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Ensemble of heterogeneous flexible neural tree for the approximation and feature-selection of Poly (Lactic-co-glycolic Acid) micro-and nanoparticle

Ojha, V. ORCID: https://orcid.org/0000-0002-9256-1192, Abraham, A. and Snasel, V. (2016) Ensemble of heterogeneous flexible neural tree for the approximation and feature-selection of Poly (Lactic-co-glycolic Acid) micro-and nanoparticle. In: Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015, Sep 9, 2015 - Sep 11, 2015, Paris - Villejuif, France, pp. 155-165, https://doi.org/10.1007/978-3-319-29504-6_16.

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To link to this item DOI: 10.1007/978-3-319-29504-6_16

Abstract/Summary

In this work, we used an adaptive feature-selection and function approximation model, called, flexible neural tree (FNT) for predicting Poly (lactic-co-glycolic acid) (PLGA) micro-and nanoparticle's dissolution-rates that bears a significant role in the pharmaceutical, medical, and drug manufacturing industries. Several factor influences PLGA nanoparticles dissolution-rate prediction. FNT model enables us to deal with feature selection and prediction simultaneously. However, a single FNT model may or may not offer a generalized solution. Hence, to build a generalized model, we used an ensemble of FNTs. In this work, we have provided a comprehensive study for examining the most significant (influencing) features that influences dissolution rate prediction.

Item Type:Conference or Workshop Item (Paper)
Refereed:Yes
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:93556
Publisher:Springer Science \mathplus Business Media

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