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Evolutionary computing enriched ridge regression model for craniofacial reconstruction

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Abstract

Craniofacial reconstruction is one of the dominating research domains having vital significance towards forensic purposes as well as archaeological investigation needs. With a goal to enable an error-resilient and swift craniofacial reconstruction model, in this paper an evolutionary computing assisted enhanced regression model has been proposed. We have developed an enhanced over-fitting resilient regression model called Ridge Regression (RR) as a statistical method to perform craniofacial reconstruction using landmark points and skull-face/(tissue) skin features. Our proposed model incorporates a hybrid evolutionary computing scheme containing Particle Swarm Optimization (PSO) and Differential Evolution (DE) to perform feature point selection and landmark count reduction. Here, the prime objective is to reduce the feature sets and landmark that can eventually make craniofacial reduction process more time efficient and accurate. The performance assessment reveals that the proposed PSO-DEFS based RRM model outperforms existing approaches such as the least square support vector regression (LSSVR) and partial least square regression (PLSR).

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Authors and Affiliations

  1. Department of Mathematics, Faculty of Science, New Valley, Assiut University, Assiut, Egypt

    Romany F. Mansour

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  1. Romany F. Mansour
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Correspondence to Romany F. Mansour.

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Mansour, R.F. Evolutionary computing enriched ridge regression model for craniofacial reconstruction. Multimed Tools Appl 79, 22065–22082 (2020). https://doi.org/10.1007/s11042-017-5015-0

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  • DOI: https://doi.org/10.1007/s11042-017-5015-0

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