Publikační činnost Katedry aplikované matematiky / Publications of Department of Computer Science (155)

Permanent URI for this collectionhttp://hdl.handle.net/10084/106673

Kolekce obsahuje bibliografické záznamy publikační činnosti (článků) akademických pracovníků Katedry aplikované matematiky (155) v časopisech registrovaných ve Web of Science od roku 2003 po současnost.
Do kolekce jsou zařazeny:
a) publikace, u nichž je v originálních dokumentech jako působiště autora (adresa) uvedena Vysoká škola báňská-Technická univerzita Ostrava (VŠB-TUO),
b) publikace, u nichž v originálních dokumentech není v adrese VŠB-TUO uvedena, ale autoři prokazatelně v době jejich zpracování a uveřejnění působili na VŠB-TUO.

Bibliografické záznamy byly původně vytvořeny v kolekci Publikační činnost akademických pracovníků VŠB-TUO, která sleduje publikování akademických pracovníků od roku 1990.

V ak. roce 2024/2025 sloučeno s katedrou 157, publikační činnost je dále vedena v kolekci Katedra managementu.

Browse

Now showing 1 - 16 out of 16 results

3D desktop virtual reality as a facilitator of the learning quality and of strengthening students’ results
(Taylor & Francis, 2024) Hvorecký, Jozef; Rozehnal, Petr; Funioková, Taťána; Gurný, Petr
Applications of Virtual Reality in education are becoming more and more frequent. A desktop-based 3D virtual reality environment has been piloted at our university. The aim was to analyse the impact of VRE implementation on results of students. Two educational experiments were conducted in order to evaluate its effect on students' outcomes in the university courses of Finance (N = 177; age 20-24) and Marketing (N = 157; age 20-24). In the case of Finance, a positive impact was demonstrated, while in Marketing, the resulting picture was inconsistent. After disclosing this disproportion, the authors discuss which factors could lead to the differences and what and how could be changed in order to increase the impact of virtual reality on students' results.
On evolving environment of 2D P colonies: ant colony simulation
(Springer Nature, 2023) Langer, Miroslav; Valenta, Daniel
P colonies are very simple membrane systems originally derived from the P systems. The 2D P colonies, as a version of P colonies with a two-dimensional environment, were introduced as a theoretical model of the multi-agent system for observ ing the behavior of a community of very simple agents living in a shared environment. Each agent is equipped with a set of programs consisting of a small number of simple rules. These programs allow the agent to act and move in the environment. Although, the 2D P colonies proved to be suitable for the simulations of various (not only) multi-agent systems, and natural phenomena, like the fash foods, there are phenomena which they are not able to simulate without some additional features or characteristics. One of the ways the agents can share the information is to use the stigmergy, which means to leave some special symbols in the environment. In this paper, we follow our previous research on the 2D P colony. We present a model of the 2D P colony with evolving environment, which allows us to simulate phenomena like the stigmergy, hence to simulate an ant colony.
Intelligence in finance and economics for predicting high-frequency data
(MDPI, 2023) Maděra, Martin; Marček, Dušan
Forecasting exchange rates is a complex problem that has benefitted from recent advances and research in machine learning. The main goal of this study is to design and implement a method to improve the learning performance of artificial neural networks with large volumes of data using population-based metaheuristics. The micro-genetic training algorithm is thoroughly analyzed using profiling tools to find bottlenecks. We compare the use of a micro-genetic algorithm to predict changes in currency exchange rates on a data set containing more than 500,000 values. To find the best parameters of neural networks, we propose an improved micro-genetic training algorithm by dividing the training data into mini batches. In this case, the improved micro-genetic algorithm proved to be much faster compared to the standard genetic algorithm, while achieving the same prediction accuracy. This allows for the use of this algorithm for just-in-time predictions of high frequency data. Here, neural network models are first created and validated on an existing data set. Then, the new data values can be added to neural network models and retrained in a short time.
Hybrid demand forecasting models: pre-pandemic and pandemic use studies
(Polskie Towarzystwo Ekonomiczne Oddział w Toruniu, Instytut Badań Gospodarczych, 2022) Kolková, Andrea; Rozehnal, Petr
Research background: In business practice and academic sphere, the question of which of the prognostic models is the most accurate is constantly present. The accuracy of models based on artificial intelligence and statistical models has long been discussed. By combining the advantages of both groups, hybrid models have emerged. These models show high accuracy. Moreover, the question remains whether data in a dynamically changing economy (for example, in a pandemic period) have changed the possibilities of using these models. The changing economy will contin-ue to be an important element in demand forecasting in the years to come. In business, where the concept of just in time already proves to be insufficient, it is necessary to open new research questions in the field of demand forecasting.Purpose of the article: The aim of the article is to apply hybrid models to bicycle sales e-shop data with a comparison of accuracy models in the pre-pandemic period and in the pandemic period. The paper examines the hypothesis that the pandemic period has changed the accuracy of hybrid models in comparison with statistical models and models based on artificial neural net-works.Models: In this study, hybrid models will be used, namely the Theta model and the new fore-castHybrid, compared to the statistical models ETS, ARIMA, and models based on artificial neural networks. They will be applied to the data of the e-shop with the cycle assortment in the period from 1.1. 2019 to 5.10 2021. Whereas the period will be divided into two parts, pre -pandemic, i.e. until 1 March 2020 and pandemic after that date. The accuracy evaluation will be based on the RMSE, MAE, and ACF1 indicators.Findings & value added: In this study, we have concluded that the prediction of the Hybrid model was the most accurate in both periods. The study can thus provide a scientific basis for any other dynamic changes that may occur in demand forecasting in the future. In other periods when there will be volatile demand, it is essential to choose models in which accuracy will decrease the least. Therefore, this study provides guidance for the use of methods in future periods as well. The stated results are likely to be valid even in an international comparison.
Virtual reality retooling humanities courses: Finance and marketing experience at a Czech university
(MDPI, 2022) Koreňová, Lilla; Gurný, Petr; Hvorecký, Jozef; Lůžek, Petr; Rozehnal, Petr
Virtual reality environments (VRE) allow users to visualize both real-life and imaginary activities. For this reason, they make appropriate training fields at universities, too. However, the positive or negative effects of VRE are still a subject of research. There is a need to verify methods of their deployment, student responses and the impact of VRE implementation. Science and medicine courses are frequently exploiting VRE, while their exploitation in humanities is much less frequent. In our paper, we describe and evaluate their application in finance and marketing courses. Both courses were designed and developed as part of a larger, potentially university-wide project. The courses were enriched by mazes including 3-D rooms with course content elements. Students could explore them and communicate with their lecturers and classmates. To allow anytime/anywhere access, the VRE does not require using any special interface. The finance course was organized as a pedagogical experiment with test and control groups. Due to organizational and scheduling reasons, the VRE in marketing served just as enrichment. At the end of the term, all students using VRE were given a questionnaire assessing their satisfaction. The majority expressed satisfaction. In the finance course, positive opinion was also supported by students' improved grades. In total, 87.5% of students agreed that the application of VRE contributed to gaining knowledge. Based on the positive experience and outcomes, the university plans to expand and to intensify its VRE-supported education.
Analysis of processes information flows and items as additional design factor in COBIT framework
(Vysoká škola ekonomická v Praze, Fakulta podnikohospodářská, 2021) Rozehnal, Petr; Novák, Vítězslav
COBIT is a process-oriented IT governance framework. In the 2019 version, the framework offers a significantly redesigned approach to prioritise processes and set process target capability levels. The design phase is very important to achieve a governance system in future. Although there are several design factors defined in the design phase of COBIT 2019, these factors do not assess process suitability and value for the governance system. Thus, connections and continuity among processes are not taken into consideration enough. Therefore, the aim of the article is to suggest another design factor based on the interrelationships of processes in the COBIT framework represented by its information flows. The nature of the proposed innovation is described, and the analysis of process information flows and items is performed. The article also publishes several inconsistencies in COBIT 2019 documentation that have been identified in the process of research. The application of process information flows and items analysis has been illustrated in two case studies. We have identified new information that can be relevant to decision making in the design phase and discussed their importance for the planned governance system. The results could help to improve the quality of the design phase by providing additional information about the context of the processes designed to ensure the governance system. Implications for Central European audience: The implications for senior managers in the Central European region will be beneficial. Optimisation and effective use of information technologies is a prerequisite for achieving long-term competitiveness. COBIT is a best practice framework, and its implementation in companies is largely based on the specifics of each organisation. Therefore, it is important to pay close attention to the implementation phase of the application of COBIT. Framework application positively supports the holistic approach to management, resource optimisation, management based on responsibility and measurability. Today, these attributes of the organisation's management are emphasised not only in Central Europe but in developed countries in general.
Aspects of distance education in combination with home offices
(MDPI, 2021) Rozehnal, Petr; Danel, Roman
This article discusses the impact of a lockdown caused by the novel coronavirus disease 2019 on the educational process at a selected faculty of a public university in the Czech Republic focused on economic education. The aim was to capture relevant aspects in the context of impacts on the management of the educational process in the organization. The unique situation brought the possibility of analyzing the flexibility of the organization, its ability to adapt. A questionnaire survey was conducted among academics. We found out how they coped with this situation, their technical equipment, support from the faculty, and whether they encountered any problems. The goal of the article was not to bring an exact evaluation of selected questions, but to show the state of the actual situation, to point out possible problems of users, and to link these things with the approach to the management of the organization. Based on the analysis, we bring suggestions and recommendations for improving the process of transition to online learning as well as distance education management and recommendation to support teaching, regardless of the teacher's workplace. The basic areas and activities that need to be managed were also identified.
Some statistical and CI models to predict chaotic high-frequency financial data
(IOS Press, 2020) Marček, Dušan
To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.
Forecasting currency pairs with RBF neural network using activation function based on generalized normal distribution experimental results
(Old City, 2019) Marček, Dušan; Babel, Jan; Falát, Lukáš
In this paper, we implement an effective way for forecasting financial time series with nonlinear relationships. We use the artificial neural network of feedforward type for making the decision-process in a company more efficient, more flexible and more accurate. The main objective of this study is to design new method for improving the performance of RBF artificial neural networks. Based on pre-experimental statistical analysis of 1225 financial time series and inspired by GARCH model, RBF neural networks with new shapes of activation functions based on generalized Normal distribution function (GED) are suggested and discussed. Within this study various types of GED activation function in RBF networks are investigated to find the best ones. Firstly, the presence of homoscedasticity and the occurrence of normality of the time series data is investigated. To test our hypothesis about the application of GED distribution in the RBF neural network, we implemented a neural network application (RBFNN) in JAVA. Using the software, we investigate the RMSE error based on the value of p parameter in GED. The optimized size of the p parameter is determined for classic and soft RBF network related to minimal prediction error. We then test our model on 25 financial datasets to explore the contribution of our suggested and implemented method. We also evaluate the forecasting performance of suggested neural network in comparison to established models based on RMSE. Our results show that the proposed approach achieves higher forecasting accuracy on the validation set than available techniques. The suggested modification form of the shape of activation function of the RBF neural network using GED distribution improves the approximation and prediction accuracy of the RBF network models used for financial time series. From performed experiments we find that the optimal size of the parameter p will likely be in the interval (1.4, 2.4) for a standard RBF and less than 2 for the soft RBF.
Forecasting of financial data: a novel fuzzy logic neural network based on error-correction concept and statistics
(Springer, 2018) Marček, Dušan
First, this paper investigates the effect of good and bad news on volatility in the BUX return time series using asymmetric ARCH models. Then, the accuracy of forecasting models based on statistical (stochastic), machine learning methods, and soft/granular RBF network is investigated. To forecast the high-frequency financial data, we apply statistical ARMA and asymmetric GARCH-class models. A novel RBF network architecture is proposed based on incorporation of an error-correction mechanism, which improves forecasting ability of feed-forward neural networks. These proposed modelling approaches and SVM models are applied to predict the high-frequency time series of the BUX stock index. We found that it is possible to enhance forecast accuracy and achieve significant risk reduction in managerial decision making by applying intelligent forecasting models based on latest information technologies. On the other hand, we showed that statistical GARCH-class models can identify the presence of leverage effects, and react to the good and bad news.
The category proliferation problem in ART neural networks
(Óbuda University, 2017) Marček, Dušan; Rojček, Michal
This article describes the design of a new model IKMART, for classification of documents and their incorporation into categories based on the KMART architecture. The architecture consists of two networks that mutually cooperate through the interconnection of weights and the output matrix of the coded documents. The architecture retains required network features such as incremental learning without the need of descriptive and input/output fuzzy data, learning acceleration and classification of documents and a minimal number of user-defined parameters. The conducted experiments with real documents showed a more precise categorization of documents and higher classification performance in comparison to the classic KMART algorithm.
Increasing the business potential of companies by ensuring continuity of the development of their information systems by current information technologies
(Taylor & Francis, 2016) Tvrdíková, Milena
This paper deals with applications of information and communication technologies in the management of companies and institutions. It also focuses on Competitive Intelligence and Business Intelligence and the description of their position in business management. The paper presents current trends in information and communication technologies with emphasis on the use of virtualization and Cloud Computing technologies. The author discusses the importance of Cloud Computing to maintain the continuity of information system of enterprises with low financial impact, thereby increasing its stability. Theoretical framework and literature support the assumption that information and communication technologies are essential for the competitiveness of small and mediumsized enterprises. Discussed are factors that affect management and use of information and communication technologies in small and medium-sized enterprises, in particular the use of cloud computing. Based on the results obtained from a questionnaire survey carried out in the Czech Republic, the author proposed methodological recommendations to facilitate the transition to cloud computing.
Statistical and soft computing methods applied to high frequency data
(Old City, 2016) Marček, Dušan; Kotillová, Alexandra
We evaluate statistical and machine learning methods for predicting different high frequency data sets. Firstly, in this paper we develop forecasting models based on the statistical (stochastic) methods, and on the soft methods using neural networks for the time series of daily exchange rates AUD currency against US dollar. Secondly, we evaluate statistical and machine learning methods for half-hourly 1-step-ahead electricity demand prediction using Australian electricity data. To illustrate the forecasting performance of these approaches the learning aspects of RBF networks are presented. We also show that an RBF neural network trained by genetic algorithm can achieved better prediction result than classic one. It is also found that the risk estimation process based on soft methods is simplified and less critical to the question whether the data is true crisp or white noise.
Academic-industrial cooperation in ICT in a transition economy - two cases from the Czech Republic
(Taylor & Francis, 2015) Ministr, Jan; Pitner, Tomáš
First, the paper presents the position of the ICT sector in the Czech Republic (CZ) as a transition economy; it pays particular attention to the ICT industry, university studies, research, and development. Then, it focuses on academic-industrial cooperation (AIC) in the CZ. As economic conditions in the CZ are different from the traditional developed economies, the AIC might not necessarily exhibit the same characteristics. Thus, the paper tries to identify potential differences on two concrete cases of two schools (faculties) at two Czech universities. The research is based on interviews with companies and stakeholders from the participating faculties. In comparison with the findings known from the literature on this issue in developed countries, the interviews revealed some differences such as a more positive attitude toward engagement of academicians in private companies and further education at universities. Specific local issues have been discovered such as small volumes of cooperation, not enough flexible researchers, and lacking business orientation at the universities.
The determinants of IT adoption in SMEs in the Czech-Polish border areas
(Taylor & Francis, 2015) Hančlová, Jana; Rozehnal, Petr; Ministr, Jan; Tvrdíková, Milena
This paper deals with the factors affecting an adoption of information technology (IT) in micro, small and medium enterprises (SMEs) within the transition economies of the Czech–Polish region. The basic determinants of IT adoption which were identified include data sources, the use of function modules in information systems, the required IT properties and the way IT operations are implemented. The results of the questionnaire survey carried out in 2012 are evaluated by the asymmetric dependence testing and estimation of ordinal regression models. The paper describes the impact of selected determinants on IT adoption. The analysis results show a different effect of determinants in relation to the size of an economic entity (individual SME segments). When we are aware of the importance of these indicators, it can help us to improve the understanding, monitoring and support of the further development of IT adoption in SMEs as an important condition for the successful economy transformation.
Modelovanie volatility a predikčné modely vysokofrekvenčných finančných dát: štatistický a neurónový prístup
(Slovenská akadémia vied. Ekonomický ústav, 2014) Marček, Dušan

Browse

Recent Submissions