Development and Analysis of Deep Learning Architectures

Description

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. Front Matter ....Pages i-xi Direct Error Driven Learning for Classification in Applications Generating Big-Data (R. Krishnan, S. Jagannathan, V. A. Samaranayake)....Pages 1-29 Deep Learning for Soft Sensor Design (Salvatore Graziani, Maria Gabriella Xibilia)....Pages 31-59 Case Study: Deep Convolutional Networks in Healthcare (Mutlu Avci, Mehmet Sarıgül, Buse Melis Ozyildirim)....Pages 61-89 Deep Domain Adaptation for Regression (Ankita Singh, Shayok Chakraborty)....Pages 91-115 Deep Learning-Based Pedestrian Detection for Automated Driving: Achievements and Future Challenges (Michelle Karg, Christian Scharfenberger)....Pages 117-143 Deep Learning in Speaker Recognition (Omid Ghahabi, Pooyan Safari, Javier Hernando)....Pages 145-169 Baby Cry Detection: Deep Learning and Classical Approaches (Rami Cohen, Dima Ruinskiy, Janis Zickfeld, Hans IJzerman, Yizhar Lavner)....Pages 171-196 Securing Industrial Control Systems from False Data Injection Attacks with Convolutional Neural Networks (Sasanka Potluri, Shamim Ahmed, Christian Diedrich)....Pages 197-222 Deep Learning for Wireless Communications (Tugba Erpek, Timothy J. O’Shea, Yalin E. Sagduyu, Yi Shi, T. Charles Clancy)....Pages 223-266 Identifying Extremism in Text Using Deep Learning (Andrew Johnston, Angjelo Marku)....Pages 267-289 Back Matter ....Pages 291-292