Publication:
Applying Artificial Neural Networks on Two-Layer Semantic Trajectories for Predicting the Next Semantic Location

No Thumbnail Available
Date
2017-01-01T00:00:00Z
Authors
Karatzoglou, Antonios
Sentuerk, HAKAN
Jablonski, Adrian
Beigl, Michael
Journal Title
Journal ISSN
Volume Title
Publisher
Research Projects
Organizational Units
Journal Issue

Metrics

Search on Google Scholar

Abstract
Location-awareness and prediction play a steadily increasing role as systems and services become more intelligent. At the same time semantics gain in importance in geolocation application. In this work, we investigate the use of artificial neural networks (ANNs) in the field of semantic location prediction. We evaluate three different ANN types: FFNN, RNN and LSTM on two different data sets on two different semantic levels each. In addition we compare each of them to a Markov model predictor. We show that neural networks perform overall well, with LSTM achieving the highest average score of 76,1%.
Description
Keywords
Karatzoglou A., Sentuerk H., Jablonski A., Beigl M., -Applying Artificial Neural Networks on Two-Layer Semantic Trajectories for Predicting the Next Semantic Location-, 26th International Conference on Artificial Neural Networks (ICANN), Alghero, İtalya, 11 - 14 Eylül 2017, cilt.10614, ss.233-241
Citation
Page Views

0

File Downloads

0

Sustainable Development Goals