Detecting COVID-19 patients based on fuzzy inference engine and Deep Neural Network.
TLDR
Experimental results have shown that the proposed HDS outperforms the other competitors in terms of the average value of accuracy, precision, recall, and F-measure in which it provides about of 97.658%, 96.756, 96.55%, and 96.615% respectively.About:
This article is published in Applied Soft Computing.The article was published on 2021-02-01 and is currently open access. It has received 60 citations till now. The article focuses on the topics: Feature selection.read more
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Artificial Intelligence in the Battle against Coronavirus (COVID-19): A Survey and Future Research Directions
TL;DR: A survey of AI methods being used in various applications in the fight against the COVID-19 outbreak is presented and the crucial roles of AI research in this unprecedented battle are outlined.
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Prediction and analysis of train arrival delay based on XGBoost and Bayesian optimization
TL;DR: A data-driven method that combines eXtreme Gradient Boosting (XGBoost) and a Bayesian optimization (BO) algorithm to predict train arrival delays outperforms other benchmark methods, especially in the prediction of long delays caused by specific abnormal events.
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Detection of COVID-19 severity using blood gas analysis parameters and Harris hawks optimized extreme learning machine
TL;DR: In this paper , a prediction framework that is based on an improved binary Harris hawk optimization (HHO) algorithm in combination with a kernel extreme learning machine is proposed in order to accurately determine the factors that play a decisive role in the early recognition and discrimination of COVID-19 severity.
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Performance of fuzzy multi-criteria decision analysis of emergency system in covid-19 pandemic. An extensive narrative review
Vicente Javier Clemente-Suárez,Eduardo Navarro-Jiménez,Pablo Ruisoto,Athanasios A. Dalamitros,Ana Isabel Beltrán-Velasco,Alberto Hormeño-Holgado,Carmen Cecilia Laborde-Cárdenas,José Francisco Tornero-Aguilera +7 more
TL;DR: In this paper, a systematic literature review of the available literature regarding the performance of the fuzzy multi-criteria decision analysis of emergency systems in the COVID-19 pandemic is presented.
References
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Fuzzy logic = computing with words
TL;DR: The point of this note is that fuzzy logic plays a pivotal role in CW and vice-versa and, as an approximation, fuzzy logic may be equated to CW.
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A Review of Coronavirus Disease-2019 (COVID-19).
TL;DR: The disease is mild in most people; in some (usually the elderly and those with comorbidities), it may progress to pneumonia, acute respiratory distress syndrome (ARDS) and multi organ dysfunction and many people are asymptomatic.
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Automated detection of COVID-19 cases using deep neural networks with X-ray images.
Tülin Öztürk,Muhammed Talo,Eylul Azra Yildirim,Ulas Baran Baloglu,Ozal Yildirim,U. Rajendra Acharya +5 more
TL;DR: A new model for automatic COVID-19 detection using raw chest X-ray images is presented and can be employed to assist radiologists in validating their initial screening, and can also be employed via cloud to immediately screen patients.
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Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks.
TL;DR: The results suggest that Deep Learning with X-ray imaging may extract significant biomarkers related to the Covid-19 disease, while the best accuracy, sensitivity, and specificity obtained is 96.78%, 98.66%, and 96.46% respectively.