A Genetic Neural Network Approach for Unusual Behavior Prediction in Smart Home

Zaineb Liouane 1 Tayeb Lemlouma 2 Philippe Roose 3 Frédéric Weis 4 Hassani Messaoud 1
3 T2I
LIUPPA - Laboratoire Informatique de l'Université de Pau et des Pays de l'Adour
4 TACOMA - TAngible COMputing Architectures
Inria Rennes – Bretagne Atlantique , IRISA-D2 - RÉSEAUX, TÉLÉCOMMUNICATION ET SERVICES
Abstract : Detect efficiently the activities of daily living of elderly people at home in order to provide a secure life and to intervene in the necessary time is an important problem we propose here an improved artificial neural network model. As we need an efficient prediction model, we propose a recurrent output neural network model (RO-NN) combined with a genetic algorithm (GA) which surely monitors and predicts the state of the concerned elderly person. Furthermore, we propose a prediction algorithm “Unusual Behavior Algorithm (UBA)” dedicated to detect the unusual activities and hold us account in the dangerous state.
Type de document :
Communication dans un congrès
Springer. 16th International Conference on Intelligent Systems Design and Applications, Dec 2016, Porto, Portugal. Springer, 557 (Springer ISBN: 978-3-319-53479-4), pp.738-748, 2016, Springer book series in Advances in Intelligent Systems and Computing (AISC). 〈10.1007/978-3-319-53480-0_73〉
Liste complète des métadonnées

Littérature citée [17 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01462993
Contributeur : Tayeb Lemlouma <>
Soumis le : jeudi 9 février 2017 - 12:05:49
Dernière modification le : mercredi 4 octobre 2017 - 14:22:16
Document(s) archivé(s) le : mercredi 10 mai 2017 - 13:16:37

Fichier

paper_103.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

Citation

Zaineb Liouane, Tayeb Lemlouma, Philippe Roose, Frédéric Weis, Hassani Messaoud. A Genetic Neural Network Approach for Unusual Behavior Prediction in Smart Home. Springer. 16th International Conference on Intelligent Systems Design and Applications, Dec 2016, Porto, Portugal. Springer, 557 (Springer ISBN: 978-3-319-53479-4), pp.738-748, 2016, Springer book series in Advances in Intelligent Systems and Computing (AISC). 〈10.1007/978-3-319-53480-0_73〉. 〈hal-01462993〉

Partager

Métriques

Consultations de la notice

329

Téléchargements de fichiers

86