Keynote Speakers

Zbigniew Michalewicz

In the modern information era, managers must recognize the competitive opportunities represented by decision-support tools. New family of such systems, based on recent advances in Artificial Intelligence, combine prediction and optimization techniques to assist decision makers in complex, rapidly changing environments. These systems address the fundamental questions: What is likely to happen in the future? and what is the best course of action? These modern AI systems include elements of data mining, predictive modelling, forecasting, optimization, and adaptability and aim at providing significant cost savings & revenue increases for businesses. The talk introduces the concepts behind construction of such systems and indicates the current challenging research issues. Several real-world examples will be shown and discussed.

Zbigniew Michalewicz is Professor in School of Computer Science at the University of Adelaide. He completed his Masters degree at Technical University of Warsaw in 1974 and he received Ph.D. degree from the Institute of Computer Science, Polish Academy of Sciences, in 1981. His last post (before arriving in Australia) was a Professor position at the University of North Carolina at Charlotte, USA, where he lectured from 1987 to 2004. Zbigniew Michalewicz also holds Professor positions at the Institute of Computer Science, Polish Academy of Sciences, the Polish-Japanese Institute of Information Technology, and the State Key Laboratory of Software Engineering of Wuhan University, China. He is also associated with the Structural Complexity Laboratory at Seoul National University, South Korea.
Zbigniew Michalewicz has published over 200 articles and 15 books on the subject of predictive data mining and logistics optimisation. These include the scientific bestseller How to Solve It: Modern Heuristics; other books include a monograph Genetic Algorithms + Data Structures = Evolution Programs (3 editions, a few translations), Handbook of Evolutionary Computation, and recent (2007-2008) three books: Adaptive Business Intelligence, Winning Credibility: A guide for building a business from rags to riches, and Puzzle-Based Learning: An Introduction to critical thinking, mathematics, and problem-solving.
Zbigniew Michalewicz has over 30 years of academic and industry experience, and possesses expert knowledge of many artificial intelligence methods and modern heuristics. He has led numerous data mining and optimisation projects for major corporations such as General Motors, Ford Motor Company, Bank of America, Wells Fargo, Dentsu, ABB Grain, Orlando Wines, Rio Tinto, and for several government agencies. Zbigniew Michalewicz has also served as the Chairman of the Technical Committee on Evolutionary Computation, and later as the Executive Vice President of IEEE Neural Network Council. His scientific and business achievements have been recognized by countless publications, including TIME Magazine, Newsweek, New York Times, Forbes, and the Associated Press among others. He serves as Chairman of the Board for SolveIT Software Pty Ltd, a company specialising in custom software solutions for demand forecasting and scheduling and supply chain optimisation.
Zbigniew Michalewicz is a Fellow of the Australian Computer Society. In 2006 he was appointed a Business Ambassador for the State of South Australia.

Alexander Gammerman

The talk presents several machine learning techniques and their applications to clinical decision-making. In many problems of computer- aided medical diagnosis and treatment a program must be capable of learning from previously accumulated past patients data records, and extrapolating to make diagnosis for new patient by considering their symptoms. Many machine learning and statisitical techniques have been developed to help in clinical decision making. Among them decision trees, the Bayesian techniques, dicriminant analysis, neural networks and many others. These techniques usually deal with conventional, small-scale, low- dimensional problems, and the application of these techniques to modern high-dimensional data sets with many thousand attributes (symptoms) usually leads to serious computational problems. Several new techniques such as Support Vector Machine (SVM) have been developed to tackle the problem of dimensionality by transferring the problem into high- dimensional space, and solving it in that space. They based on so-called kernal methods and can very often solve some high-dimensional problems These techniques perform very well with good accuracy. However, a typical drawback of techniques such as the SVM is that they usually do not provide any useful measure of confidence of new, unclassified examples (new pattients). Recently a new set of techniques, called Conformal Predictors, have been developed that allows to make predictions with valid measures of confidence. The approach is based on approximations to the universal measures of confidence given by the algorithmic theory of randomness and allows us to compute diagnostic classes and estimate confidence of the diagnostics for high-dimensional data. The talk will present Conformal Predictors and their applications in medicine.

Alexander Gammerman is Professor of Computer Science and Director of the Computer Learning Research Centre (CLRC) at Royal Holloway, University of London.
Professor Gammerman has been a Fellow of the Royal Statistical Society since 1985. His research interest lies in the field of computational aspects of Inference and Data Analysis. His current work is on Conformal Predictors based on Algorithmic Randomness Theory and its applications to machine learning. Professor Gammerman has also been interested in the applications of these and other probabilistic techniques to a variety of subject areas such as medicine (medical diagnosis and clinical decision-making), bioinformatics(regulatory site analysis and promoter prediction), environment (prediction of pollution level), and forensic science (offender profiling). Alex Gammerman has published over two hundred research papers and five books on computational learning and probabilistic reasoning.
Details of Professor Gammerman's research can be found at:http://clrc.rhul.ac.uk.

Masoud Mohammadian

Artificial Intelligence (AI) techniques have been successfully applied in many areas of software engineering. The complexity of software systems has limited the application of AI techniques in many real world applications. This talk provides an insight into applications of AI techniques in software engineering and how innovative application of AI can assist in achieving ever competitive and firm schedules for software development projects as well as Information Technology (IT) management. The pros and cons of using AI techniques are investigated and specifically the application of AI in IT management, software application development and software security is considered.
Organisations that build software applications do so in an environment characterised by limited resources, increased pressure to reduce cost and development schedules. Organisations demand to build software applications adequately and quickly. One approach to achieve this is to use automated software development tools from the very initial stage of software design up to the software testing and installation. Considering software testing as an example, automated software systems can assist in most software testing phases.
On the hand data security, availability, privacy and integrity are very important issues in the success of a business operation. Data security and privacy policies in business are governed by business requirements and government regulations. AI can also assist in software security, privacy and reliability. Implementing data security using data encryption solutions remain at the forefront for data security. Many solutions to data encryption at this level are expensive, disruptive and resource intensive. AI can be used for data classification in organizations. It can assist in identifying and encrypting only the relevant data thereby saving time and processing power. Without data classification organizations using encryption process would simply encrypt everything and consequently impact users more than necessary. Data classification is essential and can assist organizations with their data security, privacy and accessibility needs. This talk explores the use of AI techniques (such as fuzzy logic) for data classification and suggests a method that can determine requirements for classification of organizations' data for security and privacy based on organizational needs and government policies. Finally the application of FCM in IT management is discussed.

Masoud Mohammadian research interests lie in adaptive self-learning systems, fuzzy logic, genetic algorithms, neural networks and their applications in industrial, financial and business problems. His current research concentrates on the application of computational intelligence techniques in software development and automation.
He has chaired twelve international conferences on computational intelligence, intelligent agents and software engineering. He has published over ninety research papers in conferences, journal and books as well as editing and co-authoring twenty books and conference proceedings. Masoud has seventeen years of academic experience and he has served as program committee member and/or co-chair of a large number of national and international conferences. He was the chair of IEEE ACT Section and he was the recipient of many Awards from IEEE from USA and Ministry of Commerce from Austria.