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: Deep Learning and Missing Data in Engineering Systems
  name:
    name part: Leke, Collins Achepsah.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Marwala, Tshilidzi.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2019
    edition: 1st ed. 2019.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XIV, 179 p. 109 illus., 84 illus. in color. online resource.
  abstract: Deep Learning and Missing Data in Engineering Systems uses deep learning and swarm intelligence methods to cover missing data estimation in engineering systems. The missing data estimation processes proposed in the book can be applied in image recognition and reconstruction. To facilitate the imputation of missing data, several artificial intelligence approaches are presented, including: deep autoencoder neural networks; deep denoising autoencoder networks; the bat algorithm; the cuckoo search algorithm; and the firefly algorithm. The hybrid models proposed are used to estimate the missing data in high-dimensional data settings more accurately. Swarm intelligence algorithms are applied to address critical questions such as model selection and model parameter estimation. The authors address feature extraction for the purpose of reconstructing the input data from reduced dimensions by the use of deep autoencoder neural networks. They illustrate new models diagrammatically, report their findings in tables, so as to put their methods on a sound statistical basis. The methods proposed speed up the process of data estimation while preserving known features of the data matrix. This book is a valuable source of information for researchers and practitioners in data science. Advanced undergraduate and postgraduate students studying topics in computational intelligence and big data, can also use the book as a reference for identifying and introducing new research thrusts in missing data estimation.
  table of contents: Introduction to Missing Data Estimation -- Introduction to Deep Learning -- Missing Data Estimation Using Bat Algorithm -- Missing Data Estimation Using Cuckoo Search Algorithm -- Missing Data Estimation Using Firefly Algorithm -- Missing Data Estimation Using Ant Colony Optimization Algorithm -- Missing Data Estimation Using Ant-Lion Optimizer Algorithm -- Missing Data Estimation Using Invasive Weed Optimization Algorithm -- Missing Data Estimation Using Swarm Intelligence Algorithms from Reduced Dimensions -- Missing Data Estimation Using Swarm Intelligence Algorithms: Deep Learning Framework Analysis -- Conclusion.
  note: by Collins Achepsah Leke, Tshilidzi Marwala.
  subject:
    topic: Computational intelligence
  subject:
    topic: Big data
  subject:
    topic: Artificial intelligence
  subject:
    topic: Computational Intelligence
  subject:
    topic: Big Data
  subject:
    topic: Artificial Intelligence
  classification: Q342
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Studies in Big Data, 48
  identifier: 9783030011802
  identifier: https://doi.org/10.1007/978-3-030-01180-2
  record info:
    record creation date: 181213
    record change date: 20191022042654.0
    record identifier: 978-3-030-01180-2