Results 101 to 110 of about 1,009,621 (180)

Site-of-Metabolism Prediction with Aleatoric and Epistemic Uncertainty Quantification

Journal of Chemical Information and Modeling
In silico metabolism prediction models have become indispensable tools to optimize the metabolic properties of xenobiotics while preserving their intended biological activity. Among these, site-of-metabolism (SOM) prediction models are particularly valuable for pinpointing metabolically labile atomic positions.
Roxane Axel Jacob, Oliver Wieder, Ya Chen 0002, Angelica Mazzolari, Andreas Bergner, Klaus-Jürgen Schleifer, Johannes Kirchmair   +6 more
openaire   +5 more sources

Bayesian Deep Learning for Uncertainty-Aware Analysis and Predictive Modeling of Graphene and MoS₂-Coated Terahertz Biosensors for Biomarker Detection in AML

Applied Sciences
In this paper, we propose a Bayesian Deep Learning (BDL) framework to model uncertainty and predict the performance of terahertz (THz) biosensors with a graphene and molybdenum disulfide (MoS2) coating for AML biomarker detection.
Arcel Kalenga Muteba, Kingsley A. Ogudo
doaj   +1 more source

Quantifying Epistemic and Aleatoric Uncertainty in the Ampair 600 Wind Turbine

, 2017
The goal of this chapter is to demonstrate a practical application of the maximum entropy method. While there are multiple approaches to quantifying uncertainty (both aleatoric and epistemic), the maximum entropy method is commonly used to study joint ...
GASTALDI, CHIARA, Chiara Gastaldi, Brett A. Robertson, Robertson Brett A., Matthew R. W. Brake, Brake Matthew R. W., Bonney Matthew S., Matthew S. Bonney   +7 more
core   +1 more source

Machine learning surrogate for charged particle beam dynamics with space charge based on a recurrent neural network with aleatoric uncertainty

Physical Review Accelerators and Beams
In this work, we develop a machine learning (ML) model with aleatoric uncertainty for the low energy beam transport (LEBT) region of the LANSCE linear accelerator in which we model the transport of a space-charge-dominated 750 keV proton beam through a ...
Cristina Garcia-Cardona, Alexander Scheinker   +1 more
doaj   +1 more source

Multimodal Uncertainty Robust Tree Cover Segmentation for High-Resolution Remote Sensing Images

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Recentadvances in semantic segmentation of multimodal remote sensing images have significantly improved the accuracy of tree cover mapping, supporting applications in urban planning, forest monitoring, and ecological assessment.
Yuanyuan Gui, Wei Li, Yinjian Wang, Xiang-Gen Xia, Mauro Marty, Christian Ginzler, Zuyuan Wang   +6 more
doaj   +1 more source

Toward a New Flood Assessment Paradigm: From Exceedance Probabilities to the Expected Maximum Floods and Damages

Water Resources Research
To assess flood risks, we seek to estimate the probability distribution of the worst possible single‐event over a contiguous period of N years rather than the cumulative losses expected over a planning horizon.
E. Todini, P. Reggiani
doaj   +1 more source

A Novel Computational Instrument Based on a Universal Mixture Density Network with a Gaussian Mixture Model as a Backbone for Predicting COVID-19 Variants’ Distributions

Mathematics
Various published COVID-19 models have been used in epidemiological studies and healthcare planning to model and predict the spread of the disease and appropriately realign health measures and priorities given the resource limitations in the field of ...
Yas Al-Hadeethi, Intesar F. El Ramley, Hiba Mohammed, Nada M. Bedaiwi, Abeer Z. Barasheed   +4 more
doaj   +1 more source

Probabilistic Neural Networks (PNNs) for Modeling Aleatoric Uncertainty in Scientific Machine Learning

IEEE Access
This paper investigates the use of probabilistic neural networks (PNNs) to model aleatoric uncertainty, which refers to the inherent variability in the input-output relationships of a system, often characterized by unequal variance or heteroscedasticity.
Farhad Pourkamali-Anaraki, Jamal F. Husseini, Scott E. Stapleton   +2 more
doaj   +1 more source

Bayesian outcome-specific ensemble modeling for football outcome prediction

Frontiers in Applied Mathematics and Statistics
This paper examines the dominant role of aleatoric uncertainty in football outcome prediction and introduces a Bayesian outcome-specific ensemble framework in which specialized hierarchical sub-models are trained independently for each outcome class and ...
Sello Dalton Pitso, Taryn Michael, Moeketsi Mosia   +2 more
doaj   +1 more source

Engineering Decisions Involving Aleatoric and Epistemic Uncertainty

, 2008
A concept we call Total Uncertainty will be used to illustrate how we combine both aleatoric (reducible) and epistemic (non-reducible) uncertainties in a given problem to assess the confidence an analyst can have in his/her decisions. In illustrating the
Timothy J. Ross
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