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

Abstract

Numerical 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.

Efficient semianalytical investigation of a fractional model describing human cornea shape

“…We begin by verifying the accuracy of the approximate solution of the FBVP in Equation (9) derived by RPSM against the numerical solution obtained by the fourth-order Runge-Kutta method (RK4) for the integer case α = 1 and the parameters A = B = 1. Substituting Equation (17) into Equation (18) gives:…”

“…Fractional differential equations have gained prominence in recent research, serving as effective tools to model natural phenomena in various fields, including biological systems, 15,16 infectious diseases, 17,18 fluid flow, 19 biochemical reactions, 20 and control theory. 21 Due to the inherent nonlocal property of fractional derivative operators, fractional models prove adept at describing memory and hereditary properties of various materials and processes.…”

“…Noteworthy among these methods are the fractional Adams-Bashforth-Moulton method, 24 the fractional predict-correct method, 25 various spectral methods, 26,27 and artificial network methods. 28,29 While these meth-ods demonstrated efficiency and accuracy, they are not without challenges, such as issues of numerical schemes and the necessity to adjust parameters to align with numerical data. 30 Among the analytical methods recently applied to solve fractional-derivative equations are the homotopy analysis method, 31 homotopy perturbation method, 32 differential transform method, 33 Green's function-fixed point method, 34 and cubic spline method.…”

A reliable semi-analytic solution for the Caputo fractional human liver model

“…Fractional differential equations pose a challenge in finding exact solutions for various reasons including nonlinearity, the choice of fractional derivative operator, and singularities. Despite this, analytical methods (see [13,14,15,16]), preferred for their precision and error tolerance, alongside adapted classical numerical methods (see [9,10,11,12]) are utilized to find approximate solutions.…”