Baoyin Sun^{1, 2}, Yantai Zhang³, Caigui Huang^{4, *}

CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 755-776, 2020, DOI:10.32604/cmes.2020.09632

Abstract Steel frames equipped with buckling restrained braces (BRBs) have been increasingly applied in earthquake-prone areas given their excellent capacity for resisting lateral forces. Therefore, special attention has been paid to the seismic risk assessment (SRA) of such structures, e.g., seismic fragility analysis. Conventional approaches, e.g., nonlinear finite element simulation (NFES), are computationally inefficient for SRA analysis particularly for large-scale steel BRB frame structures. In this study, a machine learning (ML)- based seismic fragility analysis framework is established to effectively assess the risk to structures under seismic loading conditions. An optimal artificial neural network model can be trained using calculated damage… More >

B. Indira^1,*, K. Valarmathi²

Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 795-805, 2020, DOI:10.32604/iasc.2020.010114

Abstract Packet classification is a major bottleneck in Software Defined Network (SDN). Each packet has to be classified based on the action specified in each rule in the given flow table. To perform classification, the system requires much of the CPU clock time. Therefore, developing an efficient packet classification algorithm is critical for high speed inter networking. Existing works make use of exact matching, range matching and longest prefix matching for classification and these techniques sometime enlarges rule databases, thus resulting in huge memory consumption and inefficient searching performance. In order to select an efficient packet classification algorithm with less memory… More >

DONG ZHAO, QIANG JI^*, SHIJIE ZHU, KAI ZHU, CHUNSHENG WANG^*

BIOCELL, Vol.44, No.3, pp. 315-321, 2020, DOI:10.32604/biocell.2020.09169

Abstract Except for the standard aortic valve replacement, no effective medical treatment is available to prevent or delay calcific aortic valve disease (CAVD) progression. Recently, macrophages and high-mobility group box 1 (HMGB1) are the most intriguing candidates in various inflammatory disorders. However, the association between serum HMGB1, CAVD, and macrophage polarization remains unclear. Therefore, we examined whether the level of serum HMGB1 is clinically associated with aortic valve calcification and whether HMGB1 treatment can promote macrophage differentiation toward M1 or M2 phenotype. This experimental study included 19 CAVD patients and 20 healthy controls whose serum HMGB1 levels were examined by ELISA… More >

Chaosheng Tang¹, Deepak Ranjan Nayak², Shuihua Wang^1,3,4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 299-313, 2020, DOI:10.32604/cmes.2020.011069

Abstract Hearing loss (HL) is a kind of common illness, which can significantly reduce the quality of life. For example, HL often results in mishearing, misunderstanding, and communication problems. Therefore, it is necessary to provide early diagnosis and timely treatment for HL. This study investigated the advantages and disadvantages of three classical machine learning methods: multilayer perceptron (MLP), support vector machine (SVM), and least-square support vector machine (LS-SVM) approach and made a further optimization of the LS-SVM model via wavelet entropy. The investigation illustrated that themultilayer perceptron is a shallowneural network,while the least square support vector machine uses hinge loss function… More >

Jia Yang¹, Haiyuan Liu^1,*, Hao He²

CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 111-123, 2020, DOI:10.32604/cmes.2020.010347

Abstract The prediction of intrinsically disordered proteins is a hot research area in bio-information. Due to the high cost of experimental methods to evaluate disordered regions of protein sequences, it is becoming increasingly important to predict those regions through computational methods. In this paper, we developed a novel scheme by employing sequence complexity to calculate six features for each residue of a protein sequence, which includes the Shannon entropy, the topological entropy, the sample entropy and three amino acid preferences including Remark 465, Deleage/Roux, and Bfactor(2STD). Particularly, we introduced the sample entropy for calculating time series complexity by mapping the amino… More >

Yongqiang Bao^{1, *}, Qi Shao¹, Xuxu Zhang¹, Jiahui Jiang¹, Yue Xie¹, Tingting Liu¹, Weiye Xu²

CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2557-2570, 2020, DOI:10.32604/cmc.2020.011241

Abstract The field of digital audio forensics aims to detect threats and fraud in audio signals. Contemporary audio forensic techniques use digital signal processing to detect the authenticity of recorded speech, recognize speakers, and recognize recording devices. User-generated audio recordings from mobile phones are very helpful in a number of forensic applications. This article proposed a novel method for recognizing recording devices based on recorded audio signals. First, a database of the features of various recording devices was constructed using 32 recording devices (20 mobile phones of different brands and 12 kinds of recording pens) in various environments. Second, the audio… More >

Xiaoyu Li¹, Qinsheng Zhu^{2, *}, Yiming Huang¹, Yong Hu², Qingyu Meng², Chenjing Su¹, Qing Yang², Shaoyi Wu², Xusheng Liu³

CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2143-2151, 2020, DOI:10.32604/cmc.2020.010865

Abstract Quantum correlation shows a fascinating nature of quantum mechanics and plays an important role in some physics topics, especially in the field of quantum information. Quantum correlations of the composite system can be quantified by resorting to geometric or entropy methods, and all these quantification methods exhibit the peculiar freezing phenomenon. The challenge is to find the characteristics of the quantum states that generate the freezing phenomenon, rather than only study the conditions which generate this phenomenon under a certain quantum system. In essence, this is a classification problem. Machine learning has become an effective method for researchers to study… More >

Li Li^1,*, Yifei Wei¹, Lianping Zhang², Xiaojun Wang³

Journal of Cyber Security, Vol.2, No.3, pp. 141-150, 2020, DOI:10.32604/jcs.2020.010764

Abstract The rapid growth of Internet content, applications and services require more computing and storage capacity and higher bandwidth. Traditionally, internet services are provided from the cloud (i.e., from far away) and consumed on increasingly smart devices. Edge computing and caching provides these services from nearby smart devices. Blending both approaches should combine the power of cloud services and the responsiveness of edge networks. This paper investigates how to intelligently use the caching and computing capabilities of edge nodes/cloudlets through the use of artificial intelligence-based policies. We first analyze the scenarios of mobile edge networks with edge computing and caching abilities,… More >

Shuo He^1,∗, Jianbin Zheng^1,†, Jiale Lin^2,‡, Tao Tang^1,§, Jintao Zhao^1,¶, Hongbao Liu^1,ll

Computer Systems Science and Engineering, Vol.35, No.3, pp. 141-149, 2020, DOI:10.32604/csse.2020.35.141

Abstract Nowadays, many payment service providers use the discounts and other marketing strategies to promote their products. This also raises the issue of people who deliberately take advantage of such promotions to reap financial benefits. These people are known as ‘scalper parties’ or ‘econnoisseurs’ which can constitute an underground industry. In this paper, we show how to use machine learning to assist in identifying abnormal scalper transactions. Moreover, we introduce the basic methods of Decision Tree and Boosting Tree, and show how these classification methods can be applied in the detection of abnormal transactions. In addition, we introduce a graph computing… More >

Marius Becherer^∗, Michael Zipperle^†, Achim Karduck^‡

Computer Systems Science and Engineering, Vol.35, No.2, pp. 81-89, 2020, DOI:10.32604/csse.2020.35.081

Abstract Machines are serviced too often or only when they fail. This can result in high costs for maintenance and machine failure. The trend of Industry 4.0 and the networking of machines opens up new possibilities for maintenance. Intelligent machines provide data that can be used to predict the ideal time of maintenance. There are different approaches to create a forecast. Depending on the method used, appropriate conditions must be created to improve the forecast. In this paper, results are compiled to give a state of the art of predictive maintenance. First, the different types of maintenance and economic relationships are… More >