The present paper addresses one of the most challenging topics in translation; namely legal translation in the framework of two different approaches; the classical (formal) and the more recent (functional). The latter approach is the outcome of the process of simplifying legal language known technically as Plain Language Movement. The advent of this movement dates back to the 1950s, in response to the widely-held complain about the awkwardness of the legal register. Within this framework, the salient features of legal language, at the various linguistic and textual levels, underwent reconsideration in favor of more publicly digested expressions. The paper then subjects two translations of a lease contract to analysis in the ligh

The present paper aims at presenting a feminist reading of Shamela. It specifically seeks to show how the patriarchal mentality behind Shamela dismembers all aspects of the positive image of the woman in Pamela so that nothing remains in the public mind but the prevalent stereotypical image.  The narrative in Pamela draws a positive picture of a truly pure, honest, moral woman; the narrative of Shamela redraws that picture by positing the stereotypical image of the woman as a lascivious temptress, false creature, immoral person, and shameless prostitute. This image is what patriarchy intends to endure as

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

The internet of medical things (IoMT), which is expected the lead to the biggest technology in worldwide distribution. Using 5th generation (5G) transmission, market possibilities and hazards related to IoMT are improved and detected. This framework describes a strategy for proactively addressing worries and offering a forum to promote development, alter attitudes and maintain people's confidence in the broader healthcare system without compromising security. It is combined with a data offloading system to speed up the transmission of medical data and improved the quality of service (QoS). As a result of this development, we suggested the enriched energy efficient fuzzy (EEEF) data offloading technique to enhance the delivery of dat

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

The internet of medical things (IoMT), which is expected the lead to the biggest technology in worldwide distribution. Using 5th generation (5G) transmission, market possibilities and hazards related to IoMT are improved and detected. This framework describes a strategy for proactively addressing worries and offering a forum to promote development, alter attitudes and maintain people's confidence in the broader healthcare system without compromising security. It is combined with a data offloading system to speed up the transmission of medical data and improved the quality of service (QoS). As a result of this development, we suggested the enriched energy efficient fuzzy (EEEF) data offloading technique to enhance the delivery of dat