Search Results - deep stacked denoising autoencoder*

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

Face recognition via Deep Stacked Denoising Sparse Autoencoders (DSDSA)

Authors: Görgel, Pelin Şimşek, Ahmet İstanbul University Cerrahpaşa Institutional Repository

Contributors: Görgel, Pelin Şimşek, Ahmet İstanbul University Cerrahpaşa Institutional Repository

Source: Applied Mathematics and Computation. 355:325-342

Subject Terms: Sparse autoencoders, 03 medical and health sciences, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, Deep learning, 02 engineering and technology, Face recognition

File Description: application/pdf

Access URL: https://ideas.repec.org/a/eee/apmaco/v355y2019icp325-342.html
https://doi.org/10.1016/j.amc.2019.02.071
http://www.sciencedirect.com/science/article/pii/S009630031930181X
https://dblp.uni-trier.de/db/journals/amc/amc355.html#GorgelS19
https://www.sciencedirect.com/science/article/abs/pii/S009630031930181X

Deep stacked denoising autoencoder for unsupervised anomaly detection in video surveillance

Authors: Sanjay Roka Manoj Diwakar

Source: Journal of Electronic Imaging. 32

A selective deep stacked denoising autoencoders ensemble with negative correlation learning for gearbox fault diagnosis

Authors: Jianbo Yu

Source: Computers in Industry. 108:62-72

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

Access URL: https://www.sciencedirect.com/science/article/abs/pii/S0166361518306870
https://doi.org/10.1016/j.compind.2019.02.015
https://dblp.uni-trier.de/db/journals/cii/cii108.html#Yu19
https://www.sciencedirect.com/science/article/pii/S0166361518306870

Deep learning framework designed for high-performance lithium-ion batteries state monitoring

Authors: Paul Takyi-Aninakwa Shunli Wang Guangchen Liu et al.

Source: Renewable and Sustainable Energy Reviews. 218:115803

Subject Terms: Lithium-ion batteries, State of charge estimation, Secondary scale feature extraction, Long short-term memory, Deep-stacked denoising autoencoder

Access URL: https://rgu-repository.worktribe.com/output/2830428

Multimodal cloud resources utilization forecasting using a Bidirectional Gated Recurrent Unit predictor based on a power efficient Stacked denoising Autoencoders.

Authors: Ikhlasse, Hamzaoui Benjamin, Duthil Vincent, Courboulay et al.

Source: Alexandria Engineering Journal. Dec2022, Vol. 61 Issue 12, p11565-11577. 13p.

Subject Terms: Deep learning, Forecasting, Time series analysis

Company/Entity: Amazon Web Services Inc.

Monitoring of complex profiles based on deep stacked denoising autoencoders

Authors: Shumei Chen Shijin Wang Jianbo Yu

Source: Computers & Industrial Engineering. 143:106402

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 0101 mathematics, 01 natural sciences

Access URL: https://www.sciencedirect.com/science/article/pii/S0360835220301364
https://doi.org/10.1016/j.cie.2020.106402

Roller bearing fault diagnosis using stacked denoising autoencoder in deep learning and Gath–Geva clustering algorithm without principal component analysis and data label.

Authors: Xu, Fan Tse, Wai tai Peter Tse, Yiu Lun

Source: Applied Soft Computing. Dec2018, Vol. 73, p898-913. 16p.

Subject Terms: Principal components analysis, Deep learning, Clustering of particles, Debugging, Entropy (Information theory)

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

Authors: Huang K Li S Dai P et al.

Source: Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2020 Jun; Vol. 126, pp. 143-152. Date of Electronic Publication: 2020 Mar 14.

Publication Type: Journal Article

Journal Info: Publisher: Pergamon Press Country of Publication: United States NLM ID: 8805018 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2782 (Electronic) Linking ISSN: 08936080 NLM ISO Abbreviation: Neural Netw Subsets: MEDLINE

MeSH Terms: Deep Learning* , Game Theory* , Neural Networks, Computer*, Algorithms ; Humans

Auxiliary Stacked Denoising Autoencoder based Collaborative Filtering Recommendation.

Authors: Ruihui Mu Xiaoqin Zeng

Source: KSII Transactions on Internet & Information Systems. Jun2020, Vol. 14 Issue 6, p2310-2332. 23p.

Subject Terms: Filtering software, Natural language processing, Recommender systems, Deep learning, Image recognition (Computer vision), Image processing

Sparsity-Penalized Stacked Denoising Autoencoders for Imputing Single-Cell RNA-Seq Data.

Authors: Chi W Deng M

Source: Genes [Genes (Basel)] 2020 May 11; Vol. 11 (5). Date of Electronic Publication: 2020 May 11.

Publication Type: Journal Article; Research Support, Non-U.S. Gov't

Journal Info: Publisher: MDPI Country of Publication: Switzerland NLM ID: 101551097 Publication Model: Electronic Cited Medium: Internet ISSN: 2073-4425 (Electronic) Linking ISSN: 20734425 NLM ISO Abbreviation: Genes (Basel) Subsets: MEDLINE

MeSH Terms: Deep Learning* , Transcriptome*, Data Compression/*methods , RNA-Seq/*methods , Single-Cell Analysis/*methods, Computer Simulation ; Datasets as Topic ; Fetal Blood/cytology ; Humans ; Leukocytes, Mononuclear/chemistry ; Proteins/analysis ; RNA/analysis

An Architecture-Oriented Analysis of Stacked Denoising Autoencoders.

Authors: Ramakotti, Raksha Paneerselvam, Surekha

Source: Procedia Computer Science. 2024, Vol. 235, p2154-2166. 13p.

Subject Terms: Signal-to-noise ratio, Artificial neural networks, Convolutional neural networks, Image processing, Inverse problems

Improved deep reinforcement learning for car-following decision-making

Authors: Yang, Xiaoxue Zou, Yajie Zhang, Hao et al.

Source: Physica A. 624

Subject Terms: Car-following model, Deep reinforcement learning, Driving behavior imitation, Stacked denoising autoencoders, Behavioral research, Decision making, Learning systems, Recurrent neural networks, Auto encoders, Car-following modeling, De-noising, Deterministics, Driving behaviour, Policy gradient, Reinforcement learnings, Stacked denoising autoencoder, Reinforcement learning

File Description: print

Access URL: https://urn.kb.se/resolve?urn=urn:nbn:se:ri:diva-65535

An ELM-based Deep SDAE Ensemble for Inter-Subject Cognitive Workload Estimation with Physiological Signals

Authors: Zheng, Zhanpeng Yin, Zhong Zhang, Jianhua

Source: Chinese Control Conference (CCC)
2020 39th Chinese Control Conference (CCC)

Subject Terms: 0209 industrial biotechnology, Cognitive workloads, Ensemble learning, Stacked denoising autoencoders, 0202 electrical engineering, electronic engineering, information engineering, Extreme learning machines, 02 engineering and technology, Electroencephalograms

File Description: application/pdf

Access URL: https://ieeexplore.ieee.org/document/9188806
https://oda.oslomet.no/oda-xmlui/handle/11250/2772135
https://hdl.handle.net/11250/2772135

Stacked Denoising Autoencoder-based Deep Collaborative Filtering using the change of similarity / 積層自己符号化器による類似性の変化に着目した協調フィルタリングの提案

Authors: Tomonobu OZAKI Yosuke SUZUKI 尾崎 知伸 et al.

Source: JSAI Technical Report, SIG-KBS. 2016, :02

Reconstructing echoes completely submerged in background noise by a stacked denoising autoencoder method for low-power EMAT testing

Authors: Jinjie Zhou Dianrui Yu Xiang Li et al.

Source: Measurement Science and Technology. 34:125910

Subject Terms: 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 01 natural sciences

Assessing cognitive mental workload via EEG signals and an ensemble deep learning classifier based on denoising autoencoders

Authors: Yang, Shuo Yin, Zhong Wang, Yagang et al.

Availability: https://hdl.handle.net/10642/8300

Stacked denoising autoencoders for sentiment analysis: a review.

Authors: Sagha, Hesam Cummins, Nicholas Schuller, Björn

Source: WIREs: Data Mining & Knowledge Discovery. Sep/Oct2017, Vol. 7 Issue 5, pn/a-N.PAG. 9p.

Subject Terms: *Sentiment analysis, *Algorithms, Deep learning, Signal denoising, Support vector machines

Identification of Cancer Mediating Biomarkers using Stacked Denoising Autoencoder Model - An Application on Human Lung Data.

Authors: Sheet, Sougata Ghosh, Anupam Ghosh, Ranjan et al.

Source: Procedia Computer Science. 2020, Vol. 167, p686-695. 10p.

Subject Terms: Gene expression profiling, Gaussian mixture models, Biomarkers, Support vector machines, Gene expression, Feature selection

栈式降噪自编码器在波形单元识别中的应用.

Alternate Title: The application ofstacked denoising autoencoders in waveform unit identification.

Authors: 陈维高 朱卫纲 唐晓婧 et al.

Source: Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban. 2018, Vol. 50 Issue 11, p94-100. 7p.