Leveraging Machine Learning for Advancements in Materials Science using

dc.contributor.authorAhlam Hacine Gherbi
dc.contributor.authorHadia Hemmami
dc.contributor.authorSalah EddineLaouini
dc.contributor.authorMohammed Taher Gherbi
dc.date.accessioned2024-06-03T09:12:01Z
dc.date.available2024-06-03T09:12:01Z
dc.date.issued2023-12-11
dc.descriptionIntervention abstract
dc.description.abstractThis study delves into applying machine learning techniques to determine and analyze the properties of nanostructured Copper Oxide (CuO) in the field of materials science. By utilizing Python, a versatile and potent programming language, we employ diverse machine learning approaches to investigate the intricate characteristics and behaviors of CuO at the nanoscale, including image preprocessing, segmentation, particle size measurement, statistical analysis, and visualization. Leveraging advanced machine learning models, we achieve a comprehensive understanding of the distinctive properties and potential applications of nano CuO, thus contributing significantly to the advancement of materials science
dc.identifier.citationAhlam Hacine Gherbi, Hadia Hemmami, Salah EddineLaouini, Mohammed Taher Gherbi. Leveraging Machine Learning for Advancements in Materials Science using. International Pluridsciplinary PhD Meeting IPPM 23. Faculty of technology. University of Eloued [visited in ../../…]. Available from[ Copy the link here]
dc.identifier.urihttps://dspace.univ-eloued.dz/handle/123456789/33070
dc.language.isoen
dc.publisherUniversity of Eloued
dc.subjectPython
dc.subjectNanostructured
dc.subjectimage preprocessing
dc.subjectparticle size
dc.subjectMachine Learning
dc.titleLeveraging Machine Learning for Advancements in Materials Science using
dc.typeIntervention

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