Data Analytics, Computational Statistics, and Operations Research for Engineers: Methodologies and Applications

Cover
Half Title
Title
Copyright
Dedication
Contents
Preface
Acknowledgments
Editor Biographies
Chapter 1 Hyperspectral Imagery Applications for Precision Agriculture: A Systemic Survey
	1.1 Introduction
		1.1.1 The Main Contribution of the Chapter
	1.2 Hyperspectral Imaging Technology
	1.3 Agricultural Applications
		1.3.1 Soil Analysis
		1.3.2 Contaminants and Nutrient Estimation
		1.3.3 Inland Water and Moisture Estimation
		1.3.4 Crop Yield Estimation
		1.3.5 Plant Disease Monitoring, Insect Pesticide Monitoring, and Invasive Plant Species
		1.3.6 Agricultural Crop Classification
	1.4 Conclusion and Future Scope
	References
Chapter 2 Early Prediction of COVID-19 Using Modified Convolutional Neural Networks
	2.1 Introduction
		2.1.1 Graph for Deaths
	2.2 What We Cover In
	2.3 Literature Survey
	2.4 Related Work
		2.4.1 Existing System
	2.5 Proposed System
	2.6 System Design and Implementation
	2.7 Paper Implementation Details
		2.7.1 System Modules
			2.7.1.1 Collecting Data Sources
			2.7.1.2 Data in Structured
			2.7.1.3 Preprocessing Datasets
			2.7.1.4 Feature Learning
		2.7.2 Implementing Using Structured Data
		2.7.3 K-Nearest Neighbor
			2.7.3.1 Data Input
			2.7.3.2 Output
			2.7.3.3 Method
			2.7.3.4 Neural Networks
			2.7.3.5 Procedure
			2.7.3.6 Step 1: Representation of Text Data
			2.7.3.7 Step 2: Convolution Layer of Text MCNN
			2.7.3.8 Step 3: POOL Layer of Text-Modified CNN
			2.7.3.9 Step 4: Full Connection Layer of Text-Modified CNN
			2.7.3.10 Step 5: Modified CNN Classifier
		2.7.4 Logical Flow of Neural Network
	2.8 Results
	2.9 Conclusion
	References
Chapter 3 Blockchain for Electronic Voting System
	3.1 Introduction
	3.2 Methods Used for Voting
		3.2.1 Paper Ballots
		3.2.2 E-Voting
		3.2.3 I-Voting
	3.3 Current E-Voting System Gaps
		3.3.1 Deploying Proprietary Software
		3.3.2 Nontransparency in Enlisting Software Version
		3.3.3 Minimal Security against Day-to-Day Attacks
		3.3.4 Incompatibility of Voting Machine and Voting Software
	3.4 Introduction to Blockchain
		3.4.1 Blockchain Network
		3.4.2 Countries that Used Blockchain for Voting
			3.4.2.1 Sierra Leone
			3.4.2.2 Russia
	3.5 Working of E-Voting Using Blockchain
		3.5.1 Requesting for Vote
		3.5.2 Casting the Vote
		3.5.3 Encrypting Votes
		3.5.4 Appending the Vote
	3.6 Blockchain as a Service
		3.6.1 Smart Contracts
		3.6.2 Noninteractive Zero Knowledge Proof
	3.7 Blockchain as a Service for E-Voting
		3.7.1 Election as a Smart Contract
		3.7.2 Election Roles
			3.7.2.1 Election administrators
			3.7.2.2 Voters
			3.7.2.3 District nodes
			3.7.2.4 Bootnodes
		3.7.3 Election Process
			3.7.3.1 Election Creation
			3.7.3.2 Voter Registration
			3.7.3.3 Vote Transaction
			3.7.3.4 Tallying the Results
			3.7.3.5 Verifying the Vote
		3.7.4 Evaluating Blockchain as a Service for E-Voting
			3.7.4.1 Exonum
			3.7.4.2 Quorum
			3.7.4.3 Geth
	3.8 Current Proposed Solutions in E-Voting System
		3.8.1 General
		3.8.2 Coin Based
		3.8.3 Integrity of the Data
		3.8.4 Consensus
		3.8.5 Competitive Consensus
		3.8.6 Proof of Work
		3.8.7 Proof of Stake
		3.8.8 Delegated Proof of Stake
		3.8.9 Noncompetitive Consensus
	3.9 Benefits of Blockchain-Based E-Voting System
		3.9.1 Challenges for Blockchain-Based E-Voting System
	3.10 Security Analysis and Legal Issues
		3.10.1 Possible Attacks on Blockchain Network
			3.10.1.1 Distributed Denial of Service
			3.10.1.2 Routing Attacks
			3.10.1.3 Sybil Attacks
		3.10.2 Anonymity
		3.10.3 Confidentiality
		3.10.4 Ballot Manipulation
		3.10.5 Transparency
		3.10.6 Auditability
		3.10.7 Nonrepudiation
	3.11 Conclusion
	References
Chapter 4 The Efficacy of AI and Big Data in Combating COVID-19
	4.1 Introduction
	4.2 COVID-19 Pandemic
	4.3 COVID-19 and AI
	4.4 How to Fight Coronavirus with the Help of AI?
	4.5 AI Making the COVID-19 Drug Development Cheaper, Quicker, and More effective
	4.5.1 Why Are Faster Trials Essential for Pharmaceutical Companies?
		4.5.1.1 The State of the Clinical Trials
	4.5.2 How AI Can Alter All Phases of Clinical Trials
		4.5.2.1 Clinical Trial Finding
			4.5.2.1.1 The EHR Interoperability Challenge
			4.5.2.1.2 Acquisitions as a Method for Obtaining Patient Information
		4.5.2.2 Enrolment Challenges
		4.5.2.3 Adherence to Drugs
			4.5.2.3.1 Visual, Auditory, and Digital Phenotyping
			4.5.2.3.2 AI and IoT for Remote Patient Monitoring
	4.5.3 How Big Tech Interrupts Clinical Trials
		4.5.3.1 Google’s Healthcare Data Platform
			4.5.3.1.1 Disrupting EHR Data Sharing
			4.5.3.1.2 What Does This Data Mean for Clinical Trials?
		4.5.3.2 The Moves of Apple and Facebook
	4.5.4 How COVID-19 Influenced Tech Adoption in Clinical Trials
		4.5.4.1 Study Design
		4.5.4.2 Virtual Trials
	4.6 AI for COVID-19 Pandemic: A Survey on the State of the Arts
		4.6.1 Understanding the Virus
		4.6.2 Monitoring the Pandemic
		4.6.3 Controlling the Pandemic
		4.6.4 Managing the Effects of the Pandemic
		4.6.5 For Pharmaceutical Studies
	4.7 Big Data for COVID-19
		4.7.1 Big Data Applications for COVID-19
		4.7.2 Big Data for COVID-19 Pandemic: A Survey on the State of the Arts
	4.8 Case Study: How India Fights COVID-19 with AI and Big Data
	4.9 Conclusion
	References
Chapter 5 Blockchain in Artificial Intelligence
	5.1 Introduction
		5.1.1 Difference between Blockchain and AI
		5.1.2 Blockchain for AI (Classification and Protection) and AI for Blockchain (Security and Straightforwardness)
		5.1.3 Blockchain and AI: A Great Match
		5.1.4 Applications of Blockchain and AI
			5.1.4.1 Smart Computing Power
			5.1.4.2 Creating Diverse Datasets
			5.1.4.3 Data Safeguarding
			5.1.4.4 Data Monetization
			5.1.4.5 Trusting AI Decision-Making
	5.2 Blockchain, Improving Machine Learning Models
		5.2.1 Some Examples of Blockchain and AI-Integrated Softwares
		5.2.2 SingularityNET
	5.3 DeepBrain Chain
	5.4 Disruptive Integration of Blockchain and AI
	5.5 Blockchain for AI
		5.5.1 Secure Data Sharing
		5.5.2 Your Records/Data, Your Cost
	5.6 Explainable AI
	5.7 AI for Blockchain
		5.7.1 Security and Scalability
	5.8 Privacy and Personalization
	5.9 Convergence of Blockchain and AI with IoT
	5.10 Convergence of Blockchain, Internet of Things, and Artificial Intelligence
	5.11 Improving Data Standardization
	5.12 Authentication in Accordance to a Blockchain-Established Identity
	5.13 Automatization by Means of Smart Contracts
	5.14 Integration of Blockchain and AI for Medical Sciences
		5.14.1 AI for Heart Medicine
		5.14.2 Blockchain in Cardiovascular Medicine
	5.15 Current Applications of Integrated Blockchain and AI
	5.16 The Prospective of Blockchain, IoT, and AI in Combination
	5.17 Conclusion
	References
Chapter 6 Big Data Analytics and Machine Learning
	6.1 Introduction: Background and Driving Forces
	6.2 Scope of Big Data Analytics
		6.2.1 Airline Industry
		6.2.2 Banking
		6.2.3 Science and Government
		6.2.4 Healthcare
	6.3 Big Data Analytics Tools
		6.3.1 Apache Kafka
		6.3.2 HBase
		6.3.3 Hive
		6.3.4 Map Reduce
		6.3.5 Pig
		6.3.6 Spark
		6.3.7 YARN
		6.3.8 Presto
	6.4 Introduction to Machine Learning
	6.5 Tools Used in Machine Learning
		6.5.1 Tensor Flow
		6.5.2 Google Cloud
		6.5.3 AWS ML
		6.5.4 Accord and Apache Mahout
		6.5.5 Shogun
		6.5.6 Oryx 2
		6.5.7 Apache Singa
		6.5.8 Google ML Kit for Mobile
		6.5.9 Apple’s Core ML
	6.6 Big Data Types and Its Classifications
		6.6.1 Structured
		6.6.2 Unstructured
		6.6.3 Semistructured
		6.6.4 Volume
		6.6.5 Variety
		6.6.6 Veracity
		6.6.7 Value
		6.6.8 Velocity
	6.7 Latest Trend in Big Data
		6.7.1 Information from Data
		6.7.2 Predictive Analysis
		6.7.3 Edge Computing
		6.7.4 Natural Language Processing
		6.7.5 Hybrid Clouds
		6.7.6 Dark Data
	6.8 Types of Machine Learning Algorithms
		6.8.1 Supervised Learning
			6.8.1.1 Categories of Supervised Learning
				6.8.1.1.1 Classification
				6.8.1.1.2 Regression
		6.8.2 Unsupervised Learning
		6.8.3 Reinforcement Learning
			6.8.3.1 Reinforcement Learning Steps
		6.8.4 Types of Reinforcement Learning
	6.9 Guidelines on Optimal Steps in Making Machine Learning Predictions
		6.9.1 Data Selection
		6.9.2 Data Exploration
		6.9.3 Data Preprocessing
		6.9.4 Feature Selection
		6.9.5 Training and Testing
		6.9.6 Prediction Using Various ML Algorithms
	6.10 Big Data Analytics and Machine Learning Fusion
	6.11 Advantage of Big Data and Machine Learning
		6.11.1 Analyzing Data in a Limited Time Frame
		6.11.2 Prediction of Real-Time Data
	6.12 Trade-Off of Bid Data and ML Combination
		6.12.1 Data Acquisition
		6.12.2 Time and Resources
		6.12.3 High Error Susceptibility
	6.13 Applications of Big Data and Machine Learning
		6.13.1 Cloud Networks
		6.13.2 Web Scraping
		6.13.3 Mixed-Initiative Systems
	6.14 Guidelines on How Machine Learning Can Be Effectively Applied to Big Data
		6.14.1 Data Warehousing
		6.14.2 Data Segmentation
		6.14.3 Outlier Detection and Imputation of Missing Values
		6.14.4 Dimensionality Reduction
		6.14.5 Descriptive Analysis
		6.14.6 Predictive Analysis
		6.14.7 Prescriptive Analysis
	6.15 Conclusion and Future Work
	References
Chapter 7 Securing IoT through Blockchain in Big Data Environment
	7.1 Introduction
	7.2 Overview of Blockchain Technology
		7.2.1 Why Is Blockchain Popular?
		7.2.2 How Does Blockchain Work?
		7.2.3 Benefits of Blockchain Technology
	7.3 Internet of Things
		7.3.1 How IoT Works
		7.3.2 IoT—Key Features
		7.3.3 IoT—Advantages
		7.3.4 IoT—Disadvantages
	7.4 The IoT Security Challenge
		7.4.1 A Spectrum of Security Considerations
		7.4.2 Unique Security Challenges of IoT Devices
		7.4.3 IoT Security Questions
	7.5 Is Blockchain the Solution to IoT Security?
	7.6 When the IoT Meets Blockchain
	7.7 IoT Architectural Pattern Based on the Blockchain Service
		7.7.1 Model of Communication
		7.7.2 A Rich Ecosystem for Leveraging Interoperability Capabilities
		7.7.3 Cohabitation between Multiple Blockchain Services
			7.7.3.1 The IoT Architecture Pattern Based on Blockchain Technology
	7.8 Features to Consider When Securing the IoT Using Blockchain Technology
		7.8.1 Scalable IoT Discovery
		7.8.2 Trusted Communication
		7.8.3 Semiautonomous Machine-to-Machine Operations
		7.8.4 IoT Configuration and Updates Controls
		7.8.5 Stable Firmware Image Distribution and Upgrade
			7.8.5.1 Firmware Reputation-Based Upgrade (Chain of Things)
	7.9 Various Ways to Strengthen IoT Security with Blockchain Technology
	7.10 Challenges in Integrating Blockchain into the IoT
	7.11 Security Recommendations
	7.12 Use Cases of Blockchain Mechanisms for IoT Security
	7.13 Conclusion
	References
Chapter 8 Spear Phishing Detection
	8.1 Introduction
	8.2 History of Phishing
	8.3 Statistics of Phishing
	8.4 Anatomy of an Attack
	8.5 Kinds of Phishing
	8.6 Dataset
	8.7 Machine Learning
		8.7.1 Basic Machine Learning Algorithms
			8.7.1.1 Support Vector Machine
			8.7.1.2 Decision Tree
			8.7.1.3 Logistic Regression
			8.7.1.4 Multinomial Naive Bayes
			8.7.1.5 K-Nearest Neighbor
			8.7.1.6 Random Forest
		8.7.2 Ensemble Learning
			8.7.2.1 Max Voting
			8.7.2.2 Averaging
			8.7.2.3 Stacking
			8.7.2.4 Bagging
			8.7.2.5 AdaBoost
			8.7.2.6 Gradient Boosting
	8.8 Conclusion
	References
Chapter 9 RFID and Operational Performances
	9.1 Introduction
	9.2 Review of Literature
		9.2.1 Theoretical Foundation
		9.2.2 First Energy Model
		9.2.3 Second Rogers’ Theory of Diffusion of Innovation
	9.3 Energy-Efficient RFID Practices in Medical Store Inside Hospitals
		9.3.1 Operational performance of stores improved through RFID (as an energy-efficient technique)
	9.4 Research Gap, Objectives, and Hypothesis
		9.4.1 Research Gap
		9.4.2 Research Objective and Hypothesis
	9.5 Methodological Foundation
	9.6 Results and Analysis
		9.6.1 Principal Component Analysis
			9.6.1.1 Correlation Analysis
			9.6.1.2 Regression Analysis
	9.7 Conclusion and Scope for Further Research
	Ethical Approval
	References
Index