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

In this paper, we propose an novel methodology aimed at simulating thelearning phenomenon of nystagmus through the application of differentialblurring on datasets. Nystagmus is a biological phenomenon that influenceshuman vision throughout life, notably by diminishing head shake from infancy toadulthood. Leveraging this concept, we address the issue of wasteclassification, a pressing global concern. The proposed framework comprises twomodules, with the second module closely resembling the original VisionTransformer, a state-of-the-art model model in classification tasks. Theprimary motivation behind our approach is to enhance the model's precision andadaptability, mirroring the real-world conditions that the human visual systemundergoes. This novel methodology surpasses the standard Vision Transformermodel in waste classification tasks, exhibiting an improvement with a margin of2%. This improvement underscores the potential of our methodology in improvingmodel precision by drawing inspiration from human vision perception. Furtherresearch in the proposed methodology could yield greater performance results,and can be extrapolated to other global issues.

Integrating Human Vision Perception in Vision Transformers for Classifying Waste Items