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  1. 561

    A supervised model to identify wolf behavior from tri-axial acceleration by Charlotte Lorand, Léa Bouet, Olivier Devineau, Marianna Chimienti, Alina L. Evans, Peggy Callahan, Mark Beckel, Timothy G. Laske, Ane Eriksen

    Published 2025-01-01
    “…Results We present: (1) a model classifying fine-scale acceleration data (32 Hz) into 12 distinct behaviors (lying, trotting, stationary, galloping, walking, chewing, sniffing, climbing, howling, shaking, digging and jumping) with a class recall of 0.77–0.99 (0.01–0.91 in cross-validation), (2) a model classifying activity data (5-min averages) into 3 behavior categories (static, locomotion and miscellaneous) with a class recall of 0.43–0.91 (0.39–0.92 in cross-validation). Although classification performance decreased following cross-validation, recall scores for lying, trotting, stationary, galloping, walking and chewing individual behaviors (as well as static and locomotion categories) remained above 0.6. …”
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    Drought Monitoring during Growth Rangeland Coverage, Ardabil Province by saideh eiyni, saeide eini

    Published 2021-03-01
    “…According to the monthly rainfall data, 4 synoptic stations of Ardebil province (Ardebil, Khalkhal, Meshgin Shahr and Parsabad Moghan) during the statistical period of 2016-1996 were used to calculate drought index (SEPI) index for 4 periods of 1, 3, 6 and 9 months. Landsat TM and OLI satellite imagery was also used to prepare landslide classification maps based on the maximum probability model and calculation of vegetation indices NDVI, EVI, SAVI and LAI. …”
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  17. 577

    Regional Epidemiological Study on the Dental Status of the First Permanent Molar in Romanian Schoolchildren by Liana Beresescu, Gabriela Felicia Beresescu, Daniela Esian, Alexandru Vlasa, Csilla Benedek, Raluca Sabau, Alexandra Mihaela Stoica

    Published 2025-01-01
    “…Among the affected molars, 41.71% had untreated caries, 9.30% were filled, and 6.25% were sealed. Boys showed a significantly higher prevalence of advanced lesions (ICDAS 4–6) compared to girls. …”
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  18. 578

    A deep learning model based on RGB and hyperspectral images for efficiently detecting tea green leafhopper damage symptoms by Yang Xu, Yilin Mao, He Li, Jiazhi Shen, Xiuxiu Xu, Shuangshuang Wang, Shah Zaman, Zhaotang Ding, Yu Wang

    Published 2025-03-01
    “…For hyperspectral image classification, models were built using UVE-SVM, CARS-SVM, SPA-SVM, NONE-SVM, UVE-LSTM, CARS-LSTM, SPA-LSTM, and NONE-LSTM. …”
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    Research on Wood Defects Feature Imbalance Optimization and Recognition by Xiao Wang

    Published 2025-01-01
    “…An overall accuracy increase of 6.6% is achieved, the accuracy on live knot has an increase of 17%, and the missing rate of defects is decreased by 15%. …”
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