ArticleScientific reports2023
A computational stochastic procedure for solving the epidemic breathing transmission system.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Dynamic modeling of PISA achievement scores: comparative analysis of artificial neural networks and differential equation systems approaches.Scientific reports · 2025Article
- Analysis of stochastic epidemic model with awareness decay and heterogeneous individuals on multi-weighted networks.Scientific reports · 2024Article
- Article
- A neural network computational structure for the fractional order breast cancer model.Scientific reports · 2023Article
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3 authors.
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Abstract
This work provides numerical simulations of the nonlinear breathing transmission epidemic system using the proposed stochastic scale conjugate gradient neural networks (SCGGNNs) procedure. The mathematical model categorizes the breathing transmission epidemic model into four dynamics based on a nonlinear stiff ordinary differential system: susceptible, exposed, infected, and recovered. Three different cases of the model are taken and numerically presented by applying the stochastic SCGGNNs. An activation function 'log-sigmoid' uses twenty neurons in the hidden layers. The precision of SCGGNNs is obtained by comparing the proposed and database solutions. While the negligible absolute error is performed around 10
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