Search Results - sparse autoencoder*

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

Authors: Quan Yuan Dezhi Li Zhenyong Wang et al.

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

Subject Terms: 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

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

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

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

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

Subject Terms: 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

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

Authors: Venkata Ramani Varanasi Razia Shaik

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

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

Authors: Xinqing Jiang Xiaojun Chen Lifeng Xu et al.

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

Subject Terms: 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

File Description: electronic resource

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

Access URL: https://doaj.org/article/4a1813752009454491f25e2b31390236

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

Authors: Abdussamad Hanita Daud Rajalingam Sokkalingam et al.

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

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

File Description: electronic resource

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

Access URL: https://doaj.org/article/2fea4da469ce4f14bd82089d355a10f4

Self-Regularization with Sparse Autoencoders for Controllable LLM-based Classification

Authors: Xuansheng Wu Wenhao Yu Xiaoming Zhai et al.

Source: Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2. :3250-3260

Subject Terms: FOS: Computer and information sciences, Computation and Language, Computation and Language (cs.CL)

Access URL: http://arxiv.org/abs/2502.14133

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

Authors: Alaa O. Khadidos Ayman Yafoz

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

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

Authors: Vít Koštejn Ladislav Peška Martin Spišák

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

Enhancing Gene Mutation Prediction With Sparse Regularized Autoencoders in Lung Cancer Radiomics Analysis

Authors: Muhammad Asif Munir Reehan Ali Shah Munwar Ali et al.

Source: IEEE Access, Vol 13, Pp 7407-7425 (2025)

Subject Terms: synthetic data generation, radiomics, Autoencoders, Electrical engineering. Electronics. Nuclear engineering, EGFR mutation, NSCLC, data augmentation, TK1-9971

Access URL: https://doaj.org/article/2422c62dbc9f4183b3a63a7607b2c83c

Regularized Stacked Autoencoder with Dropout-Layer to Overcome Overfitting in Numerical High-Dimensional Sparse Data

Authors: Abdussamad Abdussamad Hanita Daud Rajalingam Sokkalingam et al.

Source: Journal of Advanced Research Design. 129:60-74

Drive-by damage detection and localization exploiting continuous wavelet transform and multiple sparse autoencoders

Authors: Lorenzo Bernardini Francesco Morgan Bono Andrea Collina

Source: Railway Engineering Science, Vol 33, Iss 4, Pp 721-745 (2025)

Subject Terms: Drive-by, Sparse autoencoder, Steel truss railway bridge, Continuous wavelet transform, Damage detection, Damage localization, Railroad engineering and operation, TF1-1620

File Description: electronic resource

Relation: https://doaj.org/toc/2662-4745; https://doaj.org/toc/2662-4753

Access URL: https://doaj.org/article/36553b70af354470bfab169610f35260

Enhancing breast cancer classification using a deep sparse wavelet autoencoder approach

Authors: Sarah A. Alzakari Salima Hassairi Amel Ali Al. Hussan et al.

Source: Scientific Reports, Vol 15, Iss 1, Pp 1-11 (2025)

Subject Terms: Breast Cancer Classification, Wavelet Networks, Deep Learning, Autoencoders, Breakhis, Sparse coding, Medicine, Science

File Description: electronic resource

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

Access URL: https://doaj.org/article/6359431cda2f42fa868e5f32bf9e4109

Optimisation of sparse deep autoencoders for dynamic network embedding

Authors: Huimei Tang Yutao Zhang Lijia Ma et al.

Source: CAAI Transactions on Intelligence Technology, Vol 9, Iss 6, Pp 1361-1376 (2024)

Subject Terms: QA76.75-76.765, network embedding, Computational linguistics. Natural language processing, 0202 electrical engineering, electronic engineering, information engineering, dynamic networks, low‐dimensional feature space, deep autoencoder, Computer software, 02 engineering and technology, P98-98.5, sparse structure

Access URL: https://doaj.org/article/f943469b92564a6dbb34831ccf9d8f76

SSC-VAE: Structured Sparse Coding Based Variational Autoencoder for Detail Preserved Image Reconstruction

Authors: Hao Wang Lu Wang Zhongyu Wang et al.

Source: Proceedings of the AAAI Conference on Artificial Intelligence. 39:7665-7673

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

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

Uncovering Branch Specialization in InceptionV1 Using K Sparse Autoencoders

Authors: Bozoukov, Matthew

Source: 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). :4809-4813

Subject Terms: FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition

Access URL: http://arxiv.org/abs/2504.11489

Analyzing Hierarchical Structure in Vision Models with Sparse Autoencoders

Authors: Olson, Matthew Lyle Hinck, Musashi Ratzlaff, Neale et al.

Source: 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). :4835-4839

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Machine Learning (cs.LG)

Access URL: http://arxiv.org/abs/2505.15970

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

Authors: null N. Savitha

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

Genotypic Data Sparse Dimensionality Reduction and Variational Autoencoder-Based Feature Extraction

Authors: Qiushi Zhang Junfeng Liu Yizhou Chen et al.

Source: 2025 28th International Conference on Computer Supported Cooperative Work in Design (CSCWD). :1634-1639

Malware Detection in Internet of Things Devices Through the Utilization of Deep Sparse Autoencoders and Signal Processing Methods

Authors: Laith Fouad Zainab Mohanad Ansaf Ahmed Hussein Ahmed et al.

Source: 2025 IEEE 4th International Conference on Computing and Machine Intelligence (ICMI). :1-5