بدائل البحث:
property optimization » process optimization (توسيع البحث), policy optimization (توسيع البحث), robust optimization (توسيع البحث)
wolf optimization » whale optimization (توسيع البحث), swarm optimization (توسيع البحث), _ optimization (توسيع البحث)
data property » taste property (توسيع البحث), peat property (توسيع البحث)
binary data » primary data (توسيع البحث), dietary data (توسيع البحث)
based wolf » based whole (توسيع البحث), based work (توسيع البحث), based well (توسيع البحث)
lens » less (توسيع البحث)
property optimization » process optimization (توسيع البحث), policy optimization (توسيع البحث), robust optimization (توسيع البحث)
wolf optimization » whale optimization (توسيع البحث), swarm optimization (توسيع البحث), _ optimization (توسيع البحث)
data property » taste property (توسيع البحث), peat property (توسيع البحث)
binary data » primary data (توسيع البحث), dietary data (توسيع البحث)
based wolf » based whole (توسيع البحث), based work (توسيع البحث), based well (توسيع البحث)
lens » less (توسيع البحث)
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Predicting Thermal Decomposition Temperature of Binary Imidazolium Ionic Liquid Mixtures from Molecular Structures
منشور في 2021"…The subset of optimal descriptors was screened by combining the genetic algorithm with the multiple linear regression method. …"
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Supplementary Material for: Penalized Logistic Regression Analysis for Genetic Association Studies of Binary Phenotypes
منشور في 2022"…Our estimate of m is the maximizer of a marginal likelihood obtained by integrating the latent log-ORs out of the joint distribution of the parameters and observed data. We consider two approximate approaches to maximizing the marginal likelihood: (i) a Monte Carlo EM algorithm (MCEM) and (ii) a Laplace approximation (LA) to each integral, followed by derivative-free optimization of the approximation. …"
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Variable Selection with Multiply-Imputed Datasets: Choosing Between Stacked and Grouped Methods
منشور في 2022"…Building on existing work, we (i) derive and implement efficient cyclic coordinate descent and majorization-minimization optimization algorithms for continuous and binary outcome data, (ii) incorporate adaptive shrinkage penalties, (iii) compare these methods through simulation, and (iv) develop an R package <i>miselect</i>. …"
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Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles
منشور في 2025"…</p><p dir="ltr"><b>Applications and Model Compatibility:</b></p><p dir="ltr">The dataset is optimized for use in supervised learning workflows and has been tested with algorithms such as:</p><p dir="ltr">Gradient Boosting Machines (GBM),</p><p dir="ltr">Support Vector Machines (SVM-RBF),</p><p dir="ltr">Random Forests, and</p><p dir="ltr">Principal Component Analysis (PCA) for feature reduction.…"