National Repository of Grey Literature 333 records found  1 - 10nextend  jump to record: Search took 0.01 seconds. 
Mendel University performance analysis through data mining
Panggam, Osunam
This thesis explores the Mendel University performance analysis and the connection between the University ranking with the news articles and reviews. The study aims to analyze media coverage and review data on the universities over the years and their impact on the university's reputation and ranking. The research methodology involves web scraping news articles and reviews related to Mendel University and using data mining and NLP techniques to analyze their sentiment and topic distribution. Further, the qualitative data collected from news articles, online students’ reviews will be correlated with the University's ranking scores data over a past-years period to identify any patterns or relationships. The findings of the study will try to find insight into the impact of media coverage on university ranking and reputation. It will also shed light on the data mining techniques to analyze textual data related to the university for interesting patterns.
Use of Data Mining in Company Processes
Měchura, Dalibor ; Kříž, Jiří (referee) ; Luhan, Jan (advisor)
This masters thesis focuses on data mining techniques and business intelligence analysis. In accordance with the analysis of the current situation in the company, a complementary solution to the problem is proposed and a view of the existing data is provided from a different perspective, namely using RapidMiner. The output of the thesis is thus concrete analytical outputs for decision support in the company.
Design of Management Reporting for a Small Enterprise Using Business Intelligence Tools
David, Pavel ; Neuwirth, Bernard (referee) ; Kříž, Jiří (advisor)
The diploma thesis deals with the implementation of Business Intelligence software in the company. The thesis is divided into three main parts. The first part contains the theoretical background for the practical part. It describes the importance of analytical applications for management, business intelligence and its individual components and some methods of risk and project management. In the second part the current reporting process of the company is analyzed. The last part of the thesis describes the development and subsequent implementation of the software itself.
Knowledge Discovery from Spatio-Temporal Data
Liptáková, Daša ; Burgetová, Ivana (referee) ; Bartík, Vladimír (advisor)
This thesis deals with knowledge discovery from spatio-temporal data. Firstly, it describes the general principles of knowledge discovery and then knowledge discovery from spatio-temporal data, where it mainly focuses on methods for detecting outlying trajectories of moving objects. In the next section, the thesis describes the design and implementation of the mining task and demonstration application. Finally, several experiments are performed over three different datasets.
Use of Data Mining for Payment Identification
Bartoš, Stanislav ; Burget, Radek (referee) ; Bartík, Vladimír (advisor)
This master thesis concentrates on design and implementation of a system for payment identification, even if the reliable identifier (e.g. variable symbol) is missing. Data mining techniques, such as classification and prediction, were used as a solution to this problem. This master thesis is company assignment for company "Platební instituce Roger a.s.".
Data Mining Techniques
Kubincová, Monika ; Bartík, Vladimír (referee) ; Burgetová, Ivana (advisor)
The Bachelor's thesis deals with the processing and analysis of data from a commercial company, aiming to create an analytical tool for regular knowledge extraction from data that assists the company with important strategic decisions. The theoretical part of the thesis describes various methods of data mining and data processing, with a significant focus on the clustering method. The thesis further describes the available datasets that were used for the analysis and implementation of the proposed tasks. The final part of the concludes results of the analysis and its future usability including suggestions for improvement.
Data Mining Based Web Analyzer of Job Advertisements
Wittner, Alex ; Dzurenda, Petr (referee) ; Sikora, Marek (advisor)
Cílem této bakalářské práce bylo vytvoření automatizovaného zadávání nových pracovních inzerátů pomocí vložení URL v rámci již existující webové aplikace https://rewire.informacni-bezpecnost.cz, jejíž cílem je shromažďování pracovních inzerátů v oblasti cybersecurity s podrobnou analýzou pracovních kompetencí. Pracovní inzeráty jsou analyzovány pomocí více vzorového vyhledávacího algoritmu Aho-Corasick, psaného v jazyce Java. K získávání informací ze zadaných pracovních inzerátů slouží Python skript využívající knihovnu Selenium. Výsledná implementace a webová stránka je vytvořena pomocí jazyka PHP a knihovny ReactJS využívající JavaScript.
Development and analysis of a database of reactions catalyzed by cytochrome P450 enzymes for machine learning applications
Komorníková, Natália ; Pluskal, Tomáš (advisor) ; Berka, Karel (referee)
Cytochrome P450 enzymes are hemoproteins showing extraordinary di- versity in the reactions they catalyze. We developed a database containing all the needed data to provide a comprehensive data source on reactions cat- alyzed by cytochrome P450 enzymes. This data mainly includes information about the substrates, products of characterized reactions, and the sequence of these enzymes. The database was developed by collecting data from reliable protein and reaction databases like UniProt and RHEA. The work presents an in-depth analysis of the created database of reactions catalyzed by cy- tochrome P450 enzymes. This database can be utilized for future machine learning approaches to predict the function of uncharacterized cytochrome P450s.
Modelling and Analysis of Logistics Processes by Applying Process and Data Mining Techniques
Rudnitckaia, Julia ; Wang, Hao (referee) ; Zendulka, Jaroslav (referee) ; Hruška, Tomáš (advisor)
In this thesis, we propose an approach for modelling hidden and unknown processes and subprocesses in the example of a seaport logistics area. Having the underlying process model makes it possible to exploit more advanced algorithms since deviations and main paths are becoming visible and better controlled. The obtained model is the foundation for the core research of this work and will be enriched with key performing indicators and their forecast by applying advanced process mining, statistics, and machine learning techniques. The main difference of the approach is that we take as a target variable not any specific value, but the object - a process variant or a process type with a set of parameters. Bottleneck analysis, from one side, and predictive analysis, on the other hand, are enforced with context-aware information, especially with these additional objective process attributes.   Furthermore, the support of the descriptive ("As is") current process model with certain notation and the integration with relevant bottleneck and predictive methods compromise the advantages of the approach. The work primarily focuses on the design of algorithms and methods for supporting logistics data analysis. However, it can be adjusted and applied to other areas accordingly, which makes the approach flexible and versatile. The result of the work is the framework for unstructured process modelling and the key process parameters predictive method. This analysis of processes with their attributes might be used for decision-making systems and process maps in future.
Data Mining for Efficient Evaluation and Management of Stock Portfolio
YUMATOVA, Angelina
This bachelor thesis is based on the analysis and searching for suitable companies so that each investor can find and add to his portfolio a company that will provide the investor a stable income throughout his life. The main goal of this work is to show methods of companies valuation using ratio indicators, intrinsic value of companies and various Data mining methods. Detailed information about the concept of Data mining and the practical usefulness of Data mining methods will be presented. The work includes the main method that an investor uses to evaluate the estimated profit for the next five or ten years, such as the return on equity (ROE) method. It will also provide a comparison of shares and funds using the Dollar-Cost Averaging strategy and determining which is more beneficial for investing. In conclusion, ratio calculations, ROE methods and various data mining methods for well-known companies around the world are presented.

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