De novo structure determination of proteins is a significant research issue of bioinformatics. Biochemical procedures for protein structure determination are costly. Use of different pattern classification techniques are proved to ease this task. In this article, the secondary structure prediction task has been mapped into a three-class problem of pattern classification, where the classes are h...

Outlier detection is of great significance in the domain data mining. Its task to find those target points that are not identical most object generation mechanisms. The existing algorithms mainly divided into density-based and distance-based algorithms. However, both approaches have some drawbacks. former struggles handle low-density modes, while latter cannot detect local outliers. Moreover, o...

We introduce a semiparametric “tubular neighborhood” of a parametric model in the multinomial setting. It consists of all multinomial distributions lying in a distance-based neighborhood of the parametric model of interest. Fitting such a tubular model allows one to use a parametric model while treating it as an approximation to the true distribution. In this paper, the Kullback–Leibler distanc...

Image registration is an important problem in computer vision and has many diverse applications. Registration is the method of aligning two or more images into the same coordinate system to achieve a one-to-one correspondence. While registration of images has been studied in the past, an area that is less explored is nonrigid registration. It turns out that nonrigid registration is very appropr...

K-nearest neighbor classification algorithm is one of the most basic algorithms in machine learning, which determines sample's category by similarity between samples. In this paper, we propose a quantum with Hamming distance. algorithm, computation firstly utilized to obtain distance parallel. Then, core sub-algorithm for searching minimum unordered integer sequence presented find out Based on ...

Instance-based regression methods generate solutions from prior solutions within a neighborhood of the input query. Their performance depends on both neighborhood selection criteria and on the method for generating new solutions from the values of prior instances. This paper proposes a new approach to addressing both problems, in which solutions are generated by an ensemble of solutions of loca...

Consider that the coordinates of N points are randomly generated along the edges of a d-dimensional hypercube (random point problem). The probability that an arbitrary point is the mth nearest neighbor to its own nth nearest neighbor (Cox probabilities) plays an important role in spatial statistics. Also, it has been useful in the description of physical processes in disordered media. Here we p...

As a research branch of data mining, clustering, as an unsupervised learning scheme, focuses on assigning objects in the dataset into several groups, called clusters, without any prior knowledge. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is one of the most widely used clustering algorithms for spatial datasets, which can detect any shapes of clusters and can automatic...