Big Data, Data Mining, Machine Learning, and Deep Learning Concepts in Crime Data

Güzel, Mehmet Serdar

doi:10.26650/jplc2020-813328

Cited by 9 publications

(2 citation statements)

References 74 publications

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“…Data mining techniques help discover hidden relationships among raw data elements, leading to inferring information and insights about the data for decision-making purposes [14]. Data Mining can be defined as a knowledge retrieval process that discovers useful and comprehensive information from massive volumes of raw data to support decision-making and problem-solving objectives in a specific domain [15].…”

Section: Theoretical and Mathematical Backgroundmentioning

confidence: 99%

Towards Detecting Influential Members and Critical Topics from Dark Web Forums

Ali,

Basheer,

Kawas

et al. 2023

JIOS

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Conventionally, the Internet consists of three parts: Surface, Deep, and Dark Webs. Inthe last two decades, a massive increase in illicit activities took place on the differentplatforms of the Dark Web. Moreover, social networks on Dark Web implicateextremism dissemination on a wide scale. In this paper, we propose an approach togenerate textual patterns from discussions on Dark Web terrorist forums employingData Mining techniques. The discovered patterns help identify the influential membersand extract critical topics. We describe our system modules that perform datapreprocessing, text preprocessing with TF-IDF weighting, outlier detection, clusteringevaluation, clustering, and clustering validation, implemented with the RapidMinertool. We apply K-Means as the Clustering method with different distance metrics,evaluate the clustering process using Elbow and Silhouette methods, and validate itusing Davies-Bouldin Index. Furthermore, we investigate the effects of altering thedistance metrics for outlier detection on the Clustering results.

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Section: Theoretical and Mathematical Backgroundmentioning

confidence: 99%

Towards Detecting Influential Members and Critical Topics from Dark Web Forums

Ali,

Basheer,

Kawas

et al. 2023

JIOS

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“…Numerous classification and clustering systems available in data mining were used for juvenile crime identification, detection, and analysis. Ates et al [12] provided a summary of the use of data mining and machine learning techniques in crimes and gave a new perception of the decision-making processes by presenting examples of the use of data mining for a crime. Saroja ota et al [13] extended the work of association rule mining of juvenile delinquency with two key risk elements, family background and education levels of children involved in crimes.…”

Section: Introductionmentioning

confidence: 99%

Juvenile Crime Monitoring and Characteristic Analysis Based on the Internet of Things and Grid Management

Gong

2022

Mobile Information Systems

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Over the past decade, researchers have identified intervention methods and program models that reduce juvenile delinquency and encourage prosocial development. Preventing juvenile delinquency not only saves young lives from being wasted but also prevents the onset of adult criminal careers and thus reduces the burden of crime on its victims and society. Aiming at this social problem, this study combines Internet of Things and grid models to study the attribution and prevention mechanisms of minor campus violence crimes. It realizes the modern network system mode of information management, collaborative operation, and grid integration in the target area through Internet of Things and grid models. In addition, based on consulting-related literature and practical research, the causes of campus violence crimes are analyzed through a self-organizing competitive neural network. Finally, this article combines the judicial practice of the management of campus violence and presents the prevention and control countermeasures of juvenile campus violence crimes to effectively prevent and control the occurrence of juvenile campus violence crimes.

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Sentiment analysis of Canadian maritime case law: a sentiment case law and deep learning approach

Abimbola,

Tan,

De La Cal Marín

2024

Int. j. inf. tecnol.

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Historical information in the Canadian Maritime Judiciary increases with time because of the need to archive data to be utilized in case references and for later application when determining verdicts for similar cases. However, such data are typically stored in multiple systems, making its reachability technical. Utilizing technologies like deep learning and sentiment analysis provides chances to facilitate faster access to court records. Such practice enhances impartial verdicts, minimizing workloads for court employees, and decreases the time used in legal proceedings for claims during maritime contracts such as shipping disputes between parties. This paper seeks to develop a sentiment analysis framework that uses deep learning, distributed learning, and machine learning to improve access to statutes, laws, and cases used by maritime judges in making judgments to back their claims. The suggested approach uses deep learning models, including convolutional neural networks (CNNs), deep neural networks, long short-term memory (LSTM), and recurrent neural networks. It extracts court records having crucial sentiments or statements for maritime court verdicts. The suggested approach has been used successfully during sentiment analysis by emphasizing feature selection from a legal repository. The LSTM + CNN model has shown promising results in obtaining sentiments and records from multiple devices and sufficiently proposing practical guidance to judicial personnel regarding the regulations applicable to various situations.

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Big Data, Data Mining, Machine Learning, and Deep Learning Concepts in Crime Data

Cited by 9 publications

References 74 publications

Towards Detecting Influential Members and Critical Topics from Dark Web Forums

Towards Detecting Influential Members and Critical Topics from Dark Web Forums

Juvenile Crime Monitoring and Characteristic Analysis Based on the Internet of Things and Grid Management

Sentiment analysis of Canadian maritime case law: a sentiment case law and deep learning approach

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