Search Results - "Graph convolutional and variational autoencoder"

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

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

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

Subject Terms: probability-based anomaly score, graph convolutional network (GCN), 0209 industrial biotechnology, QA1-939, 0202 electrical engineering, electronic engineering, information engineering, statistical feature, variational autoencoder (VAE), 02 engineering and technology, anomaly detection, Mathematics

Access URL: https://doaj.org/article/c1d52f19b28848b1becfec82e190f2fb

GC-AVAE: A Graph Convolutional Adversarial Variational Autoencoder for Node Classification

Authors: Mohadeseh Ghayekhloo Ahmad Nickabadi

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

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

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

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

Subject Representation Learning from EEG using Graph Convolutional Variational Autoencoders

Authors: Mishra, Aditya Samin, Ahnaf Mozib Etemad, Ali et al.

Index Terms: Electrical Engineering and Systems Science - Signal Processing, Computer Science - Machine Learning, text

URL: http://arxiv.org/abs/2501.16626

Uncertainty-aware probabilistic travel demand prediction for mobility-on-demand services.

Authors: Peng, Tao Gao, Jie Cats, Oded

Source: Transportation Research Part C: Emerging Technologies. Dec2025, Vol. 181, pN.PAG-N.PAG. 1p.

Subject Terms: *FORECASTING, *STOCHASTIC analysis, DEMAND forecasting, DEEP learning, PROBABILITY density function, SPATIOTEMPORAL processes, PARATRANSIT services, LATENT variables

Deep learning for multivariate time series anomaly detection: an evaluation of reconstruction-based methods.

Authors: Yahya, Mohammed A. Moya, Antonio R. Ventura, Sebastián

Source: Artificial Intelligence Review; Dec2025, Vol. 58 Issue 12, p1-66, 66p

Machine learning and generative AI in the rational design of DNA gyrase-targeted antibacterials.

Authors: Gayathri, Krishnamurthy Ganga¹ (AUTHOR) gayathrikg@mgintelligence.org

Source: Journal of Molecular Graphics & Modelling. Jan2026, Vol. 142, pN.PAG-N.PAG. 1p.

Subject Terms: *MACHINE learning, *DRUG discovery, *GRAPH neural networks, *GENERATIVE artificial intelligence, *MOLECULAR docking, *ANTIBACTERIAL agents, *DNA topoisomerase II, *BOOSTING algorithms

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

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

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

Subject Terms: SOLAR cells, RENEWABLE energy sources, PHOTOVOLTAIC power systems, SOLAR energy, AUTOENCODERS

Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions.

Authors: Li, Fang Nian, Yi Sun, Zenan et al.

Source: Yearbook of Medical Informatics; 2023, Vol. 32 Issue 1, p215-224, 10p

Advanced Intelligent Computing Technology and Applications

Authors: De-Shuang Huang Haiming Chen Bo Li et al.

Resource Type: eBook.

Subjects: Computational intelligence--Congresses

Categories: COMPUTERS / Artificial Intelligence / General