Additional feet-on-the-street deployment method for indexed crime prevention initiative

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 1st to Dec 31st 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

Contour Based Tracking for Driveway Entrance Counting System

Managing vehicle in free-flow entrance is tiring to do manually by a guard control especially due to the increase in transportation demand. Providing an accurate vehicle counting approach is vital for traffic management and it will surely be an essential part in tomorrow's smart cities. Therefore, the main objective of this paper is to propose a more accurate vehicle counter by using the tracking and heuristic rules approaches. EzCam v1.0 is a vehicle surveillance system for a free-flow entrance where a module of vehicle counting based on proposed idea has been applied. The proposed method does not require high computational resources more than any relatively affordable non task specific hardware. It employs single threshold, contour extraction and sequential frame analysis and finally, vehicle counting process subsequently. The tracking-based method employs foreground object detection method and a mechanism for object filtering approach as compared to Chris Dahms approach which does not consider any object rejection and accept all contour information as relevant to be counted as vehicles. As a result, EzCam v1.0 which utilizes the exploited contour-based approach is able to achieve up to 94 percent of accuracy rate and outperforms the classic Chris Dahms method which obtained an accuracy of 88 percent. Therefore, the Exploited Contour based tracking method helps vehicle counting system to perform better accuracy in comparison to Chris Dahms approach

The Growth and Development of the Malaysian media landscape in shaping media regulation.

An increase in the demand of digital media and information communication technology in a borderless world has encouraged Malaysia to move to a new era of knowledge creation and fast-moving competitive advantages especially in the media sector. This phenomenon has blurred boundaries between the broadcasting and computing industries in terms of their roles, functions and economic scale. The new technological environment in Malaysia has resulted in conflicts and posed challenges to the country. It may affect the current regulatory media approach and technology acceptance in harmonizing digital intellectual property, market power, content values, and diversion of cultures. The purpose of this paper is to identify the mechanism, concepts, and implementation of self-regulation in the Malaysian media environment. In-depth interviews were conducted with informants who were responsible for practicing the Content Code. The primary regulatory reference for the study is the Communication Act 1998. The media industry players in this study are media organizations that are governed by the Communication and Multimedia Act 1998, and members of the Communication and Multimedia Content Forum (CMCF). The focus on self-regulation and its procedures is based on the Content Code developed by the Content Forum of the media industry. This study provides useful insights for analyzing the development of Malaysian legislation, cyber policies and the implementation and practices of Malaysian media players. This study helps to shed information on the relevance and usefulness of local legislations and policies to local media practitioners and industry

Predicting the success of suicide terrorist attacks using different machine learning algorithms

Extremism has become one of the major threats throughout the world over the past few decades. In the last two decades, there has been a sharp increase in extremism and terrorist attacks. Nowadays, terrorism concerns all nations in terms of national security and is considered one of the most priority research topics. In order to support the national defense system, academics and researchers are analyzing various datasets to determine the reasons behind these attacks, their patterns, and how to predict their success. The main objective of our paper is to predict different types of attacks, such as successful suicide attacks, successful non-suicide attacks, unsuccessful suicide attacks, and unsuccessful non-suicide attacks. For this purpose, various machine learning algorithms, namely Random Forest, K Nearest Neighbor, Decision Tree, LightGBM Boosting, and a feedforward Artificial Neural Network called Multilayer Perceptron (MLP), are used to determine the success of suicide terrorist attacks. With an accuracy rate of 98.4% and an AUC-ROC score of 99.9%, the Random Forest classifier was the most accurate among all other algorithms. This model is more trustworthy than previous work and provides a useful comparison between machine learning methods and an artificial neural network because it is less dependent and has a multiclass target feature

Allelopathic effects of litter Axonopus compressus against two weedy species and its persistence in soil.

This study investigated the allelopathic effect of Axonopus compressus litter on Asystasia gangetica and Pennisetum polystachion. In experiment 1 the bioassays with 0, 10, 30, and 50 g L−1 of aqueous A. compressus litter leachate were conducted. Experiment 2 was carried out by incorporating 0, 10, 20, 30, 40, and 50 g L−1 of A. compressus litter leachate into soil. In experiment 3, the fate of A. compressus litter leachate phenolics in the soil was investigated. A. compressus leachates did not affect the germination percentage of A. gangetica and P. polystachion, but delayed germination of A. gangetica seeds and decreased seed germination time of P. polystachion. A. compressus litter leachates affected weeds hypocotyl length. Hypocotyl length reductions of 18 and 31% were observed at the highest concentration (50 g L−1) compared to the control in A. gangetica and P. polystachion, respectively. When concentration of A. compressus litter leachate-amended soil increased A. gangetica and P. polystachion seedling shoot length, root length, seedling weight and chlorophyll concentration were not affected. The 5-week decomposition study of A. compressus showed that the phenolic compounds in A. compressus litter abruptly decreased about 52% after two weeks and remained steady until the end of the incubation

Textile-based flexible linear-to-circular polarizing surface for s-band pico-satellites

This paper presents a single layered textile-based flexible linear-to-circular polarizing surface. The proposed structure is designed based on a rectangular ring structure for CubeSat application in the S-band. Each unit cell is sized at 0.35λ×0.33λ×0.2λ for operation centered at 2.2 GHz. This unit cell is then multiplied into a 9x10 array to form the polarizing surface. It features a 3 dB axial ratio bandwidth (ARBW) of 34.73%, with a minimum AR of 0.28 dB. Besides that, it also offers a 90 % conversion efficiency bandwidth of up to 47.34%. The proposed structure’s performance is validated by placing it in front of a patch antenna operating at 2.2 GHz. The antenna performance indicated an increase in terms of gain from 3.14 dBi to 7.33 dBi when integrated with the polarizing surface, besides successfully converting linearly-polarized waves to circularly-polarized

Enhancement in Monitoring for Integrated Project Implementation

AbstractIntegrated project for Year III has been introduced by the Department of Chemical and Process Engineering since 2007/2008 session. This project integrates three or four compulsory subject for each semester. The purpose of this project was to minimize student work load and help student to understand how each courses are related. Project monitoring is one of the procedures to evaluate the performance of integrated project. Since implementation of this project, the coordinator of integrated project will give feedbacks to the students on their performance after they have completed presenting their project. Even though this method is quite sufficient for student to learn their mistake unfortunately similar mistakes was repeated during their final year design project in final year. In order to overcome this problem, student self-assessment for integrated project was introduced during Semester II, Session 2011/2012. The results show that the students were able to detect their mistakes and errors, and some correction was done to their project

Machine Learning Methods for Breast Cancer Diagnostic

This chapter discusses radio-pathological correlation with recent imaging advances such as machine learning (ML) with the use of technical methods such as mammography and histopathology. Although criteria for diagnostic categories for radiology and pathology are well established, manual detection and grading, respectively, are tedious and subjective processes and thus suffer from inter-observer and intra-observer variations. Two most popular techniques that use ML, computer aided detection (CADe) and computer aided diagnosis (CADx), are presented. CADe is a rejection model based on SVM algorithm which is used to reduce the False Positive (FP) of the output of the Chan-Vese segmentation algorithm that was initialized by the marker controller watershed (MCWS) algorithm. CADx method applies the ensemble framework, consisting of four-base SVM (RBF) classifiers, where each base classifier is a specialist and is trained to use the selected features of a particular tissue component. In general, both proposed methods offer alternative decision-making ability and are able to assist the medical expert in giving second opinion on more precise nodule detection. Hence, it reduces FP rate that causes over segmentation and improves the performance for detection and diagnosis of the breast cancer and is able to create a platform that integrates diagnostic reporting system