Developing multimodal AI systems for comprehensive threat detection and geospatial risk mitigation
1 Independent Researcher, Canada.
2 Babcock University, Ilishan-Remo, Ogun State, Nigeria.
3 M and M Technical Services, Nigeria.
4 Independent Researcher, Lagos Nigeria.
5 Amstek Nigeria Limited, Nigeria
6 Independent Researcher, Nigeria.
Review
Open Access Research Journal of Science and Technology, 2022, 06(01), 093-101.
Article DOI: 10.53022/oarjst.2022.6.1.0063
Publication history:
Received on 01 August 2022; revised on 10 September 2022; accepted on 13 September 2022
Abstract:
Multimodal AI systems represent a groundbreaking approach to comprehensive threat detection and geospatial risk mitigation, integrating diverse data modalities such as textual, visual, and geospatial information. These systems overcome the limitations of traditional methods by synthesizing complex datasets, enabling accurate predictions and real-time decision-making. This paper explores multimodal AI systems' theoretical foundations, applications, challenges, and opportunities. Key insights include their transformative potential in disaster response, cybersecurity, urban planning, and technical hurdles like data heterogeneity, scalability, and ethical concerns such as privacy and algorithmic bias. The paper concludes with actionable recommendations for researchers, developers, and policymakers, emphasizing innovation, interdisciplinary collaboration, and the integration of these systems into existing risk management frameworks. By addressing current limitations and leveraging emerging technologies, multimodal AI systems can play a pivotal role in building resilient and sustainable societies.
Keywords:
Multimodal AI; Threat Detection; Geospatial Risk Mitigation; Data Integration; Predictive Analytics; Ethical AI Systems
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Copyright © 2022 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0