Artificial Intelligence Center

Function of the Center

Our research encompasses three distinct areas: theoretical exploration focused on the optimization of complex networks, advances in sensor systems and machine vision, and the application of artificial intelligence (AI) models for in-depth economic and business analysis. Our mission is to push the boundaries of AI, creating innovative solutions that not only enhance process efficiency but also contribute to societal and economic advancement. As a nexus for experts from diverse fields, we are dedicated to developing solutions that foster resilience, transparency, and sustainability in our future.

Head of the Center: Prof. Dr. Ferenc Friedler

Contact – e-mail address: f.friedler@ga.sze.hu

Research-professional areas

Digital corporate reporting

The research addresses the transformation of traditional corporate reporting into digital formats, such as the eXtensibe Business Reporting Language (XBRL). This involves utilizing technology to improve the transparency, accessibility, and comprehensiveness of corporate financial information. Through the retrieval, processing, and analysis of a large volume of reports, our goal is to provide stakeholders with state-of-the-art insights using digital reports. This approach reduces the potential for human error and provides comprehensive fundamental data.

Sustainability and ESG accounting

Our research focuses on the integration of Environmental, Social, and Governance (ESG) factors into accounting practices. This area examines how companies can report on sustainability issues, measure their impact, and communicate their performance in these critical areas to investors and the public, aligning financial strategies with sustainable development goals.

Theoretical research and development of artificial intelligence devices

The computational resource needs of artificial intelligence devices have been increasing by the more complicated tools and the stricter requirements. Only the most efficient algorithms-based artificial intelligence devices corresponds to practical-applicability. We develop mathematical modelling methodologies, which are based on the structural examination of engineering systems. The methodology increase the efficiency and effectivity of artificial intelligence tools. We consider important the direct engineering or economic applicability of the theoretical results. Our main research areas are the controllability of systems, energy efficiency of systems and sustainability of systems.

Computer vision

Our researchers develop computer vision algorithms by the processing of the data of independent sensor platforms. The vehicles and road-sections are detected by these independent sensor platforms. These platforms operate with different image processing methods and these systems operate between different circumstance in space and time. We accomplish sensor-tests by different vehicles – ground vehicle or aircraft – at night or in the daytime. The processing of the independently-obtained information from each other is expedient together, so we obtain more accurate and more reliable information. On the basis of all these, we examine the coherence between different sensing-environments and our aim is the most accurate recognition of vehicles, road-sections by artificial intelligence devices.

Analysis and optimization of economic processes

We analyse and optimize economic process-systems related to the economic companies, commercial and service companies. The development and application of artificial intelligence devices is justified by the changes of economic process-systems, the stricter regulations and the claim of the recognition of regular/irregular operation.

Digital corporate reporting

The research addresses the transformation of traditional corporate reporting into digital formats, such as the eXtensibe Business Reporting Language (XBRL). This involves utilizing technology to improve the transparency, accessibility, and comprehensiveness of corporate financial information. Through the retrieval, processing, and analysis of a large volume of reports, our goal is to provide stakeholders with state-of-the-art insights using digital reports. This approach reduces the potential for human error and provides comprehensive fundamental data.

Sustainability and ESG accounting

Our research focuses on the integration of Environmental, Social, and Governance (ESG) factors into accounting practices. This area examines how companies can report on sustainability issues, measure their impact, and communicate their performance in these critical areas to investors and the public, aligning financial strategies with sustainable development goals.

Theoretical research and development of artificial intelligence devices

The computational resource needs of artificial intelligence devices have been increasing by the more complicated tools and the stricter requirements. Only the most efficient algorithms-based artificial intelligence devices corresponds to practical-applicability. We develop mathematical modelling methodologies, which are based on the structural examination of engineering systems. The methodology increase the efficiency and effectivity of artificial intelligence tools. We consider important the direct engineering or economic applicability of the theoretical results. Our main research areas are the controllability of systems, energy efficiency of systems and sustainability of systems.

Computer vision

Our researchers develop computer vision algorithms by the processing of the data of independent sensor platforms. The vehicles and road-sections are detected by these independent sensor platforms. These platforms operate with different image processing methods and these systems operate between different circumstance in space and time. We accomplish sensor-tests by different vehicles – ground vehicle or aircraft – at night or in the daytime. The processing of the independently-obtained information from each other is expedient together, so we obtain more accurate and more reliable information. On the basis of all these, we examine the coherence between different sensing-environments and our aim is the most accurate recognition of vehicles, road-sections by artificial intelligence devices.