بدائل البحث:
larger decrease » marked decrease (توسيع البحث)
values decrease » values increased (توسيع البحث), largest decrease (توسيع البحث)
learning data » learning dataset (توسيع البحث), learning a (توسيع البحث)
data decrease » rate decreased (توسيع البحث), a decrease (توسيع البحث), deaths decreased (توسيع البحث)
a larger » a large (توسيع البحث), _ larger (توسيع البحث), _ large (توسيع البحث)
i values » _ values (توسيع البحث)
larger decrease » marked decrease (توسيع البحث)
values decrease » values increased (توسيع البحث), largest decrease (توسيع البحث)
learning data » learning dataset (توسيع البحث), learning a (توسيع البحث)
data decrease » rate decreased (توسيع البحث), a decrease (توسيع البحث), deaths decreased (توسيع البحث)
a larger » a large (توسيع البحث), _ larger (توسيع البحث), _ large (توسيع البحث)
i values » _ values (توسيع البحث)
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The introduction of mutualisms into assembled communities increases their connectance and complexity while decreasing their richness.
منشور في 2025"…Parameter values: interaction strengths were drawn from a half-normal distribution of zero mean and a standard deviation of 0.2, and strength for consumers was made no larger than the strength for resources. …"
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Scheme of g-λ model with larger values λ.
منشور في 2024"…The stress-deformation model of the single uncoupled joint (g-λ model with λ ≥ 1) is employed to depict the nonlinearity of uncoupled joints, with a greater value of the parameter λ signifying a lower degree of non-linearity in the joint model curve. …"
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Biases in larger populations.
منشور في 2025"…<p>(<b>A</b>) Maximum absolute bias vs the number of neurons in the population for the Bayesian decoder. …"
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The MAE value of the model under raw data.
منشور في 2025"…Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. 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. …"
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Three error values under raw data.
منشور في 2025"…Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. 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. …"
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Comparison of environmental perception time results at different learning rates.
منشور في 2025الموضوعات: -
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