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Machine Learning Approach for Cloud Data Analytics in IoT 1st Edition by Sachi Nandan Mohanty, Jyotir Moy Chatterjee, Monika Mangla, Suneeta Satpathy, Sirisha Potluri ISBN 9781119785804 1119785804

Name: Machine Learning Approach for Cloud Data Analytics in IoT 1st Edition by Sachi Nandan Mohanty, Jyotir Moy Chatterjee, Monika Mangla, Suneeta Satpathy, Sirisha Potluri ISBN 9781119785804 1119785804
SKU: EB-33798232
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Machine Learning Approach for Cloud Data Analytics in IoT 1st Edition Sachi Nandan Mohanty (Editor) Digital Instant Download

Author(s): Sachi Nandan Mohanty (Editor), Jyotir Moy Chatterjee (Editor), Monika Mangla (Editor), Suneeta Satpathy (Editor), Sirisha Potluri (Editor)

ISBN(s): 9781119785804, 1119785804

Edition: 1

File Details: PDF, 36.98 MB

Year: 2021

Language: English

SKU: EB-33798232 Category: Computers - Computer Science Tags: Jyotir Moy Chatterjee (Editor), Monika Mangla (Editor), Sachi Nandan Mohanty (Editor), Sirisha Potluri (Editor), Suneeta Satpathy (Editor)

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Machine Learning Approach for Cloud Data Analytics in IoT 1st Edition by Sachi Nandan Mohanty, Jyotir Moy Chatterjee, Monika Mangla, Suneeta Satpathy, Sirisha Potluri – Ebook PDF Instant Download/Delivery: 9781119785804 ,1119785804
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Product details:
ISBN 10: 1119785804
ISBN 13: 9781119785804
Author: Sachi Nandan Mohanty, Jyotir Moy Chatterjee, Monika Mangla, Suneeta Satpathy, Sirisha Potluri

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.

Machine Learning Approach for Cloud Data Analytics in IoT 1st Edition Table of contents:

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

2.1 Introduction

2.2 Some Associated Impactful Terms

2.3 Cloud Rationality Representation

2.4 Integration of IoT With Cloud

2.5 The Concepts That Rules Over

2.6 Related Work

2.7 Methodology

2.8 Discussions and Implications

2.9 Conclusion

References

3 Employing Machine Learning Approaches for Predictive Data Analytics in Retail Industry

3.1 Introduction

3.2 Related Work

3.3 Predictive Data Analytics in Retail

3.4 Proposed Model

3.5 Conclusion and Future Scope

References

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

5.1 Introduction

5.2 Data in Cloud

5.3 Security Challenges in Cloud Computing for Data

5.4 Cross-Cutting Issues Related to Network in Cloud

5.5 Protection of Data

5.6 Tighter IAM Controls

5.7 Conclusion and Future Scope

References

6 Cloud Cryptography for Cloud Data Analytics in IoT

6.1 Introduction

6.2 Cloud Computing Software Security Fundamentals

6.3 Security Management

6.4 Cryptography Algorithms

6.5 Secure Communications

6.6 Identity Management and Access Control

6.7 Autonomic Security

6.8 Conclusion

References

7 Issues and Challenges of Classical Cryptography in Cloud Computing

7.1 Introduction

7.2 Cryptography

7.3 Security in Cloud Computing

7.4 Classical Cryptography for Cloud Computing

7.5 Homomorphic Cryptosystem

7.6 Implementation

7.7 Conclusion and Future Scope

References

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

9.1 Introduction

9.2 Workflow Model

9.3 System Computing Model

9.4 Major Objective of Scheduling

9.5 Task Computational Attributes for Scheduling

9.6 Performance Metrics

9.7 Heuristic Task Scheduling Algorithms

9.8 Performance Analysis and Results

9.9 Conclusion

References

10 Smart Environment Monitoring Models Using Cloud-Based Data Analytics: A Comprehensive Study

10.1 Introduction

10.2 Background and Motivation

10.3 Conclusion

References

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

13.1 Introduction

13.2 Existing System

13.3 Proposed System

13.4 Objectives of the Concept

13.5 Operational Requirements

13.6 Non-Operational Requirements

13.7 Depiction Design Description

13.8 System Architecture

13.9 Design Diagrams

13.10 Comparison and Screenshot

13.11 Conclusion

References

14 Prediction of the Stock Market Using Machine Learning–Based Data Analytics

14.1 Introduction of Stock Market

14.2 Related Works

14.3 Financial Prediction Systems Framework

14.4 Implementation and Discussion of Result

14.5 Conclusion

References

Web Citations

15 Pehchaan: Analysis of the ‘Aadhar Dataset’ to Facilitate a Smooth and Efficient Conduct of the Upcoming NPR

15.1 Introduction

15.2 Basic Concepts

15.3 Study of Literature Survey and Technology

15.4 Proposed Model

15.5 Implementation and Results

15.6 Conclusion

References

16 Deep Learning Approach for Resource Optimization in Blockchain, Cellular Networks, and IoT: Open Challenges and Current Solutions

16.1 Introduction

16.2 Background

16.3 Deep Learning for Resource Management in Blockchain, Cellular, and IoT Networks

16.4 Future Research Challenges

16.5 Conclusion and Discussion

References

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

18.1 Introduction

18.2 Gamification in Transitional Healthcare: A New Model

18.3 Existing Related Work

18.4 The Framework

18.5 Implementation

18.6 Conclusion

References

Index

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Tags: Sachi Nandan Mohanty, Jyotir Moy Chatterjee, Monika Mangla, Suneeta Satpathy, Sirisha Potluri, Machine Learning