The 21st International Conference on Artificial Intelligence and Soft Computing

June 19-23, 2022

Invited Talks

Rafal Angryk "Machine Learning Opportunities in Heliophysics: Big Picture with Some Examples"

Rafal Angryk
Rafal Angryk "Machine Learning Opportunities in Heliophysics: Big Picture with Some Examples" Georgia State University, Atlanta, USA


With the recently publicized USA’s ambitions to enable human exploration of the Moon and Mars in the next decade, combined with the NASA’s long-standing open data policy, we can observe an increase in opportunities for performing interesting and impactful machine learning research on the rich space weather data, which includes the Sun’s magnetic field data, and other measurements coming from multiple spacecrafts or ground-based observatories. These research opportunities can be further enriched with another exciting source of big data, which comes from numerous heliophysics-related scientific simulations.
We hope to increase awareness about these promising machine learning research opportunities through this seminar. Firstly, we will briefly discuss the impacts of transient solar events on the Earth and space travel to clarify the importance of accurate space weather predictions. While these topics have always been central to heliophysicists’ research interests, they are nowadays intensively investigated by the operational community, involving major federal institutions, such as National Oceanic and Atmospheric Administration (NOAA), the United States Space Force, NASA, and many dynamically growing industries related to communication satellites, air travels, power grids, precision agriculture, oil drilling, and (recently growing) space tourism. Secondly, we will discuss some aspects of freely available big data coming from the Sun-pointing satellites (NASA, NOAA, ESA), multi-institutional networks of ground observatories (e.g., GONG H-alpha Network, hosted by National Solar Observatory), as well as computationally demanding high-resolution solar activity simulations conducted by the heliophysicists-led academic units, governmental and private research labs, as well as small businesses. Finally, we will present some examples of the space weather-related research performed in the interdisciplinary Data Mining Lab at Georgia State University (https://dmlab.cs.gsu.edu/), where we try to serve, through collaborative initiatives, both research- and operations-focused space weather communities with big data-driven machine learning research and applications.

Sanghamitra Bandyopadhyay "Multiobjective Optimization and Multimodality: Algorithms and Applications"

Sanghamitra Bandyopadhyay
Sanghamitra Bandyopadhyay "Multiobjective Optimization and Multimodality: Algorithms and Applications" Indian Statistical Institute, India

Prof. Sanghamitra Bandyopadhyay did her B Tech, M Tech and Ph.D. in Computer Science from Calcutta University, IIT Kharagpur and Indian Statistical Institute respectively. She then joined the Indian Statistical Institute as a faculty member, and became the Director in 2015. Since 2020 she is continuing in her second tenure as the Director of the Institute. Her research interests include computational biology, soft and evolutionary computation, artificial intelligence and machine learning. She has authored/co-authored several books and numerous articles in journals, book chapters, and conference proceedings and has a citation h-index of 57. Prof. Bandyopadhyay has worked in many Institutes and Universities worldwide. She is the recipient of several awards including the Shanti Swarup Bhatnagar Prize in Engineering Science, TWAS Prize, Infosys Prize, JC Bose Fellowship, Swarnajayanti fellowship, INAE Silver Jubilee award, INAE Woman Engineer of the Year award (academia), IIT Kharagpur Distinguished Alumni Award, Humboldt Fellowship from Germany, Senior Associateship of ICTP, Italy, young engineer/scientist awards from INSA, INAE and ISCA, and Dr. Shanker Dayal Sharma Gold Medal and Institute Silver from IIT, Kharagpur, India. She is a Fellow of the Indian National Science Academy (INSA), National Academy of Sciences, India (NASI), Indian National Academy of Engineers (INAE), Institute of Electrical and Electronic Engineers (IEEE), The World Academy of Sciences (TWAS), International Association for Pattern Recognition (IAPR) and West Bengal Academy of Science and Technology. She serves as a member of the Science, Technology and Innovation Advisory Council of the Prime Minister of India (PM-STIAC). In 2022, she has been selected for the conferment of the Padma Shri award,the fourth highest civilian award of the Government of India.


Multi-objective optimization problems (MOPs) are ones that require simultaneous optimization of multiple conflicting objectives to attain the state of Pareto-optimality, where improving solutions in terms of one objective leads to deterioration in terms of one or more of the other objectives. MOPs galore in diverse real-life situations, and several algorithms have been developed for solving them. Multi-modal MOPs (MMMOPs) are those problems where a many-to-one mapping exists from solution space to objective space. As a result, multiple subsets of the Pareto-optimal set could independently generate the same Pareto-Front. The discovery of such equivalent solutions across the different subsets is essential during decision-making to facilitate the analysis of their non-numeric, domain-specific attributes.
In this talk, we will first provide a brief introduction to MOPs and an algorithm for solving them, followed by an application to the real-life problem of drug design. This will be followed by a discussion on the basic concept of multi-modality in MOPs. We then identify a problem of the existing approaches for solving MMMOPs, which is referred to as the crowding illusion problem. A method for solving MMMOPs with a graph Laplacian-based Optimization using Reference vector assisted Decomposition (LORD) will thereafter be discussed. The talk will conclude with the mention of an application of MMMOPs to the problem of building energy optimization.

Włodzisław Duch "New developments in EEG analysis for diagnosis, biofeedback and brain-computer interfaces"

Włodzisław Duch
Włodzisław Duch "New developments in EEG analysis for diagnosis, biofeedback and brain-computer interfaces" Neurocognitive Laboratory, Center for Modern Interdisciplinary Technologies, and Department of Informatics, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University, Toruń, Poland
Link to CV: http://www.is.umk.pl/~duch/cv/cv.html

New approaches to extract useful information from EEG are the key to use this technique for diagnosis, biofeedback and brain-computer interfaces. Techniques based on event related potentials, motor imagery and steady state visually evoked potentials (SSVEP) are still dominating the field, but most interesting developments are in investigation of neurodynamics, observing information flow through the brain connectomes. Many new mathematical techniques have been proposed, but development of methods useful in clinical practice is still a great challenge. I will review most interesting new approaches in this areas and summarize our own attempts in analysis of frequency-based fingerprints, new spatial filters, recurrence-based nonlinear features.

[1] Rykaczewski, K, Nikadon, J, Duch, W, Piotrowski, T. (2021). SupFunSim: spatial filtering toolbox for EEG. Neuroinformatics 19, 107–125
[2] M.K. Komorowski, K. Rykaczewski, T. Piotrowski, K. Jurewicz, J. Wojciechowski, A. Keitel, J. Dreszer, W. Duch (2021) ToFFi - Toolbox for Frequency-based Fingerprinting of Brain Signals (submitted to Neurocomputing).

Tingwen Huang "Efficient Computational Approaches and Applications to Some Optimization Problems in Smart Grid"

 Nikola Kasabov
Tingwen Huang "Efficient Computational Approaches and Applications to Some Optimization Problems in Smart Grid" Texas A&M University

Prof. Tingwen Huang's research focuses on dynamics of nonlinear systems including neural networks, complex networks and multi-agent and their applications to smart grids and cybersecurity. He is a Highly Cited Researcher by Clarivate Analytics, formerly Thomson Reuters.

He is very actively involving in professional service. He serves/served as the Past-President (2021), President (2020), President-Elect (2019) for Asia Pacific Neural Network Society, as an associate editor for a dozen international journals, as a guest editor for 12 special issues publishing in 9 leading journals.

He is a Member of the European Academy of Sciences and Arts, an Academician of the International Academy for Systems and Cybernetic Sciences, a Fellow of IEEE and AAIA (Asia-Pacific Artificial Intelligence Association).


In a smart grid context, a demand response strategy of electric vehicle charging is modelled by a stochastic game, where a big data analytic framework is proposed for controlling the electric vehicle charging behaviours. We will also look at Plug-In Electric Vehicles (PEVs) Charging: Feeder Overload Control problem. Moreover, a two-stage stochastic game theoretical model is proposed for energy trading problem in a multi-energy microgrid system. Concerning the privacy, a research branch of reinforcement learning (RL) that dominates distributed learning for years will be presented by making the first attempt to apply RL-based algorithms in the energy trading game among smart microgrids where no information concerning the distribution of payoffs is a priori available and the strategy chosen by each microgrid is private to opponents, even trading partners. To solve this challenge, a new energy trading framework based on the repeated game that enables each microgrid to individually and randomly choose a strategy with probability to trade the energy in an independent market so as to maximize his/her average revenue.

Janusz Kacprzyk "AI-assisted/enabled decision making, decision aid and decision support for solving complex problems"

Janusz Kacprzyk
Janusz Kacprzyk "AI-assisted/enabled decision making, decision aid and decision support for solving complex problems" Fellow, IEEE, IET, EurAI, IFIP, IFSA, SMIA
Full member, Polish Academy of Sciences
Member, Academia Europaea
Member, European Academy of Sciences and Arts
Member, European Academy of Sciences
Member, International Academy for Systems and Cybernetic Sciences (IASCYS)
Foreign member, Bulgarian Academy of Sciences
Foreign member, Finnish Society of Sciences and Letters
Foreign member, Royal Flemish Academy of Belgium for Sciences and the Arts (KVAB)
Foreign member, Spanish Royal Academy of Economic and Financial Sciences (RACEF)

Systems Research Institute, Polish Academy of Sciences
Ul. Newelska 6, 01-447 Warsaw, Poland
Email: kacprzyk@ibspan.waw.pl
Google: kacprzyk

Janusz Kacprzyk is 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.


Decision making – which is meant as a selection of an option, or a set of options, or a course of action, or courses of action – is presumably the most common and frequent human activity. More and more often decision making proceeds in complex settings in the sense that there are more and more stakeholders, much uncertain, imprecise and missing information, all characteristics and specifications change over time, multiple criteria, goals or aspects are to be accounted for. Moreover, an explict human centricity is pronounced in the sense of a crucial role of the human being in the decision process. These complications clearly suggest that the humans involved in the decision making process should be aided or supported by some computer based tools and techniques.
In most cases a more effective and efficient setting is to assume the decision making to proceed, first, in a setting in which there is a domain expert, the so called judge, who has a deep expertise in his domain, e.g., city transportation, but not in the solution tools, e.g. optimization, and he/she commissions an additional expert, the so-called advisor, who has a deep expertise in the solution methods for some class of formally stated problems but not necessarily in the domain. For such a setting we present the so-called judge-advisor type approaches, and consider in particular issues related to advice giving, taking, and rejecting. We show that AI, notably via machine learning but also via analyses of argumentation or explanation, can help improve this judge-advisor scheme.
Then, we propose as a convenient, „democratic” solution the decision support systems (DSSs) which can be used by less qualified users, and we show that data driven approaches open here new vistas. In this contaxt of a wide use of algorithms for automating many tasks, we consider the so-called algorithm aversion, notably its aspects related to a need for comprehensible and trustworthy results of such approaches, as well as when, for which problems, etc. these new autonomous AI-enabled decision support systems can be used.
Finally, we briefly summarize new qualities that can be obtained by involving the concepts, and tools and techniques of AI-assisted decision making and AI-enabled decision support. Some examples on socioeconomic planning are mentioned.

Nikola Kasabov "Deep Learning, Deep Knowledge Representation and Knowledge Transfer with Brain-Inspired Neural Network Architectures"

 Nikola Kasabov
Nikola Kasabov "Deep Learning, Deep Knowledge Representation and Knowledge Transfer with Brain-Inspired Neural Network Architectures" Fellow IEEE, Fellow RSNZ, DV Fellow RAE UK
Director, Knowledge Engineering and Discovery Research Institute,
Auckland University of Technology, Auckland, New Zealand, nkasabov@aut.ac.nz,
Advisory/Visiting Professor Shanghai Jiao Tong University, Robert Gordon University UK

Professor Nikola Kasabov is Fellow of IEEE, Fellow of the Royal Society of New Zealand, Fellow of the INNS College of Fellows, DVF of the Royal Academy of Engineering UK and the Scottish Computer Association. He is the Founding Director of the Knowledge Engineering and Discovery Research Institute (KEDRI), Auckland and Professor at the School of Engineering, Computing and Mathematical Sciences at Auckland University of Technology. Kasabov is the 2019 President of the Asia Pacific Neural Network Society (APNNS) and Past President of the International Neural Network Society (INNS). He is member of several technical committees of IEEE Computational Intelligence Society and Distinguished Lecturer of IEEE (2012-2014). He is Editor of Springer Handbook of Bio-Neuroinformatics, Springer Series of Bio-and Neurosystems and Springer journal Evolving Systems. He is Associate Editor of several journals, including Neural Networks, IEEE TrNN, Tr CDS, Information Sciences, Applied Soft Computing. Kasabov holds MSc and PhD from TU Sofia, Bulgaria. His main research interests are in the areas of neural networks, intelligent information systems, soft computing, bioinformatics, neuroinformatics. He has published more than 620 publications. He has extensive academic experience at various academic and research organisations in Europe and Asia, including: TU Sofia Bulgaria; University of Essex UK; University of Otago, NZ; Advisory Professor at Shanghai Jiao Tong University, Visiting Professor at ETH/University of Zurich and Robert Gordon University UK. Prof. Kasabov has received a number of awards, among them: Doctor Honoris Causa from Obuda University, Budapest; INNS Ada Lovelace Meritorious Service Award; NN Best Paper Award for 2016; APNNA ‘Outstanding Achievements Award’; INNS Gabor Award for ‘Outstanding contributions to engineering applications of neural networks’; EU Marie Curie Fellowship; Bayer Science Innovation Award; APNNA Excellent Service Award; RSNZ Science and Technology Medal; 2015 AUT Medal; Honorable Member of the Bulgarian and the Greek Societies for Computer Science. More information of Prof. Kasabov can be found on the KEDRI web site: http://www.kedri.aut.ac.nz


The talk argues and demonstrates that the third generation of artificial neural networks, the spiking neural networks (SNN), can be used to design brain-inspired architectures that are not only capable of deep learning of temporal or spatio-temporal data, but also enabling the extraction of deep knowledge representation from the learned data. Similarly to how the brain learns time-space data, these SNN models do not need to be restricted in number of layers, neurons in each layer, etc. as it is the case with the traditional deep neural network architectures. When the SNN model is designed to follow a brain template, knowledge transfer between humans and machines in both directions becomes possible through the creation of brain-inspired BCI. The presented approach is illustrated on an exemplar SNN architecture NeuCube (free software and open source available from www.kedri.aut.ac.nz/neucube) and case studies of brain and environmental data modelling and knowledge representation using incremental and transfer learning algorithms These include predictive modelling of EEG and fMRI data measuring cognitive processes and response to treatment, AD prediction, BCI, human-human and human-VR communication, hyper-scanning and other. More details can be found in the recent book: Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence, Springer,2019, https://www.springer.com/gp/book/9783662577134.

Adam Krzyzak "On the rate of convergence of image classifiers based on deep convolutional neural networks"

Adam Krzyzak
Adam Krzyzak "On the rate of convergence of image classifiers based on deep convolutional neural networks " Concordia University, Montreal, Canada

Adam Krzyzak received the M.Sc. and Ph.D. degrees in computer engineering from the Wroc law University of Science and Technology, Poland, in 1977 and 1980, respectively, D.Sc. degree (habilitation) in computer engineering from the Warsaw University of Technology, Poland in 1998 and the Title of Professor from the President of Poland in 2003. Since 1983, he has been with the Department of Computer Science and Software Engineering, Concordia University, Montreal, Canada, where he is currently a Professor. In 1983, he held an International Scientific Exchange Award in the School of Computer Science, McGill University, Montreal, Canada, in 1991, the Vineberg Memorial Fellowship at the Technion{Israel Institute of Technology and, in 1992, Humboldt Research Fellowship at the University of Erlangen-Nuremberg, Germany. He visited the University of California Irvine, Information Systems Laboratory at Stanford University, Riken Frontiers Research Laboratory, Japan, Stuttgart University, Technical University of Berlin, University of Saarlandes and Technical University Darmstadt. His current research interests include nonparametric statistics, deep learning theory and applications and classification. He has been an associate editor of IEEE Transactions on Neural Networks and IEEE Transactions on Information Theory and is presently an Associate Editor-in-Chief of Pattern Recognition Journal. He is a co-author of the book A Distribution-Free Theory of Nonparametric Regression, New York: Springer, 2002. He has been a General Co-chair of the IAPR Workshop on Statistical, Syntactic and Structural Pattern Recognition 2022, Co-chair of the Program Committee of the 10-th IEEE International Conference on Advanced Video and Signal-Based Surveillance 2013 and has served on program committees of numerous international conferences. He is a Fellow of the IEEE and a Fellow of the IAPR.


Image classifiers based on convolutional neural networks are defined, and the rate of convergence of the misclassification risk of the estimates towards the optimal misclassification risk is analyzed. Under suitable assumptions on the smoothness and structure of a posteriori probability, the rate of convergence is shown which is independent of the dimension of the image. This proves that in image classification, it is possible to circumvent the curse of dimensionality by convolutional neural networks. Furthermore, the obtained result gives an indication why convolutional neural networks are able to outperform the standard feedforward neural networks in image classification. Our classifiers are compared with various other classification methods using simulated data. Furthermore, the performance of our estimates is also tested on real images.

Ujjwal Maulik "AI and Data Science: Path Traversed and Ahead"

 Ujjwal Maulik
Ujjwal Maulik "AI and Data Science: Path Traversed and Ahead" Professor of Computer Science and Engineering, Jadavpur University, India

Dr. Ujjwal Maulik is a Professor in the Dept. of Comp. Sc. and Engg., Jadavpur University since 2004. He was also the former Head of the same Department. He also held the position of the Principal in charge and the Head of the Dept. of Comp. Sc. and Engg., Kalayni Govt. Engg. College. Dr. Maulik has worked in many universities and research laboratories around the world as visiting Professor/ Scientist including Los Alamos National Lab., USA in 1997, Univ. of New South Wales, Australia in 1999, Univ. of Texas at Arlington, USA in 2001, Univ. of Maryland at Baltimore County, USA in 2004, Fraunhofer Institute for Autonome Intelligent Systems, St. Augustin, Germany in 2005, Tsinghua Univ., China in 2007, Sapienza Univ., Rome, Italy in 2008, Univ. of Heidelberg, Germany in 2009, German Cancer Research Center (DKFZ), Germany in 2010, 2011 and 2012, Grenoble INP, France in 2010, 2013 and 2016, University of Warsaw in 2013 and 2019, University of Padova, Italy in 2014 and 2016, Corvinus University, Budapest, Hungary in 2015 and 2016, University of Ljubljana, Slovenia in 2015 and 2017, International Center for Theoretical Physics (ICTP), Trieste, Italy in 2014, 2017 and 2018. He is the recipient of Alexander von Humboldt Fellowship during 2010, 2011 and 2012 and Senior Associate of ICTP, Italy during 2012-2018. He is the Fellow of Indian National Academy of Engineers (INAE), India, National Academy of Science India (NASI), International Association for Pattern Recognition (IAPR), USA and The Institute of Electrical and Electronics Engineers (IEEE), USA. He is also the Distinguish Member of the ACM. He is Distinguish Speaker of IEEE as well as ACM. His research interest include Machine Learning, Data Science, Bioinformatics, Multi-objective Optimization, Social Networking, IoT and Autonomous Car. In these areas he has published ten books, more than three hundred fifty papers, filed several patents and guided twenty two doctoral students. He is mentoring a couple of Start-Ups in the area - AI for Healthcare. His other interest include Sports and Classical Music.


In this lecture first we will describe fundamental and current trends in Artificial Intelligence (AI), and Data Science. In this regard we will demonstrate applications of different machine learning algorithms including Deep Learning and Graph Neural Network in real life application like Intelligence Car and healthcare. We will discuss the important of explainable and trusted AI. Finally we will also discuss issues and challenges related to Big Data.

Christian Napoli "Human-Centered Artificial Intelligence & Ambient Assisted Living: actual challenges and future perspectives in Europe"

Christian Napoli class=
Christian Napoli "Human-Centered Artificial Intelligence & Ambient Assisted Living: actual challenges and future perspectives in Europe" Sapienza University of Rome, Italy

Christian Napoli is Associate Professor with the Department of Computer, Control, and Management Engineering "Antonio Ruberti", Sapienza University of Rome, since 2019, where he also collaborates with the department of Physics and the Faculty of Medicine and Psychology, as well as holding the office of Scientific Director of the International School of Advanced and Applied Computing (ISAAC). He received the B.Sc. degree in Physics from the Department of Physics and Astronomy, University of Catania, in 2010, where he also got the M.Sc. degree in Astrophysics in 2012 and the Ph.D. in Computer Science in 2016 at the Department of Mathematics and Computer Science.
Christian Napoli has been Research Associate with the Department of Mathematics and Computer Science, University of Catania, from 2018 to 2019, while, previously, Research Fellow and Adjunct Professor with the same department from 2015 to 2018. He has been a Student Research Fellow with the Department of Electrical, Electronics, and Informatics Engineering, University of Catania, from 2009 to 2016, a collaborator of the Astrophysical Observatory of Catania and the National Institute for Nuclear Physics, since 2010. He has been several time Invited Professor at the Silesian University of Technology, Visiting Academic at the New York University, and responsible of many different institutional topics from 2011 until now for Undegraduate, Graduate and PhD students in Computer Science, Computer Engineering and Electronics Engineering. His teaching activity focused on Artificial Intelligence, Neural Networks, Machine Learning, Computing Systems, Computer Architectures, Distributed Systems, and High Performance Computing. He is involved in several international research projects, serves as reviewer and member of the board program committee for major international journals and international conferences. His current research interests include neural networks, artificial intelligence, human-computer interaction and computational neuropsychology.


Ambient Assisted Living (AAL) is evolving rapidly and has undoubtedly become a topic of great relevance given the aging of the world population. Advances in networking, sensors, and embedded devices have made it possible to monitor and provide assistance to people in their homes. Such an enormous growth of AAL-related AI applications has prompted recommendations that AI techniques should be "human-centered". There is no clear definition of what is meant by Human Centered Artificial Intelligence. Unfortunately, while we can define several components that compose an Human-Centered AI agent, we probably face a quite long road in order to workaround a noticeable series of formal problems in order to obtain such a standardized definition. Europe's diverse AI community are bringing together individuals and organizations that are interested in contributing to, or benefitting from, today's AI capabilities and that are interested on a formal definition and widespread adoption of standards for Human-Centered AI systems. An entire AI ecosystem is nowaday growing moving forward the frontiers of our knowledge and application capabilities on this field.

Witold Pedrycz "Federated Learning, Knowledge Transfer, and Knowledge Distillation: Towards Green and Granular Machine Learning"

Witold Pedrycz
Witold Pedrycz "Federated Learning, Knowledge Transfer, and Knowledge Distillation: Towards Green and Granular Machine Learning" Department of Electrical & Computer Engineering, University of Alberta, Canada
Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland


Granular Computing is about representing knowledge by means of information granules, constructing information granules, their processing, and realizing communication and interpretation carried out in the framework of information granules.

Information granules are abstract constructs that bring together individual entities because of their closeness, similarity, or resemblance. The level of abstraction makes a description of the problem manageable and problem solving strategies feasible and efficient.

We offer a rationale behind emergence of information granules, offer examples and present a variety of frameworks (sets, intervals, fuzzy sets, probabilities, rough sets, random sets, intuitionistic sets…) using which they are formally represented.

Main motivating factors are advocated. General ways of designing and evaluating information granules are discussed. A role of a variety of clustering techniques treated as a prerequisite for the formation of information granules is demonstrated. The evaluation of the quality of information granules is case ion the granulation-degranulation scheme.

We deliver a comprehensive approach to the development of information granules by means of the principle of justifiable granularity; here various construction scenarios are discussed. In the sequel, we look at the generative and discriminative aspects of information granules supporting their further usage in the formation of granular models. A symbolic manifestation of information granules is put forward and analyzed from the perspective of semantically sound descriptors of data and relationships among data. The principle provides a way to build an information granule such that it is legitimate from the perspective of coverage (experimental legitimacy of the granule) and its semantics (meaning). Along with the generic construct, discussed are various augmentations of the principle. We carefully look at the generative and discriminative aspects of information granules supporting their further usage in the formation of granular artifacts. The considerations are carried out following a general knowledge representation scheme:

data -› numeric prototypes -› information granules -› symbols

Furthermore, a symbolic characterization of information granules is put forward and analyzed from the perspective of semantically sound descriptors of data. Their linguistic summarization is offered as well. The diversity of information granules is also captured by more advanced constructs of information granules of higher type and higher order.

Some selected topics of data analytics in which information granularity is visible such as (i) imputation, (ii) time series prediction, (ii) data stream analysis, (iii) imputation, (iv) association analysis and associative memories, and (v) transfer learning are formulated and discussed.

The tutorial is made self-contained; all required prerequisite material will be made an initial part of the presentation.

Roman Šenkeřík "Recent Trends in Evolutionary Computation: From good practices in bench-marking to explainable AI"

Roman Senkerik
Roman Šenkeřík "Recent Trends in Evolutionary Computation: From good practices in bench-marking to explainable AI" Tomas Bata University in Zlin

Roman Senkerik is an Associate Professor and Head of the A.I.Lab with the Department of Informatics and Artificial Intelligence, and Leader of Evolutionary computing research group at Tomas Bata University in Zlin, Czech Republic. He is the author of 50 journal papers, 300 conference papers, and several book chapters as well as editorial notes. His research interests are soft computing methods and their interdisciplinary applications in optimization, development and benchmarking of evolutionary algorithms, machine learning, data science, theory of chaos and complex systems, and AI in cyber-security. Roman Senkerik is a recognized reviewer for many leading journals in computer science/computational intelligence, and associated editor of EAAI and SWEVO. He was a part of the organizing teams for special sessions/symposiums at GECCO, IEEE WCCI, CEC, and SSCI evolutionary computation events. He is IEEE member, IEEE CIS member, and IEEE CIS Task Force on Benchmarking member.


Undoubtedly, it can be said that nowadays, evolutionary algorithms (EA) or so-called evolutionary computational techniques (ECT) represent the most popular metaheuristics that allow solving complex optimization problems. Over time, two large groups of algorithms have formed, distinguished by different philosophies of function (mainly due to different inspirations in nature). Thus, in the literature and the professional community, one can find these two essential groups labeled as "classical EA" and so-called "swarm" algorithms. Both groups of algorithms, together with recently published findings and results, carry specific challenges and also criticisms.
This talk aims to focus specifically on the recent challenges in the ECT field, algorithm development trends, and analysis that could eliminate many of the problems and resolve the open challenges. In recent years, incremental algorithm performance and efficiency advances have been made primarily on benchmark sets, but benchmarking is not always done correctly. Thus, the recent trend of so-called good practices in benchmarking will be discussed. Furthermore, possible approaches and SW tools (frameworks) for algorithm autoconfiguration, i.e., the appropriate setting of algorithm hyperparameters for given instances of the optimization problem. Another important topic that will be mentioned is also the possibility of linking the development of ECT and explainable AI, in particular, how the notion of explainability can be understood in relation to ECT.
The lecture is designed to be self-explanatory, without requiring knowledge of ECT theory.

Program Committee

General chairman

Leszek Rutkowski Leszek Rutkowski

Rafał Scherer Rafał Scherer
Technical Chair
Marcin Korytkowski Marcin Korytkowski

Area Chairs

Fuzzy Systems
Witold Pedrycz Witold Pedrycz
Evolutionary Algorithms
Zbigniew Michalewicz Zbigniew Michalewicz
Neural Networks
Jinde Cao Jinde Cao

Computer Vision
Dacheng Tao Dacheng Tao
Machine Learning
Nikhil R. Pal Nikhil R. Pal
Artificial Intelligence with Applications
Janusz Kacprzyk Janusz Kacprzyk

International Liaison

Jacek M. Żurada Jacek M. Żurada

Finance Chair

Marcin Gabryel Marcin Gabryel

International Program Committee

  • Hojjat Adeli - USA
  • Cesare Alippi - Italy
  • Shun-ichi Amari - Japan
  • Rafal A. Angryk - USA
  • Robert Babuska - Netherlands
  • James C. Bezdek - Australia
  • Piero P. Bonissone - USA
  • Bernadette Bouchon-Meunier - France
  • Jinde Cao - China
  • Juan Luis Castro - Spain
  • Yen-Wei Chen - Japan
  • Andrzej Cichocki - Japan
  • Krzysztof Cios - USA
  • Ian Cloete - Germany
  • Oscar Cordón - Spain
  • Bernard De Baets - Belgium
  • Włodzisław Duch - Poland
  • Meng Joo Er - Singapore
  • Pablo Estevez - Chile
  • David B. Fogel - USA
  • Tom Gedeon - Australia
  • Erol Gelenbe - United Kingdom
  • Jerzy W. Grzymala-Busse - USA
  • Hani Hagras - UK
  • Saman Halgamuge - Australia
  • Yoichi Hayashi - Japan
  • Tim Hendtlass - Australia
  • Francisco Herrera - Spain
  • Kaoru Hirota - Japan
  • Tingwen Huang - USA
  • Hisao Ishibuchi - Japan
  • Mo Jamshidi - USA
  • Robert John - UK
  • Janusz Kacprzyk - Poland
  • Nikola Kasabov - New Zealand
  • Okyay Kaynak - Turkey
  • Vojislav Kecman - USA
  • James M. Keller - USA
  • Etienne Kerre - Belgium
  • Frank Klawonn - Germany
  • Robert Kozma - USA
  • László Kóczy - Hungary
  • Józef Korbicz - Poland
  • Rudolf Kruse - German
  • Adam Krzyzak - Canada
  • Věra Kůrková - Czech Republic
  • Soo-Young Lee - Korea
  • Simon M. Lucas - UK
  • Luis Magdalena - Spain
  • Jerry M. Mendel - USA
  • Radko Mesiar - Slovakia
  • Zbigniew Michalewicz - Australia
  • Javier Montero - Spain
  • Eduard Montseny - Spain
  • Kazumi Nakamatsu - Japan
  • Detlef D. Nauck - Germany
  • Ngoc Thanh Nguyen - Poland
  • Erkki Oja - Finland
  • Nikhil R. Pal - India
  • Witold Pedrycz - Canada
  • Leonid Perlovsky - USA
  • Marios M. Polycarpou - Cyprus
  • Danil Prokhorov - USA
  • Vincenzo Piuri - Italy
  • Sarunas Raudys - Lithuania
  • Olga Rebrova - Russia
  • Vladimir Red’ko - Russia
  • Raúl Rojas - Germany
  • Imre J. Rudas - Hungary
  • Norihide Sano - Japan
  • Rudy Setiono - Singapore
  • Jennie Si - USA
  • Peter Sincak - Slovakia
  • Andrzej Skowron - Poland
  • Roman Słowiński - Poland
  • Pilar Sobrevilla - Spain
  • Janusz Starzyk - USA
  • Jerzy Stefanowski - Poland
  • Vitomir Štruc - Slovenia
  • Ron Sun - USA
  • Johan Suykens - Belgium
  • Ryszard Tadeusiewicz - Poland
  • Hideyuki Takagi - Japan
  • Dacheng Tao - Australia
  • Vicenç Torra - Spain
  • Burhan Turksen - Canada
  • Shiro Usui - Japan
  • Deliang Wang - USA
  • Jun Wang - Hong Kong
  • Lipo Wang - Singapore
  • Paul Werbos - USA
  • Bernard Widrow - USA
  • Kay C. Wiese - Canada
  • Bogdan M. Wilamowski - USA
  • Donald C. Wunsch - USA
  • Ronald R. Yager - USA
  • Xin-She Yang - United Kingdom
  • Gary Yen - USA
  • Sławomir Zadrożny - Poland
  • Jacek Zurada - USA
Committee on Informatics of the Polish Academy of Sciences

Contact - ICAISC 2022 Organizing Committee

General Chairman
Leszek Rutkowski Leszek Rutkowski email: leszek.rutkowski.a_t.pcz.pl
Rafał Scherer Rafał Scherer email: rafal.scherer.a_t.pcz.pl
Technical Chair
Marcin Korytkowski Marcin Korytkowski email: marcin.korytkowski.a_t.pcz.pl
Finance Chair
Marcin Gabryel Marcin Gabryel email: marcin.gabryel.a_t.pcz.pl

Organizing Committee Office

Institute of Computational Intelligence Częstochowa University of Technology
al. Armii Krajowej 36 42-200 Częstochowa, Poland
Tel: +48 (34) 3250546 Fax: +48 (34) 3250546 E-mail: icaisc@pcz.pl ICAISC 2022 Web page: http://icaisc.eu

International Liaison

Prof. Jacek M. Żurada Dept. of Electr. and Comp. Engineering
University of Louisville, Louisville, KY 40292, USA
Tel: (502) 852-6314 Fax: (502) 852-3940 E-mail: jacek.zurada@louisville.edu URL: http://ci.uofl.edu/zurada