A decision support system for detecting serial crimes

Chi, Huisheng; Lin, Zhihong; Jin, Huidong; Bing, Xu; Qi, Mingliang

doi:10.1016/j.knosys.2017.02.017

Cited by 25 publications

(10 citation statements)

References 36 publications

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“…They compared iterative classification tree, logistic regression, and Bayesian analysis for linking stranger sexual assaults and highlighted that future research should benchmark usability, accuracy, and suitability for practice. Chi, Lin, Jin, Xu, and Qi () developed a decision support system for detecting serial crimes—their underlying technique is pairwise classification based on similarity, which they state is interpretable and easy to tune. Chamikara et al () use fuzzy logic for matching sets of behavioral features.…”

Section: Technological Solutionsmentioning

confidence: 99%

Forensic intelligence and the analytical process

Oatley

Chapman

Speers

2020

WIREs Data Min & Knowl

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A review was undertaken of the developments made with integrating forensic evidence into the analytical process to support police investigations. Evidence such as DNA, fingerprints, fibers, accelerants, tyre marks, and so forth, can support to differing degrees the various working theories or hypotheses about the nature of the alleged crime, the persons of interest and the modus operandi. Investigators however, either forensic or detective, bring various biases to evidence capture and analysis, biases which are better understood in the intelligence community. Structured analytical techniques have a long history in intelligence analysis, for example analysis of competing hypotheses, which serves several purposes: information sharing, clarity of communication, and to highlight the common forms of bias brought to bear in an investigation.We illustrate the representation of links based on traces and intelligence, and how these can be stored in databases permitting better "reasoning" with evidence. We also present some recommendations for integration of forensic intelligence into the investigative analytic process and review information systems in this area.

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Section: Technological Solutionsmentioning

confidence: 99%

Forensic intelligence and the analytical process

Oatley

Chapman

Speers

2020

WIREs Data Min & Knowl

View full text Add to dashboard Cite

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“…Chi et al present a decision support system for crime linkage based on various, including behavioral, features of criminal cases. This system is based on feature similarity algorithms to calculate the pairwise similarities and build up a classifier to determine whether or not a case pair should belong to a criminal series.…”

Section: Related Workmentioning

confidence: 99%

A Dempster‐Shafer theory and uninorm‐based framework of reasoning and multiattribute decision‐making for surveillance system

Liu

Luo

et al. 2019

Int J Intell Syst

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Closed-circuit television and sensor-based intelligent surveillance systems have attracted considerable attentions in the field of public security affairs. To provide real-time reaction in the case of a huge volume of the surveillance data, researchers have proposed eventreasoning frameworks for modeling and inferring events of interest. However, they do not support decisionmaking, which is very important for surveillance operators. To this end, this paper incorporate a function of decision-making in an event-reasoning framework, so that our model not only can perform event-reasoning but also can predict, rank, and alarm threats according to uncertain information from multiple heterogeneous sources. In particular, we propose a multiattribute decision-making model, in which an object being watched is modeled as a multiattribute event, where each attribute corresponds to a specific source, and the information from each source can be used to elicit a local threat degree of different malicious situations with respect to the corresponding attribute. Moreover, to assess an overall threat degree of an object being observed, we also propose a method to fuse the conflict threat degrees regarding all the relevant attributes.

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“…However in recent past not very much work is reported towards extracting crime linkages from textual data. The more recent example includes linking serial crimes [29] through behavioral information. They developed a decision support system consisting of similarity algorithms, a classification model, a feature selection and parameter learning algorithm to link the serial crimes.…”

Section: Associations Extraction For Crime Analysismentioning

confidence: 99%