Predictive Modeling of Yield Sooting Index Using Machine Learning with Uncertainty Estimation

This study explores the development of two predictive models for the yield sooting index (YSI) of various fuels using the advanced capabilities of machine learning (ML), particularly multilayer perceptron (MLP) networks. Quantitative structure–property relationship (QSPR) methodology, which connects...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Zied Hosni (21516040) (author)
مؤلفون آخرون:	Xike Chen (11480552) (author), Sofiene Achour (21516043) (author), Fatma Saadi (21516046) (author)
منشور في:	2025
الموضوعات:	Medicine Inorganic Chemistry Plant Biology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified yield sooting index various fuels using provided deeper insights future computational advancements different significance levels connects molecular structures certain 2d matrix efficient fuel alternatives two predictive models significantly impact ysi gini importance model enables accurate predictions developed predictive models predicting fuel properties gini importance fuel properties predictive modeling fuel research fuel behavior work highlights uncertainty estimation thereby enhancing study explores strong relationship potent synergy octane numbers machine learning kinematic viscosity including ysi ignition temperature hyperparameter tuning genetic algorithms fresh perspective feature selection developing sustainable comprehensive approach broader goal based descriptors also sets advanced capabilities
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Predictive Modeling of Yield Sooting Index Using Machine Learning with Uncertainty Estimation

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