@ARTICLE{Al-Kaltakchi_Musab_T.S._DNA_2024,
 author={Al-Kaltakchi, Musab T.S. and Abdulla, Hasan A. and Al-Nima, Raid Rafi Omar},
 volume={vol. 70},
 number={No 2},
 journal={International Journal of Electronics and Telecommunications},
 pages={481-487},
 howpublished={online},
 year={2024},
 publisher={Polish Academy of Sciences Committee of Electronics and Telecommunications},
 abstract={DNA, a significant physiological biometric, is present in 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.},
 type={Article},
 title={DNA recognition using Novel Deep Learning Model},
 URL={http://journals.pan.pl/Content/131810/29_4388_Kaltakchi_sk1.pdf},
 doi={10.24425/ijet.2024.149569},
 keywords={DNA Recognition, Deep Learning, DNA Identification},
}