rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Cognitive Computing for Big Data Systems Over IoT
    sub title: Frameworks, Tools and Applications
  name:
    name part: Sangaiah, Arun Kumar.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Thangavelu, Arunkumar.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Meenakshi Sundaram, Venkatesan.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2018
    edition: 1st ed. 2018.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVI, 375 p. 81 illus., 51 illus. in color. online resource.
  abstract: This 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.
  table of contents: Beyond 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). .
  note: edited by Arun Kumar Sangaiah, Arunkumar Thangavelu, Venkatesan Meenakshi Sundaram.
  subject:
    topic: Computational intelligence
  subject:
    topic: Data mining
  subject:
    topic: Computational Intelligence
  subject:
    topic: Data Mining and Knowledge Discovery
  classification: Q342
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Lecture Notes on Data Engineering and Communications Technologies, 14
  identifier: 9783319706887
  identifier: https://doi.org/10.1007/978-3-319-70688-7
  record info:
    record creation date: 171230
    record change date: 20191026101947.0
    record identifier: 978-3-319-70688-7