Generative Adversarial Networks Research Articles

Artificial Intelligence-based Fair Allocation in NOMA Technique: AReview

: Non-Orthogonal Multiple Access (NOMA) is an innovation that has great potential in wireless communication. It permits multiple users to efficiently allot a frequency band by adjusting their power allocations. Nevertheless, attaining fair power allocation in NOMA structures presents complex challenges that require specific models, extensive training data, and addressing issues of generalization. This review aims to explore the applications of Artificial Intelligence (AI) and Deep Learning (DL) methods to tackle the challenges associated with fair power allocation in NOMA systems. The focus is on developing strong AI-DL models and creative optimization methods specifically designed for dynamic environments to improve transparency and interpretability. This study explores a wide range of techniques, including Reinforcement Learning, Convolutional Neural Networks (CNN) for power allocation, Generative Adversarial Networks, Deep Reinforcement Learning, and Transfer Learning. The goal is to enhance various aspects, such as power allocation, user coupling, scheduling strategies, interference cancellation, user mobility, security, and deeplearning- based NOMA. Despite the difficulties, impartial power allocation algorithms based on AI and DL show promise in improving user performance and promoting fair power distribution in NOMA systems. This study emphasizes the significance of continuous research efforts to overcome current obstacles, enhance efficiency, and strengthen the dependability of wireless communication systems. This highlights the significance of NOMA as an advanced innovation for upcoming wireless generations that go beyond 5G. Future areas of study involve investigating federated learning and novel techniques for gathering data and utilizing interpretable AI-DL models to address existing constraints. Overall, this review highlights the potential of AI and DL techniques in achieving fair power distribution in NOMA systems. However, further investigation is crucial to addressing obstacles and fully exploring the capabilities of NOMA technology.

Enhanced multi-ethnic speech recognition using pitch shifting generative adversarial networks

<p><span lang="EN-US">Research in the field of speech recognition is a challenging research area. Various approaches have been applied to build robust models. A problem faced in speech recognition research is overfitting, especially if there is insufficient data to train the model. A large enough amount of data can train the model well, resulting in high accuracy. Data augmentation is an approach often used to increase the quantity of dataset. This research uses a data augmentation approach, namely pitch shifting, to increase the quantity of speech dataset, which is then processed into spectrogram data and then classified using a generative adversarial network (GAN). Using the pitch shifting-generative adversarial network (PS-GAN) model, this research produces high accuracy performance in multi-ethnic speech recognition, namely 98.43%, better than several similar studies.</span></p>

53731 Use of generative adversarial neural networks to remove markers from skin lesion images

53731 Use of generative adversarial neural networks to remove markers from skin lesion images

Detection of cyberattacks using bidirectional generative adversarial network

Due to the progress of communication technologies, diverse information is transmitted in distributed systems via a network model. Concurrently, with the evolution of communication technologies, the attacks have broadened, raising concerns about the security of networks. For dealing with different attacks, the analysis of intrusion detection system (IDS) has been carried out. Conventional IDS rely on signatures and are time-consuming for updation, often lacking coverage for all kinds of attacks. Deep learning (DL), specifically generative methods demonstrate potential in detecting intrusions through network data analysis. This work presents a bidirectional generative adversarial network (BiGAN) for the detection of cyberattacks using the IoT23 database. This BiGAN model efficiently detected different attacks and the accuracy and F-score values achieved were 98.8% and 98.2% respectively.

Deep Generative Adversarial Networks For Noise Reduction in Medical Images: A Review

Deep Generative Adversarial Networks For Noise Reduction in Medical Images: A Review

Deep learning for the harmonization of structural MRI scans: a survey.

Medical imaging datasets for research are frequently collected from multiple imaging centers using different scanners, protocols, and settings. These variations affect data consistency and compatibility across different sources. Image harmonization is a critical step to mitigate the effects of factors like inherent differences between various vendors, hardware upgrades, protocol changes, and scanner calibration drift, as well as to ensure consistent data for medical image processing techniques. Given the critical importance and widespread relevance of this issue, a vast array of image harmonization methodologies have emerged, with deep learning-based approaches driving substantial advancements in recent times. The goal of this review paper is to examine the latest deep learning techniques employed for image harmonization by analyzing cutting-edge architectural approaches in the field of medical image harmonization, evaluating both their strengths and limitations. This paper begins by providing a comprehensive fundamental overview of image harmonization strategies, covering three critical aspects: established imaging datasets, commonly used evaluation metrics, and characteristics of different scanners. Subsequently, this paper analyzes recent structural MRI (Magnetic Resonance Imaging) harmonization techniques based on network architecture, network learning algorithm, network supervision strategy, and network output. The underlying architectures include U-Net, Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), flow-based generative models, transformer-based approaches, as well as custom-designed network architectures. This paper investigates the effectiveness of Disentangled Representation Learning (DRL) as a pivotal learning algorithm in harmonization. Lastly, the review highlights the primary limitations in harmonization techniques, specifically the lack of comprehensive quantitative comparisons across different methods. The overall aim of this review is to serve as a guide for researchers and practitioners to select appropriate architectures based on their specific conditions and requirements. It also aims to foster discussions around ongoing challenges in the field and shed light on promising future research directions with the potential for significant advancements.

The data recovery strategy on machine learning against false data injection attacks in power cyber physical systems

During the transmission of power measurement data through communication networks from remote terminal unit (RTU) to the state estimator in Supervisory Control and Data Acquisition (SCADA), power cyber-physical systems (PCPSs) are more susceptible to cyber-attacks. To mitigate that threat, this paper is concerned with a new data recovery strategy on machine learning against false data injection attacks (FDIAs) in PCPSs. Firstly, in view of the limited resources (such as limited energy) of adversaries and system protections, a sparse target false data injection attack (FDIA) is constructed. Then, the FDIA detection problem is transformed into a tripartite separation problem, and the alternating direction method of multipliers on proximal exchange (ADMM-PE) is adopted to complete the intrusion detection of FDIAs. In addition, with the help of reliable mask information and real incomplete measurement data provided by the FDIA detection, a similar supervised generative adversarial imputation networks (GAIN) is proposed to complete the measurement data recovery after FDIAs. Specifically, the pseudo labels generated by data analysis methods such as k-means clustering and support vector machine (SVM) to improve the accuracy of measurement data recovery. Finally, the experimental results of PCPSs show the effectiveness and superiority of the proposed data recovery strategy against FDIAs.

Generative Adversarial Networks in Anomaly Detection and Malware Detection: A Comprehensive Survey

Generative Adversarial Networks in Anomaly Detection and Malware Detection: A Comprehensive Survey

Internal Thread Defect Generation Algorithm and Detection System Based on Generative Adversarial Networks and You Only Look Once

Internal Thread Defect Generation Algorithm and Detection System Based on Generative Adversarial Networks and You Only Look Once

Generative Adversarial Network Optimization Algorithm Based on Adaptive Data Augmentation

Generative Adversarial Network Optimization Algorithm Based on Adaptive Data Augmentation

DEGAN: Detail-Enhanced Generative Adversarial Network for Monocular Depth based 3D Reconstruction

DEGAN: Detail-Enhanced Generative Adversarial Network for Monocular Depth based 3D Reconstruction

Cvstgan: A Controllable Generative Adversarial Network for Video Style Transfer of Chinese Painting

Cvstgan: A Controllable Generative Adversarial Network for Video Style Transfer of Chinese Painting

Generative Adversarial Networks with Radiomics Supervision for Lung Lesion Generation.

Data-driven methods for lesion generation are quickly emerging due to the need for realistic imaging targets for image quality assessment and virtual clinical trials. We proposed a generative adversarial network (GAN) architecture for conditional generation of lung lesions based on user-specified classes of lesion size and solidity. The network consists of two discriminators, one for volumetric lesion data, and one for radiomics features derived from the lesion volume. A Wasserstein loss with gradient penalty was adopted for each discriminator. Training data were drawn from contoured and annotated lesions from a public lung CT database. Four quantitative evaluation methods were devised to assess the network performance: 1) overfitting (similarity between generated and real lesions), 2) diversity (similarity among generated lesions), 3) conditional consistency (capability of generating lesions according to user-specified classes), and 4) similarity in distributions of various lesion properties between the generated and real lesions. Ablation studies were also performed to investigate the importance of individual network component. The proposed network was found to generate lesions that resemble real lesions by visual inspection. Solid lesions are distinct from non-solid ones, and lesion sizes largely correspond to their specified classes. With a classifier trained on real lesions, the classification accuracies of generated and real lesions in both solid and non-solid classes are similar. Radiomics features of generated and real lesions were found to have similar distributions, indicated by the relatively low Kullback-Leibler (KL) divergence values. Furthermore, the correlations between pairwise radiomics features in generated lesions were comparable to those of real lesions. The proposed network presents a promising approach for generating realistic lesions with clinically relevant features crucial for the comprehensive assessment of medical imaging systems.

Enhancing Intelligent Processing System with Generative Adversarial Networks

Enhancing Intelligent Processing System with Generative Adversarial Networks

OFPP-GAN: One-Shot Federated Personalized Protection–Generative Adversarial Network

OFPP-GAN: One-Shot Federated Personalized Protection–Generative Adversarial Network

Advancing Architectural Design Through Generative Adversarial Network Deep Learning Technology

Advancing Architectural Design Through Generative Adversarial Network Deep Learning Technology

Brain Magnetic Resonance Image Inpainting via Deep Edge Region-based Generative Adversarial Network

Brain Magnetic Resonance Image Inpainting via Deep Edge Region-based Generative Adversarial Network

DeepCGAN: early Alzheimer's detection with deep convolutional generative adversarial networks

DeepCGAN: early Alzheimer's detection with deep convolutional generative adversarial networks

Research and Practice of Interior Design Based on Deep Convolutional Generative Adversarial Networks Algorithms

Research and Practice of Interior Design Based on Deep Convolutional Generative Adversarial Networks Algorithms

Adversarial Variational Autoencoders to Extend and Improve Generative Model

Generative artificial intelligence (GenAI) has been advancing with many notable achievements like ChatGPT and Bard. The deep generative model (DGM) is a branch of GenAI, which is preeminent in generating raster data such as image and sound due to the strong role of deep neural networks (DNNs) in inference and recognition. The built-in inference mechanism of DNN, which simulates and aims at synaptic plasticity of the human neuron network, fosters the generation ability of DGM, which produces surprising results with the support of statistical flexibility. Two popular approaches in DGM are the variational autoencoder (VAE) and generative adversarial network (GAN). Both VAE and GAN have their own strong points although they share and imply the underlying theory of statistics as well as significant complex via hidden layers of DNN when DNN becomes effective encoding/decoding functions without concrete specifications. This research unifies VAE and GAN into a consistent and consolidated model called the adversarial variational autoencoder (AVA) in which the VAE and GAN complement each other; for instance, the VAE is a good data generator by encoding data via the excellent ideology of Kullback–Leibler divergence and the GAN is a significantly important method to assess the reliability of data as to whether it is real or fake. In other words, the AVA aims to improve the accuracy of generative models; besides, the AVA extends the function of simple generative models. In methodology, this research focuses on the combination of applied mathematical concepts and skillful techniques of computer programming in order to implement and solve complicated problems as simply as possible.