Results 41 to 50 of about 218,844 (266)
Deep Sparse Learning for Automatic Modulation Classification Using Recurrent Neural Networks
Sensors, 2021 Deep learning models, especially recurrent neural networks (RNNs), have been successfully applied to automatic modulation classification (AMC) problems recently.
Ke Zang, Wenqi Wu, Wei Luo
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Exploiting more robust and efficacious deep learning techniques for modeling wind power with speed
Energy Reports, 2022 Sound analyses of the nonlinear relationship between wind speed and power generation are crucial for the advancement of wind energy optimization. As an emerging artificial intelligence technology, deep learning has received growing attention from energy ...
Hao Chen, Reidar Staupe-Delgado
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Deep Randomized Neural Networks [PDF]
, 2020 Randomized Neural Networks explore the behavior of neural systems where the majority of connections are fixed, either in a stochastic or a deterministic fashion. Typical examples of such systems consist of multi-layered neural network architectures where the connections to the hidden layer(s) are left untrained after initialization.
Gallicchio C., Scardapane S.
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Deep Learning-Based Intrusion Detection With Adversaries
IEEE Access, 2018 Deep neural networks have demonstrated their effectiveness in most machine learning tasks, with intrusion detection included. Unfortunately, recent research found that deep neural networks are vulnerable to adversarial examples in the image ...
Zheng Wang
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Deep oscillatory neural network
Scientific ReportsWe propose a novel, brain-inspired deep neural network model known as the Deep Oscillatory Neural Network (DONN). Deep neural networks like the Recurrent Neural Networks indeed possess sequence processing capabilities but the internal states of the network are not designed to exhibit brain-like oscillatory activity.
Nurani Rajagopal Rohan +5 more
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Performance analysis of different DCNN models in remote sensing image object detection
EURASIP Journal on Image and Video Processing, 2022 In recent years, deep learning, especially deep convolutional neural networks (DCNN), has made great progress. Many researchers use different DCNN models to detect remote sensing targets. Different DCNN models have different advantages and disadvantages.
Huaijin Liu +3 more
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Deep Morphological Neural Networks
CoRR, 2019 Mathematical morphology is a theory and technique to collect features like geometric and topological structures in digital images. Given a target image, determining suitable morphological operations and structuring elements is a cumbersome and time-consuming task.
Yucong Shen +2 more
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Reciprocal control of viral infection and phosphoinositide dynamics
FEBS Letters, EarlyView.Phosphoinositides, although scarce, regulate key cellular processes, including membrane dynamics and signaling. Viruses exploit these lipids to support their entry, replication, assembly, and egress. The central role of phosphoinositides in infection highlights phosphoinositide metabolism as a promising antiviral target.
Marie Déborah Bancilhon, Bruno Mesmin
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FEBS Letters, EarlyView.We identified a systemic, progressive loss of protein S‐glutathionylation—detected by nonreducing western blotting—alongside dysregulation of glutathione‐cycle enzymes in both neuronal and peripheral tissues of Taiwanese SMA mice. These alterations were partially rescued by SMN antisense oligonucleotide therapy, revealing persistent redox imbalance as ...
Sofia Vrettou, Brunhilde Wirth
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Copulas and deep learning: a review
Dependence ModelingIn the last two decades, there has been a surge in the research on neural networks, and particularly on deep learning. At the same time, copulas as a statistical modeling tool for multivariate distributions became more and more popular.
Coblenz Maximilian +3 more
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