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

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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Publication Details

Journal
International Journal of Scientific Research in Computer Science Engineering and Information Technology
Published
2025-03-06
DOI
https://doi.org/10.32628/cseit25112375
Primary Topic
Green IT and Sustainability
Type
article
Field-Weighted Citation Impact
0.00
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article

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

Kamal Gupta
International Journal of Scientific Research in Computer Science Engineering and Information Technology
Green IT and Sustainability
article

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

Kamal Gupta
article en

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.

International Journal of Scientific Research in Computer Science Engineering and Information TechnologyVol. 11(2)
University of Birmingham (GB)
Decent work and economic growth
Openalex Percentile: Top 97%
Green IT and Sustainability
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