Evaluation of the COVID-19 Era by Using Machine Learning and Interpretation of Confidential Dataset

Various research approaches to COVID-19 are currently being developed by machine learning (ML) techniques and edge computing, either in the sense of identifying virus molecules or in anticipating the risk analysis of the spread of COVID-19. Consequently, these orientations are elaborating datasets t...

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Bibliographic Details
Published in:Electronics (Basel) 2021-12, Vol.10 (23), p.2910
Main Authors: Andreou, Andreas, Mavromoustakis, Constandinos X., Mastorakis, George, Batalla, Jordi Mongay, Pallis, Evangelos
Format: Article
Language:English
Subjects:
Algorithms
Cloud computing
Coronaviruses
COVID-19
Data analysis
Datasets
Edge computing
Electronic devices
Fatalities
Machine learning
Mortality
Optimization
Real time
Risk analysis
Viral diseases
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Summary:Various research approaches to COVID-19 are currently being developed by machine learning (ML) techniques and edge computing, either in the sense of identifying virus molecules or in anticipating the risk analysis of the spread of COVID-19. Consequently, these orientations are elaborating datasets that derive either from WHO, through the respective website and research portals, or from data generated in real-time from the healthcare system. The implementation of data analysis, modelling and prediction processing is performed through multiple algorithmic techniques. The lack of these techniques to generate predictions with accuracy motivates us to proceed with this research study, which elaborates an existing machine learning technique and achieves valuable forecasts by modification. More specifically, this study modifies the Levenberg–Marquardt algorithm, which is commonly beneficial for approaching solutions to nonlinear least squares problems, endorses the acquisition of data driven from IoT devices and analyses these data via cloud computing to generate foresight about the progress of the outbreak in real-time environments. Hence, we enhance the optimization of the trend line that interprets these data. Therefore, we introduce this framework in conjunction with a novel encryption process that we are proposing for the datasets and the implementation of mortality predictions.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics10232910