RaDSS V02: A Radiolarian Classifier Using Convolutional Neural Network

dc.contributor.author	Quisote, Micah
dc.date.accessioned	2019-08-17T13:06:27Z
dc.date.available	2019-08-17T13:06:27Z
dc.date.issued	2018-05
dc.identifier.uri	http://dspace.cas.upm.edu.ph:8080/xmlui/handle/123456789/460
dc.description.abstract	Radiolarian assemblages have played a signi ficant role as a biostratigraphic and paleoenvironmental tool used in age-dating, correlation, and studying deep-sea sedimentary rocks that lacks calcareous fossils. The species rapid classi fication would allow micropaleontologists to proceed further into studying the structure and way of living of these Radiolarians. RaDSS V02 is a deep learning based system that could help researchers in classifying Radiolarian species' microfossil images through image processing and convolutional neural network.	en_US
dc.language.iso	en	en_US
dc.subject	Radiolarian	en_US
dc.subject	Deep Learning	en_US
dc.subject	Convolutional Neural Networks	en_US
dc.subject	Image Recognition	en_US
dc.subject	Image processing	en_US
dc.title	RaDSS V02: A Radiolarian Classifier Using Convolutional Neural Network	en_US
dc.type	Thesis	en_US