| Title |
COVID-19 Prediction Model Empowered with Fused Computational Intelligence Technique |
| Authors |
(Muhammad Adnan Khan) ; (Iftikhar Naseer) ; (Muhammad Nadeem Ali) ; (Byung-Seo Kim) |
| DOI |
https://doi.org/10.5573/IEIESPC.2025.14.1.109 |
| Keywords |
Covid-19; Fusion; Computational intelligence; Convolutional neural network; Support vector machine |
| Abstract |
The novel Coronavirus (COVID-19) spread rapidly around the world and caused overwhelming effects on the health and economy of the world. It first appeared in Wuhan city of China and was declared a pandemic by the World Health Organization (WHO). Many researchers, as well as experts in clinical and artificial intelligence experts, are working together to control the rapid spread of COVID-19 with early detection. This study focused on intelligent prediction for coronavirus using computational intelligence approaches (IPC-FCIA) like convolutional neural networks, support vector machines, and fuzzy logic techniques. The proposed IPC-FCIA model is based on two sections namely the training section and the validation section. Features fusion and decision-level fusion are used in this study to enhance the performance of the recommended IPC-FCIA model. The proposed model predicts the early detection of COVID-19 in two types COVID-negative and COVID-positive. The benchmark results of the model show an accuracy of 97.66 % on decision-level fusion in the detection of COVID-19. The proposed model can be helpful for medical experts as well as COVID-19-affected patients. |