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Showing 21 - 40 results of 60 for search '(( binary based process identification algorithm ) OR ( binary b process optimization algorithm ))', query time: 1.15s Refine Results
  1. 21
    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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  2. 22
    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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  3. 23
    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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  4. 24
    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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  5. 25
    Hierarchical clustering to infer a binary tree with <i>K</i> = 4 sampled populations.

    Hierarchical clustering to infer a binary tree with <i>K</i> = 4 sampled populations. by Tristan Mary-Huard (3864)

    Published 2023
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  6. 26
    Image processing workflow.

    Image processing workflow. by Denis Tamiev (7404980)

    Published 2020
    “…<p>Raw fluorescent microscope images (a) were processed with a binary segmentation algorithm, and clusters of bacterial cells were manually annotated. …”
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  7. 27
    An overview of our ceRNAR package.

    An overview of our ceRNAR package. by Yi-Wen Hsiao (13767916)

    Published 2022
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  8. 28
    Performance Comparison of State-of-the-Art WPT-MEC Offloading Methods.

    Performance Comparison of State-of-the-Art WPT-MEC Offloading Methods. by Hend Bayoumi (22693738)

    Published 2025
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  9. 29
    CDF of task latency, approximated as the inverse of the achieved computation rate.

    CDF of task latency, approximated as the inverse of the achieved computation rate. by Hend Bayoumi (22693738)

    Published 2025
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  10. 30
    The evolution of the Wireless Power Transfer (WPT) time fraction β over simulation frames.

    The evolution of the Wireless Power Transfer (WPT) time fraction β over simulation frames. by Hend Bayoumi (22693738)

    Published 2025
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  11. 31
    An Example of a WPT-MEC Network.

    An Example of a WPT-MEC Network. by Hend Bayoumi (22693738)

    Published 2025
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  12. 32
    Related Work Summary.

    Related Work Summary. by Hend Bayoumi (22693738)

    Published 2025
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  13. 33
    Simulation parameters.

    Simulation parameters. by Hend Bayoumi (22693738)

    Published 2025
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  14. 34
    Training losses for N = 10.

    Training losses for N = 10. by Hend Bayoumi (22693738)

    Published 2025
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  15. 35
    Normalized computation rate for N = 10.

    Normalized computation rate for N = 10. by Hend Bayoumi (22693738)

    Published 2025
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  16. 36
    Summary of Notations Used in this paper.

    Summary of Notations Used in this paper. by Hend Bayoumi (22693738)

    Published 2025
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  17. 37
    Application of ABC SMC to the fitting of the binary flip with cusp model.

    Application of ABC SMC to the fitting of the binary flip with cusp model. by Elena Camacho-Aguilar (10906327)

    Published 2021
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  18. 38
    Natural language processing and machine learning algorithm to identify brain MRI reports with acute ischemic stroke

    Natural language processing and machine learning algorithm to identify brain MRI reports with acute ischemic stroke by Chulho Kim (622686)

    Published 2019
    “…</p><p>Conclusions</p><p>Supervised ML based NLP algorithms are useful for automatic classification of brain MRI reports for identification of AIS patients. …”
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  19. 39
    Illustration of the steps used to construct the mask for identifying corner regions in Hi-C maps.

    Illustration of the steps used to construct the mask for identifying corner regions in Hi-C maps. by Luka Maisuradze (8791229)

    Published 2024
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  20. 40
    Validation of image processing steps and metric results using images of a microfluidic device.

    Validation of image processing steps and metric results using images of a microfluidic device. by Gavrielle R. Untracht (11820311)

    Published 2021
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