{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T10:40:21Z","timestamp":1784544021133,"version":"3.55.0"},"reference-count":30,"publisher":"Wiley","license":[{"start":{"date-parts":[[2023,12,22]],"date-time":"2023-12-22T00:00:00Z","timestamp":1703203200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Computational Intelligence and Soft Computing"],"published-print":{"date-parts":[[2023,12,22]]},"abstract":"<jats:p>The COVID-19 pandemic influenced the whole world and changed social life globally. Social distancing is an effective strategy adopted by all countries to prevent humans from being infected. Al-Quran is the holy book of Muslims and its listening and reading is one of the obligatory activities. Close contact is essential in traditional learning system; however, most of the Al-Quran learning schools were locked down to minimize the spread of COVID-19 infection. To address this limitation, in this paper, we propose a novel system using deep learning to identify the correct recitation of individual alphabets, words from a recited verse and a complete verse of Al-Quran to assist the reciter. Moreover, in the proposed approach, if the user recites correctly, his\/her voice is also added to the existing dataset to leverage proposed approach effectiveness. We employ mel-frequency cepstral coefficients (MFCC) to extract voice features and long short-term memory (LSTM), a recurrent neural network (RNN) for classification. The said approach is validated using the Al-Quran dataset. The results demonstrate that the proposed system outperforms the state-of-the-art approaches with an accuracy rate of 97.7%. This system will help the Muslim community all over the world to recite the Al-Quran in the right way in the absence of human help due to similar future pandemics.<\/jats:p>","DOI":"10.1155\/2023\/5541699","type":"journal-article","created":{"date-parts":[[2023,12,22]],"date-time":"2023-12-22T16:05:05Z","timestamp":1703261105000},"page":"1-9","source":"Crossref","is-referenced-by-count":4,"title":["An Intelligent Framework Based on Deep Learning for Online Quran Learning during Pandemic"],"prefix":"10.1155","volume":"2023","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6517-666X","authenticated-orcid":true,"given":"Natasha","family":"Nigar","sequence":"first","affiliation":[{"name":"Department of Computer Science (RCET), University of Engineering and Technology, Lahore, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amna","family":"Wajid","sequence":"additional","affiliation":[{"name":"Department of Computer Science (RCET), University of Engineering and Technology, Lahore, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7010-5540","authenticated-orcid":true,"given":"Sunday Adeola","family":"Ajagbe","sequence":"additional","affiliation":[{"name":"Department of Computer and Industrial Production Engineering, First Technical University, Ibadan, Nigeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6256-5865","authenticated-orcid":true,"given":"Matthew O.","family":"Adigun","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Zululand, Richards Bay, South Africa"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1007\/s11125-020-09464-3"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1111\/tmi.13383"},{"key":"3","volume-title":"A Grammar of the Arabic Language","author":"W. 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