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  • Open Access


    MII: A Novel Text Classification Model Combining Deep Active Learning with BERT

    Anman Zhang1, Bohan Li1, 2, 3, *, Wenhuan Wang1, Shuo Wan1, Weitong Chen4

    CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1499-1514, 2020, DOI:10.32604/cmc.2020.09962

    Abstract Active learning has been widely utilized to reduce the labeling cost of supervised learning. By selecting specific instances to train the model, the performance of the model was improved within limited steps. However, rare work paid attention to the effectiveness of active learning on it. In this paper, we proposed a deep active learning model with bidirectional encoder representations from transformers (BERT) for text classification. BERT takes advantage of the self-attention mechanism to integrate contextual information, which is beneficial to accelerate the convergence of training. As for the process of active learning, we design an More >

  • Open Access


    Left or Right Hand Classification from Fingerprint Images Using a Deep Neural Network

    Junseob Kim1, Beanbonyka Rim1, Nak-Jun Sung1, Min Hong2, *

    CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 17-30, 2020, DOI:10.32604/cmc.2020.09044

    Abstract Fingerprint security technology has attracted a great deal of attention in recent years because of its unique biometric information that does not change over an individual’s lifetime and is a highly reliable and secure way to identify a certain individuals. AFIS (Automated Fingerprint Identification System) is a system used by Korean police for identifying a specific person by fingerprint. The AFIS system, however, only selects a list of possible candidates through fingerprints, the exact individual must be found by fingerprint experts. In this paper, we designed a deep learning system using deep convolution network to… More >

  • Open Access


    Defend Against Adversarial Samples by Using Perceptual Hash

    Changrui Liu1, Dengpan Ye1, *, Yueyun Shang2, Shunzhi Jiang1, Shiyu Li1, Yuan Mei1, Liqiang Wang3

    CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1365-1386, 2020, DOI:10.32604/cmc.2020.07421

    Abstract Image classifiers that based on Deep Neural Networks (DNNs) have been proved to be easily fooled by well-designed perturbations. Previous defense methods have the limitations of requiring expensive computation or reducing the accuracy of the image classifiers. In this paper, we propose a novel defense method which based on perceptual hash. Our main goal is to destroy the process of perturbations generation by comparing the similarities of images thus achieve the purpose of defense. To verify our idea, we defended against two main attack methods (a white-box attack and a black-box attack) in different DNN-based More >

  • Open Access


    Growing and Pruning Based Deep Neural Networks Modeling for Effective Parkinson’s Disease Diagnosis

    Kemal Akyol1, *

    CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.2, pp. 619-632, 2020, DOI:10.32604/cmes.2020.07632

    Abstract Parkinson’s disease is a serious disease that causes death. Recently, a new dataset has been introduced on this disease. The aim of this study is to improve the predictive performance of the model designed for Parkinson’s disease diagnosis. By and large, original DNN models were designed by using specific or random number of neurons and layers. This study analyzed the effects of parameters, i.e., neuron number and activation function on the model performance based on growing and pruning approach. In other words, this study addressed the optimum hidden layer and neuron numbers and ideal activation More >

  • Open Access


    Parameters Compressing in Deep Learning

    Shiming He1, Zhuozhou Li1, Yangning Tang1, Zhuofan Liao1, Feng Li1, *, Se-Jung Lim2

    CMC-Computers, Materials & Continua, Vol.62, No.1, pp. 321-336, 2020, DOI:10.32604/cmc.2020.06130

    Abstract With the popularity of deep learning tools in image decomposition and natural language processing, how to support and store a large number of parameters required by deep learning algorithms has become an urgent problem to be solved. These parameters are huge and can be as many as millions. At present, a feasible direction is to use the sparse representation technique to compress the parameter matrix to achieve the purpose of reducing parameters and reducing the storage pressure. These methods include matrix decomposition and tensor decomposition. To let vector take advance of the compressing performance of More >

  • Open Access


    An Image Classification Method Based on Deep Neural Network with Energy Model

    Yang Yang1,*, Jinbao Duan1, Haitao Yu1, Zhipeng Gao1, Xuesong Qiu1

    CMES-Computer Modeling in Engineering & Sciences, Vol.117, No.3, pp. 555-575, 2018, DOI:10.31614/cmes.2018.04249

    Abstract The development of deep learning has revolutionized image recognition technology. How to design faster and more accurate image classification algorithms has become our research interests. In this paper, we propose a new algorithm called stochastic depth networks with deep energy model (SADIE), and the model improves stochastic depth neural network with deep energy model to provide attributes of images and analysis their characteristics. First, the Bernoulli distribution probability is used to select the current layer of the neural network to prevent gradient dispersion during training. Then in the backpropagation process, the energy function is designed More >

  • Open Access


    A Hybrid Model for Anomalies Detection in AMI System Combining K-means Clustering and Deep Neural Network

    Assia Maamar1,*, Khelifa Benahmed2

    CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 15-39, 2019, DOI:10.32604/cmc.2019.06497

    Abstract Recently, the radical digital transformation has deeply affected the traditional electricity grid and transformed it into an intelligent network (smart grid). This mutation is based on the progressive development of advanced technologies: advanced metering infrastructure (AMI) and smart meter which play a crucial role in the development of smart grid. AMI technologies have a promising potential in terms of improvement in energy efficiency, better demand management, and reduction in electricity costs. However the possibility of hacking smart meters and electricity theft is still among the most significant challenges facing electricity companies. In this regard, we… More >

  • Open Access


    Computational Machine Learning Representation for the Flexoelectricity Effect in Truncated Pyramid Structures

    Khader M. Hamdia2, Hamid Ghasemi3, Xiaoying Zhuang4,5, Naif Alajlan1, Timon Rabczuk1,2,*

    CMC-Computers, Materials & Continua, Vol.59, No.1, pp. 79-87, 2019, DOI:10.32604/cmc.2019.05882

    Abstract In this study, machine learning representation is introduced to evaluate the flexoelectricity effect in truncated pyramid nanostructure under compression. A Non-Uniform Rational B-spline (NURBS) based IGA formulation is employed to model the flexoelectricity. We investigate 2D system with an isotropic linear elastic material under plane strain conditions discretized by 45×30 grid of B-spline elements. Six input parameters are selected to construct a deep neural network (DNN) model. They are the Young's modulus, two dielectric permittivity constants, the longitudinal and transversal flexoelectric coefficients and the order of the shape function. The outputs of interest are the More >

  • Open Access


    Estimating the Properties of Ground-Waste-Brick Mortars Using DNN and ANN

    Abdulkadir Karaci1,*, Hasbi Yaprak2, Osman Ozkaraca3, Ilhami Demir4, Osman Simsek5

    CMES-Computer Modeling in Engineering & Sciences, Vol.118, No.1, pp. 207-228, 2019, DOI:10.31614/cmes.2019.04216

    Abstract In this study, deep-neural-network (DNN)- and artificial-neural-network (ANN)-based models along with regression models have been developed to estimate the pressure, bending and elongation values of ground-brick (GB)-added mortar samples. This study is aimed at utilizing GB as a mineral additive in concrete in the ratios 0.0%, 2.5%, 5.0%, 7.5%, 10.0%, 12.5% and 15.0%. In this study, 756 mortar samples were produced for 84 different series and were cured in tap water (W), 5% sodium sulphate solution (SS5) and 5% ammonium nitrate solution (AN5) for 7 days, 28 days, 90 days and 180 days. The developed… More >

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