Search alternatives:
design optimization » bayesian optimization (Expand Search)
codon optimization » wolf optimization (Expand Search)
primary data » primary care (Expand Search)
step codon » stop codon (Expand Search)
design optimization » bayesian optimization (Expand Search)
codon optimization » wolf optimization (Expand Search)
primary data » primary care (Expand Search)
step codon » stop codon (Expand Search)
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A portfolio selection model based on the knapsack problem under uncertainty
Published 2019“…The resulted model is converted into a parametric linear programming model in which the decision maker is able to determine the optimism threshold. Finally, a discrete firefly algorithm is designed to find the near optional solutions in large dimensions. …”
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Big Data Model Building Using Dimension Reduction and Sample Selection
Published 2023“…To achieve this goal, the selection of training subdata becomes pivotal in retaining essential characteristics of the full data. Recently, several procedures have been proposed to select “optimal design points” as training subdata under pre-specified models, such as linear regression and logistic regression. …”
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Data used to drive the Double Layer Carbon Model in the Qinling Mountains.
Published 2024“…It relies on comprehensive input data, including initial SOC stocks, climate data, and vegetation production to drive these simulations.…”
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Table_1_Enriching the Study Population for Ischemic Stroke Therapeutic Trials Using a Machine Learning Algorithm.pdf
Published 2022“…</p>Methods<p>A retrospective study was performed using 41,970 qualifying patient encounters with ischemic stroke from inpatient visits recorded from over 700 inpatient and ambulatory care sites. Patient data were extracted from electronic health records and used to train and test a gradient boosted machine learning algorithm (MLA) to predict the patients' risk of experiencing ischemic stroke from the period of 1 day up to 1 year following the patient encounter. …”
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Image_1_Enriching the Study Population for Ischemic Stroke Therapeutic Trials Using a Machine Learning Algorithm.pdf
Published 2022“…</p>Methods<p>A retrospective study was performed using 41,970 qualifying patient encounters with ischemic stroke from inpatient visits recorded from over 700 inpatient and ambulatory care sites. Patient data were extracted from electronic health records and used to train and test a gradient boosted machine learning algorithm (MLA) to predict the patients' risk of experiencing ischemic stroke from the period of 1 day up to 1 year following the patient encounter. …”
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Supplementary Material for: The Therapeutic Evaluation of Steroids in IgA Nephropathy Global (TESTING) Study: Trial Design and Baseline Characteristics
Published 2021“…We report the trial design as well as the baseline characteristics of study participants. …”
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Cluster Analysis of Cardiovascular Phenotypes in Patients With Type 2 Diabetes and Established Atherosclerotic Cardiovascular Disease: A Potential Approach to Precision Medicine
Published 2021“…Further cardiovascular phenotyping is warranted to inform patient care and optimize clinical trial designs.</p>…”
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A construction method of a multidimensional and multilingual association network for earth surface system science data
Published 2025“…Existing methodologies are primarily designed for single data sources, limited dimensions, and specific linguistic domains, limiting their applicability and effectiveness. …”
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