DEEP LEARNING MODELS APPLIED TO ARABIC QURANIC TEXT
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Date
2024-06
Journal Title
Journal ISSN
Volume Title
Publisher
UNIVERSITY OF MOHAMED BOUDIAF – MSILA, FACULTY OF MATHMATICS AND COMPUTER SCIENCE, DEPARTEMENT OF COMPUTER SCIENCE
Abstract
Deep learning has seen significant growth and development in natural language
processing (NLP) for many languages, including Arabic. However, the unique
characteristics of Arabic represent many challenges for deep learning models.
The aim of this research is to develop accurate and effective systems for
classifying Arabic verses of the Holy Quran using recurrent neural networks (RNN)
and convolutional neural networks (CNN).
The proposed model achieved promising results on the data set used, demonstrating
its effectiveness in classifying the verses of the Holy Quran with high accuracy. This
research contributes to the promotion of Quranic text classification techniques and
opens new avenues for exploring deep learning applications in Islamic studies.
Description
Keywords
HUMANITIES and RELIGION::Languages and linguistics::Other languages::Arabic language, Holy Quran, Deep Learning, Natural Language, Processing (NLP), CNN, RNN