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Prediction rule mining in an Ambient Intelligence context

Abstract : This thesis deals with the subject of Ambient Intelligence, the fusion between Artificial Intelligence and the Internet of Things. The goal of this work is to extract prediction rules from the data provided by connected objects in an environment, in order to propose automation to users. Our main concern relies on privacy, user interactions, and the explainability of the system’s operation. In this context, several contributions were made. The first is an ambient intelligence architecture that operates locally, and processes data from a single connected environment. The second is a discretization process without a priori on the input data, allowing to take into account different kinds of data from various objects. The third is a new algorithm for searching rules over a time series, which avoids the limitations of stateoftheart algorithms. The approach was validated by tests on two real databases. Finally, prospects for future developments in the system are presented
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Submitted on : Wednesday, September 9, 2020 - 12:08:45 PM
Last modification on : Tuesday, June 1, 2021 - 2:08:10 PM
Long-term archiving on: : Thursday, December 3, 2020 - 12:45:59 AM


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  • HAL Id : tel-02934428, version 1


Benoit Vuillemin. Prediction rule mining in an Ambient Intelligence context. Artificial Intelligence [cs.AI]. Université de Lyon, 2020. English. ⟨NNT : 2020LYSE1120⟩. ⟨tel-02934428⟩



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