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Showing 1 - 6 results of 6 for search '(( binary likely design optimization algorithm ) OR ( binary image whale optimization algorithm ))*', query time: 0.54s Refine Results
  1. 1

    Classification baseline performance. by Doaa Sami Khafaga (21463870)

    Published 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. …”
  2. 2

    Feature selection results. by Doaa Sami Khafaga (21463870)

    Published 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. …”
  3. 3

    ANOVA test result. by Doaa Sami Khafaga (21463870)

    Published 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

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

    Published 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

    MCLP_quantum_annealer_V0.5 by Anonymous Anonymous (4854526)

    Published 2025
    “…This paper first proposes the QUBO-MCLP algorithm workflow and designs the Transformation Operator for Inequality Constraints Considering the Capacity of Accessible Providers (TOICCAP), which accounts for the scale of accessible supply points. …”
  6. 6

    Models and Dataset by M RN (9866504)

    Published 2025
    “…The algorithm does not rely on predefined control parameters like crossover or mutation rates, which makes it lightweight and easy to implement for various feature selection and optimization tasks.…”