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Showing 1 - 20 results of 33 for search '(( binary based early classification algorithm ) OR ( binary 2 whale optimization algorithm ))', query time: 0.64s Refine Results
  1. 1
    The classification algorithm flow chart for binary classification among NC, EMCI, LMCI and AD groups.

    The classification algorithm flow chart for binary classification among NC, EMCI, LMCI and AD groups. by Qian Zhang (49333)

    Published 2022
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  2. 2
    For each classification algorithm, the classification accuracies for binary subject groups NC versus EMCI, NC versus LEMC, EMCI versus LEMC, EMCI versus AD, LMCI versus AD, AD versus NC.

    For each classification algorithm, the classification accuracies for binary subject groups NC versus EMCI, NC versus LEMC, EMCI versus LEMC, EMCI versus AD, LMCI versus AD, AD vers... by Qian Zhang (49333)

    Published 2022
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  3. 3
    Explicability results of the crowding classification model.

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

    Published 2025
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  4. 4
    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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  5. 5
    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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  6. 6
    Structure of the proposed hybrid CNN-SVD-based ensemble model for lung cancer classification.

    Structure of the proposed hybrid CNN-SVD-based ensemble model for lung cancer classification. by Md. Sabbir Hossain (9958939)

    Published 2025
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  7. 7
    Association between maxillary protrusion and oral habits.

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

    Published 2025
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  8. 8
    Association between crowding and oral habits.

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

    Published 2025
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  9. 9
    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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  10. 10
    Association between deep bite and oral habits.

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

    Published 2025
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  11. 11
    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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  12. 12
    Representative photographic image.

    Representative photographic image. by Kengo Oka (1420585)

    Published 2025
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  13. 13
    Breakdown of participants by residential area.

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

    Published 2025
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  14. 14
    Flowchart of study participants and design.

    Flowchart of study participants and design. by Kengo Oka (1420585)

    Published 2025
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  15. 15
    Breakdown of participants by annual household income.

    Breakdown of participants by annual household income. by Kengo Oka (1420585)

    Published 2025
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  16. 16
    Performance metrics of the machine learning models on the test dataset.

    Performance metrics of the machine learning models on the test dataset. by Kengo Oka (1420585)

    Published 2025
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  17. 17
    Each variable for the dataset.

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

    Published 2025
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  18. 18
    Random forest model performs better than support vector machine algorithms and when it primarily uses spontaneous photopic ERG of 60-s duration in humans.

    Random forest model performs better than support vector machine algorithms and when it primarily uses spontaneous photopic ERG of 60-s duration in humans. by Ramsés Noguez Imm (6658637)

    Published 2023
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  19. 19
    Hyperparameters of the LSTM Model.

    Hyperparameters of the LSTM Model. by Ahmed M. Elshewey (21463867)

    Published 2025
    “…The capacity to confront and overcome this obstacle is where machine learning and metaheuristic algorithms shine. This study introduces the Adaptive Dynamic Particle Swarm Optimization enhanced with the Guided Whale Optimization Algorithm (AD-PSO-Guided WOA) for rainfall prediction. …”
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  20. 20
    The AD-PSO-Guided WOA LSTM framework.

    The AD-PSO-Guided WOA LSTM framework. by Ahmed M. Elshewey (21463867)

    Published 2025
    “…The capacity to confront and overcome this obstacle is where machine learning and metaheuristic algorithms shine. This study introduces the Adaptive Dynamic Particle Swarm Optimization enhanced with the Guided Whale Optimization Algorithm (AD-PSO-Guided WOA) for rainfall prediction. …”
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