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Hybrid of deep learning and exponential smoothing for enhancing crime forecasting accuracy. [PDF]
PLoS ONE, 2022 The continued urbanization poses several challenges for law enforcement agencies to ensure a safe and secure environment. Countries are spending a substantial amount of their budgets to control and prevent crime.
Umair Muneer Butt +3 more
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A systematic review on spatial crime forecasting [PDF]
Crime Science, 2020 Background Predictive policing and crime analytics with a spatiotemporal focus get increasing attention among a variety of scientific communities and are already being implemented as effective policing tools.
Ourania Kounadi +3 more
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Crime forecasting: a machine learning and computer vision approach to crime prediction and prevention [PDF]
Visual Computing for Industry, Biomedicine, and Art, 2021 A crime is a deliberate act that can cause physical or psychological harm, as well as property damage or loss, and can lead to punishment by a state or other authority according to the severity of the crime.
Neil Shah, Nandish Bhagat, Manan Shah
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A simple crime hotspot forecasting algorithm [PDF]
Annals of computer science and information systems, 2020 Crime hotspot forecasting is an important part of crime prevention and reducing the delay between a 911 call and the physical intervention. Current developments in the field focus on enriching the historical data and sophisticated point process analysis methods with a fixed grid.
Robert Bogucki +2 more
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Big data analytics and smart cities: applications, challenges, and opportunities [PDF]
Frontiers in Big Data, 2023 Urban environments continuously generate larger and larger volumes of data, whose analysis can provide descriptive and predictive models as valuable support to inspire and develop data-driven Smart City applications.
Eugenio Cesario
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Lightweight deep learning model for crime pattern recognition based on transformer with simulated annealing sparsity and CNN [PDF]
Scientific ReportsThis study addresses the pressing need for high efficiency and low resource consumption in crime pattern recognition within public safety governance by proposing a lightweight deep learning model known as the lightweight crime recognition network (LCRNet)
HongYuan Lu +3 more
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ACSAformer: A crime forecasting model based on sparse attention and adaptive graph convolution
Frontiers in PhysicsIntroductionCrime forecasting is crucial for urban safety management, as it facilitates the optimization of police resource allocation, crime prevention, and the enhancement of public security.
Zhenkai Qin +9 more
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IEEE Access, 2021 Crime and violation are the threat to justice and meant to be controlled. Accurate crime prediction and future forecasting trends can assist to enhance metropolitan safety computationally. The limited ability of humans to process complex information from
Wajiha Safat +2 more
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Money laundering rate modelling
Вестник университета, 2023 The article continues the analysis of the work of the model developed by the authors of forecasting money laundering rate. The purpose of the study is to compare the results of the forecast with the actual data. Authors used official data by the Ministry
J. M. Beketnova, E. S. Anisimov
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Predicting Crime and Other Uses of Neural Networks in Police Decision Making
Frontiers in Psychology, 2021 Neural networks are a machine learning method that excel in solving classification and forecasting problems. They have also been shown to be a useful tool for working with big data oriented environments such as law enforcement.
Steven Walczak
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