A first step towards a machine learning-based framework for bloodstain classification in forensic science.

Autor: Jung H; Department of Computer Science and Engineering, Sungkyunkwan University, Suwon, Gyeonggi-Do 16419, Republic of Korea. Electronic address: hyeonah214@skku.edu., Jo YS; Department of Forensic Sciences, Sungkyunkwan University, Suwon, Gyeonggi-Do 16419, Republic of Korea. Electronic address: joys98@skku.edu., Ahn Y; Department of Computer Science and Engineering, Sungkyunkwan University, Suwon, Gyeonggi-Do 16419, Republic of Korea. Electronic address: ahnjs124@skku.edu., Jeong J; Department of Computer Science and Engineering, Sungkyunkwan University, Suwon, Gyeonggi-Do 16419, Republic of Korea. Electronic address: pauljeong@skku.edu., Lim SK; Department of Forensic Sciences, Sungkyunkwan University, Suwon, Gyeonggi-Do 16419, Republic of Korea. Electronic address: sikeun.lim@skku.edu.
Jazyk: angličtina
Zdroj: Forensic science international [Forensic Sci Int] 2024 Dec; Vol. 365, pp. 112278. Date of Electronic Publication: 2024 Oct 31.
DOI: 10.1016/j.forsciint.2024.112278
Abstrakt: Bloodstains found at a crime scene can help estimate the events that occurred during the crime. Reconstructing the crime scene by analyzing the bloodstain pattern contributes to understanding the bloody event. Therefore, it is essential to classify bloodstains through bloodstain pattern analysis (BPA) and accurately estimate the actions that took place at that time. In this study, we investigate the potential of using machine learning and deep learning to determine an action related to bloodstain data through the accessment of the corresponding bloodstain type by creating a prototype classification model. There are 14 types of bloodstain according to the classification system based on appearance. In this study, we test the classification potential of each bloodstain data for three bloodstain patterns such as Swing, Cessation, and Impact. Through experiments, it is shown that our prototype classification model for the selected bloodstains is developed and the accuracy of the resulting model is evaluated to be 80 %.
Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: The corresponding author reports financial support was provided by his government. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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