بدائل البحث:
dose optimization » based optimization (توسيع البحث), model optimization (توسيع البحث), wolf optimization (توسيع البحث)
binary first » bars first (توسيع البحث), binary pairs (توسيع البحث)
first global » from global (توسيع البحث)
binary data » primary data (توسيع البحث), dietary data (توسيع البحث)
data dose » data due (توسيع البحث), data de (توسيع البحث)
dose optimization » based optimization (توسيع البحث), model optimization (توسيع البحث), wolf optimization (توسيع البحث)
binary first » bars first (توسيع البحث), binary pairs (توسيع البحث)
first global » from global (توسيع البحث)
binary data » primary data (توسيع البحث), dietary data (توسيع البحث)
data dose » data due (توسيع البحث), data de (توسيع البحث)
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Image1_Applying the Hubbard-Stratonovich Transformation to Solve Scheduling Problems Under Inequality Constraints With Quantum Annealing.TIF
منشور في 2021"…<p>Quantum annealing is a global optimization algorithm that uses the quantum tunneling effect to speed-up the search for an optimal solution. …"
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Image3_Applying the Hubbard-Stratonovich Transformation to Solve Scheduling Problems Under Inequality Constraints With Quantum Annealing.TIF
منشور في 2021"…<p>Quantum annealing is a global optimization algorithm that uses the quantum tunneling effect to speed-up the search for an optimal solution. …"
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Image2_Applying the Hubbard-Stratonovich Transformation to Solve Scheduling Problems Under Inequality Constraints With Quantum Annealing.TIF
منشور في 2021"…<p>Quantum annealing is a global optimization algorithm that uses the quantum tunneling effect to speed-up the search for an optimal solution. …"
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DataSheet1_Applying the Hubbard-Stratonovich Transformation to Solve Scheduling Problems Under Inequality Constraints With Quantum Annealing.pdf
منشور في 2021"…<p>Quantum annealing is a global optimization algorithm that uses the quantum tunneling effect to speed-up the search for an optimal solution. …"
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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.…"