Leidner, Jochen L.; Reiche, Michael (2024)
Development Methodologies for Big Data Analytics Systems.
A number of machine learning process models (SEMMA, KDD, CRISP-DM, CRISP-ML, Data-to-Value1 etc.) have been recently proposed to facilitate the development of machine learning models in their organizational context. While the existing proposals vary with respect to complexity and suitability for particular tasks, it would be desirable to have software tools that embody or support these process models, and make it easier for project teams to capture, share among team members and stakeholders and preserve the relevant project information pertaining to the various process stages. In particular, recorded past statistics may be applied to predict the duration of stages or the overall project effort.
Presently, to the best of our knowledge, no requirement analysis exists that stipulates the detailed needs. To this end, we present a first collection and analysis of a requirements document for the software tooling for machine learning process models. We describe the functional and non-functional requirements of a Computer-Aided Machine Learning Modeling (CAMLM) tool, the soft-computing world’s counter-part to a CASE (Computer Aided Software Engineering) tool.
Various software cover sub-areas such as team management and communication management (Confluence, Jira, Slack, Zoom...) or project management (CRISP-DM, Scrum, Kanban-Board...) or data and information management (model management [Weber, Christian; Hirmer, Pascal; Reimann, Peter; Schwarz, Holger (2019): A New Process Model for the Comprehensive Management of Machine Learning Models. In: Proceedings of the 21st International Conference on Enterprise Information Systems: SCITEPRESS - Science and Technology Publications.] ). What is not available to our knowledge, however, is software that covers the entire sub-areas and the entire life cycle of machine learning projects in detail.
Freiberger, Annika; Andonian, Caroline; Beckmann, Jürgen ; Freilinger, Sebastian; Ewert, Peter; Kaemmerer, Harald; Kohls, Niko; Richter, Cristina; Huber, M. (2024)
Freiberger, Annika; Andonian, Caroline; Beckmann, Jürgen ; Freilinger, Sebastian...
International Journal of Behavioral Medicine.
DOI: 10.1007/s12529-024-10332-z
Roßteutscher, Immanuel; Blaschke, Oliver; Dötzer, Florian; Uphues, Thorsten; Drese, Klaus Stefan (2024)
Roßteutscher, Immanuel; Blaschke, Oliver; Dötzer, Florian; Uphues, Thorsten...
Sensors 2024/24, 7114 (22).
DOI: 10.3390/s24227114
This study is focused on optimizing electromagnetic acoustic transducer (EMAT) sensors for enhanced ultrasonic guided wave signal generation in steel cables using CAD and modern manufacturing to enable contactless ultrasonic signal transmission and reception. A lab test rig with advanced measurement and data processing was set up to test the sensors’ ability to detect cable damage, like wire breaks and abrasion, while also examining the effect of potential disruptors such as rope soiling. Machine learning algorithms were applied to improve the damage detection accuracy, leading to significant advancements in magnetostrictive measurement methods and providing a new standard for future development in this area. The use of the Vision Transformer Masked Autoencoder Architecture (ViTMAE) and generative pre-training has shown that reliable damage detection is possible despite the considerable signal fluctuations caused by rope movement.
Dehghani, Ali; Salaar, Hamza; Srinivasan, Priya; Zhou, Lixian; Arbeiter, Georg; Lindner, Alisa; Patiño Studencki, Lucila (2024)
Dehghani, Ali; Salaar, Hamza; Srinivasan, Priya; Zhou, Lixian; Arbeiter, Georg...
SAE International Journal of Connected and Automated Vehicles 2025, 8 (3).
DOI: 10.4271/12-08-03-0023
Lohrenscheit , Claudia (2024)
In: Rost, Sebastian/ Bloch, Bianca/ Kaiser, Anna-Katharina/ Kaul, Ina (Hg.): Bildung für nachhaltige Entwicklung in der Kindheitspädagogik. Beiträge zur Disziplin, Profession und Praxis der Pädagogik der frühen Kindheit. Weinheim/Basel. Beltz Juventa. S. 49-63 2024, S. 49-63.
Lohrenscheit , Claudia (2024)
2024.
Lohrenscheit , Claudia (2024)
2024.
Meißner, Karin (2024)
TV Oberfranken.
Meißner, Karin (2024)
Chinesische Medizin / Chinese Medicine 39 (2).
DOI: 10.1007/s00052-024-00130-x
Meißner, Karin (2024)
Welt am Sonntag (Interview).
Wilde, Mathias; Riedelbauch, Lukas (2024)
Internationales Verkehrswesen 76 (3), S. 48-50.
Charzyńska , Edyta; Offenbächer, M.; Halverson, k; Hirsch, J. K.; Kohls, Niko; Hanshans, Christian ; Sirois, F.; Toussaint, L. (2024)
Charzyńska , Edyta; Offenbächer, M.; Halverson, k; Hirsch, J. K.; Kohls, Niko...
British Journal of Health Psychology 2024, S. 1-23.
Leyendecker, Matthia; Zagel, Christian (2024)
AHFE International - The Human Side of Service Engineering 143, S. 167-177.
DOI: 10.54941/ahfe1005095
Wilde, Mathias; Böhringer, Michael; Schubert, Jennifer (2024)
AKP - Fachzeitschrift für Alternative Kommunal Politik (4), S. 42-43.
Srinivasan, Priya; Tribulowski, Paul; Lindner, Alisa (2024)
Advances in Human Factors of Transportation 2024, S. 161-171.
DOI: 10.54941/ahfe1005206
Grosch, Christian (2024)
The Human Side of Service Engineering 143, 143, S. 185-191.
DOI: 10.54941/ahfe1005097
Stübinger, Johannes ; Stübinger, Johannes (2024)
The Human Side of Service Engineering. AHFE (2024) International Conference 2024 (143), S. 160-166.
DOI: 10.54941/ahfe1005094
Lohrenscheit , Claudia ; Schmelz, Andrea ; Schmitt, Caroline; Straub, Ute (2024)
, S. 7-19.
Lohrenscheit , Claudia ; Schmelz, Andrea ; Schmitt, Caroline; Straub, Ute (2024)
, S. 205-211.
Demmler, Uwe (2024)
Steuerrecht aktuell 2024 (1), S. 31-33.
Friedrich-Streib-Str. 2
96450 Coburg