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Human Pathogenic Monkeypox Disease Recognition Using Q-Learning Approach
While the world is working quietly to repair the damage caused by COVID-19’s widespread transmission, the monkeypox virus threatens to become a global pandemic. There are several nations that report new monkeypox cases daily, despite the virus being less deadly and contagious than COVID-19. Monkeypo...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10137862/ https://www.ncbi.nlm.nih.gov/pubmed/37189591 http://dx.doi.org/10.3390/diagnostics13081491 |
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author | Velu, Malathi Dhanaraj, Rajesh Kumar Balusamy, Balamurugan Kadry, Seifedine Yu, Yang Nadeem, Ahmed Rauf, Hafiz Tayyab |
author_facet | Velu, Malathi Dhanaraj, Rajesh Kumar Balusamy, Balamurugan Kadry, Seifedine Yu, Yang Nadeem, Ahmed Rauf, Hafiz Tayyab |
author_sort | Velu, Malathi |
collection | PubMed |
description | While the world is working quietly to repair the damage caused by COVID-19’s widespread transmission, the monkeypox virus threatens to become a global pandemic. There are several nations that report new monkeypox cases daily, despite the virus being less deadly and contagious than COVID-19. Monkeypox disease may be detected using artificial intelligence techniques. This paper suggests two strategies for improving monkeypox image classification precision. Based on reinforcement learning and parameter optimization for multi-layer neural networks, the suggested approaches are based on feature extraction and classification: the Q-learning algorithm determines the rate at which an act occurs in a particular state; Malneural networks are binary hybrid algorithms that improve the parameters of neural networks. The algorithms are evaluated using an openly available dataset. In order to analyze the proposed optimization feature selection for monkeypox classification, interpretation criteria were utilized. In order to evaluate the efficiency, significance, and robustness of the suggested algorithms, a series of numerical tests were conducted. There were 95% precision, 95% recall, and 96% f1 scores for monkeypox disease. As compared to traditional learning methods, this method has a higher accuracy value. The overall macro average was around 0.95, and the overall weighted average was around 0.96. When compared to the benchmark algorithms, DDQN, Policy Gradient, and Actor–Critic, the Malneural network had the highest accuracy (around 0.985). In comparison with traditional methods, the proposed methods were found to be more effective. Clinicians can use this proposal to treat monkeypox patients and administration agencies can use it to observe the origin and current status of the disease. |
format | Online Article Text |
id | pubmed-10137862 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-101378622023-04-28 Human Pathogenic Monkeypox Disease Recognition Using Q-Learning Approach Velu, Malathi Dhanaraj, Rajesh Kumar Balusamy, Balamurugan Kadry, Seifedine Yu, Yang Nadeem, Ahmed Rauf, Hafiz Tayyab Diagnostics (Basel) Article While the world is working quietly to repair the damage caused by COVID-19’s widespread transmission, the monkeypox virus threatens to become a global pandemic. There are several nations that report new monkeypox cases daily, despite the virus being less deadly and contagious than COVID-19. Monkeypox disease may be detected using artificial intelligence techniques. This paper suggests two strategies for improving monkeypox image classification precision. Based on reinforcement learning and parameter optimization for multi-layer neural networks, the suggested approaches are based on feature extraction and classification: the Q-learning algorithm determines the rate at which an act occurs in a particular state; Malneural networks are binary hybrid algorithms that improve the parameters of neural networks. The algorithms are evaluated using an openly available dataset. In order to analyze the proposed optimization feature selection for monkeypox classification, interpretation criteria were utilized. In order to evaluate the efficiency, significance, and robustness of the suggested algorithms, a series of numerical tests were conducted. There were 95% precision, 95% recall, and 96% f1 scores for monkeypox disease. As compared to traditional learning methods, this method has a higher accuracy value. The overall macro average was around 0.95, and the overall weighted average was around 0.96. When compared to the benchmark algorithms, DDQN, Policy Gradient, and Actor–Critic, the Malneural network had the highest accuracy (around 0.985). In comparison with traditional methods, the proposed methods were found to be more effective. Clinicians can use this proposal to treat monkeypox patients and administration agencies can use it to observe the origin and current status of the disease. MDPI 2023-04-20 /pmc/articles/PMC10137862/ /pubmed/37189591 http://dx.doi.org/10.3390/diagnostics13081491 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Velu, Malathi Dhanaraj, Rajesh Kumar Balusamy, Balamurugan Kadry, Seifedine Yu, Yang Nadeem, Ahmed Rauf, Hafiz Tayyab Human Pathogenic Monkeypox Disease Recognition Using Q-Learning Approach |
title | Human Pathogenic Monkeypox Disease Recognition Using Q-Learning Approach |
title_full | Human Pathogenic Monkeypox Disease Recognition Using Q-Learning Approach |
title_fullStr | Human Pathogenic Monkeypox Disease Recognition Using Q-Learning Approach |
title_full_unstemmed | Human Pathogenic Monkeypox Disease Recognition Using Q-Learning Approach |
title_short | Human Pathogenic Monkeypox Disease Recognition Using Q-Learning Approach |
title_sort | human pathogenic monkeypox disease recognition using q-learning approach |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10137862/ https://www.ncbi.nlm.nih.gov/pubmed/37189591 http://dx.doi.org/10.3390/diagnostics13081491 |
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