CovidXNet: An Ensemble of Deep Neural Networks for Non-invasive Binary Identification of COVID-19 through Chest Radiographs

This study compared five different image classification algorithms, namely VGG16, VGG19, AlexNet, DenseNet, and ConVNext, based on their ability to detect and classify COVID-19-related cases given chest X-ray images. Using performance metrics like accuracy, F1 score, precision, recall, and MCC compared these intelligent classification algorithms. Upon testing these algorithms, the accuracy for each model was quite unsatisfactory, ranging from 80.00% to 92.50%, provided it is for medical application. As such, an ensemble learning-based image classification model, made up of AlexNet and VGG19 called CovidXNet, was proposed to detect COVID-19 through chest X-ray images discriminating between health and pneumonic lung images. CovidXNet achieved an accuracy of 97.00%, which was significantly better considering past results. Further studies may be conducted to increase the accuracy, particularly for identifying and classifying chest radiographs for COVID-19-related cases, since the current model may still provide false negatives, which may be detrimental to the prevention of the spread of the virus. © 2022 IEEE.

COVID-19 Vaccine and Booster Shots Compatibility Analyzer using CMOS-based Monochrome Encoding

The continuous increase in COVID-19 positive cases in the Philippines might further weaken the local healthcare system. As such, an efficient system must be implemented to further improve the immunization efforts of the country. In this paper, a contactless digital electronic device is proposed to assess the vaccine and booster brand compatibility. Using Logisim 2.7.1, the logic diagrams were designed and simulated with the help of truth tables and Boolean functions. Moreover, the finalized logic circuit design was converted into its equivalent complementary metal-oxide semiconductor (CMOS) and stick diagrams to help contextualize how the integrated circuits will be designed. Results have shown that the proposed device was able to accept three inputs of the top three COVID-19 vaccine brands (Sinovac, AstraZeneca, and Pfizer) and assess the compatibility of heterologous vaccinations. With the successful results of the circuit, it can be concluded that this low-power device can be used to manufacture a physical prototype for use in booster vaccination sites. © 2022 IEEE.

Impact of COVID-19 on Air Quality in Hanoi and Ho Chi Minh City, Vietnam

Vietnam has had one of the fastest growing economies in Asia over the years. However, the COVID-19 pandemic has proven to be a major hindrance to this growth as the country's GDP plummeted significantly. Air pollution can further amplify the impact of the pandemic since residents exposed to high levels of pollution are likely to increasingly suffer from respiratory illnesses, such as asthma. This paper investigates the impact of COVID-19 on air quality and how air quality can influence the spread of the virus. Finally, the paper proposes suitable machine learning practices for predicting air quality, based on historical trends, using spatial and temporal data. © 2021 IEEE.