Computational and mathematical methods in medicine
34721656
Public health and its related facilities are crucial for thriving cities and societies. The optimum utilization of health resources saves money and time, but above all, it saves precious lives. It has become even more evident in the present as the pa...
Computational and mathematical methods in medicine
34646332
The aim of this work is to introduce a stochastic solver based on the Levenberg-Marquardt backpropagation neural networks (LMBNNs) for the nonlinear host-vector-predator model. The nonlinear host-vector-predator model is dependent upon five classes, ...
Computational and mathematical methods in medicine
34594396
There have been remarkable changes in our lives and the way we perceive the world with advances in computing technology. Healthcare sector is evolving with the intervention of the latest computer-driven technology and has made a remarkable change in ...
Computational and mathematical methods in medicine
34557257
Artificial Intelligence (AI) is the domain of computer science that focuses on the development of machines that operate like humans. In the field of AI, medical disease detection is an instantly growing domain of research. In the past years, numerous...
Non-Markovian models of stochastic biochemical kinetics often incorporate explicit time delays to effectively model large numbers of intermediate biochemical processes. Analysis and simulation of these models, as well as the inference of their parame...
Many uncertain factors exist in the water resource systems, leading to dynamic characteristics of the water distribution process. Especially for the watershed including irrigation area with multiple water sources and water users, it is complicated th...
Artificial neural networks, taking inspiration from biological neurons, have become an invaluable tool for machine learning applications. Recent studies have developed techniques to effectively tune the connectivity of sparsely-connected artificial n...
Proceedings of the National Academy of Sciences of the United States of America
34675075
In this paper, we introduce the , a nonconvex, yet analytically tractable, optimization program, in a quest to better understand deep neural networks that are trained for a sufficiently long time. As the name suggests, this model is derived by isolat...
We show analytically that training a neural network by conditioned stochastic mutation or neuroevolution of its weights is equivalent, in the limit of small mutations, to gradient descent on the loss function in the presence of Gaussian white noise. ...
IEEE transactions on neural networks and learning systems
34106867
This article studies the problem of the stability for Markovian neural networks (MNNs) with time delay. The transition rate is considered to be generally hybrid, which treats those existing ones as its special cases. The introduced generally hybrid t...