Cloud-based Intelligent Informative Engineering for Society 5.0

Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Preface
Editors
Contributors
1. Managing Information System with the Help of Cloud Computing
	1.1 Introduction
		1.1.1 What Is Cloud Computing?
		1.1.2 History of Cloud Computing
		1.1.3 Basics of Cloud Computing
		1.1.4 Deployment Models
			1.1.4.1 Service Models
	1.2 What Is Cloud Computing in the Management Information System?
	1.3 Need for MIS
		1.3.1 Cloud Storage
		1.3.2 Why Use Cloud Storage
		1.3.3 Working of Cloud Storage
		1.3.4 Review on Management Information System with the Help of Cloud Computing
			How Does Cloud Computing Change Management?
	1.4 Data Management in Cloud Computing
	1.5 Data Security in Cloud
	1.6 Cloud-Based E-Learning Systems
		1.6.1 Cloud-Based College-Enterprise Classroom Training Method
	1.7 Cloud-Based Employee Management System
		1.7.1 Employee Management System
		1.7.2 Cloud-Based Human Resource Management System
	1.8 Cloud-Based Health Management System
	1.9 Supply Chain Management
		1.9.1 Cloud Computing Paradigms
	1.10 Conceptual Framework in Designing Cloud Computing Management Information System in Academic Area
	1.11 Cloud Computing and Its Amalgamation with Information Science
		1.11.1 Information Networks
		1.11.2 Information System
		1.11.3 Knowledge Lattice and Networks
		1.11.4 Information Center and Data Center
		1.11.5 Information Analysis Center
	1.12 Cloud Computing: Challenges
		1.12.1 Security
		1.12.2 Data Possession
		1.12.3 Standard Architecture
		1.12.4 Need for Internet Connectivity
		1.12.5 Compatibility
	1.13 Cloud Computing Life Cycle
		1.13.1 Methodology
	1.14 Future Scope
	1.15 Conclusion
	References
2. Wireless Networks Based in the Cloud That Support 5G
	2.1 Introduction
		2.1.1 The Emergence of Wireless Networking Technology
			2.1.1.1 Capacity for Connectivity
			2.1.1.2 Performance of the Network
			2.1.1.3 Resource Optimization
		2.1.2 Wireless Networks Capable of 5G
			2.1.2.1 The Cost of Using the Internet (Energy Consumption by Existing Technologies)
			2.1.2.2 Sufficient Speed and Capacity
			2.1.2.3 Friendliness
			2.1.2.4 Accessibility
			2.1.2.5 Economy
			2.1.2.6 Personality
		2.1.3 5G and Mobile Cloud Computing
		2.1.4 Mobile Cloud Computing Issues MCC Applications Encounter These Issues
			2.1.4.1 Availability
			2.1.4.2 Bandwidth
			2.1.4.3 Heterogeneity
	2.2 Networking That Are Hosted on the Cloud
		2.2.1 The Virtualization of the Network Foundation
		2.2.2 Radio Access Networks Hosted in the Cloud
		2.2.3 Cloud Networking on Mobile Devices
		2.2.4 MCN's Aims
	2.3 Networking Platforms on the Cloud
		2.3.1 OpenNebule
		2.3.2 OpenStack
	2.4 5G Wireless Mobile Network Adopts Deep Learning Architecture
		2.4.1 Convolution Neural Network
	2.5 Conclusion
	References
3. Implications of Cloud Computing for Health Care
	3.1 Introduction
		3.1.1 Definition of Cloud
		3.1.2 What Is Cloud Computing?
	3.2 Important Aspects of Cloud Computing
		3.2.1 Benefits of Cloud Computing (CC)
		3.2.2 Below Are the Working Models for CC
		3.2.3 Public Cloud
		3.2.4 Private Cloud
		3.2.5 Hybrid Cloud
		3.2.6 Community Cloud
	3.3 Service Models
		3.3.1 Infrastructure as a Service (IaaS)
		3.3.2 Platform as a Service (PaaS)
		3.3.3 Software as a Service (SaaS)
		3.3.4 Advantages of Cloud Computing in Healthcare System
	3.4 Collaboration
		3.4.1 Security
		3.4.2 Cost
		3.4.3 Speed
		3.4.4 Scalability and Flexibility
	3.5 Applications of Cloud Computing in Health Care
		3.5.1 Dynamic Scalability of Infrastructure
		3.5.2 Information Sharing
		3.5.3 Availability in CC
		3.5.4 Benefits of Adopting CC for Healthcare Organizations
		3.5.5 Impacts of Cloud Computing on Healthcare Sector
		3.5.6 Ease of Interoperability
		3.5.7 Access to Powerful Analytics
	3.6 Ownership of Consumer (Patient) Information
		3.6.1 Telemedicine Function
	3.7 Barriers in Using CC in Healthcare Systems Sectors
		3.7.1 Security Concerns
		3.7.2 Complaisance with Safety Standards
		3.7.3 System Downtime
		3.7.4 World Market for CC in Health Sectors
		3.7.5 Availability and Control
		3.7.6 Security Threats
		3.7.7 Legal and Compliance Risks
	3.8 Conclusion
	References
4. Cloud Computing in Artificial Neural Network
	4.1 Introduction
	4.2 Characteristics of Cloud Computing
	4.3 Scope of Cloud Computing in Artificial Neural Network
		4.3.1 Basics of BNN
	4.4 Basics of ANN
		4.4.1 Services of Cloud Computing Inherited in Artificial Neural Network
		4.4.2 Cloud Service as Software in ANN
		4.4.3 ANN in Job Scheduling
		4.4.4 ANN in Textiles
		4.4.5 Cloud Service as Infrastructure in ANN
		4.4.6 Supervised Learning
		4.4.7 Unsupervised Learning
		4.4.8 Cloud Service as Platform in ANN
		4.4.9 How the Security Applies in Cloud Data by Using ANN
	4.5 Reviews
	4.6 Proposed Model
	4.7 Conclusion
	4.8 Future Scope
	References
5. Cloud Computing in Blockchain
	5.1 Introduction
		5.1.1 Blockchain Model Blocks Include
			5.1.1.1 Blockchain
			5.1.1.2 Blockchain Security
		5.1.2 Ad Hoc Mobile Cloud Infrastructure
		5.1.3 Bitcoin
		5.1.4 Cloud Computing Authentication
		5.1.5 Blockchain Specifications
			5.1.5.1 E-Cash and Its Security
			5.1.5.2 Access Control
			5.1.5.3 Blockchain and Cloud Computing Security
	5.2 Cloud Computing
		5.2.1 Cloud Deployment Models
		5.2.2 Community Cloud
		5.2.3 Data Security
		5.2.4 Restrictions
		5.2.5 Reputation
		5.2.6 No-Vendor Legal Liability
		5.2.7 Cloud-Based Research
			5.2.7.1 Reliability
			5.2.7.2 Requirement
			5.2.7.3 SLAs
			5.2.7.4 Cloud Data Management
			5.2.7.5 Data Encryption
			5.2.7.6 Interoperability
	5.3 Blockchain Technology
		5.3.1 Emergence of Blockchain-Bitcoin
		5.3.2 Differentials
			5.3.2.1 Decentralisation
			5.3.2.2 Persistence
			5.3.2.3 Auditability
			5.3.2.4 Anonymity
			5.3.2.5 Autonomous
			5.3.2.6 Immunity
			5.3.2.7 Transparency
			5.3.2.8 Traceability
		5.3.3 Blockchain Types
			5.3.3.1 Public Blockchain
			5.3.3.2 Public Blockchain
			5.3.3.3 Consortium Blockchain
		5.3.4 Blockchain Phases
			5.3.4.1 First-Generation Blockchain
			5.3.4.2 Second-Generation Blockchain
			5.3.4.3 Third-Generation Blockchain
			5.3.4.4 Mining
			5.3.4.5 Blockchain Nodes
			5.3.4.6 Blockchain Layers
			5.3.4.7 Hashing
			5.3.4.8 Smart Contracts
		5.3.5 Digital Signatures
		5.3.6 Blockchain Performance Analysis
			5.3.6.1 Bitcoin and Ethereum Performance Comparison
			5.3.6.2 Hyperledger and Ethereum Comparison
		5.3.7 Blockchain Applications
			5.3.7.1 Financial Blockchain
			5.3.7.2 Healthcare Blockchain
			5.3.7.3 Blockchain in Data Provenance
			5.3.7.4 5G Blockchain
			5.3.7.5 Aviation Blockchain
			5.3.7.6 Supply Chain Blockchain
			5.3.7.7 Blockchain in Smart Homes
			5.3.7.8 Blockchain in Smart Property
			5.3.7.9 Blockchain Elsewhere
		5.3.8 Blockchain Architecture
			5.3.8.1 Blockchain's Workings
			5.3.8.2 Consensus Algorithms
			5.3.8.3 Proof of Work
			5.3.8.4 Proof of Stake
			5.3.8.5 Practical Byzantine Fault Tolerance (PBFT)
			5.3.8.6 Delegated Stake Proof
			5.3.8.7 Ripple
			5.3.8.8 Tendermint
			5.3.8.9 Node Identity Management
			5.3.8.10 Energy Saving
			5.3.8.11 Tolerated Adversary Power
		5.3.9 Blockchain Challenges
			5.3.9.1 Scalability
			5.3.9.2 Privacy Leak
			5.3.9.3 Laws
			5.3.9.4 Governing
	5.4 Support Blockchain for Cloud Computing
		5.4.1 Interoperability
		5.4.2 Data Encryption
		5.4.3 SLAs
		5.4.4 Cloud Data Management
		5.4.5 Blockchain–Cloud Analysis
	5.5 Conclusion
	References
6. Cloud Computing for Machine Learning and Cognitive Application
	6.1 Introduction
		6.1.1 Cloud Computing
		6.1.2 Software as a Service
		6.1.3 Platform as a Service
		6.1.4 Infrastructure as a Service
	6.2 Machine Learning
		6.2.1 Supervised Learning
		6.2.2 Unsupervised Learning
	6.3 Literature Review
		6.3.1 Cloud Computing
		6.3.2 Multitenancy
		6.3.3 Huge Scalability
		6.3.4 Elasticity
		6.3.5 Pay-as-You-Go
		6.3.6 Self-Provision of Resources
	6.4 The SPI Framework for Cloud Computing
		6.4.1 The Cloud Services Delivery Model
			6.4.1.1 The Software as a Service Model
			6.4.1.2 The Platform as a Service Model
			6.4.1.3 The Infrastructure as a Service Model
			6.4.1.4 Cloud Deployment Model
		6.4.2 Public Clouds
		6.4.3 Private Clouds
		6.4.4 Hybrid Clouds
		6.4.5 The Impact of Cloud Computing on Users
		6.4.6 Individual Business
		6.4.7 Individual Customers
		6.4.8 Start-Ups
		6.4.9 Small- and Medium–Sized Business
		6.4.10 Enterprise Businesses
	6.5 Conclusions
	6.6 Future Scope
	References
7. Edge Cloud Computing-Based Model for IoT
	7.1 Introduction
		7.1.1 Cloud Computing
		7.1.2 Software-as-a-Service (SaaS)
		7.1.3 Platform-as-a-Service (PaaS)
		7.1.4 Infrastructure-as-a-Service (IaaS)
		7.1.5 Cloud Computing at the Edge Offers Many Benefits for LSD-IoT
			7.1.5.1 Scalable
			7.1.5.2 Performance
			7.1.5.3 Data Size
			7.1.5.4 Availability
			7.1.5.5 Effectiveness
	7.2 Edge Computing: Why You Need It
		7.2.1 Push From the Cloud Services
		7.2.2 Push From the IoT
			7.2.2.1 Go From Data Consumer to Data Creator
	7.3 Related Work
		7.3.1 Edge Computing Architecture
		7.3.2 Cloudlet Computing
		7.3.3 Fog Computing
		7.3.4 Virtualization
	7.4 Models of IoT Communication
		7.4.1 Device to Device Communication (D2D)
		7.4.2 Device to Cloud Communication (D2C)
		7.4.3 Device to Gateway Communication (D2G)
	7.5 Edge Computing Architecture
		7.5.1 Far End
		7.5.2 Near End
	7.6 Cloud Architecture Based on IoT
		7.6.1 IoT Applications in Detail
			7.6.1.1 Smart Cities
			7.6.1.2 Smart Security
			7.6.1.3 Smart Medical Field
			7.6.1.4 Intelligent Agriculture
			7.6.1.5 Smart Industrial Control
			7.6.1.6 Smart Entertainment and Media
			7.6.1.7 Smart Legal System
	7.7 Benefits of the Internet of Things
		7.7.1 Communication
		7.7.2 Storage
		7.7.3 Processing Capabilities
		7.7.4 New Abilities
	7.8 Advantages of IoT and Cloud Computing Integration
		7.8.1 Analysis
		7.8.2 Scalability
		7.8.3 Visualization
		7.8.4 Flexibility
		7.8.5 Fast Reaction Time
		7.8.6 Automation
		7.8.7 Multitenancy
	7.9 Future Work
	7.10 Conclusion
	References
8. Cloud-Based License Plate Recognition for Smart City Using Deep Learning
	8.1 Introduction
		8.1.1 Related Technologies
			8.1.1.1 Deep Learning
			8.1.1.2 Cloud Computing
		8.1.2 Literature Review
	8.2 Proposed Model
		8.2.1 Image Acquisition
		8.2.2 Horizontal Flipping
		8.2.3 Color Augmentation
			8.2.3.1 Brightness
			8.2.3.2 Contrast
			8.2.3.3 Saturation
			8.2.3.4 Hue
		8.2.4 Cropping
		8.2.5 Data Pre-Processing
			8.2.5.1 Smoothing
			8.2.5.2 Scaling
			8.2.5.3 Data Cleaning
	8.3 Segmentation
		8.3.1 Segmentation Approaches
		8.3.2 Segmenting Images
		8.3.3 Segmentation Based on Thresholds
		8.3.4 Segmentation Based on Location
		8.3.5 Clustering by Merging
		8.3.6 Divisive Splitting or Clustering by Division
	8.4 Segmentation Using an Artificial Neuronal Network
	8.5 Optical Character Recognition
	8.6 Convolutional Neural Networks
	8.7 Evaluation Parameters for the Proposed Model
	8.8 Conclusion
	8.9 Future Work
	References
9. Sentimental Analysis Using Cloud Dictionary and Machine Learning Approach
	9.1 Introduction
	9.2 Literature Review
		9.2.1 Machine Learning Approach
		9.2.2 Supervised Learning
		9.2.3 Decision Tree Classifier
		9.2.4 Linear Classification
		9.2.5 Support Vector Machine (SVM)
	9.3 Lexicon-Based Approach
	9.4 Methodology
		9.4.1 Dictionary Based Approach
			9.4.1.1 Text Data From Snscrape (SNS)
		9.4.2 Data Pre-Processing
			9.4.2.1 Tokenization
			9.4.2.2 Stop Words Removal
			9.4.2.3 Case Normalization
		9.4.3 Data Polarization
	9.5 Machine Learning Based Approach
		9.5.1 Dataset: Contains
		9.5.2 Data Pre-Processing and Cleaning
	9.6 Binary Classifier Using LSTM
		9.6.1 Class Prediction
	9.7 Result and Discussion
	9.8 Conclusion
	References
10. Impact of Cloud Computing on Entrepreneurship, Cost, and Security
	10.1 Introduction
		10.1.1 Theoretical Background
		10.1.2 Cloud Computing
	10.2 The Technical Part of the Cloud
		10.2.1 SAAS (SaaS)
		10.2.2 PAAS (PaaS)
		10.2.3 IAAS (IaaS)
		10.2.4 Public Cloud
		10.2.5 Hybrid Cloud
	10.3 Case Studies Abroad
		10.3.1 Google.com
		10.3.2 Amazon.com
		10.3.3 Microsoft
		10.3.4 Apple
		10.3.5 Adoption of Cloud Computing in Europe
		10.3.6 Potential Benefits of Cloud Computing
	10.4 Concerns and Challenges
		10.4.1 Cost Benefits
		10.4.2 Cost Impact
	10.5 Security Risks
		10.5.1 Security Impact
	10.6 Data Collection
	10.7 Cloud Computing on Investments
	10.8 Conclusions
	References
11. Green Cloud Computing
	11.1 Introduction
		11.1.1 Infrastructure as a Service (IaaS)
	11.2 Amazon Web Services
		11.2.1 AWS Storage Services
		11.2.2 Amazon Glacier
		11.2.3 Elastic Block Storage (EBS)
		11.2.4 AWS Computing Service
	11.3 Platforms as a Service (PaaS)
		11.3.1 Public Cloud
		11.3.2 Private Cloud
			11.3.2.1 Security
			11.3.2.2 Long-Term Savings
			11.3.2.3 Regulatory Governance
		11.3.3 Community Cloud
		11.3.4 Hybrid Cloud
	11.4 Literature Review
	11.5 Existing Approaches
		11.5.1 Advantages and Disadvantages
	11.6 Conclusions and Future Work
	References
12. Study of Issues with Cloud Security
	12.1 Introduction
		12.1.1 Cloud Computing
		12.1.2 The Cloud Model Consists of Five Key Features
	12.2 Literature Survey
	12.3 Cloud Models and Their Security Issues
		12.3.1 Service Models
		12.3.2 Deployment Models
	12.4 Cloud Security Issues
		12.4.1 Deployment Models Security Issues
		12.4.2 Service Models Security Issues
	12.5 Countermeasures
	12.6 Conclusion
	References
Index