Monerah Alawadh^*, Ahmed Barnawi

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4995-5015, 2024, DOI:10.32604/cmc.2024.048762 - 20 June 2024

Abstract Association rule learning (ARL) is a widely used technique for discovering relationships within datasets. However, it often generates excessive irrelevant or ambiguous rules. Therefore, post-processing is crucial not only for removing irrelevant or redundant rules but also for uncovering hidden associations that impact other factors. Recently, several post-processing methods have been proposed, each with its own strengths and weaknesses. In this paper, we propose THAPE (Tunable Hybrid Associative Predictive Engine), which combines descriptive and predictive techniques. By leveraging both techniques, our aim is to enhance the quality of analyzing generated rules. This includes removing irrelevant… More >

Yuhao Yang¹, Yingjun Wang^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.26, No.2, pp. 1-1, 2023, DOI:10.32604/icces.2023.09068

Abstract In the intelligent structural optimization, designers can obtain a high-performance design scheme automatically with the help of topology optimization (TO). Since computer aided design (CAD) and computer aided engineering (CAE) models have different geometric representations in TO, the optimized results must be reconstructed to generate CAD models, which is complicated and time-consuming. To address this issue, the isogeometric analysis (IGA) is employed in TO to replace the finite element method (FEM), and such TO is termed as isogeometric TO (ITO). ITO is an advanced TO method with high efficiency and accuracy. It uses the same… More >

Yiming Zhang^1,*, Zhiran Gao¹, Xueya Wang¹, Qi Liu²

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 821-833, 2023, DOI:10.32604/cmes.2022.022088 - 31 August 2022

Abstract A large amount of data can partly assure good fitting quality for the trained neural networks. When the quantity of experimental or on-site monitoring data is commonly insufficient and the quality is difficult to control in engineering practice, numerical simulations can provide a large amount of controlled high quality data. Once the neural networks are trained by such data, they can be used for predicting the properties/responses of the engineering objects instantly, saving the further computing efforts of simulation tools. Correspondingly, a strategy for efficiently transferring the input and output data used and obtained in More >

B. Priestly Shan¹, O. Jeba Shiney¹, Sharzeel Saleem², V. Rajinikanth³, Atef Zaguia⁴, Dilbag Singh^5,*

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2749-2763, 2022, DOI:10.32604/cmc.2022.023057 - 29 March 2022

Abstract Low contrast of Magnetic Resonance (MR) images limits the visibility of subtle structures and adversely affects the outcome of both subjective and automated diagnosis. State-of-the-art contrast boosting techniques intolerably alter inherent features of MR images. Drastic changes in brightness features, induced by post-processing are not appreciated in medical imaging as the grey level values have certain diagnostic meanings. To overcome these issues this paper proposes an algorithm that enhance the contrast of MR images while preserving the underlying features as well. This method termed as Power-law and Logarithmic Modification-based Histogram Equalization (PLMHE) partitions the histogram More >

Ziqing Yan^1,2, Pengpeng Yang^1,2, Rongrong Ni^1,2,*, Yao Zhao^1,2, Hairong Qi³

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3205-3216, 2021, DOI:10.32604/cmc.2021.020324 - 24 August 2021

Abstract As one of the most popular digital image manipulations, contrast enhancement (CE) is frequently applied to improve the visual quality of the forged images and conceal traces of forgery, therefore it can provide evidence of tampering when verifying the authenticity of digital images. Contrast enhancement forensics techniques have always drawn significant attention for image forensics community, although most approaches have obtained effective detection results, existing CE forensic methods exhibit poor performance when detecting enhanced images stored in the JPEG format. The detection of forgery on contrast adjustments in the presence of JPEG post processing is… More >

Sheng Bai, Feng Bao^*, Fengzhi Zhao

FDMP-Fluid Dynamics & Materials Processing, Vol.17, No.1, pp. 39-47, 2021, DOI:10.32604/fdmp.2021.010376 - 09 February 2021

Abstract The present study aims to improve the efficiency of typical procedures used for post-processing flow field data by applying a neural-network technology. Assuming a problem of aircraft design as the workhorse, a regression calculation model for processing the flow data of a FCN-VGG19 aircraft is elaborated based on VGGNet (Visual Geometry Group Net) and FCN (Fully Convolutional Network) techniques. As shown by the results, the model displays a strong fitting ability, and there is almost no over-fitting in training. Moreover, the model has good accuracy and convergence. For different input data and different grids, the More >

Dae-Hyun Park¹, Hyun-Hee Park², Yoon Kim¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.11, No.4, pp. 113-114, 2009, DOI:10.3970/icces.2009.011.113

Abstract In this paper, we propose a post-processing visual enhancement technique to reduce the blocking artifacts in block based DCT decoded image for mobile devices that has allocation of the restricted resource. This algorithm uses the adaptive deblocking filter to remove grid noise and ringing noise in monotone areas. To decide whether monotone region or not, we introduce a notion of Flatness. Also, a new directional filter is utilized to get rid of staircase noise and preserve the original edge component. The directional filter is applied according to the direction of edge, which is corrected in More >

K. Lee¹, H. Park², S.W. Lee³

CMES-Computer Modeling in Engineering & Sciences, Vol.32, No.3, pp. 113-122, 2008, DOI:10.3970/cmes.2008.032.113

Abstract A post-processing scheme is presented to accurately determine transverse shear and normal stresses in composite panels undergoing geometrically nonlinear deformation under dynamic loading conditions. Transverse stresses are assumed through thickness at a point of interest and are recovered via a one-dimensional finite element method. The finite element method is based on the least square functional of the error in the equilibrium equation along the thickness direction and utilizes the in-plane stresses and resultant transverse shear forces per unit length obtained by a shell element analysis. Numerical results demonstrate that, with minimal addition of computational efforts, More >