ICBET 2024          Full Paper Submission Deadline: 20 March, 2024     Conference Dates: 14th - 17th June 2024      Conference Place: Seoul, South Korea



Seoul, South Korea





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.




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. 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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