prenode as Research Advisor: Activities in Advisory Councils of Cross-Domain Projects
January 1, 2020
Contributing Expert Knowledge
prenode actively contributes know-how on machine learning and AI development in various research projects. Through our position on the advisory boards of two collaborative projects, we can exchange cross-domain knowledge. This enables us to provide expertise on tech topics and learn about the needs and requirements of the involved partners.
Specifically, we are involved in the “Intelligent Diagnostics” program of the Innovation Alliance BW and the “IMPRESS” project from Hahn-Schickard and Hohenstein Institute of Textile Innovation.
Intelligent Diagnostics in Medicine
The project “Intelligent Diagnostics” aims to detect skin tumors with the help of AI. Traditionally, these medical diagnoses require specifically trained staff. An AI-assisted approach could make such screenings acessible for a broader audience. However, the development requires high-quality medical data. This is crucial for model training and is especially important, because the patients’ data has to be kept private. At this point, prenode’s decentralized approach comes into play: by using Federated Learning and Transfer Learning, we enable the development of ML models at separate locations without the centralization of sensitive data.
More information on the project at the innBW website.
The project is carried out conjointly by
- FZI – Research Center for Information Technology
- ILM – Institute for Laser Technologies in Medicine and Measurement Technology
- Hahn-Schickard Society
- NMI – Natural Science and Medical Institute
The project is funded by the Baden-Württemberg Ministry of Economics, Labor and Tourism.
Monitoring in Commercial Laundry
The “IMPRESS” project focuses on process automation in commercial laundries. For the development of an intelligent monitoring concept, a system of sensors is combined with an AI-based control component.
The AI-based steering could increase both the process quality and resource efficiency of the laundries. prenode helps in the development of ML models across different facilities. Ultimately, successful implementation of the system could pave the way for further intelligent automation in this sector.
Find out more about the project at the website of the Hahn-Schickard Society.
Project partners
Funding is provided by the Federal Ministry for Economic Affairs and Energy.
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