Over the past years, notable progress has been made towards fighting misinformation spread over social media, encouraging the development of many fact-checking systems. However, systems that operate over Arabic content are scarce. In this work, we bridge this gap by proposing Tahaqqaq (Verify), an Arabic real-time system that helps users verify claims over Twitter with several functionalities, such as identifying check-worthy claims, estimating credibility of users in terms of spreading fake news, and finding authoritative accounts. Tahaqqaq has a friendly online Web interface that supports various real-time user scenarios. In the same breath, we enable public access to Tahaqqaq services through a handy RESTful API. Finally, in terms of performance, multiple components of Tahaqqaq outperform the state-of-the-art models on Arabic datasets.

Smart health systems have become the need of the hour taking into account the rising number of patients and the limited facilities available to treat them. This paper has profited by the advantages of two major fields of research in the recent times; deep learning and edge computing. A deep learning method for epileptic seizure detection using mobile edge computing is proposed. EEG signals are used for seizure detection. A novel deep Convolutional Neural Network (CNN) with six convolutional layer, two fully connected layer with softmax activation is used to classify the EEG signal into either seizure or non-seizure category. A three tier architecture with an edge layer between the end device and the cloud is used. Out of all the advantages the edge layer can enhance privacy, this paper is mainly focused on using the edge to reduce the size of the data being sent to the cloud. A dataset with 500 samples from healthy and seizure activities is used. The raw data is represented in both time and frequency domain. The best model proposed can achieve an accuracy of 92 % in time domain, 99.22 % in frequency domain.