Application of Support Vector Machine Algorithm Incorporating Slime Mould Algorithm Strategy in Ancient Glass Classification

Glass products are important evidence of early East–West cultural exchanges. Ancient glass in China mostly consisted lead glass, and potassium is widely believed to be imported abroad. In order figure out the origin artefacts, it crucial define type accurately. contemporary research on chemical composition ancient products, separated from primarily by weight ratio oxides or proportion lead-containing compounds. This approach can excessively subjective prone mistakes while calculating mass fraction compounds containing potassium. So, better find link between glass’s its classifications during weathering process develop an effective classification model using machine learning techniques. this research, we suggest employing slime mould optimise parameters a support vector examine 69-group dataset. addition, results proposed algorithm compared those commonly used models: decision trees (DT), random forests (RF), machines (SVM), optimised genetic algorithms (GA-SVM). The indicated that method with sticky strategy most effective. On training set, 100% accuracy was attained, test 97.50% attained research. demonstrate combining capable providing trustworthy reference for future artefacts.