Rodyti trumpą aprašą

dc.contributor.authorSledevič, Tomyslav
dc.date.accessioned2023-09-18T17:05:57Z
dc.date.available2023-09-18T17:05:57Z
dc.date.issued2018
dc.identifier.urihttps://etalpykla.vilniustech.lt/handle/123456789/119515
dc.description.abstractThe article presents the classification of images with pollen bearing bees using convolutional neural network (CNN). The aim is to find out a sufficient configuration of CNN required for future implementation on low-cost FPGA. A new dataset with bee images was collected on the entrances to several beehives. hidden layers with up to 15 15 x 15 down to 3x3 filter sizes. The CNN configured to three hidden layers 7–7, 5–5, 3–3 was selected for future application as a trade off between accuracy 94% and number of required arithmetic operations.eng
dc.formatPDF
dc.format.extentp. 1-4
dc.format.mediumtekstas / txt
dc.language.isoeng
dc.relation.isreferencedbyConference Proceedings Citation Index - Science (Web of Science)
dc.relation.isreferencedbyIEEE Xplore
dc.relation.isreferencedbyScopus
dc.source.urihttps://ieeexplore.ieee.org/document/8592464
dc.titleThe application of convolutional neural network for pollen bearing bee classification
dc.typeStraipsnis konferencijos darbų leidinyje Web of Science DB / Paper in conference publication in Web of Science DB
dcterms.references16
dc.type.pubtypeP1a - Straipsnis konferencijos darbų leidinyje Web of Science DB / Article in conference proceedings Web of Science DB
dc.contributor.institutionVilniaus Gedimino technikos universitetas
dc.contributor.facultyElektronikos fakultetas / Faculty of Electronics
dc.subject.researchfieldT 001 - Elektros ir elektronikos inžinerija / Electrical and electronic engineering
dc.subject.researchfieldT 007 - Informatikos inžinerija / Informatics engineering
dc.subject.researchfieldN 001 - Matematika / Mathematics
dc.subject.vgtuprioritizedfieldsIK0202 - Išmaniosios signalų apdorojimo ir ryšių technologijos / Smart Signal Processing and Telecommunication Technologies
dc.subject.ltspecializationsL106 - Transportas, logistika ir informacinės ir ryšių technologijos (IRT) / Transport, logistic and information and communication technologies
dc.subject.enconvolutional neural network
dc.subject.engradient descent training
dc.subject.enfeature extraction
dc.subject.enpollen bearing bee
dcterms.sourcetitle2018 IEEE. 6th workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE), November 8-10, 2018 Vilnius, Lithuania : proceedings / edited by: Dalius Navakauskas, Andrejs Romanovs, Darius Plonis
dc.publisher.nameIEEE
dc.publisher.cityNew York
dc.identifier.doi2-s2.0-85061510135
dc.identifier.doi000458738600027
dc.identifier.doi10.1109/AIEEE.2018.8592464
dc.identifier.elaba33370786


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