Search Results - sparse stacked denoising autoencoder

An Anomaly Detection Method for Nonlinear Industrial Process Using Sparse Stacked Denoising Autoencoder

Authors: Mingwei Yang YanHua Liu Hong Chen et al.

Source: 2023 9th International Conference on Systems and Informatics (ICSAI). :1-6

A novel multimodal framework for automatic recognition of individual cattle based on hybrid features using sparse stacked denoising autoencoder and group sparse representation techniques

Authors: Santosh Kumar Sunil Kumar Mehak Shafi et al.

Source: Multimedia Tools and Applications. 81:31075-31106

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

Network intrusion detection based on Contractive Sparse Stacked Denoising Autoencoder

Authors: Yang Yang Li Wencui Meng Huiping et al.

Source: 2021 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB). :1-6

A Nonnegativity-Constraint Sparse Stacked Denoising Autoencoder for Anomaly Detection in Electric Power Communication Network

Authors: Zhuo Tao Yuanyuan Wang Yang Yang et al.

Source: 2020 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB). :1-6

Subject Terms: 9. Industry and infrastructure, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

A sparse stacked denoising autoencoder with optimized transfer learning applied to the fault diagnosis of rolling bearings

Authors: Ping Liu Peng Fan Hui Wang et al.

Source: Measurement. 146:305-314

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Access URL: https://ui.adsabs.harvard.edu/abs/2019Meas..146..305S/abstract
https://www.sciencedirect.com/science/article/pii/S0263224119305950

Prediction of Recurrence using a Stacked Denoising Autoencoder and Multifaceted Feature Analysis of Pretreatment MRI in Patients with Nasopharyngeal Carcinoma.

Authors: Liu Y Wang X Li J et al.

Source: Current radiopharmaceuticals [Curr Radiopharm] 2025; Vol. 18 (3), pp. 224-243.

Publication Type: Journal Article

Journal Info: Publisher: Bentham Science Publishers Country of Publication: United Arab Emirates NLM ID: 101468718 Publication Model: Print Cited Medium: Internet ISSN: 1874-4729 (Electronic) Linking ISSN: 18744710 NLM ISO Abbreviation: Curr Radiopharm Subsets: MEDLINE

MeSH Terms: Nasopharyngeal Carcinoma*/diagnostic imaging , Nasopharyngeal Carcinoma*/radiotherapy , Nasopharyngeal Carcinoma*/pathology , Magnetic Resonance Imaging*/methods , Neoplasm Recurrence, Local*/diagnostic imaging , Nasopharyngeal Neoplasms*/diagnostic imaging , Nasopharyngeal Neoplasms*/radiotherapy , Nasopharyngeal Neoplasms*/pathology, Humans ; Female ; Male ; Middle Aged ; Adult ; Retrospective Studies ; Radiotherapy, Intensity-Modulated ; Aged ; Deep Learning ; Autoencoder

A novel multimodal framework for automatic recognition of individual cattle based on hybrid features using sparse stacked denoising autoencoder and group sparse representation techniques.

Authors: Kumar, Santosh Kumar, Sunil Shafi, Mehak et al.

Source: Multimedia Tools & Applications; Sep2022, Vol. 81 Issue 21, p31075-31106, 32p

Subject Terms: ENDANGERED species, FEATURE extraction, FACE, EXTRACTION techniques

Deep Learning Approach Based on Tensor-Train for Sparse Signal Recovery

Authors: Cong Zou Fang Yang

Source: IEEE Access, Vol 7, Pp 34753-34761 (2019)

Subject Terms: Compressive sensing, deep learning, Tensor-Train, stacked denoising autoencoder, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

File Description: electronic resource

Relation: https://ieeexplore.ieee.org/document/8663363/; https://doaj.org/toc/2169-3536

Access URL: https://doaj.org/article/7eb7a8bf675a46849f2b1b0a47dd7467

A Semi-supervised Stacked Autoencoder Approach for Network Traffic Classification

Authors: Aouedi, Ons Piamrat, Kandaraj Bagadthey, Dhruvjyoti et al.

Contributors: Aouedi, Ons Piamrat, Kandaraj Bagadthey, Dhruvjyoti et al.

Source: https://hal.science/hal-02933689 ; 2020.

Subject Terms: Stacked De- noising Autoencoder, Traffic classification, Feature extraction, Deep learning, Machine learning, Stacked Autoencoder, Stacked Denoising Autoencoder, Dropout, Semi-supervised learning, Index Terms-Traffic classification, [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI]

Availability: https://hal.science/hal-02933689
https://hal.science/hal-02933689v2/document
https://hal.science/hal-02933689v2/file/CR2_SAE.pdf

Feature-Alignment-Based Cross-Platform Question Answering Expert Recommendation

Authors: Bin Tang Qinqin Gao Xin Cui et al.

Source: Mathematics, Vol 11, Iss 9, p 2174 (2023)

Subject Terms: community question answering, expert recommendation, cross-domain recommendation, stacked denoising autoencoder, feature alignment, Mathematics, QA1-939

Relation: https://www.mdpi.com/2227-7390/11/9/2174; https://doaj.org/toc/2227-7390; https://doaj.org/article/b0d685528ade41e1ac629af49aa9234a

Availability: https://doi.org/10.3390/math11092174
https://doaj.org/article/b0d685528ade41e1ac629af49aa9234a

Blind Denoising Autoencoder

Authors: Majumdar, Angshul

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

URL: http://arxiv.org/abs/1912.07358

Relational stacked denoising autoencoder for tag recommendation

Authors: Wang, Hao Shi, Xingjian Yeung, Dit Yan

Index Terms: Conference paper

URL: http://repository.ust.hk/ir/Record/1783.1-72866
http://lbdiscover.ust.hk/uresolver?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rfr_id=info:sid/HKUST:SPI&rft.genre=article&rft.issn=&rft.volume=&rft.issue=&rft.date=2015&rft.spage=3052&rft.aulast=Wang&rft.aufirst=Hao&rft.atitle=Relational+stacked+denoising+autoencoder+for+tag+recommendation&rft.title=Proceedings+of+the+Twenty-Ninth+AAAI+Conference+on+Artificial+Intelligence%2C+Austin%2C+Texas%2C+USA
http://www.scopus.com/record/display.url?eid=2-s2.0-84958767160&origin=inward
http://gateway.isiknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=LinksAMR&SrcApp=PARTNER_APP&DestLinkType=FullRecord&DestApp=WOS&KeyUT=000485625503013
http://repository.ust.hk/ir/bitstream/1783.1-72866/1/621310-ConfP-17-fulltext-pre.pdf

Blind Denoising Autoencoder

Authors: Angshul Majumdar

Source: IEEE Transactions on Neural Networks and Learning Systems. 30:312-317

Subject Terms: Signal Processing (eess.SP), FOS: Computer and information sciences, Computer Science - Machine Learning, FOS: Electrical engineering, electronic engineering, information engineering, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, Electrical Engineering and Systems Science - Signal Processing, Machine Learning (cs.LG)

Access URL: http://arxiv.org/pdf/1912.07358
https://pubmed.ncbi.nlm.nih.gov/29994276
http://arxiv.org/abs/1912.07358

Analysis and Review of Extraordinary Machine Learning Approaches

Authors: Tyagi, Nitin Gupta, Sandeep Pratap Srivastava, Arun et al.

Source: International Journal of Engineering and Technology; Vol. 7 No. 4.39 (2018): Special Issue 39; 915-920 ; 2227-524X ; 10.14419/ijet.v7i4.39

Subject Terms: Random Recursive Support Vector Machine (R2SVM), Stacked Sparse autoencoder (SSAE), Stacked Denoising Autoencoder (SDAE), Convolutional Neural Network (CNN), Support Vector Machine (SVM)

File Description: application/pdf

Relation: http://www.sciencepubco.com/index.php/IJET/article/view/27728/14398; http://www.sciencepubco.com/index.php/IJET/article/view/27728

Availability: http://www.sciencepubco.com/index.php/IJET/article/view/27728
https://doi.org/10.14419/ijet.v7i4.39.27728

Learning social representations with deep autoencoder for recommender system.

Authors: Pan, Yiteng He, Fazhi Yu, Haiping

Source: World Wide Web. Jul2020, Vol. 23 Issue 4, p2259-2279. 21p.

Subject Terms: *SOCIAL learning, *COLLECTIVE representation, *RECOMMENDER systems, *MATRIX decomposition, *SOCIAL influence, *SOCIAL interaction, *SOCIAL problems

Deep stacked denoising autoencoder for unsupervised anomaly detection in video surveillance.

Authors: Roka, Sanjay Diwakar, Manoj

Source: Journal of Electronic Imaging; May/Jun2023, Vol. 32 Issue 3, p33015-033015-23, 1p

Subject Terms: VIDEO surveillance, ANOMALY detection (Computer security), IMAGE denoising, OPTICAL flow, CONVOLUTIONAL neural networks, DEEP learning

基于栈式降噪自编码的变压器“快速发展型” 沿面放电状态评估.

Alternate Title: Evaluation of "rapidly developing" creeping discharge states of transformers based on stacked denoising autoencoder.

Authors: 程林 杨旭 刘正阳 et al.

Source: Journal of Nanchang University (Natural Science). 2022, Vol. 46 Issue 2, p226-238. 7p.

Subject Terms: *ELECTRIC transformers, *TRANSFORMER insulation, *SUPPORT vector machines, *EVALUATION methodology, *INSULATING oils, *PARTIAL discharges

SDARE: A stacked denoising autoencoder method for game dynamics network structure reconstruction.

Authors: Huang, Keke Li, Shuo Dai, Penglin et al.

Source: Neural Networks. Jun2020, Vol. 126, p143-152. 10p.

Subject Terms: *DEEP learning, *GREEDY algorithms, *INVERSE problems

基于优化堆叠降噪自编码器的 水轮发电机组故障诊断. (Chinese)

Alternate Title: Fault Diagnosis of Hydro-Generator Set Based on Optimized Stacked Denoising Autoencoder. (English)

Authors: 肖发厚 钟波 张彬桥 et al.

Source: China Rural Water & Hydropower; 2025, Issue 8, p119-125, 7p

Subject Terms: FAULT diagnosis, HYDROELECTRIC generators, ALGORITHMS, OPTIMIZATION algorithms, ARTIFICIAL neural networks, MATHEMATICAL optimization

DC Disturbance Classification Method Based on Compressed Sensing and Encoder.

Authors: Yu, Huanan Zhang, Xiang Wang, Jian

Source: Energy Engineering; 2025, Vol. 122 Issue 12, p5055-5071, 17p