Yuanjing Luo¹, Jiaohua Qin^{1, *}, Xuyu Xiang¹, Yun Tan¹, Zhibin He¹, Neal N. Xiong²

CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 1281-1295, 2020, DOI:10.32604/cmc.2020.010867

Abstract To resist the risk of the stego-image being maliciously altered during transmission, we propose a coverless image steganography method based on image segmentation. Most existing coverless steganography methods are based on whole feature mapping, which has poor robustness when facing geometric attacks, because the contents in the image are easy to lost. To solve this problem, we use ResNet to extract semantic features, and segment the object areas from the image through Mask RCNN for information hiding. These selected object areas have ethical structural integrity and are not located in the visual center of the image, reducing the information loss… More >

Mo Chen¹, Xiaojuan Wang^{1, *}, Mingshu He¹, Lei Jin¹, Khalid Javeed², Xiaojun Wang³

CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 941-959, 2020, DOI:10.32604/cmc.2020.09802

Abstract Attacks on websites and network servers are among the most critical threats in network security. Network behavior identification is one of the most effective ways to identify malicious network intrusions. Analyzing abnormal network traffic patterns and traffic classification based on labeled network traffic data are among the most effective approaches for network behavior identification. Traditional methods for network traffic classification utilize algorithms such as Naive Bayes, Decision Tree and XGBoost. However, network traffic classification, which is required for network behavior identification, generally suffers from the problem of low accuracy even with the recently proposed deep learning models. To improve network… More >

Hongping Wu¹, Yuling Liu^{1, *}, 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 >

Ravi Samikannu^{1, *}, Rohini Ravi², Sivaram Murugan³, Bakary Diarra⁴

CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1133-1142, 2020, DOI:10.32604/cmc.2020.08578

Abstract The identification of brain tumors is multifarious work for the separation of the similar intensity pixels from their surrounding neighbours. The detection of tumors is performed with the help of automatic computing technique as presented in the proposed work. The non-active cells in brain region are known to be benign and they will never cause the death of the patient. These non-active cells follow a uniform pattern in brain and have lower density than the surrounding pixels. The Magnetic Resonance (MR) image contrast is improved by the cost map construction technique. The deep learning algorithm for differentiating the normal brain… More >

Shiqi Wang¹, Yimin Yang^{1, *}, Ruizhong Wei¹, Qingming Jonathan Wu²

CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1081-1091, 2020, DOI:10.32604/cmc.2020.09648

Abstract Human motion recognition plays a crucial role in the video analysis framework. However, a given video may contain a variety of noises, such as an unstable background and redundant actions, that are completely different from the key actions. These noises pose a great challenge to human motion recognition. To solve this problem, we propose a new method based on the 3-Dimensional (3D) Bag of Visual Words (BoVW) framework. Our method includes two parts: The first part is the video action feature extractor, which can identify key actions by analyzing action features. In the video action encoder, by analyzing the action… More >

Caining Zhang¹, Xiaopeng Guo², Xiaoya Guo³, David Molony⁴, Huaguang Li², Habib Samady⁴, Don P. Giddens^4,5, Lambros Athanasiou⁶, Dalin Tang^1*,7, Rencan Nie^2,*, Jinde Cao⁸

CMES-Computer Modeling in Engineering & Sciences, Vol.123, No.2, pp. 631-646, 2020, DOI:10.32604/cmes.2020.09718

Abstract Optical coherence tomography (OCT) is a new intravascular imaging technique with high resolution and could provide accurate morphological infor￾mation for plaques in coronary arteries. However, its segmentation is still com￾monly performed manually by experts which is time-consuming. The aim of this study was to develop automatic techniques to characterize plaque components and quantify plaque cap thickness using 3 machine learning methods including convolutional neural network (CNN) with U-Net architecture, CNN with Fully convolutional DenseNet (FC-DenseNet) architecture and support vector machine (SVM). In vivo OCT and intravascular ultrasound (IVUS) images were acquired from two patients at Emory University with informed consent… More >

Binjie Gu^{1, *}, 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 >

Ruohan Meng^{1, 2}, Qi Cui^{1, 2}, Zhili Zhou^{1, 2}, Chengsheng Yuan^{1, 2, 3}, Xingming Sun^{1, 2, *}

CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 183-196, 2020, DOI:10.32604/cmc.2020.05317

Abstract Information hiding tends to hide secret information in image area where is rich texture or high frequency, so as to transmit secret information to the recipient without affecting the visual quality of the image and arousing suspicion. We take advantage of the complexity of the object texture and consider that under certain circumstances, the object texture is more complex than the background of the image, so the foreground object is more suitable for steganography than the background. On the basis of instance segmentation, such as Mask R-CNN, the proposed method hides secret information into each object's region by using the… More >

Jieren Cheng^{1, 2}, Canting Cai^{1, *}, Xiangyan Tang¹, Victor S. Sheng³, Wei Guo¹, Mengyang Li¹

CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 131-150, 2020, DOI:10.32604/cmc.2020.06175

Abstract Traditional distributed denial of service (DDoS) detection methods need a lot of computing resource, and many of them which are based on single element have high missing rate and false alarm rate. In order to solve the problems, this paper proposes a DDoS attack information fusion method based on CNN for multi-element data. Firstly, according to the distribution, concentration and high traffic abruptness of DDoS attacks, this paper defines six features which are respectively obtained from the elements of source IP address, destination IP address, source port, destination port, packet size and the number of IP packets. Then, we propose… More >

Ling Ding^{1, 2}, Huyin Zhang^{1, *}, Jinsheng Xiao^{3, *}, Cheng Shu³, Shejie Lu²

CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.3, pp. 1039-1053, 2020, DOI:10.32604/cmes.2020.08268

Abstract This paper proposes a novel method of lane detection, which adopts VGG16 as the basis of convolutional neural network to extract lane line features by cavity convolution, wherein the lane lines are divided into dotted lines and solid lines. Expanding the field of experience through hollow convolution, the full connection layer of the network is discarded, the last largest pooling layer of the VGG16 network is removed, and the processing of the last three convolution layers is replaced by hole convolution. At the same time, CNN adopts the encoder and decoder structure mode, and uses the index function of the… More >