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  • Open Access

    ARTICLE

    A Rub-Impact Recognition Method Based on Improved Convolutional Neural Network

    Weibo Yang1, *, Jing Li2, Wei Peng2, Aidong Deng3

    CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 283-299, 2020, DOI:10.32604/cmc.2020.07511

    Abstract Based on the theory of modal acoustic emission (AE), when the convolutional neural network (CNN) is used to identify rotor rub-impact faults, the training data has a small sample size, and the AE sound segment belongs to a single channel signal with less pixel-level information and strong local correlation. Due to the convolutional pooling operations of CNN, coarse-grained and edge information are lost, and the top-level information dimension in CNN network is low, which can easily lead to overfitting. To solve the above problems, we first propose the use of sound spectrograms and their differential features to construct multi-channel image… More >

  • Open Access

    ARTICLE

    Human Action Recognition Based on Supervised Class-Specific Dictionary Learning with Deep Convolutional Neural Network Features

    Binjie Gu1, *, Weili Xiong1, Zhonghu Bai2

    CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 243-262, 2020, DOI:10.32604/cmc.2020.06898

    Abstract Human action recognition under complex environment is a challenging work. Recently, sparse representation has achieved excellent results of dealing with human action recognition problem under different conditions. The main idea of sparse representation classification is to construct a general classification scheme where the training samples of each class can be considered as the dictionary to express the query class, and the minimal reconstruction error indicates its corresponding class. However, how to learn a discriminative dictionary is still a difficult work. In this work, we make two contributions. First, we build a new and robust human action recognition framework by combining… More >

  • Open Access

    ARTICLE

    DDoS Attack Detection via Multi-Scale Convolutional Neural Network

    Jieren Cheng1, 2, Yifu Liu1, *, Xiangyan Tang1, Victor S. Sheng3, Mengyang Li1, Junqi Li1

    CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1317-1333, 2020, DOI:10.32604/cmc.2020.06177

    Abstract Distributed Denial-of-Service (DDoS) has caused great damage to the network in the big data environment. Existing methods are characterized by low computational efficiency, high false alarm rate and high false alarm rate. In this paper, we propose a DDoS attack detection method based on network flow grayscale matrix feature via multiscale convolutional neural network (CNN). According to the different characteristics of the attack flow and the normal flow in the IP protocol, the seven-tuple is defined to describe the network flow characteristics and converted into a grayscale feature by binary. Based on the network flow grayscale matrix feature (GMF), the… More >

  • Open Access

    ARTICLE

    Advanced Feature Fusion Algorithm Based on Multiple Convolutional Neural Network for Scene Recognition

    Lei Chen1, #, Kanghu Bo2, #, Feifei Lee1, *, Qiu Chen1, 3, *

    CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.2, pp. 505-523, 2020, DOI:10.32604/cmes.2020.08425

    Abstract Scene recognition is a popular open problem in the computer vision field. Among lots of methods proposed in recent years, Convolutional Neural Network (CNN) based approaches achieve the best performance in scene recognition. We propose in this paper an advanced feature fusion algorithm using Multiple Convolutional Neural Network (MultiCNN) for scene recognition. Unlike existing works that usually use individual convolutional neural network, a fusion of multiple different convolutional neural networks is applied for scene recognition. Firstly, we split training images in two directions and apply to three deep CNN model, and then extract features from the last full-connected (FC) layer… More >

  • Open Access

    ARTICLE

    A Novel Combinational Convolutional Neural Network for Automatic Food-Ingredient Classification

    Lili Pan1, Cong Li1, *, Samira Pouyanfar2, Rongyu Chen1, Yan Zhou1

    CMC-Computers, Materials & Continua, Vol.62, No.2, pp. 731-746, 2020, DOI:10.32604/cmc.2020.06508

    Abstract With the development of deep learning and Convolutional Neural Networks (CNNs), the accuracy of automatic food recognition based on visual data have significantly improved. Some research studies have shown that the deeper the model is, the higher the accuracy is. However, very deep neural networks would be affected by the overfitting problem and also consume huge computing resources. In this paper, a new classification scheme is proposed for automatic food-ingredient recognition based on deep learning. We construct an up-to-date combinational convolutional neural network (CBNet) with a subnet merging technique. Firstly, two different neural networks are utilized for learning interested features.… More >

  • Open Access

    ARTICLE

    Research on Privacy Disclosure Detection Method in Social Networks Based on Multi-Dimensional Deep Learning

    Yabin Xu1, 2, *, Xuyang Meng1, Yangyang Li3, Xiaowei Xu4, *

    CMC-Computers, Materials & Continua, Vol.62, No.1, pp. 137-155, 2020, DOI:10.32604/cmc.2020.05825

    Abstract In order to effectively detect the privacy that may be leaked through social networks and avoid unnecessary harm to users, this paper takes microblog as the research object to study the detection of privacy disclosure in social networks. First, we perform fast privacy leak detection on the currently published text based on the fastText model. In the case that the text to be published contains certain private information, we fully consider the aggregation effect of the private information leaked by different channels, and establish a convolution neural network model based on multi-dimensional features (MF-CNN) to detect privacy disclosure comprehensively and… More >

  • Open Access

    ARTICLE

    Median Filtering Forensics Scheme for Color Images Based on Quaternion Magnitude-Phase CNN

    Jinwei Wang1, *, Yang Zhang1

    CMC-Computers, Materials & Continua, Vol.62, No.1, pp. 99-112, 2020, DOI:10.32604/cmc.2020.04373

    Abstract In the paper, a convolutional neural network based on quaternion transformation is proposed to detect median filtering for color images. Compared with conventional convolutional neural network, color images can be processed in a holistic manner in the proposed scheme, which makes full use of the correlation between RGB channels. And due to the use of convolutional neural network, it can effectively avoid the one-sidedness of artificial features. Experimental results have shown the scheme’s improvement over the state-of-the-art scheme on the accuracy of color image median filtering detection. More >

  • Open Access

    ARTICLE

    Digital Forensics for Recoloring via Convolutional Neural Network

    Zhangyi Shen1, Feng Ding2, *, Yunqing Shi1

    CMC-Computers, Materials & Continua, Vol.62, No.1, pp. 1-16, 2020, DOI:10.32604/cmc.2020.08291

    Abstract As a common medium in our daily life, images are important for most people to gather information. There are also people who edit or even tamper images to deliberately deliver false information under different purposes. Thus, in digital forensics, it is necessary to understand the manipulating history of images. That requires to verify all possible manipulations applied to images. Among all the image editing manipulations, recoloring is widely used to adjust or repaint the colors in images. The color information is an important visual information that image can deliver. Thus, it is necessary to guarantee the correctness of color in… More >

  • Open Access

    ARTICLE

    IDSH: An Improved Deep Supervised Hashing Method for Image Retrieval

    Chaowen Lu1,a, Feifei Lee1,a,*, Lei Chen1, Sheng Huang1, Qiu Chen2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.121, No.2, pp. 593-608, 2019, DOI:10.32604/cmes.2019.07796

    Abstract Image retrieval has become more and more important because of the explosive growth of images on the Internet. Traditional image retrieval methods have limited image retrieval performance due to the poor image expression abhility of visual feature and high dimension of feature. Hashing is a widely-used method for Approximate Nearest Neighbor (ANN) search due to its rapidity and timeliness. Meanwhile, Convolutional Neural Networks (CNNs) have strong discriminative characteristics which are used for image classification. In this paper, we propose a CNN architecture based on improved deep supervised hashing (IDSH) method, by which the binary compact codes can be generated directly.… More >

  • Open Access

    ARTICLE

    Digital Vision Based Concrete Compressive Strength Evaluating Model Using Deep Convolutional Neural Network

    Hyun Kyu Shin1, Yong Han Ahn2, Sang Hyo Lee3, Ha Young Kim4,*

    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 911-928, 2019, DOI:10.32604/cmc.2019.08269

    Abstract Compressive strength of concrete is a significant factor to assess building structure health and safety. Therefore, various methods have been developed to evaluate the compressive strength of concrete structures. However, previous methods have several challenges in costly, time-consuming, and unsafety. To address these drawbacks, this paper proposed a digital vision based concrete compressive strength evaluating model using deep convolutional neural network (DCNN). The proposed model presented an alternative approach to evaluating the concrete strength and contributed to improving efficiency and accuracy. The model was developed with 4,000 digital images and 61,996 images extracted from video recordings collected from concrete samples.… More >

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