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Showing 1 - 20 results of 95 for search '(( binary image early detection algorithm ) OR ( binary b based optimization algorithm ))', query time: 0.52s Refine Results
  1. 1
    One advantage of our algorithm is that it is not binary in nature.

    One advantage of our algorithm is that it is not binary in nature. by Yong Kuk Kim (13955214)

    Published 2022
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  2. 2
    Melanoma Skin Cancer Detection Using Deep Learning Methods and Binary GWO Algorithm

    Melanoma Skin Cancer Detection Using Deep Learning Methods and Binary GWO Algorithm by Hussein Ali Bardan (21976208)

    Published 2025
    “…The binary GWO algorithm identifies the most relevant features from </p><p dir="ltr">dermatological images, eliminating redundancy and reducing the computational burden. …”
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  3. 3
    Optimization of tracking parameters based on <i>P</i><sub><i>swap</i></sub>.

    Optimization of tracking parameters based on <i>P</i><sub><i>swap</i></sub>. by Benjamin Gallois (10131683)

    Published 2021
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  4. 4
    Performance of the proposed method for binary classification of lung cancer.

    Performance of the proposed method for binary classification of lung cancer. by Md. Sabbir Hossain (9958939)

    Published 2025
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  5. 5
    Graphical comparison of binary class lung cancer classification with different approaches.

    Graphical comparison of binary class lung cancer classification with different approaches. by Md. Sabbir Hossain (9958939)

    Published 2025
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  6. 6
    Data_Sheet_1_Pneumonia detection by binary classification: classical, quantum, and hybrid approaches for support vector machine (SVM).pdf

    Data_Sheet_1_Pneumonia detection by binary classification: classical, quantum, and hybrid approaches for support vector machine (SVM).pdf by Sai Sakunthala Guddanti (17739363)

    Published 2024
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  7. 7
    Representative photographic image.

    Representative photographic image. by Kengo Oka (1420585)

    Published 2025
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  8. 8
    Proposed Algorithm.

    Proposed Algorithm. by Hend Bayoumi (22693738)

    Published 2025
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  9. 9
    (a) Accuracy and (b) loss curves of training and validation during binary class classification for 512 features extracted by the CNN.

    (a) Accuracy and (b) loss curves of training and validation during binary class classification for 512 features extracted by the CNN. by Md. Sabbir Hossain (9958939)

    Published 2025
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  10. 10
    Comparisons between ADAM and NADAM optimizers.

    Comparisons between ADAM and NADAM optimizers. by Hend Bayoumi (22693738)

    Published 2025
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  11. 11
    ROC curves of the proposed method for binary class classification: (A) GNB, (B) GBM, (C) KNN, (D) SVM, (E) RF, and (F) Ensemble learning.

    ROC curves of the proposed method for binary class classification: (A) GNB, (B) GBM, (C) KNN, (D) SVM, (E) RF, and (F) Ensemble learning. by Md. Sabbir Hossain (9958939)

    Published 2025
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  12. 12
    Association between maxillary protrusion and oral habits.

    Association between maxillary protrusion and oral habits. by Kengo Oka (1420585)

    Published 2025
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  13. 13
    Association between crowding and oral habits.

    Association between crowding and oral habits. by Kengo Oka (1420585)

    Published 2025
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  14. 14
    Guide frame that appears on the smartphone application.

    Guide frame that appears on the smartphone application. by Kengo Oka (1420585)

    Published 2025
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  15. 15
    Association between deep bite and oral habits.

    Association between deep bite and oral habits. by Kengo Oka (1420585)

    Published 2025
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  16. 16
    Performance metrics of the logistic regression models on the test dataset.

    Performance metrics of the logistic regression models on the test dataset. by Kengo Oka (1420585)

    Published 2025
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  17. 17
    Explicability results of the deep bite classification model.

    Explicability results of the deep bite classification model. by Kengo Oka (1420585)

    Published 2025
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  18. 18
    Explicability results of the crowding classification model.

    Explicability results of the crowding classification model. by Kengo Oka (1420585)

    Published 2025
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  19. 19
    Breakdown of participants by residential area.

    Breakdown of participants by residential area. by Kengo Oka (1420585)

    Published 2025
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  20. 20
    Explicability results of the maxillary protrusion classification model.

    Explicability results of the maxillary protrusion classification model. by Kengo Oka (1420585)

    Published 2025
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