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020 _a9783319940304
024 7 _a10.1007/978-3-319-94030-4
_2doi
040 _bspa
_dES-MaUEC
_cES-MaUEC
050 4 _aQA76.9 .D343
_b2019 EB
245 0 0 _aMachine learning paradigms :
_badvances in data analytics
_cedited by George A. Tsihrintzis, Dionisios N. Sotiropoulos, Lakhmi C. Jain
264 1 _aCham
_bSpringer International Publishing :
_bImprint: Springer
_c2019
300 _a1 recurso en línea (XVI, 370 páginas)
_b131 ilustraciones, 110 ilustraciones a color
336 _2rdacontent
_aTexto
_btxt
337 _2rdamedia
_aelectrónico
_bc
338 _2rdacarrier
_arecurso electrónico
_bcr
347 _atext file
_bPDF
_2rda
490 0 _aIntelligent Systems Reference Library
_x1868-4394
_v149
490 0 _aIntelligent Technologies and Robotics (Springer-42732)
505 0 _aData Analytics in the Medical, Biological and Signal Sciences -- Recommender System of Medical Reports Leveraging Cognitive Computing and Frame Semantics -- Classification Methods in Image Analysis with a Special Focus on Medical Analytics -- Medical Data Mining for Heart Diseases and the Future of Sequential Mining in Medical Field -- Machine Learning Methods for the Protein Fold Recognition Problem. .
520 3 _aThis book explores some of the emerging scientific and technological areas in which the need for data analytics arises and is likely to play a significant role in the years to come. At the dawn of the 4th Industrial Revolution, data analytics is emerging as a force that drives towards dramatic changes in our daily lives, the workplace and human relationships. Synergies between physical, digital, biological and energy sciences and technologies, brought together by non-traditional data collection and analysis, drive the digital economy at all levels and offer new, previously-unavailable opportunities. The need for data analytics arises in most modern scientific disciplines, including engineering; natural-, computer- and information sciences; economics; business; commerce; environment; healthcare; and life sciences. Coming as the third volume under the general title MACHINE LEARNING PARADIGMS, the book includes an editorial note (Chapter 1) and an additional 12 chapters, and is divided into five parts: (1) Data Analytics in the Medical, Biological and Signal Sciences, (2) Data Analytics in Social Studies and Social Interactions, (3) Data Analytics in Traffic, Computer and Power Networks, (4) Data Analytics for Digital Forensics, and (5) Theoretical Advances and Tools for Data Analytics. This research book is intended for both experts/researchers in the field of data analytics, and readers working in the fields of artificial and computational intelligence as well as computer science in general who wish to learn more about the field of data analytics and its applications. An extensive list of bibliographic references at the end of each chapter guides readers to probe further into the application areas of interest to them.
988 _aPrimersemestre_2019_Robotics
650 7 _2embne
_aAprendizaje automático
_9166090
650 7 _2embne
_aData mining
_9162648
700 1 _aTsihrintzis, George A.
_eeditor literario
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_9100283
700 1 _aSotiropoulos, Dionisios N.
_eeditor literario
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 _aJain, Lakhmi C.
_eeditor literario
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_996931
776 0 8 _iPrinted edition:
_z9783319940298
776 0 8 _iPrinted edition:
_z9783319940311
776 0 8 _iPrinted edition:
_z9783030067779
856 4 0 _uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-94030-4
_zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942 _2lcc
_cLE
998 _aSI
_cm
_dz
_feng
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_h0
_b10/2019
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