M. R. Hematiyan^1,*, B. Jamshidi¹, M. Mohammadi²

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1349-1369, 2022, DOI:10.32604/cmes.2022.018235 - 30 December 2021

Abstract The method of fundamental solutions (MFS) is a boundary-type and truly meshfree method, which is recognized as an efficient numerical tool for solving boundary value problems. The geometrical shape, boundary conditions, and applied loads can be easily modeled in the MFS. This capability makes the MFS particularly suitable for shape optimization, moving load, and inverse problems. However, it is observed that the standard MFS lead to inaccurate solutions for some elastostatic problems with stress concentration and/or highly anisotropic materials. In this work, by a numerical study, the important parameters, which have significant influence on the… More >

J. Haritha^1,*, R. S. Valarmathi², M. Kalamani³

Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1867-1880, 2022, DOI:10.32604/iasc.2022.022547 - 09 December 2021

Abstract Urea is the most common fertilizer used by the farmers. In this study, the variation of mid-infrared transmittance spectra with addition of urea in soil was studied for five different concentrations of urea. 150 gm of soil is taken and dried in a hot air oven for 5 h at 80°C and then samples are prepared by adding urea and water to it. The spectral signature of soil with urea is obtained by using an Infrared Spectrometer that reads the spectra in the mid infra-red region. The analysis is done using Partial Least Square Regression… More >

Walid Aydi^1,3,*, Fuad S. Alduais^2,4

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 4033-4050, 2022, DOI:10.32604/cmc.2022.023119 - 07 December 2021

Abstract The Weibull distribution is regarded as among the finest in the family of failure distributions. One of the most commonly used parameters of the Weibull distribution (WD) is the ordinary least squares (OLS) technique, which is useful in reliability and lifetime modeling. In this study, we propose an approach based on the ordinary least squares and the multilayer perceptron (MLP) neural network called the OLSMLP that is based on the resilience of the OLS method. The MLP solves the problem of heteroscedasticity that distorts the estimation of the parameters of the WD due to the… More >

Yi Liang^1,2, Haichao Wang^3,*, Wei-Chiang Hong⁴

Energy Engineering, Vol.119, No.1, pp. 163-188, 2022, DOI:10.32604/EE.2022.017396 - 22 November 2021

Abstract Photovoltaics (PV) has been combined with many other industries, such as agriculture. But there are many problems for the sustainability of PV agriculture. Timely and accurate sustainability evaluation of modern photovoltaic agriculture is of great significance for accelerating the sustainable development of modern photovoltaic agriculture. In order to improve the timeliness and accuracy of evaluation, this paper proposes an evaluation model based on interval type-2 Fuzzy AHP-TOPSIS and least squares support vector machine optimized by fireworks algorithm. Firstly, the criteria system of modern photovoltaic agriculture sustainability is constructed from three dimensions including technology sustainability, economic… More >

I. Kalphana^1,*, T. Kesavamurthy²

Computer Systems Science and Engineering, Vol.41, No.1, pp. 171-185, 2022, DOI:10.32604/csse.2022.019799 - 08 October 2021

Abstract Higher transmission rate is one of the technological features of prominently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among an effective solution for channel estimation in wireless communication system, specifically in different environments is Deep Learning (DL) method. This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifier is one among Deep Learning (DL) algorithm, in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal More >

Mohd Ahmed^1,*, Devender Singh², Saeed Al Qadhi¹, Nguyen Viet Thanh³

CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.1, pp. 167-189, 2021, DOI:10.32604/cmes.2021.014672 - 24 August 2021

Abstract The performance of a-posteriori error methodology based on moving least squares (MLS) interpolation is explored in this paper by varying the finite element error recovery parameters, namely recovery points and field variable derivatives recovery. The MLS interpolation based recovery technique uses the weighted least squares method on top of the finite element method's field variable derivatives solution to build a continuous field variable derivatives approximation. The boundary of the node support (mesh free patch of influenced nodes within a determined distance) is taken as circular, i.e., circular support domain constructed using radial weights is considered. The… More >

Gamal M. Ibrahim¹, Amal S. Hassan², Ehab M. Almetwally^3,*, Hisham M. Almongy⁴

Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 353-371, 2021, DOI:10.32604/iasc.2021.017586 - 16 June 2021

Abstract We introduce a new two-parameter lifetime model, referred to alpha power transformed inverted Topp-Leone, derived by combining the alpha power transformation-G family with the inverted Topp-Leone distribution. Structural properties of the proposed distribution are implemented like; quantile function, residual and reversed residual life, Rényi entropy measure, moments and incomplete moments. The maximum likelihood, weighted least squares, maximum product of spacing, and Bayesian methods of estimation are considered. A simulation study is worked out to evaluate the restricted sample properties of the proposed distribution. Numerical results showed that the Bayesian estimates give more accurate results than… More >

Gang Yu^1,4,5, Jian Tang^2,*, Jian Zhang³, Zhonghui Wang⁶

Intelligent Automation & Soft Computing, Vol.29, No.1, pp. 273-290, 2021, DOI:10.32604/iasc.2021.016981 - 12 May 2021

Abstract Kernel learning based on structure risk minimum can be employed to build a soft measuring model for analyzing small samples. However, it is difficult to select learning parameters, such as kernel parameter (KP) and regularization parameter (RP). In this paper, a soft measuring method is investigated to select learning parameters, which is based on adaptive multi-layer selective ensemble (AMLSEN) and least-square support vector machine (LSSVM). First, candidate kernels and RPs with K and R numbers are preset based on prior knowledge, and candidate sub-sub-models with K*R numbers are constructed through utilizing LSSVM. Second, the candidate More >

Ibrahim M. Almanjahie^1,2, Amer Ibrahim Al-Omari^3,*, Emmanuel J. Ekpenyong⁴, Mir Subzar⁵

Computer Systems Science and Engineering, Vol.38, No.1, pp. 1-15, 2021, DOI:10.32604/csse.2021.015933 - 01 April 2021

Abstract In estimation theory, the researchers have put their efforts to develop some estimators of population mean which may give more precise results when adopting ordinary least squares (OLS) method or robust regression techniques for estimating regression coefficients. But when the correlation is negative and the outliers are presented, the results can be distorted and the OLS-type estimators may give misleading estimates or highly biased estimates. Hence, this paper mainly focuses on such issues through the use of non-conventional measures of dispersion and a robust estimation method. Precisely, we have proposed generalized estimators by using the… More >

Haibo Liu^1,*, Yujie Dong², Fuzhong Wang¹

Energy Engineering, Vol.118, No.3, pp. 679-689, 2021, DOI:10.32604/EE.2021.014630 - 22 March 2021

Abstract For the problems of nonlinearity, uncertainty and low prediction accuracy in the gas outburst prediction of coal mining face, the least squares support vector machine (LSSVM) is proposed to establish the prediction model. Firstly, considering the inertia coefficients as global parameters lacks the ability to improve the solution for the traditional particle swarm optimization (PSO), an improved PSO (IPSO) algorithm is introduced to adjust different inertia weights in updating the particle swarm and solve the fitness to stagnate. Secondly, the penalty factor and kernel function parameter of LSSVM are searched automatically, and the regression accuracy More >