Detection of COVID-19 patient based on attention segmental recurrent neural network (ASRNN) Archimedes optimization algorithm using ultra-low-dose CT images

In this article, the detection of COVID-19 patient based on attention segmental recurrent neural network (ASRNN) with Archimedes optimization algorithm (AOA) using ultra-low-dose CT (ULDCT) images is proposed. Here, the ultra-low-dose CT images are gathered via real time dataset. The input images are preprocessed with the help of convolutional auto-encoder to recover the ULDCT images quality by removing noises. The preprocessed images are given to generalized additive models with structured interactions (GAMI) for extracting the radiomic features. The radiomic features, such as morphologic, gray scale statistic, Haralick texture are extracted using GAMI-Net. The ASRNN classifier, whose weight parameters optimized with Archimedes optimization algorithm enables COVID-19 ULDCT images classification as COVID-19 or normal. The proposed approach is activated in MATLAB platform. The proposed ASRNN-AOA-ULDCT attains accuracy 22.08%, 24.03%, 34.76%, 34.65%, 26.89%, 45.86%, and 32.14%;precision 23.34%, 26.45%, 34.98%, 27.06%, 35.87%, 34.44%, and 22.36% better than the existing methods, such as DenseNet-HHO-ULDCT, ELM-DNN-ULDCT, EDL-ULDCT, ResNet 50-ULDCT, SDL-ULDCT, CNN-ULDCT, and DRNN-ULDCT, respectively. © 2023 John Wiley & Sons, Ltd.

Analysis on COVID-19 Infection Spread Rate during Relief Schemes Using Graph Theory and Deep Learning.

The novel coronavirus 2019 (COVID-19) disease is a pandemic which affects thousands of people throughout the world. It has rapidly spread throughout India since the first case in India was reported on 30 January 2020. The official report says that totally 4, 11,773 cases are positive, 2, 28,307 recovered, and the country reported 12,948 deaths as of 21 June 2020. Vaccination is the only way to prevent the spreading of COVID-19 disease. Due to various reasons, there is vaccine hesitancy across many people. Hence, the Indian government has the solution to avoid the spread of the disease by instructing their citizens to maintain social distancing, wearing masks, avoiding crowds, and cleaning your hands. Moreover, lots of poverty cases are reported due to social distancing, and hence, both the center government and the respective state governments decide to issue relief funds to all its citizens. The government is unable to maintain social distancing during the relief schemes as the population is huge and available support staffs are less. In this paper, the proposed algorithm makes use of graph theory to schedule the timing of the relief funds so that with the available support staff, the government would able to implement its relief scheme while maintaining social distancing. Furthermore, we have used LSTM deep learning model to predict the spread rate and analyze the daily positive COVID cases.

COVID-19: a behavioural surveillance study in 100 wards of Madurai Corporation, Tamil Nadu

The main aim of the study is to understand the behavioural practices of people in Madurai Corporation in the context of COVID-19 (Coronavirus Disease). The major findings of the study are that a little more than two-third of the respondents (67.2 per cent) wash hands frequently, more than two-third of the respondents (68.6 per cent) wear masks every time, a little more than one-fifth of the respondents (21 per cent) maintain 6 feet of distancing from others, nearly two-fifth of the respondents (38.7 per cent) report to the nearest government health Centre, if developed any ailments.

Remote monitoring of indoor/outdoor movement in epidemiological situations utilising UWB transceivers

A global pandemic has reduced mobility, impacting the healthcare assessment, and diagnostics on co-morbid patients. Various challenges for location finding and indoor signalling issues give rise to the necessity for compact and low-cost ultra-wideband sensor technology. Lockdown has played a vital role during crisis in engaging with specific conditions like position estimation in areas under LOS and NLOS caused by diffraction and reflection of UWB signals across obstacles. UWB-based localisation system with edge/fog computing is used to analyse the indoor/outdoor usage of quarantined patients reducing the transmission, which becomes a threat for society and healthcare personals. The mathematical models to symbolise various patterns utilising UWB sensors with anchors and tags were used to determine the flow of all rapid and random movements within the specified clustered locations. The proposed system achieves high classification accuracy with SVM to classify the patterns and assist frontline medical workers in containing pandemics through human interaction. Copyright © 2021 Inderscience Enterprises Ltd.

Pediatric and geriatric immunity network mobile computational model for COVID-19

PurposeThe computational model proposed in this work uses the data's of COVID-19 cases in India. From the analysis, it can be observed that the proposed immunity model decides the recovery rate of COVID −19 patients;moreover, the recovery rate does not depend on the age of the patients. These analytic models can be used by public health professionals, hospital administrators and epidemiologists for strategic decision-making to enhance health requirements based on various demographic and social factors of those affected by the pandemic. Mobile-based computational model can be used to compute the travel history of the affected people by accessing the near geographical maps of the path traveled.Design/methodology/approachIn this paper, the authors developed a pediatric and geriatric person’s immunity network-based mobile computational model for COVID-19 patients. As the computational model is hard to analyze mathematically, the authors simplified the computational model as general COVID-19 infected people, the computational immunity model. The model proposed in this work used the data's of COVID-19 cases in India.FindingsThis study proposes a pediatric and geriatric people immunity network model for COVID- 19 patients. For the analysis part, the data's on COVID-19 cases in India was used. In this model, the authors have taken two sets of people (pediatric and geriatric), both are facing common symptoms such as fever, cough and myalgia. From the analysis, it was observed and also proved that the immunity level of patients decides the recovery rate of COVID-19 patients and the age of COVID-19 patients has no significant influence on the recovery rate of the patient.Originality/valueCOVID-19 has created a global health crisis that has had a deep impact on the way we perceive our world and our everyday lives. Not only the rate of contagion and patterns of transmission threatens our sense of agency, but the safety measures put in place to contain the spread of the virus also require social distancing. The novel model in this work focus on the Indian scenario and thereby may help Indian health organizations for future planning and organization. The factors model in this work such as age, immunity level, recovery rate can be used by machine leaning models for predicting other useful outcomes.

IoT role in prevention of COVID-19 and health care workforces behavioural intention in India-an empirical examination

Purpose This paper aims to address the role of Internet of Things (IoT) in preventing COVID-19. The IoT devices can be used in various ways to track the patients and suspected person. Remote data collection can be done with the help of IoT and sensors. Later, the data can be analyzed with the help of data science engineers and researchers to predict and prevent the COVID-19. Design/methodology/approach IoT is a creative mean of amalgamating clinical gadgets and their applications to associate with the human services and data innovation frameworks. An investigation on the conceivable outcomes of defying progressive COVID-19 pandemic by implementing the IoT approach while offering treatment to all classes of patient without any partiality in poor and rich. The information sharing, report checking, patient tracking, data social affair, investigation, cleanliness clinical consideration and so forth are the different cloud-based administrations of IoT. It can totally change the working format of the medical services while rewarding the huge volume of patients with a predominant degree of care and more fulfilment, particularly during this pandemic of COVID-19 lockdown. Health workers can quickly focus on patient zero and identify everyone who has come into contact with the infected person and move these people to quarantine/isolation. As COVID-19 has emerged from the Wuhan province of China, IoT tools such as geographic information system could be used as an effective tool to curb the spread of pandemics by acting as an early warning system. Scanners at airports across the world could be used to monitor temperature and other symptoms. This paper addresses the role of IoT in preventing COVID-19. Findings In the period of continuous pandemic of COVID-19, IoT offers many propelled cloud-based administrations and offices to serve a greater number of patients effectively. The remote medicinal services framework provides a lot of significance in such a crucial time of lockdown. The powerful interconnected arrangement of gadgets, applications, Web, database and so on encourages the consumers to benefit the administrations in smart way. IoT additionally advances its administrations by building up the quality culture of perceptive medicinal services or portable centre. It is a "distinct advantage innovation," which may totally change the practices universally. Indeed, even its quality administrations in this extreme time make this methodology progressively productive and beneficial. IoT helps in observing and tracking more recognized people and patients in remote areas for their human service prerequisites. The customary medicinal services are probably going to observe a huge change in perspective sooner rather than later, as the computerized revolution would place cutting-edge innovation and its associated items in the possession of the patients and give both patients and doctors in remote areas better access to quality clinical services. Originality/value The contemporary exploration study focuses on the proposed IoT system for the treatment of patients in this progressing COVID-19. The working principle of IoT approach incorporates the mix of human services apparatuses, clinical treatment framework, Web organize, programming and administrations. IoT framework empowers the information assortment, report observing, understanding database, testing pictures and investigation and so forth. Data has been collected through online mode;in this study, the authors adopted empirical research design. Total 150 (118/150 = 78. 66% respondent response ratio) online questionnaires were sent in the Chennai city of Tamilnadu, India. The participated nature of work is clinical examination in critical care division.