Sentence examples for models we achieved from inspiring English sources

By developing SVM-based models we achieved maximum accuracy of 94.14%, which is almost similar to five-input features (predicted SVM scores of different compositional features) based hybrid approach of RNAcon (93.97% accuracy) (Table  1).

By fusing scores of the detection and verification models, we achieve the state-of-the-art results.

In addition, it is observed that using FunFold-cdd, FunFold-InterPro, LogEFold and GeoFold models, we achieve competitive results compared with the Taxfold webserver (Yang and Chen, 2011).

Using the implicit shape model, we achieved an accuracy of 96.25% in the case of cars vs vans, 89.39% in the case of sedans vs taxis, and 85.66% in the case of sedans vs vans vs taxis.

For the baseline model we achieved a 25% rate of acceptance and for the most complex model (8 parameters), a rate of approx 10%.

Using the 50 ANN sub-models, we achieved classification accuracy of 92.34% on the blind set with only one misclassification.

Using the model, we achieved a significant improvement in wait time of patients in the clinic, which the clinic management initially had difficulty achieving through manual interventions.

For the RF model, we achieved the best results using 100 trees without restricting maximal depth using nine random features per node.

In a sheep model, we achieved intrauterine growth restriction experimentally by maternal nutrient restriction (R) that involved a 40% reduction in food intake through late gestation.

By generating our datasets through an evolution strategy not wedded to any specific model, we achieved datasets that represent the multiplicity of models possible under the desired attribute construction of high-order interaction without low-order interaction.

Referring to our final model, we achieve an adjusted R2 of 37%%.