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Forensic Linguistics and DBN: Automating the Analysis of Witness Statements and Testimonies in AI-Driven Policing
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Author(s): Abhinash Mohanta (KIIT University, India), Sneha (XIM University, Bhubaneswar, India), Kyvalya Garikapati (KIIT University, India), Sanskruti Mohanty (Utkal University, India)and Shivani Samanta (South Asian University, India)
Copyright: 2026
Pages: 32
Source title:
AI-Driven Policing and Urban Security in Smart Cities
Source Author(s)/Editor(s): Mamdooh Abdelhameed Abdelmottlep (University of St. Thomas, USA)
DOI: 10.4018/979-8-3373-0245-4.ch007
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Abstract
The integration of Deep Belief Networks (DBNs) into forensic linguistics enhances AI-driven policing in smart cities by automating witness testimony analysis. DBNs address limitations of traditional methods, such as subjectivity and inefficiency, by detecting linguistic markers of deception and inconsistency. AI tools have accelerated investigations by analyzing digital evidence, though specific architectures like DBNs were not disclosed in these cases. Ethical considerations, including algorithmic bias and data privacy, are addressed through strategies like adversarial debiasing and synthetic data augmentation. Collaboration between law enforcement, AI experts, and policymakers ensures ethical deployment and inclusive dataset curation. Future directions include expanding DBN applications to real-time threat detection and multilingual forensic analysis. DBNs improve urban security by enhancing testimony credibility assessments, fostering community trust, and aligning AI-driven policing with ethical standards.
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