Showing 801 - 820 results of 1,453 for search '(( algorithm within function ) OR ((( algorithm python function ) OR ( algorithm both function ))))', query time: 0.43s Refine Results
  1. 801

    Gene expression feature importance ranking. by Ruiyu Zhan (21602031)

    Published 2025
    “…We performed comparative analyses against other methodologies across various functions and public datasets to assess their effectiveness. …”
  2. 802

    BP schematic diagram. by Ruiyu Zhan (21602031)

    Published 2025
    “…We performed comparative analyses against other methodologies across various functions and public datasets to assess their effectiveness. …”
  3. 803

    DNAmethylationfeature importance ranking. by Ruiyu Zhan (21602031)

    Published 2025
    “…We performed comparative analyses against other methodologies across various functions and public datasets to assess their effectiveness. …”
  4. 804

    Pearson correlation heatmap of DNAmethylation. by Ruiyu Zhan (21602031)

    Published 2025
    “…We performed comparative analyses against other methodologies across various functions and public datasets to assess their effectiveness. …”
  5. 805
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  7. 807

    Right Knee fNIRS MI Dataset by Hammad Gilani (8060012)

    Published 2025
    “…<p><br></p><p dir="ltr">This dataset contains functional near-infrared spectroscopy (fNIRS) signals recorded during motor imagery (MI) tasks of lower limb movements. …”
  8. 808

    Left Knee fNIRS MI Dataset by Hammad Gilani (8060012)

    Published 2025
    “…<p><br></p><p dir="ltr">This dataset contains functional near-infrared spectroscopy (fNIRS) signals recorded during motor imagery (MI) tasks of lower limb movements. …”
  9. 809

    Left Ankle fNIRS MI Dataset by Hammad Gilani (8060012)

    Published 2025
    “…<p><br></p><p dir="ltr">This dataset contains functional near-infrared spectroscopy (fNIRS) signals recorded during motor imagery (MI) tasks of lower limb movements. …”
  10. 810
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  14. 814

    Two datasets. by Salem A. Alyami (10572611)

    Published 2024
    “…<div><p>This article examines the estimate of <i>ϑ</i> = <i>P</i> [<i>T</i> < <i>Q</i>], using both Bayesian and non-Bayesian methods, utilizing progressively first-failure censored data. …”
  15. 815

    Histogram of MCMC estimates of <i>ϑ</i>. by Salem A. Alyami (10572611)

    Published 2024
    “…<div><p>This article examines the estimate of <i>ϑ</i> = <i>P</i> [<i>T</i> < <i>Q</i>], using both Bayesian and non-Bayesian methods, utilizing progressively first-failure censored data. …”
  16. 816

    Trace plot of MCMC estimates of <i>ϑ</i>. by Salem A. Alyami (10572611)

    Published 2024
    “…<div><p>This article examines the estimate of <i>ϑ</i> = <i>P</i> [<i>T</i> < <i>Q</i>], using both Bayesian and non-Bayesian methods, utilizing progressively first-failure censored data. …”
  17. 817

    The POFIF-C samples from the real data sets. by Salem A. Alyami (10572611)

    Published 2024
    “…<div><p>This article examines the estimate of <i>ϑ</i> = <i>P</i> [<i>T</i> < <i>Q</i>], using both Bayesian and non-Bayesian methods, utilizing progressively first-failure censored data. …”
  18. 818
  19. 819

    General Chemically Intuitive Atom- and Bond-Level DFT Descriptors for Machine Learning Approaches to Reaction Condition Prediction by Miguel Nouman (21557202)

    Published 2025
    “…Remarkably, the best performing neural network trained on hybrid embeddings outperforms the best purely structural model investigated despite the latter benefiting from of an embedding strategy with 267 times more data points than the one used for generating and embedding hybrid descriptors, with both strategies being unsupervised learning algorithms that share considerable conceptual and architectural similarities.…”
  20. 820

    General Chemically Intuitive Atom- and Bond-Level DFT Descriptors for Machine Learning Approaches to Reaction Condition Prediction by Miguel Nouman (21557202)

    Published 2025
    “…Remarkably, the best performing neural network trained on hybrid embeddings outperforms the best purely structural model investigated despite the latter benefiting from of an embedding strategy with 267 times more data points than the one used for generating and embedding hybrid descriptors, with both strategies being unsupervised learning algorithms that share considerable conceptual and architectural similarities.…”