We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Machine Learning Approach for Material Analytics and Classification - Insights Based on a Criminal Forensic Investigation Data.
- Authors
Raja, K. Venkatesh; Ayshwarya, B.; Geetha, D. Mohana; Nagaraj, V.; Ramkumar, R.
- Abstract
Glass is a non-crystalline chalcogenide amorphous solid that is often transparent and has widespread practical, technological, and decorative usage in, for example, windowpanes, tableware, and optoelectronics. Each type of glass has different material compositions to better suit the required application. Composition of glass has various material compositions like Si, Na, Mg, Al, Ca, Ba and so on, based of which the type of glass is classified. This research work primarily focuses on assessing the capability of Machine Learning models for predicting the type of glass left in a crime scene which can further be utilized for higher levels of criminological investigations. The proposed research process incorporates collection of data set from records of forensic investigation. Further, the data is processed for any errors and processed with the aid of popular machine learning algorithms viz. Regression, decision trees, k-means clustering and random forest classifier. The proposed data set has seven different types of glass attributes with 224 sample instances are used in this study for classification. From the results it is evident that, random forest algorithm performs well with higher magnitudes of accuracy.
- Subjects
FORENSIC sciences; MACHINE learning; CRIMINAL investigation; RANDOM forest algorithms; AMORPHOUS substances; FORENSIC psychiatry
- Publication
International Journal of Safety & Security Engineering, 2023, Vol 13, Issue 2, p359
- ISSN
2041-9031
- Publication type
Article
- DOI
10.18280/ijsse.130218