George A. Papakostas

International Hellenic University Greece

George A. Papakostas received the Diploma, M.Sc. and Ph.D. in Electrical and Computer Engineering in 1999, 2002 and 2007, respectively, from the Democritus University of Thrace (DUTh), Greece. He has 15 years of experience in large-scale systems design as a senior software engineer and technical manager. He is the Head of the Machine Learning and Vision (MLV) Research Group. He is a Tenured Full Professor in the Department of Informatics at DUTh, Greece. Prof. Papakostas has (co) authored more than 250 publications in indexed journals, international conferences, book chapters, one book (in Greek), three edited books, and eight journal special issues. His publications have over 4900 citations with an h-index 38 (Google Scholar). He has been included in the World's Top 2% of Scientists' Stanford list for 2022 and 2023 years in the field of "Artificial Intelligence & Image Processing". His research interests include machine learning, computer/machine vision, pattern recognition, and computational intelligence.

George A. Papakostas

1books edited

3chapters authored

Latest work with IntechOpen by George A. Papakostas

The academic interest in artificial intelligence (AI) has grown exponentially in recent years. The rapid development of AI technologies and the interdisciplinary nature of research in AI and its applications have contributed considerably to the global popularity of this research field. This volume deals with three key areas of the advancements in AI: machine learning and data mining, computer vision, and multi-agent systems. The increasing availability of vast data sets and powerful computing resources has enabled the development of more complex algorithms and models to address real-world challenges. In addition, deep learning has revolutionized the field of artificial intelligence, with computer vision being at the forefront of innovations. Multi-agent systems (MAS) have also proven to be the best fitting state-of-the-art within the AI framework for raising distributed AI technologies and applications such as smart cities and the Internet of (every)thing(s). Extended with machine learning, MAS have become very popular for researchers in every field, especially in autonomous vehicular technologies. This book should serve as a valuable resource not just to scientists dealing with AI research but also to anyone interested in its broad application areas across various disciplines.

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