Papers by Keyword: FNT

Abstract: In this paper we intend to apply a new method to predict tertiary structure. A novel hybrid feature adopted is composed of physicochemical composition (PCC), recurrence quantification analysis (RQA) and pseudo amino acid composition (PseAA). We use the Error Correcting Output Coding (ECOC) based on three flexible neural tree models as the classifiers. 640 dataset is selected to our experiment. The predict accuracy with our method on this data set is 60.23%, higher than some other methods on the 640 datasets. So, our method is feasible and effective in some extent.

Abstract: In this paper we intend to apply a new method to predict tertiary structure. Several feature extraction methods adopted are physicochemical composition, recurrence quantification analysis (RQA) , pseudo amino acid composition (PseAA) and Distance frequency. We construct the binary tree Classification model, and adopt flexible neural tree models as the classifiers. We will train a number of based classifiers through different features extraction methods for every node of binary tree, then employ the selective ensemble method to ensemble them. 640 dataset is selected to our experiment. The predict accuracy with our method on this data set is 63.58%, higher than some other methods on the 640 datasets. So, our method is feasible and effective in some extent.

Abstract: A dual-butterfly parallel access constant geometry pipeline radix-2 FNT (Fermat Number Transform) is proposed to enhance the computing performance of FNT. By the extending the conventional constant geometry FNT, two radix-2 butterflies could be calculated simultaneously in each stage, and the address generating method for parallel access without conflicts is deduced to make the dual-butterfly’s four operators fetched and stored at the same time. Compared with other single data stream FNT, the efficiency is enhanced by 3 times. Compared with the traditional convolution and the convolution based on conventional FNT, the convolution based on the proposed algorithm has the advantage in computing efficiency, which also indicates the efficiency of the proposed algorithm.