Abstract
With the escalation of cybercriminal activities, the demand for forensic investigations into these crimes has grown significantly. However, the concept of systematic pre-preparation for potential forensic examinations during the software design phase, known as forensic readiness, has only recently gained attention. Against the backdrop of surging urban crime rates, this study aims to conduct a rigorous and precise analysis and forecast of crime rates in Los Angeles, employing advanced Artificial Intelligence (AI) technologies. This research amalgamates diverse datasets encompassing crime history, various socio-economic indicators, and geographical locations to attain a comprehensive understanding of how crimes manifest within the city. Leveraging sophisticated AI algorithms, the study focuses on scrutinizing subtle periodic patterns and uncovering relationships among the collected datasets. Through this comprehensive analysis, the research endeavors to pinpoint crime hotspots, detect fluctuations in frequency, and identify underlying causes of criminal activities. Furthermore, the research evaluates the efficacy of the AI model in generating productive insights and providing the most accurate predictions of future criminal trends. These predictive insights are poised to revolutionize the strategies of law enforcement agencies, enabling them to adopt proactive and targeted approaches. Emphasizing ethical considerations, this research ensures the continued feasibility of AI use while safeguarding individuals' constitutional rights, including privacy. The anticipated outcomes of this research are anticipated to furnish actionable intelligence for law enforcement, policymakers, and urban planners, aiding in the identification of effective crime prevention strategies. By harnessing the potential of AI, this research contributes to the promotion of proactive strategies and data-driven models in crime analysis and prediction, offering a promising avenue for enhancing public security in Los Angeles and other metropolitan areas.
Author supplied keywords
Cite
CITATION STYLE
Mihna, F. K. H., Habeeb, M. A., Khaleel, Y. L., Ali, Y. H., & Al-Saeedi, L. A. E. (2024). Using Information Technology for Comprehensive Analysis and Prediction in Forensic Evidence. Mesopotamian Journal of CyberSecurity, 4(1), 4–16. https://doi.org/10.58496/MJCS/2024/002
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.