Suchergebnisse - stacked variational autoencoder

Stacked Variational Autoencoder in the Classification of Cardiac Arrhythmia using ECG Signals with 2D-ECG Images

Autoren: S Nithya M. Mary Shanthi Rani

Quelle: 2022 International Conference on Intelligent Innovations in Engineering and Technology (ICIIET). :222-226

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

SVAD: Stacked Variational Autoencoder Deep Neural Network -Based Dimensionality Reduction and classification of Small Sample Size and High Dimensional Data

Autoren: Neha Srivastava Devendra K. Tayal

Quelle: SN Computer Science. 5

Schlagwörter: 0301 basic medicine, 0303 health sciences, 03 medical and health sciences

SVDTI: Stacked variational autoencoder with SMILES-based drug representations for identifying drug-target interaction.

Autoren: Ha, Jihwan¹ (AUTHOR) jhha@pknu.ac.kr

Quelle: Neurocomputing. Jan2026, Vol. 661, pN.PAG-N.PAG. 1p.

Schlagwörter: *DRUG discovery, *PREDICTION models, *MATRIX decomposition, *DRUG receptors, *PREDICTIVE tests, *CHEMICAL formulas, *RECURRENT neural networks

A novel 3D indoor localization method integrating deep spatial feature augmentation and attention-based denoising

Autoren: Chunyuan Liu Xiaomin Yu Peilong Wu et al.

Quelle: Scientific Reports, Vol 15, Iss 1, Pp 1-16 (2025)

Schlagwörter: 3D indoor positioning, Stacked variational autoencoder, Wasserstein generative adversarial network, Attention mechanism, Denoising autoencoder, Medicine, Science

Dateibeschreibung: electronic resource

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

Zugangs-URL: https://doaj.org/article/7d6129d2fbb942f7bfc1b0de39fd8270

Text feature extraction based on stacked variational autoencoder

Autoren: Liang Wang Lei Che Xiaoping Yang

Quelle: Microprocessors and Microsystems. 76:103063

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

Zugangs-URL: https://www.sciencedirect.com/science/article/abs/pii/S0141933120300879
https://dblp.uni-trier.de/db/journals/mam/mam76.html#CheYW20

Semi-supervised Deep Learning in Motor Imagery-Based Brain-Computer Interfaces with Stacked Variational Autoencoder

Autoren: Zhenghui Gu Zhu Liang Yu Junjian Chen

Quelle: Journal of Physics: Conference Series. 1631:012007

Schlagwörter: 03 medical and health sciences, 0302 clinical medicine

Zugangs-URL: https://robots.iopscience.iop.org/article/10.1088/1742-6596/1631/1/012007/pdf
https://iopscience.iop.org/article/10.1088/1742-6596/1631/1/012007/pdf
https://ui.adsabs.harvard.edu/abs/2020JPhCS1631a2007C/abstract
https://robots.iopscience.iop.org/article/10.1088/1742-6596/1631/1/012007

DurIAN-E 2: Duration Informed Attention Network with Adaptive Variational Autoencoder and Adversarial Learning for Expressive Text-to-Speech Synthesis

Autoren: Gu, Yu Zhu, Qiushi Lei, Guangzhi et al.

Index Begriffe: Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Sound, text

URL: http://arxiv.org/abs/2410.13288

A New Deep Stacked Architecture for Multi-Fault Machinery Identification With Imbalanced Samples

Autoren: Hanen Karamti Maha M. A. Lashin Fadwa M. Alrowais et al.

Quelle: IEEE Access, Vol 9, Pp 58838-58851 (2021)

Schlagwörter: Fault diagnosis, imbalanced samples, logistic regression, rotating machinery, sparse autoencoders, variational autoencoder, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Dateibeschreibung: electronic resource

Relation: https://ieeexplore.ieee.org/document/9399084/; https://doaj.org/toc/2169-3536

Zugangs-URL: https://doaj.org/article/c3635dce090a46ff85f0d90aa7823bf1

SVAD: Stacked Variational Autoencoder Deep Neural Network-Based Dimensionality Reduction and Classification of Small Sample Size and High Dimensional Data

Autoren: Srivastava, Neha Tayal, Devendra K. Department of Science and Technology, Ministry of Science and Technology, India

Weitere Verfasser: Srivastava, Neha Tayal, Devendra K. Department of Science and Technology, Ministry of Science and Technology, India

Quelle: SN Computer Science ; volume 5, issue 7 ; ISSN 2661-8907

Verfügbarkeit: http://dx.doi.org/10.1007/s42979-024-03294-2
https://link.springer.com/content/pdf/10.1007/s42979-024-03294-2.pdf
https://link.springer.com/article/10.1007/s42979-024-03294-2/fulltext.html

Enhancing wind power prediction with self-attentive variational autoencoders: A comparative study

Autoren: Fouzi Harrou Abdelkader Dairi Abdelhakim Dorbane et al.

Quelle: Results in Engineering, Vol 23, Iss , Pp 102504- (2024)

Schlagwörter: Wind power forecasting, Data-driven, Deep learning, Self-attentive VAE, RNN, Technology

Dateibeschreibung: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S259012302400759X; https://doaj.org/toc/2590-1230

Zugangs-URL: https://doaj.org/article/721e9dd10236449e925de32a119933b1

Predicting chemical structure using reinforcement learning with a stack-augmented conditional variational autoencoder

Autoren: Hwanhee Kim Soohyun Ko Byung Ju Kim et al.

Quelle: Journal of Cheminformatics, Vol 14, Iss 1, Pp 1-12 (2022)

Schlagwörter: De novo drug design, Reinforcement learning, Conditional Variational AutoEencoder, Sorafenib, Raf kinases, Information technology, T58.5-58.64, Chemistry, QD1-999

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/1758-2946

Zugangs-URL: https://doaj.org/article/01327ec5eb24459cbd7fb1c48ad25b1b

Estimating daily global solar radiation using deep learning

Autoren: Hamid Hamdaouy El Mahjoub Benghoulam Mohamed Chaibi et al.

Quelle: Results in Engineering, Vol 27, Iss , Pp 106132- (2025)

Schlagwörter: Daily global solar radiation, Long-short term memory, Gated recurrent units, Variational AutoEncoder, Convolutional neural network, Transformer, Technology

Dateibeschreibung: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2590123025022042; https://doaj.org/toc/2590-1230

Zugangs-URL: https://doaj.org/article/4eeed0ada789418f9779759eff0af1b7

A combination method of stacked autoencoder and 3D deep residual network for hyperspectral image classification

Autoren: Jinling Zhao Lei Hu Yingying Dong et al.

Quelle: International Journal of Applied Earth Observations and Geoinformation, Vol 102, Iss , Pp 102459- (2021)

Schlagwörter: Deep learning, Hyperspectral remote sensing, Residual network, 3D convolutional neural network, Spectral-spatial features, Stacked autoencoder, Physical geography, GB3-5030, Environmental sciences, GE1-350

Dateibeschreibung: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S0303243421001665; https://doaj.org/toc/1569-8432

Zugangs-URL: https://doaj.org/article/4549b09e51ee4d4cbd419b3cbe97ef77

All SMILES Variational Autoencoder

Autoren: Alperstein, Zaccary Cherkasov, Artem Rolfe, Jason Tyler

Index Begriffe: Computer Science - Machine Learning, Statistics - Machine Learning, text

URL: http://arxiv.org/abs/1905.13343

Reliable Fault Diagnosis of Bearings Using an Optimized Stacked Variational Denoising Auto-Encoder

Autoren: Xiaoan Yan Yadong Xu Daoming She et al.

Quelle: Entropy, Vol 24, Iss 1, p 36 (2021)

Schlagwörter: stacked variational denoising auto-encoder, seagull optimization algorithm, rolling bearing, fault diagnosis, Science, Astrophysics, QB460-466, Physics, QC1-999

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/1099-4300/24/1/36; https://doaj.org/toc/1099-4300

Zugangs-URL: https://doaj.org/article/9e53ea89bc54445888360548273ed1cc

SPECTRAL-SPATIAL CLASSIFICATION OF HYPERSPECTRAL REMOTE SENSING IMAGES USING VARIATIONAL AUTOENCODER AND CONVOLUTION NEURAL NETWORK

Autoren: A. Belwalkar A. Nath O. Dikshit

Quelle: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLII-5, Pp 613-620 (2018)

Schlagwörter: Technology, Engineering (General). Civil engineering (General), TA1-2040, Applied optics. Photonics, TA1501-1820

Dateibeschreibung: electronic resource

Relation: https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-5/613/2018/isprs-archives-XLII-5-613-2018.pdf; https://doaj.org/toc/1682-1750; https://doaj.org/toc/2194-9034

Zugangs-URL: https://doaj.org/article/9fa40909cf78469fbcb26d7a6e690e76

A novel 3D indoor localization method integrating deep spatial feature augmentation and attention-based denoising.

Autoren: Liu, Chunyuan Yu, Xiaomin Wu, Peilong et al.

Quelle: Scientific Reports; 9/26/2025, Vol. 15 Issue 1, p1-16, 16p

Schlagwörter: INDOOR positioning systems, DEEP learning, GENERATIVE adversarial networks, WIRELESS geolocation systems, SIGNAL denoising, SPACE perception

Loop closure detection of visual SLAM based on variational autoencoder.

Autoren: Shibin Song Fengjie Yu Xiaojie Jiang et al.

Quelle: Frontiers in Neurorobotics; 2024, p1-13, 13p

Schlagwörter: DEEP learning, IMAGE representation, HANDICRAFT

Simulation of Spring Discharge Using Deep Learning, Considering the Spatiotemporal Variability of Precipitation.

Autoren: Ma, Chunmei Jiao, Haoyu Hao, Yonghong et al.

Quelle: Water Resources Research; Apr2025, Vol. 61 Issue 4, p1-24, 24p

Schlagwörter: STANDARD deviations, SPRING, PRECIPITATION variability, DATA augmentation, AUTOENCODERS, DEEP learning

Computational detection and interpretation of heart disease based on conditional variational auto-encoder and stacked ensemble-learning framework.

Autoren: Abdellatif, Abdallah Mubarak, Hamza Abdellatef, Hamdan et al.

Quelle: Biomedical Signal Processing & Control. Feb2024:Part A, Vol. 88, pN.PAG-N.PAG. 1p.

Schlagwörter: Heart diseases, Support vector machines, Video coding, Deep learning, Cardiovascular diseases, Machine learning

Geografische Kategorien: Cleveland (Ohio)