Simulation and Analysis of Mathematical Methods in Real-Time Engineering Applications
Herausgeber: Ananthkumar, T.; Jaisakthi, S M; Robinson, Y Harold; Julie, E Golden
Simulation and Analysis of Mathematical Methods in Real-Time Engineering Applications
Herausgeber: Ananthkumar, T.; Jaisakthi, S M; Robinson, Y Harold; Julie, E Golden
- Gebundenes Buch
- Merkliste
- Auf die Merkliste
- Bewerten Bewerten
- Teilen
- Produkt teilen
- Produkterinnerung
- Produkterinnerung
SIMULATIONS AND ANALYSIS of Mathematical Methods 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 applications of computer science using mathematics. 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…mehr
- Masud MansuripurMathematical Methods in Science and Engineering346,99 €
- Ferdinand CapMathematical Methods in Physics and Engineering with Mathematica235,99 €
- Masud MansuripurMathematical Methods in Science and Engineering128,99 €
- Advances in Applied Mathematical Analysis and Applications135,99 €
- D. GoelevenVariational and Hemivariational Inequalities Theory, Methods and Applications107,99 €
- Fuzzy Logic Applications in Computer Science and Mathematics201,99 €
- Cuthbert DanielApplications of Statistics to Industrial Experimentation340,99 €
-
-
-
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
- Produktdetails
- Verlag: Wiley
- Seitenzahl: 368
- Erscheinungstermin: 8. September 2021
- Englisch
- Abmessung: 237mm x 161mm x 24mm
- Gewicht: 649g
- ISBN-13: 9781119785378
- ISBN-10: 1119785375
- Artikelnr.: 60895949
- Verlag: Wiley
- Seitenzahl: 368
- Erscheinungstermin: 8. September 2021
- Englisch
- Abmessung: 237mm x 161mm x 24mm
- Gewicht: 649g
- ISBN-13: 9781119785378
- ISBN-10: 1119785375
- Artikelnr.: 60895949
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
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