يعرض 1 - 11 نتائج من 11 نتيجة بحث عن '(( binary art design optimization algorithm ) OR ( binary data wolf optimization algorithm ))', وقت الاستعلام: 0.34s تنقيح النتائج
  1. 1
  2. 2

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

    منشور في 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. …"
  3. 3

    Classification baseline performance. حسب Doaa Sami Khafaga (21463870)

    منشور في 2025
    "…To overcome these limitations, this study introduces a comprehensive deep learning framework enhanced with the innovative bio-inspired Ocotillo Optimization Algorithm (OcOA), designed to improve the accuracy and efficiency of bone marrow cell classification. …"
  4. 4

    Feature selection results. حسب Doaa Sami Khafaga (21463870)

    منشور في 2025
    "…To overcome these limitations, this study introduces a comprehensive deep learning framework enhanced with the innovative bio-inspired Ocotillo Optimization Algorithm (OcOA), designed to improve the accuracy and efficiency of bone marrow cell classification. …"
  5. 5

    ANOVA test result. حسب Doaa Sami Khafaga (21463870)

    منشور في 2025
    "…To overcome these limitations, this study introduces a comprehensive deep learning framework enhanced with the innovative bio-inspired Ocotillo Optimization Algorithm (OcOA), designed to improve the accuracy and efficiency of bone marrow cell classification. …"
  6. 6

    Summary of literature review. حسب Doaa Sami Khafaga (21463870)

    منشور في 2025
    "…To overcome these limitations, this study introduces a comprehensive deep learning framework enhanced with the innovative bio-inspired Ocotillo Optimization Algorithm (OcOA), designed to improve the accuracy and efficiency of bone marrow cell classification. …"
  7. 7

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

    منشور في 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. …"
  8. 8

    Topic description. حسب Muhammad Ayyaz Sheikh (18610943)

    منشور في 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. …"
  9. 9

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

    منشور في 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. …"
  10. 10

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

    منشور في 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. …"
  11. 11

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

    منشور في 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. …"