Recurrent neuralnetworks (RNNs) are traditionally used for machinelearningapplications for temporal sequences such as natural language processing. Its application to imageprocessing is relatively new. In this pape...
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ISBN:
(纸本)9781665462839
Recurrent neuralnetworks (RNNs) are traditionally used for machinelearningapplications for temporal sequences such as natural language processing. Its application to imageprocessing is relatively new. In this paper, we apply RNNs to denoise images corrupted by mixed Poisson and Gaussian noise. The motivation for using an RNN comes from viewing the denoising of the Poisson-Gaussian realization as a temporal process. The network then attempts to trace back the steps that create the noisy realization in order to arrive at the noiseless reconstruction. Numerical experiments demonstrate that our proposed RNN approach outperforms convolutional autoencoder methods for denoising and upsampling low-resolution images from the CIFAR-10 dataset.
Knowledge Graphs (KG) are repositories of structured, machine-readable data stored as relational triples. DBpedia, Freebase, and YAGO are examples of KGs that have been playing a vital role in many applications, inclu...
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In an era dominated by data-driven decision-making, the insufficiency of high-quality and diverse datasets presents a profound challenge for researchers, businesses, and organizations. The scarcity of data, particular...
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Text is one of humankind's most significant inventions essential for communication and collaboration in modern society. Extracting text from images, especially for languages with cursive and connected scripts like...
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Target contour extraction is a key task in the field of imageprocessing, which is of great significance for applications such as image segmentation, object detection, and scene understanding. Traditional methods are ...
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Deep neuralnetworks have recently seen a significant surge in adoption for different Artificial Intelligence technologies due to the development of powerful computer systems. However, because of the growing security ...
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Deep neuralnetworks have recently seen a significant surge in adoption for different Artificial Intelligence technologies due to the development of powerful computer systems. However, because of the growing security concerns, they are susceptible to dangerous risks. Adversarial instances were initially discovered in the field of computer vision (CV), where systems were deceived by altering their initial inputs. In the field of natural language processing (NLP), additionally they occur. Several approaches are put up to address this gap and handle an extensive variety of NLP applications. We give an organized survey of these works in this *** text is distinct and meaningful in nature, in contrast to the image, which makes the creation of hostile assaults much more challenging. In this study, we present a thorough analysis of adversarial attacks and counterattacks in the textual domain. In order to make the essay self-contained, we examine related important works in computer vision and cover the fundamentals of NLP. We explore unresolved concerns to close the gap between current advancements and increasingly powerful adversarial assaults on NLP DNNs in our survey's conclusion.
neuralnetworks with relatively shallow layers and simple structures may have limited ability in accurately identifying pneumonia. In addition, deep neuralnetworks also have a large demand for computing resources, wh...
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Covid-19, a respiratory disease caused by the SARS-CoV-2 virus, manifests in individuals with varying degrees of severity. Chest X-rays serve as initial screening procedures for suspected Covid-19 infections, aiding i...
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ISBN:
(纸本)9798400716553
Covid-19, a respiratory disease caused by the SARS-CoV-2 virus, manifests in individuals with varying degrees of severity. Chest X-rays serve as initial screening procedures for suspected Covid-19 infections, aiding in the detection of abnormalities. Various approaches utilizing deep learning models like Convolutional neuralnetworks (CNN) have been proposed for Covid-19 detection through the analysis of chest radiograph images. However, the availability of radiographic Chest X-ray images for Covid-19 remains limited and not easily accessible to the global research community. This scarcity of data poses a significant challenge for further research in the diagnosis of Covid-19 using radiographic images. This research aims to overcome this obstacle by the generation of synthetic Chest X-ray images through the use of Generative Adversarial networks (GANs) which produce more realistic images compared to traditional data augmentation methods like rotation, scaling, and flipping. The research analyzes and compares the results to address the limited availability of Covid-19 Chest X-ray data. By augmenting the existing dataset, this research has aimed to improve the performance of Covid-19 diagnosis models based on past research findings.
Recent advancements in deep neuralnetworks have shown remarkable improvements in image quality during the demosaicking process, surpassing conventional algorithms. However, these deep neural network techniques are of...
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In recent years, the rapid development of computer vision and artificial intelligence has significantly advanced agricultural applications, particularly in the quality detection and grading of navel oranges. This revi...
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