Belief Rule Based Expert System to Identify the Crime Zones

• URL: http://arxiv.org/abs/2005.04570v1
• Date: Sun, 10 May 2020 04:07:38 GMT
• Title: Belief Rule Based Expert System to Identify the Crime Zones
• Authors: Abhijit Pathak and Abrar Hossain Tasin
• Abstract summary: This paper focuses on Crime zone Identification. Then, it clarifies how we conducted the Belief Rule Base algorithm to produce interesting frequent patterns for crime hotspots. The paper also shows how we used an expert system to forecast potential types of crime.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper focuses on Crime zone Identification. Then, it clarifies how we conducted the Belief Rule Base algorithm to produce interesting frequent patterns for crime hotspots. The paper also shows how we used an expert system to forecast potential types of crime. In order to further analyze the crime datasets, the paper introduces an analysis study by combining our findings of the Chittagong crime dataset with demographic information to capture factors that could affect neighborhood safety. The results of this solution could be used to raise awareness of the dangerous locations and to help agencies predict future crimes at a specific location in a given time.

Related papers

• An Event-centric Framework for Predicting Crime Hotspots with Flexible Time Intervals [15.749155522310941]
  FlexiCrime is a novel event framework for predicting crime hotspots with flexible time intervals. It incorporates a continuous-time attention network to capture correlations between crime events, which learns crime context features. It measures the risk of specific crime types at a given time and location by considering frequency of past crime events.
  arXiv  Detail & Related papers  (2024-11-02T04:38:10Z)
• Deep learning based black spot identification on Greek road networks [52.77024349608834]
  Black spot identification, atemporal phenomenon, involves analyzing geographical location and time-based occurrence of road accidents. This study focused on traffic accidents in Greek road networks to recognize black spots, utilizing data from police and government-issued car crash reports.
  arXiv  Detail & Related papers  (2023-06-19T07:08:30Z)
• Crime Prediction using Machine Learning with a Novel Crime Dataset [0.0]
  Bangladesh has a high crime rate due to poverty, population growth, and many other socio-economic issues. For law enforcement agencies, understanding crime patterns is essential for preventing future criminal activity. This paper introduces a novel crime dataset that contains temporal, geographic, weather, and demographic data about 6574 crime incidents of Bangladesh.
  arXiv  Detail & Related papers  (2022-11-03T01:55:52Z)
• Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction [60.508960752148454]
  This work proposes a Spatial-Temporal Hypergraph Self-Supervised Learning framework to tackle the label scarcity issue in crime prediction. We propose the cross-region hypergraph structure learning to encode region-wise crime dependency under the entire urban space. We also design the dual-stage self-supervised learning paradigm, to not only jointly capture local- and global-level spatial-temporal crime patterns, but also supplement the sparse crime representation by augmenting region self-discrimination.
  arXiv  Detail & Related papers  (2022-04-18T23:46:01Z)
• Spatial-Temporal Sequential Hypergraph Network for Crime Prediction [56.41899180029119]
  We propose Spatial-Temporal Sequential Hypergraph Network (ST-SHN) to collectively encode complex crime spatial-temporal patterns. In particular, to handle spatial-temporal dynamics under the long-range and global context, we design a graph-structured message passing architecture. We conduct extensive experiments on two real-world datasets, showing that our proposed ST-SHN framework can significantly improve the prediction performance.
  arXiv  Detail & Related papers  (2022-01-07T12:46:50Z)
• The effect of differential victim crime reporting on predictive policing systems [84.86615754515252]
  We show how differential victim crime reporting rates can lead to outcome disparities in common crime hot spot prediction models. Our results suggest that differential crime reporting rates can lead to a displacement of predicted hotspots from high crime but low reporting areas to high or medium crime and high reporting areas.
  arXiv  Detail & Related papers  (2021-01-30T01:57:22Z)
• Fairness Evaluation in Presence of Biased Noisy Labels [84.12514975093826]
  We propose a sensitivity analysis framework for assessing how assumptions on the noise across groups affect the predictive bias properties of the risk assessment model. Our experimental results on two real world criminal justice data sets demonstrate how even small biases in the observed labels may call into question the conclusions of an analysis based on the noisy outcome.
  arXiv  Detail & Related papers  (2020-03-30T20:47:00Z)
• Crime Prediction Using Spatio-Temporal Data [8.50468505606714]
  Supervised learning technique is used to predict crimes with better accuracy. The proposed system is feed with a criminal-activity data set of twelve years of San Francisco city.
  arXiv  Detail & Related papers  (2020-03-11T16:19:19Z)
• Exploring Spatio-Temporal and Cross-Type Correlations for Crime Prediction [48.1813701535167]
  We perform crime prediction exploiting the cross-type and-temporal correlations of urban crimes. We propose a coherent framework to mathematically model these correlations for crime prediction. Further experiments have been conducted to understand the importance of different correlations in crime prediction.
  arXiv  Detail & Related papers  (2020-01-20T00:34:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.