A Cloud-Based Diagnosis Framework Of Dyslexia For The Arabic Language using Large Scale Ontologies Reasoning over Apache Spark
Mohamed Oubezza, Ali El Hore, Jamal El Kafi · 2020
In this paper we present our Cloud-based Framework for Knowledge Sharing on Early Diagnosis of Dyslexia for the Arabic Language. The Framework allows the intelligent generation of sentences containing forms of dyslexia that are susceptible to being mispronounced by the child. The Framework also extracts the forms of dyslexia present in a sentence and evaluates the patient's achievements. We exploit the innovative techniques of knowledge representation and parallel processing of big data to ensure the efficiency and speed necessary for such real time applications. The Framework architecture is based on Apache HDFS, Apache SPARK and HBase. Our system is designed to be easily integrated into mobile games and applications. The results of the tests carried out are encouraging.