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http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31345Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Lawal, Kehinde Hussein | - |
| dc.contributor.author | Foluso, Ayeni | - |
| dc.contributor.author | Nafiu, L.A | - |
| dc.date.accessioned | 2026-05-19T15:24:59Z | - |
| dc.date.available | 2026-05-19T15:24:59Z | - |
| dc.date.issued | 2024 | - |
| dc.identifier.uri | http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31345 | - |
| dc.description.abstract | This study investigates the integration of Artificial Intelligence (AI) into Nigeria’s national security operations, focusing on real-time analysis of crowdsourced intelligence. Using a mixed-methods approach, data was collected through semi-structured interviews with security personnel and surveys with community respondents, alongside testing an AI prototype on crowdsourced datasets. AI technologies, including machine learning and natural language processing (NLP), were employed to identify patterns in multimodal data, optimizing decision-making and resource allocation. Challenges such as infrastructural limitations, data quality, and ethical concerns are explored, with practical recommendations for overcoming these barriers. This research contributes to the growing body of knowledge on leveraging AI for security in resource-constrained environments. | en_US |
| dc.description.sponsorship | self | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | International Conference on Information and Communication Technology (2024) Owerri | en_US |
| dc.subject | Artificial Intelligence, Crowdsourced Intelligence, Real-time Threat Detection, Security Operations, Algorithmic Bias, Data Quality, AI Adoption Challenges, National Security, Machine Learning, Ethical Considerations | en_US |
| dc.title | Enhancing Nigeria’s National Security Operations: AI-Driven Framework for Real-time Analysis of Crowdsourced Intelligence | en_US |
| dc.type | Presentation | en_US |
| Appears in Collections: | Computer Science | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| ICICT_2024_paper_33 (1).pdf | 867.16 kB | Adobe PDF | View/Open |
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