dc.contributor.advisor | Chakravorty, Antorweep | |
dc.contributor.author | Heidari, Paria | |
dc.date.accessioned | 2019-10-07T07:55:54Z | |
dc.date.available | 2019-10-07T07:55:54Z | |
dc.date.issued | 2019-06-23 | |
dc.identifier.uri | http://hdl.handle.net/11250/2620511 | |
dc.description | Master's thesis in Computer science | nb_NO |
dc.description.abstract | The 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.iso | eng | nb_NO |
dc.publisher | University of Stavanger, Norway | nb_NO |
dc.relation.ispartofseries | Masteroppgave/UIS-TN-IDE/2019; | |
dc.subject | informasjonsteknologi | nb_NO |
dc.subject | Random Forest | nb_NO |
dc.subject | datateknikk | nb_NO |
dc.subject | machine learning | nb_NO |
dc.subject | maskinlæring | nb_NO |
dc.subject | datateknologi | nb_NO |
dc.subject | data analysis | nb_NO |
dc.subject | regression analysis | nb_NO |
dc.subject | dimensionality reduction | nb_NO |
dc.subject | principal component analysis | nb_NO |
dc.title | Intelligent supply and demand for marine protein factory (based on MindSphere platform) | nb_NO |
dc.type | Master thesis | nb_NO |
dc.subject.nsi | VDP::Technology: 500::Information and communication technology: 550 | nb_NO |