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