(EBook PDF) Cognitive Big Data Intelligence with a Metaheuristic Approach Cognitive Data Science in Sustainable Computing 1st edition by Sushruta Mishra, Hrudaya Kumar Tripathy, Pradeep Kumar Mallick, Arun Kumar Sangaiah, Gyoo Soo Chae 0323851185 9780323851183 full chapters

Cognitive Big Data Intelligence with a Metaheuristic Approach Cognitive Data Science in Sustainable Computing 1st edition by Sushruta Mishra, Hrudaya Kumar Tripathy, Pradeep Kumar Mallick, Arun Kumar Sangaiah, Gyoo-Soo Chae – Ebook PDF Instant Download/DeliveryISBN: 0323851185, 9780323851183 

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Product details:

ISBN-10:   0323851185

ISBN-13 :  9780323851183 

Author :  Sushruta Mishra, Hrudaya Kumar Tripathy, Pradeep Kumar Mallick, Arun Kumar Sangaiah, Gyoo-Soo Chae

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.

Cognitive Big Data Intelligence with a Metaheuristic Approach Cognitive Data Science in Sustainable Computing 1st Table of contents:

Chapter 1. A discourse on metaheuristics techniques for solving clustering and semisupervised learning models
1. Introduction
2. Overview of clustering
3. Conclusion

Chapter 2. Metaheuristics in classification, clustering, and frequent pattern mining
1. Introduction
2. Metaheuristics in classification
3. Metaheuristics in clustering
4. Metaheuristics in frequent pattern mining
5. Conclusion

Chapter 3. Impacts of metaheuristic and swarm intelligence approach in optimization
1. Introduction
2. Concepts of Metaheuristic
3. Metaheuristic techniques
4. Swarm intelligence techniques
5. Impacts of metaheuristic and swarm intelligence approach in optimization
6. Conclusion

Chapter 4. A perspective depiction of heuristics in virtual reality
1. Introduction to virtual reality
2. Heuristics in brief
3. Virtual reality–enabled case studies
4. Performance evaluation and discussion
5. Conclusion

Chapter 5. A heuristic approach of web users decision-making using deep learning models
1. Introduction
2. Analysis of user online behavior using deep learning models
3. Greedy algorithm as the heuristic
4. Background study
5. Description of the dataset
6. Implementation and discussion
7. Conclusion

Chapter 6. Inertia weight strategies for task allocation using metaheuristic algorithm
1. Introduction
2. Related work
3. Standard PSO
4. Model of task allocation in VM
5. Inertia weight strategy
6. Performance evaluation
7. Conclusion and future work

Chapter 7. Big data classification with IoT-based application for e-health care
1. Introduction
2. State of the art
3. Big data in health care
4. Classification techniques
5. IoT-based smart biomedical data acquisition and processing system
6. Multiagent system for biomedical data processing
7. Detection of cardiac abnormalities
8. Results and discussion
9. Conclusion

Chapter 8. Study of bio-inspired neural networks for the prediction of liquid flow in a process control system
1. Introduction
2. Related work
3. Experimental setup
4. Preliminary details of the algorithm
5. Proposed model
6. Results and discussion
7. Conclusions and future work

Chapter 9. Affordable energy-intensive routing using metaheuristics
1. Introduction
2. Literature survey
3. Problem description
4. Routing
5. Routing algorithms
6. Routing table
7. Metaheuristics
8. Metaheuristics for efficient routing
9. Proposed solution using metaheuristics
10. Conclusion

Chapter 10. Semantic segmentation for self-driving cars using deep learning: a survey
1. Introduction
2. Semantic segmentation for autonomous driving
3. Deep learning
4. Related work
5. Experimental results
6. Conclusion

Chapter 11. Cognitive big data analysis for E-health and telemedicine using metaheuristic algorithms
1. Introduction
2. Cognitive computing technologies for E-health care
3. Cognitive big data analytics for E-health care
4. Need for cognitive big data analytics in E-health care
5. Advantages of cognitive big data analytics in E-health care
6. Challenges of cognitive big data analytics in E-health care
7. Metaheuristic approach for optimization of cognitive big data healthcare
8. Cognitive big data analytics use cases in E-health care
9. Future of cognitive big data analytics in E-health care
10. Market analysis of cognitive big data analytics in E-health care
11. Cognitive big data players in E-health care

Chapter 12. Multicriteria recommender system using different approaches
1. Introduction
2. Related work
3. Working principle
4. Proposed approaches
5. Experimental data analysis
6. Result
7. Conclusion

Chapter 13. Optimization-based energy-efficient routing scheme for wireless body area network
1. Introduction
2. Related work
3. Case study on an energy-efficient hybrid C-means donkey-smuggler optimization-based routing technique for a wireless sensor network
4. Analysis of the previous approach
5. Conclusion

Chapter 14. Livestock health monitoring using a smart IoT-enabled neural network recognition system
1. Introduction
2. System architecture
3. Recognition of a diseased bird by the central monitoring unit using Raspberry Pi
4. Results and discussion
5. Conclusion

Chapter 15. Preserving healthcare data: from traditional encryption to cognitive deep learning perspective
1. Introduction
2. Related works
3. Encryption algorithms
4. Performance evaluation
5. Future challenges of cognitive encryption models in healthcare
6. Conclusion

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