ABSTRACT
Facemask detection is a need of time as we are suffering in a pandemic situation of COVID-19, and facemask is considered the best preventive measure to stop the rapid spread. The vast majority of the world population is still unvaccinated, especially young and kids. Moreover, despite the vaccination, people are still getting Covid positive, and the majority are due to the Delta variant. So, we still need to have strict SOP implementation. The best way is to have some autonomous system to monitor SOP compliance and alert the authority to take countermeasures. Many people wear the mask, but the mask is usually on the chin and does not serve the purpose because the facemask must cover the mouth and nose to stop the spread. This study has proposed the improved version of the YOLOv4 model for the robust detection of face masks and checks whether the mask is worn in the recommended way. 2D convolutions of Yolov4 are replaced with the spatially separable convolutional in YOLOv4 to reduce the parameters so that the model can work in real-time. We have achieved an accuracy of 86.61% in terms of proper mask-wearing. Unlike other proposed approaches, our model is not only detecting the mask but also determines that whether the mask is worn in the recommended manner.
ABSTRACT
The catastrophic outbreak of SARS-CoV-2 or COVID-19 has taken the world to uncharted waters. Detecting such an outbreak at its early stages is crucial to minimize its spread but is very difficult as well. The pandemic situation is not yet under control as the virus tends to evolve and develop mutations. This further complicates the development of machine learning or AI models that can automatically detect the disease in the general public. However, researchers worldwide have been putting their incredible efforts into devising mechanisms that help analyze and control the pandemic situation. Many prediction models have been developed to predict COVID-19 infection risk that helps in mitigating the burden on the healthcare system. These models help the medical staff, especially when healthcare resources are limited. As a contribution to society's well-being, this research work deploys a machine learning prediction model that predicts COVID-19 patients with COVID-19 symptoms. Key pieces of information from RT-PCR test data results by the Israeli ministry of health publicly available have been distilled, preprocessed, and then used to train our prediction model. The model is trained on eight features, out of which five are the primary clinical symptoms of this fatal virus: cough, sore throat, fever, headache, breath shortness;and the other three features are gender, test indication, and age. Machine learning models can be considered for COVID-19 testing, especially when resources are limited. We have achieved highly accurate results in COVID-19 prediction with our prediction model. The model is best suited in urgent situations where there is a limitation of testing resources.
ABSTRACT
The goals of a health-care system are, to provide effective treatment with minimum cost and risk factors, delivery of medical services on time, and be ready to counter any emergency situation. To achieve these goals, a health-care system must be well prepared. The preparation is depending upon data, and the data scientists to analyze that data. Artificial Intelligence (AI) introduced a novelty in health-care, with its different tools which are built upon Machine Learning (ML) and Deep Learning (DL) algorithms. Besides clinical procedures and treatments, these algorithms are used for analyzing data and provides help in decision making. Similarly, to tackle this pandemic Novel Coronavirus disease 2019 (COVID-19) the computer scientists played their role individually and with the help of biological scientists to provide the solutions related to risk management (How much this pandemic can spread and affect the people), diagnosis of this disease with minimum time and cost, making vaccine and, suggesting that which medical treatment should adopt. © 2022 IEEE.