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Development and Analysis of Deep Learning Architectures

  • Book
  • © 2020

Overview

Editors:
  1. Witold Pedrycz

    1. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada

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    You can also search for this editor in PubMed   Google Scholar

  2. Shyi-Ming Chen

    1. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  • Provides a comprehensive and up-to-date overview of deep learning by discussing a range of methodological and algorithmic issues
  • Addresses implementations and case studies, identifying the best design practices and assessing business models and methodologies encountered in industry, health care, science, administration, and business
  • Serves as a unique and well-structured reference resource for graduate and senior undergraduate students in areas such as computational intelligence, pattern recognition, computer vision, knowledge acquisition and representation, and knowledge-based systems

Part of the book series: Studies in Computational Intelligence (SCI, volume 867)

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Table of contents (10 chapters)

  1. Front Matter

    Pages i-xi
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  2. Direct Error Driven Learning for Classification in Applications Generating Big-Data

    • R. Krishnan, S. Jagannathan, V. A. Samaranayake
    Pages 1-29

  3. Deep Learning for Soft Sensor Design

    • Salvatore Graziani, Maria Gabriella Xibilia
    Pages 31-59

  4. Case Study: Deep Convolutional Networks in Healthcare

    • Mutlu Avci, Mehmet Sarıgül, Buse Melis Ozyildirim
    Pages 61-89

  5. Deep Domain Adaptation for Regression

    • Ankita Singh, Shayok Chakraborty
    Pages 91-115

  6. Deep Learning-Based Pedestrian Detection for Automated Driving: Achievements and Future Challenges

    • Michelle Karg, Christian Scharfenberger
    Pages 117-143

  7. Deep Learning in Speaker Recognition

    • Omid Ghahabi, Pooyan Safari, Javier Hernando
    Pages 145-169

  8. Baby Cry Detection: Deep Learning and Classical Approaches

    • Rami Cohen, Dima Ruinskiy, Janis Zickfeld, Hans IJzerman, Yizhar Lavner
    Pages 171-196

  9. Securing Industrial Control Systems from False Data Injection Attacks with Convolutional Neural Networks

    • Sasanka Potluri, Shamim Ahmed, Christian Diedrich
    Pages 197-222

  10. Deep Learning for Wireless Communications

    • Tugba Erpek, Timothy J. O’Shea, Yalin E. Sagduyu, Yi Shi, T. Charles Clancy
    Pages 223-266

  11. Identifying Extremism in Text Using Deep Learning

    • Andrew Johnston, Angjelo Marku
    Pages 267-289

  12. Back Matter

    Pages 291-292
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Keywords

About this book

This book offers a timely reflection on the remarkable range of algorithms and applications that have made the area of deep learning so attractive and heavily researched today. Introducing the diversity of learning mechanisms in the environment of big data, and presenting authoritative studies in fields such as sensor design, health care, autonomous driving, industrial control and wireless communication, it enables readers to gain a practical understanding of design. The book also discusses systematic design procedures, optimization techniques, and validation processes.

Editors and Affiliations

  • Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada

    Witold Pedrycz

  • Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Shyi-Ming Chen

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