ABSTRACT
Internet of the Everything (IoE) along with different technologies like Cloud and Arduino UNO is of great use throughout the disaster. We have used the MLX90614 Sensor to send the temperature of the person on the cloud with the help of Arduino UNO. This sensor can assist in discovering all of the humans who have come into touch with the infected people. This IoE generation is also useful in tracking sufferers. For those who are having a high probability of getting infected, their information can be supplied to the healthcare workforce to take further action. The COVID-19 epidemic can be controlled by IoE-stimulated frameworks. IoE answers at the side of far-flung health monitoring. If the disease is found in a suspected person then this information is passed through IoE to the health care team. IoE technology which is used here can be a fantastic way to control epidemics. Here we have maintained the privacy and protection issues in our project. IoE is very well analyzed with growing requirements and for multi-tasking practices. © 2021 IEEE.
ABSTRACT
New reality imposed by months of quarantine days caused by the global spread of COVID-19 affected mostly every industry. In this new digital age, new technologies are being incorporated continuously introducing new shapes of businesses. Companies able to adapt have a high probability of survival but, even now, digital transformation is still a challenge for traditional nondigital companies. A digital business strategy could guide the inevitable transformations that digital technologies trigger. This paper presents a systematic review of current literature on digital (business and/or transformation) strategies with the objective to identify, evaluate and interpret published research that could bring some light to the relevant aspects and pitfalls that should be considered in a digital transformation initiative. The review investigates what is known about the risks and multi-industry aspects in a digital strategy and the results show statistical data, gaps in current research and models of successful implementation. © 2022 IEEE Computer Society. All rights reserved.
ABSTRACT
The COVID-19 pandemic is continuously evolving with drastically changing epidemiological situations which are approached with different decisions: from the reduction of fatalities to even the selection of patients with the highest probability of survival in critical clinical situations. Motivated by this, a battery of mortality prediction models with different performances has been developed to assist physicians and hospital managers. Logistic regression, one of the most popular classifiers within the clinical field, has been chosen as the basis for the generation of our models. Whilst a standard logistic regression only learns a single model focusing on improving accuracy, we propose to extend the possibilities of logistic regression by focusing on sensitivity and specificity. Hence, the log-likelihood function, used to calculate the coefficients in the logistic model, is split into two objective functions: one representing the survivors and the other for the deceased class. A multi-objective optimization process is undertaken on both functions in order to find the Pareto set, composed of models not improved by another model in both objective functions simultaneously. The individual optimization of either sensitivity (deceased patients) or specificity (survivors) criteria may be conflicting objectives because the improvement of one can imply the worsening of the other. Nonetheless, this conflict guarantees the output of a battery of diverse prediction models. Furthermore, a specific methodology for the evaluation of the Pareto models is proposed. As a result, a battery of COVID-19 mortality prediction models is obtained to assist physicians in decision-making for specific epidemiological situations. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The COVID-19 pandemic is far from over. The government has carried out several policies to suppress the development of COVID-19 is no exception in Bogor Regency. However, the public still has to be vigilant especially now we will face a year-end holiday that can certainly be a trigger for the third wave of COVID-19. Therefore, researchers aim to make predictions of the increase in positive cases, especially in the Bogor Regency area to help the government in making policies related to COVID-19. The algorithms used are Gaussian Process, Linear Regression, and Random Forest. Each Algorithm is used to predict the total number of COVID-19 cases for the next 21 days. Researchers approached the Time Series Forecasting model using datasets taken from the COVID-19 Information Center Coordinationn Center website. The results obtained in this study, the method that has the highest probability of accurate and appropriate data contained in the Gaussian Process method. Prediction data on the Linear Regression method has accurate results with actual data that occur with Root Mean Square Error 1202.6262. © 2021 IEEE.
ABSTRACT
Traditional molecular techniques for COVID-19 viral detection are time-consuming and can exhibit a high probability of false negatives. In this work, we present a computational study of COVID-19 detection using plasmonic gold nanoparticles. The resonance wavelength of a COVID-19 virion was recently estimated to be in the near-infrared region. By engineering gold nanospheres to bind with the outer surface of the COVID-19 virus specifically, the resonance frequency can be shifted to the visible range (380 nm-700 nm). Moreover, we show that broadband absorption will emerge in the visible spectrum when the virus is partially covered with gold nanoparticles at a certain percentage. This broadband absorption can be used to guide the development of an efficient and accurate colorimetric plasmon sensor for COVID-19 detection. © 2021 IEEE.