Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Velu, Malathi, Dhanaraj, Rajesh Kumar, Balusamy, Balamurugan, Kadry, Seifedine, Yu, Yang, Nadeem, Ahmed, Rauf, Hafiz Tayyab
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
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
_version_ 1785032568587943936
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
work_keys_str_mv AT velumalathi humanpathogenicmonkeypoxdiseaserecognitionusingqlearningapproach
AT dhanarajrajeshkumar humanpathogenicmonkeypoxdiseaserecognitionusingqlearningapproach
AT balusamybalamurugan humanpathogenicmonkeypoxdiseaserecognitionusingqlearningapproach
AT kadryseifedine humanpathogenicmonkeypoxdiseaserecognitionusingqlearningapproach
AT yuyang humanpathogenicmonkeypoxdiseaserecognitionusingqlearningapproach
AT nadeemahmed humanpathogenicmonkeypoxdiseaserecognitionusingqlearningapproach
AT raufhafiztayyab humanpathogenicmonkeypoxdiseaserecognitionusingqlearningapproach