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Advances in Machine Learning/Deep Learning-based Technologies - Selected Papers in Honour of Professor Nikolaos G. Bourbakis - Vol. 2 (Paperback, 1st ed. 2022)
Loot Price: R5,234
Discovery Miles 52 340
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Advances in Machine Learning/Deep Learning-based Technologies - Selected Papers in Honour of Professor Nikolaos G. Bourbakis - Vol. 2 (Paperback, 1st ed. 2022)
Series: Learning and Analytics in Intelligent Systems, 23
Expected to ship within 10 - 15 working days
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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.
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