Semantic place prediction from crowd-sensed mobile phone data

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dc.authoridIncel, Ozlem Durmaz/0000-0002-6229-7343
dc.authorwosidÇelik, Selek Ceren/AAW-7201-2020
dc.authorwosidIncel, Ozlem Durmaz/K-2570-2012
dc.contributor.authorCelik, Selek Ceren
dc.contributor.authorIncel, Ozlem Durmaz
dc.date.accessioned2024-07-18T20:42:23Z
dc.date.available2024-07-18T20:42:23Z
dc.date.issued2018
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description.abstractSemantic place prediction problem is the process of giving semantic names to locations. While the localization problem is about predicting the exact position, i.e. the coordinates, of a place, the aim here is to semantically characterize the location, such as home, school, restaurant. In order to solve the problem, phone usage patterns of crowds and the performed activities at different places can be utilized. In this study, we aim to semantically classify places visited by smart phone users utilizing the data collected from sensors and wireless interfaces available on the phones as well as phone usage patterns, such as battery level, and time-related information, with machine learning algorithms. For this purpose, we collected data from 15 participants at Galatasaray University for a duration of 1 month, in April 2016, and two of the users continued to collect 1 more month of data, which makes it 17 participants in total. We extract various set of features from the collected data and analyse the efficiency of features with different classification algorithms such as, decision tree, random forest, k-nearest neighbour, naive Bayes and multi-layer perceptron. We observe that, by fusing features extracted from different sources of data, better success rates are achieved. Moreover, we explore the relationship between places and activities, which was not explored in previous studies, and show that activities are important source of information for characterizing the places. Additionally, we observe that, while a generalized classifier performs reasonably well, using person-specific data and classification can help to improve the success rate.en_US
dc.description.sponsorshipGalatasaray University [15.401.004]; Tubitak [113E271]en_US
dc.description.sponsorshipThis work is supported by the Galatasaray University Research Fund under Grant Number 15.401.004 and by Tubitak under Grant Number 113E271.en_US
dc.identifier.doi10.1007/s12652-017-0549-6
dc.identifier.endpage2124en_US
dc.identifier.issn1868-5137
dc.identifier.issn1868-5145
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-85049603474en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage2109en_US
dc.identifier.urihttps://doi.org/10.1007/s12652-017-0549-6
dc.identifier.urihttps://hdl.handle.net/11411/7266
dc.identifier.volume9en_US
dc.identifier.wosWOS:000446730900026en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofJournal of Ambient Intelligence and Humanized Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMobile Phone Sensingen_US
dc.subjectSemantic Place Predictionen_US
dc.subjectClassificationen_US
dc.titleSemantic place prediction from crowd-sensed mobile phone dataen_US
dc.typeArticleen_US

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