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  • Open Access


    Incomplete Image Completion through GAN

    Biying Deng1 , Desheng Zheng1, *, Zhifeng Liu1 , Yanling Lai1, Zhihong Zhang2

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

    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 are quickly generated using the generative… More >

  • Open Access


    A Hybrid Intrusion Detection Model Based on Spatiotemporal Features

    Linbei Wang1 , Zaoyu Tao1, Lina Wang2,*, Yongjun Ren3

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

    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 the temporal characteristics. The data… More >

  • Open Access


    Tissue Segmentation in Nasopharyngeal CT Images Using TwoStage Learning

    Yong Luo1, Xiaojie Li2, Chao Luo2, Feng Wang1, Xi Wu2, Imran Mumtaz3, Cheng Yi1, *

    CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1771-1780, 2020, DOI:10.32604/cmc.2020.010069

    Abstract Tissue segmentation is a fundamental and important task in nasopharyngeal images analysis. However, it is a challenging task to accurately and quickly segment various tissues in the nasopharynx region due to the small difference in gray value between tissues in the nasopharyngeal image and the complexity of the tissue structure. In this paper, we propose a novel tissue segmentation approach based on a two-stage learning framework and U-Net. In the proposed methodology, the network consists of two segmentation modules. The first module performs rough segmentation and the second module performs accurate segmentation. Considering the training time and the limitation of… More >

  • Open Access


    Accurate Multi-Scale Feature Fusion CNN for Time Series Classification in Smart Factory

    Xiaorui Shao1, Chang Soo Kim1, *, Dae Geun Kim2

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 543-561, 2020, DOI:10.32604/cmc.2020.011108

    Abstract Time series classification (TSC) has attracted various attention in the community of machine learning and data mining and has many successful applications such as fault detection and product identification in the process of building a smart factory. However, it is still challenging for the efficiency and accuracy of classification due to complexity, multi-dimension of time series. This paper presents a new approach for time series classification based on convolutional neural networks (CNN). The proposed method contains three parts: short-time gap feature extraction, multi-scale local feature learning, and global feature learning. In the process of short-time gap feature extraction, large kernel… More >

  • Open Access


    Sentence Similarity Measurement with Convolutional Neural Networks Using Semantic and Syntactic Features

    Shiru Zhang1, Zhiyao Liang1, *, Jian Lin2

    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 943-957, 2020, DOI:10.32604/cmc.2020.08800

    Abstract Calculating the semantic similarity of two sentences is an extremely challenging problem. We propose a solution based on convolutional neural networks (CNN) using semantic and syntactic features of sentences. The similarity score between two sentences is computed as follows. First, given a sentence, two matrices are constructed accordingly, which are called the syntax model input matrix and the semantic model input matrix; one records some syntax features, and the other records some semantic features. By experimenting with different arrangements of representing the syntactic and semantic features of the sentences in the matrices, we adopt the most effective way of constructing… More >

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