Plenary Speakers

Professor Saeid Nahavandi Hide Sasaki
Saeid Nahavandi
Deakin University Centre
for Intelligent Systems
Research Geelong, Australia
Václav Snášel
VSB-Technical University
of Ostrava, Czech Republic
Hideyasu Sasaki
The Chinese University of Hong Kong, Hong Kong
 GAURI S. MITTAL, P.Eng    aditya  
Gauri S. Mittal
University of Guelph, Canada
Emilia I. Barakova
Eindhoven University of
Technology, Netherlands
Aditya K. Ghose
University of Wollongong,
Australia
 Nikitas Sgouros
 Elpida Tzafestas  
Nikitas Sgouros
University of Piraeus,
Greece
Elpida Tzafestas
University of Athens,
Greece

Sankar K. Pal
Indian Statistical Institute,
India

 Andre de Carvalho
Andre de Carvalho
University of Sao Paulo,
Brazil

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Professor Saeid Nahavandi

Knowledge Management in Process Control using Simulation and Modelling techniques
Saeid Nahavandi
Deakin University Centre for Intelligent Systems Research Geelong, Australia 

 [Abstract] Retaining knowledge in companies often is a major challenge as there are very few formal tools available to achieve this. Capturing the appropriate knowledge about the organisation on the other hand has proved to be one of the greatest barriers. This talk will highlight challenges and devise a mechanism on how this can be achieved through simulation and modelling techniques for complex engineered systems. Through real world industry case studies the concept will be demonstrated step by step, highlighting all aspects of data capture, information processing and knowledge management for key decision-making processes demonstrating their effect on a company's bottom line.

 [Biography] Saeid Nahavandi received his BSc (Hons), MSc and PhD in Control Engineering from Durham University, UK in 1985, 1986 and 1991 respectively.

Saeid is an Alfred Deakin Professor and the Director for the Centre for Intelligent Systems Research at Deakin University in Australia.

Professor Nahavandi is a Fellow member of IET. IEAust and Senior Member of IEEE and has published over 350 refereed papers and been awarded several competitive Australian Research Council (ARC) grants over the past five years. He received the Research collaboration / initiatives award from Japan (2000) and Prince & Princess of Wales Science Award in 1994. He won the title of Young Engineer of the Year Award in 1996 and holds two patents. In 2002 Professor Nahavandi served as a consultant to Jet Propulsion Lab (NASA) during his visit to JPL Labs. In 2006 he received the title of Alfred Deakin Professor, the highest honour at Deakin University for his contribution to fundamental research.

Professor Nahavandi is the founder for the Centre for Intelligent Systems Research with 55 full time researchers at Deakin University. He actively contributes and leads four major research projects in three Cooperative Research Centres with over 50 major international companies as partners. In modelling and simulation of complex systems he received awards from several organisations to focus on simulation based optimization of manufacturing processes, airport operations, logistics and distribution centres. He has carried out industry based research with several major international companies such as GM, Ford, Holden, Nissan, Bosch, Futuris, Boeing, Vestas just to name a few. For his contribution in haptics and robotics he won two major research grants from the Australian Department of Defence on haptically enabled counter explosive robot design.

Professor Nahavandi has been the chairman of eight International conferences and the General Chair for World Manufacturing Congress series and the International Congress on Autonomous Intelligent Systems. He also holds the position of Editor for the International Journal Intelligent Automation and Soft Computing (South Pacific region), International Journal of Computational Intelligence and Associate Editor - IEEE Systems Journal, International Journal of Innovative Computing & Information Control.

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Václav Snášel
VSB-Technical University of Ostrava, Czech Republic
 [Abstract]

 [Biography] Vaclav Snasel's research and development experience includes over 25 years in the Industry and Academia. He works in a multi-disciplinary environment involving artificial intelligence, multidimensional data indexing, conceptual lattice, information retrieval, semantic web, knowledge management, data compression, machine intelligence, neural network, web intelligence, data mining and applied to various real world problems. He has given more than 10 plenary lectures and conference tutorials in these areas. He has authored/co-authored several refereed journal/conference papers and book chapters. He has published more than 400 papers (147 is recorded at Web of Science). He has supervised many Ph.D. students from Czech Republic, Jordan, Yemen, Slovakia, Ukraine and Vietnam. 
From 2001 he is a visiting scientist in the Institute of Computer Science, Academy of Sciences of the Czech Republic. From 2003 he is vice-dean for Research and Science at Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czech Republic. He is full professor since 2006. Before turning into a full time academic, he was working with industrial company where he was involved in different industrial research and development projects for nearly 8 years. He received Ph.D. degree in Algebra and Geometry from Masaryk University, Brno, Czech Republic and a Master of Science degree from Palacky University, Olomouc, Czech Republic. 
Besides, the Editor-in-Chief of two journals, he also serves the editorial board of some reputed International journals. He is actively involved in the International Conference on Computational Aspects of Social Networks (CASoN) ; Computer Information Systems and Industrial Management (CISIM); Evolutionary Techniques in Data Processing (ETID) series of International conferences. He is a Member of IEEE, ACM, AMS and SIAM.

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Hide Sasaki

Human-Machine Interaction in Time-Critical Communications
Hideyasu Sasaki
The Chinese University of Hong Kong, Hong Kong

 [Abstract] Managing time-critical transactions is a challenging problem in communication systems which involve human interventions. Tracking the human-machine interaction in information communications often anticipates technical challenges derived from human misapprehensions or the limitations of human perception. This talk will commence with discussion on a well-known heuristic approach for tackling with the communication problem in human-machine systems and its limitations of applications to real practice. We then highlight a non-heuristic approach using stochastic analysis of human behavior. The introduced approach dramatically improves human-machine interaction in time-critical communications.

 [Biography] Prof. Sasaki has given invited talks at renowned conferences and institutes including SPIE Defense, Security and Sensing (DSS) and The Energy and Resources Institute of India (TERI). His research interests include Human-Machine Systems, Collective Intelligence, Soft Computing and Decision Making. His primary concern is time-critical analysis on decision making. Prof. Sasaki is the founding Editor-in-Chief of International Journal of Organizational and Collective Intelligence (IJOCI), IRMA, N.J., United States. He is active in several international program committees including IEEE SMC 2011 Part B Human-Machine Systems. Prof. Sasaki is a Fellow member of IARIA and has been awarded best paper awards for his presentations twice consecutively, and the 4th Annual Excellence in Research Journal Award from IRMA for his co-authored journal article in IJSSOE about a steganography technique using artificial neural networks in 2010. Dr. Sasaki has been awarded competitive Japan Society for Science Promotion (JSPS) grants over the past six years from the very beginning of his tenure professorship. He received the Microsoft Research Grant in 2005.Dr. Sasaki is an Associate Professor of Computer Science at Ritsumeikan University in Kyoto, Japan. He has been tenured there since 2005. Prof. Sasaki received his BA, LLB from the University of Tokyo, Japan, LLM from University of Chicago Law School, MS (Hons) and PhD (Highest Hons) in Computer and Information Sciences in 1992, 1994, 1999, 2001 and 2003 respectively. In his graduate research, he won the title of Keio Engineering Society Fellow (2001). He has experienced lawyering and litigations as an Attorney-at-Law in New York since 1999.

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GAURI S. MITTAL, P.Eng

Sensors and sensor networks in health, food safety and quality detection
Gauri S. Mittal
Systems Engineering at the School of Engineering, University of Guelph, Guelph, Ontario, Canada

 [Abstract] Various sensors and sensor fusion are presented in this talk based on our research at the University of Guelph. These sensors and networks were developed to sense health, and food safety and quality. Various techniques such as image processing, impedance spectroscopy, signal processing, near-infrared, audio signals, and ultrasound and microwave pulses were used.
Using image processing and impedance, sensing system was developed to detect crack and internal quality of egg for grading, respectively. The image processing algorithm, for the detection of cracks on the exterior of the egg, was designed to capture the image from 10 MP camera, identify and extract egg shape from the background, and then perform edge detection functions on the extracted image. The edge detection functions, Sobel & Canny, look for discontinuities in image brightness on the edge surface and returned a binary matrix of pixels where 1’s are identified as edges and 0’s as non-edges. Any egg surface produces more than zero edges is classified as a cracked egg. 100% accuracy was obtained. A resonant LC circuit was used for internal egg quality detection. It detected capacitance changes at high sensitivity which is related with the egg quality. This can also be used in detecting fruit ripeness and moisture content of baked goods.
The feasibility of the ultrasound based technology to detect bone and plastic pieces in ground beef and pork is confirmed. A series of experiments was conducted to investigate the ultrasound penetration depth in the ground beef/pork and the ultrasound signal characteristics of the ground meat with and without extraneous matters. Mechanical setups and calibration were conducted and software was developed to complete the experiments. We confirmed detection of bone fragments down to 1/8” and plastic down to ?” at a penetration depth of 3”.
Extraneous matter in wet products, such as cheese, was detected using ultrasound and in dry materials containing a lot of air, such as cereal, using microwaves. We have been successful in detecting glass in orange juice, apple juice and even higher density products like tomato juice and pumpkin puree. This is something that cannot be seen by x-ray. With ultrasound and microwave, the density difference between the foreign material and the product is not important. The attenuation of the signal and the change in pressure of the waves is being measured. Advanced signal processing techniques, hardware and software were developed. 

 [Biography] GAURI S. MITTAL, P.Eng., Professor of Systems Engineering at the School of Engineering, University of Guelph, Guelph, Ontario, Canada. An author of more than 250 refereed journal research papers and 210 other publications, as well as three books, e.g. ”Computerized Controls in Food Industry”. He is a member of many technical societies. He is the recipient of the 1994 John Clark Award presented by the Canadian Society of Bioengineering, the 1994 Membro Benemerito Award given by the Colombian Association of Food Engineers, International Best Researcher award 2005 & 2007 by Japanese Association of Food Machinery Manufacturers, and Fellow (2010) of Canadian Society of Bioengineering. A registered professional engineer, professor Mittal received the B.Sc. (Engg.) (1969) from India, M.Sc. (1976) from the University of Manitoba, Winnipeg, Canada, and the Ph.D. (1979) from the Ohio State University, Columbus, USA.


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Brain-inspired robots for social training of autistic children
Emilia I. Barakova
Eindhoven University of Technology,  Eindhoven
Area, Netherlands

 [Abstract] Social robotics is a field that deals with simulating social behavior on robots with the aim of making the robots cope with the interactive aspects of autonomy while they interact with humans or another embodied autonomous agents. Due to the many layers of social interaction and the complexity of the autonomous social behavior, the observed social behaviors are simulated. We aim at augmenting the social interaction behaviors with elements of brain-inspired mechanisms that cause social behavior. So far we use a combination of observed and emulated social intelligence.

Autistic children have atypical social behavior and the origin of that can be traced back to the difficulties in performing simple behaviors such as eye contact, turn taking, and imitation. In addition, at present, understanding the underlying mechanisms of intentions and emotions is getting within the reach of contemporary science.  We use a combination of methods consisting of functional brain modeling, behavioral robotics, and human centered design in social scenarios that comply to the modern therapies for autistic children such as Applied Behavioral Analysis and Pivotal Response Training. The behaviors are tested with human subjects (especially children with autism). The user group of children with autism was chosen, because they do not only benefit from the outcome of the research but also help us to generate knowledge on how social interaction is developing in typically developing and socially impaired (autistic) children. The results of the experiments with humans facilitate knowledge discovery and the obtained results are fed back as a research input for novel robot behaviors, and interaction scenarios for behavioral training. 
 [Biography] Dr. Ir. Emilia I. Barakova is affiliated with the Department of Industrial Design at the Eindhoven University of Technology, The Netherlands, and simultaneously holds a Visiting Researcher position at RIKEN Brain Science Institute in Japan. She has: Masters Degree in Electronics and Automation from Technical University of Sofia (Bulgaria) and PhD in Mathematics and Physics from Groningen University (The Netherlands, 1999).

Barakova has expertise in behavioral robotics and functional brain modeling based on behavioral data from mice, monkeys, and humans, on learning methods, and human centered interaction design. Currently she is working on human-robot social interaction, robotics for behavioral training of autistic children and  on prediction of conflicts in social groups, which includes measuring, and analyzing human behavior and interaction, use of machine learning and brain-inspired computational models to create robot interactive behaviors and human-centered design to design interactive scenarios that are based on advanced therapeutic practices.

Barakova has worked at different research institutes: the RIKEN Brain Science Institute (Japan), the GMD-Japan Research Laboratory (Japan), Groningen University (The Netherlands), the Eindhoven University of Technology (The Netherlands), Starlab (Belgium), and the Bulgarian Academy of Science. She has closely collaborated with Honda Research Institute (Asimo), Fraunhover AiS (Germany), Philips Research, Noldus, and TiViPE (The Netherlands), and ARC Cambridge (UK). She has been an interim Scientific Director of GMD Japan research laboratory, a project leader of several multidisciplinary projects, and coordinated the Robotics and Social robots educational and research team at ID department of TU/e. Barakova is an Associate editor of Journal of Integrative Neuroscience, and has organized several scientific workshops, special issues of international journals. She has organized  international conferences and has served as a program chair of IEEE and ACM conferences. Barakova is an author of over 100 scientific papers, conference proceedings, and one book.

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The Optimizing Web: Leveraging efficiencies from collaborative services
Aditya K. Ghose
University of Wollongong,Australia.

 [Abstract] We live in a world where the pressure to be more efficient has never been greater. Carbon mitigation is a key driver for this imperative, as is the need to do more with less. This talk will bring together several distinct threads of research. First, it will argue the case for leveraging formal service engineering techniques, and indeed formal computing techniques in modeling, designing, delivering and monitoring services in the most general sense. Second, it will argue that generating efficiencies from such services requires us to leverage optimization techniques, both in the design and operation of such services. Third, it will argue that piecemeal optimization is inadequate and that we must design networks of collaboratiing services to effectively maximize efficiency opportunities. These threads will be brought together in the context of the Optimizing Web project that provides the infrastructure for large, ubiquitous networks of local optimizers to collaborate to improve solutions relative to a shared (and arguably global) objective function. This has implications for our reponse to the climate change challenge (where the global objective is the minimization of the carbon footprint), but also in the context of service engineering at the bottom of the pyramid. 
[Biography] Aditya Ghose is Professor of Computer Science at the University of Wollongong and Director of its Decision Systems Lab. He holds PhD and MSc degrees in Computing Science from the University of Alberta, Canada (he also spent parts of his PhD candidature at the Beckman Institute, University of Illinois at Urbana Champaign and the University of Tokyo) and a Bachelor of Engineering degree in Computer Science and Engineering from Jadavpur University, Kolkata, India. While at the University of Alberta, he received the Jeffrey Sampson Memorial Award. His research has been funded by the Australian Research Council, the Canadian Natural Sciences and Engineering Research Council, the Japanese Institute for Advanced Information Technology (AITEC) and various Australian government agencies as well as companies such as Bluescope Steel, CSC and Pillar Administration. His research has been published in the top venues in service-oriented computing (SCC and ICSOC), software modelling (ER), software evolution (IWSSD, IWPSE) and AI (Artificial Intelligence Journal, AAAI, AAMAS and ECAI). He has been an invited speaker at the Schloss Dagstuhl Seminar Series in Germany and the Banff International Research Station in Canada. He has also been a keynote speaker at several conferences, and program/general chair of several others. He is a senior technical advisor to several companies in the areas of constraint programming and business process management, both in Australia and Canada. He reviews for well-regarded journals such as Artificial Intelligence, the IBM Systems Journal and the Journal of Autonomous Agents and Multi-Agent Systems, serves as assessor (Ozreader) for the Australian Research Council and as an external reviewer for the Natural Sciences and Engineering Research Council (NSERC) of Canada and the Science Foundation of Ireland. Professor Ghose is a Research Leader in the Australian Cooperative Research Centre for Smart Services, Co-Director of the Centre for Oncology Informatics at the Illawarra Health and Medical Research Institute, Co-Leader of the University of Wollongong Carbon-Centric Computing Initiative and Co-Convenor of the Australian Computer Society NSW SIG on Green ICT. He is also Vice-President of CORE, Australia's apex body for computing academics.

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Means of Expression, Rendering and Analysis of Collective Reactions in Social Interaction Environments
Nikitas Sgouros
University of Piraeus, Greece

 [Abstract] In recent years the development of a multitude of environments for social interaction has provided unprecedented opportunities for mass participation in social activities on a global scale. Participation in social action can take many forms from well-structured dialogues such as those taking place in scientific conferences to spontaneous crowd reactions similar to those occurring in sports or mass entertainment venues. Our research focuses on the creation of methods for expression, rendering and analysis of collective reactions in social activities. Collective in this context refers to a number of reactions with similar content, referring to the same situation and posted by a significant number of people at approximately the same point in time. We examine the types of such reactions and propose a number of rendering methods that take into account their magnitude, persistence and the aesthetics of the environment they appear in. We also describe analysis tools for tracking the emergence and evolution of such phenomena and discovering their causes. Finally, we propose methods by which the results of this analysis can be used in the creation of richer and more engaging social interaction experiences. 
[Biography] Professor Nikitas M. Sgouros holds a PhD in Computer Science from Northwestern University, USA (1994) a M.Sc. with distinction in Artificial Intelligence from the University of Edinburgh, UK (1990) and a Diploma in EECS from the National Technical University of Athens, Greece (1988). Currently, he is Professor in the Department of Digital Systems at the University of Piraeus, Greece. His main research interests include multimedia systems, artificial intelligence and entertainment computing. Dr. Sgouros has participated in a number of national and EU research projects. He is the author of more than 50 publications in scientific journals and conferences.

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Elpida Tzafestas Constraints and Effects of Partner Selection on the Emergence of Structures in Social Environments
Elpida Tzafestas
University of Athens, Greece

 [Abstract] In this work, we are exploring spatial and social dynamics and clashes in social simulations involving partner selection. We are considering three different social settings: (a) a modified Axelrod cultural simulation model extended with a Moore neighborhood, heterogeneous sets of cultural features per agent and a number of psychologically realistic, basic and more advanced, conceptual models of cultural affinity perception and imitation, (b) a proto-imitation model where agents imitate unconditionally those they happen to interact with because perceived external signals are replicated impulsively without associating with objects of reference, and (c) a model of social noisy IPD interaction with an additional attraction mechanism that makes agents unconditionally cooperative toward attractive opponents. In all these models, a simple mechanism of partner selection has been found to modify the social environment by allowing different types of social structures to emerge, for example fast built cultural homogeneous groups in the case of cultural simulation or groups or interacting cooperative agents that are attracted by one another in the case of IPD with attraction. We are identifying a number of cognitive factors that are used to model partner selection, namely memory depth, learning speed and openness, and how they relate to both the type of the social environment at hand (all-to-all, networked ot grid-based) and the phenomena obtained. We are finally discussing how these factors may be studied and taken into account when designing complex sociotechnical systems, such as social networking collaborative environments with human participants, so as to accomodate the diversity of the social and cultural background of participants. 
[Biography] Elpida Tzafestas is an Associate Professor of Artificial Intelligence in the Department of Philosophy and History of Science, University of Athens, Greece. She finished her Electrical and Computer Engineering degree from  NTU, Athens, M.Sc. and Ph.D. on Artificial Intelligence (Univ. Paris VI, France). She has been a senior researcher in the Institute of Communication and Computer Systems (NTU, Athens) and has taught a number of undergraduate courses on computer science and graduate courses on intelligent, complex and biological systems. She has been the principal investigator in numerous national and european R&D projects and has authored over eighty articles in journals, books and conference proceedings, at least half of them as a single author. She serves on several editorial boards and is frequent reviewer for journals, conferences and research grants. Her research interests lie on the intersection of biological, complex and cognitive systems and their application to intelligent human-computer interaction, logistics and sustainable development.

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Machine Intelligence, Granular Mining and Image Analysis: F-granulation, Rough-fuzzy Approach and Challenges
Sankar K. Pal
Indian Statistical Institute, India

 [Abstract] Different components of machine intelligence are explained. The role of rough sets in uncertainty handling and granular computing is described. The significance of its integration with other soft computing tools and the relevance of rough-fuzzy computing, as a stronger paradigm for uncertainty handling, are explained. Different applications of rough granules, significance of f-granulation and certain important issues in their implementations are stated. Generalized rough sets using the concept of fuzziness in granules and sets are defined both for equivalence and tolerance relations. These are followed by definitions of various entropy measures. Different tasks such as case generation, class-dependent rough-fuzzy granulation for classification, rough-fuzzy clustering and defining various image ambiguity measures for mining are then addressed in this regard, explaining the nature and characteristics of granules used therein.
While the method of case generation with variable reduced dimension is useful for mining data sets with large dimension and size, class dependent granulation coupled with neighborhood rough sets for feature selection is efficient in modeling overlapping classes. Superiority of rough-fuzzy clustering is illustrated for brain MRI segmentation problem. Image ambiguity measures, which take into account both the fuzziness in boundary regions, and the rough resemblance among nearby gray levels and nearby pixels, are useful for various image analysis operations. Merits of generalization in rough sets, as well as the incorporation of the concept of rough granulation on the top of fuzziness in gray level are extensively demonstrated for image segmentation problem.
The talk concludes with stating the future directions of research such as in bioinformatics and web intelligence, and the challenging issues. 

[Biography] Sankar K. Pal (www.isical.ac.in/~sankar) is a Distinguished Scientist of the Indian Statistical Institute and a former Director. He is also a J.C. Bose Fellow of the Govt. of India. He founded the Machine Intelligence Unit and the Center for Soft Computing Research: A National Facility in the Institute in Calcutta. He received a Ph.D. in Radio Physics and Electronics from the University of Calcutta in 1979, and another Ph.D. in Electrical Engineering along with DIC from Imperial College, University of London in 1982. He joined his Institute in 1975 as a CSIR Senior Research Fellow where he later became a Full Professor in 1987, Distinguished Scientist in 1998 and the Director for the term 2005-10.
He worked at the University of California, Berkeley and the University of Maryland, College Park in 1986-87; the NASA Johnson Space Center, Houston, Texas in 1990-92 & 1994; and in US Naval Research Laboratory, Washington DC in 2004. Since 1997 he has been serving as a Distinguished Visitor of IEEE Computer Society (USA) for the Asia-Pacific Region, and held several visiting positions in Italy, Poland, Hong Kong and Australian universities.
Prof. Pal is a Fellow of the IEEE, USA, the Academy of Sciences for the Developing World (TWAS), Italy, International Association for Pattern recognition, USA, International Association of Fuzzy Systems, USA, and all the four National Academies for Science/Engineering in India. He is a co-author of seventeen books and more than four hundred research publications in the areas of Pattern Recognition and Machine Learning, Image Processing, Data Mining and Web Intelligence, Soft Computing, Neural Nets, Genetic Algorithms, Fuzzy Sets, Rough Sets and Bioinformatics.
He has received the 1990 S.S. Bhatnagar Prize (which is the most coveted award for a scientist in India), and many prestigious awards in India and abroad including the 1999 G.D. Birla Award, 1998 Om Bhasin Award, 1993 Jawaharlal Nehru Fellowship, 2000 Khwarizmi International Award from the Islamic Republic of Iran, 2000-2001 FICCI Award, 1993 Vikram Sarabhai Research Award, 1993 NASA Tech Brief Award (USA), 1994 IEEE Trans. Neural Networks Outstanding Paper Award (USA), 1995 NASA Patent Application Award (USA), 1997 IETE-R.L. Wadhwa Gold Medal, the 2001 INSA-S.H. Zaheer Medal, 2005-06 Indian Science Congress-P.C. Mahalanobis Birth Centenary Award (Gold Medal) for Lifetime Achievement, 2007 J.C. Bose Fellowship of the Government of India and 2008 Vigyan Ratna Award from Science & Culture Organization, West Bengal.
Prof. Pal is/ was an Associate Editor of IEEE Trans. Pattern Analysis and Machine Intelligence (2002-06), IEEE Trans. Neural Networks [1994-98 & 2003-06], Neurocomputing (1995-2005), Pattern Recognition Letters (1993-2011), Int. J. Pattern Recognition & Artificial Intelligence, Applied Intelligence, Information Sciences, Fuzzy Sets and Systems, Fundamenta Informaticae, LNCS Trans. On Rough Sets, Int. J. Computational Intelligence and Applications, IET Image Processing, J. Intelligent Information Systems, and Proc. INSA-A; Editor-in-Chief, Int. J. Signal Processing, Image Processing and Pattern Recognition; a Book Series Editor, Frontiers in Artificial Intelligence and Applications, IOS Press, and Statistical Science and Interdisciplinary Research, World Scientific; a Member, Executive Advisory Editorial Board, IEEE Trans. Fuzzy Systems, Int. Journal on Image and Graphics, Int. J. Computational Science & Engineering, and Int. J. Approximate Reasoning; and Guest Editor, IEEE Computer, and Theoretical Computer Science - C.

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Andre de Carvalho Using metalearning for technique recommendation
Andre de Carvalho
University of Sao Paulo, Brazil

 [Abstract] One of the main chalenges for the use of intelligent techniques to solve real problems is the selection of the most suited technique. Metalearning allows the use of learning algorithms for the recommendation of the techniques with the best potential to provide a good model.
In this talk I will discuss how metalearning can support the development of intelligent systems by recommending the most promising intelligent techniques. Real problems will illustrate the usefulness of using metalearning. 

[Biography] Prof. André C. Ponce de Leon F. de Carvalho is a Full Professor in the Department of Computer Science, University of S?o Paulo, Brazil, where he was head of Department from 2008 to 2010. He received his B.Sc. and M.Sc. degrees in Computer Science from the Universidade Federal de Pernambuco, Brazil. He received his Ph.D. degree in Electronic Engineering from the University of Kent, UK. He has published around 80 Journal and 200 Conference refereed papers. He has been involved in the organization of several conferences and journal special issues. His main interests are Machine Learning, Data Mining and Hybrid Intelligent Systems. He is in the editorial board of several journals and was a member of the Brazilian Computing Society, SBC, Council. He was until July 2011 the editor of the SBC/Elsevier textbook series.

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