Suchergebnisse - convolutional neural network-autoencoder (architecture OR architektura)~

Advanced ECG signal denoising using deep learning architectures: convolutional neural networks, autoencoders, and generative adversarial networks

Autoren: Arjun Singh Vijoriya Yogesh Parmar Gaurav Varshney et al.

Quelle: IET Conference Proceedings. 2025:1760-1764

Plantar Pressure-Based Gait Recognition with and Without Carried Object by Convolutional Neural Network-Autoencoder Architecture.

Autoren: Wu, Chin-Cheng Tsai, Cheng-Wei Wu, Fei-En et al.

Quelle: Biomimetics (2313-7673). Feb2025, Vol. 10 Issue 2, p79. 19p.

Schlagwörter: *CONVOLUTIONAL neural networks, *AUTOENCODERS, *FEATURE extraction

Recreating Fingerprint Images by Convolutional Neural Network Autoencoder Architecture

Autoren: Sergio Saponara Abdussalam Elhanashi Qinghe Zheng

Quelle: IEEE Access, Vol 9, Pp 147888-147899 (2021)

Schlagwörter: autoencoder, convolution neural networks, General Computer Science, feature extraction, General Engineering, Fingerprint images, 02 engineering and technology, Autoencoder, Convolution neural networks, Feature extraction, System identification, TK1-9971, 0202 electrical engineering, electronic engineering, information engineering, General Materials Science, Electrical engineering. Electronics. Nuclear engineering, Knowmad Institut, system identification

Zugangs-URL: https://doaj.org/article/dcba48f5a187474189ffe3a7e3b2a461
https://dblp.uni-trier.de/db/journals/access/access9.html#SaponaraEZ21
https://ieeexplore.ieee.org/document/9598889/
https://www.openaccessrepository.it/record/142261

Artificial and Convolutional Neural Network Architectures for Childhood Stunting Classification: Design, Evaluation, and Optimization.

Autoren: Hendy, Abdelaziz Ibrahim, Rasha Kadri Abdelaliem, Sally Mohammed Farghaly et al.

Quelle: SAGE Open; July-Sep2025, Vol. 15 Issue 3, p1-19, 19p

Schlagwörter: STUNTED growth, ARTIFICIAL neural networks, NUTRITIONAL assessment, HEALTH status indicators, CONVOLUTIONAL neural networks, DATA analysis, MACHINE learning

Geografische Kategorien: EGYPT

NEURAL NETWORK AUTOENCODER MODEL FOR FORMING REDUCED VECTOR CHARACTERISTICS OF ECG SIGNALS

Autoren: Anton Mnevec Natalia Ivanushkina

Quelle: Bulletin of Kyiv Polytechnic Institute. Series Instrument Making. :93-105

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

CAD-Skin: A Hybrid Convolutional Neural Network–Autoencoder Framework for Precise Detection and Classification of Skin Lesions and Cancer.

Autoren: Khan, Abdullah Sajid, Muhammad Zaheer Khan, Nauman Ali et al.

Quelle: Bioengineering (Basel); Apr2025, Vol. 12 Issue 4, p326, 31p

Schlagwörter: MACHINE learning, CONVOLUTIONAL neural networks, ARTIFICIAL intelligence, DATA augmentation, SKIN cancer, DEEP learning

A Review of Neural Network-Based Image Noise Processing Methods.

Autoren: Volkov, Anton A. Kozlov, Alexander V. Cheremkhin, Pavel A. et al.

Quelle: Sensors (14248220); Oct2025, Vol. 25 Issue 19, p6088, 37p

Schlagwörter: CONVOLUTIONAL neural networks, DIGITAL image processing, SIGNAL filtering, AUTOENCODERS, ARTIFICIAL neural networks, GENERATIVE adversarial networks, IMAGE quality analysis

Dynamic forecasting of China's carbon market prices by the coupling of macroeconomic indicators and LSTM model.

Autoren: Wang, Yinuo Huo, Haoyu Liu, Xingchen et al.

Quelle: Scientific Reports; 11/19/2025, Vol. 15 Issue 1, p1-16, 16p

Schlagwörter: FORECASTING, LONG short-term memory, CARBON offsetting, ARTIFICIAL neural networks, MULTISENSOR data fusion, SUSTAINABLE development, ECONOMIC indicators, ECONOMIC forecasting

Geografische Kategorien: CHINA

NEURAL NETWORK AUTOENCODER MODEL FOR FORMING REDUCED VECTOR CHARACTERISTICS OF ECG SIGNALS

Quelle: Bulletin of Kyiv Polytechnic Institute. Series Instrument Making; No. 69(1) (2025); 93-105
Вестник Киевского политехнического института. Серия приборостроение; № 69(1) (2025); 93-105
Вісник Київського політехнічного інституту. Серія Приладобудування; № 69(1) (2025); 93-105

Schlagwörter: autoencoder, пізні потенціали передсердь, стиснення даних, electrocardiography, автоенкодер, ventricular late potentials, atrial late potentials, пізні потенціали шлуночків, електрокардіографія, data compression

Dateibeschreibung: application/pdf

Zugangs-URL: https://visnykpb.kpi.ua/article/view/331920

基于深度学习的短时交通流预测研究综述. (Chinese)

Alternate Title: Short-Term Traffic Flow Prediction Based on Deep Learning. (English)

Autoren: 熊章友 李卫军 朱晓娟 et al.

Quelle: Journal of Computer Engineering & Applications; Jun2025, Vol. 61 Issue 11, p67-82, 16p

Schlagwörter: CONVOLUTIONAL neural networks, RECURRENT neural networks, INTELLIGENT transportation systems, TRAFFIC flow, AUTOENCODERS, DEEP learning

Encoding Multivariate Time Series of Gas Turbine Data as Images to Improve Fault Detection Reliability.

Autoren: Losi, Enzo Venturini, Mauro Manservigi, Lucrezia et al.

Quelle: Machines; Oct2025, Vol. 13 Issue 10, p943, 42p

Schlagwörter: GAS turbines, FAULT diagnosis, CONVOLUTIONAL neural networks, IMAGE reconstruction, CONDITION-based maintenance, OUTLIER detection

Enhanced Kinship Verification through Ear Images: A Comparative Study of CNNs, Attention Mechanisms, and MLP Mixer Models.

Autoren: Cao, Thien-Tan Duong, Huu-Thanh Le, Viet-Tuan et al.

Quelle: Computers, Materials & Continua; 2025, Vol. 83 Issue 3, p4373-4391, 19p

Schlagwörter: TRANSFORMER models, RECOGNITION (Psychology), VERNACULAR architecture, FACIAL expression, CONVOLUTIONAL neural networks, EAR

Enhance real-time activity recognition of disabled individuals using hybridisation of convolutional neural network with attention mechanism.

Autoren: Alabdan, Rana AlAqil, Mohammed A. Alanazi, Eman M. et al.

Quelle: Signal, Image & Video Processing; Nov2025, Vol. 19 Issue 14, p1-15, 15p

Explainable AI and vision transformers for detection and classification of brain tumor: a comprehensive survey.

Autoren: Hosny, Khalid M. Mohammed, Mahmoud A.

Quelle: Artificial Intelligence Review; Sep2025, Vol. 58 Issue 9, p1-60, 60p

Schlagwörter: TRANSFORMER models, CONVOLUTIONAL neural networks, ARTIFICIAL intelligence, BRAIN tumors, CANCER diagnosis, DEEP learning

Implementation of Kolmogorov–Arnold Networks for Efficient Image Processing in Resource-Constrained Internet of Things Devices.

Autoren: Shaushenova, Anargul Kuznetsov, Oleksandr Nurpeisova, Ardak et al.

Quelle: Technologies (2227-7080); Apr2025, Vol. 13 Issue 4, p155, 21p

Schlagwörter: COMPUTER vision, INDUSTRIAL robots, CONVOLUTIONAL neural networks, IMAGE processing, COMPUTER networks, DEEP learning

SpaVGN: A hybrid deep learning framework for high-resolution spatial transcriptomics data reconstruction and spatial domain identification.

Autoren: Wang, Haiyan Zhang, Yanping Zhang, Yangyang et al.

Quelle: PLoS ONE; 8/14/2025, Vol. 20 Issue 8, p1-15, 15p

Schlagwörter: DEEP learning, GRAPH neural networks, GENE expression, CONVOLUTIONAL neural networks, GENE expression profiling, INTERPOLATION, TISSUES, PATTERN perception

An Adaptive Temporal Convolutional Network Autoencoder for Malicious Data Detection in Mobile Crowd Sensing.

Autoren: Owoh, Nsikak Riley, Jackie Ashawa, Moses et al.

Quelle: Sensors (14248220); Apr2024, Vol. 24 Issue 7, p2353, 23p

Schlagwörter: CONVOLUTIONAL neural networks, CROWDSENSING, SECURITY systems, DATA integrity, TRUST

Convolutional Neural Network-Based Reconstruction of Experimental Velocity Fields in an Impinging Jet Flow.

Autoren: Lakdar, Omar Elsoufi, Louay Alkheir, Marwan et al.

Quelle: International Journal of Heat & Technology; Aug2025, Vol. 43 Issue 4, p1229-1234, 6p

Recent Landscape of Deep Learning Intervention and Consecutive Clustering on Biomedical Diagnosis.

Autoren: Mukherji, Ayan Mondal, Arindam Banerjee, Rajib et al.

Quelle: Artificial Intelligence & Applications (2811-0854); Oct2025, Vol. 3 Issue 4, p359-377, 19p

Schlagwörter: DEEP learning, DNA methylation, EPIGENETICS, MEDICAL informatics, METHYLATION, CLUSTER analysis (Statistics), DIAGNOSIS, ARTIFICIAL neural networks