Multi-Stage Temporal Convolution Network for COVID-19 Variant Classification
Waseem Ullah,
Amin Ullah,
Khalid Mahmood Malik,
Abdul Khader Jilani Saudagar,
Muhammad Badruddin Khan,
Mozaherul Hoque Abul Hasanat,
Abdullah AlTameem,
Mohammed AlKhathami
Affiliations
Waseem Ullah
Department of Software, Sejong University, Seoul 05006, Korea
Amin Ullah
CORIS Institute, Oregon State University, Corvallis, OR 97331, USA
Khalid Mahmood Malik
Department of Computer Science and Engineering, Oakland University, Rochester, MI 48309, USA
Abdul Khader Jilani Saudagar
Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11564, Saudi Arabia
Muhammad Badruddin Khan
Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11564, Saudi Arabia
Mozaherul Hoque Abul Hasanat
Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11564, Saudi Arabia
Abdullah AlTameem
Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11564, Saudi Arabia
Mohammed AlKhathami
Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11564, Saudi Arabia
The outbreak of the novel coronavirus disease COVID-19 (SARS-CoV-2) has developed into a global epidemic. Due to the pathogenic virus’s high transmission rate, accurate identification and early prediction are required for subsequent therapy. Moreover, the virus’s polymorphic nature allows it to evolve and adapt to various environments, making prediction difficult. However, other diseases, such as dengue, MERS-CoV, Ebola, SARS-CoV-1, and influenza, necessitate the employment of a predictor based on their genomic information. To alleviate the situation, we propose a deep learning-based mechanism for the classification of various SARS-CoV-2 virus variants, including the most recent, Omicron. Our model uses a neural network with a temporal convolution neural network to accurately identify different variants of COVID-19. The proposed model first encodes the sequences in the numerical descriptor, and then the convolution operation is applied for discriminative feature extraction from the encoded sequences. The sequential relations between the features are collected using a temporal convolution network to classify COVID-19 variants accurately. We collected recent data from the NCBI, on which the proposed method outperforms various baselines with a high margin.
