ABSTRACT
The early detection of COVID-19 is one of the current challenges in developing effective diagnosis and treatment mechanisms for patients who are at a high risk for community contagion. Computed Tomography (CT) is an essential support for detecting the infection pattern that causes this disease. CT scans provide relevant information on the morphological appearance of the infected parenchymal tissue, known as ground-glass opacities. Artificial Intelligence (AI) can assist in the quick evaluation of CT scans to differentiate COVID-19 findings in suggestive clinical cases. In this context, AI in the form of, Convolutional Neural Networks (CNN), has achieved successful results in the analysis and classification of medical images. A deep CNN architecture is proposed in this study to diagnose COVID-19 based on the classification of Chest Computed Tomography (CCT) images. In this study 8,624 CCTs of Ecuadorian patients affected by COVID-19 in the first quarter of 2021, were examined. The initial review of CCTs was performed by medical experts to discriminate the CCTs against other chronic lung diseases not associated with COVID-19. The CCTs were pre-processed by techniques such as morphological segmentation, erosion, dilation, and adjustment. After training the model reached an overall F1-score of 97%. © 2021 ACM.
ABSTRACT
With the promotion of electrification of transportation, fuel cell electric vehicles (FCEVs) begin to flourish in recent years. FCEVs operate with zero emission and excellent fuel economy, but high cost and incomplete infrastructure hinder the popularization further. Targeting resources are poured into this area by some governments worldwide. To foster the development, it is essential to study the use of FCEVs. Based on the Service and Management center for EVs (SMC-EV), this work conducts a statistical analysis of the market scales, the operation conditions, such as user login statistics, driving distance and refueling behavior and the impact of the occurrence of the Covid-19 pandemic. The analysis results provide essential support to predict the subsequent development of FCEVs and guide the policymaking and the construction of hydrogen refueling stations. © 2022 SPIE. All rights reserved.