It is a well-known fact that some criminals follow perpetual methods of operations known as modi operandi. Modus operandi is a commonly used term to describe the habits in committing crimes. These modi operandi are used in relating criminals to crimes for which the suspects have not yet been recognized. This paper presents the design, implementation and evaluation of a new method to find connections between crimes and criminals using modi operandi. The method involves generating a feature matrix for a particular criminal based on the flow of events of his/her previous convictions. Then, based on the feature matrix, two representative modi operandi are generated: complete modus operandi and dynamic modus operandi. These two representative modi operandi are compared with the flow of events of the crime at hand, in order to generate two other outputs: completeness probability (CP) and deviation probability (DP). CP and DP are used as inputs to a fuzzy inference system to generate a score which is used in providing a measurement for the similarity between the suspect and the crime at hand. The method was evaluated using actual crime data and ten other open data sets. In addition, comparison with nine other classification algorithms showed that the proposed method performs competitively with other related methods proving that the performance of the new method is at an acceptable level.
The manual crime recording and investigation systems in police stations all around the world are generating piles of crime documents which make storage and retrieval of reliable crime information extremely difficult as well as inefficient. Furthermore, investigators of central authorities have to manually search through these documents and communicate between the relevant police stations to obtain required information. Eventually, delays in information flow between investigators of crime incidents cannot be avoided. Sri Lanka Police too have been facing the same set of issues over many years. To get rid of pilling of large number of documents annually in police stations, Sri Lanka Police is allowed to destroy the documents related to solved crimes which are older than five years. This may destroy not only "closed files", but also very valuable information that can be used in future crime investigations.To overcome this problem, this paper proposes a web-based framework with geographical information support which contains a centralized database for crime data storage and retrieval. Geographical capabilities of the framework support not only spatial analysis but also provide an efficient solution to current manual crime map generation. Our highly secured and user friendly framework follows the state of the art layered architecture which provides an optimized data model for fast access and easy analysis of crime data. The solution consists of an affluent set of data mining tools which are essential in any crime investigation process. Security of data is ensured with data encryption for sensitive information and by limiting access to the data through a role based access method. Further the data is only accessible through a virtual private network (VPN) connecting all the police stations and other relevant departments of the Police. The The manual crime recording and investigation systems in police stations all around the world 42 are generating piles of crime documents which make storage and retrieval of reliable crime 43 information extremely difficult as well as inefficient. Furthermore, investigators of central 44 authorities have to manually search through these documents and communicate between the 45 relevant police stations to obtain required information. Eventually, delays in information flow 46 between investigators of crime incidents cannot be avoided. Sri Lanka Police too have been 47 facing the same set of issues over many years. To get rid of pilling of large number of 48 documents annually in police stations, Sri Lanka Police is allowed to destroy the documents 49 related to solved crimes which are older than five years. This may destroy not only "closed 50 files", but also very valuable information that can be used in future crime investigations.To 51 overcome this problem, this paper proposes a web-based framework with geographical 52 information support which contains a centralized database for crime data storage and 53 retrieval. Geographical capabilities of the framework support not only spatial ...
It is a well-known fact that some criminals follow perpetual methods of operations, known as modus operandi (MO) which is commonly used to describe the habits in committing something especially in the context of criminal investigations. These modus operandi are then used in relating criminals to other crimes where the suspect has not yet been recognized. This paper presents a method which is focused on identifying the perpetual modus operandi of criminals by analyzing their previous convictions. The method involves in generating a feature matrix for a particular suspect based on the flow of events. Then, based on the feature matrix, two representative modus operandi are generated: complete modus operandi and dynamic modus operandi. These two representative modus operandi will be compared with the flow of events of the crime in order to investigate and relate a particular criminal. This comparison uses several operations to generate two other outputs: completeness probability and deviation probability. These two outcomes are used as inputs to a fuzzy inference system to generate a score value which is used in providing a measurement for the similarity between the suspect and the crime at hand. The method was evaluated using actual crime data and four other open data sets. Then ROC analysis was performed to justify the validity and the generalizability of the proposed method. In addition, comparison with five other classification algorithms showed that the proposed method performs competitively with other related methods.
The manual crime recording and investigation systems in police stations all around the world are generating piles of crime documents which make storage and retrieval of reliable crime information extremely difficult as well as inefficient. Furthermore, investigators of central authorities have to manually search through these documents and communicate between the relevant police stations to obtain required information. Eventually, delays in information flow between investigators of crime incidents cannot be avoided. Sri Lanka Police too have been facing the same set of issues over many years. To get rid of pilling of large number of documents annually in police stations, Sri Lanka Police is allowed to destroy the documents related to solved crimes which are older than five years. This may destroy not only "closed files", but also very valuable information that can be used in future crime investigations.To overcome this problem, this paper proposes a web-based framework with geographical information support which contains a centralized database for crime data storage and retrieval. Geographical capabilities of the framework support not only spatial analysis but also provide an efficient solution to current manual crime map generation. Our highly secured and user friendly framework follows the state of the art layered architecture which provides an optimized data model for fast access and easy analysis of crime data. The solution consists of an affluent set of data mining tools which are essential in any crime investigation process. Security of data is ensured with data encryption for sensitive information and by limiting access to the data through a role based access method. Further the data is only accessible through a virtual private network (VPN) connecting all the police stations and other relevant departments of the Police. The proposed framework was evaluated by conducting an experimental study and the results are promising.
Understanding community structure helps to interpret the role of actors in a social network. Actor has close ties to actors within a community than actors outside of its community. Community structure reveals important information such as central members in communities and bridges members who connect communities. Clustering algorithms like hierarchical clustering, affinity propagation, modularity and spectral graph clustering had been applied in social network clustering to identify community structures in it. This study proposes a novel method for distance measurement between nodes and centroids. Distance is measured based on the shortest path length and number of common nearest neighbors with one path length. This measure, "Proportional closeness" is used to assign nodes to the closest centroid. A fuzzy system is also applied to find the closest centroid to a node when similar proportional closeness values are present for multiple centroids. The method has been applied to two artificial networks and one real world network data to test its accuracy on membership identification. The results revealed that the method successfully assigns members to its nearest centroid and leave neutral members aside without assigning to any centroid.
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