
Gradient boosting model
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This article conducts an exhaustive investigation into the utilization of machine learning (ML) methods for forecasting the maximum load capacity (MLC) of circular reinforced concrete columns (CRCC) using Fiber-Reinforced Polymer (FRP). Extreme Gradient Boosting (XGB) algorithm is combined with novel metaheuristic algorithms, namely Sailfish Optimizer and Aquila Optimizer, to fine-tune its hyperparameters.
18p
viengfa
28-10-2024
2
2
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In this study, we aim to delineate landslide susceptibility zones within Dien Bien province, Vietnam, leveraging the capabilities of various machine learning models including Light Gradient Boosting Machine (LGBM), K-Nearest Neighbors (KNN), and Gradient Boosting (GB).
19p
viengfa
28-10-2024
4
2
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This study delves into the application of machine learning (ML), specifically a Gradient Boosting (GB) model, for predicting the punching shear strength (PSS) of two-way reinforced concrete flat slabs.
16p
viengfa
28-10-2024
3
2
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The Axial Load Capacity (ALC) of Concrete-Filled Steel Tubular (CFST) structural members is regarded as one of the most crucial technical factors for the design of these composite structures.
17p
viengfa
28-10-2024
2
2
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This study focuses on optimizing the hyperparameters of the XGB model by a grid search to find an optimal XGB predictive model. In addition, the effect of input parameters on the SCCRF's CS is studied using Shapley Additive exExplanations (SHAP) values technique.
14p
viengfa
28-10-2024
6
2
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Light Gradient Boosting Machine is a new machine learning technique developed by Microsoft corporation which has been proposed in the present study to determine the CBR of stabilized expansive soils. Model performance of the ML model are evaluated by different criteria such as correlation coefficient R, root mean square error RMSE and mean absolute error MAE.
8p
viengfa
28-10-2024
4
2
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This paper presents a novel approach to enhancing the accuracy of precipitation estimation in Central Vietnam using the Extreme Gradient Boosting (XGBoost) machine learning model. The proposed method integrates multi-source data, combining satellite imagery from Himawari-8, atmospheric reanalysis from ERA-5, and digital elevation models from ASTER DEM to train the model.
9p
tuetuebinhan000
23-01-2025
2
1
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Bài viết tập trung vào việc đánh giá và so sánh hiệu quả của các mô hình học máy dựa trên cây (Tree-based machine learning models) trong việc dự báo gian lận thẻ tín dụng. Các mô hình được xét gồm Decision Tree, Random Forest, Gradient Boosting Machines (GBM) và Extreme Gradient Boosting (XGBoost).
17p
gaupanda068
02-01-2025
35
5
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