Human motion monitoring for falling detection using artificial intelligence

Basit Öğe Kaydı
dc.contributor.advisorAtilla, Doğu Çağdaş
dc.contributor.authorAl-Hassani, Raghad Tariq Abdulrazzaq
dc.date.accessioned2023-12-28T08:09:03Z
dc.date.available2023-12-28T08:09:03Z
dc.date.issued2023en_US
dc.date.submitted2023
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalıen_US
dc.description.abstractHospitals must continually monitor their patients' actions to lower the chance of accidents such as patient falls and slides. Human behaviour is difficult to track due to the complexity of human activities and the unpredictable nature of their conduct. As a result, creating a static link that is used to influence human behaviour is challenging since it is hard to forecast how individuals will think or act in response to a certain event. Deep learning and data mining enable turbulence prediction models. Industry progressed greatly. This study anticipated four motion turbulence types using auto encoding and two other methods. To improve accuracy. The AEC's forecasts average 0.01 inaccuracy. Consequently, the 4SFNN method divides the data into four areas for each classifier. The system now predicts 98.6025 percent. PSO optimization boosts system performance, according to our research. Bayesian Regularization training enhanced the recommended system's performance to 98.63 percent accuracy and 0.015 percent MAE.en_US
dc.identifier.citationAl-Hassani, R. T. A. (2023). Human motion monitoring for falling detection using artificial intelligence. (Yayınlanmamış doktora tezi). Altınbaş Üniversitesi, Lisansüstü Eğitim Enstitüsü, İstanbul.en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12939/4471
dc.identifier.yoktezid826004
dc.institutionauthorAl-Hassani, Raghad Tariq Abdulrazzaq
dc.language.isoen
dc.publisherAltınbaş Üniversitesi / Lisansüstü Eğitim Enstitüsüen_US
dc.relation.publicationcategoryTezen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subject4SFNNen_US
dc.subjectPSOen_US
dc.subjectAECen_US
dc.subjectBayesianen_US
dc.subjectPredictionen_US
dc.subjectAccuracyen_US
dc.subjectMAEen_US
dc.subjectFallingen_US
dc.subjectBSCen_US
dc.subjectSDLen_US
dc.subjectFOLen_US
dc.subjectFKLen_US
dc.titleHuman motion monitoring for falling detection using artificial intelligence
dc.typeDoctoral Thesis

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