Výsledky vyhledávání - "convolutional neural network autoencoder"

Adaptive hybrid convolutional neural network-autoencoder framework for backdoor detection in GenAI-driven semantic communication systems

Autoři: Hassan El Alami Danda B. Rawat

Zdroj: ITU Journal on Future and Evolving Technologies. 6:309-321

Data-driven modeling of transonic nonlinear flutter via convolutional neural network autoencoder

Autoři: Zhijie Peng Xiangjie Yao Haojie Liu a další

Zdroj: Nonlinear Dynamics. 113:15741-15760

Cascade Convolutional Neural Network - Autoencoder Method for Parkinson’s Disease Detection Using Multilingual Voice Signal

Autoři: Pandit Vivek Kumar Pandey Sitanshu Sekhar Sahu

Zdroj: SN Computer Science. 6

Unveiling Irregular Heart Rhythms: Utilizing a Pretrained ResNet50 Convolutional Neural Network Autoencoder Strategy for Detecting Anomalies in ECG Signals

Autoři: Muskan Agarwal Kanwarpartap Singh Gill Priyanshi Aggarwal a další

Zdroj: 2024 2nd World Conference on Communication & Computing (WCONF). :1-4

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

Autoři: Chin-Cheng Wu Cheng-Wei Tsai Fei-En Wu a další

Zdroj: Biomimetics (Basel)
Biomimetics, Vol 10, Iss 2, p 79 (2025)

Témata: gait recognition, Technology, plantar pressure, convolutional neural network-autoencoder architecture, Article

Přístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/39997101
https://doaj.org/article/d1e45a784e0949d78698f9ef82f80918

A 3d convolutional neural network autoencoder for predicting solvent configuration changes in condensed phase biomass reactions

Autoři: Anjana Puliyanda Arul Mozhi Devan Padmanathan Samir H. Mushrif a další

Zdroj: Digital Discovery. 3:1130-1143

Témata: 01 natural sciences, 0104 chemical sciences

High-Resolution Daily XCH4 Prediction Using New Convolutional Neural Network Autoencoder Model and Remote Sensing Data

Autoři: Mohamad M. Awad Saeid Homayouni

Zdroj: Atmosphere, Vol 16, Iss 7, p 806 (2025)

Témata: monitoring, remote sensing, Meteorology. Climatology, prediction, QC851-999, global, CNN, XCH4

Přístupová URL adresa: https://doaj.org/article/2c7c11b3c4a8441dad9326bca9cf8246

Anomaly-Based Drone Classification Using a Model Trained Convolutional Neural Network Autoencoder on Radar Micro-Doppler

Autoři: Alexander Karlsson Magnus Jansson Mikael Hämäläinen

Zdroj: 2023 IEEE International Radar Conference (RADAR). :1-6

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

Autoři: Abdullah Khan Muhammad Zaheer Sajid Nauman Ali Khan a další

Zdroj: Bioengineering (Basel)
Bioengineering, Vol 12, Iss 4, p 326 (2025)

Témata: convolutional neural network (CNN), Technology, quantum support vector machine (QSVM), QH301-705.5, skin lesion classification, deep learning, Biology (General), artificial intelligence in healthcare, Article, Internet of Things (IoT)

Přístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/40281686
https://doaj.org/article/bbacd5c436614f4a87c7597d8a50a5a8

Recreating Fingerprint Images by Convolutional Neural Network Autoencoder Architecture

Autoři: Sergio Saponara Abdussalam Elhanashi Qinghe Zheng

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

Témata: 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

Přístupová URL adresa: 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

Anomaly Detection of Hydropower Bearing Based on Convolutional Neural Network Autoencoder

Autoři: Liyong Ma Hongteng Wang Lianfang Wang

Zdroj: 2021 IEEE 4th International Conference on Computer and Communication Engineering Technology (CCET). :430-433

Témata: 13. Climate action, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 7. Clean energy

Přístupová URL adresa: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=9544462
http://xplorestaging.ieee.org/ielx7/9544087/9544108/09544462.pdf?arnumber=9544462

Anomaly-Based Drone Classification Using a Model Trained Convolutional Neural Network Autoencoder on Radar Micro-Doppler

Autoři: Karlsson, Alexander Jansson, Magnus Hamalainen, Mikael

Témata: bird, classification, deep learning, drone, high dimensional anomaly detection, QDA, radar, RCS modeling, Probability Theory and Statistics, Sannolikhetsteori och statistik, Conference paper, info:eu-repo/semantics/conferenceObject, text

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-342796
2023 IEEE International Radar Conference, RADAR 2023

Optimizing solar power generation forecasting in smart grids: a hybrid convolutional neural network -autoencoder long short-term memory approach

Autoři: Ahsan Zafar Yanbo Che Moeed Sehnan a další

Zdroj: Physica Scripta. 99:095249

Témata: 9. Industry and infrastructure, 13. Climate action, 11. Sustainability, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 7. Clean energy

Multivariate time-series cyberattack detection in the distributed secondary control of AC microgrids with convolutional neural network autoencoder ensemble

Autoři: Behshad Roshanzadeh Jeewon Choi Ali Bidram a další

Zdroj: Sustainable Energy, Grids and Networks. 38:101374

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

Autoři: Awad, Mohamad M. Homayouni, Saeid

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

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

Data-driven modeling of transonic nonlinear flutter via convolutional neural network autoencoder.

Autoři: Peng, Zhijie Yao, Xiangjie Liu, Haojie a další

Zdroj: Nonlinear Dynamics; Jul2025, Vol. 113 Issue 13, p15741-15760, 20p

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

Autoři: Wu, Chin-Cheng Tsai, Cheng-Wei Wu, Fei-En a další

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

Témata: CONVOLUTIONAL neural networks, AUTOENCODERS, FEATURE extraction

Model order reduction of building energy simulation models using a convolutional neural network autoencoder

Autoři: Farzan Banihashemi Manuel Weber Werner Lang

Zdroj: Building and Environment. 207:108498

Témata: 0301 basic medicine, 03 medical and health sciences, 0211 other engineering and technologies, 02 engineering and technology

Přístupová URL adresa: https://api.elsevier.com/content/article/PII:S0360132321008933?httpAccept=text/xml

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

Autoři: Khan, Abdullah Sajid, Muhammad Zaheer Khan, Nauman Ali a další

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

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

Optimization of time-of-use electricity tariffs for multi-type consumers using price-based demand response and consumer classification model

Autoři: Yuhua Lv Yuqian Fang

Témata: Pharmacology, Biotechnology, Ecology, Science Policy, Biological Sciences not elsewhere classified, Mathematical Sciences not elsewhere classified, Information Systems not elsewhere classified, Consumer classification, convolutional neural network autoencoder, demand response, hierarchical clustering algorithm, time-of-use tariff optimization

Dostupnost: https://doi.org/10.6084/m9.figshare.29143830.v1
https://figshare.com/articles/journal_contribution/Optimization_of_time-of-use_electricity_tariffs_for_multi-type_consumers_using_price-based_demand_response_and_consumer_classification_model/29143830