Ganesan and Veeramani [Fuzzy linear programs with trapezoidal fuzzy numbers, Annals of Operations Research 143 (2006) 305-315.] proposed a new method for solving a special type of fuzzy linear programming problems. In this paper a new method, named as Mehar’s method, is proposed for solving the same type of fuzzy linear programming problems and it is shown that it is easy to apply the Mehar’s m...

Efficient indexing and retrieval of digital video is an important aspect of video databases. One powerful index for retrieval is the text appearing in them. It enables content based browsing. In this paper, we describe a system for detecting and extracting text appearing in video frames A supervised learning method based on color and edge information is used to detect text regions. After an uns...

Mamdani Fuzzy Model is an important technique in Computational Intelligence (CI) study. This paper presents an implementation of a supervised learning method based on membership function training in the context of Mamdani fuzzy models. Specifically, auto zoom function of a digital camera is modelled using Mamdani technique. The performance of control method is verified through a series of simul...

This paper presents an integration framework for image segmentation. The proposed method is based on Fuzzy c-means clustering (FCM) and level set method. In this framework, firstly Chan and Vese’s level set method (CV) and Bayes classifier based on mixture of density models are utilized to find a prior membership value for each pixel. Then, a supervised kernel based fuzzy c-means clustering (SK...

3] and [1] opened the possibility of using an unlabeled corpus through co-training, a semi-supervised learning algorithm, to classify named entities. Our approach to solve the problem of Korean named entity classification also adopted a co-training method called DL-CoTrain. However, we use only a part-of-speech tagger and a simple noun phrase chunker instead of a full parser to extract the cont...

Article history: Received 31 July 2005 Received in revised form 27 September 2005 Accepted 5 October 2005 The implementations of both the supervised and unsupervised fuzzy c-means classification algorithms require a priori selection of the fuzzy exponent parameter. This parameter is a weighting exponent and it determines the degree of fuzziness of the membership grades. The determination of an ...