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Coronavirus disease (COVID‐19) is a pandemic that has caused thousands of casualties and impacts all over the world. Most countries are facing a shortage of COVID‐19 test kits in hospitals due to the daily increase in the number of cases. Early detection of COVID‐19 can protect people from severe infection. Unfortunately, COVID‐19 can be misdiagnosed as pneumonia or other illness and can lead to patient death. Therefore, in order to avoid the spread of COVID‐19 among the population, it is necessary to implement an automated early diagnostic system as a rapid alternative diagnostic system. Several researchers have done very well in detecting COVID‐19; however, most of them have lower accuracy and overfitting issues that make early screening of COVID‐19 difficult. Transfer learning is the most successful technique to solve this problem with higher accuracy. In this paper, we studied the feasibility of applying transfer learning and added our own classifier to automatically classify COVID‐19 because transfer learning is very suitable for medical imaging due to the limited availability of data. In this work, we proposed a CNN model based on deep transfer learning technique using six different pre‐trained architectures, including VGG16, DenseNet201, MobileNetV2, ResNet50, Xception, and EfficientNetB0. A total of 3886 chest X‐rays (1200 cases of COVID‐19, 1341 healthy and 1345 cases of viral pneumonia) were used to study the effectiveness of the proposed CNN model. A comparative analysis of the proposed CNN models using three classes of chest X‐ray datasets was carried out in order to find the most suitable model. Experimental results show that the proposed CNN model based on VGG16 was able to accurately diagnose COVID‐19 patients with 97.84% accuracy, 97.90% precision, 97.89% sensitivity, and 97.89% of F1‐score. Evaluation of the test data shows that the proposed model produces the highest accuracy among CNNs and seems to be the most suitable choice for COVID‐19 classification. We believe that in this pandemic situation, this model will support healthcare professionals in improving patient screening. [ABSTRACT FROM AUTHOR]

In this paper, we study double cascade dressed optical metal oxide semiconductor field-effect transistor (MOSFET) by exploiting enhancement and suppression for mixed-phase (hexagonal + tetragonal) of Eu 3+ :YPO 4 and different phases (hexagonal + tetragonal and pure tetragonal) of Pr 3+ :YPO 4 crystals. We report variation of fine structure energy levels in different doped ions (Eu 3+ and Pr 3+ ) in the host YPO crystal. We compared multi-level energy transition from a single dressing laser with single level energy transition from double cascade dressing lasers. Gate delay facilitates multi-energy level dressed transition and is modeled through a Hamiltonian. Based on the results of double cascade dressing, we have realized MOSFET for logic gates (inverter and logic not and gate) with a switching contrast of about 92% using a mixed phase of Pr 3+ :YPO 4 ., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)