book

Machine Learning Approach for Cloud Data Analytics in IoT

by Sachi Nandan Mohanty, Jyotir Moy Chatterjee, Monika Mangla, Suneeta Satpathy, Sirisha Potluri

July 2021

Intermediate to advanced

528 pages

13h 35m

English

Wiley-Scrivener

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1.1 Introduction1.2 Machine Learning for the Internet of Things Using Data Analysis1.3 Machine Learning Applied to Data Analysis1.4 Practical Issues in Machine Learning1.5 Data Acquisition1.6 Understanding the Data Formats Used in Data Analysis Applications1.7 Data Cleaning1.8 Data Visualization1.9 Understanding the Data Analysis Problem-Solving Approach1.10 Visualizing Data to Enhance Understanding and Using Neural Networks in Data Analysis1.11 Statistical Data Analysis Techniques1.12 Text Analysis and Visual and Audio Analysis1.13 Mathematical and Parallel Techniques for Data Analysis1.14 ConclusionReferences
2.1 Introduction2.2 Some Associated Impactful Terms2.3 Cloud Rationality Representation2.4 Integration of IoT With Cloud2.5 The Concepts That Rules Over2.6 Related Work2.7 Methodology2.8 Discussions and Implications2.9 ConclusionReferences
3.1 Introduction3.2 Related Work3.3 Predictive Data Analytics in Retail3.4 Proposed Model3.5 Conclusion and Future ScopeReferences
4.1 Introduction4.2 History of Cloud Computing4.3 Core Attributes of Cloud Computing4.4 Cloud Computing Models4.5 Core Components of Cloud Computing Architecture: Hardware and Software4.6 Factors Need to Consider for Cloud Adoption4.7 Transforming Business Through Cloud4.8 Key Emerging Trends in Cloud Computing4.9 Case Study: Moving Data Warehouse to Cloud Boosts Performance for Johnson & Johnson4.10 ConclusionReferences
5.1 Introduction5.2 Data in Cloud5.3 Security Challenges in Cloud Computing for Data5.4 Cross-Cutting Issues Related to Network in Cloud5.5 Protection of Data5.6 Tighter IAM Controls5.7 Conclusion and Future ScopeReferences

6.1 Introduction6.2 Cloud Computing Software Security Fundamentals6.3 Security Management6.4 Cryptography Algorithms6.5 Secure Communications6.6 Identity Management and Access Control6.7 Autonomic Security6.8 ConclusionReferences
7.1 Introduction7.2 Cryptography7.3 Security in Cloud Computing7.4 Classical Cryptography for Cloud Computing7.5 Homomorphic Cryptosystem7.6 Implementation7.7 Conclusion and Future ScopeReferences
8.1 Introduction8.2 Environmental Monitoring for Smart Buildings8.3 Smart Health8.4 Digital Network 5G and Broadband Networks8.5 Emergent Smart Cities Communication Networks8.6 Smart City IoT Platforms Analysis System8.7 Smart Management of Car Parking in Smart Cities8.8 Smart City Systems and Services Securing: A Risk-Based Analytical Approach8.9 Virtual Integrated Storage System8.10 Convolutional Neural Network (CNN)8.11 Challenges and Issues8.12 Future Trends and Research Directions in Big Data Platforms for the Internet of Things8.13 Case Study8.14 ConclusionReferences
9.1 Introduction9.2 Workflow Model9.3 System Computing Model9.4 Major Objective of Scheduling9.5 Task Computational Attributes for Scheduling9.6 Performance Metrics9.7 Heuristic Task Scheduling Algorithms9.8 Performance Analysis and Results9.9 ConclusionReferences
10.1 Introduction10.2 Background and Motivation10.3 ConclusionReferences
11.1 Introduction11.2 Survey on Architectural WBAN11.3 Suggested Strategies11.4 CNN-Based Image Segmentation (UNet Model)11.5 Emerging Trends in IoT Healthcare11.6 Tier Health IoT Model11.7 Role of IoT in Big Data Analytics11.8 Tier Wireless Body Area Network Architecture11.9 ConclusionReferences
12.1 Introduction12.2 Cloud Computing12.3 Features of Cloud Computing12.4 Green Computing12.5 Green Cloud Computing12.6 Models of Cloud Computing12.7 Models of Cloud Services12.8 Cloud Deployment Models12.9 Green Cloud Architecture12.10 Cloud Service Providers12.11 Features of Green Cloud Computing12.12 Advantages of Green Cloud Computing12.13 Limitations of Green Cloud Computing12.14 Cloud and Sustainability Environmental12.15 Statistics Related to Cloud Data Centers12.16 The Impact of Data Centers on Environment12.17 Virtualization Technologies12.18 Literature Review12.19 The Main Objective12.20 Research Gap12.21 Research Methodology12.22 Conclusion and Suggestions12.23 Scope for Further ResearchReferences
13.1 Introduction13.2 Existing System13.3 Proposed System13.4 Objectives of the Concept13.5 Operational Requirements13.6 Non-Operational Requirements13.7 Depiction Design Description13.8 System Architecture13.9 Design Diagrams13.10 Comparison and Screenshot13.11 ConclusionReferences
14.1 Introduction of Stock Market14.2 Related Works14.3 Financial Prediction Systems Framework14.4 Implementation and Discussion of Result14.5 ConclusionReferencesWeb Citations
15.1 Introduction15.2 Basic Concepts15.3 Study of Literature Survey and Technology15.4 Proposed Model15.5 Implementation and Results15.6 ConclusionReferences
16.1 Introduction16.2 Background16.3 Deep Learning for Resource Management in Blockchain, Cellular, and IoT Networks16.4 Future Research Challenges16.5 Conclusion and DiscussionReferences
17.1 Introduction17.2 Applications of Machine Learning in Data Management Possibilities17.3 Solutions to Improve Unsupervised Learning Using Machine Learning17.4 Open Source Platform for Cutting Edge Unsupervised Machine Learning17.5 Applications of Unsupervised Learning17.6 Applications Using Machine Learning AlgosReferences
18.1 Introduction18.2 Gamification in Transitional Healthcare: A New Model18.3 Existing Related Work18.4 The Framework18.5 Implementation18.6 ConclusionReferences