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Personalized Treatment of Respiratory Diseases Using Artificial Intelligence and Interoperability with e-Health Systems
Myška, Vojtěch ; Drotár,, Peter (referee) ; Brezany, Peter (referee) ; Burget, Radim (advisor)
Corticosteroid (CS) treatment in patients with Long COVID aims to prevent the progression from active post-inflammatory changes to fibrosis scarring. However, CS have side effects, which may sometimes be severe. Some patients might not require any treatment as their post-inflammatory changes resolve spontaneously. This dissertation thesis aims to develop an artificial intelligence (AI) based approach that allows personalized treatment of patients with Long COVID and a design of modular architecture allowing seamless interoperability of AI models with the information systems used in healthcare facilities. The first part of the thesis deals with the foundation of the state-of-the-art of using AI algorithms to recommend CS treatment in patients with Long COVID, who have the risk of permanent lung damage. This study examines how various parameters from different examinations influence the accuracy of the AI models. The most effective model achieves an accuracy of 73.68 %, a balanced accuracy of 73.52 %, and an AUC of 0.7469. These results prove that a trained AI model on a correctly chosen set of parameters from various medical examinations is effective and can be used as a decision-support tool for further treatment courses. The second part focuses on developing a modular architecture that allows interoperability between AI models and the information system of health facilities. Its specific implementation for early COVID-19 detection, incorporating DeepCovidXR models, is presented. In the performance test, the average processing time of X-ray images is 11.53 seconds using the CPU and 2.78 seconds with the GPU. Both values meet the maximum permissible analysis time set at 20 seconds. The results presented in both sections have been implemented and are currently used at the Olomouc University Hospital.
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Management of water reservoir storage function using methods of artificial intelligence
Urbanec, Patrik ; Matoušek, Petr (referee) ; Kozel, Tomáš (advisor)
The subject of this thesis is to control the storage function of the reservoir using artificial intelligence methods, including the construction of the appropriate control algorithm. The thesis is divided into the theoretical part and the part of the application of reservoir storage function control. The theoretical part describes the control algorithm and the prediction model. The following are basic optimization methods and artificial intelligence methods. The second part presents the historical data used for the prediction model. The following is a description of calibration and validation of the control module and evaluation of the application results. Finally, there is a comparison and summary of individual results, control algorithm and prediction model. According to the results, the control algorithm can be recommended for further investigation.
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The Business Plan of the Porterhouse BeerPub
Švanda, Martin ; Trlida, Martin (referee) ; Luňáček, Jiří (advisor)
The diploma thesis focuses on creating a real budiness plan for establishment a porter house, that will offer beer from production of small breweries. Part of the thesis is also a making of prediction models for sales and costs in the branch. It also contains a financial plan and evaluation of the investment effectivity.
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Time-Series Analysis and Prediction by Means of Neural Networks
Kňažovič, Martin ; Jaroš, Jiří (referee) ; Bidlo, Michal (advisor)
This thesis deals with stock price prediction based on the creation of prediction models for selected stocks (BRK-A, GOOG, and MSFT), which can help investors in the creation of their financial decisions or by replacing other stock prediction models in existing prediction systems. Models created in this thesis are presented in two types - univariate model and multivariate model, which are in their final version presented in two architectures, one-layer architecture and two-layer architecture. Discussed models are created by means of neural networks, specifically recurrent neural networks with its extension - Long short-term memory. The output of the presented models is a forecast of the next-day stock price, which can be used for evaluating the right time to buy or sell a given stock. The quality of individual prediction models is evaluated via the mean squared error of the validation or testing dataset or alternatively based on stock price trend prediction.
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Evaluation of the financial situation of the company using systems of financial indicators
HRBOVÁ, Veronika
The aim of this thesis is to evaluate the financial situation of the company using parallel, pyramid and purpose systems of financial indicators. The selected company of this thesis is the company Kores Europe s.r.o., which deals mainly with the production of glue sticks. The goal was achieved through various methods of financial analysis - a parallel set of indicators, a pyramid set of indicators, purposely selected set of financial indicators and an intercompany comparison. The analysed period is from 2016 to 2020. The results are interpreted and compared with a sample of companies from the Bisnode Albertina database. Finally, potential sources of inefficient finance and recommendations for improving the financial situation of the company are identified.
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Financial analysis of the company
KAPLANOVÁ, Lucie
The objective of this work is to evaluate the development of the financial situation of a selected company, to identify possible sources of inefficiency and to propose measures to eliminate them. The theoretical part is focused on the main objectives, subject and methods financial analysis. The practical part deals with the analysis of the selected company based on financial statements using horizontal and vertical analyses, an analysis of differences and ratios, the breakdown of return on equity, predictive and diagnostic models. The values of selected indicators are further compared with companies from the same industry.
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Management of water reservoir storage function using methods of artificial intelligence
Urbanec, Patrik ; Matoušek, Petr (referee) ; Kozel, Tomáš (advisor)
The subject of this thesis is to control the storage function of the reservoir using artificial intelligence methods, including the construction of the appropriate control algorithm. The thesis is divided into the theoretical part and the part of the application of reservoir storage function control. The theoretical part describes the control algorithm and the prediction model. The following are basic optimization methods and artificial intelligence methods. The second part presents the historical data used for the prediction model. The following is a description of calibration and validation of the control module and evaluation of the application results. Finally, there is a comparison and summary of individual results, control algorithm and prediction model. According to the results, the control algorithm can be recommended for further investigation.
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Decision making of the user of the financial statements about the financial position of the enterprise
VALDMANOVÁ, Dominika
The financial health of a company is important for the decision-making of a financial statement user for various reasons. It may be important for future investors to decide if they can invest in the company. In addition, it may also be important for a bank to decide whether the company can provide a credit. However, there are other cases where the user needs to know if the company is financially health. For this evaluation, there are selected the methods used to detect and evaluate the manipulation of financial statements. As these methods are selected the CFEBT model, the Beneish model and the Jones model of non-discretion accrual. Creditworthy models are used to determine whether the company is making any value for the future and there is any danger of bankruptcy. The creditworthiness index, the Tamari model and the IN05 model are selected as these models. In the diploma thesis, these methods are applied in six companies. There are the parent company named ABC a.s. and its five subsidiaries named with Roman numerals I-V. Names of companies are invention. In the practical part of the diploma thesis, the companies are analysed according to individual models and then there is determined the influence of subsidiaries on the parent company according the correlation coefficient. In the end the hypotheses are confirmed or refuted. The first hypothesis says that the whole consolidation unit is financially health. The second hypothesis assumes that the results of subsidiaries influence the results of parent company positively.
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