Book description
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
- Cover
- Title page
- Copyright
- Preface
- Acknowledgment
-
1 Machine Learning–Based Data Analysis
- 1.1 Introduction
- 1.2 Machine Learning for the Internet of Things Using Data Analysis
- 1.3 Machine Learning Applied to Data Analysis
- 1.4 Practical Issues in Machine Learning
- 1.5 Data Acquisition
- 1.6 Understanding the Data Formats Used in Data Analysis Applications
- 1.7 Data Cleaning
- 1.8 Data Visualization
- 1.9 Understanding the Data Analysis Problem-Solving Approach
- 1.10 Visualizing Data to Enhance Understanding and Using Neural Networks in Data Analysis
- 1.11 Statistical Data Analysis Techniques
- 1.12 Text Analysis and Visual and Audio Analysis
- 1.13 Mathematical and Parallel Techniques for Data Analysis
- 1.14 Conclusion
- References
- 2 Machine Learning for Cyber-Immune IoT Applications
- 3 Employing Machine Learning Approaches for Predictive Data Analytics in Retail Industry
-
4 Emerging Cloud Computing Trends for Business Transformation
- 4.1 Introduction
- 4.2 History of Cloud Computing
- 4.3 Core Attributes of Cloud Computing
- 4.4 Cloud Computing Models
- 4.5 Core Components of Cloud Computing Architecture: Hardware and Software
- 4.6 Factors Need to Consider for Cloud Adoption
- 4.7 Transforming Business Through Cloud
- 4.8 Key Emerging Trends in Cloud Computing
- 4.9 Case Study: Moving Data Warehouse to Cloud Boosts Performance for Johnson & Johnson
- 4.10 Conclusion
- References
- 5 Security of Sensitive Data in Cloud Computing
- 6 Cloud Cryptography for Cloud Data Analytics in IoT
- 7 Issues and Challenges of Classical Cryptography in Cloud Computing
-
8 Cloud-Based Data Analytics for Monitoring Smart Environments
- 8.1 Introduction
- 8.2 Environmental Monitoring for Smart Buildings
- 8.3 Smart Health
- 8.4 Digital Network 5G and Broadband Networks
- 8.5 Emergent Smart Cities Communication Networks
- 8.6 Smart City IoT Platforms Analysis System
- 8.7 Smart Management of Car Parking in Smart Cities
- 8.8 Smart City Systems and Services Securing: A Risk-Based Analytical Approach
- 8.9 Virtual Integrated Storage System
- 8.10 Convolutional Neural Network (CNN)
- 8.11 Challenges and Issues
- 8.12 Future Trends and Research Directions in Big Data Platforms for the Internet of Things
- 8.13 Case Study
- 8.14 Conclusion
- References
- 9 Performance Metrics for Comparison of Heuristics Task Scheduling Algorithms in Cloud Computing Platform
- 10 Smart Environment Monitoring Models Using Cloud-Based Data Analytics: A Comprehensive Study
-
11 Advancement of Machine Learning and Cloud Computing in the Field of Smart Health Care
- 11.1 Introduction
- 11.2 Survey on Architectural WBAN
- 11.3 Suggested Strategies
- 11.4 CNN-Based Image Segmentation (UNet Model)
- 11.5 Emerging Trends in IoT Healthcare
- 11.6 Tier Health IoT Model
- 11.7 Role of IoT in Big Data Analytics
- 11.8 Tier Wireless Body Area Network Architecture
- 11.9 Conclusion
- References
-
12 Study on Green Cloud Computing—A Review
- 12.1 Introduction
- 12.2 Cloud Computing
- 12.3 Features of Cloud Computing
- 12.4 Green Computing
- 12.5 Green Cloud Computing
- 12.6 Models of Cloud Computing
- 12.7 Models of Cloud Services
- 12.8 Cloud Deployment Models
- 12.9 Green Cloud Architecture
- 12.10 Cloud Service Providers
- 12.11 Features of Green Cloud Computing
- 12.12 Advantages of Green Cloud Computing
- 12.13 Limitations of Green Cloud Computing
- 12.14 Cloud and Sustainability Environmental
- 12.15 Statistics Related to Cloud Data Centers
- 12.16 The Impact of Data Centers on Environment
- 12.17 Virtualization Technologies
- 12.18 Literature Review
- 12.19 The Main Objective
- 12.20 Research Gap
- 12.21 Research Methodology
- 12.22 Conclusion and Suggestions
- 12.23 Scope for Further Research
- References
- 13 Intelligent Reclamation of Plantae Affliction Disease
- 14 Prediction of the Stock Market Using Machine Learning–Based Data Analytics
- 15 Pehchaan: Analysis of the ‘Aadhar Dataset’ to Facilitate a Smooth and Efficient Conduct of the Upcoming NPR
- 16 Deep Learning Approach for Resource Optimization in Blockchain, Cellular Networks, and IoT: Open Challenges and Current Solutions
-
17 Unsupervised Learning in Accordance With New Aspects of Artificial Intelligence
- 17.1 Introduction
- 17.2 Applications of Machine Learning in Data Management Possibilities
- 17.3 Solutions to Improve Unsupervised Learning Using Machine Learning
- 17.4 Open Source Platform for Cutting Edge Unsupervised Machine Learning
- 17.5 Applications of Unsupervised Learning
- 17.6 Applications Using Machine Learning Algos
- References
- 18 Predictive Modeling of Anthropomorphic Gamifying Blockchain-Enabled Transitional Healthcare System
- Index
- End User License Agreement
Product information
- Title: Machine Learning Approach for Cloud Data Analytics in IoT
- Author(s):
- Release date: July 2021
- Publisher(s): Wiley-Scrivener
- ISBN: 9781119785804
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