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Found 11 papers, 4 papers with code
AI-Enabled Software and System Architecture Frameworks: Focusing on smart Cyber-Physical Systems (CPS)
Therefore, they failed to address the architecture viewpoints and views responsive to the concerns of the data science community.
Model-Driven Quantum Federated Learning (QFL)
Recently, several studies have proposed frameworks for Quantum Federated Learning (QFL).
MDE for Machine Learning-Enabled Software Systems: A Case Study and Comparison of MontiAnna & ML-Quadrat
In this paper, we propose to adopt the MDE paradigm for the development of Machine Learning (ML)-enabled software systems with a focus on the Internet of Things (IoT) domain.
Enabling Automated Machine Learning for Model-Driven AI Engineering
Developing smart software services requires both Software Engineering and Artificial Intelligence (AI) skills.
MDE4QAI: Towards Model-Driven Engineering for Quantum Artificial Intelligence
Over the past decade, Artificial Intelligence (AI) has provided enormous new possibilities and opportunities, but also new demands and requirements for software systems.
A Model-Driven Approach to Machine Learning and Software Modeling for the IoT
In particular, we implement the proposed approach, called ML-Quadrat, based on ThingML, and validate it using a case study from the IoT domain, as well as through an empirical user evaluation.
ML-Quadrat & DriotData: A Model-Driven Engineering Tool and a Low-Code Platform for Smart IoT Services
In this paper, we present ML-Quadrat, an open-source research prototype that is based on the Eclipse Modeling Framework (EMF) and the state of the art in the literature of Model-Driven Software Engineering (MDSE) for smart Cyber-Physical Systems (CPS) and the Internet of Things (IoT).
Supporting AI Engineering on the IoT Edge through Model-Driven TinyML
We focus on a sub-discipline of AI, namely Machine Learning (ML) and propose the delegation of data analytics and ML to the IoT edge.
Data Analytics and Machine Learning Methods, Techniques and Tool for Model-Driven Engineering of Smart IoT Services
Moreover, we support the APIs of several ML libraries and frameworks for the automated generation of the source code of the target software in Python and Java.
ThingML+ Augmenting Model-Driven Software Engineering for the Internet of Things with Machine Learning
In this paper, we present the current position of the research project ML-Quadrat, which aims to extend the methodology, modeling language and tool support of ThingML - an open source modeling tool for IoT/CPS - to address Machine Learning needs for the IoT applications.
From Things' Modeling Language (ThingML) to Things' Machine Learning (ThingML2)
In this paper, we illustrate how to enhance an existing state-of-the-art modeling language and tool for the Internet of Things (IoT), called ThingML, to support machine learning on the modeling level.
