The DOI matches its DataCite record; 6 of the 6 references it cites were found in a registry or an index.
A Mobile Intelligent Interruption Management System
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“Thus, the developers of mobile apps can rely on machine learning to create smart apps that can understand human behavior, support, and entertain users [107, 137, 140].”
From Machine learning: Algorithms, real-world applications and research directions (2021) · cites this paper as [140]
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Citations
Cites
The cost of interrupted work: more speed and stress
Mark, G., Gudith, D., & Klocke, U. (2008) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1357054.1357072 · Cited by 728
Bailey, B. P., & Konstan, J. A. (2006) · Computers in Human Behavior · doi:10.1016/j.chb.2005.12.009 · Cited by 600
Using context-aware computing to reduce the perceived burden of interruptions from mobile devices
Ho, J., & Intille, S. S. (2005) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1054972.1055100 · Cited by 367
Bailey, B. P., & Iqbal, S. T. (2008) · ACM Transactions on Computer-Human Interaction · doi:10.1145/1314683.1314689 · Cited by 325
BusyBody: creating and fielding personalized models of the cost of interruption
Horvitz, E., Koch, P., & Apacible, J. (2004) · Proceedings of the 2004 ACM conference on Computer supported cooperative work · doi:10.1145/1031607.1031690 · Cited by 150
Lilsys: Sensing Unavailability
Begole, J. "., Matsakis, N. E., & Tang, J. C. (2004) · Proceedings of the 2004 ACM conference on Computer supported cooperative work · doi:10.1145/1031607.1031691 · Cited by 136
Cited by
Machine learning: Algorithms, real-world applications and research directions
Sarker, I. H. (2021) · SN Computer Science · doi:10.1007/s42979-021-00592-x · Cited by 5,583
Sarker, I. H., Kayes, A. S. M., & Watters, P. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0219-y · Cited by 287
Mobile Data Science and Intelligent Apps: Concepts, AI-Based Modeling and Research Directions
Sarker, I. H., Hoque, M. M., Uddin, M. K., & Alsanoosy, T. (2021) · Mobile Networks and Applications · doi:10.1007/s11036-020-01650-z · Cited by 207
A machine learning based robust prediction model for real-life mobile phone data
Sarker, I. H. (2019) · Internet of Things · doi:10.1016/j.iot.2019.01.007 · Cited by 113
Context-aware rule learning from smartphone data: survey, challenges and future directions
Sarker, I. H. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0258-4 · Cited by 105
Metadata
- Authors
- Stamm, K., Madiraju, P., Ahamed, S. I., & Zulkernain, S.
- Venue
- Verlag der Technischen Universität Graz
- Publisher
- Verlag der Technischen Universität Graz
- Published
- 2010
- References
- 6
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A Mobile Intelligent Interruption Management System. https://app.cochat.ai/papers/10.3217/jucs-016-15-2060
- The paper
- Stamm, K., Madiraju, P., Ahamed, S. I., & Zulkernain, S. (2010). A Mobile Intelligent Interruption Management System. Verlag der Technischen Universität Graz. https://doi.org/10.3217/jucs-016-15-2060
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
