Patterns of Terror: A Comparative Predictive Model
This study aims to develop an intelligent model for counter-terrorism operations using a dataset of 50,064 entries from the Armed Conflict Location and Event Data (ACLED) covering terrorism activities from 1997 to 2022. The entropy-based feature engineering approach, utilizing information gain, was employed for its robustness to noisy data and ability to prioritize the most predictive features. Three machine learning classifiers—REPtrree, AdaBoost, and Meta-Stacking—were compared, with REPtree achieving the highest accuracy of 96.81% in predicting terrorist incidents. This study contributes to enhancing national security by leveraging machine learning techniques to improve counter-terrorism efforts.
Authors
- Ojei Harrison Onyijen (ORCID: https://orcid.org/0000-0001-9237-2271)
- Akinola Samson Olayinka (ORCID: https://orcid.org/0000-0002-3022-449X)
- Wilson Nwankwo (ORCID: https://orcid.org/0000-0003-1655-1440)
- Chukwuemeka Pascal Nwankwo (ORCID: https://orcid.org/0009-0001-9967-2856)
- Duke Oghorodi (ORCID: https://orcid.org/0009-0000-9497-1016)
- Kingsley Ukaoha (ORCID: https://orcid.org/0000-0002-6147-2314)
- Tosin Comfort Olayinka (ORCID: https://orcid.org/0000-0001-5987-7390)
- Kizito Eluemunor Anazia
- Lawal Adeniyi
Institutions
- Delta State University (NG)
- Wellspring University (NG)
- Delta State Polytechnic Ogwashi-Uku (NG)
- National Open University of Nigeria (NG)
Publication Details
- Published
- 2024-11-26
- DOI
- https://doi.org/10.1109/nigercon62786.2024.10927324
- Primary Topic
- Terrorism, Counterterrorism, and Political Violence
- Type
- article
- Field-Weighted Citation Impact
- 0.00