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Showing 1 - 20 results of 323 for search '(( binary a guide optimization algorithm ) OR ( primary data based optimization algorithm ))', query time: 0.69s Refine Results
  1. 1
    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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  2. 2
    Features selected by optimization algorithms.

    Features selected by optimization algorithms. by Afnan M. Alhassan (18349378)

    Published 2024
    “…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …”
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  3. 3
    The convergence curve of each optimization algorithm under the test function.

    The convergence curve of each optimization algorithm under the test function. by Hao Yang (328526)

    Published 2025
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  4. 4
    Data_Sheet_1_How can age-based vaccine allocation strategies be optimized? A multi-objective optimization framework.ZIP

    Data_Sheet_1_How can age-based vaccine allocation strategies be optimized? A multi-objective optimization framework.ZIP by Hao Wu (65943)

    Published 2022
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  5. 5
    S1 Data -

    S1 Data - by Guangwei Liu (181992)

    Published 2024
    “…<div><p>This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). …”
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  6. 6
    Parameter settings for algorithms.

    Parameter settings for algorithms. by Guangwei Liu (181992)

    Published 2024
    “…<div><p>This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). …”
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  7. 7
    Parameter settings for algorithms.

    Parameter settings for algorithms. by Guangwei Liu (181992)

    Published 2024
    “…<div><p>This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). …”
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  8. 8
    Average runtime of different algorithms.

    Average runtime of different algorithms. by Guangwei Liu (181992)

    Published 2024
    “…<div><p>This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). …”
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  9. 9
    Average runtime of different algorithms.

    Average runtime of different algorithms. by Guangwei Liu (181992)

    Published 2024
    “…<div><p>This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). …”
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  10. 10
    Flowchart of GJO-GWO algorithm.

    Flowchart of GJO-GWO algorithm. by Guangwei Liu (181992)

    Published 2024
    “…<div><p>This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). …”
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  11. 11
    Routing policy based on path satisfaction.

    Routing policy based on path satisfaction. by Yang Yu (4292)

    Published 2025
    “…These enhancements aim to achieve optimal routing scheduling based on risk information. …”
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  12. 12
    Hybrid feature selection algorithm of CSCO-ROA.

    Hybrid feature selection algorithm of CSCO-ROA. by Afnan M. Alhassan (18349378)

    Published 2024
    “…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …”
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  13. 13
    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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  14. 14
    Prediction results of individual models.

    Prediction results of individual models. 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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  15. 15
    FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology

    FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology by Pieter B. Burger (4172578)

    Published 2024
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  16. 16
    FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology

    FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology by Pieter B. Burger (4172578)

    Published 2024
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  17. 17
    FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology

    FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology by Pieter B. Burger (4172578)

    Published 2024
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  18. 18
    FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology

    FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology by Pieter B. Burger (4172578)

    Published 2024
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  19. 19
    FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology

    FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology by Pieter B. Burger (4172578)

    Published 2024
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  20. 20
    FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology

    FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology by Pieter B. Burger (4172578)

    Published 2024
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