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, cartea 23
Editat de George A. Tsihrintzis, Maria Virvou, Lakhmi C. Jainen Limba Engleză Paperback – 8 aug 2022
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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Paperback (1) | 1111.65 lei 6-8 săpt. | |
Springer International Publishing – 8 aug 2022 | 1111.65 lei 6-8 săpt. | |
Hardback (1) | 1117.65 lei 6-8 săpt. | |
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Specificații
ISBN-13: 9783030767969
ISBN-10: 3030767965
Pagini: 224
Ilustrații: XVI, 224 p. 85 illus., 70 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.35 kg
Ediția:1st ed. 2022
Editura: Springer International Publishing
Colecția Springer
Seria Learning and Analytics in Intelligent Systems
Locul publicării:Cham, Switzerland
ISBN-10: 3030767965
Pagini: 224
Ilustrații: XVI, 224 p. 85 illus., 70 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.35 kg
Ediția:1st ed. 2022
Editura: Springer International Publishing
Colecția Springer
Seria Learning and Analytics in Intelligent Systems
Locul publicării:Cham, Switzerland
Cuprins
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.
Recenzii
“The trilogy is useful to either the specialized researcher seeking information on specific sub areas within these disciplines or the newcomer who seeks to get involved in these disciplines. … I warmly congratulate the editors for their superb work. I highly and wholeheartedly recommend the trilogy to professors, graduate students, practitioners and other specialists in artificial intelligence-based technologies and assistive technologies, and to general readers, all of whom, I am sure, will benefit greatly from it in their research endeavor.” (Du Zhang, Intelligent Decision Technologies, Vol. 16 (1), 2022)
Textul de pe ultima copertă
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 ofthe 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.
The book at hand aims at exposing its readers to some ofthe 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.
Caracteristici
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