Suchergebnisse - "sparse autoencoder"

Non-Line-of-Sight Multipath Classification Method for BDS Using Convolutional Sparse Autoencoder with LSTM

Autoren: Qin, Yahang Li, Zhenni Xie, Shengli et al.

Quelle: Tsinghua Science and Technology. 30:68-86

Schlagwörter: Satellites, Multipath classification, BeiDou Navigation Satellite System (BDS), Solid modeling, Three-dimensional displays, Long short-term memory (LSTM), Convolutional sparse autoencoder, Interference, Urban areas, Convolution, Long short term memory

Channel Estimation and Pilot Design for Uplink Sparse Code Multiple Access System Based on Complex-Valued Sparse Autoencoder

Autoren: Quan Yuan Dezhi Li Zhenyong Wang et al.

Quelle: IEEE Access, Vol 13, Pp 123559-123569 (2025)

Schlagwörter: Sparse code multiple access, Internet of Things, channel estimation, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, massive connection, sparse pilot structure, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, complex-valued sparse autoencoder, TK1-9971

Zugangs-URL: https://ieeexplore.ieee.org/ielx7/6287639/6514899/08667100.pdf
https://doaj.org/article/fbe5140ccf6b4575b898c315f16c8e7b
https://ieeexplore.ieee.org/document/8667100/

ESAE-SDA: ensemble sparse autoencoder framework for epigenomics-informed snoRNA-disease associations prediction

Autoren: Xinqing Jiang Xiaojun Chen Lifeng Xu et al.

Quelle: BMC Bioinformatics, Vol 26, Iss 1, Pp 1-18 (2025)

Schlagwörter: Epigenomics, snoRNA-disease associations (SDAs), Ensemble learning framework, Sparse autoencoder, Dynamically sampling, Artificial intelligence (AI), Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/1471-2105

Zugangs-URL: https://doaj.org/article/4a1813752009454491f25e2b31390236

Intelligent guidance system for the visually impaired person using a lightweight YOLOv4-based object detection and sparse autoencoder framework

Autoren: Alaa O. Khadidos Ayman Yafoz

Quelle: Journal of the Chinese Institute of Engineers. :1-21

SAGEA: Sparse Autoencoder-based Group Embeddings Aggregation for Fairness-Preserving Group Recommendations

Autoren: Vít Koštejn Ladislav Peška Martin Spišák

Quelle: Proceedings of the Nineteenth ACM Conference on Recommender Systems. :1290-1295

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

Real-Time Cloud Intrusion Detection with SpinalSAENet: A Sparse Autoencoder Approach with Focal Loss Optimization

Autoren: null N. Savitha

Quelle: Journal of Information Systems Engineering and Management. 10:54-69

Research on a face data processing method based on sparse autoencoder neural networks

Autoren: Zhipeng Chen Shenda Yu

Quelle: IET Conference Proceedings. 2024:325-330

XAI-DSCSA: explainable-AI-based deep semi-supervised convolutional sparse autoencoder for facial expression recognition

Autoren: M. Mohana P. Subashini George Ghinea

Quelle: Signal, Image and Video Processing. 19

Schlagwörter: autoencoder, semi-supervised learning, convolutional sparse autoencoder, facial expression recognition, feature representation, unsupervised learning

Zugangs-URL: https://bura.brunel.ac.uk/handle/2438/31141

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

Time-Inferred Sparse Autoencoder for Improved Full-Field Reconstruction from Sparse Measurements

Autoren: Nitin Nagesh Kulkarni Alessandro Sabato

Quelle: Computer Vision & Laser Vibrometry, Vol. 6 ISBN: 9788743804277

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

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

Quality‐related process monitoring approach based on sparse autoencoder and comprehensive KPLS

Autoren: Yikai Xue Haipeng Pan Ping Wu et al.

Quelle: The Canadian Journal of Chemical Engineering. 103:4939-4951

An ECG Signal Encryption and Classification Using Sparse Autoencoder and Optimized Neural Network Techniques

Autoren: Sumathi Shanmugasundaram Murukesh Chinnasamy Lakshmi Sangeetha Annaman

Quelle: Traitement du Signal. 42:557-568

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

Sparse Autoencoder Insights on Voice Embeddings

Autoren: Pluth, Daniel Zhou, Yu Gurbani, Vijay K.

Quelle: 2025 Conference on Artificial Intelligence x Multimedia (AIxMM). :1-6

Schlagwörter: FOS: Computer and information sciences, Computer Science - Computation and Language, Computation and Language (cs.CL)

Zugangs-URL: http://arxiv.org/abs/2502.00127

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