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

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 pe...

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Bibliographic Details
Published in:AI & society 2020-06, Vol.35 (2), p.459-467
Main Authors: Sarker, Iqbal H., Kayes, A. S. M., Furhad, Md Hasan, Islam, Mohammad Mainul, Islam, Md Shohidul
Format: Article
Language:English
Subjects:
Ambient intelligence
Artificial Intelligence
Cellular telephony
Computer Science
Control
Decision trees
Engineering Economics
Ensemble learning
Learning algorithms
Logistics
Machine learning
Management
Marketing
Mechatronics
Methodology of the Social Sciences
Model accuracy
Open Forum
Organization
Performing Arts
Predictions
Robotics
Telephone calls
Telephony
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Summary: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.
ISSN:0951-5666
1435-5655
DOI:10.1007/s00146-019-00898-8