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Uninformed optimization improved model performance by 29% compared to expert opinion-informed model, while sensitivity-analysis informed optimization improved model performance by 54%.
We assessed model performance by simulating the standing crop of pendulous lichens (Bryoria spp).
The proposed model is applied to a real-world corporation and results show model performance by analyzing different scenarios.
In this paper we present a new, alternative approach for assessing wave model performance by applying new parameterisation to the frequency wave spectrum.
In order to determine objectively the fractal behaviour of a time series, and to facilitate potential future attempts to assess model performance by incorporating fractal behaviour, a multi-order robust detrended fluctuation analysis (r-DFAn) procedure is developed herein.
This paper addresses the subject of a new deterministic model calibration technique based on simulated annealing, which improves the model performance by means of a few pilot measurements.
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Through regression analysis, we showed that features extracted from an EEG-based BCI could improve QoE models performance by as much as 12.5 and 14.45 for hands-free communication and TTS systems, respectively.
Model derivation and assessment of model performance was performed by using R software version 2.6.0.
However, model performance varied by measure of performance, season, and location.
Model performance, assessed by receiver-operating-characteristics (ROC) indexes, evidences fairly good reproduction of the observed event.
We calculated 95% confidence intervals for the model performance characteristics by bootstrapping.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com