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

Emerging Trends in Engineering Polymers: A Paradigm Shift in Material Engineering DOI
Mohammad Harun‐Ur‐Rashid, Abu Bin Imran

Recent Progress in Materials, Journal Year: 2024, Volume and Issue: 06(03), P. 1 - 37

Published: Sept. 30, 2024

Emerging Trends in Engineering Polymers signify a pivotal transformation material engineering, marking departure from traditional materials towards innovative, multifunctional, and sustainable polymers. This review delineates the forefront of advancements polymer materials, including high-performance, bio-based, biodegradable, functional Highlighting their enhanced mechanical properties, thermal stability, chemical resistance showcases these materials' role driving technological progress. The exploration extends to advanced manufacturing techniques such as 3D printing, electrospinning, fabrication nanocomposites, underscoring impact on customizing product properties scaling production. Central this discourse is sustainability environmental stewardship sector, addressing recycling methodologies, circular economy, regulatory frameworks guiding practices. juxtaposes emerging processes, illuminating path toward more cycles. Furthermore, it ventures into applications across diverse sectors energy, electronics, healthcare, automotive, aerospace, elucidating transformative potential engineering polymers domains. Challenges spanning technical, economic, environmental, landscapes are critically examined, setting stage for future directions research development. culminates forward-looking perspective, advocating interdisciplinary collaboration science innovation navigate modern challenges' complexities. Through comprehensive analysis, articulates narrative evolution opportunity within polymers, poised redefine decades come.

Language: Английский

Citations

5
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