Search Results - stacking denoising sparse autoencoder~

A neural convolutional network intrusion detection model based on autoencoder dimension reduction

Authors: SUN Jing DING Jiawei FENG Guanghui

Source: Dianxin kexue, Vol 41, Pp 129-138 (2025)

Subject Terms: network attack, intrusion detection model, stacking denoising sparse autoencoder, convolutional attention mechanism, residual network, Telecommunication, TK5101-6720, Technology

File Description: electronic resource

Relation: https://doaj.org/toc/1000-0801

Access URL: https://doaj.org/article/342a07987715452a83672954a94cd12f

Research of stacked denoising sparse autoencoder

Authors: Jian Zhang Shifei Ding Nan Zhang et al.

Source: Neural Computing and Applications. 30:2083-2100

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

Access URL: https://link.springer.com/article/10.1007/s00521-016-2790-x
https://dblp.uni-trier.de/db/journals/nca/nca30.html#MengD0018

一种基于自编码器降维的神经卷积网络入侵检测模型. (Chinese)

Alternate Title: A neural convolutional network intrusion detection model based on autoencoder dimension reduction. (English)

Authors: 孙敬 丁嘉伟 冯光辉

Source: Telecommunications Science; 2025, Vol. 41 Issue 2, p129-138, 10p

Research of stacked denoising sparse autoencoder.

Authors: Meng, Lingheng Ding, Shifei Zhang, Nan et al.

Source: Neural Computing & Applications; Oct2018, Vol. 30 Issue 7, p2083-2100, 18p

Subject Terms: SIGNAL denoising, ARTIFICIAL intelligence, MACHINE learning, DEEP learning, ENCODING

Study of anomaly registration detection based on multilayer kernel autoencoder extreme learning machine model.

Authors: Gu, Zhengmin Guo, Lang Huang, Jie et al.

Source: Scientific Reports. 11/21/2025, Vol. 15 Issue 1, p1-14. 14p.

Subject Terms: *ANOMALY detection (Computer security), *MACHINE learning, *DEEP learning, *RESOURCE allocation, *MEDICAL informatics, *DIMENSIONAL reduction algorithms, *FEATURE extraction, *AUTOENCODERS

Incipient fault detection and process monitoring of thermal power plant pulverizing system based on deep representation learning.

Authors: Zeng, Lei Zhang, Xin Yu, Ke et al.

Source: Transactions of the Institute of Measurement & Control. Nov2025, Vol. 47 Issue 15, p3111-3123. 13p.

Subject Terms: *FAULT diagnosis, *DEEP learning, *STEAM power plants, *K-nearest neighbor classification, *PROCESS control systems, *FEATURE extraction

Building feature space of extreme learning machine with sparse denoising stacked-autoencoder.

Authors: Cao, Le-le Huang, Wen-bing Sun, Fu-chun

Source: Neurocomputing. Jan2016 Part A, Vol. 174, p60-71. 12p.

Subject Terms: *FEATURE extraction, *MACHINE learning, *SIGNAL denoising, *GENERALIZATION, *MATHEMATICAL transformations, *REGRESSION analysis

Leakage aperture identification of natural gas pipeline based on compressed acquisition and DSAE.

Authors: Sun, Jiedi Liu, Zhao Wen, Jiangtao et al.

Source: Structural Health Monitoring; Jul2023, Vol. 22 Issue 4, p2856-2867, 12p

Subject Terms: ARTIFICIAL neural networks, NATURAL gas pipelines, GAS leakage, LEAKAGE, FEATURE extraction, LEAK detection

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

A stacking denoising auto-encoder with sample weight approach for order remaining completion time prediction in complex discrete manufacturing workshop.

Authors: Daoyuan Liu Yu Guo Shaohua Huang et al.

Source: International Journal of Production Research; May2023, Vol. 61 Issue 10, p3346-3259, 14p

Subject Terms: DATA distribution, PREDICTION models, FORECASTING, PRODUCTION planning, INTERNET of things

Deep autoencoder-driven feature learning and meta-heuristic optimized machine learning modelling for crop water stress identification.

Authors: Subeesh, A. Chauhan, Naveen Chandel, Narendra Singh et al.

Source: Evolving Systems; Sep2025, Vol. 16 Issue 3, p1-19, 19p

Unsupervised abnormality detection through mixed structure regularization (MSR) in deep sparse autoencoders.

Authors: Freiman, Moti Manjeshwar, Ravindra Goshen, Liran

Source: Medical Physics; May2019, Vol. 46 Issue 5, p2223-2231, 9p

Subject Terms: MATHEMATICAL regularization, ABNORMALITIES in animals, RANDOM noise theory, CORONARY disease, SPARSE approximations, DATA corruption, COMPUTED tomography

Fault Diagnosis of Rolling Bearing Based on Multiscale Intrinsic Mode Function Permutation Entropy and a Stacked Sparse Denoising Autoencoder.

Authors: Dai, Juying Tang, Jian Shao, Faming et al.

Source: Applied Sciences (2076-3417); Jul2019, Vol. 9 Issue 13, p2743, 28p

Subject Terms: HILBERT-Huang transform, FAULT diagnosis, ENTROPY (Information theory), PERMUTATIONS, DEEP learning

Company/Entity: INTERNATIONAL Monetary Fund, SOCIETY of Automotive Engineers

Radar HRRP target recognition based on stacked denosing sparse autoencoder.

Authors: Tai, Guangxing Wang, Yanhua Li, Yang et al.

Source: Journal of Engineering; Nov2019, Vol. 2019 Issue 22, p7945-7949, 5p

Subject Terms: RADAR target recognition, SIGNAL denoising, RADAR resolution, RADAR signal processing, SIGNAL resolution

Static hand gesture recognition using stacked Denoising Sparse Autoencoders.

Authors: Kumar, Varun Nandi, G.C. Kala, Rahul

Source: 2014 Seventh International Conference on Contemporary Computing (IC3); 2014, p99-104, 6p

An intelligent fusion-based transfer learning model with artificial protozoa optimiser for enhancing gesture recognition to aid visually impaired people.

Authors: Mengash, Hanan Abdullah Alqadi, Basma S. Marzouk, Radwa

Source: Scientific Reports; 11/21/2025, Vol. 15 Issue 1, p1-20, 20p

Subject Terms: PEOPLE with visual disabilities, ARTIFICIAL intelligence, HUMAN-computer interaction, IMAGE processing, SIGN language, DEEP learning, TRANSFER of training, HUMAN activity recognition

SSAE‐KPLS: A quality‐related process monitoring via integrating stacked sparse autoencoder with kernel partial least squares.

Authors: Ye, Zhenyu Wu, Ping He, Yuchen et al.

Source: Canadian Journal of Chemical Engineering; Oct2023, Vol. 101 Issue 10, p5858-5873, 16p

Subject Terms: LEAST squares, FEATURE extraction, MANUFACTURING processes, PRODUCT quality

Automated multi-class skin cancer classification using white shark optimizer with ensemble learning classifier on dermoscopy images.

Authors: Arumugam, R. Vijay Saravanan, S.

Source: Multimedia Tools & Applications; Mar2025, Vol. 84 Issue 8, p4857-4879, 23p

Subject Terms: ENSEMBLE learning, COMPUTER-aided diagnosis, ARTIFICIAL intelligence, DEEP learning, AUTOENCODERS

Representation learning via an integrated autoencoder for unsupervised domain adaptation.

Authors: Zhu, Yi Wu, Xindong Qiang, Jipeng et al.

Source: Frontiers of Computer Science; Oct2023, Vol. 17 Issue 5, p1-13, 13p

Artificial neural network technique for improving prediction of credit card default: A stacked sparse autoencoder approach.

Authors: Ebiaredoh-Mienye, Sarah. A. Esenogho, E. Swart, Theo G.

Source: International Journal of Electrical & Computer Engineering (2088-8708); Oct2021, Vol. 11 Issue 5, p4392-4402, 11p

Subject Terms: ARTIFICIAL neural networks, DEFAULT (Finance), CREDIT cards, MACHINE learning, MACHINE performance