ABSTRACT
With an ongoing episode of Covid, the world health security and precaution need reformation and a new approach to be dealt with. The health concerns of the individual is a topic of utmost importance for every nation fighting the pandemic. With limited healthcare staff and the large public to look after, the assistance of Computer vision and AI is needed. Social distancing is a very effective way of containing the spread of a pandemic. Social distancing becomes difficult when dealing with a number of subjects like at gateways of offices, Airports, and many other sectors that have significant footfall in a day. In this paper we have tried to compare the different models for the recognition of mask on the face, for doing so we have used Real world masked face dataset (RMFD) (Iqbal et al, Renewable power for sustainable growth, Springer Nature, Berlin, LNEE, 2020) and Kaggle (Tomar et al, Machine learning, advances in computing, renewable energy and communication, vol 768. Springer Nature, Berlin, LNEE, 2020) dataset. At first we gather the images where face have actual mask on it and also augmented the image with editing the image of unmasked face with mask so that model can learn very details of the image and result will come more accurate and clean. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Machine Learning is ever advancing field and as more and more research is being done in the field, more applications are being developed for this field and it is now being used in all fields. Also, nowadays people are facing multiples diseases posing danger to human life. This prompted researchers to critically analyse and work to apply Machine learning in the use of prediction of these diseases and using this analysis to assist the medical industry. The idea is to find various datasets of different Diseases like Dengue, Covid-19. Perform analysis on the datasets of these diseases to understand more about them and how much they affect us. There are various models available like KNN, SVM, etc. The task is to work with different models and find out how they perform with data of different diseases and which models are most affective and accurate. © 2022 IEEE.
ABSTRACT
Background: SARS-CoV-2 is well known disorder to affect respiratory system, although it can also influence several extrapulmonary organs through variety of pathological mechanism. In this study, we aimed to discuss the prevalence of atypical and/or extrapulmonary manifestations in COVID-19, therefor action for early isolation and diagnosis can be initiated to prevent spread of infection. Methods: This retrospective observational study included 4200 admitted COVID-19 patients. The patient's data concerning medical history, clinical symptoms at presentation and during course of hospitalization, laboratory and radiological diagnosis and underlying chronic medical illness were extracted from their medical records. Data of extrapulmonary and/or atypical presentations of COVID-19 were compiled and tabulated to know prevalence of these manifestations. Results: In this study, 1260 patients (30%) had symptomatic presentation. Major extrapulmonary clinical manifestation includes fatigue in 72.22% patients, impaired sense of taste (ageusia) in 58.73%, loss of appetite in 52.78%, impaired sense of smell (anosmia) in 46.83%, palpitation in 33.33%, headache in 33.17%, nausea/vomiting in 31.43%, diarrhoea in 25.40% patients. Among symptomatic COVID-19 patients, 95.56% patients had sinus tachycardia, 38.49% had lymphocytopenia, 36.83% had hepatitis, 35.48% had leukopenia, 27.83% had gastroenteritis, 22.22% had sepsis, 20.87% had proteinuria, 17.30% had coronary artery disease and 16.34% had acute kidney injury in decreasing order. Prevalence of coagulation defect associated disorder were found to be deep venous thrombosis in 15.56% patients, acute coronary syndrome in 7.78%, brain infarct in 6.35%, pulmonary artery thrombosis in 3.25% and SMA thrombosis in 0.32% of symptomatic patients. Conclusion: Patients of SARS-CoV-2 had widespread organ-specific manifestations with involvement of almost all organ system of body. Clinicians must have knowledge of these extrapulmonary symptoms or atypical presentation of COVID-19 as it assists in early diagnosis, isolation of suspected patients and limit the transmission of infection in the hospital settings. © 2022 Journal of Association of Physicians of India. All rights reserved.
ABSTRACT
The world and its ways of living have seen a massive change post the Covid lockdown in multiple aspects as, new practices, new trends and a new lifestyle of maintaining social distancing has wholly altered the world's standard. This change has severely affected the ways of learning, teaching and schooling as in-person classes have shifted to online live courses. But the available platforms of online classes don't provide a classroom-like feel, thereby creating hindrance in students learning process and diminishing the quality of education. This study provides a novel paradigm with complete architecture and implementation of a platform that caters explicitly to online learning and lives classes with provisions to provide class like atmosphere. It aims to do so with graded feature break down aided by a detailed description and plans to set up, build maintain such a paradigm from both technical administration fronts. In addition to the above, it further provides a framework that helps colleges education institutes to conduct "in-person classes"after pandemic enervates while following norms of social distancing. © 2021 IEEE.