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In this era of IoT, edge devices generate gigantic data during every fraction of a second. The main aim of these networks is to infer some meaningful information from the collected data. For the same, the huge data is transmitted to the cloud which is highly expensive and time-consuming. Hence, it needs to devise some efficient mechanism to handle this huge data, thus necessitating efficient data handling techniques.  Sustainable computing paradigms like cloud and fog are expedient to capably handle the issues of performance, capabilities allied to storage and processing, maintenance, security, efficiency, integration, cost, energy and latency. However, it requires sophisticated analytics tools so as to address the queries in an optimized time. Hence, rigorous research is taking place in the direction of devising effective and efficient framework to garner utmost advantage.

Machine learning has gained unmatched popularity for handling massive amounts of data and has applications in a wide variety of disciplines, including social media.

Machine Learning Approach for Cloud Data Analytics in IoT details and integrates all aspects of IoT, cloud computing and data analytics from diversified perspectives. It reports on the state-of-the-art research and advanced topics, thereby bringing readers up to date and giving them a means to understand and explore the spectrum of applications of IoT, cloud computing and data analytics.

Table of Contents

  1. Cover
  2. Title page
  3. Copyright
  4. Preface
  5. Acknowledgment
  6. 1 Machine Learning–Based Data Analysis
    1. 1.1 Introduction
    2. 1.2 Machine Learning for the Internet of Things Using Data Analysis
    3. 1.3 Machine Learning Applied to Data Analysis
    4. 1.4 Practical Issues in Machine Learning
    5. 1.5 Data Acquisition
    6. 1.6 Understanding the Data Formats Used in Data Analysis Applications
    7. 1.7 Data Cleaning
    8. 1.8 Data Visualization
    9. 1.9 Understanding the Data Analysis Problem-Solving Approach
    10. 1.10 Visualizing Data to Enhance Understanding and Using Neural Networks in Data Analysis
    11. 1.11 Statistical Data Analysis Techniques
    12. 1.12 Text Analysis and Visual and Audio Analysis
    13. 1.13 Mathematical and Parallel Techniques for Data Analysis
    14. 1.14 Conclusion
    15. References
  7. 2 Machine Learning for Cyber-Immune IoT Applications
    1. 2.1 Introduction
    2. 2.2 Some Associated Impactful Terms
    3. 2.3 Cloud Rationality Representation
    4. 2.4 Integration of IoT With Cloud
    5. 2.5 The Concepts That Rules Over
    6. 2.6 Related Work
    7. 2.7 Methodology
    8. 2.8 Discussions and Implications
    9. 2.9 Conclusion
    10. References
  8. 3 Employing Machine Learning Approaches for Predictive Data Analytics in Retail Industry
    1. 3.1 Introduction
    2. 3.2 Related Work
    3. 3.3 Predictive Data Analytics in Retail
    4. 3.4 Proposed Model
    5. 3.5 Conclusion and Future Scope
    6. References
  9. 4 Emerging Cloud Computing Trends for Business Transformation
    1. 4.1 Introduction
    2. 4.2 History of Cloud Computing
    3. 4.3 Core Attributes of Cloud Computing
    4. 4.4 Cloud Computing Models
    5. 4.5 Core Components of Cloud Computing Architecture: Hardware and Software
    6. 4.6 Factors Need to Consider for Cloud Adoption
    7. 4.7 Transforming Business Through Cloud
    8. 4.8 Key Emerging Trends in Cloud Computing
    9. 4.9 Case Study: Moving Data Warehouse to Cloud Boosts Performance for Johnson & Johnson
    10. 4.10 Conclusion
    11. References
  10. 5 Security of Sensitive Data in Cloud Computing
    1. 5.1 Introduction
    2. 5.2 Data in Cloud
    3. 5.3 Security Challenges in Cloud Computing for Data
    4. 5.4 Cross-Cutting Issues Related to Network in Cloud
    5. 5.5 Protection of Data
    6. 5.6 Tighter IAM Controls
    7. 5.7 Conclusion and Future Scope
    8. References
  11. 6 Cloud Cryptography for Cloud Data Analytics in IoT
    1. 6.1 Introduction
    2. 6.2 Cloud Computing Software Security Fundamentals
    3. 6.3 Security Management
    4. 6.4 Cryptography Algorithms
    5. 6.5 Secure Communications
    6. 6.6 Identity Management and Access Control
    7. 6.7 Autonomic Security
    8. 6.8 Conclusion
    9. References
  12. 7 Issues and Challenges of Classical Cryptography in Cloud Computing
    1. 7.1 Introduction
    2. 7.2 Cryptography
    3. 7.3 Security in Cloud Computing
    4. 7.4 Classical Cryptography for Cloud Computing
    5. 7.5 Homomorphic Cryptosystem
    6. 7.6 Implementation
    7. 7.7 Conclusion and Future Scope
    8. References
  13. 8 Cloud-Based Data Analytics for Monitoring Smart Environments
    1. 8.1 Introduction
    2. 8.2 Environmental Monitoring for Smart Buildings
    3. 8.3 Smart Health
    4. 8.4 Digital Network 5G and Broadband Networks
    5. 8.5 Emergent Smart Cities Communication Networks
    6. 8.6 Smart City IoT Platforms Analysis System
    7. 8.7 Smart Management of Car Parking in Smart Cities
    8. 8.8 Smart City Systems and Services Securing: A Risk-Based Analytical Approach
    9. 8.9 Virtual Integrated Storage System
    10. 8.10 Convolutional Neural Network (CNN)
    11. 8.11 Challenges and Issues
    12. 8.12 Future Trends and Research Directions in Big Data Platforms for the Internet of Things
    13. 8.13 Case Study
    14. 8.14 Conclusion
    15. References
  14. 9 Performance Metrics for Comparison of Heuristics Task Scheduling Algorithms in Cloud Computing Platform
    1. 9.1 Introduction
    2. 9.2 Workflow Model
    3. 9.3 System Computing Model
    4. 9.4 Major Objective of Scheduling
    5. 9.5 Task Computational Attributes for Scheduling
    6. 9.6 Performance Metrics
    7. 9.7 Heuristic Task Scheduling Algorithms
    8. 9.8 Performance Analysis and Results
    9. 9.9 Conclusion
    10. References
  15. 10 Smart Environment Monitoring Models Using Cloud-Based Data Analytics: A Comprehensive Study
    1. 10.1 Introduction
    2. 10.2 Background and Motivation
    3. 10.3 Conclusion
    4. References
  16. 11 Advancement of Machine Learning and Cloud Computing in the Field of Smart Health Care
    1. 11.1 Introduction
    2. 11.2 Survey on Architectural WBAN
    3. 11.3 Suggested Strategies
    4. 11.4 CNN-Based Image Segmentation (UNet Model)
    5. 11.5 Emerging Trends in IoT Healthcare
    6. 11.6 Tier Health IoT Model
    7. 11.7 Role of IoT in Big Data Analytics
    8. 11.8 Tier Wireless Body Area Network Architecture
    9. 11.9 Conclusion
    10. References
  17. 12 Study on Green Cloud Computing—A Review
    1. 12.1 Introduction
    2. 12.2 Cloud Computing
    3. 12.3 Features of Cloud Computing
    4. 12.4 Green Computing
    5. 12.5 Green Cloud Computing
    6. 12.6 Models of Cloud Computing
    7. 12.7 Models of Cloud Services
    8. 12.8 Cloud Deployment Models
    9. 12.9 Green Cloud Architecture
    10. 12.10 Cloud Service Providers
    11. 12.11 Features of Green Cloud Computing
    12. 12.12 Advantages of Green Cloud Computing
    13. 12.13 Limitations of Green Cloud Computing
    14. 12.14 Cloud and Sustainability Environmental
    15. 12.15 Statistics Related to Cloud Data Centers
    16. 12.16 The Impact of Data Centers on Environment
    17. 12.17 Virtualization Technologies
    18. 12.18 Literature Review
    19. 12.19 The Main Objective
    20. 12.20 Research Gap
    21. 12.21 Research Methodology
    22. 12.22 Conclusion and Suggestions
    23. 12.23 Scope for Further Research
    24. References
  18. 13 Intelligent Reclamation of Plantae Affliction Disease
    1. 13.1 Introduction
    2. 13.2 Existing System
    3. 13.3 Proposed System
    4. 13.4 Objectives of the Concept
    5. 13.5 Operational Requirements
    6. 13.6 Non-Operational Requirements
    7. 13.7 Depiction Design Description
    8. 13.8 System Architecture
    9. 13.9 Design Diagrams
    10. 13.10 Comparison and Screenshot
    11. 13.11 Conclusion
    12. References
  19. 14 Prediction of the Stock Market Using Machine Learning–Based Data Analytics
    1. 14.1 Introduction of Stock Market
    2. 14.2 Related Works
    3. 14.3 Financial Prediction Systems Framework
    4. 14.4 Implementation and Discussion of Result
    5. 14.5 Conclusion
    6. References
    7. Web Citations
  20. 15 Pehchaan: Analysis of the ‘Aadhar Dataset’ to Facilitate a Smooth and Efficient Conduct of the Upcoming NPR
    1. 15.1 Introduction
    2. 15.2 Basic Concepts
    3. 15.3 Study of Literature Survey and Technology
    4. 15.4 Proposed Model
    5. 15.5 Implementation and Results
    6. 15.6 Conclusion
    7. References
  21. 16 Deep Learning Approach for Resource Optimization in Blockchain, Cellular Networks, and IoT: Open Challenges and Current Solutions
    1. 16.1 Introduction
    2. 16.2 Background
    3. 16.3 Deep Learning for Resource Management in Blockchain, Cellular, and IoT Networks
    4. 16.4 Future Research Challenges
    5. 16.5 Conclusion and Discussion
    6. References
  22. 17 Unsupervised Learning in Accordance With New Aspects of Artificial Intelligence
    1. 17.1 Introduction
    2. 17.2 Applications of Machine Learning in Data Management Possibilities
    3. 17.3 Solutions to Improve Unsupervised Learning Using Machine Learning
    4. 17.4 Open Source Platform for Cutting Edge Unsupervised Machine Learning
    5. 17.5 Applications of Unsupervised Learning
    6. 17.6 Applications Using Machine Learning Algos
    7. References
  23. 18 Predictive Modeling of Anthropomorphic Gamifying Blockchain-Enabled Transitional Healthcare System
    1. 18.1 Introduction
    2. 18.2 Gamification in Transitional Healthcare: A New Model
    3. 18.3 Existing Related Work
    4. 18.4 The Framework
    5. 18.5 Implementation
    6. 18.6 Conclusion
    7. References
  24. Index
  25. End User License Agreement
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