Invited Talks
Luís A. Alexandre "Title TBA"
http://www.di.ubi.pt/~lfbaa/
TBA
Włodzisław Duch "Understanding real brain neurodynamics using recurrence analysis"
Link to CV: http://www.is.umk.pl/~duch/cv/cv.html
Brain networks may be modeled using attractor neural networks. Dynamics of such networks is characterized by metastable states. Recurrence analysis is very well suited to find such states in neuroimaging or in electrophysiological EEG or MEG signals. Interpretation of brain signals has high diagnostic value, opening also new doors to therapeutic interventions. Unfortunately such signals are nonlinear, noisy, oscillatory, and nonstationary, therefore very difficult to interpret. One promising approach is based on Recurrence Quantification Analysis (RQA) that provides useful features for classification of EEG and other brain signals. RQA measures have been better suited for the quasi-periodic dynamics and require some adjustment in application to the attractor dynamics. To characterize global states of the brain signals in short time windows are represented by power spectra. We have recently showed that this method generates useful RQA features, and that using these features in combination with SVM analysis we can find reduced number of electrodes/features that lead to a high accuracy classification of these complex signals. Identification of recurrent states and analysis of transition sequences between these states provides important information about specific, frequently occurring states, including their spatial locations and dominating oscillation frequency. Connections between observed states and probability of transitions between them can be displayed in graphical form, and linked to the sources in the brain that generate signals measured on the scalp. Some examples of the usefulness of such analysis in diagnostics of mental disorders will be provided. We are developing a BrainPulse software package to automatize such analysis.
Furman Ł, Tołpa K, Minati L, Duch W. (2022) Short-Time Fourier Transform and Embedding Method for Recurrence Quantification Analysis of EEG Time Series.
European Physical Journal Special Topics, 1-15
Nikola Kasabov "Title TBA"
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
https://kedri.aut.ac.nz/staff/staff-profiles/professor-nikola-kasabov
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
TBA
Zbigniew Michalewicz "Advanced AI-based business applications for transforming data into decisions"
Zbigniew Michalewicz received the Master of Science degree from the Technical University of Warsaw, Warsaw, Poland, in 1974; the Ph.D. degree from the Institute of Computer Science, Polish Academy of Sciences, Warsaw, in 1981, and the D.Sc. degree in Computer Science from the Polish Academy of Science in 1997. He is currently Emeritus Professor of Computer Science at the University of Adelaide, Australia. He is also a Professor with the Institute of Computer Science, Polish Academy of Sciences, the Polish-Japanese Institute of Information Technology, Warsaw, and the State Key Laboratory of Software Engineering, Wuhan University, Wuhan, China. He is also associated with the Structural Complexity Laboratory, Seoul National University, South Korea. Zbigniew Michalewicz is the Chief Scientific Officer at Complexica (www.complexica.com), a leading provider of software applications that harness the power of Artificial Intelligence and big data to improve the effectiveness of sales & marketing activities. For many years his research interests were in the field of evolutionary computation. He published several books, including a monograph Genetic Algorithms + Data Structures = Evolution Programs (3 editions, a few translations, over 20,000 citations, source: Google Scholar), and over 300 technical papers in journals and conference proceedings that are cited widely (50,000 citations, source: Google Scholar). Other books include Adaptive Business Intelligence and How to Solve It: Modern Heuristics (both published by Springer, Berlin, 2006 and 2004, respectively), Puzzle-based Learning (Hybrid Publishers, Melbourne, 2008), Winning Credibility: A Guide for Building a Business from Rags to Riches (Hybrid Publishers, Melbourne, 2007), where he described his business experiences over the last years.
Zbigniew Michalewicz was one of the editors-in-chief of the Handbook of Evolutionary Computation and the general chairman of the First IEEE International Conference on Evolutionary Computation held in Orlando, June 1994. In December 2013 Zbigniew was awarded (by the President of Poland, Mr. Bronislaw Komorowski) the Order of the Rebirth of Polish Polonia Restituta – the second highest Polish state decoration civilian for outstanding achievements in the field of education, science, sports, culture, arts, economy, national defence, social activities, the civil service and the development of good relations with other countries.
The talk would cover a few AI-based business applications for transforming data into decisions, based on work done for three companies (NuTech Solutions, SolveIT Software, and Complexica) over the last 20 years. A few general concepts (Adaptive Business Intelligence, Travelling Thief Problem, Larry – the Digital Analyst) would be discussed and illustrated by a few examples. The final part of the talk would present Complexica’s approach for increasing revenue, margin, and customer engagement through automated analysis.
Witold Pedrycz "Title TBA"
and
Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland
TBA
