Intro
Preface
Organization
Contents
Artificial Intelligence
Student Performance Monitoring System Using Artificial Intelligence Models
1 Introduction
2 Collaboration Through Decision-Making
3 Artificial Intelligence
3.1 Impact of AI on Education
3.2 Machine Learning
3.3 Decision-Making
4 Proposed Model of 4 -Tier DSS Using AI
4.1 Decision-Making Systems
5 Methodology
5.1 Decision-Making with Data Mining
5.2 Typical Attributes
6 Result and Discussion
6.1 Accuracy of Prediction
6.2 DSS in E-Learning
6.3 Decision-Making in Education

7 Conclusion
References
On Assaying the T-score Value for the Detection and Classification of Osteoporosis Using AI Learning Techniques
1 Introduction
2 Background
3 Analysis
4 Discussion
5 Conclusion
References
Evaluation of Healthcare Data in Machine Learning Model Used in Fraud Detection
1 Introduction
2 Literature Review
3 Evaluation Method
3.1 Accuracy
3.2 Precision or Positive Predicted Value
3.3 Negative Predicted Value
3.4 Sensitivity or Recall or TPR
3.5 Specificity or True Negative Rate
3.6 False Positive Rate

3.7 False Negative Rate
3.8 FDR
3.9 F 1 Score or Beta
3.10 GLC
3.11 F2 Score or Beta 2
3.12 ROC (Receiver Operating Characteristic) Curve
3.13 AUC
3.14 Average Precision or PR AUC Score
3.15 Brier Score
4 Evaluation Measure
5 Conclusion
References
A New Approach to Heart Disease Prediction Using Clustering and Classification Algorithms
1 Introduction
2 Literature Review
3 Clustering Algorithms
4 Classification Algorithms
5 Problem Statement
6 Methodology
6.1 K-Nearest Neighbors (KNN) and K-Means Algorithm
6.2 Logistic Regression (LR)

7 Results and Discussions
8 Conclusion
References
Skin Lesion Classification: Scrutiny of Learning-Based Methods
1 Introduction
2 Skin Lesion Imaging Techniques
2.1 Optical Coherence Tomography (OCT)
2.2 Reflectance Confocal Microscopy (RCM)
2.3 Dermoscopy
2.4 Ultrasound
3 Benchmark Dataset
3.1 Hospital Pedro Hispano (PH2)
3.2 International Skin Imaging Collaboration (ISIC) Archive
4 Deep Learning-Based Skin Lesion Detection Systems
5 Challenges and Future Recommendations
6 Conclusion
References

Lung Disease Classification Using CNN-Based Trained Models from CXR Image
1 Introduction
2 Related work
3 Methodology
3.1 System Architecture
3.2 AlexNet
3.3 SqueezeNet
3.4 Feature Selection
4 Dataset
5 Preprocessing
6 Proposed Work
7 Result and Discussion
8 Evaluation
9 Conclusion
References
A Novel Framework for Satellite Image Denoising and Super Resolution Using CNN-GAN
1 Introduction
2 Related Work
2.1 SAR Image Multiplicative Noise Degradation Model
2.2 CNNs for SAR Image Despeckling
2.3 Residual Learning
3 Proposed Model