AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Stochastic Processes

Showing 81 to 90 of 243 articles

Clear Filters

Exponential synchronization of neural networks with time-varying delays and stochastic impulses.

Neural networks : the official journal of the International Neural Network Society
This paper concentrates on the exponential synchronization problem of the delayed neural networks (DNNs) with stochastic impulses. First, the impulsive Halanay differential inequality is further extended to the case that the impulsive strengths are r...

Stochastic DCA for minimizing a large sum of DC functions with application to multi-class logistic regression.

Neural networks : the official journal of the International Neural Network Society
We consider the large sum of DC (Difference of Convex) functions minimization problem which appear in several different areas, especially in stochastic optimization and machine learning. Two DCA (DC Algorithm) based algorithms are proposed: stochasti...

Exponential synchronization of stochastic delayed memristive neural networks via a novel hybrid control.

Neural networks : the official journal of the International Neural Network Society
This paper investigates the exponential synchronization issue of stochastic delayed memristive neural networks (SDMNNs) via a novel hybrid control (HC), where impulsive instants are determined by the state-dependent trigger condition. The switching a...

Research and Verification of Convolutional Neural Network Lightweight in BCI.

Computational and mathematical methods in medicine
With the increasing of depth and complexity of the convolutional neural network, parameter dimensionality and volume of computing have greatly restricted its applications. Based on the SqueezeNet network structure, this study introduces a block convo...

A high throughput machine-learning driven analysis of Ca spatio-temporal maps.

Cell calcium
High-resolution Ca imaging to study cellular Ca behaviors has led to the creation of large datasets with a profound need for standardized and accurate analysis. To analyze these datasets, spatio-temporal maps (STMaps) that allow for 2D visualization ...

Intermittent boundary stabilization of stochastic reaction-diffusion Cohen-Grossberg neural networks.

Neural networks : the official journal of the International Neural Network Society
Cohen-Grossberg neural networks (CGNNs) play an important role in many applications and the stabilization of this system has been well studied. This study considers the exponential stabilization for stochastic reaction-diffusion Cohen-Grossberg neura...

Fixed-time synchronization of stochastic memristor-based neural networks with adaptive control.

Neural networks : the official journal of the International Neural Network Society
In this study, we consider the fixed-time synchronization problem for stochastic memristor-based neural networks (MNNs) via two different controllers. First, a new stochastic differential equation is established using differential inclusions and set-...

Delay-distribution-dependent state estimation for neural networks under stochastic communication protocol with uncertain transition probabilities.

Neural networks : the official journal of the International Neural Network Society
In this paper, the protocol-based remote state estimation problem is considered for a kind of delayed artificial neural networks. The random time-varying delays fall into certain intervals with known probability distributions. For the sake of reducin...

Viewpoint on Time Series and Interrupted Time Series Optimum Modeling for Predicting Arthritic Disease Outcomes.

Current rheumatology reports
PURPOSE OF REVIEW: The propose of this viewpoint is to improve or facilitate the clinical decision-making in the management/treatment strategies of arthritis patients through knowing, understanding, and having access to an interactive process allowin...

Physics-informed neural networks for solving nonlinear diffusivity and Biot's equations.

PloS one
This paper presents the potential of applying physics-informed neural networks for solving nonlinear multiphysics problems, which are essential to many fields such as biomedical engineering, earthquake prediction, and underground energy harvesting. S...