{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T12:04:29Z","timestamp":1777291469537,"version":"3.51.4"},"reference-count":19,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T00:00:00Z","timestamp":1685577600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats: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.<\/jats:p>","DOI":"10.2478\/acss-2023-0014","type":"journal-article","created":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T06:46:01Z","timestamp":1692341161000},"page":"148-155","source":"Crossref","is-referenced-by-count":0,"title":["Intelligent Mobile User Profiling for Maximum Performance"],"prefix":"10.2478","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0096-2552","authenticated-orcid":false,"given":"Adnan","family":"Muhammad","sequence":"first","affiliation":[{"name":"Department of Computer Science , FAST-NUCES , Lahore , Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sher","family":"Afghan","sequence":"additional","affiliation":[{"name":"Department of Computer Science , UET , Lahore , Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7997-8196","authenticated-orcid":false,"given":"Afzal","family":"Muhammad","sequence":"additional","affiliation":[{"name":"Department of Computer Science , Qarshi University , Lahore , Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2023,8,17]]},"reference":[{"key":"2026042709094038562_j_acss-2023-0014_ref_001","doi-asserted-by":"crossref","unstructured":"M. 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Malerba, \u201cCatch-up and the entry strategies of latecomers: Chinese firms in the mobile phone sector,\u201d Industrial and Corporate Change, vol. 30, no. 1, pp. 189\u2013213, Feb. 2021. https:\/\/doi.org\/10.1093\/icc\/dtaa061","DOI":"10.1093\/icc\/dtaa061"},{"key":"2026042709094038562_j_acss-2023-0014_ref_004","unstructured":"S. M. Jacob and B. Issac, \u201cThe mobile devices and its mobile learning usage analysis,\u201d arXiv preprint, arXiv:1410.4375, Oct. 2014. https:\/\/doi.org\/10.48550\/arXiv.1410.4375"},{"key":"2026042709094038562_j_acss-2023-0014_ref_005","doi-asserted-by":"crossref","unstructured":"M. Qiu, Z. Chen, L. T. Yang, X. Qin and B. Wang, \u201cTowards power efficient smartphones by energy-aware dynamic task scheduling,\u201d in 2012 IEEE 14th International Conference on High Performance Computing and Communication & 2012 IEEE 9th International Conference on Embedded Software and Systems, Liverpool, UK, 2012, pp. 1466\u20131472. https:\/\/doi.org\/10.1109\/HPCC.2012.214","DOI":"10.1109\/HPCC.2012.214"},{"key":"2026042709094038562_j_acss-2023-0014_ref_006","doi-asserted-by":"crossref","unstructured":"T. Fjellheim, S. Milliner, M. Dumas, and J. Vayssi\u00e8re, \u201cA process-based methodology for designing event-based mobile composite applications,\u201d Data & Knowledge Engineering, vol. 61, no. 1, pp. 6\u201322, Apr. 2007. https:\/\/doi.org\/10.1016\/j.datak.2006.04.004","DOI":"10.1016\/j.datak.2006.04.004"},{"key":"2026042709094038562_j_acss-2023-0014_ref_007","doi-asserted-by":"crossref","unstructured":"M. Igarashi et al., \u201cA 28 nm high-k\/MG heterogeneous multicore mobile application processor with 2 GHz cores and low-power 1 GHz cores,\u201d IEEE Journal of Solid-State Circuits, vol. 50, no. 1, pp. 92\u2013101, Jan. 2015. https:\/\/doi.org\/10.1109\/JSSC.2014.2347353","DOI":"10.1109\/JSSC.2014.2347353"},{"key":"2026042709094038562_j_acss-2023-0014_ref_008","doi-asserted-by":"crossref","unstructured":"P. T. Palomino, A. M. Toda, L. Rodrigues, W. Oliveira, L. Nacke, and S. Isotani, \u201cAn ontology for modelling user\u2019 profiles and activities in gamified education,\u201d Research and Practice in Technology Enhanced Learning, vol. 18, Feb. 2023, Art. no. 018. https:\/\/doi.org\/10.58459\/rptel.2023.18018","DOI":"10.58459\/rptel.2023.18018"},{"key":"2026042709094038562_j_acss-2023-0014_ref_009","doi-asserted-by":"crossref","unstructured":"H. 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Nian, \u201cPersonalized recommendation system based on knowledge embedding and historical behavior,\u201d Applied Intelligence, vol. 52, pp. 954\u2013966, 2022. https:\/\/doi.org\/10.1007\/s10489-021-02363-w","DOI":"10.1007\/s10489-021-02363-w"},{"key":"2026042709094038562_j_acss-2023-0014_ref_016","doi-asserted-by":"crossref","unstructured":"I. Tochukwu, L. Hederman, and P. J. Wall, \u201cDesign processes for user engagement with mobile health: A systematic review,\u201d International Journal of Advanced Computer Science and Applications, vol. 13, no. 2, 2022. https:\/\/doi.org\/10.14569\/IJACSA.2022.0130235","DOI":"10.14569\/IJACSA.2022.0130235"},{"key":"2026042709094038562_j_acss-2023-0014_ref_017","doi-asserted-by":"crossref","unstructured":"M. Hosseini, N. Abdolvand, and S. R. Harandi, \u201cTwo-dimensional analysis of customer behavior in traditional and electronic banking,\u201d Digital Business, vol. 2, no. 2, 2022, Art. no. 100030. https:\/\/doi.org\/10.1016\/j.digbus.2022.100030","DOI":"10.1016\/j.digbus.2022.100030"},{"key":"2026042709094038562_j_acss-2023-0014_ref_018","doi-asserted-by":"crossref","unstructured":"A. Bhutoria, \u201cPersonalized education and Artificial Intelligence in the United States, China, and India: A systematic review using a Human-In-The-Loop model,\u201d Computers and Education: Artificial Intelligence, vol. 3, 2022, Art. no. 100068. https:\/\/doi.org\/10.1016\/j.caeai.2022.100068","DOI":"10.1016\/j.caeai.2022.100068"},{"key":"2026042709094038562_j_acss-2023-0014_ref_019","doi-asserted-by":"crossref","unstructured":"S. Banabilah, M. Aloqaily, E. Alsayed, N. Malik, and Y. 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