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  • Open Access

    ARTICLE

    Convolution Neural Networks and Support Vector Machines for Automatic Segmentation of Intracoronary Optical Coherence Tomography

    Caining Zhang1, Huaguang Li2, Xiaoya Guo3, David Molony4, Xiaopeng Guo2, Habib Samady4, Don P. Giddens4,5, Lambros Athanasiou6, Rencan Nie2,*, Jinde Cao3,*, Dalin Tang1,*,7

    Molecular & Cellular Biomechanics, Vol.16, No.2, pp. 153-161, 2019, DOI:10.32604/mcb.2019.06873

    Abstract Cardiovascular diseases are closely associated with deteriorating atherosclerotic plaques. Optical coherence tomography (OCT) is a recently developed intravascular imaging technique with high resolution approximately 10 microns and could provide accurate quantification of coronary plaque morphology. However, tissue segmentation of OCT images in clinic is still mainly performed manually by physicians which is time consuming and subjective. To overcome these limitations, two automatic segmentation methods for intracoronary OCT image based on support vector machine (SVM) and convolutional neural network (CNN) were performed to identify the plaque region and characterize plaque components. In vivo IVUS and OCT coronary… More >

  • Open Access

    ARTICLE

    Street-Level Landmarks Acquisition Based on SVM Classifiers

    Ruixiang Li1,2, Yingying Liu3, Yaqiong Qiao1,2, Te Ma1,2, Bo Wang4, Xiangyang Luo1,2,*

    CMC-Computers, Materials & Continua, Vol.59, No.2, pp. 591-606, 2019, DOI:10.32604/cmc.2019.05208

    Abstract High-density street-level reliable landmarks are one of the important foundations for street-level geolocation. However, the existing methods cannot obtain enough street-level landmarks in a short period of time. In this paper, a street-level landmarks acquisition method based on SVM (Support Vector Machine) classifiers is proposed. Firstly, the port detection results of IPs with known services are vectorized, and the vectorization results are used as an input of the SVM training. Then, the kernel function and penalty factor are adjusted for SVM classifiers training, and the optimal SVM classifiers are obtained. After that, the classifier sequence More >

  • Open Access

    ARTICLE

    A DDoS Attack Situation Assessment Method via Optimized Cloud Model Based on Influence Function

    Xiangyan Tang1, Qidong Zheng1,*, Jieren Cheng1,2, Victor S. Sheng3, Rui Cao1, Meizhu Chen1

    CMC-Computers, Materials & Continua, Vol.60, No.3, pp. 1263-1281, 2019, DOI:10.32604/cmc.2019.06173

    Abstract The existing network security situation assessment methods cannot effectively assess the Distributed denial-of-service (DDoS) attack situation. In order to solve these problems, we propose a DDoS attack situation assessment method via optimized cloud model based on influence function. Firstly, according to the state change characteristics of the IP addresses which are accessed by new and old user respectively, this paper defines a fusion feature value. Then, based on this value, we establish a V-Support Vector Machines (V-SVM) classification model to analyze network flow for identifying DDoS attacks. Secondly, according to the change of new and… More >

  • Open Access

    ARTICLE

    An Improved Lung Sound De-noising Method by Wavelet Packet Transform with Pso-Based Threshold Selection

    Qing-Hua Hea, Bin Yub, Xin Honga, Bo Lva, Tao Liub, Jian Ranb, Yu-Tian Bia

    Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 223-230, 2018, DOI:10.1080/10798587.2016.1261957

    Abstract Lung abnormalities and respiratory diseases increase with the development of urban life. Lung sound analysis provides vital information of the present condition of the pulmonary. But lung sounds are easily interfered by noises in the transmission and record process, then it cannot be used for diagnosis of diseases. So the noised sound should be processed to reduce noises and to enhance the quality of signals received. On the basis of analyzing wavelet packet transform theory and the characteristics of traditional wavelet threshold de-noising method, we proposed a modified threshold selection method based on Particle Swarm More >

  • Open Access

    ARTICLE

    Electro-Mechanical Impedance Based Position Identification of Bolt Loosening Using LibSVM

    Yuxiang Zhanga, Xin Zhanga, Jiazhao Chenb, Jianhai Yanga

    Intelligent Automation & Soft Computing, Vol.24, No.1, pp. 81-88, 2018, DOI:10.1080/10798587.2016.1267245

    Abstract Bolt loosening is a common structural failure, which received extensive attention from many industrial departments. Because the uneven stress on different directions of a bolt is the common reason that the bolt becomes loose, it is quite important to carry out the research about sensitive detection of bolt loosening. Using Agilent 4924A instrument, this paper precedes the loosening test of bolts based on Electro-Mechanical Impedance (EMI), on an aluminum plate instead of flange plate for simplification. And the electromechanical admittance is given according to the fundamental equation of PiezoMaterial Lead Zirconate Titanate (PZT). Specifically, the More >

  • Open Access

    ARTICLE

    Analyzing and Assessing Reviews on Jd.com

    Jie Liua,b,c,d, Xiaodong Fud, Jin Liua,b,c, Yunchuan Suna,e

    Intelligent Automation & Soft Computing, Vol.24, No.1, pp. 73-80, 2018, DOI:10.1080/10798587.2016.1267244

    Abstract Reviews are contents written by users to express opinions on products or services. The information contained in reviews is valuable to users who are going to make decisions on products or services. However, there are numbers of reviews for popular products, and the quality of reviews is not always good. It’s necessary to pick out reviews, which are in high quality from numbers of reviews to assist user in making decision. In this paper, we collected 21,501 reviews flagged as good from 499,253 products on JD.com. We observed the level of users is an important More >

  • Open Access

    ARTICLE

    Comparisons of MFDFA, EMD and WT by Neural Network, Mahalanobis Distance and SVM in Fault Diagnosis of Gearboxes

    Jinshan Lina*, Chunhong Doub, Qianqian Wanga

    Sound & Vibration, Vol.52, No.2, pp. 11-15, 2018, DOI:10.32604/sv.2018.03653

    Abstract A method for gearbox fault diagnosis consists of feature extraction and fault identification. Many methods for feature extraction have been devised for exposing nature of vibration data of a defective gearbox. In addition, features extracted from gearbox vibration data are identified by various classifiers. However, existing literatures leave much to be desired in assessing performance of different combinatorial methods for gearbox fault diagnosis. To this end, this paper evaluated performance of several typical combinatorial methods for gearbox fault diagnosis by associating each of multifractal detrended fluctuation analysis (MFDFA), empirical mode decomposition (EMD) and wavelet transform More >

  • Open Access

    ARTICLE

    New Method for Computer Identification Through Electromagnetic Radiation

    Jun Shi1, Zhujun Zhang2, Yangyang Li1,*, Rui Wang1, Hao Shi1, Xile Li3

    CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 69-80, 2018, DOI:10.32604/cmc.2018.03688

    Abstract The electromagnetic waves emitted from devices can be a source of information leakage and can cause electromagnetic compatibility (EMC) problems. Electromagnetic radiation signals from computer displays can be a security risk if they are intercepted and reconstructed. In addition, the leaks may reveal the hardware information of the computer, which is more important for some attackers, protectors and security inspection workers. In this paper, we propose a statistical distribution based algorithm (SD algorithm) to extracted eigenvalues from electromagnetic radiate video signals, and then classified computers by using classifier based on Bayesian and SVM. We can More >

  • Open Access

    ARTICLE

    Fingerprint Liveness Detection from Different Fingerprint Materials Using Convolutional Neural Network and Principal Component Analysis

    Chengsheng Yuan1,2,3, Xinting Li3, Q. M. Jonathan Wu3, Jin Li4,5, Xingming Sun1,2

    CMC-Computers, Materials & Continua, Vol.53, No.4, pp. 357-372, 2017, DOI:10.3970/cmc.2017.053.357

    Abstract Fingerprint-spoofing attack often occurs when imposters gain access illegally by using artificial fingerprints, which are made of common fingerprint materials, such as silicon, latex, etc. Thus, to protect our privacy, many fingerprint liveness detection methods are put forward to discriminate fake or true fingerprint. Current work on liveness detection for fingerprint images is focused on the construction of complex handcrafted features, but these methods normally destroy or lose spatial information between pixels. Different from existing methods, convolutional neural network (CNN) can generate high-level semantic representations by learning and concatenating low-level edge and shape features from… More >

  • Open Access

    ARTICLE

    A Machine Learning Approach for MRI Brain Tumor Classification

    Ravikumar Gurusamy1, Dr Vijayan Subramaniam2

    CMC-Computers, Materials & Continua, Vol.53, No.2, pp. 91-108, 2017, DOI:10.3970/cmc.2017.053.091

    Abstract A new method for the denoising, extraction and tumor detection on MRI images is presented in this paper. MRI images help physicians study and diagnose diseases or tumors present in the brain. This work is focused towards helping the radiologist and physician to have a second opinion on the diagnosis. The ambiguity of Magnetic Resonance (MR) image features is solved in a simpler manner. The MRI image acquired from the machine is subjected to analysis in the work. The real-time data is used for the analysis. Basic preprocessing is performed using various filters for noise More >

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