Consequently, the search efficiency of this method is comparatively low.
The accuracy and validity of each method were comparatively studied and discussed.
The SVM method is comparatively new learning system that is used in both forecasting and classification problems.
Henceforth, in general, the proposed method is comparatively superior than Li's [27] model for the scaling experiment.
On Heart dataset, classification accuracy found on the proposed method is comparatively low with random forest, KNN and SVM classifier.
Theoretical results show that this method is comparatively more accurate than Tikhonov regularization and truncated singular value decomposition.
On the other hand, when the quality factor is lesser than 50, the proposed method is comparatively superior than that of Patra's [7] model.
In addition, the results of the Ismo method are comparatively good, in part because the prediction of the signal became easy.
In contrast, the acid precipitation method is comparatively inexpensive and reported for extraction of lipopeptide biosurfactant like surfactin, where lipopeptide biosurfactants that are not soluble under highly acidic conditions (pH 2.0 4.0) are precipitated (Makkar and Cameotra, 1997).
