Systematic Literature Review of Crime Prediction and Data Mining

Falade, Adesola; Azeta, A. A.; Oni, Aderonke; Odun-Ayo, Isaac

doi:10.18280/rces.060302

Cited by 9 publications

(3 citation statements)

References 24 publications

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“…A systematic literature review (SLR) is a tool used to assess and interpret all available research related to a particular research question, topic area, or phenomenon of interest [22]. Likewise, SLR studies aim to identify relevant primary documents, extract the required data, and analyze and synthesize results to obtain a broader and deeper view of the investigated domain [23]. Additionally, regarding the usefulness of conducting SLR studies, there are several recent investigations [23][24][25][26][27], which show the benefits of this study type.…”

Section: Study Typementioning

confidence: 99%

“…Likewise, SLR studies aim to identify relevant primary documents, extract the required data, and analyze and synthesize results to obtain a broader and deeper view of the investigated domain [23]. Additionally, regarding the usefulness of conducting SLR studies, there are several recent investigations [23][24][25][26][27], which show the benefits of this study type. In consequence, the importance of carrying out this SLR study lies in the need to know in depth about the state of information security in the banking sector.…”

Section: Study Typementioning

confidence: 99%

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Information Security in the Banking Sector: A Systematic Literature Review on Current Trends, Issues, and Challenges

Ubaldo¹,

Barreto²,

Albines³

et al. 2023

IJSSE

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In recent years, information security has become a very important aspect, since it creates serious trouble for organizations that do not take it into consideration. In this regard, it was detected that many entities in the banking sector experience multiple problems that are mainly related to the way in which they protect their data, so it is necessary to pay close attention to this issue. For this reason, in this investigation it was decided to carry out a systematic review of the literature, obtaining 2,787 articles through searches in electronic databases. Likewise, the PRISMA method was used, which allowed the identification of 15 relevant articles to form the synthesis of the investigation. In the same way, a bibliometric analysis was carried out, which allowed knowing the gaps in knowledge. Finally, in the conclusions emphasis is placed on several aspects; for example, it is highlighted that cybercriminals constantly attack the banking sector and it is mentioned that all the research questions posed were successfully answered.

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Section: Study Typementioning

confidence: 99%

Section: Study Typementioning

confidence: 99%

Information Security in the Banking Sector: A Systematic Literature Review on Current Trends, Issues, and Challenges

Ubaldo¹,

Barreto²,

Albines³

et al. 2023

IJSSE

View full text Add to dashboard Cite

show abstract

“…Crime is a social issue that hinders the economic growth of a nation. Crime has always existed, and violent crime is the greatest threat to society [2]. Population growth and urbanization have dramatically increased criminal activity [3], particularly in urban areas [4].…”

Section: Introductionmentioning

confidence: 99%

Crime Prediction Model using Three Classification Techniques: Random Forest, Logistic Regression, and LightGBM

Alsubayhin,

Ramzan,

Alzahrani

2024

IJACSA

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Predicting the likelihood of a crime occurring is difficult, but machine learning can be used to develop models that can do so. Random forest, logistic regression, and LightGBM are three well-known classification methods that can be applied to crime prediction. Random forest is an ensemble learning algorithm that predicts by combining multiple decision trees. It is an effective method for classification tasks, and it is frequently employed for crime prediction because it handles imbalanced datasets well. Logistic regression is a linear model that can be used to predict the probability of a binary outcome, such as the occurrence of a crime. It is a relatively straightforward technique that can be effective for crime prediction if the features are carefully chosen. LightGBM is a gradient-boosting decision tree algorithm with a reputation for speed and precision. It is a relatively new algorithm, but because it can achieve high accuracy on even small datasets, it has rapidly gained popularity for crime prediction. The experimental results show that the LightGBM performs best for binary classification, followed by Random Forest and Logistic Regression.

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ExplainEx: An Explainable Artificial Intelligence Framework for Interpreting Predictive Models

Udenwagu

Azeta

Misra

et al. 2021

Hybrid Intelligent Systems

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Artificial Intelligence (AI) systems are increasingly dependent on machine learning models which lack interpretability and algorithmic transparency, and hence may not be trusted by its users. The fear of failure in these systems is driving many governments to demand more explanation and accountability. Take, for example, the "Right of Explanation" rule proposed in the European Union in 2019, which gives citizens the right to demand an explanation from AI-based predictions. Explainable Artificial Intelligence (XAI) is an attempt to open up the "black box" and create more explainable systems which create predictive models whose results are easily understandable to humans. This paper describes an explanation model called ExplainEx which automatically generates natural language explanation for predictive models by consuming REST API provided by ExpliClas open-source web service. The classification model consists of four main decision tree algorithms including J48, Random Tree, RepTree and FURIA. The user interface was designed based on Microsoft.Net Framework programming platform. At the background is a software engine automating a seamless interaction between Expliclas API and the trained datasets, to provide natural language explanation to users. Unlike other studies, our proposed model is both a stand-alone and client-server based system capable of providing global explanations for any decision tree classifier. It supports multiple concurrent users in a client-server environment and can apply all four algorithms concurrently on a single dataset, returning both precision score and explanation. It is a ready tool for researchers who have datasets and classifiers prepared for explanation. This work bridges the gap between prediction and explanation, thereby allowing researchers to concentrate on data analysis and building state-of-the-art predictive models.

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Systematic Literature Review of Crime Prediction and Data Mining

Cited by 9 publications

References 24 publications

Information Security in the Banking Sector: A Systematic Literature Review on Current Trends, Issues, and Challenges

Information Security in the Banking Sector: A Systematic Literature Review on Current Trends, Issues, and Challenges

Crime Prediction Model using Three Classification Techniques: Random Forest, Logistic Regression, and LightGBM

ExplainEx: An Explainable Artificial Intelligence Framework for Interpreting Predictive Models

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