Suchergebnisse - "Auto encoder–decoder"

A Deep-learning-based Auto Encoder-Decoder Model for Denoising Electrocardiogram Signals

Autoren: Maumita Das Bikash Chandra Sahana

Quelle: IETE Journal of Research. 71:326-340

Microwave-based Breast Cancer Detection by using Auto Encoder-Decoder Probabilistic Neural Network

Autoren: Mehran Taghipour-Gorjikolaie Navid Ghavami Gianluigi Tiberi et al.

Quelle: 2024 18th International Symposium on Medical Information and Communication Technology (ISMICT). :47-52

Deep Artificial Denoising Auto-Encoder-Decoder Pain Recognition System

Autoren: H. Perry Fordson Adam Anderson Eve Derosa

Quelle: 2023 International Conference on Machine Learning and Cybernetics (ICMLC). :75-81

Redefining Retinal Lesion Segmentation: A Quantum Leap With DL-UNet Enhanced Auto Encoder-Decoder for Fundus Image Analysis

Autoren: B. Naveen Kumar T. R. Mahesh G. Geetha et al.

Quelle: IEEE Access, Vol 11, Pp 70853-70864 (2023)

Schlagwörter: Diabetic retinopathy, fundus images, encoder-decoder network, patch generation, lesion segmentation, convolutional neural network, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Zugangs-URL: https://doaj.org/article/a789036994cc45ffabfbdabc6d685ecf

Lite-Deep : Improved Auto Encoder-Decoder for Polyp Segmentation

Autoren: S Geetha Arun Sreenivas C Gopakumar et al.

Quelle: 2021 8th International Conference on Smart Computing and Communications (ICSCC). :6-11

Schlagwörter: 03 medical and health sciences, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 3. Good health

Zugangs-URL: https://ieeexplore.ieee.org/document/9528124

Wavelet-Based Deep Auto Encoder-Decoder (WDAED)-Based Image Compression

Autoren: Rajat Kumar Singh Dipti Mishra Satish K. Singh

Quelle: IEEE Transactions on Circuits and Systems for Video Technology. 31:1452-1462

Schlagwörter: 03 medical and health sciences, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: https://ieeexplore.ieee.org/document/9144534
https://doi.org/10.1109/TCSVT.2020.3010627
https://dblp.uni-trier.de/db/journals/tcsv/tcsv31.html#MishraSS21

Audio signal quality enhancement using multi-layered convolutional neural network based auto encoder–decoder

Autoren: Shivangi Raj P. Prakasam Shubham Gupta

Quelle: International Journal of Speech Technology. 24:425-437

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

Zugangs-URL: https://link.springer.com/article/10.1007/s10772-021-09809-z

An Auto Encoder-Decoder Approach to Classify the Bird Sounds Using Deep Learning Techniques

Autoren: Bandi Vamsi Mohan Mahanty Bhanu Prakash Doppala

Quelle: SN Computer Science. 4

Eadn: Enhanced Auto Encoder Decoder Network Ensembled With Boosting Technique for Feature Selection of Parkinson’s Disease Detection

Autoren: Navya Sre S. Srinivasa

Developing a novel hybrid Auto Encoder Decoder Bidirectional Gated Recurrent Unit model enhanced with empirical wavelet transform and Boruta-Catboost to forecast significant wave height

Autoren: Masoud Karbasi Mehdi Jamei Mumtaz Ali et al.

Quelle: Journal of Cleaner Production. 379:134820

Schlagwörter: Boosted regression tree, 13. Climate action, Wave energy, Auto encoder decoder Bi-Directional GRU, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, Boruta-Cat Boost, 02 engineering and technology, Empirical wavelet transform, 7. Clean energy, Significant wave height

Infrared image colorization using an edge aware auto encoder decoder with the multi-resolution fusion

Autoren: Ziyue Dong Weidong Qu

Quelle: 2019 Chinese Automation Congress (CAC). :1011-1016

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

Zugangs-URL: http://xplorestaging.ieee.org/ielx7/8966769/8996152/08996588.pdf?arnumber=8996588
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=8996588

Image Steganography Using Auto Encoder-Decoder Based Deep Learning Method

Autoren: Somaya Al-Maadeed M. Samir Abou El-Seoud Omar Elharrouss et al.

Quelle: Advances in Intelligent Systems and Computing ISBN: 9783030672089

Zugangs-URL: https://link.springer.com/chapter/10.1007/978-3-030-67209-6_56
https://link.springer.com/content/pdf/10.1007%2F978-3-030-67209-6_56.pdf

Surgical tool segmentation using a hybrid deep CNN-RNN auto encoder-decoder

Autoren: Saeid Nahavandi Mohamed Attia Mohammed Hossny et al.

Quelle: 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC). :3373-3378

Schlagwörter: 03 medical and health sciences, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Two Stream Auto-encoder Decoder Network for Kidney and Tumor Segmentation

Autoren: Moradi, Pouria Azad, Reza Asadi-Aghbolaghi, Maryam

Quelle: Submissions to the 2019 Kidney Tumor Segmentation Challenge: KiTS19.

Schlagwörter: 03 medical and health sciences, 0302 clinical medicine

Zugangs-URL: https://kits.lib.umn.edu/wp-content/uploads/2019/11/rezazad68_1ee.pdf

Forecasting weekly reference evapotranspiration using Auto Encoder Decoder Bidirectional LSTM model hybridized with a Boruta-CatBoost input optimizer

Autoren: Karbasi, Masoud Jamei, Mehdi Ali, Mumtaz et al.

Quelle: Computers and Electronics in Agriculture. 198:107121

Schlagwörter: FAO-56 Penman-Monteith, 13. Climate action, Bi-LSTM, 0208 environmental biotechnology, 0207 environmental engineering, Reference evapotranspiration, 02 engineering and technology, Auto Decoder Encoder, Boruta-Catboost, 7. Clean energy, 6. Clean water, Forecasting

A Deep-learning-based Auto Encoder-Decoder Model for Denoising Electrocardiogram Signals.

Autoren: Das, Maumita Sahana, Bikash Chandra

Quelle: IETE Journal of Research; Jan-Jun2025, Vol. 71 Issue 1, p326-340, 15p

Schlagwörter: CONVOLUTIONAL neural networks, ADDITIVE white Gaussian noise, MEAN square algorithms, SIGNAL-to-noise ratio, SIGNAL denoising

Efficient Wheat Disease Identification Using Hybrid Swin-SHARP Vision Model

Autoren: Waqar Khalid Yazeed Alkhrijah Syed Muhammad Usman et al.

Quelle: IEEE Access, Vol 13, Pp 141781-141797 (2025)

Schlagwörter: Wheat disease, Swin-SHARP, ViT, SVH model, customized convolutional auto-encoder decoder, softmax, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Dateibeschreibung: electronic resource

Relation: https://ieeexplore.ieee.org/document/11121865/; https://doaj.org/toc/2169-3536

Zugangs-URL: https://doaj.org/article/e3693229b0dc49ec932d168764843d5c

Forecasting weekly reference evapotranspiration using Auto Encoder Decoder Bidirectional LSTM model hybridized with a Boruta-CatBoost input optimizer

Autoren: M Karbasi M Jamei M Ali et al.

Schlagwörter: Agriculture, Multidisciplinary, ALGORITHM, Auto Decoder Encoder, Bi-LSTM, Boruta-Catboost, Computer Science, Interdisciplinary Applications, FAO-56 Penman-Monteith, Forecasting, GAUSSIAN PROCESS REGRESSION, Life Sciences & Biomedicine, LIMITED METEOROLOGICAL DATA, MULTISTEP, NEURAL-NETWORKS, REFERENCE CROP EVAPOTRANSPIRATION, Reference evapotranspiration, Science & Technology, SVM, Technology

Relation: http://hdl.handle.net/10779/DRO/DU:21063733.v2

Verfügbarkeit: http://hdl.handle.net/10779/DRO/DU:21063733.v2
https://figshare.com/articles/journal_contribution/Forecasting_weekly_reference_evapotranspiration_using_Auto_Encoder_Decoder_Bidirectional_LSTM_model_hybridized_with_a_Boruta-CatBoost_input_optimizer/21063733

Wavelet-Based Deep Auto Encoder-Decoder (WDAED)-Based Image Compression.

Autoren: Mishra, Dipti Singh, Satish Kumar Singh, Rajat Kumar

Quelle: IEEE Transactions on Circuits & Systems for Video Technology; Apr2021, Vol. 31 Issue 4, p1452-1462, 11p

Schlagwörter: WAVELET transforms, SIGNAL convolution, IMAGE compression, ALGORITHMS, MACHINE learning

Audio signal quality enhancement using multi-layered convolutional neural network based auto encoder–decoder.

Autoren: Raj, Shivangi Prakasam, P. Gupta, Shubham

Quelle: International Journal of Speech Technology; Jun2021, Vol. 24 Issue 2, p425-437, 13p

Schlagwörter: CONVOLUTIONAL neural networks, INTELLIGIBILITY of speech