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The results surprisingly show that charge distribution based model was able to predict the selectivity coefficients of MIP based potentiometric sensors.

Overall, PSSM based model was able to achieve an accuracy of 98.5% as compared to 95% by hybrid-6.

However, the prediction accuracy on independent dataset; representing a true unknown prediction showed that PSSM based model was able to predict 53 out of 54 sequences (as compared to 50 by hybrid-6 and HMM profiles of Pfam database).

Subsequently, Wang and Zheng [7] coupled a GA and SA to obtain the optimal pumping rate for a groundwater system, finding that their GA-based model was able to obtain almost identical solutions to other programming methods (or even offer improvements).

The results from their study showed that ANN-based model was able to predict the system behavior clearly showing lipolytic activity of 7.69 U/mL.

Once quasi-steady state has been reached, we find a good agreement in terms of the chemical-compound concentrations between the two approaches inside the reactive zones, whereas the exposure-time based model is not able to capture reactions occurring in the zones with zero electron-donor release.

The PSSM based SVM model was able to predict 3 out of 4 sequences, apart from correctly predicting all other 50; making a total of 53 correct predictions out of 54.

With respect to aggregate network parameters, the probability matrix based on the best statistical model was able to predict only nestedness, based on both 'NODF' and 'weighted nestedness' algorithms (Table  2).

Based on the ROC curves, the model was able to classify tumor and normal tissue with 77% sensitivity and 81% specificity (Youden's index).

Based on these known interactions our model was able to infer 3,195 new interactions.

The CFD based empirical model is able to predict corrosion rate of pipelines, with the modeling results validated by actual measurements.