Crime is a social menace that impacts negatively on social economic development of a nation. Crime has been in existence from time immemorial and violent crime is the main enemy of the society. One of the primary responsibilities of any government is security of life and properties which translates to reduction of crime rate and provisioning of adequate security to its citizenry. To this end, government must wake up to its responsibilities by reducing crime rate and provide adequate security to its citizenry through effective, efficient and proactive policing. Any research in this direction that can help in analyzing and predicting the future occurrence of violent crime by using crime dataset is laudable. Predicting future occurrence of crime from crime dataset is well reported in literature, therefore it has become imperative to come up with an overview of the present state of the art on crime prediction and control. The systematic review present in this study focuses on crime prediction and data mining as well as the techniques employed in the past studies. The existing work is classified and grouped into different categories and are presented by using visualization approach. It is found that more studies adopted supervised learning approaches to crime prediction and control compared to other methods. The challenges encountered were also reported. Crime prediction has become hot research area in recent time because of its intending benefits to socio-economic development of a nation.
Learning management systems (LMS) logs all actions taken on the system. These logs provide additional data about the activities and behaviour of users. Educational process mining techniques can use these data to unveil useful information to help instructors, educators and administrators accurately monitor, analyze and improve the online learning patterns of students. This research work presents a framework that uses process mining approach to analyse event log data generated within educational information systems, such as LMSs. In this framework, digital twin concept is employed to present a virtual representation of the students’ activities on the LMS. This framework also used inductive and fuzzy miner algorithms to produce a process model which was represented using virtual model of student’s learning patterns. This model was then evaluated for conformance with the activities observed in the log. The analysis conducted during this study showed the disparity between the behaviours of students that passed a particular course and students that failed the course. Findings also showed that the using the inductive and the fuzzy miner algorithms produced better fitness levels for the process model when compared with other previously used algorithms such as the heuristic miner and alpha miner algorithms. This paper concluded by recommending that the development of educational process mining specific tools can help domain experts better understand students’ learning patterns.
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