An Overview of Data Mining for Combating Crime

Nissan, Ephraim

doi:10.1080/08839514.2012.713309

Cited by 15 publications

(15 citation statements)

References 33 publications

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“…In matters linked to careful deployment, the concept may eliminate bias instilled in the decision-making processes of humans. Further, AI is more successful in recognizing white-collar crimes like credit card fraud [9]. Many people within the years have grown wry of cyber security but machine learning helped police detect these crimes.…”

Section: Crime Investigationmentioning

confidence: 99%

“…These technologies are in form of surveillance cameras, which easily identify anomalies that may result in crime, predictive policing applications, and drones [9]. It is apparent that AI may allow more targeted policing since it will undergo usage only when necessary [9]. In matters linked to careful deployment, the concept may eliminate bias instilled in the decision-making processes of humans.…”

Section: Crime Investigationmentioning

confidence: 99%

“…Owing to this, most cities not only in the United States but also around the world are integrating artificial intelligence technologies to aid in fighting crime. These technologies are in form of surveillance cameras, which easily identify anomalies that may result in crime, predictive policing applications, and drones [9]. It is apparent that AI may allow more targeted policing since it will undergo usage only when necessary [9].…”

Section: Crime Investigationmentioning

confidence: 99%

“…Many people within the years have grown wry of cyber security but machine learning helped police detect these crimes. The artificial intelligent tools also allow the police to control crime scenes or the rescue and search proceedings by assisting the commanders to concentrate on specific tasks and allocate the necessary resources, even though these mechanisms are not ready to automate such activities [9].…”

Section: Crime Investigationmentioning

confidence: 99%

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Artificial Intelligence – Consumers and Industry Impact

Nadimpalli¹

2017

Int J Econ Manag Sci

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Section: Crime Investigationmentioning

confidence: 99%

Section: Crime Investigationmentioning

confidence: 99%

Section: Crime Investigationmentioning

confidence: 99%

Section: Crime Investigationmentioning

confidence: 99%

See 2 more Smart Citations

Artificial Intelligence – Consumers and Industry Impact

Nadimpalli¹

2017

Int J Econ Manag Sci

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“…They concentrated on crime spatial analysis and visualization. Nissan (2012) surveyed on forensic applications of data mining to fraud and crime intelligence or investigation within law enforcement. Roth et al (2013) presented a survey on current practices and unmet needs for spatiotemporal crime analysis in U.S. law enforcement agencies.…”

Section: Introductionmentioning

confidence: 99%

A Review on a Classification Framework for Supporting Decision Making in Crime Prevention

No¹,

Hamid²,

Mohemad³

et al. 2014

J. of Artificial Intelligence

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Increasing volume of crimes has brought a serious problem to many countries across the world. Crime prevention is an important component of an overall strategy to reduce crime and to strengthen public safety. Although, Supporting Decision Making (SDM) in crime prevention is an important topic but a comprehensive literature review on the subject has yet to be implemented. Thus, this study presents a systematic and comprehensive review on a classification framework for SDM in crime prevention. Forty four journal articles on the subject published between 2000 and October, 2015 were analyzed and classified into two categories of index crime (violent crime and property crime) and six classes of data mining techniques (prediction, classification, visualization, regression, clustering and outlier detection). The results of this study clearly show that data mining especially prediction and clustering techniques have been applied most extensively in both index crime categories. The main data mining techniques used for SDM in crime prevention are Bayesian, neural network and nearest neighbor. This study also addresses the gaps between SDM in crime prevention and the needs of practitioners to encourage more researches in crime analysis. Finally, it concludes with some suggestions for future research on SDM in crime prevention.

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