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Center for Advanced Research in Applied Informatics

Classification Craiova Data Mining Decision support systems, Expert systems Evolutionary computation Machine Learning Natural Language Processing

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.

 

Applied Computational Intelligence Group

Bioinformatics Cluj-Napoca Computational Linguistics Data Mining Medical data mining Natural Language Processing Text mining

The Applied Computational Intelligence Group within the Babeș-Bolyai University is lead by Prof. Horia Pop, Ph.D. The group's research interests focus on Intelligent Data Analysis Methods, Formal Concept Analysis, Bioinformatics, Computational Linguistics, Medical Data Mining, Search-based Software Engineering and using Machine Learning techniques in Software Engineering.

 

The Center for Computational Linguistics

Bucharest Cities Computational Linguistics Information retrieval Natural Language Processing Research domains

The Center of Computational Linguistics, lead by Prof. Liviu Dinu, Ph.D. and Prof. Emil Ionescu, Ph.D., is a multi-disciplinary research area that addresses the automatic processing of human language for a range of tasks. We are a high-level research center inside the University of Bucharest, with our members having gained experience (as graduates, PhD, master students, or full employees) from the Faculty of Letters, the Faculty of Foreign Languages and Literatures, and the Faculty of Mathematics and Computer Science.

 
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Knowledge Engineering Group

Classification Cluj-Napoca Data Mining Information retrieval Knowledge Representation and Reasoning Machine Learning Medical data mining Natural Language Processing Social Network Analysis Text mining

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.

 
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