2020 IEEE 3rd International Conference and Workshop in Óbuda on Electrical and Power Engineering (CANDO-EPE)最新文献

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Rapid COVID-19 Diagnosis Using Deep Learning of the Computerized Tomography Scans 利用计算机断层扫描的深度学习快速诊断COVID-19

2020 IEEE 3rd International Conference and Workshop in Óbuda on Electrical and Power Engineering (CANDO-EPE) Pub Date : 2020-09-21 DOI: 10.1109/CANDO-EPE51100.2020.9337794

Hamed Tabrizchi, A. Mosavi, Á. Szabó-Gali, I. Felde, L. Nádai

{"title":"Rapid COVID-19 Diagnosis Using Deep Learning of the Computerized Tomography Scans","authors":"Hamed Tabrizchi, A. Mosavi, Á. Szabó-Gali, I. Felde, L. Nádai","doi":"10.1109/CANDO-EPE51100.2020.9337794","DOIUrl":"https://doi.org/10.1109/CANDO-EPE51100.2020.9337794","url":null,"abstract":"Several studies suggest that COVID-19 may be accompanied by symptoms such as a dry cough, muscle aches, sore throat, and mild to moderate respiratory illness. The symptoms of this disease indicate the fact that COVID-19 causes noticeable negative effects on the lungs. Therefore, considering the health status of the lungs using X-rays and CT scans of the chest can significantly help diagnose COVID-19 infection. Due to the fact that most of the methods that have been proposed to COVID-19 diagnose deal with the lengthy testing time and also might give more false positive and false negative results, this paper aims to review and implement artificial intelligence (AI) image-based diagnosis methods in order to detect coronavirus infection with zero or near to zero false positives and false negatives rates. Besides the already existing AI image-based medical diagnosis method for the other well-known disease, this study aims on finding the most accurate COVID-19 detection method among AI methods such as machine learning (ML) and artificial neural network (ANN), ensemble learning (EL) methods.","PeriodicalId":201378,"journal":{"name":"2020 IEEE 3rd International Conference and Workshop in Óbuda on Electrical and Power Engineering (CANDO-EPE)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130165587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 19

Modeling and Sensitivity Analysis of Coronavirus Disease (COVID-19) Outbreak Prediction 冠状病毒病(COVID-19)暴发预测建模及敏感性分析

2020 IEEE 3rd International Conference and Workshop in Óbuda on Electrical and Power Engineering (CANDO-EPE) Pub Date : 2020-07-29 DOI: 10.1109/CANDO-EPE51100.2020.9337772

A. Sedaghat, S. A. A. Oloomi, Ashtian Malayer, A. Mosavi

{"title":"Modeling and Sensitivity Analysis of Coronavirus Disease (COVID-19) Outbreak Prediction","authors":"A. Sedaghat, S. A. A. Oloomi, Ashtian Malayer, A. Mosavi","doi":"10.1109/CANDO-EPE51100.2020.9337772","DOIUrl":"https://doi.org/10.1109/CANDO-EPE51100.2020.9337772","url":null,"abstract":"Susceptible-infectious-recovered-deceased (SIRD) model is an essential model for outbreak prediction. This paper evaluates the performance of the SIRD model for the outbreak of COVID-19 in Kuwait, which initiated on 24 February 2020 by five patients in Kuwait. This paper investigates the sensitivity of the SIRD model for the development of COVID-19 in Kuwait based on the duration of the progressed days of data. For Kuwait, we have fitted the SIRD model to COVID-19 data for 20, 40, 60, 80, 100, and 116 days of data and assessed the sensitivity of the model with the number of days of data. The parameters of the SIRD model are obtained using an optimization algorithm (lsqcurvefit) in MATLAB. The total population of 50,000 is equally applied for all Kuwait time intervals. Results of the SIRD model indicate that after 40 days, the peak infectious day can be adequately predicted. Although error percentage from sensitivity analysis suggests that different exposed population sizes are not correctly predicted. SIRD type models are too simple to robustly capture all features of COVID-19, and more precise methods are needed to tackle the correct trends of a pandemic.","PeriodicalId":201378,"journal":{"name":"2020 IEEE 3rd International Conference and Workshop in Óbuda on Electrical and Power Engineering (CANDO-EPE)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126564601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

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