Deep Learning Techniques and Optimization Strategies in Big Data Analytics

Deep Learning Techniques and Optimization Strategies in Big Data Analytics
PDF, ePUB
  • eBook:
    Deep Learning Techniques and Optimization Strategies in Big Data Analytics
  • Author:
    J Joshua Thomas, Pinar Karagoz, B Bazeer Ahamed
  • Edition:
    -
  • Categories:
  • Data:
    October 14, 2019
  • ISBN:
    1799811921
  • ISBN-13:
    9781799811923
  • Language:
    English
  • Pages:
    355 pages
  • Format:
    PDF, ePUB

Book 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.

Content

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: A Review
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: A Review and Applications of Graph Autoencoder in Chemoinformatics
Chapter 8. Deep Learning: A Recent Computing Platform for Multimedia Information Retrieval
Chapter 9. Deep Learning Techniques and Optimization Strategies in Big Data Analytics: Automated Transfer Learning of Convolutional Neural Networks Using Enas Algorithm
Chapter 10. Dimensionality Reduction With Multi-Fold Deep Denoising Autoencoder
Chapter 11. Fake News Detection Using Deep Learning: Supervised Fake News Detection Analysis in Social Media With Semantic Similarity Method
Chapter 12. Heuristic Optimization Algorithms for Power System Scheduling Applications: Multi-Objective Generation Scheduling With PSO
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: A Review With Focus on Deep Learning
Methods
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

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