Boron Nanoparticle Image Analysis using Machine Learning Algorithms

The effort of digital image processing involves efficient computation aimed at developing an economical, fasterand more accurate, and cost-effective automated system. The objective of this paper is to ascertain and categorizeBoron nanoparticles (BNP) using digital image processing techniques. The spatial features are unsheathed fromthe Boron nanoparticle Transmission Electron Microscope (TEM) images using different segmentationtechniques, namely; Fuzzy C -means (FCM) and K-means. The size of Boron nanoparticles is determined andcategorized based on the area(size) in the microsize.The synthesization and characterization of Boronnanoparticles play an important role as an elementary procedure for the formation of Boron nanoparticles. Theresults are analyzed, interpreted and comparison is done with the manual values to observe the efficacy of theresults. It is observed that the K-means segmentation technique yields a smaller amountof error (5.87%) ascompared with Fuzzy C-mean(16.78%). Hence, it is considered that the K-Means is the most relevantsegmentation technique for Boron nanoparticle image analysis and categorization. The statistical test ofsignificance is applied using the Chi-square testing method (at 5% of significance level) to check the relationshipbetween the manual results and the algorithm results.The proposed study also establishes collaborative researchwork between Chemistry and Computer Science departments to develop computational research on theseplatforms