Showing 1 - 20 results of 382 for search '((((melta OR melina) OR meld) OR ((elms OR ease) OR esa)) OR menba)', query time: 0.18s Refine Results
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    ELM embedded discriminative dictionary learning for image classification by Zeng, Yijie, Li, Yue, Chen, Jichao, Jia, Xiaofan, Huang, Guang-Bin

    Published 2022
    “…In this work we propose a simple and effective framework called ELM-DDL to address these issues. Specifically, we represent input features with Extreme Learning Machine (ELM) with orthogonal output projection, which enables diverse representation on nonlinear hidden space and task specific feature learning on output space. …”
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    Journal Article
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    NOx measurements in vehicle exhaust using advanced deep ELM networks by Ouyang, Tinghui, Wang, Chongwu, Yu, Zhangjun, Stach, Robert, Mizaikoff, Boris, Huang, Guang-Bin, Wang, Qijie

    Published 2021
    “…Moreover, to further improve the regression performance the proposed deep ELM was provided with features derived from supervised learning improving its ability to address target constituents. …”
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    Journal Article
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    Streamlining electronic reporting of serious adverse events (SAEs) using the REDCap data collection system: the eSAE Project by Black, J, Julier, P, Eldridge, L, Barber, V

    Published 2024
    “…This functionality ‘The eSAE Project’ is now an active project for all of our new trials where data collection is undertaken using the REDCap system.…”
    Journal article
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    Beyond MELD Score: Association of Machine Learning-derived CT Body Composition with 90-Day Mortality Post Transjugular Intrahepatic Portosystemic Shunt Placement by Elhakim, Tarig, Mansur, Arian, Kondo, Jordan, Omar, Omar M. F., Ahmed, Khalid, Tabari, Azadeh, Brea, Allison, Ndakwah, Gabriel, Iqbal, Shams, Allegretti, Andrew S., Fintelmann, Florian J., Wehrenberg-Klee, Eric, Bridge, Christopher, Daye, Dania

    Published 2024
    “…Multivariable logistic regression showed that SMA (OR = 0.97, p < 0.01), SMI (OR = 0.94, p = 0.03), SFA (OR = 0.99, p = 0.01), and VFA (OR = 0.99, p = 0.02) remained significant predictors of 90-day mortality when adjusted for MELD score. ROC curve analysis demonstrated that including SMA, SFA, and VFA improves the predictive power of MELD score in predicting 90-day mortality after TIPS (AUC, 0.84; 95% CI: 0.77, 0.91; p = 0.02). …”
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    Article
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    Generic Object Recognition with Local Receptive Fields Based Extreme Learning Machine by Bai, Zuo, Kasun, Liyanaarachchi Lekamalage Chamara, Huang, Guang-Bin

    Published 2015
    Subjects: “…Generic object recognition; local receptive fields; Extreme Learning Machine (ELM)…”
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    Journal Article
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