Neural network based image and video coding technologies by Chuanmin JIA, Zhenghui ZHAO, Shanshe WANG, Siwei MA

“…Deep neural networks have achieved tremendous success in artificial intelligence,which makes the broad and in-depth research of neural network resurge in recent years.Recently,the neural network based image and video coding has become one of the front-edge topics.A systematic and comprehensive review of neural network based image and video coding approaches based on network structure and coding modules were provided.The development of neural network based image compression,e.g.multi-layer perceptron,random neural network,convolutional neural network,recurrent neural network and generative adversarial network based image compression methods and neural network based video compression tools were introduced respectively.Moreover,the future trends in neural network based compression were also envisioned and discussed.…”
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Global-Local Self-Attention-Based Long Short-Term Memory with Optimization Algorithm for Speaker Identification by Pravin Marotrao Ghate, Bhagvat D. Jadhav, Shriram Sadashiv Kulkarni, Pravin Balaso Chopade, Prabhakar N. Kota

“…The GLSA-LSTM with EN-GWO method acquires an accuracy of 99.36% on the TIMIT dataset, and an accuracy of 93.45% on the VoxCeleb 1 datasets, while compared to SincNet and Generative Adversarial Network (SincGAN) and Hybrid Neural Network – Support Vector Machine (NN-SVM). …”
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The backtrack Hölder gradient method with application to min-max and min-min problems by Bolte, Jérôme, Glaudin, Lilian, Pauwels, Edouard, Serrurier, Mathieu

“…We apply our findings on simple Generative Adversarial Network (GAN) problems obtaining promising numerical results. …”
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Research on PAPR reduction algorithm based on CWGAN-SLM for multi-wavelet OFDM system by Guang YANG, Zhaoyang WU, Min NIE, Xiaohong YAN, Fan JIANG

“…In order to meet the demand for low peak to average ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) technology in the future 6G satellite-ground integrated system, an algorithm combining selective mapping (SLM) algorithm and multi-wavelet OFDM technology was proposed firstly.However, the PAPR reduction was limited and the computational complexity was high.To solve this problem, a multi-wavelet OFDM PAPR reduction algorithm based on conditional Wasserstein generative adversarial network (CWGAN) and SLM was proposed, which was called CWGAN-SLM algorithm.CWGAN was introduced to generate more time-domain alternative signals to reduce the PAPR in the CWGAN-SLM algorithm.Simulation results indicate that the CWGAN-SLM algorithm greatly reduces the PAPR of the system and the computational complexity, and has a lower bit error rate.Compared with the GAN and WGAN, the CWGAN has the advantages of easy training, strong stability and good PAPR performance.…”
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Fusion reconstruction mechanism and contrast learning method for WSN abnormal node detection by YE Miao, CHENG Jin, HUANG Yuan, JIANG Qiuxiang, WANG Yong

“…Firstly, this method provided sufficient positive and negative example information representation for the reconstruction model by using contrastive learning methods, and combined with generative adversarial network (GAN) to generate negative examples with diverse characteristics. …”
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Machine Learning Classifiers and Data Synthesis Techniques to Tackle with Highly Imbalanced COVID-19 Data by Avaz Naghipour, Mohammad Reza Abbaszadeh Bavil Soflaei, mostafa ghader-zefrehei

“…To this end, we demonstrate the capability of two advanced data synthesis algorithms, Conditional Tabular Generative Adversarial Network (CTGAN) and Tabular Variational Autoencoder (TVAE), in addressing the class imbalance inherent in the dataset. …”
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Two-layer optimization model of distribution network line loss considering the uncertainty of new energy access by Xiping Ma, Xiping Ma, Xiaoyang Dong, Haitao Xiao, Yaxin Li, Rui Xu, Kai Wei, Juanjuan Cai, Juan Wei

“…This article proposes a scenario generation method using a generative adversarial network (GAN) to handle the uncertainty associated with DGs and constructs a two-layer optimization model for the distribution network. …”
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Enhanced Landslide Susceptibility Assessment in Western Sichuan Utilizing DCGAN-Generated Samples by Yuanxin Tong, Hongxia Luo, Zili Qin, Hua Xia, Xinyao Zhou

“…To address this issue, this study introduces a novel approach leveraging a deep convolutional generative adversarial network (DCGAN) for data augmentation aimed at enhancing the efficacy of various machine learning methods in LSA, including support vector machines (SVMs), convolutional neural networks (CNNs), and residual neural networks (ResNets). …”
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Adversarial sample generation algorithm for vertical federated learning by Xiaolin CHEN, Daoguang ZAN, Bingchao WU, Bei GUAN, Yongji WANG

“…To adapt to the scenario characteristics of vertical federated learning (VFL) applications regarding high communication cost, fast model iteration, and decentralized data storage, a generalized adversarial sample generation algorithm named VFL-GASG was proposed.Specifically, an adversarial sample generation framework was constructed for the VFL architecture.A white-box adversarial attack in the VFL was implemented by extending the centralized machine learning adversarial sample generation algorithm with different policies such as L-BFGS, FGSM, and C＆amp;W.By introducing deep convolutional generative adversarial network (DCGAN), an adversarial sample generation algorithm named VFL-GASG was designed to address the problem of universality in the generation of adversarial perturbations.Hidden layer vectors were utilized as local prior knowledge to train the adversarial perturbation generation model, and through a series of convolution-deconvolution network layers, finely crafted adversarial perturbations were produced.Experiments show that VFL-GASG can maintain a high attack success while achieving a higher generation efficiency, robustness, and generalization ability than the baseline algorithm, and further verify the impact of relevant settings for adversarial attacks.…”
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An overview of skin cancer classification based on deep learning by Toqa A. Sadoon

“…Finally, some opportunities for skin cancer, such as data imbalance and limitation, generative adversarial network, various data sets, and data augmentation, are summarized.…”
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Distributed abnormal traffic detection method for SDN based on deep learning by WANG Kun, FU Yu, DUAN Xueyuan, YU Yihan, LIU Taotao

“…This method constructed a “one-to-many” distributed generative adversarial network (D-VAE-WGAN) with a discriminator deployed on a cloud server and multiple generators deployed on SDN controllers. …”
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Research on unsupervised domain adaptive bearing fault diagnosis method by WU ShengKai, SHAO Xing, WANG CuiXiang, GAO Jun

“…Then, the feature distributions output of the source domain and the target domain were converged by the method of reversing labels in the generative adversarial network. Finally, the classifier of the source domain was exploited to complete the bearing fault diagnosis task under different working conditions. …”
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Network threat situation assessment based on unsupervised multi-source data feature analysis by Hongyu YANG, Fengyan WANG

“…Aiming at the limitations of supervised neural network in the network threat testing task relying on data category tagging,a network threat situation evaluation method based on unsupervised multi-source data feature analysis was proposed.Firstly,a variant auto encoder-generative adversarial network (V-G) for security threat assessment was designed.The training data set containing only normal network traffic was input to the network collection layer of V-G to perform the model training,and the reconstruction error of the network output of each layer was calculated.Then,the reconstruction error learning was performed by the three-layer variation automatic encoder of the output layer,and the training abnormal threshold was obtained.The packet threat was tested by using the test data set containing the abnormal network traffic,and the probability of occurrence of the threat of each group of tests was counted.Finally,the severity of the network security threat was determined according to the probability of threat occurrence,and the threat situation value was calculated according to the threat impact to obtain the network threat situation.The simulation results show that the proposed method has strong characterization ability for network threats,and can effectively and intuitively evaluate the overall situation of network threat.…”
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Research on image generation technology based on deep learning by Li Jinchen

“…This paper mainly introduces two main methods: generating adversarial network (GAN) and variational autoencoder (VAE). …”
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Prediction of viscosity of blast furnace slag based on NRBO-DNN model by Zhe Li, Meng Wang, Rui Xu, Juanjuan Jiang, Jie Li, Zunqian Zhang, Aimin Yang

“…Data preprocessing included isolation forest outlier detection, missing data imputation, normalization, and Generative Adversarial Network (GAN)-based data augmentation, ensuring high-quality data. …”
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Multi-stage detection method for APT attack based on sample feature reinforcement by Lixia XIE, Xueou LI, Hongyu YANG, Liang ZHANG, Xiang CHENG

“…Given the problems that the current APT attack detection methods were difficult to perceive the diversity of stage flow features and generally hard to detect the long duration APT attack sequences and potential APT attacks with different attack stages, a multi-stage detection method for APT attack based on sample feature reinforcement was proposed.Firstly, the malicious flow was divided into different attack stages and the APT attack identification sequences were constructed by analyzing the characteristics of the APT attack.In addition, sequence generative adversarial network was used to simulate the generation of identification sequences in the multi-stage of APT attacks.Sample feature reinforcement was achieved by increasing the number of sequence samples in different stages, which improved the diversity of multi-stage sample features.Finally, a multi-stage detection network was proposed.Based on the multi-stage perceptual attention mechanism, the extracted multi-stage flow features and identification sequences were calculated by attention to obtain the stage feature vectors.The feature vectors were used as auxiliary information to splice with the identification sequences.The detection model’s perception ability in different stages was enhanced and the detection accuracy was improved.The experimental results show that the proposed method has remarkable detection effects on two benchmark datasets and has better effects on multi-class potential APT attacks than other models.…”
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A hybrid machine learning model for intrusion detection in wireless sensor networks leveraging data balancing and dimensionality reduction by Md. Alamin Talukder, Majdi Khalid, Nasrin Sultana

“…For the TON-IoT dataset, it achieves 99.97% accuracy and an f1-score of 99.97%, outperforming traditional SMOTE TomekLink and Generative Adversarial Network-based data balancing techniques. …”
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Image Inpainting of Portraits Artwork Design and Implementation by Zhang Hongting

“…Additionally, a Generative Adversarial Network (GAN), which has shown promising results on other datasets, is used as a baseline for comparison. …”
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How Big Data Has Changed Technology Roadmapping: A Review on Data-Driven Roadmapping by Jinhong Kim, Gamunnarbi Park, Myoungkyun Woo, Youngjung Geum

“…Apart from simple trend analysis to support decision-making, it has evolved to generate the technology roadmap using generative AI techniques such as generative adversarial network (GAN).…”
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Unsupervised inter-domain transformation for virtually stained high-resolution mid-infrared photoacoustic microscopy using explainable deep learning by Eunwoo Park, Sampa Misra, Dong Gyu Hwang, Chiho Yoon, Joongho Ahn, Donggyu Kim, Jinah Jang, Chulhong Kim

“…The explainable deep learning-based framework is proposed for this transformation, wherein an unsupervised generative adversarial network is primarily employed and then a saliency constraint is added for better explainability. …”
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