Results 171 to 180 of about 2,535,602 (380)
Advanced Intelligent Systems, EarlyView.A portable, wearable device based on metabolic heat integrated sensing and deep learning enables continuous blood glucose (BG) monitoring. The system uses a gate recurrent unit model for real‐time BG prediction, achieving accuracy comparable to commercial noninvasive meters.
Haolin Wang +12 more
wiley +1 more source
Modifying Bayesian Networks by Probability Constraints
, 2005 Proceedings of the 21st Conference on Uncertainty in Artificial ...
Peng, Yun, Ding, Zhongli
openaire +2 more sources
Predicting Performance of Hall Effect Ion Source Using Machine Learning
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.This study introduces HallNN, a machine learning tool for predicting Hall effect ion source performance using a neural network ensemble trained on data generated from numerical simulations. HallNN provides faster and more accurate predictions than numerical methods and traditional scaling laws, making it valuable for designing and optimizing Hall ...
Jaehong Park +8 more
wiley +1 more source
Advanced Intelligent Systems, EarlyView.In this article, an energy‐efficient hardware implementation of spiking‐restricted Boltzmann machines using the pseudo‐synaptic sampling (PS2) method is presented. In the PS2 method, superior area and energy efficiency over previous approaches, such as the random walk method, are demonstrated, achieving a 94.94% reduction in power consumption during on‐
Hyunwoo Kim +10 more
wiley +1 more source
Posterior probability intervals in Bayesian wavelet estimation [PDF]
, 2004 C. Semadeni
openalex +1 more source
Deep Learning Methods in Soft Robotics: Architectures and Applications
Advanced Intelligent Systems, EarlyView.Soft robotics has seen intense research over the past two decades and offers a promising approach for future robotic applications. However, standard industrial methods may be challenging to apply to soft robots. Recent advances in deep learning provide powerful tools to analyze and design complex soft machines that can operate in unstructured ...
Tomáš Čakurda +3 more
wiley +1 more source
Assessing Students’ Difficulties with Conditional Probability and Bayesian Reasoning [PDF]
, 2007 Carmen Pena Díaz +1 more
openalex +1 more source
Generative Inverse Design of Metamaterials with Functional Responses by Interpretable Learning
Advanced Intelligent Systems, EarlyView.This work introduces random‐forest‐based interpretable generative inverse design (RIGID), a new single‐shot inverse design method for metamaterials using interpretable machine learning and Markov chain Monte Carlo sampling. Once trained on a small dataset, RIGID can estimate the likelihood of designs achieving target behaviors (e.g., wave‐based ...
Wei (Wayne) Chen +4 more
wiley +1 more source
Bayesian inference: more than Bayes’s theorem
Frontiers in Astronomy and Space SciencesBayesian inference gets its name from Bayes’s theorem, expressing posterior probabilities for hypotheses about a data generating process as the (normalized) product of prior probabilities and a likelihood function.
Thomas J. Loredo, Robert L. Wolpert
doaj +1 more source
Machine Learning‐Assisted Simulations and Predictions for Battery Interfaces
Advanced Intelligent Systems, EarlyView.This review summarizes machine learning (ML)‐assisted simulations and predictions at battery interfaces. It highlights how employing ML algorithms with machine vision, enables the lithium dendrite growth simulation, the solid–electrolyte interphase formation, and other interfacial dynamics.
Zhaojun Sun +4 more
wiley +1 more source