Clinical Named Entity Recognition is a part of Task 1b, organised by CLEF eHealth organisation in 2015. The aim is to automatically identify clinically relevant entities in medical text in French. A supervised learning approach has been used for training the tagger. For the purpose of training, Conditional Random Fields(CRF) has been used. An extensive set of features was used for training. Pre...

The Arrow index of a fuzzy choice function C is a measure of the degree to which C satisfies the Fuzzy Arrow Axiom, a fuzzy version of the classical Arrow Axiom. The main result of this paper shows that A(C) characterizes the degree to which C is full rational. We also obtain a method for computing A(C). The Arrow index allows to rank the fuzzy choice functions with respect to their rationality...

Insufficiency of labeled training data is a major obstacle for automatic video annotation. Semi-supervised learning is an effective approach to this problem by leveraging a large amount of unlabeled data. However, existing semi-supervised learning algorithms have not demonstrated promising results in largescale video annotation due to several difficulties, such as large variation of video conte...

Image segmentation is the process of finding out all non-overlapping distinct regions from the given image based on certain criteria such as intensity, color, texture or shape. This paper proposes a two level hybrid non classical model for image segmentation based on pixel color and texture features of the image. The first level uses Fuzzy C-Means (FCM) unsupervised method to form a clustering ...

In wireless technologies, there are various types of networks having different parameters and their specifications and thus having different QoS (Quality of Service). When the MS moves from one network to another network, process is known as vertical handoff. In this paper we designed the simulation model having eight parameters for vertical handoff between WWAN and cellular network with the he...

In medical imaging uncertainty is widely present in data, because of the noise in acquisition and of the partial volume effects originating from the low resolution of sensors. In particular, borders between tissues are not exactly defined and memberships in the boundary regions are intrinsically fuzzy. Therefore, computer assisted unsupervised fuzzy clustering methods turn out to be particularl...

A large number of accuracy measures for crisp supervised classification have been developed in supervised image classification literature. Overall accuracy, Kappa index, Kappa location, Kappa histo and user accuracy are some well-known examples. In this work, we will extend and analyze some of these measures in a fuzzy framework to be able to measure the goodness of a given classifier in a supe...

When the number of training data is limited, the performance of supervised learning could be improved if valuable samples are selected for training. In this work, we propose a novel data selection method based on fuzzy clustering. Our method first partitions all the data which need to be classified into clusters. Then training data are selected from each cluster based on their membership degree...