Results 51 to 60 of about 80,384 (252)
Hybrid Optimization Algorithm for Bayesian Network Structure Learning
Information, 2019 Since the beginning of the 21st century, research on artificial intelligence has made great progress. Bayesian networks have gradually become one of the hotspots and important achievements in artificial intelligence research.
Xingping Sun +5 more
doaj +1 more source
Advanced Functional Materials, EarlyView.We present a fully automated Bayesian optimization (BO) protocol for the parameterization of nonbonded interactions in coarse‐grain CG force fields (BACH). Using experimental thermophysical data, we apply the protocol to a broad range of liquids, spanning linear, branched, and unsaturated hydrocarbons, esters, triglycerides, and water.
Janak Prabhu +3 more
wiley +1 more source
Improving the performance of Bayesian networks in non-ignorable missing data imputation
Kuwait Journal of Science, 2013 The issue of missing data may arise for researchers who deal with data gathering problems. Bayesian networks are one of the proposed methods that have been recently used in missing data imputation.
P. NILOOFAR +2 more
doaj
Advanced Healthcare Materials, EarlyView.We developed a micro‐sized, biocompatible implant for postoperative sustained delivery of anti‐fibrotic antibodies in glaucoma surgery. Machine learning‐guided optimization of polymer composition, implant geometry, and porosity enabled precise control of drug release.
Mengqi Qin +5 more
wiley +1 more source
Advanced Materials, EarlyView.Metal‐free carbon catalysts enable the sustainable synthesis of hydrogen peroxide via two‐electron oxygen reduction; however, active site complexity continues to hinder reliable interpretation. This review critiques correlation‐based approaches and highlights the importance of orthogonal experimental designs, standardized catalyst passports ...
Dayu Zhu +3 more
wiley +1 more source
Advanced Materials, EarlyView.Electrochemical CO2RR is a key technology for converting CO2 into chemicals, but there remains a gap between “laboratory science” and “engineering practice” in current research. This review establishes a multi‐scale research framework, encompassing atomic‐level characterization, microenvironment regulation, external field‐assisted optimization, and AI ...
Ping Hong +3 more
wiley +1 more source
Uniqueness of the Level Two Bayesian Network Representing a Probability Distribution
International Journal of Mathematics and Mathematical Sciences, 2011 Bayesian Networks are graphic probabilistic models through which we can acquire, capitalize on, and exploit knowledge. they are becoming an important tool for research and applications in artificial intelligence and many other fields in the last decade ...
Linda Smail
doaj +1 more source
Advanced Materials, EarlyView.A machine learning‐guided bio‐interfacial design strategy resolves the long‐standing strength–toughness–functionality trade‐off in nanocomposites. By efficiently mapping high‐performance regions in the composition–processing space, the approach delivers hierarchically entangled, nanosheet‐pinned architectures that combine mechanical robustness ...
Hao Wang +10 more
wiley +1 more source
CausalTrail: Testing hypothesis using causal Bayesian networks [version 1; referees: 2 approved]
F1000Research, 2015 Summary Causal Bayesian Networks are a special class of Bayesian networks in which the hierarchy directly encodes the causal relationships between the variables. This allows to compute the effect of interventions, which are external changes to the system,
Daniel Stöckel +3 more
doaj +1 more source