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An Empirical Study on Paddy Harvest and Rice Demand Prediction for an Optimal Distribution Plan

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dc.contributor.authorRankothge, W. H
dc.date.accessioned2022-07-20T07:30:13Z
dc.date.available2022-07-20T07:30:13Z
dc.date.issued2022-03-04
dc.description.abstractThis book provides the state-of-the-art applications of Machine Learning in IoT environment. The most common use cases for machine learning and IoT data are predictive maintenance, followed by analyzing CCTV surveillance, smart home applications, smart-healthcare, in-store ‘contextualized marketing’ and intelligent transportation systems. Readers will gain an insight into the integration of Machine Learning with IoT in various application domains.en_US
dc.identifier.doihttps://doi.org/10.1002/9781119763499.ch2en_US
dc.identifier.isbn978-1-119-76047-4
dc.identifier.urihttps://rda.sliit.lk/handle/123456789/2815
dc.language.isoenen_US
dc.publisherwileyen_US
dc.relation.ispartofseriesMachine Learning Paradigm for Internet of Things Applications;chapter 2
dc.subjectEmpirical Studyen_US
dc.subjectPaddy Harvesten_US
dc.subjectRice Demanden_US
dc.subjectPredictionen_US
dc.subjectOptimal Distributionen_US
dc.subjectDistribution Planen_US
dc.titleAn Empirical Study on Paddy Harvest and Rice Demand Prediction for an Optimal Distribution Planen_US
dc.typeBook chapteren_US
dspace.entity.typePublication

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