Results 81 to 90 of about 1,651,457 (304)
Organic Materials of Tomorrow: Horizons of Artificial Intelligence
Advanced Materials, EarlyView.This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena +3 more
wiley +1 more source
Determination of Maximum Bayesian Entropy Probability Distribution [PDF]
Journal of Sciences, Islamic Republic of Iran, 2005 In this paper, we consider the determination methods of maximum entropy multivariate distributions with given prior under the constraints, that the marginal distributions or the marginals and covariance matrix are prescribed.
doaj
Chemical Engineering Transactions, 2016 Process plants are particularly subjected to major accidental events, whose catastrophic escalations, triggered by external factors and characterized by very high impact and low probability, can affect both workers and population in the nearby of a ...
V. Villa, V. Cozzani
doaj +1 more source
Fault diagnosis of mine hoist based on fuzzy fault tree and Bayesian network
Gong-kuang zidonghua, 2020 In order to solve problems of low efficiency and poor accuracy of existing mine hoist fault diagnosis methods, a fault diagnosis method of mine hoist based on fuzzy fault tree and Bayesian network was proposed.
ZHANG Mei +3 more
doaj +1 more source
Data‐Driven Materials Science for Energy‐Sustainable Applications
Advanced Materials, EarlyView.Data‐driven approaches powered by artificial intelligence are transforming materials discovery for energy sustainability. This review examines how auto‐generated high‐quality materials databases and domain‐specific language models accelerate research in photovoltaics, thermoelectrics, batteries and magnetic materials. Applications involve extraction of
Jacqueline M. Cole
wiley +1 more source
BMC Medical Research Methodology, 2010 Background Automatic variable selection methods are usually discouraged in medical research although we believe they might be valuable for studies where subject matter knowledge is limited.
Steineck Gunnar +3 more
doaj +1 more source
Artificial Intelligence Meets Micro/Nanorobotics
Advanced Materials, EarlyView.Artificial intelligence is transforming micro‐ and nanorobots from externally controlled, task‐specific machines into adaptive, autonomous systems. Machine learning, multimodal perception, digital twins, AI‐guided materials and geometry design enhance propulsion, localization, decision‐making, whichaccelerates clinical and environmental applications ...
Fatma M. Yurtsever +6 more
wiley +1 more source
A Software Tool for Estimating Uncertainty of Bayesian Posterior Probability for Disease
DiagnosticsThe role of medical diagnosis is essential in patient care and healthcare. Established diagnostic practices typically rely on predetermined clinical criteria and numerical thresholds.
Theodora Chatzimichail +1 more
doaj +1 more source
Being Bayesian about Categorical Probability
CoRR, 2020 Neural networks utilize the softmax as a building block in classification tasks, which contains an overconfidence problem and lacks an uncertainty representation ability. As a Bayesian alternative to the softmax, we consider a random variable of a categorical probability over class labels. In this framework, the prior distribution explicitly models the
Taejong Joo, Uijung Chung, Min-Gwan Seo
openaire +4 more sources
On a Gibbs sampler based random process in Bayesian nonparametrics [PDF]
We define and investigate a new class of measure-valued Markov chains by resorting to ideas formulated in Bayesian nonparametrics related to the Dirichlet process and the Gibbs sampler.
Stefano Favaro +2 more
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