Deep Learning Applications and Intelligent Decision Making in Engineering

✍ Scribed by Balamurugan Shanmugam (editor), Dinesh Goyal (editor), Iyswarya Annapoorani (editor), Ravi Samikannu (editor)

• Publisher: IGI Global
• Year: 2020
• Tongue: English
• Leaves: 346
• Series: Advances in Computational Intelligence and Robotics
• Edition: 1
• Category: Library

✦ Synopsis

Deep learning includes a subset of machine learning for processing the unsupervised data with artificial neural network functions. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. When applied to engineering, deep learning can have a great impact on the decision-making process.

Deep Learning Applications and Intelligent Decision Making in Engineering is a pivotal reference source that provides practical applications of deep learning to improve decision-making methods and construct smart environments. Highlighting topics such as smart transportation, e-commerce, and cyber physical systems, this book is ideally designed for engineers, computer scientists, programmers, software engineers, research scholars, IT professionals, academicians, and postgraduate students seeking current research on the implementation of automation and deep learning in various engineering disciplines.

✦ Table of Contents

Title Page
Copyright Page
Book Series
Table of Contents
Detailed Table of Contents
Preface
Chapter 1: Deep Learning in IoT
Chapter 2: Deep Learning Architectures and Tools
Chapter 3: Facial Emotion Recognition System Using Entire Feature Vectors and Supervised Classifier
Chapter 4: KidNet
Chapter 5: Liver Disease Detection Using Grey Wolf Optimization and Random Forest Classification
Chapter 6: Deep Neural Network-Based Android Malware Detection (D-AMD)
Chapter 7: Deep Learning With Conceptual View in Meta Data for Content Categorization
Chapter 8: A Fully Automated Crop Disease Monitoring and Management System Based on IoT
Chapter 9: Analysis of Heart Disorder by Using Machine Learning Methods and Data Mining Techniques
Chapter 10: Deep Learning in Engineering Education
Chapter 11: Deep Learning Solutions for Agricultural and Farming Activities
Compilation of References
About the Contributors
Index