Results 191 to 200 of about 207,453 (329)
Inferring neuronal functional connectivity using dynamic Bayesian networks
BMC Neuroscience, 2008 Jin Rong +3 more
doaj +1 more source
A Dynamic Bayesian Network Based Merge Mechanism for Autonomous Vehicles
, 2019 Kherroubi Zine el abidine +2 more
openalex +2 more sources
Autonomous AI‐Driven Design for Skin Product Formulations
Advanced Intelligent Discovery, EarlyView.This review presents a comprehensive closed‐loop framework for autonomous skin product formulation design. By integrating artificial intelligence‐driven experiment selection with automated multi‐tiered assays, the approach shifts development from trial‐and‐error to intelligent optimisation.
Yu Zhang +5 more
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.A machine learning framework simultaneously predicts four critical properties of monomers for emulsion polymerization: propagation rate constant, reactivity ratios, glass transition temperature, and water solubility. These tools can be used to systematically identify viable bio‐based monomer pairs as replacements for conventional formulations, with ...
Kiarash Farajzadehahary +1 more
wiley +1 more source
Dynamic Bayesian network-based acoustic models incorporating speaking rate effects
, 2004 隆宏 篠崎 +3 more
openalex +1 more source
Dynamic Bayesian network model on two opposite types of sensory adaptation
, 2010 Kazuyuki Aihara
openalex +1 more source
Prognostic Modelling with Dynamic Bayesian Networks
, 2009 In this paper, we review the application of dynamic Bayesian networks to prognostic modelling. An example is provided for illustration. With this example, we show how the equipment’s reliability decays over time in the situation where repair is not possible and then how a simple change to the model allows us to represent different maintenance policies ...
McNaught, K., Zagorecki, A.
openaire +1 more source
AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective
Advanced Intelligent Discovery, EarlyView.The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley +1 more source