Development and Analysis of Deep Learning Architectures

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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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.

• Computational Intelligence
• Machine Learning
• Computer Vision
• Natural Language Processing
• Deep Learning Architectures
• Development Paradigms
• Pattern Recognition
• Image Processing
• Knowledge Representation
• AI

• 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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