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Analytics and Decision Support Systems- Artificial Intelligence technologies

Decision Support Systems Help SOlve Crime.JPG
Decision Support Systems Help SOlve Crime.JPG

There have been different milestones being achieved with the adoption of different artificial tech inventions. We have covered such past applications here. We have also had a look at other review in relation to the topic such as the Movie Review on AI. All of these just highlight the application of Artificial Intelligence in our lives. In the next article, the writer sought to analyze the different artificial Intelligence technologies that are aimed at empowering police to tackle crime by relying on analytics and decision support system.

Whereas geographic information systems (GISs) have been triumphant at monitoring criminal activity, hands-on law enforcement requires systems that predict the surfacing of criminal activity (Olligschlaeger, 2006). One similar system under development at the Pittsburgh (PA) Bureau of Police and Carnegie Mellon University is an early warning system built to incorporate a GIS system, previously engineered to track unlawful activity, along with a relatively modern technology — artificial neural networks — to forecast the emergence of drug hot-spot region (Gruff & LaVigne, 2006). [blur] While scientists studying intelligence agree that one’s IQ levels are considerably influenced genes, one’s environment also largely influences it shaping how an individual will respond to both the society and their environement (Ellis, 2006). [/blur]

[blur] The Artificial Neural Network application for murder and rape offenders’ classification demonstrates an algorithm for examining and comparing huge databases of crime data. This computer aided tracking and characterization tool was built to offer crime analysts advanced means for construing huge databases of crime data. It assists in investigations by evaluating probable characteristics of unknown criminals, through linking a specific crime cases to others, and by using a tool that clusters similar criminal cases attributed to the same offenders (Ellis, 2006). [/blur]

[blur] Some of the personal characteristics useful in neural network application in establishing susceptibility to crime include impulsiveness, which refers to individuals’ varying tendencies to act issues without presenting much thought to the consequences. Although it varies from individual to individual and circumstance involved, one can be impulsive without crossing the crime/non-crime threshold. However, impulsive persons are found more often in criminal populations than in the general population (Ellis, 2006). [/blur]

[blur] Negative emotionality, on the other hand, is personalities attribute referring to the tendency to experience various situations as aversive and to respond to them with anger and irritation more readily than with positive affective states (Ellis, 2006). Another trait is sensation seeking, which turns to the active desire for varied, novel, and extreme experiences and sensations, often to the peak of taking social and physical risks to achieve them (Ellis, 2006). [/blur]

[blur] Throughout history, there have been attempts to explain what the root of odd social behavior is, including crime. By the 21st century, criminologists considered a wide range of demographic factors though to influence an individual’s future towards crime (Olligschlaeger, 2006). These factors included psychological, social, biological, and economic reasons. [/blur]

[blur] Most criminologists today believe that individuals commit crime due to anger, jealousy, pride, revenge, or greed. On the other hand, the desire for material gain may lead to crimes such as burglaries, auto thefts robberies and white-collar crimes. In addition, the desire for revenge, power, or control results to crimes such as rapes, assaults, and murders. [/blur]

[blur] Throughout history, there have been attempts to explain what the root of odd social behavior is, including crime. By the 21st century, criminologists considered a wide range of demographic factors though to influence an individual’s future towards crime (Olligschlaeger, 2006). These factors included psychological, social, biological, and economic reasons. Most criminologists today believe that individuals commit crime due to anger, jealousy, pride, revenge, or greed. On the other hand, the desire for material gain may lead to crimes such as burglaries, auto thefts robberies and white-collar crimes. In addition, the desire for revenge, power, or control results to crimes such as rapes, assaults, and murders. [/blur]

[blur] According to numerous research studies carried out on the subject of genes and criminology, genetic factors are undoubtedly correlated with a variety of criminality measures. Studies on criminal behavior in present and former mental health patients have been biological criminologists’ way of acknowledging the expanding field of criminology. [/blur]

[blur] Those in support of rational choice theories believe that taking part in a criminal behavior is rational and coherent choice made by the offender, and not their genetic characteristics. Additionally, deterrence theory discounts genetic considerations. On the other hand, though it does not completely accept that genetics lead to crime, general strain theory acknowledges a correlation between the offender’s genetic traits and the crime committed. [/blur]

References

Ellis (2006). Psycho social Theories: Individual Traits and Criminal Behavior. In A. W. Ellis, Criminology: An Interdisciplinary Approach (p. 169). SAGE Publications.

Gruff, E. R., & LaVigne, N. G. (2006). Forecasting the Future Of Predictive Crime Mapping. Crime Prevention Studies , pp. 29-57.

Olligschlaeger, A. M. (2006). Artificial Neural Networks and Crime Mapping. Pittsburgh: Carnegie Mellon University.

Analytics and Decision Support Systems- Artificial Intelligence technologies

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