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1.
IEEE Access ; : 1-1, 2022.
Article in English | Scopus | ID: covidwho-1961361

ABSTRACT

Recently, healthcare stakeholders have orchestrated steps to strengthen and curb the COVID-19 wave. There has been a surge in vaccinations to curb the virus wave, but it is crucial to strengthen our healthcare resources to fight COVID-19 and like pandemics. Recent researchers have suggested effective forecasting models for COVID-19 transmission rate, spread, and the number of positive cases, but the focus on healthcare resources to meet the current spread is not discussed. Motivated from the gap, in this paper, we propose a scheme, <italic>ABV-CoViD</italic> (Availibility of Beds and Ventilators for COVID-19 patients), that forms an ensemble forecasting model to predict the availability of beds and ventilators (ABV) for the COVID-19 patients. The scheme considers a region-wise demarcation for the allotment of beds and ventilators (BV), termed resources, based on region-wise ABV and COVID-19 positive patients (inside the hospitals occupying the BV resource). We consider an integration of artificial neural network (ANN) and auto-regressive integrated neural network (ARIMA) model to address both the linear and non-linear dependencies. We also consider the effective wave spread of COVID-19 on external patients (not occupying the BV resources) through a θ- ARNN model, which gives us long-term complex dependencies of BV resources in the future time window. We have considered the COVID-19 healthcare dataset on 3 USA regions (Illinois, Michigan, and Indiana) for testing our ensemble forecasting scheme from January 2021 to May 2022. We evaluated our scheme in terms of statistical performance metrics and validated that ensemble methods have higher accuracy. In simulation, for linear modelling, we considered the <italic>ARIMA</italic>(1, 0, 12) model, and <italic>N</italic>8-3-2 model for ANN modelling. We considered the θ- <italic>ARNN</italic>(12, 6) forecasting. On a population of 2, 93, 90, 897, the obtained mean absolute error (MAE) on average for 3 regions is 170.5514. The average root means square error (RMSE) of θ-ARNN is 333.18, with an accuracy of 98.876%, which shows the scheme’s efficacy in ABV measurement over conventional and manual resource allocation schemes. Author

2.
2nd International Conference on Computing, Communications, and Cyber-Security, IC4S 2020 ; 203 LNNS:787-797, 2021.
Article in English | Scopus | ID: covidwho-1340428

ABSTRACT

Technologies play an essential role in mitigating the physical human need and replacing this with robots (bots). Hence reducing social involvement results in a reduction in COVID-19 patients. This proofs to be safe for the human generation and humanity too. The technological aspects of cloud computing resource management and bots can be used for the management and security of the patient’s data and incorporating intelligent decision support in case of the massive reporting of the patients. As the healthcare sector continues to offer life-critical services while working to improve treatment and patient care with new technologies, criminals and cyber threat actors look to exploit the vulnerabilities that are coupled with this expertise. Healthcare organizations collect and store vast amounts of personal information, making them a primary target for cyber-criminals. In this paper, we will explore and discover the security implications and privacy issues of these health care technologies related to the management of patient’s data. We also describe various security breaches in medical data and used a framework called as C2B-SCHMS which usages machine learning-based isolation graph for handling anomaly. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

3.
Bioscience Biotechnology Research Communications ; 13(14):361-363, 2020.
Article in English | Web of Science | ID: covidwho-1257411

ABSTRACT

COVID-19 is a disease caused by a new strain of coronavirus. With the rapid increase in the spread of COVID-19, many people are getting affected by it. As the disease cannot be detected until the affected person doesn't take the COVID-19 test, the person remains unaware that he has been infected by the coronavirus. The person would travel, go to shops, do other activities as well thereby infecting the other people and a potential threat to society. Thus it becomes difficult to trace all the people who have been infected. It is necessary to report all the close contacts of the infected person in the last 14 days. With the increase in the COVID-19 cases, it is very difficult to manually monitor and track down all the contacts of a COVID-19 positive patient. This calls for an autonomous application that will provide information about the person's traces, the people with whom he came in contact with and the places he visited in the last 14 days. This application will help in collecting the data for traces of a COVID-19 positive patient.

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