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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Omar Al-Nima, Raid Rafi
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Coronavirus risk factor by Sugeno fuzzy logic Qasim Hasan, Saba; Omar Al-Nima, Raid Rafi; Esmail Mahmmod, Sahar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1420-1429

Abstract

World recently faced big challenges with the pandemic of coronavirus disease 2019 (COVID-19). Governments suffer from the problem of appropriately identifying the risk factor of this virus and establishing their safety procedures accordingly. This paper concentrates on designing a coronavirus risk factor (CRF) by the power of Sugeno fuzzy logic (SFL). The main advantage of the CRF is that it can provides a quick and suitable risk evaluation. According to the degree of severity, three essential parameters are considered: number of infected cases, number of people in intensive care units (ICU) and number of deaths. All of these parameters are provided per population. Such interesting and promising outcomes are attained, where the total effect is found equal to 95.3%.
Identifying deoxyribonucleic acids of individuals based on their chromosomes by proposing a special deep learning model Omar Al-Nima, Raid Rafi; Salahaldeen Al-Kaltakchi, Musab Tahseen; A. Abdulla, Hasan
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.6198

Abstract

One of the most significant physiological biometrics is the deoxyribonucleic acid (DNA). It can be found in every human cell as in hair, blood, and skin. In this paper, a special DNA deep learning (SDDL) is proposed as a novel machine learning (ML) model to identify persons depending on their DNAs. The proposed model is designed to collect DNA chromosomes of parents for an individual. It is flexible (can be enlarged or reduced) and it can identify one or both parents of a person, based on the provided chromosomes. The SDDL is so fast in training compared to other traditional deep learning models. Two real datasets from Iraq are utilized called: Real Iraqi Dataset for Kurd (RIDK) and Real Iraqi Dataset for Arab (RIDA). The results yield that the suggested SDDL model achieves 100% testing accuracy for each of the employed datasets.
Co-Authors A. Abdulla, Hasan Esmail Mahmmod, Sahar Qasim Hasan, Saba Salahaldeen Al-Kaltakchi, Musab Tahseen