There are two interesting methods, in the literature, for solving fuzzy linear programming problems in which the elements of coefficient matrix of the constraints are represented by real numbers and rest of the parameters are represented by symmetric trapezoidal fuzzy numbers. The first method, named as fuzzy primal simplex method, assumes an initial primal basic feasible solution is at hand. T...

Hybrid neuro-fuzzy systems have been in evidence during the past few years, due to its attractive combination of the learning capacity of arti2cial neural networks with the interpretability of the fuzzy systems. This article proposes a new hybrid neuro-fuzzy model, named hierarchical neuro-fuzzy quadtree (HNFQ), which is based on a recursive partitioning method of the input space named quadtree...

This study proposes a new single-solution based metaheuristic, namely the Vortex Search algorithm (VS), for fuzzy clustering of ECG beats. The newly proposed metaheuristic is quite simple and highly competitive when compared to the population-based metaheuristics. In order to study the performance of the proposed method a number of experiments are performed over a dataset which is created by us...

In this paper an optimized method for unsupervised image clustering is proposed. Generally a Novel Fuzzy C Means (FCM) or FCM based clustering algorithm are used for clustering based image segmentation but these algorithms have a disadvantage of depending upon supervised user inputs such as number of clusters. Our proposed algorithm enhances an unsupervised preliminary process known as Double C...

Machine Learning (ML) based approaches for the Named Entity Recognition (NER) task require Named Entity (NE) annotated data to train the classifier. If amount of NE annotated data is not sufficient, the classifier may not yield good result. NE annotated data is scarce, especially for resource poor languages. But in most cases large raw corpora are available. In this paper we describe a novel ap...

Named entity disambiguation concerns linking a potentially ambiguous mention of named entity in text to an unambiguous identifier in a standard database. One approach to this task is supervised classification. However, the availability of training data is often limited, and the available data sets tend to be imbalanced and, in some cases, heterogeneous. We propose a new method that distinguishe...