Utilization of machine learning approach for production of optimized PLGA nanoparticles for drug delivery applications DOI Creative Commons
Khaled Almansour,

Arwa Sultan Alqahtani

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 14, 2025

This study investigates utilization of machine learning for the regression task predicting size PLGA (Poly lactic-co-glycolic acid) nanoparticles. Various inputs including category and numeric were considered building model to predict optimum conditions preparation nanosized particles drug delivery applications. The proposed methodology employs Leave-One-Out (LOO) categorical feature transformation, Local Outlier Factor (LOF) outlier detection, Bat Optimization Algorithm (BA) hyperparameter optimization. A comparative analysis compares K-Nearest Neighbors (KNN), ensemble methods such as Bagging Adaptive Boosting (AdaBoost), novel Small-Size Bat-Optimized KNN Regression (SBNNR) model, which uses generative adversarial networks deep extraction improve performance on sparse datasets. Results demonstrate that ADA-KNN outperforms other models Particle Size prediction with a test R² 0.94385, while SBNNR achieves superior accuracy in Zeta Potential 0.97674. These findings underscore efficacy combining advanced preprocessing, optimization, techniques robust modeling. contributions this work include development validation BA's optimization capabilities, comprehensive evaluation methods. method provides reliable framework using material science applications, particularly nanoparticle characterization.

Language: Английский

Advances in Nanoparticles in Targeted Drug Delivery- A Review DOI Creative Commons
Safiul Islam, Md Mir Shakib Ahmed, Mohammad Aminul Islam

et al.

Results in Surfaces and Interfaces, Journal Year: 2025, Volume and Issue: unknown, P. 100529 - 100529

Published: April 1, 2025

Language: Английский

Citations

0
Utilization of machine learning approach for production of optimized PLGA nanoparticles for drug delivery applications DOI Creative Commons
Khaled Almansour,

Arwa Sultan Alqahtani

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 14, 2025

This study investigates utilization of machine learning for the regression task predicting size PLGA (Poly lactic-co-glycolic acid) nanoparticles. Various inputs including category and numeric were considered building model to predict optimum conditions preparation nanosized particles drug delivery applications. The proposed methodology employs Leave-One-Out (LOO) categorical feature transformation, Local Outlier Factor (LOF) outlier detection, Bat Optimization Algorithm (BA) hyperparameter optimization. A comparative analysis compares K-Nearest Neighbors (KNN), ensemble methods such as Bagging Adaptive Boosting (AdaBoost), novel Small-Size Bat-Optimized KNN Regression (SBNNR) model, which uses generative adversarial networks deep extraction improve performance on sparse datasets. Results demonstrate that ADA-KNN outperforms other models Particle Size prediction with a test R² 0.94385, while SBNNR achieves superior accuracy in Zeta Potential 0.97674. These findings underscore efficacy combining advanced preprocessing, optimization, techniques robust modeling. contributions this work include development validation BA's optimization capabilities, comprehensive evaluation methods. method provides reliable framework using material science applications, particularly nanoparticle characterization.

Language: Английский

Citations

0