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Smoking has emerged as one of the leading global factors that is the source of health issues. It damages almost all of the body's organs. It damages various muscles and causes lung cancer. Additionally, it causes ulcers, pulmonary disease, and vascular deterioration. Except for the financial benefit to tobacco companies, manufacturers, and marketing companies, smoking has no advantages. Due to these factors, the present study exploited a feed forward neural networking based global optimization procedure with a local scheme to solve a mathematical model of smoking. A genetic based algorithm and sequential quadratic programming (GA-SQP) are utilized as hybridized global and local strategies. The model is categorized into five classes: potential smokers, occasional smokers, smokers, temporary quit, and permanent quit smokers. An objective optimization function is constructed to minimize the mean square error using the designed smoking model in form of feed forward neural networking. The comparative evaluation of hybrid GA-SQP and Adam numerical scheme is also assessed to authenticate the precision and correctness of the solution of the smoking model. The robustness, perfection, and convergence stability of GA-SQP are verified by establishing various statistical performance indicators. The quantitative analysis provides the minimum, mean, and semi-inter quartile range values for absolute errors up to 6 to 13 decimal places, demonstrating the worthiness and precision of the proposed GA-SQP. [ABSTRACT FROM AUTHOR]

This investigation aims to investigate the pine wilt disease model (PWDM) employing hybrid bio-inspired algorithm. The artificial neural networks-based genetic algorithm (ANNs-GA) as global search and sequential quadratic programming (SQP) serve as local search framework. The model consists of two populations, i.e. host (h) and vector (v). There are four classes in host population representing susceptible host (S h) , exposed host (E h) , asymptomatic host (A h) and infectious host (I h) whereas in vector susceptible (S v) and infectious (I v) class are present. Activation function is introduced for the formulation of the fitness-based function as mean squared error by using nonlinear PWD equations for the accomplishment of ANNs-GASQP paradigm. The stability, robustness and effectiveness of proposed paradigm is comparatively evaluated through Adam numerical scheme with absolute error analysis. Computational complexity of GASQP is determined by convergence criteria of best global weight, fitness evaluation, time, generations, iterations, function counts and mean square error. Moreover, the statistical analysis is performed via Theil's inequality coefficients (TICs), mean of absolute deviation (MAD) and root mean squared error (RMSE) for multiple trials of ANNs-GASQP. Results reveal that accuracy is obtained up to 3–11 decimal places which proves the reliability of proposed ANNs-GASQP solver. [ABSTRACT FROM AUTHOR]

• A hybrid algorithm of Genetics Optimization and Sequential Quadratic Programming is designed. • Constructed and analyzed the mathematical model for the dynamics of Lassa fever in Nigeria. • The log sigmoid function as an objective function based on mean squared error is constructed. • The trustworthiness, resilience, and effectiveness of the recommended framework is evaluated. This paper presents an innovative artificial neural networks (ANNs) based hybrid algorithm of genetics optimization and sequential quadratic programming (AGOSQP) to construct the mathematical model for the dynamics of Lassa fever (DLF) in Nigeria. The model designated by the transmission of disease between two populations: human population i.e. susceptible S h , exposed E h , infectious I h and recovered R h humans and rodent population i.e. susceptible S r and infectious I r rodents. The log sigmoid function as an objective function based on mean squared error is constructed to optimize AGOSQP where genetic algorithm work as global searching optimization and SQP serve as the local searching optimization. To assess the correctness, robustness and convergence stability, the comparison between state of art Adam method and proposed AGOSQP is established. The Theil's inequality coefficient (TIC), root mean square error (RMSE) and mean absolute deviation (MAD) are also computed to authenticate the efficiency of proposed AGOSQP to solve the model for the dynamics of Lassa fever. [ABSTRACT FROM AUTHOR]