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largest decrease » larger decrease (Expand Search), marked decrease (Expand Search)
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_ largest » _ large (Expand Search)
largest decrease » larger decrease (Expand Search), marked decrease (Expand Search)
values decrease » values increased (Expand Search)
learning test » learning task (Expand Search), learning tasks (Expand Search), learning rates (Expand Search)
test decrease » teer decrease (Expand Search), cost decreased (Expand Search), mean decrease (Expand Search)
_ largest » _ large (Expand Search)
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Median SHAP values (calculated for CSCHF at age 5000 days) for binary features in the competing risks RSF model for different disease categories. The features are sorted in decreasing order by absolute value for the cancer outcomes.
Published 2025“…The features are sorted in decreasing order by absolute value for the cancer outcomes.…”
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The MAE value of the model under raw data.
Published 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.
Published 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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Means for STEM competence for pre- and post-test.
Published 2024“…After the Challenge, participants increased in awareness of global issues, and engagement with others, but also showed a small but significant decrease in respect for people from other cultural backgrounds. …”
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TITAN thresholds and percentile estimates for benthic macroinvertebrate and diatom communities deemed to be sensitive decreasers or tolerant increasers. The thresholds represent the largest fsum <i>z</i> value in the main data analysis run (i.e., the median), whereas the 5<sup>th</sup> and 95<sup>th</sup> percentile change points are determined from 500 bootstrap replicate runs....
Published 2025“…<p>TITAN thresholds and percentile estimates for benthic macroinvertebrate and diatom communities deemed to be sensitive decreasers or tolerant increasers. The thresholds represent the largest fsum <i>z</i> value in the main data analysis run (i.e., the median), whereas the 5<sup>th</sup> and 95<sup>th</sup> percentile change points are determined from 500 bootstrap replicate runs. …”
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RMSE versus learning rate.
Published 2025“…Field experiments demonstrate that the predicted values from the LSTM model closely align with the measured values, maintaining short-term shape error prediction accuracy within 3 mm. …”