AIMC Topic: Stochastic Processes

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Numerical computation of the stochastic hepatitis B model using feed forward neural network and real data.

Scientific reports
Hepatitis B is a global health burden and can persist for years, with nearly two billion infections worldwide, where its spread is influenced by environmental heterogeneity, host-pathogen interactions, and vaccination-induced immune variability. Prop...

Building the connectome of a small brain with a simple stochastic developmental generative model.

Proceedings of the National Academy of Sciences of the United States of America
The architectures of biological neural networks result from developmental processes shaped by genetically encoded rules, biophysical constraints, stochasticity, and learning. Understanding these processes is crucial for comprehending neural circuits'...

Dynamic forecasting and mechanisms of volatility synchronization in complex financial systems.

PloS one
Synchronization, which has been a common natural phenomenon, occurs frequently in complex financial systems and is an important contagion mechanism for systemic financial risks and even financial crises. In view of this, we construct a coupled stocha...

Stabilizing machine learning for reproducible and explainable results: A novel validation approach to subject-specific insights.

Computer methods and programs in biomedicine
INTRODUCTION: Machine Learning (ML) is transforming medical research by enhancing diagnostic accuracy, predicting disease progression, and personalizing treatments. While general models trained on large datasets identify broad patterns across populat...

Computer Vision in Monitoring Fruit Browning: Neural Networks vs. Stochastic Modelling.

Sensors (Basel, Switzerland)
As human labour is limited and therefore expensive, computer vision has emerged as a solution with encouraging results for monitoring and sorting tasks in the agrifood sector, where conventional methods for inspecting fruit browning that are generall...

Solving two-stage stochastic integer programs via representation learning.

Neural networks : the official journal of the International Neural Network Society
Solving stochastic integer programs (SIPs) is extremely intractable due to the high computational complexity. To solve two-stage SIPs efficiently, we propose a conditional variational autoencoder (CVAE) for scenario representation learning. A graph c...

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

PloS one
OBJECTIVES: 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 susce...

Control of medical digital twins with artificial neural networks.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
The objective of precision medicine is to tailor interventions to an individual patient's unique characteristics. A key technology for this purpose involves medical digital twins, computational models of human biology that can be personalized and dyn...

Loss formulations for assumption-free neural inference of SDE coefficient functions.

NPJ systems biology and applications
Stochastic differential equations (SDEs) are one of the most commonly studied probabilistic dynamical systems, and widely used to model complex biological processes. Building upon the previously introduced idea of performing inference of dynamical sy...

Practical X-ray gastric cancer diagnostic support using refined stochastic data augmentation and hard boundary box training.

Artificial intelligence in medicine
Endoscopy is widely used to diagnose gastric cancer and has a high diagnostic performance, but it must be performed by a physician, which limits the number of people who can be diagnosed. In contrast, gastric X-rays can be taken by radiographers, thu...