A machine learning computational approach for the mathematical anthrax disease system in animals

• Published in: PloS one Vol. 20; no. 4; p. e0320327
• Main Authors: Sabir, Zulqurnain, Simbawa, Eman
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.04.2025; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Animal diseases; Animals; Anthrax; Anthrax - veterinary; Bacillus anthracis; Bayes Theorem; Bayesian analysis; Biology and Life Sciences; Care and treatment; Computer and Information Sciences; Construction; Diagnosis; Diseases; Error correction; Health aspects; Illnesses; Learning algorithms; Machine Learning; Mathematical models; Mathematics; Medicine and Health Sciences; Neural networks; Neurons; Optimization; Pathogens; Regression coefficients; Regularization; Regularization methods; Research and Analysis Methods; Runge-Kutta method; Statistical analysis; Statistical models; Stochastic Processes; Vaccination; Zoonoses; Saudi Arabia
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0320327

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. 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. 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. The designed scheme is not applied before to get the numerical results of the anthrax disease system in animals.