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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Rajendrani, Mukherjee | - |
| dc.contributor.author | Aurghyadip, Kundu | - |
| dc.contributor.author | Indrajit, Mukherjee | - |
| dc.date.accessioned | 2023-03-31T02:51:39Z | - |
| dc.date.available | 2023-03-31T02:51:39Z | - |
| dc.date.issued | 2023 | - |
| dc.identifier.uri | https://link.springer.com/article/10.1007/s00607-021-00951-9 | - |
| dc.identifier.uri | https://dlib.phenikaa-uni.edu.vn/handle/PNK/7365 | - |
| dc.description | CC BY | vi |
| dc.description.abstract | COVID - 19 affected severely worldwide. The pandemic has caused many causalities in a very short span. The IoT-cloud-based healthcare model requirement is utmost in this situation to provide a better decision in the covid-19 pandemic. In this paper, an attempt has been made to perform predictive analytics regarding the disease using a machine learning classifier. This research proposed an enhanced KNN (k NearestNeighbor) algorithm eKNN, which did not randomly choose the value of k. However, it used a mathematical function of the dataset’s sample size while determining the k value. The enhanced KNN algorithm eKNN has experimented on 7 benchmark COVID-19 datasets of different size, which has been gathered from standard data cloud of different countries (Brazil, Mexico, etc.). | vi |
| dc.language.iso | en | vi |
| dc.publisher | Springer | vi |
| dc.subject | NearestNeighbor) | vi |
| dc.subject | eKNN | vi |
| dc.title | IoT-cloud based healthcare model for COVID-19 detection an enhanced k-Nearest Neighbour classifier based approach | vi |
| dc.type | Book | vi |
| Appears in Collections | OER - Công nghệ thông tin | |
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