Advances in machine learning/deep learning-based technologies : selected papers in honour of Professor Nikolaos G. Bourbakis. Vol. 2 / George A. Tsihrintzis, Maria Virvou, Lakhmi C. Jain, editors.
Tsihrintzis, George A., editor.; Virvou, Maria, editor.; Jain, L. C., editor.; Bourbakis, Nikolaos G., honouree.
2022
Q325.5 .A38 2022
Available Online

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Title Advances in machine learning/deep learning-based technologies : selected papers in honour of Professor Nikolaos G. Bourbakis. Vol. 2 / George A. Tsihrintzis, Maria Virvou, Lakhmi C. Jain, editors.
ISBN 9783030767945 (electronic bk.)
3030767949 (electronic bk.)
9783030767938
3030767930
DOI https://doi.org/10.1007/978-3-030-76794-5
Published Cham : Springer, [2022]
Copyright ©2022
Language English
Description 1 online resource : illustrations (chiefly color)
Item Number 10.1007/978-3-030-76794-5 doi
Call Number Q325.5 .A38 2022
Dewey Decimal Classification 006.3/1
Summary 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 the most 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.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Digital File Characteristics text file
PDF
Source of Description Online resource; title from PDF title page (SpringerLink, viewed August 20, 2021).
Added Author Tsihrintzis, George A., editor.
Virvou, Maria, editor.
Jain, L. C., editor.
Bourbakis, Nikolaos G., honouree.
Series Learning and analytics in intelligent systems ; v. 23. 2662-3447
Available in Other Form Advances in machine learning/deep learning-based technologies.
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Part I: Machine Learning/Deep Learning in Socializing and Entertainment
Part II: Machine Learning/Deep Learning in
Part III: Machine Learning/Deep Learning in Security
Part IV: Machine Learning/Deep Learning in Time Series Forecasting
Part V: Machine Learning in Video Coding and Information Extraction.

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