Yixin Tang^*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1195-1212, 2023, DOI:10.32604/cmc.2023.039182

Abstract Visual illustration transformation from real-world to cartoon images is one of the famous and challenging tasks in computer vision. Image-to-image translation from real-world to cartoon domains poses issues such as a lack of paired training samples, lack of good image translation, low feature extraction from the previous domain images, and lack of high-quality image translation from the traditional generator algorithms. To solve the above-mentioned issues, paired independent model, high-quality dataset, Bayesian-based feature extractor, and an improved generator must be proposed. In this study, we propose a high-quality dataset to reduce the effect of paired training samples on the model’s performance.… More >

Hassam Tahir¹, Muhammad Shahbaz Khan¹, Fawad Ahmed², Abdullah M. Albarrak³, Sultan Noman Qasem³, Jawad Ahmad^4,*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3517-3535, 2023, DOI:10.32604/cmc.2023.035410

Abstract The COVID-19 outbreak began in December 2019 and was declared a global health emergency by the World Health Organization. The four most dominating variants are Beta, Gamma, Delta, and Omicron. After the administration of vaccine doses, an eminent decline in new cases has been observed. The COVID-19 vaccine induces neutralizing antibodies and T-cells in our bodies. However, strong variants like Delta and Omicron tend to escape these neutralizing antibodies elicited by COVID-19 vaccination. Therefore, it is indispensable to study, analyze and most importantly, predict the response of SARS-CoV-2-derived t-cell epitopes against Covid variants in vaccinated and unvaccinated persons. In this… More >