Abstract: Criminal investigations are complex and require law enforcement agencies to gather and analyze large amounts of data to identify suspects and solve crimes. Traditional approaches have relied on human intuition and experience, which can be time-consuming and prone to errors. With advancements in technology, there is an opportunity to improve criminal investigations by using data-driven approaches. This research proposes a criminal investigation tracker with suspect prediction using machine learning techniques to improve the accuracy and efficiency of criminal investigations. This paper reviews previous studies that have used machine learning algorithms in criminal investigations and presents our proposed methodology, which involves the use of a criminal investigation tracker that integrates data from various sources such as criminal records, social media, and crime scene evidence. We discuss the machine learning algorithms that will be used and the performance metrics that will be used to evaluate the system. Finally, we conclude that our proposed system has the potential to improve the accuracy and efficiency of criminal investigations.
Keywords: criminal investigations, machine learning, suspect prediction, data-driven, crime scene evidence
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DOI:
10.17148/IJARCCE.2023.12350
