Suchergebnisse - "stacked autoencoder (SAE)"

Classification of autistic subjects employing modified volume local binary pattern (MVLBP) and stacked Autoencoder (SAE) on functional magnetic resonance imaging (fMRI)

Autoren: Kaviya Elakkiya M. null Dejey

Quelle: Multimedia Tools and Applications. 84:22161-22186

Detection of sea‐surface target of coastal defense radar based on Stacked Autoencoder (SAE) algorithm

Autoren: He Yan Chao Chen Guodong Jin et al.

Quelle: IET Radar, Sonar & Navigation, Vol 16, Iss 2, Pp 291-305 (2022)

Schlagwörter: Stacked Autoencoder (SAE), Cell Averaging Constant False Alarm Rate (CA‐CFAR), Telecommunication, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, TK5101-6720, 02 engineering and technology, 14. Life underwater, coastal defense radar system

Zugangs-URL: https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/rsn2.12183
https://doaj.org/article/c840ec18856c45ef96cb36851ea7fd36
https://ietresearch.onlinelibrary.wiley.com/doi/full/10.1049/rsn2.12183

Performance Evaluation of Support Vector Machine and Stacked Autoencoder for Hyperspectral Image Analysis

Autoren: Brahim Jabir Bendaoud Nadif Isabel De la Torre Díez et al.

Quelle: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 18, Pp 16429-16444 (2025)

Schlagwörter: Ocean engineering, remote sensing, Active learning, QC801-809, hyperspectral data classification, Geophysics. Cosmic physics, deep learning, support vector machine (SVM), TC1501-1800, stacked autoencoder (SAE)

Zugangs-URL: https://doaj.org/article/5602b4c04aa6495993f0a42a57ad9363

Stressed Vegetation Identification Under Natural Gas Microleakage From Hyperspectral Images Using Stacked Autoencoder and Multiscale Three-Dimensional Convolutional Neural Network

Autoren: Kangni Xiong Jinbao Jiang Weiwei Ran et al.

Quelle: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 18, Pp 16334-16346 (2025)

Schlagwörter: natural gas microleakage, Ocean engineering, hyperspectral imagery, vegetation stress, QC801-809, Geophysics. Cosmic physics, Convolution neural network, TC1501-1800, stacked autoencoder (SAE)

Zugangs-URL: https://doaj.org/article/7663de844d86404e971d9788f0b8a659

Classification of autistic subjects employing modified volume local binary pattern (MVLBP) and stacked Autoencoder (SAE) on functional magnetic resonance imaging (fMRI).

Autoren: M., Kaviya Elakkiya Dejey

Quelle: Multimedia Tools & Applications; Jun2025, Vol. 84 Issue 20, p22161-22186, 26p

Schlagwörter: FUNCTIONAL magnetic resonance imaging, INDEPENDENT component analysis, AUTISM spectrum disorders, IMAGE processing, DEEP learning

Stacked encoding and AutoML-based identification of lead–zinc small open pit active mines around Rampura Agucha in Rajasthan state, India

Autoren: Abhishek Kumar Ojha Raja Biswas Akhouri Pramod Krishna

Quelle: Scientific Reports, Vol 15, Iss 1, Pp 1-20 (2025)

Schlagwörter: Automated machine learning (AutoML), Stacked autoencoder (SAE), Extra tree classifier (et), Light gradient boosting machine (lightgbm), Random forest classifier (rf), Medicine, Science

Dateibeschreibung: electronic resource

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

Zugangs-URL: https://doaj.org/article/5810a8a5c34f405c842f6ed2a02ba34e

Deep Cascade Gradient RBF Networks With Output-Relevant Feature Extraction and Adaptation for Nonlinear and Nonstationary Processes

Autoren: Tong Liu Zeyue Tian Sheng Chen et al.

Quelle: IEEE Transactions on Cybernetics. 53:4908-4922

Schlagwörter: Output-relevant features, Gradient radial basis function (GRBF) network, Data models, Adaptation models, Computational modeling, Deep learning, 02 engineering and technology, Radial basis function networks, Adaptive systems, Predictive models, Online adaptation, High-dimensional and nonstationary processes, Stacked autoencoder (SAE), 0202 electrical engineering, electronic engineering, information engineering, Feature extraction

Dateibeschreibung: text

Zugangs-URL: https://eprints.soton.ac.uk/454982/1/DGRBFlv_re3.pdf
https://pubmed.ncbi.nlm.nih.gov/35239499
https://eprints.soton.ac.uk/454982/

Rock mass quality classification based on deep learning: A feasibility study for stacked autoencoders

Autoren: Danjie Sheng Jin Yu Fei Tan et al.

Quelle: Journal of Rock Mechanics and Geotechnical Engineering, Vol 15, Iss 7, Pp 1749-1758 (2023)

Schlagwörter: Stacked autoencoder (SAE), Back propagation algorithm, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, TA703-712, Deep learning, Engineering geology. Rock mechanics. Soil mechanics. Underground construction, 02 engineering and technology, Rock mass quality classification, 01 natural sciences, 0105 earth and related environmental sciences

Zugangs-URL: https://doaj.org/article/6478b6bd19cd457cbec576a0d6ea39b8

Generalized Zero-Shot Domain Adaptation for Unsupervised Cross-Domain PolSAR Image Classification

Autoren: Rong Gui Xin Xu Rui Yang et al.

Quelle: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 270-283 (2022)

Schlagwörter: unsupervised domain adaptation (uDA), QC801-809, Geophysics. Cosmic physics, 0211 other engineering and technologies, Generalized zero-shot learning (GZSL), 02 engineering and technology, 15. Life on land, 01 natural sciences, polarimetric synthetic aperture radar (PolSAR) image, scattering component semantics (SCS), Ocean engineering, TC1501-1800, stacked autoencoder (SAE), 0105 earth and related environmental sciences

Zugangs-URL: https://ieeexplore.ieee.org/ielx7/4609443/4609444/09647973.pdf
https://doaj.org/article/10fdb577de704dd486100af3ff4ec890

Background Purification Framework With Extended Morphological Attribute Profile for Hyperspectral Anomaly Detection

Autoren: Ju Huang Kang Liu Mingliang Xu et al.

Quelle: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 8113-8124 (2021)

Schlagwörter: trening, hyperspectral imaging, Geophysics. Cosmic physics, 0211 other engineering and technologies, sparse representation (SR), Anomaly detection, 02 engineering and technology, rekonstrukcija slik, optika, hyperspectral image (HSI), spektralna analiza, 0202 electrical engineering, electronic engineering, information engineering, stacked autoencoder, background purification, sparse representation, TC1501-1800, detectors, dictionaries, training, QC801-809, extended attribute profile (EAP), image reconstruction, extended attribute profile, anomaly detection, optics, detektorji, Ocean engineering, zaznavanje anomalij, slovarji, stacked autoencoder (SAE)

Dateibeschreibung: application/pdf; text/url

Zugangs-URL: https://ieeexplore.ieee.org/ielx7/4609443/4609444/09511250.pdf
https://doaj.org/article/75cf6c9d8cad45d0b85987f1ed765ec9
http://ui.adsabs.harvard.edu/abs/2021IJSTA..14.8113H/abstract
https://dblp.uni-trier.de/db/journals/staeors/staeors14.html#HuangLXPL21

Detection of sea‐surface target of coastal defense radar based on Stacked Autoencoder (SAE) algorithm.

Autoren: Yan, He Chen, Chao Jin, Guodong et al.

Quelle: IET Radar, Sonar & Navigation (Wiley-Blackwell); Feb2022, Vol. 16 Issue 2, p291-305, 15p

Schlagwörter: COAST defenses, RADAR signal processing, ALGORITHMS, MACHINE learning, FALSE alarms

A Fault Identification Method for Metal Oxide Arresters Combining Suppression of Environmental Temperature and Humidity Interference with a Stacked Autoencoder

Autoren: Shengwen Shu Xiaoyao Zhang Guobin Wang et al.

Quelle: Energies, Vol 16, Iss 24, p 8033 (2023)

Schlagwörter: metal oxide arrester (MOA), fault identification, environmental temperature and humidity, interference suppression, stacked autoencoder (SAE), Technology

Relation: https://www.mdpi.com/1996-1073/16/24/8033; https://doaj.org/toc/1996-1073; https://doaj.org/article/889099d062104a7b8024ca67e2dba73e

Verfügbarkeit: https://doi.org/10.3390/en16248033
https://doaj.org/article/889099d062104a7b8024ca67e2dba73e

Target Detection in Clutter/Interference Regions Based on Deep Feature Fusion for HFSWR

Autoren: Maokai Wu Ling Zhang Jiong Niu et al.

Quelle: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 5581-5595 (2021)

Schlagwörter: Convolutional neural network (CNN), extremum detection, feature fusion, high-frequency surface wave radar (HFSWR), stacked autoencoder (SAE), target detection, Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809

Dateibeschreibung: electronic resource

Relation: https://ieeexplore.ieee.org/document/9436001/; https://doaj.org/toc/2151-1535

Zugangs-URL: https://doaj.org/article/fdddac9749c14e7eaa07c932fa7e035b

Analysis of AI-Based Single-View 3D Reconstruction Methods for an Industrial Application

Autoren: Julia Hartung Patricia M. Dold Andreas Jahn et al.

Quelle: Sensors, Vol 22, Iss 6425, p 6425 (2022)

Schlagwörter: three-dimensional reconstruction, single view, stacked autoencoder (SAE), generative adversarial network (GAN), U-Net, stacked dilated U-Net (SDU-Net), Chemical technology, TP1-1185

Relation: https://www.mdpi.com/1424-8220/22/17/6425; https://doaj.org/toc/1424-8220; https://doaj.org/article/a133fba263a443f3a127321736bd6c75

Verfügbarkeit: https://doi.org/10.3390/s22176425
https://doaj.org/article/a133fba263a443f3a127321736bd6c75

Sensing OFDM Signal: A Deep Learning Approach

Autoren: Qingqing Cheng Zhenguo Shi Diep N. Nguyen et al.

Quelle: IEEE Transactions on Communications. 67:7785-7798

Schlagwörter: Spectrum sensing, stacked autoencoder SAE, 0202 electrical engineering, electronic engineering, information engineering, deep learning, 02 engineering and technology, OFDM

Zugangs-URL: https://opus.lib.uts.edu.au/handle/10453/140061
https://opus.cloud.lib.uts.edu.au/handle/10453/140061
https://ieeexplore.ieee.org/document/8827563
https://dblp.uni-trier.de/db/journals/tcom/tcom67.html#ChengSND19

Health Indicators Construction and Remaining Useful Life Estimation for Concrete Structures Using Deep Neural Networks

Autoren: Viet Tra Tuan-Khai Nguyen Cheol-Hong Kim et al.

Quelle: Applied Sciences, Vol 11, Iss 4113, p 4113 (2021)

Schlagwörter: concrete structures, remaining useful life (RUL), stacked autoencoder (SAE), deep neural network (DNN), long short-term memory (LSTM), Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

Relation: https://www.mdpi.com/2076-3417/11/9/4113; https://doaj.org/toc/2076-3417; https://doaj.org/article/0e169cac49f442dda9b2f51013750bb9

Verfügbarkeit: https://doi.org/10.3390/app11094113
https://doaj.org/article/0e169cac49f442dda9b2f51013750bb9

SAR Target Recognition with Feature Fusion Based on Stacked Autoencoder

Autoren: Kang Miao Ji Kefeng Leng Xiangguang et al.

Quelle: Leida xuebao, Vol 6, Iss 2, Pp 167-176 (2017)

Schlagwörter: Synthetic Aperture Rader (SAR), Target recognition, Feature fusion, Stacked AutoEncoder (SAE), Moving and Stationary Target Acquisition and Recognition (MSTAR), Electricity and magnetism, QC501-766

Dateibeschreibung: electronic resource

Relation: http://radars.ie.ac.cn/CN/html/R16112.htm; https://doaj.org/toc/2095-283X

Zugangs-URL: https://doaj.org/article/c3e47e9fb46a4e05a4a4638a5a0ef2a0

Soft Sensor Modeling Method by Maximizing Output-Related Variable Characteristics Based on a Stacked Autoencoder and Maximal Information Coefficients

Autoren: Yanzhen Wang Xuefeng Yan

Quelle: International Journal of Computational Intelligence Systems, Vol 12, Iss 2 (2019)

Schlagwörter: Soft sensor, Deep learning, Stacked autoencoder (SAE), Maximal information coefficient (MIC), Modeling method, Electronic computers. Computer science, QA75.5-76.95

Dateibeschreibung: electronic resource

Relation: https://www.atlantis-press.com/article/125917186/view; https://doaj.org/toc/1875-6883

Zugangs-URL: https://doaj.org/article/539bb6067c8a42dcb2fc328ac25b6d2f

A Hybrid Autoencoder Network for Unsupervised Image Clustering

Autoren: Pei-Yin Chen Jih-Jeng Huang

Quelle: Algorithms, Vol 12, Iss 6, p 122 (2019)

Schlagwörter: image clustering, convolutional autoencoder (CAE), adversarial autoencoder (AAE), stacked autoencoder (SAE), Industrial engineering. Management engineering, T55.4-60.8, Electronic computers. Computer science, QA75.5-76.95

Relation: https://www.mdpi.com/1999-4893/12/6/122; https://doaj.org/toc/1999-4893; https://doaj.org/article/caae9c3398a34723a735c3518ada5b7f

Verfügbarkeit: https://doi.org/10.3390/a12060122
https://doaj.org/article/caae9c3398a34723a735c3518ada5b7f

SAE-based Encrypted Traffic Identification Method

Autoren: WANG Pan,CHEN Xuejiao

Quelle: Jisuanji gongcheng, Vol 44, Iss 11, Pp 140-147,153 (2018)

Schlagwörter: encrypted traffic identification, deep learning, stacked autoencoder (sae), traffic classification, multilayer perceptron (mlp), convolutional neural network (cnn), Computer engineering. Computer hardware, TK7885-7895, Computer software, QA76.75-76.765

Dateibeschreibung: electronic resource

Relation: https://www.ecice06.com/fileup/1000-3428/PDF/1811140.pdf; https://doaj.org/toc/1000-3428

Zugangs-URL: https://doaj.org/article/b504b2d72d124e95838787e5382660b7