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Advances in machine learnin...
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
Monografía
monografia Rebiun36894341 https://catalogo.rebiun.org/rebiun/record/Rebiun36894341 m o d cr |n||||||||| 210811s2022 sz a ob 010 0 eng d 1263874929 1287778094 9783030767945 electronic bk.) 3030767949 electronic bk.) 9783030767938 3030767930 10.1007/978-3-030-76794-5 doi AU@ 000070097693 Springer YDX eng rda pn YDX GW5XE EBLCP OCLCO OCLCF DCT OCLCQ COM OCLCO OCLCQ AUD OCLCO OCLCL OCLCQ OCLCO UYQ bicssc TEC009000 bisacsh UYQ thema 006.3/1 23 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 Cham Springer [2022] Cham Cham Springer 2022 1 online resource illustrations (chiefly color) 1 online resource Text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF Learning and analytics in intelligent systems 2662-3447 volume 23 Includes bibliographical references 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 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 Machine learning Apprentissage automatique Machine learning. Festschriften. Festschriften. Tsihrintzis, George A. editor. https://id.oclc.org/worldcat/entity/E39PCjKp3VyCDJ3DvqRfXGY7BK Virvou, Maria editor. https://id.oclc.org/worldcat/entity/E39PCjwJ4gyXb7qCyTdTrpHxTb Jain, L. C. editor. https://id.oclc.org/worldcat/entity/E39PCjFYPJwhcTjgf6hywj3QD3 Bourbakis, Nikolaos G. honouree Print version Advances in machine learning/deep learning-based technologies. Cham : Springer, [2022] 3030767930 9783030767938 (OCoLC)1247667748 Learning and analytics in intelligent systems v. 23. 2662-3447