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Showing 1 - 20 results of 21 for search '(( binary orange surface optimization algorithm ) OR ( primary late based optimization algorithm ))', query time: 0.62s Refine Results
  1. 1
    Table_8_Preliminary prediction of semen quality based on modifiable lifestyle factors by using the XGBoost algorithm.docx

    Table_8_Preliminary prediction of semen quality based on modifiable lifestyle factors by using the XGBoost algorithm.docx by Mingjuan Zhou (12880019)

    Published 2022
    “…</p>Conclusion<p>The preliminary lifestyle-based model developed for semen quality prediction by using the XGBoost algorithm showed potential for clinical application and further optimization with larger training datasets.…”
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  2. 2
    Table_5_Preliminary prediction of semen quality based on modifiable lifestyle factors by using the XGBoost algorithm.docx

    Table_5_Preliminary prediction of semen quality based on modifiable lifestyle factors by using the XGBoost algorithm.docx by Mingjuan Zhou (12880019)

    Published 2022
    “…</p>Conclusion<p>The preliminary lifestyle-based model developed for semen quality prediction by using the XGBoost algorithm showed potential for clinical application and further optimization with larger training datasets.…”
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  3. 3
    Table_1_Preliminary prediction of semen quality based on modifiable lifestyle factors by using the XGBoost algorithm.docx

    Table_1_Preliminary prediction of semen quality based on modifiable lifestyle factors by using the XGBoost algorithm.docx by Mingjuan Zhou (12880019)

    Published 2022
    “…</p>Conclusion<p>The preliminary lifestyle-based model developed for semen quality prediction by using the XGBoost algorithm showed potential for clinical application and further optimization with larger training datasets.…”
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  4. 4
    Table_9_Preliminary prediction of semen quality based on modifiable lifestyle factors by using the XGBoost algorithm.docx

    Table_9_Preliminary prediction of semen quality based on modifiable lifestyle factors by using the XGBoost algorithm.docx by Mingjuan Zhou (12880019)

    Published 2022
    “…</p>Conclusion<p>The preliminary lifestyle-based model developed for semen quality prediction by using the XGBoost algorithm showed potential for clinical application and further optimization with larger training datasets.…”
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  5. 5
    Table_7_Preliminary prediction of semen quality based on modifiable lifestyle factors by using the XGBoost algorithm.docx

    Table_7_Preliminary prediction of semen quality based on modifiable lifestyle factors by using the XGBoost algorithm.docx by Mingjuan Zhou (12880019)

    Published 2022
    “…</p>Conclusion<p>The preliminary lifestyle-based model developed for semen quality prediction by using the XGBoost algorithm showed potential for clinical application and further optimization with larger training datasets.…”
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  6. 6
    Table_4_Preliminary prediction of semen quality based on modifiable lifestyle factors by using the XGBoost algorithm.docx

    Table_4_Preliminary prediction of semen quality based on modifiable lifestyle factors by using the XGBoost algorithm.docx by Mingjuan Zhou (12880019)

    Published 2022
    “…</p>Conclusion<p>The preliminary lifestyle-based model developed for semen quality prediction by using the XGBoost algorithm showed potential for clinical application and further optimization with larger training datasets.…”
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  7. 7
    Table_2_Preliminary prediction of semen quality based on modifiable lifestyle factors by using the XGBoost algorithm.docx

    Table_2_Preliminary prediction of semen quality based on modifiable lifestyle factors by using the XGBoost algorithm.docx by Mingjuan Zhou (12880019)

    Published 2022
    “…</p>Conclusion<p>The preliminary lifestyle-based model developed for semen quality prediction by using the XGBoost algorithm showed potential for clinical application and further optimization with larger training datasets.…”
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  8. 8
    Table_6_Preliminary prediction of semen quality based on modifiable lifestyle factors by using the XGBoost algorithm.docx

    Table_6_Preliminary prediction of semen quality based on modifiable lifestyle factors by using the XGBoost algorithm.docx by Mingjuan Zhou (12880019)

    Published 2022
    “…</p>Conclusion<p>The preliminary lifestyle-based model developed for semen quality prediction by using the XGBoost algorithm showed potential for clinical application and further optimization with larger training datasets.…”
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  9. 9
    Table_10_Preliminary prediction of semen quality based on modifiable lifestyle factors by using the XGBoost algorithm.docx

    Table_10_Preliminary prediction of semen quality based on modifiable lifestyle factors by using the XGBoost algorithm.docx by Mingjuan Zhou (12880019)

    Published 2022
    “…</p>Conclusion<p>The preliminary lifestyle-based model developed for semen quality prediction by using the XGBoost algorithm showed potential for clinical application and further optimization with larger training datasets.…”
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  10. 10
    Table_3_Preliminary prediction of semen quality based on modifiable lifestyle factors by using the XGBoost algorithm.docx

    Table_3_Preliminary prediction of semen quality based on modifiable lifestyle factors by using the XGBoost algorithm.docx by Mingjuan Zhou (12880019)

    Published 2022
    “…</p>Conclusion<p>The preliminary lifestyle-based model developed for semen quality prediction by using the XGBoost algorithm showed potential for clinical application and further optimization with larger training datasets.…”
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  11. 11
    Surround suppression of naturalistic textures and noise.

    Surround suppression of naturalistic textures and noise. by Xu Pan (517723)

    Published 2023
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  12. 12
    Data_Sheet_1_Optimizing Spatio-Temporal Allocation of the COVID-19 Vaccine Under Different Epidemiological Landscapes.pdf

    Data_Sheet_1_Optimizing Spatio-Temporal Allocation of the COVID-19 Vaccine Under Different Epidemiological Landscapes.pdf by Wen Cao (696121)

    Published 2022
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  13. 13
    Data_Sheet_1_Hierarchical multi-class Alzheimer’s disease diagnostic framework using imaging and clinical features.docx

    Data_Sheet_1_Hierarchical multi-class Alzheimer’s disease diagnostic framework using imaging and clinical features.docx by Yao Qin (385509)

    Published 2022
    “…Clinical Dementia Rating (CDR) was the primary clinical variable associated with AD-related populations. …”
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  14. 14
    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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  15. 15
    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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  16. 16
    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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  17. 17
    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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  18. 18
    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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  19. 19
    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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  20. 20
    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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