Show simple item record

dc.contributor.advisorChakravorty, Antorweep
dc.contributor.authorHeidari, Paria
dc.date.accessioned2019-10-07T07:55:54Z
dc.date.available2019-10-07T07:55:54Z
dc.date.issued2019-06-23
dc.identifier.urihttp://hdl.handle.net/11250/2620511
dc.descriptionMaster's thesis in Computer sciencenb_NO
dc.description.abstractThe purpose of this project is to collect Internet Of Things data from available sources in the value chain. Our viewpoint to observe IoT data streams from all different processes in the factory as well as get an understanding of the different available features and the relation between them, by learning about the process with Siemens engineers and the factory. According to the captured data, the motivation is to model and predict production efficiency by using some appropriate machine learning algorithm. The models provide insights and correlations between features from the input parameters, and also analyze the data to produce the output. Data Pre-processing and re-sampling techniques are necessary to provide a deeper understanding of the essential features and to know which parameters are highly influential on production efficiency. In this experiment, captured data contains the different essential features of fish oil and meal production machines that are provided by Siemens. The significant part of the project is to analyze the data that has performed selecting features, validations, statistical analysis as well as presenting some graphs to decide which model can provide the best prediction with less error and finally propose a model for prediction of one of the critical key performance indicator.nb_NO
dc.language.isoengnb_NO
dc.publisherUniversity of Stavanger, Norwaynb_NO
dc.relation.ispartofseriesMasteroppgave/UIS-TN-IDE/2019;
dc.subjectinformasjonsteknologinb_NO
dc.subjectRandom Forestnb_NO
dc.subjectdatateknikknb_NO
dc.subjectmachine learningnb_NO
dc.subjectmaskinlæringnb_NO
dc.subjectdatateknologinb_NO
dc.subjectdata analysisnb_NO
dc.subjectregression analysisnb_NO
dc.subjectdimensionality reductionnb_NO
dc.subjectprincipal component analysisnb_NO
dc.titleIntelligent supply and demand for marine protein factory (based on MindSphere platform)nb_NO
dc.typeMaster thesisnb_NO
dc.subject.nsiVDP::Technology: 500::Information and communication technology: 550nb_NO


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record