In this paper, we define a new cosine similarity between two interval valued neutrosophic sets based on Bhattacharya’s distance [19]. The notions of interval valued neutrosophic sets (IVNS, for short) will be used as vector representations in 3D-vector space. Based on the comparative analysis of the existing similarity measures for IVNS, we find that our proposed similarity measure is better an...

In this paper, we discussed Visual Based Image Retrieval System to retrieve set of relevant images for the given input image from the large generic image database. We proposed HSV color space model and Haar transform to extract color and texture features. The images are transformed into set of features. These features are used as inputs in Self Organizing Maps (SOM) to train the network for gen...

Traditional real estate appraisal methods obtain estimates of real estate by using mathematical modeling to analyze the existing sample data. However, the information of sample data sometimes cannot fully reflect the real-time quotes. For example, in a thin real estate market, the correlated sample data for estimated object is lacking, which limits the estimates of these traditional methods. In...

Several applications for example, study of biological tissue movement and organ growth require shape correspondence with a physical basis, especially for shapes or regions lacking distinctive features. For this purpose, we propose the adaptation of mechanical strain, a well-established physical measure for deformation, to the problem of constructing shape correspondence and measuring similarity...

The neutrosophic cubic set can contain much more information to express its interval neutrosophic numbers and single-valued neutrosophic numbers simultaneously in indeterminate environments. Hence, it is a usual tool for expressing much more information in complex decision-making problems. Unfortunately, there has been no research on similarity measures of neutrosophic cubic sets so far. Since ...

A dual probability model is constructed for the Latent Semantic Indexing (LSI) using the cosine similarity measure. Both the document-document similarity matrix and the term-term similarity matrix naturally arise from the maximum likelihood estimation of the model parameters, and the optimal solutions are the latent semantic vectors of of LSI. Dimensionality reduction is justiied by the statist...

We present a novel unified framework for both static and space-time saliency detection. Our method is a bottom-up approach and computes so-called local regression kernels (i.e., local descriptors) from the given image (or a video), which measure the likeness of a pixel (or voxel) to its surroundings. Visual saliency is then computed using the said "self-resemblance" measure. The framework resul...

We present a novel method for efficiently searching top-k neighbors for documents represented in high dimensional space of terms based on the cosine similarity. Mostly, documents are stored as bagof-words tf-idf representation. One of the most used ways of computing similarity between a pair of documents is cosine similarity between the vector representations, but cosine similarity is not a met...

Similarity measures are an important tool in pattern recognition and medical diagnosis. To overcome some disadvantages of existing cosine similarity measures for simplified neutrosophic sets (SNSs) in vector space, this paper proposes improved cosine similarity measures for SNSs based on the cosine function, including single valued neutrosophic cosine similarity measures and interval neutrosoph...

Feature space high dimensionality is a well-known problem in text classification and web mining domains, it caused mainly by the large number of vocabularies contained within documents. Several methods were applied to select most useful important features over years; however, performance such still improvable from different aspects as computational cost accuracy. This research presents an enhan...