Forecasting the Risk Factors of COVID-19 through AI Feature Fitting Learning Process (FfitL-CoV19)
V. Kakulapati, A. Jayanthiladevi
Asian Journal of Advanced Research and Reports · pp. 29–35 · Published 10 Mar 2023
10.9734/ajarr/2023/v17i3471Abstract
In the present day situation, decreasing COVID-19 risk elements also can moreover need to probably check the abilities to provide and test AI-primarily based total models for COVID-19 severity prediction. This work aimed to select the maximum affecting abilities of COVID-19 chance elements and enhance the functionality of the AI method for COVID-19 chance elements primarily based totally on the chosen abilities. In this study, proposed a method for determining whether or not a patient has a chance of contracting COVID-19 by making use of an AI characteristic that becomes incorporated into the feature-fitting learning process (FfitL-CoV19), while also taking a number of symptoms into consideration. Textual data has been divided into traditional and ensemble system learning algorithms as part of the AI characteristic that is becoming a part of the learning process that is being offered. Feature engineering has become completed the use of the AI technique and functions skills have been provided to conventional and ensemble system learning classifiers. The hybrid approach that was made is a big step up from what had been done before, and it can be very effective in all situations. Using the method shown, a structural model could be made that shows how COV-19 infections can spread and cause more infections.
References (10)
- 1 Cardiovascular Implications of Fatal Outcomes of Patients With Coronavirus Disease 2019 (COVID-19) [DOI]
- 2 Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks [DOI]
- 3 Efficient deep learning approach for augmented detection of Coronavirus disease [DOI]
- 4 Optimal governance and implementation of vaccination programmes to contain the COVID-19 pandemic [DOI]
- 5 Detecting SARS-CoV-2 From Chest X-Ray Using Artificial Intelligence [DOI]
- 6 Proceedings of International Conference on Trends in Computational and Cognitive Engineering [DOI]
- 7 COVID-19 Recognition based on Deep Transfer Learning [DOI]
- 8 COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images [DOI]
- 9 2016 24th Mediterranean Conference on Control and Automation (MED) [DOI]
- 10 Proceedings of the 30th Conference of Open Innovations Association FRUCT [DOI]
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