Performance comparison of CNN models with different region sizes and other baseline models.</p
Performance comparison of the machine learning models regarding the use of KNN imputer.</p
Comparison of the performance of the models using area under the curve (AUC) of ROC.</p
Comparative model performance: Low complexity choices, base rate of comparison: 50%.</p
<p>Performance comparisons between three different models using breast cancer datasets.</p
CNN segmentation performance metrics for training, validation, and test sets.</p
CNN segmentation performance metrics for training, validation, and test sets.</p
Classification performances obtained with four CNN models for the real-world testing dataset.</p
Performance comparison of Bayesian network classifiers using validation dataset.</p
Comparison of the computational-time for both the CWT-based and VMD-based CNN frameworks.</p
Comparison of the computational-time for both the CWT-based and VMD-based CNN frameworks.</p
<p>The performance comparison of the models trained with different sequence lengths.</p
Comparison of out-of-sample results for stock chart images using the SC-CNN model.</p
Receiver Operating Characteristic curve for our CNN model and the transfer-learned Inception v3 mode...
Comparison of out-of-sample results for fusion chart images using the SC-CNN model.</p
Performance comparison of a species-specific predictor using the test dataset.</p
Performance comparison of the machine learning models regarding the use of KNN imputer.</p
Comparison of the performance of the models using area under the curve (AUC) of ROC.</p
Comparative model performance: Low complexity choices, base rate of comparison: 50%.</p
<p>Performance comparisons between three different models using breast cancer datasets.</p
CNN segmentation performance metrics for training, validation, and test sets.</p
CNN segmentation performance metrics for training, validation, and test sets.</p
Classification performances obtained with four CNN models for the real-world testing dataset.</p
Performance comparison of Bayesian network classifiers using validation dataset.</p
Comparison of the computational-time for both the CWT-based and VMD-based CNN frameworks.</p
Comparison of the computational-time for both the CWT-based and VMD-based CNN frameworks.</p
<p>The performance comparison of the models trained with different sequence lengths.</p
Comparison of out-of-sample results for stock chart images using the SC-CNN model.</p
Receiver Operating Characteristic curve for our CNN model and the transfer-learned Inception v3 mode...
Comparison of out-of-sample results for fusion chart images using the SC-CNN model.</p
Performance comparison of a species-specific predictor using the test dataset.</p
Performance comparison of the machine learning models regarding the use of KNN imputer.</p
Comparison of the performance of the models using area under the curve (AUC) of ROC.</p
Comparative model performance: Low complexity choices, base rate of comparison: 50%.</p
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