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As one of the most popular stochastic modeling methods, the previous several decades have witnessed remarkable developments and extensive applications of Wiener-process-based methods.
One reason may be geographic differences in the effects of source reduction methods (the previous study was conducted in Cambodia).
This may be due to the different sampling methods used (the previous study used trunk blood and brain homogenate collected following decapitation) or different data analysis methods (the previous study used the brain homogenate-to-plasma concentration ratio rather than the brain dialysate AUC-to-blood dialysate AUC ratio).
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As shown in Table 1, the performance of the proposed method was the best as compared to the single classifier, other fusion methods, and the previous method.
Thus, it is not possible to compare this method with the previous methods as earlier methods have been developed for predicting BCEs only.
The NMF method, the previous CWT method, and the proposed AS-CWT method can affect the conversion of all emotional voice datasets.
When compared with the standard greedy relay selection method, the previous stage decision is more accurate and the global optimum can be approached more closely.
Thus in contrast to our method, the previous method is not truly non-destructive.
Our proposal consists of an efficient hybrid method using the previous methods.
Experimental results show that the proposed method outperforms the previous methods including some algorithms which are based on the least square predictors.
The proposed method and the previous methods are applied on those data sets.
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CEO of Professional Science Editing for Scientists @ prosciediting.com