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005			20230102113108.0
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008			171230s2018 gw | s |||| 0|eng d
020			_a9783319706887
024	7		_a10.1007/978-3-319-70688-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.B45 2018 EB
245	1	0	_aCognitive Computing for Big Data Systems Over IoT _bFrameworks, Tools and Applications _cedited by Arun Kumar Sangaiah, Arunkumar Thangavelu, Venkatesan Meenakshi Sundaram.
264		1	_aCham _bSpringer International Publishing _c2018
300			_a1 recurso en línea (XVI, 375 páginas 81 ilustraciones, 51 ilustraciones a color)
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aLecture Notes on Data Engineering and Communications Technologies _x2367-4512 _v14
490	0		_aEngineering (Springer-11647)
505	0		_aBeyond Automation: The Cognitive IoT - Artificial Intelligence Brings Sense to the Internet of Things -- Cybercrimes Investigation and Intrusion Detection in Internet of Things Based on Data Science Methods -- Modeling and Analysis of Multi-Objective Service Selection Scheme in IoT-Cloud Environment -- Cognitive data science automatic fraud detection solution, based on Benford' s law, fuzzy logic with elements of machine learning -- Reliable Cross Layer Design for E-health Applications - IoT Perspective -- Erasure Codes for Reliable Communication in Internet-ofThings (IoT) embedded with Wireless Sensors -- Review: Security and Privacy Issues of Fog Computing -- A Review on Security and Privacy Challenges of Big Data -- Recent Developments in Deep Learning with Applications -- High-Level Knowledge Representation and Reasoning in a Cognitive IoT/WoT Context -- Applications of IoT in Healthcare -- Security Stipulations on IoT Networks -- A Hyper Heuristic Localization Based Cloned Node Detection Technique using GSA Based Simulated Annealing in Sensor Networks -- Review on Analysis of the Application Areas and Algorithms used in Data Wrangling in Big Data -- An innovation model for Smart Traffic Management System Using Internet of Things(IoT). .
520	3		_aThis book brings a high level of fluidity to analytics and addresses recent trends, innovative ideas, challenges and cognitive computing solutions in big data and the Internet of Things (IoT). It explores domain knowledge, data science reasoning and cognitive methods in the context of the IoT, extending current data science approaches by incorporating insights from experts as well as a notion of artificial intelligence, and performing inferences on the knowledge The book provides a comprehensive overview of the constituent paradigms underlying cognitive computing methods, which illustrate the increased focus on big data in IoT problems as they evolve. It includes novel, in-depth fundamental research contributions from a methodological/application in data science accomplishing sustainable solution for the future perspective. Mainly focusing on the design of the best cognitive embedded data science technologies to process and analyze the large amount of data collected through the IoT, and aid better decision making, the book discusses adapting decision-making approaches under cognitive computing paradigms to demonstrate how the proposed procedures as well as big data and IoT problems can be handled in practice. This book is a valuable resource for scientists, professionals, researchers, and academicians dealing with the new challenges and advances in the specific areas of cognitive computing and data science approaches.
988			_aEBSPRINGER_2018
650		7	_aDatos masivos _9495511 _2embne
650		7	_aData mining _9162648 _2embne
700	1		_aSangaiah, Arun Kumar. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _1http://viaf.org/viaf/14147423058644881603/
700	1		_aThangavelu, Arunkumar. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _0http://id.loc.gov/authorities/names/n2016065109 _1http://viaf.org/viaf/103148209322900460000/
700	1		_aMeenakshi Sundaram, Venkatesan. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
776	0	8	_iEdición impresa: _z9783319706870
776	0	8	_iEdición impresa: _z9783319706894
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-70688-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2019 _dz _ek _feng _ggw _h0