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Clustering and Classification of Fungal Cells and PMMA Microparticles: Unsupervised and Supervised Learning using K-Means, PCA, Logistic Regression and Spiking Neural Networks on Event-Based Cytometry Datasets

 
dc.contributor.authorGouda, Muhammed
dc.contributor.authorAbreu, Steve
dc.contributor.authorBienstman, Peter
dc.contributor.imecauthorGouda, Muhammed
dc.contributor.imecauthorBienstman, Peter
dc.contributor.orcidimecBienstman, Peter::0000-0001-6259-464X
dc.date.accessioned2025-05-13T10:23:40Z
dc.date.available2024-12-06T16:45:48Z
dc.date.available2025-05-13T10:23:40Z
dc.date.issued2024
dc.description.wosFundingTextThis work was performed in the context of the European projects Neoteric (grant agreement 871330), Prometheus (grant agreement 101070195), and in the Flemish FWO project G006020N and the Belgian EOS project G0H1422N.
dc.identifier.doi10.1109/ICTON62926.2024.10647743
dc.identifier.eisbn979-8-3503-7732-3
dc.identifier.isbn979-8-3503-7733-0
dc.identifier.issn2162-7339
dc.identifier.urihttps://imec-publications.be/handle/20.500.12860/44924
dc.publisherIEEE
dc.source.conference24th International Conference on Transparent Optical Networks (ICTON)
dc.source.conferencedateJUL 14-18, 2024
dc.source.conferencelocationBari
dc.source.journalN/A
dc.source.numberofpages4
dc.title

Clustering and Classification of Fungal Cells and PMMA Microparticles: Unsupervised and Supervised Learning using K-Means, PCA, Logistic Regression and Spiking Neural Networks on Event-Based Cytometry Datasets

dc.typeProceedings paper
dspace.entity.typePublication
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