hypedarhypedar
feedtrendsdiscovershowcasearchive
login
login
login
FeedTrendsDiscoverShowcaseArchiveDashboard
Submit Showcase

Trending now

Security + Agents + Infrastructure60Multimodal + Inference46Fine Tuning38
View all trends →

hypedar

AI trend radar for developers. Catch emerging papers, repos, and discussions before the hype peaks.

AboutGitHubDiscord

By the makers of hypedar

Codepawl

Open-source tools for developers.

Explore our tools →
AboutPrivacyTermsX

© 2026 Codepawl

Built by Codepawl·© 2026

About·Terms·Privacy·Security

GitHub·Discord·X

feedtrendsdiscovershowcasearchive
← trends

Logistics + Prediction

10.0

Implement a Graph Neural Network (GNN) model that predicts delivery delays by considering the network topology of logistics chains. It offers a cleaner, more spatial approach than standard time-series models.

+0
emergingimplementation gap
logisticspredictiongraph-neural-networksml

Signals (2)

arXiv2d ago

EAGLE: Edge-Aware Graph Learning for Proactive Delivery Delay Prediction in Smart Logistics Networks

arXiv50m ago

Toward Reducing Unproductive Container Moves: Predicting Service Requirements and Dwell Times