Yasemin Onal^*

Computer Systems Science and Engineering, Vol.41, No.1, pp. 141-156, 2022, DOI:10.32604/csse.2022.020367 - 08 October 2021

Abstract Predicting the power obtained at the output of the photovoltaic (PV) system is fundamental for the optimum use of the PV system. However, it varies at different times of the day depending on intermittent and nonlinear environmental conditions including solar irradiation, temperature and the wind speed, Short-term power prediction is vital in PV systems to reconcile generation and demand in terms of the cost and capacity of the reserve. In this study, a Gaussian kernel based Support Vector Regression (SVR) prediction model using multiple input variables is proposed for estimating the maximum power obtained from… More >

P. Vinayagam^1,*, P. Anandan², N. Kumaratharan³

Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 869-879, 2021, DOI:10.32604/iasc.2021.016761 - 20 August 2021

Abstract Recent advances in the development of image denoising applications for eliminating the various sources of noise in digital images have employed hardware platforms based on field programmable gate arrays for attaining speed and efficiency, which are essential factors in real-time applications. However, image denoising providing for maximum denoising performance, speed, and efficiency on these platforms is subject to constant innovation. To this end, the present work proposes a high-throughput fixed-point adaptive edge noise filter architecture to denoise digital images with additive white Gaussian noise in realtime using a nonlinear modified pixel-likeness weighted-frame technique. The proposed More >

Dejia Shi¹, Hanzhong Zheng^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.1, pp. 161-181, 2021, DOI:10.32604/cmes.2021.014917 - 28 June 2021

Abstract ICU patients are vulnerable to medications, especially infusion medications, and the rate and dosage of infusion drugs may worsen the condition. The mortality prediction model can monitor the real-time response of patients to drug treatment, evaluate doctors’ treatment plans to avoid severe situations such as inverse Drug-Drug Interactions (DDI), and facilitate the timely intervention and adjustment of doctor’s treatment plan. The treatment process of patients usually has a time-sequence relation (which usually has the missing data problem) in patients’ treatment history. The state-of-the-art method to model such time-sequence is to use Recurrent Neural Network (RNN).… More >

Masoud Muhammed Hassan¹, Adel Sabry Eesa^1,*, Ahmed Jameel Mohammed², Wahab Kh. Arabo¹

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 451-469, 2021, DOI:10.32604/cmc.2021.018280 - 04 June 2021

Abstract Learning from imbalanced data is one of the greatest challenging problems in binary classification, and this problem has gained more importance in recent years. When the class distribution is imbalanced, classical machine learning algorithms tend to move strongly towards the majority class and disregard the minority. Therefore, the accuracy may be high, but the model cannot recognize data instances in the minority class to classify them, leading to many misclassifications. Different methods have been proposed in the literature to handle the imbalance problem, but most are complicated and tend to simulate unnecessary noise. In this More >

Taher M. Ghazal^1,2, Marrium Anam³, Mohammad Kamrul Hasan¹, Muzammil Hussain^4,*, Muhammad Sajid Farooq⁵, Hafiz Muhammad Ammar Ali⁴, Munir Ahmad⁶, Tariq Rahim Soomro⁷

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 191-203, 2021, DOI:10.32604/cmc.2021.015436 - 04 June 2021

Abstract Hepatitis C is a contagious blood-borne infection, and it is mostly asymptomatic during the initial stages. Therefore, it is difficult to diagnose and treat patients in the early stages of infection. The disease’s progression to its last stages makes diagnosis and treatment more difficult. In this study, an AI system based on machine learning algorithms is presented to help healthcare professionals with an early diagnosis of hepatitis C. The dataset used for our Hep-Pred model is based on a literature study, and includes the records of 1385 patients infected with the hepatitis C virus. Patients More >

Annan Jiang^*, Xinping Guo, Shuai Zheng, Mengfei Xu

CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.3, pp. 1177-1199, 2021, DOI:10.32604/cmes.2021.014199 - 24 May 2021

Abstract Due to the geological body uncertainty, the identification of the surrounding rock parameters in the tunnel construction process is of great significance to the calculation of tunnel stability. The ubiquitous-joint model and three-dimensional numerical simulation have advantages in the parameter identification of surrounding rock with weak planes, but conventional methods have certain problems, such as a large number of parameters and large time consumption. To solve the problems, this study combines the orthogonal design, Gaussian process (GP) regression, and difference evolution (DE) optimization, and it constructs the parameters identification method of the jointed surrounding rock.… More >

Xueping Fan^*, Guanghong Yang, Zhipeng Shang, Xiaoxiong Zhao, Yuefei Liu^*

Structural Durability & Health Monitoring, Vol.15, No.1, pp. 69-83, 2021, DOI:10.32604/sdhm.2021.011922 - 22 March 2021

Abstract This article presented a new data fusion approach for reasonably predicting dynamic serviceability reliability of the long-span bridge girder. Firstly, multivariate Bayesian dynamic linear model (MBDLM) considering dynamic correlation among the multiple variables is provided to predict dynamic extreme deflections; secondly, with the proposed MBDLM, the dynamic correlation coefficients between any two performance functions can be predicted; finally, based on MBDLM and Gaussian copula technique, a new data fusion method is given to predict the serviceability reliability of the long-span bridge girder, and the monitoring extreme deflection data from an actual bridge is provided to More >

Dah-Jing Jwo^*, Jui-Tao Lee

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 857-876, 2021, DOI:10.32604/cmc.2021.016894 - 22 March 2021

Abstract This paper investigates the kernel entropy based extended Kalman filter (EKF) as the navigation processor for the Global Navigation Satellite Systems (GNSS), such as the Global Positioning System (GPS). The algorithm is effective for dealing with non-Gaussian errors or heavy-tailed (or impulsive) interference errors, such as the multipath. The kernel minimum error entropy (MEE) and maximum correntropy criterion (MCC) based filtering for satellite navigation system is involved for dealing with non-Gaussian errors or heavy-tailed interference errors or outliers of the GPS. The standard EKF method is derived based on minimization of mean square error (MSE)… More >

Farouq Mohammad A. Alam¹, Sharifah Alrajhi¹, Mazen Nassar^1,2, Ahmed Z. Afify^3,*

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2185-2202, 2021, DOI:10.32604/cmc.2021.015089 - 05 February 2021

Abstract The main objective of this paper is to discuss a general family of distributions generated from the symmetrical arcsine distribution. The considered family includes various asymmetrical and symmetrical probability distributions as special cases. A particular case of a symmetrical probability distribution from this family is the Arcsine–Gaussian distribution. Key statistical properties of this distribution including quantile, mean residual life, order statistics and moments are derived. The Arcsine–Gaussian parameters are estimated using two classical estimation methods called moments and maximum likelihood methods. A simulation study which provides asymptotic distribution of all considered point estimators, 90% and… More >

Dah-Jing Jwo^1,*, Chien-Hao Tseng²

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1555-1575, 2021, DOI:10.32604/cmc.2021.014875 - 05 February 2021

Abstract This paper evaluates the state estimation performance for processing nonlinear/non-Gaussian systems using the cubature particle filter (CPF), which is an estimation algorithm that combines the cubature Kalman filter (CKF) and the particle filter (PF). The CPF is essentially a realization of PF where the third-degree cubature rule based on numerical integration method is adopted to approximate the proposal distribution. It is beneficial where the CKF is used to generate the importance density function in the PF framework for effectively resolving the nonlinear/non-Gaussian problems. Based on the spherical-radial transformation to generate an even number of equally… More >