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ACSOS 2020
Mon 17 - Fri 21 August 2020
Tue 18 Aug 2020 20:17 - 20:29 at Presentation Room A - Poster and Demo Session Chair(s): Ioan Petri, Gabriele Valentini

The problem of adaptation space reduction has been tackled recently using machine learning. While the result has been interesting, the accuracy of the predictions was not outstanding. In this paper, we propose a deep learning approach using a convolutional neural network (CNN) to tackle the adaptation space reduction. As the inherent nature of deep learning models to behave like a black box makes them difficult to follow and to interpret, we also explore the use of Explainable AI (XAI) in the process of the learning and prediction. XAI helps to build trust in the system by explaining the predictions and the behavior of the deep learning model. We plan to evaluate our approach on two simulated IoT applications for smart environment monitoring.

Tue 18 Aug
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20:05 - 21:05: Posters and Demos - Poster and Demo Session at Presentation Room A
Chair(s): Ioan PetriCardiff University, Gabriele ValentiniArizona State University
acsos-2020-posters-and-demos20:05 - 20:17
acsos-2020-posters-and-demos20:17 - 20:29
acsos-2020-posters-and-demos20:29 - 20:41
Martin PfannemüllerUniversity of Mannheim, Martin BreitbachUniversity of Mannheim, Christian KrupitzerUniversity of Würzburg, Germany, Christian BeckerUniversity of Mannheim, Andy SchürrTU Darmstadt
acsos-2020-posters-and-demos20:41 - 20:53
acsos-2020-posters-and-demos20:53 - 21:05
Jelle Van De Sijpe, Danny WeynsLinnaeus University, Sweden