcile highly distinct levels of abstraction (numeric-qualitative) and with this regard information granules play a pivotal role. We offer a taxonomy of knowledge by distinguishing between scientific and common-sense knowledge and elaborate on a spectrum of ensuing knowledge representation scheme. In the sequel, the main categories of knowledge-oriented ML design are discussed including physics-informed ML (with the reliance of scientific knowledge), an augmentation of data driven models through knowledge-oriented constraints (regularization), a development of granular expansion of the data-driven model and ways of building ML models in the presence of knowledge conveyed by rules. When analyzing the proposed categories, it is also clearly explained how the new ML environment helps avoid a detrimental effect of data blinding. Selected schemes of the KD unified environment and ensuing learning schemes are discussed including a study on LLM-based knowledge acquisition.

SHORT BIO ABOUT THE AUTHOR :

Witold Pedrycz is a Professor and Canada Research Chair (CRC) in Computational Intelligence in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. He is also with the Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland. He also holds an appointment of special professorship in the School of Computer Science, University of Nottingham, UK. In 2009 Dr. Pedrycz was elected a foreign member of the Polish Academy of Sciences. In 2012 he was elected a Fellow of the Royal Society of Canada. Witold Pedrycz has been a member of numerous program committees of IEEE conferences in the area of fuzzy sets and neurocomputing. In 2007 he received a prestigious Norbert Wiener award from the IEEE Systems, Man, and Cybernetics Council. He is a recipient of the IEEE Canada Computer Engineering Medal 2008. In 2009 he has received a Cajastur Prize for Soft Computing from the European Centre for Soft Computing for “pioneering and multifaceted contributions to Granular Computing”. In 2013 has was awarded a Killam Prize. In the same year he received a Fuzzy Pioneer Award 2013 from the IEEE Computational Intelligence Society. His main research directions involve Computational Intelligence, fuzzy modeling and Granular Computing, knowledge discovery and data mining, fuzzy control, pattern recognition, knowledge-based neural networks, relational computing, and Software Engineering. He has published numerous papers in this area. He is also an author of 15 research monographs covering various aspects of Computational Intelligence, data mining, and Software Engineering. Dr. Pedrycz is intensively involved in editorial activities. He is an Editor-in-Chief of Information Sciences and Editor-in-Chief of WIREs Data Mining and Knowledge Discovery (Wiley). He currently serves as an Associate Editor of IEEE Transactions on Fuzzy Systems and is a member of a number of editorial boards of other international journals

ABSTRACT: APPROXIMATION AND INTERPOLATION THEOREMS FOR FUZZY SYSTEMS: CONSEQUENCES AND APPLICATIONS.

Similar to neural networks (NN), fuzzy logic systems (FLSs) with center of gravity (c.o.g.) defuzzifier are universal approximators. Even more, FLSs with a c.o.g. defuzzifier are interpolators for well-behaved functions. These general properties have wide applicability in engineering, decision making, and economics. In many cases, the type of fuzzy logic used in the FLS does not play an important role in the universal approximation or interpolation properties, thus leaving much space for the FLSs optimization. The lecture will review key results about approximation and interpolation with FLSs and will discuss various applications.

SHORT BIO ABOUT THE AUTHOR :

Dept. ETTI, Technical University “Gheorghe Asachi” of Iasi, Iasi, Romania, Dept. Computer Science, “Al.I. Cuza” University of Iasi, Romania, and Romanian Academy. His publications cover topics in artificial intelligence, applied mathematics, stochastic processes, nonlinear dynamics, communications, control, electronics and applied physics, including fuzzy and neuro-fuzzy systems, cellular automata, speech technology, artificial intelligence applications, circuit performance, communication nodes, control loop, dielectric constant, electromagnetic interference, form of equation, internet of things applications, stochastic partial differential equations, access control, acoustic waves, actuators, sensors, communication paths, communication protocol, control applications, critical applications, data packets, electromagnetic compatibility, electromagnetic field, electronic circuits, emotional prosody, and medical applications. Horia-Nicolai L. Teodorescu has served as a Vice-Rector (International) of “Gheorghe Asachi” Technical University of Iasi, and as Director of the Institute of Computer Science of the Romanian Academy, Iasi. He has been an invited Professor at Kyushu Institute of Technology in Japan, the Swiss Federal Institute of Technology in Lausanne, and Leon University in Spain and, for four years, a Visiting Professor at the University of South Florida, Tampa. He has (co)authored or (co)edited more than 20 books published by Springer, CRC Press, Kluwer, and other publishers, as well as more than 300 journal and conference papers. He is an inventor or co-inventor for about 30 patents in the U.S., European Union, Japan, and Romania. He has served as an invited/guest (co)editor to several journal special issues. Dr. Teodorescu is a full member (fellow) of the Romanian Academy and held the honorary position of Vice-Chair of the Science for Peace and Security–Information Technology (SPS-IT) North Atlantic Treaty Organization panel. He has served as the Chairman of several international conference committees and as a member of scientific committees for more than 40 international conferences, several of them organized or supported by IEEE. He served as the Co-chair of several IEEE Symposia and conferences organized in Romania. He has served as an invited speaker at about 20 international conferences and symposia. He received two honorary doctorates (honoris causa).

ABSTRACT: FUZZY LOGIC IN LINGUISTICS.

In this study we analyze what do we gain from searching for the manifestation of fuzzy thinking and fuzzy logic in the language-speech level? First of all, we can say that during this search, the relationships and essences hidden in the deep levels (potential) of language-speech activity dazzle with their richness. The rich potential of language-speech activity once again gains the opportunity to be re-examined. New perspectives emerge. The relationships between language-speech units shine in a new light. Additionally, the hidden synergistic relationships within each of these units allow for the discovery of new perspectives. We apply fuzzy logic principles to investigate different levels of language, namely phonology, morphology, lexical and sentence levels.

SHORT BIO ABOUT THE AUTHOR :

Kamal Abdullayev (PhD 1977, Doctor of Sciences 1984) is Rector and Professor at Azerbaijan University of Languages, Baku, Azerbaijan. He is also a full member of Azerbaijan National Academy of Sciences. The study of syntax and text has always been in focus since early years of his academic career as a linguist. Later mythology and epic texts in particular “The Book of Dede Korkut” from the perspectives of linguistics, semiotics and literary theory have become a significant part of his research agenda. His more recent research interests deal with the role of fuzzy logic in humanities, especially in literature and in linguistics. His numerous monographs, textbooks and scholarly articles on various theoretical and applied aspects of these issues have been published in Azerbaijan and abroad including Turkey, Russia and Germany. Kamal Abdulla is also a prominent Azerbaijani writer. He is a Popular Writer of Azerbaijan. Kamal Abdulla`s novels have been published in many languages worldwide.

ABSTRACT: UNCERTAIN DYNAMIC SYSTEMS: A LOOK AT MULTIPLE SCLEROSIS (MS).

Uncertainty is intrinsic to how real-world systems evolve, especially in complex biological contexts. In modeling dynamic systems—systems that change over time—uncertainty appears not only in initial conditions and parameters but also in the system's structure. This keynote offers a conceptual reflection on uncertain dynamic systems, drawing on the case of Multiple Sclerosis (MS) as a biological phenomenon shaped by unpredictability, variability, and incomplete knowledge. We explore key forms of uncertainty in dynamic modeling:

– Stochastic uncertainty, where randomness drives system variation

– Fuzzy dynamics, where definitions are vague or linguistically expressed

– Interval and incomplete models, where data is partial or bounded

– Epistemic uncertainty, where the trustworthiness of available information is itself in question

MS, as a progressive neurological disease, illustrates all of these forms. From the unpredictability of immune response to the variability in lesion formation and symptom expression across individuals, modeling MS dynamics requires a framework that accepts and encodes uncertainty rather than eliminating it. The talk reflects on how mathematics—particularly soft and uncertain methods—provides tools not just for prediction but for interpreting uncertainty in evolving systems. Ultimately, this lecture invites a broader dialogue between mathematics, biology, and uncertainty. Through the lens of MS, we examine what it means to model life processes under uncertainty and how dynamic systems thinking can bridge the gap between data, meaning, and decision-making in the face of the unknown.

SHORT BIO ABOUT THE AUTHOR :

Tofigh Allahviranloo is a Professor of Applied Mathematics at Istinye University in Istanbul, Türkiye. An accomplished mathematician and computer scientist, Prof. Allahviranloo is dedicated to multi- and interdisciplinary research efforts. His expertise lies primarily in fundamental research in applied fuzzy mathematics, with a special focus on dynamical systems and pioneering applications in applied biological sciences.
Prof. Allahviranloo has made significant scientific contributions, including authoring over 16 international books in English and 10 books in Farsi, as well as approximately 450 publications with renowned publishers such as Elsevier, Springer, Wiley, and Taylor & Francis. He has published more than 250 peer-reviewed journal papers over the past 15 years.
He is the lead editor of the book series, Uncertainty, Computational Techniques and Decision Intelligence, published by ELSEVIER. In addition to his extensive writing activities, Prof. Allahviranloo plays an important role in the academic community as Associate Editor and Editorial Board Member of several prestigious journals. These include Information Sciences opens in new tab/window (ELSEVIER), Fuzzy Sets and Systems (ELSEVIER), Journal of Intelligent and Fuzzy Systems (IOS Press), Iranian Journal of Fuzzy Systems, Mathematical Sciences (Springer), Granular Computing (Springer), Journal of Mathematics and Computer Science (ISRP), and Journal of Computational Methods for Differential Equations (University of Tabriz).
He is currently Executive Editor-in-Chief of Information Sciences, Editor-in-Chief of Transactions on Fuzzy Sets and Systems, Editor-in-Chief of International Journal of Industrial Mathematics, Chairman of International Conference on Decision Sciences (IDS) and Managing Editor of The Journal of Mathematics and Computer Science (International Scientific Research Publications).
In addition, Prof. Allahviranloo is a member of the program committee for the FUZZ-IEEE, NAFIPS Annual Meeting, and IFSA conferences, where he brings his extensive knowledge and experience to these key events in the field of fuzzy systems and applied mathematics.

ABSTRACT: APPLICATION OF AI IN AUTOMATION AND CONTROL SYSTEMS: FOURIER-BASED TYPE-2 FUZZY NEURAL NETWORKS.

One of the key trends in information technology is the rise of artificial intelligence (AI) and machine learning. AI has the potential to revolutionize industries by automating processes, improving decision-making, and enhancing customer experiences. Machine learning algorithms are being used to analyze vast amounts of data and extract valuable insights, enabling organizations to make more informed decisions and drive innovation. On the other hand, intelligent automation and control systems are a trend that has primarily hit the manufacturing and production units and is estimated to only grow more in the coming years. Intelligent automation has also enabled processes to work faster and would allow companies to reach their goals much more efficiently. In this talk, first, the AI systems and majors are defined, the main application and challenges of AI in control systems are summarized, and a new approach of intelligent fuzzy systems is presented as a solution to deal with high dimensional problems. The concept of Fourier-based type-2 fuzzy neural networks is presented and by some examples such as face recognition problem, English handwriting digit recognition, and modeling problem with real-world data its effectiveness is illustrated.

SHORT BIO ABOUT THE AUTHOR :

Prof. Ardashir Mohammadzadeh is a professor at Sakarya University, Turkey. He also leading a researching team in field of intelligent control systems in China. As reported by Stanford University, in 2021-2024, he was listed among the top 2% of the best researchers in the field of artificial intelligence. He was also listed among the top 1% of highly cited researchers in 2023 based on the ESI database. His research interests include control theory, fuzzy logic systems, machine learning, neural networks, intelligent control systems, electric vehicles, power system control systems, chaotic systems, and medical control systems.

ABSTRACT: TYPE-3 FUZZY SYSTEMS AND THEIR APPLICATIONS.

(This article was written in collaboration with Mr. Rafik Aliev.)

In the real world, many complex dynamic processes are characterized by uncertainty. Addressing the complexity of these processes and accurately modeling uncertainties is a major challenge. Traditional type-1 fuzzy systems, which rely on crisp membership functions, are often inadequate for fully capturing the uncertainty involved. To overcome these limitations, type-2 and type-n fuzzy sets were introduced, in which the membership grades themselves are fuzzy, allowing for a more detailed representation of uncertainty. However, even type-2 fuzzy systems may be insufficient for modeling the intricate uncertainties found in many real-world scenarios. This has led to the development of type-3 fuzzy systems, which future extend the capabilities of fuzzy modeling. Type-3 fuzzy systems are particularly effective in handling scenarios involving linguistic uncertainty, statistical uncertainty, and contextual uncertainty, where both primary and secondary membership functions need to be fuzzy to adequately capture the underlying complexity.

One of the most challenging aspects of Type-3 fuzzy systems is the inference process and knowledge acquisition. The inference process requires type-reduction and defuzzification operations. One of efficient approach to automate knowledge acquisition is the use of neural networks, which can learn and adapt fuzzy rules from data. In this context, the design of a type-3 fuzzy neural networks is presented, where the system demonstrates its ability to model deep uncertainty and improve decision-making performance.

SHORT BIO ABOUT THE AUTHOR :

Rahib H.Abiyev is a Professor in the Department of Computer Engineering, at Near East University, North Cyprus. In 2001, he founded Applied Artificial Intelligence Research Centre and in 2008, he created “Robotics” research group in Near East University. He is currently chair of Applied Artificial Intelligence Institute and chair of Computer Engineering Department. His current research interests include computational intelligence, fuzzy systems, control systems, and signal processing. He has published set of research papers in related subjects. R.H.Abiyev is listed in the ”World’s 2% Top Scientists” in the field of Artificial Intelligence for 2022, 2023, 2024 and 2025, published by Elsevier BV & Stanford University.

ABSTRACT: SYSTEM, LANGUAGE, FUZZY LOGIC.

After focusing on the systems approach in general, I will show how systems and language are indispensable for each other. I will try to identify the theoretical and practical contribution of fuzzy logic in the relationship between systems and language. I will conclude by emphasizing the implications of all this for current and future work in artificial intelligence. In a sense, this is a continuation of the ideas I tried to express last year. That the contribution of fuzzy logic and artificial intelligence studies to contemporary rhetoric can actually be made much more useful by revealing the common ground of the insights gained in these subsystems of the system. It is a view on how the theoretical and practical knowledge we have gained in these fields can move us forward.This is actually an attempt to place fuzzy logic where it should be beyond mathematical discussions.

SHORT BIO ABOUT THE AUTHOR :

Av.Dr. Rüştü Burak Eke graduated from Istanbul University, Faculty of Law in 1982. He received master’s degree (Thesis: "Transfer of Technology through Foreign Investments) and PhD degree (Thesis: Patent Right and License Agreement) at the same university where he worked as a research assistant in the Conflict of Laws Department between 1983-1990 and lecturer at the Faculty of Business Administration between 1990-1995. He was on the board of Başak Insurance Company, an affiliate of state-owned bank Ziraat Bankası between 1995-1996 and thereafter he worked as CEO of Ziraat Leasing, also affiliate of Ziraat Bankası from 1995 to 2003. R. Burak Eke is a member of Istanbul Bar since1985. Currently, he is working as advisor and trainer for numerous companies on the subjects of Communication and Organizational Learning along with his professional activities as an attorney at law. He lectures on contemporary rhetoric at MEF University in Istanbul. He is the founder and trustee of Dil ve Sistem Vakfi (The Language and System Foundation)