Suchergebnisse - sparse convolutional autoencoder ((same OR sage))*

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

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

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

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

Feature extraction of fields of fluid dynamics data using sparse convolutional autoencoder

Autoren: Wataru Obayashi Hikaru Aono Tomoaki Tatsukawa et al.

Quelle: AIP Advances, Vol 11, Iss 10, Pp 105211-105211-6 (2021)

Schlagwörter: Physics, QC1-999

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/2158-3226

Zugangs-URL: https://doaj.org/article/7d4dc31e6e08442fba36ddd862379ce8

Feature extraction of fields of fluid dynamics data using sparse convolutional autoencoder.

Autoren: Obayashi, Wataru Aono, Hikaru Tatsukawa, Tomoaki et al.

Quelle: AIP Advances; Oct2021, Vol. 11 Issue 10, p1-6, 6p

Schlagwörter: FLUID dynamics, FEATURE extraction, PROPER orthogonal decomposition, NAVIER-Stokes equations, CONVOLUTIONAL neural networks, UNSTEADY flow, COMPRESSIBLE flow

Sparse convolutional autoencoder‐based fault location for drive circuits in nuclear reactors.

Autoren: Yang, Cheng Yuan, Yannan Wang, Fu et al.

Quelle: Quality & Reliability Engineering International; Mar2024, Vol. 40 Issue 2, p819-837, 19p

Schlagwörter: FAULT location (Engineering), NUCLEAR reactors, NUCLEAR reactor control, FAULT diagnosis, ELECTRONIC equipment, LINE drivers (Integrated circuits)

Convolutional autoencoder-based deep learning for intracerebral hemorrhage classification using brain CT images.

Autoren: Nageswara Rao, B. Acharya, U. Rajendra Tan, Ru-San et al.

Quelle: Cognitive Neurodynamics; 5/19/2025, Vol. 19 Issue 1, p1-13, 13p

A Cylindrical Near-Field Acoustical Holography Method Based on Cylindrical Translation Window Expansion and an Autoencoder Stacked with 3D-CNN Layers.

Autoren: Wang, Jiaxuan Zhang, Weihan Zhang, Zhifu et al.

Quelle: Sensors (14248220). Apr2023, Vol. 23 Issue 8, p4146. 22p.

Schlagwörter: *HOLOGRAPHY, *CONVOLUTIONAL neural networks, *ERROR rates, *INTERPOLATION algorithms

A 3D Sparse Autoencoder for Fully Automated Quality Control of Affine Registrations in Big Data Brain MRI Studies.

Autoren: Thadikemalla, Venkata Sainath Gupta Focke, Niels K. Tummala, Sudhakar

Quelle: Journal of Digital Imaging; Feb2024, Vol. 37 Issue 1, p412-427, 16p

Schlagwörter: RECEIVER operating characteristic curves, MAGNETIC resonance imaging, QUALITY control, DATA analytics, DESCRIPTIVE statistics, DIAGNOSIS of brain abnormalities

Enhanced heart sound anomaly detection via WCOS: a semi-supervised framework integrating wavelet, autoencoder and SVM.

Autoren: Zeng, Peipei Kang, Shuimiao Fan, Fan et al.

Quelle: Frontiers in Neuroinformatics; 2025, p1-15, 15p

Schlagwörter: ANOMALY detection (Computer security), HEART murmurs, SUPPORT vector machines, CONGENITAL heart disease, AUTOENCODERS

Multi-scale asynchronous correlation and 2D convolutional autoencoder for adolescent health risk prediction with limited fMRI data.

Autoren: Di Gao Guanghao Yang Jiarun Shen et al.

Quelle: Frontiers in Computational Neuroscience; 2024, p01-12, 12p

Schlagwörter: HEALTH risk assessment, FUNCTIONAL magnetic resonance imaging, VALUE engineering, EXTRACTION techniques, DATA mining

DSCAE: a denoising sparse convolutional autoencoder defense against adversarial examples.

Autoren: Ye, Hongwei Liu, Xiaozhang Li, Chunlai

Quelle: Journal of Ambient Intelligence & Humanized Computing; Mar2022, Vol. 13 Issue 3, p1419-1429, 11p

Investigating Beta-Variational Convolutional Autoencoders for the Unsupervised Classification of Chest Pneumonia.

Autoren: Akila, Serag Mohamed Imanov, Elbrus Almezhghwi, Khaled

Quelle: Diagnostics (2075-4418); Jul2023, Vol. 13 Issue 13, p2199, 14p

Schlagwörter: MACHINE learning, PNEUMONIA, ANOMALY detection (Computer security), LUNG infections, DIAGNOSIS, KLEBSIELLA infections

3D Segmentation of Perivascular Spaces on T1-Weighted 3 Tesla MR Images With a Convolutional Autoencoder and a U-Shaped Neural Network.

Autoren: Boutinaud, Philippe Tsuchida, Ami Laurent, Alexandre et al.

Quelle: Frontiers in Neuroinformatics; 6/18/2021, Vol. 15, p1-21, 21p

Schlagwörter: MAGNETIC resonance imaging, DEEP learning, ALGORITHMS, BASAL ganglia, YOUNG adults, VOXEL-based morphometry

Application of Artificial Intelligence Technologies as an Intervention for Promoting Healthy Eating and Nutrition in Older Adults: A Systematic Literature Review.

Autoren: Kalu, Kingsley Ataguba, Grace Onifade, Oyepeju et al.

Quelle: Nutrients. Oct2025, Vol. 17 Issue 20, p3223. 33p.

Human Activity Recognition Using Convolutional Autoencoder and Advanced Preprocessing.

Autoren: Zaoui, Chaimae Benabbou, Faouzia Ettaoufik, Abdelaziz et al.

Quelle: International Journal of Online & Biomedical Engineering; 2024, Vol. 20 Issue 4, p144-159, 16p

Deep spectral unmixing framework via 3D denoising convolutional autoencoder.

Autoren: Jia, Peiyuan Zhang, Miao Shen, Yi

Quelle: IET Image Processing (Wiley-Blackwell); May2021, Vol. 15 Issue 7, p1399-1409, 11p

Schlagwörter: HYPERSPECTRAL imaging systems, IMAGE denoising, IMAGE processing, PIXELS, GEOMETRIC analysis, DATA

Lameness detection in dairy cows from overhead view: high-precision keypoint localization and multi-feature fusion classification.

Autoren: Duan, Weijun Wang, Fang Li, Honghui et al.

Quelle: Frontiers in Veterinary Science. 2025, p1-19. 19p.

Classification of Atypical White Blood Cells in Acute Myeloid Leukemia Using a Two-Stage Hybrid Model Based on Deep Convolutional Autoencoder and Deep Convolutional Neural Network.

Autoren: Elhassan, Tusneem A. Mohd Rahim, Mohd Shafry Siti Zaiton, Mohd Hashim et al.

Quelle: Diagnostics (2075-4418); Jan2023, Vol. 13 Issue 2, p196, 20p

Schlagwörter: LEUKOCYTES, ACUTE myeloid leukemia, CONVOLUTIONAL neural networks, MYELOID cells, MORPHOLOGY

Automated Video-EEG Analysis in Epilepsy Studies: A Narrative Review of Advances and Challenges.

Autoren: Zuev VA Salmagambetova EG Djakov SN et al.

Quelle: Journal of medical systems [J Med Syst] 2025 Oct 20; Vol. 49 (1), pp. 142. Date of Electronic Publication: 2025 Oct 20.

Publikationsart: Journal Article; Review

Info zur Zeitschrift: Publisher: Kluwer Academic/Plenum Publishers Country of Publication: United States NLM ID: 7806056 Publication Model: Electronic Cited Medium: Internet ISSN: 1573-689X (Electronic) Linking ISSN: 01485598 NLM ISO Abbreviation: J Med Syst Subsets: MEDLINE

MeSH-Schlagworte: Electroencephalography*/methods , Epilepsy*/diagnosis , Epilepsy*/physiopathology , Video Recording*/methods, Humans ; Machine Learning

Multi-frequency and multi-domain human activity recognition based on SFCW radar using deep learning.

Autoren: Jia, Yong Guo, Yong Wang, Gang et al.

Quelle: Neurocomputing. Jul2021, Vol. 444, p274-287. 14p.

Schlagwörter: *HUMAN activity recognition, *DEEP learning, *CONVOLUTIONAL neural networks, *RADAR, *VIDEO coding, *SPECTROGRAMS, *SURVEILLANCE radar

Electroencephalogram Emotion Recognition Based on 3D Feature Fusion and Convolutional Autoencoder.

Autoren: An, Yanling Hu, Shaohai Duan, Xiaoying et al.

Quelle: Frontiers in Computational Neuroscience; 10/18/2021, Vol. 15, p1-13, 13p

Schlagwörter: EMOTION recognition, BRAIN-computer interfaces, SIGNAL convolution, DIFFERENTIAL entropy, CONVOLUTIONAL neural networks, ELECTROENCEPHALOGRAPHY, EMOTIONS, FEATURE extraction