Deep Learning and Parallel Computing Environment for Bioengineering Systems delivers a significant forum for the technical advancement of deep learning in parallel computing environment across bio-engineering diversified domains and its applications. Pursuing an interdisciplinary approach, it focuses on methods used to identify and acquire valid, potentially useful knowledge sources. Managing the gathered knowledge and applying it to multiple domains including health care, social networks, mining, recommendation systems, image processing, pattern recognition and predictions using deep learning paradigms is the major strength of this book. This book integrates the core ideas of deep learning and its applications in bio engineering application domains, to be accessible to all scholars and academicians. The proposed techniques and concepts in this book can be extended in future to accommodate changing business organizations’ needs as well as practitioners’ innovative ideas.
1. Cover image - p. iv 2. Title page - p. iv 3. Table of Contents - p. iv 4. Copyright - p. iv 5. Preface - p. vii 6. Foreword - p. xi 7. Acknowledgment - p. xiii 8. Chapter 1: Parallel Computing, Graphics Processing Unit (GPU) and New Hardware for Deep Learning in Computational Intelligence Research - p. xiii 9. Chapter 2: Big Data Analytics and Deep Learning in Bioinformatics With Hadoop - p. 15 10. Chapter 3: Image Fusion Through Deep Convolutional Neural Network - p. 36 11. Chapter 4: Medical Imaging With Intelligent Systems: A Review - p. 52 12. Chapter 5: Medical Image Analysis With Deep Neural Networks - p. 73 13. Chapter 6: Deep Convolutional Neural Network for Image Classification on CUDA Platform - p. 97 14. Chapter 7: Efficient Deep Learning Approaches for Health Informatics - p. 122 15. Chapter 8: Deep Learning and Semi-Supervised and Transfer Learning Algorithms for Medical Imaging - p. 137 16. Chapter 9: Survey on Evaluating the Performance of Machine Learning Algorithms: Past Contributions and Future Roadmap - p. 151 17. Chapter 10: Miracle of Deep Learning Using IoT - p. 164 18. Chapter 11: Challenges in Storing and Processing Big Data Using Hadoop and Spark - p. 178 19. Chapter 12: An Efficient Biography-Based Optimization Algorithm to Solve the Location Routing Problem With Intermediate Depots for Multiple Perishable Products - p. 187 20. Chapter 13: Evolutionary Mapping Techniques for Systolic Computing System - p. 205 21. Chapter 14: Varied Expression Analysis of Children With ASD Using Multimodal Deep Learning Technique - p. 223 22. Chapter 15: Parallel Machine Learning and Deep Learning Approaches for Bioinformatics - p. 243 23. Index - p. 257
Deep Learning and Parallel Computing Environment for Bioengineering Systems delivers a significant forum for the technical advancement of deep learning in parallel computing environment across bio-engineering diversified domains and its applications. Pursuing an interdisciplinary approach, it focuses on methods used to identify and acquire valid, potentially useful knowledge sources. Managing the gathered knowledge and applying it to multiple domains including health care, social networks, mining, recommendation systems, image processing, pattern recognition and predictions using deep learning paradigms is the major strength of this book. This book integrates the core ideas of deep learning and its applications in bio engineering application domains, to be accessible to all scholars and academicians. The proposed techniques and concepts in this book can be extended in future to accommodate changing business organizations’ needs as well as practitioners’ innovative ideas.
1. Cover image - p. iv 2. Title page - p. iv 3. Table of Contents - p. iv 4. Copyright - p. iv 5. Preface - p. vii 6. Foreword - p. xi 7. Acknowledgment - p. xiii 8. Chapter 1: Parallel Computing, Graphics Processing Unit (GPU) and New Hardware for Deep Learning in Computational Intelligence Research - p. xiii 9. Chapter 2: Big Data Analytics and Deep Learning in Bioinformatics With Hadoop - p. 15 10. Chapter 3: Image Fusion Through Deep Convolutional Neural Network - p. 36 11. Chapter 4: Medical Imaging With Intelligent Systems: A Review - p. 52 12. Chapter 5: Medical Image Analysis With Deep Neural Networks - p. 73 13. Chapter 6: Deep Convolutional Neural Network for Image Classification on CUDA Platform - p. 97 14. Chapter 7: Efficient Deep Learning Approaches for Health Informatics - p. 122 15. Chapter 8: Deep Learning and Semi-Supervised and Transfer Learning Algorithms for Medical Imaging - p. 137 16. Chapter 9: Survey on Evaluating the Performance of Machine Learning Algorithms: Past Contributions and Future Roadmap - p. 151 17. Chapter 10: Miracle of Deep Learning Using IoT - p. 164 18. Chapter 11: Challenges in Storing and Processing Big Data Using Hadoop and Spark - p. 178 19. Chapter 12: An Efficient Biography-Based Optimization Algorithm to Solve the Location Routing Problem With Intermediate Depots for Multiple Perishable Products - p. 187 20. Chapter 13: Evolutionary Mapping Techniques for Systolic Computing System - p. 205 21. Chapter 14: Varied Expression Analysis of Children With ASD Using Multimodal Deep Learning Technique - p. 223 22. Chapter 15: Parallel Machine Learning and Deep Learning Approaches for Bioinformatics - p. 243 23. Index - p. 257
