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Showing 1 - 11 results of 11 for search '(( binary data wolf optimization algorithm ) OR ( primary care yet optimization algorithm ))', query time: 1.04s Refine Results
  1. 1
    The flowchart of the proposed algorithm.

    The flowchart of the proposed algorithm. by Muhammad Ayyaz Sheikh (18610943)

    Published 2024
    “…To overcome this limitation, recent advancements have introduced multi-objective evolutionary algorithms for ATS. This study proposes an enhancement to the performance of ATS through the utilization of an improved version of the Binary Multi-Objective Grey Wolf Optimizer (BMOGWO), incorporating mutation. …”
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  2. 2
    The relationship between the prevalence of PE and failure rate of each diagnostic strategy.

    The relationship between the prevalence of PE and failure rate of each diagnostic strategy. by Geert-Jan Geersing (331049)

    Published 2022
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  3. 3
    Forest plot of failure rate and efficiency of the diagnostic strategies across healthcare settings.

    Forest plot of failure rate and efficiency of the diagnostic strategies across healthcare settings. by Geert-Jan Geersing (331049)

    Published 2022
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  4. 4
    The relationship between the prevalence of PE and efficiency of each diagnostic strategy.

    The relationship between the prevalence of PE and efficiency of each diagnostic strategy. by Geert-Jan Geersing (331049)

    Published 2022
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  5. 5
    Patient characteristics across different healthcare settings.

    Patient characteristics across different healthcare settings. by Geert-Jan Geersing (331049)

    Published 2022
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  6. 6
    Sensitivity and specificity of diagnostic strategies across healthcare settings.

    Sensitivity and specificity of diagnostic strategies across healthcare settings. by Geert-Jan Geersing (331049)

    Published 2022
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  7. 7
    Summary of literature review.

    Summary of literature review. by Muhammad Ayyaz Sheikh (18610943)

    Published 2024
    “…To overcome this limitation, recent advancements have introduced multi-objective evolutionary algorithms for ATS. This study proposes an enhancement to the performance of ATS through the utilization of an improved version of the Binary Multi-Objective Grey Wolf Optimizer (BMOGWO), incorporating mutation. …”
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  8. 8
    Topic description.

    Topic description. by Muhammad Ayyaz Sheikh (18610943)

    Published 2024
    “…To overcome this limitation, recent advancements have introduced multi-objective evolutionary algorithms for ATS. This study proposes an enhancement to the performance of ATS through the utilization of an improved version of the Binary Multi-Objective Grey Wolf Optimizer (BMOGWO), incorporating mutation. …”
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  9. 9
    Notations along with their descriptions.

    Notations along with their descriptions. by Muhammad Ayyaz Sheikh (18610943)

    Published 2024
    “…To overcome this limitation, recent advancements have introduced multi-objective evolutionary algorithms for ATS. This study proposes an enhancement to the performance of ATS through the utilization of an improved version of the Binary Multi-Objective Grey Wolf Optimizer (BMOGWO), incorporating mutation. …”
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  10. 10
    Detail of the topics extracted from DUC2002.

    Detail of the topics extracted from DUC2002. by Muhammad Ayyaz Sheikh (18610943)

    Published 2024
    “…To overcome this limitation, recent advancements have introduced multi-objective evolutionary algorithms for ATS. This study proposes an enhancement to the performance of ATS through the utilization of an improved version of the Binary Multi-Objective Grey Wolf Optimizer (BMOGWO), incorporating mutation. …”
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  11. 11
    Table_1_An efficient decision support system for leukemia identification utilizing nature-inspired deep feature optimization.pdf

    Table_1_An efficient decision support system for leukemia identification utilizing nature-inspired deep feature optimization.pdf by Muhammad Awais (263096)

    Published 2024
    “…Next, a hybrid feature extraction approach is presented leveraging transfer learning from selected deep neural network models, InceptionV3 and DenseNet201, to extract comprehensive feature sets. To optimize feature selection, a customized binary Grey Wolf Algorithm is utilized, achieving an impressive 80% reduction in feature size while preserving key discriminative information. …”
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