Orthogonal Procrustes and Machine Learning: Predicting Bill of Materials errors on time

Author:

Schramm SimonORCID,Pieper Matthias,Vogl StefanieORCID

Funder

Bayerische Motoren Werke AG

Publisher

Elsevier BV

Subject

General Engineering,General Computer Science

Reference39 articles.

1. Generating integrated bill of materials using mask R-CNN artificial intelligence model;Automation in Construction,2023

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3. Customer churn prediction in telecom using machine learning in big data platform;Ahmad;Journal of Big Data,2019

4. Artificial neural network classification of high dimensional data with novel optimization approach of dimension reduction;Aziz;Annals of Data Science,2018

5. Towards traceability in data ecosystems using a bill of materials model;Barclay,2019

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