Ahlam Hacine GherbiHadia HemmamiSalah EddineLaouiniMohammed Taher Gherbi2024-06-032024-06-032023-12-11Ahlam 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]https://dspace.univ-eloued.dz/handle/123456789/33070Intervention 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 scienceenPythonNanostructuredimage preprocessingparticle sizeMachine LearningIntervention