Huanrong Tang¹, Aoming Peng¹, Dongming Zhang², Tianming Liu³, Jianquan Ouyang^{1, *}

CMC-Computers, Materials & Continua, Vol.62, No.1, pp. 293-307, 2020, DOI:10.32604/cmc.2020.06427

Abstract With the improvement of the national economic level, the number of vehicles is still increasing year by year. According to the statistics of National Bureau of Statics, the number is approximately up to 327 million in China by the end of 2018, which makes urban traffic pressure continues to rise so that the negative impact of urban traffic order is growing. Illegal parking-the common problem in the field of transportation security is urgent to be solved and traditional methods to address it are mainly based on ground loop and manual supervision, which may miss detection… More >

Yabin Xu^{1, 2, *}, Xuyang Meng¹, Yangyang Li³, Xiaowei Xu^{4, *}

CMC-Computers, Materials & Continua, Vol.62, No.1, pp. 137-155, 2020, DOI:10.32604/cmc.2020.05825

Abstract In order to effectively detect the privacy that may be leaked through social networks and avoid unnecessary harm to users, this paper takes microblog as the research object to study the detection of privacy disclosure in social networks. First, we perform fast privacy leak detection on the currently published text based on the fastText model. In the case that the text to be published contains certain private information, we fully consider the aggregation effect of the private information leaked by different channels, and establish a convolution neural network model based on multi-dimensional features (MF-CNN) to More >

Youqin Huang¹, Jiayong Li¹, Jiyang Fu^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.121, No.3, pp. 845-875, 2019, DOI:10.32604/cmes.2019.07653

Abstract In last few years, big data and deep learning technologies have been successfully applied in various fields of civil engineering with the great progress of machine learning techniques. However, until now, there has been no comprehensive review on its applications in civil engineering. To fill this gap, this paper reviews the application and development of artificial intelligence in civil engineering in recent years, including intelligent algorithms, big data and deep learning. Through the work of this paper, the research direction and difficulties of artificial intelligence in civil engineering for the past few years can be More >

Wei Song^1,2,*, Jing Yu³, Xiaobing Zhao^1,2, Antai Wang⁴

CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1189-1204, 2019, DOI:10.32604/cmc.2019.06361

Abstract In the current era of multimedia information, it is increasingly urgent to realize intelligent video action recognition and content analysis. In the past few years, video action recognition, as an important direction in computer vision, has attracted many researchers and made much progress. First, this paper reviews the latest video action recognition methods based on Deep Neural Network and Markov Logic Network. Second, we analyze the characteristics of each method and the performance from the experiment results. Then compare the emphases of these methods and discuss the application scenarios. Finally, we consider and prospect the More >

Duyen Le Hien Nguyen¹, Dieu Thi Thanh Do², Jaehong Lee², Timon Rabczuk³, Hung Nguyen-Xuan^1,4,*

CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 951-977, 2019, DOI:10.32604/cmc.2019.08001

Abstract We in this paper exploit time series algorithm based deep learning in forecasting damage mechanics problems. The methodologies that are able to work accurately for less computational and resolving attempts are a significant demand nowadays. Relied on learning an amount of information from given data, the long short-term memory (LSTM) method and multi-layer neural networks (MNN) method are applied to predict solutions. Numerical examples are implemented for predicting fracture growth rates of L-shape concrete specimen under load ratio, single-edge-notched beam forced by 4-point shear and hydraulic fracturing in permeable porous media problems such as storage-toughness More >

Hyun Kyu Shin¹, Yong Han Ahn², Sang Hyo Lee³, Ha Young Kim^4,*

CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 911-928, 2019, DOI:10.32604/cmc.2019.08269

Abstract Compressive strength of concrete is a significant factor to assess building structure health and safety. Therefore, various methods have been developed to evaluate the compressive strength of concrete structures. However, previous methods have several challenges in costly, time-consuming, and unsafety. To address these drawbacks, this paper proposed a digital vision based concrete compressive strength evaluating model using deep convolutional neural network (DCNN). The proposed model presented an alternative approach to evaluating the concrete strength and contributed to improving efficiency and accuracy. The model was developed with 4,000 digital images and 61,996 images extracted from video More >

Yanzhen Wang¹, Xuefeng Yan^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.121, No.1, pp. 123-144, 2019, DOI:10.32604/cmes.2019.07052

Abstract A single model cannot satisfy the high-precision prediction requirements given the high nonlinearity between variables. By contrast, ensemble models can effectively solve this problem. Three key factors for improving the accuracy of ensemble models are namely the high accuracy of a submodel, the diversity between subsample sets and the optimal ensemble method. This study presents an improved ensemble modeling method to improve the prediction precision and generalization capability of the model. Our proposed method first uses a bagging algorithm to generate multiple subsample sets. Second, an indicator vector is defined to describe these subsample sets. More >

Kazuhiko Kakuda^1,*, Tomoyuki Enomoto¹, Shinichiro Miura²

CMES-Computer Modeling in Engineering & Sciences, Vol.118, No.1, pp. 1-14, 2019, DOI:10.31614/cmes.2019.04676

Abstract The nonlinear activation functions in the deep CNN (Convolutional Neural Network) based on fluid dynamics are presented. We propose two types of activation functions by applying the so-called parametric softsign to the negative region. We use significantly the well-known TensorFlow as the deep learning framework. The CNN architecture consists of three convolutional layers with the max-pooling and one fully-connected softmax layer. The CNN approaches are applied to three benchmark datasets, namely, MNIST, CIFAR-10, and CIFAR-100. Numerical results demonstrate the workability and the validity of the present approach through comparison with other numerical performances. More >

Xu Zhang¹, Wenpeng Lu^1,*, Fangfang Li^2,3, Xueping Peng³, Ruoyu Zhang¹

CMC-Computers, Materials & Continua, Vol.61, No.2, pp. 601-616, 2019, DOI:10.32604/cmc.2019.06045

Abstract Sentence semantic matching (SSM) is a fundamental research in solving natural language processing tasks such as question answering and machine translation. The latest SSM research benefits from deep learning techniques by incorporating attention mechanism to semantically match given sentences. However, how to fully capture the semantic context without losing significant features for sentence encoding is still a challenge. To address this challenge, we propose a deep feature fusion model and integrate it into the most popular deep learning architecture for sentence matching task. The integrated architecture mainly consists of embedding layer, deep feature fusion layer, More >

Tomoyuki Enomoto, Kazuhiko Kakuda, Shinichiro Miura

The International Conference on Computational & Experimental Engineering and Sciences, Vol.21, No.2, pp. 36-39, 2019, DOI:10.32604/icces.2019.05292

Abstract In this paper, the nonlinear activation functions based on fluid dynamics are presented. We propose two types of activation functions by applying the so-called parametric softsign to the negative region. We apply the activation function to CNN (Convolutional Neural Network) which performs image recognition and approaches from multiple benchmark datasets such as MNIST, CIFAR-10. Numerical results demonstrate the workability and the validity of the present approach through comparison with other numerical performances. More >