Search alternatives:
process optimization » model optimization (Expand Search)
dose optimization » based optimization (Expand Search), model optimization (Expand Search), wolf optimization (Expand Search)
binary layer » boundary layer (Expand Search), final layer (Expand Search), linear layer (Expand Search)
age process » same process (Expand Search), a process (Expand Search), use process (Expand Search)
linear age » linear range (Expand Search), linear rate (Expand Search), linear layer (Expand Search)
layer dose » lower dose (Expand Search), layer wise (Expand Search), layer se (Expand Search)
process optimization » model optimization (Expand Search)
dose optimization » based optimization (Expand Search), model optimization (Expand Search), wolf optimization (Expand Search)
binary layer » boundary layer (Expand Search), final layer (Expand Search), linear layer (Expand Search)
age process » same process (Expand Search), a process (Expand Search), use process (Expand Search)
linear age » linear range (Expand Search), linear rate (Expand Search), linear layer (Expand Search)
layer dose » lower dose (Expand Search), layer wise (Expand Search), layer se (Expand Search)
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Flowchart of this study.
Published 2024“…A total of 160 variables were included in the machine learning (ML) models, and feature scaling and one-hot encoding were employed for data processing. Ten supervised ML algorithms were utilized, namely logistic regression (LR), support vector machine (SVM), random forest (RF), Gaussian naive Bayes (GNB), linear discriminant analysis (LDA), k-nearest neighbors (KNN), gradient boosting machine (GBM), extreme gradient boosting (XGB), cat boost (CAT), and light gradient boosting machine (LGBM). …”