ABSTRACT
Control of the spread of COVID-19 must be encouraged, even though this is a new normal era. Rapid screening for COVID-19 detection must be carried out to control the spread of COVID-19. This research develops a website for COVID-19 detection based on chest X-Ray images and compares the CNN-BiLSTM model. This study divides X-ray images of the chest into three categories: COVID-19, Normal, and Viral Pneumonia. When compared to other models, the Resnet50-BiLSTM model produces the highest accuracy. The accuracy of the Resnet50-BiLSTM model was 98.51%. Then, in order, the following models were used: Resnet50, VGG19-BiLSTM, VGG19, AlexNet-BiLSTM, and AlexNet. The comparison of Precision, Recall, and F1-Measure findings also demonstrate that Resnet50-BiLSTM has the highest score when compared to other approaches. The website was also developed using the Flask framework for automatic COVID-19 detection. © 2022 IEEE.
ABSTRACT
With the spread of the epidemic, the development of digital industry will be rapidly enhanced in the new situation and opportunities. The combination of mathematical model and graph makes it possible to predict the trend of infectious diseases according to the different transmission speed, spatial scope, transmission route diversity, dynamic mechanism and other factors. The visualization technology of infectious disease model data also plays an important role in epidemic data detection. In this paper, real-time monitoring, quantitative analysis, dynamic prediction and assessment of the current severe situation of the COVID-19 epidemic were conducted to obtain relevant information on the development of the epidemic, objectively estimate the current situation of the COVID-19 epidemic, and predict the development trend in the future. The innovative drag-and-drop recalculation, data viewing, distance roaming and other functions in this paper have greatly improved user experience and enabled users to have the ability of data mining and integration. It provides a one-stop solution, which is from the template, ORM, Session and the Authentication background. It is very convenient to use. The monitoring and trend prediction platform has also naturally become a powerful helper for the government to realize comprehensive monitoring and decision-making on COVID-19. © 2022 IEEE.
ABSTRACT
Generally, purchaser wishes to attempt all jewels on them and share those photos on social media to get a recommendation. But now, in the present situation, all are avoiding touching activities unnecessarily. It is not so safe due to COVID-19. So without stopping purchaser jewellery attempts, a solution is provided by using an application called virtual ornament room. The proposed scheme is based on creating a Web application that identifies where the human face is located in the frame and superimposes the chosen jewellery on the face using the HAAR Cascade Algorithm. By using the concepts of augmented reality, the digital objects are estimated and placed onto the frame in real time. The system is implemented by using Flask framework and OpenCV, a Python module. This application works with an attached camera, Internet, and a Web browser. From the results, it can be seen all the jewellery can be easily selected by the customers from their home itself during the pandemic situation. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.