George A. Tsihrintzis (editor) - Advances in Machine Learning/Deep Learning-based Technologies: Selected Papers in Honour of Professor Nikolaos G. Bourbakis – Vol. 2 (Learning and Analytics in Intelligent Systems, 23)

Machine Learning can be considered as a part of the Artificial Intelligence field.

In 1959, Arthur Samuel [1, 2] introduced the term Machine Learning to refer to research efforts to develop algorithms and procedures which, when incorporated into machines, would allow them to improve their performance on specific tasks, i.e., to learn in ways that mimic human learning [3]. More recent efforts have been inspired by biological neural structures and have being receiving significant research attention worldwide. These approaches form a sub-area of Machine Learning, termed Deep Learning, and include various computing paradigms of the artificial neural network type, such as convolutional neural networks, recurrent neural networks, and deep belief networks [4].

In the six decades since publication of Samuels nominal paper, Machine Learning, in general, and Deep Learning, in particular, have grown into one of the most active research fields worldwide. These research efforts have met with success in many technological application areas and increasingly affect many aspects of everyday life, the workplace, and human relationships [59]. Of course, such a broad impact also comes with risks and threats in security, privacy, safety, transparency, business, competition, the job market, fundamental rights, democracy, or even human existence itself [1012], which are hard to ignore and care must be taken to prevent them.

Professor Nikolaos G. Bourbakis stands out as one of the main contributors to various applications of Machine Learning/Deep Learning throughout his long and fruitful research career at various posts. Currently, Nikolaos is a Distinguished Professor of Information & Technology and the Director of the Center of Assistive Research Technologies (CART) at Wright State University, Ohio, USA, after receiving a B.S. degree in Mathematics from the National and Kapodistrian University of Athens, Greece, a Certificate in Electrical Engineering from the University of Patras, Greece, and a Ph.D. degree in Computer Engineering and Informatics (awarded with excellence), from the Department of Computer Engineering & Informatics, University of Patras, Greece. His many achievements in Machine Learning/Deep Learning-based Technologies have been recognized via many distinctions and awards, including elevation to IEEE Fellow (1996); IEEE Computer Society Technical Research Achievement Award; Member of the New York Academy of Sciences; Diploma of Honor in Artificial Intelligence, School of Engineering, University of Patras, Greece; ASC Outstanding Scientists & Engineers Research Award; Dr. F. Russ IEEE Biomedical Engineering Award, Dayton Ohio; Recognition Award for Outstanding Scholarly Achievements and Contributions in the field of Computer Science, University of Piraeus, Greece; IEEE EMBS-GR Award of Achievements; IEEE Computer Society 30 years ICTAI Outstanding Service & Leadership Recognition; Honorary Doctorate Degree of the University of Piraeus, Greece (2020).

Professors George A. Tsihrintzis, Maria Virvou, and Lakhmi C. Jain recently undertook a dual task. On one hand they are editing a special book in Prof. Nikolaos G. Bourbakis honor and on the other hand they are attempting to update the relevant research communities, in computer science-related disciplines, as well as the general reader from other disciplines, on the most recent advances in Machine Learning/Deep Learning-based technological applications. They are handing to us a book consisting of 11 chapters, each of which has been written by active and recognized researchers and reports on recent research and development findings. Overall, the book is well structured as, besides an editorial note (introductory chapter), it has been further divided into five parts devoted to