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Intelligent systems are characterized by learning processes that allow the acquisition of new skills and deal with a large amount of heterogeneous, uncertain and probabilistic data. Due to the complexity of the real-world situations represented by them, the automatic abstraction of information from data, the formation of appropriate representative models, and the semantic processing of existing information is essential to build intelligent systems for digital transformation, such as mobility To enable industry, medicine and education.

Projekte

AutoML MOOC

Online Course at AI-Campus.org about Automated Machine Learning

 

 

 

BacData

Building an analytics framework for precision microbiology to fight biofilm-associated infections

 

 

 

 

BIAS

Bias and Discrimination in Big Data and Algorithmic Processing. Philosophical Assessments, Legal Dimensions, and Technical Solutions

 

 

 

 

Big Data for Cochlea Implants

Understanding Cochlear Implant Outcome Variability using Big Data and Machine Learning Approaches

BigMedilytics

 Application of Big data technologies for supporting more efficient and effective healthcare systems

BOOST4.0 - Big Data for Factories

Big Data Value Spaces for COmpetitiveness of European COnnected Smart FacTories 4.0

 

 

BRENDA II

BRaunschweig ENzyme DAtabase ist das weltweit größte Informationssystem für biochemische, molekularbiologische und funktionale Enzymdaten

 

CHORUS

A highly optimized hardware/software module library for intelligent sensor systems in highly automated driver assistance applications based on the reconfigurable Dream Chip Technologies DCT10A SoM platform

 

 

Cleopatra

Cross-lingual Event-centric Open Analytics Research Academy

 

 

 

 

 

Coding of Sequencing Data

New coding methods allow sequencing data to be streamed