Maizatul Akmar Ismail

A Systematic Review on Educational Data Mining

Presently, educational institutions compile and store huge volumes of data, such as student enrolment and attendance records, as well as their examination results. Mining such data yields stimulating information that serves its handlers well. Rapid growth in educational data points to the fact that distilling massive amounts of data requires a more sophisticated set of algorithms. This issue led to the emergence of the field of educational data mining (EDM). Traditional data mining algorithms cannot be directly applied to educational problems, as they may have a specific objective and function. This implies that a preprocessing algorithm has to be enforced first and only then some specific data mining methods can be applied to the problems. One such preprocessing algorithm in EDM is clustering. Many studies on EDM have focused on the application of various data mining algorithms to educational attributes. Therefore, this paper provides over three decades long (1983–2016) systematic literature review on clustering algorithm and its applicability and usability in the context of EDM. Future insights are outlined based on the literature reviewed, and avenues for further research are identified.

Automated Bone Age Assessment: Motivation, Taxonomies, and Challenges

Bone age assessment (BAA) of unknown people is one of the most important topics in clinical procedure for evaluation of biological maturity of children. BAA is performed usually by comparing an X-ray of left hand wrist with an atlas of known sample bones. Recently, BAA has gained remarkable ground from academia and medicine. Manual methods of BAA are time-consuming and prone to observer variability. This is a motivation for developing automated methods of BAA. However, there is considerable research on the automated assessment, much of which are still in the experimental stage. This survey provides taxonomy of automated BAA approaches and discusses the challenges. Finally, we present suggestions for future research.

Information Infrastructure Capability and Organisational Competitive Advantage: Framework

Purpose – The purpose of this paper is to propose an inclusive research model to overcome the single perspective issues of the previous research which were looking at either on knowledge management (KM) activity, information technology (IT) applications or information infrastructure capability (IIC) independently. Design/methodology/approach – This paper reviewed and categorised five knowledge management (KM) frameworks: first, KM foundation studies; second, resource-based view studies; third, IIC studies; fourth, competitive advantage (CA) studies; fifth, organisational information processing theory studies to propose research model. Case studies based on face-to-face interviews were conducted to empirically analyse the proposed research model. Findings – An inclusive research model was suggested to redress the key limitation of past studies in this research field. Research limitations/implications – Since Asian countries are at present heading for the creation of a knowledge economy, the present study i...

Knowledge sharing behaviour of postgraduate students in University of Malaya

Purpose – This research aims to examine the knowledge sharing factors that influence postgraduate students during research. The main objective is to identify the differences in knowledge sharing behaviour among the postgraduate students with the behaviour commonly found in corporate organisations. Design/methodology/approach – This study utilises an exploratory approach to examine these phenomena. Related documents were analysed to get an overview of the factors that have been identified and examined within the study. Survey instrument was used to collect data in order to get a first-hand view of the contributing factors to knowledge sharing amongst postgraduate students. Findings – This study found that the motivating factors for knowledge sharing among postgraduate students differ from what is found in the corporate world, due to the difference in goals of students. Factors such as extrinsic rewards had no impact on the knowledge sharing behaviour of the respondents. In addition, the institutional repos...

A collaborative approach for research paper recommender system

Research paper recommenders emerged over the last decade to ease finding publications relating to researchers’ area of interest. The challenge was not just to provide researchers with very rich publications at any time, any place and in any form but to also offer the right publication to the right researcher in the right way. Several approaches exist in handling paper recommender systems. However, these approaches assumed the availability of the whole contents of the recommending papers to be freely accessible, which is not always true due to factors such as copyright restrictions. This paper presents a collaborative approach for research paper recommender system. By leveraging the advantages of collaborative filtering approach, we utilize the publicly available contextual metadata to infer the hidden associations that exist between research papers in order to personalize recommendations. The novelty of our proposed approach is that it provides personalized recommendations regardless of the research field and regardless of the user’s expertise. Using a publicly available dataset, our proposed approach has recorded a significant improvement over other baseline methods in measuring both the overall performance and the ability to return relevant and useful publications at the top of the recommendation list.

A Review on Soft Set-Based Parameter Reduction and Decision Making

Many real world decision making problems often involve uncertainty data, which mainly originating from incomplete data and imprecise decision. The soft set theory as a mathematical tool that deals with uncertainty, imprecise, and vagueness is often employed in solving decision making problem. It has been widely used to identify irrelevant parameters and make reduction set of parameters for decision making in order to bring out the optimal choices. In this paper, we present a review on different parameter reduction and decision making techniques for soft set and hybrid soft sets under unpleasant set of hypothesis environment as well as performance analysis of the their derived algorithms. The review has summarized this paper in those areas of research, pointed out the limitations of previous works and areas that require further research works. Researchers can use our review to quickly identify areas that received diminutive or no attention from researchers so as to propose novel methods and applications.

A modified Fuzzy k-Partition based on indiscernibility relation for categorical data clustering

Categorical data clustering has been adopted by many scientific communities to classify objects from large databases. In order to classify the objects, Fuzzy k-Partition approach has been proposed for categorical data clustering. However, existing Fuzzy k-Partition approaches suffer from high computational time and low clustering accuracy. Moreover, the parameter maximize of the classification likelihood function in Fuzzy k-Partition approach will always have the same categories, hence producing the same results. To overcome these issues, we propose a modified Fuzzy k-Partition based on indiscernibility relation. The indiscernibility relation induces an approximation space which is constructed by equivalence classes of indiscernible objects, thus it can be applied to classify categorical data. The novelty of the proposed approach is that unlike previous approach that use the likelihood function of multivariate multinomial distributions, the proposed approach is based on indescernibility relation. We performed an extensive theoretical analysis of the proposed approach to show its effectiveness in achieving lower computational complexity. Further, we compared the proposed approach with Fuzzy Centroid and Fuzzy k-Partition approaches in terms of response time and clustering accuracy on several UCI benchmark and real world datasets. The results show that the proposed approach achieves lower response time and higher clustering accuracy as compared to other Fuzzy k-based approaches.

An Alternative Approach to Normal Parameter Reduction Algorithm for Soft Set Theory

The soft set theory is a mathematical tool that deals with uncertainty, imprecise, and vagueness in decision systems. It has been widely used to identify irrelevant parameters and make reduction set of parameters for decision making in order to bring out the optimal choices of the decision systems. Many normal parameter reduction algorithms exist to handle parameter reduction and maintain consistency of decision choices. However, they require much time to repeatedly run the algorithm to reduce unnecessary parameters using either parameter important degree or oriented parameter sum. In this paper, we propose an alternative algorithm for parameter reduction and decision making based on soft set theory. We show that the proposed algorithm can reduce the computational complexity and run time compared with baseline algorithms. To evaluate the proposed algorithm, we perform thorough experiments on a binary-valued data set. The experimental result shows that the proposed algorithm is feasible and has relatively reduced the computational complexity and running time. In addition, the algorithm is relatively easy to understand compared with the state of the art of normal parameter reduction algorithm. The proposed algorithm is able to avoid the use of parameter important degree, decision partition, and finding the multiple of the universe within the sets.

A Quantitative Study for Developing a Computerized System for Bone Age Assessment in University of Malaya Medical Center

A quantitative study was conducted to direct the design and development of a computerized system for bone age assessment (BAA) in University of Malaya Medical Center (UMMC). Bone age assessment is a clinical procedure performed in pediatric radiology for evaluation the stage of skeletal maturation. It is usually performed by comparing an x-ray of a child’s left hand with a standard of known samples. The current methods utilized in clinical environment to estimate bone age are time consuming and prone to observer variability. This is motivation for developing a computerized method for BAA. A primary analysis shows the current method used by UMMC radiologists for bone age assessment, their feedbacks, problems encountered and their opinions about new approach for BAA. Our study also extracts user requirements for designing and developing a computerized method for BAA.

An overview on the state-of-the-art comparison of the three-contextualization paradigms

In this paper, we present the concept of context and the three-contextualization paradigms for incorporating contextual information in the recommendation process. We provide a comprehensive overview on the several novel approaches of contextual pre-filtering, contextual post-filtering and contextual modelling approaches. We then present state-of-the-art comparison across these three paradigms and raised some key concerns that are not fully addressed in the literature. This will help academicians and practitioners in comparing these three approaches to choose the best option according to their market strategy.