SIAKAD machine learning for correcting errors in speaking Arabic

Using Machine Learning Algorithms for Automatic Cyber Bullying Detection in Arabic Social Media

Social media allows people interact to express their thoughts or feelings about different subjects. However, some of users may write offensive twits to other via social media which known as cyber bullying. Successful prevention depends on automatically detecting malicious messages. Automatic detection of bullying in the text of social media by analyzing the text "twits" via one of the machine l...

Machine learning for Arabic text categorization

Evaluation of Machine Translation Errors in English and Iraqi Arabic

Errors in machine translations of English-Iraqi Arabic dialogues were analyzed at two different points in the systems‟ development using HTER methods to identify errors and human annotations to refine TER annotations. Although the frequencies of errors in the more mature systems were lower, the proportions of error types exhibited little change. Results include high frequencies of pronoun error...

Obesity in Arabic-Speaking Countries

Obesity has reached epidemic proportions throughout the globe, and this has also impacted people of the Arabic-speaking countries, especially those in higher-income, oil-producing countries. The prevalence of obesity in children and adolescents ranges from 5% to 14% in males and from 3% to 18% in females. There is a significant increase in the incidence of obesity with a prevalence of 2%-55% in...

A Multi-Agent System for Detecting and Correcting "Hidden" Spelling Errors in Arabic Texts

: In this paper, we address the problem of detecting and correcting hidden spelling errors in Arabic texts. Hidden spelling errors are morphologically valid words and therefore they cannot be detected or corrected by conventional spell checking programs. In the work presented here, we investigate this kind of errors as they relate to the Arabic language. We start by proposing a classification o...