Search Results - stack sparse autoencoder deep learning model (((suae OR sua) OR sae))

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

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

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

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

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

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

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

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

Subject Terms: *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.

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

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

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

Digital Twin Applications in the Water Sector: A Review.

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

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

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

Deep Learning Model of Object Classification Feature Based on HOG

Authors: HE Xiping,ZHANG Qionghua,LIU Bo

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

Subject Terms: 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

File Description: electronic resource

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

Access URL: https://doaj.org/article/da67fc497ffa422f9cc31b645bd74a65

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

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

Deep Feature Learning Architectures for Daily Reservoir Inflow Forecasting.

Authors: Li, Chuan Bai, Yun Zeng, Bo

Source: Water Resources Management; Nov2016, Vol. 30 Issue 14, p5145-5161, 17p

Subject Terms: RESERVOIRS, BODIES of water, HYDRAULIC structures, ARTIFICIAL neural networks, MATHEMATICAL models

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

Authors: Juying Dai Jian Tang Faming Shao et al.

Source: Applied Sciences, Vol 9, Iss 13, p 2743 (2019)

Subject Terms: IMFs, multiscale permutation entropy, stacked sparse denoising autoencoder, fault diagnosis, 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/2076-3417/9/13/2743; https://doaj.org/toc/2076-3417

Access URL: https://doaj.org/article/513f8bf811eb491dab82fad9ce1ea4c2

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

Effects of Automatic Hyperparameter Tuning on the Performance of Multi-Variate Deep Learning-Based Rainfall Nowcasting.

Authors: Amini, Amirmasoud Dolatshahi, Mehri Kerachian, Reza

Source: Water Resources Research; Jan2023, Vol. 59 Issue 1, p1-27, 27p

Subject Terms: ARTIFICIAL neural networks, RUNOFF, NUMERICAL weather forecasting, HUMIDITY, STORMS

Geographic Terms: TEHRAN (Iran)

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

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

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

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

Automatic Malignant and Benign Skin Cancer Classification Using a Hybrid Deep Learning Approach.

Authors: Bassel, Atheer Abdulkareem, Amjed Basil Alyasseri, Zaid Abdi Alkareem et al.

Source: Diagnostics (2075-4418); Oct2022, Vol. 12 Issue 10, p2472-N.PAG, 15p

Subject Terms: SKIN cancer, DEEP learning, TUMOR classification, SUPPORT vector machines, SKIN imaging, SPEECH processing systems

End-to-end multidimensional interpretable tourism demand combined forecasting model based on feature fusion.

Authors: Wu, Binrong Lin, Jiacheng Lv, Sheng-Xiang et al.

Source: Applied Intelligence; Aug2025, Vol. 55 Issue 12, p1-32, 32p

Subject Terms: ARTIFICIAL neural networks, LONG short-term memory, MULTI-objective optimization, OPTIMIZATION algorithms, AURORAS, DEMAND forecasting, DEEP learning