Publikationen 2024

Scheurer, L., Leukel, J., Zimpel, T., Werner, J., Perdana-Decker, S., & Dickhoefer, U. (2024). Predicting herbage biomass on small-scale farms by combining sward height with different aggregations of weather data. Agronomy Journal, 116(6), 3205-3221. https://doi.org/10.1002/agj2.21705

Leukel, J., Scheurer, L., & Sugumaran, V. (2024). Machine learning models for predicting physical properties in asphalt road construction: a systematic review. Construction and Building Materials, 440, 137397. https://doi.org/10.1016/j.conbuildmat.2024.137397

Riekert, M. (2024). Automatische Prozessüberwachung in der Nutztierhaltung: Gestaltung eines Verfahrens zur Extraktion von Prozessindikatoren aus Bilddaten mittels Deep Learning. Dissertation, Universität Hohenheim, 24.04.2024. https://doi.org/10.60848/10815

Leukel, J., Özbek, G., & Sugumaran, V. (2024). Application of logistic regression to explain internet use among older adults: a review of the empirical literature. Universal Access in the Information Society, 23, 621-635. https://doi.org/10.1007/s10209-022-00960-1

Stumpe, C., Leukel, J., & Zimpel, T. (2024). Prediction of pasture yield using machine learning-based optical sensing: a systematic review. Precision Agriculture, 25(1), 430-459. https://doi.org/10.1007/s11119-023-10079-9

Publikationen 2023

Zimpel, T., Perdana-Decker, S., Leukel, J., Scheurer, L., Dickhoefer, U., & Werner, J. (2023). P42 Estimating pasture yield using machine learning and weather data: effect of small and large prediction horizons. Animal-science proceedings, 14(4), 628-629. https://doi.org/10.1016/j.anscip.2023.04.137

Leukel, J., González, J., & Riekert, M. (2023). Machine learning-based failure prediction in industrial maintenance: improving performance by sliding window selection. International Journal of Quality & Reliability Management, 40(6), 1449-1462. https://doi.org/10.1108/IJQRM-12-2021-0439

Leukel, J., Zimpel, T., & Stumpe, C. (2023). Machine learning technology for early prediction of grain yield at the field scale: a systematic review. Computers and Electronics in Agriculture, 207, 107721. https://doi.org/10.1016/j.compag.2023.107721

Leukel, J., Schehl, B., & Sugumaran, V. (2023). Digital inequality among older adults: explaining differences in the breadth of internet use. Information, Communication & Society, 26(1), 139-154. https://doi.org/10.1080/1369118X.2021.1942951

Publikationen 2022

Zimpel, T., Wild, A., Schrade, H., & Kirn, S. (2022). Association rule mining to study process-related cause-effect-relationships in pig farming. In Proceedings of the Workshop on Process Management in the AI Era 2022 (PMAI 2022) co-located with 31st International Joint Conference on Artificial Intelligence and the 25th European Conference on Artificial Intelligence (IJCAI-ECAIS 2022), Vienna, Austria, July 23, 2022. CEUR Workshop Proceedings 3310. https://ceur-ws.org/Vol-3310

Hubl, M. (2022). Modeling an agricultural process coordination problem to enhance efficiency and resilience with methods of artificial intelligence. In J. Michael, J. Pfeiffer & A. Wortmann (Eds.), Modellierung 2022 Satellite Events. DL. GI. https://doi.org/10.18420/modellierung2022ws-003

Zimpel, T. (2022). Modeling pig rearing as a digital shadow. In J. Michael, J. Pfeiffer, & A. Wortmann (Eds.), Modellierung 2022 Satellite Events. GI. https://doi.org/10.18420/modellierung2022ws-014

Leukel, J., & Sugumaran, V. (2022). How novice analysts understand supply chain process models: an experimental study of using diagrams and texts. Journal of Enterprise Information Management, 35(3), 757-773. https://doi.org/10.1108/JEIM-11-2020-0478

Publikationen 2021

Kösebay, M., Kirn, S., Wallrafen, S., Leukel, J., & Gierl, F. (Hrsg.) (2021). Stadt der Zukunft – Smartes Stadtmobiliar für mehr Teilhabe im Alter. medhochzwei.

Kirn, S., & Kösebay, M. (2021). Das Projekt UrbanLife+: Digitale Technologien für lebenswerte Stadtquartiere im demografischen Wandel. In M. Kösebay, S. Kirn, S. Wallrafen, J. Leukel & F. Gierl (Hrsg.), Stadt der Zukunft – Smartes Stadtmobiliar für mehr Teilhabe im Alter (S. 3-7). medhochzwei.

Hubl, M., Zimpel, T., Widmer, T., & Kirn, S. (2021). Digitale Transformation des urbanen Raums. In M. Kösebay, S. Kirn, S. Wallrafen, J. Leukel & F. Gierl (Hrsg.), Stadt der Zukunft – Smartes Stadtmobiliar für mehr Teilhabe im Alter (S. 63-73). medhochzwei.

Leukel, J., & Wallrafen, S. (2021). Situation älterer Menschen in Deutschland. In M. Kösebay, S. Kirn, S. Wallrafen, J. Leukel & F. Gierl (Hrsg.), Stadt der Zukunft – Smartes Stadtmobiliar für mehr Teilhabe im Alter (S. 77-83). medhochzwei.

Leukel, J., Schehl, B., & Wallrafen, S. (2021). Bürgerbefragung 65+ in Mönchengladbach. In M. Kösebay, S. Kirn, S. Wallrafen, J. Leukel & F. Gierl (Hrsg.), Stadt der Zukunft – Smartes Stadtmobiliar für mehr Teilhabe im Alter (S. 104-118). medhochzwei.

Zimpel, T., Hubl, M., Widmer, T., Kafurke, L., Wolschewski, A., Braun, M., & Kirn, S. (2021). UrbanLife+-Szenarien: Safety durch smarte städtebauliche Objekte. In M. Kösebay, S. Kirn, S. Wallrafen, J. Leukel & F. Gierl (Hrsg.), Stadt der Zukunft – Smartes Stadtmobiliar für mehr Teilhabe im Alter (S. 121-164). medhochzwei.

Kösebay, M., & Kirn, S. (2021). Ausblick. In M. Kösebay, S. Kirn, S. Wallrafen, J. Leukel & F. Gierl (Hrsg.), Stadt der Zukunft – Smartes Stadtmobiliar für mehr Teilhabe im Alter (S. 197-199). medhochzwei.

Hubl, M. (2021) Smarte Städtebauliche Objekte für eine adaptive Stadt: Ein Verfahren der Künstlichen Intelligenz zur Erhöhung der Wohlfahrt. Dissertation, Universität Hohenheim, 16.06.2021. URL: http://opus.uni-hohenheim.de/volltexte/2021/1932/

Leukel, J., Gonzalez, J., & Riekert, M. (2021). Adoption of machine learning technology for failure prediction in industrial maintenance: a systematic review. Journal of Manufacturing Systems, 61, 87-96. https://doi.org/10.1016/j.jmsy.2021.08.012

Riekert, M., Opderbeck, S., Wild, A., & Gallmann, E. (2021). Model selection for 24/7 pig position and posture detection by 2D camera imaging and deep learning. Computers and Electronics in Agriculture, 187, 106213. https://doi.org/10.1016/j.compag.2021.106213

Müller-Hengstenberg, C. D., & Kirn, S. (2021). Haftung des Betreibers von autonomen Softwareagents. MMR Zeitschrift für IT-Recht und Recht der Digitalisierung, 24:5, 376-380.

Riekert, M., Riekert, M. & Klein, A. (2021). Simple baseline machine learning text classifiers for small datasets. SN Computer Science, 2, 178. https://doi.org/10.1007/s42979-021-00480-4

Zimpel, T., Riekert, M., Klein, A., & Hoffmann, C. (2021). Machine learning for predicting animal welfare risks in pig farming. Agricultural Engineering, 76(1), 24-35. https://doi.org/10.15150/lt.2021.3261

Zimpel, T., Riekert, M., Klein, A., & Hoffmann, C. (2021). Maschinelle Lernverfahren zur Prognose von Tierwohlrisiken in der Schweinehaltung. LANDTECHNIK, 76(1), 24-35. https://doi.org/10.15150/lt.2021.3261

Widmer, T., Karaenke, P., & Sugumaran, V. (2021). Two‐sided service markets: effects of quality differentiation on market efficiency. Managerial and Decison Economics, 42, 588-604. https://doi.org/10.1002/mde.3256

Schehl, B. (2021). Older adults’ Internet use, outdoor activity, and the urban environment: empirical analysis. Dissertation, Universität Hohenheim, 02.03.2021. URL: http://opus.uni-hohenheim.de/frontdoor.php?source_opus=1899&la=de

Publikationen 2020

Schehl, B., & Leukel, J. (2020). Associations between individual factors, environmental factors, and outdoor independence in older adults. European Journal of Ageing, 17(3), 291-298. https://doi.org/10.1007/s10433-020-00553-y

Leukel, J., Schehl, B., & Sugumaran, V. (2020). To do or not to do: how socio-demographic characteristics of older adults are associated with online activities. In Q. Gao & J. Zhou (Eds.), Proceedings of the 6th International Conference on Human Aspects of IT for the Aged Population (ITAP 2020) (pp. 255-268). LNCS 12209. Springer. https://doi.org/10.1007/978-3-030-50232-4_18

Riekert, M., Klein, A., Adrion, F., Hoffmann, C., & Gallmann, E. (2020). Automatically detecting pig position and posture by 2D camera imaging and deep learning. Computers and Electronics in Agriculture, 174, 105391. https://doi.org/10.1016/j.compag.2020.105391

Merkt, O. (2020). Predictive models for maintenance optimization: an analytical literature survey of industrial maintenance strategies. In E. Ziemba (Ed.), Information technology for management: Current research and future directions. AITM 2019, ISM 2019 (pp. 135-154). LNBIP 380. Springer. https://doi.org/10.1007/978-3-030-43353-6_8

Zimpel, T., Riekert, M., & Wild, A. (2020). Designing a smart farming platform for sustainable decision making. Proceedings of the 15th International Conference on Wirtschaftsinformatik (WI 2020). Potsdam, Germany. https://doi.org/10.30844/wi_2020_x3-zimpel

Riekert, M., Zimpel, T., Hoffmann, C., Wild, A., Gallmann, E., & Klein, A. (2020). Towards animal welfare monitoring in pig farming using sensors and machine learning. In Referate der 40. Jahrestagung der Gesellschaft für Informatik in der Land-, Forst- und Ernährungswirtschaft. Fokus: Digitalisierung für Mensch, Umwelt und Tier (pp. 271-276). Freising, Germany. LNI P-299. GI.

Zimpel, T., Riekert, M., Hoffmann, C., & Wild, A. (2020). Maschinelle Lernverfahren zur frühzeitigen Prognose der Handelsklasse. In Referate der 40. Jahrestagung der Gesellschaft für Informatik in der Land-, Forst- und Ernährungswirtschaft. Fokus: Digitalisierung für Mensch, Umwelt und Tier (pp. 361-366). Freising, Germany. LNI P-299. GI.

Schehl, B. (2020). Outdoor activity among older adults: exploring the role of informational Internet use. Educational Gerontology, 46(1), 36-45. https://doi.org/10.1080/03601277.2019.1698200

Publikationen 2019

Klein, A., Riekert, M., & Dinev, V. (2019). Accurate retrieval of corporate reputation from online media using machine learning. In Proceedings of the 14th Federated Conference on Computer Science and Information Systems (FedCSIS 2019). Leipzig, Germany. https://doi.org/10.15439/2019F169

Merkt, O. (2019). On the use of predictive models for improving the quality of industrial maintenance: an analytical literature review of maintenance strategies. In Proceedings of the 14th Federated Conference on Computer Science and Information Systems (FedCSIS 2019). Leipzig, Germany. https://doi.org/10.15439/2019F101

Skowron, P., Aleithe, M., Wallrafen, S., Hubl, M., Fietkau, J., & Franczyk, B. (2019). Smart urban design space. In Proceedings of the 14th Federated Conference on Computer Science and Information Systems (FedCSIS 2019). Leipzig, Germany. https://doi.org/10.15439/2019F80

Wachter, P., Widmer, T., & Klein, A. (2019). Predicting automotive sales using pre-purchase online search data. In Proceedings of the 14th Federated Conference on Computer Science and Information Systems (FedCSIS 2019). Leipzig, Germany. https://doi.org/10.15439/2019F239

Zimpel, T., & Hubl, M. (2019). Smart urban objects to enhance safe participation in major events for the elderly. In Proceedings of the 14th Federated Conference on Computer Science and Information Systems (FedCSIS 2019). Leipzig, Germany. https://doi.org/10.15439/2019F180

Schehl, B., Leukel, J., & Sugumaran, V. (2019). Understanding differentiated internet use in older adults: a study of informational, social and instrumental online activities. Computers in Human Behavior, 97, 222-230. https://doi.org/10.1016/j.chb.2019.03.031

Hubl, M. (2019). An adaptive park bench system to enhance availability of appropriate seats for the elderly: a safety engineering approach for Smart City. In Proceedings of the 21st IEEE Conference on Business Informatics (CBI 2019) (pp. 373-382). https://doi.org/10.1109/CBI.2019.00049

Widmer, T., Klein, A., Wachter, P., & Meyl, S. (2019). Predicting material requirements in the automotive industry using data mining. In Proceedings of the 22nd International Conference on Business Information Systems (BIS 2019) (pp. 147-161). Leipzig, Germany. LNBIP 354. Springer. https://doi.org/10.1007/978-3-030-20482-2_13

Leukel, J., & Sugumaran, V. (2019). Supplement to the article: how product representation influences the understanding of supply chain process models. Social Science Research Network (SSRN). http://dx.doi.org/10.2139/ssrn.3386680

Klein, A., Dinev, V., & Riekert, M. (2019). Cryptocurrency crashes: a dataset for measuring the effect of regulatory news in online media. In Proceedings of the 1st Workshop on Systemic Risks in Global Networks (SysRisk2019) (pp. 85-88). Siegen, Germany. CEUR Workshop Proceedings 2397. http://ceur-ws.org/Vol-2397/