Design of an evolutionary optimization networks for transmission dynamics and control of bovine brucellosis in cattle

Animals worldwide are harmed by the bacterial illness brucellosis, which is dangerous, expensive to treat, and contagious. In this paper, we provide a numerical solution for studying bovine brucellosis in cattle using a neural network (NN). This method combines genetic algorithms (GA) with sequential quadratic programming (SQP) and a log-sigmoid activation function, i.e. NN-GASQP-LS. Several researchers have explored the brucellosis model in their research articles by using many conventional methods. The present work makes extensive use of hybrid recommended methods to address this problem, highlighting the innovative nature of the study. To examine the viability and effectiveness of NN-GASQP-LS, Adam’s technique is used. Theil’s inequality coefficients, root mean squared error, and mean of absolute deviation values have all been evaluated analytically. Statistical procedures are used to further confirm the convergence and accuracy of the NN-GASQP-LS technique.