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Center for Advanced Research in Applied Informatics

The Center for Advanced Research in Applied Informatics (CAIA) at University of Craiova, lead by Prof. Ion Iancu, Ph.D., has as main research interests the following domains: Data Mining, Decision Support Systems, Natural Language Processing, Evolutionary Computation, Support Vector Machines and Artificial Neural Networks.

Database and Artificial Intelligence Group

The Database and Artificial Intelligence Group within the Politechnica University of Timisoara, is lead by Prof. Eng. Stefan Holban, Ph.D. The group focuses on Modeling and Simulation, Neural Networks, Image Processing and Pattern Recognition, Wireless Networks and Cyber-Physical Systems, Data mining, Expert Systems, Fuzzy Logic, Adaptive Systems and Dynamic Reconfigurable Systems.

The Intelligent Systems Group

The Intelligent Systems Group within the Technical University of Cluj-Napoca is lead by Prof. Ioan Alfred Letia, Ph.D. The main areas of intrest are: norm compliance (verifying business processes against norm compliance and quality standards), support for dispute resolution for Small and Medium Enterprises in case of contract breach, semantic-based business process re-engineering, Decision Support…


Artificial Intelligence and Bioinformatics Research Group

The Artificial Intelligence and Bioinformatics Research Group, lead by researcher Liviu Badea, Ph.D., has as main areas of interest Machine Learning, Data Mining, Inductive Logic Programming, Multi-relational Learning, Bioinformatics, Semantic Web,Image Processing, Knowledge Representation and Expert Systems.

Knowledge Engineering Group

The Knowledge Engineering Group (KEG), lead by Prof. Rodica Potolea, PhD, is addressing problems for knowledge extraction, representation, storage and management that the information era has brought in various segments of human activity due to data overload.

The main fundamental theoretical aspects our group focuses on are: dealing with problem-specific features extraction from both structured data, pre-processing techniques for handling noisy and/or incomplete data, learning from balanced/unbalanced and structured/unstructured data.