Show simple item record

dc.contributor.authorÖzekes, Serhat
dc.contributor.authorErguzel, Turker Tekin
dc.date.accessioned2016-04-06T07:49:36Z
dc.date.available2016-04-06T07:49:36Z
dc.date.issued2014
dc.identifier.citationErguzel, T. T. & Ozekes, S. (2014) Artificial intelligence approaches in psychiatric disorders. The Journal of Neurobehavioral Sciences, 1 (2), 52-53.tr_TR
dc.identifier.issn2148-4325
dc.identifier.urihttp://earsiv.uskudar.edu.tr/xmlui/handle/123456789/542
dc.description.abstractPotential utility of machine learning (ML) methods can be used as a clinical tool in administering diagnosis and therapy to a targeted group of subjects suffering from psychiatric diseae. The use of ML methodology is more potentially useful to the clinician as a classification or treatment response prediction tool . It is worth using feature selection algorithms to raise the sensitivity and accuracy of the models to contribute to the hybrid approach of artificial intelligence methodologies.tr_TR
dc.language.isoengtr_TR
dc.publisherÜsküdar Üniversitesitr_TR
dc.relation.isversionof10.5455/JNBS.1405259279tr_TR
dc.subjectMachine learningtr_TR
dc.subjectfeature selectiontr_TR
dc.subjectpsychiatric diseasestr_TR
dc.titleArtificial intelligence approaches in psychiatric disorderstr_TR
dc.title.alternativePSİKİYATRİK BOZUKLUKLARDA YAPAY ZEKA YAKLAŞIMLARItr_TR
dc.typeOthertr_TR
dc.relation.journalThe Journal of Neurobehavioral Sciencestr_TR
dc.contributor.departmentÜsküdar University, Faculty of Engineering and Natural Sciences, Department of Computer Engineeringtr_TR
dc.identifier.volume1tr_TR
dc.identifier.issue2tr_TR
dc.identifier.startpage52tr_TR
dc.identifier.endpage53tr_TR
dc.contributor.authorIDTR29371tr_TR
dc.contributor.authorIDTR19915tr_TR


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record