Results 121 to 130 of about 7,255,480 (346)

Weaving Intelligence: Thermally Drawn Multimaterial Fibers Toward AI‐Enabled Smart Textiles

Advanced Materials, EarlyView.
Thermally drawn multimaterial fibers are rapidly advancing as intelligent structural units for next‐generation smart textiles. Integrating multimaterial architectures with neuromorphic and spiking‐neural‐network principles enables fabrics that can sense, compute, and adapt autonomously.
Vuong Dinh Trung, Jiaxiang Yi, Hieu Nguyen‐Duc, Tien Nu Hoang Lo, Weili Zhao, Chaoqun Dong, Fabien Sorin, Baris Caglar, Lei Wei, Tung Nguyen‐Dang   +9 more
wiley   +1 more source

Structural learning of bayesian networks using statistical constraints [PDF]

, 2012
Bayesian Networks are probabilistic graphical models that encode in a compact manner the conditional probabilistic relations over a set of random variables.
Venco, Francesco
core  

Real-time information processing of environmental sensor network data using Bayesian Gaussian processes

, 2012
In this article, we consider the problem faced by a sensor network operator who must infer, in real time, the value of some environmental parameter that is being monitored at discrete points in space and time by a sensor network.
Osborne, Michael A., Roberts, Stephen, Osborne, MA, Rogers, A, Jennings, Nicholas R., Roberts, Stephen J., Rogers, Alex, Roberts, SJ, Jennings, NR   +8 more
core   +1 more source

Spatial Component Analysis of MRI data for Alzheimer's Disease Diagnosis: a Bayesian network approach

Frontiers in Computational Neuroscience, 2014
This work presents a spatial-component (SC) based approach to aid the diagnosis of Alzheimer's disease (AD) using magnetic resonance images. In this approach, the whole brain image is subdivided in regions or spatial components, and a Bayesian network is
Ignacio eA. Illán, Juan Manuel Gorriz, Javier eRamírez, Anke eMeyer-Baese   +3 more
doaj   +1 more source

Ecological Risk of Water Resource Use to the Wellbeing of Macroinvertebrate Communities in the Rivers of KwaZulu-Natal, South Africa

Frontiers in Water, 2020
The rivers of KwaZulu-Natal, South Africa, are being impacted by various anthropogenic activities that threaten their sustainability. Our study demonstrated how Bayesian networks could be used to conduct an environmental risk assessment of ...
Olalekan A. Agboola, Colleen T. Downs, Gordon O'Brien, Gordon O'Brien   +3 more
doaj   +1 more source

Machine Learning Accelerated Computational Design of Bio‐Inspired Catalysts in the Nitrogen Reduction Reaction

Advanced Materials, EarlyView.
We introduce a computational workflow that combines quantum chemical calculations and machine learning techniques to predict the catalytic performance of a wide range of catalysts in the nitrogen reduction reaction (NRR). The analysis of the trained models provides insights into the complex structure–activity relationship in experimental catalytic ...
Leonardo Di Ciano, Zihan You, Haoran Chen, Qifan Zhong, Rong‐Zhen Liao, Shaoqi Zhan   +5 more
wiley   +1 more source

Object-Oriented Bayesian Networks for a Decision Support System [PDF]

We study an economic decision problem where the actors are two rms and the Antitrust Authority whose main task is to monitor and prevent rms potential anti-competitive behaviour.
Paola Vicard, Julia Mortera, Cecilia Vergari   +2 more
core  

Rock typing and causality analysis in unconventional formation using Bayes nets

Petroleum Research
Unconventional formations are characterized by high heterogeneity and anisotropy, making it difficult to interpret logging data, perform core analysis, and create accurate petrophysical models.
Evgeny Chekhonin, Alexander Nikitin, Dimitri Pissarenko, Yuri Popov, Raissa Romushkevich, Dzhulia Zagranovskaya   +5 more
doaj   +1 more source

Hybrid time Bayesian networks

International Journal of Approximate Reasoning, 2015
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Manxia Liu, Arjen Hommersom, Maarten van der Heijden, Peter J. F. Lucas   +3 more
openaire   +7 more sources

Bidirectional Process Prediction in the Laser‐Induced‐Graphene Production Using Blackbox Deep Learning

Advanced Materials Technologies, EarlyView.
This study shows that a lightweight blackbox neural network provides a practical, cost‐effective solution for bidirectional process prediction in laser‐induced graphene (LIG) fabrication. Achieving high predictive performance with minimal overhead, the approach democratizes machine learning (ML) for resource‐limited environments.
Maxim Polomoshnov, Nitheesh M. Nair, Gerardo Hernández‐Sosa, Markus Reischl   +3 more
wiley   +1 more source