Published Paper Details:

Dyslexia is a learning disorder characterized by lack of reading and /or writing skills, difficulty in rapid word naming and also poor in spelling. Dyslexic individuals have great difficulty to read and interpret words or letters. The most common reading disability is dyslexia. This reading disability encompasses various symptoms such as poor spelling outcomes, reading fluency and difficulties in expressing oneself. Failure to diagnose children coping with dyslexia is a potential risk of discrimination and social exclusion. Dyslexia is the most widely recognized neurological learning disability. All these can influence scholastic achievement, confidence, and social-emotional development. Studies have demonstrated that the earlier dyslexia is recognized and backing is given in education and training, the more its negative impacts can be alleviated. Subsequently, building up a solid and target screening technique to analyze dyslexia at an early age would be of most extreme significance. The work is carried out to classify dyslexic from non-dyslexics by various approaches such as machine learning, image processing, understanding the brain behavior through psychology, studying the differences in anatomy of brain. In this work, brain images are used for screening individuals who have high risk to dyslexia. This work also motivates the application of machine learning in distributed environment. The proposed predictive model uses the machine-learning algorithms like Decision Tree(DT) and Random Forest (RF). The model is classified using Weka tool and Python implementation.