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CONFERENCE TO BE HELD IN
Seoul, South Korea
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KEYNOTE
SPEAKERS
Prof. Irene Yu-Hua Gu
Chalmers University of Technology, Sweden
Dr. Irene Yu-Hua Gu received Ph.D. degree
in electrical engineering from Eindhoven University of
Technology, Eindhoven, The Netherlands, in 1992. From 1992
to 1996, she was Research Fellow at Philips Research
Institute IPO, Eindhoven, The Netherlands, post dr. at
Staffordshire University, Staffordshire, U.K., and Lecturer
at the University of Birmingham, Birmingham, U.K. Since
1996, she has been with the Department of Electrical
Engineering (previous name: Department of Signals and
Systems), Chalmers University of Technology, Gothenburg,
Sweden, where she has been a Professor since 2004. Her
research interests include statistical image and video
processing, video object tracking and recognition, machine
learning and deep learning, and signal processing with
applications. During the last several years her main
research has been focused on biomedical image analysis and
deep learning. Dr. Gu was an Associate Editor for IEEE
Transactions on Systems, Man, and Cybernetics, Part A:
Systems and Humans, and Part B: Cybernetics from 2000 to
2005, and Associate Editor for EURASIP Journal on Advances
in Signal Processing from 2005 to 2016, Editorial Board of
the Journal of Ambient Intelligence and Smart Environments
from 2011 to 2019. She is a Senior Area Editor for IEEE
Signal Processing Letters since 2021. She was elected as the
Chair of the IEEE Swedish Signal Processing Chapter from
2001 to 2004. She is a senior member of IEEE. She has
coauthored over 200 papers, and has been ranked as the top
50 scientists in the field of Computer Science and
Electronics in Sweden in the 6th Edition of 2020 ranking by
Guide2Research team.
Prof. Stephen Kwok-Wing Tsui (h-index: 58)
The Chinese University of Hong Kong, Hong Kong
Stephen Kwok-Wing Tsui is currently a
Professor and the Associate Director (Research) in the
School of Biomedical Sciences. He is also the Director of
Hong Kong Bioinformatics Centre in the Chinese University of
Hong Kong (CUHK). In 1995, he received his PhD degree in
Biochemistry at CUHK. He was then appointed as an Assistant
Professor in the Biochemistry Department in 1997 and
promoted to the professorship in 2004. He was also a former
member of the International HapMap Consortium and worked on
the single nucleotide polymorphisms of human chromosome 3p.
During the SARS outbreak in 2003, his team was one of the
earliest teams that cracked the complete genome of the
SARS-coronavirus and facilitated the emergence of real-time
PCR assay for the virus. Totally, he has published more than
240 scientific papers in international journals, including
Nature, Nature Machine Intelligence, New England Journal
of Medicine, Lancet, PNAS, Nucleic Acids Research, Genome
Biology and Bioinformatics. His h-index is 58 and the
citations of his publications are over 20,000. His major
research interests are next generation sequencing,
bioinformatics and metagenomics in human diseases.
INVITED
SPEAKERS
Assoc. Prof. Giovanni Pappalettera
Polytechnic University of Bari, Italy
Giovanni Pappalettera is an Associate
Professor at Polytechnic University of Bari, where he also
lectures in the Bachelor Degree in Industrial Design and the
Master Degree of Mechanical Engineering. He earned his PhD
in Mechanical and Biomechanical Design at Politecnico di
Bari and in Biomechanics and Bioengineering at Scuola
Interpolitecnica di Dottorato di Torino. His primary
research interests include material characterization and
experimental mechanics, encompassing optical methods,
acoustic emission, and residual stress analysis. He is
actively involved in biomechanics research including
ultrasound-induced fatigue effects on cells, mechanical
characterization of dental devices, and 3D dental element
reconstruction. In 2007 and 2008, he was a visiting
researcher at Politechnika Warsawska – Warszawa, and in
2009, he was a visiting researcher at the Northern Illinois
University - DeKalb. Giovanni serves on the PhD committee in
Mechanical and Management Engineering at Politecnico di
Bari. He has authored over a hundred papers in international
journals and conference proceedings and is a co-inventor of
three Italian patents. He is also a co-author of three books
and a member of international associations, including the
Society of Experimental Mechanics, the European Society of
Experimental Mechanics, and the Italian Association of
Stress Analysis. Since 2003, he has been a research
associate at the National Institute of Nuclear Physics
(INFN).
Speech Title: "Effects of OUT
(Onco-Ultrasound-Tripsy) in-vitro Treatment on Cancer and
Healthy Cells"
Assoc. Prof. Guanghui (Richard) Wang
Toronto Metropolitan University, Canada
Guanghui (Richard) Wang received the
Ph.D. degree in engineering from the University of Waterloo,
Waterloo, ON, Canada, in 2014. He is currently an Associate
Professor with the Department of Computer Science, Toronto
Metropolitan University (TMU), Toronto, ON. He is also the
director of the Computer Vision and Intelligent Systems
Laboratory at TMU. From 2014 to 2020, he was an Assistant
Professor and then an Associate Professor in the Department
of Electrical Engineering and Computer Science at the
University of Kansas (KU), Lawrence, KS, USA. He has
authored one book “Guide to Three Dimensional Structure and
Motion Factorization”, published by Springer-Verlag. He has
authored or co-authored more than 180 papers in
peer-reviewed journals and conferences. His research
interests include computer vision, image analysis, machine
learning, and intelligent systems.
Speech Title: "Polyp Segmentation
from Colonoscopy Images Using Enhanced Neural Networks"
Abstract: Colonoscopy is a crucial
procedure for detecting colorectal polyps, which stand as
the primary precursors to colorectal cancer. However,
accurately segmenting polyps presents a significant
challenge due to their diverse shapes, sizes, colors, and
texture variations. To address these challenges and enhance
polyp segmentation performance, we propose two enhanced
neural network structures. The first network enhances
semantic information through a novel Semantic Feature
Enhancement Module (SFEM) and an Adaptive Global Context
Module (AGCM). By integrating these modules, we
progressively refine feature quality across layers, thereby
enhancing the final feature representation. The second
network introduces a novel Fuzzy Attention module, designed
to prioritize difficult pixels, particularly those near the
boundaries that pose a greater challenge for prediction.
This attention module can be seamlessly incorporated into
any backbone network. We evaluated its efficacy with three
backbone networks: Res2Net, ConvNext, and Pyramid Vision
Transformer. Our proposed approaches are rigorously
evaluated across five colonoscopy datasets, showcasing
superior performance compared to other state-of-the-art
models.
Assoc. Prof. Md. Altaf-Ul-Amin
NARA Institute of Science and Technology, Japan
Md. Altaf-Ul-Amin received B.Sc. degree
in Electrical and Electronic Engineering from Bangladesh
University of Engineering and Technology (BUET), Dhaka,
M.Sc. degree in Electrical, Electronic and Systems
Engineering from Universiti Kebangsaan Malaysia (UKM) and
PhD degree from Nara Institute of Science and Technology
(NAIST), Japan. He received the best student paper award in
the IEEE 10th Asian Test Symposium. Also, he received two
other best paper awards as a co-author of journal articles.
He previously worked in several universities in Bangladesh,
Malaysia and Japan. Currently he is working as an associate
professor in Computational Systems Biology Lab of NAIST. He
is conducting research on Network Biology, Systems Biology,
Cheminformatics and Biological Databases. He published
around 90 peer reviewed papers in international journals and
conference proceedings. Current google scholar citation
index of his publications is more than 7900.
Speech Title: "Computational Approaches to
Predict Natural Antibiotics based on Traditional Herbal
Medicines"
Abstract: Antibiotic resistance is a
major public health threat and there is an urgent need for
new antibiotics. Traditional herbal medicine systems, such
as Jamu, Unani, and Traditional Chinese Medicine, have been
used for centuries to treat bacterial infections. Machine
learning methods have been shown to be effective for
predicting potential natural antibiotic candidates based on
traditional herbal medicine systems. In this study, we used
machine learning methods to predict potential natural
antibiotic candidates at plant and metabolite levels. We
evaluated different machine learning algorithms and
preprocessing techniques to obtain the best prediction
accuracy. For Jamu, we achieved an accuracy of 91.10% using
the Random Forest model. For Unani, we achieved an accuracy
of 83% using a multilayer perceptron model with SMOTE
preprocessing. In total, we predicted 42 potential plant
candidates and 201 candidate metabolites as potential
natural antibiotics. Many of these candidates have been
validated based on published literature mentioning their
antibacterial properties. Some others are structurally
similar to known antibiotics. Our findings suggest that
machine learning methods can be used to effectively predict
potential natural antibiotic candidates utilizing
traditional herbal medicines. This approach has the
potential to accelerate the development of new antibiotics
to combat antibiotic-resistant pathogens.
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