An Intelligent Model for Analyzing the (I‘rāb) Syntax Parsing of the First Three Parts (Juz’) of the Holy Quran

Abstract

This research paper aims to design and develop an intelligent model based on deep neural networks and long-term memory (LTM) for the automated analysis of Qur'anic texts. This facilitates a more accurate understanding of the grammatical structures of the Qur'anic text. The first three parts of the Holy Qur'an were used as the data set, and a long-term memory network (LSTM) was employed after converting the Qur'anic words from their textual form into numerical representations that could be trained and processed by the proposed model. The results showed the model's ability to predict and analyze the grammatical structure of the words with 96% accuracy, demonstrating the effectiveness of long-term memory networks in analyzing words within the complex grammatical and morphological structure of the Arabic language.