English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper presents methods to identify the plastic waste based on its resinidentification code to provide an efficient recycling of post-consumer plasticwaste. We propose the design, training and testing of different machinelearning techniques to (i) identify a plastic waste that belongs to the knowncategories of plastic waste when the system is trained and (ii) identify a newplastic waste that do not belong the any known categories of plastic wastewhile the system is trained. For the first case,we propose the use of one-shotlearning techniques using Siamese and Triplet loss networks. Our proposedapproach does not require any augmentation to increase the size of the databaseand achieved a high accuracy of 99.74%. For the second case, we propose the useof supervised and unsupervised dimensionality reduction techniques and achievedan accuracy of 95% to correctly identify a new plastic waste.

Application of Computer Vision Techniques for Segregation of PlasticWaste based on Resin Identification Code