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001442118 0247_ $$a10.1007/978-3-030-76794-5$$2doi
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001442118 049__ $$aISEA
001442118 050_4 $$aQ325.5$$b.A38 2022
001442118 08204 $$a006.3/1$$223
001442118 24500 $$aAdvances in machine learning/deep learning-based technologies :$$bselected papers in honour of Professor Nikolaos G. Bourbakis.$$nVol. 2 /$$cGeorge A. Tsihrintzis, Maria Virvou, Lakhmi C. Jain, editors.
001442118 264_1 $$aCham :$$bSpringer,$$c[2022]
001442118 264_4 $$c©2022
001442118 300__ $$a1 online resource :$$billustrations (chiefly color)
001442118 336__ $$atext$$btxt$$2rdacontent
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001442118 4901_ $$aLearning and analytics in intelligent systems,$$x2662-3447 ;$$vvolume 23
001442118 504__ $$aIncludes bibliographical references.
001442118 5050_ $$aPart I: Machine Learning/Deep Learning in Socializing and Entertainment -- Part II: Machine Learning/Deep Learning in -- Part III: Machine Learning/Deep Learning in Security -- Part IV: Machine Learning/Deep Learning in Time Series Forecasting -- Part V: Machine Learning in Video Coding and Information Extraction.
001442118 506__ $$aAccess limited to authorized users.
001442118 520__ $$aAs 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.
001442118 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed August 20, 2021).
001442118 650_0 $$aMachine learning.
001442118 650_6 $$aApprentissage automatique.
001442118 655_7 $$aFestschriften.$$2fast$$0(OCoLC)fst01941036
001442118 655_7 $$aFestschriften.$$2lcgft
001442118 655_0 $$aElectronic books.
001442118 7001_ $$aTsihrintzis, George A.,$$eeditor.
001442118 7001_ $$aVirvou, Maria,$$eeditor.
001442118 7001_ $$aJain, L. C.,$$eeditor.
001442118 7001_ $$aBourbakis, Nikolaos G.,$$ehonouree.
001442118 77608 $$iPrint version:$$tAdvances in machine learning/deep learning-based technologies.$$dCham : Springer, [2022]$$z3030767930$$z9783030767938$$w(OCoLC)1247667748
001442118 830_0 $$aLearning and analytics in intelligent systems ;$$vv. 23.$$x2662-3447
001442118 852__ $$bebk
001442118 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-76794-5$$zOnline Access$$91397441.1
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