Invited Speakers
S1: Title: TBA
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Prof. PhD. DrSc. A.Kaklauskas Member of the Research Council of Lithuania Member of the Lithuanian Academy of Science Vilnius Gediminas Technical University Sauletekio al. 11, LT-10223 Vilnius, Lithuania |
Abstract: TBA
Biography: Ph.D Dr.Sc Arturas Kaklauskas is a professor at Vilnius Gediminas Technical University, in Lithuania; Head of the Department of Construction Management and Property; laureate of the Lithuanian Science Prize; member of the Lithuanian Academy of Sciences, editor of Engineering Applications of Artificial Intelligence, an international journal and associate editor of journal “Ecological Indicators”. He contributed to nine Framework and five Horizon 2020 programmes projects and participated in over 30 other projects in the EU, US, Africa and Asia. The Belarusian State Technological University (Minsk, Belarus) awarded him an Honorary Doctorate in 2014. His publications include nine books and 143 papers in Web of Science Journals. Fifteen PhD students successfully defended their theses under his supervision. The Web of Science H-Index of Prof. A. Kaklauskas is 27. Web of Science Journals have cited him 2471 times and average citations per article - 17.28. His areas of interest include affective computing, intelligent tutoring systems; affective intelligent tutoring systems; Massive open online courses (MOOCS); affective Internet of Things; smart built environment; intelligent event prediction, opinion mining, intelligent decision support systems, life cycle analyses of built environments, energy, climate change, resilience management, healthy houses, sustainable built environments, big data and text analytics, intelligent library, internet of things, etc.
S2: Title: Second Order Data Mining of Financial Transaction Time-Series for Fraud Detection and Improved Customer Relationship Management
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Dr. Pawan Lingras |
Abstract:Clustering is an unsupervised learning technique. Since it does not require an expert categorization of patterns, it is one of the first data mining techniques applied to a new dataset. It helps a data scientist to identify patterns in a dataset. There is no correct solution for a clustering problem. The aim of clustering is to assign objects that are alike to the same cluster and ensure that different clusters are well separated from each other. This paper proposes a novel technique for time-series data mining that uses multiple and sequential application of a number of data mining techniques to gain insights into financial transaction dataset that can be used for fraud detection and other behavioral aspects of a customer.
An important first step in creating unsupervised profiles of a customer are finding an appropriate representation. Time-series of financial transactions offer us multiple alternatives:
Spending distribution from month to month (Jan, Feb, …, Nov, Dec)
Spending distribution from day to day in a week (Monday, Tuesday, …, Saturday, Sunday)
Spending distribution during a day (morning, afternoon, evening, night)
Spending distribution among various categories (grocery, household, restaurants)
Geographical distribution of spending:
Spending in City of Residence
Spending in Province of Residence
Spending in Country of Residence
Online spending
Histogram of spending based on distance of vendor from the residence
Each one of these representations describe different behavioral aspects of a customer. To generate meaningful insights from this first order of data mining, the project proposes a second order data mining. The five set of profiles can be used as attributes in a derived dataset. The dataset can then be further analyzed using various data mining techniques including business intelligence, association mining, supervised learning (classification), and second order unsupervised learning (clustering).
Business intelligence will help us compare summary statistics of different profiles against the population. For example, "value conscious" customers spend a greater percentage of their money in groceries than average.
The association mining will provide rules such as:
- Those who spend more in summer tend to spend more on weekends
- Those who do not spend in summer tend to spend more on hotels in February
The supervised learning will use known incidences of frauds and anomalous behavior in the financial transaction data and create models that predict the chances of frauds and anomalies based on the profiles of the customers.
The second order unsupervised learning will also help us understand the correlations between different profiles. For example, "summer spenders" tend to be "non-local spenders".
The resulting insights will help us identify:
Unusual spending pattern such as a client who never spends at night having a large transaction
Building an inference system that uses the association rules that make up normal behavior. Any transaction that fall outside normal behavior can be easily assigned a probability of potential fraud
Additional insights may include knowledge such as:
The customer only uses this card for restaurants and hotels. That means, she must be using a different card for other spending needs
A customer with this profile should be spending more on travel. The low or absence of travel spending on this card may suggest a use of another card
These insights can be used to encourage customer to shift spending from other cards to this card.
Biography: Pawan Lingras is a graduate of IIT Bombay with graduate studies from University of Regina. He is currently a Professor and Director of Computing and Data Analytics at Saint Marys University, Halifax. He is also internationally active having served as a visiting professor at Munich University of Applied Sciences, IIT Gandhinagar, as a research supervisor at Institut Superieur de Gestion de Tunis, as a Scholar-in-Residence, and as a Shastri Indo-Canadian scholar. He has delivered more than 40 invited talks at various institutions around the world. He has authored more than 210 research papers in various international journals and conferences. He has also co-authored three textbooks, and co-edited two books and eight volumes of research papers. His academic collaborations/co-authors include academics from Canada, Chile, China, Germany, India, Poland, Tunisia, U.K. and USA. His areas of interests include artificial intelligence, information retrieval, data mining, web intelligence, and intelligent transportation systems. He has served as the general co-chair, program co-chair, review committee chair, program committee member, and reviewer for various international conferences on artificial intelligence and data mining. He is also on editorial boards of a number of international journals. His research has been supported by Natural Science and Engineering Research Council (NSERC) of Canada for twenty-five years, as well as other funding agencies including NRC-IRAP and MITACS. He has also served on the NSERC's Computer Science peer review committee. He has been awarded an Alumni association excellence in teaching award, Student union's faculty of science teaching award, and President's award for excellence in research at Saint Mary's University.
S3: Title: Anomaly Detection for Clusters of Data
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Prof. Dr. Stephen Huang, Department of Computer Science, University of Houston, USA |
Abstract: Intrusion detection systems attempts to detect attacks by comparing data to predefined malicious activities (signature-based IDS) or to a model or profile of normal behavior (anomaly-Based IDS). These profile-based approaches can also be applied to detections anomaly of clusters of data, such as blood cells, other than network intrusions. However, most of the current detections assume that the data set consists of (multi-attribute) data points in a multi-dimensional space. Sometimes each individual data point does not carry much information. It is the collection of data that contains inherited information. This talk will show two examples of how to detect the behavior of clusters of data. In detecting intruders at the end of stepping-stone chain, it is common to study gaps between packets. But each individual gap means nothing. It is the collection of the gap that characterize the chain. In the case of computational pathology, we have a very similar issue. A single cell in the blood sample tells us nothing while a collection of cells (size, shape, etc.) that can help us in identifying cancer patients. This talk will discuss the issues involved in finding solutions for this types of problems.
Biography:Dr. Shou-Hsuan Stephen Huang is a full professor of Computer Science at the University of Houston, Houston, Texas, USA. He has 38 years of experience in Computer Science teaching, research and administration. Dr. Huang’s research interests include Computer Security, Intrusion Detection, Algorithms and Data Structures, and Bioinformatics. He is currently working on detecting intruders by studying the cyber behavior of the intruders. He has authored and co-authored more than 100 peer-reviewed journal and conference papers. He also served as one of the delegates from the U.S. universities to the US-EC Consortium on "Towards a Common Computer Science Curriculum and Mutual Degree Recognition" meeting in Nice, France, in 1999 funded in part by the US Department of Education. Dr. Huang also served as a National Research Council (NRC)-NASA Senior Research Associate at NASA Goddard Space Flight Center, Greenbelt, MD in 1989-90. Prof. Huang received his MS and Ph.D. degrees in Computer Science from The University of Texas-Austin in 1979 and 1981 respectively. Dr. Huang has served in several administrative capacities at the University of Houston including Director of Graduate Studies and Department Chairman.
S4: Title: Edge Detection method using Soft Computing techniques applied to Pattern Recognition
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Prof. Patricia Melin, Ph.D., D.Sc. Tijuana Institute of Technology, Tijuana, Mexico |
Abstract: Edge detection is a fundamental part in many computer vision and image processing applications, such as pattern recognition, medical image processing, object recognition, and motion analysis. Edge detection process is widely used in pattern recognition and computer vision systems because it is helpful in obtaining satisfactory results when applied in the preprocessing phase. The process of finding the edges is not easy, especially if the image is blurry or distorted by noise; these phenomena frequently occurs when the image is captured by any acquisition hardware, factors like the distance, quality, and resolution of cameras, environment, and illumination variation tend to produce images with ambiguous or incomplete data. The issue of determining what is an image edge and what is not becomes more critical by the fact that edges are partially distorted or hidden. In order to solve this issue for image edge detection, in recent years, various approaches that involve soft computing methods have been proposed, including the principles of fuzzy set theory, which is one of the main topics in this Talk. In the past few decades most research has concentrated on designing edge detector algorithms for grayscale images; however, color image processing applications have recently been receiving increasing attention because color images provide more information about the objects in a scene than grayscale images and this information can be used to improve the performance of image processing systems. Edge detection in color images is computationally more complex than in grayscale images, but there are many advantages to using color images. For example, the increase in the amount of information can lead to more accurate object location and the possibility of processing images that are more complex. Fuzzy techniques for edge detection have gained importance because they offer a good alternative to enhance the accuracy in the edge detection process. In this talk we will focus in various edge detection techniques using Type 1 fuzzy logic, interval type-2 fuzzy logic and General type-2 fuzzy logic applied in gray scale and color images and how to use in pattern recognition.
Biography:Prof. Patricia Melin holds the Doctor in Science degree (Doctor Habilitatus D.Sc.) in Computer Science from the Polish Academy of Sciences. She is a Professor of Computer Science in the Graduate Division, Tijuana Institute of Technology, Tijuana, Mexico, since 1998. In addition, she is serving as Director of Graduate Studies in Computer Science and head of the research group on Hybrid Neural Intelligent Systems (2000-present). She is past President of NAFIPS (North American Fuzzy Information Processing Society) 2019-2020. Prof. Melin is the founding Chair of the Mexican Chapter of the IEEE Computational Intelligence Society. She is member of the IEEE Neural Network Technical Committee (2007 to present), the IEEE Fuzzy System Technical Committee (2014 to present) and is Chair of the Task Force on Hybrid Intelligent Systems (2007 to present) and she is currently Associate Editor of the Journal of Information Sciences and IEEE Transactions on Fuzzy Systems. She is member of NAFIPS, IFSA, and IEEE. She belongs to the Mexican Research System with level III. Her research interests are in Modular Neural Networks, Type-2 Fuzzy Logic, Pattern Recognition, Fuzzy Control, Neuro-Fuzzy and Genetic-Fuzzy hybrid approaches. She has published over 220 journal papers, 10 authored books, 22 edited books, 103 chapter books and more than 300 papers in conference proceedings with h-index of 51 in Scopus. She has served as Guest Editor of several Special Issues in the past, in journals like: Applied Soft Computing, Intelligent Systems, Information Sciences, Non-Linear Studies, JAMRIS, Fuzzy Sets and Systems. Prof. Melin is Associate Editor of the IEEE Transactions of Fuzzy Systems, Journal of Information Sciences, and Journal of Complex and Intelligent Systems. She has been recognized as Highly Cited Researcher in 2017 and 2018 by Clarivate Analytics because of having multiple highly cited papers in Web of Science.
S5: Title: Optimization of Type-2 Fuzzy Systems: Theory and Applications
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Prof. Oscar Castillo, Ph.D., D.Sc. Tijuana, Institute of Technology Tijuana, Mexico |
Abstract: Type-2 fuzzy systems are powerful intelligent models based on the theory of fuzzy sets, originally proposed by Prof. Zadeh. Most real-world applications up to now are based on type-1 fuzzy systems, which are built based on the original (type-1) fuzzy sets that extend the concept of classical sets. Type-2 fuzzy sets extend type-1 fuzzy sets by allowing the membership to be fuzzy, in this way allowing a higher level of uncertainty management. Even with the current successful applications of type-1 fuzzy systems, now several papers have shown that type-2 is able to outperform type-1 in control, pattern recognition, manufacturing and other areas. The key challenge in dealing with type-2 fuzzy models is that their design has a higher level of complexity, and in this regard the use of bio-inspired optimization techniques is of great help in finding the optimal structure and parameters of the type-2 fuzzy systems for particular applications, like in control, robotics, manufacturing and others. Methodologies for designing type-2 fuzzy systems using bio-inspired optimization in different areas of application are presented as illustration. In particular, we will cover Bee Colony Optimization, Particle Swarm Optimization, Gravitational Search and similar approaches to the optimization of fuzzy systems in control applications, robotics and pattern recognition. Finally, we will also consider using fuzzy logic for enhancing the performance of metaheuristics, where also good results have been achieved.
Biography: Oscar Castillo holds the Doctor in Science degree (Doctor Habilitatus) in Computer Science from the Polish Academy of Sciences (with the Dissertation “Soft Computing and Fractal Theory for Intelligent Manufacturing”). He is a Professor of Computer Science in the Graduate Division, Tijuana Institute of Technology, Tijuana, Mexico. In addition, he is serving as Research Director of Computer Science and head of the research group on Hybrid Fuzzy Intelligent Systems. Currently, he is President of HAFSA (Hispanic American Fuzzy Systems Association) and Past President of IFSA (International Fuzzy Systems Association). Prof. Castillo is also Chair of the Mexican Chapter of the Computational Intelligence Society (IEEE). He also belongs to the Technical Committee on Fuzzy Systems of IEEE and to the Task Force on “Extensions to Type-1 Fuzzy Systems”. He is also a member of NAFIPS, IFSA and IEEE. He belongs to the Mexican Research System (SNI Level 3). His research interests are in Type-2 Fuzzy Logic, Fuzzy Control, Neuro-Fuzzy and Genetic-Fuzzy hybrid approaches. He has published over 300 journal papers, 10 authored books, 40 edited books, 200 papers in conference proceedings, and more than 300 chapters in edited books, in total 840 publications according to Scopus (H index=56), and more than 940 publications according to Research Gate (H index=68 in Google Scholar). He has been Guest Editor of several successful Special Issues in the past, like in the following journals: Applied Soft Computing, Intelligent Systems, Information Sciences, Non-Linear Studies, Fuzzy Sets and Systems, JAMRIS and Engineering Letters. He is currently Associate Editor of the Information Sciences Journal, Applied Soft Computing Journal, Engineering Applications of Artificial Intelligence, Granular Computing Journal and the International Journal on Fuzzy Systems. Finally, he has been elected IFSA Fellow in 2015 and MICAI Fellow member in 2017. He has been recognized as Highly Cited Researcher in 2017 and 2018 by Clarivate Analytics because of having multiple highly cited papers in Web of Science.