The results exhibit a brilliant prediction performance of the developed fuzzy model.
The analytical results revealed that the metaheuristic optimization within machine learning-based classification system exhibited a groutability prediction accuracy of 95.42%, seismic prediction accuracy of 93.96%, soil liquefaction prediction accuracy of 95.18%, and soil collapse prediction accuracy of 95.45%.
The final selected model 4-11-1 4-11-1 4-11-1ering appropriate transfer function and learning rule, exhibited a good prediction capability with a high linear considering coefficient of 0.9964 appropriateean squared error for transfer testing.
It was observed that the proposed SVR-based model significantly exhibited an enhanced prediction and generalization performance.
State-of-the-art metrics - just as ML-based metrics - exhibit a decrease in the prediction performance that is related to the presence of distortions that were not involved in the setup of the prediction system.
While models for HLA class I binding prediction exhibit a high accuracy, proteasomal cleavage models still leave room for improvements [ 36].
Results from this study show that model predictions exhibit a strong scale-dependency, with percent clay, sum of bases, and total carbon having the highest R2-adj and lowest root mean square error (RMSE) at coarse neighborhood extents (i.e., 150 to 300 m) both between soil variables and across soil depths.
Gene prediction programs give a reasonable set of gene structures, but the best of them only predict ∼80% of the complete gene structure correctly (10) and although the best gene prediction programs exhibit a similar overall level of sensitivity, they differ in which particular genes are correctly predicted.
Therefore, the gene prediction algorithms exhibit a false negative rate of 31 to 42%, which is the portion of the experimentally validated coding regions (clusters) (MS and EST) that are completely missed by individual prediction methods.
Obviously, all prediction models exhibit a considerable constraint regarding their ability of finding potential target sites.
