We study the importance of noncognitive skills in explaining differences in the labor market performance of individuals by means of machine learning techniques. Unlike previous em- pirical approaches centering around the within-sample explanatory power of noncognitive skills our approach focuses...

The focus of the research study is to investigate and analyses the current data processing and analytics issues in health focusing in diabetes data order to make sense of the data and use it to improve the health system. Within the investigation of the set of data from the ministry of health of...

This paper proposes a new tool in the field of telemedicine, defined as a specific branch where IT supports medicine, in case distance impairs the proper care to be delivered to a patient. All the information contained into medical texts, if properly extracted, may be suitable for searching,...

Purpose: This research paper aims to create an environment which enables robots to learn from humans by algorithms of Computer Vision and Machine Learning for object detection and gripping. The proposed concept transforms manual picking to highly automated picking performed by robots....

Purpose: This paper aims to give an overview about the current state of research in the field of machine learning methods in demand planning. A cross-industry analysis for current machine learning approaches within the field of demand planning provides a decision-making support for the...

Purpose: In recent years, a number of practical logistic applications of machine learning (ML) have emerged, especially in Supply Chain Management (SCM). By linking applied ML methods to the SCM task model, the paper indicates the current applications in SCM and visualises potential research...

Purpose: Automobile manufacturers are highly dependent on supply chain performance which is endangered by risks. They are not yet able to proactively manage these risks, often requiring reactive bottleneck management. A proactive and digitalized early warning method is needed. Methodology: The...

We develop the case of two-stage least squares estimation (2SLS) in the general framework of Athey et al. (Generalized Random Forests, Annals of Statistics, Vol. 47, 2019) and provide a software implementation for R and C++. We use the method to revisit the classic application of instrumental...

Extended Characteristic Surface Model (eCSM) is a theoretical tool of general application designed for computing coefficients in stochastic (Monte Carlo) simulations in particular in multi equation stochastic econometric models. Econometric models are most often used for economic analysis of...

The interest collected by the main borrowers is collected to pay back the principal borrowed from the depositary bank. In financial risk management, credit risk assessment is becoming a significant sector. For the credit risk assessment of client data sets, many credit risk analysis methods are...