Partial derivative Nonlinear Global Pandemic Machine Learning prediction of COVID 19
| Autor: | Manikandan Ramachandran, Rizwan Patan, Durga Prasad Kavadi, Amir H. Gandomi |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Karush–Kuhn–Tucker conditions
Global Pandemic Computer science General Mathematics General Physics and Astronomy Nonlinear Machine learning computer.software_genre 01 natural sciences Article 010305 fluids & plasmas Machine Learning Progressive Kuhn-tucker 0103 physical sciences Pandemic Linear regression Feature (machine learning) Partial Derivative 010301 acoustics business.industry Applied Mathematics Linear Regression Statistical and Nonlinear Physics Regression Identification (information) Nonlinear system Partial derivative Artificial intelligence business computer |
| Zdroj: | Chaos, Solitons & Fractals Chaos, Solitons, and Fractals |
| ISSN: | 0960-0779 |
| DOI: | 10.1016/j.chaos.2020.110056 |
| Popis: | Highlights • The novel COVID-19 opened new challenges for the research community. • Partial derivative regression and nonlinear machine learning is proposed. • Two different models were employed for comparisons and benchmarking. • Progressive Partial Derivative Linear Regression for improving the features normalization. • The results confirm the proposed approach is robust and has accurate predictions. The recent worldwide outbreak of the novel coronavirus disease 2019 (COVID-19) opened new challenges for the research community. Machine learning (ML)-guided methods can be useful for feature prediction, involved risk, and the causes of an analogous epidemic. Such predictions can be useful for managing and intercepting the outbreak of such diseases. The foremost advantages of applying ML methods are handling a wide variety of data and easy identification of trends and patterns of an undetermined nature.In this study, we propose a partial derivative regression and nonlinear machine learning (PDR-NML) method for global pandemic prediction of COVID-19. We used a Progressive Partial Derivative Linear Regression model to search for the best parameters in the dataset in a computationally efficient manner. Next, a Nonlinear Global Pandemic Machine Learning model was applied to the normalized features for making accurate predictions. The results show that the proposed ML method outperformed state-of-the-art methods in the Indian population and can also be a convenient tool for making predictions for other countries. |
| Databáze: | OpenAIRE |
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