In this paper, we propose a new filtration method, called Transformation-based Database Filtration method (TDF), to screen out those data sequences of a DNA sequence database which cannot satisfy a given query sequence. Our proposed method consists of two phases. First, we divide each data sequence into several windows (blocks), each of which is transformed into a data feature vector using the ...

Multi-point distance minimization problems (M-DMP) pose a number of theoretical challenges and simultaneously represent a number of practical applications, particularly in navigational and layout design problems. When the Euclidean distance measure is minimized for each target point, the resulting problem has a trivial solution, however such a consideration limits its application in practice. S...

Particle swarm optimization (PSO) has been employed on several optimization problems, including the clustering problem. PSO has also been employed in the clustering of data of different structure and dimensionality. In this paper it is employed in the clustering of nucleic acid sequences. The application of clustering, as a statistical tool, in the analysis of data of varied complexity has been...

A Manhattan network for a finite set P of n points in the plane is a geometric graph such that its vertex set contains P , its edges are axis-parallel and non-crossing and, for any two points p and q in P , there exists a path in the network connecting p and q whose length equals the l1-distance between p and q. The problem of computing a Manhattan network of minimum total edge length for a giv...

Many important functions over strings can be represented as finite-state string transducers. In this paper, we present an automatatheoretic technique for algorithmically verifying that such a function is robust to uncertainty. A function encoded as a transducer is defined to be robust if for each small (i.e., bounded) change to any input string, the change in the transducer’s output is proporti...

The Fuzzy Hyperline Segment Neural Network (FHLSNN) pattern classifier utilizes fuzzy set as pattern classes in which each fuzzy set is a union of fuzzy set hyperline segments. The Euclidean distance metric is used to compute the distances to decide the degree of membership function. In this paper, the use of other various distance metrics such as Manhattan, Squared Euclidean, Canberra and Cheb...

We showed in this work how the Hassanat distance metric enhances the performance of the nearest neighbour classifiers. The results demonstrate the superiority of this distance metric over the traditional and most-used distances, such as Manhattan distance and Euclidian distance. Moreover, we proved that the Hassanat distance metric is invariant to data scale, noise and outliers. Throughout this...

Graphical representation of DNA sequences is one of the most popular techniques for alignment-free sequence comparison. Here, we propose a new method for the feature extraction of DNA sequences represented by binary images, by estimating the similarity between DNA sequences using the frequency histograms of local bitmap patterns of images. Our method shows linear time complexity for the length ...

A new general algorithm for computing distance transforms of digital images is presented. The algorithm consists of two phases. Both phases consist of two scans, a forward and a backward scan. The first phase scans the image column-wise, while the second phase scans the image row-wise. Since the computation per row (column) is independent of the computation of other rows (columns), the algorith...