A deep neural network model for paternity testing based on 15-loci STR for Iraqi families
Paternity testing using a deoxyribose nucleic acid (DNA) profile is an essential branch of forensic science, and DNA short tandem repeat (STR) is usually used for this purpose. Nowadays, in third-world countries, conventional kinship analysis techniques used in forensic investigations result in inad...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2023-11-01
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| Series: | Journal of Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/jisys-2023-0041 |
| _version_ | 1827762793326051328 |
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| author | Khalid Donya A. Nafea Nasser |
| author_facet | Khalid Donya A. Nafea Nasser |
| author_sort | Khalid Donya A. |
| collection | DOAJ |
| description | Paternity testing using a deoxyribose nucleic acid (DNA) profile is an essential branch of forensic science, and DNA short tandem repeat (STR) is usually used for this purpose. Nowadays, in third-world countries, conventional kinship analysis techniques used in forensic investigations result in inadequate accuracy measurements, especially when dealing with large human STR datasets; they compare human profiles manually so that the number of samples is limited due to the required human efforts and time consumption. By utilizing automation made possible by AI, forensic investigations are conducted more efficiently, saving both time conception and cost. In this article, we propose a new algorithm for predicting paternity based on the 15-loci STR-DNA datasets using a deep neural network (DNN), where comparisons among many human profiles are held regardless of the limitation of the number of samples. For the purpose of paternity testing, familial data are artificially created based on the real data of individual Iraqi people from Al-Najaf province. Such action helps to overcome the shortage of Iraqi data due to restricted policies and the secrecy of familial datasets. About 53,530 datasets are used in the proposed DNN model for the purpose of training and testing. The Keras library based on Python is used to implement and test the proposed system, as well as the confusion matrix and receiver operating characteristic curve for system evaluation. The system shows excellent accuracy of 99.6% in paternity tests, which is the highest accuracy compared to the existing works. This system shows a good attempt at testing paternity based on a technique of artificial intelligence. |
| first_indexed | 2024-03-11T10:41:47Z |
| format | Article |
| id | doaj.art-7524891344474b2da65070db20054e26 |
| institution | Directory Open Access Journal |
| issn | 2191-026X |
| language | English |
| last_indexed | 2024-03-11T10:41:47Z |
| publishDate | 2023-11-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Intelligent Systems |
| spelling | doaj.art-7524891344474b2da65070db20054e262023-11-14T08:29:35ZengDe GruyterJournal of Intelligent Systems2191-026X2023-11-01321201402522210.1515/jisys-2023-0041A deep neural network model for paternity testing based on 15-loci STR for Iraqi familiesKhalid Donya A.0Nafea Nasser1College of Information Engineering, Al-Nahrain University, Baghdad 10011, IraqCollege of Information Engineering, Al-Nahrain University, Baghdad 10011, IraqPaternity testing using a deoxyribose nucleic acid (DNA) profile is an essential branch of forensic science, and DNA short tandem repeat (STR) is usually used for this purpose. Nowadays, in third-world countries, conventional kinship analysis techniques used in forensic investigations result in inadequate accuracy measurements, especially when dealing with large human STR datasets; they compare human profiles manually so that the number of samples is limited due to the required human efforts and time consumption. By utilizing automation made possible by AI, forensic investigations are conducted more efficiently, saving both time conception and cost. In this article, we propose a new algorithm for predicting paternity based on the 15-loci STR-DNA datasets using a deep neural network (DNN), where comparisons among many human profiles are held regardless of the limitation of the number of samples. For the purpose of paternity testing, familial data are artificially created based on the real data of individual Iraqi people from Al-Najaf province. Such action helps to overcome the shortage of Iraqi data due to restricted policies and the secrecy of familial datasets. About 53,530 datasets are used in the proposed DNN model for the purpose of training and testing. The Keras library based on Python is used to implement and test the proposed system, as well as the confusion matrix and receiver operating characteristic curve for system evaluation. The system shows excellent accuracy of 99.6% in paternity tests, which is the highest accuracy compared to the existing works. This system shows a good attempt at testing paternity based on a technique of artificial intelligence.https://doi.org/10.1515/jisys-2023-0041dnastrpaternity testingartificial intelligencednn |
| spellingShingle | Khalid Donya A. Nafea Nasser A deep neural network model for paternity testing based on 15-loci STR for Iraqi families Journal of Intelligent Systems dna str paternity testing artificial intelligence dnn |
| title | A deep neural network model for paternity testing based on 15-loci STR for Iraqi families |
| title_full | A deep neural network model for paternity testing based on 15-loci STR for Iraqi families |
| title_fullStr | A deep neural network model for paternity testing based on 15-loci STR for Iraqi families |
| title_full_unstemmed | A deep neural network model for paternity testing based on 15-loci STR for Iraqi families |
| title_short | A deep neural network model for paternity testing based on 15-loci STR for Iraqi families |
| title_sort | deep neural network model for paternity testing based on 15 loci str for iraqi families |
| topic | dna str paternity testing artificial intelligence dnn |
| url | https://doi.org/10.1515/jisys-2023-0041 |
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