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A performance comparison of supervised machine learning models for Covid-19 tweets sentiment analysis
A performance comparison of supervised machine learning models for Covid-19 tweets sentiment analysis
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A performance comparison of supervised machine learning models for Covid-19 tweets sentiment analysis

PloS one, 2021-02, Vol.16 (2), p.e0245909-e0245909 [Peer Reviewed Journal]

COPYRIGHT 2021 Public Library of Science ;COPYRIGHT 2021 Public Library of Science ;2021 Rustam et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2021 Rustam et al 2021 Rustam et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0245909 ;PMID: 33630869

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Not getting in too deep: A practical deep learning approach to routine crystallisation image classification
Not getting in too deep: A practical deep learning approach to routine crystallisation image classification
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Not getting in too deep: A practical deep learning approach to routine crystallisation image classification

PloS one, 2023-03, Vol.18 (3), p.e0282562-e0282562 [Peer Reviewed Journal]

Copyright: © 2023 Milne et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. ;COPYRIGHT 2023 Public Library of Science ;2023 Milne et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2023 Milne et al 2023 Milne et al ;2023 Milne et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0282562 ;PMID: 36893084

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A machine learning model on Real World Data for predicting progression to Acute Respiratory Distress Syndrome (ARDS) among COVID-19 patients
A machine learning model on Real World Data for predicting progression to Acute Respiratory Distress Syndrome (ARDS) among COVID-19 patients
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A machine learning model on Real World Data for predicting progression to Acute Respiratory Distress Syndrome (ARDS) among COVID-19 patients

PloS one, 2022-07, Vol.17 (7), p.e0271227-e0271227 [Peer Reviewed Journal]

COPYRIGHT 2022 Public Library of Science ;2022 Lazzarini et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2022 Lazzarini et al 2022 Lazzarini et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0271227 ;PMID: 35901089

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Short k-mer abundance profiles yield robust machine learning features and accurate classifiers for RNA viruses
Short k-mer abundance profiles yield robust machine learning features and accurate classifiers for RNA viruses
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Short k-mer abundance profiles yield robust machine learning features and accurate classifiers for RNA viruses

PloS one, 2020-09, Vol.15 (9), p.e0239381-e0239381 [Peer Reviewed Journal]

COPYRIGHT 2020 Public Library of Science ;COPYRIGHT 2020 Public Library of Science ;2020 Alam, Chowdhury. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2020 Alam, Chowdhury 2020 Alam, Chowdhury ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0239381 ;PMID: 32946529

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