Showing 1 - 20 results of 14,118 for search '(( significantly increased decrease ) OR ( significant clusters processing ))', query time: 0.53s Refine Results
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    Summary of the effect of MPDD on SDLP across all participants, and also participants categorized by driving styles (“NS” (no significant), “+” (significant increase), and “-” (significant decrease)). by Mobina Faqani (22783963)

    Published 2025
    “…<p>Summary of the effect of MPDD on SDLP across all participants, and also participants categorized by driving styles (“NS” (no significant), “+” (significant increase), and “-” (significant decrease)).…”
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    Summary of the effect of MPDD on ART and TIBL across all participants, and also participants categorized by driving styles (“NS” (no significant), “+” (significant increase), and “-” (significant decrease). by Mobina Faqani (22783963)

    Published 2025
    “…<p>Summary of the effect of MPDD on ART and TIBL across all participants, and also participants categorized by driving styles (“NS” (no significant), “+” (significant increase), and “-” (significant decrease).…”
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    Summary map of all contacts with statistically significant SVM classifications. by Alexander P. Rockhill (6053618)

    Published 2024
    “…Yellow indicates an increase in time-frequency clusters during number trials versus inter-trial intervals while blue indicates a decrease in time-frequency clusters. …”
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    Tissue, days post-infection (dpi) and the top 10 most significant genes with increased and decreased expression with valid gene symbols for the response contrasts. by Gillian P. McHugo (8965919)

    Published 2025
    “…<p>Tissue, days post-infection (dpi) and the top 10 most significant genes with increased and decreased expression with valid gene symbols for the response contrasts.…”
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    K-means clustering processing. by Xiaohan Yu (7525940)

    Published 2025
    “…Thirdly, a graph-based formulation is developed, in which words are represented as nodes, and the relations between them are defined using GATs to modify the features of nodes based on their significance in the context. Finally, unsupervised sentiment labelling, or K-Means clustering, is used to classify sentiment. …”
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