Crime Analysis and Prediction using Optimized K-Means Algorithm

Krishnendu, S.; Puzhankara, Lakshmi; Nitha, L.

doi:10.1109/iccmc48092.2020.iccmc-000169

Cited by 16 publications

(2 citation statements)

References 3 publications

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“…The method proposed by Aldossari et al in 2020 [34] used Chicago Police Department's CLEAR as the processed data, selected nine features from the dataset, and verified that the decision tree outperformed the Bayesian network. The method proposed by Krishnendu et al in 2020 [35] analyses the region and age in which more crimes occur. And the Kaggle dataset on India crime data was used, and the dataset spans from 2001 to 2010, including 1053 values.…”

Section: ) Methods Based On Probabilitymentioning

confidence: 99%

Crime Prediction Methods Based on Machine Learning: A Survey

Yin¹

2023

Computers, Materials &Amp; Continua

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The objective of crime prediction, one of the most important technologies in social computing, is to extract useful information from many existing criminal records to predict the next process-related crime. It can aid the police in obtaining criminal information and warn the public to be vigilant in certain areas. With the rapid growth of big data, the Internet of Things, and other technologies, as well as the increasing use of artificial intelligence in forecasting models, crime prediction models based on deep learning techniques are accelerating. Therefore, it is necessary to classify the existing crime prediction algorithms and compare in depth the attributes and conditions that play an essential role in the analysis of crime prediction algorithms. Existing crime prediction methods can be roughly divided into two categories: those based on conventional machine learning and those based on contemporary deep learning. This survey analyses the fundamental theories and procedures. The most frequently used data sets are then enumerated, and the fundamental procedures of various algorithms are also analyzed in this paper. In light of the insufficient scale of existing data in this field, the ambiguity of data types used to predict crimes, and the absence of public data sets that have a significant impact on the research of algorithm models, this survey proposes the construction of a machine learning-based big data research model to address these issues. Future researchers who will enter this field are provided with a guide to the direction of future research development.

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Section: ) Methods Based On Probabilitymentioning

confidence: 99%

Crime Prediction Methods Based on Machine Learning: A Survey

Yin¹

2023

Computers, Materials &Amp; Continua

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“…This section describes the experimental tools, functions and assumptions that were considered to implement and evaluate the proposed scheme. Python programming language was utilized for this, due to its powerful capabilities [ 45 ]. The Shapely package, a Python package that eases work with polygons [ 46 ], was employed to create polygons in the smart city and perform geometric operations.…”

Section: Experimental Setupmentioning

confidence: 99%

EDTD-SC: An IoT Sensor Deployment Strategy for Smart Cities

Alablani

Alenazi

2020

Sensors

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A smart city is a geographical area that uses modern technologies to facilitate the lives of its residents. Wireless sensor networks (WSNs) are important components of smart cities. Deploying IoT sensors in WSNs is a challenging aspect of network design. Sensor deployment is performed to achieve objectives like increasing coverage, strengthening connectivity, improving robustness, or increasing the lifetime of a given WSN. Therefore, a sensor deployment method must be carefully designed to achieve such objective functions without exceeding the available budget. This study introduces a novel deployment algorithm, called the Evaluated Delaunay Triangulation-based Deployment for Smart Cities (EDTD-SC), which targets not only sensor distribution, but also sink placement. Our algorithm utilizes Delaunay triangulation and k-means clustering to find optimal locations to improve coverage while maintaining connectivity and robustness with obstacles existence in sensing area. The EDTD-SC has been applied to real-world areas and cities, such as Midtown Manhattan in New York in the United States of America. The results show that the EDTD-SC outperforms random and regular deployments in terms of area coverage and end-to-end-delay by 29.6% and 29.7%, respectively. Further, it exhibits significant performance in terms of resilience to attacks.

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