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Showing 161 - 180 results of 11,094 for search '(( algorithms risk function ) OR ((( algorithm python function ) OR ( algorithm model function ))))', query time: 0.66s Refine Results
  1. 161
    Vehicle parameters.

    Vehicle parameters. by Honglei Pang (22693724)

    Published 2025
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  2. 162
    HIL test results of curved road: (a)Vehicle velocity; (b) Braking distance; (c) Lateral displacement; (d) Lateral acceleration; (e)Vehicle sideslip angle; (f) Yaw rate.

    HIL test results of curved road: (a)Vehicle velocity; (b) Braking distance; (c) Lateral displacement; (d) Lateral acceleration; (e)Vehicle sideslip angle; (f) Yaw rate. by Honglei Pang (22693724)

    Published 2025
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  3. 163
    Comparison of average reward values in various simulation scenarios.

    Comparison of average reward values in various simulation scenarios. by Honglei Pang (22693724)

    Published 2025
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  4. 164
    HIL test results of split-μ road: (a)Vehicle velocity; (b) Braking distance; (c) Lateral displacement; (d) Lateral acceleration; (e)Vehicle sideslip angle; (f) Yaw rate.

    HIL test results of split-μ road: (a)Vehicle velocity; (b) Braking distance; (c) Lateral displacement; (d) Lateral acceleration; (e)Vehicle sideslip angle; (f) Yaw rate. by Honglei Pang (22693724)

    Published 2025
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  5. 165
    Differences in Immune Signatures Between High and Low-Risk Groups.

    Differences in Immune Signatures Between High and Low-Risk Groups. by Jie Chu (293795)

    Published 2025
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  6. 166
    The speciation of ANEAT model evolution.

    The speciation of ANEAT model evolution. by Wenbing Shi (5806160)

    Published 2025
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  7. 167
    The fitness of ANEAT model evolution.

    The fitness of ANEAT model evolution. by Wenbing Shi (5806160)

    Published 2025
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  8. 168
    The fitness of NANEAT model evolution.

    The fitness of NANEAT model evolution. by Wenbing Shi (5806160)

    Published 2025
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  9. 169
    The speciation of NANEAT model evolution.

    The speciation of NANEAT model evolution. by Wenbing Shi (5806160)

    Published 2025
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  10. 170
    GSEA Analysis of Differentially Expressed Genes in High and Low-Risk Groups.

    GSEA Analysis of Differentially Expressed Genes in High and Low-Risk Groups. by Jie Chu (293795)

    Published 2025
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  11. 171
    GSVA analysis of differentially expressed genes in high and low risk groups.

    GSVA analysis of differentially expressed genes in high and low risk groups. by Jie Chu (293795)

    Published 2025
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  12. 172
    The GA iteration result.

    The GA iteration result. by Jiaqing Huang (5018492)

    Published 2025
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  13. 173
    GA crossover and mutation process.

    GA crossover and mutation process. by Jiaqing Huang (5018492)

    Published 2025
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  14. 174
    The structure for the FNN.

    The structure for the FNN. by Jiaqing Huang (5018492)

    Published 2025
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  15. 175
    Table 1_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx

    Table 1_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx by Jingjing Chen (293564)

    Published 2025
    “…A multi-algorithm machine learning framework was employed to optimize predictive performance, with model interpretability achieved through SHapley Additive exPlanations and diagnostic accuracy validated by receiver operating characteristic curves. …”
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  16. 176
    Table 4_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx

    Table 4_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx by Jingjing Chen (293564)

    Published 2025
    “…A multi-algorithm machine learning framework was employed to optimize predictive performance, with model interpretability achieved through SHapley Additive exPlanations and diagnostic accuracy validated by receiver operating characteristic curves. …”
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  17. 177
    Table 5_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx

    Table 5_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx by Jingjing Chen (293564)

    Published 2025
    “…A multi-algorithm machine learning framework was employed to optimize predictive performance, with model interpretability achieved through SHapley Additive exPlanations and diagnostic accuracy validated by receiver operating characteristic curves. …”
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  18. 178
    Table 2_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx

    Table 2_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx by Jingjing Chen (293564)

    Published 2025
    “…A multi-algorithm machine learning framework was employed to optimize predictive performance, with model interpretability achieved through SHapley Additive exPlanations and diagnostic accuracy validated by receiver operating characteristic curves. …”
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  19. 179
    Table 3_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx

    Table 3_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx by Jingjing Chen (293564)

    Published 2025
    “…A multi-algorithm machine learning framework was employed to optimize predictive performance, with model interpretability achieved through SHapley Additive exPlanations and diagnostic accuracy validated by receiver operating characteristic curves. …”
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  20. 180
    Flowchart of the AH-NSAFSA.

    Flowchart of the AH-NSAFSA. by Jihui Hu (19812736)

    Published 2024
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