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Manage. Inf. Syst."],"published-print":{"date-parts":[[2022,3,31]]},"abstract":"<jats:p>\n            (\n            <jats:bold>Aim<\/jats:bold>\n            ) COVID-19 has caused more than 2.28 million deaths till 4\/Feb\/2021 while it is still spreading across the world. This study proposed a novel artificial intelligence model to diagnose COVID-19 based on chest CT images. (\n            <jats:bold>Methods<\/jats:bold>\n            ) First, the two-dimensional fractional Fourier entropy was used to extract features. Second, a custom\n            <jats:bold>deep stacked sparse autoencoder (DSSAE)<\/jats:bold>\n            model was created to serve as the classifier. Third, an improved multiple-way data augmentation was proposed to resist overfitting. (\n            <jats:bold>Results<\/jats:bold>\n            ) Our DSSAE model obtains a micro-averaged F1 score of 92.32% in handling a four-class problem (COVID-19, community-acquired pneumonia, secondary pulmonary tuberculosis, and healthy control). (\n            <jats:bold>Conclusion<\/jats:bold>\n            ) Our method outperforms 10 state-of-the-art approaches.\n          <\/jats:p>","DOI":"10.1145\/3451357","type":"journal-article","created":{"date-parts":[[2021,10,5]],"date-time":"2021-10-05T18:37:36Z","timestamp":1633459056000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":21,"title":["DSSAE: Deep Stacked Sparse Autoencoder Analytical Model for COVID-19 Diagnosis by Fractional Fourier Entropy"],"prefix":"10.1145","volume":"13","author":[{"given":"Shui-Hua","family":"Wang","sequence":"first","affiliation":[{"name":"University of Leicester, Leicester, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Fourth People's Hospital of Huai'an, Huai'an, Jiangsu Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-Dong","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Leicester, Leicester, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,10,5]]},"reference":[{"issue":"2","key":"e_1_3_1_2_2","first-page":"9744","article-title":"Does living in previously exposed malaria or warm areas is associated with a lower risk of severe COVID-19 infection in Italy?","volume":"11","author":"Carta M. 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