Search alternatives:
model optimization » codon optimization (Expand Search), global optimization (Expand Search), based optimization (Expand Search)
dose optimization » based optimization (Expand Search), wolf optimization (Expand Search), design optimization (Expand Search)
ongoing model » encoding model (Expand Search), bagging model (Expand Search)
binary data » primary data (Expand Search), dietary data (Expand Search)
data dose » data due (Expand Search), data de (Expand Search)
model optimization » codon optimization (Expand Search), global optimization (Expand Search), based optimization (Expand Search)
dose optimization » based optimization (Expand Search), wolf optimization (Expand Search), design optimization (Expand Search)
ongoing model » encoding model (Expand Search), bagging model (Expand Search)
binary data » primary data (Expand Search), dietary data (Expand Search)
data dose » data due (Expand Search), data de (Expand Search)
-
1
Table_1_Machine Learning Techniques in Blood Pressure Management During the Acute Phase of Ischemic Stroke.DOCX
Published 2022“…Decision trees were constructed by a hierarchical binary recursive partitioning algorithm to predict the BP-lowering of 10–30% off the maximal value when antihypertensive treatment was given in patients with an extremely high BP (above 220/110 or 180/105 mmHg for patients receiving thrombolysis), according to the American Heart Association/American Stroke Association (AHA/ASA), the European Society of Cardiology, and the European Society of Hypertension (ESC/ESH) guidelines. …”
-
2
Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles
Published 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.…”