Publication:
Similarity Segmentation Approach For Sensor-Based Human Activity Recognition

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dc.contributor.authorBaraka, Abdulrahman M. A.,
dc.date.accessioned2025-10-13T07:34:55Z
dc.date.available2025-10-13T07:34:55Z
dc.date.issued2024-01
dc.description.abstractThe researchers attempted to enhance the segmentation method by proposing various techniques. However, most of them focus on each window’s features, and few consider the temporal relationships between the adjacent windows. Therefore, an analysis of the impact of window size on the performance of basic and transitional activity recognition is performed using a deep learning model.
dc.identifier.urihttps://erepo.usm.my/handle/123456789/22761
dc.language.isoen
dc.subjectSensor networks
dc.titleSimilarity Segmentation Approach For Sensor-Based Human Activity Recognition
dc.typeResource Types::text::thesis::doctoral thesis
dspace.entity.typePublication
oairecerif.author.affiliationUniversiti Sains Malaysia
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