PloS one · 2025
A machine learning computational approach for the mathematical anthrax disease system in animals.
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Sabir Zulqurnain, Simbawa Eman
Аннотация
OBJECTIVES: The current research investigations present the numerical solutions of the anthrax disease system in animals by designing a machine learning stochastic procedure. The mathematical anthrax disease system in animals is classified into susceptible, infected, recovered and vaccinated.
METHOD: A Runge-Kutta solver is applied to collect the dataset, which decreases the mean square error by dividing into training as 78%, testing 12% and verification 10%. The proposed stochastic computing technique is performed through the logistic sigmoid activation function, and a single hidden layer construction, twenty-seven numbers of neurons, and optimization through the Bayesian regularization for the mathematical anthrax disease system in animals.
FINDING: The designed procedure's correctness is authenticated through the results overlapping and reducible absolute error, which are calculated around 10-05 to 10-08 for each case of the model. The best training performances are performed as 10-10 to 10-12 of the model. Moreover, the statistical performances in terms of regression coefficient, error histogram, and state transition values enhance the reliability of the proposed stochastic machine learning approach.
NOVELTY: The designed scheme is not applied before to get the numerical results of the anthrax disease system in animals.
METHOD: A Runge-Kutta solver is applied to collect the dataset, which decreases the mean square error by dividing into training as 78%, testing 12% and verification 10%. The proposed stochastic computing technique is performed through the logistic sigmoid activation function, and a single hidden layer construction, twenty-seven numbers of neurons, and optimization through the Bayesian regularization for the mathematical anthrax disease system in animals.
FINDING: The designed procedure's correctness is authenticated through the results overlapping and reducible absolute error, which are calculated around 10-05 to 10-08 for each case of the model. The best training performances are performed as 10-10 to 10-12 of the model. Moreover, the statistical performances in terms of regression coefficient, error histogram, and state transition values enhance the reliability of the proposed stochastic machine learning approach.
NOVELTY: The designed scheme is not applied before to get the numerical results of the anthrax disease system in animals.