Attention-based networks for analyzing inappropriate speech in Arabic text

Abstract Analyzing social media posts and comments has become a critical task to prevent cyberbullying and hate speech. In this work we present a classification models based on the attention mechanism to analyze Arabic posts and filter out all kinds of inappropriate speech including Religious based hate speech, offensive and abusive content in different Arabic dialects. The attention-based models show promising results for four Arabic datasets. The results are presented and compared in terms of accuracy and training time

Arabic dialects identification:North African dialects case study

Abstract Arabic is the fourth most used language on the Internet and the official language of more than 20 countries around the world. It has three main varieties, Modern Standard Arabic, which is used in books, news and education, local Dialects that vary from region to another, and Classical Arabic, the written language of the Quran. Maghrebi dialect is the Arabic dialect language used in North African countries, where internet users from these countries feel more comfortable using local slangs than native Arabic. In this study, we present a large dataset of regional dialects of three countries, namely Algeria, Tunisia, and Morocco, then we investigate the identification of each dialect using a machine learning classifiers with TF-IDF features. The approach shows promising results, where we achieved accuracy up to 96%

COVID-19 detection from Xray and CT scans using transfer learning

Abstract Since the novel coronavirus SARS-CoV-2 outbreak, intensive research has been conducted to find suitable tools for diagnosis and identifying infected people in order to take appropriate action. Chest imaging plays a significant role in this phase where CT and Xrays scans have proven to be effective in detecting COVID-19 within the lungs. In this research, we propose deep learning models using Transfer learning to detect COVID-19. Both X-ray and CT scans were considered to evaluate the proposed methods