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

  • Antonios Karatzoglou
  • Harun Sentürk
  • Adrian Jablonski
  • Michael Beigl
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10614)

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%.

Keywords

Feed-forward-, Recurrent-, LSTM- Artificial Neural Networks Markov chains Semantic trajectories Location prediction 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Antonios Karatzoglou
    • 1
    • 2
  • Harun Sentürk
    • 1
  • Adrian Jablonski
    • 1
  • Michael Beigl
    • 1
  1. 1.Karlsruhe Institute of TechnologyKarlsruheGermany
  2. 2.Robert Bosch GmbH, Corporate Sector Research and Advance EngineeringStuttgartGermany

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