| 000 | 01740naa a22003497a 4500 | ||
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| 001 | 00005823 | ||
| 003 | ES-MaONT | ||
| 005 | 20211221160753.0 | ||
| 008 | 200318s2020 xxk |||fst|||i00| 0 eng d | ||
| 024 |
_2doi _a10.1017/ice.2020.61 |
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| 040 | _aES-MaONT | ||
| 100 | 1 |
_aRao, Arni S.R. Srinivasa _94288 |
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| 245 | 1 | 0 |
_aIdentification of COVID-19 Can be Quicker through Artificial Intelligence framework using a Mobile Phone-Based Survey in the Populations when Cities/Towns Are Under Quarantine _c/ Arni S.R. Srinivasa Rao and Jose A. Vazquez |
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_a[Cambridge] : _bCambridge University Press, _c2020 |
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| 300 |
_a[18] p.; _c1 documento PDF |
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_atexto (visual) _2isbdcontent |
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_aelectrónico _2isbdmedia |
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_arecurso en línea _2rdacarrier |
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| 520 | _aWe are proposing to use machine learning algorithms to be able to improve possible case identifications of COVID-19 more quicker when we use a mobile phone-based web survey. This will also reduce the spread in the susceptible populations | ||
| 650 | 0 |
_aTecnologías habilitadoras digitales _918 |
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| 653 | _aAI | ||
| 653 | _acoronavirus | ||
| 653 | _aartificial intelligence | ||
| 653 | _aCOVID-19 | ||
| 653 | _ahealthcare | ||
| 653 | _amobile phone | ||
| 653 | _aquarantine | ||
| 700 | 1 |
_aVazquez, Jose A. _94289 |
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| 710 | 2 |
_aCambridge University Press _94290 |
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| 856 | 4 |
_uhttps://www.cambridge.org/core/services/aop-cambridge-core/content/view/7151059680918EF9B8CDBCC4EF19C292/S0899823X20000616a.pdf/identification_of_covid19_can_be_quicker_through_artificial_intelligence_framework_using_a_mobile_phonebased_survey_in_the_populations_when_citiestowns_are_under_quarantine.pdf _x0 _zAcceso al documento _qpdf |
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_2z _cART |
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_c5823 _d5823 |
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