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Deep Learning and Missing Data in Engineering Systems

  • Collins Achepsah Leke
  • Tshilidzi Marwala

Part of the Studies in Big Data book series (SBD, volume 48)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Collins Achepsah Leke, Tshilidzi Marwala
    Pages 1-20
  3. Collins Achepsah Leke, Tshilidzi Marwala
    Pages 21-40
  4. Collins Achepsah Leke, Tshilidzi Marwala
    Pages 41-56
  5. Collins Achepsah Leke, Tshilidzi Marwala
    Pages 57-71
  6. Collins Achepsah Leke, Tshilidzi Marwala
    Pages 73-89
  7. Collins Achepsah Leke, Tshilidzi Marwala
    Pages 91-102
  8. Collins Achepsah Leke, Tshilidzi Marwala
    Pages 103-114
  9. Collins Achepsah Leke, Tshilidzi Marwala
    Pages 115-128
  10. Collins Achepsah Leke, Tshilidzi Marwala
    Pages 129-146
  11. Collins Achepsah Leke, Tshilidzi Marwala
    Pages 147-171
  12. Collins Achepsah Leke, Tshilidzi Marwala
    Pages 173-177
  13. Back Matter
    Pages 179-179

About this book

Introduction

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.

Keywords

Artificial Intelligence Missing Data Estimation Deep Learning Swarm Intelligence Machine Learning Model Parameter Estimation

Authors and affiliations

  • Collins Achepsah Leke
    • 1
  • Tshilidzi Marwala
    • 2
  1. 1.Faculty of Engineering and Built EnvironmentUniversity of JohannesburgAuckland ParkSouth Africa
  2. 2.Faculty of Engineering and Built EnvironmentUniversity of JohannesburgAuckland ParkSouth Africa

Bibliographic information