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The world has altered since the World Health Organization (WHO) designated (COVID-19) a worldwide epidemic. Everything in society, from professions to routines, has shifted to accommodate the new reality. The World Health Organization warns that future pandemics of infectious diseases are likely and that people should be ready for the worst. Therefore, this study presents a framework for tracking and monitoring COVID-19 using a Deep Learning (DL) perfect. The suggested framework utilises UAVs (such as a quadcopter or drone) equipped with artificial intelligence (AI) and the Internet of Things (IoT) to keep an eye on and combat the spread of COVID-19. AI/IoT for COVID-19 nursing and a drone-based IoT scheme for sterilisation make up the bulk of the infrastructure. The proposed solution is based on the use of a current camera installed in a face-shield or helmet for use in emergency situations like pandemics. The developed AI algorithm processes the thermal images that have been detected using multi-scale similar convolution blocks (MPCs) and Res blocks that are trained using residual learning. When infected cases are detected, the helmet's embedded Internet of Things system can trigger the drone system to intervene. The infected population is eradicated with the help of the drone's sterilisation process. The developed system undergoes experimental evaluation, and the findings are presented. The developed outline delivers a novel and well-organized arrangement for monitoring and combating COVID-19 and additional future epidemics, as evidenced by the results. © 2023 IEEE.
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Introduction: The world is currently experiencing the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [COVID-19], however, this is not a new phenomenon;it occurred in 2009-2010 in the form of novel influenza A. (H1N1). The H1N1 virus primarily afflicted people between the ages of 26 and 50, but SARS-CoV-2 primarily afflicted those over the age of 60, increasing the number of deaths owing to their weakened immunity. The report provides a case study of the impact of H1N1 and SARS-CoV-2 in India. Methods: Data is obtained from The Hindustan Times newspaper, GoI press releases and World Health Organization (WHO) reports. Results: The incidence rate was initially low and it was only by the 10-15th week that it started increasing. There is an initial upward trend before levelling out followed by a second wave and third wave. COVID-19 exhibited a steeper growth, where the steps taken by the Government were ineffective leading to higher death cases. Kerala was affected due to the travellers returning from the Middle East, while Maharashtra and Delhi saw large incidence rates due to the migrant influx and communal gathering. Conclusion: The most effective and practical approach is to test the symptomatic patients and aggressive testing to contain the transmission. Awareness campaigns to educate the public about social distancing and personal hygiene is more practical. There is still scope of improvement with regards to the public health care support, preparedness and response. Lockdown measures could have been avoided if the initial screening was conducted properly. © 2022 UPM Press. All rights reserved.
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Everyday life and the global economy have been negatively impacted by COVID-19 (Coronavirus). Slowing the spread of coronaviruses through social distance is proven to be an effective strategy in the war against COVID-19. The social distancing is the best way to stop the spread of COVID-19, as it prevents people from coming into intimate touch with each other. Recently, due to the fast spreading outbreak of the COVID-19, one of the obligatory preventive measures to avoid physical contact has become social distance. Surveillance methods that use Deep Learning, Open-CV and Computer vision to follow pedestrians and prevent congestion are the focus of this article. Closed-circuit television (CCTV) and drones can be used for implementation, where the camera will use object detection to identify the crowd and compute the distance between the humans. Local law enforcement will be notified if the Euclidean distance between two persons is less than the standard distance, which is determined by converting it to pixels and comparing it to that value.
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All over the world new and fast spreading disease have been affecting human health. This led to degradation of the living being's health immunity, support system, mental and physical stability. So the need of assistance requirement for all bed ridden patients is an increasing demand on daily basis. In our present situation the COVID-19 virus's crisis have caused a drastic change to the world. An enormous number of fields such as destruction of employment, economic losses, pandemic growth, food supplies and etc. have been affected. In this paper, an initiative step towards the replacement of man power supply to take care of COVID-19 patients have been proposed. A flex sensor glove wore by a patient would be able to communicate commands to the nurse or a care taker. The system consists of a 2.2 and 4.5 inches' flex sensors fixed over the right hand of the patient, connected with a microcontroller and wireless transmission unit. A corresponding receiver unit would be placed in the nurse station or to the care taker. The movement of patient's hand is associated to different commands. This reduces the need of the nurse to accompany a patient for 24 hours, in this crisis situation and also supports them with an electronic assistance. This flex controlled assist device have been designed and tested with an accuracy of 82.85%. © 2022 Author(s).
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Radiologists are faced with a challenging problem whenever they have to classify the anomalies shown on chest x-rays. Because of this, throughout the course of the last few decades, computer aided diagnostic (CAD) systems have been created to extract meaningful information from X-rays in order to assist medical professionals in gaining a quantitative understanding of an X-ray.Because radiology is such an important field, most of the time the analysis of radiologist images is carried out by trained medical professionals. This is due to the fact that patients seek the highest possible level of treatment in addition to the highest possible quality, regardless of how much it costs.However, its complexity and the subjective nature of the visuals limit its usefulness. There is a great deal of diversity between different translators and a great deal of exhaustion in human professional image processing. Our main goal is to classify lung disorders utilizing diagnostic X-ray images analysed using deep learning and images exploited using Pandas, Keras, Open CV, Tensor Flow, etc. Chest radiographs are still diagnosed by doctors and radiologists using manual and visual methods. As a result, a system capable of diagnosing chest X-rays must be developed that is both smart and automated. The goal of this study is to classify chest X-ray images into normal and pathological using a deep neural network model called Pneumonia Net. It is trained and evaluated using chest X-rays taken from publicly available databases that include both normal and pathological radiographs. Due to their capacity to automatically extract high-level representations from large data sets, CNN-based deep learning categorization approaches outperform existing picture classification methods in this regard. Three different network models are compared depending on their performance. In experiments, it was found that the Pneumonia Net model had a good generalisation capacity in identifying unseen chest X-rays as normal or anomalous, and that its performance was better than that of other network models.
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Background: The COVID-19 pandemic is an ongoing global health care challenge. Upto 1/3rd of hospitalised patients develop severe pulmonary complications and ARDS. Our study aims to evaluate the pulmonary function in COVID-19 pneumonia patients at 6 months follow up. Methods: Prospective cohort study in 30 hospitalised patients with a confirmed diagnosis of COVID-19 pneumonia;belonging to mild, moderated, severe categories - 6 months after discharge. The study consists of assessing the pulmonary function with pre and post bronchodilator spirometry and 6-minute walking test for post exercise desaturation. Results: Lung function test results showed 27% patients had a normal FEV1/FVC ratio with reduction in FVC (forced vital capacity). The mean basal saturation before the 6-MWT was 96+ or - 2%. Exercise oxygen desaturation was observed in 6% cases. Conclusion: This study shows that post infection with SARS-CoV-2, severe or critical covid-19 pneumonia patients showed higher prevalence of abnormal spirometry , with a mainly restrictive pattern when compared to non severe pneumonia patients.
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Background: COVID-19 vaccines shows good efficacy in the course of COVID19 pandemic, but some people still become infected with SARS-CoV-2 even after vaccination. The present study is to identify the disease progression in SARS -CoV2 infection among post-vaccinated patients with first and second dose of vaccinations. Methods: This is a retrospective observational study of 108 vaccinated, hospitalized confirmed covid-19 patients at tertiary care hospital from July,2021 to August,2021. Patients data in terms of vaccine status, type of vaccine, duration from last dose, inflammatory markers , comorbidities, severity of illness are collected and analyzed. Result: Total 500 patients of which 21% (108) got vaccinated. 68% (74) patients received single dose of vaccination and remaining 32% (34) patients completed two doses. 74% (80) patients were diagnosed through RAT/RTPCR and 26% (28) patients based on HRCT-Chest. 59% (64) patients had co-morbidities of which 65% (42) patients taken single dose and 35% (22) patients completed two doses. 35% (38) patients required O2 support, out of which 73% (28) has taken single dose and 27% (10) completed two doses. Average duration of hospital stay is 7 days in non oxygen dependent patients, 13 days in O2 / NIV support patients. Total 8%(8)deaths were reported of which 50%(4) received single dose of vaccination and 50%(4)completed two doses. Conclusion: In vaccinated patients disease severity is mild. In patients with co-morbidities who received single dose, requirement of O2/NIV was more when compared to patients who received both doses. Mortality is seen in patients with co-morbidities, even with complete vaccination.