In recent years, gamification is becoming popular in education development to enrich students experience in classroom. However, there is still lack of awareness among educators and the concern of whether the gamification technique is acceptable by students. This paper presents the effectiveness of gamification technique to improve students' engagement in Database Design subject at Polytechnic Muadzam Shah Pahang, Malaysia. A framework to implement the gamification technique in higher education is also described. As for the evaluation, an empirical investigation method is adapted and data was collected based on Technology Acceptance Model (TAM) and Student Course Engagement Questionnaire (SCEQ). The evaluation results indicate that the students positively inclined towards gamification caused by the ease of the platform used rather than the benefits that they can obtain from the gamification, concluding that Perceived Ease of Use (PEOU) is a better indicator for students' attitude towards gamification.
Automated inspection has proven to be of great importance in increasing the quality of timber products, optimising raw material resources, increasing productivity as well as reducing error related to human labour. This paper reviews automated inspection of timber surface defects with a special focus on vision inspection. Previous works on sensors utilised are presented and can be used as a reference for future researchers. General approaches to solving the problem of wood surface defect detection can be categorised into segmentation and non-segmenting approaches. The weaknesses and strengths of each approach are discussed along with feature extraction techniques and classifiers implemented in timber surface defect detection. Furthermore, insights into the practicality of implementing automated vision inspection of timber defects were also discussed. This paper shall benefit researchers and practitioners in understanding different approaches, sensors, feature extraction techniques as well as classifiers that have been implemented in automated inspection of timber surface defects, thus providing some direction for future research.
Abstract. Clustering is one of most commonly used approach in the literature of Pattern recognition and Machine Learning. K-means clustering algorithm is a fast and simple method in the clustering approaches. However, due to random selection of center of clusters and the adherence to preliminary results of center of clusters, the risk of trapping to a local minimum ever exist.in this study, we have taken help of effective hybrid of optimization algorithms, artificial bee colony (ABC) and differential evolution (DE), is proposed as a method to mentioned problems. The proposed method consists of two main steps. In first step, Seed Cluster Center Algorithm employed to best initial cluster centers. The combined evolutionary algorithm explores the solution space to find global solution. The performance of proposed method evaluated with standard data set. The evaluation results of the proposed algorithm and its comparison with other alternative algorithms in literature confirms its superior performance and higher efficiency.
This study aims to investigate healthcare practitioner behaviour in adopting Health Information Systems which could affect patients’ safety and quality of health. A qualitative study was conducted based on a semi-structured interview protocol on 31 medical doctors in three Malaysian government hospitals implementing the Total Hospital Information Systems. The period of study was between March and May 2015. A thematic qualitative analysis was performed on the resultant data to categorize them into relevant themes. Four themes emerged as healthcare practitioners’ behaviours that influence the unsafe use of Hospital Information Systems. The themes include (1) carelessness, (2) workarounds, (3) noncompliance to procedure, and (4) copy and paste habit. By addressing these behaviours, the hospital management could further improve patient safety and the quality of patient care.
Selecting important features in classifying wood defects remains a challenging issue to the automated visual inspection domain. This study aims to address the extraction and analysis of features based on statistical texture on images of wood defects. A series of procedures including feature extraction using the Grey Level Dependence Matrix (GLDM) and feature analysis were executed in order to investigate the appropriate displacement and quantisation parameters that could significantly classify wood defects. Samples were taken from the KembangSemangkuk (KSK), Meranti and Merbau wood species. Findings from visual analysis and classification accuracy measures suggest that the feature set with the displacement parameter, d=2, and quantisation level, q=128, shows the highest classification accuracy. However, to achieve less computational cost, the feature set with quantisation level, q=32, shows acceptable performance in terms of classification accuracy.
The objective of this study is to identify factors influencing unsafe use of hospital information systems in Malaysian government hospitals. Semi-structured interviews with 31 medical doctors in three Malaysian government hospitals implementing total hospital information systems were conducted between March and May 2015. A thematic qualitative analysis was performed on the resultant data to deduce the relevant themes. A total of five themes emerged as the factors influencing unsafe use of a hospital information system: (1) knowledge, (2) system quality, (3) task stressor, (4) organization resources, and (5) teamwork. These qualitative findings highlight that factors influencing unsafe use of a hospital information system originate from multidimensional sociotechnical aspects. Unsafe use of a hospital information system could possibly lead to the incidence of errors and thus raises safety risks to the patients. Hence, multiple interventions (e.g. technology systems and teamwork) are required in shaping high-quality hospital information system use.
This paper presents an analysis of the statistical texture representation of the Local Binary Pattern (LBP) variants in the classification of wood defect images. The basic and variants of the LBP feature set that was constructed from a stage of feature extraction processes with the Basic LBP, Rotation Invariant LBP, Uniform LBP, and Rotation Invariant Uniform LBP. For significantly discriminating, the wood defect classes were further evaluated with the use of different classifiers. By comparing the results of the classification performances that had been conducted across the multiple wood species, the Uniform LBP was found to have demonstrated the highest accuracy level in the classification of the wood defects.
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