بدائل البحث:
derived optimization » driven optimization (توسيع البحث), required optimization (توسيع البحث), design optimization (توسيع البحث)
access optimization » process optimization (توسيع البحث), stress optimization (توسيع البحث), process optimisation (توسيع البحث)
also derived » blood derived (توسيع البحث), ipsc derived (توسيع البحث)
binary water » binary data (توسيع البحث), lunar water (توسيع البحث)
water access » water stress (توسيع البحث), care access (توسيع البحث)
derived optimization » driven optimization (توسيع البحث), required optimization (توسيع البحث), design optimization (توسيع البحث)
access optimization » process optimization (توسيع البحث), stress optimization (توسيع البحث), process optimisation (توسيع البحث)
also derived » blood derived (توسيع البحث), ipsc derived (توسيع البحث)
binary water » binary data (توسيع البحث), lunar water (توسيع البحث)
water access » water stress (توسيع البحث), care access (توسيع البحث)
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1
Hyperparameters of the LSTM Model.
منشور في 2025"…The capacity to confront and overcome this obstacle is where machine learning and metaheuristic algorithms shine. This study introduces the Adaptive Dynamic Particle Swarm Optimization enhanced with the Guided Whale Optimization Algorithm (AD-PSO-Guided WOA) for rainfall prediction. …"
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2
The AD-PSO-Guided WOA LSTM framework.
منشور في 2025"…The capacity to confront and overcome this obstacle is where machine learning and metaheuristic algorithms shine. This study introduces the Adaptive Dynamic Particle Swarm Optimization enhanced with the Guided Whale Optimization Algorithm (AD-PSO-Guided WOA) for rainfall prediction. …"
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3
Prediction results of individual models.
منشور في 2025"…The capacity to confront and overcome this obstacle is where machine learning and metaheuristic algorithms shine. This study introduces the Adaptive Dynamic Particle Swarm Optimization enhanced with the Guided Whale Optimization Algorithm (AD-PSO-Guided WOA) for rainfall prediction. …"
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4
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5
Supplementary Material for: Penalized Logistic Regression Analysis for Genetic Association Studies of Binary Phenotypes
منشور في 2022"…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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6
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7
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.…"