A-XGBoost: a resilient machine learning technique for predicting crimes against women across cultures on low cardinality crime data

A-XGBoost: a resilient machine learning technique for predicting crimes against women across cultures on low cardinality crime data

Violence against women is a global problem requiring innovative preventive measures. This research leverages Accelerated XGBoost (A-XGBoost), to predict crime against women in two culturally distinct countries: Finland and the United Arab Emirates (UAE). In culturally sensitive regions, public repor...

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Bibliographic Details
Main Authors: Mathew Nicho, Ahmed Hamed, Tarek Gaber, Jamal Hamad Al Arimi
Format: Article
Language:English
Published: Taylor & Francis Group 2025-12-01
Series:Cogent Social Sciences
Subjects:
Violence against women
predicting crimes
machine learning
accelerated XGBoost
victim profiling
Artificial Intelligence
Online Access:https://www.tandfonline.com/doi/10.1080/23311886.2025.2527392
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