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The simulation results show that, compared to traditional algorithm, the classification accuracy of small sample categories was significantly improved; the forecast energy consumption value and the standard values are 7.67% in private cloud and 2.93% in public cloud; the energy saving reaches 2.22% to 16.23% in 3G and 7.35% to 20.74% in Wi-Fi.<\/jats:p>","DOI":"10.1155\/2019\/7430860","type":"journal-article","created":{"date-parts":[[2019,1,23]],"date-time":"2019-01-23T18:59:28Z","timestamp":1548269968000},"page":"1-15","source":"Crossref","is-referenced-by-count":9,"title":["A Novel Resource Deployment Approach to Mobile Microlearning: From Energy-Saving Perspective"],"prefix":"10.1155","volume":"2019","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0932-8788","authenticated-orcid":true,"given":"Ruijuan","family":"Zheng","sequence":"first","affiliation":[{"name":"College of Information Engineering, Henan University of Science and Technology, Luoyang 471023, 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