Results 181 to 190 of about 1,708,308 (346)

A binary-domain recurrent-like architecture-based dynamic graph neural network

Autonomous Intelligent Systems
The integration of Dynamic Graph Neural Networks (DGNNs) with Smart Manufacturing is crucial as it enables real-time, adaptive analysis of complex data, leading to enhanced predictive accuracy and operational efficiency in industrial environments.
Zi-chao Chen, Sui Lin
doaj   +1 more source

Xenes for Sustainable Energy: A Roadmap From First‐Principles Design to Practical Deployment

Advanced Materials Interfaces, EarlyView.
Emerging 2D Xenes are advancing from theoretical predictions toward practical energy‐storage and conversion technologies through the integration of first‐principles modelling, experimental synthesis, electrochemical validation, and AI‐assisted materials design, enabling accelerated discovery of high‐performance and sustainable electrochemical systems ...
Onur Karaman, Ceren Karaman
wiley   +1 more source

Path-Link Graph Neural Network for IP Network Performance Prediction

, 2021
Publisher Copyright: © 2021 IFIP.Dynamic resource provisioning and quality assurance for the plethora of end-to-end slices running over 5G and B5G networks require advanced modeling capabilities.
Raisanen, Vilho, Kong, Yangzhe, Petrov, Dmitry, Ilin, Alexander   +3 more
core  

Two‐Way Shape Memory Polymer Composite Gripper for Adaptive Robotic Applications

Advanced Materials Technologies, EarlyView.
A two‐way shape memory polymer (SMP) composite is developed with intrinsic shape‐changing capability driven solely by temperature, eliminating external actuation loads. Embedding the SMP in a low‐stiffness elastomeric matrix enabled reversible transformations during heating and cooling cycles.
Aamna Hameed, Kamran Ahmed Khan
wiley   +1 more source

Getting NBA Shots in Context: Analysing Basketball Shots with Graph Embeddings

International Journal of Computer Science in Sport
Evaluating the quality of shots in basketball is crucial and requires considering the context in which they are taken. We introduce a graph neural network to process a graph based on player and ball tracking data to compute expected shot quality.
Schmid Marc, Schöpf Moritz, Kolbinger Otto   +2 more
doaj   +1 more source

Intelligent recommendation system for College English courses based on graph convolutional networks

Heliyon
With the rapid development of international communication, the number of English courses has shown an explosive growth trend, which has caused a serious problem of information overload, resulting in poor teaching performance of recommended English ...
Chen Lilan, Jianqi Zhong
doaj   +1 more source

End‐to‐End Sensing Systems for Breast Cancer: From Wearables for Early Detection to Lab‐Based Diagnosis Chips

Advanced Materials Technologies, EarlyView.
This review explores advances in wearable and lab‐on‐chip technologies for breast cancer detection. Covering tactile, thermal, ultrasound, microwave, electrical impedance tomography, electrochemical, microelectromechanical, and optical systems, it highlights innovations in flexible electronics, nanomaterials, and machine learning.
Neshika Wijewardhane, Lyn I. Jones, Richard M. Martin, Helen Winter, Faezeh Arab Hassani   +4 more
wiley   +1 more source

Transducers Across Scales and Frequencies: A System‐Level Framework for Multiphysics Integration and Co‐Design

Advanced Materials Technologies, EarlyView.
Transducers convert physical signals into electrical and optical representations, yet each mechanism is bounded by intrinsic trade‐offs across bandwidth, sensitivity, speed, and energy. This review maps transduction mechanisms across physical scale and frequency, showing how heterogeneous integration and multiphysics co‐design transform isolated ...
Aolei Xu, Mengyao Xiao, Zhouli Sui, Yinpeng Wang, Daoye Zheng, Yuxin Liu, Luwei Wang, Huajun Liu, Chengkuo Lee   +8 more
wiley   +1 more source

Graph Neural Networks for Bipartite Graphs

Bipartite graphs are a special type of graph data structure where vertices can be divided into two disjoint and independent sets, and each edge connects a vertex from one set to a vertex in the other set. They can be used to model many real-world applications such as user-item interaction networks, authorship networks, and product-customer networks ...
openaire   +2 more sources

Memorization in Graph Neural Networks

CoRR
Deep neural networks (DNNs) have been shown to memorize their training data, yet similar analyses for graph neural networks (GNNs) remain largely under-explored. We introduce NCMemo (Node Classification Memorization), the first framework to quantify label memorization in semi-supervised node classification.
Jamadandi, Adarsh, Xu, Jing, Dziedzic, Adam, Boenisch, Franziska   +3 more
openaire   +3 more sources