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dc.contributor.authorAnagnostopoulos, Theodoros-
dc.contributor.authorAnagnostopoulos, Christos-
dc.contributor.authorHadjiefthymiades, Stathes-
dc.date.accessioned2024-07-05T07:48:48Z-
dc.date.available2024-07-05T07:48:48Z-
dc.date.issued2011-06-01-
dc.identifierscopus-79960013279-
dc.identifier.issn1572-8129-
dc.identifier.issn1068-9605-
dc.identifier.other79960013279-
dc.identifier.urihttps://uniwacris.uniwa.gr/handle/3000/2676-
dc.description.abstractContext-awareness is viewed as one of the most important aspects in the emerging pervasive computing paradigm. Mobile context-aware applications are required to sense and react to changing environment conditions. Such applications, usually, need to recognize, classify and predict context in order to act efficiently, beforehand, for the benefit of the user. In this paper, we propose a novel adaptive mobility prediction algorithm, which deals with location context representation and trajectory prediction of moving users. Machine Learning (ML) is used for trajectory classification. Our algorithm adopts spatial and temporal on-line clustering, and relies on Adaptive Resonance Theory (ART) for trajectory prediction. The proposed algorithm applies a Hausdorff-like distance over the extracted trajectories handling location prediction. Since our approach is time-sensitive, the Hausdorff distance is considered more advantageous than a simple Euclidean norm. Two learning methods (non-reinforcement and reinforcement learning) are presented and evaluated. Finally, we compare our algorithm with Offline kMeans and Online kMeans algorithms. Our findings are very promising for the use of the proposed algorithm in mobile context aware applications.en_US
dc.language.isoenen_US
dc.relation.ispartofInternational Journal of Wireless Information Networksen_US
dc.subjectAdaptive resonance theoryen_US
dc.subjectContext-awarenessen_US
dc.subjectLocation predictionen_US
dc.subjectMachine learningen_US
dc.subjectOnline clustering and classificationen_US
dc.titleAn adaptive machine learning algorithm for location predictionen_US
dc.typeArticleen_US
dc.identifier.doi10.1007/s10776-011-0142-4en_US
dc.identifier.scopus2-s2.0-79960013279-
dcterms.accessRights0en_US
dc.relation.deptDepartment of Business Administrationen_US
dc.relation.facultySchool of Administrative, Economics and Social Sciencesen_US
dc.relation.volume18en_US
dc.relation.issue2en_US
dc.identifier.spage88en_US
dc.identifier.epage99en_US
dc.collaborationUniversity of West Attica (UNIWA)en_US
dc.subject.fieldSocial Sciencesen_US
dc.journalsOpen Accessen_US
dc.publicationPeer Revieweden_US
dc.countryGreeceen_US
local.metadatastatusverifieden_US
item.cerifentitytypePublications-
item.languageiso639-1en-
item.openairetypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextNo Fulltext-
item.grantfulltextnone-
crisitem.author.deptDepartment of Business Administration-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.orcid0000-0002-5587-2848-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
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