Revolutionizing Forensic Investigations Using q Rung Orthopair Fuzzy Logic to Trace Vehicle Parts Post Accident

Introduction

In the intricate world of forensic investigations, determining the origin of mechanical parts at accident scenes is crucial for legal clarity and justice. A recent study introduces a novel methodology using q-rung orthopair fuzzy numbers (q-ROFNs) within a multi-criteria decision-making (MCDM) framework—reshaping how experts deal with uncertainty in forensic evidence evaluation.
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Advanced MCDM in Forensic Science: A Breakthrough Approach

This study, conducted by Mehmet Akif Yerlikaya and Yunus Sayan, proposes a cutting-edge technique to analyze mechanical components from vehicle accidents:

• Objective: To determine the likely vehicle of origin for mechanical parts using six forensic criteria.
• Method Used: MCDM integrated with q-ROFNs a powerful fuzzy logic extension that handles expert uncertainty and ambiguous data more effectively than traditional approaches.
• Key Criteria Evaluated:
  • Compatibility with vehicle models
  • Damage consistency
  • Serial number and markings
  • Color/material compatibility
  • Wear patterns
  • Environmental residue

Read the full study at: https://doi.org/10.29328/journal.jfsr.1001067

q-Rung Orthopair Fuzzy Numbers: Why It Matters

q-ROFNs go beyond traditional fuzzy sets like intuitionistic and Pythagorean by allowing greater flexibility in modeling expert hesitation and indeterminacy. This innovation ensures:

• Nuanced Judgment Expression: Experts can express both their confidence and uncertainty.
• Higher Accuracy: Enhances decision reliability in the face of conflicting or incomplete evidence.
• Legal Strength: Improves the defensibility of forensic conclusions in court.

The American Academy of Forensic Sciences (AAFS) underscores the importance of continuous innovation in forensic methodologies to maintain the credibility and objectivity of legal proceedings.

Step-by-Step Methodology Explained

This novel MCDM process uses structured steps to ensure a transparent, systematic approach:

Define Alternatives and Criteria – Potential vehicle matches and forensic benchmarks.

Collect Expert Evaluations – Judgments are expressed as q-ROFNs.

1. Assign Weights – Based on legal relevance and expert consensus.
2. Aggregate Evaluations – Using q-ROFWA operators for robust comparisons.
3. Score & Rank – Alternatives are ranked to identify the most likely vehicle source.

A detailed analysis can be found in our main journal article.

Implications in Legal and Forensic Practice

• Enhanced Accuracy: Reduces the chances of misattribution in accident reconstructions.
• Greater Transparency: Clear, mathematical evaluations can be peer-reviewed.
• Adaptability: Methodology can extend to other forensic scenarios like digital forensics or biological trace analysis.

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