Suchergebnisse - novel stack deep convolutional autoencoder*

SDCA: a novel stack deep convolutional autoencoder – an application on retinal image denoising

Autoren: Swarup Kr Ghosh Biswajit Biswas Anupam Ghosh

Quelle: IET Image Processing. 13:2778-2789

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

Zugangs-URL: https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ipr.2018.6582
https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.6582
https://dblp.uni-trier.de/db/journals/iet-ipr/iet-ipr13.html#GhoshBG19
https://ietresearch.onlinelibrary.wiley.com/doi/abs/10.1049/iet-ipr.2018.6582

Epileptic Seizure Recognition Using Reduced Deep Convolutional Stack Autoencoder and Improved Kernel RVFLN From EEG Signals

Autoren: Mrutyunjaya Sahani Susanta Kumar Rout Pradipta Kishor Dash

Quelle: IEEE Transactions on Biomedical Circuits and Systems. 15:595-605

Schlagwörter: Epilepsy, Seizures, 0202 electrical engineering, electronic engineering, information engineering, Humans, Electroencephalography, Signal Processing, Computer-Assisted, Neural Networks, Computer, 02 engineering and technology, Child, Algorithms

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/34156948
https://ieeexplore.ieee.org/document/9462442/
https://europepmc.org/article/MED/34156948

Deep Convolutional Stack Autoencoder of Process Adaptive VMD Data With Robust Multikernel RVFLN for Power Quality Events Recognition

Autoren: Mrutyunjaya Sahani Pradipta Kishore Dash

Quelle: IEEE Transactions on Instrumentation and Measurement. 70:1-12

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

Zugangs-URL: https://ieeexplore.ieee.org/document/9335941
http://ieeexplore.ieee.org/iel7/19/9259274/09335941.pdf
https://dblp.uni-trier.de/db/journals/tim/tim70.html#SahaniD21

Precise single step and multistep short-term photovoltaic parameters forecasting based on reduced deep convolutional stack autoencoder and minimum variance multikernel random vector functional network.

Autoren: Sahani, Mrutyunjaya Choudhury, Sasmita Siddique, Marif Daula et al.

Quelle: Engineering Applications of Artificial Intelligence. Oct2024:Part A, Vol. 136, pN.PAG-N.PAG. 1p.

Schlagwörter: *FIELD programmable gate arrays, *SOLAR temperature, *SOLAR power plants, *STATISTICAL correlation, *ENERGY shortages

BrainNet: a fusion assisted novel optimal framework of residual blocks and stacked autoencoders for multimodal brain tumor classification

Autoren: Muhammad Sami Ullah Muhammad Attique Khan Nouf Abdullah Almujally et al.

Quelle: Scientific Reports, Vol 14, Iss 1, Pp 1-20 (2024)

Schlagwörter: Brain tumor, Explinable AI, MRI, Residual blocks, Stack encoders, Feature selection, Medicine, Science

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/2045-2322

Zugangs-URL: https://doaj.org/article/46675dc185ec43f2a8fded212bdc5c11

SDCA: a novel stack deep convolutional autoencoder – an application on retinal image denoising.

Autoren: Ghosh, Swarup Kr Biswas, Biswajit Ghosh, Anupam

Quelle: IET Image Processing (Wiley-Blackwell); Dec2019, Vol. 13 Issue 14, p2778-2789, 12p

Deep Convolutional Neural Networks for Plant Disease Detection: A Mobile Application Approach (Agri Bot).

Autoren: Ibrahim, Hegazi Ahmed, Abdelmoty M. Bouallegue, Belgacem et al.

Quelle: International Journal of Intelligent Systems; 5/30/2025, Vol. 2025, p1-19, 19p

Schlagwörter: CONVOLUTIONAL neural networks, PLANT diseases, AGRICULTURE, AUTOENCODERS, CROP yields

Heart Diseases Prediction Using Deep Convolutional Neural Network with Deformable Angular Alignment Module.

Autoren: Kumar, Voruganti Naresh Ramatenki, Sateesh Kumar Marri, Sireesha et al.

Quelle: International Journal of Intelligent Engineering & Systems; 2025, Vol. 18 Issue 11, p551-565, 15p

Schlagwörter: HEART diseases, CONVOLUTIONAL neural networks, PREDICTIVE validity, COMPUTER-assisted image analysis (Medicine), FEATURE extraction, DEEP learning, ELECTROCARDIOGRAPHY

Epileptic Seizure Recognition Using Reduced Deep Convolutional Stack Autoencoder and Improved Kernel RVFLN From EEG Signals.

Autoren: Sahani, Mrutyunjaya Rout, Susanta Kumar Dash, Pradipta Kishor

Quelle: IEEE Transactions on Biomedical Circuits & Systems; Jun2021, Vol. 16 Issue 3, p595-605, 11p

DAB-LSTMNN: A novel respiratory disease diagnosis deep CNN framework based on attention scheme and BLSTM neural networks.

Autoren: Dai, Zhaoji Zheng, Xianwei Lin, Junqi et al.

Quelle: International Journal of Wavelets, Multiresolution & Information Processing; Jan2025, Vol. 23 Issue 1, p1-24, 24p

Schlagwörter: CONVOLUTIONAL neural networks, LONG short-term memory, ARTIFICIAL intelligence, COVID-19, ASTHMATICS, DEEP learning

A Novel Attention-Based Parallel Blocks Deep Architecture for Human Action Recognition.

Autoren: Jadoon, Yasir Khan Khalid, Yasir Noman Khan, Muhammad Attique et al.

Quelle: Computer Modeling in Engineering & Sciences (CMES); 2025, Vol. 144 Issue 1, p1143-1164, 22p

Schlagwörter: HUMAN activity recognition, DEEP learning, NEURAL computers, COMPUTER performance, MOTION analysis, ARTIFICIAL neural networks, MATHEMATICAL optimization

Inpainting non-anatomical objects in brain imaging using enhanced deep convolutional autoencoder network.

Autoren: Kumar, Puranam Revanth Shilpa, B Jha, Rajesh Kumar et al.

Quelle: Sādhanā: Academy Proceedings in Engineering Sciences; Jun2024, Vol. 49 Issue 2, p1-13, 13p

Wavelet-Based Fusion for Image Steganography Using Deep Convolutional Neural Networks.

Autoren: Khalifa, Amal Yadav, Yashi

Quelle: Electronics (2079-9292); Jul2025, Vol. 14 Issue 14, p2758, 13p

Schlagwörter: DEEP learning, CONVOLUTIONAL neural networks, WAVELET transforms, CRYPTOGRAPHY, IMAGE processing

Accident Impact Prediction Based on a Deep Convolutional and Recurrent Neural Network Model.

Autoren: Sajadi, Pouyan Qorbani, Mahya Moosavi, Sobhan et al.

Quelle: Urban Science; Aug2025, Vol. 9 Issue 8, p299, 30p

Schlagwörter: TRAFFIC accidents, PREDICTION models, DEEP learning, REAL-time computing, TRAFFIC safety, CONVOLUTIONAL neural networks, LONG short-term memory

Geografische Kategorien: LOS Angeles County (Calif.)

Parkinson's disease Severity Detection Based on Fused features with tuned Residual Deep Convolutional Neural Network.

Autoren: Vishwakarma, Gagan Nandanwar, Amit Kumar Thakur, Ghanshyam Singh

Quelle: Multimedia Tools & Applications; Apr2025, Vol. 84 Issue 14, p13111-13131, 21p

Schlagwörter: CONVOLUTIONAL neural networks, PARKINSON'S disease, ARTIFICIAL intelligence, FEATURE extraction, OPTIMIZATION algorithms

Automatic lung nodule classification using deep learning: CNN architecture.

Autoren: Mukil, A. Andrews, L. J. B. Lai, N. S. et al.

Quelle: AIP Conference Proceedings. 2024, Vol. 3161 Issue 1, p1-10. 10p.

Schlagwörter: *ARTIFICIAL neural networks, *CONVOLUTIONAL neural networks, *PULMONARY nodules, *COMPUTER-aided diagnosis, *DEEP learning, *LUNGS

SDCAE: Stack Denoising Convolutional Autoencoder Model for Accident Risk Prediction Via Traffic Big Data

Autoren: Xiaoliang Fan Lujing Xiao Chuanpan Zheng et al.

Quelle: 2018 Sixth International Conference on Advanced Cloud and Big Data (CBD). :328-333

Schlagwörter: 11. Sustainability, 0502 economics and business, 05 social sciences, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 3. Good health

Zugangs-URL: http://xplorestaging.ieee.org/ielx7/8529158/8530796/08530861.pdf?arnumber=8530861
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=8530861

Improving automated latent fingerprint detection and segmentation using deep convolutional neural network.

Autoren: Chhabra, Megha Ravulakollu, Kiran Kumar Kumar, Manoj et al.

Quelle: Neural Computing & Applications; Mar2023, Vol. 35 Issue 9, p6471-6497, 27p

Schlagwörter: CONVOLUTIONAL neural networks, FORENSIC fingerprinting, DEEP learning, HUMAN fingerprints, OBJECT recognition (Computer vision), IMAGE segmentation, DATABASES

Deep convolutional transformer network for hyperspectral unmixing.

Autoren: Hadi, Fazal Yang, Jingxiang Farooque, Ghulam et al.

Quelle: European Journal of Remote Sensing; Dec2023, Vol. 56 Issue 1, p1-18, 18p

Schlagwörter: TRANSFORMER models, CONVOLUTIONAL neural networks, DEEP learning, IMAGE analysis

Vessel trajectory classification via transfer learning with Deep Convolutional Neural Networks.

Autoren: Kim, Hwan Choi, Mingyu Park, Sekil et al.

Quelle: PLoS ONE; 8/26/2024, Vol. 19 Issue 8, p1-15, 15p

Schlagwörter: CONVOLUTIONAL neural networks, NAVIGATION in shipping, AUTOMATIC identification, MARITIME safety, CLASSIFICATION, DEEP learning