University of Bahrain
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Deep Learning based Analysis of Covid-19 Mortality risk

Show simple item record Iqbal, Touseef Qureshi, Shaima 2022-03-09T11:55:23Z 2022-03-09T11:55:23Z 2022-03-09
dc.identifier.issn 2210-142X
dc.description.abstract Viral infectious diseases such as Covid-19 present a major threat to public health. Despite extreme research efforts, how, when and where such new outbreaks appear is still a source of substantial uncertainty. Deep learning (DL) is playing an increasingly important role in our lives. This paper presents one of the popular deep learning technique, Long Short Term Memory (LSTM) for prediction of Corona-Virus cases. The handcrafted feature extraction of traditional methods is less scalable on large data-sets, but deep learning algorithms perform extremely well on large data-sets, because of automatic feature extraction. Deep learning has already made a huge impact in areas, such as cancer diagnosis, precision medicine, self-driving cars, predictive forecasting, and speech recognition. This paper highlights the approaches where deep learning can be helpful to tackle the Covid-19 virus and similar outbreaks. This paper also discusses the structure and functioning of Covid-19. The utilization of different deep learning concepts like Convolutional Neural Networks, Transfer Learning for this pandemic is also highlighted. en_US
dc.language.iso en en_US
dc.publisher University of Bahrain en_US
dc.subject Covid-19 en_US
dc.subject Deep Learning (DL) en_US
dc.subject Convolutional Neural Networks (CNNs) en_US
dc.subject Long Short Term Memory (LSTM) en_US
dc.title Deep Learning based Analysis of Covid-19 Mortality risk en_US
dc.volume 11 en_US
dc.issue 1 en_US
dc.pagestart 1 en_US
dc.pageend 8 en_US
dc.contributor.authoraffiliation Department of Computer Science and Engineering, National Institute of Technology Srinagar, J&K, India en_US
dc.source.title International Journal of Computing and Digital Systems en_US
dc.abbreviatedsourcetitle IJCDS en_US

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