Masahiko Demura^1,*, Junya Sakurai^1,2, Masayoshi Yamazaki¹, Junya Inoue^1,2

The International Conference on Computational & Experimental Engineering and Sciences, Vol.22, No.2, pp. 123-123, 2019, DOI:10.32604/icces.2019.05303

Abstract Creep is a complicated and time-dependent phenomenon, which is affected by the initial state and the degradation of microstructures. It is thus considered that the information about the microstructure is essential to predict the creep rupture time. On the other hand, there is a strong, practical need for the prediction without the investigation of microstructures nor the disclosure of the detailed process that should control the initial microstructures. In this study, we examined how modern machine learning technique can help to predict the creep rupture time in heat-resistant ferrite-type steels without the direct information about… More >

Qiang Liu^1,*, Yanyun Zou^2,3, Xiaodong Liu⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.3, pp. 617-637, 2019, DOI:10.32604/cmes.2019.06272

Abstract Haze-fog, which is an atmospheric aerosol caused by natural or man-made factors, seriously affects the physical and mental health of human beings. PM_2.5 (a particulate matter whose diameter is smaller than or equal to 2.5 microns) is the chief culprit causing aerosol. To forecast the condition of PM_2.5, this paper adopts the related the meteorological data and air pollutes data to predict the concentration of PM_2.5. Since the meteorological data and air pollutes data are typical time series data, it is reasonable to adopt a machine learning method called Single Hidden-Layer Long Short-Term Memory Neural Network (SSHL-LSTMNN)… More >

Yuxi Jia¹, Feng Li^1,2, Fei Wang^1,2,*, Yan Gui^1,2,3

Journal of Information Hiding and Privacy Protection, Vol.1, No.1, pp. 11-21, 2019, DOI:10.32604/jihpp.2019.05979

Abstract The Brain-Computer Interfaces (BCIs) had been proposed and used in therapeutics for decades. However, the need of time-consuming calibration phase and the lack of robustness, which are caused by little-labeled data, are restricting the advance and application of BCI, especially for the BCI based on motor imagery (MI). In this paper, we reviewed the recent development in the machine learning algorithm used in the MI-based BCI, which may provide potential solutions for addressing the issue. We classified these algorithms into two categories, namely, and enhancing the representation and expanding the training set. Specifically, these methods More >

A. Joshuva¹, V. Sugumaran²

Structural Durability & Health Monitoring, Vol.13, No.2, pp. 181-203, 2019, DOI:10.32604/sdhm.2019.00287

Abstract Wind turbine blades are generally manufactured using fiber type material because of their cost effectiveness and light weight property however, blade get damaged due to wind gusts, bad weather conditions, unpredictable aerodynamic forces, lightning strikes and gravitational loads which causes crack on the surface of wind turbine blade. It is very much essential to identify the damage on blade before it crashes catastrophically which might possibly destroy the complete wind turbine. In this paper, a fifteen tree classification based machine learning algorithms were modelled for identifying and detecting the crack on wind turbine blades. The More >

Maha Al-Yahya^{1, *}

CMC-Computers, Materials & Continua, Vol.60, No.2, pp. 421-433, 2019, DOI:10.32604/cmc.2019.06209

Abstract The purpose of this study is to evaluate the performance of five supervised machine learning methods for the task of automated genre identification of classical Arabic texts using text most frequent words as features. We design an experiment for comparing five machine-learning methods for the genre identification task for classical Arabic text. We set the data and the stylometric features and vary the classification method to evaluate the performance of each method. Of the five machine learning methods tested, we can conclude that Support Vector Machine (SVM) are generally the most effective. The contribution of More >

Hangjun Zhou^1,2,*, Guang Sun^1,3, Sha Fu¹, Wangdong Jiang¹, Juan Xue¹

CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 179-192, 2019, DOI:10.32604/cmc.2019.05214

Abstract With the rapid development of mobile Internet and finance technology, online e-commerce transactions have been increasing and expanding very fast, which globally brings a lot of convenience and availability to our life, but meanwhile, chances of committing frauds also come in all shapes and sizes. Moreover, fraud detection in online e-commerce transactions is not totally the same to that in the existing areas due to the massive amounts of data generated in e-commerce, which makes the fraudulent transactions more covertly scattered with genuine transactions than before. In this article, a novel scalable and comprehensive approach More >

Assia Maamar^1,*, 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 >

Alanoud Alhussain¹, Heba Kurdi^1,*, Lina Altoaimy²

CMC-Computers, Materials & Continua, Vol.59, No.3, pp. 805-815, 2019, DOI:10.32604/cmc.2019.05848

Abstract Edge devices in Internet of Things (IoT) applications can form peers to communicate in peer-to-peer (P2P) networks over P2P protocols. Using P2P networks ensures scalability and removes the need for centralized management. However, due to the open nature of P2P networks, they often suffer from the existence of malicious peers, especially malicious peers that unite in groups to raise each other's ratings. This compromises users' safety and makes them lose their confidence about the files or services they are receiving. To address these challenges, we propose a neural network-based algorithm, which uses the advantages of More >

Dae-Young Kim¹, Seokhoon Kim^2,*

CMC-Computers, Materials & Continua, Vol.59, No.2, pp. 375-387, 2019, DOI:10.32604/cmc.2019.06077

Abstract Communication is important for providing intelligent services in connected vehicles. Vehicles must be able to communicate with different places and exchange information while driving. For service operation, connected vehicles frequently attempt to download large amounts of data. They can request data downloading to a road side unit (RSU), which provides infrastructure for connected vehicles. The RSU is a data bottleneck in a transportation system because data traffic is concentrated on the RSU. Therefore, it is not appropriate for a connected vehicle to always attempt a high speed download from the RSU. If the mobile network… More >

Khader M. Hamdia², Hamid Ghasemi³, Xiaoying Zhuang^4,5, Naif Alajlan¹, Timon Rabczuk^1,2,*

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 >