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

T. Ananth Kumar, PhD, is an assistant professor at the IFET College of Engineering, Anna University, Chennai. He received his PhD 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 doctorate 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 has more than 15 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 xvAcknowledgments xix1 Certain Investigations on Different Mathematical Models in Machine Learning and Artificial Intelligence 1Ms. Akshatha Y and Dr. S Pravinth Raja1.1 Introduction 21.1.1 Knowledge-Based Expert Systems 21.1.2 Problem-Solving Techniques 31.2 Mathematical Models of Classification Algorithm of Machine Learning 41.2.1 Tried and True Tools 51.2.2 Joining Together Old and New 61.2.3 Markov Chain Model 71.2.4 Method for Automated Simulation of Dynamical Systems 71.2.5 kNN is a Case-Based Learning Method 91.2.6 Comparison for KNN and SVM 101.3 Mathematical Models and Covid-19 121.3.1 SEIR Model (Susceptible-Exposed-Infectious-Removed) 131.3.2 SIR Model (Susceptible-Infected-Recovered) 141.4 Conclusion 15References 152 Edge Computing Optimization Using Mathematical Modeling, Deep Learning Models, and Evolutionary Algorithms 17P. Vijayakumar, Prithiviraj Rajalingam and S. V. K. R. Rajeswari2.1 Introduction to Edge Computing and Research Challenges 182.1.1 Cloud-Based IoT and Need of Edge Computing 182.1.2 Edge Architecture 192.1.3 Edge Computing Motivation, Challenges and Opportunities 212.2 Introduction for Computational Offloading in Edge Computing 242.2.1 Need of Computational Offloading and Its Benefit 252.2.2 Computation Offloading Mechanisms 272.2.2.1 Offloading Techniques 292.3 Mathematical Model for Offloading 302.3.1 Introduction to Markov Chain Process and Offloading 312.3.1.1 Markov Chain Based Schemes 322.3.1.2 Schemes Based on Semi-Markov Chain 322.3.1.3 Schemes Based on the Markov Decision Process 332.3.1.4 Schemes Based on Hidden Markov Model 332.3.2 Computation Offloading Schemes Based on Game Theory 332.4 QoS and Optimization in Edge Computing 342.4.1 Statistical Delay Bounded QoS 352.4.2 Holistic Task Offloading Algorithm Considerations 352.5 Deep Learning Mathematical Models for Edge Computing 362.5.1 Applications of Deep Learning at the Edge 362.5.2 Resource Allocation Using Deep Learning 372.5.3 Computation Offloading Using Deep Learning 392.6 Evolutionary Algorithm and Edge Computing 392.7 Conclusion 41References 413 Mathematical Modelling of Cryptographic Approaches in Cloud Computing Scenario 45M. Julie Therese, A. Devi, P. Dharanyadevi and Dr. G. Kavya3.1 Introduction to IoT 463.1.1 Introduction to Cloud 463.1.2 General Characteristics of Cloud 473.1.3 Integration of IoT and Cloud 473.1.4 Security Characteristics of Cloud 473.2 Data Computation Process 493.2.1 Star Cubing Method for Data Computation 493.2.1.1 Star Cubing Algorithm 493.3 Data Partition Process 513.3.1 Need for Data Partition 523.3.2 Shamir Secret (SS) Share Algorithm for Data Partition 523.3.3 Working of Shamir Secret Share 533.3.4 Properties of Shamir Secret Sharing 553.4 Data Encryption Process 563.4.1 Need for Data Encryption 563.4.2 Advanced Encryption Standard (AES) Algorithm 563.4.2.1 Working of AES Algorithm 573.5 Results and Discussions 593.6 Overview and Conclusion 63References 644 An Exploration of Networking and Communication Methodologies for Security and Privacy Preservation in Edge Computing Platforms 69Arulkumaran G, Balamurugan P and Santhosh JIntroduction 704.1 State-of-the-Art Edge Security and Privacy Preservation Protocols 714.1.1 Proxy Re-Encryption (PRE) 724.1.2 Attribute-Based Encryption (ABE) 734.1.3 Homomorphic Encryption (HE) 734.2 Authentication and Trust Management in Edge Computing Paradigms 764.2.1 Trust Management in Edge Computing Platforms 774.2.2 Authentication in Edge Computing Frameworks 784.3 Key Management in Edge Computing Platforms 794.3.1 Broadcast Encryption (BE) 804.3.2 Group Key Agreement (GKA) 804.3.3 Dynamic Key Management Scheme (DKM) 804.3.4 Secure User Authentication Key Exchange 814.4 Secure Edge Computing in IoT Platforms 814.5 Secure Edge Computing Architectures Using Block Chain Technologies 844.5.1 Harnessing Blockchain Assisted IoT in Edge Network Security 864.6 Machine Learning Perspectives on Edge Security 874.7 Privacy Preservation in Edge Computing 884.8 Advances of On-Device Intelligence for Secured Data Transmission 914.9 Security and Privacy Preservation for Edge Intelligence in Beyond 5G Networks 924.10 Providing Cyber Security Using Network and Communication Protocols for Edge Computing Devices 954.11 Conclusion 96References 965 Nature Inspired Algorithm for Placing Sensors in Structural Health Monitoring System - Mouth Brooding Fish Approach 99P. Selvaprasanth, Dr. J. Rajeshkumar, Dr. R. Malathy, Dr. D. Karunkuzhali and M. Nandhini5.1 Introduction 1005.2 Structural Health Monitoring 1015.3 Machine Learning 1025.3.1 Methods of Optimal Sensor Placement 1025.4 Approaches of ML in SHM 1035.5 Mouth Brooding Fish Algorithm 1165.5.1 Application of MBF System 1185.6 Case Studies On OSP Using Mouth Brooding Fish Algorithms 1205.7 Conclusions 126References 1286 Heat Source/Sink Effects on Convective Flow of a Newtonian Fluid Past an Inclined Vertical Plate in Conducting Field 131Raghunath Kodi and Obulesu Mopuri6.1 Introduction 1316.2 Mathematic Formulation and Physical Design 1336.3 Discusion of Findings 1386.3.1 Velocity Profiles 1386.3.2 Temperature Profile 1396.3.3 Concentration Profiles 1446.4 Conclusion 144References 1477 Application of Fuzzy Differential Equations in Digital Images Via Fixed Point Techniques 151D. N. Chalishajar and R. Ramesh7.1 Introduction 1517.2 Preliminaries 1537.3 Applications of Fixed-Point Techniques 1547.4 An Application 1597.5 Conclusion 160References 1608 The Convergence of Novel Deep Learning Approaches in Cybersecurity and Digital Forensics 163Ramesh S, Prathibanandhi K, Hemalatha P, Yaashuwanth C and Adam Raja Basha A8.1 Introduction 1648.2 Digital Forensics 1668.2.1 Cybernetics Schemes for Digital Forensics 1678.2.2 Deep Learning and Cybernetics Schemes for Digital Forensics 1698.3 Biometric Analysis of Crime Scene Traces of Forensic Investigation 1708.3.1 Biometric in Crime Scene Analysis 1708.3.1.1 Parameters of Biometric Analysis 1728.3.2 Data Acquisition in Biometric Identity 1728.3.3 Deep Learning in Biometric Recognition 1738.4 Forensic Data Analytics (FDA) for Risk Management 1748.5 Forensic Data Subsets and Open-Source Intelligence for Cybersecurity 1778.5.1 Intelligence Analysis 1778.5.2 Open-Source Intelligence 1788.6 Recent Detection and Prevention Mechanisms for Ensuring Privacy and Security in Forensic Investigation 1798.6.1 Threat Investigation 1798.6.2 Prevention Mechanisms 1808.7 Adversarial Deep Learning in Cybersecurity and Privacy 1818.8 Efficient Control of System-Environment Interactions Against Cyber Threats 1848.9 Incident Response Applications of Digital Forensics 1858.10 Deep Learning for Modeling Secure Interactions Between Systems 1868.11 Recent Advancements in Internet of Things Forensics 1878.11.1 IoT Advancements in Forensics 1888.11.2 Conclusion 189References 1899 Mathematical Models for Computer Vision in Cardiovascular Image Segmentation 191S. Usharani, K. Dhanalakshmi, P. Manju Bala, M. Pavithra and R. Rajmohan9.1 Introduction 1929.1.1 Computer Vision 1929.1.2 Present State of Computer Vision Technology 1939.1.3 The Future of Computer Vision 1939.1.4 Deep Learning 1949.1.5 Image Segmentation 1949.1.6 Cardiovascular Diseases 1959.2 Cardiac Image Segmentation Using Deep Learning 1969.2.1 MR Image Segmentation 1969.2.1.1 Atrium Segmentation 1969.2.1.2 Atrial Segmentation 2009.2.1.3 Cicatrix Segmentation 2019.2.1.4 Aorta Segmentation 2019.2.2 CT Image Segmentation for Cardiac Disease 2019.2.2.1 Segmentation of Cardiac Substructure 2029.2.2.2 Angiography 2039.2.2.3 CA Plaque and Calcium Segmentation 2049.2.3 Ultrasound Cardiac Image Segmentation 2059.2.3.1 2-Dimensional Left Ventricle Segmentation 2059.2.3.2 3-Dimensional Left Ventricle Segmentation 2069.2.3.3 Segmentation of Left Atrium 2079.2.3.4 Multi-Chamber Segmentation 2079.2.3.5 Aortic Valve Segmentation 2079.3 Proposed Method 2089.4 Algorithm Behaviors and Characteristics 2099.5 Computed Tomography Cardiovascular Data 2129.5.1 Graph Cuts to Segment Specific Heart Chambers 2129.5.2 Ringed Graph Cuts with Multi-Resolution 2139.5.3 Simultaneous Chamber Segmentation using Arbitrary Rover 2149.5.3.1 The Arbitrary Rover Algorithm 2159.5.4 Static Strength Algorithm 2179.6 Performance Evaluation 2199.6.1 Ringed Graph Cuts with Multi-Resolution 2199.6.2 The Arbitrary Rover Algorithm 2209.6.3 Static Strength Algorithm 2209.6.4 Comparison of Three Algorithm 2219.7 Conclusion 221References 22110 Modeling of Diabetic Retinopathy Grading Using Deep Learning 225Balaji Srinivasan, Prithiviraj Rajalingam and Anish Jeshvina Arokiachamy10.1 Introduction 22510.2 Related Works 22810.3 Methodology 23110.4 Dataset 23610.5 Results and Discussion 23610.6 Conclusion 243References 24311 Novel Deep-Learning Approaches for Future Computing Applications and Services 247M. Jayalakshmi, K. Maharajan, K. Jayakumar and G. Visalaxi11.1 Introduction 24811.2 Architecture 25011.2.1 Convolutional Neural Network (CNN) 25211.2.2 Restricted Boltzmann Machines and Deep Belief Network 25211.3 Multiple Applications of Deep Learning 25411.4 Challenges 26411.5 Conclusion and Future Aspects 265References 26612 Effects of Radiation Absorption and Aligned Magnetic Field on MHD Cassion Fluid Past an Inclined Vertical Porous Plate in Porous Media 273Raghunath Kodi, Ramachandra Reddy Vaddemani and Obulesu Mopuri12.1 Introduction 27412.2 Physical Configuration and Mathematical Formulation 27512.2.1 Skin Friction 27912.2.2 Nusselt Number 28012.2.3 Sherwood Number 28012.3 Discussion of Result 28012.3.1 Velocity Profiles 28012.3.2 Temperature Profiles 28412.3.3 Concentration Profiles 28412.4 Conclusion 289References 29013 Integrated Mathematical Modelling and Analysis of Paddy Crop Pest Detection Framework Using Convolutional Classifiers 293R. Rajmohan, M. Pavithra, P. Praveen Kumar, S. Usharani, P. Manjubala and N. Padmapriya13.1 Introduction 29413.2 Literature Survey 29513.3 Proposed System Model 29513.3.1 Disease Prediction 29613.3.2 Insect Identification Algorithm 29713.4 Paddy Pest Database Model 30813.5 Implementation and Results 30913.6 Conclusion 312References 31314 A Novel Machine Learning Approach in Edge Analytics with Mathematical Modeling for IoT Test Optimization 317D. Jeya Mala and A. Pradeep Reynold14.1 Introduction: Background and Driving Forces 31814.2 Objectives 31914.3 Mathematical Model for IoT Test Optimization 31914.4 Introduction to Internet of Things (IoT) 32014.5 IoT Analytics 32114.5.1 Edge Analytics 32214.6 Survey on IoT Testing 32414.7 Optimization of End-User Application Testing in IoT 32714.8 Machine Learning in Edge Analytics for IoT Testing 32714.9 Proposed IoT Operations Framework Using Machine Learning on the Edge 32814.9.1 Case Study 1 - Home Automation System Using IoT 32914.9.2 Case Study 2 – A Real-Time Implementation of Edge Analytics in IBM Watson Studio 33514.9.3 Optimized Test Suite Using ML-Based Approach 33814.10 Expected Advantages and Challenges in Applying Machine Learning Techniques in End-User Application Testing on the Edge 33914.11 Conclusion 342References 343Index 345