ARTIFICIAL INTELLIGENCE TO DEAL WITH THE POST COVID-19 FRACTAL DYNAMICS LINKED WITH ECONOMY

Yingzi Jiang, Mohamed Abdelsabour Fahmy, Naima Amin, Ayesha Sohail, Fatima Alam and Taher A. Nofal
Additional contact information
Yingzi Jiang: School of Mathematics and Statistics, Xuzhou University of Technology, Xuzhou 2221018, P. R. China
Mohamed Abdelsabour Fahmy: ��Adham University College, Umm Al-Qura University, Adham 28653, Makkah, Saudi Arabia‡Faculty of Computers and Informatics, Suez Canal University, New Campus, 41522 Ismailia, Egypt
Naima Amin: �Department of Physics, Comsats University Islamabad, Lahore Campus 54000, Pakistan
Ayesha Sohail: �Department of Mathematics, Comsats University Islamabad, Lahore Campus 54000, Pakistan
Fatima Alam: �Department of Mathematics, Comsats University Islamabad, Lahore Campus 54000, Pakistan
Taher A. Nofal: ��Department of Mathematics, College of Science, Taif University, P. O. Box 11099, Taif 21944, Saudi Arabia

FRACTALS (fractals), 2023, vol. 31, issue 10, 1-10

Abstract: Data-based studies have always provided useful insights of the research problem, provided that correct statistical modeling and inference strategies are adopted. For bi-directional studies or longitudinal datasets, it is always difficult to analyze the dependence of variables and their impact on the key features. With the advancement in the fields of data science and the applied mathematical modeling, these difficulties are well resolved. The machine learning algorithms can help to streamline the data-based studies in a robust manner. During this research, the COVID-19 data are analyzed with the aid of machine learning classification tools to identify the predictors, directly influenced by the pandemic. The impact of COVID-19 on the world’s economy can be better interpreted with the aid of data-based research. The data linked to the unemployment rates, during the frequent waves of COVID-19, are extracted from different sources and analyzed during this research for better forecasting measures. The nonlinear dynamical analysis can be visualized with the aid of the 2D fractal pattern generation approach. Thus the current research is an attempt to connect the outcomes of the classification analysis to the 2D fractal generation for better visual interpretation of lengthy large datasets.

Keywords: 2D Fractal Patterns; Classification Analysis; Inflation; COVID-19 Economy Data; Socioeconomic Parametric Optimization (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0218348X23400029

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