Papers by Roliana Ibrahim
International Journal of Management Excellence, 2013
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Lecture Notes in Computer Science, 2013
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Advances in Intelligent Systems and Computing, 2014
ABSTRACT We consider the prioritization problem in cases where the number of requirements to prio... more ABSTRACT We consider the prioritization problem in cases where the number of requirements to prioritize is large using a clustering technique. Clustering is a method used to find classes of data elements with respect to their attributes. K-Means, one of the most popular clustering algorithms, was adopted in this research. To utilize k-means algorithm for solving requirements prioritization problems, weights of attributes of requirement sets from relevant project stakeholders are required as input parameters. This paper showed that, the output of running k-means algorithm on requirement sets varies depending on the weights provided by relevant stakeholders. The proposed approach was validated using a requirement dataset known as RALIC. The results suggested that, a synthetic method with scrambled centroids is effective for prioritizing requirements using k-means clustering.
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Expert Systems with Applications, 2015
ABSTRACT The profiling of background knowledge is essential in scholar’s recommender systems. Exi... more ABSTRACT The profiling of background knowledge is essential in scholar’s recommender systems. Existing ontology-based profiling approaches employ a pre-built reference ontology as a backbone structure for representing the scholar’s preferences. However, such singular reference ontologies lack sufficient ontological concepts and are unable to represent the hierarchical structure of scholars’ knowledge. They rather encompass general-purpose topics of the domain and are inaccurate in representing the scholars’ knowledge. This paper proposes a method for integrating of multiple domain taxonomies to build a reference ontology, and exploits this reference ontology for profiling scholars’ background knowledge. In our approach, various topics of Computer Science domain from Web taxonomies are selected, transformed by DBpedia, and merged to construct a reference ontology. We demonstrate the effectiveness of our approach by measuring five quality-based metrics as well as application-based evaluation against the developed reference ontology. The empirical results show an improvement over the existing reference ontologies in terms of completeness, richness, and coverage.
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International Journal of Computer Science & Engineering Survey, 2011
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International Journal of Digital Content Technology and its Applications, 2012
Recommender systems for digital libraries have been received increasing attention since they assi... more Recommender systems for digital libraries have been received increasing attention since they assist scholars to find the most appropriate articles for research purposes. Many attempts have been conducted to model the user interests and suggest new articles based on scholar’s preferences. However, a major problem of such systems is that they don’t subsume user background knowledge into the recommendation process and scholars typically have to sift manually irrelevant articles retrieved from digital libraries. Therefore, a great challenging task is how to collect and incorporate sufficient scholar’s knowledge into personalization process to improve the recommendation accuracy. To address this issue, a novel cascade recommender framework which consolidates scholar’s background knowledge based on ontological modeling is proposed. The framework exploits Wikipedia as a lexicographic database for concept disambiguation and semantic similarity mapping. The primary experiment with the assistant a group of scholars at UTM over CiteSeerX digital library indicates an improvement in accuracy of recommendation.
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ABSTRACT Information integration plays an important role in academic environments since it provid... more ABSTRACT Information integration plays an important role in academic environments since it provides a comprehensive view of education data and enables mangers to analyze and evaluate the effectiveness of education processes. However, the problem in the traditional information integration is the lack of personalization due to weak information resource or unavailability of analysis functionality. In this research the layered service-oriented framework was proposed which augmented recommendation approaches with components of semantic based integration to provide adaptive, flexible, and context based information integration and analysis for decision makers in Higher Education Institutes. This framework encompassed the integration of structured information from internal data sources as well as unstructured data from the Web. The main objective of this paper was to adapt the content as well as appropriate services for personalized information analysis. In addition, the framework could enable administrators to analyze instances of education information and receive recommendation of new information sources as well as web services based on the current education status. Service orientation paradigm provides adaptive, flexible, and scalable means of communication for service interoperability and interaction among the framework components. Semantic web technologies help to overcome the heterogeneity among information sources and facilitate on-demand web service discovery and invocation for efficient information analysis.
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... Ali Selamat, Roliana Ibrahim Faculty of computer Science& Information System Universi... more ... Ali Selamat, Roliana Ibrahim Faculty of computer Science& Information System University Technology Malaysia 81310 UTM Johor Bahru {aselamat, roliana}@utm ... Jason Weston “A User's Guide to Support Vector Machines “ 2008 [6] Yuh-Jye Lee and Su-Yun Huang “Reduced ...
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Papers by Roliana Ibrahim