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...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
|
| Series: | Cogent Social Sciences |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/23311886.2025.2527392 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849716072549187584 |
|---|---|
| author | Mathew Nicho Ahmed Hamed Tarek Gaber Jamal Hamad Al Arimi |
| author_facet | Mathew Nicho Ahmed Hamed Tarek Gaber Jamal Hamad Al Arimi |
| author_sort | Mathew Nicho |
| collection | DOAJ |
| description | 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 reporting of violence against women is limited, leading to low-cardinality datasets. A-XGBoost addresses this by using Conditional GANs to generate realistic data samples while maintaining original feature distributions. This novel study proposes a culturally adaptive AI framework combining CGAN-based augmentation with optimized XGBoost , . Experimental results using A-XGBoost demonstrate 92.4% and 89.3% accuracy on UAE and Finland datasets respectively, with 6.7% and 3.1% improvements over traditional models. Under noisy conditions, A-XGBoost shows minimal performance degradation of 6.1% and 5.6%, outperforming similar studies. Its performance is further enhanced by automated hyperparameter tuning through grid search and cross-validation, ensuring optimal model configuration without excessive complexity. In Finland, the model highlighted intimate partner violence and trafficking, while in the UAE, public and sexual violence were dominant. This replicable framework tackles regional challenges, ensures strong predictive accuracy and upholding ethical standards by avoiding offender profiling. A-XGBoost maintains manageable time complexity, supporting real-world deployments. Hence predictive AI supports proactive interventions, resource optimisation, and evidence-based policymaking. |
| format | Article |
| id | doaj-art-25d696349f794930824bc980eeedb4d2 |
| institution | DOAJ |
| issn | 2331-1886 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Social Sciences |
| spelling | doaj-art-25d696349f794930824bc980eeedb4d22025-08-20T03:13:08ZengTaylor & Francis GroupCogent Social Sciences2331-18862025-12-0111110.1080/23311886.2025.2527392A-XGBoost: a resilient machine learning technique for predicting crimes against women across cultures on low cardinality crime dataMathew Nicho0Ahmed Hamed1Tarek Gaber2Jamal Hamad Al Arimi3Research and Innovation Centre, Rabdan Academy, Abu Dhabi, United Arab Emirates;Department of Computer Science, Faculty of Computers and Information, Damanhour University, Damanhour, EgyptSchool of Science, Engineering and Environment, University of Salford, Manchester, UKStrategy and Institutional Development Center, Abu Dhabi Police, Abu Dhabi, United Arab EmiratesViolence 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 reporting of violence against women is limited, leading to low-cardinality datasets. A-XGBoost addresses this by using Conditional GANs to generate realistic data samples while maintaining original feature distributions. This novel study proposes a culturally adaptive AI framework combining CGAN-based augmentation with optimized XGBoost , . Experimental results using A-XGBoost demonstrate 92.4% and 89.3% accuracy on UAE and Finland datasets respectively, with 6.7% and 3.1% improvements over traditional models. Under noisy conditions, A-XGBoost shows minimal performance degradation of 6.1% and 5.6%, outperforming similar studies. Its performance is further enhanced by automated hyperparameter tuning through grid search and cross-validation, ensuring optimal model configuration without excessive complexity. In Finland, the model highlighted intimate partner violence and trafficking, while in the UAE, public and sexual violence were dominant. This replicable framework tackles regional challenges, ensures strong predictive accuracy and upholding ethical standards by avoiding offender profiling. A-XGBoost maintains manageable time complexity, supporting real-world deployments. Hence predictive AI supports proactive interventions, resource optimisation, and evidence-based policymaking.https://www.tandfonline.com/doi/10.1080/23311886.2025.2527392Violence against womenpredicting crimesmachine learningaccelerated XGBoostvictim profilingArtificial Intelligence |
| spellingShingle | Mathew Nicho Ahmed Hamed Tarek Gaber Jamal Hamad Al Arimi A-XGBoost: a resilient machine learning technique for predicting crimes against women across cultures on low cardinality crime data Cogent Social Sciences Violence against women predicting crimes machine learning accelerated XGBoost victim profiling Artificial Intelligence |
| title | A-XGBoost: a resilient machine learning technique for predicting crimes against women across cultures on low cardinality crime data |
| title_full | A-XGBoost: a resilient machine learning technique for predicting crimes against women across cultures on low cardinality crime data |
| title_fullStr | A-XGBoost: a resilient machine learning technique for predicting crimes against women across cultures on low cardinality crime data |
| title_full_unstemmed | A-XGBoost: a resilient machine learning technique for predicting crimes against women across cultures on low cardinality crime data |
| title_short | A-XGBoost: a resilient machine learning technique for predicting crimes against women across cultures on low cardinality crime data |
| title_sort | a xgboost a resilient machine learning technique for predicting crimes against women across cultures on low cardinality crime data |
| topic | Violence against women predicting crimes machine learning accelerated XGBoost victim profiling Artificial Intelligence |
| url | https://www.tandfonline.com/doi/10.1080/23311886.2025.2527392 |
| work_keys_str_mv | AT mathewnicho axgboostaresilientmachinelearningtechniqueforpredictingcrimesagainstwomenacrossculturesonlowcardinalitycrimedata AT ahmedhamed axgboostaresilientmachinelearningtechniqueforpredictingcrimesagainstwomenacrossculturesonlowcardinalitycrimedata AT tarekgaber axgboostaresilientmachinelearningtechniqueforpredictingcrimesagainstwomenacrossculturesonlowcardinalitycrimedata AT jamalhamadalarimi axgboostaresilientmachinelearningtechniqueforpredictingcrimesagainstwomenacrossculturesonlowcardinalitycrimedata |