Exploring Ocean Animal Trajectory Pattern via Deep Learning

Handle URI:
http://hdl.handle.net/10754/610580
Title:
Exploring Ocean Animal Trajectory Pattern via Deep Learning
Authors:
Wang, Su ( 0000-0002-8980-0818 )
Abstract:
We trained a combined deep convolutional neural network to predict seals’ age (3 categories) and gender (2 categories). The entire dataset contains 110 seals with around 489 thousand location records. Most records are continuous and measured in a certain step. We created five convolutional layers for feature representation and established two fully connected structure as age’s and gender’s classifier, respectively. Each classifier consists of three fully connected layers. Treating seals’ latitude and longitude as input, entire deep learning network, which includes 780,000 neurons and 2,097,000 parameters, can reach to 70.72% accuracy rate for predicting seals’ age and simultaneously achieve 79.95% for gender estimation.
Advisors:
Zhang, Xiangliang ( 0000-0002-3574-5665 )
Committee Member:
Gao, Xin ( 0000-0002-7108-3574 ) ; Moshkov, Mikhail ( 0000-0003-0085-9483 )
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science
Program:
Computer Science
Issue Date:
23-May-2016
Type:
Thesis
Appears in Collections:
Theses; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.advisorZhang, Xiangliangen
dc.contributor.authorWang, Suen
dc.date.accessioned2016-05-23T13:06:11Z-
dc.date.available2016-05-23T13:06:11Z-
dc.date.issued2016-05-23-
dc.identifier.urihttp://hdl.handle.net/10754/610580-
dc.description.abstractWe trained a combined deep convolutional neural network to predict seals’ age (3 categories) and gender (2 categories). The entire dataset contains 110 seals with around 489 thousand location records. Most records are continuous and measured in a certain step. We created five convolutional layers for feature representation and established two fully connected structure as age’s and gender’s classifier, respectively. Each classifier consists of three fully connected layers. Treating seals’ latitude and longitude as input, entire deep learning network, which includes 780,000 neurons and 2,097,000 parameters, can reach to 70.72% accuracy rate for predicting seals’ age and simultaneously achieve 79.95% for gender estimation.en
dc.language.isoenen
dc.subjectdeep learningen
dc.subjectanimal trajectoryen
dc.subjectconvolutional neural networken
dc.subjectfeature representationen
dc.titleExploring Ocean Animal Trajectory Pattern via Deep Learningen
dc.typeThesisen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Scienceen
thesis.degree.grantorKing Abdullah University of Science and Technologyen_GB
dc.contributor.committeememberGao, Xinen
dc.contributor.committeememberMoshkov, Mikhailen
thesis.degree.disciplineComputer Scienceen
thesis.degree.nameMaster of Scienceen
dc.person.id132680en
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