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In this study, a supervised learning agent architecture and service selection algorithm are proposed to solve this problem. Adaptation is performed online, on a mobile device. Information about the context, task description, the decision made and its results such as power consumption are stored and constitute training data for a supervised learning algorithm, which updates the knowledge used to determine the optimal location for the execution of a given type of task. To verify the solution proposed, appropriate software has been developed and a series of experiments have been conducted. Results show that as a result of the experience gathered and the learning process performed, the decision module has become more efficient in assigning the task to either the mobile device or cloud resources.<\/jats:p>","DOI":"10.1007\/s11277-020-07657-9","type":"journal-article","created":{"date-parts":[[2020,8,13]],"date-time":"2020-08-13T06:03:01Z","timestamp":1597298581000},"page":"1839-1867","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Adaptive Context-Aware Energy Optimization for Services on Mobile Devices with Use of Machine Learning"],"prefix":"10.1007","volume":"115","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4512-9337","authenticated-orcid":false,"given":"Piotr","family":"Nawrocki","sequence":"first","affiliation":[]},{"given":"Bartlomiej","family":"Sniezynski","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,8,13]]},"reference":[{"issue":"3","key":"7657_CR1","doi-asserted-by":"publisher","first-page":"371","DOI":"10.7494\/csci.2016.17.3.371","volume":"17","author":"P Nawrocki","year":"2016","unstructured":"Nawrocki, P., & Sliwa, A. 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