Ein Ziel der Arbeitsgruppe Künstliche Intelligenz1 am Fachbereich Wirtschaftder Hochschule Wismar ist die praktische Anwendung der Methodenund Techniken der Künstlichen Intelligenz in der betriebswirtschaftlichenPraxis. Der Trend hin zum Einsatz von Wissen in entsprechendenIT-Lösungen und...

Im Interview mit ChatGPT geht es um den Einsatz von Künstlicher Intelligenz (KI) an Hochschulen, z. B. wie KI in der Lehre eingesetzt werden kann, um Lernprozesse zu verbessern und Studierende zu unterstützen, wie KI in der Forschung genutzt werden kann, um die Effizienz von Prozessen zu...

Galician Abstract: O impacto socio-económico do turismo está atraendo a atención dos estudios de políticas públicas. A actividade turística pode producir relevantes beneficios económicos, pero pode tamén producir impactos negativos nas comunidades locais, o seu patrimonio cultural así...

How will superhuman artificial intelligence (AI) affect human decision making? And what will be the mechanisms behind this effect? We address these questions in a domain where AI already exceeds human performance, analyzing more than 5.8 million move decisions made by professional Go players...

Outliers are observations that deviate significantly from the norm, and their detection has been a critical topic in various research areas and application domains, such as video surveillance, network intrusion detection, and disease outbreak detection. In recent years, deep learning-based...

There is increasing deployment of machine learning algorithms by financial institutions during and after the coronavirus pandemic. However, majority of these models are being implemented for credit risk management, anti-fraud and anti-money laundering use cases. Moreover, previous research and...

When merging firms across large databases in the absence of common identifiers, text algorithms can help. I propose a high-performance fuzzy firm name matching algorithm that uses existing computational methods and works even under hardware restrictions. The algorithm consists of four steps,...

This study addresses the challenge of sovereign external debt sustainability by employing a cointegration test, machine-learning classifiers, and explainable models. Focusing on 22 middle-income countries during the period 2000-2021, our study aims to provide accurate insights into debt...

As the amount of economic and other data generated worldwide increases vastly, a challenge for future generations of econometricians will be to master efficient algorithms for inference in empirical models with large information sets. This Chapter provides a review of popular estimation...

This research proposes new estimations of the Fama-French three- and five-factor models via a machine learning approach. Speci fically, it uses a Bayesian optimization-support vector regression (BSVR) approach to obtain predictions of portfolio returns. On data from fi ve industries' portfolio...