Our experimental analysis on two different dynamic saliency benchmark datasets reveal that our models achieve better performance than the individual features.
The reviews about theoretical models that support it and tools they develop does not provide conclusive evidence to support the chronic care models achieve better results than another alternatives of care.
Simulation results show that the ILP models achieve better performance in terms of the number of frequency slots and hop counts than the proposed spectrum-space-divided spectrum allocation approaches with and without dedicated-path protection under the static scenario of connection requests.
As seen in this table, the combination of our improved Dice coefficient and the tri-gram model achieved better performance than the normal Dice coefficient with the tri-gram model.
This BN model achieved better performance (AUC = 0.82), compared to the aforementioned NBc model.
Furthermore, the intact model achieved better signal vs rest discrimination than the two control models, notably avoiding false alarms.
This prediction model achieved better accuracy than MLP and also it was fast in comparison with MLP model.
Compared with GeneMarkS, Metagene, Orphelia, and Heuristic Approach methods, our model achieved better prediction performance in short gene classification.
In summary, the RF model achieved better results than Rosetta in 57% of the cases generally in all the loop length ranges.
