Results 41 to 50 of about 3,934 (193)

Decoding Uncertainty Quantification for Oncology—An Illustration Using Radiomics

Diagnostics
While AI models are developed in oncology for predicting different clinical outcomes, the focus is often on accuracy and many fail to adequately communicate the degree of certainty in these predictions.
Florian van Daalen, Balu Krishna Sasidharan, C. Praveenraj, Amal Joseph Varghese, Andre Dekker, Leonard Wee, Rianne Fijten, Aparna Irodi, Hannah Mary T. Thomas   +8 more
doaj   +1 more source

Incoherence: A Generalized Measure of Complexity to Quantify Ensemble Divergence in Multi-Trial Experiments and Simulations

Entropy
Complex systems pose significant challenges to traditional scientific and statistical methods due to their inherent unpredictability and resistance to simplification.
Timothy Davey
doaj   +1 more source

Towards Reliable Parameter Extraction in MEMS Final Module Testing Using Bayesian Inference

Sensors, 2022
In micro-electro-mechanical systems (MEMS) testing high overall precision and reliability are essential. Due to the additional requirement of runtime efficiency, machine learning methods have been investigated in recent years.
Monika E. Heringhaus, Yi Zhang, André Zimmermann, Lars Mikelsons   +3 more
doaj   +1 more source

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, Guentae Doh, Dongho Lee, Youngho Kim, Changmin Shin, Su‐Jin Shin, Young‐Chul Ghim, Sanghoo Park, Wonho Choe   +8 more
wiley   +1 more source

Recalibration of Aleatoric and EpistemicRegression Uncertainty in Medical Imaging

Machine Learning for Biomedical Imaging, 2021
The consideration of predictive uncertainty in medical imaging with deep learning is of utmost importance. We apply estimation of both aleatoric and epistemic uncertainty by variational Bayesian inference with Monte Carlo dropout to regression tasks and show that predictive uncertainty is systematically underestimated.
Max-Heinrich Laves, Sontje Ihler, Jacob F. Fast, Lüder A. Kahrs, Tobias Ortmaier   +4 more
openaire   +2 more sources

Accelerating Materials Discovery: A Review of Machine Learning in X‐Ray Absorption Spectroscopy

Advanced Intelligent Systems, EarlyView.
This review systematically details how machine learning transforms X‐ray absorption spectroscopy (XAS) analysis. It covers advanced deep learning architectures for structure‐spectra mapping and inverse tasks, while discussing key challenges like the simulation‐to‐reality gap.
Melaku Lake Tegegne, Haodong Yao, Liyuan Wu, Yihe Pang, Fazhi Qi, Lina Zhao   +5 more
wiley   +1 more source

Vertical NAND Flash Memory‐Based Bayesian Neural Networks With Intrinsic Gaussian Weight Sampling

Advanced Intelligent Systems, EarlyView.
Commercial vertical NAND flash memory supports Bayesian neural networks by using paired cells to produce bell‐shaped weight distributions. Read voltage controls their spread without additional memory cells or changes to the array structure. Simulations based on device measurements demonstrate improved uncertainty estimation and accuracy gains of up to ...
Sung‐Ho Park, Mingyun Oh, Dongbeen Shin, Jaehyeon Kim, Jonghyun Ko, Jiseong Im, Yeongheon Yang, Gyuweon Jung, Jong‐Ho Lee   +8 more
wiley   +1 more source

Uncertainty Quantification for Full-Flight Data Based Engine Fault Detection with Neural Networks

Machines, 2022
Current state-of-the-art engine condition monitoring is based on a minimum of one steady-state data point per flight. Due to the scarcity of available data points, there are difficulties distinguishing between random scatter and an underlying fault ...
Matthias Weiss, Stephan Staudacher, Jürgen Mathes, Duilio Becchio, Christian Keller   +4 more
doaj   +1 more source

Experimental methods in chemical engineering: The scientific method versus engineering design methodology

The Canadian Journal of Chemical Engineering, EarlyView.
Abstract Engineering graduate students confuse the scientific method and problem solving when presenting their pre‐doctoral exams and theses: Science discourse adopts question and hypotheses to assert and establish a study's rationale, while it is problem and objectives in engineering design.
Gregory S. Patience, Alexander R. P. Harrison, Nicolas A. Patience, Paul A. Patience, Robert S. Weber   +4 more
wiley   +1 more source

Evaluating Aleatoric Uncertainty via Conditional Generative Models

, 2022
Aleatoric uncertainty quantification seeks for distributional knowledge of random responses, which is important for reliability analysis and robustness improvement in machine learning applications.
Lam, Henry, Huang, Ziyi, Zhang, Haofeng
core