Suchergebnisse - stacked sparse autoencoder((sae OR sage))~

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

A Comparative Study of Lightweight, Sparse Autoencoder-Based Classifiers for Edge Network Devices: An Efficiency Analysis of Feed-Forward and Deep Neural Networks.

Autoren: Jo, Mi Young Kim, Hyun Jung

Quelle: Sensors (14248220); Oct2025, Vol. 25 Issue 20, p6439, 22p

Schlagwörter: EDGE computing, AUTOENCODERS, RESEARCH evaluation, ANOMALY detection (Computer security), FEEDFORWARD neural networks, ARTIFICIAL neural networks

An Enhanced Intrusion Detection System Using Attention-Based Stacked Sparse Autoencoder Feature Extraction.

Autoren: Varanasi, Venkata Ramani Razia, Shaik

Quelle: Engineering, Technology & Applied Science Research; Aug2025, Vol. 15 Issue 4, p24436-24441, 6p

Schlagwörter: INTRUSION detection systems (Computer security), FEATURE extraction, SIGNAL denoising, LONG short-term memory, ARTIFICIAL neural networks, BIG data, 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

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

An Innovative Study for Tool Wear Prediction Based on Stacked Sparse Autoencoder and Ensemble Learning Strategy.

Autoren: He, Zhaopeng Shi, Tielin Chen, Xu

Quelle: Sensors (14248220); Apr2025, Vol. 25 Issue 8, p2391, 14p

Schlagwörter: ARTIFICIAL neural networks, MACHINE learning, ENSEMBLE learning, NUMERICAL control of machine tools, AUTOENCODERS, DEEP learning

Early identification of out-of-roundness damage wheels in railway freight vehicles using a wayside system and a stacked sparse autoencoder.

Autoren: Jorge, T. Magalhães, J. Silva, R. et al.

Quelle: Vehicle System Dynamics; Feb2025, Vol. 63 Issue 2, p232-257, 26p

Schlagwörter: FEATURE extraction, RAILROAD trains, AUTOENCODERS, ARTIFICIAL intelligence, MULTISENSOR data fusion, ROLLING stock

Social Media Platform Based Evaluation of Teaching Style on Online Education System using Heuristic Search with Stacked Sparse Autoencoder Model.

Autoren: Fouda, Walaa Mirzaliev, Sanjar Abokhoza, Reneh

Quelle: Journal of Intelligent Systems & Internet of Things; 2025, Vol. 14 Issue 2, p115-126, 12p

Schlagwörter: SOCIAL media in education, VIRTUAL classrooms, ONLINE education, OPTIMIZATION algorithms, TEACHING methods

Stacked Sparse Autoencoder-Based Deep Network for Fault Diagnosis of Rotating Machinery

Autoren: Yumei Qi Changqing Shen Dong Wang et al.

Quelle: IEEE Access, Vol 5, Pp 15066-15079 (2017)

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, autoregressive model, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, fault diagnosis, ensemble empirical mode decomposition, Sparse autoencoder, TK1-9971

Zugangs-URL: https://doaj.org/article/da13decb21fb43e09782631267e8200c
https://dblp.uni-trier.de/db/journals/access/access5.html#QiSWSJZ17
http://ieeexplore.ieee.org/document/7983338
https://ieeexplore.ieee.org/abstract/document/7983338
https://doi.org/10.1109/ACCESS.2017.2728010

Remaining Useful Life Prediction Based on Stacked Sparse Autoencoder and Echo State Network

Autoren: Dandan Yao Yinghua Yang Xiaozhi Liu

Quelle: 2020 39th Chinese Control Conference (CCC). :5922-5926

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: https://ieeexplore.ieee.org/document/9188550/

End-to-end deep SAE-DNN model for predicting Egyptian buffalo calf sex, weight, and daily milk yield.

Autoren: Issa, Sali Ali, Montaser Elsayed Wang, Qi et al.

Quelle: Tropical Animal Health & Production. Dec2025, Vol. 57 Issue 9, p1-9. 9p.

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

Six Phase Transmission Line Protection Using Bat Algorithm Tuned Stacked Sparse Autoencoder.

Autoren: Rao Althi, Tirupathi Koley, Ebha Ghosh, Subhojit et al.

Quelle: Electric Power Components & Systems; 2023, Vol. 51 Issue 2, p113-130, 18p

Schlagwörter: ELECTRIC lines, ALGORITHMS, DIGITAL computer simulation, ELECTRIC fault location

Fault Diagnosis for Power Batteries Based on a Stacked Sparse Autoencoder and a Convolutional Block Attention Capsule Network.

Autoren: Zhou, Juan Zhang, Shun Wang, Peng

Quelle: Processes; Apr2024, Vol. 12 Issue 4, p816, 19p

Schlagwörter: CAPSULE neural networks, FAULT diagnosis, ELECTRIC vehicle batteries, ELECTRIC vehicles, ELECTRIC power failures, ELECTRIC fault location, FAILURE mode & effects analysis

A COMPARATIVE STUDY OF DIVERSE AUTOENCODER MODELS IN LOCAL GEAR PITTING FAULT DIAGNOSIS.

Autoren: YURTSEVER, Mustafa ÜMÜTLÜ, Rafet Can ÖZTÜRK, Hasan

Quelle: Konya Journal of Engineering Sciences / Konya Mühendislik Bilimleri Dergisi; Mar2025, Vol. 13 Issue 1, p59-74, 16p

Schlagwörter: GEARBOXES, FAULT diagnosis, POWER transmission, DEEP learning, AUTOENCODERS

An Automatic Feature Learning and Fault Diagnosis Method Based on Stacked Sparse Autoencoder

Autoren: Jie Liu Dading Li Zhongkui Zhu et al.

Quelle: Lecture Notes in Electrical Engineering ISBN: 9789811057670

Zugangs-URL: https://rd.springer.com/chapter/10.1007/978-981-10-5768-7_39
https://link.springer.com/chapter/10.1007%2F978-981-10-5768-7_39

CoCoChain: A Concept-Aware Consensus Protocol for Secure Sensor Data Exchange in Vehicular Ad Hoc Networks.

Autoren: Juárez, Rubén Nicolas-Sans, Ruben Tamames, José Fernández

Quelle: Sensors (14248220); Oct2025, Vol. 25 Issue 19, p6226, 51p

Schlagwörter: VEHICULAR ad hoc networks, BLOCKCHAINS, MOBILE communication systems, DATA transmission systems, DATA integrity, AUTOENCODERS

基于堆叠稀疏判别自编码的滚动轴承 智能故障诊断方法. (Chinese)

Alternate Title: Intelligent Fault Diagnosis Method for Rolling Bearings Based on Stacked Sparse Discriminative Autoencoders. (English)

Autoren: 曾梦洁 李舜酩 李冉冉 et al.

Quelle: Bearing; Mar2024, Issue 3, p77-98, 8p

Optimization Strategy of a Stacked Autoencoder and Deep Belief Network in a Hyperspectral Remote-Sensing Image Classification Model.

Autoren: Dai, Xiaoai Cheng, Junying Guo, Shouheng et al.

Quelle: Discrete Dynamics in Nature & Society; 4/28/2023, p1-20, 20p

Schlagwörter: DEEP learning, IMAGE recognition (Computer vision), REMOTE-sensing images, BOLTZMANN machine, REMOTE sensing, FEATURE extraction, ELECTRONIC data processing