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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

    FSPAM: A Feature Construction Method to Identifying Cell Populations in ScRNA-seq Data

    Amin Einipour1, Mohammad Mosleh1, *, Karim Ansari-Asl1, 2

    CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.1, pp. 377-397, 2020, DOI:10.32604/cmes.2020.08496

    Abstract The emergence of single-cell RNA-sequencing (scRNA-seq) technology has introduced new information about the structure of cells, diseases, and their associated biological factors. One of the main uses of scRNA-seq is identifying cell populations, which sometimes leads to the detection of rare cell populations. However, the new method is still in its infancy and with its advantages comes computational challenges that are just beginning to address. An important tool in the analysis is dimensionality reduction, which transforms high dimensional data into a meaningful reduced subspace. The technique allows noise removal, visualization and compression of high-dimensional data. This paper presents a new… More >

  • Open Access

    ARTICLE

    Fire Detection Method Based on Improved Fruit Fly Optimization-Based SVM

    Fangming Bi1, 2, Xuanyi Fu1, 2, Wei Chen1, 2, 3, *, Weidong Fang4, Xuzhi Miao1, 2, Biruk Assefa1, 5

    CMC-Computers, Materials & Continua, Vol.62, No.1, pp. 199-216, 2020, DOI:10.32604/cmc.2020.06258

    Abstract Aiming at the defects of the traditional fire detection methods, which are caused by false positives and false negatives in large space buildings, a fire identification detection method based on video images is proposed. The algorithm first uses the hybrid Gaussian background modeling method and the RGB color model to perform fire prejudgment on the video image, which can eliminate most non-fire interferences. Secondly, the traditional regional growth algorithm is improved and the fire image segmentation effect is effectively improved. Then, based on the segmented image, the dynamic and static features of the fire flame are further analyzed and extracted… More >

  • Open Access

    ARTICLE

    A Face Recognition Algorithm Based on LBP-EHMM

    Tao Li1, Lingyun Wang1, Yin Chen1,*, Yongjun Ren1, Lei Wang1, Jinyue Xia2

    Journal on Artificial Intelligence, Vol.1, No.2, pp. 59-68, 2019, DOI:10.32604/jai.2019.06346

    Abstract In order to solve the problem that real-time face recognition is susceptible to illumination changes, this paper proposes a face recognition method that combines Local Binary Patterns (LBP) and Embedded Hidden Markov Model (EHMM). Face recognition method. The method firstly performs LBP preprocessing on the input face image, then extracts the feature vector, and finally sends the extracted feature observation vector to the EHMM for training or recognition. Experiments on multiple face databases show that the proposed algorithm is robust to illumination and improves recognition rate. More >

  • Open Access

    ARTICLE

    Texture Feature Extraction Method for Ground Nephogram Based on Contourlet and the Power Spectrum Analysis Algorithm

    Xiaoying Chen1, 2, *, Shijun Zhao2, Xiaolei Wang2, Xuejin Sun2, Jing Feng2, Nan Ye3

    CMC-Computers, Materials & Continua, Vol.61, No.2, pp. 861-875, 2019, DOI:10.32604/cmc.2019.06230

    Abstract It is important to extract texture feature from the ground-base cloud image for cloud type automatic detection. In this paper, a new method is presented to capture the contour edge, texture and geometric structure of cloud images by using Contourlet and the power spectrum analysis algorithm. More abundant texture information is extracted. Cloud images can be obtained a multiscale and multidirection decomposition. The coefficient matrix from Contourlet transform of ground nephogram is calculated. The energy, mean and variance characteristics calculated from coefficient matrix are composed of the feature information. The frequency information of the data series from the feature vector… More >

  • Open Access

    ABSTRACT

    Evaluation of Statistical Feature Encoding Techniques on Iris Images

    Chowhan S.S.1, G.N. Shinde2

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.9, No.1, pp. 67-74, 2009, DOI:10.3970/icces.2009.009.067

    Abstract Feature selection, often used as a pre-processing step to machine learning, is designed to reduce dimensionality, eliminate irrelevant data and improve accuracy. Iris Basis is our first attempt to reduce the dimensionality of the problem while focusing only on parts of the scene that effectively identify the individual. Independent Component Analysis (ICA) is to extract iris feature to recognize iris pattern. Principal Component Analysis (PCA) is a dimension-reduction tool that can be used to reduce a large set of variables to a small set that still contains most of the information in the large set. Image quality is very important… More >

  • Open Access

    ARTICLE

    Ground-Based Cloud Recognition Based on Dense_SIFT Features

    Zhizheng Zhang1, Jing Feng1,*, Jun Yan2, Xiaolei Wang1, Xiaocun Shu1

    Journal of New Media, Vol.1, No.1, pp. 1-9, 2019, DOI:10.32604/jnm.2019.05937

    Abstract Clouds play an important role in modulating radiation processes and climate changes in the Earth's atmosphere. Currently, measurement of meteorological elements such as temperature, air pressure, humidity, and wind has been automated. However, the cloud's automatic identification technology is still not perfect. Thus, this paper presents an approach that extracts dense scale-invariant feature transform (Dense_SIFT) as the local features of four typical cloud images. The extracted cloud features are then clustered by K-means algorithm, and the bag-of-words (BoW) model is used to describe each ground-based cloud image. Finally, support vector machine (SVM) is used for classification and recognition. Based on… More >

  • Open Access

    ARTICLE

    Brake Fault Diagnosis Through Machine Learning Approaches – A Review

    Alamelu Manghai T.M.1, Jegadeeshwaran R2, Sugumaran V.3

    Structural Durability & Health Monitoring, Vol.11, No.1, pp. 43-67, 2017, DOI:10.3970/sdhm.2017.012.043

    Abstract Diagnosis is the recognition of the nature and cause of a certain phenomenon. It is generally used to determine cause and effect of a problem. Machine fault diagnosis is a field of finding faults arising in machines. To identify the most probable faults leading to failure, many methods are used for data collection, including vibration monitoring, thermal imaging, oil particle analysis, etc. Then these data are processed using methods like spectral analysis, wavelet analysis, wavelet transform, short-term Fourier transform, high-resolution spectral analysis, waveform analysis, etc., The results of this analysis are used in a root cause failure analysis in order… More >

  • Open Access

    ARTICLE

    A GLCM-Feature-Based Approach for Reversible Image Transformation

    Xianyi Chen1,2,*, Haidong Zhong1,2, Zhifeng Bao3

    CMC-Computers, Materials & Continua, Vol.59, No.1, pp. 239-255, 2019, DOI:10.32604/cmc.2019.03572

    Abstract Recently, a reversible image transformation (RIT) technology that transforms a secret image to a freely-selected target image is proposed. It not only can generate a stego-image that looks similar to the target image, but also can recover the secret image without any loss. It also has been proved to be very useful in image content protection and reversible data hiding in encrypted images. However, the standard deviation (SD) is selected as the only feature during the matching of the secret and target image blocks in RIT methods, the matching result is not so good and needs to be further improved… More >

  • Open Access

    ARTICLE

    Gender-Specific Multi-Task Micro-Expression Recognition Using Pyramid CGBP-TOP Feature

    Chunlong Hu1,*, Jianjun Chen1, Xin Zuo1, Haitao Zou1, Xing Deng1, Yucheng Shu2

    CMES-Computer Modeling in Engineering & Sciences, Vol.118, No.3, pp. 547-559, 2019, DOI:10.31614/cmes.2019.04032

    Abstract Micro-expression recognition has attracted growing research interests in the field of compute vision. However, micro-expression usually lasts a few seconds, thus it is difficult to detect. This paper presents a new framework to recognize micro-expression using pyramid histogram of Centralized Gabor Binary Pattern from Three Orthogonal Panels (CGBP-TOP) which is an extension of Local Gabor Binary Pattern from Three Orthogonal Panels feature. CGBP-TOP performs spatial and temporal analysis to capture the local facial characteristics of micro-expression image sequences. In order to keep more local information of the face, CGBP-TOP is extracted based on pyramid sub-regions of the micro-expression video frame.… More >

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