Search Results - sparse deep convolutional autoencoder*

Enhancing breast cancer classification using a deep sparse wavelet autoencoder approach.

Authors: Alzakari SA Hassairi S Hussan AAA et al.

Source: Scientific reports [Sci Rep] 2025 Jul 19; Vol. 15 (1), pp. 26194. Date of Electronic Publication: 2025 Jul 19.

Publication Type: Journal Article

Journal Info: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE

MeSH Terms: Breast Neoplasms*/diagnostic imaging , Breast Neoplasms*/classification , Breast Neoplasms*/pathology , Breast Neoplasms*/diagnosis , Wavelet Analysis* , Deep Learning* , Image Processing, Computer-Assisted*/methods, Humans ; Female ; Algorithms ; Image Interpretation, Computer-Assisted/methods ; Mammography/methods ; Autoencoder

Sparse deep encoded features with enhanced sinogramic red deer optimization for fault inspection in wafer maps.

Authors: Altantawy, Doaa A. Yakout, Mohamed A.

Source: Journal of Intelligent Manufacturing. Jun2025, Vol. 36 Issue 5, p3359-3397. 39p.

Subject Terms: *MANUFACTURING processes, *INFORMATION processing, CONVOLUTIONAL neural networks, OPTIMIZATION algorithms, AUTOENCODERS

SPAER: Sparse Deep Convolutional Autoencoder Model to Extract Low Dimensional Imaging Biomarkers for Early Detection of Breast Cancer Using Dynamic Thermography

Authors: Bardia Yousefi Hamed Akbari Michelle Hershman et al.

Source: Applied Sciences, Vol 11, Iss 7, p 3248 (2021)

Subject Terms: breast cancer, thermography, sparse deep convolutional autoencoder, matrix factorization, dimensionality reduction, thermomics, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

File Description: electronic resource

Relation: https://www.mdpi.com/2076-3417/11/7/3248; https://doaj.org/toc/2076-3417

Access URL: https://doaj.org/article/3c70757effa743f3b9bef5e4d264eb2f

Self‐Attention‐Based Deep Convolutional Sparse Dense Autoencoder Model With Chebyshev‐Based Osprey Algorithm for Cardiovascular Disease Detection

Authors: N. J. Divya N. Suresh Kumar R. Kanniga Devi

Source: Transactions on Emerging Telecommunications Technologies. 36

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

Authors: M. Mohana P. Subashini George Ghinea

Source: Signal, Image and Video Processing. 19

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

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

DOA estimation method for sparse arrays based on deep convolutional autoencoder and deep convolutional neural network

Authors: Shuhan Guo Qin Zhang Xiaolong Fu et al.

Source: Digital Signal Processing. 168:105627

A novel multichannel sparse convolutional autoencoder for electrocardiogram signal compression.

Authors: Bekiryazıcı, Tahir Damkacı, Mehmet Aydemir, Gürkan et al.

Source: Journal of Electrocardiology. Nov2025, Vol. 93, pN.PAG-N.PAG. 1p.

Findings from Department of Computer Sciences and Engineering Yields New Data on Cardiovascular Diseases and Conditions (Self-attention-based Deep Convolutional Sparse Dense Autoencoder Model With Chebyshev-based Osprey Algorithm for...).

Source: Cardiovascular Week; 10/13/2025, p364-364, 1p

Subject Terms: CARDIOVASCULAR diseases, CONVOLUTIONAL neural networks, FEATURE extraction, DATA analysis, STATISTICAL accuracy, DEEP learning, ELECTROCARDIOGRAPHY, MEDICAL screening

Efficient Mapping Between Void Shapes and Stress Fields Using Deep Convolutional Neural Networks With Sparse Data

Authors: Anindya Bhaduri Nesar Ramachandra Sandipp Krishnan Ravi et al.

Source: Journal of Computing and Information Science in Engineering. 24

Subject Terms: 0209 industrial biotechnology, 0203 mechanical engineering, 02 engineering and technology

Level-crossing processing and deep convolutional neural network for arrhythmia classification in telehealth services.

Authors: Hussain SF Mian Qaisar S Sherjeel M

Source: Physical and engineering sciences in medicine [Phys Eng Sci Med] 2025 Nov 03. Date of Electronic Publication: 2025 Nov 03.

Publication Type: Journal Article

Journal Info: Publisher: Springer Country of Publication: Switzerland NLM ID: 101760671 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2662-4737 (Electronic) Linking ISSN: 26624729 NLM ISO Abbreviation: Phys Eng Sci Med Subsets: MEDLINE

SPAER: Sparse Deep Convolutional Autoencoder Model to Extract Low Dimensional Imaging Biomarkers for Early Detection of Breast Cancer Using Dynamic Thermography.

Authors: Yousefi, Bardia Akbari, Hamed Hershman, Michelle et al.

Source: Applied Sciences (2076-3417); Apr2021, Vol. 11 Issue 7, p3248, 15p

Subject Terms: EARLY detection of cancer, THERMOGRAPHY, MATRIX decomposition, DEEP learning, INFRARED imaging, NONNEGATIVE matrices

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

Authors: Mohana, M. Subashini, P. Ghinea, George

Source: Signal, Image & Video Processing; May2025, Vol. 19 Issue 5, p1-18, 18p

Automatic Baseline Correction of 1D Signals Using a Parameter-Free Deep Convolutional Autoencoder Algorithm.

Authors: Górski, Łukasz Jakubowska, Małgorzata

Source: Applied Sciences (2076-3417); Nov2025, Vol. 15 Issue 22, p12069, 18p

Subject Terms: AUTOENCODERS, ANALYTICAL chemistry, DEEP learning, ALGORITHMS, SIGNAL processing

Self‐Attention‐Based Deep Convolutional Sparse Dense Autoencoder Model With Chebyshev‐Based Osprey Algorithm for Cardiovascular Disease Detection.

Authors: Divya, N. J. Kumar, N. Suresh Devi, R. Kanniga

Source: Transactions on Emerging Telecommunications Technologies; Sep2025, Vol. 36 Issue 9, p1-17, 17p

Truncation Artifact Reduction in Stationary Inverse-Geometry Digital Tomosynthesis Using Deep Convolutional Generative Adversarial Network.

Authors: Kim, Burnyoung Lee, Seungwan

Source: Applied Sciences (2076-3417); Jul2025, Vol. 15 Issue 14, p7699, 16p

Subject Terms: GENERATIVE adversarial networks, TOMOSYNTHESIS, AUTOENCODERS, MEDICAL technology, DEEP learning, SIGNAL-to-noise ratio, IMAGE reconstruction

Optimisation of sparse deep autoencoders for dynamic network embedding.

Authors: Tang, Huimei Zhang, Yutao Ma, Lijia et al.

Source: CAAI Transactions on Intelligence Technology; Dec2024, Vol. 9 Issue 6, p1361-1376, 16p

Subject Terms: ARTIFICIAL neural networks, AUTOENCODERS, COMPUTATIONAL complexity, TRAINING needs, DYNAMIC models

Integrating multi-source monitoring data and deep convolutional autoencoder technology for slope failure pattern recognition.

Authors: Han, Nana Miao, Wending Li, Mingzhi et al.

Source: Frontiers in Earth Science; 2025, p1-11, 11p

Subject Terms: PATTERN recognition systems, SLOPES (Soil mechanics), AUTOENCODERS, INDUSTRIAL safety, FAILURE mode & effects analysis, LANDSLIDES

A Deep Convolutional Autoencoder–Enabled Channel Estimation Method in Intelligent Wireless Communication Systems.

Authors: Tian, Xinyu Jiang, Weiwei

Source: International Journal of Intelligent Systems; 10/30/2024, Vol. 2024, p1-12, 12p

Subject Terms: WIRELESS communications, CHANNEL estimation, SIGNAL detection, DEEP learning, DEMODULATION, WIRELESS channels, TRANSMITTERS (Communication)

DLSIA: Deep Learning for Scientific Image Analysis

Authors: Roberts, Eric J Chavez, Tanny Hexemer, Alexander et al.

Source: Journal of Applied Crystallography. 57(2)

Subject Terms: 3402 Inorganic Chemistry (for-2020), 34 Chemical Sciences (for-2020), 3406 Physical Chemistry (for-2020), 51 Physical Sciences (for-2020), 5104 Condensed Matter Physics (for-2020), Bioengineering (rcdc), Biomedical Imaging (rcdc), Machine Learning and Artificial Intelligence (rcdc), Networking and Information Technology R&D (NITRD) (rcdc), deep learning, convolutional neural networks, X-ray scattering, tomography, data compression, X-ray scattering, convolutional neural networks, data compression, deep learning, tomography, 01 Mathematical Sciences (for), 02 Physical Sciences (for), 09 Engineering (for), Inorganic & Nuclear Chemistry (science-metrix), 3402 Inorganic chemistry (for-2020), 3406 Physical chemistry (for-2020), 5104 Condensed matter physics (for-2020)

File Description: application/pdf

Access URL: https://escholarship.org/uc/item/966220w4
https://escholarship.org/content/qt966220w4/qt966220w4.pdf

Intelligent Fault Detection Scheme for Rolling Bearing Based on Generative Adversarial Network and AutoEncoders Using Convolutional Neural Network.

Authors: Rathore, Maan Singh Harsha, S. P.

Source: Journal of Vibration Engineering & Technologies; Dec2024, Vol. 12 Issue 8, p8979-8991, 13p

Subject Terms: CONVOLUTIONAL neural networks, GENERATIVE adversarial networks, DATA augmentation, ARTIFICIAL intelligence, AUTOENCODERS, DEEP learning, MONITORING of machinery, ROLLER bearings