We build a model using finite element analysis software to the test piece model analysis, the analysis result consistent with the experimental results.
We built a model using four species of endemic passerine birds in order to evaluate the case in an Atlantic Forest area, a biodiversity hotspot.
In this paper, we build a model using a configuration based on a simplified variation of System F6 architecture that is being developed as a part of a DARPA program on fractionated spacecraft.
The results showed that the validation results differed from each other when different data-pretreatment methods combined with near-infrared spectroscopy (NIRS) to build a model using artificial neural network (ANN) for quality control of paracetamol in coldrex.
Since all of these data, except the assessment data, are also available for the unassessed stocks, they built a model using all these data to calculate the stock size for these unevaluated stocks.
The authors built a model using seven states corresponding to HAQ-scores also including death with a time horizon of 10 years and a cycle-length of one year.
They might create from a simple standpoint some SQL queries, start to build a model using python or R, but once they create that model, one they can predict a user is leaving business, what do they do with it?
The latest report was in 2013 [76], the authors built a model using DT method with 1289 compounds which could accurately predict 78.4/76.1/79.1% of H/M/L compounds on the training set and 78.6/71.1/77.6% on the test set.
In 2011 [77], Pham et al. built a model using linear discriminant analysis (LDA) method with 674 molecules which reported results: MCC = 0.62, Accuracy = 81.56% (training set), Accuracy = 83.94% (test set).
