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<p><span>Optical burst switching (OBS) is a transporting network for the optical internet in the years ahead vision. As OBS depends on statistical multiplexing, the effect of contention resolution is the main issue to achieve a low probability losing of burst. Basically, there are different ways to resolve contentions in the OBS networks like wavelength conversion, deflection routing, burst segmentation and optical buffering using fiber delay lines. Burst segmentation and deflection routing as well as fiber delay lines technologies, are discussed among the various accessible conflict resolution techniques in this study. However, the main aim of this study is to demonstrate that, the performance of fiber delay line (FDL) is better than other techniques to resolve contention by comparing the performance of these different schemes based on burst losing probabilities and the data handling capability. To evaluate the performance, appropriate mathematical formulae were used. Under the MATLAB environment, the performance was measured based on the probability of burst loss versus incoming traffic (load). The outcomes suggest that deflection routing outperforms fiber delay lines and burst segmentation in the OBS network in terms of resolve the contention. </span></p>

Breast cancer is the greatest challenging health complexities that medical science is facing. Most cases can be prevented by early detection and diagnosis which are the best way to cure breast cancer to decrease the mortality rate. The aim of this research is to obtain a method for enhancing the mammography images by using the proposed method which is incorporating the Local Contrast with Contrast Limited Adaptive Histogram Equalization (LC-CLAHE) to improve the appearance and to increase the contrast of the image and then de-noised by 2D wiener filter techniques. To extract the region of interest (tumor), we used region growing technique for the segmentation process. The standard Mammographic Image Analysis Society (MIAS) database images are considered for the evaluation. Efficiency is measured by Root Mean Square Error (RMSE) and Peak Signal to Noise Ratio (PSNR). It is observed that the proposed method with wiener filter gives higher (PSNR) and lower (RMSE), with a significant filter mask [3 3].

The selection process of the kernel parameters and the relevant features are very crucial to enhance the classification tasks. Thus, in this work, a genetic algorithm that mimics the biological evaluation is used to optimize the support vector machine kernel parameters in order to achieve a high classification accuracy of an acute leukemia diagnosis. The results proved that the combination of genetic algorithm with support vector machine increased the classification accuracy of acute leukemia diagnosis to 99.19%, compared with the value of 89.43% obtained under default support vector machine kernel parameters. This can be directly attributed to the elimination of the irrelevant features and the suitable selection of the kernel parameters. This implies that the genetic algorithm model can be adequately used to solve the optimization problem and features subset selection that gives the optimal accuracy.