Introduction

Estimating the Post-Mortem Interval (PMI) is a critical task in forensic investigations, but current methods often rely on time-consuming manual calculations. This research aims to automate PMI estimation using computational algorithms to enhance accuracy and efficiency in forensic science.

Key Findings

• Develop a computational tool that automates PMI estimation using mathematical models.
• Integrate environmental variables like temperature and humidity into the calculations.
• Minimize human error and improve the precision of time-of-death determinations.

The project employs computational frameworks to automate established forensic models such as the Glaister Equation and Newton’s Law of Cooling. Tools like SQL, AWS, and API integrations are used to manage forensic datasets and retrieve environmental data.

Expected Impact

Current Status: The project has completed foundational work, including literature reviews, preliminary database schemas, and algorithm prototyping.

Expected Impact: By automating PMI calculations, this research aims to streamline forensic investigations, reduce processing times, and improve overall accuracy in time-of-death determinations.

References

• Barash et al. (2023). Machine learning applications in forensic DNA profiling.
• England, D. B. (2006). Post Mortem Interval and Decomposition Rates.
• Shrestha, R. (2023). Methods of estimation of time since death.