درخت حوزه‌های تخصصی

Considering students expectations and rivalry among higher education institutions, novel practical ways through adaptation of market-oriented mechanisms as well as business mechanisms in higher education are required. The present paper, therefore, aims to examine mechanisms for positioning in Iranian higher education. In doing so, phenomenological approach was applied. A purposive sample of 10 informants consisting of higher education experts and deans was selected. To analyze data, open, axial, and selective coding system was applied and the findings were organized under mechanisms for positioning in higher education. With regard to target market in higher education, attracting factors and success factors were yielded. With regard to positioning in higher education, 4 categories were identified including emphasis on relative advantage, emphasis on Basic resource, emphasis on marketing techniques, and emphasis on customers’ perception. Barriers and constraints were categorized under 6 main categories: political, administrative, institutional, cultural and social, economic, and academic. Based on literature and findings, conceptual framework on mechanisms for positioning in higher education was developed. Considering students expectations and rivalry among higher education institutions, novel practical ways through adaptation of market-oriented mechanisms as well as business mechanisms in higher education are required. The present paper, therefore, aims to examine mechanisms for positioning in Iranian higher education. In doing so, phenomenological approach was applied. A purposive sample of 10 informants consisting of higher education experts and deans was selected. To analyze data, open, axial, and selective coding system was applied and the findings were organized under mechanisms for positioning in higher education. With regard to target market in higher education, attracting factors and success factors were yielded. With regard to positioning in higher education, 4 categories were identified including emphasis on relative advantage, emphasis on Basic resource, emphasis on marketing techniques, and emphasis on customers’ perception. Barriers and constraints were categorized under 6 main categories: political, administrative, institutional, cultural and social, economic, and academic. Based on literature and findings, conceptual framework on mechanisms for positioning in higher education was developed.

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.