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  • 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 >

  • Open Access

    ABSTRACT

    Characterization of Coronary Atherosclerotic Plaque Composition Based on Convolutional Neural Network (CNN)

    Yifan Yin1, Chunliu He1, Biao Xu2, Zhiyong Li1,*

    Molecular & Cellular Biomechanics, Vol.16, Suppl.1, pp. 57-57, 2019, DOI:10.32604/mcb.2019.05732

    Abstract The tissue composition and morphological structure of atherosclerotic plaques determine its stability or vulnerability. Intravascular optical coherence tomography (IVOCT) has rapidly become the method of choice for assessing the pathology of the coronary arterial wall in vivo due to its superior resolution. However, in clinical practice, the analysis of plaque composition of OCT images mainly relies on the interpretation of images by well-trained experts, which is a time-consuming, labor-intensive procedure and it is also subjective. The purpose of this study is to use the Convolutional neural network (CNN) method to automatically extract the best feature information from the OCT images… More >

  • Open Access

    ARTICLE

    A Multi-Scale Network with the Encoder-Decoder Structure for CMR Segmentation

    Chaoyang Xia1, Jing Peng1, Zongqing Ma2, Xiaojie Li1,*

    Journal of Information Hiding and Privacy Protection, Vol.1, No.3, pp. 109-117, 2019, DOI:10.32604/jihpp.2019.07198

    Abstract Cardiomyopathy is one of the most serious public health threats. The precise structural and functional cardiac measurement is an essential step for clinical diagnosis and follow-up treatment planning. Cardiologists are often required to draw endocardial and epicardial contours of the left ventricle (LV) manually in routine clinical diagnosis or treatment planning period. This task is time-consuming and error-prone. Therefore, it is necessary to develop a fully automated end-to-end semantic segmentation method on cardiac magnetic resonance (CMR) imaging datasets. However, due to the low image quality and the deformation caused by heartbeat, there is no effective tool for fully automated end-to-end… More >

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