بدائل البحث:
algorithm python » algorithm within (توسيع البحث), algorithms within (توسيع البحث), algorithm both (توسيع البحث)
algorithms risk » algorithms less (توسيع البحث), algorithms real (توسيع البحث), algorithms across (توسيع البحث)
python function » protein function (توسيع البحث)
risk function » link function (توسيع البحث), loss function (توسيع البحث), cost function (توسيع البحث)
algorithm a » algorithm _ (توسيع البحث), algorithm b (توسيع البحث), algorithms _ (توسيع البحث)
a function » _ function (توسيع البحث)
algorithm python » algorithm within (توسيع البحث), algorithms within (توسيع البحث), algorithm both (توسيع البحث)
algorithms risk » algorithms less (توسيع البحث), algorithms real (توسيع البحث), algorithms across (توسيع البحث)
python function » protein function (توسيع البحث)
risk function » link function (توسيع البحث), loss function (توسيع البحث), cost function (توسيع البحث)
algorithm a » algorithm _ (توسيع البحث), algorithm b (توسيع البحث), algorithms _ (توسيع البحث)
a function » _ function (توسيع البحث)
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Table1_v1_Optimum Design of High-Strength Concrete Mix Proportion for Crack Resistance Using Artificial Neural Networks and Genetic Algorithm.DOCX
منشور في 2020"…On this basis, using the widely used shrinkage and creep models, the functional relationship between the concrete cracking risk coefficient and the mix proportion is derived, and finally genetic algorithm is used to optimize the concrete mix proportion to improve its crack resistance. …"
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Image_1_Identification of energy metabolism-related biomarkers for risk prediction of heart failure patients using random forest algorithm.TIFF
منشور في 2022"…Random-forest and support vector machine algorithm eventually identified ten diagnostic markers (MEF2D, RXRA, PPARA, FOXO1, PPARD, PPP3CB, MAPK14, CREB1, MEF2A, PRMT1) for risk prediction of HF patients, and the proposed nomogram resulted in good predictive performance (GSE66360, AUC = 0.91; GSE59867, AUC = 0.84) and the clinical usefulness in HF patients. …"
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Table_1_Identification of energy metabolism-related biomarkers for risk prediction of heart failure patients using random forest algorithm.XLSX
منشور في 2022"…Random-forest and support vector machine algorithm eventually identified ten diagnostic markers (MEF2D, RXRA, PPARA, FOXO1, PPARD, PPP3CB, MAPK14, CREB1, MEF2A, PRMT1) for risk prediction of HF patients, and the proposed nomogram resulted in good predictive performance (GSE66360, AUC = 0.91; GSE59867, AUC = 0.84) and the clinical usefulness in HF patients. …"
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