Cognitive Big Data Intelligence with a Metaheuristic Approach presents an exact and compact organization of content relating to the latest metaheuristics methodologies based on new challenging big data application domains and cognitive computing. The combined model of cognitive big data intelligence with metaheuristics methods can be used to analyze emerging patterns, spot business opportunities, and take care of critical process-centric issues in real-time. Various real-time case studies and implemented works are discussed in this book for better understanding and additional clarity.
This book presents an essential platform for the use of cognitive technology in the field of Data Science. It covers metaheuristic methodologies that can be successful in a wide variety of problem settings in big data frameworks.
1. Cognitive Big Data Intelligence with a Metaheuristic Approach - p. iii 2. Cover - p. iii 3. Title Page - p. iii 4. Copyright Page - p. iv 5. Table of Contents - p. iv 6. Contributors - p. xiii 7. Preface - p. xv 8. Chapter 1 A discourse on metaheuristics techniques for solving clustering and semisupervised learning models - p. 1 9. Chapter 2 Metaheuristics in classification, clustering, and frequent pattern mining - p. 21 10. Chapter 3 Impacts of metaheuristic and swarm intelligence approach in optimization - p. 71 11. Chapter 4 A perspective depiction of heuristics in virtual reality - p. 101 12. Chapter 5 A heuristic approach of web users decision-making using deep learning models - p. 117 13. Chapter 6 Inertia weight strategies for task allocation using metaheuristic algorithm - p. 131 14. Chapter 7 Big data classification with IoT-based application for e-health care - p. 147 15. Chapter 8 Study of bio-inspired neural networks for the prediction of liquid flow in a process control system - p. 173 16. Chapter 9 Affordable energy-intensive routing using metaheuristics - p. 193 17. Chapter 10 Semantic segmentation for self-driving cars using deep learning - p. 211 18. Chapter 11 Cognitive big data analysis for E-health and telemedicine using metaheuristic algorithms - p. 239 19. Chapter 12 Multicriteria recommender system using different approaches - p. 259 20. Chapter 13 Optimization-based energy-efficient routing scheme for wireless body area network - p. 279 21. Chapter 14 Livestock health monitoring using a smart IoT-enabled neural network recognition system - p. 305 22. Chapter 15 Preserving healthcare data - p. 323 23. Index - p. 347
Cognitive Big Data Intelligence with a Metaheuristic Approach presents an exact and compact organization of content relating to the latest metaheuristics methodologies based on new challenging big data application domains and cognitive computing. The combined model of cognitive big data intelligence with metaheuristics methods can be used to analyze emerging patterns, spot business opportunities, and take care of critical process-centric issues in real-time. Various real-time case studies and implemented works are discussed in this book for better understanding and additional clarity.
This book presents an essential platform for the use of cognitive technology in the field of Data Science. It covers metaheuristic methodologies that can be successful in a wide variety of problem settings in big data frameworks.
1. Cognitive Big Data Intelligence with a Metaheuristic Approach - p. iii 2. Cover - p. iii 3. Title Page - p. iii 4. Copyright Page - p. iv 5. Table of Contents - p. iv 6. Contributors - p. xiii 7. Preface - p. xv 8. Chapter 1 A discourse on metaheuristics techniques for solving clustering and semisupervised learning models - p. 1 9. Chapter 2 Metaheuristics in classification, clustering, and frequent pattern mining - p. 21 10. Chapter 3 Impacts of metaheuristic and swarm intelligence approach in optimization - p. 71 11. Chapter 4 A perspective depiction of heuristics in virtual reality - p. 101 12. Chapter 5 A heuristic approach of web users decision-making using deep learning models - p. 117 13. Chapter 6 Inertia weight strategies for task allocation using metaheuristic algorithm - p. 131 14. Chapter 7 Big data classification with IoT-based application for e-health care - p. 147 15. Chapter 8 Study of bio-inspired neural networks for the prediction of liquid flow in a process control system - p. 173 16. Chapter 9 Affordable energy-intensive routing using metaheuristics - p. 193 17. Chapter 10 Semantic segmentation for self-driving cars using deep learning - p. 211 18. Chapter 11 Cognitive big data analysis for E-health and telemedicine using metaheuristic algorithms - p. 239 19. Chapter 12 Multicriteria recommender system using different approaches - p. 259 20. Chapter 13 Optimization-based energy-efficient routing scheme for wireless body area network - p. 279 21. Chapter 14 Livestock health monitoring using a smart IoT-enabled neural network recognition system - p. 305 22. Chapter 15 Preserving healthcare data - p. 323 23. Index - p. 347
