Results 61 to 70 of about 37,604 (258)

Bayesian Graph Convolutional Neural Networks via Tempered MCMC

IEEE Access, 2021
Deep learning models, such as convolutional neural networks, have long been applied to image and multi-media tasks, particularly those with structured data.
Rohitash Chandra, Ayush Bhagat, Manavendra Maharana, Pavel N. Krivitsky   +3 more
doaj   +1 more source

At Home Detection of Ovarian Health Biomarker in Menstruation Blood

Advanced Materials Technologies, EarlyView.
A lateral flow assay enables the detection of anti‐Müllerian hormone directly in unprocessed menstrual blood using silica‐gold nanoshells and smartphone‐assisted machine learning analysis. The platform supports decentralized, user‐operated testing in wearable and dipstick formats, highlighting the potential of menstrual blood as a non‐invasive matrix ...
Lucas Dosnon, Thomas Rduch, Salma Sherif Azer, Inge K. Herrmann   +3 more
wiley   +1 more source

Linear graph convolutional networks [PDF]

, 2020
Many neural networks for graphs are based on the graph convolution operator, proposed more than a decade ago. Since then, many alternative definitions have been proposed, that tend to add complexity (and non-linearity) to the model.
Erb W., Pasa L., Sperduti A., Navarin N.
core  

Data‐Driven Bulldozer Blade Control for Autonomous Terrain Leveling

Advanced Robotics Research, EarlyView.
A simulation‐driven framework for autonomous bulldozer leveling is presented, combining high‐fidelity terramechanics simulation with a neural‐network‐based reduced‐order model. Gradient‐based optimization enables efficient, low‐level blade control that balances leveling quality and operation time.
Harry Zhang, Ganesh Arivoli, Huzaifa Unjhawala, Luning Bakke, Radu Serban, Dan Negrut   +5 more
wiley   +1 more source

Graph Neural Networks: A Bibliometric Mapping of the Research Landscape and Applications

Information
Graph neural networks (GNNs) are deep learning algorithms that process graph-structured data and are suitable for applications such as social networks, physical models, financial markets, and molecular predictions.
Annielle Mendes Brito da Silva, Natiele Carla da Silva Ferreira, Luiza Amara Maciel Braga, Fabio Batista Mota, Victor Maricato, Luiz Anastacio Alves   +5 more
doaj   +1 more source

Dynamic gesture recognition based on 2D convolutional neural network and feature fusion

Scientific Reports, 2022
Gesture recognition is one of the most popular techniques in the field of computer vision today. In recent years, many algorithms for gesture recognition have been proposed, but most of them do not have a good balance between recognition efficiency and ...
Jimin Yu, Maowei Qin, Shangbo Zhou
doaj   +1 more source

Exact combinatorial optimization with graph convolutional neural networks

, 2019
Combinatorial optimization problems are typically tackled by the branch-and-bound paradigm. We propose a new graph convolutional neural network model for learning branch-and-bound variable selection policies, which leverages the natural variable ...
Lodi A., Charlin L., Gasse M., Chetelat D., Ferroni N.   +4 more
core   +1 more source

Intelligent Maintenance Review for Robots: Multimodal Information, Deep Diagnosis and Embodied Artificial Intelligence

Advanced Robotics Research, EarlyView.
This review maps the methods to monitor robots’ health by fusing vibration, sound, control signals, vision, force, and oil information with artificial intelligence. It identifies deep learning, transfer learning, digital twins, and physics‐informed models as key methodological pathways enabling earlier diagnosis, safer human–robot collaboration, and ...
Yuting Qiao, Hongbo Wang, Naipeng Li, Shuo Wang, Yaguo Lei, Tonghai Wu, Junyi Cao   +6 more
wiley   +1 more source

Sample and Aggregate Voronoi Neighborhood Weighted Graph Neural Network (SAGE-Voronoi) and Its Capability for City-Sized Vehicle Traffic Time Series Prediction

Applied Sciences
The application of graph convolutional neural networks for traffic prediction is a standard procedure; however, this approach is rarely used under the assumption that the exact city plan is unknown and the prediction area is a city-sized region.
Przemysław Bielecki, Tomasz Hachaj, Jarosław Wąs   +2 more
doaj   +1 more source

Learning‐Based Soft Robotic Grasping: Recent Progress and Remaining Challenges

Advanced Robotics Research, EarlyView.
This review analyzes learning‐based soft robotic grasping from a pipeline‐oriented perspective, encompassing soft gripper design, multimodal sensing, and learning‐based planning and control. It surveys key neural network architectures and benchmark datasets and identifies critical challenges such as sim‐to‐real transfer, generalization, and continual ...
Arnab Majumder, Jelizaveta Konstantinova, Graham Deacon, Kaspar Althoefer   +3 more
wiley   +1 more source