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008			210805s2022 sz | s |||| 0|eng d
020			_a9783030767945
024	7		_a10.1007/978-3-030-76794-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2022 EB
245	0	0	_aAdvances in Machine Learning/Deep Learning-based Technologies : _bSelected Papers in Honour of Professor Nikolaos G. Bourbakis - Vol. 2 _cedited by George A. Tsihrintzis, Maria Virvou, Lakhmi C. Jain.
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XVI, 224 páginas) _b85 ilustraciones, 70 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aLearning and Analytics in Intelligent Systems _x2662-3455 _v23
505	0		_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.
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.
988			_aSpringer_Robotics_2022
650		7	_2embne _9166090 _aAprendizaje automático
776	0	8	_iPrinted edition: _z9783030767938
776	0	8	_iPrinted edition: _z9783030767952
776	0	8	_iPrinted edition: _z9783030767969
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-76794-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2023 _dz _eu _zSI