Malaysia citizens are categorised into three different income groups which are the Top 20 Percent (T20), Middle 40 Percent (M40), and Bottom 40 Percent (B40). One of the focus areas in the Eleventh Malaysia Plan (11MP) is to elevate the B40 household group towards the middle-income society. Based on recent studies by the World Bank, Malaysia is expected to enter the high-income economy status no later than the year 2024. Thus, it is essential to clarify the B40 population through a predictive classification as a prerequisite towards developing a comprehensive action plan by the government. This paper is aimed at identifying the best machine learning models using Naive Bayes, Decision Tree and k-Nearest Neighbors algorithm for classifying the B40 population. Several data pre-processing task such as data cleaning, feature engineering, normalisation, feature selection: Correlation Attribute, Information Gain Attribute and Symmetrical Uncertainty Attribute and sampling methods using SMOTE has been conducted to the raw dataset to ensure the quality of the training data. Each classifier is then optimized using different tuning parameter with 10-Fold Cross Validation for achieving the optimal values before the performance of the three classifiers are compared to each other. For the experiments, a dataset from National Poverty Data Bank called eKasih obtained from the Society Wellbeing Department, Implementation Coordination Unit of Prime Minister's Department (ICU JPM), consisting of 99,546 households from 3 different states: Johor, Terengganu and Pahang are used to train each of the machine learning model. The experimental results using 10-Fold Cross-Validation method demonstrates that the overall performance of Decision Tree model outperformed the other models and the significance test specified the result is statistically significance.
Abstract-This paper presents the bees algorithm for vehicle routing problems within time windows (VRPTW). The VRPTW aims to determine the optimal route for a number of vehicles when serving a set of customers within a predefined time interval (the time window). The objective in VRPTW is to minimize overall transportation cost. Various heuristic and metaheuristic approaches have been developed in literature to produce high-quality solutions for this problem because of its high complication rate and extensive implementation in real-life applications. This research investigates the use of bee algorithms (BA) for VRPTW and identifying the strengths and weaknesses.Index Terms-Foraging behaviour, bees algorithm, vehicle routing problem with time windows.
<p>Cultural heritage reflects a society’s identity, hence should be protected and preserved for the future generation. Digital preservation is significant for cultural heritage since there are a lot of important knowledge and collections of manuscripts and artefacts which need to be preserved to ensure sustainability for future generations. However, there is still a lack in digital preservation methods for cultural heritage especially intangible cultural heritage. This paper discusses cultural heritage and results of a study on intangible cultural heritage preservation. An interview with five experts in intangible cultural heritage domain has been carried out. Results show that level of awareness of preserving intangible cultural heritage is still low. In addition, the heritage practitioners - artisans and craftsmen keep the knowledge and skills in their memory as preservation method. Thus the knowledge depend on individual practitioners since no documentation is made. Informants are also aware of the importance of digitalization of intangible cultural heritage knowledge for the preservation and safeguard.</p>
Traditional rehabilitation is a tedious task which typically reduces the patient's motivation to perform rehabilitation exercises. Patients therefore need a program that can entice them to do rehabilitation exercises continuously. The proposed game includes two different types of game and three different types of movement for interacting with the game. The game was designed and developed based on the elements of a rehabilitation game and the types of movement in rehabilitation exercises. The interface was developed with the aim of increasing the motivation of players, and the design was based on an analysis of the technology constraints faced by post-stroke patients. Since these patients experience physical limitations, Microsoft Kinect was used for interaction in this game. Using Kinect, the patient is not bound by the controller to interact with the game. Therefore, rehabilitation exercise games that support multi-player will provide a higher motivation than the single-player. Since most stroke patients suffer from cognitive impairment, cognitive challenge levels are also the key factors in the design of the game so that it does not become an obstacle for the recovery process. This research develops a prototype of a rehabilitation exercise game that contains aspects of the social context, the type of movement and cognitive challenges. It also provides usability in game design, according to a post-stroke stage so that they can perform recovery activities based on their ability. In addition, this study highlights technology and rehabilitation exercise games in Malaysia.The game also adds a social context that gives patients the opportunity to have a friend to play either by competition or cooperation. The contribution of this research is to measure the effectiveness of Microsoft's Kinect game console and this game can help in recovery the post-stroke patients do additional exercises at home without the supervision of therapist.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.