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Browsing by study line "Yleinen opintosuunta"

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  • Lampinen, Sebastian (2022)
    Modeling customer engagement assists a business in identifying the high risk and high potential customers. A way to define high risk and high potential customers in a Software-as-a-Service (SaaS) business is to define them as customers with high potential to churn or upgrade. Identifying the high risk and high potential customers in time can help the business retain and grow revenue. This thesis uses churn and upgrade prediction classifiers to define a customer engagement score for a SaaS business. The classifiers used and compared in the research were logistic regression, random forest and XGBoost. The classifiers were trained using data from the case-company containing customer data such as user count and feature usage. To tackle class imbalance, the models were also trained with oversampled training data. The hyperparameters of each classifier were optimised using grid search. After training the models, performance of the classifiers on a test data was evaluated. In the end, the XGBoost classifiers outperformed the other classifiers in churn prediction. In predicting customer upgrades, the results were more mixed. Feature importances were also calculated, and the results showed that the importances differ for churn and upgrade prediction.
  • Pyykölä, Sara (2022)
    This thesis regards non-Lambertian surfaces and their challenges, solutions and study in computer vision. The physical theory for understanding the phenomenon is built first, using the Lambertian reflectance model, which defines Lambertian surfaces as ideally diffuse surfaces, whose luminance is isotropic and the luminous intensity obeys Lambert's cosine law. From these two assumptions, non-Lambertian surfaces violate at least the cosine law and are consequently specularly reflecting surfaces, whose perceived brightness is dependent from the viewpoint. Thus non-Lambertian surfaces violate also brightness and colour constancies, which assume that the brightness and colour of same real-world points stays constant across images. These assumptions are used, for example, in tracking and feature matching and thus non-Lambertian surfaces pose complications for object reconstruction and navigation among other tasks in the field of computer vision. After formulating the theoretical foundation of necessary physics and a more general reflectance model called the bi-directional reflectance distribution function, a comprehensive literature review into significant studies regarding non-Lambertian surfaces is conducted. The primary topics of the survey include photometric stereo and navigation systems, while considering other potential fields, such as fusion methods and illumination invariance. The goal of the survey is to formulate a detailed and in-depth answer to what methods can be used to solve the challenges posed by non-Lambertian surfaces, what are these methods' strengths and weaknesses, what are the used datasets and what remains to be answered by further research. After the survey, a dataset is collected and presented, and an outline of another dataset to be published in an upcoming paper is presented. Then a general discussion about the survey and the study is undertaken and conclusions along with proposed future steps are introduced.