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

✍ Scribed by T. Ananth Kumar (editor), E. Golden Julie (editor), Y. Harold Robinson (editor), S. M. Jaisakthi (editor)

• Publisher: Wiley-Scrivener
• Year: 2021
• Tongue: English
• Leaves: 372
• Edition: 1
• Category: Library

✦ Synopsis

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. 

✦ Table of Contents

Title page
Copyright
Preface
Acknowledgments
1 Certain Investigations on Different Mathematical Models in Machine Learning and Artificial Intelligence
1.1 Introduction
1.2 Mathematical Models of Classification Algorithm of Machine Learning
1.3 Mathematical Models and Covid-19
1.4 Conclusion
References
2 Edge Computing Optimization Using Mathematical Modeling, Deep Learning Models, and Evolutionary Algorithms
2.1 Introduction to Edge Computing and Research Challenges
2.2 Introduction for Computational Offloading in Edge Computing
2.3 Mathematical Model for Offloading
2.4 QoS and Optimization in Edge Computing
2.5 Deep Learning Mathematical Models for Edge Computing
2.6 Evolutionary Algorithm and Edge Computing
2.7 Conclusion
References
3 Mathematical Modelling of Cryptographic Approaches in Cloud Computing Scenario
3.1 Introduction to IoT
3.2 Data Computation Process
3.3 Data Partition Process
3.4 Data Encryption Process
3.5 Results and Discussions
3.6 Overview and Conclusion
References
4 An Exploration of Networking and Communication Methodologies for Security and Privacy Preservation in Edge Computing Platforms
Introduction
4.1 State-of-the-Art Edge Security and Privacy Preservation Protocols
4.2 Authentication and Trust Management in Edge Computing Paradigms
4.3 Key Management in Edge Computing Platforms
4.4 Secure Edge Computing in IoT Platforms
4.5 Secure Edge Computing Architectures Using Block Chain Technologies
4.6 Machine Learning Perspectives on Edge Security
4.7 Privacy Preservation in Edge Computing
4.8 Advances of On-Device Intelligence for Secured Data Transmission
4.9 Security and Privacy Preservation for Edge Intelligence in Beyond 5G Networks
4.10 Providing Cyber Security Using Network and Communication Protocols for Edge Computing Devices
4.11 Conclusion
References
5 Nature Inspired Algorithm for Placing Sensors in Structural Health Monitoring System - Mouth Brooding Fish Approach
5.1 Introduction
5.2 Structural Health Monitoring
5.3 Machine Learning
5.4 Approaches of ML in SHM
5.5 Mouth Brooding Fish Algorithm
5.6 Case Studies On OSP Using Mouth Brooding Fish Algorithms
5.7 Conclusions
References
6 Heat Source/Sink Effects on Convective Flow of a Newtonian Fluid Past an Inclined Vertical Plate in Conducting Field
6.1 Introduction
6.2 Mathematic Formulation and Physical Design
6.3 Discusion of Findings
6.4 Conclusion
References
7 Application of Fuzzy Differential Equations in Digital Images Via Fixed Point Techniques
7.1 Introduction
7.2 Preliminaries
7.3 Applications of Fixed-Point Techniques
7.4 An Application
7.5 Conclusion
References
8 The Convergence of Novel Deep Learning Approaches in Cybersecurity and Digital Forensics
8.1 Introduction
8.2 Digital Forensics
8.3 Biometric Analysis of Crime Scene Traces of Forensic Investigation
8.4 Forensic Data Analytics (FDA) for Risk Management
8.5 Forensic Data Subsets and Open-Source Intelligence for Cybersecurity
8.6 Recent Detection and Prevention Mechanisms for Ensuring Privacy and Security in Forensic Investigation
8.7 Adversarial Deep Learning in Cybersecurity and Privacy
8.8 Efficient Control of System-Environment Interactions Against Cyber Threats
8.9 Incident Response Applications of Digital Forensics
8.10 Deep Learning for Modeling Secure Interactions Between Systems
8.11 Recent Advancements in Internet of Things Forensics
References
9 Mathematical Models for Computer Vision in Cardiovascular Image Segmentation
9.1 Introduction
9.2 Cardiac Image Segmentation Using Deep Learning
9.3 Proposed Method
9.4 Algorithm Behaviors and Characteristics
9.5 Computed Tomography Cardiovascular Data
9.6 Performance Evaluation
9.7 Conclusion
References
10 Modeling of Diabetic Retinopathy Grading Using Deep Learning
10.1 Introduction
10.2 Related Works
10.3 Methodology
10.4 Dataset
10.5 Results and Discussion
10.6 Conclusion
References
11 Novel Deep-Learning Approaches for Future Computing Applications and Services
11.1 Introduction
11.2 Architecture
11.3 Multiple Applications of Deep Learning
11.4 Challenges
11.5 Conclusion and Future Aspects
References
12 Effects of Radiation Absorption and Aligned Magnetic Field on MHD Cassion Fluid Past an Inclined Vertical Porous Plate in Porous Media
12.1 Introduction
12.2 Physical Configuration and Mathematical Formulation
12.3 Discussion of Result
12.4 Conclusion
References
13 Integrated Mathematical Modelling and Analysis of Paddy Crop Pest Detection Framework Using Convolutional Classifiers
13.1 Introduction
13.2 Literature Survey
13.3 Proposed System Model
13.4 Paddy Pest Database Model
13.5 Implementation and Results
13.6 Conclusion
References
14 A Novel Machine Learning Approach in Edge Analytics with Mathematical Modeling for IoT Test Optimization
14.1 Introduction: Background and Driving Forces
14.2 Objectives
14.3 Mathematical Model for IoT Test Optimization
14.4 Introduction to Internet of Things (IoT)
14.5 IoT Analytics
14.6 Survey on IoT Testing
14.7 Optimization of End-User Application Testing in IoT
14.8 Machine Learning in Edge Analytics for IoT Testing
14.9 Proposed IoT Operations Framework Using Machine Learning on the Edge
14.10 Expected Advantages and Challenges in Applying Machine Learning Techniques in End-User Application Testing on the Edge
14.11 Conclusion
References
Index
End User License Agreement