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In these days, smartphones become much more used than the personal computers because of the various categories of applications downloadable from the store. The vendors of smartphones support different platforms hence to reach as many users as possible, the developer has to develop the same application for all these platforms using the different tools and programming languages provided by each platform vendor. Therefore the cross-platform mobile applications development solutions were introduced to develop the application once and run it everywhere. The cross-platform solutions use different approaches for native development such as cross-compilation, Model-Driven Development ... etc. None of these approaches claim that it provides a complete solution as they are still under research and development. This paper introduces a new integrated cross-platform mobile development solution that merges between different approaches to benefit from the advantages and minimize the drawbacks of each approach. The main contributions include: explore the approaches used in designing the new solution, explain the research methodology and the new solution architecture along with the implementation, and evaluate the limitations of the new proposed architecture and implementation compared to known solutions. The results show substantial improvement over existing solutions.

In this paper, a new technique is proposed for automatic segmentation of multiple sclerosis (MS) lesions from brain magnetic resonance imaging (MRI). The technique uses textural features to describe the blocks of each MRI slice along with position and neighborhood features. A trained support vector machine (SVM) is used to discriminate between the blocks in regions of MS lesions and the blocks in non-MS lesion regions based on mainly the textural features with aid of the other features. The MRI slice blocks' classification is used to provide an initial segmentation. A comprehensive post processing module is then utilized to refine and improve the quality of the initial segmentation. The main contribution of the proposed technique described in this paper is the use of textural features to detect MS lesions in a fully automated process without the need to manually define regions of interest (ROIs). In addition, the post processing module is generic enough to be applied to the results of any other MS segmentation technique to improve the segmentation quality. This technique is evaluated using ten real MRI data-sets with 10% used in the training of the textural-based SVM. The average results for the performance evaluation of the presented technique were 0.79 for dice similarity, 0.68 for sensitivity and 0.9 for the percentage of the detected lesion load. These results indicate that the proposed method would be useful in clinical practice for the detection of MS lesions from MRI.