ORCID Profile
0000-0003-0690-4732
Current Organisation
University of Technology Sydney
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Publisher: IEEE
Date: 06-2009
Publisher: Springer International Publishing
Date: 2014
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Springer Science and Business Media LLC
Date: 21-10-2008
Publisher: Association for Computing Machinery (ACM)
Date: 13-11-2020
DOI: 10.1145/3420034
Abstract: Online data stream mining is of great significance in practice because of its ubiquity in many real-world scenarios, especially in the big data era. Traditional data mining algorithms cannot be directly applied to data streams due to (1) the possible change of underlying data distribution over time (i.e., concept drift ) and (2) delayed, short, or even no labels for streaming data in practice. A new research area, named unsupervised concept drift detection , has emerged to tackle this difficulty mainly based on two-s le hypothesis tests, such as the Kolmogorov–Smirnov test. However, it is surprising that none of the existing methods in this area exploit the Bayesian nonparametric hypothesis test, which has clear interpretability and straightforward prior knowledge encoding ability and no strict or unrealistic requirement of prefixing the form for the underlying data distribution. In this article, we present a Bayesian nonparametric unsupervised concept drift detection method based on the Polya tree hypothesis test. The basic idea is to decompose the underlying data distribution into a multi-resolution representation that transforms the whole distribution hypothesis test into recursive and simple binomial tests. Also, an incremental mechanism is especially designed to improve its efficiency in the stream setting. The method effectively detect drifts, and it also locates where a drift happens and the posteriors of hypotheses. The experiments on synthetic data verify the desired properties of the proposed method, and the experiments on real-world data show the better performance of the method for data stream mining compared with its frequentist counterpart in the literature.
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Elsevier BV
Date: 09-2009
Publisher: Atlantis Press
Date: 2007
Publisher: Springer Science and Business Media LLC
Date: 11-10-2007
Publisher: Elsevier BV
Date: 06-2016
Publisher: Springer Science and Business Media LLC
Date: 07-03-2006
Publisher: Emerald
Date: 19-06-2009
DOI: 10.1108/17440080910968463
Abstract: Matching relevant ontology data for integration is vitally important as the amount of ontology data increases along with the evolving Semantic web, in which data are published from different in iduals or organizations in a decentralized environment. For any domain that has developed a suitable ontology, its ontology annotated data (or simply ontology data) from different sources often overlaps and needs to be integrated. The purpose of this paper is to develop intelligent web ontology data matching method and framework for data integration. This paper develops an intelligent matching method to solve the issue of ontology data matching. Based on the matching method, it also proposes a flexible peer‐to‐peer framework to address the issue of ontology data integration in a distributed Semantic web environment. The proposed matching method is different from existing data matching or merging methods applied to data warehouse in that it employs a machine learning approach and more similarity measurements by exploring ontology features. The proposed method and framework will be further tested for some more complicated real cases in the future. The experiments show that this proposed intelligent matching method increases ontology data matching accuracy.
Publisher: Association for Computing Machinery (ACM)
Date: 29-04-2020
DOI: 10.1145/3379500
Abstract: Estimating causal effects by making causal inferences from observational data is common practice in scientific studies, business decision-making, and daily life. In today’s data-driven world, causal inference has become a key part of the evaluation process for many purposes, such as examining the effects of medicine or the impact of an economic policy on society. However, although the literature contains some excellent models, there is room to improve their representation power and their ability to capture complex relationships. For these reasons, we propose a novel prior called Causal DP and a model called CDP. The prior captures the complex relationships between covariates, treatments, and outcomes in observational data using a rational probabilistic dependency structure. The model is Bayesian, nonparametric, and generative and is not based on the assumption of any parametric distribution. CDP is designed to estimate various kinds of causal effects—average, conditional average, average treated, quantile, and so on. It performs well with missing covariates and does not suffer from overfitting. Comparative experiments on synthetic datasets against several state-of-the-art methods demonstrate that CDP has a superior ability to capture complex relationships. Further, a simple evaluation to infer the effect of a job training program on trainee earnings from real-world data shows that CDP is both effective and useful for causal inference.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2015
Publisher: Atlantis Press
Date: 2007
Publisher: Hindawi Limited
Date: 15-07-2011
DOI: 10.1002/INT.20495
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: Elsevier BV
Date: 05-2021
Publisher: Atlantis Press
Date: 2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2021
Publisher: Springer US
Date: 2008
Publisher: IEEE
Date: 11-2010
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: Elsevier BV
Date: 04-2014
Publisher: Elsevier BV
Date: 06-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2018
Publisher: IEEE
Date: 07-2014
Publisher: Elsevier BV
Date: 04-2009
Publisher: Elsevier BV
Date: 06-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2023
Publisher: Springer Science and Business Media LLC
Date: 20-05-2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2014
Publisher: Elsevier BV
Date: 11-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 2006
Publisher: IEEE
Date: 11-2010
Publisher: World Scientific Pub Co Pte Lt
Date: 06-2007
DOI: 10.1142/S0219622007002459
Abstract: Within the framework of any bilevel decision problem, a leader's decision at the upper level is influenced by the reaction of their follower at the lower level. When multiple followers are involved in a bilevel decision problem, the leader's decision will not only be affected by the reactions of those followers, but also by the relationships among those followers. One of the popular situations within this framework is where these followers are uncooperatively making decisions while having cross reference of decision information, called a referential-uncooperative situation in this paper. The well-known branch and bound algorithm has been successfully applied to a one-leader-and-one-follower linear bilevel decision problem. This paper extends this algorithm to deal with the above-mentioned linear bilevel multi-follower decision problem by means of a linear referential-uncooperative bilevel multi-follower decision model. It then proposes an extended branch and bound algorithm to solve this problem with a set of illustrative ex les in a referential-uncooperative situation.
Publisher: Emerald
Date: 08-04-2014
Publisher: Springer Berlin Heidelberg
Date: 25-07-2005
DOI: 10.1007/11004011_15
Publisher: Springer Nature Singapore
Date: 2023
Publisher: Springer New York
Date: 2008
Publisher: Springer Berlin Heidelberg
Date: 02-11-2014
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Springer Berlin Heidelberg
Date: 02-11-2014
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11941439_163
Publisher: WORLD SCIENTIFIC
Date: 08-2016
Publisher: Springer Science and Business Media LLC
Date: 2016
Publisher: Elsevier BV
Date: 09-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2015
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: IEEE
Date: 10-2013
DOI: 10.1109/SMC.2013.593
Publisher: IEEE
Date: 06-2008
Publisher: IEEE
Date: 10-2013
DOI: 10.1109/SMC.2013.594
Publisher: Elsevier BV
Date: 04-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2014
DOI: 10.1109/TKDE.2013.78
Publisher: Hindawi Limited
Date: 2007
DOI: 10.1002/INT.20206
Publisher: Association for Computing Machinery (ACM)
Date: 09-06-2016
DOI: 10.1145/2903719
Abstract: The evolution potential estimation of news events can support the decision making of both corporations and governments. For ex le, a corporation could manage its public relations crisis in a timely manner if a negative news event about this corporation is known with large evolution potential in advance. However, existing state-of-the-art methods are mainly based on time series historical data, which are not suitable for the news events with limited historical data and bursty properties. In this article, we propose a purely content-based method to estimate the evolution potential of the news events. The proposed method considers a news event at a given time point as a system composed of different keywords, and the uncertainty of this system is defined and measured as the Semantic Uncertainty of this news event. At the same time, an uncertainty space is constructed with two extreme states: the most uncertain state and the most certain state. We believe that the Semantic Uncertainty has correlation with the content evolution of the news events, so it can be used to estimate the evolution potential of the news events. In order to verify the proposed method, we present detailed experimental setups and results measuring the correlation of the Semantic Uncertainty with the Content Change of news events using collected news events data. The results show that the correlation does exist and is stronger than the correlation of value from the time-series-based method with the Content Change. Therefore, we can use the Semantic Uncertainty to estimate the evolution potential of news events.
Publisher: Hindawi Limited
Date: 2007
DOI: 10.1002/INT.20205
Publisher: Springer Science and Business Media LLC
Date: 08-06-2014
Publisher: Springer Science and Business Media LLC
Date: 08-05-2008
Publisher: Inderscience Publishers
Date: 2008
Publisher: Elsevier BV
Date: 07-2020
Publisher: Springer International Publishing
Date: 2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: IEEE
Date: 2010
DOI: 10.1109/AINA.2010.78
Publisher: Elsevier BV
Date: 06-2021
Publisher: Elsevier BV
Date: 03-2014
Publisher: IEEE
Date: 04-2011
Publisher: Elsevier BV
Date: 05-2016
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: IEEE
Date: 04-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2013
Publisher: Springer International Publishing
Date: 2014
Publisher: Elsevier BV
Date: 11-2016
Publisher: Elsevier BV
Date: 27-06-2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2019
Publisher: IEEE
Date: 12-2014
Publisher: Inderscience Publishers
Date: 2007
Publisher: Elsevier BV
Date: 2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IEEE
Date: 07-2016
Publisher: IEEE
Date: 07-2016
Publisher: IEEE
Date: 07-2016
Publisher: IEEE
Date: 07-2016
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Springer Berlin Heidelberg
Date: 2015
Publisher: IEEE
Date: 11-2007
Publisher: Elsevier BV
Date: 2009
Publisher: Elsevier BV
Date: 12-2016
Publisher: Springer Berlin Heidelberg
Date: 2015
Publisher: Springer Berlin Heidelberg
Date: 2015
Publisher: Springer Berlin Heidelberg
Date: 2015
Publisher: Springer Berlin Heidelberg
Date: 2015
Publisher: Elsevier BV
Date: 10-2019
Publisher: Springer Science and Business Media LLC
Date: 11-2003
Publisher: IEEE
Date: 08-2010
DOI: 10.1109/GRC.2010.165
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Hindawi Limited
Date: 10-04-2015
DOI: 10.1002/INT.21728
Publisher: IEEE
Date: 04-2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2019
Publisher: Springer Science and Business Media LLC
Date: 2008
Publisher: IEEE
Date: 08-2015
Publisher: WORLD SCIENTIFIC
Date: 21-07-2014
Publisher: WORLD SCIENTIFIC
Date: 21-07-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2017
Publisher: Wiley
Date: 05-2011
Publisher: Emerald
Date: 08-06-2010
Publisher: Hindawi Limited
Date: 05-2015
DOI: 10.1002/INT.21735
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2011
Publisher: Springer Berlin Heidelberg
Date: 2004
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: IEEE
Date: 05-2013
Publisher: World Scientific Pub Co Pte Lt
Date: 03-2013
DOI: 10.1142/S146902681350003X
Abstract: Machine learning methods, such as neural network (NN) and support vector machine, assume that the training data and the test data are drawn from the same distribution. This assumption may not be satisfied in many real world applications, like long-term financial failure prediction, because the training and test data may each come from different time periods or domains. This paper proposes a novel algorithm known as fuzzy bridged refinement-based domain adaptation to solve the problem of long-term prediction. The algorithm utilizes the fuzzy system and similarity concepts to modify the target instances' labels which were initially predicted by a shift-unaware prediction model. The experiments are performed using three shift-unaware prediction models based on nine different settings including two main situations: (1) there is no labeled instance in the target domain (2) there are a few labeled instances in the target domain. In these experiments bank failure financial data is used to validate the algorithm. The results demonstrate a significant improvement in the predictive accuracy, particularly in the second situation identified above.
Publisher: Elsevier BV
Date: 09-2015
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Springer Science and Business Media LLC
Date: 19-10-2014
Publisher: WORLD SCIENTIFIC
Date: 08-2016
Publisher: WORLD SCIENTIFIC
Date: 07-2006
Publisher: WORLD SCIENTIFIC
Date: 21-07-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2022
Publisher: Springer Science and Business Media LLC
Date: 29-08-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2014
Publisher: Wiley
Date: 20-03-2014
DOI: 10.1111/COIN.12008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2018
Publisher: IEEE
Date: 06-2011
Publisher: IEEE
Date: 07-2011
Publisher: Elsevier BV
Date: 05-2014
Publisher: IEEE
Date: 06-2015
Publisher: IEEE
Date: 07-2016
Publisher: Elsevier BV
Date: 05-2005
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2018
Publisher: Springer Science and Business Media LLC
Date: 08-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2015
Publisher: ACM
Date: 08-03-2009
Publisher: World Scientific Pub Co Pte Ltd
Date: 03-2007
DOI: 10.1142/S1793005707000665
Abstract: Customer classification is one of the major tasks in customer relationship management. Customers often have both static characteristics and dynamic behavioral features. Using both kinds of data to conduct comprehensive analysis can enhance the reasonability of customer classification. In the proposed classification method, customer dynamic data is clustered using a hybrid genetic algorithm. The result is then combined with customer static data to give reasonable customer segmentation supported by neural network technique. A bank dataset-based experiment shows that applying the proposed method can obviously improve the accuracy of customer classification comparing with the traditional methods where only static data is used.
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: IEEE
Date: 06-2013
Publisher: Elsevier BV
Date: 04-2013
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Elsevier BV
Date: 08-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2017
Publisher: Emerald
Date: 20-02-2007
DOI: 10.1108/17410390710725760
Abstract: This study aims to develop a decision making model and approach for logistics planning problem which naturally involves two or more decision units at a hierarchical structure. Such a decision problem in practice often involves uncertain and imprecise factors with the parameters of a bilevel decision model, either in the objective functions or constraints. This paper proposes a fuzzy bilevel decision making model for a general logistics planning problem and develops a fuzzy number based K th‐best approach to find an optimal solution for the proposed fuzzy bilevel decision problem. The proposed approach illustrates an optimal solution in logistics management, which meets maximally/minimally the objectives of both supplier and distributor (or other parts of the logistics chain). The proposed fuzzy bilevel decision approach can have a wide range of logistics management applications. The decision model, approach and system will be further tested for some more complicated real cases in the future. The proposed fuzzy bilevel decision model and approach are new, which offer theoretical and practice help to logistics management.
Publisher: Springer Science and Business Media LLC
Date: 25-09-2009
Publisher: World Scientific Pub Co Pte Lt
Date: 09-2014
DOI: 10.1142/S1469026814500175
Abstract: Fault tree analysis for nuclear power plant probabilistic safety assessment is an intricate process. Personal computer-based software systems have therefore been developed to conduct this analysis. However, all existing fault tree analysis software systems only accept quantitative data to characterized basic event reliabilities. In real-world applications, basic event reliabilities may not be represented by quantitative data but by qualitative justifications. The motivation of this work is to develop an intelligent system by fuzzy reliability algorithm in fault tree analysis, which can accept not only quantitative data but also qualitative information to characterized reliabilities of basic events. In this paper, a newly-developed system called InFaTAS-NuSA is presented and its main features and capabilities are discussed. To benchmark the applicability of the intelligent concept implemented in InFaTAS-NuSA, a case study is performed and the analysis results are compared to the results obtained from a well-known fault tree analysis software package. The results confirm that the intelligent concept implemented in InFaTAS-NuSA can be very useful to complement conventional fault tree analysis software systems.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2021
Publisher: WORLD SCIENTIFIC
Date: 08-2016
Publisher: WORLD SCIENTIFIC
Date: 10-2012
Publisher: WORLD SCIENTIFIC
Date: 10-2012
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: IEEE
Date: 02-2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2023
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: IEEE
Date: 2005
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: IEEE
Date: 10-2015
DOI: 10.1109/SMC.2015.487
Publisher: Elsevier BV
Date: 11-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2023
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Springer International Publishing
Date: 2020
Publisher: IEEE
Date: 2010
Publisher: Springer Berlin Heidelberg
Date: 2014
Publisher: Springer Science and Business Media LLC
Date: 2008
Publisher: Elsevier BV
Date: 11-2011
Publisher: IEEE
Date: 06-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Elsevier BV
Date: 08-2007
Publisher: Elsevier BV
Date: 2015
Publisher: Elsevier BV
Date: 07-2003
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2012
Publisher: Elsevier BV
Date: 11-2015
Publisher: Elsevier BV
Date: 2016
Publisher: IEEE
Date: 11-2010
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: IEEE
Date: 11-2016
DOI: 10.1109/ICDM.2015.19
Publisher: ACM
Date: 14-12-2009
Publisher: Atlantis Press
Date: 2013
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11875581_6
Publisher: Elsevier BV
Date: 05-2015
Publisher: China Science Publishing & Media Ltd.
Date: 2007
DOI: 10.1360/CRAD20070101
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 06-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2022
Publisher: IEEE
Date: 11-2015
DOI: 10.1109/ISKE.2015.29
Publisher: World Scientific Pub Co Pte Lt
Date: 12-2012
DOI: 10.1142/S1469026812500228
Abstract: Reliability data is essential for a nuclear power plant probabilistic safety assessment by fault tree analysis to assess the performance of the safety-related systems. The limitation of conventional reliability data arises from insufficient historical data for probabilistic calculation. This study describes a new approach to calculate nuclear event reliability data by utilizing the concept of failure possibilities, which are expressed in qualitative natural languages, mathematically represented by membership functions of fuzzy numbers, and subjectively justified by a group of experts based on their working experience and expertise. We also propose an area defuzzification technique to convert the membership function into nuclear event reliability data. The actual event reliability data, which are collected from the operational experiences of the reactor protection system in Babcock & Wilcox pressurized water reactor between 1984 and 1998, are then compared with the reliability data calculated from the new approach. The results show that fuzzy failure rates can be used as alternatives for probabilistic failure rates when nuclear event historical data are insufficient or unavailable for probabilistic calculation. This study also confirms that our proposed area defuzzification technique is a suitable technique to defuzzify failure possibilities into nuclear event reliability data.
Publisher: WORLD SCIENTIFIC
Date: 08-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2023
Publisher: IEEE
Date: 12-2013
DOI: 10.1109/CSE.2013.152
Publisher: Wiley
Date: 24-05-2013
Publisher: Elsevier BV
Date: 12-2009
Publisher: WORLD SCIENTIFIC
Date: 08-2008
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: IEEE
Date: 07-2014
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: SciTePress - Science and and Technology Publications
Date: 2010
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: IEEE
Date: 08-2015
Publisher: World Scientific Pub Co Pte Lt
Date: 06-2012
Publisher: IEEE
Date: 08-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2022
Publisher: Springer Berlin Heidelberg
Date: 23-11-2013
Publisher: WORLD SCIENTIFIC
Date: 08-2008
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: IEEE
Date: 10-2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2012
Publisher: Elsevier BV
Date: 2005
Publisher: IEEE
Date: 10-2009
Publisher: Springer Science and Business Media LLC
Date: 2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2021
Publisher: Springer Berlin Heidelberg
Date: 2014
Publisher: WORLD SCIENTIFIC
Date: 07-2006
Publisher: Elsevier BV
Date: 11-2007
Publisher: Atlantis Press
Date: 2013
Publisher: Elsevier BV
Date: 02-2020
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2022
Publisher: IEEE
Date: 05-2010
Publisher: Elsevier BV
Date: 03-06-2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2020
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: IEEE
Date: 07-2014
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Emerald
Date: 12-2003
DOI: 10.1108/09564230310500237
Abstract: This paper first presents a research framework for e‐service evaluation within four categories: cost, benefit, functions and development, each incorporating a number of factors. Through data analysis and hypotheses testing, inter‐relationships among the factors of the four categories are examined. The results show that the development type of an e‐service has a significant effect on the degree of user satisfaction. Expertise, technique and expense are the principle factors limiting current e‐service adoption. The most significant finding is that, in the development of e‐services, certain cost factors are significantly more important than others in relation to certain benefit factors. The finding is presented as a cost‐benefit factor‐relation model. This provides an insight into whether investment in certain areas of e‐service applications is more important than in others for particular business objectives. These results have the potential to improve the strategic planning of companies by determining more effective investment areas and adopting more suitable development activities where e‐services are concerned.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2021
Publisher: WORLD SCIENTIFIC
Date: 08-2004
Publisher: WORLD SCIENTIFIC
Date: 08-2004
Publisher: Springer International Publishing
Date: 2016
Publisher: WORLD SCIENTIFIC
Date: 07-2010
Publisher: Springer Berlin Heidelberg
Date: 2015
Publisher: WORLD SCIENTIFIC
Date: 07-2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2020
Publisher: Springer Berlin Heidelberg
Date: 2014
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11552413_38
Publisher: IEEE
Date: 06-2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2020
Publisher: Springer Berlin Heidelberg
Date: 24-07-2014
Publisher: Elsevier BV
Date: 06-2016
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: Springer Berlin Heidelberg
Date: 24-07-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2022
Publisher: IEEE
Date: 12-2006
DOI: 10.1109/WI.2006.100
Publisher: IEEE
Date: 12-2014
Publisher: IEEE
Date: 06-2007
Publisher: IEEE
Date: 12-2011
Publisher: IEEE
Date: 11-2015
DOI: 10.1109/ISKE.2015.86
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2015
Publisher: WORLD SCIENTIFIC
Date: 07-2010
Publisher: Elsevier BV
Date: 03-2014
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: World Scientific Pub Co Pte Lt
Date: 03-2009
DOI: 10.1142/S0219622009003284
Abstract: In a bilevel decision problem, both the leader and the follower may have multiple objectives, and the coefficients involved in these objective functions or constraints may be described by some uncertain values. To express such a situation, a fuzzy multi-objective bilevel (FMOBL) programming model and related solution methods are introduced. This research develops a FMOBL decision support system through implementing the proposed FMOBL methods.
Publisher: IEEE
Date: 11-2010
Publisher: Springer International Publishing
Date: 2022
Publisher: WORLD SCIENTIFIC
Date: 08-2008
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2015
Publisher: Springer Science and Business Media LLC
Date: 03-09-2009
Publisher: IEEE
Date: 11-2015
DOI: 10.1109/ISKE.2015.98
Publisher: International Academy Publishing (IAP)
Date: 08-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2020
Publisher: Elsevier BV
Date: 05-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2023
Publisher: IEEE
Date: 06-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2020
Publisher: IEEE
Date: 2007
Publisher: Elsevier BV
Date: 02-2010
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2010
Publisher: Elsevier BV
Date: 06-2015
Publisher: Elsevier BV
Date: 02-2010
Publisher: IEEE
Date: 07-2014
Publisher: Springer Berlin Heidelberg
Date: 2015
Publisher: Springer Berlin Heidelberg
Date: 2015
Publisher: Springer Berlin Heidelberg
Date: 2015
Publisher: Springer Berlin Heidelberg
Date: 2015
Publisher: Springer Berlin Heidelberg
Date: 2015
Publisher: Springer Berlin Heidelberg
Date: 2015
Publisher: Springer Berlin Heidelberg
Date: 2015
Publisher: Springer Berlin Heidelberg
Date: 2015
Publisher: Springer Berlin Heidelberg
Date: 2015
Publisher: Inderscience Publishers
Date: 2011
Publisher: WORLD SCIENTIFIC
Date: 07-2006
Publisher: Springer Science and Business Media LLC
Date: 2003
Publisher: Springer Science and Business Media LLC
Date: 11-09-2010
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2019
Publisher: IEEE
Date: 07-2013
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: IEEE
Date: 08-2015
Publisher: World Scientific Pub Co Pte Lt
Date: 12-2011
DOI: 10.1142/S1469026811003173
Abstract: H5N1 avian influenza outbreak detection is a significant issue for early warning of epidemics. This paper proposes domain knowledge-based joint one class classification model for avian influenza outbreak. Instead of focusing on manipulations of the one class classification model, we delve into the one class avian influenza dataset, ide it into sub-classes by domain knowledge, train the sub-class classifiers and unify the result of each classifier. The proposed joint method solves the one class classification and features selection problems together. The experiment results demonstrate that the proposed joint model definitely outperforms the normal one class classification model on the animal avian influenza dataset.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IEEE
Date: 08-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2017
Publisher: Springer Science and Business Media LLC
Date: 2010
Publisher: Springer Science and Business Media LLC
Date: 07-2004
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2015
Publisher: Springer Netherlands
Date: 2007
Publisher: Association for Computing Machinery (ACM)
Date: 25-01-2019
DOI: 10.1145/3291044
Abstract: Bayesian (machine) learning has been playing a significant role in machine learning for a long time due to its particular ability to embrace uncertainty, encode prior knowledge, and endow interpretability. On the back of Bayesian learning’s great success, Bayesian nonparametric learning (BNL) has emerged as a force for further advances in this field due to its greater modelling flexibility and representation power. Instead of playing with the fixed-dimensional probabilistic distributions of Bayesian learning, BNL creates a new “game” with infinite-dimensional stochastic processes. BNL has long been recognised as a research subject in statistics, and, to date, several state-of-the-art pilot studies have demonstrated that BNL has a great deal of potential to solve real-world machine-learning tasks. However, despite these promising results, BNL has not created a huge wave in the machine-learning community. Esotericism may account for this. The books and surveys on BNL written by statisticians are overcomplicated and filled with tedious theories and proofs. Each is certainly meaningful but may scare away new researchers, especially those with computer science backgrounds. Hence, the aim of this article is to provide a plain-spoken, yet comprehensive, theoretical survey of BNL in terms that researchers in the machine-learning community can understand. It is hoped this survey will serve as a starting point for understanding and exploiting the benefits of BNL in our current scholarly endeavours. To achieve this goal, we have collated the extant studies in this field and aligned them with the steps of a standard BNL procedure—from selecting the appropriate stochastic processes through manipulation to executing the model inference algorithms. At each step, past efforts have been thoroughly summarised and discussed. In addition, we have reviewed the common methods for implementing BNL in various machine-learning tasks along with its erse applications in the real world as ex les to motivate future studies.
Publisher: WORLD SCIENTIFIC
Date: 10-2009
Publisher: Elsevier BV
Date: 09-2006
Publisher: IEEE
Date: 06-2013
Publisher: Elsevier BV
Date: 05-2007
Publisher: Springer Science and Business Media LLC
Date: 11-2020
DOI: 10.1007/S40747-020-00212-W
Abstract: Recommender systems provide personalized service support to users by learning their previous behaviors and predicting their current preferences for particular products. Artificial intelligence (AI), particularly computational intelligence and machine learning methods and algorithms, has been naturally applied in the development of recommender systems to improve prediction accuracy and solve data sparsity and cold start problems. This position paper systematically discusses the basic methodologies and prevailing techniques in recommender systems and how AI can effectively improve the technological development and application of recommender systems. The paper not only reviews cutting-edge theoretical and practical contributions, but also identifies current research issues and indicates new research directions. It carefully surveys various issues related to recommender systems that use AI, and also reviews the improvements made to these systems through the use of such AI approaches as fuzzy techniques, transfer learning, genetic algorithms, evolutionary algorithms, neural networks and deep learning, and active learning. The observations in this paper will directly support researchers and professionals to better understand current developments and new directions in the field of recommender systems using AI.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2020
Publisher: IEEE
Date: 06-2013
Publisher: IEEE
Date: 08-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: World Scientific Pub Co Pte Ltd
Date: 06-2011
DOI: 10.1142/S1469026811003069
Abstract: The time series prediction of avian influenza epidemics is a complex issue, because avian influenza has latent seasonality which is difficult to identify. Although researchers have applied a neural network (NN) model and the Box-Jenkins model for the seasonal epidemic series research area, the results are limited. In this study, we develop a new prediction seasonal auto-regressive-based support vector regression (SAR-SVR) model which combines the seasonal auto-regressive (SAR) model with a support vector regression (SVR) model to address this prediction problem to overcome existing limitations. Fast Fourier transformation is also merged into this method to identify the latent seasonality inside the time series. The experiments demonstrate that the developed SAR-SVR method out-performs SVR, Box-Jenkins models and two layer feed forward NN model-both in accuracy and stability in the avian influenza epidemic disease time series prediction.
Publisher: MDPI AG
Date: 08-2008
Publisher: WORLD SCIENTIFIC
Date: 10-2009
Publisher: IEEE
Date: 12-2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2022
Publisher: Elsevier BV
Date: 02-2008
Publisher: Elsevier BV
Date: 02-2010
Publisher: Springer Science and Business Media LLC
Date: 23-10-2020
DOI: 10.1007/S40747-019-00124-4
Abstract: Data-driven decision-making ( $$\\mathrm {D^3}$$ D 3 M) is often confronted by the problem of uncertainty or unknown dynamics in streaming data. To provide real-time accurate decision solutions, the systems have to promptly address changes in data distribution in streaming data—a phenomenon known as concept drift. Past data patterns may not be relevant to new data when a data stream experiences significant drift, thus to continue using models based on past data will lead to poor prediction and poor decision outcomes. This position paper discusses the basic framework and prevailing techniques in streaming type big data and concept drift for $$\\mathrm {D^3}$$ D 3 M. The study first establishes a technical framework for real-time $$\\mathrm {D^3}$$ D 3 M under concept drift and details the characteristics of high-volume streaming data. The main methodologies and approaches for detecting concept drift and supporting $$\\mathrm {D^3}$$ D 3 M are highlighted and presented. Lastly, further research directions, related methods and procedures for using streaming data to support decision-making in concept drift environments are identified. We hope the observations in this paper could support researchers and professionals to better understand the fundamentals and research directions of $$\\mathrm {D^3}$$ D 3 M in streamed big data environments.
Publisher: IEEE
Date: 07-2013
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2016
Publisher: WORLD SCIENTIFIC
Date: 10-2009
Publisher: WORLD SCIENTIFIC
Date: 08-2008
Publisher: IEEE
Date: 12-2010
Publisher: Springer International Publishing
Date: 2020
Publisher: Elsevier BV
Date: 12-2014
DOI: 10.1016/J.AAP.2014.09.008
Abstract: In 2008 a runaway chemical reaction caused an explosion at a methomyl unit in West Virginia, USA, killing two employees, injuring eight people, evacuating more than 40,000 residents adjacent to the facility, disrupting traffic on a nearby highway and causing significant business loss and interruption. Although the accident was formally investigated, the role of the situation awareness (SA) factor, i.e., a correct understanding of the situation, and appropriate models to maintain SA, remain unexplained. This paper extracts details of abnormal situations within the methomyl unit and models them into a situational network using dynamic Bayesian networks. A fuzzy logic system is used to resemble the operator's thinking when confronted with these abnormal situations. The combined situational network and fuzzy logic system make it possible for the operator to assess such situations dynamically to achieve accurate SA. The findings show that the proposed structure provides a useful graphical model that facilitates the inclusion of prior background knowledge and the updating of this knowledge when new information is available from monitoring systems.
Publisher: Elsevier BV
Date: 08-2015
Publisher: IMPERIAL COLLEGE PRESS
Date: 05-2007
DOI: 10.1142/P505
Publisher: Atlantis Press
Date: 2007
DOI: 10.2991/ISKE.2007.29
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Elsevier BV
Date: 03-2006
Publisher: World Scientific Pub Co Pte Lt
Date: 08-2008
DOI: 10.1142/S0218488508005510
Abstract: Bilevel programming deals with hierarchical optimization problems in which the leader at the upper level attempts to optimize his or her objectives, but subject to a set of constraints and the follower's reactions. Typical bilevel programming considers one leader one follower situation and supposes each of them has only one objective. In real world situations, multiple followers may be involved and they may be with different relationships such as sharing decision variables or not, sharing objectives or not. Therefore, the leader's decision will be affected not only by those followers' reactions but also by their relationships. In addition, any of the leader and/or these followers may have multiple conflict objectives that should be optimized simultaneously. Furthermore, the parameters of a bilevel programming model may be described by uncertain values. This paper addresses all these three issues as a whole by particularly focusing on the situation of sharing decision variables among followers. It first proposes a set of fuzzy multi-objective multi-follower bilevel programming (FMMBP) models to describe the complex issue. It then presents an approximation branch-and-bound algorithm to solve the FMMBP problems. Finally, two ex les illustrate the proposed models and algorithm.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2021
Publisher: Atlantis Press
Date: 2012
Publisher: Springer International Publishing
Date: 2018
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: IEEE
Date: 04-2008
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2022
Publisher: Atlantis Press
Date: 2010
Publisher: IEEE
Date: 11-2008
Publisher: Elsevier BV
Date: 06-2007
Publisher: Elsevier BV
Date: 05-2021
Publisher: Elsevier BV
Date: 06-2007
Publisher: IEEE
Date: 04-2011
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: Oxford University Press (OUP)
Date: 02-01-2017
Publisher: Springer International Publishing
Date: 2015
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Springer Science and Business Media LLC
Date: 14-08-2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2023
Publisher: Springer Science and Business Media LLC
Date: 22-09-2009
Publisher: IEEE
Date: 11-2010
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: World Scientific Pub Co Pte Lt
Date: 12-2008
DOI: 10.1142/S0219622008003113
Abstract: Bilevel programming techniques are developed for decentralized decision problems with decision makers located in two levels. Both upper and lower decision makers, termed as leader and follower, try to optimize their own objectives in solution procedure but are affected by those of the other levels. When a bilevel decision model is built with fuzzy coefficients and the leader and/or follower have goals for their objectives, we call it fuzzy goal bilevel (FGBL) decision problem. This paper first proposes a λ-cut set based FGBL model. A programmable λ-cut approximate algorithm is then presented in detail. Based on this algorithm, a FGBL software system is developed to reach solutions for FGBL decision problems. Finally, two ex les are given to illustrate the application of the proposed algorithm.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2022
Publisher: IEEE
Date: 07-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2017
Publisher: Springer Science and Business Media LLC
Date: 10-03-2015
Publisher: IEEE
Date: 08-2015
Publisher: IEEE
Date: 2005
DOI: 10.1109/WI.2005.126
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2019
Publisher: Elsevier BV
Date: 12-2012
Publisher: IEEE
Date: 2005
DOI: 10.1109/WI.2005.12
Publisher: Elsevier BV
Date: 03-2010
Publisher: IEEE
Date: 11-2015
DOI: 10.1109/ISKE.2015.21
Publisher: Springer Science and Business Media LLC
Date: 12-2005
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: IEEE
Date: 06-2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2022
Publisher: Springer Berlin Heidelberg
Date: 2009
Start Date: 2005
End Date: 2007
Funder: Australian Research Council
View Funded ActivityStart Date: 2005
End Date: 2009
Funder: Australian Research Council
View Funded ActivityStart Date: 2008
End Date: 2010
Funder: Australian Research Council
View Funded ActivityStart Date: 2014
End Date: 2016
Funder: Australian Research Council
View Funded ActivityStart Date: 2002
End Date: 2004
Funder: Australian Research Council
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