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Revolutionizing Crime Investigation: The Role of Deep Learning in Forensic Intelligence
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Author(s): Nituja Singh (Symbiosis International University, India), Mumtaz Zabeen Khan (Rayat College of Law, India), Balwinder Kaur (Rayat College of Law, India)and Ajitabh Mishra (Rayat College of Law, India)
Copyright: 2025
Pages: 20
Source title:
Forensic Intelligence and Deep Learning Solutions in Crime Investigation
Source Author(s)/Editor(s): Christian Kaunert (Dublin City University, Ireland), Anjali Raghav (Sharda University, India), Kamalesh Ravesangar (Tunku Abdul Rahman University of Management and Technology, Malaysia)and Bhupinder Singh (Sharda University, India)
DOI: 10.4018/979-8-3693-9405-2.ch014
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Abstract
As law enforcement agencies face increasingly intricate criminal cases, the incorporation of deep learning presents remarkable capabilities in data analysis, pattern recognition, and predictive modeling. This chapter commences by delineating the core principles of deep learning and its unique benefits compared to traditional forensic methods, particularly its proficiency in efficiently processing extensive datasets. The discourse further explores various applications of deep learning within forensic science, such as digital forensics, biometric identification, and crime scene reconstruction. By utilizing convolutional neural networks (CNNs) and other sophisticated algorithms, investigators can improve image analysis, automate the classification of evidence, and enhance the reliability of eyewitness identifications. Additionally, the chapter examines the challenges linked to the adoption of these technologies, including concerns regarding data privacy and algorithmic bias.
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