Leveraging Machine Learning for Advancements in Materials Science using - Ahlam Hacine Gherbi; Hadia Hemmami; Salah EddineLaouini; Mohammed Taher Gherbi (University of Eloued, 2023)
This 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
Intervention abstract
Keywords: Python; Nanostructured; image preprocessing; particle size; Machine Learning