Deep Learning Techniques and Optimization Strategies in Big Data Analytics

Description

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. 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