Support Vector Machine and Relevance Vector Machine for Prediction of Alumina and Pore Volume Fraction in Bioceramics

Authors

  • Kangeyanallore Govindaswamy Shanmugam Gopinath,

    1. Mechanical Engineering Department, Jaya Suriya Engineering College, Anna University, Chennai, India
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  • Soumen Pal,

    Corresponding author
    1. Manufacturing Division, School of Mechanical and Building Sciences, VIT University, Vellore, Tamil Nadu, India
    2. Centre for Disaster Mitigation and Management, VIT University, Vellore, Tamil Nadu, India
    3. Nuclear and Medical Physics Division, School of Advanced Sciences, VIT University, Vellore, Tamil Nadu, India
    • Mechanical Engineering Department, Jaya Suriya Engineering College, Anna University, Chennai, India
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  • Pijush Samui,

    1. Manufacturing Division, School of Mechanical and Building Sciences, VIT University, Vellore, Tamil Nadu, India
    2. Centre for Disaster Mitigation and Management, VIT University, Vellore, Tamil Nadu, India
    3. Nuclear and Medical Physics Division, School of Advanced Sciences, VIT University, Vellore, Tamil Nadu, India
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  • Bimal Kumar Sarkar

    1. Manufacturing Division, School of Mechanical and Building Sciences, VIT University, Vellore, Tamil Nadu, India
    2. Centre for Disaster Mitigation and Management, VIT University, Vellore, Tamil Nadu, India
    3. Nuclear and Medical Physics Division, School of Advanced Sciences, VIT University, Vellore, Tamil Nadu, India
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soumenpal12@gmail.com

Abstract

The determination of wt% alumina (wa) and pore volume fraction (pv) in alumina-based bioceramics is important in ceramic engineering. This article adopts support vector machine (SVM) and relevance vector machine (RVM) for prediction of wa and pv based on SiC. SVM is firmly based on theory of statistical learning. RVM is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. The developed SVM and RVM give equations for prediction of wa and pv. This article gives robust models based on SVM and RVM for prediction of wa and pv.

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