Lili Pan¹, Cong Li^{1, *}, Samira Pouyanfar², Rongyu Chen¹, Yan Zhou¹

CMC-Computers, Materials & Continua, Vol.62, No.2, pp. 731-746, 2020, DOI:10.32604/cmc.2020.06508

Abstract With the development of deep learning and Convolutional Neural Networks (CNNs), the accuracy of automatic food recognition based on visual data have significantly improved. Some research studies have shown that the deeper the model is, the higher the accuracy is. However, very deep neural networks would be affected by the overfitting problem and also consume huge computing resources. In this paper, a new classification scheme is proposed for automatic food-ingredient recognition based on deep learning. We construct an up-to-date combinational convolutional neural network (CBNet) with a subnet merging technique. Firstly, two different neural networks are… More >

Xing Deng^{1, 2}, Haijian Shao^{1, *}, Chunlong Hu¹, Dengbiao Jiang¹, Yingtao Jiang³

CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.1, pp. 273-301, 2020, DOI:10.32604/cmes.2020.08768 - 01 January 2020

Abstract Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved… More >

Hee-Jin Yoon¹, Kang-Jik Kim¹, Jun-Chul Chun^1,*

CMC-Computers, Materials & Continua, Vol.62, No.1, pp. 459-472, 2020, DOI:10.32604/cmc.2020.08799

Abstract Recently, virtual reality technology that can interact with various data is used for urban design and analysis. Reality, one of the most important elements in virtual reality technology, means visual expression so that a person can experience threedimensional space like reality. To obtain this realism, real-world data are used in the various fields. For example, in order to increase the realism of 3D modeled building textures real aerial images are utilized in 3D modelling. However, the aerial image captured during the day can be shadowed by the sun and it can cause the distortion or… More >

Chaochao Luo¹, Shen Su^{2, *}, Yanbin Sun², Qingji Tan³, Meng Han⁴, Zhihong Tian^{2, *}

CMC-Computers, Materials & Continua, Vol.62, No.1, pp. 399-411, 2020, DOI:10.32604/cmc.2020.06507

Abstract Since the web service is essential in daily lives, cyber security becomes more and more important in this digital world. Malicious Uniform Resource Locator (URL) is a common and serious threat to cybersecurity. It hosts unsolicited content and lure unsuspecting users to become victim of scams, such as theft of private information, monetary loss, and malware installation. Thus, it is imperative to detect such threats. However, traditional approaches for malicious URLs detection that based on the blacklists are easy to be bypassed and lack the ability to detect newly generated malicious URLs. In this paper, More >

Shiming He¹, Zhuozhou Li¹, Yangning Tang¹, Zhuofan Liao¹, Feng Li^{1, *}, 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 >

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 >