Zhongping Chen¹, Dong Wang^{1, *}, Gang Chen², Yanxi Ren³, Danjie Du⁴

CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1781-1793, 2020, DOI:10.32604/cmc.2020.010902

Abstract Due to the unique steering mechanism and driving characteristics of the articulated vehicle, a hybrid path planning method based on the articulated vehicle model is proposed to meet the demand of obstacle avoidance and searching the path back and forth of the articulated vehicle. First, Support Vector Machine (SVM) theory is used to obtain the two-dimensional optimal zero potential curve and the maximum margin, and then, several key points are selected from the optimal zero potential curves by using Longest Accessible Path (LAP) method. Next, the Cubic Bezier (CB) curve is adopted to connect the curve that satisfies the curvature… More >

Bao Le Nguyen¹, E. Laxmi Lydia², Mohamed Elhoseny³, Irina V. Pustokhina⁴, Denis A. Pustokhin⁵, Mahmoud Mohamed Selim⁶, Gia Nhu Nguyen^{7, 8}, K. Shankar^{9, *}

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 87-107, 2020, DOI:10.32604/cmc.2020.011599

Abstract In present digital era, an exponential increase in Internet of Things (IoT) devices poses several design issues for business concerning security and privacy. Earlier studies indicate that the blockchain technology is found to be a significant solution to resolve the challenges of data security exist in IoT. In this view, this paper presents a new privacy-preserving Secure Ant Colony optimization with Multi Kernel Support Vector Machine (ACOMKSVM) with Elliptical Curve cryptosystem (ECC) for secure and reliable IoT data sharing. This program uses blockchain to ensure protection and integrity of some data while it has the technology to create secure ACOMKSVM… More >

Dingwen Wang, Ming Zhao^*

Journal of Cyber Security, Vol.2, No.2, pp. 63-68, 2020, DOI:10.32604/jcs.2020.07229

Abstract Mobile crowdsensing is a new paradigm with powerful performance for data collection through a large number of smart devices. It is essential to obtain high quality data in crowdsensing campaign. Most of the existing specs ignore users’ diversity, focus on solving complicated optimization problem, and consider devices as instances of intelligent software agents which can make reasonable choices on behalf of users. Thus, the efficiency and quality of contributed data cannot be preserved simultaneously. In this paper, we propose a new scheme for improving the quality of contributed data, which recommends tasks to users based on calculated score that jointly… More >

Hossein ElahiShirvan¹, MohammadReza Ghotbi-Ravandi², Sajad Zare^3,*, Mostafa Ghazizadeh Ahsaee⁴

Sound & Vibration, Vol.54, No.2, pp. 99-112, 2020, DOI:10.32604/sv.2020.08839

Abstract Workers’ exposure to excessive noise is a big universal work-related challenges. One of the major consequences of exposure to noise is permanent or transient hearing loss. The current study sought to utilize audiometric data to weigh and prioritize the factors affecting workers’ hearing loss based using the Support Vector Machine (SVM) algorithm. This cross sectional-descriptive study was conducted in 2017 in a mining industry in southeast Iran. The participating workers (n = 150) were divided into three groups of 50 based on the sound pressure level to which they were exposed (two experimental groups and one control group). Audiometric tests… More >

Amin Bemani¹, Alireza Baghban², Shahaboddin Shamshirband^{3, 4, *}, Amir Mosavi^{5, 6, 7}, Peter Csiba⁷, Annamaria R. Varkonyi-Koczy^{5, 7}

CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1175-1204, 2020, DOI:10.32604/cmc.2020.07723

Abstract In the present work, a novel machine learning computational investigation is carried out to accurately predict the solubility of different acids in supercritical carbon dioxide. Four different machine learning algorithms of radial basis function, multi-layer perceptron (MLP), artificial neural networks (ANN), least squares support vector machine (LSSVM) and adaptive neuro-fuzzy inference system (ANFIS) are used to model the solubility of different acids in carbon dioxide based on the temperature, pressure, hydrogen number, carbon number, molecular weight, and the dissociation constant of acid. To evaluate the proposed models, different graphical and statistical analyses, along with novel sensitivity analysis, are carried out.… More >

Jianhua Dong¹, Lifeng Wu², Xiaogang Liu^{1, *}, Cheng Fan¹, Menghui Leng³, Qiliang Yang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.123, No.1, pp. 49-73, 2020, DOI: 10.32604/cmes.2020.09014

Abstract Solar radiation is an important parameter in the fields of computer modeling, engineering technology and energy development. This paper evaluated the ability of three machine learning models, i.e., Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM) and Multivariate Adaptive Regression Splines (MARS), to estimate the daily diffuse solar radiation (R_d). The regular meteorological data of 1966-2015 at five stations in China were taken as the input parameters (including mean average temperature (T_a), theoretical sunshine duration (N), actual sunshine duration (n), daily average air relative humidity (RH), and extra-terrestrial solar radiation (R_a)). And their estimation accuracies were subjected to comparative analysis.… More >

Jiaqi Zhen^{1, *}

CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 445-455, 2020, DOI:10.32604/cmc.2020.06385

Abstract In array signal processing, number of signals is often a premise of estimating other parameters. For the sake of determining signal number in the condition of strong additive noise or a little sample data, an algorithm for detecting number of wideband signals is provided. First, technique of focusing is used for transforming signals into a same focusing subspace. Then the support vector machine (SVM) can be deduced by the information of eigenvalues and corresponding eigenvectors. At last, the signal number can be determined with the obtained decision function. Several simulations have been carried on verifying the proposed algorithm. More >

Jingmin Guo¹, Xiu Cheng¹, Duanpo Wu^{2, 3, *}

CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.2, pp. 721-741, 2020, DOI:10.32604/cmes.2020.07470

Abstract The grading of hypoxic-ischemic encephalopathy (HIE) contributes to the clinical decision making for neonates with HIE. In this paper, an automated grading method based on electroencephalogram (EEG) data is proposed to describe the severity of HIE infants, namely mild asphyxia, moderate asphyxia and severe asphyxia. The automated grading method is based on a multi-class support vector machine (SVM) classifier, and the input features of SVM classifier include long-term features which are extracted by decomposing the EEG data into different 64 s epoch data and short-term features which are extracted by segmenting the 64 s epoch data into 8 s epoch… More >

Fangming Bi^{1, 2}, Xuanyi Fu^{1, 2}, Wei Chen^{1, 2, 3, *}, Weidong Fang⁴, Xuzhi Miao^{1, 2}, Biruk Assefa^{1, 5}

CMC-Computers, Materials & Continua, Vol.62, No.1, pp. 199-216, 2020, DOI:10.32604/cmc.2020.06258

Abstract Aiming at the defects of the traditional fire detection methods, which are caused by false positives and false negatives in large space buildings, a fire identification detection method based on video images is proposed. The algorithm first uses the hybrid Gaussian background modeling method and the RGB color model to perform fire prejudgment on the video image, which can eliminate most non-fire interferences. Secondly, the traditional regional growth algorithm is improved and the fire image segmentation effect is effectively improved. Then, based on the segmented image, the dynamic and static features of the fire flame are further analyzed and extracted… More >

Yixiong Yu^*

Sound & Vibration, Vol.53, No.5, pp. 237-243, 2019, DOI:10.32604/sv.2019.07887

Abstract Mining aeroengine operational data and developing fault diagnosis models for aeroengines are to avoid running aeroengines under undesired conditions. Because of the complexity of working environment and faults of aeroengines, it is unavoidable that the monitored parameters vary widely and possess larger noise levels. This paper reports the extrapolation of a diagnosis model for 20 gas path faults of a double-spool turbofan civil aeroengine. By applying support vector machine (SVM) algorithm together with genetic algorithm (GA), the fault diagnosis model is obtained from the training set that was based on the deviations of the monitored parameters superimposed with the noise… More >