Prediction of the effects of environmental factors towards COVID-19 outbreak using AI-based models

Mahmoud, Khalid; Bebiş, Hatice; Usman, A. G.; Salihu, Armend; Gaya, Muhammad Sani; Dalhat, Umar Farouk; Abdulkadir, Rabiu Aliyu; Jibril, M. B.; Abba, S.I.

doi:10.11591/ijai.v10.i1.pp35-42

Cited by 15 publications

(12 citation statements)

References 19 publications

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“…Comparison of meta-heuristic algorithms for fuzzy modelling of … (Nur Azieta Mohamad Aseri) 51 It was found that using the fuzzy system is a suitable approach in identifying to identify the severity of the COVID-19 illness as it implements fuzzy logic and approximate reasoning [26], [27]. In addition, the fuzzy system also uses expert knowledge to ensure the system performs better.…”

Section: Int J Artif Intell Issn: 2252-8938mentioning

confidence: 99%

Comparison of meta-heuristic algorithms for fuzzy modelling of COVID-19 illness’ severity classification

Aseri

Ismail

Fakharudin

et al. 2022

IJ-AI

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The world health organization (WHO) proclaimed the COVID-19, commonly known as the coronavirus disease 2019, was a pandemic in March 2020. When people are in close proximity to one another, the virus spreads mostly through the air. It causes some symptoms in the affected person. COVID-19 symptoms are quite variable, ranging from none to severe sickness. As a result, the fuzzy method is seen favourably as a tool for determining the severity of a person’s COVID-19 sickness. However, when applied to a large situation, manually generating a fuzzy parameter is challenging. This could be because of the identification of a large number of fuzzy parameters. A mechanism, such as an automatic procedure, is consequently required to identify the right fuzzy parameters. The metaheuristic algorithm is regarded as a viable strategy. Five meta-heuristic algorithms were analyzed and utilized in this article to classify the severity of COVID-19 sickness data. The performance of the five meta-heuristic algorithms was evaluated using the COVID-19 symptoms dataset. The COVID-19 symptom dataset was created in accordance with WHO and the Indian ministry of health and family welfare criteria. The findings provide the average classification accuracy for each approach.

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Section: Int J Artif Intell Issn: 2252-8938mentioning

confidence: 99%

Comparison of meta-heuristic algorithms for fuzzy modelling of COVID-19 illness’ severity classification

Aseri

Ismail

Fakharudin

et al. 2022

IJ-AI

View full text Add to dashboard Cite

show abstract

“…As a result, the generally used error metrics are used to evaluate the outputs of prediction models as well as to compare them to one another. Metrics such as Coefficient of determination (R 2 ), Correlation coefficient (R), Mean square error (MSE) and Root mean square error (RMSE) were used to compare the performance success of the forecasting models used in this study more information on performance evaluation can be found in the following references [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [23], [40], [41], [42], [43], [44], [45], [46] and [47].…”

Section: Performance Evaluationmentioning

confidence: 99%

Implementation of adaptive neuro fuzzy inference system and back propagation neural network for the appraisal of power system contingency analysis

Yakubu¹,

B²,

Najashi³

et al. 2022

GSC Adv. Eng. Technol.

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Power System Security and Contingency analysis is one of the most important tasks in power systems. In operation, contingency analysis assists engineers to operate the power system at a secure and safe operating point where equipment are loaded within their safe operating area (SOA). Power is dispatched to customers with acceptable quality standards. The results of off-line load flow calculations are used to estimate performance indices (PI flow, PI V). MATLAB toolbox was the proposed methodology used for the implementation. The proposed approach for contingency analysis was found to be appropriate for screening and ranking fast voltage and line flow contingencies.

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“…The use of supervised learning to reduce the differences between computed and desired values is common. [22]. The learning rate is significant because it determines the network's intelligence by minimizing the probability of a local minimum by converging the network structure.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

The impact of online education due to COVID-19 outbreak as a cause of anxiety among university students in Northern Cyprus using cross- sectional survey: A data driven based approach

Ibrahim¹,

Nabegu²,

Ahseen³

2021

GSC Adv. Eng. Technol.

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Introduction: Coronavirus disease (COVID-19) or severe acute respiratory syndrome is an infectious disease caused by a newly found coronavirus strain. SARS-CoV-2 coronavirus is a type of virus that causes respiratory illnesses in people. Online learning system was switched due to the COVID-19 and GAD-7 Scales were used to measure the correlation and impact of online education as a reason for anxiety among university students in Northern Cyprus. Methods: The Generalized anxiety disorder (GAD-7) scale was used to assess anxiety level. An e-questionnaire was formed using google form and shared on social media. An online survey was done, it was participated by 209 students of universities in Northern Cyprus in which the data analysis was done using statistical package for social science (SPSS), MLR, ANN, and ANFIS. Results: 209 participants 64.6% of the participants had minimal to mild anxiety and 34.6% had moderate to severe anxiety according to generalized anxiety disorder (GAD-7) scale. Almost all Students with medium economic status have minimal to mild anxiety levels. The obtained data driven approach performance results indicates the ability of the hybrid techniques over the single models in simulation of the anxiety level of the students based on the performance evaluation indices used in the current research Conclusion: The COVID-19 Outbreak imposes a serious challenge to university students undergoing online classes in Northern Cyprus as majority of the students with medium income reported to had minimal to mild anxiety which require the attention of the concern authorities to provide a solution. The proposed hybrid techniques prove to have the necessary efficiency in boosting the performance of the single models towards anxiety level of the student’s simulation.

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Prediction of the effects of environmental factors towards COVID-19 outbreak using AI-based models

Cited by 15 publications

References 19 publications

Comparison of meta-heuristic algorithms for fuzzy modelling of COVID-19 illness’ severity classification

Comparison of meta-heuristic algorithms for fuzzy modelling of COVID-19 illness’ severity classification

Implementation of adaptive neuro fuzzy inference system and back propagation neural network for the appraisal of power system contingency analysis

The impact of online education due to COVID-19 outbreak as a cause of anxiety among university students in Northern Cyprus using cross- sectional survey: A data driven based approach

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