ANN Assisted-IoT Enabled COVID-19 Patient Monitoring
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/..
COVID-19 is an extremely dangerous disease because of its highly infectious nature. In order to provide a quick and immediate identification of infection, a proper and immediate clinical support is needed. Researchers have proposed various Machine Learning and smart IoT based schemes for categorizing the COVID-19 patients. Artificial Neural Networks (ANN) that are inspired by the biological concept of neurons are generally used in various applications including healthcare systems. The ANN scheme provides a viable solution in the decision making process for managing the healthcare information. This manuscript endeavours to illustrate the applicability and suitability of ANN by categorizing the status of COVID-19 patients' health into infected (IN), uninfected (UI), exposed (EP) and susceptible (ST). In order to do so, Bayesian and back propagation algorithms have been used to generate the results. Further, viterbi algorithm is used to improve the accuracy of the proposed system. The proposed mechanism is validated over various accuracy and classification parameters against conventional Random Tree (RT), Fuzzy C Means (FCM) and REPTree (RPT) methods.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:9 |
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| Enthalten in: |
IEEE access : practical innovations, open solutions - 9(2021) vom: 28., Seite 42483-42492 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Rathee, Geetanjali [VerfasserIn] |
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| Links: |
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| Themen: |
Artificial neural network |
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| Anmerkungen: |
Date Revised 03.04.2024 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1109/ACCESS.2021.3064826 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM333239938 |
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| 520 | |a This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/. | ||
| 520 | |a COVID-19 is an extremely dangerous disease because of its highly infectious nature. In order to provide a quick and immediate identification of infection, a proper and immediate clinical support is needed. Researchers have proposed various Machine Learning and smart IoT based schemes for categorizing the COVID-19 patients. Artificial Neural Networks (ANN) that are inspired by the biological concept of neurons are generally used in various applications including healthcare systems. The ANN scheme provides a viable solution in the decision making process for managing the healthcare information. This manuscript endeavours to illustrate the applicability and suitability of ANN by categorizing the status of COVID-19 patients' health into infected (IN), uninfected (UI), exposed (EP) and susceptible (ST). In order to do so, Bayesian and back propagation algorithms have been used to generate the results. Further, viterbi algorithm is used to improve the accuracy of the proposed system. The proposed mechanism is validated over various accuracy and classification parameters against conventional Random Tree (RT), Fuzzy C Means (FCM) and REPTree (RPT) methods | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Artificial neural network | |
| 650 | 4 | |a COVID 19 patients’ identification | |
| 650 | 4 | |a back propagation network | |
| 650 | 4 | |a multi-perceptron layer | |
| 650 | 4 | |a security in healthcare | |
| 700 | 1 | |a Garg, Sahil |e verfasserin |4 aut | |
| 700 | 1 | |a Kaddoum, Georges |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Yulei |e verfasserin |4 aut | |
| 700 | 1 | |a K Jayakody, Dushantha Nalin |e verfasserin |4 aut | |
| 700 | 1 | |a Alamri, Atif |e verfasserin |4 aut | |
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