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

Waste recycling is an important way of saving energy and materials in theproduction process. In general cases recyclable objects are mixed withunrecyclable objects, which raises a need for identification andclassification. This paper proposes a convolutional neural network (CNN) modelto complete both tasks. The model uses transfer learning from a pretrainedResnet-50 CNN to complete feature extraction. A subsequent fully connectedlayer for classification was trained on the augmented TrashNet dataset [1]. Inthe application, sliding-window is used for image segmentation in thepre-classification stage. In the post-classification stage, the labelled samplepoints are integrated with Gaussian Clustering to locate the object. Theresulting model has achieved an overall detection rate of 48.4% in simulationand final classification accuracy of 92.4%.

Recyclable Waste Identification Using CNN Image Recognition and Gaussian Clustering