Suchergebnisse - graph convolutional autoencoder

Multi-modal graph convolutional network for vessel trajectory prediction based on cooperative intention enhance using conditional variational autoencoder

Autoren: Jiang, Junhao Zuo, Yi Li, Zhiyuan

Quelle: Reliability Engineering and System Safety. 267

Schlagwörter: Graph convolutional network, Trajectory prediction, Cooperative intention constructor, Intelligent situational awareness systems, Conditional variational autoencoder, Multi-modal interaction extractor

Zugangs-URL: https://research.chalmers.se/publication/549377

Deep Graph Convolutional Autoencoder With Conditional Normalizing Flow for Power Distribution Systems Fault Classification and Location

Autoren: Mohsen Saffari Mahdi Khodayar Mohammad E. Khodayar et al.

Quelle: IEEE Transactions on Artificial Intelligence. 6:2448-2463

Drug-target interaction prediction based on graph convolutional autoencoder with dynamic weighting residual GCN

Autoren: Ming Zeng Min Wang Fuqiang Xie et al.

Quelle: BMC Bioinformatics, Vol 26, Iss 1, Pp 1-29 (2025)

Schlagwörter: Drug-target interaction, Dynamic weighting convolutional residual connection, Dual self-supervised joint training mechanism, Graph convolutional autoencoder, Generative adversarial network, Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/1471-2105

Zugangs-URL: https://doaj.org/article/c9b441325e6d409dbc981ebfca8af0c6

Blockchain-Powered Secure and Scalable Threat Intelligence System With Graph Convolutional Autoencoder and Reinforcement Learning Feedback Loop

Autoren: Mohamad Khayat Ezedin Barka Mohamed Adel Serhani et al.

Quelle: IEEE Access, Vol 13, Pp 24736-24748 (2025)

Schlagwörter: blockchain, reinforcement learning, cybersecurity, hybrid optimization, Autoencoder, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Zugangs-URL: https://doaj.org/article/f8c8f1f864b04ea3b3ecfcbc0e75dde8

Batch Process Monitoring with Attention-Based Two-Dimensional Stacked Graph Convolutional Autoencoder

Autoren: Xingke Gao Zheng Zhang Furong Gao et al.

Quelle: 2024 6th International Conference on Industrial Artificial Intelligence (IAI). :1-6

Derivative-informed Graph Convolutional Autoencoder with Phase Classification for the Lifshitz-Petrich Model

Autoren: Chen, Yanlai Ji, Yajie Xu, Zhenli

Schlagwörter: Machine Learning, FOS: Computer and information sciences, Numerical Analysis, FOS: Mathematics, Numerical Analysis (math.NA), Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2509.11293

Temporal Graph Convolutional Autoencoder based Fault Detection for Renewable Energy Applications

Autoren: Murshedul Arifeen Andrei Petrovski

Quelle: 2024 IEEE 7th International Conference on Industrial Cyber-Physical Systems (ICPS). :1-6

Graph-Convolutional Autoencoder Frameworks for Aerodynamic Shape Predictions of the Agard Wing

Autoren: David Massegur Sampietro Gabriele Immordino Andrea Vaiuso et al.

Quelle: AIAA SCITECH 2025 Forum.

Seismic damage identification by graph convolutional autoencoder using adjacency matrix based on structural modes

Autoren: Minkyu Kim Junho Song

Quelle: Earthquake Engineering & Structural Dynamics. 53:815-837

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 0201 civil engineering

MSGCA: Drug-Disease Associations Prediction Based on Multi-Similarities Graph Convolutional Autoencoder

Autoren: Ying Wang Ying-Lian Gao Juan Wang et al.

Quelle: IEEE Journal of Biomedical and Health Informatics. 27:3686-3694

Schlagwörter: Humans, Reproducibility of Results, Algorithms

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/37163398

Predicted Drug-Drug Interactions in Diabetes Mellitus with Graph Convolutional Autoencoder

Autoren: Rashini Maduka Rupika Wijesinghe Ruvan Weerasinghe

Quelle: Proceedings of the 2023 7th International Conference on Computational Biology and Bioinformatics. :48-52

Drug-target interaction prediction based on spatial consistency constraint and graph convolutional autoencoder

Autoren: Peng Chen Haoran Zheng

Quelle: BMC Bioinformatics, Vol 24, Iss 1, Pp 1-21 (2023)

Schlagwörter: Drug-target interaction, Spatial consistency constraint, Graph convolutional autoencoder, Deep learning, Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/1471-2105

Zugangs-URL: https://doaj.org/article/79c43c3a72d742d6a41c5871de422089

Lip-Synchronized 3D Facial Animation Using Audio-Driven Graph Convolutional Autoencoder

Autoren: Ivaylo Bozhilov Krasimir Tonchev Nikolay Neshov et al.

Quelle: 2023 IEEE 12th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS). :346-351

Self-supervised community detection in multiplex networks with graph convolutional autoencoder

Autoren: Xingyu Liu Junwei Cheng Hao Cheng et al.

Quelle: 2023 26th International Conference on Computer Supported Cooperative Work in Design (CSCWD). :1378-1383

A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

Autoren: Pichi, Federico Moya, Beatriz Hesthaven, Jan S.

Index Begriffe: info:eu-repo/semantics/article, info:eu-repo/semantics/publishedVersion

URL: http://zaguan.unizar.es/record/132810
info:eu-repo/grantAgreement/ES/MICINN-AEI/PID2020-113463RB-C31/AEI/10.13039/501100011033
info:eu-repo/grantAgreement/ES/UZ-IBERCAJA-CAI/IT1-21

SGEGCAE: A Sparse Gating Enhanced Graph Convolutional Autoencoder for Multi-omics Data Integration and Classification

Autoren: Junliang Shang Limin Zhang Linqian Zhao et al.

Quelle: Lecture Notes in Computer Science ISBN: 9789819756889

Graph-Convolutional Autoencoder Ensembles for the Humanities, Illustrated with a Study of the American Slave Trade

Autoren: Lippincott, Tom

Index Begriffe: Computer Science - Machine Learning, Computer Science - Computation and Language, text

URL: http://arxiv.org/abs/2401.00824

ScMOGAE: A Graph Convolutional Autoencoder-Based Multi-omics Data Integration Framework for Single-Cell Clustering

Autoren: Benjie Zhou Hongyang Jiang Yuezhu Wang et al.

Quelle: Lecture Notes in Computer Science ISBN: 9789819751273

Semi-supervised overlapping community detection in attributed graph with graph convolutional autoencoder

Autoren: Chaobo He Yulong Zheng Junwei Cheng et al.

Quelle: Information Sciences. 608:1464-1479

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Nonlinear reduced-order modeling with a Graph Convolutional Autoencoder for time-domain electromagnetics

Autoren: Filippin, Carlotta Strazzullo, Maria Lanteri, Stéphane et al.

Weitere Verfasser: Filippin, Carlotta Strazzullo, Maria Lanteri, Stéphane et al.

Schlagwörter: nanophotonique, nanophotonics électromagnétisme computationnel, réseaux de neurones, nonlinear phenomena, graph neural networks, computational electromagnetics, [MATH.MATH-NA] Mathematics [math]/Numerical Analysis [math.NA], phénomènes non linéaires, modélisation d'ordre réduit, reduced order modeling

Dateibeschreibung: application/pdf

Zugangs-URL: https://hal.science/hal-04867474v1