The week's pick
Reseach Article
Multifaceted Computational Framework for COVID-19 Variant Classification using Advanced Machine Learning, Signal Processing, and High-Dimensional Feature Reduction Techniques
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 70 |
| Year of Publication: 2025 |
| Authors: Love Fadia, Vatsal Shah, Mohammad Hassanzadeh, Majid Ahmadi, Jonathan Wu |
10.5120/ijca2025924499
|
Love Fadia, Vatsal Shah, Mohammad Hassanzadeh, Majid Ahmadi, Jonathan Wu . Multifaceted Computational Framework for COVID-19 Variant Classification using Advanced Machine Learning, Signal Processing, and High-Dimensional Feature Reduction Techniques. International Journal of Computer Applications. 186, 70 ( Mar 2025), 1-8. DOI=10.5120/ijca2025924499
Abstract
The coronavirus pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), had an extensive global impact, causing widespread disruptions to public health. The early and accurate identification of the virus and its various strains is imperative for safeguarding lives. Over the past few years, multifarious machine learning and deep learning techniques were used to classify genomic sequences . However, existing methods face several limitations. Many approaches struggle with dataset imbalance, leading to biased and unreliable models. Traditional neural network-based methods are computationally intensive, requiring significant time and resources. Moreover, existing techniques often fail to achieve consistently high classification accuracy across properly balanced datasets. To address these gaps, this article presents an efficient method for classifying coronavirus variants’ DNA sequences using a combination of machine learning and signal processing. The DNA sequences are first converted into numbers using Electron-Ion Interaction Potential, Numeric, and Complex coding techniques. After that signal processing methods; Discrete Cosine Transform II, Discrete Cosine Transform III, Fast Fourier Transform, Haar Wavelet Transform, and Coiflet Wavelet Transform are applied to extract features from the coded data. The high dimensionality is reduced using Linear Discriminant Analysis and Principal Component Analysis. For the classification task, machine learning models such as Decision Tree, Support Vector Classifier, and a fusion of Light-Gradient Boosting Machine, AdaBoost, and Random Forest are employed. The proposed approach achieves an impressive accuracy of 99.8%, which surpasses the state of the art using a different combination of transformations with Numeric coding and Voting Classifier.
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