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020 _a9783319706887
_9978-3-319-70688-7
024 7 _a10.1007/978-3-319-70688-7
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aTEC009000
_2bisacsh
072 7 _aUYQ
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082 0 4 _a006.3
_223
245 1 0 _aCognitive Computing for Big Data Systems Over IoT
_h[electronic resource] :
_bFrameworks, Tools and Applications /
_cedited by Arun Kumar Sangaiah, Arunkumar Thangavelu, Venkatesan Meenakshi Sundaram.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aXVI, 375 p. 81 illus., 51 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes on Data Engineering and Communications Technologies,
_x2367-4520 ;
_v14
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 _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.
650 0 _aComputational intelligence.
_97716
650 0 _aData mining.
_93907
650 1 4 _aComputational Intelligence.
_97716
650 2 4 _aData Mining and Knowledge Discovery.
_955789
700 1 _aSangaiah, Arun Kumar.
_eeditor.
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_1https://orcid.org/0000-0002-0229-2460
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700 1 _aThangavelu, Arunkumar.
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_955791
700 1 _aMeenakshi Sundaram, Venkatesan.
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_4http://id.loc.gov/vocabulary/relators/edt
_955792
710 2 _aSpringerLink (Online service)
_955793
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319706870
776 0 8 _iPrinted edition:
_z9783319706894
830 0 _aLecture Notes on Data Engineering and Communications Technologies,
_x2367-4520 ;
_v14
_955794
856 4 0 _uhttps://doi.org/10.1007/978-3-319-70688-7
912 _aZDB-2-ENG
912 _aZDB-2-SXE
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