In this letter, we derive the cumulative density function (CDF) of the nearest neighbor and contact distance distributions of the Matérn cluster process (MCP) in R. These results will be useful in the performance analysis of many realworld wireless networks that exhibit inter-node attraction. Using these results, we concretely demonstrate that the contact distance of the MCP stochastically domi...

The κ-Nearest Neighbors ( κNN) query is an important spatial query in mobile sensor networks. In this work we extend κNN to include a distance constraint, calling it a l-distant κ-nearest-neighbors (l-κNN) query, which finds the κ sensor nodes nearest to a query point that are also at or greater distance from each other. The query results indicate the objects nearest to the area of interest tha...

K-nearest neighbor (k-NN) classification is a powerful and simple method for classification. k-NN classifiers approximate a Bayesian classifier for a large number of data samples. The accuracy of k-NN classifier relies on the distance metric used for calculating nearest neighbor and features used for instances in training and testing data. In this paper we use deep neural networks (DNNs) as a f...

This paper presents new approach Hesitant Fuzzy K-nearest neighbour (HFK-nn) based document classification and numerical results analysis. The proposed classification Hesitant Fuzzy K-nearest neighbour (HFKnn) approach is based on hesitant Fuzzy distance. In this paper we have used hesitant Fuzzy distance calculations for document classification results. The following steps are used for classif...

The nearest point map of a real algebraic variety with respect to Euclidean distance is an algebraic function. For instance, for varieties of low rank matrices, the Eckart-Young Theorem states that this map is given by the singular value decomposition. This article develops a theory of such nearest point maps from the perspective of computational algebraic geometry. The Euclidean distance degre...