Showing 301 - 320 results of 862 for search 'S14 (classification)', query time: 0.05s Refine Results
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    Social Attitudes toward Cerebral Palsy and Potential Uses in Medical Education Based on the Analysis of Motion Pictures by Marek Jóźwiak, Brian Po-Jung Chen, Bartosz Musielak, Jacek Fabiszak, Andrzej Grzegorzewski

    Published 2015-01-01
    “…The CP incidences of different Gross Motor Function Classification System (GMFCS) levels in real world and in movies, respectively, are 40–51%, 47% (Level I + II); 14–19%, 12% (Level III); 34–41%, 41% (Level IV + V). …”
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    Feature Extraction of Ship-Radiated Noise Based on Hierarchical Dispersion Entropy by Leilei Xiao

    Published 2022-01-01
    “…The classification and recognition of ship-radiated noise (SRN) is of great significance to the processing of underwater acoustic signals. …”
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    THE INFLUENCE OF SAMPLE SIZE AND SELECTION OF FINANCIAL RATIOS IN BANKRUPTCY MODEL ACCURACY by Yusuf Ali Al-Hroot

    Published 2015-05-01
    “…The study sample is divided into three sub-samples counting 6, 10 and 14 companies respectively; each sample is composed of bankrupt companies and the solvent ones during the period from 2000 to 2013. …”
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    THE INFLUENCE OF SAMPLE SIZE AND SELECTION OF FINANCIAL RATIOS IN BANKRUPTCY MODEL ACCURACY by Yusuf Ali Al-Hroot

    Published 2015-05-01
    “…The study sample is divided into three sub-samples counting 6, 10 and 14 companies respectively; each sample is composed of bankrupt companies and the solvent ones during the period from 2000 to 2013. …”
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    Article
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    Kombinasi K-Means dan Support Vector Machine (SVM) untuk Memprediksi Unsur Sara pada Tweet by Wiga Maulana Baihaqi, Muliasari Pinilih, Miftakhul Rohmah

    Published 2020-05-01
    “…Based on the results obtained to improve the results, the k-means process data were reprocessed with linguists, the results obtained were 139 positive SARA tweets and 62 SARA negative tweets, the results of which increased to 70.15% and 71.14%. From the results obtained, Twitter can be used as a source to create a corpus about SARA sentences, and methods that have succeeded in labeling and classification sentiments, but still need to improve the results of accuracy. …”
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    Coronary Heart Disease Risk Prediction Model Based on Machine Learning by YUE Haitao, HE Chanchan, CHENG Yuyou, ZHANG Sencheng, WU You, MA Jing

    Published 2025-02-01
    “…Using random oversampling, the models achieved classification accuracies of 62.5%, 68.5%, 69.0%, 60.2%, and 70.1%; recall rates of 70.0%, 69.5%, 71.9%, 69.0%, and 67.6%; precision of 15.8%, 18.4%, 19.1%, 14.8%, and 19.0%; F-values of 0.258, 0.291, 0.302, 0.244, and 0.297; and AUC values of 0.80, 0.77, 0.72, 0.72, and 0.83, respectively. …”
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