Modeling of COVID-19 death rate using various air pollutants: A multiple linear regression approach

Air pollution is a significant health risk, especially for vulnerable populations such as children, people with chronic illnesses, the elderly, and the economically and socially disadvantaged. Furthermore, air pollution has enormous social costs that we all bear in the form of premature deaths, low productivity, sick leave, and other strains on the healthcare system. The primary sources of air pollution are traffic, home fires, and industry. Measuring NO2 levels in air pollution reveals the extent of pollution caused by traffic, particularly diesel vehicles, which are the primary source of NO2. COVID-19 rates are rising in areas with high levels of air pollution, according to mounting evidence. Toxic contaminants can make people more susceptible to COVID-19. The causal relationship between air pollution and COVID-19 cases has yet to be established, but experts warn that long-term exposure will undoubtedly make people more susceptible to lung infections. Air pollution has been linked to an increase in cancer, heart disease, stroke, diabetes, asthma, and other comorbidities by inducing cellular damage and inflammation throughout the body. All of these factors increase the risk of death in COVID-19 patients. As a result, air quality parameters must be predicted and monitored. To predict results, this study proposes a statistical-based machine learning approach. Using multiple linear regression (MLR), Decision Tree (D.T.), and Random Forest (R.F.), the experimental results achieved 80%, 73%, and 65% accuracy on the dataset, respectively. © 2023 Wiley Periodicals LLC.

Detection of Face Mask: A Systematic Approach

The COVID-19 coronavirus pandemic is causing health crises around the world. According to the World Health Organization (WHO), wearing a face mask is an effective means of protection in public places. In most public gatherings such as shopping centers, theaters, parks, it is increasingly necessary to make sure that people in the crowd are wearing masks. Developing an artificial intelligence solution that determines regardless of whether an individual is wearing a cover and letting it in will be great help for the society. In this case, a simple face mask detection system is built using deep learning techniques such as machine learning and persuasive neural network. The model is built with machine learning and OpenCV libraries often used for real-time applications. This model can also be used to develop complete software that scans each person before going to a public meeting © 2022 IEEE.

Ultrasonic Sensor Based Physical Distance and Temperature Tracking System

Now a days maintaining the social distance has become mandatory to decrease the spread of corona. So a novel way of finding pairs automatically of people in a crowded environment that does not participate in the block of public space, that is, about 6 feet of space among them. This Will-making method does not think about crowded traffic or pedestrian directions. Here a moving robot with sensory inputs, a camera to perform non-collision navigation Jump and measure the distance among the adjacent people found in the camera view field. Moreover, this equips the robot with a hot camera that transmits hot wireless images to safety / health workers who watches when someone shows a higher temperature than required. In these situations, the robot integrated with static cameras to improve social distance maintenance Remote Culprits detected, precisely following pedestrians etc. Social segregation measures are important to reduce the spread of Covid. In order to break the chain of transmission, public distribution is strictly followed as usual. This paper centralizes a useful thing to monitor the populated such as ATMs, supermarkets and hospitals for any violations of social segregation. By using the system, I have proposed will be possible to monitor queue world who maintain social isolation in a protected area and to alert individuals in the event. © 2022 IEEE.

Programmable Detection of COVID-19 Infection Using Chest X-Ray Images through Transfer Learning

The novel Coronavirus (COVID-19), which has been designated a pandemic by the World Health Organization, has infected over 1 million individuals and killed many. COVID-19 infection may progress to pneumonia, which can be diagnosed via a chest X-ray. This research work proposes a novel technique for automatically detecting COVID-19 infection using chest X-rays. This research used 500 X-rays of patients diagnosed with coronavirus and 500 X-rays of healthy individuals to generate a data set. Due to the scarcity of publicly accessible pictures of COVID-19 patients, this research study has been attempted via the lens of knowledge transmission. Also, this research work integrates different convolutional neural network (CNN) architectures trained on Image Net to function as X-ray image feature extractors. After that, integrate CNN with well-established machine learning methods such as k Nearest Neighbor, Bayes, Random Forest, Multilayer Perceptron (MLP). The findings indicate that the most successful extractor-classifier combination for one of the data sets is the InceptionV3 architecture, which has an SVM classifier with a linear kernel that achieves an accuracy of 99.421 percent. Another benchmark, the best combination, is ResNet50 with MLP, which has 97.461%accuracy. As a result, the suggested technique demonstrates the efficacy of detecting COVID-19 using X-rays. © 2021 IEEE.

Forecasting the spread of COVID-19 pandemic with prophet

COVID-19 pandemic shook the whole world with its brutality, and the spread has been still rising on a daily basis, causing many nations to suffer seriously. This paper presents a medical stance on research studies of COVID-19, wherein we estimated a time-series data-based statistical model using prophet to comprehend the trend of the current pandemic in the coming future after July 29, 2020 by using data at a global level. Prophet is an open-source framework discovered by the Data Science team at Facebook for carrying out forecasting based operations. It aids to automate the procedure of developing accurate forecasts and can be customized according to the use case we are solving. The Prophet model is easy to work because the official repository of prophet is live on GitHub and is open for contributions and can be fitted effortlessly. The statistical data presented on the paper refers to the number of daily confirmed cases officially for the period January 22, 2020, to July 29, 2020. The estimated data produced by the forecast models can then be used by Governments and medical care departments of various countries to manage the existing situation, thus trying to flatten the curve in various nations as we believe that there is minimal time to do this. The inferences made using the model can be clearly comprehended without much effort. Furthermore, it tries to give an understanding of the past, present, and future trends by showing graphical forecasts and statistics. Compared to other models, prophet specifically holds its own importance and innovativeness as the model is fully automated and generates quick and precise forecasts that can be tunable additionally. © 2021 Lavoisier. All rights reserved.