Specification of the department's research focus

Research focus:

Integrated Approaches and Tools for Supporting Decision-Making and Cognitive Processes through Algorithmization, Data Science, Quantitative Methods, and ICT

 

Annotation:

Modern science and advanced technologies require multidisciplinary and integrated approaches to solve complex real-world problems through algorithmization, data analysis and quantitative methods. This research direction requires interdisciplinary collaboration. It focuses on applying advanced algorithms, analytical tools and artificial intelligence methods to solve problems with practical applications. Important areas include data processing and analysis, modelling, localization issues and decision support using innovative ICT tools. An essential part of the solution with practical outcomes is the matter of security and data anonymization, which ensures the secure use of information technology.

The idea behind integrated approaches is to combine traditional and innovative methodologies that are applied to various application areas, ranging from economic analysis to education, social issues and artificial intelligence. This approach requires interdisciplinary solutions and advanced mathematical, statistical, and computer science methods to optimize processes and solve complex problems. 

In algorithmization, the emphasis is on developing and testing algorithms that respond to the needs of specific application domains and different data types. This approach includes big data processing (for example, image data or time series data), numerical solutions to mathematical problems, probabilistic simulations, and methods for indoor localization. Proposed approaches are based on robust ICT technologies that enable their effective implementation in real-world environments. 

Statistical and mathematical models are applied in data science to enable a deeper understanding of the dynamics of systems and processes in various application domains. These include, for example, time series modelling, data filtering, and the use of discrete and numerical mathematics methods, as well as optimization in economic, managerial, transport, or other contexts. The resulting solutions are inspired by real data from external collaborations and open space for artificial intelligence applications, including machine learning, computer vision and natural language processing, thus expanding the possibilities for interpreting and using big data in the digital society.

 

Subtopics:

• Algorithmization
  • Development and testing of original algorithms responding to the needs of external solutions.
  • Data, image and time series processing with emphasis on anonymization and data security.
  • Probabilistic simulations and numerical methods for solving mathematical problems.
• Data Science and Data Analysis
  • ICT tools for data analysis with emphasis on econometric models and time series modelling.
  • Projects focused on practical applications (e.g. water management, transport systems, smart parking).
  • Data Warehouse Methodologies and Data Lakes.
• Quantitative methods in AI
  • Machine learning and neural networks (classification, detection, natural language processing).
  • Advanced methods of graphics, computer vision and information extraction from visual data.
• Cognitive Processes and ICT
  • Supporting cognitive processes with ICT.
  • Projects related to sustainable economy and interdisciplinary cooperation.
  • ICT support in modern educational methods (gamification, AI, content visualization)
• Smart technology and indoor localization
  • Smart approaches to localization, data analysis and mobile user support.
  • Methods for refining indoor localization through a combination of fingerprint data and contextual information.
  • Developing smart systems using location data, contextual information and mobile devices to improve services and decision-making processes.

 

Implemented projects:

• Project name: Research and Development of AI Deployment in Production Planning

Project number: CZ.01.01.01/01/22_002/0001106, Call for proposals: MPO ČR – Aplikace I., Contracting authority: RDD, s.r.o., Period: 01/2024 - 03/2026 (FIM involved from 11/2024), Principal investigator for FIM: Prof. RNDr. PhDr. Antonín Slabý, CSc.

 

• Project name: Research and Development of AI Deployment in Production Planning

Project number: MK 75969/2023 OUKKO, Guarantor: Hradec Králové Region, Department of Culture, Period: 11/2023 - 05/2025, From FIM involved: Ing. Tereza Otčenášková, BA, Ph.D., doc. RNDr. Pavel Pražák, Ph.D., a team of students of FIM UHK                                                                                                                                         

• Project name: Automatic Identification of Keywords

Project number: CZ.01.1.02/0.0/0.0/20_358/0028191, Contracting authority: Near Future s.r.o. / Innovation Voucher/ VI, Period: 04/2023 - 07/2023. Investigators: Ing. Tereza Otčenášková, BA, Ph.D., Mgr. Jiří Haviger, Ph.D., Ing. Michal Dobrovolný, Ing. Martin Konvička, Bc. Michael Bartoš.

 

• Project name: Smart Parking & Charging

Project number: CZ.01.1.02/0.0/0.0/20_321/0024477, Call: MPO ČR, OP Podnikání a inovace pro konkurenceschopnost – program Aplikace, výzva VIII, Contracting authority: Vigour Alfa spol. s r.o., Period: 01/2021 - 05/2023, Co-investigator for FIM: prof. RNDr. Petra Poulová, Ph.D.

 

• Project name: Innovation of Tourism Management Systems Using the Process Management Tools

Project number: TL01000191, Contracting authority: TAČR ÉTA, Period: 03/2018-03/2021, Principal investigator: doc. Ing. Zdeněk Ulrych, Ph.D. - University of West Bohemia in Pilsen, Co-investigator for FIM: prof. RNDr. Petra Poulová, Ph.D.

 

• Project name: Development of a unique specialized database for e-commerce solutions

Project number: CZ.01.1.02/0.0/0.0/19_262/0020308, Call for proposals: MIT of the Czech Republic, OP Enterprise and Innovation for Competitiveness, Programme Applications, Contracting authority: FG Forrest, a.s., Period: 7/2020 - 6/2021, Principal investigator: prof. RNDr. Petra Poulová, Ph.D.                 

 

International cooperation:

• University of Zilina, Slovakia
• Wroclaw University of Technology, Poland
• Academia Sinica, Taipei, Taiwan
• Constantine the Philosopher University in Nitra, Slovakia
• Hong Kong Metropolitan University, Hong Kong

 

Topics offered in doctoral studies:

• Algorithmic solutions for security and data anonymization in smart systems
  • Focus on the development and optimization of algorithms to ensure data protection and privacy in smart technology environments
• Advanced machine learning and dynamic data analysis methods
  • Researching new machine learning techniques applied to large-scale datasets to improve predictions and optimize decision-making processes
• Smart technology for indoor location and personalised services
  • Developing methods for refining indoor location, integrating fingerprint data and contextual information, with application to personalised services and smart applications
• Optimizing data infrastructure to support interdisciplinary research
  • Research on efficient methods for managing and analyzing large data sets, with an emphasis on their use in application research
• Combining Artificial Intelligence and Graph Algorithms for the Analysis of Complex Systems
  • Exploring innovative methods linking graph algorithms and AI with applications to the analysis of complex networks such as transport systems, water management or socio-economic networks