Although Machine Learning (ML) in supply chain management (SCM) has become a popular topic, predictive uses of ML in …

This paper applies machine learning to forecast business cycles in the German economy using a high-dimensional dataset … setting shows that many indicators are stable across time and business cycles. Machine learning models prove particularly … diminishes. The findings contribute to the ongoing discussion on the use of machine learning in economic forecasting, especially …

‘learning from external stakeholders’ took place. Projects and stakeholders appeared to benefit from this learning (e.g. by … as indicators of learning. Our contribution is threefold: first, we create a better understanding of learning by … cases. Third, we present propositions that contribute to increasing the value of public infrastructure projects by learning …

Employment Research. We use a machine learning based methodology, which 1) improves the record linkage of datasets without unique … identifiers, and 2) evaluates the quality of the record linkage. The machine learning algorithms are trained on a synthetic …

outcomes for the most vulnerable. Objective: To evaluate whether machine learning methods outperform linear regression in … predicting dropouts from a telemonitoring program. Methods: Use of linear regression and machine learning to predict dropouts … classification of dropouts compared to neural networks with a sensitivity of 56%. All machine learning algorithms outperformed linear …