Leading technology firms and research institutions are continuously exploring new techniques in artificial intelligence and machine learning. As such, deep learning has now been recognized in various real-world applications such as computer vision, image processing, biometrics, pattern recognition, and medical imaging. The deep learning approach has opened new opportunities that can make such real-life applications and tasks easier and more efficient.
The Handbook of Research on Deep Learning Innovations and Trends is an essential scholarly resource that presents current trends and the latest research on deep learning and explores the concepts, algorithms, and techniques of data mining and analysis. Highlighting topics such as computer vision, encryption systems, and biometrics, this book is ideal for researchers, practitioners, industry professionals, students, and academicians
Author(s): Aboul Ella Hassanien, Ashraf Darwish Helwan, Chiranji Lal Chowdhary
Series: Advances in Computational Intelligence and Robotics (ACIR)
Publisher: Engineering Science Reference
Year: 2019
Language: English
Pages: 374
City: Hershey
Title Page
Copyright Page
Book Series
Editorial Advisory Board
List of Contributors
Table of Contents
Detailed Table of Contents
Preface
Section 1: Deep Learning Applications
Chapter 1: Detection of Blood-Related Diseases Using Deep Neural Nets
Chapter 2: Enhanced Footsteps Generation Method for Walking Robots Based on Convolutional Neural Networks
Chapter 3: CNN Customizations With Transfer Learning for Face Recognition Task
Chapter 4: Application of Deep Learning in Speech Recognition
Chapter 5: Sensation of Deep Learning in Image Processing Applications
Chapter 6: Transfer Learning in 2.5D Face Image for Occlusion Presence and Gender Classification
Chapter 7: Deep Learning
Chapter 8: A Survey on Deep Learning Techniques Used for Quality Process
Chapter 9: Deep Learning in Early Detection of Alzheimer's
Section 2: Advanced Deep Learning Techniques
Chapter 10: Deep Clustering
Chapter 11: Deep Reinforcement Learning for Optimization
Chapter 12: A Similarity-Based Object Classification Using Deep Neural Networks
Chapter 13: Cognitive Deep Learning
Section 3: Security in Deep Learning
Chapter 14: Malware Classification and Analysis Using Convolutional and Recurrent Neural Network
Chapter 15: Anomaly Detection Using Deep Learning With Modular Networks
Glossary
Compilation of References
Related References
About the Contributors
Index
