Summary of Advancing Crime Linkage Analysis with Machine Learning: a Comprehensive Review and Framework For Data-driven Approaches, by Vinicius Lima and Umit Karabiyik
Advancing Crime Linkage Analysis with Machine Learning: A Comprehensive Review and Framework for Data-Driven Approaches
by Vinicius Lima, Umit Karabiyik
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper aims to understand the challenges faced by machine learning approaches in crime linkage, which is the process of analyzing criminal behavior data to determine whether a pair or group of crime cases are connected or belong to a series of offenses. The study surveys the main literature on the topic and develops a general framework for crime linkage processes, describing each step thoroughly. The goal is to unify insights from diverse fields, such as sociology, psychology, statistics, computer science, and artificial intelligence, into a shared terminology to enhance the research landscape. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how machine learning can help link crimes together. It looks at what’s been done so far in this area and tries to figure out where the biggest challenges are. To do this, it surveys existing work on crime linkage and creates a general plan for how this process works. The goal is to bring together ideas from different fields, like sociology and computer science, to make it easier for researchers to study this topic. |
Keywords
* Artificial intelligence * Machine learning
