{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T21:33:40Z","timestamp":1775511220704,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,8,14]],"date-time":"2023-08-14T00:00:00Z","timestamp":1691971200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia","award":["20\/18"],"award-info":[{"award-number":["20\/18"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Religious studies are a rich land for Natural Language Processing (NLP). The reason is that all religions have their instructions as written texts. In this paper, we apply NLP to Islamic Hadiths, which are the written traditions, sayings, actions, approvals, and discussions of the Prophet Muhammad, his companions, or his followers. A Hadith is composed of two parts: the chain of narrators (Sanad) and the content of the Hadith (Matn). A Hadith is transmitted from its author to a Hadith book author using a chain of narrators. The problem we solve focuses on the classification of Hadiths based on their origin of narration. This is important for several reasons. First, it helps determine the authenticity and reliability of the Hadiths. Second, it helps trace the chain of narration and identify the narrators involved in transmitting Hadiths. Finally, it helps understand the historical and cultural contexts in which Hadiths were transmitted, and the different levels of authority attributed to the narrators. To the best of our knowledge, and based on our literature review, this problem is not solved before using machine\/deep learning approaches. To solve this classification problem, we created a novel Author-Based Hadith Classification Dataset (ABCD) collected from classical Hadiths\u2019 books. The ABCD size is 29 K Hadiths and it contains unique 18 K narrators, with all their information. We applied machine learning (ML), and deep learning (DL) approaches. ML was applied on Sanad and Matn separately; then, we did the same with DL. The results revealed that ML performs better than DL using the Matn input data, with a 77% F1-score. DL performed better than ML using the Sanad input data, with a 92% F1-score. We used precision and recall alongside the F1-score; details of the results are explained at the end of the paper. We claim that the ABCD and the reported results will motivate the community to work in this new area. Our dataset and results will represent a baseline for further research on the same problem.<\/jats:p>","DOI":"10.3390\/bdcc7030141","type":"journal-article","created":{"date-parts":[[2023,8,14]],"date-time":"2023-08-14T10:20:14Z","timestamp":1692008414000},"page":"141","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Hadiths Classification Using a Novel Author-Based Hadith Classification Dataset (ABCD)"],"prefix":"10.3390","volume":"7","author":[{"given":"Ahmed","family":"Ramzy","sequence":"first","affiliation":[{"name":"Faculty of Computers and Artificial Intelligence, Cairo University, Giza 12613, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6149-1718","authenticated-orcid":false,"given":"Marwan","family":"Torki","sequence":"additional","affiliation":[{"name":"Department of Computer and Systems Engineering, Alexandria University, Alexandria 21526, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9326-8409","authenticated-orcid":false,"given":"Mohamed","family":"Abdeen","sequence":"additional","affiliation":[{"name":"Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi Arabia"}]},{"given":"Omar","family":"Saif","sequence":"additional","affiliation":[{"name":"Faculty of Hadith and Islamic Studies, Islamic University of Madinah, Madinah 42351, Saudi Arabia"},{"name":"Faculty of Sharia and Islamic Studies, Alqasimia University, Sharjah 63000, United Arab Emirates"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2215-8736","authenticated-orcid":false,"given":"Mustafa","family":"ElNainay","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Engineering, Alamein International University, New Alamein City 51718, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6080-5236","authenticated-orcid":false,"given":"AbdAllah","family":"Alshanqiti","sequence":"additional","affiliation":[{"name":"Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3729-5079","authenticated-orcid":false,"given":"Emad","family":"Nabil","sequence":"additional","affiliation":[{"name":"Faculty of Computers and Artificial Intelligence, Cairo University, Giza 12613, Egypt"},{"name":"Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1007\/s10462-016-9458-x","article-title":"Hadith data mining and classification: A comparative analysis","volume":"46","author":"Saloot","year":"2016","journal-title":"Artif. 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