Machine Learning based criminal short listing using Modus Operandi features

Munasinghe, Malith; Perera, Hemamali; Udeshini, Shanika; Weerasinghe, Ruvan

doi:10.1109/icter.2015.7377669

Cited by 7 publications

(2 citation statements)

References 8 publications

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“…We might locate an offender within the pattern they can discover offenders proportionately inside the pattern, which will aid in criminal shortlisting. Out of the 21 grave crime categories in Sri Lanka, housebreaking and stealing is considered the category with the most unique MOs, hence the focus of the research is on that specific category [4].…”

Section: Literature Reviewmentioning

confidence: 99%

Survey: Crime Prediction using Machine Learning Approach

Khalaf,

Taresh

2022

J. Al-Qadisiyah Comp. Sci. Math.

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The presented studies have proven that machine learning algorithms The field of machine learning (ML) is expanding as more people realize how important it can be in a variety of crucial applications, including data mining, natural language processing, picture recognition, and expert systems. As it is well known that ML offers possible answers in all these domains and more, it is destined to be one of the pillars of our future civilization. This article presents an outline of the function of machine learning in prediction. algorithms have excelled in solving prediction and classification problems. Below We highlight the machine learning algorithms and techniques used in predicting crimes in particular, and the accuracy of the results obtained by each study or research. We see the challenges faced by a study or researcher who used machine learning algorithms and we hope with this paper, providing the researcher, in particular, with information covering the most important studies or research presented during the past five years to abbreviate the time of the researcher, and in the interest of his effort.

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Section: Literature Reviewmentioning

confidence: 99%

Survey: Crime Prediction using Machine Learning Approach

Khalaf,

Taresh

2022

J. Al-Qadisiyah Comp. Sci. Math.

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“…In order to identify crimes that have been committed by the same criminals, previous studies relied on spatial-temporal information and criminals' MO. Recent works [5,6,[26][27][28] used different clustering methods, such as k-means or hierarchical clustering, to perform the task of crime linkage. Two recent works performed crime linkage based on police reports.…”

Section: Crime Linkagementioning

confidence: 99%

Crime Linkage Based on Textual Hebrew Police Reports Utilizing Behavioral Patterns

Solomon

Magen

Hanouna

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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The identification of criminals' behavioral patterns can be helpful for solving crimes. Currently, in order to perform this task, police investigators manually extract criminals' behavioral patterns (also referred to as criminals' modus operandi) from a large corpus of police reports. These patterns are compared to the patterns observed in an ongoing criminal investigation to identify similarities that may link the suspect to other documented crimes. Due to the large number of historical cases, this manual process is time consuming, very costly in terms of police resources, and limits the investigators' ability to solve open cases. In this study, we propose an automatic and language independent method for extracting behavioral patterns from police reports. Relying on the extracted behavioral patterns as input, we utilize a Siamese neural network to identify burglaries committed by the same criminals. Experiments performed using a large dataset of police reports written in Hebrew provided by the Israel Police demonstrate the proposed method's high performance, achieving an AUC above 0.9. Using our method, we are also able to identify potential suspects for 22.41% of the open burglary cases in Israel. CCS CONCEPTS • Applied computing → Document management and text processing; Law, social and behavioral sciences; • Information systems → Document representation.

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