Showing 1 - 20 results of 2,805 for search '(((( learning ((we decrease) OR (a decrease)) ) OR ( _ stem decrease ))) OR ( _ largest decrease ))', query time: 0.47s Refine Results
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    Measures. by Esther Kim (2996622)

    Published 2024
    Subjects:
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    Data from: Colony losses of stingless bees increase in agricultural areas, but decrease in forested areas by Malena Sibaja Leyton (18400983)

    Published 2025
    “…On average, meliponiculturists lost 43.4 % of their stingless bee colonies annually, 33.3 % during the rainy season, and 22.0 % during the dry season. We found that colony losses during the rainy season decreased with higher abundance of forested areas and increased with higher abundance of agricultural area around meliponaries. …”
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    Overview of the WeARTolerance program. by Ana Beato (20489933)

    Published 2024
    “…The quantitative results from Phase 1 demonstrated a decreasing trend in all primary outcomes. In phase 2, participants acknowledged the activities’ relevance, reported overall satisfaction with the program, and showed great enthusiasm and willingness to learn more. …”
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    Development of a machine learning method for predicting neutrophil-specific functional genes. by Guihua Wang (286390)

    Published 2025
    “…<p>(A) NeuRGI model training workflow involved: 1) extracting gene features from various databases. 2) using genes of neutrophil-related genes as positives and PU-learning as negatives. 3) balancing the training set with under-sampling and training the NeuRGI random forest model with 10-fold cross-validation, then employing a Gaussian Mixture Model (GMM) with NeuRGI scores to identify potential positives. 4) using OntoVAE for <i>in silico</i> knockout of GMM-classified genes to find key regulatory factors for guiding follow-up experiments. …”
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    Evaluation of the effectiveness of double task. by Fan Yang (1413)

    Published 2025
    “…The Spatial Attention Based Dual-Branch Information Fusion Block links these branches, enabling mutual benefit. Furthermore, we present a structured pruning method grounded in channel attention to decrease parameter count, mitigate overfitting, and uphold segmentation accuracy. …”