applying rf/nb/knn classification techniques on thermal and mechanical properties of different pmma/nano-silica

Different compositions of bone cement having various amounts of solid and liquid phase with nano-silica as a filler are provided. after mixing for 30 seconds, the compositions are injected into 2.5 ml plastic syringes. after that, the syringes are placed in an oven at 37°c for 24 hours. impact and compressive strengths are also attained. all the outcomes are examined by random forest (rf), k- nearest neighbor (knn) and naive bayes (nb) classification techniques and it is seen that the specimens which are categorized according to their features are devided into two collections of with filler and without filler.