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

This paper presents an investigation into machine learning techniques forviolence detection in videos and their adaptation to a federated learningcontext. The study includes experiments with spatio-temporal features extractedfrom benchmark video datasets, comparison of different methods, and proposal ofa modified version of the "Flow-Gated" architecture called "Diff-Gated."Additionally, various machine learning techniques, including super-convergenceand transfer learning, are explored, and a method for adapting centralizeddatasets to a federated learning context is developed. The research achievesbetter accuracy results compared to state-of-the-art models by training thebest violence detection model in a federated learning context.

Balancing Accuracy and Training Time in Federated Learning for Violence Detection in Surveillance Videos: A Study of Neural Network Architectures