Integrating prior epidemiological knowledge embedded within mechanistic models with the
data-mining capabilities of artificial intelligence (AI) offers transformative potential for …

Differential equations-based epidemic compartmental models and deep neural networks-
based artificial intelligence (AI) models are powerful tools for analyzing and fighting the …

This study develops an Artificial Neural Network (ANN)-based framework to model the
transmission dynamics and long-term disability outcomes of Ebola Virus Disease (EVD) …

The study of disease spread often relies on compartmental models based on nonlinear
differential equations, which typically require computationally intensive numerical …

Non-linearities in simulation arise from the time variance in wire-less mobile networks when
integrated with human in the loop, human in the plant (HIL-HIP) physical systems under …

Malaria is one of the deadliest diseases in the world, every year millions of people become
victims of this disease and many even lose their lives. Medical professionals and the …

This paper investigates the application of Physics-Informed Neural Network (PINN)
technique into the uncertainty identification and control issue for reentry vehicles (RV) …

We present three deep-learning methods to analyze different COVID-19 epidemic models.
The first method, an epidemiology-informed neural network, is developed to learn the model …

Background: This study investigates the impact of memory effects and nonlocality on COVID-
19 World Data. The primary objective is to explore the dynamics of the pandemic using a …