Sentence examples for models performance by from inspiring English sources

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.

This paper extends the work presented in [18] by providing a deeper insight on the comparison of the models' performance, by addressing the SD resolution in addition to the HD one, by sharpening the analysis of the degradation-type impact on audiovisual quality, and by analyzing the quality impact of the audiovisual content type.

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.

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.

This approach evaluates model performance by assessing the accuracy of pairwise predictions, comparing compound pairs in a manner similar to that done by medicinal chemists.

Kappes et al. (2011) assessed the Flow-R model performance by means of a comparison of the potentially affected areas with the footprints of the past events.

In this paper we present a new, alternative approach for assessing wave model performance by applying new parameterisation to the frequency wave spectrum.

We also propose a performance projection model that projects and model performance by changing different processor architecture parameters such as the number of cores/threads, memory bandwidth, memory size, cycles-per-instruction (CPI) and memory latency [18, 19].

To develop confidence in the application of the model this paper also evaluates the reliability of hydrologic model performance by comparing basin discharge, a product of the runoff and recharge values generated by the BCM with streamgage data.