Search Results - Stacked sparse autoencoder data reconstruction

Multimode system condition monitoring using sparsity reconstruction for quality control

Authors: Wafa Bougheloum Mounir Bekaik Sofiane Gherbi

Subject Terms: Diagnosis, Process monitoring multivariate statistical process control, Stacked sparse autoencoder data reconstruction

Relation: https://zenodo.org/records/7827354; oai:zenodo.org:7827354

Availability: https://doi.org/10.11591/ijece.v13i3.pp2711-2720
https://zenodo.org/records/7827354

Lithium exploration targeting through robust variable selection and deep anomaly detection: An integrated application of sparse principal component analysis and stacked autoencoders.

Authors: Esmaeiloghli, Saeid Lima, Alexandre Sadeghi, Behnam

Source: Geochemistry / Chemie der Erde. Nov2024, Vol. 84 Issue 4, pN.PAG-N.PAG. 1p.

Subject Terms: Principal components analysis, Renewable energy sources, Employee reviews, Signal reconstruction, Metal industry

Hyperband Tuned Deep Neural Network With Well Posed Stacked Sparse AutoEncoder for Detection of DDoS Attacks in Cloud

Authors: Aanshi Bhardwaj Veenu Mangat Renu Vig

Source: IEEE Access, Vol 8, Pp 181916-181929 (2020)

Subject Terms: Machine learning, intrusion detection, artificial neural network, cloud computing, distributed denial of service attack, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

File Description: electronic resource

Relation: https://ieeexplore.ieee.org/document/9212425/; https://doaj.org/toc/2169-3536

Access URL: https://doaj.org/article/37ba09dfdb5841b7a8e095ef9657e1f6

Improved stacked sparse auto encoder by adaptive shuffled shepherd optimization algorithm for ERT image reconstruction

Authors: Ammaiappan, Sathesh Liang, Guanghui Dong, Feng et al.

Contributors: Ammaiappan, Sathesh Liang, Guanghui Dong, Feng et al.

Source: Measurement Science and Technology ; volume 36, issue 6, page 065406 ; ISSN 0957-0233 1361-6501

Availability: https://doi.org/10.1088/1361-6501/adda70
https://iopscience.iop.org/article/10.1088/1361-6501/adda70
https://iopscience.iop.org/article/10.1088/1361-6501/adda70/pdf

Electricity theft detection based on stacked sparse denoising autoencoder

Authors: Qifeng Xu Yifan Huang

Source: International Journal of Electrical Power & Energy Systems. 125:106448

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

Access URL: https://www.sciencedirect.com/science/article/pii/S014206151933666X

An intrusion detection method based on stacked sparse autoencoder and improved gaussian mixture model.

Authors: Zhang, Tianyue Chen, Wei Liu, Yuxiao et al.

Source: Computers & Security. May2023, Vol. 128, pN.PAG-N.PAG. 1p.

Subject Terms: *CLUSTER analysis (Statistics), GAUSSIAN mixture models, ANOMALY detection (Computer security), PEARSON correlation (Statistics), FEATURE selection, THRESHOLD energy

Battle damage assessment based on an improved Kullback-Leibler divergence sparse autoencoder.

Authors: Qi, Zong-feng Liu, Qiao-qiao Wang, Jun et al.

Source: Frontiers of Information Technology & Electronic Engineering; Dec2017, Vol. 18 Issue 12, p1991-2000, 10p

Anomaly Feature Learning for Unsupervised Change Detection in Heterogeneous Images: A Deep Sparse Residual Model

Authors: Redha Touati Max Mignotte Mohamed Dahmane

Source: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 13, Pp 588-600 (2020)

Subject Terms: Ocean engineering, QC801-809, multimodal anomaly detector, Geophysics. Cosmic physics, heterogeneous remote sensing, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, deep learning, feature space reconstruction, 02 engineering and technology, TC1501-1800, Anomalous patterns, change detection (CD)

Access URL: https://ieeexplore.ieee.org/ielx7/4609443/8994817/08966599.pdf
https://doaj.org/article/e84f6d6127b14886a67167740702e8a6
http://ui.adsabs.harvard.edu/abs/2020IJSTA..13..588T/abstract
https://dblp.uni-trier.de/db/journals/staeors/staeors13.html#TouatiMD20
https://ieeexplore.ieee.org/document/8966599
https://doaj.org/article/e84f6d6127b14886a67167740702e8a6

A deep learning framework with hybrid stacked sparse autoencoder for type 2 diabetes prediction.

Authors: Abdussamad Daud, Hanita Sokkalingam, Rajalingam et al.

Source: Scientific Reports; 10/21/2025, Vol. 15 Issue 1, p1-22, 22p

Subject Terms: TYPE 2 diabetes, FEATURE selection, FORECASTING, COMPUTER performance, ARTIFICIAL neural networks, MEDICAL care, DEEP learning

Latent Feature‐Based Type 2 Diabetes Prediction Using a Hybrid Stacked Sparse Autoencoder and Machine Learning Models.

Authors: Abdussamad Daud, Hanita Sokkalingam, Rajalingam et al.

Source: Engineering Reports; Sep2025, Vol. 7 Issue 9, p1-17, 17p

Subject Terms: TYPE 2 diabetes, MACHINE learning, CLINICAL decision support systems, METADATA, FORECASTING, FEATURE extraction

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

Authors: Jorge, T. Magalhães, J. Silva, R. et al.

Source: Vehicle System Dynamics; Feb2025, Vol. 63 Issue 2, p232-257, 26p

Subject Terms: FEATURE extraction, RAILROAD trains, AUTOENCODERS, ARTIFICIAL intelligence, MULTISENSOR data fusion, ROLLING stock

An Innovative Study for Tool Wear Prediction Based on Stacked Sparse Autoencoder and Ensemble Learning Strategy.

Authors: He, Zhaopeng Shi, Tielin Chen, Xu

Source: Sensors (14248220); Apr2025, Vol. 25 Issue 8, p2391, 14p

Subject Terms: ARTIFICIAL neural networks, MACHINE learning, ENSEMBLE learning, NUMERICAL control of machine tools, AUTOENCODERS, DEEP learning

The optimally designed autoencoder network for compressed sensing

Authors: Zufan Zhang Yunfeng Wu Chenquan Gan et al.

Source: EURASIP Journal on Image and Video Processing, Vol 2019, Iss 1, Pp 1-12 (2019)

Subject Terms: Compressed sensing, Stacked sparse denoising autoencoder, Deep learning, Multiple nonlinear measurement, Signal reconstruction, Electronics, TK7800-8360

File Description: electronic resource

Relation: http://link.springer.com/article/10.1186/s13640-019-0460-5; https://doaj.org/toc/1687-5281

Access URL: https://doaj.org/article/8a403d77e24e4e6189172e0d05faf8f3

Intelligent evaluation method for identifying favorable shale oil areas based on improved stacked sparse autoencoder.

Authors: Rui Xu Tie Yan Shihui Sun et al.

Source: Oil Shale; 2025, Vol. 42 Issue 1, p79-114, 36p

Subject Terms: SHALE oils, SUPERVISED learning, PETROLEUM prospecting, AUTOENCODERS, MACHINE learning

Unsupervised Domain Adaptation via Stacked Convolutional Autoencoder

Authors: Yi Zhu Xinke Zhou Xindong Wu

Source: Applied Sciences, Vol 13, Iss 1, p 481 (2022)

Subject Terms: domain adaptation, convolutional autoencoder, sparse autoencoder, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

File Description: electronic resource

Relation: https://www.mdpi.com/2076-3417/13/1/481; https://doaj.org/toc/2076-3417

Access URL: https://doaj.org/article/491024e2261147178e7df1a59356e59a

RETRACTED: Optimization using Internet of Agent based Stacked Sparse Autoencoder Model for Heart Disease Prediction.

Authors: Baviskar, Vaishali Verma, Madhushi Chatterjee, Pradeep et al.

Source: Expert Systems; Dec2024, Vol. 41 Issue 12, p1-20, 20p

Subject Terms: CONVOLUTIONAL neural networks, MACHINE learning, OPTIMIZATION algorithms, NOSOLOGY, CLASSIFICATION algorithms

An extreme rainfall-induced landslide susceptibility assessment using autoencoder combined with random forest in Shimane Prefecture, Japan

Authors: Kounghoon Nam Fawu Wang

Source: Geoenvironmental Disasters, Vol 7, Iss 1, Pp 1-16 (2020)

Subject Terms: Stacked autoencoder, Sparse autoencoder, Support vector machine, Random forest, Landslide susceptibility, Disasters and engineering, TA495, Environmental sciences, GE1-350

File Description: electronic resource

Relation: https://doaj.org/toc/2197-8670

Access URL: https://doaj.org/article/87ae8b521da74432b7704408d4b4a9b4

Visual Motion Segmentation in Crowd Videos Based on Spatial-Angular Stacked Sparse Autoencoders.

Authors: Hafeezallah, Adel Al-Dhamari, Ahlam Abu-Bakar, Syed Abd Rahman

Source: Computer Systems Science & Engineering; 2023, Vol. 47 Issue 1, p593-611, 19p

Subject Terms: COLLECTIVE behavior, IMAGE segmentation, DEEP learning, DATA analysis, ACCURACY

An Abnormal Data Processing Method Based on An Ensemble Algorithm for Early Warning of Wind Turbine Failure

Authors: Qiang Zhao Kunkun Bao Zhenfan Wei et al.

Source: IEEE Access, Vol 9, Pp 69044-69060 (2021)

Subject Terms: Density ratio, early warning, serialization ensemble, stacked autoencoder, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

File Description: electronic resource

Relation: https://ieeexplore.ieee.org/document/9366474/; https://doaj.org/toc/2169-3536

Access URL: https://doaj.org/article/d4afc64709684ba3b5912d918ab5bf4e

Web-S4AE: a semi-supervised stacked sparse autoencoder model for web robot detection.

Authors: Jagat, Rikhi Ram Sisodia, Dilip Singh Singh, Pradeep

Source: Neural Computing & Applications; Aug2023, Vol. 35 Issue 24, p17883-17898, 16p

Subject Terms: BLOGS, ROBOTS, DEEP learning, COMPUTER software, RANDOM forest algorithms, DECISION trees, SUPERVISED learning