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We propose a two-stage Convolutional Neural Network (CNN) based classification framework for detecting COVID-19 and Community Acquired Pneumonia (CAP) using the chest Computed Tomography (CT) scan images. In the first stage, an infection -COVID-19 or CAP, is detected using a pre-trained DenseNet architecture. Then, in the second stage, a fine-grained three-way classification is done using EfficientNet architecture. The proposed COVID+CAP-CNN framework achieved a slice-level classification accuracy of over 94% at identifying COVID-19 and CAP. Further, the proposed framework has the potential to be an initial screening tool for differential diagnosis of COVID-19 and CAP, achieving a validation accuracy of over 89.3% at the finer three-way COVID-19, CAP, and healthy classification. Within the IEEE ICASSP 2021 Signal Processing Grand Challenge (SPGC) on COVID-19 Diagnosis, our proposed two-stage classification framework achieved an overall accuracy of 90% and sensitivity of .857, .9, and .942 at distinguishing COVID-19, CAP, and normal individuals respectively, to rank first in the evaluation. Code and model weights are available at https://github.com/shubhamchaudhary2015/ ct_covid19_cap_cnn

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Recently fuel cell has been paid significant attention because of their several advantages namely higher efficiency and eco-friendly technology and finds applications in vehicles, various house hold applications, electronic devices, and to support the electricity grids. In this paper various fuel cell technologies, their working principle, and applications are presented. Also the mathematical modeling and Simulink model of Proton Exchange Membrane fuel cell (PEMFC) and Solid Oxide fuel cell (SOFC), and their operating characteristics are presented in details. The effect of various parameters on PEMFC performance is also assessed under various operating conditions in detail. The results prove the validity of developed Matlab/Simulink models of FCs.