Papers by Mohammed Ariff Abdullah
Jurnal Teknologi, 2018
Serial crime recognition is a critical task. Usually, police officer investigates the serial crim... more Serial crime recognition is a critical task. Usually, police officer investigates the serial crime behavior based on their heuristics, evidence or prior information from public. Sometimes, the police officer makes inadequate decision when handling the serial crime problems due to lack of preliminary study on relationship between serial crime and amenities. Therefore, this study explores k-means to identify pattern of surroundings area at serial comersial crime scene. In Malaysia, precisely Selangor, Wilayah Persekutuan Kuala Lumpur and Wilayah Persekutuan Putrjaya, a set data of serial crime including index and non-index, and its surroundings area at crime scene are being investigated. Experimental result shows that ‘hot spot’ amenities such as bank, commercial center, restorant, place of worship, resident and school are highly involved with three types of crime namely house breaking at night, day and robbery without firearm. Furthermore, radius distance with 0.2 km and 0.3 km betwe...
International Journal of Advanced Computer Science and Applications, 2015
The prediction of the next serial criminal time is important in the field of criminology for prev... more The prediction of the next serial criminal time is important in the field of criminology for preventing the recurring actions of serial criminals. In the associated dynamic systems, one of the main sources of instability and poor performances is the time delay, which is commonly predicted based on nonlinear methods. The aim of this study is to introduce a dynamic neural network model by using nonlinear autoregressive time series with exogenous (external) input (NARX) and Back Propagation Through Time (BPTT), which is verified intensively with MATLAB to predict and model the crime times for the next distance of serial cases. Recurrent neural networks have been extensively used for modeling of nonlinear dynamic systems. There are different types of recurrent neural networks such as Time Delay Neural Networks (TDNN), layer recurrent networks, NARX, and BPTT. The NARX model for the two cases of inputoutput modeling of dynamic systems and time series prediction draw more attention. In this study, a comparison of two models of NARX and BPTT used for the prediction of the next serial criminal time illustrates that the NARX model exhibits better performance for the prediction of serial cases than the BPTT model. Our future work aims to improve the NARX model by combining objective functions.
Delivering experience, Smart City development is an orchestration of a system scaling from home t... more Delivering experience, Smart City development is an orchestration of a system scaling from home to community, precinct, city and nation. Smart City method of Crime Busting has been introduced in the Malaysian context timely when the attention given towards crime has been taking center stage. Through National Key Result Area Safe City Program, crimes are mapped into a centralized Geographical Information System database hence enabling the visualization of crime hot spots. The concept from this study presents possibly a major capability upgrade to the existing Safe City Program by introducing the ability to predict the next location crimes will be committed. Crime profiling is done based on geographical attributes with machine learning to produce reliable prediction of the next crime location. It is hoped the ability to predict the next crime location can lead to another form of proactive crime busting.
Jurnal Pengurusan, 2018
Under the National Key Result Area (NKRA) Safe City Program's (SCP) Safe City Monitoring System (... more Under the National Key Result Area (NKRA) Safe City Program's (SCP) Safe City Monitoring System (SCMS) initiative, the Royal Malaysian Police (RMP) manages the deployment of feet-on-the-street via the indexed crime hotspots. Working on an approach known as the Repeat Location Finder (RLF), the RMP determines the displacement of indexed crime on the hotspots and may deploy feet-on-the-streets at the identified displacement areas as crime prevention measures. This paper introduces another deployment capability by shifting the focus from the hotspots to the identified serial suspects. Displacement models work on the concentration of crime incidents and the propensity location where the concentration might shift to the surrounding immediate hotspots. This additional method on the other hand, works on the identified suspects and identifies the next location where the suspects might surface, which may take place beyond the distance and boundaries of the hotspots. The objective of this paper is to identify the spatial features that positively contribute towards this new method. The solutions to the objective have been tested on a dataset made available by the RMP comprising 74 serial criminal suspects around the areas of Selangor, Kuala Lumpur and Putrajaya, spanning from Jan 1 st to Dec 31 st 2013. The identification capability moves as high as 92.86%. The RMP has been presented with the results of this paper and it was concluded that this method may be applicable as another capability in managing the deployment of feet-on-the-street resources.
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Papers by Mohammed Ariff Abdullah