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  • Open Access


    An Opinion Spam Detection Method Based on Multi-Filters Convolutional Neural Network

    Ye Wang1, Bixin Liu2, Hongjia Wu1, Shan Zhao1, Zhiping Cai1, *, Donghui Li3, *, Cheang Chak Fong4

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 355-367, 2020, DOI:10.32604/cmc.2020.09835

    Abstract With the continuous development of e-commerce, consumers show increasing interest in posting comments on consumption experience and quality of commodities. Meanwhile, people make purchasing decisions relying on other comments much more than ever before. So the reliability of commodity comments has a significant impact on ensuring consumers’ equity and building a fair internet-trade-environment. However, some unscrupulous online-sellers write fake praiseful reviews for themselves and malicious comments for their business counterparts to maximize their profits. Those improper ways of self-profiting have severely ruined the entire online shopping industry. Aiming to detect and prevent these deceptive comments effectively, we construct a model… More >

  • Open Access


    Predicting Concrete Compressive Strength Using Deep Convolutional Neural Network Based on Image Characteristics

    Sanghyo Lee1, Yonghan Ahn2, Ha Young Kim3, *

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 1-17, 2020, DOI:10.32604/cmc.2020.011104

    Abstract In this study, we examined the efficacy of a deep convolutional neural network (DCNN) in recognizing concrete surface images and predicting the compressive strength of concrete. A digital single-lens reflex (DSLR) camera and microscope were simultaneously used to obtain concrete surface images used as the input data for the DCNN. Thereafter, training, validation, and testing of the DCNNs were performed based on the DSLR camera and microscope image data. Results of the analysis indicated that the DCNN employing DSLR image data achieved a relatively higher accuracy. The accuracy of the DSLR-derived image data was attributed to the relatively wider range… More >

  • Open Access


    Identification of Weather Phenomena Based on Lightweight Convolutional Neural Networks

    Congcong Wang1, 2, 3, Pengyu Liu1, 2, 3, *, Kebin Jia1, 2, 3, Xiaowei Jia4, Yaoyao Li1, 2, 3

    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 2043-2055, 2020, DOI:10.32604/cmc.2020.010505

    Abstract Weather phenomenon recognition plays an important role in the field of meteorology. Nowadays, weather radars and weathers sensor have been widely used for weather recognition. However, given the high cost in deploying and maintaining the devices, it is difficult to apply them to intensive weather phenomenon recognition. Moreover, advanced machine learning models such as Convolutional Neural Networks (CNNs) have shown a lot of promise in meteorology, but these models also require intensive computation and large memory, which make it difficult to use them in reality. In practice, lightweight models are often used to solve such problems. However, lightweight models often… More >

  • Open Access


    Intelligent Detection Model Based on a Fully Convolutional Neural Network for Pavement Cracks

    Duo Ma1, 2, 3, Hongyuan Fang1, 2, 3, *, Binghan Xue1, 2, 3, Fuming Wang1, 2, 3, Mohammed A. Msekh4, Chiu Ling Chan5

    CMES-Computer Modeling in Engineering & Sciences, Vol.123, No.3, pp. 1267-1291, 2020, DOI:10.32604/cmes.2020.09122

    Abstract The crack is a common pavement failure problem. A lack of periodic maintenance will result in extending the cracks and damage the pavement, which will affect the normal use of the road. Therefore, it is significant to establish an efficient intelligent identification model for pavement cracks. The neural network is a method of simulating animal nervous systems using gradient descent to predict results by learning a weight matrix. It has been widely used in geotechnical engineering, computer vision, medicine, and other fields. However, there are three major problems in the application of neural networks to crack identification. There are too… More >

  • Open Access


    Acoustic Emission Recognition Based on a Two-Streams Convolutional Neural Network

    Weibo Yang1, Weidong Liu2, *, Jinming Liu3, Mingyang Zhang4

    CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 515-525, 2020, DOI:10.32604/cmc.2020.09801

    Abstract The Convolutional Neural Network (CNN) is a widely used deep neural network. Compared with the shallow neural network, the CNN network has better performance and faster computing in some image recognition tasks. It can effectively avoid the problem that network training falls into local extremes. At present, CNN has been applied in many different fields, including fault diagnosis, and it has improved the level and efficiency of fault diagnosis. In this paper, a two-streams convolutional neural network (TCNN) model is proposed. Based on the short-time Fourier transform (STFT) spectral and Mel Frequency Cepstrum Coefficient (MFCC) input characteristics of two-streams acoustic… More >

  • Open Access


    Retraction Notice to: Automatic Arrhythmia Detection Based on Convolutional Neural Networks

    Zhong Liu, Xin’an Wang, Kuntao Lu, David Su

    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 1079-1079, 2020, DOI:10.32604/cmc.2020.04882

    Abstract This article has no abstract. More >

  • Open Access


    Sentence Similarity Measurement with Convolutional Neural Networks Using Semantic and Syntactic Features

    Shiru Zhang1, Zhiyao Liang1, *, Jian Lin2

    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 943-957, 2020, DOI:10.32604/cmc.2020.08800

    Abstract Calculating the semantic similarity of two sentences is an extremely challenging problem. We propose a solution based on convolutional neural networks (CNN) using semantic and syntactic features of sentences. The similarity score between two sentences is computed as follows. First, given a sentence, two matrices are constructed accordingly, which are called the syntax model input matrix and the semantic model input matrix; one records some syntax features, and the other records some semantic features. By experimenting with different arrangements of representing the syntactic and semantic features of the sentences in the matrices, we adopt the most effective way of constructing… More >

  • Open Access


    Towards No-Reference Image Quality Assessment Based on Multi-Scale Convolutional Neural Network

    Yao Ma1, Xibiao Cai1, *, Fuming Sun2

    CMES-Computer Modeling in Engineering & Sciences, Vol.123, No.1, pp. 201-216, 2020, DOI:10.32604/cmes.2020.07867

    Abstract Image quality assessment has become increasingly important in image quality monitoring and reliability assuring of image processing systems. Most of the existing no-reference image quality assessment methods mainly exploit the global information of image while ignoring vital local information. Actually, the introduced distortion depends on a slight difference in details between the distorted image and the non-distorted reference image. In light of this, we propose a no-reference image quality assessment method based on a multi-scale convolutional neural network, which integrates both global information and local information of an image. We first adopt the image pyramid method to generate four scale… More >

  • Open Access


    A Rub-Impact Recognition Method Based on Improved Convolutional Neural Network

    Weibo Yang1, *, Jing Li2, Wei Peng2, Aidong Deng3

    CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 283-299, 2020, DOI:10.32604/cmc.2020.07511

    Abstract Based on the theory of modal acoustic emission (AE), when the convolutional neural network (CNN) is used to identify rotor rub-impact faults, the training data has a small sample size, and the AE sound segment belongs to a single channel signal with less pixel-level information and strong local correlation. Due to the convolutional pooling operations of CNN, coarse-grained and edge information are lost, and the top-level information dimension in CNN network is low, which can easily lead to overfitting. To solve the above problems, we first propose the use of sound spectrograms and their differential features to construct multi-channel image… More >

  • Open Access


    Human Action Recognition Based on Supervised Class-Specific Dictionary Learning with Deep Convolutional Neural Network Features

    Binjie Gu1, *, Weili Xiong1, Zhonghu Bai2

    CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 243-262, 2020, DOI:10.32604/cmc.2020.06898

    Abstract Human action recognition under complex environment is a challenging work. Recently, sparse representation has achieved excellent results of dealing with human action recognition problem under different conditions. The main idea of sparse representation classification is to construct a general classification scheme where the training samples of each class can be considered as the dictionary to express the query class, and the minimal reconstruction error indicates its corresponding class. However, how to learn a discriminative dictionary is still a difficult work. In this work, we make two contributions. First, we build a new and robust human action recognition framework by combining… More >

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