Читать книгу Fundamentals and Methods of Machine and Deep Learning - Pradeep Singh - Страница 4

1 Chapter 1Figure 1.1 Linear regression [3].Figure 1.2 Height vs. weight graph [6].Figure 1.3 Logistic regression [3].Figure 1.4 SVM [11].Figure 1.5 Decision tree.

2 Chapter 2Figure 2.1 A high-level representation of Bayes optimal classifier.Figure 2.2 A high-level representation of Bootstrap aggregating.Figure 2.3 A high-level representation of Bayesian model averaging (BMA).Figure 2.4 A high-level representation of Bayesian classifier combination (BCC).Figure 2.5 A high-level representation of bucket of models.Figure 2.6 A high-level representation of stacking.

3 Chapter 3Figure 3.1 ML/DL model deployment process.Figure 3.2 Residual.Figure 3.3 Confusion metric.Figure 3.4 Confusion metric interpretation.Figure 3.5 Metric derived from confusion metric.Figure 3.6 Precision-recall trade-off.Figure 3.7 AUC-ROC curve.Figure 3.8 Precision-recall curve.Figure 3.9 Confusion metric example.Figure 3.10 Cosine similarity projection.Figure 3.11 (a) Cosine similarity. (b) Soft cosine similarity.Figure 3.12 Intersection and union of two sets A and B.Figure 3.13 Confusion metric.

4 Chapter 4Figure 4.1 Cases in Karnataka, India.Figure 4.2 Cases trend in Karnataka, India.Figure 4.3 Modified SEIR.Figure 4.4 LSTM cell.Figure 4.5 (a) Arrangement of data set in 3D tensor. (b) Mapping of the 3D and 2...Figure 4.6 RMSLE value vs. number of epochs.Figure 4.7 Cases in Karnataka.Figure 4.8 SEIR Model fit for test cases.Figure 4.9 Cases predicted for next 10 days.Figure 4.10 Testing results.Figure 4.11 Next 10 days Prediction using LSTM model.Figure 4.12 Prediction error curve.Figure 4.13 Prediction error and RMSLE curve.

5 Chapter 5Figure 5.1 Classification of feature extraction methods.

6 Chapter 6Figure 6.1 Relationship between AI, ML, and DL.Figure 6.2 Image segmentation process flow.Figure 6.3 The visual representation of clinical data generation to natural lang...

7 Chapter 7Figure 7.1 Extraction of exudates.Figure 7.2 Extraction of blood vessels.Figure 7.3 Extraction of microaneurysms.Figure 7.4 Extraction of hemorrhages.Figure 7.5 Working of AdaBoost model.Figure 7.6 Working of AdaNaive model.Figure 7.7 Working of AdaSVM model.Figure 7.8 Working of AdaForest model.Figure 7.9 Representative retinal images of DR in their order of increasing seve...Figure 7.10 Comparison of classifiers using ROC curve (Binary classification).Figure 7.11 Comparison of classifiers (Binary Classification).Figure 7.12 Comparison of classifiers (Multi Classification).

8 Chapter 8Figure 8.1 Workflow model of proposed system.Figure 8.2 Architecture of proposed system.Figure 8.3 Original dataset distribution.Figure 8.4 Resampling using SMOTE.Figure 8.5 Target class distribution.Figure 8.6 Resampled distribution applying SMOTE.Figure 8.7 Feature ranking using Extra tree classifier.Figure 8.8 p-values of the features.Figure 8.9 Performance evaluation of models under study 1 with dataset size = 1,...Figure 8.10 Performance evaluation of models under study 2 with data size = 1,00...Figure 8.11 Performance evaluation of models under study 3 With dataset size = 1...Figure 8.12 Correlation between follow-up time and death event.Figure 8.13 Performance evaluation of models on different classifiers.Figure 8.14 Performance evaluation of models on dataset size = 508.Figure 8.15 Performance evaluation of models on dataset size = 1,000.

9 Chapter 9Figure 9.1 File level defect prediction process.Figure 9.2 A basic convolutional neural network (CNN) architecture.Figure 9.3 Overall network architecture of proposed NCNN model.Figure 9.4 Description regarding confusion matrix.Figure 9.5 Confusion matrix analysis for the data sets (KC1, KC3, PC1, and PC2).Figure 9.6 Model accuracy and model loss analysis for the data sets (KC1, KC3, P...Figure 9.7 Performance comparison of different models for software defect predic...Figure 9.8 Model accuracy analysis for the data sets (KC1, KC3, PC1, and PC2).Figure 9.9 Confusion rate analysis for the data sets (KC1, KC3, PC1, and PC2).

10 Chapter 10Figure 10.1 Hierarchical video representation.Figure 10.2 Overall architecture of the proposed framework.Figure 10.3 Blocking pattern.Figure 10.4 Key frame extraction.Figure 10.5 Training and testing process.Figure 10.6 Predicted output frames from advertisement videos.Figure 10.7 Predicted output frames from non-advertisement videos.

11 Chapter 11Figure 11.1 Flowchart of proposed architecture.Figure 11.2 Architecture of proposed combinational CNN+LSTM model.Figure 11.3 Overall XceptionNet architecture.Figure 11.4 Proposed CNN model’s accuracy graph on (a) MindBig dataset and (b) P...Figure 11.5 Proposed CNN+LSTM model’s accuracy graph on (a) MindBig dataset and ...

12 Chapter 12Figure 12.1 Flow diagram of the credit card fraudulent transaction detection.Figure 12.2 Correlation matrix for the credit card dataset showing correlation b...Figure 12.3 Oversampling of the fraud transactions.Figure 12.4 Undersampling of the no-fraud transactions.Figure 12.5 SMOTE [26].Figure 12.6 Optimal hyperplane and maximum margin [29].Figure 12.7 Support vector classifier.Figure 12.8 Binary decision tree [31].Figure 12.9 (a) Five-fold cross-validation technique and (b) GridSearchCV.Figure 12.10 (a) ROC curve [39]. (b) Precision recall curve for no skill and log...Figure 12.11 Outline of implementation and results.

13 Chapter 13Figure 13.1 The architecture crack detection system.Figure 13.2 (a) Thermal image. (b) Digital image. (c) Thermal image. (d) Digital...Figure 13.3 CNN layers in learning process.

14 Chapter 14Figure 14.1 Raw images (Band 2 and Band 5, respectively).Figure 14.2 Band combination 3-4-6 and 3-2-1, respectively.Figure 14.3 Spectral signatures after atmospheric correction.Figure 14.4 Pictorial representation of Euclidean and Manhattan distances.Figure 14.5 Discriminant functions.Figure 14.6 Result of ML classifier.Figure 14.7 Result of k-NN classifier.

15 Chapter 15Figure 15.1 Digital medical images: (a) X-ray of chest, (b) MRI imaging of brain...Figure 15.2 Scheme of image processing [12].Figure 15.3 Anatomy-wise breakdown of papers in each year (2016–2020).Figure 15.4 Year-wise breakdown of papers (2016–2020) based on the task.

16 Chapter 16Figure 16.1 Prototype 1:16 scale car.Figure 16.2 Image processing pipeline.Figure 16.3 Original Image.Figure 16.4 Canny edge output.Figure 16.5 Hough lines overlaid on original image.Figure 16.6 CNN model architecture.Figure 16.7 Experimental track used for training and testing.Figure 16.8 Accuracy vs. training time (hours) plot of Model 1 that uses classif...Figure 16.9 Loss vs. training time (hours) plot of Model 1 that uses classificat...Figure 16.10 MSE vs. steps plot of Model 2 that uses classification method with ...Figure 16.11 MSE vs. steps plot of Model 8 that uses classification method with ...Figure 16.12 Accuracy vs. steps plot of Model 5 that uses classification method ...Figure 16.13 Loss vs. steps plot of Model 5 that uses classification method with...Figure 16.14 Input image given to CNN.Figure 16.15 Feature map at second convolutional layer.Figure 16.16 Feature map at the fifth convolutional layer.

17 Chapter 17Figure 17.1 An architecture of simple obstacle detection and avoidance framework...Figure 17.2 A prototype of a wearable system with image to tactile rendering fro...Figure 17.3 DG5-V hand glove developed for Arabic sign language recognition [40]...

18 Chapter 18Figure 18.1 Land cover classification using CNN.Figure 18.2 Remote sensing image classifier using stacked denoising autoencoder.Figure 18.3 Gaussian-Bernoulli RBM for hyperspectral image classification.Figure 18.4 GAN for pan-sharpening with multispectral and panchromatic images.Figure 18.5 Change detection on multi-temporal images using RNN.