Ing. Petr Pulc, Ph.D.

2016

The project is focused on data mining of non-structured data from large-scale sources. The data are represented by textual and image/video information. To porcess the data, selected methods of artificial intelligence such as neural networks/deep learning, evolutionary algorithms and methods of image processing will be used. The need of these topics is evident from the fact that each year, the volume of data is doubling. At the same time, only 20 % of the available data are processed.

2020 - 2022

The project focuses on research of modern algorithms and techniques in knowledge engineering in a broader sense. The topic of the project represents a promising and constantly expanding area both in terms of theoretical research and in terms of potential for practical applications. In particular, the project will focus on methods in data processing, extraction of information-intensive knowledge from data, and automatic search for explanations for phenomena occurring in the data. The important issue of contemporary artificial intelligence focused on knowledge processing is the limited ability or inability to give explanations leading to a certain decision, and thus often the decision cannot be formally supported and justified. The whole project through all sub-themes will therefore reflect this question. This is a doctoral project covering the diverse research interests of PhD students, but providing a general underlying question that encompasses all sub-themes. The project specifically aims to develop algorithms and techniques in neural networks, evolutionary algorithms, image and text processing, and motion information processing. The project is a direct continuation of a finishing SGS project from data processing area.

2017 - 2019

The project focuses on perspective and ever-expanding field of data processing and extraction of information of valuable content from the data. The methods for information extraction are increasingly using selected methods of artificial intelligence such as neural networks and evolutionary algorithms. Combined models or algorithms for text processing (text mining) come to the fore. The need of these topics is evident from the fact that each year, the volume of data is doubling. At the same time, only 20 % of the available data are processed.