Sökning: "cross-validation"

Visar resultat 16 - 20 av 252 uppsatser innehållade ordet cross-validation.

1. 16. Rotor temperature estimation in Induction Motors with Supervised Machine Learning

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Christopher Gauffin; [2023]
   Nyckelord :Induction motors; Supervised Machine learning; Power converters; Parameter estimation; Embedded systems; Induktionsmotorer; Övervakad maskininlärning; Strömkonverterare; Parameterestimering; Inbyggda system;

   Sammanfattning : The electrification of the automotive industry and artificial intelligence are both growing rapidly and can be greatly beneficial for a more sustainable future when combined. Induction machines exhibit many complex relationships between physical and electromagnetic properties that must be calculated in order to produce the correct quantities of torque and speed commanded by the driver. LÄS MER

3. 17. Machine Learning based Predictive Data Analytics for Embedded Test Systems

   Kandidat-uppsats, Mälardalens universitet/Akademin för innovation, design och teknik

   Författare :Fayad Al Hanash; [2023]
   Nyckelord :Machine learning; Artificial Intelligence; Predictive data analytics; Embedded test systems; Confusion matrix; Predictive maintenance; Support vector machines; Random forest; Gradient Boosting; Multi-layer perceptron; Binary classification; Multi-class classification;

   Sammanfattning : Organizations gather enormous amounts of data and analyze these data to extract insights that can be useful for them and help them to make better decisions. Predictive data analytics is a crucial subfield within data analytics that make accurate predictions. Predictive data analytics extracts insights from data by using machine learning algorithms. LÄS MER

4. 18. Toward an application of machine learning for predicting foreign trade in services – a pilot study for Statistics Sweden

   Master-uppsats, Stockholms universitet/Statistiska institutionen

   Författare :Tea Unnebäck; [2023]
   Nyckelord :foreign trade in services; sampling; sampling frame; statistics; machine learning; random forest; predicting; extreme gradient boosting; k nearest neighbors; k-nn; official statistics; statistics sweden;

   Sammanfattning : The objective of this thesis is to investigate the possibility of using machine learn- ing at Statistics Sweden within the Foreign Trade in Services (FTS) statistic, to predict the likelihood of a unit to conduct foreign trade in services. The FTS survey is a sample survey, for which there is no natural frame to sample from. LÄS MER

5. 19. Evaluating Membership Inference Attacks on Synthetic Data Generated With Formal Privacy Guarantees

   Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Elliot Beskow; Erik Lindé; [2023]
   Nyckelord :;

   Sammanfattning : Synthetic data generation using generative machine learning has been increasinglypublicized as a new tool for data anonymization. It promises to offer privacy whilemaintaining the statistical properties of the original dataset. This study focuses on the riskswith synthetic data by looking mainly at two aspects: privacy and utility. LÄS MER

7. 20. Data-Driven Success in Infrastructure Megaprojects. : Leveraging Machine Learning and Expert Insights for Enhanced Prediction and Efficiency

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :David E.G. Nordmark; [2023]
   Nyckelord :Megaproject; Small sample size; Project management; Random forest; Critical success factors; Feature selection; Recursive feature elimination; Megaprojekt; Små dataurval; Projektledning; Random forest; Kritiska framgångsfaktorer; Variabel urval; Rekursiv variabel eliminering;

   Sammanfattning : This Master's thesis utilizes random forest and leave-one-out cross-validation to predict the success of megaprojects involving infrastructure. The goal was to enhance the efficiency of the design and engineering phase of the infrastructure and construction industries. LÄS MER