AI-Enabled Deep Learning Model for COVID-19 Identification Leveraging Internet of Things
Addressing the COVID-19 epidemic since December 2019 has emphasized the criticality of timely and error-free identification of infected COVID-19 individuals in medical settings. To effectively combat the epidemic, the utilization of deep TL-enabled automated COVID-19 identification on CXRs is paramo...
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| Format: | Article |
| Language: | English |
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College of Computer and Information Technology – University of Wasit, Iraq
2023
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| Online Access: | http://umpir.ump.edu.my/id/eprint/38906/ http://umpir.ump.edu.my/id/eprint/38906/1/AI-Enabled%20Deep%20Learning%20Model%20for%20COVID-19%20Identification%20Leveraging%20Internet%20of%20Things.pdf |
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| author | Mohd Arfian, Ismail Siti Nur Fathin Najwa, Mustaffa Abed, Munther H. |
| author_facet | Mohd Arfian, Ismail Siti Nur Fathin Najwa, Mustaffa Abed, Munther H. |
| author_sort | Mohd Arfian, Ismail |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Addressing the COVID-19 epidemic since December 2019 has emphasized the criticality of timely and error-free identification of infected COVID-19 individuals in medical settings. To effectively combat the epidemic, the utilization of deep TL-enabled automated COVID-19 identification on CXRs is paramount. This study recommended a real-time IoT system employing ensemble deep TL to enable early identification of infected COVID-19 individuals. The system allows for real-time transmission and identification of COVID-19 suspicious individuals. The suggested IoT model incorporates several DL models, including InceptionResNetV2, VGG16, ResNet152V2, and DenseNet201. These models, stored on a cloud server, are utilized in conjunction with medical sensors to gather chest X-ray data and detect infections. A chest X-ray dataset is used to compare the deep ensemble model against six transfer learning algorithms. The comparative investigation demonstrates that the suggested approach facilitates swift and effective diagnosis of COVID-19 suspicious patients, providing valuable support to radiologists. This work highlights the significance of leveraging deep transfer learning and IoT in achieving early identification of suspected COVID-19 patients. The proposed system, incorporating a deep ensemble model, offers a practical solution for assisting radiologists in efficiently diagnosing COVID-19 cases |
| first_indexed | 2025-11-15T03:31:58Z |
| format | Article |
| id | ump-38906 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:31:58Z |
| publishDate | 2023 |
| publisher | College of Computer and Information Technology – University of Wasit, Iraq |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-389062023-10-17T03:47:10Z http://umpir.ump.edu.my/id/eprint/38906/ AI-Enabled Deep Learning Model for COVID-19 Identification Leveraging Internet of Things Mohd Arfian, Ismail Siti Nur Fathin Najwa, Mustaffa Abed, Munther H. QA75 Electronic computers. Computer science Addressing the COVID-19 epidemic since December 2019 has emphasized the criticality of timely and error-free identification of infected COVID-19 individuals in medical settings. To effectively combat the epidemic, the utilization of deep TL-enabled automated COVID-19 identification on CXRs is paramount. This study recommended a real-time IoT system employing ensemble deep TL to enable early identification of infected COVID-19 individuals. The system allows for real-time transmission and identification of COVID-19 suspicious individuals. The suggested IoT model incorporates several DL models, including InceptionResNetV2, VGG16, ResNet152V2, and DenseNet201. These models, stored on a cloud server, are utilized in conjunction with medical sensors to gather chest X-ray data and detect infections. A chest X-ray dataset is used to compare the deep ensemble model against six transfer learning algorithms. The comparative investigation demonstrates that the suggested approach facilitates swift and effective diagnosis of COVID-19 suspicious patients, providing valuable support to radiologists. This work highlights the significance of leveraging deep transfer learning and IoT in achieving early identification of suspected COVID-19 patients. The proposed system, incorporating a deep ensemble model, offers a practical solution for assisting radiologists in efficiently diagnosing COVID-19 cases College of Computer and Information Technology – University of Wasit, Iraq 2023 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/38906/1/AI-Enabled%20Deep%20Learning%20Model%20for%20COVID-19%20Identification%20Leveraging%20Internet%20of%20Things.pdf Mohd Arfian, Ismail and Siti Nur Fathin Najwa, Mustaffa and Abed, Munther H. (2023) AI-Enabled Deep Learning Model for COVID-19 Identification Leveraging Internet of Things. Wasit Journal of Computer and Mathematics Science, 2 (2). pp. 33-39. ISSN 2788-5879. (Published) https://wjcm.uowasit.edu.iq/index.php/wjcm/article/view/146/94 |
| spellingShingle | QA75 Electronic computers. Computer science Mohd Arfian, Ismail Siti Nur Fathin Najwa, Mustaffa Abed, Munther H. AI-Enabled Deep Learning Model for COVID-19 Identification Leveraging Internet of Things |
| title | AI-Enabled Deep Learning Model for COVID-19 Identification Leveraging Internet of Things |
| title_full | AI-Enabled Deep Learning Model for COVID-19 Identification Leveraging Internet of Things |
| title_fullStr | AI-Enabled Deep Learning Model for COVID-19 Identification Leveraging Internet of Things |
| title_full_unstemmed | AI-Enabled Deep Learning Model for COVID-19 Identification Leveraging Internet of Things |
| title_short | AI-Enabled Deep Learning Model for COVID-19 Identification Leveraging Internet of Things |
| title_sort | ai-enabled deep learning model for covid-19 identification leveraging internet of things |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/38906/ http://umpir.ump.edu.my/id/eprint/38906/ http://umpir.ump.edu.my/id/eprint/38906/1/AI-Enabled%20Deep%20Learning%20Model%20for%20COVID-19%20Identification%20Leveraging%20Internet%20of%20Things.pdf |