Jerry Chun-Wei Lin, Western Norway
University of Applied Sciences

Title: Mining the interesting patterns to aid the education system


Jerry Chun-Wei Lin received his Ph.D. from the Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan in 2010. He is currently a full Professor with the Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway. He has published more than 500+ research articles in refereed journals (with 60+ ACM/IEEE transactions journals) and international conferences (IEEE ICDE, IEEE ICDM, PKDD, PAKDD), 16 edited books, as well as 33 patents (held and filed, 3 US patents). His research interests include data mining and analytics, natural language processing (NLP), soft computing, IoTs, bioinformatics, artificial intelligence/machine learning, and privacy preserving and security technologies. He is the Editor-in-Chief of the International Journal of Data Science and Pattern Recognition, the Associate Editor for IEEE TNNLS, IEEE TCYB, IEEE TDSC, INS, JIT, AIHC, IJIMAI, HCIS, IDA, PlosOne, IEEE Access, and the Guest Editor for several IEEE/ACM journals such as IEEE TFS, IEEE TII, IEEE TIST, IEEE JBHI, ACM TMIS, ACM TOIT, ACM TALLIP, and ACM JDIQ. He has recognized as the most cited Chinese Researcher respectively in 2018, 2019, 2020, and 2021 by Scopus/Elsevier. He is the Fellow of IET (FIET), ACM Distinguished Member (Scientist), and IEEE Senior Member.


As a large amount of data is collected daily from individuals, businesses, and other organizations or applications, various algorithms have been developed to identify interesting and useful patterns in the data that meet a set of requirements specified by a user. The main purpose of data analysis and data mining is to find new, potentially useful patterns that can be used in real-world applications. For example, analyzing customer transactions in a retail store can reveal interesting patterns in customer buying behavior that can then be used for decision making. Several pattern mining approaches have then been extensively developed and used in various domains and applications, such as cross-marketing, e-commerce, finance, education, medical and biomedical applications. In this talk, I will first highlight the advantages of using pattern mining models for knowledge discovery and pattern analysis. Then, I will explain how pattern mining helps to improve the effectiveness of studying/learning in an education scheme.



Irfan Mehmood, University of Bradford

Title: Machine Learning based Small Bowel Video Capsule Endoscopy Analysis: Challenges
and Opportunities


Irfan Mehmood (M’16) has been involved in IT industry and academia in Pakistan, South Korea, and
UK for over 10 years. He is now serving as a Lecturer in Applied Artificial Intelligence, School of
Electrical Engineering and Computer Science, Media, Design and Technology, University of
Bradford, UK. His sustained contribution at various research and industry-collaborative projects give
him an extra edge to meet the current challenges faced in the field of multimedia analytics.
Specifically, he has made significant contribution in the areas of visual surveillance, information
mining and data encryption. He has published 90+ papers in peer-reviewed international journals and
conferences such as Information Fusion, Neurocomputing, IEEE Access, IEEE Transactions on
Industrial Informatics, IEEE Internet of Things Journal, International Journal of Information
Management, Future Generation Computer Systems, Sensors, Journal of Visual Communication and
Image Representation, Multimedia Tools and Applications, Computers in Human Behavior,
EURASIP Journal on Image and Video Processing, Mobile Networks and Applications, Computers in
Biology and Medicine, Journal of Medical Systems, Signal, Image and Video Processing, Bio-
Medical Materials and Engineering, KSII Transactions on Internet and Information Systems, NBIS
2015, MITA 2015, PlatCon 2016, SKIMA 2019, and IWFCV 2020. He is serving as a professional
reviewer for numerous well-reputed journals such as Journal of Visual Communication and Image
Representation, Future Generation Computer Systems, IEEE Access, Journal of SuperComputing,
Signal Image and Video Processing, Multimedia Tools and Applications, ACM Transactions on
Embedded Computing Systems, and Enterprise Information Systems. He acted as GE/LGE in several
special issues of SCI/SCIE indexed journals and is currently involved in editing of several other
special issues. Contact at [email protected]
Google Scholar:
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Artificial Intelligence (AI) prolifically reciprocates impact on its boundaries with diagnostic medical
imaging systems. The complex and diverse nature of medical imaging data confers an intricate
application domain to explore the associated pitfalls algorithms, hence validate the machine learning
based visual analytics techniques. In this talk, an earnest attempt will be made to reflect on recent
visual computing approaches for machine learning driven automatic analysis of small bowel capsule
endoscopy videos (VCE). Majority of frameworks developed for general medical imaging analysis
and capsule endoscopy in particular tend to be polarized either towards computational or clinical
domain overlooking the essential gaps required to fill for a prospective clinical solution. In this talk, a
detailed comparative and critical analysis of existing research methodologies for small bowel capsule
endoscopic video will be presented and these methods are also evaluated from the aspects considered
significant by clinical experts such as population study, biasness, data split type, validation method
and prospective nature of experiments. The gaps between AI driven computational methodologies
presented by AI experts and clinical knowledge basis established by expert gastroenterologists arecritically highlighted to be addressed in future.