بدائل البحث:
after optimization » based optimization (توسيع البحث), model optimization (توسيع البحث), path optimization (توسيع البحث)
wolf optimization » whale optimization (توسيع البحث), swarm optimization (توسيع البحث), _ optimization (توسيع البحث)
primary data » primary care (توسيع البحث)
binary time » binary image (توسيع البحث)
time after » mice after (توسيع البحث)
after optimization » based optimization (توسيع البحث), model optimization (توسيع البحث), path optimization (توسيع البحث)
wolf optimization » whale optimization (توسيع البحث), swarm optimization (توسيع البحث), _ optimization (توسيع البحث)
primary data » primary care (توسيع البحث)
binary time » binary image (توسيع البحث)
time after » mice after (توسيع البحث)
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S1 Data -
منشور في 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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Parameter settings for algorithms.
منشور في 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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Parameter settings for algorithms.
منشور في 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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Average runtime of different algorithms.
منشور في 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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Average runtime of different algorithms.
منشور في 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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Flowchart of GJO-GWO algorithm.
منشور في 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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Detailed information of benchmark functions.
منشور في 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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Evaluation metrics of the models’ performance.
منشور في 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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Detailed information of datasets.
منشور في 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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Friedman test results.
منشور في 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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Average number of selected features.
منشور في 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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Wilcoxon rank sum test results.
منشور في 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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Wilcoxon rank sum test results.
منشور في 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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Average number of selected features.
منشور في 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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Table_1_Machine Learning Techniques in Blood Pressure Management During the Acute Phase of Ischemic Stroke.DOCX
منشور في 2022"…Decision trees were constructed by a hierarchical binary recursive partitioning algorithm to predict the BP-lowering of 10–30% off the maximal value when antihypertensive treatment was given in patients with an extremely high BP (above 220/110 or 180/105 mmHg for patients receiving thrombolysis), according to the American Heart Association/American Stroke Association (AHA/ASA), the European Society of Cardiology, and the European Society of Hypertension (ESC/ESH) guidelines. …"
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PathOlOgics_RBCs Python Scripts.zip
منشور في 2023"…</p><p dir="ltr">In terms of classification, a second algorithm was developed and employed to preliminary sort or group the individual cells (after excluding the overlapping cells manually) into different categories using five geometric measurements applied to the extracted contour from each binary image mask (see PathOlOgics_script_2; preliminary shape measurements). …"