Publikationen

Gesamtübersicht KI-Publikationen

• Anderie, Lutz (2020): Quick Guide Game Hacking, Blockchain und Monetarisierung. Wie Sie mit Künstlicher Intelligenz Wertschöpfung generieren. https://doi.org/10.1007/978-3-662-60859-3 [Stand: 14.01.2025]
• Winkel, Fabian; Geierhos, Michaela; and Fink, Josef, "Evaluating Embedding Models for Retrieving ESG Information from Annual Business Reports" (2024). ICIS 2024 Proceedings. 23. https://aisel.aisnet.org/icis2024/aiinbus/aiinbus/23
• Paschalidou, Anastasia (2024): Wie wollen wir mit KI leben? - Systeme Künstlicher Intelligenz in der Sozialen Arbeit. In: Klein, Barbara (Hrsg.): Künstliche Intelligenz im Health-Care Sektor, Frankfurt University of Applied Sciences, 39-44. Online unter: https://doi.org/10.48718/1cw9-3c06  [Stand: 31.01.2024]
• Alig, Olivia, KI im Konfliktmanagement – rechtliche & ethische Aspekte, in: Klein, Barbara, Frankfurt University of Applied Sciences (Hrsg.), Künstliche Intelligenz im Healthcare-Sektor, Frankfurt/M. 2024, S. 101ff: Link zum E-Book
• F. S. Butt, M. F. Wagner, J. Schäfer and D. G. Ullate, "Toward Automated Feature Extraction for Deep Learning Classification of Electrocardiogram Signals," in IEEE Access, vol. 10, pp. 118601-118616, 2022, doi: 10.1109/ACCESS.2022.3220670.
  Abstract: Many recent studies have focused on the automatic classification of electrocardiogram (ECG) signals using deep learning (DL) methods. Most rely on existing complex DL methods, such as transfer learning or providing the models with carefully designed extracted features based on domain knowledge. A common assumption is that the deeper and more complex the DL model is, the better it learns. In this study, we propose two different DL models for automatic feature extraction from ECG signals for classification tasks: A CNN-LSTM hybrid model and an attention/transformer-based model with wavelet transform for the dimensional embedding. Both of the models extract the features from time series at the initial layers of the neural networks and can obtain performance at least equal to, if not greater than, many contemporary deep neural networks. To validate our hypothesis, we used three publicly available data-sets to evaluate the proposed models. Our model achieved a benchmark accuracy of 99.92% for fall detection and 99.93% for the PTB database for myocardial infarction versus normal heartbeat classification.
• F. S. Butt, J. Schäfer, M. F. Wagner, D. Stegelmeyer and D. G. -U. Oteiza, "Application of CRISP-DM and DMME to a Case Study of Condition Monitoring of Lens Coating Machines," 2023 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT), Brescia, Italy, 2023, pp. 409-414, doi: 10.1109/MetroInd4.0IoT57462.2023.10180178.
  Abstract: Condition monitoring industrial machinery by using data mining in industrial projects has been extensively extended in the last few years. Applying data science to industrial processes should be straightforward in theory, but very few instances in the literature deal with the actual practical issues encountered while carrying out industrial data science projects. The case study discussed in this paper was pursued in accordance with the steps outlined in the standard CRISP-DM (CRoss-Industry Standard Process for Data Mining) including its latest holistic approach for engineering applications called DMME (Data Mining Methodology for Engineering Applications). The industrial data was acquired as part of industrial cooperation from multiple anti-reflective lens coating machines. Various deep learning (DL) models like long short-term memory (LSTM), and machine learning (ML) models like Decision Trees and Support Vector Machines (SVM) were used as proof of concept for confirming the domain understanding of the process experts. Our main contribution is the description of deficiencies and gaps of the standard process framework CRISP-DM based on the issues faced in implementing each phase of the process in a real world case study. In addition, we propose future research ideas to close these gaps. This complements findings in the literature on gaps in CRISP-DM and DMME.
• P. Weber „Anti-reflex coating and process control - Gaining control of process parameters by digital means. An approach to reduce scrap and rework.”, MAFO Ophtalmic Labs & Industry, Vol. 5-2203, p.20-25 https://issuu.com/eyepressfachmedien/docs/mafo_2305/20

• Fachartikel über die wissenschaftliche Veröffentlichung 1. Rheinmaintv „Brillenglasproduktion am Standort Deutschland“ Bericht über das Forschungsprojekt der FG INDAS mit optovision und Bühler GmbH (s. 2 u. 3): https://www.rheinmaintv.de/sendungen/beitrag-video/brillenglasproduktion-am-standort-deutschland-attraktiv-und-rentabel-gestalten/vom-19.03.2024/

• Hofmann, P., Lämmermann, L., & Urbach, N. (2024). Managing artificial intelligence applications in healthcare: Promoting information processing among stakeholders. International Journal of Information Management, 75, 102728.
• Duda, S., Hofmann, P., Urbach, N., Völter, F. and Zwickel, A. (2023) The Impact of Resource Allocation on the Machine Learning Lifecycle: Bridging the Gap between Software Engineering and Management, Business & Information Systems Engineering (BISE), forthcoming.
• Gimpel, H., Hall, K., Decker, S., Eymann, T., Lämmermann, L., Mädche, A., ... Urbach, N., Vandrik, S. (2023). Unlocking the power of generative AI models and systems such as GPT-4 and ChatGPT for higher education: A guide for students and lecturers. Hohenheim Discussion Papers in Business, Economics and Social Sciences.
• Guggenberger, T., Lämmermann, L., Urbach, N., Walter, A. and Hofmann, P. (2023) Task delegation from AI to humans: A principal-agent perspective, Proceedings of the 44th International Conference on Information Systems, December 10-13, Hyderabad, India.
• Hinsen, S., Hofmann, P., Jöhnk, J. and Urbach, N. (2022) How Can Organizations Design Purposeful Human-AI Interactions: A Practical Perspective From Existing Use Cases and Interviews, Proceedings of the 55th Hawaii International Conference on System Sciences (HICSS-55), January 4-7, Maui, Hawaii.
• Lämmermann, L., Richter, P., Zwickel, A., & Markgraf, M. (2022). AI Fairness at Subgroup Level–A Structured Literature Review. Proceedings of the European Conference on Information Systems 2022. Timisoara, Romania
• Urbach, N. and Hofmann, P. (2022) Warum wir nicht nur von Künstlicher Intelligenz sprechen sollten, transfer – Zeitschrift für Werbung, Kommunikation und Markenführung, 68, 3, 10-13.
• Geske, F., Hofmann, P., Lämmermann, L., Schlatt, V. and Urbach, N. (2021) Gateways to Artificial Intelligence: Developing a Taxonomy for AI Service Platforms, Proceedings of the 29th European Conference on Information Systems (ECIS 2021), June 14-16, Marrakesh, Marocco.
• Hofmann, P., Rückel, T. and Urbach, N. (2021) Innovating with Artificial Intelligence: Capturing the Constructive Functional Capabilities of Deep Generative Learning, Proceedings of the 54th Hawaii International Conference on System Sciences (HICSS-54), January 5-8, Koloa, Hawaii.
• Urbach, N., Häckel, B., Hofmann, P., Fabri, L., Ifland, S., Karnebogen, P., Krause, S., Lämmermann, L., Protschky, D., Markgraf, M. and Willburger, L. (2021) KI-basierte Services intelligent gestalten – Einführung des KI-Service-Canvas. Projektgruppe Wirtschaftsinformatik des Fraunhofer-Instituts für Angewandte Informationstechnik FIT, Hochschule Augsburg, Universität Bayreuth, Frankfurt University of Applied Sciences, Bayreuth, Augsburg und Frankfurt.
• Hofmann, P., Jöhnk, J., Protschky, D. and Urbach, N. (2020) Developing Purposeful AI Use Cases – A Structured Method and its Application in Project Management, Proceedings of the 15th International Conference on Wirtschaftsinformatik (WI 2020), March 9-11, Potsdam, Germany
• Hofmann, P., Oesterle, S., Rust, P. and Urbach, N. (2019) Machine Learning Approaches along the Radiology Value Chain – Rethinking Value Propositions, Proceedings of the 27th European Conference on Information Systems (ECIS 2019), June 8-14, 2019, Stockholm, Sweden.
• Urbach, N., Hofmann, P. and Protschky, D. (2019) KI – Eine Aufgabe für das ganze Unternehmen, CIO-Jahrbuch 2020: Prognosen zur Zukunft der IT, IDG Business Media, München: 60-63.