Handbook of Intelligent Computing and Optimization for Sustainable Development. Группа авторов

Conclusion 31.8 Future Work References 32 Intelligent Computing for Precision Agriculture 32.1 Introduction 32.2 Technology in Agriculture References 33 Intelligent Computing for Green Sustainability 33.1 Introduction 33.2 Modified DEMATEL 33.3 Weighted Sum Model 33.4 Weighted Product Model 33.5 Weighted Aggregated Sum Product Assessment 33.6 Grey Relational Analysis 33.7 Simple Multi-Attribute Rating Technique 33.8 Criteria Importance Through Inter-Criteria Correlation 33.9 Entropy 33.10 Evaluation Based on Distance From Average Solution 33.11 MOORA 33.12 Interpretive Structural Modeling 33.13 Conclusions 33.14 Limitations of the Study 33.15 Suggestions for Future Research References

      12  Part V AI IN HEALTHCARE 34 Bayesian Estimation of Gender Differences in Lipid Profile, Among Patients With Coronary Artery Disease 34.1 Introduction 34.2 Methods 34.3 Statistical Analysis 34.4 Results 34.5 Discussion 34.6 Conclusion Acknowledgements References 35 Reconstruction of Dynamic MRI Using Convolutional LSTM Techniques 35.1 Introduction 35.2 Methodologies 35.3 Problem Formulation 35.4 Network Architecture 35.5 Results 35.6 Discussion 35.7 Conclusion References 36 Gender Classification Using Multispectral Imaging: A Comparative Performance Analysis Between Affine Hull and Wavelet Fusion 36.1 Introduction 36.2 Literature Review 36.3 Multispectral Face Database 36.4 Methodology 36.5 Experiments 36.6 Results and Discussion 36.7 Conclusions Acknowledgments References 37 Polyp Detection Using Deep Neural Networks 37.1 Introduction 37.2 Literature Survey 37.3 Proposed Methodology 37.4 Implementation and Results 37.5 Conclusion and Future Work References 38 Boundary Exon Prediction in Human Sequences Using External Information Sources 38.1 Introduction 38.2 Proposed Exon Prediction Model 38.3 Homology-Based Exon Prediction 38.4 Results and Discussion 38.5 Conclusion 38.6 Motivation and Limitations of the Research 38.7 Major Findings of the Research References 39 Blood Glucose Prediction Using Machine Learning on Jetson Nanoplatform 39.1 Introduction 39.2 Sample Preparation 39.3 Methodology 39.4 Results and Discussion 39.5 Discussion 39.6 Conclusion 39.7 Future Scope Acknowledgement References