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Showing 101 - 120 results of 464 for search '(( binary data binary classification algorithm ) OR ( binary a based optimization algorithm ))', query time: 0.66s Refine Results
  1. 101
    Summary of the results obtained using the dataset4 (SMOTE and feature selected).

    Summary of the results obtained using the dataset4 (SMOTE and feature selected). by Rajan Vohra (10689820)

    Published 2022
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  2. 102
    Prevalence of mild, moderate and severe anemia by age.

    Prevalence of mild, moderate and severe anemia by age. by Rajan Vohra (10689820)

    Published 2022
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  3. 103
    Prevalence of mild, moderate and severe anemia by age.

    Prevalence of mild, moderate and severe anemia by age. by Rajan Vohra (10689820)

    Published 2022
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  4. 104
    Summary of the results obtained using the dataset2 (feature selected).

    Summary of the results obtained using the dataset2 (feature selected). by Rajan Vohra (10689820)

    Published 2022
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  5. 105
    Framework of the proposed model.

    Framework of the proposed model. by Rajan Vohra (10689820)

    Published 2022
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  6. 106
    Summary of the results obtained using the dataset1 (Anemia original dataset).

    Summary of the results obtained using the dataset1 (Anemia original dataset). by Rajan Vohra (10689820)

    Published 2022
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  7. 107
    Prevalence of normocytic, microcytic and macrocytic by age.

    Prevalence of normocytic, microcytic and macrocytic by age. by Rajan Vohra (10689820)

    Published 2022
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  8. 108
    Feature selection.

    Feature selection. by Rajan Vohra (10689820)

    Published 2022
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  9. 109
    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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  10. 110
    Using the Accuracy metric, three model types—naive deep learning models, advanced deep learning models, and the proposed framework—are compared for the binary classification of normal and abnormal classes in an unbalanced CTG test dataset.

    Using the Accuracy metric, three model types—naive deep learning models, advanced deep learning models, and the proposed framework—are compared for the binary classification of nor... by Amin Ullah (12015113)

    Published 2025
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  11. 111
    Small-scale dataset comparative analysis using classification accuracy for population sizes 50 and 100.

    Small-scale dataset comparative analysis using classification accuracy for population sizes 50 and 100. by Olaide N. Oyelade (14047002)

    Published 2023
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  12. 112
    Medium-scale dataset comparative analysis using classification accuracy for population sizes 50 and 100.

    Medium-scale dataset comparative analysis using classification accuracy for population sizes 50 and 100. by Olaide N. Oyelade (14047002)

    Published 2023
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  13. 113
    Large-scale dataset comparative analysis using classification accuracy for population sizes 50 and 100.

    Large-scale dataset comparative analysis using classification accuracy for population sizes 50 and 100. by Olaide N. Oyelade (14047002)

    Published 2023
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  14. 114
    Characteristics of the data sets used to evaluate Machine Learning and Deep Learning models for arboviral diseases classification.

    Characteristics of the data sets used to evaluate Machine Learning and Deep Learning models for arboviral diseases classification. by Sebastião Rogério da Silva Neto (11944944)

    Published 2022
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  15. 115
    Binary leaf image rotated anti-clock wise from the origin degree to 40<sup><i>o</i></sup>, 80<sup><i>o</i></sup>, 120<sup><i>o</i></sup>, and 160<sup><i>o</i></sup> from left to right respectively to verify rotation invariance with their corresponding curve-fitted data.

    Binary leaf image rotated anti-clock wise from the origin degree to 40<sup><i>o</i></sup>, 80<sup><i>o</i></sup>, 120<sup><i>o</i></sup>, and 160<sup><i>o</i></sup> from left to ri... by Ali Ahmed (2567584)

    Published 2020
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  16. 116
    Classification report based on HaPi.

    Classification report based on HaPi. by Akib Mohi Ud Din Khanday (19065631)

    Published 2024
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  17. 117
    Using the Recall, Precision, and F1-score metrics, three model types are compared: naïve deep learning models, advanced deep learning models, and the proposed framework for binary classification of normal class in an imbalanced CTG test dataset.

    Using the Recall, Precision, and F1-score metrics, three model types are compared: naïve deep learning models, advanced deep learning models, and the proposed framework for binary... by Amin Ullah (12015113)

    Published 2025
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  18. 118
    Using the Recall, Precision, and F1-score metrics, three model types are compared: naïve deep learning models, advanced deep learning models, and the proposed framework for binary classification of abnormal class in an imbalanced CTG test dataset.

    Using the Recall, Precision, and F1-score metrics, three model types are compared: naïve deep learning models, advanced deep learning models, and the proposed framework for binary... by Amin Ullah (12015113)

    Published 2025
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  19. 119
    A classification scheme of bipartite randomization algorithms, based on whether the matrix row and columns sums are preserved exactly (Fixed, <i>F</i>), preserved on average (Proportional, <i>P</i>), or unconstrained (Equiprobable, <i>E</i>).

    A classification scheme of bipartite randomization algorithms, based on whether the matrix row and columns sums are preserved exactly (Fixed, <i>F</i>), preserved on average (Propo... by Zachary P. Neal (9946331)

    Published 2024
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  20. 120
    Attributes found in the data sets.

    Attributes found in the data sets. by Sebastião Rogério da Silva Neto (11944944)

    Published 2022
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