An anticrime information support system design: Application of K-means-VMD-BiGRU in the city of Chicago

Zhu, Qing; Zhang, Fan; Liu, Shan; Li, Yuze

doi:10.1016/j.im.2019.103247

Cited by 27 publications

(17 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the BiGRU, two GRU inputs in opposite directions are provided at the same time at each time t. GRUs in the two directions are not directly connected, and the output is jointly determined by two unidirectional GRUs. e BiGRU model has good prediction performance in nonlinear time series data [24,25]. e structure of the BiGRU is shown in Figure 3.…”

Section: Scientific Programmingmentioning

confidence: 99%

A Method of Amino Acid Terahertz Spectrum Recognition Based on the Convolutional Neural Network and Bidirectional Gated Recurrent Network Model

Luo

et al. 2021

Scientific Programming

View full text Add to dashboard Cite

In order to improve the accuracy of amino acid identification, a model based on the convolutional neural network (CNN) and bidirectional gated recurrent network (BiGRU) is proposed for terahertz spectrum identification of amino acids. First, we use the CNN to extract the feature information of the terahertz spectrum; then, we use the BiGRU to process the feature vector of the amino acid time-domain spectrum, describe the time series dynamic change information, and finally achieve amino acid identification through the fully connected network. Experiments are carried out on the terahertz spectra of various amino acids. The experimental results show that the CNN-BiGRU model proposed in this study can effectively realize the terahertz spectrum identification of amino acids and will provide a new and effective analysis method for the identification of amino acids by terahertz spectroscopy technology.

show abstract

Section: Scientific Programmingmentioning

confidence: 99%

A Method of Amino Acid Terahertz Spectrum Recognition Based on the Convolutional Neural Network and Bidirectional Gated Recurrent Network Model

Luo

et al. 2021

Scientific Programming

View full text Add to dashboard Cite

show abstract

“…Both NNs are ambitious in attempting to predict a specific crime happening at a specific time or a generalized location of a crime. A more precise location prediction is not attempted since prior research has shown that there is no basis for short term crime forecasting due to evidence from data showing that spatial heterogeneity and time lag cannot accurately be reflected in short-term prediction (Zhu et al, 2019).…”

Section: Nn To Predict Crime Locationmentioning

confidence: 99%

“…Crime is a global concern that impacts individuals and society on a daily basis and negatively affects society (Costa, 2010). The ever increasing population along with the rise in urbanization has led to dramatic increases in criminal activities (Fajnzylber et al, 2002;Zhang, 2016), particularly in urban settings (Tumulak and Espinosa, 2017;Stebbins, 2019;Zhu et al, 2019). While increased police presence has been documented to reduce increases in crime (Chalfin and McCrary, 2018), police need to be able to plan for and respond effectively to criminal events, especially those that affect personal or public safety.…”

Section: Introductionmentioning

confidence: 99%

Predicting Crime and Other Uses of Neural Networks in Police Decision Making

Walczak

2021

Front. Psychol.

View full text Add to dashboard Cite

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. This article reviews and examines existing research on the utilization of neural networks for forecasting crime and other police decision making problem solving. Neural network models to predict specific types of crime using location and time information and to predict a crime’s location when given the crime and time of day are developed to demonstrate the application of neural networks to police decision making. The neural network crime prediction models utilize geo-spatiality to provide immediate information on crimes to enhance law enforcement decision making. The neural network models are able to predict the type of crime being committed 16.4% of the time for 27 different types of crime or 27.1% of the time when similar crimes are grouped into seven categories of crime. The location prediction neural networks are able to predict the zip code location or adjacent location 31.2% of the time.

show abstract

“…One solution to improve security in cities is to provide more support in public spaces to stay safe [5]. Increasing the number of crimes Becomes a problem in social aspects such as work, health, and education [6]. The main challenge that is corrected by all police organizations where data is recorded and corrected is not necessary to increase crime data.…”

Section: Introductionmentioning

confidence: 99%

“…Grouping crime is a good strategy in improving planning and improving crime prevention [6]. The emergence of the field of prediction of crime, find facts that show geographical patterns.…”

Section: Introductionmentioning

confidence: 99%

Implementation of K-Means Clustering and Weighted Products in Determining Crime-Prone Locations

Rahmatika

Sediyono

Widodo

2020

KINETIK

View full text Add to dashboard Cite

Crime is an act that violates the law. The number of criminal acts that occur becomes a social problem that makes the community and the police uneasy. Increasing the number of crimes is a problem in the social aspect. This research aims to build an information system to provide information on areas prone to a crime that can help the police to speed up the crime resolution process. The grouping process uses the k-means method used to classify based on the level of vulnerability of the area, grouping crime is a good strategy in improving crime prevention planning. In addition to the k-means clustering method, we also use the weighted product method which functions as a recommendation ranking for crime selection. The grouping results obtained from this study are that there is 1 very vulnerable area, 5 areas in the vulnerable category and 3 safe areas. While the weighted product method produces melatilor kudus city 'as a prone to beating areas with a score of 0.182093.

show abstract

An anticrime information support system design: Application of K-means-VMD-BiGRU in the city of Chicago

Cited by 27 publications

References 40 publications

A Method of Amino Acid Terahertz Spectrum Recognition Based on the Convolutional Neural Network and Bidirectional Gated Recurrent Network Model

A Method of Amino Acid Terahertz Spectrum Recognition Based on the Convolutional Neural Network and Bidirectional Gated Recurrent Network Model

Predicting Crime and Other Uses of Neural Networks in Police Decision Making

Implementation of K-Means Clustering and Weighted Products in Determining Crime-Prone Locations

Contact Info

Product

Resources

About