Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (1,446)
  • Open Access

    ARTICLE

    A Distributed Approach of Big Data Mining for Financial Fraud Detection in a Supply Chain

    Hangjun Zhou1, *, Guang Sun1, 2, Sha Fu1, Xiaoping Fan1, Wangdong Jiang1, Shuting Hu1, Lingjiao Li1

    CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 1091-1105, 2020, DOI:10.32604/cmc.2020.09834

    Abstract Supply Chain Finance (SCF) is important for improving the effectiveness of supply chain capital operations and reducing the overall management cost of a supply chain. In recent years, with the deep integration of supply chain and Internet, Big Data, Artificial Intelligence, Internet of Things, Blockchain, etc., the efficiency of supply chain financial services can be greatly promoted through building more customized risk pricing models and conducting more rigorous investment decision-making processes. However, with the rapid development of new technologies, the SCF data has been massively increased and new financial fraud behaviors or patterns are becoming more covertly scattered among normal… More >

  • Open Access

    ARTICLE

    Modeling Multi-Targets Sentiment Classification via Graph Convolutional Networks and Auxiliary Relation

    Ao Feng1, Zhengjie Gao1, *, Xinyu Song1, Ke Ke2, Tianhao Xu1, Xuelei Zhang1

    CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 909-923, 2020, DOI:10.32604/cmc.2020.09913

    Abstract Existing solutions do not work well when multi-targets coexist in a sentence. The reason is that the existing solution is usually to separate multiple targets and process them separately. If the original sentence has N target, the original sentence will be repeated for N times, and only one target will be processed each time. To some extent, this approach degenerates the fine-grained sentiment classification task into the sentencelevel sentiment classification task, and the research method of processing the target separately ignores the internal relation and interaction between the targets. Based on the above considerations, we proposes to use Graph Convolutional… More >

  • Open Access

    ARTICLE

    Corpus Augmentation for Improving Neural Machine Translation

    Zijian Li1, Chengying Chi1, *, Yunyun Zhan2, *

    CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 637-650, 2020, DOI:10.32604/cmc.2020.010265

    Abstract The translation quality of neural machine translation (NMT) systems depends largely on the quality of large-scale bilingual parallel corpora available. Research shows that under the condition of limited resources, the performance of NMT is greatly reduced, and a large amount of high-quality bilingual parallel data is needed to train a competitive translation model. However, not all languages have large-scale and high-quality bilingual corpus resources available. In these cases, improving the quality of the corpora has become the main focus to increase the accuracy of the NMT results. This paper proposes a new method to improve the quality of data by… More >

  • Open Access

    ARTICLE

    A Fast Two-Stage Black-Box Deep Learning Network Attacking Method Based on Cross-Correlation

    Deyin Li1, 2, Mingzhi Cheng3, Yu Yang1, 2, *, Min Lei1, 2, Linfeng Shen4

    CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 623-635, 2020, DOI:10.32604/cmc.2020.09800

    Abstract Deep learning networks are widely used in various systems that require classification. However, deep learning networks are vulnerable to adversarial attacks. The study on adversarial attacks plays an important role in defense. Black-box attacks require less knowledge about target models than white-box attacks do, which means black-box attacks are easier to launch and more valuable. However, the state-of-arts black-box attacks still suffer in low success rates and large visual distances between generative adversarial images and original images. This paper proposes a kind of fast black-box attack based on the cross-correlation (FBACC) method. The attack is carried out in two stages.… More >

  • Open Access

    ARTICLE

    Outlier Detection for Water Supply Data Based on Joint Auto-Encoder

    Shu Fang1, Lei Huang1, Yi Wan2, Weize Sun1, *, Jingxin Xu3

    CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 541-555, 2020, DOI:10.32604/cmc.2020.010066

    Abstract With the development of science and technology, the status of the water environment has received more and more attention. In this paper, we propose a deep learning model, named a Joint Auto-Encoder network, to solve the problem of outlier detection in water supply data. The Joint Auto-Encoder network first expands the size of training data and extracts the useful features from the input data, and then reconstructs the input data effectively into an output. The outliers are detected based on the network’s reconstruction errors, with a larger reconstruction error indicating a higher rate to be an outlier. For water supply… More >

  • Open Access

    ARTICLE

    Visual Relationship Detection with Contextual Information

    Yugang Li1, 2, *, Yongbin Wang1, Zhe Chen2, Yuting Zhu3

    CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1575-1589, 2020, DOI:10.32604/cmc.2020.07451

    Abstract Understanding an image goes beyond recognizing and locating the objects in it, the relationships between objects also very important in image understanding. Most previous methods have focused on recognizing local predictions of the relationships. But real-world image relationships often determined by the surrounding objects and other contextual information. In this work, we employ this insight to propose a novel framework to deal with the problem of visual relationship detection. The core of the framework is a relationship inference network, which is a recurrent structure designed for combining the global contextual information of the object to infer the relationship of the… More >

  • Open Access

    ARTICLE

    Joint Deep Matching Model of OCT Retinal Layer Segmentation

    Mei Yang1, Yuanjie Zheng1, 2, *, Weikuan Jia1, *, Yunlong He3, Tongtong Che1, Jinyu Cong1

    CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1485-1498, 2020, DOI:10.32604/cmc.2020.09940

    Abstract Optical Coherence Tomography (OCT) is very important in medicine and provide useful diagnostic information. Measuring retinal layer thicknesses plays a vital role in pathophysiologic factors of many ocular conditions. Among the existing retinal layer segmentation approaches, learning or deep learning-based methods belong to the state-of-art. However, most of these techniques rely on manual-marked layers and the performances are limited due to the image quality. In order to overcome this limitation, we build a framework based on gray value curve matching, which uses depth learning to match the curve for semi-automatic segmentation of retinal layers from OCT. The depth convolution network… More >

  • Open Access

    ARTICLE

    Review of Text Classification Methods on Deep Learning

    Hongping Wu1, Yuling Liu1, *, Jingwen Wang2

    CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1309-1321, 2020, DOI:10.32604/cmc.2020.010172

    Abstract Text classification has always been an increasingly crucial topic in natural language processing. Traditional text classification methods based on machine learning have many disadvantages such as dimension explosion, data sparsity, limited generalization ability and so on. Based on deep learning text classification, this paper presents an extensive study on the text classification models including Convolutional Neural Network-Based (CNN-Based), Recurrent Neural Network-Based (RNN-based), Attention Mechanisms-Based and so on. Many studies have proved that text classification methods based on deep learning outperform the traditional methods when processing large-scale and complex datasets. The main reasons are text classification methods based on deep learning… More >

  • Open Access

    ARTICLE

    Feature Fusion Multi-View Hashing Based on Random Kernel Canonical Correlation Analysis

    Junshan Tan1, Rong Duan1, Jiaohua Qin1, *, Xuyu Xiang1, Yun Tan1

    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 675-689, 2020, DOI:10.32604/cmc.2020.07730

    Abstract Hashing technology has the advantages of reducing data storage and improving the efficiency of the learning system, making it more and more widely used in image retrieval. Multi-view data describes image information more comprehensively than traditional methods using a single-view. How to use hashing to combine multi-view data for image retrieval is still a challenge. In this paper, a multi-view fusion hashing method based on RKCCA (Random Kernel Canonical Correlation Analysis) is proposed. In order to describe image content more accurately, we use deep learning dense convolutional network feature DenseNet to construct multi-view by combining GIST feature or BoW_SIFT (Bag-of-Words… More >

  • Open Access

    ARTICLE

    Method to Appraise Dangerous Class of Building Masonry Component Based on DC-YOLO Model

    Hongrui Zhang1, Wenxue Wei1, *, Xinguang Xiao1, Song Yang1, Wanlu Shao1

    CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 457-468, 2020, DOI:10.32604/cmc.2020.06988

    Abstract This DC-YOLO Model was designed in order to improve the efficiency for appraising dangerous class of buildings and avoid manual intervention, thereby making the appraisal results more objective. It is an automated method designed based on deep learning and target detection algorithms to appraise the dangerous class of building masonry component. Specifically, it (1) adopted K-means clustering to obtain the quantity and size of the prior boxes; (2) expanded the grid size to improve identification to small targets; (3) introduced in deformable convolution to adapt to the irregular shape of the masonry component cracks. The experimental results show that, comparing… More >

Displaying 1391-1400 on page 140 of 1446. Per Page