"Virtual Learning System (Miqra’ah) for Quran Recitations for Sighted and Blind Students"
written by Samir A. Elsagheer Mohamed, Allam Shehata Hassanin, Mohamed Taher Ben Othman,
published by Journal of Software Engineering and Applications, Vol.7 No.4, 2014
has been cited by the following article(s):
  • Google Scholar
  • CrossRef
[1] Predicting Quranic Audio Clips Reciters Using Classical Machine Learning Algorithms: A Comparative Study
2020
[2] An efficient holy quran recitation recognizer based on svm learning model
2020
[3] Arabic audio clips: Identification and discrimination of authentic Cantillations from imitations
2020
[4] The Omani Distance Learning Program to Teach the Holy Quran: Analytical Descriptive Study
2019
[5] Development model of digital Quran based on technology Acceptance in learning performance
Journal of Fundamental and Applied Sciences, 2018
[6] Towards an accurate speaker-independent Holy Quran acoustic model
2017
[7] DESIGN OF INTELLIGENT QIRA'AT IDENTIFICATION ALGORITHM
2017
[8] Strategies for implementing an optimal ASR system for quranic recitation recognition
International Journal of Computer Applications, 2017
[9] UI design recommendation for illiterate/semi-literate
2016
[10] UWBLOCALIZATION FOR ARABIC INDOOR NAVIGATION SYSTEM FOR BLINDS
2016
[11] Building CMU Sphinx language model for the Holy Quran using simplified Arabic phonemes
Egyptian Informatics Journal, 2016
[12] Analysis on Mel Frequency Cepstral Coefficients and Linear Predictive Cepstral Coefficients as Feature Extraction on Automatic Accents Identification
International Journal of Applied Engineering Research [IJAER], 2016
[13] Towards Using CMU Sphinx Tools for the Holy Quran Recitation Verification
2015
[14] Exploring Qur'an by using Aspects and Dependencies
2015
[15] Feature extraction using Spectral Centroid and Mel Frequency Cepstral Coefficient for Quranic Accent Automatic Identification
Research and Development (SCOReD), 2014 IEEE Student Conference on, 2014