This paper examines some syntactic features of English legal texts, and the changes that may be reflected on these features when they are translated into Arabic. For example, passivization, nominalization, complex sentences and modality. The researcher tries to demonstrate why it is difficult to suggest a specific translation of each syntactic feature, especially the modal verbs. The researcher also attempts to provide translations for some legal sentences written in some charters and international organizations. The descriptive methodology is used to identify the characteristics of these syntactic features in order to provide a proper translation of each legal sentence. It has been concluded that the translator has to be aware of the precise meaning of the legislator before deciding the translation, and that the meaning of any specific modal verb is translated differently from English into Arabic according to the linguistic system and organizations in which it is used.
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Calcifying epithelial odontogenic tumour (CEOT) is a benign odontogenic neoplasm of epithelial origin that secretes an amyloid‐like protein tending towards calcification. This study aims to describe a case series from Iraq of one of the rarest odontogenic tumours.
Clinical and histopathological analysis of Calcifying epithelial odontogenic tumour cases that are archived at the oral pathology laboratory of the college of dentistry (Baghdad University) from 2000 to 2019.
Six cases of CEOT were regi
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Text based-image clustering (TBIC) is an insufficient approach for clustering related web images. It is a challenging task to abstract the visual features of images with the support of textual information in a database. In content-based image clustering (CBIC), image data are clustered on the foundation of specific features like texture, colors, boundaries, shapes. In this paper, an effective CBIC) technique is presented, which uses texture and statistical features of the images. The statistical features or moments of colors (mean, skewness, standard deviation, kurtosis, and variance) are extracted from the images. These features are collected in a one dimension array, and then genetic algorithm (GA) is applied for image clustering.
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Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class
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The growth of developments in machine learning, the image processing methods along with availability of the medical imaging data are taking a big increase in the utilization of machine learning strategies in the medical area. The utilization of neural networks, mainly, in recent days, the convolutional neural networks (CNN), have powerful descriptors for computer added diagnosis systems. Even so, there are several issues when work with medical images in which many of medical images possess a low-quality noise-to-signal (NSR) ratio compared to scenes obtained with a digital camera, that generally qualified a confusingly low spatial resolution and tends to make the contrast between different tissues of body are very low and it difficult to co
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Between decline and appearing dichotomy, art history comes to announce birth of an era that glories past and find new names that are emerged from yearning to past and represented by neo-classical, By refusing the previous approaches and create topics that touché culture and derived from it through s revitalizing ideal beauty standards. One of neo-classical artists, who tried to simulate the classical works, is (Jean-Auguste-Dominique Ingres), who put framework for semantic aesthetic of the art form by revitalizing past glories and deeply searching myths and cultures through finding special artistic features that emphasizes artist own stylistics and identity. This research studies artistic features of women form in (Jean-Auguste-D
Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class
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