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

Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/2815

Full metadata record

DC Field	Value	Language
dc.contributor.author	Rankothge, W. H	-
dc.date.accessioned	2022-07-20T07:30:13Z	-
dc.date.available	2022-07-20T07:30:13Z	-
dc.date.issued	2022-03-04	-
dc.identifier.isbn	978-1-119-76047-4	-
dc.identifier.uri	http://rda.sliit.lk/handle/123456789/2815	-
dc.description.abstract	This 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.language.iso	en	en_US
dc.publisher	wiley	en_US
dc.relation.ispartofseries	Machine Learning Paradigm for Internet of Things Applications;chapter 2	-
dc.subject	Empirical Study	en_US
dc.subject	Paddy Harvest	en_US
dc.subject	Rice Demand	en_US
dc.subject	Prediction	en_US
dc.subject	Optimal Distribution	en_US
dc.subject	Distribution Plan	en_US
dc.title	An Empirical Study on Paddy Harvest and Rice Demand Prediction for an Optimal Distribution Plan	en_US
dc.type	Book chapter	en_US
dc.identifier.doi	https://doi.org/10.1002/9781119763499.ch2	en_US
Appears in Collections:	Book Chapters Chapters

Files in This Item:

File	Description	Size	Format
Machine Learning Paradigm for Internet of Things Applications 1.pdf Until 2050-12-31		3.3 MB	Adobe PDF	View/Open Request a copy