Výsledky vyhledávání - "Deep convolutional neural networks"

Conditioned fully convolutional denoising autoencoder for multi-target NILM

Autoři: García, Diego Pérez, Daniel Papapetrou, Panagiotis a další

Zdroj: Neural Computing & Applications. 37(17):10491-10505

Témata: Non-intrusive Load Monitoring, Energy Efficiency, Deep Convolutional Neural Networks, Interpretability, Multi-target NILM models, data- och systemvetenskap, Computer and Systems Sciences

Popis souboru: print

Přístupová URL adresa: https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-237967
https://doi.org/10.1007/s00521-024-10552-0

GAN-Based Evasion Attack in Filtered Multicarrier Waveforms Systems

Autoři: Zerhouni, Kawtar Singh Gaba, Gurjot, Dr. Hedabou, Mustapha a další

Zdroj: IEEE Transactions on Machine Learning in Communications and Networking. 2:210-220

Témata: Conditional generative adversarial networks, deep convolutional neural networks, evasion attack, filtered multicarrier waveforms

Popis souboru: electronic

Přístupová URL adresa: https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-201155
https://doi.org/10.1109/tmlcn.2024.3361834

End-to-end deep fusion of hyperspectral imaging and computer vision techniques for rapid detection of wheat seed quality

Autoři: Tingting Zhang Jing Li Jinpeng Tong a další

Zdroj: Artificial Intelligence in Agriculture, Vol 15, Iss 3, Pp 537-549 (2025)

Témata: Hyperspectral imaging, Deep convolutional neural networks, Seed quality, Computer vision, Agriculture, Data fusion

Přístupová URL adresa: https://doaj.org/article/12de7b0b19d8419bab87564eb203abb7

Reducing False Alarm in Target Sidelobes Using Time-Varying Trajectories in Range-Doppler Diagram With Conditional 3D-DCNN

Autoři: Jiyeon Choi Daegun Oh Youngwook Kim

Zdroj: IEEE Access, Vol 13, Pp 127518-127525 (2025)

Témata: frequency-modulated continuous wave (FMCW) radar, low-radar cross section (RCS) target, constant false alarm rate (CFAR), Electrical engineering. Electronics. Nuclear engineering, false alarm (FA) suppression, Conditional-three dimensional-deep convolutional neural networks (C-3D-DCNN), TK1-9971

Přístupová URL adresa: https://doaj.org/article/ff8cfa0ae10c428c9ee1124a28461461

Using Deep Convolutional Neural Networks for Earthquake and Explosion Classification

Autoři: Mingquan Hong Hongcai Zhang Lihua Wu a další

Zdroj: IEEE Access, Vol 13, Pp 56144-56159 (2025)

Témata: Earthquake, deep learning, Electrical engineering. Electronics. Nuclear engineering, explosion, deep convolutional neural networks, TK1-9971

Přístupová URL adresa: https://doaj.org/article/d766d21df17f4006836a01163cba7713

Deep learning based automated estimation of urban green space index from satellite image: A case study

Autoři: Rahaman, G. M. Atiqur Längkvist, Martin Loutfi, Amy

Zdroj: Urban Forestry & Urban Greening. 97

Témata: Deep convolutional neural networks (CNN), Green infrastructure, Green index, Semantic segmentation, Urban greenery, Urban planning

Popis souboru: print

Přístupová URL adresa: https://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-114996
https://doi.org/10.1016/j.ufug.2024.128373

BCDCNN: breast cancer deep convolutional neural network for breast cancer detection using MRI images

Autoři: D. E. Martina Jaincy V. Pattabiraman

Zdroj: Scientific Reports, Vol 15, Iss 1, Pp 1-26 (2025)

Témata: Breast cancer detection (BCD), Adaptive kalman filter (AKF), Pyramid scene parsing network (PSPNet), Jellyfsh search optimizer (JSO), Deep convolutional neural networks, Medicine, Science

Popis souboru: electronic resource

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

Přístupová URL adresa: https://doaj.org/article/d72f6b0250d941bca5dec042a2842137

Classifying and diagnosing Alzheimer’s disease with deep learning using 6735 brain MRI images

Autoři: Seyed Mohammad Mousavi Khadijeh Moulaei Leila Ahmadian

Zdroj: Scientific Reports, Vol 15, Iss 1, Pp 1-12 (2025)

Témata: Alzheimer, Artificial intelligence, Deep convolutional neural networks, Deep learning, Medicine, Science

Popis souboru: electronic resource

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

Přístupová URL adresa: https://doaj.org/article/24edf3d80071457b8880c925ab700b02

IMPROVED SEGMENTATION AND DEFORMITY CORRECTION IN MEDICAL IMAGING SYSTEMS

Autoři: M.Dattatreya Goud

Témata: Medical Image Segmentation, Deep Convolutional Neural Networks (DCNNs), Computer-Aided Diagnosis, Segmentation Accuracy

Lumbar Spinal Stenosis Grading in Multiple Level Magnetic Resonance Imaging Using Deep Convolutional Neural Networks

Autoři: Dongkyu Won Hyun-Joo Lee Suk-Joong Lee a další

Zdroj: Global Spine J

Témata: spinal stenosis, 03 medical and health sciences, 0302 clinical medicine, grading, multiple level, deep convolutional neural networks, lumbar, Original Research

Přístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/39487037

Brain Tumor Detection Using RestNet50 Architecture

Autoři: Shadab Manzar Khan

Témata: Artificial Intelligence, Magnetic Resonance Imaging (MRI), Deep Convolutional Neural Networks (CNNs), Brain Tumour Detection,ResNet50

Decalcify cardiac CT: unveiling clearer images with deep convolutional neural networks

Autoři: Gopinath Nagarajan Anandh Rajasekaran Balaji Nagarajan a další

Zdroj: Front Med (Lausanne)
Frontiers in Medicine, Vol 12 (2025)

Témata: cardiac classification, Medicine (General), machine learning, deep convolutional neural networks (CNN), R5-920, decalcification, deep learning, DenseNet, Medicine

Přístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/40351458
https://doaj.org/article/b00ea8e0e4394bb281be643c36fad0af

A lightweight deep convolutional neural network development for soybean leaf disease recognition

Autoři: Yakun Zhang Ruofei Bao Mengxin Guan a další

Zdroj: Frontiers in Plant Science, Vol 16 (2025)

Témata: soybean leaf diseases, disease diagnosis, multiscale feature extraction fusion, dense connectivity, deep convolutional neural networks, Plant culture, SB1-1110

Popis souboru: electronic resource

Relation: https://www.frontiersin.org/articles/10.3389/fpls.2025.1655564/full; https://doaj.org/toc/1664-462X

Přístupová URL adresa: https://doaj.org/article/4b6c3f93521e411e8b529c822601ba99

Scoring facial attractiveness with deep convolutional neural networks: How training on standardized images reduces the bias of facial expressions

Autoři: Obwegeser, Dorothea Timofte, Radu Mayer, Christoph a další

Zdroj: Orthod Craniofac Res
Orthodontics & Craniofacial Research, 27 (S2)

Témata: Adult, Digital Orthodontics, 05 social sciences, facial expressions, artificial intelligence, emotions, facial attractiveness, Facial Expression, Beauty, Young Adult, 03 medical and health sciences, 0302 clinical medicine, Face, Photography, Image Processing, Computer-Assisted, Humans, Female, 0501 psychology and cognitive sciences, Neural Networks, Computer, deep convolutional neural networks, Algorithms

Popis souboru: application/application/pdf

Přístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/38825845
http://hdl.handle.net/20.500.11850/677335

Fine-Tuning Approach to Configuration and Data Selection for Path Loss Prediction in Different Geographical Environments

Autoři: Takahiro Hayashi Koichi Ichige

Zdroj: IEEE Open Journal of Antennas and Propagation, Vol 6, Iss 5, Pp 1595-1608 (2025)

Témata: Path loss prediction, transfer learning, fine-tuning, machine learning, deep convolutional neural networks, cyber-physical systems, Telecommunication, TK5101-6720

Popis souboru: electronic resource

Relation: https://ieeexplore.ieee.org/document/11098504/; https://doaj.org/toc/2637-6431

Přístupová URL adresa: https://doaj.org/article/f8a7843d85a647aa86b7dea157bcaa52

Semi-supervised semantic labeling of remote sensing images with improved image-level selection retraining

Autoři: Qiongqiong Hu Yuechao Wu Ying Li

Zdroj: Alexandria Engineering Journal, Vol 94, Iss, Pp 235-247 (2024)

Témata: 0209 industrial biotechnology, Deep convolutional neural networks, Semantic labeling, Semi-supervised learning, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, TA1-2040, Engineering (General). Civil engineering (General), Remote sensing images

Přístupová URL adresa: https://doaj.org/article/0233f965be344769afda65ba0e02a51b

ECARRNet: An Efficient LSTM-Based Ensembled Deep Neural Network Architecture for Railway Fault Detection

Autoři: Salman Ibne Eunus Shahriar Hossain A. E. M. Ridwan a další

Zdroj: AI, Vol 5, Iss 2, Pp 482-503 (2024)

Témata: xception, Electronic computers. Computer science, inceptionV3, 11. Sustainability, 0202 electrical engineering, electronic engineering, information engineering, ensemble learning, QA75.5-76.95, 02 engineering and technology, deep convolutional neural networks, RNN, inceptionResNetV2

Přístupová URL adresa: https://doaj.org/article/52b549285e7546c7ba8548875d68c16f

Towards Explaining Satellite Based Poverty Predictions with Convolutional Neural Networks

Autoři: Sarmadi, Hamid Rögnvaldsson, Thorsteinn Carlsson, Nils Roger a další

Zdroj: 2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA). :1-10

Témata: FOS: Computer and information sciences, Poverty prediction, Deep Convolutional Neural Networks, Computer Vision and Pattern Recognition (cs.CV), Explainable AI, Signal Processing, 1. No poverty, Satellite Images, Computer Science - Computer Vision and Pattern Recognition, Signalbehandling

Popis souboru: application/pdf

Přístupová URL adresa: http://arxiv.org/abs/2312.00416
http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-51353

Semantic Image Segmentation Based on Superpixel-Pooling DCNN With Random Walks on Graph and Application to Intrusion Detection of Railway

Autoři: Wang Fuzhi Song Changlin

Zdroj: IEEE Access, Vol 12, Pp 100086-100101 (2024)

Témata: superpixel-pooling, 0202 electrical engineering, electronic engineering, information engineering, random walks, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, deep convolutional neural networks, Semantic segmentation, TK1-9971

Přístupová URL adresa: https://doaj.org/article/c73485211c2248e5830aace0eb7374ce

Efficient Channel-Temporal Attention for Boosting RF Fingerprinting

Autoři: Hanqing Gu Lisheng Su Yuxia Wang a další

Zdroj: IEEE Open Journal of Signal Processing, Vol 5, Pp 478-492 (2024)

Témata: Channel attention, 0202 electrical engineering, electronic engineering, information engineering, RF fingerprinting, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, deep convolutional neural networks, temporal attention, TK1-9971

Přístupová URL adresa: https://doaj.org/article/110a91bce3cd44ed9cff9b2fb045d6b1