Now showing 1 - 5 of 5
  • Publication
    Shape preserving data interpolation using rational cubic ball functions
    ( 2015)
    Ayser Nasir Hassan Tahat
    ;
    Abd Rahni Mt Piah
    ;
    A smooth curve interpolation scheme for positive, monotone, and convex data is developed.This scheme uses rational cubic Ball representation with four shape parameters in its description. Conditions of two shape parameters are derived in such a way that they preserve the shape of the data, whereas the other two parameters remain free to enable the user to modify the shape of the curve. The degree of smoothness is 𝐶1.The outputs from a number of numerical experiments are presented.
  • Publication
    Iteration Variational Method for Solving Two-Dimensional Partial Integro-Differential Equations
    ( 2020-08-13)
    Hussain A.K.
    ;
    Fadhel F.S.
    ;
    ;
    The two-dimensional integro-differential partial equations is one of the so difficult problems to be solved analytically and/or approximately, and therefore, a method that is efficient for solving such type of problems seems to be necessary. Therefore, in this paper, the iteration methods, which is so called the variational iteration method have been used to provide a solution to such type of problems approximately, in which the obtained results are very accurate in comparison with the exact solution for certain well selected examples which are constructed so that the exact solution exist. Main results of this work is to derive first the variational iteration formula and then analyzing analytically the error term and prove its convergence to zero as the number of iteration increases.
  • Publication
    Hybrid Mahalanobis Taguchi System with Binary Whale Optimisation Feature Selection for the Wisconsin Breast Cancer Dataset
    (Semarak Ilmu Publishing, 2023)
    Chow Yong Huan
    ;
    ; ; ; ;
    Tan Xiao Jian
    The Mahalanobis-Taguchi System (MTS) is a statistical approach used in breast cancer research to facilitate early detection and promote efficient treatment. The technique analyses mammogram images for significant features using a multivariate statistical analysis technique. It combines the Mahalanobis distance (MD) and Taguchi's method to determine the differences between benign and malignant samples. While orthogonal array (OA) has been widely used in MTS, it has been criticised for providing suboptimal results due to insufficient coverage of feature combinations during the feature optimisation process. To address this issue, the Binary Whale Optimisation Algorithm (BWOA) is proposed as an improved search algorithm for MTS. This paper aims to develop a novel hybrid method that enhances the efficiency of the Mahalanobis Taguchi System (MTS). The performance of feature selection ability due to different MTS hybrid algorithms were also compared. BWOA simulates the hunting behaviour of humpback whales and works by exploring new regions of the solution space, gradually narrowing the search space, and fine-tuning the solution. MTS-BWOA demonstrated its enhanced capability in feature optimisation compared to traditional MTS methods and has the potential to be applied in other medical imaging domains.
  • Publication
    A Modified Artificial Bee Colony Based Test Suite Generation Strategy for Uniform T-Way Testing
    Today, t-way testing has been widely known with the ability to reduce test suite size compared to exhaustive testing. At the same time, it has been proven by many researchers to provide maximum bug detection capability. Thus, various t-way strategies were developed since the past three decades. The paper proposed a new test generation strategy, named Modified Artificial Bee Colony T-Way Test Suite Generation (MABCTS). It supports uniform strength t-way testing. Experimentation results are compared with present strategies and produced comparable results. Since t-way testing is considered an NP-hard problem, there are no strategies that can be demanded to produce the best results.
      1  18
  • Publication
    A survey on improvement of Mahalanobis Taguchi system and its application
    ( 2023-11-01)
    Tan L.M.
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    ; ; ; ;
    Ramlie F.
    ;
    Harudin N.
    ;
    Abu M.Y.
    ;
    Tan X.J.
    Mahalanobis Taguchi System (MTS) is used for pattern recognition and classification, diagnosis, and prediction of a multivariate data set. Mahalanobis Distance (MD), orthogonal array (OA), and signal-to-noise ratio (SNR) are used in traditional MTS in order to identify and optimize the variables. However, the high correlation among variables shows an effect on the inverse of the correlation matrix that uses in the calculation of MD and hence affects the accuracy of the MD. Therefore, Mahalanobis-Taguchi-Gram-Schmidt (MTGS) system is proposed in order to solve the problem of multicollinearity. The value of MD can be calculated by using the Gram-Schmidt Orthogonalization Process (GSOP). Besides, the computational speed and the accuracy in optimization using OA and SNR are other issues that are concerned the authors. Hence, the combination of MTS and other methods such as Binary Particles Swarm Optimization (BPSO) and Binary Ant Colony Optimization (NBACO) is proposed to improve the computational speed and the accuracy in optimization. The purpose of this paper is to review and summarize some works that developed and used the hybrid methodology of MTS as well as its application in several fields. Moreover, a discussion about the future work that can be done related to MTS is carried out.
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