Hybrid two-level protection system for preserving pre-trained DNN models ownership
Sign Up to like & getrecommendations! Published in 2024 at "Neural Computing and Applications"
DOI: 10.1007/s00521-024-10304-0
Abstract: Recent advancements in deep neural networks (DNNs) have made them indispensable for numerous commercial applications. These include healthcare systems and self-driving cars. Training DNN models typically demands substantial time, vast datasets and high computational costs.… read more here.
Keywords: protection; pre trained; attack; trained dnn ... See more keywords
Influence of cognitive networks and task performance on fMRI-based state classification using DNN models
Sign Up to like & getrecommendations! Published in 2025 at "Scientific Reports"
DOI: 10.1038/s41598-025-05690-x
Abstract: Deep neural networks (DNNs) excel at extracting insights from complex data across various fields, however, their application in cognitive neuroscience remains limited, largely due to the lack of approaches with interpretability. Here, we employ two… read more here.
Keywords: classification; bilstm; dnn models; performance ... See more keywords
Prediction of the inhibitory concentrations of chloroquine derivatives using deep neural networks models
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Biomolecular Structure and Dynamics"
DOI: 10.1080/07391102.2020.1714486
Abstract: Abstract In recent years, deep neural networks have begun to receive much attention, which has obvious advantages in feature extraction and modeling. However, in the using of deep neural networks for the QSAR modeling process,… read more here.
Keywords: using deep; deep neural; neural networks; prediction inhibitory ... See more keywords
Design and Analysis of Convolutional Neural Layers: A Signal Processing Perspective
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Access"
DOI: 10.1109/access.2023.3258399
Abstract: Convolutional layers (CLs) are ubiquitous in contemporary deep neural network (DNN) models, commonly used for automatic feature extraction. A CL performs cross-correlation between the input to the layer and a set of learnable kernels to… read more here.
Keywords: cls; design analysis; signal processing; dnn models ... See more keywords
Visual Diagnostics of Parallel Performance in Training Large-Scale DNN Models.
Sign Up to like & getrecommendations! Published in 2023 at "IEEE transactions on visualization and computer graphics"
DOI: 10.1109/tvcg.2023.3243228
Abstract: Diagnosing the cluster-based performance of large-scale deep neural network (DNN) models during training is essential for improving training efficiency and reducing resource consumption. However, it remains challenging due to the incomprehensibility of the parallelization strategy… read more here.
Keywords: training; large scale; dnn models; model ... See more keywords
Deep learning-based prognosis of major adverse cardiac events in patients with acute myocardial infarction: a retrospective observational study in the Republic of Korea
Sign Up to like & getrecommendations! Published in 2025 at "Osong Public Health and Research Perspectives"
DOI: 10.24171/j.phrp.2025.0120
Abstract: Objectives This study developed deep neural network (DNN) models capable of accurately classifying major adverse cardiac events (MACE) in patients with acute myocardial infarction (AMI) after hospital discharge, across 3 follow-up intervals: 1, 6, and… read more here.
Keywords: adverse cardiac; patients acute; major adverse; dnn models ... See more keywords