The advent of UNHCR reports has given rise to the uniqueness of its distinctive way of image representation and using semiotic features. So, there are a lot of researches that have investigated UNHCR reports, but no research has examined images in UNHCR reports of displaced Iraqis from a multimodal discourse perspective. The present study suggests that the images are, like language, rich in many potential meanings and are governed by clearly visual grammar structures that can be employed to decode these multiple meanings. Seven images are examined in terms of their representational, interactional and compositional aspects. Depending on the results, this study concludes that the findings support the visual grammar theory and highlight the value of images as semiotic resources in conveying multi-layered meanings. Applying Kress and Van Leeuwen"s (2006) Multimodal Discourse Analysis (MDA) of analyzing the images in UNHCR reports on the displaced Iraqis succeed in revealing their semiotic structures. The analysis of the selected images shows various relations existed between the participants and the viewers on the visual level through employing different visual modes.
במחקר הזה ניתחנו מספר נאומים של שמעון פרס, אנחנו התמקדנו בהשפעה והשכנוע אצל שמעון פרס ואיך הוא יכול להעביר את המסרים של נאומיו בסגנון פרגמטי כדי להגיע ללבו של הציבור.
גם כן, התמקדנו בסגנון הפוליטי שהוא חושב כי התחום הזה צריך להיות ברור מול הציבור וגם כן מול דעת הקהל הבינלאומי מתוך השימוש במונחים בעלי השפעה ושכנוע להגיע למטרות המבוקשות.
במלים אחרות, שמעון פרס, לעתים, מתמקד בשפה מליצית ויעמוד הרב
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As cultures are mainly divided into collectivistic and individualistic, members tend to emphasize, through communication, either their position as part of their group or their independence from the group. This emphasis is manifested in using the pragmatic concepts of positive politeness and negative politeness. The present study looks into the reflection of these two cultures in Rockstar’s renowned video game, Red Dead Redemption 2 (2018). It aims at identifying the two cultures as present in the game and showing their significance to its narrative. It fills the gap in the studies of language used within video games as well as its cultural reflections. The study addresses the following question: What are the positive and negative
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A content-based image retrieval (CBIR) is a technique used to retrieve images from an image database. However, the CBIR process suffers from less accuracy to retrieve images from an extensive image database and ensure the privacy of images. This paper aims to address the issues of accuracy utilizing deep learning techniques as the CNN method. Also, it provides the necessary privacy for images using fully homomorphic encryption methods by Cheon, Kim, Kim, and Song (CKKS). To achieve these aims, a system has been proposed, namely RCNN_CKKS, that includes two parts. The first part (offline processing) extracts automated high-level features based on a flatting layer in a convolutional neural network (CNN) and then stores these features in a
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Graphite nanoparticles were successfully synthesized using mixture of H2O2/NH4OH with three steps of oxidation. The process of oxidations were analysis by XRD and optics microscopic images which shows clear change in particle size of graphite after every steps of oxidation. The method depend on treatments the graphite with H2O2 in two steps than complete the last steps by reacting with H2O2/NH4OH with equal quantities. The process did not reduces the several sheets for graphite but dispersion the aggregates of multi-sheets carbon when removed the Van Der Waals forces through the oxidation process.
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LK Abood, RA Ali, M Maliki, International Journal of Science and Research, 2015 - Cited by 2
<p>Analyzing X-rays and computed tomography-scan (CT scan) images using a convolutional neural network (CNN) method is a very interesting subject, especially after coronavirus disease 2019 (COVID-19) pandemic. In this paper, a study is made on 423 patients’ CT scan images from Al-Kadhimiya (Madenat Al Emammain Al Kadhmain) hospital in Baghdad, Iraq, to diagnose if they have COVID or not using CNN. The total data being tested has 15000 CT-scan images chosen in a specific way to give a correct diagnosis. The activation function used in this research is the wavelet function, which differs from CNN activation functions. The convolutional wavelet neural network (CWNN) model proposed in this paper is compared with regular convol
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<p>Analyzing X-rays and computed tomography-scan (CT scan) images using a convolutional neural network (CNN) method is a very interesting subject, especially after coronavirus disease 2019 (COVID-19) pandemic. In this paper, a study is made on 423 patients’ CT scan images from Al-Kadhimiya (Madenat Al Emammain Al Kadhmain) hospital in Baghdad, Iraq, to diagnose if they have COVID or not using CNN. The total data being tested has 15000 CT-scan images chosen in a specific way to give a correct diagnosis. The activation function used in this research is the wavelet function, which differs from CNN activation functions. The convolutional wavelet neural network (CWNN) model proposed in this paper is compared with regular convol
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In this paper, a compression system with high synthetic architect is introduced, it is based on wavelet transform, polynomial representation and quadtree coding. The bio-orthogonal (tap 9/7) wavelet transform is used to decompose the image signal, and 2D polynomial representation is utilized to prune the existing high scale variation of image signal. Quantization with quadtree coding are followed by shift coding are applied to compress the detail band and the residue part of approximation subband. The test results indicate that the introduced system is simple and fast and it leads to better compression gain in comparison with the case of using first order polynomial approximation.
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