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  • 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 >

  • Open Access

    ARTICLE

    Prediction of Concrete Cubic Compressive Strength Using ANN Based Size Effect Model

    Q.W. Yang1, S.G. Du1,2

    CMC-Computers, Materials & Continua, Vol.47, No.3, pp. 217-236, 2015, DOI:10.3970/cmc.2015.047.217

    Abstract Size effect is a major issue in concrete structures and occurs in concrete in any loading conditions. In this study, size effect on concrete cubic compressive strength is modeled with a back-propagation neural network. The main advantage in using an artificial neural network (ANN) technique is that the network is built directly from experimental data without any simplifying assumptions via the self-organizing capabilities of the neural network. The proposed ANN model is verified by using 27 experimental data sets collected from the literature. For the large specimens, a modified ANN is developed in the paper More >

  • Open Access

    ARTICLE

    Research on Activated Carbon Supercapacitors Electrochemical Properties Based on Improved PSO-BP Neural Network

    Xiaoyi Liang1, Zhen Yang1,2, Xingsheng Gu3, Licheng Ling1

    CMC-Computers, Materials & Continua, Vol.13, No.2, pp. 135-152, 2009, DOI:10.3970/cmc.2009.013.135

    Abstract Supercapacitors, also called electrical double-layer capacitors (EDLCs), occupy a region between batteries and dielectric capacitors on the Ragone plot describing the relation between energy and power. BET specific surface area and specific capacitance are two important electrochemical property parameters for activated carbon EDLCs, which are usually tested by experimental method. However, it is misspent time to repeat lots of experiments for EDLCs' studies. In this investigation, we developed one theoretical model based on improved particle swarm optimization algorithm back propagation (PSO-BP) neural network (NN) to simulate and optimize BET specific surface area and specific capacitance. More >

  • Open Access

    ARTICLE

    Comparison of New Formulations for Martensite Start Temperature of Fe-Mn-Si Shape Memory Alloys Using Geneting Programming and Neural Networks

    CMC-Computers, Materials & Continua, Vol.10, No.1, pp. 65-96, 2009, DOI:10.3970/cmc.2009.010.065

    Abstract This work proposed an alternative formulation for the prediction of martensite start temperature (Ms) of Fe-Mn-Si shape memory alloys (SMAs) depending on the various compositions and heat treatment techniques by using Neural Network (NN) and genetic programming (GP) soft computing techniques. The training and testing patterns of the proposed NN and GP formulations are based on well established experimental results from the literature. The NN and GP based formulation results are compared with experimental results and found to be quite reliable with a very high correlation (R2=0.955 for GEP and 0.999 for NN). More >

  • Open Access

    ARTICLE

    Role of Coupling Terms in Constitutive Relationships of Magnetostrictive Materials

    D. P. Ghosh1, S. Gopalakrishnan2

    CMC-Computers, Materials & Continua, Vol.1, No.3, pp. 213-228, 2004, DOI:10.3970/cmc.2004.001.213

    Abstract Anhysteretic, coupled, linear and nonlinear constitutive relationship for magnetostrictive material is studied in this paper. Constitutive relationships of magnetostrictive material are represented through two equations, one for actuation and other for sensing, both of which are coupled through magneto-mechanical coefficient. Coupled model is studied without assuming any explicit direct relationship with magnetic field. In linear-coupled model, which is assumed to preserve the magnetic flux line continuity, the elastic modulus, the permeability and magneto-elastic constant are assumed as constant. In nonlinear-coupled model, the nonlinearity is decoupled and solved separately for the magnetic domain and mechanical domain More >

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