Models and algorithms of data analysis in forensic proceedings

Авторы

  • Mahmudjanov Sarvar Ulugbekovich Tashkent University of Information Technologies named after Muhammad al-Khorazmi
  • Akbarov Navruz Jahongir ogli Ferghana branch of Tashkent University of Information Technologies named after Muhammad al -Khwarazmi

Kalit so'zlar:

Forensic Data Analysis, Algorithms, Machine Learning, Statistical Models, Data Science, Investigative Accuracy, Predictive Modeling, Crime Analysis, Forensic Technology, Artificial Intelligence.

Annotatsiya

This paper explores the integral role of models and algorithms in data analysis within forensic proceedings, emphasizing their critical impact on enhancing investigative accuracy, efficiency, and reliability. It begins with a comprehensive review of traditional and modern forensic data analysis techniques, including statistical, machine learning, and artificial intelligence approaches. A detailed examination of various analytical models and algorithms reveals their operational mechanisms and applications in real-world case studies. The research employs robust evaluation metrics to assess the performance of these data-driven methods, highlighting their advantages and limitations. Additionally, the paper addresses the implications of these tools for forensic practice and suggests pathways for future research. Ultimately, the findings underscore the transformative potential of advanced data analysis techniques in advancing forensic science and improving legal outcomes.

Библиографические ссылки

1. Peterson, J. S., & Keating, S. (2019). *Data Analytics in Forensic Science: Principles and Practices*. New York: Academic Press.

2. McGowan, M. L., & Lentz, K. (2020). Ethical Issues in the Application of Data Science to Forensic Investigations. *Journal of Forensic Sciences*, 65(2), 412-422. https://doi.org/10.1111/1556-4029.14123

3. Zhang, Y., & Chen, L. (2021). Machine Learning Applications in Crime Prediction and Prevention: A Review. *International Journal of Digital Crime and Forensics*, 13(4), 1-17. https://doi.org/10.4018/IJDCF.2021100101

4. National Institute of Justice. (2022). *Advancing Forensic Science Through Data Analytics*. Washington, DC: U.S. Department of Justice.

5. Taylor, J. H., & Best, V. (2023). The Role of Big Data in Modern Forensic Investigation. *Forensic Science International*, 320, 110692. https://doi.org/10.1016/j.forsciint.2023.110692

6. Thoma, M., & Hellman, M. (2020). Navigating Ethical Dilemmas in Forensic Data Analysis: Balancing Innovation and Integrity. *Forensic Research & Criminology International Journal*, 8(3), 1-10. https://doi.org/10.15406/frcij.2020.08.00288

7. Smith, R. T., & Johnson, A. B. (2023). Trends in Forensic Data Science: A Critical Review. *Journal of Analytical Toxicology*, 37(1), 23-34. https://doi.org/10.1093/jat/bkac055

8. American Academy of Forensic Sciences. (2021). *Guidelines for Data Analysis in Forensic Science*. Colorado Springs, CO: AAFS.

Опубликован

2024-09-27

Как цитировать

Mahmudjanov, S., & Akbarov, N. (2024). Models and algorithms of data analysis in forensic proceedings. Research and Implementation, 2(9), 36–40. извлечено от https://rai-journal.uz/index.php/rai/article/view/428

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