Mehwish Zafar¹, Javeria Amin², Muhammad Sharif¹, Muhammad Almas Anjum³, Seifedine Kadry^4,5,6, Jungeun Kim^7,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2779-2793, 2023, DOI:10.32604/cmc.2023.035860

Abstract Worldwide cotton is the most profitable cash crop. Each year the production of this crop suffers because of several diseases. At an early stage, computerized methods are used for disease detection that may reduce the loss in the production of cotton. Although several methods are proposed for the detection of cotton diseases, however, still there are limitations because of low-quality images, size, shape, variations in orientation, and complex background. Due to these factors, there is a need for novel methods for features extraction/selection for the accurate cotton disease classification. Therefore in this research, an optimized features fusion-based model is proposed,… More >

Lei Ling¹, Lijun Huang², Jie Wang², Li Zhang², Yue Wu², Yizhang Jiang¹, Kaijian Xia^2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2353-2379, 2023, DOI:10.32604/cmes.2023.028828

Abstract In recent years, the soft subspace clustering algorithm has shown good results for high-dimensional data, which can assign different weights to each cluster class and use weights to measure the contribution of each dimension in various features. The enhanced soft subspace clustering algorithm combines interclass separation and intraclass tightness information, which has strong results for image segmentation, but the clustering algorithm is vulnerable to noisy data and dependence on the initialized clustering center. However, the clustering algorithm is susceptible to the influence of noisy data and reliance on initialized clustering centers and falls into a local optimum; the clustering effect… More >

Nur Laila Ab Ghani^1,2,*, Izzatdin Abdul Aziz^1,2, Said Jadid AbdulKadir^1,2

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4649-4668, 2023, DOI:10.32604/cmc.2023.035987

Abstract Clustering high dimensional data is challenging as data dimensionality increases the distance between data points, resulting in sparse regions that degrade clustering performance. Subspace clustering is a common approach for processing high-dimensional data by finding relevant features for each cluster in the data space. Subspace clustering methods extend traditional clustering to account for the constraints imposed by data streams. Data streams are not only high-dimensional, but also unbounded and evolving. This necessitates the development of subspace clustering algorithms that can handle high dimensionality and adapt to the unique characteristics of data streams. Although many articles have contributed to the literature… More >

Ying Sun¹, Yanfei Sun^1,2,*, Jin Qi¹, Fei Wu¹, Xiao-Yuan Jing^1,3, Yu Xue⁴, Zixin Shen⁵

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3373-3389, 2021, DOI:10.32604/cmc.2021.016539

Abstract Cross-project defect prediction (CPDP) aims to predict the defects on target project by using a prediction model built on source projects. The main problem in CPDP is the huge distribution gap between the source project and the target project, which prevents the prediction model from performing well. Most existing methods overlook the class discrimination of the learned features. Seeking an effective transferable model from the source project to the target project for CPDP is challenging. In this paper, we propose an unsupervised domain adaptation based on the discriminative subspace learning (DSL) approach for CPDP. DSL treats the data from two… More >

Yuan Zhu^{1, 2}, Xin Ning^{1, 2}, Yao Zhang^{1, 2}, Yuwan Yin^{1, 2}

The International Conference on Computational & Experimental Engineering and Sciences, Vol.23, No.1, pp. 11-12, 2021, DOI:10.32604/icces.2021.08311

Abstract High performance of the spacecraft structure is required in the special environment, it includes mechanical performance and operational performance, etc. When performing tasks, the spaceborne equipment requires high precision. Therefore, the design of lightweight, high stability and high reliability structure is essential for spacecraft. Topology optimization is widely used in structural design. However, there are some problems in the structure after macro topology optimization, such as checkerboard, local optimal solution and other phenomena. Despite a long calculation period, the obtained structure is often not smooth enough and hard to manufacture. Aiming to this issue, this paper proposes a combined method… More >

Geng Guo^1,*, Leiwen Chen¹, Ji Li², Shu Yan³, Wenxiang Wu⁴, Lingxu Li⁵, Hongda Li⁶

Energy Engineering, Vol.117, No.2, pp. 79-88, 2020, DOI:10.32604/EE.2020.010529

Abstract Featured by high energy density, low combustion pollution and large quantity, natural gas hydrate has become one of the research hotspots in Sanlutian Field of Muri Coalfield since 2008, when China first drilled natural gas hydrate samples in the permafrost area of Qilian Mountains, Qinghai-Tibet Plateau. However, the study on the controlling factors of gas hydrate accumulation is still shallow, which hinders the exploration and development of natural gas hydrate resources. The controlling factors of gas hydrate accumulation mainly include temperature and pressure conditions, gas source conditions, sedimentary conditions and structural conditions, among which structural conditions are the important one.… More >

Taku Itoh^1,*, Soichiro Ikuno²

The International Conference on Computational & Experimental Engineering and Sciences, Vol.21, No.2, pp. 44-44, 2019, DOI:10.32604/icces.2019.05521

Abstract The purpose of this study is to efficiently solve N by N linear systems Ax = b obtained by eXtended Element-free Galerkin method (X-EFG). X-EFG is one of the meshless methods for discretizing partial differential equations, and the linear systems have asymmetric saddle point structure. In this study, the block Krylov subspace methods have been applied for solving linear systems obtained by X-EFG. Note that the block Krylov subspace methods are usually employed for solving linear systems with multiple right hand sides, AX = B, where X = [x₁, x₂, …, x_L], B = [b₁, b₂, …, b_L], and L… More >

Akira Matsumoto^1,*, Taku Itoh², Soichiro Ikuno¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.21, No.2, pp. 43-43, 2019, DOI:10.32604/icces.2019.05496

Abstract In this study, an improvement technique of convergence property of Communication Avoiding (CA) Kyrlov subspace method is proposed, and parallelization efficiencies of CA Krylov subspace method is numerically investigated. As is known that most of all the procedures of the Krylov subspace method are constituted by addition of vectors, inner products and multiplication of matrices and vectors. These operations are very easy to derive a parallelization efficiency. However, the candidate coefficient matrices of linear system obtained from the numerical analysis such as Finite Element Method are large sparse matrices, and communications occur between Processing Units at short intervals in parallel… More >

Chein-Shan Liu ¹

CMC-Computers, Materials & Continua, Vol.33, No.2, pp. 175-198, 2013, DOI:10.3970/cmc.2013.033.175

Abstract An optimal m-vector descent iterative algorithm in a Krylov subspace is developed, of which the m weighting parameters are optimized from a properly defined objective function to accelerate the convergence rate in solving an ill-posed linear problem. The optimal multi-vector iterative algorithm (OMVIA) is convergent fast and accurate, which is verified by numerical tests of several linear inverse problems, including the backward heat conduction problem, the heat source identification problem, the inverse Cauchy problem, and the external force recovery problem. Because the OMVIA has a good filtering effect, the numerical results recovered are quite smooth with small error, even under… More >

Chein-Shan Liu¹, Satya N. Atluri²

CMES-Computer Modeling in Engineering & Sciences, Vol.104, No.1, pp. 1-39, 2015, DOI:10.3970/cmes.2015.104.001

Abstract A double optimal solution of an n-dimensional system of linear equations Ax = b has been derived in an affine m « n. We further develop a double optimal iterative algorithm (DOIA), with the descent direction z being solved from the residual equation Az = r₀ by using its double optimal solution, to solve ill-posed linear problem under large noise. The DOIA is proven to be absolutely convergent step-by-step with the square residual error ||r||² = ||b - Ax||² being reduced by a positive quantity ||Az_k||² at each iteration step, which is found to be better than those algorithms based… More >