An Empirical Analysis on Big Analytics for e-Healthcare and Agriculture
Lecture Notes in Electrical Engineering, ISSN: 1876-1119, Vol: 758, Page: 409-417
2022
- 7Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Captures7
- Readers7
Conference Paper Description
There is a lot being said and done in the field of data analytics. Using large amounts of data for analytics has become one of the rising trends in the business world but, implementing this business intelligence into different sectors of government hasn’t still progressed well. We have discussed two major applications of data analytics in government sectors where the government and eventually the citizens could benefit from all the available big data. The applications include (i) Agriculture, where the big data analytics could result into better crop planning, yield analysis, improved soil health and irrigation as well as reduce the support cost incurred. (ii) The section on data analytics in healthcare mainly points out the importance of predictive analytics in improving personalized healthcare and healthcare infrastructure as a whole. It also talks about how the government can unlock value through big data and machine learning to provide better health insurance than the existing ones and how data analytics is helping with fraud detection while providing the health insurances.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85142691699&origin=inward; http://dx.doi.org/10.1007/978-981-16-2183-3_40; https://link.springer.com/10.1007/978-981-16-2183-3_40; https://dx.doi.org/10.1007/978-981-16-2183-3_40; https://link.springer.com/chapter/10.1007/978-981-16-2183-3_40
Springer Science and Business Media LLC
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