Accurate Machine Learning Algorithm for Monkey Pox Based on Covid-19
2023 IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023
; : 380-383, 2023.
Article
in English
| Scopus | ID: covidwho-2319810
ABSTRACT
The Covid-19 virus is still marching all over the world. Many people are getting infected and a few are fatal to death. This research paper expressed that supervised learning has revealed supreme results than unsupervised learning in machine learning. Within supervised learning, random forest regression outplays all other algorithms like logistic regression (LR), support vector machine (SVM), decision tree (DT), etc. Now monkeypox is escalating in other countries at present. This virus is allied to human orthopox viruses. It can expand from one to one through contact person having rash or body fluids etc. The symptoms of monkeypox are much similar to covid19 virus-like fever, cold, fatigue, and body pains. Herewith we concluded that random forest regression shows possible foremost (97.15%) accuracy. © 2023 IEEE.
Covid-19; Machine Learning (ML); monkey pox; Supervised Learning (SL); Unsupervised Learning (USL); Body fluids; Decision trees; Learning algorithms; Learning systems; Logistic regression; Support vector machines; Unsupervised learning; Logistics regressions; Machine learning; Machine learning algorithms; Machine-learning; Random forests; Research papers; Supervised learning; Viruses
Full text:
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
2023 IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023
Year:
2023
Document Type:
Article
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