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complementary method » complementary tool (Expand Search), complementary nature (Expand Search)
method algorithm » network algorithm (Expand Search), means algorithm (Expand Search), mean algorithm (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)
complementary method » complementary tool (Expand Search), complementary nature (Expand Search)
method algorithm » network algorithm (Expand Search), means algorithm (Expand Search), mean algorithm (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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Data Sheet 1_Exploring immune-inflammation markers in psoriasis prediction using advanced machine learning algorithms.pdf
Published 2025“…Subsequently, nine classification algorithms were developed using the processed training set, including random forest, neural networks, XGBoost, k-nearest neighbors, gradient boosting, logistic regression, naïve Bayes, AdaBoost, and SVMs. …”
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Comparison of Proposed Model using Unseen Data.
Published 2025“…Various models have successfully identified beta-thalassemia carriers using CBC data alone, but these models perform classification and prediction based on normalized data. …”
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Comparison of proposed model using unseen data.
Published 2025“…Various models have successfully identified beta-thalassemia carriers using CBC data alone, but these models perform classification and prediction based on normalized data. …”
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The structure of genetic algorithm (GA).
Published 2024“…Then, radial basis functions (RBFNNs), multilayer perceptron (MLPNNs), hybrid genetic algorithm (GA-NNs), and particle swarm optimization (PSO-NNs) neural networks were utilized to develop PTFs and compared their accuracy with the traditional regression model (MLR) using statistical indices. …”
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