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

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