Search Results - Deep autoencoder Gaussian mixture model

Communication Security Protection Method Based on Deep Autoencoder Gaussian Mixture Model

Authors: Yu, Guanghua Xia, Kuiliang

Source: Advances in Transdisciplinary Engineering ISBN: 9781643686097

On the Achievable Rate of Satellite Quantum Communication Channel using Deep Autoencoder Gaussian Mixture Model

Authors: Chakraborty, Mouli Chandra, Subhash Nag, Avishek et al.

Subject Terms: Signal Processing (eess.SP), Signal Processing, FOS: Electrical engineering, electronic engineering, information engineering

Access URL: http://arxiv.org/abs/2507.23695

Unsupervised detection of multivariate geochemical anomalies using a high-performance deep autoencoder Gaussian mixture model

Authors: Xuemei Wang Yongliang Chen

Source: Journal of Geochemical Exploration. 271:107671

An Unsupervised Self-Organizing Map Assisted Deep Autoencoder Gaussian Mixture Model for IoT Anomaly Detection

Authors: Kristy Saha Md. Mushfiqur Rahman Fakir M. M. A Hashem

Source: 2021 5th International Conference on Electrical Information and Communication Technology (EICT). :1-6

Denoising Deep Autoencoder Gaussian Mixture Model and Its Application for Robust Nonlinear Industrial Process Monitoring

Authors: Yin-Chang Zhou Meng-Qian Li Long-Bin Ji

Source: 2021 International Conference on Computer Information Science and Artificial Intelligence (CISAI). :67-73

Subject Terms: 02 engineering and technology, 0210 nano-technology, 01 natural sciences, 0104 chemical sciences

Hybrid Deep Learning Framework for Anomaly Detection in Power Plant Systems.

Authors: Wang, Shuchong Zhao, Changxiang Liu, Xingchen et al.

Source: Algorithms; Nov2025, Vol. 18 Issue 11, p704, 18p

Subject Terms: AUTOENCODERS, TRANSFORMER models, ENSEMBLE learning, OUTLIER detection, ELECTRIC power plants, GAUSSIAN mixture models

Aircraft Trajectory Clustering in Terminal Airspace Based on Deep Autoencoder and Gaussian Mixture Model

Authors: Weili Zeng Zhengfeng Xu Zhipeng Cai et al.

Source: Aerospace, Vol 8, Iss 9, p 266 (2021)

Subject Terms: air traffic control, trajectory clustering, deep autoencoder, Gaussian mixture model, Motor vehicles. Aeronautics. Astronautics, TL1-4050

File Description: electronic resource

Relation: https://www.mdpi.com/2226-4310/8/9/266; https://doaj.org/toc/2226-4310

Access URL: https://doaj.org/article/c21568fff1af4f60995f67019ffabef2

Research on unsupervised anomaly data detection method based on improved automatic encoder and Gaussian mixture model

Authors: Xiangyu Liu Shibing Zhu Fan Yang et al.

Source: Journal of Cloud Computing: Advances, Systems and Applications, Vol 11, Iss 1, Pp 1-16 (2022)

Subject Terms: Cloud security, Unsupervised machine learning, Anomalous data detection, Memory module, Deep autoencoder, Gaussian mixture model, Computer engineering. Computer hardware, TK7885-7895, Electronic computers. Computer science, QA75.5-76.95

File Description: electronic resource

Relation: https://doaj.org/toc/2192-113X

Access URL: https://doaj.org/article/63403ce30347435183b26b652a7a7f35

scGMAI: a Gaussian mixture model for clustering single-cell RNA-Seq data based on deep autoencoder.

Authors: Yu, Bin Chen, Chen Qi, Ren et al.

Source: Briefings in Bioinformatics. Jul2021, Vol. 22 Issue 4, p1-10. 10p.

Subject Terms: *GAUSSIAN mixture models, *INDEPENDENT component analysis, *RNA sequencing, *GENE expression, *GENE regulatory networks

Deep Belief Network Model for Detection of an Outlier in Healthcare Data

Authors: Dhabliya, Dharmesh Gupta, Ankur Dandavate, Abhijit et al.

Source: International Journal of Intelligent Systems and Applications in Engineering; Vol. 11 No. 11s (2023); 468-479

Subject Terms: Variational Autoencoder, Gaussian Mixture Model, Deep Belief Network, Healthcare, Curse of Dimensionality

File Description: application/pdf

Access URL: https://www.ijisae.org/index.php/IJISAE/article/view/3549

Steam turbine anomaly detection: an unsupervised learning approach using enhanced long short-term memory variational autoencoder.

Authors: Xu, Weiming Zhang, Peng

Source: Applied Thermal Engineering. Nov2025:Part A, Vol. 278, pN.PAG-N.PAG. 1p.

Subject Terms: STEAM-turbines, OUTLIER detection, DEEP learning, GAUSSIAN mixture models, METADATA, FEATURE extraction, MACHINE learning

Geochemical anomaly recognition based on Gaussian mixture model

Authors: Linfu Xue Xiangjin Ran Haijun Wang

Source: 2021 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS). :821-824

Access URL: https://ieeexplore.ieee.org/abstract/document/9525884

Measure of Similarity between GMMs Based on Autoencoder-Generated Gaussian Component Representations

Authors: Vladimir Kalušev Branislav Popović Marko Janev et al.

Source: Axioms, Vol 12, Iss 6, p 535 (2023)

Subject Terms: Gaussian mixture models, autoencoders, KL divergence, classification, machine learning, Mathematics, QA1-939

File Description: electronic resource

Relation: https://www.mdpi.com/2075-1680/12/6/535; https://doaj.org/toc/2075-1680

Access URL: https://doaj.org/article/2b3286ea82ff44b6937ba034f509463e

DVAEGMM: Dual Variational Autoencoder With Gaussian Mixture Model for Anomaly Detection on Attributed Networks

Authors: Wasim Khan Mohammad Haroon Ahmad Neyaz Khan et al.

Source: IEEE Access, Vol 10, Pp 91160-91176 (2022)

Subject Terms: Anomaly detection, attributed networks, deep learning, dual variational autoencoder, Gaussian mixture model, graph convolution network, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

File Description: electronic resource

Relation: https://ieeexplore.ieee.org/document/9866699/; https://doaj.org/toc/2169-3536

Access URL: https://doaj.org/article/93e0e6ab6a674e3989f9079de7322ef9

Anomaly Detection on Laminated Composite Plate Using Self-Attention Autoencoder and Gaussian Mixture Model

Authors: Olivier Munyaneza Jung Woo Sohn

Source: Mathematics, Vol 13, Iss 15, p 2445 (2025)

Subject Terms: composite laminates, unsupervised machine learning, structural healthy monitoring, self-attention, Gaussian mixture model, generative adversarial network, Mathematics, QA1-939

File Description: electronic resource

Relation: https://www.mdpi.com/2227-7390/13/15/2445; https://doaj.org/toc/2227-7390

Access URL: https://doaj.org/article/69426417c24d4ce98ab0bb68b5719e7f

Detection of Health Data Based on Gaussian Mixture Generative Model

Authors: ZHU Zhuangzhuang, ZHOU Zhiping

Source: Jisuanji kexue yu tansuo, Vol 16, Iss 5, Pp 1128-1135 (2022)

Subject Terms: variational autoencoder (vae), deep brief network (dbn), gaussian mixture model (gmm), health data, anomaly detection, Electronic computers. Computer science, QA75.5-76.95

File Description: electronic resource

Relation: http://fcst.ceaj.org/fileup/1673-9418/PDF/1652926867722-1195079126.pdf; https://doaj.org/toc/1673-9418

Access URL: https://doaj.org/article/70f4b1c474644ad484c46029cf01dcdd

Toward Adaptive Unsupervised and Blind Image Forgery Localization with ViT-VAE and a Gaussian Mixture Model

Authors: Haichang Yin KinTak U Jing Wang et al.

Source: Mathematics, Vol 13, Iss 14, p 2285 (2025)

Subject Terms: variational autoencoder, Gaussian mixture models, blind image forgery localization, Mathematics, QA1-939

File Description: electronic resource

Relation: https://www.mdpi.com/2227-7390/13/14/2285; https://doaj.org/toc/2227-7390

Access URL: https://doaj.org/article/a25b6a902628499aa7e8274ab700a9ad

Unsupervised deep feature embeddings for speaker diarization.

Authors: AHMAD, Rehan ZUBAIR, Syed

Source: Turkish Journal of Electrical Engineering & Computer Sciences. 2019, Vol. 27 Issue 4, p3138-3149. 12p.

Subject Terms: *GAUSSIAN mixture models, *EMBEDDINGS (Mathematics), *ERROR rates

Gaussian Mixture Variational Autoencoder for Semi-Supervised Topic Modeling

Authors: Cangqi Zhou Hao Ban Jing Zhang et al.

Source: IEEE Access, Vol 8, Pp 106843-106854 (2020)

Subject Terms: Topic model, variational autoencoder, semi-supervised learning, Gaussian mixture model, deep generative learning, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

File Description: electronic resource

Relation: https://ieeexplore.ieee.org/document/9112154/; https://doaj.org/toc/2169-3536

Access URL: https://doaj.org/article/7e478776d5ef40bf95e175c244396031

scGMM-VGAE: a Gaussian mixture model-based variational graph autoencoder algorithm for clustering single-cell RNA-seq data

Authors: Eric Lin Boyuan Liu Leann Lac et al.

Source: Machine Learning: Science and Technology, Vol 4, Iss 3, p 035013 (2023)

Subject Terms: Gaussian mixture model, variational graph autoencoder, single cell RNA-seq, scGMM-VGAE, cell type, model integration, Computer engineering. Computer hardware, TK7885-7895, Electronic computers. Computer science, QA75.5-76.95

File Description: electronic resource

Relation: https://doaj.org/toc/2632-2153

Access URL: https://doaj.org/article/2ad3fa2850704c3a942a46b003492d03