ORCID Profile
0000-0001-5182-3558
Current Organisation
Murdoch University
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Publisher: IEEE
Date: 07-2011
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: IEEE
Date: 07-2012
Publisher: Springer Berlin Heidelberg
Date: 2000
Publisher: IEEE
Date: 07-2011
Publisher: IEEE
Date: 07-2008
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11739685_93
Publisher: IEEE
Date: 10-2014
Publisher: IEEE
Date: 2002
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: IEEE
Date: 07-2014
Publisher: Elsevier BV
Date: 10-2008
Publisher: Asia University
Date: 26-01-2015
DOI: 10.1155/2015/925935
Abstract: Challenges for text processing in ancient document images are mainly due to the high degree of variations in foreground and background. Image binarization is an image segmentation technique used to separate the image into text and background components. Although several techniques for binarizing text documents have been proposed, the performance of these techniques varies and depends on the image characteristics. Therefore, selecting binarization techniques can be a key idea to achieve improved results. This paper proposes a framework for selecting binarizing techniques of palm leaf manuscripts using Support Vector Machines (SVMs). The overall process is ided into three steps: (i) feature extraction: feature patterns are extracted from grayscale images based on global intensity, local contrast, and intensity (ii) treatment of imbalanced data: imbalanced dataset is balanced by using Synthetic Minority Overs ling Technique as to improve the performance of prediction and (iii) selection: SVM is applied in order to select the appropriate binarization techniques. The proposed framework has been evaluated with palm leaf manuscript images and benchmarking dataset from DIBCO series and compared the performance of prediction between imbalanced and balanced datasets. Experimental results showed that the proposed framework can be used as an integral part of an automatic selection process.
Publisher: IEEE
Date: 1997
Publisher: IEEE
Date: 2006
Publisher: Springer International Publishing
Date: 2014
Publisher: IEEE
Date: 11-2005
Publisher: IEEE
Date: 2007
Publisher: Springer Berlin Heidelberg
Date: 2003
Publisher: Informa UK Limited
Date: 20-10-2004
Publisher: IEEE
Date: 07-2009
Publisher: IEEE
Date: 06-2015
Publisher: IEEE
Date: 11-2009
Publisher: Springer International Publishing
Date: 2016
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: IEEE
Date: 07-2011
Publisher: IEEE
Date: 2000
Publisher: IEEE
Date: 06-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 1997
DOI: 10.1109/19.650798
Publisher: Springer Berlin Heidelberg
Date: 1997
DOI: 10.1007/BFB0032490
Publisher: ACM
Date: 24-02-2017
Publisher: Springer International Publishing
Date: 27-06-2019
Publisher: IEEE
Date: 2005
Publisher: Springer International Publishing
Date: 2018
Publisher: IEEE
Date: 11-2005
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11736639_58
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: IEEE
Date: 10-2011
Publisher: Elsevier BV
Date: 08-2009
Publisher: IEEE
Date: 12-2018
Publisher: IEEE
Date: 10-2007
Publisher: Elsevier BV
Date: 12-2010
Publisher: EJournal Publishing
Date: 2020
Publisher: IEEE
Date: 1994
Publisher: IEEE
Date: 10-2012
Publisher: IEEE
Date: 12-2014
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: IEEE
Date: 05-2012
Publisher: IEEE
Date: 2013
Publisher: IEEE
Date: 1997
Publisher: IEEE
Date: 11-2017
Publisher: Springer Science and Business Media LLC
Date: 05-2003
Publisher: IEEE
Date: 06-2010
DOI: 10.1109/SNPD.2010.34
Publisher: International Association of Online Engineering (IAOE)
Date: 06-12-2019
DOI: 10.3991/IJET.V14I23.11066
Abstract: Effective system development to support higher education institutions is important. There are two steps in developing a good system and they are system architecture analysis and design. The first task of this research is to design the use case diagrams, system overview and the system architecture. The second is to evaluate the system architecture of a proposed Student Relationship Management (SRM) system using Internet of Things (IoT) to collect digital footprint of higher education institutions. The outcome of this research include the system architecture of the proposed student relationship management system (SRMS)-IoT consists of six main parts: 1) service stations, 2) system identification, 3) system integration API, 4) SRM internal system, 5) report analytic and 6) web server and database server. Evaluation of the results shows an overall appropriateness at a very high level: the overall appropriateness of usability result was also at a very high level, which shows that this research is appropriate to be used as a guideline for further system development to support student services, and to promote learning and analysis of student behaviours in higher education institutions.
Publisher: IEEE
Date: 06-2012
Publisher: Springer Science and Business Media LLC
Date: 14-09-2016
Publisher: IEEE
Date: 2001
Publisher: ACM
Date: 16-09-2011
Publisher: IEEE
Date: 1999
Publisher: IEEE
Date: 11-2017
Publisher: Springer Berlin Heidelberg
Date: 1998
DOI: 10.1007/BFB0095276
Publisher: IEEE
Date: 07-2010
Publisher: IEEE
Date: 06-2015
Publisher: IEEE
Date: 09-2022
Publisher: IEEE
Date: 08-2007
Publisher: Fuji Technology Press Ltd.
Date: 20-01-2017
DOI: 10.20965/JACIII.2017.P0031
Abstract: Data mining is the analytics and knowledge discovery process of analyzing large volumes of data from various sources and transforming the data into useful information. Various disciplines have contributed to its development and is becoming increasingly important in the scientific and industrial world. This article presents a review of data mining techniques and applications from 1996 to 2016. Techniques are ided into two main categories: predictive methods and descriptive methods. Due to the huge number of publications available on this topic, only a selected number are used in this review to highlight the developments of the past 20 years. Applications are included to provide some insights into how each data mining technique has evolved over the last two decades. Recent research trends focus more on large data sets and big data. Recently there have also been more applications in area of health informatics with the advent of newer algorithms.
Publisher: IEEE
Date: 10-2017
Publisher: IEEE
Date: 08-2007
Publisher: IEEE
Date: 08-2011
Publisher: Inderscience Publishers
Date: 2012
Publisher: IEEE
Date: 2010
Publisher: Elsevier BV
Date: 02-2018
Publisher: IEEE
Date: 12-2008
Publisher: IEEE
Date: 06-2009
Publisher: IEEE
Date: 1998
Publisher: Springer US
Date: 2008
Publisher: IEEE
Date: 07-2016
Publisher: Springer International Publishing
Date: 2017
Publisher: Springer International Publishing
Date: 2017
Publisher: IEEE
Date: 10-2011
Publisher: ACM
Date: 19-09-2007
Publisher: IEEE
Date: 11-2005
Publisher: Springer Science and Business Media LLC
Date: 11-2007
Publisher: IEEE
Date: 07-2009
Publisher: IEEE
Date: 06-2006
Publisher: Springer International Publishing
Date: 2017
Publisher: IEEE
Date: 10-2019
Publisher: IEEE
Date: 09-2012
Publisher: IEEE
Date: 06-2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 1997
DOI: 10.1109/19.668276
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 1997
DOI: 10.1109/19.668273
Publisher: Academy of Taiwan Information Systems Research
Date: 12-2013
DOI: 10.7903/IJECS.1038
Publisher: IEEE
Date: 2010
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Springer Science and Business Media LLC
Date: 22-02-2010
Publisher: IEEE
Date: 2002
Publisher: Fuji Technology Press Ltd.
Date: 20-04-2010
DOI: 10.20965/JACIII.2010.P0297
Abstract: In most classification problems, sometimes in order to achieve better results, data cleaning is used as a preprocessing technique. The purpose of data cleaning is to remove noise, inconsistent data and errors in the training data. This should enable the use of a better and representative data set to develop a reliable classification model. In most classification models, unclean data could sometime affect the classification accuracies of a model. In this paper, we investigate the use of misclassification analysis for data cleaning. In order to demonstrate our concept, we have used Artificial Neural Network (ANN) as the core computational intelligence technique. We use four benchmark data sets obtained from the University of California Irvine (UCI) machine learning repository to investigate the results from our proposed data cleaning technique. The experimental data sets used in our experiment are binary classification problems, which are German credit data, BUPA liver disorders, Johns Hopkins Ionosphere and Pima Indians Diabetes. The results show that the proposed cleaning technique could be a good alternative to provide some confidence when constructing a classification model.
Publisher: IEEE
Date: 07-2013
Publisher: IEEE
Date: 08-2007
Publisher: IEEE
Date: 07-2013
Publisher: Association for Computing Machinery (ACM)
Date: 04-2006
Abstract: The advanced computational capabilities in modern personal computers have made it possible for consumers to experience simulations with a high degree of verisimilitude through simulation games (a.k.a. Sims). In recent years, the cross-boundary technology exchange between game and simulation technology, along with other factors, has contributed to the confusion as to what makes a simulation game and what makes a simulator. This article provides a user's and designer's perspective on a definitive comparison of the similarities and differences between games in general, simulation games, and simulators. It also introduces a method that can be easily used to distinguish games and simulation games from simulators by using observable design characteristics. On the other hand, the convergence of functionality and technology in simulation games and simulators has created new applications of simulation. One such application is in serious games. Serious games and simulation games are confusingly similar in many ways. However, they greatly differ in functionality. This article also provides a method to distinguish serious games from simulation games, to clarify the strict categorization between these two applications of simulation.
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11558651_40
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2003
Publisher: IEEE
Date: 07-2009
Publisher: MDPI AG
Date: 26-05-2202
DOI: 10.3390/A13060131
Abstract: Feature Selection (FS) methods have been studied extensively in the literature, and there are a crucial component in machine learning techniques. However, unsupervised text feature selection has not been well studied in document clustering problems. Feature selection could be modelled as an optimization problem due to the large number of possible solutions that might be valid. In this paper, a memetic method that combines Differential Evolution (DE) with Simulated Annealing (SA) for unsupervised FS was proposed. Due to the use of only two values indicating the existence or absence of the feature, a binary version of differential evolution is used. A dichotomous DE was used for the purpose of the binary version, and the proposed method is named Dichotomous Differential Evolution Simulated Annealing (DDESA). This method uses dichotomous mutation instead of using the standard mutation DE to be more effective for binary purposes. The Mean Absolute Distance (MAD) filter was used as the feature subset internal evaluation measure in this paper. The proposed method was compared with other state-of-the-art methods including the standard DE combined with SA, which is named DESA in this paper, using five benchmark datasets. The F-micro, F-macro (F-scores) and Average Distance of Document to Cluster (ADDC) measures were utilized as the evaluation measures. The Reduction Rate (RR) was also used as an evaluation measure. Test results showed that the proposed DDESA outperformed the other tested methods in performing the unsupervised text feature selection.
Publisher: IEEE
Date: 07-2009
Publisher: MDPI AG
Date: 31-05-2017
DOI: 10.3390/NANO7060131
Publisher: IEEE
Date: 07-2009
Publisher: IEEE
Date: 2005
DOI: 10.1109/ICEBE.2005.4
Publisher: IEEE
Date: 10-2010
Publisher: IEEE
Date: 10-2006
Publisher: Fuji Technology Press Ltd.
Date: 20-03-2007
DOI: 10.20965/JACIII.2007.P0289
Abstract: Use of relevance feedback (RF) in the feature vector model has been one of the most widely used approaches to fine tuning queries for content-based image retrieval (CBIR). We propose a framework that extends RF to capturing the inter-query relationship between current and previous queries. Using the feature vector model, this avoids the need to “memorize” actual retrieval relationships between actual image indexes and the previous queries. This approach is suited to image database applications in which images are frequently added and removed. In the previous work [1], we developed a feature vector framework for inter-query learning using statistical discriminant analysis. One weakness of the previous framework is that the criteria for exploring and merging with an existing visual group are based on two constant thresholds, which are selected through trial and error. Another weakness is that it is not suited to mutually interrelated data clusters. Instead of using constant values, we have further extended the framework using positive feedback s le size as a factor for determining thresholds. Experiments demonstrated that our proposed framework outperforms the previous framework.
Publisher: IEEE
Date: 02-2010
Publisher: Springer Science and Business Media LLC
Date: 05-12-2016
Publisher: Springer International Publishing
Date: 2017
Publisher: IEEE
Date: 04-2013
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11596981_160
Publisher: IEEE
Date: 06-2009
Publisher: Springer International Publishing
Date: 2014
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: IEEE
Date: 07-2201
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: IEEE
Date: 2003
Publisher: IEEE
Date: 02-2017
Publisher: IEEE
Date: 1999
Publisher: ACM
Date: 10-12-2018
No related grants have been discovered for Chun Che Fung.