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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

This master thesis describes an algorithm for automated categorization ofscientific documents using deep learning techniques and compares the results tothe results of existing classification algorithms. As an additional goal areusable API is to be developed allowing the automation of classification tasksin existing software. A design will be proposed using a convolutional neuralnetwork as a classifier and integrating this into a REST based API. This isthen used as the basis for an actual proof of concept implementation presentedas well in this thesis. It will be shown that the deep learning classifierprovides very good result in the context of multi-class document categorizationand that it is feasible to integrate such classifiers into a larger ecosystemusing REST based services.

Towards the Improvement of Automated Scientific Document Categorization by Deep Learning