• norsk
    • English
  • English 
    • norsk
    • English
  • Login
View Item 
  •   Home
  • Fakultet for teknologi, kunst og design (TKD)
  • TKD - Institutt for maskin, elektronikk og kjemi
  • View Item
  •   Home
  • Fakultet for teknologi, kunst og design (TKD)
  • TKD - Institutt for maskin, elektronikk og kjemi
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Comparison of Probabilistic Models and Neural Networks on Prediction of Home Sensor Events

Casagrande, Flavia Dias; Tørresen, Jim; Zouganeli, Evi
Conference object
Accepted version
Thumbnail
View/Open
comparison_of_probabilistic_models.pdf (990.7Kb)
URI
https://hdl.handle.net/10642/8490
Date
2019-09-30
Metadata
Show full item record
Collections
  • TKD - Institutt for maskin, elektronikk og kjemi [265]
Original version
Casagrande FDC, Tørresen J, Zouganeli E P: Comparison of Probabilistic Models and Neural Networks on Prediction of Home Sensor Events. In: Jayne C, Somogyvári. 2019 International Joint Conference on Neural Networks (IJCNN) , 2019. IEEE   https://dx.doi.org/10.1109/IJCNN.2019.8851746
Abstract
We present results and comparative analysis on the prediction of sensor events in a smart home environment with a limited number of binary sensors. We apply two probabilistic methods, namely Sequence Prediction via Enhanced Episode Discovery - SPEED, and Active LeZi - ALZ, as well as Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) in order to predict the next sensor event in a sequence. Our dataset has been collected from a real home with one resident over a period of 30 weeks. The binary sensor events are converted to two different text sequences as dictated by SPEED and ALZ, which are also used as inputs for the LSTM networks. We compare the performance of the algorithms regarding the number of preceding sensor events required to predict the next one, the required amount of data for the model to reach peak accuracy and stability, and the execution time. In addition, we analyze these for two different sets of sensors. Our best implementation achieved a peak accuracy of 83% for a set with fifteen sensors including motion, magnetic and power sensors, and 87% for seven motion sensors.
Publisher
IEEE
Series
International Joint Conference on Neural Networks (IJCNN); 2019 International Joint Conference on Neural Networks (IJCNN)

Contact Us | Send Feedback

Privacy policy
DSpace software copyright © 2002-2019  DuraSpace

Service from  Unit
 

 

Browse

ArchiveCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsDocument TypesJournalsThis CollectionBy Issue DateAuthorsTitlesSubjectsDocument TypesJournals

My Account

Login

Statistics

View Usage Statistics

Contact Us | Send Feedback

Privacy policy
DSpace software copyright © 2002-2019  DuraSpace

Service from  Unit