Authors : Dr. Swarupa Amey Wagh
DOI : 10.46335/IJIES.2025.10.7.13
Abstract –The Rapid advancements in deep learning have significantly transformed DNA sequencing and forensic identification. Traditional forensic DNA analysis relies on STR profiling, but deep learning techniques offer enhanced accuracy, speed, and automation. This research explores the integration of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer models in analyzing DNA sequences for criminal identification.
Our approach involves preprocessing DNA datasets, extracting genetic markers, and classifying genetic profiles to match individuals in forensic databases. The study evaluates various deep learning models on forensic DNA datasets, highlighting their effectiveness in identifying individuals from complex DNA mixtures. The results demonstrate that deep learning-based DNA profiling surpasses conventional forensic methods in accuracy and scalability. Future research could further optimize models for degraded and low-quality DNA samples.
