Comparative analysis of the data for T30, using Akaike's Information Criteria method, confirmed that the two-site competition model fits the data better than a one-site competition model with a >99.99% probability that it is correct.
The present method confirmed this.
The results of the cross-validation test (see Methods) confirmed that the CAT model provided a better fit to the amino acid data than the JTT model (Additional file 6: Figure S3).
The performance of our method was significantly better than these two results, and thus, the effectiveness of our method was confirmed.
Although the Tiansi mask did not do as well as the proposed method, we confirm that it performed better than Canny at (alpha=0.1) for both Linear and Non-linear images and (alpha=0.4) for the Medical image.
The receiver operating characteristic (ROC) analysis of the two methods, Wv and HRC confirms their better performance (Fig. 2), with only PROSPECTR [ 16] having a higher sensitivity (0.90) than Wv and HRC.
We also design an improved random forest classifier based on these network topological features, and a cross-validation test confirms that our method performs better than previous similar studies.
In section Numerical Examples, we have solved three problems considered, the results obtained are presented in Tables 1, 2, and 3, and Figs. 2, 3, and 4, the comparison of results confirms the better accuracy with this method.
In the computational experiments, we confirmed that the proposed method achieved better prediction accuracy and more efficient computational time than the other methods.
This confirms the better sensitivity of the developed method which can make it to be applicable for the routine analysis of food containing coffee alkaloids.
