Machine Learning Approach for Cloud Data Analytics in IoT

Cover
Half-Title Page
Series Page
Title Page
Copyright Page
Contents
Preface
Acknowledgment
1 Machine Learning–Based Data Analysis
	1.1 Introduction
	1.2 Machine Learning for the Internet of Things Using Data Analysis
		1.2.1 Computing Framework
		1.2.2 Fog Computing
		1.2.3 Edge Computing
		1.2.4 Cloud Computing
		1.2.5 Distributed Computing
	1.3 Machine Learning Applied to Data Analysis
		1.3.1 Supervised Learning Systems
		1.3.2 Decision Trees
		1.3.3 Decision Tree Types
		1.3.4 Unsupervised Machine Learning
		1.3.5 Association Rule Learning
		1.3.6 Reinforcement Learning
	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
		1.11.1 Hypothesis Testing
		1.11.2 Regression Analysis
	1.12 Text Analysis and Visual and Audio Analysis
	1.13 Mathematical and Parallel Techniques for Data Analysis
		1.13.1 Using Map-Reduce
		1.13.2 Leaning Analysis
		1.13.3 Market Basket Analysis
	1.14 Conclusion
	References
2 Machine Learning for Cyber-Immune IoT Applications
	2.1 Introduction
	2.2 Some Associated Impactful Terms
		2.2.1 IoT
		2.2.2 IoT Device
		2.2.3 IoT Service
		2.2.4 Internet Security
		2.2.5 Data Security
		2.2.6 Cyberthreats
		2.2.7 Cyber Attack
		2.2.8 Malware
		2.2.9 Phishing
		2.2.10 Ransomware
		2.2.11 Spear-Phishing
		2.2.12 Spyware
		2.2.13 Cybercrime
		2.2.14 IoT Cyber Security
		2.2.15 IP Address
	2.3 Cloud Rationality Representation
		2.3.1 Cloud
		2.3.2 Cloud Data
		2.3.3 Cloud Security
		2.3.4 Cloud Computing
	2.4 Integration of IoT With Cloud
	2.5 The Concepts That Rules Over
		2.5.1 Artificial Intelligent
		2.5.2 Overview of Machine Learning
		2.5.3 Applications of Machine Learning in Cyber Security
		2.5.4 Applications of Machine Learning in Cybercrime
		2.5.5 Adherence of Machine Learning With Cyber Security in Relevance to IoT
		2.5.6 Distributed Denial-of-Service
	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
		3.3.1 ML for Predictive Data Analytics
		3.3.2 Use Cases
		3.3.3 Limitations and Challenges
	3.4 Proposed Model
		3.4.1 Case Study
	3.5 Conclusion and Future Scope
	References
4 Emerging Cloud Computing Trends for Business Transformation
	4.1 Introduction
		4.1.1 Computing Definition Cloud
		4.1.2 Advantages of Cloud Computing Over On-Premises IT Operation
		4.1.3 Limitations of Cloud Computing
	4.2 History of Cloud Computing
	4.3 Core Attributes of Cloud Computing
	4.4 Cloud Computing Models
		4.4.1 Cloud Deployment Model
		4.4.2 Cloud Service Model
	4.5 Core Components of Cloud Computing Architecture: Hardware and Software
	4.6 Factors Need to Consider for Cloud Adoption
		4.6.1 Evaluating Cloud Infrastructure
		4.6.2 Evaluating Cloud Provider
		4.6.3 Evaluating Cloud Security
		4.6.4 Evaluating Cloud Services
		4.6.5 Evaluating Cloud Service Level Agreements (SLA)
		4.6.6 Limitations to Cloud Adoption
	4.7 Transforming Business Through Cloud
	4.8 Key Emerging Trends in Cloud Computing
		4.8.1 Technology Trends
		4.8.2 Business Models
		4.8.3 Product Transformation
		4.8.4 Customer Engagement
		4.8.5 Employee Empowerment
		4.8.6 Data Management and Assurance
		4.8.7 Digitalization
		4.8.8 Building Intelligence Cloud System
		4.8.9 Creating Hyper-Converged Infrastructure
	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
		5.1.1 Characteristics of Cloud Computing
		5.1.2 Deployment Models for Cloud Services
		5.1.3 Types of Cloud Delivery Models
	5.2 Data in Cloud
		5.2.1 Data Life Cycle
	5.3 Security Challenges in Cloud Computing for Data
		5.3.1 Security Challenges Related to Data at Rest
		5.3.2 Security Challenges Related to Data in Use
		5.3.3 Security Challenges Related to Data in Transit
	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
		6.4.1 Types of Cryptography
	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
		7.1.1 Problem Statement and Motivation
		7.1.2 Contribution
	7.2 Cryptography
		7.2.1 Cryptography Classification
	7.3 Security in Cloud Computing
		7.3.1 The Need for Security in Cloud Computing
		7.3.2 Challenges in Cloud Computing Security
		7.3.3 Benefits of Cloud Computing Security
		7.3.4 Literature Survey
	7.4 Classical Cryptography for Cloud Computing
		7.4.1 RSA
		7.4.2 AES
		7.4.3 DES
		7.4.4 Blowfish
	7.5 Homomorphic Cryptosystem
		7.5.1 Paillier Cryptosystem
		7.5.2 RSA 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
		8.2.1 Smart Environments
	8.3 Smart Health
		8.3.1 Description of Solutions in General
		8.3.2 Detection of Distress
		8.3.3 Green Protection
		8.3.4 Medical Preventive/Help
	8.4 Digital Network 5G and Broadband Networks
		8.4.1 IoT-Based Smart Grid Technologies
	8.5 Emergent Smart Cities Communication Networks
		8.5.1 RFID Technologies
		8.5.2 Identifier Schemes
	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)
		8.10.1 IEEE 802.15.4
		8.10.2 BLE
		8.10.3 ITU-T G.9959 (Z-Wave)
		8.10.4 NFC
		8.10.5 LoRaWAN
		8.10.6 Sigfox
		8.10.7 NB-IoT
		8.10.8 PLC
		8.10.9 MS/TP
	8.11 Challenges and Issues
		8.11.1 Interoperability and Standardization
		8.11.2 Customization and Adaptation
		8.11.3 Entity Identification and Virtualization
		8.11.4 Big Data Issue in Smart Environments
	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
		9.7.1 Heterogeneous Earliest Finish Time (HEFT) Algorithm
		9.7.2 Critical-Path-on-a-Processor (CPOP) Algorithm
		9.7.3 As Late As Possible (ALAP) Algorithm
		9.7.4 Performance Effective Task Scheduling (PETS) Algorithm
	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
		10.1.1 Internet of Things
		10.1.2 Cloud Computing
		10.1.3 Environmental Monitoring
	10.2 Background and Motivation
		10.2.1 Challenges and Issues
		10.2.2 Technologies Used for Designing Cloud-Based Data Analytics
		10.2.3 Cloud-Based Data Analysis Techniques and Models
		10.2.4 Data Mining Techniques
		10.2.5 Machine Learning
		10.2.6 Applications
	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
		11.3.1 System Overview
		11.3.2 Motivation
		11.3.3 DSCB Protocol
	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
		12.2.1 Cloud Computing: On-Request Outsourcing-Pay-as-You-Go
	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
		13.8.1 Module Characteristics
		13.8.2 Convolutional Neural System
		13.8.3 User Application
	13.9 Design Diagrams
		13.9.1 High-Level Design
		13.9.2 Low-Level Design
		13.9.3 Test Cases
	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
		14.1.1 Impact of Stock Prices
	14.2 Related Works
	14.3 Financial Prediction Systems Framework
		14.3.1 Conceptual Financial Prediction Systems
		14.3.2 Framework of Financial Prediction Systems Using Machine Learning
		14.3.3 Framework of Financial Prediction Systems Using Deep Learning
	14.4 Implementation and Discussion of Result
		14.4.1 Pharmaceutical Sector
		14.4.2 Banking Sector
		14.4.3 Fast-Moving Consumer Goods Sector
		14.4.4 Power Sector
		14.4.5 Automobiles Sector
		14.4.6 Comparison of Prediction Using Linear Regression Model and Long-Short-Term Memory Model
	14.5 Conclusion
		14.5.1 Future Enhancement
	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
		16.1.1 Aim
		16.1.2 Research Contribution
		16.1.3 Organization
	16.2 Background
		16.2.1 Blockchain
		16.2.2 Internet of Things (IoT)
		16.2.3 5G Future Generation Cellular Networks
		16.2.4 Machine Learning and Deep Learning Techniques
		16.2.5 Deep Reinforcement Learning
	16.3 Deep Learning for Resource Management in Blockchain, Cellular, and IoT Networks
		16.3.1 Resource Management in Blockchain for 5G Cellular Networks
		16.3.2 Deep Learning Blockchain Application for Resource Management in IoT Networks
	16.4 Future Research Challenges
		16.4.1 Blockchain Technology
		16.4.2 IoT Networks
		16.4.3 5G Future Generation Networks
		16.4.4 Machine Learning and Deep Learning
		16.4.5 General Issues
	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
		17.2.1 Terminology of Basic Machine Learning
		17.2.2 Rules Based on Machine Learning
		17.2.3 Unsupervised vs. Supervised Methodology
	17.3 Solutions to Improve Unsupervised Learning Using Machine Learning
		17.3.1 Insufficiency of Labeled Data
		17.3.2 Overfitting
		17.3.3 A Closer Look Into Unsupervised Algorithms
		17.3.4 Singular Value Decomposition (SVD)
		17.3.5 Dictionary Learning
		17.3.6 The Latent Dirichlet Allocation
	17.4 Open Source Platform for Cutting Edge Unsupervised Machine Learning
		17.4.1 TensorFlow
		17.4.2 Keras
		17.4.3 Scikit-Learn
		17.4.4 Microsoft Cognitive Toolkit
		17.4.5 Theano
		17.4.6 Caffe
		17.4.7 Torch
	17.5 Applications of Unsupervised Learning
		17.5.1 Regulation of Digital Data
		17.5.2 Machine Learning in Voice Assistance
		17.5.3 For Effective Marketing
		17.5.4 Advancement of Cyber Security
		17.5.5 Faster Computing Power
		17.5.6 The Endnote
	17.6 Applications Using Machine Learning Algos
		17.6.1 Linear Regression
		17.6.2 Logistic Regression
		17.6.3 Decision Tree
		17.6.4 Support Vector Machine (SVM)
		17.6.5 Naive Bayes
		17.6.6 K-Nearest Neighbors
		17.6.7 K-Means
		17.6.8 Random Forest
		17.6.9 Dimensionality Reduction Algorithms
		17.6.10 Gradient Boosting Algorithms
	References
18 Predictive Modeling of Anthropomorphic Gamifying Blockchain-Enabled Transitional Healthcare System
	18.1 Introduction
		18.1.1 Transitional Healthcare Services and Their Challenges
	18.2 Gamification in Transitional Healthcare: A New Model
		18.2.1 Anthropomorphic Interface With Gamification
		18.2.2 Gamification in Blockchain
		18.2.3 Anthropomorphic Gamification in Blockchain: Motivational Factors
	18.3 Existing Related Work
	18.4 The Framework
		18.4.1 Health Player
		18.4.2 Data Collection
		18.4.3 Anthropomorphic Gamification Layers
		18.4.4 Ethereum
		18.4.5 Reward Model
		18.4.6 Predictive Models
	18.5 Implementation
		18.5.1 Methodology
		18.5.2 Result Analysis
		18.5.3 Threats to the Validity
	18.6 Conclusion
	References
Index