FOREWARD

Abstract

By the grace of Allah, it is a great pleasure to introduce this issue of The International Journal on Islamic Applications in Computer Science and Technology. During the 11th year of the publication of this Journal, this issue is the 43rd of this journal. We thank Allah for enabling us to continue all through these years. With the wide specialization of this Journal, it attracted contributions from researchers from all over the world. We pray to Allah to put his “Baraka” in the contents of the Journal and spread the fruits of its contents in the future. This issue contains two papers. The first one is entitled Was the Quran Written by the Prophet? A Stylometric Investigation Using the Interrogative Form In this investigation, we try to see whether the holy Quran could have been written or dictated by the Prophet (Pbuh), thanks to a stylometric discriminative analysis of the corresponding Author styles. The originality of this research work lies in the use of a new set of linguistic features based on 26 interrogative features and a special fusion, which we called logarithmic feature fusion or LFF. The experiments have shown that the proposed features, with their fusion, are interesting. Furthermore, the application of discrimination on the Quran and Hadith has shown a great difference in Author style between the two books, which confirms that the Quran could not be written or dictated by the Prophet. The second paper is entitled Topic Modeling for Hadith Corpus: A Comparison of Latent Dirichlet Allocation (LDA), Non-Negative Matrix Factorization (NMF), and BERTopic with AraBERT, XLM-R, MARBERT, and CAMeLBERT The primary source of Islamic law, following the Holy Qur'an, is the collection of authentic Hadith attributed to the prophet of God, peace be upon him (PBUH). The status of the prophet's Hadith is evident in its being an explanation of the Qur'an and its abstract topics. With that, this research presents different topic modeling techniques to examine their performance on the authentic Hadith. Topic modeling is the process of clustering documents and words automatically in a textual domain. LDA and NMF are the most widely used topic modeling techniques. BERTopic is a modern technique based on BERT using pre-trained transformer based language models for topic modeling. This study aims to apply the topic modeling approaches to the "Matn" part of the authentic Hadith. Then, we compare the performance of BERTopic using state-of-the-art pre-trained Arabic language models to LDA and NMF approaches. We finally evaluate the topic coherence of topic modeling methods using normalized pointwise mutual information (NPMI). The findings of this study indicate that the BERTopic model outperforms the LDA and NMF techniques in terms of overall performance.