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

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  • Ilmumisaeg: 16-Aug-2021
  • Kirjastus: Wiley-Scrivener
  • Keel: eng
  • ISBN-13: 9781119785514
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Simulation and Analysis of Mathematical Methods in Real-Time Engineering ApplicationsSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 16-Aug-2021
  • Kirjastus: Wiley-Scrivener
  • Keel: eng
  • ISBN-13: 9781119785514
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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 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.

Preface xv
Acknowledgments xix
1 Certain Investigations on Different Mathematical Models in Machine Learning and Artificial Intelligence
1(16)
Ms. Akshatha Y
Dr. S. Pravinth Raja
1.1 Introduction
2(2)
1.1.1 Knowledge-Based Expert Systems
2(1)
1.1.2 Problem-Solving Techniques
3(1)
1.2 Mathematical Models of Classification Algorithm of Machine Learning
4(8)
1.2.1 Tried and True Tools
5(1)
1.2.2 Joining Together Old and New
6(1)
1.2.3 Markov Chain Model
7(1)
1.2.4 Method for Automated Simulation of Dynamical Systems
7(2)
1.2.5 kNN is a Case-Based Learning Method
9(1)
1.2.6 Comparison for KNN and SVM
10(2)
1.3 Mathematical Models and Covid-19
12(3)
1.3.1 SEIR Model (Susceptible-Exposed-Infectious-Removed)
13(1)
1.3.2 SIR Model (Susceptible-Infected-Recovered)
14(1)
1.4 Conclusion
15(2)
References
15(2)
2 Edge Computing Optimization Using Mathematical Modeling, Deep Learning Models, and Evolutionary Algorithms
17(28)
P. Vijayakumar
Prithiviraj Rajalingam
S. V. K. R. Rajeswari
2.1 Introduction to Edge Computing and Research Challenges
18(6)
2.1.1 Cloud-Based IoT and Need of Edge Computing
18(1)
2.1.2 Edge Architecture
19(2)
2.1.3 Edge Computing Motivation, Challenges and Opportunities
21(3)
2.2 Introduction for Computational Offloading in Edge Computing
24(6)
2.2.1 Need of Computational Offloading and Its Benefit
25(2)
2.2.2 Computation Offloading Mechanisms
27(2)
2.2.2.1 Offloading Techniques
29(1)
2.3 Mathematical Model for Offloading
30(4)
2.3.1 Introduction to Markov Chain Process and Offloading
31(1)
2.3.1.1 Markov Chain Based Schemes
32(1)
2.3.1.2 Schemes Based on Semi-Markov Chain
32(1)
2.3.1.3 Schemes Based on the Markov Decision Process
33(1)
2.3.1.4 Schemes Based on Hidden Markov Model
33(1)
2.3.2 Computation Offloading Schemes Based on Game Theory
33(1)
2.4 QoS and Optimization in Edge Computing
34(2)
2.4.1 Statistical Delay Bounded QoS
35(1)
2.4.2 Holistic Task Offloading Algorithm Considerations
35(1)
2.5 Deep Learning Mathematical Models for Edge Computing
36(3)
2.5.1 Applications of Deep Learning at the Edge
36(1)
2.5.2 Resource Allocation Using Deep Learning
37(2)
2.5.3 Computation Offloading Using Deep Learning
39(1)
2.6 Evolutionary Algorithm and Edge Computing
39(2)
2.7 Conclusion
41(4)
References
41(4)
3 Mathematical Modelling of Cryptographic Approaches in Cloud Computing Scenario
45(24)
M. Julie Therese
A. Devi
P. Dharanyadevi
Dr. G. Kavya
3.1 Introduction to IoT
46(3)
3.1.1 Introduction to Cloud
46(1)
3.1.2 General Characteristics of Cloud
47(1)
3.1.3 Integration of IoT and Cloud
47(1)
3.1.4 Security Characteristics of Cloud
47(2)
3.2 Data Computation Process
49(2)
3.2.1 Star Cubing Method for Data Computation
49(1)
3.2.1.1 Star Cubing Algorithm
49(2)
3.3 Data Partition Process
51(5)
3.3.1 Need for Data Partition
52(1)
3.3.2 Shamir Secret (SS) Share Algorithm for Data Partition
52(1)
3.3.3 Working of Shamir Secret Share
53(2)
3.3.4 Properties of Shamir Secret Sharing
55(1)
3.4 Data Encryption Process
56(3)
3.4.1 Need for Data Encryption
56(1)
3.4.2 Advanced Encryption Standard (AES) Algorithm
56(1)
3.4.2.1 Working of AES Algorithm
57(2)
3.5 Results and Discussions
59(4)
3.6 Overview and Conclusion
63(6)
References
64(5)
4 An Exploration of Networking and Communication Methodologies for Security and Privacy Preservation in Edge Computing Platforms
69(30)
Arulkumaran G
Balamurugan P
Santhosh J
Introduction
70(1)
4.1 State-of-the-Art Edge Security and Privacy Preservation Protocols
71(5)
4.1.1 Proxy Re-Encryption (PRE)
72(1)
4.1.2 Attribute-Based Encryption (ABE)
73(1)
4.1.3 Homomorphic Encryption (HE)
73(3)
4.2 Authentication and Trust Management in Edge Computing Paradigms
76(3)
4.2.1 Trust Management in Edge Computing Platforms
77(1)
4.2.2 Authentication in Edge Computing Frameworks
78(1)
4.3 Key Management in Edge Computing Platforms
79(2)
4.3.1 Broadcast Encryption (BE)
80(1)
4.3.2 Group Key Agreement (GKA)
80(1)
4.3.3 Dynamic Key Management Scheme (DKM)
80(1)
4.3.4 Secure User Authentication Key Exchange
81(1)
4.4 Secure Edge Computing in IoT Platforms
81(3)
4.5 Secure Edge Computing Architectures Using Block Chain Technologies
84(3)
4.5.1 Harnessing Blockchain Assisted IoT in Edge Network Security
86(1)
4.6 Machine Learning Perspectives on Edge Security
87(1)
4.7 Privacy Preservation in Edge Computing
88(3)
4.8 Advances of On-Device Intelligence for Secured Data Transmission
91(1)
4.9 Security and Privacy Preservation for Edge Intelligence in Beyond 5G Networks
92(3)
4.10 Providing Cyber Security Using Network and Communication Protocols for Edge Computing Devices
95(1)
4.11 Conclusion
96(3)
References
96(3)
5 Nature Inspired Algorithm for Placing Sensors in Structural Health Monitoring System -- Mouth Brooding Fish Approach
99(32)
P. Selvaprasanth
Dr. J. Rajeshkumar
Dr. R. Malathy
Dr. D. Karunkuzhali
M. Nandhini
5.1 Introduction
100(1)
5.2 Structural Health Monitoring
101(1)
5.3 Machine Learning
102(1)
5.3.1 Methods of Optimal Sensor Placement
102(1)
5.4 Approaches of ML in SHM
103(13)
5.5 Mouth Brooding Fish Algorithm
116(4)
5.5.1 Application of MBF System
118(2)
5.6 Case Studies On OSP Using Mouth Brooding Fish Algorithms
120(6)
5.7 Conclusions
126(5)
References
128(3)
6 Heat Source/Sink Effects on Convective Flow of a Newtonian Fluid Past an Inclined Vertical Plate in Conducting Field
131(20)
Raghunath Kodi
Obulesu Mopuri
6.1 Introduction
131(2)
6.2 Mathematic Formulation and Physical Design
133(5)
6.3 Discusion of Findings
138(6)
6.3.1 Velocity Profiles
138(1)
6.3.2 Temperature Profile
139(5)
6.3.3 Concentration Profiles
144(1)
6.4 Conclusion
144(7)
References
147(4)
7 Application of Fuzzy Differential Equations in Digital Images Via Fixed Point Techniques
151(12)
D. N. Chalishajar
R. Ramesh
7.1 Introduction
151(2)
7.2 Preliminaries
153(1)
7.3 Applications of Fixed-Point Techniques
154(5)
7.4 An Application
159(1)
7.5 Conclusion
160(3)
References
160(3)
8 The Convergence of Novel Deep Learning Approaches in Cyber security and Digital Forensics
163(28)
Ramesh S
Prathibanandhi K
Hemalatha P
Yaashuwanth C
Adam Raja Basha A
8.1 Introduction
164(2)
8.2 Digital Forensics
166(4)
8.2.1 Cybernetics Schemes for Digital Forensics
167(2)
8.2.2 Deep Learning and Cybernetics Schemes for Digital Forensics
169(1)
8.3 Biometric Analysis of Crime Scene Traces of Forensic Investigation
170(4)
8.3.1 Biometric in Crime Scene Analysis
170(2)
8.3.1.1 Parameters of Biometric Analysis
172(1)
8.3.2 Data Acquisition in Biometric Identity
172(1)
8.3.3 Deep Learning in Biometric Recognition
173(1)
8.4 Forensic Data Analytics (FDA) for Risk Management
174(3)
8.5 Forensic Data Subsets and Open-Source Intelligence for Cybersecurity
177(2)
8.5.1 Intelligence Analysis
177(1)
8.5.2 Open-Source Intelligence
178(1)
8.6 Recent Detection and Prevention Mechanisms for Ensuring Privacy and Security in Forensic Investigation
179(2)
8.6.1 Threat Investigation
179(1)
8.6.2 Prevention Mechanisms
180(1)
8.7 Adversarial Deep Learning in Cybersecurity and Privacy
181(3)
8.8 Efficient Control of System-Environment Interactions Against Cyber Threats
184(1)
8.9 Incident Response Applications of Digital Forensics
185(1)
8.10 Deep Learning for Modeling Secure Interactions Between Systems
186(1)
8.11 Recent Advancements in Internet of Things Forensics
187(4)
8.11.1 IoT Advancements in Forensics
188(1)
8.11.2 Conclusion
189(1)
References
189(2)
9 Mathematical Models for Computer Vision in Cardiovascular Image Segmentation
191(34)
S. Usharani
K. Dhanalakshmi
P. Manju Bala
M. Pavithra
R. Rajmohan
9.1 Introduction
192(4)
9.1.1 Computer Vision
192(1)
9.1.2 Present State of Computer Vision Technology
193(1)
9.1.3 The Future of Computer Vision
193(1)
9.1.4 Deep Learning
194(1)
9.1.5 Image Segmentation
194(1)
9.1.6 Cardiovascular Diseases
195(1)
9.2 Cardiac Image Segmentation Using Deep Learning
196(12)
9.2.1 MR Image Segmentation
196(1)
9.2.1.1 Atrium Segmentation
196(4)
9.2.1.2 Atrial Segmentation
200(1)
9.2.1.3 Cicatrix Segmentation
201(1)
9.2.1.4 Aorta Segmentation
201(1)
9.2.2 CT Image Segmentation for Cardiac Disease
201(1)
9.2.2.1 Segmentation of Cardiac Substructure
202(1)
9.2.2.2 Angiography
203(1)
9.2.2.3 CA Plaque and Calcium Segmentation
204(1)
9.2.3 Ultrasound Cardiac Image Segmentation
205(1)
9.2.3.1 2-Dimensional Left Ventricle Segmentation
205(1)
9.2.3.2 3-Dimensional Left Ventricle Segmentation
206(1)
9.2.3.3 Segmentation of Left Atrium
207(1)
9.2.3.4 Multi-Chamber Segmentation
207(1)
9.2.3.5 Aortic Valve Segmentation
207(1)
9.3 Proposed Method
208(1)
9.4 Algorithm Behaviors and Characteristics
209(3)
9.5 Computed Tomography Cardiovascular Data
212(7)
9.5.1 Graph Cuts to Segment Specific Heart Chambers
212(1)
9.5.2 Ringed Graph Cuts with Multi-Resolution
213(1)
9.5.3 Simultaneous Chamber Segmentation using Arbitrary Rover
214(1)
9.5.3.1 The Arbitrary Rover Algorithm
215(2)
9.5.4 Static Strength Algorithm
217(2)
9.6 Performance Evaluation
219(2)
9.6.1 Ringed Graph Cuts with Multi-Resolution
219(1)
9.6.2 The Arbitrary Rover Algorithm
220(1)
9.6.3 Static Strength Algorithm
220(1)
9.6.4 Comparison of Three Algorithm
221(1)
9.7 Conclusion
221(4)
References
221(4)
10 Modeling of Diabetic Retinopathy Grading Using Deep Learning
225(22)
Balaji Srinivasan
Prithiviraj Rajalingam
Anish Jeshvina Arokiachamy
10.1 Introduction
225(3)
10.2 Related Works
228(3)
10.3 Methodology
231(5)
10.4 Dataset
236(1)
10.5 Results and Discussion
236(7)
10.6 Conclusion
243(4)
References
243(4)
11 Novel Deep-Learning Approaches for Future Computing Applications and Services
247(26)
M. Jayalakshmi
K. Maharajan
K. Jayakumar
G. Visalaxi
11.1 Introduction
248(2)
11.2 Architecture
250(4)
11.2.1 Convolutional Neural Network (CNN)
252(1)
11.2.2 Restricted Boltzmann Machines and Deep Belief Network
252(2)
11.3 Multiple Applications of Deep Learning
254(10)
11.4 Challenges
264(1)
11.5 Conclusion and Future Aspects
265(8)
References
266(7)
12 Effects of Radiation Absorption and Aligned Magnetic Field on MHD Cassion Fluid Past an Inclined Vertical Porous Plate in Porous Media
273(20)
Raghunath Kodi
Ramachandra Reddy Vaddemani
Obulesu Mopuri
12.1 Introduction
274(1)
12.2 Physical Configuration and Mathematical Formulation
275(5)
12.2.1 Skin Friction
279(1)
12.2.2 Nusselt Number
280(1)
12.2.3 Sherwood Number
280(1)
12.3 Discussion of Result
280(9)
12.3.1 Velocity Profiles
280(4)
12.3.2 Temperature Profiles
284(1)
12.3.3 Concentration Profiles
284(5)
12.4 Conclusion
289(4)
References
290(3)
13 Integrated Mathematical Modelling and Analysis of Paddy Crop Pest Detection Framework Using Convolutional Classifiers
293(24)
R. Rajmohan
M. Pavithra
P. Praveen Kumar
S. Usharani
P. Manjubala
N. Padmapriya
13.1 Introduction
294(1)
13.2 Literature Survey
295(1)
13.3 Proposed System Model
295(13)
13.3.1 Disease Prediction
296(1)
13.3.2 Insect Identification Algorithm
297(11)
13.4 Paddy Pest Database Model
308(1)
13.5 Implementation and Results
309(3)
13.6 Conclusion
312(5)
References
313(4)
14 A Novel Machine Learning Approach in Edge Analytics with Mathematical Modeling for IoT Test Optimization
317(28)
D. Jeya Mala
A. Pradeep Reynold
14.1 Introduction: Background and Driving Forces
318(1)
14.2 Objectives
319(1)
14.3 Mathematical Model for IoT Test Optimization
319(1)
14.4 Introduction to Internet of Things (IoT)
320(1)
14.5 IoT Analytics
321(3)
14.5.1 Edge Analytics
322(2)
14.6 Survey on IoT Testing
324(3)
14.7 Optimization of End-User Application Testing in IoT
327(1)
14.8 Machine Learning in Edge Analytics for IoT Testing
327(1)
14.9 Proposed IoT Operations Framework Using Machine Learning on the Edge
328(11)
14.9.1 Case Study 1 - Home Automation System Using IoT
329(6)
14.9.2 Case Study 2 -- A Real-Time Implementation of Edge Analytics in IBM Watson Studio
335(3)
14.9.3 Optimized Test Suite Using ML-Based Approach
338(1)
14.10 Expected Advantages and Challenges in Applying Machine Learning Techniques in End-User Application Testing on the Edge
339(3)
14.11 Conclusion
342(3)
References
343(2)
Index 345
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.
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