Daily Food Demand Forecast with Artificial Neural Networks: Kirikkale University Case

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dc.contributor.authorCetinkaya Z.
dc.contributor.authorErdal E.
dc.date.accessioned2020-06-25T15:18:06Z
dc.date.available2020-06-25T15:18:06Z
dc.date.issued2019
dc.departmentKırıkkale Üniversitesi
dc.description4th International Conference on Computer Science and Engineering, UBMK 2019 -- 11 September 2019 through 15 September 2019 -- -- 154916
dc.description.abstractIn food service organizations, demand estimation is very important in planning of production. When an accurate demand forecast is made, the resources are used more efficiently, and the production wants to be lost in some places. In the institutions where the number of people to make a request is not known clearly, the demand forecasts of the quantitative and qualitative targets are made. It has been proposed a model of artificial neural networks to estimate the daily meal demand. Artificial neural networks are a qualitative method that targets a predetermined target by using the previous example data using a predefined demand estimate. Kirikkale University cafeteria, where a selection should be made, the demand can affect, your criteria, MATLAB program was prepared a suitable model was created and the data were analyzed in the meantime. © 2019 IEEE.en_US
dc.identifier.doi10.1109/UBMK.2019.8907105
dc.identifier.endpage238en_US
dc.identifier.isbn9781728139647
dc.identifier.scopus2-s2.0-85076224368
dc.identifier.scopusqualityN/A
dc.identifier.startpage233en_US
dc.identifier.urihttps://doi.org/10.1109/UBMK.2019.8907105
dc.identifier.urihttps://hdl.handle.net/20.500.12587/2625
dc.identifier.wosWOS:000609879900044
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofUBMK 2019 - Proceedings, 4th International Conference on Computer Science and Engineering
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectDemand Forecasten_US
dc.subjectFood Forecasten_US
dc.titleDaily Food Demand Forecast with Artificial Neural Networks: Kirikkale University Caseen_US
dc.typeConference Object

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