Computer science Books

Book SynopsisThis book provides a scientific modeling approach for conducting metrics-based quantitative risk assessments of cybersecurity vulnerabilities and threats. This book provides a scientific modeling approach for conducting metrics-based quantitative risk assessments of cybersecurity threats. The author builds from a common understanding based on previous class-tested works to introduce the reader to the current and newly innovative approaches to address the maliciously-by-human-created (rather than by-chance-occurring) vulnerability and threat, and related cost-effective management to mitigate such risk. This book is purely statistical data-oriented (not deterministic) and employs computationally intensive techniques,such as Monte Carlo and Discrete Event Simulation. The enriched JAVA ready-to-go applications and solutions to exercises provided by the author at the book's specifically preserved website will enable readers to utilize the course related problems. EnTable of ContentsPrologue xiv Reviews xv Preface xxi Acknowledgments and Dedication xxix About the Author xxxi 1 Metrics, Statistical Quality Control, and Basic Reliability in Cyber-Risk 1 1.1 Deterministic and Stochastic Cyber-Risk Metrics 1 1.2 Statistical Risk Analysis 2 1.2.1 Introduction to Statistical Hypotheses 2 1.2.2 Decision Rules 3 1.2.3 One-Tailed Tests 4 1.2.4 Two-Tailed Tests 4 1.2.5 Decision Errors 6 1.2.6 Applications to One-Tailed Tests Associated with Both Type I and Type II Errors 7 1.2.7 Applications to Two-Tailed Tests (Normal Distribution Assumption) 11 1.3 Acceptance Sampling in Quality Control 16 1.3.1 Introduction 16 1.3.2 Definition of an Acceptance Sampling Plan 16 1.3.3 The OC Curve 16 1.4 Poisson and Normal Approximation to Binomial in Quality Control 19 1.4.1 Approximations to Binomial Distribution 19 1.4.2 Approximation of Binomial to Poisson Distribution 19 1.4.3 Approximation to Normal Distribution 20 1.4.4 Comparisons of Normal and Poisson Approximations to the Binomial 21 1.5 Basic Statistical Reliability Concepts and Mc Simulators 21 1.5.1 Fundamental Equations for Reliability, Hazard, and Statistical Notions 23 1.5.2 Fundamentals for Reliability Block Diagramming and Redundancy 27 1.5.3 Solving Basic Reliability Questions by Using Student-Friendly Pedagogical Examples 30 1.5.4 MC Simulators for Commonly Used Distributions in Reliability 47 1.6 Discussions and Conclusion 52 1.7 Exercises 52 References 60 2 Complex Network Reliability Evaluation and Estimation in Cyber-Risk 61 2.1 Introduction 61 2.2 Overlap Technique to Calculate Complex Network Reliability 62 2.2.1 Network State Enumeration and Example 1 63 2.2.2 Generating Minimal Paths and Example 2 64 2.2.3 Overlap Method Algorithmic Rules and Example 3 68 2.3 The Overlap Method: Monte Carlo and Discrete Event Simulation 70 2.4 Multistate System Reliability Evaluation 71 2.4.1 Simple Series System with Single Derated States 73 2.4.2 Active Parallel System 73 2.4.3 Simple Series–Parallel System 74 2.4.4 A Simple Series–Parallel System with Multistate Components 75 2.4.5 A Combined System: Power Plant Example 76 2.4.6 Large Network Examples Using Multistate Overlap Technique 77 2.5 Weibull Time Distributed Reliability Evaluation 78 2.5.1 Motivation behind Weibull Probability Modeling 78 2.5.2 Weibull Parameter Estimation Methodology 79 2.5.3 Overlap Algorithm Applied to Weibull Distributed Components 80 2.5.4 Estimating Weibull Parameters 80 2.5.5 Fifty-Two-Node Weibull Example for Estimating Weibull Parameters 85 2.5.6 A Weibull Network Example from an Oil Rig System 90 2.6 Discussions and Conclusion 90 Appendix 2.A Overlap Algorithm and Example 93 2.A.1 Algorithm 93 2.A.2 Example 95 2.7 Exercises 101 References 103 3 Stopping Rules for Reliability and Security Tests in Cyber-Risk 105 3.1 Introduction 105 3.2 Methods 107 3.2.1 Lgm by Verhulst 108 3.2.2 Compound Poisson Model 110 3.3 Examples Merging Both Stopping Rules: Lgm and Cpm 114 3.3.1 The DR5 Data Set Example 114 3.3.2 The Dr4 Data Set Example 118 3.3.3 The Supercomputing Cloud Historical Failure Data—Case Study 119 3.3.4 Appendix for Section 3.3 121 3.4 Stopping Rule for Testing in the Time Domain 131 3.4.1 Review of Compound Poisson Process and Stopping Rule 131 3.4.2 Empirical Bayes Analysis for the Poisson^Geometric Stopping Rule 132 3.4.3 Howden’s Model for Stopping Rule 135 3.4.4 Computational Example for Stopping-Rule Algorithm in Time Domain 136 3.5 Discussions and Conclusion 139 3.6 Exercises 143 References 144 4 Security Assessment and Management in Cyber-Risk 147 4.1 Introduction 147 4.1.1 What Other Scoring Methods Are Available? 148 4.2 Security Meter (Sm) Model Design 152 4.3 Verification of the Probabilistic Security Meter (Sm) Method by Monte Carlo Simulation and Math-Statistical Triple-Product Rule 154 4.3.1 The Triple-Product Rule of Uniforms 156 4.3.2 Data Analysis on the Total Residual Risk of the Security Meter Design 158 4.3.3 Triple-Product Rule Discussions 169 4.4 Modifying the SM Quantitative Model for Categorical, Hybrid, and Nondisjoint Data 170 4.5 Maintenance Priority Determination for 3 × 3 × 2 Sm 178 4.6 Privacy Meter (PM): How to Quantify Privacy Breach 183 4.6.1 Methodology 184 4.6.2 Privacy Risk-Meter Assessment and Management Examples 185 4.7 Polish Decoding (Decompression) Algorithm 187 4.8 Discussions and Conclusion 189 4.9 Exercises 190 References 199 5 Game-Theoretic Computing in Cyber-Risk 201 5.1 Historical Perspective to Game Theory’s Origins 201 5.2 Applications of Game Theory to Cyber-Security Risk 203 5.3 Intuitive Background: Concepts, Definitions, and Nomenclature 204 5.3.1 A Price War Example 205 5.4 Random Selection for Nash Mixed Strategy 208 5.4.1 Random Probabilistic Selection 208 5.4.2 Does Nash Equilibrium (NE) Exist for the Company A/B Problem in Table 5.1? 209 5.4.3 An Example: Matching Pennies 210 5.4.4 Another Game: The Prisoner’s Dilemma 210 5.4.5 Games with Multiple NE (Terrorist Game: Bold Strategy Result in Domination) 211 5.5 Adversarial Risk Analysis Models by Banks, Rios, and Rios 213 5.6 An Alternative Model: Sahinoglu’s Security Meter for Neumann and Nash Mixed Strategy 215 5.7 Other Interdisciplinary Applications of Risk Meters 220 5.8 Mixed Strategy for Risk Assessment and Management-University Server and Social Network Examples 221 5.8.1 University Server’s Security Risk-Meter Example 221 5.8.2 Social Networks’ Privacy and Security Risk-Meter (RM) Example 222 5.8.3 Clarification of Risk Assessment and Management Algorithm for Social Networks 224 5.9 Application to Hospital Healthcare Service Risk 226 5.10 Application to Environmetrics and Ecology Risk 229 5.11 Application to Digital Forensics Security Risk 234 5.12 Application to Business Contracting Risk 239 5.13 Application to National Cybersecurity Risk 245 5.14 Application to Airport Service Quality Risk 253 5.15 Application to Offshore Oil-Drilling Spill and Security Risk 257 5.16 Discussions and Conclusion 264 5.17 Exercises 266 References 271 6 Modeling and Simulation in Cyber-Risk 277 6.1 Introduction and a Brief History to Simulation 277 6.2 Generic Theory: Case Studies on Goodness of Fit for Uniform Numbers 278 6.3 Why Crucial to Manufacturing and Cyber Defense 279 6.4 A Cross Section of Modeling and Simulation in Manufacturing Industry 280 6.4.1 Modeling and Simulation of Multistate Production Units and Systems in Manufacturing 281 6.4.2 Two-State SL Probability Model of Units with Closed-Form Solution 283 6.4.3 Extended Three-State SL Probability Model of Up–Down –Derated Units with Mc Simulation 284 6.4.4 Statistical Simulation of Three-State Units to Estimate the Density of Up–Down –Der 289 6.4.5 How to Generate Random Numbers from Sl pdf to Simulate Component and System Behavior 296 6.4.6 Example of Sl Simulation for Modeling Network of 2-in-Simple-Series Two-State (Up–Dn) Units 297 6.4.7 Example of Sl Simulation for Modeling a Network of 7-in-Complex-Topology Two-State (Up–Dn) Units 300 6.5 A Review of Modeling and Simulation in Cyber-Security 301 6.5.1 MC Value-at-Risk Approach by Kim et al. in Cloud Computing 301 6.5.2 MC and DES in Security Meter (Sm) Risk Model 302 6.6 Application of Queuing Theory and Multichannel Simulation to Cyber-Security 306 6.6.1 Example 1: One Recovery-Crew Case for Cyber-Security Queuing Simulation 306 6.6.2 Example 2: Two Recovery-Crew Case for Cyber-Security Queuing Simulation 308 6.7 Discussions and Conclusion 308 Appendix 6.A 311 6.8 Exercises 315 References 335 7 Cloud Computing in Cyber-Risk 339 7.1 Introduction and Motivation 339 7.2 Cloud Computing Risk Assessment 342 7.3 Motivation and Methodology 343 7.3.1 History of Theoretical Developments on CLOUD Modeling 343 7.3.2 Notation 344 7.3.3 Objectives 344 7.3.4 Frequency and Duration Method for the Loss of Load or Service 345 7.3.5 Nbd as a Compound Poisson Model 346 7.3.6 Nbd for the Loss of Load or Loss of Cloud Service Expected 348 7.4 Various Applications to Cyber Systems 349 7.4.1 Small Sample Experimental Systems 349 7.4.2 Large Cyber Systems 353 7.5 Large Cyber Systems Using Statistical Methods 357 7.6 Repair Crew and Product Reserve Planning to Manage Risk Cost Effectively Using Cyberrisksolver Cloud Management Java Tool 359 7.6.1 Cloud Resource Management Planning for Employment of Repair Crews 360 7.6.2 Cloud Resource Management Planning by Production Deployment 365 7.7 Remarks for “Physical Cloud” Employing Physical Products (Servers, Generators, Communication Towers, Etc.) 368 7.8 Applications to “Social (Human Resources) Cloud” 372 7.8.1 Numerical Example for Social Cloud (200 Employees Performing) 376 7.8.2 Input Wizard Example for Social Cloud (200 Employees Performing) 379 7.9 Stochastic Cloud System Simulation 379 7.9.1 Introduction and Methodology 381 7.9.2 Numerical Applications for Ss to Verify Non-Ss 385 7.9.3 Details of Probability Distributions Used in Stochastic Simulation 387 7.9.4 Varying Product Repair and Failure Date with Empirical Bayesian Posterior Gamma Approach 393 7.9.5 Varying Link Repair and Failure Using Gamma Distribution 393 7.9.6 Ss Applied to a Power or Cyber Grid 394 7.9.7 Error Checking or Flagging 396 7.10 Cloud Risk Meter Analysis 397 7.10.1 Risk Assessment and Management Clarifications for Figures 7.72 and 7.73 402 7.11 Discussions and Conclusion 405 7.12 Exercises 407 References 416 8 Software Reliability Modeling and Metrics in Cyber-Risk 421 8.1 Introduction, Motivation, and Methodology 421 8.2 History and Classification of Software Reliability Models 422 8.2.1 Time-between-Failures Models 422 8.2.2 Failure-Counting Models 422 8.2.3 Bayesian Model 423 8.2.4 Static (Nondynamic) Models 423 8.2.5 Others 424 8.3 Software Reliability Models in Time Domain 424 8.4 Software Reliability Growth Models 425 8.4.1 Negative Exponential Class of Failure Times 425 8.4.2 J–M De-eutrophication Model (Binomial Type) 425 8.4.3 Moranda’s Geometric Model (Poisson Type) 426 8.4.4 Goel–Okumoto Nonhomogeneous Poisson Process (Poisson Type) 427 8.4.5 Musa’s Basic Execution Time Model (Poisson Type) 428 8.4.6 Musa–Okumoto Logarithmic Poisson Execution Time Model (Poisson Type) 429 8.4.7 L–V Bayesian Model 431 8.4.8 Sahinoglu’s Compound Poisson^Geometric and Poisson^Logarithmic Series Models 433 8.4.9 Gamma, Weibull, and Other Classes of Failure Times 435 8.4.10 Duane Model (Poisson Type) 439 8.5 Numerical Examples Using Pedagogues 440 8.5.1 Example 1 440 8.5.2 Example 2 441 8.6 Recent Trends in Software Reliability 441 8.7 Discussions and Conclusion 442 8.8 Exercises 444 References 445 9 Metrics for Software Reliability Failure-Count Models in Cyber-Risk 451 9.1 Introduction and Methodology on Failure-Count Estimation in Software Reliability 451 9.1.1 Statistical Estimation Models, Computational Formulas, and Examples 452 9.1.2 Interpretations of Numerical Examples and Discussions 464 9.2 Predictive Accuracy to Compare Failure-Count Models 466 9.2.1 Classical Distribution Approach 468 9.2.2 Prior Distribution Approach 469 9.2.3 Applications to Data Sets and Comparisons 472 9.3 Discussions and Conclusion 473 appendix 9.A 477 9.4 Exercises 478 References 482 10 Practical Hands-On Lab Topics in Cyber-Risk 483 10.1 System Hardening 483 10.1.1 General 483 10.1.2 Windows Servers 484 10.1.3 Wireless 484 10.1.4 Firewalls, Routers, and Switches 485 10.2 Email Security 486 10.2.1 Identifying Fake Emails 486 10.2.2 Emotion Responses 486 10.3 MS-DOS Commands 487 10.3.1 Mapping Intel 488 10.4 Logging 492 10.4.1 Policy 493 10.4.2 Understanding Logs 494 10.5 Firewall 495 10.5.1 Traditional Firewalls 495 10.5.2 Ngfs 496 10.5.3 Host-Based Firewalls 496 10.6 Wireless Networks 496 10.7 Discussions and Conclusion 499 Appendix 10.A 500 10.8 Exercises 501 10.8.1 System Hardening 501 10.8.2 Email 501 10.8.3 Ms-Dos 502 10.8.4 Logging 503 10.8.5 Firewall 503 10.8.6 Wireless 505 10.8.7 Comprehensive Exercises 505 10.8.8 Cryptology Projects 507 References 509 What the Cyber-Risk Informatics Textbook and the Author are About? 511 Index 513

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Book Synopsis30 tutorials and more than 100 exercises in chemoinformatics, supported by online software and data sets Chemoinformatics is widely used in both academic and industrial chemical and biochemical research worldwide. Yet, until this unique guide, there were no books offering practical exercises in chemoinformatics methods.Table of ContentsList of Contributors xv Preface xvii About the Companion Website xix Part 1 Chemical Databases 1 1 Data Curation 3 Gilles Marcou and Alexandre Varnek Theoretical Background 3 Software 5 Step‐by‐Step Instructions 7 Conclusion 34 References 36 2 Relational Chemical Databases: Creation, Management, and Usage 37 Gilles Marcou and Alexandre Varnek Theoretical Background 37 Step‐by‐Step Instructions 41 Conclusion 65 References 65 3 Handling of Markush Structures 67 Timur Madzhidov, Ramil Nugmanov, and Alexandre Varnek Theoretical Background 67 Step‐by‐Step Instructions 68 Conclusion 73 References 73 4 Processing of SMILES, InChI, and Hashed Fingerprints 75 João Montargil Aires de Sousa Theoretical Background 75 Algorithms 76 Step‐by‐Step Instructions 78 Conclusion 80 References 81 Part 2 Library Design 83 5 Design of Diverse and Focused Compound Libraries 85 Antonio de la Vega de Leon, Eugen Lounkine, Martin Vogt, and Jürgen Bajorath Introduction 85 Data Acquisition 86 Implementation 86 Compound Library Creation 87 Compound Library Analysis 90 Normalization of Descriptor Values 91 Visualizing Descriptor Distributions 92 Decorrelation and Dimension Reduction 94 Partitioning and Diverse Subset Calculation 95 Partitioning 95 Diverse Subset Selection 97 Combinatorial Libraries 98 Combinatorial Enumeration of Compounds 98 Retrosynthetic Approaches to Library Design 99 References 101 Part 3 Data Analysis and Visualization 103 6 Hierarchical Clustering in R 105 Martin Vogt and Jürgen Bajorath Theoretical Background 105 Algorithms 106 Instructions 107 Hierarchical Clustering Using Fingerprints 108 Hierarchical Clustering Using Descriptors 111 Visualization of the Data Sets 113 Alternative Clustering Methods 116 Conclusion 117 References 118 7 Data Visualization and Analysis Using Kohonen Self‐Organizing Maps 119 João Montargil Aires de Sousa Theoretical Background 119 Algorithms 120 Instructions 121 Conclusion 126 References 126 Part 4 Obtaining and Validation QSAR/QSPR Models 127 8 Descriptors Generation Using the CDK Toolkit and Web Services 129 João Montargil Aires de Sousa Theoretical Background 129 Algorithms 130 Step‐by‐Step Instructions 131 Conclusion 133 References 134 9 QSPR Models on Fragment Descriptors 135 Vitaly Solov’ev and Alexandre Varnek Abbreviations 135 Data 136 ISIDA_QSPR Input 137 Data Split Into Training and Test Sets 139 Substructure Molecular Fragment (SMF) Descriptors 139 Regression Equations 142 Forward and Backward Stepwise Variable Selection 142 Parameters of Internal Model Validation 143 Applicability Domain (AD) of the Model 143 Storage and Retrieval Modeling Results 144 Analysis of Modeling Results 144 Root‐Mean Squared Error (RMSE) Estimation 148 Setting the Parameters 151 Analysis of n‐Fold Cross‐Validation Results 151 Loading Structure‐Data File 153 Descriptors and Fitting Equation 154 Variables Selection 155 Consensus Model 155 Model Applicability Domain 155 n‐Fold External Cross‐Validation 155 Saving and Loading of the Consensus Modeling Results 155 Statistical Parameters of the Consensus Model 156 Consensus Model Performance as a Function of Individual Models Acceptance Threshold 157 Building Consensus Model on the Entire Data Set 158 Loading Input Data 159 Loading Selected Models and Choosing their Applicability Domain 160 Reporting Predicted Values 160 Analysis of the Fragments Contributions 161 References 161 10 Cross‐Validation and the Variable Selection Bias 163 Igor I. Baskin, Gilles Marcou, Dragos Horvath, and Alexandre Varnek Theoretical Background 163 Step‐by‐Step Instructions 165 Conclusion 172 References 173 11 Classification Models 175 Igor I. Baskin, Gilles Marcou, Dragos Horvath, and Alexandre Varnek Theoretical Background 176 Algorithms 178 Step‐by‐Step Instructions 180 Conclusion 191 References 192 12 Regression Models 193 Igor I. Baskin, Gilles Marcou, Dragos Horvath, and Alexandre Varnek Theoretical Background 194 Step‐by‐Step Instructions 197 Conclusion 207 References 208 13 Benchmarking Machine‐Learning Methods 209 Igor I. Baskin, Gilles Marcou, Dragos Horvath, and Alexandre Varnek Theoretical Background 209 Step‐by‐Step Instructions 210 Conclusion 222 References 222 14 Compound Classification Using the scikit‐learn Library 223 Jenny Balfer, Jürgen Bajorath, and Martin Vogt Theoretical Background 224 Algorithms 225 Step‐by‐Step Instructions 230 Naïve Bayes 230 Decision Tree 231 Support Vector Machine 234 Notes on Provided Code 237 Conclusion 238 References 239 Part 5 Ensemble Modeling 241 15 Bagging and Boosting of Classification Models 243 Igor I. Baskin, Gilles Marcou, Dragos Horvath, and Alexandre Varnek Theoretical Background 243 Algorithm 244 Step by Step Instructions 245 Conclusion 247 References 247 16 Bagging and Boosting of Regression Models 249 Igor I. Baskin, Gilles Marcou, Dragos Horvath, and Alexandre Varnek Theoretical Background 249 Algorithm 249 Step‐by‐Step Instructions 250 Conclusion 255 References 255 17 Instability of Interpretable Rules 257 Igor I. Baskin, Gilles Marcou, Dragos Horvath, and Alexandre Varnek Theoretical Background 257 Algorithm 258 Step‐by‐Step Instructions 258 Conclusion 261 References 261 18 Random Subspaces and Random Forest 263 Igor I. Baskin, Gilles Marcou, Dragos Horvath, and Alexandre Varnek Theoretical Background 264 Algorithm 264 Step‐by‐Step Instructions 265 Conclusion 269 References 269 19 Stacking 271 Igor I. Baskin, Gilles Marcou, Dragos Horvath, and Alexandre Varnek Theoretical Background 271 Algorithm 272 Step‐by‐Step Instructions 273 Conclusion 277 References 278 Part 6 3D Pharmacophore Modeling 279 20 3D Pharmacophore Modeling Techniques in Computer‐Aided Molecular Design Using LigandScout 281 Thomas Seidel, Sharon D. Bryant, Gökhan Ibis, Giulio Poli, and Thierry Langer Introduction 281 Theory: 3D Pharmacophores 283 Representation of Pharmacophore Models 283 Hydrogen‐Bonding Interactions 285 Hydrophobic Interactions 285 Aromatic and Cation‐π Interactions 286 Ionic Interactions 286 Metal Complexation 286 Ligand Shape Constraints 287 Pharmacophore Modeling 288 Manual Pharmacophore Construction 288 Structure‐Based Pharmacophore Models 289 Ligand‐Based Pharmacophore Models 289 3D Pharmacophore‐Based Virtual Screening 291 3D Pharmacophore Creation 291 Annotated Database Creation 291 Virtual Screening‐Database Searching 292 Hit‐List Analysis 292 Tutorial: Creating 3D‐Pharmacophore Models Using LigandScout 294 Creating Structure‐Based Pharmacophores From a Ligand‐Protein Complex 294 Description: Create a Structure‐Based Pharmacophore Model 296 Create a Shared Feature Pharmacophore Model From Multiple Ligand‐Protein Complexes 296 Description: Create a Shared Feature Pharmacophore and Align it to Ligands 297 Create Ligand‐Based Pharmacophore Models 298 Description: Ligand‐Based Pharmacophore Model Creation 300 Tutorial: Pharmacophore‐Based Virtual Screening Using LigandScout 301 Virtual Screening, Model Editing, and Viewing Hits in the Target Active Site 301 Description: Virtual Screening and Pharmacophore Model Editing 302 Analyzing Screening Results with Respect to the Binding Site 303 Description: Analyzing Hits in the Active Site Using LigandScout 305 Parallel Virtual Screening of Multiple Databases Using LigandScout 305 Virtual Screening in the Screening Perspective of LigandScout 306 Description: Virtual Screening Using LigandScout 306 Conclusions 307 Acknowledgments 307 References 307 Part 7 The Protein 3D‐Structures in Virtual Screening 311 21 The Protein 3D‐Structures in Virtual Screening 313 Inna Slynko and Esther Kellenberger Introduction 313 Description of the Example Case 314 Thrombin and Blood Coagulation 314 Active Thrombin and Inactive Prothrombin 314 Thrombin as a Drug Target 314 Thrombin Three‐Dimensional Structure: The 1OYT PDB File 315 Modeling Suite 315 Overall Description of the Input Data Available on the Editor Website 315 Exercise 1: Protein Analysis and Preparation 316 Step 1: Identification of Molecules Described in the 1OYT PDB File 316 Step 2: Protein Quality Analysis of the Thrombin/Inhibitor PDB Complex Using MOE Geometry Utility 320 Step 3: Preparation of the Protein for Drug Design Applications 321 Step 4: Description of the Protein‐Ligand Binding Mode 325 Step 5: Detection of Protein Cavities 328 Exercise 2: Retrospective Virtual Screening Using the Pharmacophore Approach 330 Step 1: Description of the Test Library 332 Step 2.1: Pharmacophore Design, Overview 333 Step 2.2: Pharmacophore Design, Flexible Alignment of Three Thrombin Inhibitors 334 Step 2.3: Pharmacophore Design, Query Generation 335 Step 3: Pharmacophore Search 337 Exercise 3: Retrospective Virtual Screening Using the Docking Approach 341 Step 1: Description of the Test Library 341 Step 2: Preparation of the Input 341 Step 3: Re‐Docking of the Crystallographic Ligand 341 Step 4: Virtual Screening of a Database 345 General Conclusion 350 References 351 Part 8 Protein‐Ligand Docking 353 22 Protein‐Ligand Docking 355 Inna Slynko, Didier Rognan, and Esther Kellenberger Introduction 355 Description of the Example Case 356 Methods 356 Ligand Preparation 359 Protein Preparation 359 Docking Parameters 360 Description of Input Data Available on the Editor Website 360 Exercises 362 A Quick Start with LeadIT 362 Re‐Docking of Tacrine into AChE 362 Preparation of AChE From 1ACJ PDB File 362 Docking of Neutral Tacrine, then of Positively Charged Tacrine 363 Docking of Positively Charged Tacrine in AChE in Presence of Water 365 Cross‐Docking of Tacrine‐Pyridone and Donepezil Into AChE 366 Preparation of AChE From 1ACJ PDB File 366 Cross‐Docking of Tacrine‐Pyridone Inhibitor and Donepezil in AChE in Presence of Water 367 Re‐Docking of Donepezil in AChE in Presence of Water 370 General Conclusions 372 Annex: Screen Captures of LeadIT Graphical Interface 372 References 375 Part 9 Pharmacophorical Profiling Using Shape Analysis 377 23 Pharmacophorical Profiling Using Shape Analysis 379 Jérémy Desaphy, Guillaume Bret, Inna Slynko, Didier Rognan, and Esther Kellenberger Introduction 379 Description of the Example Case 380 Aim and Context 380 Description of the Searched Data Set 381 Description of the Query 381 Methods 381 Rocs 381 VolSite and Shaper 384 Other Programs for Shape Comparison 384 Description of Input Data Available on the Editor Website 385 Exercises 387 Preamble: Practical Considerations 387 Ligand Shape Analysis 387 What are ROCS Output Files? 387 Binding Site Comparison 388 Conclusions 390 References 391 Part 10 Algorithmic Chemoinformatics 393 24 Algorithmic Chemoinformatics 395 Martin Vogt, Antonio de la Vega de Leon, and Jürgen Bajorath Introduction 395 Similarity Searching Using Data Fusion Techniques 396 Introduction to Virtual Screening 396 The Three Pillars of Virtual Screening 397 Molecular Representation 397 Similarity Function 397 Search Strategy (Data Fusion) 397 Fingerprints 397 Count Fingerprints 397 Fingerprint Representations 399 Bit Strings 399 Feature Lists 399 Generation of Fingerprints 399 Similarity Metrics 402 Search Strategy 404 Completed Virtual Screening Program 405 Benchmarking VS Performance 406 Scoring the Scorers 407 How to Score 407 Multiple Runs and Reproducibility 408 Adjusting the VS Program for Benchmarking 408 Analyzing Benchmark Results 410 Conclusion 414 Introduction to Chemoinformatics Toolkits 415 Theoretical Background 415 A Note on Graph Theory 416 Basic Usage: Creating and Manipulating Molecules in RDKit 417 Creation of Molecule Objects 417 Molecule Methods 418 Atom Methods 418 Bond Methods 419 An Example: Hill Notation for Molecules 419 Canonical SMILES: The Canon Algorithm 420 Theoretical Background 420 Recap of SMILES Notation 420 Canonical SMILES 421 Building a SMILES String 422 Canonicalization of SMILES 425 The Initial Invariant 427 The Iteration Step 428 Summary 431 Substructure Searching: The Ullmann Algorithm 432 Theoretical Background 432 Backtracking 433 A Note on Atom Order 436 The Ullmann Algorithm 436 Sample Runs 440 Summary 441 Atom Environment Fingerprints 441 Theoretical Background 441 Implementation 443 The Hashing Function 443 The Initial Atom Invariant 444 The Algorithm 444 Summary 447 References 447 Index 449

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Book SynopsisWritten by a team of experts at the forefront of the cyber-physical systems (CPS) revolution, this book provides an in-depth look at security and privacy, two of the most critical challenges facing both the CPS research and development community and ICT professionals. It explores, in depth, the key technical, social, and legal issues at stake, and it provides readers with the information they need to advance research and development in this exciting area. Cyber-physical systems (CPS) are engineered systems that are built from, and depend upon the seamless integration of computational algorithms and physical components. Advances in CPS will enable capability, adaptability, scalability, resiliency, safety, security, and usability far in excess of what today's simple embedded systems can provide. Just as the Internet revolutionized the way we interact with information, CPS technology has already begun to transform the way people interact with engineered systems. In the years aheTable of ContentsList of Contributors xvii Foreword xxiii Preface xxv Acknowledgments xxix 1 Overview of Security and Privacy in Cyber-Physical Systems 1Glenn A. Fink, ThomasW. Edgar, Theora R. Rice, Douglas G. MacDonald and Cary E. Crawford 1.1 Introduction 1 1.2 Defining Security and Privacy 1 1.2.1 Cybersecurity and Privacy 2 1.2.2 Physical Security and Privacy 3 1.3 Defining Cyber-Physical Systems 4 1.3.1 Infrastructural CPSs 5 1.3.1.1 Example: Electric Power 5 1.3.2 Personal CPSs 5 1.3.2.1 Example: Smart Appliances 6 1.3.3 Security and Privacy in CPSs 6 1.4 Examples of Security and Privacy in Action 7 1.4.1 Security in Cyber-Physical Systems 7 1.4.1.1 Protecting Critical Infrastructure from Blended Threat 8 1.4.1.2 Cyber-Physical Terrorism 8 1.4.1.3 Smart Car Hacking 9 1.4.1.4 Port Attack 10 1.4.2 Privacy in Cyber-Physical Systems 11 1.4.2.1 Wearables 11 1.4.2.2 Appliances 12 1.4.2.3 Motivating Sharing 12 1.4.3 Blending Information and Physical Security and Privacy 12 1.5 Approaches to Secure Cyber-Physical Systems 14 1.5.1 Least Privilege 14 1.5.2 Need-to-Know 15 1.5.3 Segmentation 15 1.5.4 Defensive Dimensionality 16 1.5.4.1 Defense-in-Depth 16 1.5.4.2 Defense-in-Breadth 16 1.5.5 User-Configurable Data Collection/Logging 17 1.5.6 Pattern Obfuscation 17 1.5.7 End-to-End Security 17 1.5.8 Tamper Detection/Security 18 1.6 Ongoing Security and Privacy Challenges for CPSs 18 1.6.1 Complexity of Privacy Regulations 18 1.6.2 Managing and Incorporating Legacy Systems 19 1.6.3 Distributed Identity and Authentication Management 20 1.6.4 Modeling Distributed CPSs 20 1.7 Conclusion 21 References 21 2 Network Security and Privacy for Cyber-Physical Systems 25Martin Henze, Jens Hiller, René Hummen, Roman Matzutt, KlausWehrle andJan H. Ziegeldorf 2.1 Introduction 25 2.2 Security and Privacy Issues in CPSs 26 2.2.1 CPS Reference Model 27 2.2.1.1 Device Level 27 2.2.1.2 Control/Enterprise Level 27 2.2.1.3 Cloud Level 28 2.2.2 CPS Evolution 28 2.2.3 Security and PrivacyThreats in CPSs 30 2.3 Local Network Security for CPSs 31 2.3.1 Secure Device Bootstrapping 32 2.3.1.1 Initial Key Exchange 33 2.3.1.2 Device Life Cycle 33 2.3.2 Secure Local Communication 34 2.3.2.1 Physical Layer 34 2.3.2.2 Medium Access 34 2.3.2.3 Network Layer 35 2.3.2.4 Secure Local Forwarding for Internet-Connected CPSs 35 2.4 Internet-Wide Secure Communication 36 2.4.1 Security Challenges for Internet-Connected CPS 37 2.4.2 Tailoring End-to-End Security to CPS 38 2.4.3 Handling Resource Heterogeneity 39 2.4.3.1 Reasonable Retransmission Mechanisms 39 2.4.3.2 Denial-of-Service Protection 40 2.5 Security and Privacy for Cloud-Interconnected CPSs 41 2.5.1 Securely Storing CPS Data in the Cloud 42 2.5.1.1 Protection of CPS Data 43 2.5.1.2 Access Control 43 2.5.2 Securely Processing CPS Data in the Cloud 44 2.5.3 Privacy for Cloud-Based CPSs 45 2.6 Summary 46 2.7 Conclusion and Outlook 47 Acknowledgments 48 References 48 3 Tutorial on Information Theoretic Metrics Quantifying Privacy in Cyber-Physical Systems 57Guido Dartmann, Mehmet Ö. Demir, Hendrik Laux, Volker Lücken, Naim Bajcinca, Gunes K. Kurt, Gerd Ascheid andMartina Ziefle 3.1 Social Perspective and Motivation 57 3.1.1 Motivation 59 3.1.2 Scenario 60 3.2 Information Theoretic Privacy Measures 62 3.2.1 Information Theoretic Foundations 62 3.2.2 Surprise and Specific Information 63 3.3 Privacy Models and Protection 64 3.3.1 k-Anonymity 65 3.4 Smart City Scenario: System Perspective 67 3.4.1 Attack without Anonymization 68 3.4.2 Attack with Anonymization of the ZIP 70 3.4.3 Attack with Anonymization of the Bluetooth ID 71 3.5 Conclusion and Outlook 71 Appendix A Derivation of the Mutual Information Based on the KLD 72 Appendix B Derivation of the Mutual Information In Terms of Entropy 73 Appendix C Derivation of the Mutual Information Conditioned onx 73 Appendix D Proof of Corollary 3.1 74 References 74 4 Cyber-Physical Systems and National Security Concerns 77Jeff Kosseff 4.1 Introduction 77 4.2 National Security Concerns Arising from Cyber-Physical Systems 79 4.2.1 Stuxnet 80 4.2.2 German Steel Mill 81 4.2.3 Future Attacks 82 4.3 National Security Implications of Attacks on Cyber-Physical Systems 82 4.3.1 Was the Cyber-Attack a “Use of Force” That Violates International Law? 83 4.3.2 If the AttackWas a Use of Force,Was That Force Attributable to a State? 86 4.3.3 Did the Use of Force Constitute an “Armed Attack” That Entitles the Target to Self-Defense? 87 4.3.4 If theUse of ForceWas an ArmedAttack, What Types of Self-Defense Are Justified? 88 4.4 Conclusion 89 References 90 5 Legal Considerations of Cyber-Physical Systems and the Internet of Things 93Alan C. Rither and Christopher M. Hoxie 5.1 Introduction 93 5.2 Privacy and Technology in Recent History 94 5.3 The Current State of Privacy Law 96 5.3.1 Privacy 98 5.3.2 Legal Background 98 5.3.3 Safety 99 5.3.4 Regulatory 100 5.3.4.1 Executive Branch Agencies 101 5.3.4.2 The Federal Trade Commission 101 5.3.4.3 The Federal Communications Commission 105 5.3.4.4 National Highway and Traffic Safety Administration 106 5.3.4.5 Food and Drug Administration 108 5.3.4.6 Federal Aviation Administration 109 5.4 Meeting Future Challenges 111 References 113 6 Key Management in CPSs 117YongWang and Jason Nikolai 6.1 Introduction 117 6.2 Key Management Security Goals and Threat Model 117 6.2.1 CPS Architecture 118 6.2.2 Threats and Attacks 119 6.2.3 Security Goals 120 6.3 CPS Key Management Design Principles 121 6.3.1 Heterogeneity 122 6.3.2 Real-Time Availability 122 6.3.3 Resilience to Attacks 123 6.3.4 Interoperability 123 6.3.5 Survivability 123 6.4 CPS Key Management 124 6.4.1 Dynamic versus Static 124 6.4.2 Public Key versus Symmetric Key 125 6.4.2.1 Public Key Cryptography 125 6.4.2.2 Symmetric Key Cryptography 127 6.4.3 Centralized versus Distributed 128 6.4.4 Deterministic versus Probabilistic 129 6.4.5 Standard versus Proprietary 130 6.4.6 Key Distribution versus Key Revocation 131 6.4.7 Key Management for SCADA Systems 131 6.5 CPS Key Management Challenges and Open Research Issues 132 6.6 Summary 133 References 133 7 Secure Registration and Remote Attestation of IoT Devices Joining the Cloud: The Stack4Things Case of Study 137Antonio Celesti,Maria Fazio, Francesco Longo, Giovanni Merlino and Antonio Puliafito 7.1 Introduction 137 7.2 Background 138 7.2.1 Cloud Integration with IoT 139 7.2.2 Security and Privacy in Cloud and IoT 139 7.2.3 Technologies 140 7.2.3.1 Hardware 140 7.2.3.2 Web Connectivity 141 7.2.3.3 Cloud 141 7.3 Reference Scenario and Motivation 142 7.4 Stack4Things Architecture 143 7.4.1 Board Side 144 7.4.2 Cloud-Side – Control and Actuation 145 7.4.3 Cloud-Side – Sensing Data Collection 146 7.5 Capabilities for Making IoT Devices Secure Over the Cloud 147 7.5.1 Trusted Computing 147 7.5.2 Security Keys, Cryptographic Algorithms, and Hidden IDs 148 7.5.3 Arduino YUN Security Extensions 149 7.6 Adding Security Capabilities to Stack4Things 149 7.6.1 Board-Side Security Extension 149 7.6.2 Cloud-Side Security Extension 150 7.6.3 Security Services in Stack4Things 150 7.6.3.1 Secure Registration of IoT Devices Joining the Cloud 151 7.6.3.2 Remote Attestation of IoT Devices 152 7.7 Conclusion 152 References 153 8 Context Awareness for Adaptive Access Control Management in IoT Environments 157Paolo Bellavista and Rebecca Montanari 8.1 Introduction 157 8.2 Security Challenges in IoT Environments 158 8.2.1 Heterogeneity and Resource Constraints 158 8.2.2 IoT Size and Dynamicity 160 8.3 Surveying Access Control Models and Solutions for IoT 160 8.3.1 Novel Access Control Requirements 160 8.3.2 Access Control Models for the IoT 162 8.3.3 State-of-the-Art Access Control Solutions 164 8.4 Access Control Adaptation:Motivations and Design Guidelines 165 8.4.1 Semantic Context-Aware Policies for Access Control Adaptation 166 8.4.2 Adaptation Enforcement Issues 167 8.5 Our Adaptive Context-Aware Access Control Solution for Smart 8.5.1 The Proteus Model 168 8.5.2 Adapting the General Proteus Model for the IoT 170 8.5.2.1 The Proteus Architecture for the IoT 172 8.5.2.2 Implementation and Deployment Issues 173 8.6 Open Technical Challenges and Concluding Remarks 174 References 176 9 Data Privacy Issues in Distributed Security Monitoring Systems 179Jeffery A. Mauth and DavidW. Archer 9.1 Information Security in Distributed Data Collection Systems 179 9.2 Technical Approaches for Assuring Information Security 181 9.2.1 Trading Security for Cost 182 9.2.2 Confidentiality: Keeping Data Private 182 9.2.3 Integrity: Preventing Data Tampering and Repudiation 186 9.2.4 Minimality: Reducing Data Attack Surfaces 188 9.2.5 Anonymity: Separating Owner from Data 188 9.2.6 Authentication: Verifying User Privileges for Access to Data 189 9.3 Approaches for Building Trust in Data Collection Systems 190 9.3.1 Transparency 190 9.3.2 Data Ownership and Usage Policies 191 9.3.3 Data Security Controls 191 9.3.4 Data Retention and Destruction Policies 192 9.3.5 Managing Data-loss Liability 192 9.3.6 Privacy Policies and Consent 192 9.4 Conclusion 193 References 193 10 Privacy Protection for Cloud-Based Robotic Networks 195Hajoon Ko, Sye L. Keoh and Jiong Jin 10.1 Introduction 195 10.2 Cloud Robot Network: Use Case, Challenges, and Security Requirements 197 10.2.1 Use Case 197 10.2.2 SecurityThreats and Challenges 199 10.2.3 Security Requirements 200 10.3 Establishment of Cloud Robot Networks 200 10.3.1 Cloud Robot Network as a Community 200 10.3.2 A Policy-Based Establishment of Cloud Robot Networks 201 10.3.3 Doctrine: A Community Specification 201 10.3.3.1 Attribute Types and User-Attribute Assignment (UAA) Policies 203 10.3.3.2 Authorization and Obligation Policies 203 10.3.3.3 Constraints Specification 205 10.3.3.4 Trusted Key Specification 206 10.3.3.5 Preferences Specification 206 10.3.3.6 Authentication in Cloud Robot Community 207 10.3.3.7 Service Access Control 207 10.4 Communication Security 207 10.4.1 Attribute-Based Encryption (ABE) 207 10.4.2 Preliminaries 208 10.4.3 Ciphertext-Policy Attribute-Based Encryption (CP-ABE) Scheme 208 10.4.4 Revocation Based on Shamir’s Secret Sharing 209 10.4.5 Cloud Robot Community’s CP-ABE Key Revocation 209 10.4.6 Integration of CP-ABE and Robot Community Architecture 210 10.5 Security Management of Cloud Robot Networks 212 10.5.1 Bootstrapping (Establishing) a Cloud Robot Community 212 10.5.2 Joining the Community 214 10.5.3 Leaving a Community 215 10.5.4 Service Access Control 216 10.6 RelatedWork 217 10.7 Conclusion 219 References 220 11 Toward Network Coding for Cyber-Physical Systems: Security Challenges and Applications 223Pouya Ostovari and JieWu 11.1 Introduction 223 11.2 Background on Network Coding and Its Applications 225 11.2.1 Background and Preliminaries 225 11.2.2 Network Coding Applications 226 11.2.2.1 Throughput/Capacity Enhancement 226 11.2.2.2 Robustness Enhancement 227 11.2.2.3 Protocol Simplification 228 11.2.2.4 Network Tomography 228 11.2.2.5 Security 229 11.2.3 Network Coding Classification 229 11.2.3.1 Stateless Network Coding Protocols 229 11.2.3.2 State-Aware Network Coding Protocols 229 11.3 Security Challenges 230 11.3.1 Byzantine Attack 230 11.3.2 Pollution Attack 230 11.3.3 Traffic Analysis 230 11.3.4 Eavesdropping Attack 231 11.3.5 Classification of the Attacks 232 11.3.5.1 Passive versus Active 232 11.3.5.2 External versus Internal 232 11.3.5.3 Effect of Network Coding 232 11.4 Secure Network Coding 233 11.4.1 Defense against Byzantine and Pollution Attack 233 11.4.2 Defense against Traffic Analysis 234 11.5 Applications of Network Coding in Providing Security 234 11.5.1 Eavesdropping Attack 234 11.5.1.1 Secure Data Transmission 234 11.5.1.2 Secure Data Storage 236 11.5.2 Secret Key Exchange 237 11.6 Conclusion 238 Acknowledgment 239 References 239 12 Lightweight Crypto and Security 243Lo’ai A. Tawalbeh and Hala Tawalbeh 12.1 Introduction 243 12.1.1 Cyber-Physical Systems CPSs 243 12.1.2 Security and Privacy 243 12.1.3 Lightweight Cryptography (LWC) 243 12.1.4 Chapter Organization 244 12.2 Cyber-Physical Systems 244 12.3 Security and Privacy in Cyber-Physical Systems 245 12.4 Lightweight Cryptography Implementations for Security and Privacy in CPSs 247 12.4.1 Introduction 247 12.4.2 Why Is Lightweight Cryptography Important? 249 12.4.3 Lightweight Symmetric and Asymmetric Ciphers Implementations 250 12.4.3.1 Hardware Implementations of Symmetric Ciphers 251 12.4.3.2 Software Implementations of Symmetric Ciphers 253 12.4.3.3 Hardware Implementations of Asymmetric Ciphers 254 12.4.3.4 Software Implementations of Asymmetric Ciphers 255 12.4.3.5 Secure Hash Algorithms (SHA) 256 12.5 Opportunities and Challenges 257 12.6 Conclusion 258 Acknowledgments 259 References 259 13 Cyber-Physical Vulnerabilities ofWireless Sensor Networks in Smart Cities 263Md. Mahmud Hasan and Hussein T. Mouftah 13.1 Introduction 263 13.1.1 The Smart City Concept and Components 263 13.2 WSN Applications in Smart Cities 265 13.2.1 Smart Home 265 13.2.2 Smart Grid Applications 267 13.2.2.1 Substation Monitoring 267 13.2.3 Intelligent Transport System Applications 268 13.2.3.1 Roadside Unit 268 13.2.3.2 Vehicular Sensor Network 269 13.2.3.3 Intelligent Sensor Network 269 13.2.4 Real-Time Monitoring and Safety Alert 270 13.3 Cyber-Physical Vulnerabilities 270 13.3.1 Possible Attacks 271 13.3.2 Impacts on Smart City Lives 272 13.3.2.1 Service Interruption 272 13.3.2.2 Damage to Property 273 13.3.2.3 Damage to Life 273 13.3.2.4 Privacy Infiltration 274 13.4 Solution Approaches 274 13.4.1 Cryptography 274 13.4.2 Intrusion Detection System 276 13.4.3 Watchdog System 277 13.4.4 GameTheoretic Deployment 277 13.4.5 Managed Security 277 13.4.6 Physical Security Measures 278 13.5 Conclusion 278 Acknowledgment 278 References 279 14 Detecting Data Integrity Attacks in Smart Grid 281Linqiang Ge,Wei Yu, Paul Moulema, Guobin Xu, David Griffith and Nada Golmie 14.1 Introduction 281 14.2 Literature Review 283 14.3 Network andThreat Models 285 14.3.1 Network Model 285 14.3.2 Threat Model 286 14.4 Our Approach 287 14.4.1 Overview 287 14.4.2 Detection Schemes 289 14.4.2.1 Statistical Anomaly-Based Detection 289 14.4.2.2 Machine Learning-Based Detection 290 14.4.2.3 Sequential Hypothesis Testing-Based Detection 291 14.5 Performance Evaluation 292 14.5.1 Evaluation Setup 292 14.5.2 Evaluation Results 294 14.6 Extension 297 14.7 Conclusion 298 References 298 15 Data Security and Privacy in Cyber-Physical Systems for Healthcare 305Aida Cauševic, Hossein Fotouhi and Kristina Lundqvist 15.1 Introduction 305 15.2 Medical Cyber-Physical Systems 306 15.2.1 Communication withinWBANs 307 15.2.1.1 Network Topology 307 15.2.1.2 Interference inWBANs 308 15.2.1.3 Challenges with LPWNs inWBANs 308 15.2.1.4 Feedback Control inWBANs 308 15.2.1.5 Radio Technologies 309 15.2.2 ExistingWBAN-Based Health Monitoring Systems 310 15.3 Data Security and Privacy Issues and Challenges inWBANs 312 15.3.1 Data Security and PrivacyThreats and Attacks 314 15.4 Existing Security and Privacy Solutions inWBAN 314 15.4.1 Academic Contributions 315 15.4.1.1 Biometric Solutions 315 15.4.1.2 Cryptographic Solutions 316 15.4.1.3 Solutions on ImplantableMedical Devices 318 15.4.2 Existing Commercial Solutions 319 15.5 Conclusion 320 References 320 16 Cyber Security of Smart Buildings 327SteffenWendzel, Jernej Tonejc, Jaspreet Kaur and Alexandra Kobekova 16.1 What Is a Smart Building? 327 16.1.1 Definition of the Term 327 16.1.2 The Design and the Relevant Components of a Smart Building 328 16.1.3 Historical Development of Building Automation Systems 330 16.1.4 The Role of Smart Buildings in Smart Cities 330 16.1.5 Known Cases of Attacks on Smart Buildings 331 16.2 Communication Protocols for Smart Buildings 332 16.2.1 KNX/EIB 333 16.2.2 BACnet 335 16.2.3 ZigBee 336 16.2.4 EnOcean 338 16.2.5 Other Protocols 339 16.2.6 Interoperability and Interconnectivity 339 16.3 Attacks 340 16.3.1 How Can Buildings Be Attacked? 340 16.3.2 Implications for the Privacy of Inhabitants and Users 340 16.3.3 Reasons for Insecure Buildings 341 16.4 Solutions to Protect Smart Buildings 342 16.4.1 Raising Security Awareness and Developing Security Know-How 342 16.4.2 Physical Access Control 343 16.4.3 Hardening Automation Systems 343 16.4.3.1 Secure Coding 343 16.4.3.2 Operating System Hardening 343 16.4.3.3 Patching 344 16.4.4 Network-Level Protection 344 16.4.4.1 Firewalls 345 16.4.4.2 Monitoring and Intrusion Detection Systems 345 16.4.4.3 Separation of Networks 345 16.4.5 Responsibility Matrix 345 16.5 Recent Trends in Smart Building Security Research 346 16.5.1 Visualization 346 16.5.2 Network Security 346 16.5.2.1 Traffic Normalization 346 16.5.2.2 Anomaly Detection 346 16.5.2.3 Novel Fuzzing Approaches 347 16.6 Conclusion and Outlook 347 References 348 17 The Internet of Postal Things: Making the Postal Infrastructure Smarter 353Paola Piscioneri, Jessica Raines and Jean Philippe Ducasse 17.1 Introduction 353 17.2 Scoping the Internet of PostalThings 354 17.2.1 The Rationale for an Internet of PostalThings 354 17.2.1.1 A Vast Infrastructure 354 17.2.1.2 Trust as a Critical Brand Attribute 355 17.2.1.3 Operational Experience in Data Collection and Analytics 356 17.2.1.4 Customer Demand for Information 356 17.2.2 Adjusting to a New Business Environment 356 17.2.2.1 Shifting from Unconnected to “Smart” Products and Services 357 17.2.2.2 Shifting from Competing on Price to Competing on Overall Value 357 17.2.2.3 Shifting from Industries to Ecosystems 357 17.2.2.4 Shifting fromWorkforce Replacement to Human-Centered Automation 357 17.3 Identifying Internet of Postal Things Applications 358 17.3.1 Transportation and Logistics 358 17.3.1.1 Predictive Maintenance 359 17.3.1.2 Fuel Management 359 17.3.1.3 Usage-Based Insurance 360 17.3.1.4 Driverless Vehicles 360 17.3.1.5 Load Optimization 360 17.3.1.6 Real-Time Dynamic Routing 360 17.3.1.7 Collaborative Last Mile Logistics 361 17.3.2 Enhanced Mail and Parcel Services: The Connected Mailbox 361 17.3.2.1 Concept and Benefits 362 17.3.2.2 The Smart Mailbox as a Potential Source of New Revenue 363 17.3.3 The Internet ofThings in Postal Buildings 364 17.3.3.1 Optimizing Energy Costs 364 17.3.3.2 The Smarter Post Office 365 17.3.4 Neighborhood Services 365 17.3.4.1 Smart Cities Need Local Partners 365 17.3.4.2 Carriers as Neighborhood Logistics Managers 366 17.3.5 Summarizing the Dollar Value of IoPT Applications 367 17.4 The Future of IoPT 367 17.4.1 IoPT Development Stages 367 17.4.2 Implementation Challenges 368 17.4.3 Building a Successful Platform Strategy 371 17.5 Conclusion 371 References 372 18 Security and Privacy Issues in the Internet of Cows 375Amber Adams-Progar, Glenn A. Fink, ElyWalker and Don Llewellyn 18.1 Precision Livestock Farming 375 18.1.1 Impact on Humans 376 18.1.1.1 Labor andWorkforce Effects 377 18.1.1.2 Food Quality and Provenance 377 18.1.1.3 Transparency and Remote Management 378 18.1.2 Impact on Animals 379 18.1.2.1 Estrus Monitoring 379 18.1.2.2 Rumen Health 380 18.1.2.3 Other Bovine Health Conditions 381 18.1.3 Impact on the Environment 382 18.1.4 Future Directions for IoT Solutions 383 18.2 Security and Privacy of IoT in Agriculture 384 18.2.1 Cyber-Physical System Vulnerabilities 385 18.2.2 Threat Models 386 18.2.2.1 Threat: Misuse of Video Data 386 18.2.2.2 Threat: Misuse of Research Data 387 18.2.2.3 Threat: Misuse of Provenance Data 387 18.2.2.4 Threat: Data Leakage via Leased Equipment and Software 388 18.2.2.5 Threat: Political Action and Terrorism 389 18.2.3 Recommendations for IoT Security and Privacy in Agriculture 390 18.2.3.1 Data Confidentiality 391 18.2.3.2 Data Integrity 393 18.2.3.3 System Availability 393 18.2.3.4 System Safety 393 18.3 Conclusion 395 References 395 19 Admission Control-Based Load Protection in the Smart Grid 399Paul Moulema, SriharshaMallapuram,Wei Yu, David Griffith, Nada Golmie and David Su 19.1 Introduction 399 19.2 RelatedWork 401 19.3 Our Approach 402 19.3.1 Load Admission Control 403 19.3.2 Load Shedding Techniques 404 19.3.2.1 Load-Size-Based Shedding – Smallest Load First: 405 19.3.2.2 Load-Size-Based Shedding – Largest Load First: 406 19.3.2.3 Priority-Based Load Shedding: 407 19.3.2.4 Fair Priority-Based Load Shedding: 408 19.3.3 Simulation Scenarios 410 19.4 Performance Evaluation 411 19.4.1 Scenario 1: Normal Operation 411 19.4.2 Scenario 2: Brutal Admission Control 413 19.4.3 Scenario 3: Load-Size-Based Admission Control 413 19.4.4 Scenario 4: Priority-Based Admission Control 416 19.4.5 Scenario 5: Fair Priority-Based Admission Control 417 19.5 Conclusion 419 References 419 Editor Biographies 423 Index 427

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Book SynopsisTable of ContentsPREFACE xvii New to the Fifth Edition xviii Audience and Scope xix Organization and Structure of the Book xx The Web Site for Ethics and Technology xxii A Note to Students xxiii Note to Instructors: A Roadmap for Using This Book xxiii A Note to Computer Science Instructors xxiv Acknowledgments xxv FOREWORD xxvii CHAPTER 1 Introduction to Cyberethics: Concepts, Perspectives, and Methodological Frameworks 1 Scenario 1–1: Hacking into the Mobile Phones of Celebrities 1 1.1 Defining Key Terms: Cyberethics and Cybertechnology 2 1.1.1 What Is Cybertechnology? 3 1.1.2 Why the Term Cyberethics? 3 1.2 The Cyberethics Evolution: Four Developmental Phases in Cybertechnology 4 1.3 Are Cyberethics Issues Unique Ethical Issues? 7 Scenario 1–2: Developing the Code for a Computerized Weapon System 8 Scenario 1–3: Digital Piracy 8 1.3.1 Distinguishing between Unique Technological Features and Unique Ethical Issues 9 1.3.2 An Alternative Strategy for Analyzing the Debate about the Uniqueness of Cyberethics Issues 10 1.3.3 A Policy Vacuum in Duplicating Computer Software 10 1.4 Cyberethics as a Branch of Applied Ethics: Three Distinct Perspectives 12 1.4.1 Perspective #1: Cyberethics as a Field of Professional Ethics 12 1.4.2 Perspective #2: Cyberethics as a Field of Philosophical Ethics 14 1.4.3 Perspective #3: Cyberethics as a Field of Sociological/Descriptive Ethics 16 Scenario 1–4: The Impact of Technology X on the Pleasantville Community 17 1.5 A Comprehensive Cyberethics Methodology 19 1.5.1 A “Disclosive” Method for Cyberethics 19 1.5.2 An Interdisciplinary and Multilevel Method for Analyzing Cyberethics Issues 21 1.6 A Comprehensive Strategy for Approaching Cyberethics Issues 21 1.7 Chapter Summary 22 Review Questions 23 Discussion Questions 23 Scenarios for Analysis 23 Endnotes 24 References 25 Further Readings 26 Online Resources 26 CHAPTER 2 Ethical Concepts And Ethical Theories: Frameworks For Analyzing Moral Issues 27 Scenario 2–1: The Case of the “Runaway Trolley”: A Classic Moral Dilemma 27 2.1 Ethics and Morality 29 2.1.1 What Is Morality? 29 2.1.2 The Study of Morality: Three Distinct Approaches for Evaluating and Justifying the Rules Comprising a Moral System 32 2.2 Discussion Stoppers as Roadblocks to Moral Discourse 35 2.2.1 Discussion Stopper #1: People Disagree on Solutions to Moral Issues 36 2.2.2 Discussion Stopper #2: Who Am I to Judge Others? 37 2.2.3 Discussion Stopper #3: Morality Is Simply a Private Matter 39 2.2.4 Discussion Stopper #4: Morality Is Simply a Matter for Individual Cultures to Decide 40 Scenario 2–2: The Price of Defending Moral Relativism 41 2.3 Why Do We Need Ethical Theories? 43 2.4 Consequence‐Based Ethical Theories 44 2.4.1 Act Utilitarianism 46 Scenario 2–3: A Controversial Policy in Newmerica 46 2.4.2 Rule Utilitarianism 46 2.5 Duty‐Based Ethical Theories 47 2.5.1 Rule Deontology 48 Scenario 2–4: Making an Exception for Oneself 48 2.5.2 Act Deontology 49 Scenario 2–5: A Dilemma Involving Conflicting Duties 50 2.6 Contract‐Based Ethical Theories 51 2.6.1 Some Criticisms of Contract‐Based Theories 52 2.6.2 Rights‐Based Contract Theories 53 2.7 Character‐Based Ethical Theories 54 2.7.1 Being a Moral Person vs. Following Moral Rules 54 2.7.2 Acquiring the “Correct” Habits 55 2.8 Integrating Aspects of Classical Ethical Theories into a Single Comprehensive Theory 56 2.8.1 Moor’s Just‐Consequentialist Theory and Its Application to Cybertechnology 57 2.8.2 Key Elements in Moor’s Just‐Consequentialist Framework 58 2.9 Chapter Summary 59 Review Questions 59 Discussion Questions 60 Scenarios for Analysis 60 Endnotes 61 References 61 Further Readings 62 CHAPTER 3 Critical Reasoning Skills for Evaluating Disputes in Cyberethics 63 SCENARIO 3–1: Reasoning About Whether to Download Software from “Sharester” 63 3.1 What Is Critical Reasoning? 64 3.1.1 Some Basic Concepts: (Logical) Arguments and Claims 64 3.1.2 The Role of Arguments 65 3.1.3 The Basic Structure of an Argument 65 3.2 Constructing an Argument 67 3.3 Valid Arguments 68 3.4 Sound Arguments 71 3.5 Invalid Arguments 73 3.6 Inductive Arguments 74 3.7 Fallacious Arguments 75 3.8 A Seven‐Step Strategy for Evaluating Arguments 77 3.9 Identifying Some Common Fallacies 79 3.9.1 Ad Hominem Argument 79 3.9.2 Slippery Slope Argument 80 3.9.3 Fallacy of Appeal to Authority 80 3.9.4 False Cause Fallacy 81 3.9.5 Fallacy of Composition/Fallacy of Division 81 3.9.6 Fallacy of Ambiguity/Equivocation 82 3.9.7 The False Dichotomy/Either–Or Fallacy/All‐or‐Nothing Fallacy 82 3.9.8 The Virtuality Fallacy 83 3.10 Chapter Summary 84 Review Questions 84 Discussion Questions 85 Scenarios for Analysis 85 Endnotes 85 References 86 Further Readings 86 CHAPTER 4 Professional Ethics, Codes of Conduct, and Moral Responsibility 87 Scenario 4–1: Fatalities Involving the Oerlikon GDF‐005 Robotic Cannon 87 4.1 What Is Professional Ethics? 88 4.1.1 What Is a Profession? 89 4.1.2 Who Is a Professional? 89 4.1.3 Who Is a Computer/IT Professional? 90 4.2 Do Computer/IT Professionals Have Any Special Moral Responsibilities? 90 4.3 Professional Codes of Ethics and Codes of Conduct 91 4.3.1 The Purpose of Professional Codes 92 4.3.2 Some Criticisms of Professional Codes 93 4.3.3 Defending Professional Codes 94 4.3.4 The IEEE‐CS/ACM Software Engineering Code of Ethics and Professional Practice 95 4.4 Conflicts of Professional Responsibility: Employee Loyalty and Whistle‐Blowing 97 4.4.1 Do Employees Have an Obligation of Loyalty to Employers? 97 4.4.2 Whistle‐Blowing 98 Scenario 4–2: NSA Surveillance and the Case of Edward Snowden 101 4.5 Moral Responsibility, Legal Liability, and Accountability 103 4.5.1 Distinguishing Responsibility from Liability and Accountability 104 4.5.2 Accountability and the Problem of “Many Hands” 105 Scenario 4–3: The Case of the Therac‐25 Machine 105 4.5.3 Legal Liability and Moral Accountability 106 4.6 Do Some Computer Corporations Have Special Moral Obligations? 107 4.7 Chapter Summary 108 Review Questions 109 Discussion Questions 109 Scenarios for Analysis 110 Endnotes 110 References 111 Further Readings 112 CHAPTER 5 Privacy and Cyberspace 113 Scenario 5–1: A New NSA Data Center 113 5.1 Privacy in the Digital Age: Who Is Affected and Why Should We Worry? 114 5.1.1 Whose Privacy Is Threatened by Cybertechnology? 115 5.1.2 Are Any Privacy Concerns Generated by Cybertechnology Unique or Special? 115 5.2 What Is Personal Privacy? 117 5.2.1 Accessibility Privacy: Freedom from Unwarranted Intrusion 118 5.2.2 Decisional Privacy: Freedom from Interference in One’s Personal Affairs 118 5.2.3 Informational Privacy: Control over the Flow of Personal Information 118 5.2.4 A Comprehensive Account of Privacy 119 Scenario 5–2: Descriptive Privacy 119 Scenario 5–3: Normative Privacy 120 5.2.5 Privacy as “Contextual Integrity” 120 Scenario 5–4: Preserving Contextual Integrity in a University Seminar 121 5.3 Why Is Privacy Important? 121 5.3.1 Is Privacy an Intrinsic Value? 122 5.3.2 Privacy as a Social Value 123 5.4 Gathering Personal Data: Surveillance, Recording, and Tracking Techniques 123 5.4.1 “Dataveillance” Techniques 124 5.4.2 Internet Cookies 124 5.4.3 RFID Technology 125 5.4.4 Cybertechnology and Government Surveillance 126 5.5 Analyzing Personal Data: Big Data, Data Mining, and Web Mining 127 5.5.1 Big Data: What, Exactly, Is It, and Why Does It Threaten Privacy? 128 5.5.2 Data Mining and Personal Privacy 128 Scenario 5–5: Data Mining at the XYZ Credit Union 129 5.5.3 Web Mining: Analyzing Personal Data Acquired from Our Interactions Online 132 5.6 Protecting Personal Privacy in Public Space 132 5.6.1 PPI vs. NPI 133 Scenario 5–6: Shopping at SuperMart 133 Scenario 5–7: Shopping at Nile.com 134 5.6.2 Search Engines and the Disclosure of Personal Information 135 5.7 Privacy Legislation and Industry Self‐Regulation 137 5.7.1 Industry Self‐Regulation and Privacy‐Enhancing Tools 137 5.7.2 Privacy Laws and Data Protection Principles 139 5.8 A Right to “Be Forgotten” (or to “Erasure”) in the Digital Age 140 Scenario 5–8: An Arrest for an Underage Drinking Incident 20 Years Ago 141 5.8.1 Arguments Opposing RTBF 142 5.8.2 Arguments Defending RTBF 143 5.8.3 Establishing “Appropriate” Criteria 144 5.9 Chapter Summary 146 Review Questions 146 Discussion Questions 147 Scenarios for Analysis 148 Endnotes 148 References 149 Further Readings 150 CHAPTER 6 Security in Cyberspace 151 Scenario 6–1: The “Olympic Games” Operation and the Stuxnet Worm 151 6.1 Security in the Context of Cybertechnology 152 6.1.1 Cybersecurity as Related to Cybercrime 153 6.1.2 Security and Privacy: Some Similarities and Some Differences 153 6.2 Three Categories of Cybersecurity 154 6.2.1 Data Security: Confidentiality, Integrity, and Availability of Information 155 6.2.2 System Security: Viruses, Worms, and Malware 156 6.2.3 Network Security: Protecting our Infrastructure 156 Scenario 6–2: The “GhostNet” Controversy 157 6.3 Cloud Computing and Security 158 6.3.1 Deployment and Service/Delivery Models for the Cloud 158 6.3.2 Securing User Data Residing in the Cloud 159 6.3.3 Assessing Risk in the Cloud and in the Context of Cybersecurity 160 6.4 Hacking and “The Hacker Ethic” 160 6.4.1 What Is “The Hacker Ethic”? 161 6.4.2 Are Computer Break‐ins Ever Ethically Justifiable? 163 6.5 Cyberterrorism 164 6.5.1 Cyberterrorism vs. Hacktivism 165 Scenario 6–3: Anonymous and the “Operation Payback” Attack 166 6.5.2 Cybertechnology and Terrorist Organizations 167 6.6 Information Warfare (IW) 167 6.6.1 Information Warfare vs. Conventional Warfare 167 6.6.2 Potential Consequences for Nations that Engage in IW 168 6.7 Chapter Summary 170 Review Questions 170 Discussion Questions 171 Scenarios for Analysis 171 Endnotes 171 References 172 Further Readings 174 CHAPTER 7 Cybercrime and Cyber‐Related Crimes 175 Scenario 7–1: Creating a Fake Facebook Account to Catch Criminals 175 7.1 Cybercrimes and Cybercriminals 177 7.1.1 Background Events: A Brief Sketch 177 7.1.2 A Typical Cybercriminal 178 7.2 Hacking, Cracking, and Counter Hacking 178 7.2.1 Hacking vs. Cracking 179 7.2.2 Active Defense Hacking: Can Acts of “Hacking Back” or Counter Hacking Ever Be Morally Justified? 179 7.3 Defining Cybercrime 180 7.3.1 Determining the Criteria 181 7.3.2 A Preliminary Definition of Cybercrime 181 7.3.3 Framing a Coherent and Comprehensive Definition of Cybercrime 182 7.4 Three Categories of Cybercrime: Piracy, Trespass, and Vandalism in Cyberspace 183 7.5 Cyber‐Related Crimes 184 7.5.1 Some Examples of Cyber‐Exacerbated vs. Cyber‐Assisted Crimes 184 7.5.2 Identity Theft 185 7.6 Technologies and Tools for Combating Cybercrime 187 7.6.1 Biometric Technologies 187 7.6.2 Keystroke‐Monitoring Software and Packet‐Sniffing Programs 188 7.7 Programs and Techniques Designed to Combat Cybercrime in the United States 189 7.7.1 Entrapment and “Sting” Operations to Catch Internet Pedophiles 189 Scenario 7–2: Entrapment on the Internet 189 7.7.2 Enhanced Government Surveillance Techniques and the Patriot Act 189 7.8 National and International Laws to Combat Cybercrime 190 7.8.1 The Problem of Jurisdiction in Cyberspace 190 Scenario 7–3: A Virtual Casino 191 Scenario 7–4: Prosecuting a Computer Corporation in Multiple Countries 192 7.8.2 Some International Laws and Conventions Affecting Cybercrime 192 Scenario 7–5: The Pirate Bay Web Site 193 7.9 Cybercrime and the Free Press: The Wikileaks Controversy 193 7.9.1 Are WikiLeaks’ Practices Ethical? 194 7.9.2 Are WikiLeaks’ Practices Criminal? 194 7.9.3 WikiLeaks and the Free Press 195 7.10 Chapter Summary 196 Review Questions 197 Discussion Questions 197 Scenarios for Analysis 198 Endnotes 199 References 199 Further Readings 200 CHAPTER 8 Intellectual Property Disputes in Cyberspace 201 Scenario 8–1: Streaming Music Online 201 8.1 What Is Intellectual Property? 202 8.1.1 Intellectual Objects 203 8.1.2 Why Protect Intellectual Objects? 203 8.1.3 Software as Intellectual Property 204 8.1.4 Evaluating a Popular Argument Used by the Software Industry to Show Why It Is Morally Wrong to Copy Proprietary Software 205 8.2 Copyright Law and Digital Media 206 8.2.1 The Evolution of Copyright Law in the United States 206 8.2.2 The Fair‐Use and First‐Sale Provisions of Copyright Law 207 8.2.3 Software Piracy as Copyright Infringement 208 8.2.4 Napster and the Ongoing Battles over Sharing Digital Music 209 8.3 Patents, Trademarks, and Trade Secrets 212 8.3.1 Patent Protections 212 8.3.2 Trademarks 213 8.3.3 Trade Secrets 214 8.4 Jurisdictional Issues Involving Intellectual Property Laws 214 8.5 Philosophical Foundations for Intellectual Property Rights 215 8.5.1 The Labor Theory of Property 215 Scenario 8–2: DEF Corporation vs. XYZ Inc. 216 8.5.2 The Utilitarian Theory of Property 216 Scenario 8–3: Sam’s e‐Book Reader Add‐on Device 217 8.5.3 The Personality Theory of Property 217 Scenario 8–4: Angela’s B++ Programming Tool 218 8.6 The “Free Software” and “Open Source” Movements 219 8.6.1 GNU and the Free Software Foundation 219 8.6.2 The “Open Source Software” Movement: OSS vs. FSF 220 8.7 The “Common Good” Approach: An Alternative Framework for Analyzing the Intellectual Property Debate 221 8.7.1 Information Wants to be Shared vs. Information Wants to be Free 223 8.7.2 Preserving the Information Commons 225 8.7.3 The Fate of the Information Commons: Could the Public Domain of Ideas Eventually Disappear? 226 8.7.4 The Creative Commons 227 8.8 Pipa, Sopa, and Rwa Legislation: Current Battlegrounds in the Intellectual Property War 228 8.8.1 The PIPA and SOPA Battles 228 8.8.2 RWA and Public Access to Health‐Related Information 229 Scenario 8–5: Elsevier Press and “The Cost of Knowledge” Boycott 229 8.8.3 Intellectual Property Battles in the Near Future 231 8.9 Chapter Summary 231 Review Questions 231 Discussion Questions 232 Scenarios for Analysis 232 Endnotes 233 References 234 Further Readings 235 CHAPTER 9 Regulating Commerce and Speech in Cyberspace 236 Scenario 9–1: Anonymous and the Ku Klux Klan 236 9.1 Introduction and Background Issues: Some Key Questions and Critical Distinctions Affecting Internet Regulation 237 9.1.1 Is Cyberspace a Medium or a Place? 238 9.1.2 Two Categories of Cyberspace Regulation: Regulating Content and Regulating Process 239 9.1.3 Four Modes of Regulation: The Lessig Model 240 9.2 Digital Rights Management (Drm) 242 9.2.1 Some Implications of DRM for Public Policy Debates Affecting Copyright Law 242 9.2.2 DRM and the Music Industry 243 Scenario 9–2: The Sony Rootkit Controversy 243 9.3 E‐Mail Spam 244 9.3.1 Defining Spam 244 9.3.2 Why Is Spam Morally Objectionable? 245 9.4 Free Speech vs. Censorship and Content Control in Cyberspace 246 9.4.1 Protecting Free Speech 247 9.4.2 Defining Censorship 247 9.5 Pornography in Cyberspace 248 9.5.1 Interpreting “Community Standards” in Cyberspace 248 9.5.2 Internet Pornography Laws and Protecting Children Online 249 9.5.3 Virtual Child Pornography 250 9.5.4 Sexting and Its Implications for Current Child Pornography Laws 252 Scenario 9–3: A Sexting Incident Involving Greensburg Salem High School 252 9.6 Hate Speech and Speech that Can Cause Physical Harm to Others 254 9.6.1 Hate Speech on the Web 254 9.6.2 Online “Speech” that Can Cause Physical Harm to Others 255 9.7 “Network Neutrality” and the Future of Internet Regulation 256 9.7.1 Defining Network Neutrality 256 9.7.2 Some Arguments Advanced by Net Neutrality’s Proponents and Opponents 257 9.7.3 Future Implications for the Net Neutrality Debate 257 9.8 Chapter Summary 258 Review Questions 259 Discussion Questions 259 Scenarios for Analysis 260 Endnotes 260 References 261 Further Readings 262 CHAPTER 10 The Digital Divide, Democracy, and Work 263 Scenario 10–1: Digital Devices, Social Media, Democracy, and the “Arab Spring” 264 10.1 The Digital Divide 265 10.1.1 The Global Digital Divide 265 10.1.2 The Digital Divide within Nations 266 Scenario 10–2: Providing In‐Home Internet Service for Public School Students 267 10.1.3 Is the Digital Divide an Ethical Issue? 268 10.2 Cybertechnology and the Disabled 270 10.3 Cybertechnology and Race 271 10.3.1 Internet Usage Patterns 272 10.3.2 Racism and the Internet 272 10.4 Cybertechnology and Gender 273 10.4.1 Access to High‐Technology Jobs 274 10.4.2 Gender Bias in Software Design and Video Games 275 10.5 Cybertechnology, Democracy, and Demotratic Ideals 276 10.5.1 Has Cybertechnology Enhanced or Threatened Democracy? 276 10.5.2 How has Cybertechnology Affected Political Elections in Democratic Nations? 279 10.6 The Transformation and the Quality of Work 280 10.6.1 Job Displacement and the Transformed Workplace 281 10.6.2 The Quality of Work Life in the Digital Era 283 Scenario 10–3: Employee Monitoring and the Case of Ontario vs. Quon 284 10.7 Chapter Summary 287 Review Questions 287 Discussion Questions 288 Scenarios for Analysis 288 Endnotes 289 References 289 Further Readings 291 CHAPTER 11 Online Communities, Virtual Reality, and Artificial Intelligence 292 Scenario 11–1: Ralph’s Online Friends and Artificial Companions 292 11.1 Online Communities and Social Networking Services 293 11.1.1 Online Communities vs. Traditional Communities 294 11.1.2 Blogs and Some Controversial Aspects of the Bogosphere 295 Scenario 11–2: “The Washingtonienne” Blogger 295 11.1.3 Some Pros and Cons of SNSs (and Other Online Communities) 296 Scenario 11–3: A Suicide Resulting from Deception on MySpace 298 11.2 Virtual Environments and Virtual Reality 299 11.2.1 What Is Virtual Reality (VR)? 300 11.2.2 Ethical Aspects of VR Applications 301 11.3 Artificial Intelligence (AI) 305 11.3.1 What Is AI? A Brief Overview 305 11.3.2 The Turing Test and John Searle’s “Chinese Room” Argument 306 11.3.3 Cyborgs and Human–Machine Relationships 307 11.4 Extending Moral Consideration to AI Entities 310 Scenario 11–4: Artificial Children 310 11.4.1 Determining Which Kinds of Beings/Entities Deserve Moral Consideration 310 11.4.2 Moral Patients vs. Moral Agents 311 11.5 Chapter Summary 312 Review Questions 313 Discussion Questions 313 Scenarios for Analysis 313 Endnotes 314 References 315 Further Readings 316 CHAPTER 12 Ethical Aspects of Emerging and Converging Technologies 317 Scenario 12–1: When “Things” Communicate with One Another 317 12.1 Converging Technologies and Technological Convergence 318 12.2 Ambient Intelligence (AmI) and Ubiquitous Computing 319 12.2.1 Pervasive Computing, Ubiquitous Communication, and Intelligent User Interfaces 320 12.2.2 Ethical and Social Aspects of AmI 321 Scenario 12–2: E. M. Forster’s “(Pre)Cautionary Tale” 322 Scenario 12–3: Jeremy Bentham’s “Panopticon/Inspection House” (Thought Experiment) 323 12.3 Nanotechnology and Nanocomputing 324 12.3.1 Nanotechnology: A Brief Overview 324 12.3.2 Ethical Issues in Nanotechnology and Nanocomputing 326 12.4 Autonomous Machines 329 12.4.1 What Is an AM? 329 12.4.2 Some Ethical and Philosophical Questions Pertaining to AMs 332 12.5 Machine Ethics and Moral Machines 336 12.5.1 What Is Machine Ethics? 336 12.5.2 Designing Moral Machines 337 12.6 A “Dynamic” Ethical Framework for Guiding Research in New and Emerging Technologies 340 12.6.1 Is an ELSI‐Like Model Adequate for New/Emerging Technologies? 340 12.6.2 A “Dynamic Ethics” Model 341 12.7 Chapter Summary 341 Review Questions 342 Discussion Questions 342 Scenarios for Analysis 343 Endnotes 343 References 344 Further Readings 346 GLOSSARY 347 INDEX 353

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Book SynopsisProvides a detailed analysis of the standards and technologies enabling applications for the wireless Internet of Things The Wireless Internet of Things: A Guide to the Lower Layers presents a practitioner's perspective toward the Internet of Things (IoT) focusing on over-the-air interfaces used by applications such as home automation, sensor networks, smart grid, and healthcare. The authora noted expert in the fieldexamines IoT as a protocol-stack detailing the physical layer of the wireless links, as both a radio and a modem, and the media access control (MAC) that enables communication in congested bands. Focusing on low-power wireless personal area networks (WPANs) the text outlines the physical and MAC layer standards used by ZigBee, Bluetooth LE, Z-Wave, and Thread. The text deconstructs these standards and provides background including relevant communication theory, modulation schemes, and access methods. The author includes a discussion on Wi-Fi aTable of ContentsPreface vii Acknowledgments ix About the Author xi 1 Introduction 1 1.1 What is the Internet of Things? 1 1.2 What is the Wireless Internet of Things? 4 1.3 Wireless Networks 5 1.4 What is the Role of Wireless Standards in the Internet of Things? 10 1.5 Protocol Stacks 10 1.6 Introduction to the Protocols for the Wireless Internet of Things 16 1.7 The Approach of this Book 17 References 18 2 Protocols of the Wireless Internet of Things 21 2.1 Bluetooth 22 2.2 ITU G.9959 29 2.3 Z-Wave 32 2.4 IEEE 802.15.4 33 2.5 The ZigBee Specification 38 2.6 Thread 40 2.7 Wi-Fi 41 References 44 3 Radio Layer 47 3.1 The Wireless System 47 3.2 Basic Transceiver Model 48 3.3 The Basics of Channels 67 3.4 Bit and Symbol Error Rate 74 3.5 Complex Channels 76 References 81 4 Modem Layer 83 4.1 The Signal Model 84 4.2 Pulse Shaping 90 4.3 Modulation Techniques 95 4.4 Synchronization 120 4.5 Spread Spectrum 132 References 137 5 MAC Layer 139 5.1 Bands and Spectrum Planning 140 5.2 Spectrum Access for the Wireless IoT 144 5.3 Multiple Access Techniques 145 5.4 Spread Spectrum as Multiple Access 153 5.5 Error Detection and Correction 154 5.6 Energy Efficiency 167 References 170 6 Conclusion 173 6.1 Selecting the Right Standard 173 6.2 Higher Layer Standardization and the Future of IoT 175 Index 177

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Book SynopsisIn the 21st century, architects and engineers are being challenged to produce work that is concurrently sustainable and resilient. Buildings need to mitigate their impact on climate change by minimising their carbon footprint, while also countering the challenging new weather conditions. Globally, severe storms, extreme droughts and rising sea levels are becoming an increasingly reoccurring feature. To respond, a design process is required that seeks to integrate resiliency by building in the capacity to absorb the impacts of these disruptive events and adapt over time to further changes, while simultaneously being part of the solution to the problem itself. This issue of AD is guest-edited by the interdisciplinary team at Stevens Institute of Technology who developed the winning entry for the 2015 US Department of Energy Solar Decathlon competition, the SU+RE House. While particular focus is paid to this student designed and built prototype home, the publication also

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Book SynopsisA thorough look at how societies can use cultural algorithms to understand human social evolution For those working in computational intelligence, developing an understanding of how cultural algorithms and social intelligence form the essential framework for the evolution of human social interaction is essential. This book, Cultural Algorithms: Tools to Model Complex Dynamic Social Systems, is the foundation of that study. It showcases how we can use cultural algorithms to organize social structures and develop socio-political systems that work. For such a vast topic, the text covers everything from the history of the development of cultural algorithms and the basic framework with which it was organized. Readers will also learn how other nature-inspired algorithms can be expressed and how to use social metrics to assess the performance of various algorithms. In addition to these topics, the book covers topics including: The CAT system Table of ContentsList of Contributors ix About the Companion Website xi 1 System Design Using Cultural Algorithms 1Robert G. Reynolds Introduction 1 The Cultural Engine 4 Outline of the Book: Cultural Learning in Dynamic Environments 6 References 10 2 The Cultural Algorithm Toolkit System 11Thomas Palazzolo CAT Overview 11 Downloading and Running CAT 14 The Repast Simphony System 15 Knowledge Sources 15 Fitness Functions 18 ConesWorld 19The Logistics Function 23 CAT Sample Runs: ConesWorld 24 CAT Sample Runs: Other Problems 32 Reference 34 3 Social Learning in Cultural Algorithms with Auctions 35Robert G. Reynolds and Leonard Kinnaird-Heether Introduction 35 Cultural Algorithms 37 Subcultured Multi-Layered, Deep Heterogeneous Networks 40 Auction Mechanisms 42 The Cultural Engine 45 ConesWorld 47 Experimental Framework 50 Results 50 Conclusions 54 References 55 4 Using Common Value Auction in Cultural Algorithm to Enhance Robustness and Resilience of Social Knowledge Distribution Systems 57Anas AL-Tirawi and Robert G. Reynolds Cultural Algorithms 57 Common Value Auction 62 ConesWorld 64 Dynamic Experimental Framework 66 Results 67 Conclusions and Future Work 73 References 73 5 Optimizing AI Pipelines: A Game-Theoretic Cultural Algorithms Approach 75Faisal Waris and Robert G. Reynolds Introduction 75 Overview of Cultural Algorithms 77 CA Knowledge Distribution Mechanisms 78 Primer on Game Theory 80 Game- Theoretic Knowledge Distribution 81 Continuous-Action Iterated Prisoner’s Dilemma 82 Test Results: Benchmark Problem 89 Test Results: Computer Vision Pipeline 92 Conclusions 95 References 96 6 Cultural Algorithms for Social Network Analysis: Case Studies in Team Formation 98Kalyani Selvarajah, Ziad Kobti, and Mehdi Kargar Introduction 98 Application of Social Network 99 Forming Successful Teams 99 Formulating TFP 100 Communication Cost 101 Personnel Cost 101 Distance Cost 102 Workload Balance 102 Why Artificial Intelligence? 103 Cultural Algorithms 103 Forming Teams in Coauthorship Network 104 Individual Representation 105 Fitness Function 107 Belief Space 107 Dataset and Observations 108 Skill Frequency 108 Forming Teams in Health-care Network 108 Individual Representation 113 Fitness Function 114 Dataset and Observation 115 Summary and Conclusion 117 References 117 7 Evolving Emergent Team Strategies in Robotic Soccer using Enhanced Cultural Algorithms 119Mostafa Z. Ali, Mohammad I. Daoud, Rami Alazrai, and Robert G. Reynolds Introduction 119 Related Work 121 The 2D Soccer Simulation Test Bed 122 Evolution of Team Strategies via Cultural Algorithm 124 Experiments and Analysis of Results 132 Conclusion 138 References 139 8 The Use of Cultural Algorithms to Learn the Impact of Climate on Local Fishing Behavior in Cerro Azul, Peru 143Khalid Kattan, Robert G. Reynolds, and Samuel Dustin Stanley Introduction 143 An Overview of the Cerro Azul Fishing Dataset 143 Data Mining at the Macro, Meso, and Micro Levels 148 Cultural Algorithms and Multiobjective Optimization 149 The Artisanal Fishing Model 153 The Experimental Results 159 Statistical Validation 163 Conclusions and Future Work 166 References 167 9 CAPSO: A Parallelized Multiobjective Cultural Algorithm Particle Swarm Optimizer 169Samuel Dustin Stanley, Khalid Kattan, and Robert G. Reynolds Introduction 169 Multiobjective Optimization 170 Cultural Algorithms 171 CAPSO Knowledge Structures 174 Tracking Knowledge Source Progress (Other than Topographic) 176 CAPSO Algorithm Pseudocode 177 Multiple Runs 180 Comparison of Benchmark Problems 180 Overall Summary of Results 192 Other Applications 192 References 193 10 Exploring Virtual Worlds with Cultural Algorithms: Ancient Alpena–Amberley Land Bridge 195Thomas Palazzolo, Robert G. Reynolds, and Samuel Dustin Stanley Archaeological Challenges 195 Generalized Framework 198 The Land Bridge Hypothesis 199 Origin and Form 204 Putting Data to Work 205 Pathfinding and Planning 215 Identifying Good Locations: The Hotspot Finder 218 Cultural Algorithms 222 Cultural Algorithm Mechanisms 225 The Composition of the Belief Space 226 Future Work 227 Path Planning Strategy 227 Local Tactics 229 Detailed Locational Information 230 Extending the CA 231 Human Presence in the Virtual World 234 Increasing the Complexity 235 Updated Path-Planning Results in Unity 236 The Fully Rendered Land Bridge 237 Pathfinder Mechanisms 239 Results 245 Conclusions 254 References 255 Index 259

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Book SynopsisA guide to intelligent decision and pervasive computing paradigms for healthcare analytics systems with a focus on the use of bio-sensors Intelligent Pervasive Computing Systems for Smarter Healthcare describes the innovations in healthcare made possible by computing through bio-sensors. The pervasive computing paradigm offers tremendous advantages in diversified areas of healthcare research and technology.The authorsnoted experts in the fieldprovide the state-of-the-art intelligence paradigm that enables optimization of medical assessment for a healthy, authentic, safer, and more productive environment. Today's computers are integrated through bio-sensors and generate a huge amount of information that can enhance our ability to process enormous bio-informatics data that can be transformed into meaningful medical knowledge and help with diagnosis, monitoring and tracking health issues, clinical decision making, early detection of infectious disease prevention, and rapid analysis of Table of ContentsList of Contributors xvii 1 Intelligent Sensing and Ubiquitous Systems (ISUS) for Smarter and Safer Home Healthcare 1 Rui Silva Moreira, José Torres, Pedro Sobral, and Christophe Soares 1.1 Introduction to Ubicomp for Home Healthcare 1 1.2 Processing and Sensing Issues 3 1.2.1 Remote Patient Monitoring in Home Environments 4 1.2.1.1 Hardware Device 5 1.2.1.2 Sensed Data Processing and Analysis 6 1.2.2 Indoor Location Using Bluetooth Low Energy Beacons 8 1.2.2.1 Bluetooth Low Energy 9 1.2.2.2 Distance Estimation 9 1.3 Integration and Management Issues 14 1.3.1 Cloud-Based Integration of Personal Healthcare Systems 15 1.3.2 SNMP-Based Integration and Interference Free Approach to Personal Healthcare 17 1.4 Communication and Networking Issues 19 1.4.1 Wireless Sensor Network for Home Healthcare 21 1.4.1.1 Home Healthcare System Architecture 21 1.4.1.2 Wireless Sensor Network Evaluation 25 1.5 Intelligence and Reasoning Issues 26 1.5.1 Intelligent Monitoring and Automation in Home Healthcare 26 1.5.2 Personal Activity Detection During Daily Living 30 1.6 Conclusion 32 Bibliography 33 2 PeMo-EC: An Intelligent, Pervasive and Mobile Platform for ECG Signal Acquisition, Processing, and Pre-Diagnostic Extraction 37 Angelo Brayner, José Maria Monteiro, and João Paulo Madeiro 2.1 Electrical System of the Heart 37 2.2 The Electrocardiogram Signal: A Gold Standard for Monitoring People Suffering from Heart Diseases 38 2.3 Pervasive and Mobile Computing: Basic Concepts 40 2.4 Ubiquitous Computing and Healthcare Applications: State of the Art 42 2.5 PeMo-EC: Description of the Proposed Framework 44 2.5.1 Acquisition Module: Biosensors and ECG Data Conditioning 44 2.5.2 Patient’s Smartphone Application: ECG Signal Processing Module 49 2.5.3 Physician’s Smartphone Application: Query/Alarm Module 54 2.5.4 The Collaborative Database: Data Integration Module 55 2.5.4.1 Motivation 55 2.5.4.2 The Design of the Collaborative Database 57 2.5.4.3 Data Mining and Pattern Recognition 59 2.6 Conclusions 61 Acknowledgements 61 Bibliography 62 3 The Impact of Implantable Sensors in Biomedical Technology on the Future of Healthcare Systems 67 Ashraf Darwish, Gehad Ismail Sayed, and Aboul Ella Hassanien 3.1 Introduction 67 3.2 Related Work 71 3.3 Motivation and Contribution 74 3.4 Fundamentals of IBANs for Healthcare Monitoring 75 3.4.1 ISs in Biomedical Systems 75 3.4.2 Applications of ISs in Biomedical Systems 78 3.4.2.1 Brain Stimulator 78 3.4.2.2 Heart Failure Monitoring 78 3.4.2.3 Blood Glucose Level 80 3.4.3 Security in Implantable Biomedical Systems 80 3.5 Challenges and Future Trends 82 3.6 Conclusion and Recommendation 85 Bibliography 86 4 Social Network’s Security Related to Healthcare 91 Fatna Elmendili, Habiba Chaoui, and Younés El Bouzekri El Idrissi 4.1 The Use of Social Networks in Healthcare 91 4.2 The Social Media Respond to a Primary Need of Security 92 4.3 The Type of Medical Data 95 4.3.1 Security of Medical Data 96 4.4 Problematic 97 4.5 Presentation of the Honeypots 98 4.5.1 Principle of Honeypots 98 4.6 Proposal System for Detecting Malicious Profiles on the Health Sector 99 4.6.1 Proposed Solution 100 4.6.1.1 Deployment of Social Honeypots 100 4.6.1.2 Data Collection 103 4.6.1.3 Classification of Users 104 4.7 Results and Discussion 108 4.8 Conclusion 111 Bibliography 111 5 Multi-Sensor Fusion for Context-Aware Applications 115 Veeramuthu Venkatesh, Ponnuraman Balakrishnan, and Pethru Raj 5.1 Introduction 115 5.1.1 What Is an Intelligent Pervasive System? 115 5.1.2 The Significance of Context Awareness for Next-Generation Smarter Environments 117 5.1.2.1 Context-Aware Characteristics 118 5.1.2.2 Context Types and Categorization Schemes 119 5.1.2.3 Context Awareness Management Design Principles 121 5.1.2.4 Context Life Cycle 122 5.1.2.5 Interval (Called Occasionally) 124 5.1.3 Pervasive Healthcare-Enabling Technologies 125 5.1.3.1 Bio-Signal Acquisition 126 5.1.3.2 Communication Technologies 126 5.1.3.3 Data Classification 128 5.1.3.4 Intelligent Agents 128 5.1.3.5 Location-Based Technologies 128 5.1.4 Pervasive Healthcare Challenges 128 5.2 Ambient Methods Used for E-Health 130 5.2.1 Body Area Networks (BANs) 130 5.2.2 Home M2M Sensor Networks 131 5.2.3 Microelectromechanical System (MEMS) 132 5.2.4 Cloud-Based Intelligent Healthcare 132 5.3 Algorithms and Methods 133 5.3.1 Behavioral Pattern Discovery 133 5.3.2 Decision Support System 134 5.4 Intelligent Pervasive Healthcare Applications 134 5.4.1 Health Information Management 134 5.4.2 Location and Context-Aware Services 136 5.4.3 Remote Patient Monitoring 136 5.4.4 Waze: Community-Based Navigation App 138 5.5 Conclusion 138 Bibliography 139 6 IoT-Based Noninvasive Wearable and Remote Intelligent Pervasive Healthcare Monitoring Systems for the Elderly People 141 Stela Vitorino Sampaio 6.1 Introduction 141 6.2 Internet of Things (IoT) and Remote Health Monitoring 141 6.3 Wearable Health Monitoring 143 6.3.1 Wearable Sensors 143 6.4 Related Work 145 6.4.1 Existing Status 146 6.5 Architectural Prototype 147 6.5.1 Data Acquisition and Processing 150 6.5.2 Pervasive and Intelligence Monitoring 151 6.5.3 Communication 153 6.5.4 Predictive Analytics 153 6.5.5 Edge Analytics 154 6.5.6 Ambient Intelligence 155 6.5.7 Privacy and Security 155 6.6 Summary 156 Bibliography 156 7 Pervasive Healthcare System Based on Environmental Monitoring 159 Sangeetha Archunan and Amudha Thangavel 7.1 Introduction 159 7.2 Intelligent Pervasive Computing System 160 7.2.1 Applications of Pervasive Computing 163 7.3 Biosensors for Environmental Monitoring 163 7.3.1 Environmental Monitoring 165 7.3.1.1 Influence of Environmental Factors on Health 167 7.4 IPCS for Healthcare 167 7.4.1 Healthcare System Architecture Based on Environmental Monitoring 171 7.5 Conclusion 174 Bibliography 174 8 Secure Pervasive Healthcare System and Diabetes Prediction Using Heuristic Algorithm 179 Patitha Parameswaran and Rajalakshmi Shenbaga Moorthy 8.1 Introduction 179 8.2 Related Work 181 8.3 System Design 182 8.3.1 Data Collector 183 8.3.2 Security Manager 183 8.3.2.1 Proxy Re-encryption Algorithm 183 8.3.2.2 Key Generator 184 8.3.2.3 Patient 185 8.3.2.4 Proxy Server 185 8.3.2.5 Healthcare Professional 185 8.3.3 Clusterer 186 8.3.3.1 Hybrid Particle Swarm Optimization K-Means (HPSO-K) Algorithm 186 8.3.4 Predictor 191 8.3.4.1 Hidden Markov Model-Based Viterbi Algorithm (HMM-VA) 191 8.4 Implementation 193 8.5 Results and Discussions 196 8.5.1 Analyzing the Performance of PRA 196 8.5.1.1 Time Taken for Encryption 196 8.5.1.2 Storage Space for Re-encrypted Data 196 8.5.1.3 Time Take for Decryption 196 8.5.2 Analyzing the Performance of HPSO-K Algorithm 197 8.5.2.1 Number of Iterations (Generations) to Cluster Patients 198 8.5.2.2 Comparison of Intra-cluster Distance 198 8.5.2.3 Comparison of Inter-cluster Distance 199 8.5.2.4 Number of Patients in Cluster 200 8.5.2.5 Comparison of Time Complexity 201 8.5.3 Analyzing the Performance of HMM-VA 201 8.5.3.1 Forecasting Diabetes 201 8.5.3.2 Comparison of Error Rate 203 8.6 Conclusion 203 Nomenclatures Used 203 Bibliography 204 9 Threshold-Based Energy-Efficient Routing Protocol for Critical Data Transmission to Increase Lifetime in Heterogeneous Wireless Body Area Sensor Network 207 Deepalakshmi Perumalsamy and Navya Venkatamari 9.1 Introduction 207 9.2 Related Works 209 9.3 Proposed Protocol: Threshold-Based Energy-Efficient Routing Protocol for Critical Data Transmission (EERPCDT) 213 9.3.1 Background and Motivation 213 9.3.2 Basic Communication Radio Model 214 9.4 System Model 215 9.4.1 Initialization Phase 216 9.4.2 Routing Phase Selection of Forwarder Node 217 9.4.3 Scheduling Phase 217 9.4.4 Data Transmission Phase 218 9.5 Analysis of Energy Consumption 218 9.6 Simulation Results and Discussions 219 9.6.1 Network Lifetime and Stability Period 219 9.6.2 Residual Energy 220 9.6.3 Throughput 221 9.7 Conclusion and Future Work 222 Bibliography 223 10 Privacy and Security Issues on Wireless Body Area and IoT for Remote Healthcare Monitoring 227 Prabha Selvaraj and Sumathi Doraikannan 10.1 Introduction 227 10.2 Healthcare Monitoring System 227 10.2.1 Evolution of Healthcare Monitoring System 227 10.3 Healthcare Monitoring System 228 10.3.1 Sensor Network 230 10.3.2 Wireless Sensor Network 230 10.3.3 Wireless Body Area Network 230 10.4 Privacy and Security 233 10.4.1 Privacy and Security Issues in Wireless Body Area Network 234 10.5 Attacks and Measures 237 10.5.1 Security Models for Various Levels 241 10.5.1.1 Security Models for Data Collection Level 241 10.5.1.2 Security Models for Data Transmission Level 242 10.5.1.3 Security Models for Data Storage and Access Level 242 10.5.2 Privacy and Security Issues Pertained to Healthcare Applications 243 10.5.3 Issues Related to Health Information Held by an Individual Organization 243 10.5.4 Categorization of Organizational Threats 244 10.6 Internet of Things 248 10.6.1 WBAN Using IoT 248 10.7 Projects and Related Works in Healthcare Monitoring System 249 10.8 Summary 251 Bibliography 251 11 Remote Patient Monitoring: A Key Management and Authentication Framework for Wireless Body Area Networks 255 Padma Theagarajan and Jayashree Nair 11.1 Introduction 255 11.2 RelatedWork 256 11.3 Proposed Framework for Secure Remote Patient Monitoring 258 11.3.1 Proposed Security Framework 259 11.3.2 Key Generation Algorithm: PQSG 260 11.3.3 Key Establishment in NetAMS: KEAMS 262 11.3.3.1 Initiation of Communication by HPA 262 11.3.3.2 Establishment of Key by HMS 263 11.3.3.3 Authentication of HMS 263 11.3.4 Key Establishment in NetSHA: KESHA 265 11.3.4.1 Initiation of Communication by WSH 265 11.3.4.2 Establishment of Key by the HPA 266 11.3.4.3 Acknowledgment by HPA 266 11.4 Performance Analysis 267 11.4.1 Randomness 267 11.4.2 Distinctiveness 268 11.4.3 Complexity 269 11.5 Discussion 271 11.6 Conclusion 272 Bibliography 273 12 Image Analysis Using Smartphones for Medical Applications: A Survey 275 Rajeswari Rajendran and Jothilakshmi Rajendiran 12.1 Introduction 275 12.2 Pervasive Healthcare Using Image-Based Smartphone Applications 276 12.3 Smartphone-Based Image Diagnosis 277 12.3.1 Diagnosis Using Built-In Camera 278 12.3.2 Diagnosis Using External Sensors/Devices 280 12.4 Libraries and Tools for Smartphone-Based Image Analysis 284 12.4.1 Open-Source Libraries for Image Analysis in Smartphones 284 12.4.2 Tools for Cross-Platform Smartphone Application Development 286 12.5 Challenges and Future Perspectives 286 12.6 Conclusion 288 Bibliography 288 13 Bounds of Spreading Rate of Virus for a Network Through an Intuitionistic Fuzzy Graph 291 Deepa Ganesan, Praba Bashyam, Chandrasekaran Vellankoil Marappan, Rajakumar Krishnan, and Krishnamoorthy Venkatesan 13.1 Intuitionistic Fuzzy Matrices Using Incoming and Outgoing Links 292 13.2 Virus Spreading Rate Between Outgoing and Incoming Links 302 13.3 Numerical Examples 305 Bibliography 310 14 Data Mining Techniques for the Detection of the Risk in Cardiovascular Diseases 313 Dinakaran Karunakaran, Vishnu Priya, and Valarmathie Palanisamy 14.1 Introduction 313 14.2 PPG Signal Analysis 315 14.2.1 Pulse Width 315 14.2.2 Pulse Area 315 14.2.3 Peak-to-Peak Interval 316 14.2.4 Pulse Interval 316 14.2.5 Augmentation Index 317 14.2.6 Large Artery Stiffness Index 317 14.2.7 Types of Photoplethysmography 319 14.3 Related Works 319 14.4 Methodology 322 14.4.1 PPG Design and Recording Setup 322 14.5 Preprocessing in PPG Signal 323 14.6 Results and Discussion 325 14.7 Conclusion 327 Bibliography 328 15 Smart Sensing System for Cardio Pulmonary Sound Signals 331 Nersisson Ruban and A.Mary Mekala 15.1 Introduction 331 15.2 Background Theory 332 15.2.1 Human Heart 333 15.2.2 Heart Sounds 334 15.2.3 Origin of Sounds 334 15.2.4 Significance of Detection 334 15.3 Heart Sound Detection 335 15.3.1 Stethoscope 335 15.4 Polyvinylidene Fluoride (PVDF) 336 15.4.1 Properties of PVDF 337 15.4.2 PVDF as Thin Film Piezoelectric Sensor 337 15.4.3 Placement of the Sensor 338 15.4.4 Development of PVDF Sensor 339 15.4.4.1 Steps Involved in the Development of Sensor 340 15.5 Hardware Implementation 341 15.5.1 Charge Amplifier 341 15.5.2 Signal Conditioning Circuits for PVDF Sensor 342 15.5.3 Hardware Circuits 343 15.5.3.1 Design of Charge Amplifier 343 15.5.3.2 Filter Design 344 15.6 LabVIEW Design 346 15.6.1 Signal Acquisition 346 15.6.1.1 Data Acquisition with LabVIEW 347 15.6.2 Fixing of the Threshold Value 348 15.6.3 Fixing the Threshold for Real-Time Signal 349 15.6.4 Fixing the Threshold in Time Scale 350 15.6.5 Separation of Peaks from Resultant Signal (Sample 1) 351 15.6.6 Separation of Peaks from Resultant Signal (Sample 2) 351 15.7 Heart Sound Segmentation 353 15.7.1 Algorithm for Signal Separation 354 15.7.1.1 Case Structure Algorithm 354 15.7.2 Segmented S1 and S2 Sounds 354 15.8 Conclusion 356 Bibliography 357 16 Anomaly Detection and Pattern Matching Algorithm for Healthcare Application: Identifying Ambulance Siren in Traffic 361 Gowthambabu Karthikeyan, Sasikala Ramasamy, and Suresh Kumar Nagarajan 16.1 Introduction 361 16.2 Related Work 364 16.2.1 Role of Sound Detection in Existing Systems 366 16.2.2 Input and Output Parameters 367 16.2.3 Features of Pattern Matching 367 16.3 Pattern Matching Algorithm for Ambulance Siren Detection 368 16.3.1 Sensors 368 16.3.2 Sensor Deviations 368 16.3.3 Traffic Signal 369 16.3.3.1 How Do Traffic Signals Work? 369 16.3.3.2 Traffic Signal 370 16.3.3.3 Sound-Detecting Sensor 370 16.3.4 Pattern Matching Algorithm: Anomaly Detection 372 16.3.4.1 Algorithm and Implementation 374 16.3.4.2 Sound Detection Module 375 16.4 Results and Conclusion 375 Bibliography 376 17 Detecting Diabetic Retinopathy from Retinal Images Using CUDA Deep Neural Network 379 Ricky Parmar, Ramanathan Lakshmanan, Swarnalatha Purushotham, and Rajkumar Soundrapandiyan 17.1 Introduction 379 17.2 Proposed Method 381 17.2.1 Preprocessing 382 17.2.2 Architecture 383 17.2.3 Digital Artifacts 386 17.2.4 Pseudo-classification 387 17.3 Experimental Results 387 17.3.1 Dataset 387 17.3.2 Performance Evaluation Measures 388 17.3.3 Validation of Datasets Using Exponential Power Distribution 388 17.3.4 Ensemble 390 17.3.5 Accuracy and Stats 390 17.4 Conclusion and Future Work 393 Bibliography 394 18 An Energy-Efficient Wireless Body Area Network Design in Health Monitoring Scenarios 397Kannan Shanmugam and Karthik Subburathinam 18.1 Wireless Body Area Network 397 18.1.1 Overview 397 18.1.2 Architectures of Wireless Body Area Network 398 18.1.2.1 Tier 1: Intra-WBAN Communication 398 18.1.2.2 Tier 2: Inter-WBAN Communication 398 18.1.2.3 Tier 3: Beyond-WBAN Communication 399 18.1.3 Challenges Faced in System Design 399 18.1.3.1 Energy Constraint 401 18.1.3.2 Interference in Communication 401 18.1.3.3 Security 401 18.1.4 Research Problems 401 18.2 Proposed Opportunistic Scheduling 402 18.2.1 Introduction 402 18.2.2 System Model and Problem Formulation 403 18.2.2.1 System Model 403 18.2.2.2 Problem Formulation 404 18.2.3 Heuristic Scheduling 404 18.2.4 Dynamic Super-Frame Length Adjustment 407 18.2.4.1 Problem Formulation 407 18.3 Performance Analysis Environment and Metrics 408 18.3.1 Heuristic Scheduling with Fixed Super-Frame Length 409 18.3.2 Heuristic Scheduling with Dynamic Super-Frame Length 410 18.4 Summary 410 Bibliography 411 Index 413

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Book SynopsisAN ESSENTIAL GUIDE TO USING BLOCKCHAIN TO PROVIDE FLEXIBILITY, COST-SAVINGS, AND SECURITY TO DATA MANAGEMENT, DATA ANALYSIS, AND INFORMATION SHARING Blockchain for Distributed Systems Securitycontains a description of the properties that underpin the formal foundations of Blockchain technologies and explores the practical issues for deployment in cloud and Internet of Things (IoT) platforms. The authorsnoted experts in the fieldpresent security and privacy issues that must be addressed for Blockchain technologies to be adopted for civilian and military domains. The book covers a range of topics including data provenance in cloud storage, secure IoT models, auditing architecture, and empirical validation of permissioned Blockchain platforms. The book's security and privacy analysis helps with an understanding of the basics of Blockchain and it explores the quantifying impact of the new attack surfaces introduced by Blockchain technologies and platforms. In addition, the book containsTable of ContentsForeword xiii Preface xv List of Contributors xix Part I Introduction to Blockchain 1 1 Introduction 3Sachin S. Shetty, Laurent Njilla, and Charles A. Kamhoua 1.1 Blockchain Overview 3 1.1.1 Blockchain Building Blocks 5 1.1.2 Blockchain Commercial Use Cases 6 1.1.3 Blockchain Military Cyber Operations Use Cases 11 1.1.4 Blockchain Challenges 13 1.2 Overview of the Book 16 1.2.1 Chapter 2: Distributed Consensus Protocols and Algorithms 16 1.2.2 Chapter 3: Overview of Attack Surfaces in Blockchain 17 1.2.3 Chapter 4: Data Provenance in Cloud Storage with Blockchain 17 1.2.4 Chapter 5: Blockchain-based Solution to Automotive Security and Privacy 18 1.2.5 Chapter 6: Blockchain-based Dynamic Key Management for IoT-Transportation Security Protection 19 1.2.6 Chapter 7: Blockchain-enabled Information Sharing Framework for Cybersecurity 19 1.2.7 Chapter 8: Blockcloud Security Analysis 20 1.2.8 Chapter 9: Security and Privacy of Permissioned and Permissionless Blockchain 20 1.2.9 Chapter 10: Shocking Public Blockchains’ Memory with Unconfirmed Transactions—New DDoS Attacks and Countermeasures 21 1.2.10 Chapter 11: Preventing Digital Currency Miners From Launching Attacks Against Mining Pools by a Reputation-Based Paradigm 21 1.2.11 Chapter 12: Private Blockchain Configurations for Improved IoT Security 22 1.2.12 Chapter 13: Blockchain Evaluation Platform 22 References 23 2 Distributed Consensus Protocols and Algorithms 25Yang Xiao, Ning Zhang, Jin Li, Wenjing Lou, and Y. Thomas Hou 2.1 Introduction 25 2.2 Fault-tolerant Consensus in a Distributed System 26 2.2.1 The System Model 26 2.2.2 BFT Consensus 28 2.2.3 The OM Algorithm 29 2.2.4 Practical Consensus Protocols in Distributed Computing 30 2.3 The Nakamoto Consensus 37 2.3.1 The Consensus Problem 38 2.3.2 Network Model 38 2.3.3 The Consensus Protocol 39 2.4 Emerging Blockchain Consensus Algorithms 40 2.4.1 Proof of Stake 41 2.4.2 BFT-based Consensus 42 2.4.3 Proof of Elapsed Time (PoET) 44 2.4.4 Ripple 45 2.5 Evaluation and Comparison 47 2.6 Summary 47 Acknowledgment 49 References 49 3 Overview of Attack Surfaces in Blockchain 51Muhammad Saad, Jeffrey Spaulding, Laurent Njilla, Charles A. Kamhoua, DaeHun Nyang, and Aziz Mohaisen 3.1 Introduction 51 3.2 Overview of Blockchain and its Operations 53 3.3 Blockchain Attacks 54 3.3.1 Blockchain Fork 54 3.3.2 Stale Blocks and Orphaned Blocks 54 3.3.3 Countering Blockchain Structure Attacks 55 3.4 Blockchain’s Peer-to-Peer System 55 3.4.1 Selfish Mining 56 3.4.2 The 51% Attack 57 3.4.3 DNS Attacks 57 3.4.4 DDoS Attacks 58 3.4.5 Consensus Delay 59 3.4.6 Countering Peer-to-Peer Attacks 59 3.5 Application Oriented Attacks 60 3.5.1 Blockchain Ingestion 60 3.5.2 Double Spending 60 3.5.3 Wallet Theft 61 3.5.4 Countering Application Oriented Attacks 61 3.6 Related Work 61 3.7 Conclusion and Future Work 62 References 62 Part II Blockchain Solutions for Distributed System Security 67 4 ProvChain: Blockchain-based Cloud Data Provenance 69Xueping Liang, Sachin S. Shetty, Deepak Tosh, Laurent Njilla, Charles A. Kamhoua, and Kevin Kwiat 4.1 Introduction 69 4.2 Background and Related Work 70 4.2.1 Data Provenance 70 4.2.2 Data Provenance in the Cloud 71 4.2.3 Blockchain 73 4.2.4 Blockchain and Data Provenance 74 4.3 ProvChain Architecture 75 4.3.1 Architecture Overview 76 4.3.2 Preliminaries and Concepts 77 4.3.3 Threat Model 78 4.3.4 Key Establishment 78 4.4 ProvChain Implementation 79 4.4.1 Provenance Data Collection and Storage 80 4.4.2 Provenance Data Validation 83 4.5 Evaluation 85 4.5.1 Summary of ProvChain’s Capabilities 85 4.5.2 Performance and Overhead 86 4.6 Conclusions and Future Work 90 Acknowledgment 91 References 92 5 A Blockchain-based Solution to Automotive Security and Privacy 95Ali Dorri, Marco Steger, Salil S. Kanhere, and Raja Jurdak 5.1 Introduction 95 5.2 An Introduction to Blockchain 98 5.3 The Proposed Framework 101 5.4 Applications 103 5.4.1 Remote Software Updates 103 5.4.2 Insurance 105 5.4.3 Electric Vehicles and Smart Charging Services 105 5.4.4 Car-sharing Services 106 5.4.5 Supply Chain 106 5.4.6 Liability 107 5.5 Evaluation and Discussion 108 5.5.1 Security and Privacy Analysis 108 5.5.2 Performance Evaluation 109 5.6 Related Works 112 5.7 Conclusion 113 References 114 6 Blockchain-based Dynamic Key Management for IoT-Transportation Security Protection 117Ao Lei, Yue Cao, Shihan Bao, Philip Asuquom, Haitham Cruickshank, and Zhili Sun 6.1 Introduction 117 6.2 Use Case 119 6.2.1 Message Handover in VCS 120 6.3 Blockchain-based Dynamic Key Management Scheme 124 6.4 Dynamic Transaction Collection Algorithm 125 6.4.1 Transaction Format 125 6.4.2 Block Format 127 6.5 Time Composition 128 6.5.1 Dynamic Transaction Collection Algorithm 129 6.6 Performance Evaluation 130 6.6.1 Experimental Assumptions and Setup 130 6.6.2 Processing Time of Cryptographic Schemes 132 6.6.3 Handover Time 133 6.6.4 Performance of the Dynamic Transaction Collection Algorithm 135 6.7 Conclusion and Future Work 138 References 140 7 Blockchain-enabled Information Sharing Framework for Cybersecurity 143Abdulhamid Adebayo, Danda B. Rawat, Laurent Njilla, and Charles A. Kamhoua 7.1 Introduction 143 7.2 The BIS Framework 145 7.3 Transactions on BIS 146 7.4 Cyberattack Detection and Information Sharing 147 7.5 Cross-group Attack Game in Blockchain-based BIS Framework: One-way Attack 149 7.6 Cross-group Attack Game in Blockchain-based BIS Framework: Two-way Attack 151 7.7 Stackelberg Game for Cyberattack and Defense Analysis 152 7.8 Conclusion 156 References 157 Part III Blockchain Security 159 8 Blockcloud Security Analysis 161Deepak Tosh, Sachin S. Shetty, Xueping Liang, Laurent Njilla, Charles A. Kamhoua, and Kevin Kwiat 8.1 Introduction 161 8.2 Blockchain Consensus Mechanisms 163 8.2.1 Proof-of-Work (PoW) Consensus 164 8.2.2 Proof-of-Stake (PoS) Consensus 165 8.2.3 Proof-of-Activity (PoA) Consensus 167 8.2.4 Practical Byzantine Fault Tolerance (PBFT) Consensus 168 8.2.5 Proof-of-Elapsed-Time (PoET) Consensus 169 8.2.6 Proof-of-Luck (PoL) Consensus 170 8.2.7 Proof-of-Space (PoSpace) Consensus 170 8.3 Blockchain Cloud and Associated Vulnerabilities 171 8.3.1 Blockchain and Cloud Security 171 8.3.2 Blockchain Cloud Vulnerabilities 174 8.4 System Model 179 8.5 Augmenting with Extra Hash Power 180 8.6 Disruptive Attack Strategy Analysis 181 8.6.1 Proportional Reward 181 8.6.2 Pay-per-last N-shares (PPLNS) Reward 184 8.7 Simulation Results and Discussion 187 8.8 Conclusions and Future Directions 188 Acknowledgment 190 References 190 9 Permissioned and Permissionless Blockchains 193Andrew Miller 9.1 Introduction 193 9.2 On Choosing Your Peers Wisely 194 9.3 Committee Election Mechanisms 196 9.4 Privacy in Permissioned and Permissionless Blockchains 199 9.5 Conclusion 201 References 202 10 Shocking Blockchain’s Memory with Unconfirmed Transactions: New DDoS Attacks and Countermeasures 205Muhammad Saad, Laurent Njilla, Charles A. Kamhoua, Kevin Kwiat, and Aziz Mohaisen 10.1 Introduction 205 10.2 Related Work 207 10.3 An Overview of Blockchain and Lifecycle 208 10.3.1 DDoS Attack on Mempools 210 10.3.2 Data Collection for Evaluation 210 10.4 Threat Model 211 10.5 Attack Procedure 212 10.5.1 The Distribution Phase 214 10.5.2 The Attack Phase 214 10.5.3 Attack Cost 214 10.6 Countering the Mempool Attack 215 10.6.1 Fee-based Mempool Design 216 10.6.2 Age-based Countermeasures 221 10.7 Experiment and Results 224 10.8 Conclusion 227 References 227 11 Preventing Digital Currency Miners from Launching Attacks Against Mining Pools Using a Reputation-based Paradigm 233Mehrdad Nojoumian, Arash Golchubian, Laurent Njilla, Kevin Kwiat, and Charles A. Kamhoua 11.1 Introduction 233 11.2 Preliminaries 234 11.2.1 Digital Currencies: Terminologies and Mechanics 234 11.2.2 Game Theory: Basic Notions and Definitions 235 11.3 Literature Review 236 11.4 Reputation-based Mining Model and Setting 238 11.5 Mining in a Reputation-based Model 240 11.5.1 Prevention of the Re-entry Attack 240 11.5.2 Technical Discussion on Detection Mechanisms 241 11.5.3 Colluding Miner’s Dilemma 243 11.5.4 Repeated Mining Game 244 11.5.5 Colluding Miners’ Preferences 245 11.5.6 Colluding Miners’ Utilities 245 11.6 Evaluation of Our Model Using Game-theoretical Analyses 246 11.7 Concluding Remarks 248 Acknowledgment 249 References 249 Part IV Blockchain Implementation 253 12 Private Blockchain Configurations for Improved IoT Security 255Adriaan Larmuseau and Devu Manikantan Shila 12.1 Introduction 255 12.2 Blockchain-enabled Gateway 257 12.2.1 Advantages 257 12.2.2 Limitations 258 12.2.3 Private Ethereum Gateways for Access Control 259 12.2.4 Evaluation 262 12.3 Blockchain-enabled Smart End Devices 263 12.3.1 Advantages 263 12.3.2 Limitations 264 12.3.3 Private Hyperledger Blockchain-enabled Smart Sensor Devices 264 12.3.4 Evaluation 269 12.4 Related Work 270 12.5 Conclusion 271 References 271 13 Blockchain Evaluation Platform 275Peter Foytik and Sachin S. Shetty 13.1 Introduction 275 13.1.1 Architecture 276 13.1.2 Distributed Ledger 276 13.1.3 Participating Nodes 277 13.1.4 Communication 277 13.1.5 Consensus 278 13.2 Hyperledger Fabric 279 13.2.1 Node Types 279 13.2.2 Docker 280 13.2.3 Hyperledger Fabric Example Exercise 281 13.2.4 Running the First Network 281 13.2.5 Running the Kafka Network 286 13.3 Measures of Performance 291 13.3.1 Performance Metrics With the Proof-of-Stake Simulation 293 13.3.2 Performance Measures With the Hyperledger Fabric Example 296 13.4 Simple Blockchain Simulation 300 13.5 Blockchain Simulation Introduction 303 13.5.1 Methodology 304 13.5.2 Simulation Integration With Live Blockchain 304 13.5.3 Simulation Integration With Simulated Blockchain 306 13.5.4 Verification and Validation 306 13.5.5 Example 307 13.6 Conclusion and Future Work 309 References 310 14 Summary and Future Work 311Sachin S. Shetty, Laurent Njilla, and Charles A. Kamhoua 14.1 Introduction 311 14.2 Blockchain and Cloud Security 312 14.3 Blockchain and IoT Security 312 14.4 Blockchain Security and Privacy 314 14.5 Experimental Testbed and Performance Evaluation 316 14.6 The Future 316 Index 319

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Book SynopsisProvides comprehensive coverage of the current state of IoT, focusing on data processing infrastructure and techniques Written by experts in the field, this book addresses the IoT technology stack, from connectivity through data platforms to end-user case studies, and considers the tradeoffs between business needs and data security and privacy throughout. There is a particular emphasis on data processing technologies that enable the extraction of actionable insights from data to inform improved decision making. These include artificial intelligence techniques such as stream processing, deep learning and knowledge graphs, as well as data interoperability and the key aspects of privacy, security and trust. Additional aspects covered include: creating and supporting IoT ecosystems; edge computing; data mining of sensor datasets; and crowd-sourcing, amongst others. The book also presents several sections featuring use cases across a range of application areas such as smartTable of ContentsAbout the Editors xi List of Contributors xiii Acknowledgments xvii 1 Introduction 1John Davies and Carolina Fortuna 1.1 Stakeholders in IoT Ecosystems 3 1.2 Human and IoT Sensing, Reasoning, and Actuation: An Analogy 4 1.3 Replicability and Re-use in IoT 5 1.4 Overview 6 References 7 2 Connecting Devices: Access Networks 9Paul Putland 2.1 Introduction 9 2.2 Overview of Access Networks 10 2.2.1 Existing Technologies are Able to Cover a Number of IoT Scenarios 10 2.3 Low-Power Wide Area Network (LPWAN) 12 2.3.1 Long-Range (LoRa) Low-Power Wide Area Network 14 2.3.2 Sigfox Low-Power Wide Area Network 14 2.3.3 Weightless Low-Power Wide Area Network 15 2.4 Cellular Technologies 15 2.4.1 Emerging 5G Cellular Technology 16 2.5 Conclusion 18 References 18 3 Edge Computing 21Mohammad Hossein Zoualfaghari, Simon Beddus, and Salman Taherizadeh 3.1 Introduction 21 3.2 Edge Computing Fundamentals 22 3.2.1 Edge Compute Strategies 22 3.2.2 Network Connectivity 25 3.3 Edge Computing Architecture 25 3.3.1 Device Overview 25 3.3.2 Edge Application Modules 26 3.3.3 IoT Runtime Environment 26 3.3.4 Device Management 27 3.3.5 Secure Runtime Environment 27 3.4 Implementing Edge Computing Solutions 28 3.4.1 Starter Configuration 28 3.4.2 Developer Tools 28 3.4.3 Edge Computing Frameworks 29 3.5 Zero-Touch Device On-boarding 30 3.6 Applying Edge Computing 32 3.7 Conclusions 33 References 33 4 Data Platforms: Interoperability and Insight 37John Davies and Mike Fisher 4.1 Introduction 37 4.2 IoT Ecosystems 38 4.3 Context 40 4.4 Aspects of Interoperability 41 4.4.1 Discovery 41 4.4.2 Access Control 43 4.4.3 Data Access 44 4.5 Conclusion 48 References 49 5 Streaming Data Processing for IoT 51Carolina Fortuna and Timotej Gale 5.1 Introduction 51 5.2 Fundamentals 52 5.2.1 Compression 52 5.2.2 Dimensionality Reduction 52 5.2.3 Summarization 53 5.2.4 Learning and Mining 53 5.2.5 Visualization 53 5.3 Architectures and Languages 54 5.4 Stream Analytics and Spectrum Sensing 56 5.4.1 Real-Time Notifications 57 5.4.2 Statistical Reporting 57 5.4.3 Custom Applications 58 5.5 Summary 59 References 60 6 Applied Machine Vision and IoT 63V. García, N. Sánchez, J.A. Rodrigo, J.M. Menéndez, and J. Lalueza 6.1 Introduction: Machine Vision and the Proliferation of Smart Internet of Things Driven Environments 63 6.2 Machine Vision Fundamentals 65 6.3 Overview of Relevant Work: Current Trends in Machine Vision in IoT 67 6.3.1 Improved Perception for IoT 67 6.3.2 Improved Interpretation and Learning for IoT 68 6.4 A Generic Deep Learning Framework for Improved Situation Awareness 69 6.5 Evaluating the Impact of Deep Learning in Different IoT Related Verticals 70 6.5.1 Sensing Critical Infrastructures Using Cognitive Drone-Based Systems 70 6.5.2 Sensing Public Spaces Using Smart Embedded Systems 71 6.5.3 Preventive Maintenance Service Comparison Based on Drone High-Definition Images 72 6.6 Best Practice 74 6.7 Summary 75 References 75 7 Data Representation and Reasoning 79Maria Maleshkova and Nicolas Seydoux 7.1 Introduction 79 7.2 Fundamentals 80 7.3 Semantic IoT and Semantic WoT (SWoT) 81 7.4 Semantics for IoT Integration 82 7.4.1 IoT Ontologies and IoT-O 83 7.4.2 The Digital Twin Approach 85 7.5 Use Case 87 7.6 Summary 88 References 89 8 Crowdsourcing and Human-in-the-Loop for IoT 91Luis-Daniel Ibáñez, Neal Reeves, and Elena Simperl 8.1 Introduction 91 8.2 Crowdsourcing 92 8.3 Human-in-the-Loop 95 8.4 Spatial Crowdsourcing 97 8.5 Participatory Sensing 99 8.6 Conclusion 100 References 101 9 IoT Security: Experience is an Expensive Teacher 107Paul Kearney 9.1 Introduction 107 9.2 Why is IoT Security Different from IT Security? 108 9.3 What is Being Done to Address IoT Security Challenges? 110 9.3.1 Governments 110 9.3.2 Standards Bodies 111 9.3.3 Industry Groups 112 9.4 Picking the Low-Hanging Fruit 113 9.4.1 Basic Hygiene Factors 113 9.4.2 Methodologies and Compliance Frameworks 115 9.4.3 Labeling Schemes and Consumer Advice 116 9.5 Summary 117 References 118 10 IoT Data Privacy 121Norihiro Okui, Vanessa Bracamonte, Shinsaku Kiyomoto, and Alistair Duke 10.1 Introduction 121 10.2 Basic Concepts in IoT Data Privacy 122 10.2.1 What is Personal Data? 122 10.2.2 General Requirements for Data Privacy 123 10.2.3 Personal Data and IoT 124 10.2.4 Existing Privacy Preservation Approaches 126 10.2.5 Toward a Standards-Based Approach in Support of PIMS Business Models 128 10.3 A Data Handling Framework Based on Consent Information and Privacy Preferences 129 10.3.1 A Data Handling Framework 129 10.3.2 Privacy Preference Manager (PPM) 130 10.3.3 Implementation of the Framework 131 10.4 Standardization for a User-Centric Data Handling Architecture 132 10.4.1 Introduction to oneM2M 132 10.4.2 PPM in oneM2M 133 10.5 Example Use Cases 133 10.5.1 Services Based on Home Energy Data 133 10.5.2 HEMS Service 133 10.5.3 Delivery Service 134 10.6 Conclusions 137 References 137 11 Blockchain: Enabling Trust on the Internet of Things 141Giampaolo Fiorentino, Carmelita Occhipinti, Antonello Corsi, Evandro Moro, John Davies, and Alistair Duke 11.1 Introduction 141 11.2 Distributed Ledger Technologies and the Blockchain 143 11.2.1 Distributed Ledger Technology Overview 143 11.2.2 Basic Concepts and Architecture 145 11.2.2.1 Consensus Algorithm 148 11.2.3 When to Deploy DLT 149 11.3 The Ledger of Things: Blockchain and IoT 150 11.4 Benefits and Challenges 150 11.5 Blockchain Use Cases 152 11.6 Conclusion 154 References 154 12 Healthcare 159Duarte Gonçalves-Ferreira, Joana Ferreira, Bruno Oliveira, Ricardo Cruz-Correia, and Pedro Pereira Rodrigues 12.1 Internet of Things in Healthcare Settings 159 12.1.1 Monitoring Patient Status in Hospitals 160 12.1.2 IoT from Healthcare to Everyday Life 160 12.1.3 Systems Interoperability 161 12.2 BigEHR: A Federated Repository for a Holistic Lifelong Health Record 163 12.2.1 Why a Federated Design? 164 12.2.2 System Architecture 164 12.3 Gathering IoT Health-Related Data 165 12.3.1 From Inside the Hospitals 166 12.3.2 Feeding Data from Outside Sources 166 12.4 Extracting Meaningful Information from IoT Data 167 12.4.1 Privacy Concerns 167 12.4.2 Distributed Reasoning 167 12.5 Outlook 168 Acknowledgments 169 References 169 13 Smart Energy 173Artemis Voulkidis, Theodore Zahariadis, Konstantinos Kalaboukas, Francesca Santori, and Matevž Vučnik 13.1 Introduction 173 13.2 Use Case Description 175 13.2.1 The Role of 5G in the Smart Grid IoT Context 177 13.3 Reference Architecture 178 13.4 Use Case Validation 182 13.4.1 AMI-Based Continuous Power Quality Assessment System 183 13.5 Conclusion 187 Acknowledgment 187 References 187 14 Road Transport and Air Quality 189Charles Carter and Chris Rushton 14.1 Introduction 189 14.2 The Air Pollution Challenge 191 14.3 Road Traffic Air Pollution Reduction Strategies 193 14.4 Monitoring Air Pollution Using IoT 194 14.5 Use Case: Reducing Emissions Through an IoT-Based Advanced Traffic Management System 196 14.6 Limitations of Average Speed Air Quality Modeling 201 14.7 Future Roadmap and Summary 202 References 203 15 Conclusion 207John Davies and Carolina Fortuna 15.1 Origins and Evolution 207 15.2 Why Now? 207 15.2.1 Falling Costs and Miniaturization 208 15.2.2 Societal Challenges and Resource Efficiency 208 15.2.3 Information Sharing Comes of Age 208 15.2.4 Managing Complexity 208 15.2.5 Technological Readiness 208 15.3 Maximizing the Value of Data 209 15.4 Commercial Opportunities 209 15.5 A Glimpse of the Future 210 References 212 Index 213

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Book SynopsisTable of ContentsIntroduction xxi Chapter 1 The Two Essential Algorithms for Making Predictions 1 Why are These Two Algorithms So Useful? 2 What are Penalized Regression Methods? 7 What are Ensemble Methods? 9 How to Decide Which Algorithm to Use 11 The Process Steps for Building a Predictive Model 13 Framing a Machine Learning Problem 15 Feature Extraction and Feature Engineering 17 Determining Performance of a Trained Model 18 Chapter Contents and Dependencies 18 Summary 20 Chapter 2 Understand the Problem by Understanding the Data 23 The Anatomy of a New Problem 24 Different Types of Attributes and Labels Drive Modeling Choices 26 Things to Notice about Your New Data Set 27 Classification Problems: Detecting Unexploded Mines Using Sonar 28 Physical Characteristics of the Rocks Versus Mines Data Set 29 Statistical Summaries of the Rocks Versus Mines Data Set 32 Visualization of Outliers Using a Quantile-Quantile Plot 34 Statistical Characterization of Categorical Attributes 35 How to Use Python Pandas to Summarize the Rocks Versus Mines Data Set 36 Visualizing Properties of the Rocks Versus Mines Data Set 39 Visualizing with Parallel Coordinates Plots 39 Visualizing Interrelationships between Attributes and Labels 41 Visualizing Attribute and Label Correlations Using a Heat Map 48 Summarizing the Process for Understanding the Rocks Versus Mines Data Set 50 Real-Valued Predictions with Factor Variables: How Old is Your Abalone? 50 Parallel Coordinates for Regression Problems—Visualize Variable Relationships for the Abalone Problem 55 How to Use a Correlation Heat Map for Regression—Visualize Pair-Wise Correlations for the Abalone Problem 59 Real-Valued Predictions Using Real-Valued Attributes: Calculate How Your Wine Tastes 61 Multiclass Classification Problem: What Type of Glass is That? 67 Using PySpark to Understand Large Data Sets 72 Summary 75 Chapter 3 Predictive Model Building: Balancing Performance, Complexity, and Big Data 77 The Basic Problem: Understanding Function Approximation 78 Working with Training Data 79 Assessing Performance of Predictive Models 81 Factors Driving Algorithm Choices and Performance—Complexity and Data 82 Contrast between a Simple Problem and a Complex Problem 82 Contrast between a Simple Model and a Complex Model 85 Factors Driving Predictive Algorithm Performance 89 Choosing an Algorithm: Linear or Nonlinear? 90 Measuring the Performance of Predictive Models 91 Performance Measures for Different Types of Problems 91 Simulating Performance of Deployed Models 105 Achieving Harmony between Model and Data 107 Choosing a Model to Balance Problem Complexity, Model Complexity, and Data Set Size 107 Using Forward Stepwise Regression to Control Overfitting 109 Evaluating and Understanding Your Predictive Model 114 Control Overfitting by Penalizing Regression Coefficients—Ridge Regression 116 Using PySpark for Training Penalized Regression Models on Extremely Large Data Sets 124 Summary 127 Chapter 4 Penalized Linear Regression 129 Why Penalized Linear Regression Methods are So Useful 130 Extremely Fast Coefficient Estimation 130 Variable Importance Information 131 Extremely Fast Evaluation When Deployed 131 Reliable Performance 131 Sparse Solutions 132 Problem May Require Linear Model 132 When to Use Ensemble Methods 132 Penalized Linear Regression: Regulating Linear Regression for Optimum Performance 132 Training Linear Models: Minimizing Errors and More 135 Adding a Coefficient Penalty to the OLS Formulation 136 Other Useful Coefficient Penalties—Manhattan and ElasticNet 137 Why Lasso Penalty Leads to Sparse Coefficient Vectors 138 ElasticNet Penalty Includes Both Lasso and Ridge 140 Solving the Penalized Linear Regression Problem 141 Understanding Least Angle Regression and Its Relationship to Forward Stepwise Regression 141 How LARS Generates Hundreds of Models of Varying Complexity 145 Choosing the Best Model from the Hundreds LARS Generates 147 Using Glmnet: Very Fast and Very General 152 Comparison of the Mechanics of Glmnet and LARS Algorithms 153 Initializing and Iterating the Glmnet Algorithm 153 Extension of Linear Regression to Classification Problems 157 Solving Classification Problems with Penalized Regression 157 Working with Classification Problems Having More Than Two Outcomes 161 Understanding Basis Expansion: Using Linear Methods on Nonlinear Problems 161 Incorporating Non-Numeric Attributes into Linear Methods 163 Summary 166 Chapter 5 Building Predictive Models Using Penalized Linear Methods 169 Python Packages for Penalized Linear Regression 170 Multivariable Regression: Predicting Wine Taste 171 Building and Testing a Model to Predict Wine Taste 172 Training on the Whole Data Set before Deployment 175 Basis Expansion: Improving Performance by Creating New Variables from Old Ones 179 Binary Classification: Using Penalized Linear Regression to Detect Unexploded Mines 182 Build a Rocks Versus Mines Classifier for Deployment 191 Multiclass Classification: Classifying Crime Scene Glass Samples 200 Linear Regression and Classification Using PySpark 203 Using PySpark to Predict Wine Taste 204 Logistic Regression with PySpark: Rocks Versus Mines 208 Incorporating Categorical Variables in a PySpark Model: Predicting Abalone Rings 213 Multiclass Logistic Regression with Meta Parameter Optimization 217 Summary 219 Chapter 6 Ensemble Methods 221 Binary Decision Trees 222 How a Binary Decision Tree Generates Predictions 224 How to Train a Binary Decision Tree 225 Tree Training Equals Split Point Selection 227 How Split Point Selection Affects Predictions 228 Algorithm for Selecting Split Points 229 Multivariable Tree Training—Which Attribute to Split? 229 Recursive Splitting for More Tree Depth 230 Overfitting Binary Trees 231 Measuring Overfit with Binary Trees 231 Balancing Binary Tree Complexity for Best Performance 232 Modifi cations for Classification and Categorical Features 235 Bootstrap Aggregation: “Bagging” 235 How Does the Bagging Algorithm Work? 236 Bagging Performance—Bias Versus Variance 239 How Bagging Behaves on Multivariable Problem 241 Bagging Needs Tree Depth for Performance 245 Summary of Bagging 246 Gradient Boosting 246 Basic Principle of Gradient Boosting Algorithm 246 Parameter Settings for Gradient Boosting 249 How Gradient Boosting Iterates toward a Predictive Model 249 Getting the Best Performance from Gradient Boosting 250 Gradient Boosting on a Multivariable Problem 253 Summary for Gradient Boosting 256 Random Forests 256 Random Forests: Bagging Plus Random Attribute Subsets 259 Random Forests Performance Drivers 260 Random Forests Summary 261 Summary 262 Chapter 7 Building Ensemble Models with Python 265 Solving Regression Problems with Python Ensemble Packages 265 Using Gradient Boosting to Predict Wine Taste 266 Using the Class Constructor for GradientBoostingRegressor 266 Using GradientBoostingRegressor to Implement a Regression Model 268 Assessing the Performance of a Gradient Boosting Model 271 Building a Random Forest Model to Predict Wine Taste 272 Constructing a RandomForestRegressor Object 273 Modeling Wine Taste with RandomForestRegressor 275 Visualizing the Performance of a Random Forest Regression Model 279 Incorporating Non-Numeric Attributes in Python Ensemble Models 279 Coding the Sex of Abalone for Gradient Boosting Regression in Python 280 Assessing Performance and the Importance of Coded Variables with Gradient Boosting 282 Coding the Sex of Abalone for Input to Random Forest Regression in Python 284 Assessing Performance and the Importance of Coded Variables 287 Solving Binary Classification Problems with Python Ensemble Methods 288 Detecting Unexploded Mines with Python Gradient Boosting 288 Determining the Performance of a Gradient Boosting Classifier 291 Detecting Unexploded Mines with Python Random Forest 292 Constructing a Random Forest Model to Detect Unexploded Mines 294 Determining the Performance of a Random Forest Classifier 298 Solving Multiclass Classification Problems with Python Ensemble Methods 300 Dealing with Class Imbalances 301 Classifying Glass Using Gradient Boosting 301 Determining the Performance of the Gradient Boosting Model on Glass Classification 306 Classifying Glass with Random Forests 307 Determining the Performance of the Random Forest Model on Glass Classification 310 Solving Regression Problems with PySpark Ensemble Packages 311 Predicting Wine Taste with PySpark Ensemble Methods 312 Predicting Abalone Age with PySpark Ensemble Methods 317 Distinguishing Mines from Rocks with PySpark Ensemble Methods 321 Identifying Glass Types with PySpark Ensemble Methods 325 Summary 327 Index 329

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Book SynopsisThe 4e,EMEA Edition of Management Information Systems promotes active learning like no other text in the market. Each chapter is comprised of tightly coupled concepts and section-level student activities that transport your students from passively learning about IS to doing IS in a realistic context.Table of ContentsPreface vii 1 Introduction to Information Systems 1 2 Organizational Strategy, Competitive Advantage, and Information Systems 33 3 Data and Knowledge Management 56 4 Telecommunications and Networking 91 5 Business Analytics 127 6 Ethics and Privacy 155 7 Information Security 176 8 Social Computing 209 9 E-Business and E-Commerce 244 10 Wireless, Mobile Computing, and Mobile Commerce 274 11 Information Systems within the Organization 306 12 Customer Relationship Management 331 13 Supply Chain Management 352 14 Acquiring Information Systems and Applications 370 Plugit 1 Business Processes and Business Process Management 398 Plugit 2 Hardware and Software 411 Plugit 3 Fundamentals of Relational Database Operations 431 Plugit 4 Cloud Computing 441 Plugit 5 Artificial Intelligence 464 Plugit 6 Project Management 477 Plugit 7 Protecting Your Information Assets 488 Index 507

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Book SynopsisThis book presents novel and efficient tools, techniques and approaches for reliability evaluation, reliability analysis, and design of reliable communication networks using graph theoretic concepts. In recent years, human beings have become largely dependent on communication networks, such as computer communication networks, telecommunication networks, mobile switching networks etc., for their day-to-day activities. In today''s world, humans and critical machines depend on these communication networks to work properly. Failure of these communication networks can result in situations where people may find themselves isolated, helpless and exposed to hazards. It is a fact that every component or system can fail and its failure probability increases with size and complexity. The main objective of this book is to devize approaches for reliability modeling and evaluation of such complex networks. Such evaluation helps to understand which network can give us better rTable of ContentsSeries Editor Preface ix Preface xiii 1 Introduction 1 1.1 Introduction 1 1.2 Network Reliability Measures 2 1.3 The Probabilistic Graph Model 4 1.4 Approaches for Network Reliability Evaluation 6 1.5 Motivation and Summary 7 2 Interconnection Networks 11 2.1 Interconnection Networks Classification 11 2.2 Multistage Interconnection Networks (MINs) 14 2.3 Research Issues in MIN Design 15 2.4 Some Existing MINs Implementations 19 2.5 Review of Topological Fault Tolerance 20 2.5.1 Redundant and Disjoint Paths 22 2.5.2 Backtracking 26 2.5.3 Dynamic Rerouting 27 2.6 MIN Topological Review on Disjoint Paths 27 2.6.1 Single-Disjoint Path Multistage Interconnection Networks 27 2.6.2 Two-Disjoint Paths Multistage Interconnection Networks 36 2.6.3 Three-Disjoint Paths Multistage Interconnection Networks 47 2.6.4 Four-Disjoint Paths Multistage Interconnection Networks 51 2.7 Hardware Cost Analysis 55 2.8 Observations 60 2.9 Summary 61 3 MIN Reliability Evaluation Techniques 63 3.1 Reliability Performance Criterion 63 3.1.1 Two Terminal or Terminal Pair Reliability (TPR) 64 3.1.2 Network or All Terminal Reliability (ATR) 64 3.1.3 Broadcast Reliability 65 3.2 Approaches for Reliability Evaluation 66 3.2.1 Continuous Time Markov Chains (CTMC) 67 3.2.2 Matrix Enumeration 67 3.2.3 Conditional Probability (CP) Method 67 3.2.4 Graph Models 69 3.2.5 Decomposition Method 70 3.2.6 Reliability Block Diagram (RBD) 71 3.2.7 Reliability Bounds 73 3.2.7.1 Lower Bound Reliability 75 3.2.7.2 Upper Bound Reliability 76 3.2.8 Monte Carlo Simulation 77 3.2.9 Path-Based or Cut-Based Approaches 78 3.3 Observations 81 4 Terminal Reliability Analysis of MIN Layouts 85 4.1 Chaturvedi and Misra Approach 87 4.1.1 Path Set Enumeration 88 4.1.2 Reliability Evaluation using MVI Techniques 96 4.1.3 Reliability Evaluation Techniques Comparison 99 4.1.3.1 Terminal Reliability of SEN, SEN+ and SEN+2 100 4.1.3.2 Broadcast Reliability of SEN, SEN +, and SEN+2 101 4.1.3.3 Comparison 102 4.2 Reliability Analysis of Multistage Interconnection Networks 104 4.3 Summary 113 5 Comprehensive MIN Reliability Paradigms Evaluation 115 5.1 Introduction 115 5.2 Reliability Evaluation Approach 119 5.2.1 Path Set Enumeration 120 5.2.1.1 Assumptions 120 5.2.1.2 Applied Approach 121 5.2.1.3 Path Tracing Algorithm (PTA) 122 5.2.1.4 Path Retrieval Algorithm (PRA) 123 5.3 Reliability Evaluation Using MVI Techniques 140 5.4 Summary 156 6 Dynamic Tolerant and Reliable Four Disjoint MIN Layouts 157 6.1 Topological Design Considerations 160 6.1.1 Topology 161 6.1.2 Switch Selection for Proposed 4DMIN 162 6.2 Proposed 4-Disjoint Multistage Interconnection Network (4DMIN) Layout 164 6.2.1 Switching Pattern 164 6.2.2 Redundant and Disjoint Paths 165 6.2.3 Routing and Dynamic Rerouting 166 6.2.4 Algorithm: Decision Making by Switches at Each Stage 168 6.2.5 Case Example 170 6.2.6 Disjoint and Dynamic Rerouting Approach in 4DMIN 172 6.2.7 Hardware Cost Analysis 172 6.3 Reliability Analysis and Comparison of MINs 174 6.4 Reliable Interconnection Network (RIN) Layout 181 6.4.1 Topology Design 185 6.4.2 Switching Pattern 187 6.4.3 Routing and Dynamic Rerouting 189 6.5 Reliability Analysis and Comparison of MINs 197 6.6 Summary 201 References 203 Index 213

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Book SynopsisA Sybex guide to Windows Security concepts, perfect for IT beginners Security is one of the most important components to every company's computer network. That's why the Security Fundamentals MTA Certification is so highly sought after. Filling IT positions is a top problem in today's businesses, so this certification could be your first step toward a stable and lucrative IT career. Security Fundamentals is your guide to developing a strong foundational understanding of Windows security, so you can take your IT career to the next level and feel confident going into the certification exam. Security Fundamentals features approachable discussion of core security concepts and topics, and includes additional learning tutorials and tools. This book covers everything you need to know about security layers, authentication, authorization, security policies, and protecting your server and client. Each chapter closes with a quiz so you can test your knowledge before moving to the next section.Table of ContentsIntroduction xix Lesson 1 Understanding Security Layers 1 Introducing Core Security Principles 3 Understanding Confidentiality 4 Understanding Integrity 4 Understanding Availability 5 Understanding the Principle of Least Privilege 7 Understanding Separation of Duties 9 Understanding an Attack Surface 10 Performing an Attack Surface Analysis 10 Understanding Social Engineering 12 Linking Cost with Security 13 Understanding Physical Security as the First Line of Defense 14 Understanding Site Security 14 Understanding Computer Security 19 Performing Threat Modeling 23 Skill Summary 25 Knowledge Assessment 27 Multiple Choice 27 Fill in the Blank 29 Matching and Identification 29 Build List 30 Business Case Scenarios 30 Scenario 1-1: Designing a Physical Security Solution 30 Scenario 1-2: Securing a Mobile Device 30 Scenario 1-3: Understanding Confidentiality, Integrity, and Availability 30 Scenario 1-4: Managing Social Engineering 30 Lesson 2 Understanding Authentication, Authorization, and Accounting 33 Starting Security with Authentication 35 Configuring Multifactor Authentication 36 Authentication Based on What a User Owns or Possesses 38 Authentication Based on a User’s Physical Traits 38 Introducing RADIUS and TACACS+ 39 Running Programs as an Administrator 40 Introducing Directory Services with Active Directory 41 Understanding Domain Controllers 42 Understanding NTLM 43 Understanding Kerberos 44 Using Organizational Units 44 Understanding Objects 46 Using Groups 49 Understanding Web Server Authentication 52 Comparing Rights and Permissions 52 Understanding NTFS 54 Using NTFS Permissions 54 Understanding Effective NTFS Permissions 56 Understanding Inheritance 60 Copying and Moving Files 62 Using Folder and File Owners 62 Sharing Drives and Folders 64 Share a Folder 64 Understanding Special Shares and Administrative Shares 66 Introducing the Registry 67 Access Registry Permissions 70 Using Encryption to Protect Data 70 Types of Encryption 71 Introducing Public Key Infrastructure (PKI) 72 Encrypting Email 78 Encrypting Files with EFS 79 Encrypting Disks in Windows 82 Understanding IPsec 87 Encrypting with VPN Technology 89 Introducing Smart Cards 92 Set Up a Virtual TPM Smart Card Environment 93 Create a Certificate Template 93 Create a TPM Virtual Smart Card 94 Enroll for the Certificate on the TPM Virtual Smart Card 94 Configuring Biometrics, Windows Hello, and Microsoft Passport 95 Set Up Windows Hello Facial Recognition 96 Set Up Windows Hello Fingerprint Reader 96 Using Auditing to Complete the Security Picture 97 Audit Files and Folders 100 Skill Summary 101 Knowledge Assessment 105 Multiple Choice 105 Fill in the Blank 107 Business Case Scenarios 108 Scenario 2-1: Understanding Biometrics 108 Scenario 2-2: Limiting Auditing 108 Scenario 2-3: Assigning NTFS Permissions 108 Scenario 2-4: Using EFS 108 Lesson 3 Understanding Security Policies 111 Using Password Policies to Enhance Security 113 Using Password Complexity to Make a Stronger Password 113 Using Account Lockout to Prevent Hacking 114 Examining Password Length 115 Using Password History to Enforce Security 115 Setting Time Between Password Changes 116 Using Password Group Policies to Enforce Password Security 118 Configuring and Applying Password Settings Objects 119 Establishing Password Procedures 121 Understanding Common Attack Methods 122 Protecting Domain User Account Passwords 125 Install Hyper-V and Isolated User Mode on Windows 10 126 Enable Device Guard and Credential Guard 126 Skill Summary 127 Knowledge Assessment 129 Multiple Choice 129 Fill in the Blank 131 Business Case Scenarios 131 Scenario 3-1: Understanding Long Passwords 131 Scenario 3-2: Using Keys and Passwords 132 Scenario 3-3: Managing User Accounts 132 Scenario 3-4: Configuring a Local Security Policy 132 Lesson 4 Understanding Network Security 133 Using Dedicated Firewalls to Protect a Network 135 Understanding the OSI Model 136 Types of Hardware Firewalls and Their Characteristics 140 Understanding When to Use a Hardware Firewall Instead of a Software Firewall 143 Understanding Stateful Inspection and Stateless Inspection 145 Using Isolation to Protect the Network 146 Understanding VLANs 146 Understanding Routing 148 Understanding Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS) 154 Understanding Honeypots 155 Understanding DMZ 156 Understanding NAT 159 Understanding VPN 160 Understanding Other VPN Protocols 162 Understanding Server and Domain Isolation 164 Protecting Data with Protocol Security 165 Understanding Tunneling 166 Understanding DNS Security Extensions (DNSSEC) 167 Understanding Protocol Spoofing 168 Understanding Network Sniffing 168 Understanding Common Attack Methods 170 Understanding Denial-of-Service (DoS) Attacks 173 Securing the Wireless Network 175 Understanding Service Set IDentifier (SSID) 176 Understanding Keys 176 Understanding MAC Filters 178 Understanding the Advantages and Disadvantages of Specific Security Types 178 Skill Summary 179 Knowledge Assessment 182 Multiple Choice 182 Fill in the Blank 184 Business Case Scenarios 185 Scenario 4-1: Using Windows Firewall 185 Scenario 4-2: Using a Routing Table 185 Scenario 4-3: Using Ports 185 Scenario 4-4: Accessing and Configuring Wireless Settings 185 Lesson 5 Protecting the Server and Client 187 Protecting the Client Computer 189 Protecting Your Computer from Malware 189 Configuring Windows Updates 196 Understanding User Account Control (UAC) 200 Using Windows Firewall 203 Using Offline Files 207 Locking Down a Client Computer 207 Managing Client Security Using Windows Defender 208 Remove a Quarantined Item 210 Schedule a Windows Defender Scan 212 Protecting Your Email 213 Managing Spam 214 Email Spoofing 215 Relaying Email 216 Securing Internet Explorer 216 Understanding Cookies and Privacy Settings 216 Using Content Zones 219 Understanding Phishing and Pharming 222 Understanding Secure Sockets Layer (SSL) and Certificates 223 Configuring Microsoft Edge 223 Protecting Your Server 225 Separating Services 225 Using a Read-Only Domain Controller (RODC) 226 Hardening Servers 226 Understanding Secure Dynamic DNS 227 Using Security Baselines 227 Using Security Templates 228 Using Security Compliance Manager 232 Locking Down Devices to Run Only Trusted Applications 235 Access AppLocker 236 Create and Test an AppLocker Rule 238 Export the Local Policy 240 Import the Local Policy 240 Managing Windows Store Apps 241 Configuring the Windows Store 242 Implementing Windows Store Apps 244 Implementing Windows Store for Business 246 Skill Summary 248 Knowledge Assessment 251 Multiple Choice 251 Fill in the Blank 254 Business Case Scenarios 255 Scenario 5-1: Enforcing Physical Security 255 Scenario 5-2: Programming Backdoors 255 Scenario 5-3: Configuring a Windows Defender Quarantine 255 Scenario 5-4: Protecting Your Resources 255 Scenario 5-5: Reviewing Windows Updates 255 Appendix Answer Key 257 Lesson 1: Understanding Security Layers 258 Answers to Knowledge Assessment 258 Answers to Business Case Scenarios 259 Lesson 2: Understanding Authentication, Authorization, and Accounting 260 Answers to Knowledge Assessment 260 Answers to Business Case Scenarios 261 Lesson 3: Understanding Security Policies 263 Answers to Knowledge Assessment 263 Answers to Business Case Scenarios 264 Lesson 4: Understanding Network Security 266 Answers to Knowledge Assessment 266 Answers to Business Case Scenarios 267 Lesson 5: Protecting the Server and Client 270 Answers to Knowledge Assessment 270 Answers to Business Case Scenarios 271 Index 273

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Book SynopsisIn the fast-developing world of Industry 4.0, which combines Extended Reality (XR) technologies, such as Virtual Reality (VR) and Augmented Reality (AR), creating location aware applications to interact with smart objects and smart processes via Cloud Computing strategies enabled with Artificial Intelligence (AI) and the Internet of Things (IoT), factories and processes can be automated and machines can be enabled with self-monitoring capabilities. Smart objects are given the ability to analyze and communicate with each other and their human co-workers, delivering the opportunity for much smoother processes, and freeing up workers for other tasks. Industry 4.0 enabled smart objects can be monitored, designed, tested and controlled via their digital twins, and these processes and controls are visualized in VR/AR. The Industry 4.0 technologies provide powerful, largely unexplored application areas that will revolutionize the way we work, collaborate and live our lives. It is importantTable of ContentsList of Figures xi List of Tables xv Foreword xvii Introduction xix Preface xxiii Acknowledgments xxv Acronyms xxvii Part I Extended Reality Education 1 Mixed Reality Use in Higher Education: Results from an International Survey 3J. Riman, N. Winters, J. Zelenak, I. Yucel, J. G. Tromp 1.1 Introduction 4 1.2 Organizational Framework 4 1.3 Online Survey About MR Usage 5 1.4 Results 6 1.4.1 Use in Classrooms 8 1.4.2 Challenges 9 1.4.3 Examples of Research in Action 10 1.4.4 Hardware and Software for Use in Classrooms and Research 10 1.4.5 Challenges Described by Researcher Respondents 12 1.4.6 Anecdotal Responses about Challenges 12 1.5 Conclusion 13 References 15 2 Applying 3D VR Technology for Human Body Simulation to Teaching, Learning and Studying 17Le Van Chung, Gia Nhu Nguyen, Tung Sanh Nguyen, Tri Huu Nguyen, Dac-Nhuong Le 2.1 Introduction 18 2.2 Related Works 18 2.3 3D Human Body Simulation System 19 2.3.1 The Simulated Human Anatomy Systems 19 2.3.2 Simulated Activities and Movements 20 2.3.3 Evaluation of the System 23 2.4 Discussion of Future Work 25 2.5 Conclusion 26 References 26 Part II Internet of Things 3 A Safety Tracking and Sensor System for School Buses in Saudi Arabia 31Samah Abbas, Hajar Mohammed, Laila Almalki Maryam Hassan, Maram Meccawy 3.1 Introduction 32 3.2 Related Work 32 3.3 Data Gathering Phase 33 3.3.1 Questionnaire 34 3.3.2 Driver Interviews 35 3.4 The Proposed Safety Tracking and Sensor School Bus System 36 3.4.1 System Analysis and Design 37 3.4.2 User Interface Design 38 3.5 Testing and Results 41 3.6 Discussion and Limitation 42 3.7 Conclusions and Future Work 42 References 42 4 A Lightweight Encryption Algorithm Applied to a Quantized Speech Image for Secure IoT 45Mourad Talbi 4.1 Introduction 46 4.2 Applications of IoT 46 4.3 Security Challenges in IoT 47 4.4 Cryptographic Algorithms for IoT 47 4.5 The Proposed Algorithm 48 4.6 Experimental Setup 50 4.7 Results and Discussion 52 4.8 Conclusion 57 References 58 Part III Mobile Technology 5 The Impact of Social Media Adoption on Entrepreneurial Ecosystem 63Bodor Almotairy, Manal Abdullah, Rabeeh Abbasi 5.1 Introduction 64 5.2 Background 65 5.2.1 Small and Medium-Sized Enterprises (SMEs) 65 5.2.2 Social Media 65 5.2.3 Social Networks and Entrepreneurial Activities 66 5.3 Analysis Methodology 66 5.4 Understanding the Entrepreneurial Ecosystem 67 5.5 Social Media and Entrepreneurial Ecosystem 69 5.5.1 Social Media Platforms and Entrepreneurship 71 5.5.2 The Drivers of Social Media Adoption 71 5.5.3 The Motivations and Benefits for Entrepreneurs to Use Social Media 71 5.5.4 Entrepreneurship Activities Analysis Techniques in Social Media Networks 71 5.6 Research Gap and Recommended Solution 73 5.6.1 Research Gap 73 5.6.2 Recommended Solution 74 5.7 Conclusion 74 References 75 6 Human Factors for E-Health Training System: UX Testing for XR Anatomy Training App 81Zhushun Timothy Cai, Oliver Medonza, Kristen Ray, Chung Van Le, Damian Schofield, Jolanda Tromp 6.1 Introduction 82 6.2 Mobile Learning Applications 82 6.3 Ease of Use and Usability 82 6.3.1 Effectiveness 83 6.3.2 Efficiency 83 6.3.3 Satisfaction 83 6.4 Methods and Materials 86 6.5 Results 89 6.5.1 Task Completion Rate (TCR) 89 6.5.2 Time-on-Task (TOT) 90 6.5.3 After-Scenario Questionnaire (ASQ) 91 6.5.4 Post-Study System Usability Questionnaire (PSSUQ) 93 6.6 Conclusion 93 References 94 Part IV Towards Digital Twins and Robotics 7 Augmented Reality at Heritage Sites: Technological Advances and Embodied Spatially Minded Interactions 101Lesley Johnston, Romy Galloway, Jordan John Trench, Matthieu Poyade, Jolanda Tromp, Hoang Thi My 7.1 Introduction 102 7.2 Augmented Reality Devices 103 7.3 Detection and Tracking 105 7.4 Environmental Variation 106 7.5 Experiential and Embodied Interactions 109 7.6 User Experience and Presence in AR 114 7.7 Conclusion 115 References 116 8 TELECI Architecture for Machine Learning Algorithms Integration in an Existing LMS 121V. Zagorskis, A. Gorbunovs, A. Kapenieks 8.1 Introduction 122 8.2 TELECI Architecture 123 8.2.1 TELECI Interface to a Real LMS 123 8.2.2 First RS Steps in the TELECI System 124 8.2.3 Real Student Data for VS Model 125 8.2.4 TELECI Interface to VS Subsystem 126 8.2.5 TELECI Interface to AI Component 128 8.3 Implementing ML Technique 128 8.3.1 Organizational Activities 128 8.3.2 Data Processing 129 8.3.3 Computing and Networking Resources 130 8.3.4 Introduction to Algorithm 130 8.3.5 Calibration Experiment 132 8.4 Learners’ Activity Issues 133 8.5 Conclusion 136 References 137 Part V Big Data Analytics 9 Enterprise Innovation Management in Industry 4.0: Modeling Aspects 141V. Babenko 9.1 Introduction 142 9.2 Conceptual Model of Enterprise Innovation Process Management 144 9.3 Formation of Restrictions for Enterprise Innovation Management Processes 147 9.4 Formation of Quality Criteria for Assessing Implementation of Enterprise Innovation Management Processes 148 9.5 Statement of Optimization Task of Implementation of Enterprise Innovation Management Processes 148 9.6 Structural and Functional Model for Solving the Task of Dynamic 150 9.7 Formulation of the Task of Minimax Program Management of Innovation Processes at Enterprises 152 9.8 General Scheme for Solving the Task of Minimax Program Management of Innovation Processes at the Enterprises 154 9.9 Model of Multicriteria Optimization of Program Management of Innovation Processes 156 9.10 Conclusion 161 References 162 10 Using Simulation for Development of Automobile Gas Diesel Engine Systems and their Operational Control 165Mikhail G. Shatrov, Vladimir V. Sinyavski, Andrey Yu. Dunin, Ivan G. Shishlov, Sergei D. Skorodelov, Andrey L. Yakovenko 10.1 Introduction 166 10.2 Computer Modeling 167 10.3 Gas Diesel Engine Systems Developed 168 10.3.1 Electronic Engine Control System 168 10.3.2 Modular Gas Feed System 169 10.3.3 Common Rail Fuel System for Supply of the Ignition Portion of Diesel Fuel 169 10.4 Results and Discussion 172 10.4.1 Results of Diesel Fuel Supply System Simulation 172 10.4.2 Results of Engine Bed Tests 181 10.5 Conclusion 183 References 184 Part VI Towards Cognitive Computing 11 Classification of Concept Drift in Evolving Data Stream 189Mashail Althabiti and Manal Abdullah 11.1 Introduction 190 11.2 Data Mining 190 11.3 Data Stream Mining 191 11.3.1 Data Stream Challenges 191 11.3.2 Features of Data Stream Methods 193 11.4 Data Stream Sources 193 11.5 Data Stream Mining Components 193 11.5.1 Input 194 11.5.2 Estimators 194 11.6 Data Stream Classification and Concept Drift 194 11.6.1 Data Stream Classification 194 11.6.2 Concept Drift 194 11.6.3 Data Stream Classification Algorithms with Concept Drift 196 11.6.4 Single Classifier 196 11.6.5 Ensemble Classifiers 197 11.6.6 Output 200 11.7 Datasets 200 11.8 Evaluation Measures 200 11.9 Data Stream Mining Tools 201 11.10 Data Stream Mining Applications 202 11.11 Conclusion 202 References 202 12 Dynamical Mass Transfer Systems in Buslaev Contour Networks with Conflicts 207Marina Yashina, Alexander Tatashev, Ivan Kuteynikov 12.1 Introduction 208 12.2 Construction of Buslaev Contour Networks 210 12.3 Concept of Spectrum 211 12.4 One-Dimensional Contour Network Binary Chain of Contours 212 12.5 Two-Dimensional Contour Network-Chainmail 214 12.6 Random Process with Restrictions on the Contour with the Possibility of Particle Movement in Both Directions 218 12.7 Conclusion 218 References 219 13 Parallel Simulation and Visualization of Traffic Flows Using Cellular Automata Theory and QuasigasDynamic Approach 223Antonina Chechina, Natalia Churbanova, Pavel Sokolov, Marina Trapeznikova, Mikhail German, Alexey Ermakov, Obidzhon Bozorov 13.1 Introduction 224 13.2 The Original CA Model 224 13.3 The Slow-to-Start Version of the CA Model 225 13.4 Numerical Realization 225 13.5 Test Predictions for the CA Model 229 13.6 The QGD Approach to Traffic Flow Modeling 230 13.7 Parallel Implementation of the QGD Traffic Model 232 13.8 Test Predictions for the QGD Traffic Model 232 13.9 Conclusion 235 References 236

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Book SynopsisThis book is intended for academic and industrial developers, exploring and developing applications in the area of big data and machine learning, including those that are solving technology requirements, evaluation of methodology advances and algorithm demonstrations. The intent of this book is to provide awareness of algorithms used for machine learning and big data in the academic and professional community. The 17 chapters are divided into 5 sections: Theoretical Fundamentals; Big Data and Pattern Recognition; Machine Learning: Algorithms & Applications; Machine Learning''s Next Frontier and Hands-On and Case Study. While it dwells on the foundations of machine learning and big data as a part of analytics, it also focuses on contemporary topics for research and development. In this regard, the book covers machine learning algorithms and their modern applications in developing automated systems. Subjects covered in detail include: Mathematical fouTable of ContentsPreface xix Section 1: Theoretical Fundamentals 1 1 Mathematical Foundation 3Afroz and Basharat Hussain 1.1 Concept of Linear Algebra 3 1.1.1 Introduction 3 1.1.2 Vector Spaces 5 1.1.3 Linear Combination 6 1.1.4 Linearly Dependent and Independent Vectors 7 1.1.5 Linear Span, Basis and Subspace 8 1.1.6 Linear Transformation (or Linear Map) 9 1.1.7 Matrix Representation of Linear Transformation 10 1.1.8 Range and Null Space of Linear Transformation 13 1.1.9 Invertible Linear Transformation 15 1.2 Eigenvalues, Eigenvectors, and Eigendecomposition of a Matrix 15 1.2.1 Characteristics Polynomial 16 1.2.1.1 Some Results on Eigenvalue 16 1.2.2 Eigendecomposition 18 1.3 Introduction to Calculus 20 1.3.1 Function 20 1.3.2 Limits of Functions 21 1.3.2.1 Some Properties of Limits 22 1.3.2.2 1nfinite Limits 25 1.3.2.3 Limits at Infinity 26 1.3.3 Continuous Functions and Discontinuous Functions 26 1.3.3.1 Discontinuous Functions 27 1.3.3.2 Properties of Continuous Function 27 1.3.4 Differentiation 28 References 29 2 Theory of Probability 31Parvaze Ahmad Dar and Afroz 2.1 Introduction 31 2.1.1 Definition 31 2.1.1.1 Statistical Definition of Probability 31 2.1.1.2 Mathematical Definition of Probability 32 2.1.2 Some Basic Terms of Probability 32 2.1.2.1 Trial and Event 32 2.1.2.2 Exhaustive Events (Exhaustive Cases) 33 2.1.2.3 Mutually Exclusive Events 33 2.1.2.4 Equally Likely Events 33 2.1.2.5 Certain Event or Sure Event 33 2.1.2.6 Impossible Event or Null Event (ϕ) 33 2.1.2.7 Sample Space 34 2.1.2.8 Permutation and Combination 34 2.1.2.9 Examples 35 2.2 Independence in Probability 38 2.2.1 Independent Events 38 2.2.2 Examples: Solve the Following Problems 38 2.3 Conditional Probability 41 2.3.1 Definition 41 2.3.2 Mutually Independent Events 42 2.3.3 Examples 42 2.4 Cumulative Distribution Function 43 2.4.1 Properties 44 2.4.2 Example 44 2.5 Baye’s Theorem 46 2.5.1 Theorem 46 2.5.1.1 Examples 47 2.6 Multivariate Gaussian Function 50 2.6.1 Definition 50 2.6.1.1 Univariate Gaussian (i.e., One Variable Gaussian) 50 2.6.1.2 Degenerate Univariate Gaussian 51 2.6.1.3 Multivariate Gaussian 51 References 51 3 Correlation and Regression 53Mohd. Abdul Haleem Rizwan 3.1 Introduction 53 3.2 Correlation 54 3.2.1 Positive Correlation and Negative Correlation 54 3.2.2 Simple Correlation and Multiple Correlation 54 3.2.3 Partial Correlation and Total Correlation 54 3.2.4 Correlation Coefficient 55 3.3 Regression 57 3.3.1 Linear Regression 64 3.3.2 Logistic Regression 64 3.3.3 Polynomial Regression 65 3.3.4 Stepwise Regression 66 3.3.5 Ridge Regression 67 3.3.6 Lasso Regression 67 3.3.7 Elastic Net Regression 68 3.4 Conclusion 68 References 69 Section 2: Big Data and Pattern Recognition 71 4 Data Preprocess 73Md. Sharif Hossen 4.1 Introduction 73 4.1.1 Need of Data Preprocessing 74 4.1.2 Main Tasks in Data Preprocessing 75 4.2 Data Cleaning 77 4.2.1 Missing Data 77 4.2.2 Noisy Data 78 4.3 Data Integration 80 4.3.1 χ2 Correlation Test 82 4.3.2 Correlation Coefficient Test 82 4.3.3 Covariance Test 83 4.4 Data Transformation 83 4.4.1 Normalization 83 4.4.2 Attribute Selection 85 4.4.3 Discretization 86 4.4.4 Concept Hierarchy Generation 86 4.5 Data Reduction 88 4.5.1 Data Cube Aggregation 88 4.5.2 Attribute Subset Selection 90 4.5.3 Numerosity Reduction 91 4.5.4 Dimensionality Reduction 95 4.6 Conclusion 101 Acknowledgements 101 References 101 5 Big Data 105R. Chinnaiyan 5.1 Introduction 105 5.2 Big Data Evaluation With Its Tools 107 5.3 Architecture of Big Data 107 5.3.1 Big Data Analytics Framework Workflow 107 5.4 Issues and Challenges 109 5.4.1 Volume 109 5.4.2 Variety of Data 110 5.4.3 Velocity 110 5.5 Big Data Analytics Tools 110 5.6 Big Data Use Cases 114 5.6.1 Banking and Finance 114 5.6.2 Fraud Detection 114 5.6.3 Customer Division and Personalized Marketing 114 5.6.4 Customer Support 115 5.6.5 Risk Management 116 5.6.6 Life Time Value Prediction 116 5.6.7 Cyber Security Analytics 117 5.6.8 Insurance Industry 118 5.6.9 Health Care Sector 118 5.6.9.1 Big Data Medical Decision Support 120 5.6.9.2 Big Data–Based Disorder Management 120 5.6.9.3 Big Data–Based Patient Monitoring and Control 120 5.6.9.4 Big Data–Based Human Routine Analytics 120 5.6.10 Internet of Things 121 5.6.11 Weather Forecasting 121 5.7 Where IoT Meets Big Data 122 5.7.1 IoT Platform 122 5.7.2 Sensors or Devices 123 5.7.3 Device Aggregators 123 5.7.4 IoT Gateway 123 5.7.5 Big Data Platform and Tools 124 5.8 Role of Machine Learning For Big Data and IoT 124 5.8.1 Typical Machine Learning Use Cases 125 5.9 Conclusion 126 References 127 6 Pattern Recognition Concepts 131Ambeshwar Kumar, R. Manikandan and C. Thaventhiran 6.1 Classifier 132 6.1.1 Introduction 132 6.1.2 Explanation-Based Learning 133 6.1.3 Isomorphism and Clique Method 135 6.1.4 Context-Dependent Classification 138 6.1.5 Summary 139 6.2 Feature Processing 140 6.2.1 Introduction 140 6.2.2 Detection and Extracting Edge With Boundary Line 141 6.2.3 Analyzing the Texture 142 6.2.4 Feature Mapping in Consecutive Moving Frame 143 6.2.5 Summary 145 6.3 Clustering 145 6.3.1 Introduction 145 6.3.2 Types of Clustering Algorithms 146 6.3.2.1 Dynamic Clustering Method 148 6.3.2.2 Model-Based Clustering 148 6.3.3 Application 149 6.3.4 Summary 150 6.4 Conclusion 151 References 151 Section 3: Machine Learning: Algorithms & Applications 153 7 Machine Learning 155Elham Ghanbari and Sara Najafzadeh 7.1 History and Purpose of Machine Learning 155 7.1.1 History of Machine Learning 155 7.1.1.1 What is Machine Learning? 156 7.1.1.2 When the Machine Learning is Needed? 157 7.1.2 Goals and Achievements in Machine Learning 158 7.1.3 Applications of Machine Learning 158 7.1.3.1 Practical Machine Learning Examples 159 7.1.4 Relation to Other Fields 161 7.1.4.1 Data Mining 161 7.1.4.2 Artificial Intelligence 162 7.1.4.3 Computational Statistics 162 7.1.4.4 Probability 163 7.1.5 Limitations of Machine Learning 163 7.2 Concept of Well-Defined Learning Problem 164 7.2.1 Concept Learning 164 7.2.1.1 Concept Representation 166 7.2.1.2 Instance Representation 167 7.2.1.3 The Inductive Learning Hypothesis 167 7.2.2 Concept Learning as Search 167 7.2.2.1 Concept Generality 168 7.3 General-to-Specific Ordering Over Hypotheses 169 7.3.1 Basic Concepts: Hypothesis, Generality 169 7.3.2 Structure of the Hypothesis Space 169 7.3.2.1 Hypothesis Notations 169 7.3.2.2 Hypothesis Evaluations 170 7.3.3 Ordering on Hypotheses: General to Specific 170 7.3.3.1 Most Specific Generalized 171 7.3.3.2 Most General Specialized 173 7.3.3.3 Generalization and Specialization Operators 173 7.3.4 Hypothesis Space Search by Find-S Algorithm 174 7.3.4.1 Properties of the Find-S Algorithm 176 7.3.4.2 Limitations of the Find-S Algorithm 176 7.4 Version Spaces and Candidate Elimination Algorithm 177 7.4.1 Representing Version Spaces 177 7.4.1.1 General Boundary 178 7.4.1.2 Specific Boundary 178 7.4.2 Version Space as Search Strategy 179 7.4.3 The List-Eliminate Method 179 7.4.4 The Candidate-Elimination Method 180 7.4.4.1 Example 181 7.4.4.2 Convergence of Candidate-Elimination Method 183 7.4.4.3 Inductive Bias for Candidate-Elimination 184 7.5 Concepts of Machine Learning Algorithm 185 7.5.1 Types of Learning Algorithms 185 7.5.1.1 Incremental vs. Batch Learning Algorithms 186 7.5.1.2 Offline vs. Online Learning Algorithms 188 7.5.1.3 Inductive vs. Deductive Learning Algorithms 189 7.5.2 A Framework for Machine Learning Algorithms 189 7.5.2.1 Training Data 190 7.5.2.2 Target Function 190 7.5.2.3 Construction Model 191 7.5.2.4 Evaluation 191 7.5.3 Types of Machine Learning Algorithms 194 7.5.3.1 Supervised Learning 196 7.5.3.2 Unsupervised Learning 198 7.5.3.3 Semi Supervised Learning 200 7.5.3.4 Reinforcement Learning 200 7.5.3.5 Deep Learning 202 7.5.4 Types of Machine Learning Problems 203 7.5.4.1 Classification 204 7.5.4.2 Clustering 204 7.5.4.3 Optimization 205 7.5.4.4 Regression 205 Conclusion 205 References 206 8 Performance of Supervised Learning Algorithms on Multi-Variate Datasets 209Asif Iqbal Hajamydeen and Rabab Alayham Abbas Helmi 8.1 Introduction 209 8.2 Supervised Learning Algorithms 210 8.2.1 Datasets and Experimental Setup 211 8.2.2 Data Treatment/Preprocessing 212 8.3 Classification 212 8.3.1 Support Vector Machines (SVM) 213 8.3.2 Naive Bayes (NB) Algorithm 214 8.3.3 Bayesian Network (BN) 214 8.3.4 Hidden Markov Model (HMM) 215 8.3.5 K-Nearest Neighbour (KNN) 216 8.3.6 Training Time 216 8.4 Neural Network 217 8.4.1 Artificial Neural Networks Architecture 219 8.4.2 Application Areas 222 8.4.3 Artificial Neural Networks and Time Series 224 8.5 Comparisons and Discussions 225 8.5.1 Comparison of Classification Accuracy 225 8.5.2 Forecasting Efficiency Comparison 226 8.5.3 Recurrent Neural Network (RNN) 226 8.5.4 Backpropagation Neural Network (BPNN) 228 8.5.5 General Regression Neural Network 229 8.6 Summary and Conclusion 230 References 231 9 Unsupervised Learning 233M. Kumara Swamy and Tejaswi Puligilla 9.1 Introduction 233 9.2 Related Work 234 9.3 Unsupervised Learning Algorithms 235 9.4 Classification of Unsupervised Learning Algorithms 238 9.4.1 Hierarchical Methods 238 9.4.2 Partitioning Methods 239 9.4.3 Density-Based Methods 242 9.4.4 Grid-Based Methods 245 9.4.5 Constraint-Based Clustering 245 9.5 Unsupervised Learning Algorithms in ML 246 9.5.1 Parametric Algorithms 246 9.5.2 Non-Parametric Algorithms 246 9.5.3 Dirichlet Process Mixture Model 247 9.5.4 X-Means 248 9.6 Summary and Conclusions 248 References 248 10 Semi-Supervised Learning 251Manish Devgan, Gaurav Malik and Deepak Kumar Sharma 10.1 Introduction 252 10.1.1 Semi-Supervised Learning 252 10.1.2 Comparison With Other Paradigms 255 10.2 Training Models 257 10.2.1 Self-Training 257 10.2.2 Co-Training 259 10.3 Generative Models—Introduction 261 10.3.1 Image Classification 264 10.3.2 Text Categorization 266 10.3.3 Speech Recognition 268 10.3.4 Baum-Welch Algorithm 268 10.4 S3VMs 270 10.5 Graph-Based Algorithms 274 10.5.1 Mincut 275 10.5.2 Harmonic 276 10.5.3 Manifold Regularization 277 10.6 Multiview Learning 277 10.7 Conclusion 278 References 279 11 Reinforcement Learning 281Amandeep Singh Bhatia, Mandeep Kaur Saggi, Amit Sundas and Jatinder Ashta 11.1 Introduction: Reinforcement Learning 281 11.1.1 Elements of Reinforcement Learning 283 11.2 Model-Free RL 284 11.2.1 Q-Learning 285 11.2.2 R-Learning 286 11.3 Model-Based RL 287 11.3.1 SARSA Learning 289 11.3.2 Dyna-Q Learning 290 11.3.3 Temporal Difference 291 11.3.3.1 TD(0) Algorithm 292 11.3.3.2 TD(1) Algorithm 293 11.3.3.3 TD(λ) Algorithm 294 11.3.4 Monte Carlo Method 294 11.3.4.1 Monte Carlo Reinforcement Learning 296 11.3.4.2 Monte Carlo Policy Evaluation 296 11.3.4.3 Monte Carlo Policy Improvement 298 11.4 Conclusion 298 References 299 12 Application of Big Data and Machine Learning 305Neha Sharma, Sunil Kumar Gautam, Azriel A. Henry and Abhimanyu Kumar 12.1 Introduction 306 12.2 Motivation 307 12.3 Related Work 308 12.4 Application of Big Data and ML 309 12.4.1 Healthcare 309 12.4.2 Banking and Insurance 312 12.4.3 Transportation 314 12.4.4 Media and Entertainment 316 12.4.5 Education 317 12.4.6 Ecosystem Conservation 319 12.4.7 Manufacturing 321 12.4.8 Agriculture 322 12.5 Issues and Challenges 324 12.6 Conclusion 326 References 326 Section 4: Machine Learning’s Next Frontier 335 13 Transfer Learning 337Riyanshi Gupta, Kartik Krishna Bhardwaj and Deepak Kumar Sharma 13.1 Introduction 338 13.1.1 Motivation, Definition, and Representation 338 13.2 Traditional Learning vs. Transfer Learning 338 13.3 Key Takeaways: Functionality 340 13.4 Transfer Learning Methodologies 341 13.5 Inductive Transfer Learning 342 13.6 Unsupervised Transfer Learning 344 13.7 Transductive Transfer Learning 346 13.8 Categories in Transfer Learning 347 13.9 Instance Transfer 348 13.10 Feature Representation Transfer 349 13.11 Parameter Transfer 349 13.12 Relational Knowledge Transfer 350 13.13 Relationship With Deep Learning 351 13.13.1 Transfer Learning in Deep Learning 351 13.13.2 Types of Deep Transfer Learning 352 13.13.3 Adaptation of Domain 352 13.13.4 Domain Confusion 353 13.13.5 Multitask Learning 354 13.13.6 One-Shot Learning 354 13.13.7 Zero-Shot Learning 355 13.14 Applications: Allied Classical Problems 355 13.14.1 Transfer Learning for Natural Language Processing 356 13.14.2 Transfer Learning for Computer Vision 356 13.14.3 Transfer Learning for Audio and Speech 357 13.15 Further Advancements and Conclusion 357 References 358 Section 5: Hands-On and Case Study 361 14 Hands on MAHOUT—Machine Learning ToolUma N. Dulhare and Sheikh Gouse 14.1 Introduction to Mahout 363 14.1.1 Features 366 14.1.2 Advantages 366 14.1.3 Disadvantages 366 14.1.4 Application 366 14.2 Installation Steps of Apache Mahout Using Cloudera 367 14.2.1 Installation of VMware Workstation 367 14.2.2 Installation of Cloudera 368 14.2.3 Installation of Mahout 383 14.2.4 Installation of Maven 384 14.2.5 Testing Mahout 386 14.3 Installation Steps of Apache Mahout Using Windows 10 386 14.3.1 Installation of Java 386 14.3.2 Installation of Hadoop 387 14.3.3 Installation of Mahout 387 14.3.4 Installation of Maven 387 14.3.5 Path Setting 388 14.3.6 Hadoop Configuration 391 14.4 Installation Steps of Apache Mahout Using Eclipse 395 14.4.1 Eclipse Installation 395 14.4.2 Installation of Maven Through Eclipse 396 14.4.3 Maven Setup for Mahout Configuration 399 14.4.4 Building the Path- 402 14.4.5 Modifying the pom.xml File 405 14.4.6 Creating the Data File 407 14.4.7 Adding External Jar Files 408 14.4.8 Creating the New Package and Classes 410 14.4.9 Result 411 14.5 Mahout Algorithms 412 14.5.1 Classification 412 14.5.2 Clustering 413 14.5.3 Recommendation 415 14.6 Conclusion 418 References 418 15 Hands-On H2O Machine Learning Tool 423Uma N. Dulhare, Azmath Mubeen and Khaleel Ahmed 15.1 Introduction 424 15.2 Installation 425 15.2.1 The Process of Installation 425 15.3 Interfaces 431 15.4 Programming Fundamentals 432 15.4.1 Data Manipulation 432 15.4.1.1 Data Types 432 15.4.1.2 Data Import 435 15.4.2 Models 436 15.4.2.1 Model Training 436 15.4.3 Discovering Aspects 437 15.4.3.1 Converting Data Frames 437 15.4.4 H2O Cluster Actions 438 15.4.4.1 H2O Key Value Retrieval 438 15.4.4.2 H2O Cluster Connection 438 15.4.5 Commands 439 15.4.5.1 Cluster Information 439 15.4.5.2 General Data Operations 441 15.4.5.3 String Manipulation Commands 442 15.5 Machine Learning in H2O 442 15.5.1 Supervised Learning 442 15.5.2 Unsupervised Learning 443 15.6 Applications of H2O 443 15.6.1 Deep Learning 443 15.6.2 K-Fold Cross-Authentication or Validation 448 15.6.3 Stacked Ensemble and Random Forest Estimator 450 15.7 Conclusion 452 References 453 16 Case Study: Intrusion Detection System Using Machine Learning 455Syeda Hajra Mahin, Fahmina Taranum and Reshma Nikhat 16.1 Introduction 456 16.1.1 Components Used to Design the Scenario Include 456 16.1.1.1 Black Hole 456 16.1.1.2 Intrusion Detection System 457 16.1.1.3 Components Used From MATLAB Simulator 458 16.2 System Design 465 16.2.1 Three Sub-Network Architecture 465 16.2.2 Using Classifiers of MATLAB 465 16.3 Existing Proposals 467 16.4 Approaches Used in Designing the Scenario 469 16.4.1 Algorithm Used in QualNet 469 16.4.2 Algorithm Applied in MATLAB 471 16.5 Result Analysis 471 16.5.1 Results From QualNet 471 16.5.1.1 Deployment 471 16.5.1.2 Detection 472 16.5.1.3 Avoidance 473 16.5.1.4 Validation of Conclusion 473 16.5.2 Applying Results to MATLAB 473 16.5.2.1 K-Nearest Neighbor 475 16.5.2.2 SVM 477 16.5.2.3 Decision Tree 477 16.5.2.4 Naive Bayes 479 16.5.2.5 Neural Network 479 16.6 Conclusion 484 References 484 17 Inclusion of Security Features for Implications of Electronic Governance Activities 487Prabal Pratap and Nripendra Dwivedi 17.1 Introduction 487 17.2 Objective of E-Governance 491 17.3 Role of Identity in E-Governance 493 17.3.1 Identity 493 17.3.2 Identity Management and its Buoyancy Against Identity Theft in E-Governance 494 17.4 Status of E-Governance in Other Countries 496 17.4.1 E-Governance Services in Other Countries Like Australia and South Africa 496 17.4.2 Adaptation of Processes and Methodology for Developing Countries 496 17.4.3 Different Programs Related to E-Governance 499 17.5 Pros and Cons of E-Governance 501 17.6 Challenges of E-Governance in Machine Learning 502 17.7 Conclusion 503 References 503 Index 505

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Book SynopsisTable of ContentsIntroduction 1 About This Book 2 Foolish Assumptions 3 Icons Used in This Book 3 How This Book Is Organized 4 Part 1: Making Cities Our Home 4 Part 2: Building a Smarter City 4 Part 3: Using Smart City Technologies 5 Part 4: Planning for an Urban Future 5 Part 5: The Part of Tens 5 Part 6: Appendixes 6 Beyond the Book 6 Where to Go from Here 7 Part 1: Making Cities Our Home 9 Chapter 1: Comprehending the Past, Present, and Future of Cities 11 Discovering the Origin of Cities 11 What is a city? 12 Building the first cities 14 Comprehending the Impact of the Industrial Revolutions 16 The first industrial revolution 16 The second industrial revolution 17 The third industrial revolution 17 The fourth industrial revolution 19 Responding to population growth 19 Urbanizing the Planet 21 Changing landscapes resulting from urbanization 23 Building megacities 23 Chapter 2: Defining Smart Cities 27 Identifying Smart Cities 27 What a smart city is 28 What a smart city is not 32 Working with digital infrastructures 34 Building the Case for Smarter Cities 35 Small cities versus large cities 36 Smart nations and other smart things 37 United Nations’ Sustainable Development Goals (SDGs) 39 Examining Examples of Smart Cities 42 Amsterdam, The Netherlands 42 Konza Technopolis, Kenya 43 Chapter 3: Responding to the Needs and Challenges of Cities 45 Mapping the Evolving Needs and Challenges of Cities 45 Economic shifts 47 Increasingly complex city requirements 47 Interdependencies between systems 48 Population changes 49 Aging infrastructure 52 Lifestyle choices 54 Environment 55 Health 56 Water management 58 Housing crisis 59 Expecting Different Results 62 Changing community behaviors and expectations 63 Expanding community engagement 64 Engaging in participatory design 65 Transforming Urbanization 66 Transportation 67 Energy 70 Buildings 71 Telecommunications 73 Sustainability 74 Part 2: Building a Smarter City 77 Chapter 4: Starting from Zero 79 Establishing a Vision 79 Identifying the role of city leadership 80 Creating a vision 83 Building a Smart City Team 85 Identifying team members 85 Creating a RACI chart 88 Getting the team on the same page 89 Chapter 5: Creating a Smart City Strategy 91 Building the Plan 92 Developing a strategic plan 93 Envisioning the envisioning process 95 Converting your vision to action 98 Codifying the Plan 100 Identifying metrics 100 Communicating the plan 102 Chapter 6: Enabling a Smart City Strategy 107 Putting the Building Blocks in Place 108 Developing policy 108 Getting started 110 Examining a few examples of smart city policies 111 Establishing regulations 112 Evaluating funding models 114 Handling procurement issues 118 Managing projects and carrying out business analyses 121 Governing the Strategy 125 Defining strategic governance 125 Managing projects with project governance 126 Regularly updating and reporting 128 Part 3: Using Smart City Technologies 131 Chapter 7: Embracing Urban Innovation 133 Defining Urban Innovation 134 Relying on urban innovation networks 136 Creating urban innovation labs 137 Implementing Urban Innovation 139 Examining the discovery process 141 Running pilots and experiments 143 Setting up living labs 144 Engaging in hackathons 145 Participating in urban challenges 149 Open innovation versus closed innovation 151 Sharing urban innovation 152 Converting ideas into projects 153 Chapter 8: Enabling Change through Technology 155 Recognizing Technological Change in Modern Cities 156 From analog to digital 157 The fourth industrial revolution 160 The fourth industrial revolution and cities 162 The Internet of Things (IoT) 163 Exploring a Variety of Urban Technologies 166 Social media and communication tools 166 Artificial intelligence (AI) 169 Blockchain technology 171 Autonomous vehicles (AVs) 175 Drones 178 Wireless communications 181 Smart street lighting 184 Smart grids and microgrids 189 Smart water 192 Digital twins 193 Digital signage 196 Application programming interfaces (APIs) 198 Chapter 9: Unleashing the Power of City Data 205 Becoming City-Data-Savvy 205 Enabling data-driven decision-making 207 Managing data 208 Developing a data strategy 209 Implementing data governance 211 Working with City Data 214 Securing data 214 Opening data 215 Making sense of data through analytics 218 Using geographic information systems (GIS) 220 Hiring a city chief data officer 223 Part 4: Planning For An Urban Future 225 Chapter 10: Building a Secure Foundation 227 Securing Your Smart City 228 Urban resilience 228 Public safety 233 Addressing Digital Security and Privacy 238 Cybersecurity 239 Privacy 241 Chapter 11: Imagining the City of the Future 245 Recognizing That the Best Is Yet to Come 246 Green cities 247 Inclusive cities 251 Healthy cities 253 Regenerative cities 256 Envisioning Big Ideas 259 Hyperloop 260 Flying cars 262 Cities without cars 264 Chapter 12: Engaging in Your City’s Future 267 Embracing an Urban Future 268 An increase in civic engagement 270 Continuous improvement in urban quality of life 274 The difference between quality of life and standard of living 275 Making a Better Tomorrow 278 It’s your community — get involved 280 Five things you can do tomorrow 281 Part 5: The Part of Tens 283 Chapter 13: Ten Smart City Pitfalls to Avoid 285 Making Your Smart City Project a Tech Program and Putting IT in Charge 286 Garnering Insufficient Support and Engagement from Stakeholders 287 Limiting Efforts to Your City Boundaries 288 Paying Insufficient Attention to Inclusiveness Issues 289 Moving Forward with Inadequate Governance 289 Working with No Clear Vision for the Program 290 Downplaying the Essential Roles of Security and Privacy 291 Sharing Successes and Failures Too Narrowly 292 Sticking Stubbornly to the Old Ways of Doing Things 293 Thinking Too Short-Term 294 Chapter 14: Ten Ways Cities Will Define Our Human Future 295 Most People Will Live, Work, and Play Their Entire Lives in Cities 296 The Increasing Demands of Sustainability Will Shape Human Behavior 297 City Interactions Will Increasingly Be Digital 298 City Data Will Drive Community Decision-Making 299 People Will Have Expanded Opportunities to Co-Create and Collaborate on Urban Solutions 300 Crime May Be Reduced Significantly 301 More Diversity Will Show Up in What Humans Do and How They Work 302 The Way People and Goods Move Will Continue to Evolve 303 The Delivery of Healthcare Will Be Transformed 305 Everything Will Be Delivered 307 Part 6: Appendixes 309 Appendix A: Smart City Strategies 311 Africa 311 Asia 312 Australia 313 Europe 315 Middle East 317 North America 319 South America 321 Appendix B: Smart City Organizations 323 Appendix C: Open Data Portals 333 Africa 333 Asia 334 Australia and New Zealand 335 Eastern Europe and Russia 336 Western Europe 337 Middle East 339 North America 340 South America 343 Appendix D: Solutions Built on Open Data 345 Appendix E: City Performance Dashboards 351 Asia 351 Australia and New Zealand 352 Europe 353 Middle East 354 North America 354 South America 356 Index 357

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Book SynopsisEfficient Single Board Computers (SBCs) and advanced VLSI systems have resulted in edge analytics and faster decision making. The QoS parameters like energy, delay, reliability, security, and throughput should be improved on seeking better intelligent expert systems. The resource constraints in the Edge devices, challenges the researchers to meet the required QoS. Since these devices and components work in a remote unattended environment, an optimum methodology to improve its lifetime has become mandatory. Continuous monitoring of events is mandatory to avoid tragic situations; it can only be enabled by providing high QoS. The applications of IoT in digital twin development, health care, traffic analysis, home surveillance, intelligent agriculture monitoring, defense and all common day to day activities have resulted in pioneering embedded devices, which can offer high computational facility without much latency and delay. The book address industrial problems in designing expert systemTable of ContentsPreface xv 1 Smart Health Care Development: Challenges and Solutions 1R. Sujatha, E.P. Ephzibah and S. Sree Dharinya 1.1 Introduction 2 1.2 ICT Explosion 3 1.2.1 RFID 4 1.2.2 IoT and Big Data 5 1.2.3 Wearable Sensors—Head to Toe 7 1.2.4 Cloud Computing 8 1.3 Intelligent Healthcare 10 1.4 Home Healthcare 11 1.5 Data Analytics 11 1.6 Technologies—Data Cognitive 13 1.6.1 Machine Learning 13 1.6.2 Image Processing 14 1.6.3 Deep Learning 14 1.7 Adoption Technologies 15 1.8 Conclusion 15 References 15 2 Working of Mobile Intelligent Agents on the Web—A Survey 21P.R. Joe Dhanith and B. Surendiran 2.1 Introduction 21 2.2 Mobile Crawler 23 2.3 Comparative Study of the Mobile Crawlers 47 2.4 Conclusion 47 References 47 3 Power Management Scheme for Photovoltaic/Battery Hybrid System in Smart Grid 49T. Bharani Prakash and S. Nagakumararaj 3.1 Power Management Scheme 50 3.2 Internal Power Flow Management 50 3.2.1 PI Controller 51 3.2.2 State of Charge 53 3.3 Voltage Source Control 54 3.3.1 Phase-Locked Loop 55 3.3.2 Space Vector Pulse Width Modulation 56 3.3.3 Park Transformation (abc to dq0) 57 3.4 Simulation Diagram and Results 58 3.4.1 Simulation Diagram 58 3.4.2 Simulation Results 63 Conclusion 65 4 Analysis: A Neural Network Equalizer for Channel Equalization by Particle Swarm Optimization for Various Channel Models 67M. Muthumari, D.C. Diana and C. Ambika Bhuvaneswari 4.1 Introduction 68 4.2 Channel Equalization 72 4.2.1 Channel Models 73 4.2.1.1 Tapped Delay Line Model 74 4.2.1.2 Stanford University Interim (SUI) Channel Models 75 4.2.2 Artificial Neural Network 75 4.3 Functional Link Artificial Neural Network 76 4.4 Particle Swarm Optimization 76 4.5 Result and Discussion 77 4.5.1 Convergence Analysis 77 4.5.2 Comparison Between Different Parameters 79 4.5.3 Comparison Between Different Channel Models 80 4.6 Conclusion 81 References 82 5 Implementing Hadoop Container Migrations in OpenNebula Private Cloud Environment 85P. Kalyanaraman, K.R. Jothi, P. Balakrishnan, R.G. Navya, A. Shah and V. Pandey 5.1 Introduction 86 5.1.1 Hadoop Architecture 86 5.1.2 Hadoop and Big Data 88 5.1.3 Hadoop and Virtualization 88 5.1.4 What is OpenNebula? 89 5.2 Literature Survey 90 5.2.1 Performance Analysis of Hadoop 90 5.2.2 Evaluating Map Reduce on Virtual Machines 91 5.2.3 Virtualizing Hadoop Containers 94 5.2.4 Optimization of Hadoop Cluster Using Cloud Platform 95 5.2.5 Heterogeneous Clusters in Cloud Computing 96 5.2.6 Performance Analysis and Optimization in Hadoop 97 5.2.7 Virtual Technologies 97 5.2.8 Scheduling 98 5.2.9 Scheduling of Hadoop VMs 98 5.3 Discussion 99 5.4 Conclusion 100 References 101 6 Transmission Line Inspection Using Unmanned Aerial Vehicle 105A. Mahaboob Subahani, M. Kathiresh and S. Sanjeev 6.1 Introduction 106 6.1.1 Unmanned Aerial Vehicle 106 6.1.2 Quadcopter 106 6.2 Literature Survey 107 6.3 System Architecture 108 6.4 ArduPilot 109 6.5 Arduino Mega 111 6.6 Brushless DC Motor 111 6.7 Battery 112 6.8 CMOS Camera 113 6.9 Electronic Speed Control 113 6.10 Power Module 115 6.11 Display Shield 116 6.12 Navigational LEDS 116 6.13 Role of Sensors in the Proposed System 118 6.13.1 Accelerometer and Gyroscope 118 6.13.2 Magnetometer 118 6.13.3 Barometric Pressure Sensor 119 6.13.4 Global Positioning System 119 6.14 Wireless Communication 120 6.15 Radio Controller 120 6.16 Telemetry Radio 121 6.17 Camera Transmitter 121 6.18 Results and Discussion 121 6.19 Conclusion 124 References 125 7 Smart City Infrastructure Management System Using IoT 127S. Ramamoorthy, M. Kowsigan, P. Balasubramanie and P. John Paul 7.1 Introduction 128 7.2 Major Challenges in IoT-Based Technology 129 7.2.1 Peer to Peer Communication Security 129 7.2.2 Objective of Smart Infrastructure 130 7.3 Internet of Things (IoT) 131 7.3.1 Key Components of Components of IoT 131 7.3.1.1 Network Gateway 132 7.3.1.2 HTTP (HyperText Transfer Protocol) 132 7.3.1.3 LoRaWan (Long Range Wide Area Network) 133 7.3.1.4 Bluetooth 133 7.3.1.5 ZigBee 133 7.3.2 IoT Data Protocols 133 7.3.2.1 Message Queue Telemetry Transport (MQTT) 133 7.3.2.2 Constrained Application Protocol (CoAP) 134 7.3.2.3 Advanced Message Queuing Protocol (AMQP) 134 7.3.2.4 Data Analytics 134 7.4 Machine Learning-Based Smart Decision-Making Process 135 7.5 Cloud Computing 136 References 138 8 Lightweight Cryptography Algorithms for IoT Resource-Starving Devices 139S. Aruna, G. Usha, P. Madhavan and M.V. Ranjith Kumar 8.1 Introduction 139 8.1.1 Need of the Cryptography 140 8.2 Challenges on Lightweight Cryptography 141 8.3 Hashing Techniques on Lightweight Cryptography 142 8.4 Applications on Lighweight Cryptography 152 8.5 Conclusion 167 References 168 9 Pre-Learning-Based Semantic Segmentation for LiDAR Point Cloud Data Using Self-Organized Map 171K. Rajathi and P. Sarasu 9.1 Introduction 172 9.2 Related Work 173 9.2.1 Semantic Segmentation for Images 173 9.3 Semantic Segmentation for LiDAR Point Cloud 173 9.4 Proposed Work 175 9.4.1 Data Acquisition 175 9.4.2 Our Approach 175 9.4.3 Pre-Learning Processing 179 9.5 Region of Interest (RoI) 180 9.6 Registration of Point Cloud 181 9.7 Semantic Segmentation 181 9.8 Self-Organized Map (SOM) 182 9.9 Experimental Result 183 9.10 Conclusion 186 References 187 10 Smart Load Balancing Algorithms in Cloud Computing—A Review 189K.R. Jothi, S. Anto, M. Kohar, M. Chadha and P. Madhavan 10.1 Introduction 189 10.2 Research Challenges 192 10.2.1 Security & Routing 192 10.2.2 Storage/Replication 192 10.2.3 Spatial Spread of the Cloud Nodes 192 10.2.4 Fault Tolerance 193 10.2.5 Algorithm Complexity 193 10.3 Literature Survey 193 10.4 Survey Table 201 10.5 Discussion & Comparison 202 10.6 Conclusion 202 References 216 11 A Low-Cost Wearable Remote Healthcare Monitoring System 219Konguvel Elango and Kannan Muniandi 11.1 Introduction 219 11.1.1 Problem Statement 220 11.1.2 Objective of the Study 221 11.2 Related Works 222 11.2.1 Remote Healthcare Monitoring Systems 222 11.2.2 Pulse Rate Detection 224 11.2.3 Temperate Measurement 225 11.2.4 Fall Detection 225 11.3 Methodology 226 11.3.1 NodeMCU 226 11.3.2 Pulse Rate Detection System 227 11.3.3 Fall Detection System 230 11.3.4 Temperature Detection System 231 11.3.5 LCD Specification 234 11.3.6 ADC Specification 234 11.4 Results and Discussions 236 11.4.1 System Implementation 236 11.4.2 Fall Detection Results 236 11.4.3 ThingSpeak 236 11.5 Conclusion 239 11.6 Future Scope 240 References 241 12 IoT-Based Secure Smart Infrastructure Data Management 243R. Poorvadevi, M. Kowsigan, P. Balasubramanie and J. Rajeshkumar 12.1 Introduction 244 12.1.1 List of Security Threats Related to the Smart IoT Network 244 12.1.2 Major Application Areas of IoT 244 12.1.3 IoT Threats and Security Issues 245 12.1.4 Unpatched Vulnerabilities 245 12.1.5 Weak Authentication 245 12.1.6 Vulnerable API’s 245 12.2 Types of Threats to Users 245 12.3 Internet of Things Security Management 246 12.3.1 Managing IoT Devices 246 12.3.2 Role of External Devices in IoT Platform 247 12.3.3 Threats to Other Computer Networks 248 12.4 Significance of IoT Security 249 12.4.1 Aspects of Workplace Security 249 12.4.2 Important IoT Security Breaches and IoT Attacks 250 12.5 IoT Security Tools and Legislation 250 12.6 Protection of IoT Systems and Devices 251 12.6.1 IoT Issues and Security Challenges 251 12.6.2 Providing Secured Connections 252 12.7 Five Ways to Secure IoT Devices 253 12.8 Conclusion 255 References 255 13 A Study of Addiction Behavior for Smart Psychological Health Care System 257V. Sabapathi and K.P. Vijayakumar 13.1 Introduction 258 13.2 Basic Criteria of Addiction 258 13.3 Influencing Factors of Addiction Behavior 259 13.3.1 Peers Influence 259 13.3.2 Environment Influence 260 13.3.3 Media Influence 262 13.3.4 Family Group and Society 262 13.4 Types of Addiction and Their Effects 262 13.4.1 Gaming Addiction 263 13.4.2 Pornography Addiction 264 13.4.3 Smart Phone Addiction 265 13.4.4 Gambling Addiction 267 13.4.5 Food Addiction 267 13.4.6 Sexual Addiction 268 13.4.7 Cigarette and Alcohol Addiction 268 13.4.8 Status Expressive Addiction 269 13.4.9 Workaholic Addiction 269 13.5 Conclusion 269 References 270 14 A Custom Cluster Design With Raspberry Pi for Parallel Programming and Deployment of Private Cloud 273Sukesh, B., Venkatesh, K. and Srinivas, L.N.B. 14.1 Introduction 274 14.2 Cluster Design with Raspberry Pi 276 14.2.1 Assembling Materials for Implementing Cluster 276 14.2.1.1 Raspberry Pi4 277 14.2.1.2 RPi 4 Model B Specifications 277 14.2.2 Setting Up Cluster 278 14.2.2.1 Installing Raspbian and Configuring Master Node 279 14.2.2.2 Installing MPICH and MPI4PY 279 14.2.2.3 Cloning the Slave Nodes 279 14.3 Parallel Computing and MPI on Raspberry Pi Cluster 279 14.4 Deployment of Private Cloud on Raspberry Pi Cluster 281 14.4.1 NextCloud Software 281 14.5 Implementation 281 14.5.1 NextCloud on RPi Cluster 281 14.5.2 Parallel Computing on RPi Cluster 282 14.6 Results and Discussions 286 14.7 Conclusion 287 References 287 15 Energy Efficient Load Balancing Technique for Distributed Data Transmission Using Edge Computing 289Karthikeyan, K. and Madhavan, P. 15.1 Introduction 290 15.2 Energy Efficiency Offloading Data Transmission 290 15.2.1 Web-Based Offloading 291 15.3 Energy Harvesting 291 15.3.1 LODCO Algorithm 292 15.4 User-Level Online Offloading Framework (ULOOF) 293 15.5 Frequency Scaling 294 15.6 Computation Offloading and Resource Allocation 295 15.7 Communication Technology 296 15.8 Ultra-Dense Network 297 15.9 Conclusion 299 References 299 16 Blockchain-Based SDR Signature Scheme With Time-Stamp 303Swathi Singh, Divya Satish and Sree Rathna Lakshmi 16.1 Introduction 303 16.2 Literature Study 304 16.2.1 Signatures With Hashes 304 16.2.2 Signature Scheme With Server Support 305 16.2.3 Signatures Scheme Based on Interaction 305 16.3 Methodology 306 16.3.1 Preliminaries 306 16.3.1.1 Hash Trees 306 16.3.1.2 Chains of Hashes 306 16.3.2 Interactive Hash-Based Signature Scheme 307 16.3.3 Significant Properties of Hash-Based Signature Scheme 309 16.3.4 Proposed SDR Scheme Structure 310 16.3.4.1 One-Time Keys 310 16.3.4.2 Server Behavior Authentication 310 16.3.4.3 Pre-Authentication by Repository 311 16.4 SDR Signature Scheme 311 16.4.1 Pre-Requisites 311 16.4.2 Key Generation Algorithm 312 16.4.2.1 Server 313 16.4.3 Sign Algorithm 313 16.4.3.1 Signer 313 16.4.3.2 Server 313 16.4.3.3 Repository 314 16.4.4 Verification Algorithm 314 16.5 Supportive Theory 315 16.5.1 Signing Algorithm Supported by Server 315 16.5.2 Repository Deployment 316 16.5.3 SDR Signature Scheme Setup 316 16.5.4 Results and Observation 316 16.6 Conclusion 317 References 317 Index 321

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Book SynopsisINTELLIGENT CONNECTIVITY AI, IOT, AND 5G Explore the economics and technology of AI, IOT, and 5G integration Intelligent Connectivity: AI, IoT, and 5G delivers a comprehensive technological and economic analysis of intelligent connectivity and the integration of artificial intelligence, Internet of Things (IoT), and 5G. It covers a broad range of topics, including Machine-to-Machine (M2M) architectures, edge computing, cybersecurity, privacy, risk management, IoT architectures, and more. The book offers readers robust statistical data in the form of tables, schematic diagrams, and figures that provide a clear understanding of the topic, along with real-world examples of applications and services of intelligent connectivity in different sectors of the economy. Intelligent Connectivity describes key aspects of the digital transformation coming with the 4th industrial revolution that will touch on industries as disparate as transportation, educatioTable of ContentsPreface Acknowledgement Introduction 1 Technology Adoption and Emerging Trends 1.1 Introduction 1.2 Trends in Business technology 1.3 AI-Fueled Organizations 1.4 Connectivity of Tomorrow 1.5 Moving Beyond Marketing 1.6 Cloud Computing 1.7 Cybersecurity, Privacy, and Risk Management 1.8 Conclusion 2 Telecommunication Transformation and Intelligent Connectivity 2.1 Introduction 2.2 Cybersecurity Concerns in the 5G World 2.3 Positive Effects of Addressing Cybersecurity Challenges in 5G 2.4 Intelligent Connectivity Use-Cases 2.5 Industrial and Manufacturing Operations 2.6 Healthcare 2.7 Public Safety and Security 2.8 Conclusion 3 The Internet of Things (IoT): Potentials and the Future Trends 3.1 Introduction 3.2 Achieving the Future of IoT 3.3 Commercial Opportunities for IoT 3.4 The Industrial Internet of Things 3.5 Future Impact of IoT in Our Industry 3.6 Data Sharing in the IoT Environment 3.7 IoT Devises Environment Operation 3.8 Interoperability Issues of IoT 3.9 IoT-Cloud –Application 3.10 Regulation and Security Issues of IoT 3.11 Achieving IoT Innovations While Tackling Security and Regulation Issues 3.12 Future of IoT 3.13 Conclusion 4 The Wild Wonders of 5G Wireless Technology 4.1 Introduction 4.2 5G Architecture 4.3 5G Applications 4.4 5G Network Architecture 4.5 Security and Issues of 5G 4.6 IoT Devices in 5G Wireless 4.7 Big Data Analytics in 5G 4.8 AI Empowers a Wide Scope of Use Cases 4.9 Conclusion 5 Artificial Intelligence Technology 5.1 Introduction 5.2 Core Concepts of Artificial Intelligence 5.3 Machine Learning and Applications 5.4 Deep Learning 5.5 Neural Networks Follow a Natural Model 5.6 Classifications of Artificial Intelligence 5.7 Trends in Artificial Intelligence 5.8 Challenges of Artificial Intelligence 5.9 Funding Trends in Artificial Intelligence 5.10 Conclusion 6 AI, 5G, & IoT: Driving Forces Towards the Industry Technology Trends 6.1 Introduction 6.2 Fifth Generation of Network Technology 6.3 Internet of Things (IoT) 6.4 Industrial Internet of Things 6.5 IoT in Automotive 6.6 IoT in Agriculture 6.7 AI, IoT, and 5G Security 6.8 Conclusion 7 Intelligent Connectivity: A New Capabilities to Bring Complex Use Cases 7.1 Introduction 7.2 Machine-to-Machine Communication and the Internet of Things 7.3 Convergence of Internet of Things, Artificial Intelligence and 5G 7.4 Intelligent Connectivity Applications 7.5 Challenges and Risks of Intelligent Connectivity 7.6 Recommendations 7.7 Conclusion 8 IoT: Laws, Policies and Regulations 8.1 Introduction 8.2 Recently Published laws and Regulations 8.3 Developing Innovation and Growing the Internet of Things (DIGIT) Act 8.4 General View 8.5 Relaxation of laws by the Federal Aviation Administration's (FAA) 8.6 Supporting Innovation of Self Driving Cars by Allowing Policies 8.7 Recommendations 8.8 Conclusion 9 Artificial Intelligence and Blockchain 9.1 Introduction 9.2 Decentralized Intelligence 9.3 Applications 9.4 How Artificial Intelligence and Blockchain will Affect Society 9.5 How Augmented Reality Works 9.6 Mixed Reality 9.7 Virtual Reality 9.8 Key Components in a Virtual Reality System 9.9 Augmented Reality Uses 9.10 Applications of Virtual Reality in Business 9.11 The Future of Blockchain 9.12 Blockchain Applications 9.13 Blockchain and the Internet of Things 9.14 Law Coordination 9.15 Collaboration for Blockchain Success 10 Digital Twin Technology 10.1 Introduction 10.2 The Timeline and History of Digital Twin Technology 10.3 Technologies Employed in Digital Twin Models 10.4 The Dimension of Digital Twins Models 10.5 Digital Twin and Other Technologies 10.6 Digital Twin Technology Implementation 10.7 Benefits of Digital Twin 10.8 Application of Digital Twins 10.9 Challenges of Digital Twins 11 Artificial Intelligence, Big Data Analytics, and IoT 11.1 Introduction 11.2 Analytic 11.3 AI Technology in Big Data and IoT 11.4 AI Technology Applications and Use Cases 11.5 AI Technology Impact on the Vertical Market 11.6 AI in Big Data and IoT Market Analysis and Forecasts 11.7 Conclusion 12 Digital Transformation Trends in the Automotive Industry 12.1 Introduction 12.2 Evolution of Automotive Industry 12.3 Data-Driven Business Model and data monetization 12.4 Services of Data-Driven Business Model 12.5 Values of New Services in the New Automotive Industry 12.6 Conclusion 13 Wireless Sensors/IoT and Artificial Intelligence for Smart Grid and Smart Home 13.1 Introduction 13.2 Wireless Sensor Networks 13.3 Power Grid Impact 13.4 Benefits of Smart Grid 13.5 Internet of Things 13.6 Internet of Things on Smart Grid 13.7 Smart Grid and Artificial Intelligence 13.8 Smart Grid Programming 13.9 Conclusion 14 Artificial Intelligence, 5G and IoT: Security 14.1 Introduction 14.2 Understanding IoT 14.3 Artificial Intelligence 14.4 5G Network 14.5 Emerging Partnership of Artificial Intelligence, IoT, 5G, and Cybersecurity 14.6 Conclusion 15 Intelligent Connectivity and Agriculture 15.1 Introduction 15.2 The Potential of Wireless Sensors and IoT in Agriculture 15.3 IoT Sensory Technology with Traditional Farming 15.4 IoT Devices and Communication Techniques 15.5 IoT and all Crop Stages 15.6 Drone in Farming Applications 15.7 Conclusion 16 Applications of Artificial Intelligence, ML, and DL 16.1 Introduction 16.2 Building Artificial Intelligence Capabilities 16.3 What is Machine Learning? 16.4 Deep Learning 16.5 Machine Learning vs. Deep Learning Comparison 16.6 Feature Engineering 16.7 Application of Machine Learning 16.8 Applications of Deep learning 16.9 Future Trends 17 Big Data and Artificial Intelligence: Strategies for Leading Business Transformation 17.1 Introduction 17.2 Big Data 17.2 Machine Learning-Based Medical Systems 17.3 Artificial Intelligence for Stock Market Prediction 17.3.1 Application of Artificial Intelligence by Investors 17.4 Trends in AI and Big Data Technologies Drive Business Innovation 17.5 Driving Innovation Through Big Data 17.6 The Convergence of AI and Big Data 17.7 How AI and Big Data Will Combine to Create Business Innovation 17.8 AI and Big Data for Technological Innovation 17.9 AI and Production 17.10 AI and ML Operations Research 17.11 Collaboration Between Machines and Human 17.12 Generative Designs 17.13 Adapting to a Changing Market 17.14 Conclusion Index

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Book SynopsisTable of ContentsPreface xi About the Author xiii 1 Overview of the Current State of Cybersecurity in the Automotive Industry 1 1.1 Cybersecurity Standards, Guidelines, and Activities 3 1.2 Process Changes, Organizational Changes, and New Solutions 6 1.3 Results from a Survey on Cybersecurity Practices in the Automotive Industry 8 1.3.1 Survey Methods 8 1.3.2 Report Results 9 1.3.2.1 Organizational Challenges 9 1.3.2.2 Technical Challenges 10 1.3.2.3 Product Development and Security Testing Challenges 11 1.3.2.4 Supply Chain and Third-Party Components Challenges 11 1.3.3 How to Address the Challenges 12 1.3.3.1 Organizational Takeaways 12 1.3.3.2 Technical Takeaways 13 1.3.3.3 Product Development and Security Testing Takeaways 13 1.3.3.4 Supply Chain and Third-Party Components Takeaways 13 1.3.3.5 Getting Started 14 1.3.3.6 Practical Examples of Organizations Who Have Started 15 1.4 Examples of Vulnerabilities in the Automotive Industry 16 1.5 Chapter Summary 18 References 19 2 Introduction to Security in the Automotive Software Development Lifecycle 23 2.1 V-Model Software Development Process 24 2.2 Challenges in Automotive Software Development 25 2.3 Security Solutions at each Step in the V-Model 26 2.3.1 Cybersecurity Requirements Review 27 2.3.2 Security Design Review 27 2.3.3 Threat Analysis and Risk Assessment 27 2.3.4 Source Code Review 28 2.3.5 Static Code Analysis 28 2.3.6 Software Composition Analysis 29 2.3.7 Security Functional Testing 29 2.3.8 Vulnerability Scanning 29 2.3.9 Fuzz Testing 30 2.3.10 Penetration Testing 30 2.3.11 Incident Response and Updates 31 2.3.12 Continuous Cybersecurity Activities 32 2.3.13 Overall Cybersecurity Management 32 2.4 New Technical Challenges 32 2.5 Chapter Summary 34 References 35 3 Automotive-Grade Secure Hardware 37 3.1 Need for Automotive Secure Hardware 39 3.2 Different Types of HSMs 41 3.3 Root of Trust: Security Features Provided by Automotive HSM 43 3.3.1 Secure Boot 44 3.3.2 Secure In-Vehicle Communication 45 3.3.3 Secure Host Flashing 46 3.3.4 Secure Debug Access 47 3.3.5 Secure Logging 47 3.4 Chapter Summary 48 References 48 4 Need for Automated Security Solutions in the Automotive Software Development Lifecycle 51 4.1 Main Challenges in the Automotive Industry 53 4.2 Automated Security Solutions During the Product Development Phases 55 4.2.1 Static Code Analysis 55 4.2.2 Software Composition Analysis 57 4.2.3 Security Testing 58 4.2.4 Automation and Traceability During Software Development 59 4.3 Solutions During Operations and Maintenance Phases 59 4.3.1 Cybersecurity Monitoring, Vulnerability Management, Incident Response, and OTA Updates 59 4.4 Chapter Summary 61 References 61 5 Static Code Analysis for Automotive Software 63 5.1 Introduction to MISRA and AUTOSAR Coding Guidelines 68 5.2 Problem Statement: MISRA and AUTOSAR Challenges 75 5.3 Solution: Workflow for Code Segmentation, Guideline Policies, and Deviation Management 79 5.3.1 Step 1: Segment the Codebase into Different Categories/Components Based on Risk 80 5.3.2 Step 2: Specify Guideline Policies (Set of Guidelines to Apply) Depending on Risk Categories 81 5.3.3 Step 3: Perform the Scan and Plan the Approach for Prioritization of Findings 82 5.3.4 Step 4: Prioritize Findings Based on the Risk Categories and Guideline Policies and Determine How to Handle Each Finding, e.g. Fix or Leave as Deviation 83 5.3.5 Step 5: Follow a Defined Deviation Management Process, Including Approval Steps 84 5.3.6 Step 6: Report on MISRA or AUTOSAR Coding Guidelines Compliance Including Deviations 86 5.4 Chapter Summary 87 References 88 6 Software Composition Analysis in the Automotive Industry 91 6.1 Software Composition Analysis: Benefits and Usage Scenarios 95 6.2 Problem Statement: Analysis of Automotive Software Open-Source Software Risks 98 6.2.1 Analysis Results 98 6.2.1.1 zlib 99 6.2.1.2 libpng 99 6.2.1.3 OpenSSL 99 6.2.1.4 curl 99 6.2.1.5 Linux Kernel 100 6.2.2 Discussion 100 6.3 Solution: Countermeasures on Process and Technical Levels 101 6.3.1 Fully Inventory Open-Source Software 101 6.3.2 Use Appropriate Software Composition Analysis Approaches 102 6.3.3 Map Open-Source Software to Known Security Vulnerabilities 102 6.3.4 Identify License, Quality, and Security Risks 103 6.3.5 Create and Enforce Open-Source Software Risk Policies 104 6.3.6 Continuously Monitor for New Security Threats and Vulnerabilities 104 6.3.7 Define and Follow Processes for Addressing Vulnerabilities in Open-Source Software 105 6.3.8 How to Get Started 106 6.4 Chapter Summary 107 References 108 7 Overview of Automotive Security Testing Approaches 111 7.1 Practical Security Testing 115 7.1.1 Security Functional Testing 117 7.1.2 Vulnerability Scanning 119 7.1.3 Fuzz Testing 121 7.1.4 Penetration Testing 122 7.2 Frameworks for Security Testing 125 7.3 Focus on Fuzz Testing 129 7.3.1 Fuzz Engine 130 7.3.2 Injector 134 7.3.3 Monitor 136 7.4 Chapter Summary 140 References 141 8 Automating Fuzz Testing of In-Vehicle Systems by Integrating with Automotive Test Tools 145 8.1 Overview of HIL Systems 147 8.2 Problem Statement: SUT Requires External Input and Monitoring 150 8.3 Solution: Integrating Fuzz Testing Tools with HIL Systems 152 8.3.1 White-Box Approach for Fuzz Testing Using HIL System 157 8.3.1.1 Example Test Setup Using an Engine ECU 159 8.3.1.2 Fuzz Testing Setup for the Engine ECU 161 8.3.1.3 Fuzz Testing Setup Considerations 165 8.3.2 Black-Box Approach for Fuzz Testing Using HIL System 166 8.3.2.1 Example Target System Setup Using Engine and Body Control Modules 168 8.3.2.2 Fuzz Testing Setup Using Duplicate Engine and Body Control Modules 171 8.3.2.3 Fuzz Testing Setup Considerations 175 8.4 Chapter Summary 176 References 177 9 Improving Fuzz Testing Coverage by Using Agent Instrumentation 179 9.1 Introduction to Agent Instrumentation 182 9.2 Problem Statement: Undetectable Vulnerabilities 183 9.2.1 Memory Leaks 184 9.2.2 Core Dumps and Zombie Processes 185 9.2.3 Considerations for Addressing Undetectable Vulnerabilities 187 9.3 Solution: Using Agents to Detect Undetectable Vulnerabilities 187 9.3.1 Overview of the Test Environment 188 9.3.2 Modes of Operation 189 9.3.2.1 Synchronous Mode 190 9.3.2.2 Asynchronous Mode 191 9.3.2.3 Hybrid Approach 192 9.3.3 Examples of Agents 193 9.3.3.1 Agent Core Dump 193 9.3.3.2 Agent Log Tailer 194 9.3.3.3 Agent Process Monitor 194 9.3.3.4 Agent PID 194 9.3.3.5 Agent Address Sanitizer 195 9.3.3.6 Agent Valgrind 195 9.3.3.7 An Example config.json Configuration File 196 9.3.4 Example Results from Agent Instrumentation 197 9.3.4.1 Bluetooth Fuzz Testing 198 9.3.4.2 Wi-Fi Fuzz Testing 199 9.3.4.3 MQTT Fuzz Testing 201 9.3.4.4 File Format Fuzz Testing 203 9.3.5 Applicability and Automation 206 9.4 Chapter Summary 207 References 208 10 Automating File Fuzzing over USB for Automotive Systems 211 10.1 Need for File Format Fuzzing 213 10.2 Problem Statement: Manual Process for File Format Fuzzing 215 10.3 Solution: Emulated Filesystems to Automate File Format Fuzzing 216 10.3.1 System Architecture Overview 217 10.3.2 Phase One Implementation Example: Prepare Fuzzed Files 219 10.3.3 Phase Two Implementation Example: Automatically Emulate Filesystems 223 10.3.4 Automating User Input 228 10.3.5 Monitor for Exceptions 231 10.4 Chapter Summary 236 References 237 11 Automation and Traceability by Integrating Application Security Testing Tools into ALM Systems 241 11.1 Introduction to ALM Systems 242 11.2 Problem Statement: Tracing Secure Software Development Activities and Results to Requirements and Automating Application Security Testing 245 11.3 Solution: Integrating Application Security Testing Tools with ALM Systems 248 11.3.1 Concept 249 11.3.1.1 Static Code Analysis – Example 249 11.3.1.2 Software Composition Analysis – Example 250 11.3.1.3 Vulnerability Scanning – Example 250 11.3.1.4 Fuzz Testing – Example 250 11.3.1.5 Concept Overview 251 11.3.2 Example Implementation 252 11.3.2.1 Defensics 252 11.3.2.2 code Beamer ALM 252 11.3.2.3 Jenkins 252 11.3.2.4 SUT 253 11.3.2.5 Implementation Overview 253 11.3.3 Considerations 258 11.4 Chapter Summary 262 References 264 12 Continuous Cybersecurity Monitoring, Vulnerability Management, Incident Response, and Secure OTA Updates 267 12.1 Need for Cybersecurity Monitoring and Secure OTA Updates 268 12.2 Problem Statement: Software Inventory, Monitoring Vulnerabilities, and Vulnerable Vehicles 271 12.3 Solution: Release Management, Monitoring and Tracking, and Secure OTA Updates 272 12.3.1 Release Management 273 12.3.2 Monitoring and Tracking 276 12.3.2.1 Solutions in Other Industries 276 12.3.2.2 Solutions in the Automotive Industry 277 12.3.2.3 Example Automotive SOC Overview 277 12.3.2.4 Example Automotive SOC Workflow 279 12.3.2.5 Newly Detected Vulnerabilities in Open-Source Software – Example 279 12.3.3 Secure OTA Updates 280 12.3.3.1 Identify Vulnerable Vehicles Targeted for OTA Updates 281 12.3.3.2 Perform Secure OTA Updates 281 12.3.3.3 Target Systems for OTA Updates 282 12.3.3.4 Overview of Secure OTA Update Process for ECUs 283 12.3.3.5 Standardization and Frameworks for OTA Updates 284 12.4 Chapter Summary 285 References 286 13 Summary and Next Steps 289 Index 293

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Book SynopsisTable of ContentsPreface xv 1 Introduction to Blockchain 1Akshay Mudgal 1.1 Introduction 1 1.1.1 Public Blockchain Architecture 5 1.1.2 Private Blockchain Architecture 5 1.1.3 Consortium Blockchain Architecture 5 1.2 The Privacy Challenges of Blockchain 6 1.3 De-Anonymization 8 1.3.1 Analysis of Network 9 1.3.2 Transaction Fingerprinting 9 1.3.3 DoS Attacks 9 1.3.4 Sybil Attacks 9 1.4 Transaction Pattern Exposure 10 1.4.1 Transaction Graph Analysis 10 1.4.2 AS-Level Deployment Analysis 10 1.5 Methodology: Identity Privacy Preservation 10 1.5.1 Mixing Services 10 1.5.2 Ring Signature 12 1.6 Decentralization Challenges Exist in Blockchain 14 1.7 Conclusion 15 1.8 Regulatory Challenges 16 1.9 Obstacles to Blockchain Regulation 16 1.10 The Current Regulatory Landscape 17 1.11 The Future of Blockchain Regulation 18 1.12 Business Model Challenges 19 1.12.1 Traditional Business Models 19 1.12.2 Manufacturer 19 1.12.3 Distributor 20 1.12.4 Retailer 20 1.12.5 Franchise 20 1.13 Utility Token Model 20 1.13.1 Right 21 1.13.2 Value Exchange 21 1.13.3 Toll 21 1.13.4 Function 21 1.13.5 Currency 22 1.13.6 Earning 22 1.14 Blockchain as a Service 22 1.15 Securities 23 1.16 Development Platforms 24 1.17 Scandals and Public Perceptions 25 1.17.1 Privacy Limitations 26 1.17.2 Lack of Regulations and Governance 26 1.17.3 Cost to Set Up 26 1.17.4 Huge Consumption of Energy 26 1.17.5 Public Perception 27 References 27 2 The Scope for Blockchain Ecosystem 29Manisha Suri 2.1 Introduction 30 2.2 Blockchain as Game Changer for Environment 32 2.3 Blockchain in Business Ecosystem 38 2.3.1 Business Ecosystem 39 2.3.1.1 What Is Business Model? 39 2.3.1.2 Business Model—Traditional 39 2.3.2 Are Blockchain Business Models Really Needed? 41 2.3.2.1 Blockchain Business Model 41 2.3.2.2 Model 1: Utility Token Model 41 2.3.2.3 Model 2: BaaS 43 2.3.2.4 Model 3: Securities 44 2.3.2.5 Model 4: Development Platforms 45 2.3.2.6 Model 5: Blockchain-Based Software Products 46 2.3.2.7 Model 6: Blockchain Professional Services 46 2.3.2.8 Model 7: Business Model—P2P 47 2.4 Is Blockchain Business Ecosystem Profitable? 48 2.5 How Do You “Design” a Business Ecosystem? 49 2.6 Redesigning Future With Blockchain 53 2.6.1 Is Earth Prepared for Blockchain? 53 2.7 Challenges and Opportunities 57 References 58 3 Business Use Cases of Blockchain Technology 59Vasudha Arora, Shweta Mongia, Sugandha Sharma and Shaveta Malik 3.1 Introduction to Cryptocurrency 60 3.2 What is a Bitcoin? 60 3.2.1 Bitcoin Transactions and Their Processing 62 3.2.2 Double Spending Problem 65 3.2.3 Bitcoin Mining 67 3.3 Bitcoin ICO 69 3.3.1 ICO Token 69 3.3.2 How to Participate in ICO 70 3.3.3 Types of Tokens 71 3.4 Advantages and Disadvantages of ICO 72 3.5 Merchant Acceptance of Bitcoin 73 References 75 4 Ethereum 77Shaveta Bhatia and S.S Tyagi 4.1 Introduction 78 4.2 Basic Features of Ethereum 78 4.3 Difference between Bitcoin and Ethereum 79 4.4 EVM (Ethereum Virtual Machine) 82 4.5 Gas 85 4.5.1 Gas Price Chart 85 4.6 Applications Built on the Basis of Ethereum 86 4.7 ETH 87 4.7.1 Why Users Want to Buy ETH? 87 4.7.2 How to Buy ETH? 88 4.7.3 Alternate Way to Buy ETH 88 4.7.4 Conversion of ETH to US Dollar 89 4.8 Smart Contracts 90 4.8.1 Government 90 4.8.2 Management 91 4.8.3 Benefits of Smart Contracts 91 4.8.4 Problems With Smart Contracts 92 4.8.5 Solution to Overcome This Problem 92 4.8.6 Languages to Build Smart Contracts 92 4.9 DApp (Decentralized Application or Smart Contract) 93 4.9.1 DApp in Ethereum 93 4.9.2 Applications of DApps 93 4.10 Conclusion 95 References 95 5 E-Wallet 97Ms. Vishawjyoti 5.1 Overview of Wallet Technology 97 5.2 Types of Wallet 98 5.2.1 Paper 98 5.2.2 Physical Bitcoins 99 5.2.3 Mobile 99 5.2.4 Web 100 5.2.5 Desktop 100 5.2.6 Hardware 100 5.2.7 Bank 101 5.3 Security of Bitcoin Wallets 101 5.4 Workings of Wallet Technology 101 5.5 Create HD Wallet From Seed 102 5.5.1 Initiation 103 5.5.2 Steps for Creating an HD Wallet From a 24-Word Seed Phrase Through Particl-qt Tool 104 5.5.3 Steps for Encrypting the HD Wallet 106 5.5.4 Utilization 108 5.5.5 Steps for Generating Address to Access Transactions on the HD Wallet 108 5.6 Navigating HD Wallet 109 5.7 Conclusion 110 References 110 6 Blockchain and Governance: Theory, Applications and Challenges 113Bhavya Ahuja Grover, Bhawna Chaudhary, Nikhil Kumar Rajput and Om Dukiya 6.1 Introduction 114 6.2 Governance: Centralized vs Decentralized 115 6.3 Blockchain’s Features Supportive of Decentralization 117 6.4 Noteworthy Application Areas for Blockchain-Based Governance 119 6.4.1 Public Service Governance 119 6.4.2 Knowledge and Shared Governance 121 6.4.3 Governance in Supply Chain 123 6.4.4 Governance of Foreign Aid 124 6.4.5 Environmental Governance 125 6.4.6 Corporate Governance 126 6.4.7 Economic Governance 128 6.5 Scopes and Challenges 128 6.6 Conclusion 136 References 137 7 Blockchain-Based Identity Management 141Abhishek Bhattacharya 7.1 Introduction 141 7.2 Existing Identity Management Systems and Their Challenges 142 7.3 Concept of Decentralized Identifiers 144 7.4 The Workflow of Blockchain Identity Management Systems 145 7.5 How Does it Contribute to Data Security? 148 7.6 Trending Blockchain Identity Management Projects 150 7.7 Why and How of Revocation 152 7.8 Points to Ponder 154 7.8.1 Comparison Between Traditional and Blockchain-Based Identity Management Systems 156 7.9 Conclusion 157 References 158 8 Blockchain & IoT: A Paradigm Shift for Supply Chain Management 159Abhishek Bhattacharya 8.1 Introduction 159 8.2 Supply Chain Management 160 8.2.1 The Aspects of a Supply Chain 161 8.2.2 Supply Chain Performance Dimensions 162 8.2.3 Supply Chain Migration Towards Digitalization 163 8.3 Blockchain and IoT 164 8.3.1 What Makes Blockchain Suitable for SCM? 166 8.3.1.1 Shared Ledger 167 8.3.1.2 Permissions 168 8.3.1.3 Consensus 168 8.3.1.4 Smart Contracts 169 8.3.2 The Role of Blockchain in Achieving the SCM Performance Dimensions 170 8.3.3 The Role of IoT in the Implementation of Blockchain Technology 171 8.4 Blockchain Technology and IoT Use Cases in Supply Chain Management 172 8.5 Benefits and Challenges in Blockchain-Based Supply Chain Management 173 8.6 Conclusion 176 References 176 9 Blockchain-Enabled Supply Chain Management System 179Sonal Pathak 9.1 Introduction 180 9.1.1 Supply Chain Management 180 9.2 Blockchain Technology 184 9.3 Blockchain Technology in Supply Chain Management 186 9.4 Elements of Blockchain That Affects Supply Chain 190 9.4.1 Bitcoin 195 9.5 Challenges in Implementation of Blockchain-Enabled Supply Chain 197 9.6 Conclusion 197 References 199 10 Security Concerns of Blockchain 201Neha Jain and Kamiya Chugh 10.1 Introduction: Security Concerns of Blockchain 201 10.2 Cryptocurrencies Scenarios 202 10.3 Privacy Challenges of Blockchain 203 10.3.1 Protection Problems in Blockchain 203 10.3.2 Privacy-Preserving Mechanisms Analysis 207 10.3.3 Data Anonymization-Mixing 207 10.4 Decentralization in Blockchain 208 10.4.1 Role of Decentralization in Blockchain 209 10.4.2 Analysis of PoS and DPoS 210 10.4.3 Problems With Decentralization 210 10.4.4 Decentralization Recovery Methods 212 10.5 Legal and Regulatory Issues in Blockchain 213 10.5.1 Legal Value of Blockchain and its Problems 214 10.6 Smart Contracts 218 10.7 Scandals of Blockchain 220 10.7.1 Blockchain Technologies as Stumbling Blocks to Financial Legitimacy 223 10.8 Is Blockchain the Rise of Trustless Trust? 223 10.8.1 Why Do We Need a System of Trust? 226 10.9 Blockchain Model Challenges 227 References 229 11 Acceptance and Adoption of Blockchain Technology: An Examination of the Security & Privacy Challenges 231Amandeep Dhaliwal and Sahil Malik 11.1 Introduction 231 11.1.1 Research Methodology 233 11.1.2 Analysis 233 11.2 Security Issues of Blockchain 233 11.2.1 The Majority Attack (51% Attacks) 233 11.2.2 The Fork Problems 234 11.2.2.1 Hard Fork 234 11.2.2.2 Soft Fork 235 11.2.3 Scale of Blockchain 235 11.2.4 Time Confirmation of Blockchain Data— Double-Spend Attack/Race Attack 235 11.2.5 Current Regulations Problems 236 11.2.6 Scalability and Storage Capacity 236 11.2.7 DOS Attack/Sybil Attack/Eclipse Attack/Bugs 237 11.2.8 Legal Issues 237 11.2.9 Security of Wallets 238 11.2.10 The Increased Computing Power 238 11.3 Privacy Challenges of Bitcoin 238 11.3.1 De-Anonymization 239 11.3.1.1 Network Analysis 239 11.3.1.2 Address Clustering 239 11.3.1.3 Transaction Finger Printing 240 11.3.2 Transaction Pattern Exposure 240 11.3.2.1 Transaction Graph Analysis 240 11.3.2.2 Autonomous System-Level Deployment Analysis 241 11.4 Blockchain Application-Based Solutions 241 11.4.1 Bitcoins 241 11.4.2 IoT 242 11.4.2.1 MyBit 242 11.4.3 Aero Token 242 11.4.4 The Chain of Things 243 11.4.5 The Modum 243 11.4.6 Twin of Things 243 11.4.7 The Blockchain of Things 244 11.4.8 Blockchain Solutions: Cloud Computing 244 11.5 Conclusion and Future Work 245 References 245 12 Deficiencies in Blockchain Technology and Potential Augmentation in Cyber Security 251Eshan Bajal, Madhulika Bhatia, Lata Nautiyal and Madhurima Hooda 12.1 Introduction 252 12.2 Security Issues in Blockchain Technology 252 12.3 Privacy Challenges 253 12.3.1 BGP Hijacking Attack 255 12.3.2 BDoS (Blockchain Denial of Service) 255 12.3.3 Forcing Other Miners to Stop Mining 256 12.4 Decentralization Challenges 256 12.5 Regulatory Challenges 260 12.5.1 Principles to Follow While Regulating 262 12.5.1.1 Flexible to Legal Innovation 262 12.5.1.2 Experimentation Should be Encouraged 263 12.5.1.3 Focus on the Immediate Implications 264 12.5.1.4 Regulators Should Engage in a Transnational Conversation 264 12.5.2 Regulatory Strategies 265 12.5.2.1 Wait-and-See 265 12.5.2.2 Imposing Narrowing and Broadening Guidance 266 12.5.2.3 Sandboxing 266 12.5.2.4 Issue a New Legislation 267 12.5.2.5 Use Blockchain in Regulation 268 12.6 Business Model Challenges 269 12.7 Scandals and Public Perception 271 12.8 Why Blockchain is Trustless 277 12.8.1 Trust Mechanism 278 12.8.2 Anonymity 279 12.8.3 Use in Digital Wallets 279 12.8.4 Forgery Resistance 279 12.9 Use of Blockchain in Cybersecurity 280 12.9.1 Blockchain Database 281 12.9.2 DNS Security 283 12.9.3 IoT Security 283 12.9.4 DDoS Prevention 286 12.9.5 CDN (Content Delivery Network) 286 12.9.6 SMS Authentication 287 References 288 13 Internet of Things and Blockchain 295Priyanka Sharma 13.1 History of ‘Internet of Things’ 296 13.2 IoT Devices 298 13.3 Sensors and Actuators 302 13.4 Cloud and Haze-Based Engineering 307 13.5 Blockchain and IoT 315 13.6 Edge Computing 321 13.7 Contextual Analyses 324 13.8 Fate of Blockchain and IoT 332 References 332 14 Blockchain Applications 337Boby Singh, Rohit Pahwa, Hari Om Tanwar and Nikita Gupta 14.1 Introduction to Blockchain 337 14.1.1 Uses of Blockchain in Administration 339 14.2 Blockchain in Big Data Predictive Task Automation 340 14.2.1 How Can Blockchain Help Big Data? 341 14.2.2 Blockchain Use Cases in Big Data 341 14.3 Digital Identity Verification 342 14.3.1 Why Digital Identity Matters? 343 14.3.2 Blockchain (Definition and its Features) 343 14.3.3 Why do we Need Blockchain in Digital Identity? 344 14.3.4 How Does a Blockchain Works? 345 14.3.5 Why is a Blockchain Secure? 345 14.3.6 What’s Blockchain Identification Management? 346 14.3.7 Advantages 347 14.4 Blockchain Government 348 14.4.1 Decentralized Government Services 349 14.4.2 Liquid Democracy and Random Sample Election 350 14.5 Blockchain Science 351 14.5.1 FoldingCoin 351 14.5.2 GridCoin (GRC) 352 14.5.3 Global Public Health 353 14.5.4 Bitcoin Genomics 354 14.6 Blockchain Health 355 14.6.1 Health Coin 355 14.6.2 EMR on Blockchain 355 14.6.3 Bit Coin Health Notary 356 14.7 Blockchain Learning 357 14.7.1 Bitcoin MOOCs 357 14.7.2 Smart Contract Literacy 357 14.7.3 LearnCoin 359 References 359 15 Advance Concepts of Blockchain 361Raj Kumar 15.1 Community Supercomputing 361 15.2 Blockchain Genomics 364 15.3 Blockchain Learning 365 15.4 Community Coin 366 15.4.1 Monetary and Non-Monetary Currencies 367 15.4.2 Difference Between Monetary and Non-Monetary Assets 369 15.4.3 Currency Multiplicity 369 15.4.4 List of Some Prominent Alternate Coins is Given Below 370 15.5 Demurrage Currencies 371 Reading List 371 Index 373

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Book SynopsisThe cognitive approach to the IoT provides connectivity to everyone and everything since IoT connected devices are known to increase rapidly. When the IoT is integrated with cognitive technology, performance is improved, and smart intelligence is obtained. Discussed in this book are different types of datasets with structured content based on cognitive systems. The IoT gathers the information from the real time datasets through the internet, where the IoT network connects with multiple devices. This book mainly concentrates on providing the best solutions to existing real-time issues in the cognitive domain. Healthcare-based, cloud-based and smart transportation-based applications in the cognitive domain are addressed. The data integrity and security aspects of the cognitive computing main are also thoroughly discussed along with validated results.Table of ContentsPreface xvii Acknowledgments xix 1 Introduction to Cognitive Computing 1Vamsidhar Enireddy, Sagar Imambi and C. Karthikeyan 1.1 Introduction: Definition of Cognition, Cognitive Computing 1 1.2 Defining and Understanding Cognitive Computing 2 1.3 Cognitive Computing Evolution and Importance 6 1.4 Difference Between Cognitive Computing and Artificial Intelligence 8 1.5 The Elements of a Cognitive System 11 1.5.1 Infrastructure and Deployment Modalities 11 1.5.2 Data Access, Metadata, and Management Services 12 1.5.3 The Corpus, Taxonomies, and Data Catalogs 12 1.5.4 Data Analytics Services 12 1.5.5 Constant Machine Learning 13 1.5.6 Components of a Cognitive System 13 1.5.7 Building the Corpus 14 1.5.8 Corpus Administration Governing and Protection Factors 16 1.6 Ingesting Data Into Cognitive System 17 1.6.1 Leveraging Interior and Exterior Data Sources 17 1.6.2 Data Access and Feature Extraction 18 1.7 Analytics Services 19 1.8 Machine Learning 22 1.9 Machine Learning Process 24 1.9.1 Data Collection 24 1.9.2 Data Preparation 24 1.9.3 Choosing a Model 24 1.9.4 Training the Model 24 1.9.5 Evaluate the Model 25 1.9.6 Parameter Tuning 25 1.9.7 Make Predictions 25 1.10 Machine Learning Techniques 25 1.10.1 Supervised Learning 25 1.10.2 Unsupervised Learning 27 1.10.3 Reinforcement Learning 27 1.10.4 The Significant Challenges in Machine Learning 28 1.11 Hypothesis Space 30 1.11.1 Hypothesis Generation 31 1.11.2 Hypotheses Score 32 1.12 Developing a Cognitive Computing Application 32 1.13 Building a Health Care Application 35 1.13.1 Healthcare Ecosystem Constituents 35 1.13.2 Beginning With a Cognitive Healthcare Application 37 1.13.3 Characterize the Questions Asked by the Clients 37 1.13.4 Creating a Corpus and Ingesting the Content 38 1.13.5 Training the System 38 1.13.6 Applying Cognition to Develop Health and Wellness 39 1.13.7 Welltok 39 1.13.8 CaféWell Concierge in Action 41 1.14 Advantages of Cognitive Computing 42 1.15 Features of Cognitive Computing 43 1.16 Limitations of Cognitive Computing 44 1.17 Conclusion 47 References 47 2 Machine Learning and Big Data in Cyber-Physical System: Methods, Applications and Challenges 49Janmenjoy Nayak, P. Suresh Kumar, Dukka Karun Kumar Reddy, Bighnaraj Naik and Danilo Pelusi 2.1 Introduction 50 2.2 Cyber-Physical System Architecture 52 2.3 Human-in-the-Loop Cyber-Physical Systems (HiLCPS) 53 2.4 Machine Learning Applications in CPS 55 2.4.1 K-Nearest Neighbors (K-NN) in CPS 55 2.4.2 Support Vector Machine (SVM) in CPS 58 2.4.3 Random Forest (RF) in CPS 61 2.4.4 Decision Trees (DT) in CPS 63 2.4.5 Linear Regression (LR) in CPS 65 2.4.6 Multi-Layer Perceptron (MLP) in CPS 66 2.4.7 Naive Bayes (NB) in CPS 70 2.5 Use of IoT in CPS 70 2.6 Use of Big Data in CPS 72 2.7 Critical Analysis 77 2.8 Conclusion 83 References 84 3 HemoSmart: A Non-Invasive Device and Mobile App for Anemia Detection 93J.A.D.C.A. Jayakody, E.A.G.A. Edirisinghe and S.Lokuliyana 3.1 Introduction 94 3.1.1 Background 94 3.1.2 Research Objectives 96 3.1.3 Research Approach 97 3.1.4 Limitations 98 3.2 Literature Review 98 3.3 Methodology 101 3.3.1 Methodological Approach 101 3.3.1.1 Select an Appropriate Camera 102 3.3.1.2 Design the Lighting System 102 3.3.1.3 Design the Electronic Circuit 104 3.3.1.4 Design the Prototype 104 3.3.1.5 Collect Data and Develop the Algorithm 104 3.3.1.6 Develop the Prototype 106 3.3.1.7 Mobile Application Development 106 3.3.1.8 Completed Device 107 3.3.1.9 Methods of Data Collection 109 3.3.2 Methods of Analysis 109 3.4 Results 110 3.4.1 Impact of Project Outcomes 110 3.4.2 Results Obtained During the Methodology 111 3.4.2.1 Select an Appropriate Camera 111 3.4.2.2 Design the Lighting System 112 3.5 Discussion 112 3.6 Originality and Innovativeness of the Research 116 3.6.1 Validation and Quality Control of Methods 117 3.6.2 Cost-Effectiveness of the Research 117 3.7 Conclusion 117 References 117 4 Advanced Cognitive Models and Algorithms 121J. Ramkumar, M. Baskar and B. Amutha 4.1 Introduction 122 4.2 Microsoft Azure Cognitive Model 122 4.2.1 AI Services Broaden in Microsoft Azure 125 4.3 IBM Watson Cognitive Analytics 126 4.3.1 Cognitive Computing 126 4.3.2 Defining Cognitive Computing via IBM Watson Interface 127 4.3.2.1 Evolution of Systems Towards Cognitive Computing 128 4.3.2.2 Main Aspects of IBM Watson 129 4.3.2.3 Key Areas of IBM Watson 130 4.3.3 IBM Watson Analytics 130 4.3.3.1 IBM Watson Features 131 4.3.3.2 IBM Watson DashDB 131 4.4 Natural Language Modeling 132 4.4.1 NLP Mainstream 132 4.4.2 Natural Language Based on Cognitive Computation 134 4.5 Representation of Knowledge Models 134 4.6 Conclusion 137 References 138 5 iParking—Smart Way to Automate the Management of the Parking System for a Smart City 141J.A.D.C.A. Jayakody, E.A.G.A. Edirisinghe, S.A.H.M. Karunanayaka, E.M.C.S. Ekanayake, H.K.T.M. Dikkumbura and L.A.I.M. Bandara 5.1 Introduction 142 5.2 Background & Literature Review 144 5.2.1 Background 144 5.2.2 Review of Literature 145 5.3 Research Gap 151 5.4 Research Problem 151 5.5 Objectives 153 5.6 Methodology 154 5.6.1 Lot Availability and Occupancy Detection 154 5.6.2 Error Analysis for GPS (Global Positioning System) 155 5.6.3 Vehicle License Plate Detection System 156 5.6.4 Analyze Differential Parking Behaviors and Pricing 156 5.6.5 Targeted Digital Advertising 157 5.6.6 Used Technologies 157 5.6.7 Specific Tools and Libraries 158 5.7 Testing and Evaluation 159 5.8 Results 161 5.9 Discussion 162 5.10 Conclusion 164 References 165 6 Cognitive Cyber-Physical System Applications 167John A., Senthilkumar Mohan and D. Maria Manuel Vianny 6.1 Introduction 168 6.2 Properties of Cognitive Cyber-Physical System 169 6.3 Components of Cognitive Cyber-Physical System 170 6.4 Relationship Between Cyber-Physical System for Human–Robot 171 6.5 Applications of Cognitive Cyber-Physical System 172 6.5.1 Transportation 172 6.5.2 Industrial Automation 173 6.5.3 Healthcare and Biomedical 176 6.5.4 Clinical Infrastructure 178 6.5.5 Agriculture 180 6.6 Case Study: Road Management System Using CPS 181 6.6.1 Smart Accident Response System for Indian City 182 6.7 Conclusion 184 References 185 7 Cognitive Computing 189T Gunasekhar and Marella Surya Teja 7.1 Introduction 189 7.2 Evolution of Cognitive System 191 7.3 Cognitive Computing Architecture 193 7.3.1 Cognitive Computing and Internet of Things 194 7.3.2 Cognitive Computing and Big Data Analysis 197 7.3.3 Cognitive Computing and Cloud Computing 200 7.4 Enabling Technologies in Cognitive Computing 202 7.4.1 Cognitive Computing and Reinforcement Learning 202 7.4.2 Cognitive Computive and Deep Learning 204 7.4.2.1 Rational Method and Perceptual Method 205 7.4.2.2 Cognitive Computing and Image Understanding 207 7.5 Applications of Cognitive Computing 209 7.5.1 Chatbots 209 7.5.2 Sentiment Analysis 210 7.5.3 Face Detection 211 7.5.4 Risk Assessment 211 7.6 Future of Cognitive Computing 212 7.7 Conclusion 214 References 215 8 Tools Used for Research in Cognitive Engineering and Cyber Physical Systems 219Ajita Seth 8.1 Cyber Physical Systems 219 8.2 Introduction: The Four Phases of Industrial Revolution 220 8.3 System 221 8.4 Autonomous Automobile System 221 8.4.1 The Timeline 222 8.5 Robotic System 223 8.6 Mechatronics 225 References 228 9 Role of Recent Technologies in Cognitive Systems 231V. Pradeep Kumar, L. Pallavi and Kolla Bhanu Prakash 9.1 Introduction 232 9.1.1 Definition and Scope of Cognitive Computing 232 9.1.2 Architecture of Cognitive Computing 233 9.1.3 Features and Limitations of Cognitive Systems 234 9.2 Natural Language Processing for Cognitive Systems 236 9.2.1 Role of NLP in Cognitive Systems 236 9.2.2 Linguistic Analysis 238 9.2.3 Example Applications Using NLP With Cognitive Systems 240 9.3 Taxonomies and Ontologies of Knowledge Representation for Cognitive Systems 241 9.3.1 Taxonomies and Ontologies and Their Importance in Knowledge Representation 242 9.3.2 How to Represent Knowledge in Cognitive Systems? 243 9.3.3 Methodologies Used for Knowledge Representation in Cognitive Systems 247 9.4 Support of Cloud Computing for Cognitive Systems 248 9.4.1 Importance of Shared Resources of Distributed Computing in Developing Cognitive Systems 248 9.4.2 Fundamental Concepts of Cloud Used in Building Cognitive Systems 249 9.5 Cognitive Analytics for Automatic Fraud Detection Using Machine Learning and Fuzzy Systems 254 9.5.1 Role of Machine Learning Concepts in Building Cognitive Analytics 255 9.5.2 Building Automated Patterns for Cognitive Analytics Using Fuzzy Systems 255 9.6 Design of Cognitive System for Healthcare Monitoring in Detecting Diseases 256 9.6.1 Role of Cognitive System in Building Clinical Decision System 257 9.7 Advanced High Standard Applications Using Cognitive Computing 259 9.8 Conclusion 262 References 263 10 Quantum Meta-Heuristics and Applications 265Kolla Bhanu Prakash 10.1 Introduction 265 10.2 What is Quantum Computing? 267 10.3 Quantum Computing Challenges 268 10.4 Meta-Heuristics and Quantum Meta-Heuristics Solution Approaches 271 10.5 Quantum Meta-Heuristics Algorithms With Application Areas 273 10.5.1 Quantum Meta-Heuristics Applications for Power Systems 277 10.5.2 Quantum Meta-Heuristics Applications for Image Analysis 281 10.5.3 Quantum Meta-Heuristics Applications for Big Data or Data Mining 282 10.5.4 Quantum Meta-Heuristics Applications for Vehicular Trafficking 285 10.5.5 Quantum Meta-Heuristics Applications for Cloud Computing 286 10.5.6 Quantum Meta-Heuristics Applications for Bioenergy or Biomedical Systems 287 10.5.7 Quantum Meta-Heuristics Applications for Cryptography or Cyber Security 287 10.5.8 Quantum Meta-Heuristics Applications for Miscellaneous Domain 288 References 291 11 Ensuring Security and Privacy in IoT for Healthcare Applications 299Anjali Yeole and D.R. Kalbande 11.1 Introduction 299 11.2 Need of IoT in Healthcare 300 11.2.1 Available Internet of Things Devices for Healthcare 301 11.3 Literature Survey on an IoT-Aware Architecture for Smart Healthcare Systems 303 11.3.1 Cyber-Physical System (CPS) for e-Healthcare 303 11.3.2 IoT-Enabled Healthcare With REST-Based Services 304 11.3.3 Smart Hospital System 304 11.3.4 Freescale Home Health Hub Reference Platform 305 11.3.5 A Smart System Connecting e-Health Sensors and Cloud 305 11.3.6 Customizing 6LoWPAN Networks Towards IoT-Based Ubiquitous Healthcare Systems 305 11.4 IoT in Healthcare: Challenges and Issues 306 11.4.1 Challenges of the Internet of Things for Healthcare 306 11.4.2 IoT Interoperability Issues 308 11.4.3 IoT Security Issues 308 11.4.3.1 Security of IoT Sensors 309 11.4.3.2 Security of Data Generated by Sensors 309 11.4.3.3 LoWPAN Networks Healthcare Systems and its Attacks 309 11.5 Proposed System: 6LoWPAN and COAP Protocol-Based IoT System for Medical Data Transfer by Preserving Privacy of Patient 310 11.6 Conclusion 312 References 312 12 Empowering Secured Outsourcing in Cloud Storage Through Data Integrity Verification 315C. Saranya Jothi, Carmel Mary Belinda and N. Rajkumar 12.1 Introduction 315 12.1.1 Confidentiality 316 12.1.2 Availability 316 12.1.3 Information Uprightness 316 12.2 Literature Survey 316 12.2.1 PDP 316 12.2.1.1 Privacy-Preserving PDP Schemes 317 12.2.1.2 Efficient PDP 317 12.2.2 POR 317 12.2.3 HAIL 318 12.2.4 RACS 318 12.2.5 FMSR 318 12.3 System Design 319 12.3.1 Design Considerations 319 12.3.2 System Overview 320 12.3.3 Workflow 320 12.3.4 System Description 321 12.3.4.1 System Encoding 321 12.3.4.2 Decoding 322 12.3.4.3 Repair and Check 323 12.4 Implementation and Result Discussion 324 12.4.1 Creating Containers 324 12.4.2 File Chunking 324 12.4.3 XORing Partitions 326 12.4.4 Regeneration of File 326 12.4.5 Reconstructing a Node 327 12.4.6 Cloud Storage 327 12.4.6.1 NC-Cloud 327 12.4.6.2 Open Swift 329 12.5 Performance 330 12.6 Conclusion 332 References 333 Index 335

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Book SynopsisMake security a priority on your team Every organization needs astrongsecurity program. One recent study estimated that a hacker attack occurs somewhere every37 seconds.Since security programs are only as effective as a team's willingness to follow their rules and protocols, it'sincreasingly necessarytohave not just awidely accessible gold standard of security,but alsoa practical plan for rolling it outand getting others on board with following it.Security AwarenessForDummiesgives you the blueprint for implementing this sort of holistic and hyper-secureprograminyour organization. Written by one of the world's most influential security professionalsand an Information Systems Security Association Hall of Famerthis pragmatic andeasy-to-followbook provides a frameworkfor creatingnew and highly effective awareness programs fromscratch,as well assteps to taketoimprove on existingones. It also covershow to measure andevaluate the successofyourprogramandhighlightits valueto management. CustomiTable of ContentsIntroduction 1 Part 1: Getting to Know Security Awareness 5 Chapter 1: Knowing How Security Awareness Programs Work 7 Chapter 2: Starting On the Right Foot: Avoiding What Doesn’t Work 19 Chapter 3: Applying the Science Behind Human Behavior and Risk Management 33 Part 2: Building a Security Awareness Program 51 Chapter 4: Creating a Security Awareness Strategy 53 Chapter 5: Determining Culture and Business Drivers 61 Chapter 6: Choosing What to Tell The Users 75 Chapter 7: Choosing the Best Tools for the Job 89 Chapter 8: Measuring Performance 107 Part 3: Putting Your Security Awareness Program Into Action 119 Chapter 9: Assembling Your Security Awareness Program 121 Chapter 10: Running Your Security Awareness Program 143 Chapter 11: Implementing Gamification 165 Chapter 12: Running Phishing Simulation Campaigns 181 Part 4: The Part of Tens 207 Chapter 13: Ten Ways to Win Support for Your Awareness Program 209 Chapter 14: Ten Ways to Make Friends and Influence People 215 Chapter 15: Ten Fundamental Awareness Topics 221 Chapter 16: Ten Helpful Security Awareness Resources 227 Appendix: Sample Questionnaire 233 Index 253

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Book SynopsisINDUSTRIAL INTERNET OF THINGS (IIOT) This book discusses how the industrial internet will be augmented through increased network agility, integrated artificial intelligence (AI) and the capacity to deploy, automate, orchestrate, and secure diverse user cases at hyperscale. Since the internet of things (IoT) dominates all sectors of technology, from home to industry, automation through IoT devices is changing the processes of our daily lives. For example, more and more businesses are adopting and accepting industrial automation on a large scale, with the market for industrial robots expected to reach $73.5 billion in 2023. The primary reason for adopting IoT industrial automation in businesses is the benefits it provides, including enhanced efficiency, high accuracy, cost-effectiveness, quick process completion, low power consumption, fewer errors, and ease of control. The 15 chapters in the book showcase industrial automation through the IoT by including case stuTable of ContentsPreface xvii 1 A Look at IIoT: The Perspective of IoT Technology Applied in the Industrial Field 1Ana Carolina Borges Monteiro, Reinaldo Padilha França, Rangel Arthur, Yuzo Iano, Andrea Coimbra Segatti, Giulliano Paes Carnielli, Julio Cesar Pereira, Henri Alves de Godoy and Elder Carlos Fernandes 1.1 Introduction 2 1.2 Relationship Between Artificial Intelligence and IoT 5 1.2.1 AI Concept 6 1.2.2 IoT Concept 10 1.3 IoT Ecosystem 15 1.3.1 Industry 4.0 Concept 18 1.3.2 Industrial Internet of Things 19 1.4 Discussion 21 1.5 Trends 23 1.6 Conclusions 24 References 26 2 Analysis on Security in IoT Devices—An Overview 31T. Nalini and T. Murali Krishna 2.1 Introduction 32 2.2 Security Properties 33 2.3 Security Challenges of IoT 34 2.3.1 Classification of Security Levels 35 2.3.1.1 At Information Level 36 2.3.1.2 At Access Level 36 2.3.1.3 At Functional Level 36 2.3.2 Classification of IoT Layered Architecture 37 2.3.2.1 Edge Layer 37 2.3.2.2 Access Layer 37 2.3.2.3 Application Layer 37 2.4 IoT Security Threats 38 2.4.1 Physical Device Threats 39 2.4.1.1 Device-Threats 39 2.4.1.2 Resource Led Constraints 39 2.4.2 Network-Oriented Communication Assaults 39 2.4.2.1 Structure 40 2.4.2.2 Protocol 40 2.4.3 Data-Based Threats 41 2.4.3.1 Confidentiality 41 2.4.3.2 Availability 41 2.4.3.3 Integrity 42 2.5 Assaults in IoT Devices 43 2.5.1 Devices of IoT 43 2.5.2 Gateways and Networking Devices 44 2.5.3 Cloud Servers and Control Devices 45 2.6 Security Analysis of IoT Platforms 46 2.6.1 ARTIK 46 2.6.2 GiGA IoT Makers 47 2.6.3 AWS IoT 47 2.6.4 Azure IoT 47 2.6.5 Google Cloud IoT (GC IoT) 48 2.7 Future Research Approaches 49 2.7.1 Blockchain Technology 51 2.7.2 5G Technology 52 2.7.3 Fog Computing (FC) and Edge Computing (EC) 52 References 54 3 Smart Automation, Smart Energy, and Grid Management Challenges 59J. Gayathri Monicka and C. Amuthadevi 3.1 Introduction 60 3.2 Internet of Things and Smart Grids 62 3.2.1 Smart Grid in IoT 63 3.2.2 IoT Application 64 3.2.3 Trials and Imminent Investigation Guidelines 66 3.3 Conceptual Model of Smart Grid 67 3.4 Building Computerization 71 3.4.1 Smart Lighting 73 3.4.2 Smart Parking 73 3.4.3 Smart Buildings 74 3.4.4 Smart Grid 75 3.4.5 Integration IoT in SG 77 3.5 Challenges and Solutions 81 3.6 Conclusions 83 References 83 4 Industrial Automation (IIoT) 4.0: An Insight Into Safety Management 89C. Amuthadevi and J. Gayathri Monicka 4.1 Introduction 89 4.1.1 Fundamental Terms in IIoT 91 4.1.1.1 Cloud Computing 92 4.1.1.2 Big Data Analytics 92 4.1.1.3 Fog/Edge Computing 92 4.1.1.4 Internet of Things 93 4.1.1.5 Cyber-Physical-System 94 4.1.1.6 Artificial Intelligence 95 4.1.1.7 Machine Learning 95 4.1.1.8 Machine-to-Machine Communication 99 4.1.2 Intelligent Analytics 99 4.1.3 Predictive Maintenance 100 4.1.4 Disaster Predication and Safety Management 101 4.1.4.1 Natural Disasters 101 4.1.4.2 Disaster Lifecycle 102 4.1.4.3 Disaster Predication 103 4.1.4.4 Safety Management 104 4.1.5 Optimization 105 4.2 Existing Technology and Its Review 106 4.2.1 Survey on Predictive Analysis in Natural Disasters 106 4.2.2 Survey on Safety Management and Recovery 108 4.2.3 Survey on Optimizing Solutions in Natural Disasters 109 4.3 Research Limitation 110 4.3.1 Forward-Looking Strategic Vision (FVS) 110 4.3.2 Availability of Data 111 4.3.3 Load Balancing 111 4.3.4 Energy Saving and Optimization 111 4.3.5 Cost Benefit Analysis 112 4.3.6 Misguidance of Analysis 112 4.4 Finding 113 4.4.1 Data Driven Reasoning 113 4.4.2 Cognitive Ability 113 4.4.3 Edge Intelligence 113 4.4.4 Effect of ML Algorithms and Optimization 114 4.4.5 Security 114 4.5 Conclusion and Future Research 114 4.5.1 Conclusion 114 4.5.2 Future Research 114 References 115 5 An Industrial Perspective on Restructured Power Systems Using Soft Computing Techniques 119Kuntal Bhattacharjee, Akhilesh Arvind Nimje, Shanker D. Godwal and Sudeep Tanwar 5.1 Introduction 120 5.2 Fuzzy Logic 121 5.2.1 Fuzzy Sets 121 5.2.2 Fuzzy Logic Basics 122 5.2.3 Fuzzy Logic and Power System 122 5.2.4 Fuzzy Logic—Automatic Generation Control 123 5.2.5 Fuzzy Microgrid Wind 123 5.3 Genetic Algorithm 123 5.3.1 Important Aspects of Genetic Algorithm 124 5.3.2 Standard Genetic Algorithm 126 5.3.3 Genetic Algorithm and Its Application 127 5.3.4 Power System and Genetic Algorithm 127 5.3.5 Economic Dispatch Using Genetic Algorithm 128 5.4 Artificial Neural Network 128 5.4.1 The Biological Neuron 129 5.4.2 A Formal Definition of Neural Network 130 5.4.3 Neural Network Models 131 5.4.4 Rosenblatt’s Perceptron 131 5.4.5 Feedforward and Recurrent Networks 132 5.4.6 Back Propagation Algorithm 133 5.4.7 Forward Propagation 133 5.4.8 Algorithm 134 5.4.9 Recurrent Network 135 5.4.10 Examples of Neural Networks 136 5.4.10.1 AND Operation 136 5.4.10.2 OR Operation 137 5.4.10.3 XOR Operation 137 5.4.11 Key Components of an Artificial Neuron Network 138 5.4.12 Neural Network Training 141 5.4.13 Training Types 142 5.4.13.1 Supervised Training 142 5.4.13.2 Unsupervised Training 142 5.4.14 Learning Rates 142 5.4.15 Learning Laws 143 5.4.16 Restructured Power System 144 5.4.17 Advantages of Precise Forecasting of the Price 145 5.5 Conclusion 145 References 146 6 Recent Advances in Wearable Antennas: A Survey 149Harvinder Kaur and Paras Chawla 6.1 Introduction 150 6.2 Types of Antennas 153 6.2.1 Description of Wearable Antennas 153 6.2.1.1 Microstrip Patch Antenna 153 6.2.1.2 Substrate Integrated Waveguide Antenna 153 6.2.1.3 Planar Inverted-F Antenna 153 6.2.1.4 Monopole Antenna 153 6.2.1.5 Metasurface Loaded Antenna 154 6.3 Design of Wearable Antennas 154 6.3.1 Effect of Substrate and Ground Geometries on Antenna Design 154 6.3.1.1 Conducting Coating on Substrate 154 6.3.1.2 Ground Plane With Spiral Metamaterial Meandered Structure 157 6.3.1.3 Partial Ground Plane 158 6.3.2 Logo Antennas 159 6.3.3 Embroidered Antenna 159 6.3.4 Wearable Antenna Based on Electromagnetic Band Gap 160 6.3.5 Wearable Reconfigurable Antenna 161 6.4 Textile Antennas 162 6.5 Comparison of Wearable Antenna Designs 168 6.6 Fractal Antennas 168 6.6.1 Minkowski Fractal Geometries Using Wearable Electro-Textile Antennas 171 6.6.2 Antenna Design With Defected Semi-Elliptical Ground Plane 172 6.6.3 Double-Fractal Layer Wearable Antenna 172 6.6.4 Development of Embroidered Sierpinski Carpet Antenna 172 6.7 Future Challenges of Wearable Antenna Designs 174 6.8 Conclusion 174 References 175 7 An Overview of IoT and Its Application With Machine Learning in Data Center 181Manikandan Ramanathan and Kumar Narayanan 7.1 Introduction 181 7.1.1 6LoWPAN 183 7.1.2 Data Protocols 185 7.1.2.1 CoAP 185 7.1.2.2 MQTT 187 7.1.2.3 Rest APIs 187 7.1.3 IoT Components 189 7.1.3.1 Hardware 190 7.1.3.2 Middleware 190 7.1.3.3 Visualization 191 7.2 Data Center and Internet of Things 191 7.2.1 Modern Data Centers 191 7.2.2 Data Storage 191 7.2.3 Computing Process 192 7.2.3.1 Fog Computing 192 7.2.3.2 Edge Computing 194 7.2.3.3 Cloud Computing 194 7.2.3.4 Distributed Computing 195 7.2.3.5 Comparison of Cloud Computing and Fog Computing 196 7.3 Machine Learning Models and IoT 196 7.3.1 Classifications of Machine Learning Supported in IoT 197 7.3.1.1 Supervised Learning 197 7.3.1.2 Unsupervised Learning 198 7.3.1.3 Reinforcement Learning 198 7.3.1.4 Ensemble Learning 199 7.3.1.5 Neural Network 199 7.4 Challenges in Data Center and IoT 199 7.4.1 Major Challenges 199 7.5 Conclusion 201 References 201 8 Impact of IoT to Meet Challenges in Drone Delivery System 203J. Ranjani, P. Kalaichelvi, V.K.G Kalaiselvi, D. Deepika Sree and K. Swathi 8.1 Introduction 204 8.1.1 IoT Components 204 8.1.2 Main Division to Apply IoT in Aviation 205 8.1.3 Required Field of IoT in Aviation 206 8.1.3.1 Airports as Smart Cities or Airports as Platforms 207 8.1.3.2 Architecture of Multidrone 208 8.1.3.3 The Multidrone Design has the Accompanying Prerequisites 208 8.2 Literature Survey 209 8.3 Smart Airport Architecture 211 8.4 Barriers to IoT Implementation 215 8.4.1 How is the Internet of Things Converting the Aviation Enterprise? 216 8.5 Current Technologies in Aviation Industry 216 8.5.1 Methodology or Research Design 217 8.6 IoT Adoption Challenges 218 8.6.1 Deployment of IoT Applications on Broad Scale Includes the Underlying Challenges 218 8.7 Transforming Airline Industry With Internet of Things 219 8.7.1 How the IoT Is Improving the Aviation Industry 219 8.7.1.1 IoT: Game Changer for Aviation Industry 220 8.7.2 Applications of AI in the Aviation Industry 220 8.7.2.1 Ticketing Systems 220 8.7.2.2 Flight Maintenance 221 8.7.2.3 Fuel Efficiency 221 8.7.2.4 Crew Management 221 8.7.2.5 Flight Health Checks and Maintenance 221 8.7.2.6 In-Flight Experience Management 222 8.7.2.7 Luggage Tracking 222 8.7.2.8 Airport Management 222 8.7.2.9 Just the Beginning 222 8.8 Revolution of Change (Paradigm Shift) 222 8.9 The Following Diagram Shows the Design of the Application 223 8.10 Discussion, Limitations, Future Research, and Conclusion 224 8.10.1 Growth of Aviation IoT Industry 224 8.10.2 IoT Applications—Benefits 225 8.10.3 Operational Efficiency 225 8.10.4 Strategic Differentiation 225 8.10.5 New Revenue 226 8.11 Present and Future Scopes 226 8.11.1 Improving Passenger Experience 226 8.11.2 Safety 227 8.11.3 Management of Goods and Luggage 227 8.11.4 Saving 227 8.12 Conclusion 227 References 227 9 IoT-Based Water Management System for a Healthy Life 229N. Meenakshi, V. Pandimurugan and S. Rajasoundaran 9.1 Introduction 230 9.1.1 Human Activities as a Source of Pollutants 230 9.2 Water Management Using IoT 231 9.2.1 Water Quality Management Based on IoT Framework 232 9.3 IoT Characteristics and Measurement Parameters 233 9.4 Platforms and Configurations 235 9.5 Water Quality Measuring Sensors and Data Analysis 239 9.6 Wastewater and Storm Water Monitoring Using IoT 241 9.6.1 System Initialization 241 9.6.2 Capture and Storage of Information 241 9.6.3 Information Modeling 241 9.6.4 Visualization and Management of the Information 243 9.7 Sensing and Sampling of Water Treatment Using IoT 244 References 246 10 Fuel Cost Optimization Using IoT in Air Travel 249P. Kalaichelvi, V. Akila, J. Ranjani, S. Sowmiya and C. Divya 10.1 Introduction 250 10.1.1 Introduction to IoT 250 10.1.2 Processing IoT Data 250 10.1.3 Advantages of IoT 251 10.1.4 Disadvantages of IoT 251 10.1.5 IoT Standards 251 10.1.6 Lite Operating System (Lite OS) 251 10.1.7 Low Range Wide Area Network (LoRaWAN) 252 10.2 Emerging Frameworks in IoT 252 10.2.1 Amazon Web Service (AWS) 252 10.2.2 Azure 252 10.2.3 Brillo/Weave Statement 252 10.2.4 Calvin 252 10.3 Applications of IoT 253 10.3.1 Healthcare in IoT 253 10.3.2 Smart Construction and Smart Vehicles 254 10.3.3 IoT in Agriculture 254 10.3.4 IoT in Baggage Tracking 254 10.3.5 Luggage Logbook 254 10.3.6 Electrical Airline Logbook 254 10.4 IoT for Smart Airports 255 10.4.1 IoT in Smart Operation in Airline Industries 257 10.4.2 Fuel Emissions on Fly 258 10.4.3 Important Things in Findings 258 10.5 Related Work 258 10.6 Existing System and Analysis 264 10.6.1 Technology Used in the System 265 10.7 Proposed System 268 10.8 Components in Fuel Reduction 276 10.9 Conclusion 276 10.10 Future Enhancements 277 References 277 11 Object Detection in IoT-Based Smart Refrigerators Using CNN 281Ashwathan R., Asnath Victy Phamila Y., Geetha S. and Kalaivani K. 11.1 Introduction 282 11.2 Literature Survey 283 11.3 Materials and Methods 287 11.3.1 Image Processing 292 11.3.2 Product Sensing 292 11.3.3 Quality Detection 293 11.3.4 Android Application 293 11.4 Results and Discussion 294 11.5 Conclusion 299 References 299 12 Effective Methodologies in Pharmacovigilance for Identifying Adverse Drug Reactions Using IoT 301Latha Parthiban, Maithili Devi Reddy and A. Kumaravel 12.1 Introduction 302 12.2 Literature Review 302 12.3 Data Mining Tasks 304 12.3.1 Classification 305 12.3.2 Regression 306 12.3.3 Clustering 306 12.3.4 Summarization 306 12.3.5 Dependency Modeling 306 12.3.6 Association Rule Discovery 307 12.3.7 Outlier Detection 307 12.3.8 Prediction 307 12.4 Feature Selection Techniques in Data Mining 308 12.4.1 GAs for Feature Selection 308 12.4.2 GP for Feature Selection 309 12.4.3 PSO for Feature Selection 310 12.4.4 ACO for Feature Selection 311 12.5 Classification With Neural Predictive Classifier 312 12.5.1 Neural Predictive Classifier 313 12.5.2 MapReduce Function on Neural Class 317 12.6 Conclusions 319 References 319 13 Impact of COVID-19 on IIoT 321K. Priyadarsini, S. Karthik, K. Malathi and M.V.V Rama Rao 13.1 Introduction 321 13.1.1 The Use of IoT During COVID-19 321 13.1.2 Consumer IoT 322 13.1.3 Commercial IoT 322 13.1.4 Industrial Internet of Things (IIoT) 322 13.1.5 Infrastructure IoT 322 13.1.6 Role of IoT in COVID-19 Response 323 13.1.7 Telehealth Consultations 323 13.1.8 Digital Diagnostics 323 13.1.9 Remote Monitoring 323 13.1.10 Robot Assistance 323 13.2 The Benefits of Industrial IoT 326 13.2.1 How IIoT is Being Used 327 13.2.2 Remote Monitoring 327 13.2.3 Predictive Maintenance 328 13.3 The Challenges of Wide-Spread IIoT Implementation 329 13.3.1 Health and Safety Monitoring Will Accelerate Automation and Remote Monitoring 330 13.3.2 Integrating Sensor and Camera Data Improves Safety and Efficiency 330 13.3.3 IIoT-Supported Safety for Customers Reduces Liability for Businesses 331 13.3.4 Predictive Maintenance Will Deliver for Organizations That Do the Work 332 13.3.5 Building on the Lessons of 2020 332 13.4 Effects of COVID-19 on Industrial Manufacturing 332 13.4.1 New Challenges for Industrial Manufacturing 333 13.4.2 Smarter Manufacturing for Actionable Insights 333 13.4.3 A Promising Future for IIoT Adoption 334 13.5 Winners and Losers—The Impact on IoT/Connected Applications and Digital Transformation due to COVID-19 Impact 335 13.6 The Impact of COVID-19 on IoT Applications 337 13.6.1 Decreased Interest in Consumer IoT Devices 338 13.6.2 Remote Asset Access Becomes Important 338 13.6.3 Digital Twins Help With Scenario Planning 339 13.6.4 New Uses for Drones 339 13.6.5 Specific IoT Health Applications Surge 340 13.6.6 Track and Trace Solutions Get Used More Extensively 340 13.6.7 Smart City Data Platforms Become Key 340 13.7 The Impact of COVID-19 on Technology in General 341 13.7.1 Ongoing Projects Are Paused 341 13.7.2 Some Enterprise Technologies Take Off 341 13.7.3 Declining Demand for New Projects/Devices/ Services 342 13.7.4 Many Digitalization Initiatives Get Accelerated or Intensified 342 13.7.5 The Digital Divide Widens 343 13.8 The Impact of COVID-19 on Specific IoT Technologies 343 13.8.1 IoT Networks Largely Unaffected 343 13.8.2 Technology Roadmaps Get Delayed 344 13.9 Coronavirus With IoT, Can Coronavirus Be Restrained? 344 13.10 The Potential of IoT in Coronavirus Like Disease Control 345 13.11 Conclusion 346 References 346 14 A Comprehensive Composite of Smart Ambulance Booking and Tracking Systems Using IoT for Digital Services 349Sumanta Chatterjee, Pabitra Kumar Bhunia, Poulami Mondal, Aishwarya Sadhu and Anusua Biswas 14.1 Introduction 350 14.2 Literature Review 353 14.3 Design of Smart Ambulance Booking System Through App 356 14.4 Smart Ambulance Booking 359 14.4.1 Welcome Page 360 14.4.2 Sign Up 360 14.4.3 Home Page 361 14.4.4 Ambulance Section 361 14.4.5 Ambulance Selection Page 362 14.4.6 Confirmation of Booking and Tracking 363 14.5 Result and Discussion 363 14.5.1 How It Works? 365 14.6 Conclusion 365 14.7 Future Scope 366 References 366 15 An Efficient Elderly Disease Prediction and Privacy Preservation Using Internet of Things 369Resmi G. Nair and N. Kumar 15.1 Introduction 370 15.2 Literature Survey 371 15.3 Problem Statement 372 15.4 Proposed Methodology 373 15.4.1 Design a Smart Wearable Device 373 15.4.2 Normalization 374 15.4.3 Feature Extraction 377 15.4.4 Classification 378 15.4.5 Polynomial HMAC Algorithm 379 15.5 Result and Discussion 382 15.5.1 Accuracy 382 15.5.2 Positive Predictive Value 382 15.5.3 Sensitivity 383 15.5.4 Specificity 383 15.5.5 False Out 383 15.5.6 False Discovery Rate 383 15.5.7 Miss Rate 383 15.5.8 F-Score 383 15.6 Conclusion 390 References 390 Index 393

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Book SynopsisTable of ContentsPreface xiii 1 A Study on Various Machine Learning Algorithms and Their Role in Agriculture 1Kalpana Rangra and Amitava Choudhury 1.1 Introduction 1 1.2 Conclusions 9 2 Smart Farming Using Machine Learning and IoT 13Alo Sen, Rahul Roy and Satya Ranjan Dash 2.1 Introduction 14 2.2 Related Work 15 2.3 Problem Identification 22 2.4 Objective Behind the Integrated Agro-IoT System 23 2.5 Proposed Prototype of the Integrated Agro-IoT System 23 2.6 Hardware Component Requirement for the Integrated Agro-IoT System 26 2.7 Comparative Study Between Raspberry Pi vs Beaglebone Black 30 2.8 Conclusions 31 2.9 Future Work 32 3 Agricultural Informatics vis-à-vis Internet of Things (IoT): The Scenario, Applications and Academic Aspects--International Trend & Indian Possibilities 35P.K. Paul 3.1 Introduction 36 3.2 Objectives 36 3.3 Methods 37 3.4 Agricultural Informatics: An Account 37 3.5 Agricultural Informatics & Technological Components: Basics & Emergence 40 3.6 IoT: Basics and Characteristics 41 3.7 IoT: The Applications & Agriculture Areas 43 3.8 Agricultural Informatics & IoT: The Scenario 45 3.9 IoT in Agriculture: Requirement, Issues & Challenges 49 3.10 Development, Economy and Growth: Agricultural Informatics Context 50 3.11 Academic Availability and Potentiality of IoT in Agricultural Informatics: International Scenario & Indian Possibilities 51 3.12 Suggestions 60 3.13 Conclusion 60 4 Application of Agricultural Drones and IoT to Understand Food Supply Chain During Post COVID-19 67Pushan Kumar Dutta and Susanta Mitra 4.1 Introduction 68 4.2 Related Work 69 4.3 Smart Production With the Introduction of Drones and IoT 72 4.4 Agricultural Drones 75 4.5 IoT Acts as a Backbone in Addressing COVID-19 Problems in Agriculture 77 4.6 Conclusion 81 5 IoT and Machine Learning-Based Approaches for Real Time Environment Parameters Monitoring in Agriculture: An Empirical Review 89Parijata Majumdar and Sanjoy Mitra 5.1 Introduction 90 5.2 Machine Learning (ML)-Based IoT Solution 90 5.3 Motivation of the Work 91 5.4 Literature Review of IoT-Based Weather and Irrigation Monitoring for Precision Agriculture 91 5.5 Literature Review of Machine Learning-Based Weather and Irrigation Monitoring for Precision Agriculture 92 5.6 Challenges 112 5.7 Conclusion and Future Work 113 6 Deep Neural Network-Based Multi-Class Image Classification for Plant Diseases 117Alok Negi, Krishan Kumar and Prachi Chauhan 6.1 Introduction 117 6.2 Related Work 119 6.3 Proposed Work 121 6.4 Results and Evaluation 124 6.5 Conclusion 127 7 Deep Residual Neural Network for Plant Seedling Image Classification 131Prachi Chauhan, Hardwari Lal Mandoria and Alok Negi 7.1 Introduction 131 7.2 Related Work 136 7.3 Proposed Work 139 7.4 Result and Evaluation 142 7.5 Conclusion 144 8 Development of IoT-Based Smart Security and Monitoring Devices for Agriculture 147Himadri Nath Saha, Reek Roy, Monojit Chakraborty and Chiranmay Sarkar 8.1 Introduction 148 8.2 Background & Related Works 150 8.3 Proposed Model 155 8.4 Methodology 160 8.5 Performance Analysis 165 8.6 Future Research Direction 166 8.7 Conclusion 167 9 An Integrated Application of IoT-Based WSN in the Field of Indian Agriculture System Using Hybrid Optimization Technique and Machine Learning 171Avishek Banerjee, Arnab Mitra and Arindam Biswas 9.1 Introduction 172 9.2 Literature Review 175 9.3 Proposed Hybrid Algorithms (GA-MWPSO) 177 9.4 Reliability Optimization and Coverage Optimization Model 179 9.5 Problem Description 181 9.6 Numerical Examples, Results and Discussion 182 9.7 Conclusion 183 10 Decryption and Design of a Multicopter Unmanned Aerial Vehicle (UAV) for Heavy Lift Agricultural Operations 189Raghuvirsinh Pravinsinh Parmar 10.1 Introduction 190 10.2 History of Multicopter UAVs 192 10.3 Basic Components of Multicopter UAV 193 10.4 Working and Control Mechanism of Multicopter UAV 207 10.5 Design Calculations and Selection of Components 210 10.6 Conclusion 218 11 IoT-Enabled Agricultural System Application, Challenges and Security Issues 223Himadri Nath Saha, Reek Roy, Monojit Chakraborty and Chiranmay Sarkar 11.1 Introduction 224 11.2 Background & Related Works 226 11.3 Challenges to Implement IoT-Enabled Systems 232 11.4 Security Issues and Measures 240 11.5 Future Research Direction 243 11.6 Conclusion 244 12 Plane Region Step Farming, Animal and Pest Attack Control Using Internet of Things 249Sahadev Roy, Kaushal Mukherjee and Arindam Biswas 12.1 Introduction 250 12.2 Proposed Work 254 12.3 Irrigation Methodology 257 12.4 Sensor Connection Using Internet of Things 259 12.5 Placement of Sensor in the Field 263 12.6 Conclusion 267 References 268 Index 271

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Book SynopsisINTELLIGENT SECURITY SYSTEMS Dramatically improve your cybersecurity using AI and machine learning In Intelligent Security Systems, distinguished professor and computer scientist Dr. Leon Reznik delivers an expert synthesis of artificial intelligence, machine learning and data science techniques, applied to computer security to assist readers in hardening their computer systems against threats. Emphasizing practical and actionable strategies that can be immediately implemented by industry professionals and computer device's owners, the author explains how to install and harden firewalls, intrusion detection systems, attack recognition tools, and malware protection systems. He also explains how to recognize and counter common hacking activities. This book bridges the gap between cybersecurity education and new data science programs, discussing how cutting-edge artificial intelligence and machine learning techniques can work for and against cybersecurity effTable of ContentsAcknowledgments ix Introduction xi 1 Computer Security with Artificial Intelligence, Machine Learning, and Data Science Combination: What? How? Why? And Why Now and Together? 1 1.1 The Current Security Landscape 1 1.2 Computer Security Basic Concepts 7 1.3 Sources of Security Threats 9 1.4 Attacks Against IoT and Wireless Sensor Networks 13 1.5 Introduction into Artificial Intelligence, Machine Learning, and Data Science 18 1.6 Fuzzy Logic and Systems 31 1.7 Machine Learning 35 1.8 Artificial Neural Networks (ANN) 43 1.9 Genetic Algorithms (GA) 50 1.10 Hybrid Intelligent Systems 51 Review Questions 52 Exercises 53 References 54 2 Firewall Design and Implementation: How to Configure Knowledge for the First Line of Defense? 57 2.1 Firewall Definition, History, and Functions: What Is It? And Where Does It Come From? 57 2.2 Firewall Operational Models or How Do They Work? 65 2.3 Basic Firewall Architectures or How Are They Built Up? 70 2.4 Process of Firewall Design, Implementation, and Maintenance or What Is the Right Way to Put All Things Together? 75 2.5 Firewall Policy Formalization with Rules or How Is the Knowledge Presented? 82 2.6 Firewalls Evaluation and Current Developments or How Are They Getting More and More Intelligent? 96 Review Questions 104 Exercises 106 References 107 3 Intrusion Detection Systems: What Do They Do Beyond the First Line of Defense? 109 3.1 Definition, Goals, and Primary Functions 109 3.2 IDS from a Historical Perspective 113 3.3 Typical IDS Architecture Topologies, Components, and Operational Ranges 116 3.4 IDS Types: Classification Approaches 121 3.5 IDS Performance Evaluation 131 3.6 Artificial Intelligence and Machine Learning Techniques in IDS Design 136 3.7 Intrusion Detection Challenges and Their Mitigation in IDS Design and Deployment 159 3.8 Intrusion Detection Tools 163 Review Questions 172 Exercises 174 References 175 4 Malware and Vulnerabilities Detection and Protection: What Are We Looking for and How? 177 4.1 Malware Definition, History, and Trends in Development 177 4.2 Malware Classification 182 4.3 Spam 214 4.4 Software Vulnerabilities 216 4.5 Principles of Malware Detection and Anti-malware Protection 219 4.6 Malware Detection Algorithms 229 4.7 Anti-malware Tools 237 Review Questions 240 Exercises 242 References 243 5 Hackers versus Normal Users: Who Is Our Enemy and How to Differentiate Them from Us? 247 5.1 Hacker’s Activities and Protection Against 247 5.2 Data Science Investigation of Ordinary Users’ Practice 273 5.3 User’s Authentication 288 5.4 User’s Anonymity, Attacks Against It, and Protection 301 Review Questions 309 Exercises 310 References 311 6 Adversarial Machine Learning: Who Is Machine Learning Working For? 315 6.1 Adversarial Machine Learning Definition 315 6.2 Adversarial Attack Taxonomy 316 6.3 Defense Strategies 320 6.4 Investigation of the Adversarial Attacks Influence on the Classifier Performance Use Case 322 6.5 Generative Adversarial Networks 327 Review Questions 333 Exercises 334 References 335 Index 337

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Book SynopsisSECURITY ISSUES AND PRIVACY CONCERNS IN INDUSTRY 4.0 APPLICATIONS Written and edited by a team of international experts, this is the most comprehensive and up-to-date coverage of the security and privacy issues surrounding Industry 4.0 applications, a must-have for any library. The scope of Security Issues and Privacy Concerns in Industry 4.0 Applications is to envision the need for security in Industry 4.0 applications and the research opportunities for the future. This book discusses the security issues in Industry 4.0 applications for research development. It will also enable the reader to develop solutions for the security threats and attacks that prevail in the industry. The chapters will be framed on par with advancements in the industry in the area of Industry 4.0 with its applications in additive manufacturing, cloud computing, IoT (Internet of Things), and many others. This book helps a researcher and an industrial specialist to reflect on the latest trends and the need for teTable of ContentsPreface xiii 1 Industry 4.0: Smart Water Management System Using IoT 1S. Saravanan, N. Renugadevi, C.M. Naga Sudha and Parul Tripathi 1.1 Introduction 2 1.1.1 Industry 4.0 2 1.1.2 IoT 2 1.1.3 Smart City 3 1.1.4 Smart Water Management 3 1.2 Preliminaries 4 1.2.1 Internet World to Intelligent World 4 1.2.2 Architecture of IoT System 4 1.2.3 Architecture of Smart City 6 1.3 Literature Review on SWMS 7 1.3.1 Water Quality Parameters Related to SWMS 8 1.3.2 SWMS in Agriculture 8 1.3.3 SWMS Using Smart Grids 9 1.3.4 Machine Learning Models in SWMS 10 1.3.5 IoT-Based SWMS 11 1.4 Conclusion 11 References 12 2 Fourth Industrial Revolution Application: Network Forensics Cloud Security Issues 15Abdullah Ayub Khan, Asif Ali Laghari, Shafique Awan and Awais Khan Jumani 2.1 Introduction 16 2.1.1 Network Forensics 16 2.1.2 The Fourth Industrial Revolution 17 2.1.2.1 Machine-to-Machine (M2M) Communication 18 2.1.3 Cloud Computing 18 2.1.3.1 Infrastructure-as-a-Service (IaaS) 19 2.1.3.2 Challenges of Cloud Security in Fourth Industrial Revolution 19 2.2 Generic Model Architecture 20 2.3 Model Implementation 24 2.3.1 OpenNebula (Hypervisor) Implementation Platform 24 2.3.2 NetworkMiner Analysis Tool 25 2.3.3 Performance Matrix Evaluation & Result Discussion 27 2.4 Cloud Security Impact on M2M Communication 28 2.4.1 Cloud Computing Security Application in the Fourth Industrial Revolution (4.0) 29 2.5 Conclusion 30 References 31 3 Regional Language Recognition System for Industry 4.0 35Bharathi V, N. Renugadevi, J. Padmapriya and M. Vijayprakash 3.1 Introduction 36 3.2 Automatic Speech Recognition System 39 3.2.1 Preprocessing 41 3.2.2 Feature Extraction 42 3.2.2.1 Linear Predictive Coding (LPC) 42 3.2.2.2 Linear Predictive Cepstral Coefficient (LPCC) 44 3.2.2.3 Perceptual Linear Predictive (PLP) 44 3.2.2.4 Power Spectral Analysis 44 3.2.2.5 Mel Frequency Cepstral Coefficients 45 3.2.2.6 Wavelet Transform 46 3.2.3 Implementation of Deep Learning Technique 46 3.2.3.1 Recurrent Neural Network 47 3.2.3.2 Long Short-Term Memory Network 47 3.2.3.3 Hidden Markov Models (HMM) 47 3.2.3.4 Hidden Markov Models - Long Short-Term Memory Network (HMM-LSTM) 48 3.2.3.5 Evaluation Metrics 49 3.3 Literature Survey on Existing TSRS 49 3.4 Conclusion 52 References 52 4 Approximation Algorithm and Linear Congruence: An Approach for Optimizing the Security of IoT-Based Healthcare Management System 55Anirban Bhowmik and Sunil Karforma 4.1 Introduction 56 4.1.1 IoT in Medical Devices 56 4.1.2 Importance of Security and Privacy Protection in IoT-Based Healthcare System 57 4.1.3 Cryptography and Secret Keys 58 4.1.4 RSA 58 4.1.5 Approximation Algorithm and Subset Sum Problem 58 4.1.6 Significance of Use of Subset Sum Problem in Our Scheme 59 4.1.7 Linear Congruence 60 4.1.8 Linear and Non-Linear Functions 61 4.1.9 Pell’s Equation 61 4.2 Literature Survey 62 4.3 Problem Domain 63 4.4 Solution Domain and Objectives 64 4.5 Proposed Work 65 4.5.1 Methodology 65 4.5.2 Session Key Generation 65 4.5.3 Intermediate Key Generation 67 4.5.4 Encryption Process 69 4.5.5 Generation of Authentication Code and Transmission File 70 4.5.6 Decryption Phase 71 4.6 Results and Discussion 71 4.6.1 Statistical Analysis 72 4.6.2 Randomness Analysis of Key 73 4.6.3 Key Sensitivity Analysis 75 4.6.4 Security Analysis 76 4.6.4.1 Key Space Analysis 76 4.6.4.2 Brute-Force Attack 77 4.6.4.3 Dictionary Attack 77 4.6.4.4 Impersonation Attack 78 4.6.4.5 Replay Attack 78 4.6.4.6 Tampering Attack 78 4.6.5 Comparative Analysis 79 4.6.5.1 Comparative Analysis Related to IoT Attacks 79 4.6.6 Significance of Authentication in Our Proposed Scheme 85 4.7 Conclusion 85 References 86 5 A Hybrid Method for Fake Profile Detection in Social Network Using Artificial Intelligence 89Ajesh F, Aswathy S U, Felix M Philip and Jeyakrishnan V 5.1 Introduction 90 5.2 Literature Survey 91 5.3 Methodology 94 5.3.1 Datasets 94 5.3.2 Detection of Fake Account 94 5.3.3 Suggested Framework 95 5.3.3.1 Pre-Processing 97 5.3.3.2 Principal Component Analysis (PCA) 98 5.3.3.3 Learning Algorithms 99 5.3.3.4 Feature or Attribute Selection 102 5.4 Result Analysis 103 5.4.1 Cross-Validation 103 5.4.2 Analysis of Metrics 104 5.4.3 Performance Evaluation of Proposed Model 105 5.4.4 Performance Analysis of Classifiers 105 5.5 Conclusion 109 References 109 6 Packet Drop Detection in Agricultural-Based Internet of Things Platform 113Sebastian Terence and Geethanjali Purushothaman 6.1 Introduction 113 6.2 Problem Statement and Related Work 114 6.3 Implementation of Packet Dropping Detection in IoT Platform 115 6.4 Performance Analysis 120 6.5 Conclusion 129 References 129 7 Smart Drone with Open CV to Clean the Railway Track 131Sujaritha M and Sujatha R 7.1 Introduction 132 7.2 Related Work 132 7.3 Problem Definition 134 7.4 The Proposed System 134 7.4.1 Drones with Human Intervention 134 7.4.2 Drones without Human Intervention 135 7.4.3 Working Model 137 7.5 Experimental Results 137 7.6 Conclusion 139 References 139 8 Blockchain and Big Data: Supportive Aid for Daily Life 141Awais Khan Jumani, Asif Ali Laghari and Abdullah Ayub Khan 8.1 Introduction 142 8.1.1 Steps of Blockchain Technology Works 144 8.1.2 Blockchain Private 144 8.1.3 Blockchain Security 145 8.2 Blockchain vs. Bitcoin 145 8.2.1 Blockchain Applications 146 8.2.2 Next Level of Blockchain 146 8.2.3 Blockchain Architecture’s Basic Components 149 8.2.4 Blockchain Architecture 150 8.2.5 Blockchain Characteristics 150 8.3 Blockchain Components 151 8.3.1 Cryptography 152 8.3.2 Distributed Ledger 153 8.3.3 Smart Contracts 153 8.3.4 Consensus Mechanism 154 8.3.4.1 Proof of Work (PoW) 155 8.3.4.2 Proof of Stake (PoS) 155 8.4 Categories of Blockchain 155 8.4.1 Public Blockchain 156 8.4.2 Private Blockchain 156 8.4.3 Consortium Blockchain 156 8.4.4 Hybrid Blockchain 156 8.5 Blockchain Applications 158 8.5.1 Financial Application 158 8.5.1.1 Bitcoin 158 8.5.1.2 Ripple 158 8.5.2 Non-Financial Applications 159 8.5.2.1 Ethereum 159 8.5.2.2 Hyperledger 159 8.6 Blockchain in Different Sectors 160 8.7 Blockchain Implementation Challenges 160 8.8 Revolutionized Challenges in Industries 163 8.9 Conclusion 170 References 172 9 A Novel Framework to Detect Effective Prediction Using Machine Learning 179Shenbaga Priya, Revadi, Sebastian Terence and Jude Immaculate 9.1 Introduction 180 9.2 ML-Based Prediction 180 9.3 Prediction in Agriculture 182 9.4 Prediction in Healthcare 183 9.5 Prediction in Economics 184 9.6 Prediction in Mammals 185 9.7 Prediction in Weather 186 9.8 Discussion 186 9.9 Proposed Framework 187 9.9.1 Problem Analysis 187 9.9.2 Preprocessing 188 9.9.3 Algorithm Selection 188 9.9.4 Training the Machine 188 9.9.5 Model Evaluation and Prediction 188 9.9.6 Expert Suggestion 188 9.9.7 Parameter Tuning 189 9.10 Implementation 189 9.10.1 Farmers and Sellers 189 9.10.2 Products 189 9.10.3 Price Prediction 190 9.11 Conclusion 192 References 192 10 Dog Breed Classification Using CNN 195Sandra Varghese and Remya S 10.1 Introduction 195 10.2 Related Work 196 10.3 Methodology 198 10.4 Results and Discussions 201 10.4.1 Training 201 10.4.2 Testing 201 10.5 Conclusions 203 References 203 11 Methodology for Load Balancing in Multi-Agent System Using SPE Approach 207S. Ajitha 11.1 Introduction 207 11.2 Methodology for Load Balancing 208 11.3 Results and Discussion 213 11.3.1 Proposed Algorithm in JADE Tool 213 11.3.1.1 Sensitivity Analysis 218 11.3.2 Proposed Algorithm in NetLogo 218 11.4 Algorithms Used 219 11.5 Results and Discussion 219 11.6 Summary 226 References 226 12 The Impact of Cyber Culture on New Media Consumers 229Durmuş KoÇak 12.1 Introduction 229 12.2 The Rise of the Term of Cyber Culture 231 12.2.1 Cyber Culture in the 21st Century 231 12.2.1.1 Socio-Economic Results of Cyber Culture 232 12.2.1.2 Psychological Outcomes of Cyber Culture 233 12.2.1.3 Political Outcomes of Cyber Culture 234 12.3 The Birth and Outcome of New Media Applications 234 12.3.1 New Media Environments 236 12.3.1.1 Social Sharing Networks 237 12.3.1.2 Network Logs (Blog, Weblog) 240 12.3.1.3 Computer Games 240 12.3.1.4 Digital News Sites and Mobile Media 240 12.3.1.5 Multimedia Media 241 12.3.1.6 What Affects the New Media Consumers’ Tendencies? 242 12.4 Result 244 References 245 Index 251

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Book SynopsisTable of ContentsPreface xvii Part 1: Introduction to Intelligent Healthcare Systems 1 1 Innovation on Machine Learning in Healthcare Services—An Introduction 3Parthasarathi Pattnayak and Om Prakash Jena 1.1 Introduction 3 1.2 Need for Change in Healthcare 5 1.3 Opportunities of Machine Learning in Healthcare 6 1.4 Healthcare Fraud 7 1.4.1 Sorts of Fraud in Healthcare 7 1.4.2 Clinical Service Providers 8 1.4.3 Clinical Resource Providers 8 1.4.4 Protection Policy Holders 8 1.4.5 Protection Policy Providers 9 1.5 Fraud Detection and Data Mining in Healthcare 9 1.5.1 Data Mining Supervised Methods 10 1.5.2 Data Mining Unsupervised Methods 10 1.6 Common Machine Learning Applications in Healthcare 10 1.6.1 Multimodal Machine Learning for Data Fusion in Medical Imaging 11 1.6.2 Machine Learning in Patient Risk Stratification 11 1.6.3 Machine Learning in Telemedicine 11 1.6.4 AI (ML) Application in Sedate Revelation 12 1.6.5 Neuroscience and Image Computing 12 1.6.6 Cloud Figuring Systems in Building AI-Based Healthcare 12 1.6.7 Applying Internet of Things and Machine-Learning for Personalized Healthcare 12 1.6.8 Machine Learning in Outbreak Prediction 13 1.7 Conclusion 13 References 14 Part 2: Machine Learning/Deep Learning-Based Model Development 17 2 A Framework for Health Status Estimation Based on Daily Life Activities Data Using Machine Learning Techniques 19Tene Ramakrishnudu, T. Sai Prasen and V. Tharun Chakravarthy 2.1 Introduction 19 2.1.1 Health Status of an Individual 19 2.1.2 Activities and Measures of an Individual 20 2.1.3 Traditional Approach to Predict Health Status 20 2.2 Background 20 2.3 Problem Statement 21 2.4 Proposed Architecture 22 2.4.1 Pre-Processing 22 2.4.2 Phase-I 23 2.4.3 Phase-II 23 2.4.4 Dataset Generation 23 2.4.4.1 Rules Collection 23 2.4.4.2 Feature Selection 24 2.4.4.3 Feature Reduction 24 2.4.4.4 Dataset Generation From Rules 24 2.4.4.5 Example 24 2.4.5 Pre-Processing 26 2.5 Experimental Results 27 2.5.1 Performance Metrics 27 2.5.1.1 Accuracy 27 2.5.1.2 Precision 28 2.5.1.3 Recall 28 2.5.1.4 F1-Score 30 2.6 Conclusion 31 References 31 3 Study of Neuromarketing With EEG Signals and Machine Learning Techniques 33S. Pal, P. Das, R. Sahu and S.R. Dash 3.1 Introduction 34 3.1.1 Why BCI 34 3.1.2 Human–Computer Interfaces 34 3.1.3 What is EEG 35 3.1.4 History of EEG 35 3.1.5 About Neuromarketing 35 3.1.6 About Machine Learning 36 3.2 Literature Survey 36 3.3 Methodology 45 3.3.1 Bagging Decision Tree Classifier 45 3.3.2 Gaussian Naïve Bayes Classifier 45 3.3.3 Kernel Support Vector Machine (Sigmoid) 45 3.3.4 Random Decision Forest Classifier 46 3.4 System Setup & Design 46 3.4.1 Pre-Processing & Feature Extraction 47 3.4.1.1 Savitzky–Golay Filter 47 3.4.1.2 Discrete Wavelet Transform 48 3.4.2 Dataset Description 49 3.5 Result 49 3.5.1 Individual Result Analysis 49 3.5.2 Comparative Results Analysis 52 3.6 Conclusion 53 References 54 4 An Expert System-Based Clinical Decision Support System for Hepatitis-B Prediction & Diagnosis 57Niranjan Panigrahi, Ishan Ayus and Om Prakash Jena 4.1 Introduction 57 4.2 Outline of Clinical DSS 59 4.2.1 Preliminaries 59 4.2.2 Types of Clinical DSS 60 4.2.3 Non-Knowledge-Based Decision Support System (NK-DSS) 60 4.2.4 Knowledge-Based Decision Support System (K-DSS) 62 4.2.5 Hybrid Decision Support System (H-DSS) 64 4.2.6 DSS Architecture 64 4.3 Background 65 4.4 Proposed Expert System-Based CDSS 65 4.4.1 Problem Description 65 4.4.2 Rules Set & Knowledge Base 66 4.4.3 Inference Engine 66 4.5 Implementation & Testing 66 4.6 Conclusion 73 References 73 5 Deep Learning on Symptoms in Disease Prediction 77Sheikh Raul Islam, Rohit Sinha, Santi P. Maity and Ajoy Kumar Ray 5.1 Introduction 77 5.2 Literature Review 78 5.3 Mathematical Models 79 5.3.1 Graphs and Related Terms 80 5.3.2 Deep Learning in Graph 80 5.3.3 Network Embedding 80 5.3.4 Graph Neural Network 81 5.3.5 Graph Convolution Network 82 5.4 Learning Representation From DSN 82 5.4.1 Description of the Proposed Model 83 5.4.2 Objective Function 84 5.5 Results and Discussion 84 5.5.1 Description of the Dataset 85 5.5.2 Training Progress 85 5.5.3 Performance Comparisons 86 5.6 Conclusions and Future Scope 86 References 87 6 Intelligent Vision-Based Systems for Public Safety and Protection via Machine Learning Techniques 89Rajitha B. 6.1 Introduction 89 6.1.1 Problems Intended in Video Surveillance Systems 90 6.1.2 Current Developments in This Area 91 6.1.3 Role of AI in Video Surveillance Systems 91 6.2 Public Safety and Video Surveillance Systems 92 6.2.1 Offline Crime Prevention 92 6.2.2 Crime Prevention and Identification via Apps 92 6.2.3 Crime Prevention and Identification via CCTV 92 6.3 Machine Learning for Public Safety 94 6.3.1 Abnormality Behavior Detection via Deep Learning 95 6.3.2 Video Analytics Methods for Accident Classification/Detection 97 6.3.3 Feature Selection and Fusion Methods 98 6.4 Securing the CCTV Data 99 6.4.1 Image/Video Security Challenges 99 6.4.2 Blockchain for Image/Video Security 99 6.5 Conclusion 99 References 100 7 Semantic Framework in Healthcare 103Sankar Pariserum Perumal, Ganapathy Sannasi, Selvi M. and Kannan Arputharaj 7.1 Introduction 103 7.2 Semantic Web Ontology 104 7.3 Multi-Agent System in a Semantic Framework 106 7.3.1 Existing Healthcare Semantic Frameworks 107 7.3.1.1 AOIS 107 7.3.1.2 SCKE 108 7.3.1.3 MASE 109 7.3.1.4 MET4 110 7.3.2 Proposed Multi-Agent-Based Semantic Framework for Healthcare Instance Data 111 7.3.2.1 Data Dictionary 111 7.3.2.2 Mapping Database 112 7.3.2.3 Decision Making Ontology 113 7.3.2.4 STTL and SPARQL-Based RDF Transformation 115 7.3.2.5 Query Optimizer Agent 116 7.3.2.6 Semantic Web Services Ontology 116 7.3.2.7 Web Application User Interface and Customer Agent 116 7.3.2.8 Translation Agent 117 7.3.2.9 RDF Translator 117 7.4 Conclusion 118 References 119 8 Detection, Prediction & Intervention of Attention Deficiency in the Brain Using tDCS 121Pallabjyoti Kakoti, Rissnalin Syiemlieh and Eeshankur Saikia 8.1 Introduction 121 8.2 Materials & Methods 123 8.2.1 Subjects and Experimental Design 123 8.2.2 Data Pre-Processing & Statistical Analysis 125 8.2.3 Extracting Singularity Spectrum from EEG 126 8.3 Results & Discussion 126 8.4 Conclusion 132 Acknowledgement 133 References 133 9 Detection of Onset and Progression of Osteoporosis Using Machine Learning 137Shilpi Ruchi Kerketta and Debalina Ghosh 9.1 Introduction 137 9.1.1 Measurement Techniques of BMD 138 9.1.2 Machine Learning Algorithms in Healthcare 138 9.1.3 Organization of Chapter 139 9.2 Microwave Characterization of Human Osseous Tissue 139 9.2.1 Frequency-Domain Analysis of Human Wrist Sample 140 9.2.2 Data Collection and Analysis 141 9.3 Prediction Model of Osteoporosis Using Machine Learning Algorithms 144 9.3.1 K-Nearest Neighbor (KNN) 144 9.3.2 Decision Tree 145 9.3.3 Random Forest 145 9.4 Conclusion 148 Acknowledgment 148 References 148 10 Applications of Machine Learning in Biomedical Text Processing and Food Industry 151K. Paramesha, Gururaj H.L. and Om Prakash Jena 10.1 Introduction 152 10.2 Use Cases of AI and ML in Healthcare 153 10.2.1 Speech Recognition (SR) 153 10.2.2 Pharmacovigilance and Adverse Drug Effects (ADE) 153 10.2.3 Clinical Imaging and Diagnostics 153 10.2.4 Conversational AI in Healthcare 154 10.3 Use Cases of AI and ML in Food Technology 154 10.3.1 Assortment of Vegetables and Fruits 154 10.3.2 Personal Hygiene 154 10.3.3 Developing New Products 155 10.3.4 Plant Leaf Disease Detection 156 10.3.5 Face Recognition Systems for Domestic Cattle 156 10.3.6 Cleaning Processing Equipment 157 10.4 A Case Study: Sentiment Analysis of Drug Reviews 158 10.4.1 Dataset 159 10.4.2 Approaches for Sentiment Analysis on Drug Reviews 159 10.4.3 BoW and TF-IDF Model 160 10.4.4 Bi-LSTM Model 160 10.4.4.1 Word Embedding 160 10.4.5 Deep Learning Model 161 10.5 Results and Analysis 164 10.6 Conclusion 165 References 166 11 Comparison of MobileNet and ResNet CNN Architectures in the CNN-Based Skin Cancer Classifier Model 169Subasish Mohapatra, N.V.S. Abhishek, Dibyajit Bardhan, Anisha Ankita Ghosh and Shubhadarshinin Mohanty 11.1 Introduction 169 11.2 Our Skin Cancer Classifier Model 171 11.3 Skin Cancer Classifier Model Results 172 11.4 Hyperparameter Tuning and Performance 174 11.4.1 Hyperparameter Tuning of MobileNet-Based CNN Model 175 11.4.2 Hyperparameter Tuning of ResNet50-Based CNN Model 175 11.4.3 Table Summary of Hyperparameter Tuning Results 176 11.5 Comparative Analysis and Results 176 11.5.1 Training and Validation Loss 177 11.5.1.1 MobileNet 177 11.5.1.2 ResNet50 177 11.5.1.3 Inferences 177 11.5.2 Training and Validation Categorical Accuracy 178 11.5.2.1 MobileNet 178 11.5.2.2 ResNet50 178 11.5.2.3 Inferences 178 11.5.3 Training and Validation Top 2 Accuracy 179 11.5.3.1 MobileNet 179 11.5.3.2 ResNet50 179 11.5.3.3 Inferences 180 11.5.4 Training and Validation Top 3 Accuracy 180 11.5.4.1 MobileNet 180 11.5.4.2 ResNet50 180 11.5.4.3 Inferences 181 11.5.5 Confusion Matrix 181 11.5.5.1 MobileNet 181 11.5.5.2 ResNet50 181 11.5.5.3 Inferences 182 11.5.6 Classification Report 182 11.5.6.1 MobileNet 182 11.5.6.2 ResNet50 182 11.5.6.3 Inferences 183 11.5.7 Last Epoch Results 183 11.5.7.1 MobileNet 183 11.5.7.2 ResNet50 183 11.5.7.3 Inferences 184 11.5.8 Best Epoch Results 184 11.5.8.1 MobileNet 184 11.5.8.2 ResNet50 184 11.5.8.3 Inferences 184 11.5.9 Overall Comparative Analysis 184 11.6 Conclusion 185 References 185 12 Deep Learning-Based Image Classifier for Malaria Cell Detection 187Alok Negi, Krishan Kumar and Prachi Chauhan 12.1 Introduction 187 12.2 Related Work 189 12.3 Proposed Work 190 12.3.1 Dataset Description 191 12.3.2 Data Pre-Processing and Augmentation 191 12.3.3 CNN Architecture and Implementation 192 12.4 Results and Evaluation 194 12.5 Conclusion 196 References 197 13 Prediction of Chest Diseases Using Transfer Learning 199S. Baghavathi Priya, M. Rajamanogaran and S. Subha 13.1 Introduction 199 13.2 Types of Diseases 200 13.2.1 Pneumothorax 200 13.2.2 Pneumonia 200 13.2.3 Effusion 200 13.2.4 Atelectasis 201 13.2.5 Nodule and Mass 202 13.2.6 Cardiomegaly 202 13.2.7 Edema 202 13.2.8 Lung Consolidation 202 13.2.9 Pleural Thickening 202 13.2.10 Infiltration 202 13.2.11 Fibrosis 203 13.2.12 Emphysema 203 13.3 Diagnosis of Lung Diseases 204 13.4 Materials and Methods 204 13.4.1 Data Augmentation 206 13.4.2 CNN Architecture 206 13.4.3 Lung Disease Prediction Model 207 13.5 Results and Discussions 208 13.5.1 Implementation Results Using ROC Curve 209 13.6 Conclusion 210 References 212 14 Early Stage Detection of Leukemia Using Artificial Intelligence 215Neha Agarwal and Piyush Agrawal 14.1 Introduction 215 14.1.1 Classification of Leukemia 216 14.1.1.1 Acute Lymphocytic Leukemia 216 14.1.1.2 Acute Myeloid Leukemia 216 14.1.1.3 Chronic Lymphocytic Leukemia 216 14.1.1.4 Chronic Myeloid Leukemia 216 14.1.2 Diagnosis of Leukemia 216 14.1.3 Acute and Chronic Stages of Leukemia 217 14.1.4 The Role of AI in Leukemia Detection 217 14.2 Literature Review 219 14.3 Proposed Work 220 14.3.1 Modules Involved in Proposed Methodology 221 14.3.2 Flowchart 222 14.3.3 Proposed Algorithm 223 14.4 Conclusion and Future Aspects 223 References 223 Part 3: Internet of Medical Things (IoMT) for Healthcare 225 15 IoT Application in Interconnected Hospitals 227Subhra Debdas, Chinmoy Kumar Panigrahi, Priyasmita Kundu, Sayantan Kundu and Ramanand Jha 15.1 Introduction 228 15.2 Networking Systems Using IoT 229 15.3 What are Smart Hospitals? 233 15.3.1 Environment of a Smart Hospital 234 15.4 Assets 236 15.4.1 Overview of Smart Hospital Assets 236 15.4.2 Exigency of Automated Healthcare Center Assets 239 15.5 Threats 241 15.5.1 Emerging Vulnerabilities 241 15.5.2 Threat Analysis 244 15.6 Conclusion 246 References 246 16 Real Time Health Monitoring Using IoT With Integration of Machine Learning Approach 249K.G. Maheswari, G. Nalinipriya, C. Siva and A. Thilakesh Raj 16.1 Introduction 250 16.2 Related Work 250 16.3 Existing Healthcare Monitoring System 251 16.4 Methodology and Data Analysis 251 16.5 Proposed System Architecture 252 16.6 Machine Learning Approach 252 16.6.1 Multiple Linear Regression Algorithm 253 16.6.2 Random Forest Algorithm 253 16.6.3 Support Vector Machine 253 16.7 Work Flow of the Proposed System 253 16.8 System Design of Health Monitoring System 256 16.9 Use Case Diagram 257 16.10 Conclusion 258 References 259 Part 4: Machine Learning Applications for COVID-19 261 17 Semantic and NLP-Based Retrieval From Covid-19 Ontology 263Ramar Kaladevi and Appavoo Revathi 17.1 Introduction 263 17.2 Related Work 264 17.3 Proposed Retrieval System 266 17.3.1 Why Ontology? 266 17.3.2 Covid Ontology 266 17.3.3 Information Retrieval From Ontology 269 17.3.4 Query Formulation 272 17.3.5 Retrieval From Knowledgebase 272 17.4 Conclusion 273 References 273 18 Semantic Behavior Analysis of COVID-19 Patients: A Collaborative Framework 277Amlan Mohanty, Debasish Kumar Mallick, Shantipriya Parida and Satya Ranjan Dash 18.1 Introduction 278 18.2 Related Work 280 18.2.1 Semantic Analysis and Topic Discovery of Alcoholic Patients From Social Media Platforms 280 18.2.2 Sentiment Analysis of Tweets From Twitter Handles of the People of Nepal in Response to the COVID-19 Pandemic 280 18.2.3 Study of Sentiment Analysis and Analyzing Scientific Papers 280 18.2.4 Informatics and COVID-19 Research 281 18.2.5 COVID-19 Outbreak in the World and Twitter Sentiment Analysis 281 18.2.6 LDA Topic Modeling on Twitter to Study Public Discourse and Sentiment During the Coronavirus Pandemic 281 18.2.7 The First Decade of Research on Sentiment Analysis 282 18.2.8 Detailed Survey on the Semantic Analysis Techniques for NLP 282 18.2.9 Understanding Text Semantics With LSA 282 18.2.10 Analyzing Suicidal Tendencies With Semantic Analysis Using Social Media 283 18.2.11 Analyzing Public Opinion on BREXIT Using Sentiment Analysis 283 18.2.12 Prediction of Indian Elections Using NLP and Decision Tree 283 18.3 Methodology 283 18.4 Conclusion 286 References 287 19 Comparative Study of Various Data Mining Techniques Towards Analysis and Prediction of Global COVID-19 Dataset 289Sachin Kamley 19.1 Introduction 289 19.2 Literature Review 290 19.3 Materials and Methods 292 19.3.1 Dataset Collection 292 19.3.2 Support Vector Machine (SVM) 292 19.3.3 Decision Tree (DT) 294 19.3.4 K-Means Clustering 294 19.3.5 Back Propagation Neural Network (BPNN) 295 19.4 Experimental Results 296 19.5 Conclusion and Future Scopes 305 References 306 20 Automated Diagnosis of COVID-19 Using Reinforced Lung Segmentation and Classification Model 309J. Shiny Duela and T. Illakiya 20.1 Introduction 309 20.2 Diagnosis of COVID-19 310 20.2.1 Pre-Processing of Lung CT Image 310 20.2.2 Lung CT Image Segmentation 311 20.2.3 ROI Extraction 311 20.2.4 Feature Extraction 311 20.2.5 Classification 311 20.3 Genetic Algorithm (GA) 311 20.3.1 Operators of GA 312 20.3.2 Applications of GA 312 20.4 Related Works 313 20.5 Challenges in GA 314 20.6 Challenges in Lung CT Segmentation 314 20.7 Proposed Diagnosis Framework 314 20.7.1 Image Pre-Processing 315 20.7.2 Proposed Image Segmentation Technique 315 20.7.3 ROI Segmentation 318 20.7.4 Feature Extraction 318 20.7.5 Modified GA Classifier 318 20.7.5.1 Gaussian Type—II Fuzzy in Classification 318 20.7.5.2 Classifier Algorithm 319 20.8 Result Discussion 319 20.9 Conclusion 321 References 321 Part 5: Case Studies of Application Areas of Machine Learning in Healthcare System 323 21 Future of Telemedicine with ML: Building a Telemedicine Framework for Lung Sound Detection 325Sudhansu Shekhar Patra, Nitin S. Goje, Kamakhya Narain Singh, Kaish Q. Khan, Deepak Kumar, Madhavi and Kumar Ashutosh Sharma 21.1 Introduction 325 21.1.1 Monitoring the Remote Patient 326 21.1.2 Intelligent Assistance for Patient Diagnosis 326 21.1.3 Fasten Electronic Health Record Retrieval Process 326 21.1.4 Collaboration Increases Among Healthcare Practitioners 326 21.2 Related Work 327 21.3 Strategic Model for Telemedicine 328 21.4 Framework for Lung Sound Detection in Telemedicine 330 21.4.1 Data Collection 330 21.4.2 Pre-Processing of Data 331 21.4.3 Feature Extraction 331 21.4.3.1 MFCC 331 21.4.3.2 Lung Sounds Using Multi Resolution DWT 332 21.4.4 Classification 334 21.4.4.1 Correlation Coefficient for Feature Selection (CFS) 334 21.4.4.2 Symmetrical Uncertainty 334 21.4.4.3 Gain Ratio 335 21.4.4.4 Modified RF Classification Architecture 335 21.5 Experimental Analysis 335 21.6 Conclusion 340 References 340 22 A Lightweight Convolutional Neural Network Model for Tuberculosis Bacilli Detection From Microscopic Sputum Smear Images 343Rani Oomman Panicker, S.J. Pawan, Jeny Rajan and M.K. Sabu 22.1 Introduction 343 22.2 Literature Review 345 22.3 Proposed Work 346 22.4 Experimental Results and Discussion 349 22.5 Conclusion 350 References 350 23 Role of Machine Learning and Texture Features for the Diagnosis of Laryngeal Cancer 353Vibhav Prakash Singh and Ashish Kumar Maurya 23.1 Introduction 353 23.2 Clinically Correlated Texture Features 358 23.2.1 Texture-Based LBP Descriptors 358 23.2.2 GLCM Features 358 23.2.3 Statistical Features 359 23.3 Machine Learning Techniques 359 23.3.1 Support Vector Machine (SVM) 359 23.3.2 k-NN (k-Nearest Neighbors) 360 23.3.3 Random Forest (RF) 361 23.3.4 Naïve Bayes 361 23.4 Result Analysis and Discussions 361 23.5 Conclusions 366 References 366 24 Analysis of Machine Learning Technologies for the Detection of Diabetic Retinopathy 369Biswabijayee Chandra Sekhar Mohanty, Sonali Mishra and Sambit Kumar Mishra 24.1 Introduction 369 24.2 Related Work 370 24.2.1 Pre-Processing of Image 371 24.2.2 Diabetic Retinopathy Detection 372 24.2.3 Grading of DR 374 24.3 Dataset Used 374 24.3.1 DIARETDB1 374 24.3.2 Diabetic-Retinopathy-Detection Dataset 376 24.4 Methodology Used 377 24.4.1 Pre-Processing 377 24.4.2 Segmentation 377 24.4.3 Feature Extraction 378 24.4.4 Classification 378 24.5 Analysis of Results and Discussion 379 24.6 Conclusion 380 References 381 Index 383

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Book SynopsisExplore the cloud-native paradigm for event-driven and service-oriented applications In Cloud-Native Computing: How to Design, Develop, and Secure Microservices and Event-Driven Applications, a team of distinguished professionals delivers a comprehensive and insightful treatment of cloud-native computing technologies and tools. With a particular emphasis on the Kubernetes platform, as well as service mesh and API gateway solutions, the book demonstrates the need for reliability assurance in any distributed environment. The authors explain the application engineering and legacy modernization aspects of the technology at length, along with agile programming models. Descriptions of MSA and EDA as tools for accelerating software design and development accompany discussions of how cloud DevOps tools empower continuous integration, delivery, and deployment. Cloud-Native Computing also introduces proven edge devices and clouds used to construct microservices-centric and real-time edge applications. Finally, readers will benefit from: Thorough introductions to the demystification of digital transformationComprehensive explorations of distributed computing in the digital era, as well as reflections on the history and technological development of cloud computingPractical discussions of cloud-native computing and microservices architecture, as well as event-driven architecture and serverless computingIn-depth examinations of the Akka framework as a tool for concurrent and distributed applications development Perfect for graduate and postgraduate students in a variety of IT- and cloud-related specialties, Cloud-Native Computing also belongs in the libraries of IT professionals and business leaders engaged or interested in the application of cloud technologies to various business operations.Table of ContentsPreface Chapter 1 - The Dawning of Digital Era Chapter 2 – Leveraging the Cloud-Native Computing Model for the Digital Era Chapter 3 - Kubernetes Architecture, Best Practices and Patterns Chapter 4 - The Resiliency and Observability Aspects of Cloud-native Applications Chapter 5 - Creating Kubernetes Clusters on Private Cloud (VMware vSphere) Chapter 6: Creating Kubernetes Clusters on Public Cloud (Microsoft Azure) Chapter 7: Design, Development and Deployment of Event-driven Microservices Practically Chapter 8 - Serverless Computing for the Cloud-native Era Chapter 9 - Demonstrating a Serverless Application using Knative on a Kubernetes Cluster Chapter 10 - Delineating Cloud-native Edge Computing Chapter 11 - Setting up a Kubernetes Cluster using Azure Kubernetes Service (AKS) Chapter 12 - Reliable Cloud-native Applications through Service Mesh Chapter 13 – Cloud-native Computing: The Security Challenges and the Solution Approaches Chapter 14 – Microservices Security: The Concerns and the Solution Approaches Chapter 15 - Apache Kafka: Setup, Monitor and Secure Kubernetes cluster.

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Book SynopsisTHE NEW ADVANCED SOCIETY Included in this book are the fundamentals of Society 5.0, artificial intelligence, and the industrial Internet of Things, featuring their working principles and application in different sectors. A 360-degree view of the different dimensions of the digital revolution is presented in this book, including the various industries transforming industrial manufacturing, the security and challenges ahead, and the far-reaching implications for society and the economy. The main objective of this edited book is to cover the impact that the new advanced society has on several platforms such as smart manufacturing systems, where artificial intelligence can be integrated with existing systems to make them smart, new business models and strategies, where anything and everything is possible through the internet and cloud, smart food chain systems, where food products can be delivered to any corner of the world at any time and in any situation, smart transTable of ContentsPreface xvii Acknowledgments xxiii 1 Post Pandemic: The New Advanced Society 1Sujata Priyambada Dash 1.1 Introduction 1 1.1.1 Themes 2 1.1.1.1 Theme: Areas of Management 2 1.1.1.2 Theme: Financial Institutions Cyber Crime 3 1.1.1.3 Theme: Economic Notion 4 1.1.1.4 Theme: Human Depression 6 1.1.1.5 Theme: Migrant Labor 7 1.1.1.6 Theme: Digital Transformation (DT) of Educational Institutions 9 1.1.1.7 School and Colleges Closures 11 1.2 Conclusions 12 References 12 2 Distributed Ledger Technology in the Construction Industry Using Corda 15Sandeep Kumar Panda, Shanmukhi Priya Daliyet, Shagun S. Lokre and Vihas Naman 2.1 Introduction 16 2.2 Prerequisites 16 2.2.1 DLT vs Blockchain 17 2.3 Key Points of Corda 18 2.3.1 Some Salient Features of Corda 20 2.3.2 States 20 2.3.3 Contract 22 2.3.3.1 Create and Assign Task (CAT) Contract 22 2.3.3.2 Request for Cash (RT) Contract 23 2.3.3.3 Transfer of Cash (TT) Contract 24 2.3.3.4 Updation of the Task (UOT) Contract 24 2.3.4 Flows 25 2.3.4.1 Flow Associated With CAT Contract 25 2.3.4.2 Flow Associated With RT Contract 26 2.3.4.3 Flow Associated With TT Contract 26 2.3.4.4 Flow Associated With UOT Contract 26 2.4 Implementation 26 2.4.1 System Overview 27 2.4.2 Working Flowchart 28 2.4.3 Experimental Demonstration 29 2.5 Future Work 35 2.6 Conclusion 36 References 37 3 Identity and Access Management for Internet of Things Cloud 43Soumya Prakash Otta and Subhrakanta Panda 3.1 Introduction 44 3.2 Internet of Things (IoT) Security 45 3.2.1 IoT Security Overview 45 3.2.2 IoT Security Requirements 46 3.2.3 Securing the IoT Infrastructure 49 3.3 IoT Cloud 49 3.3.1 Cloudification of IoT 50 3.3.2 Commercial IoT Clouds 52 3.3.3 IAM of IoT Clouds 54 3.4 IoT Cloud Related Developments 55 3.5 Proposed Method for IoT Cloud IAM 58 3.5.1 Distributed Ledger Approach for IoT Security 59 3.5.2 Blockchain for IoT Security Solution 60 3.5.3 Proposed Distributed Ledger-Based IoT Cloud IAM 62 3.6 Conclusion 64 References 65 4 Automated TSR Using DNN Approach for Intelligent Vehicles 67Banhi Sanyal, Piyush R. Biswal, R.K. Mohapatra, Ratnakar Dash and Ankush Agarwalla 4.1 Introduction 68 4.2 Literature Survey 69 4.3 Neural Network (NN) 70 4.4 Methodology 71 4.4.1 System Architecture 71 4.4.2 Database 71 4.5 Experiments and Results 71 4.5.1 FFNN 74 4.5.2 RNN 76 4.5.3 CNN 76 4.5.4 CNN 76 4.5.5 Pre-Trained Models 79 4.6 Discussion 79 4.7 Conclusion 80 References 88 5 Honeypot: A Trap for Attackers 91Anjanna Matta, G. Sucharitha, Bandlamudi Greeshmanjali, Manji Prashanth Kumar and Mathi Naga Sarath Kumar 5.1 Introduction 92 5.1.1 Research Honeypots 93 5.1.2 Production Honeypots 93 5.2 Method 94 5.2.1 Low-Interaction Honeypots 94 5.2.2 Medium-Interaction Honeypots 95 5.2.3 High-Interaction Honeypots 95 5.3 Cryptanalysis 96 5.3.1 System Architecture 96 5.3.2 Possible Attacks on Honeypot 97 5.3.3 Advantages of Honeypots 98 5.3.4 Disadvantages of Honeypots 99 5.4 Conclusions 99 References 100 6 Examining Security Aspect in Industrial-Based Internet of Things 103Rohini Jha 6.1 Introduction 104 6.2 Process Frame of IoT Before Security 105 6.2.1 Cyber Attack 107 6.2.2 Security Assessment in IoT 107 6.2.2.1 Security in Perception and Network Frame 108 6.3 Attacks and Security Assessments in IIoT 111 6.3.1 IoT Security Techniques Analysis Based on its Merits 111 6.4 Conclusion 116 References 119 7 A Cooperative Navigation for Multi-Robots in Unknown Environments Using Hybrid Jaya-DE Algorithm 123D. Chandrasekhar Rao 7.1 Introduction 124 7.2 Related Works 126 7.3 Problem Formulation 130 7.4 Multi-Robot Navigation Employing Hybrid Jaya-DE Algorithm 134 7.4.1 Basic Jaya Algorithm 134 7.5 Hybrid Jaya-DE 136 7.5.1 Mutation 136 7.5.2 Crossover 136 7.5.3 Selection 137 7.6 Simulation Analysis and Performance Evaluation of Jaya-DE Algorithm 139 7.7 Total Navigation Path Deviation (TNPD) 147 7.8 Average Unexplored Goal Distance (AUGD) 148 7.9 Conclusion 159 References 159 8 Categorization Model for Parkinson’s Disease Occurrence and Severity Prediction 163Prashant Kumar Shrivastava, Ashish Chaturvedi, Megha Kamble and Megha Jain 8.1 Introduction 164 8.2 Applications 166 8.2.1 Machine Learning in PD Diagnosis 166 8.2.2 Challenges of PD Detection 169 8.2.3 Structuring of UPDRS Score 170 8.3 Methodology 173 8.3.1 Overview of Data Driven Intelligence 173 8.3.2 Comparison Between Deep Learning and Traditional Machine 175 8.3.3 Deep Learning for PD Diagnosis 176 8.3.4 Convolution Neural Network for PD Diagnosis 176 8.4 Proposed Models 178 8.4.1 Classification of Patient and Healthy Controls 178 8.4.2 Severity Score Classification 181 8.5 Results and Discussion 184 8.5.1 Performance Measures 185 8.5.2 Graphical Results 187 8.6 Conclusion 187 References 187 9 AI-Based Smart Agriculture Monitoring Using Ground-Based and Remotely Sensed Images 191Shounak Chakraborty, Nikumani Choudhury and Indrajit Kalita 9.1 Introduction 192 9.2 Automatic Land-Cover Classification Techniques Using Remotely Sensed Images 194 9.3 Deep Learning-Based Agriculture Monitoring 196 9.4 Adaptive Approaches for Multi-Modal Classification 197 9.4.1 Unsupervised DA 199 9.4.2 Semi-Supervised DA 200 9.4.3 Active Learning-Based DA 201 9.5 System Model 202 9.6 IEEE 802.15.4 204 9.6.1 802.15.4 MAC 204 9.6.2 DSME MAC 205 9.6.3 TSCH MAC 206 9.7 Analysis of IEEE 802.15.4 for Smart Agriculture 207 9.7.1 Effect of Device Specification 207 9.7.1.1 Low-Power 208 9.7.2 Effect of MAC Protocols 208 9.8 Experimental Results 209 9.9 Conclusion & Future Directions 212 References 212 10 Car Buying Criteria Evaluation Using Machine Learning Approach 223Samdeep Kumar Panda 10.1 Introduction 224 10.2 Literature Survey 225 10.3 Proposed Method 226 10.4 Dataset 227 10.5 Exploratory Data Analysis 227 10.6 Splitting of Data Into Training Data and Test Data 230 10.7 Pre-Processing 232 10.8 Training of Our Models 232 10.8.1 Gaussian Naïve Bayes 233 10.8.2 Decision Tree Classifier 234 10.8.3 Tuning the Model 235 10.8.4 Karnough Nearest Neighbor Classifier 236 10.8.5 Tuning the Model 237 10.8.6 Neural Network 238 10.8.7 Tuning the Model 239 10.9 Result Analysis 240 10.9.1 Confusion Matrix 240 10.9.2 Gaussian Naïve Bayes 241 10.9.3 Decision Tree Classifier 242 10.9.4 Karnough Nearest Neighbor Classifier 242 10.9.5 Neural Network 242 10.9.6 Accuracy Scores 243 10.10 Conclusion and Future Work 244 References 244 11 Big Data, Artificial Intelligence and Machine Learning: A Paradigm Shift in Election Campaigns 247Md. Safiullah and Neha Parveen 11.1 Introduction 248 11.2 Big Data Reveals the Voters’ Preference 249 11.2.1 Use of Software Applications in Election Campaigns 251 11.2.1.1 Team Joe App 252 11.2.1.2 Trump 2020 252 11.2.1.3 Modi App 253 11.3 Deep Fakes and Election Campaigns 254 11.3.1 Deep Fake in Delhi Elections 254 11.4 Social Media Bots 256 11.5 Future of Artificial Intelligence and Machine Learning in Election Campaigns 259 References 259 12 Impact of Optimized Segment Routing in Software Defined Network 263Amrutanshu Panigrahi, Bibhuprasad Sahu, Satya Sobhan Panigrahi, Ajay Kumar Jena and Md. Sahil Khan 12.1 Introduction 264 12.2 Software-Defined Network 266 12.3 SDN Architecture 268 12.4 Segment Routing 270 12.5 Segment Routing in SDN 272 12.6 Traffic Engineering in SDN 274 12.7 Segment Routing Protocol 275 12.8 Simulation and Result 277 12.9 Conclusion and Future Work 278 References 283 13 An Investigation into COVID-19 Pandemic in India 289Shubhangi V. Urkude, Vijaykumar R. Urkude, S. Vairachilai and Sandeep Kumar Panda 13.1 Introduction 289 13.1.1 Symptoms of COVID-19 292 13.1.2 Precautionary Measures 292 13.1.3 Ways of Spreading the Coronavirus 294 13.2 Literature Survey 295 13.3 Technologies Used to Fight COVID-19 296 13.3.1 Robots 296 13.3.2 Drone Technology 297 13.3.3 Crowd Surveillance 297 13.3.4 Spraying the Disinfectant 298 13.3.5 Sanitizing the Contaminated Areas 298 13.3.6 Monitoring Temperature Using Thermal Camera 298 13.3.7 Delivering the Essential Things 298 13.3.8 Public Announcement in the Infected Areas 298 13.4 Impact of COVID-19 on Business 299 13.4.1 Impact on Financial Markets 299 13.4.2 Impact on Supply Side 299 13.4.3 Impact on Demand Side 300 13.4.4 Impact on International Trade 300 13.5 Impact of COVID-19 on Indian Economy 300 13.6 Data and Result Analysis 300 13.7 Conclusion and Future Scope 304 References 304 14 Skin Cancer Classification: Analysis of Different CNN Models via Classification Accuracy 307Poonam Biswal, Monali Saha, Nishtha Jaiswal and Minakhi Rout 14.1 Introduction 307 14.2 Literature Survey 308 14.3 Methodology 310 14.3.1 Dataset Preparation 310 14.3.2 Dataset Loading and Data Pre-Processing 311 14.3.3 Creating Models 312 14.4 Models Used 312 14.5 Simulation Results 313 14.5.1 Changing Size of MaxPool2D(n,n) 314 14.5.2 Changing Size of AveragePool2D(n,n) 314 14.5.3 Changing Number of con2d(32n–64n) Layers 315 14.5.4 Changing Number of con2d-32*n Layers 315 14.5.5 ROC Curves and MSE Curves 318 14.6 Conclusion 321 References 321 15 Route Mapping of Multiple Humanoid Robots Using Firefly-Based Artificial Potential Field Algorithm in a Cluttered Terrain 323Abhishek Kumar Kashyap, Anish Pandey and Dayal R. Parhi 15.1 Introduction 324 15.2 Design of Proposed Algorithm 328 15.2.1 Mechanism of Artificial Potential Field 328 15.2.1.1 Potential Field Generated by Attractive Force of Goal 329 15.2.1.2 Potential Field Generated by Repulsive Force of Obstacle 331 15.2.2 Mechanism of Firefly Algorithm 332 15.2.2.1 Architecture of Optimization Problem Based on Firefly Algorithm 335 15.2.3 Dining Philosopher Controller 337 15.3 Hybridization Process of Proposed Algorithm 339 15.4 Execution of Proposed Algorithm in Multiple Humanoid Robots 339 15.5 Comparison 344 15.6 Conclusion 346 References 346 16 Innovative Practices in Education Systems Using Artificial Intelligence for Advanced Society 351Vinutha D.C., Kavyashree S., Vijay C.P. and G.T. Raju 16.1 Introduction 352 16.2 Literature Survey 353 16.2.1 AI in Auto-Grading 354 16.2.2 AI in Smart Content 356 16.2.3 AI in Auto Analysis on Student’s Grade 356 16.2.4 AI Extends Free Intelligent Tutoring 357 16.2.5 AI in Predicting Student Admission and Drop-Out Rate 359 16.3 Proposed System 359 16.3.1 Data Collection Module 360 16.3.2 Data Pre-Processing Module 364 16.3.3 Clustering Module 364 16.3.4 Partner Selection Module 366 16.4 Results 368 16.5 Future Enhancements 370 16.6 Conclusion 370 References 371 17 PSO-Based Hybrid Weighted k-Nearest Neighbor Algorithm for Workload Prediction in Cloud Infrastructures 373N. Yamuna, J. Antony Vijay and B. Gomathi 17.1 Introduction 374 17.2 Literature Survey 375 17.2.1 Machine Learning 378 17.3 Proposed System 379 17.3.1 Load Aware Cloud Computing Model 379 17.3.2 Wavelet Neural Network 379 17.3.3 Evaluation Using LOOCV Model 380 17.3.4 k-Nearest Neighbor (k-NN) Algorithm 381 17.3.5 Particle Swarm Optimization (PSO) Algorithm 382 17.3.6 HWkNN Optimization Algorithm Based on PSO 383 17.3.7 PSO-Based HWkNN (PHWkNN) Load Prediction Algorithm 384 17.4 Experimental Results 385 17.5 Conclusion 390 References 391 18 An Extensive Survey on the Prediction of Bankruptcy 395Sasmita Manjari Nayak and Minakhi Rout 18.1 Introduction 395 18.2 Literature Survey 397 18.2.1 Data Pre-Processing 397 18.2.1.1 Balancing of Imbalanced Dataset 397 18.2.1.2 Outlier Data Handling 410 18.2.2 Classifiers 418 18.2.3 Ensemble Models 422 18.3 System Architecture and Simulation Results 438 18.4 Conclusion 438 References 443 19 Future of Indian Agriculture Using AI and Machine Learning Tools and Techniques 447Manoj Kumar, Pratibha Maurya and Rinki Verma 19.1 Introduction 448 19.2 Overview of AI and Machine Learning 450 19.3 Review of Literature 452 19.4 Application of AI & Machine Learning in Agriculture 456 19.5 Current Scenario and Emerging Trends of AI and ML in Indian Agriculture Sector 460 19.6 Opportunities for Agricultural Operations in India 465 19.7 Conclusion 466 References 467 Index 473

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Book SynopsisA must-read exploration of lock and physical security from a renowned author and expert In Tobias on Locks and Insecurity Engineering, renowned investigative attorney and physical security expert Marc Weber Tobias delivers a comprehensive and insightful exploration of how locks are designed, built, and ultimately defeated by criminals, spies, hackers, and even lockpickers. In the book, you'll discover the myriad ways that security experts and bad actors have compromised physical locks using everything from the newest 3D printers to 99-cent ballpoint pens. The book explores the origins of different lock designs and the mistakes that design engineers make when they create new locks. It explains the countless ways that locks remain at risk for attack. The author explains the latest lock designs and technology, as well as how to assess whether a specific solution will work for you depending on your individual security requirements and use case. You'll also find: Ways to differentiate between fatally flawed locks and solid, secure optionsSeveral relevant and real-world case examples of catastrophic lock design failures that led to monetary loss, property damage, or bodily harmExaminations of lock security from the perspectives of forced entry, covert entry, and key-control An instructive and indispensable roadmap to locks and physical security, Tobias on Locks and Insecurity Engineering is the perfect guide for security and information technology professionals, design engineers, risk managers, law enforcement personnel, intelligence agents, regulators, policymakers, investigators, lawyers, and more.

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Book SynopsisCYBER-PHYSICAL SYSTEMS The 13 chapters in this book cover the various aspects associated with Cyber-Physical Systems (CPS) such as algorithms, application areas, and the improvement of existing technology such as machine learning, big data and robotics. Cyber-Physical Systems (CPS) is the interconnection of the virtual or cyber and the physical system. It is realized by combining three well-known technologies, namely Embedded Systems, Sensors and Actuators, and Network and Communication Systems. These technologies combine to form a system known as CPS. In CPS, the physical process and information processing are so tightly connected that it is hard to distinguish the individual contribution of each process from the output. Some exciting innovations such as autonomous cars, quadcopter, spaceships, sophisticated medical devices fall under CPS. The scope of CPS is tremendous. In CPS, one sees the applications of various emerging technologies such as artificial intelTable of ContentsPreface xv Acknowledgement xix 1 A Systematic Literature Review on Cyber Security Threats of Industrial Internet of Things 1 Ravi Gedam and Surendra Rahamatkar 1.1 Introduction 2 1.2 Background of Industrial Internet of Things 3 1.3 Literature Review 6 1.4 The Proposed Methodology 13 1.5 Experimental Requirements 14 1.6 Conclusion 15 References 16 2 Integration of Big Data Analytics Into Cyber-Physical Systems 19 Nandhini R.S. and Ramanathan L. 2.1 Introduction 19 2.2 Big Data Model for Cyber-Physical System 21 2.2.1 Cyber-Physical System Architecture 22 2.2.2 Big Data Analytics Model 22 2.3 Big Data and Cyber-Physical System Integration 23 2.3.1 Big Data Analytics and Cyber-Physical System 23 2.3.1.1 Integration of CPS With BDA 24 2.3.1.2 Control and Management of Cyber-Physical System With Big Data Analytics 24 2.3.2 Issues and Challenges for Big Data-Enabled Cyber-Physical System 25 2.4 Storage and Communication of Big Data for Cyber-Physical System 26 2.4.1 Big Data Storage for Cyber-Physical System 27 2.4.2 Big Data Communication for Cyber-Physical System 28 2.5 Big Data Processing in Cyber-Physical System 29 2.5.1 Data Processing 29 2.5.1.1 Data Processing in the Cloud and Multi-Cloud Computing 29 2.5.1.2 Clustering in Big Data 31 2.5.1.3 Clustering in Cyber-Physical System 32 2.5.2 Big Data Analytics 32 2.6 Applications of Big Data for Cyber-Physical System 33 2.6.1 Manufacturing 33 2.6.2 Smart Grids and Smart Cities 34 2.6.3 Healthcare 35 2.6.4 Smart Transportation 35 2.7 Security and Privacy 36 2.8 Conclusion 37 References 38 3 Machine Learning: A Key Towards Smart Cyber-Physical Systems 43 Rashmi Kapoor, Chandragiri Radhacharan and Sung-ho Hur 3.1 Introduction 44 3.2 Different Machine Learning Algorithms 46 3.2.1 Performance Measures for Machine Learning Algorithms 48 3.2.2 Steps to Implement ML Algorithms 49 3.2.3 Various Platforms Available for Implementation 50 3.2.4 Applications of Machine Learning in Electrical Engineering 50 3.3 ML Use-Case in MATLAB 51 3.4 ML Use-Case in Python 56 3.4.1 ML Model Deployment 59 3.5 Conclusion 60 References 60 4 Precise Risk Assessment and Management 63 Ambika N. 4.1 Introduction 64 4.2 Need for Security 65 4.2.1 Confidentiality 65 4.2.2 Integrity 66 4.2.3 Availability 66 4.2.4 Accountability 66 4.2.5 Auditing 67 4.3 Different Kinds of Attacks 67 4.3.1 Malware 67 4.3.2 Man-in-the Middle Assault 69 4.3.3 Brute Force Assault 69 4.3.4 Distributed Denial of Service 69 4.4 Literature Survey 70 4.5 Proposed Work 75 4.5.1 Objective 75 4.5.2 Notations Used in the Contribution 76 4.5.3 Methodology 76 4.5.4 Simulation and Analysis 78 4.6 Conclusion 80 References 80 5 A Detailed Review on Security Issues in Layered Architectures and Distributed Denial Service of Attacks Over IoT Environment 85 Rajarajan Ganesarathinam, Muthukumaran Singaravelu and K.N. Padma Pooja 5.1 Introduction 86 5.2 IoT Components, Layered Architectures, Security Threats 89 5.2.1 IoT Components 89 5.2.2 IoT Layered Architectures 90 5.2.2.1 3-Layer Architecture 91 5.2.2.2 4-Layer Architecture 91 5.2.2.3 5-Layer Architecture 93 5.2.3 Associated Threats in the Layers 93 5.2.3.1 Node Capture 93 5.2.3.2 Playback Attack 93 5.2.3.3 Fake Node Augmentation 93 5.2.3.4 Timing Attack 94 5.2.3.5 Bootstrap Attack 94 5.2.3.6 Jamming Attack 94 5.2.3.7 Kill Command Attack 94 5.2.3.8 Denial-of-Service (DoS) Attack 94 5.2.3.9 Storage Attack 94 5.2.3.10 Exploit Attack 95 5.2.3.11 Man-In-The-Middle (MITM) Attack 95 5.2.3.12 XSS Attack 95 5.2.3.13 Malicious Insider Attack 95 5.2.3.14 Malwares 95 5.2.3.15 Zero-Day Attack 95 5.3 Taxonomy of DDoS Attacks and Its Working Mechanism in IoT 97 5.3.1 Taxonomy of DDoS Attacks 99 5.3.1.1 Architectural Model 99 5.3.1.2 Exploited Vulnerability 100 5.3.1.3 Protocol Level 101 5.3.1.4 Degree of Automation 101 5.3.1.5 Scanning Techniques 101 5.3.1.6 Propagation Mechanism 102 5.3.1.7 Impact Over the Victim 102 5.3.1.8 Rate of Attack 103 5.3.1.9 Persistence of Agents 103 5.3.1.10 Validity of Source Address 103 5.3.1.11 Type of Victim 103 5.3.1.12 Attack Traffic Distribution 103 5.3.2 Working Mechanism of DDoS Attack 104 5.4 Existing Solution Mechanisms Against DDoS Over IoT 105 5.4.1 Detection Techniques 105 5.4.2 Prevention Mechanisms 108 5.5 Challenges and Research Directions 113 5.6 Conclusion 115 References 115 6 Machine Learning and Deep Learning Techniques for Phishing Threats and Challenges 123 Bhimavarapu Usharani 6.1 Introduction 124 6.2 Phishing Threats 124 6.2.1 Internet Fraud 124 6.2.1.1 Electronic-Mail Fraud 125 6.2.1.2 Phishing Extortion 126 6.2.1.3 Extortion Fraud 127 6.2.1.4 Social Media Fraud 127 6.2.1.5 Tourism Fraud 128 6.2.1.6 Excise Fraud 129 6.2.2 Phishing 129 6.3 Deep Learning Architectures 131 6.3.1 Convolution Neural Network (CNN) Models 131 6.3.1.1 Recurrent Neural Network 131 6.3.1.2 Long Short-Term Memory (LSTM) 134 6.4 Related Work 135 6.4.1 Machine Learning Approach 135 6.4.2 Neural Network Approach 136 6.4.3 Deep Learning Approach 138 6.5 Analysis Report 139 6.6 Current Challenges 140 6.6.1 File-Less Malware 140 6.6.2 Crypto Mining 140 6.7 Conclusions 140 References 141 7 Novel Defending and Prevention Technique for Man-in-the-Middle Attacks in Cyber-Physical Networks 147 Gaurav Narula, Preeti Nagrath, Drishti Hans and Anand Nayyar 7.1 Introduction 148 7.2 Literature Review 150 7.3 Classification of Attacks 152 7.3.1 The Perception Layer Network Attacks 152 7.3.2 Network Attacks on the Application Control Layer 153 7.3.3 Data Transmission Layer Network Attacks 153 7.3.3.1 Rogue Access Point 154 7.3.3.2 ARP Spoofing 155 7.3.3.3 DNS Spoofing 157 7.3.3.4 mDNS Spoofing 160 7.3.3.5 SSL Stripping 161 7.4 Proposed Algorithm of Detection and Prevention 162 7.4.1 ARP Spoofing 162 7.4.2 Rogue Access Point and SSL Stripping 168 7.4.3 DNS Spoofing 169 7.5 Results and Discussion 173 7.6 Conclusion and Future Scope 173 References 174 8 Fourth Order Interleaved Boost Converter With PID, Type II and Type III Controllers for Smart Grid Applications 179 Saurav S. and Arnab Ghosh 8.1 Introduction 179 8.2 Modeling of Fourth Order Interleaved Boost Converter 181 8.2.1 Introduction to the Topology 181 8.2.2 Modeling of FIBC 182 8.2.2.1 Mode 1 Operation (0 to d1 Ts) 182 8.2.2.2 Mode 2 Operation (d1 Ts to d2 Ts) 184 8.2.2.3 Mode 3 Operation (d2 Ts to d3 Ts) 186 8.2.2.4 Mode 4 Operation (d3 Ts to Ts) 188 8.2.3 Averaging of the Model 190 8.2.4 Small Signal Analysis 190 8.3 Controller Design for FIBC 193 8.3.1 PID Controller 193 8.3.2 Type II Controller 194 8.3.3 Type III Controller 195 8.4 Computational Results 197 8.5 Conclusion 204 References 205 9 Industry 4.0 in Healthcare IoT for Inventory and Supply Chain Management 209 Somya Goyal 9.1 Introduction 210 9.1.1 RFID and IoT for Smart Inventory Management 210 9.2 Benefits and Barriers in Implementation of RFID 212 9.2.1 Benefits 213 9.2.1.1 Routine Automation 213 9.2.1.2 Improvement in the Visibility of Assets and Quick Availability 215 9.2.1.3 SCM-Business Benefits 215 9.2.1.4 Automated Lost and Found 216 9.2.1.5 Smart Investment on Inventory 217 9.2.1.6 Automated Patient Tracking 217 9.2.2 Barriers 218 9.2.2.1 RFID May Interfere With Medical Activities 218 9.2.2.2 Extra Maintenance for RFID Tags 218 9.2.2.3 Expense Overhead 218 9.2.2.4 Interoperability Issues 218 9.2.2.5 Security Issues 218 9.3 IoT-Based Inventory Management—Case Studies 218 9.4 Proposed Model for RFID-Based Hospital Management 220 9.5 Conclusion and Future Scope 225 References 226 10 A Systematic Study of Security of Industrial IoT 229 Ravi Gedam and Surendra Rahamatkar 10.1 Introduction 230 10.2 Overview of Industrial Internet of Things (Smart Manufacturing) 231 10.2.1 Key Enablers in Industry 4.0 233 10.2.2 OPC Unified Architecture (OPC UA) 234 10.3 Industrial Reference Architecture 236 10.3.1 Arrowgead 237 10.3.2 FIWARE 237 10.3.3 Industrial Internet Reference Architecture (IIRA) 238 10.3.4 Kaa IoT Platform 238 10.3.5 Open Connectivity Foundation (OCF) 239 10.3.6 Reference Architecture Model Industrie 4.0 (RAMI 4.0) 239 10.3.7 ThingsBoard 240 10.3.8 ThingSpeak 240 10.3.9 ThingWorx 240 10.4 FIWARE Generic Enabler (FIWARE GE) 241 10.4.1 Core Context Management GE 241 10.4.2 NGSI Context Data Model 242 10.4.3 IDAS IoT Agents 244 10.4.3.1 IoT Agent-JSON 246 10.4.3.2 IoT Agent-OPC UA 247 10.4.3.3 Context Provider 247 10.4.4 FIWARE for Smart Industry 248 10.5 Discussion 249 10.5.1 Solutions Adopting FIWARE 250 10.5.2 IoT Interoperability Testing 251 10.6 Conclusion 252 References 253 11 Investigation of Holistic Approaches for Privacy Aware Design of Cyber-Physical Systems 257 Manas Kumar Yogi, A.S.N. Chakravarthy and Jyotir Moy Chatterjee 11.1 Introduction 258 11.2 Popular Privacy Design Recommendations 258 11.2.1 Dynamic Authorization 258 11.2.2 End to End Security 259 11.2.3 Enrollment and Authentication APIs 259 11.2.4 Distributed Authorization 260 11.2.5 Decentralization Authentication 261 11.2.6 Interoperable Privacy Profiles 261 11.3 Current Privacy Challenges in CPS 262 11.4 Privacy Aware Design for CPS 263 11.5 Limitations 265 11.6 Converting Risks of Applying AI Into Advantages 266 11.6.1 Proof of Recognition and De-Anonymization 267 11.6.2 Segregation, Shamefulness, Mistakes 267 11.6.3 Haziness and Bias of Profiling 267 11.6.4 Abuse Arising From Information 267 11.6.5 Tips for CPS Designers Including AI in the CPS Ecosystem 268 11.7 Conclusion and Future Scope 269 References 270 12 Exposing Security and Privacy Issues on Cyber-Physical Systems 273 Keshav Kaushik 12.1 Introduction to Cyber-Physical Systems (CPS) 273 12.2 Cyber-Attacks and Security in CPS 277 12.3 Privacy in CPS 281 12.4 Conclusion & Future Trends in CPS Security 284 References 285 13 Applications of Cyber-Physical Systems 289 Amandeep Kaur and Jyotir Moy Chatterjee 13.1 Introduction 289 13.2 Applications of Cyber-Physical Systems 291 13.2.1 Healthcare 291 13.2.1.1 Related Work 293 13.2.2 Education 295 13.2.2.1 Related Works 295 13.2.3 Agriculture 296 13.2.3.1 Related Work 297 13.2.4 Energy Management 298 13.2.4.1 Related Work 299 13.2.5 Smart Transportation 300 13.2.5.1 Related Work 301 13.2.6 Smart Manufacturing 301 13.2.6.1 Related Work 303 13.2.7 Smart Buildings: Smart Cities and Smart Houses 303 13.2.7.1 Related Work 304 13.3 Conclusion 304 References 305 Index 311

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Book SynopsisTable of ContentsPreface xiii 1 Introduction to Brain–Computer Interface: Applications and Challenges 1 Jyoti R. Munavalli, Priya R. Sankpal, Sumathi A. and Jayashree M. Oli 1.1 Introduction 1 1.2 The Brain – Its Functions 3 1.3 BCI Technology 3 1.3.1 Signal Acquisition 5 1.3.1.1 Invasive Methods 6 1.3.1.2 Non-Invasive Methods 8 1.3.2 Feature Extraction 10 1.3.3 Classification 11 1.3.3.1 Types of Classifiers 12 1.4 Applications of BCI 13 1.5 Challenges Faced During Implementation of BCI 17 References 21 2 Introduction: Brain–Computer Interface and Deep Learning 25 Muskan Jindal, Eshan Bajal and Areeba Kazim 2.1 Introduction 26 2.1.1 Current Stance of P300 BCI 28 2.2 Brain–Computer Interface Cycle 29 2.3 Classification of Techniques Used for Brain–Computer Interface 38 2.3.1 Application in Mental Health 38 2.3.2 Application in Motor-Imagery 38 2.3.3 Application in Sleep Analysis 39 2.3.4 Application in Emotion Analysis 39 2.3.5 Hybrid Methodologies 40 2.3.6 Recent Notable Advancements 41 2.4 Case Study: A Hybrid EEG-fNIRS BCI 46 2.5 Conclusion, Open Issues and Future Endeavors 47 References 49 3 Statistical Learning for Brain–Computer Interface 63 Lalit Kumar Gangwar, Ankit, John A. and Rajesh E. 3.1 Introduction 64 3.1.1 Various Techniques to BCI 64 3.1.1.1 Non-Invasive 64 3.1.1.2 Semi-Invasive 65 3.1.1.3 Invasive 67 3.2 Machine Learning Techniques to BCI 67 3.2.1 Support Vector Machine (SVM) 69 3.2.2 Neural Networks 69 3.3 Deep Learning Techniques Used in BCI 70 3.3.1 Convolutional Neural Network Model (CNN) 72 3.3.2 Generative DL Models 73 3.4 Future Direction 73 3.5 Conclusion 74 References 75 4 The Impact of Brain–Computer Interface on Lifestyle of Elderly People 77 Zahra Alidousti Shahraki and Mohsen Aghabozorgi Nafchi 4.1 Introduction 78 4.2 Diagnosing Diseases 79 4.3 Movement Control 84 4.4 IoT 85 4.5 Cognitive Science 86 4.6 Olfactory System 88 4.7 Brain-to-Brain (B2B) Communication Systems 89 4.8 Hearing 90 4.9 Diabetes 91 4.10 Urinary Incontinence 92 4.11 Conclusion 93 References 93 5 A Review of Innovation to Human Augmentation in Brain-Machine Interface – Potential, Limitation, and Incorporation of AI 101 T. Graceshalini, S. Rathnamala and M. Prabhanantha Kumar 5.1 Introduction 102 5.2 Technologies in Neuroscience for Recording and Influencing Brain Activity 103 5.2.1 Brain Activity Recording Technologies 104 5.2.1.1 A Non-Invasive Recording Methodology 104 5.2.1.2 An Invasive Recording Methodology 104 5.3 Neuroscience Technology Applications for Human Augmentation 106 5.3.1 Need for BMI 106 5.3.1.1 Need of BMI Individuals for Re-Establishing the Control and Communication of Motor 107 5.3.1.2 Brain-Computer Interface Noninvasive Research at Wadsworth Center 107 5.3.1.3 An Interface of Berlin Brain-Computer: Machine Learning-Dependent of User-Specific Brain States Detection 107 5.4 History of BMI 108 5.5 BMI Interpretation of Machine Learning Integration 111 5.6 Beyond Current Existing Methodologies: Nanomachine Learning BMI Supported 116 5.7 Challenges and Open Issues 119 5.8 Conclusion 120 References 121 6 Resting-State fMRI: Large Data Analysis in Neuroimaging 127 M. Menagadevi , S. Mangai, S. Sudha and D. Thiyagarajan 6.1 Introduction 128 6.1.1 Principles of Functional Magnetic Resonance Imaging (fMRI) 128 6.1.2 Resting State fMRI (rsfMRI) for Neuroimaging 128 6.1.3 The Measurement of Fully Connected and Construction of Default Mode Network (DMN) 129 6.2 Brain Connectivity 129 6.2.1 Anatomical Connectivity 129 6.2.2 Functional Connectivity 130 6.3 Better Image Availability 130 6.3.1 Large Data Analysis in Neuroimaging 131 6.3.2 Big Data rfMRI Challenges 133 6.3.3 Large rfMRI Data Software Packages 134 6.4 Informatics Infrastructure and Analytical Analysis 137 6.5 Need of Resting-State MRI 137 6.5.1 Cerebral Energetics 137 6.5.2 Signal to Noise Ratio (SNR) 137 6.5.3 Multi-Purpose Data Sets 138 6.5.4 Expanded Patient Populations 138 6.5.5 Reliability 138 6.6 Technical Development 138 6.7 rsfMRI Clinical Applications 139 6.7.1 Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD) 139 6.7.2 Fronto-Temporal Dementia (FTD) 140 6.7.3 Multiple Sclerosis (MS) 141 6.7.4 Amyotrophic Lateral Sclerosis (ALS) and Depression 143 6.7.5 Bipolar 144 6.7.6 Schizophrenia 145 6.7.7 Attention Deficit Hyperactivity Disorder (ADHD) 147 6.7.8 Multiple System Atrophy (MSA) 147 6.7.9 Epilepsy/Seizures 147 6.7.10 Pediatric Applications 149 6.8 Resting-State Functional Imaging of Neonatal Brain Image 149 6.9 Different Groups in Brain Disease 151 6.10 Learning Algorithms for Analyzing rsfMRI 151 6.11 Conclusion and Future Directions 154 References 154 7 Early Prediction of Epileptic Seizure Using Deep Learning Algorithm 157 T. Jagadesh, A. Reethika, B. Jaishankar and M.S. Kanivarshini 7.1 Introduction 158 7.2 Methodology 164 7.3 Experimental Results 169 7.4 Taking Care of Children with Seizure Disorders 172 7.5 Ketogenic Diet 172 7.6 Vagus Nerve Stimulation (VNS) 172 7.7 Brain Surgeries 173 7.8 Conclusion 173 References 175 8 Brain–Computer Interface-Based Real-Time Movement of Upper Limb Prostheses Topic: Improving the Quality of the Elderly with Brain-Computer Interface 179 S. Vairaprakash and S. Rajagopal 8.1 Introduction 180 8.1.1 Motor Imagery Signal Decoding 181 8.2 Literature Survey 182 8.3 Methodology of Proposed Work 184 8.3.1 Proposed Control Scheme 185 8.3.2 One Versus All Adaptive Neural Type- 2 Fuzzy Inference System (OVAANT2FIS) 187 8.3.3 Position Control of Robot Arm Using Hybrid BCI for Rehabilitation Purpose 187 8.3.4 Jaco Robot Arm 189 8.3.5 Scheme 1: Random Order Positional Control 189 8.4 Experiments and Data Processing 192 8.4.1 Feature Extraction 195 8.4.2 Performance Analysis of the Detectors 197 8.4.3 Performance of the Real Time Robot Arm Controllers 198 8.5 Discussion 200 8.6 Conclusion and Future Research Directions 202 References 203 9 Brain–Computer Interface-Assisted Automated Wheelchair Control Management-Cerebro: A BCI Application 205 Sudhendra Kambhamettu, Meenalosini Vimal Cruz, Anitha S., Sibi Chakkaravarthy S. and K. Nandeesh Kumar 9.1 Introduction 206 9.1.1 What is a BCI? 207 9.2 How Do BCI’s Work? 207 9.2.1 Measuring Brain Activity 208 9.2.1.1 Without Surgery 208 9.2.1.2 With Surgery 208 9.2.2 Mental Strategies 209 9.2.2.1 Ssvep 210 9.2.2.2 Neural Motor Imagery 210 9.3 Data Collection 211 9.3.1 Overview of the Data 211 9.3.2 EEG Headset 213 9.3.3 EEG Signal Collection 214 9.4 Data Pre-Processing 215 9.4.1 Artifact Removal 216 9.4.2 Signal Processing and Dimensionality Reduction 217 9.4.3 Feature Extraction 217 9.5 Classification 218 9.5.1 Deep Learning (DL) Model Pipeline 219 9.5.2 Architecture of the DL Model 220 9.5.3 Output Metrics of the Classifier 221 9.5.4 Deployment of DL Model 221 9.5.5 Control System 223 9.5.6 Control Flow Overview 223 9.6 Control Modes 223 9.6.1 Speech Mode 223 9.6.2 Blink Stimulus Mapping 223 9.6.3 Text Interface 225 9.6.4 Motion Mode 225 9.6.5 Motor Arrangement 225 9.6.6 Imagined Motion Mapping 226 9.7 Compilation of All Systems 226 9.8 Conclusion 226 References 227 10 Identification of Imagined Bengali Vowels from EEG Signals Using Activity Map and Convolutional Neural Network 231 Rajdeep Ghosh, Nidul Sinha and Souvik Phadikar 10.1 Introduction 232 10.1.1 Electroencephalography (EEG) 233 10.1.2 Imagined Speech or Silent Speech 233 10.2 Literature Survey 234 10.3 Theoretical Background 238 10.3.1 Convolutional Neural Network 238 10.3.2 Activity Map 240 10.4 Methodology 242 10.4.1 Data Collection 243 10.4.2 Pre-Processing 244 10.4.3 Feature Extraction 245 10.4.4 Classification 247 10.5 Results 249 10.6 Conclusion 252 Acknowledgment 252 References 252 11 Optimized Feature Selection Techniques for Classifying Electrocorticography Signals 255 B. Paulchamy, R. Uma Maheshwari, D. Sudarvizhi AP(Sr. G), R. Anandkumar AP(Sr. G) and Ravi G. 11.1 Introduction 256 11.1.1 Brain–Computer Interface 256 11.2 Literature Study 258 11.3 Proposed Methodology 260 11.3.1 Dataset 261 11.3.2 Feature Extraction Using Auto-Regressive (AR) Model and Wavelet Transform 261 11.3.2.1 Auto-Regressive Features 261 11.3.2.2 Wavelet Features 262 11.3.2.3 Feature Selection Methods 262 11.3.2.4 Information Gain (IG) 263 11.3.2.5 Clonal Selection 263 11.3.2.6 An Overview of the Steps of the Clonalg 264 11.3.3 Hybrid CLONALG 265 11.4 Experimental Results 268 11.4.1 Results of Feature Selection Using IG with Various Classifiers 272 11.4.2 Results of Optimizing Support Vector Machine Using CLONALG Selection 274 11.5 Conclusion 276 References 277 12 BCI – Challenges, Applications, and Advancements 279 R. Remya and Sumithra, M.G. 12.1 Introduction 279 12.1.1 BCI Structure 280 12.2 Related Works 281 12.3 Applications 282 12.4 Challenges and Advancements 297 12.5 Conclusion 299 References 299 Index 303

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Book SynopsisFrom 5G to 6G Understand the transition to the sixth generation of wireless with this bold introduction The transition from the fifth generation of wireless communication (5G) to the coming sixth generation (6G) promises to be one of the most significant phases in the history of telecommunications. The technological, social, and logistical challenges promise to be significant, and meeting these challenges will determine the future of wireless communication. Experts and professionals across dozens of fields and industries are beginning to reckon seriously with these challenges as the 6G revolution approaches. From 5G to 6G provides an overview of this transition, offering a snapshot of a moment in which 5G is establishing itself and 6G draws ever nearer. It focuses on recent advances in wireless technology that brings 6G closer to reality, as well as the near-term challenges that still have to be met for this transition to succeed. The result is an essential book for anyone wishing to understand the future of wireless telecommunications in an increasingly connected world. From 5G to 6G readers will also find: 6G applications to both AI and Machine Learning, technologies which loom ever larger in wireless communicationDiscussion of subjects including smart healthcare, cybersecurity, extended reality, and moreTreatment of the ongoing infrastructural and technological requirements for 6G From 5G to 6G is essential for researchers and academics in wireless communication and computer science, as well as for undergraduates in related subjects and professionals in wireless-adjacent fields.Table of ContentsAbout the Author xiii Preface xv 1 Technologies and Development for the Next Information Age 1 1.1 Introduction 1 1.2 Roadmap to 6G 1 1.2.1 Society 5.0 4 1.2.2 Extended Reality 4 1.2.3 Wireless Brain-Computer 5 1.2.4 Haptic Communication 5 1.2.5 Smart Healthcare 5 1.2.6 Five-Sense Information 6 1.2.7 The Internet of Everything 6 1.2.8 5G to 6G 6 1.3 AI and Cybersecurity: Paving the Way for the Future 10 1.4 Fusion of IoT, AI, and Cybersecurity 10 1.4.1 Where Did AI Begin? 12 1.4.2 Role of AI 12 1.4.3 Disadvantages of AI 12 1.4.4 Advantages of AI 12 1.4.5 Threats from Hackers 14 1.5 How AI Can Help Solve These Problems 15 1.6 Connected Devices and Cybersecurity 16 1.7 Solutions for Data Management in Cybersecurity 17 1.8 Conclusion 17 References 18 2 Networks of the Future 21 2.1 Introduction 21 2.2 The Motive for Energy-Efficient ICTs 22 2.2.1 Approaches 23 2.3 Wireless Networks 24 2.3.1 Wi-Fi 26 2.3.2 Lte 28 2.3.3 Heterogeneous Networks 29 2.3.4 Femtocell Repeater 29 2.3.5 The Dawn of 5G Wireless Systems 30 2.3.6 Advancing from 5G to 6G Networks 32 2.4 Cognitive Networking 33 2.4.1 Zero-Touch Network and Service Management 34 2.4.2 Zero-Trust Networking 35 2.4.3 Information-Centric Networking 35 2.4.3.1 Basic Concepts of ICN 36 2.4.4 In-Network Computing 36 2.4.5 Active Networking 36 2.5 Mobile Edge Computing 37 2.6 Quantum Communications 37 2.6.1 Quantum Computing and 6G Wireless 38 2.7 Cybersecurity of 6G 38 2.8 Massive Machine-Type Communications (MTC) 39 2.9 Edge-Intelligence and Pervasive Artificial Intelligence in 6G 40 2.10 Blockchain: Foundations and Role in 6G 40 2.11 Role of Open-Source Platforms in 6G 40 2.11.1 PHY Technologies for 6G Wireless 40 2.11.2 Reconfigurable Intelligent Surface for 6G Wireless Networks 41 2.11.3 Millimeter-Wave and Terahertz Spectrum for 6G Wireless 41 2.11.4 Challenges in Transport Layer for Terabit Communications 41 2.11.5 High-Capacity Backhaul Connectivity for 6G Wireless 42 2.11.6 Cloud-Native Approach for 6G Wireless Networks 42 2.11.7 Machine Type Communications in 6G 42 2.11.8 Impact of 5G and 6G on Health and Environment 42 2.12 Integration of 5G with AI and IoT and Roadmap to 6G 43 2.13 3gpp 47 2.14 Conclusion 49 References 49 3 The Future of Wireless Communication with 6G 53 3.1 Introduction 53 3.2 Recent Trends Leading to 6G Technology Evolution 53 3.3 Security and Privacy Challenges in 6G Wireless Communications 53 3.4 The Impact of 6G on Healthcare Systems 56 3.5 The Impact of 6G on Space Technology and Satellite Communication 58 3.6 The Impact of 6G on Other Industries 60 3.7 Terahertz Wireless Systems and Networks with 6G 61 3.8 The Future of 6G and Its Role in IT 62 References 62 4 Artificial Intelligence and Machine Learning in the Era of 5G and 6G Technology 65 4.1 Artificial Intelligence and Machine Learning: Definitions, Applications, and Challenges 66 4.1.1 Application of Machine Learning and Artificial Intelligence 66 4.1.2 Challenges for Machine Learning and Artificial Intelligence 66 4.2 Artificial Intelligence: Laws, Regulations, and Ethical Issues 67 4.2.1 Ethical Governance in Artificial Intelligence 67 4.2.2 The Future of Regulation for AI 67 4.3 Potentials of Artificial Intelligence in Wireless 5G and 6G: Benefits and Challenges 68 4.3.1 Artificial Intelligence in Wireless 5G and 6G 68 4.3.2 Benefits and Challenges of AI in 5G and 6G 68 4.3.3 How Can AI Be Used to Enhance 6G Wireless Security? 68 4.3.4 The 6G Era’s Edge Intelligence and Cloudification 69 4.3.5 Distributed Artificial Intelligence in 6G Security 69 4.4 Cybersecurity Issues in Advanced 5G and 6G 70 4.5 Benefits and Challenges of Using AI in Cybersecurity: Help or Hurt? 70 4.6 How Can AI Be Used by Hackers Attacking Networks? 71 4.7 Conclusion 72 References 72 5 6G Wireless Communication Systems: Emerging Technologies, Architectures, Challenges, and Opportunities 73 5.1 Introduction 73 5.2 Important Aspects of Sixth-Generation Communication Technology 73 5.2.1 A Much Higher Data Rate 74 5.2.2 A Much Lower Latency 74 5.2.3 Network Reliability and Accuracy 74 5.2.4 Energy Efficiency 74 5.2.5 Focus on Machines as Primary Users 74 5.2.6 AI Wireless Communication Tools 74 5.2.7 Personalized Network Experience 74 5.3 Enabling Technologies Behind the Drive for 6G 76 5.3.1 Artificial Intelligence 76 5.3.2 Terahertz Communications 78 5.3.3 Optical Wireless Technology 78 5.4 Extreme Performance Technologies in 6G Connectivity 79 5.4.1 Quantum Communication and Quantum ml 79 5.4.2 Blockchain 80 5.4.2.1 Internal Network Operations 80 5.4.2.2 Ecosystem for Productive Collaboration 80 5.4.2.3 Tactile Internet 80 5.4.2.4 Spectrum Sharing (FDSS) and Free Duplexing 80 5.5 6G Communications Using Intelligent Platforms 81 5.5.1 Integrated Intelligence 82 5.5.2 Satellite-Based Integrated Network 82 5.5.3 Wireless Information and Energy Transfer Are Seamlessly Integrated 83 5.6 Artificial Intelligence and a Data-Driven Approach to Networks 83 5.6.1 Zero-Touch Network 84 5.6.2 AI by Design 85 5.6.3 Technological Fundamentals for Zero-Touch Systems 85 5.7 Sensing for 6G 85 5.7.1 A Bandwidth as Well as Carrier Frequency Rise 85 5.7.2 Chip Technologies of the Future 86 5.7.3 Models of Consistent Channels 86 5.7.4 X-Haul and Transport Network for 6G 87 5.8 Applications 87 5.9 Innovative 6G Network Architectures 89 5.10 Conclusion 89 References 90 6 6G: Architecture, Applications, and Challenges 91 6.1 Introduction 91 6.2 6G Network Architecture Vision 93 6.2.1 6G Use Cases, Requirements, and Metrics 94 6.2.2 What 5G Is Currently Covering 95 6.3 6th Generation Networks: A Step Beyond 5G 97 6.3.1 6G and the Fundamental Features 98 6.4 Emerging Applications of 6G Wireless Networks 99 6.4.1 Virtual, Augmented, and Mixed Reality 99 6.4.2 Holographic Telepresence 100 6.4.3 Automation: The Future of Factories 101 6.4.4 Smart Lifestyle with the Integration of the Internet of Things 101 6.4.5 Autonomous Driving and Connected Devices 101 6.4.6 Healthcare 101 6.4.7 Nonterrestrial Communication 101 6.4.8 Underwater Communication 102 6.4.9 Disaster Management 102 6.4.10 Environment 102 6.5 The Requirements and KPI Targets of 6G 102 6.5.1 Extremely Low Latency 102 6.5.2 Low Power Consumption 102 6.5.3 High Data Rates 103 6.5.4 High-Frequency Bands 103 6.5.5 Ultra-Reliability 103 6.5.6 Security and Privacy 103 6.5.7 Massive Connection Density 104 6.5.8 Extreme Coverage Extension 104 6.5.9 Mobility 104 6.6 6G Applications 104 6.7 Challenges in 6G: Standardization, Design, and Deployment 104 References 106 7 Cybersecurity in Digital Transformation Era: Security Risks and Solutions 109 7.1 Introduction 109 7.2 Digital Transformation and Mesh Networks of Networks 109 7.3 Security as the Enemy of Digital Transformation 111 7.4 The Current State of Cybercrime 113 7.5 Security and Technologies of the Digital Transformation Economy 115 7.6 Tackling the Cybersecurity Maturity Challenges to Succeed with Digital Transformation 116 7.7 Security Maturity and Optimization: Perception versus Reality 117 7.7.1 Why Cybersecurity Maturity Is Not What It Should Be in the Digital Business and Transformation Reality 118 7.7.2 Why Cybersecurity Maturity and Strategy Are Lagging 119 7.8 Changing Security Parameters and Cyber Risks Demand a Holistic Security Approach for Digital Business 120 7.9 Cybersecurity Challenges and Digital Risks for the Future 121 7.10 Conclusion 122 References 122 8 Next Generations Networks: Integration, Trustworthiness, Privacy, and Security 125 8.1 Introduction 125 8.2 The State of 5G Networks 127 8.2.1 Applications and Services of 5G Technologies 128 8.3 6G: Key Technologies 130 8.4 6G: Application and Services 134 8.5 Benefits of 6G over 5G: A Comparison 135 8.5.1 Artificial Intelligence in 5G and 6G: Benefits and Challenges 135 8.5.2 Artificial Intelligence and Cybersecurity 136 8.5.3 Benefits and Challenges of AI and 6G for Cybersecurity as Defense and Offense 136 8.6 6G: Integration and Roadmap 137 8.7 Key Words in Safeguarding 6G 137 8.7.1 Trust 137 8.7.2 Security 137 8.7.3 Privacy 138 8.8 Trustworthiness in 6G 138 8.8.1 Is Trust Networking Needed? 138 8.8.2 Benefits of Trust Networking for 6G 138 8.8.3 Constraints of Trust Networking in 6G 138 8.8.4 Principles for Trust Networking 139 8.8.5 Challenges in Trust Networking for 6G 139 8.9 Network Security Architecture for 6G 140 8.9.1 Privacy and Security in IoT for 6G 140 8.10 6G Wireless Systems 141 8.10.1 Advances 141 8.10.2 Physical Layer Security as a Means of Confidentiality 142 8.10.3 Challenges of Implementing Federated Learning 143 8.10.4 Physical Layer Security for Six-Generation Connectivity 143 8.10.5 Physical Layer Security Using Light Communications 144 8.10.6 Challenges for Physical Layer Security 144 8.10.7 Privacy Requirements for 6G 145 8.10.8 Is Personal Information Really Personal? 145 8.11 Fifth Generation vs. Sixth Generation 145 8.12 Conclusion 146 References 147 9 Artificial Intelligence: Cybersecurity and Security Threats 149 9.1 Introduction 149 9.2 5G and 6G 150 9.3 Cybersecurity in Its Current State 151 9.4 AI as a Concept 153 9.5 AI: A Solution for Cybersecurity 154 9.6 AI: New Challenges in Cybersecurity 154 9.7 Conclusion 156 References 156 10 Impact of Artificial Intelligence and Machine Learning on Cybersecurity 159 10.1 Introduction 159 10.2 What Is Artificial Intelligence (AI)? 160 10.2.1 Reactive Machines 160 10.2.2 Limited Memory 160 10.2.3 Theory of Mind 160 10.2.4 Self-Awareness 161 10.3 The Transformative Power of AI 161 10.4 Understanding the Relationship Between AI and Cybersecurity 161 10.5 The Promise and Challenges of AI for Cybersecurity 162 10.5.1 Risks and Impacts of AI on Cybersecurity (Threats and Solutions) 163 10.5.1.1 Domestic Risks 164 10.5.1.2 Local Risks 164 10.5.1.3 National Risks 164 10.5.1.4 Why Prediction and Prevention 164 10.6 Broad Domain of AI Security (Major Themes in the AI Security Landscape) 164 10.6.1 Digital/Physical 165 10.6.2 Protection from Malicious Use of AI and Automated Cyberattacks 165 10.6.3 Other Technologies with AI and Their Integration 165 10.6.4 Political 165 10.6.5 Manipulation and Disinformation Protection 165 10.6.6 Infrastructure Based on AI and Digital Expertise of Government 166 10.6.6.1 Economic 166 10.6.6.2 Labor Displacement and Its Mitigation 166 10.6.6.3 Promotion of AI R&D 166 10.6.6.4 Education and Training That Is Updated 167 10.7 Transparency of Artificial Intelligence and Accountability Societal Aspects 167 10.7.1 Rights of Privacy and Data 167 10.8 Global AI Security Priorities 168 10.8.1 Global Economy 168 10.8.2 Global Privacy and Data Rights 168 10.8.2.1 AI and Ethics 169 10.8.3 Automation of Cyberattacks or Social Engineering Attacks 170 10.8.4 Target Prioritizing with Machine Learning 170 10.9 Automation of Services in Cybercriminal Offense 170 10.9.1 Increased Scale of Attacks 170 10.10 The Future of AI in Cybersecurity 171 10.11 Conclusion 171 References 172 11 AI and Cybersecurity: Paving the Way for the Future 175 11.1 Introduction 175 11.2 IoT Security and the Role of AI 176 11.3 Cybercrime and Cybersecurity 179 11.4 How Can AI Help Solve These Problems? 181 11.5 The Realm of Cyberspace 181 11.6 Connected Devices and Cybersecurity 182 11.7 Solutions for Data Management in Cybersecurity 183 11.8 Conclusion 183 References 184 12 Future 6G Networks 185 12.1 Introduction 185 12.2 Vision, Challenges, and Key Features for Future 6G Networks 186 12.2.1 Fourth Generation Long-Term Evolution (4G-LTE) 187 12.3 Rationale for 6G Networks with Prevailing and Future Success of 5G 188 12.4 Missing Units from LTE and 5G That 6G Will Integrate 189 12.5 Features of 6G Networks 189 12.5.1 Large Bandwidth 189 12.5.2 Artificial Intelligence 189 12.5.3 Operational Intelligence 190 12.6 Wireless Networks 190 12.6.1 Beyond 5G and Toward 6G 190 12.6.2 Visible-Light Communications 191 12.6.3 E-MBB Plus 191 12.6.4 Big Communications 191 12.6.5 Secure Ultra-Reliable Low-Latency Communications 192 12.6.6 Three-Dimensional Integrated Communications 192 12.6.7 Underwater Communication 193 12.6.8 Space Communication 194 12.6.9 UAV-Based Communication 194 12.6.10 Unconventional Data Communications 194 12.6.11 Tactical Communications 195 12.6.12 Holographic Communications 195 12.6.13 Human-Bond Communications 196 12.7 Challenges for 6G Networks 196 12.7.1 Potential Health Issues 196 12.7.2 Security and Privacy Concerns 197 12.7.3 Research Activities and Trends 197 12.8 Conclusion 198 References 200 Index 203

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Book SynopsisCONVERGENCE of CLOUD with AI for BIG DATA ANALYTICS This book covers the foundations and applications of cloud computing, AI, and Big Data and analyses their convergence for improved development and services. The 17 chapters of the book masterfully and comprehensively cover the intertwining concepts of artificial intelligence, cloud computing, and big data, all of which have recently emerged as the next-generation paradigms. There has been rigorous growth in their applications and the hybrid blend of AI Cloud and IoT (Ambient-intelligence technology) also relies on input from wireless devices. Despite the multitude of applications and advancements, there are still some limitations and challenges to overcome, such as security, latency, energy consumption, service allocation, healthcare services, network lifetime, etc. Convergence of Cloud with AI for Big Data Analytics: Foundations and Innovation details all these technologies and how theyTable of ContentsPreface xv 1 Integration of Artificial Intelligence, Big Data, and Cloud Computing with Internet of Things 1 Jaydip Kumar 1.1 Introduction 2 1.2 Roll of Artificial Intelligence, Big Data and Cloud Computing in IoT 3 1.3 Integration of Artificial Intelligence with the Internet of Things Devices 4 1.4 Integration of Big Data with the Internet of Things 6 1.5 Integration of Cloud Computing with the Internet of Things 6 1.6 Security of Internet of Things 8 1.7 Conclusion 10 References 10 2 Cloud Computing and Virtualization 13 Sudheer Mangalampalli, Pokkuluri Kiran Sree, Sangram K. Swain and Ganesh Reddy Karri 2.1 Introduction to Cloud Computing 14 2.1.1 Need of Cloud Computing 14 2.1.2 History of Cloud Computing 14 2.1.3 Definition of Cloud Computing 15 2.1.4 Different Architectures of Cloud Computing 16 2.1.4.1 Generic Architecture of Cloud Computing 16 2.1.4.2 Market Oriented Architecture of Cloud Computing 17 2.1.5 Applications of Cloud Computing in Different Domains 18 2.1.5.1 Cloud Computing in Healthcare 18 2.5.1.2 Cloud Computing in Education 19 2.5.1.3 Cloud Computing in Entertainment Services 19 2.5.1.4 Cloud Computing in Government Services 19 2.1.6 Service Models in Cloud Computing 19 2.1.7 Deployment Models in Cloud Computing 21 2.2 Virtualization 22 2.2.1 Need of Virtualization in Cloud Computing 22 2.2.2 Architecture of a Virtual Machine 23 2.2.3 Advantages of Virtualization 24 2.2.4 Different Implementation Levels of Virtualization 25 2.2.4.1 Instruction Set Architecture Level 25 2.2.4.2 Hardware Level 26 2.2.4.3 Operating System Level 26 2.2.4.4 Library Level 26 2.2.4.5 Application Level 26 2.2.5 Server Consolidation Using Virtualization 26 2.2.6 Task Scheduling in Cloud Computing 27 2.2.7 Proposed System Architecture 31 2.2.8 Mathematical Modeling of Proposed Task Scheduling Algorithm 31 2.2.9 Multi Objective Optimization 34 2.2.10 Chaotic Social Spider Algorithm 34 2.2.11 Proposed Task Scheduling Algorithm 35 2.2.12 Simulation and Results 36 2.2.12.1 Calculation of Makespan 36 2.2.12.2 Calculation of Energy Consumption 37 2.3 Conclusion 37 References 38 3 Time and Cost-Effective Multi-Objective Scheduling Technique for Cloud Computing Environment 41 Aida A. Nasr, Kalka Dubey, Nirmeen El-Bahnasawy, Gamal Attiya and Ayman El-Sayed 3.1 Introduction 42 3.2 Literature Survey 44 3.3 Cloud Computing and Cloudlet Scheduling Problem 46 3.4 Problem Formulation 47 3.5 Cloudlet Scheduling Techniques 49 3.5.1 Heuristic Methods 50 3.5.2 Meta-Heuristic Methods 51 3.6 Cloudlet Scheduling Approach (CSA) 52 3.6.1 Proposed CSA 52 3.6.2 Time Complexity 53 3.6.3 Case Study 54 3.7 Simulation Results 56 3.7.1 Simulation Environment 56 3.7.2 Evaluation Metrics 56 3.7.2.1 Performance Evaluation with Small Number of Cloudlets 57 3.7.2.2 Performance Evaluation with Large Number of Cloudlets 57 3.8 Conclusion 64 References 64 4 Cloud-Based Architecture for Effective Surveillance and Diagnosis of COVID- 19 69 Shweta Singh, Aditya Bhardwaj, Ishan Budhiraja, Umesh Gupta and Indrajeet Gupta 4.1 Introduction 70 4.2 Related Work 71 4.2.1 Proposed Cloud-Based Network for Management of COVID- 19 73 4.3 Research Methodology 75 4.3.1 Sample Size and Target 76 4.3.1.1 Sampling Procedures 77 4.3.1.2 Response Rate 77 4.3.1.3 Instrument and Measures 77 4.3.2 Reliability and Validity Test 78 4.3.3 Exploratory Factor Analysis 78 4.4 Survey Findings 80 4.4.1 Outcomes of the Proposed Scenario 82 4.4.1.1 Online Monitoring 82 4.4.1.2 Location Tracking 82 4.4.1.3 Alarm Linkage 82 4.4.1.4 Command and Control 82 4.4.1.5 Plan Management 82 4.4.1.6 Security Privacy 83 4.4.1.7 Remote Maintenance 83 4.4.1.8 Online Upgrade 83 4.4.1.9 Command Management 83 4.4.1.10 Statistical Decision 83 4.4.2 Experimental Setup 83 4.5 Conclusion and Future Scope 85 References 86 5 Smart Agriculture Applications Using Cloud and IoT 89 Keshav Kaushik 5.1 Role of IoT and Cloud in Smart Agriculture 89 5.2 Applications of IoT and Cloud in Smart Agriculture 94 5.3 Security Challenges in Smart Agriculture 97 5.4 Open Research Challenges for IoT and Cloud in Smart Agriculture 100 5.5 Conclusion 103 References 103 6 Applications of Federated Learning in Computing Technologies 107 Sambit Kumar Mishra, Kotipalli Sindhu, Mogaparthi Surya Teja, Vutukuri Akhil, Ravella Hari Krishna, Pakalapati Praveen and Tapas Kumar Mishra 6.1 Introduction 108 6.1.1 Federated Learning in Cloud Computing 108 6.1.1.1 Cloud-Mobile Edge Computing 109 6.1.1.2 Cloud Edge Computing 111 6.1.2 Federated Learning in Edge Computing 112 6.1.2.1 Vehicular Edge Computing 113 6.1.2.2 Intelligent Recommendation 113 6.1.3 Federated Learning in IoT (Internet of Things) 114 6.1.3.1 Federated Learning for Wireless Edge Intelligence 114 6.1.3.2 Federated Learning for Privacy Protected Information 115 6.1.4 Federated Learning in Medical Computing Field 116 6.1.4.1 Federated Learning in Medical Healthcare 117 6.1.4.2 Data Privacy in Healthcare 117 6.1.5 Federated Learning in Blockchain 118 6.1.5.1 Blockchain-Based Federated Learning Against End-Point Adversarial Data 118 6.2 Advantages of Federated Learning 119 6.3 Conclusion 119 References 119 7 Analyzing the Application of Edge Computing in Smart Healthcare 121 Parul Verma and Umesh Kumar 7.1 Internet of Things (IoT) 122 7.1.1 IoT Communication Models 122 7.1.2 IoT Architecture 124 7.1.3 Protocols for IoT 125 7.1.3.1 Physical/Data Link Layer Protocols 125 7.1.3.2 Network Layer Protocols 127 7.1.3.3 Transport Layer Protocols 128 7.1.3.4 Application Layer Protocols 129 7.1.4 IoT Applications 130 7.1.5 IoT Challenges 132 7.2 Edge Computing 133 7.2.1 Cloud vs. Fog vs. Edge 134 7.2.2 Existing Edge Computing Reference Architecture 135 7.2.2.1 FAR-EDGE Reference Architecture 135 7.2.2.2 Intel-SAP Joint Reference Architecture (RA) 135 7.2.3 Integrated Architecture for IoT and Edge 136 7.2.4 Benefits of Edge Computing Based IoT Architecture 138 7.3 Edge Computing and Real Time Analytics in Healthcare 140 7.4 Edge Computing Use Cases in Healthcare 148 7.5 Future of Healthcare and Edge Computing 151 7.6 Conclusion 151 References 152 8 Fog-IoT Assistance-Based Smart Agriculture Application 157 Pawan Whig, Arun Velu and Rahul Reddy Nadikattu 8.1 Introduction 158 8.1.1 Difference Between Fog and Edge Computing 159 8.1.1.1 Bandwidth 163 8.1.1.2 Confidence 164 8.1.1.3 Agility 164 8.1.2 Relation of Fog with IoT 165 8.1.3 Fog Computing in Agriculture 167 8.1.4 Fog Computing in Smart Cities 169 8.1.5 Fog Computing in Education 170 8.1.6 Case Study 171 Conclusion and Future Scope 173 References 173 9 Internet of Things in the Global Impacts of COVID-19: A Systematic Study 177 Shalini Sharma Goel, Anubhav Goel, Mohit Kumar and Sachin Sharma 9.1 Introduction 178 9.2 COVID-19 – Misconceptions 181 9.3 Global Impacts of COVID-19 and Significant Contributions of IoT in Respective Domains to Counter the Pandemic 183 9.3.1 Impact on Healthcare and Major Contributions of IoT 183 9.3.2 Social Impacts of COVID-19 and Role of IoT 187 9.3.3 Financial and Economic Impact and How IoT Can Help to Shape Businesses 188 9.3.4 Impact on Education and Part Played by IoT 191 9.3.5 Impact on Climate and Environment and Indoor Air Quality Monitoring Using IoT 194 9.3.6 Impact on Travel and Tourism and Aviation Industry and How IoT is Shaping its Future 197 9.4 Conclusions 198 References 198 10 An Efficient Solar Energy Management Using IoT-Enabled Arduino-Based MPPT Techniques 205 Rita Banik and Ankur Biswas List of Symbols 206 10.1 Introduction 206 10.2 Impact of Irradiance on PV Efficiency 210 10.2.1 PV Reliability and Irradiance Optimization 211 10.2.1.1 PV System Level Reliability 211 10.2.1.2 PV Output with Varying Irradiance 211 10.2.1.3 PV Output with Varying Tilt 212 10.3 Design and Implementation 212 10.3.1 The DC to DC Buck Converter 215 10.3.2 The Arduino Microcontroller 217 10.3.3 Dynamic Response 219 10.4 Result and Discussions 220 10.5 Conclusions 223 References 224 11 Axiomatic Analysis of Pre-Processing Methodologies Using Machine Learning in Text Mining: A Social Media Perspective in Internet of Things 229 Tajinder Singh, Madhu Kumari, Daya Sagar Gupta and Nikolai Siniak 11.1 Introduction 230 11.2 Text Pre-Processing – Role and Characteristics 232 11.3 Modern Pre-Processing Methodologies and Their Scope 234 11.4 Text Stream and Role of Clustering in Social Text Stream 241 11.5 Social Text Stream Event Analysis 242 11.6 Embedding 244 11.6.1 Type of Embeddings 244 11.7 Description of Twitter Text Stream 250 11.8 Experiment and Result 251 11.9 Applications of Machine Learning in IoT (Internet of Things) 251 11.10 Conclusion 252 References 252 12 APP-Based Agriculture Information System for Rural Farmers in India 257 Ashwini Kumar, Dilip Kumar Choubey, Manish Kumar and Santosh Kumar 12.1 Introduction 258 12.2 Motivation 259 12.3 Related Work 260 12.4 Proposed Methodology and Experimental Results Discussion 262 12.4.1 Mobile Cloud Computing 266 12.4.2 XML Parsing and Computation Offloading 266 12.4.3 Energy Analysis for Computation Offloading 267 12.4.4 Virtual Database 269 12.4.5 App Engine 270 12.4.6 User Interface 272 12.4.7 Securing Data 273 12.5 Conclusion and Future Work 274 References 274 13 SSAMH – A Systematic Survey on AI-Enabled Cyber Physical Systems in Healthcare 277 Kamalpreet Kaur, Renu Dhir and Mariya Ouaissa 13.1 Introduction 278 13.2 The Architecture of Medical Cyber-Physical Systems 278 13.3 Artificial Intelligence-Driven Medical Devices 282 13.3.1 Monitoring Devices 282 13.3.2 Delivery Devices 283 13.3.3 Network Medical Device Systems 283 13.3.4 IT-Based Medical Device Systems 284 13.3.5 Wireless Sensor Network-Based Medical Driven Systems 285 13.4 Certification and Regulation Issues 285 13.5 Big Data Platform for Medical Cyber-Physical Systems 286 13.6 The Emergence of New Trends in Medical Cyber-Physical Systems 288 13.7 Eminence Attributes and Challenges 289 13.8 High-Confidence Expansion of a Medical Cyber-Physical Expansion 290 13.9 Role of the Software Platform in the Interoperability of Medical Devices 291 13.10 Clinical Acceptable Decision Support Systems 291 13.11 Prevalent Attacks in the Medical Cyber-Physical Systems 292 13.12 A Suggested Framework for Medical Cyber-Physical System 294 13.13 Conclusion 295 References 296 14 ANN-Aware Methanol Detection Approach with CuO-Doped SnO 2 in Gas Sensor 299 Jitendra K. Srivastava, Deepak Kumar Verma, Bholey Nath Prasad and Chayan Kumar Mishra 14.1 Introduction 300 14.1.1 Basic ANN Model 300 14.1.2 ANN Data Pre- and Post-Processing 303 14.1.2.1 Activation Function 304 14.2 Network Architectures 305 14.2.1 Feed Forward ANNs 305 14.2.2 Recurrent ANNs Topologies 307 14.2.3 Learning Processes 308 14.2.3.1 Supervised Learning 308 14.2.3.2 Unsupervised Learning 308 14.2.4 ANN Methodology 309 14.2.5 1%CuO–Doped SnO 2 Sensor for Methanol 309 14.2.6 Experimental Result 311 References 327 15 Detecting Heart Arrhythmias Using Deep Learning Algorithms 331 Dilip Kumar Choubey, Chandan Kumar Jha, Niraj Kumar, Neha Kumari and Vaibhav Soni 15.1 Introduction 332 15.1.1 Deep Learning 333 15.2 Motivation 334 15.3 Literature Review 334 15.4 Proposed Approach 366 15.4.1 Dataset Descriptions 367 15.4.2 Algorithms Description 369 15.4.2.1 Dense Neural Network 369 15.4.2.2 Convolutional Neural Network 370 15.4.2.3 Long Short-Term Memory 372 15.5 Experimental Results of Proposed Approach 376 15.6 Conclusion and Future Scope 379 References 380 16 Artificial Intelligence Approach for Signature Detection 387 Amar Shukla, Rajeev Tiwari, Saurav Raghuvanshi, Shivam Sharma and Shridhar Avinash 16.1 Introduction 387 16.2 Literature Review 390 16.3 Problem Definition 392 16.4 Methodology 392 16.4.1 Data Flow Process 394 16.4.2 Algorithm 395 16.5 Result Analysis 397 16.6 Conclusion 399 References 399 17 Comparison of Various Classification Models Using Machine Learning to Predict Mobile Phones Price Range 401 Chinu Singla and Chirag Jindal 17.1 Introduction 402 17.2 Materials and Methods 403 17.2.1 Dataset 403 17.2.2 Decision Tree 403 17.2.2.1 Basic Algorithm 404 17.2.3 Gaussian Naive Bayes (GNB) 404 17.2.3.1 Basic Algorithm 405 17.2.4 Support Vector Machine 405 17.2.4.1 Basic Algorithm 406 17.2.5 Logistic Regression (LR) 407 17.2.5.1 Basic Algorithm 407 17.2.6 K-Nearest Neighbor 408 17.2.6.1 Basic Algorithm 409 17.2.7 Evaluation Metrics 409 17.3 Application of the Model 410 17.3.1 Decision Tree (DT) 411 17.3.2 Gaussian Naive Bayes 411 17.3.3 Support Vector Machine 412 17.3.4 Logistic Regression 412 17.3.5 K Nearest Neighbor 413 17.4 Results and Comparison 413 17.5 Conclusion and Future Scope 418 References 418 Index 421

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Book SynopsisTable of ContentsForeword xxv Introduction xxix Chapter 1 Why Are We (Still) Here? 1 A Brief History of Diversity in the U.S. Labor Workforce 2 The Current United States Labor Workforce 2 Examining Labor Workforce Dimensions 3 The Murder of George Floyd, the Rise of the Black Lives Matter Movement, and the Corporate Response 7 Why Haven’t We Made More Progress? 9 Fixed Practices: Reluctance to Let Go of Entrenched Formulas 13 Fixed Attitudes: Continued Pervasiveness of Ingrained Personal Ideas and Beliefs 14 The COVID- 19 Pandemic: DEI Response to Long- term Structural Impacts 15 Why Diversity Matters 18 The Business Case for Diversity 19 The Moral and Ethical Imperative for Diversity 20 What Got Us Here Won’t Get Us There: The Diversity- Innovation Paradigm 21 Conclusion 22 Summary 22 Chapter 2 Defining Diversity, Equity, and Inclusion 23 What Do Diversity, Equity, and Inclusion Actually Mean? 23 What Is Diversity? 24 What Is Equity? 24 Equity ≠ Equality 24 Equity > Compensation 25 What Is Inclusion? 26 Inclusion, Explained Further 26 Diversity Can’t Thrive Without Equity and Inclusion 27 What Is a Diversity, Equity, and Inclusion (DEI) Initiative? 27 How Leaders Shortchange DEI 28 Common DEI Pitfalls 28 No Overarching Strategy 28 No Commitment from Leadership 29 DEI Work as a Checklist 29 DEI Work as a Human Resources Function Only 29 Ignoring Intersectionality 30 Not Establishing (The Right) Metrics 30 DEI as a Marketing Campaign 30 Ineffective Recruiting Practices 31 Ineffective Diversity Training 31 Ineffective Talent Development Programs 32 No Accountability 32 No or Low Compensation and Recognition for DEI Work 33 Not Listening to Employees 33 No Transparency 34 “Copying and Pasting” DEI Initiatives from Other Organizations 34 Going Alone 34 The Consequences of Ineffective DEI Initiatives 35 Conclusion 35 Summary 36 Chapter 3 The Virtuous Cycle of Innovation and Diversity 37 The Power of Innovation 38 Why Companies Get Stuck 39 Innovation Is Simply Not Prioritized 39 Inertia Is the Mortal Enemy of Original Thinking 40 The Power of Humility Is Overlooked and Undervalued 40 Diversity Drives Innovation 42 Innovation Principles 45 Chapter 4 Courage 46 Risk- Taking 48 Trust 49 Collaboration 52 Leadership 53 Conclusion 58 Summary 58 Innovating the Apprenticeship Model to Advance Diversity in Tech 59 The problem: Recruiting and retaining more tech talent from diverse and military backgrounds 60 “It’s time to get creative!” 60 “We can do better” 62 What Needed to Change? 64 The Solution 66 Adding Structure 66 Engaging Partners 68 Improving the Apprenticeship Selection Process 69 First Pilot Outcomes 70 The Importance of Consistency to a Good Start 70 The Role of Battle Buddies 71 The Citi Salutes Impact 71 Glitches Along the Way 73 Unsuccessful Recruitment Choices 73 Supervisor Limitations 74 Skill Mismatches 74 Two- Year Outcomes at Citi 78 From Pilot to Operationalizing: Expanding Irving Success across Citi 78 Conclusion 82 Summary 83 Chapter 5 Creating High-Impact Mentoring Programs 85 Coca- Cola’s Journey to DEI Success 86 The Costs of Inaction and Not Listening to Employees 87 Positivity from Turmoil 88 Measurable Results 89 Moving on from the Past 91 Mentoring Innovation at Zendesk 93 Zendesk’s Women Mentorship Program: Initial Pilot Program 95 Application and Matching Process 95 Support 96 Measurements for Success and Feedback 96 Pilot Observations: What Worked and What Didn’t 96 Chapter 6 Improving on Success 97 Measurable Results 97 Conclusion 99 Summary 99 Looking Beyond Traditional Talent Sources for “Hard to Find” Roles 101 Northrop Grumman and Tessco: Shifting Long- Standing Perceptions of Who Can Succeed 102 Northrop Grumman: Focus on Novel Thinking and New Talent 103 Roadblocks and Pathways 105 Shared Values Shaped by Common Experiences— and a “Secret Mission” 106 Selecting the Right Talent 107 Onboarding and Upskilling: “Building Software Engineers” 108 “Relentless Focus on Culture” 110 Week 12: The Beginning of a New Employment Pathway 113 How Success Ultimately Looked 114 Tessco: Reinstalling the First Rung of a Career Ladder 115 The IT Helpdesk Solution 116 A Career with Upward Mobility 117 Building a Successful Talent Incubator 118 Conclusion 123 Summary 124 Chapter 7 Innovations for DEI in Small Business 125 The Challenge for Small Businesses Implementing DEI Programs 126 DEI - Not Impossible for Small Businesses 128 Setbacks and Progress: Online Optimism’s DEI Journey 129 Turning Point: Developing a Deeper Appreciation for DEI 130 Blackout Tuesday: Paving the Road for Meaningful DEI Innovation 131 Today’s Outcomes, Leading to Future Impact 133 From Concept to Realization: Creating DEI for Small Businesses 136 Conclusion 139 Summary 140 Chapter 8 Rethinking Retention Through the Lens of DEI 141 Understanding Employee Retention and Turnover 142 Contributing Factors for Recent Turnover Trends 143 Compensation 144 Limited Career Development or Advancement 145 Inflexible Workplaces 145 Retention, Turnover, and DEI 146 Promoting Diverse, Mid- Career Talent 147 Promoting from Within: Target Engineering Manager Immersion Program 150 Retaining and Promoting Women in Engineering 151 Outcomes: Increased Representation of Women and Focus on Other Groups 152 The Positive Impact of Remote Work for DEI 153 What’s Needed for Hybrid and Remote Work Success 156 Compensation: Innovate with Employee Benefits Programs 159 Conclusion 163 Summary 163 Chapter 9 The Inescapable, Undeniable Role of Executive Leaders 165 Words and Actions: Do Behaviors Match the Script? 166 A Focus on Systems Thinking 168 National Hockey League: DEI as a Movement, Not a Moment 169 Employment: Building an NHL Talent Strategy to Reflect Local Communities 170 Youth Participation: Diversity as a Growth Mindset 172 Systems as a Movement- Making Innovation 173 World Wide Technology: The Power of Successfully Scaling Culture 174 The Story of WWT’s Culture and Values Begins with its Founders 174 Codifying and Scaling Culture at WWT 176 Key Business Concepts 177 How the Principles of WWT’s Culture Supports Innovation for Diversity 178 Training as Innovation 179 Radical Listening as an Innovation Pathway 180 Creating Career Pathways for All Employees 181 Conclusion 185 Summary 186 Chapter 10 Final Thoughts and Next Steps 187 Lessons in Innovating for Diversity 188 Revisiting the Virtuous Cycle of Innovation and Diversity 192 Applying Innovation Principles in Your Organization 194 Index 197

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Book SynopsisThe digital turn in architecture has gone through several stages and phases, and Architectural Design ( AD) has captured them all.Table of Contents8 Introduction Twenty Years of Digital Design 15 Architecture After the Age of Printing (1992) Visions Unfolding: Architecture in the Age of Electronic Media AD September–October 1992Peter Eisenman The Affects of Singularity AD November–December 1992Peter Eisenman 28 Folding in Architecture (1993) Architectural Curvilinearity: The Folded, the Pliant and the Supple AD March–April 1993Greg Lynn Shoei Yoh, Prefectura Gymnasium AD March–April 1993Greg Lynn 48 The Architectural Relevance of Cyberspace (1995) The Architectural Relevance of Cyberspace AD November–December 1995John Frazer Architectural Experiments AD November–December 1995John Frazer 57 The Digital and the Global (1996) Yokohama International Port Terminal AD July–August 1996 Foreign Office Architects 62 Field Conditions (1997) From Object to Field AD May–June 1997Stan Allen 80 Nonlinear Architecture (1997) Nonlinear Architecture: New Science = New Architecture? AD September–October 1997Charles Jencks Landform Architecture: Emergent in the Nineties AD September–October 1997Charles Jencks 108 Hypersurfaces (1998) Motor Geometry AD May–June 1998Lars Spuybroek Salt Water Live: Behaviour of the Salt Water Pavilion AD May–June 1998Kas Oosterhuis 124 Embryologic Houses© (2000) Embryologic Houses© AD May–June 2000Greg Lynn 131 Versioning (2002) Introduction to Versioning: Evolutionary Techniques in Architecture AD September–October 2002SHoP/Sharples Holden Pasquarelli Eroding the Barriers AD September–October 2002SHoP/Sharples Holden Pasquarelli 146 Topological Architecture (1998–2003) Bernard Cache/Objectile: Topological Architecture and the Ambiguous Sign AD May–June 1998Stephen Perrella Philibert De L’Orme Pavilion: Towards an Associative Architecture AD March–April 2003Bernard Cache 158 Morphogenesis and Emergence (2004–2006) Introduction to Emergence: Morphogenetic Design Strategies AD May–June 2004Michael Hensel, Achim Menges and Michael Weinstock Polymorphism AD March–April 2006Achim Menges 182 Scripting (2006) 20 Years of Scripted Space AD July–August 2006Malcolm McCullough 188 Collective Intelligence (2006) Introduction to Collective Intelligence in Design AD September–October 2006Christopher Hight and Chris Perry Computational Intelligence: The Grid as a Post-Human Network AD September–October 2006Philippe Morel 208 Elegance (2007) The Economies of Elegance, Migrating Coastlines: Residential Tower, Dubai AD January–February 2007Ali Rahim and Hina Jamelle Deus ex Machina: From Semiology to the Elegance of Aesthetics AD January–February 2007Mark Foster Gage 226 Building Information Modelling (2009) Optimisation Stories: The Impact of Building Information Modelling on Contemporary Design Practice AD March–April 2009Richard Garber 240 A New Global Style (2009) Parametricism: A New Global Style for Architecture and Urban Design AD July–August 2009Patrik Schumacher 258 Index

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Book SynopsisComputers are ubiquitous throughout all life-cycle stages of engineering, from conceptual design to manufacturing maintenance, repair and replacement. It is essential for all engineers to be aware of the knowledge behind computer-based tools and techniques they are likely to encounter. The computational technology, which allows engineers to carry out design, modelling, visualisation, manufacturing, construction and management of products and infrastructure is known as Computer-Aided Engineering (CAE). Engineering Informatics: Fundamentals of Computer-Aided Engineering, 2nd Edition provides the foundation knowledge of computing that is essential for all engineers. This knowledge is independent of hardware and software characteristics and thus, it is expected to remain valid throughout an engineering career. This Second Edition is enhanced with treatment of new areas such as network science and the computational complexity of distributed systems. Key features:Table of ContentsForeword to the First Edition xiii Preface to the First Edition xvii Preface to the Second Edition xxi 1 Fundamental Logic and the Definition of Engineering Tasks 1 1.1 Three Types of Inference 1 1.2 Engineering Tasks 3 1.3 A Model of Information and Tasks 5 1.4 Another Task Definition 8 1.5 The Five Orders of Ignorance 9 1.6 Summary 9 Exercises 10 References 10 2 Algorithms and Complexity 11 2.1 Algorithms and Execution Time of Programs 12 2.1.1 Program Execution Time versus Task Size 12 2.2 ‘Big Oh’ Notation 14 2.2.1 Definition of the Big Oh Notation 15 2.2.2 Big Oh and Tightness of Bound 16 2.2.3 Classification of Functions 20 2.2.4 Examples 21 2.2.5 Tractability and Algorithm Optimality 30 2.3 Practical Methods for Determining the Complexity of Algorithms 30 2.4 P, NP and NP-Completeness 34 2.4.1 Zero–One Integer Programming (ZOIP) Problem 35 2.4.2 Classes of NP-Complete Problems 36 2.5 Summary 37 Exercises 37 Reference 40 Further Reading 40 3 Data Structures 41 3.1 Introduction 41 3.2 Definitions 42 3.3 Derived Data Types 42 3.3.1 Examples of Derived Data Types 43 3.3.2 User-Defined Data Types 45 3.4 Abstract Data Types 46 3.4.1 Linked Lists 47 3.4.2 Graphs 50 3.4.3 Trees 52 3.4.4 Stacks 56 3.4.5 Queues 60 3.5 An Example: Conceptual Structural Design of Buildings 63 3.6 Network Science 70 3.6.1 Types of Networks 71 3.7 Hashing 73 3.8 Summary 74 Exercises 74 Further Reading 79 4 Object Representation and Reasoning 81 4.1 Introduction 81 4.2 Grouping Data and Methods 82 4.3 Definitions and Basic Concepts 83 4.3.1 Classes and Objects 83 4.3.2 Object-Oriented Programming (OOP) 84 4.3.3 Messages 84 4.4 Important Characteristics of Objects 84 4.4.1 Encapsulation of Data and Methods 84 4.4.2 Message-Passing Mechanism 85 4.4.3 Abstraction Hierarchy 86 4.4.4 Secondary Features of Object Representation 88 4.4.5 Decomposition versus Abstraction 89 4.5 Applications Outside Programming 90 4.5.1 Knowledge Representation 91 4.5.2 User Interfaces 91 4.5.3 Off-the-Shelf Components 91 4.5.4 Product Models 91 4.6 An Object-Oriented Design Methodology 93 4.6.1 Single versus Multiple Inheritance 93 4.6.2 Message-Passing Architecture 94 4.7 Summary 95 Exercises 95 References 101 Further Reading 101 5 Database Concepts 103 5.1 Introduction 103 5.2 Basic Concepts 104 5.2.1 Initial Definitions 104 5.2.2 Evolution of Types of Databases 104 5.2.3 The Three-Level Architecture 106 5.3 Relational Database Systems 106 5.3.1 The Relational Model 107 5.3.2 Limitations of Relational Databases 111 5.3.3 Accessing Data in Relational Databases 112 5.4 Relational Database Design 114 5.4.1 First Normal Form 114 5.4.2 Second Normal Form 115 5.4.3 Third Normal Form 118 5.4.4 Boyce-Codd and Higher Normal Forms 119 5.4.5 Importance of Database Design 120 5.5 Transaction Processing 120 5.5.1 Definition of Transaction 121 5.5.2 Implementing Transactions 122 5.5.3 Properties of Transactions 124 5.6 Other Types of Database 124 5.6.1 Object-Oriented Databases 124 5.6.2 Geographical Databases 124 5.6.3 Multimedia Database Systems 125 5.6.4 Distributed Databases 125 5.7 Summary 126 Exercises 127 Transaction A 131 Transaction B 131 Reference 131 Further Reading 131 6 Computational Mechanics 133 6.1 Introduction 133 6.1.1 Challenges of Computational Mechanics 134 6.2 From Physical Principles to Practical Systems 135 6.3 Methods for Finding Solutions 137 6.3.1 Galerkin Method 137 6.3.2 Remarks 139 6.4 Issues in Computer-Aided Engineering 139 6.4.1 Accuracy 140 6.4.2 Speed 141 6.4.3 User Interaction 142 6.5 Summary 142 References 142 Further Reading 142 7 Constraint-Based Reasoning 143 7.1 Introduction 143 7.2 Terminology 145 7.3 Constraint-Solving Methods 146 7.3.1 Levels of Consistency for Label Propagation 147 7.3.2 Global Consistency in Label Propagation 148 7.3.3 Constraint Propagation 149 7.4 Reasoning with Constraints on Discrete Variables 149 7.4.1 CSP Complexity for Discrete Variables 151 7.5 Reasoning with Constraints on Continuous Variables 151 7.5.1 Constraint-Based Support for Collaborative Work 152 7.6 Summary 156 References 156 8 Optimization and Search 157 8.1 Introduction 157 8.2 Basic Concepts 158 8.2.1 Types of Optimization Problem 160 8.2.2 Formulating Optimization Tasks 161 8.2.3 Representing Search Spaces 163 8.2.4 Representing Constraints 164 8.2.5 Some Optimization Problems 165 8.3 Classification of Methods 167 8.4 Deterministic Optimization and Search 169 8.4.1 Special Cases 169 8.4.2 Deterministic Methods 174 8.5 Stochastic Methods 179 8.5.1 Pure Global Random Search 182 8.5.2 Local Search with Multiple Random Starts 182 8.5.3 Simulated Annealing 182 8.5.4 Genetic Algorithms 184 8.5.5 Controlled Random Search 184 8.5.6 PGSL 185 8.6 A Closer Look at Genetic Algorithms 188 8.6.1 Representation: Genetic Encoding 188 8.6.2 Evaluating an Individual 189 8.6.3 Creating the Initial Population 189 8.6.4 The Fitness Function 190 8.6.5 Reproduction 190 8.6.6 Mutation 192 8.7 Summary of Methods 192 Exercises 193 References 198 Further Reading 198 9 Knowledge Systems for Decision Support 199 9.1 Introduction 199 9.2 Important Characteristics of Knowledge Systems 200 9.3 Representation of Knowledge 202 9.3.1 Representation of Knowledge in Knowledge Systems 204 9.4 Reasoning with Knowledge 205 9.4.1 Rule Selection and Conflict Resolution 207 9.5 Importance of the User Interface 207 9.6 Maintenance of Knowledge 208 9.7 Model-based Reasoning 209 9.8 Case-Based Reasoning 209 9.8.1 Stages of Case-Based Reasoning 210 9.9 Summary 215 Reference 215 Further Reading 215 10 Machine Learning 217 10.1 Introduction 217 10.2 Improving Performance with Experience 218 10.3 Formalizing the Learning Task 220 10.3.1 Searching Hypothesis Spaces 224 10.4 Learning Algorithms 224 10.4.1 Rote Learning 225 10.4.2 Statistical Learning Techniques 226 10.4.3 Deductive Learning 230 10.4.4 Exploration and Discovery 231 10.5 A Closer Look at Artificial Neural Networks 231 10.5.1 Types of Neural Network 235 10.5.2 Learning in Neural Networks 236 10.5.3 Summary of Neural Networks 237 10.6 Support Vector Machines 237 10.6.1 Support Vector Classification 237 10.6.2 Support Vector Regression 240 10.7 Summary 240 Exercises 241 References 242 Further Reading 242 11 Geometric Modelling 243 11.1 Introduction 243 11.2 Engineering Applications 244 11.2.1 Criteria for Evaluating Representations 244 11.3 Mathematical Models for Representing Geometry 245 11.3.1 Two-Dimensional Representation of Simple Shapes 245 11.3.2 Curves Without Simple Mathematical Representations 247 11.3.3 B´ezier Curves 248 11.3.4 Mathematical Representation of Simple Surfaces 249 11.3.5 B´ezier Patches 250 11.3.6 Mathematical Representation of Regular-Shaped Solids 251 11.4 Representing Complex Solids 252 11.4.1 Primitive Instancing 252 11.4.2 Mesh Representations 253 11.4.3 Sweep Representations 255 11.4.4 Boundary Representations 257 11.4.5 Decomposition Models 258 11.4.6 Constructive Solid Geometry (CSG) 260 11.5 Applications 263 11.5.1 Estimation of Volume 263 11.5.2 Finite Element Mesh for a Spread Footing 264 11.5.3 3D Graphical View of a Structure 266 11.6 Summary 267 Further Reading 267 12 Computer Graphics 269 12.1 Introduction 269 12.2 Tasks of Computer Graphics 270 12.3 Display Devices 270 12.3.1 Types of Display Device 271 12.3.2 From Geometric Representations to Graphical Displays 272 12.4 Representing Graphics 272 12.4.1 Representing Colours 273 12.4.2 Coordinate System 273 12.4.3 Bitmap Representations 274 12.4.4 Higher-Level Representations 275 12.5 The Graphics Pipeline 276 12.5.1 Modelling Transformations 276 12.5.2 Viewing Transformations 280 12.5.3 Scan Conversion 285 12.6 Interactive Graphics 287 12.7 Graphical User Interfaces (GUI) and Human–Computer Interaction (HCI) 288 12.7.1 Engineer–Computer Interaction 288 12.8 Applications 289 12.8.1 4D Simulations 289 12.8.2 Navigating Multidimensional Solution Spaces 289 12.8.3 Computer Vision and Image Processing 290 12.8.4 Laser Scanning 290 12.9 Summary 292 References 292 Further Reading 292 13 Distributed Applications and the Web 293 13.1 Introduction 293 13.1.1 A Simple Example of a Client–Server System 294 13.1.2 Definitions 295 13.1.3 Trends Driving C/S Architecture 296 13.2 Examples of Client–Server Applications 297 13.2.1 File Servers 297 13.2.2 FTP Servers 298 13.2.3 Database Servers 298 13.2.4 Groupware Servers 298 13.2.5 Object Servers 298 13.2.6 Operating System Servers 299 13.2.7 Display Servers 299 13.2.8 Web Servers 300 13.2.9 Application Servers 300 13.3 Distinctive Features of C/S Systems 300 13.3.1 Asymmetrical Protocol 300 13.3.2 Message-Based Mechanism 301 13.3.3 Why are Protocols Important? 304 13.4 Client–server System Design 304 13.4.1 Three-Tier Architecture 305 13.4.2 Application Partitioning 306 13.5 Advantages of Client–Server Systems 307 13.6 Developing Client–Server Applications 307 13.6.1 TCP/IP Sockets 308 13.6.2 Other Middleware Options 309 13.7 The World Wide Web 309 13.7.1 Limitations of Exchanging Only Static Information 310 13.7.2 Common Gateway Interface 310 13.7.3 Engineering Applications on the Web 311 13.7.4 Other Models for Dynamic Information Exchange 311 13.8 Peer-to-Peer Networks 312 13.8.1 Information Interchange Through P2P Networks 314 13.8.2 P2P Networks for Engineering Applications 314 13.8.3 Advantages of Peer-to-Peer Networks 315 13.8.4 Issues and Challenges 315 13.9 Agent Technology 316 13.9.1 Issues in Multi-Agent Systems 317 13.10 Cloud Computing 318 13.11 Complexity 319 13.12 Summary 319 Reference 320 Further Reading 320 Index 321

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Book SynopsisA sweeping history of data and its technical, political and ethical impact on our worldTrade Review"In a tour-de-force, Wiggins and Jones put data in context so that we can see the values, politics, and controversies that shape our present reality. This book is truly a semester-long class bottled into a narrative fit for vacation." -- Danah Boyd, founder and president, Data & Society Research Institute"Sometimes the best way to understand the present and prepare for the future is to look to the past. This insight is at the core of How Data Happened, an ambitious and thoughtful work. Wiggins and Jones have worked together—as data scientist and historian—to write a book that will reshape how you will see the relationship between data and society." -- Matthew J. Salganik, Professor, Department of Sociology, Princeton University, and author of Bit by Bit: Social Research in the Digital Age"A leading data scientist and a historian of science walk into a classroom resulting in this ambitious and bold book packed with stories about the role of data in our society. Wiggins and Jones plainly and forcefully trace why we ended up with the big data mess that we have now and what we might do about it. Instead of platitudes, they argue how today’s fights over surveillance capitalism, government access to data, and Big Tech could shape the future of data’s power in society. How Data Happened is a must read for everyone interested in how data is changing our lives." -- Gina Neff, Executive Director, Minderoo Centre for Technology and Democracy, University of Cambridge"This is the first comprehensive look at the history of data and how power has played a critical role in shaping the history. It’s a must read for any data scientist about how we got here and what we need to do to ensure that data works for everyone." -- DJ Patil, former U.S. Chief Data Scientist

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Book SynopsisDiscover the new cybersecurity landscape of the interconnected software supply chain In Software Transparency: Supply Chain Security in an Era of a Software-Driven Society, a team of veteran information security professionals delivers an expert treatment of software supply chain security. In the book, you'll explore real-world examples and guidance on how to defend your own organization against internal and external attacks. It includes coverage of topics including the history of the software transparency movement, software bills of materials, and high assurance attestations. The authors examine the background of attack vectors that are becoming increasingly vulnerable, like mobile and social networks, retail and banking systems, and infrastructure and defense systems. You'll also discover: Use cases and practical guidance for both software consumers and suppliers Discussions of firmware and embedded software, as well as cloud and connected APIsTable of ContentsForeword xxi Introduction xxv Chapter 1 Background on Software Supply Chain Threats 1 Incentives for the Attacker 1 Threat Models 2 Threat Modeling Methodologies 3 Stride 3 Stride- LM 4 Open Worldwide Application Security Project (OWASP) Risk- Rating Methodology 4 Dread 5 Using Attack Trees 5 Threat Modeling Process 6 Landmark Case 1: SolarWinds 14 Landmark Case 2: Log4j 18 Landmark Case 3: Kaseya 21 What Can We Learn from These Cases? 23 Summary 24 Chapter 2 Existing Approaches— Traditional Vendor Risk Management 25 Assessments 25 SDL Assessments 28 Application Security Maturity Models 29 Governance 30 Design 30 Implementation 31 Verification 31 Operations 32 Application Security Assurance 32 Static Application Security Testing 33 Dynamic Application Security Testing 34 Interactive Application Security Testing 35 Mobile Application Security Testing 36 Software Composition Analysis 36 Hashing and Code Signing 37 Summary 39 Chapter 3 Vulnerability Databases and Scoring Methodologies 41 Common Vulnerabilities and Exposures 41 National Vulnerability Database 44 Software Identity Formats 46 Cpe 46 Software Identification Tagging 47 Purl 49 Sonatype OSS Index 50 Open Source Vulnerability Database 51 Global Security Database 52 Common Vulnerability Scoring System 54 Base Metrics 55 Temporal Metrics 57 Environmental Metrics 58 CVSS Rating Scale 58 Critiques 59 Exploit Prediction Scoring System 59 EPSS Model 60 EPSS Critiques 62 CISA’s Take 63 Common Security Advisory Framework 63 Vulnerability Exploitability eXchange 64 Stakeholder- Specific Vulnerability Categorization and Known Exploited Vulnerabilities 65 Moving Forward 69 Summary 70 Chapter 4 Rise of Software Bill of Materials 71 SBOM in Regulations: Failures and Successes 71 NTIA: Evangelizing the Need for SBOM 72 Industry Efforts: National Labs 77 SBOM Formats 78 Software Identification (SWID) Tags 79 CycloneDX 80 Software Package Data Exchange (SPDX) 81 Vulnerability Exploitability eXchange (VEX) and Vulnerability Disclosures 82 VEX Enters the Conversation 83 VEX: Adding Context and Clarity 84 VEX vs. VDR 85 Moving Forward 88 Using SBOM with Other Attestations 89 Source Authenticity 89 Build Attestations 90 Dependency Management and Verification 90 Sigstore 92 Adoption 93 Sigstore Components 93 Commit Signing 95 SBOM Critiques and Concerns 95 Visibility for the Attacker 96 Intellectual Property 97 Tooling and Operationalization 97 Summary 98 Chapter 5 Challenges in Software Transparency 99 Firmware and Embedded Software 99 Linux Firmware 99 Real- Time Operating System Firmware 100 Embedded Systems 100 Device- Specific SBOM 100 Open Source Software and Proprietary Code 101 User Software 105 Legacy Software 106 Secure Transport 107 Summary 108 Chapter 6 Cloud and Containerization 111 Shared Responsibility Model 112 Breakdown of the Shared Responsibility Model 112 Duties of the Shared Responsibility Model 112 The 4 Cs of Cloud Native Security 116 Containers 118 Kubernetes 123 Serverless Model 128 SaaSBOM and the Complexity of APIs 129 CycloneDX SaaSBOM 130 Tooling and Emerging Discussions 132 Usage in DevOps and DevSecOps 132 Summary 135 Chapter 7 Existing and Emerging Commercial Guidance 137 Supply Chain Levels for Software Artifacts 137 Google Graph for Understanding Artifact Composition 141 CIS Software Supply Chain Security Guide 144 Source Code 145 Build Pipelines 146 Dependencies 148 Artifacts 148 Deployment 149 CNCF’s Software Supply Chain Best Practices 150 Securing the Source Code 152 Securing Materials 154 Securing Build Pipelines 155 Securing Artifacts 157 Securing Deployments 157 CNCF’s Secure Software Factory Reference Architecture 157 The Secure Software Factory Reference Architecture 158 Core Components 159 Management Components 160 Distribution Components 160 Variables and Functionality 160 Wrapping It Up 161 Microsoft’s Secure Supply Chain Consumption Framework 161 S2C2F Practices 163 S2C2F Implementation Guide 166 OWASP Software Component Verification Standard 167 SCVS Levels 168 Level 1 168 Level 2 169 Level 3 169 Inventory 169 Software Bill of Materials 170 Build Environment 171 Package Management 171 Component Analysis 173 Pedigree and Provenance 173 Open Source Policy 174 OpenSSF Scorecard 175 Security Scorecards for Open Source Projects 175 How Can Organizations Make Use of the Scorecards Project? 177 The Path Ahead 178 Summary 178 Chapter 8 Existing and Emerging Government Guidance 179 Cybersecurity Supply Chain Risk Management Practices for Systems and Organizations 179 Critical Software 181 Security Measures for Critical Software 182 Software Verification 186 Threat Modeling 187 Automated Testing 187 Code- Based or Static Analysis and Dynamic Testing 188 Review for Hard-Coded Secrets 188 Run with Language- Provided Checks and Protection 189 Black- Box Test Cases 189 Code- Based Test Cases 189 Historical Test Cases 189 Fuzzing 190 Web Application Scanning 190 Check Included Software Components 190 NIST’s Secure Software Development Framework 191 SSDF Details 192 Prepare the Organization (PO) 193 Protect the Software (PS) 194 Produce Well- Secured Software (PW) 194 Respond to Vulnerabilities (RV) 196 NSAs: Securing the Software Supply Chain Guidance Series 197 Security Guidance for Software Developers 197 Secure Product Criteria and Management 199 Develop Secure Code 202 Verify Third- Party Components 204 Harden the Build Environment 206 Deliver the Code 207 NSA Appendices 207 Recommended Practices Guide for Suppliers 209 Prepare the Organization 209 Protect the Software 210 Produce Well- Secured Software 211 Respond to Vulnerabilities 213 Recommended Practices Guide for Customers 214 Summary 218 Chapter 9 Software Transparency in Operational Technology 219 The Kinetic Effect of Software 220 Legacy Software Risks 222 Ladder Logic and Setpoints in Control Systems 223 ICS Attack Surface 225 Smart Grid 227 Summary 228 Chapter 10 Practical Guidance for Suppliers 229 Vulnerability Disclosure and Response PSIRT 229 Product Security Incident Response Team (PSIRT) 231 To Share or Not to Share and How Much Is Too Much? 236 Copyleft, Licensing Concerns, and “As- Is” Code 238 Open Source Program Offices 240 Consistency Across Product Teams 242 Manual Effort vs. Automation and Accuracy 243 Summary 244 Chapter 11 Practical Guidance for Consumers 245 Thinking Broad and Deep 245 Do I Really Need an SBOM? 246 What Do I Do with It? 250 Receiving and Managing SBOMs at Scale 251 Reducing the Noise 253 The Divergent Workflow— I Can’t Just Apply a Patch? 254 Preparation 256 Identification 256 Analysis 257 Virtual Patch Creation 257 Implementation and Testing 258 Recovery and Follow- up 258 Long- Term Thinking 259 Summary 259 Chapter 12 Software Transparency Predictions 261 Emerging Efforts, Regulations, and Requirements 261 The Power of the U.S. Government Supply Chains to Affect Markets 267 Acceleration of Supply Chain Attacks 270 The Increasing Connectedness of Our Digital World 272 What Comes Next? 275 Index 283

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Book SynopsisDEEP LEARNING AND ITS APPLICATIONS USING PYTHON This practical book gives a detailed description of deep learning models and their implementation using Python programming relating to computer vision, natural language processing, and other applications. This book thoroughly explains deep learning models and how to use Python programming to implement them in applications such as NLP, face detection, face recognition, face analysis, and virtual assistance (chatbot, machine translation, etc.). It provides hands-on guidance in using Python for implementing deep learning application models. It also identifies future research directions for deep learning. Readers/users will discover A precise description of deep learning history, fundamental concepts, and background information relating to deep learning;A detailed introduction to several concepts including tensorflow and keras, starting from the fundamentals to the application-based concept implementation using Python;Explanations of multilayTable of ContentsPreface ix 1 Introduction to Deep Learning 1 1.1 History of Deep Learning 1 1.2 A Probabilistic Theory of Deep Learning 4 1.3 Back Propagation and Regularization 14 1.4 Batch Normalization and VC Dimension 17 1.5 Neural Nets--Deep and Shallow Networks 18 1.6 Supervised and Semi-Supervised Learning 19 1.7 Deep Learning and Reinforcement Learning 21 2 Basics of TensorFlow 25 2.1 Tensors 25 2.2 Computational Graph and Session 27 2.3 Constants, Placeholders, and Variables 28 2.4 Creating Tensor 32 2.5 Working on Matrices 35 2.6 Activation Functions 36 2.7 Loss Functions 39 2.8 Common Loss Function 39 2.9 Optimizers 40 2.10 Metrics 41 3 Understanding and Working with Keras 45 3.1 Major Steps to Deep Learning Models 45 3.2 Load Data 47 3.3 Pre-Process Data 48 3.4 Define the Model 48 3.5 Compile the Model 49 3.6 Fit and Evaluate the Mode 51 3.7 Prediction 52 3.8 Save and Reload the Model 52 3.9 Additional Steps to Improve Keras Models 53 3.10 Keras with TensorFlow 55 4 Multilayer Perceptron 57 4.1 Artificial Neural Network 57 4.2 Single-Layer Perceptron 60 4.3 Multilayer Perceptron 61 4.4 Logistic Regression Model 61 4.5 Regression to MLP in TensorFlow 63 4.6 TensorFlow Steps to Build Models 63 4.7 Linear Regression in TensorFlow 63 4.8 Logistic Regression Mode in TensorFlow 67 4.9 Multilayer Perceptron in TensorFlow 69 4.10 Regression to MLP in Keras 72 4.11 Log-Linear Model 72 4.12 Keras Neural Network for Linear Regression 73 4.13 Keras Neural Network for Logistic Regression 73 4.14 MLPs on the Iris Data 75 4.15 MLPs on MNIST Data (Digit Classification) 76 4.16 MLPs on Randomly Generated Data 78 5 Convolutional Neural Networks in Tensorflow 81 5.1 CNN Architectures 81 5.2 Properties of CNN Representations 82 5.3 Convolution Layers, Pooling Layers - Strides - Padding and Fully Connected Layer 82 5.4 Why TensorFlow for CNN Models? 84 5.5 TensorFlow Code for Building an Image Classifier for MNIST Data 84 5.6 Using a High-Level API for Building CNN Models 88 5.7 CNN in Keras 88 5.8 Building an Image Classifier for MNIST Data in Keras 88 5.9 Building an Image Classifier with CIFAR-10 Data 89 5.10 Define the Model Architecture 90 5.11 Pre-Trained Models 91 6 RNN and LSTM 95 6.1 Concept of RNN 95 6.2 Concept of LSTM 96 6.3 Modes of LSTM 97 6.4 Sequence Prediction 98 6.5 Time-Series Forecasting with the LSTM Model 99 6.6 Speech to Text 100 6.7 Examples Using Each API 102 6.8 Text-to-Speech Conversion 105 6.9 Cognitive Service Providers 106 6.10 The Future of Speech Analytics 107 7 Developing Chatbot's Face Detection and Recognition 109 7.1 Why Chatbots? 109 7.2 Designs and Functions of Chatbot's 109 7.3 Steps for Building a Chatbot's 110 7.4 Best Practices of Chatbot Development 116 7.5 Face Detection 116 7.6 Face Recognition 117 7.7 Face Analysis 117 7.8 OpenCV--Detecting a Face, Recognition and Face Analysis 117 7.8.1 Face Detection 117 7.8.2 Face Recognition 120 7.9 Deep Learning-Based Face Recognition 124 7.10 Transfer Learning 127 7.11 API's 131 8 Advanced Deep Learning 133 8.1 Deep Convolutional Neural Networks (AlexNet) 133 8.2 Networks Using Blocks (VGG) 137 8.3 Network in Network (NiN) 140 8.4 Networks with Parallel Concatenations (GoogLeNet) 144 8.5 Residual Networks (ResNet) 148 8.6 Densely Connected Networks (DenseNet) 151 8.7 Gated Recurrent Units (GRU) 154 8.8 Long Short-Term Memory (LSTM) 156 8.9 Deep Recurrent Neural Networks (D-RNN) 158 8.10 Bidirectional Recurrent Neural Networks (Bi-RNN) 159 8.11 Machine Translation and the Dataset 160 8.12 Sequence to Sequence Learning 161 9 Enhanced Convolutional Neural Network 167 9.1 Introduction 167 9.2 Deep Learning-Based Architecture for Absence Seizure Detection 178 9.3 EEG Signal Pre-Processing Strategy and Channel Selection 180 9.4 Input Formulation and Augmentation of EEG Signal for Deep Learning Model 188 9.5 Deep Learning Based Feature Extraction and Classification 196 9.6 Performance Analysis 200 9.7 Summary 201 10 Conclusion 205 10.1 Introduction 205 10.2 Future Research Direction and Prospects 205 10.3 Research Challenges in Deep Learning 210 10.4 Practical Deep Learning Case Studies 210 10.4.1 Medicine: Epilepsy Seizure Onset Prediction 219 10.4.2 Using Data from Test Drills to Predict where to Drill for Oil 232 10.5 Summary 235 References 235 Index 239

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Book SynopsisARTIFICAL INTELLIGENCE for SUSTAINABLE APPLICATIONS The objective of this book is to leverage the significance of artificial intelligence in achieving sustainable solutions using interdisciplinary research through innovative ideas. With the advent of recent technologies, the demand for Information and Communication Technology (ICT)-based applications such as artificial intelligence (AI), machine learning (ML), Internet of Things (IoT), health care, data analytics, augmented reality/virtual reality, cyber-physical systems, and future generation networks, has increased drastically. In recent years, artificial intelligence has played a more significant role in everyday activities. While AI creates opportunities, it also presents greater challenges in the sustainable development of engineering applications. Therefore, the association between AI and sustainable applications is an essential field of research. Moreover, the applications of sustainable products have Table of ContentsPreface xv Part I: Medical Applications 1 1 Predictive Models of Alzheimer's Disease Using Machine Learning Algorithms -- An Analysis 3Karpagam G. R., Swathipriya M., Charanya A. G. and Murali Murugan 1.1 Introduction 3 1.2 Prediction of Diseases Using Machine Learning 4 1.3 Materials and Methods 5 1.4 Methods 6 1.5 ML Algorithm and Their Results 7 1.6 Support Vector Machine (SVM) 11 1.7 Logistic Regression 11 1.8 K Nearest Neighbor Algorithm (KNN) 12 1.9 Naive Bayes 15 1.10 Finding the Best Algorithm Using Experimenter Application 17 1.11 Conclusion 18 1.12 Future Scope 19 2 Bounding Box Region-Based Segmentation of COVID-19 X-Ray Images by Thresholding and Clustering 23Kavitha S. and Hannah Inbarani 2.1 Introduction 23 2.2 Literature Review 24 2.3 Dataset Used 26 2.4 Proposed Method 26 2.5 Experimental Analysis 29 2.6 Conclusion 33 3 Steering Angle Prediction for Autonomous Vehicles Using Deep Learning Model with Optimized Hyperparameters 37Bineeshia J., Vinoth Kumar B., Karthikeyan T. and Syed Khaja Mohideen 3.1 Introduction 38 3.2 Literature Review 39 3.3 Methodology 41 3.4 Experiment and Results 46 3.5 Conclusion 51 4 Review of Classification and Feature Selection Methods for Genome-Wide Association SNP for Breast Cancer 55L.R. Sujithra and A. Kuntha 4.1 Introduction 56 4.2 Literature Analysis 58 4.3 Comparison Analysis 66 4.4 Issues of the Existing Works 70 4.5 Experimental Results 70 4.6 Conclusion and Future Work 73 5 COVID-19 Data Analysis Using the Trend Check Data Analysis Approaches 79Alamelu M., M. Naveena, Rakshitha M. and M. Hari Prasanth 5.1 Introduction 79 5.2 Literature Survey 80 5.3 COVID-19 Data Segregation Analysis Using the Trend Check Approaches 81 5.4 Results and Discussion 83 5.5 Conclusion 86 6 Analyzing Statewise COVID-19 Lockdowns Using Support Vector Regression 89Karpagam G. R., Keerthna M., Naresh K., Sairam Vaidya M., Karthikeyan T. and Syed Khaja Mohideen 6.1 Introduction 90 6.2 Background 91 6.3 Proposed Work 98 6.4 Experimental Results 104 6.5 Discussion and Conclusion 110 7 A Systematic Review for Medical Data Fusion Over Wireless Multimedia Sensor Networks 117John Nisha Anita and Sujatha Kumaran 7.1 Introduction 118 7.2 Literature Survey Based on Brain Tumor Detection Methods 118 7.3 Literature Survey Based on WMSN 122 7.4 Literature Survey Based on Data Fusion 123 7.5 Conclusions 125 Part II: Data Analytics Applications 127 8 An Experimental Comparison on Machine Learning Ensemble Stacking-Based Air Quality Prediction System 129P. Vasantha Kumari and G. Sujatha 8.1 Introduction 130 8.2 Related Work 133 8.3 Proposed Architecture for Air Quality Prediction System 134 8.4 Results and Discussion 140 8.5 Conclusion 145 9 An Enhanced K-Means Algorithm for Large Data Clustering in Social Media Networks 147R. Tamilselvan, A. Prabhu and R. Rajagopal 9.1 Introduction 148 9.2 Related Work 149 9.3 K-Means Algorithm 151 9.4 Data Partitioning 152 9.5 Experimental Results 154 9.6 Conclusion 159 10 An Analysis on Detection and Visualization of Code Smells 163Prabhu J., Thejineaswar Guhan, M. A. Rahul, Pritish Gupta and Sandeep Kumar M. 10.1 Introduction 164 10.2 Literature Survey 165 10.3 Code Smells 168 10.4 Comparative Analysis 170 10.5 Conclusion 174 11 Leveraging Classification Through AutoML and Microservices 177M. Keerthivasan and V. Krishnaveni 11.1 Introduction 178 11.2 Related Work 179 11.3 Observations 181 11.4 Conceptual Architecture 181 11.5 Analysis of Results 190 11.6 Results and Discussion 193 Part III: E-Learning Applications 197 12 Virtual Teaching Activity Monitor 199Sakthivel S. and Akash Ram R.K. 12.1 Introduction 199 12.2 Related Works 203 12.3 Methodology 206 12.4 Results and Discussion 213 12.5 Conclusions 215 13 AI-Based Development of Student E-Learning Framework 219S. Jeyanthi, C. Sathya, N. Uma Maheswari, R. Venkatesh and V. Ganapathy Subramanian 13.1 Introduction 220 13.2 Objective 220 13.3 Literature Survey 221 13.4 Proposed Student E-Learning Framework 222 13.5 System Architecture 223 13.6 Working Module Description 224 13.7 Conclusion 228 13.8 Future Enhancements 228 Part IV: Networks Application 231 14 A Comparison of Selective Machine Learning Algorithms for Anomaly Detection in Wireless Sensor Networks 233Arul Jothi S. and Venkatesan R. 14.1 Introduction 234 14.2 Anomaly Detection in WSN 236 14.3 Summary of Anomaly Detections Techniques Using Machine Learning Algorithms 237 14.4 Experimental Results and Challenges of Machine Learning Approaches 238 14.5 Performance Evaluation 244 14.6 Conclusion 246 15 Unique and Random Key Generation Using Deep Convolutional Neural Network and Genetic Algorithm for Secure Data Communication Over Wireless Network 249S. Venkatesan, M. Ramakrishnan and M. Archana 15.1 Introduction 250 15.2 Literature Survey 252 15.3 Proposed Work 253 15.4 Genetic Algorithm (GA) 253 15.5 Conclusion 261 Part V: Automotive Applications 265 16 Review of Non-Recurrent Neural Networks for State of Charge Estimation of Batteries of Electric Vehicles 267R. Arun Chendhuran and J. Senthil Kumar 16.1 Introduction 267 16.2 Battery State of Charge Prediction Using Non -Recurrent Neural Networks 268 16.3 Evaluation of Charge Prediction Techniques 272 16.3 Conclusion 273 17 Driver Drowsiness Detection System 275G. Lavanya, N. Sunand, S. Gokulraj and T.G. Chakaravarthi 17.1 Introduction 275 17.2 Literature Survey 276 17.3 Components and Methodology 277 17.4 Conclusion 281 Part VI: Security Applications 283 18 An Extensive Study to Devise a Smart Solution for Healthcare IoT Security Using Deep Learning 285Arul Treesa Mathew and Prasanna Mani 18.1 Introduction 285 18.2 Related Literature 286 18.3 Proposed Model 291 18.4 Conclusions and Future Works 292 19 A Research on Lattice-Based Homomorphic Encryption Schemes 295Anitha Kumari K., Prakaashini S. and Suresh Shanmugasundaram 19.1 Introduction 295 19.2 Overview of Lattice-Based HE 296 19.3 Applications of Lattice HE 299 19.4 NTRU Scheme 301 19.5 GGH Signature Scheme 303 19.6 Related Work 304 19.5 Conclusion 308 20 Biometrics with Blockchain: A Better Secure Solution for Template Protection 311P. Jayapriya, K. Umamaheswari and S. Sathish Kumar 20.1 Introduction 311 20.2 Blockchain Technology 313 20.3 Biometric Architecture 317 20.4 Blockchain in Biometrics 320 20.4.1 Template Storage Techniques 322 20.5 Conclusion 324 References 324 Index 329

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Book SynopsisOPTIMIZED PREDICTIVE MODELS IN HEALTH CARE USING MACHINE LEARNING This book is a comprehensive guide to developing and implementing optimized predictive models in healthcare using machine learning and is a required resource for researchers, healthcare professionals, and students who wish to know more about real-time applications. The book focuses on how humans and computers interact to ever-increasing levels of complexity and simplicity and provides content on the theory of optimized predictive model design, evaluation, and user diversity. Predictive modeling, a field of machine learning, has emerged as a powerful tool in healthcare for identifying high-risk patients, predicting disease progression, and optimizing treatment plans. By leveraging data from various sources, predictive models can help healthcare providers make informed decisions, resulting in better patient outcomes and reduced costs. Other essential features of the book include: provides detailed guidance on data collec

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Book SynopsisDiscover the next major revolution in data science and AI and how it applies to your organization In Causal Artificial Intelligence: The Next Step in Effective, Efficient, and Practical AI, a team of dedicated tech executives delivers a business-focused approach based on a deep and engaging exploration of the models and data used in causal AI. The book's discussions include both accessible and understandable technical detail and business context and concepts that frame causal AI in familiar business settings. Useful for both data scientists and business-side professionals, the book offers: Clear and compelling descriptions of the concept of causality and how it can benefit your organization Detailed use cases and examples that vividly demonstrate the value of causality for solving business problems Useful strategies for deciding when to use correlation-based approaches and when to use causal inference An enlightening and easTable of ContentsForeword xix Preface xxiii Introduction xxix Chapter 1 Setting the Stage for Causal AI 1 Why Causality Is a Game Changer 2 Causal AI in Perspective with Analytics 7 Analytical Sophistication Model 8 Analytics Enablers 10 Analytics 10 Advanced Analytics 11 Scope of Services to Support Causal AI 11 The Value of the Hybrid Team 13 The Promise of AI 14 Understanding the Core Concepts of Causal AI 15 Explainability and Bias Detection 15 Explainability 17 Detecting Bias in a Model 17 Directed Acyclic Graphs 18 Structural Causal Model 19 Observed and Unobserved Variables 20 Counterfactuals 21 Confounders 21 Colliders 22 Front- Door and Backdoor Paths 23 Correlation 24 Causal Libraries and Tools 25 Propensity Score 25 Augmented Intelligence and Causal AI 26 Summary 27 Note 27 Chapter 2 Understanding the Value of Causal AI 29 Defining Causal AI 30 The Origins of Causal AI 33 Why Causality? 34 Expressing Relationships 37 The Ladder of Causation 38 Rung 1: Association, or Passive Observation 40 Rung 2: Intervention, or Taking Action 40 Rung 3: Counterfactuals, or Imagining What If 42 Why Causal AI Is the Next Generation of AI 43 Deep Learning and Neural Networks 43 Neural Networks 44 Establishing Ground Truth 45 The Business Imperative of a Causal Model 46 The Importance of Augmented Intelligence 51 The Importance of Data, Visualization, and Frameworks 52 Getting the Appropriate Data 52 Applying Data and Model Visualization 55 Applying Frameworks After Creating a Model 56 Getting Started with Causal AI 57 Summary 58 Notes 59 Chapter 3 Elements of Causal AI 61 Conceptual Models 62 Correlation vs. Causal Models 63 Correlation- Based AI 63 Causal AI 63 Understanding the Relationship Between Correlation and Causality 64 Process Models 66 Correlation- Based AI Process Model 67 Causal- Based AI Process Model 69 Collaboration Between Business and Analytics Professionals 72 The Fundamental Building Blocks of Causal AI Models 75 The Relations Between DAGs and SCMs 76 Explaining DAGs 76 Causal Notation: The Language of DAGs 78 Operationalizing a DAG with an SCM 79 The Elements of Visual Modeling 81 Nodes 83 Variables 83 Endogenous and Exogenous Variables 83 Observed and Unobserved Variables 84 Paths/Relationships 84 Weights 86 Summary 88 Notes 89 Chapter 4 Creating Practical Causal AI Models and Systems 91 Understanding Complex Models 92 Causal Modeling Process: Part 1 94 Step 1: What Are the Intended Outcomes? 95 Step 2: What Are the Proposed Interventions? 97 Step 3: What Are the Confounding Factors? 99 Step 4: What Are the Factors Creating the Effects and Changes? 102 Common/Universal Effects in a Causal Model 102 Refined Effects in a Causal Model 103 Step 5: Creating a Directed Acyclic Graph 105 Step 6: Paths and Relationships 105 Types of Paths 106 Path Connecting an Unobserved Variable 107 Front- Door Paths 108 Backdoor Paths 108 Modeling for Simplicity to Understand Complexity 109 Step 7: Data Acquisition 110 Causal- Based Approach: Part 2 112 Step 8: Data Integration 113 Step 9: Model Modification 114 Step 10: Data Transformation 115 Step 11: Preparing for Deployment in Business 118 Summary 121 Notes 122 Chapter 5 Creating a Model with a Hybrid Team 125 The Hybrid Team 126 Why a Hybrid Team? 127 The Benefits of a Hybrid Team 128 Establishing the Hybrid Team as a Center of Excellence 129 How Teams Collaborate 131 But Why? 132 Defining Roles 134 Leaders and Business Strategists 137 Subject- Matter Experts 138 Data Experts 140 Software Developers 142 Business Process Analysts 143 Information Technology Expertise 143 Project Manager(s) 144 The Basics Steps for a Hybrid Team Project 145 An Overview of Model Creation 146 It Depends on Your Destination 150 Understanding the Root Cause of a Problem 151 Understanding What Happened and Why 153 The Importance of the Iterative Process 154 Summary 155 Chapter 6 Explainability, Bias Detection, and AI Responsibility in Causal AI 157 Explainability 158 The Ramifications of the Lack of Explainability 159 What Is Explainable AI in Causal AI Models? 161 Black Boxes 162 Internal Workings of Black-Box Models 162 Deep Learning at the Heart of Black Boxes 163 Is Code Understandable? 163 The Value of White-Box Models 166 Understanding Causal AI Code 167 Techniques for Achieving Explainability 169 Challenges of Complex Causal Models 169 Methods for Understanding and Explaining Complex Causal AI Models 171 The Importance of the SHAP Explainability Method 172 Detecting Bias and Ensuring Responsible AI 175 Bias in Causal AI Systems 176 Responsible AI: Trust and Fairness 178 How Causal AI Addresses Bias Detection 180 Tools for Assessing Fairness and Bias 182 The Human Factor in Bias Detection and Responsible AI 183 Summary 184 Note 184 Chapter 7 Tools, Practices, and Techniques to Enable Causal AI 185 The Causal AI Pipeline 187 Define Business Objectives 190 Model Development 193 Data Identification and Collection 195 Data Privacy, Governance, and Security 197 Synthetic Data 198 Model Validation 199 Deployment/Production 201 Monitor and Evaluate 203 Update and Iterate 205 Continuous Learning 208 The Importance of Synthetic Data 210 Why Create Synthetic Data? 210 Overcoming Data Limitations 211 Enhancing Data Privacy and Security 211 Model Validation and Testing 211 Expanding the Range of Possible Scenarios 212 Reducing the Cost of Data Collection 212 Improving Data Imbalance 213 Encouraging Collaboration and Openness 213 Streamlining Data Preprocessing 213 Supporting Counterfactual Analysis 213 Fostering Innovation and Experimentation 214 Creating Synthetic Data 214 Generative Models 214 Agent-Based Modeling 215 Data Augmentation 215 Data Synthesis Tools and Platforms 215 Conditional Synthetic Data Generation 216 Synthetic Data from Text 216 The Limitations of Synthetic Data 217 Current State of Tools and Software in Causal AI 218 The Role of Open Source in Causal AI 218 Commercial Causal AI Software 221 CausaLens 221 Geminos Software 223 Summary 223 Chapter 8 Causal AI in Action 225 Enterprise Marketing in a Business- to- Consumer Scenario 226 DDCo Marketing Causal Model: Annual Pricing Review and Update Cycle 228 Incorporating Internal and External Factors in the Model and DAG 230 Easily Enabling Iterating 231 End-User-Driven Exploration 232 Bench Testing 234 DDCo Marketing Causal Model: Semiannual Product Planning Cycle 236 Always Consider Model Reuse 237 Give and Take in Building a New Model 239 Typical Model and Process Operation: Iterating 239 Keeping the Process/Model Scope Manageable and Understandable 240 Moving from Strategy to Building and Implementing Causal AI Solutions 241 Agriculture: Enhancing Crop Yield 242 Key Causal Variables 244 Creating the DAG 246 Moving from the DAG to Implementing the Causal AI Model 247 Commercial Real Estate: Valuing Warehouse Space 250 Key Causal Variables 251 Implementing the Causal AI Model 253 Video Streaming: Enhancing Content Recommendations 254 Key Causal Variables 255 Implementing the Causal AI Model 256 Healthcare: Reducing Infant Mortality 258 Key Causal Variables 259 Implementing the Causal AI Model 261 Retail: Providing Executives Actionable Information 263 Key Causal Variables 264 Implementing the Causal Model 265 Summary 267 Chapter 9 The Future of Causal AI 271 Where We Stand Today 271 Foundations of Causal AI 273 The Causal AI Journey 274 Causal AI Today 274 What’s Next for Causal AI 276 Integrating Causal AI and Traditional AI 278 The Imperative for Managing Data 279 Ensembles of Data 279 Generative AI Is Emerging as a Game Changer for Causal AI 281 The Future of Causal Discovery 282 The Emergence of Causal AI Reinforcement Learning Will Accelerate Model Training 284 Causal AI as a Common Language Between Business Leaders and Data Scientists 284 The Emergence of Probabilistic Programming Languages 286 The Predictable Model Evolution Cycle 286 The Emergence of the Digital Twin 287 Improving the Ability to Understand Ground Truth 289 The Development of More Sophisticated DAGs 289 Visualizing Complex Relationships in the DAGs 290 The Merging of Causal and Traditional AI Models 291 The Future of Explainability 291 The Evolution of Responsible AI 292 Advances in Data Security and Privacy 293 Integration Will Be Between Models and Business Applications 294 Summary 295 Glossary 299 Appendix 313 Selected Resources 329 Acknowledgments 331 About the authors 335 About the contributor 339 Index 341

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Book SynopsisAI Applications to Communications and Information Technologies Apply the technology of the future to networking and communications. Artificial intelligence, which enables computers or computer-controlled systems to perform tasks which ordinarily require human-like intelligence and decision-making, has revolutionized computing and digital industries like few other developments in recent history. Tools like artificial neural networks, large language models, and deep learning have quickly become integral aspects of modern life. With research and development into AI technologies proceeding at lightning speeds, the potential applications of these new technologies are all but limitless. AI Applications to Communications and Information Technologies offers a cutting-edge introduction to AI applications in one particular set of disciplines. Beginning with an overview of foundational concepts in AI, it then moves through numerous possible extensions of this technol

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Book SynopsisHUMAN-MACHINE INTERFACE The book contains the latest advances in healthcare and presents them in the frame of the Human-Machine Interface (HMI). The Human-Machine Interface (HMI) industry has witnessed the evolution from a simple push button to a modern touch-screen display. HMI is a user interface that allows humans to operate controllers for machines, systems, or instruments. Most medical procedures are improved by HMI systems, from calling an ambulance to ensuring that a patient receives adequate treatment on time. This book describes the scenario of biomedical technologies in the context of the advanced HMI, with a focus on direct brain-computer connection. The book describes several HMI tools and related techniques for analyzing, creating, controlling, and upgrading healthcare delivery systems, and provides details regarding how advancements in technology, particularly HMI, ensure ethical and fair use in patient care. Audience The target audiTable of ContentsForeword xxiii Preface xxv Acknowledgement xxvii Part I: Advanced Patient Care with HMI 1 1 Introduction to Human-Machine Interface 3 Shama Mujawar, Aarohi Deshpande, Aarohi Gherkar, Samson Eugin Simon and Bhupendra Prajapati 1.1 Introduction 4 1.2 Types of HMI 6 1.2.1 The Pushbutton Replacer 6 1.2.2 The Data Handler 7 1.2.3 The Overseer 7 1.3 Transformation of HMI 7 1.4 Importance and COVID Relevance With HMI 9 1.5 Applications 11 1.5.1 Biological Applications 12 1.5.1.1 HMI Signal Detection and Procurement Method 12 1.5.1.2 Healthcare and Rehabilitation 12 1.5.1.3 Magnetoencephalography 13 1.5.1.4 Flexible Hybrid Electronics (FHE) 13 1.5.1.5 Robotic-Assisted Surgeries 13 1.5.1.6 Flexible Microstructural Pressure Sensors 14 1.5.1.7 Biomedical Applications 14 1.5.1.8 Cb-hmi 15 1.5.1.9 HMI in Medical Devices 15 1.5.2 Industrial Applications 15 1.5.2.1 Metal Industries 16 1.5.2.2 Video Game Industry 16 1.5.2.3 Aerospace and Defense 16 1.5.2.4 Water Purification Plant HMI Based on Multi-Agent Systems (MAS) 17 1.5.2.5 Virtual and Haptic Interfaces 17 1.5.2.6 Space Crafts 17 1.5.2.7 Car Wash System 18 1.5.2.8 Pharmaceutical Processing and Industries 18 1.6 Challenges 18 1.7 Conclusion and Future Prospects 19 References 20 2 Improving Healthcare Practice by Using HMI Interface 25 Vaibhav Verma, Vivek Dave and Pranay Wal 2.1 Background of Human-Machine Interaction 26 2.2 Introduction 26 2.2.1 Healthcare Practice 26 2.2.2 Human-Machine Interface System in Healthcare 26 2.3 Evolution of HMI Design 27 2.3.1 HMI Design 1.0 27 2.3.2 HMI Design 2.0 28 2.3.3 HMI Design 3.0 28 2.3.4 HMI Design 4.0 28 2.4 Anatomy of Human Brain 28 2.5 Signal Associated With Brain 31 2.5.1 Evoked Signals 31 2.5.2 Spontaneous Signals 32 2.5.3 Hybrid Signals 32 2.6 HMI Signal Processing and Acquisition Methods 32 2.7 Human-Machine Interface–Based Healthcare System 36 2.7.1 Healthcare Practice System 36 2.7.1.1 Healthcare Practice 36 2.7.1.2 Current State of Healthcare Provision 37 2.7.1.3 Concerns With Domestic Healthcare 38 2.7.2 Medical Education System 38 2.7.2.1 Traditional and Modern Way of Providing Medical Education 38 2.8 Working Model of HMI 38 2.9 Challenges and Limitations of HMI Design 40 2.10 Role of HMI in Healthcare Practice 40 2.10.1 Simple to Clean 41 2.10.2 High Chemical Tolerance 41 2.10.3 Transportable and Light 41 2.10.4 Enhancing Communication 41 2.11 Application of HMI Technology in Medical Fields 42 2.11.1 Medical and Rehabilitative Engineering Using HMI 42 2.11.2 Controls for Robotic Surgery and Human Prosthetics 45 2.11.3 Sensory Replacement Mechanism 47 2.11.4 Wheelchairs and Moving Robots Along With Neurological Interface 48 2.11.5 Cognitive Improvement 49 2.12 Conclusion and Future Perspective 51 References 52 3 Human-Machine Interface and Patient Safety 59 Arun Kumar Singh and Rishabha Malviya 3.1 Introduction 59 3.2 Detecting Anesthesia-Related Drug Administration Errors and Predicting Their Impact 60 3.2.1 Methodological Difficulties in Studying Rare, Dangerous Phenomena 61 3.2.2 Consequences of Errors 63 3.2.3 Lessons From Other Industries 65 3.2.4 The Double-Human Interface 66 3.2.5 The Culture of Denial and Effort 67 3.2.6 Poor Labeling 68 3.3 Systematic Approaches to Improve Patient Safety During Anesthesia 69 3.3.1 Design Principles 69 3.3.2 Evidence of Safety Gains 70 3.3.3 Consistent Color-Coding 71 3.3.4 The Codonics Label System 72 3.4 The Triumph of Software 73 3.4.1 Software in Hospitals 74 3.4.2 Software in Anesthesia 75 3.4.3 The Alarm Problem 76 3.5 Environments that Audit Themselves 77 3.6 New Risks and Dangers 77 3.7 Conclusion 78 References 79 4 Human-Machine Interface Improving Quality of Patient Care 89 Rishav Sharma and Rishabha Malviya 4.1 Introduction 90 4.2 An Advanced Framework for Human-Machine Interaction 92 4.2.1 A Simulated Workplace Safety and Health Program 92 4.3 Human–Computer Interaction (HCI) 93 4.4 Multimodal Processing 95 4.5 Integrated Multimodality at a Lower Order (Stimulus Orientation) 96 4.6 Higher-Order Multimodal Integration (Perceptual Binding) 96 4.7 Gains in Performance From Multisensory Stimulation 97 4.8 Amplitude Envelope and Alarm Design 98 4.9 Recent Trends in Alarm Tone Design for Medical Devices 99 4.10 Percussive Tone Integration in Multimodal User Interfaces 99 4.11 Software in Hospitals 100 4.12 Brain–Machine Interface (BCI) Outfit 101 4.13 BCI Sensors and Techniques 101 4.13.1 Eeg 102 4.13.2 ECoG 102 4.13.3 Ecg 102 4.13.4 Emg 103 4.13.5 Meg 103 4.13.6 Fmri 103 4.14 New Generation Advanced Human-Machine Interface 104 4.15 Conclusion 105 References 106 5 Smart Patient Engagement through Robotics 115 Rakhi Mohan, A. Arun Prakash, Uma Devi N., Anjali Sharma S., Aiswarya Babu N. and Thennarasi P. 5.1 Introduction 116 5.1.1 Robotics in Healthcare 116 5.1.2 Patient Engagement Tasks (Front End) 118 5.1.2.1 Robotics in Nursing, Patient Handling, and Support 118 5.1.2.2 Robotics in Patient Reception 119 5.1.2.3 Robotics in Ambulance Services 120 5.1.2.4 Robotics in Serving (Food and Medicine) 120 5.1.2.5 Robotics in Surgery and Surgical Assistance 121 5.1.2.6 Robotics in Cleaning, Moping, Spraying and Disinfecting 122 5.1.2.7 Robotics in Physiotherapy, Radiology, Lab Diagnostics and Rehabilitation (Exoskeletons) 122 5.1.2.8 Robotics in Tele-Presence 122 5.1.2.9 Robotics in Hospital Kitchen and Pantry Management 123 5.1.2.10 Robotics in Outdoor Medicine Delivery 123 5.1.2.11 Robotics in Home Healthcare 123 5.1.3 Documentation and Other Hospital Management Tasks (Back End) 124 5.1.3.1 Robotics in Patient Data Feeding and Storing 124 5.1.3.2 Robotics in Data Mining 124 5.1.3.3 Robotics in Job Allocation to Hospital Staffs 125 5.1.3.4 Robotics in Payroll Management 125 5.1.3.5 Robotics in Medicine and Medical Equipment Logistics 126 5.1.3.6 Robotics in Medical Waste Residual Management 126 5.2 Theoretical Framework 126 5.3 Objectives 127 5.4 Research Methodology 127 5.5 Primary and Secondary Data 127 5.6 Factors for Consideration 127 5.6.1 Patient Demographics 127 5.6.2 Hospital/Health Institutes Demographics 127 5.6.3 Patient Perception Factors 128 5.6.4 Hospital’s Feasibility Factors and Hospital’s Economic Factors for Implementation 128 5.7 Robotics Implementation 128 5.8 Tools for Analysis 129 5.9 Analysis of Patient’s Perception 129 5.10 Review of Literature 129 5.11 Hospitals Considered for the Study (Through Indirect Sources) 131 5.12 Analysis and Interpretation 133 5.12.1 Crosstabulation 133 5.12.2 Regression and Model Fit 137 5.12.3 Factor Analysis 140 5.12.4 Regression Analysis 147 5.12.5 Descriptive Statistics 149 5.13 Conclusion 153 References 153 Annexure 154 6 Accelerating Development of Medical Devices Using Human-Machine Interface 161 Dipanjan Karati, Swarupananda Mukherjee, Souvik Roy and Bhupendra G. Prajapati 6.1 Introduction 162 6.2 HMI Machineries 164 6.3 Brain–Computer Interface and HMI 165 6.4 HMI for a Mobile Medical Exoskeleton 166 6.5 Human Artificial Limb and Robotic Surgical Treatment by HMI 167 6.6 Cognitive Enhancement by HMI 170 6.7 Soft Electronics for the Skin Using HMI 171 6.8 Safety Considerations 173 6.9 Conclusion 174 References 174 7 The Role of a Human-Machine Interaction (HMI) System on the Medical Devices 183 Zahra Alidousti Shahraki and Mohsen Aghabozorgi Nafchi 7.1 Introduction 184 7.2 Machine Learning for HCI Systems 185 7.3 Patient Experience 187 7.4 Cognitive Science 190 7.5 HCI System Based on Image Processing 192 7.5.1 Patient’s Facial Expression 193 7.5.2 Gender and Age 194 7.5.3 Emotional Intelligence 199 7.6 Blockchain 201 7.7 Virtual Reality 203 7.8 The Challenges in Designing HCI Systems for Medical Devices 206 7.9 Conclusion 207 References 208 8 Human-Machine Interaction in Leveraging the Concept of Telemedicine 211 Dipa K. Israni and Nandita S. Chawla 8.1 Introduction 212 8.2 Innovative Development in HMI Technologies and Its Use in Telemedicine 213 8.2.1 Nanotechnology 214 8.2.2 The Internet of Things (IoT) 215 8.2.3 Internet of Medical Things (IoMT) 216 8.2.3.1 Motion Detection Sensors 217 8.2.3.2 Pressure Sensors 217 8.2.3.3 Temperature Sensors 217 8.2.3.4 Monitoring Cardiovascular Disease 217 8.2.3.5 Glucose Level Monitoring 217 8.2.3.6 Asthma Monitoring 217 8.2.3.7 GPS Smart Soles and Motion Detection Sensors 218 8.2.3.8 Wireless Fetal Monitoring 218 8.2.3.9 Smart Clothing 218 8.2.4 Ai 219 8.2.5 Machine Learning Techniques 220 8.2.6 Deep Learning 221 8.2.7 Home Monitoring Devices, Augmented and Virtual 222 8.2.8 Drone Technology 223 8.2.9 Robotics 223 8.2.9.1 Robotics in Healthcare 224 8.2.9.2 History of Robotics 224 8.2.9.3 Tele-Surgery/Remote Surgery 224 8.2.10 5G Technology 225 8.2.11 6g 225 8.2.12 Big Data 226 8.2.13 Cloud Computing 226 8.2.14 Blockchain 227 8.2.14.1 Clinical Trials 228 8.2.14.2 Patient Records 228 8.2.14.3 Drug Tracking 228 8.2.14.4 Device Tracking 229 8.3 Advantages of Utilizing HMI in Healthcare for Telemedicine 230 8.3.1 Emotive Telemedicine 230 8.3.2 Ambient Assisted Living 232 8.3.2.1 Wearable Sensors for AAL 232 8.3.3 Monitoring and Controlling Intelligent Self-Management and Wellbeing 233 8.3.4 Intelligent Reminders for Treatment, Compliance, and Adherence 233 8.3.5 Personalized and Connected Healthcare 233 8.4 Obstacles to the Utilize, Accept, and Implement HMI in Telemedicine 234 8.4.1 Data Inconsistency and Disintegration 234 8.4.2 Standards and Interoperability are Lacking 234 8.4.3 Intermittent or Non-Existent Network Connectivity 234 8.4.4 Sensor Data Unreliability and Invalidity 235 8.4.5 Privacy, Confidentiality, and Data Consistency 235 8.4.6 Scalability Issues 235 8.4.7 Health Consequences 235 8.4.8 Clinical Challenges 236 8.4.9 Nanosensors and Biosensors Offer Health Risks 236 8.4.10 Limited Computing Capability and Inefficient Energy Use 236 8.4.11 Memory Space is Limited 237 8.4.12 Models of Digital Technology are Rigid and Sophisticated 237 8.4.13 Regulatory Frameworks 237 8.4.14 Incorporated IT Infrastructure 237 8.4.15 Misalignment with Nations’ e-Health Policies 238 8.4.16 Implementing Costs 238 8.4.17 Operational and Systems Challenges 238 8.4.18 Logistical Challenges 239 8.4.19 Communication Barriers 239 8.4.20 Unique Challenges 239 8.5 Conclusions 239 References 240 9 Making Hospital Environment Friendly for People: A Concept of HMI 247 Rihana Begum P., Badrud Duza Mohammad, Saravana Kumar A. and Muhasina K.M. 9.1 Introduction 248 9.2 A Scenario for Ubiquitous Computing and Ambient Intelligence 249 9.3 Emergence of Ambient Intelligence 250 9.4 Framework for Advanced Human-Machine Interfaces 251 9.5 Brain Computer Interface (BCI) 252 9.5.1 The BCI System: An Introduction 252 9.5.2 The Characteristics of a BCI 253 9.5.2.1 Dependent and Independent BCIs 253 9.5.2.2 Motor Disabilities: Options for Restoring Function 253 9.5.3 Components of BCI 254 9.5.4 Structure of the Human Brain and Its Signals 254 9.5.4.1 A Signal That is Evoked 256 9.5.4.2 Spontaneous Signals 256 9.5.4.3 Hybrid Signals 257 9.6 Development in MHI Technologies and Their Applications 257 9.7 Techniques of Signal Acquisition and Processing Applied to HMI 258 9.8 Hospital-Friendly Environment for Patients 260 9.8.1 Physiological Study State 260 9.8.1.1 Nature 260 9.8.1.2 Music 260 9.8.2 Pain State 260 9.8.2.1 Nature 260 9.8.2.2 Natural Light 261 9.8.3 Sleep 261 9.8.3.1 Nature Images 261 9.8.4 Patient Experience 261 9.8.4.1 Patient’s Satisfaction 261 9.8.4.2 Interaction 262 9.9 Applications of HMI for Patient-Friendly Hospital Environment 263 9.9.1 Healthcare and Engineering 263 9.9.2 Controls for Robotic Surgery and Human Prosthetics 265 9.9.3 Sensory Substitution System 266 9.9.4 Mobile Robots and Wheelchairs With Neural Interfaces 267 9.9.5 Technology on Biometric System 268 9.9.6 Enhancement of Cognition Level 269 9.9.7 fNIRS-EEG Multimodal BCI as a Future Perspective 270 9.10 Conclusion 270 References 271 Part II : Emerging Application and Regulatory Prospects of HMI in Healthcare 279 10 HMI: Disruption in the Neural Healthcare Industry 281 Preetam L. Nikam, Amol U. Gayke, Pavan S. Avhad, Rahul B. Bhabad and Rishabha Malviya 10.1 Introduction 282 10.2 Stimulation of Muscles 283 10.3 Cochlear Implants 283 10.3.1 Implants for Cochlear 283 10.3.2 Prosthetics for Ears 284 10.4 Peripheral Nervous System Interaction 284 10.5 Sleeve Electrodes 285 10.6 Flat-Interfaced Nerve Electrodes 287 10.7 Transverse and Longitudinal Intrafascicular Electrode (LIFE and TIME) 287 10.8 Multi-Channel Arrays That Penetrate 288 10.8.1 Numerous-Channel Arrays That Penetrate 288 10.9 Spinal Cord Stimulation and Central Nervous System Interaction 289 10.9.1 Cortical Connections 289 10.9.2 Stimulation of the Auditory Nucleus and Ganglions 290 10.9.3 Stimulation of the Deep Brain 290 10.10 Computer–Brain Interfaces 290 10.11 Conclusion 291 References 291 11 Dynamics of EHR in M-Healthcare Application 295 Eva Kaushik and Rohit Kaushik 11.1 Introduction 296 11.1.1 Why EHR is Needed in the Nation? 296 11.1.2 Empowering Patients in Healthcare Management 297 11.1.3 Data Management in EHR 298 11.1.4 Long-Term Architectural Approach 298 11.2 Background Related Work 299 11.3 Methodology 300 11.3.1 Use-Cases on Ground Base Reality 300 11.3.2 Integration of Technology to Solve Healthcare Issues 301 11.3.3 Workflow 302 11.4 Tools and Technologies 303 11.5 Limitations 304 11.6 Future Scope 305 11.6.1 Personalized EHR Cards 305 11.7 Discussion 306 11.7.1 Electronic Health Records and Personal Health Records 306 11.7.2 Physicians’ Review Toward EHR 307 11.7.3 Interoperability 307 11.8 Conclusion 308 References 308 12 Role of Human-Machine Interface in the Biomedical Device Development to Handle COVID-19 Pandemic Situation in an Efficient Way 311 Soma Datta and Nabendu Chaki 12.1 Introduction: Background and Driving Forces 312 12.1.1 Observed Scenario During May 2021 314 12.1.1.1 Transmission Medium 314 12.1.2 Limitation of Vaccine Technology 314 12.1.3 Adverse Effect of Protective Measure 314 12.1.4 Revoking of Restrictions Causes Surges in Pandemic 315 12.2 Methods 315 12.2.1 Determine Major Influencing Factors 316 12.2.2 Analyzed the Selected Influencing Factor 317 12.2.2.1 Evidence 1 318 12.2.2.2 Evidence 2 318 12.2.2.3 Evidence 3 320 12.2.3 Managing Mechanism to Reduce the Spreading Rate of COVID- 19 320 12.2.4 The Households Health Safety Systems to Disinfect Outdoor Cloths 321 12.2.4.1 Present Households Disinfect Systems for Cloth and Personal Belonging 321 12.2.4.2 The Outline of Households Health Safety Systems to Disinfect Outdoor Clothes 322 12.2.5 Upgradation of Individual Room Air Conditioning System 324 12.2.5.1 The Outline of the AI-Based Room Ventilator System 324 12.2.6 Design of Next-Generation Mask 324 12.3 Results 325 12.4 Conclusion 325 Acknowledgment 325 References 326 13 Role of HMI in the Drug Manufacturing Process 329 Biswajit Basu, Kevinkumar Garala and Bhupendra G. Prajapati 13.1 Introduction 330 13.1.1 Dialogue Systems 331 13.2 Types of HMI 333 13.3 Advantages and Disadvantages of HMI 334 13.4 Roles of HMI in the Pharmaceutical Manufacturing Process 339 13.5 Common Applications for Human-Machine Interfaces 343 13.5.1 Automotive Dashboards 343 13.5.2 Monitoring of Machinery and Equipment 344 13.5.3 Digital Displays 344 13.5.4 Building Automation 344 13.5.5 Video and Audio Production 344 13.6 Healthcare System-Based Human–Computer Interaction 345 13.6.1 Healthcare System 345 13.6.2 Teaching of Medicine and Physiology 346 13.7 Performance Test of Healthcare System Based on HCI 349 13.7.1 HCI-Based Medical Teaching System 349 13.8 Human-Machine Interface for Healthcare and Rehabilitation 349 13.8.1 Ambient Intelligence and Ubiquitous Computing Scenario 349 13.8.2 The Advanced Human-Machine Interface Framework 350 13.9 Human-Machine Interface for Research Reactor: Instrumentation and Control System 351 13.10 Future Scope of Human-Machine Interface (HMI) 352 13.11 Conclusion 353 References 353 14 Breaking the Silence: Brain–Computer Interface for Communication 357 Preetam L. Nikam, Sheetal Wagh, Sarika Shinde, Abhishek Mokal, Smita Andhale, Prathmesh Wagh, Vivek Bhosale and Rishabha Malviya 14.1 Introduction 358 14.2 Survey of BCI 359 14.3 Techniques of BCI 361 14.3.1 Potentials Associated With an Event 361 14.3.2 Cortical Gradual Potentials 361 14.3.3 Evoked Visual Possibilities 361 14.3.4 Sensorimotor Rhythms 362 14.3.5 Motor Imagery 362 14.4 BCI Components 362 14.4.1 Signal Acquisition 363 14.4.2 Signal Processing 363 14.4.3 Extraction of Features 363 14.4.4 Signal Categorization 363 14.5 BCI Signal Acquisition Methods 364 14.6 BCI Invasion 364 14.7 BCI With Limited Invasion 364 14.8 BCI Not Invasive 364 14.9 BCI Applications 365 14.9.1 Movement 365 14.9.2 Recreation 365 14.9.3 Reconstruction 366 14.9.4 Interaction 366 14.9.5 Interaction With Others 366 14.9.6 Diagnosis and Treatment of Depression 366 14.9.7 Reduces Healthcare Costs 367 14.10 BCI Healthcare Challenges 367 14.10.1 Ethical Difficulties 367 14.10.2 Goodwill 367 14.10.3 Legality 368 14.10.4 Freedom of Privacy 368 14.10.5 Issues With Standardization 368 14.10.6 Problems With Reliability 368 14.10.7 Prolonged Training Process 369 14.10.8 Expensive Acquisition and Control 369 14.11 Conclusion 370 References 370 15 Regulatory Perspective: Human-Machine Interfaces 375 Artiben Patel, Ravi Patel, Rakesh Patel, Bhupendra Prajapati and Shivani Jani Abbreviations 376 15.1 Introduction 376 15.2 Why are Regulations Needed? 377 15.2.1 Safety 378 15.2.2 Uniform Requirements 378 15.2.3 Promote Innovation 378 15.2.4 Free Movement of Goods 378 15.2.5 Compensation 379 15.2.6 Fostering Innovation 379 15.3 US Regulatory Perspective 379 15.3.1 History of Medical Device Regulation and Its Supervision in the United States 380 15.3.2 Classification of Medical Devices 384 15.3.3 Reclassification 385 15.3.4 How to Determine if the Product is a Medical Device or How to Classify the Medical Device 385 15.3.5 Device Development Process 387 15.3.6 Overview of Device Regulations 391 15.3.7 Quality and Compliance of Medical Devices 393 15.3.8 Human Factors and Medical Devices 395 15.3.9 Continuous Improvement of Regulations 402 15.4 Conclusion 407 References 407 16 Towards the Digitization of Healthcare Record Management 411 Shivani Patel, Bhavinkumar Gayakvad, Ravisinh Solanki, Ravi Patel and Dignesh Khunt 16.1 Introduction 412 16.2 Digital Health Records: Concept and Organization 416 16.3 Mechanism and Operation of Digital Health Record 419 16.3.1 Physician-Hosted EHR 420 16.3.2 Remotely-Hosted EHR 420 16.3.2.1 Subsidized System 420 16.3.2.2 Dedicated Hosted System 421 16.3.2.3 Cloud-Based or Internet-Based Computing 421 16.4 Benefits of Digital Health Records 426 16.4.1 Security 426 16.4.2 Costs 427 16.4.3 Access 427 16.4.4 Storage 427 16.4.5 Accuracy and Readability 427 16.4.6 Practice Management 428 16.4.7 Quality of Care 428 16.5 Limitations of Digital Health Records 428 16.5.1 Completeness 428 16.5.2 Correctness 429 16.5.3 Complexity 429 16.5.4 Acceptability 430 16.5.4.1 People 430 16.5.4.2 Hardware, Software and Network 430 16.5.4.3 Procedure 430 16.6 Risk & Problems Associated With the System 431 16.6.1 Lack of Concord 431 16.6.2 Privacy and Data Security Issues 431 16.6.3 Problems in Patient Matching 432 16.6.4 Alteration of Algorithms in Decision-Support Models 432 16.6.5 Increased Workload of Clinicians 432 16.7 Future Benefits 432 16.8 Miscellaneous 434 16.8.1 Policies Regarding Data Exchange 434 16.8.1.1 Directed Exchange 435 16.8.1.2 Query-Based Exchange 435 16.8.1.3 Consumer-Mediated Exchange 435 16.8.2 Current Practices of Digital Health Records 438 16.8.2.1 India 438 16.8.2.2 Australia 439 16.8.2.3 Canada 439 16.8.2.4 USA 440 16.8.2.5 China 440 16.8.3 Data Analysis 442 16.8.4 Role and Benefits to the Stakeholders 443 16.8.4.1 Advantages to the Patient 443 16.8.4.2 Advantages to the Healthcare Providers 444 16.8.4.3 Advantages to the Society 444 16.9 Conclusion 445 References 446 17 Intelligent Healthcare Supply Chain 449 Chirag Kalaria, Shambhavi Singh and Bhupendra G. Prajapati 17.1 Introduction 450 17.2 Supply Chain – Method Networking? 451 17.3 Healthcare Supply Chain and Steps Involved 451 17.4 Importance of HSC 452 17.5 Risks and Complexities Affecting the Globally Distributed HSC 453 17.5.1 Legacy HSC 453 17.5.1.1 SWOT Analysis of Legacy HSC 454 17.5.2 What is an Intelligent Supply Chain? 454 17.5.3 Difference Between Legacy HSC and Intelligent HSC 456 17.6 Technologies Come to Aid to Build an Intelligent HSC 457 17.6.1 Hmi 457 17.6.2 Ai 458 17.6.3 Ml/dl 459 17.7 Blockchain 460 17.8 Robotics 461 17.9 Cloud Computing 463 17.10 Big Data Analytics (BDA) 465 17.11 Industry 4.0 465 17.12 Internet of Things (IoT) 467 17.13 Digital Twins 469 17.14 Supply Chain Control Tower 470 17.15 Predictive Maintenance 472 17.16 A Digital Transformation Roadmap 473 17.17 Prerequisite for Designing Intelligent HSC 475 17.18 HMI—Usage in HSC Management 476 17.19 HMI—A Face of the Supply Chain Control Tower 477 17.20 The Intelligent Future of the Healthcare Industry 478 17.21 Conclusion 480 References 481 Index 483

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Book SynopsisEmbrace emerging AI trends and integrate your operations with cutting-edge solutions Enterprise AI in the Cloud: A Practical Guide to Deploying End-to-End Machine Learning and ChatGPT Solutions is an indispensable resource for professionals and companies who want to bring new AI technologies like generative AI, ChatGPT, and machine learning (ML) into their suite of cloud-based solutions. If you want to set up AI platforms in the cloud quickly and confidently and drive your business forward with the power of AI, this book is the ultimate go-to guide. The author shows you how to start an enterprise-wide AI transformation effort, taking you all the way through to implementation, with clearly defined processes, numerous examples, and hands-on exercises. You'll also discover best practices on optimizing cloud infrastructure for scalability and automation. Enterprise AI in the Cloud helps you gain a solid understanding of: AI-First Strategy: Adopt a comprehensive approach to implementing corporate AI systems in the cloud and at scale, using an AI-First strategy to drive innovationState-of-the-Art Use Cases: Learn from emerging AI/ML use cases, such as ChatGPT, VR/AR, blockchain, metaverse, hyper-automation, generative AI, transformer models, Keras, TensorFlow in the cloud, and quantum machine learningPlatform Scalability and MLOps (ML Operations): Select the ideal cloud platform and adopt best practices on optimizing cloud infrastructure for scalability and automationAWS, Azure, Google ML: Understand the machine learning lifecycle, from framing problems to deploying models and beyond, leveraging the full power of Azure, AWS, and Google Cloud platformsAI-Driven Innovation Excellence: Get practical advice on identifying potential use cases, developing a winning AI strategy and portfolio, and driving an innovation cultureEthical and Trustworthy AI Mastery: Implement Responsible AI by avoiding common risks while maintaining transparency and ethicsScaling AI Enterprise-Wide: Scale your AI implementation using Strategic Change Management, AI Maturity Models, AI Center of Excellence, and AI Operating Model Whether you're a beginner or an experienced AI or MLOps engineer, business or technology leader, or an AI student or enthusiast, this comprehensive resource empowers you to confidently build and use AI models in production, bridging the gap between proof-of-concept projects and real-world AI deployments. With over 300 review questions, 50 hands-on exercises, templates, and hundreds of best practice tips to guide you through every step of the way, this book is a must-read for anyone seeking to accelerate AI transformation across their enterprise.Table of ContentsIntroduction xvii Part I: Introduction Chapter 1: Enterprise Transformation with AI in the Cloud 3 Chapter 2: Case Studies of Enterprise AI in the Cloud 19 Part II: Strategizing and Assessing for Ai Chapter 3: Addressing the Challenges with Enterprise AI 31 Chapter 4: Designing AI Systems Responsibly 41 Chapter 5: Envisioning and Aligning Your AI Strategy 50 Chapter 6: Developing An AI Strategy and Portfolio 57 Chapter 7: Managing Strategic Change 66 Part III: Planning and Launching a Pilot Project Chapter 8: Identifying Use Cases for Your AI/ml Project 79 Chapter 9: Evaluating AI/ml Platforms and Services 106 Chapter 10: Launching Your Pilot Project 152 Part IV: Building and Governing Your Team Chapter 11: Empowering Your People Through Org Change Management 163 Chapter 12: Building Your Team 173 Part V: Setting Up Infrastructure and Managing Operations Chapter 13: Setting Up An Enterprise AI Cloud Platform Infrastructure 187 Chapter 14: Operating Your AI Platform with Mlops Best Practices 217 Part VI: Processing Data and Modeling Chapter 15: Process Data and Engineer Features in The Cloud 243 Chapter 16: Choosing Your AI/ml Algorithms 268 Chapter 17: Training, Tuning, and Evaluating Models 315 Part VII: Deploying and Monitoring Models Chapter 18: Deploying Your Models Into Production 345 Chapter 19: Monitoring Models 361 Chapter 20: Governing Models for Bias and Ethics 377 Part VIII: Scaling and Transforming AI Chapter 21: Using the AI Maturity Framework to Transform Your Business 391 Chapter 22: Setting Up Your AI Coe 407 Chapter 23: Building Your AI Operating Model and Transformation Plan 416 Part IX: Evolving and Maturing AI Chapter 24: Implementing Generative AI Use Cases With Chatgpt for the Enterprise 433 Chapter 25: Planning for the Future of AI 465 Chapter 26: Continuing Your AI Journey 479Index 485

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Book SynopsisAUTOMATED SECURE COMPUTING FOR NEXT-GENERATION SYSTEMS This book provides cutting-edge chapters on machine-empowered solutions for next-generation systems for today's society. Security is always a primary concern for each application and sector. In the last decade, many techniques and frameworks have been suggested to improve security (data, information, and network). Due to rapid improvements in industry automation, however, systems need to be secured more quickly and efficiently. It is important to explore the best ways to incorporate the suggested solutions to improve their accuracy while reducing their learning cost. During implementation, the most difficult challenge is determining how to exploit AI and ML algorithms for improved safe service computation while maintaining the user's privacy. The robustness of AI and deep learning, as well as the reliability and privacy of data, is an important part of modern computing. It is essential to determine the securiTable of ContentsPreface xvii Acknowledgements xix Part 1: Fundamentals 1 1 Digital Twin Technology: Necessity of the Future in Education and Beyond 3Robertas Damasevicius and Ligita Zailskaite-Jakste 1.1 Introduction 3 1.2 Digital Twins in Education 5 1.3 Examples and Case Studies 8 1.4 Discussion 12 1.5 Challenges and Limitations 13 1.6 Conclusion 17 2 An Intersection Between Machine Learning, Security, and Privacy 23Hareharan P.K., Kanishka J. and Subaasri D. 2.1 Introduction 23 2.2 Machine Learning 24 2.3 Threat Model 27 2.4 Training in a Differential Environment 30 2.5 Inferring in Adversarial Attack 33 2.6 Machine Learning Methods That Are Sustainable, Private, and Accountable 36 2.7 Conclusion 40 3 Decentralized, Distributed Computing for Internet of Things-Based Cloud Applications 43Roopa Devi E.M., Shanthakumari R., Rajadevi R., Kayethri D. and Aparna V. 3.1 Introduction to Volunteer Edge Cloud for Internet of Things Utilising Blockchain 44 3.2 Significance of Volunteer Edge Cloud Concept 45 3.3 Proposed System 46 3.4 Implementation of Volunteer Edge Control 49 3.5 Result Analysis of Volunteer Edge Cloud 52 3.6 Introducing Blockchain-Enabled Internet of Things Systems Using the Serverless Cloud Platform 53 3.7 Introducing Serverless Cloud Platforms 54 3.8 Serverless Cloud Platform System Design 55 3.9 Evaluation of HCloud 60 3.10 HCloud-Related Works 61 3.11 Conclusion 62 4 Artificial Intelligence–Blockchain-Enabled–Internet of Things-Based Cloud Applications for Next-Generation Society 65V. Hemamalini, Anand Kumar Mishra, Amit Kumar Tyagi and Vijayalakshmi Kakulapati 4.1 Introduction 65 4.2 Background Work 69 4.3 Motivation 71 4.4 Existing Innovations in the Current Society 72 4.5 Expected Innovations in the Next-Generation Society 72 4.6 An Environment with Artificial Intelligence–Blockchain-Enabled–Internet of Things-Based Cloud Applications 73 4.7 Open Issues in Artificial Intelligence–Blockchain-Enabled–Internet of Things-Based Cloud Applications 74 4.8 Research Challenges in Artificial Intelligence–Blockchain-Enabled–Internet of Things-Based Cloud Applications 75 4.9 Legal Challenges in Artificial Intelligence–Blockchain-Enabled–Internet of Things-Based Cloud Applications 76 4.10 Future Research Opportunities Towards Artificial Intelligence–Blockchain-Enabled–Internet of Things-Based Cloud Applications 77 4.11 An Open Discussion 78 4.12 Conclusion 79 5 Artificial Intelligence for Cyber Security: Current Trends and Future Challenges 83Meghna Manoj Nair, Atharva Deshmukh and Amit Kumar Tyagi 5.1 Introduction: Security and Its Types 83 5.2 Network and Information Security for Industry 4.0 and Society 5.0 86 5.3 Internet Monitoring, Espionage, and Surveillance 89 5.4 Cyber Forensics with Artificial Intelligence and without Artificial Intelligence 91 5.5 Intrusion Detection and Prevention Systems Using Artificial Intelligence 92 5.6 Homomorphic Encryption and Cryptographic Obfuscation 94 5.7 Artificial Intelligence Security as Adversarial Machine Learning 95 5.8 Post-Quantum Cryptography 96 5.9 Security and Privacy in Online Social Networks and Other Sectors 98 5.10 Security and Privacy Using Artificial Intelligence in Future Applications/Smart Applications 99 5.11 Security Management and Security Operations Using Artificial Intelligence for Society 5.0 and Industry 4.0 101 5.12 Digital Trust and Reputation Using Artificial Intelligence 103 5.13 Human-Centric Cyber Security Solutions 104 5.14 Artificial Intelligence-Based Cyber Security Technologies and Solutions 106 5.15 Open Issues, Challenges, and New Horizons Towards Artificial Intelligence and Cyber Security 107 5.16 Future Research with Artificial Intelligence and Cyber Security 109 5.17 Conclusion 110 Part 2: Methods and Techniques 115 6 An Automatic Artificial Intelligence System for Malware Detection 117Ahmad Moawad, Ahmed Ismail Ebada, A.A. El-Harby and Aya M. Al-Zoghby 6.1 Introduction 117 6.2 Malware Types 119 6.3 Structure Format of Binary Executable Files 121 6.4 Malware Analysis and Detection 124 6.5 Malware Techniques to Evade Analysis and Detection 128 6.6 Malware Detection With Applying AI 130 6.7 Open Issues and Challenges 134 6.8 Discussion and Conclusion 135 7 Early Detection of Darknet Traffic in Internet of Things Applications 139Ambika N. 7.1 Introduction 139 7.2 Literature Survey 143 7.3 Proposed Work 147 7.4 Analysis of the Work 149 7.5 Future Work 150 7.6 Conclusion 151 8 A Novel and Efficient Approach to Detect Vehicle Insurance Claim Fraud Using Machine Learning Techniques 155Anand Kumar Mishra, V. Hemamalini, Amit Kumar Tyagi, Piyali Saha and Abirami A. 8.1 Introduction 155 8.2 Literature Survey 156 8.3 Implementation and Analysis 157 8.4 Conclusion 174 9 Automated Secure Computing for Fraud Detection in Financial Transactions 177Kuldeep Singh, Prasanna Kolar, Rebecca Abraham, Vedantam Seetharam, Sireesha Nanduri and Divyesh Kumar 9.1 Introduction 177 9.2 Historical Perspective 180 9.3 Previous Models for Fraud Detection in Financial Transactions 181 9.4 Proposed Model Based on Automated Secure Computing 182 9.5 Discussion 184 9.6 Conclusion 185 10 Data Anonymization on Biometric Security Using Iris Recognition Technology 191Aparna D. K., Malarkodi M., Lakshmanaprakash S., Priya R. L. and Ajay Nair 10.1 Introduction 191 10.2 Problems Faced in Facial Recognition 194 10.3 Face Recognition 197 10.4 The Important Aspects of Facial Recognition 199 10.5 Proposed Methodology 201 10.6 Results and Discussion 202 10.7 Conclusion 202 11 Analysis of Data Anonymization Techniques in Biometric Authentication System 205Harini S., Dharshini R., Agalya N., Priya R. L. and Ajay Nair 11.1 Introduction 205 11.2 Literature Survey 207 11.3 Existing Survey 209 11.4 Proposed System 212 11.5 Implementation of AI 219 11.6 Limitations and Future Works 220 11.7 Conclusion 221 Part 3: Applications 223 12 Detection of Bank Fraud Using Machine Learning Techniques 225Kalyani G., Anand Kumar Mishra, Diya Harish, Amit Kumar Tyagi, Sajidha S. A. and Shashank Pandey 12.1 Introduction 225 12.2 Literature Review 226 12.3 Problem Description 227 12.4 Implementation and Analysis 228 12.5 Results 238 12.6 Conclusion 238 12.7 Future Works 240 13 An Internet of Things-Integrated Home Automation with Smart Security System 243Md. Sayeduzzaman, Touhidul Hasan, Adel A. Nasser and Akashdeep Negi 13.1 Introduction 244 13.2 Literature Review 246 13.3 Methodology and Working Procedure with Diagrams 249 13.4 Research Analysis 252 13.5 Establishment of the Prototype 256 13.6 Results and Discussions 265 13.7 Conclusions 270 14 An Automated Home Security System Using Secure Message Queue Telemetry Transport Protocol 275P. Rukmani, S. Graceline Jasmine, M. Vergin Raja Sarobin, L. Jani Anbarasi and Soumitro Datta 14.1 Introduction 275 14.2 Related Works 277 14.3 Proposed Solution 278 14.4 Implementation 285 14.5 Results 290 14.6 Conclusion and Future Work 292 15 Machine Learning-Based Solutions for Internet of Things-Based Applications 295Varsha Bhatia and Bhavesh Bhatia 15.1 Introduction 295 15.2 IoT Ecosystem 296 15.3 Importance of Data in IoT Applications 298 15.4 Machine Learning 299 15.5 Machine Learning Algorithms 302 15.6 Applications of Machine Learning in IoT 304 15.7 Challenges of Implementing ML for IoT Solutions 313 15.8 Emerging Trends in IoT 314 15.9 Conclusion 315 16 Machine Learning-Based Intelligent Power Systems 319Kusumika Krori Dutta, S. Poornima, R. Subha, Lipika Deka and Archit Kamath 16.1 Introduction 319 16.2 Machine Learning Techniques 321 16.3 Implementation of ML Techniques in Smart Power Systems 334 16.4 Case Study 340 16.5 Conclusion 341 Part 4: Future Research Opportunities 345 17 Quantum Computation, Quantum Information, and Quantum Key Distribution 347Mohanaprabhu D., Monish Kanna S. P., Jayasuriya J., Lakshmanaprakash S., Abirami A. and Amit Kumar Tyagi 17.1 Introduction 347 17.2 Literature Work 352 17.3 Motivation Behind this Study 353 17.4 Existing Players in the Market 354 17.5 Quantum Key Distribution 356 17.6 Proposed Models for Quantum Computing 356 17.7 Simulation/Result 361 17.8 Conclusion 365 18 Quantum Computing, Qubits with Artificial Intelligence, and Blockchain Technologies: A Roadmap for the Future 367Amit Kumar Tyagi, Anand Kumar Mishra, Aswathy S. U. and Shabnam Kumari 18.1 Introduction to Quantum Computing and Its Related Terms 368 18.2 How Quantum Computing is Different from Security? 374 18.3 Artificial Intelligence—Blockchain-Based Quantum Computing? 375 18.4 Process to Build a Quantum Computer 378 18.5 Popular Issues with Quantum Computing in this Smart Era 379 18.6 Problems Faced with Artificial Intelligence–Blockchain-Based Quantum Computing 379 18.7 Challenges with the Implementation of Quantum Computers in Today's Smart Era 380 18.8 Future Research Opportunities with Quantum Computing 381 18.9 Future Opportunities with Artificial Intelligence–Blockchain-Based Quantum Computing 382 18.10 Conclusion 383 19 Qubits, Quantum Bits, and Quantum Computing: The Future of Computer Security System 385Harini S., Dharshini R., Praveen R., Abirami A., Lakshmanaprakash S. and Amit Kumar Tyagi 19.1 Introduction 385 19.2 Importance of Quantum Computing 387 19.3 Literature Survey 388 19.4 Quantum Computing Features 390 19.5 Quantum Algorithms 394 19.6 Experimental Results 399 19.7 Conclusion 400 20 Future Technologies for Industry 5.0 and Society 5.0 403Mani Deepak Choudhry, S. Jeevanandham, M. Sundarrajan, Akshya Jothi, K. Prashanthini and V. Saravanan 20.1 Introduction 404 20.2 Related Work 407 20.3 Comparative Analysis of I4.0 to I5.0 and S4.0 to S5.0 409 20.4 Risks and Prospects 412 20.5 Conclusion 412 21 Futuristic Technologies for Smart Manufacturing: Research Statement and Vision for the Future 415Amit Kumar Tyagi, Anand Kumar Mishra, Nalla Vedavathi, Vijayalakshmi Kakulapati and Sajidha S. A. 21.1 Introduction About Futuristic Technologies 415 21.2 Related Work Towards Futuristic Technologies 418 21.3 Related Work Towards Smart Manufacturing 419 21.4 Literature Review Towards Futuristic Technology 420 21.5 Motivation 421 21.6 Smart Applications 422 21.7 Popular Issues with Futuristic Technologies for Emerging Applications 424 21.8 Legal Issues Towards Futuristic Technologies 427 21.9 Critical Challenges with Futuristic Technology for Emerging Applications 428 21.10 Research Opportunities for Futuristic Technologies Towards Emerging Applications 430 21.11 Lesson Learned 433 21.12 Conclusion 434 References 434 Index 443

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