An Intelligent Framework Based on Deep Learning for Online Quran Learning during Pandemic
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...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2023-01-01
|
| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2023/5541699 |
| _version_ | 1826992877348061184 |
|---|---|
| author | Natasha Nigar Amna Wajid Sunday Adeola Ajagbe Matthew O. Adigun |
| author_facet | Natasha Nigar Amna Wajid Sunday Adeola Ajagbe Matthew O. Adigun |
| author_sort | Natasha Nigar |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T18:32:07Z |
| format | Article |
| id | doaj.art-c9c7b845f9ef40b79bd903a261ce831b |
| institution | Directory Open Access Journal |
| issn | 1687-9732 |
| language | English |
| last_indexed | 2025-02-18T08:56:19Z |
| publishDate | 2023-01-01 |
| publisher | Hindawi Limited |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj.art-c9c7b845f9ef40b79bd903a261ce831b2024-11-02T23:55:12ZengHindawi LimitedApplied Computational Intelligence and Soft Computing1687-97322023-01-01202310.1155/2023/5541699An Intelligent Framework Based on Deep Learning for Online Quran Learning during PandemicNatasha Nigar0Amna Wajid1Sunday Adeola Ajagbe2Matthew O. Adigun3Department of Computer Science (RCET)Department of Computer Science (RCET)Department of Computer and Industrial Production EngineeringDepartment of Computer ScienceThe 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.http://dx.doi.org/10.1155/2023/5541699 |
| spellingShingle | Natasha Nigar Amna Wajid Sunday Adeola Ajagbe Matthew O. Adigun An Intelligent Framework Based on Deep Learning for Online Quran Learning during Pandemic Applied Computational Intelligence and Soft Computing |
| title | An Intelligent Framework Based on Deep Learning for Online Quran Learning during Pandemic |
| title_full | An Intelligent Framework Based on Deep Learning for Online Quran Learning during Pandemic |
| title_fullStr | An Intelligent Framework Based on Deep Learning for Online Quran Learning during Pandemic |
| title_full_unstemmed | An Intelligent Framework Based on Deep Learning for Online Quran Learning during Pandemic |
| title_short | An Intelligent Framework Based on Deep Learning for Online Quran Learning during Pandemic |
| title_sort | intelligent framework based on deep learning for online quran learning during pandemic |
| url | http://dx.doi.org/10.1155/2023/5541699 |
| work_keys_str_mv | AT natashanigar anintelligentframeworkbasedondeeplearningforonlinequranlearningduringpandemic AT amnawajid anintelligentframeworkbasedondeeplearningforonlinequranlearningduringpandemic AT sundayadeolaajagbe anintelligentframeworkbasedondeeplearningforonlinequranlearningduringpandemic AT matthewoadigun anintelligentframeworkbasedondeeplearningforonlinequranlearningduringpandemic AT natashanigar intelligentframeworkbasedondeeplearningforonlinequranlearningduringpandemic AT amnawajid intelligentframeworkbasedondeeplearningforonlinequranlearningduringpandemic AT sundayadeolaajagbe intelligentframeworkbasedondeeplearningforonlinequranlearningduringpandemic AT matthewoadigun intelligentframeworkbasedondeeplearningforonlinequranlearningduringpandemic |