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.The number a...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.The number and forms of criminal activities are increasing at an alarming rate,forcing agencies to develop efficient methods to take preventive measures.In the current scenario of rapidly increasing crime,traditional crime-solving techniques are unable to deliver results,being slow paced and less efficient.Thus,if we can come up with ways to predict crime,in detail,before it occurs,or come up with a“machine”that can assist police officers,it would lift the burden of police and help in preventing crimes.To achieve this,we suggest including machine learning(ML)and computer vision algorithms and techniques.In this paper,we describe the results of certain cases where such approaches were used,and which motivated us to pursue further research in this field.The main reason for the change in crime detection and prevention lies in the before and after statistical observations of the authorities using such techniques.The sole purpose of this study is to determine how a combination of ML and computer vision can be used by law agencies or authorities to detect,prevent,and solve crimes at a much more accurate and faster rate.In summary,ML and computer vision techniques can bring about an evolution in law agencies.展开更多
The nineteenth session of the Standing Committee of the Eleventh National People＇s Congress in February 25, 2011 approved the ＂PRC Criminal Law Amendment （eight）＂, which for the first time added the provision of ...The nineteenth session of the Standing Committee of the Eleventh National People＇s Congress in February 25, 2011 approved the ＂PRC Criminal Law Amendment （eight）＂, which for the first time added the provision of the lenient punishment for the elderly crimes. The provision filled the legislative blank of the criminal responsibility of this special group of the old people in the criminal law of our country. But at the same time, we should also clearly see the deficiencies of the amendment （eight）. This paper attempts to sort out the rationality of the lenient application of the punishment on the older people from the aspect of the criminal policy oftemperingjustice with mercy and other aspects, and to construct and perfect the specific legislative proposals that the amendment （eight） puts forward.展开更多
One of the computer crimes is the voice-mail robbery.Some “computer hackers sell unused voice-mail boxes forprofits.Voice-mail boxes are an ideal means of communicationfor those who want to do business legal or illeg...One of the computer crimes is the voice-mail robbery.Some “computer hackers sell unused voice-mail boxes forprofits.Voice-mail boxes are an ideal means of communicationfor those who want to do business legal or illegal,without be-ing found out.These people can use voice-mail systems tomake long distance calls by dialling a local or free phone num-ber without being charged.The systems manufactured by展开更多
Crime hotspot detection is essential for law enforcement agencies to allocate resources effectively,predict potential criminal activities,and ensure public safety.Traditional methods of crime analysis often rely on ma...Crime hotspot detection is essential for law enforcement agencies to allocate resources effectively,predict potential criminal activities,and ensure public safety.Traditional methods of crime analysis often rely on manual,time-consuming processes that may overlook intricate patterns and correlations within the data.While some existing machine learning models have improved the efficiency and accuracy of crime prediction,they often face limitations such as overfitting,imbalanced datasets,and inadequate handling of spatiotemporal dynamics.This research proposes an advanced machine learning framework,CHART(Crime Hotspot Analysis and Real-time Tracking),designed to overcome these challenges.The proposed methodology begins with comprehensive data collection from the police database.The dataset includes detailed attributes such as crime type,location,time and demographic information.The key steps in the proposed framework include:Data Preprocessing,Feature Engineering that leveraging domain-specific knowledge to extract and transform relevant features.Heat Map Generation that employs Kernel Density Estimation(KDE)to create visual representations of crime density,highlighting hotspots through smooth data point distributions and Hotspot Detection based on Random Forest-based to predict crime likelihood in various areas.The Experimental evaluation demonstrated that CHART shows superior performance over benchmark methods,significantly improving crime detection accuracy by getting 95.24%for crime detection-I(CD-I),96.12%for crime detection-II(CD-II)and 94.68%for crime detection-III(CD-III),respectively.By designing the application with integrating sophisticated preprocessing techniques,balanced data representation,and advanced feature engineering,the proposed model provides a reliable and practical tool for real-world crime analysis.Visualization of crime hotspots enables law enforcement agencies to strategize effectively,focusing resources on high-risk areas and thereby enhancing overall crime prevention and response efforts.展开更多
Crime scene investigation(CSI)image is key evidence carrier during criminal investiga-tion,in which CSI image retrieval can assist the public police to obtain criminal clues.Moreover,with the rapid development of deep...Crime scene investigation(CSI)image is key evidence carrier during criminal investiga-tion,in which CSI image retrieval can assist the public police to obtain criminal clues.Moreover,with the rapid development of deep learning,data-driven paradigm has become the mainstreammethod of CSI image feature extraction and representation,and in this process,datasets provideeffective support for CSI retrieval performance.However,there is a lack of systematic research onCSI image retrieval methods and datasets.Therefore,we present an overview of the existing worksabout one-class and multi-class CSI image retrieval based on deep learning.According to theresearch,based on their technical functionalities and implementation methods,CSI image retrievalis roughly classified into five categories:feature representation,metric learning,generative adversar-ial networks,autoencoder networks and attention networks.Furthermore,We analyzed the remain-ing challenges and discussed future work directions in this field.展开更多
This paper presents a detailed statistical exploration of crime trends in Chicago from 2001 to 2023, employing data from the Chicago Police Department’s publicly available crime database. The study aims to elucidate ...This paper presents a detailed statistical exploration of crime trends in Chicago from 2001 to 2023, employing data from the Chicago Police Department’s publicly available crime database. The study aims to elucidate the patterns, distribution, and variations in crime across different types and locations, providing a comprehensive picture of the city’s crime landscape through advanced data analytics and visualization techniques. Using exploratory data analysis (EDA), we identified significant insights into crime trends, including the prevalence of theft and battery, the impact of seasonal changes on crime rates, and spatial concentrations of criminal activities. The research leveraged a Power BI dashboard to visually represent crime data, facilitating an intuitive understanding of complex patterns and enabling dynamic interaction with the dataset. Key findings highlight notable disparities in crime occurrences by type, location, and time, offering a granular view of crime hotspots and temporal trends. Additionally, the study examines clearance rates, revealing variations in the resolution of cases across different crime categories. This analysis not only sheds light on the current state of urban safety but also serves as a critical tool for policymakers and law enforcement agencies to develop targeted interventions. The paper concludes with recommendations for enhancing public safety strategies and suggests directions for future research, emphasizing the need for continuous data-driven approaches to effectively address and mitigate urban crime. This study contributes to the broader discourse on urban safety, crime prevention, and the role of data analytics in public policy and community well-being.展开更多
The network is a major platform for implementing new cyber-telecom crimes.Therefore,it is important to carry out monitoring and early warning research on new cyber-telecom crime platforms,which will lay the foundation...The network is a major platform for implementing new cyber-telecom crimes.Therefore,it is important to carry out monitoring and early warning research on new cyber-telecom crime platforms,which will lay the foundation for the establishment of prevention and control systems to protect citizens’property.However,the deep-learning methods applied in the monitoring and early warning of new cyber-telecom crime platforms have some apparent drawbacks.For instance,the methods suffer from data-distribution differences and tremendous manual efforts spent on data labeling.Therefore,a monitoring and early warning method for new cyber-telecom crime platforms based on the BERT migration learning model is proposed.This method first identifies the text data and their tags,and then performs migration training based on a pre-training model.Finally,the method uses the fine-tuned model to predict and classify new cyber-telecom crimes.Experimental analysis on the crime data collected by public security organizations shows that higher classification accuracy can be achieved using the proposed method,compared with the deep-learning method.展开更多
Crimes are expected to rise with an increase in population and the rising gap between society’s income levels.Crimes contribute to a significant portion of the socioeconomic loss to any society,not only through its i...Crimes are expected to rise with an increase in population and the rising gap between society’s income levels.Crimes contribute to a significant portion of the socioeconomic loss to any society,not only through its indirect damage to the social fabric and peace but also the more direct negative impacts on the economy,social parameters,and reputation of a nation.Policing and other preventive resources are limited and have to be utilized.The conventional methods are being superseded by more modern approaches of machine learning algorithms capable of making predictions where the relationships between the features and the outcomes are complex.Making it possible for such algorithms to provide indicators of specific areas that may become criminal hot-spots.These predictions can be used by policymakers and police personals alike to make effective and informed strategies that can curtail criminal activities and contribute to the nation’s development.This paper aims to predict factors that most affected crimes in Saudi Arabia by developing a machine learning model to predict an acceptable output value.Our results show that FAMD as features selection methods showed more accuracy on machine learning classifiers than the PCA method.The naïve Bayes classifier performs better than other classifiers on both features selections methods with an accuracy of 97.53%for FAMD,and PCA equals to 97.10%.展开更多
In this paper we aim to identify certain social factors that influence,and thus can be used to predict,the occurrence of crimes.The factors under consideration for this analytic are social demographics such as age,sex...In this paper we aim to identify certain social factors that influence,and thus can be used to predict,the occurrence of crimes.The factors under consideration for this analytic are social demographics such as age,sex,poverty,etc.,train ridership,traffic density and the number of business licenses per community area in Chicago,IL.A factor will be considered pertinent if there is high correlation between it and the number of crimes of a particular type in that community area.展开更多
文摘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.The number and forms of criminal activities are increasing at an alarming rate,forcing agencies to develop efficient methods to take preventive measures.In the current scenario of rapidly increasing crime,traditional crime-solving techniques are unable to deliver results,being slow paced and less efficient.Thus,if we can come up with ways to predict crime,in detail,before it occurs,or come up with a“machine”that can assist police officers,it would lift the burden of police and help in preventing crimes.To achieve this,we suggest including machine learning(ML)and computer vision algorithms and techniques.In this paper,we describe the results of certain cases where such approaches were used,and which motivated us to pursue further research in this field.The main reason for the change in crime detection and prevention lies in the before and after statistical observations of the authorities using such techniques.The sole purpose of this study is to determine how a combination of ML and computer vision can be used by law agencies or authorities to detect,prevent,and solve crimes at a much more accurate and faster rate.In summary,ML and computer vision techniques can bring about an evolution in law agencies.
文摘The nineteenth session of the Standing Committee of the Eleventh National People＇s Congress in February 25, 2011 approved the ＂PRC Criminal Law Amendment （eight）＂, which for the first time added the provision of the lenient punishment for the elderly crimes. The provision filled the legislative blank of the criminal responsibility of this special group of the old people in the criminal law of our country. But at the same time, we should also clearly see the deficiencies of the amendment （eight）. This paper attempts to sort out the rationality of the lenient application of the punishment on the older people from the aspect of the criminal policy oftemperingjustice with mercy and other aspects, and to construct and perfect the specific legislative proposals that the amendment （eight） puts forward.
文摘One of the computer crimes is the voice-mail robbery.Some “computer hackers sell unused voice-mail boxes forprofits.Voice-mail boxes are an ideal means of communicationfor those who want to do business legal or illegal,without be-ing found out.These people can use voice-mail systems tomake long distance calls by dialling a local or free phone num-ber without being charged.The systems manufactured by
基金appreciation to King Saud University for funding this work through Researchers Supporting Project number(RSPD2025R685),King Saud University,Riyadh,Saudi Arabia.
文摘Crime hotspot detection is essential for law enforcement agencies to allocate resources effectively,predict potential criminal activities,and ensure public safety.Traditional methods of crime analysis often rely on manual,time-consuming processes that may overlook intricate patterns and correlations within the data.While some existing machine learning models have improved the efficiency and accuracy of crime prediction,they often face limitations such as overfitting,imbalanced datasets,and inadequate handling of spatiotemporal dynamics.This research proposes an advanced machine learning framework,CHART(Crime Hotspot Analysis and Real-time Tracking),designed to overcome these challenges.The proposed methodology begins with comprehensive data collection from the police database.The dataset includes detailed attributes such as crime type,location,time and demographic information.The key steps in the proposed framework include:Data Preprocessing,Feature Engineering that leveraging domain-specific knowledge to extract and transform relevant features.Heat Map Generation that employs Kernel Density Estimation(KDE)to create visual representations of crime density,highlighting hotspots through smooth data point distributions and Hotspot Detection based on Random Forest-based to predict crime likelihood in various areas.The Experimental evaluation demonstrated that CHART shows superior performance over benchmark methods,significantly improving crime detection accuracy by getting 95.24%for crime detection-I(CD-I),96.12%for crime detection-II(CD-II)and 94.68%for crime detection-III(CD-III),respectively.By designing the application with integrating sophisticated preprocessing techniques,balanced data representation,and advanced feature engineering,the proposed model provides a reliable and practical tool for real-world crime analysis.Visualization of crime hotspots enables law enforcement agencies to strategize effectively,focusing resources on high-risk areas and thereby enhancing overall crime prevention and response efforts.
文摘Crime scene investigation(CSI)image is key evidence carrier during criminal investiga-tion,in which CSI image retrieval can assist the public police to obtain criminal clues.Moreover,with the rapid development of deep learning,data-driven paradigm has become the mainstreammethod of CSI image feature extraction and representation,and in this process,datasets provideeffective support for CSI retrieval performance.However,there is a lack of systematic research onCSI image retrieval methods and datasets.Therefore,we present an overview of the existing worksabout one-class and multi-class CSI image retrieval based on deep learning.According to theresearch,based on their technical functionalities and implementation methods,CSI image retrievalis roughly classified into five categories:feature representation,metric learning,generative adversar-ial networks,autoencoder networks and attention networks.Furthermore,We analyzed the remain-ing challenges and discussed future work directions in this field.
文摘This paper presents a detailed statistical exploration of crime trends in Chicago from 2001 to 2023, employing data from the Chicago Police Department’s publicly available crime database. The study aims to elucidate the patterns, distribution, and variations in crime across different types and locations, providing a comprehensive picture of the city’s crime landscape through advanced data analytics and visualization techniques. Using exploratory data analysis (EDA), we identified significant insights into crime trends, including the prevalence of theft and battery, the impact of seasonal changes on crime rates, and spatial concentrations of criminal activities. The research leveraged a Power BI dashboard to visually represent crime data, facilitating an intuitive understanding of complex patterns and enabling dynamic interaction with the dataset. Key findings highlight notable disparities in crime occurrences by type, location, and time, offering a granular view of crime hotspots and temporal trends. Additionally, the study examines clearance rates, revealing variations in the resolution of cases across different crime categories. This analysis not only sheds light on the current state of urban safety but also serves as a critical tool for policymakers and law enforcement agencies to develop targeted interventions. The paper concludes with recommendations for enhancing public safety strategies and suggests directions for future research, emphasizing the need for continuous data-driven approaches to effectively address and mitigate urban crime. This study contributes to the broader discourse on urban safety, crime prevention, and the role of data analytics in public policy and community well-being.
基金supported in part by the Basic Public Welfare Research Program of Zhejiang Province under Grant LGF20G030001.
文摘The network is a major platform for implementing new cyber-telecom crimes.Therefore,it is important to carry out monitoring and early warning research on new cyber-telecom crime platforms,which will lay the foundation for the establishment of prevention and control systems to protect citizens’property.However,the deep-learning methods applied in the monitoring and early warning of new cyber-telecom crime platforms have some apparent drawbacks.For instance,the methods suffer from data-distribution differences and tremendous manual efforts spent on data labeling.Therefore,a monitoring and early warning method for new cyber-telecom crime platforms based on the BERT migration learning model is proposed.This method first identifies the text data and their tags,and then performs migration training based on a pre-training model.Finally,the method uses the fine-tuned model to predict and classify new cyber-telecom crimes.Experimental analysis on the crime data collected by public security organizations shows that higher classification accuracy can be achieved using the proposed method,compared with the deep-learning method.
文摘Crimes are expected to rise with an increase in population and the rising gap between society’s income levels.Crimes contribute to a significant portion of the socioeconomic loss to any society,not only through its indirect damage to the social fabric and peace but also the more direct negative impacts on the economy,social parameters,and reputation of a nation.Policing and other preventive resources are limited and have to be utilized.The conventional methods are being superseded by more modern approaches of machine learning algorithms capable of making predictions where the relationships between the features and the outcomes are complex.Making it possible for such algorithms to provide indicators of specific areas that may become criminal hot-spots.These predictions can be used by policymakers and police personals alike to make effective and informed strategies that can curtail criminal activities and contribute to the nation’s development.This paper aims to predict factors that most affected crimes in Saudi Arabia by developing a machine learning model to predict an acceptable output value.Our results show that FAMD as features selection methods showed more accuracy on machine learning classifiers than the PCA method.The naïve Bayes classifier performs better than other classifiers on both features selections methods with an accuracy of 97.53%for FAMD,and PCA equals to 97.10%.
文摘In this paper we aim to identify certain social factors that influence,and thus can be used to predict,the occurrence of crimes.The factors under consideration for this analytic are social demographics such as age,sex,poverty,etc.,train ridership,traffic density and the number of business licenses per community area in Chicago,IL.A factor will be considered pertinent if there is high correlation between it and the number of crimes of a particular type in that community area.
