Suchergebnisse - stacked sparse autoencoder*

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

Autoren: Venkata Ramani Varanasi Razia Shaik

Quelle: Engineering, Technology & Applied Science Research. 15:24436-24441

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

Autoren: Abdussamad Hanita Daud Rajalingam Sokkalingam et al.

Quelle: Scientific Reports, Vol 15, Iss 1, Pp 1-22 (2025)

Schlagwörter: Autoencoder, Deep learning, Diabetes prediction, Feature selection, Machine learning, Sparse data, Medicine, Science

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/2045-2322

Zugangs-URL: https://doaj.org/article/2fea4da469ce4f14bd82089d355a10f4

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, Vol 42, Iss 1, Pp 79-114 (2025)

Schlagwörter: autoencoder, semi-supervised learning, Technology, Q1-390, Science (General), batch normalization, shale oil, favorable area

GRU‐based stacked sparse autoencoder with attention mechanism for process monitoring

Autoren: Zengdi Miao Ping Wu Zhenquan Wu et al.

Quelle: The Canadian Journal of Chemical Engineering. 103:3767-3785

Recognition of Human Activities via SSAE Algorithm: Implementing Stacked Sparse Autoencoder

Autoren: Jeffry Jeffry Firman Aziz Sri Kurniyan Sari et al.

Quelle: Journal of System and Computer Engineering (JSCE). 6:117-123

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

Autoren: Walaa Walaa Sanjar Mirzaliev Reneh Abokhoza

Quelle: Journal of Intelligent Systems and Internet of Things. 14:115-126

Research on Dung Beetle Optimization Based Stacked Sparse Autoencoder for Network Situation Element Extraction

Autoren: Yongchao Yang Pan Zhao

Quelle: IEEE Access, Vol 12, Pp 24014-24026 (2024)

Schlagwörter: Dung beetle optimization, network security, stacked sparse autoencoder, 0202 electrical engineering, electronic engineering, information engineering, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, network situation element extraction, TK1-9971

Zugangs-URL: https://doaj.org/article/1673678679c54e25ad55d197aad24b5e

Optimized Stacked Sparse Autoencoder for IoT-Cloud Smart Healthcare Networks

Autoren: Baba Fakruddin Ali B H L. Guganathan Prasanta Kumar Parida et al.

Quelle: 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL). :256-262

Tool wear monitoring for robotic milling based on multi-dimensional stacked sparse autoencoders and bidirectional LSTM networks with singularity features

Autoren: Chang’an Zhou Kaixing Zhang Jiawei Xu et al.

Quelle: The International Journal of Advanced Manufacturing Technology. 136:3543-3557

Network Anomaly Detection With Stacked Sparse Shrink Variational Autoencoders and Unbalanced XGBoost

Autoren: Jing Bi Ziyue Guan Haitao Yuan et al.

Quelle: IEEE Transactions on Sustainable Computing. 10:28-38

Latent Feature‐Based Type 2 Diabetes Prediction Using a Hybrid Stacked Sparse Autoencoder and Machine Learning Models

Autoren: null Abdussamad Hanita Daud Rajalingam Sokkalingam et al.

Quelle: Engineering Reports. 7

Industrial Internet of Things Cyber Threats Detection Through Deep Feature Learning and Stacked Sparse Autoencoder Based Classification

Autoren: R. Vijay Anand G. Magesh I. Alagiri et al.

Quelle: Transactions on Emerging Telecommunications Technologies. 36

Research on Deep Adaptive Clustering Method Based on Stacked Sparse Autoencoders for Concrete Truck Mixers Driving Conditions

Autoren: Ying Huang Fachao Jiang Haiming Xie

Quelle: World Electric Vehicle Journal, Vol 16, Iss 10, p 581 (2025)

Schlagwörter: concrete truck mixers, stacked sparse autoencoder, deep features extraction, adaptive clustering algorithm, Electrical engineering. Electronics. Nuclear engineering, TK1-9971, Transportation engineering, TA1001-1280

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/2032-6653/16/10/581; https://doaj.org/toc/2032-6653

Zugangs-URL: https://doaj.org/article/438be03da83f4e4b8087e7267170ff5e

Flatness pattern recognition based on stacked sparse denoising autoencoder and improved Osprey optimisation algorithm kernel-extreme learning machine

Autoren: Yaluo Zhou Shaochuan Zhang Wenguang Liu et al.

Quelle: Ironmaking & Steelmaking: Processes, Products and Applications.

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

Autoren: Hari Vinayak Madhavan Nair Vasanthamma Jarin Thankaswamy

Quelle: Iran Journal of Computer Science.

An Integrated Stacked Sparse Autoencoder and CNN-BLSTM Model for Ultra-Short-Term Wind Power Forecasting with Advanced Feature Learning

Autoren: Jinjie Liu Behzad Kazemtabrizi Hailiang Du et al.

Quelle: IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society. :1-6

Improved Human Activity Recognition Using Stacked Sparse Autoencoder (SSAE) Algorithm

Autoren: Firman Aziz Nurul Fathanah Mustamin Muhammad Rijal et al.

Quelle: JOIV: International Journal on Informatics Visualization, Vol 9, Iss 4, Pp 1469-1475 (2025)

Schlagwörter: classification, har, smartphones, svm, ssae, Computer software, QA76.75-76.765

Dateibeschreibung: electronic resource

Relation: https://joiv.org/index.php/joiv/article/view/3079; https://doaj.org/toc/2549-9610; https://doaj.org/toc/2549-9904

Zugangs-URL: https://doaj.org/article/b3862fba0473469baccb6c99d6abb913

Novel Pelican Optimization Algorithm (POA) With Stacked Sparse Autoencoder (SSAE) Based IDS for Network Security

Autoren: R. Kanimozhi A. Neela Madheswari

Quelle: Transactions on Emerging Telecommunications Technologies. 36

Utilizing chaos game representation for enhanced classification of SARS-CoV-2 variants with stacked sparse autoencoders

Autoren: Maria G. F. Coutinho Gabriel B. M. Câmara Raquel de M. Barbosa et al.

Quelle: Neural Computing and Applications. 36:19823-19837

Multi-Classification Decision Fusion Based on Stacked Sparse Shrink AutoEncoder and GS-Tabnet for Network Intrusion Detection

Autoren: Ziqi Wang Ziyue Guan Xiangxi Wu et al.

Quelle: 2024 10th International Conference on Control, Decision and Information Technologies (CoDIT). :2560-2565