Suchergebnisse - "Convolution autoencoder"

GaitAL-EI: a dual-branch CNN integrating gait energy images and video sequences for person identification

Autoren: Tran-Chi, Cuong Tran, Hanh T. M. Ho-Phuoc, Tien

Quelle: The University of Danang - Journal of Science and Technology; Vol. 23, No. 10B, 2025; 70-75 ; Tạp chí Khoa học và Công nghệ - Đại học Đà Nẵng; Vol. 23, No. 10B, 2025; 70-75 ; 1859-1531

Schlagwörter: Gait Recognition, Convolution Autoencoder, Gait Energy Image

Dateibeschreibung: application/pdf

Relation: https://jst-ud.vn/jst-ud/article/view/10294/6706

Verfügbarkeit: https://jst-ud.vn/jst-ud/article/view/10294
https://doi.org/10.31130/ud-jst.2025.23(10B).640E

Enhanced Prediction of the Remaining Useful Life of Rolling Bearings Under Cross-Working Conditions via an Initial Degradation Detection-Enabled Joint Transfer Metric Network

Autoren: Lingfeng Qi Jiafang Pan Tianping Huang et al.

Quelle: Applied Sciences, Vol 15, Iss 12, p 6401 (2025)

Schlagwörter: remaining useful life prediction, reconstruction along projection pathway, deep adversarial convolution autoencoder network, joint domain adaptation loss, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/2076-3417/15/12/6401; https://doaj.org/toc/2076-3417

Zugangs-URL: https://doaj.org/article/e5b80ed94f004b17a3ca4adee50fd0fc

Performance enhancement in hydroponic and soil compound prediction by deep learning techniques

Autoren: Mustufa Haider Abidi Sanjay Chintakindi Ateekh Ur Rehman et al.

Quelle: PeerJ Computer Science, Vol 10, p e2101 (2024)

Schlagwörter: Hydroponic and soil compound prediction, Plant growth, Weighted features, Iteration-assisted enhanced mother optimization algorithm, Multi-scale feature fusion-based convolution autoencoder with gated recurrent unit, Electronic computers. Computer science, QA75.5-76.95

Dateibeschreibung: electronic resource

Relation: https://peerj.com/articles/cs-2101.pdf; https://peerj.com/articles/cs-2101/; https://doaj.org/toc/2376-5992

Zugangs-URL: https://doaj.org/article/23660b5531f14c868a86f5405575af7e

Semi-2DCAE: a semi-supervision 2D-CNN AutoEncoder model for feature representation and classification of encrypted traffic

Autoren: Jun Cui Longkun Bai Guangxu Li et al.

Quelle: PeerJ Computer Science, Vol 9, p e1635 (2023)

Schlagwörter: Feature representation, Encrypted traffic classification, 2-Dimensional convolution AutoEncoder, FlowSpectrum, Electronic computers. Computer science, QA75.5-76.95

Dateibeschreibung: electronic resource

Relation: https://peerj.com/articles/cs-1635.pdf; https://peerj.com/articles/cs-1635/; https://doaj.org/toc/2376-5992

Zugangs-URL: https://doaj.org/article/16f256760da84abd8a6d7a90d12209be

Multi-Step Short-Term Power Consumption Forecasting Using Multi-Channel LSTM With Time Location Considering Customer Behavior

Autoren: Xiaorui Shao Chang Soo Kim

Quelle: IEEE Access, Vol 8, Pp 125263-125273 (2020)

Schlagwörter: Short-term load forecasting (STLF), multi-step forecasting, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, smart grid, LSTM, convolution autoencoder, 7. Clean energy, TK1-9971

Zugangs-URL: https://ieeexplore.ieee.org/ielx7/6287639/8948470/09133384.pdf
https://doaj.org/article/8c54eef622e44e629a9a1d31429d4b75
https://dblp.uni-trier.de/db/journals/access/access8.html#ShaoK20
https://ieeexplore.ieee.org/document/9133384

Lightweight Reconstruction Network for Surface Defect Detection Based on Texture Complexity Analysis

Autoren: Hui Shi Gangyan Li Hanwei Bao

Quelle: Electronics, Vol 12, Iss 3617, p 3617 (2023)

Schlagwörter: defect detection, deep learning, convolution autoencoder, loss function, texture complexity, Electronics, TK7800-8360

Relation: https://www.mdpi.com/2079-9292/12/17/3617; https://doaj.org/toc/2079-9292; https://doaj.org/article/52035489cc2c46ada847f03aefcbb33f

Verfügbarkeit: https://doi.org/10.3390/electronics12173617
https://doaj.org/article/52035489cc2c46ada847f03aefcbb33f

Gaussian Mixture with Max Expectation Guide for Stacked Architecture of Denoising Autoencoder and DRBM for Medical Chest Scans and Disease Identification

Autoren: Mona Jamjoom Abeer M. Mahmoud Safia Abbas et al.

Quelle: Electronics, Vol 12, Iss 1, p 105 (2022)

Schlagwörter: deep learning, pneumonia prediction, Gaussian mixture, convolution autoencoder, Boltzmann machine, Electronics, TK7800-8360

Relation: https://www.mdpi.com/2079-9292/12/1/105; https://doaj.org/toc/2079-9292; https://doaj.org/article/a38a0b1d3de44d3ca1881344c08f8535

Verfügbarkeit: https://doi.org/10.3390/electronics12010105
https://doaj.org/article/a38a0b1d3de44d3ca1881344c08f8535

Multi-Label Extreme Learning Machine (MLELMs) for Bangla Regional Speech Recognition

Autoren: Prommy Sultana Hossain Amitabha Chakrabarty Kyuheon Kim et al.

Quelle: Applied Sciences, Vol 12, Iss 5463, p 5463 (2022)

Schlagwörter: Bangla regional speech classification, Stacked Convolution Autoencoder (SCAE), Multi-Label Extreme Learning machine (MLELMs), Mel Frequency Energy Coefficients (MFECs), Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

Relation: https://www.mdpi.com/2076-3417/12/11/5463; https://doaj.org/toc/2076-3417; https://doaj.org/article/e261f25a60514cd9996bdef4d6b9cc18

Verfügbarkeit: https://doi.org/10.3390/app12115463
https://doaj.org/article/e261f25a60514cd9996bdef4d6b9cc18

Sentiment analysis using attention-based convolution autoencoder (SAABCA)

Autoren: Sharma, Neha

Schlagwörter: Deep Learning, Attention based Convolution Autoencoder, Sentiment Analysis, Autoencoder

Online Adaptive Prediction of Human Motion Intention Based on sEMG

Autoren: Zhen Ding Chifu Yang Zhipeng Wang et al.

Quelle: Sensors ; Volume 21 ; Issue 8 ; Pages: 2882

Schlagwörter: motion intention prediction, sEMG, convolution autoencoder, online adaptation, wearable sensors

Dateibeschreibung: application/pdf

Relation: Intelligent Sensors; https://dx.doi.org/10.3390/s21082882

Verfügbarkeit: https://doi.org/10.3390/s21082882

Unsupervised change detection using hierarchical convolutional autoencoder

Autoren: Bergamasco, Luca Bovolo, Francesca Bruzzone, Lorenzo

Quelle: Image and Signal Processing for Remote Sensing XXVI. :11

Schlagwörter: Convolution autoencoder, Deep learning, Hierarchical loss function, Multi-scale change detection, Multi-temporal analysis, Unsupervised learning, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Dateibeschreibung: application/pdf

Zugangs-URL: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11533/115330D/Unsupervised-change-detection-using-hierarchical-convolutional-autoencoder/10.1117/12.2571313.full
https://www.scilit.net/article/056f9a458615c25515777fe1a48a026e

Automatická klasifikace obrazů

Automatic image classification

Autoren: Ševčík, Zdeněk

Thesis Advisors: Miklánek, Štěpán, Sikora, Pavel

Schlagwörter: automatic image classification, feature extraction, Aglomerativ clustering, unsupervised learning, automatická klasifikace obrazů, strojové učení, K-means, BRISK, K-majority, machine learning, Aglomerativní shlukování, konvoluční autoenkodér, extrakce příznaků, BoVW, BIRCH, ORB, učení bez učitele, OBR, convolution autoencoder

Verfügbarkeit: http://www.nusl.cz/ntk/nusl-413248

Automatická klasifikace obrazů ; Automatic image classification

Autoren: Ševčík, Zdeněk Sikora, Pavel Miklánek, Štěpán

Weitere Verfasser: Ševčík, Zdeněk Sikora, Pavel Miklánek, Štěpán

Schlagwörter: Aglomerativní shlukování, automatická klasifikace obrazů, BIRCH, BoVW, BRISK, extrakce příznaků, K-majority, K-means, konvoluční autoenkodér, OBR, strojové učení, učení bez učitele, Aglomerativ clustering, automatic image classification, convolution autoencoder, feature extraction, machine learning, ORB, unsupervised learning

Dateibeschreibung: application/pdf; application/zip; text/html

Relation: ŠEVČÍK, Z. Automatická klasifikace obrazů [online]. Brno: Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. 2020.; 126059; http://hdl.handle.net/11012/189399

Verfügbarkeit: http://hdl.handle.net/11012/189399

Automatická klasifikace obrazů

Autoren: Ševčík, Zdeněk Sikora, Pavel Miklánek, Štěpán

Weitere Verfasser: Ševčík, Zdeněk Sikora, Pavel Miklánek, Štěpán

Schlagwörter: strojové učení, extrakce příznaků, feature extraction, automatická klasifikace obrazů, BoVW, konvoluční autoenkodér, učení bez učitele, Aglomerativní shlukování, unsupervised learning, automatic image classification, OBR, machine learning, ORB, BIRCH, K-majority, K-means, Aglomerativ clustering, convolution autoencoder, BRISK

Dateibeschreibung: application/pdf; application/zip; text/html

Zugangs-URL: http://hdl.handle.net/11012/189399

Automatická klasifikace obrazů

Autoren: Sikora, Pavel Miklánek, Štěpán Ševčík, Zdeněk

Index Begriffe: Aglomerativní shlukování, automatická klasifikace obrazů, BIRCH, BoVW, BRISK, extrakce příznaků, K-majority, K-means, konvoluční autoenkodér, OBR, strojové učení, učení bez učitele, Aglomerativ clustering, automatic image classification, convolution autoencoder, feature extraction, machine learning, ORB, unsupervised learning, Text

URL: http://hdl.handle.net/11012/189399

Automatická klasifikace obrazů

Autoren: Sikora, Pavel Miklánek, Štěpán Ševčík, Zdeněk

Index Begriffe: Aglomerativní shlukování, automatická klasifikace obrazů, BIRCH, BoVW, BRISK, extrakce příznaků, K-majority, K-means, konvoluční autoenkodér, OBR, strojové učení, učení bez učitele, Aglomerativ clustering, automatic image classification, convolution autoencoder, feature extraction, machine learning, ORB, unsupervised learning, Text

URL: http://hdl.handle.net/11012/189399

Automatická klasifikace obrazů

Autoren: Sikora, Pavel Miklánek, Štěpán

Index Begriffe: Aglomerativní shlukování, automatická klasifikace obrazů, BIRCH, BoVW, BRISK, extrakce příznaků, K-majority, K-means, konvoluční autoenkodér, OBR, strojové učení, učení bez učitele, Aglomerativ clustering, automatic image classification, convolution autoencoder, feature extraction, machine learning, ORB, unsupervised learning, Text

URL: http://hdl.handle.net/11012/189399

Automatická klasifikace obrazů

Autoren: Sikora, Pavel Miklánek, Štěpán

Index Begriffe: Aglomerativní shlukování, automatická klasifikace obrazů, BIRCH, BoVW, BRISK, extrakce příznaků, K-majority, K-means, konvoluční autoenkodér, OBR, strojové učení, učení bez učitele, Aglomerativ clustering, automatic image classification, convolution autoencoder, feature extraction, machine learning, ORB, unsupervised learning, Text

URL: http://hdl.handle.net/11012/189399

Automatická klasifikace obrazů

Autoren: Sikora, Pavel Miklánek, Štěpán Ševčík, Zdeněk

Index Begriffe: Aglomerativní shlukování, automatická klasifikace obrazů, BIRCH, BoVW, BRISK, extrakce příznaků, K-majority, K-means, konvoluční autoenkodér, OBR, strojové učení, učení bez učitele, Aglomerativ clustering, automatic image classification, convolution autoencoder, feature extraction, machine learning, ORB, unsupervised learning, Text

URL: http://hdl.handle.net/11012/189399