Search Results - stack sparse autoencoder deep learning model (SSAE)~

A novel NMF-DiCCA deep learning method and its application in wind turbine blade icing failure identification.

Authors: Chuangyan Yang Peng Li Xun Lang et al.

Source: Maintenance & Reliability / Eksploatacja i Niezawodność; 2024, Vol. 26 Issue 4, p1-14, 14p

Subject Terms: MACHINE learning, SINGULAR value decomposition, LATENT variables, AUTOENCODERS, NONNEGATIVE matrices, WIND turbine blades

一种电网调控防误优化方法研究及应用. (Chinese)

Alternate Title: Research and Application of an Optimization Method for Power Grid Error Prevention and Control. (English)

Authors: 孟成真 郝方舟 李欢欢 et al.

Source: Control Engineering of China; Nov2025, Vol. 32 Issue 11, p2073-2080, 8p

Fault diagnosis of brushless DC motor based on Stack Sparse Autoencoder.

Authors: Du, Qiang Zhu, Danjiang Ni, Ming

Source: Journal of Physics: Conference Series; Nov2023, Vol. 2674 Issue 1, p1-8, 8p

On-line surface roughness classification for multiple CNC milling conditions based on transfer learning and neural network.

Authors: Deng, Congying Ye, Bo Lu, Sheng et al.

Source: International Journal of Advanced Manufacturing Technology; Sep2023, Vol. 128 Issue 3/4, p1063-1076, 14p

Subject Terms: SURFACE roughness, PREDICTION models, CLASSIFICATION

Microscopic segmentation and classification of COVID‐19 infection with ensemble convolutional neural network.

Authors: Amin, Javeria Anjum, Muhammad Almas Sharif, Muhammad et al.

Source: Microscopy Research & Technique; Jan2022, Vol. 85 Issue 1, p385-397, 13p

SDCA: a novel stack deep convolutional autoencoder – an application on retinal image denoising.

Authors: Ghosh, Swarup Kr Biswas, Biswajit Ghosh, Anupam

Source: IET Image Processing (Wiley-Blackwell); Dec2019, Vol. 13 Issue 14, p2778-2789, 12p

Feature Analysis Network: An Interpretable Idea in Deep Learning.

Authors: Li, Xinyu Gao, Xiaoguang Wang, Qianglong et al.

Source: Cognitive Computation; May2024, Vol. 16 Issue 3, p803-826, 24p

Systematic review of automated sleep apnea detection based on physiological signal data using deep learning algorithm: a meta-analysis approach.

Authors: Tyagi, Praveen Kumar Agarwal, Dheeraj

Source: Biomedical Engineering Letters; Aug2023, Vol. 13 Issue 3, p293-312, 20p

Remaining useful life prognosis of turbofan engines based on deep feature extraction and fusion.

Authors: Peng, Cheng Chen, Yufeng Gui, Weihua et al.

Source: Scientific Reports; 4/21/2022, Vol. 12 Issue 1, p1-14, 14p

Subject Terms: TURBOFAN engines, FEATURE extraction, REMAINING useful life

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

Deep extreme learning machine with knowledge augmentation for EEG seizure signal recognition.

Authors: Xiongtao Zhang Shuai Dong Qing Shen et al.

Source: Frontiers in Neuroinformatics; 2023, p1-13, 13p

Subject Terms: ELECTROENCEPHALOGRAPHY, MACHINE learning, SIGNAL classification, EPILEPSY, SEIZURES (Medicine), DEEP learning

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

A hybrid stacked sparse autoencoder and LightGBM framework for high-performance intrusion detection in IoT networks.

Authors: Madhavan Nair Vasanthamma, Hari Vinayak Thankaswamy, Jarin

Source: Iran Journal of Computer Science; Dec2025, Vol. 8 Issue 4, p2175-2198, 24p

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

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

SSAE‐MLP: Stacked sparse autoencoders‐based multi‐layer perceptron for main bearing temperature prediction of large‐scale wind turbines.

Authors: Xiao, Xiaocong Liu, Jianxun Liu, Deshun et al.

Source: Concurrency & Computation: Practice & Experience; 9/10/2021, Vol. 33 Issue 17, p1-15, 15p

Subject Terms: DEEP learning, WIND turbines, SUPERVISED learning, MACHINE learning, SUPERVISORY control systems, MAINTENANCE costs, GREEDY algorithms

Piranha Foraging Optimization Algorithm with Deep Learning Enabled Fault Detection in Blockchain-Assisted Sustainable IoT Environment.

Authors: Assiri, Haitham

Source: Sustainability (2071-1050); Feb2025, Vol. 17 Issue 4, p1362, 24p

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

Construction of Transformer Fault Diagnosis and Prediction Model Based on Deep Learning.

Authors: Xiaomeng Li

Source: Journal of Computing & Information Technology; Dec2022, Vol. 30 Issue 4, p223-238, 16p

Subject Terms: DEEP learning, FAULT diagnosis, TRANSFORMER models, PREDICTION models, SHORT-term memory, LONG-term memory