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This article examines perceptions of social media use (e.g. the WhatsApp application), in particular looking at how Bahraini women use such technology and how mobile communication is used by such a segment of the population. Mobile devices are very accessible to Bahraini women and this needs even further study to learn ways of using applications for information and other things. This analysis is based on an online survey, conducted among 1137 Bahraini women, using a nonrepresentative sample (snowball). Such data were analyzed using a deep learning approach which utilizes, in particular, the Fuzzy Proximity Knowledge Mining technique to examine the provided answers. The study found that WhatsApp has enabled Bahraini women to communicate and share information with others. They spent 2–3 h daily sending and enjoying comics and entertainment clips and important and rare news stories. Social interaction, communication, and escapism featured strongly as the most popular reasons for using WhatsApp. Overall, WhatsApp served as a platform used to participate in social and communication activities. [ABSTRACT FROM AUTHOR]

The coronavirus pandemic and its unprecedented consequences globally has spurred the interest of the artificial intelligence research community. A plethora of published studies have investigated the role of imaging such as chest X-rays and computer tomography in coronavirus disease 2019 (COVID-19) automated diagnosis. Οpen repositories of medical imaging data can play a significant role by promoting cooperation among institutes in a world-wide scale. However, they may induce limitations related to variable data quality and intrinsic differences due to the wide variety of scanner vendors and imaging parameters. In this study, a state-of-the-art custom U-Net model is presented with a dice similarity coefficient performance of 99.6% along with a transfer learning VGG-19 based model for COVID-19 versus pneumonia differentiation exhibiting an area under curve of 96.1%. The above was significantly improved over the baseline model trained with no segmentation in selected tomographic slices of the same dataset. The presented study highlights the importance of a robust preprocessing protocol for image analysis within a heterogeneous imaging dataset and assesses the potential diagnostic value of the presented COVID-19 model by comparing its performance to the state of the art. [ABSTRACT FROM AUTHOR]