The generative adversarial network (GAN), which has received considerable notice for its outstanding data generating abilities, is one of the most intriguing fields of artificial intelligence study. Large volumes of d...
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The generative adversarial network (GAN), which has received considerable notice for its outstanding data generating abilities, is one of the most intriguing fields of artificial intelligence study. Large volumes of data are required to develop generalizable deep learning models. GANs are a highly strong class of networks capable of producing believable new pictures from unlabeled source prints and labeled medical imaging data is scarce and costly to produce. Despite GAN's remarkable outcomes, steady training remains a challenge. The goal of this study is to perform a complete evaluation of the GAN-related literature and to present a succinct summary of existing knowledge on GAN, including the theory following it, its intended purpose, potential base model alterations, and latest breakthroughs in the area. This article will aid you in gaining a comprehensive grasp of GAN and provide an overview of GAN and its many model types, as well as common implementations, measurement parameter suggestions, and GAN applications in imageprocessing. It will also go over the several applications of GANs in imageprocessing, as well as their benefits and limitations, as well as its prospective reach.
artificialneuralnetworks and Fuzzy Classifiers have emerged as powerful tools for processing Synthetic Aperture Radar images, addressing the challenges of SAR image interpretation and classification. This study prop...
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Deep learning is a powerful multi-layer architecture that has important applications in imageprocessing and text classification. This paper first introduces the development of deep learning and two important algorith...
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ISBN:
(纸本)9798350312935
Deep learning is a powerful multi-layer architecture that has important applications in imageprocessing and text classification. This paper first introduces the development of deep learning and two important algorithms of deep learning: convolutional neuralnetworks and recurrent neuralnetworks. The paper then introduces three applications of deep learning for image recognition, image detection, and image forensics, as well as three text classification methods based on convolutional neuralnetworks, recurrent neuralnetworks, and other text classification methods. Finally, the development trend of deep learning in the field of text and imageprocessing and the difficulties to be further researched are summarised and prospected.
The recent surge in the use of Deep neuralnetworks (DNNs) has also made its mark in the field of Audio Enhancement (AE), providing much better quality than the classical methods. Although, there are dedicated audio p...
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The recent surge in the use of Deep neuralnetworks (DNNs) has also made its mark in the field of Audio Enhancement (AE), providing much better quality than the classical methods. Although, there are dedicated audio processing DNNs, yet, many recent models of AE have utilized U-Net: a DNN based on Convolutional neural Network (CNN), fundamentally developed for image segmentation. It is found that the useful features hidden in the time domain are highlighted when the audio signal is converted to a spectrogram, which can be treated as an image. In this article, we will review the recent work, utilizing U-Nets for different AE applications. Different than other published reviews, this review focuses entirely on AE techniques based on image U-Nets. We will discuss the need for AE, U-Net comparison to other DNNs, the benefits of converting the audio to 2D, input representations that are useful for different AE applications, the architecture of vanilla U-Net and the pre-trained models, variations in vanilla architecture incorporated in different E models, and the state-of-the-art AE algorithms based on U-Net in various applications. Apart from speech and music, this article discusses a wide range of audio signals e.g. environmental, biomedical, bioacoustics, and industrial sounds, not covered collectively in a single article in previously published studies. The article ends with the discussion of colored spectrograms in future AE applications.
In this study, a fully connected pre-layer that dynamically assigns weights to input features is developed to improve classification performance of artificialneuralnetworks. The Hadamard product of the input vector ...
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ISBN:
(纸本)9798350388978;9798350388961
In this study, a fully connected pre-layer that dynamically assigns weights to input features is developed to improve classification performance of artificialneuralnetworks. The Hadamard product of the input vector and the output of the pre-layer is transferred to the following hidden layer so as to preserve the feature subspace. In this respect, a two-stage training mechanism is devised to learn pre-layer parameters that will yield feature representations enabling the network to make more accurate predictions. Applying the proposed approach on a sentiment detection task for product reviews compiled in two different languages, its positive contribution to the classification performance is empirically shown, and furthermore, the conception that the customized layer may be used as an independent feature weighting scheme is supported by experimental results obtained with several other fundamental classification models.
By utilizing hybrid quantum-classical neuralnetworks (HNNs), this research aims to enhance the efficiency of image classification tasks. HNNs allow us to utilize quantum computing to solve machine learning problems, ...
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By utilizing hybrid quantum-classical neuralnetworks (HNNs), this research aims to enhance the efficiency of image classification tasks. HNNs allow us to utilize quantum computing to solve machine learning problems, which can be highly power-efficient and provide significant computation speedup compared to classical operations. This is particularly relevant in sustainable applications where reducing computational resources and energy consumption is crucial. This study explores the feasibility of a novel architecture by leveraging quantum devices as the first layer of the neural network, which proved to be useful for scaling HNNs' training process. Understanding the role of quanvolutional operations and how they interact with classical neuralnetworks can lead to optimized model architectures that are more efficient and effective for image classification tasks. This research investigates the performance of HNNs across different datasets, including CIFAR100 and Satellite images of Hurricane Damage by evaluating the performance of HNNs on these datasets in comparison with the performance of reference classical models. By evaluating the scalability of HNNs on diverse datasets, the study provides insights into their applicability across various real-world scenarios, which is essential for building sustainable machine learning solutions that can adapt to different environments. Leveraging transfer learning techniques with pre-trained models such as ResNet, EfficientNet, and VGG16 demonstrates the potential for HNNs to benefit from existing knowledge in classical neuralnetworks. This approach can significantly reduce the computational cost of training HNNs from scratch while still achieving competitive performance. The feasibility study conducted in this research assesses the practicality and viability of deploying HNNs for real-world image classification tasks. By comparing the performance of HNNs with classical reference models like ResNet, EfficientNet, and VGG-16, th
We extensively survey applications of Clifford Geometric algebra in recent years (mainly 2019-2022). This includes engineering;electric engineering;optical fibers;geographic information systems;geometry;molecular geom...
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We extensively survey applications of Clifford Geometric algebra in recent years (mainly 2019-2022). This includes engineering;electric engineering;optical fibers;geographic information systems;geometry;molecular geometry;protein structure;neuralnetworks;artificial intelligence;encryption;physics;signal, image, and video processing;and software.
The emerging technology of adversarial information hiding can generate adversarial example by embedding useful information instead of meaningless noise into the host image. The obtained image can deceive classificatio...
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The emerging technology of adversarial information hiding can generate adversarial example by embedding useful information instead of meaningless noise into the host image. The obtained image can deceive classification models and therefore equip the function of privacy protection. However, existing research mainly focuses on the effectiveness of attack rather than preserving the integrity of the protected image. It cannot meet the requirements in the fields such as medical imaging, financial transactions, and copyright protection where users' raw data should not be damaged. Therefore, we propose a novel reversible adversarial information hiding based on interpretability of neuralnetworks. The Grad-CAM-generated heatmap is employed to identify a minimal set of high-impact pixels for embedding, ensuring that minor modifications can induce significant misclassification. Then, the user-defined secret bits are embedded into the identified pixels by using difference expansion. After extraction, the original image can be perfectly restored. The technologies of adversarial example and reversible information hiding are combined to accommodate wider applications. The proposed method is tested on CIFAR-10 with three different neural architectures (NiN, AlexNet, ResNet). Experimental results show that the proposed method can completely restore the cover image while ensuring the extraction of the embedded data and misleading the neural network.
In modern healthcare, medical imageprocessing plays a vital role in enabling early disease detection, treatment planning, and improved patient care. However, traditional methods face challenges such as handling big d...
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In modern healthcare, medical imageprocessing plays a vital role in enabling early disease detection, treatment planning, and improved patient care. However, traditional methods face challenges such as handling big data, scalability, and computational intensity. To address these issues, this paper proposes a Convolutional Global Gated Recurrent-based Adaptive Gazelle (CGGR-AG) algorithm for medical imageprocessingapplications. The CGGR-AG algorithm detects abnormalities and classifies specific objects within images by leveraging Convolutional neuralnetworks (CNNs) for feature extraction and Gated Recurrent Units (GRUs) for capturing sequential patterns. Additionally, the Adaptive Gazelle Optimization algorithm fine-tunes parameters to enhance the effectiveness of the CGGR-AG method. Experimental validation is conducted on Tuberculosis and heart disease datasets, evaluating performance metrics including recall, specificity, accuracy, Area Under the Curve - Receiver Operating Characteristic (AUC-ROC), precision, and F1-score. Comparative analysis with state-of-the-art methods demonstrates the effectiveness of the CGGR-AG method in medical imageprocessingapplications.
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