يعرض 1 - 20 نتائج من 6,642 نتيجة بحث عن '(((( learning test decrease ) OR ( a marker decrease ))) OR ( i values decrease ))', وقت الاستعلام: 0.58s تنقيح النتائج
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    Validation of genetic markers for risk of OA or knee OA for decrease in minJSW. حسب Mieke L. M. Bentvelzen (21594442)

    منشور في 2025
    "…<p><b>(A)</b> Manhattan plot of minJSW decrease at 24 months. The Manhattan plots show the -log10(P) values of all ~ 1,5 million SNPs against their position. …"
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    Image 1_Exploration of the diagnostic and prognostic roles of decreased autoantibodies in lung cancer.tif حسب Ying Ye (72583)

    منشور في 2025
    "…</p>Results<p>In total, 15 types of decreased autoantibodies were identified, and 6 of them were constructed into a predictive model for early lung cancer, reaching a sensitivity of 76.19% and a specificity of 55.74%. …"
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    Class-wise performance on the test set. حسب Muhammad Zubair (728141)

    منشور في 2025
    الموضوعات:
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    The MAE value of the model under raw data. حسب Xiangjuan Liu (618000)

    منشور في 2025
    "…Further integration of Spearman correlation analysis and PCA dimensionality reduction created multidimensional feature sets, revealing substantial accuracy improvements: The BiLSTM model achieved an 83.6% cumulative MAE reduction from 1.65 (raw data) to 0.27 (STL-PCA), while traditional models like Prophet showed an 82.2% MAE decrease after feature engineering optimization. Finally, the Beluga Whale Optimization (BWO)-tuned STL-PCA-BWO-BiLSTM hybrid model delivered optimal performance on test sets (RMSE = 0.22, MAE = 0.16, MAPE = 0.99%, ), exhibiting 40.7% higher accuracy than unoptimized BiLSTM (MAE = 0.27). …"
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    Three error values under raw data. حسب Xiangjuan Liu (618000)

    منشور في 2025
    "…Further integration of Spearman correlation analysis and PCA dimensionality reduction created multidimensional feature sets, revealing substantial accuracy improvements: The BiLSTM model achieved an 83.6% cumulative MAE reduction from 1.65 (raw data) to 0.27 (STL-PCA), while traditional models like Prophet showed an 82.2% MAE decrease after feature engineering optimization. Finally, the Beluga Whale Optimization (BWO)-tuned STL-PCA-BWO-BiLSTM hybrid model delivered optimal performance on test sets (RMSE = 0.22, MAE = 0.16, MAPE = 0.99%, ), exhibiting 40.7% higher accuracy than unoptimized BiLSTM (MAE = 0.27). …"
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