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estimation algorithm » optimization algorithms (Expand Search), maximization algorithm (Expand Search), detection algorithm (Expand Search)
phase optimization » whale optimization (Expand Search), based optimization (Expand Search), path optimization (Expand Search)
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Showing 1 - 11 results of 11 for search '(( binary b robust estimation algorithm ) OR ( binary d phase optimization algorithm ))*', query time: 0.49s Refine Results
  1. 1
    Reverse engineering information flow using stimulus-encoding and stimulus-transfer estimation algorithms.

    Reverse engineering information flow using stimulus-encoding and stimulus-transfer estimation algorithms. by Gabriel Matías Lorenz (21094672)

    Published 2025
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  2. 2
    Fig 2 -

    Fig 2 - by Olaide N. Oyelade (14047002)

    Published 2023
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  3. 3
    An illustration and comparison of the fitness convergence curves for the Large-scale dataset (WaveformEW), medium-scale dataset (Zoo dataset), and Small-scale (Wine dataset) using 50 and 100 population sizes.

    An illustration and comparison of the fitness convergence curves for the Large-scale dataset (WaveformEW), medium-scale dataset (Zoo dataset), and Small-scale (Wine dataset) using... by Olaide N. Oyelade (14047002)

    Published 2023
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  4. 4
    An illustration and comparison of the cost convergence curves for the Large-scale dataset (WaveformEW), medium-scale dataset (Zoo dataset), and Small-scale (Wine dataset) using 50 and 100 population sizes.

    An illustration and comparison of the cost convergence curves for the Large-scale dataset (WaveformEW), medium-scale dataset (Zoo dataset), and Small-scale (Wine dataset) using 50... by Olaide N. Oyelade (14047002)

    Published 2023
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  5. 5
    A radar plot illustrating the comparison of the classification accuracy, cost values, and fitness values for the hybrids of BEOSA when applied to some high-dimensional datasets using a variation of 50 and 100 population sizes.

    A radar plot illustrating the comparison of the classification accuracy, cost values, and fitness values for the hybrids of BEOSA when applied to some high-dimensional datasets usi... by Olaide N. Oyelade (14047002)

    Published 2023
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  6. 6
    A radar plot illustrating the comparison of the classification accuracy, cost values, and fitness values for the hybrids of BEOSA when applied to some medium-sized dimensional datasets using a variation of 50 and 100 population sizes.

    A radar plot illustrating the comparison of the classification accuracy, cost values, and fitness values for the hybrids of BEOSA when applied to some medium-sized dimensional data... by Olaide N. Oyelade (14047002)

    Published 2023
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  7. 7
    A radar plot illustrating the comparison of the classification accuracy, cost values, and fitness values for the hybrids of BEOSA when applied to some small-sized dimensional datasets using a variation of 50 and 100 population sizes.

    A radar plot illustrating the comparison of the classification accuracy, cost values, and fitness values for the hybrids of BEOSA when applied to some small-sized dimensional datas... by Olaide N. Oyelade (14047002)

    Published 2023
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  8. 8
    Overview of the segmentation algorithm.

    Overview of the segmentation algorithm. by Michael C. Robitaille (11949174)

    Published 2022
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  9. 9
    Fig 1 -

    Fig 1 - by Jakub Stoklosa (3315042)

    Published 2023
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  10. 10
    Comparisons of prediction of phenotypes by the microbiome.

    Comparisons of prediction of phenotypes by the microbiome. by Daphna Rothschild (561439)

    Published 2022
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  11. 11
    Data_Sheet_1_Alzheimer’s Disease Diagnosis and Biomarker Analysis Using Resting-State Functional MRI Functional Brain Network With Multi-Measures Features and Hippocampal Subfield and Amygdala Volume of Structural MRI.pdf

    Data_Sheet_1_Alzheimer’s Disease Diagnosis and Biomarker Analysis Using Resting-State Functional MRI Functional Brain Network With Multi-Measures Features and Hippocampal Subfield... by Uttam Khatri (12689072)

    Published 2022
    “…Finally, we implemented and compared the different feature selection algorithms to integrate the structural features, brain networks, and voxel features to optimize the diagnostic identifications of AD using support vector machine (SVM) classifiers. …”
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