Integrated Neuro-Evolution-Based Computing Paradigm to Study the COVID-19 Transposition and Severity in Romania and Pakistan

Abstract

AbstractNumerical treatment of the COVID-19 transposition and severity in Romania and Pakistan has been presented in this study, i.e., ANN-GA-SQP through artificial neural network genetic algorithms (ANN-GA) and sequential quadratic programming (SQP), a design of an integrated computational intelligent paradigm, COVID-19 is widely considered to be the greatest health threat humanity has ever faced. In terms of both health and economics, COVID-19 is a huge disaster. Many academics have looked at the COVID-19 model in their research papers, although they use different traditional techniques to represent it. The use of hybrid suggested solutions to solve this issue in the present article is significant, demonstrating the study's novelty. The SIR model of COVID-19 consists of a susceptible, infectious, and recovered class of population. The activation function for the construction of functions based on fitness in mean squared error sense is developed using nonlinear equations of the COVID-19 SIR model for the best performance of ANN-GA-SQP with the combined potential of GA and SQP of a network. While detailed refining is done with efficient local search with SQP, GAs operates as a global search. In addition, a neuron analysis will be presented to verify the effectiveness and complexity of the proposed method. Adam’s numerical methodology is applied to compare the sustainability and efficacy of the presented paradigm. Analytical evaluations of mean, median, and semi-interquartile range values, as well as Theil’s inequality coefficients, root mean squared error, and mean of absolute deviation) values have been observed. The convergence and correctness of the ANN-GA-SQP approach are further validated by statistical analyses.