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dc.contributor.authorEl-Amin, Mohamed F.
dc.date.accessioned2023-11-16T09:55:55Z
dc.date.available2023-11-16T09:55:55Z
dc.date.issued2023-06-23
dc.identifier.isbn978-0-323-90511-4en_US
dc.identifier.doihttps://doi.org/10.1016/B978-0-323-90511-4.00009-5en_US
dc.identifier.urihttp://hdl.handle.net/20.500.14131/1122
dc.description.abstractMachine learning is a branch of artificial intelligence concerned with creating and developing algorithms that enable computers to learn behaviors or patterns from empirical data. The aim of this chapter is the implementation of machine learning algorithms in predicting nanoparticle transport in the oil reservoir. We used Jupyter Notebook for the implementation, which utilizes Python programming language. Jupyter Notebook is an open-source web tool that allows you to write live code while creating statistics and machine learning models. This chapter contains selected machine learning techniques that can be used for nanoparticle transport in porous media. It starts with the fundamentals of a number of machine learning methods, followed by basic metrics that are frequently used. After that, we discuss datasets and their analysis. Finally, we explain how to implement machine learning techniques in the Jupyter Notebook environment using Python programming language.en_US
dc.publisherElsevieren_US
dc.title11 - Machine learning techniques for nanoparticles transporten_US
dc.source.booktitleNumerical Modeling of Nanoparticle Transport in Porous Media MATLAB/PYTHON Approachen_US
dc.source.pages303-339en_US
dc.contributor.researcherNo Collaborationen_US
dc.contributor.labEnergy Laben_US
dc.subject.KSAENERGYen_US
dc.contributor.ugstudent0en_US
dc.contributor.alumnae0en_US
dc.source.indexScopusen_US
dc.contributor.departmentNSMTUen_US
dc.contributor.pgstudent0en_US
dc.contributor.firstauthorEl-Amin, Mohamed F.


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