Intro
Preface
Organization
Contents
Improvisation of Predictive Modeling Using Different Classifiers for Predicting Thyroid Disease in Patients
1 Introduction
1.1 Data Analytics
1.2 Prediction and Predictive Analysis
2 Literature Review
3 Exploratory Data Analysis
4 Proposed Model
4.1 k-NN Classifier
4.2 Random Forest Classifier
4.3 XGBoost Classifier
4.4 CatBoost Classifier
5 Results
6 Conclusion
References
Application of IoT for Proximity Analysis and Alert Generation for Maintaining Social Distancing
1 Introduction
2 Related Work

3 Materials and Methods
3.1 Methodology and Block Diagram of System
4 Experimental Setup
5 Mathematical Modeling
6 Theoretical Results
7 Device Simulation and Result
8 Conclusions and Future Scope
References
Analysis and Optimization of Fault
Tolerant Behaviour of Motors in Electric Vehicular Systems
1 Introduction
2 Overview of Motors in Electric Vehicular Systems
3 EV Modelling
4 Exploratory Data Analysis of PMSM Characteristics Using Real - Time Data
4.1 About Dataset
4.2 Key Points About the Dataset
4.3 Determining Correlations

4.4 Generating Heatmap
4.5 Observations
5 Modelling of the Dynamics of Electric Vehicle
6 Fault Analysis
6.1 Normal Operation
6.2 Abnormal Operation
6.3 Observation Table
7 Optimization Technique of PMSM for Electric Vehicular Application
7.1 Normal Operation
7.2 Abnormal Operation
7.3 Observation Table
8 Conclusion
References
Transfer Learning of Mammogram Images Using Morphological Bilateral Subtraction and Enhancement Filter
1 Introduction
2 Contrast Enhancement
3 Histogram Equalization (HE)

4 Contrast Limited Adaptive Histogram Equalization (CLAHE)
4.1 Histogram Modified - Local Contrast Enhancement (HM-LCE)
5 Related Works
6 Image Segmentation
6.1 Mathematical Morphological Operations
7 Proposed Contrast Enhancement Using Modified Bi-level Histogram with Homomorphic Filter (MBH-HF)
7.1 Modified Bi-level Histogram
8 Experimental Analysis
8.1 Dataset Used
8.2 Experimental Result Analysis
9 Conclusion
References
Deep Learning Based Bengali Image Caption Generation
1 Introduction
2 Related Works
3 Proposed Methodology

3.1 Processing of Dataset
3.2 Extraction of Feature
3.3 Embedding
3.4 Encoder
3.5 Decoder
4 Experimental Setup
4.1 Output Generation
4.2 Result Analysis
5 Comparison
6 Future Scope and Conclusion
References
Analyzing Deep Neural Network Algorithms for Recognition of Emotions Using Textual Data
1 Introduction
2 Related Work
3 Research Methodology
3.1 Collection of Dataset
3.2 Data Pre-processing
3.3 Feature Extraction
3.4 Emotion Detection Using Deep Learning Models
4 Result and Analysis
5 Conclusion and Future Work
References