DC FieldValueLanguage
dc.contributor.authorAnagnostopoulos, Theodoros-
dc.date.accessioned2024-07-05T14:41:51Z-
dc.date.available2024-07-05T14:41:51Z-
dc.date.issued2013-05-31-
dc.identifierscopus-84944200002-
dc.identifier.isbn9781466640399-
dc.identifier.isbn9781466640382-
dc.identifier.other84944200002-
dc.identifier.urihttps://uniwacris.uniwa.gr/handle/3000/2687-
dc.description.abstractMobile 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 chapter, the authors propose a mobility prediction model, which deals with context representation and location prediction of moving users. Machine Learning (ML) techniques are used for trajectory classification. Spatial and temporal on-line clustering is adopted. They rely on Adaptive Resonance Theory (ART) for location prediction. Location prediction is treated as a context classification problem. The authors introduce a novel classifier that applies a Hausdorff-like distance over the extracted trajectories handling location prediction. Two learning methods (non-reinforcement and reinforcement learning) are presented and evaluated. They compare ART with Self-Organizing Maps (SOM), Offline kMeans, and Online kMeans algorithms. Their findings are very promising for the use of the proposed model in mobile context aware applications.en_US
dc.language.isoenen_US
dc.relation.ispartofIntelligent Technologies and Techniques for Pervasive Computingen_US
dc.subjectArtificial intelligenceen_US
dc.subjectComputer science & ITen_US
dc.subjectEngineering science referenceen_US
dc.subjectUbiquitous & pervasive computingen_US
dc.titleReinforcement and non-reinforcement machine learning classifiers for user movement predictionen_US
dc.typeBook Chapteren_US
dc.identifier.doi10.4018/978-1-4666-4038-2.ch012en_US
dc.identifier.scopus2-s2.0-84944200002-
dcterms.accessRights0en_US
dc.relation.deptDepartment of Business Administrationen_US
dc.relation.facultySchool of Administrative, Economics and Social Sciencesen_US
dc.identifier.spage218en_US
dc.identifier.epage237en_US
dc.collaborationUniversity of West Attica (UNIWA)en_US
dc.journalsSubscriptionen_US
dc.publicationPeer Revieweden_US
dc.countryGreeceen_US
local.metadatastatusverifieden_US
item.cerifentitytypePublications-
item.languageiso639-1en-
item.openairetypeBook Chapter-
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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