KEYNOTE/INVITED SPEAKER
Keynote Speakers |
Dr. Roumiana Koutcheva, S.R.
TK Engineering, Bulgaria
Roumiana Kountcheva is Vice President of TK Engineering. She got her MSc and PhD at the Technical University of Sofia, Bulgaria, and became Senior Researcher (SR) at TIE, in 1993.
R. Kountcheva had two post-doc trainings in Japan (Fujitsu, 1977 and Fanuc, 1980). She has more than 200 publications (including 32 book chapters and 5 patents), and presented 23 plenary speeches at international conferences and workshops. She is a member of IRIEM, IDSAI, IJBST Journal Group, and Bulgarian Association for Pattern Recognition.
R. Kountcheva participated as PI, Co-PI and team member of 47 scientific research projects. She is a reviewer of WSEAS conferences and journals, and edited several books for Springer SIST series. Her scientific areas of interest are in image processing and analysis, image watermarking and data hiding, multidimensional image representation, tensor representation, pyramidal image decompositions, image and video compression, CNC, programmable controllers, etc.
Title: Intelligent Invariant object representation
Prof. Nashwa El-Bendary
Arab Academy for Science, Technology, and Maritime Transport (AASTMT), Egypt
Prof. Nashwa El-Bendary received her Ph.D. in information technology from the Faculty of Computers and Artificial Intelligence, Cairo University - Egypt in 2008. Since October 2020, Prof. El-Bendary has served as the Dean of the College of Computing and Information Technology (South Valley – Aswan) at the Arab Academy for Science, Technology, and Maritime Transport (AASTMT) in Egypt. In addition, she holds the position of Director of the China-Arab States Technology Transfer Center (CASTTC) at AASTMT since 2016, a testament to her leadership and expertise in fostering technology transfer and collaborations between China and Arab countries. Prof. El-Bendary actively contributes to the academic community as a committee member of various international journals and conferences. She serves as an area editor for the Elsevier Applied Soft Computing Journal since 2017, and she is an IEEE Senior membership since 2019.
Prof. El-Bendary is the author of more than 100 scientific articles published in prestigious indexed and highly ranked international journals and conferences, with main research interests include Artificial Intelligence, Machine Learning, Pattern Recognition, Image and Signal Processing, and Data Analytics. Moreover, she has actively participated in both internationally and locally funded scientific research projects, as well as mobility projects, further enhancing her collaborative efforts and expanding her global network. Acknowledging her exceptional contributions, Prof. El-Bendary has received several notable accolades. In 2014, she was honored with the UNESCO-ALECSO Award for creativity and technical innovation, recognizing her groundbreaking work as a young researcher. In 2015, she was awarded the L'Oréal-UNESCO for Women in Science Fellowship, a prestigious distinction that highlights her significant contributions to scientific research.
Title: Revolutionizing the Medical Metaverse: The Power of AI in Diagnosis, Discovery, and Personalized Medicine
Abstract: In this talk we also shall explore how generative AI models are redefining the boundaries of medical practice. Witness the potential of AI-powered approaches to synthesize missing or corrupted medical images, facilitating accurate diagnosis and timely intervention. Explore how personalized treatment plans tailored to the unique needs of each patient, leveraging AI's ability to analyze vast amounts of data and generate actionable insights, and enabling physicians to make informed diagnoses with unprecedented clarity. Through the immense potential of AI-powered tools, it shapes a healthier future for all, where data creation fuels personalized medicine, groundbreaking discoveries, and ultimately a world transformed by the ingenuity of machine and human collaboration. Delve into the realm of accelerated drug discovery, where virtual libraries of potential molecules are created and tested with remarkable speed and precision. However, ethical considerations cannot be ignored. To ensure the ethical and responsible development and deployment of AI-powered technologies, especially Generative AI tools, in healthcare, patient safety and well-being must be prioritized. By addressing the challenges and opportunities presented by this technology, we can empower healthcare professionals to improve the quality of life, particularly for seniors, including those living with dementia.
Prof.Adil Baykasoglu Dokuz Eylul University, Turkey Adil Baykasoglu received his B.Sc., M.Sc. and Ph.D. degrees from Mechanical and Industrial Engineering areas in Turkey (Gaziantep) and England (Nottingham). He is presently a full Professor at the Industrial Engineering Department at the Dokuz Eylul University. He has organized many academic conferences and published numerous academic papers, 3 books and edited several conference books on operational research, computational intelligence, engineering management, and manufacturing systems design. He is an active editor and editorial member for many scientific journals. He is also an active member of many academic and professional institutions including the International Society of Agile Manufacturing, Turkish Chamber of Mechanical Engineers, Turkish Operational Research Association, etc. He has received awards from Turkish Academy of Sciences (TUBA), Scientific and Technological Research Council of Turkey (TUBITAK), Middle East Technical University (METU), and several other institutions for his scientific contributions. Title: State of metaheuristic algorithms Abstract: Metaheuristics are versatile, stochastic search algorithms that work independently of specific problem types and have been successfully applied across diverse scientific fields. Their popularity has led to the introduction of over 250 algorithms in the literature, often marketed as innovative, high-performing, or promising. However, this abundance has drawn substantial criticism within the research community. The primary critiques include limited novelty, as many algorithms share similar structures; insufficient validation and justification that sometimes obscure a lack of real effectiveness; and the use of metaphorical names that don't clearly align with classical optimization concepts. Nonetheless, researchers often continue to search for new natural phenomena to imitate rather than focusing on refining the optimization field or improving existing metaheuristics. This trend may be partly driven by the No Free Lunch Theorem (NFLT), which posits that all metaheuristic algorithms perform equally when averaged over all possible problems, potentially encouraging the development of competitive new algorithms for complex optimization problems. Despite this trend, there is considerable scope to enhance current metaheuristics. Key areas for improvement include developing intelligent step-size adjustment techniques, refined search direction mechanisms, effective constraint-handling methods, and coding strategies for complex discrete optimization problems. Additional opportunities for advancement include strategies for stochastic and fuzzy optimization, dynamic optimization, integration of learning algorithms, memory management, and parallelization of population-based metaheuristics. Moreover, combining metaheuristics with mathematical programming techniques offers a promising path for future progress. |
