Showing 1 - 20 results of 804 for search '(( third ((larger decrease) OR (((we decrease) OR (_ decrease)))) ) OR ( shows mae decrease ))', query time: 0.58s Refine Results
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    The MAE value of the model under raw data. by Xiangjuan Liu (618000)

    Published 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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    DataSheet1_Decreasing viscosity and increasing accessible load by replacing classical diluents with a hydrotrope in liquid–liquid extraction.docx by Asmae El Maangar (19690522)

    Published 2025
    “…We show that using hydrotropes as a diluent decreases the viscosity of solutions by more than a factor of ten, even under high load by extracted cations. …”
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    Maternal group B <i>Streptococcus</i> decreases infant length and alters the early-life microbiome: a prospective cohort study by Shanshan Li (114847)

    Published 2024
    “…</p> <p>GBS exposure is associated with decreased infant length growth, with altered microbiota and metabolites potentially mediating the effects of maternal GBS on offspring length growth, offering potential targets for predicting and addressing growth impairment.…”
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    Testing set error. by Xiangjuan Liu (618000)

    Published 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. …”