<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The use of smartphones and their applications is expanding rapidly, thereby increasing the demand of computational power and other hardware resources of the smartphones. On the other hand, these small devices can have limited resources of computation power, battery backup, RAM memory, and storage space due to their small size. These devices need to reconcile resource hungry applications. This research focuses on solving issues of power and efficiency of smart devices by adapting intelligently to mobile usage by profiling the user intelligently. Our designed architecture makes a smartphone smarter by intelligently utilizing its resources to increase the battery life. Our developed application makes profiles of the applications usage at different time intervals. These stored usage profiles are utilized to make intelligent resource allocation for next time interval. We implemented and evaluated the profiling scheme for different brands of android smartphone. We implemented our approach with Naive Bayes and Decision Tree for performance and compared it with conventional approach. The results show that the proposed approach based on decision trees saves 31 % CPU and 60 % of RAM usage as compared to the conventional approach.</p>
</abstract>

The use of smartphones and their applications is expanding rapidly, thereby increasing the demand of computational power and other hardware resources of the smartphones. On the other hand, these small devices can have limited resources of computation power, battery backup, RAM memory, and storage space due to their small size. These devices need to reconcile resource hungry applications. This research focuses on solving issues of power and efficiency of smart devices by adapting intelligently to mobile usage by profiling the user intelligently. Our designed architecture makes a smartphone smarter by intelligently utilizing its resources to increase the battery life. Our developed application makes profiles of the applications usage at different time intervals. These stored usage profiles are utilized to make intelligent resource allocation for next time interval. We implemented and evaluated the profiling scheme for different brands of android smartphone. We implemented our approach with Naive Bayes and Decision Tree for performance and compared it with conventional approach. The results show that the proposed approach based on decision trees saves 31 % CPU and 60 % of RAM usage as compared to the conventional approach.

Intelligent Mobile User Profiling for Maximum Performance

Applied Computer Systems

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Intelligent Mobile User Profiling for Maximum Performance"}

The use of smartphones and their applications is expanding rapidly, thereby increasing the demand of computational power and other hardware resources of the...

Intelligent Mobile User Profiling for Maximum Performance

Data publikacji: 17 sie 2023

Zakres stron: 148 - 155

DOI: https://doi.org/10.2478/acss-2023-0014

Słowa kluczowe
Decision trees, intelligent mobile, maximum performance, Naive Bayes, user profiling

© 2023 Adnan Muhammad et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Intelligent Mobile User Profiling for Maximum Performance

Data publikacji: 17 sie 2023

Zakres stron: 148 - 155

DOI: https://doi.org/10.2478/acss-2023-0014

Słowa kluczoweDecision trees, intelligent mobile, maximum performance, Naive Bayes, user profiling

© 2023 Adnan Muhammad et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Decision trees, intelligent mobile, maximum performance, Naive Bayes, user profiling