A Generative Adversarial Network Approach to Predict Nanoparticle Size in Microfluidics DOI

Sara Mihandoost,

Sima Rezvantalab, Roger M. Pallares

et al.

ACS Biomaterials Science & Engineering, Journal Year: 2024, Volume and Issue: 11(1), P. 268 - 279

Published: Dec. 12, 2024

To achieve precise control over the properties and performance of nanoparticles (NPs) in a microfluidic setting, profound understanding influential parameters governing NP size is crucial. This study specifically delves into poly(lactic-co-glycolic acid) (PLGA)-based NPs synthesized through microfluidics that have been extensively explored as drug delivery systems (DDS). A comprehensive database, containing more than 11 hundred data points, curated an extensive literature review, identifying potential effective features. Initially, we employed tabular generative adversarial network (TGAN) to enhance sets, increasing reliability obtained results elevating prediction accuracy. Subsequently, was performed using different machine learning (ML) techniques including decision tree (DT), random forest (RF), deep neural networks (DNN), linear regression (LR), support vector (SVR), gradient boosting (GB). Among these ensembles, DT emerges most accurate algorithm, yielding average error 8%. Further simulations underscore pivotal role synthesis method, poly(vinyl alcohol) (PVA) concentration, lactide-to-glycolide (LA/GA) ratio PLGA copolymers primary determinants influencing size.

Language: Английский

Explainable AI Chatbots Towards XAI ChatGPT: A Review DOI Creative Commons
Attila Kővári

Heliyon, Journal Year: 2025, Volume and Issue: 11(2), P. e42077 - e42077

Published: Jan. 1, 2025

Advances in artificial intelligence (AI) have had a major impact on natural language processing (NLP), even more so with the emergence of large-scale models like ChatGPT. This paper aims to provide critical review explainable AI (XAI) methodologies for chatbots, particular focus Its main objectives are investigate applied methods that improve explainability identify challenges and limitations within them, explore future research directions. Such goals emphasize need transparency interpretability systems build trust users allow accountability. While integrating such interdisciplinary methods, as hybrid combining knowledge graphs ChatGPT, enhancing explainability, they also highlight industry needs user-centred design. will be followed by discussion balance between performance, then role human judgement, finally verifiable AI. These avenues through which insights can used guide development transparent, reliable efficient chatbots.

Language: Английский

Citations

2
Dual-directional small-sampling deep-learning modelling on co-flowing microfluidic droplet generation DOI
Ji‐Xiang Wang,

Jian Qian,

Hongmei Wang

et al.

Chemical Engineering Journal, Journal Year: 2024, Volume and Issue: 485, P. 149467 - 149467

Published: Feb. 10, 2024

Language: Английский

Citations

7
Machine learning and physics-driven modelling and simulation of multiphase systems DOI Creative Commons
Nausheen Basha, Rossella Arcucci, Panagiota Angeli

et al.

International Journal of Multiphase Flow, Journal Year: 2024, Volume and Issue: 179, P. 104936 - 104936

Published: Aug. 1, 2024

We highlight the work of a multi-university collaborative programme, PREMIERE (PREdictive Modelling with QuantIfication UncERtainty for MultiphasE Systems), which is at intersection multi-physics and machine learning, aiming to enhance predictive capabilities in complex multiphase flow systems across diverse length time scales. Our contributions encompass variety approaches, including Design Experiments nanoparticle synthesis optimisation, Generalised Latent Assimilation models drop coalescence prediction, Bayesian regularised artificial neural networks, eXtreme Gradient Boosting microdroplet formation sub-sampling based adversarial network predicting slug behaviour two-phase pipe flows. Additionally, we introduce generalised latent assimilation technique, Long Short-Term Memory networks sequence forecasting mixing performance stirred static mixers, active learning via optimisation recover model parameters high current density electrolysers, Gaussian process regression size distribution predictions sprays, acoustic emission signal inversion using gradient boosting machines characterise particle fluidised beds. also offer perspectives on development shape framework that leverages use multi-fidelity emulator. The results presented have applications chemical synthesis, microfluidics, product manufacturing, green hydrogen generation.

Language: Английский

Citations

7
Explainable AI model for predicting equivalent viscous damping in dual frame–wall resilient system DOI Creative Commons
Chuandong Xie, Jin-Wei Hu, George Vasdravellis

et al.

Journal of Building Engineering, Journal Year: 2024, Volume and Issue: 96, P. 110564 - 110564

Published: Aug. 28, 2024

A prominent challenge in applying the direct displacement-based design (DDBD) method to proposed dual frame–wall lateral force-resisting system lies determining equivalent viscous damping ratio (EVDR). However, strong nonlinearity and complexity behind procedure lead limited choice, mostly trial error based on experience, explain predict EVDR context of traditional research. This study employs XGBoost unravel intricate relationships using over 5 million data points from nonlinear time-history (NLTH) analyses, encompassing various parameters including fundamental period, ductility, subsystem stiffness ratios, post-yielding ratios subsystems ground motion types. SHapley Additive exPlanations (SHAP) values consistently identify critical features relevant procedure. Comprehensive feature ablation tests further illuminate robustness susceptibility each model. Additionally, incorporation Local Interpretable Model-agnostic Explanations (LIME) for local interpretability provides insights into decision-making mechanisms inherent model's predictions. Both predicting results machine learning (ML) are also compared. Findings highlight relative importance present a refined prediction It underscores pivotal role model reinforcing confidence complex models advocates leveraging ML techniques enhance effectiveness efficiency DDBD structural design.

Language: Английский

Citations

4
Additive-feature-attribution methods: A review on explainable artificial intelligence for fluid dynamics and heat transfer DOI Creative Commons
A. Cremades, Sergio Hoyas, Ricardo Vinuesa

et al.

International Journal of Heat and Fluid Flow, Journal Year: 2024, Volume and Issue: 112, P. 109662 - 109662

Published: Dec. 9, 2024

Language: Английский

Citations

4
Optimizing Droplet Coalescence Dynamics in Microchannels: A Comprehensive Study Using Response Surface Methodology and Machine Learning Algorithms DOI Creative Commons
Seyed Morteza Javadpour, Erfan Kadivar,

Zienab Heidary Zarneh

et al.

Heliyon, Journal Year: 2025, Volume and Issue: 11(1), P. e41510 - e41510

Published: Jan. 1, 2025

Droplet coalescence in microchannels is a complex phenomenon influenced by various parameters such as droplet size, velocity, liquid surface tension, and droplet-droplet spacing. In this study, we thoroughly investigate the impact of these control on dynamics within sudden expansion microchannel using two distinct numerical methods. Initially, employ boundary element method to solve Brinkman integral equation, providing detailed insights into underlying physics coalescence. Furthermore, integrate Response Surface Methodology (RSM) effectively optimize dynamics, harnessing power machine learning algorithms. Our results showcase efficacy computational techniques enhancing experimental efficiency. Through rigorous evaluation utilizing Regression Coefficient Mean Absolute Error metrics, ascertain accuracy our estimations. findings highlight significant influence key parameters, specifically non-dimensional initial distance droplets (D), viscosity ratio ( μ ), Capillary number (Ca), width (w), identified final spacing (DD), velocity first (VFD), second (VBD), respectively. This comprehensive approach provides valuable phenomena offers robust framework for optimizing microfluidic systems. The most influential DD are values Ad D, while has lowest DD. channel width, whereas Ca have least velocity. comparison different algorithms indicates that best ones predicting DD, VFD, VBD function, SMOreg, Lazy-IBK, Meta-Bagging,

Language: Английский

Citations

0
A novel hybrid group method of data handling and Levenberg Marquardt model for estimating total organic carbon in source rocks with explainable artificial intelligence DOI
Christopher N. Mkono, Chuanbo Shen,

Alvin K. Mulashani

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 144, P. 110137 - 110137

Published: Jan. 27, 2025

Language: Английский

Citations

0
Modeling and analysis of droplet generation in microchannels using interpretable machine learning methods DOI
Mengqi Liu, Haoyang Hu, Yongjin Cui

et al.

Chemical Engineering Journal, Journal Year: 2025, Volume and Issue: unknown, P. 161972 - 161972

Published: March 1, 2025

Language: Английский

Citations

0
Striking a balance: navigating the trade-offs between predictive accuracy and interpretability in machine learning models DOI Creative Commons

Miguel Arantes,

Wenceslao González‐Manteiga, Javier Martínez Martínez

et al.

Electronic Research Archive, Journal Year: 2025, Volume and Issue: 33(4), P. 2092 - 2117

Published: Jan. 1, 2025

Language: Английский

Citations

0
Enhancing Survival Analysis Model Selection through XAI(t) in Healthcare DOI Creative Commons
Francesco Berloco,

Pietro Maria Marvulli,

Vladimiro Suglia

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(14), P. 6084 - 6084

Published: July 12, 2024

Artificial intelligence algorithms have become extensively utilized in survival analysis for high-dimensional, multi-source data. However, due to their complexity, these methods often yield poorly interpretable outcomes, posing challenges the of several conditions. One conditions is obstructive sleep apnea, a disorder characterized by simultaneous occurrence comorbidities. Survival provides potential solution assessing and categorizing severity aiding personalized treatment strategies. Given critical role time such scenarios considering limitations model interpretability, time-dependent explainable artificial been developed recent years direct application basic Machine Learning models, as Cox regression random forest. Our work aims enhance selection OSA using XAI Deep models. We an end-to-end pipeline, training models selecting best performers. top models—Cox regression, time, logistic hazard—achieved good performance, with C-index scores 0.81, 0.78, 0.77, Brier 0.10, 0.12, 0.11 on test set. applied SurvSHAP hazard investigate behavior. Although showed similar our established that results log were more reliable useful clinical practice compared those scenarios.

Language: Английский

Citations

2