Xinlong Wu¹, Desheng Zheng^1,*, Kexin Zhang¹, Yanling Lai¹, Zhifeng Liu¹, Zhihong Zhang²

Journal of Quantum Computing, Vol.3, No.4, pp. 127-136, 2021, DOI:10.32604/jqc.2021.017251 - 10 January 2022

Abstract Image conversion refers to converting an image from one style to another and ensuring that the content of the image remains unchanged. Using Generative Adversarial Networks (GAN) for image conversion can achieve good results. However, if there are enough samples, any image in the target domain can be mapped to the same set of inputs. On this basis, the Cycle Consistency Generative Adversarial Network (CycleGAN) was developed. This article verifies and discusses the advantages and disadvantages of the CycleGAN model in image style conversion. CycleGAN uses two generator networks and two discriminator networks. The purpose… More >

Biying Deng¹ , Desheng Zheng^{1, *}, Zhifeng Liu¹ , Yanling Lai¹, Zhihong Zhang²

Journal of Quantum Computing, Vol.3, No.3, pp. 119-126, 2021, DOI:10.32604/jqc.2021.017250 - 21 December 2021

Abstract There are two difficult in the existing image restoration methods. One is that the method is difficult to repair the image with a large damaged, the other is the result of image completion is not good and the speed is slow. With the development and application of deep learning, the image repair algorithm based on generative adversarial networks can repair images by simulating the distribution of data. In the process of image completion, the first step is trained the generator to simulate data distribution and generate samples. Then a large number of falsified images More >

Linbei Wang¹ , Zaoyu Tao¹, Lina Wang^2,*, Yongjun Ren³

Journal of Quantum Computing, Vol.3, No.3, pp. 107-118, 2021, DOI:10.32604/jqc.2021.016857 - 21 December 2021

Abstract With the accelerating process of social informatization, our personal information security and Internet sites, etc., have been facing a series of threats and challenges. Recently, well-developed neural network has seen great advancement in natural language processing and computer vision, which is also adopted in intrusion detection. In this research, a hybrid model integrating MultiScale Convolutional Neural Network and Long Short-term Memory Network (MSCNN-LSTM) is designed to conduct the intrusion detection. Multi-Scale Convolutional Neural Network (MSCNN) is used to extract the spatial characteristics of data sets. And Long Short-term Memory Network (LSTM) is responsible for processing More >

Hengyang Wang, Jin Liu^*, Haoliang Ren

Journal on Big Data, Vol.3, No.3, pp. 119-126, 2021, DOI:10.32604/jbd.2021.017184 - 22 November 2021

Abstract The past decade has seen the rapid development of text detection based on deep learning. However, current methods of Chinese character detection and recognition have proven to be poor. The accuracy of segmenting text boxes in natural scenes is not impressive. The reasons for this strait can be summarized into two points: the complexity of natural scenes and numerous types of Chinese characters. In response to these problems, we proposed a lightweight neural network architecture named CTSF. It consists of two modules, one is a text detection network that combines CTPN and the image feature More >

Yang Dai^*, Zhiyuan Luo

Journal of New Media, Vol.3, No.4, pp. 129-136, 2021, DOI:10.32604/jnm.2021.023981 - 05 November 2021

Abstract Person re-identification (re-ID) aims to match images of the same pedestrian across different cameras. It plays an important role in the field of security and surveillance. Although it has been studied for many years, it is still considered as an unsolved problem. Since the rise of deep learning, the accuracy of supervised person re-ID on public datasets has reached the highest level. However, these methods are difficult to apply to real-life scenarios because a large number of labeled training data is required in this situation. Pedestrian identity labeling, especially cross-camera pedestrian identity labeling, is heavy More >

Liang Mu, Hong Zhao^*, Yan Li, Xiaotong Liu, Junzheng Qiu, Chuanlong Sun

CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.2, pp. 465-483, 2021, DOI:10.32604/cmes.2021.017276 - 08 October 2021

Abstract Traffic flow statistics have become a particularly important part of intelligent transportation. To solve the problems of low real-time robustness and accuracy in traffic flow statistics. In the DeepSort tracking algorithm, the Kalman filter (KF), which is only suitable for linear problems, is replaced by the extended Kalman filter (EKF), which can effectively solve nonlinear problems and integrate the Histogram of Oriented Gradient (HOG) of the target. The multi-target tracking framework was constructed with YOLO V5 target detection algorithm. An efficient and long-running Traffic Flow Statistical framework (TFSF) is established based on the tracking framework.… More >

Wenqian Li¹, Xing Deng^1,2,*, Haijian Shao¹, Xia Wang³

CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.1, pp. 65-98, 2021, DOI:10.32604/cmes.2021.016981 - 24 August 2021

Abstract The COVID-19 has resulted in catastrophic situation and the deaths of millions of people all over the world. In this paper, the predictions of epidemiological propagation models, such as SIR and SEIR, are introduced to analyze the earlier COVID-19 propagation. The deep learning methods combined with transfer learning are familiar with classification-detection approaches based on chest X-ray and CT images are presented in detail. Besides, deep learning approaches have also been applied to lung ultrasound (LUS), which has been shown to be more sensitive than chest X-ray and CT images in detecting COVID-19. In the… More > Graphic Abstract

Yonghun Jang, Chang-Hyeon Park, Dong-Gun Lee, Yeong-Seok Seo^*

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3563-3579, 2021, DOI:10.32604/cmc.2021.018901 - 24 August 2021

Abstract Following the development of communication techniques and smart devices, the era of Artificial Intelligence (AI) and big data has arrived. The increased connectivity, referred to as hyper-connectivity, has led to the development of smart cities. People in these smart cities can access numerous online contents and are always connected. These developments, however, also lead to a lack of standardization and consistency in the propagation of information throughout communities due to the consumption of information through social media channels. Information cannot often be verified, which can confuse the users. The increasing influence of social media has… More >

Sujeet More¹, Jimmy Singla¹, Oh-Young Song^2,*, Usman Tariq³, Sharaf Malebary⁴

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3127-3143, 2021, DOI:10.32604/cmc.2021.018230 - 24 August 2021

Abstract Medical Resonance Imaging (MRI) is a noninvasive, nonradioactive, and meticulous diagnostic modality capability in the field of medical imaging. However, the efficiency of MR image reconstruction is affected by its bulky image sets and slow process implementation. Therefore, to obtain a high-quality reconstructed image we presented a sparse aware noise removal technique that uses convolution neural network (SANR_CNN) for eliminating noise and improving the MR image reconstruction quality. The proposed noise removal or denoising technique adopts a fast CNN architecture that aids in training larger datasets with improved quality, and SARN algorithm is used for More >

Yu-Dong Zhang¹, Muhammad Attique Khan², Ziquan Zhu³, Shui-Hua Wang^4,*

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3145-3162, 2021, DOI:10.32604/cmc.2021.018040 - 24 August 2021

Abstract (Aim) COVID-19 is an ongoing infectious disease. It has caused more than 107.45 m confirmed cases and 2.35 m deaths till 11/Feb/2021. Traditional computer vision methods have achieved promising results on the automatic smart diagnosis. (Method) This study aims to propose a novel deep learning method that can obtain better performance. We use the pseudo-Zernike moment (PZM), derived from Zernike moment, as the extracted features. Two settings are introducing: (i) image plane over unit circle; and (ii) image plane inside the unit circle. Afterward, we use a deep-stacked sparse autoencoder (DSSAE) as the classifier. Besides, multiple-way… More >