Suchergebnisse - Graph convolutional and variational 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

Graph-Variational Convolutional Autoencoder-Based Fault Detection and Diagnosis for Photovoltaic Arrays

Autoren: Murshedul Arifeen Andrei Petrovski Md Junayed Hasan et al.

Quelle: Machines, Vol 12, Iss 12, p 894 (2024)

Schlagwörter: solar array, photovoltaic array, fault detection, fault diagnosis, graph convolutional network, variational autoencoder, Mechanical engineering and machinery, TJ1-1570

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/2075-1702/12/12/894; https://doaj.org/toc/2075-1702

Zugangs-URL: https://doaj.org/article/a68e9bb8f0bd42d6b8c9842c6321237e

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

Autoren: Liang, Guojun, Postdoktor Tiwari, Prayag Nowaczyk, Sławomir et al.

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

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

Dateibeschreibung: print

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

Anomaly Detection Based on Graph Convolutional Network–Variational Autoencoder Model Using Time-Series Vibration and Current Data

Autoren: Seung-Hwan Choi Dawn An Inho Lee et al.

Quelle: Mathematics, Vol 12, Iss 23, p 3750 (2024)

Schlagwörter: anomaly detection, graph convolutional network (GCN), variational autoencoder (VAE), statistical feature, probability-based anomaly score, Mathematics, QA1-939

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/2227-7390/12/23/3750; https://doaj.org/toc/2227-7390

Zugangs-URL: https://doaj.org/article/c1d52f19b28848b1becfec82e190f2fb

Prediction of microbe–drug associations based on a modified graph attention variational autoencoder and random forest

Autoren: Bo Wang Fangjian Ma Xiaoxin Du et al.

Quelle: Frontiers in Microbiology, Vol 15 (2024)

Schlagwörter: microbe–drug association prediction, variational autoencoder, modified graph convolutional neural network, graph attention network, random forest classifier, computational prediction model, Microbiology, QR1-502

Dateibeschreibung: electronic resource

Relation: https://www.frontiersin.org/articles/10.3389/fmicb.2024.1394302/full; https://doaj.org/toc/1664-302X

Zugangs-URL: https://doaj.org/article/5e963e31a3a540a0a7d0d21975537bff

Synergistic Integration of Image-Text Modalities in Telemedicine: VAE-Infused Graph Convolutional Networks for Diagnostic Decision-Making

Autoren: Yirui Kuang Hui Jin Jiayu Yang et al.

Quelle: IEEE Access, Vol 13, Pp 63093-63107 (2025)

Schlagwörter: Telemedicine, multimodal fusion, graph neural networks, variational autoencoders, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Dateibeschreibung: electronic resource

Relation: https://ieeexplore.ieee.org/document/10955270/; https://doaj.org/toc/2169-3536

Zugangs-URL: https://doaj.org/article/9c3b4c47a53c4b2a87210659c816f623

Improving Variational Autoencoders for New Physics Detection at the LHC With Normalizing Flows

Autoren: Jawahar, Pratik Aarrestad, Thea Chernyavskaya, Nadezda et al.

Schlagwörter: Data Management and Data Science, Information and Computing Sciences, Information Systems, anomaly detection, variational auto encoder, normalizing flow, Large Hadron Collider, new physics beyond standard model, graph convolutional network, convolutional neural net, Data management and data science, Information systems

Dateibeschreibung: application/pdf

Zugangs-URL: https://escholarship.org/uc/item/9d87s2q3
https://escholarship.org/content/qt9d87s2q3/qt9d87s2q3.pdf

VHGAE: Drug-Target Interaction Prediction Model Based on Heterogeneous Graph Variational Autoencoder.

Autoren: Zhang C Sun J Xing L et al.

Quelle: Interdisciplinary sciences, computational life sciences [Interdiscip Sci] 2025 Aug 21. Date of Electronic Publication: 2025 Aug 21.

Publikationsart: Journal Article

Info zur Zeitschrift: Publisher: Springer-Verlag Country of Publication: Germany NLM ID: 101515919 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1867-1462 (Electronic) Linking ISSN: 18671462 NLM ISO Abbreviation: Interdiscip Sci Subsets: MEDLINE

Graph convolutional network based on self-attention variational autoencoder and capsule contrastive learning for aspect-based sentiment analysis.

Autoren: Wang, Xinyue Liu, Long Chen, Zhuo et al.

Quelle: Expert Systems with Applications. Jun2025, Vol. 279, pN.PAG-N.PAG. 1p.

Schlagwörter: *CAPSULE neural networks, *AUTOENCODERS, *SENTIMENT analysis, *SOURCE code, *LEARNING modules

Graph-Variational Convolutional Autoencoder-Based Fault Detection and Diagnosis for Photovoltaic Arrays.

Autoren: Arifeen, Murshedul Petrovski, Andrei Hasan, Md Junayed et al.

Quelle: Machines; Dec2024, Vol. 12 Issue 12, p894, 15p

Schlagwörter: SOLAR cells, RENEWABLE energy sources, PHOTOVOLTAIC power systems, SOLAR energy, AUTOENCODERS

GVDTI: graph convolutional and variational autoencoders with attribute-level attention for drug–protein interaction prediction.

Autoren: Xuan, Ping Fan, Mengsi Cui, Hui et al.

Quelle: Briefings in Bioinformatics. Jan2022, Vol. 23 Issue 1, p1-12. 12p.

Schlagwörter: *PROTEIN-protein interactions, *CONVOLUTIONAL neural networks, *PROTEIN drugs

Anomaly Detection Based on Graph Convolutional Network–Variational Autoencoder Model Using Time-Series Vibration and Current Data.

Autoren: Choi, Seung-Hwan An, Dawn Lee, Inho et al.

Quelle: Mathematics (2227-7390); Dec2024, Vol. 12 Issue 23, p3750, 16p

Schlagwörter: INDUSTRIAL robots, ANOMALY detection (Computer security), AUTOENCODERS, SYSTEM failures, FEATURE extraction, DEEP learning

VAE-GCN: Deep Graph Convolutional Network via Variational Autoencoder for Multivariable Time Series Classification

Autoren: Li, Lu Wu, Gongqing

Quelle: Lecture Notes in Computer Science ; Advanced Intelligent Computing Technology and Applications ; page 199-210 ; ISSN 0302-9743 1611-3349 ; ISBN 9789819698806 9789819698813

Verfügbarkeit: https://doi.org/10.1007/978-981-96-9881-3_17
https://link.springer.com/content/pdf/10.1007/978-981-96-9881-3_17

Enhancing Gas Turbine Fault Diagnosis Using a Multi-Scale Dilated Graph Variational Autoencoder Model

Autoren: Zhang Kun Li Hongren Wang Xin et al.

Quelle: IEEE Access, Vol 12, Pp 104818-104832 (2024)

Schlagwörter: Gas turbine, fault diagnosis, MG-VAE, noise robustness, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Dateibeschreibung: electronic resource

Relation: https://ieeexplore.ieee.org/document/10613765/; https://doaj.org/toc/2169-3536

Zugangs-URL: https://doaj.org/article/c986c7c9e0e34b09bbf948eb168f8205

HHGNN: Hyperbolic Hypergraph Convolutional Neural Network based on variational autoencoder.

Autoren: Mei, Zhangyu Bi, Xiao Wen, Yating et al.

Quelle: Neurocomputing. Oct2024, Vol. 601, pN.PAG-N.PAG. 1p.

Schlagwörter: *CONVOLUTIONAL neural networks, *GRAPH neural networks, *HYPERBOLIC spaces, *REPRESENTATIONS of graphs, *DIAGNOSIS of diabetes

Graph Representation Learning on Street Networks.

Autoren: Neira, Mateo Murcio, Roberto

Quelle: ISPRS International Journal of Geo-Information; Aug2025, Vol. 14 Issue 8, p284, 18p

Schlagwörter: REPRESENTATIONS of graphs, URBAN morphology, PATTERN perception, SPATIAL behavior, LATENT variables, ROAD construction, TRAFFIC patterns, CONVOLUTIONAL neural networks

Hierarchical Graph-Convolutional Variational AutoEncoding for Generative Modelling of Human Motion

Autoren: Bourached, Anthony Gray, Robert Guan, Xiaodong et al.

Index Begriffe: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Mathematics - Probability, text

URL: http://arxiv.org/abs/2111.12602

一种基于图神经网络的比特币非法交易检测方法. (Chinese)

Alternate Title: A Bitcoin Illegal Transaction Detection Method Based on Graph Neural Networks. (English)

Autoren: 姜贤波 邢桂东 康艳荣

Quelle: Forensic Science & Technology; Dec2025, Vol. 50 Issue 6, p563-568, 6p

Schlagwörter: BITCOIN, GRAPH neural networks, AUTOENCODERS, ANONYMITY, BLACK market, DETECTION algorithms

scVGAE: A Novel Approach using ZINB-Based Variational Graph Autoencoder for Single-Cell RNA-Seq Imputation

Autoren: Inoue, Yoshitaka

Index Begriffe: Quantitative Biology - Genomics, Computer Science - Computational Engineering, Finance, and Science, text

URL: http://arxiv.org/abs/2403.08959

VGE: Gene-Disease Association by Variational Graph Embedding

Autoren: Peng Han Xiangliang Zhang

Quelle: International Journal of Crowd Science, Vol 8, Iss 2, Pp 95-99 (2024)

Schlagwörter: disease-gene association, graph convolutional network (gcn), variational antoencoder (vae), Technology, Engineering (General). Civil engineering (General), TA1-2040

Dateibeschreibung: electronic resource

Relation: https://www.sciopen.com/article/10.26599/IJCS.2024.9100004; https://doaj.org/toc/2398-7294

Zugangs-URL: https://doaj.org/article/433f523c1b194c51bb094eaeb00d9154