The Experts below are selected from a list of 87 Experts worldwide ranked by ideXlab platform
Mohammadreza Ebrahimi - One of the best experts on this subject based on the ideXlab platform.
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detecting and Investigating Crime by means of data mining a general Crime matching framework
Procedia Computer Science, 2011Co-Authors: Mohammad Reza Keyvanpour, Mostafa Javideh, Mohammadreza EbrahimiAbstract:Abstract Data mining is a way to extract knowledge out of usually large data sets; in other words it is an approach to discover hidden relationships among data by using artificial intelligence methods. The wide range of data mining applications has made it an important field of research. Criminology is one of the most important fields for applying data mining. Criminology is a process that aims to identify Crime characteristics. Actually Crime analysis includes exploring and detecting Crimes and their relationships with criminals. The high volume of Crime datasets and also the complexity of relationships between these kinds of data have made criminology an appropriate field for applying data mining techniques. Identifying Crime characteristics is the first step for developing further analysis. The knowledge that is gained from data mining approaches is a very useful tool which can help and support police forces. An approach based on data mining techniques is discussed in this paper to extract important entities from police narrative reports which are written in plain text. By using this approach, Crime data can be automatically entered into a database, in law enforcement agencies. We have also applied a SOM clustering method in the scope of Crime analysis and finally we will use the clustering results in order to perform Crime matching process.
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WCIT - Detecting and Investigating Crime by means of data mining: a general Crime matching framework
Procedia Computer Science, 2011Co-Authors: Mohammad Reza Keyvanpour, Mostafa Javideh, Mohammadreza EbrahimiAbstract:Abstract Data mining is a way to extract knowledge out of usually large data sets; in other words it is an approach to discover hidden relationships among data by using artificial intelligence methods. The wide range of data mining applications has made it an important field of research. Criminology is one of the most important fields for applying data mining. Criminology is a process that aims to identify Crime characteristics. Actually Crime analysis includes exploring and detecting Crimes and their relationships with criminals. The high volume of Crime datasets and also the complexity of relationships between these kinds of data have made criminology an appropriate field for applying data mining techniques. Identifying Crime characteristics is the first step for developing further analysis. The knowledge that is gained from data mining approaches is a very useful tool which can help and support police forces. An approach based on data mining techniques is discussed in this paper to extract important entities from police narrative reports which are written in plain text. By using this approach, Crime data can be automatically entered into a database, in law enforcement agencies. We have also applied a SOM clustering method in the scope of Crime analysis and finally we will use the clustering results in order to perform Crime matching process.
Dirk Neumann - One of the best experts on this subject based on the ideXlab platform.
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ECIS - Investigating Crime-TO-TWITTER RELATIONSHIPS IN URBAN ENVIRONMENTS - FACILITATING A VIRTUAL NEIGHBORHOOD WATCH
2014Co-Authors: Johannes Bendler, Tobias Brandt, Sebastian Wagner, Dirk NeumannAbstract:Social networks offer vast potential for marketing agencies, as members freely provide private information, for instance on their current situation, opinions, tastes, and feelings. The use of social networks to feed into Crime platforms has been acknowledged to build a kind of a virtual neighborhood watch. Current attempts that tried to automatically connect news from social networks with Crime platforms have concentrated on documentation of past events, but neglected the opportunity to use Twitter data as a decision support system to detect future Crimes. In this work, we attempt to unleash the wisdom of crowds materialized in tweets from Twitter. This requires to look at Tweets that have been sent within a vicinity of each other. Based on the aggregated Tweets traffic we correlate them with Crime types. Apparently, Crimes such as disturbing the peace or homicide exhibit different Tweet patterns before the Crime has been committed. We show that these tweet patterns can strengthen the explanation of criminal activity in urban areas. On top of that, we go beyond pure explanatory approaches and use predictive analytics to provide evidence that Twitter data can improve the prediction of Crimes.
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Investigating Crime to twitter relationships in urban environments facilitating a virtual neighborhood watch
European Conference on Information Systems, 2014Co-Authors: Johannes Bendler, Tobias Brandt, Sebastian Wagner, Dirk NeumannAbstract:Social networks offer vast potential for marketing agencies, as members freely provide private information, for instance on their current situation, opinions, tastes, and feelings. The use of social networks to feed into Crime platforms has been acknowledged to build a kind of a virtual neighborhood watch. Current attempts that tried to automatically connect news from social networks with Crime platforms have concentrated on documentation of past events, but neglected the opportunity to use Twitter data as a decision support system to detect future Crimes. In this work, we attempt to unleash the wisdom of crowds materialized in tweets from Twitter. This requires to look at Tweets that have been sent within a vicinity of each other. Based on the aggregated Tweets traffic we correlate them with Crime types. Apparently, Crimes such as disturbing the peace or homicide exhibit different Tweet patterns before the Crime has been committed. We show that these tweet patterns can strengthen the explanation of criminal activity in urban areas. On top of that, we go beyond pure explanatory approaches and use predictive analytics to provide evidence that Twitter data can improve the prediction of Crimes.
Mohammad Reza Keyvanpour - One of the best experts on this subject based on the ideXlab platform.
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detecting and Investigating Crime by means of data mining a general Crime matching framework
Procedia Computer Science, 2011Co-Authors: Mohammad Reza Keyvanpour, Mostafa Javideh, Mohammadreza EbrahimiAbstract:Abstract Data mining is a way to extract knowledge out of usually large data sets; in other words it is an approach to discover hidden relationships among data by using artificial intelligence methods. The wide range of data mining applications has made it an important field of research. Criminology is one of the most important fields for applying data mining. Criminology is a process that aims to identify Crime characteristics. Actually Crime analysis includes exploring and detecting Crimes and their relationships with criminals. The high volume of Crime datasets and also the complexity of relationships between these kinds of data have made criminology an appropriate field for applying data mining techniques. Identifying Crime characteristics is the first step for developing further analysis. The knowledge that is gained from data mining approaches is a very useful tool which can help and support police forces. An approach based on data mining techniques is discussed in this paper to extract important entities from police narrative reports which are written in plain text. By using this approach, Crime data can be automatically entered into a database, in law enforcement agencies. We have also applied a SOM clustering method in the scope of Crime analysis and finally we will use the clustering results in order to perform Crime matching process.
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WCIT - Detecting and Investigating Crime by means of data mining: a general Crime matching framework
Procedia Computer Science, 2011Co-Authors: Mohammad Reza Keyvanpour, Mostafa Javideh, Mohammadreza EbrahimiAbstract:Abstract Data mining is a way to extract knowledge out of usually large data sets; in other words it is an approach to discover hidden relationships among data by using artificial intelligence methods. The wide range of data mining applications has made it an important field of research. Criminology is one of the most important fields for applying data mining. Criminology is a process that aims to identify Crime characteristics. Actually Crime analysis includes exploring and detecting Crimes and their relationships with criminals. The high volume of Crime datasets and also the complexity of relationships between these kinds of data have made criminology an appropriate field for applying data mining techniques. Identifying Crime characteristics is the first step for developing further analysis. The knowledge that is gained from data mining approaches is a very useful tool which can help and support police forces. An approach based on data mining techniques is discussed in this paper to extract important entities from police narrative reports which are written in plain text. By using this approach, Crime data can be automatically entered into a database, in law enforcement agencies. We have also applied a SOM clustering method in the scope of Crime analysis and finally we will use the clustering results in order to perform Crime matching process.
Johannes Bendler - One of the best experts on this subject based on the ideXlab platform.
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ECIS - Investigating Crime-TO-TWITTER RELATIONSHIPS IN URBAN ENVIRONMENTS - FACILITATING A VIRTUAL NEIGHBORHOOD WATCH
2014Co-Authors: Johannes Bendler, Tobias Brandt, Sebastian Wagner, Dirk NeumannAbstract:Social networks offer vast potential for marketing agencies, as members freely provide private information, for instance on their current situation, opinions, tastes, and feelings. The use of social networks to feed into Crime platforms has been acknowledged to build a kind of a virtual neighborhood watch. Current attempts that tried to automatically connect news from social networks with Crime platforms have concentrated on documentation of past events, but neglected the opportunity to use Twitter data as a decision support system to detect future Crimes. In this work, we attempt to unleash the wisdom of crowds materialized in tweets from Twitter. This requires to look at Tweets that have been sent within a vicinity of each other. Based on the aggregated Tweets traffic we correlate them with Crime types. Apparently, Crimes such as disturbing the peace or homicide exhibit different Tweet patterns before the Crime has been committed. We show that these tweet patterns can strengthen the explanation of criminal activity in urban areas. On top of that, we go beyond pure explanatory approaches and use predictive analytics to provide evidence that Twitter data can improve the prediction of Crimes.
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Investigating Crime to twitter relationships in urban environments facilitating a virtual neighborhood watch
European Conference on Information Systems, 2014Co-Authors: Johannes Bendler, Tobias Brandt, Sebastian Wagner, Dirk NeumannAbstract:Social networks offer vast potential for marketing agencies, as members freely provide private information, for instance on their current situation, opinions, tastes, and feelings. The use of social networks to feed into Crime platforms has been acknowledged to build a kind of a virtual neighborhood watch. Current attempts that tried to automatically connect news from social networks with Crime platforms have concentrated on documentation of past events, but neglected the opportunity to use Twitter data as a decision support system to detect future Crimes. In this work, we attempt to unleash the wisdom of crowds materialized in tweets from Twitter. This requires to look at Tweets that have been sent within a vicinity of each other. Based on the aggregated Tweets traffic we correlate them with Crime types. Apparently, Crimes such as disturbing the peace or homicide exhibit different Tweet patterns before the Crime has been committed. We show that these tweet patterns can strengthen the explanation of criminal activity in urban areas. On top of that, we go beyond pure explanatory approaches and use predictive analytics to provide evidence that Twitter data can improve the prediction of Crimes.
Tobias Brandt - One of the best experts on this subject based on the ideXlab platform.
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ECIS - Investigating Crime-TO-TWITTER RELATIONSHIPS IN URBAN ENVIRONMENTS - FACILITATING A VIRTUAL NEIGHBORHOOD WATCH
2014Co-Authors: Johannes Bendler, Tobias Brandt, Sebastian Wagner, Dirk NeumannAbstract:Social networks offer vast potential for marketing agencies, as members freely provide private information, for instance on their current situation, opinions, tastes, and feelings. The use of social networks to feed into Crime platforms has been acknowledged to build a kind of a virtual neighborhood watch. Current attempts that tried to automatically connect news from social networks with Crime platforms have concentrated on documentation of past events, but neglected the opportunity to use Twitter data as a decision support system to detect future Crimes. In this work, we attempt to unleash the wisdom of crowds materialized in tweets from Twitter. This requires to look at Tweets that have been sent within a vicinity of each other. Based on the aggregated Tweets traffic we correlate them with Crime types. Apparently, Crimes such as disturbing the peace or homicide exhibit different Tweet patterns before the Crime has been committed. We show that these tweet patterns can strengthen the explanation of criminal activity in urban areas. On top of that, we go beyond pure explanatory approaches and use predictive analytics to provide evidence that Twitter data can improve the prediction of Crimes.
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Investigating Crime to twitter relationships in urban environments facilitating a virtual neighborhood watch
European Conference on Information Systems, 2014Co-Authors: Johannes Bendler, Tobias Brandt, Sebastian Wagner, Dirk NeumannAbstract:Social networks offer vast potential for marketing agencies, as members freely provide private information, for instance on their current situation, opinions, tastes, and feelings. The use of social networks to feed into Crime platforms has been acknowledged to build a kind of a virtual neighborhood watch. Current attempts that tried to automatically connect news from social networks with Crime platforms have concentrated on documentation of past events, but neglected the opportunity to use Twitter data as a decision support system to detect future Crimes. In this work, we attempt to unleash the wisdom of crowds materialized in tweets from Twitter. This requires to look at Tweets that have been sent within a vicinity of each other. Based on the aggregated Tweets traffic we correlate them with Crime types. Apparently, Crimes such as disturbing the peace or homicide exhibit different Tweet patterns before the Crime has been committed. We show that these tweet patterns can strengthen the explanation of criminal activity in urban areas. On top of that, we go beyond pure explanatory approaches and use predictive analytics to provide evidence that Twitter data can improve the prediction of Crimes.
