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  • Open Access


    Mathematical Modelling of Quantum Kernel Method for Biomedical Data Analysis

    Mahmoud Ragab1,2,3, Ehab Bahauden Ashary4, Maha Farouk S. Sabir5, Adel A. Bahaddad5, Romany F. Mansour6,*

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5441-5457, 2022, DOI:10.32604/cmc.2022.024545

    Abstract This study presents a novel method to detect the medical application based on Quantum Computing (QC) and a few Machine Learning (ML) systems. QC has a primary advantage i.e., it uses the impact of quantum parallelism to provide the consequences of prime factorization issue in a matter of seconds. So, this model is suggested for medical application only by recent researchers. A novel strategy i.e., Quantum Kernel Method (QKM) is proposed in this paper for data prediction. In this QKM process, Linear Tunicate Swarm Algorithm (LTSA), the optimization technique is used to calculate the loss function initially and is aimed… More >

  • Open Access


    Industrial Food Quality Analysis Using New k-Nearest-Neighbour methods

    Omar Fetitah1, Ibrahim M. Almanjahie2,3, Mohammed Kadi Attouch1,*, Salah Khardani4

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2681-2694, 2021, DOI:10.32604/cmc.2021.015469

    Abstract The problem of predicting continuous scalar outcomes from functional predictors has received high levels of interest in recent years in many fields, especially in the food industry. The k-nearest neighbor (k-NN) method of Near-Infrared Reflectance (NIR) analysis is practical, relatively easy to implement, and becoming one of the most popular methods for conducting food quality based on NIR data. The k-NN is often named k nearest neighbor classifier when it is used for classifying categorical variables, while it is called k-nearest neighbor regression when it is applied for predicting noncategorical variables. The objective of this paper is to use the… More >

  • Open Access


    Prediction of Time Series Empowered with a Novel SREKRLS Algorithm

    Bilal Shoaib1, Yasir Javed2, Muhammad Adnan Khan3,*, Fahad Ahmad4, Rizwan Majeed5, Muhammad Saqib Nawaz1, Muhammad Adeel Ashraf6, Abid Iqbal2, Muhammad Idrees7

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1413-1427, 2021, DOI:10.32604/cmc.2021.015099

    Abstract For the unforced dynamical non-linear statespace model, a new Q1 and efficient square root extended kernel recursive least square estimation algorithm is developed in this article. The proposed algorithm lends itself towards the parallel implementation as in the FPGA systems. With the help of an ortho-normal triangularization method, which relies on numerically stable givens rotation, matrix inversion causes a computational burden, is reduced. Matrix computation possesses many excellent numerical properties such as singularity, symmetry, skew symmetry, and triangularity is achieved by using this algorithm. The proposed method is validated for the prediction of stationary and non-stationary MackeyGlass Time Series, along… More >

  • Open Access


    Privacy-Preserving Recommendation Based on Kernel Method in Cloud Computing

    Tao Li1, Qi Qian2, Yongjun Ren3,*, Yongzhen Ren4, Jinyue Xia5

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 779-791, 2021, DOI:10.32604/cmc.2020.010424

    Abstract The application field of the Internet of Things (IoT) involves all aspects, and its application in the fields of industry, agriculture, environment, transportation, logistics, security and other infrastructure has effectively promoted the intelligent development of these aspects. Although the IoT has gradually grown in recent years, there are still many problems that need to be overcome in terms of technology, management, cost, policy, and security. We need to constantly weigh the benefits of trusting IoT products and the risk of leaking private data. To avoid the leakage and loss of various user data, this paper developed a hybrid algorithm of… More >

  • Open Access


    Solving a Class of PDEs by a Local Reproducing Kernel Method with An Adaptive Residual Subsampling Technique

    H. Rafieayan Zadeh1, M. Mohammadi1,2, E. Babolian1

    CMES-Computer Modeling in Engineering & Sciences, Vol.108, No.6, pp. 375-396, 2015, DOI:10.3970/cmes.2015.108.375

    Abstract A local reproducing kernel method based on spatial trial space spanned by the Newton basis functions in the native Hilbert space of the reproducing kernel is proposed. It is a truly meshless approach which uses the local sub clusters of domain nodes for approximation of the arbitrary field. It leads to a system of ordinary differential equations (ODEs) for the time-dependent partial differential equations (PDEs). An adaptive algorithm, so-called adaptive residual subsampling, is used to adjust nodes in order to remove oscillations which are caused by a sharp gradient. The method is applied for solving the Allen-Cahn and Burgers’ equations.… More >

  • Open Access


    A State Space Differential Reproducing Kernel Method for the Buckling Analysis of Carbon Nanotube-Reinforced Composite Circular Hollow Cylinders

    Chih-Ping Wu1,2, Ruei-Yong Jiang1

    CMES-Computer Modeling in Engineering & Sciences, Vol.97, No.3, pp. 239-279, 2014, DOI:10.3970/cmes.2014.097.239

    Abstract A state space differential reproducing kernel (DRK) method is developed for the three-dimensional (3D) buckling analysis of simply-supported, carbon nanotube-reinforced composite (CNTRC) circular hollow cylinders and laminated composite ones under axial compression. The single-walled carbon nanotubes (CNTs) and polymer are used as the reinforcements and matrix, respectively, to constitute the CNTRC cylinder. Three different distributions of CNTs varying in the thickness direction are considered (i.e., the uniform distribution and functionally graded rhombus-, and X-type ones), and the through-thickness distributions of effective material properties of the cylinder are determined using the rule of mixtures. The 3D linear buckling theory is used,… More >

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