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developing forest » developing stress (Expand Search), developing potent (Expand Search), developing robust (Expand Search)
using algorithm » using algorithms (Expand Search), routing algorithm (Expand Search), fusion algorithm (Expand Search)
data algorithm » data algorithms (Expand Search), update algorithm (Expand Search), atlas algorithm (Expand Search)
element data » settlement data (Expand Search), relevant data (Expand Search), movement data (Expand Search)
developing forest » developing stress (Expand Search), developing potent (Expand Search), developing robust (Expand Search)
using algorithm » using algorithms (Expand Search), routing algorithm (Expand Search), fusion algorithm (Expand Search)
data algorithm » data algorithms (Expand Search), update algorithm (Expand Search), atlas algorithm (Expand Search)
element data » settlement data (Expand Search), relevant data (Expand Search), movement data (Expand Search)
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Pseudocode for the missForestPredict algorithm.
Published 2025“…The algorithm iteratively imputes variables using random forests until a convergence criterion, unified for continuous and categorical variables, is met. …”
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Feature selection using Boruta algorithm.
Published 2025“…</p><p>Methods</p><p>Multiple machine learning (ML) algorithms were applied to data from the 2022 Bangladesh Demographic Health Survey, including Random Forest, Decision Tree, K-Nearest Neighbors, Logistic Regression, Support Vector Machine, XGBoost, LightGBM and Neural Networks. …”
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Types of machine learning algorithms.
Published 2024“…<div><p>Background and objectives</p><p>Child undernutrition is a leading global health concern, especially in low and middle-income developing countries, including Bangladesh. Thus, the objectives of this study are to develop an appropriate model for predicting the risk of undernutrition and identify its influencing predictors among under-five children in Bangladesh using explainable machine learning algorithms.…”
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Ranking of ML algorithms.
Published 2025“…For this purpose, well-known Machine Learning (ML) algorithms such as Random Forest (RF), Adaptive Boosting (AB), and Gradient Boosting (GB) were utilized. …”
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Feature selection using the Boruta algorithm.
Published 2025“…We extracted baseline characteristics, laboratory parameters, and clinical outcomes. The Boruta algorithm was employed for feature selection to identify variables significantly associated with in-hospital mortality, and 16 machine learning models, including logistic regression, random forest, gradient boosting, and neural networks, were developed and compared using receiver operating characteristic (ROC) curves and area under the curve (AUC) analysis. …”
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Explained variance ration of the PCA algorithm.
Published 2025“…The spectral coefficients are based on an orthogonal system of Legendre type smooth polynomials. We developed the mathematical theory to calculate spectral moment in Legendre polynomails space and use these moments to train traditional classifier like SVM and random forest for a classification task. …”
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Comparison of different optimization algorithms.
Published 2025Subjects: “…crayfish optimization algorithm…”
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The overview of the ML algorithms’ flowchart.
Published 2025“…For this purpose, well-known Machine Learning (ML) algorithms such as Random Forest (RF), Adaptive Boosting (AB), and Gradient Boosting (GB) were utilized. …”
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Algorithmic experimental parameter design.
Published 2024“…The results of numerical simulations and sea trial experimental data indicate that the use of subarrays comprising 5 and 3 array elements, respectively, is sufficient to effectively estimate 12 source angles. …”
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Genome-wide identification of candidate regions associated with birth weight in Lori-Bakhtiari sheep using Random Forest algorithm
Published 2025“…This study was conducted to identify genetic loci associated with birth weight in a meat-type sheep using a Random Forest (RF) algorithm applied to genomic data. …”
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Spatial spectrum estimation for three algorithms.
Published 2024“…The results of numerical simulations and sea trial experimental data indicate that the use of subarrays comprising 5 and 3 array elements, respectively, is sufficient to effectively estimate 12 source angles. …”
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Performance of models by different algorithms.
Published 2025“…To facilitate early diagnosis and intervention, this study aims to develop an efficient and reliable prediction model for MASLD using machine learning algorithm.…”
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