Simulation and Analysis of Mathematical Methods in Real-Time Engineering Applications

This breakthrough edited volume highlights the security, privacy, artificial intelligence, and practical approaches needed by engineers and scientists in all fields of science and technology. It highlights the current research, which is intended to advance not only mathematics but all areas of science, research, and development, and where these disciplines intersect. As the book is focused on emerging concepts in machine learning and artificial intelligence algorithmic approaches and soft computing techniques, it is an invaluable tool for researchers, academicians, data scientists, and technology developers.

The newest and most comprehensive volume in the area of mathematical methods for use in real-time engineering, this groundbreaking new work is a must-have for any engineer or scientist’s library. Also useful as a textbook for the student, it is a valuable contribution to the advancement of the science, both a working handbook for the new hire or student, and a reference for the veteran engineer.

T. Ananth Kumar, PhD, is an assistant professor at the IFET College of Engineering, Anna University, Chennai. He received his Ph.D. degree in VLSI design from Manonmaniam Sundaranar University, Tirunelveli. He is the recipient of the Best Paper Award at INCODS 2017. He is a life member of ISTE, has numerous patents to his credit and has written many book chapters for a variety of well-known publishers. 

E. Golden Julie, PhD, is a senior assistant professor in the Department of Computer Science and Engineering, Anna university, Regional campus, Tirunelveli. She earned her doctorare in information and communication engineering from Anna University, Chennai in 2017.  She has over twelve years of teaching experience and has published over 34 papers in various international journals and presented more than 20 papers at technical conferences. She has written ten book chapters for multiple publishers and is a reviewer for many scientific and technical journals. 

Y.  Harold Robinson, PhD, is currently teaching at the School of Information Technology and Engineering, Vellore Institute of Technology, Vellore. He earned his doctorate in information and communication engineering from Anna University, Chennai in 2016. He is having more than fifteen years of experience in teaching and has published more than 50 papers in various international journals.  He has also presented more than 45 papers at technical conferences and has written four book chapters.  He is a reviewer for many scientific journals, as well.  

S. M. Jaisakthi, PhD, is an associate professor at the School of Computer Science & Engineering, at the Vellore Institute of Technology. She earned her doctorate from Anna University, Chennai. She has published many research publications in refereed international journals and in proceedings of international conferences.

Preface xv

Acknowledgments xix

1 Certain Investigations on Different Mathematical Models in Machine Learning and Artificial Intelligence 1
Ms. Akshatha Y and Dr. S Pravinth Raja

1.1 Introduction 2

1.1.1 Knowledge-Based Expert Systems 2

1.1.2 Problem-Solving Techniques 3

1.2 Mathematical Models of Classification Algorithm of Machine Learning 4

1.2.1 Tried and True Tools 5

1.2.2 Joining Together Old and New 6

1.2.3 Markov Chain Model 7

1.2.4 Method for Automated Simulation of Dynamical Systems 7

1.2.5 kNN is a Case-Based Learning Method 9

1.2.6 Comparison for KNN and SVM 10

1.3 Mathematical Models and Covid-19 12

1.3.1 SEIR Model (Susceptible-Exposed-Infectious-Removed) 13

1.3.2 SIR Model (Susceptible-Infected-Recovered) 14

1.4 Conclusion 15

References 15

2 Edge Computing Optimization Using Mathematical Modeling, Deep Learning Models, and Evolutionary Algorithms 17
P. Vijayakumar, Prithiviraj Rajalingam and S. V. K. R. Rajeswari

2.1 Introduction to Edge Computing and Research Challenges 18

2.1.1 Cloud-Based IoT and Need of Edge Computing 18

2.1.2 Edge Architecture 19

2.1.3 Edge Computing Motivation, Challenges and Opportunities 21

2.2 Introduction for Computational Offloading in Edge Computing 24

2.2.1 Need of Computational Offloading and Its Benefit 25

2.2.2 Computation Offloading Mechanisms 27

2.2.2.1 Offloading Techniques 29

2.3 Mathematical Model for Offloading 30

2.3.1 Introduction to Markov Chain Process and Offloading 31

2.3.1.1 Markov Chain Based Schemes 32

2.3.1.2 Schemes Based on Semi-Markov Chain 32

2.3.1.3 Schemes Based on the Markov Decision Process 33

2.3.1.4 Schemes Based on Hidden Markov Model 33

2.3.2 Computation Offloading Schemes Based on Game Theory 33

2.4 QoS and Optimization in Edge Computing 34

2.4.1 Statistical Delay Bounded QoS 35

2.4.2 Holistic Task Offloading Algorithm Considerations 35

2.5 Deep Learning Mathematical Models for Edge Computing 36

2.5.1 Applications of Deep Learning at the Edge 36

2.5.2 Resource Allocation Using Deep Learning 37

2.5.3 Computation Offloading Using Deep Learning 39

2.6 Evolutionary Algorithm and Edge Computing 39

2.7 Conclusion 41

References 41

3 Mathematical Modelling of Cryptographic Approaches in Cloud Computing Scenario 45
M. Julie Therese, A. Devi, P. Dharanyadevi and Dr. G. Kavya

3.1 Introduction to IoT 46

3.1.1 Introduction to Cloud 46

3.1.2 General Characteristics of Cloud 47

3.1.3 Integration of IoT and Cloud 47

3.1.4 Security Characteristics of Cloud 47

3.2 Data Computation Process 49

3.2.1 Star Cubing Method for Data Computation 49

3.2.1.1 Star Cubing Algorithm 49

3.3 Data Partition Process 51

3.3.1 Need for Data Partition 52

3.3.2 Shamir Secret (SS) Share Algorithm for Data Partition 52

3.3.3 Working of Shamir Secret Share 53

3.3.4 Properties of Shamir Secret Sharing 55

3.4 Data Encryption Process 56

3.4.1 Need for Data Encryption 56

3.4.2 Advanced Encryption Standard (AES) Algorithm 56

3.4.2.1 Working of AES Algorithm 57

3.5 Results and Discussions 59

3.6 Overview and Conclusion 63

References 64

4 An Exploration of Networking and Communication Methodologies for Security and Privacy Preservation in Edge Computing Platforms 69
Arulkumaran G, Balamurugan P and Santhosh J

Introduction 70

4.1 State-of-the-Art Edge Security and Privacy Preservation Protocols 71

4.1.1 Proxy Re-Encryption (PRE) 72

4.1.2 Attribute-Based Encryption (ABE) 73

4.1.3 Homomorphic Encryption (HE) 73

4.2 Authentication and Trust Management in Edge Computing Paradigms 76

4.2.1 Trust Management in Edge Computing Platforms 77

4.2.2 Authentication in Edge Computing Frameworks 78

4.3 Key Management in Edge Computing Platforms 79

4.3.1 Broadcast Encryption (BE) 80

4.3.2 Group Key Agreement (GKA) 80

4.3.3 Dynamic Key Management Scheme (DKM) 80

4.3.4 Secure User Authentication Key Exchange 81

4.4 Secure Edge Computing in IoT Platforms 81

4.5 Secure Edge Computing Architectures Using Block Chain Technologies 84

4.5.1 Harnessing Blockchain Assisted IoT in Edge Network Security 86

4.6 Machine Learning Perspectives on Edge Security 87

4.7 Privacy Preservation in Edge Computing 88

4.8 Advances of On-Device Intelligence for Secured Data Transmission 91

4.9 Security and Privacy Preservation for Edge Intelligence in Beyond 5G Networks 92

4.10 Providing Cyber Security Using Network and Communication Protocols for Edge Computing Devices 95

4.11 Conclusion 96

References 96

5 Nature Inspired Algorithm for Placing Sensors in Structural Health Monitoring System - Mouth Brooding Fish Approach 99
P. Selvaprasanth, Dr. J. Rajeshkumar, Dr. R. Malathy, Dr. D. Karunkuzhali and M. Nandhini

5.1 Introduction 100

5.2 Structural Health Monitoring 101

5.3 Machine Learning 102

5.3.1 Methods of Optimal Sensor Placement 102

5.4 Approaches of ML in SHM 103

5.5 Mouth Brooding Fish Algorithm 116

5.5.1 Application of MBF System 118

5.6 Case Studies On OSP Using Mouth Brooding Fish Algorithms 120

5.7 Conclusions 126

References 128

6 Heat Source/Sink Effects on Convective Flow of a Newtonian Fluid Past an Inclined Vertical Plate in Conducting Field 131
Raghunath Kodi and Obulesu Mopuri

6.1 Introduction 131

6.2 Mathematic Formulation and Physical Design 133

6.3 Discusion of Findings 138

6.3.1 Velocity Profiles 138

6.3.2 Temperature Profile 139

6.3.3 Concentration Profiles 144

6.4 Conclusion 144

References 147

7 Application of Fuzzy Differential Equations in Digital Images Via Fixed Point Techniques 151
D. N. Chalishajar and R. Ramesh

7.1 Introduction 151

7.2 Preliminaries 153

7.3 Applications of Fixed-Point Techniques 154

7.4 An Application 159

7.5 Conclusion 160

References 160

8 The Convergence of Novel Deep Learning Approaches in Cybersecurity and Digital Forensics 163
Ramesh S, Prathibanandhi K, Hemalatha P, Yaashuwanth C and Adam Raja Basha A

8.1 Introduction 164

8.2 Digital Forensics 166

8.2.1 Cybernetics Schemes for Digital Forensics 167

8.2.2 Deep Learning and Cybernetics Schemes for Digital Forensics 169

8.3 Biometric Analysis of Crime Scene Traces of Forensic Investigation 170

8.3.1 Biometric in Crime Scene Analysis 170

8.3.1.1 Parameters of Biometric Analysis 172

8.3.2 Data Acquisition in Biometric Identity 172

8.3.3 Deep Learning in Biometric Recognition 173

8.4 Forensic Data Analytics (FDA) for Risk Management 174

8.5 Forensic Data Subsets and Open-Source Intelligence for Cybersecurity 177

8.5.1 Intelligence Analysis 177

8.5.2 Open-Source Intelligence 178

8.6 Recent Detection and Prevention Mechanisms for Ensuring Privacy and Security in Forensic Investigation 179

8.6.1 Threat Investigation 179

8.6.2 Prevention Mechanisms 180

8.7 Adversarial Deep Learning in Cybersecurity and Privacy 181

8.8 Efficient Control of System-Environment Interactions Against Cyber Threats 184

8.9 Incident Response Applications of Digital Forensics 185

8.10 Deep Learning for Modeling Secure Interactions Between Systems 186

8.11 Recent Advancements in Internet of Things Forensics 187

8.11.1 IoT Advancements in Forensics 188

8.11.2 Conclusion 189

References 189

9 Mathematical Models for Computer Vision in Cardiovascular Image Segmentation 191
S. Usharani, K. Dhanalakshmi, P. Manju Bala, M. Pavithra and R. Rajmohan

9.1 Introduction 192

9.1.1 Computer Vision 192

9.1.2 Present State of Computer Vision Technology 193

9.1.3 The Future of Computer Vision 193

9.1.4 Deep Learning 194

9.1.5 Image Segmentation 194

9.1.6 Cardiovascular Diseases 195

9.2 Cardiac Image Segmentation Using Deep Learning 196

9.2.1 MR Image Segmentation 196

9.2.1.1 Atrium Segmentation 196

9.2.1.2 Atrial Segmentation 200

9.2.1.3 Cicatrix Segmentation 201

9.2.1.4 Aorta Segmentation 201

9.2.2 CT Image Segmentation for Cardiac Disease 201

9.2.2.1 Segmentation of Cardiac Substructure 202

9.2.2.2 Angiography 203

9.2.2.3 CA Plaque and Calcium Segmentation 204

9.2.3 Ultrasound Cardiac Image Segmentation 205

9.2.3.1 2-Dimensional Left Ventricle Segmentation 205

9.2.3.2 3-Dimensional Left Ventricle Segmentation 206

9.2.3.3 Segmentation of Left Atrium 207

9.2.3.4 Multi-Chamber Segmentation 207

9.2.3.5 Aortic Valve Segmentation 207

9.3 Proposed Method 208

9.4 Algorithm Behaviors and Characteristics 209

9.5 Computed Tomography Cardiovascular Data 212

9.5.1 Graph Cuts to Segment Specific Heart Chambers 212

9.5.2 Ringed Graph Cuts with Multi-Resolution 213

9.5.3 Simultaneous Chamber Segmentation using Arbitrary Rover 214

9.5.3.1 The Arbitrary Rover Algorithm 215

9.5.4 Static Strength Algorithm 217

9.6 Performance Evaluation 219

9.6.1 Ringed Graph Cuts with Multi-Resolution 219

9.6.2 The Arbitrary Rover Algorithm 220

9.6.3 Static Strength Algorithm 220

9.6.4 Comparison of Three Algorithm 221

9.7 Conclusion 221

References 221

10 Modeling of Diabetic Retinopathy Grading Using Deep Learning 225
Balaji Srinivasan, Prithiviraj Rajalingam and Anish Jeshvina Arokiachamy

10.1 Introduction 225

10.2 Related Works 228

10.3 Methodology 231

10.4 Dataset 236

10.5 Results and Discussion 236

10.6 Conclusion 243

References 243

11 Novel Deep-Learning Approaches for Future Computing Applications and Services 247
M. Jayalakshmi, K. Maharajan, K. Jayakumar and G. Visalaxi

11.1 Introduction 248

11.2 Architecture 250

11.2.1 Convolutional Neural Network (CNN) 252

11.2.2 Restricted Boltzmann Machines and Deep Belief Network 252

11.3 Multiple Applications of Deep Learning 254

11.4 Challenges 264

11.5 Conclusion and Future Aspects 265

References 266

12 Effects of Radiation Absorption and Aligned Magnetic Field on MHD Cassion Fluid Past an Inclined Vertical Porous Plate in Porous Media 273
Raghunath Kodi, Ramachandra Reddy Vaddemani and Obulesu Mopuri

12.1 Introduction 274

12.2 Physical Configuration and Mathematical Formulation 275

12.2.1 Skin Friction 279

12.2.2 Nusselt Number 280

12.2.3 Sherwood Number 280

12.3 Discussion of Result 280

12.3.1 Velocity Profiles 280

12.3.2 Temperature Profiles 284

12.3.3 Concentration Profiles 284

12.4 Conclusion 289

References 290

13 Integrated Mathematical Modelling and Analysis of Paddy Crop Pest Detection Framework Using Convolutional Classifiers 293
R. Rajmohan, M. Pavithra, P. Praveen Kumar, S. Usharani, P. Manjubala and N. Padmapriya

13.1 Introduction 294

13.2 Literature Survey 295

13.3 Proposed System Model 295

13.3.1 Disease Prediction 296

13.3.2 Insect Identification Algorithm 297

13.4 Paddy Pest Database Model 308

13.5 Implementation and Results 309

13.6 Conclusion 312

References 313

14 A Novel Machine Learning Approach in Edge Analytics with Mathematical Modeling for IoT Test Optimization 317
D. Jeya Mala and A. Pradeep Reynold

14.1 Introduction: Background and Driving Forces 318

14.2 Objectives 319

14.3 Mathematical Model for IoT Test Optimization 319

14.4 Introduction to Internet of Things (IoT) 320

14.5 IoT Analytics 321

14.5.1 Edge Analytics 322

14.6 Survey on IoT Testing 324

14.7 Optimization of End-User Application Testing in IoT 327

14.8 Machine Learning in Edge Analytics for IoT Testing 327

14.9 Proposed IoT Operations Framework Using Machine Learning on the Edge 328

14.9.1 Case Study 1 - Home Automation System Using IoT 329

14.9.2 Case Study 2 – A Real-Time Implementation of Edge Analytics in IBM Watson Studio 335

14.9.3 Optimized Test Suite Using ML-Based Approach 338

14.10 Expected Advantages and Challenges in Applying Machine Learning Techniques in End-User Application Testing on the Edge 339

14.11 Conclusion 342

References 343

Index 345

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