The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
A conceptual framework for context-driven self-adaptive intelligent user interface based on Android
Open accessAbstract
Adaptive User Interface (AUI) can change its layout, appearance, and/or elements based on the needs of its user requirements and current usage context. The AUIs are used in state-of-the-art software products, applications for mobile devices, and websites. Moreover, AUI is an emerging research field in a mobile context, as it can enhance usability, performance, and user satisfaction. This study aims to propose a conceptual framework for developing a real-time self-adaptive user interface based on the Android Operating System (OS). Furthermore, the focus is on developing the core algorithms for the modules of the proposed framework. To evaluate the performance of the proposed framework, three case studies have been designed based on the daily and weekly activities of the user. Moreover, an expert-based validation approach is employed to obtain the expert’s feedback regarding the proposed framework. The result indicates that the proposed framework helps improve user satisfaction and experience by making an intelligent mobile device interface. The results of the framework’s evaluation and validation show the proposed framework’s feasibility and effectiveness. We conclude that the current work is beneficial in filling the identified research gap. Moreover, this research shows the significance of an adaptive interface in an Android OS-based context. In addition, it not only helps in improving the user interest and satisfaction but also enhances the overall performance of the mobile device.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“Furthermore, current research often lacks comprehensive validation processes for proposed AUI models, tending to focus more on single-product solutions (Ali et al. 2024).”
From AdaptUI: A Framework for the development of Adaptive User Interfaces in Smart Product-Service Systems (2024) · cites this paper as Ali et al. 2024
The cited source says what the claim attributes to it.
In the citing paper
“An observation also found in recent research (Ali et al. 2024) reinforces the existing gap in the field; many studies in this domain have failed to conduct a validation process for their proposed models or frameworks and have often focused exclusively on a single specific product.”
From AdaptUI: A Framework for the development of Adaptive User Interfaces in Smart Product-Service Systems (2024) · cites this paper as Ali et al. 2024
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
1 retracted
Khan, I. U., & Khusro, S. (2022) · Soft Computing · doi:10.1007/s00500-021-06705-1 · Cited by 10
RetractedModeling and Analyzing MAPE-K Feedback Loops for Self-Adaptation
Arcaini, P., Riccobene, E., & Scandurra, P. (2015) · International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS) · doi:10.1109/seams.2015.10 · Cited by 242
Applying Machine Learning in Self-adaptive Systems
Gheibi, O., Weyns, D., & Quin, F. (2020) · ACM Transactions on Autonomous and Adaptive Systems · doi:10.1145/3469440 · Cited by 233
Model-based adaptive user interface based on context and user experience evaluation
Hussain, J., Hassan, A. U., Bilal, H. S. M., Ali, R., Afzal, M., Hussain, S., Bang, J., Baños, O., & Lee, S. (2018) · Journal on Multimodal User Interfaces · doi:10.1007/s12193-018-0258-2 · Cited by 178
Integrated model-driven development of self-adaptive user interfaces
Yigitbas, E., Jovanovikj, I., Biermeier, K., Sauer, S., & Engels, G. (2020) · Software & Systems Modeling · doi:10.1007/s10270-020-00777-7 · Cited by 69
Khan, A., & Khusro, S. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-018-7094-y · Cited by 63
Software development with feature toggles: practices used by practitioners
Mahdavi-Hezaveh, R., Dremann, J., & Williams, L. A. (2021) · Empirical Software Engineering · doi:10.1007/s10664-020-09901-z · Cited by 47
BlindSense: An Accessibility-inclusive Universal User Interface for Blind People
Khan, A., Khusro, S., & Alam, I. (2018) · Engineering, Technology & Applied Science Research · doi:10.48084/etasr.1895 · Cited by 44
A Conceptual framework for Adaptive User Interfaces for older adults
Machado, E., Singh, D., Cruciani, F., Chen, L., Hanke, S., Salvago, F., Kropf, J., & Holzinger, A. (2018) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomw.2018.8480407 · Cited by 39
Mobile Devices Interface Adaptivity Using Ontologies
Iqbal, M. W., Naqvi, M. R., Khan, M. A., Khan, F. N., & Whangbo, T. K. (2022) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.023239 · Cited by 37
A Framework for Adaptive User Interface Generation based on User Behavioural Patterns
Rathnayake, N., Meedeniya, D., Perera, I., & Welivita, A. (2019) · Moratuwa Engineering Research Conference (MERCon) · doi:10.1109/mercon.2019.8818825 · Cited by 35
Towards the Design of Context-Aware Adaptive User Interfaces to Minimize Drivers’ Distractions
Khan, I. U., & Khusro, S. (2020) · Mobile Information Systems · doi:10.1155/2020/8858886 · Cited by 34
An adaptive user interface based on spatiotemporal structure learning
Lee, H., Choi, Y. S., & Kim, Y. J. (2011) · IEEE Communications Magazine · doi:10.1109/mcom.2011.5783996 · Cited by 33
Can Adaptive Interfaces Improve the Usability of Mobile Applications?
Wesson, J. L., Singh, A. K., & van Tonder, B. (2010) · IFIP International Federation for Information Processing/IFIP · doi:10.1007/978-3-642-15231-3_19 · Cited by 32
Yigitbas, E., Sauer, S., & Engels, G. (2017) · ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS) · doi:10.1145/3102113.3102144 · Cited by 31
Perspectives on the Design, Challenges, and Evaluation of Smart TV User Interfaces
Khan, M., Khusro, S., Alam, I., Ali, S., & Khan, I. U. (2022) · Scientific Programming · doi:10.1155/2022/2775959 · Cited by 22
On the Importance of Spatial Perception for the Design of Adaptive User Interfaces
Deuschel, T., & Scully, T. (2016) · IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO) · doi:10.1109/saso.2016.13 · Cited by 21
Towards the design of personalized adaptive user interfaces for smart TV viewers
Khan, M., & Khusro, S. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.101777 · Cited by 20
Improving the user experience using an intelligent Adaptive User Interface in mobile applications
Raheel, S. (2016) · IEEE International Multidisciplinary Conference on Engineering Technology (IMCET) · doi:10.1109/imcet.2016.7777428 · Cited by 19
Using smartphone logging to gain insight about phone use in traffic
Ahlstrom, C. H. G., Wachtmeister, J., Nyman, M., Nordenström, A., & Kircher, K. (2019) · Cognition Technology & Work · doi:10.1007/s10111-019-00547-6 · Cited by 18
Component-based development of adaptive user interfaces
Yigitbas, E., Josifovska, K., Jovanovikj, I., Kalinci, F., Anjorin, A., & Engels, G. (2019) · ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS) · doi:10.1145/3319499.3328229 · Cited by 18
Energy efficient adaptation engines for android applications
Cañete, Á., Horcas, J.-M., Ayala, I., & Fuentes, L. (2019) · Information and Software Technology · doi:10.1016/j.infsof.2019.106220 · Cited by 17
Towards Adaptive user Interfaces for Mobile-Phone in Smart World
Iqbal, M. W., Ahmad, N., Shahzad, S. K., Feroz, I., & Ali, N. (2018) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2018.091177 · Cited by 16
Yigitbas, E., Hottung, A., Rojas, S. M., Anjorin, A., Sauer, S., & Engels, G. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3331161 · Cited by 16
Intelligent Context-Aware and Adaptive Interface for Mobile LBS
Feng, J., & Liu, Y. (2015) · Computational Intelligence and Neuroscience · doi:10.1155/2015/489793 · Cited by 15
Naqvi, N. Z., Devlieghere, J., Preuveneers, D., & Berbers, Y. (2016) · Hawaii International Conference on System Sciences (HICSS) · doi:10.1109/hicss.2016.705 · Cited by 14
An Ontology-Based Approach for User Interface Adaptation
Soui, M., Diab, S., Ouni, A., Essayeh, A., & Abed, M. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-45991-2_13 · Cited by 11
Dynamic adaptation and reconfiguration of security in mobile devices
Amoud, M., & Roudiès, O. (2017) · doi:10.1109/cyberincident.2017.8054639 · Cited by 10
An adaptive smartphone anomaly detection model based on data mining
Hu, X. L., Zhang, L. C., & Wang, Z. (2018) · EURASIP Journal on Wireless Communications and Networking · doi:10.1186/s13638-018-1158-6 · Cited by 10
Adaptive Deployment Infrastructure for Android Applications
Casquina, J. C., Eleuterio, J. D. A. S., & Rubira, C. M. F. (2016) · European Dependable Computing Conference · doi:10.1109/edcc.2016.25 · Cited by 9
Adaptive Security Based on MAPE-K: A Survey
Lara, E., Aguilar, L., Sanchez, M. A., & García, J. A. (2019) · Studies in systems, decision and control · doi:10.1007/978-3-030-17985-4_7 · Cited by 9
An adaptive system architecture for devising adaptive user interfaces for mobile ERP apps
Omar, K., & Gómez, J. M. (2017) · doi:10.1109/it-dreps.2017.8277812 · Cited by 6
A self-adaptive framework for enhancing energy efficiency in mobile applications
Moghaddam, F. A., Simaremare, M. E. S., Lago, P., & Grosso, P. (2017) · Sustainable Internet and ICT for Sustainability (SustainIT) · doi:10.23919/sustainit.2017.8379811 · Cited by 6
Ali, M., Khan, S. U. R., & Hussain, S. (2021) · Data & Knowledge Engineering · doi:10.1016/j.datak.2021.101929 · Cited by 6
Self-citationAutonomic Adaptation of Multimedia Content Adhering to Application Mobility
Velázquez-García, F. J., Halvorsen, P., Stensland, H. K., & Eliassen, F. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-93767-0_11 · Cited by 4
Development Support of User Interfaces Adaptive to Use Environment
Tanaka, S., Iwata, H., Shirogane, J., & Fukazawa, Y. (2019) · International Conference on Software and Computer Applications (ICSCA) · doi:10.1145/3316615.3316663 · Cited by 4
Khan, M., & Khusro, S. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2250054 · Cited by 4
An adaptive user interface model for mobile devices based on perceptual control theory
Wang, X., Hua, Q. Y., Zou, F., & Guo, L. (2014) · IEEE International Conference on Software Engineering and Service Science (ICSESS) · doi:10.1109/icsess.2014.6933712 · Cited by 3
A Framework to Decide Adaptive Functionalities by Considering User Emotions and the Context
Wattearachchi, W. D., Hewagamage, K. P., & Hettiarachchi, E. (2020) · International Conference on Advances in ICT for Emerging Regions · doi:10.1109/icter51097.2020.9325497 · Cited by 3
Enabling Active User Participation in Self-Adaptive Applications
Evers, C., & Geihs, K. (2014) · Springer eBooks · doi:10.1007/978-3-319-05044-7_5 · Cited by 2
Research works printed but not linked: 1
- Braham A, Buendía F, Khemaja M, Gargouri F (2019) Generation of adaptive mobile applications based on design patterns for user interfaces. Multidiscip Digit Publ Inst Proc 31(1):19
Cited by
Sorted by citations
Grand Challenges of Smart Technology for Older Adults
Zhou, J., Salvendy, G., Boot, W. R., Charness, N. H., Czaja, S. J., Gao, Q., Holzinger, A., Ntoa, S., Rau, P. P., Rogers, W. A., et al. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2457003 · Cited by 54
Carrera-Rivera, A., Larrinaga, F., Lasa, G., Martinez-Arellano, G., & Unamuno, G. (2024) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-024-09414-0 · Cited by 30
Ge, Y., Li, M., Huang, X., Hu, Y., Wang, Q., Sun, X., & Guo, W. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714238 · Cited by 8
A Systematic Review of Self-Adaptive Mobile Applications with Cooperative Dimension
Gebreselassie, B. A., García, N. M., & Midekso, D. (2025) · IoT · doi:10.3390/iot7010006 · Cited by 1
Human–Machine Synergy in Learning-Driven Data Fabrics
Saravanan, T. R., Samuel, P., Kanimozhi, N., & Poongothai, E. (2026) · Springer eBooks · doi:10.1007/978-3-032-09090-4_15 · Cited by 1
Metadata
- Authors
- Ali, M., Khan, S. U. R., Mashkoor, A., & Taskeen, A.
- Affiliations
- COMSATS University Islamabad (PK)
- Shifa Tameer-e-Millat University (PK)
- Johannes Kepler University of Linz (AT)
- Corresponding author
- Mashkoor, Atif
- Venue
- Cognition, Technology & Work
- Published
- 2024-01-03
- Volume
- 26
- Issue
- 1
- Pages
- 83-106
- ISSN
- 1435-5558 (ISSN-L)
- 1435-5566
- OpenAlex
- W4390539103
- Cited by
- 20
- References
- 40
- Citations by year
- 2026: 13 · 2025: 5 · 2024: 2
- FWCI
- 4.32
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Cognition Technology & Work (publishedVersion, cc-by)
- University Library Linz repository (Johannes Kepler Universitat Linz) (submittedVersion, cc-by)
- Funders
- Johannes Kepler University Linz
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Software › Advanced Software Engineering Methodologies
- Keywords
- adaptive user interfaces, context-aware computing, Android, mobile user interface, conceptual framework, real-time adaptation, user satisfaction, expert validation, user activity patterns
About these checks
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
