Suchergebnisse - Sparse stacked autoencoder

GRU‐based stacked sparse autoencoder with attention mechanism for process monitoring

Autoren: Zengdi Miao Ping Wu Zhenquan Wu et al.

Quelle: The Canadian Journal of Chemical Engineering. 103:3767-3785

Novel semi-supervised sparse stacked autoencoder integrated with local linear embedding for industrial soft sensing

Autoren: Yan-Lin He Yu Jiang Hui-Hui Gao et al.

Quelle: ISA Transactions.

Cervical cancer classification using sparse stacked autoencoder and fuzzy ARTMAP

Autoren: Lawrence Chuin Ming Liaw Shing Chiang Tan Pey Yun Goh et al.

Quelle: Neural Computing and Applications. 36:13895-13913

Schlagwörter: RC71-78.7 Examination. Diagnosis

Dateibeschreibung: text

Hybrid Feature Embedded Sparse Stacked Autoencoder and Manifold Dimensionality Reduction Ensemble for Mental Health Speech Recognition

Autoren: Hong Chen Yuan Lin Yongming Li et al.

Quelle: IEEE Access, Vol 9, Pp 28729-28741 (2021)

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, ensemble learning, feature fusion, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, speech mental health recognition, Embedded hybrid feature sparse stacked autoencoder, L1 regularization, TK1-9971, 3. Good health

Zugangs-URL: https://ieeexplore.ieee.org/ielx7/6287639/9312710/09348883.pdf
https://doaj.org/article/4e1029e37cad4c62884db95a4caa020b
https://dblp.uni-trier.de/db/journals/access/access9.html#ChenLLWWL21
https://ieeexplore.ieee.org/document/9348883

Fault classification based on variable‐weighted dynamic sparse stacked autoencoder for industrial processes

Autoren: Jie Dong Yanan Tu Kaixiang Peng

Quelle: The Canadian Journal of Chemical Engineering. 101:420-430

Schlagwörter: 02 engineering and technology, 0204 chemical engineering, 0210 nano-technology

GRU‐based stacked sparse autoencoder with attention mechanism for process monitoring.

Autoren: Miao, Zengdi Wu, Ping Wu, Zhenquan et al.

Quelle: Canadian Journal of Chemical Engineering; Aug2025, Vol. 103 Issue 8, p3767-3785, 19p

Schlagwörter: RECURRENT neural networks, AUTOENCODERS, BLAST furnaces, MANUFACTURING processes, INDUSTRIAL safety, DEEP learning

Improved Diagnostic Approach for BRB Detection and Classification in Inverter-Driven Induction Motors Employing Sparse Stacked Autoencoder (SSAE) and LightGBM

Autoren: Muhammad Amir Khan Bilal Asad Toomas Vaimann et al.

Quelle: Electronics. 13:1292

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

Fusion of hyperspectral and LiDAR data using sparse stacked autoencoder for land cover classification with 3D-2D convolutional neural network

Autoren: Manoj Kumar Singh Shashank Mohan Brajesh Kumar

Quelle: Journal of Applied Remote Sensing. 16

Schlagwörter: 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

DS2AN: Deep stacked sparse autoencoder for secure and fast authentication in HetNets

Autoren: Hemavathi S. Akhila Samreen Zubeda et al.

Quelle: SECURITY AND PRIVACY. 5

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

[Psychosis speech recognition algorithm based on deep embedded sparse stacked autoencoder and manifold ensemble]

Autoren: Pin Wang Xiaolin Qin Zuwei Zhang et al.

Quelle: Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi. 38(4)

Schlagwörter: Databases, Factual, Psychotic Disorders, 0202 electrical engineering, electronic engineering, information engineering, Speech Perception, Humans, Speech, 02 engineering and technology, Algorithms

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/34459164
https://www.ncbi.nlm.nih.gov/pubmed/34459164

Early identification of out-of-roundness damage wheels in railway freight vehicles using a wayside system and a stacked sparse autoencoder

Autoren: Jorge, T Magalhaes, J Silva, R et al.

Dateibeschreibung: application/pdf

Relation: 0042-3114

Verfügbarkeit: https://hdl.handle.net/10216/166712

Spectral-spatial classification method for hyperspectral images using stacked sparse autoencoder suitable in limited labelled samples situation

Autoren: Seyyed Ali Ahmadi Nasser Mehrshad Seyyed Mohammadali Arghavan

Quelle: Geocarto International, Vol 37, Iss 7, Pp 2031-2054 (2022)

Schlagwörter: classification, hyperspectral image, limited labelled samples, deep learning, stacked autoencoder, gabor feature, extended multi-attribute profile, Physical geography, GB3-5030

Relation: http://dx.doi.org/10.1080/10106049.2020.1797188; https://doaj.org/toc/1010-6049; https://doaj.org/toc/1752-0762; https://doaj.org/article/fc81f5c807e44a22b0771904d910a1d2

Verfügbarkeit: https://doi.org/10.1080/10106049.2020.1797188
https://doaj.org/article/fc81f5c807e44a22b0771904d910a1d2

Identificação de defeitos em veículos ferroviários de mercadorias baseado num sistema wayside e com recurso a um Autoencoder

Autoren: Jorge, Tomás Simões Ribeiro, Diogo Rodrigo Ferreira REPOSITÓRIO P.PORTO

Weitere Verfasser: Jorge, Tomás Simões Ribeiro, Diogo Rodrigo Ferreira REPOSITÓRIO P.PORTO

Quelle: urn:tid:203377842

Schlagwörter: Danos nas rodas OOR, Desequilíbrio de cargas, Deteção e classificação de danos, Comboios de mercadorias, Sistemas de monitorização wayside, Autoencoder esparso empilhado, Inteligência artificial, OOR wheel damage, Unbalance loads, Damage detection and classification, Freight trains, Wayside monitoring systems, Stacked sparse autoencoder, Artificial intelligence

Dateibeschreibung: application/pdf

Verfügbarkeit: http://hdl.handle.net/10400.22/23828

Sparse stacked autoencoder network for complex system monitoring with industrial applications

Autoren: Keke Huang Ziwei Deng Zhaohui Tang et al.

Quelle: Chaos, Solitons & Fractals. 137:109838

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

Zugangs-URL: http://ui.adsabs.harvard.edu/abs/2020CSF...13709838D/abstract
https://ideas.repec.org/a/eee/chsofr/v137y2020ics0960077920302381.html
https://ui.adsabs.harvard.edu/abs/2020CSF...137j9838D/abstract
https://www.sciencedirect.com/science/article/pii/S0960077920302381

Comparing Functional and Effective Connectivity Features in Diagnosis of Autism Spectrum Disorder Using Stacked Autoencoder by Resting-State fMRI Data.

Autoren: Navaei, Pegah Khadem, Ali Zaferani, Effat Jalaeian

Quelle: Frontiers in Biomedical Technologies; Autumn2025, Vol. 12 Issue 4, p830-843, 14p

Schlagwörter: AUTISM spectrum disorders, FUNCTIONAL connectivity, AUTOENCODERS, ARTIFICIAL neural networks, FUNCTIONAL magnetic resonance imaging, GRANGER causality test

Improved Diagnostic Approach for BRB Detection and Classification in Inverter-Driven Induction Motors Employing Sparse Stacked Autoencoder (SSAE) and LightGBM.

Autoren: Khan, Muhammad Amir Asad, Bilal Vaimann, Toomas et al.

Quelle: Electronics (2079-9292); Apr2024, Vol. 13 Issue 7, p1292, 20p

Schlagwörter: INDUCTION motors, RECEIVER operating characteristic curves, FAULT diagnosis, FEATURE extraction

Cervical cancer classification using sparse stacked autoencoder and fuzzy ARTMAP.

Autoren: Liaw, Lawrence Chuin Ming Tan, Shing Chiang Goh, Pey Yun et al.

Quelle: Neural Computing & Applications; Aug2024, Vol. 36 Issue 22, p13895-13913, 19p

Schlagwörter: TUMOR classification, SUPERVISED learning, CERVICAL cancer, MACHINE learning, EARLY diagnosis, DEEP learning

Decision letter for 'Fault classification based on variable‐weighted dynamic sparse stacked autoencoder for industrial processes'

Cervical cancer classification using sparse stacked autoencoder and fuzzy ARTMAP

Autoren: LCM Liaw SC Tan PY Goh et al.

Schlagwörter: Engineering, Information and computing sciences, Artificial intelligence, Computer vision and multimedia computation, Machine learning, Cognitive and computational psychology, Uncategorised value

Relation: http://hdl.handle.net/10779/DRO/DU:28739858.v1; https://figshare.com/articles/journal_contribution/Cervical_cancer_classification_using_sparse_stacked_autoencoder_and_fuzzy_ARTMAP/28739858

Verfügbarkeit: http://hdl.handle.net/10779/DRO/DU:28739858.v1
https://figshare.com/articles/journal_contribution/Cervical_cancer_classification_using_sparse_stacked_autoencoder_and_fuzzy_ARTMAP/28739858

Review for 'Fault classification based on variable‐weighted dynamic sparse stacked autoencoder for industrial processes'