Výsledky vyhledávání - "autoencoder convolutional neural network"

BEARING FAULT DETECTION BY USING AUTOENCODER CONVOLUTIONAL NEURAL NETWORK

Autoři: M. K. Kysarin

Zdroj: Radio Electronics, Computer Science, Control. :116-125

BEARING FAULT DETECTION BY USING AUTOENCODER CONVOLUTIONAL NEURAL NETWORK

Zdroj: Radio Electronics, Computer Science, Control; No. 2 (2025): Radio Electronics, Computer Science, Control; 116-125
Радиоэлектроника, информатика, управление; № 2 (2025): Радиоэлектроника, информатика, управление; 116-125
Радіоелектроніка, iнформатика, управління; № 2 (2025): Радіоелектроніка, інформатика, управління; 116-125

Témata: несправність підшипника, autoencoder, bearing fault, binary classification, автокодувальник, згорткова нейронна мережа, convolutional neural network, навчання з нуля, zero-shot learning, бінарна класифікація

Popis souboru: application/pdf

Přístupová URL adresa: https://ric.zp.edu.ua/article/view/332949

AutoEncoder Convolutional Neural Network for Pneumonia Detection

Autoři: Nosa-Omoruyi, Michael Oghenekaro, Linda U.

Zdroj: International Journal of Artificial Intelligence & Applications. 15:21-32

Témata: Image and Video Processing (eess.IV), FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Image and Video Processing

Přístupová URL adresa: http://arxiv.org/abs/2409.02142

BEARING FAULT DETECTION BY USING AUTOENCODER CONVOLUTIONAL NEURAL NETWORK

Zdroj: Radio Electronics, Computer Science, Control; No. 2 (2025): Radio Electronics, Computer Science, Control; 116-125; 116-125; 116-125; 2313-688X; 1607-3274

Témata: bearing fault, autoencoder, convolutional neural network, zero-shot learning, binary classification, info:eu-repo/semantics/article, info:eu-repo/semantics/publishedVersion

URL: https://ric.zp.edu.ua/article/view/332949/321885
https://ric.zp.edu.ua/article/view/332949/321885

Image dehazing using autoencoder convolutional neural network

Autoři: Richa Singh Ashwani Kumar Dubey Rajiv Kapoor

Zdroj: International Journal of System Assurance Engineering and Management. 13:3002-3016

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

Classification of MR Images pertaining to with artifact / without artifact using Deep Autoencoder Convolutional Neural Network

Autoři: Shiju Samuel Rohini S. Ochawar M.S.S Rukmini

Autoencoder Convolutional Neural Network-Based Brain Tumor Segmentation

Autoři: Singaravelan S Balaganesh S Gopalsamy P a další

An Autoencoder Convolutional Neural Network Framework for Sarcopenia Detection Based on Multi-frame Ultrasound Image Slices

Autoři: Pintelas, Emmanuel Livieris, Ioannis Barotsis, Nikolaos a další

Přispěvatelé: Pintelas, Emmanuel Livieris, Ioannis Barotsis, Nikolaos a další

Zdroj: IFIP Advances in Information and Communication Technology ISBN: 9783030791490

Témata: 03 medical and health sciences, 0302 clinical medicine, Image classification, Ultrasound, 0202 electrical engineering, electronic engineering, information engineering, Deep learning, Computer vision, 02 engineering and technology, [INFO] Computer Science [cs], AutoEncoders, Transfer learning, Sarcopenia detection

Popis souboru: application/pdf

Přístupová URL adresa: https://link.springer.com/chapter/10.1007/978-3-030-79150-6_17
https://hal.inria.fr/IFIP-AICT-627/hal-03287711
https://dblp.uni-trier.de/db/conf/ifip12/aiai2021.html#PintelasLB0P21
https://rd.springer.com/chapter/10.1007/978-3-030-79150-6_17
https://hal.archives-ouvertes.fr/hal-03287711v1

Autoencoder-convolutional neural network-based embedding and extraction model for image watermarking

Autoři: Debolina Mahapatra Preetam Amrit Om Prakash Singh a další

Zdroj: Journal of Electronic Imaging. 32

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

Deep reference autoencoder convolutional neural network for damage identification in parallel steel wire cables

Autoři: Songling Xue Yidan Sun Teng Su a další

Zdroj: Structures. 57:105316

Rapid and Accurate Prediction of Soil Texture Using an Image-Based Deep Learning Autoencoder Convolutional Neural Network Random Forest (DLAC-CNN-RF) Algorithm

Autoři: Zhuan Zhao Wenkang Feng Jinrui Xiao a další

Zdroj: Agronomy, Vol 12, Iss 12, p 3063 (2022)

Témata: soil texture, identification, DLAC-CNN-RF model, accuracy, Agriculture

Popis souboru: electronic resource

Relation: https://www.mdpi.com/2073-4395/12/12/3063; https://doaj.org/toc/2073-4395

Přístupová URL adresa: https://doaj.org/article/7999737dc36c4c81859244f7c55bece1

Peak detection of spectrally-overlapped fibre Bragg gratings using an autoencoder convolutional neural network

Autoři: Gabriel Rudloff Marcelo A. Soto

Zdroj: European Workshop on Optical Fibre Sensors (EWOFS 2023). :112

Computer-Aided Diagnosis of Acute Lymphoblastic Leukemiaby Using a Novel CAE-CNN Framework

Autoři: Mohammed Mansoor Alhammadi

Zdroj: Iraqi Journal for Computers and Informatics, Vol 50, Iss 2, Pp 186-206 (2024)

Témata: Technology, acute lymphoblastic leukemia, convolutional autoencoder, convolutional neural network, feature extraction, computer-aided diagnosis

Přístupová URL adresa: https://doaj.org/article/e9df85c84bfc4fee8a5a49bf1b184bee

BEARING FAULT DETECTION BY USING AUTOENCODER CONVOLUTIONAL NEURAL NETWORK.

Alternate Title: ВИЯВЛЕННЯ НЕСПРАВНОСТІ ПІДШИПНИКА ЗА ДОПОМОГОЮ ЗГОРТКОВОЇ НЕЙРОННОЇ МЕРЕЖІ АВТОКОДУВАЛЬНИКА. (Ukrainian)

Autoři: K., Kysarin M.

Zdroj: Radio Electronics, Computer Science, Control; 2025, Issue 2, p116-125, 10p

Témata: AUTOENCODERS, CONVOLUTIONAL neural networks, MACHINE learning, OPTIMIZATION algorithms, FAULT diagnosis, MECHANICAL vibration research, NONDESTRUCTIVE testing

Intersection of AI and Cybersecurity: A Data-Driven Approach to Proactive Risk Management in ETL Processes

Autoři: Shiva Kumar Vuppala

Zdroj: International Journal of Intelligent Systems and Applications in Engineering; Vol. 13 No. 1 (2025); 191-204

Témata: Artificial Intelligence, cybersecurity, ETL processes, anomaly detection, autoencoder, Convolutional Neural Network, Gated Recurrent Unit

Popis souboru: application/pdf

Přístupová URL adresa: https://www.ijisae.org/index.php/IJISAE/article/view/7567

A DenseU-Net framework for Music Source Separation using Spectrogram Domain Approach

Autoři: Vinitha George E

Zdroj: International Journal of Intelligent Systems and Applications in Engineering; Vol. 12 No. 4 (2024); 77-85

Témata: Autoencoder, Convolutional Neural Network, Deep learning, DenseNet, Music source separation, ResNet, U-Net architecture

Popis souboru: application/pdf

Přístupová URL adresa: https://www.ijisae.org/index.php/IJISAE/article/view/6175

Rapid and Accurate Prediction of Soil Texture Using an Image-Based Deep Learning Autoencoder Convolutional Neural Network Random Forest (DLAC-CNN-RF) Algorithm.

Autoři: Zhao, Zhuan Feng, Wenkang Xiao, Jinrui a další

Zdroj: Agronomy; Dec2022, Vol. 12 Issue 12, p3063, 16p

Témata: DEEP learning, CROP quality, SOIL texture, CONVOLUTIONAL neural networks, COMPUTER vision, RANDOM forest algorithms, GRAPHICAL user interfaces, SAND waves

Image dehazing using autoencoder convolutional neural network.

Autoři: Singh, Richa Dubey, Ashwani Kumar Kapoor, Rajiv

Zdroj: International Journal of Systems Assurance Engineering & Management; Dec2022, Vol. 13 Issue 6, p3002-3016, 15p

Seismic Random Noise Attenuation Using a Tied-Weights Autoencoder Neural Network

Autoři: Huailai Zhou Yangqin Guo Ke Guo

Zdroj: Minerals, Vol 11, Iss 10, p 1089 (2021)

Témata: autoencoder convolutional neural network, noise suppression, seismic data, tied weights, self-supervised learning, Mineralogy, QE351-399.2

Relation: https://www.mdpi.com/2075-163X/11/10/1089; https://doaj.org/toc/2075-163X; https://doaj.org/article/cbdef19ae3d04a5c823f633e54853862

Dostupnost: https://doi.org/10.3390/min11101089
https://doaj.org/article/cbdef19ae3d04a5c823f633e54853862

Vision-Based Damage Detection in CFRP Beams Using Optical Flow and Mahalanobis-Enhanced Deep Learning Models.

Autoři: Hacıefendioğlu, Kemal Kahya, Volkan Şimşek, Sebahat a další

Zdroj: Journal of Nondestructive Evaluation; Dec2025, Vol. 44 Issue 4, p1-28, 28p

Témata: DEEP learning, STRUCTURAL health monitoring, MATHEMATICAL statistics, OPTICAL flow, COMPOSITE construction, ROBUST statistics, OUTLIER detection