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    Research on the Prediction of Pipelines Corrosion Rate Based on GA-LSSVM by CHEN Yong-hong, SU Yong-sheng, HU Ping

    Published 2021-01-01
    Subjects: “…least squares support vector machine(lssvm); genetic algorithm(ga); corrosion rate; prediction…”
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    Article
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    Hybrid extreme learning machine for real-time rate of penetration prediction by Abdelhamid Kenioua, Omar Djebili, Ammar Touati Brahim

    Published 2025-08-01
    “…Abstract This study presents a comparative analysis of hybrid Extreme Learning Machine (ELM) models optimized with metaheuristic algorithms Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), and Grey Wolf Optimizer (GWO) for real-time Rate of Penetration (ROP) prediction in drilling operations. …”
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    The Local Path Planning Algorithm for Amphibious Robots Based on an Improved Dynamic Window Approach by Xiaoqiang Dai, Chengye Liu, Qiang Lai, Xin Huang, Qingjun Zeng, Ming Liu

    Published 2025-02-01
    “…The speed resolution adaptive adjustment algorithm improves the ability to pass through a complex multiple-obstacle area, and the dynamic obstacle prediction algorithm optimizes obstacle avoidance paths. …”
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    Article
  12. 1372

    Study on Short Term Temperature Forecast Model in Jiangxi Province based on LightGBM Machine Learning Algorithm by Kanghui SUN, An XIAO, Houjie XIA

    Published 2024-12-01
    “…In order to achieve further improvement in the forecast accuracy of station temperatures and enhance the forecast capability for extreme temperatures, this study establishes a 24-hour national station daily maximum (minimum) temperature forecast model for Jiangxi Province based on the LightGBM machine-learning algorithm and the MOS forecast framework by using the surface observation data of 91 national stations in Jiangxi Province and the upper-air and surface forecast data of the ECMWF model from 2017 to 2019.The results of the 2020 evaluation show that the LightGBM model daily maximum (minimum) temperature forecast is consistent with the observed trend, and the annual average forecast is better than that of three numerical models, ECMWF, CMA-SH9 and CMA-GFS, two machine learning products, RF and SVM, and subjective revision products.In terms of the spatial and temporal distribution of forecast errors, the model's daily maximum (minimum) temperature forecast errors in winter and spring are slightly larger than those in summer and autumn; the daily maximum temperature forecast errors show the spatial distribution characteristics of "larger in the south and smaller in the north, and larger in the periphery than in the centre", while the opposite is true for the daily minimum temperatures.In terms of important weather processes, the LightGBM model has the best prediction effect among the seven products in the high temperature process; in the strong cold air process, the LightGBM model is still better than the three numerical model products and the other two machine-learning models, but the prediction effect of the daily minimum temperature is not as good as that of the subjective revision products.After a simple empirical correction for the low-temperature forecast error in the strong cold air process, the model low-temperature forecast effect is close to that of the subjective revision product.The model significance analysis shows that the recent surface observation features also contribute to the model construction, and the results can be used as a reference for model improvement and temperature forecast product development.At present, the LightGBM model temperature forecast products have been applied to meteorological operations in Jiangxi Province.…”
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  13. 1373

    A predictive model for functional cure in chronic HBV patients treated with pegylated interferon alpha: a comparative study of multiple algorithms based on clinical data by Ya-mei Ye, Yong Lin, Fang Sun, Wen-yan Yang, Lina Zhou, Chun Lin, Chen Pan

    Published 2024-12-01
    “…Predictor variables were identified (LASSO), followed by multivariate analysis and logistic regression analysis. Subsequently, predictive models were developed via logistic regression, random forest (RF), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and support vector machine (SVM) algorithms. …”
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    Research on Path-Following Technology of a Single-Outboard-Motor Unmanned Surface Vehicle Based on Deep Reinforcement Learning and Model Predictive Control Algorithm by Bin Cui, Yuanming Chen, Xiaobin Hong, Hao Luo, Guanqiao Chen

    Published 2024-12-01
    “…This paper proposes a path-tracking control method for a single-outboard-motor USV based on a Deep Deterministic Policy Gradient (DDPG) algorithm and model predictive control (MPC) algorithm. …”
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    Development of a MVI associated HCC prognostic model through single cell transcriptomic analysis and 101 machine learning algorithms by Jiayi Zhang, Zheng Zhang, Chenqing Yang, Qingguang Liu, Tao Song

    Published 2025-03-01
    “…Additionally, we affirmed the predictive precision and superiority of our model through a meta-analysis against existing HCC models. …”
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    Article
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