Smart Hybrid Machine Learning System for Antibacterial Activity Prediction of Metapenaeus Monoceros Nanoparticles

This paper explains the process of making and antimicrobial assay of the chitosan nanoparticles (chNP) produced using Metapenaeus monoceros. The X-ray diffraction (XRD) revealed that the nanoparticles were semi-crystalline. The chNPs synthesized displayed a good concentrationdependent antibacterial activity on ten bacteria strains. Antibacterial experiments were conducted with ten bacterial strains at four doses (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{2 5 - 1 0 0} \boldsymbol{\mu} \mathbf{g} / \mathbf{m L}$</tex>), yielding 40 experimental examples The highest inhibition zone was observed with Escherichia coli (21 mm) followed by Salmonella typhi (19 mm) and moderate with Klebsiella pneumoniae and Streptococcus pyogenes shows relatively lower with Vibrio species, which has selective antimicrobial potential.. In order to optimize predictive analysis three machine learning algorithms: Random Forest (RF), Support Vector regression (SVR), and Elastic Net Regression (ENR) were used to predict experimental variables with the achievement of antibacterial efficacy. The RF model also proved to be the most effective, in terms of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{R}^{\mathbf{2}}=\mathbf{0. 9 4 7}$</tex>), RMSE (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$=0.602$</tex>), MAE <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$=0.478$</tex>, being better than either SVR model proves <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R^{2}=0.862$</tex> or <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$E N R R^{2}=0.774$</tex>. Experimental validation combined with computational modeling is a good strategy to predict the antibacterial efficacy and further develop marine-derived bioactive nanomaterials in biomedical fields.