UTS site search

Professor Jie Lu

Biography

Professor Jie Lu is the Associate Dean Research in the Faculty of Engineering and Information Technology (FEIT) at the University of Technology, Sydney (UTS). She was Head of School of Software in the FEIT. She is also the Director of Decision Systems & e-Service Intelligence lab in Centre for QCIS.

Her main research interests lie in the area of decision support systems, recommender systems, knowledge-based prediction and warning systems, fuzzy information processing and e-Service intelligence. She has published 6 research books and 350 papers in refereed journals and conference proceedings. She has won seven Australian Research Council (ARC) Discovery Project grants and 10 other research grants. She received the first UTS Research Excellence Medal for Teaching and Research Integration in 2010.

She serves as Editor-In-Chief for Knowledge-Based Systems (Elsevier), Editor-In-Chief for International Journal on Computational Intelligence Systems (Atlantis), Associate Editor for IEEE Trans on Fuzzy Systems, editor for book series on Intelligent Information Systems (World Scientific), and has served as a guest editor of ten special issues for international journals, chairs for ten international conferences as well as having delivered many keynote speeches at international conferences.

Professional

Editor-In-Chief for international journal “Knowledge-Based Systems” (Elsevier) (2010--)

Editor-In-Chief for International Journal on Computational Intelligence Systems (Atlantis Press, Taylor and Francis) (9/2011--)

Associate Editor, IEEE Transactions on Systems, Man, and Cybernetics: Systems (2013--)

Associate Editor, IEEE Transactions on Fuzzy Systems (2013--)

Series Editor for World Scientific Book Series on “Intelligent Information Systems” (World Scientific) (2007--)

Series Editor for World Scientific Proceedings Series on “Computer Engineering and Information Science” (World Scientific) (9/2011--)

Series Editor for book series on “Atlantis Computational Intelligence Systems” (Atlantis press & Taylor and Francis) (9/2011--)

Board member for European Society for Fuzzy Logic and Technology (EUSFLAT) (2013-)

CHAIR OF CONFERENCES

Program Chair for The 11th International FLINS Conference on Decision Making and Soft Computing (FLINS2014), 17-20 August, 2014, João Pessoa (Paraíba), Brazil

General Chair for Intelligent Systems and Knowledge Engineering 2013, Nov 20-23, Shenzhen, China

Image of Jie Lu
Associate Dean (Research), Faculty of Engineering & Information Technology
Core Member, Joint Research Centre in Intelligent Systems Membership
Core Member, QCIS - Quantum Computation and Intelligent Systems
Director, Global Big Data Technologies
Core Member, Global Big Data Technologies
BSc (Hebei), MEdu (Hebei), MAppSc (BIT), PhD (Curtin)
 
Phone
+61 2 9514 1838

Research Interests

Decision support systems, e-services, e-business, e-government, group decision making, resource planning, database design and development, system modeling, web-based information systems, intelligent decision support systems, fuzzy optimization, fuzzy decision making, business intelligence, personalised recommender systems, system evaluation

Can supervise: Yes

Currently supervise 10 PhD students

Have supervised over 20 PhD students in the last 10 years

Decision support systems, Business intelligence, databases

Books

Zhang, G., Lu, J. & Gao, Y. 2015, Multi-Level Decision Making Models, Methods and Applications, Springer.
View/Download from: UTS OPUS
This book addresses an important decision making area—multi-level decisionmaking. To help readers understand the following chapters of this book, this chapter presents fundamental concepts, models, and techniques of decision making ...
Lu, J., Jain, L.C. & Zhang, G. 2012, Handbook on Decision Making Vol 2: Risk Management in Decision Making, Springer Science & Business Media.
Vol 2: Risk Management in Decision Making Jie Lu, Lakhmi C Jain, Guangquan Zhang. An example of the risk analysis framework proposed by Li et al. (2007) including the risk factor system, data standards for risk factors, weights of risk ...
Niu, L., Lu, J. & Zhang, G. 2009, Cognition-Driven Decision Support for Business Intelligence - Models, Techniques, Systems and Applications, 1st, Springer, Berlin/Heidelberg.
View/Download from: UTS OPUS or Publisher's site
Cognition-driven decision support system (DSS) has been recognized as a paradigm in the research and development of business intelligence (BI). Cognitive decision support aims to help managers in their decision making from human cognitive aspects, such as thinking, sensing, understanding and predicting, and fully reuse their experience. Among these cognitive aspects, decision makers situation awareness (SA) and mental models are considered to be two important prerequisites fordecision making , particularly in ill-structured and dynamic decision situations with uncertainties, time pressure and high personal stake. In todays business domain,decision making is becoming increasingly complex. To make a successful decision, managers SA about their business environments becomes a critical factor. This book presents theoretical models as well practical techniques of cognitiondriven DSS. It first introduces some important concepts of cognition orientation indecision making process and some techniques in related research areas including DSS, data warehouse and BI, offering readers a preliminary for moving forward in this book. It then proposes a cognition-driven decision process (CDDP) model which incorporates SA and experience (mental models) as its central components. The goal of the CDDP model is to facilitate cognitive decision support to managers on the basis of BI systems. It also presents relevant techniques developed to support the implementation of the CDDP model in a BI environment
Lu, J., Zhang, G., Ruan, D. & Wu, F. 2007, Multi-objective group decision making: methods, software and applications, 1, Imperial College Press, London, UK.
View/Download from: UTS OPUS
Lu, J., Ruan, D. & Zhang, G. 2007, E-Service Intelligence Methodologies, Technologies and Applications, Springer Science & Business Media.
E-Service. Intelligence: An. Introduction. # # Jie Lu, Da Ruan*, and Guangquan Zhang #Faculty of Information Technology, University of Technology, Sydney ( UTS) PO Box 123, Broadway, NSW 2007, Australia. Email: {jielu ...

Chapters

Zhang, G., Lu, J. & Gao, Y. 2015, 'Fuzzy Multi-objective Bi-level Decision Making', pp. 207-228.
View/Download from: UTS OPUS or Publisher's site
© Springer-Verlag Berlin Heidelberg 2015. we presented a set of solution approaches and related algorithms to solve a fuzzy bi-level programming problem. This chapter extends the results given in Chap. 7 by adding the capability to handle the multi-objective issue, that is, the leader, or the follower, or both have multiple objectives.
Zhang, G., Lu, J. & Gao, Y. 2015, 'Fuzzy Bi-level Decision Making', pp. 175-205.
View/Download from: UTS OPUS or Publisher's site
© Springer-Verlag Berlin Heidelberg 2015. Various uncertain issues naturally appear in organizational bi-level decision problems. Fuzzy sets and fuzzy systems can be used to handle uncertainties. This chapter introduces related definitions, theorems and models of fuzzy bi-level decision-making (FBLDM) and develops related algorithms to solve the uncertain issues in bi-level decision-making.
Zhang, G., Lu, J. & Gao, Y. 2015, 'Bi-level Decision Making in Railway Transportation Management' in Multi-Level Decision Making: Models, Methods and Applications, Springer Science and Business Media Deutschland GmbH, Germany, pp. 337-356.
View/Download from: UTS OPUS or Publisher's site
Transportation management is an important application field of bi-level decision-making. For example, transportation facilities, resources planning and moving, as well as staff relocation all involve sub-optimization and optimization problems, that is, the decision entities are often at two decision levels. This chapter presents two real applications of the bi-level decision techniques in railway transportation management.
Zhang, G., Lu, J. & Gao, Y. 2015, 'Fuzzy Bi-level and Tri-level Decision Support Systems', pp. 289-314.
View/Download from: UTS OPUS or Publisher's site
© Springer-Verlag Berlin Heidelberg 2015. This chapter presents two multi-level decision support systems that implement related algorithms developed in previous chapters to support decision making in practice.
Zhang, G., Jie, L. & Ya, G. 2015, 'Decision Making and Decision Support Systems', pp. 3-24.
View/Download from: UTS OPUS or Publisher's site
© Springer-Verlag Berlin Heidelberg 2015. This book addresses an important decision making area—multi-level decision-making. To help readers understand the following chapters of this book, this chapter presents fundamental concepts, models, and techniques of decision making and decision support systems (DSS), thus providing an introduction for the remaining chapters of this book.
Memon, T., Hussain, F.K. & Lu, J. 2014, 'Human-Centric Cognitive Decision Support System for Ill-Structured Problems' in Guo, P. & Pedrycz, W. (eds), Human-Centric Decision-Making Models for Social Sciences, Springer, Germany, pp. 289-313.
View/Download from: UTS OPUS or Publisher's site
The solutions to ill-structured decision problems greatly rely upon the intuition and cognitive abilities of a decision maker because of the vague nature of such problems. To provide decision support for these problems, a decision support system (DSS) must be able to support a user's cognitive abilities, as well as facilitate seamless communication of knowledge and cognition between itself and the user. This study develops a cognitive decision support system (CDSS) based on human-centric semantic de-biased associations (SDA) model to improve ill-structured decision support. The SDA model improves ill-structured decision support by refining a user's cognition through reducing or eliminating bias and providing the user with validated domain knowledge. The use of semantics in the SDA model facilitates the natural representation of the user's cognition, thus making the transfer of knowledge/cognition between the user and system a natural and effortless process. The potential of semantically defined cognition for effective ill-structured decision support is discussed from a human-centric perspective. The effectiveness of the approach is demonstrated with a case study in the domain of sales.
Naderpour, M. & Lu, J. 2013, 'A Human Situation Awareness Support System to Avoid Technological Disasters' in Vitoriano, B., Montero, J. & Ruan, D. (eds), Decision Aid Models for Disaster Management and Emergencies, Atlantis Press, Paris, France, pp. 307-325.
View/Download from: UTS OPUS or Publisher's site
In many complex technological systems, accidents have primarily been attributed to human error. In the majority of these accidents the human operators were striving against significant challenges. They have to face data overload, the challenge of working with a complex system and the stressful task of understanding what is going on in the situation. Therefore, to design and implement complex technological systems where the information flow is quite high, and poor decisions may lead to serious consequences, Situation Awareness (SA) should be appropriately considered. A level 1 SA is highly supported in these systems through the various heterogeneous sensors and signal-processing methods but, for levels 2 and 3 there is still a need for concepts and methods. This work develops a system called the Human Situation Awareness Support System (HSASS) that supports the safety operators in an ever increasing amount of available risky status and alert information. The proposed system includes a new dynamic situation assessment method based on risk, which has the ability to support the operators understanding of the current state of the system, predict the near future, and suggest appropriate actions. The proposed system does not control the course of action and allows the human to act at his/her discretion in specific contexts.
Lu, J., Jain, L. & Zhang, G. 2012, 'Risk Management in Decision Making' in Lu, J., jain, L.C. & Zhang, G. (eds), Handbook on Decision Making, Springer, Berlin; New York, pp. 3-7.
View/Download from: UTS OPUS or Publisher's site
Organizational decision making often occurs in the face of uncertainty about whether a decision makers choices will lead to benefit or disaster. Risk is the potential that a decision will lead to a loss or an undesirable outcome. In fact, almost any human decision carries some risk, but some decisions are much more risky than others. Risk and decision making are two inter-related factors in organizational management, and they are both related to various uncertainties.
Amailef, K. & Lu, J. 2012, 'Mobile-Based Emergency Response System Using Ontology-Supported Information Extraction' in Lu, J., Jain, L.C. & Zhang, G. (eds), Handbook on Decision Making, Springer, Berlin; New York, pp. 429-449.
View/Download from: UTS OPUS or Publisher's site
This chapter describes an algorithm within a Mobile-based Emergency Response System (MERS) to automatically extract information from Short Message Service (SMS). The algorithm is based on an ontology concept, and a maximum entropy statistical model. Ontology has been used to improve the performance of an information extraction system. A maximum entropy statistical model with various predefined features offers a clean way to estimate the probability of certain token occurring with a certain SMS text. The algorithm has four main functions: to collect unstructured information from an SMS emergency text message; to conduct information extraction and aggregation; to calculate the similarity of SMS text messages; and to generate query and results presentation
Demong, N.A. & Lu, J. 2012, 'Risk-Based Decision Making Framework for Investment in the Real Estate Industry' in Lu, J., Jain, L.C. & Zhang, G. (eds), Handbook on Decision Making, Springer, The Netherlands, pp. 259-283.
View/Download from: UTS OPUS or Publisher's site
Investment in the real estate industry is subject to high risk, especially when there are a large number of uncertainty factors in a project. Risk analysis has been widely used to make decisions for real estate investment. Accordingly, risk-based decision making is a vital process that should be considered when a list of projects and constraints are being assessed. This chapter proposes a risk-based decision making (RBDM) framework for risk analysis of investment in the real estate industry, based on a review of the research. The framework comprises the basic concepts, process, sources and factors, techniques/approaches, and issues and challenges of RBDM. The framework can be applied to problem solving different issues involved in the decision making process when risk is a factor. Decision makers need to understand the terms and concepts of their problems and be familiar with the processes involved in decision making. They also need to know the source of their problems and the relevant factors involved before selecting the best and most suitable technique to apply to solve their problems. Furthermore, decision makers need to recognize the issues and challenges related to their problems to mitigate future risk by monitoring and controlling risk sources and factors. This framework provides a comprehensive analysis of risk-based decision making and supports decision makers to enable them to achieve optimal decisions.
Ma, J., Zhang, G. & Lu, J. 2011, 'A Fuzzy Hierarchical Multiple Criteria Group Decision Support System - Decider - And Its Applications' in Kacprzyk, P.J. (ed), Studies In Fuzziness And Soft Computing, Springer Verlag, Berlin, Germany, pp. 383-403.
View/Download from: UTS OPUS or Publisher's site
Decider is a Fuzzy Hierarchical Multiple Criteria Group Decision Support System (FHMC-GDSS) designed for dealing with subjective, in particular linguistic, information and objective information simultaneously to support group decision making particularly on evaluation. In this chapter, the fuzzy aggregation decision model, functions and structure of Decider are introduced. The ideas to resolve decision and evaluation problems we have faced in the development and application of Decider are presented. Two real applications of the Decider system are briefly illustrated. Finally, we discuss our further research in this area.
Lu, P., Zhang, G. & Lu, J. 2010, 'Detecting Change via Competence Model' in Goebel, R., Siekmann, J.R., Wahlster, W., Bichindaritz, I. & Montani, S. (eds), Lecture Notes in Artificial Intelligence 6176 - Case-Based Reasoning, Springer, Germany, pp. 201-212.
View/Download from: UTS OPUS or Publisher's site
In real world applications, interested concepts are more likely to change rather than remain stable, which is known as concept drift. This situation causes problems on predictions for many learning algorithms including case-base reasoning (CBR). When learning under concept drift, a critical issue is to identify and determine âwhenâ and âhowâ the concept changes. In this paper, we developed a competence-based empirical distance between case chunks and then proposed a change detection method based on it. As a main contribution of our work, the change detection method provides an approach to measure the distribution change of cases of an infinite domain through finite samples and requires no prior knowledge about the case distribution, which makes it more practical in real world applications. Also, different from many other change detection methods, we not only detect the change of concepts but also quantify and describe this change.
Purba, J., Lu, J., Ruan, D. & Zhang, G. 2010, 'A Hybrid Approach for Fault Tree Analysis Combining Probabilistic Method with Fuzzy Numbers' in Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A. & Zurada, J.M. (eds), Lecture Notes in Artificial Intelligence 6113 - Artificial Intelligence and Soft Computing ICAISC 20, Springer, Germany, pp. 194-201.
View/Download from: UTS OPUS or Publisher's site
Conventional fault tree analysis in safety analysis of complex engineering systems calculates the occurrence probability of the top undesired event using probabilistic failure rates. However, it is often very difficult to obtain those failure rates well in advance due to insufficient data, environment changing or new components. Fuzzy numbers can be applied to estimate failure rates by handling linguistic terms. This study proposes a hybrid approach of Fuzzy Numbers and Fault Tree Analysis to solve the conventional problem and describes its procedures using a case study of emergency core cooling system of a typical nuclear power plant.
Zhang, G., Zhang, G., Gao, Y. & Lu, J. 2010, 'Evolutionary Computation Methods for Fuzzy Decision Making on Load Dispatch Problems' in Da Ruan (ed), Computational Intelligence In Complex Decision Systems, Atlantis Press, Paris, France, pp. 301-323.
View/Download from: UTS OPUS
This chapter introduces basic concepts relating to a day-ahead market in a power system. A load dispatch model considers a ramp rate and valve-point-loading effects. An environment/economic load dispatch model is presented to handle uncertainty factors. The model provides theoretical foundations for the research on operations and decision making in the electric power market. To solve load dispatch problems from day-ahead markets in power systems, a hybrid evolutionary computation method with a quasi-simplex technique, a weight point method for multi-objective programming, and a fuzzy-number-ranking-based optimization method for fuzzy multi-objective non-linear programming are developed.
Zhang, G., Lu, J. & Dillon, T.S. 2008, 'Solution concepts and an approximation Kuhu-Tucker approach for fuzzy multi-objective linear programming' in Pardalos, P. & Chinchuluun, A. (eds), Pareto Optimality, Game Theory and Equilibria, Springer, Springer Berlin, pp. 457-480.
View/Download from: UTS OPUS
Zhang, G., Lu, J. & Dillon, T.S. 2008, 'An approximation Kuhn-Tucker approach for fuzzy linear bilevel decision making' in Jain, L. & Wren, G. (eds), Intelligent Decision Making, Springer, The Netherlands, pp. 157-171.
View/Download from: UTS OPUS or Publisher's site
In bilevel decision making, the leader aims to achieve an optimal solution by considering the follower's optimized strategy to react each of his/her possible decisions. In a real-world bilevel decision environment, uncertainty must be considered when modeling the objective functions and constraints of the leader and the follower. Following our previous work, this chapter proposes a fuzzy bilevel decision making model to describe bilevel decision making under uncertainty. After giving the definitions of optimal solutions and related theorems for fuzzy bilevel decision problems this chapter develops an approximation KuhnTucker approach to solve the problem. Finally, an example of reverse logistics management illustrates the application of this proposed fuzzy bilevel decision making approach.
Lu, J., Zhang, G. & Ruan, D. 2008, 'Fuzzy multi-objective decision making models and approaches' in Fuzzy Multi-Criteria Decision-Making Theory and Applications with Recent Developments, Springer, Berlin, pp. 483-522.
View/Download from: UTS OPUS
Lu, J., Ruan, D. & Zhang, G. 2008, 'Fuzzy set techniques in e-service applications' in NA (ed), Fuzzy Sets and Their Extensions: Representation, Aggregation and Models--Intelligent Systems from De, Springer, Berlin Heidelberg New York, pp. 555-569.
View/Download from: UTS OPUS or Publisher's site
E-services involve various types, delivery systems, advanced information technologies, methodologies and applications of online services that are provided by e-government, e-business, e-commerce, e-market, e-finance, e-learning systems, to name a few. They offer great opportunities and challenges for many areas, such as government, business, commerce, marketing, finance and education. E-service intelligence is a new research field that deals with fundamental roles, social impacts and practical applications of various intelligent technologies on the Internet based e-services. This chapter aims to offer a thorough introduction and systematic overview of the new field e-service intelligence mainly based on fuzzy set related techniques. It covers the state-of-the-art of the research and development in various aspects including both theorems and applications, of e-service intelligence by applying fuzzy set theory. Moreover, it demonstrates how adaptations of existing intelligent technologies benefit from the development of e-service applications in online customer decision, personalised services, web mining, online searching/data retrieval, online pattern recognition/image processing, and web-based e-logistics/planning.
Niu, L., Lu, J. & Zhang, G. 2008, 'Cognitive Orientation in Business Intelligence Systems' in Ruan, D., Harderman, F. & van der Meer, K. (eds), Intelligent Decision and Policy Making Support Systems, Springer, Berlin, Germany, pp. 55-72.
View/Download from: UTS OPUS or Publisher's site
With the increasing importance of cognitive aspects in decision making, this research addresses how human cognitive abilities, mainly situation awareness and mental models, can be used to drive the decision process in complex decision situations. Cognitive orientation has long been regarded as an important consideration in the development and application of decision support systems (DSS). Rather than cognitive orientation, a data-driven DSS emphasizes access to and manipulation of a series of company internal and external data, compared to a model-driven DSS underpinned by statistical, financial, optimization or simulation models. A business intelligence (BI) system is essentially a kind of data-driven DSS therefore shares the similar drawbacks with traditional DSS. A framework of cognitive BI system is firstly developed. A model of cognition-driven decision process is then proposed based on the system framework. In this framework and decision model, data retrieval, information filtering and knowledge presentation are based on the tacit knowledge elicited from the decision-maker. The final decision is no longer the direct output of a computer system, but the result of decision-making cycles of human-machine interaction.
Laes, E., Meskens, G., Ruan, D., Lu, J., Zhang, G., Wu, F., D'haeseleer, W. & Weiler, R. 2008, 'Fuzzy-set Decision Support for a Belgian Long-Term Sustainable Energy Strategy' in Ruan, D., Hardeman, F. & Meer, K.V.D. (eds), Intelligent Decision and Policy Making Support Systems, Springer, Berlin, Germany, pp. 319-350.
View/Download from: UTS OPUS or Publisher's site
This chapter addresses the methodological challenges of developing relevant scientific knowledge for a sustainable energy system transition in an innovative way. We argue that scientific contributions to sustainable development do not follow the `linear procedure from empirical knowledge production to policy advice. Instead, they consist of problem-oriented combinations of explanatory, orientationand action-guiding knowledge. Society and policy makers not only have to be `provided with action-guiding knowledge, but also with an awareness of the manner in which this knowledge is to be interpreted, and where the inevitable uncertainties lie. Since the sustainability question is inherently multi-dimensional, participation of social groups is an essential element of a strategy aimed at sustainable development. Multi-criteria decision support provides a platform to accommodate a process of arriving at a judgment or a solution for the sustainability question based on the input and feedback of multiple individuals. At the same time in practice, multi-criteria problems at tactical and strategic levels often involve fuzziness in their criteria and decision makers judgments. Therefore, we argue in favor of the use of fuzzy-logic based multi-criteria group decision support as a decision support tool for long-term strategic choices in the context of Belgian sustainable energy policy.
Lu, J., Bai, C. & Zhang, G. 2007, 'E-service cost benefit evaluation and analysis' in Lu, J., Ruan, D. & Zhang, G. (eds), E-Service Intelligence-Methodologies, Technologies & Application, Springer, Deblick, Germany, pp. 389-409.
View/Download from: UTS OPUS or Publisher's site
Lu, J., Ruan, D. & Zhang, G. 2007, 'E-service Intelligence: An introduction' in Lu, J., Ruan, D. & Zhang, G. (eds), E-Service Intelligence-Methodologies, Technologies & Application, Springer, Deblick, Germany, pp. 1-33.
View/Download from: UTS OPUS or Publisher's site
E-service intelligence is a new research field that deals with fundamental roles, social impacts and practical applications of various intelligent technologies on the Internet based e-service applications that are provided by e-government, e-business, e-commerce, e-market, e-finance, and e-learning systems, to name a few. This chapter offers a thorough introduction and systematic overview of the new field e-service intelligence mainly based on computational intelligence techniques. It covers the state both theorems and applications of e-service intelligence. Moreover, it demonstrates how adaptations of existing computational intelligent technologies benefit from the development of e-service applications in online customer decision, personalized services, web mining, online searching/ data retrieval, and various web-based support systems.
Lu, Z., Zhang, J., Han, B., Deng, Z. & Lu, J. 2007, 'The development of urban E-Government in China' in Al-Hakim, L. (ed), Global E-Government: Theory, applications and benchmarking, IGP, Hershey, USA, pp. 214-237.
View/Download from: UTS OPUS
The chapter assesses and cognizes the development of urban e-government in China from two main aspects: functionality and complexity. Tofunctionality, nine Web sites of urban governments in China at three levels were selectedfor this assessment. Data needed for the study were tracked and recorded continuously for six weeks from these Web sites. The influence of e-govemment to urban modality and evolution is explored. Result shows that e-government has a leading role to the gathering and decentralization of urban space, the organization of material (people) flows, and the informal exchange in internal cities.
Zhang, G., Lu, J. & Dillon, T.S. 2007, 'Fuzzy linear bilevel optimization: Solution concept, approaches and applications' in Wang, P.P., Ruan, D. & Kerre, E.E. (eds), Fuzzy Logic - A Spectrum of Theoretical & Proctical Issues, Springer, Germany, pp. 351-379.
View/Download from: UTS OPUS or Publisher's site
Bilevel programming provides a means of supporting two level non-cooperative decision-making. When a decision maker at the upper level (the leader) attempts to optimize an objective, the decision maker at the lower level (the follower) tries to find an optimized strategy according to each of the possible decisions made by the leader. A bilevel decision model is normally based on experts understanding of possible choices made by decision makers at both levels. The parameters, either in the objective functions or constraints of the leader or the follower in a bilevel decision model, are therefore hard to characterize by precise values. Hence this study proposes a fuzzy parameter linear bilevel programming model and its solution concept. It then develops three approaches to solve the proposed fuzzy linear bilevel programming problems by applying fuzzy set techniques. Finally, a numerical example and a case study illustrate the applications of the proposed three approaches.
Lu, J. & Zhang, G. 2005, 'Personalised multi-stage decision support in reverse logistics management' in Ruan, D., Chen, G.Q., Kerre, E.E. & Wets, G. (eds), Intelligent Data Mining - Techniques and Applications, Springer, New York, USA, pp. 293-312.
View/Download from: UTS OPUS
Guo, X. & Lu, J. 2005, 'Effectiveness of E-Government Online Services in Australia' in Hang, W., Siau, K. & Wei, K.K. (eds), Electronic Government Strategies and Implementation, IDEA Group Publishing, Hereshy,USA, pp. 214-241.
View/Download from: UTS OPUS
Zhang, G. & Lu, J. 2004, 'Using General Fuzzy Number to handle uncertainty and imprecision in group decision-making' in Ruan, D. & Zeng, X. (eds), Intelligent Sensory Evaluation: Methodologies and Applications, Springer, Berlin, Germany, pp. 51-70.
View/Download from: UTS OPUS

Conferences

Zhang, Y., Chen, H., Zhang, G., Zhu, D. & Lu, J. 2015, 'Multiple Science Data-oriented Technology Roadmapping Method', Proceedings of the 2015 Portland International Conference on Management of Engineering and Technology, 2015 Portland International Conference on Management of Engineering and Technology, IEEE, Portland, USA, pp. 2278-2287.
View/Download from: UTS OPUS or Publisher's site
Since its first engagement with industry decades ago, Technology Roadmapping (TRM) is taking a more and more important role for technical intelligence in current R&D planning and innovation tracking. Important topics for both science policy and engineering management researchers evolves with the approaches that refer to the real-world problems, explore value-added information from the complex data sets, fuse the analytic results and expert knowledge effectively and reasonable, and demonstrate to the decision makers visually and understandable. Moreover, the growing variety of science data sources in the Big Data Age increases these challenges and opportunities. Addressing these concerns, this paper proposes a TRM composing method with a clustering-based topic identification model, a multiple science data sources integration model, and a semi-automated fuzzy set-based TRM composing model with expert aid. We focus on a case study on computer science related R&D. Empirical data from the United States National Science Foundation Award data (innovative research ideas and proposals) and Derwent Innovation Index data source (patents emphasizing technical products) provide vantage points at two stages of the R&D process. The understanding gained will assist in description of computer science macro-trends for R&D decision makers.
Chen, H., Zhang, Y.I., Zhang, G., Zhu, D. & Lu, J. 2015, 'Modeling Technological Topic Changes in Patent Claims', Proceeding of 2015 Portland International Conference on Management of Engineering and Technology (PICMET), 2015 Portland International Conference on Management of Engineering and Technology (PICMET), IEEE, Portland, USA, pp. 2049-2059.
View/Download from: UTS OPUS or Publisher's site
Patent claims usually embody the most essential terms and the core technological scope to define the protection of an invention, which makes them the ideal resource for patent content and topic change analysis. However, manually conducting content analysis on massive technical terms is very time consuming and laborious. Even with the help of traditional text mining techniques, it is still difficult to model topic changes over time, because single keywords alone are usually too general or ambiguous to represent a concept. Moreover, term frequency which used to define a topic cannot separate polysemous words that are actually describing a different theme. To address this issue, this research proposes a topic change identification approach based on Latent Dirichlet Allocation to model and analyze topic changes with minimal human intervention. After textual data cleaning, underlying semantic topics hidden in large archives of patent claims are revealed automatically. Concepts are defined by probability distributions over words instead of term frequency, so that polysemy is allowed. A case study using patents published in the United States Patent and Trademark Office (USPTO) from 2009 to 2013 with Australia as their assignee country is presented to demonstrate the validity of the proposed topic change identification approach. The experimental result shows that the proposed approach can be used as an automatic tool to provide machine-identified topic changes for more efficient and effective R&D management assistance.
Zuo, H., Zhang, G., Behbood, V. & Lu, J. 2015, 'Feature Spaces-based Transfer Learning', PROCEEDINGS OF THE 2015 CONFERENCE OF THE INTERNATIONAL FUZZY SYSTEMS ASSOCIATION AND THE EUROPEAN SOCIETY FOR FUZZY LOGIC AND TECHNOLOGY, 16th World Congress of the International Fuzzy Systems Association (IFSA) and the 9th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), Atlantis Press, Gijon, Spain, pp. 1000-1005.
View/Download from: UTS OPUS or Publisher's site
Transfer learning provides an approach to solve target tasks more quickly and effectively by using previously-acquired knowledge learned from source tasks. Most of transfer learning approaches extract knowledge of source domain in the given feature space. The issue is that single perspective can t mine the relationship of source domain and target domain fully. To deal with this issue, this paper develops a method using Stacked Denoising Autoencoder (SDA) to extract new feature spaces for source domain and target domain, and define two fuzzy sets to analyse the variation of prediction ac-curacy of target task in new feature spaces
Chen, H., Zhang, G., Lu, J. & Zhu, D. 2015, 'A Fuzzy Approach for Measuring Development of Topics in Patents Using Latent Dirichlet Allocation', 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2015), The 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2015), IEEE, Istanbul, Turkey, pp. 1-7.
View/Download from: UTS OPUS or Publisher's site
Technology progress brings the very rapid growth of patent publications, which increases the difficulty of domain experts to measure the development of various topics, handle linguistic terms used in evaluation and understand massive technological content. To overcome the limitations of keyword-ranking type of text mining result in existing research, and at the same time deal with the vagueness of linguistic terms to assist thematic evaluation, this research proposes a fuzzy set-based topic development measurement (FTDM) approach to estimate and evaluate the topics hidden in a large volume of patent claims using Latent Dirichlet Allocation. In this study, latent semantic topics are first discovered from patent corpus and measured by a temporal-weight matrix to reveal the importance of all topics in different years. For each topic, we then calculate a temporal-weight coefficient based on the matrix, which is associated with a set of linguistic terms to describe its development state over time. After choosing a suitable linguistic term set, fuzzy membership functions are created for each term. The temporal-weight coefficients are then transformed to membership vectors related to the linguistic terms, which can be used to measure the development states of all topics directly and effectively. A case study using solar cell related patents is given to show the effectiveness of the proposed FTDM approach and its applicability for estimating hidden topics and measuring their corresponding development states efficiently.
Xue, S., Lu, J., Zhang, G. & Xiong, L. 2015, 'Heterogeneous feature space based task selection machine for unsupervised transfer learning', The 10th International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2015), IEEE, Taipei Taiwan, pp. 46-51.
View/Download from: UTS OPUS or Publisher's site
Transfer learning techniques try to transfer knowledge from previous tasks to a new target task with either fewer training data or less training than traditional machine learning techniques. Since transfer learning cares more about relatedness between tasks and their domains, it is useful for handling massive data, which are not labeled, to overcome distribution and feature space gaps, respectively. In this paper, we propose a new task selection algorithm in an unsupervised transfer learning domain, called as Task Selection Machine (TSM). It goes with a key technical problem, i.e., feature mapping for heterogeneous feature spaces. An extended feature method is applied to feature mapping algorithm. Also, TSM training algorithm, which is main contribution for this paper, relies on feature mapping. Meanwhile, the proposed TSM finally meets the unsupervised transfer learning requirements and solves the unsupervised multi-task transfer learning issues conversely.
Xue, S., Lu, J., Zhang, G. & Xiong, L. 2015, 'SEIR immune strategy for instance weighted Naive bayes classification', Neural Information Processing (LNCS), 22nd International Conference on Neural Information Processing (ICONIP2015), Springer, Istanbul, Turkey, pp. 283-292.
View/Download from: UTS OPUS or Publisher's site
Naive Bayes (NB) has been popularly applied in many classification tasks. However, in real-world applications, the pronounced advantage of NB is often challenged by insufficient training samples. Specifically, the high variance may occur with respect to the limited number of training samples. The estimated class distribution of a NB classier is inaccurate if the number of training instances is small. To handle this issue, in this paper, we proposed a SEIR (Susceptible, Exposed, Infectious and Recovered) immune-strategy-based instance weighting algorithm for naive Bayes classification, namely SWNB. The immune instance weighting allows the SWNB algorithm adjust itself to the data without explicit specification of functional or distributional forms of the underlying model. Experiments and comparisons on 20 benchmark datasets demonstrated that the proposed SWNB algorithm outperformed existing state-of-the-art instance weighted NB algorithm and other related computational intelligence methods.
Zhang, Q., Zhang, G., Lu, J. & Wu, D. 2015, 'A framework of hybrid recommender system for personalized clinical prescription', Proceedings of the 10th International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2015), The 10th International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2015), IEEE, Taipei Taiwan, pp. 189-195.
View/Download from: UTS OPUS or Publisher's site
General practitioners are faced with a great challenge of clinical prescription owing to the increase of new drugs and their complex functions to different diseases. A personalized recommender system can help practitioners deal with mass of medical knowledge hidden in history medical records. To support practitioner's decision making in prescription, this paper proposes a framework of a hybrid recommender system which integrates artificial neural network and case-based reasoning. Three issues are considered in this system framework: (1) to define a patient's need by giving his/her symptom, (2) to mine features from free text in medical records and (3) to analyze temporal efficiency of drugs. The proposed recommender system is expected to help general practitioners to improve their efficiency and reduce risks of making errors in daily clinical consultation with patients.
Xuan, J., Lu, J., Zhang, G., Xu, R.Y.D. & Luo, X. 2015, 'Infinite author topic model based on mixed gamma-negative binomial process', Proceedings - IEEE International Conference on Data Mining, ICDM, 2015 IEEE International Conference on Data Mining, IEEE, Atlantic City, USA, pp. 489-498.
View/Download from: UTS OPUS or Publisher's site
Incorporating the side information of text corpus, i.e., authors, time stamps, and emotional tags, into the traditionaltext mining models has gained significant interests in the area of information retrieval, statistical natural language processing, andmachine learning. One branch of these works is the so-called Author Topic Model (ATM), which incorporates the authors'sinterests as side information into the classical topic model. However, the existing ATM needs to predefine the number of topics, which is difficult and inappropriate in many real-world settings. In this paper, we propose an Infinite Author Topic (IAT) modelto resolve this issue. Instead of assigning a discrete probability on fixed number of topics, we use a stochastic process to determinethe number of topics from the data itself. To be specific, we extend a gamma-negative binomial process to three levels in orderto capture the author-document-keyword hierarchical structure. Furthermore, each document is assigned a mixed gamma processthat accounts for the multi-author's contribution towards this document. An efficient Gibbs sampling inference algorithm witheach conditional distribution being closed-form is developed for the IAT model. Experiments on several real-world datasets showthe capabilities of our IAT model to learn the hidden topics, authors' interests on these topics and the number of topicssimultaneously.
Han, J., Zhang, G., Hu, Y. & Lu, J. 2015, 'Solving tri-level programming problems using a particle swarm optimization algorithm', Proceedings of the 2015 IEEE 10th Conference on Industrial Electronics and Applications (ICIEA), IEEE Conference on Industrial Electronics and Applications (ICIEA), IEEE, Auckland, New Zealand, pp. 569-574.
View/Download from: UTS OPUS or Publisher's site
Tri-level programming, a special case of multilevel programming, arises to deal with decentralized decision-making problems that feature interacting decision entities distributed throughout three hierarchical levels. As tri-level programming problems are strongly NP-hard and the existing solution approaches lack universality in solving such problems, the purpose of this study is to propose an intelligence-based heuristic algorithm to solve tri-level programming problems involving linear and nonlinear versions. In this paper, we first propose a general tri-level programming problem and discuss related theoretical properties. A particle swarm optimization (PSO) algorithm is then developed to solve the tri-level programming problem. Lastly, a numerical example is adopted to illustrate the effectiveness of the proposed PSO algorithm.
Han, J., Hu, Y., Zhang, G. & Lu, J. 2015, 'A Compromise-Based Particle Swarm Optimization Algorithm for Solving Bi-Level Programming Problems with Fuzzy Parameters', Intelligent Systems and Knowledge Engineering (ISKE), 2015 10th International Conference on, Intelligent Systems and Knowledge Engineering (ISKE), 2015 10th International Conference on, IEEE, Taipei, pp. 214-221.
View/Download from: UTS OPUS or Publisher's site
Bi-level programming has arisen to handle decentralized decision-making problems that feature interactive decision entities distributed throughout a bi-level hierarchy. Fuzzy parameters often appear in such a problem in applications and this is called a fuzzy bi-level programming problem. Since the existing approaches lack universality in solving such problems, this study aims to develop a particle swarm optimization (PSO) algorithm to solve fuzzy bi-level programming problems in the linear and nonlinear versions. In this paper, we first present a general fuzzy bi-level programming problem and discuss related theoretical properties based on a fuzzy number ranking method commonly used. A PSO algorithm is then developed to solve the fuzzy bi-level programming problem based on different compromised selections by decision entities on the feasible degree for constraint conditions under fuzziness. Lastly, an illustrative numerical example and two benchmark examples are adopted to state the effectiveness of the compromise-based PSO algorithm.
Zuo, H., Zhang, G., Behbood, V., Lu, J. & Meng, X. 2015, 'Transfer Learning in Hierarchical Feature Spaces', Procedings of the 10th International Conference on Intelligent Systems and Knowledge Engineering, 2015 10th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), IEEE, Taipei, Taiwan, pp. 183-188.
View/Download from: UTS OPUS or Publisher's site
Transfer learning provides an approach to solve target tasks more quickly and effectively by using previously acquired knowledge learned from source tasks. As one category of transfer learning approaches, feature-based transfer learning approaches aim to find a latent feature space shared between source and target domains. The issue is that the sole feature space can't exploit the relationship of source domain and target domain fully. To deal with this issue, this paper proposes a transfer learning method that uses deep learning to extract hierarchical feature spaces, so knowledge of source domain can be exploited and transferred in multiple feature spaces with different levels of abstraction. In the experiment, the effectiveness of transfer learning in multiple feature spaces is compared and this can help us find the optimal feature space for transfer learning
Ramezani, F., Naderpour, M. & Lu, J. 2015, 'Handling Uncertainty in Cloud Resource Management Using Fuzzy Bayesian Networks', Proceedings of the 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, Istanbul, Turkey.
View/Download from: UTS OPUS or Publisher's site
The success of cloud services depends critically on the effective management of virtualized resources. This paper aims to design and implement a decision support method to handle uncertainties in resource management from the cloud provider perspective that enables underlying complexity, automates resource provisioning and controls client-perceived quality of service. The paper includes a probabilistic decision making module that relies upon a fuzzy Bayesian network to determine the current situation status of a cloud infrastructure, including physical and virtual machines, and predicts the near future state, that will help the hypervisor to migrate or expand the VMs to reduce execution time and meet quality of service requirements. First, the framework of resource management is presented. Second, the decision making module is developed. Lastly, a series of experiments to investigate the performance of the proposed module is implemented. Experiments reveal the efficiency of the module prototype.
Pratama, M., Lu, J. & Zhang, G.Q. 2015, 'A Novel Meta-Cognitive Extreme Learning Machine to Learning from Data Streams', Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2015 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE, Kowloon, pp. 2792-2797.
View/Download from: UTS OPUS or Publisher's site
Extreme Learning Machine (ELM) is an answer to an increasing demand for a low-cost learning algorithm to handle big data applications. Nevertheless, existing ELMs leave four uncharted problems: complexity, uncertainty, concept drifts, curse of dimensionality. To correct these issues, a novel incremental meta-cognitive ELM, namely Evolving Type-2 Extreme Learning Machine (eT2ELM), is proposed. Et2Elm is built upon the three pillars of meta-cognitive learning, namely what-to-learn, how-to-learn, when-to-learn, where the notion of ELM is implemented in the how-to-learn component. On the other hand, eT2ELM is driven by a generalized interval type-2 Fuzzy Neural Network (FNN) as the cognitive constituent, where the interval type-2 multivariate Gaussian function is used in the hidden layer, whereas the nonlinear Chebyshev function is embedded in the output layer. The efficacy of eT2ELM is proven with four data streams possessing various concept drifts, comparisons with prominent classifiers, and statistical tests, where eT2ELM demonstrates the most encouraging learning performances in terms of accuracy and complexity.
Lu, J., Mao, M.S., Zhang, G.Q. & Zhang, J.L. 2015, 'A Fuzzy Content Matching-based e-Commerce Recommendation Approach', Proceedings of the 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, Istanbul, Turkey, pp. 1-8.
View/Download from: UTS OPUS or Publisher's site
E-Commerce products often come with rich and tree-structured content information describing the attributes. To well utilize the content information, this study proposed a fuzzy content matching-based recommendation approach to assist e-Commerce customers to choose their truly interested items. In this paper, users' ratings and preferences are represented using fuzzy numbers to remain uncertainties. Tree-structured content information is transformed to a set of descriptors, and users' preferences on these descriptors are derived from fuzzy ratings by using fuzzy number operations. A kind of preference dependence relations is established between descriptors to explore the relations of different content features, and as a base to sketch the complete profile of users. While the extended preference profile of a user is established, given a new item, the fuzzy match degree of the user preference and the item content information is carried out, and then a fuzzy Topsis ranking method is proposed to able to rank all candidate items according to the fuzzy match degrees, and the highest ranked items are recommended to the target user. We conduct empirical experiments on Yelp and MovieLens datasets. The results indicate that the proposed approach improve recommendation performance in terms of both coverage and accuracy.
Pratama, M., Lu, J. & Zhang, G.Q. 2015, 'An Incremental Interval Type-2 Neural Fuzzy Classifier', Proceedings of the 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, Istanbul, Turkey, pp. 1-8.
View/Download from: UTS OPUS or Publisher's site
Most real world classification problems involve a high degree of uncertainty, unsolved by a traditional type-1 fuzzy classifier. In this paper, a novel interval type-2 classifier, namely Evolving Type-2 Classifier (eT2Class), is proposed. The eT2Class features a flexible working principle built upon a fully sequential and local working principle. This learning notion allows eT2Class to automatically grow, adapt, prune, recall its knowledge from data streams in the single-pass learning fashion, while employing loosely coupled fuzzy sub-models. In addition, eT2Class introduces a generalized interval type-2 fuzzy neural network architecture, where a multivariate Gaussian function with uncertain non-diagonal covariance matrixes constructs the rule premise, while the rule consequent is crafted by a local non-linear Chebyshev polynomial. The efficacy of eT2Class is numerically validated by numerical studies with four data streams characterizing non-stationary behaviors, where eT2Class demonstrates the most encouraging learning performance in achieving a tradeoff between accuracy and complexity.
Chen, H., Zhang, G., Lu, J. & Zhu, D. 2012, 'A Two-steps Agglomerative Hierarchical Clustering Method for Patent Time-dependent Data', The 7th International Conference on Intelligent Systems and Knowledge Engineering, The seventh International Conference on Intelligent Systems and Knowledge Engineering (ISKE2012), Springer, Beijing, China, pp. 111-121.
View/Download from: UTS OPUS or Publisher's site
Patent data has time-dependent property and also semantic attributes. Technology clustering based on patent time-dependent data which processed by trend analysis has been used to help technologies relationships identification. However, the raw patent data carries more features than processed data. This paper aims to develop a new methodology to cluster patent frequency data based on its time-related properties. To handle time-dependent attributes of patent data, this study first compares it with typical time-series data to propose preferable similarity measurement approach. It then presents a two-steps agglomerative hierarchical technology clustering method to cluster original patent time-dependent data directly. Finally, a case study using communication-related patents is given to illustrate the clustering method.
Mao, M., Zhang, G., Lu, J. & Zhang, J. 2012, 'A Signed Trust-based Recommender Approach for Personalized Government-to-Business e-Services', The seventh International Conference on Intelligent Systems and Knowledge Engineering (ISKE2012), International Conference on Intelligent Systems and Knowledge Engineering, Springer, Beijing, China, pp. 91-101.
View/Download from: UTS OPUS or Publisher's site
Recently recommender systems are introduced into the web-based government applications which expect to provide personalized Government-to-Business (G2B) e-Services. For more personalization, we illustrate a subjective signed trust relationship between users, and based on such trust we proposed a recommendation framework for G2B e-services. A case study is conducted as an example of implementing our approach in e-government applications. Empirical analysis is also conducted to compare our approach with other models, which shows that our approach is of the highest. In conclusion, the signed trust relationship can reflect the real preferences of users, and the proposed recommendation framework is believed to be reliable and applicable.
Komkhao, M., Lu, J., Li, Z. & Halang, W.A. 2012, 'Improving group recommendations by identifying homogeneous subgroups', International Conference on Intelligent System and Knowledge Engineering, International Conference on Intelligent Systems and Knowledge Engineering, Springer, Beijing, China, pp. 453-462.
View/Download from: UTS OPUS or Publisher's site
Recommender systems have proven their effectiveness in supporting personalised purchasing decisions and e-service intelligence. In order to support members in user groups of recommender systems, recently designed group recommender systems search for data relevant to all group members and discover the agreements between members of online communities. This paper focuses on achieving common satisfaction for groups or communities by, e.g. finding a restaurant for a family or shoes for a group of cheerleaders. It establishes an algorithm, called I-GRS, to devise group recommender systems based on incremental model-based collaborative filtering and applying the Mahalanobis distance and fuzzy membership to create groups of users with similar interests. Finally, an algorithm and related design strategy to build group recommender systems is proposed. A set of experiments is set up to evaluate the performance of the I-GRS algorithm in group recommendations. The results show its effectiveness vis-à-vis the recommendations made by classical recommender systems to single or groups of individuals.
Ramezani, F., Lu, J. & Hussain, F.K. 2012, 'Task Based System Load Balancing Approach in Cloud Environments', Knowledge Engineering and Management, Seventh International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2012), Springer Berlin Heidelberg, Beijing, China, pp. 31-42.
View/Download from: UTS OPUS or Publisher's site
Live virtual machine (VM) migration is a technique for transferring an active VM from one physical host to another without disrupting the VM. This technique has been proposed to reduce the downtime for migrated overload VMs. As VMs migration takes much more times and cost in comparison with tasks migration, this study develops a novel approach to confront with the problem of overload VM and achieving system load balancing, by assigning the arrival task to another similar VM in a cloud environment. In addition, we propose a multi-objective optimization model to migrate these tasks to a new VM host applying multi-objective genetic algorithm (MOGA). In the proposed approach, there is no need to pause VM during migration time. In addition, as contrast to tasks migration, VM live migration takes longer to complete and needs more idle capacity in host physical machine (PM), the proposed approach will significantly reduce time, downtime memory, and cost consumption.
Liu, A., Zhang, G. & Lu, J. 2014, 'A Novel Weighting Method for Online Ensemble Learning with the Presence of Concept Drift', Proceedings of the 11th Internationaal FLINS Conference, Decision Making and Soft Computing, World Scientific Publishing Co. Pte. Ltd., Brazil, pp. 550-555.
View/Download from: UTS OPUS or Publisher's site
Ensemble of classifiers is a very popular method for online and incremental learning in non-stationary environment, as it improves the accuracy of single classifiers and is able to recover from drifting concept without explicit drift detection. However, current ensemble weighing methods do not consider the relationship between a test instance and each ensemble member's training domain. As a result, a locally correct ensemble member may be reduced weight unfairly because that its prediction result of an out of domain test instance is wrong. These inaccuracies will increases when there is a significant concept change. In this paper, therefore, we proposed a fuzzy online ensemble weighting method which takes the consideration of the degree of membership of each instance in each ensemble member and a modified majority voting method to improve the ability of ensembles on handling online classification tasks with concept drift
Wang, W., Lu, J. & Zhang, G. 2013, 'A New Similarity Measure-Based Collaborative Filtering Approach for Recommender Systems', Foundations of Intelligent Systems, Eighth International Conference on Intelligent Systems and Knowledge Engineering, Springer Berlin Heidelberg, Shenzhen,China, pp. 443-452.
View/Download from: UTS OPUS or Publisher's site
Dianshuang Wu, Guangquan Zhang & Jie Lu 2014, 'A fuzzy tree matching-based personalised e-learning recommender system', 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, Beijing, China, pp. 1898-1904.
View/Download from: UTS OPUS or Publisher's site
The rapid development of e-learning systems provides learners great opportunities to access the learning activities online, which greatly supports and enhances learning practices. However, too many learning activities are emerging in the e-learning system, which makes it difficult for learners to select proper ones for their particular situations since there is no personalised service function. Recommender systems, which aim to provide personalised recommendations, can be used to solve this issue. However, e-learning systems have two features to handle: (1) data of learners and leaning activities often present tree structures; (2) data are often vague and uncertain in practice. In this study, a fuzzy tree-structured data model is proposed to comprehensively describe the complex learning activities and learner profiles. A tree matching method is then developed to match the similar learning activities or learners. To deal with the uncertain category issues, a fuzzy category tree and relevant similarity measure are developed. A hybrid recommendation approach, which considers precedence relations between learning activities and combines the semantic and collaborative filtering similarities between learners, is developed. The proposed approach can handle the special requirements in e-learning environment and make proper recommendations in e-learning systems.
Kaur, P., Goyal, M.L. & Lu, J. 2014, 'A price prediction model for online auctions using fuzzy reasoning techniques', Proceedings of the IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014, 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, Beijing, China, pp. 1311-1318.
View/Download from: UTS OPUS or Publisher's site
E-consumers are urged to opt for the best bidding strategies to excel in the competitive environment of multiple and simultaneous online auctions for same or similar items. It becomes very complicated for the bidders to make the decisions of selecting which auction to participate in, place single or multiple bids, early or late bidding and how much to bid. In this paper, we present the design of an autonomous dynamic bidding agent (ADBA) that makes these decisions on behalf of the buyers according to their bidding behaviors. The agent develops a comprehensive methodology for initial price estimation and an integrated model for final price prediction. The initial price estimation methodology selects an auction to participate in and assesses the value (initial price) of the auctioned item. Then the final price prediction model forecasts the bid amount by designing different bidding strategies using fuzzy reasoning techniques. The experimental results demonstrated improved initial price prediction outcomes by proposing a clustering based approach. Also, the results show the proficiency of the fuzzy bidding strategies in terms of their success rate and expected utility.
Pratama, M., Jie Lu, P., Sreenatha Anavatti, D. & Jose Antonio Iglesias, D. 2014, 'A Recurrent Meta-Cognitive-Based Scaffolding Classifier from Data Streams', IEEE Symposium on Evolving and Automomous Learning, 2014 IEEE Symposium on Evolving and Automomous Learning Systems, IEEE, Orlando, Florida, USA, pp. 132-139.
View/Download from: UTS OPUS or Publisher's site
a novel incremental meta-cognitive-based Scaffolding algorithm is proposed in this paper crafted in a recurrent network based on fuzzy inference system termed recurrent classifier (rClass). rClass features a synergy between schema and scaffolding theories in the how-to-learn part, which constitute prominent learning theories of the cognitive psychology. In what-to-learn component, rClass amalgamates the new online active learning concept by virtue of the Bayesian conflict measure and dynamic sampling strategy, whereas the standard sample reserved strategy is incorporated in the when-to-learn constituent. The inference scheme of rClass is managed by the local recurrent network, sustained by the generalized fuzzy rule. Our thorough empirical study has ascertained the efficacy of rClass, which is capable of producing reliable classification accuracies, while retaining the amenable computational and memory burdens
Zhang, T., Lu, J., Zhang, G. & Gu, J. 2014, 'A novel evaluation approach for power distribution system planning based on linear programming model and ELECTRE III', IEEE International Conference on Fuzzy Systems, Institute of Electrical and Electronics Engineers Inc., pp. 1921-1928.
View/Download from: UTS OPUS or Publisher's site
To evaluate solutions of power distribution system planning (PDSP) is an essential task in smart grid and requires multi-criteria decision making (MCDM). However, the vagueness of attribute values and the fuzziness of weights of criteria need integrate fuzzy techniques with MCDM. In order to incorporate the issues with uncertainty in PDSP evaluation, this paper proposes a novel PDSP approach based on linear programming model and ELECTRE IIL The incomplete weight preference information of decision-maker is elicited and expressed by a group of weight constraint functions, combined these functions with the simple multi-attribute rating technique, a linear programming model is set up to obtain the weights for each solution. Then with the weights and a PDSP model based on ELECTRE III model, the outranking score of each solution compared with other solutions can be calculated, and a net present score for each solution will be obtained for ranking these solutions, DM can choose one desired. A case is demonstrated to show the evaluation process using this approach and the results indicate that this approach incorporating the issues with uncertainty is robust for PDSP evaluation. The results are acceptable to DM.
Dong, F., Lu, J., Zhang, G. & Li, K. 2014, 'A MODIFIED LEARN++.NSE ALGORITHM FOR DEALING WITH CONCEPT DRIFT', Decision Making and Soft Computing, 11 FLINS Conference, World Scientific, Brazil, pp. 556-561.
View/Download from: UTS OPUS or Publisher's site
Concept drift is a very pervasive phenomenon in real world applications. By virtue of variety change types of concept drift, it makes more difficult for learning algorithm to track the concept drift very closely. Learn++.NSE is an incremental ensemble learner without any assumption on change type of concept drift. Even though it has good performance on handling concept drift, but it costs high computation and needs more time to recover from accuracy drop. This paper proposed a modified Learn++.NSE algorithm. During learning instances in data stream, our algorithm first identifies where and when drift happened, then uses instances accumulated by drift detection method to create a new base classifier, and finally organized all existing classifiers based on Learn++.NSE weighting mechanism to update ensemble learner. This modified algorithm can reduce high computation cost without any performance drop and improve the accuracy recover speed when drift happened. Read More: http://www.worldscientific.com/doi/abs/10.1142/9789814619998_0092
Mao, M., Lu, J., Zhang, G. & Zhang, J. 2014, 'Hybridizing Social Filtering for Recommender Systems', Foundations of Intelligent Systems, International Conference on Intelligent Systems and Knowledge Engineering, Springer Berlin Heidelberg, Shenzhen, China.
View/Download from: UTS OPUS or Publisher's site
Users send requests to recommender systems for getting suggested products or services. Collaborative filtering is a popular technique for making such suggestions efficiently, but it suffers from a drawback known as 'cold-start problem. Social filtering may succeed for such users, since it utilize the extra social relations of users. It gives us opportunities to eliminate the limitations by hybridizing social filtering into traditional collaborative filtering. To handle this issue, differing from previous fusion models that only combine the final results, this paper proposed a new neighborhood fusion model to make hybridization at an earlier and deeper stage. Experiment-based comparative analyses are also conducted. The results show that our model is of a higher recommendation quality, on different datasets.
Zhang, Y., Zhang, G., Porter, A., Zhu, D. & Lu, J. 2014, 'Science, Technology & Innovation Textual Data – Oriented Topic Analysis and Forecasting: Model and A Case Study', 5th International Conference on Future-Oriented Technology Analysis (FTA), Brussels.
View/Download from: UTS OPUS
Not only the external quantities, but also the potential topics of current Science, Technology & Innovation (ST&I) are changing all the time, and their induced accumulative innovation or, even, disruptive revolution, should be able to heavily influence the whole society in the near future. Addressing and predicting the changes, this paper proposes an analytic method (1) to cluster associated terms and phrases to constitute meaningful technological topics and (2) to identify changing topical emphases, the results of which we carry forward to present mechanisms to forecast prospective developments via Technology Roadmapping approaches. Furthermore, an empirical case study of Award data in the United States National Science Foundation Division of Computer and Communication Foundations is performed to demonstrate the proposed method and the resulting knowledge could hold interests for R&D management and science policy in practice.
Han, J., Zhang, G., Lu, J., Hu, Y. & Ma, S. 2014, 'Model and Algorithm for Multi-follower Tri-level Hierarchical Decision-Making', Springer International Publishing, pp. 398-406.
View/Download from: UTS OPUS or Publisher's site
Han, J., Lu, J., Zhang, G. & Ma, S. 2014, 'Multi-follower tri-level decision making with uncooperative followers', Decision Making and Soft Computing, 11th International FLINS Conference, WORLD SCIENTIFIC, pp. 524-529.
View/Download from: UTS OPUS or Publisher's site
Junyu Xuan, Jie Lu, Guangquan Zhang & Xiangfeng Luo 2014, 'Extension of similarity measures in VSM: From orthogonal coordinate system to affine coordinate system', Neural Networks (IJCNN), 2014 International Joint Conference on, Neural Networks (IJCNN), 2014 International Joint Conference on, pp. 4084-4091.
View/Download from: UTS OPUS
Similarity measures are the foundations of many research areas, e.g. information retrieval, recommender system and machine learning algorithms. Promoted by these application scenarios, a number of similarity measures have been proposed and proposing. In these state-of-the-art measures, vector-based representation is widely accepted based on Vector Space Model (VSM) in which an object is represented as a vector composed of its features. Then, the similarity between two objects is evaluated by the operations on two corresponding vectors, like cosine, extended jaccard, extended dice and so on. However, there is an assumption that the features are independent of each others. This assumption is apparently unrealistic, and normally, there are relations between features, i.e. the co-occurrence relations between keywords in text mining area. In this paper, a space geometry-based method is proposed to extend the VSM from the orthogonal coordinate system (OVSM) to affine coordinate system (AVSM) and OVSM is proved to be a special case of AVSM. Unit coordinate vectors of AVSM are inferred by the relations between features which are considered as angles between these unit coordinate vectors. At last, five different similarity measures are extended from OVSM to AVSM using unit coordinate vectors of AVSM. Within the numerous application fields of similarity measures, the task of text clustering is selected to be the evaluation criterion. Documents are represented as vectors in OVSM and AVSM, respectively. The clustering results show that AVSM outweighs the OVSM.
Xuan, J., Lu, J., Zhang, G. & Luo, X. 2014, 'Release 'Bag-of-Words' Assumption of Latent Dirichlet Allocation', Springer Berlin Heidelberg, pp. 83-92.
View/Download from: UTS OPUS or Publisher's site
Rodriguez, J.T., De Los Rios, C.F., Montero, J. & Lu, J. 2014, 'Paired Structures in Logical and Semiotic Models of Natural Language', Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2014, Springer International Publishing, Montpellier, France, pp. 566-575.
View/Download from: UTS OPUS or Publisher's site
The evidence coming from cognitive psychology and linguistics shows that pairs of reference concepts (as e.g. good/bad, tall/short, nice/ugly, etc.) play a crucial role in the way we everyday use and understand natural languages in order to analyze reality and make decisions. Different situations and problems require different pairs of landmark concepts, since they provide the referential semantics in which the available information is understood accordingly to our goals in each context. In this way, a semantic valuation structure or system emerges from a pair of reference concepts and the way they oppose each other. Such structures allow representing the logic of new concepts according to the semantics of the references. We will refer to these semantic valuation structures as paired structures. Our point is that the semantic features of a paired structure could essentially depend on the semantic relationships holding between the pair of reference concepts from which the valuation structure emerges. Different relationships may enable the representation of different types of neutrality, understood here as an epistemic hesitation regarding the references. However, the standard approach to natural languages through logical models usually assumes that reference concepts are just each other complement. In this paper, we informally discuss more deeply about these issues, claiming in a positional manner that an adequate logical study and representation of the features and complexity of natural languages requires to consider more general semantic relationships between references.
Liu, A., Zhang, G. & Lu, J. 2014, 'Concept Drift Detection Based on Anomaly Analysis', Neural Information Processing, 21st International Conference on Neural Information Processing, Springer International Publishing, Kuching, Sarawak, Malaysia, pp. 263-270.
View/Download from: UTS OPUS
Abstract In online machine learning, the ability to adapt to new concept quickly is highly desired. In this paper, we propose a novel concept drift detection method, which is called Anomaly Analysis Drift Detection (AADD), to improve the performance of machine learning algorithms under non-stationary environment. The proposed AADD method is based on an anomaly analysis of learner's accuracy associate with the similarity between learners' training domain and test data. This method first identifies whether there are conflicts ...
Wu, D., Zhang, G. & Lu, J. 2013, 'A Fuzzy Tree Similarity Based Recommendation Approach for Telecom Products', 2013 Joint IFSA World Congress NAFIPS Annual Meeting (IFSA/NAFIPS), Joint IFSA World Congress NAFIPS Annual Meeting, IEEE, Edmonton, Canada, pp. 813-818.
View/Download from: UTS OPUS or Publisher's site
Due to the huge product assortments and complex descriptions of telecom products, it is a great challenge for customers to select appropriate products. A fuzzy tree similarity based hybrid recommendation approach is proposed to solve this issue. In this study, fuzzy techniques are used to deal with the various uncertainties existing within the product and customer data. A fuzzy tree similarity measure is developed to evaluate the semantic similarity between tree structured products or user profiles. The similarity measures for items and users both integrate the collaborative filtering (CF) and semantic similarities. The final recommendation hybridizes item-based and user-based CF recommendation techniques. A telecom product recommendation case study is given to show the effectiveness of the proposed approach.
Naderpour, M. & Lu, J. 2013, 'A Hybrid Bayesian Network for Safety of Chemical Plants', Pacific Asia Conference on Information Systems 2013 Proceedings, Pacific Asia Conference on Information Systems, AIS Electronic Library (AISeL, Jeju Island, Korea, pp. 1-12.
View/Download from: UTS OPUS
In today's process systems, operators must consider an overwhelming amount of information which is passed to them via automated systems, and make decisions very quickly. Since the decision-making in a time-critical situation is extremely complicated, the use of automated systems to aid decision making is highly recommended. This paper proposes a hybrid Bayesian network (HBN) to support process operators in hazardous situations. The proposed HBN includes three parts: an evidence preparation, a situational network, and risk estimation. The evidence preparation part provides soft evidence based on the online conditions and process monitoring system. The situational network is developed based on dynamic Bayesian networks to model the hazardous situations, and the risk estimation part calculates the risk level of every situation dynamically to show whether the risk level of situations is acceptable or not. The threefold HBN is explained through a case from U.S. Chemical Safety Board (CSB) investigation report. According to the CSB report, following an operator error at a paint manufacturing plant, the explosion and subsequent fire destroyed a facility, injured ten residents, and heavily damaged dozens of nearby homes and businesses. Finally a sensitivity analysis is presented to evaluate the proposed HBN.
Behbood, V., Lu, J. & Zhang, G. 2013, 'Text categorization by fuzzy domain adaptation', 2013 IEEE International Conference on Fuzzy Systems, IEEE International Conference on Fuzzy Systems, IEEE, Hyderabad, India, pp. 1841-1848.
View/Download from: UTS OPUS or Publisher's site
Machine learning methods have attracted attention of researches in computational fields such as classification/categorization. However, these learning methods work under the assumption that the training and test data distributions are identical. In some real world applications, the training data (from the source domain) and test data (from the target domain) come from different domains and this may result in different data distributions. Moreover, the values of the features and/or labels of the data sets could be non-numeric and contain vague values. In this study, we propose a fuzzy domain adaptation method, which offers an effective way to deal with both issues. It utilizes the similarity concept to modify the target instances' labels, which were initially classified by a shift-unaware classifier. The proposed method is built on the given data and refines the labels. In this way it performs completely independently of the shift-unaware classifier. As an example of text categorization, 20Newsgroup data set is used in the experiments to validate the proposed method. The results, which are compared with those generated when using different baselines, demonstrate a significant improvement in the accuracy
Jiang, J., Lu, J., Zhang, G. & Long, G. 2013, 'Optimal Cloud Resource Auto-scaling for Web Application', IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, IEEE, Delft, Netherlands, pp. 58-65.
View/Download from: UTS OPUS or Publisher's site
In the on-demand cloud environment, web application providers have the potential to scale virtual resources up or down to achieve cost-effective outcomes. True elasticity and cost-effectiveness in the pay-per-use cloud business model, however, have not yet been achieved. To address this challenge, we propose a novel cloud resource auto-scaling scheme at the virtual machine (VM) level for web application providers. The scheme automatically predicts the number of web requests and discovers an optimal cloud resource demand with cost-latency trade-off. Based on this demand, the scheme makes a resource scaling decision that is up or down or NOP (no operation) in each time-unit re-allocation. We have implemented the scheme on the Amazon cloud platform and evaluated it using three real-world web log datasets. Our experiment results demonstrate that the proposed scheme achieves resource auto-scaling with an optimal cost-latency trade-off, as well as low SLA violations.
Naderpour, M., Lu, J. & Zhang, G. 2013, 'A Fuzzy Dynamic Bayesian Network-Based Situation Assessment Approach', The Proceeding of IEEE International Conference on Fuzzy Systems, The 22th IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, Hyderabad, India, pp. 1-8.
View/Download from: UTS OPUS or Publisher's site
Situation awareness (SA), a state in the mind of a human, is essential to conduct decision-making activities. It is about the perception of the elements in the environment, the comprehension of their meaning, and the projection of their status in the near future. Two decades of investigation and analysis of accidents have showed that SA was behind of many serious large-scale technological systems accidents. This emphasizes the importance of SA support systems development for complex and dynamic environments. This paper presents a fuzzy dynamic Bayesian network-based situation assessment approach to support the operators in decision making process in hazardous situations. The approach includes a dynamic Bayesian network-based situational network to model the hazardous situations where the existence of the situations can be inferred by sensor observations through the SCADA monitoring system using a fuzzy quantizer method. In addition to generate the assessment result, a fuzzy risk estimation method is proposed to show the risk level of situations. Ultimately a hazardous environment from U.S. Chemical Safety Board investigation reports has been used to illustrate the application of proposed approach.
Ramezani, F., Lu, J. & Hussain, F.K. 2013, 'Task Scheduling Optimization in Cloud Computing Applying Multi-Objective Particle Swarm Optimization', Lecture Notes in Computer Science, Springer, Berlin, Germany, pp. 237-251.
View/Download from: UTS OPUS or Publisher's site
Optimizing the scheduling of tasks in a distributed heterogeneous computing environment is a nonlinear multi-objective NP-hard problem which is playing an important role in optimizing cloud utilization and Quality of Service (QoS). In this paper, we develop a comprehensive multi-objective model for optimizing task scheduling to minimize task execution time, task transferring time, and task execution cost. However, the objective functions in this model are in conflict with one another. Considering this fact and the supremacy of Particle Swarm Optimization (PSO) algorithm in speed and accuracy, we design a multi-objective algorithm based on multi-objective PSO (MOPSO) method to provide an optimal solution for the proposed model. To implement and evaluate the proposed model, we extend Jswarm package to multi-objective Jswarm (MO-Jswarm) package. We also extend Cloudsim toolkit applying MO-Jswarm as its task scheduling algorithm. MO-Jswarm in Cloudsim determines the optimal task arrangement among VMs according to MOPSO algorithm. The simulation results show that the proposed method has the ability to find optimal trade-off solutions for multi-objective task scheduling problems that represent the best possible compromises among the conflicting objectives, and significantly increases the QoS.
Ramezani, F., Hussain, F.K. & Lu, J. 2013, 'A Fuzzy Predictable Load Balancing Approach in Cloud Computing', Proceedings of the International Conference on Grid & Cloud Computing and Applications GCA'13, The 2013 International Conference on Grid & Cloud Computing and Applications, WorldComp, Las Vegas, Nevada, USA.
View/Download from: UTS OPUS
Cloud computing is a new paradigm for hosting and delivering services on demand over the internet where users access services. It is an example of an ultimately virtualized system, and a natural evolution for data centers that employ automated systems management, workload balancing, and virtualization technologies. Live virtual machine (VM) migration is a technique to achieve load balancing in cloud environment by transferring an active overload VM from one physical host to another one without disrupting the VM. In this study, to eliminate whole VM migration in load balancing process, we propose a Fuzzy Predictable Load Balancing (FPLB) approach which confronts with the problem of overload VM, by assigning the extra tasks from overloaded VM to another similar VM instead of whole VM migration. In addition, we propose a Fuzzy Prediction Method (FPM) to predict VMs migration time. This approach also contains a multi-objective optimization model to migrate these tasks to a new VM host. In proposed FPLB approach there is no need to pause VM during migration time. Furthermore, considering this fact that VM live migration contrast to tasks migration takes longer to complete and needs more idle capacity in host physical machine (PM), the proposed approach will significantly reduce time, idle memory and cost consumption.
Ramezani, F., Lu, J. & Hussain, F.K. 2013, 'An Online Fuzzy Decision Support System for Resource Management in Cloud Environments', Proceedings of the 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 2013 Joint IFSA World Congress NAFIPS Annual Meeting (IFSA/NAFIPS), IEEE, Edmonton, Canada, pp. 754-760.
View/Download from: UTS OPUS or Publisher's site
Cloud computing is a large-scale distributed computing paradigm driven by economies of scale, in which a pool of abstracted, virtualized, dynamically-scalable, managed computing power, storage, platforms, and services are delivered on demand to external customers over the Internet. Although a significant amount of studies have been developed to optimize resource management and task scheduling in cloud computing, none of them considered the impact of task scheduling patterns on resource management and vice versa. To overcome this drawback, and considering the lack of resources in cloud environments and growing customer demands for cloud services, this paper proposes an Online Resource Management Decision Support System (ORMDSS) that addresses both tasks scheduling and resource management optimization in a unique system. In addition, ORMDSS contains a fuzzy prediction method for predicting VM workload patterns and VM migration time by applying neural networks and fuzzy expert systems. This ORMDSS helps cloud providers to automatically allocate scare resources to the applications and services in an optimal way. It is expected that the ORMDSS not only increases cloud utilization and QoS, but also decreases cost and response time.
Wu, D., Zhang, G. & Lu, J. 2013, 'A Fuzzy Tree Similarity Measure and Its Application in Telecom Product Recommendation', 2013 IEEE International Conference on Systems, Man and Cybernetics (SMC), IEEE International Conference on Systems, Man and Cybernetics, IEEE, Manchester, pp. 3483-3488.
View/Download from: UTS OPUS or Publisher's site
The recommender systems field has been well developed in the last few years to provide item recommendations to related users. Existing recommendation approaches, however, assume that an item is described by a single value or a vector. Unfortunately, some items in real world applications, such as telecom products, could have a tree structure. This paper aims to handle this issue by developing a comprehensive fuzzy tree similarity measure. The fuzzy tree similarity measure compares both the concepts and values in two trees of items. The focus of this study is primarily on the fuzzy value similarity between two trees. In the similarity measure, each attribute is associated with a set of linguistic terms to express the value granularly. The node values are first transformed to membership vectors related to the linguistic terms, and the values of the conceptual corresponding nodes are then compared. These local similarities are aggregated into the final fuzzy value similarity between the two trees. A telecom product recommendation case study shows the effectiveness of the proposed fuzzy tree similarity measure and its applicability for telecom product recommendations
Chen, H., Zhang, G. & Lu, J. 2013, 'A Time-Series-Based Technology Intelligence Framework by Trend Prediction Functionality', 2013 IEEE International Conference on Systems, Man, and Cybernetics, IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE, Manchester, UK, pp. 3477-3482.
View/Download from: UTS OPUS or Publisher's site
Technology Intelligence (TI) indicates the concept and applications that transform data hidden in patents or scientific literature into technical insight for technology development planning and strategies formulation. Although much effort has been put into technology trend analysis in existing research, the majority of the results are still obtained from expert opinions on the basis of historical trends presented by content-based Technology Intelligence tools. To improve this situation, this paper proposes a time-series-based framework for TI that enables the system to be more effective when dealing with trend prediction requirements. Time-series analysis module is first applied in TI framework to process patent time series for technology trend predictions in a real sense, at the same time overcome the problem that prediction of future data points' values is insufficient to support TI construction. Based on explicit patent attributes and unknown patterns learned from the historical data, the framework combines the "trend" and "content" knowledge by analyzing both time-related property and semantic attributes of patent data, to support technology development planning more efficiently and satisfactorily. A case study is presented to demonstrate the validity of trend prediction functionality, which is the emphasis of the whole framework
Ma, J., Lin, H., Lu, J. & Zhang, G. 2013, 'A hybrid model for migrating customer segmentation with missing attributes', 2013 Joint IFSA World Congress NAFIPS Annual Meeting (IFSA/NAFIPS), Joint IFSA World Congress NAFIPS Annual Meeting, IEEE, Edmonton, Canada, pp. 825-830.
View/Download from: UTS OPUS or Publisher's site
Due to missing attributes in an enterprise's database, migrating customer segmentation results from external dataset to enterprise database in difficult. In this paper, a hybrid model, called HMCS model, is presented. This model artificially generates values of missing attributes based on external dataset and populates them to enterprise database. Based on this model, an application in a telecom application is reported. Application indicates the presented model can produce acceptable segmentation results on the enterprise dataset which is with missing attributes.
Xuan, J., Luo, X. & Lu, J. 2013, 'Mining Websites Preferences on Web Events in Big Data Environment', 2013 IEEE 16th International Conference on Computational Science and Engineering (CSE), IEEE 16th International Conference on Computational Science and Engineering, IEEE, Sydney, Australia, pp. 1043-1050.
View/Download from: UTS OPUS or Publisher's site
On the web, there are numerous websites publishing web pages to cover the events occurring in society. The web events data satisfies the well-accepted attributes of big data: Volume, Velocity, Variety and Value. As a great value of web events data, website preferences can help the followers of web events, e.g. peoples or organizations, to select the proper websites to follow their interested aspects of web events. However, the big volume, fast evolution speed, multisource and unstructured data all together make the value of website preferences mining very challenging. In this paper, website preference is formally defined at first. Then, according to the hierarchical attribute of web events data, we propose a hierarchical network model to organize big data of a web event from different organizations, different areas and different nations at a given time stamp. With this hierarchical network structure in hand, two strategies are proposed to mine the value of websites preferences from web events data. The first straightforward strategy utilizes the communities of keyword level network and the mapping relations between websites and keywords to unveil the Value in them. By taking the whole hierarchical network structure into consideration, an iterative algorithm is proposed in second strategy to refine the keyword communities like the first strategy. At last, an evaluation criteria of website preferences is designed to compare the performances of two proposed strategies. Experimental results show the proper combination of horizontal relations (each level network) with vertical relations (mapping relations between three level networks) can extract more value from web events data and then improve the efficiency on website preferences mining.
Ramezani, F., Lu, J. & Hussain, F.K. 2013, 'Task Scheduling Optimization in Cloud Computing Applying Multi-Objective Particle Swarm Optimization', Proceedings of the 11th International Conference on Service-Oriented Computing, 11th International Conference on Service-Oriented Computing, Springer Verlag, Berlin, Germany, pp. 237-251.
View/Download from: UTS OPUS
Purba, J., Lu, J. & Zhang, G. 2012, 'An Area Defuzzification Technique and Essential Fuzzy Rules for Defuzzifying Nuclear Event Failure Possibilities into Reliability Data', Uncertainty Modeling in Knowledge Engineering and Decision Making, The 10th International FLINS Conference, World Scientific Publishing Co. Pte. Lt., Istanbul, Turkey, pp. 1208-1213.
Naderpour, M. & Lu, J. 2012, 'A Fuzzy Dual Expert System for Managing Situation Awareness in a Safety Supervisory System', 2012 IEEE International Conference on Fuzzy Systems, The 2012 IEEE World Congress on Computational Intelligence (IEEE-WCCI 2012), IEEE, Brisbane, Australia, pp. 715-721.
View/Download from: UTS OPUS
Safety supervisory systems continue to increase in degree of automation and complexity as operators are decreasing. As a result, each operator must be able to comprehend and respond to an ever increasing amount of available risky status and alert information. They generally have no difficulty in performing their tasks physically but they are stressed by the task of understanding what is going on in the situation. So in the last two decades, situation awareness has been recognized as a critical foundation for successful decision making across a broad range of complex and dynamic systems. This paper develops a fuzzy dual expert system based approach to enhance situation awareness. The proposed approach has ability to support the operators understanding and assessing the situations, and to deal with uncertainties, applying fuzzy risk assessment concepts.
Purba, J., Lu, J., Zhang, G. & Ruan, D. 2012, 'A Failure Possibility-Based Reliability Algorithm for Nuclear Safety Assessment by Fault Tree Analysis', The 1st International Workshop on Safety & Security Risk Assessment and Organisational Cultures (SSRAOC2012), The Belgian Nuclear Research Centre SCKâ¢CEN, Antwerp - Belgium, pp. 1-8.
Reliability data are essential for nuclear power plant probabilistic safety assessment by fault tree analysis. The limitation of conventional reliability data comes from the insufficient reliable historical data for probabilistic calculation. This paper proposes an algorithm to calculate nuclear event reliability data using possibility approach as an alternative to probabilistic approach. Nuclear events are evaluated by a group of experts based on their working experience and expertise, which are expressed in qualitative natural languages and mathematically represented by membership functions of fuzzy numbers. Every expert is given a justification weight to represent his/her knowledge level of the safety system under investigation. A case study on emergency core cooling system of a typical nuclear power plant is given to mathematically verify the proposed algorithm. The results show that this proposed algorithm is a very good alternative to assess nuclear event data when historical data for probabilistic calculation is not available.
Naderpour, M. & Lu, J. 2012, 'Supporting Situation Awareness Using Neural Network and Expert System', Uncertainty Modeling in Knowledge Engineering and Decision Making, The 10th International FLINS Conference on Uncertainty Modeling in Knowledge Engineering and Decision Making, World Scientific, Istanbul-Turkey, pp. 993-998.
View/Download from: UTS OPUS or Publisher's site
Situation awareness (SA) is a critical factor for human decision making and performance in dynamic environments. Actually SA is a mental model of the current state of the environment and includes many types of complex systems such as safety supervisory systems. The current paper employs two focus areas including neural network and expert system for maintaining SA in a safety supervisory system. The neural network components provide adaptive mechanisms for perception, and the expert system offers the ability to support comprehension and projection.
Ramezani, F. & Lu, J. 2012, 'A COGNITIVE GROUP DECISION SUPPORT SYSTEM FOR PROJECTS EVALUATION', Uncertainty Modeling in Knowledge Engineering and Decision Making, FLINS 2012, World Scientific, Istanbul-Turkey, pp. 231-236.
View/Download from: UTS OPUS
In any organization there are some main goals and lots of projects for achieving these goals. For any organization, it is important to determine how much these projects affect on achieving the main goals. This paper proposes a new fuzzy multiple attribute-based decision support system (DSS) for evaluating projects in promoting the goals as such a selection may involve both quantitative and qualitative assessment attributes. In addition the proposed DSS has ability to choose the most appropriate fuzzy ranking method for solving given MADM problem. Also it contains sensitivity analysis system which provides opportunity for analyzing the impacts of attributesâ weights and project sâ performance on achieving organizationsâ goals, and assess the reliability of the decision making process. The proposed DSS can be applied for solving every FMADM problem which needs to rank some alternatives according to some attributes.
Demong, N.A., Lu, J. & Hussain, F.K. 2012, 'Multidimensional Analysis and Data Mining for Property Investment Risk Analysis', International Conference on Information Systems (ICIS 2012), International Conference on Information Systems (ICIS 2012), World Academy of Science, Engineering and Technology, Penang, Malaysia, pp. 118-123.
View/Download from: UTS OPUS
Property investment in the real estate industry has a high risk due to the uncertainty factors that will affect the decisions made and high cost. Analytic hierarchy process has existed for some time in which referred to an expertâs opinion to measure the uncertainty of the risk factors for the risk analysis. Therefore, different level of expertsâ experiences will create different opinion and lead to the conflict among the experts in the field. The objective of this paper is to propose a new technique to measure the uncertainty of the risk factors based on multidimensional data model and data mining techniques as deterministic approach. The propose technique consist of a basic framework which includes four modules: user, technology, end-user access tools and applications. The property investment risk analysis defines as a micro level analysis as the features of the property will be considered in the analysis in this paper.
Espinilla, M., Lu, J., Ma, J. & Martinez, L. 2012, 'An Extended Version of the Fuzzy Multicriteria Group Decision-Making Method in Evaluation Processes', 14th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems IPMU 2012, Springer, Catania, Italy, pp. 191-200.
View/Download from: UTS OPUS
Evaluation processes are a key element used in quality inspection, marketing and other fields in industrial companies. In these processes, it is very common that a group of evaluators assess a set of evaluated elements, according to a set of criteria, which may have different nature and usually present uncertainty. In this context, the fuzzy multicriteria group decision-making (FMCGDM) method has been successfully applied to different evaluation problems. This method provides a closeness coefficient of each evaluated element in order to generate a final raking. However, its applications to complex evaluation processes that requires the understandability of the closeness coefficient drive us to propose the use of the linguistic 2-tuple representation model to extend the FMCGDM method, in order to provide linguistic closeness coefficients, which are easy to understand. Moreover, we apply the extended version of the FMCGDM method in an evaluation process of fabric hand.
An, H., Lu, J. & Zhang, G. 2012, 'A JUDGEMENT METHOD FOR EARTHQUAKE EARLY WARNING INFORMATION', The 10th International FLINS Conference on Uncertainty Modeling in Knowledge Engineering and Decision Making (FLINS 2012), The 10th International FLINS Conference on Uncertainty Modeling in Knowledge Engineering and Decision Making (FLINS 2012), World Scientific, Istanbul, Turkey, pp. 333-338.
View/Download from: UTS OPUS
Earthquake early warning (EEW) is very important to earthquake mitigation, but EEW information before an earthquake is usually ignored. China Public Safety Early Warning Information Systems (CPSEWIS) including EEW information before, amid and after an earthquake is introduced. More important is that a method is designed to judge credibility of EEW information and help people mitigate
Memon, T., Lu, J. & Hussain, F.K. 2012, 'Semantic De-biased Associations (SDA) Model to Improve Ill-Structured Decision Support', Proceedings of the 19th International Conference on Neural Information Processing, 19th International Conference on Neural Information Processing, Springer Verlag, Doha, Qatar, pp. 483-490.
View/Download from: UTS OPUS
Wu, D., Zhang, G., Lu, J. & Halang, W.A. 2012, 'A Similarity Measure on Tree Structured Business Data', ACIS 2012 : Location, location, location : Proceedings of the 23rd Australasian Conference on Information Systems 2012, 23rd Australasian Conference on Information Systems, ACIS, Geelong, Vic., pp. 1-10.
View/Download from: UTS OPUS
In many business situations, products or user profile data are so complex that they need to be described by use of tree structures. Evaluating the similarity between tree-structured data is essential in many applications, such as recommender systems. To evaluate the similarity between two trees, concept corresponding nodes should be identified by constructing an edit distance mapping between them. Sometimes, the intension of one concept includes the intensions of several other concepts. In that situation, a one-to-many mapping should be constructed from the point of view of structures. This paper proposes a tree similarity measure model that can construct this kind of mapping. The similarity measure model takes into account all the information on nodes concepts, weights, and values. The conceptual similarity and the value similarity between two trees are evaluated based on the constructed mapping, and the final similarity measure is assessed as a weighted sum of their conceptual and value similarities. The effectiveness of the proposed similarity measure model is shown by an illustrative example and is also demonstrated by applying it into a recommender system
Lu, P., Lu, J. & Zhang, G. 2012, 'Case-Base Maintenance for Concept Drift', WASET Issue 72: Proceedings of the International Conference on Information Systems, International Conference on Information Systems, WASET, Penang, Malaysia, pp. 333-340.
View/Download from: UTS OPUS
As the evolving nature of a real data stream, so called concept drift, and its accumulating volume, any deployed Case-Based Reasoning (CBR) system will nead to have procedures for Case-Base Maintenance (CBM). Traditional CBM methods for handling concept drift cannot well distinguish between noise and true concept drift, some of them may even run under the risk of losing case-base competence. Motivated by these two problems, wc present a twostage CBM approach. We propose a Noise-Enhanced Fast Context Switch (NEFCS) algorithm, which targets to remove noise under dynamic environment as Stage 1. We also propose a Recursive Conservative Redundancy Removal (RCRR) algorithm, which removes redundant cases in a recursively unifonn way while keeping the casebase coveragc as Stage 2. Experimental evaluations on realworld datasels show that our approach significant improves the perfonnance compared with other CBM approaches.
Komkhao, M., Li, Z., Halang, W.A. & Lu, J. 2012, 'An incremental collaborative filtering algorithm for recommender systems', the 10th International FLINS Conference on Uncertainty Modeling in Knowledge Engineering and Decision Making (FLINS 2012), World Scientific, Istanbul, Turkey, pp. 327-332.
View/Download from: UTS OPUS
Ramezani, F. & Lu, J. 2012, 'A Fuzzy Group Decision Support System for Projects Evaluation', 14th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2012, IPMU 2012, Springer, CATANIA, ITALY, pp. 160-169.
View/Download from: UTS OPUS or Publisher's site
In any organization there are some main goals and lots of projects for achieving these goals. For any organization, it is important to determine how much these projects affect on achieving the main goals. This paper proposes a new fuzzy multiple attribute-based decision support system (DSS) for evaluating projects in promoting the goals as such a selection may involve both quantitative and qualitative assessment attributes. There are many fuzzy ranking methods available to solve multi-attribute decision making (MADM) problems. Some are more suitable than other for particular decision problems. The proposed DSS has ability to choose the most appropriate fuzzy ranking method for solving given MADM problem. In addition it contains sensitivity analysis system which provides opportunity for analyzing the impacts of attributes weights and projects performance on achieving organizations goals. A DSS software prototype has been developed on the basis of the proposed DSS which can be applied for solving every FMADM problem which needs to rank some alternatives according to some attributes.
Kaur, P., Goyal, M.L. & Lu, J. 2012, 'Price Forecasting Using Dynamic Assessment of Market Conditions and Agent's Bidding Behavior', Lecture Notes in Computer Science, Springer-Verlag, Doha, Qatar, pp. 100-108.
View/Download from: UTS OPUS or Publisher's site
Multiple online auctions need complex bidding decisions for selecting which auction to participate in, whether to place single or multiple bids, do early or late bidding and how much to bid. This paper designs a novel fuzzy dynamic bidding agent (FDBA) which uses a comprehensive method for initial price estimation and price forecasting. First, FDBA selects an auction to participate in and calculates its initial price based on clustering and bid selection approach. Then the price of the auction is forecasted based on the estimated initial price, attitude of the bidders to win the auction and the competition assessment for the late bidders using fuzzy reasoning technique. The experiments demonstrated improved price forecasting outcomes using the proposed approach.
Komkhao, M., Lu, J. & Zhang, L. 2012, 'Determining pattern similarity in a medical recommender system', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 103-114.
© Springer-Verlag Berlin Heidelberg 2012. As recommender systems have proven their effectiveness in other areas, it is aimed to transfer this approach for use in medicine. Particularly, the diagnoses of physicians made in rural hospitals of developing countries, in remote areas or in situations of uncertainty are to be complemented by machine recommendations drawing on large bases of expert knowledge in order to reduce the risk to patients. Recommendation is mainly based on finding known patterns similar to a case under consideration. To search for such patterns in rather large databases, a weighted similarity distance is employed, which is specially derived for medical knowledge. For collaborative filtering an incremental algorithm, called W-InCF, is used working with the Mahalanobis distance and fuzzy membership. W-InCF consists of a learning phase, in which a cluster model of patients' medical history is constructed incrementally, and a prediction phase, in which the medical pattern of each patient considered is compared with the model to determine the most similar cluster. Fuzzy sets are employed to cope with possible confusion of decision making on overlapping clusters. The degrees of membership to these fuzzy sets is expressed by a weighted Mahalanobis radial basis function, and the weights are derived from risk factors identified by experts. The algorithm is validated using data on cephalopelvic disproportion.
Behbood, V. & Lu, J. 2011, 'Fuzzy Refinement-based Transductive Transfer Learning for Bank Failure Prediction', World Conference on Soft Computing, IEEE, USA.
Behbood, V. & Lu, J. 2011, 'Efficiency Prediction in Decision Making Units Merger using Data Envelopment Analysis and Neural Network', 19th Triennial Conference of the International Federation of Operational Research Societies, IFORS, Australia.
Behbood, V. & Lu, J. 2011, 'Financial Early Warning System: Adaptive Inference-based Fuzzy Neural Network', 19th Triennial Conference of the International Federation of Operational Research Societies, IFORS, Australia.
Kaur, P., Goyal, M.L. & Lu, J. 2011, 'Pricing Analysis in Online Auctions Using Clustering and Regression Tree Approach', Lecture Notes in Artificial Intelligence 5980 - Agents and Data Mining Interaction, 7th InternationalWorkshop, ADMI 2011, Springer-Verlag Berlin / Heidelberg, Taipei, Taiwan, pp. 248-257.
View/Download from: UTS OPUS
Auctions can be characterized by distinct nature of their feature space. This feature space may include opening price, closing price, average bid rate, bid history, seller and buyer reputation, number of bids and many more. In this paper, a price forecasting agent (PFA) is proposed using data mining techniques to forecast the end-price of an online auction for autonomous agent based system. In the proposed model, the input auction space is partitioned into groups of similar auctions by kmeans clustering algorithm. The recurrent problem of finding the value of k in k-means algorithm is solved by employing elbow method using one way analysis of variance (ANOVA). Based on the transformed data after clustering, bid selector nominates the cluster for the current auction whose price is to be forecasted. Regression trees are employed to predict the end-price and designing the optimal bidding strategies for the current auction. Our results show the improvements in the end price prediction using clustering and regression tree approach.
Demong, N.A. & Lu, J. 2011, 'Personalized Multidimensional Process Framework For Dynamic Risk Analysis In The Real Estate Industry', Sixth International Conference on Construction in the 21st Century (CITC-VI) Construction Challenges in the New Decade, 6th International Conference On Construction In The 21st Century, Greenville, Kuala Lumpar, Malaysia, pp. 111-118.
View/Download from: UTS OPUS
The risk analysis for real estate property investment is subject to high risk. It is qualitatively and quantitatively assessed by various techniques such as the analytical hierarchy process (AHP) and the analytic network process (ANP) which determine the risk factors based on expert survey, weight and rank the factors using algorithm and mathematical formula and decide the best investment based on performance index of the alternatives given. However, experts from the field have different opinions and judgments about the environment of the real estate industry and this scenario will affect the result of the risk factor weight and ranking. Moreover, different investors have different goals and objectives to be achieve. Thus, this paper will propose a new personalized multidimensional process (PMP) framework based on knowledge discovery. This framework comprises of two new methods namely the personalized association mapping (PAM) method and the personalized multidimensional sensitivity analysis (PM-SA) method. The innovations of this research are the justification of risk factor weight and ranking. It will be based on deterministic approach using historical data driven to decision support using knowledge discovery in database and the heuristic approach which is refers to investors personalization of the risk factors which fulfil their requirements.
Ramezani, F., Memariani, A. & Lu, J. 2011, 'A Dynamic Fuzzy Multi-Criteria Group Decision Support System for Manager Selection', Foundations of Intelligent Systems, International Conference on Intelligent Systems and Knowledge Engineering, Springer-Verlag Berlin / Heidelberg, Shanghai, China, pp. 265-274.
View/Download from: UTS OPUS
In any organization, because of the importance of management responsibility and its effect on efficiency improvement, the selection of the appropriate person as a manager is one of the important decision making subjects. This paper proposes a new fuzzy multiple attribute-based decision support system (DSS) for choosing suitable managers as such a selection may involve both quantitative and qualitative assessment attributes. There are many fuzzy ranking methods available to solve multi-attribute decision making (MADM) problems. Some are more suitable than other for particular decision problems. The proposed DSS has ability to choose the most appropriate fuzzy ranking method for solving given MADM problem, based on the type of attributes and the size of the problem, considering the least computation and time consumption for ranking alternatives. A DSS software prototype has been developed on the basis of the proposed DSS which can be applied for solving every FMADM problem which needs to rank some alternatives according to some attributes.
Ramezani, F. & Lu, J. 2011, 'A new approach for choosing the most appropriate fuzzy ranking algorithm for solving MADM problems', Autonomous Systems: Developments and Trends, Workshop Autonomous Systems, Springer-Verlag Berlin / Heidelberg, Mallorca, Spain, pp. 13-24.
View/Download from: UTS OPUS or Publisher's site
There are many fuzzy ranking algorithms available to solve multi-attribute decision making (MADM) problems. Some are more suitable than others for particular decision problems. This paper proposes a new method for choosing the most appropriate fuzzy ranking algorithm for solving MADM problems based on the type and number of attributes and the number of alternatives, considering the least time consumption and the least computation for ranking alternatives. In addition, we develop a software to simulate three main fuzzy ranking algorithms: SAW, Negi, and Chen and Hwang (Chen and Hwang 1992). This software can be used in any MADM decision support system.
Behbood, V., Lu, J. & Zhang, G. 2011, 'Long Term Bank Failure Prediction using Fuzzy Refinement-based Transductive Transfer Learning', 2011 IEEE International Conference on Fuzzy Systems (FUZZ), International Conference on Fuzzy Systems (FUZZ), IEEE, Taipei, Taiwan, pp. 2676-2683.
View/Download from: UTS OPUS or Publisher's site
Machine learning algorithms, which have been considered as robust methods in different computational fields, assume that the training and test data are drawn from the same distribution. This assumption may be violated in many real world applications like bank failure prediction because training and test data may come from different time periods or domains. An efficient novel algorithm known as Fuzzy Refinement (FR) is proposed in this paper to solve this problem and improve the performance. The algorithm utilizes the fuzzy system and similarity concept to modify the instances' labels in target domain which was initially predicted by shift-unaware Fuzzy Neural Network (FNN) proposed by [1]. The experiments are performed using bank failure financial data of United States to evaluate the algorithm performance. The results address a significant improvement in the predictive accuracy of FNN due to applying the proposed algorithm.
Shambour, Q.Y. & Lu, J. 2011, 'Government-to-Business Personalized e-Services Using Semantic-Enhanced Recommender System', Lecture Notes in Computer Science - Electronic Government and the Information Systems Perspective: Proceedings of Second International Conference, EGOVIS 2011, Toulouse, France, August 29 September 2, 2011., Springer, Toulouse, France, pp. 197-211.
View/Download from: UTS OPUS or Publisher's site
The information overload problem results in the under-use of some existing e-Government services. Recommender systems have proven to be an effective solution to the information overload problem by providing users with information and services specific to their needs, rather than an undifferentiated mass of information. This paper focuses on how e-Governments can support businesses, which are seeking `one-to-one e-services, on the problem of finding adequate business partners. For this purpose, a Hybrid Semantic-enhanced Collaborative Filtering (HSeCF) recommendation approach to provide personalized Government-to-Business (G2B) e-services, and in particular, business partner recommendation e-services for Small to Medium Businesses is proposed. Experimental results on two data sets, MovieLens and BizSeeker, show that the proposed HSeCF approach significantly outperforms the benchmark item-based CF algorithms, especially in dealing with sparsity or cold-start item problems
Naderpour, M., Lu, J. & Kerre, E.E. 2011, 'A Conceptual Model for Risk-Based Situation Awareness', Foundations of Intelligent Systems: Proceedings of the Sixth International Conference on Intelligent Systems and Knowledge Engineering, Shanghai, China, Dec 2011 (ISKE2011), International Conference on Intelligent Systems and Knowledge Engineering, Springer, Shanghai, China, pp. 297-306.
View/Download from: UTS OPUS or Publisher's site
Situation Awareness is the perception of the elements in the environment within a volume of time and space, the comprehension of their meaning and the projection of their status in the near future. It is a crucial factor in decision-making in a dynamic environment particularly with certain degrees of risk, called risk-based situation awareness. In this paper we first explore the most popular models in situation awareness, data fusion and risk assessment. We show how they complement each other in developing a conceptual model for risk-based situation awareness. We will also demonstrate how this model can be used to support decision-making in a dynamic environment.
Behbood, V. & Lu, J. 2011, 'Intelligent Financial Warning Model Using Fuzzy Neural Network and Case-Based Reasoning', IEEE Symposium on Computational Intelligence for Financial Engineering & Economics, Computational Intelligence for Financial Engineering and Economics (CIFEr), 2011 IEEE Symposium on, IEEE, France, pp. 9-15.
View/Download from: UTS OPUS or Publisher's site
Creating an applicable and precise financial early warning model is highly desirable for decision makers and regulators in the financial industry. Although Business Failure Prediction (BFP) especially banks has been extensively a researched area since late 1960s, the next critical step which is the decision making support scheme has been ignored. This paper presents a novel model for financial warning which combines a fuzzy inference system with the learning ability of neural network as a Fuzzy Neural Network (FNN) to predict organizational financial status and also applies reasoning capability of Fuzzy Case-Based Reasoning (FCBR) to support decision makers measuring appropriate solutions. The proposed financial warning model generates an adaptive fuzzy rule base to predict financial status of target case and then if it is predicted to fail, the FCBR is used to find similar survived cases. Finally according similar cases and a fuzzy rule base, the model provides financial decisions to change particular features as company goals in upcoming year to avoid future financial distress.
Kaur, P., Goyal, M.L. & Lu, J. 2011, 'Data Mining Driven agents for Predicting Online Auction's End Price', 2011 IEEE Symposium Series on Computational Intelligence Proceedings, IEEE Symposium on Computational Intelligence and Data Mining, IEEE Computational Intelligence Society, Paris, France, pp. 141-147.
View/Download from: UTS OPUS
Abstract Auctions can be characterized by distinct nature of their feature space. This feature space may include opening price, closing price, average bid rate, bid history, seller and buyer reputation, number of bids and many more. In this paper, a clustering based method is used to forecast the end-price of an online auction for autonomous agent based system. In the proposed model, the input auction space is partitioned into groups of similar auctions by kmeans clustering algorithm. The recurrent problem of finding the value of k in k-means algorithm is solved by employing elbow method using one way analysis of variance (ANOVA). Then k numbers of regression models are employed to estimate the forecasted price of an online auction. Based on the transformed data after clustering and the characteristics of the current auction, bid selector nominates the regression model for the current auction whose price is to be forecasted. Our results show the improvements in the end price prediction for each cluster which support in favor of the proposed clustering based model for the bid prediction in the online auction environment.
Al-hassan, M.W., Lu, H. & Lu, J. 2010, 'Personalized e-Government Services: Tourism Recommender System Framework', Web Information Systems and Technologies - Lecture Notes in Computer Science Vol 75 Part III, International Conference WEBIST, Springer, Valencia, Spain, pp. 173-178.
View/Download from: UTS OPUS or Publisher's site
Most governments around the globe use the internet and information technologies to deliver information and services for citizens and businesses. One of the main directions in the current e-government (e-Gov) development strategy is to provide better online services to citizens such that the required information can be located by citizens with less time and effort. Tourism is one of the main focused areas of e-Gov development strategy because it is one of the major profitable industries. Significant efforts have been devoted by governments to improve tourism services. However, the current e-Gov tourism services are limited to simple online presentation; intelligent e-Gov tourism services are highly desirable. Personalization techniques, particularly recommendation systems, are the most promising techniques to deliver personalized e-Gov (Pe-Gov) tourism services. This study proposes ontology-based personalized e-Gov tourism recommender system framework, which would enable tourism information seekers to locate the most interesting destinations and find the most preferable attractions and activities with less time and effort. The main components of the proposed framework and some outstanding features are presented along with a detailed description of a scenario.
Perez, I.J., Alonso, S., Cabrerizo, F., Lu, J. & Herrera-Viedma, E. 2011, 'Modelling Heterogeneity among Experts in Multi-criteria Group Decision Making Problems', Lecture Notes in Computer Science: Proceedings of the 8th International Conference, Modeling Decisions for Artificial Intelligence MDAI 2011, Springer, Changsha, China, pp. 55-66.
View/Download from: UTS OPUS or Publisher's site
Heterogeneity in group decision making problems has been recently studied in the literature. Some instances of these studies include the use of heterogeneous preference representation structures, heterogeneous preference representation domains and heterogeneous importance degrees. On this last heterogeneity level, the importance degrees are associated to the experts regardless of what is being assessed by them, and these degrees are fixed through the problem. However, there are some situations in which the experts importance degrees do not depend only on the expert. Sometimes we can find sets of heterogeneously specialized experts, that is, experts whose knowledge level is higher on some alternatives and criteria than it is on any others. Consequently, their importance degree should be established in accordance with what is being assessed. Thus, there is still a gap on heterogeneous group decision making frameworks to be studied. We propose a new fuzzy linguistic multi-criteria group decision making model which considers different importance degrees for each expert depending not only on the alternatives but also on the criterion which is taken into account to evaluate them.
Jiang, J., Lu, J., Zhang, G. & Long, G. 2011, 'Scaling-Up Item-Based Collaborative Filtering Recommendation Algorithm Based on Hadoop', 2011 IEEE World Congress on Services (SERVICES 2011), IEEE World Congress on Services, IEEE, Washington, DC, pp. 490-497.
View/Download from: UTS OPUS
Collaborative filtering (CF) techniques have achieved widespread success in E-commerce nowadays. The tremendous growth of the number of customers and products in recent years poses some key challenges for recommender systems in which high quality recommendations are required and more recommendations per second for millions of customers and products need to be performed. Thus, the improvement of scalability and efficiency of collaborative filtering (CF) algorithms become increasingly important and difficult. In this paper, we developed and implemented a scaling-up item-based collaborative filtering algorithm on MapReduce, by splitting the three most costly computations in the proposed algorithm into four Map-Reduce phases, each of which can be independently executed on different nodes in parallel. We also proposed efficient partition strategies not only to enable the parallel computation in each Map-Reduce phase but also to maximize data locality to minimize the communication cost. Experimental results effectively showed the good performance in scalability and efficiency of the item-based CF algorithm on a Hadoop cluster.
Zhang, J., Lu, J. & Zhang, G. 2011, 'Combining one class classification models for avian influenza outbreaks', IEEE SSCI 2011 - Symposium Series on Computational Intelligence - MCDM 2011: 2011 IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making, pp. 190-196.
View/Download from: Publisher's site
The prediction of avian influenza outbreak animal cases is a genuine one class classification issue because the real world outliers are impractical to obtain. In this paper, a new combining one class classification method has been presented and illustrated on the avian influenza outbreak dataset. The presented combining methods outperform the previous combining methods both on the original avian influenza outbreak dataset and dimension reduction one. The new one classification combining model can be adapted to the warning surveillance purpose and proved to be practical on the avian influenza outbreak prediction tasks. © 2011 IEEE.
Shambour, Q. & Lu, J. 2011, 'Integrating Multi-Criteria Collaborative Filtering and Trust filtering for personalized Recommender Systems', IEEE SSCI 2011 - Symposium Series on Computational Intelligence - MCDM 2011: 2011 IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making, pp. 44-51.
View/Download from: Publisher's site
Recommender Systems are information systems that attempt to recommend items of interest to particular users based on their explicit and implicit preferences. Multi-Criteria Decision Making (MCDM) aims at assisting the decision maker in the decision making process, or giving the decision maker a recommendation, concerning a set of actions, alternatives, items etc. Thus, despite their differences, Recommender Systems and Multi-Criteria Decision Making share the same objective which is supporting the decision making process and reducing information overload. In this paper we propose a novel hybrid Multi-Criteria Trust-enhanced CF (MC-TeCF) approach. The proposed MC-TeCF approach combines the MC user-based CF and the MC user-based Trust filtering approaches to alleviate the standard Single-Criteria user-based CF limitations. Empirical results demonstrate the significance and effectiveness of the proposed MC-TeCF approach in terms of improving accuracy, as well as in dealing with very sparse data sets or cold start users compared with the standard Single-Criteria user-based CF approach. © 2011 IEEE.
Shambour, Q. & Lu, J. 2011, 'A hybrid Multi-Criteria Semantic-enhanced collaborative filtering approach for personalized recommendations', Proceedings - 2011 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2011, pp. 71-78.
View/Download from: Publisher's site
Recommender systems aim to assist web users to find only relevant information to their needs rather than an undifferentiated mass of information. Collaborative filtering (CF) techniques are probably the most popular and widely adopted techniques in recommender systems. Despite of their success in various applications, CF-based techniques still encounter two major limitations, namely sparsity and coldstart problems. More recently, semantic information of items has been successfully used in recommender systems to alleviate such problems. Moreover, the incorporation of multi-criteria ratings in recommender systems can help to produce more accurate recommendations. Thereby, in this paper, we propose a hybrid Multi-Criteria Semantic-enhanced CF (MC-SeCF) approach. The MC-SeCF approach integrates the enhanced MC item-based CF and the item-based semantic filtering approaches to alleviate current limitations of the item-based CF techniques. Experimental results demonstrate the effectiveness of the proposed MC-SeCF approach in terms of improving accuracy, as well as in dealing with very sparse data sets or cold-start items compared to benchmark item-based CF techniques. © 2011 IEEE.
Gao, Y., Zhang, G., Lu, J. & Wee, H. 2009, 'A Bi-level Pricing Model and a PSO Based Algorithm in Supply Chains', Intelligent Decision Making systems: Proceedings of the 4th International ISKE Conference, International Conference on Intelligent Systems and Knowledge Engineering, World Scientific, Hasselt, Belgium, pp. 394-401.
View/Download from: UTS OPUS or Publisher's site
Due to rapid technological innovation and severe competition, in hi-tech industries such as computers and communication, the upstream component price and the downstream product cost usually decline significantly with time. In such a background, an effective pricing supply chain model becomes crucial. This paper first establishes a bi-level pricing model for pricing problems for a buyer and a vendor in a supply chain. Then, a particle swarm optimization Q>SO) based algorithm is developed to solve the problem defined by this model. Experiments illustrate that this algorithm can achieve more profits for both a buyer and a vendor compared with the existing methods.
Amailef, K. & Lu, J. 2009, 'An Ontology-Supported CBR System for a Mobile-Based Emergency response system', Intelligent Decision Making Systems: Proceedings of the 4th International ISKE Conference, International Conference on Intelligent Systems and Knowledge Engineering, World Scientific, Hasselt, Belgium, pp. 261-266.
View/Download from: UTS OPUS or Publisher's site
A mobile-based emergency response system (MERS), as one of the important Mobile Government (m-Government) services, aims to reduce risks in an emergency situation. This paper presents a system basedon case-based reasoning (CBR) approach combined with domain ontology to support emergency decision makers for the MERS. The benefit of using this system is to let the retrieving process more convenient in order to depict conclusions and to give recommendations based on theknowledge from thepast disaster event occurs. The system mainly consists of five components: data acquisition; ontology; knowledge base; CBR system; and situation assessment.
Shambour, Q.Y. & Lu, J. 2009, 'A Recommender System for Personalized G2B E-services Using Metadata-Based Ontology and Focused Web Crawler', Intelligent Decision Making Systems: Proceedings of the 4th International Conference on Intelligent Systems and Knowledge Engineering (ISKE2009), International Conference on Intelligent Systems and Knowledge Engineering, World Scientific Publishing Co. Pte. Ltd., Hasselt, Belgium, pp. 332-337.
View/Download from: Publisher's site
Providing personalized online services to businesses is one of the main challenges in current e- Government development. Recommendation techniques can provide a possible solution for this issue. This study presents an e-Government to Business Recommender System (eGBRS) based on the Multi-Attribute Utility Theory (MAUT) to handle personalized government-to-Business (G2B) e-Services. Specifically, the proposed system uses web information crawling and metadata-based ontology techniques for building a business-based knowledge base with multiattribute recommendation capabilities. The proposed eGBRS can be used by e-government agencies to provide business partner matching recommendation services to their business users according to their needs and preferences.
Al- Hassan, M.W., Lu, H. & Lu, J. 2010, 'A Framework for Delivering Personalized E-Government Tourism Services', WEBIST 2010 - Proceedings of the 6th International Conference on Web Information Systems and Technology, WEBIST, Institute for Systems and Technologies of Information, Control and Communication, Valencia, Spain, pp. 263-270.
View/Download from: UTS OPUS
E-government (e-Gov) has become one of the most important parts of government strategies. Significant efforts have been devoted to e-Gov tourism services in many countries because tourism is one of the major profitable industries. However, the current e-Gov tourism services are limited to simple online presentation of tourism information. Intelligent e-Gov tourism services, such as the personalized e-Gov (Pe-Gov) tourism services, are highly desirable for helping users decide âwhere to go, and what to do/seeâ amongst massive number of destinations and enormous attractiveness and activities. This paper proposes a framework of Pe-Gov tourism services using recommender system techniques and semantic ontology. This framework has the potential to enable tourism information seekers to locate the most interesting destinations with the most suitable activities with the least search efforts. Its workflow and some outstanding features are depicted with an example.
Xiao, J., Lu, J., Chin, K., Xu, J. & Yao, J. 2010, 'Cross-Cultural Learning Challenges and Teaching Strategies for First-Year Asian Students in Australian Universities', CSEDU 2010 - Proceedings of the 2nd International Conference on Computer Supported Education, International Conference on Computer Supported Education, Institute for Systems and Technologies of Information, Control and Communication, Valencia, Spain, pp. 297-303.
View/Download from: UTS OPUS
With the dramatic increase in the number of Asian students in the past few years, the cross-cultural teaching and learning situation becomes an important issue in Australian universities. To tickle this issue, we conducted a survey to students studying Information Technology and Business courses in five Australian universities. A total of 639 international students and 387 local students completed the questionnaire survey. Our survey results revealed a number of leaning challenges facing international students, especially first year Asian students. Student and staff interviews were also conducted to discover further facts that may not be covered by the questionnaire, and to check whether or not the survey (and interview) results represent the similar view from the staff side. The initial interview outcome, based on an incomplete number of interviews, generally supported the findings from the student survey. This paper is to examine the challenges, especially those from language and cultural aspects that face Asian students studying in Australian universities, and summarize some responses to relevant survey/interview questions from both local and international students. Some teaching strategies on how to improve language ability and classroom skills for first-year Asian international students are initiated.
Purba, J., Lu, J., Ruan, D. & Zhang, G. 2010, 'Probabilistic Safety Assessment in Nuclear Power Plants by Fuzzy Numbers', Proceedings of the 9th FLINS Conference: Computational Intelligence: Foundations and Applications (2010), International FLINS Conference on Computational Intelligence: Foundations and Applications, World Scientific Publishing Co. Pte. Ltd., Chengdu, China, pp. 256-262.
View/Download from: UTS OPUS
Probabilistic safety assessment in nuclear power plants (NPPs) greatly considers plant safety and optimal plant design. Plant specific data are usually recommended to analyze safety in NPPs. However, such NPP specific data are not always available in practice. This paper presents an approach by combining fuzzy numbers and expert justification to assess an NPP probabilistic failure rate in the absence of statistical data. The proposed approach illustrates a case study for high pressure core spray systems of boiling water reactors.
Zhang, G., Dillon, T.S., Cai, K., Ma, J. & Lu, J. 2010, 'Delta-Equalities of Complex Fuzzy Relations', Proceedings 24th IEEE International Conference on Advanced Information Networking and Applications, IEEE International Conference on Advanced Information Networking and Applications, IEEE Computer Society Conference Publishing Services (CPS), Perth, Australia, pp. 1218-1224.
View/Download from: UTS OPUS or Publisher's site
A complex fuzzy relation is defined as a fuzzy relation whose membership function takes values in the unit circle on a complex plane. This paper first investigates various operation properties of a complex fuzzy relation. It then defines the distance measure of two complex fuzzy relations that can measure the differences between the grades as well as the phases of two complex fuzzy relations. This distance measure is used to define δ-equalities of complex fuzzy relations that coincide with those of fuzzy relations already defined in the literature if complex fuzzy relations reduce to real-valued fuzzy relations. Two complex fuzzy relations are said to be δ-equal if the distance between them is less than 1-δ. This paper shows how various operations between complex fuzzy relations, including T-norms and S-norms, affect given δ-equalities of complex fuzzy relations. Finally, fuzzy inference is examined in the framework of δ-equalities of complex fuzzy relations.
Nguyen, T., Lu, H. & Lu, J. 2010, 'Ontology-Style Web Usage Model for Semantic Web Applications', Proc. of the 10th International Conference on Intelligent Systems Design and Applications (ISDA 2010), International Conference on Intelligent Systems Design and Applications, IEEE, Egypt, pp. 784-789.
View/Download from: UTS OPUS or Publisher's site
Current semantic recommender systems aim to exploit the website ontologies to produce valuable web recommendations. However, Web usage knowledge for recommendation is presented separately and differently from the domain ontology, this leads to the complexity of using inconsistent knowledge resources. This paper aims to solve this problem by proposing a novel ontology-style model of Web usage to represent the non-taxonomic visiting relationship among the visited pages. The output of this model is an ontology-style document which enables the discovered web usage knowledge to be sharable and machine-understandable in semantic Web applications, such as recommender systems. A case study is presented to show how this model is used in conjunction of the web usage mining and web recommendation. Two real-world datasets are used in the case study.
Sanati, F. & Lu, J. 2010, 'LifeEvent Ontology Oriented E-government Service Integration', Proc. of 2010 IEEE International Conference on Service-Oriented Computing and Applications (SOCA), IEEE International Conference on Service-Oriented Computing and Applications, IEEE, Perth, Australia, pp. 1-6.
View/Download from: UTS OPUS or Publisher's site
From e-government integration viewpoint, LifeEvent is a collection of actions including at least one public service, which executed in its designated workflow to fulfil request of a citizen arising from a new real-life situation. The purpose of this study is to provide technical guidelines for extending Ontology Web Language for Services (OWL-S), which can provide technical support for LifeEvent Ontology Oriented Service Integration within the e-government domain. This study suggests a framework based on ontological analysis and modelling. Proposed framework is based on the extensive use of LifeEvent concept to achieve dynamically configured automated delivery of integrated e-government services. This paper proposes the LifeEvent Ontology that is a logical extension of OWL-S for implementation of EService Integration Modelling framework proposed in prior research.
Shambour, Q.Y. & Lu, J. 2010, 'A framework of hybrid recommendation system for government-to-business personalized e-services', ITNG2010 - 7th International Conference on Information Technology: New Generations, International Conference on Information Technology: New Generations, IEEE, Las Vegas, USA, pp. 592-597.
View/Download from: UTS OPUS or Publisher's site
One of the challenges facing e-governments is how to provide businesses with services and information specific to their needs, rather than an undifferentiated mass of information. One way to achieve this is through the design and development of personali
Wu, D., Lu, J. & Zhang, G. 2010, 'A hybrid recommendation approach for hierarchical items', Proceedings of 2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2010, International Conference on Intelligent Systems and Knowledge Engineering, IEEE, Hangzhou, China, pp. 492-497.
View/Download from: UTS OPUS or Publisher's site
Recommender systems aim to recommend items that are likely to be of interest to the user. In many business situations, complex items are described by hierarchical tree structures, which contain rich semantic information. To recommend hierarchical items a
Tao, W. & Lu, J. 2010, 'Trust-based decision making in dynamic environments', Proceedings - 2010 IEEE International Conference on Granular Computing, GrC 2010, International Conference on Granular Computing, IEEE, San Jose, CA, pp. 465-470.
View/Download from: UTS OPUS or Publisher's site
Recent developments in information technology shift the computing paradigm towards greater dynamism and unpredictability, which raises new challenges. In dynamic computing environments, the relationship between transacting entities is not pre-determined,
Zhang, T., Zhang, G., Lu, J. & Ding, Q. 2010, 'Fault Diagnosis of Transformer using Association Rule Mining and Knowledge Base', Proceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications (ISDA), International Conference on Intelligent Systems Design and Applications, IEEE, Cairo, Egypt, pp. 737-742.
View/Download from: UTS OPUS or Publisher's site
Association rule mining makes interesting associations and/or correlations among large sets of data. Those associations can be refined as decision rules to be used and stored in a knowledge base system. In this paper, an approach based on association rule and knowledge base is proposed and implemented in the fault diagnosis of a transformer system. According to the features of association rule, the Apriori algorithm is adopted and modified to generate decision rules from power transformer information for building knowledge base, then the rules can be refined to diagnose the fault of the transformer through reasoning, and a prototype system is developed. This approach based on association rule is described in detail and the application is illustrated by an example. A comparison with the IEC (International Electrotechnical Commission) three-ratio method shows the proposed method can provide better accuracy in performance.
Zhang, R., Wei, J., Lu, J. & Zhang, G. 2010, 'A Decision Support System for Ore Blending Cost Optimization Problem of Blast Furnaces', Advances in Intelligent Decision Technologies - Proceedings of the Second KES International Symposium IDT 2010, The Second KES International Symposium IDT, Springer-Verlag, Baltimore, USA, pp. 143-152.
View/Download from: UTS OPUS or Publisher's site
In iron and steel enterprises, it is difficult to obtain the lowest-cost optimal solution to an ore blending problem for blast furnaces by using the traditional trial-fault-trial (TFT) method because of the complexity of materials and burden of workflow. Here, we develop a set of decision support systems (DSS) software to solve the problem. Using the basics of analyzing business flow and the working process of ore blending, we pre-process the data for materials and elements, abstract a non-linear model of ore blending for a blast furnace, design the architecture for ore blending cost optimization DSS which integrates a database, a model base and a knowledge base, and solve the problem. The system has made economic gains since it was implemented in Xiangtan Iron & Steel Group Co. Ltd., China, in September 2008.
Zhang, T., Liang, L., Lin, H., Lu, J. & Zhang, G. 2010, 'A knowledge-based efficiency assessment system for distribution network using data envelopment analysis', Proceedings of 2010 Fourth International Conference on Research Challenges in Information Science, International Conference on Research Challenges in Information Science, IEEE Computer Society Press, Nice, France, pp. 331-336.
View/Download from: UTS OPUS or Publisher's site
Distribution network is the most important asset in electric utilities, to increase its efficiency, there is a need to effectively assess the efficiency of distribution network and provide solution for improvement. This paper presents a knowledge-based efficiency assessment system for distribution network using data envelopment analysis (DEA). From an input-output view, DEA method is used to calculate the efficiency of distribution lines and obtain gap information. Knowledge base is used to store facts and rules, facts include input-outputs data of DEA and other information about structure and operation of distribution lines. The rules can be empirical rules from domain expert or extracted from industry guidelines or government standards by knowledge worker. Considering the service requirements of power supply in rules, DEA assessment results can be effectively used or provided in the solution of improvement by reasoning. The knowledge base technology and DEA integration represents a step towards a real challenge of the near future. In this system, the final decision is based on DEA assessment results and reasoning. The suggested solution can assist decision maker in making planning to further strengthen distribution network in an efficient and effective manner.
Behbood, V., Lu, J. & Zhang, G. 2010, 'Adaptive Inference-based Learning and Rule Generation Algorithms in Fuzzy Neural Network for Failure Prediction', The Proceedings of 2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2010), IEEE International Conference on Intelligent Systems and Knowledge Engineering, IEEE, China, pp. 33-38.
View/Download from: UTS OPUS or Publisher's site
highly desirable for decision makers and regulators in the finance industry. This study develops a new Failure Prediction (FP) approach which effectively integrates a fuzzy logic-based adaptive inference system with the learning ability of a neural network to generate knowledge in the form of a fuzzy rule base. This FP approach uses a preprocessing phase to deal with the imbalanced data-sets problem and develops a new Fuzzy Neural Network (FNN) including an adaptive inference system in the learning algorithm along with its network structure and rule generation algorithm as a means to reduce prediction error in the FP approach.
Behbood, V., Lu, J. & Zhang, G. 2010, 'Intelligent financial warning support system', International Conference on Applied Statistics and Financial Mathematics, International Conference on Applied Statistics and Financial Mathematics, IOS Press, Hong Kong.
Lu, J., Zhang, G. & Behbood, V. 2010, 'Decision Support and Warning Systems for Business Intelligence', 10th International Conference on Information (ICI10), Egypt.
Al Qahtani, A., Lu, H. & Lu, J. 2010, 'Towards Semantic-Aware and Ontology-Based e-Government Service Integration - An Applicative Case Study of Saudi Arabia's King Abdullah Scholarship Program', Advances in Intelligent Decision Technologies - Proceedings of the Second KES International Symposium IDT 2010, The Second KES International Symposium IDT, Springer-Verlag, Baltimore, USA, pp. 403-411.
View/Download from: UTS OPUS or Publisher's site
By improving the quality of e-government services by enabling access to services across different government agencies through one portal, services integration plays a key role in e-government development. This paper proposes a conceptual framework of ontology based e-government service integration, using Saudi Arabia's King Abdullah Scholarship Program (SAKASP) as a case study. SAKASP is a multi-domain program in which students must collect information from various Ministries to complete applications and the administering authority must verify the information supplied by the Ministries. The current implementation of SAKASP is clumsy because it is a mixture of online submission and manual collection and verification of information; its time-consuming and tedious procedures are inconvenient for the applicants and inefficient for the administrators. The proposed framework provides an integrated service by employing semantic web service (SWS) and ontology, improving the current implementation of SAKASP by automatically collecting and processing the related information for a given application. The article includes a typical scenario that demonstrates the workflow of the framework. This framework is applicable to other multi-domain e-government services.
Tao, W., Lu, J. & Yang, J. 2010, 'Trusted Interaction With Multi-Criteria Decision Support In Dynamic Environment', Proceedings of the 9th International FLINS Conference, International FLINS Conference, World Scientific, Chengdu, China, pp. 1065-1071.
View/Download from: UTS OPUS or Publisher's site
Recent developments information technology shifts the computing paradigm towards more dynamic, which also raises some new challenges. Based on our previous research work MobiPass, this paper proposes a technique which can help transacting entities select the most suitable transacting entities by establishing trusted interaction in dynamic environments in a real time manner by using Multi Criteria Decision Support System(MCDSS) as well as MobiPass framework,
Amailef, K., Lu, J. & Ma, J. 2009, 'Text Information Extraction and Aggregation in a mobile-Based Emergency Response System', New Perspectives on Risk Analysis and Crisis Response: Proceedings of the 2nd International Conference on Risk Analysis and Crisis Response, International Conference on Risk Analysis and Crisis Response, Atlantis Press, Beijing, China, pp. 186-191.
View/Download from: UTS OPUS
Wee, H., Lu, J., Zhang, G., Chiao, H. & Gao, Y. 2009, 'A decision making model for vendor-buyer inventory systems', Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing: Lecture Notes In Artificial Intelligence; Vol. 5908, International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, Springer, Delhi, India, pp. 336-343.
View/Download from: UTS OPUS or Publisher's site
In a vendor-buyer supply chain, the buyer's economic order quantity and the vendor's optimal number of deliveries are derived either independently or collaboratively. In this paper, we establish a two-stage vendor-buyer inventory system decision model by using bi-level decision making approach. The experimental result shows that the proposed bi-level decision model can effectively handle two-stage vendor-buyer inventory problems and obtain better results than the existing methods.
Zhang, J., Yin, W., Pongpanich, N., Lu, J. & Zhang, G. 2009, 'Case-based reasoning in avian influenza risk early warning', The Second Conference on Risk Analysis and Crisis Response (RACR) 2009, Atlantis press, Beijing, P. R. China, pp. 246-251.
View/Download from: UTS OPUS
NA
Lu, J., Shambour, Q.Y. & Zhang, G. 2009, 'Recommendation Technique-based Government-to-Business Personalized E-services', The 28th North American Fuzzy Information Processing Society Annual Conference (NAFIPS2009), North American Fuzzy Information Processing Society Conference, Institute of Electrical and Electronics Engineers (IEEE), Cincinnati, Ohio, USA, pp. 1-6.
View/Download from: UTS OPUS or Publisher's site
One of the new directions in current e-government development is to provide personalized online services to citizens and businesses. Recommendation techniques can bring a possible solution for this issue. This study proposes a hybrid recommendation approach to provide personalized government to business (G2B) e-services. The approach integrates fuzzy setsbased semantic similarity and traditional item-based collaborative filtering methods to improve recommendation accuracy. A recommender system named Intelligent Business Partner Locator (IBPL) is designed to apply the proposed recommendation approach for supporting government agencies to recommend business partners.
Ma, J., Zhang, G., Lu, J. & Ruan, D. 2009, 'Impute missing assessments by opinion clustering in multi-criteria group decision making problems', The 13th IFSA World Congress and the 6th Conference of EUSFLAT, IFSA World Congress, IFSA/EUSFLAT, Lisbon, Portugal, pp. 555-560.
View/Download from: UTS OPUS
Multi-criteria group decision-making and evaluation (MCGDME) method typically aggregates information in evaluation tables. For various reasons, evaluation tables (decision matrix) often include missing data that highly affect correct decision-making and evaluation. Most existing imputation methods of missing data are based on statistical features which do not exist in an MCGDME setting. This paper proposes an imputation method of missing data (IMD) in evaluation tables. The IMD method measures the similarity betweent two evaluators' mental models. Evaluators are then classed into several groups based on their similarities by using fuzzy clustering methods.
Ma, J., Zhang, G. & Lu, J. 2009, 'An approximate reasoning based linguistic multi-criteria group decision-making method', International Conference on Quantitative Logic and Quantification of Software, Quantitative Logic and Quantification of Software, Global-Link Publisher, Shanghai, China, pp. 145-156.
View/Download from: UTS OPUS
NA
Gao, Y., Zhang, G., Lu, J. & Wee, H. 2010, 'A Fuzzy Bi-level Pricing Model and a PSO Based Algorithm in Supply Chains', Proceedings of the 16th International Conference on Neural Information Processing: Lecture Notes in Computer Science Vol 5864, International Conference on Neural Information Processing, Springer, Bangkok, Thailand, pp. 226-233.
View/Download from: UTS OPUS or Publisher's site
Due to rapid technological innovation and severe competition, the upstream component price and the downstream product cost in hi-tech industries usually decline significantly with time. In building a pricing supply chain model, some coefficients are generally obtained from experiments and cannot be defined as crisp numbers. Thus, an effective fuzzy pricing supply chain model becomes crucial. This paper establishes a fuzzy bi-level pricing model for buyers and vendors in supply chains. Then, a particle swarm optimization (PSO) based algorithm is developed to solve problems defined by this model. Experiments show that this PSO-based algorithm can solve fuzzy bi-level pricing problems effectively.
Zhang, G., Zhang, G., Gao, Y. & Lu, J. 2009, 'A Fuzzy Bilevel Model and a PSO-based Algorithm for Day-ahead Electricity Market Strategy Making', Knowledge-Based and Intelligent Information and Engineering Systems - 13th International Conference, KES 2009 Lecture Notes in Computer Science Vol 5712, International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, IOS Press, Santiago, Chile, pp. 736-744.
View/Download from: UTS OPUS or Publisher's site
This paper applies bilevel optimization techniques and fuzzy set theory to model and support bidding strategy making in electricity markets. By analyzing the strategic bidding behavior of generating companies, we build up a fuzzy bilevel optimization model for day-ahead electricity market strategy making. In this model, each generating company chooses the bids to maximize the individual profit. A market operator solves an optimization problem based on the minimization purchase electricity fare to determine the output power for each unit and uniform marginal price. Then, a particle swarm optimization (PSO)-based algorithm is developed for solving problems defined by this model.
Zhang, G., Zhang, G., Gao, Y. & Lu, J. 2009, 'A Bilevel Optimization Model and a PSO-based Algorithm in Day-ahead Electricity Markets', Proceedings of the 2009 IEEE International Conference on Systems, Man, and Cybernetics, IEEE Conference on Systems, Man and Cybernetics, IEEE, Texas, USA, pp. 611-616.
View/Download from: UTS OPUS or Publisher's site
Strategic bidding problems are becoming key issues in competitive electricity markets. This paper applies bilevel optimization theory to deal with this issue. We first analyze generating company strategic bidding behaviors and build a bilevel optimization model for a day-ahead electricity market. In this bilevel optimization model, each generating company will choose their bids in order to maximize their individual profits. A market operator will determine the output power for each unit and uniform marginal price based on the minimization purchase electricity fare. For solving this competitive strategic bidding problem described by the bilevel optimization model, a particle swarm optimization (PSO)-based algorithm is. Experiment results have demonstrated the validity of the PSO-based algorithm in solving the competitive strategic bidding problems for a day-ahead electricity market
Gao, Y., Zhang, G. & Lu, J. 2009, 'A Particle Swarm Optimization Based Algorithm for Fuzzy Bilevel Decision Making with Constraints-shared Followers', Proceedings of the 2009 ACM Symposium on Applied Computing, ACM Symposium on Applied Computing, ACM, Hawaii, USA, pp. 1075-1079.
View/Download from: UTS OPUS or Publisher's site
In a bilevel decision problem, decision making may involve multiple followers and fuzzy demands. This research focuses on the problem of fuzzy linear bilevel decision making with multiple followers who share common constraints (FBCSF). Based on the ranking relationship among fuzzy sets defined by cut set and satisfactory degree , a FBCSF model is presented and a particle swarm optimization based algorithm is developed. The experiments reveal that solutions obtained by this algorithm are reasonable and stable.
Lu, P., Lu, J. & Zhang, G. 2009, 'An Integrated Knowledge Adaption Framework for Case-based Reasoning Systems', Knowledge-Based and Intelligent Information and Engineering Systems: Lecture Notes in Artificial Intelligence Vol 5712, International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, Springer, Santiago, Chile, pp. 372-379.
View/Download from: UTS OPUS or Publisher's site
The development of effective knowledge adaption techniques is one of the promising solutions to improve the performance of case-based reasoning (CBR) systems. Case-base maintenance becomes a powerful method to refine knowledge in CBR systems. This paper proposes an integrated knowledge adaption framework for CBR systems which contains a meta database component and a maintenance strategies component. The meta database component can help track changes of interested concepts and therefore enable a CBR system to signal a need for maintenance or to invoke adaption on its own. The maintenance strategies component can perform cross-container maintenance operations in a CBR system. This paper also illustrates how the proposed integrated knowledge adaption framework assists decision makers to build dynamic prediction and decision capabilities.
Zhang, G., Zhang, J. & Lu, J. 2009, 'A hybrid knowledge-based prediction method for avian influenza early warning', Proceedings of the 2009 IEEE International Conference on Systems, Man, and Cybernetics, IEEE Conference on Systems, Man and Cybernetics, IEEE, San Antonio, Texas, USA, pp. 617-622.
View/Download from: UTS OPUS or Publisher's site
High pathogenic avian influenza remains rampant and the epidemic size has been growing in the world. The early warning system (EWS) for avian influenza becomes increasingly essential to militating against the risk of outbreak crisis. An EWS can generate timely early warnings to support decision makers in identifying underlying vulnerabilities and implementing relevant strategies. This paper addresses this crucial issue and focuses on how to make full use of previous events to perform comprehensive forecasting and generate reliable warning signals. It proposes a hybrid knowledge-based prediction (HKBP) method which combines case-based reasoning (CBR) with the fuzzy logic technique. The method can improve the prediction accuracy for avian influenza in a specific region at a specific time. An example is presented to illustrate the capabilities and procedures of the HKBP method.
Al- Hassan, M.W., Lu, H. & Lu, J. 2009, 'A Framework for Delivering Personalized e-Government Services from a Citizen-Centric Approach', Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services (iiWAS2009), Information Integration and Web-based Applications and Services, ACM in cooperation with the Austrian Computer Society, Kuala Lumpur, Malaysia, pp. 434-438.
View/Download from: UTS OPUS or Publisher's site
E-government is becoming more attentive towards providing intelligent personalized online services to citizens so that citizens can receive better services with less time and effort. This paper proposes a new conceptual framework for delivering personalized e-government services to citizens from a citizen-centric approach, called Pe-Gov service framework. This framework outlines the main components and their interconnections. Detailed explanations about these components are given and the special features of this framework are highlighted. This framework has the potential to outperform the existing e-Gov service systems as illustrated by two real life examples.
Lu, P., Lu, J. & Zhang, G. 2009, 'Maintaining Footprint-Based Retrieval for Case Deletion', Neural Information Processing: Lecture Notes in Computer Science Vol 5864, International Conference on Neural Information Processing, Springer, Bangkok, Thailand, pp. 318-325.
View/Download from: UTS OPUS or Publisher's site
The effectiveness and efficiency of case-based reasoning (CBR) systems depend largely on the success of case-based retrieval. The case-base maintenance (CBM) issues become imperative and important especially for modern societies. This paper proposes a new competence model and a new maintenance procedure for the proposed competence model. Based on the proposed competence maintenance procedure, footprint-based retrieval (FBR), a competence-based case base retrieval method, is able to preserve its own retrieval effectiveness and efficiency.
Lu, J., Chin, K., Yao, J., Xu, J. & Xiao, J. 2009, 'Cross-Cultural Teaching and Learning Methodology Analysis for Asian Students in Australian Universities', International Conference on Innovation in Teaching and Management of Higher Education (ICITM 2009), Innovation in Teaching and Management of Higher Education, UPENA, Malaysia, pp. 1-12.
View/Download from: UTS OPUS
The number of Asian background international students in Australian universities has been increasing dramatically in the last ten years. The cultural factors may affect the learning outcomes of international students and the teaching approaches adopted by Australian lecturing staff. Therefore, the cross-cultural teaching and learning environment becomes an important issue in some education units of Australian universities. This study has completed a questionnaire survey of 1026 students from five universities in Australia. The questionnaire includes 55 main questions within six sections. Within the 1026 students, there are 639 Asian background international students and 387 others. These students mainly study in Information Technology field (28.5%) and Business field (52.5%). We have conducted statistical analysis including frequency and Chi-Square to test 59 hypotheses we defined. We have also analyzed the open questions in the questionnaire. This paper mainly presents the research methodology, main data analysis results and interesting findings of this project.
Gao, Y., Zhang, G. & Lu, J. 2008, 'A Particle Swarm Optimization based Algorithm for Fuzzy Bilevel Decision Making', WCCI 2008 Proceedings: Proceedings of the IEEE Conference on Fuzzy Systems, IEEE International Conference on Fuzzy Systems, IEEE, Kong Kong, China, pp. 1452-1457.
View/Download from: UTS OPUS or Publisher's site
Bilevel decision techniques are developed for decentralized planning problems with decision makers located in a two-level system. This study develops a particle swarm optimization based algorithm to solve fuzzy linear bilevel (FLBL) decision problems. A main advantage of this algorithm is that the optimization technique is adopted directly on FLBL problems by fully considering the original information carried by the fuzzy parameters, thus minimizing information loss. Experiments reveal that this algorithm can effectively solve the fuzzy linear bilevel decision problems.
Sanati, F. & Lu, J. 2008, 'Semantic Web for E-Government Service Delivery Integration', Information Technology: New Generations, 2008. ITNG 2008. Fifth International Conference on, International Conference on Information Technology: New Generations, IEEE, Las Vegas, NV, USA, pp. 459-464.
View/Download from: UTS OPUS or Publisher's site
Repeatability is one of the most fundamental components of any methodology or framework in any engineering discipline. Many research projects attempting to formulate some modelling strategies as the new technologies and development techniques are being p
Zhang, J., Lu, J. & Zhang, G. 2008, 'An Integrated Framework of Early Warning Systems', Computational Intelligence in Decision and Control Proceedings of 8th FLINS Conference, International Fuzzy Logic and Intelligent technologies in Nuclear Science Conference, World Scientific, Madrid, Spain, pp. 683-688.
View/Download from: UTS OPUS
An early warning system (EWS) is critical to saving lives and mitigating loss from disasters. Literature addresses specific technical issues of EWSs in different hazard domains, however, only a few discussions on framework and standards. The paper proposes a set of practical designing standards and a comprehensive EWS integration framework. The framework takes considerations of human factors, lead-time and feed-back issues, therefore more suitable for a wide range of applications in practice.
Gao, Y., Zhang, G. & Lu, J. 2008, 'A Particle Swarm Optimization based Algorithm for Fuzzy Bilevel Decision Making with Objective-shared Followers', Simulated Evolution and Learning, Asia-Pacific Conference on Simulated Evolution and Learning, Springer, Melbourne, Australia, pp. 190-199.
View/Download from: UTS OPUS or Publisher's site
A bilevel decision problem may have multiple followers as the lower decision units and have fuzzy demands simultaneously. This paper focuses on problems of fuzzy linear bilevel decision making with multiple followers who share a common objective but have different constraints (FBOSF). Based on the ranking relationship among fuzzy sets defined by cut set and satisfactory degree, a FBOSF model is presented and a particle swarm optimization based algorithm is developed.
Niu, L., Lu, J. & Zhang, G. 2008, 'Improved business intelligence analytics on manager's experience', WCCI 2008 Proceedings: Proceedings of the World Congress on Evolutionary Computing, IEEE Congress on Evolutionary Computation, IEEE, Hong Kong, pp. 726-730.
View/Download from: UTS OPUS or Publisher's site
Gao, Y., Zhang, G., Lu, J. & Goyal, M.L. 2008, 'A Decision Support System for Fuzzy Bilevel Decision Making', Computational Intelligence in Decision and Control: Proceedings of the 8th International FLINS Conference, International Fuzzy Logic and Intelligent technologies in Nuclear Science Conference, World Scientific Publishing Co., Inc., Madrid, Spain, pp. 763-768.
View/Download from: UTS OPUS
Bilevel decision techniques are developed for decentralized decision problems, which may be defined by fuzzy coefficients. Based on a fuzzy linear bilevel (FLBL) model and two FLBL algorithms, this research develops a FLBL decision support system (FLBLDS
Wang, C., Lu, J. & Zhang, G. 2008, 'An ontology data matching method for web information integration', Proceedings of The tenth International Conference on Information Integration and Web-based Applications & Services, Information Integration and Web-based Applications and Services, ACM, Linz, Austria, pp. 208-213.
View/Download from: UTS OPUS
Lu, J., Deng, X., Zeng, X., Vroman, P., Wu, F. & Zhang, G. 2008, 'A fuzzy multi-objective decision support system for nonwoven products experiment design', Computational Intelligence in Decision and Control Proceedings of 8th FLINS Conference, International Fuzzy Logic and Intelligent technologies in Nuclear Science Conference, World Scientific, Madrid, Spain, pp. 787-792.
View/Download from: UTS OPUS
Experiment design often involves multiple objectives and uncertain data in its optimizing process. Fuzzy multi-objective linear programming (FMOLP) is an appropriate method to handle this problem. For the case of modeling nonwoven-based resilient product
Lu, J., Zhu, Y., Zeng, X., koehl, L., Ma, J. & Zhang, G. 2008, 'A fuzzy multi-criteria group decision support system for textile material fabric-hand evaluation', Computational Intelligence in Decision and Control Proceedings of 8th FLINS Conference, International Fuzzy Logic and Intelligent technologies in Nuclear Science Conference, World Scientific, Madrid, Spain, pp. 1129-1134.
View/Download from: UTS OPUS
Fabric-hand evaluation is one of the key features and measures in textile material selection for fashion design. Fabric-hand evaluation requires considering multiple criteria with in a group of evaluators, The evaluation process often involves fuzziness
Zhang, G., Yang, X. & Lu, J. 2008, 'A SVM-based classification approach for early warning systems', Computational Intelligence in Decision and Control Proceedings of 8th FLINS Conference, International Fuzzy Logic and Intelligent technologies in Nuclear Science Conference, World Scientific, Madrid, Spain, pp. 549-554.
View/Download from: UTS OPUS
Amailef, K. & Lu, J. 2008, 'm-Government: A Framework of Mobile-based Emergency Response Systems', Proceedings of 2008 3rd International Conference on Intelligent System and Knowledge Engineering, International Conference on Intelligent Systems and Knowledge Engineering, Institute of Electrical and Electronics Engineers, Inc., Xiamen, China, pp. 1398-1403.
View/Download from: UTS OPUS or Publisher's site
Mobile government (m-Government) is the next inevitable direction of evolution of e-Government. A mobile-based emergency response system (MERS) is one of the important m-Government services. A MERS under m-Government platform is a mobile-based information system designed to let people get help from the government in an emergency situation. It also makes the use of mobile technologies to assist the government to get information and make decisions in responding disasters anytime and anywhere. This paper presents a framework of MERS which has five main components (register, monitoring, analysis, decision support, and warning) aiming to provide a new function and service to m-Government. The proposed MERS framework would also offer a new opportunity to interact between government, citizens, responders, and other non-government agencies in emergency situations.
Lu, J., Zhu, Y., Zeng, X., koehl, L., Ma, J. & Zhang, G. 2008, 'A fuzzy decision support system for garment new product development', Lecture Notes in Artificial Intelligence Vol 5360: AI 2008: Advances in Artificial Intelligence, Australasian Joint Conference on Artificial Intelligence, Springer, New Zealand, pp. 532-543.
View/Download from: UTS OPUS or Publisher's site
Garment new product development (NPD) evaluation requires considering multiple criteria under a hierarchical structure. The evaluation process often involves uncertainty and fuzziness in both the relationships between criteria and the judgments of evaluators. This study first presents a garment NPD evaluation model under a well-being concept. It then proposes a fuzzy multi-criteria group decision-making (FMCGDM) method to evaluate garment NPD. The advantages of the FMCGDM method include handling criteria in a hierarchical structure, dealing with three kinds of uncertainties simultaneously, and using suitable types of fuzzy numbers to describe linguistic terms. A fuzzy multi-criteria group decision support system (FMCGDSS) is developed to implement the proposed method. Finally a garment NPD evaluation case study demonstrates the proposed method and software system.
Lu, J., Deng, X., Vroman, P., Zeng, X., Ma, J. & Zhang, G. 2008, 'A fuzzy multi-criteria group decision support system for nonwoven based cosmetic product development evaluation', 2008 Proceedings IEEE International Conference on Fuzzy Systems, IEEE International Conference on Fuzzy Systems, IEEE, Kong Kong, China, pp. 1700-1707.
View/Download from: UTS OPUS or Publisher's site
Product prototype evaluation is an important phase in new product development (NPD). Such evaluation often requires multiple criteria that are within a hierarchy and a group of evaluators. The evaluation process and these evaluation criteria often involve uncertain and fuzzy data in the weights of these criteria and the judgments of these evaluators. To evaluate nonwoven cosmetic product prototypes, this study first develops a NPD evaluation model, which has evaluation criteria within three levels, based on the features of nonwoven products. It then proposes a fuzzy (multi-level) multi-criteria group decision-making (FMCGDM) method for supporting the evaluation task. A fuzzy multi-criteria group decision support system (FMCGDSS) is developed to implement the proposed method and applied in nonwoven cosmetic product development evaluation.
Ma, J., Lu, J. & Zhang, G. 2007, 'A rule-map based technique for information inconsistency verification', International Conference on Information, Decision and Control, IEEE Conference on Information, Decision and Control, IEEE, Adelaide, Australia, pp. 296-301.
View/Download from: UTS OPUS or Publisher's site
This paper focuses on the problem of verifying information inconsistencies in acquired information. A rule-map based technique for data inconsistency is presented, where rule-map is used to describe hierarchical structure of rules and estimate judgment standard for consistency dynamically. Moreover, a state-based knowledge representation technique for logical inconsistency is investigated, in which knowledge is illustrated as states set of related objects and logical inconsistency is determined by the relationships between those state-sets. To illustrate the presented techniques, two examples are given.
Wu, R.C. & Lu, J. 2006, 'Enterprise integration strategy of interoperability', Advances and Innovations in Systems, Computing Sciences and Software Engineering, International Conference on Systems, Computing Sciences and Software Engineering, Springer, University of Bridgeport, pp. 369-374.
View/Download from: UTS OPUS or Publisher's site
In this new computing age of high complexity, a common weakness in the interoperability between business and IT leaves IT far behind the direction business is taking; poor business responsiveness and IT governance makes it even harder to achieve the enterprise goal. To cope with this common issue, we introduce the enterprise interoperability to integrate the metadata between business, service and information layers, this create visibility of vertical alignment within enterprise architecture and use metadata configuration to construct the mapping between each layer.
Wang, C., Lu, J. & Zhang, G. 2007, 'A constrained clustering approach to duplicate detection among relational data', Advances in Knowledge Discovery and Data Mining (Lecture Notes in computer Science (4426)), Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer Berlin / Heidelberg, Nanjing, China, pp. 308-319.
View/Download from: UTS OPUS or Publisher's site
This paper proposes an approach to detect duplicates among relational data. Traditional methods for record linkage or duplicate detection work on a set of records which have no explicit relations with each other. These records can be formatted into a single database table for processing. However, there are situations that records from different sources can not be flattened into one table and records within one source have certain (semantic) relations between them. The duplicate detection issue of these relational data records/instances can be dealt with by formatting them into several tables and applying traditional methods to each table. However, as the relations among the original data records are ignored, this approach generates poor or inconsistent results. This paper analyzes the characteristics of relational data and proposes a particular clustering approach to perform duplicate detection. This approach incorporates constraint rules derived from the characteristics of relational data and therefore yields better and more consistent results, which are revealed by our experiments.
Le Roux, F., Ranjeet, E., Ghai, V., Gao, Y. & Lu, J. 2007, 'A Course Recommender System Using Multiple Criteria Decision Making Method', 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE2007), International Conference on Intelligent Systems and Knowledge Engineering, Atlantis Press, Chengdu, China, pp. 346-350.
View/Download from: UTS OPUS or Publisher's site
A recommender system is a specific type of information filtering technique that presents the user-relevant information, which is implemented by creating a user's profile and comparing it to the other existing reference characteristics stored in the database. This paper developed a course recommender system capable of helping prospective students to choose relevant post graduate courses by multiple criteria decision making method. First, the multiple criteria decision making method was given. Then, the system prototype, which aimed at amalgamating the multiple criteria decision making model and the collaborative filtering recommendation system, was described. Finally the system architecture was illustrated
Niu, L., Lu, J., Chew, E.K. & Zhang, G. 2007, 'An exploratory cognitive business intelligence system', Proceedings 2007 IEEE / WIC /ACM International Conference on Web Intelligence. 2007, IEEE/WIC/ACM international Conference on Web Intelligence and Intelligent Agent Technology, IEEE, Silicon Valley, USA, pp. 812-815.
View/Download from: UTS OPUS or Publisher's site
An exploratory study of web-based cognitive business intelligence systems (CBIS) is presented in this paper. The underpinning concepts and theories are situation awareness, mental model, and naturalistic decision making (NDM). The CBIS is an extension of the traditional business intelligence system with cognitive orientation. It focuses on developing, enriching, and utilizing the executive's situation awareness, mental models, and other past experience during human-computer interaction, which drives the decision process to approach a naturalistic decision.
Wang, C., Lu, J., Zhang, G. & Zeng, X. 2007, 'Creating and Managing Ontology Data on the Web: A semantic Wiki for Semantic Approach', The 8th International Conference on Web Information Systems Engineering, International Conference on Web Information Systems Engineering, Springer, Nancy, France, pp. 513-522.
View/Download from: UTS OPUS or Publisher's site
The creation of ontology data on web sites and proper management of them would help the growth of the semantic web. This paper proposes a semantic wiki approach to tackle this issue. Desirable functions that a semantic wiki approach should implement to offer a better solution to this issue are discussed. Along with that, some key problems such as usability, data reliability and data quality are identified and analyzed. Based on that, a system framework is presented to show how such functions are designed. These functions are further explained along with the description of our implemented prototype system. By addressing the identified key problems, our semantic wiki approach is expected to be able to create and manage web ontology data more effectively.
Lu, J., Li, Z. & Ruan, D. 2007, 'Fuzzy multi-criteria group decision support in teaching performance evaluation', 2007 Three-Rivers Workshop on Soft Computing in Industrial Applications, Workshop on Soft Computing in Industrial Applications, IEEE, University of Passau, Germany, pp. 145-150.
View/Download from: UTS OPUS
Zhang, G., Lu, J. & Dillon, T.S. 2007, 'Models and algorithm for fuzzy multi-objective multi-follower linear bilevel programming', IEEE International Conference on Fuzzy Systems, IEEE International Conference on Fuzzy Systems, IEEE, Imperial College, London, UK, pp. 1-6.
View/Download from: UTS OPUS or Publisher's site
Basic bilevel programming deals with hierarchical optimization problems in which the leader at the upper level attempts to optimize his/her objective, subject to a set of constraints and his/her follower's solution, and the follower at the lower level tries to find an optimized strategy according to each of possible decisions made by the leader. Three issues may be involved in a basic bilevel decision problem. One is that bilevel decision making model may involve uncertain parameters which appear either in the objective functions or constraints of the leader or the follower or both. Second, the leader and the follower may have multiple conflict objectives that should be optimized simultaneously. Third, there may have multiple followers in a real decision situation. Following our previous work, this study proposes a set of fuzzy multi-objective multi-follower linear bilevel programming models to describe the three issues. It also develops an approximation branch-and-bound algorithm to solve such kinds of problems.
Wu, F., Lu, J., Zhang, G. & Ruan, D. 2007, 'The development of a fuzzy multi-objective group decision support system', IEEE International Conference on Fuzzy Systems, IEEE International Conference on Fuzzy Systems, IEEE, Imperial College, London, UK, pp. 670-675.
View/Download from: UTS OPUS or Publisher's site
This paper deals with multi-objective decisionmaking problem with fuzzy parameters under a group environment, called fuzzy multi-objective group decisionmaking (FMOGDM). It first presents an FMOGDM method, which integrates fuzzy multi-objective linear programming (FMOLP) with fuzzy group decision making techniques. Based on the method, a fuzzy multiple objective group decision support system (FMOGDSS) is developed. Finally, it gives a case-based example to demonstrate how an FMOLP problem is solved in a group supported by the FMOGDSS.
Ruan, D., Lu, J., Laes, E., Zhang, G., Wu, F. & Hardeman, F. 2007, 'Fuzzy multi-criteria group decision support in long-term options of Belgian energy policy', IEEE North American Fuzzy Information Processing Society Conference 2007, North American Fuzzy Information Processing Society Conference, IEEE, San Diego, CA, pp. 496-501.
View/Download from: UTS OPUS or Publisher's site
Decision making requires multiple perspectives of different people as one single decision maker may have not enough knowledge to well solve a problem alone. This is particularly true when the decision environment becomes more complex. More organizational decisions are made now in groups than ever before. Group decision making is thus a process of arriving at a judgment or a solution for a decision problem based on the input and feedback of multiple individuals. At the same time in practice, multi-criteria problems at tactical and strategic levels often involve fuzziness in their criteria and decision makers' judgments. Relevant alternatives are evaluated according to a number of criteria. Fuzzy logic based multi-criteria group decision support is justified to analysis long-term options for Belgian energy policy in this paper.
Ma, J., Lu, J. & Zhang, G. 2007, 'A two-level information filtering model for warning systems', IEEE Symposium on Computational Intelligence in Multi-criteria Decision-Making, IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making, IEEE, Hawaii, USA, pp. 354-359.
View/Download from: UTS OPUS or Publisher's site
Information filtering is an important component in warning systems. This paper proposes a two-level information filtering model for generating warning information. In this model, information is represented by n-tuple, whose elements are values of information features. The features of information are divided into critical and uncritical features. Within this model, the collected information is filtered in two stages by users at different levels. At the first stage, exceptions are separated from normal information. And at the second stage, critical exceptions are separated from uncritical information. To illustration the proposed model, an example is discussed
Fizard, S. & Lu, J. 2007, 'An optimal Intelligent Framework for Integrating e-Government Service Delivery', eGov-INTEROP'07 conference, Paris France.
Cheng, Y. & Lu, J. 2007, 'Intellectual Properties Data Mining over Internet', The 33rd Annual conference of the IEEE Industrial Electronics Society (IEEE IECON'07), IECON 2007, IEEE, Taipei, Taiwan, pp. 117-118.
View/Download from: Publisher's site
This research is based on needs from enterprises spread in each different area of expertise. No matter car manufacture, government, high-tech area, media/publication, research institute, academics and other areas, people have the desire of knowing what the top research and discovery today is. For sure, industrial company would not think the same as academic organizations while they both see the same report about latest intellectual properties. However, it is undeniable to conclude that knowledge of newest intellectual properties is so important for company that wants to dominate the market in next generation, academics that wants to win prizes in coming year and so on. And related researches in this area are many, such as 'Sameer Singh, Maneesha Singh, Chid Apte, Petra Perner' [1], 'Xue Li, Shuliang Wang, Zhao Yang Dong'[2], 'Petra Perner, Atsushi Imiya' [3] and so on. This activity of research must have potential of helping any desires of data mining over Internet in any area in the future.
Khosravi, A., Lu, J., Zheng, J. & Barzamini, R. 2007, 'Skew-Tree based Multistage fuzzy controller for nonlinear systems', EUSFLAT, Conference of the European Society for Fuzzy Logic and Technology, University of Ostrava, Ostravo, Czech Republic, pp. 137-142.
View/Download from: UTS OPUS
Niu, L., Lu, J. & Zhang, G. 2007, 'Enriching executives situation awareness and mental models: a conceptual ESS framework', 9th ICEIS, International Conference on Enterprise Information Systems, INSTICC, Funchal, Madeira, Portugal, pp. 510-515.
View/Download from: UTS OPUS
Gao, Y., Zhang, G., Lu, J. & Zeng, X. 2007, 'A Lambda-cut approximate approach to supporting fuzzy goal based bilevel decision making in risk management', The First International Conference on Risk Analysis and Crisis Response, International Conference on Risk Analysis and Crisis Response, Atlantis Press, Shanghai, China, pp. 132-137.
View/Download from: UTS OPUS
Niu, L., Lu, J. & Zhang, G. 2007, 'Cognition-driven decision support system framework', The First International Conference on Risk Analysis and Crisis Response, International Conference on Risk Analysis and Crisis Response, Atlantis Press, Shanghai, China, pp. 353-357.
View/Download from: UTS OPUS
Zhang, G., Lu, J. & Zeng, X. 2007, 'Models and algorithms for fuzzy multi-objective multi-follower linear bilevel programming in a partial cooperative situation', International Conference on Intelligent Systems and Knowledge Engineering (ISKE2007), International Conference on Intelligent Systems and Knowledge Engineering, Atlantis Press, Chengdu, China, pp. 382-389.
View/Download from: UTS OPUS or Publisher's site
Basic bilevel programming deals with hierarchical optimization problems in which the leader at the upper level attempts to optimize his/her objective, subject to a set of constraints and his/her followers solution, and the follower at the lower level tries to find an optimized strategy according to each of possible decisions made by the leader. Three issues may be involved in a basic bilevel decision problem. One is that bilevel decision making model may involve uncertain parameters which appear either in the objective functions or constraints of the leader or the follower or both. Second, the leader and the follower may have multiple conflict objectives that should be optimized simultaneously. Third, there may have multiple followers and partial shared their decision variables among followers in a real decision situation. Following our previous work, this study proposes a set of fuzzy multi-objective multi-follower linear bilevel programming models to describe the three issues. It also develops an approximation branch-and-bound algorithm to solve such kinds of problems
Gao, Y., Lu, J., Zhang, G. & Gao, S. 2007, 'A bilevel model for railway train set organizing optimization', International Conference on Intelligent Systems and Knowledge Engineering (ISKE2007), International Conference on Intelligent Systems and Knowledge Engineering, Atlantis Press, Chengdu, China, pp. 777-782.
View/Download from: UTS OPUS or Publisher's site
Train Set Organization (TSO) is to arrange the train set in railway freight transportation. Bilevel programming techniques were proposed to solve the Stackelberg game in which play is sequential and cooperation is not permitted. In this paper, an op- timizing model for TSO is developed by the bilevel techniques. First, we analyzed the multiple level nature of management on TSO and simplified it into two levels. Then, a bilevel model for TSO was develop. Finally, this model was further illustrated by applying it on a railway station.
Wang, C., Lu, J. & Zhang, G. 2007, 'Generation and Matching of Ontology Data for the Semantic Web in a Peer-to-peer Framework', The Joint Conference of The 9th Asia-Pacific Web Conference and The 8th International Conference on Web-Age Information Management (APWeb/WAIM 2007), International Conference on Web-Age Information Management, Springer, Huangshan, China, pp. 136-143.
View/Download from: UTS OPUS or Publisher's site
The abundance of ontology data is very crucial to the emerging semantic web. This paper proposes a framework that supports the generation of ontology data in a ptop environment. It not only enables users to convert existing structured data to ontology data aligned with given ontology schemas, but also allows them to publish new ontology data with ease. Besides ontology data generation, the common issue of data overlapping over the peers is addressed by the process of ontology data matching in the framework. This process helps turn the implicitly related data among the peers caused by overlapping into explicitly interlinked ontology data, which increases the overall quality of the ontology data. To improve matching accuracy, we explore ontology related features in the matching process. Experiments show that adding these features achieves better overall performance than using traditional features only.
Liu, B., Lu, J., Zhang, G., Hao, Z. & Gao, Y. 2007, 'A support vector machine model for the situation awareness system', The First International Conference on Risk Analysis and Crisis Response, International Conference on Risk Analysis and Crisis Response, Atlantis Press, Shanghai, China, pp. 244-248.
View/Download from: UTS OPUS
Wu, R. & Lu, J. 2007, 'Semantic interoperability and transformation in enterprise integration', Proceedings of the 2nd International Conference on Internet Technologies and Applications, ITA 07, pp. 28-34.
The main purpose of enterprise interoperability try to cope with changeable business and variety of technologies by means of component identification, design, interconnectivity and service transformation. A rough roadmap initiated by consolidating all heterogeneous environment and system silo into component based foundation, and apply some particular methodology, framework and design to implement their common practices, the transformation requires different technologies and vision to achieve service virtualization which is a new area in future service industry to form a common main route, the service grid concept will provision a transparent and highly integrated framework to provide global service infrastructure which has no limitation in geography or system we use.
Lu, J. & Zhang, G. 2006, 'Information integration based team situation assessment in an uncertain environment', Applied Artificial Intelligence, International Fuzzy Logic and Intelligent technologies in Nuclear Science Conference, World Scientific Publ Co Pte Ltd, Genova, ITALY, pp. 441-448.
View/Download from: UTS OPUS
Understanding a situation requires integrating many pieces of information which can be obtained by a group of data collectors from multiple data sources. Uncertainty is involved in situation assessment. How to integrate multi-source multi-member uncertai
Lu, J., Zhang, G., Yan, X. & Zhang, J. 2006, 'An intergrated analysis method for bank customer clarification', Applied Artificial Intelligence - Proceedings of the 7th International Fuzzy Logic and Intelligent Technologies in Nuclear Science Conference FLINS, International Fuzzy Logic and Intelligent technologies in Nuclear Science Conference, World Scientific, Genova, Italy, pp. 247-252.
View/Download from: UTS OPUS
NA
Goyal, M.L., Lu, J. & Zhang, G. 2005, 'Negotiating Multi-Issue e-Market negotiation through fuzzy attitudes', Proceedings of International Conference on Computational Intelligence for Modelling, Control and Automation 2004, International Conference on Computational Intelligence for Modelling, Control and Automation, IEEE, Vienna, Austria, pp. 922-927.
View/Download from: UTS OPUS or Publisher's site
The online auctions are one of the most effective ways of negotiation of salable goods over the Internet. To be successful in open multi-agent environments, agents must be capable of adapting different strategies and tactics to their prevailing circumstances. This paper presents a software test-bed for studying autonomous bidding strategies in simulated auctions for procuring goods. It shows that agents' bidding strategy explore the attitudes and behaviors that help agents to manage dynamic assessment of alternative prices of goods given the different scenario conditions
Lu, J. 2006, 'T-Service personalisation: Concepts, systems and applications', ISKE 2006, International Conference on Intelligent Systems and Knowledge Engineering, Donghua University, Shanghai, China, pp. 17-17.
View/Download from: UTS OPUS
Lu, J., Zhang, G., Hao, Z., Wen, W. & Yang, X. 2006, 'A fast data processing procedure for support vector regression', Intelligent data engineering and automated learning 2006, International Conference on Intelligent Data Engineering and Automated Learning, Springer-Verlag, BURJOS, Spain, pp. 48-56.
View/Download from: UTS OPUS or Publisher's site
A fast data preprocessing procedure (FDPP) for support vector regression (SVR) is proposed in this paper. In the presented method, the dataset is firstly divided into several subsets and then K-means clustering is implemented in each subset. The clusters are classified by their group size. The centroids with small group size are eliminated and the rest centroids are used for SVR training. The relationships between the group sizes and the noisy clusters are discussed and simulations are also given. Results show that FDPP cleans most of the noises, preserves the useful statistical information and reduces the training samples. Most importantly, FDPP runs very fast and maintains the good regression performance of SVR.
Zhang, G., Dillon, T.S. & Lu, J. 2006, 'An approximation branch-and-bound algorithms for fuzzy bilevel decision making problems', Proceedings of the international multiconfernce computer science and information echnology, Autumn meeting point of Polish information processing society, PTI, Wisla, Poland, pp. 223-231.
View/Download from: UTS OPUS
Tran, T.P., Lu, J., Wang, D., Chen, S. & Tolentino, M. 2006, 'A neutral network classifier based design support system (NNCDSS) for network intrusion detection and response', ISKE 2006, International Conference on Intelligent Systems and Knowledge Engineering, Donghua University, Shanghai, China, pp. 1-7.
View/Download from: UTS OPUS
Lu, Z., Lu, J., Bai, C. & Zhang, G. 2006, 'Customer online shopping behaviours analysis using Bayesian networks', AI 2006: Advances in artificial intelligence, Australasian Joint Conference on Artificial Intelligence, Springer, Hobart, Australia, pp. 1293-1297.
View/Download from: UTS OPUS or Publisher's site
This study applies Bayesian network technique to analyse the relationships among customer online shopping behaviours and customer requirements. This study first proposes an initial behaviour-requirement relationship model as domain knowledge. Through conducting a survey customer data is collected as evidences for inference of the relationships among the factors described in the model. After creating a graphical structure, this study calculates conditional probability distribution among these factors, and then conducts inference by using the Junction-tree algorithm. A set of useful findings has been obtained for customer online shopping behaviours and their requirements with motivations. These findings have potential to help businesses adopting more suitable online system development.
Wang, C., Lu, J. & Zhang, G. 2006, 'Integration of ontology data through learning instance matching', 2006 IEEE/WIC/ACM international Conference on Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM international Conference on Web Intelligence and Intelligent Agent Technology, IEEE Computer Society, HK, China, pp. 536-539.
View/Download from: UTS OPUS or Publisher's site
Information integration with the aid of ontology can roughly be divided into two levels: schema level and data level. Most research has been focused on the schema level, i.e., mapping/matching concepts and properties in different ontologies with each other. However, the data level integration is equally important, especially in the decentralized semantic Web environment. Noticing that ontology data (in the form of instances of concepts) from different sources often have different perspectives and may overlap with each other, we develop a matching method that utilizes the features of ontology and employs the machine learning approach to integrate those instances. By exploring ontology features, this method performs better than other general methods, which is revealed in our experiments. Through the process that implements the matching method, ontology data can be integrated together to offer more sophisticated services
Zhang, G., He, Q., Lu, J., Shi, Z. & Zheng, Z. 2006, 'Rule sets based bilevel decision model', Australasian Computer Science Conference 2006, Australasian Computer Science Conference, ACM Digital Library, Hobart, Australia, pp. 113-120.
View/Download from: UTS OPUS
Bilevel decision addresses the problem in which two levels of decision makers, each tries to optimize their individual objectives under constraints, act and react in an uncooperative, sequential manner. Such a bilevel optimization structure appears naturally in many aspects of planning, management and policy making. However, bilevel decision making may involve many uncertain factors in a real world problem. Therefore it is hard to determine the objective functions and constraints of the leader and the follower when build a bilevel decision model. To deal with this issue, this study explores the use of rule sets to format a bilevel decision problem by establishing a rule sets based model. After develop a method to construct a rule sets based bilevel model of a real-world problem, an example to illustrate the construction process is presented.
Khosravi, A. & Lu, J. 2006, 'Fault modeling for non-linear systems using ANFIS', Proceedings of the international multiconference on computer science and information technology, Autumn meeting of Polish information Processing Society, Prokom Softwares, Wilsa, Poland, pp. 75-83.
View/Download from: UTS OPUS
Zhang, G., Zhang, G., Lu, J. & Lu, H. 2006, 'Environmental/economic dispatch using genetic algorithm and fuzzy number ranking method', Applied Artificial Intelligence - Proceedings of the 7th International Fuzzy Logic and Intelligent Technologies in Nuclear Science Conference FLINS, International Fuzzy Logic and Intelligent technologies in Nuclear Science Conference, World Scientific, Genova, Italy, pp. 59-65.
View/Download from: UTS OPUS
NA
Zhang, G., Lu, J. & Dillon, T.S. 2006, 'An extended branch-and-bound algorithm for fuzzy linear bilevel programming', Applied Artificial Intelligence - Proceedings of the 7th International FLINS Conference, International Fuzzy Logic and Intelligent technologies in Nuclear Science Conference, World scientific, Genova, Italy, pp. 291-298.
View/Download from: UTS OPUS
This paper presents an extended Branch-and-Bound algorithm for solving fuzzy linear bilevel programming problems. In a fuzzy bilevel programming model, the leader attempts to optimize his/her fuzzy objective with a consideration of overall satisfaction,
Goyal, M.L., Lu, J. & Zhang, G. 2006, 'A novel fuzzy attitude based bidding strategy for multi-attribute auctions', Web intelligence and intelligent agent technology WI IAT 06 workshop proceedings, IEEE/WIC/ACM international Conference on Web Intelligence and Intelligent Agent Technology, Conference publicity services, Hong Kong, China, pp. 535-539.
View/Download from: UTS OPUS or Publisher's site
Auctions have recently commanded a lot of attention in the field of multi-agent systems. To be successful in open multi-attribute auctions, agents must be capable of adapting different strategies and tactics to their prevailing circumstances. This paper presents a software test-bed for studying autonomous bidding strategies in simulated auctions for procuring goods. It shows that agents' bidding strategy explore the attitudes and behaviors that help agents to manage dynamic assessment of alternative prices of goods given the different scenario conditions. Our agent also uses fuzzy techniques for the decision making: to make decisions about the outcome of auctions, and to alter the agent's bidding strategy in response to the different criteria and market conditions
Guo, X. & Lu, J. 2005, 'STEF: Personalized trade exhibition recommendation', Proceedings Of the 8th Joint Conference On Information Sciences, Vols 1-3, Joint Conference on Information Sciences, JCIS, Salt Lake City, USA, pp. 1538-1541.
View/Download from: UTS OPUS
In this study, a novel recommendation technique is proposed by integrating the user-based and item-based recommendation approaches. A recommender system, called Smart Trade Exhibition Finder (STEF), is proposed to tailor relevant trade exhibition informa
Zhang, G., Lu, J., Steele, R.J. & Shi, C. 2005, 'An extended Kth-best approach for fuzzy linear bilevel problems', Proceedings Of the 8th Joint Conference On Information Sciences, Vols 1-3, Joint Conference on Information Sciences, University of Utah, Salt Lake City, UT, USA, pp. 46-49.
View/Download from: UTS OPUS
Organizational bilevel decision-making often involves uncertain factors. The parameters shown in a bilevel programming model, either in the objective functions or constraints, are thus often imprecise, which is called the fuzzy parameter bilevel programm
Wang, C.C., Lu, J. & Zhang, G. 2005, 'Mining key information of web pages', Proceedings Of the 8th Joint Conference On Information Sciences, Vols 1-3, Joint Conference on Information Sciences, Joint Conference On Information Sciences, Salt Lake City, UT, USA, pp. 1573-1576.
View/Download from: UTS OPUS
Key information, in the form of distinctive menu items, navigation indicators provided by web site constructors, can classify the main contents of web pages and reflect certain taxonomy knowledge. Mining such information is significant as it can be used
Guo, X. & Lu, J. 2005, 'Recommending trade exhibitions by integrating semantic information with collaborative filtering', proceedings The 2005 IEEE/WIC/ACM international conference on web intelligence WI 2005, IEEE/WIC/ACM international Conference on Web Intelligence and Intelligent Agent Technology, IEEE, Paris, France, pp. 747-750.
View/Download from: UTS OPUS or Publisher's site
Recommender systems have gained successfully applications for the past ten years, particular in E-commerce domain. However, existing recommendation approaches can not effectively deal with recommendation issue of one-and-only items occurred in government-to-business services, e.g. recommendation of trade exhibitions. Thus, in this study, we propose a novel approach by integrating semantic information with the traditional item-based collaborative filtering, and attempt to help the businesses choose the right trade exhibitions at the right time. The outcome of this study will have tremendous significance in overcoming the new item problem of existing recommendation approaches.
Lu, J., Shi, C., Zhang, G. & Ruan, D. 2005, 'Multi-Follower Linear Bilevel Programming: Model and Kuhn-Tucker Approach', Proceedings of The IADIS International Conference Applied Computing 2005, IADIS International Conference Applied Computing, IADIS, Algarve, Portugal, pp. 81-88.
View/Download from: UTS OPUS
Guo, X., Lu, J. & Simoff, S.J. 2005, 'Applying web personalisation techniques in E-government services', Proceedings of AusWeb05, Australian World Wide Web Conference, Southern Cross University, Gold Coast, Australia, pp. 233-238.
View/Download from: UTS OPUS
Many E-commerce websites attempt to develop personalized features to encourage users' repetitive visits. Yet, there is less attention about the applications of personalization technologies in E-government services. In this study, we present a classification of personalization techniques. Also, a novel recommendation approach is proposed to improve the existing techniques by the integration of user-based and item-based collaborative filtering recommendation techniques. A recommender system prototype, named Smart Trade Exhibitions Finder, is developed to help companies choosing the right trade exhibitions. The outcome of this study will have tremendous significance in overcoming the drawback of existing recommendation approaches.
Shi, C., Lu, J. & Zhang, G. 2005, 'A Web-Based Decision Support System for Linear Multi Follower Problems without Shared Variables', Proceedings Of the 8th Joint Conference On Information Sciences, Vols 1-3, Joint Conference on Information Sciences, University of Utah, Salt lake City, USA, pp. 1565-1568.
View/Download from: UTS OPUS
Lu, Z., Wang, R., Zhang, Z., Han, B., Deng, Z. & Lu, J. 2005, 'The Virtual Boundary Violation Faced by China in Information Society', Frontiers of Information Systems Research and Applications in China, Frontiers of Information Systems Research and Applications in China, Hua Publisher, China, Beijing, China, pp. 636-640.
View/Download from: UTS OPUS
Wu, F., Lu, J. & Zhang, G. 2005, 'A Decision Support System Prototype for Fuzzy Multiple objetcive Optimization', Joint EUSFLAT-LFA 2005, European Society for Fuzzy logic and Technology and 11 Rencontres Francophones Sur La logique Floue et ses Applications, Universitat politecnica De Catalunya, Barcelona, Spain, pp. 985-990.
View/Download from: UTS OPUS
Shi, C., Lu, J., Zhang, G. & Zhou, H. 2005, 'An Extended Kuhn-Tucker Approach for Linear Bilevel Multifollower Programming with Partial Shared Variables among Followers.', Proceedings of IEEE Systems, Man and Cyberntetics Conference 2005, IEEE Conference on Systems, Man and Cybernetics, IEEE Publisher, Hawaii, USA, pp. 3350-3357.
View/Download from: UTS OPUS or Publisher's site
In a real world bilevel decision-making, the lower level of a bilevel decision usually involves multiple decision units. This paper proposes an extended Kuhn-Tucker approach for linear bilevel multifollower programming problems with partial shared variables among followers. Finally numeric examples are given to show how the Kuhn-Tucker approach works.
Zhang, G. & Lu, J. 2005, '8-Equalities of Sequences of Fuzzy Sets', Eleventh International Fuzzy Systems Association World Congress, IFSA World Congress, Tsinghua University Press, Springer, Beijing, China, pp. 126-130.
View/Download from: UTS OPUS
Wang, C., Lu, J. & Zhang, G. 2005, 'A semantic classification approach for online product reviews', Proceedings 2005 IEEE/WIC/ACM International Conference on web intelligence, IEEE/WIC/ACM international Conference on Web Intelligence and Intelligent Agent Technology, IEEE, France, pp. 276-279.
View/Download from: UTS OPUS or Publisher's site
With the fast growth of e-commerce, product reviews on the Web have become an important information source for customersý decision making when they plan to buy products online. As the reviews are often too many for customers to go through, how to automatically classify them into different semantic orientations (i.e. recommend/not recommend) has become a research problem. Different from traditional approaches that treat a review as a whole, our approach performs semantic classifications at the sentence level by realizing reviews often contain mixed feelings or opinions. In this approach, a typical feature selection method based on sentence tagging is employed and a nave bayes classifier is used to create a base classification model, which is then combined with certain heuristic rules for review sentence classification. Experiments show that this approach achieves better results than using general nave bayes classifiers.
Wang, C., Lu, J. & Zhang, G. 2005, 'A Framework for capturing domain knowledge via the web', Proceedings of AusWeb 05, Australian World Wide Web Conference, Southern Cross University, Gold Coast, Aust, pp. 248-255.
View/Download from: UTS OPUS
Cornelis, C., Guo, X., Lu, J. & Zhang, G. 2005, 'A Fuzzy Relational Approach to Event Recommendation', Proceedings on The 2nd Indian International Conference on Artificial Intelligence, Indian International Conference on Artificial Intelligence, IICAI, Pune, INDIA, pp. 2231-2243.
View/Download from: UTS OPUS
Zhang, G., Bai, C., Lu, J. & Zhang, C. 2004, 'Bayesian Network based Cost Benefit Factor Inference in Eservices', Proceedings of 2nd International Conference on Information Technology and Applications, International Conference on Information Technology and Applications, Macquarie Scientific Publishing, Harbin, China, pp. 464-469.
View/Download from: UTS OPUS
Lu, J. 2004, 'A Personalized e-Learning material Recommender System', Proceedings of 2nd International Conference on Information Technology and Applications, International Conference on Information Technology and Applications, Macquarie Scientific Publishing, Harbin, China, pp. 374-379.
View/Download from: UTS OPUS
Lu, J. & Zhang, G. 2004, 'Uncertain Information Processing Framework for Situation Awareness and Emergency Decision-Making', Proceedings of the Third International Conference on Information, International Conference on Information, International Information Institute, Tokoyo,Japan, pp. 265-268.
View/Download from: UTS OPUS
Lu, J., Guo, X., Huynh, A. & Benkovich, L. 2004, 'SRS: A Subject Recommender System to Enhance E-learning Personalisation', Proceedings of the Third International Conference on Information (Info'2004), International Conference on Information, International Information Institute, Tokyo, Japan, pp. 253-256.
View/Download from: UTS OPUS
Lu, J. & Shi, C. 2004, 'A Word-Document Mode for Text Mining by Multi-objective Programming Technology', Proceedings of the Third International Conference on Information and Management Sciences, International Conference on Information and Management Sciences, California Polytechnic State University, Dunhuang,China, pp. 455-460.
View/Download from: UTS OPUS
Shi, C. & Lu, J. 2004, 'A Hybrid Algorithm for Linear Bilevel Programming Problems', Proceedings of the Third International Conference on Information and Management Sciences, International Conference on Information and Management Sciences, California Polytechnic State University, Dunhuang,China, pp. 227-231.
Shi, C. & Lu, J. 2004, 'A Text Mining Model by Using Weighting Technology', Proceedings of the Tenth Americas Conference on Information Systems, Americas Conference on Information Systems, AMCIS, New York, USA, pp. 1904-1912.
View/Download from: UTS OPUS
Shi, C., Lu, J. & Zhang, G. 2004, 'A Web-based decision Support System for Linear Bilevel Problems', Proceedings of the Third International Conference on Information (Info 2004), International Conference on Information, International Information Institute, Tokyo,Japan, pp. 257-260.
View/Download from: UTS OPUS
Guo, X., Lu, J. & Zhang, G. 2004, 'A Recommender system by two-level collaborative filtering', Proceedings of the 8th IASTED International Conference Software Engineering and Applications, IASTED International Conference Software Engineering and Applications, ACTA Press, Cambridge,USA, pp. 65-69.
Guo, X. & Lu, J. 2004, 'Towards a conceptual framework of dynamic personalisation', Proceedings of the Third International Conference on Information and Management Sciences, International Conference on Information and Management Sciences, California Polytechnic State University, USA, Dunhuang, China, pp. 348-354.
Wu, F., Lu, J. & Zhang, G. 2004, 'A decision support system for multiple objective linear programming with fuzzy parameters', Proceedings 2004 IEEE International Conference on e-Technology, e-Commerce and e-Service EEE 2004, IEEE International Conference on e-Technology, e-Commerce and e-Service, IEEE, Taipei, Taiwan, pp. 425-428.
View/Download from: UTS OPUS
A new approximate algorithm has been developed by Wuet al. for solving fuzzy multiple objective linearprogramming (FMOLP) problems with fuzzy parameters inany form of membership function in both objectivefunctions and constraints. Based on the approximatealgorithm, a fuzzy multiple objective decision supportsystem (FMODSS) is developed. This paper focuses on thedescription of use for FMODSS in detail, and an example ispresented for demonstrating how to solve a FMOLPproblem by the FMODSS.
Wu, F., Lu, J. & Zhang, G. 2004, 'A Fuzzy Goal Approximate Algorithm to Fuzzy Multiple Objective Decision-Making', Proceedings of the Third International Conference on Information and Management Sciences, International Conference on Information and Management Sciences, California Polytechnic State University, Dunhuang,China, pp. 182-187.
View/Download from: UTS OPUS
Wu, F., Lu, J. & Zhang, G. 2004, 'A fuzzy goal approximate algorithm for solving multiple objective linear programming problems with fuzzy parameters', Applied Computational Intelligence - Proceedings of the 6th International Fuzzy Logic and Intelligent technologies in Nuclear Science Conference FLINS, International Fuzzy Logic and Intelligent technologies in Nuclear Science Conference, World Scientific, Blankenberghe, Belgium, pp. 304-307.
View/Download from: UTS OPUS or Publisher's site
NA
Wu, F., Lu, J. & Zhang, G. 2004, 'FMODSS: A decision support system for solving multiple objective linear programming problem with fuzzy parameter', The 2004 IFIP International Conference on Decision Support Systems (DSS2004) Conference Proceedings, IFIP International Conference on Decision Support Systems, Monash University, Prato,Italy, pp. 0-0.
View/Download from: UTS OPUS
Wu, F., Lu, J. & Zhang, G. 2004, 'An fuzzy goal approximate algorithm for fuzzy multiple objective linear programming problems', Proceedings of the Third International Conference on Information (Info 2004), International Conference on Information, International Information Institute, Tokyo, Japan, pp. 261-264.
View/Download from: UTS OPUS
Shi, C., Zhang, G. & Lu, J. 2004, 'An Algorithm for Linear Bilevel Programming Problems', Applied Computational Intelligence Proceedings of the 6th International FLINS Conference, International Fuzzy Logic and Intelligent technologies in Nuclear Science Conference, World Scientific, Blankenberghe, Belgium, pp. 300-303.
View/Download from: UTS OPUS or Publisher's site
For linear bilevel programming problems, the branch and bound algorithm is the most successful algorithm to deal with the complementary constraints arising from Kuhn-Tucker conditions. This paper proposes a new branch and bound algorithm for linear bilevel programming problems. Based on this result, a web-based bilevel decision support system is developed.
Zhang, G., Bai, C., Lu, J. & Zhang, C. 2004, 'Bayesian network based cost benefit factor inference in e-services', Proceedings of the Second International Conference on Information Technology and Applications (ICITA 2004), pp. 404-409.
This paper applies Bayesian network technique to model and inference the uncertain relationships among cost factors and benefit factors in E-services. A cost-benefit factor-relation model proposed in our previous study is considered as domain knowledge and the data collected through a survey is as evidence to conduct inference. Through calculating conditional probability distribution among factors and conducting inference, this paper identifies that certain cost factors are significantly more Important than others to certain benefit factors. In particular, this study found that 'increased investment in maintaining E-services' would significantly contribute to 'enhancing perceived company image' and 'gaining competitive advantages', and 'increased investment In staff training' would significant contribute to 'realizing business strategies'. 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 Eservices are concerned.
Lu, J. 2004, 'A personalized e-learning material recommender system', Proceedings of the Second International Conference on Information Technology and Applications (ICITA 2004), pp. 23-28.
E-learning environments are mainly based on a range of delivery and interactive services. Web-based personalized learning recommender systems can, as a kind of services in e-learning environment, provide learning recommendations to students. This research proposes a framework of a personalized learning recommender system, which aims to help students find learning materials they would need to read. Two related technologies are developed under the framework: one is a multi-attribute evaluation method to justify a student's need, and another is a fuzzy matching method to find suitable learning materials to best meet each student need. The implementation of this proposed personalized learning recommender system can support students online learning more effectively and assist large class online teaching with multi-background students.
Zhang, G. & Lu, J. 2003, 'A Group Decision Making Approach for Dealing with Fuzziness in Decision Process', The Third International Conference on Electronic Business (ICEB 2003), International Conference on e-Business, National University of Singapore, Singapore, pp. 76-78.
Lu, J. & Shi, C. 2003, 'A Web-based Decision Support System for Multi-objective Decision Making', Proceedings of International Conference on Pacific Rim Management 13th Annual Meeting, ACME Transactions 2003 - International Conference on Pacific Rim Management 13th Annual Meeting, ACME Transactions, Seattle, USA, pp. 933-938.
Lin, L., Ling, H., Lu, J., Zhang, C., Song, L. & Xue, H. 2003, 'Case-based Reasoning Integrating with Direct-Case-Linkage for Tacit Knowledge Management', Proceedings of the Seventh Pacific Asia Conference on Information Systems, Pacific Asia Conference on Information Systems, University of South Australia, Adelaide, Australia, pp. 1724-1733.
View/Download from: UTS OPUS
Zhang, G. & Lu, J. 2003, 'Using General Fuzzy Number to Handle Fuzziness in Group Decision Making', Proceedings 7th Joint Conference on Information Sciences - JCIS 2003, Joint Conference on Information Sciences, Association for Intelligent Machinery Inc, North Carolina, USA, pp. 175-179.
View/Download from: UTS OPUS
Lu, J., Zhang, G. & Shi, C. 2003, 'Framework and Implementation of A Web-based Multi-objective Decision Support System: WMODSS', WI/IAT 2003 Workshop on Applications, Products and Services of Web-based Support Systems, WSS and WI/IAT Workshop on Applications, Products and Services of Web-based Support Systems, Department of Mathematics and Computing Science, Saimt Mary's University, Halifax, Canada, pp. 7-11.
View/Download from: UTS OPUS
Lin, L., Lu, J., Song, L., Huang, W. & Ling, H. 2003, 'Enhancing the Quality of e-Service in Consulting Industry Using Case-Reference-Net CBR Technique', ACME Transactions - Proceedings of the International Conference on Pacific Rim Management 13th Annual Meeting, ACME Transactions - International Conference on Pacific Rim Management 13th Annual Meeting, ACME, Seattle, USA, pp. 950-955.
View/Download from: UTS OPUS
Zhang, B., Zhang, G. & Lu, J. 2004, 'A System for Solving Fuzzy Linear Programming Problems by Multi-Objective Linear Programming', Proceedings of International Conference on Fuzzy Information Processing Theories and Applications Volume II, International Conference on Fuzzy Information Processing Theories and Applications, Tsinghua University Press, Beijing, China, pp. 675-680.
View/Download from: UTS OPUS
Zhang, G. & Lu, J. 2003, 'A Group Decision Making Method with Fuzzy Weights for Decision Makers, Fuzzy Preferences for Alternatives and Fuzzy Judgements for Selection Criteria', Proceedings of International Conference on Fuzzy Information Processing Theories and Applications Volume II, International Conference on Fuzzy Information Processing Theories and Applications, Tsinghua University Press, Beijing, China, pp. 655-661.
View/Download from: UTS OPUS
Wu, F., Lu, J. & Zhang, G. 2003, 'A New Approximation Algorithm for Solving Multiple Objective Linear Programming with Fuzzy Parameters', The Third International Conference on Electronic Business (ICEB 2003), International Conference on e-Business, National University of Singapore, Singapore, pp. 532-534.
View/Download from: UTS OPUS
Guo, X. & Lu, J. 2003, 'Building Intelligent e-Government: A Strategic Development Model in Context of Australia', Building Society Through e-Commerce: e-Government, e-Business and e-Learning, Collaborative Electronic Commerce Technology and Research, Collector Latam Editions, Santiago, Chile, pp. 35-45.
Shi, C. & Lu, J. 2003, 'An Information Retrieval Model by Using Weighting Technology', Proceedings of the Second International Conference on Information and Management Sciences. Volume 2, Series of Information and Management Sciences, International Conference on Information Management Sciences, California Polytechnic State University, Chengdu, China, pp. 427-430.
View/Download from: UTS OPUS
Shi, C. & Lu, J. 2003, 'Choosing LSI Dimensions by Word Vector Linear Association Analysis', The 7th World Multiconference on Systematics, Cybernetics and Informatics. Proceedings Volume 1 Information Systems, Technologies and Applications, World Multi-Conference on Systematics, Cybernetics and Informatics, IIS - International Institute of Informatics and Systemics, Orlando, Florida, USA, pp. 260-265.
View/Download from: UTS OPUS
Shi, C. & Lu, J. 2003, 'Choosing LSI Dimensions by Document Linear Association Analysis', Proceedings of the International Conference on Information and Knowledge Engineering Volume II, International Conference on Information and Knowledge Engineering, CSREA Press, Las Vegas, Nevada, USA, pp. 615-621.
View/Download from: UTS OPUS
Wu, F., Lu, J. & Zhang, G. 2003, 'An Extension of Scalarization-Based Approach to Fuzzy Multiple Objective Linear Programming with Fuzzy Parameters', Proceedings of the Second International Conference on Information Management Sciences. Volume 2, Series of Information Management Sciences, International Conference on Information Management Sciences, California Polytechnic State University, Chengdu, China, pp. 420-426.
View/Download from: UTS OPUS
Guo, X. & Lu, J. 2002, 'An Evaluation for the Adoption of Government e-Services in Australia', 21st Century E-commerce Integration and Innovation, 21st Century E-commerce Integration and Innovation The Second Wuhan International Conference on Electronic Commerce, Science and Technology Progress and Policy, Wuhan, China, pp. 393-401.
View/Download from: UTS OPUS
Guo, X. & Lu, J. 2002, 'E-service Adoption in Australia Government Agencies', Proceedings of the International Conference on e-Business, International Conference on e-Business, Beijing Institute of Technology Press, Beijing, China, pp. 103-107.
View/Download from: UTS OPUS
Lu, Z. & Lu, J. 2002, 'Tourism Website Development and Customer Requirement in China', Procs of the Second International Conference E-Business, 2nd International Conference on E-Business, NA, Taipei, Taiwan, pp. 239-241.
View/Download from: UTS OPUS
Lu, J. & Zhang, G. 2002, 'Which Factors are Affecting e-Service Benefit - A research Framework', Proceedings of International Conference on E-Business (ICEB2002), International Conference on e-Business, Beijing Institute of Technology Press, Beijing, China, pp. 128-135.
Lu, J. & Lu, Z. 2002, 'Development, Distribution and Classification of Online Tourism Services in China', 3rd International WE-B Conference 2002 Proceedings, We-B Conference 2002, We-B Centre, School of MIS, Edith Cowan University, Perth, Australia, pp. 405-414.
View/Download from: UTS OPUS
Lu, J. 2001, 'Measuring Costs/Benefits of E-Business Applications & Customer Satisfaction', 2nd International We-B Conference '01, 2nd International We-B Conference '01, We-B Centre, School of Management Information systems, Edith Cowan University, Perth, WA, Australia.
View/Download from: UTS OPUS
Lu, J. & Quaddus, M. 2001, 'A Prototype of Multi-Objective Group decision support system with a group aggregation method base', Proceedings of the 12th Australasian Conference in Information Systems, Australasian Conference on Information Systems, ACIS Conference, Coffs Harbour, NSW, pp. 387-394.
View/Download from: UTS OPUS

Journal articles

Lu, N., Lu, J., Zhang, G. & Lopez De Mantaras, R. 2016, 'A concept drift-tolerant case-base editing technique', Artificial Intelligence, vol. 230, pp. 108-133.
View/Download from: UTS OPUS or Publisher's site
© 2015 Elsevier B.V. All rights reserved. The evolving nature and accumulating volume of real-world data inevitably give rise to the so-called "concept drift" issue, causing many deployed Case-Based Reasoning (CBR) systems to require additional maintenance procedures. In Case-base Maintenance (CBM), case-base editing strategies to revise the case-base have proven to be effective instance selection approaches for handling concept drift. Motivated by current issues related to CBR techniques in handling concept drift, we present a two-stage case-base editing technique. In Stage 1, we propose a Noise-Enhanced Fast Context Switch (NEFCS) algorithm, which targets the removal of noise in a dynamic environment, and in Stage 2, we develop an innovative Stepwise Redundancy Removal (SRR) algorithm, which reduces the size of the case-base by eliminating redundancies while preserving the case-base coverage. Experimental evaluations on several public real-world datasets show that our case-base editing technique significantly improves accuracy compared to other case-base editing approaches on concept drift tasks, while preserving its effectiveness on static tasks.
Zhang, Y., Robinson, D.K.R., Porter, A.L., Zhu, D., Zhang, G. & Lu, J. 2016, 'Technology roadmapping for competitive technical intelligence', Technological Forecasting and Social Change.
View/Download from: UTS OPUS or Publisher's site
© 2015 Elsevier Inc. Understanding the evolution and emergence of technology domains remains a challenge, particularly so for potentially breakthrough technologies. Though it is well recognized that emergence of new fields is complex and uncertain, to make decisions amidst such uncertainty, one needs to mobilize various sources of intelligence to identify known-knowns and known-unknowns to be able to choose appropriate strategies and policies. This competitive technical intelligence cannot rely on simple trend analyses because breakthrough technologies have little past to inform such trends, and positing the directions of evolution is challenging. Neither do qualitative tools, embracing the complexities, provide all the solutions, since transparent and repeatable techniques need to be employed to create best practices and evaluate the intelligence that comes from such exercises. In this paper, we present a hybrid roadmapping technique that draws on a number of approaches and integrates them into a multi-level approach (individual activities, industry evolutions and broader global changes) that can be applied to breakthrough technologies. We describe this approach in deeper detail through a case study on dye-sensitized solar cells. Our contribution to this special issue is to showcase the technique as part of a family of approaches that are emerging around the world to inform strategy and policy.
Pratama, M., Lu, J., Lughofer, E., Zhang, G. & Anavatti, S. 2016, 'Scaffolding type-2 classifier for incremental learning under concept drifts', Neurocomputing.
View/Download from: UTS OPUS or Publisher's site
© 2016 Elsevier B.V. The proposal of a meta-cognitive learning machine that embodies the three pillars of human learning: what-to-learn, how-to-learn, and when-to-learn, has enriched the landscape of evolving systems. The majority of meta-cognitive learning machines in the literature have not, however, characterized a plug-and-play working principle, and thus require supplementary learning modules to be pre-or post-processed. In addition, they still rely on the type-1 neuron, which has problems of uncertainty. This paper proposes the Scaffolding Type-2 Classifier (ST2Class). ST2Class is a novel meta-cognitive scaffolding classifier that operates completely in local and incremental learning modes. It is built upon a multivariable interval type-2 Fuzzy Neural Network (FNN) which is driven by multivariate Gaussian function in the hidden layer and the non-linear wavelet polynomial in the output layer. The what-to-learn module is created by virtue of a novel active learning scenario termed the uncertainty measure; the how-to-learn module is based on the renowned Schema and Scaffolding theories; and the when-to-learn module uses a standard sample reserved strategy. The viability of ST2Class is numerically benchmarked against state-of-the-art classifiers in 12 data streams, and is statistically validated by thorough statistical tests, in which it achieves high accuracy while retaining low complexity.
Naderpour, M., Lu, J. & Zhang, G. 2016, 'A safety-critical decision support system evaluation using situation awareness and workload measures', Reliability Engineering and System Safety, vol. 150, pp. 147-159.
View/Download from: UTS OPUS or Publisher's site
© 2016 Elsevier Ltd. To ensure the safety of operations in safety-critical systems, it is necessary to maintain operators' situation awareness (SA) at a high level. A situation awareness support system (SASS) has therefore been developed to handle uncertain situations [1]. This paper aims to systematically evaluate the enhancement of SA in SASS by applying a multi-perspective approach. The approach consists of two SA metrics, SAGAT and SART, and one workload metric, NASA-TLX. The first two metrics are used for the direct objective and subjective measurement of SA, while the third is used to estimate operator workload. The approach is applied in a safety-critical environment called residue treater, located at a chemical plant in which a poor human-system interface reduced the operators' SA and caused one of the worst accidents in US history. A counterbalanced within-subjects experiment is performed using a virtual environment interface with and without the support of SASS. The results indicate that SASS improves operators' SA, and specifically has benefits for SA levels 2 and 3. In addition, it is concluded that SASS reduces operator workload, although further investigations in different environments with a larger number of participants have been suggested.
Lu, J., Han, J., Hu, Y. & Zhang, G. 2016, 'Multilevel decision-making: A survey', Information Sciences, vol. 346-347, pp. 463-487.
View/Download from: UTS OPUS or Publisher's site
© 2016 Elsevier Inc. All rights reserved. Multilevel decision-making techniques aim to deal with decentralized management problems that feature interactive decision entities distributed throughout a multiple level hierarchy. Significant efforts have been devoted to understanding the fundamental concepts and developing diverse solution algorithms associated with multilevel decision-making by researchers in areas of both mathematics/computer science and business areas. Researchers have emphasized the importance of developing a range of multilevel decision-making techniques to handle a wide variety of management and optimization problems in real-world applications, and have successfully gained experience in this area. It is thus vital that a high quality, instructive review of current trends should be conducted, not only of the theoretical research results but also the practical developments in multilevel decision-making in business. This paper systematically reviews up-to-date multilevel decision-making techniques and clusters related technique developments into four main categories: bi-level decision-making (including multi-objective and multi-follower situations), tri-level decision-making, fuzzy multilevel decision-making, and the applications of these techniques in different domains. By providing state-of-the-art knowledge, this survey will directly support researchers and practical professionals in their understanding of developments in theoretical research results and applications in relation to multilevel decision-making techniques.
Zhang, G., Han, J. & Lu, J. 2016, 'Fuzzy Bi-level Decision-Making Techniques: A Survey', International Journal of Computational Intelligence Systems, vol. 9, pp. 25-34.
View/Download from: UTS OPUS or Publisher's site
© 2016 the authors. Bi-level decision-making techniques aim to deal with decentralized management problems that feature interactive decision entities distributed throughout a bi-level hierarchy. A challenge in handling bi-level decision problems is that various uncertainties naturally appear in decision-making process. Significant efforts have been devoted that fuzzy set techniques can be used to effectively deal with uncertain issues in bi-level decision-making, known as fuzzy bi-level decision-making techniques, and researchers have successfully gained experience in this area. It is thus vital that an instructive review of current trends in this area should be conducted, not only of the theoretical research but also the practical developments. This paper systematically reviews up-to-date fuzzy bi-level decisionmaking techniques, including models, approaches, algorithms and systems. It also clusters related technique developments into four main categories: basic fuzzy bi-level decision-making, fuzzy bi-level decision-making with multiple optima, fuzzy random bi-level decision-making, and the applications of bi-level decision-making techniques in different domains. By providing state-of-the-art knowledge, this survey paper will directly support researchers and practitioners in their understanding of developments in theoretical research results and applications in relation to fuzzy bi-level decision-making techniques.
Martínez, L. & Lu, J. 2016, 'A Humble Tribute to 50 Years of Fuzzy Sets', International Journal of Computational Intelligence Systems, vol. 9, pp. 1-2.
View/Download from: UTS OPUS or Publisher's site
Wang, W., Zhang, G. & Lu, J. 2016, 'Member contribution-based group recommender system', Decision Support Systems, vol. 87, pp. 80-93.
View/Download from: UTS OPUS or Publisher's site
© 2016.Developing group recommender systems (GRSs) is a vital requirement in many online service systems to provide recommendations in contexts in which a group of users are involved. Unfortunately, GRSs cannot be effectively supported using traditional individual recommendation techniques because it needs new models to reach an agreement to satisfy all the members of this group, given their conflicting preferences. Our goal is to generate recommendations by taking each group member's contribution into account through weighting members according to their degrees of importance. To achieve this goal, we first propose a member contribution score (MCS) model, which employs the separable non-negative matrix factorization technique on a group rating matrix, to analyze the degree of importance of each member. A Manhattan distance-based local average rating (MLA) model is then developed to refine predictions by addressing the fat tail problem. By integrating the MCS and MLA models, a member contribution-based group recommendation (MC-GR) approach is developed. Experiments show that our MC-GR approach achieves a significant improvement in the performance of group recommendations. Lastly, using the MC-GR approach, we develop a group recommender system called GroTo that can effectively recommend activities to web-based tourist groups.
Xuan, J., Luo, X., Zhang, G., Lu, J. & Xu, Z. 2016, 'Uncertainty Analysis for the Keyword System of Web Events', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 46, no. 6, pp. 829-842.
View/Download from: UTS OPUS or Publisher's site
© 2015 IEEE.Webpage recommendations for hot Web events can assist people to easily follow the evolution of these Web events. At the same time, there are different levels of semantic uncertainty underlying the amount of Webpages for a Web event, such as recapitulative information and detailed information. Apparently, the grasp of the semantic uncertainty of Web events could improve the satisfactoriness of Webpage recommendations. However, traditional hit-rate-based or clustering-based Webpage recommendation methods have overlooked these different levels of semantic uncertainty. In this paper, we propose a framework to identify the different underlying levels of semantic uncertainty in terms of Web events, and then utilize these for Webpage recommendations. Our idea is to consider a Web event as a system composed of different keywords, and the uncertainty of this keyword system is related to the uncertainty of the particular Web event. Based on keyword association linked network Web event representation and Shannon entropy, we identify the different levels of semantic uncertainty, and construct a semantic pyramid (SP) to express the uncertainty hierarchy of a Web event. Finally, an SP-based Webpage recommendation system is developed. Experiments show that the proposed algorithm can significantly capture the different levels of the semantic uncertainties of Web events and it can be applied to Webpage recommendations.
Chen, H., Zhang, G., Zhu, D. & Lu, J. 2015, 'A patent time series processing component for technology intelligence by trend identification functionality', Neural Computing and Applications, vol. 26, no. 2, pp. 345-353.
View/Download from: UTS OPUS or Publisher's site
Naderpour, M., Lu, J. & Zhang, G.Q. 2015, 'An Abnormal Situation Modeling Method to Assist Operators in Safety-Critical Systems', Reliability Engineering and System Safety, vol. 133, pp. 33-47.
View/Download from: UTS OPUS or Publisher's site
One of the main causes of accidents in safety-critical systems is human error. In order to reduce human errors in the process of handling abnormal situations that are highly complex and mentally taxing activities, operators need to be supported, from a cognitive perspective, in order to reduce their workload, stress, and the consequent error rate. Of the various cognitive activities, a correct understanding of the situation, i.e. situation awareness (SA), is a crucial factor in improving performance and reducing errors. Despite the importance of SA in decision-making in time- and safety-critical situations, the difficulty of SA modeling and assessment means that very few methods have as yet been developed. This study confronts this challenge, and develops an innovative abnormal situation modeling (ASM) method that exploits the capabilities of risk indicators, Bayesian networks and fuzzy logic systems. The risk indicators are used to identify abnormal situations, Bayesian networks are utilized to model them and a fuzzy logic system is developed to assess them. The ASM method can be used in the development of situation assessment decision support systems that underlie the achievement of SA. The performance of the ASM method is tested through a real case study at a chemical plant.
Lu, J., Zheng, Z., Zhang, G., He, Q. & Shi, Z. 2015, 'A new solution algorithm for solving rule-sets based bilevel decision problems', Concurrency Computation Practice and Experience, vol. 27, no. 4, pp. 830-854.
View/Download from: UTS OPUS or Publisher's site
Bilevel decision addresses compromises between two interacting decision entities within a given hierarchical complex system under distributed environments. Bilevel programming typically solves bilevel decision problems. However, formulation of objectives and constraints in mathematical functions is required, which are difficult, and sometimes impossible, in real-world situations because of various uncertainties. Our study develops a rule-set based bilevel decision approach, which models a bilevel decision problem by creating, transforming and reducing related rule sets. This study develops a new rule-sets based solution algorithm to obtain an optimal solution from the bilevel decision problem described by rule sets. A case study and a set of experiments illustrate both functions and the effectiveness of the developed algorithm in solving a bilevel decision problem. © 2012 John Wiley & Sons, Ltd.
Zhang, G., Lu, J. & Gao, Y. 2015, 'Bi-level Multi-follower Decision Making', Intelligent Systems Reference Library, vol. 82, pp. 65-104.
View/Download from: UTS OPUS or Publisher's site
© Springer-Verlag Berlin Heidelberg 2015. A bi-level decision problem may involve multiple decision entities (decision units or decision makers) at the lower level, and these followers may have different reactions for a possible decision made by the leader.
Zhang, G., Lu, J. & Gao, Y. 2015, 'Optimization Models', Intelligent Systems Reference Library, vol. 82, pp. 25-46.
View/Download from: UTS OPUS or Publisher's site
© Springer-Verlag Berlin Heidelberg 2015. To model and solve a bi-level or multi-level optimization problem, we have to first understand basic single-level optimization models and related solution methods. This chapter introduces related concepts, models and solution methods of basic single-level optimization including linear programming, non-linear programming, multi-objective programming, goal programming, Stackelberg game theory, and particle swarm optimization. These knowledge will be used in the rest of the book.
Zhang, G., Lu, J. & Gao, Y. 2015, 'Bi-level Multi-leader Decision Making', Intelligent Systems Reference Library, vol. 82, pp. 105-120.
View/Download from: UTS OPUS or Publisher's site
© Springer-Verlag Berlin Heidelberg 2015. In real-world applications, a bi-level decision problem may involve multiple decision entities on the upper level, that is, the bi-level decision problem has multiple leaders. The leaders may have their individual decision variables, objective functions and/or constraint conditions. This kind of bi-level decision problem is called a bi-level multi-leader (BLML) decision problem.
Zhang, G., Lu, J. & Gao, Y. 2015, 'Tri-level Multi-follower Decision Making', Intelligent Systems Reference Library, vol. 82, pp. 121-171.
View/Download from: UTS OPUS or Publisher's site
© Springer-Verlag Berlin Heidelberg 2015. In a tri-level hierarchical decision problem, each decision entity at one level has its objective, constraints and decision variables affected in part by the decision entities at the other two levels. The choice of values for its variables may allow it to influence the decisions made at other levels, and thereby improve its own objective. We called this a tri-level decision problem. When multiple decision entities are involved at the middle and bottom levels, the top-level entity's decision will be affected not only by these followers' individual reactions but also by the relationships among the followers. We call this problem a tri-level multi-follower (TLMF) decision.
Zhang, G., Lu, J. & Gao, Y. 2015, 'Rule-Set-Based Bi-level Decision Making', Intelligent Systems Reference Library, vol. 82, pp. 251-286.
View/Download from: UTS OPUS or Publisher's site
© Springer-Verlag Berlin Heidelberg 2015. As discussed in previous chapters, bi-level decision-making problems are normally modeled by bi-level programming.
Zhang, G., Lu, J. & Gao, Y. 2015, 'Bi-level Programming for Competitive Strategic Bidding Optimization in Electricity Markets', Intelligent Systems Reference Library, vol. 82, pp. 315-324.
View/Download from: UTS OPUS or Publisher's site
© Springer-Verlag Berlin Heidelberg 2015. We focus on the application of bi-level programming in electricity markets (power market) in this chapter. Competitive strategic bidding optimization of electric power plants (companies) is becoming one of the key issues in electricity markets. This chapter presents a strategic bidding optimization technique developed by applying the bi-level programming. By analyzing the strategic bidding behavior of power plants, we understand that this bidding problem includes several power plants and only one market operator respectively known as multiple leaders and single follower.
Zhang, G., Lu, J. & Gao, Y. 2015, 'Bi-level Programming Models and Algorithms', Intelligent Systems Reference Library, vol. 82, pp. 47-62.
View/Download from: UTS OPUS or Publisher's site
© Springer-Verlag Berlin Heidelberg 2015. This chapter introduces basic definitions, theorems, models and algorithms for bi-level programming (bi-level decision-making) and also basic models of multi-level programming, which will be used in the remaining chapters of this book.
Zhang, G., Lu, J. & Gao, Y. 2015, 'Bi-level Pricing and Replenishment in Supply Chains', Intelligent Systems Reference Library, vol. 82, pp. 325-336.
View/Download from: UTS OPUS or Publisher's site
© Springer-Verlag Berlin Heidelberg 2015. Effective pricing and replenishment strategies in supply chain management are the keys to business success. Notably, with rapid technological innovation and strong competition in hi-tech industries such as computer and communication organizations, the upstream component price and the down-stream product cost usually decline significantly with time. As a result, effective pricing and replenishment decision models are very important in supply chain management. This chapter first establishes a bi-level pricing and replenishment strategy optimization model in hi-tech industry. Then, two bi-level pricing models for pricing problems, in which the buyer and the vendor in a supply chain are respectively designated as the leader and the follower, are presented. Experiments illustrate that bi-level decision techniques can solve problems defined by these models and can achieve a profit increase under some situations, compared with the existing methods.
Zhang, G., Lu, J. & Gao, Y. 2015, 'Fuzzy Multi-objective Bi-level Goal Programming', Intelligent Systems Reference Library, vol. 82, pp. 229-247.
View/Download from: UTS OPUS or Publisher's site
© Springer-Verlag Berlin Heidelberg 2015. we presented the definitions, solutions, and algorithms for the fuzzy multi-objective bi-level programming (FMO-BLP) problems. This chapter still addresses the fuzzy multi-objective bi-level problem but applies a goal programming approach. We call it fuzzy multi-objective bi-level goal programming (FMO-BLGP). This chapter will discuss related definitions, solution concepts, and algorithms for the FMO-BLGP problem and will focus on the linear version of the FMO-BLGP problem. First, a fuzzy ranking method is used to give a mathematical definition for a FMO-BLGP problem, and then, based on a fuzzy vectors distance measure definition, a fuzzy bi-level goal programming (FBLGP) model is proposed. An algorithm for solving the FMO-BLGP problem is also developed.
Lu, J., Behbood, V., Hao, P., Zuo, H., Xue, S. & Zhang, G. 2015, 'Transfer Learning using Computational Intelligence: A Survey', Knowledge-Based Systems, vol. 80, pp. 14-23.
View/Download from: UTS OPUS
Abstract Transfer learning aims to provide a framework to utilize previously-acquired knowledge to solve new but similar problems much more quickly and effectively. In contrast to classical machine learning methods, transfer learning methods exploit the knowledge accumulated from data in auxiliary domains to facilitate predictive modeling consisting of different data patterns in the current domain. To improve the performance of existing transfer learning methods and handle the knowledge transfer process in real-world systems, ...
Ramezani, F., Lu, J., Taheri, J. & Hussain, F.K. 2015, 'Evolutionary algorithm-based multi-objective task scheduling optimization model in cloud environments', World Wide Web, vol. 18, no. 6, pp. 1737-1757.
View/Download from: UTS OPUS or Publisher's site
© 2015, Springer Science+Business Media New York. Optimizing task scheduling in a distributed heterogeneous computing environment, which is a nonlinear multi-objective NP-hard problem, plays a critical role in decreasing service response time and cost, and boosting Quality of Service (QoS). This paper, considers four conflicting objectives, namely minimizing task transfer time, task execution cost, power consumption, and task queue length, to develop a comprehensive multi-objective optimization model for task scheduling. This model reduces costs from both the customer and provider perspectives by considering execution and power cost. We evaluate our model by applying two multi-objective evolutionary algorithms, namely Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Genetic Algorithm (MOGA). To implement the proposed model, we extend the Cloudsim toolkit by using MOPSO and MOGA as its task scheduling algorithms which determine the optimal task arrangement among VMs. The simulation results show that the proposed multi-objective model finds optimal trade-off solutions amongst the four conflicting objectives, which significantly reduces the job response time and makespan. This model not only increases QoS but also decreases the cost to providers. From our experimentation results, we find that MOPSO is a faster and more accurate evolutionary algorithm than MOGA for solving such problems.
Lu, J., Wu, D., Mao, M., Wang, W. & Zhang, G. 2015, 'Recommender system application developments: A survey', Decision Support Systems, vol. 74, pp. 12-32.
View/Download from: UTS OPUS or Publisher's site
© 2015 Elsevier B.V. A recommender system aims to provide users with personalized online product or service recommendations to handle the increasing online information overload problem and improve customer relationship management. Various recommender system techniques have been proposed since the mid-1990s, and many sorts of recommender system software have been developed recently for a variety of applications. Researchers and managers recognize that recommender systems offer great opportunities and challenges for business, government, education, and other domains, with more recent successful developments of recommender systems for real-world applications becoming apparent. It is thus vital that a high quality, instructive review of current trends should be conducted, not only of the theoretical research results but more importantly of the practical developments in recommender systems. This paper therefore reviews up-to-date application developments of recommender systems, clusters their applications into eight main categories: e-government, e-business, e-commerce/e-shopping, e-library, e-learning, e-tourism, e-resource services and e-group activities, and summarizes the related recommendation techniques used in each category. It systematically examines the reported recommender systems through four dimensions: recommendation methods (such as CF), recommender systems software (such as BizSeeker), real-world application domains (such as e-business) and application platforms (such as mobile-based platforms). Some significant new topics are identified and listed as new directions. By providing a state-of-the-art knowledge, this survey will directly support researchers and practical professionals in their understanding of developments in recommender system applications.
Han, J., Lu, J., Hu, Y. & Zhang, G. 2015, 'Tri-level decision-making with multiple followers: Model, algorithm and case study', INFORMATION SCIENCES, vol. 311, pp. 182-204.
View/Download from: UTS OPUS or Publisher's site
Xuan, J., Lu, J., Zhang, G. & Luo, X. 2015, 'Topic Model for Graph Mining', IEEE Transactions on Cybernetics, vol. 45, no. 2, pp. 2792-2803.
View/Download from: UTS OPUS or Publisher's site
Graph mining has been a popular research area because of its numerous application scenarios. Many unstructured and structured data can be represented as graphs, such as, documents, chemical molecular structures, and images. However, an issue in relation to current research on graphs is that they cannot adequately discover the topics hidden in graph-structured data which can be beneficial for both the unsupervised learning and supervised learning of the graphs. Although topic models have proved to be very successful in discovering latent topics, the standard topic models cannot be directly applied to graphstructured data due to the 'bag-of-word assumption. In this paper, an innovative graph topic model (GTM) is proposed to address this issue, which uses Bernoulli distributions to model the edges between nodes in a graph. It can, therefore, make the edges in a graph contribute to latent topic discovery and further improve the accuracy of the supervised and unsupervised learning of graphs. The experimental results on two different types of graph datasets show that the proposed GTM outperforms the latent Dirichlet allocation on classification by using the unveiled topics of these two models to represent graphs.
Wang, W., Zhang, G. & Lu, J. 2015, 'Collaborative filtering with entropy-driven user similarity in recommender systems', International Journal of Intelligent Systems, vol. 30, no. 8, pp. 854-870.
View/Download from: UTS OPUS or Publisher's site
© 2015 Wiley Periodicals, Inc. Collaborative filtering (CF) is the most popular approach in personalized recommender systems. Although CF approaches have successfully been used and have the advantage in that it is unnecessary to analyze item content when generating recommendations, they nevertheless suffer from problems with accuracy. In this paper, we propose a new CF approach to improve recommendation performance. First, a new information entropy-driven user similarity measure model is proposed to measure the relative difference between ratings. A Manhattan distance-based model is then developed to address the fat tail problem by estimating the alternative active user average rating. The effectiveness of the proposed approach is analyzed on public and private data sets. As a result of the introduction of the new similarity measure and average rating estimation, we demonstrate that the proposed new CF recommendation approach provides better recommendations.
Behbood, V., Lu, J., Zhang, G. & Pedrycz, W. 2015, 'Multi-Step Fuzzy Bridged Refinement Domain Adaptation Algorithm and Its Application to Bank Failure Prediction', IEEE Transactions on Fuzzy Systems, vol. 23, no. 6, pp. 1917-1935.
View/Download from: UTS OPUS or Publisher's site
Machine learning plays an important role in data classification and data-based prediction. In some real world applications, however, the training data (coming from the source domain) and test data (from the target domain) come from different domains or time periods, and this may result in the different distributions of some features. Moreover, the values of the features and/or labels of the data sets might be non-numeric and involve vague values. Traditional learning-based prediction and classification methods cannot handle these two issues. In this study, we propose a multi-step fuzzy bridged refinement domain adaptation algorithm, which offers an effective way to deal with both issues. It utilizes a concept of similarity to modify the labels of the target instances that were initially predicted by a shift-unaware model. It then refines the labels using instances that are most similar to a given target instance. These instances are extracted from mixture domains composed of source and target domains. The proposed algorithm is built on a basis of some data and refines the labels, thus performing completely independently of the shift-unaware prediction model. The algorithm uses a fuzzy set-based approach to deal with the vague values of the features and labels. Four different data sets are used in the experiments to validate the proposed algorithm. The results, which are compared with those generated by the existing domain adaptation methods, demonstrate a significant improvement in prediction accuracy in both the above-mentioned data sets.
Wu, D., Zhang, G. & Lu, J. 2015, 'A fuzzy preference tree-based recommender system for personalized business-to-business e-services', IEEE Transactions on Fuzzy Systems, vol. 23, no. 1, pp. 29-43.
View/Download from: UTS OPUS or Publisher's site
© 2014 IEEE. The Web creates excellent opportunities for businesses to provide personalized online services to their customers. Recommender systems aim to automatically generate personalized suggestions of products/services to customers (businesses or individuals). Although recommender systems have been well studied, there are still two challenges in the development of a recommender system, particularly in real-world B2B e-services: 1) items or user profiles often present complicated tree structures in business applications, which cannot be handled by normal item similarity measures and 2) online users' preferences are often vague and fuzzy, and cannot be dealt with by existing recommendation methods. To handle both these challenges, this study first proposes a method for modeling fuzzy tree-structured user preferences, in which fuzzy set techniques are used to express user preferences. A recommendation approach to recommending tree-structured items is then developed. The key technique in this study is a comprehensive tree matching method, which can match two tree-structured data and identify their corresponding parts by considering all the information on tree structures, node attributes, and weights. Importantly, the proposed fuzzy preference tree-based recommendation approach is tested and validated using an Australian business dataset and the MovieLens dataset. Experimental results show that the proposed fuzzy tree-structured user preference profile reflects user preferences effectively and the recommendation approach demonstrates excellent performance for tree-structured items, especially in e-business applications. This study also applies the proposed recommendation approach to the development of a web-based business partner recommender system.
Wu, D., Lu, J. & Zhang, G. 2015, 'A Fuzzy Tree Matching-Based Personalized E-Learning Recommender System', IEEE Transactions on Fuzzy Systems, vol. 23, no. 6, pp. 2412-2426.
View/Download from: UTS OPUS or Publisher's site
© 1993-2012 IEEE. The rapid development of e-learning systems provides learners with great opportunities to access learning activities online, and this greatly supports and enhances the learning practices. However, an issue reduces the success of application of e-learning systems; too many learning activities (such as various leaning materials, subjects, and learning resources) are emerging in an e-learning system, making it difficult for individual learners to select proper activities for their particular situations/requirements because there is no personalized service function. Recommender systems, which aim to provide personalized recommendations for products or services, can be used to solve this issue. However, e-learning systems need to be able to handle certain special requirements: 1) leaning activities and learners' profiles often present tree structures; 2) learning activities contain vague and uncertain data, such as the uncertain categories that the learning activities belong to; 3) there are pedagogical issues, such as the precedence relations between learning activities. To deal with the three requirements, this study first proposes a fuzzy tree-structured learning activity model, and a learner profile model to comprehensively describe the complex learning activities and learner profiles. In the two models, fuzzy category trees and related similarity measures are presented to infer the semantic relations between learning activities or learner requirements. Since it is impossible to have two completely same trees, in practice, a fuzzy tree matching method is carefully discussed. A fuzzy tree matching-based hybrid learning activity recommendation approach is then developed. This approach takes advantage of both the knowledge-based and collaborative filtering-based recommendation approaches, and considers both the semantic and collaborative filtering similarities between learners. Finally, an e-learning recommender system prototype is well designed and...
Pedrycz, W. & Lu, J. 2015, 'Web-Based Intelligent Support Systems-Preface to the Special Section', IEEE TRANSACTIONS ON FUZZY SYSTEMS, vol. 23, no. 1, pp. 1-2.
View/Download from: UTS OPUS or Publisher's site
Naderpour, M., Nazir, S. & Lu, J. 2015, 'The role of situation awareness in accidents of large-scale technological systems', Process Safety and Environmental Protection, vol. 97, pp. 13-24.
View/Download from: UTS OPUS or Publisher's site
© 2015 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved. In the last two decades, several serious accidents at large-scale technological systems that have had grave consequences, such as that at Bhopal, have primarily been attributed to human error. However, further investigations have revealed that humans are not the primary cause of these accidents, but have inherited the problems and difficulties of working with complex systems created by engineers. The operators have to comprehend malfunctions in real time, respond quickly, and make rapid decisions to return operational units to normal conditions, but under these circumstances, the mental workload of operators rises sharply, and a mental workload that is too high increases the rate of error. Therefore, cognivitive human features such as situation awareness (SA) - one of the most important prerequisite for decision-making - should be considered and analyzed appropriately. This paper applys the SA Error Taxonomy methodology to analyze the role of SA in three different accidents: (1) A runaway chemical reaction at Institute, West Virginia killing two employees, injuring eight people, and requiring the evacuation of more than 40,000 residents adjacent to the facility, (2) The ignition of a vapor cloud at Bellwood, Illinois that killed one person, injured two employees, and caused significant business interruption, and (3) An explosion at Ontario, California injuring four workers and caused extensive damage to the facility. In addition, the paper presents certain requirements for cognitive operator support system development and operator training under abnormal situations to promote operators' SA in the process industry.
Naderpour, M., Lu, J. & Zhang, G. 2015, 'A human-system interface risk assessment method based on mental models', Safety Science, vol. 79, pp. 286-297.
View/Download from: UTS OPUS or Publisher's site
© 2015 Elsevier Ltd. In many safety-critical systems, it is necessary to maintain operators' situation awareness at a high level to ensure the safety of operations. Today, in many such systems, operators have to rely on the principles and design of human-system interfaces (HSIs) to observe and comprehend the overwhelming amount of process data. Thus, poor HSIs may cause serious consequences, such as occupational accidents and diseases including stress, and they have therefore been considered an emerging risk. Despite the importance of this, very few methods have as yet been developed to assess the risk of HSIs. This paper presents a new risk assessment method that relies upon operators' mental models, human reliability analysis (HRA) event tree, and the situation awareness global assessment technique (SAGAT) to produce a risk profile for the intended HSI. In the proposed method, the operator's understanding (i.e. mental models) about possible abnormal situations in the intended plant is modeled on the basis of the capabilities of Bayesian networks. The situation models are combined with the HRA event tree, which paves the way for the incorporation of operator responses in the assessment method. Probe questions in line with the SAGAT through simulated scenarios in a virtual environment are then administrated to gather operator responses. Finally, the proposed method determines a risk level for the HSI by assigning the operator responses to the developed situational networks. The performance of the proposed method is investigated through a case study at a chemical plant.
Shambour, Q. & Lu, J. 2015, 'An effective recommender system by unifying user and item trust information for B2B applications', Journal of Computer and System Sciences, vol. 81, no. 7, pp. 1110-1126.
View/Download from: UTS OPUS or Publisher's site
© 2015 Elsevier Inc. Although Collaborative Filtering (CF)-based recommender systems have received great success in a variety of applications, they still under-perform and are unable to provide accurate recommendations when users and items have few ratings, resulting in reduced coverage. To overcome these limitations, we propose an effective hybrid user-item trust-based (HUIT) recommendation approach in this paper that fuses the users' and items' implicit trust information. We have also considered and computed user and item global reputations into this approach. This approach allows the recommender system to make an increased number of accurate predictions, especially in circumstances where users and items have few ratings. Experiments on four real-world datasets, particularly a business-to-business (B2B) case study, show that the proposed HUIT recommendation approach significantly outperforms state-of-the-art recommendation algorithms in terms of recommendation accuracy and coverage, as well as significantly alleviating data sparsity, cold-start user and cold-start item problems.
Li, T., Lu, J. & Lõpez, L.M. 2015, 'Preface: Intelligent techniques for data science', International Journal of Intelligent Systems, vol. 30, no. 8, pp. 851-853.
View/Download from: UTS OPUS or Publisher's site
Al-Hassan, M., Lu, H. & Lu, J. 2015, 'A semantic enhanced hybrid recommendation approach: A case study of e-Government tourism service recommendation system', Decision Support Systems, vol. 72, pp. 97-109.
View/Download from: UTS OPUS or Publisher's site
© 2015 Elsevier B.V.All rights reserved. Recommender systems are effectively used as a personalized information filtering technology to automatically predict and identify a set of interesting items on behalf of users according to their personal needs and preferences. Collaborative Filtering (CF) approach is commonly used in the context of recommender systems; however, obtaining better prediction accuracy and overcoming the main limitations of the standard CF recommendation algorithms, such as sparsity and cold-start item problems, remain a significant challenge. Recent developments in personalization and recommendation techniques support the use of semantic enhanced hybrid recommender systems, which incorporate ontology-based semantic similarity measure with other recommendation approaches to improve the quality of recommendations. Consequently, this paper presents the effectiveness of utilizing semantic knowledge of items to enhance the recommendation quality. It proposes a new Inferential Ontology-based Semantic Similarity (IOBSS) measure to evaluate semantic similarity between items in a specific domain of interest by taking into account their explicit hierarchical relationships, shared attributes and implicit relationships. The paper further proposes a hybrid semantic enhanced recommendation approach by combining the new IOBSS measure and the standard item-based CF approach. A set of experiments with promising results validates the effectiveness of the proposed hybrid approach, using a case study of the Australian e-Government tourism services.
Zhang, Y., Zhang, G., Chen, H., Porter, A.L., Zhu, D. & Lu, J. 2015, 'Topic analysis and forecasting for science, technology and innovation: Methodology with a case study focusing on big data research', Technological Forecasting and Social Change.
View/Download from: UTS OPUS or Publisher's site
© 2016.The number and extent of current Science, Technology & Innovation topics are changing all the time, and their induced accumulative innovation, or even disruptive revolution, will heavily influence the whole of society in the near future. By addressing and predicting these changes, this paper proposes an analytic method to (1) cluster associated terms and phrases to constitute meaningful technological topics and their interactions, and (2) identify changing topical emphases. Our results are carried forward to present mechanisms that forecast prospective developments using Technology Roadmapping, combining qualitative and quantitative methodologies. An empirical case study of Awards data from the United States National Science Foundation, Division of Computer and Communication Foundation, is performed to demonstrate the proposed method. The resulting knowledge may hold interest for R&D management and science policy in practice.
Pratama, M., Anavatti, S.G. & Lu, J. 2015, 'Recurrent Classifier Based on an Incremental Metacognitive-Based Scaffolding Algorithm', IEEE Transactions on Fuzzy Systems, vol. 23, no. 6, pp. 2048-2066.
View/Download from: UTS OPUS or Publisher's site
Lu, P., Lin, H., Lu, J. & Zhang, G. 2014, 'A Customer Churn Prediction Model in Telecom Industry Using Boosting', IEEE Transaction on Industrial Informatics, vol. 10, no. 2, pp. 1-7.
View/Download from: UTS OPUS or Publisher's site
Lu, P., Zhang, G. & Lu, J. 2014, 'Concept drift detection via competence models', Artificial Intelligence Journal, vol. 209, pp. 11-28.
View/Download from: UTS OPUS or Publisher's site
Naderpour, M., Lu, J. & Zhang, G. 2014, 'An Intelligent Situation Awareness Support System for Safety-Critical Environments', Decision Support Systems, vol. 59, pp. 325-340.
View/Download from: UTS OPUS or Publisher's site
Operators handling abnormal situations in safety-critical environments need to be supported from a cognitive perspective to reduce their workload, stress, and consequent error rate. Of the various cognitive activities, a correct understanding of the situation, i.e. situation awareness (SA), is a crucial factor in improving performance and reducing error.However, existing systemsafety researches focus mainly on technical issues and often neglect SA. This study presents an innovative cognition-driven decision support system called the situation awareness support system (SASS) to manage abnormal situations in safety-critical environments in which the effect of situational complexity on human decision-makers is a concern. To achieve this objective, a situational network modeling process and a situation assessment model that exploits the specific capabilities of dynamic Bayesian networks and risk indicators are first proposed. The SASS is then developed and consists of fourmajor elements: 1) a situation data collection component that provides the current state of the observable variables based on online conditions and monitoring systems, 2) a situation assessment component based on dynamic Bayesian networks (DBN) to model the hazardous situations in a situational network and a fuzzy risk estimation method to generate the assessment result, 3) a situation recovery component that provides a basis for decision-making to reduce the risk level of situations to an acceptable level, and 4) a human-computer interface. The SASS is partially evaluated by a sensitivity analysis, which is carried out to validate DBN-based situational networks, and SA measurements are suggested for a full evaluation of the proposed system. The performance of the SASS is demonstrated by a case taken from US Chemical Safety Board reports, and the results demonstrate that the SASS provides a useful graphical, mathematically consistent system for dealing with incomplete and uncertain information to he...
Ma, J., Lu, J. & Zhang, G. 2014, 'A three-level-similarity measuring method of participant opinions in multiple-criteria group decision supports', Decision Support Systems, vol. 59, pp. 74-83.
View/Download from: UTS OPUS or Publisher's site
Measuring opinion similarity between participants is an important strategy to reduce the chance of making and applying inappropriate decisions in multi-criteria group decision making applications. Due to the small-sized opinion data and the varieties of opinion representations, measuring the similarity between opinions is difficult and has not been well-studied in developing decision support. Considering that the similarity changes with the number of concerned criteria, this paper develops a gradual aggregation algorithmand establishes a three-levelsimilarity measuring (TLSM) method based on it to measure the opinion similarity at the assessment level, the criterion level and the problem level. Two applications of the TLSM method on social policy selection and energy policy evaluation are conducted. The study indicates that the TLSM method can effectively measure the similarity between opinions in small-sizewith possiblymissing values and simulate the dynamic generation of a decision.
Naderpour, M., Lu, J. & Zhang, G. 2014, 'A Situation Risk Awareness Approach for Process Systems Safety', Safety Science, vol. 64, pp. 173-189.
View/Download from: UTS OPUS or Publisher's site
Promoting situation awareness is an important design objective for a wide variety of domains, especially for process systems where the information flow is quite high and poor decisions may lead to serious consequences. In todays process systems, operators are often moved to a control room far away from the physical environment, and increasing amounts of information are passed to them via automated systems, they therefore need a greater level of support to control and maintain the facilities in safe conditions. This paper proposes a situation risk awareness approach for process systems safety where the effect of ever-increasing situational complexity on human decision-makers is a concern. To develop the approach, two important aspects addressing hazards that arise from hardware failure and reducing human error through decision-making have been considered. The proposed situation risk awareness approach includes two major elements: an evidence preparation component and a situation assessment component. The evidence preparation component provides the soft evidence, using a fuzzy partitioning method, that is used in the subsequent situation assessment component. The situation assessment component includes a situational network based on dynamic Bayesian networks to model the abnormal situations, and a fuzzy risk estimation method to generate the assessment result. A case from U.S. Chemical Safety Board investigation reports has been used to illustrate the application of the proposed approach.
Ramezani, F., Lu, J. & Hussain, F.K. 2014, 'Task-Based System Load Balancing in Cloud Computing Using Particle Swarm Optimization', International Journal of Parallel Programming, vol. 42, pp. 1-12.
View/Download from: UTS OPUS or Publisher's site
Live virtual machine (VM) migration is a technique for achieving system load balancing in a cloud environment by transferring an active VM from one physical host to another. This technique has been proposed to reduce the downtime for migrating overloaded VMs, but it is still time- and cost-consuming, and a large amount of memory is involved in the migration process. To overcome these drawbacks, we propose a Task-based System Load Balancing method using Particle Swarm Optimization (TBSLB-PSO) that achieves system load balancing by only transferring extra tasks from an overloaded VM instead of migrating the entire overloaded VM. We also design an optimization model to migrate these extra tasks to the new host VMs by applying Particle Swarm Optimization (PSO). To evaluate the proposed method, we extend the cloud simulator (Cloudsim) package and use PSO as its task scheduling model. The simulation results show that the proposed TBSLB-PSO method significantly reduces the time taken for the load balancing process compared to traditional load balancing approaches. Furthermore, in our proposed approach the overloaded VMs will not be paused during the migration process, and there is no need to use the VM pre-copy process. Therefore, the TBSLB-PSO method will eliminate VM downtime and the risk of losing the last activity performed by a customer, and will increase the Quality of Service (QoS) experienced by cloud customers.
Behbood, V., Lu, J. & Zhang, G. 2014, 'Fuzzy Refinement Domain Adaptation for Long Term Prediction in Banking Ecosystem', IEEE Transaction on Industrial Informatics, vol. 10, no. 2, pp. 1637-1646.
View/Download from: UTS OPUS or Publisher's site
Long-term bank failure prediction is a challenging real world problem in banking ecosystem and machine learning methods have been recently applied to improve the prediction accuracy. However, traditional machine learning methods assume that the training data and the test data are drawn from the same distribution, which is hard to be met in real world banking applications. This paper proposes a novel algorithm known as fuzzy refinement domain adaptation to solve this problem based on the ecosystem-oriented architecture. The algorithm utilizes the fuzzy system and similarity/dissimilarity concepts to modify the target instances' labels which were initially predicted by a shift-unaware prediction model. It employs a classifier to modify the label values of target instances based on their similarity/dissimilarity to the candidate positive and negative instances in mixture domains. Thirty six experiments are performed using three different shift-unaware prediction models. In these experiments bank failure financial data is used to evaluate the algorithm. The results demonstrate that the proposed algorithm significantly improves predictive accuracy and outperforms other refinement algorithms.
Purba, J., Lu, J., Zhang, G. & Pedrycz, W. 2014, 'A fuzzy reliability assessment of basic events of fault trees through qualitative data processing', Fuzzy Sets and Systems, vol. 243, no. 16, pp. 50-69.
View/Download from: UTS OPUS
Probabilistic approaches are common in the analysis of reliability of complex engineering systems. However, they require quantitative historical failure data for determining reliability characteristics. In many real-world areas, such as e.g., nuclear engineering, quantitative historical failure data are unavailable or become inadequate and only qualitative data such as expert opinions, which are described in linguistic terms, can be collected and then used to assess system reliability. Moreover, experts are more comfortable justifying event failure likelihood using linguistic terms, which capture uncertainties rather than by expressing judgments in a quantitative manner. New techniques are therefore needed that will help construct models of reliability of complex engineering system without being confined to quantitative historical failure data. The objective of this study is to develop a fuzzy reliability algorithm to effectively generate basic event failure probabilities without reliance on quantitative historical failure data through qualitative data processing. The originality of this study comes with an introduction of linguistic values articulated in terms of component failure possibilities in order to qualitatively assess basic event failure possibilities treated as inputs of the proposed model and generate basic event failure probabilities as its outputs. To demonstrate the feasibility and effectiveness of the proposed algorithm, actual basic event failure probabilities collected from nuclear power plant operating experiences are compared with the failure probabilities generated by the algorithm. The results demonstrate that the proposed fuzzy reliability algorithm arises as a suitable alternative for the probabilistic reliability approach when quantitative historical failure data are unavailable.
Ramezani, F. & Lu, J. 2014, 'An intelligent group decision-support system and its application for project performance evaluation', Journal of Enterprise Information Management, vol. 27, no. 3, pp. 278-291.
View/Download from: UTS OPUS
Purpose In any organization there are main goals, with lots of projects designed to achieve these goals. It is important for any organization to determine how much these projects affect the achievement of these goals. The purpose of this paper is to develop a fuzzy multiple attribute-based group decision-support system (FMAGDSS) to evaluate projects performance in promoting the organization's goals utilizing simple additive weighting (SAW) algorithm and technique for order of preference by similarity to ideal solution (TOPSIS) algorithm. The proposed FMAGDSS deals with choosing the most appropriate fuzzy ranking algorithm for solving a given fuzzy multi attribute decision making (FMADM) problem with both qualitative and quantitative criteria (attributes), and uncertain judgments of decision makers. Design/methodology/approach In this paper, a FMAGDSS model is designed to determine scores and ranks of every project in promoting the organization's goals. In the first step of FMAGDSS model, all projects are assessed by experts based on evaluation criteria and the organization's goals. The proposed FMAGDSS model will then choose the most appropriate fuzzy ranking method to solve the given FMADM problem. Finally, a sensitivity analysis system is developed to assess the reliability of the decision-making process and provide an opportunity to analyze the impacts of criteria weights and projects performance on evaluating projects in achieving the organizations goals, and to assess the reliability of the decision-making process. In addition, a software prototype has been developed on the basis of FMAGDSS model that can be applied to solve every FMADM problem that needs to rank alternatives according to certain attributes. Findings The result of this study simplifies and accelerates the evaluation process. The proposed system not only helps organizations to choose the most efficient projects for sustainable development, but also helps them to assess the reliability of...
Naderpour, M., Lu, J. & Zhang, G. 2014, 'The explosion at Institute: Modeling and analyzing the situation awareness factor.', Accident; analysis and prevention, vol. 73C, pp. 209-224.
View/Download from: UTS OPUS or Publisher's site
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.
Gao, Y., Zhang, G., Lu, J. & Ma, J. 2014, 'A bi-level decision model for customer churn analysis', Computational Intelligence, vol. 30, no. 3, pp. 583-599.
View/Download from: UTS OPUS or Publisher's site
This paper develops a bi-level decision model and a solution approach to optimizing service features for a company to reduce its customer churn rate. First, a bi-level decision model, together with its modeling approach, are developed to describe the gaming relationship between decision makers in a company (service provider) and its customers. Then, a practical solution approach to reaching solutions for the bi-level-modeled customer churn problem is developed. Finally, experiments and case studies are conducted to illustrate the bi-level decision model and the solution approach. © 2013 Wiley Periodicals, Inc.
Alqahtani, A., Lu, H. & Lu, J. 2014, 'Knowledge-based life event model for e-government service integration with illustrative examples', Intelligent Decision Technologies, vol. 8, no. 3, pp. 189-205.
View/Download from: UTS OPUS or Publisher's site
The advancement of information and communications technology and web services offers an opportunity for e-government service integration, which can help improve the availability and quality of services offered. However, few of the potential service integration applications have been adopted by governments to increase the accessibility of and satisfaction with government services and information for citizens. Recently, the 'life event' concept was introduced as the core element of integrating complexity of service delivery to improve the efficiency and reusability of e-government services, web-based information management systems. In addition, a semantic web-based ontology is considered to be the most powerful conceptual approach for dealing with challenges associated with developing seamless systems in distributed environments. Among these challenges are interoperability, which can be loosely defined as the technical capability for interoperation. Despite the conceptual emergence of semantic web-based ontology for life events, the question remains of what methodology to use when designing a semantic web-based ontology for life events. This paper proposes a semantic web-based ontology model for life events for e-government service integration created using a methodology that implements the model using the ontology modelling tool Protégé and evaluates the model using Pellet Reasoner and the SPARQL query language. In addition, this model is illustrated by two examples, the Saudi Arabia King Abdullah Scholarship and Hafiz, to show the advantages of integrated systems compared with standalone systems. These examples show that the new model can effectively support the integration of standalone e-government services automatically so that citizens do not need to manually execute individual services. This can significantly improve the accessibility of e-government services and citizen's satisfaction. © 2014-IOS Press.
Nguyen, T.T.S., Lu, H.H. & Lu, J. 2014, 'Web-Page Recommendation Based on Web Usage and Domain Knowledge', IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 10, pp. 2574-2587.
View/Download from: UTS OPUS or Publisher's site
Purba, J., Lu, J. & Zhang, G. 2014, 'An intelligent system by fuzzy reliability algorithm in fault tree analysis for nuclear power plant probabilistic safety assessment', International Journal of Computational Intelligence and Applications, vol. 13, no. 03, pp. 1450017-1450017.
View/Download from: UTS OPUS or Publisher's site
Demong, N.A.R., Lu, J. & Hussain, F.K. 2014, 'Personalised property investment risk analysis model in the real estate industry', Studies in Computational Intelligence, vol. 502, pp. 369-390.
View/Download from: Publisher's site
Property investment in the real estate industry entails high cost and high risk, but provides high yield for return on investment. Risk factors in the real estate industry are mostly uncertain and change dynamically with the surrounding developments. There are many existing risk analysis tools or techniques that help investors to find better solutions. Most techniques available refer to expert's opinions in ranking and weighting the risk factors. As a result, they create misinterpretation and varying judgments from the experts. In addition, investment purposes differ between investors for both commercial and residential properties. There is therefore a need for personalisation elements to enable investors to interact with the analysis. This chapter presents a personalised risk analysis model that enables investors to analyse the risk of their property investments and make correct decisions. The model has three main components: investor, decision support technologies, and the data. Real world data from the Australian real estate industry is used to validate the proposed model. © 2014 Springer-Verlag Berlin Heidelberg.
Lu, J., Niu, L. & Zhang, G. 2013, 'A Situation Retrieval Model for Cognitive Decision Support in Digital Business Ecosystems', IEEE Transactions On Industrial Electronics, vol. 60, no. 3, pp. 1059-1069.
View/Download from: UTS OPUS or Publisher's site
This paper presents a novel situation retrieval (SR) model for supporting cognition-driven decision processes in digital business ecosystems. Cognitive decision support in digital ecosystems is concerned with decision makers cognitive processes. This study aims to facilitate cognitive decision support to decision makers on the basis of current business intelligence (BI) platform. Underlying foundations of the SR model are two types of mental constructs: situation awareness (SA) and mental models of decision makers and the model of naturalistic decision making (NDM). These mental constructs and NDM are integrated into the BI application framework. Our experiments showed that the SR model was playing a nontrivial role to help decision makers develop enhanced SA and reuse their past experience to make better decisions.
Komkhao, M., Lu, J., Li, Z. & Halang, W.A. 2013, 'Incremental collaborative filtering based on Mahalanobis distance and fuzzy membership for recommender systems', International Journal of General Systems, vol. 42, no. 1, pp. 41-66.
View/Download from: UTS OPUS or Publisher's site
Recommender systems, as an effective personalization approach, can suggest best-suited items (products or services) to particular users based on their explicit and implicit preferences by applying information filtering technology. Collaborative filtering (CF) method is currently the most popular and widely adopted recommendation approach. It works by collecting user ratings for items in a given domain and by computing the similarity between the profiles of several users in order to recommend items. Current similarity measures and models updated by traditional model-based CF have, however, shortcomings with respect to accuracy of prediction and scalability of recommender systems. To overcome these problems, here an incremental CF algorithm based on the Mahalanobis distance is presented. The algorithm has two phases: the learning phase, in which models of similar users are constructed incrementally, and the prediction phase, in which interested users are clustered by measuring their similarity to existing clusters in a model. To handle confusion of decision making on overlapping clusters, fuzzy sets are employed, and the degree of membership to them is expressed by the Mahalanobis radial basis function. Experimental results demonstrate that the proposed algorithm leads to improved prediction accuracy and prevents the scalability problem in recommendation systems.
Niu, L., Lu, J., Zhang, G. & Wu, D. 2013, 'FACETS: A Cognitive Business Intelligence System', Information Systems, vol. 38, no. 6, pp. 835-862.
View/Download from: UTS OPUS or Publisher's site
A cognitive decision support system called FACETS was developed and evaluated based on the situation retrieval (SR) model. The aim of FACETS is to provide decision makers cognitive decision support in ill-structured decision situations. The design and de
Amailef, K. & Lu, J. 2013, 'Ontology-supported Case-based Reasoning Approach For Intelligent M-government Emergency Response Services', Decision Support Systems, vol. 55, no. 1, pp. 79-97.
View/Download from: UTS OPUS or Publisher's site
There is a critical need to develop a mobile-based emergency response system (MERS) to help reduce risks in emergency situations. Existing systems only provide short message service (SMS) notifications, and the decision support is weak, especially in man-made disaster situations. This paper presents a MERS ontology-supported case-based reasoning (OS-CBR) method, with implementation, to support emergency decision makers to effectively respond to emergencies. The advantages of the OS-CBR approach is that it builds a case retrieving process, which provides a more convenient system for decision support based on knowledge from, and solutions provided for past disaster events. The OS-CBR approach includes a set of algorithms that have been successfully implemented in four components: data acquisition; ontology; knowledge base; and reasoning; as a sub-system of the MERS framework. A set of experiments and case studies validated the OS-CBR approach and application, and demonstrate its efficiency
Zhang, Z., Lin, H., Liu, K., Wu, D., Zhang, G. & Lu, J. 2013, 'A Hybrid Fuzzy-based Personalized Recommender System For Telecom Products/services', Information Sciences, vol. 235, no. 1, pp. 117-129.
View/Download from: UTS OPUS or Publisher's site
The Internet creates excellent opportunities for businesses to provide personalized online services to their customers. Recommender systems are designed to automatically generate personalized suggestions of products/services to customers. Because various uncertainties exist within both product and customer data, it is a challenge to achieve high recommendation accuracy. This study develops a hybrid recommendation approach which combines user-based and item-based collaborative filtering techniques with fuzzy set techniques and applies it to mobile product and service recommendation. It particularly implements the proposed approach in an intelligent recommender system software called Fuzzy-based Telecom Product Recommender System (FTCP-RS). Experimental results demonstrate the effectiveness of the proposed approach and the initial application shows that the FTCP-RS can effectively help customers to select the most suitable mobile products or services
Behbood, V., Lu, J. & Zhang, G. 2013, 'Fuzzy bridged refinement domain adaptation: Long-term bank failure prediction', International Journal of Computational Intelligence and Applications, vol. 12, no. 1, pp. 1-17.
View/Download from: UTS OPUS or Publisher's site
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
Kaur, P., Goyal, M.L. & Lu, J. 2013, 'A Proficient and Dynamic Bidding Agent for Online Auctions', Lecture Notes in Computer Science, vol. 7607, no. 1, pp. 178-190.
View/Download from: UTS OPUS or Publisher's site
E-consumers face biggest challenge of opting for the best bidding strategies for competing in an environment of multiple and simultaneous online auctions for same or similar items. It becomes very complicated for the bidders to make decisions of selecting which auction to participate in, place single or multiple bids, early or late bidding and how much to bid. In this paper, we present the design of an autonomous dynamic bidding agent (ADBA) that makes these decisions on behalf of the buyers according to their bidding behaviors. The agent develops a comprehensive method for initial price prediction and an integrated model for bid forecasting. The initial price prediction method selects an auction to participate in and then predicts its closing price (initial price). Then the bid forecasting model forecasts the bid amount by designing different bidding strategies followed by the late bidders. The experimental results demonstrated improved initial price prediction outcomes by proposing a clustering based approach. Also, the results show the proficiency of the bidding strategies amongst the late bidders with desire for bargain
Lu, J., Shambour, Q.Y., Xu, Y., Lin, Q. & Zhang, G. 2013, 'A web-based personalized business partner recommendation system using fuzzy semantic techniques', Computational Intelligence, vol. 29, no. 1, pp. 37-69.
View/Download from: UTS OPUS or Publisher's site
The web provides excellent opportunities to businesses in various aspects of development such as finding a business partner online. However, with the rapid growth of web information, business users struggle with information overload and increasingly find it difficult to locate the right information at the right time. Meanwhile, small and medium businesses (SMBs), in particular, are seeking one-to-one e-services from government in current highly competitive markets. How can business users be provided with information and services specific to their needs, rather than an undifferentiated mass of information? An effective solution proposed in this study is the development of personalized e-services. Recommender systems is an effective approach for the implementation of Personalized E-Service which has gained wide exposure in e-commerce in recent years. Accordingly, this paper first presents a hybrid fuzzy semantic recommendation (HFSR) approach which combines item-based fuzzy semantic similarity and item-based fuzzy collaborative filtering (CF) similarity techniques. This paper then presents the implementation of the proposed approach into an intelligent recommendation system prototype called Smart BizSeeker, which can recommend relevant business partners to individual business users, particularly for SMBs. Experimental results show that the HFSR approach can help overcome the semantic limitations of classical CF-based recommendation approaches, namely sparsity and new cold start item problems.
Memon, T., Lu, J. & Hussain, F.K. 2013, 'An Enhanced Mental Model Elicitation Technique to Improve Mental Model Accuracy', Lecture Notes in Computer Science, vol. 8226, pp. 82-89.
View/Download from: UTS OPUS or Publisher's site
The causal mental model representation has been used extensively in decision support. Due to limited information requirements of this representation, that is concepts and relationships, the users are required to articulate only the mental models, without invoking the corresponding experiential knowledge stored in associative memory. The elicitation of mental models without being endorsed by experiential knowledge may lead to inaccurate, invalidated or biased mental models, and espoused theories, being stored for decision making. We introduce SDA articulation/ elicitation cycle, which invokes a users associative memory during the articulation/elicitation process to validate mental models. It is argued in this paper that by engaging associative memory during the mental model articulation/elicitation process, the accuracy and validity of mental models can be improved, the biases can be reduced, and the theories-in-use can be elicited rather than the espoused theories. A case study is presented to demonstrate the working and contributions of the SDA articulation/elicitation cycle.
Memon, T., Lu, J., Hussain, F.K. & Rauniyar, R.K. 2013, 'Subject-Oriented Semantic Knowledge Warehouse (SSKW) to Support Cognitive DSS', Lecture Notes in Computer Science, vol. 8185, pp. 291-299.
View/Download from: UTS OPUS or Publisher's site
The communication between cognitive DSS and data warehouse tends to be inefficient due to their contradictory knowledge/data oriented nature. Data-to-knowledge conversion requires specialized techniques, whereas knowledge-to-data conversion results in loss of knowledge. To address these issues, a subject-oriented semantic knowledge warehouse (SSKW) is proposed, to provide relevant and precise knowledge to CDSS. The SSKW consists of: a) object/process/event/relationship (OPER) model to store domain knowledge in a unified fashion; and, b) a subjective view database, containing opinions of stakeholders about various OPER knowledge elements. A case study to compare the performance of the SSKW-based CDSS against a DW-based CDSS is presented. The results show that SSKW improves communication efficiency, provides relevant and precise domain knowledge to CDSS in less decision cycles, minimizes the loss of knowledge, and helps decision maker to quickly grasp the decision situation through its human-centric nature.
Kaur, P., Goyal, M. & Lu, J. 2012, 'An Integrated Model for a Price Forecasting Agent in Online Auctions', Journal of Internet Commerce, vol. 11, pp. 208-225.
View/Download from: UTS OPUS
Zhang, T.T., Zhang, G., Lu, J., Feng, X. & Yang, W. 2012, 'A New Index And Classification Approach For Load Pattern Analysis Of Large Electricity Customers', IEEE Transactions on Power Systems, vol. 27, no. 1, pp. 153-160.
View/Download from: UTS OPUS or Publisher's site
Conducting load pattern analysis is an important task in obtaining typical load profiles (TLPs) of customers and grouping them into classes according to their load characteristics. When using clustering techniques to obtain the load patterns of electrici
Ma, J., Zhang, G. & Lu, J. 2012, 'A Method For Multiple Periodic Factor Prediction Problems Using Complex Fuzzy Sets', Ieee Transactions On Fuzzy Systems, vol. 20, no. 1, pp. 32-45.
View/Download from: UTS OPUS or Publisher's site
Multiple periodic factor prediction (MPFP) problems exist widely in multisensor data fusion applications. Development of an effective prediction method should integrate information for multiple periodically changing factors. Because the uncertainty and p
Purba, J., Lu, J., Zhang, G. & Ruan, D. 2012, 'An Area Defuzzification Technique to Assess Nuclear Event Reliability Data from Failure Possibilities', International Journal of Computational Intelligence and Applications, vol. 11, no. 4, pp. 1-16.
View/Download from: UTS OPUS or Publisher's site
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
Sanati, F. & Lu, J. 2012, 'An Ontology For E-government Service Integration', International Journal of Computer Systems Science And Engineering, vol. 27, no. 2, pp. 89-101.
View/Download from: UTS OPUS
Composition and delivery of e-government web services is a challenging task. The lack of semantics in the current Web Services Description Language (WSDL) prevents automatic discovery and hence, automatic invocation and composition of those services. Thi
Lu, J., Zhang, G., Montero, J. & Garmendia, L. 2012, 'Multifollower Trilevel Decision Making Models And System', IEEE Transactions On Industrial Informatics, vol. 8, no. 4, pp. 974-985.
View/Download from: UTS OPUS or Publisher's site
In a trilevel hierarchical decision problem, the objectives and variables of each decision entity at one level are controlled, in part, by the decision entities at other levels. The choice of values for the decision variables at each level may influence
Shambour, Q.Y. & Lu, J. 2012, 'A trust-semantic fusion-based recommendation approach for e-business applications', Decision Support Systems, vol. 54, no. 1, pp. 768-780.
View/Download from: UTS OPUS or Publisher's site
Collaborative Filtering (CF) is the most popular recommendation technique but still suffers from data sparsity, user and item cold-start problems, resulting in poor recommendation accuracy and reduced coverage. This study incorporates additional information from the users' social trust network and the items' semantic domain knowledge to alleviate these problems. It proposes an innovative TrustSemantic Fusion (TSF)-based recommendation approach within the CF framework. Experiments demonstrate that the TSF approach significantly outperforms existing recommendation algorithms in terms of recommendation accuracy and coverage when dealing with the above problems. A business-to-business recommender system case study validates the applicability of the TSF approach.
Lu, J. & Li, T. 2012, 'Guest editors' introduction: Special issue on computational intelligence for policy making and risk governance: A tribute to professor Dr da Ruan', International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems, vol. 20, no. SUPPL. 1, pp. vii-viii.
View/Download from: Publisher's site
Zhang, G., Zhang, G., Gao, Y. & Lu, J. 2011, 'Competitive Strategic Bidding Optimization In Electricity Markets Using Bilevel Programming And Swarm Technique', Ieee Transactions On Industrial Electronics, vol. 58, no. 6, pp. 2138-2146.
View/Download from: UTS OPUS or Publisher's site
AbstractCompetitive strategic bidding optimization is now a key issue in electricity generator markets. Digital ecosystems provide a powerful technological foundation and support for the implementation of the optimization. This paper presents a new strategic bidding optimization technique which applies bilevel programming and swarm intelligence. In this paper, we first propose a general multileader-one-follower nonlinear bilevel (MLNB) optimization concept and related definitions based on the generalized Nash equilibrium. By analyzing the strategic bidding behavior of generating companies, we create a specific MLNB decision model for day-ahead electricity markets.
Lu, J., Ma, J., Zhang, G., Zhu, Y., Zeng, X. & koehl, L. 2011, 'Theme-Based Comprehensive Evaluation In New Product Development Using Fuzzy Hierarchical Criteria Group Decision-Making Method', Ieee Transactions On Industrial Electronics, vol. 58, no. 6, pp. 2236-2246.
View/Download from: UTS OPUS or Publisher's site
AbstractOne of the features of the digital ecosystem is the integration of human cognition and socio-economic themes into the process of new product development (NPD). In a socio-economic theme-based NPD, ranking a set of product prototypes that have been designed always requires the participation of multiple evaluators and consideration of multiple evaluation criteria. Using the well-being theme-based garment NPD as a background, this paper first presents a fuzzy hierarchical criteria group decision-making (FHCGDM) method which can effectively calculate final ranking results through fusing all assessment data from human beings and machines. It then presents a garment NPD comprehensive evaluation model with hierarchical criteria under the well-being theme through identifying a set of marketing tactics from a consumer acceptance survey. It further provides an establishment process for an NPD evaluation model under the digital ecosystem framework. Finally, a garment NPD case study further demonstrates the proposed well-being NPD comprehensive evaluation model and the FHCGDM method. The advantages of the proposed evaluation method include successfully handling criteria in a hierarchical structure, automatically processing both objective measurements from machines and subjective assessments from human evaluators, and using the most suitable type of fuzzy numbers to describe linguistic terms.
Zhang, G., Zheng, Z., Lu, J. & He, Q. 2011, 'An Algorithm For Solving Rule Sets-Based Bilevel Decision Problems', Computational Intelligence, vol. 27, no. 2, pp. 235-259.
View/Download from: UTS OPUS or Publisher's site
Bilevel decision addresses the problem in which two levels of decision makers each tries to optimize their individual objectives under certain constraints, and to act and react in an uncooperative and sequential manner. Given the difficulty of formulatin
Zhang, J., Lu, J. & Zhang, G. 2011, 'A Hybrid Knowledge-Based Risk Prediction Method Using Fuzzy Logic And Cbr For Avian Influenza Early Warning', Journal of Multiple-Valued Logic and Soft Computing, vol. 17, no. 4 Special Issue, pp. 363-386.
View/Download from: UTS OPUS
The threat of highly pathogenic avian influenza persists, with the size of the epidemic growing worldwide. Various methods have been applied to measure and predict the threat. This paper outlines our research which develops a knowledge-based method that makes full use of previous knowledge to perform a comprehensive forecast of the risk of avian influenza and generate reliable warning signals for a specific region at a specific time. The method contains a risk estimation model and a knowledge-based prediction method using fuzzy logic and case-based reasoning (CBR) to generate timely early warnings to support decision makers to identify underlying vulnerabilities and implement relevant strategies. An example is presented that illustrates the capabilities and procedures of the proposed method in avian influenza early warning systems.
Gao, Y., Zhang, G., Lu, J. & Wee, H. 2011, 'Particle swarm optimization for bi-level pricing problems in supply chains', Journal Of Global Optimization, vol. 51, no. 2, pp. 245-254.
View/Download from: UTS OPUS or Publisher's site
With rapid technological innovation and strong competition in hi-tech industries such as computer and communication organizations, the upstream component price and the downstream product cost usually decline significantly with time. As a result, an effective pricing supply chain model is very important. This paper first establishes two bi-level pricing models for pricing problems with the buyer and the vendor in a supply chain designated as the leader and the follower, respectively. A particle swarm optimization (PSO) based algorithm is developed to solve problems defined by these bi-level pricing models. Experiments illustrate that this PSO based algorithm can achieve a profit increase for buyers or vendors if they are treated as the leaders under some situations, compared with the existing methods.
Zhang, R., Lu, J. & Zhang, G. 2011, 'An information presentation method based on tree-like super entity component', Journal of Systems and Software, vol. 84, no. 8, pp. 1306-1318.
View/Download from: UTS OPUS or Publisher's site
Information systems are increasingly oriented in the direction of large-scale integration due to the explosion of multi-source information. It is therefore important to discuss how to reasonably organize and present information from multiple structures and sources on the same information system platform. In this study, we propose a 3C (Components, Connections, Container) component model by combining white-box and black-box methods, design a tree-like super entity based on the model, present its construction and related algorithm, and take a tree-like super entity as the information organization method for multi-level entities. In order to represent structural, semi-structural and non-structural data on the same information system platform, an information presentation method based on an editable e-book component has been developed by combining the tree-like super entity component, QQ-style menu and 1/K switch connection component, which has been successfully applied in the Flood Protection Project Information System of the Yangtze River in China.
Yang, X., Zhang, G., Lu, J. & Ma, J. 2011, 'A kernel fuzzy c-means clustering based fuzzy support vector machine algorithm for classification problems with outliers or noises', IEEE Transactions on Fuzzy Systems, vol. 19, no. 1, pp. 105-115.
View/Download from: UTS OPUS or Publisher's site
The support vector machine (SVM) has provided higher performance than traditional learning machines and has been widely applied in real-world classification problems and nonlinear function estimation problems. Unfortunately, the training process of the SVM is sensitive to the outliers or noises in the training set. In this paper, a common misunderstanding of Gaussianfunction- based kernel fuzzy clustering is corrected, and a kernel fuzzy c-means clustering-based fuzzy SVM algorithm (KFCMFSVM) is developed to deal with the classification problems with outliers or noises. In the KFCM-FSVM algorithm, we first use the FCM clustering to cluster each of two classes from the training set in the high-dimensional feature space. The farthest pair of clusters, where one cluster comes from the positive class and the other from the negative class, is then searched and forms one new training set with membership degrees. Finally, we adopt FSVM to induce the final classification results on this new training set. The computational complexity of the KFCM-FSVM algorithm is analyzed. A set of experiments is conducted on six benchmarking datasets and four artificial datasets for testing the generalization performance of the KFCM-FSVM algorithm. The results indicate that the KFCM-FSVM algorithm is robust for classification problems with outliers or noises.
Wu, D., Lu, J. & Zhang, G. 2011, 'Similarity measure models and algorithms for hierarchical cases', Expert Systems with Applications, vol. 38, no. 12, pp. 15049-15056.
View/Download from: UTS OPUS or Publisher's site
Many business situations such as events, products and services, are often described in a hierarchical structure. When we use case-based reasoning (CBR) techniques to support business decision-making, we require a hierarchical-CBR technique which can effectively compare and measure similarity between two hierarchical cases. This study first defines hierarchical case trees (HC-trees) and discusses related features. It then develops a similarity evaluation model which takes into account all the information on nodes structures, concepts, weights, and values in order to comprehensively compare two hierarchical case trees. A similarity measure algorithm is proposed which includes a node concept correspondence degree computation algorithm and a maximum correspondence tree mapping construction algorithm, for HC-trees. We provide two illustrative examples to demonstrate the effectiveness of the proposed hierarchical case similarity evaluation model and algorithms, and possible applications in CBR systems
Zhang, J., Lu, J. & Zhang, G. 2011, 'A Seasonal Auto-regressive Model Based Support Vector Regression Prediction Method For H5n1 Avian Influenza Animal Events', International Journal of Computational Intelligence and Applications, vol. 10, no. 2, pp. 199-230.
View/Download from: UTS OPUS or Publisher's site
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 SARSVR 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.
Zhang, J., Lu, J. & Zhang, G. 2011, 'Joint Sub-classifiers One Class Classification Model For Avian Influenza Outbreak Detection', International Journal of Computational Intelligence and Applications, vol. 10, no. 4, pp. 425-443.
View/Download from: UTS OPUS or Publisher's site
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, divide it into subclasses 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.
Shambour, Q. & Lu, J. 2011, 'A Hybrid Trust-enhanced Collaborative Filtering Recommendation Approach For Personalized Government-to-business E-services', International Journal of Intelligent Systems, vol. 26, no. 9, pp. 814-843.
View/Download from: UTS OPUS or Publisher's site
The information overload on the World Wide Web results in the underuse of some existing egovernment services within the business domain. Small-to-medium businesses (SMBs), in particular, are seeking 'one-to-one' e-services from government in current highly competitive markets, and there is an imperative need to develop Web personalization techniques to provide business users with information and services specific to their needs, rather than an undifferentiated mass of information. This paper focuses on how e-governments can support businesses on the problem of selecting a trustworthy business partner to perform reliable business transactions. In the business partner selection process, trust or reputation information is crucial and has significant influence on a business user's decision regarding whether or not to do business with other business entities. For this purpose, an intelligent trust-enhanced recommendation approach to provide personalized government-to-business (G2B) e-services, and in particular, business partner recommendation e-services for SMBs is proposed. Accordingly, in this paper, we develop (1) an implicit trust filtering recommendation approach and (2) an enhanced user-based collaborative filtering (CF) recommendation approach. To further exploit the advantages of the two proposed approaches, we develop (3) a hybrid trust-enhanced CF recommendation approach (TeCF) that integrates both the proposed implicit trust filtering and the enhanced user-based CF recommendation approaches. Empirical results demonstrate the effectiveness of the proposed approaches, especially the hybrid TeCF recommendation approach in terms of improving accuracy, as well as in dealing with very sparse data sets and cold-start users.
Amailef, K. & Lu, J. 2011, 'A Mobile-based Emergency Response System For Intelligent M-government Services', Journal of Enterprise Information Management, vol. 24, no. 4, pp. 338-359.
View/Download from: UTS OPUS or Publisher's site
Purpose: The purpose of this paper is to present an intelligent mobile based emergency response system (MERS) framework, a text information extraction and aggregation algorithm to integrate information from multiple sources in the MERS system, and an ontology-supported case-based reasoning system for the MERS system. Design/methodology/approach: The paper explains the components of information extraction and aggregation process, and a CBR-Ontology approach for the MERS system. Findings: The result of this study will offer a new opportunity to the interaction between government, citizens, responders, and other non-government agencies in emergency situations, and therefore improve the services of the government in an emergency situation. Originality/value: The paper indicates the need for usage of mobile technologies to assist the government to get information and make decisions in responding to disasters anytime and anywhere.
Purba, J., Lu, J., Ruan, D. & Zhang, G. 2011, 'Failure possibilities for nuclear safety assessment by fault tree analysis', International Journal of Nuclear Knowledge Management, vol. 5, no. 2, pp. 162-177.
View/Download from: UTS OPUS or Publisher's site
Fault tree analysis (FTA) is a deductive tool to assess the safety of nuclear power plants. This analysis can only be implemented if all basic events in the tree have their corresponding failure rates. Therefore, safety analysts have to provide those failure rates well in advance. However, it is often difficult to obtain those failure rates due to insufficient data, changing environment or new components. This paper proposes a failure possibility based FTA approach to overcome the limitation of the conventional FTA for nuclear safety assessment. It utilises the concept of failure possibilities to evaluate basic event failure without historical data, fuzzy numbers to map component failure possibilities into mathematical form and defuzzification algorithms to convert fuzzy numbers into component failure rates. A case study on evaluating a typical high pressure core spray system of a boiling water reactor illustrates the applicability of the proposed approach.
Zhang, R., Lu, J. & Zhang, G. 2011, 'A knowledge-based multi-role decision support system for ore blending cost optimization of blast furnaces', European Journal Of Operational Research, vol. 215, no. 1, pp. 194-203.
View/Download from: UTS OPUS or Publisher's site
Literature illustrates the difficulties in obtaining the lowest-cost optimal solution to an ore blending problem for blast furnaces by using the traditional trial-and-error method in iron and steel enterprises. To solve this problem, we developed a cost optimization model which we have implemented in a multi-role-based decision support system (DSS). On the basis of analyzing the business flow and working process of ore blending, we propose an architecture of DSS which is built based on multi-roles. This DSS construction pre-processes the data for materials and elements, builds a general database, abstracts the related optimal operations research models and introduces the reasoning mechanism of an expert system. A non-linear model of ore blending for blast furnaces and its solutions are provided. A database, a model base and a knowledge base are integrated into the expert system-based multi-role DSS to meet the different demands of data, information and decision-making knowledge for the various roles of users. A comparison of the results for the DSS and the trial-and-error method is provided. The system has produced excellent economic benefits since it was implemented at the Xiangtan Iron & Steel Group Co. Ltd., China.
Lu, J. & Zhang, G. 2011, 'Guest editorial: Optimization Techniques for Business Intelligence', Journal Of Global Optimization, vol. 51, no. 2, pp. 185-187.
View/Download from: Publisher's site
Zhang, J., Lu, J. & Zhang, G. 2011, 'A hybrid knowledge-based risk prediction method using fuzzy logic and CBR for avian influenza early warning', Journal of Multiple-Valued Logic and Soft Computing, vol. 17, no. 4, pp. 363-386.
The threat of highly pathogenic avian influenza persists, with the size of the epidemic growing worldwide. Various methods have been applied to measure and predict the threat. This paper outlines our research which develops a knowledge-based method that makes full use of previous knowledge to perform a comprehensive forecast of the risk of avian influenza and generate reliable warning signals for a specific region at a specific time. The method contains a risk estimation model and a knowledge-based prediction method using fuzzy logic and case-based reasoning (CBR) to generate timely early warnings to support decision makers to identify underlying ulnerabilities and implement relevant strategies. An example is presented that illustrates the capabilities and procedures of the proposed method in avian influenza early warning systems. © 2011 Old City Publishing, Inc.
Sanati, F. & Lu, J. 2010, 'Life-event modelling framework for e-government integration', Electronic Government: An International Journal, vol. 7, no. 2, pp. 183-202.
View/Download from: UTS OPUS
Ability to offer a citizen-centric view of government model is the key to a successful e-government service. Life-event model is the most widely adopted paradigm supporting the idea of composing a single complex e-government service that corresponds to an event in a citizen's life. Elementary building blocks of Life-event are based on atomic services offered from multiple government agencies. This study found that methodological mechanics of service integration and in particular the requirements engineering for composite services has been overlooked. Purpose of this study is to define obstacles of achieving e-government service delivery integration, and suggests a framework based on ontological analysis and modelling. Proposed framework that shall be called E-Service Integration Modelling (E-SIM) is based on the extensive use of Life-event concept. This paper proposes a top-down abstraction approach in requirements elicitation and modelling to define and implement the phenomenon of Life-event in context of e-government.
Ma, J., Zhang, G. & Lu, J. 2010, 'A state-based knowledge representation approach for information logical inconsistency detection in warning systems', Knowledge-Based Systems, vol. 23, no. 2, pp. 125-131.
View/Download from: UTS OPUS or Publisher's site
Detecting logical inconsistency in collected information is a vital function when deploying a knowledge-based warning system to monitor a specific application domain for the reason that logical inconsistency is often hidden from seemingly consistent information and may lead to unexpected results. Existing logical inconsistency detection methods usually focus on information stored in a knowledge base by using a well-defined general purpose knowledge representation approach, and therefore cannot fulfill the demands of a domain-specific situation. This paper first proposes a state-based knowledge representation approach, in which domain-specific knowledge is expressed by combinations of the relevant objects states. Based on this approach, a method for information logical inconsistency detection (ILID) is developed which can flexibly handle the demands of various domain-specific situations through reducing part of restrictions in existing methods. Finally, two real-case based examples are presented to illustrate the ILID method and its advantages.
Ma, J., Lu, J. & Zhang, G. 2010, 'Team situation awareness measure using semantic utility functions for supporting dynamic decision-making', Soft Computing - A Fusion of Foundations, Methodologies and Applications, vol. 14, no. 12, pp. 1305-1316.
View/Download from: UTS OPUS or Publisher's site
Team decision-making is a remarkable feature in a complex dynamic decision environment, which can be supported by team situation awareness. In this paper, a team situation awareness measure (TSAM) method using a semantic utility function is proposed. The semantic utility function is used to clarify the semantics of qualitative information expressed in linguistic terms. The individual and team situation awareness are treated as linguistic possibility distributions on the potential decisions in a dynamic decision environment. In the TSAM method, team situation awareness is generated through reasoning and aggregating individual situation awareness based on a multi-level hierarchy mental model of the team. Individual and team mental models are composed of key drivers and significant variables. An illustrative example in telecoms customer churn prediction is given to explain the effectiveness and the main steps of the TSAM method.
Lu, J., Wang, C., Zhang, G. & Ma, J. 2010, 'Collaborative management of web ontology data with flexible access control', Expert Systems with Applications, vol. 37, no. 5, pp. 3737-3746.
View/Download from: UTS OPUS or Publisher's site
The creation and management of ontology data on web sites (e.g. instance data that is used to annotate web pages) is important technical support for the growth of the semantic web. This study identifies some key issues for web ontology data management and describes an ontology data management system, called robinet, to perform the management. This paper presents the structure of the system and introduces a Web ontology data management model that enables a flexible access control mechanism. This model adds rules into the robinet system to utilize the semantics of ontology for controlling the access to ontology data. The implementation of the rule-based access control mechanism and related testing are also discussed
Zhang, T.T., Zhang, G., Ma, J. & Lu, J. 2010, 'Power Distribution System Planning Evaluation By A Fuzzy Multi-Criteria Group Decision Support System', International Journal of Computational Intelligence Systems, vol. 3, no. 4, pp. 474-485.
View/Download from: UTS OPUS
The evaluation of solutions is an important phase in power distribution system planning (PDSP) which allows issues such as quality of supply, cost, social service and environmental implications to be considered and usually involves the judgments of a group of experts. The planning problem is thus suitable for the multi-criteria group decision-making (MCGDM) method. The evaluation process and evaluation criteria often involve uncertainties incorporated in quantitative analysis with crisp values and qualitative judgments with linguistic terms; therefore, fuzzy sets techniques are applied in this study. This paper proposes a fuzzy multi-criteria group decision-making (FMCGDM) method for PDSP evaluation and applies a fuzzy multi-criteria group decision support system (FMCGDSS) to support the evaluation task. We introduce a PDSP evaluation model, which has evaluation criteria within three levels, based on the characteristics of a power distribution system. A case-based example is performed on a test distribution network and demonstrates how all the problems in a PDSP evaluation are addressed using FMCGDSS. The results are acceptable to expert evaluators.
Lu, J., Shambour, Q.Y., Xu, Y., Lin, Q. & Zhang, G. 2010, 'Bizseeker - A Hybrid Semantic Recommendation System For Personalised Government-To-Business E-Services', Internet Research, vol. 20, no. 3, pp. 342-365.
View/Download from: UTS OPUS or Publisher's site
Purpose - The purpose of this paper is to develop a hybrid semantic recommendation system to provide personalized government to business (G2B) e-services, in particular, business partner recommendation e-services for Australian small to medium enterprises (SMEs). Design/methodology/approach - The study first proposes a product semantic relevance model. It then develops a hybrid semantic recommendation approach which combines item-based collaborative filtering (CF) similarity and item-based semantic similarity techniques. This hybrid approach is implemented into an intelligent business-partner-locator recommendation-system prototype called BizSeeker. Findings - The hybrid semantic recommendation approach can help overcome the limitations of existing recommendation techniques. The recommendation system prototype, BizSeeker, can recommend relevant business partners to individual business users (e.g. exporters), which therefore will reduce the time, cost and risk of businesses involved in entering local and international markets. Practical implications - The study would be of great value in e-government personalization research. It would facilitate the transformation of the current G2B e-services into a new stage wherein the e-government agencies offer personalized e-services to business users. The study would help government policy decision-makers to increase the adoption of e-government services. Originality/value - Providing personalized e-services by e-government can be seen as an evolution of the intentions-based approach and will be one of the next directions of government e-services. This paper develops a new recommender approach and systems to improve personalization of government e-services.
Ruan, D., Lu, J., Laes, E., Zhang, G., Ma, J. & Meskens, G. 2010, 'Multi-Criteria Group Decision Support With Linguistic Variables In Long-Term Scenarios For Belgian Energy Policy', Journal of Universal Computer Science, vol. 16, no. 1, pp. 103-120.
View/Download from: UTS OPUS
Real world decisions often made in the presence of multiple, conflicting, and incommensurate criteria. Decision making requires multiple perspectives of different individuals as more decisions are made now in groups than ever before. This is particularly true when the decision environment becomes more complex such as sustainability policies study in environmental and energy sectors. Group decision making processes judgments or solutions for decision problems based on the input and feedback of multiple individuals. Multi-criteria decision and evaluation problems at tactical and strategic levels in practice involve fuzziness in terms of linguistic variables vis-à-vis criteria, weights, and decision maker judgments. Relevant alternatives or scenarios are evaluated according to a number of desired criteria. A fuzzy multi-criteria group decision software tool is developed to analyze long-term scenarios for Belgian energy policy in this paper.
Zhang, G., Lu, J., Montero, J. & Zeng, Y. 2010, 'Model, Solution Concept, And Kth-Best Algorithm For Linear Trilevel Programming', Information Sciences, vol. 180, no. 4, pp. 481-492.
View/Download from: UTS OPUS or Publisher's site
Trilevel programming refers to hierarchical optimization problems in which the top-level, middle-level, and bottom-level decision entities all attempt to optimize their individual objectives, but are impacted by the actions and partial control exercised by decision entities located at other levels. To solve this complex problem, in this study first we propose the use of a general linear trilevel programming (LTLP) subsequently, we develop a trilevel Kth-best algorithm to solve LTLP problems. A user-friendly trilevel decision support tool is also developed. A case study further illustrates the effectiveness of the proposed method.
Gao, Y., Zhang, G., Ma, J. & Lu, J. 2010, 'A Lambda-Cut And Goal-Programming-Based Algorithm For Fuzzy-Linear Multiple-Objective Bilevel Optimization', IEEE Transactions on Fuzzy Systems, vol. 18, no. 1, pp. 1-13.
View/Download from: UTS OPUS or Publisher's site
Bilevel-programming techniques are developed to handle decentralized problems with two-level decision makers, which are leaders and followers, who may have more than one objective to achieve. This paper proposes a λ-cut and goalprogramming-based algorithm to solve fuzzy-linear multipleobjective bilevel (FLMOB) decision problems. First, based on the definition of a distance measure between two fuzzy vectors using λ-cut, a fuzzy-linear bilevel goal (FLBG) model is formatted, and related theorems are proved. Then, using a λ-cut for fuzzy coefficients and a goal-programming strategy for multiple objectives, a λ-cut and goal-programming-based algorithm to solve FLMOB decision problems is presented.Acase study for a newsboy problem is adopted to illustrate the application and executing procedure of this algorithm. Finally, experiments are carried out to discuss and analyze the performance of this algorithm.
Zhang, G. & Lu, J. 2010, 'Fuzzy bilevel programming with multiple objectives and cooperative multiple followers', Journal Of Global Optimization, vol. 47, no. 3, pp. 403-419.
View/Download from: UTS OPUS or Publisher's site
Classic bilevel programming deals with two level hierarchical optimization problems in which the leader attempts to optimize his/her objective, subject to a set of constraints and his/her followerâs solution. In modelling a real-world bilevel decision problem, some uncertain coefficients often appear in the objective functions and/or constraints of the leader and/or the follower. Also, the leader and the follower may have multiple conflicting objectives that should be optimized simultaneously. Furthermore, multiple followers may be involved in a decision problem and work cooperatively according to each of the possible decisions made by the leader, but with different objectives and/or constraints. Following our previous work, this study proposes a set of models to describe such fuzzy multi-objective, multi-follower (cooperative) bilevel programming problems. We then develop an approximation Kth-best algorithm to solve the problems.
Yang, X., Lu, J. & Zhang, G. 2010, 'Adaptive pruning algorithm for least squares support vector machine classifier', Soft Computing - A Fusion of Foundations, Methodologies and Applications, vol. 14, no. 7, pp. 667-680.
View/Download from: UTS OPUS or Publisher's site
As a new version of support vector machine (SVM), least squares SVM (LS-SVM) involves equality instead of inequality constraints and works with a least squares cost function. A well-known drawback in the LSSVM applications is that the sparseness is lost. In this paper, we develop an adaptive pruning algorithm based on the bottom-to-top strategy, which can deal with this drawback. In the proposed algorithm, the incremental and decremental learning procedures are used alternately and a small support vector set, which can cover most of the information in the training set, can be formed adaptively. Using this set, one can construct the final classifier. In general, the number of the elements in the support vector set is much smaller than that in the training set and a sparse solution is obtained. In order to test the efficiency of the proposed algorithm, we apply it to eight UCI datasets and one benchmarking dataset. The experimental results show that the presented algorithm can obtain adaptively the sparse solutions with losing a little generalization performance for the classification problems with no-noises or noises, and its training speed is much faster than sequential minimal optimization algorithm (SMO) for the large-scale classification problems with no-noises.
Ma, J., Lu, J. & Zhang, G. 2010, 'Decider: A fuzzy multi-criteria group decision support system', Knowledge-based Systems, vol. 23, no. 1, pp. 23-31.
View/Download from: UTS OPUS or Publisher's site
Multi-criteria group decision making (MCGDM) aims to support preference-based decision over the available alternatives that are characterized by multiple criteria in a group. To increase the level of overall satisfaction for the final decision across the group and deal with uncertainty in decision process, a fuzzy MCGDM process (FMP) model is established in this study. This FMP model can also aggregate both subjective and objective information under multi-level hierarchies of criteria and evaluators. Based on the FMP model, a fuzzy MCGDM decision support system (called Decider) is developed, which can handle information expressed in linguistic terms, boolean values, as well as numeric values to assess and rank a set of alternatives within a group of decision makers. Real applications indicate that the presented FMP model and the Decider software are able to effectively handle fuzziness in both subjective and objective information and support group decision-making under multi-level criteria with a higher level of satisfaction by decision makers.
Lu, J., Ruan, D. & Zhang, G. 2010, 'A special issue on Intelligent Decision Support and Warning Systems', Knowledge-Based Systems, vol. 23, no. 1, pp. 1-2.
View/Download from: Publisher's site
Yang, X., Lu, J. & Zhang, G. 2010, 'Adaptive pruning algorithm for least squares support vector machine classifier', Soft Computing, vol. 14, no. 7, pp. 667-680.
View/Download from: Publisher's site
As a new version of support vector machine (SVM), least squares SVM (LS-SVM) involves equality instead of inequality constraints and works with a least squares cost function. A well-known drawback in the LS-SVM applications is that the sparseness is lost. In this paper, we develop an adaptive pruning algorithm based on the bottom-to-top strategy, which can deal with this drawback. In the proposed algorithm, the incremental and decremental learning procedures are used alternately and a small support vector set, which can cover most of the information in the training set, can be formed adaptively. Using this set, one can construct the final classifier. In general, the number of the elements in the support vector set is much smaller than that in the training set and a sparse solution is obtained. In order to test the efficiency of the proposed algorithm, we apply it to eight UCI datasets and one benchmarking dataset. The experimental results show that the presented algorithm can obtain adaptively the sparse solutions with losing a little generalization performance for the classification problems with no-noises or noises, and its training speed is much faster than sequential minimal optimization algorithm (SMO) for the large-scale classification problems with no-noises. © Springer-Verlag 2009.
Egea, K., Lu, J., Xiao, J. & Clear, T. 2010, 'Internationalisation and Cross Cultural Issues in Computing Education', Conferences in Research and Practice in Information Technology Series, vol. 103, pp. 25-31.
Lu, J. & Zhang, G. 2010, 'A special issue on decision intelligence with soft computing', SOFT COMPUTING, vol. 14, no. 12, pp. 1253-1254.
View/Download from: Publisher's site
Lu, J., Chin, K.L., Yao, J., Xu, J. & Xiao, J. 2010, 'Cross-cultural education: Learning methodology and behaviour analysis for asian students in IT field of australian universities', Conferences in Research and Practice in Information Technology Series, vol. 103, pp. 117-125.
Australian tertiary education of information technology (IT) has attracted a large number of international students, particularly from Asia. Cultural factors have affected the quality of learning of international students and the teaching approaches adopted by Australian lecturers. Therefore, cross-cultural teaching and learning situations have become an important issue in Australian universities. This study intends to improve the understanding of Asian students' cultural backgrounds, their previous learning approaches and theirperspectives on Australian culture and educational mode, with the objective of helping international students from different cultural backgrounds to overcome the difficulties of cross-cultural study. This study has completed a questionnaire survey of 1026 students, including 292 Information Technology (28.5%) students from five universities in Australia. Among these IT students, there are 100 (34.25%) local students and 192 (65.75%) international students from 39 other countries. The questionnaire contains 55 questions within six question sections and one information section. This paper presents comparison-based data analysis results of this survey on learning methodology and behaviours of Asian students in IT field of Australian universities. It particularly reveals the main difference for students between the universities in their home countries and in Australia, also the difficulties of these students during their study in Australian university through qualitative analysis on open questions of the survey. This paper also reports the research methodology and main findings in cross-culture teaching and learning generated from this study. This work was fully supported by Australian Learning and Teaching Council (CG7-494). © 2010, Australian Computer Society, Inc.
Lu, J., Zhu, Y., Zeng, X., koehl, L., Ma, J. & Zhang, G. 2009, 'A linguistic multi-criteria group decision support system for fabric hand evaluation', Fuzzy Optimization and Decision Making, vol. 8, no. 4, pp. 395-413.
View/Download from: UTS OPUS or Publisher's site
Fabric hand evaluation (FHE) is the main measure in textile material selection for fashion design and development. Fabric hand evaluation requires considering multiple evaluation aspects/criteria by a group of evaluators. Some fabric features can also be measured using instruments. The evaluation often uses linguistic terms in the weights of criteria, and the weights and judgments of evaluators. To support a FHE-based material selection, this study first develops a fabric hand-based textile material evaluation model. It then proposes a human-machine measure integrated fuzzy multi-criteria group decision-making method. A software tool is also developed, which implements the proposed method and is applied in fabric hand-based textile material evaluation.
Zheng, Z., Lu, J., Zhang, G. & He, Q. 2009, 'Rule sets based bilevel decision model and algorithm', Expert Systems with Applications, vol. 36, no. 1, pp. 18-26.
View/Download from: UTS OPUS or Publisher's site
Bilevel decision addresses the problem in which two levels of decision makers, each tries to optimize their individual objectives under certain constraints, act and react in an uncooperative, sequential manner. As bilevel decision making often involves many uncertain factors in real world problems, it is hard to formulate the objective functions and constraints of the leader and the follower in modelling a real bilevel decision problem. This study explores a new approach that uses rule sets to formulate a bilevel decision problem. It first develops related theories to prove the feasibility to model a bilevel decision problem by rule sets. It then proposes an algorithm to describe the modelling process. A case study is discussed to illustrate the functions and effectiveness of the proposed rule sets based bilevel decision modelling algorithm.
Ma, J., Lu, J. & Zhang, G. 2009, 'Information inconsistencies detection using a rule-map technique', Expert Systems with Applications, vol. 36, no. 10, pp. 12510-12519.
View/Download from: UTS OPUS or Publisher's site
Timely detecting information inconsistencies (anomalies) in real-time information provides strong support for decision-making in a dynamic decision-making situation. Existing techniques for information inconsistencies detection mainly focus on stored information by using a single structured-fixed descriptive model which always requires support from sufficient prior knowledge. The aim of this study is to develop a method for information inconsistencies detection for real-time information in dynamic decision-making situation where prior knowledge is insufficient by using multiple descriptive models. First, a rule-map technique is presented. A rule-map is a hierarchical directed graph, whose vertexes are selected descriptive models and whose arcs represent the covering relationship between descriptive models. A rule-map provides a strategy for selecting detecting descriptive models by means of the covering relationship and its structure is adjustable with the change in a situation. Then, a real-time information inconsistencies detection method, named RMDID, is developed based on the rule-map technique, which can take full advantage of multiple descriptive models. Finally, the proposed RMDID method is tested through two real cases. Experiments indicate that the proposed rule-map technique can trace the changes of a dynamic decision-making situation and the developed RMDID method can efficiently detect potential anomalies in real-time information.
Zhang, G. & Lu, J. 2009, 'A linguistic intelligent user guide for method selection in multi-objective decision support systems', Information Sciences, vol. 179, no. 14, pp. 2299-2308.
View/Download from: UTS OPUS or Publisher's site
Some multi-objective decision-making (MODM) methods are more effective than others for particular decision problems and/or particular decision makers. It is therefore necessary to provide a set of MODM methods in a multi-objective decision support system (MODSS) to support a wide range of problem solving. However, it is always difficult for decision makers to select the most suitable method for individual cases because MODM methods involve a deep knowledge of mathematics. To handle this difficulty, this study develops a MODM method selection guide supported by a fuzzy matching optimization method. In this paper, we first present the modelling process for the knowledge of characteristics of the main MODM methods. We then present related matching techniques between the characteristics of a real-world decision-making situation and a set of predefined situation descriptions (characteristics of a MODM method) where the elements of the two sets may be expressed by linguistic terms. Based on this process, a fuzzy matching optimization-based MODM method selection approach is proposed. The approach applies general fuzzy numbers, fuzzy distance, fuzzy multi-criteria decision-making concepts, and rule-based inference techniques to recommend the most suitable method from a MODM method-base. The approach is adopted in a linguistic intelligent user guide within a MODSS. Experiments have shown that the development of the linguistic intelligent user guide can increase the ability of the MODSS to support decision makers in arriving at a satisfactory solution in a most effective way.
Lu, J., Bai, C. & Zhang, G. 2009, 'Cost-benefit factor analysis in e-services using bayesian networks', Expert Systems with Applications, vol. 36, no. 3, pp. 4617-4625.
View/Download from: UTS OPUS or Publisher's site
This study applies Bayesian network techniques to analyze and verify the relationships among cost factors and benefit factors in e-service systems. This study first establishes a Bayesian network for e-service cost-benefit factor relationships based on our previous study [Lu, J. & Zhang, G. Q (2003). Cost benefit factor analysis in e-services. International Journal of Service Industry Management (IJSIM), 14(5), 570-5951. It then calculates conditional probability distributions among these factors shown in the Bayesian network. Finally it runs a junction-tree algorithm to conduct inference for verifying these cost-benefit factor relationships, and the data collected through a survey is as evidences in the inference process. Through the above application of Bayesian network techniques a set of useful findings is obtained for the costs involved in e-service developments against the benefits received by adopting these e-service systems. The case of 'increased investments in maintaining e-services' would significantly contribute to 'enhancing perceived company image', and the case of 'increased investments in security of e-service systems' would bring high benefits in 'building customer relationships' and 'improving cooperation between companies'. These findings have great potential to improve the strategic planning of businesses by determining more effective investments items and adopting more suitable development activities in e-service systems and applications.
Zhang, G., Dillon, T.S., Cai, K., Ma, J. & Lu, J. 2009, 'Operation properties and delta-equalities of complex fuzzy sets', International Journal of Approximate Reasoning, vol. 50, no. 8, pp. 1227-1249.
View/Download from: UTS OPUS or Publisher's site
A complex fuzzy set is a fuzzy set whose membership function takes values in the unit circle in the complex plane. This paper investigates various operation properties and proposes a distance measure for complex fuzzy sets. The distance of two complex fuzzy sets measures the difference between the grades of two complex fuzzy sets as well as that between the phases of the two complex fuzzy sets. This distance measure is then used to define ?-equalities of complex fuzzy sets which coincide with those of fuzzy sets already defined in the literature if complex fuzzy sets reduce to real-valued fuzzy sets. Two complex fuzzy sets are said to be ?-equal if the distance between them is less than 1-?. This paper shows how various operations between complex fuzzy sets affect given ?-equalities of complex fuzzy sets. An example application of signal detection demonstrates the utility of the concept of ?-equalities of complex fuzzy sets in practic
Wang, C., Lu, J. & Zhang, G. 2009, 'Web ontology data matching for integration: Method and framework', International Journal of Web Information Systems, vol. 5, no. 2, pp. 220-238.
View/Download from: UTS OPUS
Purpose 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 individuals 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. Design/methodology/approach 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. Findings 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. Research limitations/implications The proposed method and framework will be further tested for some more complicated real cases in the future. Originality/value The experiments show that this proposed intelligent matching method increases ontology data matching accuracy.
Gao, Y., Zhang, G. & Lu, J. 2009, 'A Fuzzy Multi-Objective Bilevel Decision Support System', International Journal of Information Technology & D..., vol. 8, no. 1, pp. 93-108.
View/Download from: UTS OPUS or Publisher's site
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.
Zhang, G., Ma, J. & Lu, J. 2009, 'Emergency management evaluation by a fuzzy multi-criteria group decision support system', Stochastic Environmental Research and Risk Assessment, vol. 23, no. 2, pp. 517-527.
View/Download from: UTS OPUS or Publisher's site
Emergency risk management (ERM) is a process which involves dealing with risks to the community arising from emergency events. Emergency management evaluation as one of the important parts of ERM aims assessing and improving social preparedness and organizational ability in identifying, analyzing, and treating emergency risks. This study first develops an emergency management evaluation model. It then proposes an extended fuzzy multi-criteria group evaluation method, which can deal with both subjective and objective criteria under multi-levels by a group of evaluators, for emergency management evaluation. A fuzzy multi-criteria group decision support system (FMCGDSS) is then developed to implement the proposed method for the case of emergency operating center/system evaluation.
Lu, J., Zhang, G. & Ruan, D. 2008, 'Intelligent multi-criteria fuzzy group decision-making for situation assessments', Soft Computing - A Fusion of Foundations, Methodologies and Applications, vol. 12, no. 3, pp. 289-299.
View/Download from: UTS OPUS or Publisher's site
Organizational decisions and situation assessment are often made in groups, and decision and assessment processes involve various uncertain factors. To increase efficiently group decision-making, this study presents a new rationalpolitical model as a systematic means of supporting group decision-making in an uncertain environment. The model takes advantage of both rational and political models and can handle inconsistent assessment, incomplete information and inaccurate opinions in deriving the best solution for the group decision under a sequential framework. The model particularly identifies three uncertain factors involved in a group decision-making process: decision makers roles, preferences for alternatives, and judgments for assessment-criteria. Based on this model, an intelligent multi-criteria fuzzy group decision-making method is proposed to deal with the three uncertain factors described by linguistic terms. The proposed method uses general fuzzy numbers and aggregates these factors into a group satisfactory decision that is in a most acceptable degree of the group. Inference rules are particularly introduced into the method for checking the consistence of individual preferences. Finally, a real case-study on a business situation assessment is illustrated by the proposed method.
Lu, J., Yan, X. & Zhang, G. 2008, 'Support vector machine-based multi-source multi-attribute information integration for situation assessment', Expert Systems with Applications, vol. 34, no. 2, pp. 1333-1340.
View/Download from: UTS OPUS or Publisher's site
Understanding any given situation requires integrating many pieces of information. Such information has in most cases multiple attributes and is obtained from multiple data sources within multiple time slots. Situation assessors' experience and preference will naturally influence the result of information integration, and hence influence the awareness generated for a situation. This study focuses on how multi-source multi-attribute information about a situation is integrated and how the awareness information for the situation is derived. A learning-based information integration approach, which embeds the fuzzy least squares support vector machine (FLS-SVM) technique, is developed in this study. This approach can assess a situation through integrating and inference obtained information and analyzing related data sources. A series of experiments show that the proposed approach has an accuracy learning ability from assessors' experience in the information integration for generating awareness for a situation.
Lu, J., Liu, B., Zhang, G., Hao, Z. & Xiao, Y. 2008, 'A situation assessment approach using support vector machine as a learning tool', International Journal of Nuclear Knowledge Management, vol. 3, no. 1, pp. 82-97.
View/Download from: UTS OPUS
In order to assess a situation and support decision makers' awareness for the situation, this study first proposes a situation assessment model with mathematical description. It then develops a Support Vector Machine based assessment approach, which has the ability to learn the rules from the previous assessment results and generate necessary warnings for a situation. Finally, a set of experiments is conducted to illustrate and validate the proposed approach.
Lu, J., Zhang, G. & Wu, F. 2008, 'Team situation awareness using web-based fuzzy group decision support systems', International Journal of Computational Intelligence Systems, vol. 1, no. 1, pp. 50-59.
View/Download from: UTS OPUS or Publisher's site
Situation awareness (SA) is an important element to support responses and decision making to crisis problems. Decision making for a complex situation often needs a team to work cooperatively to get consensus awareness for the situation. Team SA is characterized including information sharing, opinion integration and consensus SA generation. In the meantime, various uncertainties are involved in team SA during information collection and awareness generation. Also, the collaboration between team members may be across distances and need web-based technology to facilitate. This paper presents a web-based fuzzy group decision support system (WFGDSS) and demonstrates how this system can provide a means of support for generating team SA in a distributed team work context with the ability of handling uncertain information.
Gao, Y., Zhang, G., Lu, J., Dillon, T.S. & Zeng, X. 2008, 'A lambda-cut Approximate Algorithm For Goal-Based Bilevel Risk Management Systems', International Journal of Information Technology & Decision Making (IJITDM), vol. 7, no. 4, pp. 589-610.
View/Download from: UTS OPUS or Publisher's site
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 lambda-cut set based FGBL model. A programmable lambda-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 examples are given to illustrate the application of the proposed algorithm
Zhang, G., Lu, J. & Gao, Y. 2008, 'Fuzzy Bilevel Programming: Multi-Objective And Multi-Follower With Shared Variables', International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 16, pp. 105-133.
View/Download from: UTS OPUS or Publisher's site
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 examples illustrate the proposed models and algorithm.
Zhang, G., Lu, J. & Gao, Y. 2008, 'An algorithm for fuzzy multi-objective multi-follower partial cooperative bilevel programming', Journal Of Intelligent & Fuzzy Systems, vol. 19, no. 4-5, pp. 303-319.
View/Download from: UTS OPUS
In a bilevel decision problem, both the leader and the follower may have multiple objectives to optimize under certain constraints. In the meantime, these objective functions and constraints may contain some uncertain parameters. In addition, there may h
Zhang, G., Shi, C. & Lu, J. 2008, 'An extended Kth-best approach for referential-uncooperative bilevel multi-follower decision making', International Journal of Computational Intelligence Systems, vol. 1, no. 3, pp. 205-214.
View/Download from: UTS OPUS or Publisher's site
Bilevel decision techniques have been mainly developed for solving decentralized management problems with decision makers in a hierarchical organization. When multiple followers are involved in a bilevel decision problem, called a bilevel multi-follower (BLMF) decision problem, the leaderâs decision will be affected, not only by the reactions of these followers, but also by the relationships among these followers. The referential-uncooperative situation is one of the popular cases of BLMF decision problems where these multiple followers donât share decision variables with each other but may take othersâ decisions as references to their decisions. This paper presents a model for the referential-uncooperative BLMF decision problem. As the kth-best approach is one of the most successful approaches in dealing with normal bilevel decision problems, this paper then proposes an extended kth-best approach to solve the referential-uncooperative BLMF problem. Finally an example of logistics planning illustrates the application of the proposed extended kth-best approach.
Goyal, M.L., Lu, J. & Zhang, G. 2008, 'Decision Making in Multi-Issue e-Market Auction Using Fuzzy Attitudes', Journal of Theoretical and Applied Electronic Commerce Research, vol. 3, no. 2, pp. 97-110.
View/Download from: UTS OPUS
Online auctions are one of the most effective ways of negotiation of salable goods over the internet. Software agents are increasingly being used to represent humans in online auctions. These agents can systematically monitor a wide variety of auctions and can make rapid decisions about what bids to place in what auctions. To be successful in open multi-agent environments, agents must be capable of adapting different strategies and tactics to their prevailing circumstances. This paper presents a software test-bed for studying autonomous bidding strategies in simulated auctions for procuring goods. It shows that agents bidding strategy explore the attitudes and behaviors that help agents to manage dynamic assessment of prices of goods given the different criteria and scenario conditions. Our agent also uses fuzzy techniques for the decision making: to make decisions about the outcome of auctions, and to alter the agents bidding strategy in response to the different criteria and market conditions.
Goyal, M., Lu, J. & Zhang, G. 2008, 'Decision making in multi-issue e-market auction using fuzzy techniques and negotiable attitudes', Journal of Theoretical and Applied Electronic Commerce Research, vol. 3, no. 2, pp. 97-110.
Online auctions are one of the most effective ways of negotiation of salable goods over the internet. Software agents are increasingly being used to represent humans in online auctions. These agents can systematically monitor a wide variety of auctions and can make rapid decisions about what bids to place In what auctions. To be successful in open multi-agent environments, agents must be capable of adapting different strategies and tactics to their prevailing circumstances. This paper presents a software test-bed for studying autonomous bidding strategies in simulated auctions for procuring goods. It shows that agents' bidding strategy explore the attitudes and behaviors that help agents to manage dynamic assessment of prices of goods given the different criteria and scenario conditions. Our agent also uses fuzzy techniques for the decision making: to make decisions about the outcome of auctions, and to alter the agent's bidding strategy in response to the different criteria and market conditions. © 2008 Universidad de Talca - Chile.
Liu, B., Hao, Z., Lu, J. & Zhang, G. 2008, 'Supervised feature extraction based on One-against-All scheme', Dynamics of Continuous, Discrete and Impulsive Systems Series B: Applications and Algorithms, vol. 15, no. 3, pp. 339-350.
Support vector machines (SVMs) as being good tools for classification problems has been proposed by Vapnik. This paper proposes a supervised feature extraction method based on One-against-All scheme for the multi-class classification problems. In this approach, after embedding all the classes into one feature space and constructing hyperplanes based on One-against-All scheme, we extract the orientation distance features between the examples and every hyperplanes in the space, and then map the new features into another feature space, finally utilize other algorithms to classify them. In order to examine the performance of the proposed approach, One-against-All, One-against-One and the introduced approach are compared using three UCI data sets. From the results, we reported that after mapping the examples two times, the training accuracy and generalization performance is enhanced more or less. Copyright © 2008 Watam Press.
Lu, J., Wu, F. & Zhang, G. 2007, 'On a generalized fuzzy goal optimization for solving fuzzy multi-objective linear programming problems', Journal of Intelligent & Fuzzy Systems, vol. 18, no. 1, pp. 83-97.
View/Download from: UTS OPUS
Many organizational decision problems can be formulated by multi-objective linear programming (MOLP) models. Referring to the imprecision inherent in human judgments, uncertainty may be incorporated in the parameters of an MOLP model when it is established, which is called a Fuzzy MOLP (FMOLP) problem. What is an optimal solution for an FMOLP problem is the first issue to deal with in this study. The second issue is how to effectively derive an optimal solution for an FMOLP problem since uncertainty is also reflected in a solution process of an FMOLP problem. By introducing three types of comparison of fuzzy numbers and an adjustable satisfactory degree alpha in this study, a new solution concept of FMOLP is given. For handling the second issue, this study develops an interactive fuzzy goal optimization method which provides an interactive fashion with decision makers during their solution process and allows decision makers to give their fuzzy goals in any forms of membership functions. An illustrative example gives the details of the solution concept and the proposed method.
Zhang, J., Lu, J. & Zhang, G. 2007, 'An Intelligent Classification Method in Bank Customer Relationship Management', New Mathematics and Natural Computation, vol. 3, no. 1, pp. 111-121.
View/Download from: UTS OPUS
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.
Lu, J., Ruan, D., Wu, F. & Zhang, G. 2007, 'An alpha-fuzzy goal approximate algorithm for solving fuzzy multiple objective linear programming problems', Soft Computing - A Fusion of Foundations, Methodologies and Applications, vol. 11, no. 3, pp. 259-267.
View/Download from: UTS OPUS
Multiple conflicting objectives in many decision making problems can be well described by multiple objective linear programming (MOLP) models. This paper deals with the vague and imprecise information in a multiple objective problem by fuzzy numbers to represent parameters of an MOLP model. This so-called fuzzy MOLP (or FMOLP) model will reflect some uncertainty in the problem solution process since most decision makers often have imprecise goals for their decision objectives. This study proposes an approximate algorithm based on a fuzzy goal optimization under the satisfactory degree alpha to handle both fuzzy and imprecise issues. The concept of a general fuzzy number is used in the proposed algorithm for an FMOLP problem with fuzzy parameters. As a result, this algorithm will allow decision makers to provide fuzzy goals in any form of membership functions.
Wu, R.C. & Lu, J. 2007, 'The validation process and component analysis in enterprise integration', International Journal of Business and Systems Research, vol. 2, no. 1, pp. 86-105.
View/Download from: UTS OPUS
In a rapidly changing development environment, componentisation is crucial as both reuse and agility are basic requirements for maintaining a consistent responsiveness between business and IT. This study proposes a framework of metadata-based component development aiming to achieve service virtualisation through multidisciplines and multisteps process. It also develops service interoperability which can identify and validate component easily. Based on the strategy, a roadmap is provided in full life cycle. The proposed techniques, the multidisciplines and dimensional services, have been applied in industry in constructing the skeleton of service-based architecture and consequently constructing the foundation of service virtualisation.
Lu, J., Ruan, D., Zhang, G. & Zimmerman, H. 2007, 'Editorial: A special issue on e-service intelligence', International Journal of Intelligent Systems, vol. 22, no. 5, pp. 397-400.
Lu, J., Ma, J. & Zhang, G. 2007, 'Warning message generation by information filtering technique', International Journal of Nuclear Knowledge Management, vol. 2, no. 4, pp. 435-448.
View/Download from: UTS OPUS
This paper proposes a two-stage model for generating warning messages by using information filtering techniques. In this model, information is represented by its attributes and processed through two stages. At the first stage, exceptions are separated from normal information by the cognitive filtering technique. At the second stage, a warning message is generated from critical exceptions by the collaborative filtering approach. An example is discussed to illustrate the proposed model.
Cornelis, C., Lu, J., Guo, X. & Zhang, G. 2007, 'One-and-only item recommendation with fuzzy logic techniques', Information Sciences, vol. 177, no. 22, pp. 4906-4921.
View/Download from: UTS OPUS or Publisher's site
Shi, C., Zhou, H., Lu, J., Zhang, G. & Zhang, Z. 2007, 'The Kth-best approach for linear bilevel multi-follower programming with partial shared variables among followers', Applied Mathematics And Computation, vol. 188, no. 2, pp. 1686-1698.
View/Download from: UTS OPUS or Publisher's site
In a real world bilevel decision-making, the lower level of a bilevel decision usually involves multiple decision units. This paper proposes the Kappa th-best approach for linear bilevel multifollower programming problems with shared variables among foll
Lu, J., Shi, C., Zhang, G. & Dillon, T.S. 2007, 'Model and extended Kuhn-Tucker approach for bilevel multi-follower decision making in a referential-uncooperative situation', International Journal of Global Optimization, vol. 38, no. 4, pp. 597-608.
View/Download from: UTS OPUS or Publisher's site
When multiple followers are involved in a bilevel decision problem, the leader's decision will be affected, not only by the reactions of these followers, but also by the relationships among these followers. One of the popular situations within this bilev
Zhang, G., Lu, J. & Dillon, T.S. 2007, 'Decentralized multi-objective bilevel decision making with fuzzy demands', Knowledge-Based Systems, vol. 20, no. 5, pp. 495-507.
View/Download from: UTS OPUS or Publisher's site
Decisions in a decentralized organization often involve two levels. The leader at the upper level attempts to optimize his/her objective but is affected by the follower; the follower at the lower level tries to find an optimized strategy according to eac
Yang, X., Lu, J. & Zhang, G. 2007, 'An effective pruning algorithm for least squares support vector machine classifier', Jisuanji Yanjiu yu Fazhan - Journal of Computer Research and Development, vol. 44, no. 7, pp. 1-8.
View/Download from: UTS OPUS or Publisher's site
Wang, C., Lu, J. & Zhang, G. 2007, 'Mining key information of web pages: a method and its application', Expert Systems with Applications, vol. 33, no. 2, pp. 425-433.
View/Download from: UTS OPUS or Publisher's site
Web content mining aims to discover useful information and generate desired knowledge from a large amount of web pages. Key information, such as distinctive menu items, navigation indicators, which is embedded in web pages, can help classify the main con
Lu, J. 2007, 'Special Issue on E-services in Social Business Applications, Preface', New Mathematics and Natural Computation, vol. 3, no. 1, p. 8.
Lu, J., Shi, C., Zhang, G. & Ruan, D. 2007, 'An extended branch and bound algorithm for bilevel multi-follower decision making in a referential-uncooperative situation', International Journal of Information Technology and Decision Making, vol. 6, no. 2, pp. 371-388.
View/Download from: UTS OPUS or Publisher's site
Lu, J., Zhang, G. & Dillon, T.S. 2007, 'Fuzzy multi-objective bilevel decision making by an approximation Kth-best approach', Journal of Multiple-Valued Logic and Soft Computing, vol. 14, pp. 205-232.
View/Download from: UTS OPUS
Many industrial decisions problems are decentralized in which decision makers are arranged at two levels, called bilevel decision problems. Bilevel decision making may involve uncertain parameters which appear either in the objective functions or constraints of the leader or the follower or both. Furthermore, the leader and the follower may have multiple conflict decision objectives that should be optimized simultaneously. This study proposes an approximation Kth-best approach to solve the fuzzy multi-objective bilevel problem. Two case based examples further illustrate how to use the approach to solve industrial decision problems.
Zhang, G. & Lu, J. 2007, 'Model and approach of fuzzy bilevel decision making for logistics planning problem', Journal of Enterprise Information Management, vol. 20, no. 2, pp. 178-197.
View/Download from: UTS OPUS
Guo, X. & Lu, J. 2007, 'Intelligent e-government services with personalized recommendation techniques', International Journal Of Intelligent Systems, vol. 22, no. 5, pp. 401-417.
View/Download from: UTS OPUS or Publisher's site
Information overload is becoming one of the problems that hinder the effectiveness of e-government services. Intelligent e-government services with personalized recommendation techniques can provide a solution for this problem. Existing recommendation ap
Lu, J. & Ruan, D. 2007, 'Intelligent knowledge engineering systems - Preface', KNOWLEDGE-BASED SYSTEMS, vol. 20, no. 5, pp. 437-438.
View/Download from: Publisher's site
Wu, F., Lu, J. & Zhang, G. 2006, 'A new approximate algorithm for solving multiple objective linear programming problems with fuzzy parameters', Applied Mathematics And Computation, vol. 174, no. 1, pp. 524-544.
View/Download from: UTS OPUS or Publisher's site
Many business decision problems involve multiple objectives and can thus be described by multiple objective linear programming (MOLP) models. When a MOLP problem is being formulated, the parameters of objective functions and constraints are normally assi
Lu, J., Shi, C. & Zhang, G. 2006, 'On bilevel multi-follower decision making General framework and solutions', Information Sciences, vol. 176, no. 11, pp. 1607-1627.
View/Download from: UTS OPUS or Publisher's site
Within the framework of any bilevel decision problem, a leader's decision is influenced by the reaction of his or her follower. When multiple followers who may have had a share in decision variables, objectives and constraints are involved in a bilevel d
Shi, C., Lu, J., Zhang, G. & Zhou, H. 2006, 'An extended branch and bound algorithm for linear bilevel programming', Applied mathemetics and computation, vol. 180, no. 2, pp. 529-537.
View/Download from: UTS OPUS or Publisher's site
Abass, A. & Lu, J. 2006, 'An ANFIS-based data-driven method for fault accommodation', International Journal of Systems Science, vol. 32, no. 4, pp. 45-54.
Since occurrence of faults in different parts of a system as a complex abnormality is inevitable and could cause a total failure, Fault Detection and Accommodation (FDA) is finding ever widening attention for both industrial practitioners as well as academic researchers. In the large majority of real implementation of FDA, analytical model of the system, if known, may exert an impact on the performance of an FDA method. However, in some cases, such analytical model cannot be obtained in advance. Under unavailability assumption of the analytical model, in this paper we develop a data-driven method to identify and model three kinds of faults in nonlinear systems. Two Adaptive Neural-Fuzzy Inference Systems (ANFISs) are employed in this method, i.e. the first one is used for building a model of the faultless plant using the historical data, and the second one for modeling the occurred faults. Parameters of the second ANFIS are adjusted in an indirect way based on minimization of difference between actual and model outputs. Simulation results for a nonlinear system are also presented to demonstrate the potentiality of the proposed method for fault identification.
Lu, J. & Quaddus, M. 2006, 'The Design and Implementation of a Knowledge-Based Guide System in an Intelligent Multiple Objective Group Decision Support System', Australian Journal of Intelligent Information Proces..., vol. 9, no. 1, pp. 54-70.
View/Download from: UTS OPUS
Lu, J. & Zhang, G. 2006, 'A support vector machine based approach in situation assessment', Proceedings of 2006 Internatioanlaconference on intelligent systems and knowledge egineering, vol. 13, no. B52, pp. 41-45.
View/Download from: UTS OPUS
Shi, C., Lu, J. & Zhang, G. 2005, 'An extended Kuhn-Tucker approach for linear bilevel programming', Applied Mathematics And Computation, vol. 162, no. 1, pp. 51-63.
View/Download from: UTS OPUS or Publisher's site
Kuhn-Tucker approach has been applied with remarkable success in linear bilevel programming (BLP). However, it still has some extent unsatisfactory and incomplete. One principle challenges is that it could not well handle a linear BLP problem when the co
Shi, C., Zhang, G. & Lu, J. 2005, 'The Kth-best approach for linear bilevel multi-follower programming', Journal Of Global Optimization, vol. 33, no. 4, pp. 563-578.
View/Download from: UTS OPUS or Publisher's site
The majority of research on bilevel programming has centered on the linear version of the problem in which only one leader and one follower are involved. This paper addresses linear bilevel multi-follower programming (BLMFP) problems in which there is no
Zhang, G. & Lu, J. 2005, 'The Definition of Optimal Solution and Extended Kuhn-Tucker Approach for Fuzzy Linear Bilevel Programming', The IEEE Intelligent Informatics Bulletin, vol. 6, no. 2, pp. 1-7.
View/Download from: UTS OPUS
Lu, J., Zhang, G. & Wu, F. 2005, 'Web-Based Mulit-criteria Group Decision Support System with Linguistic Term Processing Function', The IEEE Intelligent Informatics Bulletin, vol. 5, no. 1, pp. 35-43.
View/Download from: UTS OPUS
Shi, C., Lu, J. & Zhang, G. 2005, 'An extended Kth-best approach for linear bilevel programming', Applied Mathematics And Computation, vol. 164, no. 3, pp. 843-855.
View/Download from: UTS OPUS or Publisher's site
Kth-best approach is one of the three popular and workable approaches for linear bilevel programming. However, it could not well deal with a linear bilevel programming problem when the constraint functions at the Upper-level are of arbitrary linear form.
Shi, C., Zhang, G. & Lu, J. 2005, 'On the definition of Linear Bilevel Programming Solution', Applied Mathematics and Computation, vol. 160, pp. 169-176.
View/Download from: UTS OPUS or Publisher's site
Linear bilevel programming theory has been studied for many years by a number of researchers from different aspects, yet it still remains to some extent unsatisfactory and incomplete. The main challenge is how to solve a linear bilevel programming problem when the upper-level's constraint functions are of arbitrary linear form. This paper proposes a definition for linear bilevel programming solution. The performance comparisons have demonstrated that the new model can solve a wider class of problems than current capabilities permit
Wu, F., Lu, J. & Zhang, G. 2005, 'Development and implementation on a fuzzy multiple objective decision support system', Knowledge-Based Intelligent Information And Engineering Systems, Pt 1, Proceedings, vol. 3681, pp. 261-267.
View/Download from: UTS OPUS
A fuzzy-goal optimization-based method has been developed for solving fuzzy multiple objective linear programming (FMOLP) problems where fuzzy parameters in both objective functions and constraints and fuzzy goals of objectives can be in any form of memb
Lu, J. & Lu, Z. 2004, 'Development Distribution and Evaluation of Online Tourism Services in China', Electronic Commerce Research, vol. 4, no. 3, pp. 221-239.
View/Download from: UTS OPUS or Publisher's site
Lu, J. 2003, 'A model for evaluating E-commerce based on cost/benefit and customer satisfaction', Information Systems Frontiers, vol. 5, no. 3, pp. 265-277.
View/Download from: UTS OPUS or Publisher's site
Zhang, G. & Lu, J. 2003, 'An integrated group decision-making method dealing with fuzzy preferences for alternatives and individual judgments for selection criteria', Group Decision And Negotiation, vol. 12, no. 6, pp. 501-515.
View/Download from: UTS OPUS or Publisher's site
Lu, J. & Zhang, G. 2003, 'Cost Benefit Factor Analysis in e-Services', International Journal of Service Industry Management, vol. 14, no. 5, pp. 570-595.
View/Download from: UTS OPUS or Publisher's site
Zhang, G., Wu, Y., Remias, M.G. & Lu, J. 2003, 'Formulation of fuzzy linear programming problems as four-objective constrained optimization problems', Applied Mathematics And Computation, vol. 139, no. 2-3, pp. 383-399.
View/Download from: UTS OPUS or Publisher's site
This paper concerns the solution of fuzzy linear programming (FLP) problems which involve fuzzy numbers in coefficients of objective functions. Firstly, a number of concepts of optimal solutions to FLP problems are introduced and investigated. Then, a number of theorems are developed so as to convert the FLP to a multi-objective optimization problem with four-objective functions. Finally, two illustrative examples are given to demonstrate the solution procedure. It also shows that our method of solution includes an existing method as a special case
Zhang, G., Lu, J. & Wu, Y. 2003, 'Formulation of linear programming problems with fuzzy coefficients of objective functions and constraints', Asian-Information - Science - Life, vol. 2, no. 1, pp. 57-68.
View/Download from: UTS OPUS
This paper concerns the solution of fuzzy linear programming problems (FLP) with fuzzy numbers in coefficients of objective functions and constraints. For constraints given by n equations / inequalities involving fuzzy numbers with isosceles triangle membership functions. we prove that the feasiible solution space is determined by 3n non-fuzzy equations / inequalities. For constraints involving fuzzy numbers with other forms of membership functions. We develop two numerical algorithms respectively for the determination of the feasible solution space and the solution of the FLP problem. An illuminative example is also given in this paper to demonstrate the validity of the methods and algorithms developed.
Zhang, G., Wu, Y. & Lu, J. 2002, 'Lattice-valued Zp-Pan-integrals I: For lattice-valued Simple Functions on Lattice', The Journal of Fuzzy mathematics, vol. 10, no. 1, pp. 213-226.
View/Download from: UTS OPUS
Zhang, G., Wu, Y. & Lu, J. 2002, 'Lattice-valued Zp-Pan-integrals II: For lattice-valued Borel functions on Lattice', The Journal of Fuzzy mathematics, vol. 10, no. 2, pp. 503-511.
View/Download from: UTS OPUS
Lu, J., Tang, S. & McCullough, G. 2001, 'An Assessment for internet-based electronic commerce development in businesses of New Zealand', Electronic Markets, vol. 11, no. 2, pp. 107-115.
View/Download from: UTS OPUS