Mobile Automated Fingerprint Identification System (MAFIS): An Android-based Criminal Tracking System using Fingerprint Minutiae Structure

Rey, William P.; Rolluqui, Gisela V.

doi:10.1145/3485768.3485773

Cited by 12 publications

(1 citation statement)

References 2 publications

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“…The ability to classify crime news articles into respective finer-grained categories, as reviewed in the previous section, could prove crucial for the automatic selection of relevant news articles for monitoring specific crime types. In addition, the ability to further extract specific common details about a crime, such as criminals, victims, actions, weapons, and root causes, could enable interesting applications in predictive policing and crime-related policymaking purposes, including criminal profiling [28], criminal tracking [29], and criminal motif analysis [30]. Given that crimes are among the most frequently covered topics by news media, existing research has been directed toward devising methods for extracting significant insights from news articles pertaining to crime events.…”

Section: B Crime Metadata Extraction From News Articlesmentioning

confidence: 99%

CAMELON: A System for Crime Metadata Extraction and Spatiotemporal Visualization From Online News Articles

Pongpaichet,

Sukosit,

Duangtanawat

et al. 2024

IEEE Access

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Crimes result in not only loss to individuals but also hinder national economic growth. While crime rates have been reported to decrease in developed countries, underdeveloped and developing nations still suffer from prevalent crimes, especially those undergoing rapid expansion of urbanization. The ability to monitor and assess trends of different types of crimes at both regional and national levels could assist local police and national-level policymakers in proactively devising means to prevent and address the root causes of criminal incidents. Furthermore, such a system could prove useful to individuals seeking to evaluate criminal activity for purposes of travel, investment, and relocation decisions. Recent literature has opted for utilizing online news articles as a reliable and timely source for crime activity information. However, most of the crime monitoring systems fueled by such news sources merely classified crimes into different types and visualized individual crimes on the map using extracted geolocations, lacking crucial information for stakeholders to make relevant, informed decisions. To better serve the unique needs of the target user groups, this paper proposes a novel comprehensive crime visualization system that mines relevant information from large-scale online news articles. The system features automatic crime-type classification and metadata extraction from news articles. The crime classification and metadata schemes are designed to serve the need for information from law enforcement and policymakers, as well as general users. Novel interactive spatiotemporal designs are integrated into the system with the ability to assess the severity and intensity of crimes in each region through the novel Criminometer index. The system is designed to be generalized for implementation in different countries with diverse prevalent crime types and languages composing the news articles, owing to the use of deep learning cross-lingual language models. The experiment results reveal that the proposed system yielded 86%, 51%, and 67% F1 in crime type classification, metadata extraction, and closed-form metadata extraction tasks, respectively. Additionally, the results of the system usability tests indicated a notable level of contentment among the target user groups. The findings not only offer insights into the possible applications of interactive spatiotemporal crime visualization tools for proactive policymaking and predictive policing but also serve as a foundation for future research that utilizes online news articles for intelligent monitoring of real-world phenomena.

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Section: B Crime Metadata Extraction From News Articlesmentioning

confidence: 99%