B. AL

Despite a proliferation of research on Islam and Muslims in the media, very little work has focused on Muslim women, a much-debated social group that merits special consideration. This article aims to investigate how Muslim women are represented in BBC News website texts using a purpose-built corpus. The research employs analytical tools from the discourse-historical, socio-cognitive, and sociosemantic approaches to critical discourse studies. These are combined with corpus-based methodologies to investigate the semantic macrostructures that tend to be associated with Muslim women and the discursive strategies employed in the representation of the hijab. The study is novel in its exhaustive approach to identifying salient and underreported issues related to Muslim women in news discourse. It also introduces a more integrated analysis that combines manual and automated techniques and tackles the quantification of qualitative results where possible. Findings suggest that Muslim women’s representations are largely restricted in terms of regional coverage. Semantic macrostructures related to conflict and crime are prevalent. The hijab remains a nodal discourse surrounding Muslim women whose function as a descriptive feature is often unclear, raising serious questions about its relevance. A number of recommendations are made for journalists to become cognizant of their own context models when reporting on Muslim women.

Deep learning techniques, which use a massive technology known as convolutional neural networks, have shown excellent results in a variety of areas, including image processing and interpretation. However, as the depth of these networks grows, so does the demand for a large amount of labeled data required to train these networks. In particular, the medical field suffers from a lack of images because the procedure for obtaining labeled medical images in the healthcare field is difficult, expensive, and requires specialized expertise to add labels to images. Moreover, the process may be prone to errors and time-consuming. Current research has revealed transfer learning as a viable solution to this problem. Transfer learning allows us to transfer knowledge gained from a previous process to improve and tackle a new problem. This study aims to conduct a comprehensive survey of recent studies that dealt with solving this problem and the most important metrics used to evaluate these methods. In addition, this study identifies problems in transfer learning techniques and highlights the problems of the medical dataset and potential problems that can be addressed in future research. According to our review, many researchers use pre-trained models on the Imagenet dataset in many applications such as skin cancer, breast cancer, and diabetic retinopathy classification tasks. These techniques require further investigation of these models, due to training them on natural, non-medical images. In addition, many researchers use data augmentation techniques to expand their dataset and avoid overfitting. However, not enough studies have shown the effect of performance with or without data augmentation. Accuracy, recall, precision, F1 score, receiver operator characteristic curve, and area under the curve were the most widely used measures in these studies. Furthermore, we identified problems in the datasets for melanoma and breast cancer and suggested corresponding solutions.

Speech is a primary means of human communication and one of the most basic features of human conduct. Voice is an important part of its subsystems. A speech disorder is a condition that affects the ability of a person to speak normally, which occasionally results in voice impairment with psychological and emotional consequences. Early detection of voice problems is a crucial factor. Computer-based procedures are less costly and easier to administer for such purposes than traditional methods. This study highlights the following issues: recent studies, methods of voice pathology detection, machine learning and deep learning (DL) methods used in data classification, main datasets utilized, and the role of Internet of things (IoT) systems employed in voice pathology diagnosis. Moreover, this study presents different applications, open challenges, and recommendations for future directions of IoT systems and artificial intelligence (AI) approaches in the voice pathology diagnosis. Finally, this study highlights some limitations of voice pathology datasets in comparison with the role of IoT in the healthcare sector, which shows the urgent need to provide efficient approaches and easy and ideal medical diagnostic procedures and treatments of disease identification for doctors and patients. This review covered voice pathology taxonomy, detection techniques, open challenges, limitations, and recommendations for future directions to provide a clear background for doctors and patients. Standard databases, including the Massachusetts Eye and Ear Infirmary, Saarbruecken Voice Database, and the Arabic Voice Pathology Database, were used in most articles reviewed in this article. The classes, features, and main purpose for voice pathology identification are also highlighted. This study focuses on the extraction of voice pathology features, especially speech analysis, extends feature vectors comprising static and dynamic features, and converts these extended feature vectors into solid vectors before passing them to the recognizer.

It might not be striking that writers expressing the same idea, or a similar reaction to an idea, would use a similar set of words if they were writing in the same language.In the examples to follow, I remain unsure as to whether I should be struck by the similarity or not; it was a nice coincidence at first, a mere "interesting observation" rather than a "striking similarity," from which I could begin an article comparing the poetics of Ibn Sīnā with the aesthetics of Kant. The observation was that three different writers, in reacting to a similarity they observed between Ibn Sīnā and other thinkers in domains related to poetics and/or imagination, used the two words "striking" and "similarity," or at least their...

Managing workplace diversity has become a priority concern among organizations in the United Arab Emirates (UAE) today. The UAE has one of the world’s largest net migration rates, and the number of workers from India, the Philippines, Indonesia, Australia, the USA, among other countries, has increased significantly in recent decades. The UAE’s cross-border mobility has resulted in the interaction of people with diverse language, customs and ethnic backgrounds. Although diversity has been shown to have a number of benefits, including enhanced employee creativity and competence, this recognition is often found more in theory than actual practice. Diversity can also lead to miscommunication, dysfunctional adaptation behaviors and the creation of barriers that reduce the benefits diversity can bring to the organization. Due to the nature of the UAE workplace, which is dominated by a foreign workforce, this study critically analyzes the benefits and challenges organizations face in the diverse workplaces of the United Arab Emirates. The study used a multi-method approach combining survey data from 450 surveys of foreign workers with qualitative data from interviews with native officials of organizations. It is an attempt to compare the views of UAE workplace experience from two different groups—non-native workers and native officials. The research found a generally favorable view toward workplace diversity from the perspective of surveyed employees. However, when asked more detailed questions about company policy, a significant segment of respondents expressed reservations about their employer’s ability to implement successful intercultural communication and diversity practices.