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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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Main Authors: Mohd Arfian, Ismail, Siti Nur Fathin Najwa, Mustaffa, Abed, Munther H.
Format: Article
Language:English
Published: College of Computer and Information Technology – University of Wasit, Iraq 2023
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://umpir.ump.edu.my/id/eprint/38906/1/AI-Enabled%20Deep%20Learning%20Model%20for%20COVID-19%20Identification%20Leveraging%20Internet%20of%20Things.pdf
http://umpir.ump.edu.my/id/eprint/38906/
https://wjcm.uowasit.edu.iq/index.php/wjcm/article/view/146/94
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spelling my.ump.umpir.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
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic QA75 Electronic computers. Computer science
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
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
format Article
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
title 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_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_sort ai-enabled deep learning model for covid-19 identification leveraging internet of things
publisher College of Computer and Information Technology – University of Wasit, Iraq
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/38906/1/AI-Enabled%20Deep%20Learning%20Model%20for%20COVID-19%20Identification%20Leveraging%20Internet%20of%20Things.pdf
http://umpir.ump.edu.my/id/eprint/38906/
https://wjcm.uowasit.edu.iq/index.php/wjcm/article/view/146/94
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score 13.23648

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