Showing 9,381 - 9,400 results of 30,886 for search '(( 50 ((we decrease) OR (mean decrease)) ) OR ( 100 ((ng decrease) OR (a decrease)) ))', query time: 1.31s Refine Results
  1. 9381

    Raw data for figures and tables in the paper. by Runhai Jiang (19871068)

    Published 2024
    “…We employed broth culture and pot experiments to investigate the effect of the inoculation of <i>P</i>. …”
  2. 9382
  3. 9383
  4. 9384

    Supplementary Material for: Epidemiology of Transthyretin Familial Amyloid Polyneuropathy in Portugal: A Nationwide Study by Inês M. (5678270)

    Published 2018
    “…Crude rates were reported per 100,000 adult inhabitants. <b><i>Results:</i></b> Over 2010–2016 period, mean incidence rates was 0.87/100,000 (95% CI 0.68–1.10) corresponding to 71 new patients yearly, that has decreased 31% in the last 7 years. …”
  5. 9385

    Imbalanced Dataset Distribution. by Mudhafar Jalil Jassim Ghrabat (22177655)

    Published 2025
    “…Every model was subjected to individual testing. The SMO_CNN model we developed demonstrated exceptional testing and training accuracies of 98.95% and 99.20% respectively, surpassing CNN, VGG19, and ResNet50 models. …”
  6. 9386

    Data Preprocessing Steps for IDC Dataset. by Mudhafar Jalil Jassim Ghrabat (22177655)

    Published 2025
    “…Every model was subjected to individual testing. The SMO_CNN model we developed demonstrated exceptional testing and training accuracies of 98.95% and 99.20% respectively, surpassing CNN, VGG19, and ResNet50 models. …”
  7. 9387

    Flowchart of Proposed SMO_CNN. by Mudhafar Jalil Jassim Ghrabat (22177655)

    Published 2025
    “…Every model was subjected to individual testing. The SMO_CNN model we developed demonstrated exceptional testing and training accuracies of 98.95% and 99.20% respectively, surpassing CNN, VGG19, and ResNet50 models. …”
  8. 9388

    IDC Breast Cancer Dataset Descriptions. by Mudhafar Jalil Jassim Ghrabat (22177655)

    Published 2025
    “…Every model was subjected to individual testing. The SMO_CNN model we developed demonstrated exceptional testing and training accuracies of 98.95% and 99.20% respectively, surpassing CNN, VGG19, and ResNet50 models. …”
  9. 9389

    Accuracy Graph. by Mudhafar Jalil Jassim Ghrabat (22177655)

    Published 2025
    “…Every model was subjected to individual testing. The SMO_CNN model we developed demonstrated exceptional testing and training accuracies of 98.95% and 99.20% respectively, surpassing CNN, VGG19, and ResNet50 models. …”
  10. 9390

    Loss Graph. by Mudhafar Jalil Jassim Ghrabat (22177655)

    Published 2025
    “…Every model was subjected to individual testing. The SMO_CNN model we developed demonstrated exceptional testing and training accuracies of 98.95% and 99.20% respectively, surpassing CNN, VGG19, and ResNet50 models. …”
  11. 9391

    Hyperparameter Tuning of the Proposed Model. by Mudhafar Jalil Jassim Ghrabat (22177655)

    Published 2025
    “…Every model was subjected to individual testing. The SMO_CNN model we developed demonstrated exceptional testing and training accuracies of 98.95% and 99.20% respectively, surpassing CNN, VGG19, and ResNet50 models. …”
  12. 9392

    Comparison of Accuracy Metric. by Mudhafar Jalil Jassim Ghrabat (22177655)

    Published 2025
    “…Every model was subjected to individual testing. The SMO_CNN model we developed demonstrated exceptional testing and training accuracies of 98.95% and 99.20% respectively, surpassing CNN, VGG19, and ResNet50 models. …”
  13. 9393

    ROC Curve for the Best Model (AUC = 0.92). by Mudhafar Jalil Jassim Ghrabat (22177655)

    Published 2025
    “…Every model was subjected to individual testing. The SMO_CNN model we developed demonstrated exceptional testing and training accuracies of 98.95% and 99.20% respectively, surpassing CNN, VGG19, and ResNet50 models. …”
  14. 9394

    Sampling Images of IDC Dataset. by Mudhafar Jalil Jassim Ghrabat (22177655)

    Published 2025
    “…Every model was subjected to individual testing. The SMO_CNN model we developed demonstrated exceptional testing and training accuracies of 98.95% and 99.20% respectively, surpassing CNN, VGG19, and ResNet50 models. …”
  15. 9395

    CNN Model Layers Summary. by Mudhafar Jalil Jassim Ghrabat (22177655)

    Published 2025
    “…Every model was subjected to individual testing. The SMO_CNN model we developed demonstrated exceptional testing and training accuracies of 98.95% and 99.20% respectively, surpassing CNN, VGG19, and ResNet50 models. …”
  16. 9396

    Training Data/Validation/Test. by Mudhafar Jalil Jassim Ghrabat (22177655)

    Published 2025
    “…Every model was subjected to individual testing. The SMO_CNN model we developed demonstrated exceptional testing and training accuracies of 98.95% and 99.20% respectively, surpassing CNN, VGG19, and ResNet50 models. …”
  17. 9397

    CNN Model Architecture. by Mudhafar Jalil Jassim Ghrabat (22177655)

    Published 2025
    “…Every model was subjected to individual testing. The SMO_CNN model we developed demonstrated exceptional testing and training accuracies of 98.95% and 99.20% respectively, surpassing CNN, VGG19, and ResNet50 models. …”
  18. 9398

    Impressive accuracy data. by Mudhafar Jalil Jassim Ghrabat (22177655)

    Published 2025
    “…Every model was subjected to individual testing. The SMO_CNN model we developed demonstrated exceptional testing and training accuracies of 98.95% and 99.20% respectively, surpassing CNN, VGG19, and ResNet50 models. …”
  19. 9399

    Performance Results of Proposed SMO_CNN Model. by Mudhafar Jalil Jassim Ghrabat (22177655)

    Published 2025
    “…Every model was subjected to individual testing. The SMO_CNN model we developed demonstrated exceptional testing and training accuracies of 98.95% and 99.20% respectively, surpassing CNN, VGG19, and ResNet50 models. …”
  20. 9400

    Means of crashes and bankruptcy rates for percentages of agents with a doubled learning rate. by Johann Lussange (18284145)

    Published 2024
    “…<p>For both plots (a) and (b), varying percentages <i>p</i> = 0%, 20%, 40%, 60%, 80% of the total agent population were analyzed, where agents had their learning rate scaled by a factor of 2 and the remaining 100 − <i>p</i>% had a learning rate <i>β</i> drawn from . …”