Results 201 to 210 of about 4,069,375 (260)
Exploiting Edge Semantics in Job Shop Scheduling Problem With Heterogeneous Graph Transformers
Advanced Intelligent Systems, EarlyView.A heterogeneous graph transformer (HGT) is introduced for reinforcement learning‐based job shop scheduling by explicitly distinguishing precedence and machine‐contention relations through edge‐type‐specific attention. The proposed framework learns richer scheduling representations, improves decision quality over homogeneous graph models, and highlights
Bulent Soykan, Fatih Kasimoglu
wiley +1 more source
Classification of alkaloids according to the starting substances of their biosynthetic pathways using graph convolutional neural networks. [PDF]
BMC Bioinformatics, 2019 Eguchi R +7 more
europepmc +1 more source
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 +5 more
wiley +1 more source
Multi-instance Deep Learning with Graph Convolutional Neural Networks for Diagnosis of Kidney Diseases Using Ultrasound Imaging. [PDF]
Uncertain Safe Util Machine Learn Med Imaging Clin Image Based Proced (2019), 2019 Yin S +9 more
europepmc +1 more source
Graph Neural Network‐Based Reinforcement Learning for Decentralized Multi‐Robot Manipulation
Advanced Intelligent Systems, EarlyView.Robot arms lifting a large object face a trade‐off: centralized controllers explode in parameters, while decentralized ones cannot coordinate. A GNN resolves this—each arm runs its own network but acts on the full team state, achieving centralized‐level coordination with decentralized execution. Trained across team sizes, a single policy scales to four‐
Tong Chen +3 more
wiley +1 more source
Reconstructing High-Quality Diffusion MRI Data from Orthogonal Slice-Undersampled Data Using Graph Convolutional Neural Networks. [PDF]
Med Image Comput Comput Assist Interv, 2019 Hong Y, Chen G, Yap PT, Shen D.
europepmc +1 more source
Arthritis &Rheumatology, EarlyView.Objective Regular imaging by conventional radiography to assess for joint damage is a cornerstone in the management of rheumatoid arthritis. Scoring systems to quantify such damage, such as the widely used Sharp/van der Heijde (SvdH) score, are limited by the requirement of time and experienced staff as well as intra‐ and interrater variability.
Thomas Deimel +6 more
wiley +1 more source
Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Use Cases delineates how graph data science significantly empowers the application of data science.
core
Business Strategy and the Environment, EarlyView.ABSTRACT Social media platforms today have become essential for consumer‐brand interactions, with visual content playing a pivotal role in shaping engagement and brand perception. Although text‐based user‐generated content (UGC) has been widely studied, the potential of visual UGC, particularly in the travel, tourism and hospitality (TTH) sector ...
Chinchu Abraham +2 more
wiley +1 more source