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

Passenger Name Records (PNRs) are at the heart of the travel industry.Created when an itinerary is booked, they contain travel and passengerinformation. It is usual for airlines and other actors in the industry tointer-exchange and access each other's PNR, creating the challenge of usingthem without infringing data ownership laws. To address this difficulty, wepropose a method to generate realistic synthetic PNRs using GenerativeAdversarial Networks (GANs). Unlike other GAN applications, PNRs consist ofcategorical and numerical features with missing/NaN values, which makes the useof GANs challenging. We propose a solution based on Cram\'{e}r GANs,categorical feature embedding and a Cross-Net architecture. The method wastested on a real PNR dataset, and evaluated in terms of distribution matching,memorization, and performance of predictive models for two real businessproblems: client segmentation and passenger nationality prediction. Resultsshow that the generated data matches well with the real PNRs without memorizingthem, and that it can be used to train models for real business applications.

Airline Passenger Name Record Generation using Generative Adversarial Networks