The most influential theory of ‘Politeness’ was formulated in 1978 and revised in 1987 by Brown and Levinson. ‘Politeness’, which represents the interlocutors’ desire to be pleasant to each other through a positive manner of addressing, was claimed to be a universal phenomenon. The gist of the theory is the intention to mitigate ‘Face’ threats carried by certain ‘Face’ threatening acts towards others.

            ‘Politeness Theory’ is based on the concept that interlocutors have ‘Face’ (i.e., self and public – image) which they consciously project, try to protect and to preserve. The theory holds that various politeness strategies are used to prot

The research aimed to identify the effects of the modern of technology on translating the media term from English language to Arabic. and try to identify the use of the impact of foreign media terminologies on the Arabic media term, and to know the effect of the translation process on Arabic media terminologies.

This research is considered an analytical study by using survey study for 111 items and the results for the study as following:

1.High percentage of the (use of foreign terms work to low the level of production) was (68.13%) and average 3.55

2.The percentage of (The multiplicity of translation of the foreign term into Arabic effects on the opinions and cognitive ideas of the Arab researcher and affects the

Early and accurate detection of COVID-19 from chest computed tomography (CT) scans are becoming essential for effective clinical decision-making and disease control. This study is proposing a robust deep learning framework that integrates a convolutional self-attention network (CSAN), gamma correction for image enhancement, and a voting-based ensemble classifier to improving diagnostic performance. The model is being evaluated on a dataset of 2,271 CT images and is achieving an accuracy of 95.12%, sensitivity of 97.25%, specificity of 98.11%, F1-score of 96.46%, and area under the curve (AUC) of 0.977. Experimental results are demonstrating that the proposed method significantly surpasses baseline models, including standalone CSAN,

We examine 10 hypothetical patients suffering from some of the symptoms of COVID 19 (modified) using topological concepts on topological spaces created from equality and similarity interactions and our information system. This is determined by the degree of accuracy obtained by weighing the value of the lower and upper figures. In practice, this approach has become clearer.

<p>Combating the COVID-19 epidemic has emerged as one of the most promising healthcare the world's challenges have ever seen. COVID-19 cases must be accurately and quickly diagnosed to receive proper medical treatment and limit the pandemic. Imaging approaches for chest radiography have been proven in order to be more successful in detecting coronavirus than the (RT-PCR) approach. Transfer knowledge is more suited to categorize patterns in medical pictures since the number of available medical images is limited. This paper illustrates a convolutional neural network (CNN) and recurrent neural network (RNN) hybrid architecture for the diagnosis of COVID-19 from chest X-rays. The deep transfer methods used were VGG19, DenseNet121