Khelifi, AyoubSahbi, RamziHalassa, Madiha: Supervisor2024-07-032024-07-032024-06https://repository.univ-msila.dz/handle/123456789/43142Deep 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.enHUMANITIES and RELIGION::Languages and linguistics::Other languages::Arabic languageHoly QuranDeep LearningNatural LanguageProcessing (NLP)CNNRNNThesis