Deep Learning Techniques and Optimization Strategies in Big Data Analytics

✍ Scribed by J. Joshua Thomas, Pinar Karagoz, B. Bazeer Ahamed, Pandian Vasant

• Publisher: Engineering Science Reference
• Year: 2019
• Tongue: English
• Leaves: 381
• Series: Advances in Systems Analysis, Software Engineering, and High Performance Computing
• Category: Library

✦ Synopsis

Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there's a need for research on the various applications and techniques of deep learning in the field of computing.

Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.

✦ Table of Contents

Cover
Title Page
Copyright Page
Book Series
Editorial Advisory Board
Table of Contents
Detailed Table of Contents
Foreword
Preface
Acknowledgment
Chapter 1: Arrhythmia Detection Based on Hybrid Features of T-Wave in Electrocardiogram
Chapter 2: A Review on Deep Learning Applications
Chapter 3: A Survey of Nature-Inspired Algorithms With Application to Well Placement Optimization
Chapter 4: Artificial Intelligence Approach for Predicting TOC From Well Logs in Shale Reservoirs
Chapter 5: Bidirectional GRU-Based Attention Model for Kid-Specific URL Classification
Chapter 6: Classification of Fundus Images Using Neural Network Approach
Chapter 7: Convolutional Graph Neural Networks
Chapter 8: Deep Learning
Chapter 9: Deep Learning Techniques and Optimization Strategies in Big Data Analytics
Chapter 10: Dimensionality Reduction With Multi-Fold Deep Denoising Autoencoder
Chapter 11: Fake News Detection Using Deep Learning
Chapter 12: Heuristic Optimization Algorithms for Power System Scheduling Applications
Chapter 13: Multiobjective Optimization of a Biofuel Supply Chain Using Random Matrix Generators
Chapter 14: Optimized Deep Learning System for Crop Health Classification Strategically Using Spatial and Temporal Data
Chapter 15: Protein Secondary Structure Prediction Approaches
Chapter 16: Recent Trends in the Use of Graph Neural Network Models for Natural Language Processing
Chapter 17: Review on Particle Swarm Optimization Approach for Optimizing Wellbore Trajectory
Compilation of References
About the Contributors
Index