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Professor, Open University of Cyprus
Title: Recommenders for Scientometic Objects?
Abstract: The area of Recommendation Systems has matured after intensive theoretical studies by researchers and practical applications by large e-commerce companies. On the other hand, Scientometrics has become an independent field, focusing in the study of laws and statistics related to scholarly publications. Nowadays, the publishing industry has accumulated big bibliographic data. Thus, the need to provide recommendations when searching in the abundance of bibliographic data has arised.
Speaker bio: Yannis Manolopoulos is Professor of the Open University of Cyprus, as well as Professor Emeritus of the Aristotle University of Thessaloniki. Moreover, he is Member of Academia Europaea, London. He has been with the University of Toronto, the University of Maryland at College Park, the University of Cyprus and the Hellenic Open University. He has served as Vice-Rector of the Open University of Cyprus, President of the Board of the University of Western Macedonia in Greece and Vice-President of the Greek Computer Society. Currently, he serves as Dean of the Faculty of Pure and Applied Sciences of the Open University of Cyprus and Member of the Board of the Research and Innovation Foundation of Cyprus. His research interest focuses in Data Management. He has co-authored 6 monographs and 10 textbooks (in Greek), as well as >350 journal and conference papers. He has received >18000 citations from >2800 distinct academic institutions from >100 countries (h-index=61). He has also received 5 best paper awards from SIGMOD, ECML/PKDD, MEDES (2) and ISSPIT conferences. Currently, he serves in the Editorial Boards of the following journals (among others): Digital (Editor-in-Chief), The Computer Journal (Deputy Editor), SN Computer Science (Section Editor-in-Chief).
Professor, Department of Mathematics & Computer Science University of Catania, Italy
Title: Metaheuristics and Machine Learning: how one enhances the other
Abstract: The integration between machine learning and metaheuristics methods
offers many advantages in all those problems where the use of purely
stochastic algorithms is no longer sufficient. The literature on the
hybridization of metaheuristics and machine learning can be broadly
categorized into two groups: research papers where machine learning is
used to enhance metaheuristics, and those where metaheuristics are
used to improve the performance of machine learning techniques.
In this talk I will present some applications where machine learning
techniques are incorporated into population-based algorithms to
improve their performance. Conversely, I will also show how
metaheuristics are very useful to machine learning to determine the
optimal neural networks configuration.
Speaker bio: He is an Associate Professor in Computer Science at the Department of Mathematics and Computer Science, University of Catania, Italy. His research focuses on the design and development of Metaheuristics applied in Combinatorial Optimization, Computational Biology, Network Sciences, and Social Networks. He is a member of several Editorial Boards for international journals and many Program Committees in international conferences and workshops. He also has extensive experience organizing successful workshops, symposiums, conferences, and summer schools. Currently, he is the Chief of the Scientific Directors of the Metaheuristics Summer School (MESS). In his scientific activities, he was also a Tutorial and Invited Speaker for several international conferences, and Editor of many special issues in fields like Artificial Life, Engineering Applications of Artificial Intelligence (EAAI), Applied Soft Computing (ASOC), International Transactions in Operational Research (ITOR), BMC Immunology, Natural Computing, and Memetic Computing.
Ibn Tofail University, Kenitra, Morocco
Title: The Power of Data: How AI is Revolutionizing Operational Models
Abstract: In today's digital age, data has emerged as a powerful asset driving transformative changes across industries. This presentation explores the profound impact of artificial intelligence (AI) on operational models within businesses. By harnessing the power of data analytics and machine learning algorithms, AI is revolutionizing traditional operational paradigms, enabling organizations to streamline processes, enhance efficiency, and drive innovation. From predictive maintenance and supply chain optimization to personalized customer experiences, AI-powered operational models are reshaping the way businesses operate and compete in the market. This abstract delves into key strategies, case studies, and future trends, illustrating how the convergence of AI and data is unlocking unprecedented opportunities for organizations to thrive in an increasingly data-driven world.
Speaker bio: Prof. Dr. Hanaa Hachimi, Ph.D in Applied Mathematics & Computer Science and a Ph.D in Mechanics & Systems Reliability, I am Full Professor at National School of Applied Sciences, Ibn Tofail University of Kenitra, Morocco. President of the Moroccan Society of Engineering Sciences and Technology (MSEST). I am the Editor in Chief of “The International Journal on Optimization and Applications” (IJOA). I am Director of the Systems Engineering Laboratory (LGS) and IEEE Senior Member. I am Lecture and Keynote Speaker of the courses: Optimization & Operational Research, Graph Theory, Statistics, Probability, Reliability and Scientific Computing. I am Member of the Moroccan Society of Applied Mathematics (SM2A). I’m the General Chair of “The International Conference on Optimization and Applications” (ICOA) & the International Competition of Innovation (Let’s Challenge). Lions Club Member and UNESCO UIT-Chair Member.
INTIS'2024 | 12th International Conference on New Technologies, Artificial Intelligence and Smart Data