يعرض 41 - 60 نتائج من 61 نتيجة بحث عن '(( binary b process optimization algorithm ) OR ( binary b based optimization algorithm ))*', وقت الاستعلام: 0.52s تنقيح النتائج
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    Table_1_bSRWPSO-FKNN: A boosted PSO with fuzzy K-nearest neighbor classifier for predicting atopic dermatitis disease.docx حسب Yupeng Li (507508)

    منشور في 2023
    "…In bSRWPSO-FKNN, the core of which is to optimize the classification performance of FKNN through binary SRWPSO.…"
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    Fig 12 - حسب Nisha Yadav (366131)

    منشور في 2024
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    Flowchart scheme of the ML-based model. حسب Noshaba Qasmi (20405009)

    منشور في 2024
    "…<b>Fii)</b> Texture information using local binary patterns. …"
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    Fig 1 - حسب Jakub Stoklosa (3315042)

    منشور في 2023
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    PathOlOgics_RBCs Python Scripts.zip حسب Ahmed Elsafty (16943883)

    منشور في 2023
    "…This process generated a ground-truth binary semantic segmentation mask and determined the bounding box coordinates (XYWH) for each cell. …"
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    Steps in the extraction of 14 coordinates from the CT slices for the curved MPR. حسب Linus Woitke (22783534)

    منشور في 2025
    "…Protruding paths are then eliminated using graph-based optimization algorithms, as demonstrated in f). …"
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    Models and Dataset حسب M RN (9866504)

    منشور في 2025
    "…</p><p dir="ltr"><br></p><p dir="ltr"><b>RAO (Rao Optimization Algorithm):</b><br>RAO is a parameter-less optimization algorithm that updates solutions based on simple arithmetic operations involving the best and worst individuals in the population. …"
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    Supplementary Material 8 حسب Nishitha R Kumar (19750617)

    منشور في 2025
    "…</li><li><b>XGboost: </b>An optimized gradient boosting algorithm that efficiently handles large genomic datasets, commonly used for high-accuracy predictions in <i>E. coli</i> classification.…"
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    Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles حسب Soham Savarkar (21811825)

    منشور في 2025
    "…</p><p dir="ltr">Encoding: Categorical variables such as surface coating and cell type were grouped into logical classes and label-encoded to enable model compatibility.</p><p dir="ltr"><b>Applications and Model Compatibility:</b></p><p dir="ltr">The dataset is optimized for use in supervised learning workflows and has been tested with algorithms such as:</p><p dir="ltr">Gradient Boosting Machines (GBM),</p><p dir="ltr">Support Vector Machines (SVM-RBF),</p><p dir="ltr">Random Forests, and</p><p dir="ltr">Principal Component Analysis (PCA) for feature reduction.…"