Suchergebnisse - stack sparse autoencoder deep learning model ((ssae OR sae))

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

Autoren: Chuangyan Yang Peng Li Xun Lang et al.

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

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

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

Autoren: Du, Qiang Zhu, Danjiang Ni, Ming

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

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

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

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

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

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

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

Schlagwörter: SURFACE roughness, PREDICTION models, CLASSIFICATION

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)

Autoren: 孟成真 郝方舟 李欢欢 et al.

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

A Deep Learning Model to Predict the ncRNA-Protein Interactions Based on Sequences Information Only.

Autoren: Sewailem, Maha FM Arif, Muhammad Alam, Tanvir

Quelle: Bioinformatics & Biology Insights; 11/10/2025, Vol. 19, p1-12, 12p

Denoising magnetic resonance spectroscopy (MRS) data using stacked autoencoder for improving signal‐to‐noise ratio and speed of MRS.

Autoren: Wang, Jing Ji, Bing Lei, Yang et al.

Quelle: Medical Physics. Dec2023, Vol. 50 Issue 12, p7955-7966. 12p.

Schlagwörter: *NUCLEAR magnetic resonance spectroscopy, *DEEP learning, *SIGNAL-to-noise ratio, *HIGH resolution imaging, *MAGNETIC resonance imaging, *BRAIN tumors, *WALKING speed

Machine-learning models for Alzheimer's disease diagnosis using neuroimaging data: survey, reproducibility, and generalizability evaluation.

Autoren: Aghdam, Maryam Akhavan Bozdag, Serdar Saeed, Fahad

Quelle: Brain Informatics; 3/21/2025, Vol. 12 Issue 1, p1-27, 27p

Schlagwörter: ALZHEIMER'S disease, MACHINE learning, DATA management, BRAIN imaging, GENERALIZABILITY theory, DIAGNOSIS, DEEP learning, REPRODUCIBLE research

Deep Learning Model of Object Classification Feature Based on HOG

Autoren: HE Xiping,ZHANG Qionghua,LIU Bo

Quelle: Jisuanji gongcheng, Vol 42, Iss 12, Pp 176-180,187 (2016)

Schlagwörter: computer vision, object classification, histogram of oriented gradients(hog) feature, stacked autoencoder(sae), deep learning, Computer engineering. Computer hardware, TK7885-7895, Computer software, QA76.75-76.765

Dateibeschreibung: electronic resource

Relation: https://www.ecice06.com/fileup/1000-3428/PDF/201612031.pdf; https://doaj.org/toc/1000-3428

Zugangs-URL: https://doaj.org/article/da67fc497ffa422f9cc31b645bd74a65

Digital Twin Applications in the Water Sector: A Review.

Autoren: Ghorbani Bam, Pooria Rezaei, Nader Roubanis, Alexander et al.

Quelle: Water (20734441); Oct2025, Vol. 17 Issue 20, p2957, 49p

Schlagwörter: DIGITAL twin, WATER management, REAL-time computing, PREDICTIVE control systems, RESOURCE allocation, RESOURCE management, ENVIRONMENTAL infrastructure

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

Autoren: Tyagi, Praveen Kumar Agarwal, Dheeraj

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

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

Autoren: Xiongtao Zhang Shuai Dong Qing Shen et al.

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

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

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

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

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

Schlagwörter: TURBOFAN engines, FEATURE extraction, REMAINING useful life

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

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

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

Schlagwörter: 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.

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

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

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

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

Autoren: Rui Xu Tie Yan Shihui Sun et al.

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

Schlagwörter: 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.

Autoren: Madhavan Nair Vasanthamma, Hari Vinayak Thankaswamy, Jarin

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

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

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

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

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

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

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

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

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

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

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

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

Schlagwörter: LEAST squares, FEATURE extraction, MANUFACTURING processes, PRODUCT quality