DNA recognition using Novel Deep Learning Model

• Autorzy: Al-Kaltakchi Musab T. S. , Abdulla Hasan A. , Al-Nima Raid Rafi Omar

Identyfikatory

DOI

10.24425/ijet.2024.149569

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

DNA recognition; deep learning; DNA identification

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2024
• Tom: Vol. 70, No. 2
• Strony: 481--487
• Opis fizyczny: Bibliogr. 28 poz., rys.

Twórcy

autor

Al-Kaltakchi Musab T. S.

• Department of Electrical Engineering, College of Engineering, Mustansiriyah University, Baghdad, Iraq

autor

Abdulla Hasan A.

• Technical Engineering College of Mosul, Northern Technical University, Iraq

autor

Al-Nima Raid Rafi Omar

• Technical Engineering College of Mosul, Northern Technical University, Iraq

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