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Showing 1 - 20 results of 55 for search '(( binary based area classification algorithm ) OR ( binary a whale optimization algorithm ))', query time: 0.95s Refine Results
  1. 1
    Urgent versus nonurgent binary classification performance.

    Urgent versus nonurgent binary classification performance. by Kyung Won Kwon (18150416)

    Published 2024
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  2. 2
    ROC curve for binary classification.

    ROC curve for binary classification. by Nicodemus Songose Awarayi (18414494)

    Published 2024
    “…The model further showed superior results on binary classification compared with existing methods. …”
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  3. 3
    Confusion matrix for binary classification.

    Confusion matrix for binary classification. by Nicodemus Songose Awarayi (18414494)

    Published 2024
    “…The model further showed superior results on binary classification compared with existing methods. …”
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  4. 4
    Accuracy assessment of 2017 binary classification and multi-classification result (OA(Overall accuracy), PA(Producer accuracy), UA(User accuracy), ML(Maximum likelihood)).

    Accuracy assessment of 2017 binary classification and multi-classification result (OA(Overall accuracy), PA(Producer accuracy), UA(User accuracy), ML(Maximum likelihood)). by Lei Liang (326409)

    Published 2023
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  5. 5
    Breakdown of participants by residential area.

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

    Published 2025
    “…The dataset was created, and AI-based binary classification models for malocclusion were developed using an automated machine learning platform (DataRobot) to construct three algorithms for determining malocclusion (deep bite, maxillary protrusion, and crowding). …”
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  6. 6
    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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  7. 7
    Explicability results of the crowding classification model.

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

    Published 2025
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  8. 8
    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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  9. 9
    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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  10. 10
    Summary of all non-clinical data (laboratory and others) presented in the data set used by the primary studies.

    Summary of all non-clinical data (laboratory and others) presented in the data set used by the primary studies. by Sebastião Rogério da Silva Neto (11944944)

    Published 2022
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  11. 11
    Models used in the works divided by the main problems.

    Models used in the works divided by the main problems. by Sebastião Rogério da Silva Neto (11944944)

    Published 2022
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  12. 12
    Study selection.

    Study selection. by Sebastião Rogério da Silva Neto (11944944)

    Published 2022
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  13. 13
    Summary of all demographic, epidemiological and clinical data presented in data set used by the primary studies.

    Summary of all demographic, epidemiological and clinical data presented in data set used by the primary studies. by Sebastião Rogério da Silva Neto (11944944)

    Published 2022
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  14. 14
    Distribution of samples per classes.

    Distribution of samples per classes. by Sebastião Rogério da Silva Neto (11944944)

    Published 2022
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  15. 15
    Overview of the primary studies.

    Overview of the primary studies. by Sebastião Rogério da Silva Neto (11944944)

    Published 2022
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  16. 16
    Metrics used to evaluate the models proposed in the literature.

    Metrics used to evaluate the models proposed in the literature. by Sebastião Rogério da Silva Neto (11944944)

    Published 2022
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  17. 17
    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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  18. 18
    Association between crowding and oral habits.

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

    Published 2025
    “…The dataset was created, and AI-based binary classification models for malocclusion were developed using an automated machine learning platform (DataRobot) to construct three algorithms for determining malocclusion (deep bite, maxillary protrusion, and crowding). …”
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  19. 19
    Association between deep bite and oral habits.

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

    Published 2025
    “…The dataset was created, and AI-based binary classification models for malocclusion were developed using an automated machine learning platform (DataRobot) to construct three algorithms for determining malocclusion (deep bite, maxillary protrusion, and crowding). …”
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  20. 20
    Each variable for the dataset.

    Each variable for the dataset. by Kengo Oka (1420585)

    Published 2025
    “…The dataset was created, and AI-based binary classification models for malocclusion were developed using an automated machine learning platform (DataRobot) to construct three algorithms for determining malocclusion (deep bite, maxillary protrusion, and crowding). …”
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