Predicting death and confirmed cases of coronavirus

Abdulraheem, Farqad H.; Al-Ridha, Moatasem Yaseen; Al-Nima, Raıd Rafı Omar

doi:10.11591/eei.v11i1.3133

Cited by 3 publications

(3 citation statements)

References 9 publications

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Section: Resultsmentioning

confidence: 99%

“…Also, the accuracy for the multiple classifications of signatures in this paper achieves 97.2, which is obviously a high value. Different state-of-the-art neural network techniques might be used in various applications as in[29],[30].Additional comparisons with state-of-the-art papers are considered, specifically with[31],[32]. Both of these papers concentrated on writer identification.…”

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confidence: 99%

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Classifications of signatures by radial basis neural network

2022

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The personal signature can be considered one of the most common behavioral biometrics. In this study, signatures are classified according to their specifications. The statistical calculation is considered for the specifications of each signature. Then, a radial basis neural network (RBNN) is adapted to apply multiple classifications for the employed signatures. A big number of signatures are utilized; they are obtained from the database called biometric ideal test (BIT). The total number of collected signatures is equally divided between the testing and training phases, where it is partitioned into 50% for the training and 50% for the testing. The proposed technique could achieve attractive performance, where each of the mean square error (MSE) and mean absolute error (MAE) attained a small value of 0.028. In addition, the proposed approach using the RBNN is compared with the different neural networks of the state-of-the-art techniques in order to demonstrate that the outcomes are acceptable and successful.

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Section: Resultsmentioning

confidence: 99%

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confidence: 99%

Classifications of signatures by radial basis neural network

2022

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“…As a result, this leaded to obtain a prediction model with effective time series. Abdulraheem et al [14] introduced a work for predicting the risk of confirmed and death incidents. The machine learning of byesian regularization backpropagation (BRB) was employed.…”

Section: Introductionmentioning

confidence: 99%

Coronavirus risk factor by Sugeno fuzzy logic

Qasim Hasan,

Omar Al-Nima,

Esmail Mahmmod

2024

IJ-AI

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<span>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%.</span>

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