Meigui Zhang^*, Kehui Zeng, Jinwei Wang

Journal of Information Hiding and Privacy Protection, Vol.2, No.1, pp. 21-34, 2020, DOI:10.32604/jihpp.2020.010467

Abstract The development of artificial intelligence makes the application of face recognition more and more extensive, which also leads to the security of face recognition technology increasingly prominent. How to design a face anti-spoofing method with high accuracy, strong generalization ability and meeting practical needs is the focus of current research. This paper introduces the research progress of face anti-spoofing algorithm, and divides the existing face anti-spoofing methods into two categories: methods based on manual feature expression and methods based on deep learning. Then, the typical algorithms included in them are classified twice, and the basic ideas, advantages and disadvantages of… More >

Soukaina Mjahed^1,*, Khadija Bouzaachane¹, Ahmad Taher Azar^2,3, Salah El Hadaj¹, Said Raghay¹

CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 459-494, 2020, DOI:10.32604/cmes.2020.010791

Abstract This paper focuses on the unsupervised detection of the Higgs boson particle using the most informative features and variables which characterize the “Higgs machine learning challenge 2014” data set. This unsupervised detection goes in this paper analysis through 4 steps: (1) selection of the most informative features from the considered data; (2) definition of the number of clusters based on the elbow criterion. The experimental results showed that the optimal number of clusters that group the considered data in an unsupervised manner corresponds to 2 clusters; (3) proposition of a new approach for hybridization of both hard and fuzzy clustering… More >

Wenyao Zhu^{1, 2}, Xue Li³, Young-Mi Shon^{4, *}

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 945-962, 2020, DOI:10.32604/cmc.2020.010089

Abstract Traditional clothing design models based on adaptive meshes cannot reflect. To solve this problem, a clothing simulation design model based on 3D image analysis technology is established. The model uses feature extraction and description of image evaluation parameters, and establishes the mapping relationship between image features and simulation results by using the optimal parameter values, thereby obtaining a three-dimensional image simulation analysis environment. On the basis of this model, by obtaining the response results of clothing collision detection and the results of local adaptive processing of clothing meshes, the cutting form and actual cutting effect of clothing are determined to… More >

Xueming Qiao¹, Yingxue Xia¹, Weiyi Zhu², Dongjie Zhu^{3, *}, Liang Kong¹, Chunxu Lin³, Zhenhao Guo³, Yiheng Sun³

Journal on Artificial Intelligence, Vol.2, No.1, pp. 29-37, 2020, DOI:10.32604/jai.2020.09955

Abstract Natural scene recognition has important significance and value in the fields of image retrieval, autonomous navigation, human-computer interaction and industrial automation. Firstly, the natural scene image non-text content takes up relatively high proportion; secondly, the natural scene images have a cluttered background and complex lighting conditions, angle, font and color. Therefore, how to extract text extreme regions efficiently from complex and varied natural scene images plays an important role in natural scene image text recognition. In this paper, a Text extremum region Extraction algorithm based on Joint-Channels (TEJC) is proposed. On the one hand, it can solve the problem that… More >

Ye Zhai^{1, *}, Dongsheng Liu¹, Celimuge Wu², Rongrong She¹

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1999-2012, 2020, DOI:10.32604/cmc.2020.09950

Abstract Reusing code fragments by copying and pasting them with or without minor adaptation is a common activity in software development. As a result, software systems often contain sections of code that are very similar, called code clones. Code clones are beneficial in reducing software development costs and development risks. However, recent studies have indicated some negative impacts as a result. In order to effectively manage and utilize the clones, we design an approach for recommending refactoring clones based on a Bayesian network. Firstly, clone codes are detected from the source code. Secondly, the clones that need to be refactored are… More >

Yongmei Zhang^{1, *}, Jianzhe Ma², Lei Hu³, Keming Yu⁴, Lihua Song^{1, 5}, Huini Chen¹

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1929-1944, 2020, DOI:10.32604/cmc.2020.010556

Abstract The prediction of particles less than 2.5 micrometers in diameter (PM2.5) in fog and haze has been paid more and more attention, but the prediction accuracy of the results is not ideal. Haze prediction algorithms based on traditional numerical and statistical prediction have poor effects on nonlinear data prediction of haze. In order to improve the effects of prediction, this paper proposes a haze feature extraction and pollution level identification pre-warning algorithm based on feature selection and integrated learning. Minimum Redundancy Maximum Relevance method is used to extract low-level features of haze, and deep confidence network is utilized to extract… More >

Jinyuan Li¹, Hao Li¹, Guorong Cui¹, Yan Kang^{1, *}, Yang Hu¹, Yingnan Zhou²

CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 925-940, 2020, DOI:10.32604/cmc.2020.09903

Abstract With continuous urbanization, cities are undergoing a sharp expansion within the regional space. Due to the high cost, the prediction of regional traffic flow is more difficult to extend to entire urban areas. To address this challenging problem, we present a new deep learning architecture for regional epitaxial traffic flow prediction called GACNet, which predicts traffic flow of surrounding areas based on inflow and outflow information in central area. The method is data-driven, and the spatial relationship of traffic flow is characterized by dynamically transforming traffic information into images through a two-dimensional matrix. We introduce adversarial training to improve performance… More >

Yang Yang^1,*, Jing Dong¹, Chao Fang², Ping Xie³, Na An³

CMES-Computer Modeling in Engineering & Sciences, Vol.123, No.3, pp. 1015-1031, 2020, DOI:10.32604/cmes.2020.09404

Abstract The development of cloud computing and virtualization technology has brought great challenges to the reliability of data center services. Data centers typically contain a large number of compute and storage nodes which may fail and affect the quality of service. Failure prediction is an important means of ensuring service availability. Predicting node failure in cloud-based data centers is challenging because the failure symptoms reflected have complex characteristics, and the distribution imbalance between the failure sample and the normal sample is widespread, resulting in inaccurate failure prediction. Targeting these challenges, this paper proposes a novel failure prediction method FP-STE (Failure Prediction… More >

Ming Wan¹, Jinfang Li¹, Jiangyuan Yao^{2, *}, Rongbing Wang^{1, 3}, Hao Luo¹

CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1415-1431, 2020, DOI:10.32604/cmc.2020.09692

Abstract In process industries, the characteristics of industrial activities focus on the integrality and continuity of production process, which can contribute to excavating the appropriate features for industrial anomaly detection. From this perspective, this paper proposes a novel state-based control feature extraction approach, which regards the finite control operations as different states. Furthermore, the procedure of state transition can adequately express the change of successive control operations, and the statistical information between different states can be used to calculate the feature values. Additionally, OCSVM (One Class Support Vector Machine) and BPNN (BP Neural Network), which are optimized by PSO (Particle Swarm… More >

Jieren Cheng^{1, 2}, Yifu Liu^{1, *}, 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 >