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.
Abstract
Currently, the “moderate discourse and civil peace” represents a rich and important topic for scholars, due the rise of waves of extremism and Islamophobia campaigns, and what that leads to in term of imbalance in relations between nations and peoples.
Based on that, the research approach was to tackle the culture of hatred and calls for the clash of civilizations.
In order to contribute to solving these problems caused by cultural and religious prejudices, I decided to address the topic of “moderate discourse and civil peace” through two essential axes:
- Features of moderate religious discourse
- The role of moderate discourse in establishing communit
Deep learning has recently received a lot of attention as a feasible solution to a variety of artificial intelligence difficulties. Convolutional neural networks (CNNs) outperform other deep learning architectures in the application of object identification and recognition when compared to other machine learning methods. Speech recognition, pattern analysis, and image identification, all benefit from deep neural networks. When performing image operations on noisy images, such as fog removal or low light enhancement, image processing methods such as filtering or image enhancement are required. The study shows the effect of using Multi-scale deep learning Context Aggregation Network CAN on Bilateral Filtering Approximation (BFA) for d
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The theoretical framework included the research problem which is determined by the following question: what are the stru