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  • 1.
    Ogeborg, Marcus
    et al.
    KTH, School of Technology and Health (STH), Medical Engineering, Computer and Electronic Engineering.
    Widerberg, Vincent
    KTH, School of Technology and Health (STH), Medical Engineering, Computer and Electronic Engineering.
    Schemaläggning med hjälp av maskininlärning2017Independent thesis Basic level (Higher Education Diploma (Fine Arts)), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    This study has been analyzing if machine learning could be useful to work-relatedscheduling. The analysis was based on predictions generated by prototypes usingbusiness calendars. The business calendars were collected from two service and installationcompanies in the Stockholm region. An analysis was conducted regardingif the application could be practically applied to devices such as a smartphone. Theanalysis was based on tests regarding the prototypes required time to perform theirtasks.Three prototypes were developed with algorithms that made them predictive. Density-based Spatial Clustering of Applications with Noise (DBSCAN), Logistic Regressionand Weighted K-Nearest Neighbors (wKNN) were the implemented algorithms.DBSCAN was the best-performing algorithm according to the tests. However, a conclusioncould not be found concerning whether machine learning could be useful.The number of successful predictions did not exceed the number of available timeson concerned days, which was assumed as unsatisfying results. In addition, the prototypesneeded a significant amount of resources which could be a problem in practicaluse.

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