DNA Recognition Using Novel Deep Learning Model
Abstract
DNA, a significant physiological biometric, is presentin all human cells like hair, blood, and skin. This research
introduces a new approach called the Deep DNA Learning
Network (DDLN) for person identification based on their DNA.
This novel Machine Learning model is designed to gather
DNA chromosomes from an individual’s parents. The model’s
flexibility allows it to expand or contract and has the capability to
determine one or both parents of an individual using the provided
chromosomes. Notably, the DDLN model offers quick training
in comparison to traditional deep learning methods. The study
employs two real datasets from Iraq: the Real Iraqi Dataset for
Kurds (RIDK) and the Real Iraqi Dataset for Arabs (RIDA). The
outcomes demonstrate that the proposed DDLN model achieves
an Equal Error Rate (EER) of 0 for both datasets, indicating
highly accurate performance.
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