{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:38:46Z","timestamp":1787027926921,"version":"build-2736575974"},"reference-count":38,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,4,20]],"date-time":"2023-04-20T00:00:00Z","timestamp":1681948800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"King Saud University, Riyadh, Saudi Arabia","award":["RSP2023R124"],"award-info":[{"award-number":["RSP2023R124"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>While the world is working quietly to repair the damage caused by COVID-19\u2019s 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\u2013Critic, 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.<\/jats:p>","DOI":"10.3390\/diagnostics13081491","type":"journal-article","created":{"date-parts":[[2023,4,21]],"date-time":"2023-04-21T01:33:43Z","timestamp":1682040823000},"page":"1491","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Human Pathogenic Monkeypox Disease Recognition Using Q-Learning Approach"],"prefix":"10.3390","volume":"13","author":[{"given":"Malathi","family":"Velu","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Panimalar Engineering College, Poonamallee, Chennai 600123, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2038-7359","authenticated-orcid":false,"given":"Rajesh","family":"Dhanaraj","sequence":"additional","affiliation":[{"name":"School of Computing Science and Engineering, Galgotias University, Greater Noida 203201, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Balamurugan","family":"Balusamy","sequence":"additional","affiliation":[{"name":"Associate Dean-Student Engagement, Shiv Nadar Institution of Eminence, Delhi-National Capital Region (NCR), Gautam Buddha Nagar 201314, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1939-4842","authenticated-orcid":false,"given":"Seifedine","family":"Kadry","sequence":"additional","affiliation":[{"name":"Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway"},{"name":"Artificial Intelligence Research Center (AIRC), Ajman University, Ajman P.O. Box 346, United Arab Emirates"},{"name":"Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Yu","sequence":"additional","affiliation":[{"name":"Centre for Infrastructure Engineering and Safety (CIES), The University of New South Wales, Sydney, NSW 2052, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2038-8367","authenticated-orcid":false,"given":"Ahmed","family":"Nadeem","sequence":"additional","affiliation":[{"name":"Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1515-3187","authenticated-orcid":false,"given":"Hafiz","family":"Rauf","sequence":"additional","affiliation":[{"name":"Independent Researcher, Bradford BD8 0HS, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1093\/cid\/cit703","article-title":"Human Monkey Pox","volume":"58","author":"McCollum","year":"2014","journal-title":"Clin. Infect. Dis."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Alakunle, E., Moens, U., Nchinda, G., and Okeke, M.I. (2020). Monkey Pox Virus in Nigeria: Infection Biology, Epidemiology, and Evolution. Viruses, 12.","DOI":"10.3390\/v12111257"},{"key":"ref_3","unstructured":"Moore, M.J., Rathish, B., and Zahra, F. (2022). Monkey Pox. StatPearls, StatPearls Publishing."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1014","DOI":"10.3201\/eid2206.150579","article-title":"Extended Human-to-Human Transmission during a Monkey Pox Outbreak in the Democratic Republic of the Congo","volume":"22","author":"Nolen","year":"2016","journal-title":"Emerg. Infect. Dis."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.3201\/eid2704.203569","article-title":"Reemergence of Human Monkey pox and Declining Population Immunity in the Context of Urbanization, Nigeria, 2017\u20132020","volume":"27","author":"Nguyen","year":"2021","journal-title":"Emerg. Infect. Dis."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"El-kenawy, E.S.M., Albalawi, F., Ward, S.A., Ghoneim, S.S.M., Eid, M.M., Abdelhamid, A.A., Bailek, N., and Ibrahim, A. (2022). Feature Selection and Classification of Transformer Faults Based on Novel Meta-Heuristic Algorithm. Mathematics, 10.","DOI":"10.3390\/math10173144"},{"key":"ref_7","first-page":"3749","article-title":"Dynamic Voting Classifier for Risk Identification in Supply Chain 4","volume":"69","author":"Abdelhameed","year":"2021","journal-title":"Comput. Mater. Contin."},{"key":"ref_8","unstructured":"Ali, S., Ahmed, M., Paul, J., Jahan, T., Sani, S., Noor, N., and Hasan, T. (2022). Monkey Pox Skin Lesion Detection Using Deep Learning Models: A Feasibility Study. arXiv."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1016\/S1473-3099(22)00228-6","article-title":"Clinical features and management of human monkey pox: A retrospective observational study in the U.K","volume":"22","author":"Adler","year":"2022","journal-title":"Lancet Infect. Dis."},{"key":"ref_10","first-page":"106","article-title":"Classification of Monkey Pox Skin Lesion Using the Explainable Artificial Intelligence Assisted Convolutional Neural Networks","volume":"40","author":"Akin","year":"2022","journal-title":"Avrupa Bilim Ve Teknol. Derg."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"125787","DOI":"10.1109\/ACCESS.2021.3111408","article-title":"Wind Speed Ensemble Forecasting Based on Deep Learning Using Adaptive Dynamic Optimization Algorithm","volume":"9","author":"Ibrahim","year":"2021","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"490","DOI":"10.3390\/make2040027","article-title":"COVID-19 Symptoms Detection Based on NasNetMobile with Explainable A.I. Using Various Imaging Modalities","volume":"2","author":"Ahsan","year":"2020","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ahsan, M.M., Alam, T.E., Trafalis, T., and Huebner, P. (2020). Deep MLP-CNN Model Using Mixed-Data to Distinguish between COVID-19 and Non-COVID-19 Patients. Symmetry, 12.","DOI":"10.3390\/sym12091526"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"35501","DOI":"10.1109\/ACCESS.2021.3061621","article-title":"Detecting SARS-CoV-2 from Chest X-Ray Using Artificial Intelligence","volume":"9","author":"Ahsan","year":"2021","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ahsan, M.M., Nazim, R., Siddique, Z., and Huebner, P. (2021). Detection of COVID-19 Patients from CT Scan and Chest X-ray Data Using Modified MobileNetV2 and LIME. Healthcare, 9.","DOI":"10.3390\/healthcare9091099"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"102289","DOI":"10.1016\/j.artmed.2022.102289","article-title":"Machine learning-based heart disease diagnosis: A systematic literature review","volume":"128","author":"Ahsan","year":"2022","journal-title":"Artif. Intell. Med."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1016\/j.compbiomed.2014.10.006","article-title":"Computer-aided diagnosis system based on fuzzy logic for breast cancer categorization","volume":"64","author":"Miranda","year":"2015","journal-title":"Comput. Biol. Med."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"103795","DOI":"10.1016\/j.compbiomed.2020.103795","article-title":"Application of deep learning technique to manage COVID-19 in routine clinical practice using C.T. images: Results of 10 convolutional neural networks","volume":"121","author":"Ardakani","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"19549","DOI":"10.1038\/s41598-020-76550-z","article-title":"COVID-Net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images","volume":"10","author":"Wang","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Goyal, V., Gupta, M., Mirjalili, S., and Trivedi, A. (2022). Diagnosis of Visible Diseases Using CNNs. Proceedings of the International Conference on Communication and Artificial Intelligence, Springer Nature. Lecture Notes in Networks and Systems.","DOI":"10.1007\/978-981-19-0976-4"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Roy, K., Chaudhuri, S.S., Ghosh, S., Dutta, S.K., Chakraborty, P., and Sarkar, R. (2019, January 18\u201320). Skin Disease detection based on different Segmentation Techniques. Proceedings of the 2019 International Conference on Opto-Electronics and Applied Optics (Optronix), Kolkata, India.","DOI":"10.1109\/OPTRONIX.2019.8862403"},{"key":"ref_22","first-page":"81542","article-title":"Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques","volume":"6","author":"Chandra","year":"2022","journal-title":"Int. J. Sci. Res. Eng. Manag."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.media.2017.05.003","article-title":"Multi-modal classification of neurodegenerative disease by progressive graph-based transductive learning","volume":"39","author":"Wang","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/j.ejcon.2020.08.001","article-title":"Hybrid data-driven fuzzy active disturbance rejection control for tower crane systems","volume":"58","author":"Roman","year":"2021","journal-title":"Eur. J. Control"},{"key":"ref_25","first-page":"1600","article-title":"Enhanced P-type Control: Indirect Adaptive Learning from Set-Point Updates","volume":"1","author":"Chi","year":"2022","journal-title":"IEEE Trans. Autom. Control."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.cmpb.2018.12.032","article-title":"Prediction of fatty liver disease using machine learning algorithms","volume":"170","author":"Wu","year":"2019","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"20991","DOI":"10.1109\/ACCESS.2019.2963053","article-title":"A Machine Learning Methodology for Diagnosing Chronic Kidney Disease","volume":"8","author":"Qin","year":"2020","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Khan, M.M.R., Arif, R.B., Siddique, M.A.B., and Oishe, M.R. (2018, January 13\u201315). Study and Observation of the Variation of Accuracies of KNN, SVM, LMNN, ENN Algorithms on Eleven Different Datasets from UCI Machine Learning Repository. Proceedings of the 2018 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT), Dhaka, Bangladesh.","DOI":"10.1109\/CEEICT.2018.8628041"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"104992","DOI":"10.1016\/j.cmpb.2019.104992","article-title":"A new machine learning technique for an accurate diagnosis of coronary artery disease","volume":"179","author":"Abdar","year":"2019","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Yu, Y., Yang, Z., Li, P., Yang, Z., and You, Y. (2019, January 3\u20136). Work-in-Progress: On the Feasibility of Lightweight Scheme of Real-Time Atrial Fibrillation Detection Using Deep Learning. Proceedings of the 2019 IEEE Real-Time Systems Symposium (RTSS), Hong Kong, China.","DOI":"10.1109\/RTSS46320.2019.00062"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1056\/NEJM186003290620901","article-title":"Remarks on Smallpox, Cowpox and Varioloid","volume":"62","author":"Hayward","year":"1860","journal-title":"Boston Med. Surg. J."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Islam, T., Hussain, M.A., Chowdhury, F.U.H., and Islam, B.M.R. (2022). A Web-scraped Skin Image Database of Monkeypox, Chickenpox, Smallpox, Cowpox, and Measles, Cold Spring Harbor Laboratory.","DOI":"10.1101\/2022.08.01.502199"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"19196","DOI":"10.1038\/s41598-020-76282-0","article-title":"Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography","volume":"10","author":"Chen","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"982","DOI":"10.4269\/ajtmh.12-0758","article-title":"Detection of human monkeypox in the republic of the congo following intensive community education","volume":"88","author":"Reynolds","year":"2013","journal-title":"Am. J. Trop. Med. Hygiene"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10916-022-01868-2","article-title":"Monkeypox virus detection using pre-trained deep learning-based approaches","volume":"46","author":"Sitaula","year":"2022","journal-title":"J. Med. Syst."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Haque, M., Ahmed, M., Nila, R.S., and Islam, S. (2022). Classification of human monkeypox disease using deep learning models and attention mechanisms. arXiv.","DOI":"10.1109\/ICCIT57492.2022.10055870"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"122","DOI":"10.18178\/joig.4.2.122-129","article-title":"Computer-Aided Model for Skin Diagnosis Using Deep Learning","volume":"4","author":"Shoieb","year":"2016","journal-title":"J. Image Graph."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Abdelhamid, A.A., El-Kenawy, E.S.M., Khodadadi, N., Mirjalili, S., Khafaga, D.S., Alharbi, A.H., and Saber, M. (2022). Classification of monkeypox images based on transfer learning and the Al-Biruni Earth Radius Optimization algorithm. Mathematics, 10.","DOI":"10.3390\/math10193614"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/8\/1491\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:19:56Z","timestamp":1760123996000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/8\/1491"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,20]]},"references-count":38,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["diagnostics13081491"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics13081491","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,20]]}}}