Intro
Foreword
Preface
Acknowledgments
Contents
Editors and Contributors
Deep Learning-Based Approach for Parkinson's Disease Detection Using Region of Interest
1 Introduction
1.1 Subject-Level Classification
1.2 Region of Interest
1.3 Model Generalizability
1.4 Data Leakage
2 Literature Survey
3 Dataset Description
4 Methodology
4.1 Data Preprocessing
4.2 Architecture
4.3 Evaluation Metrics
5 Experiments and Results
5.1 Subject-Level Classification
5.2 Region of Interest
5.3 Model Generalizability
5.4 Effect of Data Leakage

6 Conclusion
References
Classification of Class-Imbalanced Diabetic Retinopathy Images Using the Synthetic Data Creation by Generative Models
1 Introduction
2 Methodology
2.1 Dataset Description
2.2 Retinal Synthetic Image Generation
2.3 CNN Classifier
3 Experimental Results and Discussions
4 Conclusion
References
A Novel Leaf Fragment Dataset and ResNet for Small-Scale Image Analysis
1 Introduction
2 Dataset Preparation
2.1 Data Collection
2.2 Image Pre-processing
2.3 Post-processing
2.4 Filename-Format

3 Residual Neural Network for Cotyledon-Type Identification and Plant Species Classification
3.1 Dataset Formulation and Feature Description
3.2 Residual Block
3.3 Methodology
4 Results
4.1 Cotyledon-Type Identification
4.2 Plant Species Classification
5 Discussion
6 Comparison Between Applied ResNet and ResNet-152 V2
7 Conclusion and Possible Future Contributions
References
Prediction of Covid 19 Cases Based on Weather Parameters
1 Introduction
2 Review of Related Literature Paper
3 Methodology Used
3.1 Dataset Used

3.2 Correlation Study of the Factors
3.3 Mean Squared Error
3.4 Models Used for Study
3.5 Analysis Performed
4 Experiment Results
4.1 Linear Regression
4.2 Decision Tree Regression
4.3 Random Forest Regression
5 Conclusion
References
CloudML: Privacy-Assured Healthcare Machine Learning Model for Cloud Network
1 Introduction
2 Related Work
3 Proposed Approaches
3.1 Unsupervised K-Means Clustering Algorithm
3.2 Secured Multi-party Addition Algorithm
3.3 Pailier Homomorphic Encryption
4 Work Done
5 Experimental Results and Discussion

5.1 Communication Overhead
5.2 Storage Overhead
5.3 Scalability
5.4 Encryption Cost
5.5 Runtime Analysis on Data Points
5.6 Runtime Analysis on Data Dimensionality
6 Limitations and Future Work
7 Concluding Remarks
References
Performance Evaluation of Hierarchical Clustering Protocols in WSN Using MATLAB
1 Introduction
2 Radio Model
3 Overview of Clustering Protocols
3.1 LEACH (Low-Energy Adaptive Clustering Hierarchy)
3.2 LEACH in Heterogeneous Environment
3.3 LEACH-C
3.4 SEP
3.5 DEEC
3.6 DDEEC
4 Simulation Scenario and Performance Metrics