Abstract Obtaining an iris image under uncontrolled and facing non friendly users, the chances of acquiring non ideal images is very high due to poor focus, off-angle, noise, and motion blur, occlusion of eyelashes and eyelids and wearing glasses. In order to improve the quality of these images, we propose a method which improves the quality of degraded iris images and... Obtaining an iris image under uncontrolled and facing non friendly users, the chances of acquiring non ideal images is very high due to poor focus, off-angle, noise, and motion blur, occlusion of eyelashes and eyelids and wearing glasses. In order to improve the quality of these images, we propose a method which improves the quality of degraded iris images and ultimately improve the recognition accuracy. In the first step the iris image is localized, and then normalized the iris image to get the fixed size. In the second step, the valid region (iris region) is extracted from the segmented iris image so that to get only the iris region. In order to get well distributed texture image, bilinear interpolation is used to the segmented valid iris gray image. The low contrast of the resulted interpolated image is enhanced by histogram equalization. The 1D Gabor filter is applied to the gray image channels and gray texture information is extracted by 1D Gabor filter. And, the Hamming distance is chosen as a metric for recognition. The NICE-II training dataset taken from UBRIS.v2 was used for the experiment. The proposed method shows more enhanced performance in the aspect of FAR (False Acceptance Rate), FRR (False Rejection Rate) and EER (Equal Error Rate). Read More Read Less

Abstract Organizing textual documents by categorizing them is important and beneficial for information retrieval; but when it comes to clustering documents containing a huge number of terms, the task become challenged. Therefore, selecting effective features is essential for reducing the feature space dimensionality and improving the clustering performances. While numerous methods have been developed for this purpose, fewer techniques considered the... Organizing textual documents by categorizing them is important and beneficial for information retrieval; but when it comes to clustering documents containing a huge number of terms, the task become challenged. Therefore, selecting effective features is essential for reducing the feature space dimensionality and improving the clustering performances. While numerous methods have been developed for this purpose, fewer techniques considered the semantic knowledge that can be incorporate into the clustering process. This paper proposes first a new semantic feature selection method SIM based on the mutual information metric, and second a novel two phase clustering mechanism. The statistical feature selection method CHIR integrates into the frequency clustering stage and then our technique SIM is used in the second stage to pilot the semantic categorization. The content based analysis allows enhancing the frequency clustering by taking the semantic relationships between the features into account. The successful evaluation of our approach demonstrates its relevancy in catching statistical and semantic pertinent features that enable better clustering accuracy in terms of F-measure and purity. Read More Read Less

Abstract The present article introduces a genetic algorithm based method to select interesting features in a clustering framework. Indeed, we used the evolutionary paradigm to explore many subsets of attributes and evaluate them according to inertia criteria when the K-Means clustering method is used in different way to data clusters. The proposed method is applied to many benchmark datasets. The experimental... The present article introduces a genetic algorithm based method to select interesting features in a clustering framework. Indeed, we used the evolutionary paradigm to explore many subsets of attributes and evaluate them according to inertia criteria when the K-Means clustering method is used in different way to data clusters. The proposed method is applied to many benchmark datasets. The experimental obtained results show the efficiency of the proposed method where features were reduced by more than 50% and the efficiency of the clustering has been improved. Read More Read Less

Abstract In this paper, we compare and investigate Naïve Bayesian Method (NB), K-Nearest Neighbor along with Neural Network method on different Arabic data sets. The bases of our comparison are the most popular text evaluation measures. The Experimental results against different Arabic text categorization data sets reveal that NB categorizer outperformed both k-NN and NN algorithms with regard to F1, Recall... In this paper, we compare and investigate Naïve Bayesian Method (NB), K-Nearest Neighbor along with Neural Network method on different Arabic data sets. The bases of our comparison are the most popular text evaluation measures. The Experimental results against different Arabic text categorization data sets reveal that NB categorizer outperformed both k-NN and NN algorithms with regard to F1, Recall and Precision measures. Read More Read Less