Suhang Li¹, Yunzuo Zhang^1,*, Ruixue Liu², Jiayu Zhang¹, Zhouchen Song¹, Yutai Wang¹

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1163-1180, 2023, DOI:10.32604/iasc.2023.037902

Abstract The communication system of high-speed trains in railway tunnels needs to be built with leaky cables fixed on the tunnel wall with special fixtures. To ensure safety, checking the regular leaky cable fixture is necessary to eliminate the potential danger. At present, the existing fixture detection algorithms are difficult to take into account detection accuracy and speed at the same time. The faulty fixture is also insufficient and difficult to obtain, seriously affecting the model detection effect. To solve these problems, an innovative detection method is proposed in this paper. Firstly, we presented the Res-Net and Wasserstein-Deep Convolution GAN (RW-DCGAN)… More >

Dina M. Ibrahim^1,2,*, Nada M. Elshennawy²

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 331-348, 2022, DOI:10.32604/cmes.2022.016419

Abstract Dates are an important part of human nutrition. Dates are high in essential nutrients and provide a number of health benefits. Date fruits are also known to protect against a number of diseases, including cancer and heart disease. Date fruits have several sizes, colors, tastes, and values. There are a lot of challenges facing the date producers. One of the most significant challenges is the classification and sorting of dates. But there is no public dataset for date fruits, which is a major limitation in order to improve the performance of convolutional neural networks (CNN) models and avoid the overfitting… More >

Seo-young Lim, Jeongho Cho^*

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 6039-6051, 2022, DOI:10.32604/cmc.2022.024862

Abstract Recently, the technology of digital image forgery based on a generative adversarial network (GAN) has considerably improved to the extent that it is difficult to distinguish it from the original image with the naked eye by compositing and editing a person's face or a specific part with the original image. Thus, much attention has been paid to digital image forgery as a social issue. Further, document forgery through GANs can completely change the meaning and context in a document, and it is difficult to identify whether the document is forged or not, which is dangerous. Nonetheless, few studies have been… 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

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 technology having been adjusted and… More >

Biying Deng¹, Ziyong Ran¹, Jixin Chen¹, Desheng Zheng^1,*, Qiao Yang², Lulu Tian³

Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 889-898, 2021, DOI:10.32604/iasc.2021.019727

Abstract In recent years, due to the popularization of deep learning technology, more and more attention has been paid to the security of deep neural networks. A wide variety of machine learning algorithms can attack neural networks and make its classification and judgement of target samples wrong. However, the previous attack algorithms are based on the calculation of the corresponding model to generate unique adversarial examples, and cannot extract attack features and generate corresponding samples in batches. In this paper, Generative Adversarial Networks (GAN) is used to learn the distribution of adversarial examples generated by FGSM and establish a generation model,… More >

Wei Fang^{1, 2}, Feihong Zhang^{1, *}, Yewen Ding¹, Jack Sheng³

CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 217-231, 2020, DOI:10.32604/cmc.2020.06395

Abstract Image recognition technology is an important field of artificial intelligence. Combined with the development of machine learning technology in recent years, it has great researches value and commercial value. As a matter of fact, a single recognition function can no longer meet people’s needs, and accurate image prediction is the trend that people pursue. This paper is based on Long Short-Term Memory (LSTM) and Deep Convolution Generative Adversarial Networks (DCGAN), studies and implements a prediction model by using radar image data. We adopt a stack cascading strategy in designing network connection which can control of parameter convergence better. This new… More >

Wei Fang^1,2, Feihong Zhang^1,*, Victor S. Sheng³, Yewen Ding¹

CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 167-178, 2018, DOI:10.32604/cmc.2018.02356

Abstract Image recognition has always been a hot research topic in the scientific community and industry. The emergence of convolutional neural networks(CNN) has made this technology turned into research focus on the field of computer vision, especially in image recognition. But it makes the recognition result largely dependent on the number and quality of training samples. Recently, DCGAN has become a frontier method for generating images, sounds, and videos. In this paper, DCGAN is used to generate sample that is difficult to collect and proposed an efficient design method of generating model. We combine DCGAN with CNN for the second time.… More >