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Articles by A. Taleb-Ahmed
Total Records ( 2 ) for A. Taleb-Ahmed
  A. Seddiki , M. Djebbouri and A. Taleb-Ahmed
  In this study, we investigate the potential of some of the well know linear block codes in reducing the peak-to-average power ratio (PAPR). The influence of combined weighting and product block coding on the PAPR is investigated. A class of constructed product block codes based on BCH block codes capable of both error correction and PAPR reduction together with several weighting functions is considered. We investigate the interplay of various weighting functions with product codes in order to minimize PAPR. Proposed scheme reduce PAPR significantly as is evident from the simulation results.
  M. Messadi , A. Bessaid and A. Taleb-Ahmed
  In this paper, a methodological approach to the classification of tumour skin lesions in dermoscopy images is presented. Melanomas are the most malignant skin tumours. They grow in melanocytes, the cells responsible for pigmentation. This type of cancer is increasing rapidly; its related mortality rate is increasing more modestly, and inversely proportional to the thickness of the tumour. The mortality rate can be decreased by earlier detection of suspicious lesions and better prevention. Using skin tumour features such as colour, symmetry and border regularity, an attempt is made to determine if the skin tumour is a melanoma or a benign tumour. In this work, we are interested in extracting specific attributes which can be used for computer-aided diagnosis of melanoma, especially among general practitioners. In the first step, we eliminate surrounding hair in order to eliminate the residual noise. In the second step, an automatic segmentation is applied to the image of the skin tumour. This method reduces a colour image into an intensity image and approximately segments the image by intensity thresholding. Then, it refines the segmentation using the image edges, which are used to localize the boundary in that area of the skin. This step is essential to characterize the shape of the lesion and also to locate the tumour for analysis. Then, a sequences of transformations is applied to the image to measure a set of attributes (A: asymmetry, B: border, C: colour and D: diameter) which contain sufficient information to differentiate a melanoma from benign lesions. Finally, the various signs of specific lesion (ABCD) are provided to an artificial neural network to differentiate between malignant tumours and benign lesions.
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