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Table of Contents
Intro
Preface
Foreword
References
Acknowledgments
Editor biographies
Mukhdeep Singh Manshahia
Igor S Litvinchev
Gerhard-Wilhelm Weber
J Joshua Thomas
Pandian Vasant
List of contributors
Chapter 1 Machine learning algorithms to improve crop evapotranspiration prediction covering a broad range of environmental gradients in agriculture 4.0: a review
1.1 Introduction
1.2 Relevant literature
1.3 Some standard methods to calculate evapotranspiration
1.4 Results and discussions on major findings
1.5 Conclusion and future work
References
Chapter 2 Iris-based biometric cryptosystem
2.1 Introduction
2.2 Eye segmentation framework
2.3 Eye segmentation methods
2.3.1 Center detection
2.3.2 Base radii detection
2.3.3 Pupil border refinement
2.4 Selecting the cryptokey embedding method
2.5 Determining the threshold probability
2.6 Description of methods
2.6.1 Decorrelation by pseudorandom shuffling
2.6.2 Bit-majority coding
2.6.3 Hadamard block coding
2.6.4 Reed-Solomon message coding
2.6.5 Additional error of code recovery
2.7 Selection of coding scheme parameters
2.8 Conclusion
References
Chapter 3 Bio-inspired approaches for a combined economic emission dispatch problem
3.1 Introduction
3.1.1 Literature review
3.1.2 Objective of the study
3.2 Problem formulation
3.2.1 Combined economic emission dispatch
3.2.2 Particle swarm optimization
3.2.3 Quantum particle swarm optimization
3.2.4 Qunatum inspired Bat algorithm
3.3 Results and discussions
3.3.1 Single objective emission dispatch problem
3.3.2 Quantum inspired Bat algorithm
3.3.3 Summary of QBA and PSO
3.3.4 Single objective economic load dispatch problem
3.3.5 Quantum inspired particle Swarm optimization
3.3.6 Summary.
3.3.7 Multi objective CEED problem
3.3.8 Quantum inspired particle Swarm optimization
3.3.9 Quantum inspired Bat algorithm
3.3.10 Summary
3.4 Conclusions and future research direction
Acknowledgement
Conflicts of interest
References
Chapter 4 Eigenvalue clustering for spectrum sensing: throughput and energy evaluation for cognitive radio-Internet of Things network
Symbols
4.1 Introduction
4.2 Background and motivation
4.3 Adopted CR-IoT scenario
4.3.1 System model
4.3.2 Conventional CSS techniques
4.4 Proposed method for CSS based on eigenvalue clustering
4.4.1 Maximum-second maximum-minimum eigenvalue clustering
4.5 Energy and throughput analysis
4.5.1 Energy analysis
4.5.2 Throughput analysis
4.5.3 Complexity analysis
4.6 Simulation results
4.6.1 Comparison of ROC performance
4.6.2 Comparison of throughput performance
4.6.3 Comparison of energy consumption performance
4.6.4 Comparison of expected lifetime performance
4.7 Discussion
4.7.1 Major findings of research
4.7.2 Limitations of research
4.8 Conclusion
References
Chapter 5 Modeling the evolution of complex networks arising in applications
5.1 Introduction
5.2 GRN networks
5.3 Hierarchy of systems
5.3.1 General
5.3.2 2D systems
5.4 3D systems
5.5 High-dimensional systems
5.5.1 4D system
5.5.2 Examples of 6D systems
5.6 Elements of reverse engineering
5.6.1 Location of a critical point
5.6.2 Creating a critical point of the desired type
5.7 Miscellaneous
5.8 Conclusions
References
Chapter 6 Computing the intelligent privacy-engineered organization: a metamodel of effective information transparency enhancing tools/technologies
6.1 Introduction
6.2 Transparency enhancing tools/technologies
6.2.1 Right to privacy and information transparency.
6.2.2 Data privacy governance frameworks overview
6.3 Modelling effective transparency enhancing tools/technologies
6.3.1 Aligning privacy frameworks in transparency
6.3.2 Transparency requirements mining
6.3.3 Transparency requirements metamodel
6.3.4 Transparency requirements classification
6.4 Leveraging privacy principles
6.5 Research findings and limitations within the scope of the goal-based requirements analysis method
6.5.1 Major research findings and contributions
6.5.2 Limitations of research
6.6 Conclusion
References
Chapter 7 A model of cells' regeneration towards smart healthcare
7.1 Introduction
7.2 Model
7.3 Result and discussion
7.4 Conclusion and highlight
7.5 Future scope and literature review
Acknowledgements
References
Chapter 8 Anomaly detection in location-based services
8.1 Introduction
8.2 Maps and navigation services
8.2.1 Navigation system
8.2.2 Mapping services
8.3 Location-based tracking services
8.3.1 Vehicle tracking services
8.3.2 Traffic tracking services
8.4 Anomaly detection in LBS
8.4.1 Route anomaly detection
8.4.2 User behavior anomaly detection
8.4.3 Fake check-in anomaly detection
8.5 Limitations
8.6 Conclusion and future enhancement
References
Chapter 9 Optimized packing soft ellipses
9.1 Introduction
9.2 The main problem
9.3 Geometric tools
9.3.1 Formulation of containment conditions
9.3.2 Formulation of non-overlapping constraints
9.4 Mathematical model
9.5 Solution strategy
9.5.1 Finding a feasible starting point
9.5.2 Compression algorithm
9.6 Computational results
9.7 Conclusions
Acknowledgements
Appendix A
References
Chapter 10 Analysis of phishing attacks
10.1 Introduction
10.2 Literature review
10.3 Methodology and used tools.
10.3.1 The text analytical SW Tovek
10.4 Statistical analysis of phishing emails
10.5 Classification of phishing emails
10.5.1 Segment business
10.5.2 Segment fund
10.5.3 Segment charity
10.5.4 Segment transfer
10.5.5 Segment other
10.6 Content analysis of phishing emails
10.6.1 Person entity
10.6.2 Phone number entity
10.6.3 City and country entity
10.6.4 Email and website entity
10.7 Research results, their limits, and further research orientation
10.8 Discussion and conclusion
References
Chapter 11 Human-assisted intelligent computing and ecological modeling (drought early warning system)
Abbreviaitons
11.l Introduction
11.1.1 Rangelands
11.1.2 Ecological modeling and early warning (theory)
11.1.3 Ecological modeling and early warning (applications)
11.2 Contribution of ecological modeling
11.2.1 Efficiency and algorithm theoretical to calibration of remote sensing data
11.2.2 Spatial and temporal analysis and polynomial regression
11.2.3 Spatial disaggregation and anomalies
11.3 Conclusion
Appendix A: Prospects
Appendix B: Ecological modeling
Appendix C: Questions for the governing bodies
Funding
Acknowledgments
References
Chapter 12 Attention mechanisms in machine vision: a survey of the state of the art
12.1 Introduction
12.1.1 Self-attention
12.1.2 Masked self-attention
12.1.3 Multi-head attention
12.2 Attention-based deep learning architectures
12.2.1 Single-channel model
12.2.2 Multi-channel model
12.2.3 Skip-layer model
12.2.4 Bottom-up or top-down model
12.2.5 Skip-layer model with multi-scale saliency network
12.3 Attention and deep learning in machine vision: broad categories
12.3.1 Attention-based CNNs
12.3.2 CNN transformer pipelines
12.3.3 Hybrid transformers.
12.4 Major research algorithms, trends, and limitations
12.5 Conclusion
Conflict of interest
Funding acknowledgement
References
Chapter 13 Sparse 2D packing in thermal deburring with shock waves acting effects
13.1 Introduction
13.2 Sparse packing
13.2.1 The main problem and mathematical model
13.2.2 Solution approach and computational results
13.3 Thermal problem formulation
13.4 The balanced layout of 2D objects with shock waves action
13.5 Conclusions and future research
Acknowledgments
References
Chapter 14 Implementation of smart manufacturing in small and medium-sized enterprises
14.1 Introduction
14.1.1 Need for smart manufacturing
14.1.2 Electronic hardware with machine and software interface
14.1.3 Management information system
14.2 Literature review
14.3 Levels of data for intelligent SME
14.3.1 Resources for SME
14.3.2 Inputs for SMEs
14.3.3 Outputs for SMEs
14.3.4 Flow diagram
14.3.5 Data collection
14.3.6 Applications for analysis and decision making
14.4 Proposed architectural framework for intelligent SMEs
14.5 Case study
14.6 Conclusions and future scope
References
Chapter 15 Performance analysis of fractal image compression methods for medical images: a review
15.1 Introduction
15.1.1 Self-similarity in fractals
15.2 Motivation of the survey
15.3 Relevant literature
15.4 Comparative survey results and discussion
15.5 Improvements on existing algorithms
15.6 Conclusion and future work
References
Chapter 16 Mobile edge computing for efficient energy management systems
16.1 Introduction
16.2 Paradigm of edge computing
16.3 Role of factors in energy consumption
16.4 Energy efficient systems
16.5 Research findings and limitations
16.6 Future research challenges
16.6.1 The healthcare domain.
16.6.2 Big data management.
Preface
Foreword
References
Acknowledgments
Editor biographies
Mukhdeep Singh Manshahia
Igor S Litvinchev
Gerhard-Wilhelm Weber
J Joshua Thomas
Pandian Vasant
List of contributors
Chapter 1 Machine learning algorithms to improve crop evapotranspiration prediction covering a broad range of environmental gradients in agriculture 4.0: a review
1.1 Introduction
1.2 Relevant literature
1.3 Some standard methods to calculate evapotranspiration
1.4 Results and discussions on major findings
1.5 Conclusion and future work
References
Chapter 2 Iris-based biometric cryptosystem
2.1 Introduction
2.2 Eye segmentation framework
2.3 Eye segmentation methods
2.3.1 Center detection
2.3.2 Base radii detection
2.3.3 Pupil border refinement
2.4 Selecting the cryptokey embedding method
2.5 Determining the threshold probability
2.6 Description of methods
2.6.1 Decorrelation by pseudorandom shuffling
2.6.2 Bit-majority coding
2.6.3 Hadamard block coding
2.6.4 Reed-Solomon message coding
2.6.5 Additional error of code recovery
2.7 Selection of coding scheme parameters
2.8 Conclusion
References
Chapter 3 Bio-inspired approaches for a combined economic emission dispatch problem
3.1 Introduction
3.1.1 Literature review
3.1.2 Objective of the study
3.2 Problem formulation
3.2.1 Combined economic emission dispatch
3.2.2 Particle swarm optimization
3.2.3 Quantum particle swarm optimization
3.2.4 Qunatum inspired Bat algorithm
3.3 Results and discussions
3.3.1 Single objective emission dispatch problem
3.3.2 Quantum inspired Bat algorithm
3.3.3 Summary of QBA and PSO
3.3.4 Single objective economic load dispatch problem
3.3.5 Quantum inspired particle Swarm optimization
3.3.6 Summary.
3.3.7 Multi objective CEED problem
3.3.8 Quantum inspired particle Swarm optimization
3.3.9 Quantum inspired Bat algorithm
3.3.10 Summary
3.4 Conclusions and future research direction
Acknowledgement
Conflicts of interest
References
Chapter 4 Eigenvalue clustering for spectrum sensing: throughput and energy evaluation for cognitive radio-Internet of Things network
Symbols
4.1 Introduction
4.2 Background and motivation
4.3 Adopted CR-IoT scenario
4.3.1 System model
4.3.2 Conventional CSS techniques
4.4 Proposed method for CSS based on eigenvalue clustering
4.4.1 Maximum-second maximum-minimum eigenvalue clustering
4.5 Energy and throughput analysis
4.5.1 Energy analysis
4.5.2 Throughput analysis
4.5.3 Complexity analysis
4.6 Simulation results
4.6.1 Comparison of ROC performance
4.6.2 Comparison of throughput performance
4.6.3 Comparison of energy consumption performance
4.6.4 Comparison of expected lifetime performance
4.7 Discussion
4.7.1 Major findings of research
4.7.2 Limitations of research
4.8 Conclusion
References
Chapter 5 Modeling the evolution of complex networks arising in applications
5.1 Introduction
5.2 GRN networks
5.3 Hierarchy of systems
5.3.1 General
5.3.2 2D systems
5.4 3D systems
5.5 High-dimensional systems
5.5.1 4D system
5.5.2 Examples of 6D systems
5.6 Elements of reverse engineering
5.6.1 Location of a critical point
5.6.2 Creating a critical point of the desired type
5.7 Miscellaneous
5.8 Conclusions
References
Chapter 6 Computing the intelligent privacy-engineered organization: a metamodel of effective information transparency enhancing tools/technologies
6.1 Introduction
6.2 Transparency enhancing tools/technologies
6.2.1 Right to privacy and information transparency.
6.2.2 Data privacy governance frameworks overview
6.3 Modelling effective transparency enhancing tools/technologies
6.3.1 Aligning privacy frameworks in transparency
6.3.2 Transparency requirements mining
6.3.3 Transparency requirements metamodel
6.3.4 Transparency requirements classification
6.4 Leveraging privacy principles
6.5 Research findings and limitations within the scope of the goal-based requirements analysis method
6.5.1 Major research findings and contributions
6.5.2 Limitations of research
6.6 Conclusion
References
Chapter 7 A model of cells' regeneration towards smart healthcare
7.1 Introduction
7.2 Model
7.3 Result and discussion
7.4 Conclusion and highlight
7.5 Future scope and literature review
Acknowledgements
References
Chapter 8 Anomaly detection in location-based services
8.1 Introduction
8.2 Maps and navigation services
8.2.1 Navigation system
8.2.2 Mapping services
8.3 Location-based tracking services
8.3.1 Vehicle tracking services
8.3.2 Traffic tracking services
8.4 Anomaly detection in LBS
8.4.1 Route anomaly detection
8.4.2 User behavior anomaly detection
8.4.3 Fake check-in anomaly detection
8.5 Limitations
8.6 Conclusion and future enhancement
References
Chapter 9 Optimized packing soft ellipses
9.1 Introduction
9.2 The main problem
9.3 Geometric tools
9.3.1 Formulation of containment conditions
9.3.2 Formulation of non-overlapping constraints
9.4 Mathematical model
9.5 Solution strategy
9.5.1 Finding a feasible starting point
9.5.2 Compression algorithm
9.6 Computational results
9.7 Conclusions
Acknowledgements
Appendix A
References
Chapter 10 Analysis of phishing attacks
10.1 Introduction
10.2 Literature review
10.3 Methodology and used tools.
10.3.1 The text analytical SW Tovek
10.4 Statistical analysis of phishing emails
10.5 Classification of phishing emails
10.5.1 Segment business
10.5.2 Segment fund
10.5.3 Segment charity
10.5.4 Segment transfer
10.5.5 Segment other
10.6 Content analysis of phishing emails
10.6.1 Person entity
10.6.2 Phone number entity
10.6.3 City and country entity
10.6.4 Email and website entity
10.7 Research results, their limits, and further research orientation
10.8 Discussion and conclusion
References
Chapter 11 Human-assisted intelligent computing and ecological modeling (drought early warning system)
Abbreviaitons
11.l Introduction
11.1.1 Rangelands
11.1.2 Ecological modeling and early warning (theory)
11.1.3 Ecological modeling and early warning (applications)
11.2 Contribution of ecological modeling
11.2.1 Efficiency and algorithm theoretical to calibration of remote sensing data
11.2.2 Spatial and temporal analysis and polynomial regression
11.2.3 Spatial disaggregation and anomalies
11.3 Conclusion
Appendix A: Prospects
Appendix B: Ecological modeling
Appendix C: Questions for the governing bodies
Funding
Acknowledgments
References
Chapter 12 Attention mechanisms in machine vision: a survey of the state of the art
12.1 Introduction
12.1.1 Self-attention
12.1.2 Masked self-attention
12.1.3 Multi-head attention
12.2 Attention-based deep learning architectures
12.2.1 Single-channel model
12.2.2 Multi-channel model
12.2.3 Skip-layer model
12.2.4 Bottom-up or top-down model
12.2.5 Skip-layer model with multi-scale saliency network
12.3 Attention and deep learning in machine vision: broad categories
12.3.1 Attention-based CNNs
12.3.2 CNN transformer pipelines
12.3.3 Hybrid transformers.
12.4 Major research algorithms, trends, and limitations
12.5 Conclusion
Conflict of interest
Funding acknowledgement
References
Chapter 13 Sparse 2D packing in thermal deburring with shock waves acting effects
13.1 Introduction
13.2 Sparse packing
13.2.1 The main problem and mathematical model
13.2.2 Solution approach and computational results
13.3 Thermal problem formulation
13.4 The balanced layout of 2D objects with shock waves action
13.5 Conclusions and future research
Acknowledgments
References
Chapter 14 Implementation of smart manufacturing in small and medium-sized enterprises
14.1 Introduction
14.1.1 Need for smart manufacturing
14.1.2 Electronic hardware with machine and software interface
14.1.3 Management information system
14.2 Literature review
14.3 Levels of data for intelligent SME
14.3.1 Resources for SME
14.3.2 Inputs for SMEs
14.3.3 Outputs for SMEs
14.3.4 Flow diagram
14.3.5 Data collection
14.3.6 Applications for analysis and decision making
14.4 Proposed architectural framework for intelligent SMEs
14.5 Case study
14.6 Conclusions and future scope
References
Chapter 15 Performance analysis of fractal image compression methods for medical images: a review
15.1 Introduction
15.1.1 Self-similarity in fractals
15.2 Motivation of the survey
15.3 Relevant literature
15.4 Comparative survey results and discussion
15.5 Improvements on existing algorithms
15.6 Conclusion and future work
References
Chapter 16 Mobile edge computing for efficient energy management systems
16.1 Introduction
16.2 Paradigm of edge computing
16.3 Role of factors in energy consumption
16.4 Energy efficient systems
16.5 Research findings and limitations
16.6 Future research challenges
16.6.1 The healthcare domain.
16.6.2 Big data management.