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Submit a ProposalSimulation and Analysis of Mathematical Methods in Real Time Engineering Applications
 | Edited by T. Ananth Kumar, E. Golden Julie, Y. Harold Robinson, and S. M. Jaisakthi Series: Modern Mathematics in Computer Science Copyright: 2021 | Status: Published ISBN: 9781119785378 | Hardcover | 368 pages Price: $225 USD  |
One Line Description
Written and edited by a group of international experts in the field, this exciting new volume covers the state of the art of real time application of computer science using mathematics.
Audience
Engineers, scientists, students, and researchers in various fields such as artificial intelligence, network security, IoT, data science, mathematical modelling, soft computing and crypto currencies
Engineers, scientists, students, and researchers in various fields such as artificial intelligence, network security, IoT, data science, mathematical modelling, soft computing and crypto currencies
Description
Written and edited by a group of renowned specialists in the field, this outstanding new volume addresses primary computational techniques for developing new technologies in soft computing. It also 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.
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Written and edited by a group of renowned specialists in the field, this outstanding new volume addresses primary computational techniques for developing new technologies in soft computing. It also 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.
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Supplementary Data
--Covers the mathematical models in machine learning and artificial intelligence
--Describes edge computing optimization using mathematical modeling, deep learning models, and evolutionary algorithms
--Explores nature-based algorithms
--Provides an in-depth analysis of the practical applications of mathematical models in real-time engineering
--Covers the mathematical models in machine learning and artificial intelligence
--Describes edge computing optimization using mathematical modeling, deep learning models, and evolutionary algorithms
--Explores nature-based algorithms
--Provides an in-depth analysis of the practical applications of mathematical models in real-time engineering
Author / Editor Details
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.
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.
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Table of Contents
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
Back to Top
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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BISAC SUBJECT HEADINGS
TEC000000 : TECHNOLOGY & ENGINEERING / General
COM077000 : COMPUTERS / Mathematical & Statistical Software
SCI003000 : SCIENCE / Applied Sciences
BIC CODES
TBJ: Maths for engineers
PDE: Maths for scientists
TJ: ELECTRONICS & COMMUNICATIONS ENGINEERING
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