E-MIIM: an ensemble-learning-based context-aware mobile telephony model for intelligent interruption management

  • Iqbal H. SarkerEmail author
  • A. S. M. Kayes
  • Md Hasan Furhad
  • Mohammad Mainul Islam
  • Md Shohidul Islam
Open Forum


Nowadays, mobile telephony interruptions in our daily life activities are common because of the inappropriate ringing notifications of incoming phone calls in different contexts. Such interruptions may impact on the work attention not only for the mobile phone owners, but also for the surrounding people. Decision tree is the most popular machine-learning classification technique that is used in existing context-aware mobile intelligent interruption management (MIIM) model to overcome such issues. However, a single-decision tree-based context-aware model may cause over-fitting problem and thus decrease the prediction accuracy of the inferred model. Therefore, in this paper, we propose an ensemble machine-learning-based context-aware mobile telephony model for the purpose of intelligent interruption management by taking into account multi-dimensional contexts and name it “E-MIIM”. The experimental results on individuals’ real-life mobile telephony data sets show that our E-MIIM model is more effective and outperforms existing MIIM model for predicting and managing individual’s mobile telephony interruptions based on their relevant contextual information.


Mobile interruptions Machine learning Classification Decision tree Ensemble learning Random forest User behavior modeling Context-aware computing Personalization Mobile service Intelligent systems IoT applications 



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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Iqbal H. Sarker
    • 1
    • 2
    Email author
  • A. S. M. Kayes
    • 3
  • Md Hasan Furhad
    • 4
  • Mohammad Mainul Islam
    • 5
  • Md Shohidul Islam
    • 6
  1. 1.Chittagong University of Engineering and TechnologyChittagongBangladesh
  2. 2.Swinburne University of TechnologyMelbourneAustralia
  3. 3.La Trobe UniversityMelbourneAustralia
  4. 4.Canberra Institute of TechnologyReidAustralia
  5. 5.Extend View Inc.Los AngelesUSA
  6. 6.University of CaliforniaRiversideUSA

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