Prediction of dyslexia using support vector machine in distributed environment

Author:

Prabha A Jothi,R Bhargavi,Ragala Ramesh

Abstract

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. Research 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 addition to it several assistive tools are developed to support dyslexics. 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 algorithm Support Vector Machine (SVM). The model is designed in Apache SPARK framework to support voluminous data. The prediction accuracy of 92.5% is achieved using SVM. 

Publisher

Science Publishing Corporation

Subject

Hardware and Architecture,General Engineering,General Chemical Engineering,Environmental Engineering,Computer Science (miscellaneous),Biotechnology

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Determining reading disorder with eye tracking and machine learning: A review of the literature;Klinik Psikoloji Dergisi;2023-09-14

2. Image-based Detection of Dyslexic Readers from 2-D Scan path using an Enhanced Deep Transfer Learning Paradigm;2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA);2022-11-30

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