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Due to the rapid growth of mobile devices, and the new generation of mobile technologies and services, terms such as 'mobile charging' and 'billing processes' arise and become a hot area for researchers and mobile telecommunications operators. Supporting the new revenue schemas and different pricing points requires a progressive improvement not only on the level of platform/applications or the physical infrastructure, but also on the level of cloud resources across different layers both IaaS and PaaS. Operators avail such cloud resources to vendors to manage the users charging transaction requires a certain performance management and enhancement. Analytical models and machine learning techniques are employed to manage, analyse the users' data/logs and to get more useful info that can be used in the management of the cloud resources in order to reach the best resources utilization with the highest revenue stream from the services users. Machine learning techniques are used to predict users charging behaviour based on their previous charging history and they are grouped into a set of clusters based on the similarity of the charging logs in a self-adaptive model that learn from old and current charging transactions as well. A detailed experiment is conducted to show how to reduce the number of charging transactions to the minimum that does not affect the revenue stream and at the same time leads to the best resources utilization. Finer tuning on this is made by applying forecasting and prediction techniques on the data to enhance the result. Several prediction techniques are applied to reach the highest accuracy level of prediction. Numerical results serve to confirm the accuracy of the proposed analytical model while providing insight on how the different parameters and designs affect cloud resources performance. Ibrahiem et al: Prediction of users charging time In cloud environment using machine learning

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Thanks to high-throughput data technology, microRNA analysis studies have evolved in early disease detection. This work introduces two complete models to detect the biomarkers of two autoimmune diseases, multiple sclerosis and rheumatoid arthritis, via miRNA analysis. Based on work the authors published previously, both introduced models involve complete pipelines of text mining methods, integrated with traditional machine learning methods, and LSTM deep learning. This work also studies the fragmentation of miRNA sequences to reduce the needed processing time and computational power. Moreover, this work studies the impact of obtaining two different library preparation kits (NEBNEXT and NEXTFLEX) on the detection accuracy for rheumatoid arthritis. Additional experiments are applied to the proposed models based on three different transcriptomic datasets. The results denote that the transcriptomic fragmentation model reported a biomarker detection accuracy of 96.45% on a sequence fragment size of 0.2, indicating a significant reduction in execution power while retaining biomarker detection accuracy. On the other hand, the LSTM model obtained a promising detection accuracy of 72%, implying savings in feature engineering processing. Additionally, the fragmentation model and the LSTM model reported 22.4% and 87.5% less execution time than work in the literature, respectively, denoting a considerable execution power reduction.

Globalization and migration patterns have led to a significant intensification of the encounters between Europeans, on one hand, and Arabs and Muslims, on the other. Increasingly, politics, economics and culture transcend national boundaries, but the relations between Europe and the Arab and Islamic worlds are threatened by polarization and hostility. Grounded on theories of sociology, pedagogy and cognition, this paper presents a mobile app that seeks to promote mutual knowledge and understanding by highlighting the associations between the two sides, driven by the commonalities and disparities between the historical characters of Richard I of England and Salah ad-Din.