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Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

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Quantum-inspired K-nearest neighbors classifier for enhanced printer source identification in forensic document

Saad M Darwish¹, Raad A Ali², Adel A Elzoghabi²

¹Department of Information Technology, Institute of Graduate Studies and Research, Alexandria University, 163 Horreya Avenue, El Shatby, P.O. Box 832, Alexandria, 21526, Egypt. saad.darwish@alexu.edu.eg.

Scientific Reports

|February 3, 2025

Summary

This summary is machine-generated.

This study introduces a quantum-inspired K-Nearest Neighbors (KNN) method for printer forensics. The quantum-inspired KNN (QKNN) improves accuracy in identifying document source printers by optimizing feature selection.

Keywords:

Classification Document source identification Feature modeling Printer forensics Quantum-inspired computing

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Area of Science:

Computer Science
Forensic Science
Quantum Computing

Background:

Document source identification is vital in forensic investigations.
Challenges include obscured artifacts and differentiating printers.
Machine learning requires robust feature identification and appropriate distance metrics for classifiers like K-Nearest Neighbors (KNN).

Purpose of the Study:

To enhance the performance of KNN classifiers in printer source identification.
To explore quantum-inspired computing for optimizing feature space calculations in noisy conditions.
To iteratively refine and select the optimal K value for KNN classification.

Main Methods:

Feature extraction using the Grey Level Co-occurrence Matrix (GLCM).
Implementation of a Quantum-inspired KNN (QKNN) classifier.
Iterative optimization of the number of neighbors (K) based on classification performance.

Main Results:

The QKNN classifier demonstrated superior performance compared to classical KNN.
Higher accuracy was achieved in identifying subtle printing artifacts.
The method proved effective even under variable and noisy conditions.

Conclusions:

Quantum-inspired approaches can significantly improve KNN performance in printer forensics.
QKNN offers a promising solution for accurate document source identification.
The GLCM feature extraction method is robust for this task.