Suchergebnisse - convolutional autoencoder-model verification*

Convolutional Autoencoder Model for Finger-Vein Verification

Autoren: Ruqiang Yan Borui Hou

Quelle: IEEE Transactions on Instrumentation and Measurement. 69:2067-2074

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: https://ieeexplore.ieee.org/document/8731996
https://dblp.uni-trier.de/db/journals/tim/tim69.html#HouY20
https://doi.org/10.1109/TIM.2019.2921135

DWSCNN Online Signature Verification Algorithm Based on CAE-MV Feature Dimensionality Reduction

Autoren: Jianbin Zheng Ziyao Chen Liping Huang et al.

Quelle: IEEE Access, Vol 12, Pp 22144-22157 (2024)

Schlagwörter: convolutional autoencoder-model verification, depth-wise separable convolution neural network, 0202 electrical engineering, electronic engineering, information engineering, deep learning, Online signature verification, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, TK1-9971

Zugangs-URL: https://doaj.org/article/69210d073ce6434f8a334c7430fb77c7

High-Resolution Daily XCH 4 Prediction Using New Convolutional Neural Network Autoencoder Model and Remote Sensing Data.

Autoren: Awad, Mohamad M. Homayouni, Saeid

Quelle: Atmosphere; Jul2025, Vol. 16 Issue 7, p806, 18p

Schlagwörter: ATMOSPHERIC methane, CONVOLUTIONAL neural networks, ENVIRONMENTAL degradation, CLIMATE change, INTERPOLATION, ENVIRONMENTAL monitoring, REMOTE sensing

Convolutional Autoencoder Model for Finger-Vein Verification.

Autoren: Hou, Borui Yan, Ruqiang

Quelle: IEEE Transactions on Instrumentation & Measurement; May2020, Vol. 69 Issue 5, p2067-2074, 8p

Schlagwörter: SUPPORT vector machines, GABOR filters

A point cloud-based mesh-independent convolutional neural network frameworks for flow field prediction on variable geometries.

Autoren: Hwang, Wontae Kim, Sooyoung Park, Donghyun et al.

Quelle: Physics of Fluids. May2025, Vol. 37 Issue 5, p1-19. 19p.

Schlagwörter: *CONVOLUTIONAL neural networks, *COMPUTATIONAL fluid dynamics, *COMPLEX geometry, *TRANSONIC flow, *AUTOENCODERS

A Survey of Finger-vein Recognition using Deep Learning: Concepts, Challenges, and Opportunities.

Autoren: KOCAKULAK, Mustafa AVCI, Adem ACIR, Nurettin

Quelle: Journal of Engineering & Smart Systems; 2025, Vol. 13 Issue 3, p71-93, 23p

Schlagwörter: CONVOLUTIONAL neural networks, CONCEPT learning, RESEARCH personnel, DEEP learning, BIOMETRY, RECOGNITION (Psychology)

Reconstruction of SMAP Soil Moisture Data Based on Residual Autoencoder Network with Convolutional Feature Extraction.

Autoren: Liu, Yaojie Fan, Haoyu Jin, Yan et al.

Quelle: Remote Sensing; Nov2025, Vol. 17 Issue 22, p3729, 32p

Schlagwörter: SOIL moisture, REMOTE sensing, CLIMATE research, FEATURE extraction, AUTOENCODERS, HYDROLOGICAL research, ELECTRONIC data processing, ENVIRONMENTAL research

Geografische Kategorien: CHINA

Unsupervised Dual Convolutional Autoencoder Models for Efficient Group Anomaly Detection of HST Bogies via Domain Adversarial Learning.

Autoren: Chang, Yuanhong Zhong, Shuncong Pan, Tongyang et al.

Quelle: Journal of Physics: Conference Series; 2025, Vol. 2999 Issue 1, p1-7, 7p

Variational Autoencoder Model Based on 3D Convolutional Neural Network for generative design framework of 3D chair.

Autoren: Park, T. min Park, J. Murdivien, S. A. et al.

Quelle: International Journal of Industrial Engineering & Management (IJIEM); Mar2024, Vol. 15 Issue 1, p1-17, 17p

Schlagwörter: CONVOLUTIONAL neural networks, THREE-dimensional modeling, CHAIR design & construction, HUMAN-machine systems, GENERATIVE artificial intelligence

Intrusion Detection for Blockchain‐Based Internet of Things Using Gaussian Mixture–Fully Convolutional Variational Autoencoder Model.

Autoren: Om Kumar, C. U. Marappan, Suguna Murugeshan, Bhavadharini et al.

Quelle: International Journal of Network Management; Nov/Dec2024, Vol. 34 Issue 6, p1-17, 17p

Schlagwörter: SMART cities, SMART devices, DEEP learning, INTERNET of things, DATA quality, INTRUSION detection systems (Computer security)

Lightweight Multi-Class Autoencoder Model for Malicious Traffic Detection in Private 5G Networks.

Autoren: Kim, Jinha Na, Seungjoon Kim, Hwankuk

Quelle: Applied Sciences (2076-3417); Nov2025, Vol. 15 Issue 22, p12242, 25p

Schlagwörter: 5G networks, AUTOENCODERS, SERVICE level agreements, REAL-time computing, RESOURCE allocation, CLASSIFICATION algorithms, COMPUTER network traffic, TRAFFIC monitoring

Evaluating statistical consistency for the ocean component of earth system models using physics informed convolutional autoencoder.

Autoren: Yu, Yangyang Zhang, Shaoqing Fu, Haohuan et al.

Quelle: Scientific Reports; 5/29/2025, Vol. 15 Issue 1, p1-14, 14p

Schlagwörter: AUTOENCODERS, COMPUTER software, HETEROGENEOUS computing, COMPUTER systems, COMPUTER science, DEEP learning

A CAE model-based secure deduplication method.

Autoren: Wang, Chunbo Zhang, Guoying Qi, Hui et al.

Quelle: Scientific Reports; 7/9/2025, Vol. 15 Issue 1, p1-11, 11p

Schlagwörter: CLOUD storage, AUTOENCODERS, DATA encryption, PRIVACY, DATA compression

Integrating image processing with deep convolutional neural networks for gene selection and cancer classification using microarray data.

Autoren: Zhang, Yuanyuan Chen, Jing Zhang, Chong

Quelle: Scientific Reports; 11/11/2025, Vol. 15 Issue 1, p1-23, 23p

Schlagwörter: CONVOLUTIONAL neural networks, DNA microarrays, FEATURE extraction, IMAGE processing, TUMOR classification, BIOMARKERS, MATHEMATICAL optimization

3D-FaIR: 3D facial imperfection regeneration with defects by fully convolutional mesh autoencoder.

Autoren: Nguyen, Phuong D. Le, Thinh D. Nguyen, Duong Q. et al.

Quelle: Applied Intelligence; Apr2025, Vol. 55 Issue 6, p1-15, 15p

Age-Invariant Cross-Age Face Verification using Transfer Learning.

Autoren: Russel, Newlin Shebiah Selvaraj, Arivazhagan S., Dhanya Devi et al.

Quelle: Inteligencia Artificial: Revista Iberoamericana de Inteligencia Artificial; Dec2024, Vol. 27 Issue 74, p37-47, 11p

Schlagwörter: FEATURE extraction, SKIN aging, SECURITY systems, HISTOGRAMS, SWINE

Cavitation state recognition method of centrifugal pump based on multi-dimensional feature fusion and convolutional gate recurrent unit.

Autoren: Zhang, Tonghe Song, Yongxing Liu, Qiang et al.

Quelle: Physics of Fluids; Oct2024, Vol. 36 Issue 10, p1-15, 15p

Schlagwörter: CONVOLUTIONAL neural networks, PRINCIPAL components analysis, FEATURE extraction, CAVITATION, TIME-frequency analysis, CAVITATION erosion, CENTRIFUGAL pumps

Comparative Analysis of Recurrent vs. Temporal Convolutional Autoencoders for Detecting Container Impacts During Quay Crane Handling.

Autoren: Jakovlev, Sergej Eglynas, Tomas Pocevicius, Edvinas et al.

Quelle: Journal of Marine Science & Engineering; Jul2025, Vol. 13 Issue 7, p1231, 23p

Schlagwörter: AUTOENCODERS, EDGE computing, CONVOLUTIONAL neural networks, CRANES (Machinery), OUTLIER detection, SHIPPING containers, REAL-time computing

A gated temporal attention based intra prediction framework for robust deepfake video detection.

Autoren: Clapten, J. Eric Balaji, v.

Quelle: Scientific Reports; 11/4/2025, Vol. 15 Issue 1, p1-28, 28p

Schlagwörter: VIDEO processing, INFORMATION technology security, CONVOLUTIONAL neural networks, AUTOENCODERS

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

Autoren: Górski, Łukasz Jakubowska, Małgorzata

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

Schlagwörter: AUTOENCODERS, ANALYTICAL chemistry, DEEP learning, ALGORITHMS, SIGNAL processing