Výsledky vyhľadávania - (dynamic OR dynamika) graph convolutional autoencoder

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

Autori: Ming Zeng Min Wang Fuqiang Xie a ďalší

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

Predmety: 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

Popis súboru: electronic resource

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

Prístupová URL adresa: https://doaj.org/article/c9b441325e6d409dbc981ebfca8af0c6

Dynamic Causal Explanation Based Diffusion-Variational Graph Neural Network for Spatiotemporal Forecasting

Autori: Liang, Guojun, Postdoktor Tiwari, Prayag Nowaczyk, Sławomir a ďalší

Zdroj: IEEE Transactions on Neural Networks and Learning Systems. 33(5):9524-9537

Predmety: Diffusion process, graph neural networks (GNNs), spatiotemporal forecasting, variational graph autoencoders (VGAEs)

Popis súboru: print

Prístupová URL adresa: https://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-55718
https://doi.org/10.1109/tnnls.2024.3415149

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

Autori: Zeng, Ming Wang, Min Xie, Fuqiang a ďalší

Zdroj: BMC Bioinformatics; 7/29/2025, Vol. 26 Issue 1, p1-29, 29p

Predmety: DRUG discovery, GRAPH neural networks, DRUG receptors, MACHINE learning

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

Autori: Minkyu Kim Junho Song

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

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

Spatio-temporal graph convolutional autoencoder for transonic wing pressure distribution forecasting

Autori: Immordino, Gabriele Vaiuso, Andrea Da Ronch, Andrea a ďalší

Popis súboru: text

Relation: https://eprints.soton.ac.uk/504263/1/1-s2.0-S1270963825005875-main.pdf; Immordino, Gabriele, Vaiuso, Andrea, Da Ronch, Andrea and Righi, Marcello (2025) Spatio-temporal graph convolutional autoencoder for transonic wing pressure distribution forecasting. Aerospace Science and Technology, 165, [110516]. (doi:10.1016/j.ast.2025.110516 ).

Dostupnosť: https://eprints.soton.ac.uk/504263/

Predicting Transonic Flowfields in Non-Homogeneous Unstructured Grids Using Autoencoder Graph Convolutional Networks

Autori: Immordino, Gabriele Vaiuso, Andrea Da Ronch, Andrea a ďalší

Témy: Computer Science - Computational Engineering, Finance, and Science, Computer Science - Machine Learning, text

URL: http://arxiv.org/abs/2405.04396

Dynamic graph convolutional autoencoder with node-attribute-wise attention for kidney and tumor segmentation from CT volumes

Autori: Ping Xuan Hui Cui Hongda Zhang a ďalší

Zdroj: Knowledge-Based Systems. 236:107360

Predmety: 03 medical and health sciences, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Prístupová URL adresa: https://www.sciencedirect.com/science/article/pii/S0950705121006225

Time Extrapolation with Graph Convolutional Autoencoder and Tensor Train Decomposition

Autori: Chen, Yuanhong Pichi, Federico Gao, Zhen a ďalší

Predmety: Numerical Analysis, Machine Learning

Relation: http://arxiv.org/abs/2511.23037

Dostupnosť: http://arxiv.org/abs/2511.23037

Deep anomaly detection in horizontal axis wind turbines using Graph Convolutional Autoencoders for Multivariate Time series

Autori: Eric Stefan Miele Fabrizio Bonacina Alessandro Corsini

Zdroj: Energy and AI, Vol 8, Iss , Pp 100145- (2022)

Predmety: Wind turbine, Condition monitoring, Deep anomaly detection, SCADA data, Graph Convolutional Autoencoder, Multivariate Time series, Electrical engineering. Electronics. Nuclear engineering, TK1-9971, Computer software, QA76.75-76.765

Popis súboru: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2666546822000076; https://doaj.org/toc/2666-5468

Prístupová URL adresa: https://doaj.org/article/24c38fb3bba94c47b4048e1cf1c518b2

Evolving graph convolutional networks for dynamic graphs

A hybrid adversarial autoencoder-graph network model with dynamic fusion for robust scRNA-seq clustering

Autori: Binhua Tang Yingying Feng Xinyu Gao

Zdroj: BMC Genomics, Vol 26, Iss 1, Pp 1-13 (2025)

Predmety: ScRNA-seq, Self-supervised clustering, GCN, Adversarial AE, Cross-attention mechanism, Biotechnology, TP248.13-248.65, Genetics, QH426-470

Popis súboru: electronic resource

Relation: https://doaj.org/toc/1471-2164

Prístupová URL adresa: https://doaj.org/article/2a5ea00c188941fbb66094bbf69d63c0

PT-TDGCN: Pre-Trained Trend-Aware Dynamic Graph Convolutional Network for Traffic Flow Prediction

Autori: Hanqing Yang Sen Wei Yuanqing Wang

Zdroj: Sensors, Vol 25, Iss 21, p 6709 (2025)

Predmety: traffic flow prediction, pre-training, dynamic graph, convolutional trend-aware attention, Chemical technology, TP1-1185

Popis súboru: electronic resource

Relation: https://www.mdpi.com/1424-8220/25/21/6709; https://doaj.org/toc/1424-8220

Prístupová URL adresa: https://doaj.org/article/31571ea2544f4762aed6ff6c7dbe219c

Lightweight Real-Time Anomaly Detection and Prediction for Surveillance Videos Using a Self-Supervised Hybrid Autoencoder and Dynamic Graph CNN-LSTM Framework

Autori: Abdulla S. AlShamsi Najah AbuAli

Zdroj: IEEE Access, Vol 13, Pp 195423-195435 (2025)

Predmety: Anomaly detection, autoencoder, graph neural networks, real-time surveillance, self-supervised learning, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Popis súboru: electronic resource

Relation: https://ieeexplore.ieee.org/document/11241206/; https://doaj.org/toc/2169-3536

Prístupová URL adresa: https://doaj.org/article/4adf79e3ac9f4aa0990e9b995cec99f6

Graph-informed convolutional autoencoder to classify brain responses during sleep

Autori: Sahar Zakeri Somayeh Makouei Sebelan Danishvar

Zdroj: Frontiers in Neuroscience, Vol 19 (2025)

Predmety: auditory stimuli, convolutional neural network, EEG, functional connectivity, graphical representation, sleep, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571

Popis súboru: electronic resource

Relation: https://www.frontiersin.org/articles/10.3389/fnins.2025.1525417/full; https://doaj.org/toc/1662-453X

Prístupová URL adresa: https://doaj.org/article/a03711a5ed214646aa5c2d62ac7f7aca

Variational Graph Convolutional Networks for Dynamic Graph Representation Learning

Autori: Aabid A. Mir Megat F. Zuhairi Shahrulniza Musa a ďalší

Zdroj: IEEE Access, Vol 12, Pp 161697-161717 (2024)

Predmety: Anomaly detection, complex networks, deep learning, graph neural networks, heterogeneous networks, network security, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Popis súboru: electronic resource

Relation: https://ieeexplore.ieee.org/document/10723293/; https://doaj.org/toc/2169-3536

Prístupová URL adresa: https://doaj.org/article/205888aa585544ec8d0e7f0c49e14058

Graph autoencoder with mirror temporal convolutional networks for traffic anomaly detection

Autori: Zhiyu Ren Xiaojie Li Jing Peng a ďalší

Zdroj: Scientific Reports, Vol 14, Iss 1, Pp 1-14 (2024)

Predmety: Medicine, Science

Popis súboru: electronic resource

Relation: https://doaj.org/toc/2045-2322

Prístupová URL adresa: https://doaj.org/article/2e41df57b73e4ace8b3d4da03250a356

GNODEVAE: a graph-based ODE-VAE enhances clustering for single-cell data

Autori: Zeyu Fu Chunlin Chen Song Wang a ďalší

Zdroj: BMC Genomics, Vol 26, Iss 1, Pp 1-29 (2025)

Predmety: Graph attention networks, Neural ordinary differential equation, Variational autoencoders, Clustering, ScRNA-seq, ScATAC-seq, Biotechnology, TP248.13-248.65, Genetics, QH426-470

Popis súboru: electronic resource

Relation: https://doaj.org/toc/1471-2164

Prístupová URL adresa: https://doaj.org/article/e02e430dd2cc4cf5a385ba4b16829290

Analysis of China’s High-Speed Railway Network Using Complex Network Theory and Graph Convolutional Networks

Autori: Zhenguo Xu Jun Li Irene Moulitsas a ďalší

Zdroj: Big Data and Cognitive Computing, Vol 9, Iss 4, p 101 (2025)

Predmety: key node identification, community detection, link prediction, small-world network, graph attention networks, variational graph autoencoder, Technology

Popis súboru: electronic resource

Relation: https://www.mdpi.com/2504-2289/9/4/101; https://doaj.org/toc/2504-2289

Prístupová URL adresa: https://doaj.org/article/e8e61df9dc424a059558d2f5b7f862d0

Dynamic Causal Explanation Based Diffusion-Variational Graph Neural Network for Spatiotemporal Forecasting

Autori: Liang, Guojun Tiwari, Prayag Nowaczyk, Sławomir a ďalší

Témy: Diffusion process, graph neural networks (GNNs), spatiotemporal forecasting, variational graph autoencoders (VGAEs), Computer Sciences, Datavetenskap (datalogi), Article in journal, info:eu-repo/semantics/article, text

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-55718
IEEE Transactions on Neural Networks and Learning Systems, 2162-237X, 2025, 33:5, s. 9524-9537

A Spatio‐Temporal Enhanced Graph‐Transformer AutoEncoder embedded pose for anomaly detection

Autori: Honglei Zhu Pengjuan Wei Zhigang Xu

Zdroj: IET Computer Vision, Vol 18, Iss 3, Pp 405-419 (2024)

Predmety: computer vision, convolutional neural nets, feature extraction, pose estimation, video surveillance, Computer applications to medicine. Medical informatics, R858-859.7, Computer software, QA76.75-76.765

Popis súboru: electronic resource

Relation: https://doaj.org/toc/1751-9632; https://doaj.org/toc/1751-9640

Prístupová URL adresa: https://doaj.org/article/41d360b7678b4b1285f23c36b7d97b4f