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Author: search.filters.author.Noeiaghdam, Samad

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    Analytical analysis of the magnetic field, heat generation and absorption, viscous dissipation on couple stress casson hybrid nano fluid over a nonlinear stretching surface
    (2022-12-01)
    Hameed, Nabila
    ;
    Noeiaghdam, Samad
    ;
    Khan, Waris
    ;
    Pimpunchat, Busayamas
    ;
    Fernandez-Gamiz, Unai
    The aim of this research paper is to study two-dimension flow of Casson hybrid nanofluid along with magnetic field, heat generation and absorption, and viscous dissipation on a nonlinear extending surface. The primary goal of this study is to improve the heat transfer relationship, which is in high demand in the manufacturing and engineering industries. The outputs of this study will be used to reduce the energy consumption in industry and other engineering fields,for example, the achievement of energy is not enough, but also to adjust the consumptions of energy and this is possible only to approve the development heat transmission liquids to mechanism the expenditures of energy and to improvement. The described similarity transformation is used to convert the non-dimensionless form of the nonlinear partial differential equation to the dimensionless form of the nonlinear ordinary differential equation. An approximate analytical method is used to solve the derived dimensionless form of nonlinear ordinary differential equations, one for velocity and the other for temperature. Graphs are used to highlight the most relevant results acquired from velocity and temperature. Tables are used to describe the skin friction coefficient and the Nusselt number.
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    Comparisons of SVM Kernels for Insurance Data Clustering
    (2022-08-01)
    Nurhidayat, Irfan
    ;
    Pimpunchat, Busayamas
    ;
    Noeiaghdam, Samad
    ;
    Fernández-Gámiz, Unai
    This paper will study insurance data clustering using Support Vector Machine (SVM) approaches. It investigates the optimum condition employing the three most popular kernels of SVM, i.e., linear, polynomial, and radial basis kernel. To explore sum insured datasets, kernel comparisons for Root Mean Square Error (RMSE) and density analysis have been provided. It employs these kernels to classify based on sum insured datasets. The objective of this research is to demonstrate to industrial researchers that data grouping may be accomplished in an organized, error-free, and efficient manner utilizing R programming and the SVM approach. In this study, we check the insurance data for the sum insured with statistical methods in the form of Model Performance Evaluation (MPE), Receiver Operating Characteristics (ROC), Area Under Curve (AUC), partial AUC (pAUC), smoothing, confidence intervals, and thresholds. Then, sum insured data are followed up to classify using SVM kernels. This paper finds new ideas for evaluating insurance data using the SVM approach with multiple kernels. This novel research emphasizes the statistical analysis methods for insurance data and uses the SVM method for more accurate data classification. Finally, it informs that this research is a pure finding, and there has never been any research on this subject. This research was conducted using the sum insured data as a sample from the Office of the Insurance Commission (OIC) in Thailand as an independent insurance institution providing actual data.
      1