Ao Zhang¹, Hongzhen Xu^1,*, Ruxin Liu²

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5159-5181, 2025, DOI:10.32604/cmc.2025.067241 - 23 October 2025

Abstract Credit card fraud is one of the primary sources of operational risk in banks, and accurate prediction of fraudulent credit card transactions is essential to minimize banks’ economic losses. Two key issues are faced in credit card fraud detection research, i.e., data category imbalance and data drift. However, the oversampling algorithm used in current research suffers from excessive noise, and the Long Short-Term Memory Network (LSTM) based temporal model suffers from gradient dispersion, which can lead to loss of model performance. To address the above problems, a credit card fraud detection method based on Random… More >

Yuan Feng¹, Yajie Si², Jianwei Zhang^3,4,*, Zengyu Cai^5,*, Hongying Zhao⁵

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4431-4449, 2025, DOI:10.32604/cmc.2025.064696 - 30 July 2025

Abstract With the rapid development of the industrial Internet, the network security environment has become increasingly complex and variable. Intrusion detection, a core technology for ensuring the security of industrial control systems, faces the challenge of unbalanced data samples, particularly the low detection rates for minority class attack samples. Therefore, this paper proposes a data enhancement method for intrusion detection in the industrial Internet based on a Self-Attention Wasserstein Generative Adversarial Network (SA-WGAN) to address the low detection rates of minority class attack samples in unbalanced intrusion detection scenarios. The proposed method integrates a self-attention mechanism… More >

Uddagiri Sirisha^1,*, Chanumolu Kiran Kumar², Sujatha Canavoy Narahari³, Parvathaneni Naga Srinivasu^4,5,6

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1757-1810, 2025, DOI:10.32604/cmc.2024.059715 - 17 February 2025

Abstract The growing spectrum of Generative Adversarial Network (GAN) applications in medical imaging, cyber security, data augmentation, and the field of remote sensing tasks necessitate a sharp spike in the criticality of review of Generative Adversarial Networks. Earlier reviews that targeted reviewing certain architecture of the GAN or emphasizing a specific application-oriented area have done so in a narrow spirit and lacked the systematic comparative analysis of the models’ performance metrics. Numerous reviews do not apply standardized frameworks, showing gaps in the efficiency evaluation of GANs, training stability, and suitability for specific tasks. In this work,… More >

Mingguan Zhao^1,2,*, Xinsheng Dong^1,2, Yang Yang^1,2, Meng Li^1,2, Hongxia Wang^1,2, Shuyang Ma^1,2, Rui Zhu³, Xiaojing Zhu³

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 2091-2109, 2025, DOI:10.32604/cmes.2025.059169 - 27 January 2025

Abstract Ice cover on transmission lines is a significant issue that affects the safe operation of the power system. Accurate calculation of the thickness of wire icing can effectively prevent economic losses caused by ice disasters and reduce the impact of power outages on residents. However, under extreme weather conditions, strong instantaneous wind can cause tension sensors to fail, resulting in significant errors in the calculation of icing thickness in traditional mechanics-based models. In this paper, we propose a dynamic prediction model of wire icing thickness that can adapt to extreme weather environments. The model expands… More >

Jiangtao Zhai^1,*, Peng Lin¹, Yongfu Cui¹, Lilong Xu¹, Ming Liu²

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 2069-2092, 2023, DOI:10.32604/cmes.2023.023764 - 06 February 2023

Abstract Encrypted traffic classification has become a hot issue in network security research. The class imbalance problem of traffic samples often causes the deterioration of Machine Learning based classifier performance. Although the Generative Adversarial Network (GAN) method can generate new samples by learning the feature distribution of the original samples, it is confronted with the problems of unstable training and mode collapse. To this end, a novel data augmenting approach called GraphCWGAN-GP is proposed in this paper. The traffic data is first converted into grayscale images as the input for the proposed model. Then, the minority… More >

Subhajit Chatterjee¹, Yung-Cheol Byun^2,*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5507-5525, 2023, DOI:10.32604/cmc.2023.032843 - 28 December 2022

Abstract The increasing penetration rate of electric kickboard vehicles has been popularized and promoted primarily because of its clean and efficient features. Electric kickboards are gradually growing in popularity in tourist and education-centric localities. In the upcoming arrival of electric kickboard vehicles, deploying a customer rental service is essential. Due to its free-floating nature, the shared electric kickboard is a common and practical means of transportation. Relocation plans for shared electric kickboards are required to increase the quality of service, and forecasting demand for their use in a specific region is crucial. Predicting demand accurately with… More >

Wei Fang^1,2,*, Enming Gu¹, Weinan Yi¹, Weiqing Wang¹, Victor S. Sheng³

Computer Systems Science and Engineering, Vol.41, No.2, pp. 689-698, 2022, DOI:10.32604/csse.2022.020176 - 25 October 2021

Abstract With the development of image restoration technology based on deep learning, more complex problems are being solved, especially in image semantic inpainting based on context. Nowadays, image semantic inpainting techniques are becoming more mature. However, due to the limitations of memory, the instability of training, and the lack of sample diversity, the results of image restoration are still encountering difficult problems, such as repairing the content of glitches which cannot be well integrated with the original image. Therefore, we propose an image inpainting network based on Wasserstein generative adversarial network (WGAN) distance. With the corresponding More >

Debapriya Hazra, Wafa Shafqat, Yung-Cheol Byun^*

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3151-3167, 2022, DOI:10.32604/cmc.2022.020143 - 27 September 2021

Abstract Renewable and nonrenewable energy sources are widely incorporated for solar and wind energy that produces electricity without increasing carbon dioxide emissions. Energy industries worldwide are trying hard to predict future energy consumption that could eliminate over or under contracting energy resources and unnecessary financing. Machine learning techniques for predicting energy are the trending solution to overcome the challenges faced by energy companies. The basic need for machine learning algorithms to be trained for accurate prediction requires a considerable amount of data. Another critical factor is balancing the data for enhanced prediction. Data Augmentation is a… More >

Xiaoli Li^1,*, Chao Ye¹, Yujia Yan², Zhenlong Du¹

Journal of New Media, Vol.1, No.2, pp. 75-85, 2019, DOI:10.32604/jnm.2019.06259

Abstract In order to improve the quality of low-dose computational tomography (CT) images, the paper proposes an improved image denoising approach based on WGAN-gp with Wasserstein distance. For improving the training and the convergence efficiency, the given method introduces the gradient penalty term to WGAN network. The novel perceptual loss is introduced to make the texture information of the low-dose images sensitive to the diagnostician eye. The experimental results show that compared with the state-of-art methods, the time complexity is reduced, and the visual quality of low-dose CT images is significantly improved. More >