Výsledky vyhledávání - stack sparse autoencoder deep learning model (sae)~

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

Autoři: Chuangyan Yang Peng Li Xun Lang a další

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

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

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

Autoři: Du, Qiang Zhu, Danjiang Ni, Ming

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

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

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

Zdroj: 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.

Autoři: Wang, Jing Ji, Bing Lei, Yang a další

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

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

Deep Learning Model of Object Classification Feature Based on HOG

Autoři: HE Xiping,ZHANG Qionghua,LIU Bo

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

Témata: 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

Popis souboru: electronic resource

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

Přístupová URL adresa: https://doaj.org/article/da67fc497ffa422f9cc31b645bd74a65

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

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

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

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

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

Autoři: Amin, Javeria Anjum, Muhammad Almas Sharif, Muhammad a další

Zdroj: 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.

Autoři: Deng, Congying Ye, Bo Lu, Sheng a další

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

Témata: SURFACE roughness, PREDICTION models, CLASSIFICATION

Digital Twin Applications in the Water Sector: A Review.

Autoři: Ghorbani Bam, Pooria Rezaei, Nader Roubanis, Alexander a další

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

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

Deep Feature Learning Architectures for Daily Reservoir Inflow Forecasting.

Autoři: Li, Chuan Bai, Yun Zeng, Bo

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

Témata: 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

Autoři: Juying Dai Jian Tang Faming Shao a další

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

Témata: 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

Popis souboru: electronic resource

Relation: https://www.mdpi.com/2076-3417/9/13/2743; https://doaj.org/toc/2076-3417

Přístupová URL adresa: https://doaj.org/article/513f8bf811eb491dab82fad9ce1ea4c2

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

Autoři: Amini, Amirmasoud Dolatshahi, Mehri Kerachian, Reza

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

Témata: ARTIFICIAL neural networks, RUNOFF, NUMERICAL weather forecasting, HUMIDITY, STORMS

Geografický termín: TEHRAN (Iran)

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

Autoři: Tyagi, Praveen Kumar Agarwal, Dheeraj

Zdroj: 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.

Autoři: Peng, Cheng Chen, Yufeng Gui, Weihua a další

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

Témata: TURBOFAN engines, FEATURE extraction, REMAINING useful life

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

Autoři: Abdussamad Daud, Hanita Sokkalingam, Rajalingam a další

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

Témata: 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.

Autoři: Abdussamad Daud, Hanita Sokkalingam, Rajalingam a další

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

Témata: 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.

Autoři: Xiongtao Zhang Shuai Dong Qing Shen a další

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

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

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

Autoři: Madhavan Nair Vasanthamma, Hari Vinayak Thankaswamy, Jarin

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

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

Autoři: Wu, Binrong Lin, Jiacheng Lv, Sheng-Xiang a další

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

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

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

Autoři: Rui Xu Tie Yan Shihui Sun a další

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

Témata: SHALE oils, SUPERVISED learning, PETROLEUM prospecting, AUTOENCODERS, MACHINE learning