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Interpretable Machine Learning for Ionic Liquid-Mediated Delignification with Applicability Domain Validation

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Origin of the OECD Principles for QSAR Validation and Their Role in Changing the QSAR Paradigm Worldwide: An Historical Overview
inferredcites →https://doi.org/10.1007/s00180-022-01207-6openalex-snapshot
Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features
inferredcites →https://doi.org/10.1007/s12155-024-10747-2openalex-snapshot
Machine Learning Modelling for Predicting the Efficacy of Ionic Liquid-Aided Biomass Pretreatment
inferredcites →https://doi.org/10.1007/s41060-025-00825-9openalex-snapshot
A comparative study of imputation techniques for missing values in healthcare diagnostic datasets
inferredcites →https://doi.org/10.1016/j.biortech.2020.124642openalex-snapshot
Machine learning prediction of cellulose-rich materials from biomass pretreatment with ionic liquid solvents
inferredcites →https://doi.org/10.1016/j.eswa.2023.120715openalex-snapshot
An empirical study of pattern leakage impact during data preprocessing on machine learning-based intrusion detection models reliability
inferredcites →https://doi.org/10.1016/j.rser.2021.111822openalex-snapshot
Lignin-enzyme interaction: A roadblock for efficient enzymatic hydrolysis of lignocellulosics
inferredcites →https://doi.org/10.1016/j.wmb.2023.12.004openalex-snapshot
RSM approach to pre-treatment of lignocellulosic waste and a statistical methodology for optimizing bioethanol production
inferredcites →https://doi.org/10.1021/acs.chemrev.4c00754openalex-snapshot
Recent Advances in the Use of Ionic Liquids and Deep Eutectic Solvents for Lignocellulosic Biorefineries and Biobased Chemical and Material Production
inferredcites →https://doi.org/10.1021/acs.iecr.5c01985openalex-snapshot
Methodological Roadmap for Machine Learning in Deep Eutectic Solvent Research: A Framework-Driven Review and Perspective
inferredcites →https://doi.org/10.1021/acssuschemeng.4c00631openalex-snapshot
High-Throughput Screening and Accurate Prediction of Ionic Liquid Viscosities Using Interpretable Machine Learning
inferredcites →https://doi.org/10.1021/acssuschemeng.5c10092openalex-snapshot
Machine Learning Prediction of Density of Ionic Liquid–Organic Solvent Systems for Green Chemistry Applications: Ensemble Model, Interpretability, and Validation
inferredcites →https://doi.org/10.1038/s41529-024-00508-zopenalex-snapshot
A novel stacking ensemble learner for predicting residual strength of corroded pipelines
inferredcites →https://doi.org/10.1039/d3ra06930copenalex-snapshot
Response surface optimization of ionic liquid pretreatments for maximizing cellulose nanofibril production
inferredcites →https://doi.org/10.1039/d4dd00250dopenalex-snapshot
MatFold: systematic insights into materials discovery models' performance through standardized cross-validation protocols
inferredcites →https://doi.org/10.1039/d5gc03051jopenalex-snapshot
Advances in ionic liquid recycling for lignocellulosic biomass pretreatment
inferredcites →https://doi.org/10.1039/d6gc00829aopenalex-snapshot
Active lignin stabilization in lignocellulosic biomass fractionation: mechanisms and advancements
inferredcites →https://doi.org/10.2196/49023openalex-snapshot
Practical Considerations and Applied Examples of Cross-Validation for Model Development and Evaluation in Health Care: Tutorial
inferredcites →https://doi.org/10.3390/recycling8040061openalex-snapshot
Lignocellulosic Agricultural Waste Valorization to Obtain Valuable Products: An Overview
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