Overview
- Presents recent research on Machine Learning/Deep Learning-based Technologies,
- Presents Selected Papers in Honour of Professor Nikolaos G. Bourbakis – Vol. 2
- Written by experts in the field
Part of the book series: Learning and Analytics in Intelligent Systems (LAIS, volume 23)
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Table of contents (11 chapters)
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Front Matter
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Machine Learning/Deep Learning in Socializing and Entertainment
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Front Matter
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Machine Learning/Deep Learning in Education
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Front Matter
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Machine Learning/Deep Learning in Security
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Front Matter
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Machine Learning/Deep Learning in Time Series Forecasting
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Front Matter
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Machine Learning in Video Coding and Information Extraction
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Front Matter
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Keywords
About this book
As the 4th Industrial Revolution is restructuring human societal organization into, so-called, “Society 5.0”, the field of Machine Learning (and its sub-field of Deep Learning) and related technologies is growing continuously and rapidly, developing in both itself and towards applications in many other disciplines. Researchers worldwide aim at incorporating cognitive abilities into machines, such as learning and problem solving. When machines and software systems have been enhanced with Machine Learning/Deep Learning components, they become better and more efficient at performing specific tasks. Consequently, Machine Learning/Deep Learning stands out as a research discipline due to its worldwide pace of growth in both theoretical advances and areas of application, while achieving very high rates of success and promising major impact in science, technology and society.
The book at hand aims at exposing its readers to some of the most significant Advances in Machine Learning/Deep Learning-based Technologies. The book consists of an editorial note and an additional ten (10) chapters, all invited from authors who work on the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into five parts, namely (i) Machine Learning/Deep Learning in Socializing and Entertainment, (ii) Machine Learning/Deep Learning in Education, (iii) Machine Learning/Deep Learning in Security, (iv) Machine Learning/Deep Learning in Time Series Forecasting, and (v) Machine Learning in Video Coding and Information Extraction.
This research book is directed towards professors, researchers, scientists, engineers and students in Machine Learning/Deep Learning-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of themost recent Machine Learning/Deep Learning-based technologies. An extensive list of bibliographic references at the end of each chapter guides the readers to probe further into the application areas of interest to them.
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Editors and Affiliations
Bibliographic Information
Book Title: Advances in Machine Learning/Deep Learning-based Technologies
Book Subtitle: Selected Papers in Honour of Professor Nikolaos G. Bourbakis – Vol. 2
Editors: George A. Tsihrintzis, Maria Virvou, Lakhmi C. Jain
Series Title: Learning and Analytics in Intelligent Systems
DOI: https://doi.org/10.1007/978-3-030-76794-5
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
Hardcover ISBN: 978-3-030-76793-8Published: 07 August 2021
Softcover ISBN: 978-3-030-76796-9Published: 08 August 2022
eBook ISBN: 978-3-030-76794-5Published: 05 August 2021
Series ISSN: 2662-3447
Series E-ISSN: 2662-3455
Edition Number: 1
Number of Pages: XVI, 224
Number of Illustrations: 15 b/w illustrations, 70 illustrations in colour