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

Similarity analysis using neural networks has emerged as a powerful techniquefor understanding and categorizing complex patterns in various domains. Byleveraging the latent representations learned by neural networks, data objectssuch as images can be compared effectively. This research explores theutilization of latent information generated by fully convolutional networks(FCNs) in similarity analysis, notably to estimate the visual resemblance ofobjects segmented in 2D pictures. To do this, the analytical scheme comprisestwo steps: (1) extracting and transforming feature patterns per 2D object froma trained FCN, and (2) identifying the most similar patterns through fuzzyinference. The step (2) can be further enhanced by incorporating a weightingscheme that considers the significance of latent variables in the analysis. Theresults provide valuable insights into the benefits and challenges of employingneural network-based similarity analysis for discerning data patternseffectively.

Object-based Probabilistic Similarity Evidence of Sparse Latent Features from Fully Convolutional Networks