Welcome to the InfoSci Platform
Reference Hub1
Optimization of Crime Scene Reconstruction Based on Bloodstain Patterns and Machine Learning Techniques

Optimization of Crime Scene Reconstruction Based on Bloodstain Patterns and Machine Learning Techniques

Samir Kumar Bandyopadhyay, Nabanita Basu
Copyright: © 2017 |Pages: 27
ISBN13: 9781522518372|ISBN10: 1522518371|EISBN13: 9781522518389
DOI: 10.4018/978-1-5225-1837-2.ch069
Cite Chapter Cite Chapter

MLA

Bandyopadhyay, Samir Kumar, and Nabanita Basu. "Optimization of Crime Scene Reconstruction Based on Bloodstain Patterns and Machine Learning Techniques." Decision Management: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2017, pp. 1497-1523. https://doi.org/10.4018/978-1-5225-1837-2.ch069

APA

Bandyopadhyay, S. K. & Basu, N. (2017). Optimization of Crime Scene Reconstruction Based on Bloodstain Patterns and Machine Learning Techniques. In I. Management Association (Ed.), Decision Management: Concepts, Methodologies, Tools, and Applications (pp. 1497-1523). IGI Global. https://doi.org/10.4018/978-1-5225-1837-2.ch069

Chicago

Bandyopadhyay, Samir Kumar, and Nabanita Basu. "Optimization of Crime Scene Reconstruction Based on Bloodstain Patterns and Machine Learning Techniques." In Decision Management: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1497-1523. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-1837-2.ch069

Export Reference

Mendeley
Favorite

Abstract

Crime scene reconstruction based on circumstantial evidence and bloodstain patterns at the scene is often affected by unwanted expert bias. Using features such as bloodstain pattern, wound analysis, size of bloodstains on objects etc., predictions could be made about the relative position of the victim/s, bystander/s and perpetrator/s. Supervised learning techniques can be used to make predictions related to the murder weapon used. Gender of an individual could also be estimated from the bloody broken plastic footprint of an individual using a suitable dataset and supervised classifier. These intermediate prediction modules are important for development of event segments. The event segments add up towards the development of the events that transpired at the crime scene. An optimal sequence of events that might have transpired at the crime scene could thereby be developed using event timestamp and logical sequencing of similar incidents that had occurred in the past using probability theory.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.