Results 91 to 100 of about 7,687 (182)

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

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

Uncertainty-Aware Fault Diagnosis of Rotating Compressors Using Dual-Graph Attention Networks

Machines
Rotating compressors are foundational in various industrial processes, particularly in the oil-and-gas sector, where reliable fault detection is crucial for maintaining operational continuity.
Seungjoo Lee, YoungSeok Kim, Hyun-Jun Choi, Bongjun Ji   +3 more
doaj   +1 more source

Aleatoric Uncertainty Medical Image Segmentation Estimation via Flow Matching

Quantifying aleatoric uncertainty in medical image segmentation is critical since it is a reflection of the natural variability observed among expert annotators. A conventional approach is to model the segmentation distribution using the generative model, but current methods limit the expression ability of generative models.
Van Nguyen, Phi, Trinh, Ngoc Huynh, Nguyen, Duy Minh Lam, Nguyen, Phu Loc, Tran, Quoc Long   +4 more
openaire   +2 more sources

Uncertainty Modelling for Tumour Cellularity Estimation in Histopathology Using Deep Learning

IEEE Access
Tumour cellularity (TC) is an important metric used in the cancer treatment journey, from monitoring the therapeutic response to guiding subsequent treatment decisions.
Riddhasree Bhattacharyya, Sushmita Mitra, Sugata Banerji   +2 more
doaj   +1 more source

Explainable & Safe Artificial Intelligence in Radiology

Journal of the Korean Society of Radiology
Artificial intelligence (AI) is transforming radiology with improved diagnostic accuracy and efficiency, but prediction uncertainty remains a critical challenge.
Synho Do
doaj   +1 more source

A Multi-Level Probabilistic Deep Learning Network Augmented With Normalizing Flow for Ambiguous Medical Image Segmentation

IEEE Access
Medical image segmentation often involves inherent uncertainty due to inter observer variability. In this case, a single deterministic mask obtained by conventional segmentation networks, such as U-Net, cannot capture the distribution of plausible expert
Satirtha Paul Shyam, Shaikh Anowarul Fattah, Mohammad Saquib   +2 more
doaj   +1 more source

Embracing Aleatoric Uncertainty: Generating Diverse 3D Human Motion

Generating 3D human motions from text is a challenging yet valuable task. The key aspects of this task are ensuring text-motion consistency and achieving generation diversity. Although recent advancements have enabled the generation of precise and high-quality human motions from text, achieving diversity in the generated motions remains a significant ...
Qin, Zheng, Wang, Yabing, Yang, Minghui, Zhou, Sanping, Yang, Ming, Wang, Le   +5 more
openaire   +2 more sources

Probabilistic Residual Learning for Aleatoric Uncertainty in Image Restoration

, 2019
this version is outdated, and we are completely reorganizing the paper and split it into several different pieces of work.
Zhang, Chen, Jin, Bangti
openaire   +2 more sources