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.