درخت حوزه‌های تخصصی

This study aims to identify the factors affecting government support for Iranian automotive industry. Initially, based on the research literature, a set of factors was identified and designed as an analytic network process model. To investigate the analytic network model using the Delphi method, the opinion of 25 experts associated with government support for the automotive industry was used to answer the designed questionnaire for pairwise comparison. These experts were chosen through snowball sampling. The research result demonstrated that in experts’ opinion, various types of government support are needed to desirably expand this industry. According to the result of this study, a list of priorities has been provided below: 1) Specific support; 2) management information support and advisors; 3) financial support; and finally, 4) legal support. With respect to the result of binary comparison, the sub-criteria of reduced tax exemption for automakers, economic incentives, joint venture and provision of global marketing services have had the greatest weight respectively in the sub-criteria of financial support, specific support, legal support and management information support and advisors.

The accuracy of brain tumor detection and segmentation are greatly affected by tumors’ location, shape, and image properties. In some situations, brain tumor detection and segmentation processes are greatly complicated and far from being completely resolved. The accuracy of the segmentation process significantly influences the diagnosis process, such as abnormal tissue detection, disease classification, and assessment. However, medical images, in particular, the Magnetic Resonance Imaging (MRI), often include undesirable artefacts such as noise, density inhomogeneity, and partial volume effects. Although many segmentation methods have been proposed, the accuracy of the segmentation results can be further improved. Subsequently, this study attempts to provide very important properties about the size, initial location and shape of tumors known as Region of Interest (RoI) to kick-start the segmentation process. The MRI consists of a sequence of images (MRI slices) of a particular person and not one image. Our method chooses the best image among them based on the tumor size, initial location and shape to avoid the partial volume effects. The selected algorithms to test our method are Active Contour and Otsu Thresholding algorithms. Several experiments are conducted in this research using the BRATS standard dataset that consist of 100 samples. These experiments comprised of MRI slices of 65 patients. The proposed method is evaluated by the similarity coefficient as a standard measure using Dice, Jaccard, and BF scores. The results revealed that the Active Contour algorithm has higher segmentation accuracy when tested across the three different similarity coefficients. Moreover, the achieved results of the two algorithms verify the ability of the proposed method to choose the best RoIs of the MRI samples.