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On Enhancement of Reading Brain Performance Using Artificial Neural Networks’ Modeling

Hassan M. H. Mustafa 1, Mohamed Abdulrahman 2, Shahinaz Mahmoud 3, Ayesha Sohail 4, and Nosipho Dladlu 5
1. Computer Engineering Dept., Faculty of Eng., Al-Baha University, Al-Baha, KSA
2. Electrical Engineering Dept., Faculty of Eng., Al-Baha University, Al-Baha, KSA
3. Instructional & Information Technology Educational Technology Dept., Women College, Ain Shams University, Cairo, Egypt
4. Dept. of Mathematics, COMSATS Institute of IT, Lahore 54000, Pakistan
5. Dept. of Computer Science Faculty of Agriculture, Science and Technology North West University, Mafikeng Campus, South Africa
Abstract—This piece of research addresses an interdisciplinary and challenging issue which originated by a discipline incorporating neuroscience, education, and cognitive sciences. More specifically, this work concerned with developing reading brain in a significant way, which based upon recent increase of sophisticated realistic artificial neural networks (ANNs) modeling. Interestingly, the role of (ANNs) modeling has been recognized by both neurological researcher as well as educationalists. In more details, this paper adopts the conceptual approach of (ANN) models inspired by functioning of highly specialized biological reading brain's neurons. The cognitive goal for reading brain is to translate visualized (orthographic word-from) into a spoken voiced word (phonological word-form). In this context, by end of presented work, obtained ANN simulation results illustrated, how ensembles of highly specialized neurons could be dynamically involved in performing the cognitive function for developing reading brain.
 
Index Terms—brain reading function, phonological and orthographic processing, artificial neural networks’ modeling, and pattern recognition

Cite: Hassan M. H. Mustafa, Mohamed Abdulrahman, Shahinaz Mahmoud, Ayesha Sohail, and Nosipho Dladlu, "On Enhancement of Reading Brain Performance Using Artificial Neural Networks’ Modeling," International Journal of Signal Processing Systems, Vol. 4, No. 3, pp. 177-184, June 2016. doi: 10.18178/ijsps.4.3.177-184
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