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maximization algorithm » optimization algorithms (Expand Search), classification algorithm (Expand Search)
proteins maximization » profit maximization (Expand Search), protein modification (Expand Search)
random optimization » codon optimization (Expand Search), from optimization (Expand Search), carbon optimization (Expand Search)
based proteins » based protein (Expand Search), based proteomics (Expand Search), capsid proteins (Expand Search)
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Showing 1 - 20 results of 56 for search '(( binary based proteins maximization algorithm ) OR ( binary _ random optimization algorithm ))*', query time: 0.49s Refine Results
  1. 1
    Examples of random events and the respective binary outcome.

    Examples of random events and the respective binary outcome. by Matheus Sant’Ana Lima (9937809)

    Published 2021
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  2. 2
    Random forest with hyperparameter optimization.

    Random forest with hyperparameter optimization. by Balraj Preet Kaur (20370832)

    Published 2024
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  3. 3
    Best optimizer results for random forest.

    Best optimizer results for random forest. by Balraj Preet Kaur (20370832)

    Published 2024
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  4. 4
    Best optimizer results of Random Forest.

    Best optimizer results of Random Forest. by Balraj Preet Kaur (20370832)

    Published 2024
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  5. 5
    Results of Random Forest.

    Results of Random Forest. by Balraj Preet Kaur (20370832)

    Published 2024
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  6. 6
    Top-performing model configuration and performance by algorithm type.

    Top-performing model configuration and performance by algorithm type. by Uğur Ejder (22683228)

    Published 2025
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  7. 7
    Effects of Class Imbalance and Data Scarcity on the Performance of Binary Classification Machine Learning Models Developed Based on ToxCast/Tox21 Assay Data

    Effects of Class Imbalance and Data Scarcity on the Performance of Binary Classification Machine Learning Models Developed Based on ToxCast/Tox21 Assay Data by Changhun Kim (682542)

    Published 2022
    “…Therefore, the resampling algorithm employed should vary depending on the data distribution to achieve optimal classification performance. …”
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  8. 8
    ROC and PR–AUC curves of the ABC–LR–RF hybrid model for IVF outcome prediction.

    ROC and PR–AUC curves of the ABC–LR–RF hybrid model for IVF outcome prediction. by Uğur Ejder (22683228)

    Published 2025
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  9. 9
    The accuracy score change matrix following model development.

    The accuracy score change matrix following model development. by Uğur Ejder (22683228)

    Published 2025
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  10. 10
    The comparison of the accuracy score of the benchmark and the proposed models.

    The comparison of the accuracy score of the benchmark and the proposed models. by Uğur Ejder (22683228)

    Published 2025
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  11. 11
    The prediction accuracy of simple versus hybrid models.

    The prediction accuracy of simple versus hybrid models. by Uğur Ejder (22683228)

    Published 2025
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  12. 12
    Flow diagram of the proposed model.

    Flow diagram of the proposed model. by Uğur Ejder (22683228)

    Published 2025
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  13. 13
    The statistical description of the original data set of the patients (<i>n</i> = 162).

    The statistical description of the original data set of the patients (<i>n</i> = 162). by Uğur Ejder (22683228)

    Published 2025
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  14. 14
    Comparison of baseline and hybrid machine learning models in predicting IVF outcomes (%).

    Comparison of baseline and hybrid machine learning models in predicting IVF outcomes (%). by Uğur Ejder (22683228)

    Published 2025
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  15. 15
    The most effective active substances for subfertility treatment.

    The most effective active substances for subfertility treatment. by Uğur Ejder (22683228)

    Published 2025
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  16. 16
    Confusion matrix of the ABC–LR–RF hybrid model for IVF outcome prediction.

    Confusion matrix of the ABC–LR–RF hybrid model for IVF outcome prediction. by Uğur Ejder (22683228)

    Published 2025
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  17. 17
    The list of parameters of the modified data set for machine learning (<i>n</i> = 162).

    The list of parameters of the modified data set for machine learning (<i>n</i> = 162). by Uğur Ejder (22683228)

    Published 2025
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  18. 18
    Flow diagram of the conversion of drugs into active substances.

    Flow diagram of the conversion of drugs into active substances. by Uğur Ejder (22683228)

    Published 2025
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  19. 19
    Calibration curve of the ABC–LR–RF hybrid model for IVF outcome prediction.

    Calibration curve of the ABC–LR–RF hybrid model for IVF outcome prediction. by Uğur Ejder (22683228)

    Published 2025
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  20. 20
    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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