As COVID-19 spreads rapidly all over the world, the lack of reliable testing kits and medical diagnoses makes the infection more vulnerable to the human population. An effective diagnosis and detection of the SARS-Cov-2 virus are required to control and prevent the COVID-19 disease. In this study, we employed a convolution neural network (CNN) to detect coronavirus-infected patients using computed tomography (CT) images. The proposed study utilized transfer learning on the three pre-trained deep CNN models to detect COVID-19 infection from the chest CT scan images. We have tuned and optimized the hyper-parameters of the pre-trained CNN models using the Bayesian Optimization technique. Further, the deep CNN architectures are incorporated with the Learning without Forgetting (LwF) technique to improve the model’s capability to recognize new Delta variants COVID-19 data. The CNN model with the LwF is evaluated on the CT images of original and the Delta-variant COVID-19 dataset. The performance of the learning, without forgetting based CNN models namely VGG16, InceptionV3, and Xception is assessed using different performance evaluation metrics in detecting COVID-19 disease. The experimental result shows that the Xception model’s performance is superior that other two developed models and effective in classifying original augmented images and new Delta-variant images with an accuracy of 98.31% and 92.32%, respectively.The empirical result shows our model performance is significantly effective in diagnosis and classification of two different variants of the SARS-CoV-2 virus and the developed CNN models can provide assistance to the medical experts for diagnosing different variants of COVID-19 disease.
Nanofluids are the combination of base fluid and nanoparticles which offer higher thermal conductivity resulting higher heat transfer. In this research article, soft computing tool is used to find the accurate Nusselt number of coiled tube heat exchanger handling Al2O3/H2O nanofluids at three different volume concentrations and at different mass flow rate in terms of Dean number. The input predictor variables used in this model are convective heat transfer coefficient, thermal conductivity of nanofluids, and Dean number and the output response variable is Nusselt number. Linear regression, generalized linear regression, and Lasso and elastic-net regularized generalized linear models methodologies are taken to predict the Nusselt number. It is observed that the linear regression method shows an accurate agreement with experimental data with root mean square error value of 0.05614 and regression coefficient value is 0.99. It is studied that the experimental data holds good accordance with the predicted data given by the trained network. The average relative errors in the prediction of Nusselt number and heat transfer coefficients are found to be 0.3% and 0.2%, respectively.
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