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Multifractal analysis and support vector machine for the classification of coronaviruses and SARS-CoV-2 variants
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Multifractal analysis and support vector machine for the classification of coronaviruses and SARS-CoV-2 variants
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Journal Article
Multifractal analysis and support vector machine for the classification of coronaviruses and SARS-CoV-2 variants
2025
Overview
This study presents a novel approach for the classification of coronavirus species and variants of SARS-CoV-2 using Chaos Game Representation (CGR) and 2D Multifractal Detrended Fluctuation Analysis (2D MF-DFA). By extracting fractal parameters from CGR images, we constructed a state space that effectively distinguishes different species and variants. Our method achieved
accuracy in species classification, with a notable
accuracy for SARS-CoV-2 variants despite their genetic similarities. Using a Support Vector Machine (SVM) as a classifier further enhanced the performance. This approach, which requires fewer steps than most existing methods, offers an efficient and effective tool for viral classification, with implications for bioinformatics, public health, and vaccine development.
