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Showing 1 - 12 results of 12 for search '(( binary basic model optimization algorithm ) OR ( primary drivers based optimization algorithm ))', query time: 0.65s Refine Results
  1. 1
    Presentation_1_Safety-oriented planning of expressway truck service areas based on driver demand.pdf

    Presentation_1_Safety-oriented planning of expressway truck service areas based on driver demand.pdf by Wenlong Ding (492768)

    Published 2022
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  2. 2
    Presentation_2_Safety-oriented planning of expressway truck service areas based on driver demand.pdf

    Presentation_2_Safety-oriented planning of expressway truck service areas based on driver demand.pdf by Wenlong Ding (492768)

    Published 2022
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  3. 3
    Data_Sheet_1_A real-time driver fatigue identification method based on GA-GRNN.ZIP

    Data_Sheet_1_A real-time driver fatigue identification method based on GA-GRNN.ZIP by Xiaoyuan Wang (492534)

    Published 2022
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  4. 4
    All online review text data.

    All online review text data. by Yuandi Jiang (16540833)

    Published 2025
    “…Based on the comprehensive perspective of typology and spatio-temporal dynamic evolution, this study not only provides empirical support for museum space optimization, but also provides new ideas and strategies for functional research and methodological insights of public spaces.…”
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  5. 5
    Image 4_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.pdf

    Image 4_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.pdf by Liping Tang (77094)

    Published 2025
    “…We then applied ten machine learning algorithm to exosomal transcriptomic data to evaluate diagnostic performance and identify the optimal classifier. …”
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  6. 6
    Image 1_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.tif

    Image 1_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.tif by Liping Tang (77094)

    Published 2025
    “…We then applied ten machine learning algorithm to exosomal transcriptomic data to evaluate diagnostic performance and identify the optimal classifier. …”
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  7. 7
    Image 7_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.tif

    Image 7_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.tif by Liping Tang (77094)

    Published 2025
    “…We then applied ten machine learning algorithm to exosomal transcriptomic data to evaluate diagnostic performance and identify the optimal classifier. …”
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  8. 8
    Image 2_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.pdf

    Image 2_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.pdf by Liping Tang (77094)

    Published 2025
    “…We then applied ten machine learning algorithm to exosomal transcriptomic data to evaluate diagnostic performance and identify the optimal classifier. …”
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  9. 9
    Image 3_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.pdf

    Image 3_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.pdf by Liping Tang (77094)

    Published 2025
    “…We then applied ten machine learning algorithm to exosomal transcriptomic data to evaluate diagnostic performance and identify the optimal classifier. …”
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  10. 10
    Image 5_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.pdf

    Image 5_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.pdf by Liping Tang (77094)

    Published 2025
    “…We then applied ten machine learning algorithm to exosomal transcriptomic data to evaluate diagnostic performance and identify the optimal classifier. …”
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  11. 11
    Image 6_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.pdf

    Image 6_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.pdf by Liping Tang (77094)

    Published 2025
    “…We then applied ten machine learning algorithm to exosomal transcriptomic data to evaluate diagnostic performance and identify the optimal classifier. …”
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  12. 12
    Data Sheet 1_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.zip

    Data Sheet 1_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.zip by Liping Tang (77094)

    Published 2025
    “…We then applied ten machine learning algorithm to exosomal transcriptomic data to evaluate diagnostic performance and identify the optimal classifier. …”
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