Intro
Preface
Organization
Contents
Artificial Intelligence
Comparative Analysis of Advanced Machine Learning Based Techniques to Identify the Lung Cancer: A Review
1 Introduction
2 Background of Cancer Stages with Machine Learning
2.1 Lung Cancer Detection in Related Works
2.2 Additional Methods
3 Background of Cancer Stages with Machine Learning
4 Performance Analysis of Existing Techniques and Discussion
5 Conclusion
References
Classification and Identification of Objects in Images Using CNN
1 Introduction
2 Novelty
3 Background

3.1 Evolution of Computer Vision
3.2 CNN Architecture
4 Computer Vision Variants
4.1 Image Classification
4.2 Object Detection
4.3 Image Classification with Localization
4.4 Semantic Segmentation
4.5 Instance Segmentation
5 Application Areas
6 Research Challenges
7 Conclusion
References
An Appraisal of Cyber-Attacks and Countermeasures Using Machine Learning Algorithms
1 Introduction
2 Machine Learning in Intrusion Detection System
2.1 History
2.2 Intrusion Detection System Detection Methods
2.3 Type of Intrusion Detection System Alerts

2.4 Benchmarks or Datasets
2.5 Machine Learning Based Intrusion Detection System
3 Common Security Attacks
4 Counter Measures
5 Conclusion
References
A Systematic Review on Autonomous Vehicle: Traffic Sign Detection and Drowsiness Detection
1 Introduction
2 Taxonomy on Autonomous Vehicle
2.1 Traffic Sign Detection
2.2 Drowsiness Detection
3 Vehicle-Focused Studies and Systems
4 Problem Statement
5 Comparative Analysis
6 Conclusion
References
Crop Disease Auto-localization and Classification
1 Introduction
2 Problem Statement

3 Literature Review
4 Proposed Methodology
4.1 Dataset Preparation
4.2 Image Segmentation Using DICOM Format
4.3 Feature Extraction
4.4 Transfer Learning
4.5 Model Analysis
4.6 Grad-CAM: For Model Performance Analysis
4.7 Bounding Box Regression
4.8 Model Deployment
5 Implementation
5.1 Augmentation and Pre-processing
5.2 DICOM Segmentation
5.3 VGG16 Model
5.4 Resnet50 Model
5.5 MobileNet V2 Model
5.6 Xception Model
5.7 Grad-CAM Analysis
5.8 Bounding Box Regression
5.9 Deployment
6 Results and Discussion

6.1 Classification: Models Analysis and Evaluation
6.2 Grad-CAM Visualization
6.3 Bounding Box Regression
6.4 Medical Imaging as a Segmentation Technique
7 Conclusion and Future Work
References
Challenges in Crop Selection Using Machine Learning
1 Introduction
2 Literature Survey
3 Proposed Method
3.1 Data Collection
3.2 Feature Engineering
3.3 Model Selection
3.4 Hyperparameter Tuning
3.5 Support Vector Machines (SVM)
4 Experimental Results
5 Conclusion
References
Handcrafted Features for Human Gait Recognition: CASIA-A Dataset