Recognition of fabric composition of clothing in an image in e-commerce using neural networks by V. V. Sorokina

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Secure federated distillation GAN for CIDS in industrial CPS by Junwei LIANG, Geng YANG, Maode MA, Sadiq Muhammad

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Modeling of Ultraviolet Aurora Intensity Associated With Interplanetary and Geomagnetic Parameters Based on Neural Networks by Ze‐Jun Hu, Bing Han, Yisheng Zhang, Huifang Lian, Ping Wang, Guojun Li, Bin Li, Xiang‐Cai Chen, Jian‐Jun Liu

Subjects: “…conditional generation adversarial network…”
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Machine-Learning-Driven Identification of Electrical Phases in Low-Sampling-Rate Consumer Data by Dilan C. Hangawatta, Ameen Gargoom, Abbas Z. Kouzani

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Unsupervised intrusion detection model based on temporal convolutional network by LIAO Jinju, DING Jiawei, FENG Guanghui

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ViT-ISRGAN: A High-Quality Super-Resolution Reconstruction Method for Multispectral Remote Sensing Images by Yifeng Yang, Hengqian Zhao, Xiadan Huangfu, Zihan Li, Pan Wang

Subjects: “…vision transformer improved super-resolution generative adversarial network (ViT-ISRGAN) model…”
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Exploring Effects of Mental Stress with Data Augmentation and Classification Using fNIRS by M. N. Afzal Khan, Nada Zahour, Usman Tariq, Ghinwa Masri, Ismat F. Almadani, Hasan Al-Nashah

Subjects: “…deep convolutional generative adversarial network (DCGAN)…”
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Advanced Detection of AI-Generated Images Through Vision Transformers by Darshan Lamichhane

“…The rapid advancement of Artificial Intelligence (AI) models such as Generative Adversarial Networks (GANs) has been a great success in the field of image synthesis and creation. …”
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DGANS:robustness image steganography model based on double GAN by Leqing ZHU, Yu GUO, Lingqiang MO, Daxing ZHANG

“…Deep convolutional neural networks can be effectively applied to large-capacity image steganography,but the research on their robustness is rarely reported.The DGANS (double-GAN-based steganography) applies the deep learning framework in image steganography,which is optimized to resist small geometric distortions so as to improve the model’s robustness.DGANS is made up of two consecutive generative adversarial networks that can hide a grayscale image into another color or grayscale image of the same size and can restore it later.The generated stego-images are augmented and used to further train and strengthen the reveal network so as to make it adaptive to small geometric distortion of input images.Experimental results suggest that DGANS can not only realize high-capacity image steganography,but also can resist geometric attacks within certain range,which demonstrates better robustness than similar models.…”
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Fault detection and diagnosis method for heterogeneous wireless network based on GAN by Xiaorong ZHU, Peipei ZHANG

“…Aiming at the problem that in the process of network fault detection and diagnosis,how to train the precise fault diagnosis and detection model based on small data volume,a fault diagnosis and detection algorithm based on generative adversarial networks (GAN) for heterogeneous wireless networks was proposed.Firstly,the common network fault sources in heterogeneous wireless network environment was analyzed,and a large number of reliable data sets was obtained based on a small amount of network fault samples through GAN algorithm.Then,the extreme gradient boosting (XGBoost) algorithm was used to select the optimal feature combination of input parameters in the fault detection stage and completed fault diagnosis and detection based on these data.Simulation results show that the algorithm can achieve more accurate and efficient fault detection and diagnosis for heterogeneous wireless networks,with an accuracy of 98.18%.…”
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Risks and countermeasures of artificial intelligence generated content technology in content security governance by Zhe QIAO

“…Recently, artificial intelligence generated content (AIGC) technology has achieved various disruptive results and has become a new trend in AI research and application, driving AI into a new era.Firstly, the development status of AIGC technology was analyzed, focusing on generative models such as generative adversarial networks and diffusion models, as well as multimodal technologies, and surveying and elaborating on the existing technological capabilities for text, speech, image and video generation.Then, the risks brought by AIGC technology in the field of content security governance were focused and analyzed, including fake information, content infringement, network and software supply chain security, data leakage and other aspects.Finally, in view of the above security risks, counter strategies were proposed from the technical, application and regulatory levels, respectively.…”
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Designing an Efficient System for Emotion Recognition Using CNN by Donia Ammous, Achraf Chabbouh, Awatef Edhib, Ahmed Chaari, Fahmi Kammoun, Nouri Masmoudi

“…Its accuracy was improved via different data augmentation tools, early stopping, and generative adversarial networks (GANs). Compared to previous methods, experimental results show that the proposed method provides a 0.55% to 35.7% gain performance.…”
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Super-resolved microstructure of pyrolyzing superlight ablators by Collin W. Foster, Sreevishnu Oruganti, Francesco Panerai

“…Abstract The microstructural evolution of superlight ablators during pyrolysis was investigated using in situ X-ray micro-computed tomography and generative adversarial networks. Superlight ablators, a type of syntactic foam thermal protection materials, are commonly employed in the backshells of planetary entry probes. …”
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Artificial Intelligence-Based Digital Media Design Effect Enhancement Mechanism by Pu Zhao

“…Specifically, we propose a method for low-light image enhancement using generative adversarial networks as a model framework. To better solve the problem, we design the following strategies in our proposed method. …”
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Application of machine learning techniques for warfarin dosage prediction: a case study on the MIMIC-III dataset by Aasim Ayaz Wani, Fatima Abeer

“…By leveraging dimensionality reduction methods such as principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), and advanced imputation techniques including denoising autoencoders (DAE) and generative adversarial networks (GAN), we achieved significant improvements in predictive accuracy. …”
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Photorealistic Texture Contextual Fill-In by Radek Richtr

“…The project combines state-of-the-art methods, including generative adversarial networks (GANs), patch-based inpainting, and manual retouching, to restore and enhance severely degraded images. …”
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Motion Deblurring in Image Color Enhancement by WGAN by Jiangfan Feng, Shuang Qi

“…To achieve this, we explore the synchronization of processing two tasks for the first time by using the framework of generative adversarial networks (GANs). We add L1 loss to the generator loss to simulate the model to match the target image at the pixel level. …”
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Advances in Image Generation Technology: Exploring GANs and MirrorGANs by Shi Lewuqiong

“…This paper is an in-depth study by delving into the latest in image generation technology, where thesis is focusing on the Generative Adversarial Networks (GANs) and MirrorGANs possibilities. …”
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Handwriting Digital Image Generation based on GAN: A Comparative Study of Basic GAN and CGAN Models by Zeng Hongzhi

“…The vast application of artificial intelligence in numerous fields—image generation being one of them—has been made possible by the quick development of deep learning. Generative Adversarial Networks (GAN) can generate high-quality images through an adversarial training mechanism. …”
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Optimizing cryptographic protocols against side channel attacks using WGAN-GP and genetic algorithms by Purushottam Singh, Prashant Pranav, Sandip Dutta

“…Abstract This research introduces a novel hybrid cryptographic framework that combines traditional cryptographic protocols with advanced methodologies, specifically Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) and Genetic Algorithms (GA). …”
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