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  1. 2641

    Cloud-Integrated Meteorological Parameter Prediction by Leveraging Multivariate Statistical Time Series and GANs by Archana Rout, Biswa Ranjan Senapati, Debahuti Mishra

    Published 2025-01-01
    “…Hence, the present work predicts six important meteorological parameters using the vector autoregression model (VAR), vector moving average model (VMA), vector autoregression moving average model (VARMA), and cointegrated vector autoregression model (CVAR). …”
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    Article
  2. 2642

    PREDICTIVE ASSESSMENT OF TECHNICAL MEANS OF FEED GRAIN PROCESSING FOR LIVESTOCK by Dmitry I. Chistyakov

    Published 2011-03-01
    “…A process model for forming technical-and-economic indices in estimating mechanical equipment for feed grain processing for livestock is offered. …”
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    Predicting distant metastasis of bladder cancer using multiple machine learning models: a study based on the SEER database with external validation by Xin Chang Zou, Xue Peng Rao, Jian Biao Huang, Jie Zhou, Hai Chao Chao, Tao Zeng

    Published 2024-12-01
    “…Based on the significant features identified, three ML algorithms were utilized to develop prediction models: logistic regression, support vector machine (SVM), and linear discriminant analysis (LDA). …”
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    Predicting the long-term viscoelastic response by short-term tests in polymers by Reza Afshar, Nikhil Ebi, Bilen Emek Abali

    Published 2025-07-01
    “…Then by using the inverse analysis, we obtain the best parameters, minimizing the error between the experiments and the predictive model. The determined parameters in the fractional Maxwell model, with the data of 16 h at three different temperatures, is then validated by predicting the response with an accuracy of ± 1% after 100 h.…”
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    A nomogram-based clinical prediction model for adverse clinical outcomes in non-HIV Pneumocystis jirovecii pneumonia patients by Dong Wang, Lujia Guan, Qian Yin, Xiaoxia Hou, Xi Zhan, Zhaohui Tong

    Published 2025-05-01
    “…Abstract Background Non-human immunodeficiency virus (HIV) immunocompromised patients with Pneumocystis jirovecii pneumonia (PJP) face rapid progression and high mortality, necessitating a predictive model to identify patients at risk of adverse clinical outcomes for timely interventions and improved stratification. …”
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