Publication:
DEVELOPING PLASTIC RECYCLING CLASSIFIER BY DEEP LEARNING AND DIRECTED ACYCLIC GRAPH RESIDUAL NETWORK

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dc.citedby2
dc.contributor.authorMohammed A.B.en_US
dc.contributor.authorAl-Mafrji A.A.M.en_US
dc.contributor.authorYassen M.S.en_US
dc.contributor.authorSabry A.H.en_US
dc.contributor.authorid57686887900en_US
dc.contributor.authorid57686888000en_US
dc.contributor.authorid57686375400en_US
dc.contributor.authorid56602511900en_US
dc.date.accessioned2023-05-29T09:40:49Z
dc.date.available2023-05-29T09:40:49Z
dc.date.issued2022
dc.description.abstractRecycling is one of the most important approaches to safeguard the environment since it aims to reduce waste in landfills while conserving natural resources. Using deep Learning networks, this group of wastes may be automatically classified on the belts of a waste sorting plant. However, a basic set of connected layers may not be adequate to give satisfactory accuracy for such multi output classifier tasks. To optimize the gradient flow and enable deeper training for network design with multi label classifier, this study suggests a residual-based deep learning convolutional neural network. For network training, ten classes have been explored. The Directed Acyclic Graph (DAG) is a structure with hidden layers that have inputs, outputs, and other layers. The DAG network�s residual-based architecture features shortcut connections that bypass some levels of the network, allowing gradients of network parameters to travel freely among the network output layers for deeper training. The methodology includes: 1. preparing the data and creating an augmented image data store; 2. defining the main serially-connected branches of the network architecture; 3. defining the residual interconnections that bypass the main branch layers; 4. defining layers, and finally; creating a residual-based deeper layer graph. The concept is to split down the multiclass classification problem into minor binary states, where every classifier performs as an expert by concentrating on discriminating between only two labels, improving total accuracy. The results achieve (2.861 %) training error and (9.76 %) a validation error. The training results of this classifier are evaluated by finding the training error, validation error, and showing the confusion matrix of validation data � Copyright � 2022, Authors. This is an open access article under the Creative Commons CC BY licenseen_US
dc.description.natureFinalen_US
dc.identifier.doi10.15587/1729-4061.2022.254285
dc.identifier.epage49
dc.identifier.issue10-116
dc.identifier.scopus2-s2.0-85130033669
dc.identifier.spage42
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85130033669&doi=10.15587%2f1729-4061.2022.254285&partnerID=40&md5=e8eb0e32ded63833958313bbfea65645
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/27197
dc.identifier.volume2
dc.publisherTechnology Centeren_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleEastern-European Journal of Enterprise Technologies
dc.titleDEVELOPING PLASTIC RECYCLING CLASSIFIER BY DEEP LEARNING AND DIRECTED ACYCLIC GRAPH RESIDUAL NETWORKen_US
dc.typeArticleen_US
dspace.entity.typePublication
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