Search Results - stacked sparse autoencoder(((sae OR sage) OR ssad))

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

Authors: Jo, Mi Young Kim, Hyun Jung

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

Subject Terms: 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.

Authors: Varanasi, Venkata Ramani Razia, Shaik

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

Subject Terms: 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.

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

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

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

Authors: He, Zhaopeng Shi, Tielin Chen, Xu

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

Subject Terms: 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.

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

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

Subject Terms: 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.

Authors: Fouda, Walaa Mirzaliev, Sanjar Abokhoza, Reneh

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

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

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

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

Source: 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.

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

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

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

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

Subject Terms: GEARBOXES, FAULT diagnosis, POWER transmission, DEEP learning, AUTOENCODERS

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

Authors: Dandan Yao Yinghua Yang Xiaozhi Liu

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

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Access URL: https://ieeexplore.ieee.org/document/9188550/

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

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

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

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

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

Authors: Yumei Qi Changqing Shen Dong Wang et al.

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

Subject Terms: Sparse autoencoder, ensemble empirical mode decomposition, autoregressive model, fault diagnosis, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

File Description: electronic resource

Relation: https://ieeexplore.ieee.org/document/7983338/; https://doaj.org/toc/2169-3536

Access URL: https://doaj.org/article/da13decb21fb43e09782631267e8200c

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

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

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

Subject Terms: 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.

Authors: Zhou, Juan Zhang, Shun Wang, Peng

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

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

Deep learning with leagues championship algorithm based intrusion detection on cybersecurity driven industrial IoT systems.

Authors: Alotaibi, Saud S. Alghamdi, Turki Ali

Source: Scientific Reports; 8/19/2025, Vol. 15 Issue 1, p1-17, 17p

Subject Terms: DEEP learning, INTRUSION detection systems (Computer security), AUTOENCODERS, ALGORITHMS, CYBER physical systems, INTERNET security, CYBERTERRORISM, FEATURE selection

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)

Authors: 曾梦洁 李舜酩 李冉冉 et al.

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

HYBRID DEEP LEARNING NETWORK-BASED APPROACH FOR AIR COMBAT MANEUVER DECISION-MAKING.

Authors: SHEEBA D. LINGESHWARI G. ANORELIN A.

Source: i-Manager's Journal on Computer Science; Sep2025, Vol. 13 Issue 2, p83-93, 11p

Subject Terms: LONG short-term memory, DEEP learning, DECISION making, METADATA, AIR warfare, ENSEMBLE learning, AUTONOMOUS robots