Sentence examples for predictions with lower from inspiring English sources

It is expected that as the system learns it gives predictions with lower uncertainty.

Generally, the predictions with lower limit fits showed better void ratio reduction and solids content increase compared to the upper limit predictions due to the afore-mentioned reasons.

To this end, predictions with high probability have a higher influence on AUPRC than predictions with lower probability, which is desirable when analyzing virtual screening results.

The possibility to detect, position, and size pores which have a low contrast-to-noise ratio increases the value of the nondestructive evaluation of welds by facilitating fatigue life predictions with lower uncertainty.

However, for all other predictions with lower confidence scores (thresholds < 0.95), CDD and FunFHMMer perform better with respect to both precision and recall.

It is thought that the brain shapes perception according to predictions that are made based on regularities; this shaping is accomplished by comparing higher-level predictions with lower-level sensory encoding of an incoming stimulus via the corticofugal (i.e., top down) system [ 2].

Experiment results show that the proposed scheme can greatly better the accuracy of traffic-load prediction with lower complex degree.

When the interaction threshold is set to the lowest number, i.e.1, at least one highly accurate function was predicted for ~9000 genes, while much fewer genes had predictions with relative lower accuracy.

For RMEL1 (Figure 4C) and RMEL3 (Figure 6C), five and three RNA structure predictions with very high probability (RNA-class probability >0.95) were generated, whereas for RMEL2 (Figure 5D) there was only one prediction and with lower probability (RNA-class probability  = 0.87), compatible with the prediction that RMEL2 is most likely a protein coding gene.

Longer follow-up increases the likelihood that clinical T2D risk factors change over time, and that as a result their baseline values will be less predictive for the development of disease, resulting in prediction models with lower AUC.

FCNs can obtain a coarse object label map from the networks by combining the final prediction layer with lower layers (skip layer) [17], where the context and localization information are available for pixel-wise labeling.