ICTA Series International Conference on Advances in Information and Communication Technology
ICTA - International Conference Series
KEYNOTE SPEAKERS
Harish Garg
Department of Mathematics, Thapar Institute of Engineering & Technology, Patiala, Punjab, India
Email: harishg58iitr@gmail.com
Preventive Maintenance Scheduling & Actions: An Optimization model
Short Biography:
Dr. Harish Garg working as an Associate Professor at Thapar Institute of Engineering & Technology, Patiala, Punjab, India. He is ranked in the World’s Top 2% Scientist List and India Rank #1 & World Rank #163 published by Stanford University in the consecutive six years 2020-2025. He received the Most Outstanding Researcher award in the field of Mathematics from Careers 360 Academy. He is also the recipient of the International Obada-Prize 2022 – Young Distinguished Researchers. He is also the recipient of the Top-Cited paper by an India-based author (2015 – 2019) from Elsevier Publisher. He also serves as an advisory board member of the Universal Scientific Education and Research Network (USERN). He is the Research Fellow of INTI International University, Malaysia; Adjunct Professor at the School of Mathematical Sciences, Sunway University, Malaysia; Adjunct Researcher at International Center for AI and Cyber Security Research and Innovations at Asia University, Taiwan.
Dr. Garg’s research interests include Computational Intelligence, Multi-criteria decision making, Evolutionary algorithms, Reliability analysis, Expert systems and decision support systems, Computing with words and Soft Computing. He has authored more than 540 papers (over 520 are SCI) published in refereed International Journals including IEEE Transactions, Elsevier, Springer etc. His Google citations are over 32750 with H-index- 95. He is one of the leading researchers in the world related to the MCDM and soft computing approaches.
Dr. Garg also serves on editorial boards of several leading international journals; this includes the Founding Editor-in-Chief of the Journal of Computational and Cognitive Engineering. He is also the Associate Editor of Alexandria Engineering Journal, Journal of Industrial & Management Optimization, Machine Intelligence Research, CAAI Transactions on Intelligence Technology, etc. For more details about him, kindly follow his webpage https://sites.google.com/site/harishg58iitr/home
Abstract:
This task centres on the periodic preventive maintenance (PM) of a system with deteriorated components by simultaneously considering the three actions, mechanical services, repair and replacement for a multi-component system. The mathematical approach behind the corrective and maintenance action scheduling is discussed. The degraded behaviour of the component is modelled by a reliability equation, and the effect of PM actions to reliability is formulated based on maximizing the maintenance-benefit analysis.
Keywords:
Preventive maintenance; Maintainability; Reliability; Scheduling actions.
Dr. Nguyen Quoc Khanh Le
Taipei Medical University (TMU), Taiwan, and Director of the TMU AIBioMed Lab
Email: khanhlee@tmu.edu.tw
Artificial Intelligence in Medical Imaging
Short Biography:
Dr. Nguyen Quoc Khanh Le is an Associate Professor at Taipei Medical University (TMU), Taiwan, and Director of the TMU AIBioMed Lab. His research focuses on artificial intelligence in biomedicine, particularly medical imaging, bioinformatics, multimodal AI, precision medicine, and translational healthcare applications.
Dr. Khanh has published more than 180 peer-reviewed papers in leading international journals and conferences spanning artificial intelligence, medical imaging, bioinformatics, and computational biology. According to the Scopus database, his publications have received over 5,000 citations with an h-index of 51. He has been included in the Stanford University/Elsevier World’s Top 2% Scientists list for multiple consecutive years and was recognized as a Rising Star in Computer Science by Research.com for his contributions to AI-driven biomedical research.
His current research interests include multimodal AI, foundation models for healthcare, radiogenomics, computational genomics, and precision medicine. He has led numerous interdisciplinary research projects and international collaborations integrating artificial intelligence with clinical and biomedical sciences.
Dr. Khanh actively serves the international scientific community as a guest editor, editorial board member, reviewer, and program committee member for leading journals and conferences. He has delivered keynote speeches, invited talks, and tutorials at international conferences and academic institutions worldwide. His contributions to research and education have been recognized through several prestigious awards, including the TMU Teaching Excellence Award, Research Paper Excellence Award, Young Scholar Research Award, and Outstanding Curriculum Award for Open Education.
Abstract:
Artificial intelligence (AI) is rapidly transforming medical imaging by enhancing disease detection, diagnosis, prognosis, and clinical decision-making. Recent advances in deep learning, multimodal learning, and foundation models have enabled AI systems to analyze complex imaging data with unprecedented accuracy and efficiency across radiology, pathology, and precision medicine applications. This keynote presentation will provide an overview of the evolution of AI in medical imaging, from traditional machine learning approaches to modern deep learning and multimodal AI frameworks. The talk will highlight representative applications in cancer imaging, image-based biomarker discovery, treatment response prediction, and radiogenomics, demonstrating how AI can support personalized healthcare and intelligent clinical workflows. In addition, emerging trends such as vision-language models, trustworthy AI, explainable AI, and multimodal integration with genomics and clinical data will be discussed. Finally, the presentation will address current challenges and future directions for translating AI-driven medical imaging technologies into real-world healthcare systems.
Keywords:
Medical Imaging, Artificial Intelligence, Deep Learning, Multimodal Learning.