Search Results - evaluation algorithms

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    A Novel Linear Evaluation of Chromatographic Peak Features in Pharmacopoeias Using an Inverse Fourier Transform Algorithm by Shuping Chen, Weiyuan Zhu, Sai Huang, Baoling Zheng

    Published 2025-06-01
    “…An inverse Fourier transform algorithm was developed to accurately evaluate chromatographic features versus a standard Gaussian peak shape. …”
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    Automated Dance Scoring Algorithm Using Alignment and Least Square Approximation with Fractional Power of Joint Features by Chen-Jhen Fan, Han-Hui Jeng, Bing-Ze Li, Jian-Jiun Ding

    Published 2025-05-01
    “…Automated motion evaluation has become popular in exercise training and entertainment. …”
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    Evaluation Method for Remaining Life of XLPE Insulated Power Cable by MA Hanchao, GAO Baoqi, LI Xiangyang, WU Suzhou, ZHANG Xiaojun

    Published 2023-06-01
    Subjects: “…particle swarm optimization algorithm…”
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    Diagrammatic prevalence index: a new algorithm to evaluate pine wilt disease prevalence at the sub-compartment scale by Yanjun Zhang, Yanjun Zhang, Siyuan Zheng, Jinjuan Bai, Jiafu Hu, Yongjun Wang, Yongjun Wang

    Published 2025-07-01
    “…This study introduces a new algorithm for evaluating PWD prevalence in Hangzhou, China, where the disease has been established for over two decades. …”
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    Physical fitness characteristics and comprehensive physical fitness evaluation model of basketball players based on association rule algorithm. by Yongkang Ding

    Published 2025-01-01
    “…The study adopted the Apriori association rule algorithm in data mining. First, the physical data of basketball players were collected and preprocessed. …”
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    Evaluating machine leaning algorithms for accuracy, stability, and among-predictors discriminability in modeling species-richness across ten datasets by Yong Cao, Tyler E. Schartel, David C. Houghton, Jared Ross, Dana M. Infante

    Published 2025-12-01
    “…While numerous machine learning (ML) algorithms for regression are available for such analyses, synthesizing outcomes across studies is challenging due to: (1) reliance on single datasets, limiting generalizability; (2) varying modeling processes; (3) inconsistent performance criteria; and (4) limited consideration of model stability and among-predictor discriminability.We addressed these issues by applying five ML algorithms—Random Forest (RF), Boosted Regression Tree (BRT), Extreme Gradient Boosting (XGB), Conditional Inference Forest (CIF), and Lasso—to ten large datasets on freshwater fish, mussels, and caddisflies. …”
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