Results 81 to 90 of about 3,284,626 (292)
Towards data-centric control of sensor networks through Bayesian dynamic linear modelling [PDF]
, 2015 Wireless sensor networks usually operate in dynamic, stochastic environments. While the behaviour of individual nodes is important, they are better seen as contributors to a larger mission, and managing the sensing quality and performance of these ...
Dobson, Simon Andrew +4 more
core +1 more source
Breaking Bottlenecks in Solid Electrolyte Discovery With Large Artificial Intelligence Models
Advanced Materials, EarlyView.Large AI models are presented as a route to break key bottlenecks in solid electrolyte discovery by linking materials databases, machine‐learning interatomic potentials, large language models, and autonomous experiments. This perspective highlights how AI can accelerate the identification of solid electrolytes with fast ion transport, stable interfaces,
Eric Jianfeng Cheng +24 more
wiley +1 more source
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 +3 more
doaj +1 more source
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 +3 more
doaj +1 more source
Advances in Bio‐Integrated Edge Computing Systems for Smart and Connected Health
Advanced Materials, EarlyView.This review explores advances in bio‐integrated edge computing for healthcare, focusing on co‐designing computing, sensing, and energy strategies to overcome integration and power challenges for optimized, decentralized operation. ABSTRACT Recent advances in bio‐integrated healthcare technologies have enabled continuous, high‐resolution monitoring and ...
Gyuwon Kim +4 more
wiley +1 more source
CoRR, 1997 A new method is developed to represent probabilistic relations on multiple random events. Where previously knowledge bases containing probabilistic rules were used for this purpose, here a probability distribution over the relations is directly represented by a Bayesian network.
openaire +4 more sources
Bayesian network classifiers in Weka [PDF]
, 2004 Various Bayesian network classifier learning algorithms are implemented in Weka [10].This note provides some user documentation and implementation details.
Remco R. Bouckaert, Bouckaert, Remco R.
core
Advanced Materials Technologies, EarlyView.This review presents multidimensional heat‐path engineering for thermal interface materials, showing how internal heat transport, interfacial contact and bondline formation, and processing‐defined pathway architectures together determine bonded‐joint thermal performance.
Jinsoo Na +7 more
wiley +1 more source
Prediction of concrete fatigue durability using Bayesian neural networks
Computer Assisted Methods in Engineering and Science, 2022 The utility of Bayesian neural networks to predict concrete fatigue durability as a function of concrete mechanical parameters of a specimen and characteristics of the loading cycle is investigated.
Marek Słoński
doaj
Advanced Robotics Research, EarlyView.Robots can learn manipulation tasks from human demonstrations. This work proposes a versatile method to identify the physical interactions that occur in a demonstration, such as sequences of different contacts and interactions with mechanical constraints.
Alex Harm Gert‐Jan Overbeek +3 more
wiley +1 more source