Nor Aniza Abdullah

Abstract: Highly scalable techniques for service discovery xD;are key to the efficient use of Cloud resources, since the xD;Cloud computing appears to be part of the mainstream xD;computing in a few years. We embarked on a preliminary xD;study on Cloud service discovery by adopting an xD;unstructured P2P paradigm. We developed an efficient xD;mechanism for routing of service requests by coupling a xD;number of components: one-hop replication, semanticaware xD;message routing, topology reorganization, and xD;supernodes. A number of experiments were carried out that xD;demonstrated the expected performance of the proposed xD;P2P search scheme.

Abstract: High dimensionality and multi-feature combinations can have negative effect on visual concept classification. In our research, we formulated a new compacted form which is Compacted Dither Pattern Code (CDPC) as a chromatic syntactic feature for visual feature extraction. The effectiveness of CDPC with Bhattacharyya classifier for irregular shapes based visual concepts depiction is reported in this paper. The proposed technique can reduce feature space and computational complexity while maintaining visual data mining and retrieval accuracy in high standard. Our system was empowered with Bhattacharyya classifier which has improved efficiency by considering one numeric value which is the Bhattacharyya coefficient. Experiments were conducted on various combinations and compared with different visual descriptors and classifiers. The first experiment illustrates the comparison of the CDPC based results with well known feature space reduction classes. The second and third experiments demonstrate the effectiveness of our approach with multiple perspectives of performance measures including various concepts.

Abstract: One of the anticipated challenges of today’s e-learning is to solve the problem of recommending from a large xD;number of learning materials. In this study, we introduce a novel architecture for an e-learning recommender xD;system. More specifically, this paper comprises the following phases i) to propose an e-learning recommender xD;system based on content-based filtering and good learners’ ratings, and ii) to compare the proposed e-learning xD;recommender system with exiting e-learning recommender systems that use both collaborative filtering and xD;content-based filtering techniques in terms of system accuracy and student’s performance. The results obtained xD;from the test data show that the proposed e-learning recommender system outperforms existing e-learning xD;recommender systems that use collaborative filtering and content-based filtering techniques with respect to xD;system accuracy of about 83.28% and 48.58%, respectively. The results further show that the learner’s xD;performance is increased by at least 12.16% when the students use the e-learning with the proposed xD;recommender system as compared to other recommendation techniques.

Abstract: The enormity of the amount of learning materials in e-learning has led to the xD;difficulty of locating suitable learning materials for a particular learning topic, creating the xD;need for recommendation tools within a learning context. In this paper, we aim to address xD;this need by proposing a novel e-learning recommender system framework that is based on xD;two conceptual foundations—peer learning and social learning theories that encourage xD;students to cooperate and learn among themselves. Our proposed framework works on the xD;idea of recommending learning materials with a similar content and indicating the quality xD;of learning materials based on good learners’ ratings. A comprehensive set of experiments xD;were conducted to measure the system accuracy and its impact on learner’s performance. xD;The obtained results show that the proposed e-learning recommender system has a significant xD;improvement in the post-test of about 12.16% with the effect size of 0.6 and xD;13.11% with the effect size of 0.53 when compared to the e-learning with a content-based xD;recommender system and the e-learning without a recommender system, respectively. xD;Furthermore, the proposed recommender system performed better in terms of having a xD;small rating deviation and a higher precision as compared to e-learning with a contentbased xD;recommender system.

Abstract: A recommender system is a piece of software that helps users to identify the most interesting and relevant learning items from a large number of items. Recommender systems may be based on collaborative filtering (by user ratings), content-based filtering (by keywords), and hybrid filtering (by both collaborative and content-based filtering). Recommender systems have been a useful tool to recommend items in many online systems, including e-learning. However, not much research has been done to measure the learning outcomes of the learners when they use e-learning with a recommender system. Instead, most of the researchers were focusing on the accuracy of the recommender system in predicting the recommendation rather than the knowledge gain by the learners. This research aims to compare the learning outcomes of the learners when they use several types of e-learning recommender systems. Based on the comparison made, we propose a new e-learning recommender system framework that uses content-based filtering and good learners' ratings to recommend learning materials, and in turn is able to increase the student's performance. The results show that students who used the proposed e-learning recommender system produced a significantly better result in the post-test. The results also show that the proposed e-learning recommender system has the highest percentage of score gain from pre-test to post-test.

Abstract: Reduction of feature space of visual descriptors has become important due to the 'curse of dimensionality' problem. This paper reports the efficiency and effectiveness of the Compacted Dither Pattern Code (CDPC) combined with the Bhattacharyya classifier over MPEG-7 Dominant Colour Descriptor (DCD). Both the CDPC and DCD syntactic features use a compact feature space for colour representation. The algorithmic comparison between the two is presented in this paper, and demonstrates that there are several competitive advantages of CDPC in feature extraction and classification stages when compared to MPEG-7 DCD. The embedded texel properties, spatial colour arrangements, high compactness, and robust feature representation of CDPC have proven its effectiveness in our experimental study. Visual description experiments were conducted for ten irregular shapes-based visual concepts in videos with three setups namely CDPC with Bhattacharyya classifier, DCD without spatial coherency and DCD with spatial coherency. The visual descriptions were performed with the TRECVID 2007 development key frame dataset. The experimental results are presented in terms of three common performance measures. The results show that CDPC with Bhattacharyya classifier provides a good generalised performance for irregular shapes-based visual description as compared to the other experimental setups.

Abstract: This paper introduces a novel architecture for an e-learning recommender system which is based on good learners' average ratings strategy and content-based filtering approach. The feasibility of the proposed system is conducted by comparing its performance against other recommender systems and an adaptive hypermedia system in order to measure the effectiveness of the proposed strategy in improving students' learning performance. Experimental result has shown that the recommender strategy can improve students' performance by at least 12.16%, as compared to other recommendation techniques. A performance evaluation with an adaptive hypermedia system that uses knowledge level as its adaptation feature also showed a positive increase of 14.99% in terms of students' performance.