Reyadh Naoum, Ala’a Al-Sabbah
College of Information Technology Middle East University, Jordan.
Abstract: The purpose of this paper is to enhance the colored images using the enhancement developed steady state genetic algorithm, SSGA, with modified fitness function to get more accurate result and less noise. In this paper the Hue Saturation Intensity (HSV) color model will be used, after enhance the S, H and V components the transformation will be made to RGB color model. We have developed three models for enhancing the colorful and chromaity of the image with different types of input – output and different type of parameter. The models are compared based on their ability to train with lowest error values. To use these models the input RGB color image is converted to an intensity image using Space Variant Luminance Map SVLM.
Keywords : Image processing; color enhancement; steady state genetic algorithm; the hue saturation intensity; space variant luminance map.
Abdullah Mamoun Hattab, Abdulameer Khalaf Hussein
Department of Computer science Middle East University, Jordan.
Abstract: Automatic Arabic content classification is an important text mining task especially with the rapid growth of the number of online Arabic documents. This system is an enhancement of the implemented machine learning classification algorithm by applying detection and correction algorithm of Non-Words in Arabic text. This detection and correction algorithm is built on morphological knowledge in form of consistent root pattern relationships, and some morpho-syntactical knowledge based on affixation and morph-graphic rules to specify the word recognition and non- word correction process. Many researchers had been focused on Arabic content classification from only morphological view such as word’s root and stemming techniques (prefixes and suffixes) which showed variant results. In this work, consider classification from a very different way which is the syntactical approach. This paper presents the results of experiments on document classification achieved on ten different Arabic domains (Economy, History, Family studies, Islamic, Sport, Health, Law, Stories, astronomy and Food articles) using statistical methodology. The performance of this classification system showed encouraging results compared with other existing systems.
Keywords : text mining; classification; Arabic text classification; Arabic language processing.
Hadi Malekpour, Reza Berangi
Computer Engineering Iran University of Science and Technology, Iran.
Abstract: Cognitive radio technology has been proposed to achieve a more efficient spectrum usage by using spectrum opportunities in time, frequency and space which is not fully used by a licensed system (primary system), but without disturbing the primary system. In this paper, we address the problem of spectrum sharing among one primary user and two secondary users. We model this problem as a game and use Cournot and Bertrand game models for spectrum allocation to secondary users. In each game model we first present the formulation of static cases when the secondary users can observe the adopted strategies and the payoff of each other. However, this assumption may not be realistic in some cognitive radio systems. Therefore, we formulate dynamic approaches in which the secondary users just communicate with the primary user. The stability conditions of the dynamic behavior for these spectrum sharing schemes is investigated.
Keywords : spectrum sharing; cognitive radio; game theory.
Sayed Jaafer Abdallah, Izzeldin Mohamed Osman, Mohamed Elhafiz Mustafa
College of Computer Science and Information Technology, Sudan University of Science and Technology, Sudan.
Abstract: This paper presents a text-independent speaker identification system based on Mel-Frequency Cepstrum Coefficient (MFCC) feature vectors and Hidden Markov Model (HMM) classifier. The implementation of the HMM is divided into two steps: feature extraction and recognition. In the feature extraction step, the paper reviews MFCCs by which the spectral features of speech signal can be estimated and shows how these features can be computed. In the recognition step, the theory and implementation of HMM are reviewed and followed by an explanation of how HMM can be trained to generate the model parameters using Forward-Backward algorithm and tested using forward algorithm. The HMM is evaluated using data of 40 speakers extracted from Switchboard corpus. Experimental results show an identification rate of about 84%.
Keywords : Speaker identification; MFCC; HMM; Feature extraction; Forward- Backward; and Switchboard.