Stacking machine learning model for predicting magnetic properties of rare-earth metals

Abstract

The bachelor's qualification work was completed by a student of the KN-417f group Hasib Ossama Ahmed Ossama. The topic is "Stacking machine learning model for predicting magnetic properties of rare-earth metals". The work is aimed at obtaining a bachelor's degree in 122 "Computer Science". The object of research is the processes of prediction the magnetic properties for alloys from rare earth metals. The subject of research is stacking machine learning approach for the prediction of magnetic remanence of Sm-Co magnets. The research is attained by increasing the prediction accuracy for the magnetic properties of alloys from rare earth metals using machine learning based ensemble model, furthermore several machine learning algorithms were employed to assess the performance of the alloys magnetic properties based on a real dataset specifically designed for magnetic property analysis. As a result of the research, A stacking machine learning models was created using the orange data mining software, The results obtained were compared and investigated its effectiveness, This system can be used in the future work to predict the magnetic properties of the alloys before its manufactured, So it can reduce the expenses and labor requirements associated with manufacturing.