František Dařena

František Daøena works as an Associate Professor and head of Information systems working group at the Department of Informatics, Faculty of Business and Economics, Mendel University Brno, he is a member of SoNet research center, author of several publications in international scientific journals, conference proceedings, and monographs, member of editorial board of international journals and editor-in-chief of International Journal in Foundations of Computer Science & Technology (IJFCST). His research areas include artificial intelligence, machine learning, and text/data mining.

Publications

Using Online Data in Predicting Stock Price Movements: Methodological and Practical Aspects
Using Online Data in Predicting Stock Price Movements: Methodological and Practical Aspects
František Dařena, Jonáš Petrovský, Jan Přichystal, Jan Žižka. © 2021. 28 pages.
A lot of research has been focusing on incorporating online data into models of various phenomena. The chapter focuses on one specific problem coming from the domain of capital...
Using Online Data in Predicting Stock Price Movements: Methodological and Practical Aspects
Using Online Data in Predicting Stock Price Movements: Methodological and Practical Aspects
František Dařena, Jonáš Petrovský, Jan Přichystal, Jan Žižka. © 2019. 35 pages.
A lot of research has been focusing on incorporating online data into models of various phenomena. The chapter focuses on one specific problem coming from the domain of capital...
Semantics-Based Document Categorization Employing Semi-Supervised Learning
Semantics-Based Document Categorization Employing Semi-Supervised Learning
Jan Žižka, František Dařena. © 2017. 29 pages.
The automated categorization of unstructured textual documents according to their semantic contents plays important role particularly linked with the ever growing volume of such...
Revealing Groups of Semantically Close Textual Documents by Clustering: Problems and Possibilities
Revealing Groups of Semantically Close Textual Documents by Clustering: Problems and Possibilities
František Dařena, Jan Žižka. © 2017. 40 pages.
The chapter introduces clustering as a family of algorithms that can be successfully used to organize text documents into groups without prior knowledge of these groups. The...
Discovering Opinions from Customers' Unstructured Textual Reviews Written in Different Natural Languages
Discovering Opinions from Customers' Unstructured Textual Reviews Written in Different Natural Languages
Jan Žižka, František Dařena. © 2015. 26 pages.
Gaining new and keeping existing clients or customers can be well-supported by creating and monitoring feedbacks: “Are the customers satisfied? Can we improve our services?” One...
Modern Computational Models of Semantic Discovery in Natural Language
Modern Computational Models of Semantic Discovery in Natural Language
Jan Žižka, František Dařena. © 2015. 335 pages.
Language—that is, oral or written content that references abstract concepts in subtle ways—is what sets us apart as a species, and in an age defined by such content, language has...
Revealing Groups of Semantically Close Textual Documents by Clustering: Problems and Possibilities
Revealing Groups of Semantically Close Textual Documents by Clustering: Problems and Possibilities
František Dařena, Jan Žižka. © 2015. 41 pages.
The chapter introduces clustering as a family of algorithms that can be successfully used to organize text documents into groups without prior knowledge of these groups. The...
Semantics-Based Document Categorization Employing Semi-Supervised Learning
Semantics-Based Document Categorization Employing Semi-Supervised Learning
Jan Žižka, František Dařena. © 2015. 29 pages.
The automated categorization of unstructured textual documents according to their semantic contents plays important role particularly linked with the ever growing volume of such...