Data Analytics, Computational Statistics, and Operations Research for Engineers

Data Analytics, Computational Statistics, and Operations Research for Engineers

by Debabrata Samanta, SK Hafizul Islam, Naveen Chilamkurti, Mohammad Hammoudeh
Released March 2022
Publisher(s): CRC Press
ISBN: 9781000550467

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Book description

This book investigates the role of data mining in computational statistics for machine learning. It offers applications that can be used in various domains and examines the role of transformation functions in optimizing problem statements.

Table of contents

  1. Cover
  2. Half Title
  3. Title
  4. Copyright
  5. Dedication
  6. Contents
  7. Preface
  8. Acknowledgments
  9. Editor Biographies
  10. Chapter 1 Hyperspectral Imagery Applications for Precision Agriculture: A Systemic Survey
    1. 1.1 Introduction
      1. 1.1.1 The Main Contribution of the Chapter
    2. 1.2 Hyperspectral Imaging Technology
    3. 1.3 Agricultural Applications
      1. 1.3.1 Soil Analysis
      2. 1.3.2 Contaminants and Nutrient Estimation
      3. 1.3.3 Inland Water and Moisture Estimation
      4. 1.3.4 Crop Yield Estimation
      5. 1.3.5 Plant Disease Monitoring, Insect Pesticide Monitoring, and Invasive Plant Species
      6. 1.3.6 Agricultural Crop Classification
    4. 1.4 Conclusion and Future Scope
    5. References
  11. Chapter 2 Early Prediction of COVID-19 Using Modified Convolutional Neural Networks
    1. 2.1 Introduction
      1. 2.1.1 Graph for Deaths
    2. 2.2 What We Cover In
    3. 2.3 Literature Survey
    4. 2.4 Related Work
      1. 2.4.1 Existing System
    5. 2.5 Proposed System
    6. 2.6 System Design and Implementation
    7. 2.7 Paper Implementation Details
      1. 2.7.1 System Modules
        1. 2.7.1.1 Collecting Data Sources
        2. 2.7.1.2 Data in Structured
        3. 2.7.1.3 Preprocessing Datasets
        4. 2.7.1.4 Feature Learning
      2. 2.7.2 Implementing Using Structured Data
      3. 2.7.3 K-Nearest Neighbor
        1. 2.7.3.1 Data Input
        2. 2.7.3.2 Output
        3. 2.7.3.3 Method
        4. 2.7.3.4 Neural Networks
        5. 2.7.3.5 Procedure
        6. 2.7.3.6 Step 1: Representation of Text Data
        7. 2.7.3.7 Step 2: Convolution Layer of Text MCNN
        8. 2.7.3.8 Step 3: POOL Layer of Text-Modified CNN
        9. 2.7.3.9 Step 4: Full Connection Layer of Text-Modified CNN
        10. 2.7.3.10 Step 5: Modified CNN Classifier
      4. 2.7.4 Logical Flow of Neural Network
    8. 2.8 Results
    9. 2.9 Conclusion
    10. References
  12. Chapter 3 Blockchain for Electronic Voting System
    1. 3.1 Introduction
    2. 3.2 Methods Used for Voting
      1. 3.2.1 Paper Ballots
      2. 3.2.2 E-Voting
      3. 3.2.3 I-Voting
    3. 3.3 Current E-Voting System Gaps
      1. 3.3.1 Deploying Proprietary Software
      2. 3.3.2 Nontransparency in Enlisting Software Version
      3. 3.3.3 Minimal Security against Day-to-Day Attacks
      4. 3.3.4 Incompatibility of Voting Machine and Voting Software
    4. 3.4 Introduction to Blockchain
      1. 3.4.1 Blockchain Network
      2. 3.4.2 Countries that Used Blockchain for Voting
        1. 3.4.2.1 Sierra Leone
        2. 3.4.2.2 Russia
    5. 3.5 Working of E-Voting Using Blockchain
      1. 3.5.1 Requesting for Vote
      2. 3.5.2 Casting the Vote
      3. 3.5.3 Encrypting Votes
      4. 3.5.4 Appending the Vote
    6. 3.6 Blockchain as a Service
      1. 3.6.1 Smart Contracts
      2. 3.6.2 Noninteractive Zero Knowledge Proof
    7. 3.7 Blockchain as a Service for E-Voting
      1. 3.7.1 Election as a Smart Contract
      2. 3.7.2 Election Roles
        1. 3.7.2.1 Election administrators
        2. 3.7.2.2 Voters
        3. 3.7.2.3 District nodes
        4. 3.7.2.4 Bootnodes
      3. 3.7.3 Election Process
        1. 3.7.3.1 Election Creation
        2. 3.7.3.2 Voter Registration
        3. 3.7.3.3 Vote Transaction
        4. 3.7.3.4 Tallying the Results
        5. 3.7.3.5 Verifying the Vote
      4. 3.7.4 Evaluating Blockchain as a Service for E-Voting
        1. 3.7.4.1 Exonum
        2. 3.7.4.2 Quorum
        3. 3.7.4.3 Geth
    8. 3.8 Current Proposed Solutions in E-Voting System
      1. 3.8.1 General
      2. 3.8.2 Coin Based
      3. 3.8.3 Integrity of the Data
      4. 3.8.4 Consensus
      5. 3.8.5 Competitive Consensus
      6. 3.8.6 Proof of Work
      7. 3.8.7 Proof of Stake
      8. 3.8.8 Delegated Proof of Stake
      9. 3.8.9 Noncompetitive Consensus
    9. 3.9 Benefits of Blockchain-Based E-Voting System
      1. 3.9.1 Challenges for Blockchain-Based E-Voting System
    10. 3.10 Security Analysis and Legal Issues
      1. 3.10.1 Possible Attacks on Blockchain Network
        1. 3.10.1.1 Distributed Denial of Service
        2. 3.10.1.2 Routing Attacks
        3. 3.10.1.3 Sybil Attacks
      2. 3.10.2 Anonymity
      3. 3.10.3 Confidentiality
      4. 3.10.4 Ballot Manipulation
      5. 3.10.5 Transparency
      6. 3.10.6 Auditability
      7. 3.10.7 Nonrepudiation
    11. 3.11 Conclusion
    12. References
  13. Chapter 4 The Efficacy of AI and Big Data in Combating COVID-19
    1. 4.1 Introduction
    2. 4.2 COVID-19 Pandemic
    3. 4.3 COVID-19 and AI
    4. 4.4 How to Fight Coronavirus with the Help of AI?
    5. 4.5 AI Making the COVID-19 Drug Development Cheaper, Quicker, and More effective
      1. 4.5.1 Why Are Faster Trials Essential for Pharmaceutical Companies?
        1. 4.5.1.1 The State of the Clinical Trials
      2. 4.5.2 How AI Can Alter All Phases of Clinical Trials
        1. 4.5.2.1 Clinical Trial Finding
          1. 4.5.2.1.1 The EHR Interoperability Challenge
          2. 4.5.2.1.2 Acquisitions as a Method for Obtaining Patient Information
        2. 4.5.2.2 Enrolment Challenges
        3. 4.5.2.3 Adherence to Drugs
          1. 4.5.2.3.1 Visual, Auditory, and Digital Phenotyping
          2. 4.5.2.3.2 AI and IoT for Remote Patient Monitoring
      3. 4.5.3 How Big Tech Interrupts Clinical Trials
        1. 4.5.3.1 Google’s Healthcare Data Platform
          1. 4.5.3.1.1 Disrupting EHR Data Sharing
          2. 4.5.3.1.2 What Does This Data Mean for Clinical Trials?
        2. 4.5.3.2 The Moves of Apple and Facebook
      4. 4.5.4 How COVID-19 Influenced Tech Adoption in Clinical Trials
        1. 4.5.4.1 Study Design
        2. 4.5.4.2 Virtual Trials
    6. 4.6 AI for COVID-19 Pandemic: A Survey on the State of the Arts
      1. 4.6.1 Understanding the Virus
      2. 4.6.2 Monitoring the Pandemic
      3. 4.6.3 Controlling the Pandemic
      4. 4.6.4 Managing the Effects of the Pandemic
      5. 4.6.5 For Pharmaceutical Studies
    7. 4.7 Big Data for COVID-19
      1. 4.7.1 Big Data Applications for COVID-19
      2. 4.7.2 Big Data for COVID-19 Pandemic: A Survey on the State of the Arts
    8. 4.8 Case Study: How India Fights COVID-19 with AI and Big Data
    9. 4.9 Conclusion
    10. References
  14. Chapter 5 Blockchain in Artificial Intelligence
    1. 5.1 Introduction
      1. 5.1.1 Difference between Blockchain and AI
      2. 5.1.2 Blockchain for AI (Classification and Protection) and AI for Blockchain (Security and Straightforwardness)
      3. 5.1.3 Blockchain and AI: A Great Match
      4. 5.1.4 Applications of Blockchain and AI
        1. 5.1.4.1 Smart Computing Power
        2. 5.1.4.2 Creating Diverse Datasets
        3. 5.1.4.3 Data Safeguarding
        4. 5.1.4.4 Data Monetization
        5. 5.1.4.5 Trusting AI Decision-Making
    2. 5.2 Blockchain, Improving Machine Learning Models
      1. 5.2.1 Some Examples of Blockchain and AI-Integrated Softwares
      2. 5.2.2 SingularityNET
    3. 5.3 DeepBrain Chain
    4. 5.4 Disruptive Integration of Blockchain and AI
    5. 5.5 Blockchain for AI
      1. 5.5.1 Secure Data Sharing
      2. 5.5.2 Your Records/Data, Your Cost
    6. 5.6 Explainable AI
    7. 5.7 AI for Blockchain
      1. 5.7.1 Security and Scalability
    8. 5.8 Privacy and Personalization
    9. 5.9 Convergence of Blockchain and AI with IoT
    10. 5.10 Convergence of Blockchain, Internet of Things, and Artificial Intelligence
    11. 5.11 Improving Data Standardization
    12. 5.12 Authentication in Accordance to a Blockchain-Established Identity
    13. 5.13 Automatization by Means of Smart Contracts
    14. 5.14 Integration of Blockchain and AI for Medical Sciences
      1. 5.14.1 AI for Heart Medicine
      2. 5.14.2 Blockchain in Cardiovascular Medicine
    15. 5.15 Current Applications of Integrated Blockchain and AI
    16. 5.16 The Prospective of Blockchain, IoT, and AI in Combination
    17. 5.17 Conclusion
    18. References
  15. Chapter 6 Big Data Analytics and Machine Learning
    1. 6.1 Introduction: Background and Driving Forces
    2. 6.2 Scope of Big Data Analytics
      1. 6.2.1 Airline Industry
      2. 6.2.2 Banking
      3. 6.2.3 Science and Government
      4. 6.2.4 Healthcare
    3. 6.3 Big Data Analytics Tools
      1. 6.3.1 Apache Kafka
      2. 6.3.2 HBase
      3. 6.3.3 Hive
      4. 6.3.4 Map Reduce
      5. 6.3.5 Pig
      6. 6.3.6 Spark
      7. 6.3.7 YARN
      8. 6.3.8 Presto
    4. 6.4 Introduction to Machine Learning
    5. 6.5 Tools Used in Machine Learning
      1. 6.5.1 Tensor Flow
      2. 6.5.2 Google Cloud
      3. 6.5.3 AWS ML
      4. 6.5.4 Accord and Apache Mahout
      5. 6.5.5 Shogun
      6. 6.5.6 Oryx 2
      7. 6.5.7 Apache Singa
      8. 6.5.8 Google ML Kit for Mobile
      9. 6.5.9 Apple’s Core ML
    6. 6.6 Big Data Types and Its Classifications
      1. 6.6.1 Structured
      2. 6.6.2 Unstructured
      3. 6.6.3 Semistructured
      4. 6.6.4 Volume
      5. 6.6.5 Variety
      6. 6.6.6 Veracity
      7. 6.6.7 Value
      8. 6.6.8 Velocity
    7. 6.7 Latest Trend in Big Data
      1. 6.7.1 Information from Data
      2. 6.7.2 Predictive Analysis
      3. 6.7.3 Edge Computing
      4. 6.7.4 Natural Language Processing
      5. 6.7.5 Hybrid Clouds
      6. 6.7.6 Dark Data
    8. 6.8 Types of Machine Learning Algorithms
      1. 6.8.1 Supervised Learning
        1. 6.8.1.1 Categories of Supervised Learning
          1. 6.8.1.1.1 Classification
          2. 6.8.1.1.2 Regression
        2. 6.8.2 Unsupervised Learning
        3. 6.8.3 Reinforcement Learning
          1. 6.8.3.1 Reinforcement Learning Steps
        4. 6.8.4 Types of Reinforcement Learning
    9. 6.9 Guidelines on Optimal Steps in Making Machine Learning Predictions
      1. 6.9.1 Data Selection
      2. 6.9.2 Data Exploration
      3. 6.9.3 Data Preprocessing
      4. 6.9.4 Feature Selection
      5. 6.9.5 Training and Testing
      6. 6.9.6 Prediction Using Various ML Algorithms
    10. 6.10 Big Data Analytics and Machine Learning Fusion
    11. 6.11 Advantage of Big Data and Machine Learning
      1. 6.11.1 Analyzing Data in a Limited Time Frame
      2. 6.11.2 Prediction of Real-Time Data
    12. 6.12 Trade-Off of Bid Data and ML Combination
      1. 6.12.1 Data Acquisition
      2. 6.12.2 Time and Resources
      3. 6.12.3 High Error Susceptibility
    13. 6.13 Applications of Big Data and Machine Learning
      1. 6.13.1 Cloud Networks
      2. 6.13.2 Web Scraping
      3. 6.13.3 Mixed-Initiative Systems
    14. 6.14 Guidelines on How Machine Learning Can Be Effectively Applied to Big Data
      1. 6.14.1 Data Warehousing
      2. 6.14.2 Data Segmentation
      3. 6.14.3 Outlier Detection and Imputation of Missing Values
      4. 6.14.4 Dimensionality Reduction
      5. 6.14.5 Descriptive Analysis
      6. 6.14.6 Predictive Analysis
      7. 6.14.7 Prescriptive Analysis
    15. 6.15 Conclusion and Future Work
    16. References
  16. Chapter 7 Securing IoT through Blockchain in Big Data Environment
    1. 7.1 Introduction
    2. 7.2 Overview of Blockchain Technology
      1. 7.2.1 Why Is Blockchain Popular?
      2. 7.2.2 How Does Blockchain Work?
      3. 7.2.3 Benefits of Blockchain Technology
    3. 7.3 Internet of Things
      1. 7.3.1 How IoT Works
      2. 7.3.2 IoT—Key Features
      3. 7.3.3 IoT—Advantages
      4. 7.3.4 IoT—Disadvantages
    4. 7.4 The IoT Security Challenge
      1. 7.4.1 A Spectrum of Security Considerations
      2. 7.4.2 Unique Security Challenges of IoT Devices
      3. 7.4.3 IoT Security Questions
    5. 7.5 Is Blockchain the Solution to IoT Security?
    6. 7.6 When the IoT Meets Blockchain
    7. 7.7 IoT Architectural Pattern Based on the Blockchain Service
      1. 7.7.1 Model of Communication
      2. 7.7.2 A Rich Ecosystem for Leveraging Interoperability Capabilities
      3. 7.7.3 Cohabitation between Multiple Blockchain Services
        1. 7.7.3.1 The IoT Architecture Pattern Based on Blockchain Technology
    8. 7.8 Features to Consider When Securing the IoT Using Blockchain Technology
      1. 7.8.1 Scalable IoT Discovery
      2. 7.8.2 Trusted Communication
      3. 7.8.3 Semiautonomous Machine-to-Machine Operations
      4. 7.8.4 IoT Configuration and Updates Controls
      5. 7.8.5 Stable Firmware Image Distribution and Upgrade
        1. 7.8.5.1 Firmware Reputation-Based Upgrade (Chain of Things)
    9. 7.9 Various Ways to Strengthen IoT Security with Blockchain Technology
    10. 7.10 Challenges in Integrating Blockchain into the IoT
    11. 7.11 Security Recommendations
    12. 7.12 Use Cases of Blockchain Mechanisms for IoT Security
    13. 7.13 Conclusion
    14. References
  17. Chapter 8 Spear Phishing Detection
    1. 8.1 Introduction
    2. 8.2 History of Phishing
    3. 8.3 Statistics of Phishing
    4. 8.4 Anatomy of an Attack
    5. 8.5 Kinds of Phishing
    6. 8.6 Dataset
    7. 8.7 Machine Learning
      1. 8.7.1 Basic Machine Learning Algorithms
        1. 8.7.1.1 Support Vector Machine
        2. 8.7.1.2 Decision Tree
        3. 8.7.1.3 Logistic Regression
        4. 8.7.1.4 Multinomial Naive Bayes
        5. 8.7.1.5 K‑Nearest Neighbor
        6. 8.7.1.6 Random Forest
      2. 8.7.2 Ensemble Learning
        1. 8.7.2.1 Max Voting
        2. 8.7.2.2 Averaging
        3. 8.7.2.3 Stacking
        4. 8.7.2.4 Bagging
        5. 8.7.2.5 AdaBoost
        6. 8.7.2.6 Gradient Boosting
    8. 8.8 Conclusion
    9. References
  18. Chapter 9 RFID and Operational Performances
    1. 9.1 Introduction
    2. 9.2 Review of Literature
      1. 9.2.1 Theoretical Foundation
      2. 9.2.2 First Energy Model
      3. 9.2.3 Second Rogers’ Theory of Diffusion of Innovation
    3. 9.3 Energy-Efficient RFID Practices in Medical Store Inside Hospitals
      1. 9.3.1 Operational performance of stores improved through RFID (as an energy-efficient technique)
    4. 9.4 Research Gap, Objectives, and Hypothesis
      1. 9.4.1 Research Gap
      2. 9.4.2 Research Objective and Hypothesis
    5. 9.5 Methodological Foundation
    6. 9.6 Results and Analysis
      1. 9.6.1 Principal Component Analysis
        1. 9.6.1.1 Correlation Analysis
        2. 9.6.1.2 Regression Analysis
    7. 9.7 Conclusion and Scope for Further Research
      1. Ethical Approval
    8. References
  19. Index

Product information

  • Title: Data Analytics, Computational Statistics, and Operations Research for Engineers
  • Author(s): Debabrata Samanta, SK Hafizul Islam, Naveen Chilamkurti, Mohammad Hammoudeh
  • Release date: March 2022
  • Publisher(s): CRC Press
  • ISBN: 9781000550467