AI-Driven App Management: Enhancing Device Optimization and Digital Wellbeing

Authors

  • Kamal Gupta University of Birmingham, United Kingdom Author

DOI:

https://doi.org/10.32628/CSEIT25112375

Keywords:

Mobile Application Management, Digital Wellbeing, Machine Learning Optimization, User Experience Design

Abstract

This article investigates the integration of machine learning techniques for intelligent application management in mobile devices, addressing the challenges of application overload and its impact on device performance and user wellbeing. The article presents a comprehensive framework that combines resource optimization with digital well-being considerations, implementing on-device processing through conditional approximate neural networks. The system analyzes user behavior patterns, resource consumption metrics, and psychological factors to provide personalized recommendations for application management. By incorporating insights from technical performance analysis and user behavior studies, the framework demonstrates significant improvements in device efficiency, user productivity, and digital well-being while maintaining high user satisfaction rates through transparent and explainable AI implementations.

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References

Javier Berrocal et al., "Early analysis of resource consumption patterns in mobile applications," Pervasive and Mobile Computing, Volume 35, February 2017, Pages 32-50. [Online]. Available: https://www.sciencedirect.com/science/article/abs/pii/S1574119216300797

Douglas A Parry et al., "Digital Wellbeing Applications: Adoption, Use and Perceived Effects," ResearchGate, September 2020. [Online]. Available: https://www.researchgate.net/publication/345913429_Digital_Wellbeing_Applications_Adoption_Use_and_Perceived_Effects

Guangli Li et al., "CoAxNN: Optimizing on-device deep learning with conditional approximate neural networks," Journal of Systems Architecture, Volume 143, October 2023, 102978. [Online]. Available: https://www.sciencedirect.com/science/article/abs/pii/S1383762123001571

Subhankar Mishra, "Learning from Usage Analysis of Mobile Devices," International Conference on Computational Intelligence and Data Science (ICCIDS 2019), Procedia Computer Science, 167 (2020) 1648–1655. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1877050920308413

Sara Thomée, "Mobile Phone Use and Mental Health. A Review of the Research That Takes a Psychological Perspective on Exposure," Int J Environ Res Public Health. 2018 Nov 29;15(12):2692. [Online]. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC6314044/

Reem S. Al-Mansoori et al., "Designing for Digital Wellbeing: From Theory to Practice a Scoping Review," Human Behavior and Emerging Technologies, Wiley, 02 August 2023. [Online]. Available: https://onlinelibrary.wiley.com/doi/10.1155/2023/9924029

Karen Church et al., "Understanding the Challenges of Mobile Phone Usage Data," MobileHCI '15: Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services, Pages 504 - 514, 24 August 2015. [Online]. Available: https://dl.acm.org/doi/10.1145/2785830.2785891

Carlo Giovine et al., "Building AI trust: The key role of explainability," McKinsey & Company, November 26, 2024. [Online]. Available: https://www.mckinsey.com/capabilities/quantumblack/our-insights/building-ai-trust-the-key-role-of-explainability

Laberiano Andrade-Arenas et al., "Mobile application: awareness of the population on the environmental impact," ResearchGate, April 2024. [Online]. Available: https://www.researchgate.net/publication/379467507_Mobile_application_awareness_of_the_population_on_the_environmental_impact

Lewend Mayiwar et al., "Determinants of digital well-being," AI & Society, 2024. [Online]. Available: https://link.springer.com/article/10.1007/s00146-024-02071-2

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Published

06-03-2025

Issue

Section

Research Articles