Results 181 to 190 of about 235,639 (329)

Risk Assessment of Hydrogen-Powered Aircraft: An Integrated HAZOP and Fuzzy Dynamic Bayesian Network Framework. [PDF]

Sensors (Basel)
Dang X, Shao Y, Liu H, Yang Z, Zhong M, Zhao H, Deng W.   +6 more
europepmc   +1 more source

Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning

Advanced Intelligent Discovery, EarlyView.
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley   +1 more source

Dynamic Bayesian network modeling for intervention mechanism

BMC Neuroscience, 2012
Sun Yan, Tang Yi-Yuan
doaj   +1 more source

Dynamic Bayesian network modeling for longitudinal data on child undernutrition in Ethiopia (2002-2016). [PDF]

Front Public Health
Begashaw GB, Zewotir T, Fenta HM.
europepmc   +1 more source

Enhancing unmanned aerial vehicles logistics for dynamic delivery: a hybrid non-dominated sorting genetic algorithm II with Bayesian belief networks [PDF]

Armin Mahmoodi, Seyed Mojtaba Sajadi, Abdellatif M. Sadeq, Masoud Narenji, Mehdi Eshaghi, Milad Jasemi   +5 more
openalex   +1 more source

Dynamic Bayesian Networks for Context-Aware Fall Risk Assessment [PDF]

, 2014
Gregory Koshmak, María Lindén, Amy Loutfi   +2 more
openalex   +1 more source

Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics

Advanced Intelligent Discovery, EarlyView.
Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong, Zijian Huang, Xuanwu Wang, Zhenghui Feng, Wei Liu, Bingding Huang   +5 more
wiley   +1 more source

Time-varying dynamic Bayesian network learning for an fMRI study of emotion processing. [PDF]

Stat Med
Sun L, Zhang A, Liang F.
europepmc   +1 more source

Language modeling using dynamic Bayesian networks

, 2004
In this paper we propose a new approach to language modeling based on dynamic Bayesian networks. The principle idea of our approach is to find the dependence relations between variables that represent different linguistic units (word, class, concept, ...) that constitutes a language model.
Deviren, Murat, Daoudi, Khalid, Smaïli, Kamel   +2 more
openaire   +1 more source

Factorization Machine with Iterative Quantum Reverse Annealing: A Python Package for Batch Black‐Box Optimization With Reverse Quantum Annealing

Advanced Intelligent Discovery, EarlyView.
Factorization machine with iterative quantum reverse annealing (FMIRA) leverages quantum reverse annealing to perform batch black‐box optimization. Factorization machine with quantum annealing (FMQA) is a widely used python package for solving black‐box optimization problems using D‐Wave quantum annealers.
Andrejs Tučs, Ryo Tamura, Koji Tsuda
wiley   +1 more source