Intro
Preface
Contents
Editors and Contributors
Neural Network Imitation Model of Realization of the Business Analysis Process
1 Introduction
2 Analysis of Recent Research and Publications
3 Setting the Objective
4 The Case Study
5 Research Method
6 Conclusions
References
Thermal Modeling of the GaN HEMT Device Using Decision Tree Machine Learning Technique
1 Introduction
2 Experimental Setup
3 Device Modeling Using Machine Learning
4 Results
5 Conclusion
References
Low-Cost FPGA-Based On-board Computer
1 Introduction

2 Related Work
3 Proposed Work
4 Results and Simulations
5 Conclusion
References
A Survey on Solution of Imbalanced Data Classification Problem Using SMOTE and Extreme Learning Machine
1 Introduction
2 Imbalanced Data
2.1 Approaches to Overcome Class Imbalance
2.2 Advantages and Disadvantages of the Approaches
3 Synthetic Minority Over-sampling Technique
4 Extreme Learning Machine (ELM)
4.1 Variants of ELM
5 Machine Learning
5.1 ML Techniques
6 Convolutional Neural Network (CNN)
7 Related Work
8 Conclusion
References

Thermal Imaging-Assisted Infection Classification (BoF) for Brinjal Crop
1 Introduction
2 Motivation
3 Material and Methods
3.1 Experimental Setup
3.2 Proposed Methodology
3.3 Classification: BoF
4 Result and Discussion
5 Conclusion
References
Preterm Delivery Prediction Using Gradient Boosting Algorithms
1 Introduction
2 Related Work
3 Backgrounds
4 Result and Discussion
5 Conclusions
References
Analysis Urban Traffic Vehicle Routing Based on Dijkstra Algorithm Optimization
1 Introduction
2 Related Work
2.1 Dijkstra Algorithm

2.2 Algorithm Optimization
3 Model Construction
4 Results and Discussion
5 Conclusion
References
A Comprehensive Overview of Quality Enhancement Approach-Based Biometric Fusion System Using Artificial Intelligence Techniques
1 Introduction
2 Background Survey
3 Biometric Quality Enhancement
4 Analysis of Existing Work
5 Conclusion and Future Work
References
Rainfall Prediction Using Deep Neural Network
1 Introduction
2 Methodology
3 Data and Data Pre-processing
3.1 Data Set
3.2 Data Pre-processing
3.3 Data Insight
3.4 Train-Test Split

3.5 Feature Scaling
4 Deep Neural Network Model
5 Result and Conclusion
5.1 Weights and Bias
5.2 Training and Validation: Loss and Accuracy
5.3 Test Loss and Test Accuracy
5.4 Comparative Analysis
6 Conclusion
References
A Comparative Analysis of Supervised Word Sense Disambiguation in Information Retrieval
1 Introduction
2 WSD Approaches
3 Supervised WSD for IR
3.1 Naïve Bayes Algorithm
3.2 Support Vector Machine (SVM) Algorithm
3.3 Decision Tree Algorithm
3.4 K-Nearest Neighbor (KNN) Algorithm
4 Comparison of Algorithms
5 Discussion